Science, Irving K. Barber Faculty of (Okanagan)
Biology, Department of (Okanagan)
DSpace
UBCO
Field, Rachel D.
Doctor of Philosophy - PhD
University of British Columbia
Ecosystem functions operate across multifunctional landscapes to produce and connect supply areas of multiple ecosystem services (ES). However, conventional approaches in spatial ES assessment typically map discrete areas that produce one or several ES, which ignores the complex functional interactions among ES. Managing a network of connected ES and maintaining their heterogenous distributions and interactions at the landscape scale is crucial for supporting the health of people and nature. However, spatial network models have not represented the functional interactions between multiple ES in large landscapes, nor have they been translated for practical governance applications. The goal of this research was to develop a novel spatial network approach to represent and quantitatively assess the functional interactions between multiple ES. First, we illustrate this approach using a small, hypothetical landscape, and provide landscape connectivity-based methodology for how to represent ES supply nodes and the functional links between them. We then evaluate the approach using existing ES supply data from a large, real-world landscape to build a spatial multi-ES network model. Our network mapping results reveal long-distance functional interaction corridors, land-use and land cover types not typically associated with a particular ES, and high-value areas that are not present on individual supply area or pairwise link maps. Quantitative network analyses show redistribution of the highest-value nodes and links when comparing single- to multi-ES networks, and that certain potentially important supply areas not detected by traditional valuation protocols are discovered through network analyses. These results show how a network approach can identify potentially important areas on the landscape that might be overlooked by conventional, single-jurisdiction ES assessments. Lastly, we use a network community detection algorithm to partition the landscape into highly functionally interacting clusters of ES supply areas, then assess the alignment of these with a social network layer represented by the spatial jurisdictions of agencies involved in relevant ES governance in the region. We identify which specific areas and among which agencies opportunities exist for multi- sector, cross-scale coordination, and find that, regardless of cluster size, all agency scales are relevant to coordinated governance of multiple ES across the multifunctional landscape.
A SPATIAL NETWORK APPROACH FOR MODELLING THE FUNCTIONAL INTERACTIONS BETWEEN MULTIPLE ECOSYSTEM SERVICES by Rachel D. Field B.Sc., University of Victoria, 2007 M.Sc., Simon Fraser University, 2011 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE COLLEGE OF GRADUATE STUDIES (Biology) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) April 2024 © Rachel Field, 2024 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: A spatial network approach for modelling the functional interactions between multiple ecosystem services submitted by Rachel D. Field in partial fulfillment of the requirements of the degree of Doctor of Philosophy . Dr. Lael Parrott, Biology and ECCC, Irving K. Barber Faculty of Science Supervisor Dr. Jason Pither, Biology, Irving K. Barber Faculty of Science Supervisory Committee Member Dr. John Janmaat, Economics, Irving K. Barber Faculty of Arts and Social Sciences Supervisory Committee Member Laura Hooker, Biology (Emerita), Irving K. Barber Faculty of Science Supervisory Committee Member Dr. Andrea Craig, Economics, Irving K. Barber Faculty of Arts and Social Sciences University Examiner Dr. Graeme Cumming, Oceans Graduate School, The University of Western Australia External Examiner iii Abstract Ecosystem functions operate across multifunctional landscapes to produce and connect supply areas of multiple ecosystem services (ES). However, conventional approaches in spatial ES assessment typically map discrete areas that produce one or several ES, which ignores the complex functional interactions among ES. Managing a network of connected ES and maintaining their heterogenous distributions and interactions at the landscape scale is crucial for supporting the health of people and nature. However, spatial network models have not represented the functional interactions between multiple ES in large landscapes, nor have they been translated for practical governance applications. The goal of this research was to develop a novel spatial network approach to represent and quantitatively assess the functional interactions between multiple ES. First, we illustrate this approach using a small, hypothetical landscape, and provide landscape connectivity-based methodology for how to represent ES supply nodes and the functional links between them. We then evaluate the approach using existing ES supply data from a large, real-world landscape to build a spatial multi-ES network model. Our network mapping results reveal long-distance functional interaction corridors, land-use and land cover types not typically associated with a particular ES, and high-value areas that are not present on individual supply area or pairwise link maps. Quantitative network analyses show redistribution of the highest-value nodes and links when comparing single- to multi-ES networks, and that certain potentially important supply areas not detected by traditional valuation protocols are discovered through network analyses. These results show how a network approach can identify potentially important areas on the landscape that might be overlooked by conventional, single-jurisdiction ES assessments. Lastly, we use a network community detection algorithm to partition the landscape into highly functionally interacting clusters of ES supply areas, then assess the alignment of these with a social network layer represented by the spatial jurisdictions of agencies involved in relevant ES governance in the region. We identify which specific areas and among which agencies opportunities exist for multi-sector, cross-scale coordination, and find that, regardless of cluster size, all agency scales are relevant to coordinated governance of multiple ES across the multifunctional landscape. iv Lay Summary In an effort to clearly define the aspects of nature upon which humans rely for their survival and well-being, the notion of ecosystem services was developed several decades ago, and incorporates the importance of healthy, functioning ecosystems in sustaining ecosystem service supply. Service supply is in part driven by interactions in the system through the underlying ecosystem functions that connect supply areas across the landscape. In this dissertation, we demonstrate a new spatial network approach for visualizing and assessing these functional interactions across a large, real-world regional landscape. We show how their governing jurisdictions can be incorporated to provide a social-ecological systems perspective. We show how spatial networks can be used to reveal different information about the distribution and importance of supply areas and their interactions relative to conventional assessments, and how networks can be used to identify opportunities for improved agency coordination. v Preface Chapter 2 and Chapter 3 are based on the following publications: (i) Field RD and Parrott L. 2017. Multi-ecosystem services networks: A new perspective for assessing landscape connectivity and resilience. Ecological Complexity 32:31-41; and (ii) Field RD and Parrott L. 2022. Multi-ecosystem services networks: Mapping the functional connectivity of ecosystem services supply across a regional landscape. eLife 11:e69395, respectively. Chapter 4 and Chapter 5 are based on submitted material currently under review as: (iii) Field RD and Parrott L. In review. Using spatial networks to evaluate the supply of and interactions between multiple ecosystem services across a regional landscape. Ecological Modelling ECOMOD-S-23-01334; and (iv) Field RD and Parrott L. In review. A spatial network approach for modelling social ecological systems and cross-scale governance of nature’s contributions to people. People and Nature PAN-23-10-543, respectively. All Chapters were produced in collaboration with my supervisor L. Parrott; therefore, I generally use first person plurals (e.g., ‘we’; ‘our’) throughout this dissertation where appropriate. For all four Chapters, I was the principal investigator responsible for the research conceptualization, design, data curation, data analyses, and writing of the original manuscript drafts, subsequent edits, and preparation and submission for publication. L. Parrott acted as my supervisor and provided invaluable support in the initial design of the research, with further ideas and recommendations throughout the research process, and substantially improved the manuscripts through her reviews and edits. vi Table of Contents Abstract ........................................................................................................................................ iii Lay Summary ............................................................................................................................... iv Preface .......................................................................................................................................... v Table of Contents .......................................................................................................................... vi List of Tables ................................................................................................................................ ix List of Figures ............................................................................................................................... xi List of Abbreviations .................................................................................................................. xiv Glossary ...................................................................................................................................... xv Acknowledgements ..................................................................................................................... xxi Dedication ................................................................................................................................. xxii Chapter 1: Introduction ................................................................................................................. 1 1.1 The current state of ecosystem services research ............................................................... 1 1.2 Conceptualizing the functional interactions between ecosystem services ............................. 2 1.3 Gaps in current spatial approaches for assessing ecosystem service interactions ................... 4 1.4 Spatial network approaches in ecosystem services assessment ............................................ 5 1.5 Governance in the context of ecosystem services interactions ............................................ 7 1.6 Objectives ................................................................................................................... 10 Chapter 2: Multi-ecosystem services networks: A new perspective for assessing landscape connectivity and resilience ........................................................................................................... 13 2.1 Synopsis ..................................................................................................................... 13 2.2 Introduction ................................................................................................................ 13 2.3 Linking concepts: connectivity, networks and ES ............................................................ 15 2.4 A new approach for multi-ES networks .......................................................................... 16 2.5 Demonstrating the multi-ES network approach .............................................................. 23 2.6 Concluding remarks .................................................................................................... 29 vii Chapter 3: Mapping the functional connectivity of ecosystem services supply across a regional landscape ..................................................................................................................................... 32 3.1 Synopsis ..................................................................................................................... 32 3.2 Introduction ................................................................................................................ 32 3.3 Materials and methods ................................................................................................. 36 3.4 Results ........................................................................................................................ 45 3.5 Discussion .................................................................................................................. 50 3.6 Conclusion .................................................................................................................. 57 Chapter 4: Using spatial networks to evaluate the supply of and functional interactions between multiple ecosystem services across a regional landscape ............................................................... 58 4.1 Synopsis ..................................................................................................................... 58 4.2 Introduction ................................................................................................................ 59 4.3 Materials and methods ................................................................................................. 64 4.4 Results ........................................................................................................................ 68 4.5 Discussion .................................................................................................................. 73 4.6 Conclusion .................................................................................................................. 82 Chapter 5: A spatial network approach for modelling social ecological systems and cross-scale governance of nature’s contributions to people ............................................................................ 83 5.1 Synopsis ..................................................................................................................... 83 5.2 Introduction ................................................................................................................ 83 5.3 Methods ...................................................................................................................... 86 5.4 Results ........................................................................................................................ 93 5.5 Discussion .................................................................................................................. 97 5.6 Conclusion ................................................................................................................ 104 Chapter 6: Conclusion ............................................................................................................... 106 viii 6.1 Significance and potential applications of the spatial ecosystem services network approach ..... ................................................................................................................................ 106 6.2 Limitations of the research and possible future directions .............................................. 109 6.3 Summary of contributions .......................................................................................... 117 Bibliography .............................................................................................................................. 119 Appendices ................................................................................................................................ 152 Appendix A. Chapter 2: Supplementary material .................................................................... 152 Appendix B. Chapter 3: Supplementary material .................................................................... 155 Appendix C. Chapter 4: Appendices ...................................................................................... 174 Appendix D. Chapter 5: Supporting information ..................................................................... 181 ix List of Tables Table 2.1. Summary of node and link (within and across ES categories) characterization methods. ......... 24 Table 2.2. Node betweenness centrality results for individual- and multi-ES networks. Nodes are further described by unique ID, ES categories, types (LULC), and weights (surface area in hectares). ................ 27 Table 3.1. Rationale behind functional interaction mechanisms, directionality, and weighting between top-value ES supply areas. Directionality is represented by the top row as originating (or ‘source’) ES supply areas; and the left column as recipient (or ‘sink’) ES supply areas (PA, WF, or LA). Functional interaction mechanisms are distinguished by Link Type (i.e., Overlap or Topographic), and their definitions and weighting rationale are provided in matrix cells. If no functional interaction exists from one ES to another, the matrix cell indicates that there is ‘None’ in either direction, or that there is a interaction in the ‘Other direction’. Superscripts denote the following references: (1) Crossman et al., 2013; (2) Power, 2010; (3) Daniel et al., 2012; (4) Zhang et al., 2007; (5) Bennett et al., 2009; (6) Poff et al., 1997; (7) Burmil et al., 1999; (8) Raudsepp-Hearne et al., 2010; (9) Nicholls and Altieri, 2012. ........ 43 Table 4.1. Summary of node and link results from traditional and network valuations. ............................. 71 Table 5.1. Management scale, types of NCP(s) managed, jurisdiction and responsibilities for different governance types and agencies within the study area. ................................................................................ 89 Table A1. Examples of functional interactions between selected provisioning, regulating and cultural ecosystem services (ES) across the landscape, which can be used to guide selection of indicators for defining landscape resistance in network link analyses. Positive links (i.e., when an increase in one ES benefits another) are identified by ‘þ’; negative links (i.e., when an increase in one ES is detrimental to another) by ‘ý’. Note that directionality of links can be inferred from table text and built into network analyses. This table is not exhaustive of all potential interactions between all ES. .................................. 152 Table B1. Summary of data sources and rationale for waterflow regulation LULC mapping prioritization (from Field et al., 2017). ............................................................................................................................ 157 x Table B2. Calculated and assumed NDVI results, infiltration rates (0%–100%), and qualitative aesthetic valuations for each LULC type. Blank (grey) NDVI values were not calculated; therefore, and infiltration rate was assumed (from Field et al., 2017). ............................................................................................... 160 Table B3. Percent of LULC overlapped by each ES supply area. ............................................................. 161 Table B4. Percent of LULC overlapped by each topographic link. .......................................................... 162 Table B5. Percent of LULC overlapped by each overlapping link. .......................................................... 163 Table B6. Summary and validation analyses data for topographic links produced by functional interaction and null models for Bellevue Creek. ......................................................................................................... 170 Table D1. List of the governance agencies, and the size of the areas under their jurisdiction, included in analyses. ..................................................................................................................................................... 183 Table D2. Data sources for governance area boundary mapping. Data sources and rationale for original NCP mapping can be found in Field et al. (2017) and in Field and Parrott (2022, 2017). ........................ 190 Table D3. Results of paired Wilcoxon signed rank test comparing agency overlap with (a) individual NCP ‘nodes’ vs. (b) network-derived NCP ‘clusters’ and the nodes therein. Total overlapped node area as a percent of the total within the entire study area was compared between the two groups; total number of local (<265 km2), regional (265-4780 km2), and supra-regional (>4780 km2) node scales were compared between the two groups for each governance type. Only NCPs directly governed by each agency were included in analyses. p-values not computed for small group sample sizes (n<30). High-valued group statistics are in bold, and corresponding governance-scales are outlined with a thicker border. ‘Mean’ is reported instead of ‘median’ to provide clearer comparison of relative group areas and numbers. .......... 195 xi List of Figures Figure 2.1. Conceptual design schematics for three alternative ES networks: (A) un-weighted nodes (circles) and links (straight lines) overlaid on a hypothetical landscape extent (black outline); (B) ES network with weighted nodes (colour gradient) and links (line thickness gradient); (C) ES bundle network with weighted, multi-ES nodes connected by links between same and different ES types; (D) inset showing node and link design detail of two nodes within network (C), with wind rose diagram slices representing presence and weights of individual within-node ES; (E) ES bundle network with multi-ES nodes connected by weighted single links. Note that these examples do not include all spatial ES network details (e.g., directional links, accurate georeferencing of node and link locations and spatial extents, etc.) or all possible ES network types. ................................................................................................................. 19 Figure 2.2. Conceptual design of two individual ES networks. Georeferenced (dotted lines) multi-ES network nodes (two ES: A = orange, B = pink), showing spatial links within-ES (grey lines), and weighted bridging links between-ES (solid black lines with thickness gradient; NB: although not shown here, weighted bridging links can be spatially explicit). ES nodes and links are nested within a non-provisioning landscape matrix (white area). Adapted from Ernstson et al. (2010). ................................... 20 Figure 3.1. Schematic and definitions for ES supply areas and functional interactions in the case study landscape. .................................................................................................................................................... 36 Figure 4.1. Graphical abstract summarizing background, results and conclusions of Chapter 4. ............... 59 Figure 4.5. Spatial distribution comparisons between a traditional metric (node weight) and network measures (NBC = A; NTD = B) of node centroids for plant agriculture (green), waterflow (blue) and aesthetics (purple). Locations are based on node polygon centroids with size ranges for top-100 nodes corresponding to relative node values. ........................................................................................................ 70 Figure 4.6. Single- vs. multi-ES network comparison for amalgamated node (NTD = A,B; NBC = C,D) and link (LBC = E,F) measures. Scatterplots include values for all individual nodes and links (A,C,E); maps include top 1% valued nodes and links within each network type (B,D,F), in addition to highlighting the highest-valued links (LBC >2; F). ......................................................................................................... 72 xii Figure 4.7. Locations and descriptions of valley bottom and wetland complex landscape highlights within the study region. ........................................................................................................................................... 76 Figure B1. Map of the distribution of major LULC types across the case study region. General LULC types in the region include, in decreasing order of area: forests (16,281 km2), grasslands (1482 km2), natural parks (2,403 km2; NB: contains several of the other listed LULC categories), shrubs (1349 km2), agricultural (842 km2), lakes (599 km2), urban residential (220 km2), rural residential (220 km2), wetlands (182 km2), rock/rubble (161 km2), exposed land (113 km2), manicured parks (45 km2), rivers (38 km2), commercial (23 km2), industrial (23 km2), urban institutional (16 km2), and reservoirs (6 km2; Field et al., 2017).- ........................................................................................................................................................ 156 Figure B2. Map of major watersheds and sub-basins within and surrounding the case study landscape in southern interior British Columbia, Canada. ............................................................................................. 165 Figure B3. Schematic outlining steps for the creation of topographic ES corridors from each origin ES supply area to downslope supply areas. (a) An initial line feature resulting from a least cost path (LCP) analysis, that is, from the origin to the goal point, amalgamated with a line from the goal point to a downstream influential landscape feature (ILF). (b) Types of LCP segment deletions addressed, including (red) segments overlapped by ES supply area polygons, (i) segments from origin points external to origin ES polygon, and (ii) segments linked between two areas of an intersected (i.e., non-origin) ES supply area. (c) Resulting ES functional interaction corridors after deletions, including feedbacks to origin ES supply area (4), links to downslope ES supply areas (1) and (3), and links to downstream ILF areas (2). ................................................................................................................................................................... 168 Figure B4. Boxplots summarizing percent overlap of buffered BC TRIM data with buffered topographic links between (a) WF; (b) WF and PA; and (c) WF and LA supply areas produced by functional interaction and null models for Bellevue Creek validation analyses. ........................................................ 171 Figure C1. Node total degree (NTD) frequencies comparison between single- and multi-ES networks. . 180 Figure D1. Location of governance area boundaries within the study area. Specific governance areas are overlaid on all agency boundaries (thin black lines) in colour for (a) the federal Department of Fisheries xiii and Oceans Canada (excluded from maps (b)-(f) for clarity); (b) municipalities; (c) regional district electoral areas; (d) First Nations reserves; (e) agriculture; (f) provincial Ministry of Forests. Note that the governance areas for the provincial Ministry of Environment and Climate Change Strategy, the Ministry of Water, Land and Resource Stewardship and the Ministry of Tourism, Arts, Culture and Sport, as well as Environment and Climate Change Canada at the national scale, span the entire study region. ............ 183 xiv List of Abbreviations ES = ecosystem service(s) LULC = land use and land cover LA = landscape aesthetics (‘AE’ used instead in Chapter 2) LBC = link betweenness centrality NCP = nature’s contributions to people NBC = node betweenness centrality NTD = node total degree PA = plant food agriculture SES = social ecological system(s) SEN = social ecological network WF = waterflow regulation xv Glossary Agency: “sets of rights, rules, and decision-making procedures [that] form a fundamental part of environmental management” (Young, 1999). Also referred to as ‘organizations’ or ‘institutions’. Betweenness: network measure that indicates how often a node lies on a path between other nodes, i.e., how much a node facilitates connecting other nodes to one another (Roldán et al., 2015). Also called ‘betweenness centrality’. Cluster(s): groups of network nodes that are highly connected to one another, and loosely connected to others. Also referred to as ‘modules’ (e.g., Felipe-Lucia et al., 2020). Collaboration: refers to ‘collaborative governance’, which can be thought of as “the processes and structures of public policy decision making and management that engage people constructively across the boundaries of public agencies, levels of government, and/or the public, private and civic spheres in order to carry out a public purpose that could not otherwise be accomplished” (Emerson et al., 2012). Can include aspects of coordination (i.e., the efficient orchestration of actors’ activities to achieve a common goal) and/or cooperation (i.e., negotiation between actors with different opinions or interests to solve problems; Bodin 2017). Cultural ES: nonmaterial benefits that people receive through their interactions with ecosystems (Dee et al., 2017). Degree: network measure used to calculate the number of links a node has (Felipe-Lucia et al., 2020). A node with a high degree has many links compared to other nodes in the network, and therefore has a central or powerful role in the network (Roldán et al., 2015). Also called ‘degree (of) centrality’. Drivers: factor(s) that impact (an) ES, and can include human activities (e.g., land use change), governance interventions, or global change (e.g., climate change; Dee et al., 2017). Ecological network: a network of ecological component nodes (e.g., species, functional groups, habitat patches) and process-based links (e.g., species interactions, dispersal-based connectivity). Ecosystem composition: the types and quantities of abiotic and biotic components within an ecosystem (Wallace, 2007). Ecosystem function(s): the movement or storage of biophysical structures, material, energy and/or processes that underpin “the capacity of ecosystems to provide goods and services that satisfy human xvi needs, directly and indirectly” (de Groot, 1992; de Groot et al., 2002; de Groot, 2010; Bellwood et al. 2019). Note these do not include the human inputs required for the actual flow or provisioning of ES to beneficiaries. We acknowledge that there is ongoing debate around the distinction between ecosystem and landscape functions and services, between ‘functions’, ‘services’ and ‘benefits’ (de Groot et al., 2010), and around the definition of ‘function’ in the environmental literature (Bellwood et al. 2019). Ecosystem processes: the interactions between the abiotic and biotic components of an ecosystem (Lyons et al., 2005). Also thought of as the means by which ES are delivered. Some ecosystem processes are also considered to be ES, e.g., waterflow regulation by vegetation (Fu et al., 2013). Ecosystem service(s) (ES): material or immaterial benefits derived from the natural world that promote and sustain human well-being (MEA, 2005). They are typically classified as provisioning (e.g., food; water); regulating (e.g., carbon sequestration; water purification); or cultural (e.g., heritage; recreation; aesthetic value). A diverse lexicon exists around such services, which are sometimes referred to as ecosystem, environmental, or natural services, benefits, or goods (Crossman et al., 2013). ES are produced through complex interactions between ecosystem functions (Maynard et al. 2010), often through additional forms of capital or other inputs (Bennett, 2017). See also glossary term ‘Nature’s Contributions to People (NCP)’. Ecosystem services bundle(s): a set of ES that repeatedly occur together in space or time. Sometimes also referred to as ‘clusters’ but note differentiation with network cluster terminology (above). In this dissertation, we refer to ES supply bundles, but note that demand bundles can also be identified using indicators related to stakeholder preferences (Saidi and Spray, 2018). Ecosystem services categories: categories defined by the latest version of the Common International Classification of Ecosystem Services (CICES): provisioning, regulating, and cultural (Haines-Young and Potschin et al., 2016). Ecosystem service(s) demand: the quantity of ES desired or required by society (Saidi and Spray, 2018). Can be based on consumption, use, or subjective needs related human well-being (Costanza et al., 2007). Demand areas can be associated with where people live, or with LULC classes that represent people's needs (Metzger et al., 2021). Ecosystem service(s) flow: the actual use or supply of ES to society, or the rate at which people use ES from supply areas. Influenced by ES supply, demand, and movement of ecological components, process and people (Mitchell et al., 2013; Tallis et al., 2012; Villamagna et al., 2013; Mitchell et al. 2015). Can be xvii defined differently for different ES categories, i.e., provisioning = quantity of goods harvested or yielded; regulating = ecological output (e.g., amount of waterflow regulation by vegetation infiltration rate); cultural = quantification of experiences without actual transfer of energy or material (e.g., number of recreational park visitors; Saidi and Spray, 2018). Also referred to as the ‘realized service’ (Villamagna et al., 2013); ‘actual service provision’ (Serna-Chavez et al., 2014); or ‘transmission of a service’ (Bagstad et al., 2013a). Ecosystem service interaction: “a situation in which the provision of one service has a direct impact on another service” (Bennett et al. 2009). Ecosystem service(s) supply: in this dissertation, we use ‘supply’ to describe an ecosystem’s capacity to produce ES based on its biophysical properties and/or on the existing infrastructure located therein. Note that ‘capacity’, ’stock’, and ‘potential’ have also been used to describe the potential production of ES, and ‘supply’ or ‘flow’ the actualized production (e.g., Saidi and Spray, 2018; Dee et al., 2017; Tallis et al., 2012, Geijzendorffer et al., 2017a). Ecosystem structure: “the distribution and arrangement of ecosystem components” (Wallace, 2007). Emergent property: property of a network that arises from the interactions of network components (Felipe-Lucia et al., 2021). Functional ecosystem service supply interaction(s): the ecosystem functions that underpin the co-occurrence or mechanistic relationship(s) between distinct supplies (distinguished either by ES type or ES areas that overlap or are separated in space) across a landscape. Note that we use the short form ‘functional interactions’ to refer to this concept throughout this thesis. See also ‘Ecosystem service interaction’ glossary term for notable distinction. Governance: policy, rules and norms that regulate who makes decisions around environmental objectives, how these decisions are made, and the means and actions to achieve them (Borrini-Feyerabend and Hill, 2015). Landscape composition: distribution and condition of landscape units or LULC types (Metzger et al., 2020). ES assessments typically have a landscape composition focus, in which an ES is often simply associated with one LULC type without considering the effects of ecological processes or interactions operating across a landscape (Metzger et al., 2021). xviii Landscape configuration: spatial arrangement of landscape units or LULC types (Metzger et al., 2020). ES assessments that consider landscape configuration consider the effects of the surrounding landscape on provisioning (e.g., isolation or proximity), usually in conjunction with landscape composition (Metzger et al., 2021). Landscape connectivity: the degree to which a landscape facilitates the movement of abiotic and biotic components and processes. It is considered essential to the healthy functioning of these characteristics and of regional landscapes in general (Shih, 2006; Taylor et al., 1993; Biggs et al., 2012; Cui et al., 2012; Phillips et al., 2015). Landscape matrix: the area of the landscape that surrounds patches of natural land cover. Typically disturbed and/or developed (Mitchell et al. 2015). Link(s): connection between two network nodes (e.g., dispersal between habitat patches; collaboration between actors). Also referred to as a ‘network link’, ‘edge’, ‘tie’, or ‘interaction’. Management: the means and actions used to achieve environmental objectives (Borrini-Feyerabend and Hill, 2015). Multifunctional landscapes: landscapes with the capacity to provide “multiple socio-economic and ecological benefits to society” (Hölting et al. 2019). Meta-network: those with multiple types of nodes and multiple types of links (Dee et al., 2017). Multiplex network: those with multiple types of nodes (Felipe-Lucia et al., 2021). Multipartite network: those with multiple types of links between nodes of the same type (Felipe-Lucia et al., 2021). Nature’s Contributions to People (NCP): an extension of the ES concept, distinguished by the recognition of the important role that culture plays in defining all links between humans and nature, and explicitly integrates indigenous and local knowledge into the understanding of NCPs (Diaz et al., 2018). Notably, we generally use ‘NCP’ in place of ‘ES' in Chapter 5 in an effort to integrate this conceptual transition, which occurred during the timeline of this dissertation, and because the nuances of the updated NCP concept better align with the social-ecological focus of that chapter. Network measure: quantifiable value(s) that represent various characteristics of a network (Brinkmeier and Schank, 2005). xix Network: simplified representation of a system of components (nodes), connected based on their interactions (links), conceptualized and/or analyzed to understand how the relationships between components affect network outcomes and/or result from underlying processes (Felipe-Lucia et al., 2021). Nodes: the components or elements of a network. Also known as ‘vertices’ (Dee et al., 2017). Ecologically, these are the different components of an ecosystem that interact and may ultimately affect human survival and well-being (Felipe-Lucia et al., 2020). Node attribute: a characteristic of a node (e.g., type of ES). Provision: the realized benefit of an ES, which results from the social-ecological capacity of a landscape to produce it (i.e., ecological and biophysical conditions; management practices), in combination with the demand for the ES (Ernstson et al. 2010, Queiroz et al. 2015). Three general approaches are used to assess ES provision, where it is either equated to: (a) only supply; (b) only flow; or (c) an integration of supply, demand and flow (Metzger et al., 2021). See also ‘Ecosystem services (ES)’ glossary term for distinction from ‘provisioning’ ESs. Resilience: a system’s capacity to deal effectively with shocks and disturbances through adaptation and/or reorganization so that it experiences change while maintaining the same overall identity, structure, function, and feedbacks (Folke, 2006). Social capital: “a multidimensional concept that incorporates diverse social phenomena such as trust, reciprocity and exchange, norms, and networks of interpersonal relationships… [and] captures the idea that social bonds, and the resources embedded within them, comprise an important asset that can be leveraged for individual or collective gain” (Barnes-Mauthe et al. 2015). Social network: network of social actor nodes (e.g., individuals, communities, institutions) and the interactions (e.g., collaboration; resource sharing) between them. Social-ecological network (SEN): a network that incorporates links within and/or between social and ecological (sub-)layers. Fully-articulated SENs include links within and between social and ecological layers; partially articulated SENs only include links between social and ecological layers (Sayles et al., 2019). Social-ecological systems (SES): a system consisting of human and natural elements that interact with each other (Gonzales and Parrott, 2012). xx Sustainability: refers to ‘social-ecological sustainability’, which is when economic and other human activities within the SES trend toward not exceeding the capacity of ecosystems to provide services (Chapin et al. 2009). We acknowledge the ongoing debate around the definition of this term (e.g., Ruggerio et al. 2021). Synergy: a positive relationship between two or more ES (Saidi and Spray, 2018). Trade-off: a negative relationship between two or more ES (Saidi and Spray, 2018). Vulnerability: “the capacity for a system to cope with threats caused by drivers” (Dee et al., 2017). xxi Acknowledgements I respectfully acknowledge the Syilx Okanagan Nation and their peoples, in whose traditional, ancestral, unceded territory UBC Okanagan is situated. I would like to whole-heartedly thank the following remarkable humans, without whom I would not have been able to complete this research nor make it to the formal finale of my PhD. First and foremost, I would like to thank Lael Parrott for being the most wholly supportive supervisor I could have ever hoped for. Over all these years, I have admired your depth of knowledge on a seemingly endless diversity of topics, your palpable positivity and excitement for our research, and your collaborative approach in helping me to become a better researcher. Thank you for being patient while I developed my GIS and coding skills, and while I expanded my proficiency into the interdisciplinary realm. Your compassionate and unwavering support through the barrage of ‘life experiences’ concurrent with my program, your grace and grounded advice in my vulnerable moments, has truly been instrumental to my healing and perseverance. For everything I will forever be indebted to you. To my committee members, Laura Hooker, Jason Pither, and John Janmaat, I am grateful for your genuine encouragement and for being so wonderfully complementary in your expertise. Your advice has helped me to be more mindful of the social, ecological and analytical realities relevant to our research, and has led me to deepen my understanding and appreciation of both the natural and human components of the landscape. To Laura, thank you for sticking with me much longer than anticipated, and making time for our work in your retirement. This dissertation would not have evolved to its final form without each of your unique and diverse perspectives. There are no adequate words, other than to say I will always be deeply grateful for your excitement and compassion through my personal highs and lows during this phase of life. To my lab, school and community networks, thank you for your tangible support, inspiring work, and much needed levity. I would particularly like to thank Katey Kyle, Rudolphe Gonzales, Bruno Carturan, Matt Bass, Ehsan Pashanejad, Shane Pedersen, and Carolina Restrepo for your friendship, collaboration, and for helping me to maintain authentic connections to campus life – often from afar. I wish we could have spent more time together and hope we will as our careers and lives tumble on. To Margaret Bakelaar and Rick Simpson, thank you for supporting this project and for your progressive and inspiring attitudes – your loss in the Okanagan is palpable. To Paul Lesack, Deepa Filatow, Morgan Brummet, and Barb Lucente, thank you for sharing your expertise and resources, and for the administrative help over the years. To Ian, Sloane, Claire and the rest of our family, thank you for your support and encouragement throughout this long arc, and for maintaining my perspective on what is truly important in life. xxii Dedication I dedicate my PhD to Sloane and Claire, my little science girls. 1 Chapter 1: Introduction 1.1 The current state of ecosystem services research The survival and well-being of every human is dependent on healthy ecosystems capable of supplying of multiple ecosystem services (ES). The degradation of ES can result in a decrease in human well-being, as well as having deleterious impacts on other ES (Howe et al., 2014). Yet our oversights have been striking over the past half-century, with unprecedented worldwide degradation and unsustainable use of ecosystems and the ES they provide (MEA, 2005). Demands for ES are projected to increase, which in turn will lead to an increased frequency of trade-offs between different ES (Howe et al., 2014). In this sense, the interactions among multiple ES can be important sources of support or hinderance to a multifunctional landscape’s ability to provide ES, and ultimately to societies’ ability to benefit from ES and limit our vulnerabilities (Schröter et al., 2019). The functional interactions between different types of ES, and between geographically separate areas that supply ES, are influenced by the ecosystem structures and processes (ecosystem functions herein) underpinning their production (e.g., pollinator habitat adjacent to crop lands can increase crop productivity; Mouchet et al., 2014), and multiple ecosystem functions and/or diverse groups of organisms are often required to supply a single ES (e.g., waterflow regulation, decomposition, and nutrient cycling drive crop supply; Schuldt et al. 2015; MEA, 2005). At the regional scale, a multifunctional landscape is a continually evolving complex social-ecological system (SES), with many interactions between humans and nature and multiple embedded land uses (Parrott and Meyer, 2012). When landscape ecological resilience (referred to as resilience herein) is low, the risk of undesired regime shifts is increased. For example, multiple ecological and social drivers and feedbacks can cause grassland ecosystems to transition to shrub- or woodlands with reduced or absent grass cover (Folke et al., 2004; Bestelmeyer et al., 2006; Bestelmeyer et al., 2018). In general, such shifts result in low-biodiversity landscapes that produce fewer ES and are more vulnerable to disturbance (Moberg and Hauge Simonsen, 2011). With the threat of climate change and uncertainty around future environmental disturbances, decision-makers recognize that action must be taken to safeguard against shocks and to enhance resilience in support of multiple, functionally interacting ES (Biggs et al., 2012; Mitchell et al., 2013; Qiu and Turner, 2013; Queiroz et al., 2015). At the same time, regional land use planning frameworks must manage a variety of land use types while meeting ecological, economic, social, and ES-related objectives; therefore, it is likely that successful future ecosystem management will need to explicitly consider landscape-level ecological functions (Parrott and Meyer, 2012; Metzger at al., 2021). To support these efforts, current approaches in spatial ES research focus on mapping the spatial distribution of ES provisioning (Egoh et al., 2008), with some studies assessing network representations 2 of ES and their correlations, connectivity or flows across the landscape (e.g., Heckmann and Schwanghart, 2013; Phillips, 2013; Peron et al., 2014; Keyes et al., 2021; Kim and Kang, 2022; Estay et al., 2023; Sadat et al., 2023; Zhang et al., 2023). Still, we lack tools that allow for the integration of landscape-level ES functional interactions into applied planning and conservation (Tallis and Polasky, 2009); specifically, we do not have sufficient approaches for spatially assessing the functional interactions between multiple ES and the social entities that govern them. 1.2 Conceptualizing the functional interactions between ecosystem services The literature broadly defines ES as the benefits derived from the natural world that promote and sustain human well-being. ES are typically divided into provisioning (e.g., food, fibre, genetic resources, biochemical, natural medicines, pharmaceuticals, fresh water), regulating (e.g., of air quality, climate, water, erosion, disease, pest, natural hazards; pollination, waste treatment, water purification), cultural (e.g., spiritual, religious and aesthetic values; recreation and ecotourism), and supporting (e.g., soil formation, photosynthesis, nutrient cycling) services categories (MEA, 2005). It has been argued that supporting services may be better classified as the processes that generate the other services categories (e.g., Van der Ploeg et al., 2010; Queiroz et al., 2015); therefore, we do not directly assess this category in this dissertation. Multiple ES can exhibit complex, unpredictable interactions to form bundles (i.e., recurring clusters of correlated ES; Rodríguez et al., 2006; Bennett et al., 2009; Queiroz et al., 2015). Synergies exist among ES when several co-benefit from increased use; conversely, trade-offs exist where the increased exploitation of one ES decreases the provision of another (Qiu and Turner, 2013). These can result from human drivers (e.g., pesticides affecting both crop provisioning and water quality) or true functional interactions (e.g., increased tree growth contributing to the supply of fresh water and carbon sequestration) between ES, and can lead to the necessity to manage trade-offs to mitigate undesirable outcomes (Queiroz et al., 2015). The supplies of multiple ES across a landscape are dependent on functional interactions with other ES areas through the ecosystem functions underpinning their production (Biggs et al., 2012; Bodin et al., 2019). Many ES rely on the flux of organisms and materials, sometimes over large distances (Tscharntke et al., 2005; Kremen, 2005; Koellner et al., 2019), in addition to biodiversity and healthy ecosystems (Fahrig, 2003). The resilience of ES provisioning is therefore likely dependent on a connected landscape that supports these characteristics (Mitchell et al., 2013). Take the case of a wetland: it might provide water and air purification, flood regulation, waste treatment, and soil formation and maintenance (Zedler and Kercher, 2005). These ES are dependent on connections to streams and lakes which, among other ES, provide fresh water for drinking and irrigation (Gordon et al., 2010). Wetlands may also depend on connections with vegetated upland areas which, for example, can provide erosion and runoff control and 3 additional water filtration (Ludwig et al., 2005). The upslope terrestrial areas that support waterflow regulation can be linked to various cultural services, including aquatic and stream-side recreation activities, oral histories, spiritual grounds (Blackstock, 2001), and landscape aesthetic value (Burmil et al., 1999). Scenic agricultural, high-retention forestry, and water provisioning areas may also contribute to recreation and aesthetic services (Daniel et al., 2012). Further, different areas providing the same type of ES may functionally interact across the landscape. For example, competition for water and pollination services may exist between agricultural and natural areas (Zhang et al., 2007). Certain ES may require a particular expanse (e.g., wetland size; Boyd and Banzhaf, 2007) or degree of functional interactions with other areas (e.g., pollination and pest regulation; Mitchell et al., 2013) to sustain the provisions required by a community or region. At larger spatial scales, the variability of ecosystem functions underpinning the production of ES typically increases, and maintaining heterogeneity in ecosystem functions and interactions increases landscape resilience (Wang et al., 2006). It is therefore critical that regional landscape management aims to conserve and restore heterogeneous distribution of multiple ES, the ecosystem functions driving their production, and the functional interactions between them (Qiu and Turner, 2013, Field and Parrott, 2022). Although the mechanisms behind these multi-ES functional interactions are sometimes discussed in ES interaction literature (e.g., Berhane Alemu et al., 2020; Li et al., 2017), to our knowledge the functional interactions between ES have not been spatially delineated on the landscape in the context of multi-ES assessments. The above examples demonstrate that ES are not produced independently, but rather exhibit interactions and spatial patterns that are aligned with concepts from landscape connectivity research (Mitchell et al., 2013; Dobson et al., 2006, Mitchell et al., 2015). Landscape connectivity conceptualizes landscapes as consisting of patches, or natural areas containing appropriate habitat or resources for a species, population, social actors, or process of interest (Chisholm et al., 2011). Patches are typically separated by a matrix of non-resource areas (Rayfield et al., 2011; Latimer and Peatt, 2014) through which connectivity corridors traverse. Corridors are important for supporting dispersal, recolonization, migrations, metapopulation persistence (Hanski, 1998), climate change-induced range shifts, and maintenance of genetic diversity to support adaptation (Minor and Urban, 2008), all of which may support the provisioning of various ES (Biggs et al., 2012). For example, well-connected river and wetland networks are important for transporting materials, organisms and information, which help maintain natural functions at the regional scale (Bunn and Arthington, 2002; Peters, 2008). Conversely, very highly connected systems can facilitate rapid and widespread propagations of natural and anthropogenic disturbances (e.g., van Nes and Scheffer, 2005; Ash and Newth, 2007; Satake et al. 2007). Ultimately, the arrangement and strength of links across a landscape are important for supporting resilience and are driven by various factors including the quality of corridors between habitat patches, 4 predator-prey relationships, and interactions among human users (Nyström and Folke, 2001). Recent conceptual frameworks have called for research that uncovers a more nuanced understanding of how landscape structure impacts the provision of multiple ES, how abiotic components impact ES provision, and which ES are more influenced by landscape connectivity (e.g., Mitchell et al., 2013; Mitchell et al., 2015; Metzger et al., 2021). However, by not explicitly considering how functional interactions contribute to the supply of ES (Fu et al., 2013), there is risk of spatial incongruence between traditional connectivity planning priority areas and the actual supply of, and functional interactions between, multiple ES across the landscape. 1.3 Gaps in current spatial approaches for assessing ecosystem service interactions To date, interactions between multiple ES have typically been represented by way of various non-functional typologies, including trade-offs, synergies, bundles, and flows to beneficiaries. Mapping ES supply areas combined with correlation analyses as a way of evaluating the relationships between multiple ES remains the most common approach in the assessment of multi-ES interactions (Agudelo et al., 2020). Bundles are defined where individual supply areas of individual ES overlap, which typically arise due to common drivers or direct interactions among ES. For example, fertilization can drive-up crop yield while decreasing water quality in an agroecosystem; intact forest ecosystems can have high recreational and aesthetic value while also providing waterflow regulation. Bundles are commonly used to inform management actions for several ES and for the post hoc assessment of their potential non-functional interactions (e.g., Qiu and Turner, 2013; Queiroz et al., 2015; Turner et al., 2014). It should be underscored that these methods do not incorporate true functional interactions between multiple ES. Instead, they use correlative relationships or geospatial associations as proxies for interactions, which may be the result of a common driver or be spurious (Vallet et al., 2018, Cord et al., 2017). A recurring criticism of the ES field in general is that assessments often lack a strong natural science foundation (Silvertown, 2015; Schröter et al., 2014; Naeem et al., 2015; Tavoni and Levin, 2014). It has been suggested that current methods for assessing ES interactions likely lead to improper identification of synergies, trade-offs and bundles as different combinations of ES may be clustered at different spatial resolutions (Shaikh et al., 2021). Further, observed associations may partially be explained by the scope of specific models used to represent ES, rather than by the functional interactions that occur in the system under study (e.g., Vallet et al., 2018). For example, the most commonly used tool in ES interactions research, InVEST, uses simple LULC look-up table-based models that automatically assign low carbon sequestration values to areas with high agricultural production; as such, these ES are forced to represent negative interactions on a landscape (Vallet et al., 2018). This does not allow for 5 spatial representation of the functional interactions between supply areas that occur in separate areas on a landscape. Ultimately, the characterizations and models currently used to assess non-functional interactions between ES are said to be inadequate for use in applied management (Spake et al., 2017; Agudelo et al., 2020), in part because understanding around the relationships between biodiversity, ecosystem functioning, and ES is typically based on small-scale field experiments rather than the larger scales relevant to decision-making (Manning et al., 2019; Chaplin-Kramer, 2023). To address this, several studies have suggested that innovative approaches are required to integrate the ecosystem functions behind ES interactions, with particular attention paid to spatially-explicit methods that account for interactions between focal area ES and the surrounding landscape (Balbi et al., 2015; Cord et al., 2017; Agudelo et al., 2020). Drawing upon interdisciplinary methods from complex systems modelling could provide a pathway to advancing this area of research (Cox et al., 2000; Colléter et al., 2015). 1.4 Spatial network approaches in ecosystem services assessment Network approaches are used to visualize and assess structural and functional relationships between components across scale hierarchies in complex systems (Rayfield et al., 2011). They are based on extensively-tested multidisciplinary science with a common language across disciplines (e.g., transportation, communication, epidemiology and, more recently, landscape ecology; Rayfield et al., 2011). Knowledge around the interactions between the ecosystem functions that give rise to ES has led to network approaches being proposed as an ideal framework for improving our understanding of ES and for assessing related ES interactions (e.g., Losey and Vaughan, 2006; Pocock et al., 2016; Bodin and Crona, 2009, Cumming et al., 2010; Dee et al., 2017). However, a gap persists between the broad foundational concepts of network theory and its application in the ES realm, and very few studies have demonstrated how spatial networks might be leveraged to examine functional interactions between multiple ES (Dee et al., 2017). At their most basic, networks are sets of components called nodes, with pairs of nodes joined by links between them. Spatial network models can be used as a tool to simplify ecosystem evaluation and monitoring (Gray et al., 2014; Pocock et al., 2016) by identifying location boundaries for component nodes (e.g., ES supply areas), and the links between them (e.g., movement of material or energy resulting in functional interactions between supply areas; Cumming et al., 2010, Erős et al., 2012). Nodes and links can also be weighted based on values of their attributes (e.g., quantity, productivity, distance; Urban et al., 2009). In addition to providing powerful visualizations (e.g., Sonderegger et al., 2020), networks can be analyzed to produce quantifiable values, known as network measures, for assessing a variety of structural and functional characteristics of a network (Brinkmeier and Schank, 2005), and to identify emergent properties that cannot be detected by studying nodes and links in isolation (Bascompte et al., 2006; 6 Thébault and Fontaine, 2010; Tylianakis et al., 2010). These measures can be used to assess system resilience, component interactions, and to identify nodes, links, or clusters (i.e., groups of interconnected nodes within a network) that are vital to overall system function (Albert et al., 2000; Fortuna et al., 2006; Janssen et al., 2006; Poole et al., 2006; Gonzalez et al., 2011; Cui et al., 2012; Erős et al., 2012; Phillips et al., 2015; Rayfield et al., 2011). Over the past few decades, several studies have used networks to incorporate ecological connectivity concepts into the assessment of provisioning, regulating and cultural ES. For example, a multi-scale analysis of water networks used for agricultural irrigation found that vegetation clearing and irrigation has negative impacts on the natural hydrological cycle, leading to water table rising and soil salinization (Janssen et al., 2006). Another study used digital elevation models and simulations for falling rocks, debris flow and fluvial processes to represent network nodes and links, respectively, which helped identify important functional interactions between upslope sediment-source areas and downslope sink areas (Heckmann and Schwanghart, 2013). An examination of the effects of landscape network fragmentation on ring-tailed lemurs (Lemur catta) revealed that certain small habitat patch nodes can play a significant role in maintaining seed dispersal ES (Janssen et al., 2006). Several types of waterflow regulation have been assessed using networks through examinations of: surface-groundwater floodplain connectivity (Poole et al., 2006); the ability of an artificial drainage network to control sea level rise (Poulter et al., 2008); and key waterflow areas important for sustaining natural growth, biodiversity, social and environmental health, while maintaining the efficient and cost-effective production of water for human consumption (e.g., Cui et al., 2009; Erős et al., 2012; Cui et al., 2012). Yu et al. (2023) leveraged the ES bundle concept to identify spatial correlation-based synergies between four water-related ES, and used centrality measures to identify ES nodes that play an essential role in overall network function. Recreation networks have also been used to identify optimal locations and types of tourist facilities (Shih, 2006). In recent years, researchers are starting to move beyond network-based examinations that focus on specific ecosystems, underlying ecosystem functions, or limited sets of ES, and are including broader systems-level perspectives and more diverse sets of ES. For example, Felipe-Lucia et al. (2020), use networks to explore how global change drivers impact correlative relationships between biodiversity, ecosystem functions, and multiple ES across a range of land use intensities within a hypothetical landscape. Keyes et al. (2021) discovered through a network assessment of storm attenuation, carbon sequestration, water filtration, fisheries, birdwatching and hunting services in a saltwater marsh ecosystem, that the secondary species indirectly supporting ES are most important to the robustness and stability of ES provisioning. Hu et al. (2023) defined various types of vegetative, climatic, landscape structure and process nodes, along with ES and related socioeconomic nodes, and linked them using 7 spatial correlation coefficients, then use network analyses to discover that scenarios where aridity is intensified threatens the stability of the ES network. These, and the aforementioned ecological ES network examples, contribute to the advancement of network applications in ES assessment. However, a key component missing from these works is spatially-explicit consideration of how the functional interactions between multiple ES, both overlapping and separated in space, might be represented and assessed for their potential impacts on network structure and outcomes. 1.5 Governance in the context of ecosystem services interactions Demand for explicitly incorporating ES concepts into landscape-level planning and policy is increasing (Bateman et al., 2013; Perrings et al., 2011). The ES concept helps capture the multifaceted contribution of nature to the well-being of people within planning schemes (Liu and Opdam, 2014); as such, it naturally aligns with and is becoming more mainstream in land use governance principles and priorities (Sitas et al., 2014; Albert et al., 2014a). However, practical attempts to enhance conservation of natural resources typically prioritizes areas of high biodiversity or a single ES (MEA, 2005). Management focus on provisioning ES is common due to their direct links to goods and services, while regulating and cultural ES are often neglected (Bennett et al., 2009; Haase et al., 2012; Deng et al., 2016). When applied across multifunctional landscapes, these reductionist approaches ignore the complex, transboundary functional interactions among various types of ES and can lead to unplanned impacts on other services (Bennett et al., 2009; Haase et al., 2012; Deng et al., 2016; Berhane Alemu et al., 2020; Zhoa et al., 2018). It is clear that governing landscapes to enhance the delivery of multiple ES, while minimizing ecosystem degradation, requires thorough understanding of the interactions between ES and the ecological functions that drive them (Fu et al., 2013; Agudelo et al., 2020). Key guiding paradigms for this are to understand the relationships between spatial patterns and ecosystem functions, and to consider not only individual components (e.g., a single ES type or supply area) but the effects stemming from their functional interactions (Wallace, 2007; Nassauer and Opdam, 2008; Fu et al., 2013). This has summoned the development of novel models and methods to support the effective management of ES (Mitchell et al., 2015). It has been argued that successful environmental governance must include coordination between overlapping jurisdictions, as well as adequate spatial and temporal alignment (or ‘fit’) with the ecosystem functions they are mandated to govern (Ekstrom and Young, 2009; Folke et al., 2005). This is especially important in the management of landscape ecological connectivity, as fragmentation can extend beyond geographic boundaries of individual agencies, thereby requiring coordination among various agencies in order to tackle the problem at the proper spatial scale (Bergsten et al., 2014). However, functional misfits between environmental institutions and the ecosystems they govern often result from ecosystem processes 8 and human actions that operate at scales outside jurisdictional reach (Bodin et al., 2019; Koellner et al., 2019). Using network approaches to visualize the distribution of social capital can pinpoint both opportunities and barriers related to resource management stakeholder relationships and communication (Barnes-Mauthe et al., 2015) and, for ES whose functional interactions operate across large swaths of a landscape, SENs can be used to evaluate whether cross-boundary social collaboration efforts are appropriate for the ES(s) of interest (Bodin, 2017; Bodin et al., 2019; Felipe-Lucia, 2021; Guerrero et al., 2015a, 2015b). Recent work by Roldán et al. (2015) found that social actors with common interests around specific ES were often not engaged in collaborative relationships, and highlighted how networks can be useful for highlighting pathways toward more effective structuring of ES governance systems. Importantly, the authors note that, in reality, shifts toward more balanced and participatory ES management may be hindered by entrenched legal decision-making structures and associated scale mis-matches (e.g., national vs. regional intervention). Along with qualitative measures (e.g., ratio of productive to unproductive relationships; percentage of time spent on a collaborative connection), network centrality has also been used to quantify coordination efficiency, though optimum centrality levels depend on the specific management context (Newig et al., 2010). Bodin et al. (2016) included several interconnected social (clans) and ecological (forest patches) in a multilevel network to predict which theoretical collaborative management structures might be most beneficial for managing particular patterns of ecological components. There is need for further development of cross-sector approaches that integrate the ecosystem functions, functional interactions and ES along with societal entities at relevant scales (Young et al., 2007) in order to identify geographic (mis)alignment of ecological and social components and opportunities for effective collaboration. SES interact through complex and dynamic mechanisms across a range of geographic and temporal scales (Berkes et al., 2003; Folke et al., 2010; Hughes et al., 2013). Environmental governance must incorporate these realities along with related social components, such as decision-making processes, stakeholder interactions, and power dynamics (Roldán et al., 2015). As network approaches allow for the identification of various relationships and processes (Janssen et al., 2006), there is an increasing volume of literature around quantitative network analyses of ecological (e.g., Guimarães, 2020), social (e.g., Knoke and Yang, 2019) and, more recently, combined social-ecological networks (SEN; e.g., Bodin et al., 2016; Dee et al., 2017; Sayles and Baggio, 2017; Egerer et al., 2020; De Jesus et al., 2023). Within SENs, social network layers are typically represented as actors or organizations (nodes) and the communication patterns, collaborations or resource exchanges between them (links, e.g., Bharwani et al., 2013; Bergsten et al., 2014). Social and ecological network layers can then be connected through the flexible process of defining SEN links based on the research question(s) and context-specific system attributes, such as spatially-aligned ecological resource use or management responsibilities (Bodin et al., 2016; Treml et al., 9 2015; Bodin, 2006; Cumming et al., 2010; Gonzalès and Parrott, 2012; Janssen et al., 2006). SENs have been used to simplify the modelling of complex processes operating within SES and, when coupled with evidence-based ecological models, can help to facilitate transparent engagement with decision-makers and stakeholders (Pocock et al., 2016). The common language of network approaches can be used to help answer myriads of multidisciplinary scientific and policy queries (Janssen et al., 2006; The Quintessence Consortium, 2015; Bohan et al., 2016), such as where critical links exist among network nodes or sub-networks (e.g., Field and Parrott, 2017). Preliminary theoretical and empirical SEN applications have used traditional models for representing ecological sub-networks, where nodes are ecosystem components such as species, habitats or aggregated resource patches (e.g., reefs; Treml et al., 2015; Bergsten et al., 2014). In recent years, several studies have conceptualized ES as nodes in an ecological sub-network layer within a SEN. For example, Dee et al. (2017) proposed a framework to operationalize ES interaction networks by integrating socioeconomic and ecological components around a management objective, i.e., particular ES(s) of interest. They propose using a meta-network framework, which is one that includes multiple types of nodes (e.g., species, people, ES, agencies) and links (e.g., trophic interactions, social relationships), and specifically identifies how ES are provided by ecosystem functions, how they are used by beneficiaries, how they are impacted directly and indirectly by drivers of change, and how they respond to management actions. The Quintessence Consortium (2016) advocate for the flexible use of network approaches in ES research, grounded in natural and ecological science, and with attempts to address the intricacies in multi-scale ecosystem function interactions. They provide two non-spatial conceptual examples of multilayer network frameworks, with the ecological network layer as the foundation (e.g., trophic interactions between species), upon which a social layer (e.g., stakeholders related to the ecological system) and an economic layer (e.g., representing costs or transactions related to ecological nodes) are based, and warn against forcing the use of overly prescriptive frameworks or necessarily including economic, social and ecological components together. Zhao et al. (2018) exemplify the flexibility of a SEN framework through various typologies and options for related sub-network components by dividing a riverine SES into beneficiary (e.g., individual, community nodes), ES (e.g., water, flood control, tourism), ecosystem (e.g., farms, forest patches, wetlands), hydrological units, management, and driver (e.g., climate change, extreme weather events, population growth) sub-layers. Importantly, they underscore the importance of careful selection of network components based on the context-specific environmental problem, and biophysical and social characteristics. Despite the advances in network-based assessments of governance and the ecological systems they manage, real-world application of network approaches is still rare in the field of ES research; rarer are attempts to combine ES and socioeconomic sub-network layers; rarer still are those that consider multiple 10 ES operating in the same geographic areas within multifunctional landscapes (Dee et al., 2017; Vialatte et al., 2019). Network visualization of SES linkages over long-distances seldom represent ES, nor do they represent links in a spatially explicit manner (Sonderegger et al., 2020). Recent literature championing the application of network theory in ES research is focused on resource governance and social network approaches, while the ecosystem functions critical to ES production remain abstract and are rarely explicitly incorporated (Bodin and Tengö, 2012; Hicks et al., 2013; Saunders et al., 2019). So far, SEN-based ES assessments are often limited to single scales of governance (e.g., local municipalities; Ernstson et al., 2010; Bergsten et al., 2014, Ekstrom and Young, 2009; Schoon et al., 2014; Rathwell and Peterson 2012), are not spatially-explicit (e.g., Firkowski et al., 2021), are purely qualitative (e.g., Metzger et al., 2020), and methods for integrating multiple ES into decision-making related to land use and planning are not well-developed (Albert et al., 2014a, MacDonald et al., 2014). In this dissertation, we present an approach to address these gaps by including spatial representations of multiple ES from several ES categories, and the jurisdictional areas of the agencies that govern them, within the same network analyses, and use this to assess institutional fit and identify opportunities for cross-scale collaboration. 1.6 Objectives This dissertation aims to deepen our spatial understanding of the ecosystem functions that underpin the supply of and functional interactions between ES by developing a novel interdisciplinary approach to landscape-level ES assessment. More specifically, the overarching theme of this dissertation is the development and evaluation of spatial network-theoretic concepts and methods for use in assessments of the functional interactions between multiple, diverse ES that are produced in, and interact across, a real-world multifunctional landscape. We achieve this objective through four chapters numbered from 2 to 5. The provisioning of ES is fundamentally spatial (Metzger et al., 2021). Further, ES assessments are by nature interdisciplinary, and therefore call for multiple analytical methods and tools. We incorporate these principles into our methodology and, with respect to existing high-level themes in ES assessment, we also focus our approaches on the distribution, condition, and connectivity of the landscape components and processes responsible for their production (Preston and Raudsepp-Hearne, 2017), and the functional interactions between multiple ES. In Chapters 3, 4 and 5, we use the Okanagan region, British Columbia (BC) as a case study area. This region is ecologically, economically, and socially diverse, and is recognized as one of the most endangered natural systems in Canada (OCCP and SOSCP, 2014). It is thus a complex multifunctional landscape, the study of which will help contribute to the understanding of multi-ES functional interactions at the regional scale. 11 Objective 1: To present a conceptual network theoretic approach for representing and assessing the functional interactions of multiple ES (Chapter 2). There are several gaps in the current literature on the interactions between multiple ES distributed across multifunctional landscapes. These gaps relate to the inclusion of multiple types of ES from several categories (i.e., provisioning, regulating, cultural) with multiple types of functional interactions between them represented and assessed within the same modelling framework. To address these, we propose a novel approach based on the interdisciplinary concepts available from network theory. Our first objective is therefore to present a position paper in which we summarize how network theory has been applied to landscape connectivity through the lens of ES and, using a small fictional landscape, to develop and evaluate an approach for how spatial network theory can be used to visualize and assess the functional interactions between multiple ES. Objective 2: To present a connectivity-based approach for mapping the functional interactions between multiple ES in a real-world multifunctional landscape (Chapter 3). The conceptual approach of spatially representing functional interactions between ES has not been demonstrated on a large, real-world landscape. We build upon the approach presented in Chapter 2 and draw on landscape connectivity concepts and theory behind the ecosystem function-based mechanisms underpinning interactions between supplies of the same (i.e., occurring in different parts of the landscape) and different ES. To map these functional interactions, we use existing spatial data for three individual ES from diverse high-level categories (i.e., provisioning = plant food agriculture; ‘PA’; regulating = waterflow regulation; ‘WF’; cultural = landscape aesthetics; ‘LA’) across the Okanagan regional landscape. Our objective is to use real-world examples of ES supply areas to demonstrate a novel approach for spatially explicit representation of the functional interactions between multiple ES across a multifunctional landscape. Objective 3: To demonstrate a network approach for assessing functional interactions between multiple ES at the landscape scale (Chapter 4). Methods for quantifying the interactions between ES typically focus on mapping functionally isolated individual or bundles of ES, which ignores the functional interactions among multiple ES. To address this gap, we use network analytic concepts and measures, based on the multi-ES functional interaction mapping created in Chapter 3, to assess ES supply and functional interactions across the Okanagan landscape. Our objective is to demonstrate how the spatial multi-ES functional interaction concepts and representations presented in Chapters 2 and 3 can be assessed using network analyses to identify important areas of supply and functional interactions across a large multifunctional landscape. 12 Objective 4: To identify opportunities for multi-sector and multi-scale governance collaboration based on network-derived clusters of highly functionally interacting ES (Chapter 5). Agencies involved in ES governance typically have narrow mandates related to a single ES, and often have minimal communication with agencies that operate in different sectors and at different scales. We develop a SEN representation to help identify potential priority areas for cross-scale, multi-sector governance. We base this on the ecological multi-ES sub-network layer built in Chapter 4, and on a social governance sub-network layer consisting of local, regional, provincial and national agencies with ES-related jurisdiction in the study region. Our objective is to show how network concepts can be leveraged to partition a large multifunctional landscape of multiple ES into smaller, highly functionally interacting communities, which can then be used to assess the spatial alignment of ES with their governing agencies, and to identify opportunities for multi-sector, cross-scale governance coordination. NB: In this chapter, we use the term nature’s contribution to people (NCP) in place of ‘ES’ as it better aligns with an explicit incorporation of social aspects with the ecological aspects of ES, and specifically engages with parts of a SES that may not be quantifiable, i.e., cultural, institutional, and social issues (Dean et al., 2021; see glossary for definition and distinction from ES). 13 Chapter 2: Multi-ecosystem services networks: A new perspective for assessing landscape connectivity and resilience 2.1 Synopsis Society is dependent on ecosystem services (ES), the delivery of which would cease without strong connectivity among the ecological functions underlying their production. However, research on the functional interactions between multiple ES is in its infancy. Preliminary studies have focused on individual ES, but research has yet to expand to incorporating the functional interactions that occur between several different types of ES, and how such complex interactions might influence landscape sustainability. Our objective is to present a viewpoint on how spatial network theory can be used to assess the functional interactions between multiple ES and landscape ecological resilience. We provide an overview of knowledge and gaps in the literature linking connectivity, networks and ES concepts. We propose a spatial network theory-based approach for the assessment of multi-ES interactions, and present how ecological function-based concepts can be used to develop spatially explicit network nodes and links between multiple ES across landscapes. To illustrate our proposed approach, we provide a design framework and assessment of a simple conceptual multi-ES network. The use of multi-ES networks can help evolve our understanding of landscape connectivity and resilience, and incorporate complex ES functional interactions into applied planning and management programs. This chapter has been published in Ecological Complexity (Field and Parrott, 2017). 2.2 Introduction No human could exist without the services ecosystems provide. However, we too often neglect the responsibility of maintaining the ability of landscapes to provide these services. With ever-increasing immigration of people into urban centres, human culture is increasingly disconnected from nature, even though our societies and economies are integral components of interconnected ecological landscapes (Moberg and Hauge Simonsen, 2014). To sustain ecological systems in the future, societies must reconnect and rebuild our appreciation of the importance of natural landscapes to our well-being and survival. Researchers participating in this paradigm shift need to recognize that such complex systems are built upon a myriad of interactions, which result in emergent dynamics equating to more than the sum of individual landscape components (Parrott and Meyer, 2012; Woodward et al., 2013). Research should realign focus away from conducting isolated ecological assessments, and toward multidisciplinary study of how interconnectivity is key to the maintenance of natural landscapes. This viewpoint article outlines a novel interdisciplinary approach to the assessment of multi-ecosystem services (ES) functional 14 interactions and resilience across the landscape, which future research can use to deepen our understanding of the ecosystems that sustain us. The concept of resilience has been increasingly applied to scientific approaches for assessing complex, interconnected systems (Moberg and Hauge Simonsen, 2014). We define ‘resilience’ as the long-term capacity of a system (e.g., a landscape) to deal with change while maintaining essentially the same structure and function, the same identity, without passing critical thresholds (Walker and Salt, 2012; Holling, 1973). Several properties contribute to system resilience, including adaptive capacity, diversity, redundancy, slow-changing variables, feedbacks, and connectivity (Biggs et al., 2012; Bennett et al., 2005; Parrott and Meyer, 2012). In general, low resilience results in low-biodiversity landscapes that produce fewer ES and are more vulnerable to disturbance (Moberg and Hauge Simonsen, 2014). Maintaining landscape-scale resilience is increasingly cited as a goal of environmental management and planning strategies (e.g., Albert et al., 2014b; MacDonald et al., 2014), however there exists no accepted method for measuring and assessing the ecological resilience of landscapes. Theory would suggest that an ecologically connected landscape that facilitates spatial movement of species and ecosystem processes should be more resilient than a fragmented landscape (Biggs et al., 2012; Lundberg and Moberg, 2003; Mitchell et al., 2013). We base our perspective on this premise and describe how including ES functional interactions in landscape assessment could facilitate quantitative, network-based analyses of landscape resilience. Traditional landscape connectivity planning focuses on species habitat corridors, and current approaches in spatial ES research focus on mapping the distribution of ES provisioning (Egoh et al., 2008; Taylor et al., 1993), with recent studies assessing networks of individual ES across the landscape (e.g., Janssen et al., 2006; Fortuna et al., 2006; Heckmann and Schwanghart, 2013; Phillips 2013; Peron et al., 2014). However, existing methods do not explore the variety of functional interactions between multiple ES and how such interactions might impact landscape resilience. It is critical to confront this void, as we predict that landscape resilience is highly dependent upon the spatial quantity, quality, diversity, and redundancy of ES, as well as the cross-scale interactions between the ecosystem functions underlying their provisioning and management. Although it has been suggested that managing multiple ES may be critical for supporting ecological resilience, there remains limited understanding of the factors that influence the spatial interactions between ES (Bennett et al., 2009; Mitchell et al., 2013; Qiu and Turner 2013, 2015). There is a call for more research on the mechanisms by which landscape connectivity influences the supply of, access to, and benefits realized from ES, and on how relevant aspects might be measured (Mitchell et al., 2013). Conceptual theoretical frameworks have been suggested to help address these questions (Cumming et al., 2010; Gonzalez et al., 2011). These studies introduce the concept of exploring multi-node ecological 15 networks within the context of links between meta-community functionality and ES provisioning, and present a framework for including both habitat and ecological interaction networks to assess their joint impact on biodiversity and associated ES, respectively. However, more work needs to be done on modelling landscape-scale networks that include multiple ES. Through these types of networks, we can begin to explore how ES interact with and influence one another across regional land use planning jurisdictions. Here, we present and evaluate a novel approach for applying network theory to assess functional interaction links between multiple ES across the landscape for improving our understanding, assessment, and management of landscape connectivity and resilience. 2.3 Linking concepts: connectivity, networks and ES Landscape connectivity, which is the degree to which a landscape facilitates or inhibits ecological, hydrological, geomorphic, and human movement, is considered an essential characteristic of landscape structure and is vitally important to ecology, hydrology and geomorphology (Taylor et al., 1993; Phillips et al., 2015). Large, regional landscapes that are structurally and functionally connected demonstrate more robust self-organization, stability, hierarchy, and resilience compared to systems where components are isolated (Parrott and Meyer, 2012; Cui et al., 2012); as such, quantifying connectivity is important for ecological conservation (Phillips et al., 2015). In a spatial network, landscape elements such as habitat patches are represented as nodes, and links between the nodes may represent physical connections such as habitat corridors, or functional connections through species and gene dispersal, movement of water, or other exchanges between the nodes. The importance of connectivity for supporting resilience is dependent on the arrangement and strength of links across the landscape. Fundamentally, connectivity enables the transfer of resources, species and human actors, which is critical for maintaining the ecosystem functions necessary for ES provisioning (Biggs et al., 2012). Further, connectivity supports resilience by supporting system recovery after disturbances (Nyström and Folke, 2001). The use of network-based methods to symbolize functional interactions between ES can support connectivity assessment and help to identify the spatial and temporal scales at which connectivity is relevant to ES provisioning. 2.3.1 ES networks in the literature Recent research has applied spatial network theory methods to ES assessment, especially in the areas of waterflow regulation (e.g., Fortuna et al., 2006; Poole et al., 2006; Cui et al., 2012, 2009; Heckmann and Schwanghart, 2013; Phillips, 2013), pests and disease (e.g., Margosian et al., 2009), seed dispersal networks (e.g., Janssen et al., 2006), and recreation (e.g., Shih, 2006). However, a similarity across all studies we reviewed was that they incorporated only one ES into network models and, although potential 16 interactions with other ES were occasionally discussed, they were not explicitly represented or analyzed within the network (see also Cui et al., 2012). Such networks include a single type of node and link, and fail to incorporate the multipartite (i.e., different types of nodes) or multiplex (i.e., different types of links between nodes) characteristics (Horvát and Zweig, 2012), which are more representative of how complex networks function in the real world. Further, although ours was not a systematic review and therefore may not have captured all papers published on network theory applications to ES, most studies we found focused on regulating ES. This points to several current gaps in the literature: the inclusion of multiple ES types (e.g., nodes representing water provisioning, soil formation, etc.) with multiple types of functional interactions between ES (e.g., timber provisioning nodes provide pollinator habitat and evapotranspiration, with links to nearby agricultural and downslope water provisioning nodes, respectively) from several ES categories (i.e., provisioning, regulating, cultural) within the same network analysis. In this viewpoint article, we address this gap by demonstrating a new method for representing a multi-ES network using a fictional landscape. For this, we represent ES supply areas as network nodes, and spatially identify their functional interactions based on relevant ecosystem function-based mechanisms that operate across a landscape. The resulting network represents multiple ES, and node betweenness centrality is examined to identify the ES nodes that contribute most to maintaining these functional interactions and, thus, may contribute to landscape resilience. This simple example serves to demonstrate the potential of the method and its applicability to studying connectivity in real-world landscapes. 2.4 A new approach for multi-ES networks The functional interactions and feedbacks between ES are influenced by multiple ecological components and processes across spatial and temporal scales (Qiu and Turner, 2013). Conceptualizing these properties is a daunting task, without even attempting to map, measure or empirically assess ES functional interactions. Below we present an approach, building on ES mapping and modelling studies and rooted in network theory, to move toward addressing these complexities and to demonstrate how multiple ES can be represented in the context of landscape connectivity and resilience assessment. 2.4.1 Defining ES nodes, links and attributes Developing a network model for ES functional interactions requires spatial definition of nodes (i.e., areas providing ES) and links (i.e., functional interactions between nodes; Fig. 2.1). Node (and, if applicable, matrix) definition can be flexible based on the nature of the study system and the available landscape data (Margosian et al., 2009). Nodes and links are assigned ‘attributes’, which are characteristics that are not directly influenced by their relationships with other nodes and links (Cumming et al., 2010). Node 17 attributes can include their spatial location, size, quality or productivity, whereas link attributes relate to functional interactions such as presence/ absence of connections, distance, or likelihood of connections between nodes (Urban et al., 2009). When assessing multiple ES in a region, the presence of at least one ES may be ubiquitous across the landscape (e.g., Queiroz et al., 2015). Alternatively, there may be clusters of ES nested within a matrix of a non-service provisioning landscape (e.g., Qiu and Turner, 2013; Fig. 2.2). We have identified two methods for constructing ES nodes, applicable in either of the above scenarios, and based on techniques employed in landscape connectivity network and ES mapping studies: (a) continuous-data delineations of polygons denoting ecological ES patch boundaries (e.g., Bangash et al., 2013); (b) clusters of a group of high ES-supplying cells in a grid-based map (e.g., Gardner, 1999; Urban et al., 2009; Qiu and Turner, 2013). Any areas lacking ES, or containing ES below a certain value threshold, can be represented as the landscape matrix. For method (a), it is necessary to define ES nodes by identifying the extent of ES supply areas. Identifiable ecological patches (such as wetlands, meadows or forest stands, e.g., Margosian et al., 2009) are good candidates for such nodes, as they will commonly contain ‘bundles’ (i.e., recurring clusters of correlated ES, e.g., Queiroz et al., 2015) whose within- and between-patch interactions may contribute to measures of ES node and link attributes (see below). Method (b) defines nodes as clusters of ES-containing cells. Criteria for cell density and spatial extent of cluster inclusion must be designated for each ES or bundle of overlapping ES. For example, cell cluster nodes may be defined by ES presence, whereby nodes are identified by the inclusion of all ES-containing cells with vertical, horizontal and diagonal connections on a raster map. Similar methods (e.g., ‘neighborhood’ or ‘nearest-neighbour’ rules) have been used to identify patches of land cover (Gardner, 1999), and several spatial clustering algorithms are available for delineating the nuances of habitat boundaries and ecotones (e.g., ‘cluster analysis’, ‘edge detection’; Fortin and Drapeau, 1995). Cell-specific weight attributes (discussed below) may also be used to delineate cell cluster nodes. ES bundles can be identified where individual ES nodes overlap on the landscape, and these bundles can help guide management interventions that seek to manipulate multiple ES, ideally with mutually beneficial outcomes (e.g., Turner et al., 2014; Queiroz et al., 2015). For example, Queiroz et al. (2015) found that, as many cultural services result from a direct interaction between humans as nature, they were often synergistically bundled in or near more densely populated areas (e.g., biodiversity appreciation, outdoor recreation, cross country skiing). This might suggest to land use managers that enhancing green infrastructure would provide more benefits in urban areas relative to rural locations. Bundles can be assessed for synergies and trade-offs among ES to weight within-node interactions (e.g., Qiu and Turner, 2013). Further, the cumulative cell quantity (e.g., agricultural yield; Andersson et al., 2015a) and/or quality (e.g., water quality status; Queiroz et al., 2015) of ES within nodes may be used for weighting 18 (e.g., Fig. 2.1B-E). Temporal scales can be incorporated into node attributes by mapping seasonal and interannual variations in ES presence (e.g., fluxes in drinking water provisioning during dry periods; pollination regulation indicated by flowering onset date). ES links represent the functional interactions between nodes. At the simplest level, links can be binary, i.e., indicate whether nodes interact or not (e.g., based on a threshold distance between nodes; Urban et al., 2009). If it is assumed that a functional interaction connects nodes via the most direct route, then Euclidean link geometry can be applied (Galpern et al., 2011). If a link represents a distance or some other quantifiable functional attribute, it can be weighted (Urban et al., 2009; Figs. 2.1 B,D,E; 2.2). For example, known threshold distance relationships between wetland water quality and adjacent node waste treatment, water filtration, and erosion control ES (e.g., Houlahan and Findlay, 2004) can be used to weight links. Functional distances may also be used to weight links based on the resistance of the matrix between nodes. Typically, for wildlife connectivity analyses, these distances are computed as ‘least-cost paths’ (LCP) based on species attributes (Urban et al., 2009). Various ES link attributes can be applied in the same analytical context. To use a simple example, recreational links may be weighted by the degree of accessibility (e.g., roads and trails) between nodes. Topographical features can be used to weight links for waterflow maintenance (e.g., connections between surface and groundwater sources; Poole et al., 2006). The degree of erosion control in one node can be linked to vegetation cover, topography and/or soil erosion potential (e.g., Crossman et al., 2013) in an adjacent node or matrix. Moreover, as several ES types can be represented by multipartite nodes, multiple link types be present in the same multiplex network. Some examples of how water provisioning nodes may functionally interact through different links types include: croplands through natural watercourses and/or irrigation infrastructure; waterflow maintenance in downstream reaches and wetlands; shoreline erosion control through water level fluctuations in manmade reservoirs; local microclimate regulation; aesthetic value of waterbodies; recreation opportunities in waterbodies; etc. In landscape networks, links are often directional due to biophysical (e.g., water currents, topography) or biological (e.g., movement from source to sink nodes) considerations; and there can be multiple positive or negative links between nodes to represent qualitatively differing connections (Zhang et al., 2007; Urban et al., 2009). Areas with multiple spatially overlapping ES nodes may have functional interactions between ES of the same type in different areas, or between different ES types in different areas (Figs. 1 C, D; 2). Cross-ES links can be defined using similar attributes to those described in the within-ES distances, threshold, or LCP analysis methods above. One can also consider ecosystem dis- 19 services (e.g., pest and disease transmission, competition for water and pollination; Zhang et al., 2007) when assigning link attributes. Figure 2.1. Conceptual design schematics for three alternative ES networks: (A) un-weighted nodes (circles) and links (straight lines) overlaid on a hypothetical landscape extent (black outline); (B) ES network with weighted nodes (colour gradient) and links (line thickness gradient); (C) ES bundle network with weighted, multi-ES nodes connected by links between same and different ES types; (D) inset showing node and link design detail of two nodes within network (C), with wind rose diagram slices representing presence and weights of individual within-node ES; (E) ES bundle network with multi-ES nodes connected by weighted single links. Note that these examples do not include all spatial ES network details (e.g., directional links, accurate georeferencing of node and link locations and spatial extents, etc.) or all possible ES network types. 20 Figure 2.2. Conceptual design of two individual ES networks. Georeferenced (dotted lines) multi-ES network nodes (two ES: A = orange, B = pink), showing spatial links within-ES (grey lines), and weighted bridging links between-ES (solid black lines with thickness gradient; NB: although not shown here, weighted bridging links can be spatially explicit). ES nodes and links are nested within a non-provisioning landscape matrix (white area). Adapted from Ernstson et al. (2010). 21 2.4.2 Using multi-ES networks to assess landscape connectivity By providing a system-level representation of ecosystem function-based interactions on a landscape, an ES network can help to inform and prioritize landscape conservation decisions. Networks can be used to identify which ES and geographic areas are most or least connected, and which ES nodes, links, and clusters may be most critical to network function and resilience. More specifically, network-based metrics can be used to quantify the strength of ES functional interactions, identify the most readily transversable routes for ES interactions to travel between provisioning areas, identify the relative contribution of an ES node to maintaining interactions, measure the efficiency of ES interactions across the landscape, assess the vulnerability of particular ES nodes and links to shocks and disease transmission, etc. (e.g., Urban et al., 2009; Heckmann et al., 2015). To assess landscape connectivity using an ES network, one must apply the appropriate network measure(s) to the modelled system. A network measure is a quantifiable value or set of values that represents a network characteristic (Brinkmeier and Schank, 2005). Below, we provide some examples of network measures and operations that can be used for evaluating the importance of ES to landscape connectivity, which is by no means exhaustive of all suitable metrics for evaluating ES functional interaction networks. Dale and Fortin (2010) provide an overview of spatial network measures that have been used in connectivity studies, and others provide summaries of network theory models applied commonly in ecological and connectivity research (e.g., Gonzalez et al., 2011; Cui et al., 2012; Phillips et al., 2015). 2.4.3 Connectivity assessment across spatial scales Connectivity in a network can be measured across spatial scales using a variety of network properties. Several measures are available for investigating the importance of individual ES nodes (i.e., habitat patch scale), including ‘betweenness centrality’, which identifies stepping stones and important dispersal pathways, ‘degree centrality’, which identifies potential accessibility and influence of a node, and ‘closeness centrality’, which indicates nodes that have a central position in the network. Individual link importance can be measured by ‘resistance distance’, which identifies the presence of alternate routes (Urban et al., 2009; Gonzalès and Parrott, 2012; Rayfield et al., 2011). At the ES cluster (i.e., local or sub-regional) scale, the isolation vulnerability of clusters can be calculated as the ‘number of cut-nodes’ and ‘number of cut-links’, which indicates the number of nodes and links whose removal would result in barriers to ES functional interactions. At the network (i.e., regional) scale, ‘network circuitry’ highlights the presence of alternative pathways for ES to interact within each network (Rayfield et al., 2011). Measures related to ‘probability of connectivity’, guided by the likelihood of movement between nodes and/or node area (or weight), are also available (Rayfield et al. 2011). This information can be used to 22 identify ‘keystone’ ES, to determine how efficient or ‘costly’ it is for ES to interact, and to balance trade-offs when attempting to enhance networks for the maintenance of ES supply and functional interactions across the landscape (Heckmann et al., 2015). 2.4.4 Using multi-ES networks to assess landscape resilience Connectivity, heterogeneity, and redundancy are the basic structural properties of landscapes that support ecological resilience (Biggs et al., 2012; Bennett et al., 2005; Parrott and Meyer, 2012). Several network characteristics can be used to assess system aspects that are related to resilience, including: connectivity, centrality, vulnerability diversity, functional redundancy, modularity, and control of flow (Gonzalès and Parrott, 2012). Connectivity can have conflicting impacts on resilience (Janssen et al., 2006). On one hand, high connectivity leads to increased resilience by allowing information to pass through many different nodes in a network. As a result, when a disturbance destroys several network links, information can continue to be transmitted via alternative pathways, thereby maintaining function and supporting subsequent recovery (Nyström and Folke, 2001). On the other hand, highly connected networks can facilitate the rapid spread of pests, diseases, pollutants, and other disturbances (e.g., van Nes and Scheffer, 2005; Ash and Newth, 2007; Satake et al., 2007; Janssen et al., 2006). Therefore, moderate levels of connectivity are likely best for maintaining resilience (Biggs et al., 2012). Centrality measures how well-connected a given node or link is in a network (Bavelas, 1948). This concept can be used to identify important hub or keystone nodes that are especially critical in maintaining network function (Janssen et al., 2006). It can be calculated using ‘betweenness centrality’, ‘degree centrality’, and ‘closeness centrality’ (see descriptions above; Urban et al., 2009; Gonzalès and Parrott, 2012; Rayfield et al., 2011). A combination of diversity in component vulnerability and redundancy in their functions increases likelihood of network persistence in the face of eliminated components (Bodin et al., 2006; Janssen et al., 2006; Carpenter et al., 2001). Although not network-specific, common ecological indices can be used to measure diversity and redundancy (e.g., Simpson and Shannon indices; Nagendra, 2002; Gonzalès and Parrott, 2012). For example, waterbodies with similar downslope proximity to fertilized agricultural lands may be identified as having similar vulnerability to eutrophication, but those providing drinking water would have different functionality than those providing water for agricultural irrigation. A particular pollinator species or habitat type may be identified as having the same vulnerability to disease outbreak across the landscape, but the location of such pollinating services will determine their function, e.g., whether they are in close enough proximity to contribute to the pollination of fruit or vegetable crops (or both). 23 The interplay between structural modularity and control of flow in a network further regulates the spread of disturbances while ensuring the flow of information, material and energy is maintained. These characteristics expand beyond single component diversity and redundancy to consider how the entire network structure and relationships between components affect resilience. A highly modular system is one in which clusters of nodes are isolated from one another, which limits or prevents the spread of disturbances to other clusters in the network. In turn, however, modularity may limit or prevent the flow of important information, material, and/or exchanges. Therefore, a balance between modularity and sub-group connectivity is important for maintaining resilience (Webb and Bodin, 2008). Several ‘clustering’ measures are used to calculate modularity and are used to determine the extent of network partitioning into sub-groups of nodes (e.g., Rayfield et al., 2011; Minor and Urban, 2008; Fortuna et al., 2006). Control of flow can be quantified using ‘betweenness centrality’ to identify which nodes act as critical bridges between modular clusters (Gonzalès and Parrott, 2012). 2.5 Demonstrating the multi-ES network approach 2.5.1 Building a conceptual network To demonstrate our proposed approach, we developed a simplified conceptual multi-ES network on a 100 km2 fictional landscape. We incorporated land use and land cover (LULC) categories that illustrate a subset of those likely to be found within social-ecological systems, including an urban core, industry, naturally vegetated areas, and freshwater distributed across variable topography (digital elevation model range of 723 m). When cell-based analyses were necessary, spatial feature conversion was based on rasterized grid of 100 m cells. We built our network using the following three ES, selected to be representative of each of three broad ES categories: fresh surface water for drinking and irrigation (provisioning); waterflow control (regulating); landscape aesthetics (cultural). For each ES, nodes were delineated by one or more explanatory landscape metrics. Water provisioning nodes were delineated as lakes that provide freshwater. For waterflow regulation, an infiltration rate threshold of 21.0 cm/h (i.e., highest infiltration rate in fictional landscape) was used to delineate polygons to be designated as nodes based on their higher relative contribution to water infiltration. Aesthetics nodes were delineated by several LULC categories, including naturally vegetated areas, industrial areas, large lakes (i.e., > 5 ha), and an urban area. Node weights for the water provisioning, waterflow regulation, and aesthetics were based on surface areas. For demonstration purposes, aesthetic node weighting was further specified as follows: natural vegetation, lakes, and urban area = 1.0; industrial areas (classified as a dis-service) = -1.0 (Table 2.1). In a real 24 landscape scenario, these hypothetical values could be calibrated to reflect the aesthetic values and preferences of the people living in the landscape. Table 2.1. Summary of node and link (within and across ES categories) characterization methods. Nodes Lakes weight: lake surface area Infiltration rate > 21.0 cm/hr weight: summed infiltration rate LULC: lakes, natural vegetation, industrial weight: LULC surface area + rank linked from linked to Water Provisioning Waterflow Landscape Aesthetics Links Water Provisioning streams flowing downslope (LCP) uni-directional weight: stream length downslope to lakes (LCP) bi-directional weight: summed sub-threshold infiltration rates direct node (lake) overlap uni-directional weight: lake surface area Waterflow downslope to lakes (LCP) bi-directional weight: summed sub-threshold infiltration rates water flowing downslope (LCP) uni-directional weight: summed sub-threshold infiltration rates direct node overlap bi-directional weight: aesthetic node summed infiltration rates Landscape Aesthetics Euclidean sightlines from urban core to lakes uni-directional weight: lake surface area direct node overlap bi-directional weight: waterflow node summed infiltration rate direct node overlap bi-directional weight: connected aesthetic node weight Links within the same ES class were defined based on mechanistic drivers of their functional interactions across the landscape. For water provisioning, links were represented by streams that connect lakes, and directionality was derived from least cost path (LCP) analysis of the downstream movement of water. Waterflow regulation uni-directional links were derived from LCP analysis of the downslope movement of water across the landscape. Aesthetic links were uni-directional Euclidean sightlines from a single viewpoint at the centroid of the urban area to surrounding aesthetic nodes. Links weights were based on quantifiable functional attributes (Urban et al., 2009), which allowed us to examine the relative impact of functional interactions between ES in network analysis results. Link weights for water provisioning, waterflow, and aesthetics were based on stream length, summed sub-threshold infiltration rates along link length, and aesthetic node weights, respectively (Table 2.1). Links between the different ES were defined based on the functional interactions between the ES types. Water provisioning and waterflow were bi-directionally linked from their nodes via the downslope LCP, as both upslope and downslope provisioning and regulating areas contribute as sources and sinks for both of these ES types. Link weight was based on summed sub-threshold infiltration rates. Water provisioning was linked uni-directionally to aesthetics by Euclidean links from the urban centre viewpoint to lakes greater than 5 ha. In the other direction, aesthetics areas were linked to water provisioning via direct overlap of lake nodes (>5 ha). For both directions, lake surface area was used to define link weight. Waterflow was linked bi-directionally to aesthetics where nodes overlapped, as the existence and 25 conservation of these ES nodes would contribute directly to the functionality of the other. However, from waterflow to aesthetics nodes, link weight was characterized by surface area of the originating waterflow node. From aesthetics to waterflow nodes, link weight was defined by the summed infiltration rate of the originating aesthetics node. All link weights were standardized on a scale from 1 to 2. Fig. 2.3 provides details on the stepwise approach used for combining spatial representations of nodes and links across the three ES categories and shows the resulting conceptual multi-ES map. Figure 2.3. Stepwise method for producing a visual representation of the spatial multi-ES network. Step A (top-down): the locations of water provisioning, waterflow, and aesthetics nodes are identified on the landscape. Step B: spatial links between each individual ES node are defined and mapped. Step C: individual ES node and link maps are combined, and across-ES links are added to produce a final, georeferenced multi-ES map. 26 2.5.2 Methods for evaluating the conceptual network From the many metrics available for assessing network resilience, we selected a single network measure, betweenness centrality (described above), to provide a simple demonstration of how a multi-ES network can be assessed using a metric related to resilience. Many other network measures, including all those discussed above, can be similarly quantified from the same network structure. We used this measure to compare the three individual ES category networks in isolation, and to assess the complete multi-ES network. ArcMap 10.4 (ESRI, 2011), R (R Development Core Team, 2013), igraph (Csardi and Nepusz, 2006), and Gephi (Bastian et al., 2009) were used to build, analyze and visualize the conceptual network. 2.5.3 Results of conceptual network analyses Analyses of individual- vs. multi-ES networks yielded very different results in terms of network function. When individual ES networks were analyzed in isolation, betweenness centrality results did not provide much information for identifying nodes that have a greater influence on network connectivity and control of flow. Of the three ES categories assessed, only one water provisioning node was identified as having a betweenness centrality >0, and therefore greater relative influence on network connectivity and flow control compared to other water provisioning nodes (Table 2.2). Both waterflow and aesthetics individual network nodes had equal betweenness centralities, which suggests that no nodes in these networks are particularly influential. 27 Table 2.2. Node betweenness centrality results for individual- and multi-ES networks. Nodes are further described by unique ID, ES categories, types (LULC), and weights (surface area in hectares). Node ID Betweenness Centrality Individual ES Network Betweenness Centrality Multi-ES Network ES Category Node Type (LULC) Node Weight (Area (ha)) 404319 0 7 Water Provisioning Lake 2.05 404327 0 0 Water Provisioning Lake 1.89 404595 0 0 Water Provisioning Lake 0.76 404665 0 0 Water Provisioning Lake 0.72 405225 0 0 Water Provisioning Lake 201.87 405248 0 0 Water Provisioning Lake 0.23 405315 1 3.5 Water Provisioning Lake 12.51 405535 0 0 Water Provisioning Lake 2.82 544284 0 0 Water Provisioning Lake 1.58 544296 0 12 Water Provisioning Lake 1.09 568732 0 0 Water Provisioning Lake 0.22 581365 0 0 Water Provisioning Lake 1.70 605415 0 4 Water Provisioning Lake 0.26 629732 0 0 Water Provisioning Lake 0.35 653904 0 0 Water Provisioning Lake 0.55 696968 0 1 Water Provisioning Lake 12.59 719770 0 0.5 Water Provisioning Lake 5.12 726519 0 0 Water Provisioning Lake 4.39 768032 0 0 Water Provisioning Lake 0.33 55550 0 0 Waterflow High Infiltration 17.50 55551 0 8 Waterflow High Infiltration 3.43 55552 0 0 Waterflow High Infiltration 3.15 55553 0 0 Waterflow High Infiltration 8.09 55554 0 0 Waterflow High Infiltration 3.56 3187605 0 0 Aesthetics Urban 176.03 405225 n/a 0 Aesthetics Lake (>5 ha) 201.87 405315 n/a 3.5 Aesthetics Lake (>5 ha) 12.51 719770 n/a 0.5 Aesthetics Lake (>5 ha) 5.12 696968 n/a 1 Aesthetics Lake (>5 ha) 12.59 77773 0 0 Aesthetics Natural Vegetation 24.26 77774 0 0 Aesthetics Natural Vegetation 5.36 77775 0 1 Aesthetics Natural Vegetation 122.92 77777 0 0 Aesthetics Natural Vegetation 6.91 77779 0 0 Aesthetics Natural Vegetation 31.80 777710 0 0 Aesthetics Natural Vegetation 37.39 66661 0 0 Aesthetics Industry -4.21 66662 0 0 Aesthetics Industry -153.51 66663 0 0 Aesthetics Industry -42.67 66664 0 0 Aesthetics Industry -64.20 66665 0 0 Aesthetics Industry -11.10 66666 0 0 Aesthetics Industry -116.95 66667 0 0 Aesthetics Industry -1.98 66668 0 0 Aesthetics Industry -373.17 66669 0 0 Aesthetics Industry -2.02 When networks were combined to form a multiplex network, nodes from all three individual ES categories emerged as having some importance to overall network connectivity (Table 2.2, Fig. 2.4). The results of the multi-ES betweenness centrality analysis suggest that lakes tend to have greater overall influence on the network relative to waterflow regulation and other aesthetics nodes. Such lakes serve to connect other lakes within the water provisioning network, contribute as waterflow sources and sinks, and 28 double as aesthetically valuable landscape features. One waterflow node served as an important connection for water provisioning, and as part of a larger naturally vegetated aesthetics node. In turn, this aesthetics node was highlighted as relatively influential because of its spatial congruence with the same waterflow node. Our results also suggest that node influence is not related to the spatial area of ES provisioning (Table 2.2). It is worth noting that variability in betweenness centrality results was likely impacted by the small node sample size and by the relative simplicity of the network. Figure 2.4. Topological representation of the multi-ES network. Nodes (circles) are colour-coded by ES categories as follows: WP (dark blue) =water provisioning; WF (orange) = waterflow; AE (pink) = aesthetics; WP + AE (light blue) = lakes ( > 5 ha) that function as both water provisioning and aesthetics nodes. Relative node size represents betweenness centrality. Directional links (lines with arrow heads) are colour-coded by within- and between-ES link types as follows: dark blue = WP~WP; orange = WF~WF; pink = AE~AE; green = WP~WF; light blue = WP~AE; red = WF~AE. Overlapping links between ES categories are depicted as loops (WP~AE only), and by the single red link (WF~AE). Link weights are represented by line thickness. 29 2.6 Concluding remarks 2.6.1 The proposed approach The application of our approach for representing ES in a network context will improve our understanding of functional interactions between multiple ES in a landscape and could provide insights into maintaining ecological resilience. One of the key advantages of applying network theory to the assessment of functional interactions between multiple ES is that it allows for multiple levels of analysis to address multi-scale questions about landscape function (Poole et al., 2006). Moreover, spatially referenced networks will help identify where key ES interact across the landscape. This information can be used by regional environmental planners to identify important areas for restoration, conservation, and monitoring of landscape characteristics that support ES provisioning. Network theory can also be a useful tool for dealing with novel disturbances or management issues, such as how to rapidly respond to threats on biological control ES from a landscape connectivity perspective (e.g., Margosian et al., 2009), and how to assess network resilience to natural- and human-induced disturbances (Minor and Urban, 2007). Network theory will aid in conceptualizing ES functional interactions with other candidates for corridor conservation (e.g., wildlife species movements), some of which require the same ecosystem functions to survive, even if ES are a fundamentally anthropogenic concept. This will contribute to a more complete understanding of ecological connectivity and further inform and increase support in planning for landscape connectivity. Incorporating ES into landscape management helps stakeholders and the general public understand and appreciate the critical value nature provides to our daily lives (Kareiva et al., 2011), and the impacts we have on our landscapes. Further, network visualizations can be used for education programs and to promote support and compliance with related connectivity policies. It has been shown that using a network approach to examine the distribution of social capital can identify barriers and opportunities for alleviating resource management issues related to stakeholder relationships and communication (Barnes-Mauthe et al., 2015). The same could be true for ES networks, as appropriate application of ES concepts encourages intellectual and emotional connections with ecosystems (e.g., Carpenter et al., 2015). Applying network analysis to ES is not without its challenges and assumptions, which must be considered when designing the network. Connectivity assessments are typically done at regional or sub-regional scales, which require the exclusion of influential factors outside the geographic extent of study (Cui et al., 2012). Overcoming this limitation will require improved management cooperation across administrative boundaries and disciplines (Yevjevich, 2001). Further, certain ES functional interactions are difficult, if not impossible, to evaluate using current network analysis methods. For example, to our knowledge there is no study that has successfully represented air purification ES spatially, or within a 30 network analysis. Although not specific to ES networks, indicator data availability and a lack of methods to adequately assess certain ES present additional challenges. Careful evaluation and validation of such ES will have to be undertaken to appropriately represent flux and interactions within an ES network. 2.6.2 The conceptual network The conceptual multi-ES network presented in this study provides a tangible example of how such networks can be designed, built, and quantitatively assessed in terms of connectivity and resilience. Our visualizations of the multi-ES network provide an example of how georeferenced node and link representations can be produced to help guide on-the-ground planning and management. By analyzing the network using a simple network measure, betweenness centrality, we provide evidence that the relative importance of ES to maintaining resilience can be measured and spatially identified. Such results can be applied to real-world management efforts that seek to identify the specific areas that are important for maintaining ES function across the landscape. Further, our conceptual case study highlights that functional interactions among and resilience within multi-ES networks provides a more complex and realistic perspective of the importance of multiple ES to landscape function relative to examining individual ES networks in isolation. We included several complexities likely to be present in reality, including ES across several categories, a spectrum of weighted nodes and links, dis-services, isolated nodes, uni- and bi-directionally linked nodes, variable spatial distribution, and elements defined based on ecosystem functions. This demonstrates the transferability of our approach to practical real-world network assessments. Our proposed approach also crosses major ecological boundaries of the aquatic and terrestrial realms, which are often studied and managed in isolation. There is the potential for a more holistic approach of incorporating trans-boundary ecological relationships into landscape planning and management through the visual representation of the spatial functional interactions between important ES, and the estimation of trans-boundary resilience measures. Although we focus on ecological ES components, we would like to emphasize that our approach could be applied across the social-ecological realm, and across various definitions of ES. In future, our approach could be extended to network optimization for planning and management, which could provide spatially explicit information on synergies and trade-offs among ES, i.e., how the conservation or maximization of one ES may have positive or negative impacts on another (e.g., Qiu and Turner, 2013). We designed our conceptual multi-ES network to be a simplified representation of realistic landscape and ES functional interaction conditions. Notably, even with a simplified network that does not take into account all complexities of ES functional interactions, node and link delineation and weighting was a time-consuming process that required careful rationale development in order for the network to approach 31 ecological validity. Thence, real-world multi-ES networks will be decidedly more challenging to build. The creation of such complex networks will be intensive in terms of data- and regionally specific knowledge-requirements and should be rigorously designed and evaluated prior to presenting them as potential management tools. 2.6.3 Multi-ES connectivity and landscape resilience Ecological resilience and landscape functioning rely on the maintenance of the ecosystem functions that underpin multiple ES within complex systems (Mitchell et al., 2013; Qiu and Turner, 2013; Queiroz et al., 2015). It is known that ES are not independent, but rather have heterogeneous and unpredictable interactions and spatial patterns; therefore, many ES are unlikely to be appropriate substitutes for others (Bennett et al., 2009). Further, variability of other influential environmental factors is typically higher at larger scales, and maintaining elevated network heterogeneity increases landscape network resilience to disturbances (Wang et al., 2006). This highlights the importance of managing to conserve and restore heterogeneous spatial distributions of and functional interactions between multiple ES at the landscape scale (Qiu and Turner, 2013). For landscapes to functionally persist in the face of such complexity, a clear and complete understanding of multi-ES functional interactions is required. Network analysis provides a promising theoretical approach with which to address this demand. 32 Chapter 3: Mapping the functional connectivity of ecosystem services supply across a regional landscape 3.1 Synopsis Sustainably managing multifunctional landscapes for production of multiple ecosystem services (ES) requires thorough understanding of the interactions between ES and the ecosystem functions that drive them. We build upon landscape connectivity theory to present a spatial approach for assessing functional interactions between multiple ES at the landscape scale and take a closer look at the concept of ES interactions by spatially representing the ecosystem functions behind them. We demonstrate application of the approach using existing ES supply mapping data for plant agriculture, waterflow regulation, and landscape aesthetics and map the functional interactions between them. We find that, when weights of all linkages were amalgamated, additional high-value areas are revealed that are not present on any individual ES supply area or pairwise link maps, which suggests that the spatial focus of planning for service provisioning may shift when functional interactions between several ES are considered. From waterflow supply areas, our modeling maps several functional interaction links that operate over both short and long distances, which highlights the importance of managing ES interactions both locally and across jurisdictions. We also found that different land use and land cover types than those associated with ES supply areas may be serving as critical corridors that connect functionally interacting ES. By providing spatial information on ES functional interactions, our approach enables local and regional environmental planning and management to take full consideration of the complex, multi-scale interactions between ecosystem functions, land use and land cover, and ES supply on a landscape. This chapter has been published in eLife (Field and Parrott, 2022). 3.2 Introduction Rapid human-driven modification of wilderness is placing increasing demands on landscapes to deliver nature’s contributions to people, or ‘ecosystem services’ (ES; Carpenter et al., 2009). These juxtaposing forces highlight an urgent need for incorporating both biodiversity and ES in land use planning, with recent research calling specifically for consideration of landscape structure and connectivity in order to achieve environmental management objectives (Mitchell et al., 2013; Ekroos et al., 2014; Werling et al., 2014; Dobbs et al., 2014). The boom in ES research over the past several decades has improved our understanding of the ecological drivers underpinning the supply of ES, but more nuanced work is necessary to meaningfully manage ES supplies and their functional interactions at the landscape scale (Kremen and Ostfeld, 2005; Tscharntke et al., 2005; Nicholson et al., 2009; Daily et al., 2009). 33 Specifically, the supply of an ES is typically mapped within fixed areas (e.g., Tallis et al., 2008) without considering the potential relevance of ecosystem function flux across the landscape for supporting ES supply (e.g., Mitchell et al., 2013) and multi-ES functional interactions. By failing to spatially represent the functional interactions between supply areas in ES assessment, we ignore ecosystem functions that may be fundamental to the maintenance of ES supplies, run the risk of overlooking potentially critical areas in landscape-scale management, and miss opportunities for uniting divergent interest groups around the concept of multifunctional landscapes (i.e. those that provide multiple ES beyond those that are primarily managed; Power, 2010). To enhance ES provisioning while minimizing potential negative effects on human well-being in the face of increased development pressures, it is critical to understand the dynamics of multi-ES supply (Lorilla et al., 2018). Connectivity is a key attribute of landscape resilience and of ES in general (e.g., Bennett et al., 2021). Loss of connectivity through fragmentation or decreases in habitat, biotic and/or abiotic supplies can have deleterious effects on the wealth of biodiversity and natural capital and ultimately lead to declines in total ES supply and in the quantity and/or quality of flows to human beneficiaries (Mitchell et al., 2015; Pal et al., 2021). Landscape fragmentation impacts the supply of ES through altering the distribution and movement of the ecological elements, structures and processes underpinning the maintenance of natural capital (Mitchell et al., 2015). Mitchell et al. (2015) discuss how loss of connectivity can be a driver of interactions between multiple ES and can impact both the size and location of ES flows (Bagstad et al., 2013b). Among key policy principles identified for enhancing ES resilience to disturbances and environmental changes is managing for connectivity among ES-related resources, species, and human actors, with specific focus on the strength and structure of these connections (Biggs et al., 2012). All this points to the importance of planning for connectivity in multifunctional landscapes (Phillips et al., 2015), while considering the potential for complex ecosystem function-based interactions among services, to successfully manage for the delivery of multiple ES (Dee et al., 2017). In simple terms, planning for landscape connectivity typically focuses on habitat patches and movement corridors, whereas ES planning focuses on the areas of the landscape with the capacity to produce the services humans need to survive and thrive (Taylor et al., 1993; Egoh et al., 2008). Recent work calling explicitly for incorporating ES into connectivity research has taken the perspective of assessing how the characteristics of landscape connectivity (i.e. how a landscape promotes or hinders movement of matter and organisms), along with composition (i.e. quantities of land use and land cover, or ‘LULC’, types), and configuration (i.e. spatial pattern of LULC), might directly or indirectly impact ES provision and related ecosystem functions (Debinski and Holt, 2001, Fahrig, 2003; Gonzalez et al., 2009; Mitchell et al., 2013). For example, adequate arrangement of adjacent natural habitat areas in agricultural landscapes can aid the movement of pollinators and pest predators to croplands, and thus promote 34 delivery of these services (Priess et al., 2007; Ricketts et al., 2008; Fleischner, 1994; Tscharntke and Brandl, 2004; Kremen et al., 2007; Tallis and Polasky, 2009; Power, 2010; Lonsdorf et al., 2011). In terms of abiotic movement, functional interactions between upstream and downstream freshwater sources can be important for maintaining quantity and quality of drinking water (Dodds and Oakes, 2008; Bangash et al., 2013), and maintenance of the natural hydrologic regime stabilizes base flows and reduces flooding, thereby promoting waterflow regulation (Poff et al., 1997). The above examples highlight that the ecosystem functions underpinning the supplies of certain ES directly influence the supplies of others, both when services co-occur in space and, sometimes, when they are produced in separate areas. Drivers behind multi-ES interactions, and the importance of such functions, are sometimes discussed in ES interaction research (e.g., Li et al., 2017; Alemu et al., 2021) but, to our knowledge, have not been explicitly delineated on the landscape in the context of multi-ES assessments. Since the seminal global appraisal of ecosystems and the ES they provide (MEA, 2005), research that assesses the interactions between multiple services has increased exponentially (Agudelo et al., 2020; Appendix B.1). However, research in this discipline commonly only considers services that co-occur in space (e.g., Queiroz et al., 2015), and assumes positively or negatively correlated ES to represent synergistic production or trade-offs, respectively (Tomscha and Gergel, 2016; Agudelo et al., 2020). Such assessments are typically based on correlation coefficients of indicators aggregated within a geographic unit (e.g., watershed, municipality) or randomly sampled across a region (Anderson et al., 2009; Qin et al., 2015; Carpenter et al., 2015). These approaches do not directly evaluate interactions based on underlying ecosystem function theory nor do they allow for spatially discrete relationships to occur, that is, they do not explicitly incorporate the ecosystem functions responsible for ES interactions, and they ignore how ES occurring in one area might have direct or indirect influence on ES in other areas. It has also been shown that simple spatial correlation analyses between pairs of ES are not necessarily a good predictor of how relationships between ES change over time (Mitchell et al., 2020), and that their interactions can vary across the LULC types found in heterogeneous landscapes (Li et al., 2017); thus, a better understanding of the ecosystem functions that underpin the spatial patterns of ES is needed to improve the sustainable management of multi-functional landscapes (Mitchell et al., 2020). Recent research has visualized the spatial connectivity between ES supply areas by modeling the movement potential of species through high-quality habitat corridors as a proxy for how biodiversity movement in general supports ES provisioning across the landscape (Peng et al., 2018). Still, this does not represent different functional interactions between ES supply, and how the supply of one type of ES directly or indirectly effects the supply of another across a landscape. Further, a recent systematic review of studies that model interactions among multiple ES between 2005 and 2019 found that the majority of studies were conducted locally while relatively few studies were done at the regional scale (Agudelo et al., 2020). 35 However, focus at the regional level may be most appropriate for reconciling the common scale mismatches between biophysical and socio-economic elements involved in sustainable ES management (e.g., Dalgaard et al., 2003; Cumming et al., 2006; Satake et al., 2008; Ingram et al., 2008), while minimizing practical issues with empirical mapping related to data gaps and indicator variability in areas larger than this (Verburg and Chen, 2000; but see de Groot et al., 2010 for examples of variability in ecological scale relevance for specific ES). As this relates to interactions between different ES, small-scale observations may be masked at larger scales (Raudsepp-Hearne and Peterson, 2016); therefore, incorporating local, grid-level data and analyses is also important for providing meaningful information to planners (Haase et al., 2012). In spite of the growing knowledge around the complex interactions and feedbacks between ES, the related suite of biotic and abiotic mechanisms, and the cruciality of incorporating this into decision-making (Qiu and Turner, 2013; Dee et al., 2017), spatial modeling of the diverse functional interactions between multiple ES (e.g. Cui et al., 2012; Kolosz et al., 2018; Agudelo et al., 2020) from several broad ES categories at the regional scale remains limited (Field et al., 2017). Several approaches used in ecological connectivity studies to identify potential spatial linkages across a landscape are promising in their applicability to multi-ES assessment. These include Euclidean distances (Cressie, 1993), least-cost path analysis (LCP; Larkin et al., 2004), least-cost corridor (LCC; Singleton, 2002), circuit theory (McRae and Beier, 2007), graph theory (Pinto and Keitt, 2008), and network flow models (Phillips et al., 2008). All these approaches are potentially amenable to assessment of multi-ES interactions but, to date, we know of no studies that have applied such methods to map the ecosystem function-driven interactions between the supplies of multiple ES in a regional context (Peng et al., 2018). Further, studies that have incorporated both landscape connectivity and ES concepts typically only focus on a single ES, are skewed toward specific types of provisioning (e.g., food) and regulating (e.g., pollination) services, and, to our knowledge, have not yet included cultural services (Mitchell et al., 2013). The purpose of this study is to present an approach to address the above research gaps, building on existing ES mapping and modeling and rooted in landscape connectivity theory, where we identify relevant functional interactions between multiple ES and demonstrate how these can be spatially represented across a regional heterogeneous landscape. We demonstrate our approach using existing grid-level data from a case study landscape in the southern interior of British Columbia, Canada, by mapping and assessing the functional interactions between ES from three broad categories: provisioning (plant food agriculture, PA), regulating (waterflow regulation, WF), and cultural (landscape aesthetics, LA; MEA, 2005; Fig. 3.1). Using these, we conceptualize ES supply areas as structural components, and the functional links between these areas as configuration elements within a landscape connectivity framework. We base our approach on existing, and relatively straightforward, spatially co-occurring ES 36 interaction and LCP corridor methods to present a first step toward representing functional interactions between multiple ES. Our multi-step approach has three specific objectives: (1) to define the ecosystem function-based interaction mechanisms between different types of ES supply; (2) to spatially map and quantify these interactions while accounting for LULC heterogeneity; (3) to compare coverages of supply areas and functional interactions across different types of LULC. Figure 3.1. Schematic and definitions for ES supply areas and functional interactions in the case study landscape. 3.3 Materials and methods Our case study area spans the Okanagan region in British Columbia (BC), Canada, which we use to demonstrate a multi-ES functional interaction mapping approach for informing landscape planning (Fig. 3.2). It is located in the south-central interior of BC, is a biodiversity hotspot within Canada and one of North America’s most endangered semi-arid ecoregions (Warman et al., 2004; Kerr and Cihlar, 2004), has a highly diverse assemblage of land use types (see Caslys Consulting Ltd, 2013; Fig. B1), and covers 21,580 km2 from ~276 to 2774 masl. The diversity of LULC and ecosystem types of this multifunctional landscape allow the results to be more widely applicable to other regions relative to a study of a more homogenous landscape. 37 Figure 3.2. Location of the case study landscape in southern interior ‘Okanagan’ region of British Columbia, Canada. Major waterbodies, elevation (masl), and the most populous cities and towns in Okanagan regional districts are indicated. 3.3.1 Approach We demonstrated a flexible approach that can be used to map the functional interactions between multiple ES on a landscape. It is flexible in that it can be adapted to various decision and research contexts (e.g., Preston and Raudsepp-Hearne, 2017), and can incorporate a variety of methods and models for mapping ES supply and flows. Figure 3.3 provides high-level guidelines for researchers with the intent of supporting standards for comprehensive ES assessments (e.g., Crossman et al., 2013). The guidelines focus on the general technical approach for producing spatial assessment tools that will inform goals of researchers and/or decision-makers; other aspects required to produce thorough ES assessments (e.g., defining issue and context; time and expertise resource logistics; communicating results) are presented elsewhere (e.g., Preston and Raudsepp-Hearne, 2017). 38 Figure 3.3. General approach guidelines for mapping the functional interactions between multiples ES within a comprehensive ES assessment framework (e.g., Preston and Raudsepp-Hearne, 2017) 39 3.3.2 Original ES supply data We obtained existing data on the spatial distribution of the ‘supply’ of three ES: (1) plant food agriculture (‘PA’ herein; provisioning = products obtained from ecosystems); (2) waterflow regulation (‘WF’; regulating = abiotic and biotic functions that moderate natural phenomena); and landscape aesthetics (‘LA’; cultural = non-material characteristics that benefit human well-being; MEA, 2005) produced for our study area by Field et al., 2017 (Figure 3.4; Field, 2021). Field et al. (2017) mapped ecosystem attributes and quantified their potential contribution to ES supply based on environmental characteristics and functions that are known to be related to ES production; on explicit incorporation of perceived benefits to humans; or a combination of the two methods (Jakeman and Letcher, 2003; Vigerstol and Aukema, 2011; Field et al., 2017). Spatial data sources for original maps included LULC indicators, remote sensing image interpretation, and were supported by some field-validations. For analytical consistency, raster data for original mapping were assigned the identical spatial projection by resampling to ~29 m x 29 m resolution based on the size of digital elevation model (DEM) cells; therefore, ES models accounted for fine-scale heterogeneity of parameters across the landscape. Original maps were created using ArcMap 10.2 and 10.4 (ESRI, 2011), and R (R Development Core Team, 2013). For full details see Field et al., 2017; data sources were all updated since 2001, and are summarized in Appendix B.3; data are available on the Open Science Framework (OSF; Field, 2021). Figure 3.4. Maps showing the original, full extent of distribution and weighting for ES supply areas in the case study landscape, including (a) plant food agriculture, (b) waterflow regulation, and (c) landscape aesthetics (Field et al., 2017). PA is an economically and culturally important ES in our study area (e.g., OVEDS, 2013; Kyle, 2018), and its supply was mapped based on the spatial extent of all crop types used directly for human nutrition, 050100150200250area_ha(a) Plant Agriculture (b) Waterflow Regulation (c) Landscape Aesthetics050100150200250area_haES Model Weight High Low 40 all of which are concentrated primarily in valley-bottom areas (Field et al., 2017). From these data, we dissolved boundaries between adjacent Agricultural Land Use Inventory (ALUI) polygons which, in some cases, resulted in different crop types being merged into a single node (MoAg, 2017). We did this to generalize the mapping of functional interactions between PA as a whole, and the other ES considered in this study, as the rationale for the interactions between PA and the other ES were consistent for all crop types. This outcome fits with our method of PA supply area valuation, which is based solely on potential crop area (ha) and not on crop type. Field et al. (2017) mapped the terrestrial areas that provide WF mapped as a function of soil texture, slope, land use and land cover (LULC)-specific perviousness (normalized difference vegetation index, or NDVI; Appendix B.3—Table B2), and functionally relevant ecosystem types including floodplains, riparian areas, wetlands, and seasonally flooded fields (‘influential landscape features’ -– ‘ILF’ herein). WF supply areas were defined as those that sustain natural hydrology in dedicated areas, and protect against flooding and droughts, both of which are persistent environmental concerns in the study region (Haughian et al., 2012). Several wetland areas were excluded from the Field et al. (2017) WF map due to the absence of soil texture data, which was one of the inputs for the waterflow infiltration model. Because wetlands are so critical to supporting WF and are relevant to the ecosystem functions that underpin the supply of several other ES, we added these areas back to our WF map by re-running the infiltration model under the assumption that all wetland areas without soils data have 100% saturated hydraulic conductivity, and then applying an ILF multiplier per Field et al. (2017). These resulting raster values ranged from 19 to 1200 (mean = 237.7; st.dev = 120.1), and wetlands coincident with mapped floodplains provided the highest-value WF supply areas in the region. Lastly, Field et al. (2017) mapped LA supply areas based on models of perceived values of different LULC types in the region, on ‘visual condition’ ranging from preserved to manicured lands, and on the visibility of areas from various viewpoints across the case study region. LA supply areas spanned both terrestrial and large aquatic (i.e., lakes, rivers, manmade reservoirs) areas. We did not separate adjacent terrestrial from aquatic LA supply areas as 13 LULC (10 terrestrial; three aquatic) values were used as input for original LA mapping, in conjunction with two other valuation methods (i.e., tourism brochure assessment; viewshed analysis), and we aimed to keep supply area delineation methodologically as consistent as possible across different ES types (e.g., for amalgamation of immediately adjacent supply areas). As LULC data overlapped in certain areas, LULC categories were ranked based on data confidence and relevance to mapped ES (Appendix B.4). We note that LULC datasets were used for mapping all three ES types in our study. We do not believe these interlinkages will impact interpretation of our results as the use of LULC indicators is common and typically the best available proxy data for ES 41 mapping (e.g., Queiroz et al., 2015), and spatial overlap is an inherent characteristic of ES (e.g., Bennett et al., 2009). 3.3.3 Mapping and valuing ES supply patches Based on the original ES mapping data produced by Field et al. (2017), we first established and valued supply area polygons – defined as spatially identifiable regions of higher-than-average-value supply potential – which serve as source and destination patches in a network (Appendix B.1). We then developed a methodology to establish and value functional interaction links between supply areas. Functional interactions were of two broad types: (1) overlapping links, which were areas where the supplies of two different types of ES occur in the same place, and there is an underlying ecosystem function-based interaction between them; and (2) topographic links, which were mapped based on the functional interactions between two ES supply areas separated in space. Links could exist in one or both directions, with unique functional interaction mechanisms operating from one ES to another. Lastly, we compared the coverage of top-value ES supplies and their linkages on the major LULC types found in the region. The details of our approach are provided below. To spatially partition the landscape into ES supply areas and establish the links between them, we developed a rationale based on interdisciplinary methods for assessing complex and connected natural systems (Bialonski et al., 2010). From the perspective of the landscapes’ capacity to provide ES, we defined subsystems as discrete areas with the greatest potential for providing ES supply (Appendix B.5), while the functional interactions between such areas were represented by spatial connections (also referred to herein as ‘links’ or ‘corridors’). We delineated ES supply areas based on approaches used in landscape connectivity and ES mapping studies: spatial polygons that represent high-value supply ES patch boundaries (e.g., Bangash et al., 2013); and the aggregation of immediately adjacent clusters of high-value supply spatial grid cells (e.g., Gardner, 1999; Urban et al., 2009; Qiu and Turner, 2013; Field and Parrott, 2017). Aggregated areas became supply area polygons and were valued based on the summed raster values therein, then normalized on a unit-less scale from 1 to 10,000. Any areas either lacking the potential for ES supply, or below a high-value supply threshold (Appendix B.5), were represented as the landscape matrix where functional interaction links between supply areas could exist (Field and Parrott, 2017). In reality, such spatial interaction networks are dynamic through time (Boesing et al., 2020), though here we consider a static snapshot of the present state of ES supply in our study region to clearly illustrate real-world application of a novel approach for mapping the ecosystem functions underpinning multiple types of ES supply. 42 3.3.4 Establishing functional interactions between ES supplies We define ES functional interactions as areas on the landscape where one ES supply area influences the supply of another via underlying ecosystem functions. We identified spatial functional interactions between ES supply areas either as those that are connected through their overlap in space, or those that traverse the landscape through the relatively low value (i.e., sub-50% threshold) ES matrix. For these two cases respectively, we applied spatial overlay analysis (e.g., Qiu and Turner, 2013), or identified interactions using a stepwise procedure involving least-cost path (LCP) analyses akin to those applied in wildlife connectivity studies based on species movement and habitat attributes (Urban et al., 2009). Movement of organisms and matter across a landscape is often specifically defined in a single direction as a result of biophysical (e.g., waterflow, topography) or biological (e.g., movement from source to destination areas) realities, with multiple link types representing qualitatively unique connections that exist between patches (Zhang et al., 2007; Urban et al., 2009). That is to say, an area on the landscape producing multiple ES supply types may have functional links between ES of the same type in different locations, between different ES types in the same location, or with different ES types in different locations. For the three ES we considered in this study, we characterized eight (8) spatial link types by the directional functional interaction links between high-value ES supply areas. The rationale behind these interaction mechanisms is summarized in Table 3.1. As model distribution and valuations were based on the original relatively high-resolution supply area mapping, they also accounted for model parameter heterogeneity across the study area (Field et al. 2017). We identified two high-level types of functional interactions: overlapping (n = 5) and topographic (n = 3). Overlapping links were defined as areas where the supplies of two different types of ES occur in the same location on the landscape and a functional interaction exists between two ES. We used the high-value ES supply area maps (Fig. 3.5b–d) to identify areas where each pair of ES overlapped (directionally) based on related ecosystem functions (Table 3.1) using a GIS clip procedure (see Appendix B.5 for step-by-step details; Field, 2021). The resulting single-part polygons of overlapping links represented the functional interactions between spatially co-occurring ES types (Fig. 3.5e–i). 43 Table 3.1. Rationale behind functional interaction mechanisms, directionality, and weighting between top-value ES supply areas. Directionality is represented by the top row as originating (or ‘source’) ES supply areas; and the left column as recipient (or ‘sink’) ES supply areas (PA, WF, or LA). Functional interaction mechanisms are distinguished by Link Type (i.e., Overlap or Topographic), and their definitions and weighting rationale are provided in matrix cells. If no functional interaction exists from one ES to another, the matrix cell indicates that there is ‘None’ in either direction, or that there is an interaction in the ‘Other direction’. Superscripts denote the following references: (1) Crossman et al., 2013; (2) Power, 2010; (3) Daniel et al., 2012; (4) Zhang et al., 2007; (5) Bennett et al., 2009; (6) Poff et al., 1997; (7) Burmil et al., 1999; (8) Raudsepp-Hearne et al., 2010; (9) Nicholls and Altieri, 2012. Supply areas linked from linked to Link Type Plant Agriculture (PA) supply area weight: potential PA crop area (ha) Waterflow Regulation (WF) supply area weight: summed WF model value Landscape Aesthetics (LA) supply area weight: area (ha) x LA model value Supply areas Plant Agriculture Overlap None WF on PA croplands4,5 link weight: summed WF model supply area values within PA supply area Other direction Topographic WF downslope4,5 link weight: summed WF model values along LCP pathway from WF to PA supply area None Waterflow Regulation Overlap PA croplands providing WF regulation1,2 link weight: all summed WF model values within entire PA supply area None LA areas providing WF regulation8,9 link weight: summed WF model values within LA supply area Topographic Other direction WF downslope6 link weight: summed WF model values along LCP pathway from WF1 to WF2 supply area Other direction Landscape Aesthetics Overlap PA cropland providing LA3 link weight: summed LA model values within PA supply area WF on LA areas7 link weight: summed WF model supply area values within LA supply area None Topographic None WF downslope7 link weight: summed WF model values along LCP pathway from WF to LA supply area Topographic links were based on the functional interactions that connect the supplies of two spatially separated ES areas across the landscape. Based on the three ES we considered, topographic links always originate at a WF supply area, and represent the influence of upslope water regulation on the maintenance of the natural hydrologic processes that help support PA (e.g., crop growth and nutrient retention; DeLaney, 1995; Nelson et al., 2009), WF (e.g., natural baseline waterflow regulation; Nelson et al., 2009), and LA (e.g., maintenance of hydrology-dependent vegetation and aquatic features deemed to have high aesthetic value) supplies in downslope areas. We developed a stepwise procedure to create topographic links between ES supply areas. First, we used least cost path analyses (LCP) to map directional ecological corridors from each WF supply area to sub-basin specific, lowest-elevation outlet (‘goal’) points based on the assumption of downslope surface waterflow over a DEM surface (e.g., Melles 44 et al., 2011). Following this, we segmented resulting LCP lines to produce separate topographic corridors between pairs of supply areas (Appendix B.5—Fig. B3). We then identified ILFs as additional WF polygons downstream of each sub-basin in the valley-bottom and associated with wetlands, floodplains, riparian areas, and/or seasonally flooded fields, which are functionally linked to upstream hydrological regulation. We connected ILFs to each upstream sub-basin outlet point, and individually merged these sub-basin lines with the LCPs for each WF supply area within that sub-basin (Appendix B.5—Fig. B3). Appendix B.5 provides details on the above approach and procedures used to address other analytical nuances; a summary of how the topographic link approach was validated is provided in Appendix B.6. In addition to spatially identifying functional interactions between pairs of ES, we quantified the weight of these interactions based on assumptions around the underlying mechanisms (e.g., Urban et al., 2009). We based valuations on the original ES supply area maps, which assigned each raster cell in the map an ES value equivalent to the results of the underlying models (Field, 2021; Field et al., 2017), and on the assumptions summarized in Table 3.1 and discussed in Appendix B.5. We acknowledge that alternative ecosystem function models could be used to produce more nuanced or accurate measures of link weightings (e.g., Cadotte et al., 2011). However, we chose to base our link quantification on high-level and readily calculable assumptions in an effort to provide simple, replicable, and easily communicated metrics to inform applied, often resource-limited, decision-making for corridor, conservation, and protected area placement. 3.3.5 Comparison with regional LULC To compare the spatial coverage of supply areas and their linkages, and to aid in our assessment of potential uses of results for on-the-ground planning and management, we calculated the proportion of several high-level LULC categories intersected by each of the high-value supply areas and eight link types identified in the above analyses. We selected several LULC categories to provide both local and regional decision-makers additional information about where on the landscape ES functional interactions are distributed, including forests, grasslands, shrubs, parks, aquatic areas, wetlands, rock and exposed land, agriculture, residential, and urban areas. We calculated the total area (ha and %) of LULC types covered by each link type, and the proportions of study area total LULC covered by each link. ArcMap 10.7.1 (ESRI, 2011) and R (version 3.6.2; R Development Core Team, 2013) packages sp 1.4–5 (Pebesma and Bivand, 2005; Bivand et al., 2013), sf 0.9–8 (Pebesma, 2018), rgdal 1.5–23 (Bivand et al., 2015), raster 3.4–5 (Hijmans and van Etten, 2012), rgeos 0.5–5 (Bivand et al., 2017), maptools 1.1–1 (Lewin-Koh et al., 2012), and stringr 1.4.0 (Wickham, 2010) were used to build, assess and visualize the ES functional interaction map. LCP analyses and subsequent stepwise link refinement were run using 45 R package gdistance 1.3–6 (van Etten, 2017). For transparency and reproducibility, data, R scripts and further details on our methodological procedures are available on the OSF (Field, 2021; Appendix B.7). 3.4 Results 3.4.1 Distribution and values of ES supply areas Based on existing ES supply area mapping from the study area (Field et al., 2017; Fig. 3.4), top-valued 50% supply areas were distributed north-to-south across our study region (Fig. 3.5b–d). Plant foods are grown primarily in valley bottom areas in the Okanagan, and thus PA supply areas (n = 1497) were concentrated in lower-elevation and population-dense regions with similar coverage to the original PA map (distribution of specific crop types detailed in Field et al., 2017). The highest-value PA supply areas were coincident with the largest farm parcels, present in the agriculture-rich areas of the south, north and east-central Okanagan. Given the extensive coverage of their original model results, top-value supply areas for both WF (n = 7350) and LA (n = 5262) were distributed fairly evenly across the entire study area. The highest-value WF supplies were associated with stream riparian areas in larger, partially protected sub-basins of the southwest, and with riparian and wetland complexes in the central- and north-east. Our results suggest that the highest value LA supplies were associated with large areas of upland forests, rivers, lakes, and protected parkland in the southwest and northeast, with relatively lower cumulative LA values in the more heavily populated valley bottom. It is worth noting that, as our method of delineating distinct LA supply areas was based on the amalgamation of immediately adjacent raster cells, there were several large LA supply areas that may or may not be subjectively interpreted by human consumers as part of a single supply area. Issues with inherent subjectivity around LA mapping and assessment are common (e.g., van Zanten et al., 2016, see also Daniel et al., 2012), and this could lead to variable results in strength and physical location of cultural supply areas and their inter- and intra-ES linkages. Even nuances within a single cultural ES valuation method can lead to complex results; for example, tourist’s aesthetic appreciation of landscape features can differ from that of residents (Beza, 2010). That said, the focus of this study is not on how to produce the most accurate spatial representation of ES and their functional interactions, but is on demonstrating a connectivity-based approach for visualizing and evaluating multi-ES interactions. The original LA value distribution map is reproduced in Figure 3.4c (Field et al., 2017). 46 Figure 3.5. Distribution and weighting of top-50%-valued ES supply areas and functional interactions on the case study landscape. Insets show (a) all top-value supply areas and links; top-value supply areas for (b) plant food agriculture (PA), (c) waterflow regulation (WF), and (d) landscape aesthetics (LA); overlapping links from (e) LA to WF, (f) PA to LA, (g) PA to WF, (h) WF to LA, and (i) WF to PA; and topographic links from (j) WF to WF, (k) WF to PA, and (l) WF to LA. Adjacent circular coxcomb charts represent the proportion of ES supply and link areas covered by major LULC types. LULC types are color-coded and include forests, agriculture, rocks/exposed areas, residential areas, shrubs, grasslands, urban areas, water, wetlands, and areas with unknown use and/or cover. 47 3.4.2 Distribution and values of functional interactions between ES supplies The spatial distribution and value of functional interactions between overlapping ES were predictable based on the extents of supply area mapping and on the rationale we applied to link weighting. Bi-directional overlap links between WF and LA (n = 9363 in each direction) were distributed across the entire study are (Fig. 3.5e,h). The highest-value links from LA to WF were associated with stream and lake riparian areas in both populated and remote valleys in the north, with riparian and wetland complexes in the central-east, and with remote stream and river riparian areas in the southwest. Similarly, the highest-value links from WF to LA were present in stream and river riparian areas in the southwest, and with stream riparian and wetland complexes in the central-east. For overlapping links from PA to LA (n = 174), link distribution was sparse throughout the valley bottom and limited to croplands with high aesthetic value; primarily associated with vineyards and orchards (Fig. 3.5f). In terms of bi-directional overlap links, the majority of PA supply areas were linked with WF regulation areas throughout the valley bottoms (WF to PA n = 1,220; PA to WF n = 1320), with highly-weighted links typically associated with cultivated lands, fields, crop transitions, vineyards, and orchards near to (or containing) riparian, floodplain, and/or wetland areas (Fig. 3.5g,i). Topographic links from high-value WF supplies to other ES supply areas revealed corridors variable in length and weight flowing across the landscape, sometimes linking ES supplies ~200 kms apart. Between pairs of spatially isolated WF areas, corridors (n = 484,602) approximated the location of watercourses (FLNRO et al., 2017), as was expected due to the elevation-based LCP resistance surface used to simulate surface waterflow. The highest-value WF to WF corridors were observed through the large central Okanagan Lake system and several of its relatively low-order tributaries; in high-order valley-bottom rivers, streams and lakes in the southwest; and in the larger valley-bottom rivers of the northeast. These observations resulted from links between WF supply areas and the influential landscape features (ILFs = floodplains, riparian areas, wetlands, seasonally flooded fields; Field et al., 2017) that are prevalent next to valley-bottom aquatic areas. From WF to LA supply areas, corridors were scattered throughout the study area (n = 2864), with the majority of links associated with the more populated valley-bottom areas in the central Okanagan basin, and with the highest-value links in higher-order streams where sub-basins contained larger numbers of WF supply areas upstream of one or several LA supply areas. Links to PA were only possible where farmlands were present within the sub-basin of the associated WF supply area, or downstream where farms were within ILF zones. Therefore, such corridors were concentrated in sub-basins along the central valley-bottom (n = 5,256), with particularly high weights in a northern agricultural valley, in the largest sub-basin in the central Okanagan watershed, and in one southern basin (Fig. 3.5l). A general trend we observed for all topographic links was the co-occurrence of higher value corridors with larger rivers and streams, rather than being associated with 48 smaller headwater streams. This was a result of the culmination of overlapping corridors from several headwater WF areas in the lower elevation stream valleys that had the largest number of supply areas for the related ES pair type. When all link types were included on a map of accumulated weights, it highlighted expansive networks of high-value functional interaction corridors between all three ES types and distributed across the entire landscape (Fig. 3.6). The highest-value link areas were found in low- and mid-elevation riparian areas across the landscape; in a mid-elevation wetland complex of the eastern-central region; in riparian and surface waterflow corridors associated with a large eastern-central sub-basin; and generally, in areas where several (or all) of the eight link types co-occurred. Notably, the accumulation map revealed that several of the highest-value areas were not coincident with the highest-value on any of the individual link-type maps (Fig. 3.5e–l), and were sometimes in relatively remote, higher elevation areas. 49 Figure 3.6. Distribution and weighting of link values amalgamated across all eight (8) overlapping and topographic link types across the case study landscape. 3.4.3 Spatial coverage of supply area and linkages across LULC types The distribution of ES supply areas and links were only in part determined by the underlying LULC types (based on biophysical and/or legal designations) included in the original model parameters defined by Field et al. (2017). In decreasing order of coverage of our study area (21,580 km2), the high-level LULC types are as follows: 76.7% forest; 11.9% park (NB: overlaps with forest, grassland, shrub, rock and exposed categories); 6.8% grassland; 5.8% shrub; 3.7% agriculture; 3.0% waterbodies (lakes, rivers, reservoirs); 1.6% residential; 1.3% rock and exposed land; 0.8% wetland; 0.3% urban; and 0.1% unknown (Appendix B.3; Fig. B1). For the subset top-valued ES areas, almost all PA supplies were, Amalgamated Link Weights High Low 50 unsurprisingly, on agricultural lands (~100.0%), but only covered 15.8% of all croplands in the region. Both WF and LA supplies were found mainly on forested lands (78.7% and 93.0%, respectively) and within parks (14.0% and 13.1%, respectively). Distribution of top-valued WF supplies covered large portions of most LULC types in the study area (19.7–50.6%; NB: 0% aquatic), including 99.6% of all mapped wetlands. Top-valued LA supplies spanned the majority of aquatic (98.8%), forested (81.9%), park (74.4%), and wetland (69.1%) LULC types (Fig. 3.5a–d). Similar trends in LULC coverage were observed for overlapping links, with links from PA to LA found mainly on agricultural lands (69.7%) and in forests (28.5%). In both directions between PA and WF, links were mainly on agricultural lands (both 98.0%) and covered 5.8% and 5.7% of all croplands in our study area from PA to WF and from WF to PA, respectively. In both directions from WF to LA and LA to WF, links were mainly on forested land (90.8% and 91.1%, respectively) and in parks (14.7% and 14.6%, respectively). Moreover, these links covered large portions of all wetlands, parks and forests (from WF to LA 68.0%, 39.2% and 37.6%; from WF to LA 68.0%, 39.2% and 37.8%, respectively) in the study region (Fig. 3.5e-i). For topographic corridors, we found that LULC coverage was less consistent with relevant ES supply area coverages. Corridors between different WF supply areas were found mainly in forested (42.9%) and aquatic (35.2%) areas, with more minor distribution in park (7.1%), grassland (6.9%), and agricultural (5.2%) LULC types. Notably, topographic WF corridors covered 24.3% of the entire aquatic areas found in our study region. From WF to PA, corridors mainly traversed forested areas (54.1%), followed by agricultural (19.8%), park (10.7%), grassland (10.6%), and residential (5.4%) LULC types. From WF to LA, corridors were mainly found in forested areas (57.3%), followed by parks (14.5%), agriculture (12.7%), and grasslands (10.2%; Fig. 3.5j-l). All LULC overlay analyses results are summarized in Appendix B.4. 3.5 Discussion We applied a novel approach to mapping and modeling the functional interactions between multiple types of ES across a regional landscape. By demonstrating the application of this approach on three ES categories for a case study area, we identified and mapped eight link types connecting ES supply areas on the landscape. The results demonstrate the variety of ways categorically distinct ES can exhibit functional interactions related to their production capacity, and to the potential importance of considering these in land use planning for ecological connectivity. 51 3.5.1 The case study: ES functional interactions across a heterogeneous regional landscape The functional interaction links we observed between ES revealed high-value multifunctional areas on the landscape that were not necessarily predictable from supply area mapping. Across all link types we found heterogeneous distribution as well as spatially distinct areas of markedly higher value, or functional interaction ‘hotspots’, relative to surrounding areas (e.g., Alemu et al., 2021). But one surprising observation is that the weighted amalgamation of all eight link types uncovered high-value areas that were not present on any of the ES supply or pairwise link maps. This finding points to nuances that can be discovered when multiple ES and multiple linkage types are assessed together and suggests that the spatial focus of planning for service provisioning may shift when functional interactions between several ES are considered. Ultimately, such multifunctional areas represent possible conservation priorities that, if degraded or lost, may cause significant disruption of ES networks. Understanding the complexity of functional interactions between multiple ES has been highlighted as a critical challenge in planning for sustainable multifunctional landscapes in the face of changing environmental conditions and management interventions (Dee et al., 2017). A recent review of studies that have modeled interactions between multiple ES found that a large proportion did so from the perspective of co-occurring production synergies and trade-offs, but that the examination of flows, and the identification and quantification of explicit functional interactions remain largely unexplored (Agudelo et al., 2020). Ultimately, simultaneously modeling multiple ES continues to be difficult in part because of methodological inadequacies and the complexity of the ecosystem functions involved (Kolosz et al., 2018). Our approach provides a new and flexible framework that can help address these challenges. From initiation points within WF supply areas, our modeling revealed several functional interactions that operate over both short and long distances. Some of these topographic corridors extended over 200 km within the boundaries of our study area and, based on the underlying ecosystem function theory, also extend across the Canada-USA border to wetlands, riparian areas, seasonally flooded agricultural fields, and service supply areas along the extent of the Columbia River to the Pacific coast confluence between Washington and Oregon states, more than 1100 km downstream from the originating supply areas in our study region. Similar long-range interactions may be observed for other water-related ES (e.g., water provisioning, water quality), as both mean-annual water volume and water quality have been found to be heavily influenced by first-order headwater catchments, even in watersheds with large high-order rivers (Alexander et al., 2007; Freeman et al., 2007). This is relevant to criticisms around the current seniority-based system of water licence rights in BC, which generally ignores environmental waterflow impacts for existing water licences (Gage 2014). Additionally, WF exhibits close- and long-range interactions with 52 many other ES not modeled in our study. For example, water extraction and damming to take advantage of freshwater provisioning supplies alters natural hydrological regimes (e.g., Jackson et al., 2001); afforestation reduces peak and maintains base waterflows (e.g., Zhang et al., 2007; Power, 2010), whereas deforestation destabilizes waterflows (Mäler et al., 2013); areas providing WF supply help to decrease pollution, flood-related turbidity, and residence time of chemicals in lakes (Burmil et al., 1999; Blackstock, 2001; Jackson et al., 2001; Bennett et al., 2009); certain pollination services can be facilitated by moving water (Biesmeijer et al., 2006); and some recreational activities are dependent on the maintenance of waterflow (e.g., fishing, kayaking; Burmil et al., 1999). Based on our observations of the potential for both short- and long-range functional interactions, ES planning for other water-related services should also consider the potential impacts of management interventions on related services areas and jurisdictions downstream. Our study demonstrates that functional interactions between ES often span several LULC categories, and that trends in dominant cover types may be unexpected relative to those associated with related supply areas. Certain areas or cover types are sometimes considered ‘hotspots’ for ES production, that is, they provide several different, often high-value, ES (e.g., Qiu and Turner, 2013). For example, wetlands provide flood and waterflow control, storm protection, erosion control, groundwater supply, water quality maintenance, nutrient waste disposal, habitat to support fishing and hunting, natural materials, biodiversity, micro-climate stabilization, carbon sequestration, recreation, and aesthetic value (Brander et al., 2006). Agricultural lands can provide many ES beyond food for humans, such as habitat and food for pollinators, biological pest control (e.g., Loos et al., 2019), and tourism (e.g., Wagner and White, 2009). We assessed the potential for LULC-associated functional interaction hotspots in our region using LULC comparisons. Forested lands clearly stand out as being important for the regulating and cultural ES we investigated. Forests are often identified as hubs for maintaining regulating and cultural ES, including surface water provisioning and quality, soil retention, nutrient retention, pollination, carbon storage, climate regulation, habitat quality, and recreation (Matson et al., 1997; Brauman et al., 2007; Qiu and Turner, 2013; Karimi et al., 2021). Notably, although land zoned as ‘parks’ make up only 11.9% of the study area, they represent important landscapes for WF and LA supply and overlapping links, and as functional interaction corridors between all ES types we investigated. Both the above observations are likely driven by the suite of ecosystem functions present in complex forest, grassland and shrub ecosystems (e.g., vegetation-mediated infiltration, Mills et al., 2004), and by the contribution of wildlands and parks to LA (Thompson, 1995). From the perspective of functional interactions, our study suggests a need to expand upon the ES ‘hotspot’ notion by considering that other LULC types beyond those associated with supply areas may be serving as critical corridors for interacting ES. A clearly delineated example of this is the ecosystem function links between terrestrial and aquatic ecosystems. Areas of land 53 adjacent to waterbodies are known to provide various regulation services in addition to WF, including erosion and water quality regulation through soil- and vegetation-mediated retention and filtration (Mills et al., 2004). Whereas the model parameters we applied for WF preclude supply area coverage within any aquatic areas, the LULC proportions we observed within upland and downslope WF corridors traversed 24.4% of all surface waterbodies in the region and demonstrated that aquatic areas represent some of the most high-value linkages between different production areas for this ES. In addition, croplands proportionally represent the third-largest cover type in the corridors between upland WF and downslope LA supplies, with the majority of these corridors found in riparian zones, or on farms adjacent to wetlands and waterbodies. The synergistic association of WF and PA supply areas has been observed in other ES interaction studies (e.g., Qiu and Turner, 2013), and stems from crops’, especially deep-rooted perennials, ability to provide a variety of hydrological benefits including increased water infiltration and recharge, reduced runoff, and mitigation of peak waterflows (Dabney, 1998; Tilman et al., 2002; Brauman et al., 2007; Power, 2010). These observations have implications for ecosystem- and habitat-based management programs as LULC types are often imposed as boundaries for interventions and/or institutions (e.g., BC Ministry of Agriculture). Especially in heterogeneous regional landscapes, our results point to potential for increased need for cross-jurisdictional coordination when planning for functional interactions in the management of multiple ES. 3.5.2 The conceptual shift: from correlative to functional interactions Our approach reveals cross-landscape functional interactions that represent important drivers of ES production that are undetectable with traditional methods for identifying ES synergies and trade-offs (e.g., Qiu and Turner, 2013; Su and Fu, 2013; Tomscha and Gergel, 2016). It can be used to represent several different types of functional interactions, for example, between different ES that occupy the same space, and abiotic movement from one ES supply area to another across the landscape. Identification of links between spatially co-occurring supply areas is similar to a representation of paired ES ‘interactions’, a concept for identifying synergies and trade-offs among services, as well as identifying groups of services that repeatedly occur together across a landscape (i.e., ‘ES bundles’; Bennett et al., 2009). Our methods take a closer look at the concept of ES interactions by explicitly representing the ecosystem function-based mechanisms behind the co-occurrence of ES in the delineation and valuation of these areas (Bennett et al., 2009). Investigation of interaction mechanisms with respect to multi-ES assessment has been highlighted as a crucial step toward providing more rigorous information to inform the management of multifunctional landscapes (Alemu et al., 2021; Thierry et al., 2021), and our study is one of the few to provide this information at the regional scale (Agudelo et al., 2020). 54 Areas of ES supply are not necessarily spatially congruent with the discrete structural components traditionally considered in landscape connectivity frameworks (e.g., habitat patches, specific LULC types); therefore, linkages between ES are also unlikely to be coincident with these components (e.g., movement of organisms and matter; Brooks, 2003). For example, the global benefit of carbon storage and sequestration depends only on the quantity of natural landcover, and not the spatial arrangement of patches (Mitchell et al., 2015). Although protected areas and intact habitat patches are important spaces for some of the plants, animals, and abiotic ecosystem components responsible for providing ES supply and functional interactions are not bound by human-defined reserve areas, and many ES are produced completely by and functionally interact with one-another in human-modified landscapes (Schröter et al., 2019). Further, functional interactions associated with certain ES will be limited by distance thresholds and/or human or ecological barriers to ecosystem functions. For example, crop production can benefit from interspersion of natural habitat throughout agricultural landscapes, which can increase pollination and pest control services delivery from species that can only move limited distances from their habitat patches (Tscharntke et al., 2005). There may be spatial congruency between existing wildlife movement corridors and certain regulating services, especially those that depend on the movement of organisms for their delivery (e.g., pollinators, disease control, pests and their predators, seed dispersal; Kremen et al., 2007), which suggests that there may be opportunities for win-win conservation initiatives for wildlife and ES together. Our approach can be used to explore this possibility, and to explicitly map and assesses the mechanisms behind distance-threshold-mediated and cross-landscape ES interactions in general. 3.5.3 Limitations and opportunities for future work We identify several limitations of our approach and suggest related opportunities for future work. Firstly, we only included three ES in our study and comparisons, a decision driven by available data and desire to clearly demonstrate a new approach while using a diverse subset of ES. A small number of ES means we are limited in the generalizations we can make, especially as they pertain to LULC- and ecosystem-relevance of the potential for ‘hotspots.’ Investigating a limited number of ES is common among studies that model interactions among ES (Agudelo et al., 2020), with data limitations, complexity of socio-ecological processes involved, and methodological gaps cited as barriers to inclusion of all ES (Kolosz et al., 2018). However, our choice to include only three ES was motivated by our goal to provide a straightforward case study of how each of the three broad ES categories (i.e., non ‘supporting’; MEA, 2005) can be represented in the same study. Our approach is easily adaptable to including an unlimited number of ES, though the complexity in representing the functional interactions between them may increase disproportionately to the number of ES included, which will result in computational challenges, and limited data and/or gaps in our understanding of mechanisms may preclude modelling of certain 55 pairwise functional interactions (Field and Parrott, 2017). As evidenced by the lack of known functional interactions between some of the ES in our study (e.g., topographic links between PA and LA; Table 3.1), some pairs of ES may not exhibit inter- or intra-ES links, although these can still be included on maps as disconnected supply areas on the landscape. Further, we note that only synergistic interactions were identified among the case study ES we included, but no trade-offs were represented. Although the latter has been identified among the ES included in our study (e.g., water extraction for agriculture can disrupt hydrologic cycles; Janssen et al., 2006), we did not model these due to lack of data on specific trade-off mechanisms. The incorporation of other services may reveal both positive and negative effects of connectivity on ES supply as a result of complex multi-ES functional interactions; for example, the rate of waterflow through riparian areas may increase filtration and water quality regulation, but decrease downstream water provisioning (Mitchell et al., 2013). The presence of potential trade-offs, as well as ecosystem dis-services (e.g., competition for water and pollination among different LULC types; spread of pests and diseases; Zhang et al., 2007), is of critical importance to informing management, as maximizing the provisioning of all ES on a landscape is usually not simultaneously possible (e.g., Qiu and Turner, 2013). We encourage future applications of our approach to represent trade-offs and negatively valued functional interactions between ES where appropriate. Our case study maps and quantifies functional interactions between ES at a snapshot in time due to lack of temporal data available for the ES included in our study. However, modifications of natural landcover can change the number, size, shape, isolation, and distribution of ecological patches across the landscape and their proximity to human beneficiaries, all of which may lead to positive, negative or neutral impacts on ES supply and flow (Mitchell et al., 2015). For example, in the face of climate change increases in dryland aridity causes grasslands and savannahs to metamorphose into shrublands as the latter grow better in sandy, nutrient-poor soils (D’Odorico et al., 2012; Phillips et al., 2019). In our study area specifically, such a shift would have implications for ES coverage and value through the dependency of model variables on underlying vegetation characteristics (Field et al., 2017), and thus influence future ES supply and functional interactions. The ultimate impacts of landscape changes on ES are dependent on the structure and quantity of such changes, and on the biophysical process, ecosystem functions, species, and human activities driving the ES supply of interest, as well as the flows to and demands of human beneficiaries (Mitchell et al., 2015). Further, it has been shown that spatial correlations between pairs of ES can exhibit interannual variability (e.g., Renard et al., 2015; Li et al., 2017), and that snapshots in time are not good predictors of how their relationships may change over time (e.g., Mitchell et al., 2020). Future studies could use the ES functional interaction framework presented here to assess how changes in LULC ultimately have cascading impacts on multiple ES across a landscape (Bagstad et al., 2013b; Grêt- 56 Regamey et al., 2017; Rieb et al., 2017), which can practically be achieved by incorporating seasonal and interannual variations in ES supply, demand and functional interactions (e.g., decreases in fresh water provisioning during dry months; Field and Parrott, 2017). We acknowledge that other existing methods for spatially identifying and evaluating connectivity may be more appropriate for certain functional interactions between other ES not included in this study. Examples include least cost corridors (Singleton, 2002), circuit theory (McRae and Beier, 2007; McRae et al., 2008), graph theory (Fall et al., 2007; Pinto and Keitt, 2008, Rayfield et al., 2011), spatial networks (Phillips et al., 2008; Parks et al., 2013), Euclidean distance mapping (Doak et al., 1992), radius buffers (Laliberté and St-Laurent, 2020), and deterministic eight models (Mark, 1984). These could also be combined with dynamic modeling approaches (e.g., Bayesian belief networks; scenario modeling) that can incorporate measures of uncertainty (e.g., Karimi et al., 2021; Sahraoui et al., 2021; Appendix B.8). Specifically, incorporating measurable changes in ES characteristics (e.g., supply quantities or distributions) will allow researchers to evaluate the degree of influence one ES supply area has on another. We chose to employ only LCP analysis mainly because the topographic ES interaction links in our study all originated at WF supply areas, corridors all were to represent water flowing downslope, and because LCP has been shown to be a valid method for approximating drainage networks while being capable of overcoming issues around topographic depressions (Melles et al., 2011). Therefore, a DEM-driven model representing water moving downslope was deemed the most appropriate for these types of ES functional interactions in our study region (which was supported by validation analyses; Appendix B.6). This provided relatively simple and accessible representations of corridors between supply areas to support the primary goal of this paper, that is, to demonstrate a novel approach for conceptualizing how ES supplies functionally interact across a landscape. Future research should compare and validate alternative spatial connectivity mapping and valuing approaches (e.g., Melles et al., 2011) for predicting ecosystem function-based movement between other ES types, with validation approaches tailored to the specific ES under study (e.g., Appendix B.6). For example, functional interactions between pollination supply areas and PA could be represented by first identifying pollinator habitat, which may encompass natural and semi-natural habitat areas within and/or adjacent to PA areas (e.g., Power, 2010). Then the functional interaction mechanism may be represented by modeling pollinator movement between habitat areas (e.g., three-dimensional surface models; Abdel Moniem and Holland, 2013). As long as the researcher clearly defines the known functional interaction mechanism(s) a priori and selects the appropriate model(s), our approach is flexible in that it allows for a variety of interactions to be spatially represented, and for the use of several methods in delineating interactions between multiple ES which can then be incorporated into the same map for spatial representation of such connections. Within the defined functional interaction mechanisms, models can allow for the incorporation of complex interactions 57 between ES, such as nonlinear relationships and threshold effects (Thierry et al., 2021). We note that certain multi-ES functional interactions may not be amenable to spatial representation or assessment using connectivity mapping (e.g. air quality with erosion control; animal agriculture with PA from the perspective of direct, non-fodder sources of food for humans, though known fertilizer contributions may allow for a directional functional interaction to be defined), but their supply areas can still be included on maps to display distribution, values, or other metrics. 3.6 Conclusion Our study provides a new approach for the assessment of multiple ES and provides important information on the spatial functional interactions between a variety of divergent types of ES across a diverse temperate landscape in southern interior British Columbia. We are confident that providing a tool for visualization of multiple ES will help address several ongoing challenges: increase awareness and understanding of how dependent humans are on nature; highlight a need to maintain landscape connectivity to support ecological functioning; advance the interdisciplinary science around the ES concept; and help move toward incorporating this science into management of natural capital (Guerry et al., 2015). As the ES concept continues to be developed and refined, considering how ES operate within the context of interconnected, complex social-ecological systems will help improve our ability to meaningfully incorporate multiple ES into decision-making and planning at the landscape scale. Overall, our methods not only allow for the explicit incorporation of the current knowledge of the ecosystem functions that drive interactions between multiple ES, but they also provide decision makers mapping tools that show where these interactions occur on the landscape and how valuable they are to ES production potential. Thus, our approach can help guide planners in predicting how intervention(s) in specific location(s) are likely to have synergistic or antagonistic impacts on ES supply areas in other, sometimes distant places. 58 Chapter 4: Using spatial networks to evaluate the supply of and functional interactions between multiple ecosystem services across a regional landscape 4.1 Synopsis The ecosystem functions that give rise to the supply of ecosystem services (ES) are distributed across multifunctional landscapes and drive the interactions between multiple ES. Maintaining these functional interactions is necessary to sustain the ES supply needed to support societies; however, research and management still focus on mapping functionally isolated individual or bundles of ES, while assessing the functional interactions among multiple ES is not well-developed. This narrow view ignores how ES supply is impacted by different, but functionally interacting ES that operate across a landscape. In this study, we use spatial network analyses to assess the supply of and interactions between multiple ES and evaluate what can be learned from these multi-ES networks relative to traditional- and single-ES evaluations. Our results show that expanding from a single- to multi-ES network leads to a redistribution of the highest-valued nodes and links across the landscape, and that network analyses highlight the importance of certain supply areas not detected by traditional ES valuations. Spatial multi-ES networks can help deepen our understanding of the functional interactions between different ES and provide a tool for the incorporation of these complex ES interactions into applied planning and management programs (Fig. 4.1). 59 Figure 4.1. Graphical abstract summarizing background, results and conclusions of Chapter 4. 4.2 Introduction Human survival and well-being relies on the maintenance of ecosystem functions, which are the interactions among biotic and abiotic components that give rise to multiple ecosystem services (ES; Lyons et al., 2005; Mitchell et al., 2013; Qiu and Turner, 2013; Queiroz et al., 2015). To sustain a landscape’s ability to produce multiple ES (Saidi and Spray, 2018) while minimizing negative effects of increased development and environmental pressures, decision-makers need a clear, nuanced and spatially-explicit understanding of the functional interactions between ES. These are the fundamental mechanisms driving the interactions between the ecosystem functions that underpin ES supply, i.e., the capacity of a landscape to produce ES (Bennett et al., 2009; Felipe-Lucia et al., 2020; Field and Parrott, 2017; IPBES, 2016; Lavorel and Grigulis, 2012; Lorilla et al., 2018; Saidi and Spray, 2018). Network-based approaches provide promising frameworks with which to assess functional interactions; however, networks of multiple interacting ES have rarely been examined empirically. Here, we address this gap by demonstrating how spatial networks of multiple diverse and functionally interacting ES can be used to visualize and evaluate their supply across a large, heterogenous landscape. Understanding and visualizing the relationships between multiple different but functionally interacting ecosystem services is important for maintaining their supply for humansWaterflow regulation (WF) is a regulating ecosystem service, i.e., abiotic and biotic processes that moderate natural phenomena Plant agriculture (PA) is a provisioning service, i.e., products obtained from ecosystems Landscape aesthetics (LA) is a cultural service, i.e., non-material characteristics that benefit human well-being The potential of the landscape to supply these three ecosystem services was modelled and mapped. These are the network nodes Functional interactions were mapped within and among these ecosystem services based on the ecological processes and mechanisms that influence their supply. These are the network links or edges A spatial network of three ecosystem services was built across a large regional landscape Network analyses highlight the importance of supply areas not detected by traditional valuations When multiple interacting services are considered in network analyses, high-value supply areas are identified that are otherwise overlooked when a single service is assessed in isolationSpatial networks can help deepen our understanding of the functional interactions between different ecosystem services, change the prioritization of conservation areas on a landscape relative to traditional and single-service assessments, and provide a tool for the incorporation of complex ecosystem service relationships into applied planning and management programs 60 Through modelling the functional interactions between multiple ES, we can quantify the co-benefits that ecosystems provide for human survival and well-being (Neugarten et al., 2018). Such interactions have traditionally been represented as synergies (i.e., when the supply of one ES positively impacts the supply of one or more other ES), trade-offs (i.e., the opposite effect; Alemu et al., 2021; Bennett et al., 2009), or bundles (i.e., ES ‘interactions’ viewed as consistent associations of several ES across space or time, not necessarily with casual links between one another, nor necessarily representing trade-offs or synergies; Raudsepp-Hearne et al., 2010; Rodríguez et al., 2006). Bundles are often used to visualize and assess interactions between multiple ES and, compared to individual ES assessment, may provide more useful information for decision-making in terms of identifying sustainability issues, helping to identify priority areas for conservation, and predicting potential changes in supply under alternative policy options (Alemu et al., 2021; Saidi and Spray, 2018). However, the bundle approach has several drawbacks. Multifunctional landscapes, those that produce multiple ES, do not necessarily form bundles (Saidi and Spray, 2018), and ES interactions identified as bundles do not typically represent functional interactions, nor do they guarantee predictable responses to driver(s) of change in ES supply (Raudsepp-Hearne et al., 2010). This presents a conceptual mismatch between these methods and how ecosystem functions operate to connect ES supply areas across a landscape, as functional interactions abound among many diverse services and habitats both where they co-occur and where they are separated in space (Field and Parrott 2017, 2022; Agudelo et al., 2020). For example, a wetland may provide water and air purification, flood abatement, waste treatment, and soil formation and maintenance (Zedler and Kercher, 2005). Those ES rely on connections with streams and lakes in other areas (which, among other services, provide water for drinking and irrigation; Gordon et al., 2010) and with upland areas (which, for example, help control erosion, runoff and provide additional vegetation-mediated water purification; Ludwig et al., 2005). The ability to assess how human interventions and changing environmental conditions will impact ES supply is made more challenging by functional interactions between multiple ES. Not fully understanding these functional interactions can result in landscape management activities that lead to unexpected and undesirable outcomes (Dee et al., 2017). Thus, there is a call for the expansion of current ES interaction modelling approaches (Englund et al., 2017) to address complexity in related ecological phenomena by representing the functional interactions between different ES (Agudelo et al., 2020; Kolosz et al., 2018) and providing metrics that can be used to quantify responses to environmental changes (Felipe-Lucia et al., 2020). Network (or graph) theory has been suggested for addressing the above challenges, although few studies have prescribed how to operationalize networks for the assessment of real-world ES functional interactions (Dee et al., 2017; but see Field and Parrott, 2017). Spatial networks provides a variety of powerful tools for assessing complex systems and for addressing the above knowledge gaps in the assessment of functional interactions between multiple ES 61 (Felipe-Lucia et al., 2020; Pocock et al., 2016). Ecological networks are made up of nodes, characterized by landscape data such as location, area coverage, quality of productivity; and links, defined by functional attributes such as presence/absence of relationships, distance, or likelihoods of interactions between nodes (Urban et al., 2009). They can be built to represent functional interactions across a landscape, and specific network analyses can be used to identify which areas are most or least connected, and which nodes, links, and clusters might be most crucial for system-wide function and resilience (Brinkmeier and Schank, 2005; Rayfield et al., 2011). Ultimately the information obtained from network analyses can help inform and prioritize landscape conservation decisions (e.g., Heckmann et al., 2015; Urban et al., 2009). Several theoretical frameworks have been proposed to introduce the concept of ecological networks to metacommunity functionality and ES supply (Cumming et al., 2010; Felipe-Lucia et al., 2022; Gonzalez et al., 2011), and several studies have applied spatial network methods to ES assessment of waterflow regulation (e.g., Cui et al., 2012, 2009; Fortuna et al., 2006; Heckmann and Schwanghart, 2013; Phillips, 2013; Poole et al., 2006), pests and disease (e.g., Margosian et al., 2009), seed dispersal networks (e.g., Janssen et al., 2006), and recreation (e.g., Shih, 2006). Specific examples include networks used to examine surface-groundwater floodplain connectivity (Poole et al., 2006); the ability of an artificial drainage network to control sea level rise (Poulter et al., 2008); and key areas important for support of ecological waterflow requirements that sustain natural growth, biodiversity, social and environmental health, while maintaining the efficient and cost-effective production of water for human consumption (e.g., Cui et al., 2012, 2009; Erós et al., 2011). Analyses of agricultural irrigation networks have found that vegetation clearing and irrigation had negative impacts on the natural hydrological cycle (Janssen et al., 2006). Recreation networks have also been assessed to identify optimal locations and types of tourist facilities (Shih, 2006). However, network-based studies typically focus on a single ES; do not evaluate functional interactions between multiple ES (see also Cui et al., 2012); and/or do not incorporate the different types of nodes or links (Horvát and Zweig, 2012) present in complex real-world networks (Field and Parrott, 2017). The above examples point to opportunities for addressing several gaps in the literature, which can be achieved by assessing multiple types of ES from diverse categories, with multiple types of functional interactions between them, within the same spatially explicit network. The aim of this paper is to demonstrate a new approach for assessing multi-ES functional interactions by developing a spatial network across a regional landscape. We provide examples of what information can be gleaned about the significance of ES supply areas and their functional interactions relative to traditional valuation approaches; specifically, we address the following questions: 1. Do the functional interactions of multi-ES networks have the potential to highlight the importance of key ES supply areas and corridors that are not detected by traditional ES valuations? 62 2. Are different areas of the landscape prioritized when a single- vs. multi-ES network are considered? For this, we partition the landscape from the perspective of ES, where nodes are represented by ES from three diverse categories (as defined by the latest version of the Common International Classification of Ecosystem Services (CICES); Haines-Young and Potschin, 2018), including: (1) plant food agriculture (‘plant agriculture’ herein; provisioning service), (2) waterflow regulation (’waterflow’; regulating), and (3) landscape aesthetics (’aesthetics’; cultural; MEA 2005). Network links between ES supply area nodes are represented by pairwise functional interactions within and among nodes separated in space (‘topographic’ links), or where they overlap on the landscape (‘overlapping’ links; Fig. 4.2). We use several network measures to assess the ability of ES supply areas to facilitate the spread of ecological influence throughout the network, and the importance of specific supply areas and corridors for maintaining functional interactions. Finally, we discuss the potential significance of our approach for management and policy in regional landscapes and show that the concept of spatial multi-ES networks provides a framework with the potential to refine and expand existing ES interactions research. 63 Figure 4.2. Conceptual schematic showing the functional interactions between waterflow (purple), plant agriculture (green) and aesthetics (orange) supply areas on a subset area of the case study landscape. All eight link types (topographic and overlap) represented in our study approach are differentiated by colour-coding and weighted by thickness gradient). ES supply areas and links are nested within a non- or low-value landscape matrix (white area). Adapted with permission from Field and Parrott (2017). 64 4.3 Materials and methods In this study we assessed functionally interacting distributions of multiple ES using spatial networks in which high-value ES supply areas (represented as network nodes) were connected via ecosystem function-based links (Fall et al., 2007). Spatial data formed the structure of the ES networks and were obtained from existing maps produced for our study area; full details on the protocols applied to the development of these data are provided in Field and Parrott (2022), Field et al. (2017), and Field (2021; Fig. 3.4; Appendix C.1). Network attributes were weighted and functional interaction links were directed based on ecosystem function models values and mechanisms, respectively. Comparative mapping and network analyses were then used to evaluate differences between traditional and network valuations, and between single- and multi-ES network representations. 4.3.1 Study area Our study area is in the Okanagan region in the south-central interior of British Columbia (BC), Canada (Fig. 3.2). It is broadly characterized by mountain ranges, highlands, foothills, and plateaus (Demarchi, 1996), and is a hotspot for species diversity (Kerr and Cihlar, 2004; Warman et al., 2004) with a climate-moderating chain of large lakes which create a critical habitat corridor that connects it to the Columbia Plateau and Great Basin in the United States (Cannings, 2000). Forested uplands cover the majority (76.7%) of the landscape, with urban centres, residential, and agriculture adjacent the shores of various large lakes throughout valley bottom areas (Field and Parrott, 2022). The Okanagan is valued for its ecological diversity, multifunctionality and agricultural production, all of which are linked to the hydrologic regimes operating throughout the region (Field and Parrott, 2022; OVEDS, 2013). Plant food agriculture is distributed throughout lower-elevation valleys and has the largest annual water volume demands of all industry and domestic consumers in the region (Fig. B1; ~77% per KWL 1990, (OBWB, 2010). A system of connected waterbodies and wetlands are distributed across the major valleys and plateaus in the region, the largest of which is Okanagan Lake (351 km2), with higher-elevation precipitation and snow melt resulting in lower-order year-round and ephemeral streams feeding valley bottom aquatic areas (FLNRO, 2017). Parks, recreation areas, golf courses, lakes, beaches, vineyards, wineries and orchards are among the landscape aesthetics-related marketing tools used to promote immigration and a robust tourism economy OVEDS, 2013). Further details on regional land use and land cover (LULC) are outlined in (Field and Parrott, 2022) and in Field et al. (2017). 65 4.3.2 Building the multi-ES network Supply areas were selected based on data availability, and to cover three broad and diverse ES categories identified by the Millennium Ecosystem Assessment (MEA, 2005): (1) provisioning, which are products obtained from ecosystems (plant food agriculture); (2) regulating, which are abiotic and biotic functions that moderate natural phenomena (waterflow regulation); and cultural, which are non-material characteristics that benefit human well-being (landscape aesthetics). We note that only three ES were mapped in order to facilitate a clear presentation of our proposed approach and based on data availability for mapped ES and related ecosystem functions in our study area (Appendix C.2). We developed network models by defining ‘nodes’ on the landscape as spatial areas that provide higher-than-average ES supply, and ‘links’ as areas where one node influences the supply of another via underlying ecosystem functions. The rationale behind these functional interaction links are summarized in Field and Parrott (2022). Network analyses were then conducted for individual (waterflow only) and multi-ES (plant agriculture, waterflow and aesthetics together), with several network measures calculated to assess how functional interactions between ES may influence the identification of highly influential areas and connections across the landscape, and comparisons were made relative to traditional ES supply valuation methods (Figs. 4.2 and 4.3). Figure 4.3. Conceptual schematic of supply areas for three different ES (ES1, ES2, ES3) on a fictional landscape (largest opaque rectangles), showing (A) traditional supply area mapping and valuations; and (B) spatial network- 66 based representation of supply area mapping and valuations. Smaller bright rectangles represent geographic extent of ES supply areas. Circles are ES supply area node centroids, with their size representing their relative weight and their colour representing each different ES type. Lines are network links, which represent the functional interactions between ES supply areas. Link directionality is indicated by arrows, link weight is indicated by thickness gradient, and link colour represents different types of links based on the modelled ecosystem function-based interactions between ES. Contrasts in traditional and network-derived valuations are demonstrated by the relative difference in node size between (A) and (B), respectively. Network nodes and links were delineated and valued based on data from existing spatial mapping (Field and Parrott, 2022; Fig. 3.4; Appendix C.1). To define network links, functional interactions between high-value supply areas were then mapped and defined as either ‘topographic’ (i.e., based on the ecosystem functions that link the supplies of two ES areas separated in space) or ‘overlapping’ (i.e., supplies of two different types of ES occur in the same location on the landscape, and there is an underlying ecosystem function-based interaction between them). In our study, topographic links always originated at an upslope waterflow node and represented the influence of waterflow on the maintenance of the natural hydrologic processes that help support supply in other downslope ES nodes (e.g., DeLaney, 1995; Nelson et al., 2009). Further details on procedures and rationale behind ES supply area delineation, functional interaction mapping, and valuations are provided in Field et al. (2017), Field and Parrott (2022), Appendix C.1; data sources are provided in Field and Parrott (2022), and related data are available on the Open Science Framework (OSF; Field, 2023a, 2021). 4.3.3 Traditional ES assessment vs. multi-ES networks Once ES networks were designed, network construction and analyses were performed using the igraph package in R to create a multi-ES network, which included all three node types and all eight link types between them (Csardi and Nepusz, 2006; R Development Core Team, 2013). To approximate how functional interactions between different ES function in the real world, we built a weighted multi-ES network with directed links based on ecosystem function mechanisms. Neither nodes nor links were assigned a ‘type’ attribute, as links in both the single- and multi-ES networks were present among nodes of the same and different types of ES (i.e., between waterflow nodes), and we wanted to maintain consistency in network frameworks to facilitate comparisons between single-ES and multi-ES networks (see below). However, prior to network analyses, both node and link weights were normalized from 1 to 100 to allow for analyses across different types of nodes and links, and individual node and link identifiers were stored through analyses to assist interpretation post hoc. We calculated several network measures to detect the importance of individual nodes and links to ES supply through the maintenance of multi-ES functional interactions and ecosystem functions across the landscape. We calculated node degree based on the number of links incident on a node (node total degree, 67 ‘NTD’ herein), which measures the ability of a supply area to spread positive (or theoretically negative) ecological influence to the wider network based on outgoing links, as well as its ability to be influenced by other supply areas based on incoming links (Fortuna et al., 2006; Minor and Urban, 2008; Rayfield et al., 2011). We also calculated node betweenness centrality (NBC) based on the number of pairwise geodesic paths (i.e., smallest number of links between two nodes) that pass through the node of interest, which highlights supply areas that are important for maintaining ES functional interactions throughout the network. We calculated link betweenness centrality (LBC) based on the number of geodesic paths passing through the link of interest, which identifies links that act as bridge-like connectors between two parts of a network, the removal of which may affect interactions between many pairs of nodes through the shortest paths between them (Minor and Urban, 2008; Rayfield et al., 2011; Urban et al., 2009). Node and link weight attributes and directed links were considered in the determination of shortest paths for both betweenness centrality analyses. All network measures were normalized from 1 to 100 prior to comparative analyses. To determine whether multi-ES network analyses may change our understanding of ES supply functionality, we compared network measures results (i.e., NTD, NBC, LBC) with values corresponding to those applied to supply areas in traditional ES assessments (i.e., supply weights; e.g., Qiu and Turner, 2013). To facilitate clear visual comparisons and to highlight the highest-value areas most likely to be selected as management priorities, we mapped pairwise node metric juxtapositions for a subset of the 100 highest-value nodes for each variable. For these maps, we used spatial points (sized by metric value) to represent nodes because polygon portrayals tended to obscure the ability to compare the spatial distribution between variables. We also calculated and mapped the net difference between traditional and network variables for node weights vs. NTD and NBC, and for link weights vs. LBC (Field, 2023a; Appendix C.3). Net differences between node and link weights and each network measure were calculated based on normalized spatial grid data for each variable. For this, network measures were subtracted from traditional weight values for each overlapping grid cell with the resulting range mapped from lowest- to highest-normalized values. 4.3.4 Single- vs. multi-ES networks To assess whether expanding ES network analyses beyond a single ES reveals new or different information about the function of supply areas and/or their functional interactions, we compared individual node and link measures between the multi-ES network and a single-ES network consisting only of waterflow nodes and the within-ES topographic links between them (i.e., isolated from the influence-of and -on other ES). We focused on waterflow for this comparison as it was the only ES that had functional interactions with other supply areas of the same ES type. At the individual node and link 68 level, we compared the valuations and spatial distributions of normalized NTD, NBC and LBC for waterflow by mapping and plotting the trends in the variable relationships between the single- and multi-ES network types. For nodes, we plotted visualizations of the trends in pairwise variable relationships between network types. As above, to visually compare the distribution of high-value nodes between network types, we also mapped pairwise node measure comparisons for a subset of the top-valued 1% of nodes and links for each variable using points (sized by metric value) and lines to represent supply area nodes and links, respectively (Field, 2023a; Fig. 4.6). Note that we provide descriptive comparisons, visualizations of trends, and mapped metric distributions, but do not present formally modelled relationships between the multi-ES network and traditional valuations or the single-ES network as our objective was to provide the reader with visualizations of the trends in the data and not to suggest generalizable modelled relationships. ArcMap 10.7.1 (ESRI, 2011) and R (version 3.6.2; R Development Core Team, 2013) package igraph 1.2.6 (Csardi and Nepusz, 2006), ggplot2 3.3.5 (Wickham, 2016), sp 1.4-5 (Bivand et al., 2013; Pebesma and Bivand, 2005), sf 0.9-8 (Pebesma, 2018), rgdal 1.5-23 (Bivand et al., 2015), raster 3.4-5 (Hijmans and van Etten, 2012), and rgeos 0.5-5 (Bivand et al., 2017) were used to build, analyze and visualize ES networks. For transparency and reproducibility, data, R scripts and further details on methodological procedures are available on the OSF (Field, 2023a; Field, 2021; Appendix C.3). 4.4 Results 4.4.1 Traditional ES assessment vs. multi-ES networks Maps of the normalized net differences between node and links weights and network measures helped to reveal the additional information we obtain by conducting network analyses as opposed to just assessing traditional valuations (Fig. 4.3). We observed spatial disparities between traditional node weights and network-derived measures across all ES types, with consistent trends in relatively higher cumulative values of NTD and NBC in the northeastern half of the study area and the largest central lake (Okanagan), and higher-values of node weights in the southwest and throughout valley bottom areas (Fig. 4.4A-E). Notably, high values for all node network measures were also present in the southwest and in a large wetland complex in the north-central region east of Okanagan Lake, but the very high node weightings resulted in net positive difference values in the southwest, and high node weighting resulted in near-neutral net values in the wetland complex (section 4.5.2). 69 Figure 4.4. Spatial distribution of total weight (A,F), network measures (NTD = B; NBC = D; LBC = G), and calculated net differences between weight and network measures (weight-NTD = C; weight-NBC = E; weight-LBC = H) for amalgamated nodes (A-E) and links (F-H). Map colour spectrums are based on lowest to highest normalized values. Spatial comparison maps for top-100 valued nodes highlighted differences among individual ES between traditional metrics and network measures. For plant agriculture, normalized node weights relative to other ES were high and distributed throughout valley bottom agricultural land (section 4.5.2.1); however, except for two (2) NBC nodes, none of the normalized highest-value network measures were associated with plant agriculture (Fig. 4.5A-B; highest-ranked PA node for NTD = 1654; NBC = 11). For waterflow, the highest-weight nodes were not consistently highly-ranked by network measures, especially 70 for NTD which identified that the majority of top-degree value waterflow nodes were not among the top 100 nodes valued by traditional valuation (NTD = 89; NBC = 51; Field, 2023a). Figure 4.5. Spatial distribution comparisons between a traditional metric (node weight) and network measures (NBC = A; NTD = B) of node centroids for plant agriculture (green), waterflow (blue) and aesthetics (purple). Locations are based on node polygon centroids with size ranges for top-100 nodes corresponding to relative node values. Net difference maps for link weights and LBC showed that the majority of link coverage had net-neutral value, but that topographic links that traverse north to south through the system of large lakes, several of its major tributaries, and various isolated segments along major south-western rivers exhibit relatively moderate or high LBC. Link weight was higher in two south-eastern sub-basins and at various isolated points throughout the central valley-bottom. The areas of highest link weight found in several of the larger sub-basins with large contiguous topographic and overlapping link areas did not exhibit high LBC, likely because they were relatively isolated and upstream, which precluded them from acting as incoming hubs for other waterflow nodes outside their sub-basin. It should be noted that the majority of normalized link weights were valued <1, which is why net difference map values were near-neutral even when spatially overlapping LBC values were zero (Field, 2023a). Node and link metrics are summarized in Table 4.1. 71 Table 4.1. Summary of node and link results from multi-ES traditional and network valuations. ES Type(s) Total number Area (km2) Node & Link weight (normalized) Node total-degree (NTD) Node betweenness centrality (NBC) Link betweenness centrality (LBC) (mean [max]) (mean) (mean [max]) (mean [max] x 104) (mean [max] x104) Nodes Plant agriculture 1497 0.08 [2.57] 1.27 5 [212] 0.61 [567] - Waterflow 7350 1.26 [670.95] 1.02 136 [2229] 3.15 [3494] - Aesthetics 5262 2.77 [6174.98] 1.00 4 [4914] 3.44 [5701] - Total (all nodes) 14109 1.69 [6174.98] 1.04 73 [4914] 2.99 [5701] - Links Overlapping Plant agriculture to Waterflow 1320 - 1.16 - - 1.81 [593] Plant agriculture to Aesthetics 174 - 1.38 - - 0.72 [36] Waterflow to Plant agriculture 1220 - 1.30 - - 1.21 [67] Waterflow to Aesthetics 9363 - 1.04 - - 2.07 [876] Aesthetics to Waterflow 9363 - 1.06 - - 2.44 [1644] Total (overlapping) 21440 - 1.07 - - 2.16 [1644] Topographic Waterflow to Plant agriculture 5256 - 2.51 - - 0.23 [84] Waterflow to Waterflow 484602 - 2.18 - - 0.01 [846] Waterflow to Aesthetics 2864 - 1.97 - - 1.3 [2294] Total (topographic) 492722 - 2.19 - - 0.02 [2294] Total (all links) 514162 - 2.14 - - 0.11 [2294] 4.4.2 Single- vs. multi-ES networks When we compared the single- and multi-ES network, several measures exhibited inconsistent valuation and spatial distribution between the single- and multi-ES network. The NTD value rankings of nodes were similar across the two network types (Fig. 4.6A); however, the highest-degree nodes occurred in valley bottom and/or downstream areas, primarily in the north-south centreline of the Okanagan watershed, whereas the top-value nodes in the multi-ES network were distributed more evenly throughout the study area (Fig. 4.6B). However, when normalized NBC values were compared, we found little consistency between the single- and multi-ES networks in node valuation rankings (Fig. 4.6C), or in the spatial distribution of top-value nodes, with top-node distribution for both networks scattered throughout the study area (Fig. 4.6D). 72 Figure 4.6. Single- vs. multi-ES network comparison for node (NTD = A,B; NBC = C,D) and topographic link (LBC = E,F) measures. Scatterplots include values for all individual nodes and links (A,C,E); maps include top 1% valued waterflow nodes and links within each network type (B,D,F), in addition to highlighting the highest-valued links (LBC >2; F). 73 Comparison of the link measure for each waterflow~waterflow link in the single- vs. multi-ES networks revealed that relative valuations of the majority of results were similar for both network types, with >99.8% of pairwise LBC valued very low (≤1.1 normalized LBC; Fig. 4.6E). Interestingly, the top-valued 1% of pairwise waterflow~waterflow links (single-ES network normalized LBC ≥ 1.0059; multi-ES normalized LBC ≥ 1.0006) tended not to represent the same individual links across network types, though these links did partially overlap spatially in several cases and were primarily present along north-south waterflow functional interaction corridors that ran through the Okanagan watershed lakes and tributaries (top 1% n = 4,846; Fig. 4.6E-F). Very few of the links in each network represented normalized LBC values >2: only 11 of the top links in the multi-ES network and 24 in the single-ES network (Fig. 4.6E-F; Field, 2023a). 4.5 Discussion Spatial network analyses of multiple ES provide new insight into the importance of particular ES supply areas and their functional interactions on a landscape beyond what is learned from mapping individual ES, bundles and/or hotspots. Preliminary studies that have integrated network concepts into ES assessment have maintained focus on underlying ecological components (e.g., Bohan et al., 2013; Hines et al., 2015; Pocock et al., 2016), whereas our primary focus was in spatially identifying and defining specific ES in a network model (Dee et al., 2017). Specifically, we demonstrate that network analyses highlight the importance of certain supply areas not detected by traditional valuation methods. We also show that expanding analyses from a single- to multi-ES network changes the distribution of relative weights and network measure values of supply areas and their functional interactions across the landscape. Our study underscores the importance of integrating multiple ES, and their underlying ecosystem function-based interactions, as a way to incorporate system-level complexity into spatially explicit assessments that can inform policy and management of large, multifunctional landscapes. 4.5.1 Key findings Our results suggest that planning for the supply of multiple ES may not be able to rely on traditional valuation methods to spatially identify and estimate the importance of ES supply areas or the functional interactions between them in a regional landscape. The network measures we calculated for individual nodes and links signify their influence on the maintenance of functional interactions through a network (Minor and Urban, 2008; Solé and Montoya, 2001; Urban et al., 2009). Similar to other studies, our results highlight areas on the landscape with higher ES supply value than adjacent areas, both for individual ES nodes and for the combined value of ES that co-occur on the landscape (e.g., Egoh et al., 2011). Unique to our work, however, is the observation that certain areas ranked as hotspots for ES 74 supply by traditional valuation methods do not correspond to supply area importance as calculated by network analyses. Our results suggest that functional interactions between high-value ES supply areas in geographically separate areas of the landscape may be important for maintaining regional ES supply, a potential that is not considered by current methods in the assessment of ES bundles (Saidi and Spray, 2018). That said, interactions between different ES types in areas where they co-occur may be more important for maintaining the ecosystem functions responsible for ES production across the landscape. In our region, even nodes separated by ~200 km exhibited functional interactions with one another, and highly influential supply areas were linked to distant nodes from several other ES categories (Field and Parrott, 2022). Corridors of connectivity across landscapes can be influenced by pinch-points, which we identify as areas on the landscape with high cumulative link values, and are considered irreplaceable routes that help maintain landscape connectivity (e.g., Peng et al., 2018). In our study region, high-elevation mountains flanking the eastern and western boundaries create a critical movement corridor for north-south species migrations (Transboundary Connectivity Group, 2016), which likely exhibits similar pinch-point connectivity dynamics for topographically-influenced ES and their functional interactions. For example, due to the topographic drivers of surface waterflow, the function of rivers, large lakes and adjacent wetlands as major waterflow corridors can also be considered pinch-points for cross-landscape functional interactions that originate at waterflow supply areas. Similarly, boundaries of sub-basins in our study area function as natural barriers to waterflow-related functional interactions among these areas and contribute to funneling interactions through the valley bottom. This is supported by our observation that, although topographic link weights were not necessarily high in the valley bottom, LBC was consistently highly valued in these areas (Fig. 4.4F,G,H). Here, lower link weights are a result of their calculations being based on original model data and the overall area coverage of each link, whereas observed high LBC values result from the presence of land cover types that control waterflow and promote aesthetics, and because of the concentration of plant agriculture supply areas at lower elevations. This suggests that valley bottom areas provide critical, albeit spatially constrained, pinch-points of multi-ES functional interactions and may be considered priority areas for conservation. Fragmentation of such areas could have negative impacts on this (flood regulation in downstream areas; Luck et al., 2009a) and/or other functionally interacting ES (e.g., mediating delivery of fresh drinking water to collection or consumption areas; Brauman et al., 2007). It is well-known that connected landscapes are required to support ecosystem functions, which in turn are essential to maintaining ES supply and delivery to beneficiaries (Biggs et al., 2012), and our method can support the identification of areas critical to the maintenance of related functional interactions. 75 Spatial and value inconsistencies revealed by comparing the multi-ES and the single-ES networks point to the risk of misrepresentations of complex networks when only partial ecological information is considered. For example, sole focus on the isolated waterflow network suggests that, in order to maintain waterflow-related functional interactions through increased access to, and influence of, top-value waterflow supply areas, management should prioritize conservation of supply areas mainly in north and central supply area concentrations (Fig. 4.6B). However, the assessment of the multi-ES network shifts prioritization to a more heterogeneous distribution of waterflow supply areas across the entire region (Fig. 4.6A-D). Both the single- and multi-ES network results suggest that, to maintain the most critical bridge-link functional interactions across the landscape between waterflow supply areas, management focus should be in valley bottom waterbodies and adjacent lower-elevation sub-basins, but the specific locations of certain top-priority areas for conservation are different when multiple ES are included in network analyses (Fig. 4.6E,F). The above points to the importance of having knowledge around functional interactions among multiple ES when land use planning programs are focused on enhancing a specific ES (e.g., Agudelo et al., 2020); specifically, it is important to empirically assess the interactions between functionally related ES when attempting to spatially identify priority areas for maintaining ES supply. This may be especially relevant for landscapes facing increasing land use intensity, as this has been shown to lead to more homogeneous and less integrated networks, and particularly a loss of strong synergies among trophic groups, ecosystem processes, and multiple ES (Felipe-Lucia et al., 2020). 4.5.2 Landscape highlights 4.5.2.1 Valley bottom areas Low-elevation valley bottom areas are the most heavily populated, contain the highest diversity of LULC types, and face the highest development pressures in the study region (Fig. 3.2.; Fig. 4.7; Fig. B1). Our results assign a relatively low cumulative value to valley bottom areas based on traditional node and link valuations (Fig. 4.4A), mainly because its only non-zero node weightings for low-elevation lakes are associated with only one of the ES (LA). However, when the influence of valley bottom areas is analyzed in the context of the multi-ES network, we find interaction values to be high, both in terms of LA supply, and as important functional interaction corridors between supply areas of multiple ES (Fig. 4.4). Low-elevation waterbodies and adjacent areas in the region contribute to various ES such as drinking water, crop irrigation, landscape aesthetics, flood attenuation to surrounding low-lying areas, recreation, tourism, fishing, and cultural heritage (Moore et al., 2004; OWSC, 2008). In 2017 following a period of rapid warming, upland snowmelt and heavy rainfall, extensive freshet flooding occurred in low-lying areas in the region which displaced 2,500 people from their homes and cost over $73 million for flood response 76 efforts (EMBC, 2018). Studies to support a management update of flood control and drought management protocols are currently underway (OBWB and Associated Environmental Consultants Ltd., 2021) and, although basin-wide hydraulic modelling and general community engagement are part of the plan, the network approach could be incorporated so that functional interactions with, and potential impacts on, other ES not directly associated with water are also contemplated. The majority of higher-elevation terrestrial areas in the region are public lands designated for ‘multiple uses’, including forestry, mining, livestock grazing, recreation, and development, all of which can have direct and detrimental impacts on ecosystem functions in downstream lower-elevation areas (OWSC, 2008). By using network analysis to quantify the importance of the valley bottom to other ES and LULC distributed throughout the region, management can specifically consider interventions that will ensure these areas can still function as corridors that support higher-elevation ES supply areas and vice versa. Figure 4.7. Locations and descriptions of valley bottom and wetland complex landscape highlights within the study region. 4.5.2.2 Wetland complex A large wetland complex located in the northeastern Thompson-Okanagan plateau ecoregion has very high aesthetics and waterflow supply value across all node metrics based on traditional node weighting and on all network measures (i.e., NBC, NTD). It spans three major watersheds (Okanagan, South Thompson, Kettle) across both the Columbia and Fraser basins, and is upstream of the most populated areas in the region and of the system of large lakes and adjacent wetland areas that traverse the central valley bottom (Fig. 4.7). We note this area is not visible from major population centres or transportation corridors in the region but, based on visibility from viewpoints, semi-qualitative LULC analyses and the 77 Provincial Visual Landscape Inventory database, and barring constraints on human access, original ES mapping determined that it has the capacity to provide aesthetic ES (Field et al., 2017). Wetlands are hotspots for ES supply, and can provide flood and storm attenuation, erosion control, surface water and groundwater supply, water quality maintenance, reduction of nutrient pollution, habitat benefits for fishing and hunting, natural materials, biodiversity, stabilization of micro-climates, carbon sequestration, recreation, aesthetic value, and buffer against the effects of climate change (Brander et al., 2006; OBWB and Associated Environmental Consultants Inc., 2019). Wetland size, location in the landscape, and proximity to other natural features or human activities determine their value to ES supply. For example, large, connected wetland areas have greater ES supply relative to small fragmented wetland patches, the latter of which are also more vulnerable to habitat loss (Pal and Talukdar, 2018; Saha and Pal, 2019). Further, agricultural practices conducted adjacent to wetlands can significantly impact a wetland’s ability to produce ES (DeLaney, 1995). Wetlands in the region are classified as sensitive ecosystems and support several rare and endangered species and provide critical habitat for resident wildlife and fish species, and have been valued at over $313 M (Parrott and Kyle, 2014). It has been estimated that 84% of low-elevation wetlands have been lost in the region, and continuing threats include land development, non-point source pollution, invasive species, and climate change (OBWB and Associated Environmental Consultants Inc., 2019; Patterson et al., 2014). Recently an action plan for wetlands has been developed in an effort to guide local governments and communities to expand or start new wetland strategies and conservation programs that seek to maintain water quality and accessibility, minimize the effects of flooding and drought, and protect rare and endangered ecosystems (OBWB and Associated Environmental Consultants Inc., 2019), but no guidance specific to ES was included. Our network approach has tangibly identified and quantified the importance of this wetland area to other ES in other areas, which can be used to help guide planners and managers toward the maintenance of the multiple ES benefits provided by wetlands. This is especially critical in a region with particular concerns around water quality, scarcity and flooding, and where maintaining related ES is vital to supporting rapid population growth and water-dependent livelihoods. 4.5.3 Management and policy implications The fundamental basis of effective environmental policy is to manage human disturbances to ecosystem functions in a way that maximizes the delivery of ES to humans, while minimizing negative ecological impacts (Wallace, 2007). However, the majority of ES management programs still rely on indicator-based mapping in specific areas on the landscape (e.g., Berdugo et al., 2017). Several recent studies have suggested various topologies for integrating network concepts into ES assessments to inform management. For example, food webs involved in fish production have been used to qualitatively predict 78 the vulnerability of ES nodes to various drivers, such as habitat degradation, overfishing, and lake eutrophication (Dee et al., 2017). The Quintessence Consortium (2016) proposed a hypothetical multilayer network framework composed of ecological (e.g., species nodes with trophic, competitive, mutualistic, etc. interaction links), social (e.g., stakeholder nodes with links based on value perceptions related to the ecological layer), and economic (e.g., institutional nodes with transactional links) sub-networks. However, we know of no studies that have applied spatially explicit multi-ES interactions, based on ecosystem function models derived from real-world data, in a network framework. Our approach can help to better inform management by complimenting other methods of ES interaction assessment through providing a tool for simplified spatial identification and quantification of the complex functional interactions between multiple ES. Beyond those used for the ES included in this study, other existing ecosystem function models are available and can be leveraged to derive input data for spatial network representations (e.g., Modeling Soil Ecosystem Services, Aitkenhead et al., 2011; hydrological feedbacks, Larsen and Harvey, 2011). In addition to the practical usefulness of providing a visual tool for decision-makers to spatially identify both key supply areas and the links between them, the approach presented here provides measures, based on a functionally interacting ES, to identifying high-value nodes and links. These can be used to inform the initial selection of target areas for precise management and monitoring to enhance multiple functionally interacting ES, and to adapt action plans as required (Dee et al., 2017; Queiroz et al., 2015; Tylianakis et al., 2010). Further, similarly to scale-specific associations of bundles, our approach can provide complementary information to local ES interventions at various governance scales, which will allow managers to predict how environmental or policy changes in other areas, or in their local area, might lead to changes in focal ES through cascading ecosystem function-based impacts across a landscape (e.g., García-Nieto et al., 2013). For example, our identification of spatial functional interaction hotspots among multiple ES shows where single management interventions could simultaneously have both local and wide-ranging co-benefits beyond the focal ES and specific area of intervention (e.g., Qiu and Turner, 2013), or how management intensity might impact network function and service hubs (e.g., Albrecht et al., 2014; Berdugo et al., 2020; De Vries and Bardgett, 2012; Palomo et al., 2016; Schöps et al., 2018). Our network approach provides a framework to evaluate how these changes and global drivers (e.g., climate change) may alter ecosystem functions and ES (Albrecht et al., 2014, Duncan et al., 2015). From another perspective, the approach can be used to monitor or predict whether land use changes in certain areas on the landscape may impact the supply of ES in other areas and/or the functional interactions between them by allowing decision-makers to evaluate the spatial- and value-based impacts of management actions on multiple ES, to identify trade-offs or other interactions, and to promote 79 mutually beneficial interactions between multiple ES (e.g., Agudelo et al., 2020; Felipe-Lucia et al., 2020). 4.5.4 Limitations and opportunities for future work In this section, we highlight several limitations of our study and future opportunities that we hope will help researchers to expand upon the capabilities of spatial network approaches in future assessments of ES functional interactions. 4.5.4.1 Negative functional interactions Deterioration of ecosystem functions, in addition to human interventions to increase the supply of certain ES, can result in unintended and significant declines in others (Bennett et al., 2009). Although we did not investigate negative functional interactions or trade-offs between the different ES in this study because the related functional interactions we defined were mutually beneficial (but see below re: complexities in ecosystem function mechanisms), future applications of the network approach should incorporate any within- and across-scale dis-services where they exist (e.g., pest and disease transmission, competition for water and pollination; Zhang et al., 2007). These types of functional interactions can be especially critical for informing the management of trade-offs between underlying ecosystem functions that have antagonistic interactions (e.g., two competitor species that provide two different ES; Bastolla et al., 2005; Felipe-Lucia et al., 2022; Windsor et al., 2021). Negative interactions can also be incorporated into social-ecological ES networks, through mutually supportive actions or antagonistic relationships between social and ecological nodes, or within the social sub-network (e.g., direction of the effects of coordinated management; Bodin et al., 2020; Felipe-Lucia et al., 2022; Guerrero et al., 2015; Kuslits et al., 2021; Metzger et al., 2021). 4.5.4.2 Complexities in underlying ecosystem function mechanisms A key motivation for undertaking this study was to integrate more of the complexities of natural systems into ES assessment (e.g., Fu et al., 2013). However, we acknowledge that comprehensive representation of all the underlying ecosystem functions was not necessarily included in the models used for the supply of or interactions between the three ES in our study. For example, not all aspects of the microclimate, vegetation (e.g., branch structure), sub-surface (e.g., root structure), human interventions (e.g., irrigation; vegetation clearing), and temporal drivers that mediate waterflow were incorporated into models, and arguably other methods may result in more accurate depictions of topographic links between ES network nodes (e.g., Braud et al., 2005; Fu et al., 2013; Janssen et al., 2006). As was done here, ecological networks commonly reduce interactions between nodes to direct pairwise relationships, which may be 80 inadequate where ES are supplied as a result of non-pairwise interactions between multiple underlying ecosystem functions or communal action. For example, pest-resistance ES can emerge a result of trait-mediated interactions among multiple species, as has been shown through the use of hypergraph network frameworks in coffee agroecosystems, where individual pest species, and the direct and indirect relationships between them, are represented as distinct network nodes and links, respectively (Battiston et al., 2020; Golubski et al., 2016). As such higher-order interactions have been shown to play a critical role in the stability and biodiversity of large ecological communities (e.g., Battiston et al., 2020; Billick and Case, 1994; Wootton, 1993), taking these types of collective interactions into account within ES networks could reveal additional information about components that contribute to system-level structure, function and resilience. Comprehensive modelling of all the ecosystem and social complexities that give rise to the supply of and functional interactions between various ES must be balanced against data availability, knowledge, resources, and practical communicability and application, so that system models can be necessarily simplified while still providing useful predictive capabilities for decision-makers (Green et al., 2005). 4.5.4.3 Investigating other network components and characteristics For clarity and simplicity, we demonstrate our approach using a few basic network measures, but many other network-compatible approaches and measures are candidates for investigating the importance of ES at various spatial scales and for addressing specific objectives. For example, beyond the individual ES supply node and link scale included here, at the local or sub-regional scale, highly-functionally interacting ‘clusters’ of nodes can be assessed for their vulnerability to isolation by calculating the number of nodes and links that could be degraded or lost before resulting in disruptions to ES supply. At the regional scale or larger, ‘global’ measures are available to assess the functionality and resilience of the network as a whole. For example, ‘network circuitry’ measures the presence of alternative interaction pathways between nodes (Rayfield et al., 2011), which can be used to inform system design options for the maintenance or enhancement of ES connectivity and supply (Heckmann et al., 2015). 4.5.4.4 Dynamics and feedbacks in ES networks The dynamic properties of a network can be used to evaluate how measures and network structure change over time and/or in response to stressors and feedbacks, and must be incorporated to assess network resilience (Bennett et al., 2009; Gonzalès and Parrott, 2012). These properties can be assigned as node and/or link attributes (e.g., seasonal variations in ES supply); or compared via changes in ES supply at various points in time (e.g., conversion of timber and wild food supply areas to agricultural lands). Future work could also consider employing random network methods and additional graph properties to facilitate 81 global network comparisons between single- and multi-ES networks (e.g., Przulj, 2007; van Wijk et al., 2010; Appendix C.4), and between the same network at different points in time or under different land use scenarios (e.g., Schröter et al., 2019). Such work could investigate how isolated drivers might have far-reaching, cumulative impacts at multiple spatial and temporal scales. A variety of network models and spatially-compatible measures used in ecological and connectivity research have been summarized in the literature (e.g., Cui et al., 2012; Dale and Fortin, 2010; Field and Parrott, 2017; Gonzalez et al., 2011; Phillips et al., 2015; Rayfield et al., 2011), and include methods for assessing changes in groups of nodes over time (e.g., Chiquet et al., 2021; Felipe-Lucia et al., 2022; Matias and Miele, 2017); for testing different theories for observed changes; and for calculating the effect size of sources of change (e.g., Snijders, 2017). This will require the ongoing collection of time-series data to support analyses, for example, of time-lagged impacts of management on ES demand and use (e.g., Rieb et al., 2017; Snäll et al., 2021). 4.5.4.5 Social-ecological ES networks The breadth of ES research can further be expanded by building interdisciplinary social-ecological network models to evaluate the complex relationships between the environmental- and human-drivers involved in ES production and delivery, where ES can be represented as nodes, links, attributes or emergent properties of a network (Felipe-Lucia et al., 2022). For example, social nodes can be added to our proposed network framework by indicating people or groups who govern or benefit from ES, with links representing the transfer of a benefit from a supply area to a user, or a specific management action (e.g., restoration) weighted by frequency or intensity of the relationship (Felipe-Lucia et al., 2022). Gonzalès and Parrott (2012) provide a detailed overview of various network approaches that can be employed to assess social-ecological system robustness and resilience, which have since been proposed for application in spatial ES network frameworks (Field and Parrott, 2017). The selection of appropriate network typologies and measures will depend on the study question(s), the management objective(s), and the social-ecological context (Fu et al., 2013). 4.5.4.6 Multilayer ES networks Our networks were designed to represent all ES types within a single network layer, but we acknowledge that analyzing ES networks structured by multiple distinct sub-network layers can yield different results around fundamental network characteristics when compared to single-layer networks (Delmas et al., 2019). For example, increased clustering in multilayer social-ecological networks can lead to a reduction in system robustness to disturbance (Baggio and Hillis, 2018), a finding in opposition to observations of single-layer representations of plant-animal mutualistic networks (Bascompte et al., 2003). Multilayer 82 network methodology and statistics need to be improved through interdisciplinary case studies and experimental work in order to investigate the relationships between network structure and ES system outcomes (Bodin et al., 2019; Felipe-Lucia et al., 2022; Kivelä et al., 2014; Sayles et al., 2019), and we hope our approach can be expanded upon to help address these gaps. General challenges and assumptions associated with applying network analysis to ES are discussed in detail in Field and Parrott (2017), and include data availability, methodological barriers associated with certain ES and/or their functional interactions, and artificial geographic boundary assignment. Additional limitations related to the mapped ES supply and interaction data used for this study (i.e., limited number of ES; methods for delineating nodes and links; source data scale and resolution; uncertainty and validation) are discussed in Field and Parrott (2022) and in Appendix C.2. 4.6 Conclusion This study demonstrates how spatial multi-ES networks can be conceptualized and quantitatively assessed to identify the importance of ES supply areas and their functional interactions across a regional landscape. By analyzing ES networks using several measures we demonstrate that the relative value of ES supplies and their functional interactions can be measured and mapped, which we hope can be applied to management efforts to identify and prioritize specific areas that are important for maintaining ES supply across the landscape. Further, multi-ES networks provide a more complex representation of the importance of multiple ES to landscape function relative to traditional spatial ES valuation approaches and compared to examining individual or bundled ES supply areas in isolation. Our work is generalizable to other assessments as it includes characteristics relevant to other regional multi-ES networks, including ES from all high-level categories, a variety of weighted nodes and links types, isolated nodes, uni- and bi-directional links, and variable distribution across a large and topographically diverse landscape, with network components defined based on ecosystem function mechanisms. The multi-ES network approach supports a clear articulation of the relationships between nature and human wellbeing, as well as biophysical quantification of ES, which is a fundamental step toward better-informing management and policy in heterogenous landscapes (Cowling et al., 2008; Egoh et al., 2011; Ferrier et al., 2016; Saidi and Spray, 2018). We hope this work will help to address several ongoing challenges: to increase awareness and understanding of how dependent humans are on nature; to advance the interdisciplinary science around the ES concept; and to facilitate sustainable management, restoration and use of natural capital (Guerry et al., 2015). 83 Chapter 5: A spatial network approach for modelling social ecological systems and cross-scale governance of nature’s contributions to people 5.1 Synopsis Maintaining the delivery of the diverse suite of ecosystem services, or nature’s contributions to people (NCPs), that support our survival and well-being requires governance frameworks to integrate both social and ecological components that interact at different scales across multifunctional landscapes. Here, we present an approach for representing supplies of multiple NCPs from three diverse categories (provisioning, regulating, cultural), their functional interactions, and multi-scale governance agencies within a spatially-explicit social-ecological network. We exemplify how network cluster algorithms can be used to partition a large landscape into communities of multiple, highly functionally interacting NCP supply areas; and assess the spatial alignment of these clusters with the multi-sector local, regional, provincial and national agencies involved in the governance of focal NCPs. We show that even the smallest clusters of NCPs are relevant across all scales of governance, and reveal where on the landscape and among which agencies opportunities exist for multidisciplinary, cross-scale NCP governance. Our methods for modelling clusters of functionally interacting NCPs could be leveraged to tackle management problems within a target geographic area by building collaborative governance frameworks among ecologically-connected agencies that operate within and external to the target area. Our approach provides a more in-depth understanding of the functional interactions between NCPs operating across multiple geographic scales and spanning jurisdictional boundaries, and what implications these interactions have for developing more effective multi-scale and multi-sector environmental governance. This will advance NCP research and help guide environmental governance and planning in large-scale complex social-ecological systems. 5.2 Introduction Humanity is now grappling with the realities of how to chart a path for the future that will ensure multifunctional landscapes will continue to supply the ecosystem services, or nature’s contributions to people (NCP), that we need to survive and thrive. Understanding the linkages between societies and NCPs is important for managing potential risks, which can stem from the capacity of ecosystems to provide NCPs as well as socio-economic use, preferences, and/or disruption of their delivery (Schröter et al., 2019). An essential element of this is the productive collaboration between governing agencies across many geographic scales, especially as many of the most threatening and complex environmental problems we face operate at regional and global scales (Bodin, 2017). In practice, governing agencies are required 84 to make strategic decisions to balance diverse ecological and societal pressures in increasingly complicated policy environments (Lindquist, 2011). This often necessitates the use of either strictly ecological or socio-political boundary units to spatially delineate NCP-providing areas (e.g., Beier et al., 2008; Chan et al., 2006; Egoh et al., 2008; Naidoo et al., 2008; Nelson et al., 2009; Raudsepp-Hearne et al., 2010), and/or a modelled reduction in the inherent complexity of natural systems and in the social components that govern them (Pocock et al., 2016). Additionally, environmental governance area boundaries are seldom spatially aligned with environmental systems and processes, which can lead to inefficient or unsuccessful resource management, though it has been suggested that this can be overcome through coordination and collaboration (Bergsten et al., 2014; Bodin, 2017; Bodin et al., 2019, 2011; Ernstson et al., 2010; Folke et al., 2005; Guerrero et al., 2013; Sayles and Baggio, 2017). However, research to date typically focusses on a single scale of governance (e.g., municipality), which fails to encompass the multi-scale (e.g., local - regional - provincial - national) governance interactions required for effective resource management in the real world (Cash et al., 2006; Gibson et al., 2000; Sayles and Baggio, 2017). Multifunctional landscape governance is further challenged by the fact that NCP are not produced in isolation. Due to the ecosystem functions underpinning their supply (e.g., waterflow regulation, decomposition, and nutrient cycling influence crop production; MEA, 2005; Schuldt et al., 2018), many NCPs have heterogeneous distribution and unpredictable interactions with each other. Demands for provisioning of NCPs are projected to rise, which will increase the need for coordinated management of synergies and trade-offs between different types of NCPs (Howe et al., 2014). Encouragingly, calls for explicitly incorporating NCPs into landscape-level planning and policy are also on the rise (Bateman et al., 2013; Perrings et al., 2011), which summons the development of new decision-support tools based on integrated interdisciplinary knowledge (Bennett et al., 2009) to inform collaborative multi-NCP management initiatives (Chan and Satterfield, 2020; Mitchell et al., 2015). The functional interactions between NCPs can occur within the same area, or when NCPs are produced in separated areas but are connected through ecosystem function mechanisms that operate across the landscape (Field and Parrott, 2022). To expand research around these concepts, recent work has applied network approaches, which are based on systems of components (nodes; e.g., social actors; species; habitat patches; areas of NCP supply) and their connections (links; e.g., communication between actors; species dispersal between patches; functional interactions between NCPs), to discover new information about complex ecological interactions and their impacts on the structural and functional processes between individual nodes and links, within highly-interacting node clusters, and across system-wide scale hierarchies (Bodin et al., 2019; Rayfield et al., 2011). Recently, spatial multi-NCP networks have been proposed, where NCP supply areas are represented as nodes, and links are based on 85 mechanistic relationships between the ecosystem functions driving the interactions between supply areas. These functional interactions can be mapped and analyzed using existing network theoretic measures to yield new information about the distribution, value and interdependence of NCP areas Chapter 4). Resulting output identifies areas of NCP supply and functional interaction assets in a region that may be candidates for protection efforts and/or offsets if they are degraded. This approach can improve spatial and functional understanding of the interactions between categorically different NCPs, thereby fostering deeper value knowledge and reflection (Chan and Satterfield, 2020) and encouraging buy-in to the NCP concept for decision-makers (Pocock et al., 2016). Social-ecological networks (SENs) are representations of the patterns of connections between both human and natural sub networks and provide a range of flexible tools and concepts for use in SES research and in sustainable resource management (Bodin, 2006; Cumming et al., 2010; Gonzalès and Parrott, 2012; Janssen et al., 2006). Recent interdisciplinary work has analyzed both human and ecological components within collaborative governance SENs (Bodin et al., 2016; Dee et al., 2017; Ekstrom and Young, 2009; Sayles and Baggio, 2017). For example, Bodin et al. (2016) empirically assessed SEN structure as it related to the management of forest patches, which were ecologically interconnected through seed dispersal, and socially interconnected through collaborative resource management. Other recent work included local and regional actors involved in salmon restoration alongside watersheds connected by river networks, and calculated various metrics to assess how the existing SEN structure and function related to scale mismatches between social and ecological sub-network layers (Sayles and Baggio, 2017). SENs have also been highlighted as a promising approach for modelling complex interactions, dependencies and feedbacks within and across the social-ecological dimensions that influence NCP supply (The Quintessence Consortium, 2016; Dee et al., 2017; Felipe-Lucia, 2021). More specifically, incorporating SENs into NCP mapping can help contextualize the influence of conservation and management actions on NCPs (Mbaru and Barnes, 2017); exchanges of information (de Lange et al., 2019); cross-scale interactions between social actors (Martín-López et al., 2019) and ecological network components (Field and Parrott, 2017); and telecoupling over long distances (Hull and Liu, 2018; Koellner et al., 2019; Seaquist and Johansson, 2019; Sonderegger et al., 2020). Additionally, and most relevant to our study, for NCPs whose functional interactions transcend large areas, SENs can be used to evaluate whether social collaboration across spatial scales (Bodin, 2017) are appropriate for the NCP(s) of interest (Bodin et al., 2019; Felipe-Lucia, 2021; Guerrero et al., 2015a, 2015b). In spite of these capabilities, the real-world application of SENs in NCP research and management remains limited (Felipe-Lucia, 2021), especially using approaches that incorporate multi-scale (e.g., local vs. regional vs. global mandates and/or NCP production and functional interaction mechanisms), multilayer (i.e., social and ecological sub-network layers), and spatially-explicit (e.g., 86 Firkowski et al., 2021) interactions within the same network. An open question for SEN researchers is, “how do multiple [NCPs] interact and what are the consequences of those interactions for their management?” (Bennett, 2017; Felipe-Lucia, 2021). In this paper, we expand upon previous research and theory to build a spatially explicit NCP governance network, considering the geographic alignment of ecological and social sub-network layers, in order to present a novel approach for representing cross-scale, multi-sector collaborative governance opportunities in multifunctional landscapes. Specifically, we (1) spatially dissect a regional network of highly functionally interacting NCP supply area nodes into smaller clusters based on network analyses of their modelled interactions; and (2) identify opportunities for cross-scale collaboration between the governance institutions with jurisdiction related to specific NCP(s) based on their social-ecological and geographic fit. We use an existing network of multiple NCPs from three diverse high-level categories (provisioning - plant food agriculture; regulating - waterflow; cultural - landscape aesthetics) built based on their ecosystem function-based interactions (Field and Parrott, 2022). We then use a quantitative network analysis to subset the regional network into smaller clusters of NCPs that exhibit the strongest functional interactions. Next, we compare these NCP clusters with relevant governance organizations operating at the local, regional, provincial, and national scale based on their spatially overlapping governance area boundaries and on their NCP-related management responsibilities to model opportunities for inter-agency coordination across scales of governance. 5.3 Methods 5.3.1 Study area and context We focus on the Okanagan regional landscape in British Columbia (BC), Canada, to assess a multi-scale social-ecological network between previously-mapped NCP supply areas and related governance jurisdictions (Fig. 3.2). The region is in the southern interior of BC, is a biodiversity hotspot, is one of the most endangered semi-arid ecoregions in North America (Kerr and Cihlar, 2004; Warman et al., 2004), has a high diversity of LULC types (Caslys Consulting, 2014), and spans 21,580 km2 from ~276 to 2,774 masl. The NCP-related resources in the region are governed by institutions at national, provincial, regional district, and municipal scales. The study area has strong engagement of regional government and non-government stakeholders, and an increasing demand for region-wide collaboration in land use planning to accommodate rapid land development in response to a high rate of human population growth (Caslys Consulting, 2014; Neale et al., 2007). We identified a total of 84 agencies that operate at the local, regional, provincial and national scale in our study area. Agencies were represented from five broad governance types, including: 17 municipalities 87 (local scale), 26 regional district electoral areas, 33 First Nations Reserves (all scales), 6 provincial government ministries, and 2 federal government departments (national). Note that there are seven First Nation tribes present in our study area, several of which are members of the Okanagan Nation Alliance tribal council (https://www.syilx.org/). We included each individual reserve area as a distinct ‘governance area’ in network analyses as they are geographically separated, although we note that several reserves may be governed by one nation or band, and traditional territories may be shared or overlap (Danesh, 2020) and may have transboundary geographic extents (e.g., https://okib.ca/uploads/files/Territory-Map1.pdf). Many agency governance areas overlap in space (Fig. D1) and have a comprehensive and sometimes overlapping range of responsibilities related to the three NCPs included here. Smaller-scale agencies in our study area are sometimes responsible for administering provincial and national legislation related to NCPs, in which case local or regional governments may permit related development or activities and only engage in passive reporting to larger-scale legislating agencies (e.g., Riparian Areas Protection Regulation legislated provincially, but permitting by local governments; Fig. 5.1; Appendix D.1). A summary of the jurisdictions and responsibilities of the different agencies in relation to NCPs is provided in Table 5.1, and a summary of governance area sizes (km2) and web-links for each agency are provided in Table D1. 88 Figure 5.1. Conceptual schematics showing social (blue) and ecological (orange) network layers. Circles represent network node centroids, with their relative size (small, medium, large) representing spatial scale of governance or extent of NCP supply for three NCPs (PA, WF, LA). Solid blue lines represent active management of NCP supply areas. Dotted blue lines represent passive reporting between management agencies, where smaller-scale governance agencies enforce larger-scale legislation and regulations. Orange lines represent functional interactions between NCP supply nodes. (a) Schematic showing spatial boundary extents for: local, regional and national spatial scales of governance (blue); three NCPs (orange); and the associated landscape map. (b) Schematics showing three management scenarios with multi-scale nodes and links for conceptual social governance sub-network layers (blue) and an ecological multi-NCP sub-network layers (orange). (i) Governance approach based on non-NCP-specific environmental mandates and limited-to-no coordination, except for passive reporting, i.e., low social-ecological alignment; high management efficiency. (ii) Non-partitioned governance approach that considers all functional interactions between all NCPs to guide opportunities for collaboration between agencies, i.e., high social-ecological alignment, low management efficiency. (iii) Partitioned governance approach, where network-based cluster analyses are used to identify the highly functionally interacting NCP supply areas (delineated by light orange shapes), which are in turn used to streamline opportunities for agency coordination, i.e., high social-ecological alignment, high management efficiency. 89 Table 5.1. Governance scale, types of NCP(s) governed, jurisdiction and responsibilities for different governance types and agencies within the study area. Governance type / Agency Scale(s) NCP(s) governed Jurisdiction and responsibilities Municipalities Local PA + WF + LA Land development permitting and enacting legislation from various levels of government, including related impacts on water (e.g., Water Sustainability Act: https://www.bclaws.gov.bc.ca/civix/document/id/complete/statreg/14015) and agricultural lands (e.g., Agricultural Land Commissions Act: https://www.bclaws.gov.bc.ca/civix/document/id/complete/statreg/02036_01); as well as managing municipal park use (e.g., City of Kelowna: www.kelowna.ca). The core areas of authority of municipal governments in BC are set out by the Community Charter (https://www2.gov.bc.ca/gov/content/governments/local-governments/facts-framework/legislative-framework) Regional Districts Regional PA + WF + LA Like municipalities but typically with differing regulations and bylaws, regional districts manage land development, and have other NCP-related responsibilities ranging from air quality; regional park planning, use and permitting; waste management; wildfire prevention; and climate action (e.g., Regional District of the Central Okanagan: www.rdco.com). The primary legislation that sets out the structure, operations, and responsibilities of Regional Districts is the Local Governments Act (https://www2.gov.bc.ca/gov/content/governments/local-governments/facts-framework/legislative-framework) First Nations Local, Regional, Provincial, National PA + WF + LA Involved in all aspects of environmental stewardship and governance related to the maintenance of NCPs on designated lands (Molnar et al., 2021); “have the right to own, use, develop and control the lands, territories and resources that they possess…”; and “to the conservation and protection of the environment and the productive capacity of their lands or territories and resources” at all scales of governance (Declaration Act 2019). Ministry of Agriculture and Food (MoAg) Provincial PA “Responsible for the production, marketing, processing and merchandising of agriculture… products, as well as supporting our province’s food security, and developing a resilient food system and economy” (https://www2.gov.bc.ca/gov/content/governments/organizational-structure/ministries-organizations/ministries/agriculture). Agricultural Land Commission (ALC) Provincial PA “Dedicated to preserving agricultural land and encouraging farming in British Columbia” (https://www2.gov.bc.ca/gov/content/industry/agriculture-seafood/agricultural-land-and-environment/agricultural-land-reserve/the-agricultural-land-reserve). They also provide support to farmers for beneficial management practices that enhance NCP provisioning on agricultural lands and/or reduce agricultural disservices to NCPs (https://www2.gov.bc.ca/gov/content/industry/agriculture-seafood/programs/beneficial-management-practices). Ministry of Tourism, Arts, Culture and Sport (TACS) Provincial LA Decisions related to recreation and cultural land use are relevant to residents and tourists across the entire province through the integration of the tourism sector with community arts, culture and sport (https://www2.gov.bc.ca/gov/content/governments/organizational 90 Governance type / Agency Scale(s) NCP(s) governed Jurisdiction and responsibilities -structure/ministries-organizations/ministries/tourism-arts-culture). Ministry of Forests (MoF) Provincial WF + LA Responsible for the stewardship of public lands and the sustainable management of forest, wildlife, water, and other land-based resources. The Forest and Range Practices Act explicitly identifies the management of scenic values, and the visual resource management program supports the coexistence of forestry and natural beauty through innovative forest-management practices (https://www2.gov.bc.ca/gov/content/governments/organizational-structure/ministries-organizations/ministries/forests). Ministry of Environment and Climate Change Strategy (ENV) Provincial WF + LA Manages, protects and conserves the province’s water, land, air and living resources, including provincial park land, and works on climate preparedness and adaptation (https://www2.gov.bc.ca/gov/content/governments/organizational-structure/ministries-organizations/ministries/environment-climate-change). Ministry of Water, Land and Resource Stewardship (LWRS) Provincial WF Established in early 2022; responsible for integrated land and natural resource management such as cumulative effects and environmental sustainability in general (https://www2.gov.bc.ca/gov/content/governments/organizational-structure/ministries-organizations/ministries/water-land-and-resource-stewardship). Environment and Climate Change Canada (ECCC) National WF NCP responsibilities related to the preservation and enhancement of water, air and soil quality (https://www.canada.ca/en/environment-climate-change/corporate/mandate.html). Department of Fisheries and Oceans Canada (DFO) National WF Responsible for aquatic habitat protection and resource management, and regulates freshwater resources and certain projects near water (https://www.dfo-mpo.gc.ca/index-eng.html). 5.3.2 Data and approach To delineate the geographic boundaries of governance jurisdictions (‘governance area(s)’ herein), we obtained spatial data for current governance areas from multiple sources (Table D2). We discovered various minor disagreements in the precise location of governance area boundaries between sources (e.g., inter- and intra-regional district electoral area boundaries from Provincial vs. local data warehouse sources). For the provincial ministries and national agencies with jurisdiction relevant to NCPs, we use provincial Agricultural Land Reserve (ALR) data and the Agricultural Land Use Inventory 2016 datasets to identify governance areas for the Ministry of Agriculture (though we note this ministry may be involved with unfarmed land within the ALR, as well as farmed land outside the ALR; City of Kelowna, 2017; Upland Agricultural Consulting Ltd. and Urban & Regional Food Strategies, 2022); cadastral, regional district land use, zoning datasets; and Provincial Freshwater Atlas datasets to identify Crown land (i.e., public parcels with ‘Crown’ ownership, or designated for resource use or forest reserves) and waterbodies owned by the government and managed by the Ministry of Forests. Lands under federal 91 Department of Fisheries and Oceans jurisdiction were defined as all aquatic and wetland areas, with an adjacent 30 m riparian buffer. The entire study area is under the management of various departments (e.g., parks and protected areas, environmental protection, water stewardship) within provincial and federal environmental ministries. Data source references and detailed data preparation procedures are provided in Table D2, Field et al. (2017) and Field and Parrott (2022). We obtained spatial data for three types of NCP supply areas and their functional interactions from previous studies (Field et al., 2017; Field and Parrott, 2022). The maps were created primarily from existing and/or publicly available data on ecological composition (biotic and abiotic elements), structure (e.g., topography, LULC distribution; Wallace, 2007), processes (e.g., water infiltration, nutrient cycle, energy cycle; Lyons et al., 2005), and/or actual NCP supply. NCP supply areas included: (1) plant food agriculture = PA (‘provisioning’ NCP = products obtained from ecosystems); (2) waterflow regulation = WF (‘regulating’ NCP = abiotic and biotic processes that moderate natural phenomena); and landscape aesthetics = LA (‘cultural’ NCP = non-material characteristics that benefit human well-being; MEA, 2005). All data were resampled to ~20 m x 20 m grid cells to account for parameter heterogeneity across the landscape and resampled to align ecological and social datasets. Methodological details on original ES mapping data are provided in Field et al. (2017) and in Field and Parrott (2022). We used multi-NCP networks built by Field and Parrott (Chapter 4). They used spatial network analyses in which high-value NCP supply nodes (PA = 1,497; WF = 7,350; LA = 5,262) were connected via functional interaction links using existing spatial data on supply (i.e., based on underlying ecosystem functions; Fall et al., 2007; Chapter 4), respectively. The ES network was directed, which means that each distinct type of functional interaction link was connected from one type of node to another type of node, and not in the opposite direction (The Quintessence Consortium, 2016). For example, topographic links between upslope WF nodes and downslope LA nodes were defined based on the function of regulating the flow of water downhill along the modelled least-cost path route that connected the pairs of these types of supply areas (Chapter 4). Rationale behind network topology and functional interaction links is summarized in Field and Parrott (2022). Related data are available on the Open Science Framework (OSF; Field, 2023a, 2023b). A cluster analysis was used to identify highly functionally interacting communities of NCP supply areas within the network; they were then compared with governance area nodes to find where NCP clusters and agency jurisdictions overlapped (Fig. 5.2). For the cluster analysis, we used edge betweenness community detection, which iteratively removes the links with the highest betweenness centrality score, a measure of the number of shortest paths traversing a particular link. Links with the highest scores are those that have a high likelihood of connecting separate modules (i.e., bridging links), and the result is a dendrogram of the graph that identifies these communities, membership in which is 92 mutually exclusive (Girvan and Newman, 2002; Newman and Girvan, 2004; Smith et al., 2020). We chose this algorithm because it is one of the few that can explicitly consider directed links (Csardi, 2013; Smith et al., 2020). Once governance areas and NCP community clusters were spatially delineated, social-ecological links between the NCP sub-network and governance agencies were defined where governance areas and clusters intersected in space, i.e., where governance area and NCP cluster polygon geometries had at least one point in common. Links within the governance sub-network were based on the results of the above analyses, and represented as all possible connections between agencies that had governance areas overlap with the same NCP cluster. Figure 5.2. Maps of (a) governance areas and (b) top-ranked NCP clusters in the study area. (a) Governance area extents with colours differentiating governance types and scales. (b) The largest NCP clusters (n = 11; >100 nodes) ranked by number of nodes (circles = node centroids) therein; colour scheme selected to differentiate boundary extents (polygons) where clusters overlap. NB: some governance areas overlap - see Fig. D1 for governance area map layers separated by agency and/or governance scale. To assess the degree of spatial alignment between NCPs and their governing agencies, we conducted spatial overlay analyses and identified the proportion of total node area (km2) overlap between (a) supply areas for each individual NCP and the governance areas of agencies with relevant mandates (i.e., isolated NCP supply-focused management approach); and (b) NCP clusters and the agencies with mandates relevant to the NCPs present within each cluster (i.e., SEN-supported multi-NCP functional interaction approach). We also assigned a scale value to each NCP node and cluster, based on area thresholds defined by the largest local municipality (City of Kelowna) and regional district (Regional District of Okanagan-Similkameen Electoral Area H) in our study area as follows: 0 < local ⩽ 265 km2; 265 < regional ⩽ 4780 93 km2; 4780 km2 < supra-regional. Then, to assess scale-mismatches between governance agencies and NCPs areas, we compared the number of scale-assigned nodes overlapped by each governance area for (c) individual NCP nodes; and for (d) NCP clusters. For these analyses, we identified specific NCPs (i.e., PA, WF and/or LA) as being directly managed by agencies based on mandates that were related to the individual NCP conceptualizations (Table 5.1). For example, municipalities, regional districts, and the provincial Ministry of Environment and Climate Change Strategy are all responsible for the management of jurisdictional park lands, in part for the maintenance of their aesthetic values (e.g., Field, 2023b; Urban Systems Ltd., 2016; NB: no National parks were located within the study area when the research was conducted). As the percent overlap area data were not normally distributed (Field, 2023b), we used paired samples Wilcoxon signed-rank tests to compare agency overlap between groups (i.e., individual NCP nodes vs. network-derived NCP clusters; MacFarland and Yates, 2016). ArcMap 10.7.1 (ESRI, 2011) and R version 3.6.2; (R Development Core Team, 2013) packages car 3.1-2 (Weisberg, 2019), data.table 1.14.2 (Dowle and Srinivasan, 2021), dplyr 1.0.7 (Wickham et al., 2021), geosphere 1.5-14 (Hijmans, 2021), ggalt 0.4.0 (Rudis et al., 2017), ggmap 3.3.5 (Kahle and Wickham, 2013), ggplot2 3.3.5 (Wickham, 2016), ggsn 0.5.0 (Santos Baquero, 2019), igraph 1.2.6 (Csardi and Nepusz, 2006), maps 3.4.0 (Becker et al., 2021), plyr 1.8.6 (Wickham, 2011), raster 3.4-5 (Hijmans and van Etten, 2012), rgdal 1.5-23 (Bivand et al., 2015), rgeos 0.5-5 (Bivand et al., 2017), rmapshaper 0.5.0 (Teucher and Russell, 2023), sp 1.4-5 (Bivand et al., 2013; Pebesma and Bivand, 2005), and sf 0.9-8 (Pebesma, 2018), tmap 3.3-2 (Tennekes, 2018) were used to build, analyze and visualize the SES networks. For transparency and reproducibility, data, R scripts and further details on methodological procedures are available on the OSF (Field, 2023a, 2023b). 5.4 Results The governance areas of the five high-level types of agencies are either socio-politically- or ecologically-derived and collectively provide full (and in some cases overlapping) spatial coverage of the study area (Fig. 5.2(a); Fig. D1). Municipalities cover 1,263 km2 and are concentrated around the populous centres in lower-elevation areas. Regional districts encompass both municipal and rural non-municipal areas, and district boundaries border one another to ensure all parts of the landscape (20,316 km2) occur within one regional-level governance area. First Nations reserves cover 635 km2 and primarily occur in lower-elevation areas. The distribution of provincial governance areas were defined based on their jurisdictional roles, where the Ministry of Agriculture covered 2,683 km2 and included lower-elevation lands designated for agricultural activities; the Ministry of Forests covered 15,303 km2 and included public (or ‘Crown’) lands and First Nations Reserves; and the Ministry of Environment and Climate Change Strategy, the Ministry of Water, Land and Resource Stewardship and the Ministry of Tourism, Arts, 94 Culture and Sport spanned the entire study region (21,580 km2). Nationally, Environment and Climate Change Canada also has ubiquitous spatial coverage, whereas Fisheries and Oceans Canada covered a total of 3,055 km2. We identified a total of 394 NCP supply node clusters distributed across the study area. Cluster area ranged from 0.0017 to 8,875 km2 and included between 2 and 3,621 nodes. The majority of clusters contained NCP node types from all 3 categories (Table 5.2). Notably, clusters with only two NCP types contained only WF and LA nodes (i.e., clusters n≥3 nodes). Table 5.2. Summary of NCP clusters by their size (i.e., # of nodes), area (ha), the number of linked agencies by governance scale, and the number of NCP type(s) within. Number of associated agencies/governance areas at each governance scale Nodes per cluster # of clusters (394) Mean cluster areas (ha) Federal (2) Provincial (6) Regional (26) Municipal (17) First Nation (33) Max # NCP types per cluster Mean # NCP types per cluster 1 2 158 24.34 2 6 20 11 3 2 1.95 2 3 60 67.36 2 6 17 8 4 3 2.12 3 4 37 233.22 2 6 15 8 3 3 2.11 4 5 27 82.34 2 6 14 4 2 3 2.30 5 6 17 419.07 2 6 12 6 2 3 2.24 6 7 10 285.33 2 6 5 3 2 3 2.30 7 8 7 2240.07 2 6 8 1 0 3 2.14 8 9 7 245.10 2 6 7 6 3 3 2.43 9 10 9 378.32 2 6 9 2 1 3 2.00 10 11 2 2760.11 2 6 4 0 1 3 2.50 11 12 9 1594.34 2 6 11 3 2 3 2.44 12 13 3 4481.50 2 6 7 1 0 3 2.33 13 14 4 3446.57 2 6 6 1 0 2 1.75 14 15 3 331.36 2 6 3 3 0 3 2.67 15 16 4 633.70 2 6 3 2 1 3 2.50 16 17 1 3035.59 2 6 1 0 0 3 3.00 17 19 2 272.16 2 6 2 3 0 3 3.00 18 20 2 144.92 2 6 1 2 1 3 2.50 19 21 5 1905.98 2 6 7 2 3 3 2.80 20 22 1 195.82 2 5 1 1 0 3 3.00 21 24 3 1488.53 2 6 3 1 1 3 2.67 22 27 2 120.46 2 6 3 1 0 3 2.50 23 28 2 3391.28 2 6 4 2 1 3 2.50 24 34 1 13126.08 2 6 3 0 1 3 3.00 25 46 1 3596.31 2 6 1 1 1 3 3.00 26 52 2 1263.22 2 6 1 3 0 3 2.50 27 70 1 207.54 2 6 2 1 1 3 3.00 28 88 1 16934.20 2 6 4 1 1 3 3.00 29 98 1 54506.06 2 6 4 0 2 2 2.00 30 99 1 37029.62 2 6 2 0 0 2 2.00 31 123 1 1577.36 2 6 2 2 1 3 3.00 32 125 1 10827.76 2 6 3 2 1 3 3.00 33 128 1 16381.71 2 6 3 0 0 2 2.00 34 137 1 10785.76 2 6 3 0 1 3 3.00 35 141 1 19762.69 2 4 5 0 0 2 2.00 36 144 1 8749.43 2 6 1 1 0 3 3.00 37 146 1 11440.78 2 6 3 3 2 3 3.00 38 436 1 19854.25 2 6 9 7 5 3 3.00 39 1829 1 87479.68 2 6 11 4 17 3 3.00 40 2797 1 35177.78 2 6 12 6 3 3 3.00 41 3621 1 12807.52 2 6 19 11 13 3 3.00 95 A total of 9448 connections between NCP clusters and governance areas were identified by spatial overlay analyses (Fig. 5.3); and at least three scales of governance, and often multiple departments and/or agencies therein, were overlapped across all cluster sizes. Regardless of cluster size, the governance areas of both federal agencies were present, as well as all provincial agencies in all but two clusters (n = 22 and 141 nodes; Table 5.2). The number of regional districts, municipalities and First Nations governance areas present in each NCP cluster was more variable across cluster sizes, but we generally found increasing mean number of agencies as cluster size increased (Fig. 5.4). Visual inspection of maps of governance areas (Fig. 5.2(a)) and a subset of the largest clusters (>100 nodes; Fig 5.2 (b)), as well as comparative analyses between individual NCP node vs. network-identified multi-NCP cluster approaches, revealed that greater total NCP areas were managed by all governance types using the multi-NCP cluster approach (significantly greater p<0.001 for all municipal, regional, and First Nations; numerically greater for provincial and national). Further, comparative analyses revealed NCP-governance spatial alignment was poor for non-local-scale governance types, although was generally improved (i.e., greater number of scale-appropriate NCP nodes overlapped) under the multi-NCP cluster approach for municipalities (p<0.001), regional districts (p<0.001) and First Nations (p<0.001 for local and regional scale; or numerically greater for supra-regional). For provincial government agencies, the spatial alignment with supra-regional scale NCPs was equal for both node- and cluster-based approaches; and for national agencies no supra-regional scale nodes (i.e., only ‘LA’) were relevant to their management jurisdictions (Table D3). 96 Figure 5.3. Number of links (i.e., opportunities for collaboration) between governance agencies at all scales, based on shared spatial overlap of NCP network clusters. 97 Figure 5.4. Average number of management agencies within (i.e., overlapped by) NCP clusters, ranked by number of nodes therein. 5.5 Discussion 5.5.1 Significance and contribution By using spatial SENs to represent the functional interactions between different NCP supply areas and their connections with the agencies that govern them, we provide tangible insight into where regulations designed for one ES could have impacts on others and highlight opportunities for collaborative governance across multiple scales. Additionally, our approach presents a framework for incorporating comprehensive ecosystem function-based interactions among multiple NCPs while also allowing for the identification of highly interacting clusters for more targeted governance coordination. We show where agencies at local, regional, provincial and national scales have opportunities for coordinating regulatory 98 responsibilities within NCP networks, even in clusters that have relatively few discrete NCP supply areas. Our approach also produces several spatial and conceptual visualization tools to facilitate engagement with non-specialist stakeholders. We illustrate our approach here using three NCPs that are produced in and interact across a large multifunctional landscape, but our framework can be applied to diverse SES and collections of NCPs beyond those contemplated here. We discuss below how our approach can be expanded to other areas of long-term research and adaptive management, and how it can be used for sophisticated quantitative network analyses. Spatial integration of functionally interacting NCP networks with multi-scale and multi-sector governance boundaries is a promising decision-support tool to complement other methods for achieving more holistic planning and management in multifunctional landscapes. 5.5.2 Potential applications To be effective, environmental collective action must be deeply connected to the complex components and processes that occur within and across geographic sociopolitical boundaries (Bodin, 2017). Specific to effective NCP management is the need for input that integrates the diversity of knowledge from relevant disciplinary and sectorial realms (Cowling et al., 2008; MEA, 2005). Decisions at local levels are often influenced by larger-scale institutions, and the management capacity of governing bodies is often contingent on spatial scale (Martín-López et al., 2019). Additionally, politically-defined jurisdictional boundaries are often mis-aligned with the spatial extent of the biophysical processes they are trying to govern (Bodin, 2017). Ultimately, the complexity of landscape-level ecological models, the large number of diverse stakeholders at the regional scale (Bodin, 2017), and budgetary, data and timeline constraints can lead to the use of simplistic and/or subjective representations of NCPs and their underlying ecosystem function-based interactions by managers (Cowling et al., 2008; Maynard et al., 2010). Rapid pushes toward collaborative governance practices has also unearthed uncertainty among actors around why and with whom they should collaborate, and can result in wasting time and resources on misguided networking without increased governance efficacy (Bodin, 2017; McAllister et al., 2014; Nohrstedt and Bodin, 2014). As we show in this study, spatial SENs have the potential to address these issues by using network conceptualizations to represent the complex interactions between ecological components and processes across the landscape, while also incorporating geographic- and mandate-based management demarcations to distill where on the landscape and among which actors lies the highest potential for collaborative governance of NCPs. Maintaining a single area of NCP supply on a landscape is unlikely to be adequate for supporting functional interactions of multiple NCPs due to spatial incongruence of underlying ecosystem functions (e.g., Alemu et al. 2021); this is one reason why researchers and institutions endorse regional-scale 99 assessment and management of natural resources (Chan et al., 2006; Grêt-Regamey and Kytzia, 2007; MEA, 2005; Raymond et al., 2009; Serrat-Capdevila et al., 2009). Additionally, ecosystem management is challenged with the need to deliver multiple NCPs (Termorshuizen and Opdam, 2009; Wood et al., 2018), while recognizing that human activities can have positive impacts on the supply of some while negatively impacting others (Bennett et al., 2009). In reality, environmental policy-makers are mandated to act within defined geographic boundaries, within sector-specific scope of practice, with limited financial and personnel resources, by balancing the public demand and preferences, all while guided by evidence-based scientific rationale. Incorporating multi-NCP functional interactions into applied management has thus far relied upon simplifications or aggregations of several NCPs, which do not truly capture the complexities of these interactions, and therefore carry the risk of misrepresenting ecological realities and ultimately being ineffective. For example, the NCP bundle concept is used to identify groups of positively or negatively interacting NCPs that repeatedly occur across a landscape in order to identify areas of multifunctionality, predict NCP responses to different management scenarios (Alemu et al., 2021), identify potentials for trade-offs and synergies among NCPs (e.g., Bennett et al., 2009; Raudsepp-Hearne et al., 2010), find common drivers that impact NCP provisioning (Spake et al., 2017), and potentially spatially assess trade-offs in terms of supply, demand and flow (Metzger et al., 2021). However, bundles ignore how an NCP occurring in one area might have direct or indirect influence on NCP(s) in other areas and how NCPs may respond differently to drivers of change (Alemu et al., 2021). By using network cluster analyses to partition near-ubiquitous multi-NCP supply areas across the landscape into separate, functionally interacting NCP supply communities, we provide a tool for dividing the landscape-scale ecological sub-network into smaller chunks that are more practical to engage in from social and economic perspectives. Cross-scale links between multiple diverse NCPs are also preserved to help guide assessment of social-ecological fit to governance sub-network structure (more on this below). Thus, our approach can be used to empirically identify actors with the highest potential to govern NCPs at different spatial scales in the context of their functional interactions with other services (Martín-López et al., 2019). It might also facilitate conversations about how to better coordinate environmental management actions across scales of governance, and may be used to help address multiscale power dynamics (Felipe-Lucia, 2021) within and across sectors. The approach also employs existing sociopolitical and governance area boundaries, which often support other government functions not directly related to, but that may have impacts on, NCPs (e.g., housing development; social/recreation support services that change people’s use of the landscape; transportation network decisions that increase or decrease traffic flow in some areas). At a higher-level, we show how a SEN can be represented by the number of connections between each agency based on where their governance areas overlap with NCP 100 clusters (e.g., Fig. 5.3). This provides a visual tool that can help communicate opportunities or priorities for coordination between agencies. Informing sustainable land use planning and management with multi-NCP assessment calls for the integration of social and ecological data (Cord et al., 2019). A benefit of our approach is that it is amenable to the incorporation of a variety of qualitative and quantitative SES data, as we show here by using GIS-based NCP data derived from ecological (e.g., vegetation cover), sociocultural (e.g., analyses of landscape aesthetic components of tourism marketing documents; Field et al., 2017), and political (e.g., governance jurisdiction boundaries) datasets. Although the inclusion of social components undoubtedly will improve understanding of multi-NCP interactions (Yalew et al., 2018), existing NCP modelling tools tend to employ primarily biophysical data and therefore have limited capability for assessing the benefits realized by humans (Saarikoski et al., 2018). Employing both social and ecological information to improve our understanding of the links between humans and NCPs is important for managing potential risks faced by NCPs (Schröter et al., 2019). Representation of the functional interactions between the variety of provisioning, regulating, cultural and supporting NCPs that multifunctional landscapes provide will help elucidate to agencies with differing responsibilities the dependency of some highly-valued and explicitly-managed NCPs on other, more obscure NCPs. For example, the importance of some regulating NCPs, such as climate regulation or soil fertility maintenance, is not immediately obvious in market-based evaluations of agricultural products and may therefore be overlooked or undervalued in NCP-based management schemes. This issue is present in management schemes in our study area, as the cultural NCP we assessed, landscape aesthetics, is rarely explicitly governed by regulatory guidelines within the legislated mandates of the agencies included in our SEN (but see e.g., City of Kelowna, 2020; FPC, 1996). Current NCP risk-management frameworks stress the importance of decision-maker’s understanding of social preferences for particular NCPs, and highlight the challenge of increasing awareness around related but ‘invisible’ NCPs (Schröter et al., 2019). But so far, regulating and cultural NCPs are not well-represented in SENs (Baggio et al., 2010; Felipe-Lucia, 2021). We demonstrate the flexibility of the spatial SEN framework by showing how different high-level NCP types (i.e., provisioning, regulating, cultural), and their functional interactions, operating at different scales can be represented therein. Using our approach, the research question(s) and data availability will guide how a SEN is constructed (Felipe-Lucia et al., 2022), and will allow a full range of priority NCP types and their interactions to be explicitly incorporated. Recent research has explored how dimensions of alignment between collaborative social networks and biophysical components and processes can impact the effectiveness of environmental management (Bodin, 2017; Bodin et al., 2016). One dimension of this social-ecological fit relates to the alignment of links between actors in the social layer (e.g., governance agencies) with the links between interacting 101 components in the ecological layer (e.g., functionally interacting NCP supply areas). An example of a good fit in the context of multiple NCPs would be when hydrological managers collaborate with agricultural agencies to encourage regional farming practices that promote flood-control services, such as maintaining wetlands and riparian buffers and planting deep-rooting perennial crops to help reduce runoff and control downstream flooding (e.g., Goldman et al., 2007). The other dimension of fit relates to the extent to which actors holistically manage the interacting components of the biophysical system (Bodin, 2017). Ideally, multifunctional landscapes should be managed as whole systems rather divided into separate ecological components. However, the latter is typical of how governance area boundaries are delineated and how environmental governance is conducted, which can lead to exacerbation of habitat change and landscape fragmentation (Bodin, 2017; Dallimer and Strange, 2015). As is true in our study area, agency jurisdictions are commonly distinguished by geographic scale (e.g., local vs. regional vs. provincial vs. national). This can also lead to poor fit of governance to ecological processes that operate at different scales (e.g., agricultural production on a local farm vs. aesthetic beauty provided by regional viewscapes vs. hydrological processes connected throughout transboundary river basins) and that functionally interact across spatial scales (e.g., a farm that is part of an aesthetically valuable mosaic farming landscape and also contributes to and benefits from watershed-scale flood mitigation; Rust et al., 2021; Sayles and Baggio, 2017). Our work confirms these observations in that the NCP supply areas and their highly functionally interacting clusters were not spatially aligned with the agencies that govern them. SEN-based investigations into such spatial scale mismatches, as was done in a recent study on local and regional agencies involved in estuary watershed restoration, can reveal potential opportunities for strengthening governance capacity in areas where there are lapses in collaborative connections, low social productivity, and over-dependence on key organizations (Sayles and Baggio, 2017). As discussed further below, a next step in applying our spatial SEN approach will be to expand the social sub-network layer(s) to characterize the connections between actors in practice, then assess existing dimensions of social-ecological fit to identify opportunities for adding or strengthening links between actors to improve the effectiveness of NCP management. 5.5.3 Challenges and future perspectives for using spatial SENs in NCP governance The specifics of environmental issues, governance structures, and the effectiveness of collaboration continually evolve over time in response to various drivers of change, such as limited funding timeframes, political turnover, and transient vs. enduring ecological problems (Bodin, 2017; Bodin and Crona, 2009). For example, the reunification of Germany in 1990 led to restructuring of jurisdictional allocations and land use zoning, which culminated in the establishment of a protected area that supports biodiversity, habitat, flood protection and recreation NCPs (Metzger et al., 2021). However, true of the reunification 102 example and of NCP management programs in general is that they do not commonly account for multiple interactions and feedback effects over time (Felipe-Lucia, 2021). Here, our approach explicitly accounts for the former, but another flexible benefit of an SEN is that its general structure can be preserved while data continuity and scope is expanded over time. Updates and amendments can then be incorporated into the already built SEN to assess and monitor the impacts and feedback effects of social and ecological elements of change (e.g., Cinner et al., 2019; Felipe-Lucia, 2021; Fortin et al., 2021; Schröter et al., 2019). Spatial SENs can be used to track how NCPs and network structure react to drivers of change such as species loss (Keyes et al., 2021), climate change, invasive species (Dee et al., 2017; Jacob et al., 2020), increased demand for NCPs (Firkowski et al., 2021), and institutional restructuring (Metzger et al., 2021; Roldán et al., 2015). Related to this is evaluating whether the actions resulting from specific collaborative governance efforts are actually effective in delivering sustainable solutions to real-world NCP problems, which in practice has ranged from extremely successful to counter-productive (Bodin, 2017). For example, global network properties in multilayer SENs have been used to discover where particular characteristics, such as governance centralization, might have negative consequences on collaborative productivity in longer-term environmental restoration efforts (e.g., Sayles and Baggio, 2017) or positive influence where specific actors can coordinate tasks in urgent or acute scenarios (e.g., Lubell et al., 2017). Various existing dynamic network models can be used to assess how groups of nodes evolve, compare different explanations for network changes, and calculate the effect sizes of different sources of network change over time (e.g., Cinner et al., 2019; Fortin et al., 2021; Snijders, 2017; Sonderegger et al., 2020). In future, the SEN approach can also be used to monitor the influences of governance actions on the ecological and social dimensions of NCPs at different spatial scales (Dee et al., 2017), which remains a sparse area of research in spite of it being identified as necessary for successful management at the regional and global level (Firkowski et al., 2021; Geijzendorffer et al., 2017b; Geijzendorffer and Roche, 2013). We show here how a spatial SEN can be used to identify potential for collaborative governance based on functional interaction mechanisms under existing conditions. Once such a network is built for a region based on present-day static data, changes in nodes and/or link weights can be adjusted and evaluated based on emerging sociopolitical changes and/or environmental threats (e.g., management restructuring; NCP supply degradation). Our SEN framework could also explicitly incorporate representations of NCP demand valuations and their links to NCP supply, and the influences that current and potential social processes exert on the production and use of NCPs over time, which are rarely accounted for in ecological monitoring schemes (Kluger et al., 2020). The SENs presented in our study do not account for demand components of NCP delivery, but we suggest future studies investigate how human demand and environmental feedbacks 103 might change network structure over time (Fortin et al., 2021; Matias and Miele, 2017), and how delayed and/or unintended impacts of management actions on NCP demand and use can be analyzed in a SEN (Rieb et al., 2017; Snäll et al., 2021). Existing approaches (e.g., structured interviews) and/or datasets (e.g., Felipe-Lucia, 2021; government population census and statistics) can be used to gather sociocultural data from stakeholders across multiple sectors (e.g., Rathwell and Peterson, 2012), which can be used to expand the spatial SEN approach to include NCP demand flows within SESs (Dunford et al., 2018; Metzger et al., 2021). Notably, we did not consider any non-government, non-profit or private sector organizations with mandates related to NCP management, of which there are several in our study area, nor landowners, researchers or other community stakeholders, all of whom could influence the design, implementation and success of governance interventions (Cumming and Epstein, 2020). Therefore, we suggest future SEN representations expand analyses to include such groups, with careful acknowledgement that power asymmetries, novel leadership challenges and the time-consuming nature of collaboration, especially among actors with divergent backgrounds and/or interests, may make collaborative governance difficult. These issues are especially pervasive when dealing with transient environmental problems that require immediate interventions (e.g., fighting a wildfire, eliminating an invasive species; Alexander et al., 2017; Bodin, 2017; Klijn et al., 2010; Rosenschöld et al., 2014; Westley et al., 2013; Zachrisson and Lindahl, 2013). Various factors may determine success in collaborative governance, including the attributes of the focal environmental system, the degree to which the environmental and governance systems are aligned, and the social patterns of collaboration (Bodin, 2017). The interplay of these factors requires ongoing interdisciplinary evaluation of a SEN, as well as built-in potential for management adaptations (e.g., Waylen et al., 2019), to ensure the limited resources involved in maintaining governance collaborations are being efficiently and effectively utilized. A general challenge of incorporating both human and ecological elements relates to the incompatibility in their underlying components, exchanges and spatial extents. For example, nodes can be individuals, agencies, or areas on the landscape; links can represent social communication, shared management responsibility, or ecosystem function-based interactions, etc. (Gonzalès and Parrott, 2012; Jackson and Fahrig, 2012). To help address this, NCP representations within a SEN can generally be separated into four typologies where NCPs are represented as either: nodes as stocks of natural capital (our approach); node attributes as social or ecological valuations (e.g., economic; management intervention; our approach); links as connections through ecological and/or social network layers; or emergent properties as results of network interactions (e.g., NCP outcomes as a result of management actions; Felipe-Lucia, 2021). Further, as in our study, operational spatial scale can vary among different NCPs and the ecosystem function underpinning their supply, and among the governance systems 104 responsible for their management. Any impacts of management interventions on NCP supply may be spatially mismatched with impacts on flows or demands (Eigenbrod, 2016), which suggests that multiple scales should be considered within a framework that seeks to understand how management impacts the suite of NCPs that interact across multifunctional landscapes (Metzger et al., 2021). Our proposed framework is a first step in tackling the above challenges by using a spatially explicit NCP-as-nodes (with value attributes) SEN typology that integrates multiple geographic scales of governance and diverse, functionally interacting NCPs. Although spatial SENs remain in early stages of development, as we show here, they have potential to provide practical information for the governance of multiple NCPs at multiple scales across SESs (Gonzalès and Parrott, 2012; Metzger et al., 2021). In addition to drawing on the flexible empirical, theoretical and methodological library of knowledge available in the network toolbox, more studies using the SEN approach across different governance, management and NCP contexts will be needed to glean generalizable insights into the patterns of human-nature interactions (Biesbroek et al., 2017; Bodin and Chen, 2023; Ferraro et al., 2019). Additional limitations related to the NCP data used for this study are discussed in Chapter 4; limitations related to NCPs, their governance, and network community detection algorithms are discussed in Appendix D.2. 5.6 Conclusion Our spatial SEN approach demonstrates how a novel spatial NCP network concept (Field and Parrott, 2022, 2017, Chapter 4) can be integrated with existing, often misaligned governance area boundaries, with the additional benefit of being a promising tool for promoting and guiding multi-sectorial agency collaboration with explicit spatial context at a range of geographic scales. Spatial SENs support multi-NCP governance by enabling clear visualization, assessment and comparison with governance area boundary distribution, all of which can guide efforts to enhance the supply of multiple NCPs and maintain their supportive functional interactions. The ability of spatial SENs to incorporate multi-scale functional connectivity information into NCP assessment has several potential benefits, including: predicting how social, economic, and environmental changes at large scales might impact local management interventions, and vice-versa; integrating local results into decision-making at larger-scales, which can help protect against uninformed actions with undesirable outcomes; aligning local, regional, and national priorities to promote coordination and co-benefits among people and nature; guidance for bottom-up and/or top-down leadership (e.g., Beckmann et al., 2019; Brauman et al., 2020; Halpern et al., 2012); and can help provide context for large-scale problems at the local level (Chaplin-Kramer et al., 2021; Fitzpatrick and Dunn, 2019; Chapter 4). We hope our approach will help build a common understanding of how multi-NCP functional interactions operate within the context of large-scale landscape governance, 105 that this will promote more effective coordination between agencies and stakeholders with diverse goals, and ultimately inform more holistic policy and management approaches that can tackle the troubling trends of a changing world. 106 Chapter 6: Conclusion 6.1 Significance and potential applications of the spatial ecosystem services network approach This dissertation contributes to the growing ES knowledge base over the past few decades by demonstrating novel methods for expanding our spatial understanding of the functional interactions between multiple, diverse ES across multifunctional landscapes. A core benefit of the multi-ES network methods presented here is the spatial network representation of functional interactions between ES supply areas, which facilitates assessment of the interactions between all supply areas, both overlapping and those separated in space. This is a different approach from conventional ES interactions research, which commonly relies on amalgamating bundles of multiple ES based on spatially concurrent and/or correlative combinations. Our results point to the nuanced information that spatial, systems-level functional interaction networks may be able to provide to real-world assessments of multiple ES. These new approaches contribute new knowledge that may support landscape-level ES management and increase our understanding around the co-benefits that ecosystems provide to societies (Bennett et al., 2009; Mouchet et al., 2014; Neugarten et al., 2018). In Chapter 2, we presented a new perspective on how spatial networks can be used to assess the functional interactions between multiple, diverse types of ES, and how this can provide insight into the ecological resilience of multifunctional landscapes. Research suggests that managing landscapes for multiple interacting ES will help support ecological resilience. However, despite advances in understanding around the ecosystem function mechanisms that drive ES supply and multi-ES interactions (Agudelo et al., 2020), ES supplies are typically mapped and assessed within fixed areas (e.g., Tallis et al., 2008), and ES ‘interactions’ are simplified into synergies or trade-offs based on positive or negative correlations between spatially co-occurring services (e.g., Queiroz et al., 2015). Thus, current approaches fail to directly assess the functional interactions among ES (Bennett et al., 2009; Mitchell et al., 2013; Qiu and Turner, 2013, 2015). Preliminary work has explored the potential for assessing habitat and ecological interaction networks to investigate the impacts of landscape structure, function and connectivity on biodiversity and ES (e.g., Cumming et al., 2010; Gonzales et al., 2011), but our review of related literature pointed to a key opportunity for further investigation: developing spatially explicit representations of multiple ES from several categories (i.e., provisioning, regulating, cultural) within the same network. The topology we presented is a first step in demonstrating how these gaps can be addressed through leveraging spatial ecological networks, and provides a working example of how to design, build and quantitatively assess multi-ES functional interactions and resilience. 107 In Chapter 3, we built upon the concepts presented in Chapter 2 to evaluate our spatial network approach for multiple functionally interacting ES using a large, diverse, real-world multifunctional landscape. By leveraging existing data on ES from three broad categories, we provided a generalizable example of how the current knowledge of ecosystem functions underpinning the supplies of a diverse group of ES and their functional interactions can be spatially depicted and quantified within a network framework. Then in Chapter 4, we use the functional interaction networks built in Chapter 3 and applied spatial network analyses to assess the supply of and interactions between the three focal ES. We showed that network analyses have the potential to identify high-value ES supply areas that are not detected by, or redistributed relative to, traditional or single-ES methods. Together, Chapters 3 and 4 showed that current approaches that focus on individual ES supply areas may not be adequate for identifying the areas critical to maintaining the functional interactions between multiple ES. With respect to our findings, it seems a difficulty may persist in terms of identifying single areas that are capable of producing and maintaining the functional interactions between multiple ES. This has been observed in other assessments of ES supply and is likely due to spatial incongruence in underlying ecosystem functions (e.g., Berhane Alemu et al., 2020). However, our identification of interaction hotspots among multiple ES showed where small-scale management interventions could extend to other, functionally interacting ES (e.g., conserving patches of both low- and high-intensity management in agricultural landscapes to help maintain multiple ES at larger scales; Prevost et al., 2022; Qiu and Turner, 2013). This kind of information could support environmental planners in identifying candidate areas for restoration, conservation, and monitoring, and help to merge the ES concept with other landscape connectivity priorities (e.g., wildlife corridors; Field and Parrott, 2017; Peng et al., 2018). Further, the methods presented not only help advance ES concepts in terms of how they interact within complex systems, but can also be used to guide thoughtful incorporation of related science into applied planning and management within a comprehensive ES assessment framework (e.g., Preston and Raudsepp-Hearne, 2017); to bolster arguments for maintaining landscape connectivity to support ecological function; and to deepen cognizance of human’s reliance on nature (Guerry et al., 2015). In Chapter 5, we expanded the ecological-centric multi-ES networks built in Chapters 3 and 4 to include a social dimension: the agencies involved in governing these specific ES at local, regional, provincial and national scales. Using a mixed-methods network approach to partition the landscape into clusters of tightly functionally interacting ES supply areas, and connecting these to spatial delineations of governance areas, we found that jurisdictional responsibility exists across all scales of government regardless of the geographic extent of highly-interacting ES clusters. Research has suggested that quantifying the relationships between environmental management structure(s) and the ES they govern is necessary to plan for the spatial arrangement of LULC and enhance ES flows to humans at the regional 108 scale (de Groot et al., 2010). Our bottom-up approach of using ecosystem function-based ES models to guide coordinated action within the existing structure of multi-scale governance systems will allow for pragmatic approaches to various scale- and resources-dependent interventions in a world where the spatial distribution and function of ecological processes is shifting at a rapidly increasing rate (Weiskopf et al., 2020; Norgaard et al., 2010). Our approach also facilitates a more targeted perspective relative to resource-intensive and sometimes action- and/or enforcement-prohibitive approaches that focus on large swaths of the landscape (e.g., Maynard et al., 2010), or uncoordinated efforts that may ignore critically connected services. For example, the identification of functionally interacting ES clusters can help pinpoint where management interventions in highly-interacting ES supply areas can simultaneously have both local and wide-ranging co-benefits beyond the focal ES and the specific area of intervention (e.g., Qiu and Turner, 2013). Additionally, by geographically connecting multidisciplinary and multi-scale governance organizations to the functionally interacting ES sub-network, we can identify the specific institutional actors that may be best suited for coordinated management at different scales and with diverse objectives, even for projects that prioritize relatively small or specific target areas on the landscape. An agency that fits well with one ecosystem may not fit well with another (Ekstrom and Young, 2009); this may also be true of institutional alignment with ES. Through providing a tool that can support the coordinated management of overlapping jurisdictions across spatial scales and based on clusters of diverse functionally interacting ES, we provide insight into ecological-institutional functional fit which, in conjunction with assessments of temporal alignment and adaptive capacity, can be useful for informing integrated, multi-sector governance (Folke et al., 2005; Crowder et al., 2006; Galaz et al., 2008; Ekstrom and Young, 2009). The next step will be to apply our tool to policy and planning in the real world, which has been substantially slower relative to accumulation of related knowledge (Schröter et al., 2019). Interdisciplinary ES assessment requires multiple analytical methods and tools, the appropriate approach dependent on the research question(s) at hand (Baciu et al., 2021). However, the most commonly-used tools are limited in their ability to assess multiple ES and the functional interactions between them (e.g., InVEST; Rieb et al., 2017; Richard et al., 2013), and instead rely on users to examine interactions between different ES post hoc. There are some tools that provide adequate frameworks for modelling the interactions between multiple ES (e.g., Artificial Intelligence for Ecosystem Services (ARIES); Bayesian networks; Villa et al., 2014, Martínez-López, et al. 2019), but they have been criticized for lacking practical applicability due to their resource-intensiveness (Balbi et al., 2015), and for not considering spatially-explicit aspects of ES interactions (Landuyt et al., 2015; Stritih et al., 2018). We used a free and globally accessible programming language tool, R, to build and evaluate our ES network models using existing data on regional ES supply areas. The resulting code is flexible enough to 109 incorporate data across a spectrum of spatially modelled ES, and to integrate output from the various ES-specific modelling tools (e.g., Sharp et al., 2014) into the delineation of network components. It could also be used to create alternative representations of functional interactions in a single-layer ES network (e.g., ecological nodes represented by species actors with inter- or intra-specific interaction links; socioeconomic nodes represented by stakeholders with management actions links; Dee et al., 2017) or multilayer ES networks (discussed below). We hope our network approach and associated code will be useful for future spatially explicit studies of ES and related functional interactions within SES. Incorporating multi-ES information into applied environmental management, planning and policy remains a challenge due to the limited understanding around how multiple ES interact with one another across complex dynamic landscapes; the variability in the ES research, modelling techniques and tools; and a failure to identify clear drivers that can guide specific interventions (Agudelo et al., 2020; Kool et al., 2013; Spake et al., 2017; Saidi and Spray, 2018). In fact, we know of no published evidence that incorporates ES interactions into actual decision-making (Saidi and Spray, 2018). This could be related to a lack of understanding around effective ways to present ES information to decision-makers, although simplified representations of complex, multi-value information are generally considered to be most useful for applied policy and management (Wright et al., 2017). Even so, the literature specifically suggests that ES interaction assessments have the potential to contribute to various policy stages, including problem identification, strategic design, monitoring, and adaptation (Saidi and Spray, 2018). Dee et al. (2017) present a stepwise approach for how to develop integrated ES interaction SENs for the assessment of management alternatives, which includes defining links between ES nodes, their underlying ecosystem function mechanisms, and relevant social network components within the management context, then identifying and assessing drivers of change and ES provisioning vulnerabilities. Our approach is readily integrable into these kinds of assessments. Ultimately, we hope it will help provide a more nuanced, spatially-explicit understanding of the complex functional interactions that drive the supplies of ES and will contribute to the development of more accessible tools and measures for decision-makers. 6.2 Limitations of the research and possible future directions There are several limitations and possible future research directions related to our approach, which we presented in detail in Chapters 2 through 5 and summarize below. We reiterate that one of our overarching objectives across all chapters was to present and evaluate a new approach for spatially representing and assessing the functional interactions between multiple-ES. We did not seek to provide a comprehensive representation of how all ES operate within our study region for use in an applied management context. As is common in studies that map ES interactions, many of the limitations we face are primarily the result of constraints around data availability, system complexity or lack of knowledge of 110 related social-ecological processes, and other methodological barriers (Agudelo et al., 2020; Kolosz et al., 2018; Field and Parrott, 2017). 6.2.1 Expanding the representations of ecosystem services provisioning and biophysical models The spatial models used to build our networks are based on various assumptions about the supply of and functional interactions between three focal ES. Although the ES proxies and models on which original mapping was based are well-supported by the literature (Field et al., 2017), and the use of LULC proxies does account for a degree of landscape heterogeneity (e.g., Qiu and Turner, 2015), we assume that model parameter values are constant across a large landscape and diversity of ecosystem types. We note that certain types of ES might be more highly valued in certain landscape contexts, such as those that show signs of decline, are deemed more critical to human survival or welfare, are fundamental parts of regional cultural identify, or are particularly valuable to a regional economy (Wolff et al., 2015). We assumed ES node and link weightings to be equivalent to those derived from original mapping modelled values, but future studies could employ multipliers for normalized model-based weightings based on regional management priorities or specific valuation contexts. In future, spatial identification of functional interactions could also be paired with other available non-spatial methods, such as correlative analyses to detect ES associations (Mouchet et al., 2014), regressions to identify relationships between two ES that could imply directional linkages (Bennett et al., 2009), qualitative models based on sociocultural or survey-based inputs (Wright et al., 2017), or data-driven modelling using empirical data and machine-learning algorithms (e.g., Bayesian networks; Agudelo et al., 2020; Barton et al., 2016). Additionally, the original ES were mapped based on existing, primarily publicly available, and relatively high-resolution data at the local scale, which we assume is representative of the operating scale of the ecosystem functions that underpin their supply (Saidi and Spray, 2018; Field et al., 2017; Field and Parrott, 2022). However, we note that valuations of the supply of and interactions between different ES can vary based on the scale of investigation, and focusing on one scale may overlook important mechanisms that unfold at different scales (Chaplin-Kramer, 2023). For example, an assessment of food production and water quality found that fertilizer application was the dominant driver at smaller-scales, whereas cumulative interactions among multiple drivers, including proactive management, land use changes, technology, and more stable climate, drove synergies and trade-offs at larger scales (Qiu et al., 2018). Indeed, mechanistic understanding around the relationships between biodiversity, ecosystem functioning, and ES is typically derived from small-scale field experiments, whereas empirical information is lacking at the larger scales relevant to decision-making (Manning et al., 2019; Chaplin- 111 Kramer, 2023). An assessment of how network results may change with the use of different spatial resolution to define ES nodes and links is beyond the scope of this study, but could be investigated in future (e.g., Schröter et al., 2018; Liu et al., 2015). We demonstrated our approach on a subset of the ES actually supplied across our study landscape, with only synergistic functional interactions between them at a snapshot in time. We note that our use of only three makes it difficult to reliably identify key functional interaction corridors, or to generalize our results across to a wider variety of ecosystems or LULC types. Further, though the potential for the trade-offs exists among the ES in our study (e.g., water extraction for plant agriculture irrigation can disrupt hydrological cycles; Janssen et al., 2006), we did not have data on related ecosystem functions or manmade infrastructure and, therefore, could not investigate negative interaction links. However, our network approach would allow for ecosystem dis-services (e.g., pest and disease transmission, competition for water and pollination; Zhang et al., 2007) to be explicitly represented by assigning negative link valuations. Additionally, we did not consider any dynamic functional interactions as temporal data for the ES we considered were not available, and scenario modelling was beyond the scope of this dissertation. Of the three focal ES supplies we considered, the spatial occurrence of waterflow regulation and landscape aesthetics have been characterized as static, whereas agricultural crops can be seasonally dynamic (Firkowski et al., 2021). We note that changes in LULC over time, management interventions, and interannual variability may directly impact the distribution, supply, flow, demand, and/or functional interactions between ES that are driven by related ecological characteristics (Mitchell et al., 2015; Renard et al., 2015; Li et al., 2017). Even standards of data collection can result in temporal data variability; for example, forest growth and carbon storage are typically measured at decadal scales, whereas recreational use might be measured annually (Firkowski et al., 2021). Social, cultural and economic drivers, such as changes in human population size, ES-related product values, and distances between resource and infrastructure, also contribute to the ES dynamics within SES (Agudelo et al., 2020). By incorporating whether the spatial and/or temporal scales of ES and related social components are static or dynamic (e.g., supply quantities or distributions; Felipe-Lucia et al., 2021; Firkowski et al., 2021), and any potential negative functional interactions into a spatial ES network, with a comprehensive enough suite of ES to satisfy management and stakeholder priorities, future research will be able to evaluate the implications of positive and negative interactions over time (e.g., Li et al., 2017; Schroter et al., 2019). This could help lower the risk of unwanted trade-offs, support management interventions seeking to maximize synergies (Fu et al., 2013), and uncover how seemingly isolated ES supply areas might have widespread or cumulative impacts over multiple spatial and temporal scales. A criticism of tools used for ES interactions research is that they tend to focus on biophysical systems and therefore have limited ability to inform how our actions might impact the actual benefits realized by 112 humans (Saarikoski et al., 2018). Although our approach uses interdisciplinary concepts, methods and data to tackle the supply part of ES networks, it does not address the demand, delivery or access components of ES provisioning (Fu et al., 2013). It has been argued that, in the absence of human demand for and access to ES, there can be no argument for existence of a valued benefit (Wieland et al., 2016; Chan and Satterfield, 2020). For example, the compulsory movement of First Nations people in BC to residential schools prevented their subsistence harvest of seaweed, which resulted in a loss of traditional knowledge related to access practices (Turner and Turner, 2008). In this scenario seaweed ES supply value is irrelevant because of the imposed barriers to demand and access. An identified challenge for current and future ES research relates to moving beyond assessments that focus solely on supply, to including components of demand, delivery (or ‘flow’) to human users, human well-being, and sustainable management within the ES system (Wang et al., 2021). This would promote a more complete understanding of how landscape structure may improve or inhibit these processes (Mitchell et al., 2015; Feurer et al., 2020) and better support decision-making (e.g., Dunford et al., 2018). For example, increasing people’s ability to access remote natural areas for fishing, hunting, recreation and wild food gathering can happen when roads and trails are built (Peres and Lake, 2003; Trombulak and Frissell, 2000). Conversely, increased access can also result in ecosystem dis-services, such as introduction of zoonotic and biotic diseases (Jackson et al., 2006; Li et al., 2012). Further, fragmentation can change how humans move and distribute themselves across the landscape and, combined with the above, this can disrupt the flow of ES to beneficiaries (Mitchell et al., 2015). Demand for ES is driven by human consumers and is not necessarily connected to the supply potentials or quantities of ES (Andersson et al., 2015b; Feurer et al., 2020), nor does it necessarily correspond spatially or mechanistically to ES supply (e.g., Tian et al., 2021). In many cases, the provisioning of ES is dependent on flows across the landscape that connect demand to areas of supply and can be impacted by various factors related to landscape composition, configuration, and processes (Aquilué et al., 2020; Fisher et al., 2009; Serna-Chavez et al., 2014). Frameworks exist to help illustrate the relationships between ES supply (and the ecosystem functions underpinning them) and the consumptive social system (e.g., Fu et al., 2013), which could be used as guidelines when developing more complete spatial ES provisioning networks. For example, Metzger et al. (2020) used a simplified SEN as a hypothetical example of how to assess changes that occurred after the implementation of payments for ES, with proximity-, quantity- or quality-based links between natural forest ‘supply’ and residential ‘demand’ area nodes. Studies that have attempted to address the demand and delivery flows of ES have found that appropriate census data are lacking (Andersson et al., 2015b). However, through spatially-explicit incorporation of the functional interactions between ES supply, demand and flow, we imagine our network approach could be leveraged to examine various research questions related to these aspects of ES provisioning. 113 In all our chapters we presented a spatially explicit approach that incorporates the influence of landscape composition (and, partially, landscape configuration through weighting of network links) on ES supply and their functional interactions. This has been identified as a necessary, but still rare, advancement in ES research (Metzger et al., 2021), but the nuances of local- and landscape-level processes could be better incorporated in future applications of our approach. For example, the influence of edge effects on particular ES could be incorporated into the delineation of network nodes and links, e.g., increased edge can reduce the number of large trees thereby negatively impacting climate regulation services (d’Albertas et al., 2018; Melito et al., 2017). Landscape-level effects related to the distribution of ES supply (and/or demand) areas could also be incorporated more explicitly, such as the impacts of proximity (e.g., isolated areas reducing the ability of human users to take advantage of recreation services), composition (e.g., balance between levels of supply and demand), configuration (e.g., impacts of interspersion of habitat and farmland on pollination and pest control; Boesing et al., 2018; González-Chaves et al., 2020), and matrix permeability (e.g., how corridors or infrastructure might impact the movement of biotic and abiotic processes between supply and/or demand areas; Metzger et al., 2021). Metzger et al. (2021) have tested these ideas using two simulations and, although they do not model the functional interactions between ES supply areas, they found that incorporating changes in these landscape characteristics changed modelled results for ES supply, demand and flow. Consideration of these effects will require collaboration among ES scientists and landscape ecologists in order to improve understanding around landscape processes, gain adequate data at fine resolution, and develop approaches, such as those presented in this dissertation, that are able to deal with the complexity of ES provisioning in a spatially explicit manner (Metzger et al., 2021). The spatial ES network approaches presented in this dissertation require knowledge of the linkages between the ecosystem functions that underpin multi-ES interactions, but complex understanding of some of these remains incomplete (e.g., Yang et al., 2018; Saunders et al., 2019). For example, there is limited knowledge of the connections between ecological communities and biogeochemical processes, and of how stressors or management interventions might change network structure and ES provisioning (e.g., Russo and Shae, 2016). We did not have the data necessary to fully integrate the inherent complexity of ecosystem functions into the models for ES supply and functional interactions used in our networks. For example, we did not include the contribution of groundwater sources and sinks to WF models due to coarse and incompatible data resolution (OBWB, 2010), but this is a logical next area of investigation for a more complete representation of this and other related ES (e.g., diverted and in situ water supply; Brauman et al., 2007). There are other models that could be used for representing functional interactions between different ES (e.g., based on modelled movement potential of pollinators between nesting habitat and adjacent plant agriculture areas (e.g., Abdel Moniem and Holland, 2013; Pashanejad et al., 2023). The 114 mechanisms behind linkages between ES need further study, especially with respect to teasing-apart the impacts of shared drivers vs. direct functional interactions between ES (Bennett et al., 2009; Kremen and Ostfeld, 2005). To provide an example related to environmental change concerns in our study region, an investigation of ecological thresholds related to increasing aridity found that landscapes have been shown to respond through multiple sequential changes in structural and functional biotic-abiotic interactions, with direct and indirect impacts on the vulnerability of related ES (Berdugo et al., 2020). However, a benefit of the ES network approach presented in this dissertation is that it is flexible in terms of which underlying models are used to represent ES supply and the functional interactions between them, and future work can integrate improved models accordingly. It is worth stating that adding complexity can be costly in terms of added difficulties around testing, validation, increased uncertainty, communication to non-specialists and end-users (Schröter et al., 2015; Voinov et al., 2014). Therefore, all studies must balance added complexity against precision, realism, generalizability, and practicality, especially when they are being developed as decision-support tools (Levins, 1966; Rieb et al., 2017). 6.2.2 Delving deeper into spatial ES network concepts We provide a general framework and initial demonstration of what can be gleaned through representing multiple ES and one aspect of a social governance system using spatial networks, but we only scratch the surface of the potential of network approaches in assessing these SES. In Chapters 2, 3 and 4, we focus on the individual ES node and link scale, and expanded to the community scale in Chapter 5 in order to partition the landscape into highly functionally interacting clusters of nodes. Networks are capable of representing ecosystem processes and functional interactions based on data from multiple scales, so that scale-dependent impacts can be assessed within the same networks or subnetworks therein (e.g., Gray et al., 2021). Thus, a framework capable of incorporating multi-scale information has several tangible benefits for applied management, including anticipating potential cascading impacts of top-down or bottom-up decisions; aligning multi-scale governance strategies and promoting collaborative management efforts; and helping to communicate the impacts of large-scale problems in local communities, and vice-versa (e.g., Beckmann et al., 2019; Brauman et al., 2020; Halpern et al., 2012; Fitzpatrick and Dunn, 2019; Chaplin-Kramer et al., 2021). Sayles and Baggio (2017) compared several well-known, global measures (density, centralization, and edge quality) of a real-world SEN to equivalent random simulations in order to assess the efficiency and effectiveness of resource management collaboration. In future, our approach can be used to assess larger network scales by using community and global measures to compare between networks (e.g., the same landscape at different points in time or under different LULC scenarios) and to assess overall system functionality and resilience (Rayfield et al., 2011; Heckmann et al., 2015; Gonzalès and Parrott, 2012; Schroter et al., 2019). 115 The spatial scope of inquiry could also be expanded to address various management and context-specific queries. Globally, functional interactions between ES operating across large scales could be represented across regions with conflicting socioeconomic status, demonstrating how resource use in affluent regions may indirectly impact marginalized populations or vice versa. Similarly, the connections between rural and natural areas and densely populated cities can be highlighted using this method, with visualizations being used as educational tools to help strengthen city dwellers’ connection with the natural areas that sustain them. From a conservation perspective, incorporating multi-scale linkage information into ES assessments has several potential benefits, including: anticipating how economic, environmental and social change at larger scales might impact local actions, and vice-versa; using smaller-scale results to inform the decisions of larger-scale actors to avoid otherwise uninformed actions; aligning local, regional, national and international goals to promote cooperation and synergies among people and nature; larger-scale data informing, and being informed by, smaller-scale studies (e.g., Beckmann et al., 2019; Brauman et al., 2020; Halpern et al., 2012); and can help communicate and provide context for large-scale problems at the local level (Chaplin-Kramer et al., 2021; Fitzpatrick and Dunn, 2019). In Chapters 2 and 4 we include analyses of network measures related to landscape ecological resilience (via the proxy of ‘robustness’, i.e., node and link betweenness centrality). As outlined in Chapter 2, the approach can be expanded to conduct a more thorough assessment of resilience using specific ecological metrics and network measures, including: connectivity, centrality, vulnerability diversity, functional redundancy, modularity, and control of flow (Gonzalès and Parrott, 2012). Changes in local and global network characteristics in multilayer SENs could then be used to assess the contribution of multiple ES to overall SES resilience, or their vulnerability to environmental shifts or management interventions (Sayles and Baggio, 2017; Kivela et al., 2014; Gonzales and Parrott, 2012; Field and Parrott, 2017). Networks are not limited by the number of layers that can be analyzed at once, and including multiple sub-network layers may allow for more accurate approximations of the functionality of complex, multilayer SESs (Bodin et al., 2016). For example, Windsor et al. (2021) conducted a multilayer ecological network analyses to identify trade-offs between pollination, pest and herbivory control ES as a result of different management practices related to non-crop plant species. Bohan et al. (2016) present an approach for building a multilayer ES network, with social and economic sub-layers grounded on an ecological interaction sub-layer, and allow for different types of intra-layer interactions. Bodin et al. (2016) use stochastic modelling, based on a class of exponential random graph models from the social sciences (e.g., Lusher et al., 2013), to assess multiple interconnected sub-network levels within a single multilayer network to identify interactive arrangements that might lead to more effective collaborative governance across various contexts (e.g., resource sharing with or without a social link; managing the 116 whole ecosystem vs. a subcomponent; collaborative vs. isolated management of interacting ecological components). Firkowski et al. (2021) present a conceptual SEN ES monitoring framework that explicitly represents potential feedback effects between ecological and social sub-layers (Ostrom, 2007), which can identify non-linear and time lagged effects that could serve as early warnings of potential shifts to alternative states (Nelson et al., 2009, Bauch et al., 2016). Feedback dynamics are seldom incorporated into current ES modelling tools (Rieb et al., 2017), in part because researchers have warned that integrating feedbacks may result in extremely complex models with less predictive capacity than the simpler ones they would replace. Decision-makers might instead find greater benefit in complementary simple exploratory models that represent general system structure, ES interactions, and their responses to change (Rieb et al., 2017). To contrast the approach presented in Chapter 5, where only the ecological network sub-layer includes detailed data on functional interactions, if a more thorough assessment of the social sub-layer(s) is a study objective, the selection of social elements and links can be directed by a priori stakeholder analyses so that connections (e.g., power; information exchange) between actors can also be evaluated (Prell et al., 2009; Gonzales and Parrott, 2011). Ultimately, although single-layer networks are generally easier to apply, more accessible to non-specialists, and might be more effective tools for communication, multilayer networks might be more appropriate for delving deeper into the intricacies of complex SES (Saunders et al., 2019). 6.2.3 Real-world application of spatial ES networks Gaps between science and policy may preclude the incorporation of ES knowledge into applied decision-making (Vallet et al., 2018). Indeed, a small minority of ES publications explicitly consider the policy context of the ES relationships being assessed (Laurans et al., 2013). Given the ever increasing interconnectedness of human actors and the environment across larger geographic and jurisdictional scales, joint assessments of social and ecological connectivity are necessary to support coordinated governance (Hughes et al., 2013; Folke et al., 2005; Klijn and Skelcher, 2007; Ansell and Gash, 2008; Brondizio et al., 2009; Walker et al., 2009; Cosens, 2013; McAllister et al., 2015; Bodin et al., 2016). Quantitative SEN approaches that allow for spatially explicit diagnoses of problematic components of the ES supply-flow-delivery chain are rare and challenging, primarily due to lack of data, high data collection costs, and complex analytical formulation, but could theoretically be helpful in identifying what governance strategies are best for addressing these problems (Metzger et al., 2020; Bodin et al., 2019). However, if the outputs of ES interactions research do not align with the needs or expectations of planners and scientific advisors, there is a risk that methods and results will simply be ignored in applied management contexts (Gasparatos, 2010; Grêt-Regamey et al., 2016; Rosenthal et al., 2015). The approaches we develop here contribute to addressing a key shortcoming identified as a barrier between 117 ES tools and meeting the needs of planners and managers: specifically, the ability to visualize and assess the complex functional interactions between multiple ES. For example, in our study area the City of Kelowna is developing an inventory of natural assets that provide ES to the community, through which they catalog assets by LULC type and various value metrics (e.g., area, tree count, watercourse length, trail length), and have developed an online exploratory dashboard in order to promote visually-effective user engagement (MNAI, 2021). Our approach could be used to directly inform such projects, by providing complementary functional interaction-based valuations and spatially-explicit visualizations of assets and their interactions. A next step will be to expand our approach to evaluating feedbacks and temporal dynamics (discussed above; Rieb et al., 2017), and to make iterative adjustments to network models when new, context-specific information emerges. This will allow for adaptive management and monitoring of the ES system under study (Dee et al., 2017; Lindenmayer and Likens, 2009, Firkowski et al., 2021). However, we acknowledge that we did not incorporate links within the governance social sub-layer presented in Chapter 5, nor did we include other stakeholder groups. ES governance often involves collective action and partnerships between public and private sectors, and encompasses policy, legislation, enforcement, decision-making processes, property rights, and markets (Metzger et al., 2020). For example, property rights can have large impacts on ES-related governance interventions, such as access, harvest, management, alteration, exclusion, or alienation, which in turn can impact ES supply, demand and flow (Galik and Jagger, 2015; Metzger et al., 2020). Therefore, expansion of the spatial ES network concept should directly integrate the input of planners, policy makers and other stakeholders at the outset, and be open to incorporating other methods that could provide unique and complementary contributions to ES-related decision-making (e.g., spatial vs. temporal vs. scenario modelling; Vallet et al., 2018). This could help tailor the data inputs and outputs in such a way that they will be communicable and useful within the policy context (Rieb et al., 2017). 6.3 Summary of contributions The ES (and NCP) concepts span social, political, economic and ecological domains (Albert et al. 2014a), and our approach contributes to a systems-level perspective that supports improved understanding of the linkages operating within these SESs. There is probable overlap between areas of ES supply, wildlife habitat (Galindo‐González et al., 2000; Cascante et al., 2002; Levey et al., 2005; Fischer et al., 2006), and high biodiversity (e.g., Nelson et al., 2009); therefore, assessing the functional interactions between ES and across ecosystem interfaces (e.g., terrestrial-aquatic; Saunders et al., 2019) may provide more robust arguments for preserving key corridors and ecosystems. Clear articulation of the relationships between nature and human wellbeing, as well as biophysical quantification of ES is essential if they are to be incorporated into policies and planning by decision-makers (Cowling et al., 2008; Egoh et al., 2011; 118 IPBES, 2016). The ES concept has the advantage of being relevant to a wide variety of stakeholders (e.g., Larondelle and Haase, 2012), and network visualizations can help to effectively communicate people’s connections with nature, conceptualize the complex interactions that occur in nature, and illuminate changes in one part of a network that might have unintended consequences on other network components, including people (Pocock et al., 2016). Thus, we hope our approach stimulates awareness and connectedness among social actors involved in ES-related policy, and leads to an increased understanding of the critical, but often hidden, functional interactions operating across multifunctional landscapes (Saunders et al. 2019). 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Note that directionality of links can be inferred from table text and built into network analyses. This table is not exhaustive of all potential interactions between all ES. Table References: [1] (Turner et al., 2014) [2] (Power, 2010) [3] (Zhang et al., 2007) [4] (Boyd and Banzhaf, 2007) [5] (Bennett et al., 2009) [6] (Mitchell et al., 2013) [7] (Crossman et al., 2013) [8] (Janssen et al., 2006) [9] (MEA, 2005), [10] (Fleischner, 1994) [11] (Tallis and Polasky, 2009) [12] (Gill, 1991) [13] (Lonsdorf et al., 2011) [14] (Batary et al., 2010) [15] (Andersson et al., 2012) [16] (Nicholls and Altieri, 2013) [17] (Marini et al., 2012) [18] (Ayers and Wescot, 1985) [19] (Qiu and Turner, 2013) [20] (Queiroz et al., 2015), [21] (Kraus, 2014) [22] (Houlahan and Findlay, 2004) [23] (Zedler and Kercher, 2005) [24] (Willemen et al., 2010) [25] (Plieninger et al., 2013) [26] (Andersson et al., 2015a) [27] (Daniel et al., 2012) [28] (Wagner and White, 2009) [29] (Raudsepp-Hearne et al., 2010) [30] (Bangash et al., 2013) [31] (Jackson et al., 2001) [32] (Klein et al., 2007) [33] (De Loë et al., 2001) [34] (Burmil et al., 1999) [35] (Mäler et al., 2013) [36] (Kremen, 2005) [37] (Bodin et al., 2006) [38] (Petz and van Oudenhoven, 2012) [39] (Maes et al., 2012) [40] (Watkins et al., 2003) [41] (Poff et al., 1997) [42] (Biesmeijer et al., 2006)[43] (Blackstock, 2001) [44] (Decourtye et al., 2010) [45] (Downing and Cuerrier, 2011) [46] (Tommasi et al., 2004) [47] (Dodds and Oakes, 2008) [48] (Ghermandi et al., 2009) [49] (Milliken and Lee, 1990) [50] (Berndtsson et al., 2006). Provisioning Regulating Cultural Agriculture Water provisioning Timber Waterflow regulation Pollination Water quality Recreation Heritage and culture Aesthetic þ livestock provides manure fertilizer for crops [1] ý plant and animal agriculture compete for water [2] ý plant agriculture areas compete with eachother for water [3] ý conversion of pasture þ irrigation water increases crop production [4][5] þ water is necessary for livestock production [4] ý agriculture accounts for ~70% of global water use, i.e., competes with other users of water [2] þ forest patches in agricultural landscapes improve crop yield [3][5] ý competition for water and light [3] decreases crop yield within 50 m of forests [6] ý clearing land for agriculture decreases forest area [1] þ regulated waterflow ensures adequate water supply for crops [3] þ regulated waterflow prevents flood inundation of fields [3][5] þ perennial crops and forage regulate water retention [2][7] ý agricultural extraction of water from watercourses reduces stream flow [2] ý agricultural diversion/channelization of natural water courses disrupts hydrologic cycle [8] ý conversion of wetlands þ pollination required for many (75% of globally significant) crops [2][11] and pasture areas [12] þ pollination increases crop yield, quality and stability [13] þ flowering crops provide food source for pollinators [2] þ cropland [2][3] and pasture [14] is habitat for some, (e.g., ground-nesting bees) pollinators þ organic farming increases pollinator success [15] ý natural pollinator habitat areas decreases cropland area [5] ý cropland decreases natural habitat for pollinators [5][16] (e.g., orchards [17]) ý agricultural insecticide use kills pollinators [2][16] þ certain grade of water quality required for crops and livestock [18] þ crop cover (especially perennial) provides water purification [2][19] ý minimizing agricultural fertilizer and insecticide use to improve water quality may decrease crop yields [20][21] ý agricultural fertilizers and insecticides pollute water [2][19][20][22][23]; NB: can be mediated by vegetated buffers [5][22] ý high livestock density and associated animal þ farms open to public provide recreation opportunities [24] (e.g., berry picking, wineries) þ grasslands (pasture) close to settlements can provide recreation areas [25] ý private farmland decreases recreation opportunities [24] þ pride of producing from the land encourages agricultural success [26] þ orchard meadows and wineries can have cultural significance [27], NB: ý not necessarily true for all regions (e.g., [24][25]) þ pastoral landscapes can have cultural significance [27] ý intensive þ 'open space' agricultural land has aesthetic value [27] (e.g., orchards; [28]) ý industrial animal agriculture discourages human settlement [29] Agriculture 153 Provisioning Regulating Cultural Agriculture Water provisioning Timber Waterflow regulation Pollination Water quality Recreation Heritage and culture Aesthetic land for crop production [1] and forests to cropland affects water regulation [9] ý grazing decreases plant cover and consequently waterflow regulation, leading to increased runoff and flash flooding [10] ý grazing decreases natural habitat areas for pollinators [10] ý grazing decreases soil litter, which can lead to delays in flowering plan phenology which negatively affects pollinators [10] ý grazed habitat is preferred by insectivorous (i.e., non-pollinating) birds [10] waste decreases water quality [10][22] ý riparian grazing increases nutrients, sedimentation, and bacterial populations in aquatic habitats [10] livestock decreases cultural heritage values [24] þ upstream water provisioning areas are sources for downstream provisioning areas [30] ý water competition among various users (e.g., domestic, agriculture, forestry, industry, energy) [2][6] ý afforestation increases evapotranspiration, which negatively impacts water provisioning for other users [5] ý water use makes aquifers more susceptible to drought [31] ý extraction of groundwater in excess of recharge rates decreases low flow [31] ý damming of reservoirs alters natural hydrological regime [31] þ optimal irrigation conditions support positive pollinator effects on crops [32] þ water quality grade determines amount of water available for various uses [18] þ water provisioning areas dilute pollution [31] ý extraction of groundwater in excess of recharge rates decreases water quality [31] ý water use makes aquifers more susceptible to contamination [31] þ lakes and rivers provide recreation sites [25][31] ý recreational tourism can lead to water shortages (e.g., wineries, golf) [28] and compete with other users for water [33] þ water provisioning areas can be important heritage sites [25] þ water provisioning areas contribute to scenic beauty of the landscape [25][27][28][29][34] ý domestic aesthetic (e.g., lawns and gardens) water use may lead to water shortages [33] Water provisioning no interaction þ afforestation reduces peak flows and maintains base flows [2][3] ý clearcutting destabilizes waterflow [35] þ retained forest patches near agricultural areas increases pollination [5][36] and seed dispersal [37] þ vegetated forest landscapes filter water [3][19][20] ý deforestation decreases water quality [22] þ afforestation increases forest recreation [1][27][38][39] þ forestry roads provide recreational access to backcountry areas [40] þ small scale forestry can have cultural significance [27] þ forested (non-harvested) landscapes contribute to aesthetic value [27][29] ý forested areas can be associated with a sense of 'scariness' [25] Timber þ natural hydrologic regime maintains baseflows and reduces overbank flows [41] þ contributes to water-mediated pollination [42] þ wetlands and riparian areas attenuate flow which improves water quality and flood control [5][31] þ instream flow regulation important for pollution abatement [34] þ reducing residence time of water in lakes decrease chemical concentrations [43] þ waterflow required for various stream recreation activities (e.g., fishing, steam-side users), NB: different optimal flow for different activity types [34] þ maintenance of waterflow is important to cultural heritage and activities [43] þ low flow and flood abatement increases aesthetic value of rivers [34] Waterflow regulation no interaction no interaction þ enhances biodiversity which provides support for outdoor recreation and agro-tourism [44] þ pollinators required for producing some important cultural sustenance foods [45] þ aesthetic urban garden areas provide pollinator habitat [46] þ enhances biodiversity which improves landscape aesthetics [44] Pollination 154 Provisioning Regulating Cultural Agriculture Water provisioning Timber Waterflow regulation Pollination Water quality Recreation Heritage and culture Aesthetic þ upstream water quality impacts water quality of downstream areas [47] þ water quality improvement provides recreation benefits [48] ý recreation activities (e.g., boating) can contaminate water [49] þ maintenance of water quality is important to cultural heritage and activities [43][45] þ perceived water quality affects aesthetic value [50] ý aesthetic features (e.g., green roofs) have negative impacts on water quality [51] Water quality no interaction þ trail building increases cultural tourism [5] þ cultural areas can provide tourism opportunities [27] þ recreation provides aesthetic experiences [27] þ areas with high aesthetic value promote recreational use [27] Recreation no interaction þ traditional culture areas provide aesthetic value [27] Heritage and culture no interaction Aesthetic 155 Appendix B. Chapter 3: Supplementary material B.1 Background information ES interactions: Other work has defined ‘interactions’ as value-based synergies (increase in supply quality and/or quantity of one ES results in supply increase of another), trade-offs (increase in supply of one ES results in decrease of another; Bennett et al., 2009), bundles (groups of ES that co-occur repeatedly across a landscape, typically linked to co-variation in LULC types; Raudsepp-Hearne et al., 2010; Lee and Lautenbach, 2016), or flows (ES interactions from the perspective of beneficiaries) between ES that occur over the same space and time (Agudelo et al., 2020). ES supply: We note that, although we use the term ‘supply' to refer to the portion of the ES provisioning delivery chain on which we are focused, two of the ES we have selected can be conceptualized as spanning both the supply and ‘flow’ aspects of this chain. ES supply refers to the ecological good(s) and service(s) produced by a natural or man-made area on the landscape (Potschin et al., 2016), whereas ES flow represents human access to ES supplies, that is, the transfer of a good and/or service from a supply to a benefit area or actor for use (Villamagna et al., 2013; Schröter et al., 2018; Schirpke et al., 2019; Vallecillo et al., 2019). For agriculture and landscape aesthetics, human action is typically required for these services to actually flow to beneficiaries, for example, produce being shipped to grocers; people venturing into nature to enjoy beautiful viewscapes. However, in the case of aesthetics, the data on which we based our mapping was informed by a viewshed analysis, which spatially quantified the potential for people to actually see areas all across the landscape, thereby incorporating an ES flow component. In the context of preventing or minimizing the impacts of flooding, waterflow regulation is provided (i.e., flows from supply to demand areas) when a supply area limits or delays the flow of water (Luck et al., 2009b), which is typically a temporal dynamic dependent on seasonal temperature and/or weather patterns. Although the existing ES maps we use in this study are static spatial representations of potential supply areas, in the cases of waterflow regulation and landscape aesthetics, the distribution of potential spatial location of flows will still be captured by this mapping. Note also that ES flows are not equivalent to ES functional interactions, the latter of which we are defining by the ecosystem function-based links between different supply areas. 156 B.2 Distribution of major LULC types across the case study region Figure B1. Map of the distribution of major LULC types across the case study region. General LULC types in the region include, in decreasing order of area: forests (16,281 km2), grasslands (1482 km2), natural parks (2,403 km2; NB: contains several of the other listed LULC categories), shrubs (1349 km2), agricultural (842 km2), lakes (599 km2), urban residential (220 km2), rural residential (220 km2), wetlands (182 km2), rock/rubble (161 km2), exposed land (113 km2), manicured parks (45 km2), rivers (38 km2), commercial (23 km2), industrial (23 km2), urban institutional (16 km2), and reservoirs (6 km2; Field et al., 2017).- . 157 B.3 Data sources for original ES supply area mapping Table B1. Summary of data sources and rationale for waterflow regulation LULC mapping prioritization (from Field et al., 2017). Map Rank Data Source Source File Name(s) Year(s) Type Fields/Classes Details 1 Agricultural Land Use Inventory (ALUI) BC Ministry of Agriculture (data user agreement) 2013 + 2014 Cover = ATVC (Anthropogenic Terrestrial Vegetated Cultivated) Ground-truthed data, not strictly based on remotely sensed image interpretation. Used ATVC category (Agricultural Terrestrial Vegetated Cultivated) class to identify specific agricultural categories – Orchards, Vineyards, Field, Other (2013/14). Agriculture - Orchards CovType = C200 (Tree Fruits) Agriculture - Field CovType = C320 (Berries) or CovType = C500 (Vegetables) Agriculture - Vineyards CovGroupType = C311 (Grapes) Agriculture - Other CovType = C100 (Cereals, Grains, Oilseeds), C400 (Forage, Pasture), C600 (Floriculture), C710 (Specialty), C720 (Turf), C730 (Nut Trees), C810 (Nursery), C820 (Tree Plantations), C900 (Other) 2 Okanagan Wetlands Strategy (OWS) Ecoscape Consulting Ltd. 2009 + 2010 + 2011 + 2014 Wetlands Compiled regional database. Includes data from the following sources: City of Kelowna WIM (2009);BC Freshwater Atlas (2014); MOE Wetland Inventory Project (2009); Alkali-Saltgrass Herbaceous Vegetation Community Assessment (2011); SEI/TEM for the study area; SHIM (BX Creek, NORD, Vaseux Creek and Oliver, Prairie Creek, Winfield Creek, various dates); FIM (Kalamalka, Wood, Mabel, Mara, Okanagan, Osoyoos, various dates); LRIM (Lower Shuswap River Inventory and Mapping, 2010); Ducks Unlimited (DU) data (various sources). 3 BC Freshwater Atlas (FWA) BC Ministry of Environment (Data Distribution Service) 2009 Lakes Lakes Rivers Rivers (polygons) Man Made Waterbodies Reservoirs 4 Municipal and Regional Zoning RDNO Open Data RDNO 2017 Urban - Commercial Urban - Institutional Urban - Industrial Urban - Residential Rural - Residential Parks Simplified and compiled zoning data from all municipal jurisdictions in the study area. Used supporting by-law documentation to limit local codes to categories: Urban – Commercial, Urban – Industrial, Urban – Institutional, Urban – Residential, Rural – Residential. (Parks were also identified in the zoning data but not incorporated until later in the model). AreaBZoning 2017 Zoning and Zoning_Des 158 Map Rank Data Source Source File Name(s) Year(s) Type Fields/Classes Details AreaCZoning 2017 Zoning and Zoning_Des AreaDZoning 2017 Zoning and Zoning_Des AreaEZoning 2017 Zoning and Zoning_Des AreaF_OCP 2017 Zoning and Zoning_Des Coldstream_Zoning 2017 Zoning and Zoning_Des Enderby_Zoning 2016 Zoning and Zoning_Des Lumby_Zoning 2012 Zoning and Zoning_Des City of Vernon Open Data Vernon_Zoning 2004 Zoning_Val and Zoning_Des RDCO (data user agreement) RDCO 2017 BrentTrepanier_FutureLandUse 2017 Ellison_FutureLandUse 2017 JoeRich_LandUse 2017 LakeCountry_FutureLandUse 2017 Peachland_LandUse 2017 RuralWestside_FutureLandUse 2017 SouthSlopes_FutureLandUse 2017 WestKelowna_LandUse 2017 City of Kelowna Open Data Kelowna_Zoning 2017 ZoningCode RDOS Open Data RDOS Zoning 2017 Designation 5 Sensitive Ecosystems Inventory (SEI) RDCO Open Data RDCO_SEI 2001 - 2015 Forest BW (Broadleaf Woodlands); MF (Mature Forest); OF (Old Forest); WD (Coniferous Woodlands) RDCO SEI coverage for RDCO portion of study area, provincial SEI coverage for north and south regional districts. Only selected polygons that contained sensitive ecosystems in the primary ground cover field. Shrub SV (Sparsely Vegetated) Grassland GR (Grasslands) BC Ministry of Environment (Data Distribution Service) SEI 2010 Forest BW (Broadleaf Woodlands); MF (Mature Forest); OF (Old Forest); WD (Coniferous Woodlands) Shrub SV (Sparsely Vegetated); AS (Antelope-Brush Steppe; SS (Sagebrush Steppe) Grassland GR (Grasslands); DG (Disturbed Grasslands) 6 Grasslands Grasslands Conservation Council (GCC) GCC Grasslands 2017 Compiled grasslands data. 7 Vegetative Resource Inventory (VRI) BC Ministry of Forests 2002 - 2017 Forest BCLCS Level 1 (BCLCS_LEVE) = V (Vegetated); BCLCS Level 4 (BCLCS_LEVE3) – TC (Treed Coniferous; TB (Treed Broadleaf); TM (Treed Mixed) VRI categories: Vegetated – Treed, Vegetated – Shrub, or Vegetated – Grasslands Grassland Shrub 8 Parks See notes on zoning data Zoning Data (all jurisdictions) 2004 - 2017 Park_Manicured See notes on zoning data Parks were subdivided into two classes – Natural and Manicured. Natural parks included provincial parks and regional parks. Manicured parks included parks from urbanized areas (Penticton, Vernon, 159 Map Rank Data Source Source File Name(s) Year(s) Type Fields/Classes Details Armstrong, RDCO). This included all of RDCO since this zoning mainly covers urban and urban fringe areas. Manicured parks also included any zoned parks in the study area that were coincident with DMTI golf course points data, even if these fell outside of the urban centres. Data Distribution Service Provincial Parks 2017 Park_Natural Data Distribution Service Conservation Lands 2013 Park_Natural RDCO Open Data RDCO Parks 2017 Park_Natural 9 Vegetative Resource Inventory (VRI) BC Ministry of Forest, Lands and Natural Resource Operations (Data Distribution Service) 2002 - 2017 Rock/Rubble BCLCS Level 4 = Rock/Rubble (1447) VRI categories: Rock/Rubble, Snow/Ice, Exposed Land, Water (Lakes, Rivers, Reservoirs) Rivers BCLCS Level 4 = Water (1069) Lake BCLCS Level 4 = Water (1069) Exposed Land BCLCS Level 4 = Exposed Land (865) Reservoir BCLCS Level 4 = Water (1069) 160 Table B2. Calculated and assumed NDVI results, infiltration rates (0%–100%), and qualitative aesthetic valuations for each LULC type. Blank (grey) NDVI values were not calculated; therefore, and infiltration rate was assumed (from Field et al., 2017). NDVI Values LULC Area (ha) Min Max Range Mean Stdev Infiltration % Aesthetic Value Roads 0.1000* 0 Urban - Industrial 2302 -0.2329 0.6099 0.8428 0.1614 0.1040 19 No Data Urban - Commercial 2319 -0.3094 0.6194 0.9287 0.2000 0.1369 32 No Data Rock/Rubble 16102 -0.3993 0.6201 1.0194 0.2149 0.1163 36 319 Urban - Residential 21952 -0.1594 0.6142 0.7736 0.2410 0.0868 45 318 Urban - Institutional 1566 -0.2074 0.6349 0.8423 0.2524 0.1309 48 No Data Exposed Land 11272 -0.2332 0.6792 0.9124 0.2528 0.1197 48 No Data Rural - Residential 21952 -0.1531 0.6326 0.7857 0.3025 0.0851 64 318 Agricultural 35291 -0.1763 0.6573 0.8335 0.3151 0.1010 68 268 Agriculture - Field 51 0.0494 0.6297 0.5803 0.3187 0.1017 69 Unknown 1291 0.0000 0.6084 0.6084 0.3264 0.0908 72 No Data Agriculture - Vineyards 4335 0.0061 0.6189 0.6128 0.3311 0.0742 73 275 Agriculture - Other 33930 -0.0636 0.6704 0.7340 0.3637 0.1231 83 Agriculture - Orchards 5802 -0.0364 0.6349 0.6713 0.4164 0.0943 100 Park - Manicured 4534 100 205 Forests 1628131 100 664 Grasslands 148236 100 319 Shrub 134873 100 319 Wetland 18207 100 661 Lake 59911 100 1000 River 3763 100 1000 Reservoir 598 100 1000 * = assumed NDVI value for paved roads based on impervious threshold (https://phenology.cr.usgs.gov/ndvi_foundation.php). No Data = no value applied in mapping due to lack of data. Gray cells = not included in analyses. 161 B.4 Summary tables of total areas and proportional coverages for major LULC types within each ES supply area and link type. Table B3. Percent of LULC overlapped by each ES supply area. High Value Supply Areas (Top 50%) Study Area PA WF LA LULC Type Total area (ha) 2158001 12606 922425 1456241 % of study area Area (ha) % of supply area % of total LULC Area (ha) % of supply area % of total LULC Area (ha) % of supply area % of total LULC Forest (incl. parks) 1654215 76.65 30.85 0.24 1.86e-03 725852.30 78.69 43.88 1354434.00 93.01 81.88 Park 255964 11.86 4.25e-06 0 0 129588.70 14.05 50.63 190476.70 13.08 74.42 Grassland 147610 6.84 0.12 9.80e-04 8.37e-05 72050.87 7.81 48.81 3783.53 0.26 2.56 Shrub 124953 5.79 0 0 0 53425.76 5.79 42.76 3889.10 0.27 3.11 Agriculture 79769 3.70 12605.44 100.00 15.80 31338.00 3.40 39.29 241.00 0.02 0.30 Water 64272 2.98 0 0 0 0 0 0 63469.20 4.36 98.75 Residential 34897 1.62 3.08e-04 0 0 10990.55 1.19 31.49 241.44 0.02 0.69 Rock / exposed 27373 1.27 2.78e-04 0 0 5387.80 0.58 19.68 779.82 0.05 2.85 Wetland 18207 0.84 9.44e-06 0 0 18128.12 1.97 99.57 12586.06 0.86 69.13 Urban 6187 0.29 0 0 0 1918.28 0.21 31.01 27.75 1.91e-03 0.45 Unknown 1291 0.06 1.72 0.01 0.13 473.51 0.05 36.67 52.74 3.62e-03 4.08 162 Table B4. Percent of LULC overlapped by each topographic link. Topographic Links Study Area WF —> PA WF —> WF WF —> LA LULC Type Total area (ha) 2158001 4079 44449 4695 % of study area Area (ha) % of link area % of total LULC Area (ha) % of link area % of total LULC Area (ha) % of link area % of total LULC Forest (incl. parks) 1654215 76.65 2205.94 54.08 0.13 19069.77 42.90 1.15 2688.56 57.26 0.16 Park 255964 11.86 438.39 10.75 0.17 3146.14 7.08 1.23 682.44 14.54 0.27 Grassland 147610 6.84 433.05 10.62 0.29 3065.90 6.90 2.08 477.85 10.18 0.32 Shrub 124953 5.79 102.67 2.52 0.08 1230.22 2.77 0.98 195.80 4.17 0.16 Agriculture 79769 3.70 807.02 19.79 1.01 2329.46 5.24 2.92 596.03 12.70 0.75 Water 64272 2.98 127.65 3.13 0.20 15664.49 35.24 24.37 184.83 3.94 0.29 Residential 34897 1.62 218.51 5.36 0.63 1368.65 3.08 3.92 304.98 6.50 0.87 Rock / exposed 27373 1.27 46.70 1.14 0.17 671.99 1.51 2.45 112.70 2.40 0.41 Wetland 18207 0.84 56.51 1.39 0.31 732.48 1.65 4.02 51.79 1.10 0.28 Urban 6187 0.29 43.92 1.08 0.71 185.53 0.42 3.00 45.69 0.97 0.74 Unknown 1291 0.06 1.07 0.03 0.08 2.83 0.01 0.22 1.72 0.04 0.13 163 Table B5. Percent of LULC overlapped by each overlapping link. Overlapping Links Study Area PA —> LA PA —> WF WF —> PA WF —> LA LA —> WF LULC Type Total area (ha) 2158001 8.57024 4747.30300 4672.03900 684301 685377 % of study area Area of overlap (ha) % of link area % of LULC area Area (ha) % of link area % of total LULC Area (ha) % of link area % of total LULC Area (ha) % of link area % of total LULC Area (ha) % of link area % of total LULC Forest (incl. parks) 1654215 76.65 2.44 28.47 1.47e-04 31.28 0.66 1.89e-03 29.11 0.62 1.76e-03 621652.40 90.84 37.58 624572.00 91.13 37.76 Park 255964 11.86 0.37 4.31 1.44e-04 14.13 0.30 0.01 14.04 0.30 0.01 100349.60 14.66 39.20 100349.60 14.64 39.20 Grassland 147610 6.84 0.24 2.75 1.59e-04 43.72 0.92 0.03 42.58 0.91 0.03 2543.49 0.37 1.72 2543.49 0.37 1.72 Shrub 124953 5.79 0.02 0.18 1.27e-05 0.64 0.01 5.15e-04 0.64 0.01 5.15e-04 2481.88 0.36 1.99 2481.88 0.36 1.99 Agriculture 79769 3.70 5.97 69.71 0.01 4652.62 98.01 5.83 4579.59 98.02 5.74 150.98 0.02 0.19 150.98 0.02 0.19 Water 64272 2.98 0.27 3.19 4.25e-04 0.50 0.01 7.84e-04 0.50 0.01 7.84e-04 2057.04 0.30 3.20 2057.04 0.30 3.20 Residential 34897 1.62 0.01 0.08 1.86e-05 12.32 0.26 0.04 12.02 0.26 0.03 124.52 0.02 0.36 124.52 0.02 0.36 Rock / exposed 27373 1.27 0.05 0.53 1.64e-04 5.40 0.11 0.02 5.11 0.11 0.02 291.40 0.04 1.06 291.40 0.04 1.06 Wetland 18207 0.84 0.16 1.92 9.02e-04 3.83 0.08 0.02 3.82 0.08 0.02 12387.53 1.81 68.04 12387.53 1.81 68.04 Urban 6187 0.29 0 0 0 2.02 0.04 0.03 1.97 0.04 0.03 14.73 2.15e-03 0.24 14.73 2.15e-03 0.24 Unknown 1291 0.06 0 0 0 0 0 0 0 0 0 0 0 0 29.40 4.29e-03 2.28 164 B.5 Methodological details Rationale and procedure for delineating top-value ES supply areas Studies that consider multiple ES have found that the distribution of at least one ES may be ubiquitous across a regional landscape (e.g., Queiroz et al., 2015), and/or isolated supply areas may be present within a non-ES-provisioning matrix (e.g., Qiu and Turner, 2013; Fig. 3.2). Both situations were true for our landscape based on the ES selected, so for the purposes of creating supply areas, we chose to only retain areas with supply values above a top 50% threshold. This was because two of the ES types we selected, WF and LA, had near-ubiquitous spatial coverage with values ranging from very low to very high, and because management applications often are most interested in maintaining the highest-value supply areas (e.g., Turner et al., 2007). For PA, we subset the top 50%-valued polygons from the original mapping; for WF and LA, we subset the top 50%-valued raster cells of each of the regional ES maps, then converted these cells to single-part polygons based on aggregating adjacent cells within a diagonal raster cell width (~29 m). We did not assess the potential sensitivity of ES supply area distribution or functional interaction results to the threshold choice as the primary goal of this study was to provide a methodological proof of concept, rather than to precisely map ES supply or interactions, though future research concerned with the latter should test sensitivity of threshold value choices. Due to the large file size of the waterflow regulation (WF) data, supply area delineation steps were run separately for identified sub-basins (n = 118) in our case study area. To identify sub-basin catchment areas, we used BC Major Watershed, Fresh Water Atlas (FWA) Watersheds, and FWA Streams datasets (FLNRO et al., 2017; see Field et al., 2017 for data source descriptions; datasets available at https://www.data.gov.bc.ca/). For major watersheds with extensive (or complete) overlap with our study area, which would result in a large number of within-basin ES supply areas and therefore potentially lead to computational limitations, nested sub-basins were identified. These major watersheds included Kettle (west), Okanagan, Similkameen, and South Thompson rivers (Fig. B2). FWA Watersheds with a common terminus into valley bottom waterbodies, verified using the FWA Streams dataset (FLNRO et al., 2017), were merged. Several of the major watersheds (Columbia, Fraser, Kettle (east), Thompson, and Washington (Coast) rivers) overlapped with our study area primarily along its border; the overlapping portions of these watersheds were clipped and added to the sub-basin dataset (see Fig. B2). The high-value WF raster was then split by sub-basins using the nearest neighbour sampling technique and Split Raster tool in ArcMap. Each major watershed was assigned a unique ‘goal’ point location for LCP analyses, that is, sub-basins within a major watershed shared the same goal point. In some cases, the mapped borders of the BC Major Watersheds and FWA Watersheds did not exhibit perfect overlap; 165 therefore, following the high-value WF split exercise, the ArcGIS Erase tool was run by erasing the spatial extent of sub-basins from other BC Major Watersheds where they overlap. This ensured that WF supply areas were assigned to sub-basins by prioritizing the more detailed FWA Watershed dataset. Figure B2. Map of major watersheds and sub-basins within and surrounding the case study landscape in southern interior British Columbia, Canada. Detailed methods for building pairwise overlapping and topographic links Theory behind the functional interaction mechanisms between overlapping pairs of ES supply areas was as follows. For PA, the presence of vegetation crops can contribute to WF through providing a variety of beneficial ecological properties (e.g., soil texture, low-slope, high-perviousness, floodplains, riparian 166 areas, and seasonally flooded fields; Power, 2010), although the weight of this positive interaction may be higher if agricultural land was allowed to return to a natural vegetated state (Roa-García et al., 2011). PA also interacts with LA by providing farmland that is recognized as being aesthetically valuable (e.g., vineyards; Wagner and White, 2009; Field et al., 2017) where these areas overlap. For WF, a direct positive influence stems from the spatial confluence of high-value WF areas on PA and LA supply areas through the maintenance of underlying hydrological processes where they co-occur (e.g., DeLaney, 1995; Nelson et al., 2009; Seavy et al., 2009). In the other direction, high-value terrestrial LA supply areas can be linked to WF areas through supportive ecological functions (e.g., pervious and water-retaining vegetated landscapes, floodplains in populated areas; Boyd and Banzhaf, 2007; Van der Ploeg et al., 2010; van Berkel and Verburg, 2014; Carpenter et al., 2015; Table 3.1). To build topographic links between pairs of ES supply areas, first a separate least cost path (LCP) analysis was run for each WF supply area polygon to identify link corridors between these and other ES supply areas (Fig. B3a) LCP analysis is a common method of mapping directional ecological corridors in landscape connectivity research, where the movement of an organism (or abiotic unit) is simulated across a resistance (cost) surface from a start to a destination point, and the lowest-accumulated resistance becomes the most likely path it will follow across a landscape (Beier et al., 2009). LCP can also be effectively used for hydrological waterflow models, where the algorithm seeks to minimize cumulative elevation along its path (e.g., Melles et al., 2011). The starting WF supply area polygon centroid was used as the ‘origin’ point for each associated LCP analysis. A single LCP ‘goal’ point was determined for each sub-basin by identifying the basin stream outlet (FLNRO et al., 2017); the LCP goal point coordinates were identified as the intersect of this stream line feature and the valley-bottom line feature of the associated major watershed (Field, 2021). If multiple outlets were present in a sub-basin (e.g., Okanagan sub-basin ‘w11213’), the furthest downstream outlet line feature was used. LCP transition functions were built based on the assumption of downslope waterflow over a DEM surface and allowed for connecting to a 16-cell neighborhood to avoid paths being terminated based only on a single depression cell (van Etten, 2017). Once initial LCPs were created, segments of LCP lines were erased where they were overlapped by a non-origin ES supply area polygon, and resulting disconnected lines were made into separate line features. Next, we deleted any lines that were deemed invalid from the perspective of real-world ES functional interactions. Specifically, any resulting lines that intersected with the sub-basin goal point were deleted as we were only interested in retaining links between pairs of ES supply areas. Certain ES polygons with an irregular shape had a centroid external to their polygon coverage, which resulted in line segments that initiated at the origin polygon centroid and terminated on the origin polygon border; these were also deleted as they did not represent links between a pair of ES supply areas. Irregular shaped 167 nodes also sometimes yielded lines that were connected between two points on the parent-polygon border. These were retained to account for functional maintenance feedback connections within a supply area; however, we ensured that any such lines associated with two borders of an intersected polygon (i.e. where the line segment was part of a non-origin polygon) were deleted to avoid duplication with feedback links identified when separate analyses were run with the intersected polygon (in this example) as the origin (Fig.B3b). We then identified influential landscape features (ILFs) as additional WF polygons downstream of each sub-basin in the valley-bottom and associated with wetlands, floodplains, riparian areas, and/or seasonally flooded fields, which are functionally linked to upstream hydrological regulation. We connected ILFs to each upstream sub-basin outlet point, and individually merged these sub-basin lines with the LCPs for each WF supply area within that sub-basin (Fig. B3). Additionally, if a sub-basin flowed into a lake or reservoir, all ILF polygons immediately adjacent to that waterbody were included in the list of ‘downstream’ supply areas. Lastly, because the DEM raster resolution was approximately 29 m, some LCPs traversed outside sub-basin boundaries between the origin polygon (typically those close to a sub-basin boundary) and the goal point. Any nodes outside the sub-basin of interest that were overlapped by such LCPs were not considered to be true ‘intersections’ and such line segments were therefore excluded from within sub-basin links. However, such LCPs were still able to become connected to downstream ILFs. For topographic corridor mapping we needed to address various rare analytical outcomes that became evident upon manual model validation. When we manually inspected preliminary spatial results, we found that the LCP analyses resulted in some connections that violated landscape topography. For example, some LCPs from WF supply areas associated with relatively flat lands in the headwaters of one sub-basin (Bellevue Creek) were found to connect south to Okanagan Lake rather than connecting north as they would in reality (Fig. B2). This was due to the necessity of balancing the smallest possible raster resolution (20 m) with the overall large size of the study area for computational efficiency. As individual sub-basins were analyzed separately for topographic links, invalid linkages between sub-basins were not possible; however, we manually inspected all LCP results and removed any LCPs that violated downslope connectivity logic from subsequent analyses. Further, we did not incorporate certain rare spatial occurrences. These included instances of a smaller supply area inside bigger one; centroids captured by the incorrect buffer due to two or more centroids occurring close to one another; and LCP segments that resulted in feedback loops that occurred across non-origin supply areas (these loops were likely retained as feedback loops when such nodes served as LCP origins). We quantified the weight of links between ES supply areas based on assumptions around the functional interactions between ES, which we discuss below. Since some of the ecological characteristics 168 of both PA and AE areas can support the maintenance of WF supply, and high-value WF lands maintain the hydrological characteristics that help support PA provision and AE, we assumed that the value of these links would approximate the available WF capacity. Therefore, the overlapping links between WF and PA, and between WF and LA, were weighted by the summed WF raster values therein. The plant food-providing agricultural areas of the Okanagan are prized by some for their beauty (Wagner and White, 2009; though this subjective evaluation is complicated, see Wagner, 2008), and we assumed the level of this significance to be equivalent to the underlying LA model value; therefore, such overlap links were unidirectionally weighted from PA to LA by the summed LA raster values therein. We assumed the contribution of upslope, high-value WF lands to the hydrologic maintenance of intersected downslope PA, WF and LA areas to be equivalent to the amount of waterflow regulation provided by landscape where water flows between these areas. Therefore, we quantified the weight of these unidirectional links by the cumulative value of all WF raster cells on the original map (i.e., not just top ES model values; Fig. 3.5c) traversed along LCP links. All link weights were obtained by extracting summed raster cell values coincident with overlap link areas or with topographic link segments, then normalized on a unit-less scale from 1 to 10,000. Additionally, raster overlay analysis was conducted, wherein cell values were summed across all eight link types to produce a weighted distribution map of all multi-ES functional interactions for our entire study region. Figure B3. Schematic outlining steps for the creation of topographic ES corridors from each origin ES supply area to downslope supply areas. (a) An initial line feature resulting from a least cost path (LCP) analysis, that is, from the origin to the goal point, amalgamated with a line from the goal point to a downstream influential landscape feature (ILF). (b) Types of LCP segment deletions addressed, including (red) segments overlapped by ES supply area polygons, (i) segments from origin points external to origin ES polygon, and (ii) segments linked between two areas of an intersected (i.e., non-origin) ES supply area. (c) Resulting ES functional interaction corridors after deletions, including feedbacks to origin ES supply area (4), links to downslope ES supply areas (1) and (3), and links to downstream ILF areas (2). Watershed boundary ES supply area Streams and lakes External origin point Intersected node feedbackLCP origin point LCP goal point ILF goal point LCP and resulting corridors ES supply overlap deletions Other deletions Resulting ES flow corridors a b c 169 B.6 Validation of topographic functional interaction mapping Methods We validated our approach for mapping the functional interactions between ES supply areas separated in space by executing a null model and comparing these and our topographic link results (all originating in WF supply areas: WF to PA, WF to WF, and WF to LA) with empirical data on surface waterflow. For this, we subset the data to a single subbasin for computational efficiency and used a random number generator to select a subbasin from those that contained all 3 types of ES supply area polygons, and therefore would have the potential to contain all 3 types of topographic links. We then ran LCP analyses using uniform resistance maps where all cells = 1, so that results approximated straight lines between the supply area centroid and the goal point, the latter of which had the same coordinates as that of the functional interaction topographic LCP analysis, as basin outflow points are well-established. We then executed the same procedures on resulting null model LCP lines that were used to produce topographic corridors between pairs of supply areas (Appendix B.5). Following this, we created a buffer equal to grid cell width (29 m) around the perimeters of (a) topographic and (b) null model links. Then, we calculated percent intersection (overlap) of each link in (a) and (b) with BC TRIM water lines data, which served as empirical data for location of surface waterflow. The WF model buffer width was applied to account for potential grid cell alignment and/or spatial resolution mismatches between ours and the TRIM source data (NB: the TRIM dataset is based on smaller cell size as minimum horizontal surface accuracy requirements are 10 m for source DEMs). A 150 m buffer width was applied around TRIM data prior to analyses, which is related to the hydrological functional role of riparian ecosystems in waterflow regulation (Fischer and Fischenich, 2000; GeoBC, 2003). To determine whether the topographic corridors included a significant proportion of TRIM waterflow data, we compared the percentage of each topographic link corridor with the percentage of each null model segment within TRIM line data using two-sample Mann-Whitney (or ‘Wilcoxon’) tests (Milton, 1964). This validation method determines whether the functional topographic corridors contained more or less surface waterflow data than expected relative to links produced by a random spatial model and assumes a relatively high proportion of topographic WF corridor area will co-occur with riparian areas associated with TRIM waterflow data (Bond et al., 2017). We did not include link weighting in our validation analyses as comparative empirical data on this is not available for our study area; nor did we incorporate a measure of uncertainty (discussed in Appendix B.8). 170 Results Bellevue Creek, a subbasin located in the east-central portion of our study area within the Okanagan watershed, was randomly selected for validation analyses. It is 87.8 km2, which covers approximately 0.4% of the total study area. The total number of resulting line segments for functional interaction vs. null model link creation were 33 and 56 for WF to WF supply areas; 62 and 34 for WF to PA; and 13 and 11 for WF to LA; respectively. For all link types, the data produced by the functional interaction models co-occurred over significantly more proportional area with TRIM waterflow data relative to links produced with the null model (Table B6; Fig. B4). This confirms that the method for producing topographic links between supply areas presented in our study results in spatially and functionally valid placement of ES functional interaction links. All validation analyses were completed using R (R Development Core Team, 2013) and ArcGIS 10.7.1 (ESRI, 2011). Table B6. Summary and validation analyses data for topographic links produced by functional interaction and null models for Bellevue Creek. Functional interaction model Null model WF~WF WF~PA WF~LA WF~WF WF~PA WF~LA Number of links 33 62 13 56 34 11 Total link area (km2) 9.05 18.23 5.12 23.21 20.06 3.58 Total overlap area (km2) with BC TRIM data 5.08 12.22 3.63 3.80 3.88 0.99 Median % overlap with BC TRIM data 42 67 69 12 25 29 Wilcoxon p-value 1.59e-03 5.29e-09 2.09e-04 - - - 171 Figure B4. Boxplots summarizing percent overlap of buffered BC TRIM data with buffered topographic links between (a) WF; (b) WF and PA; and (c) WF and LA supply areas produced by functional interaction and null models for Bellevue Creek validation analyses. 172 B.7 R script files The following code files are available on the OSF (Field, 2021). 1. TOP-VALUE SUPPLY AREA NODE CREATION (file name: ‘nodes_waterflow_FORMAT.R’) 2.OVERLAP LINK CREATION (file name: ‘links(overlap)_waterflow<>plantag_MASTER_FORMAT.R’) 3. SUPPLY AREA CENTROID CREATION (PREP FOR LEAST COST PATH ‘LCP’ ANALYSES) (file name: ‘links_waterflow_ALL_centroids_FORMAT.R’) 4. CREATE LCP GOAL POINTS FOR EACH SUB-BASIN/MAJOR WATERSHED (file name: ‘links_waterflow_ALL_LCP goal pts_FORMAT.R’) 5. LEAST COST PATH ‘LCP’ ANALYSIS FOR TOPOGRAPHIC LINK CREATION (file name: ‘links_waterflow_okanagan_LCPs_SUBBASIN_FORMAT.R’) 6.TOPOGRAPHIC LINK CREATION (file name: ‘links_waterflow_okanagan_1_MASTER_FORMAT.R’) 7.INFLUENTIAL LANDSCAPE FEATURES (ILF) LINKS (file name: ‘links_waterflow_okanagan_LCPs_ILF_FORMAT.R’) B.8 Discussion of uncertainty We did not incorporate a measurement of uncertainty into our approach. For example, we did not attempt to directly assess spatial autocorrelation of our functional interaction models with any other, potentially influential ecosystem functions, as we were interested in providing straightforward and replicable rationale for mapping linkages; however, this precluded us from being able to parse the presence of shared drivers and potential artefacts in proxy or primary data. Additionally, the location and value of the identified functional interaction corridors may be driven by the assumptions of original ES mapping and the threshold (top 50%) we used to delineate high-value supply areas. Several publications have suggested that incorporating uncertainty measures is necessary for producing reliable results to support decision making and will lead to improved understanding of the system under study through identification of the most compelling findings (Seppelt et al., 2011; Hamel and Bryant, 2017; Stritih et al., 2019). Sources of uncertainty considered in ES assessments are related to models of ecological processes; subjective choices of researchers and/or participants; and practical modelling skills and data quality (Gos and Lavorel, 2012; Crossman et al., 2013; Hou et al., 2018; Wang et al., 2018). To date, only a limited number of studies on ES interactions have incorporated measures of uncertainty and/or model validation (Boerema et al., 2017; Agudelo et al., 2020). As unconfirmed results are difficult to reliably assess, they are not as useful for direct practical applications (Agudelo et al., 2020). Studies with the express purpose 173 of providing guidance for on-the-ground multi-ES planning should therefore incorporate metrics of uncertainty and model validation procedures. 174 Appendix C. Chapter 4: Appendices C.1 Background on original ES supply and functional interaction mapping Full methodological details on original ES supply and functional interaction mapping are provided in (Field and Parrott, 2022). In summary, high-value plant agriculture supply areas were mapped based on the spatial extent of all crop types used directly for human consumption. Terrestrial areas that supply waterflow were defined as those that maintain water delivery in specific areas and buffer against flooding and droughts (Haughian et al., 2012), and were modelled using data on soil texture, slope, LULC-specific perviousness, and functionally important ecosystems including floodplains, riparian areas, wetlands, and seasonally flooded fields. Aesthetics supply areas included both terrestrial and large aquatic areas that have the potential to improve people’s well-being through visual enjoyment and were modelled based on regionally specific semi-quantitative valuations of different LULC types, on provincial ‘visual conditions’, ranging from preserved to manicured lands, and on a viewscape analysis which ranked the visibility of areas from various viewpoints (Field et al., 2017). From the above ES data, Field and Parrott (2022) subset high-value supply areas based on the top-50% of model values and mapped the functional interactions between these areas. Values (or ‘weights’) were quantified for plant agriculture based on the size of each continuous area (ha), and for waterflow and aesthetics areas based on summed model values of map raster cells (~29 x 29 m) that fell within each continuous area. These areas became the ‘nodes’ and their values became the node ‘weight’ attribute in the ES networks. We identified 1,497 plant agriculture nodes, 7,350 waterflow nodes, and 5,262 aesthetics nodes (total # nodes = 14,109). The functional interactions between high-value supply areas were then mapped and defined as either ‘topographic’ (i.e., based on the ecosystem functions that connect the supplies of two ES areas separated in space) or ‘overlapping’ (i.e., supplies of two different types of ES occur in the same location on the landscape, and there is an underlying functional interaction between them). Overlapping links were delineated by sub-setting areas where each pair of ES overlapped (directionally) high-value ES supply area maps. Topographic links were defined based on least cost path (LCP) analyses, which were used to represent the most likely path of water downslope over a landscape based on a digital elevation model surface (i.e., ‘resistance’ or ‘cost’ surface; Beier et al., 2009; Melles et al., 2011). Details on the stepwise procedures used to identify topographic link corridors are provided in (Field and Parrott, 2022). The specific rationale behind each functional link and their delineation are discussed in detail in Field and Parrott (2022). In total, five overlapping (i.e., plant agriculture to waterflow; plant agriculture to aesthetics; waterflow to plant agriculture; waterflow to aesthetics; aesthetics to waterflow) and three topographic (i.e., waterflow to plant agriculture; waterflow to waterflow; waterflow to aesthetics) functional interactions represented the directional links in our networks. For overlapping links, there were 175 1,320 from plant agriculture to waterflow; 1,220 from waterflow to plant agriculture; 174 from plant agriculture to aesthetics, and 9,363 in both directions between waterflow and aesthetics (total # overlapping links = 21,440). There were 5,256 topographic links from waterflow to plant agriculture; 484,602 from waterflow to waterflow; and 2,864 from waterflow to aesthetics (total # topographic links = 492,722; total # network links = 514,162). It is worth noting that, although the ecosystem function-based indicators and models used to represent ES supply areas and their linkages are support by the literature, we assume that biophysical indicators are representative of complex functional interactions between multiple ES. This limitation is particularly relevant to hydrological models, as models did not consider groundwater contributions; evaporation; or urban, recreational, agricultural environmental use and water governance constraints that might directly impact waterflow (e.g., Torell et al., 2022). Future applications could consider more complex models where data and/or an improved understanding of relational ecosystem functions are tested and accessible. Related to the above, although the underlying mechanisms between ES co-occurrences (Bennett et al., 2009) and topographic interactions are represented in the network models evaluated here, rationale behind functional interaction links is based on extrapolations of evidence in the literature and may require further refinement and validation (but see Field and Parrott, 2022). This is especially important where network results will be relied on by decision-makers for accurate spatial- and value-based interventions. In reality, such networks are dynamic through time (e.g., Boesing et al., 2020), though we consider a static snapshot of the recent condition of ES within our case study region to demonstrate a novel network-based approach. 176 C.2 Study caveats Limited number of ES We note that our research includes only a subset of the ES operating in our study region, and that there are undoubtedly several other ES that exhibit functional interactions with the ES included here. This is because our objective was to demonstrate a new approach for representing multi-ES functional interactions, rather than to develop a representative model for total ES supply in the region. We therefore caution the incorporation of our results to on-the-ground planning decisions and the generalization of our results to a different set of ES and encourage the use of regionally specific empirical validations in future applications of this approach. Ultimately, building networks to inform management objectives will require detailed, regionally specific knowledge of ES and the ecosystem functions responsible for their production, which will be best supported by integrated research programs that measure a variety of relevant species, trophic groups, processes, and ES (Felipe-Lucia et al., 2020). Spatial delineation of nodes and links When applying network theoretic methods to the assessment of complex natural systems, some basic assumptions are that nodes are representative of distinct subsystems, and links represent subsystem interactions. Design challenges arise when trying to meaningfully and realistically delineate nodes and links in space, and when interactions for the ecosystem functions that drive links are unknown or unmeasurable (Bialonski et al., 2010). Both challenges are relevant when designing spatial ES networks. To delineate ES nodes, many of which were based on near-ubiquitous spatial coverage of ES-supply, Field and Parrott (2022) applied a top-50% supply value threshold above which node borders were drawn. Further, network links were defined based on models amenable to existing, primarily publicly available data rather than study-specific experimental approaches, and utilize data for topographic links based on methods that constrain them to grid cell-width corridors (Field and Parrott, 2022). Networks are dependent on the availability and quality of information that informs the representation of nodes and links; incomplete data and/or knowledge is prevalent in other ecological network studies and may lead to inaccurate results (e.g., Gray et al., 2021). We defend our approach by reiterating that our primary goal was to illustrate a theoretical framework for ES networks by utilizing existing and publicly available data from a real-world landscape. Future studies that apply our methods will be able to base node delineation on defined ES supply/value targets, where any data required to support node and/or link definition could be collected in the field. 177 Source data scale and resolution A common concern in the ES interaction literature relates to the scale and resolution of the data used for mapping, and what impacts this might have on policy (Saidi and Spray, 2018). We assume the scale at which ES supply areas were originally mapped is representative of the operating scale for each ES as source data were selected to represent environmental components related to the ecosystem functions underpinning these services, but note that some source data were resampled to achieve alignment for original ES mapping (Field et al., 2017). A comparative study on the underlying data used to create ES bundles concluded that large changes in observational scale could result in reconfiguration of bundles, likely due to scale-dependence and heterogenous distributions of individual ES across the landscape (Raudsepp-Hearne and Peterson, 2016). Additionally, interactions among ES are likely driven by specific ecoregions (Marsboom et al., 2018). Therefore, future studies can consider gathering primary data (e.g., remote sensing, field sampling, participatory GIS; Saidi and Spray, 2018) at different spatial resolutions and scales to represent different ES and their functional interactions, ideally guided by their research objective(s) and study system characteristics. For example, ES may be supported by small-scale habitat and organismal components, such as biodiversity provided by remnant vegetation strips (Díaz-Forestier et al., 2021) and soil nutrient cycling provided by bacterial diversity (Felipe-Lucia et al., 2020). At the other end of the spectrum, larger-scale and telecoupled interactions, such as influences of global trade and tourism (Liu et al., 2015; Schröter et al., 2018), can also be investigated using ES networks. The spatial network approach allows multiple scales and interactions to be functionally (rather than correlatively) represented, so that scale-dependent impacts of various drivers could be examined within the same network or across subnetworks therein (e.g., Gray et al., 2021). This will help address gaps in ES research related to scale mismatches between underlying processes, human interpretation, management jurisdictions, supply and demand (e.g., Saidi and Spray, 2018; Zhou et al., 2022), which are especially challenging to tackle due to the multitude of social-ecological processes and infrastructure responsible for service supply (Andersson et al., 2015b; Cumming et al., 2006; Raudsepp-Hearne and Peterson, 2016; Schneider, 2001; Scholes, 2017). For example, spatial scale-dependent interactions between food production and water quality have been observed, where fertilizer application is the dominant driver at smaller-scales, and cumulative interactions among multiple drivers, including proactive management, land use changes, technology, and more stable climate, drive synergies and trade-offs at larger scales (Qiu et al., 2018). From a conservation perspective, incorporating multi-scale linkage information into ES assessments has several potential benefits, including: anticipating how economic, environmental and social change at larger scales might impact local actions, and vice-versa; using smaller-scale results to inform the decisions of larger-scale corporate, non-governmental, and supranational organization actors to avoid making otherwise uninformed actions; aligning local, regional, national and international goals to 178 promote cooperation and synergies among people and nature; larger-scale data informing, and being informed by, smaller-scale studies (e.g., Beckmann et al., 2019; Brauman et al., 2020; Halpern et al., 2012); and can help communicate and provide context for large-scale problems at the local level (Chaplin-Kramer et al., 2021; Fitzpatrick and Dunn, 2019). Model uncertainty and validation As our objective was simply to provide an operational example of how ES functional interactions between multiple diverse ES can be assessed in a spatial network framework, we did not include any assessments of model uncertainty or validation. A critical review of the ES interaction literature found that a limited number of studies have incorporated model validation, and fewer still have included measures of uncertainty (Agudelo et al., 2020; Boerema et al., 2017). Here for example, we did not assess spatial autocorrelation of models used to delineate ES nodes or links with any other, potentially related ecosystem functions, which did not allow us to identify the existence of shared drivers or potential artefacts in the original ES mapping data. Further, the location and value of network nodes and links may be driven by the assumptions of ES mapping data and the threshold (top 50%) used for high-value supply areas. Various studies insist that explicit inclusion of uncertainty measures in ES assessments are essential for improved credibility of results, better understanding of landscape systems in general, and more successful management decisions (Hamel and Bryant, 2017; Seppelt et al., 2011; Stritih et al., 2019). Although this remains a rare component of ES assessments, main potential sources of uncertainty include the modelling ecosystem processes; subjectivity around researcher or participant preferences and expertise; and the quality-relative issues present in practical acquisition of data and modelling (Crossman et al., 2013; Gos and Lavorel, 2012; Hou et al., 2018; Wang et al., 2018). Note that model validation was performed for the least cost path-based topographic links used in our networks, based on an assessment of whether links contained more or less empirical surface waterflow data than expected relative to paths produced by a random spatial model, and assumed a relatively high proportion of topographic waterflow corridor area will co-occur with riparian areas associated with TRIM waterflow data (Bond et al., 2017; Field and Parrott, 2022). However, no weighting, node, overlapping link, or structural network validation was performed because comparative empirical data on these are not available for our study area. Future multi-ES network studies designed for application in applied management and policy should attempt to incorporate uncertainty measures and validation procedures for underlying ecosystem function and network models. 179 C.3 R script files The following code files are available on the OSF (Field, 2023b). 1. Spatial ES network build and analyses (file name: ‘network_spatial_OSF.R’) 2. Plotting of spatial difference maps (file name: ‘network_spatial_difference maps_OSF.R’) 3. Comparisons between multi-ES network and single-ES (waterflow only) network (file name: ‘network_ALL.vs.WF_OSF.R’) 4. Assessment of pairwise variable relationships between network types using nonparametric local regression (loess) (file name: ‘network_ALL.vs.WF_LOESS_span_OSF.R’) C.4 Global topology comparisons between the single- vs. multi-ES networks We assessed the global topology for single- and multi-ES networks based on their degree distributions (i.e., node total degree; NTD), which is a frequency plot commonly used to depict general trends in the network structure of complex systems (Wang and Chen, 2003). The network degree distributions of both ES networks peaked at low node degree (k = 2 for multi-ES; k = 0 for single-ES), then approximate exponential decay (Fig. A5). This demonstrates that there are a few highly central nodes connected to many other nodes (Aarstad et al., 2013). There were several minor exceptions to this trend, where summed k frequencies approach ~5%, specifically: multi-ES NTD (299 ≤ total k ≤ 308 and 1240 ≤ total k ≤ 1241); and single-ES NTD (297 ≤ total k ≤ 306 and total k = 1238; Fig. A5). These anomalies appear to be driven by WF nodes, which make up 52% of the nodes in the multi-ES network. The first NTD degree frequency exception in the multi-ES network are all waterflow nodes. They had relatively small individual and cumulative spatial coverage and are primarily located in the centre of our study area surrounding and to the west of the central and largest lake in our region (Okanagan Lake), but not necessarily in valley bottom areas. The second and larger degree frequency exception is also driven entirely by WF nodes. These nodes occur in valley bottom areas surrounding Okanagan Lake, and do not have high normalized node weights or node betweenness centrality (NBC) scores. Degree distribution anomalies make up a relatively minor proportion of the degree rankings in the multi-ES network, which suggests that the expansion from single- to multi-ES networks results in a topology that more closely approximates a power law form that may have scale free network properties, such as increased vulnerability to targeted attacks on high-degree nodes (Albert et al., 2000). This indicates that, if nodes are added to the network, they would preferentially be connected to ES nodes with higher degrees (Barabási and Albert, 1999; Wang and Chen, 2003). However, we note that comparing networks with different numbers of nodes and/or degrees, such as the ones presented here, can yield 180 spurious results (e.g., Bialonski et al., 2010; van Wijk et al., 2010); therefore, these plots are included to depict general single- and multi-ES network structure as a record for future related studies, rather than to definitively assess the structure of the single- vs. multi-ES networks. Figure C1. Node total degree (NTD) frequencies comparison between single- and multi-ES networks. 181 Appendix D. Chapter 5: Supporting information D.1 Study limitations D.1.1 Limited number of Nature’s Contributions to People It is important to note that the types of Nature’s Contributions to People (NCPs) included in a network will influence the spatial distribution of NCP clusters. Our study is illustrative of a novel method for building and analyzing spatial social-ecological networks (SENs) and therefore draws on existing spatial information on the distribution and functional interactions for three NCPs. Here, the waterflow regulation (WF) and landscape aesthetics (LA) network models were partially based on topographic metrics (i.e., water flowing downhill and landscape visible from various low-elevation viewpoints along transportation corridors, respectively); therefore, clusters of highly interacting nodes within and across NCP types were often spatially constrained by watershed boundaries. This results in some clusters containing only WF and LA nodes but no plant food agriculture (PA) nodes; in such cases, consultation with agricultural governance agencies may be important for certain management objective(s) but might not be relevant to other focal environmental issues. Ideally, for a more holistic representation of NCP functional interactions and their relevance to collaborative governance, as many NCPs as operate in a region should be mapped and assessed in the network. D.1.2 NCP supply, demand and flow Our study uses interdisciplinary concepts, methods and data to tackle the supply part of NCP networks. The supply of NCPs corresponds to the potential for the natural capital of ecosystems to provide a benefit to humans, whether it is actually exploited in reality. Yet, the provisioning of NCPs is dependent on three linked concepts: supply, demand, and flow. NCP demand is driven by human requirements, values, cultures, and infrastructure and refers to the amount of an NCP that is needed or wanted by a society (Mitchell et al., 2015). Flow from an NCP supply to a demand ‘area’ only occurs when humans actually interact with the relevant ecosystems to produce a benefit (Mitchell et al., 2015). For example, fresh water is only realized as a benefit when it flows across the landscape from a supply area to one of collection for human consumption (Brauman et al., 2007). NCPs at each of these three phases of provisioning can respond differently to the ecological composition, configuration, and processes relevant to connectivity on a landscape (Mitchell et al., 2015), and thus may be modelled differently depending on the research question(s). Several models exist for assessing relationships between NCP supply and demand (e.g., InVEST, ARIES, MIMES; Shaaban et al., 2021). However, the differentiation between supply and flow for certain NCPs can be obscure, and flows can also be a result of facilitating or limiting movement of 182 matter. For example, flood and/or drought mitigation is provided as an NCP when an ecosystem with the capacity to provide WF (supply) actually functions to maintain natural hydrologic processes by restricting or promoting water movement, respectively (flow; Luck et al., 2009a). A next step for future studies will be to investigate how incorporating spatial demand and flow components of NCP provisioning may impact the distribution of SEN links and the potential for multi-scale governance agencies and other stakeholders to engage in effective applied management. D.1.3 Nuances in NCP governance There are likely to be several nuances present in governance structures and approaches that make it difficult to assign an individual NCP to a single agency. Firstly, as is true in our study area, it is important to note that certain governance mandates and programs pre-date or have not necessarily directly or fully adopted ecosystem services and/or NCP concepts. Additionally, different agencies across different scales may separately manage various aspects of the same NCP and their underlying ecosystem functions, either directly or indirectly. For example, the federal Department of Fisheries and Oceans manages riparian habitat for its benefits to fish which, in addition to directly supporting fish habitat services, has indirect implications for supporting the maintenance of flood and drought control (i.e., WF) NCPs. Local municipalities and regional districts have regulations related to the management of WF by setting limits on the development of non-permeable surface areas on private property. There may also be agency overlap in the governance of an NCP, but not necessarily with any active coordination. For example, in some cases permits are submitted to separate agencies who ensure all regulations are being followed and, if so, there is no active internal communication between agencies (i.e., 'passive reporting’; e.g., Riparian Areas Protection Regulation is governed by the provincial Ministry of Forests, but related riparian area development permit applications are submitted directly to local governments: https://www2.gov.bc.ca/gov/content/environment/plants-animals-ecosystems/fish/aquatic-habitat-management/riparian-areas-regulation#:~:text=The%20RAPR%20calls%20on%20local,based%20assessment%20of %20proposed%20activities). Some agencies may arguably be involved in indirect management or governance of NCPs (e.g., farms that install waterflow control structures to retain water for irrigation and/or minimize nutrient runoff, which may indirectly impact WF), and, if we had included these, results would likely skew to greater NCP alignment for some agencies. As explained in Chapter 5, for the purposes of a straightforward spatial overlay analyses, we assigned NCP types based on evidence of their direct governance within agency mandate and program descriptions. 183 D.2 List of governance agencies and maps of governance area boundaries Figure D1. Location of governance area boundaries within the study area. Specific governance areas are overlaid on all agency boundaries (thin black lines) in colour for: (a) the federal Department of Fisheries and Oceans Canada (excluded from maps (b)-(f) for clarity); (b) municipalities; (c) regional district electoral areas; (d) First Nations reserves; (e) agriculture; (f) provincial Ministry of Forests. Note that the governance areas for the provincial Ministry of Environment and Climate Change Strategy, the Ministry of Water, Land and Resource Stewardship and the Ministry of Tourism, Arts, Culture and Sport, as well as Environment and Climate Change Canada at the national scale, span the entire study region. 184 Table D1. List of the governance agencies, and the size of the areas under their jurisdiction, included in analyses. Governance scale Governance type Agency name Acronym / Abbreviated name First Nation Affiliation Governance area (km2)* Link Local (n=17) Municipality City of Armstrong Armstrong n/a 5.233 https://www.cityofarmstrong.bc.ca/ Municipality City of Enderby Enderby n/a 4.217 https://www.cityofenderby.com/ Municipality City of Kelowna Kelowna n/a 264.829 https://www.kelowna.ca/ Municipality City of Penticton Penticton n/a 46.077 https://www.penticton.ca/ Municipality City of Salmon Arm Salmon Arm n/a 0.126 https://www.salmonarm.ca/ Municipality City of Vernon Vernon n/a 116.605 https://www.vernon.ca/ Municipality City of West Kelowna West Kelowna n/a 154.097 www.westkelownacity.ca Municipality District of Coldstream Coldstream n/a 68.294 https://www.coldstream.ca/ Municipality District of Lake Country Lake Country n/a 164.387 https://www.lakecountry.bc.ca/ Municipality District of Peachland Peachland n/a 18.174 https://www.peachland.ca/ Municipality District of Summerland Summerland n/a 76.734 https://www.summerland.ca/ Municipality Town of Oliver Oliver n/a 5.862 https://www.oliver.ca/ Municipality Town of Osoyoos Osoyoos n/a 9.952 https://www.osoyoos.ca/ Municipality Town of Princeton Princeton n/a 63.555 https://princeton.ca/ Municipality Township of Spallumcheen Spallumcheen n/a 256.796 https://www.spallumcheentwp.bc.ca/ Municipality Village of Keremeos Keremeos n/a 2.192 https://www.keremeos.ca/home Municipality Village of Lumby Lumby n/a 6.028 https://lumby.ca/ Local / regional / provincial / First Nation Alexis 9 n/a Lower Similkameen Indian Band 1.693 https://www.lsib.net/ 185 Governance scale Governance type Agency name Acronym / Abbreviated name First Nation Affiliation Governance area (km2)* Link national (n=33) First Nation Ashnola 10 n/a Lower Similkameen Indian Band 35.719 https://www.lsib.net/ First Nation Blind Creek 6 n/a Lower Similkameen Indian Band 1.621 https://www.lsib.net/ First Nation Blind Creek 6A n/a Lower Similkameen Indian Band 0.001 https://www.lsib.net/ First Nation Chopaka 7 & 8 n/a Lower Similkameen Indian Band 15.541 https://www.lsib.net/ First Nation Chuchuwayha 2 n/a Upper Similkameen Indian Band 21.329 https://usib.ca/ First Nation Chuchuwayha 2C n/a Upper Similkameen Indian Band 1.213 https://usib.ca/ First Nation Duck Lake 7 n/a Okanagan Indian Band 1.844 https://okib.ca/ First Nation Enderby 2 n/a Splatsin First Nation 22.405 https://splatsin.ca/ First Nation Harris 3 n/a Okanagan Indian Band 0.616 https://okib.ca/ First Nation Iltcoola Indian Reserve No. 7 n/a Upper Similkameen Indian Band 0.174 https://usib.ca/ First Nation Keremeos Forks 12 & 12A n/a Lower Similkameen Indian Band 4.958 https://www.lsib.net/ First Nation Lower Similkameen 2 n/a Lower Similkameen Indian Band 13.133 https://www.lsib.net/ First Nation Lulu 5 n/a Upper Similkameen Indian Band 0.187 https://usib.ca/ First Nation Medicine Hill 11 n/a Westbank First Nation 5.167 https://www.wfn.ca/ First Nation Medicine Creek 12 n/a Westbank First Nation 6.644 https://www.wfn.ca/ First Nation Mission Creek 8 n/a Westbank First Nation 0.020 https://www.wfn.ca/ First Nation Narcisse’s Farm 4 n/a Lower Similkameen Indian Band 7.445 https://www.lsib.net/ 186 Governance scale Governance type Agency name Acronym / Abbreviated name First Nation Affiliation Governance area (km2)* Link First Nation Nine Mile Creek 4 n/a Upper Similkameen Indian Band 0.820 https://usib.ca/ First Nation Okanagan 1 n/a Okanagan Indian Band 89.083 https://okib.ca/ First Nation Osoyoos 1 n/a Osoyoos Indian Band 130.100 http://oib.ca/ First Nation Osoyoos 3 n/a Osoyoos Indian Band 0.647 http://oib.ca/ First Nation Otter Lake 2 n/a Okanagan Indian Band 0.258 https://okib.ca/ First Nation Penticton 1 n/a Penticton Indian Band 192.759 https://pib.ca/ First Nation Penticton 2 n/a Penticton Indian Band 0.131 https://pib.ca/ First Nation Penticton 3A n/a Penticton Indian Band 1.467 https://pib.ca/ First Nation Priest’s Valley 6 n/a Okanagan Indian Band 0.354 https://okib.ca/ First Nation Range 13 n/a Lower Similkameen Indian Band 67.913 https://www.lsib.net/ First Nation Salmon River 1 n/a Splatsin First Nation 0.035 https://splatsin.ca/ First Nation Swan Lake 4 n/a Okanagan Indian Band 0.321 https://okib.ca/ First Nation Tsinstikeptum 9 n/a Westbank First Nation 6.146 https://www.wfn.ca/ First Nation Tsinstikeptum 10 n/a Westbank First Nation 2.960 https://www.wfn.ca/ First Nation Wolf Creek 3 n/a Upper Similkameen Indian Band 2.007 https://usib.ca/ Regional (n=26) Electoral Area Columbia-Shuswap Regional District Electoral Area B CSRD EA B n/a 0.500 https://www.csrd.bc.ca/ Electoral Area Columbia-Shuswap Regional District Electoral Area D CSRD EA D n/a 86.950 https://www.csrd.bc.ca/ Electoral Area Columbia-Shuswap Regional District Electoral Area E CSRD EA E n/a 2.371 https://www.csrd.bc.ca/ 187 Governance scale Governance type Agency name Acronym / Abbreviated name First Nation Affiliation Governance area (km2)* Link Electoral Area Fraser Valley Regional District Electoral Area B FVRD EA B n/a 11.322 https://www.fvrd.ca/ Electoral Area Regional District of Central Kootenay Electoral Area K RDCK EA K n/a 2.310 https://www.rdck.ca/ Electoral Area Regional District of Central Okanagan Electoral Area E RDCO EA E n/a 1279.160 https://www.rdco.com/en/index.aspx Electoral Area Regional District of Central Okanagan Electoral Area W RDCO EA W n/a 1264.970 https://www.rdco.com/en/index.aspx Electoral Area Regional District of Kootenay Boundary Electoral Area D RDKB EA D n/a 0.236 https://rdkb.com/ Electoral Area Regional District of Kootenay Boundary Electoral Area E RDKB EA E n/a 57.861 https://rdkb.com/ Electoral Area Regional District of North Okanagan Electoral Area B RDNO EA B n/a 642.589 https://www.rdno.ca/ Electoral Area Regional District of North Okanagan Electoral Area C RDNO EA C n/a 301.606 https://www.rdno.ca/ Electoral Area Regional District of North Okanagan Electoral Area D RDNO EA D n/a 1809.770 https://www.rdno.ca/ Electoral Area Regional District of North Okanagan Electoral Area E RDNO EA E n/a 2639.870 https://www.rdno.ca/ Electoral Area Regional District of North Okanagan Electoral Area F RDNO EA F n/a 1858.860 https://www.rdno.ca/ Electoral Area Regional District of Okanagan-Similkameen Electoral Area A RDOS EA A n/a 305.572 https://www.rdos.bc.ca/ 188 Governance scale Governance type Agency name Acronym / Abbreviated name First Nation Affiliation Governance area (km2)* Link Electoral Area Regional District of Okanagan-Similkameen Electoral Area B RDOS EA B n/a 273.704 https://www.rdos.bc.ca/ Electoral Area Regional District of Okanagan-Similkameen Electoral Area C RDOS EA C n/a 542.618 https://www.rdos.bc.ca/ Electoral Area Regional District of Okanagan-Similkameen Electoral Area D RDOS EA D n/a 593.907 https://www.rdos.bc.ca/ Electoral Area Regional District of Okanagan-Similkameen Electoral Area E RDOS EA E n/a 543.582 https://www.rdos.bc.ca/ Electoral Area Regional District of Okanagan-Similkameen Electoral Area F RDOS EA F n/a 672.681 https://www.rdos.bc.ca/ Electoral Area Regional District of Okanagan-Similkameen Electoral Area G RDOS EA G n/a 2157.930 https://www.rdos.bc.ca/ Electoral Area Regional District of Okanagan-Similkameen Electoral Area H RDOS EA H n/a 4779.890 https://www.rdos.bc.ca/ Electoral Area Regional District of Okanagan-Similkameen Electoral Area I RDOS EA I n/a 476.576 https://www.rdos.bc.ca/ Electoral Area Thompson-Nicola Regional District Electoral Area L TNRD EA L n/a 2.950 https://www.tnrd.ca/ Electoral Area Thompson-Nicola Regional District Electoral Area M TNRD EA M n/a 7.741 https://www.tnrd.ca/ Electoral Area Thompson-Nicola Regional District Electoral Area N TNRD EA N n/a 0.361 https://www.tnrd.ca/ Provincial (n=6) Provincial Ministry Agricultural Land Commission ALC n/a 2683.500 https://www.alc.gov.bc.ca/ 189 Governance scale Governance type Agency name Acronym / Abbreviated name First Nation Affiliation Governance area (km2)* Link Provincial Ministry BC Ministry of Environment and Climate Change Strategy ENV n/a 21580.000 https://www2.gov.bc.ca/gov/content/governments/organizational-structure/ministries-organizations/ministries/environment-climate-change Provincial Ministry BC Ministry of Water, Land and Resource Stewardship LWRS n/a 21580.000 https://www2.gov.bc.ca/gov/content/governments/organizational-structure/ministries-organizations/ministries/water-land-and-resource-stewardship#:~:text=The%20Ministry%20of%20Water%2C%20Land,peoples%2C%20environmental%20sustainability%2C%20and%20economic Provincial Ministry BC Ministry of Agriculture MoAg n/a 2683.500 https://www2.gov.bc.ca/gov/content/governments/organizational-structure/ministries-organizations/ministries/agriculture Provincial Ministry BC Ministry of Forests MoF n/a 15421.600 https://www2.gov.bc.ca/gov/content/governments/organizational-structure/ministries-organizations/ministries/forests Provincial Ministry BC Ministry of Tourism, Arts, Culture and Sport TACS n/a 15364.759 https://www2.gov.bc.ca/gov/content/governments/organizational-structure/ministries-organizations/ministries/tourism-arts-culture National (n=2) Federal Department Fisheries and Oceans Canada DFO n/a 3054.549 https://www.dfo-mpo.gc.ca/index-eng.html Federal Department Environment and Climate Change Canada ECCC n/a 21580.013 https://www.canada.ca/en/environment-climate-change.html *defined by discrete governance areas and reserve lands considered in this study; does not account for expanse of traditional territories managed by First Nations; includes only the portion of governance area that falls within Study Area boundary. 190 D.3 Data sources Table D2. Data sources for governance area boundary mapping. Data sources and rationale for original NCP mapping can be found in Field et al. (2017) and in Field and Parrott (2022, 2017). Source Data source Year(s) Mapped governance types and agencies Details and data preparation procedures Link GeoBC (BC Data Catalogue) BC Major Watersheds 2003 ECCC + ENV + LWRS + MoF + TACS Major watersheds of BC tagged with the first 3 digits of the Watershed Code. NB: Okanagan watershed (code: 310) used in conjunction with RDNO + RDCO + RDOS boundaries for study area extent. https://catalogue.data.gov.bc.ca/dataset/bc-major-watersheds BC Freshwater Atlas Lakes 2008 DFO + Regional Districts Lake polygons for the province. Add to Regional District shape files where they were not included in zoning and/or OCP data (e.g., RDNO). DFO governance areas defined as all aquatic and wetland areas, with an adjacent 30 m riparian buffer https://catalogue.data.gov.bc.ca/dataset/freshwater-atlas-lakes BC Freshwater Atlas Rivers River polygons for the province. Add to Regional District shape files where they were not included in zoning and/or OCP data (e.g., RDNO). DFO governance areas defined as all aquatic and wetland areas, with an adjacent 30 m riparian buffer https://catalogue.data.gov.bc.ca/dataset/freshwater-atlas-rivers BC Freshwater Atlas Steam Network Stream lines for the province. Add to Regional District shape files where they were not included in zoning and/or OCP data (e.g., RDNO). DFO governance areas defined as all aquatic and wetland areas, with an adjacent 30 m riparian buffer https://catalogue.data.gov.bc.ca/dataset/freshwater-atlas-stream-network BC Freshwater Atlas Manmade Waterbodies Manmade waterbody polygons for the province. Add to Regional District shape files where they were not included in zoning and/or OCP data (e.g., RDNO). DFO governance areas defined as all aquatic and wetland areas, with an adjacent 30 m riparian buffer https://catalogue.data.gov.bc.ca/dataset/freshwater-atlas-manmade-waterbodies Indian Reserves - Administrative Boundaries 2005 First Nations Administrative boundaries (extent) of Canada Lands which includes Indian Reserves. Administrative boundaries were compiled from Legal Surveys Division's cadastral datasets and survey records archived in the Canada Lands Survey Records. See the Natural Resource Canada's GeoGratis website, Aboriginal Lands. https://catalogue.data.gov.bc.ca/dataset/indian-reserves-administrative-boundaries 191 Source Data source Year(s) Mapped governance types and agencies Details and data preparation procedures Link BC Parks (BC Data Catalogue) BC Parks, Ecological Reserves, and Protected Areas 2008 ENV Parks and protected areas managed for important conservation values and are dedicated for the preservation of their natural environments for the inspiration, use and enjoyment of the public. Places of special ecological importance are designated as ecological reserves for scientific research and educational purposes. Removed from Municipal and Regional agency boundaries; retained for ENV. https://catalogue.data.gov.bc.ca/dataset/bc-parks-map BC Governance and Structure (BC Data Catalogue) Municipalities - Legally Defined Administrative Areas of BC 2016 MoF + Municipalities Legally defined Municipal polygons were drawn from metes and bounds descriptions as written in Letters Patent for Municipalities in the province of British Columbia. In the event of a discrepancy in the data, the metes and bounds description will prevail. AA_TYPE = Municipality erased from remainder of study area (i.e., Crown lands). https://catalogue.data.gov.bc.ca/dataset/e3c3c580-996a-4668-8bc5-6aa7c7dc4932 Regional Districts - Legally Defined Administrative Areas of BC 2016 ECCC + ENV + LWRS + MoF + Regional Districts Legally defined Regional District polygons were drawn from metes and bounds descriptions as written in Letters Patent for Regional Districts in the province of British Columbia. In the event of a discrepancy in the data, the metes and bounds description will prevail. NB: Okanagan watershed (code: 310) used in conjunction with RDNO + RDCO + RDOS boundaries for study area extent. AA_TYPE = Municipality erased from remainder of study area (i.e., Crown lands). https://catalogue.data.gov.bc.ca/dataset/d1aff64e-dbfe-45a6-af97-582b7f6418b9 Ecoscape Consulting Ltd. Okanagan Wetlands Strategy (OWS) 2009 - 2014 DFO Compiled regional database. Includes data from the following sources: City of Kelowna WIM (2009); BC Freshwater Atlas (2014); MOE Wetland Inventory Project (2009); Alkali-Saltgrass Herbaceous Vegetation Community Assessment (2011); SEI/TEM for the study area; SHIM (BX Creek, NORD, Vaseux Creek and Oliver, Prairie Creek, Winfield Creek, various dates); FIM (Kalamalka, Wood, Mabel, Mara, Okanagan, Osoyoos, various dates); LRIM (Lower Shuswap River Inventory and Mapping, 2010); Ducks Unlimited (DU) data (various sources). DFO governance areas defined as all aquatic and wetland areas, with an adjacent 30 m riparian buffer data user agreement 192 Source Data source Year(s) Mapped governance types and agencies Details and data preparation procedures Link BC Ministry of Agriculture (data user agreement) Agricultural Land Use Inventory (ALUI) 2013 - 2014 MoAg + ALC Ground-truthed data, not strictly based on remotely sensed image interpretation. data user agreement CSRD Open Data CSRD OCP Designations 2017 CSRD + MoF + TACS Simplified and compiled zoning and Official Community Plan (for areas without zoning) data from all municipal jurisdictions in the study area. Used supporting by-law documentation to identify land use types relevant to non-municipal agencies (e.g., Resource Areas = added to Provincial ministry-managed Crown land extents; Urban - Commercial areas removed from TACS extent). https://data-csrd.opendata.arcgis.com/datasets/CSRD::ocp-designations/explore CSRD Zoning https://data-csrd.opendata.arcgis.com/datasets/zoning/explore RDCO (data user agreement) RDCO Zoning 2017 RDCO + MoF + TACS data user agreement RDCO Open Data RDCO Rural Land Use (Joe Rich) 2014 https://gis-rdco.hub.arcgis.com/datasets/RDCO::rdco-rural-land-use-joe-rich/explore?location=49.884922%2C-119.180050%2C12.35 RDCO Future Land Use - Ellison 2018 https://gis-rdco.hub.arcgis.com/datasets/RDCO::future-land-use-ellison/explore RDCO Future Land Use - South Slopes https://gis-rdco.hub.arcgis.com/datasets/RDCO::future-land-use-south-slopes/explore RDCO Future Land Use - Rural Westside https://gis-rdco.hub.arcgis.com/datasets/RDCO::future-land-use-rural-westside/explore RDCO Future Land Use - Brent Trepanier https://gis-rdco.hub.arcgis.com/datasets/RDCO::future-land-use-brent-trepanier/explore?location=49.781878%2C-119.765900%2C12.55 RDNO Open Data Coldstream OCP Zoning 2021 RDNO + MoF + TACS https://regional-district-of-north-okanagan-gis-open-data-site-rdnogis.hub.arcgis.com/maps/coldstream-ocp-zoning 193 Source Data source Year(s) Mapped governance types and agencies Details and data preparation procedures Link Enderby OCP Zoning https://regional-district-of-north-okanagan-gis-open-data-site-rdnogis.hub.arcgis.com/maps/enderby-ocp-zoning Lumby OCP Zoning https://regional-district-of-north-okanagan-gis-open-data-site-rdnogis.hub.arcgis.com/maps/lumby-ocp-zoning Spallumcheen OCP Zoning https://regional-district-of-north-okanagan-gis-open-data-site-rdnogis.hub.arcgis.com/maps/spallumcheen-ocp-zoning Electoral Area B OCP Zoning https://regional-district-of-north-okanagan-gis-open-data-site-rdnogis.hub.arcgis.com/maps/electoral-area-b-ocp-zoning Electoral Area C OCP Zoning https://regional-district-of-north-okanagan-gis-open-data-site-rdnogis.hub.arcgis.com/maps/electoral-area-c-ocp-zoning Electoral Area D OCP Zoning https://regional-district-of-north-okanagan-gis-open-data-site-rdnogis.hub.arcgis.com/maps/electoral-area-d-ocp-zoning Electoral Area E OCP Zoning https://regional-district-of-north-okanagan-gis-open-data-site-rdnogis.hub.arcgis.com/maps/electoral-area-e-ocp-zoning Electoral Area F OCP Zoning https://regional-district-of-north-okanagan-gis-open-data-site-rdnogis.hub.arcgis.com/maps/electoral-area-f-ocp-zoning RDOS Open Data RDOS Official Community Plan 2020 RDOS + MoF + TACS https://hub-rdos.opendata.arcgis.com/maps/officialcommunityplan RDOS Zoning https://hub-rdos.opendata.arcgis.com/maps/zoning-1 194 Source Data source Year(s) Mapped governance types and agencies Details and data preparation procedures Link UBC Data Archives RDKB Cadastre 2017 RDKB + MoF + TACS Used to identify properties in most southeast portion of the study area within RDKB EA E identified as resource/Crown land. data user agreement FVRD Online Mapping FVRD web map 2020 FVRD + MoF + TACS Verify and convert non-Crown land area polygons in ArcMap. Trace polygon shapes to define non-resource areas. https://maps.fvrd.ca/portal/apps/webappviewer/index.html?id=eae55e6da5f14e11a9a5e07a78f339c5 RDCK Public Map Service RDCK web map 2020 MoF + TACS Verify Crown land parcels. No zoning information present on map; verified on bcassessment.ca - all non-cadastral lands so assumed to be Crown lands. https://gis.rdck.bc.ca/portal/apps/webappviewer/index.html?id=e15a66aba317469481f584a71350f2ad TNRD Online Mapping TNRD web map 2020 MoF + TACS Verify that portion of TNRD within study area is all Crown land and agricultural parcels. Area L = AF-1 (Agriculture/Forestry). Areas M & N = RL-1 = Rural (which includes forestry). See https://tnrd.civicweb.net/filepro/documents/4078 https://portal.tnrd.ca/MyRegionView/ Acronyms: Agricultural Land Commission (ALC); British Columbia (BC); Columbia Shuswap Regional District (CSRD); Environment and Climate Change Strategy (ECCC); BC Ministry of Environment (ENV); Fraser Valley Regional District (FVRD); BC Ministry of Land, Water and Resource Stewardship (LWRS); BC Ministry of Agriculture (MoAg); BC Ministry of Forests (MoF); Official Community Plan (OCP); Regional District of Central Kootenay (RDCK); Regional District of Kootenay Boundary (RDKB); Regional District of the Central Okanagan (RDCO); Regional District of Central Kootenay (RDCK) ; Regional District of the North Okanagan (RDNO); Regional District of Okanagan-Similkameen (RDOS); BC Ministry of Tourism, Arts and Culture (TACS); Thompson Nicola Regional District (TNRD). 195 D.4 Comparisons of agency overlap with NCP nodes vs. clusters Table D3. Results of paired Wilcoxon signed rank test comparing agency overlap with (a) individual NCP ‘nodes’ vs. (b) network-derived NCP ‘clusters’ and the nodes therein. Total overlapped node area as a percent of the total within the entire study area was compared between the two groups; total number of local (<265 km2), regional (265- 4780 km2), and supra-regional (>4780 km2) node scales were compared between the two groups for each governance type. Only NCPs directly governed by each agency were included in analyses. p values not computed for small group sample sizes (n<30). High-valued group statistics are in bold, and corresponding governance-scales are outlined with a thicker border. ‘Mean’ is reported instead of ‘median’ to provide clearer comparison of relative group areas and numbers. Overlap % of total NCP area (mean km2) Overlap of local-scale NCPs (mean #) Overlap of regional-scale NCPs (mean #) Overlap of supra-regional-scale NCPs (mean #) Governance type (group n for directly governed NCPs) Nodes Clusters (p-value) Nodes Clusters (p-value) Nodes Clusters (p-value) Nodes Clusters (p-value) All agencies (238) 4.9 33.3 (<0.001) Municipalities (51) 1.2 29.9 (<0.001) 56.2 1361.0 (<0.001) 0.3 1.6 (<0.001) 0.2 1.0 (<0.001) Regional (78) 3.1 39.0 (<0.001) 159.0 1697.0 (<0.001) 0.5 2.1 (<0.001) 0.3 0.3 (0.149) First Nations (99) 0.1 23.8 (<0.001) 6.8 910.0 (<0.001) 0.1 1.2 (<0.001) 0.1 0.2 (0.346) Provincial (8) 90.1 98.3 4348 4612 4.1 4.9 0.8 0.8 National (2) 60.9 99.9 5339 6769 7 7 n/a n/a
Thesis/Dissertation
eng
Biology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Parrott, Lael
A spatial network approach for modelling the functional interactions between multiple ecosystem services
Text