Spatial Capture-recapture Models Research Articles

Evaluating streamflow and temperature effects on Bull Trout migration and survival with linear spatial capture–recapture models

AbstractObjectiveIn the U.S. Pacific Northwest, climate change is increasing air temperatures, decreasing warm season (April–September) streamflow, and increasing cool season (October–March) streamflow. Warmer water temperatures may alter conditions for migratory coldwater fishes like the Bull Trout Salvelinus confluentus. Consequently, an understanding of Bull Trout migration and survival is critical for species conservation and restoration. In the Salmon River basin, Idaho, 1992 and 1993 transpired to be two of the most opposing extreme years among the past three decades for warm season water temperature and streamflow. These extremes provided a unique opportunity to retrospectively compare Bull Trout survival and migration under potential climate change scenarios.MethodsWe evaluated prespawning and postspawning migrations and survival of fluvial Bull Trout that were radio‐tagged and tracked from 1992 to 1994. We used a Cormack–Jolly–Seber linear spatial capture–recapture model to simultaneously model the migration and survival of radio‐tagged prespawn (n = 58) and postspawn (n = 23) Bull Trout among weeks and river reaches with streamflow, water temperature, and habitat covariates.ResultMost individual prespawning migrations were similar among tagged fish, whereas postspawn fish adopted multiple migration and overwintering strategies. Movements of prespawn Bull Trout were larger when (1) weekly average daily maximum streamflow increased and (2) weekly average daily maximum water temperature increased. The model estimated that at least 52% of spawners survived to spawning, and mean weekly prespawning apparent survival was higher in the low‐streamflow year (1992) than in the year with higher and more variable streamflow (1993). Survival of 1992–1994 fish during the 38‐week postspawning period was intermediate to that in the prespawning period. Detections of prespawn Bull Trout were generally higher at sites with more complex habitats, less large woody debris, and fewer undercut banks.ConclusionWe found that the prespawn life stage can represent a shorter time frame (14–18 weeks) with increased mortality compared to the longer postspawning period (38 weeks). Bull Trout apparent survival increased with lower streamflow variability, indicating that expected future changes in climate may adversely affect Bull Trout.

A flexible framework for spatial capture-recapture with unknown identities.

Camera traps or acoustic recorders are often used to sample wildlife populations. When animals can be individually identified, these data can be used with spatial capture-recapture (SCR) methods to assess populations. However, obtaining animal identities is often labor-intensive and not always possible for all detected animals. To address this problem, we formulate SCR, including acoustic SCR, as a marked Poisson process, comprising a single counting process for the detections of all animals and a mark distribution for what is observed (eg, animal identity, detector location). The counting process applies equally when it is animals appearing in front of camera traps and when vocalizations are captured by microphones, although the definition of a mark changes. When animals cannot be uniquely identified, the observed marks arise from a mixture of mark distributions defined by the animal activity centers and additional characteristics. Our method generalizes existing latent identity SCR models and provides an integrated framework that includes acoustic SCR. We apply our method to estimate density from a camera trap study of fisher (Pekania pennanti) and an acoustic survey of Cape Peninsula moss frog (Arthroleptella lightfooti). We also test it through simulation. We find latent identity SCR with additional marks such as sex or time of arrival to be a reliable method for estimating animal density.

Evaluating density-weighted connectivity of black bears (Ursus americanus) in Glacier National Park with spatial capture–recapture models

BackgroundImproved understanding of wildlife population connectivity among protected area networks can support effective planning for the persistence of wildlife populations in the face of land use and climate change. Common approaches to estimating connectivity often rely on small samples of individuals without considering the spatial structure of populations, leading to limited understanding of how individual movement links to demography and population connectivity. Recently developed spatial capture-recapture (SCR) models provide a framework to formally connect inference about individual movement, connectivity, and population density, but few studies have applied this approach to empirical data to support connectivity planning.MethodsWe used mark-recapture data collected from 924 genetic detections of 598 American black bears (Ursus americanus) in 2004 with SCR ecological distance models to simultaneously estimate density, landscape resistance to movement, and population connectivity in Glacier National Park northwest Montana, USA. We estimated density and movement parameters separately for males and females and used model estimates to calculate predicted density-weighted connectivity surfaces.ResultsModel results indicated that landscape structure influences black bear density and space use in Glacier. The mean density estimate was 16.08 bears/100 km2 (95% CI 12.52–20.6) for females and 9.27 bears/100 km2 (95% CI 7.70–11.14) for males. Density increased with forest cover for both sexes. For male black bears, density decreased at higher grizzly bear (Ursus arctos) densities. Drainages, valley bottoms, and riparian vegetation decreased estimates of landscape resistance to movement for male and female bears. For males, forest cover also decreased estimated resistance to movement, but a transportation corridor bisecting the study area strongly increased resistance to movement presenting a barrier to connectivity.ConclusionsDensity-weighed connectivity surfaces highlighted areas important for population connectivity that were distinct from areas with high potential connectivity. For black bears in Glacier and surrounding landscapes, consideration of both vegetation and valley topography could inform the placement of underpasses along the transportation corridor in areas characterized by both high population density and potential connectivity. Our study demonstrates that the SCR ecological distance model can provide biologically realistic, spatially explicit predictions to support movement connectivity planning across large landscapes.

Rapid recovery of invasive wild pig (Sus scrofa) populations following density reduction

Trapping is an effective strategy for control of invasive wild pig (Sus scrofa), but eradication of populations via trapping alone typically is impractical throughout much of the species’ introduced range. Consequently, recovery of wild pig populations to pre-trapping levels following control is a major impediment to success of long-term control. In this study, we conducted camera-trap surveys to monitor recovery of two wild pig populations immediately following density reductions achieved via trapping on two sites on the Savannah River Site, SC. We used spatial capture-recapture models to estimate wild pig density during seven 3 week periods spanning 12 months to: (1) determine the time required for populations to recover to pre-trapping levels; and (2) evaluate trends in population growth, density, and distribution during recovery. Recovery of both wild pig populations started immediately after trapping ceased, with populations reaching pre-trapping levels in 3 and 5 months. Threeweek population growth rates for each wild pig population during recovery averaged 1.28 (range = 1.11–1.39 on site 1) and 1.24 (range = 0.64–1.92 on site 2), which corresponded to a 345% and 132% increase in abundance (an increase of 141 and 126 individual wild pigs on site 1 and 2, respectively) of each population by the last survey period. Rapid recovery and growth beyond pre-trapping levels of wild pig populations likely were driven by residual wild pigs not removed by trapping and compensatory reproduction and immigration. Our study is one of the first to provide information on how quickly wild pig populations can recover following overall density reductions of 54–68% and indicates practitioners should be prepared to reinitiate trapping efforts in as little as 3 months to prevent recovery of wild pig populations following control.

Forecasting wildlife movement with spatialcapture–recapture

AbstractWildlife movement is an important process affecting species population biology and community interactions in myriad ways. Studies of wildlife movement have focused on retrospectively estimating movements of small numbers of individuals by outfitting them with GPS and telemetry tags. Recent developments in spatial capture–recapture modelling permit the integration of movement models that can estimate the movement of untagged and undetected individuals. Additionally, hidden Markov movement models provide a framework for forecasting individuals' movements, which may be valuable in the conservation of threatened species facing risks that vary across space and time.We describe maximum likelihood estimators for spatial capture–recapture models integrated with simple, biased and correlated random walk movement models formulated as hidden Markov models. Additionally, we demonstrate how to forecast wildlife movement based on these models and hidden Markov model algorithms. We conducted a simulation study to test the performance of the models' abundance estimators and movement forecasts when fit to data simulated under different movement models. We also fit the models to spatial capture–recapture data collected on North Atlantic right whales off the Atlantic Coast of the southeastern United States.Random walk movement models improved abundance estimation and movement forecasts in our simulation study and received greater support from the data in the right whale case study than did activity centre movement models.Forecasts of wildlife movement made under integrated spatial capture–recapture movement models will be most valuable when individuals have been observed recently, when sampling for individuals is extensive and efficient, and when the scale of individuals' movements is small relative to the scale of the study area and sampling process.

Sexual segregation results in pronounced sex-specific density gradients in the mountain ungulate, Rupicapra rupicapra

Sex-specific differences in habitat selection and space use are common in ungulates. Yet, it is largely unknown how this behavioral dimorphism, ultimately leading to sexual segregation, translates to population-level patterns and density gradients across landscapes. Alpine chamois (Rupicapra rupicapra r.) predominantly occupy habitat above tree line, yet especially males may also take advantage of forested habitats. To estimate male and female chamois density and determinants thereof, we applied Bayesian spatial capture-recapture (SCR) models in two contrasting study areas in the Alps, Germany, during autumn. We fitted SCR models to non-invasive individual encounter data derived from genotyped feces. Sex-specific densities were modeled as a function of terrain ruggedness, forest canopy cover, proportion of barren ground, and site severity. We detected pronounced differences in male and female density patterns, driven primarily by terrain ruggedness, rather than by sex-specific effects of canopy cover. The positive effect of ruggedness on density was weaker for males which translated into a higher proportion of males occupying less variable terrain, frequently located in forests, compared to females. By estimating sex-specific variation in both detection probabilities and density, we were able to quantify and map how individual behavioral differences scale up and shape spatial patterns in population density.

An integrated spatial capture–recapture approach reveals the distribution of a cryptic carnivore in a protected area

AbstractQuantifying animal abundance, density, and distributions affords the opportunity to understand the effects of landscape structure and change on species of conservation interest, but estimating these parameters can be difficult for rare and cryptic species. Noninvasive sampling methods, such as remote cameras or scat DNA, can mitigate the challenges of studying rare and cryptic species while also minimizing effects on species of conservation interest or concern. Data derived from these methods can be integrated into robust, contemporary quantitative methods, including spatial capture–recapture (SCR) models, which provide a hierarchical framework for jointly estimating animal abundance, distribution, and, consequently, density. Herein, we developed an integrated SCR model to estimate the abundance, density, and distribution of a large carnivore of conservation interest, the cougar (Puma concolor), in a rugged and remote protected area, Yosemite National Park, California, USA. We combined spatial encounter data from DNA‐based individual identification of scats with detection count data derived from remote cameras to estimate cougar density and detection probability in Yosemite. We further estimated how cougar density and detection probability varied as a result of vegetation, topography, anthropogenic and natural linear features, and survey effort. Using data collected in 2019 and 2020, we estimated a median of 31 (SD = 3.96, 95% credible intervals = 24–39) cougars in Yosemite across the two years, with higher densities associated with productive, vegetated areas. We found detection probability by scent detection teams was higher for females than males and positively correlated with survey effort, proximity to trails, and distance farther from roads and streams. Our study illustrates the utility of noninvasive survey methods that yield individual identities in rugged and remote environments, where capture and handling of cryptic, low‐density animals is logistically challenging and cost prohibitive. Integrated modeling approaches, as used here, allow ecologists to leverage empirical data using a robust quantitative framework that can effectively address conservation objectives. Through this work, we demonstrate the importance of large, contiguous, and heterogeneous ecosystems to the ecology of wide‐ranging species that occur in dynamic landscapes.

Considerations on effort, precision and accuracy for long-term monitoring of African lions (Panthera leo), when using Bayesian spatial explicit capture-recapture models, in fenced protected areas.

Intensive management is frequently required in fenced wildlife areas to reduce deleterious effects of isolation. Decisions on how best to manage such wildlife are ideally informed by regular and reliable estimates of spatiotemporal fluctuations in population size and structure. However, even in small, fenced areas, it is difficult and costly to regularly monitor key species using advanced methods. This is particularly the case for large carnivores, which typically occur at low density and are elusive yet are central to management decision-making due to their top-down effects in ecosystems and attracting tourism. In this study, we aimed to provide robust estimates of population parameters for African lions (Panthera leo) and use the data to inform a resource-efficient long-term monitoring programme. To achieve this, we used unstructured spatial sampling to collect data on lions in Pilanesberg National Park, a small (~550 km2) fenced protected area in South Africa. We used Bayesian spatial capture-recapture models to estimate density, abundance, sex ratio and home range size of lions over the age of 1 year. Finally, to provide guidance on resource requirements for regular monitoring, we rarefied our empirical data set incrementally and analysed the subsets. Lion density was estimated to be 8.8 per 100 km2 (posterior SD = 0.6), which was lower than anticipated by park management. Sex ratio was estimated close to parity (0.9♀:1♂), consistent with emerging evidence in fenced lion populations, yet discordant with unfenced populations, which are usually ~2♀:1♂ in healthy, source populations. Our rarefied data suggest that a minimum of 4000 km search effort needs to be invested in future monitoring to obtain accurate and precise estimates, while assuming similar detection rates. This study demonstrates an important utility of Bayesian spatial explicit capture-recapture methods for obtaining robust estimates of lion densities and other important parameters in fence-protected areas to inform decision-making.

Using heterogeneous camera-trapping sites to obtain the first density estimates for the transboundary Eurasian lynx (Lynx lynx) population in the Dinaric Mountains

Estimating abundance of wild animal populations is crucial for their management and conservation. While spatial capture-recapture models are becoming increasingly common to assess the densities of elusive species, recent studies have indicated potential bias that can be introduced by unaccounted spatial variation of detectability. We used camera-trapping data collected in collaboration with local hunters from a transnational population survey of the Eurasian lynx (Lynx lynx) in Slovenia and Croatia, to provide the first density estimate for the threatened Eurasian lynx population in the Northern Dinaric Mountains. Population density was 0.83 (95% CI: 0.60–1.16) lynx/100 km2, which is comparable to other reintroduced Eurasian lynx populations in Europe. Furthermore, we showed that baseline detection rate was influenced by the type of site used, as well as by sex of the individual and local behavioural response. Scent-marking sites had on average a 1.6- and 2.5-times higher baseline detection rate compared to roads and other locations, respectively. Scent-marking behaviour is common for several mammals, and selecting sites that attracts the targeted species is used to increase detection rates, especially for rare and cryptic species. But we show that the use of different location types for camera trapping can bias density estimates if not homogenously distributed across the surveyed area. This highlights the importance of incorporating not only individual characteristics (e.g., sex), but also information on the type of site used in camera trapping surveys into estimates of population densities.

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.

Population dynamics of the last leopard population of eastern Indochina in the context of improved law enforcement

Poaching, a major threat to wildlife worldwide, is pushing species toward extinction. To reduce poaching pressure and combat biodiversity loss, improved law-enforcement efforts reportedly are required; the effectiveness of which can be determined through rigorous monitoring of wildlife populations, particularly of endangered large carnivores. In the Eastern Plains Landscape of Cambodia, law-enforcement efforts increased to counter the severe threats from illegal activities; however, it is unknown if these strategies are benefiting the population of the Critically Endangered Indochinese leopard (Panthera pardus delacouri), the last large felid population of eastern Indochina. We used open population spatial capture-recapture models to estimate density, survival, per-capita recruitment, and population growth rates of leopard using data from 7 camera-trap surveys conducted over 11 years (2009–2019). We found that the population (a) declined by over 82 % (from 1.5 to 0.3 leopard/100 km2), (b) had low survival probability (0.58) and low recruitment rates (males: 0.04, females: 0.24), and (c) is expected to continue declining. An additional survey in 2021 failed to detect leopard, suggesting the species now is functionally extinct, if not fully extirpated, from the landscape. Over the study period, there was a drastic increase in human activity, with a 20-fold increase in detection frequencies of humans and a 1000-fold increase in lethal-trap encounter rate. The rise in anthropogenic pressures, particularly snaring, appeared to be the primary reason for the leopard decline, indicating the last decade of management interventions was insufficient to conserve the species, which now appears to be extirpated in all of Cambodia, Laos and Vietnam. This has implications for leopard conservation in the wider region, notably that efforts should now focus on populations within the two remaining strongholds. Our results suggest that increases in law-enforcement efforts alone are unlikely to protect wildlife in eastern Indochina; thus, additional strategies are needed to address the region's snaring crisis, including legislative reforms, community engagement, and programs that reduce demand for wildlife meat and products. Long-term studies of remaining Indochinese leopard populations coupled with timely and effective conservation actions are needed to avoid the complete demise of this subspecies.

Cover Image

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.

Spatial variation in red deer density in a transboundary forest ecosystem

Forests in Europe are exposed to increasingly frequent and severe disturbances. The resulting changes in the structure and composition of forests can have profound consequences for the wildlife inhabiting them. Moreover, wildlife populations in Europe are often subjected to differential management regimes as they regularly extend across multiple national and administrative borders. The red deer Cervus elaphus population in the Bohemian Forest Ecosystem, straddling the Czech-German border, has experienced forest disturbances, primarily caused by windfalls and bark beetle Ips typographus outbreaks during the past decades. To adapt local management strategies to the changing environmental conditions and to coordinate them across the international border, reliable estimates of red deer density and abundance are highly sought-after by policymakers, wildlife managers, and stakeholders. Covering a 1081-km2 study area, we conducted a transnational non-invasive DNA sampling study in 2018 that yielded 1578 genotyped DNA samples from 1120 individual red deer. Using spatial capture-recapture models, we estimated total and jurisdiction-specific abundance of red deer throughout the ecosystem and quantified the role of forest disturbance and differential management strategies in shaping spatial heterogeneity in red deer density. We hypothesised that (a) forest disturbances provide favourable habitat conditions (e.g., forage and cover), and (b) contrasting red deer management regimes in different jurisdictions create a differential risk landscape, ultimately shaping density distributions. Overall, we estimated that 2851 red deer (95% Credible Interval = 2609–3119) resided in the study area during the sampling period, with a relatively even overall sex ratio (1406 females, 95% CI = 1229–1612 and 1445 males, 95% CI = 1288–1626). The average red deer density was higher in Czechia (3.5 km−2, 95% CI = 1.2–12.3) compared to Germany (2 km−2, 95% CI = 0.2–11). The effect of forest disturbances on red deer density was context-dependent. Forest disturbances had a positive effect on red deer density at higher elevations and a negative effect at lower elevations, which could be explained by partial migration and its drivers in this population. Density of red deer was generally higher in management units where hunting is prohibited. In addition, we found that sex ratios differed between administrative units and were more balanced in the non-intervention zones. Our results show that the effect of forest disturbances on wild ungulates is modulated by additional factors, such as elevation and ungulate management practices. Overall density patterns and sex ratios suggested strong gradients in density between administrative units. With climate change increasing the severity and frequency of forest disturbances, population-level monitoring and management are becoming increasingly important, especially for wide-ranging species as both wildlife and global change transcend administrative boundaries.

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.

Counting the Capital's cats: Estimating drivers of abundance of free-roaming cats with a novel hierarchical model.

Free-roaming cats are a conservation concern in many areas but identifying their impacts and developing mitigation strategies requires a robust understanding of their distribution and density patterns. Urban and residential areas may be especially relevant in this process because free-roaming cats are abundant in these anthropogenic landscapes. Here, we estimate the occupancy and density of free-roaming cats in Washington D.C. and relate these metrics to known landscape and social factors. We conducted an extended camera trap survey of public and private spaces across D.C. and analyzed data collected from 1483 camera deployments from 2018 to 2020. We estimated citywide cat distribution by fitting hierarchical occupancy models and further estimated cat abundance using a novel random thinning spatial capture-recapture model that allows for the use of photos that can and cannot be identified to individual. Within this model, we utilized individual covariates that provided identity exclusions between photos of unidentifiable cats with inconsistent coat patterns, thus increasing the precision of abundance estimates. This combined model also allowed for unbiased estimation of density when animals cannot be identified to individual at the same rate as for free-roaming cats whose identifiability depended on their coat characteristics. Cat occupancy and abundance declined with increasing distance from residential areas, an effect that was more pronounced in wealthier neighborhoods. There was noteworthy absence of cats detected in larger public spaces and forests. Realized densities ranged from 0.02 to 1.75 cats/ha in sampled areas, resulting in a district-wide estimate of ~7296 free-roaming cats. Ninety percent of cat detections lacked collars and nearly 35% of known individuals were ear-tipped, indicative of district Trap-Neuter-Return (TNR) programs. These results suggest that we mainly sampled and estimated the unowned cat subpopulation, such that indoor/outdoor housecats were not well represented. The precise estimation of cat population densities is difficult due to the varied behavior of subpopulations within free-roaming cat populations (housecats, stray and feral cats), but our methods provide a first step in establishing citywide baselines to inform data-driven management plans for free-roaming cats in urban environments.

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.