Arts, Faculty of
Geography, Department of
DSpace
UBCV
Matechuk, Landen
Master of Science - MSc
University of British Columbia
Beavers inhabit and alter freshwater ecosystems through the construction of beaver dams, lodges, ponds and canals. Beaver-controlled stream systems exhibit increased sediment and water storage, creating pools and important habitats for fish and invertebrates. However, determining historic and or suitable beaver habitats poses a challenge due to factors such as trapping, land use disturbances, and climate change. As interest in restoring riverine and stream ecosystems grows, the reintroduction of beavers and construction of beaver dam analogs—man-made structures designed to mimic natural beaver dams—are increasingly being used to rehabilitate impaired riverine habitats. A mapping approach that identifies suitable topographic and environmental conditions associated with successful beaver habitats would therefore be a useful guide for future freshwater ecosystem restoration efforts. In this study, beaver dam locations were identified and used to train a random forest machine learning model to assess stream, wetland and river beaver habitat suitability. The model uses provincially available topographic, biological, and hydrologic variables to test their importance for predicting the presence or absence of beaver dams. This procedure produced a measure of habitat suitability with an accuracy score of 97%. The research discussed here can contribute to informed decision-making regarding the reintroduction of beavers and the restoration of stream systems, promoting ecological balance and enhancing the natural state of riverine ecosystems.
Predictive Modelling of Beaver Habitats Using Machine Learning by Landen Matechuk B.Sc., The University of Victoria, 2020 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Geography) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) August 2024 © Landen Matechuk, 2024ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Predictive Modelling of Beaver Habitats Using Machine Learning submitted by Landen Matechuk in partial fulfilment of the requirements for the degree of Master of Science in Geography Examining Committee: Marwan Hassan, Professor, Department of Geography, University of British Columbia. Supervisor Mike Miles, Principal, M. Miles and Associates Ltd. Supervisory Committee Member Dave Reid, Senior Restoration Biologist, District of Fisheries and Oceans Canada Additional Examiner iii Abstract Beavers inhabit and alter freshwater ecosystems through the construction of beaver dams, lodges, ponds and canals. Beaver-controlled stream systems exhibit increased sediment and water storage, creating pools and important habitats for fish and invertebrates. However, determining historic and or suitable beaver habitats poses a challenge due to factors such as trapping, land use disturbances, and climate change. As interest in restoring riverine and stream ecosystems grows, the reintroduction of beavers and construction of beaver dam analogs—man-made structures designed to mimic natural beaver dams—are increasingly being used to rehabilitate impaired riverine habitats. A mapping approach that identifies suitable topographic and environmental conditions associated with successful beaver habitats would therefore be a useful guide for future freshwater ecosystem restoration efforts. In this study, beaver dam locations were identified and used to train a random forest machine learning model to assess stream, wetland and river beaver habitat suitability. The model uses provincially available topographic, biological, and hydrologic variables to test their importance for predicting the presence or absence of beaver dams. This procedure produced a measure of habitat suitability with an accuracy score of 97%. The research discussed here can contribute to informed decision-making regarding the reintroduction of beavers and the restoration of stream systems, promoting ecological balance and enhancing the natural state of riverine ecosystems. iv Lay Summary Beavers, through their dam-building activities, significantly alter freshwater ecosystems, creating habitats beneficial for various aquatic species. This study uses machine learning to identify suitable beaver habitats in British Columbia, with an accuracy of 97%. The findings can guide efforts that aim to restore ecosystems by reintroducing beavers or constructing beaver dam analogs. v Preface This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Geography at the University of British Columbia, Vancouver. The research was conducted under the supervision of Dr. Marwan Hassan and includes contributions from Mike Miles P.Geo. and Chelton VanGeloven R.P.F. vi Table of Contents Abstract ....................................................................................................................................................... iii Lay Summary ............................................................................................................................................. iv Preface .......................................................................................................................................................... v Table of Contents ....................................................................................................................................... vi List of Tables ............................................................................................................................................. viii List of Figures ............................................................................................................................................. ix List of Abbreviations ................................................................................................................................... x List of Equations ........................................................................................................................................ xi Acknowledgements ................................................................................................................................... xii Dedication ..................................................................................................................................................xiii 1. Introduction ............................................................................................................................................. 1 1.1 Beavers in North America ................................................................................................................ 2 1.2 Ecosystem Impact of Beavers........................................................................................................... 2 1.3 Suitable Beaver Habitats as Described by Previous Studies ......................................................... 5 1.4 Previous Beaver Habitat Modelling Studies ................................................................................... 7 1.5 Random Forest Machine Learning.................................................................................................. 9 1.6 Explanatory Variables .................................................................................................................... 10 1.7 Study Area ....................................................................................................................................... 11 2. Methodology .......................................................................................................................................... 16 2.1.1 Presence Points: Beaver Dam Locations .................................................................................... 18 2.1.2 Randomly Generated Points ....................................................................................................... 19 2.2 Explanatory Variables .................................................................................................................... 19 2.2.1 Elevation ................................................................................................................................... 21 2.2.2 Topographic Wetness Index .................................................................................................... 21 2.2.3 Slope .......................................................................................................................................... 22 2.2.4 Vegetation Inventory ................................................................................................................ 22 2.2.5 Proximity to Hydrological Features ....................................................................................... 23 2.2.6 Baseflow .................................................................................................................................... 23 2.3 Processing in R ................................................................................................................................ 23 2.3.1 Model Development and Training .............................................................................................. 24 3. Results .................................................................................................................................................... 26 3.1 Model Performance ........................................................................................................................ 26 vii 3.2 Importance of Variables ................................................................................................................. 27 3.3 Preferred Habitat Based on Explanatory Variable Values .......................................................... 28 3.4 Model Output .................................................................................................................................. 33 4. Discussion............................................................................................................................................... 35 4.1 Comparison with previous models ................................................................................................ 35 4.2 Importance of Environmental Variables ....................................................................................... 37 4.3 Reproducibility and Applicability to Other Watersheds ............................................................. 37 4.4 Application ....................................................................................................................................... 38 4.5 Limitations ....................................................................................................................................... 39 5. Conclusion ............................................................................................................................................. 41 References .................................................................................................................................................. 43 viii List of Tables Table 1.1: Beaver Habitat Preferences as Described by Previous Studies .................................................... 6 Table 2.1: Overview of data used to train the machine learning model. ..................................................... 20 Table 3.1: Model performance metrics calculated in R…………………………………………………... 27 Table 3.2: Importance value of explanatory variables used in the analysis. ............................................... 28 ix List of Figures Figure 1.1:The Nechako River Watershed………………………………………………………………. . 13 Figure 2.1: Workflow programmed and processed in R…………………………………………………. . 17 Figure 2.2: Satellite imagery of beaver dams dams. ................................................................................... 18 Figure 3.1: Distribution of Topographic Wetness Index ............................................................................. 30 Figure 3.2: Distribution of Baseflow .......................................................................................................... 31 Figure 3.3: Distribution of Elevation .......................................................................................................... 31 Figure 3.4: Distribution of Slope ................................................................................................................ 32 Figure 3.5: Distribution of Land Classification .......................................................................................... 33 Figure 3.6: Model output ............................................................................................................................ 34 x List of Abbreviations BCLCS - British Columbia Land Classification System BCMF - British Columbia Ministry of Forests BRAT - Beaver Restoration Assessment Tool COSEWIC - Committee on the Status of Endangered Wildlife in Canada CRAN - Comprehensive R Archive Network CSTC - Carrier Sekani Tribal Council DEM - Digital Elevation Model EEAGER - Neural Network Model for Finding Beaver Complexes ESRI - Environmental Systems Research Institute F1 - F1 Score (harmonic mean of precision and recall) FP - False Positive FWS - Fish and Wildlife Service GIS - Geographic Information System HSI - Habitat Suitability Index MASL - Meters Above Sea Level NAD - North American Datum PCIC - Pacific Climate Impacts Consortium PH - Potential Hydrogen (acidity/basicity) RGB - Red, Green, Blue TP - True Positive TRIM - Terrain Resource Information Management TWI - Topographic Wetness Index USGS - United States Geological Survey VIC - Variable Infiltration Capacity VRI - Vegetation Resource Inventory xi List of Equations Equation 1: Topographic Wetness Index .................................................................................................... 21 Equation 2: Precision .................................................................................................................................. 26 Equation 3: Recall ....................................................................................................................................... 26 Equation 4: F1 Score .................................................................................................................................. 26 xii Acknowledgements I would like to express my sincere gratitude to my supervisor, Dr. Marwan Hassan, for his guidance and support throughout this project. I am deeply appreciative of the invaluable contributions from Mike Miles, P.Geo., and Chelton VanGeloven, R.P.F. My thanks also extend to the members of my research lab for their insightful feedback and encouragement during this research. Additionally, I am grateful to Joan Grau, A.Ag., who co-created the initial beaver dam inventory dataset used for training in the analysis. Funding for this project was provided by MiTACS 1,M. Miles and Associates Ltd., the Lheidli Tenneh First Nation2, the BC Salmon Restoration Fund3 and the Indigenous Watershed Initiative4 . This support is greatly appreciated. 1 https://www.mitacs.ca/ 2 https://www.lheidli.ca/ 3 https://www.dfo-mpo.gc.ca/fisheries-peches/initiatives/fish-fund-bc-fonds-peche-cb/index-eng.html 4 https://www.indigenouswatersheds.ca/ xiii Dedication I dedicate this research to my family, whose unwavering support has been invaluable throughout this journey. Their sacrifices and commitment to my education and scientific endeavors have been a constant source of inspiration. I hope my dedication to learning and discovery honors their love and perseverance. 1 1. Introduction Beavers (castor canadensis) can play an important role in shaping the landscape of freshwater ecosystems through their construction of dams, lodges, ponds, and canals. When present, these features can significantly alter the hydrology, biology, and geomorphology of riverine and riparian habitats (Wilson & Reeder, 2005). This impact is particularly evident in the formation of pools and habitats used by various aquatic species including fish and invertebrates. The significance of these activities extends beyond immediate habitat modification, as they influence sediment dynamics and enhance water storage capabilities within stream systems, which can affect the ecological health and resilience of these environments. Beaver populations and beaver habitats have been significantly impacted by factors such as trapping, land use changes, and climate change, presenting a substantial challenge in identifying historical or suitable locations for conservation and restoration efforts (Brazier et al., 2021; Wohl, 2015). As efforts to restore riverine ecosystems continue, the importance of beavers to these systems has become better understood (Goldfarb, 2019). This work has led to the increased reintroduction of beaver to areas where they have been extirpated and the construction of beaver dam analogs as viable methods to achieve ecological restoration goals (Wheaton, Bennett, Bouwes, Maestas, & Shahverdian, 2019). The present study focuses on the Nechako watershed in British Columbia, a region where beaver dam locations offer valuable insights into habitat suitability. Over 8800 beaver dam locations have been identified through satellite imagery analysis and field visits. A random forest machine learning model, was trained on these locations and used to assesses stream, wetland, and river habitat attributes at known beaver dam locations. This model employs a comprehensive range of provincially available topographic, biological, and hydrological data. Its purpose is to evaluate the factors which influence the presence or absence of beaver dams or ponds and to provide a quantifiable measure of habitat suitability. This contributes significantly to efforts aimed at promoting ecological balance and restoring the natural state of riverine ecosystems historically inhabited by beavers. 2 1.1 Beavers in North America Historically, beavers inhabited North America in significant numbers, with their population estimated to range between 60 and 400 million (Seton, 1929). However, extensive trapping of beavers between 1600 and 1900 reduced their population to less than one percent of the estimated pre-colonial population (Backhouse, 2015; Seton, 1929). Once ranging from the Arctic Tundra in the north to as far south as Mexico, their population is now rebounding, with estimates suggesting a current number between 6-12 million (Naiman, Johnston, & Kelley, 1988). Despite this recovery, their potential habitats face limitations due to pressures such as agriculture, timber harvesting, and land development. When assessing wetlands and stream systems, it's potentially important to recognize the historical role of beavers as the absence of beaver dams can influence sediment load, water storage, and riparian health (Brazier et al., 2021). Given that historical photographs and records primarily date back to the early 1900s, a significant knowledge gap exists regarding the precise location and complete historical distribution of beaver dams and habitats that may have been suitable for beaver populations in the past. 1.2 Ecosystem Impact of Beavers Beavers are recognized as a keystone species in some freshwater ecosystems due to their influence on the environment, particularly through their creation of habitats through dam construction, and the establishment of beaver ponds (Brazier et al., 2021). Beaver dams act as barriers to sediment, woody debris and pollutants, effectively regulating the transport of these materials throughout the channel network (Law, McLean, & Willby, 2016). The interception of pollutants, storage of sediment and the decomposition of stored woody debris provide downstream benefits including an increase in invertebrate species profusion (Redin & Sj€oberg, 2013; Clifford et al., 1993). However, the storage of sediment can increase siltation, impacting the spawning gravels of salmonids and trout immediately upstream of the dam (Collen & Gibson, 2000; Knudsen, 1962). Therefore, the effects of sediment storage on fish health should be evaluated based on conditions such as gradient and the presence of spawning beds (Collen & 3 Gibson, 2000). An increase in nutrient availability due to the decomposition and storage of organic matter affects filter feeders both upstream and downstream of beaver-controlled areas (Law, McLean, & Willby, 2016; Redin & Sj€oberg, 2013; Rolauffs et al., 2001; Margolis et al., 2001; Nummi, 1989). The increased abundance of invertebrates can positively affect the nutrient availability for aerial and aquatic species such as fish and bats (Nummi et al., 2011; Kemp et al., 2011). Increased wetland connectivity from beaver activity creates preferred habitats for various amphibian and fish species (Cunningham, Calhoun, & Glanz, 2007; Schlosser & Kallemeyn, 2000). However, beaver dams can act as a barrier to migratory fish during periods of low flows impacting autumn spawning fish such as brook trout and brown trout (Collen & Gibson, 2000; Grasse & Putnam, 1955). Fish can cross beaver dams at high flows, through side channels, or directly through the dam depending on the deterioration of the dam and the size of the fish (Collen & Gibson, 2000; Gard, 1961; Grasse & Putnam, 1955). Studies by Lokteff et al. (2013) on brook trout and Bryant (1984) on Coho salmon found that both fish species were able to cross dams as high as 2 meters. Flooding and pool creation caused by the beaver dams can also significantly influence riparian vegetation. A study by Wright et al. (2002) found that beaver-controlled stream sections promote herbaceous vegetation growth, contributing to increased species richness within the riparian zone. The shade provided by riparian vegetation around beaver ponds helps regulate water temperature, creating a more stable environment for aquatic life, particularly in areas facing rising temperatures due to climate change and land use disturbances such as logging (Reeder, 2011). However, beaver activity can also increase water temperature by exposing impounded water to solar radiation for longer periods and removing the shade provided by riparian vegetation through felling (Cook, 1940; Kemp et al., 2012). Knudsen (1962) described an increase in water temperature in streams where beavers cropped riparian vegetation or flooded riparian areas, causing the degradation of shade-producing trees. The increased temperature reached levels lethal to Brook Trout and impacted the populations within the beaver-dammed 4 stream (Knudsen, 1962). Therefore, the effects of beaver activity on temperature can vary based on local environmental conditions and should be assessed according to local species needs. Geomorphologically, beaver dam construction can transform some static single thread channels into dynamic multi channel drainage networks with increased floodplain connectivity and increased sediment deposition (Grudzinski et al., 2022; Kramer, Wohl, & Harry, 2012; Polvi & Wohl, 2012). The water impounded by beaver dams saturates the soil, providing a valuable reservoir during dry periods that can regulate stream flow during periods of high discharge (Bergstrom, 1985; Brazier et al., 2021). Building on these geomorphological changes, Fairfax and Whittle (2020) studied how increased floodplain connectivity, water storage, and soil moisture, as a result of beaver dams, enhance floodplain resilience to wildfires. Areas with saturated floodplains and lush riparian vegetation, due to beaver activity, often exhibit greater resistance to wildfires than those without such beaver-induced modifications (Fairfax & Whittle, 2020). The absence of beaver-controlled stream sections, resulting from the removal of beaver dams, can lead to significant changes in riparian vegetation, flow regimes, stream ecology, and channel morphology (Westbrook, 2009). The study by Green and Westbrook (2009) examined the effects of beaver dam removal on Sundown Creek in British Columbia, specifically evaluating the impact on a stream section where dams were removed to facilitate fish passage. The floodplain initially had beaver ponds, open vegetation and a multithread channel. Sixteen years after the dam removal the stream was transformed into a single thread channel with dense vegetation including willow and coniferous saplings (Westbrook, 2009). The hydraulic geometry of the channel underwent significant alterations following the removal of beaver dams, which included a reduction in average channel width, loss of beaver ponds, and a decrease in frictional resistance (Westbrook, 2009). These changes not only affected the channel's structure but also its functionality, leading to an 81% increase in the mean velocity at bank-full discharge and a 92% rise in cross-sectional stream power (Westbrook, 2009). 5 Beaver activity can also lead to the flooding of infrastructure such as roads and agricultural land, resulting in damage to both public and private property (Collen & Gibson, 2000; Wheaton et al., 2019). This flooding can necessitate increased maintenance costs and pose potential safety hazards, highlighting the need for careful management of beaver populations within urban or agricultural areas (Wheaton et al., 2019). 1.3 Suitable Beaver Habitats as Described by Previous Studies Previous studies have identified specific environmental characteristics and conditions conducive to suitable beaver habitats, which are essential for assessing habitat suitability and evaluating their potential impact across fresh water ecosystems. Preferred food sources for beavers include the leaves, twigs and bark of the aspen, cottonwood and alder trees (Denney, 1952). Additionally, beavers prefer herbaceous vegetation and aquatic (Jenkins, 1981). Although these types of tree species and aquatic vegetation are preferred, beavers can adapt to and thrive in areas where these species are absent or uncommon (Jenkins, 1975). Moreover, a sufficient food source must be available throughout the year for a beaver colony to exist productively (Slough and Sadleir 1977). This considered, types of food are less important in assessing habitat suitability than topographic and hydrologic conditions (Jenkins, 1975). Additionally, habitat suitability is dependent on access to dam-building material, with materials typically felled and collected within 30m of the water, though foraging distances of up to 200m have been documented (Bradt, 1938). Trees of all sizes are felled close to the water regardless of species, however as distance from the water increases the stem size and types of species foraged decrease (Jenkins, 1980). Preferred habitats consist of low-lying marshy areas with a consistent flow of water to allow for damming and channelization (Allen, 1983). Through their dam-building behaviours, beavers can modulate water levels and maintain productive hydrologic conditions in smaller water bodies such as ponds, streams, and lakes (Slough and Sadleir 1977). In contrast, the channel width and inherent variability of discharge and water levels of larger rivers and lakes typically render them less ideal or even unsuitable habitats for beaver dam construction (Slough and Sadleir 1977). In freshwater ecosystems, the slope or gradient of streams 6 predominantly shapes their morphology and plays a pivotal role in determining their suitability for beaver habitation (Slough and Sadleir, 1977). Data from Retzer et al. (1956) indicated that in Colorado, 68% of identified beaver colonies were located in valleys with a gentle stream gradient of under 6%. Meanwhile, 28% were found in areas with stream gradients ranging from 7% to 12%. Only 4% were situated near streams that exhibited gradients between 13% and 14% (Retzer et al., 1956). Retzer et al. (1956) reported that streams with steep gradients of 15% or higher were devoid of beaver colonies. In terms of landscape characteristics, narrow valleys equivalent in width to the stream channel were not favoured by beavers for dam construction (Retzer et al., 1956). In contrast, broader valleys exceeding the stream's width were typically inhabited with particular preference for valleys spanning 46 meters or wider (Retzer et al., 1956). Table 1.1: Beaver Habitat Preferences as Described by Previous Studies Category Habitat Preference Description Topography Stream Gradient Preferred in valleys with gentle gradients (<6%); some in 7%-12%; few in 13%-14%; none in >15% (Retzer et al. 1956). Valley Width Narrow valleys equivalent to stream width are not favoured; preference for valleys wider than 46 meters (Retzer et al. 1956). High Suitability Areas Low-lying marshy areas with a consistent water flow for damming and channelization (Allen, 1983). Hydrology Water Body Suitability Smaller water bodies (ponds, streams, lakes) are ideal; larger rivers and lakes are less suitable due to variability in discharge and water levels (Murray, 1961; Slough & Sadleir, 1977). Dam-Building Material Access Materials typically collected within 30m of water, but can forage up to 200m. Trees of all sizes felled close to water; species and size decrease with distance (Bradt,1938; Jenkins, 1980). 7 Category Habitat Preference Description Biology/Vegetation Preferred Food Sources Leaves, twigs, and bark of aspen, cottonwood, and alder trees; herbaceous vegetation and aquatic vegetation like duck potato, pondweed, water weed, and water lilies (Denney, 1952; Jenkins, 1981). Food Adaptability Beavers can adapt to areas where preferred food sources are absent or uncommon (Jenkins, 1975). Year-Round Food Source Necessary for a productive beaver colony (Slough & Sadlier 1977). Importance of Food vs. Topography/Hydrology Topographic and hydrologic conditions are more critical than food types for habitat suitability (Jenkins, 1975). 1.4 Previous Beaver Habitat Modelling Studies Habitat identification and suitability studies have been conducted in the United States. However, the use of random forest machine learning for beaver habitat suitability assessment is a novel approach in this research. Notable previous examples include a habitat suitability model developed by Allen (1983) for the United States Fish and Wildlife Service. Additionally, contemporary models, such as the EEAGER image recognition machine learning model, which identifies beaver complexes from aerial and satellite imagery (Fairfax et al., 2023), and the Beaver Restoration Assessment Tool (BRAT; Macfarlane et al., 2017), have made significant contributions to the field. While these models were developed for and implemented using data exclusive to the United States, their methodologies and frameworks provided valuable insights and guidance for the present study. The Habitat Suitability Index (HSI) serves as a foundational piece in understanding habitat information that is invaluable for impact assessment and habitat management. Allen (1983) presents quantifiable links between key environmental variables and habitat suitability, thereby providing a basis for subsequent HSI models (Allen, 1983). At its core, the HSI model offers a synthesis of habitat use information tailored for practical field application. The input includes data from field 8 observations in three main categories: stream gradient, vegetation, and water supply (Allen, 1983). The model employs a scale that translates into an index ranging from 0.0, representing unsuitable habitat, to 1.0, symbolizing optimal habitat conditions. Crucially, this model not only provides descriptions of the habitat's geographical ranges and seasonal application but also outlines the current verification status and offers a detailed listing of model variables accompanied by recommended measurement methodologies (Allen, 1983). Despite its foundational value, it's essential to note that the model primarily presents species-habitat interrelationships, rather than definitively stating cause and effect dynamics (Allen, 1983). Its applicability has been tested and referenced in various contexts, but like any model, its reliability can differ across different environments and geographical locations. The EEAGER model was created to provide a solution to the time extensive task of manually identifying beaver dams from aerial and satellite imagery. The EEAGER model uses a neural network deep-learning algorithm to identify beaver dams by detecting distinct features such as edges, textures, and abstract geometries (Fairfax et al., 2023). The training dataset included locations of 13,344 manually mapped beaver dams across seven states in the Western United States. Explanatory data used for the study included RGB (Red, Green, Blue) imagery at 0.2m resolution, which captures visible light in three color bands, and a digital elevation model at 0.2m resolution (Fairfax et al., 2023). The output of the model is a polygon data layer identifying predictions of beaver dam locations (Fairfax et al., 2023). The performance of the model was evaluated through measurements of accuracy, recall and precision. The model scored high across all three metrics with an overall accuracy of 98.5% (Fairfax et al., 2023). The value of the EEAGER model is in its accuracy in predicting existing beaver dams and beaver habitat locations. Although the exact data used for the study is limited to the United States, the application of these methods to British Columbia would provide impactful data on beaver dam locations). However, the identification of existing beaver habitats does not provide information on the underlying topographic, hydrologic, and environmental factors contributing to the presence or absence of beaver habitats. 9 Comparatively, the BRAT model established a weighted assessment of areas suitable for dam-building based on hydrological, vegetative, and topographical characteristics (Macfarlane et al., 2017). The model considered seven factors: water availability, suitable riparian vegetation, potential for dam construction, flood resistance, stream gradient appropriateness, and river size compatibility (Macfarlane et al., 2017). To represent these characteristics, the model was developed from datasets widely available in the United States, including USGS baseflow, 2-year peak flow values, a 10m resolution DEM, and land cover data (Macfarlane et al., 2017). Applied to 40,561 km of streams in Utah, the model predicted a potential for 356,294 dams (Macfarlane et al., 2017). These analyses can guide decisions on where increased beaver dam activities might bolster channel-floodplain and wetland connectivity. 1.5 Random Forest Machine Learning Forest-based regression and classification represents an evolution in the field of machine learning, building upon foundational techniques to offer enhanced analytical capabilities. This method, a derivative of the classification and regression trees algorithm, was introduced by Leo Breiman in 2001 and has since become an important tool for complex data analysis. Its application in ecological studies illustrates its versatility in handling diverse and intricate datasets (Cutler et al., 2007). The method employs an ensemble learning approach, where hundreds to thousands of decision trees are produced during training (Breiman, 2001). A decision tree consists of three main components: the decision node, which represents a tested attribute, a leaf or terminal node which uses regression analysis or other criteria to guide the decision-making process towards a likely outcome, and an end node where the prediction is made (Kamiński, Jakubczyk, & Szufel, 2018). Each tree is constructed from a subset of the data, which is created by random samples of the original data, and then used to generate its prediction (Breiman, 2001). Instead of relying on a singular decision tree as in conventional methods, the forest-based model aggregates predictions from all trees to yield a more robust and often more accurate outcome (Breiman, 2001). The ensemble method provides several advantages over traditional machine learning techniques. Notably, it is highly effective in handling large datasets with numerous variables, tolerates missing data, 10 and can account for non-linear relationships (Breiman, 2001). Moreover, it offers inherent feature selection, identifying the most influential predictors among the data variables (Breiman, 2001). The ability to identify influential predictors and make a feature selection is especially valuable when considering the diverse factors influencing beaver habitats, such as landscape characteristics, environmental conditions, and anthropogenic activities. 1.6 Explanatory Variables In this study, six key explanatory variables were selected to predict habitat suitability for beavers: Topographic Wetness Index (TWI), proximity to hydrological features, elevation, slope, land classification, and baseflow. These choices were informed by insights from previous research, including studies by Allen (1983), Macfarlane et al. (2017), and Fairfax et al. (2023) as well as preliminary testing on variable importance to the accuracy of the model. The Topographic Wetness Index (TWI) is a steady-state wetness index that integrates local upslope contributing area and slope to model the spatial distribution of soil moisture and identify areas with potential for water accumulation (Sørensen, Zinko, & Seibert, 2006). Regions with high TWI values typically exhibit increased water accumulation potential, elevated soil moisture, and dense vegetation—traits indicative of wetland and riverine ecosystems (Sørensen, Zinko, & Seibert, 2006). In this model, TWI is used specifically to identify areas likely to support wetland and riverine ecosystems, as these environments provide the necessary conditions for beaver habitats (Naiman, Johnston, & Kelley, 1988). Moreover, as the Topographic Wetness Index (TWI) assesses variations in hydrological and topographic conditions, its integration with a measure of proximity to hydrological features such as wetlands and rivers aims to distinguish between suitable and unsuitable beaver habitats. This differentiation is based on the distance in meters from environments that have the potential to provide food, shelter, habitat connectivity and dam-building materials that support migration between systems and territorial establishment (Naiman, Johnston, & Kelley, 1988). Topography is modeled using both elevation in meters above sea level (MASL), similar to the EEAGER model by Fairfax et al. (2023), and slope, as utilized by Allen (1983) and Macfarlane (2017). Biological conditions 11 are represented by the British Columbia Land Classification dataset collected and curated as part of the Vegetation Resource inventory from the British Columbia Ministry of Forests (2023). The use of this data set aims to model land cover types as was included by Allen (1983) with the “Land Capability Classification System” from Slough and Sadlier (1977) and the LANDFIRE “Existing Vegetation Type Layer” used by the BRAT model by Macfarlane et al. (2017). Hydrological data is represented by a baseflow model from the Pacific Climate Impacts Consortium (2020), which is used in place of the USGS baseflow model by Wilkowske et al. (2011) used in the BRAT model (2017). 1.7 Study Area The Nechako watershed is located within the Interior Plateau East of the Coast Mountains and extends eastward to the Fraser River near Prince George, British Columbia, (Holland, 1964; Matthews, Picketts, Déry, Parkes, & Sharma, 2015). Central to the watershed, the Nechako River has 102 mapped tributaries flows from west to east, draining an area of 51,900 km² (Helm et al., 1980). The Nechako River has an average gradient of 0.098% and experiences an elevation drop from 853 meters to 564 meters above sea level (MASL). The average annual discharge is 163 m3/s as measured at the Isle Pierre hydrometric station located 34 kilometers west of Prince George (Déry et al., 2012; Helm et al., 1980; Water Survey of Canada, 2023). However, Kenney dam was constructed on Nechako River in 1952 and diverts flow from the Nechako Reservoir to the Kitimat River at Kemano, located south of Kitimat (Kendrick,1987). The mean annual discharge at the Kemano Tailrace varies but typically ranges between 40 m3/s and 55m3/s (Government of Canada, n.d.). Flows in the Nechako River are therefore regulated, and this has resulted in reduced peak flows, a reduction in channel width, detrimental effects on both salmon and sturgeon populations, as well as other aquatic or riparian species (Christensen, 1995). The geological composition of the watershed encompasses an array of rocks from the Permian period to the Holocene period, illustrating a long history of volcanic activity (Helm et al., 1980). The geology features basalts, coarse-grained granite, andesite, dacite, agglomerate, and granodiorite which 12 highlight the diverse volcanic and intrusive rock formations within the watershed (Helm et al., 1980). Additionally, the landscape and geology has been shaped by the impact of glacial processes, as evidenced by the widespread presence of glacial deposits and erosional features (Helm et al., 1980). These deposits vary in thickness and consist of till, outwash, drumlins, and lacustrine sediments. Remnants of the glaciers and sediments associated with former glacial lakes are found through-out this region (Helm et al., 1980; Plouffe & Levson, 2001). This landscape includes distinct meltwater landforms, including channels, eskers, and kettle holes, which play significant roles in shaping the topography and influencing water storage patterns (Margold, Jansson, Kleman, & Stroeven, 2011; Plouffe & Levson, 2001). The climate of the Nechako River watershed features substantial seasonal variations, characterized by cold, long winters and short, dry summers (BCMF, 1998; Benke & Cushing, 2005; Picketts et al., 2016). Heavy precipitation and cold temperatures create a snowpack in the winter, resulting in high freshet flows throughout the spring and into the early summer as temperatures increase (BCMF, 1998; Picketts et al., 2016). The hydrological dynamics of the Nechako River Watershed are fundamentally influenced by these spring freshet flows, although occasional summer thunderstorms in July can provide intermittent hydrological input (Helm, 1980). The bio-geoclimatic classification of the watershed is primarily sub boreal spruce, which is characterized by vast coniferous forests and wetlands dominated by a hybrid of white spruce sub alpine fir and lodgepole pine (BCMoF, 1998). The hydrological, climatic, biological, topographical, and geological characteristics of the watershed have collectively shaped a diverse range of environmental conditions. This environmental diversity has fostered a rich biodiversity, offering habitats for a variety of species including trout, salmon, moose, caribou, elk, and a genetically unique population of white sturgeon (Matthews, Picketts, Déry, Parkes, & Sharma, 2015). 13 Figure 1.1:The Nechako River Watershed, highlighting topographical and hydrological features, including the largest city, Prince George, and the Kenney Dam, which diverts 50% of the Nechako's flow for aluminum smelting (Picketts et al., 2016). 14 The Carrier (Dakelh) Indigenous peoples have resided in the Nechako watershed since time immemorial, enduring the arrival of Europeans in the 19th and 20th centuries, along with the ensuing disease epidemics (CSTC, 2011; Picketts et al., 2015). The governing bands of the Carrier people include the Tl'azt'en, Nak'azdil, Saik'uz, Takla Lake, Stellat'en, Lheidli T'enneh, and Cheslatta First Nations (Picketts., et al 2016; Sanderson et al. 2015; CSTC 2016). The landscape of the Nechako watershed began to change significantly in the 19th and 20th centuries due to resource extraction activities like mining, agriculture, hydroelectric damming, and forestry (Picketts et al., 2016). The construction of the Kenney Dam by the Aluminum Company of Canada in the early 1950s is a notable example of continued human impact (Picketts et al., 2016). Additional anthropogenic disturbances, such as agriculture, cattle grazing, forest harvesting, forest fires, and Mountain Pine beetle infestation, have dramatically altered the region's ecosystems, impacting both human and animal populations (Picketts., 2020). Fish populations have been classified as endangered by the Committee on the Status of Endangered wildlife in Canada (COSEWIC), which has consequently affected indigenous fishing practices (Forsite Consultants Ltd., 2019). Environmental groups, residents, and landowners have expressed concerns about these ecological changes, noting the loss of biodiversity and shifts in ecosystem control. Forsite's (2019) report includes observations of overbank flooding, persistent erosion, and riparian vegetation loss. Further impacts include channel instability and additional vegetation loss due to cattle grazing, road development, timber harvesting, and forage production. Efforts to mitigate habitat degradation include stream bank stabilization projects and the designation of protected areas like provincial parks, Ungulate Winter Ranges, Fishery Sensitive Watersheds, and Old Growth Management Areas (Forsite Consultants Ltd., 2019). Despite these initiatives, disturbances, particularly on private lands in valley-flat areas near rivers and streams, continue to be a challenge (Forsite Consultants Ltd., 2019). While previous research has mapped current conditions, significant information gaps remain due to evolving governmental objectives and reduced data collection and monitoring. These 15 gaps highlight the need for further research into both current and historical watershed conditions to develop effective recovery and mitigation strategies (Forsite Consultants Ltd., 2019). 16 2. Methodology This section details the methodology for identifying beaver habitats using satellite imagery and field visits, the development of the predictive machine learning model in R and Arc GIS, and the evaluation of the model through the calculation of accuracy metrics. The workflow in Figure 4.1 outlines the use of explanatory variables to represent environmental characteristics, with dependent variables comprising beaver dam locations and random points tested against known beaver dam locations. Data processing is conducted in R to normalize datasets, followed by training a random forest model with 500 trees and a 70%-30% training split. The results are then overlaid onto a raster to generate habitat suitability scores ranging from 0 to 1. 17 Figure 2.1: Workflow programmed and processed in R using the machine learning R package “randomForest” developed by Liaw & Wiener in 2022. 18 2.1.1 Presence Points: Beaver Dam Locations The beaver habitat dataset was created in part with the British Columbia Provincial government to assess the distribution of beavers throughout the Nechako Watershed. The beaver dam locations were identified using both ESRI and Google satellite imagery (8941) as well as field visits (22) for dates between 2021 to 2023. The location of each beaver dam was stored as a point location and catalogued into a dataset in ArcGIS pro. In Figure 2.2, beaver dams are identified using high-resolution satellite imagery with spatially referenced data points created to store their location. Beaver dams are distinguishable by the build-up of materials such as mud and woody debris, often covered in vegetation, which disrupts the flow of streams or rivers (Fairfax & Whittle, 2020). This disruption causes water to disperse, saturating the surrounding areas and pooling at the foot of the dam (Fairfax & Whittle, 2020). Figure 2.2: Right frame shows satellite imagery of beaver dams identified by the pooling of water and structure spanning the stream. Left frame shows the data points used to identify the locations of beaver dams. 19 Field visits were focused on areas of interest around the Chilako River, Nechako River, and Murray Creek watershed where a total of 22 beaver dams were identified. Additionally, seven dams identified from satellite imagery were validated in the field. 2.1.2 Randomly Generated Points A set of randomly generated points was created to represent areas absent of beaver dams to test against the known beaver dam locations and train the model. These points were generated within the same boundaries as the known beaver dam locations but without actual dam presence. This approach in R allowed for a direct comparison between points with and without beaver dams, facilitating a more nuanced understanding of the environmental variables influencing beaver dam construction. The inclusion of these 'absence' points enables the model's ability to test for correlation between environmental values and suitable and unsuitable locations for beaver dams. 2.2 Explanatory Variables In the model training process, six explanatory variables were utilized: elevation, topographic wetness index, slope, land classification, proximity to hydrological features, and baseflow. To maintain consistency with the source data, all layers were converted to a 25m resolution, matching that of the TRIM digital elevation model obtained from the British Columbia Data Catalogue (B.C. Ministry of Forests., 2022). Large lakes (exceeding 0.30 km²) and major rivers, deemed unsuitable for beaver dam construction, were excluded from the analysis. This exclusion is based on beaver habitat preferences of small lakes, river and streams and locations of existing beaver dams (Slough and Sadleir 1977). Furthermore, all layers were confined to the boundary of the Nechako river watershed. Table 2.1 provides an overview of each dataset used for the analysis as well as a description and the component of the habitat in which it is used to represent. 20 Table 2.1: Overview of data used to train the machine learning model. Explanatory Variable Description Formula Habitat Component Source Topographic Wetness Index (TWI) A function of slope and the upstream contributing area, used to identify areas with potential for water accumulation based on topography 𝑇𝑜𝑝𝑜𝑔𝑟𝑎𝑝ℎ𝑖𝑐 𝑊𝑒𝑡𝑛𝑒𝑠𝑠 𝐼𝑛𝑑𝑒𝑥 = ln ((𝑎 + 1 ∗ 𝑐𝑒𝑙𝑙 𝑠𝑖𝑧𝑒)𝑡𝑎𝑛 𝛽 Hydrology and Topography British Columbia TRIM 25m Resolution Digital Elevation Model. Distance to Hydrological Features Distance of Beaver Dam or Random point to river, stream, pond, or lake. 𝑑= (√(𝑥2 − 𝑥1)2 + √(𝑦2 − 𝑦1)2 Hydrology British Columbia Fresh Water Atlas. Elevation Height above sea level Topography British Columbia TRIM 25m Resolution Digital Elevation Model. Slope Steepness 𝑚 = 𝑦2 − 𝑦1𝑥2 − 𝑥1 Topography British Columbia TRIM 25m Resolution Digital Elevation Model. Topographic Wetness Index (TWI) Potential water accumulation (𝑇𝑊𝐼) = ln ((𝑎+1∗𝑐𝑒𝑙𝑙 𝑠𝑖𝑧𝑒)𝑡𝑎𝑛 𝛽) Topography British Columbia TRIM 25m Resolution Digital Elevation Model. Land Classification Classification of dominant land type for each polygon Biological British Columbia Vegetation Resource Inventory. Baseflow Portion of river discharge sustained by groundwater seeping into the stream channel (Hawley, Thomas, & Acosta, 2023). Hydrology and Topography Pacific Climate Impacts Consortium, Gridded Hydrologic Model Output. Proximity to Hydrological Feature Proximity of location to hydrological feature 𝑃𝑟𝑜𝑥𝑖𝑚𝑖𝑡𝑦 = min √(𝑥2 − 𝑥1)2(𝑦2 − 𝑦1)2 Topography and Hydrology British Columbia Fresh Water Atlas 21 2.2.1 Elevation The foundational data layer for this study—a digital elevation model (DEM) derived from the TRIM dataset—was chosen for its comprehensive coverage and suitable resolution of 25 meters, aligning with the context of average beaver dam lengths ranging from 10-20m (Zavyalov, 2014). While lidar and other high-resolution datasets provide finer detail, their limited coverage of the study area, along with considerations of public access and resolution requirements, justified the use of the TRIM topographic data. Nonetheless, it's important to acknowledge that LiDAR and photogrammetrically derived DEMs offer greater accuracy in modeling topography and stream topology, making them preferable for focused analyses within smaller areas of interest (Hopkinson, Hayashi, & Peddle, 2009). 2.2.2 Topographic Wetness Index Topographic wetness index (TWI) is a function of slope and the upstream contributing area, and used to identify areas with potential for water accumulation based on topography (Sorensen et al., 2006). 𝑇𝑜𝑝𝑜𝑔𝑟𝑎𝑝ℎ𝑖𝑐 𝑊𝑒𝑡𝑛𝑒𝑠𝑠 𝐼𝑛𝑑𝑒𝑥 (𝑇𝑊𝐼) = ln ((𝑎+1∗𝑐𝑒𝑙𝑙 𝑠𝑖𝑧𝑒)𝑡𝑎𝑛 𝛽) Equation 1: Topographic Wetness Index (Sorenson et al., 2006) In equation (1) 𝑎 represents the cumulative upslope area draining through a point, the cell size of the digital elevation data was set to 25m, and β represents the slope in radian units (Bystron, 2018). While TWI offers invaluable insights, it is important to note its limitations as it primarily predicts potential water accumulation based on topography, but actual water distribution can be influenced by other factors, such as soil composition, bedrock permeability, or human interventions (Sorensen et al., 2006). A TWI tool was developed in ArcGIS based on equation 1. This tool was used to calculate the TWI layer for the analysis in R. 22 2.2.3 Slope Topographic slope and stream slope were used to characterize terrain attributes and stream networks. The general topographic slope was determined using Ritter's (1987) method, which calculates each raster pixel's inclination as a plane relative to the horizontal plane by deducing the normal vector from the elevation values of surrounding pixels. The slope thus represents the ratio of vertical change (rise) to the horizontal extent (run) across the pixel. These calculations were executed in R using the terrain package, which incorporates Ritter's method (Mahoney, Beier, & Ackerman, 2022). In conjunction with the topographic slope, stream slope was calculated and applied to the centerline of stream networks within the study area. To determine stream slope, elevations at the start and end points of stream segments, ranging between 200 to 300 meters in length, were measured. The size of stream segment was chosen as this follows the stream calculation process as part of the BRAT tool (Macfarlane et al., 2017). The stream slope is then calculated by dividing the difference in these elevation points by the length of the stream segment. This method yields the change in height per unit length, providing a measure of upstream to downstream slope. The stream slope raster, values along a centerline derived from the Freshwater Atlas stream network (Government of British Columbia, n.d.), was overlaid onto the calculated topographic slope layer to produce the combined slope layer used for the analysis. 2.2.4 Vegetation Inventory Land classification is used to identify areas by their predominant attributes which can include land use, vegetation type, or dominant hydrological features. In this analysis, an assessment of five land classification layers (BCLCS) from the BC Vegetation Resource Inventory was completed to determine their relevance to the presence or absence of beaver dams. These layers varied in detail, offering between 3 to 34 categories of land classification. Of these, the BCLCS level 4, which comprises of 14 different dominant land cover classifications, was identified as having the highest significance in identifying beaver dam presence or absence (British Columbia Ministry of Forests, 2023). BCLCS 4 includes the 23 classification of forested areas based on the dominant type of tree, shrub cover, herb cover, Bryoid cover as well as rock, or exposed land. In areas that were data was not available for the BCLCS 4 classification, BCLCS 5 classification data was inserted. 2.2.5 Proximity to Hydrological Features Hydrological features such as wetlands, lakes, rivers and streams were accessed from the BC Fresh Water Atlas (Government of British Columbia, 2022) and used to calculate a distance to feature raster in ArcGIS representing meters from each cell of the raster to a hydrological feature. 2.2.6 Baseflow Baseflow data was sourced from the Pacific Climate Impacts Consortium (PCIC) Gridded Hydrologic Model Output (Pacific Climate Impacts Consortium, 2020). This dataset provided a 30𝑘𝑚2 resolution gridded baseflow projection, which covered the entirety of the Nechako watershed. The PCIC baseflow data was generated using an enhanced version of the Variable Infiltration Capacity (VIC-GL) model, originally developed by Liang et al. (1994). This model is designed to simulate essential land surface processes, including surface water infiltration, evaporation, and runoff (Liang et al., 1994). An average baseflow was calculated for thirty years from 1982 to 2012 and converted to a higher resolution of 25 meters, to match the other five layers used in the analysis. Using higher resolution hydrological data could yield a more detailed hydrological characteristic such as discharge for individual streams to be included in the model. However, this potential is constrained by the limited number of active stream gauges within the study area and the absence of open-source models for sub-watersheds. All extractions, calculations and processing were completed in R. 2.3 Processing in R All point location data (beaver dam locations and randomly generated points) and explanatory variables were re-projected in the NAD 1983 BC Environment Albers coordinate system. This step guarantees 24 spatial alignment and accuracy across the employed datasets. Explanatory variable datasets, originally in various spatial resolutions and formats, were systematically converted into rasters. The rasters were then resampled to a uniform resolution of 25m x 25m, to ensure consistency, computational efficiency and spatial detail necessary for identifying landscape features relevant to beaver dam locations. To address potential biases in model training and prediction, an equal number of known beaver dam locations (8,963) and randomly generated absence points were used. This approach mitigates the skewness often encountered in presence-only datasets, leading to a more robust and reliable predictive model. 2.3.1 Model Development and Training Utilizing the “randomForest” package (Liaw & Wiener, 2002), the model was trained to predict the binary presence or absence of beaver dams based on six explanatory variables. The model first calculates the probability of each prediction being correct, which is then used to generate a suitability score ranging from 0 to 1. This score indicates the likelihood of an area being suitable for beaver dam presence based on the provided data. The data was randomly partitioned, allocating 70% for training and 30% for testing. Such a split ensures ample data for learning while retaining a substantial subset for unbiased evaluation of the model (Breiman, 2001). The model was configured to operate with 500 trees. Given that Random Forest models benefit from larger numbers of trees, this choice aims to enhance the model's ability to learn complex patterns and interactions among explanatory variables without overfitting (Breiman, 2001). The first output was a classification-type model, calculating a binary outcome (presence or absence of beaver dams). The probability of each prediction was calculated to provide a range of values from 0 to 1 to represent suitability. The trained model was then applied to the areas within the borders of the Nechako watershed, projecting a suitability score of 0 to 1. The values were averaged by 3 x 3 grids and applied to centerlines from the stream routes dataset from the freshwater atlas (Government of British Columbia, 2022). To calculate the relative importance of each explanatory variable, the model was configured with the 25 importance parameter set to true. This setting enabled the calculation of the Mean Decrease in Accuracy which quantifies each explanatory variable’s importance to the predictive accuracy of the model (Breiman, 2001). The Mean Decrease in Accuracy is calculated by permuting the values of each feature across the dataset and observing the resultant decrease in the model's predictive accuracy (Breiman, 2001). This decrease in accuracy highlights the impact of each feature on the overall model performance, with a larger decrease indicating a more important feature. The insights gained from these importance measures were instrumental in understanding the contribution of each environmental variable to the model's ability to predict suitable locations for beaver dam construction. After conducting these permutations, the importance scores were normalized to express the relative contribution of each feature as a percentage. It's important to note that while these metrics provide a valuable overview of feature importance, they should be interpreted cautiously, especially in datasets with mixed types of features, due to potential biases towards features with more categories or continuous variables. This method, executed within the R environment, provided a quantifiable understanding of how each environmental characteristic influenced the model’s ability to accurately predict suitable locations for beaver dam construction. 26 3. Results The results include accuracy metrics on the model, explanatory characteristic importance values, a raster layer indicating dam location suitability for streams throughout the Nechako watershed and summary statistics for the dam locations. 3.1 Model Performance In the evaluation of the model, performance metrics were calculated to assess its accuracy and reliability. The model's predictive performance was evaluated using accuracy metrics precision, recall, and the F1-score as outlined by Breiman (2001). Precision is calculated as the ratio of true positive predictions (TP) to the sum of true positives and false positives (FP), defined by the equation (2). 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 =𝑇𝑃(𝑇𝑃 + 𝐹𝑃) Equation 2: Precision (Breiman, 2001) Recall, also known as sensitivity, measures the ratio of true positives to the sum of true positives and false negatives (FN), calculated by equation (3). 𝑅𝑒𝑐𝑎𝑙𝑙 = 𝑇𝑃𝑇𝑃 + 𝐹𝑁 Equation 3: Recall (Breiman, 2001) The F1-score provides a balance between precision and recall, calculated by the equation (4). 𝐹1 𝑆𝑐𝑜𝑟𝑒 = 2 ∗ (𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 ∗ 𝑅𝑒𝑐𝑎𝑙𝑙)(𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 + 𝑅𝑒𝑐𝑎𝑙𝑙) Equation 4: F1 Score (Breiman, 2001) The mean of precision and recall offers a comprehensive metric for assessing the model's accuracy in correctly predicting beaver dam locations. The model achieved an accuracy of 97%, indicating a high rate of correct predictions overall. Precision was measured at 95%, which indicates that 27 95% of the predicted positive instances were true positives. The recall of the model was 98%, showing its capability to correctly identify the majority of actual positive cases. The F1-score, which is a balanced measure of precision and recall, was calculated to be 97%. These metrics collectively suggest that the model is effective in classifying cases accurately, demonstrating a balanced performance between identifying positive cases and correctly ruling out negative cases. The accuracy metrics are calculated based on a binary prediction of areas absent of beaver dams or areas with beaver dams present. The accuracy is based on a binary prediction of absence or presence for each 25m x 25m cell. Table 3.1: Model performance metrics calculated in R. Metric Result Description Accuracy 97% This is a measure of how often the model is correct. Precision 95% Out of all the positive predictions made, how many were actually correct. Recall 98% Out of all the actual positives, how many did we predict correctly. F1-Score 97% This is the mean of precision and recall and is a metric that tries to balances the two. 3.2 Importance of Variables In the development of a machine learning model for predicting suitable locations for beaver dam construction, various environmental features were evaluated for their importance in the model's decision-making process. In machine learning, "importance" refers to the contribution of each variable in making accurate predictions. It quantifies the extent to which each feature improves the model's performance, measured by the increase in prediction accuracy when a feature is used in the model. The importance value of each variable is outlined in Table 3.2. 28 Table 3.2: Importance value of explanatory variables used in the analysis. Metric Importance Data Source Slope 31.6% Calculated from DEM Proximity to Hydrological Features 23.0% From British Columbia Fresh Water Atlas Land Classification 14.7% Sourced from Vegetation Resource Inventory BCLCS 4 and 5 Elevation 25m 14.4% British Columbia TRIM Digital Elevation Model Baseflow 9.2% From Pacific Climate Impacts Consortium Topographic Wetness Index 7.2% Calculated from DEM Slope, derived from the British Columbia TRIM Digital Elevation Model (British Columbia Ministry of Forests, Lands, Natural Resource Operations and Rural Development, 2022), emerged as a major factor with an importance of 31.59%, suggesting its critical influence on site suitability. Proximity to hydrological features, informed by the British Columbia Fresh Water Atlas (Government of British Columbia, 2022), was identified as another significant factor with an importance of 22.97%. Land classification data, sourced from the Vegetation Resource Inventory BCLCS 4 and 5 (British Columbia Ministry of Forests, 2023), accounted for 14.65% of the model's predictive power. Elevation, derived from the same DEM, contributed 14.35% to the model's predictions, underscoring its significance in determining suitable dam locations. Baseflow information from the Pacific Climate Impacts Consortium contributed 9.22% to the model’s predictive ability. The Topographic Wetness Index, calculated from the same DEM, had an importance of 7.21%, indicating its role, albeit lesser than elevation, in the model's analysis. These metrics collectively offer a comparative ranking of the importance of the parameters investigated used to identify potential sites for beaver dam construction. 3.3 Preferred Habitat Based on Explanatory Variable Values This section discusses the potential physical significance of the descriptive statistics for six parameters investigated in this study: elevation, Topographic Wetness Index (TWI), slope, proximity to hydrological features, baseflow, and land classification. These values give an insight into the 29 environmental characteristics that make a location suitable for beaver dams. The identified beaver dams located within the Nechako watershed range in elevation between 639 to 1481 meters above sea level (m asl), with the average elevation at beaver dam locations being 875 meters. The elevation range of the entire Nechako watershed varies between 550 to 2750 meters, with an average elevation of 993 meters. This analysis suggests that beaver dams are primarily situated in the lower elevation areas of the watershed (see Figure 3.3). However, the lack of beaver dams in some lower elevation areas could reflect land use development or the effects of historic trapping activities. Similarly, the absence of beavers at higher elevation sites could reflect increasing slopes, lack of suitable vegetation, or other factors. The Topographic Wetness Index (TWI) values at beaver dam locations range from 2.5 to 16.5, with a mean of 8.8, indicating that areas with beaver dam locations are characterized by wet conditions and high-water accumulation potential (see Figure 3.1). At beaver dam locations, the slope was minimal, averaging 0 degrees with a maximum value of 0.5 degrees, indicating a preference for areas with predominantly flat to gently sloping terrain (see Figure 3.4). The average distance of beaver dams from hydrological features was 10.7 meters, ranging between 0 and 50 meters, suggesting that these structures are principally located in close vicinity to, or directly within, rivers, streams, ponds, and lakes as classified by the BC Freshwater Atlas (Government of British Columbia, 2022). The mean baseflow at beaver dam locations was 0.3 cubic meters per second (m³/s), indicating the presence of perennial water availability at most beaver dam locations. However, the range from 0.1 m³/s to 24 m³/s indicates a diversity in hydrological conditions (see Figure 3.2). Beaver dams were located in areas predominantly featuring shrubby vegetation, with vegetation classifications at these locations including Low Shrub, characterized by an average height under two meters (2321 locations), and Tall Shrub, with heights of two meters or more (2635 locations), as classified by the British Columbia Land Classification system (British Columbia Ministry of Forests, 2023). The locations of beaver dams also frequently included Treed Coniferous vegetation (1768 30 locations) as well as grassy and herbaceous zones, classified as Herb Graminoids (896 locations) and Herb (836 locations). Treed Broadleaf (76 locations), Treed Mixed (254 locations), and Herb Forbs (7 locations) were found in fewer beaver dam locations. The presence of Rock Rubble (1 location), Byroid (2 locations), and Exposed Land (27 locations) was minimal. Small Lake/River vegetation classifications were observed in 106 beaver dam locations (see Figure 3.5). Figure 3.1: Distribution of Topographic Wetness Index 31 Figure 3.2: Distribution of Baseflow Figure 3.3: Distribution of Elevation 32 Figure 3.4: Distribution of Slope 33 Figure 3.5: Distribution of Land Classification 3.4 Model Output Figure 3.6 presents the Beaver Habitat Suitability Model overlaid on an area of interest within the Nechako Watershed. The model output assigns a suitability score from 0.0 to 1.0 to the streams, visualized through a gradient of colors from red, indicating the highest suitability, to purple, showing the least suitable areas for beaver habitation and dam construction. The scores are derived from an average taken over a 3 x 3 cell grid with each cell measuring 25 meters, providing an assessment that accounts for the immediate environmental context. 34 Figure 3.6: Model with a beaver habitat suitability score extracted along the centerlines of streams, rivers, and ponds. 35 4. Discussion The model developed for predicting beaver dam location suitability in the Nechako watershed exhibited an accuracy of 97%, with precision, recall, and F1-score values also above 95%. These metrics indicate a high level of measured accuracy in the model's predictions, suggesting its effectiveness in distinguishing between suitable and unsuitable locations for beaver dams. The balance between precision and recall in the model is particularly noteworthy, as it reflects its capability to correctly identify suitable locations while minimizing errors. The model identified slope (31.6 %) and proximity to hydrological features (23.0 %) as significant variables influencing beaver dam location suitability. This finding aligns with known preferences of beavers for flatter terrains and proximity to water bodies as described by Slough and Sadleir (1977). Elevation, Topographic Wetness Index, land classification, and baseflow also contributed to the model, albeit to a lesser extent, indicating that in this context topography is more important determining habitat suitability. Additionally, the analysis of environmental conditions at beaver dam locations revealed a broad range in elevation (639 to 1481m), suggesting the range of elevations that beaver dams can be constructed. The average Topographic Wetness Index (8.8) and its range (2.5 – 16.5) indicate a preference for wet conditions and areas with high water accumulation potential. The minimal average slope at beaver dam locations, consistently measured at 0 degrees, underscores the preference for flat or nearly flat terrain, which is ideally suited for the construction and persistence of beaver dams. These statistics provide a comprehensive view of the environmental characteristics prevalent at beaver dam locations throughout the Nechako River Watershed. Future research could explore the applicability of the model in other geographical contexts or include additional environmental variables. 4.1 Comparison with previous models The application of a random forest machine learning model to assess beaver habitat suitability in the Nechako watershed represents a novel approach within this area of research. However, this method 36 builds upon the conceptual frameworks established by earlier models such as the Beaver Habitat Suitability Index model developed by Allen (1983) and the BRAT model by Macfarlane et al (2017). The Habitat Suitability Index (HSI) model developed by Allen (1983) serves as an early outline for the ecological modeling of beaver habitats. The Machine learning method incorporates biological, topographical, and hydrological variables as outlined by Allen (1983), yet leverages advancements in GIS technology to build upon the HSI's suitability scale. The use of GIS in this context allows for the ability to incorporate a wider array of environmental variables on a larger scale, enabling the development of a suitability model that can be applied remotely across extensive areas such as the Nechako River Watershed. The EEAGER model developed by Fairfax (2023) and the Beaver Restoration Assessment Tool (BRAT) developed by Macfarlane (2017) are examples of contemporary habitat modeling approaches that utilize GIS technologies as integral components of their models. The EEAGER model (2023) employs neural networks to analyze high-resolution imagery to identify beaver dams and beaver complexes. The current study diverges from the EEAGER model, which identifies beaver dam locations using satellite imagery and a digital elevation model, by instead predicting habitat suitability through calculated values of preferred hydrological, biological, and topographical environmental factors (Fairfax et al. 2023). Similarly, the BRAT model, with its analysis of hydrological, vegetative, and topographical data, aligns closely with the approach of this study (Macfarlane et al., 2017). Both models incorporate a variety of environmental variables to predict habitat suitability. However, the current model benefits from the random forest algorithm's robust handling of complex, non-linear relationships between variables. Data resolution and availability present challenges in British Columbia. Unlike the BRAT (2017) and EEAGER (2023) models, which employ high-resolution data from the United States, this study adjusts to the data resolutions and datasets available within British Columbia. A significant limitation is the absence of stream scale hydrological data for the Nechako watershed—a contrast to the BRAT model, which incorporates baseflow stream power and two-year flood stream power (Macfarlane et al., 2017). 37 4.2 Importance of Environmental Variables The results of the model provide a nuanced understanding of the various environmental factors that influence beaver habitat suitability. Notably, slope is identified as the most significant variable with a weight of 31.6 %, aligning with ecological studies that emphasize the preference of beavers for flatter terrains conducive to dam building (Slough and Sadleir, 1977). This finding underscores the need to consider topographical features in habitat assessments for beaver re-establishment projects. Proximity to hydrological features, carrying a weight of 23.0 %, also plays a substantial role. This reflects beavers' reliance on water bodies for habitat, a factor highlighted in literature on beaver ecology (Bradt, 1938; Brazier et al, 2021; Jenkins, 1975). Land classification (14.7 %) and elevation (14.4 %) also contribute significantly to habitat suitability. These variables account for the diversity of landscapes and altitudes at which beavers can construct dams, suggesting a broad ecological adaptability to food sources, building materials and elevation. The emphasis on land classification aligns with findings by Denney (1952), who noted the impact of vegetation type such as willows, alders and cottonwoods on beaver habitat selection. The Topographic Wetness Index (7.2 %) and baseflow (9.2 %) have lesser but still notable influences. The Wetness Index, indicating potential water accumulation areas, and baseflow, reflecting underlying hydrological processes, both play roles in determining suitable habitats, albeit to a lesser extent than the more directly observable physical characteristics. This distribution of variable importance is a key finding, offering a comprehensive perspective on the multifaceted nature of beaver habitat suitability and providing a blueprint for similar ecological modeling efforts. 4.3 Reproducibility and Applicability to Other Watersheds The analysis in this study utilized topographic and ecological data, which are available across British Columbia. However, for broader application beyond the Nechako watershed, it's recommended to modify certain data measures. Specifically, elevation data should be transformed into a relative elevation metric to ensure relevance in province wide geographic regions. While the baseflow dataset covers a 38 substantial part of British Columbia, its availability is not universal across the province. This limitation necessitates cautious application or adaptation of the model in areas where baseflow data may be incomplete or absent. Beaver dam locations, as currently inventoried, are specific to the Nechako watershed. Before extending the model's application to other regions, it's crucial to compare the environmental characteristics of these known dam locations with potential sites elsewhere. This comparison will help determine if the model's predictive power remains valid in other regions. There is an opportunity to map and include additional beaver dam locations to enhance the model's accuracy and relevance in areas outside the Nechako watershed. Incorporating these new data points will not only refine the model's predictive capabilities but also provide a more comprehensive understanding of beaver habitat preferences across varied landscapes. This expansion of data scope, coupled with adaptations to the model's parameters, is essential for ensuring its effectiveness and accuracy when applied to different geographical contexts. 4.4 Application The methodologies employed in this study offer a reproducible framework for habitat suitability assessment that can be applied to any region with available environmental data. The versatility of the model provides an opportunity to extend its use beyond the Nechako watershed, which could significantly broaden its scope and enhance its utility in large-scale environmental management. The insights gained regarding beaver habitat preferences can be juxtaposed with findings from other studies, potentially enriching the collective understanding of beaver ecology. Such comparisons may reveal consistencies or discrepancies that could guide future research directions and contribute to more informed ecological decision-making. The predictive power of the model is especially pertinent to restoration initiatives as it can serve as a strategic tool to easily identify suitable sites for beaver re-introduction or for the construction of beaver dam analogs or other measures to assist in the re-establishment of beaver dam like habitats. This application directly supports the objectives of conservation organizations, as exemplified by the British Columbia Wildlife Federation's initiative to 39 construct 100 beaver dam analogs across the province (B.C. Wildlife Federation, 2023). By aligning with these restoration efforts, the model not only emphasizes the importance of scientific research in ecological restoration but also highlights the practical implications of machine learning in resource management and conservation strategies. 4.5 Limitations The primary limitations of this model stem from the availability and resolution of data, particularly regarding discharge information at a stream scale. As of 2024, there are fifteen active hydrometric stations in the Nechako River watershed positioned primarily on major rivers such as the Nechako and Stuart Rivers (Government of Canada, 2024). The limited number of monitoring stations restricts to the availability of site-specific discharge data, which is used for modeling of stream and river flow metrics. Although discharge rates have been modeled at a sub-watershed level and are accessible through the Freshwater Atlas British Columbia Watershed Tool, comprehensive data for large areas, such as the Nechako watershed, remain inaccessible (Government of British Columbia, 2024). As a result, the model currently relies on baseflow modeling data from the Pacific Climate Impacts Consortium which has been converted from a 30 𝑘𝑚2 grid to a 25𝑚2 grid (Pacific Climate Impacts Consortium, University of Victoria, 2020). While this approach provides valuable insights, it cannot fully replace the need for direct, stream-level discharge data. A more refined model incorporating discharge information at the level of individual streams is recommended to assist future model development. Such improvements would not only increase the accuracy of the model but also offer a more nuanced understanding of hydrological dynamics within the Nechako watershed. Pursuing higher-resolution data and advancing hydrological modeling techniques are crucial for enhancing the predictive power of future model iterations. The model's current reliance on available discharge data may lead to some areas with unsuitable discharge rates being indicated as highly suitable due to favorable values for other variables. 40 The layers used in the model have been standardized to a resolution of 25 meters, applied to stream centerlines at the same resolution. However, it is important to note that slope measurements were calculated for longer segments (200-300 meters), which should be considered in the model's output. Currently, the model was trained and validated within the Nechako watershed. Its accuracy has been assessed based on this specific geographic area. Future studies should evaluate the model's performance in other regions. This limitation highlights the need for cautious application of the model's findings outside the Nechako watershed until further validation is completed. 41 5. Conclusion This study employed a random forest machine learning approach to assess the suitability of habitats for beaver dams within the Nechako watershed in British Columbia. The model, which incorporated a comprehensive range of environmental data, demonstrated high accuracy in predicting identified locations for beaver dams, as evidenced by its 97% accuracy score along with high precision, recall, and F1-score values. Environmental variables such as slope, proximity to hydrological features, elevation, land classification, and baseflow were analyzed for their influence on habitat suitability. The results indicated that slope and proximity to water bodies were particularly significant in determining suitable locations for beaver dam construction. The descriptive statistics of these environmental conditions at existing beaver dam locations provided further insights into the habitat preferences of beavers, highlighting their adaptability and the diverse range of environmental conditions they inhabit. The findings of this study have important implications for ecological restoration and conservation efforts, especially in the context of beaver reintroduction and beaver dam analog construction strategies. By identifying areas most suitable for beaver habitation, this research aids in making informed decisions that could enhance the success of reintroduction projects and contribute to the ecological balance of freshwater ecosystems. The use of a machine learning model in this context demonstrates the potential of such technologies in ecological studies and conservation planning. While the study presents a comprehensive analysis, future research could extend its findings by applying the model to other regions, incorporating additional environmental variables, or conducting long-term monitoring of reintroduced beaver populations. Such efforts would further validate and refine the model, enhancing its applicability and utility in broader ecological contexts. In conclusion, this research underscores the significance of advanced predictive modelling in understanding wildlife habitat preferences and offers a valuable tool for informing wildlife management and habitat restoration practices. The successful application of the random forest model in this study contributes to the growing body of knowledge on the integration of 42 technology and ecological research, aiding in the support of informed and effective conservation strategies. 43 References Allen, Arthur W. Habitat Suitability Index Models: Beaver. U.S. Fish and Wildlife Service, 1983. FWS/OBS-W10.30 Revised. 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Thesis/Dissertation
eng
Geography
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Hassan, Marwan A.
Predictive modelling of beaver habitats using machine learning
Text