Forestry, Faculty of
DSpace
UBCV
Stewart, Laura Nicole
Master of Science - MSc
University of British Columbia
Monitoring animals in northern Canada is especially difficult, and there is a need for cost-effective methods and sampling designs. Camera traps and autonomous recording units (ARUs) are both promising tools, but they are rarely combined. This thesis focuses on two areas of integration: sampling design and habitat modeling. Data were collected in Ts’udé Nilįné Tuyeta, an Indigenous and Territorial Protected Area in the Northwest Territories. Hierarchical sampling designs, where sensors are deployed in clusters, are common for ARUs but rarely used for camera traps. I evaluated a hierarchical design for camera traps using resampling to determine the sample size required to estimate detection rate, habitat associations, and species richness across our study area. I found that 4 cameras per cluster across 35 clusters were sufficient for most of the metrics tested, but up to 13 cameras per cluster were needed to precisely estimate detection rate of rarer species. Cluster-scale species richness was unreliable even with up to 18 cameras per cluster, suggesting richness is best estimated at a larger scale. In my third chapter, I evaluated the use of ARUs and camera traps to create habitat models for sandhill crane Antigone canadensis. I tested for differences in the spatial scale of inference by performing model selection using two buffer sizes, and whether a model that integrated both data types would improve predictive performance. I found that ARUs made the best inferences at the landscape scale, while cameras made the best inferences at the home range scale. The integrated model did show evidence for hierarchical habitat selection by cranes, but did not improve predictive performance. Overall, I found that each data source had merits of its own, but that, in this case, integration into one model was not an improvement over modeling data separately. These two data chapters showcase two benefits of pairing camera traps and ARUs: first, improving response variables by carefully considering sampling design; and second, improving our understanding of habitat selection by using data from both sensors. Efforts should continue to apply and test these and other methods for effective wildlife monitoring in Canada’s rapidly changing northern environments.
INTEGRATING CAMERA TRAPS AND AUTONOMOUS RECORDING UNITS FOR WILDLIFEMONITORING IN THE NORTHWEST TERRITORIESbyLAURA NICOLE STEWARTB.Sc., The University of British Columbia, 2018A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinTHE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES(Forestry)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)August 2023© Laura Nicole Stewart, 2023The following individuals certify that they have read, and recommend to the Faculty of Graduateand Postdoctoral Studies for acceptance, the thesis entitled:Integrating camera traps and autonomous recording units for wildlife monitoring in theNorthwest Territoriessubmitted by Laura Nicole Stewart in partial fulfillment of the requirements forthe degree of Master of Sciencein ForestryExamining Committee:Dr. Cole Burton, Associate Professor, Forestry, UBCSupervisorDr. Samuel Haché, Boreal Ecologist, Government of CanadaSupervisory Committee MemberClaudia Haas, Protected Areas Biologist, Government of the Northwest TerritoriesSupervisory Committee MemberDr. Kaitlyn Gaynor, Assistant Professor, Zoology, UBCAdditional ExaminerAdditional Supervisory Committee Members:Dr. Kathy Martin, Professor, Forestry, UBCSupervisory Committee MemberiiAbstractMonitoring animals in northern Canada is especially difficult, and there is a need forcost-effective methods and sampling designs. Camera traps and autonomous recording units(ARUs) are both promising tools, but they are rarely combined. This thesis focuses on two areasof integration: sampling design and habitat modeling. Data were collected in Ts’udé NilįnéTuyeta, an Indigenous and Territorial Protected Area in the Northwest Territories.Hierarchical sampling designs, where sensors are deployed in clusters, are common for ARUsbut rarely used for camera traps. I evaluated a hierarchical design for camera traps usingresampling to determine the sample size required to estimate detection rate, habitatassociations, and species richness across our study area. I found that 4 cameras per clusteracross 35 clusters were sufficient for most of the metrics tested, but up to 13 cameras percluster were needed to precisely estimate detection rate of rarer species. Cluster-scale speciesrichness was unreliable even with up to 18 cameras per cluster, suggesting richness is bestestimated at a larger scale.In my third chapter, I evaluated the use of ARUs and camera traps to create habitat models forsandhill crane Antigone canadensis. I tested for differences in the spatial scale of inference byperforming model selection using two buffer sizes, and whether a model that integrated bothdata types would improve predictive performance. I found that ARUs made the best inferencesat the landscape scale, while cameras made the best inferences at the home range scale. Theintegrated model did show evidence for hierarchical habitat selection by cranes, but did notimprove predictive performance. Overall, I found that each data source had merits of its own,but that, in this case, integration into one model was not an improvement over modeling dataseparately.These two data chapters showcase two benefits of pairing camera traps and ARUs: first,improving response variables by carefully considering sampling design; and second, improvingour understanding of habitat selection by using data from both sensors. Efforts should continueto apply and test these and other methods for effective wildlife monitoring in Canada’s rapidlychanging northern environments.iiiLay summaryMonitoring animals is important to understand where they are living and how they are changing.Wildlife cameras and audio recording units (ARUs) are both good tools for this because theylast a long time, make observations day and night, and are not invasive.Using clusters of cameras gives us a better chance of getting pictures of animals in the area. Ifound that using clusters of 4 cameras were good for common animals, but clusters of 13 wereneeded for rare animals.I integrated sandhill crane information from cameras and ARUs. I found that camera informationwas better for understanding crane habitat within 300 m to the camera, but ARU informationwas better for understanding crane habitat in a larger area. The integrated information wasn’tbetter than looking at ARU and camera information separately.Overall, using both ARUs and cameras was good for studying more animals and understandinganimal habitats.ivPrefaceAll data used in this thesis were collected as a pilot study in Ts’udé Nilįné Tuyeta Indigenousand Territorial Protected Area by the Northwest Territories Biodiversity Monitoring Program.Partners in this collaboration include the UBC WildCo Lab, Government of the NorthwestTerritories (GNWT), Fort Good Hope Ɂehdzo Got’ı̨nę, Government of Canada, and WilfridLaurier University. Some of the constituting organizations from the K’áhshó Got’ı̨nę Party to theestablishment of Ts’ud’e Nilįné Tuyeta that pre-dated the K’áhshó Got’ı̨nę Foundation wereengaged at the outset of the project. The K’áhshó Got’ı̨nę Foundation, once established,supported some of this work.The field program and sampling design was led by Claudia Haas with GNWT, some members ofthe K’áhshó Got’ı̨nę Party, and Dr. Samuel Haché with ECCC. The K’áhshó Got’ı̨nę monitors,staff from GNWT, staff from ECCC, Dr. Chris Beirne (WildCo Lab), and I assisted with fielddeployments and/or retrievals. Data collection was authorized under Wildlife Permits WL500886and WL500836 issued by the GNWT.Camera trap image tagging was led by me, with assistance from other members of the WildlifeCoexistence Lab at UBC. Autonomous recording unit data transcription and management wasled by Dr. Samuel Haché and Jocelyn Gregoire at ECCC.Research questions were identified by Dr. Cole Burton, Dr. Chris Beirne, and Dr. Samuel Haché,and I conducted all analyses in this thesis. I completed all writing and all revisions of my thesis,with feedback from my committee members and Dr. Chris Beirne as well as staff of the GNWT,and staff of K’áhshó Got’ı̨nę Foundation.I intend to submit material from Chapters 2 and 3 of this thesis for publication in peer-reviewedjournals. These chapters are included in their original forms as publishable manuscripts, exceptthat repeated information (study area, survey design) was consolidated and presented only inChapter 1.vTable of ContentsAbstract....................................................................................................................................... iiiLay summary...............................................................................................................................ivPreface..........................................................................................................................................vTable of Contents........................................................................................................................viList of Tables.............................................................................................................................viiiList of Figures............................................................................................................................. ivList of Abbreviations..................................................................................................................viAcknowledgements...................................................................................................................viiChapter 1: Introduction...............................................................................................................11.1 The importance of cost-effective and non-invasive wildlife monitoring............................11.2 Ts’udé Nilįné Tuyeta Indigenous and Territorial Protected Area...................................... 21.3 Biodiversity monitoring in Ts’udé Nilįné Tuyeta.................................................................41.4 Thesis objectives............................................................................................................. 6Chapter 2: Hierarchical Sampling Designs Improve Estimates of Detection Rate andSpecies Richness from Camera Traps...................................................................................... 82.1 Introduction...................................................................................................................... 82.2 Methods......................................................................................................................... 102.2.1 Image processing....................................................................................................102.2.2 Detection rate..........................................................................................................102.2.3 Habitat model.......................................................................................................... 112.2.4 Species richness.....................................................................................................122.3 Results...........................................................................................................................132.3.1 Detection rate..........................................................................................................132.3.2 Sandhill crane habitat model...................................................................................152.3.3 Species richness.....................................................................................................172.3.4 Synthesis................................................................................................................ 202.4 Discussion..................................................................................................................... 212.4.1 Detection rate..........................................................................................................212.4.2 Habitat model..........................................................................................................222.4.3 Species richness.....................................................................................................232.4.4 Sampling design optimization................................................................................. 242.5 Conclusion..................................................................................................................... 25Chapter 3: Integrating data from camera traps and autonomous recording units toimprove habitat models............................................................................................................ 263.1 Introduction.................................................................................................................... 263.2 Methods......................................................................................................................... 293.2.1 Audio transcription.................................................................................................. 293.2.2 Habitat model..........................................................................................................30vi3.2.2.1 Detection histories............................................................................................... 303.2.2.2 Occupancy and detection covariates...................................................................313.2.2.3 Model specifications and selection...................................................................... 313.3 Results...........................................................................................................................333.4 Discussion..................................................................................................................... 403.5 Conclusion..................................................................................................................... 43Chapter 4: Conclusions............................................................................................................ 444.1 Working together for conservation..................................................................................444.2 Strengths and limitations............................................................................................... 454.3 Applications and future directions..................................................................................46Works Cited................................................................................................................................48Appendix A: Supporting information for Chapter 2............................................................... 62Appendix B: Supporting information for Chapter 3............................................................... 73viiList of TablesTable 2.1. Model selection for sandhill crane. The top model (bolded) is the one which bestexplains the observed variation. Model selection was based on Aikaike’s Information Criterion(AIC; Burnham et al., 2002). ΔAIC indicates the difference between the AIC value for eachmodel compared to the top model. Each model also included a random effect for cluster..........27Table 2.2. Summary of results for all three sections. NR indicates targets that were not reachedwithin our available sample size.................................................................................................. 31Table 3.1. Selection of occupancy models for sandhill crane habitat associations, based on ARUdata, camera data, and both. ‘Wetland’ indicates all wetland classes added together (marsh,swamp, fen, and bog). All models include a random intercept for cluster. Buffer radius and WAIC(Widely Applicable Information Criterion; (Hooten & Hobbs, 2015) are shown and the top modelis bolded. Stars (*) indicate where the residual value of a linear regression between buffer sizeswas used instead of the habitat cover within 2000 m.................................................................. 45Table 3.2. Results of k-fold cross-validation using k = 4. Deviance scores were calculatedseparately for each data source when both data sets were used for the testing data. Deviance isshown for cross-validation using only ARU data and only camera data, respectively................. 49viiiList of FiguresFigure 1.1. Aerial photos of Tuyeta showing wetlands (left) and seismic lines (right). Photos byChris Beirne.................................................................................................................................14Figure 1.2. On-the-ground habitat photos from time lapse images taken by camera traps at 4different sites in Tuyeta................................................................................................................15Figure 1.3. A schematic of the Boreal Optimal Sampling Strategy. Pink hexagons are PrimarySampling Units that have been selected for monitoring (middle panel) and pink points areSecondary Sampling Units (right panel). From Van Wilgenburg et al. (2020)............................. 17Figure 1.4. Ts’udé Nilįné Tuyeta, Northwest Territories (NWT), Canada. A) Location of the studyarea (green box) within NWT. B) Locations of clustered camera traps and ARUs, and thehexagons that delineate them. Contour lines are shown in orange. C) Locations of camera trapsand ARUs within one example cluster. Hexagons were used in accordance with the BorealOptimal Sampling Strategy (Van Wilgenburg et al., 2020), the primary sampling design in thisstudy............................................................................................................................................ 17Figure 2.1. Number of clusters needed across the study area for a precise estimate of detectionrate at the protected area scale, for all species with at least 5 detections. Number of clusters isplotted against the proportion of camera traps at which each species was detected (top) andbody mass (bottom; log scale). See Table A1 for scientific names..............................................25Figure 2.2. Number of cameras needed to obtain precise estimates of detection rate at thecluster level. Boxplots are shown, which represent the range of values across all clusters whereeach species was detected..........................................................................................................26Figure 2.3. Assessment of sandhill crane habitat models built on subsampled datasets ofcamera trap detections. Each pixel represents an average across 50 models fitted to differentsubsets of randomly selected cameras. Pixel colours represent the precision, accuracy (relativeto the model with full dataset) or failure rate of the resultant models. In all cases, green and bluevalues represent values that meet our targets, and stars (*) indicate the smallest sample sizeswhere each target was reliably reached. Model failure represents the percent of times, out of 50,that the model failed to generate an effect size at all. Gray pixels in the first two panelsrepresent subsets where over half of the models failed. All coloured and gray pixels show therange of sample size scenarios that were available in the dataset..............................................28Figure 2.4. Species accumulation curves for the whole protected area, for all species and byspecies groups. Curves were fit using iNEXT (Hsieh et al., 2016). Black dots indicate where100% of asymptotic species richness was reached, and shaded areas indicate 95% confidenceintervals. Dashed gray lines indicate the number of species expected based on range maps(IUCN, 2022). The sample size is the total number of cameras across the study area, regardlessof clustering................................................................................................................................. 29Figure 2.5. Changes in observed and estimated species richness (mean and precision). Eachpixel represents an average across 50 random subsets of the full dataset. Estimated speciesrichness is the asymptotic species richness and estimated precision is the size of the associatedconfidence interval. Pixels in green or light blue represent the sample size scenarios that meetivour targets, and stars (*) indicate the smallest sample sizes where each target was reliablyreached. Yellow to red pixels show underestimates of richness or very large confidenceintervals, and dark blue pixels show overestimates of species richness. Gray pixels indicatesample sizes where there was not enough data to generate predictions. All coloured and graypixels show the range of sample size scenarios that were available in the dataset....................30Figure 2.6. Cluster-level species richness estimates for all species and their associated SE,ordered by the magnitude of species richness estimates. Colours represent whether the targetspecies richness (90% of the asymptotic richness estimate) was obtained via interpolation orextrapolation. Numbers show the number of cameras in each cluster........................................ 31Figure 3.1. Both ARUs and camera traps record the presence of cranes at a given time andlocation. Above, spectrogram of a sandhill crane rattle call; below, an image of two adult andtwo juvenile sandhill cranes collected by sampling in T’sudé Nilįné Tuyeta Indigenous andTerritorial Protected Area, Northwest Territories, Canada........................................................... 39Figure 3.2. Locations of clustered ARUs and cameras within Ts’udé Nilįné Tuyeta Indigenousand Territorial Protected Area. Those with sandhill crane detections are coloured in blue......... 44Figure 3.3. Coefficients for estimated effects of wetland variables at two spatial scales onoccupancy probability of sandhill cranes. Occupancy coefficients for the top-ranked ARU,camera, and integrated models are shown. The buffer size is listed after each land covervariable. Means and 95% credible intervals are shown on the logit scale. Stars (*) indicatewhere the residual value of a linear regression was used instead of the percent cover within2000 m.........................................................................................................................................47Figure 3.4. Coefficients for estimated effects of variables at two spatial scales on detectionprobability of sandhill cranes. Detection coefficients for the top ARU, camera, and integratedmodel are shown. The integrated model estimates detection coefficients separately for eachdata source; therefore, two panels are shown for the integrated model. Means and 95% credibleintervals are shown on the logit scale..........................................................................................48Figure 3.5. Predicted occupancy of sandhill crane across Ts’udé Nilįné Tuyeta Indigenous andTerritorial Protected Area, from the top-ranking ARU, camera, and integrated models. Shownare occupancy values predicted from habitat information around points spaced 1 km apart......50Figure 3.6. Correlations among predicted occupancy probabilities across Tuyeta for ARU only,camera only, and integrated models. Points represent the predicted occupancy based on habitatvalues for a 1 km by 1 km grid of points, shown in Figure 6........................................................51vList of AbbreviationsARU: Autonomous Recording UnitBOSS: Boreal Optimal Sampling Strategy (from Van Wilgenburg et al., 2020)ECCC: Environment and Climate Change CanadaGNWT: Government of the Northwest TerritoriesIPCA: Indigenous Protected and Conserved AreaUBC: University of British ColumbiaviAcknowledgementsThis work occurred on K’áhshó Got’ı̨nę territory and the Sahtu Dene Métis Land Claim. Irespectfully acknowledge the initial involvement of the Yamoga Land Corporation and the FortGood Hope Ɂehdzo Got'ı̨nę (Renewable Resources Council), and later the K’áhshó Got’ı̨nęFoundation for their contributions to this work and for welcoming me as a visitor to Fort GoodHope and Ts’udé Nilįné Tuyeta Indigenous and Territorial Protected Area. Mahsí Cho.Thank you to my committee, Samuel Haché, Claudia Haas, and Kathy Martin, and to mysupervisor, Cole Burton, for their guidance, assistance, and mentorship. Thank you especially toChris Beirne, who patiently mentored me through my initial phase of ignorance of the cameratrapping world. Thank you to everyone who offered me professional or personal support: NigelBocking, Kevin Chan, Kristie Leach, Micheal Procko, Brad Woodworth, Liz & Alan Stewart, ErinTattersall, and Dani Thompson.To everyone who helped make this program happen, whether planning, support, or field work inchallenging northern conditions, Mashí Cho for your hard work and diligence: Steven Andersen,Chris Beirne, Lilith Brook, James Caesar, Lawrence Caesar, Stephen Crerar, Connon Gould,Leslie Drybones, Jaylin Edgi, Twyla Edgi-Masuzumi, Blaine Grandjambe, Michel Grandjambe,Jocelyn Gregoire, Claudia Haas, Samuel Haché, Robert Hessian, Kirk Hesketh, ChristopherJenner, Lauren King, Marie-Josée Lacroix, Clayton Lafferty, Burli Lafferty, Joel Lafferty, AdamMalgokaka-Inuktalik, Tyra Manuel, Lawrence Manuel, Cody McNeely, Junior McNeely, RyanMutz, Michael Minoza, Mike Palmer, Tanner Pelletier, Lary Penner, Harvey Pierrot, CharlesOudzi, Amélie Roberto-Charron, Nigel Rossouw, Henry Sabourin, Julien Schroder, MitchellShae, Mia Smith, Kelly Stein, Kelsey Stewart, John Tobac, Joseph Tobac, Evan Tobac, JesseTobac, Frank T’Seleie, and Montana T’Seleie-King. Thank you as well to Genevieve Hurd,Debra Sinatra, and Christopher Moser-Purdy for many hours spent transcribing images andrecordings.This work was funded in part by the Government of the Northwest Territories and by the NaturalSciences and Engineering Research Council of Canada.viiChapter 1: Introduction1.1 The importance of cost-effective and non-invasive wildlife monitoringIn the context of climate change and the global biodiversity crisis (Díaz et al., 2019), biologicalmonitoring is more important than ever. An estimated 3 billion birds have been lost from the USand Canada since 1970 (Rosenberg et al., 2019), and large-bodied land mammals have beenextirpated from 75% of their natural ranges (Faurby & Svenning, 2015). Northern Canada hassome of the most intact wilderness in the world (Watson et al., 2018), and large northernprotected areas can be a safe haven for animals that are sensitive to human disturbances.However, the arctic and boreal forest are experiencing the most rapid climate change on thecontinent (Bush & Lemmen, 2019). Biodiversity monitoring efforts are the basis for providingadequate baseline data, tracking population changes, determining species at risk, and makingmanagement decisions (Balmford & Gaston, 1999). However, there are gaps in knowledge ofboth birds and mammals in northern Canada (Kays et al., 2020; North American BirdConservation Initiative Canada, 2019; Pearce & Venier, 2005).Across northern Canada, there are vast areas with few roads and towns, making monitoringanimals in this part of the world especially difficult. The boreal forest is poorly monitored byconventional research protocols, which have historically relied on proximity to roads andvolunteer citizen science. For example, the Alberta Biodiversity Monitoring Institute (ABMI)sampling design is a systematic grid that covers the whole of Alberta, where there are manyroads, and the Breeding Bird Survey sampling design is a collection of roadside point countsconducted by volunteers (Alberta Biodiversity Monitoring Institute, 2018; Smith & Edwards,2021). Researchers with limited time and budgets can rarely implement systematic samplingdesigns to survey wildlife over large and remote areas. The resulting data gap is a big problemfor researchers and managers who need to manage and evaluate statuses of northernpopulations of animals (Ferrier, 2002; Greenberg & Matsuoka, 2010; McKinnon et al., 2017).One way to meet the challenge of collecting data in remote locations is by using in-situ sensingtechnologies such as camera traps and autonomous recording units (ARUs). Camera traps andARUs can be used to study mammal and songbird distribution, abundance, and changes overtime (Burton et al., 2015; Burton & Chetkiewicz, 2021; Shonfield & Bayne, 2017). They are bothminimally invasive and cost-effective technologies that gather large quantities of dataautonomously. Pairing the deployment of ARUs with cameras is logistically easy, reduces costscompared to operating monitoring programs independently, and expands the number of taxabeing monitored. Despite the ease of pairing camera traps and ARUs in deployment, fewstudies have incorporated the two types of data for analysis (Buxton et al., 2018; Garland et al.,2020). With increasing interest and new tools available for data integration (Isaac et al., 2020;Miller et al., 2019), there are new opportunities to integrate camera trap and ARU data to meetresearch goals.11.2 Ts’udé Nilįné Tuyeta Indigenous and Territorial Protected AreaThe establishment of Indigenous Protected and Conserved Areas (IPCAs) follows under theGovernment of Canada’s mandate to protect 30% of its lands and oceans by 2030 (Governmentof Canada, 2023). Ts’udé Nilįné Tuyeta (Tuyeta, for short) is a 10,060 km2 Indigenous andTerritorial Protected Area near Fort Good Hope, Northwest Territories, Canada. Anestablishment agreement for the protected area was signed in 2019 and ratified in 2022. It isco-managed by the Government of the Northwest Territories and the K’áhshó Got’ı̨nęFoundation, (Fort Good Hope Dene Band, Yamoga Land Corporation, Ayoni Keh LandCorporation, Behdzi Ahda, and Fort Good Hope Métis Local 54). It is a culturally important areafor the K’áhshó Got’ı̨nę Dene and Métis and is used for activities like cultural gatherings,hunting, fishing, foraging, and trapping (Government of the Northwest Territories, 2022).Figure 1.1. Aerial photos of Tuyeta showing wetlands (left) and seismic lines (right). Photos byChris Beirne.2Figure 1.2. On-the-ground habitat photos from time lapse images taken by camera traps at 4different sites in Tuyeta. Bottom left photo from the Taiga Cordillera; other photos from the TaigaPlains.Tuyeta is relatively undisturbed by industrial development, but does have some historical cutlines, mainly created for hydrocarbon exploration in the 1960s and 1970s (Canadian WildlifeService, 2007; Figure 1.1). Its climate is subarctic, with a mean annual temperature of -6℃ andannual precipitation of 200 to 600 mm, about half of which falls as snow. It is located mainlywithin the Taiga Plains ecozone, which is characterized by a low elevational gradient andpermafrost (Ecosystem Classification Group, 2009). Tuyeta contains a patchwork of upland andlowland habitat, with uplands being dominated by black spruce Picea mariana, Jack pine Pinusbanksiana, and white birch Betula papyrifera, and lowlands being dominated by open water,Sphagnum bogs, and sedge marshes (Figure 1.2; see also Appendix A). This area contains theRamparts River and Wetlands, which is considered to be an important bird area because of thehigh numbers of ducks that breed in the area, especially scaup and scoters (Latour, 2008). Theland cover present in Tuyeta is highly affected by wildlfires, as is typical for this ecozone(Canadian Wildlife Service, 2007). In the southern portion of Tuyeta, it transitions throughfoothills to the Mackenzie Mountain Range in the Taiga Cordillera ecozone. The transition to themountains also represents a key ecological gradient which separates the ranges of severalspecies, including mountain and boreal caribou Rangifer tarandus, grizzly bears Ursus arctos,and Dall’s sheep Ovis dalli. A small portion of alpine tundra is present within Tuyeta, but there isno arctic tundra.31.3 Biodiversity monitoring in Ts’udé Nilįné TuyetaBiodiversity monitoring may be particularly important in Indigenous Protected Areas, wheremaintaining the ecological integrity of the land is essential not only for biodiversity, but for foodsecurity and overall physical, cultural, and spiritual well-being (ICE, 2018; Popp et al., 2020;Schuster et al., 2019). This is because many Indigenous people often do not see themselvesas separate from “nature” but as living together with it as a family might live together (Gareau,2022). Furthermore, Indigenous cultures have special relationships with the plants, fish,mammals, and other beings that they harvest for food or materials, and the act of harvesting isessential to Indigenous culture and Indigenous knowledge (Moller et al., 2004). Indigenouspeople have long histories of knowing the land and the animals on it; however, there areopportunities for scientific and Indigenous knowledge to work together for the benefit of all,especially in the face of land use change and climate change (Bartlett et al., 2012; Peacock etal., 2020; Popp et al., 2020).To that end, the camera trap and ARU deployment in Tuyeta in 2020 and 2021 was a pilot studyto test scientific methods for wildlife monitoring in protected areas in the Northwest Territories,and to improve scientific baseline knowledge of biodiversity in these areas. The Government ofthe Northwest Territories (GNWT), Environment and Climate Change Canada (ECCC), theUniversity of British Columbia (UBC), and the Fort Good Hope Renewable Resources Councilformed the initial Northwest Territories Biodiversity Monitoring Program (NWTBMP), which hasexpanded to include the K’áhshó Got’ı̨nę Foundation, once the organization was established,and many more partners and has deployed camera traps and ARUs at study sites across theterritory (Environment and Natural Resources, 2023).Camera traps and ARUs were deployed in pairs at 250 locations throughout Tuyeta using threedistinct study designs. The initial deployment in March 2020 selected from locations that werepart of two existing sampling designs, for logistical ease and expediency. First, pairs of sensors,i.e., one camera trap and one ARU, were deployed at 157 locations selected by the BorealOptimal Sampling Strategy (BOSS; Van Wilgenburg et al., 2020). This is a protocol designed tomake inferences across ecoregions within the Canadian boreal forest (Figure 1.3). It first selectsprimary sampling units, in the shape of hexagons 5 km in diameter, based on access cost,spatial balance, and habitat representation for each ecoregion within the boreal forest. Withineach primary sampling unit, it selects four grids of 9 points, each point spaced 300 m apart,based on habitat representation at the ecoregion scale. Field staff then choose two of the fourgrids to survey depending on field conditions, access, and safety. Because of additionalconstraints in the field, staff were not always able to access all 9 points in the grid; in thosecases, locations from any of the four grids were substituted (Figure 1.4). The 157 locationssampled were across 13 primary sampling units. Second, 66 locations across 33 clusters (e.g.spatially proximate groups of sensor locations) were re-surveys of avian survey locations(Canadian Wildlife Service, 2007). These were originally surveyed in 2005 and 2006, and wereselected based on summer site access and using a random design stratified by habitat type.This sampling design clustered locations in groups of three, spaced 300 m apart in a triangularformation. Again, because of challenges in the field, pairs of sensors were not always deployed4at all three locations within a cluster. Because some of the clusters from the historical survey fellwithin BOSS primary sampling units, these two designs together were considered to represent42 unique clusters for the subsampling question in chapter 2.Figure 1.3. A schematic of the Boreal Optimal Sampling Strategy (BOSS). The BOSS design isa random stratified design based on habitat type and access cost at the ecoregion level. Withina given ecoregion (left panel), Primary Sampling Units (PSUs) will be selected for monitoring(pink hexagons in middle panel). Within those, points will be selected (pink points in right panel).Of the four grids shown in the rightmost panel, two would be chosen for deployment based onaccessibility in the field. Figure reproduced with permission from Van Wilgenburg et al. (2020).Third, 27 additional locations were targeted along two culturally important river basins(Ramparts and Hume Rivers). These were deployed in July 2020 in clusters of nine samplinglocations to mimic the Boreal Optimal Sampling Strategy. Two of the three clusters fell within thesame 5 km hexagon, for a total of two hexagons.5Figure 1.4. Ts’udé Nilįné Tuyeta, Northwest Territories (NWT), Canada. A) Location of the studyarea (green box) within NWT, relative to the capital city, Yellowknife. B) Locations of clusteredcamera traps and ARUs, and the hexagons that delineate them. Contour lines are shown inorange. C) Locations of camera traps and ARUs within one example cluster. Hexagons wereused in accordance with the Boreal Optimal Sampling Strategy (Van Wilgenburg et al., 2020),the primary sampling design in this study.All pairs of sensors were left in place for one year (351 – 379 days; Figure A9). In total, therewere 44 clusters with 1 to 19 pairs of sensors (5.6, ± 6.2 SD) across the study area. Within acluster, pairs of sensors were placed 150 m to 2,700 m apart from each other (mean 330 m, ±172 SD). For both data chapters, I defined clusters using hexagons 5 km in diameter forconsistency with the Boreal Optimal Sampling Strategy protocol (Van Wilgenburg et al., 2020).For details of the land cover types that were sampled in Tuyeta, see Appendix A.We used Reconyx Hyperfire HP2X camera traps, which we attached to trees at 1 meter abovethe ground, angled slightly down and facing north. Cameras were programmed to take 3pictures per motion trigger, with no quiet period, high trigger sensitivity, high flash, and to take adaily time lapse image at noon to evaluate whether the camera was functioning appropriately.Wildlife Acoustics Song Meter SM4 and Song Meter Mini ARUs were programmed to takerecordings at dawn, dusk, and night periods throughout the spring and summer. Throughout aportion of the summer in Tuyeta, the sun does not set and there is no period of darkness ortwilight; during that time, recordings were made at the times of day when the sun was lowest inits altitude. ARUs recorded in stereo WAV file format, with a sampling rate of 44100 Hz andfactory default settings for the microphone preamplifier.1.4 Thesis objectivesMy thesis objective was to advance methodologies for wildlife monitoring in large and remoteboreal landscapes through the integration of camera traps and ARUs. I did this by examiningtwo aspects of pairing camera traps and ARUs: sampling design and habitat modeling.6Determining sampling design and sample size requirements is an important step because ifcamera traps and ARUs are to be deployed together, a given sampling design for paireddeployment must be suitable for both methods. Hierarchical (i.e., clustered) sampling designsare relatively common for ARUs (Garland et al., 2020; A. C. Smith & Edwards, 2021; VanWilgenburg et al., 2020), and work has been done to optimize designs in terms of survey effortper ARU and number of ARUs per cluster (Van Wilgenburg et al., 2023). However, a limitednumber of studies have evaluated the same for camera traps (Clark, 2019). Studies that doprovide sample size recommendations for camera trap monitoring usually assume non-clustereddeployments, and recommendations vary considerably among different sites and studies, from40 to 90 cameras per study area recommended by different practitioners (Beaudrot et al., 2019;Colyn et al., 2018; Kays et al., 2020). For example, Kays et al. (2020) found that tropical studyareas typically needed fewer camera traps than temperate study areas. Therefore, in mysecond chapter, I used resampling methods to determine the number of cameras per clusterand number of clusters required for accurate and precise estimates of species richness,detection rate, and to fit a habitat model. These results can inform future camera trapdeployments in the Northwest Territories, with applications for similar monitoring requirements inother regions.For my third chapter, I used both data types to make a habitat model for sandhill crane Antigonecanadensis, a large-bodied migratory bird species well-monitored by both sensors. My objectivewas to test whether integrating camera trap and ARU data would improve predictiveperformance of a habitat model for sandhill crane. I also tested whether the most appropriatespatial scale (300 m vs 2 km) of inference would differ between the two methods. For theintegrated model, I used a newly developed integrated occupancy model in a Bayesianframework, and included both buffer sizes (Doser et al., 2022). These results can haveimplications not only for non-invasive management of sandhill cranes, but could also be appliedto other large, terrestrial, and vocal species, such as the biologically similar but endangeredwhooping crane Grus americana.7Chapter 2: Hierarchical Sampling Designs Improve Estimates of DetectionRate and Species Richness from Camera Traps2.1 IntroductionMonitoring animal populations is important to identify species at risk, understand how ecologicalcommunities are changing, inform conservation efforts, and meet legal requirements formanagement (Nichols & Williams, 2006). Species conservation is most effective when actionsare taken before populations are imperiled (Taylor et al., 2005). Proactive interventions requirethe availability of accurate and precise estimates of population trends and habitat associationsto alert land managers to declines and inform management actions. To this end, the samplingdesign chosen for a monitoring strategy must be effective and appropriate for the monitoringobjectives, study area, and survey methods (Hanson et al., 2023).Camera traps are an effective and increasingly popular method to monitor populations ofmedium to large-bodied terrestrial animals, especially cryptic or nocturnal species (Linkie et al.,2013; O’Connell et al., 2011; Wearn & Glover-Kapfer, 2019). They can also be useful whenmethods that involve handling animals do not align with local Indigenous values (Wong et al.,2020). Camera traps have a wide range of uses, from focused studies targeting single speciesto large-scale community monitoring (Burton & Chetkiewicz, 2021). A variety of responsevariables can be calculated with camera trap data, including measures of relative abundance,community diversity, animal behaviour, sex and age ratios, animal health, and populationdensity (Chen et al., 2022; Murray et al., 2021; Wearn & Glover-Kapfer, 2017). However, all ofthese measures are sensitive to changes in sampling design and scale of inference (Kolowski &Forrester, 2017).Of particular concern to camera traps is ‘imperfect detection’: cameras may not detect a specieseven if they are deployed within an animal's home range (Burton et al., 2015; Rowcliffe et al.,2011). Detectability may vary across cameras for several reasons, including heterogeneousanimal movement and microsite effects (Hofmeester et al., 2019). Several studies haveexplored the implications of heterogeneity in camera detections by placing ten or more camerasin dense arrays and found important variation among cameras, even when they were placed asclose as 10 m apart (Kays et al., 2021; Kolowski & Forrester, 2017; O’Connor et al., 2017). Evencameras placed on the same tree, but facing different directions, had important differences inanimal detections (Pease et al., 2016). Microsite characteristics such as roads, trails, and logswithin the camera’s relatively small viewshed (approximately 10 m in front of the camera in a 40°arc) can have a significant impact on species detectability, although the magnitude and directionof the effect are both context- and species-specific (Cusack et al., 2015; Fonteyn et al., 2021;Kolowski & Forrester, 2017). This body of research suggests that data from any given cameramay be limited in the inferences that can be generated, either by stochastic animal movement orby systematic biases in specific camera placement. Biased or inaccurate data from a singlecamera will carry over into any further analyses, potentially resulting in erroneous inferences.This is a serious concern given that many researchers often make inferences using response8variables calculated from a single camera per sampling site (Naidoo & Burton, 2020; Rovero etal., 2014; Stewart, Volpe, Eaton, et al., 2019).Using a hierarchical (i.e. nested or clustered) sampling design is a promising solution toovercome local heterogeneity in animal detections from camera traps, ensure that variation inmicrosites is sampled representatively at a local scale, and provide robust estimates ofresponse variables. Such designs have been successfully applied to other taxa and surveymethods, such as for molluscs (Gray et al., 2005) and birds (Pavlacky et al., 2012), and can beparticularly useful in vast and remote areas (e.g. reducing travel costs and increasing field staffsafety relative to single sampling sites; Van Wilgenburg et al., 2020). Clustering of cameras hasalready been shown to improve detection probabilities of both common and rare species(O’Connor et al., 2017; Stokeld et al., 2015). Despite the advantages of hierarchical samplingdesigns, single-camera deployments are currently the norm in camera trap research (Burton etal., 2015). In fact, many camera trap practitioners purposely place cameras far apart in order tomaintain independence between them (Rovero et al., 2013). However, in a modeling framework,non-independence can be accounted for by including a random effect (Bolker et al., 2009) orspatial autocorrelation (Doser et al., 2022), among other methods.Few studies have made general recommendations for camera trap survey effort required for astudy area (Beaudrot et al., 2019; Colyn et al., 2018; Kays et al., 2020; Rovero et al., 2013), butrequirements vary by region, species, and response metric. Other studies have explored samplesize requirements at the cluster scale to maximize species-specific detection probability(O’Connor et al., 2017; Stokeld et al., 2015); these have also shown differences in region andspecies. To our knowledge, these two studies are the only ones to have empirically evaluatedsample size requirements for clustered camera trap designs. Stockeld et al. (2015) foundclusters of five cameras improved detection probability of invasive feral cats in Australia, andO’Connor et al. (2017) recommend at least two cameras per cluster to improve detectionprobability of four common species in a temperate forest in the United States.All response variables derived from camera traps suffer from similar challenges related toimperfect detection. Here, we chose to focus on two commonly used metrics: detection rate andspecies richness. Species-specific detection rates derived from camera traps, defined as thenumber of detections per unit time (also referred to as capture rate or capture frequency), areoften used as an index of relative abundance for unmarked animals (Burton & Chetkiewicz,2021; Kolowski et al., 2021). Using detection rate as an index of relative abundance has beencriticized because it often assumes that detectability and movement rate are the same acrosscameras (Broadley et al., 2019; Sollmann et al., 2013; Stewart et al., 2018); however, it hasbeen tied empirically to animal abundance (Palmer et al., 2018; Parsons et al., 2017; Rovero &Marshall, 2009), can be used to track spatio-temporal changes in abundance (Liu et al., 2013;Palmer et al., 2018), and can be used as a response variable in habitat modeling (Bowkett etal., 2008; Davis et al., 2011; Hurtado & Burton, 2022; Tattersall et al., 2020). An accurateestimate of species richness, on the other hand, is often a primary goal of multispecies surveys,not only to determine the number of species present, but also to identify exactly which species9are present in a given area. For example, it might be critical to determine whether a specificspecies at risk is present or not in a study area (Burton et al., 2011).In this study, we used empirical camera trap data from a hierarchical sampling design with threespatial scales of sampling units: camera, cluster, and protected area. Data was collected inTs’udé Nilįné Tuyeta Indigenous and Territorial Protected Area, Northwest Territories, Canada.This study area is in the boreal forest of northern Canada, which is part of a vast biomeunder-represented in the camera trap literature (Kays et al., 2021). We determined the numberof cameras needed at both the cluster and protected area scales to provide: 1) preciseestimates of species-specific detection rates; 2) an accurate, precise, and reliable habitat modelbased on detection rate; and 3) accurate and precise estimates of species richness. We aimedto make inferences about large-bodied mammals, medium-bodied mammals, and upland gamebirds, and to test whether sample size requirements would differ among these three groups.2.2 MethodsCamera trap sampling design, including clustering, is described in section 1.3.2.2.1 Image processingImage processing involved removing blank images using automated tagging software, taggingthe remaining images manually, and assessing field of view to determine survey effort at eachcamera. We used Microsoft Megadetector AI v4 (Microsoft, 2020) with a conservativeconfidence threshold of 20% to pre-filter blank images (Fennell et al., 2022). Trained staffreviewed the remaining images using the WildTrax platform to identify animals and determineminimum group size. Images were grouped in sequences based on a 5-minute threshold: ifthere was a quiet period of more than 5 minutes between subsequent images, it was considereda new sequence and a new group count was determined. All species identifications and groupcounts were verified by another observer. Staff also assessed whether the field of view of eachimage was acceptable to detect wildlife (i.e., camera functioning with an unobstructed lens).For analysis, we followed the common practice of using a 30-minute independence window(Burton et al., 2015), so if multiple groups of the same species at the same camera locationwere detected within that threshold, we took the maximum group count. Survey effort for eachcamera was calculated by the amount of time, in decimal days, that the camera was active andtaking acceptable images. We included all mammal and game bird species over 200 g in mass,and separated them into large mammals (>2000 g), medium mammals (200 - 2000 g), andupland game birds (>500 g). The PanTHERIA database (Jones et al., 2009) and Birds of theWorld (2022) were used to determine body mass.2.2.2 Detection rateTo determine the sample size needed for precise estimates of detection rate, we usedsubsampling to examine the effect of varying number of clusters and cameras per cluster on10detection rate estimates at both spatial scales (protected area and cluster) separately. Wedefined detection rate as the sum of independent group counts per 100 active camera days, andwe included all species with at least 5 independent detections (Table A1). Since cameras withina cluster were on average only 330 m apart, we assumed that cameras within a cluster werenon-independent, and calculated a species-specific average detection rate per cluster. We thensubsampled the clusters by randomly selecting c clusters (1 to 43), without replacement, andrepeated the random selection 50 times to create 50 individual subsamples at each c (i.e., werandomly selected 1 cluster, 50 times, then we randomly selected 2 clusters, 50 times, etc.). Foreach level of c, we calculated the mean detection rate and standard error for all 50 subsamples.As per Kays et al. (2020), we defined our target precision as the sample size at which thestandard error was less than 10% of the mean. Across all species, we used linear regression totest whether there was a relationship between proportion of camera locations detected andsample size requirements, or between body mass and sample size requirements. We alsoperformed a one-way ANOVA to examine the effect of species group on sample sizerequirements.To determine the number of cameras per cluster needed for precise estimates of detection ratesat the cluster scale, we repeated this process using clusters with at least 5 cameras (in total, 14clusters). We analyzed data from each cluster individually. For each cluster, we randomlyselected n number of cameras (1 to the maximum number of cameras in each cluster), andrepeated the random selection 50 times for each n. For each level of n, we calculated the meandetection rate and standard error across all 50 subsamples. We used the same target forprecision as in our estimated protected area scale detection rates.2.2.3 Habitat modelTo investigate how the inferences made by habitat models would change with different samplesizes, we created a habitat model for one focal species. We chose sandhill crane Antigonecanadensis because they are of moderate mass and are seasonally common in our study area.We also chose sandhill crane because camera trap sampling in Tuyeta was concurrent withacoustic sampling, and one of the aims of the project is to monitor across multiple taxa(mammals and birds; Environment and Natural Resources, 2023). Since sandhill cranes aremigratory in the Northwest Territories, we only included cameras that were active in May throughAugust (231 cameras). We calculated effort as the number of active camera days during thosemonths. We used negative binomial generalized linear mixed models with detection rate percamera as the response variable, log-transformed active camera days as an offset, and clusteras a random intercept. For predictor variables, we used eight land cover classes: open water,marsh, fen, bog, swamp, upland deciduous, upland conifer, and upland other (Smith et al.,2007). This dataset was derived based on Sentinel-1 radar and Sentinel-2 optical imagery at a10 m resolution (Ducks Unlimited Canada, 2021). We calculated percent cover of each landcover class within the surveyed hexagon (5 km in diameter). We also calculated the overallpercent cover of wetland (marsh, bog, fen, and swamp) within the hexagon. We did not includeland cover classes that were highly correlated in any given model (Figure A8).11All models were fitted with the package lme4 in R (Bates et al., 2015). We used the full data set(all clusters and all cameras with spring and summer data) to fit multiple models with differentcombinations of those eight land cover classes. We selected the top-performing model as theone with the lowest Aikaike’s Information Criterion value (AIC; Burnham et al., 2002). We thensubsampled the data by randomly including c clusters (1 to 43) and n cameras per cluster (1 to19), and repeated the random selection 50 times for each sample size scenario. We applied thetop-performing model to each subsample and extracted estimates for the fixed effect sizes andstandard errors. For each model, we calculated a modified version of the coefficient of variation,i.e. the standard error of the effect size divided by the effect size, to represent the precision ofthe model. We used this approach because standard error is more closely tied to significancetests than standard deviation (Altman & Bland, 2005). The effect size for a given model isstatistically significant at 95% confidence level if the coefficient of variation was <51%, so weused this as our target for precision.For each model made with subsampled data, we calculated the percent difference between thefixed effect sizes and the corresponding values from the model made with the full dataset. Thiswas considered a measure of each model’s relative accuracy, assuming that the model usingthe full dataset was our most accurate. Here, we used a target for precision of < 20%. Weaveraged the percent difference and coefficient of variation across the 50 subsamples for eachsample size scenario. Finally, we calculated the proportion of times, out of 50, that the modelfailed to generate effect sizes, as a measure of how reliably that sample size scenario could beused to fit a model. The target for reliability was a failure rate of less than 10%.2.2.4 Species richnessFor species richness, we included all species regardless of number of detections (Table A1). Weused the R package iNEXT to generate species accumulation curves (i.e. rarefaction curves)and estimates of asymptotic species richness (Chao et al., 2014; Hsieh et al., 2016). We fittedcurves for all species and for each species group separately (large mammals, mediummammals, and upland game birds). First, we fitted curves for the entire protected area, usingcameras as the sampling unit. We used cameras as opposed to duration-based sampling units,such as camera-days; while there was considerable variation in survey effort among all cameras(Figure A9), we considered this typical of a camera deployment in a remote area. We chose atarget of 100% of asymptotic species richness (rounded down to the nearest integer) at theprotected area scale, because it is important to provide a comprehensive list of species usingthe protected area to inform management and conservation planning. We first fitted curvesusing the full dataset, then examined the uncertainty around each curve and whether our targetrichness was reached via interpolation (within the sample size) or extrapolation. We alsocompared asymptotic species richnesses to the number of species expected in Tuyeta based onrange maps developed by the IUCN Red List (IUCN, 2022). We then created subsets of thedata similarly to the habitat model analysis, including n cameras per cluster and c clusters, tosee how the number of species observed and the asymptotic species richness estimatechanged. For subsampling, we chose the same target (100% of the asymptotic speciesrichness, calculated using the full data set) for observed and estimated species richness. For12precision in the estimated species richness, we chose a target of a confidence interval <20% ofthe mean.We repeated this process at the cluster level by fitting accumulation curves separately for eachcluster. We chose a target of 90% of estimated asymptotic species richness at this scalebecause we shouldn’t expect all species present in the broader area to be detected, but enoughto reliably assess spatial variation in richness. For this analysis, we only included clusters with atleast 3 cameras (214 cameras across 23 clusters), because at least three samples are requiredto fit a species accumulation curve. Again, we examined the uncertainty around each curve andwhether our target richness was reached via interpolation or extrapolation. To determinewhether there was a bias in results across clusters, we used linear regression to test therelationship between number of cameras in a cluster and asymptotic richness estimates.2.3 ResultsThe total sampling effort across all 249 camera traps was 63,996 days, with cameras active andtaking acceptable images for a mean of 257 ± 106 (SD) days. Causes of camera failuresincluded lenses snowed over, batteries failing at extremely cold winter temperatures, and SDcards reaching storage capacity. We identified 58 vertebrate species, 22 of which met our taxonand mass requirements for inclusion in species richness analysis; of those, 20 had at least 5detections for inclusion in the protected area-scale detection analysis and 18 were detected atclusters with at least 5 cameras for inclusion in the cluster-scale detection rate analysis (TableA1).2.3.1 Detection rateThe number of clusters needed for a precise (i.e. SE of subsamples < 10% of the mean)estimate of detection rate at the protected area scale was 12 on average, but ranged from 3 formarten Martes americana to 28 for muskrat Ondatra zibethicus. Species with fewer detectionstended to need more clusters to meet our precision target (n = 20, β = -40, SE = 8.0, p < 0.01).However, we found no relationship between body size and sample size requirements when weconsidered body size as a continuous variable (n = 20, β = -0.013, SE = 0.014, p = 0.37) (Figure2.1, A3). There was also no significant difference in sample size needed among the threespecies groups (F2,17 = 0.38, p = 0.69).13Figure 2.1. Number of clusters needed across the study area for a precise estimate of detectionrate at the protected area scale, for all species with at least 5 detections. Number of clusters isplotted against the proportion of camera traps at which each species was detected (top) andbody mass (bottom; log scale). See Table A1 for scientific names.The average number of cameras needed to obtain a precise estimate of detection rate at thecluster scale was 8 ± 2 (SD), and ranged from 4 for moose Alces alces to 13 for beaver Castorcanadensis (Figures 2.2, A4). Again, we found no significant difference among species groups(F2,138 = 1.31, p = 0.27).14Figure 2.2. Number of cameras needed to obtain precise estimates of detection rate at thecluster level. Boxplots are shown, which represent the range of values across all clusters whereeach species was detected.2.3.2 Sandhill crane habitat modelUsing the full data set, our top-ranked model for sandhill crane was the marsh-only model (Table2.1). The estimated effect size for marsh was 35.6 ± 7.2 (SE). Acceptable levels of precision(coefficient of variation < 51%) and accuracy (difference < 20% from full data model) werereliably reached with at least 3 cameras per cluster across at least 17 clusters, and the bestmodel reliability (failure rate < 10%) was found with at least 4 cameras per cluster across 11clusters, or at least 13 cameras per cluster across 4 clusters (Figure 2.3).15Table 2.1. Model selection for sandhill crane. The top model (bolded) is the one which bestexplains the observed variation. Model selection was based on Aikaike’s Information Criterion(AIC; Burnham et al., 2002). ΔAIC indicates the difference between the AIC value for eachmodel compared to the top model. Each model also included a random effect for cluster.Fixed Effects ΔAICMarsh 0.00Marsh + Swamp + Bog 2.30Marsh + Swamp + Bog + Fen 3.99Marsh + Fen + Bog + Upland Conifer + Burn 5.23Fen 13.901 (Null model) 19.98Wetland 21.9416Figure 2.3. Assessment of sandhill crane habitat models built on subsampled datasets ofcamera trap detections. Each pixel represents an average across 50 models fitted to differentsubsets of randomly selected cameras. Pixel colours represent the precision, accuracy (relativeto the model with full dataset) or failure rate of the resultant models. In all cases, green and bluevalues represent values that meet our targets, and stars (*) indicate the smallest sample sizeswhere each target was reliably reached. Model failure represents the percent of times, out of 50,that the model failed to generate an effect size at all. Gray pixels in the first two panelsrepresent subsets where over half of the models failed. All coloured and gray pixels show therange of sample size scenarios that were available in the dataset.2.3.3 Species richnessThe estimated species richness for the entire study area was 22 (95% CI: 22 - 30), the same asthe number of species actually observed using camera traps, suggesting that all species in thetarget groups were detected (or almost all, considering the confidence interval; Figure 2.4). Theexpected species richness using IUCN range maps was 27 species, and for all species groups,that was within one or two species of what was observed (Figure 2.4). We did not detect anyspecies that were unexpected according to IUCN maps.17Figure 2.4. Species accumulation curves for the whole protected area, for all species and byspecies groups. Curves were fit using iNEXT (Hsieh et al., 2016). Black dots indicate where100% of asymptotic species richness was reached, and shaded areas indicate 95% confidenceintervals. Dashed gray lines indicate the number of species expected based on range maps(IUCN, 2022). The sample size is the total number of cameras across the study area, regardlessof clustering.We determined that at least 2 cameras per cluster across at least 35 clusters were needed toobserve all 22 species (Figure 2.5). Assuming that the asymptotic species richness estimate forthe full dataset (22 species) is correct, the most accurate estimate was reached with 3 or morecameras in 17 or more clusters; however, an acceptable level of precision (confidence intervalwidth less than 20% of mean; 0-4) around this estimate was never reached with subsampling.18Figure 2.5. Changes in observed and estimated species richness (mean and precision). Eachpixel represents an average across 50 random subsets of the full dataset. Estimated speciesrichness is the asymptotic species richness and estimated precision is the size of the associatedconfidence interval. Pixels in green or light blue represent the sample size scenarios that meetour targets, and stars (*) indicate the smallest sample sizes where each target was reliablyreached. Yellow to red pixels show underestimates of richness or very large confidenceintervals, and dark blue pixels show overestimates of species richness. Gray pixels indicatesample sizes where there was not enough data to generate predictions. All coloured and graypixels show the range of sample size scenarios that were available in the dataset.The cluster-level observed species richness ranged from 3 to 14 and cluster-level estimatedspecies richness ranged from 3 (95% CI: 3 - 7) to 22 (95% CI: 12 - 99; Figure 2.6). Severalclusters required extrapolation beyond twice the sample size to reach the target (90% ofasymptotic species richness), and many had large uncertainties associated with speciesrichness estimates (Figure 2.6, A1). Additionally, species richness estimates were significantlysmaller when they came from smaller clusters (β = 0.38, p = 0.01), indicating that thoseestimates may be biased (Figure 2.6). Overall, even when there were as many as 18 camerasper cluster, the cluster-level species richness estimate was not precise enough to be useful. For19that reason, we did not continue any further with subsampling analysis for species richnessestimates at the cluster scale.Figure 2.6. Cluster-level species richness estimates for all species and their associated SE,ordered by the magnitude of species richness estimates. Colours represent whether the targetspecies richness (90% of the asymptotic richness estimate) was obtained via interpolation orextrapolation. Numbers show the number of cameras in each cluster.2.3.4 SynthesisWe found that camera trap studies can effectively characterize species richness and detectionrate for all three groups of species that we included, but found that aquatic animals may bepoorly surveyed (e.g., high sample sizes shown in Figures Figures 2.1 and 2.2). As shown inTable 2.2, all of our detection rate and habitat model targets were reached with our samplingdesign. Precision of the species richness estimate across the study area was not reached andneither were any of the targets for species richness at the cluster scale.20Table 2.2. Summary of results for all three sections. NR indicates targets that were not reachedwithin our available sample size.Response Measure Target Cameras percluster (n)Clusters (c)Detection rate(all species)Precision Standard error less than 10%of the mean13 28Habitat model(sandhill crane)Precision Coefficient of variation lessthan 51%3 14Accuracy Model difference less than20%3 17Reliability Failure rate less than 10% 4 11Species richness,protected areaAccuracy(observed)Observed 100% of species 2 35Accuracy(estimated)Richness estimate is 100%of species observed3 17Precision(estimated)95% confidence interval lessthan 20% of the meanNR NRSpecies richness,cluster scaleAccuracy(observed)Observed 90% of species NR NRPrecision(estimated)95% confidence interval lessthan 20% of the meanNR NR2.4 DiscussionTo our knowledge, this study provides the first evaluation of the value of hierarchical cameratrap deployments to estimate three key variables: detection rates, species-habitat associations,and species richness. We found that both the number of clusters and the number of camerasneeded within a cluster varied depending on the scale of the inference (protected area vs.cluster scale), the target species, and the response variables of choice. For all metrics, a singlecamera per cluster was insufficient, highlighting the value of hierarchical sampling designs incamera trap research. Below we discuss the implications of this variation for camera trap surveydesigns, with a particular focus on remote protected areas.2.4.1 Detection rateThere was substantial among-species variation in sample size requirements for preciseestimates of detection rate (ranging from 4 to 13 cameras per cluster across 3 to 28 clusters),which was mainly explained by the proportion of locations detected. This result was21unsurprising, and has also been found by other camera trap studies (Beaudrot et al., 2019;Kays et al., 2020; Shannon et al., 2014) and also by general sampling design studies(Mackenzie & Royle, 2005). Another possible explanation is species ecology; the species withthe greatest sample size requirements were aquatic species, indicating that these may bepoorly surveyed by most multispecies camera trap studies. Camera traps may haveundersampled the habitat of aquatic species for two reasons: 1) aquatic features such asriparian habitat were not specifically included in the sampling design; and 2) they cannot bedeployed across the range of environments an aquatic species would use (i.e., open water farfrom shore, underwater). If we exclude aquatic species from our assessment, the sample sizeneeded for a precise estimate of detection rate would be 10 cameras per cluster across 22clusters.In general, species that needed more clusters also needed more cameras per cluster. Twonotable exceptions were porcupine Erethizon dorsatum and grizzly bear Ursus arctos, whichneeded relatively more clusters (>15) but relatively fewer cameras per cluster (4 - 7) than otherspecies. Porcupine and grizzly bear were considered rare in our study area, but locally commonwithin river valleys and mountainous areas, respectively. This emphasizes the need toadequately survey the full range of land cover types of interest in a given study area andunderstand the specific habitat or movement characteristics of any particular species of interest(Rovero et al., 2013).2.4.2 Habitat modelFor an accurate, precise, and reliable habitat model for sandhill crane, at least 4 cameras percluster across 17 clusters were needed (68 cameras total). The model also performed well withmore cameras per cluster and fewer clusters (e.g. 14 cameras per cluster across 7 clusters; 98cameras total). However, these sampling designs would not be ideal because they need morecameras and provide less spatial coverage.With only one camera per cluster, even up to 43 clusters, the model failed 100% of the time.This shows the importance of clustering cameras in general. Models were sufficient in accuracyand precision at 3 cameras per cluster, but were not reliable until a 4th camera per cluster wasadded. Our response variable (detection rate of sandhill crane) contained a disproportionatenumber of 0s, as is typical for ecological data, particularly in low productivity environmentswhere animal densities are low (Martin et al., 2005). In our case, models tend to fail because ofthe random chance of choosing all or most cameras with counts of 0 for the focal species.Hofmeester et al. (2019) described six levels of processes at different spatial scales that affectan animal’s detection probability on any given camera trap. Given that an animal’s home rangeintersects a cluster of cameras and that those cameras were set up consistently, two levels ofprocesses might apply: 1) animal use of the specific habitat patch where the camera wasplaced; and 2) microsite conditions such as trails or logs within the camera’s viewshed. Ourhabitat model does not include camera-level habitat or detectability covariates, and therefore,we do not discriminate between these two sources of variation. Adding these covariates could22improve model performance (Hofmeester et al., 2019), but in practice, it could be difficult todirectly attribute the observed variation to one source or the other. Instead, our modelingframework infers the number of cameras needed to make accurate and precise inferences atthe protected area scale regardless of exactly where those cameras are placed within a givencluster.While we only modeled habitat associations for one species, we expect that a similar patternwould be shown as for detection rate, with apparently rarer species needing a greater samplesize than more common species. The detection rate subsampling results for sandhill craneshowed more cameras per cluster would be needed than for the habitat model, but fewerclusters (8 cameras per cluster across 7 clusters). We speculate that this difference may bebecause fewer cameras per cluster would be needed to model the protected area-scaledifference in detection rate with respect to habitat type, while more cameras might be needed toestimate a truly precise cluster-scale estimate of relative abundance. However, both needed asimilar total number of cameras, indicating that the results from both methods were generallycomparable.2.4.3 Species richnessFor species richness estimates at the protected area scale, we found that all three speciesgroups (medium mammals, large mammals, and upland game birds) were well-surveyed interms of accuracy, but an acceptable level of precision was not reached with the full dataset. Anacceptable level of precision was also never achieved with subsampling. This indicates that agreater sample size than we were able to test (either more cameras per cluster or moreclusters) would be needed to meet these targets.Sampling designs must sample all the land cover types present in a study area in order todetect rarer species and habitat specialists. For example, all of our detections of muskratOndatra zibethicus, an aquatic mammal, were at cameras that directly faced open water. Thismay also explain the discrepancy between species expected based on range maps and thespecies actually detected (Tables 2.1, A1, and A2). All of the species the cameras failed todetect are tundra specialists, either Arctic (Arctic fox Vulpes lagopus), alpine (Dall’s sheep Ovisdalli, white-tailed ptarmigan Lagopus leucura), or both (Arctic ground squirrel Urocitellus parryii,rock ptarmigan Lagopus muta). Arctic tundra is absent from Tuyeta, calling into question theaccuracy of the range map for Arctic fox. Alpine tundra is rare in Tuyeta, and was notextensively surveyed (Figures A6, A7). That being said, range maps may overestimate speciesrichness because they typically show the entire area that a species might inhabit, and a speciesmight not occur everywhere within a drawn range (Hurlbert & White, 2005). Furthermore, threeof the five species above (Dall’s sheep, Arctic fox, and white-tailed ptarmigan) would be at theedges of their presumed ranges, where turnover and local extinction is typically high and densityis typically low (Doherty et al., 2003).At the cluster scale, we saw a very large variation in estimated species richness from rarefactioncurves. We found that clusters of 3 cameras often produced a lower estimate of richness than23clusters with more cameras, even though asymptotic species richness is meant to standardizedeployments with different survey effort. This is likely because clusters of 3 gave incompleteinformation about the community present at the cluster scale. Clusters with more cameras oftenhad higher estimates of richness, but also had very large errors associated with thoseestimates. It is known that species rarefaction curves give unreliable estimates whenextrapolation is taken beyond two or three times the sample size (Chao et al., 2014; Colwell etal., 2004), and this was the case in several of our clusters. Based on this, we caution againstmaking cluster-scale inferences about species richness, and strongly caution against using datafrom a single camera to represent the full community of species present. This is also moreconsistent with the relevant scale for assessing mammal communities: the cluster scalerepresents species co-occurrences within local habitat use, whereas communities representinteracting populations within a larger landscape.2.4.4 Sampling design optimizationTo date, sampling design assessments have been limited for camera traps, although somerecommendations exist in the literature. Kays et al. (2020) recommended running cameras for 3to 5 weeks across 40 to 60 locations (840 - 2100 camera days). Based on a study in SouthAfrica, Colyn et al. (2018) recommended spacing cameras in a systematic 1 km x 1 km gridacross a study area and running surveys until a total of 1000 camera days was reached. Roveroet al. (2013) also recommend 1000 - 2000 camera days for an initial inventory; Beaudrot et al.(2019) utilize 60 to 90 cameras for 30 days each in a tropical study area (1800 - 2700 cameradays). In Tuyeta, we needed approximately the same number of cameras as other studies havesuggested, depending on the metric of interest (42 - 220 cameras), but the duration of samplingwas an order of magnitude higher (given 257 days on average per camera, 12,000 - 61,000camera days in total to reach our targets). This shows that survey design recommendationsmade in one part of the world may not be generalizable to other ecosystems. High latitude, lowproductivity study areas such as Tuyeta are notably absent from survey designrecommendations in the literature, and we show that these areas need much more samplingthan previously recommended. In addition to being much larger than study areas from literaturementioned here, detection rates in Tuyeta are much lower; Kays et al. (2020) report overalldetection rates almost one hundred times larger than we observed in Tuyeta.In this analysis, we did not specifically test how the duration or season of sampling wouldchange inferences made from camera trap data. We expect that most animals in Tuyeta wouldexhibit seasonal changes in presence or detectability due to hibernation, migration, seasonalfood hoarding, etc., based on other studies in temperate ecosystems (Kays et al., 2020;Kolowski & Forrester, 2017). Work has been done linking vegetation phenology extracted fromcamera traps’ timelapse photos to wildlife resource use in a northern ecosystem (Sun et al.,2021); future work could examine the impacts this would have on inferences derived fromcamera trap data collected in different seasons. In the meantime, we recommend thatmultispecies camera trap sampling take place over the course of a full year in seasonal areas.24The practice of camera trapping would benefit from more work on optimization of survey design,across all of the above response variables and in a variety of study areas across gradients ofproductivity. Optimization can be done with respect to cost (Gálvez et al., 2016; Hanson et al.,2023), duration of sampling (Field et al., 2005), and spatial arrangement of survey locations aswe have done here (Clark, 2019). Very few camera trap studies explore the usefulness ofclustering cameras, although it has been demonstrated to be critical in obtaining adequatedetection probability for feral cats in Australia, in which case 5 cameras per cluster were needed(Stokeld et al., 2015) and for common species in the United States, in which case 2 to 8cameras per cluster were needed (O’Connor et al., 2017). Additionally, survey designoptimization will vary depending on the response variable chosen; common response variablesthat we did not test here include occupancy models (Mackenzie & Royle, 2005; Shannon et al.,2014) and various forms of density estimation (Clarke et al., 2023; Gilbert et al., 2021; Rovero etal., 2013), although a simulation study by Clark (2019) showed clustering could improve densityestimates for a variety of sensors.2.5 ConclusionWe found that camera trap studies can effectively characterize species richness and detectionrate for large mammals (>2 kg), medium mammals (200 g - 2 kg), and upland game birds (>500g), with the caveat that aquatic animals may be poorly surveyed. We show that clusteringcameras in groups of at least 4 cameras significantly improved our estimates of protected-areaspecies richness and habitat models, and clustering in groups of up to 13 was needed forcluster-scale estimates of detection rate. Species richness was never reliably estimated at thecluster scale, even up to 18 cameras per cluster. In our high latitude study area of 10,060 km2,we needed 35 independent clusters to meet our protected area scale targets for all threemetrics (3.5 clusters per 1000 km2). This study shows the utility of hierarchical sampling designsto generate more robust inferences and improve the quality of the information provided bycamera trap studies to inform conservation science. Current and future camera trap monitoringprograms in other parts of the world could use a similar approach to evaluate optimal samplingdesign for their metrics of interest.25Chapter 3: Integrating data from camera traps and autonomous recordingunits to improve habitat models3.1 IntroductionTargeted monitoring of keystone and endangered species in an ecosystem is critical for theirsuccessful management and recovery, especially when monitoring is linked to adaptivemanagement (Nichols & Williams, 2006). Conservation has the best chance at success whenmanagement actions are taken before species’ populations have declined drastically (Taylor etal., 2005). Monitoring programs capable of detecting early warnings of species declines aretherefore needed for successful conservation. Monitoring can be particularly challenging in lessproductive northern environments, like the boreal forest of Canada, because small numbers ofindividuals might occur across vast, remote areas (Allen, 1952; Ausband et al., 2014). In thiscase, species distribution modeling is particularly useful to make inferences across large areasand detect changes in species occurrences or abundances, and such models are commonproducts of monitoring programs (Beirne et al., 2021; Bobrowski et al., 2021; Van Wilgenburg etal., 2020). It is important that the sampling methods chosen for data collection are effective inmeeting these objectives within the context of a given study area and monitoring program(Hanson et al., 2023).Scientists are often under pressure to choose data collection methods that will not only provideaccurate and precise information, but also be less invasive, more cost-effective, and more in linewith local Indigenous values than some traditional sampling methods (Hanson et al., 2023;Soulsbury et al., 2020; Wong et al., 2020). Many common wildlife survey techniques may notmeet all these criteria. For example, aerial surveys can be problematic because they can disturbthe target species (Bleich et al., 1994; Brambilla & Brivio, 2018; Ditmer et al., 2015) and canresult in significant under-estimates of population sizes due to low sightability (Fleming &Tracey, 2008; Schlossberg et al., 2016). Methods that involve handling animals, such astelemetry, may be against cultural values of wildlife co-management partners (Spak, 2005;Wong et al., 2020). Furthermore, tracking, tagging or marking animals can result in changes inbehaviour or reduced fitness (Northrup et al., 2014; Soulsbury et al., 2020). Even call-responsesurveys, which broadcast recorded animal calls to elicit a response, may disrupt naturalbehaviour and potentially violate assumptions made when modeling those data (Conway &Gibbs, 2005; see also the use of baited stations; Stewart et al., 2019). All these samplingmethods provide very important information on species distribution, abundance, and populationdynamics, but there is a need to complement these traditional methods with new methods thatare more cost-effective and less invasive.Camera traps (hereafter “cameras”) and autonomous recording units (ARUs) are bothcost-effective and minimally invasive sampling methods to collect wildlife data (Burton et al.,2015; Shonfield & Bayne, 2017). They can both be used for targeted studies and long-termmonitoring, and can be integrated with other sampling methods. For example, ARU surveys canbe integrated with in-person avian point count surveys for more comprehensive sampling design26(Drake et al., 2021; Van Wilgenburg et al., 2017). Deployments of cameras and ARUs can alsobe combined (Buxton et al., 2018) to monitor a wider range of species and ecologicalprocesses, and leveraging funding across a wider range of collaborators with shared priorities(ABMI, 2023; Environment and Natural Resources, 2023). Furthermore, ARUs are not onlyuseful for studying birds, but have also been shown to successfully detect some mammals(Barber-Meyer et al., 2020; Palacios et al., 2022), amphibians (Acevedo & Villanueva-Rivera,2006), and human activities (Buxton et al., 2018; Dobbins et al., 2020). Similarly, cameras arenot only useful for studying mammals, but have been shown to effectively detect some birdspecies (Beirne et al., 2021; Fontúrbel et al., 2020; Vaughan et al., 2022) and seasonal changesand phenology (Sun et al., 2021). This wide range of ecological processes that can bemonitored by using camera traps and ARUs can also favor large collaborative initiatives, whichincrease the resources and expertise available to maximize the information gleaned from anygiven study area.Despite the ease of pairing cameras and ARUs, there has been relatively little research onintegrating the two types of data in a single modeling framework. Studies have compared bothsampling methods for occupancy and detectability of gray wolves (Garland et al., 2020),seasonal distribution and detectability of right whales (Rayment et al., 2018), diversity ofunderstorey birds (Fontúrbel et al., 2020), and detectability of hoary bats (Gorresen et al.,2017). Most of these studies concluded that either one method or the other was superior from astand-alone point of view, but that detection probabilities were highest when both sensors weredeployed together. ARUs and cameras have also been paired to examine the effects ofanthropogenic disturbance on animals: for example, hunting pressure as measured throughgunfire (Dobbins et al., 2020) and the effects of anthropogenic noise on seabirds and mammals(Buxton et al., 2017; Kleist et al., 2020). With increasing interest in data integration and newstatistical methods, there are opportunities to improve single or multi-species habitat modelsand better inform multi-species conservation planning (Isaac et al., 2020; Miller et al., 2019).However, to our knowledge, such an integrated species habitat modeling framework using bothcamera and ARU data have not been evaluated.Sandhill crane Antigone canadensis is an ideal species to test the usefulness of integrating ARUand camera data (Figure 3.1). Sandhill cranes are very vocal; they advertise their breedingterritory with rattle calls that can be heard up to 4 km away (Gerber et al., 2020), making themeasily detected by ARUs. Sandhill cranes even possess tracheal elongation, which causes theircalls to be louder than expected for their body size (Fitch, 1999). They are also large-bodiedand travel almost exclusively on the ground before their chicks are able to fly (Gerber et al.,2020), making them a good candidate for monitoring using camera traps. In Canada, sandhillcranes are harvested (Migratory Birds Convention Act, 1994) and monitoring change in relativeabundance could inform adaptive management through hunting regulations such as bag limits,timing and length of hunting season, etc. A framework to generate habitat models for sandhillcrane that would include both ARU and camera data could be applicable to a large number oflarge-bodied waterbirds to help inform status assessments and conservation actions, includingthe whooping crane Grus americana, an endangered species (IUCN, 2022; Species at Risk Act,2002).27Habitat selection is a hierarchical process that occurs across different spatial scales (Toews etal., 2017; Wiens, 1989). Therefore, the scale of measurement, in terms of both spatial resolutionand extent, should match the targeted spatial scale of inference and analyses should beconducted at multiple spatial scales to better understand these processes (Mayor et al., 2009;Toews et al., 2017; Wheatley & Johnson, 2009). Inferences made based on ARU and cameradata are expected to reflect habitat association at different spatial scales because ARUseffectively survey larger areas than cameras (Becker et al., 2022; Sólymos et al., 2013) andintegrating these methods may allow for multiple scales to be analyzed concurrently.28Figure 3.1. Both ARUs and camera traps record the presence of cranes at a given time andlocation. Above, spectrogram of a sandhill crane rattle call; below, an image of two adult andtwo juvenile sandhill cranes collected by sampling in Ts’udé Nilįné Tuyeta Indigenous andTerritorial Protected Area, Northwest Territories, Canada.In this study, we compared predictive performance of different species habitat models usingARU and camera data to better understand habitat associations and distributions of sandhillcranes nesting in Ts’udé Nilįné Tuyeta Indigenous and Territorial Protected Area, NorthwestTerritories, Canada. We tested whether 1) the most appropriate spatial scale of inference differsfor ARU data model, camera data model, and integrated ARU and camera data model; and 2)an integrated model would have better predictive performance than ARU data and camera datamodels. Models included variables that represented the species’ home range (300 m radius)and a broader landscape context (2000 m radius). We hypothesized that camera models wouldmake stronger inferences at the home range scale than ARUs because they have a smallereffective detection distance (Becker et al., 2022), and therefore more effectively pinpoint thelocations of cranes, while ARUs would make stronger inferences at a larger spatial scalebecause of the loud crane vocalizations. We also hypothesized that the integrated model wouldhave the best predictive power because including more data should provide the best informationavailable. We used our results to provide general recommendations for sampling strategies thatuse both methods.3.2 Methods3.2.1 Audio transcriptionEight three-minute recordings were transcribed per ARU. All recordings were in the month ofJune, to target the peak of singing activity for most bird species. Most ARUs (142) tookrecordings in 2020, but 18 took recordings in 2021. For each ARU, four recordings each wererandomly selected in early (1 to 15) and late (16 to 31) June. The recordings were randomlyselected to represent early morning (0200-0459), mid-morning (0500-0659), late morning(0700-0859), and “night” (2030 - 0130). In cases where the selected recording was unsuitabledue to bad weather (rain or wind), another randomly selected suitable recording from the sametime and month category or, if not suitable, within the same time category, but in the other monthcategory. However, we never selected two recordings from the same day; because of thislimitation, there were some cases where fewer than 8 recordings were transcribed per ARU. Weused the hour of the day to categorize the recordings instead of time relative to sunrise,because the sun does not set for most of the month of June in Tuyeta; details of sampling effortare shown in Figure B5. Transcription was done on the WildTrax platform, by one humanobserver who classified all species in each recording.Of the 250 locations with paired sensors, we obtained data from our target period (May throughJuly for cameras, June for ARUs) for 229 cameras and 160 ARUs, representing 246 uniquelocations. Causes of camera failure included cold temperature malfunctions and reachingbattery and/or memory capacity. ARUs experienced a high failure rate due to firmware29malfunctions in extremely cold winter temperatures. We were able to transcribe the full 8recordings for 154 of the 160 ARUs, and the remaining 6 ARUs had at least 2 recordingstranscribed. Cameras were active and taking acceptable images for an average of 84 days(range = 5 - 90) throughout our target period.3.2.2 Habitat modelFrom both sensors, we used detection-nondetection data to estimate sandhill crane occupancy,i.e. the probability that a crane occurs at a given location (MacKenzie et al., 2018; Stiffler et al.,2018; Wevers et al., 2021). We fit occupancy models with the package spOccupancy (Doser etal., 2022) in R Statistical Software (R Core Team, 2022). This modeling framework assumes thatthe true presence (1) or absence (0) of a species follows a Bernoulli process described by theoccupancy probability (ψ). Occupancy probability itself is modeled based on the occupancycovariates and coefficients, using a logit link function. The model also assumes that thedetection (1) or nondetection (0) of a species across multiple visits (i.e., repeat surveys) at thesame sampling location also follows a Bernoulli process, described by the detection probability(p), conditional on the true occupancy at a given location. Detection probability is modeledbased on visit-specific detection covariates and coefficients, also following a logit link function.This modeling framework is also capable of integrating multiple data types; it does this bymodeling detection probability separately for each data source, and modeling occupancyprobability jointly (Doser et al., 2022; MacKenzie et al., 2018). There are five key assumptions tooccupancy models (Mackenzie, 2018): 1) locations are “closed” throughout the duration of asampling period, i.e. there are no changes in occupancy between visits; 2) locations and visitsare independent; 3) there are no false detections (no false positives); 4) detection probability isconstant across locations, or modeled adequately with covariates; and 5) occupancy probabilityis constant across locations, or modeled adequately with covariates.3.2.2.1 Detection historiesOur response variable was binary detection-nondetection of sandhill cranes for each visit ateach location, which we summarized in the form of detection histories. Detection histories forARUs were detection or non-detection on each 3-minute recording; all of these recordings werewithin the month of June. For camera data, we used weekly detection histories throughout thetarget period. While sandhill cranes are present in Tuyeta beyond the end of July, we did notinclude any later times in our analysis because chicks will fledge in August and family groupswill begin to abandon their territories (Gerber et al., 2020). While there is a difference in thesampling period between the two methods, the assumption of closure should hold throughoutthis period because sandhill crane pairs successfully raising chicks will typically occupy thesame territory throughout the breeding season (Gerber et al., 2020). Furthermore, it may benecessary for cameras and ARUs on sandhill cranes’ breeding grounds to target different timeswithin the cranes’ phenology, as peak vocalization occurs before incubation, while peakmovement rate occurs once the chicks have hatched (Gerber et al., 2020). Thus, our samplingperiods reflect appropriate sampling conditions for each method.303.2.2.2 Occupancy and detection covariatesTo test whether occupancy of cranes at a sampling location was explained by habitatcharacteristics, we extracted habitat variables using two buffer sizes centered on each ARU orcamera. The territories of sandhill cranes are relatively small; they occupy territories rangingfrom 0.17 to 1.4 km2, depending on the study area (Drewien, 1973; Reed, 1988; Walkinshaw,1973); therefore, we chose a 300 m radius to represent habitat selection for a typical homerange of a pair of sandhill cranes. Despite their small territories, sandhill crane calls areextremely loud; while a specific effective detection radius (which describes the relationshipbetween detection probability and distance; Matsuoka et al., 2012) has not been published, it isbelieved that their calls can be heard up to 4 km away (Gerber et al., 2020). A maximumdetection distance is typically twice (or more) the size of the effective detection radius (Bucklandet al., 2001), so we reasoned it would be approximately 2 km, and therefore used this size forthe larger buffer. We considered this buffer to represent habitat selection within a largerlandscape context, and we refer to it as the “landscape” scale for simplicity.For occupancy covariates, we used marsh, fen, bog, swamp, and total wetland areas based onthe Ducks Unlimited Canada wetland classifications (Ducks Unlimited Canada, 2021; Smith etal., 2007). We focussed on the four wetland land cover types given previously reported habitatassociation for this species (Gerber et al., 2020; Saunders et al., 2019). We calculated percentcover of each land cover class within both buffers for each location and scaled (i.e., normalized)them to investigate changes in crane habitat use per unit change in habitat cover. To reducemulticollinearity, we did not include variables with a correlation of greater than 0.7 in the samemodel, and therefore did not include fen and marsh in the same model at the landscape scale(Figure B4). We used cluster as a random effect to account for the non-independence ofrepeated measurements within the cluster (i.e. spatial proximity of sampling locations).Detection covariates for ARU data were Julian day, Julian day squared, and time of day. Wechose these covariates because there are peaks in sandhill crane detectability (i.e., peak time ofvocalization) throughout the month of June and early in the morning (Gerber et al., 2020).Detection covariates for camera data were the number of active camera days, week of the year,and week of the year squared. Number of active camera days within each week would directlyaffect detection probability, and there is a peak in movement rates that would affect detectabilitywithin the period we surveyed.3.2.2.3 Model specifications and selectionWe fit models in a Bayesian framework using noninformative priors and default initial values forregression coefficients. Specifically, priors were set with hypermeans of 0 and hypervariances of2.72 for both occupancy and detection covariates (Lunn et al., 2013). We began with 3 chainseach of 5000 Markov Chain Monte Carlo (MCMC) samples with a burn-in of 1000 samples anda thinning rate of 2. We increased the number of samples until the Gelman-Rubin diagnosticwas 1.1 or less for all estimated coefficients, indicating convergence (Brooks & Gelman, 1998).31We also performed posterior predictive checks as an assessment of goodness of fit using theFreeman-Tukey fit statistic (Kéry & Royle, 2016).To find the most parsimonious model for each data type, we ran multiple models and chose thetop-ranking one using the Widely Applicable Information Criterion (WAIC; Hooten & Hobbs,2015). Because of computational limitations, model selection was iterative. First, we rancompeting models that included all wetland types (full model), each wetland type separately,and all wetland types added together, using the 2000 m buffer. We then chose the twotop-performing models for this first pass of model selection, and ran those models with allpermutations of the corresponding covariates and spatial scales. We then compared all resultingmodels for each data type and chose one top-performing model.For the integrated model, we integrated both data types and spatial scales. We reducedmulticollinearity between percent cover at each buffer size by using a similar approachdescribed by Rhodes et al. (2009). In short, we performed a linear regression between the samehabitat type at both buffer sizes, using percent cover within home range scale as the x-variableand percent cover with the landscape scale as the y-variable. Each individual location with onepair of sensors (n = 246) was one data point. We then calculated the residual value for eachpoint (Figure B2), which represents whether there was more or less cover of each habitat typeat the landscape scale than would be expected based on the home range value. In our models,we included the value for the home range scale and the residual value.We used 4-fold cross-validation (Hooten & Hobbs, 2015) to examine the predictive performanceof the integrated model relative to the camera only and ARU only models. Cross-validation canbe used to compare the predictive performance of different models, but only if the testing datacomes from the same dataset (Hooten & Hobbs, 2015). Therefore, we couldn’t directly comparethe predictive performance of the top-ranking camera and ARU models. However, we assessedwhether the addition of each data type in the integrated model improved predictive performancecompared to using one data source alone. We first cross-validated the top-ranking ARU model,choosing testing data randomly from all locations and fitting the model to the remaining data.We compared the resulting deviance score to cross-validation done on the top-rankingintegrated model, where we chose testing data randomly only from the ARU dataset, and fit themodel with the remaining ARU data plus all camera data. We repeated this process with thetop-ranking camera model, and compared that score to cross-validation done on the topintegrated model, where we held out camera data only for testing.For each data source, we compared the size and precision of the detection and occupancyparameter estimates. We calculated an overall detection probability for each data source usingp* = 1 - (1 - p)k, where p is the detection probability for a single visit, and p* is the probability ofdetecting a crane at least once across k visits, given that the location is occupied (MacKenzie etal., 2018). For k, we used the average number of surveys each data source completed, whichwas k = 8 recordings for ARUs and k = 12 weeks for cameras. To visualize how inferenceswould change depending on the data source, we also mapped predicted occupancy acrossTuyeta based on the top-performing model from each data source. To do this, we created a 132km by 1 km grid of points across the study area. At each point, we extracted percent cover ofwetland land cover classes within a 300 m and 2000 m buffer. Based on that information, wepredicted point-level occupancy using the coefficient estimates from the top-performing ARUmodel, camera model, and integrated model, respectively, keeping detectability at a meanvalue. We then examined each map for differences and similarities in patterns of occupancyprobability. Finally, we calculated the correlations among predictions made based on each datasource.3.3 ResultsSandhill cranes were detected on 59% of ARUs, 18% of cameras, and 48% of the total locations(naive occupancy; Figure 3.2). Of the 143 locations where data was obtained from both ARUsand cameras, cranes were detected 63 times by ARUs only, 17 times by both methods, and 9times by cameras only. Posterior predictive checks resulted in Bayesian p-values of 0.45 for thetop ARU model, 0.12 for the top camera model, and 0.32 and 0.00 for the ARU and cameraportions of the integrated model, respectively (Figure B3). The ideal p-value is 0.5, whichindicates that the predicted and observed values match.All top models included percent cover of marsh and swamp, but fen and/or bog were alsoincluded in the camera and integrated models (Table 3.1). As expected, the top ARU modelincluded habitat variables at the 2000 m buffer, while the top camera model included the 300 mbuffer, and the top integrated model included variables from both spatial scales (Table 3.1). Thetop ARU model was only slightly better than the null model (ΔWAIC = 1.17), but the top cameraand integrated models were notably better than the null models (ΔWAIC = 37.26 and ΔWAIC =53.39, respectively).33Figure 3.2. Locations of clustered ARUs and cameras within Ts’udé Nilįné Tuyeta Indigenousand Territorial Protected Area. Those with sandhill crane detections are coloured in blue.Table 3.1. Selection of occupancy models for sandhill crane habitat associations, based on ARUdata, camera data, and both. ‘Wetland’ indicates all wetland classes added together (marsh,swamp, fen, and bog). All models include a random intercept for cluster. Buffer radius and WAIC(Widely Applicable Information Criterion; (Hooten & Hobbs, 2015) are shown and the top modelis bolded. Stars (*) indicate where the residual value of a linear regression between buffer sizeswas used instead of the habitat cover within 2000 m.Data type Occupancy covariates Buffer radius (m) WAIC ΔWAICARU Marsh + Swamp + Bog 2000 935.38 0.00Swamp 2000 936.26 0.88Bog 2000 936.39 1.01Marsh 2000 936.51 1.14Fen + Bog 2000 936.81 1.43Fen 2000 937.25 1.871 (Null model) - 937.28 1.91Swamp 300 938.91 3.53Marsh + Swamp + Fen + Bog 300 939.01 3.63Marsh + Swamp + Bog 300 939.09 3.72Wetland 2000 939.60 4.22Camera Marsh + Swamp + Fen + Bog 300 581.34 0.00Bog 300 602.08 20.74Marsh + Swamp + Bog 2000 609.34 28.00Bog 2000 612.19 30.85Marsh 2000 612.89 31.56Fen + Bog 2000 613.77 32.43Wetland 2000 616.09 34.7534Swamp 2000 618.06 36.731 (Null model) - 618.60 37.26Fen 2000 619.76 38.42Integrated Marsh + Swamp + Bog 300 + 2000* 1610.89 0.00Marsh + Swamp + Bog 2000 1618.06 7.17Marsh + Swamp + Fen + Bog 300 1633.83 22.94Marsh + Swamp + Bog 300 1642.51 31.62Wetland 2000 1646.53 35.64Marsh 2000 1651.69 40.80Fen + Bog 2000 1656.59 45.70Fen 2000 1661.39 50.50Bog 2000 1661.45 50.56Swamp 2000 1662.82 51.931 (Null model) - 1664.28 53.39Sandhill crane occupancy increased with percent cover of marsh and bog, except within the2000 m residual for cover of bog within the integrated model. The effect of swamp on occupancywas always negative, but did overlap zero in the ARU and camera only models (Figure 3.3). Thecamera model had the lowest intercept, indicating lowest average sandhill crane occupancy,and the intercept was highest for the integrated model. Marsh and swamp were included in theARU model, all four wetland classes were included in the camera model, and bog, swamp andmarsh were included in the integrated model.35Figure 3.3. Coefficients for estimated effects of wetland variables at two spatial scales onoccupancy probability of sandhill cranes. Occupancy coefficients for the top-ranked ARU,camera, and integrated models are shown. The buffer size is listed after each land covervariable. Means and 95% credible intervals are shown on the logit scale. Stars (*) indicatewhere the residual value of a linear regression was used instead of the percent cover within2000 m.36Figure 3.4. Coefficients for estimated effects of variables at two spatial scales on detectionprobability of sandhill cranes. Detection coefficients for the top ARU, camera, and integratedmodel are shown. The integrated model estimates detection coefficients separately for eachdata source; therefore, two panels are shown for the integrated model. Means and 95% credibleintervals are shown on the logit scale.Detection probability was also lower for the camera model than for the ARU model (Figure 3.4).For ARU data detection probability was p = 0.22 and detection probability across all repeatedvisits was p* = 0.87. For camera data, p = 0.08 and p* = 0.62. Highest detection probability forARUs was found to be earlier in June (negative value for Julian day) and later in the day(positive value for time of day), while highest detection probability for cameras was found to belater in the summer period (positive value for week of the year), and on cameras with moreactive days per week (positive value for active days; Figure 3.4). The integrated model37estimated very similar detection coefficients, but with slightly lower intercepts for both ARU andcamera data; the estimated detection probability for ARUs was p = 0.22 and p* = 0.86 and theestimated detection probability for cameras was p = 0.03 and p* = 0.31.Surprisingly, the deviance score (predictive power) for the camera model was higher than thecorresponding integrated model, but the deviance score for the ARU model was lower (Table3.2). In other words, including ARU data in the integrated model improved predictiveperformance for the camera data, but including camera data in the integrated model did notimprove predictive performance for ARU data.Table 3.2. Results of k-fold cross-validation using k = 4. Deviance scores were calculatedseparately for each data source when both data sets were used for the testing data. Deviance isshown for cross-validation using only ARU data and only camera data, respectively.Model Testing data DevianceCamera Camera 639Integrated Camera only 608ARU ARU 961Integrated ARU only 987The predicted occupancy for the top ARU, camera, and integrated models show similar patterns(Figure 3.5). The camera model predicts smaller areas of high occupancy, while the ARU andintegrated models predict overall higher occupancy across the study area. Across all points, thecorrelations among predicted occupancy were 0.31 for ARU and camera, 0.88 for ARU andintegrated, and 0.46 for the camera and integrated models.38Figure 3.5. Predicted occupancy of sandhill crane across Ts’udé Nilįné Tuyeta Indigenous andTerritorial Protected Area, from the top-ranking ARU, camera, and integrated models. Shownare occupancy values predicted from habitat information around points spaced 1 km apart.39Figure 3.6. Correlations among predicted occupancy probabilities across Tuyeta for ARU only,camera only, and integrated models. Points represent the predicted occupancy based on habitatvalues for a 1 km by 1 km grid of points, shown in Figure 3.5.3.4 DiscussionThere are two key differences in the detection process between ARUs and cameras: theduration of sampling, which is much larger for cameras, and the effective area sampled, which ismuch larger for ARUs. Only eight 3-minute recordings were transcribed per ARU, for a total of24 minutes of sampling, while cameras were active around the clock for an average of 84 days(5,000 times longer sampling period). Given an estimated effective detection radius of 2 km forthe call of the sandhill crane, the effective area sampled for ARUs would be approximately 12.5km2 (Sólymos et al., 2013). The effective area surveyed by the camera, however, could beestimated as ~10 m in front of the camera in a 40° arc, equaling 125 m2 (Becker et al., 2022).The effective area surveyed is a key distinction, especially for occupancy models, because thedefinition of occupancy is dependent on scale (MacKenzie et al., 2018). For the same number ofindividuals across a study area, choosing a larger scale to represent one “site” will result in agreater estimation of occupancy (Miller et al., 2019). This may partly explain the higheroccupancy estimate derived from ARUs versus cameras.The loudness and frequency of the sandhill crane vocalizations make this species more likely tobe detected on ARUs than on cameras. In fact, sandhill cranes were so common on ARU datathat the models were more difficult to fit; they required many more iterations (200,000 versus50,000) to reach convergence, and the WAIC value of the top model was closer to the nullmodel than it was for the camera model. However, the Bayesian p-value for the ARU model wasclosest to 0.5, indicating the best model fit of the three data sources.Work by Baker et al. (1995) found no significant habitat associations beyond a 200 m bufferaround sandhill crane nests in Michigan, USA, and recommended that the scale of habitatselection should be carefully considered for this species. This could be a concern for habitat40relationships derived from ARUs, because an ARU that detects a crane might actually falloutside of its home range and preferred habitat type; ARUs may also detect multiple pairs ofcranes occupying different home ranges. Nonetheless, the ARU models provide information onhabitat selection of cranes at the landscape scale, because our top model was slightly betterthan the null model, and it could be most useful for making inferences about the presence ofcranes in larger areas. More work is needed to better understand the proper spatial scale ofinference when using ARU data to model occupancy of sandhill crane and ecological similarspecies.In contrast, the top-performing camera model estimated lower overall occupancy and includedcovariates at the smaller buffer size (300 m). These differences suggest that cameras showoccupancy at a smaller scale and infer habitat selection within home ranges. Camera datashows the exact location of crane adults and offspring, as well as their behaviour (Figure 3.1).The predicted occupancy map is patchier and shows lower occupancy across the study area.This should be interpreted as areas of very high-quality habitat. Hence, given the differentinformation collected by both sampling methods, it was not surprising that the camera modelestimated lower occupancy than the ARU model. The pattern of the camera map is somewhatsimilar to the pattern of the ARU map, indicating general agreement between the two methods.The Bayesian p-value of the top camera model is lower than ideal, but above 0.1, indicating anacceptable model fit (Hobbs & Hooten, 2015).The highest overall occupancy was found for the integrated model. This is because each sensorhad at least one sandhill crane detection at a location that the other did not (Figure 3.2).Garland et al. (2020) found the same result (higher occupancy and detection probability forARUs over cameras, but highest occupancy for the integrated data set) when comparing ARUand camera data for wolves (Canis lupus) in northern Alberta, and also posited that this wasbecause of the difference in effective area surveyed. Our top integrated model includedcovariates at both spatial scales, indicating that both data sources contributed to improving themodels, with cameras including smaller-scale information than ARUs.In wildlife surveys, a species' true abundance or occupancy across a study area can rarely beobserved directly, only estimated from patterns observed with the data available. Therefore, amodel’s true predictive performance cannot be evaluated other than by comparing to othermodels fit to the same data. Thus, we were not able to evaluate which model is the “best”;however, we did compare them qualitatively and determined whether including a second dataset in an integrated model framework improved predictive performance for the first dataset overa model that was fit using only the first dataset. In this case, K-fold cross-validation showed thatintegrating data sources did not improve predictive performance for ARU data, which is to saythat the integrated model did not predict ARU data better than the ARU only model did. Incontrast, the integrated model did improve predictive performance for camera data (Table 3.2).The pattern of the integrated map is very similar to the pattern of the ARU map (Figure 3.5).However, it does help tease apart habitat patch versus landscape scale characteristics, and themap shows specific low-quality habitat patches areas within high quality landscape habitat (light41points within darker areas). The Bayesian p-value of 0.00 for the camera fit in the integratedmodel is likely due to the difference in naive occupancy (proportion of locations detected)between ARU and camera detections; these goodness of fit metrics are still an active area ofresearch in integrated models and the values shown here may not be reliable estimates ofmodel fit (Doser et al., 2022).Detection probability was lower in the integrated model than for each data source alone. This isbecause while detection probability is modeled separately for each data source in an integratedmodel, it is conditional on the latent occurrence of both data sources combined, and eachmethod detected cranes at at least one location that the other missed. This was especiallyimpactful for the detection probability of the camera data, which was decreased quite a bit bythe addition of the ARU data. This could indicate that camera data has a higher probability offalse negatives; however, as discussed above, it may be biased by the addition of cranedetections by ARUs whose territories do not overlap the location of the pair of sensors.Our results show that sandhill crane occupancy is positively associated with the percent coverof marsh, fen, and bog around a location, and negatively associated with the percent cover ofswamp. Positive association with wetland habitat types, particularly fen and bog, is inaccordance with previous studies (Baker et al., 1995; Kruse et al., 2017; Melvin et al., 1990).However, a study by Kruse et al. (2017) during nesting season in Colorado, USA, showedpositive associations with swamp. This discrepancy may be explained by how land cover ischaracterized by different wetland classification systems; the U.S. National VegetationClassification (2016) includes peatlands in their definition of swamp, which may be classified asbog by the Ducks Unlimited Wetland Classification (Smith et al., 2007).Despite the apparent redundancy of our integrated model compared to ARU data alone, bothsampling methods have their own merits. Each sensor could be useful in different managementapplications, either of sandhill cranes or of other species such as whooping cranes Grusamericana. For example, ARUs could be used to detect range expansions of whooping cranesback into their historical breeding range. Cameras, on the other hand, could help uncoverspecific wetland types cranes are using in order to inform wetland habitat managementdecisions such as installing water control devices. Future work could examine whetherhierarchical habitat models based on camera data, either alone or in conjunction with ARU data,could make inferences about habitat selection of specific patches within a crane’s home rangeby including point-level habitat covariates (Johnson, 1980).Furthermore, cameras and ARUs have a range of uses that we did not explore here, whichcould be applied to many species. For example, cameras can be used to quantify behaviourwith respect to environmental stressors (Burton et al., 2022); they could also be used in amulti-state occupancy framework that separates breeding individuals from non-breeding (J. D.Nichols et al., 2007) or healthy individuals from unhealthy (Murray et al., 2021). Camera timelapse images have also been used to link vegetation phenology to sandhill crane occurrence(Sun et al., 2021). ARUs, in some cases, can discriminate individuals by unique acoustic cues,enabling more accurate density estimation (Larsen et al., 2022). Both methods can be used to42monitor across multiple species and collect information on human disturbance (Buxton et al.,2018).In this study, we focused on sandhill cranes. However, many other species have the potential tobe studied using both methods. Several birds and mammals are seasonally vocal in Tuyetaoutside of the time period that was transcribed, including moose, lynx, grouse, ptarmigans, andwolves. Targeted audio transcription during the peaks of vocalization of these species could beused to augment or compare with camera data. For these species, ARUs and cameras mayalso be able to provide different types of inferences. For example, ARUs could be used toidentify the locations of sharp-tailed grouse Tympanuchus phasianellus leks, which areimportant to conserve for the long-term persistence of the species (Connelly et al., 2020).Cameras, however, might better inform year-round habitat use because sharp-tailed grouse arelargely silent away from leks (Connelly et al., 2020). For very rare or endangered species, theadditional detections obtained by both sampling methods could be of high value at limitedadditional costs, especially in remote regions (Mallory et al., 2018). For example, we obtaineddetections of the endangered rusty blackbird Euphagus carolinus on 10 cameras and 10 ARUs,representing 18 unique locations, so the addition of a second sensor could be non-trivial. Futureresearch could explore how ARU and camera trap data could complement each other for alarger number of species with different ecological characteristics (e.g., scales of movement,habitat specificity, detectability).3.5 ConclusionHabitat models made with ARU data inferred patterns of occupancy at a larger scale than thosemade with camera trap data, while the integrated model included both spatial scales. ARUmodels had higher detection probability than camera trap models. The integrated model did notimprove predictive performance over ARU data alone, and its predictions of occupancyprobability across the study area were similar to the ARU model. While our study does notprovide evidence that an integrated model using data from both sampling methods improvesinferences for sandhill crane habitat use, it does lend some merit to the multi-scale approach ofhabitat modeling. Additionally, data from each method do provide complementary informationabout the sandhill crane ecology. We recommend that ARUs be used to achieve the highestdetection probability or when examining landscape-scale factors in occupancy. We recommendthat camera traps be used when examining factors affecting habitat-patch scale occupancy.Overall, both ARUs and camera traps are effective and non-invasive alternatives for modelingspecies distributions and habitat selection, and can form an important part of monitoringprograms in a cost-effective way.43Chapter 4: Conclusions4.1 Pairing camera traps and ARUs to improve wildlife monitoringIn the face of current environmental challenges, many groups and agencies must collaborate tosuccessfully conserve wildlife (Kark et al., 2015; Mace et al., 2000). Multi-partner projects,where multiple public governments, Indigenous Governments, Indigenous organizations, andacademic institutions collaborate to achieve a common goal, allow for more efficient datacollection, management, analyses, and reporting by taking advantage of shared capacity andexpertise (Environment and Natural Resources, 2023; Peacock et al., 2020). Such partnershipsoffer unique opportunities for linking different information systems (Buxton et al., 2018). In thisthesis, I explored one such opportunity: the pairing and deployment of camera traps and ARUsby multiple collaborating groups. I acknowledge that my thesis focuses on scientific knowledge;however, these results could be used going forward to link scientific and Indigenous knowledgesystems for co-management of IPCAs (Peacock et al., 2020).The goals of my thesis were to examine two aspects of pairing camera traps and ARUs. Thefirst addresses sampling design. In Tuyeta, camera traps were placed at clustered locationschosen for ARUs. One initial goal was to evaluate whether this sampling strategy was suitablefor camera traps as well, and this became an opportunity to test the utility of hierarchicalsampling designs for camera trapping research in general. The idea of clustering camera trapsto improve detection probability and reduce stochasticity in detection rates was timely, givenrecent criticism against detections from camera traps, specifically detection rate (Kolowski et al.,2021). I found that clustered sampling did improve estimates for three response variables:species richness, detection rate, and habitat associations. Clusters of 2 to 5 cameras weresufficient for species richness, detection rate of common species, and habitat associations ofsandhill crane, but clusters of up to 13 were needed for rarer species. Across Tuyeta, a largeprotected area with low overall detection rates, we needed 35 clusters for all three metrics (3.5clusters / 1000km2). Examining clusters of cameras also brought to light the large uncertaintyassociated with species richness estimates at the cluster scale, which serves as a caution tocalculating this variable at a small scale.One reason for deploying multiple types of sensors in one place is to include more taxa in amonitoring scheme. Camera trap studies often exclude birds from analysis, but others haveshown that some birds are well detected by cameras (Beirne et al., 2021; Fontúrbel et al.,2020). To that end, in my second chapter, I also tested whether sample size requirements woulddiffer among three species groups: large mammals, medium mammals, and upland game birds(grouse and cranes). I found no difference among the three groups, and concluded thatmedium-sized mammals and upland game birds are well-monitored by camera traps and shouldbe included in multispecies analyses, where appropriate. Aquatic species, however, wereinadequately surveyed by our methods, indicating that these should be excluded from mostanalyses or specifically targeted with different camera trap placement (i.e., surveyingshorelines).44The second aspect of pairing camera traps and ARUs is data integration. Specifically, Iexamined whether the two data types could be combined to create a single-species habitatmodel for sandhill crane. I also examined the differences between the ARU-only model, thecamera-only model, and the integrated model. I concluded that the camera model made betterinferences at a smaller scale, which I mapped to the home range scale of habitat selection. TheARU model made better inferences at a larger scale, which I mapped to the landscape scale ofhabitat selection. Overall occupancy and detection probability were higher for ARU data than forcamera data, but highest for the integrated data because each data source had at least onedetection that the other missed. The integrated model did not improve predictive performancecompared to the ARU data alone, so I concluded that while each data source has its ownmerits, a habitat model created with integrated data was no better than creating separatemodels.4.2 Strengths and limitationsIn this thesis, I made use of a large dataset of paired camera traps and ARUs in the borealforest, a biome under-represented in both the camera trap and songbird literature (Kays et al.,2020; Machtans et al., 2014). The Tuyeta project was a pilot study which aimed to explore theresearch opportunities of deploying these sensors within large northern protected areas. Thepairing of camera traps and ARUs is fairly novel, with few published studies pertaining to thepairing of sensors or integrating of the two data types (Buxton et al., 2018; Dobbins et al., 2020).The challenges of field work in Tuyeta, with its remoteness and extreme cold, did cause somelimitations. The challenges of accessing predefined sampling locations resulted in manylocations being eliminated or substituted for others, and the end result was a sampling designthat was first, uneven with respect to numbers of locations per cluster, and second, more difficultto delineate clusters. Specifically, the number of “clusters” with only one location werechallenging to analyze. This did result in a limited interpretation of the data in chapter 2. For asingle response variable, while each target (precision and accuracy) alone may have beenachieved within the available subsampling scenarios, in some cases all targets together werenot (Figure 2.3). The cold weather also affected the sensors themselves. 46% of ARUs faileddue to a software glitch causing power surges at cold temperatures. Camera traps fared better:only 25% failed before retrieval a year after deployment. This did cause an imbalance betweencamera traps and ARUs that may have affected results from chapter 3; however, this makes aneven stronger case for considering multiple data types, because one sensor could act as asafety net for the other in case of failure. Future studies should be sure to plan for the potentialof equipment failures, and build redundancy into designs where possible.The sampling design used in Tuyeta was a combination of designs, and was not necessarilyideal for inferences to be made for the protected area specifically. For example, the BorealOptimal Sampling Strategy was designed for the much larger boreal region (Van Wilgenburg etal., 2020). There was added value that was gained by deploying these sites, which tie intobroader research programs. However, in this case, Tuyeta deployment was dominated by thisresearch protocol. This resulted in poor spatial coverage, with a lack of sampling in the western45portion of Tuyeta (Figure 1.4). However, the range of available land cover types was sampledadequately, with a possible under-representation of alpine tundra (Appendix A). This made itdifficult to say whether alpine species undetected on cameras, such as Dall’s sheep, are trulyabsent from Tuyeta.4.3 Reflections on working with multiple governments within Indigenous ProtectedAreasGiven that Ts’udé Nilįné Tuyeta is an Indigenous Protected and Conserved Area (IPCA), andthat there are several similar projects on other IPCAs in the Northwest Territories, future workshould better link Indigenous and scientific knowledge systems for true co-monitoring andco-management. Coexistence or co-production models of linking knowledge systems, whereeach type of knowledge retains its integrity, are the best practice (Abu et al., 2020; McGregor,2002). For coexistence and co-production to truly function, there must be collaboration at everystep of the scientific and land management process (Cooke et al., 2021). One successfulexample of this includes the co-monitoring and co-management of muskox Ovibos moschatusand caribou Rangifer tarandus using several indicators of animal population health based onthree different knowledge types: interviews, harvest-based sampling, and conventional science(Peacock et al., 2020). Other successful examples involve asking for community input onsampling locations or important biodiversity areas, as was done in the Great Bear Rainforest ofBritish Columbia, Canada (Artelle et al., 2021). This was done in Tuyeta with the deployment in2021 of 18 locations chosen by community members. For example, future studies could furtherexamine the optimal location of camera traps and ARUs to improve wildlife detection in theprotected area using both Indigenous and scientific knowledge to inform sampling design. TheK’ahsho Got’ı̨nę have thousands of years of knowledge about wildlife in the protected area.Despite these examples of successful linking of knowledge systems, there are still challenges toovercome. Differences in beliefs are a barrier to successful co-management, such as the idea ofwhat place humans have in the ecosystem or in nature, the timescale of monitoring, and thespiritual and cultural connection to the land (Adams et al., 2014). Often, these differences inbeliefs can be resolved by focusing on the common ground among all stakeholders (Bohensky& Maru, 2011). A large barrier to co-management and linking knowledge is the long history ofcolonization in Canada, which has set a foundation of deep-seated racism towards Indigenouspeople and distrust between them and those who work in academic and government positions.Good working relationships based on mutual respect and trust take time to develop, even in thebest of cases. It can be especially difficult to find the time needed to build trust-basedrelationships in the context of academic institutions, which operate on short deadlines and havea high student turnover rate (Cooke et al., 2021). To remedy this, the time it takes to developthese relationships should be budgeted for, both in terms of time and money (Adams et al.,2014; Castleden et al., 2012; Wong et al., 2020). Another significant challenge in linkingknowledge systems is the capacity of any given Indigenous community. These communities areoften very small and can be overwhelmed by numerous scientific projects wanting to collaborate(Adams et al., 2014). Offering compensation for the time that Indigenous community members46spend in knowledge co-production is an important step to help address capacity barriers(Indigenous Leadership Initiative, 2022).Based on my personal experience, a few specific recommendations I would make for graduatestudents who are conducting studies within Indigenous Protected Areas are as follows. Firstly,Indigenous governments and organizations should be engaged at all stages of the researchprocess, including sampling design (e.g., when choosing where to put camera traps and ARUs).It is critical that there is a formal data sharing agreement in place, signed by the relevant parties,that outlines the terms for research, including type and frequency of engagement,communications, resulting publications, and reporting results to the Indigenous government ororganization. This agreement should follow ownership, control, access, and possessionprinciples (OCAP®; First Nations Information and Governance Centre, 2023). Secondly, resultsshould be communicated to the community frequently, clearly, concisely, in plain language, andin-person whenever possible. In-person meetings may be preferable to video conferencingbecause of large group sizes, poor internet connectivity in remote communities, and languagebarriers. Students should not rely solely on government partners to communicate results.Thirdly, students should visit the community as often as possible, ideally at least three times ayear. These visits may require additional funding and planning (particularly for remotecommunities). These steps were not all followed during the course of this research, and someresulting miscommunications or misunderstandings impeded timely completion and publicationof the research.4.4 Applications and future directionsThe results of chapter 2 can be applied to future camera trap sampling designs, in Canada’snorth and across the world. In fact, the camera trap deployments in Thaidene Nëné NationalPark Reserve in 2022 were partly informed by the results of this data chapter, in terms ofhighlighting the importance of spatial balance in survey design, habitat representation, andchoosing the number of clusters they decided to keep for continuous monitoring after the initialdeployments. As discussed in chapter 2, sampling design recommendations and sample sizerequirements might vary considerably from one part of the world to another (Colyn et al., 2018;Kays et al., 2020). However, the utility of hierarchical sampling designs should hold acrossmany ecosystems. In other ecosystems with higher or lower animal diversity or density, a similarsubsampling approach using a clustered dataset from the study area in question could informoptimal sampling designs in those ecosystems.The field of optimizing sampling design for camera trap and paired camera trap / ARUdeployments would benefit from more work across different ecosystems and from differentresponse variables than I tested. Ideally, a monitoring program such as the Northwest TerritoriesBiodiversity Monitoring Program would track changes in relative or absolute abundance overtime; for this, a required sample size and design could be calculated using a power analysis(Cohen, 1992) or by using various density estimations (Clarke et al., 2023) as a responsevariable in subsampling.47Chapter 3 focuses on sandhill crane, but there are management implications for other speciesthat could be monitored by both methods. Garland et al, (2020), for example, found similarresults for gray wolves Canis lupus. Specifically, there are clear implications for the endangeredwhooping crane. Whooping cranes share many characteristics with sandhill cranes, and wouldvery likely be well-detected by ARUs and cameras because they are both louder and larger thansandhill cranes (Urbanek & Lewis, 2020). Future work could apply these findings to the contextof whooping cranes that breed in the Northwest Territories and Alberta, to understand whathabitat types are most important, to detect range shifts, and to monitor over time.More generally, future directions on camera trap and ARU integration could consider multi-taxaanalyses, such as joint species distribution models (Doser et al., 2022; Warton et al., 2015).These types of analysis could be used in applications such as the umbrella species concept,i.e., testing whether protection or management actions directed towards one species benefitsothers as well (Barlow et al., 2020; Drever et al., 2019). 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Towardsreconciliation: 10 Calls to Action to natural scientists working in Canada. Facets, 5(1),769–783. https://doi.org/10.1139/FACETS-2020-000562Appendix A: Supporting information for Chapter 2Table A1. Species detected at Tuyeta which we included in our analyses. These includemammals and upland game birds (cranes and grouse) over 200 g in mass.Common Name Scientific Name Guild Number ofindependentdetectionsProportionof camerasdetectedMoose Alces alces Large mammal 538 0.446Caribou Rangifer tarandus Large mammal 345 0.301Black Bear Ursus americanus Large mammal 314 0.317Gray Wolf Canis lupus Large mammal 88 0.100Lynx Lynx canadensis Large mammal 69 0.153Wolverine Gulo gulo Large mammal 56 0.149Porcupine Erethizon dorsatum Large mammal 42 0.036Mink Neovison vison Large mammal 9 0.032Grizzly Bear Ursus arctos Large mammal 8 0.024Beaver Castor canadensis Large mammal 7 0.012Red Fox Vulpes vulpes Large mammal 2 0.008River Otter Lontra canadensis Large mammal 1 0.004Red Squirrel TamiasciurushudsonicusMedium mammal 437 0.161Snowshoe Hare Lepus americanus Medium mammal 279 0.189Marten Martes americana Medium mammal 224 0.369Muskrat Ondatra zibethicus Medium mammal 25 0.020Ermine Mustela erminea Medium mammal 10 0.036Sandhill Crane Anitgone canadensis Game bird 166 0.165Sharp-tailedGrouseTympanuchusphasianellusGame bird 148 0.26563Spruce Grouse FalcipenniscanadensisGame bird 54 0.064Ruffed Grouse Bonasa umbellus Game bird 5 0.016WillowPtarmiganLagopus lagopus Game bird 5 0.016Table A2. Species expected based on range maps (IUCN, 2022) which we did not detect withcamera traps.Common Name Scientific Name GuildDall’s Sheep Ovis dalli Large mammalArctic Fox Vulpes lagopus Large mammalArctic Ground Squirrel Urocitellus parryii Medium mammalWhite-tailed Ptarmigan Lagopus leucura Upland game birdRock Ptarmigan Lagopus muta Upland game bird64Figure A1. Species accumulation curves for all clusters. Note that the top panel has a smallerscale than the bottom panel. The red dashed line represents extrapolation beyond double thesample size, which is out of the bounds of typical extrapolation.65Figure A3. Detection rate estimates from resampling, at the protected area scale. Gray dotsindicate means for each random subsample. The black line indicates the overall mean acrossthe 50 subsamples, and the blue lines are the associated 95% confidence intervals. The verticalblack line is the sample size at which the target accuracy is reached (the standard error lessthan 10% of the mean).666768Figure A4. Detection rate estimates from resampling at the cluster level. Cluster IDs are printedin the top left corner and species are printed on the top right of each plot. Gray dots indicatemeans for each random subsample. The black line indicates the overall mean across the 50subsamples, and the blue lines are the associated 95% confidence intervals. The vertical blackline is the sample size at which the target accuracy is reached (standard error less than 10% ofthe mean).69Figure A5. Left, ecoregions present in Tuyeta. Middle, hexagons available and sampled. Right,barplots showing number of available and sampled hexagons within each ecoregion. When ahexagon intersected with more than one ecoregion, it was assigned one by majority rules.Abbreviations are as follows: FMP, Fort MacPherson Plain; MMO, Mackenzie Mountains; MRP,Mackenzie River Plain; PRP, Peel River Plateau.70Figure A6. Left, Ducks Unlimited Canada land cover classifications across Tuyeta (DucksUnlimited Canada, 2021; K. B. Smith et al., 2007). Middle, dominant land cover types ofavailable and sampled hexagons. Right, barplots showing number of available and sampledhexagons of each dominant land cover type.71Figure A7. Left, Ducks Unlimited Canada land cover classifications across Tuyeta and locationsof camera traps (Ducks Unlimited Canada, 2021; K. B. Smith et al., 2007). Right, barplotsshowing available habitat (number of raster cells) and point-level sampled habitat (number ofcamera traps) within each habitat type.72Figure A8. Correlations among land cover classes within sampled hexagons. We did not includetwo variables that had a correlation of greater than 0.6 or less than -0.6 in the same model.7374Figure A9. Survey effort across all camera traps. Black lines denote times when each camerawas active and taking acceptable images. White spaces indicate times when each camera wasinactive (battery run down, lens snowed over, storage space full, etc.).75Appendix B: Supporting information for Chapter 3Figure B1. Ducks Unlimited Land Cover Classification for Ts’udé Nilįné Tuyeta Indigenous andTerritorial Protected Area, Northwest Territories, Canada. For more information, see DucksUnlimited Canada (2021) and Smith et al (2007).76Figure B2. To incorporate both buffer sizes in the same model, a score for each land cover typeat the larger buffer was calculated to reduce multicollinearity. This was done by fitting a linearregression that describes the relationship between the two variables, shown by the diagonal linein the graph above. This line represents the percent cover of marsh within the 2000 m bufferthat would be expected based on a given percent cover of marsh within the 300 m buffer. Theexpected value was subtracted from the actual value for marsh cover within 2000 m, and thisscore was used as a habitat covariate in the integrated model. The vertical lines represent thescores for each data point; they are negative below the linear regression line and positive aboveit. Negative values indicate locations where there is less marsh than expected in a 2000 mradius, and positive values indicate locations where there is more marsh than expected.77Figure B3. Model fit statistics for top ARU, camera, and integrated models. Bayesian p-values(posterior predictive checks based on the Freeman-Tukey test statistic) are reported andvisualized (Kéry & Royle, 2016). The p-value represents the proportion of fitted, or predictedvalues, that are greater than the true, or actual values in the data. The ideal p-value is 0.5. Forthe integrated model, values are reported separately for each data source. Orange circlesrepresent fitted values that are larger than the true values, and blue circles represent theopposite.78Figure B4. Correlations among land cover classes at two spatial scales (300 m and 2000 mbuffers around each point). For modeling, we considered a correlation of greater than 0.6 or lessthan -0.6 to be too high. To eliminate correlation among the same land cover classes at differentspatial scales, we used the residual value of a linear regression between the land cover class ateach buffer size (Figure B2). Red values are negative and blue values are positive. More detailsare in the methods.79Figure B5. Sampling effort for ARUs, classified by hour of the day (top) and sun altitude(bottom).80
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Integrating camera traps and autonomous recording units for wildlife monitoring in the Northwest Territories
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