Spatial Capture-recapture Data Research Articles

Individual‐level biotic interactions and species distribution models

AbstractAimAccounting for biotic interactions in species distribution models is complicated by the fact that interactions occur at the individual‐level at unknown spatial scales. Standard approaches that ignore individual‐level interactions and focus on aggregate scales are subject to the modifiable aerial unit problem (MAUP) in which incorrect inferences may arise about the sign and magnitude of interspecific effects.LocationGlobal (simulation) and North Carolina, United States (case study).TaxonNone (simulation) and Aves (case study).MethodsWe present a hierarchical species distribution model that includes a Markov point process in which the locations of individuals of one species are modelled as a function of both abiotic variables and the locations of individuals of another species. We applied the model to spatial capture‐recapture (SCR) data on two ecologically similar songbird species—hooded warbler (Setophaga citrina) and black‐throated blue warbler (Setophaga caerulescens)—that segregate over a climate gradient in the southern Appalachian Mountains, USA.ResultsA simulation study indicated that the model can identify the effects of environmental variation and biotic interactions on co‐occurring species distributions. In the case study, there were strong and opposing effects of climate on spatial variation in population densities, but spatial competition did not influence the two species' distributions.Main ConclusionsUnlike existing species distribution models, the framework proposed here overcomes the MAUP and can be used to investigate how population‐level patterns emerge from individual‐level processes, while also allowing for inference on the spatial scale of biotic interactions. Our finding of minimal spatial competition between black‐throated blue warbler and hooded warbler adds to the growing body of literature suggesting that abiotic factors may be more important than competition at low‐latitude range margins. The model can be extended to accommodate count data and binary data in addition to SCR data.

Demography and space-use of Eastern Red-backed Salamanders (Plethodon cinereus) between mature and successional forests.

Space-use and demographic processes are critical to the persistence of populations across space and time. Despite their importance, estimates of these processes are often derived from a limited number of populations spanning broad habitat or environmental gradients. With increasing appreciation of the role fine-scale environmental variation in microgeographic adaptation, there is a need and value to assessing within-site variation in space-use and demographic patterns. In this study, we analyze 3 years of spatial capture-recapture data on the Eastern Red-backed Salamander collected from a mixed-use deciduous forest site in central Ohio, USA. Study plots were situated in both a mature forest stand and successional forest stand separated by <100-m distance. Our results showed that salamander density was reduced on successional plots, which corresponded with greater distance between nearest neighbors, less overlap in core use areas, greater space-use, and greater shifts in activity centers when compared to salamanders occupying the mature habitat. By contrast, individual growth rates of salamanders occupying the successional forest were significantly greater than salamanders in the mature forest. These estimates result in successional plot salamanders reaching maturity more than 1 year earlier than salamanders on the mature forest plots and increasing their estimated lifetime fecundity by as much as 43%. The patterns we observed in space-use and individual growth are likely the result of density-dependent processes, potentially reflecting differences in resource availability or quality. Our study highlights how fine-scale, within-site variation can shape population demographics. As research into the demographic and population consequences of climate change and habitat loss and alteration continue, future research should take care to acknowledge the role that fine-scale variation may play, especially for abiotically sensitive organisms with limited vagility.

Protection status, human disturbance, snow cover and trapping drive density of a declining wolverine population in the Canadian Rocky Mountains

Protected areas are important in species conservation, but high rates of human-caused mortality outside their borders and increasing popularity for recreation can negatively affect wildlife populations. We quantified wolverine (Gulo gulo) population trends from 2011 to 2020 in > 14,000 km2 protected and non-protected habitat in southwestern Canada. We conducted wolverine and multi-species surveys using non-invasive DNA and remote camera-based methods. We developed Bayesian integrated models combining spatial capture-recapture data of marked and unmarked individuals with occupancy data. Wolverine density and occupancy declined by 39%, with an annual population growth rate of 0.925. Density within protected areas was 3 times higher than outside and declined between 2011 (3.6 wolverines/1000 km2) and 2020 (2.1 wolverines/1000 km2). Wolverine density and detection probability increased with snow cover and decreased near development. Detection probability also decreased with human recreational activity. The annual harvest rate of ≥ 13% was above the maximum sustainable rate. We conclude that humans negatively affected the population through direct mortality, sub-lethal effects and habitat impacts. Our study exemplifies the need to monitor population trends for species at risk—within and between protected areas—as steep declines can occur unnoticed if key conservation concerns are not identified and addressed.

Special Feature: Linking capture-recapture and movement.

Special Feature: Linking capture-recapture and movement.

Integrated animal movement and spatial capture-recapture models: Simulation, implementation, and inference.

Over the last decade, spatial capture-recapture (SCR) models have become widespread for estimating demographic parameters in ecological studies. However, the underlying assumptions about animal movement and space use are often not realistic. This is a missed opportunity because interesting ecological questions related to animal space use, habitat selection, and behavior cannot be addressed with most SCR models, despite the fact that the data collected in SCR studies - individual animals observed at specific locations and times - can provide a rich source of information about these processes and how they relate to demographic rates. We developed SCR models that integrated more complex movement processes that are typically inferred from telemetry data, including a simple random walk, correlated random walk (i.e., short-term directional persistence), and habitat-driven Langevin diffusion. We demonstrated how to formulate, simulate from, and fit these models with standard SCR data using data-augmented Bayesian analysis methods. We evaluated their performance through a simulation study, in which we varied the detection, movement, and resource selection parameters. We also examined different numbers of sampling occasions and assessed performance gains when including auxiliary location data collected from telemetered individuals. Across all scenarios, the integrated SCR movement models performed well in terms of abundance, detection, and movement parameter estimation. We found little difference in bias for the simple random walk model when reducing the number of sampling occasions from T = 25 to T = 15. We found some bias in movement parameter estimates under several of the correlated random walk scenarios, but incorporating auxiliary location data improved parameter estimates and significantly improved mixing during model fitting. The Langevin movement model was able to recover resource selection parameters from standard SCR data, which is particularly appealing because it explicitly links the individual-level movement process with habitat selection and population density. We focused on closed population models, but the movement models developed here can be extended to open SCR models. The movement process models could also be easily extended to accommodate additional "building blocks" of random walks, such as central tendency (e.g., territoriality) or multiple movement behavior states, thereby providing a flexible and coherent framework for linking animal movement behavior to population dynamics, density, and distribution.

Confronting spatial capture-recapture models with realistic animal movement simulations.

Spatial capture-recapture (SCR) models have emerged as a robust method to estimate the population density of mobile animals. However, model evaluation has generally been based on data simulated from simplified representations of animal space use. Here, we generated data from animal movement simulated from a mechanistic individual-based model, in which movement emerges from the individual's response to a changing environment (i.e., from the bottom-up), driven by key ecological processes (e.g., resource memory and territoriality). We drew individual detection data from simulated movement trajectories and fitted detection data sets to a basic, resource selection and transience SCR model, as well as their variants accounting for resource-driven heterogeneity in density and detectability. Across all SCR models, abundance estimates were robust to multiple, but low-degree violations of the specified movement processes (e.g., resource selection). SCR models also successfully captured the positive effect of resource quality on density. However, covariate models failed to capture the finer scale effect of resource quality on detectability and space use, which may be a consequence of the low temporal resolution of SCR data sets and/or model misspecification. We show that home-range size is challenging to infer from the scale parameter alone, compounded by reliance on conventional measures of "true" home-range size that are highly sensitive to sampling regime. Additionally, we found the transience model challenging to fit, probably due to data sparsity and violation of the assumption of normally distributed inter-occasion movement of activity centers, suggesting that further development of the model is required for general applicability. Our results showed that further integration of complex movement into SCR models may not be necessary for population estimates of abundance when the level of individual heterogeneity induced by the underlying movement process is low, but appears warranted in terms of accurately revealing finer scale patterns of ecological and movement processes. Further investigation into whether this holds true in populations with other types of realistic movement characteristics is merited. Our study provides a framework to generate realistic SCR data sets to develop and evaluate more complex movement processes in SCR models.

Does the punishment fit the crime? Consequences and diagnosis of misspecified detection functions in Bayesian spatial capture-recapture modeling.

Spatial capture–recapture (SCR) analysis is now used routinely to inform wildlife management and conservation decisions. It is therefore imperative that we understand the implications of and can diagnose common SCR model misspecifications, as flawed inferences could propagate to policy and interventions. The detection function of an SCR model describes how an individual's detections are distributed in space. Despite the detection function's central role in SCR, little is known about the robustness of SCR‐derived abundance estimates and home range size estimates to misspecifications. Here, we set out to (a) determine whether abundance estimates are robust to a wider range of misspecifications of the detection function than previously explored, (b) quantify the sensitivity of home range size estimates to the choice of detection function, and (c) evaluate commonly used Bayesian p‐values for detecting misspecifications thereof. We simulated SCR data using different circular detection functions to emulate a wide range of space use patterns. We then fit Bayesian SCR models with three detection functions (half‐normal, exponential, and half‐normal plateau) to each simulated data set. While abundance estimates were very robust, estimates of home range size were sensitive to misspecifications of the detection function. When misspecified, SCR models with the half‐normal plateau and exponential detection functions produced the most and least reliable home range size, respectively. Misspecifications with the strongest impact on parameter estimates were easily detected by Bayesian p‐values. Practitioners using SCR exclusively for density estimation are unlikely to be impacted by misspecifications of the detection function. However, the choice of detection function can have substantial consequences for the reliability of inferences about space use. Although Bayesian p‐values can aid the diagnosis of detection function misspecification under certain conditions, we urge the development of additional custom goodness‐of‐fit diagnostics for Bayesian SCR models to identify a wider range of model misspecifications.

Fragmentation and low density as major conservation challenges for the southernmost populations of the European wildcat.

Knowledge of population dynamics of threatened species in the wild is key to effective conservation actions. However, at present, there are many examples of endangered animals for which their current situation is unknown, and not just in remote areas and less developed countries. We have explored this topic by studying the paradigmatic case of the European wildcat (Felis silvestris silvestris), an endangered small carnivore whose status has been subjectively established on the basis of non-systematic approaches and opportunistic records. Little is known about its demographic situation, prompting the need for information to improve conservation measures. However, the secretive behaviour of felines along with its low density in natural conditions have prevented the gathering of sufficient data. We developed a field sampling strategy for one of the largest populations (Andalusia, South Spain, 87,268 km2), based on a logistically viable systematic non-intrusive survey by camera-trapping. This study offers the first large-scale estimation for any European wildcat population, based on analytical approaches applied on Species Distribution Models. A hierarchical approach based on a Maxent model for distribution estimation was used, along with Generalised Linear Models for density estimation from explicit spatial capture-recapture data. Our results show that the distribution range is smaller and more highly fragmented than previously assumed. The overall estimated density was very low (0.069 ±0.0019 wildcats/km2) and the protected areas network seems to be insufficient to cover a significant part of the population or a viable nucleus in demographic terms. Indeed, the most important areas remain unprotected. Our main recommendations are to improve the protected area network and/or vigilance programs in hunting estates, in addition to studying and improving connectivity between the main population patches.

Consequences of ignoring group association in spatial capture–recapture analysis

Many models in population ecology, including spatial capture–recapture (SCR) models, assume that individuals are distributed and detected independently of one another. In reality, this is rarely the case – both antagonistic and gregarious relationships lead to non-independent spatial configurations, with territorial exclusion at one end of the spectrum and group-living at the other. Previous simulation studies suggest that grouping has limited impact on the outcome of SCR analyses. However, group associations entail not only spatial clustering of activity centers but also coordinated space use by group members, potentially impacting both ecological and observation processes underlying SCR analysis. We simulated SCR scenarios with different strengths of aggregation (clustering of individuals into groups with shared activity centers) and cohesion (synchronization of detection patterns of members of a group). We then fit SCR models to the simulated data sets and evaluated the effect of aggregation and cohesion on parameter estimates. Low to moderate aggregation and cohesion did not impact the bias and precision of estimates of density and the scale parameter of the detection function. However, non-independence between individuals led to high levels of overdispersion. Overdispersion strongly decreased the coverage of confidence intervals around parameter estimates, thereby increasing the probability of erroneous predictions. Our results indicate that SCR models are robust to moderate levels of aggregation and cohesion. Nonetheless, spatial dependence between individuals can lead to false inference. We recommend that practitioners 1) test for the presence of overdispersion in SCR data caused by aggregation and cohesion, and, if necessary, 2) correct their variance estimates using the overdispersion factor ĉ . Approaches for doing both are described in this paper. We also urge the development of SCR models that incorporate spatial associations between individuals not only to account for overdispersion but also to obtain quantitative information about social aspects of study populations.

Incorporating citizen science data in spatially explicit integrated population models.

Information about population abundance, distribution, and demographic rates is critical for understanding a species' ecology and for effective conservation and management. To collect data over large spatial and temporal extents for such inferences, especially for species with low densities or wide distributions, citizen science can be an efficient approach. Integrated models have also emerged as an important methodology to estimate population parameters by combining multiple types of data, including citizen science data. We developed a spatially explicit integrated model that combines opportunistically collected presence-absence (PA) data, commonly collected in citizen science efforts, with systematically collected spatial capture-recapture (SCR) data, which are often limited to small spatial and temporal extents. We conducted single and multi-season simulations with parameters informed by North American black bear (Ursus americanus) populations, to evaluate the influence of varying amounts of opportunistic PA data collected at larger spatial and temporal extents on the estimation of population-level parameters. Integrating opportunistic PA data increased the precision and accuracy of posterior estimates of abundance, and survival and recruitment rates. In some cases, adding PA locations improved abundance estimates more than increasing PA detection probability. Posterior estimates were as precise and unbiased as when higher quality, but sparse, SCR data were available. We also applied the integrated model to SCR and citizen science PA data collected on black bears in New York, with results consistent with our simulations. Our findings indicate that citizen science in integrated models can be a cost-efficient way to improve estimates of population parameters and increase the spatiotemporal extent of inference. Continued developments with integrated models and citizen science data will offer additional ways to improve our understanding of population structure and demographics.

Estimating density and detection of bobcats in fragmented midwestern landscapes using spatial capture–recapture data from camera traps

ABSTRACTCamera‐trapping data analyzed with spatially explicit capture–recapture (SCR) models can provide a rigorous method for estimating density of small populations of elusive carnivore species. We sought to develop and evaluate the efficacy of SCR models for estimating density of a presumed low‐density bobcat (Lynx rufus) population in fragmented landscapes of west‐central Illinois, USA. We analyzed camera‐trapping data from 49 camera stations in a 1,458‐km2 area deployed over a 77‐day period from 1 February to 18 April 2017. Mean operational time of cameras was 52 days (range = 32–67 days). We captured 23 uniquely identifiable bobcats 113 times and recaptured these same individuals 90 times; 15 of 23 (65.2%) individuals were recaptured at ≥2 camera traps. Total number of bobcat capture events was 139, of which 26 (18.7%) were discarded from analyses because of poor image quality or capture of only a part of an animal in photographs. Of 113 capture events used in analyses, 106 (93.8%) and 7 (6.2%) were classified as positive and tentative identifications, respectively; agreement on tentative identifications of bobcats was high (71.4%) among 3 observers. We photographed bobcats at 36 of 49 (73.5%) camera stations, of which 34 stations were used in analyses. We estimated bobcat density at 1.40 individuals (range = 1.00–2.02)/100 km 2. Our modeled bobcat density estimates are considerably below previously reported densities (30.5 individuals/100 km 2) within the state, and among the lowest yet recorded for the species. Nevertheless, use of remote cameras and SCR models was a viable technique for reliably estimating bobcat density across west‐central Illinois. Our research establishes ecological benchmarks for understanding potential effects of colonization, habitat fragmentation, and exploitation on future assessments of bobcat density using standardized methodologies that can be compared directly over time. Further application of SCR models that quantify specific costs of animal movements (i.e., least‐cost path models) while accounting for landscape connectivity has great utility and relevance for conservation and management of bobcat populations across fragmented Midwestern landscapes. © 2019 The Wildlife Society.

Characterizing spatio-temporal variation in survival and recruitment with integrated population models

ABSTRACT Efforts to understand population dynamics and identify high-quality habitat require information about spatial variation in demographic parameters. However, estimating demographic parameters typically requires labor-intensive capture–recapture methods that are difficult to implement over large spatial extents. Spatially explicit integrated population models (IPMs) provide a solution by accommodating spatial capture–recapture (SCR) data collected at a small number of sites with survey data that may be collected over a much larger extent. We extended the spatial IPM framework to include a spatio-temporal point process model for recruitment, and we applied the model to 4 yr of SCR and distance-sampling data on Canada Warblers (Cardellina canadensis) near the southern extent of the species' breeding range in North Carolina, USA, where climate change is predicted to cause population declines and distributional shifts toward higher elevations. To characterize spatial variation in demographic parameters ...

Data integration for inference about spatial processes: A model-based approach to test and account for data inconsistency.

Recently-developed methods that integrate multiple data sources arising from the same ecological processes have typically utilized structured data from well-defined sampling protocols (e.g., capture-recapture and telemetry). Despite this new methodological focus, the value of opportunistic data for improving inference about spatial ecological processes is unclear and, perhaps more importantly, no procedures are available to formally test whether parameter estimates are consistent across data sources and whether they are suitable for integration. Using data collected on the reintroduced brown bear population in the Italian Alps, a population of conservation importance, we combined data from three sources: traditional spatial capture-recapture data, telemetry data, and opportunistic data. We developed a fully integrated spatial capture-recapture (SCR) model that included a model-based test for data consistency to first compare model estimates using different combinations of data, and then, by acknowledging data-type differences, evaluate parameter consistency. We demonstrate that opportunistic data lend itself naturally to integration within the SCR framework and highlight the value of opportunistic data for improving inference about space use and population size. This is particularly relevant in studies of rare or elusive species, where the number of spatial encounters is usually small and where additional observations are of high value. In addition, our results highlight the importance of testing and accounting for inconsistencies in spatial information from structured and unstructured data so as to avoid the risk of spurious or averaged estimates of space use and consequently, of population size. Our work supports the use of a single modeling framework to combine spatially-referenced data while also accounting for parameter consistency.

A hierarchical model for estimating the spatial distribution and abundance of animals detected by continuous-time recorders.

MotivationSeveral spatial capture-recapture (SCR) models have been developed to estimate animal abundance by analyzing the detections of individuals in a spatial array of traps. Most of these models do not use the actual dates and times of detection, even though this information is readily available when using continuous-time recorders, such as microphones or motion-activated cameras. Instead most SCR models either partition the period of trap operation into a set of subjectively chosen discrete intervals and ignore multiple detections of the same individual within each interval, or they simply use the frequency of detections during the period of trap operation and ignore the observed times of detection. Both practices make inefficient use of potentially important information in the data.Model and data analysisWe developed a hierarchical SCR model to estimate the spatial distribution and abundance of animals detected with continuous-time recorders. Our model includes two kinds of point processes: a spatial process to specify the distribution of latent activity centers of individuals within the region of sampling and a temporal process to specify temporal patterns in the detections of individuals. We illustrated this SCR model by analyzing spatial and temporal patterns evident in the camera-trap detections of tigers living in and around the Nagarahole Tiger Reserve in India. We also conducted a simulation study to examine the performance of our model when analyzing data sets of greater complexity than the tiger data.BenefitsOur approach provides three important benefits: First, it exploits all of the information in SCR data obtained using continuous-time recorders. Second, it is sufficiently versatile to allow the effects of both space use and behavior of animals to be specified as functions of covariates that vary over space and time. Third, it allows both the spatial distribution and abundance of individuals to be estimated, effectively providing a species distribution model, even in cases where spatial covariates of abundance are unknown or unavailable. We illustrated these benefits in the analysis of our data, which allowed us to quantify differences between nocturnal and diurnal activities of tigers and to estimate their spatial distribution and abundance across the study area. Our continuous-time SCR model allows an analyst to specify many of the ecological processes thought to be involved in the distribution, movement, and behavior of animals detected in a spatial trapping array of continuous-time recorders. We plan to extend this model to estimate the population dynamics of animals detected during multiple years of SCR surveys.

Likelihood analysis of spatial capture‐recapture models for stratified or class structured populations

We develop a likelihood analysis framework for fitting spatial capture‐recapture (SCR) models to data collected on class structured or stratified populations. Our interest is motivated by the necessity of accommodating the problem of missing observations of individual class membership. This is particularly problematic in SCR data arising from DNA analysis of scat, hair or other material, which frequently yields individual identity but fails to identify the sex. Moreover, this can represent a large fraction of the data and, given the typically small sample sizes of many capture‐recapture studies based on DNA information, utilization of the data with missing sex information is necessary. We develop the class structured likelihood for the case of missing covariate values, and then we address the scaling of the likelihood so that models with and without class structured parameters can be formally compared regardless of missing values. We apply our class structured model to black bear data collected in New York in which sex could be determined for only 62 of 169 uniquely identified individuals. The models containing sex‐specificity of both the intercept of the SCR encounter probability model and the distance coefficient, and including a behavioral response are strongly favored by log‐likelihood. Estimated population sex ratio is strongly influenced by sex structure in model parameters illustrating the importance of rigorous modeling of sex differences in capture‐recapture models.

Modelling non‐Euclidean movement and landscape connectivity in highly structured ecological networks

Summary Movement is influenced by landscape structure, configuration and geometry, but measuring distance as perceived by animals poses technical and logistical challenges. Instead, movement is typically measured using Euclidean distance, irrespective of location or landscape structure, or is based on arbitrary cost surfaces. A recently proposed extension of spatial capture‐recapture (SCR) models resolves this issue using spatial encounter histories of individuals to calculate least‐cost paths (ecological distance: Ecology, 94, 2013, 287) thereby relaxing the Euclidean assumption. We evaluate the consequences of not accounting for movement heterogeneity when estimating abundance in highly structured landscapes, and demonstrate the value of this approach for estimating biologically realistic space‐use patterns and landscape connectivity. We simulated SCR data in a riparian habitat network, using the ecological distance model under a range of scenarios where space‐use in and around the landscape was increasingly associated with water (i.e. increasingly less Euclidean). To assess the influence of miscalculating distance on estimates of population size, we compared the results from the ecological and Euclidean distance based models. We then demonstrate that the ecological distance model can be used to estimate home range geometry when space use is not symmetrical. Finally, we provide a method for calculating landscape connectivity based on modelled species‐landscape interactions generated from capture‐recapture data. Using ecological distance always produced unbiased estimates of abundance. Explicitly modelling the strength of the species‐landscape interaction provided a direct measure of landscape connectivity and better characterised true home range geometry. Abundance under the Euclidean distance model was increasingly (negatively) biased as space use was more strongly associated with water and, because home ranges are assumed to be symmetrical, produced poor characterisations of home range geometry and no information about landscape connectivity. The ecological distance SCR model uses spatially indexed capture‐recapture data to estimate how activity patterns are influenced by landscape structure. As well as reducing bias in estimates of abundance, this approach provides biologically realistic representations of home range geometry, and direct information about species‐landscape interactions. The incorporation of both structural (landscape) and functional (movement) components of connectivity provides a direct measure of species‐specific landscape connectivity.

Trap Configuration and Spacing Influences Parameter Estimates in Spatial Capture-Recapture Models

An increasing number of studies employ spatial capture-recapture models to estimate population size, but there has been limited research on how different spatial sampling designs and trap configurations influence parameter estimators. Spatial capture-recapture models provide an advantage over non-spatial models by explicitly accounting for heterogeneous detection probabilities among individuals that arise due to the spatial organization of individuals relative to sampling devices. We simulated black bear (Ursus americanus) populations and spatial capture-recapture data to evaluate the influence of trap configuration and trap spacing on estimates of population size and a spatial scale parameter, sigma, that relates to home range size. We varied detection probability and home range size, and considered three trap configurations common to large-mammal mark-recapture studies: regular spacing, clustered, and a temporal sequence of different cluster configurations (i.e., trap relocation). We explored trap spacing and number of traps per cluster by varying the number of traps. The clustered arrangement performed well when detection rates were low, and provides for easier field implementation than the sequential trap arrangement. However, performance differences between trap configurations diminished as home range size increased. Our simulations suggest it is important to consider trap spacing relative to home range sizes, with traps ideally spaced no more than twice the spatial scale parameter. While spatial capture-recapture models can accommodate different sampling designs and still estimate parameters with accuracy and precision, our simulations demonstrate that aspects of sampling design, namely trap configuration and spacing, must consider study area size, ranges of individual movement, and home range sizes in the study population.

Spatially explicit integrated population models

Summary Studies of demographic processes are typically restricted to small geographic areas and short time periods due to the costs of marking and monitoring individuals. However, environmental changes are occurring at much broader spatial and temporal scales, and thus, inferences about the mechanisms governing population dynamics need to be scaled accordingly. Recently developed integrated population models (IPMs) represent an approach for doing so, by jointly analysing survey data and capture–recapture data. Although promising, several shortcomings of conventional IPMs exist, including difficulties accounting for spatial variation in demographic, movement and detection parameters; limited ability to make spatially explicit predictions of abundance or vital rates; and a requirement that the survey data and the capture–recapture data are independent. We demonstrate how each of these limitations can be resolved by adopting a spatial population dynamics model upon which both the survey data and the capture–recapture data are conditioned. We applied the model to 6 years of hair data collected on the threatened Louisiana black bear Ursus americanus luteolus. For years in which the hair samples were genotyped, the resulting data are information‐rich (but expensive) spatial capture–recapture (SCR) data. For the remaining years, the data are binary detection data, of the type often analysed using occupancy models. We compared estimates of demographic parameters and annual abundance using various combinations of the SCR and detection data, and found that combining the SCR data and the detection data resulted in more precise estimates of abundance relative to estimates that did not use the detection data. A simulation study provided additional evidence of increased precision, as well as evidence that the estimators of annual abundance are approximately unbiased. The ability to combine survey data and capture–recapture data using a spatially explicit model opens many possibilities for designing cost effective studies and scaling up inferences about the demographic processes influencing spatial and temporal population dynamics.

Bayes and Empirical Bayes Estimators of Abundance and Density from Spatial Capture-Recapture Data

In capture-recapture and mark-resight surveys, movements of individuals both within and between sampling periods can alter the susceptibility of individuals to detection over the region of sampling. In these circumstances spatially explicit capture-recapture (SECR) models, which incorporate the observed locations of individuals, allow population density and abundance to be estimated while accounting for differences in detectability of individuals. In this paper I propose two Bayesian SECR models, one for the analysis of recaptures observed in trapping arrays and another for the analysis of recaptures observed in area searches. In formulating these models I used distinct submodels to specify the distribution of individual home-range centers and the observable recaptures associated with these individuals. This separation of ecological and observational processes allowed me to derive a formal connection between Bayes and empirical Bayes estimators of population abundance that has not been established previously. I showed that this connection applies to every Poisson point-process model of SECR data and provides theoretical support for a previously proposed estimator of abundance based on recaptures in trapping arrays. To illustrate results of both classical and Bayesian methods of analysis, I compared Bayes and empirical Bayes esimates of abundance and density using recaptures from simulated and real populations of animals. Real populations included two iconic datasets: recaptures of tigers detected in camera-trap surveys and recaptures of lizards detected in area-search surveys. In the datasets I analyzed, classical and Bayesian methods provided similar – and often identical – inferences, which is not surprising given the sample sizes and the noninformative priors used in the analyses.

Separating mortality and emigration: modelling space use, dispersal and survival with robust‐design spatial capture–recapture data

Summary Capture–recapture (CR) techniques are commonly used to gain information about population dynamics, demography and life‐history traits of populations. However, traditional CR models cannot separate mortality from emigration. Recently developed spatial–capture–recapture (SCR) models explicitly incorporate spatial information into traditional CR models, thus allowing for individuals' movements to be modelled explicitly. In this paper, we extend SCR models using robust‐design data to allow for both processes in which individuals can disappear from the population, mortality and dispersal, to be estimated separately. We formulate a general robust‐design spatial capture–recapture (RD‐SCR) model, explore the properties of the model in a simulation study and compare the results to a Cormack–Jolly–Seber model and a non‐spatial robust‐design model with temporary emigration. In the case study, we fit several versions of the general model to data on field voles (Microtus agrestis) and compare the results with those from the non‐spatial models fitted to the same data. We also evaluate assumptions of the fitted models with a series of simulation‐based posterior predictive goodness‐of‐fit checks that are applicable to the SCR models in general and the RD‐SCR model in particular. The simulation results show that the model preforms well under a wide range of dispersal distances. Our model outperforms the traditional CR models in terms of both accuracy and precision for survival. The case study showed that adult females have an c. 3·5 times higher mortality rate than adult males. Males have larger home ranges and disperse longer distances than females, but both males and females mostly move their activity centres within their previous home range between trapping sessions at 3‐week intervals. Our RD‐SCR model has several advantages compared to other approaches to estimate ‘true’ survival instead of only ‘apparent’ survival. Additionally, the model extracts information about space use and dispersal distributions that are relevant for behavioural studies as well as studies of life‐history variation, population dynamics and management. The model can be widely applied due to the flexible framework, and other variations of the model could easily be implemented.