Spatial Capture-recapture Research Articles

Density and genetic diversity of grizzly bears at the northern edge of their distribution

AbstractSpecies at the periphery of their range are typically limited in density by poor habitat quality. As a result, the central–marginal hypothesis (CMH) predicts a decline in genetic diversity of populations toward the periphery of a species' range. Grizzly bears (Ursus arctos) once ranged throughout most of North America but have been extirpated from nearly half of their former range, mainly in the south. They are considered a species at risk even in Canada's remote North, where they occupy the northernmost edge of the species' continental distribution in a low‐productivity tundra environment. With climate change, one of their main prey species in the tundra (caribou), which has always shown yearly fluctuations, is declining, but simultaneously, grizzlies appear to be expanding their range northward in the same tundra environment. Yet, a lack of population density estimates across the North is hindering effective conservation action. The CMH has implications for the viability of peripheral populations, and the links between population fluctuations, potential bottlenecks, and genetic diversity need to be determined to contribute to species' conservation. Using noninvasive genetic sampling from 2012 to 2014 and autosomal DNA genotyping (via microsatellites), we estimated bear density using a spatial capture–recapture framework and analyzed genetic diversity using observed heterozygosity (Ho), allelic richness (AR), and expected heterozygosity (He). We compared our findings to other studies that used comparable methodologies on grizzly bears and a related species (black bears; Ursus americanus). We found densities of grizzly bears that were low for the species but characteristic for the region (5.9 ± 0.4 bears/1000 km2), but with high Ho (0.81 ± 0.05), AR (7 ± 0.78), and He (0.71 ± 0.03), despite a signal of recent bottlenecks. In both species, peripherality was not correlated with Ho but was negatively correlated with density. We suggest that the apparent growth of this expanding population of grizzlies offsets the negative impacts of recent bottlenecks on Ho. Indigenous knowledge provides historical context (on the order of centuries, e.g., arctic large mammal fluctuations, grizzly bear bottlenecks) for the current bear population dynamics (on the order of decades, e.g., climate change, northern grizzly bear expansion).

Estimating density of forest bats and their long-term trends in a climate refuge.

For many species, estimating density is challenging, but it is important for conservation planning and understanding the functional role of species. Bats play key ecological roles, yet little is known about their free-ranging density. We used a long-term banding study of four species caught in an extensively forested climate refuge and spatial capture-recapture models (SCR) to estimate density and its change over time. Between 1999 and 2020, there were 3671 captures of four bat species, which were all edge-space foragers. Recaptures represented 16% (n = 587) of all captures, of which 89 were between-trap-cluster movements. Closed spatial mark-recapture models estimated plausible densities that varied with elevation. Preferred elevations differed between species, with density averaging 0.63 ha-1 for Vespadelus darlingtoni (high elevation), 0.43 ha-1 for V. pumilus (low elevation), 0.19 ha-1 for Chalinolobus morio (high elevation), and 0.08 ha-1 for V. regulus (high elevation). Overall, densities were higher than most previous published estimates for bats. Forest disturbance history (past timber harvesting) had no detectable effect on density. Density also varied substantially across years, and although annual maximum temperature and rainfall were not supported in models, some time periods showed an apparent relationship between density and annual rainfall (+ve) and/or annual maximum temperature (-ve). The most notable change was an increase in the density of V. pumilus after 2013, which tracked an increase in annual temperature at the site, reflecting a warming climate. Bat densities in forests outside of climate refugia are likely to be more sensitive to climate change, but more studies are needed in different habitats and continents and outside climate refugia to place the densities we estimated into a broader context.

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EcosphereVolume 14, Issue 5 e4123 COVER IMAGEOpen Access Cover Image First published: 07 May 2023 https://doi.org/10.1002/ecs2.4123AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Graphical Abstract COVER PHOTO: To understand how environmental variables influence white-tailed deer (Odocoileus virginianus) fawn survival, Engebretsen et al. (“Noninvasive camera data and spatial capture–recapture models reveal strong temporal variation in fawn survival,” Ecosphere, Volume 14, Issue 4, Article e4497; doi: 10.1002/ecs2.4497) deployed 180 cameras throughout the Florida Panther National Wildlife Refuge and the Big Cypress National Preserve. The authors were able to identify 271 fawns, such as the one shown here traveling with its mother through a cypress dome in the Big Cypress National Preserve. They used the camera data to create a dataset for a spatial capture–recapture analysis that revealed differences in survival to 180 days between fawns born in 2015 and those born in 2016. Their data suggest that extreme weather events likely contributed to lower recruitment in south Florida, USA. Photo credit: the authors. Volume14, Issue5May 2023e4123 RelatedInformation

Monitoring distribution, density and introgression in European wildcats in Switzerland

Monitoring the population dynamics of elusive hybridizing species in human-dominated landscapes helps conservation and management of these species. This is particularly relevant when wild species are potentially threatened by hybridization with a domestic species, as is the case for European wildcats and domestic cats. We estimated the occupancy of European wildcats in 2010 and in 2020, based on genetic analysis of single hairs, systematically collected at 121 1 km2 sites, evenly distributed across the Jura (4307 km2). We mapped the current density of European wildcats in Switzerland based on a spatial capture recapture model using data from 246 1 km2 sites, evenly distributed across the Jura, the Plateau and the Prealps. We estimated the level of admixture between European wildcats and domestic cats in Switzerland in 2010 and 2020. The proportion of occupied sites in the Swiss Jura doubled from 15 % to 31 % within ten years. The current wildcat population size in Switzerland is estimated to be around 1100 individuals. Gene flow from domestic cats into the wildcat gene pool increased from 0.02 to 0.03 migrants per generation between 2010 and 2020. The results suggest that the European wildcat distribution in Switzerland expanded to regions of increased human presence and thus presumably also of increased domestic cat presence. In this context, the threat of introgression of domestic genes becomes more relevant. Thus, continued monitoring of population dynamics using molecular tools is recommended. Long-term systematic genetic survey of population dynamics proves to be an important tool to manage elusive hybridizing mammals.

Noninvasive camera data and spatial capture–recapture models reveal strong temporal variation in fawn survival

AbstractIn south Florida, white‐tailed deer (Odocoileus virginianus) are the primary prey of the endangered Florida panther (Puma concolor coryi). Deer populations in some regions of south Florida have declined in recent years, and the role of fawn survival and recruitment in these declines is unknown. Determining known‐fate survival of fawns is challenging, requires invasive and costly methods, and often has a limited geographic scope. We deployed 180 cameras throughout the Florida Panther National Wildlife Refuge and the Big Cypress National Preserve to understand how environmental variables influence fawn survival. We identified 271 fawns from 12,715 photographs in 2015 and 2016. We utilized a noninvasive sampling method coupled with a spatial capture–recapture model to estimate the number of fawns born, the spatial distribution of birth locations, and the number of fawns that survived to recruitment (180 days old) during two fawning seasons. We found strong evidence of temporal variation in survival, but little evidence of spatial variation. Within the 10,941‐ha study area, we estimated that 305 (95% CI: 245–385) fawns were born in 2015 and 278 (212–381) fawns were born in 2016. In 2015, 36% (110) of the estimated 305 fawns survived to 180 days. However, in 2016, only 13% (36) of the estimated 278 fawns survived to 180 days. The large difference in recruitment between years was likely driven by record flooding in 2016. Our data suggest that extreme weather events, coupled with high adult mortality, likely contributed to recent deer population decline in south Florida through reduced fawn recruitment. Unlike studies of known‐fate fawn survival that require labor‐intensive and invasive capture of both adults and neonates, our approach relies exclusively on camera data, which makes it possible to conduct studies over broad spatiotemporal scales in challenging environments to illuminate the drivers of variation in juvenile survival.

Ipsecr: An R package for awkward spatial capture–recapture data

AbstractSome capture–recapture models for population estimation cannot easily be fitted by the usual methods (maximum likelihood and Markov‐chain Monte Carlo). For example, there is no straightforward probability model for the capture of animals in traps that hold a maximum of one individual (‘single‐catch traps’), yet such data are commonly collected. It is usual to ignore the limit on individuals per trap and analyse with a competing‐risk ‘multi‐catch’ model that gives unbiased estimates of average density. However, that approach breaks down for models with varying density.Simulation and inverse prediction was suggested by Efford (2004) for estimating population density with data from single‐catch traps, but the method has been little used, in part because the existing software allows only a narrow range of models. I describe a new R package that refines the method and extends it to include models with varying density, trap interference and other sources of non‐independence among detection histories.The method depends on (i) a function of the data that generates a proxy for each parameter of interest and (ii) functions to simulate new datasets given values of the parameters. By simulating many datasets, it is possible to infer the relationship between proxies and parameters and, by inverting that relationship, to estimate the parameters from the observed data.The method is applied to data from a trapping study of brushtail possumsTrichosurus vulpeculain New Zealand. A feature of these data is the high frequency of non‐capture events that disabled traps (interference). Allowing for a time‐varying interference process in a model fitted by simulation and inverse prediction increased the steepness of inferred year‐on‐year population decline. Drawbacks and possible extensions of the method are discussed.

Modelling spatially autocorrelated detection probabilities in spatial capture-recapture using random effects

Spatial capture-recapture (SCR) models are now widely used for estimating density from repeated individual spatial encounters. SCR accounts for the inherent spatial autocorrelation in individual detections by modelling detection probabilities as a function of distance between the detectors and individual activity centres. However, additional spatial heterogeneity in detection probability may still creep in due to environmental or sampling characteristics. if unaccounted for, such variation can lead to pronounced bias in population size estimates.In this paper, we address this issue by describing three Bayesian SCR models that use generalized linear mixed modelling (GLMM) approach to account for latent heterogeneity in baseline detection probability across detectors with: independent random effects (RE), spatially autocorrelated random effects (SARE) with components of prior covariance matrix modelled as a decreasing function of inter-detector distance, and a two-group finite mixture model (FM) to identify latent detectability classes of each detector. We test these models using a simulation study and an empirical application to a non-invasive genetic monitoring data set of female brown bears (Ursus arctos) in central Sweden.In the simulation study, all three models largely succeeded in mitigating the biasing effect of spatially heterogeneous detection probability on population size estimates. Overall, SARE provided the least biased population size estimates (median RB: -9% – 6%). When spatial autocorrelation in detection probability was high, SARE also performed best at predicting the spatial pattern of heterogeneity in detection probability. At intermediate levels of autocorrelation, spatially-explicit estimates of detection probability obtained with FM were more accurate than those generated by SARE and RE. The empirical example revealed patterns consistent with the results from the simulation study. We found that ignoring spatial heterogeneity in detection probability led to at least 22% lower estimate of bear population size compared to models that accounted for it (i.e., SARE and RE models). When the number of detections per detector is low (≤1), the GLMMs considered here may require dimension reduction of the random effects by pooling baseline detection probability parameters across neighbouring detectors (“aggregation”) to avoid over-parameterization.The added complexity and computational overhead associated with SCR-GLMMs may only be justified in extreme cases of spatial heterogeneity, e.g., large clusters of inactive detectors unbeknownst to the investigator. However, even in less extreme cases, detecting and estimating spatially heterogeneous detection probability may assist in planning or adjusting monitoring schemes.

An evaluation of spatial capture‐recapture models applied to ungulate non‐invasive genetic sampling data

AbstractNon‐invasive genetic sampling (NGS) methods are becoming a mainstay in wildlife monitoring and can be used with spatial capture‐recapture (SCR) methods to estimate population density. Yet SCR based on NGS remains relatively underused for ungulate population monitoring, despite the importance of robust density estimates for this ecologically and economically important group of species. This may be in part attributed to biological characteristics of ungulate species and data collection methods that lead to violations of SCR model assumptions. We conducted a simulation study to evaluate the robustness of SCR methods to spatially heterogeneous density (i.e., configuration of individuals into groups of variable sizes and composition), individual heterogeneity in space‐use patterns, and adaptive sampling (i.e., variation in detectability across space that correlates with density). We evaluated each violation separately and in combination. We parameterized our simulations based on published information and preliminary analyses of NGS data sets of 3 ungulate species: chamois (Rupicapra rupicapra), red deer (Cervus elaphus), and wild boar (Sus scrofa). While SCR estimates were robust to grouping and adaptive sampling, abundance estimates could be negatively biased (up to 10% in our simulations) in the presence of unaccounted individual heterogeneity in space use. The degree to which abundance estimates were underestimated depended mostly on the amount of variation in space use and detectability among age classes. This bias was also accompanied by a reduction in precision and coverage probability of the SCR estimators. We discuss the implications of these findings, possible approaches to identify problematic violations in available data sets (goodness‐of‐fit tests), and potential further developments of SCR models to ensure reliable abundance estimates for ungulate populations from NGS data.

Wildlife Population Assessment: Changing Priorities Driven by Technological Advances

Advances in technology are having a large effect on the priorities for innovation in statistical ecology. Collaborations between statisticians and ecologists have always been important in driving methodological development, but increasingly, expertise from computer scientists and engineers is also needed. We discuss changes that are occurring and that may occur in the future in surveys for estimating animal abundance. As technology advances, we expect classical distance sampling and capture-recapture to decrease in importance, as camera (still and video) survey, acoustic survey, spatial capture-recapture and genetic methods continue to develop and find new applications. We explore how these changes are impacting the work of the statistical ecologist.

Integrating opportunistic and structured non-invasive surveys with spatial capture-recapture models to map connectivity of the Pyrenean brown bear population

Connectivity, in the sense of the persistence of movements between habitat patches, is key to maintain endangered populations and has to be evaluated in management plans. In practice, connectivity is difficult to quantify especially for rare and elusive species. Here, we use spatial capture-recapture (SCR) models with an ecological detection distance to identify barriers to movement. We focused on the transnational critically endangered Pyrenean brown bear (Ursus arctos) population, which is distributed over Spain, France and Andorra and is divided into two main cores areas following translocations. We integrate structured monitoring from camera traps and hair snags with opportunistic data gathered after depredation events. While structured monitoring focuses on areas of regular bear presence, the integration of opportunistic data allows us to obtain information in a wider range of habitat, which is especially important for ecological inference. By estimating a resistance parameter from encounter data, we show that the road network impedes movements, leading to smaller home ranges with increasing road density. Although the quantitative effect of roads is context-dependent (i.e. varying according to landscape configuration), our model predicts that a brown bear with a home range located in an area with relatively high road density (8.29 km/km2) has a home range size reduced by 1.4-fold for males and 1.6-fold for females compared to a brown bear with a home range located in an area with low road density (1.38 km/km2). When assessing connectivity, spatial capture-recapture modeling offers an alternative to the use of experts' opinion when telemetry data are not available.

Estimating spatially variable and density-dependent survival using open-population spatial capture-recapture models.

Open-population spatial capture-recapture (OPSCR) models use the spatial information contained in individual detections collected over multiple consecutive occasions to estimate not only occasion-specific density, but also demographic parameters. OPSCR models can also estimate spatial variation in vital rates, but such models are neither widely used nor thoroughly tested. We developed a Bayesian OPSCR model that not only accounts for spatial variation in survival using spatial covariates but also estimates local density-dependent effects on survival within a unified framework. Using simulations, we show that OPSCR models provide sound inferences on the effect of spatial covariates on survival, including multiple competing sources of mortality, each with potentially different spatial determinants. Estimation of local density-dependent survival was possible but required more data due to the greater complexity of the model. Not accounting for spatial heterogeneity in survival led to up to 10% positive bias in abundance estimates. We provide an empirical demonstration of the model by estimating the effect of country and density on cause-specific mortality of female wolverines (Gulo gulo) in central Sweden and Norway. The ability to make population-level inferences on spatial variation in survival is an essential step toward a fully spatially explicit OPSCR model capable of disentangling the role of multiple spatial drivers of population dynamics.

Density-habitat relationships of white-tailed deer (Odocoileus virginianus) in Finland.

In heterogeneous landscapes, resource selection constitutes a crucial link between landscape and population-level processes such as density. We conducted a non-invasive genetic study of white-tailed deer in southern Finland in 2016 and 2017 using fecal DNA samples to understand factors influencing white-tailed deer density and space use in late summer prior to the hunting season. We estimated deer density as a function of landcover types using a spatial capture-recapture (SCR) model with individual identities established using microsatellite markers. The study revealed second-order habitat selection with highest deer densities in fields and mixed forest, and third-order habitat selection (detection probability) for transitional woodlands (clear-cuts) and closeness to fields. Including landscape heterogeneity improved model fit and increased inferred total density compared with models assuming a homogenous landscape. Our findings underline the importance of including habitat covariates when estimating density and exemplifies that resource selection can be studied using non-invasive methods.

Assessing Red Fox (Vulpes vulpes) Demographics to Monitor Wildlife Diseases: A Spotlight on Echinococcus multilocularis

The assessment of red fox population density is considered relevant to the surveillance of zoonotic agents vectored by this species. However, density is difficult to estimate reliably, since the ecological plasticity and elusive behavior of this carnivore hinder classic methods of inference. In this study, red fox population density was estimated using a non-invasive molecular spatial capture-recapture (SCR) approach in two study areas: one in a known hotspot of the zoonotic cestode Echinococcus multilocularis, and another naïve to the parasite. Parasitological investigations on collected samples confirmed the presence of the parasite exclusively in the former area; the SCR results indicated a higher fox population density in the control area than in the hotspot, suggesting either that the relationship between fox density and parasite prevalence is not linear and/or the existence of other latent factors supporting the parasitic cycle in the known focus. In addition, fox spotlight count data for the two study areas were used to estimate the index of kilometric abundance (IKA). Although this method is cheaper and less time-consuming than SCR, IKA values were the highest in the areas with the lower molecular SCR density estimates, confirming that IKA should be regarded as a relative index only.

Bobcats in southern California respond to urbanization at multiple scales

Urban areas impact wildlife populations through multiple pathways, notably habitat fragmentation and degradation. Wildlife can respond to development and other human-driven environmental change at multiple scales, from the individual to the population level, ultimately driving biodiversity patterns and processes in built and adjacent natural landscapes. However, multiple scales of space use are rarely considered in an integrated framework. Focusing on bobcats, a species associated with intermediate sensitivity to development, we explored factors affecting multi-scale space use and population density across San Diego County, a region in southern California characterized by a pronounced development gradient. We used remote camera data and GPS collar data in an integrated spatial capture-recapture + resource selection function approach. At the individual scale, bobcats selected against developed areas, while at the population level, bobcat densities were higher in habitat patches surrounded by development, suggesting that bobcat populations in these areas may be experiencing home range pile-up, a manifestation of the fence effect. Our results provide an integrated multi-scale view of the impacts of human-modified landscapes on wildlife, confirming that research on space use at the individual and population levels should be considered jointly to advance understanding of the mechanisms driving wildlife response to development. This approach has promise to improve conservation and management efforts for urban wildlife.

Identification of familial networks reveals sex-specific density dependence in the dispersal and reproductive success of an endangered ungulate

Density is an important demographic parameter that is commonly overlooked in studies of wild populations. Here, we examined the effects of variable spatially explicit density on a range of demographic parameters in a wild population of a cryptic ungulate, boreal woodland caribou (Rangifer tarandus caribou). Using non-invasive genetic sampling, we applied spatial capture–recapture methods with landscape covariates to estimate the density of boreal woodland caribou across a 108,806 km2 study area. We then created a familial network from the reconstructed parent–offspring relationships to determine whether spatial density influenced sex-specific individual reproductive success, female pregnancy status, and dispersal distance. We found that animal density varied greatly in response to land cover types and disturbance; animal density was most influenced by landscape composition and distance to roads varying from 0 in areas with >20% deciduous cover to 270 caribou per 1,000 km2 in areas presenting contiguous older coniferous cover. We found that both male and female reproductive success varied with density, with males showing a higher probability of having offspring in higher-density areas, and the opposite for females. No differences were found in female pregnancy rates occurring in high- and low-density areas. Dispersal distances varied with density, with offspring moving shorter distances when parents were found in higher-density areas. Familial networks showed lower-closeness centrality and lower-degree centrality for females in higher-density areas, indicating that females found in higher-density areas tend to be less broadly associated with animals across the range. Although high-density areas do reflect good-quality caribou habitat, the observed decreased closeness and degree centrality measures, dispersal rates, and lower female recruitment rates suggest that remnant habitat patches across the landscape may create population sinks.

A flexible and efficient Bayesian implementation of point process models for spatial capture-recapture data.

Spatial capture-recapture (SCR) is now routinely used for estimating abundance and density of wildlife populations. A standard SCR model includes sub-models for the distribution of individual activity centers (ACs) and for individual detections conditional on the locations of these ACs. Both sub-models can be expressed as point processes taking place in continuous space, but there is a lack of accessible and efficient tools to fit such models in a Bayesian paradigm. Here, we describe a set of custom functions and distributions to achieve this. Our work allows for more efficient model fitting with spatial covariates on population density, offers the option to fit SCR models using the semi-complete data likelihood (SCDL) approach instead of data augmentation, and better reflects the spatially continuous detection process in SCR studies that use area searches. In addition, the SCDL approach is more efficient than data augmentation for simple SCR models while losing its advantages for more complicated models that account for spatial variation in either population density or detection. We present the model formulation, test it with simulations, quantify computational efficiency gains, and conclude with a real-life example using non-invasive genetic sampling data for an elusive large carnivore, the wolverine (Gulo gulo) in Norway.

Effects of anthropogenic disturbance on group densities of Thomas' langurs (Presbytis thomasi) within a lowland tropical forest, north Sumatra

AbstractEcosystems around the globe are facing irreversible impacts due to climate change, habitat destruction, hunting, and an ever‐increasing human population. Estimating densities of species across their geographical range helps us to understand natural variation and anthropogenic effects on species densities and to assess the effectiveness of existing conservation measures. Various methods have been used to produce accurate and precise population density estimates, each with associated limitations. Acoustic surveys for species producing loud calls have become common due to their ease of use, low cost, and reduced timescale. Relative to many other mammal taxa, primate species have been studied extensively, producing a wealth of data on socioecology and behavior, but for most species, density estimates over large geographical ranges are still lacking. We used an acoustic spatial capture–recapture model to estimate group density of unhabituated Thomas' langurs (Presbytis thomasi), a primate endemic to the Indonesian island of Sumatra, over a 60 km2 area of lowland dipterocarp forest. We then assessed if vegetation structure and distance from human habitation affected density estimates. Estimates of group density differed almost threefold between survey locations (from 2.79 to 8.08 groups/km2); there was no clear relationship with forest structure, but there was a significantly positive relationship between group density and distance from human habitation, with an increase of 0.38 groups/km2 for every km of distance. Although large‐scale logging within the Sikundur region ceased ~30 years ago, the impacts of logging continue to have detrimental effects on the species within the area.

Evaluating the prevalence and spatial distribution of giraffes injured by non‐target poaching

AbstractIllegal harvest (poaching) is a geographically widespread threat to animal populations. Wire snares are a common poaching technique used in the Global South and are indiscriminate with respect to species, age, and sex of individual animals that they capture. When caught, relatively large mammals frequently break free from the snares, suffering injuries in the process. The prevalence of snaring injuries as well as the ecological and conservation implications of such patterns have not been widely investigated for many animal populations. We evaluated the prevalence and spatial distribution of giraffes (Giraffa camelopardalis) with snaring injuries in Murchison Falls National Park, Uganda, a system experiencing high rates of wire snare poaching. We conducted photographic spatial encounter surveys along five fixed road transects. We photographed detected giraffes, recorded whether a snaring injury was present, and identified individuals via autonomous image processing of pelage patterns. We fitted a spatial capture‐recapture (SCR) model to the encounter history data to estimate abundance and predict the spatial distribution of injured giraffes. We detected 1306 (sub) adult giraffes from the surveys and predicted 1939 (±80) via the SCR model. We encountered 26 giraffes with snaring injuries, corresponding to a population‐level snaring rate of 1.4%. Individuals with higher movement scales were more at risk of being snared. Though not fatal across the three‐month duration of this study, the injuries likely lead to secondary infections, disability, and potentially death in the long term. We explore the implications of bearing snaring injuries for ecology, behavior, and conservation of affected species.

Comparative spatially explicit approach for testing effects of soil chemicals on terrestrial wildlife bioindicator demographics

Wildlife species are often used as bioindicators to evaluate the extent and severity of environmental contamination and the effectiveness of remediation practices. A common approach for investigating population- or community-level impacts on bioindicators compares demographic parameter estimates (e.g., population size or density) between sites that were subjected to different levels of contamination. However, the traditional analytical method used in such studies is nonspatial capture-recapture, which results in conclusions about potential relationships between demographics and contaminants being inferred indirectly. Here, we extend this comparative approach to the spatially explicit framework, allowing direct estimation of said relationships and comparisons between study areas, by applying spatial capture-recapture (SCR) models to bioindicator (deer mice [Peromyscus spp.]) detection data from two study areas that were subjected to different industrial activities and remediation practices. Bioindicator density differed by 178% between the neighboring study areas, and the area with the highest soil concentrations of polychlorinated biphenyls, chromium, and zinc had the highest bioindicator density. Under the traditional nonspatial approach, we might have concluded that soil chemical levels had negligible influences on demographics. However, by modeling density as a spatial function of select chemical concentrations using SCR models, we found strong support for a positive relationship between density and soil chromium concentrations in one study area (β = 0.82), which was not masked by or associated with habitat-related metrics. To obtain reliable inferences about potential effects of environmental contamination on bioindicator demographics, we contend that a comparative spatially explicit approach using SCR ought to become standard.

Use of aerial distance sampling to estimate abundance of tule elk across a gradient of canopy cover and comparison to a concurrent fecal DNA spatial capture-recapture survey

Historically, aerial surveys have been used widely to monitor abundance of large mammals in the western United States. In California, such surveys have typically served as minimum count indices rather than true abundance estimates. Here, we evaluated the utility of aerial multiple covariate distance sampling (MCDS) to estimate abundance of three populations of tule elk (Cervus canadensis nannodes) in northern California. We also compared estimates and costs with published results from a concurrent fecal DNA spatial capture-recapture (SCR) survey. During December 2018 and 2019, we flew line transects for distance sampling of tule elk in Colusa and Lake counties. We modeled detection functions and evaluated effects of group size, canopy cover, and survey year. We averaged the top models comprising ≥0.95 of Akaike Model Weight and estimated abundance of both total and discrete populations. Detection probability increased with increasing group size and decreasing canopy cover. We estimated a two-year average total population size of N̂ = 674 elk (90% CI = 501–907) in our survey area which was similar to N̂ = 653 elk (90% CI = 573–745) from SCR estimates. Overall precision was greater (CV = 0.08; range = 0.11–0.30 by population) for SCR than for MCDS (CV = 0.18; range = 0.22–0.43 by population). Although estimates differed somewhat between methods for the individual populations, the combined estimate across the study region compared favorably. Total cost of SCR and MCDS surveys was $98,326 and $147,324, respectively. While SCR efforts were more precise and less expensive overall, our MCDS approach reduced staff time by 64% (587 person-hours) and the number of survey days by 87% (64 days). Our results suggest MCDS methods can produce reliable abundance estimates across a gradient of canopy cover, particularly when observations can be pooled across populations to decrease variance. We recommend future research to assess use of hybrid models, such as mark-recapture distance sampling or hierarchical distance sampling, to improve precision and estimation of detection probability.