Forestry, Faculty of
DSpace
UBCV
Clement, Sarah Elizabeth
Master of Science - MSc
University of British Columbia
In the era of global climate change and increasing urbanization, anthropogenic disturbances and habitat degradation are ubiquitous environmental problems. One tool to combat these issues is ecological restoration. Restoration of aquatic ecosystems is a key area of study, as streams face a multitude of concurrent sources of degradation, such as urbanization, invasive species, pollutants, and increasing temperatures. The cumulative effects of these stressors are having a major impact on these ecosystems. There is currently limited knowledge on long-term impacts of restoration on stream functions. Therefore, it is important to assess impacts of previous projects to ensure the success of future projects. This study used a space-for-time substitution to analyze long-term impacts of riparian and stream restoration on stream ecosystems. The primary research question of this study was: Do structural and functional indicators become more similar to reference ecosystems as years since restoration increases? Twelve restored sites of various ages (2-25 years since restoration) were compared to 2 highly degraded sites and 3 reference sites. All sites are located in Metro Vancouver, BC, Canada. The structural indicators I measured include water temperature, canopy cover, tree diversity and density, water nutrient concentrations, and in-stream flow characteristics. Ecosystem functions included estimates of leaf decomposition and periphyton production. Tree species richness and basal area (cm²/hectare) showed significant responses to restoration. Tree species richness was 3x higher at restored sites compared to degraded sites, and basal area increased with age since restoration in restored sites. For leaf decomposition, there was a significant difference in the decay rates between treatments. Decay rate (g/g/degree day) was highest in the reference sites (mean = 0.0036), and lowest in the degraded sites (mean = 0.0014). For periphyton production, the difference between treatments was not significant but there was a significant increase with years since restoration. There was a great deal of variation for other variables, so there was no significant difference between restored, reference, and degraded sites. Evidence thus far suggests that one ecosystem function, leaf decomposition rate, does increase over time following restoration.
ASSESSING LONG-TERM OUTCOMES OF STREAM AND RIPARIAN RESTORATION PROJECTS IN URBAN AND SUBURBAN AREAS by Sarah Elizabeth Clement B.S., University of Georgia, 2017 A THESIS SUBMITTED IN PARTIAL FULLFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Forestry) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2024 © Sarah Elizabeth Clement, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis entitled: Assessing long-term outcomes of stream and riparian restoration projects in urban and suburban areas Submitted by Sarah Elizabeth Clement in partial fulfillment of the requirements for the degree of Master of Science in Forestry Examining Committee: John Richardson, Professor Emeritus, Forestry, UBC Supervisor Jennifer Williams, Associate Professor, Geography, UBC Supervisory Committee Member Marwan Hassan, Professor, Geography, UBC Additional Examiner Additional Supervisory Committee Members: Brett Eaton, Professor, Geography, UBC Supervisory Committee Member iii Abstract In the era of global climate change and increasing urbanization, anthropogenic disturbances and habitat degradation are ubiquitous environmental problems. One tool to combat these issues is ecological restoration. Restoration of aquatic ecosystems is a key area of study, as streams face a multitude of concurrent sources of degradation, such as urbanization, invasive species, pollutants, and increasing temperatures. The cumulative effects of these stressors are having a major impact on these ecosystems. There is currently limited knowledge on long-term impacts of restoration on stream functions. Therefore, it is important to assess impacts of previous projects to ensure the success of future projects. This study used a space-for-time substitution to analyze long-term impacts of riparian and stream restoration on stream ecosystems. The primary research question of this study was: Do structural and functional indicators become more similar to reference ecosystems as years since restoration increases? Twelve restored sites of various ages (2-25 years since restoration) were compared to 2 highly degraded sites and 3 reference sites. All sites are located in Metro Vancouver, BC, Canada. The structural indicators I measured include water temperature, canopy cover, tree diversity and density, water nutrient concentrations, and in-stream flow characteristics. Ecosystem functions included estimates of leaf decomposition and periphyton production. Tree species richness and basal area (cm2/hectare) showed significant responses to restoration. Tree species richness was 3x higher at restored sites compared to degraded sites, and basal area increased with age since restoration in restored sites. For leaf decomposition, there was a significant difference in the decay rates between treatments. Decay rate (g/g/degree day) was highest in the reference sites (mean = 0.0036), and lowest in the degraded sites (mean = 0.0014). iv For periphyton production, the difference between treatments was not significant but there was a significant increase with years since restoration. There was a great deal of variation for other variables, so there was no significant difference between restored, reference, and degraded sites. Evidence thus far suggests that one ecosystem function, leaf decomposition rate, does increase over time following restoration. v Lay Summary Freshwater ecosystems are impacted by land use changes, pollutants on land, and changes to watercourses themselves. Freshwater ecological restoration is the practice of attempting to mitigate or reverse these impacts. There is currently a lack of data on the outcomes of freshwater restoration practices, particularly regarding ecosystem functions. Ecosystem functions are processes that maintain ecosystem habitats and support life within the ecosystem. My research focused on how restoration impacts structural features and ecosystem functions. I compared two types of sites that had not been restored (degraded and reference) to sites restored at different times, primarily by planting of streamside trees, to see how these impacts progress over time. Some structural features showed expected changes over time, such as increases in average tree size; some did not. One ecosystem function (decomposition) was highest in references site and lowest in degraded sites, following expected trends. Algae growth was not different between treatments. vi Preface Sarah Clement is the primary author of this thesis. All field work was conducted under Sarah’s supervision, along with a team of undergraduate assistants, lab members, and friends. Sarah completed the majority of the lab processing, with assistance from undergraduate assistants. Water nutrient samples were analyzed by the BC Ministry of Environment and Climate Change Strategy’s Analytical Chemistry Research Laboratory in Victoria, BC. Sarah completed chlorophyll-a extraction, with the assistance of undergraduate assistants Cole Munro and Matthew Appelbaum. Sarah completed all data analysis with guidance from Dr. John Richardson. Research questions were created by Sarah under the supervision of Dr. John Richardson. The leaf decomposition experiment was conducted in collaboration with Dr. Chelsea Little and Pierre Banville at Simon Fraser University. Dr. Chelsea Little and Pierre Banville conducted a cotton strip decomposition experiment concurrently to compare decomposition rates. Results from the two experiments will be edited, submitted for peer-review, and published. Results from this study will be compiled into manuscript(s) for future publication. Additional data were collected during fieldwork but that has yet to be processed and analyzed due to the scope of this study. This additional data will be incorporated into future publications. vii Table of Contents Abstract ........................................................................................................................................................ iii Lay Summary ................................................................................................................................................ v Preface ......................................................................................................................................................... vi Table of Contents ........................................................................................................................................ vii List of Tables ............................................................................................................................................... ix List of Figures ............................................................................................................................................... x Acknowledgements ..................................................................................................................................... xii Dedication .................................................................................................................................................. xiii 1 Introduction ........................................................................................................................................... 1 1.1 Importance of stream restoration ................................................................................................. 1 1.2 Stream ecosystem functions ........................................................................................................ 4 1.3 Importance of riparian vegetation ................................................................................................ 5 1.4 Knowledge gaps in stream and riparian restoration .................................................................... 6 1.5 Stream restoration in the Pacific Northwest ................................................................................ 9 1.6 Research goals and hypotheses .................................................................................................. 11 2 Biological and Physical Indicators of Ecosystem Conditions ............................................................. 17 2.1 Introduction ............................................................................................................................... 17 2.2 Methods ..................................................................................................................................... 19 2.2.1 Sites ....................................................................................................................................... 19 2.2.2 Temperature data ................................................................................................................... 21 2.2.3 Canopy cover ........................................................................................................................ 22 2.2.4 Tree data ................................................................................................................................ 22 2.2.5 Nutrients ................................................................................................................................ 23 2.2.6 Stream flow ........................................................................................................................... 23 2.2.7 Statistical analysis ................................................................................................................. 23 2.3 Results ....................................................................................................................................... 25 2.3.1 Temperature data ................................................................................................................... 25 2.3.2 Canopy cover ........................................................................................................................ 26 2.3.3 Tree data ................................................................................................................................ 27 2.3.4 Nutrients ................................................................................................................................ 28 2.3.5 Stream velocity ...................................................................................................................... 29 2.3.6 Factor analysis of mixed data ................................................................................................ 29 2.4 Discussion .................................................................................................................................. 31 viii 2.4.1 Overview ............................................................................................................................... 31 2.4.2 Temperature data ................................................................................................................... 32 2.4.3 Canopy cover ........................................................................................................................ 32 2.4.4 Tree data ................................................................................................................................ 33 2.4.5 Nutrients ................................................................................................................................ 36 2.4.6 Stream velocity ...................................................................................................................... 38 2.4.7 Compiled analysis of environmental gradients ..................................................................... 38 3 Ecosystem Functions .......................................................................................................................... 62 3.1 Introduction ............................................................................................................................... 62 3.2 Methods ..................................................................................................................................... 65 3.2.1 Decomposition ...................................................................................................................... 65 3.2.2 Periphyton production ........................................................................................................... 68 3.2.3 Statistical analysis ................................................................................................................. 69 3.3 Results ....................................................................................................................................... 69 3.3.1 Decomposition ...................................................................................................................... 69 3.3.2 Periphyton ............................................................................................................................. 70 3.4 Discussion .................................................................................................................................. 71 3.4.1 Decomposition ...................................................................................................................... 71 3.4.2 Periphyton ............................................................................................................................. 75 4 General Discussion ............................................................................................................................. 87 4.1 Restoration response .................................................................................................................. 87 4.2 Study limitations ........................................................................................................................ 92 4.3 Future research and recommendations ...................................................................................... 94 References ................................................................................................................................................... 95 Appendices ................................................................................................................................................ 109 Appendix A. Historical Land Uses in Reference Sites ......................................................................... 109 ix List of Tables Table 1.1 Research questions and response variables. ................................................................................ 15 Table 2.1 Research sites. Site refers to the name of the stream. Abbreviation refers to the names by which sites will be referred. Year restored refers to the year that the restoration project began. In cases where ranges or multiple dates were listed, restoration projects had multiple phases or continuous restoration practices. For restored sites, age refers to the years since restoration commenced at the time of research. For degraded sites, age is 0 to represent degraded conditions. For reference sites, age represents the years since the last major disturbance. ................................................................................................................. 57 Table 2.2 Average and maximum stream temperatures recorded by HOBO data loggers in riffles. All temperatures are ± 0.3 °C. Loggers were placed between 12-21 July 2022 and collected 27 September 2022. A second set of loggers was deployed between 13-14 October 2022 and collected between 24-25 November 2022. ND indicates that a logger was lost, and no data were available. Bold indicates values above water quality standards for streams with unknown fish distribution. ............................................... 59 Table 2.3 Nutrient concentrations from water samples collected September 27, 2022, at all sites, during low flow. Concentrations below detection limits (<0.100 mg/L) are signified as <DL. Bold numbers indicate values above the BC Water Quality standards for chronic exposure for aquatic life. ................... 60 Table 2.4 Wetted width (m), riffle velocity (m/s), and number of pools and riffles for 14 of the 17 research sites. ND represents no data. Pools and riffles could not be counted at MN due to blackberry brambles blocking access to stream. Pools and riffles could not not be measured at HI and AR due to exceptionally high water levels. At MQ riffles were contiguous throughout the reach without discernable beginnings and ends, so riffles are counted as ALL; all pools at MQ were side pools. Only one riffle velocity measurement was taken at HI and AR, so no standard deviation values are available. N = 10 ± 1 for all other width and velocity measurements. Measurements were taken on June 12, 16, and 23, 2023. 61 Table 3.1 Models of decomposition rate (g/g/degree day) for red alder (Alnus rubra) leaves placed in study streams October 2023. Variables indicate variables included in the model. ..................................... 85 Table 3.2 Models of chlorophyll-a (µ/cm2) concentrations. Chlorophyll-a was extracted from tiles placed in streams between 27 July – 16 August 2022. Tiles were left in streams approximately 30 days. Variables indicate variables included in the model. Some data were missing for some variables, which lead to some sites being excluded from the models. Sites removed refers to the number of sites that were excluded from the model due to missing data. .................................................................................................................... 86 x List of Figures Figure 1.1 Examples of impacts of woody riparian vegetation on stream ecosystems and streambanks. .. 14 Figure 2.1 Map of research sites, located throughout Metro Vancouver, BC, Canada. Marker dots are not to scale. ....................................................................................................................................................... 41 Figure 2.2 Average percent canopy cover in research reaches (n = 10 ± 6 per site). Error bars represent standard deviations. Photos used for analysis were taken between August – September 2022, or June 2023. Age represents years since restoration for restored sites, or years since last major disturbance for reference sites.............................................................................................................................................. 42 Figure 2.3 Box plots of percent canopy cover by treatment. Bars represent median value. Vertical lines represent range, excluding outliers. Dots represent outliers. Canopy cover represents the amount area above the stream that is shaded. Canopy cover was measured by taking upward fisheye photos in the streams that were analyzed in Gap Light Analyzer software. Photos used for canopy cover analysis were taken between August – September 2022, or June 2023. ........................................................................... 43 Figure 2.4 Average percent canopy cover (n = 10 ± 6 per site) between restored sites, based on age class (n = 6 for <6 years, n = 4 for 6-10, n = 2 for 10+). Photos used for canopy cover analysis were taken between August – September 2022, or in June 2023. ................................................................................. 44 Figure 2.5 Tree species richness within 10 m of research reach on both banks. Willows (Salix spp.) and other shrubs species were not included. Trees were surveyed between June-August 2022. Letters signify significant differences based on Tukey’s honestly significant difference test (p = 0.039). ........................ 45 Figure 2.6 Tree density (trees/hectare) within 10 m of research reach on both banks. Willows (Salix spp.) and other shrubs species were not included. Trees were surveyed between June-August 2022. Letters signify significant differences based on Tukey’s honestly significant difference test (p = 0.007 for degraded-restored and 0.08 for degraded-reference). ................................................................................. 46 Figure 2.7 Comparison of basal area (m2/hectare) values by treatment. Differences between treatments were not significant. Trees within 10 m of research reach on both banks were measured for DBH, and then total basal area was calculated based on the DBH values. Willow (Salix spp.) and other shrub species were not included. Trees were surveyed between June-August 2022......................................................... 47 Figure 2.8 Basal area (m2/hectare) over time for all sites. Trees within 10 m of research reach on both banks were measured for DBH, and then total basal area was calculated based on the DBH values. Willows (Salix spp.) and other shrub species were not included. Trees were surveyed between June-August 2022. ............................................................................................................................................... 48 Figure 2.9 Tree species richness in restored sites over time. Tree species richness was measured within 10 m of research reach on both banks. Willows (Salix spp.) and other shrubs species were not included. Trees were surveyed between June-August 2022. ................................................................................................ 49 Figure 2.10 Average diameter (± SD) at breast height (cm) versus year since restoration for all restored sites. Trees within 10 m of research reach on both banks were measured for DBH. Willows (Salix spp.) and other shrubs species were not included. Measurements were taken between June-August 2022. ....... 50 Figure 2.11 Basal area (m2/hectare) (± SD) versus years since restoration for all restored sites. Basal area measurements based off DBH (cm) measurements of all trees within 10 m of research reach, on both banks (n = 12). Willows (Salix spp.) and other shrubs were not included, due to difficulty measuring DBH with multiple trunks. DBH measured between June-August 2022. ............................................................ 51 Figure 2.12 Percent canopy cover (± SD) versus basal area (cm2/hectare) for all 17 sites. Basal area measurements based on DBH (cm) measurements of all trees within 10 m of research reach, on both banks (n = 12). Willows (Salix spp.) and other shrubs were not included, due to difficulty measuring DBH with multiple trunks. DBH measured between June-August 2022. Canopy cover measurements based off xi photos taken with fish-eye lens. Canopy cover photos were taken between August-September 2022, or in June 2023. ................................................................................................................................................... 52 Figure 2.13 Factor Analysis of Mixed Variables graph of quantitative variables. Length of line represents how well variables are explained by dimensions. Variables that are close together (< 90°) are positively related, whereas variables that are far apart (> 90°) are negatively related. Variables that are approximately 90° apart are unrelated. Missing data were imputed using missMDA package in R. Blue color represents supplementary variable. .................................................................................................... 53 Figure 2.14 Factor Analysis of Mixed Variables contributions of variables. Colors represent combined contribution to both dimensions. ................................................................................................................. 54 Figure 2.15 Cos2 values for each site in Factor Analysis of Mixed Variables. High cos2 values indicate that sites are well represented by both dimensions. .................................................................................... 55 Figure 2.16 Factor Analysis of Mixed Variables individual sites clustered by treatment. ......................... 56 Figure 3.1 Experimental setup for summer decomposition experiment. Mesh bags contain red alder (Alnus rubra) leaves and a wooden stick (wooden stick decomposition was not included in this study due to too little decomposition). Cotton strips and HOBO temperature logger are attached to brick. ........................ 78 Figure 3.2 Experimental design for fall decomposition experiment. Wavy lines represent riffles. Grids with leaf represents leaf packs. ................................................................................................................... 79 Figure 3.3 Decay rates (g/g/day) for summer red alder (Alnus rubra) decomposition experiment, conducted at research sites from July – August 2022. Leaves were placed in stream in mesh bags. Error bars represent standard error. Asterisks represent significant results, based on regression of natural log of the percent mass remaining and the number of days in the stream. Restored sites are ordered from most recently restored to oldest restored sites. .................................................................................................... 80 Figure 3.4 Decay rates (g/g/degree day) for red alder (Alnus rubra) leaves placed in study streams October 2023. Averages per site based on 8 samples. N = 2 for degraded sites, N = 8 for restored sites, and N = 3 for reference sites. Letters (A, B) represent significant differences detected by Tukey test (p = 0.013 for degraded and 0.022 for restored sites). ........................................................................................ 81 Figure 3.5 Decay rate (g/g/degree day) versus years since last major disturbance, or years since restoration for red alder (Alnus rubra) leaves placed in study streams October 2022. Averages per site based on 8 samples. N = 2 for degraded sites, N = 8 for restored sites, and N = 3 for reference sites. ...... 82 Figure 3.6 Chlorophyll-a (µg/cm2) concentrations by site. Chlorophyll-a was extracted from tiles placed in streams between 27 July – 16 August 2022. Tiles were left in streams approximately 30 days. N = 2-8 per site. Sites are arranged by year since last major disturbance or restoration, from most recently disturbed/restored to longest time since disturbance. ................................................................................. 83 Figure 3.7 Chlorophyll-a (µg/cm2) concentrations for restored sites, showing difference with age since restoration. Chlorophyll-a was extracted from tiles placed in streams between 27 July – 16 August 2022. Tiles were left in streams approximately 30 days. Error bar indicated standard deviation. N = 6 ± 3. ...... 84 xii Acknowledgements I would like to thank my supervisor, Dr. John Richardson, for continued guidance and support throughout my master’s degree. I appreciate the flexibility you gave me to pursue my own research interests. Financial support for this project was provided through a Natural Science and Engineering Research Council (Canada) Discovery Grant to John Richardson. I appreciate the feedback from my committee members, Dr. Jennifer Williams, and Dr. Brett Eaton. Your guidance helped me direct my focus and made the whole process much more manageable. I am tremendously grateful for my cohort of assistants, lab mates, friends, and collaborators who helped me throughout field and lab work. This project would not be possible without each and every one of you: Paulina Chalmers, Aline Isabelle, Gretchen MacNaughton, Anika Lesko, Rebecca Halasy, Alonn Castle, Pierre Banville, Ethan Vayman, Cole Munro, Matthew Appelbaum, Kelsey Tikka, Jennifer Fisher, Kay Colletti, Valerie Russell, and Maxwell Kristen. xiii Dedication To my mom, for supporting my decision to move so far from home to a place I’d never even visited in pursuit of my education. To my dad, who would be so proud to see all that I’ve accomplished. Lastly, to my loving partner, Maxwell Kristen, for the constant encouragement, emergency field support, and hours of editing. 1 Introduction 1.1 Importance of stream restoration The United Nations declared 2021-2030 the United Nations Decade on Ecosystem Restoration, “with the aim of supporting and scaling up efforts to prevent, halt and reverse the degradation of ecosystems worldwide and raise awareness of the importance of successful ecosystem restoration” (UN 2019). Given the impetus to conduct restoration in this decade, there is a need to assess the ecological outcomes of previous projects to better guide future practices. The Society for Ecological Restoration defines ecological restoration as “the process of assisting the recovery of an ecosystem that has been degraded, damaged, or destroyed” (SER 2004). The need for ecological restoration is increasing as ecological degradation has been occurring at an increasingly fast pace over the last half-century (IPBES 2019). For example, the quantity of urbanized land has increased two-fold since 1992, and urbanization is associated with increased pollution, fewer natural areas, and overall reduced ecological function (Meyer et al. 2005, Paul and Meyer 2000, IPBES 2019). Additionally, there has been a 40% increase in the cumulative global records of invasive species in the last four decades, which is associated with a decrease in native biodiversity (IPBES 2019). While the field of ecological restoration is almost a century old, as Aldo Leopold conducted ecological restoration in the 1930s, there are major gaps in the knowledge of the efficacy of restoration practices (Leopold 1933). The knowledge gaps are largely due to a lack of long-term monitoring after project completion and a lack of clearly defined project goals upon which to measure success (Nilsson et al. 2015, Palmer et al. 2005, Rumps et al. 2007, Wortley et al. 2013). Restoration of freshwater systems is critically important. Freshwater ecosystems make up approximately 2.3% of the total land area, yet more than 9.5% of animal species inhabit these 2 ecosystems (Balian et al. 2008, Lehner and Dӧll 2004, Reid et al. 2019). Loss of biodiversity in freshwater systems is occurring at a much faster rate than loss of biodiversity in terrestrial and marine systems. For example, the 2020 Living Planet Index found an average 68% decrease in populations for all measured vertebrate species between 1970 and 2016, whereas within freshwater species there was a decline of 84% (WWF 2020). This degradation of freshwater is associated with a multitude of anthropogenic land-use changes (Allan 2004). Streams in urbanized areas exhibit signs of degradation such as increased pollutant loads, decreased channel stability, and reduced ecological functions (Meyer et al 2005, Paul and Meyer 2000, IPBES 2019). Examples of ways in which freshwater ecosystems have been negatively impacted include changes in riparian vegetation (e.g., deforestation and urbanization or conversion to agriculture), flow modification (e.g., dam construction), channel modification (e.g., diking and piping underground), and nutrient pollution (e.g., agricultural runoff and stormwater runoff). Given the different sources of stress on freshwater ecosystems, there are several different categories of stream and riparian restoration practices. These include in-stream habitat modification (e.g., addition of large wood or gravel), channel modification (e.g., removal of dikes and daylighting streams), flow naturalization (e.g., removal of dams), and riparian vegetation restoration (e.g., replanting native vegetation along streambanks) (Beechie et al. 2010). In streams where habitat lacks complexity, addition of logs and boulders can help to create flow heterogeneity (Beechie et al. 2010, Bilby and Ward 1991, Katz et al. 2007, Negishi and Richardson 2003). Removal of dikes can also help to create flow heterogeneity and reconnect the stream to the riparian zone. Manmade structures such as dams and culverts can create barriers to movement for migrating fish and other large stream organisms. These structures reduce overall connectivity of stream systems. When connectivity is a major issue, 3 culvert repair or dam removal can increase accessible habitat to migrating fish (Beechie et al. 2010, Roni et al. 2002). Riparian vegetation restoration projects are useful anywhere that riparian vegetation has been lost or degraded (e.g., due to an increase in invasive species). Riparian vegetation plays a role in many physical and biochemical processes in streams, which will be explained in detail in section 1.3. The focus of this study will be riparian vegetation restoration projects, which are projects that focus on replanting the areas surrounding an impacted stream or river. Restoration strategies can be further divided into two categories: process-based restoration and structural restoration. Process-based restoration aims to enhance or recreate the natural processes within the stream or watershed that create or maintain ecosystem structure and function (Beechie et al. 2010, Roni et al. 2002). Structural restoration, by contrast, is focused on physically recreating habitat structures. For instance, adding cobble in a reach where fine sediment has infiltrated the original cobble or gravel would be an example of structural restoration, whereas process-based restoration would focus on eliminating the source of sedimentation (e.g., through increasing riparian vegetation). Structural restoration can be shorter-lasting (e.g., substrate replacement), and the efficacy of these practices remains unclear (Beechie et al. 2010, Larson et al. 2001, Palmer et al. 2010, Roni et al. 2002). It should be noted that process-based restoration often includes structural modifications; the difference is that process-based restoration also focuses on natural regeneration of structures and processes in order to address causes of degradation rather than outcomes of degradation (e.g., in addition to adding large wood to create pools, adding riparian vegetation to replace wood in stream and to prevent sedimentation from filling in pools) (Beechie et al. 2010). In simple terms, process-based restoration aims to address the problem at the source, whereas structural restoration merely improves the symptoms. 4 1.2 Stream ecosystem functions Stream ecosystem functions are dynamic processes that create or modify habitat, move resources, and support life through the supply of essential food and nutrients within an ecosystem. Examples of stream ecosystem functions include organic matter decomposition, nutrient cycling, and ecosystem metabolism (Bernhardt et al. 2018, Gessner and Chauvet 2002, Palmer and Febria 2012). Ecosystem functions involve interactions between multiple organisms or physical features within an ecosystem. For example, leaf decomposition involves microbes, as well as shredder invertebrates, and is mediated by physical factors such as nutrients and water temperature (Benfield et al. 2001, Ferreira et al 2015, Richardson 1992, Woodward et al. 2012). Leaf decomposition is an important function because small streams generally derive more energy from allochthonous resource inputs from surrounding forest than is produced in stream via photosynthetic activity (Lepori et al. 2005, Wallace et al. 1997). Therefore, impaired leaf decomposition can limit production of higher trophic levels (Wallace et al. 1997). Studying ecosystem functions, in addition to ecosystem structures, can help to show a more holistic view of the ecosystem. Ecosystem structures (such as presence or absence of keystone and indicator species) are an indication of the state of degradation. Ecosystem functions can help to determine which interactions are influencing the degradation (Gessner and Chauvet 2002, Palmer and Febria 2012). For example, a structural index such as benthic macroinvertebrate community composition might show a decline in shredder macroinvertebrates, which could reduce nutrient cycling and limit production of predators that rely on macroinvertebrates for food. To determine what the limiting factors could be, one might look at organic matter retention to determine if the shredders’ food source is being kept in the system long enough for them to utilize it. 5 Furthermore, the time scale over which ecosystem structures and ecosystem functions respond to or recover from stress is not always equal within the same system (Gessner and Chauvet 2002). For example, one study that analyzed both ecosystem structure and ecosystem functions in response to logging found slower recovery of canopy cover (structure) than decomposition (function) but hypothesized that water chemistry (structure) was the first variable to recover (Yeung et al. 2017). Given the different recovery timescales and trajectories, analyzing ecosystem structures and functions in tandem can give a clearer understanding of how the system is changing. 1.3 Importance of riparian vegetation Riparian plants play an important role in stream ecosystem health (Figure 1.1). They modify physical conditions of the stream by providing shade which maintains lower water temperatures and limits primary production, and by stabilizing stream banks which reduces sediment inputs from erosion (Allan et al. 2021, Martens et al. 2019, Naiman and Décamps 1997, Richardson et al. 2007, Wondzell et al. 2019). Riparian vegetation also provides benefits to terrestrial organisms, such as by providing food for herbivores, nesting sites for birds, overwintering habitat for insects, and host plants for pollinators (Knopf and Samson 1994, Naiman et al. 2005, Pollock et al. 2012, Ramey and Richardson 2017, Richardson et al. 2007, Ringold et al. 2008, Stewardship Centre for British Columbia 2018). Riparian vegetation can also help to improve water quality by increasing or maintaining infiltration rates and removing excessive nutrients (Naiman et al 2005, Richardson and Danehy 2007, Richardson et al. 2007, WWF 2020). Riparian vegetation also plays an important role in terms of organic matter inputs to streams. Large wood in streams, such as fallen trees, provide important habitat for salmon fry as well as other stream organisms (Beechie et al. 2010, Bilby and Ward 1991, Martens et al. 2019, Pollock 6 et al. 2012). Large wood and debris dams help to create flow heterogeneity, increasing the number of pools in streams (Allan et al. 2021, Beechie et al 2010, Coe et al. 2009, Larson et al. 2001). Leaf litter inputs from riparian plants provide an important food source to stream macroinvertebrates, microbes, fish, and amphibians (Martens et al. 2019, Richardson et al. 2010). The species composition of riparian vegetation greatly impacts the effect it will have on the stream. For example, non-native grasses do not have as deep root systems as native grasses or woody vegetation, and therefore are not able to stabilize stream banks as effectively (Stover et al. 2018, Zaimes et al. 2021). Herbaceous plants and small shrubs cannot supply large diameter wood pieces to streams to modify habitat. Non-native invasive species can reduce overall riparian vegetation biodiversity, by outcompeting native vegetation (Fryer 2021, Maceda-Veiga et al. 2016, Richardson et al. 2007, Ringold et al. 2008). This can reduce pollinator species’ access to host plants, reduce overall food availability, and reduce access to early-season or late-season food and habitat. 1.4 Knowledge gaps in stream and riparian restoration In the field of stream and riparian restoration, there is a lack of follow-up data to determine the outcomes of projects. A global literature review of 169 riparian vegetation restoration projects found that more than 33% of projects were only monitored for one year after project completion, and 87% of projects were monitored for fewer than 6 years (Gonzalez et al. 2015). An interview study with stream restoration practitioners in the Pacific Northwest found that 19% of interviewees did not conduct any follow-up monitoring (Rumps et al. 2007). Given that streams are not static ecosystems, short-term monitoring is unlikely to capture evidence of ecosystem-wide effects (Beechie et al. 2010, Hasselquist et al. 2017, Matzek et al. 2016, Nilsson et al 2015). 7 There are several reasons for the lack of empirical evidence of the outcomes of different practices, such as financial constraints, temporal scale of previous monitoring efforts, and a lack of clearly defined project objectives (Feio et al. 2021, Hasselquist et al. 2017, Kuglerová et al. 2023, Matzek et al. 2016, Nilsson et al 2015, Palmer et al. 2005, Rumps et al. 2007). Many stream and riparian restoration projects are conducted by restoration practitioners and engineers, rather than researchers, so there is little financial incentive to expend efforts monitoring previous projects for many years, when resources could instead be used to conduct additional projects. When monitoring is required, it often fails to adequately capture the effect that restoration is having on in-stream ecosystem processes, such as decomposition, nutrient cycling, and respiration (Palmer et al. 2014, Palmer et al. 2005). This is because most projects only require compliance monitoring, where the aim is to ensure projects were completed as planned (DFO 2016). Compliance monitoring does not require any pre-restoration monitoring, and there is no requirement for long-term data (DFO 2012). Furthermore, compliance monitoring has been described as “not science based”, and therefore provides very little useful data (DFO 2019). For example, when trees are planted, compliance monitoring often involves checking for tree survival (Rumps et al. 2007). Such monitoring fails to detect whether the rates of soil stabilization, quantity of leaf litter input, and percent canopy cover are increasing in magnitude, and whether these metrics are approaching a state expected in a properly functioning ecosystem. Another reason is that most monitoring requirements do not set out a sufficiently lengthy temporal scale to detect the true ecosystem effects (Hasselquist et al. 2017, Matzek et al. 2016, Nilsson et al 2015). A meta-analysis on offsetting strategies for freshwater fish found that the average monitoring time for riparian restoration was less than 2 years (Theis et al. 2022). The Rumps et al. (2007) interview study found that of the practitioners that conducted follow-up 8 monitoring, 18% only monitored once, and only 29% monitored for more than 10 years. While monitoring one-year post-restoration might answer some questions about restoration outcomes (e.g., were boulder placements able to modify stream velocity (Negishi and Richardson 2003)), other questions cannot be answered in that time frame (e.g., is the habitat created by boulder additions maintained for the life cycle of a salmonid species). Furthermore, some studies have shown opposing results when comparing long-term and short-term data. For example, an early study looking at the outcome of log additions in a Colorado stream indicated that the project had only concentrated fish around the new habitat, whereas a second study conducted 20 years post restoration showed that populations had increased (Roni 2019, Gowan and Fausch 1996, White et al. 2011). Many studies have noted that the temporal scale of monitoring was not long enough to detect true effects (Collier et al. 2001, Louhi et al. 2011, Matzek et al. 2016, Nilsson et al 2015, Palmer et al. 2005), but few studies have clearly defined what an adequate temporal scale would be. Collier and colleagues (2001) forecasted that a stable state would be reached in 15 years in New Zealand, in terms of water temperature, sediment yields, and changes in the Macroinvertebrate Community Index. More research is needed to determine the time required in the Pacific Northwest for functional and structural metrics to approach pre-disturbance levels; this timeframe will vary by project depending on location, level of disturbance and restoration goals (Beechie et al. 2010, Roni 2019). Historical successional models of this region have predicted peak canopy shading 40-80 years after a major disturbance, but these models are based on natural stand regeneration rather than restoration (Gregory et al. 1987, Minshall et al. 1989, Warren et al. 2016). The third reason is that many projects lack clearly defined restoration objectives (Palmer et al. 2005, Rumps et al. 2007). If objectives are not clearly defined, then it is impossible to determine 9 a project’s success in meeting those objectives. Clear objectives should include numeric targets (e.g., percent increase in canopy cover) with specific timelines, so that monitoring efforts can focus on those specific metrics, and monitoring timelines can be created in the project planning phase. Clear numerical objectives also provide guidance for baseline monitoring, so that restoration metrics can be compared to pre-restoration values (Feio et al. 2021, Kuglerová et al. 2023). 1.5 Stream restoration in the Pacific Northwest A substantial portion of freshwater and riparian restoration projects in the Pacific Northwest (PNW), and throughout Canada, are compensatory projects required by the Fisheries Act (1986). The Fisheries Act requires that any projects that will result in ‘‘harmful alteration, disruption, or destruction to fish habitat’’ (HADD) must include a restoration project to offset the HADD, with a goal of “no net loss of habitat productivity” (NNL). Quigley and Harper (2006) conducted a national review of the effectiveness of HADD-offset restoration projects. The authors noted that a very small proportion of Fisheries and Oceans Canada (DFO)’s time is spent testing efficacy of restoration methods. Results from the national review found that 64% of in-stream projects and 75% of riparian projects did not meet spatial compensation requirements. Furthermore, only 37% of projects achieved NNL requirements (Quigley and Harper 2006). While monitoring is required for these projects, the requirements generally only include compliance monitoring, and the time scale required is usually less than 5 years. Monitoring is conducted by third parties and submitted to DFO. Recently, DFO has highlighted a need to increase effectiveness monitoring (DFO 2016). Effectiveness monitoring differs from the typical compliance monitoring in that it includes pre-restoration assessments and includes comparison to a control site (DFO 2012). However, 10 standards for cost-effective, reliable effectiveness monitoring protocols are still being developed (DFO 2016). A major focus of restoration projects in the PNW is in-stream habitat modification, such as the addition of large wood (Beechie et al. 2010, Larson et al. 2001, Rumps et al. 2007). The goal of such projects is to increase habitat heterogeneity, working under the assumption that increases in habitat heterogeneity will increase biodiversity; however, there is limited evidence to support this assumption (Beechie et al. 2010, Katz et al. 2007, Larson et al. 2001, Palmer et al. 1997, Palmer et al. 2010). The motivation of many of these projects is to improve fish habitat, but few studies test effects on fish populations (Booth et al. 2016, Coe et al. 2009, Katz et al. 2007, Lavelle et al. 2021). Studies that looked at effects of in-stream habitat modification on fish populations often found an increase in density; however, increases in density could be due to preferential selection of modified habitats rather than an increase in the total population (Lavelle et al. 2021, van Zyll De Jong et al. 1997). Roni (2019) published a review of recent literature analyzing whether increases in fish density following restoration are a sign of preferential habitat selection or increasing population. The evidence he provided supported the idea that total populations increased but he concluded that more evidence is needed on the topic (Roni 2019). A study comparing restoration projects across the Pacific Northwest region of the United States also found that in-stream habitat modification projects had some of the highest average costs, with an average cost of $697,000 USD (2007) per project between 1984-2004, whereas the average cost of riparian restoration projects was an order of magnitude lower (Katz et al 2007). Another study in the Pacific Northwest found that some physical indicators, such as stream temperature, were able to recover to levels similar to those found in minimally disturbed second-growth forests after 18 years of passive restoration, but that the amount of degradation pre-11 restoration influenced the speed at which the indicator recovered (Martens et al. 2019). However, other indicators, such as canopy cover, fish abundance, and in-stream wood, did not recover in that time period (Martens et al 2019). Given that active restoration aims to accelerate the recovery rate, it is expected that actively restored sites, such as those in this study, should recover sooner. However, the time frame within which this change will occur is still unknown. 1.6 Research goals and hypotheses The goal of this study was to increase understanding of outcomes of stream restoration practices in Metro Vancouver and, more broadly, the Pacific Northwest. By assessing ecosystem processes, in addition to structural responses, I aimed to gain a better understanding of the restoration trajectory. I compared restored sites of different ages, using a space-for-time substitution design. I compared restored sites to reference sites and degraded sites, with a total of 17 sites (though some data were not available for some sites – details on missing sites are provided below for each variable or study). Reference sites represent minimally disturbed second-growth conditions, and degraded sites represent highly degraded conditions. For reference sites, I selected mature second-growth forests rather than old-growth forests, because reaching old-growth status is not attainable within a reasonable time frame and because old-growth sites were not available. In the Pacific Northwest region of the United States, restoration goals are often based on mature second-growth forests rather than old-growth forests (Pollock et al. 2012). Given that extensive logging occurred in the Lower Mainland in the late 1800s, few old-growth forests remain (Boyle et al. 1997). I aimed to select sites where ecosystem structural and functional measures were representative of a properly functioning ecosystem. However, it is not realistic that these sites 12 would be completely unimpacted, in terms of invasive species’ presence, erosion, or nutrient pollution. For this study I looked at riparian and in-stream structural metrics: plant diversity and community structure, percent canopy cover, percent streambank erosion, water temperature, and average tree size. I also looked at in-stream functional metrics: decomposition rate and periphyton production rate. By assessing multiple functional and structural indices, I sought to compare the rate at which different metrics recover following restoration. My goal was to determine the most effective way to evaluate future restoration projects. I predicted that some structural indices such as canopy cover, which is restrained by the slow growth of trees, would take longer to recover than some functional metrics (Martens et al. 2019). Functional metrics, such as decomposition, are mediated by several factors with varying recovery rates (i.e., light availability, water chemistry, microbes, and macroinvertebrates) and previous studies have demonstrated recovery of decomposition rates when only some of the mediating factors have recovered to pre-disturbance levels (e.g., Martens et al. 2019, Yeung et al. 2017). The first chapter will explore structural indices, the second chapter will explore functional metrics, and I will conclude my thesis with a general discussion of the outcomes of this study. The primary research question of this study was: Do indicators of ecosystem function and ecosystem structural metrics become more similar to reference ecosystems as years since restoration increases? My overarching hypothesis was: 13 that structural and functional metrics of degraded, restored, and reference sites would be significantly different as restoration practices aim to improve structural and functional metrics from degraded conditions to pre-degradation conditions. Furthermore, I hypothesized that as years since restoration increased, restored sites would become more similar to reference sites as vegetation becomes more established and the ecosystem has time to recover. My research includes 7 questions within this overarching question, relating to stream temperatures, percent canopy cover, average tree size, water nutrient concentrations, stream velocity, organic matter decomposition, and periphyton production rate. See Table 1 for the full list of research questions. 14 Figure 1.1 Examples of impacts of woody riparian vegetation on stream ecosystems and streambanks. Woody Riparian VegetationIncreased soil stabilityOrganic matter inputsIncreased shading of streamReduced sedimentation and erosion Food and habitat for aquatic animals Maintains lower water temperatures Increased groundwater nutrient removal Restricts algae growth 15 Table 1.1 Research questions and response variables. Research Questions Response Measure Environment Prediction Do stream temperatures differ between restored and unrestored sites, and does the magnitude of the difference change with years since restoration? Water temperature In Stream Highest in degraded, lowest in reference Is percent canopy cover significantly different at restored sites versus control sites, and does the magnitude of difference vary with years since restoration? Percent canopy cover In Stream and Riparian Zone Highest in reference, lowest in degraded Does average tree size, basal area, tree density and tree species richness differ between control and restored sites, and does the difference vary with years since restoration? Diameter at breast height of all trees within 10 m of stream Riparian Zone DBH: highest in reference, lowest in restored; Basal area & richness: highest in reference lowest in restored; Density: Highest in restored lowest in degraded Do nutrient concentrations differ between control and restored sites, and does the difference vary with years since restoration? Nutrient concentrations (NO3, PO4, and SO4) In Stream Highest in degraded, lowest in reference Does stream velocity and flow heterogeneity differ between restored and unrestored sites, and does the magnitude of the difference vary with years since restoration? Stream width, riffle velocity, number of riffles and number of pools In Stream Highest heterogeneity in reference, lowest in degraded Do rates of decomposition of organic matter differ between restored and unrestored sites, and does this response change with years since restoration? Rate of decomposition of red alder (Alnus rubra) leaves In Stream Highest in reference, lowest in degraded 16 Research Questions Response Measure Environment Prediction Does periphyton production rate differ between restored sites and unrestored sites, and does the magnitude of difference vary with years since restoration? Periphyton production on ceramic tiles incubated in stream In Stream Highest in degraded, lowest in reference 17 2 Biological and Physical Indicators of Ecosystem Conditions 2.1 Introduction Biological and physical indicators, such as canopy cover, water temperatures, riparian forest composition, nutrient concentrations, and streambank erosion are commonly used to assess the ecological integrity of watersheds or the capacity of the watersheds to support native wildlife (Martens et al. 2019, Naiman et al. 1992). Each of these indicators have direct and indirect effects on stream ecosystem functions and animals living within the stream (Beechie et al. 2010, Tolkkinen et al. 2020). Canopy cover affects water temperatures, as shade from the canopy helps maintain lower water temperatures, and primary production, as it affects the amount of photosynthetically active radiation (PAR) reaching the plants, algae, and other microorganisms living in the stream (Martens et al. 2019, Moore et al. 2005, Naiman et al. 1992). Water temperatures affect the metabolisms of animals living in the streams and impacts timing of spawning and emergence (Naiman et al. 1992). Riparian forests create canopy cover and are responsible for significant organic matter inputs into streams in the forms of leaf litter and woody material (Beechie et al. 2010, Tolkkinen et al. 2020). Leaf litter provides food for animals in the stream. Large woody material creates habitat heterogeneity by causing log dams that slow flows and create pools (Allan et al. 2021, Beechie et al. 2010, Coe et al. 2009, Larson et al. 2001). The habitats created by large wood in streams are important for early life stages of salmonids and influence sediment transport by trapping gravels (Beechie et al. 2010, Martens et al. 2019, Pollock et al. 2012). The composition of the forest, such as percent deciduous species, impacts the quantity and quality of leaf litter inputs, and the seasonal variations in canopy cover (Richardson et al. 2004). Moreover, 18 riparian plants influence nutrient dynamics by absorbing nutrients before they enter the stream (Naiman et al. 1992, Naiman et al. 2005, Richardson et al. 2007, WWF 2020). High concentrations of nutrients can have negative impacts on stream organisms (Nessel et al. 2023, Woodward et al. 2012). Increased nutrients are associated with greater algal growth and higher turbidity (Coe et al. 2009, Murdock et al. 2004). Long term decreases in macroinvertebrate populations have been attributed to nutrient pollution (Nessel et al. 2023). Studies looking at how decomposition rates are affected by increased nutrients have had mixed results, some showing increases or decreases in decomposition rates and some showing no effect at all (Ferriera et al. 2015, Rosemond et al. 2015, Royer and Minshall 2001, Woodward et al. 2012). Salmonids and other fish lay eggs in the interstitial space in cobble substrate and many macroinvertebrates inhabit these spaces (Allan et al. 2021, Lapointe et al. 2004, McAdam 2011). Fine sediment increases turbidity and reduces interstitial space in substrate (Parkhill and Gulliver 2002). The roots of riparian plants help to stabilize the streambank, reducing the amount of fine sediment entering the stream (Allan et al. 2021, Martens et al. 2019, Tolkkinen et al. 2020). For this study, I compared canopy cover, water temperatures, riparian forest composition, nutrient concentrations, and stream velocity for all 17 sites except in some cases where data were lost or unavailable at certain sites. I predicted that percent canopy cover would be highest at reference sites and lowest at degraded sites, and that stream temperatures would follow the opposite trend, due to the role of canopy cover in maintaining lower stream temperatures (Martens et al. 2019, Naiman and Décamps 1997, Richardson et al. 2007, Wondzell et al. 2019). If old growth forests were used as reference sites, rather than second-growth forests, then I would predict lower canopy cover and greater canopy variance compared to the oldest restored sites, due to gap formation and root competition in mature forests (Martens et al. 2019, Warren et 19 al. 2013). I predicted that tree species richness would be highest in restored sites, due to the greater potential for restored sites to have invasive or introduced species, and lowest in degraded sites, due to the loss of woody riparian vegetation (Allan et al. 2021, USEPA 2016). I predicted that average DBH and basal area would be highest in reference sites, intermediate in degraded sites, and lowest in restored sites, due to the recently planted trees in restored sites. I predicted that nutrient concentrations would be highest in degraded sites and lowest in reference sites, given that degraded sites had fewer woody riparian plants that would reduce the amount of nutrients entering the streams (Naiman et al 2005, Richardson et al. 2007, WWF 2020). I predicted that stream velocity variance would be highest in restored sites and that the number of pools would be significantly higher in restored sites than degraded sites, given that additions of instream structures was another common restoration technique, and these structures are intended to increase the number of pools and increase flow heterogeneity (Allan et al. 2021, Beechie et al. 2010, Coe et al. 2009, Larson et al. 2001). For all of these variables, I predicted that restored sites would become more similar to references sites as years since restoration increased. 2.2 Methods 2.2.1 Sites Restoration sites were selected using the following criteria: 1) Riparian planting was a major component of the project; 2) The project had been completed more than 2 years prior to sampling, and details of the restoration practice(s) used were available; and 3) Restoration took place on both sides of the stream (except in one case where the other side was already protected). All projects were located within Metro Vancouver, BC, Canada. Reference and degraded sites were chosen based on visual assessments of riparian vegetation, stream habitat complexity, streambank erosion, and substrate. For example, riparian vegetation 20 was ranked based on the number and size of trees, as well as the presence of invasive species. Stream habitat complexity was assessed by the sinuosity of the stream and the number of pools and riffles. Streambank erosion was assessed based on evidence of recent erosion and incised banks. When I analyzed substrate, it was noted whether cobble and large boulders were present, or if substrate was largely covered by fine sediment. Degraded sites showed very poor conditions in these categories (i.e., few large trees, straight channels, incised banks), whereas reference sites showed superior conditions in these categories. Reference and degraded sites had not received any major restoration activities. However, a community group had removed invasive species from one of the reference sites (Hastings Creek), but no vegetation was added. For reference sites, the age since the last major disturbance is recorded in Table 2.1. Historic records of land use were used to determine these values. Only major land-clearing activities are included as major disturbances. Logging occurred in the Hastings Creek (HC) watershed throughout the early 1900s. The logging company that most recently logged in the area dissolved in 1926, so that is considered the date of the last major disturbance for the sake of this study (Kaher 1988). The last major land clearing around Third Branch Creek (TB) occurred in 1960 (Northcote and Luksun 1992). For Anderson Creek (AC) the records of when the last land clearing occurred are not clear: There was urban encroachment around the stream between 1966-1976 (Shupe, unpublished data). For the purposes of this study, 1975 is considered the date of the last major disturbance. See Appendix A for further details on the history of land clearing in reference sites. While care was taken to ensure that reference streams had relatively similar conditions, there were inherent differences based on the municipality of the reference site. For example, North Vancouver is mountainous whereas Surrey and Langley are relatively flat. The selected stream segments in North Vancouver were not in the mountains, and therefore there 21 were not major notable differences in stream slope (based on visual assessments), but the stream headwaters were in the mountains and therefore likely had steep slopes. Given that there were several restored sites in North Vancouver and Surrey/Langley, I thought it was important to include reference sites in both locations, despite these differences. At each site, a 100 m reach was selected (henceforth referred to as research reaches), except in the cases where the scope of the restoration project was less than 100 m (Table 2.1). For most sites, the 100 m reaches encompassed the entire restored area, or the longest contiguous restored area with flowing water. In cases where more than 100 m were restored, reaches were selected based on ease of accessibility, and to maximize representation of restoration techniques (i.e., to include both armored and unarmored banks). 2.2.2 Temperature data Water temperatures were recorded using HOBO data loggers. Loggers were placed in stream at the start of the summer decomposition experiment (12-21 July 2022) and collected when nutrient samples were collected (27 September 2022). A second set of HOBO data loggers was placed in each stream for the duration of the fall decomposition experiment (13 October – 18 November 2022). Summer temperature data were not available for 3 sites (EB, PR, and TB). Fall temperature data were not available for 3 sites (BA, EB, and SH) (Table 2.2). Temperature data are missing for sites where loggers were lost and sites that were not included in the decomposition study due to time constraints or study permit restrictions. Average monthly temperatures and maximum monthly temperatures were calculated and compared to BC Water Quality Standards for Aquatic Life. For streams with unknown fish distributions, the maximum mean weekly temperature standard is 18 °C and the maximum daily 22 temperature standard is 19 °C. During the fall, maximum incubation temperature standard is 12 °C (BC ENV 2001a). Average temperatures were compared to these standards. Daily temperature change was also calculated for summer loggers. Average daily temperature change for July-August was calculated. 2.2.3 Canopy cover Ten photos were taken at random locations (based on a random number generator) within the research reach using a fisheye lens. Photos were taken midstream, at breast height, facing upwards. Photos were then processed using Gap Light Analyzer v2 software (Simon Fraser University, Burnaby, BC, Canada) to determine canopy openness. Percent canopy cover was calculated as 100 minus percent canopy openness. Photos that included significant lens flares were excluded from analysis, so some sites had less than 10 canopy cover values. Photos were taken in August-September 2022, before significant leaf drop which usually occurs in October. Two sites were missed in the 2022 sampling and photos were taken June 2023. 2.2.4 Tree data Tree data were collected at all 17 sites. All trees within 10 m of the stream (both banks) within the research reach were identified to species and measured at breast height. Circumference (m) was recorded and used to calculate diameter at breast height. Trees that were dead but standing were measured and noted as dead. Short trees (≤ 0.5 m) were excluded. Tree species richness was calculated. Density (trees/ha) was calculated based on the total number of trees measured, divided by the area of the site in hectares. Basal area was calculated using the following formula: 𝐵𝑎𝑠𝑎𝑙 𝐴𝑟𝑒𝑎 =∑ 𝜋(𝑑𝑏ℎ2 )2𝑎𝑟𝑒𝑎 23 Where dbh = diameter at breast height (m), and area = total area samples (hectares) (modified from British Columbia Ministry of Forestry and Range 2009) 2.2.5 Nutrients Water samples were collected in 50 mL falcon tubes from 16 sites on 27 September 2022. EB was dry at the time of sampling, so nutrient concentration data were not available for that site. Samples were stored on ice and shipped to the BC Ministry of Environment Analytical Laboratory where they were analyzed for anion concentrations using standard methods. Detection limits for all nutrients were 100 µg/L. 2.2.6 Stream flow Wetted width of streams was measured on 12, 16, or 23 June 2023. Measurements were taken every 10 m, for a total of 10 width measurements per research reach. The starting point was determined by generating a random number between 0-10 (i.e., for the randomly generated number 7 measurements were taken at 7, 17, 27, etc. meters). Stream flow was measured in the nearest riffle using a velocity meter. All riffles and pools within the research reach were counted via visual inspection. Wetted width data and pool and riffle data were not available for 3 sites (BA, EB, and HC) because sites were inaccessible at time of sampling. Pool and riffle data were not available for 3 additional sites (AR, HI and MN) because water levels were too high to accurately determine pools or because portions of the research reach were inaccessible. 2.2.7 Statistical analysis All statistical analysis was carried out in R version 4.2.2. For all variables, averages were calculated per site. I compared averages per site rather than using site as a random variable 24 because I expected some degree of variation within sites and therefore wanted to compare the individual sites using one metric rather than focusing on variance among the sites. I was more interested in variance between treatments than variance within sites. For variables where the variation itself was of interest (i.e., canopy cover, where gaps in canopy can have a large effect) I included variation as a separate variable in additional statistical analysis. A Shapiro-Wilk test was used to test for normality of all variables, using the olsrr package in R (Hebbali 2020). In cases where variables were not normally distributed, values were log transformed, natural log transformed, or square root transformed and tested for normality again. When variables were normally distributed, site averages were compared using one-way analysis of variance (ANOVA) based on treatment. When ANOVA tests yielded significant results, Tukey’s honestly significant difference test was used to determine which variables were significantly different. When variables were not normally distributed, even after transformation, Kruskal-Wallis test was used instead of ANOVA. Results with a p-value <0.05 were considered significant, whereas results with a p-value <0.1 were described as marginally significant. To determine differences over time, regression analysis was used with years since restoration (for restored sites) or years since disturbance (for reference and degraded sites). Linear regression was the first regression analysis used. In cases where linear regression was not significant or variables were expected to level off, asymptotic regression was used as well, and p-values for the regression analyses were compared. Biological and physical indicator variables were then input into Factor Analysis of Mixed Data (FAMD) using the FactoMineR package (Lê 2008). The intention of the FAMD was to determine if there was clustering by treatment that was not detected via the other means of analysis (ANOVA, regression) and to determine if certain variables were highly related. Determining if certain variables were highly related would help to narrow down which biological 25 and physical indicators would later be used as covariates in models of ecosystem functions. FAMD was used in order to incorporate both quantitative variables and categorical variables. Supplementary variables, which did not affect the outcome of the analysis, were also included, so that the relationship between these variables and the principal components could be analyzed without creating false clustering (e.g., including age as a primary variable could cause clustering by treatment, so age is included as a supplementary variable). Municipality was the only categorical variable. Quantitative variables in the model included basal area (m2/hectare), percent canopy cover, canopy cover variance, tree species richness, percent deciduous species, average DBH (cm), average summer daily temperature change (°C), watershed area (square hectares), wetted width (m), average riffle velocity (m/s), NO3 concentration (µg/L), PO4 concentrations (µg/L), and SO4 concentration (µg/L). Treatment and years since last major disturbance or restoration were included as supplementary variables. Missing data were handled using the missMDA package, which imputes predicted values to minimize effects on analysis (Josse and Husson 2016). Finally, the factoextra package was used to visualize data (Kassambara and Mundt 2020). 2.3 Results 2.3.1 Temperature data Stream temperatures were high from July – September, with a peak value of 22.2 °C in July. In the summer, average monthly temperatures exceeded BC Water Quality maximum weekly mean temperature standards for streams with unknown fish distribution (18 °C) in 3 streams (HI, CC, and MQ). Maximum temperatures exceeded BC Water Quality maximum daily temperatures for fish with unknown fish distribution (19 °C) in 11 streams. 26 In the fall, average monthly temperatures exceeded BC Water Quality Standard maximum incubation temperature (12 °C) in 5 streams (Table 2.2). Maximum monthly temperatures exceeded water quality standards for incubation in 11 streams. When daily temperature changes were compared by treatment, there was no significant difference between treatments (ANOVA, F2,11 = 0.19, p = 0.83). There was no significant relation between average daily temperature change and canopy cover (regression, R2 = 0.012, p = 0.71). 2.3.2 Canopy cover Percent canopy cover showed a nonlinear relationship with years since disturbance or restoration (Figure 2.2). Percent canopy cover was highest in reference sites (mean = 79.38 ± 10.62 [SD]), lowest in degraded sites (53.47 ± 43.95), and intermediate in restored sites (74.47 ± 16.69) (Figure 2.3). Percent canopy cover values were not normally distributed, based on Shapiro-Wilk test results (p < 0.001), and were still not normally distributed after natural log transformation or square root transformation. The difference between treatments was not significant (Kruskal-Wallis, Χ2 = 1.46, dfx = 2, p = 0.48). When comparing canopy cover between age classes of restored sites (2-5 years since restoration, 6-10 years since restoration, 10+ years since restoration), the most recently restored sites had the lowest percent canopy cover (66.86 ± 18.00), sites restored 6-10 years ago had the highest percent canopy cover (86.81 ± 1.43) and the oldest restored sites had intermediate percent canopy cover (85.56 ± 3.80) though the difference between groups was not significant (Kruskal-Wallis, chi-squared = 1.67, df = 2, p = 0.43) (Figure 2.4). Canopy cover values in restored sites ranged from very low coverage (36.6% at AR, a site experiencing erosion and beaver issues) to higher coverage than any of the reference sites (88.9% at ST). 27 There was a high degree of inter-site variability in canopy cover values in the degraded sites. MN was overgrown with invasive Himalayan blackberry (Rubus armeniacus) brambles throughout the research reach and had an average canopy cover of 84.6 (± 7.0). The other degraded site, HI, had much lower average canopy cover, and much greater variation (22.4% ± 20.0). 2.3.3 Tree data Species richness was highest in restored sites (9.33 ± 2.99), intermediate in reference sites (8 ± 3.61) and lowest in degraded sites (3.00 ± 1.41) (Figure 2.5). Species richness values were significantly different (ANOVA, F2, 14 = 3.83, p = 0.047) and post-hoc Tukey test determined the significant difference was between restored and degraded sites (p = 0.039) (Figure 2.5). Restored sites had the greatest tree density (1258 ± 1148 trees/ha) (Figure 2.6) and intermediate basal area (31.7 ± 23.2 m2/ha) (Figure 2.7); reference sites had intermediate tree density (665 ± 509 trees/ha) and highest basal area (49.00 ± 18.9 m2/ha); degraded sites had the lowest tree density (85 ± 14.1 trees/ha) and lowest basal area (5.46 ± 6.81 m2/ha). Tree density was not normally distributed (Shapiro-Wilk’s test, p = 0.001), so values were log-transformed. Log-transformed density values were significantly different (ANOVA, F2,14 = 6.7, p = 0.009) (Figure 2.6). Basal area was not significantly different (ANOVA, F2, 14 = 2.38, p = 0.13) (Figure 2.7). DBH values were not significantly different (restored =12.73 cm ± 5.56, degraded = 20.09 cm ± 14.31, reference = 20.26 cm ± 7.80, ANOVA, F2, 14 = 2.07, p = 0.16). Basal area was modelled as a function of age via linear regression and via asymptotic regression. Model fits were compared using Akaike Information Criterion (AIC) and the asymptotic regression was found to be a better fit, based on the lower AIC score (Figure 2.8). 28 When analyzing restored sites on their own, both tree species richness (Figure 2.9) (R2 = 0.32, df = 10, p = 0.056) and average DBH (cm) (Figure 2.10) (R2 = 0.53, df = 10, p < 0.001) increased with years since restoration. However, the relationship between basal area (m2/ha) and years since restoration was not significant (R2 = 0.12, df = 10, p = 0.27) (Figure 2.11). Basal area and canopy cover were not significantly related (R2 = 0.14, p = 0.13) (Figure 2.12). 2.3.4 Nutrients Nutrient concentrations were highly variable between sites (Table 2.3). There was no significant difference by treatment for NO3, PO4, or SO4 concentrations (ANOVA). NO2 was below the detectable limit for all samples. Reference sites had the highest average concentrations of NO3 (5.331 mg/L ± 3.460) and SO4 (13.658 mg/L ± 6.590), but concentrations of PO4 were below detection limits (0.100 mg/L) at all reference sites. Degraded sites had the lowest average concentrations of NO3 (1.908 mg/L ± 0.033) and SO4 (9.198 mg/L ± 8.087), and concentrations of PO4 were below detection limits. For the 6 restored sites where PO4 levels were above detectable limits, the average concentration was 0.309 mg/L ± 0.076. NO3 and SO4 concentrations had high variability at restored sites (2.510 mg/L ± 1.917 and 10.680 mg/L ± 7.102, respectively). When comparing restored sites by age class, there were no significant differences in nutrient concentrations. When comparing nutrient concentrations for all sites based on municipality, there was no significant difference for NO3 or PO4 (ANOVA, F4, 11 = 0.88, p = 0.51, and F2, 4 = 0.94, p = 0.46, respectively). However, SO4 values were significantly different based on municipality (ANOVA, F4, 11 = 5.11, p = 0.014). Concentrations of SO4 in Langley sites (20.625 mg/L ± 0.366) was significantly higher (p = 0.007) than North Vancouver streams (4.864 mg/L ± 1.659). Concentrations of SO4 were also marginally significantly higher (p = 0.072) in Langley than in Surrey (10.679 µg/L ± 6.78). 29 For SO4, water quality guidelines are dependent on the hardness of the water (CaCO3 concentration), which was not measured in this study (BC ENV 2021). For NO3, all of the values were below the BC Water Quality Standards for Aquatic Life Short-Term Acute Exposure. However, 5 sites (WB, PR, SH, AC, and TB) had values above the BC Water Quality Standards for Aquatic Life Long-Term Chronic Exposure (British Columbia Ministry of Environment and Climate Change Strategy 2021) (Table 2.3). BC Ministry of Environment defines chronic exposure as a 30-day average, based on 5 or more samples. More samples would therefore be necessary to determine if values chronically exceeded water quality standards. 2.3.5 Stream velocity Riffle velocity was not significantly different by treatment. The variance (standard deviation) in riffle velocity was not significantly different but was slightly higher in restored sites (SD = 0.26 m/s) compared to reference sites (SD = 0.20 m/s) and degraded sites (SD = 0.10 m/s) (Table 2.4). There was no significant change in riffle velocity variance with years since restoration, based on linear regression analysis. The number of pools and riffles were not significantly different by treatment. When comparing restored sites on their own, there was a significant difference in the number of riffles (ANOVA F2, 5 = 7.13, p = 0.03) between age classes. Post-hoc Tukey test found that the most recently restored sites had significantly more riffles than the sites restored more than 10 years prior (p = 0.03). 2.3.6 Factor analysis of mixed data The factors included in the FAMD were municipality, basal area, canopy cover, canopy variance, tree species richness, percent deciduous trees, watershed area, stream width, velocity, and nutrient concentrations. Treatment and years since last major disturbance or restoration were included as supplemental variables so they did not affect the outcome of the analysis but could 30 be plotted to show where they would fit with other variables. The FAMD results showed that Dimensions 1 and 2 only collectively explained 40.5% of variance (Figure 2.13). Therefore, patterns among variables were not very clear. The main contributing variables for Dimension 1 were canopy cover, SO4, watershed area, municipality, canopy variance, DBH and tree species richness. Canopy cover and tree species richness were negatively associated with the first dimension, while SO4, watershed area, canopy variance and DBH were positively associated (Figure 2.13). Contributing variables for Dimension 2 were PO4, stream width, municipality, summer daily temperature change, basal area, and NO3. PO4, summer daily temperature change, and NO3 were positively associated with the second dimension, while stream width and basal area were negatively associated (Figure 2.14). For the tree variables, canopy cover, species richness, and percent deciduous species were positively associated with each other, but negatively associated with DBH and canopy variance (Figure 2.14). Basal area was not associated with any of the other tree variables, but age and basal area were closely, positively associated, as seen by the proximity of the basal area and age lines in Figure 2.13. Watershed area and stream width were positively associated, but watershed area and velocity were negatively associated. Summer daily temperature change was positively associated with all three nutrient variables. Basal area and summer daily temperature change were negatively associated, but canopy cover and summer daily temperature change were not associated (Figure 2.14). The representation of the FAMD in dimensions 1 and 2 is indicated by the cos2 of individual sites plot (Figure 2.15). The two dimensions explain well the reference sites TC and AC, but do not explain well the reference site TB (Table 2.1). The two dimensions explain well degraded site HI and do not explain well degraded site MN. Restored sites had variable cos2 values, 31 indicating that some restored sites were well represented but others were not. There was no clear trend between age group and cos2 values. Plots of individual sites along dimensions 1 and 2 did not show clustering by treatment (Figure 2.16). 2.4 Discussion 2.4.1 Overview There was a high degree of variability across all sites. While some variability between watersheds is common (Naiman et al. 1992), it is possible that more significant differences would have been detected had there been a greater number of degraded and reference sites. Finding sites that had not received any kind of restoration was a major challenge when designing this study. Furthermore, historical and current land uses between the districts in this study (Burnaby, Port Moody, North Vancouver, Surrey, and Langley) impacted the physical and biological variables in the streams. For example, Langley and Surrey are centers of agriculture in the region and, therefore, have a greater history of nutrient pollution and land clearing for agriculture (Boyle et al. 1997, Shupe 2017). It should be noted that the year before the study began, there was a record-setting heatwave in the summer followed by historical flooding in November (Government of Canada 2021). This combination of major weather events in the months prior likely impacted many of the physical and biological variables measured. In particular, there were signs of recent damage at reference site AC. There were many recently fallen trees, evident from the dying leaves still present on the branches of the fallen trunks. If these trees had not fallen, basal area and canopy cover values would have been higher at this site. AC had the lowest basal area and canopy cover values of the three reference sites, so the differences between the reference sites and the other treatments might have been more pronounced if it were not for the recently fallen trees. 32 2.4.2 Temperature data Temperatures were compared to BC Water Quality Standards for unknown fish distribution, as fish were not surveyed for this study. However, salmonids were observed at 6 sites throughout the field work (HI, AR, SH, ST, GB, and AC). While I predicted that average daily temperature change would be negatively related to canopy cover, I found no significant relationship between percent canopy cover and average daily temperature change. My results were not in congruence with available literature on the relationship between canopy cover and stream temperature. Bower et al. (2012) conducted a meta-analysis of studies looking at effects of riparian vegetation on stream temperatures and found that average and maximum temperatures were lower in streams with higher canopy cover. Wondzell et al. (2019) used the Heat Source stream temperature model to predict potential stream temperature changes based on different climate change scenarios: changes in canopy cover, +4 °C change in air temperature, and -30% change in stream discharge. The results of the model indicated that differences in canopy cover had the greatest impact on predicted stream temperatures (Wondzell et al. 2019). 2.4.3 Canopy cover All sites restored 6+ years prior to the onset of this study had canopy cover values comparable to reference sites. Other studies in this region comparing canopy cover values in reference and restored sites have found similar trends. A study conducted in the Olympic Peninsula, WA, USA found that passively restored riparian forests had higher percent canopy cover than reference forests after 18 years of passive restoration (Martens et al. 2019). Given that the sites in my study were actively restored rather than passively, one would expect faster increases in canopy cover. It should be noted that canopy cover values in the passively restored sites in Martens and 33 colleagues’ (2019) study (mean = 95%) were higher than canopy cover values at my restored sites (mean = 74.5%, mean for sites restored 6+ years prior = 86.4%). However, reference canopy cover values in Martens et al. (2019) were the same as reference canopy cover values in this study (79% and 79.4%). Warren and colleagues (2013) compared canopy coverage and its variance in second-growth forests (aged 40-60 years) to old growth forests in Oregon, US. Second-growth forests in their study had measured canopy cover values between 87.7% - 93.5%. While the canopy cover values in their study were greater than canopy cover values of second-growth reference forests in this study, the canopy variance (standard deviation) values were similar (2.3 - 6.3 in this study vs. 3.1 - 6.6). Old-growth forests had canopy cover values between 77.9% - 89.1%, which was similar to the canopy cover values in my restored sites when sites restored <6 years prior were excluded (Warren et al. 2013). The differences in canopy cover values in the two degraded sites (MN = 84.6% ± 7.0 vs. HI = 22.4% ± 20.0). highlights the difficulties in comparing degraded sites, particularly when additional variables (i.e., plant species) are not accounted for. For example, while Himalayan blackberry provides dense shade, that is often at the expense of biodiversity. The presence of Himalayan blackberry may also increase rates of erosion as it outcompetes species with larger root systems that are more adept at preventing erosion (Gaire et al. 2015, ISCBC 2014). 2.4.4 Tree data Tree species richness was highest in restored sites; a recent study by Burgess et al. (2023) in the Pacific Northwest that compared restored sites to paired control sites found similar results, where restored sites had higher plant species richness. However, the magnitude of difference in my 34 study (3x higher) was much higher than the magnitude of difference in theirs (1.3x higher). It should be noted that the paired controlled sites represented pre-restoration conditions, rather than reference conditions (Burgess et al. 2023). Higher tree species richness in restored sites compared to reference sites could be due to the presence of some non-native tree species in restored sites (e.g., European Mountain Ash [Sorbus aucuparia] and Japanese Maple [Acer palmatum]), or related to the intermediate disturbance hypothesis, which states that species diversity is highest at intermediate levels of disturbance (Connell 1978). Viers et al. (2012) analyzed native riparian plant diversity at multiple scales in California and found highest woody riparian plant diversity in moderately disturbed sites, consistent with the intermediate disturbance hypothesis. The same study highlighted the need to focus both on fine-scale species diversity patterns and landscape-scale species diversity patterns when planning for restoration projects (Viers et al. 2012). The higher species diversity at restored sites could therefore also be due to planting plans focused on region-wide species diversity, including plants that would not normally be found within the same small area. Tree density was also highest in restored sites, though the difference between restored and reference sites was not significant. Lennox et al. (2011) analyzed long-term riparian restoration outcomes in northern California and found that woody plant density followed a polynomial trend, with a peak density around 18-25 years. While the highest tree density occurred at sites restored 5 – 10 years prior in my study, the higher density at restored sites could be a sign that tree density has not hit the peak value yet, after which it will decline. Based on the differences in values between age classes and the high variability in values, I believe the tree density had not yet reached a stable state. 35 Basal area was not significantly different between treatments. Burgess and colleagues (2023) measured tree cover for each individual tree (not basal area directly), and also found no significant difference between restored and control sites. The high variability for basal area is likely one reason there was no significant difference between treatments. As previously stated, one of the reference sites (AC) had degraded vegetation, possibly due to harsh conditions the previous year, and the low basal area at AC contributed to the variability of reference sites. Pollock and colleagues (2012) sought to define reference conditions for riparian restoration projects in the Pacific Northwest and found an average basal area of 88.4 m2/ha (± 20.3) for their defined reference conditions. The reference sites in this study had basal areas of 27.2 m2/ha (AC), 59.6 m2/ha (HC) and 60.4 m2/ha (TB). Therefore, none of the reference sites in this study met the reference conditions defined by Pollock et al. (2012). However, it should be noted that the forests used to define reference conditions by Pollock et al. (2012) were 80-200 years old, whereas the forests in my study were 46-96 years old, because older second-growth forests were not available in this region. Reference conditions for tree density was defined as 372.8 (± 97.3) live trees per hectare (Pollock et al. 2012). Density for reference sites in this study ranged from 220 – 1220 trees per hectare, again possibly due to differences in age, as tree density tends to be lower in old-growth forests (Martens et al. 2019, Warren et al. 2013). For restored sites, different planting prescriptions influenced differences in tree density and basal area. At many sites, more willows (Salix spp.), dogwoods (Cornaceae spp.) and other shrubs were planted than tree species. Given that shrubs were not included in tree measurements, the tree density and the basal area values were not representative of the total woody vegetation, and this underrepresentation was especially pronounced at restored sites where shrubs were planted in greater abundance than trees. The number of shrubs is also likely a reason that the R2 36 value was so low for the linear regression between basal area and canopy cover. Willow dogwood live stakes were planted along the stream bank of many restored sites, where they have a greater influence on canopy cover. 2.4.5 Nutrients Nutrient concentrations were high at the time samples were collected, which was at the end of the dry season, when water levels were lowest. The month following nutrient sample collection was also unusually dry, with little rainfall until mid-October. Therefore, nutrient concentrations would not have been diluted with increased streamflow until after the start of the fall decomposition experiment. When compared to regional nutrient data concentrations were extremely high. For example, dissolved nitrate/nitrite has been sampled monthly in the Fraser River since 2008, and the concentration has never exceeded 0.400 mg/L, whereas NO3 concentrations at 5 sites in this study (AC, WB, PR, SH, and TB) were an order of magnitude greater than that (Table 2.3) (Environment Canada 2023). The Fraser River is not directly comparable to the streams in this study, as it is a major river rather than a small stream, but many of the streams in this study ultimately enter the Fraser River. However, for comparison, a study in the Okanagan region of British Columbia compared nitrate concentrations in small tributaries to the main stem downstream of confluences and found a relationship between 0.6:1 and 1.8:1 for concentrations in the tributary versus main stem (Tavernini and Richardson 2020). Therefore, such a large difference between rivers and tributaries is not expected. Additionally, nitrate and nitrite concentrations were monitored monthly in the North Alouette River in Maple Ridge, British Columbia from 2005 to 2014, and concentrations never exceeded 0.350 mg/L (BC ENV 2015). 37 Historical and current land uses likely had an impact on nutrient concentrations. Langley sites had significantly higher concentrations of SO4 (mean = 20.625 mg/L ± 0.366), and the highest concentrations of NO3 (4.195 mg/L ± 4.053), which was possibly caused by nutrient runoff from current agricultural practices in the region, particularly if ammonium sulphate was being used as a fertilizer (Boyle et al. 1997, BC ENV 2008, Shupe 2017). In addition to increasing nutrient concentrations via direct runoff, agriculture can reduce denitrification rates, thereby reducing nitrogen removal in streams. Agricultural practices can deplete soil of organic carbon, which provides an energy source for the microbes involved in denitrification (Lammers and Bledsoe 2017, Watson et al. 2010). While I hypothesized that nutrient concentrations in restored sites would become more similar to reference conditions as age since restoration increased, previous studies analyzing effects of stream restoration on nutrient dynamics have shown mixed results. A review by Palmer and colleagues (2014) reported 88% success rate for riparian restoration projects improving nutrient processes. Conversely, a more recent review reported that riparian restoration can have inconsistent effects on nutrient dynamics (Lammers and Bledsoe 2017). Riparian buffers can be net phosphorous sources, due to increases in groundwater phosphorous associated with iron reduction occurring in vegetated soil. However, bank stabilization can reduce phosphorous inputs if streambank phosphorous concentrations are high (Lammers and Bledsoe 2017). Therefore, if reducing phosphorous concentrations is a restoration goal, it would be useful to first determine if streambank erosion and high riparian soil phosphorous concentrations are a contributing factor. While riparian buffers may increase phosphorous concentrations, riparian buffers have been shown to be effective in reducing nitrate. Mayer et al. (2005) reported results of 22 studies where forested riparian buffers were able to remove 58-100% of nitrogen influent 38 (mean = 90% removal). Furthermore, nitrogen removal rates generally increase as stand age increases (King et al 2016, Lammer and Bledsoe 2017). Therefore it is important to conduct long-term monitoring to determine effectiveness of riparian restoration in nitrogen removal. 2.4.6 Stream velocity I was not able to test my hypothesis that restored sites would have more pools than degraded sites, due to difficulties accessing and counting the pools in degraded sites. However, the fact that more recently restored sites had significantly more riffles than sites restored 10+ years ago could support the critique that several papers have made (i.e., Beechie et al. 2010) that instream habitat modifications may not be long lasting. Alternatively, the difference in the number of riffles between older restored sites and more recently restored sites could be due to changes in restoration practices over time. It should be noted that instream habitat modifications were not the primary subject of this study, and therefore additional studies directly comparing instream habitat modifications would be necessary to give further evidence for these findings. 2.4.7 Compiled analysis of environmental gradients The FAMD showed some expected trends, while some were unexpected. For example, the clustering of tree variables was expected but basal area was not related to other tree variables, which was unexpected. The lack of clustering by treatment was also unexpected as variables were hypothesized to vary by treatment. However, two of the variables that contributed strongly to both dimensions, municipality and watershed area, are things that cannot be affected by restoration (Figure 2.13). The strong influence of these two variables could indicate variation was more related to locations than to treatments. Had it been possible to find enough restored, reference, and degraded sites within the same municipality, then there might have been more clustering by treatment. For example, Maztek et al. (2016) were able to conduct a 39 chronosequence study in different riparian areas along the same waterbody, the Sacremento River. Restricting the study to one waterbody or one watershed would have reduced the inter-site variability, but such a study was not possible in this region. To my knowledge, at the time of writing this, another study using FAMD or Principal Component Analysis (PCA) to assess outcomes of stream restoration projects in the Pacific Northwest region has not been published. However, a study in Virginia used PCA to analyze biological (e.g., EPT taxa richness) and physical (e.g., dissolved oxygen) variables as well as ecosystem functions (e.g., ecosystem metabolism) in restored and unrestored streams in a coal mining region (Northington et al. 2011). They did not find any significant differences between treatments for the three principal components derived from the PCA despite the three components collectively explaining 87% of variation (Northington et al. 2011). The fact that they did not find signficant differences between treatments, despite their principal components explaining twice as much of the variation as my principal components, indicates potential for difficulty in detecting restoration treatment effects between different streams via PCA or FAMD. This type of analysis might be better suited during restoration planning to determine which degraded conditions are correlated and, therefore, what restoration actions would be most impactful for the specific restoration objectives. For example, the FAMD results showed a strong positive relation between canopy cover and riparian tree diversity. Therefore, if increasing canopy cover were a restoration objective, then I would recommend planting a diverse mix of riparian trees. Furthermore, if pre-restoration monitoring included studies on ecosystem functions (i.e., primary productivity), then the FAMD analysis could help to distinguish the correlary strength of canopy cover as compared with that of nutrients to determine which variable to prioritize (i.e., if primary productivity were strongly negatively related with canopy 40 cover, but not related with nutrients, then focusing on canopy cover would improve restoration outcomes). In summary, FAMD results were useful in detecting relationships between a large number of variables but were unable to detect differences by treatment. Therefore, FAMD might be a useful tool for restoration planning, but might not be suited for analyzing restoration outcomes. 41 Figure 2.1 Map of research sites, located throughout Metro Vancouver, BC, Canada. Marker dots are not to scale. 42 Figure 2.2 Average percent canopy cover in research reaches (n = 10 ± 6 per site). Error bars represent standard deviations. Photos used for analysis were taken between August – September 2022, or June 2023. Age represents years since restoration for restored sites, or years since last major disturbance for reference sites. 43 Figure 2.3 Box plots of percent canopy cover by treatment. Bars represent median value. Vertical lines represent range, excluding outliers. Dots represent outliers. Canopy cover represents the amount area above the stream that is shaded. Canopy cover was measured by taking upward fisheye photos in the streams that were analyzed in Gap Light Analyzer software. Photos used for canopy cover analysis were taken between August – September 2022, or June 2023. 44 Figure 2.4 Average percent canopy cover (n = 10 ± 6 per site) between restored sites, based on age class (n = 6 for <6 years, n = 4 for 6-10, n = 2 for 10+). Photos used for canopy cover analysis were taken between August – September 2022, or in June 2023. 45 Figure 2.5 Tree species richness within 10 m of research reach on both banks. Willows (Salix spp.) and other shrubs species were not included. Trees were surveyed between June-August 2022. Letters signify significant differences based on Tukey’s honestly significant difference test (p = 0.039). 46 Figure 2.6 Tree density (trees/hectare) within 10 m of research reach on both banks. Willows (Salix spp.) and other shrubs species were not included. Trees were surveyed between June-August 2022. Letters signify significant differences based on Tukey’s honestly significant difference test (p = 0.007 for degraded-restored and 0.08 for degraded-reference). 47 Figure 2.7 Comparison of basal area (m2/hectare) values by treatment. Differences between treatments were not significant. Trees within 10 m of research reach on both banks were measured for DBH, and then total basal area was calculated based on the DBH values. Willow (Salix spp.) and other shrub species were not included. Trees were surveyed between June-August 2022. 48 Figure 2.8 Basal area (m2/hectare) over time for all sites. Trees within 10 m of research reach on both banks were measured for DBH, and then total basal area was calculated based on the DBH values. Willows (Salix spp.) and other shrub species were not included. Trees were surveyed between June-August 2022. 49 Figure 2.9 Tree species richness in restored sites over time. Tree species richness was measured within 10 m of research reach on both banks. Willows (Salix spp.) and other shrubs species were not included. Trees were surveyed between June-August 2022. 50 Figure 2.10 Average diameter (± SD) at breast height (cm) versus year since restoration for all restored sites. Trees within 10 m of research reach on both banks were measured for DBH. Willows (Salix spp.) and other shrubs species were not included. Measurements were taken between June-August 2022. 51 Figure 2.11 Basal area (m2/hectare) (± SD) versus years since restoration for all restored sites. Basal area measurements based off DBH (cm) measurements of all trees within 10 m of research reach, on both banks (n = 12). Willows (Salix spp.) and other shrubs were not included, due to difficulty measuring DBH with multiple trunks. DBH measured between June-August 2022. 52 Figure 2.12 Percent canopy cover (± SD) versus basal area (cm2/hectare) for all 17 sites. Basal area measurements based on DBH (cm) measurements of all trees within 10 m of research reach, on both banks (n = 12). Willows (Salix spp.) and other shrubs were not included, due to difficulty measuring DBH with multiple trunks. DBH measured between June-August 2022. Canopy cover measurements based off photos taken with fish-eye lens. Canopy cover photos were taken between August-September 2022, or in June 2023. 53 Figure 2.13 Factor Analysis of Mixed Variables graph of quantitative variables. Length of line represents how well variables are explained by dimensions. Variables that are close together (< 90°) are positively related, whereas variables that are far apart (> 90°) are negatively related. Variables that are approximately 90° apart are unrelated. Missing data were imputed using missMDA package in R. Blue color represents supplementary variable. 54 Figure 2.14 Factor Analysis of Mixed Variables contributions of variables. Colors represent combined contribution to both dimensions. 55 Figure 2.15 Cos2 values for each site in Factor Analysis of Mixed Variables. High cos2 values indicate that sites are well represented by both dimensions. 56 Figure 2.16 Factor Analysis of Mixed Variables individual sites clustered by treatment. 57 Table 2.1 Research sites. Site refers to the name of the stream. Abbreviation refers to the names by which sites will be referred. Year restored refers to the year that the restoration project began. In cases where ranges or multiple dates were listed, restoration projects had multiple phases or continuous restoration practices. For restored sites, age refers to the years since restoration commenced at the time of research. For degraded sites, age is 0 to represent degraded conditions. For reference sites, age represents the years since the last major disturbance. Site Abbreviation Treatment Year Restored Years Since Restoration or Last Major Disturbance Municipality Research Reach Length (m) Highland Creek HI Degraded NA 0 Surrey 100 Mahood Creek MN Degraded NA 0 Surrey 100 Wills Brook Tributary WB Restored 2020 2 Surrey 85 Thames Creek (Evergreen Basin) EB Restored 2018 4 North Vancouver 70 McNair Basin MB Restored 2018 4 North Vancouver 50 Price Creek PR Restored 2018 4 Surrey 100 Little Campbell River AR Restored 2017 5 Surrey 100 Bon Accord Creek BA Restored 2017 5 Surrey 100 Schoolhouse Creek SH Restored 2016 6 Port Moody 100 Suter Brook Creek Tributary ST Restored 2015 7 Port Moody 100 Cougar Creek CC Restored 2014 8 Surrey 100 58 Site Abbreviation Treatment Year Restored Years Since Restoration or Last Major Disturbance Municipality Research Reach Length (m) Taylor Creek TC Restored 2014 8 North Vancouver 100 Gordon Brook GB Restored 2001-2010 21 Langley 100 Mosquito Creek MQ Restored 1997, 2003 25 North Vancouver 100 Anderson Creek AC Reference NA 46 Langley 100 Third Beach Creek TB Reference NA 62 Burnaby 100 Hastings Creek HC Reference NA 96 North Vancouver 100 59 Table 2.2 Average and maximum stream temperatures recorded by HOBO data loggers in riffles. All temperatures are ± 0.3 °C. Loggers were placed between 12-21 July 2022 and collected 27 September 2022. A second set of loggers was deployed between 13-14 October 2022 and collected between 24-25 November 2022. ND indicates that a logger was lost, and no data were available. Bold indicates values above water quality standards for streams with unknown fish distribution. Site Treatment Jul Avg °C Jul Max °C Aug Avg °C Aug Max °C Sep Avg °C Sep Max °C Oct Avg °C Oct Max °C Nov Avg °C Nov Max °C HI Degraded 18.7 22.2 19.0 21.3 16.1 19.8 12.0 13.7 7.5 11.9 MN Degraded 17.0 22.1 17.7 21.2 15.7 21.4 14.5 16.6 11.7 14.5 WB Restored 16.5 20.0 17.1 19.3 15.5 18.2 12.5 14.6 8.8 12.6 EB Restored 16.4 20.7 17.4 21.6 15.5 19.1 11.7 15.0 7.5 10.0 PR Restored ND ND ND ND ND ND 12.2 15.2 8.7 11.6 AR Restored 16.1 20.0 16.0 18.4 13.9 16.6 10.8 12.2 6.0 10.4 BA Restored 12.5 15.4 12.6 15.7 11.7 14.2 ND ND ND ND SH Restored 15.7 18.1 15.7 17.5 13.9 16.6 ND ND ND ND ST Restored 16.7 19.9 16.8 19.2 14.6 18.1 11.3 13.1 7.1 10.4 CC Restored 18.0 22.1 18.6 21.2 16.2 20.2 12.2 15.1 7.7 11.6 TC Restored 16.3 19.2 17.2 18.7 15.8 19.5 12.4 15.1 7.4 10.7 GB Restored 17.5 21.0 17.0 19.7 14.1 17.5 9.9 11.4 4.7 9.7 MQ Restored 16.8 20.9 18.0 20.3 15.5 19.5 ND ND ND ND AC Reference 12.3 14.6 12.1 13.9 11.4 12.9 10.6 11.4 7.3 10.2 TB Reference ND ND ND ND ND ND 12.0 13.4 8.9 11.7 HC Reference 17.7 20.2 17.5 20.0 15.4 18.9 11.4 14.3 7.0 9.9 60 Table 2.3 Nutrient concentrations from water samples collected September 27, 2022, at all sites, during low flow. Concentrations below detection limits (<0.100 mg/L) are signified as <DL. Bold numbers indicate values above the BC Water Quality standards for chronic exposure for aquatic life. Site Treatment NO3 (mg/L) PO4 (mg/L) SO4 (mg/L) HI Degraded 1.931 <DL 14.916 MN Degraded 1.884 0.232 3.479 WB Restored 6.908 0.339 18.271 EB Restored 1.448 <DL 4.403 PR Restored 4.798 0.287 14.905 AR Restored 2.446 <DL 20.735 BA Restored 1.664 0.201 11.020 SH Restored 4.022 <DL 8.071 ST Restored 1.572 0.250 9.649 CC Restored 1.275 0.388 1.486 TC Restored 0.845 <DL 3.043 GB Restored 1.310 0.389 20.924 MQ Restored 1.320 <DL 4.972 AC Reference 8.829 <DL 20.216 TB Reference 5.252 <DL 13.723 HC Reference 1.911 <DL 7.036 61 Table 2.4 Wetted width (m), riffle velocity (m/s), and number of pools and riffles for 14 of the 17 research sites. ND represents no data. Pools and riffles could not be counted at MN due to blackberry brambles blocking access to stream. Pools and riffles could not not be measured at HI and AR due to exceptionally high water levels. At MQ riffles were contiguous throughout the reach without discernable beginnings and ends, so riffles are counted as ALL; all pools at MQ were side pools. Only one riffle velocity measurement was taken at HI and AR, so no standard deviation values are available. N = 10 ± 1 for all other width and velocity measurements. Measurements were taken on June 12, 16, and 23, 2023. Site Treatment Average Width (m) (SD) Average Riffle Velocity (m/s) (SD) Pools Riffles HI Degraded 4.64 (1.18) 0.98 (ND) ND ND MN Degraded 1.41 (0.33) 0.21 (0.10) ND ND WB Restored 0.77 (0.30) 0.28 (0.09) 23 22 EB Restored 2.12 (0.77) 0.39 (0.16) 15 15 PR Restored 1.62 (0.64) 0.62 (0.38) 20 19 AR Restored 6.50 (1.00) 0.48 (ND) ND ND SH Restored 2.55 (0.71) 0.99 (0.45) 1 7 ST Restored 1.66 (0.74) 0.71 (0.25) 7 13 CC Restored 1.34 (0.84) 0.28 (0.12) 17 15 GB Restored 3.05 (1.26) 0.61 (0.33) 14 8 MQ Restored 7.94 (1.37) 1.11 (0.26) 7 ALL AC Reference 4.35 (1.12) 0.63 (0.21) 10 10 TB Reference 1.81 (0.39) 0.85 (0.27) 5 18 TC Reference 2.08 (0.67) 0.41 (0.12) 7 11 62 3 Ecosystem Functions 3.1 Introduction There is a growing body of literature highlighting the importance of assessing impacts of restoration on ecosystem functions, such as rates of decomposition, algae growth rates, or ecosystem metabolism, in addition to assessments of biological and physical indicators (Bernhardt et al. 2018, Blersch et al. 2019, Bunn and Davie 2000, Beechie 2010, Palmer and Febria 2012). Assessing ecosystem functions is useful because functions can respond quicker to restoration actions than biological and physical variables (Beechie et al. 2010). For example, the recovery of benthic invertebrate community structure (a biological indicator) can be hindered by the presence of highly tolerant benthic invertebrates which prevent colonization of more sensitive species (Eglesfield et al. 2023). Additionally, there can be a great degree of temporal variation in biological and physical variables (Bunn and Davies 2000). Ecosystem functions, therefore, can help to document the progression of the changes in the ecosystem, if they signify improved conditions before other biological (e.g., canopy cover) and physical (e.g., stream temperatures) variables respond. Assessing ecological functional processes is especially beneficial when pre-restoration data are not available. For example, a site might have historical erosion issues. If one were to simply measure the eroded streambanks there would still be a high indication of degradation, whereas measuring the rate of erosion would show if the problem were getting worse. Due to the importance of leaf litter within stream ecosystems, leaf litter decomposition, or breakdown rate, has widely been used as an indicator of ecosystem function (Chauvet et al. 2016, Gessner and Chauvet 2002, Palmer et al. 2005, Young and Collier 2009). Decomposition rates 63 are useful because they provide a metric of an ecosystem process, rather than a structural measure (Gessner and Chauvet 2002, Palmer and Febria 2012). Leaf litter is an important resource subsidy for stream ecosystems. Leaf litter provides a food source for aquatic biota, such as shredder macroinvertebrates, and contributes to the dissolved organic carbon (DOC) present in streams (Richardson et al. 2010). The quality and quantity of leaf litter inputs can cause bottom-up controls on stream consumer species richness and abundance (LeRoy and Marks 2006, Wallace et al. 1997). The decomposition of leaf litter is controlled by both biotic factors (e.g., microbial growth rate) and abiotic factors (e.g., temperature and nutrient concentrations) (Benfield et al. 2001, Ferreira et al 2015, Richardson 1992, Woodward et al. 2012). Breakdown rates can help to determine how quickly these allochthonous resources are being utilized and incorporated into the ecosystem. Periphyton is an important autotrophic food source at the base of aquatic food webs (Allan et al. 2021, Melody and Richardson 2004, Roon et al. 2022). Periphyton assemblages are controlled by many factors, including PAR, stream temperatures, nutrient concentrations, and flood events (Coe et al. 2009, Murdock et al. 2004, add other sources). During major flood events, periphyton standing stocks can be scoured away (Bernhardt et al. 2018, Coe et al. 2009, Murdock et al. 2004). Growth rates of algae can be used to determine how quickly food sources will be available following scouring events, and how quickly periphyton biomass will reach nuisance levels (Murdock et al. 2004). Given the increasing frequency of major flooding in the study region, the rate at which food sources recover from scouring events is important to consider, particularly in cases where increasing productivity is a restoration goal (Government of Canada 2021). The BC Water Quality Guidelines report a maximum recommended short-term biomass value of 100 mg/m2 of chlorophyll-a for the protection of aquatic life (BC ENV 2001b). 64 Chlorophyll-a values are often used to report algae biomass, as chlorophyll-a extraction is the most common method by which algae is quantified (BC ENV 2001b, Steinman et al 2017). A recent study that focused on forest thinning as a restoration method in the PNW found mixed results for effects on periphyton. While periphyton production on ceramic tiles was higher in thinned areas compared to upstream and downstream control reaches, periphyton standing stock was not significantly different before and after thinning treatment (Roon et al. 2022). Periphyton production was not measured pre-treatment, so before-after treatment comparisons are not available (Roon et al. 2022). A similar study in the PNW looking at changes in periphyton following active reforestation has not been published, to my knowledge. However, several studies have looked at effects of forest harvesting practices or experimental shading on periphyton accrual (see Kiffney et al. 2003, Kiffney et al. 2004, Melody and Richardson 2007). One would expect faster results from thinning or harvest treatments compared to reforestation, given that the changes in PAR are immediate with forest thinning, but changes in PAR are slower for reforestation, as canopy cover increases slowly over time. I predicted a gradient of decomposition rates: highest at reference sites to lowest at degraded sites, with restored sites becoming more similar to reference sites over time as the riparian vegetation becomes more established. However, previous studies in BC have found that red alder (Alnus rubra) leaves break down faster in streams with a higher percent deciduous tree composition (Kominoski et al. 2011). Since later successional states in BC forests typically have a higher percent coniferous forest (Yeung et al. 2017), decomposition rates could be lower in reference sites as they represent a later successional state than restored sites and therefore would likely have a lower percent deciduous tree composition (Kominoski et al. 2011). If decomposition rates were highest at degraded sites, this could be due to nutrient pollution 65 (Ferreira et al 2015). However, nutrient pollution does not have a unidirectional effect on decomposition rates. Moderate levels of nutrient enrichment can increase decomposition rates while higher levels of nutrient enrichment can decrease decomposition rates (Rosemond et al. 2015, Woodward et al. 2012). I predicted that periphyton production would be highest in degraded sites, because periphyton production rate is inversely related with canopy cover, and I predicted degraded sites would have the lowest canopy cover. I predicted that periphyton production would be lowest in reference sites, and that restored sites would become more similar to reference sites as years since restoration increased. 3.2 Methods For the leaf decomposition and periphyton production studies results were compared between three treatments: restored sites, reference sites, and degraded sites. Reference sites represent minimally disturbed second growth forest ecosystems. Degraded sites represent highly degraded ecological conditions. Restored sites of various ages (2-25 years since restoration) were selected to compare restoration outcomes over time. 3.2.1 Decomposition Two studies of decomposition rates were conducted: one in July 2022 (referred to as summer study) and one in October 2022 (referred to as fall study). Lab processing and leaf pack preparation were the same for both studies: Red alder leaves were collected from Malcolm Knapp Research Forest following fall abscission. Leaves were then placed in the drying oven at 60 °C for 48 hours to ensure all moisture had evaporated. Leaves were weighed into 3.00 g (± 0.03 g total) packs and placed in plastic tubs for transport to field sites. Once in the field, leaves were wetted to prevent breakage and stuffed into coarse mesh bags (1 cm mesh size). Packs were 66 tied to rebar, or attached to bricks where rebar could not be inserted into streambank, and weighted down with rocks to ensure they remained within the flow (Figure 3.1). HOBO temperature loggers (Onset Computer Corporation HOBO Pendant Temperature/Light Data Logger) were placed in the streams to record water temperatures (Figure 3.1). Leaves were left to decompose in the stream for allotted time periods (detailed below) and then collected from the streams and placed into Ziploc bags. Leaf packs were transported back to the lab on ice. Leaves were removed from the bags, rinsed over a 500 µm sieve, and macroinvertebrates were collected from the leaves and stored in 70% ethanol (Richardson 2020). Leaf packs were processed within 48 hours of collection and stored in the refrigerator in the interim. Following initial processing, leaves were placed in the drying oven at 60 °C for 48+ hours and then weighed for dry mass. Leaves were then placed in a muffle furnace at 550 °C for 3+ hours until completely combusted. Leaves were then reweighed to determine ash-free dry mass (AFDM). Control packs were used to determine handling losses and initial leaching, and to correct for differences between dry mass and AFDM. Leaves were brought to the stream, wetted, and stuffed into bags. Bags were dipped in the stream and then removed. Control packs were then brought back to the lab, rinsed, and dried and combusted following the same protocol. Once the AFDM of the control packs was calculated, the average percent mass loss for the control packs was subtracted from the initial mass values, therefore correcting for initial mass loss and the difference between the dry weight and the AFDM. For the summer study, three leaf packs were placed in one riffle per site, and 2 leaf packs were placed in one pool per site, for a total of 5 leaf packs per stream. Leaf packs were deployed between 13-21 July 2022 and collected approximately 4 weeks later, between 8-15 August 2022. Both riffle and pool leaf packs were collected on the same date. The summer decomposition 67 project had major disruptions. The leaves decomposed much faster than expected at some sites, which resulted in many samples being too decomposed to provide useful data. At the same time, samples at some sites barely broke down in that time period, particularly in pools. Many of the samples were also tampered with or lost on site, as people found the packs and removed them. The twine used to secure the packs also broke down, which was unexpected, as it had been used in many other leaf decomposition experiments without any issue. For the fall decomposition experiment, these issues were taken into account, and the study design was modified. For the fall study, leaf packs were placed at 2 riffle sites per stream, with 4 leaf packs per riffle, for a total of 8 leaf packs per stream (Figure 3.2). Leaf packs were deployed 13 & 14 October 2022. Half of the leaf packs were collected approximately 3 weeks later (2 & 5 November 2022, respectively), that is, 2 leaf packs were collected per riffle per site (n = 4). The remaining 4 leaf packs were collected 5 weeks after launch date (17 & 18 November 2022). Mass loss (control corrected initial mass – final AFDM) and percent loss (AFDM/control corrected initial mass) were calculated on a per day basis and a per degree day basis. Degree day was calculated as average daily temperature * number of days, for all days where the average daily temperature exceeded 0 °C. Decay rate was calculate using the formula: ln[𝑀𝑡] = ln [𝑀0 × 𝑒−𝑘𝑡] Where Mt = percent remaining at time t, t = days or degree days, and k = decay rate (g/g/day or g/g/degree day) (Bärlocher 2020). Outliers were removed before calculating decay rates. Outliers were defined as packs where grams lost per day were ≥3 standard deviations away from the mean value for the site. Once the decay rate was calculated per site, average rates were calculated per treatment. 68 3.2.2 Periphyton production Unglazed ceramic tiles (7.5 x 7.5 cm) were placed in each stream between 27 July – 16 August 2023. Six tiles were placed in the streams, all in the same riffle. Tiles were collected approximately 30 days later. Tiles were wrapped in aluminum foil and transported back to the lab on ice. Tiles were then frozen until analysis. Tiles were rinsed with distilled water and scraped with a wire brush for 3 minutes to remove periphyton. The distilled water and algae mix was then vacuum filtered through a 0.7 µm glass microfiber filter (Whatman™ GF/F: 47 mm diameter). For some samples, a subsample of the water and algae mix was filtered to reduce filtration times, to prevent degradation of chlorophyll during filtration. Samples were mixed using a Vortex Mixer (Fisher Science) before subsampling, to ensure samples were homogenous. Filters were extracted in 5 mL of 90% acetone for 24+ hours. A 1 mL subsample of the extract was mixed with 7 mL of 90% acetone and then chlorophyll-a concentrations (µg/L) were determined using a fluorometer (Turner Designs™ 10-AU) (Steinman et al. 2017). When readings were too high to detect (>200 µg/L), a dilution was carried out by mixing 1 mL of the extract and acetone mixture with 7 mL of 90% acetone. If the reading was still too high, this dilution was repeated. The chlorophyll-a per surface area was then calculated using the formula: 𝐶ℎ𝑙 𝑎µg𝐿∗1 𝐿1000 𝑚𝐿∗𝑒𝑥𝑡𝑟𝑎𝑐𝑡 (𝑚𝐿)𝑡𝑖𝑙𝑒 𝑎𝑟𝑒𝑎 (𝑚2)∗ 𝑑𝑖𝑙𝑢𝑡𝑖𝑜𝑛(𝑚𝐿) = 𝐶ℎ𝑙 𝑎µg𝐿∗1 𝐿1000 𝑚𝐿∗5 𝑚𝐿58.5 𝑐𝑚2∗7 𝑚𝐿1 𝑚𝐿 Some tiles were lost in the field, and some samples could not be processed within an appropriate amount of time due to equipment malfunctioning, so a subset of 12 sites were analyzed: 2 reference sites, 2 degraded sites, and 8 restored sites. 69 3.2.3 Statistical analysis Statistical analysis was carried out in R version 4.2.2 in base R, unless otherwise listed (R Core Team 2019). For both studies, ANOVA was used to determine if there was a significant difference between treatments (as defined in Section 3.2 and Chapter 2), and then a post-hoc Tukey’s test was used to determine where significant differences lie. For the leaf decay study, restored sites were also compared by age class (2-5 years, 6-10 years, 10+ years). Following the ANOVA test, linear models were created in R to determine contributing factors. Variables included in the leaf decomposition models were canopy cover, watershed area, percent deciduous species, basal area, and age. Variables in the algae accrual models included degree days, average riffle velocity, percent canopy cover, canopy cover variance, and age. For each variable, a single factor model was created without the categorical (treatment) variable. For the decomposition study, 5 additional models were created with treatment as the primary explanatory variable plus one of the variables as covariates, for a total of 11 models. For the algae production study additional models were created to include one variable plus treatment and/or degree days, for a total of 19 models. Akaike Information Criterion (AIC) scores were calculated and compared for all models using the R-package AICcmodavg (Mazerolle 2023). 3.3 Results 3.3.1 Decomposition Summer decomposition rates were highly variable. When calculating decay rates, the objective was to calculate one decay rate per stream per flow type (riffle vs pool) (e.g., CC riffle decay rate and CC pool decay rate). However, the sample size per stream per flow type was too small (n = 3 per riffle and 2 per pool), and many packs were lost during the study. When I calculated the decomposition rates via regression of the natural log of percent mass remaining and the number 70 of days (or degree days) in the stream, some regressions were not significant. Therefore, for some streams, decay rates cannot be confidently estimated for one or both flow types. Decomposition was marginally significantly faster in riffles than pools (t-test, t = -1.78, df = 22.94, p = 0.09). However, the highest decay rate (0.10 g/g/day) occurred in a pool at restored site AR (Table 2.1) (Figure 3.3). Pool decay rates generally had higher variance. Since all packs from one of the degraded sites were lost and decay rate calculations (via regression) from one of the two reference sites were not significant, there were not enough replicates (sites) per treatment to conduct ANOVA between degraded, restored, and reference decay rates. For the fall decomposition study, there was a significant difference in the degree day decay rates between treatments (ANOVA, F2,10 = 4.42, p = 0.04) (Figure 3.4). Decay rate in reference sites (mean = 0.0036 ± 0.0013 [SD]), was significantly higher than restored sites (post-hoc Tukey test, p = 0.04; restored sites mean = 0.0024 ± 0.0007), and degraded sites (p = 0.013; degraded sites mean = 0.0014 ± 0.00017). Decay rates did not increase linearly with years since restoration or disturbance (Figure 3.5) (linear regression R2 = 0.13, p = 0.23). Based on AIC model selection the model including treatment and age was the best fit (AIC score = -142.40), followed by the model with treatment as the single explanatory variable (AIC score = -137.14) (Table 3.1). When restored sites were compared by age class (2-5 years, 6-10 years, 10+ years) there was no significant difference between age classes for degree day decay rates or daily decay rates (ANOVA, F2,5 = 1.07, p = 0.41, and F2,6 = 1.78, p = 0.25, respectively). 3.3.2 Periphyton Chlorophyll-a values did not follow expected trends. Chlorophyll-a values (µg/cm2) were highest in reference sites (1113.56 ± 1665.97), intermediate in restored sites (462.15 ± 491.64) and 71 lowest in degraded sites (39.66 ± 21.01) (Figure 3.6). Differences between treatments were not significant (ANOVA F2,9 = 0.46, p = 0.63). There was high intra-site variability, and high variability within treatments. Given that ANOVA compares variance within treatments to variance between treatments, high variability within treatments would reduce the likelihood of finding significant differences between treatments. The best-fit model was the linear model including degree days and velocity (AIC score = 145.56); however, this model did not include any reference sites and only included one of the degraded sites (Table 3.2). The coefficients for both variables were positive, however degree days was only marginally significant (p = 0.08), and velocity was not significant (p = 0.19). The best-fitting model that was not missing any sites was the linear model with canopy cover as the independent variable, which had an AIC score that was 70 greater than the AIC score for the model with degree day and riffle velocity. The single factor models with velocity as the independent variable (AIC score =168.23) and degree days as the independent variable (AIC score = 182.68) ranked higher than other models, and were only missing 3 and 2 sites, respectively. When analyzing restored sites on their own, chlorophyll-a values were positively related with years since restoration (R2 = 0.62, p = 0.021) (Figure 3.7). However, when age was added as a variable for models including all treatments, the model fit did not improve. 3.4 Discussion 3.4.1 Decomposition A major lesson learned in the summer experiment was that, if resources are limited (such as time or quantity of leaves), then it would be best to focus on the same flow type. For the summer 72 experiment, regressions used to calculate decay rates calculated at several sites and flow types (i.e., k value calculated for PR pools) were not significant, likely due to the smaller sample size per flow type. Additionally, when comparing many sites, having multiple collection dates can be useful; this is why leaves were collected on two dates in the fall study. Decay rates for the fall study generally followed expected trends, where rates were highest in restored sites and lowest in degraded sites, but rates were not significantly different between restored and degraded sites. This lack of significant difference could be due to the high degree of variability within the restored sites, or the fact that most sites had been restored less than 10 years ago. Given that decay rates were expected to increase with years since restoration, more recently restored sites were expected to be more similar to degraded sites. Additionally, samples from the oldest restored sites (MQ and GB) (Table 2.2) were either lost or potentially underestimated. At MQ, samples were found washed up on a rock after a major storm and were therefore not usable. Decay rate was lowest at GB. When retrieving the samples from GB, it was noted that some leaf bags had shifted out of the main flow into a pool, which might have contributed to the slow decomposition. When the packs from the oldest restored sites were excluded from analysis, the decomposition rates followed the expected trend that decay rates become more similar to reference sites as time since restoration increased, but the differences in decay rates between recently restored (2-5 years) and intermediate aged sites (6-10 years) were not significant. A study in Sweden compared decomposition rates of birch leaves between restored sites of different ages (9 and 25 years) and different restoration treatments (basic and enhanced) to reference and degraded sites. The basic restoration treatments included streambank, channel, and substrate restoration; the enhanced restoration treatments included wood and boulder additions in conjunction with basic restoration practices. They found that time since 73 restoration did not affect decomposition rate, but type of restoration treatment (basic or enhanced) did. Decomposition rates were higher in enhanced restoration sites than basic restoration sites. (Frainer et al. 2017). Therefore, differences in decay rates between restored sites could be due to minor differences in restoration techniques. While care was taken when selecting sites to ensure restoration actions were similar, there is bound to be some differences in restoration treatments between different restoration planners and practitioners. Additionally, many practitioners mentioned that their restoration techniques had grown and developed over time, so more recently restored sites could have superior restoration techniques despite the overall objectives being similar (e.g., Mike Pearson, Pearson Ecological, Personal Communications 2022). Decay rates in reference sites (0.0036 g/g/degree day) were similar to decay rates of red alder leaves in other recent studies in nearby second-growth forests. For example, Becu and Richardson (2023) had the same mean decay rate (0.0036 g/g/degree day) for decomposition in stream ecosystems when they compared decomposition rates in streams, lakes, and ponds. The mean decay rates for degraded sites (0.0014 g/g/degree day) were similar to the decay rates in lakes in Becu and Richardson’s study (0.0012 g/g/degree day). One of the degraded sites, MN (Table 2.1), had the lowest riffle velocity (Table 2.4) of any of the sites, and at the start of the fall study there was very little stream flow. Therefore, flow conditions at MN could have been more similar to lentic environments in the fall. Becu and Richardson (2023) also found that shredder abundance was significantly higher in stream leaf packs compared to lake and pond leaf packs, so the low decay rates in the degraded sites could be due to a lack of shredder invertebrates. A study by Lecerf and Richardson (2010) compared red alder decay rates in streams with various logging practices (clear cutting, 10 m buffers, 30 m buffers, and thinning) and streams with 74 second growth forests outside Metro Vancouver. They found no significant difference in decay rates between the various logging practices, but decay rates were significantly higher in reference sites (~0.0023 g/g/degree day). Decay rates at their reference sites were comparable to decay rates at restored sites in this study, indicating that decay rates at restored sites might be approaching a normal range. However, as their study was conducted slightly later in the year (November – January), the lower decay rates in their study could also be related to differences in stream biota during that time period, given that rates were already temperature corrected. The decay rates in logged streams were all <0.001 g/g/degree day, which is slightly lower than the decay rates in degraded sites in this study. However, the magnitude of difference in decay rates between treatment and reference sites in their study was comparable to the magnitude of difference between reference and degraded sites in this study. Other studies that have compared leaf litter breakdown rates along gradients of impairment have recorded similar differences between reference and impacted streams (Chauvet et al. 2016). For example, Lecerf et al. (2006) compared highly eutrophic streams to reference streams and found that decomposition rates were 2.8-9.1x higher in reference sites, whereas my decomposition rates were 2.6x higher in reference sites compared to degraded sites. Hladyz et al. (2011) compared breakdown of native leaf litter (alder and oak species) between streams that had been heavily invaded with non-native Rhododendron ponticum and reference streams and reported 2.0-2.27x faster decomposition in reference streams. Studies comparing other sources of contamination such as acidification, mine discharge, and copper pollution have found similar breakdown ratios between degraded and reference sites, indicating that a multitude of stressors can lead to a similar result (Baudoin et al. 2008, Chauvet et al. 2016, Medeiros et al. 2008, Roussel et al. 2009). 75 3.4.2 Periphyton The fact that canopy cover had such low explanatory power was surprising. Other experimental stream studies in the region where light was manipulated and other variables held constant showed a strong effect on periphyton, where chlorophyll-a increased as light increased (Kiffney et al. 2004). A corresponding study that compared periphyton production in streams with various logging treatments, and therefore various changes in canopy cover, also showed increases in chlorophyll-a with increasing canopy openness, though the increases were non-linear (Kiffney et al. 2003). However, the difference in canopy cover in those studies was greater than the difference in canopy cover at the sites in this study. For example, the lowest canopy cover in the studies by Kiffney and colleagues (2003 and 2004) represented clear-cut logging conditions, whereas the sites in this study ranged from 22%-89% canopy cover. Another study conducted in interior BC experimentally manipulated light inputs and measured periphyton biomass, chlorophyll-a, and algal filament length. While they found no significant differences in biomass or chlorophyll-a based on light treatment, they did find significant differences in filament length (Melody and Richardson 2004). Therefore, differences in canopy cover could have affected other aspects of algae growth that were not directly measured in this study. It is also possible that growth was inhibited at some of the more impacted sites. While the difference between treatments was not significant, the degraded sites, which had low canopy cover, had chlorophyll-a values that were an order of magnitude lower than reference and restored averages. Other studies have shown that sedimentation from erosion can reduce periphyton production (e.g., Atkinson et al. 2008, Parkhill and Gulliver 2002, Yamada and Nakamura 2002). Therefore, the low chlorophyll-a values at the degraded sites could be due to 76 erosion. The chlorophyll-a values for the degraded sites were also much lower than the 30-day chlorophyll-a values in the studies by Kiffney and colleagues (2003, 2004), which gives further support for growth inhibition. Biggs et al. (1998) looked at the relationship between velocity and various growth forms of periphyton. They found that some growth forms had the highest chlorophyll-a accrual at velocities around 0.2 m/s, some were unaffected by velocity, and some were dislodged by velocities above 0.2 m/s (Biggs et al. 1998). The highest chlorophyll-a value in this study occurred at a velocity of 0.26 m/s. Therefore, it is possible that the dominant algal growth forms in these streams were those that were not dislodged at velocities above 0.2 m/s, and that the higher velocities could have increased nutrient delivery rates, thus increasing growth rates (Borchardt 1996). Other studies that have investigated periphyton production in response to restoration have shown mixed results. For example, Roon et al. (2022) compared periphyton production on ceramic tiles and periphyton stocks on natural substrates within the stream before and after a forest thinning restoration project in Oregon, US. They found that growth increased significantly, but that stocks within the stream did not change (Roon et al. 2022). Turunen et al. (2017) compared algae accrual in streams that had been restored with wood additions, streams restored with boulder additions, reference streams, and degraded streams. They did not observe any significant differences in algae accrual between treatments. However, their non-significant results followed the opposite trend from mine: growth was highest in degraded sites, lowest in reference sites, and intermediate in restored streams (Turunen et al. 2017). 77 Despite periphyton production not showing a significant difference between treatments in my study, I still would conclude that it is a useful tool in measuring restoration progress. If increasing stream productivity is a restoration goal, measuring periphyton production is a straightforward way to determine if the project achieves the intended outcome. If periphyton production does not respond to the restoration activities, that could indicate that the root of the problem was not addressed. 78 Figure 3.1 Experimental setup for summer decomposition experiment. Mesh bags contain red alder (Alnus rubra) leaves and a wooden stick (wooden stick decomposition was not included in this study due to too little decomposition). Cotton strips and HOBO temperature logger are attached to brick. 79 Figure 3.2 Experimental design for fall decomposition experiment. Wavy lines represent riffles. Grids with leaf represents leaf packs. 80 Figure 3.3 Decay rates (g/g/day) for summer red alder (Alnus rubra) decomposition experiment, conducted at research sites from July – August 2022. Leaves were placed in stream in mesh bags. Error bars represent standard error. Asterisks represent significant results, based on regression of natural log of the percent mass remaining and the number of days in the stream. Restored sites are ordered from most recently restored to oldest restored sites. 81 Figure 3.4 Decay rates (g/g/degree day) for red alder (Alnus rubra) leaves placed in study streams October 2023. Averages per site based on 8 samples. N = 2 for degraded sites, N = 8 for restored sites, and N = 3 for reference sites. Letters (A, B) represent significant differences detected by Tukey test (p = 0.013 for degraded and 0.022 for restored sites). 82 Figure 3.5 Decay rate (g/g/degree day) versus years since last major disturbance, or years since restoration for red alder (Alnus rubra) leaves placed in study streams October 2022. Averages per site based on 8 samples. N = 2 for degraded sites, N = 8 for restored sites, and N = 3 for reference sites. 83 Figure 3.6 Chlorophyll-a (µg/cm2) concentrations by site. Chlorophyll-a was extracted from tiles placed in streams between 27 July – 16 August 2022. Tiles were left in streams approximately 30 days. N = 2-8 per site. Sites are arranged by year since last major disturbance or restoration, from most recently disturbed/restored to longest time since disturbance. 84 Figure 3.7 Chlorophyll-a (µg/cm2) concentrations for restored sites, showing difference with age since restoration. Chlorophyll-a was extracted from tiles placed in streams between 27 July – 16 August 2022. Tiles were left in streams approximately 30 days. Error bar indicated standard deviation. N = 6 ± 3. 85 Table 3.1 Models of decomposition rate (g/g/degree day) for red alder (Alnus rubra) leaves placed in study streams October 2023. Variables indicate variables included in the model. Variables AIC Score Treatment & Years Since Disturbance/Restoration -142.4 Treatment -137.14 Treatment & Basal Area -136.81 Years Since Disturbance/Restoration -135.05 Treatment & % Deciduous Trees -134.36 % Canopy Cover -134.04 % Deciduous Trees -133.58 Basal Area -133.48 Watershed Area -133.24 Treatment & Watershed Area -131.83 Treatment & % Canopy Cover -131.57 86 Table 3.2 Models of chlorophyll-a (µ/cm2) concentrations. Chlorophyll-a was extracted from tiles placed in streams between 27 July – 16 August 2022. Tiles were left in streams approximately 30 days. Variables indicate variables included in the model. Some data were missing for some variables, which lead to some sites being excluded from the models. Sites removed refers to the number of sites that were excluded from the model due to missing data. Variables AIC score Sites Removed Degree Days & Velocity 145.56 5 Velocity 168.23 3 Degree Days 182.68 2 Treatment, Degree Days & Velocity 186.31 5 Treatment & Velocity 186.61 3 Degree Days & Canopy Variance 187.96 2 Degree Days & Canopy Cover 188.11 2 Degree Days & Years Since Disturbance/Restoration 188.57 2 Treatment & Degree Days 192.32 2 Treatment, Degree Days & Years Since Disturbance/Restoration 202.11 2 Treatment, Degree Days & Canopy Variance 207.28 2 Treatment, Degree Days & Canopy Cover 207.31 2 Canopy Cover 215.56 0 Canopy Variance 215.63 0 Years Since Disturbance/Restoration 215.86 0 Treatment 219.49 0 Treatment & Years Since Disturbance/Restoration 220.12 0 Treatment & Canopy Cover Variance 225.49 0 Treatment & Canopy Cover 225.57 0 87 4 General Discussion 4.1 Restoration response In general, the restored sites showed some response to riparian vegetation restoration, both in terms of the physical and biological structure and the ecosystem functions. Of the seven research questions I asked related to stream temperatures, canopy cover, trees, water chemistry, streamflow, leaf decomposition, and algae growth, only two metrics showed significant differences between treatments and changes over time: tree species richness and leaf litter decomposition. Several metrics (canopy cover, DBH, basal area, and algae growth) showed changes over time since restoration or since major disturbance, either within the restored sites or among all sites. Tree density was the only variable that showed significant differences between treatments without an observed trend over time. These results show the value of analyzing both changes over time and comparisons to reference and degraded sites. If sites were not compared temporally, then many of the restoration effects would have been missed because several of the significant relationships were observed through regression with years since restoration or disturbance; and if restored sites were not compared to degraded or reference sites, then there would be no indication of where they were on the restoration trajectory. While many variables showed differences related to years since restoration or disturbance, few showed indications that variables had reached reference values. For canopy cover, there appears to be a levelling off after 6 years (Figure 2.4), and canopy cover values became similar to reference site values after 6 years. This levelling off could indicate that 6 years is an adequate amount of time to measure canopy cover response to restoration or that 6 years is approximately how long it takes actively restored riparian canopies to approach levels found in minimally disturbed second-growth forests. The canopy cover values at references sites and sites restored 88 6+ years prior were similar to reference canopy cover values from one study in this region (Martens et al. 2019) but lower than reference values in another (Warren et al. 2013). Matzek and colleagues (2016) found that riparian canopy cover still had not recovered to values in remnant forest patches more than 15 years post-restoration in northern California. Therefore, canopy cover values might have become similar to my reference sites after 6 years, but the canopy cover values might still have been below minimally disturbed second growth conditions. Additionally, of the projects included in the global literature review of riparian vegetation restoration projects by Gonzalez et al. (2015), only 13% were monitored for more than 6 years, so if canopy cover values had levelled off after 6 years it would not have been detected in 87% of the studies. Given that restoration compliance monitoring only requires 5 years of monitoring, none of the variables in this study would have reached a stable state or approached reference values within the required monitoring period (DFO 2012, DFO 2016). Tree density was the only variable where there was a significant difference detected between treatments, but Tukey’s test showed no significant difference between restored and reference sites (Figure 2.6). The lack of significant difference between restored and reference sites might be interpreted as reaching a stable state. This could indicate that most restored sites were successfully planted to a density resembling that of a minimally disturbed second growth forest or that density approached reference conditions through successional changes in restored sites over time. However, among the restored sites, the average density increased from the sites restored less than 5 years prior (1241 ± 797 trees/ha) to sites restored 5 – 10 years prior (1344 ± 1527 trees/ha) and then decreased in sites restored more than 10 years prior (1035 ± 891). Based on the differences in values between age classes and the high variability in values within age classes, I believe the tree density had not yet reached a stable state. Furthermore, Pollock et al. 89 (2012) defined reference conditions for second growth forests in this region as 372.8 trees/hectare, and Lennox et al. (2011) found that woody plant density followed a polynomial trend following riparian restoration, where density eventually started to decrease rather than reaching an asymptote. Given that my restored sites still had a tree density that was more than 2.75x predefined reference conditions for the region (Pollock et al. 2012), and that density is expected to rise and then fall following restoration, there is not strong evidence that tree density had reached a stable state or approached reference conditions. Other riparian restoration studies have found no significant differences in riparian tree density between restored sites and reference or control sites, despite riparian planting being a restoration practice. For example, Hammer and Gunn (2021) found no significant difference in native stem density between restored (planted) and control (non-planted) sites, but did find that restored sites had a lower density of invasive species as a result of competitive exclusion. Kaase and Katz (2012) compared reaches restored via riparian planting to reference and degraded reaches within the same streams and found no difference in stem density between treatments, while other variables such as basal area and canopy cover were significantly higher in reference reaches. In conclusion, it does not appear that any of the variables measured in this study approached reference conditions. Other chronosequence (space-for-time) studies that assessed ecosystem functions and structural indicators in response to stream restoration have found that some variables did recover within a similar time frame, while others did not. For example, Matzek et al. (2016) found that herbaceous and understory biomass approached reference conditions within 6 and 14 years, respectively, while tree biomass was significantly lower in the oldest restored sites (>15 years post restoration) compared to reference sites. Of the ecosystem functions measured by Matzek et al. (2016), terrestrial leaf litter decomposition showed no significant 90 difference between treatments, while C mineralization rates (a metric of nutrient cycling) in the soil were significantly higher in reference sites than restored sites. Hasselquist et al. (2015) compared streams that were restored 5-25 years prior via de-channelization to channelized control streams in northern Sweden. When all restored sites were grouped together, they found no differences in plant species richness or density, but significant differences in total plant biomass. However, when time since restoration was accounted for there were significant increases in species diversity over time. It should be noted that planting was not part of the restoration practices in the Hasselquist et al. (2015) study. Suganuma and Durigan (2015) used a chronosequence study of riparian restoration projects to model recovery times for several riparian vegetation variables in a semideciduous tropical forest in Brazil. When comparing to second-growth reference forests, they estimated that tree species richness would recover after 16 years, basal area would recover after 10 years, canopy cover would take 24 years, and tree density would be comparable after 37 years. For some biological and structural indicators, it was clear that individual variables provided limited data and multiple variables or interactions between variables needed to be considered in order to interpret results. For example, based on only the canopy cover value, the riparian vegetation at degraded site MN would appear to be in good condition. However, the high canopy cover value was largely the result of overgrowth of the invasive species Himalayan blackberry. Other invasive species, such as Rhododendron ponticum have also been recorded to create dense canopy cover and degraded ecological conditions (Hladyz et al. 2011). Analyzing the canopy cover values at MN in tandem with the basal area measurements or tree species richness provided a much clearer picture of the state of the ecosystem. Other studies have noted the complex nature of interpreting environmental assessment results (e.g., Hladyz et al. 2011, 91 Woodward et al. 2012). For example, Bunting et al. (2012) studied long term effects of dense seeding as a restoration method for cottonwood-willow communities. While dense seeding was successful in terms of cottonwood establishment, it was at the expense of plant diversity and resulted in structurally simple trees (i.e., single-stemmed trees with reduced foliage). This need to incorporate multiple variables into data analysis is one reason why studying ecosystem functions is so useful: Ecosystem functions (or processes) incorporate many ecosystem components into one measurement as functions involve interactions between multiple organisms or physical variables (Bernhardt et al. 2018, Gessner and Chauvet 2002, Palmer and Febria 2012, Woodward et al. 2012). The ecosystem function studies showed mixed responses to restoration. While the decomposition study showed significant effect by treatment, the algae growth study did not. However, both studies showed some change over time: (1) The best-fit model for the decomposition experiment included treatment and years since disturbance/restoration and (2) the chlorophyll-a values increased significantly with years since restoration. The difference in observable treatment effects between the two studies could indicate that factors contributing to decomposition were better incorporated into the restoration plans. For example, restoration efforts could have provided better habitat for shredder macroinvertebrates, which would increase breakdown rates but would not directly impact algae production. Given that many of the samples were lost in the algae growth study, the lack of treatment effect for periphyton could simply be due to an insufficient amount of data. Other studies that have compared multiple ecosystem functions between restored, reference, and degraded sites have also shown mixed results. For example, Lepori et al. (2005) found that leaf retention was significantly higher in restored sites than reference sites and degraded sites, but that leaf breakdown rate was not significantly different 92 between treatments. Turunen and colleagues (2017) found no differences in leaf decomposition rates or algae accrual between restored, reference, and degraded sites in northern Finland, despite some restoration treatments resulting in significant differences in biotic variables (i.e., riparian plant assemblages). Sudduth et al. (2011) compared stream metabolism and nitrate uptake between restored urban streams, degraded urban streams, and reference forested streams in North Carolina, USA and found that metabolism did not differ between treatments and nitrate uptake rates were only significantly different in the summer, when restored sites had the highest uptake. Another factor influencing the mixed results in this study was the reference sites. Characteristics of reference sites differed across municipalities. There are two reasons why reference sites differed across municipalities: natural differences based on the locations (i.e., streams draining from mountains versus streams in relatively flat locations) and different land use histories causing different levels of degradation (Allan 2004). Therefore, restoration trajectories within the different municipalities might not follow the same trends. Monitoring the same variables and repeating the same studies over a longer time span would help to show how the restoration trajectory compares among sites. Unfortunately, repeated studies were not possible within the scope of this project. Additional analysis of geomorphology of the streams could also provide potential explanations for differences in restoration outcomes and trajectories. 4.2 Study limitations One of the major difficulties in this study design was finding restored sites of various ages, where similar restoration techniques were utilized. During personal communications with multiple restoration practitioners and government employees, I was told that their restoration techniques had changed over time. I had to be very selective to ensure that projects were comparable, in order to increase confidence that differences were related to age since restoration, 93 rather than differences in restoration practices. This is one of the factors that likely contributed to the large temporal gap, with no restoration projects that had been completed 10-20 years prior. It is also likely that there was a large decline in restoration projects during that period. There was a marked political shift in 2006 following the federal election which resulted in a nationwide reduction in environmental policy, including a change in 2012 that limited the application of the Fisheries Act to commercially important habitats (Lakanen 2018). Additionally, in 2008 there was a financial crisis which limited available funding for projects. The large temporal gap is therefore reflective of the ways in which social, political, and economic shifts can impact conservation efforts. Having projects within the 10–20-year time scale would have given a more complete outlook on the restoration progression. A major barrier to finding sites for this study was that details of most restoration projects in the region are not publicly available. For 11 out of 12 of the restored sites, information was provided directly from government employees or restoration practitioners and would not have been available without industry contacts. If project information and post-restoration monitoring reports were publicly available, site selection would have been much easier, and large-scale meta-analysis of projects would be possible. Pre-restoration data would have also been extremely beneficial, as it would have allowed for before-after-control-impact (BACI) analysis. However, as previously stated, pre-restoration data is not required for compliance monitoring; sites that had previously been monitored only had compliance monitoring data. The 2019 changes to the Fisheries Act included creating a public database of all HADDs, but information is generally limited to a single paragraph summary of the HADD itself, not the restoration required (DFO 2021). If the database were expanded to include restoration plans, goals, and outcomes then this information could be used in future restoration planning. 94 4.3 Future research and recommendations Comparing leaf decomposition rates proved to be an effective way of analyzing response to restoration, given the significant differences between treatments. Considering that these experiments are inexpensive, easily repeatable, and require minimal field effort and laboratory processing, these experiments could provide a useful standardized monitoring protocol for restoration projects. However, the most effective monitoring plan is one that addresses the specific goals of the restoration project. Given that most stream and riparian restoration projects do not have specific, measurable goals, it is difficult to create effective monitoring plans (Kuglerová et al. 2023, Palmer et al. 2005). Therefore, the first step to creating standardized monitoring protocols is to standardize the practice of creating specific, measurable goals. Creating goals in the planning phase allows for long-term monitoring planning that addresses the specific desired outcomes of the project and allows restoration planners to budget for monitoring at the onset of the project. Having a public database of restoration projects in British Columbia, or in all of Canada, would increase our collective ability to analyze the effects of restoration techniques as scientists. In the US, the National River Restoration Science Synthesis is a database that includes information on stream and riparian restoration projects across the country, and these data have been used in 16 national and regional studies (e.g., Katz et al. 2007, Palmer et al. 2005, Rumps et al. 2007). A public database would allow for the sharing of knowledge between restoration practitioners and academics, which would make it easier to conduct long-term monitoring and meta-analysis, and to determine trends along relevant gradients such as urbanization, stream order, or elevation. Having more data on specific outcomes and trends would allow for a more targeted, research-based approach to restoration. 95 References Allan, J. D. (2004). Landscapes and riverscapes: The influence of land use on stream ecosystems. Annual Review of Ecology, Evolution, and Systematics, 35. https://doi.org/10.1146/annurev.ecolsys.35.120202.110122 Allan, J. D., Castillo, M. M., & Capps, K. A. (2021). Stream Ecology Structure and Function of Running Waters (3rd ed.). Springer Nature Switzerland AG. Atkinson, B. L., Grace, M. R., Hart, B. T., & Vanderkruk, K. E. N. (2008). Sediment instability affects the rate and location of primary production and respiration in a sand-bed stream. Journal of the North American Benthological Society, 27(3). https://doi.org/10.1899/07-143.1 Balian, E. v., Segers, H., Lévèque, C., & Martens, K. (2008). The Freshwater Animal Diversity Assessment: An overview of the results. Hydrobiologia, 595(1). https://doi.org/10.1007/s10750-007-9246-3 Bärlocher, F. (2020). Leaf mass loss estimated by the litter bag technique. In F. Bärlocher, M. O. Gessner & M. A. S. Graça (Eds.), Methods to Study Litter Decomposition (2nd ed.) (p. 43-51). Springer. Baudoin, J.M., Guérold, F., Felten, V., Chauvet, E., Wagner P., Rouselle, P. (2008). Elevated aluminum concentration in acidified headwater streams lowers aquatic hyphomycete diversity and impairs leaf-litter breakdown. Microbial Ecology, 56, 260-269 Beechie, T. J., Sear, D. A., Olden, J. D., Pess, G. R., Buffington, J. M., Moir, H., Roni, P., & Pollock, M. M. (2010). Process-based principles for restoring river ecosystems. BioScience, 60(3), 209–222. https://doi.org/10.1525/bio.2010.60.3.7 Benfield, E. F., Webster, J. R., Tank, J. L., & Hutchens, J. J. (2001). Long-term patterns in leaf breakdown in streams in response to watershed logging. International Review of Hydrobiology, 86, 4–5. https://doi.org/10.1002/1522-2632(200107)86:4/5<467::AID-IROH467>3.0.CO;2-1 Bernhardt, E. S., Heffernan, J. B., Grimm, N. B., Stanley, E. H., Harvey, J. W., Arroita, M., Appling, A. P., Cohen, M. J., McDowell, W. H., Hall, R. O., Read, J. S., Roberts, B. J., Stets, E. G., & Yackulic, C. B. (2018). The metabolic regimes of flowing waters. Limnology and Oceanography 63, (S99–S118). https://doi.org/10.1002/lno.10726 Biggs, B. J. F., Goring, D. G., & Nikora, V. I. (1998). Subsidy and stress responses of stream periphyton to gradients in water velocity as a function of community growth form. Journal of Phycology, 34(4), 598–607. https://doi.org/10.1046/j.1529-8817.1998.340598.x Bilby, R. E., & Ward, J. W. (1991). Characteristics and function of large woody debris in streams draining old-growth, clear-cut, and second-growth forests in southwestern 96 Washington. Canadian Journal of Fisheries and Aquatic Sciences, 48(12). https://doi.org/10.1139/f91-291 Blersch, S. S., Blersch, D. M., & Atkinson, J. F. (2019). Metabolic variance: a metric to detect shifts in stream ecosystem function as a result of stream restoration. Journal of the American Water Resources Association, 55(3), 608–621. https://doi.org/10.1111/1752-1688.12753 Booth, D. B., Scholz, J. G., Beechie, T. J., & Ralph, S. C. (2016). Integrating limiting-factors analysis with process-based restoration to improve recovery of endangered salmonids in the Pacific Northwest, USA. Water (Switzerland), 8(5). https://doi.org/10.3390/w8050174 Borchardt, M. A., Hoffmann, J. P., & Cook, P. W. (1994). Phosphorus uptake kinetics of Spirogyra fluviatilis (Charophyceae) in flowing water. Journal of Phycology, 30(3), 403–417. https://doi.org/10.1111/j.0022-3646.1994.00403.x Bowler, D. E., Mant, R., Orr, H., Hannah, D. M., & Pullin, A. S. (2012). What are the effects of wooded riparian zones on stream temperature? Environmental Evidence, 1(3). http://www.environmentalevidencejournal.org/content/1/1/3 Boyle, C. A., Lavkulich, L., Schreier, H., & Kiss, E. (1997). Changes in Land Cover and Subsequent Effects on Lower Fraser Basin Ecosystems from 1827 to 1990. British Columbia Ministry of Environment (BC ENV). (2001a). Water Quality Guidelines for Temperature. Retrieved from British Columbia Ministry of Environment website: https://www2.gov.bc.ca/assets/gov/environment/air-land-water/water/waterquality/water-quality-guidelines/approved-wqgs/temperature-or.pdf British Columbia Ministry of Environment (BC ENV). (2001b). Water Quality Criteria for Nutrients and Algae. Retrieved from British Columbia Ministry of Environment website: https://www2.gov.bc.ca/assets/gov/environment/air-land-water/water/waterquality/water-quality-guidelines/approved-wqgs/nutrients-or.pdf British Columbia Ministry of Environment (BC ENV). (2008). Little Campbell River Watershed Water Quality Monitoring: 2005-2007. Retrieved from British Columbia Ministry of Environment website: https://www2.gov.bc.ca/assets/gov/environment/air-land-water/water/waterquality/monitoring-water-quality/south-coast-wq-docs/littlecr-wq-monitor2005.pdf British Columbia Ministry of Environment (BC ENV). (2015). Station Summary of Trends in Water Quality (2005-2014): North Alouette River at 132nd Ave Bridge, near Maple Ridge (BC08MH0269). Retrieved from British Columbia Ministry of Environment website: https://www.nrs.gov.bc.ca/ecms/CanadaBCWQReports/TrendReports/BC08MH0269_SiteReport.html British Columbia Ministry of Forestry and Range (2009). Vegetation Resource Inventory Sample Data Compilation Process. Retrieved from British Columbia Ministry of Forestry and 97 Range website: https://www2.gov.bc.ca/assets/gov/farming-natural-resources-and-industry/forestry/stewardship/forest-analysis-inventory/vri/standards-and-procedures/ground-sampling/vri_sample_data_compilation_process_41.pdf British Columbia Ministry of Water, Land and Air Protection. (2001). Water Quality Criteria for Nutrients and Algae: under the Environmental Management Act, 1981. Retrieved from British Columbia Ministry of Water, Land and Air Protection website: https://www2.gov.bc.ca/assets/gov/environment/air-land-water/water/waterquality/water-quality-guidelines/approved-wqgs/nutrients-or.pdf Bunn, S. E., & Davies, P. M. (2000). Biological processes in running water and their implications for the assessments of ecological integrity. In M. Junggwirth, S. Muhar, & S. Schmutz (Eds.), Assessing the Ecological Integrity of Running Waters (p. 61-70). Kluwer Academic Publishers Bunting, D. P., Kurc, S., & Grabau, M. (2013). Long-term vegetation dynamics after high-density seedling establishment: Implications for riparian restoration and management. River Research and Applications, 29(9), 1119–1130. https://doi.org/10.1002/rra.2613 Burgess, S., Clark, C., Ross, K., Krall, M., & Roni, P. (2023). Evaluation of riparian enhancement actions in the Columbia River Basin. Ecological Engineering, 189. https://doi.org/10.1016/j.ecoleng.2023.106897 Chauvet, E., Ferreira, V., Giller, P. S., McKie, B. G., Tiegs, S. D., Woodward, G., Elosegi, A., Dobson, M., Fleituch, T., Graça, M. A. S., Gulis, V., Hladyz, S., Lacoursière, J. O., Lecerf, A., Pozo, J., Preda, E., Riipinen, M., Rîşnoveanu, G., Vadineanu, A., … Gessner, M. O. (2016). Litter decomposition as an indicator of stream ecosystem functioning at local-to-continental scales: Insights from the European RivFunction Project. Advances in Ecological Research 55, 99–182. https://doi.org/10.1016/bs.aecr.2016.08.006 Coe, H. J., Kiffney, P. M., Pess, G. R., Kloehn, K. K., & McHenry, M. L. (2009). Periphyton and invertebrate response to wood placement in large Pacific Coastal rivers. River Research and Applications, 25(8), 1025–1035. https://doi.org/10.1002/rra.1201 Collier, K. J., Rutherford, J. C., Quinn, J. M., & Davies-Colley, R. J. (2001). Forecasting rehabilitation outcomes for degraded New Zealand pastoral streams. Water Science and Technology, 43(9). https://doi.org/10.2166/wst.2001.0534 Connell, J. H. (1978). Diversity in Tropical Rain Forests and Coral Reefs. Science, 199(4335), 1302–1310. http://www.jstor.org/stable/1745369 Eglesfield, I. B., McIntosh, A. R., & Warburton, H. J. (2023). Biotic interactions could control colonization success during stream restoration. Freshwater Science, 42(4), 363–374. https://doi.org/10.1086/728054 Environment Canada (2023). Fraser River (Main Arm) at Gravesend Reach – Buoy (BC08MH0453) Long Term Trend Monitoring. Retrieved via Environment Canada website: 98 https://aquatic.pyr.ec.gc.ca/WQMSDOnlineNationalData2019/en/Home/Details/BC08MH0453 Feio, M. J., Hughes, R. M., Callisto, M., Nichols, S. J., Odume, O. N., Quintella, B. R., Kuemmerlen, M., Aguiar, F. C., Almeida, S. F. P., Alonso-Eguíalis, P., Arimoro, F. O., Dyer, F. J., Harding, J. S., Jang, S., Kaufmann, P. R., Lee, S., Li, J., Macedo, D. R., Mendes, A., … Yates, A. G. (2021). The biological assessment and rehabilitation of the world’s rivers: An overview. Water (Switzerland) 13(3). MDPI AG. https://doi.org/10.3390/w13030371 Ferreira, V., Castagneyrol, B., Koricheva, J., Gulis, V., Chauvet, E., & Graça, M. A. S. (2015). A meta-analysis of the effects of nutrient enrichment on litter decomposition in streams Biological Reviews, 90(3). https://doi.org/10.1111/brv.12125ï Fisheries and Oceans Canada (DFO). (2012). Assessing the Effectiveness of Fish Habitat Compensation Activities in Canada: Monitoring Design and Metrics. Retrieved from Fisheries and Oceans Canada website: https://www.dfo-mpo.gc.ca/csas-sccs/Publications/SAR-AS/2012/2012_060-eng.html Fisheries and Oceans Canada (DFO). (2016) Public Report on the Fisheries Protection Program Monitoring Efforts. Retrieved from Fisheries and Oceans Canada website: https://www.dfo-mpo.gc.ca/species-especes/publications/pnw-ppe/fpp-ppp/index-eng.html Fisheries and Oceans Canada (DFO). (2021) Introducing Canada’s modernized Fisheries Act. Retrieved from Fisheries and Oceans Canada website: https://www.dfo-mpo.gc.ca/campaign-campagne/fisheries-act-loi-sur-les-peches/introduction-eng.html Fryer, J. L. 2021. Rubus armeniacus, R. bifrons: Himalayan blackberry. In: Fire Effects Information System, [Online]. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Missoula Fire Sciences Laboratory (Producer). Available: www.fs.usda.gov/database/feis/plants/shrub/rubspp/all.html [2023, July 21]. Gaire R., Astley C., Upadhyaya M. K., Clements D. R., & Bargen M. 2015. The biology of Canadian weeds. 154. Himalayan blackberry. Canadian Journal of Plant Science. 95(3): 557-570. https://doi.org/10.4141/cjps-2014-402 Gessner, M. O., & Chauvet, E. (2002). A case for using litter breakdown to assess functional stream integrity. Ecological Applications, 12(2). https://doi.org/10.1890/1051-0761(2002)012[0498:ACFULB]2.0.CO;2 Gjerløv, C., & Richardson, J. S. (2010). Experimental increases and reductions of light to streams: Effects on periphyton and macroinvertebrate assemblages in a coniferous forest landscape. Hydrobiologia, 652(1). https://doi.org/10.1007/s10750-010-0331-7 González, E., Sher, A. A., Tabacchi, E., Masip, A., & Poulin, M. (2015). Restoration of riparian vegetation: A global review of implementation and evaluation approaches in the 99 international, peer-reviewed literature. In Journal of Environmental Management, 158. https://doi.org/10.1016/j.jenvman.2015.04.033 Government of Canada (2010). Canadian Climate Normals 1981 – 2010 Station Data: Vancouver International Airport. Temperature and Precipitation Graph. Accessed from Government of Canada website: https://climate.weather.gc.ca/climate_normals/results_1981_2010_e.html?searchType=stnProx&txtRadius=25&optProxType=city&selCity=49%7C17%7C123%7C8%7CVancouver&selPark=&txtCentralLatDeg=&txtCentralLatMin=0&txtCentralLatSec=0&txtCentralLongDeg=&txtCentralLongMin=0&txtCentralLongSec=0&txtLatDecDeg=&txtLongDecDeg=&stnID=889&dispBack=0 Government of Canada. (2021). Canada’s Top 10 Weather Stories in 2021. Retrieved from Government of Canada website: https://www.canada.ca/en/environment-climate-change/services/top-ten-weather-stories/2021.html#toc3 Gowan, C., & Fausch, K. D. (1996). Long-term demographic responses of trout populations to habitat manipulation in six Colorado streams. Ecological Applications, 6(3). https://doi.org/10.2307/2269496 Gregory, S. V, Lamberti, G. A., Erman, D. C., Koski, K. v, Murphy, M. L., & Sedell, J. R. (1987). Influences of forest practices on aquatic production. Streamside Management: Forestry and Fishery Interactions. Proceedings of a Symposium. Hammer, C. F., & Gunn, J. S. (2021). Planting native trees to restore riparian forests increases biotic resistance to nonnative plant invasions. Invasive Plant Science and Management, 14(2), 126–133. https://doi.org/10.1017/inp.2021.11 Hasselquist, E. M., Hasselquist, N. J., Sparks, J. P., & Nilsson, C. (2017). Recovery of nitrogen cycling in riparian zones after stream restoration using δ 15N along a 25-year chronosequence in northern Sweden. Plant and Soil, 410, 1–2. https://doi.org/10.1007/s11104-016-3038-3 Hebbali, A. (2020). olsrr: Tools for Building OLS Regression Models. R package version 0.5.3. https://CRAN.R-project.org/package=olsrr Hladyz, S., Åbjörnsson, K., Giller, P. S., & Woodward, G. (2011). Impacts of an aggressive riparian invader on community structure and ecosystem functioning in stream food webs. Journal of Applied Ecology, 48(2), 443–452. https://doi.org/10.1111/j.1365-2664.2010.01924.x Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES). (2019). Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. S. Díaz, J. Settele, E. S. Brondízio E.S., H. T. Ngo, M. Guèze, J. Agard, A. Arneth, P. Balvanera, K. A. Brauman, S. H. M. Butchart, K. M. A. Chan, L. A. Garibaldi, K. Ichii, J. Liu, S. M. Subramanian, G. F. Midgley, P. Miloslavich, Z. Molnár, D. Obura, A. Pfaff, S. Polasky, A. Purvis, J. Razzaque, B. Reyers, R. Roy Chowdhury, Y. 100 J. Shin, I. J. Visseren-Hamakers, K. J. Willis, and C. N. Zayas (eds.). IPBES secretariat, Bonn, Germany. 56 pages. Invasive Species Counsil of British Columbia (ISCBC) (2019). Himalayan Blackberry Factsheet. https://bcinvasives.ca/wp-content/uploads/2021/01/Himalayan_Blackberry_Factsheet_20190220.pdf Josse, J. & Husson, F. (2016). missMDA: A Package for Handling Missing Values in Multivariate Data Analysis. Journal of Statistical Software, 70(1), 1-31. doi:10.18637/jss.v070.i01 Kaase, C. T., & Katz, G. L. (2012). Effects of stream restoration on woody riparian vegetation of southern Appalachian Mountain streams, North Carolina, U.S.A. Restoration Ecology, 20(5), 647–655. https://doi.org/10.1111/j.1526-100X.2011.00807.x Kahrer, A. G. (1988). Logging and landscape change on the north shore of Burrard Inlet, British Columbia, 1860’s to 1930’s (T). University of British Columbia. Retrieved from https://open.library.ubc.ca/collections/ubctheses/831/items/1.0097716 Kassambara, A. & Mundt, F. (2020). factoextra: Extract and Visualize the Results of Multivariate Data Analyses. R package version 1.0.7. https://CRAN.R-project.org/package=factoextra Katz, S. L., Barnas, K., Hicks, R., Cowen, J., & Jenkinson, R. (2007). Freshwater habitat restoration actions in the Pacific Northwest: A decade’s investment in habitat improvement. Restoration Ecology, 15(3), 494–505. https://doi.org/10.1111/j.1526-100X.2007.00245.x Kuglerová, L., Muotka, T., Chellaiah, D., Jyväsjärvi, J., & Richardson, J. S. (2024). Protecting our streams by defining measurable targets for riparian management in a forestry context. Journal of Applied Ecology, 61(2), 206–214. https://doi.org/10.1111/1365-2664.14549 Kiffney, P. M., Richardson, J. S., & Bull, J. P. (2003). Responses of periphyton and insects to experimental manipulation of riparian buffer width along forest streams. Journal of Applied Ecology, 40(6), 1060–1076. https://doi.org/10.1111/j.1365-2664.2003.00855.x Kiffney, P. M., Richardson, J. S., & Bull, J. P. (2004). Establishing light as a causal mechanism structuring stream communities in response to experimental manipulation of riparian buffer width. Journal of the North American Benthological Society 23(3), 542–555. https://doi.org/10.1899/0887-3593(2004)023<0542:ELAACM>2.0.CO;2 King, S. E., Osmond, D. L., Smith, J., Burchell, M. R., Dukes, M., Evans, R. O., Knies, S., & Kunickis, S. (2016). Effects of riparian buffer vegetation and width: a 12‐year longitudinal study. Journal of Environmental Quality, 45(4): 1243–1251. https://doi.org/10.2134/jeq2015.06.0321 101 Knopf, F. L., & Samson, F. B. 1994. Scale perspectives on avian diversity in western riparian ecosystems. Conservation Biology 8, 669–676 https://dio.org/10.1046/j.1523-1739.1994.08030669.x Kominoski, J. S., Marczak, L. B., & Richardson, J. S. (2011). Riparian forest composition affects stream litter decomposition despite similar microbial and invertebrate communities. Ecology, 92(1). https://doi.org/10.1890/10-0028.1 Lakanen, R. (2018). Dissent and descent: tracing Canada’s environmental governance from regulatory beginnings to dismissal and reversals by the Harper government. Local Environment, 23(5). https://doi.org/10.1080/13549839.2018.1444589 Lammers, R. W., & Bledsoe, B. P. (2017). What role does stream restoration play in nutrient management? Critical Reviews in Environmental Science and Technology, 47(6), 335–371. https://doi.org/10.1080/10643389.2017.1318618 Lapointe, M. F., Bergeron, N. E., Bérubé, F., Pouliot, M. A., & Johnston, P. (2004). Interactive effects of substrate sand and silt contents, redd-scale hydraulic gradients, and interstitial velocities on egg-to-emergence survival of Atlantic salmon (Salmo salar). Canadian Journal of Fisheries and Aquatic Sciences, 61(12), 2271–2277. https://doi.org/10.1139/f04-236 Larson, M. G., Booth, D. B., & Morley, S. A. (2001). Effectiveness of large woody debris in stream rehabilitation projects in urban basins. Ecological Engineering, 18. www.elsevier.com/locate/ecoleng Lavelle, A. M., Chadwick, M. A., Chadwick, D. D. A., Pritchard, E. G., & Bury, N. R. (2021). Effects of habitat restoration on fish communities in urban streams. Water (Switzerland), 13(16). https://doi.org/10.3390/w13162170 Lê, S., Josse, J. & Husson, F. (2008). FactoMineR: An R Package for Multivariate Analysis. Journal of Statistical Software. 25(1), 1-18. Lecerf, A., Usseglio-Polater, P., Charcosset, J. Y. L., Bracht, B. & Chauvet, E. (2006). Assessment of functional integrity of eutrophic streams using litter breakdown and benthic macroinvertebrates. Fundamental and Applied Limnology. 165(1), 105-126. https://doi.org/10.1127/0003-9136/2006/0165-0105 Lepori, F., Palm, D., & Malmqvist, B. (2005). Effects of stream restoration on ecosystem functioning: Detritus retentiveness and decomposition. Journal of Applied Ecology 42(2), 228–238. https://doi.org/10.1111/j.1365-2664.2004.00965.x Lehner, B., & Döll, P. (2004). Development and validation of a global database of lakes, reservoirs and wetlands. Journal of Hydrology, 296, 1–4. https://doi.org/10.1016/j.jhydrol.2004.03.028 102 LeRoy, C. J., & Marks, J. C. (2006). Litter quality, stream characteristics and litter diversity influence decomposition rates and macroinvertebrates. Freshwater Biology, 51(4). https://doi.org/10.1111/j.1365-2427.2006.01512.x Lennox, M. S., Lewis, D. J., Jackson, R. D., Harper, J., Larson, S., & Tate, K. W. (2011). Development of Vegetation and Aquatic Habitat in Restored Riparian Sites of California’s North Coast Rangelands. Restoration Ecology, 19(2), 225–233. https://doi.org/10.1111/j.1526-100X.2009.00558.x Louhi, P., Mykrä, H., Paavola, R., Huusko, A., Vehanen, T., Mäki-Petäys, A., & Muotka, T. (2011). Twenty years of stream restoration in Finland: Little response by benthic macroinvertebrate communities. Ecological Applications 21(6), 1950–1961. https://doi.org/10.1890/10-0591.1 Maceda-Veiga, A., Basas, H., Lanzaco, G., Sala, M., de Sostoa, A., & Serra, A. (2016). Impacts of the invader giant reed (Arundo donax) on riparian habitats and ground arthropod communities. Biological Invasions, 18(3), 731–749. https://doi.org/10.1007/s10530-015-1044-7 Martens, K. D., Devine, W. D., Minkova, T. v., & Foster, A. D. (2019). Stream conditions after 18 years of passive riparian restoration in small fish-bearing watersheds. Environmental Management, 63(5), 673–690. https://doi.org/10.1007/s00267-019-01146-x Matzek, V., Warren, S., & Fisher, C. (2016). Incomplete recovery of ecosystem processes after two decades of riparian forest restoration. Restoration Ecology, 24(5). https://doi.org/10.1111/rec.12361 Mayer, P.M., Reynolds, S.K., Canfield, T.J., McCutchen, M.D. (2005). Riparian buffer width, vegetative cover, and nitrogen removal effectiveness: A review of current science and regulations. EPA/600/R-05/118, U.S. Environmental Protection Agency, Cincinnati, Ohio. Mazerolle, M.J. (2023). _AICcmodavg: Model selection and multimodel inference based on (Q)AIC(c). R package version 2.3.2, https://cran.r-project.org/package=AICcmodavg McAdam, S. O. (2011). Effects of substrate condition on habitat use and survival by white sturgeon (Acipenser transmontanus) larvae and potential implications for recruitment. Canadian Journal of Fisheries and Aquatic Sciences, 68(5), 812–822. https://doi.org/10.1139/f2011-021 Medeiros, A.O., Rocha, P., Rosa, C.A., Graça, M.A.S., (2008). Litter breakdown in a stream affected by drainage from a gold mine. Fundamental and Applied Limnology 172, 59-70. https://doi.org/10.1127/1863-9135/2008/0172-0059 Melody, K. J., & Richardson, J. S. (2004). Responses of invertebrates and algae of a boreal coniferous forest stream to experimental manipulation of leaf litter inputs and shading. Hydrobiologia 519, 197-206. 103 Meyer, J. L., Paul, M. J., & Taulbee, W. K. (2015). Stream ecosystem function in urbanizing landscapes. Journal of the North American Benthological Society, 24(3). https://doi.org/10.1899/04-021.1 Minshall, W. G., Brock, J. T., & Varley, J. D. (1989). Wildfires and Stream Yellowstone Ecosystems A temporal perspective shows that aquatic recovery parallels forest succession. BioScience, 39(10). Moore, R.D., Spittlehouse, D., Story, A. (2005). Riparian microclimate and stream temperature response to forest harvesting: a review. Journal of the American Water Resources Association 41, 813-834. Murdock, J., Roelke, D., & Gelwick, F. (2004). Interactions between flow, periphyton, and nutrients in a heavily impacted urban stream: Implications for stream restoration effectiveness. Ecological Engineering, 22(3), 197–207. https://doi.org/10.1016/j.ecoleng.2004.05.005 Naiman, R. J., Beechie, T. J., Benda, L. E., Berg, D. R., Bisson, P. A., MacDonald, L. H., O’Connor, M. D., Olson, P. L., & Steel, E. A. (1992). Fundamental Elements of Ecologically Healthy Watersheds in the Pacific Northwest Coastal Ecoregion. In Watershed Management. https://doi.org/10.1007/978-1-4612-4382-3_6 Naiman, R. J., & Décamps, H. (1997). The ecology of interfaces: Riparian zones. Annual Review of Ecology and Systematics, 28. https://doi.org/10.1146/annurev.ecolsys.28.1.621 Naiman, R. J., Décamps, H., & McClain, M. E. (2005). Riparia: Ecology, Conservation and Management of Streamside Communities (1st ed.). Elsevier Academic Press. Negishi, J.N. & J.S. Richardson. 2003. Responses of organic matter and macroinvertebrates to placements of boulder clusters in a small stream of southwestern British Columbia, Canada. Canadian Journal of Fisheries and Aquatic Sciences 60: 247-258. Nessel, M. P., Konnovitch, T., Romero, G. Q., & González, A. L. (2023). Decline of insects and arachnids driven by nutrient enrichment: A meta-analysis. Ecology, 104(2). https://doi.org/10.1002/ecy.3897 Nilsson, C., Polvi, L. E., Gardeström, J., Hasselquist, E. M., Lind, L., & Sarneel, J. M. (2015). Riparian and in-stream restoration of boreal streams and rivers: Success or failure? Ecohydrology, 8(5), 753–764. https://doi.org/10.1002/eco.1480 Northcote, T. G., & Luksun, B. (1992). Restoration and environmental sustainability of a small British Columbia urban lake. Water Quality Research Journal 27(2): 341-364. https://doi.org/10.2166/wqrj.1992.024 Northington, R. M., Benfield, E. F., Schoenholtz, S. H., Timpano, A. J., Webster, J. R., & Zipper, C. (2011). An assessment of structural attributes and ecosystem function in restored Virginia coalfield streams. Hydrobiologia, 671(1), 51–63. https://doi.org/10.1007/s10750-011-0703-7 104 Palumbo, J. E., & Brown, L. C. (2014). Assessing the Performance of Reaeration Prediction Equations. Journal of Environmental Engineering, 140(3). https://doi.org/10.1061/(asce)ee.1943-7870.0000799 Palmer, M. A., Ambrose, R. F., & Poff, N. L. R. (1997). Ecological theory and community restoration ecology. Restoration Ecology, 5(4), 291–300. https://doi.org/10.1046/j.1526-100X.1997.00543.x Palmer, M. A., Bernhardt, E. S., Allan, J. D., Lake, P. S., Alexander, G., Brooks, S., Carr, J., Clayton, S., Dahm, C. N., Follstad Shah, J., Galat, D. L., Loss, S. G., Goodwin, P., Hart, D. D., Hassett, B., Jenkinson, R., Kondolf, G. M., Lave, R., Meyer, J. L., … Sudduth, E. (2005). Standards for ecologically successful river restoration. Journal of Applied Ecology, 42(2). https://doi.org/10.1111/j.1365-2664.2005.01004.x Palmer, M. A., & Febria, C. M. (2012). Ecology: The heartbeat of ecosystems. Science 336(6087), 1393–1394. https://doi.org/10.1126/science.1223250 Palmer, M. A., Hondula, K. L., & Koch, B. J. (2014). Ecological restoration of streams and rivers: Shifting strategies and shifting goals. Annual Review of Ecology, Evolution, and Systematics, 45. https://doi.org/10.1146/annurev-ecolsys-120213-091935 Palmer, M. A., Menninger, H. L., & Bernhardt, E. (2010). River restoration, habitat heterogeneity and biodiversity: A failure of theory or practice? Freshwater Biology, 55(SUPPL. 1), 205–222. https://doi.org/10.1111/j.1365-2427.2009.02372.x Parkhill, K. L., & Gulliver, J. S. (2002). Effect of inorganic sediment on whole-stream productivity. Hydrobiologia 472, 5-17. Paul, M. J., & Meyer, J. L. (2001). Streams in the urban landscape. Annual Review of Ecology and Systematics, 32. https://doi.org/10.1146/annurev.ecolsys.32.081501.114040 Pollock, M. M., Beechie, T. J., & Imaki, H. (2012). Using reference conditions in ecosystem restoration: an example for riparian conifer forests in the Pacific Northwest. Ecosphere, 3(11), art98. https://doi.org/10.1890/es12-00175.1 R Core Team. 2019. R: A language and environment for statistical computing. (v4.2.2) [Computer software] R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/. Reid, A. J., Carlson, A. K., Creed, I. F., Eliason, E. J., Gell, P. A., Johnson, P. T. J., Kidd, K. A., MacCormack, T. J., Olden, J. D., Ormerod, S. J., Smol, J. P., Taylor, W. W., Tockner, K., Vermaire, J. C., Dudgeon, D., & Cooke, S. J. (2019). Emerging threats and persistent conservation challenges for freshwater biodiversity. Biological Reviews, 94(3), 849–873. https://doi.org/10.1111/brv.12480 Richardson, D. M., Holmes, P. M., Esler, K. J., Galatowitsch, S. M., Stromberg, J. C., Kirkman, S. P., Pyšek, P., & Hobbs, R. J. (2007). Riparian vegetation: Degradation, alien plant 105 invasions, and restoration prospects. Diversity and Distributions, 13(1). https://doi.org/10.1111/j.1366-9516.2006.00314.x Richardson, J.S. & R.J. Danehy. (2007). A synthesis of the ecology of headwater streams and their riparian zones in temperate forests. Forest Science 53,131-147. Richardson, J. S. (1992). Coarse particulate detritus dynamics in small, montane streams of southwestern British Columbia. Canadian Journal of Fisheries and Aquatic Sciences, 49(2). https://doi.org/10.1139/f92-038 Richardson, J. S., Shaughnessy, C. R., & Harrison, P. G. (2004). Litter breakdown and invertebrate association with three types of leaves in a temperate rainforest stream. Archiv Fur Hydrobiologie, 159(3), 309–325. https://doi.org/10.1127/0003-9136/2004/0159-0309 Richardson, J. S., Zhang, Y., & Marczak, L. B. (2010). Resource subsidies across the land-freshwater interface and responses in recipient communities. River Research and Applications, 26(1). https://doi.org/10.1002/rra.1283 Ringold, P. L., Magee, T. K., & Peck, D. V. (2008). Twelve invasive plant taxa in US western riparian ecosystems. Journal of the North American Benthological Society, 27(4), 949–966. https://doi.org/10.1899/07-154.1 Roni, P. 2019. Does river restoration increase fish abundance and survival or concentrate fish? The effects of project scale, location, and fish life history. Fisheries, 44(1), 7–19. https://doi.org/10.1002/fsh.10180 Roni, P., Beechie, T. J., Bilby, R. E., Leonetti, F. E., Pollock, M. M., & Pess, G. R. (2002). A review of stream restoration techniques and a hierarchical strategy for prioritizing restoration in Pacific Northwest watersheds. North American Journal of Fisheries Management, 22(1), 1–20. https://doi.org/10.1577/1548-8675(2002)022<0001:arosrt>2.0.co;2 Roon, D. A., Dunham, J. B., Bellmore, J. R., Olson, D. H., & Harvey, B. C. (2022). Influence of riparian thinning on trophic pathways supporting stream food webs in forested watersheds. Ecosphere, 13(9). https://doi.org/10.1002/ecs2.4219 Rosemond, A. D., Benstead, J. P., Bumpers, P. M., Gulis, V., Kominoski, J. S., Manning, D. W. P., Suberkropp, K., & Wallace, J. B. (2015). Experimental nutrient additions accelerate terrestrial carbon loss from stream ecosystems. Science, 347(6226). https://doi.org/10.1126/science.aaa1958 Roussel, H., Chauvet, E., & Bonzom, J. M. (2009). Alteration of leaf decomposition in copper-contaminated freshwater mesocosms. Environmental Toxicology and Chemistry 27(3), 637-644. https://doi.org/10.1897/07-168.1 106 Royer, T. V., & Minshall, G. W. (2001). Effects of nutrient enrichment and leaf quality on the breakdown of leaves in a hardwater stream. Freshwater Biology, 46(5). https://doi.org/10.1046/j.1365-2427.2001.00694.x Rumps, J. M., Katz, S. L., Barnas, K., Morehead, M. D., Jenkinson, R., Clayton, S. R., & Goodwin, P. (2007). Stream restoration in the Pacific Northwest: Analysis of interviews with project managers. Restoration Ecology, 15(3). https://doi.org/10.1111/j.1526-100X.2007.00246.x Shupe, S. M. (2017). High resolution stream water quality assessment in the Vancouver, British Columbia region: a citizen science study. Science of the Total Environment, 603–604, 745–759. https://doi.org/10.1016/j.scitotenv.2017.02.195 Simon Fraser University. (1999). Gap Light Analyzer (v2) [Computer software]. https://www.sfu.ca/rem/forestry/downloads/gap-light-analyzer.html Steinman, A. D., Lamberti, G. A., Leavitt, P. R., & Uzarski, D. G. (2017). Biomass and Pigments of Benthic Algae. In Methods in Stream Ecology: Third Edition (Vol. 1, pp. 223–241). Elsevier Inc. https://doi.org/10.1016/B978-0-12-416558-8.00012-3 Stewardship Centre for British Columbia. (2018). Land near water: riparian restoration & enhancement. Pearson, M., Zehnder, D. & Blair, D. G. Stover, J. E., Keller, E. A., Dudley, T. L., & Langendoen, E. J. (2018). Fluvial geomorphology, root distribution, and tensile strength of the invasive giant reed, Arundo donax and its role on stream bank stability in the Santa Clara River, southern California. Geosciences (Switzerland), 8(8). https://doi.org/10.3390/geosciences8080304 Sudduth, E. B., Hassett, B. A., Cada, P., & Bernhardt, E. S. (2011). Testing the field of dreams hypothesis: Functional responses to urbanization and restoration in stream ecosystems. Ecological Applications, 21(6), 1972–1988. https://doi.org/10.1890/10-0653.1 Suganuma, M. S., & Durigan, G. (2015). Indicators of restoration success in riparian tropical forests using multiple reference ecosystems. Restoration Ecology, 23(3), 238–251. https://doi.org/10.1111/rec.12168 Society for Ecological Restoration International Science & Policy Working Group. (2004). The SER International Primer on Ecological Restoration. www.ser.org & Tucson: Society for Ecological Restoration International. https://cdn.ymaws.com/www.ser.org/resource/resmgr/custompages/publications/ser_publications/ser_primer.pdf Tavernini, D. A., & Richardson, J. S. (2020). Effects of tributary size on the resource supply and physical habitat at tributary junctions along two mainstem rivers. Canadian Journal of Fisheries and Aquatic Sciences, 77(8), 1393–1408. https://doi.org/10.1139/cjfas-2019-0435 107 Thackston, E. L., & Dawson III, J. W. (2001). Recalibration of a Reaeration Equation. Journal of Environmental Engineering, 127(4). https://doi.org/10.1061/(asce)0733-9372(2001)127:4(317) Theis, S., Koops, M. A., & Poesch, M. S. (2022). A Meta-analysis on the Effectiveness of Offsetting Strategies to Address Harm to Freshwater Fishes. Environmental Management, 70(5), 793–807. https://doi.org/10.1007/s00267-022-01703-x Tolkkinen, M. J., Heino, J., Ahonen, S. H. K., Lehosmaa, K., & Mykrä, H. (2020). Streams and riparian forests depend on each other: A review with a special focus on microbes. Forest Ecology and Management 462. https://doi.org/10.1016/j.foreco.2020.117962 Turunen, J., Aroviita, J., Marttila, H., Louhi, P., Laamanen, T., Tolkkinen, M., Luhta, P. L., Kløve, B., & Muotka, T. (2017). Differential responses by stream and riparian biodiversity to in-stream restoration of forestry-impacted streams. Journal of Applied Ecology, 54(5), 1505–1514. https://doi.org/10.1111/1365-2664.12897 United Nations General Assembly (2019). United Nations Decade on Ecosystems Restoration (2021-2030). ADD NEW LINK US Environmental Protection Agency (2016). National Rivers and Streams Assessment 2008-2009. Retrieved from US Environmental Protection Agency website: https://www.epa.gov/sites/default/files/2016-03/documents/nrsa_0809_march_2_final.pdf van Zyll De Jong, M. C., Cowx, I. G., & Scruton, D. A. (1997). An evaluation of in-stream habitat restoration techniques on salmonid populations in a Newfoundland stream. Regulated Rivers: Research and Management 13(6), 603–614. https://doi.org/10.1002/(sici)1099-1646(199711/12)13:6<603::aid-rrr487>3.0.co;2-y Vannote, R. L., Minshall, G. W., Cummins, K. W., Sedell, J. R., & Cushing, C. E. (1980). The river continuum concept. Canadian Journal of Fish & Aquatic Sciences 37, 130 – 137. Viers, J. H., Fremier, A. K., Hutchinson, R. A., Quinn, J. F., Thorne, J. H., & Vaghti, M. G. (2012). Multiscale patterns of riparian plant diversity and implications for restoration. Restoration Ecology, 20(2), 160–169. https://doi.org/10.1111/j.1526-100X.2011.00787.x Wallace, J. B., Eggert, S. L., Meyer, J. L., & Webster, J. R. (1997). Multiple trophic levels of a forest stream linked to terrestrial litter inputs. Science, 277(5322). https://doi.org/10.1126/science.277.5322.102 Warren, D. R., Keeton, W. S., Bechtold, H. A., & Rosi-Marshall, E. J. (2013). Comparing streambed light availability and canopy cover in streams with old-growth versus early-mature riparian forests in western Oregon. Aquatic Sciences, 75(4), 547–558. https://doi.org/10.1007/s00027-013-0299-2 Warren, D. R., Keeton, W. S., Kiffney, P. M., Kaylor, M. J., Bechtold, H. A., & Magee, J. (2016). Changing forests-changing streams: Riparian forest stand development and 108 ecosystem function in temperate headwaters. Ecosphere, 7(8). https://doi.org/10.1002/ecs2.1435 Watson, T.K., Kellog, D.Q., Addy, K., Gold, A.J., Stolt, M. H., Donohue, S.W. & Groffman, P.M. (2010). Groundwater denitrification capacity of riparian zones in suburban and agricultural watersheds. Journal of American Water Resources Association, 46, 237-245. https.doi.org/10.1111/j.1752-1688.2010.00418.x White, S. L., Gowan, C., Fausch, K. D., Harris, J. G., & Saunders, W. C. (2011). Response of trout populations in five Colorado streams two decades after habitat manipulation. Canadian Journal of Fisheries and Aquatic Sciences, 68(12). https://doi.org/10.1139/F2011-125 Wondzell, S. M., Diabat, M., & Haggerty, R. (2019). What matters most: Are future stream temperatures more sensitive to changing air temperatures, discharge, or riparian vegetation? Journal of the American Water Resources Association, 55(1), 116–132. https://doi.org/10.1111/1752-1688.12707 Woodward, G., Gessner, M. O., Giller, P. S., Gulis, V., Hladyz, S., Lecerf, A., Malmqvist, B., McKie, B. G., Tiegs, S. D., Cariss, H., Dobson, M., Elosegi, A., Ferreira, V., Graça, M. A. S., Fleituch, T., Lacoursière, J. O., Nistorescu, M., Pozo, J., Risnoveanu, G., … Chauvet, E. (2012). Continental-scale effects of nutrient pollution on stream ecosystem functioning. Science, 336(6087). https://doi.org/10.1126/science.1219534 Wortley, L., Hero, J. M., & Howes, M. (2013). Evaluating ecological restoration success: A review of the literature. Restoration Ecology, 21(5). https://doi.org/10.1111/rec.12028 WWF (2020) Living Planet Report 2020 - Bending the curve of biodiversity loss. Almond, R.E.A., Grooten M. and Petersen, T. (Eds). WWF, Gland, Switzerland. Yamada, H. and Nakamura, F. (2002). Effect of fine sediment deposition and channel works on periphyton biomass in the Makomanai River, northern Japan. River Research and Applications, 18, 481-493. https://doi.org/10.1002/rra.688 Yeung, A. C. Y., Lecerf, A., & Richardson, J. S. (2017). Assessing the long-term ecological effects of riparian management practices on headwater streams in a coastal temperate rainforest. Forest Ecology and Management, 384, 100–109. https://doi.org/10.1016/j.foreco.2016.10.044 Young, R. G., & Collier, K. J. (2009). Contrasting responses to catchment modification among a range of functional and structural indicators of river ecosystem health. Freshwater Biology, 54(10). https://doi.org/10.1111/j.1365-2427.2009.02239.x Zaimes, G., Tamparopoulos, A. E., Tufekcioglu, M., & Schultz, R. C. (2021). Understanding stream bank erosion and deposition in Iowa, USA: A seven year study along streams in different regions with different riparian land-uses. Journal of Environmental Management, 287. https://doi.org/10.1016/j.jenvman.2021.112352 109 Appendices Appendix A. Historical Land Uses in Reference Sites Logging occurred in the Hastings Creek (HC) watershed in the early 1900s. There were two major companies logging in the area: Seymour Lumber Company and Hastings Shingle Company. In 1912, there was a fire at the Seymour Lumber Company mill which destroyed much of the forest surrounding the creek. Seymour Lumber Company ceased operations shortly after the mill fire. The exact date of the final harvest is unclear as records state that Hasting Shingle Company cut down second growth forests following initial clearing of the area. Hastings Shingle Company dissolved in 1926, so the last land-clearing likely occurred before 1926. The area was then closed to logging in the mid-1930s (Kahrer 1988). For the sake of this study, 1926 is considered the date of the last major disturbance. For Third Beach Creek (TB), the last major disturbance was land clearing for the Oakalla Prison Farm in 1960 (Northcote and Luksun 1992). For Anderson Creek (AC) the records of when the last land clearing occurred are not as clear: There was urban encroachment around the stream between 1966-1976 (Shupe, unpublished data). For the purposes of this study, 1975 is considered the date of the last major disturbance.
Thesis/Dissertation
eng
Forestry
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Richardson, John Stuart
Assessing long-term outcomes of stream and riparian restoration projects in urban and suburban areas
Text