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Quantifying the distribution of daily activity is an important component of behavioral ecology. Historically, it has been difficult to obtain data on activity patterns, especially for elusive species. However, the development of affordable camera traps and their widespread usage has led to an explosion of available data from which activity patterns can be estimated. Continuous‐time spatial capture–recapture (CT SCR) models drop the occasion structure seen in traditional spatial and nonspatial capture–recapture (CR) models and use the actual times of capture. In addition to estimating density, CT SCR models estimate expected encounters through time. Cyclic splines can be used to allow flexible shapes for modeling cyclic activity patterns, and the fact that SCR models also incorporate distance means that space–time interactions can be explored. This method is applied to a jaguar dataset. Jaguars in Belize are most active and range furthest in the evening and early morning and when they are located closer to the network of trails. There is some evidence that females have a less variable pattern than males. The comparison between sexes demonstrates how CT SCR can be used to explore hypotheses about animal behavior within a formal modeling framework. SCR models were developed primarily to estimate and model density, but the models can be used to explore processes that interact across space and time, especially when using the CT SCR framework that models the temporal dimension at a finer resolution. Quantifying the distribution of daily activity is an important component of behavioral ecology, but historically it has been difficult to obtain data on activity patterns. The emergence of camera traps as a mainstream tool in ecological monitoring has led to an explosion of available data from which activity patterns can be estimated. Continuous‐time spatial capture–recapture (CT SCR) models model the encounter process at an arbitrarily fine temporal resolution and so allow users to investigate how activity patterns and space use change continuously over time.

DNA-based mark–recapture has become a methodological cornerstone of research focused on bear species. The objective of such studies is often to estimate population size; however, doing so is frequently complicated by movement of individual bears. Movement affects the probability of detection and the assumption of closure of the population required in most models. To mitigate the bias caused by movement of individuals, population size and density estimates are often adjusted using ad hoc methods, including buffering the minimum polygon of the trapping array. We used a hierarchical, spatial capture–recapture model that contains explicit components for the spatial-point process that governs the distribution of individuals and their exposure to (via movement), and detection by, traps. We modeled detection probability as a function of each individual's distance to the trap and an indicator variable for previous capture to account for possible behavioral responses. We applied our model to a 2006 hair-snare study of a black bear (Ursus americanus) population in northern New York, USA. Based on the microsatellite marker analysis of collected hair samples, 47 individuals were identified. We estimated mean density at 0.20 bears/km2. A positive estimate of the indicator variable suggests that bears are attracted to baited sites; therefore, including a trap-dependence covariate is important when using bait to attract individuals. Bayesian analysis of the model was implemented in WinBUGS, and we provide the model specification. The model can be applied to any spatially organized trapping array (hair snares, camera traps, mist nests, etc.) to estimate density and can also account for heterogeneity and covariate information at the trap or individual level.

Recently developed spatial capture–recapture (SCR) models represent a major advance over traditional capture–recapture (CR) models because they yield explicit estimates of animal density instead of population size within an unknown area. Furthermore, unlike nonspatial CR methods, SCR models account for heterogeneity in capture probability arising from the juxtaposition of animal activity centers and sample locations. Although the utility of SCR methods is gaining recognition, the requirement that all individuals can be uniquely identified excludes their use in many contexts. In this paper, we develop models for situations in which individual recognition is not possible, thereby allowing SCR concepts to be applied in studies of unmarked or partially marked populations. The data required for our model are spatially referenced counts made on one or more sample occasions at a collection of closely spaced sample units such that individuals can be encountered at multiple locations. Our approach includes a spatial point process for the animal activity centers and uses the spatial correlation in counts as information about the number and location of the activity centers. Camera-traps, hair snares, track plates, sound recordings, and even point counts can yield spatially correlated count data, and thus our model is widely applicable. A simulation study demonstrated that while the posterior mean exhibits frequentist bias on the order of 5–10% in small samples, the posterior mode is an accurate point estimator as long as adequate spatial correlation is present. Marking a subset of the population substantially increases posterior precision and is recommended whenever possible. We applied our model to avian point count data collected on an unmarked population of the northern parula (Parula americana) and obtained a density estimate (posterior mode) of 0.38 (95% CI: 0.19–1.64) birds/ha. Our paper challenges sampling and analytical conventions in ecology by demonstrating that neither spatial independence nor individual recognition is needed to estimate population density—rather, spatial dependence can be informative about individual distribution and density.

Reliable estimates of animal density are fundamental to understanding ecological processes and population dynamics. Furthermore, their accuracy is vital to conservation because wildlife authorities rely on estimates to make decisions. However, it is notoriously difficult to accurately estimate density for wideranging carnivores that occur at low densities. In recent years, significant progress has been made in density estimation of Asian carnivores, but the methods have not been widely adapted to African carnivores, such as lions (Panthera leo). Although abundance indices for lions may produce poor inferences, they continue to be used to estimate density and inform management and policy. We used sighting data from a 3-month survey and adapted a Bayesian spatially explicit capture-recapture (SECR) model to estimate spatial lion density in the Maasai Mara National Reserve and surrounding conservancies in Kenya. Our unstructured spatial capture-recapture sampling design incorporated search effort to explicitly estimate detection probability and density on afine spatial scale, making our approach robust in the context of varying detection probabilities. Overall posterior mean lion density was estimated to be 17.08 (posterior SD 1.310) lions > 1 year old/100 km², and the sex ratio was estimated at 2.2 females to 1 male. Our modeling framework and narrow posterior SD demonstrate that SECR methods can produce statistically rigorous and precise estimates of population parameters, and we argue that they should be favored over less reliable abundance indices. Furthermore, our approach is flexible enough to incorporate different data types, which enables robust population estimates over relatively short survey periods in a variety of systems. Trend analyses are essential to guide conservation decisions but are frequently based on surveys of differing reliability. We therefore call for a unified framework to assess lion numbers in key populations to improve management and policy decisions. Las estimaciones confiables de la densidad animal son fundamentales para el entendimiento de los procesos ecológicos y las dinámicas poblacionales. Más allá, su certeza es vital para la conservación porque las autoridades de la vida silvestre dependen de las estimaciones para tomar decisiones. Sin embargo, es notoria la dificultad que existe para estimar con certeza la densidad de los carnívoros con una extensión amplia que están presentes en densidades bajas. En años recientes, se ha avanzado significativamente en la estimación de densidad de los carnívoros asiáticos, pero los métodos no han sido adaptados ampliamente para los carnívoros africanos como los leones (Panthera leo,). Aunque los índices de abundancia para los leones pueden producir inferencias pobres, todavía se usan para estimar la densidad e informar al manejo y a la política. Utilizamos datos de avistamientos de un censo de tres meses y adaptamos un modelo bayesiano de captura-recaptura espacialmente explícito (CREE) para estimar la densidad espacial de los leones en la Reserva Nacional Maasai las reservas circundantes. Nuestro muestreo desestructurado de capturarecaptura espacial incorporó esfuerzos de búsqueda para estimar explícitamente la probabilidad de detección y la densidad en una escala espacial fina, lo que hizo a nuestra estrategia convincente en el contexto de las probabilidades de detección variantes. En general, se estimó que la densidad media de leones era 17.08 (DS posterior 1.310) leones > 1 año de edad/100 km², y se estimó que la proporción de sexos era 2.2 hembras por 1 macho. Nuestro marco de trabajo de la modelación y la DS posterior estrecha demuestran que los métodos CREE pueden producir estimaciones de los parámetros poblacionales estadísticamente rigurosas y precisas, y argumentamos que deberían ser favorecidos por encima de los índices de abundancia menos confiables. Además, nuestra estrategia es lo suficientemente flexible para incorporar diferentes tipos de datos, lo que habilita estimaciones poblacionales convincentes con periodos relativamente cortos de censos en una variedad de sistemas. Los análisis de las tendencias son esenciales para guiar a las decisiones de la conservación pero están basadas frecuentemente en censos de confianza discrepante. Por lo tanto hacemos un llamado por un marco de trabajo unificado para valorar los números de leones en poblaciones clave para mejorar las decisiones de manejo y política.

Spatial capture‐recapture (SCR) models provide estimates of animal density from spatially referenced encounter data and has become the most widely adopted approach for estimating density. Despite the rapid growth in the development and application of spatial capture‐recapture methods, approaches for assessing model fit have received very little attention when compared to other classes of hierarchical models in ecology. Here, we develop an approach for testing goodness‐of‐fit (GoF) for frequentist SCR models using Monte Carlo simulations. We derive probability distributions of activity centres from the fitted model. From these, we calculate the expected encounters in the capture history based on the SCR parameter estimates, propagating the uncertainty of the estimates and the activity centre locations via Monte Carlo simulations. Aggregating these test statistics result in count data, allowing us to test fit with Freeman‐Tukey tests. These tests are based on summary statistics of the total encounters of each individual at each trap (FT‐ind‐trap), total encounters of each individual (FT‐individuals) and total encounters at each trap (FT‐traps). We assess the ability of these GoF tests to diagnose lack of fit under a range of assumption violating scenarios. FT‐traps had the strongest response to unmodelled spatial and trap heterogeneity in detection probability (power = 0.53–0.56), while FT‐ind‐traps had the strongest responses to random individual variation in detectability (power = 0.88) and non‐spatial discrete variation in g0 (power = 0.35). The tests, designed to diagnose poor fit in the detection parameters, were insensitive to unmodelled heterogeneity in density (power = <0.001). They also demonstrated low false positive rates (<0.001) when the correct models were fitted; therefore, it is very unlikely that they will provide false indications of poor model fit. We demonstrate that these GoF tests are capable of detecting lack‐of‐fit when unmodelled heterogeneity is present in the detection sub‐model. When used jointly, the combinations of test results are also able to infer the type of lack‐of‐fit in certain cases. Our Monte Carlo sampling methods may be extended to a wider range of GoF tests, thereby providing a platform for developing more GoF methods for SCR.

The Virginia opossum (Didelphis virginiana), North America's only marsupial, has a range extending from southern Ontario, Canada, to the Yucatan Peninsula, Mexico, and from the Atlantic seaboard to the Pacific. Despite the Virginia opossum's taxonomic uniqueness in relation to other mammals in North America and rapidly expanding distribution, its ecology remains relatively understudied. Our poor understanding of the ecology of this important mesopredator is especially pronounced in the rural southeastern United States. Our goal was to estimate effects of habitat on opossum density within an extensive multi-year spatial capture-recapture study. Additionally, we compared the results of this spatial capture-recapture analysis with a simple relative abundance index. Opossum densities in the relatively underdeveloped regions of the southeastern United States were lower compared to the more human-dominated landscapes of the Northeast and Midwest. In the southeastern United States, Virginia opossums occurred at a higher density in bottomland swamp and riparian hardwood forest compared to upland pine (Pinus spp.) plantations and isolated wetlands. These results reinforce the notion that the Virginia opossum is commonly associated with land cover types adjacent to permanent water (bottomland swamps, riparian hardwood). The relatively low density of opossums at isolated wetland sites suggests that the large spatial scale of selection demonstrated by opossums gives the species access to preferable cover types within the same landscape.

Open population capture-recapture models are widely used to estimate population demographics and abundance over time. Bayesian methods exist to incorporate open population modeling with spatial capture-recapture (SCR), allowing for estimation of the effective area sampled and population density. Here, open population SCR is formulated as a hidden Markov model (HMM), allowing inference by maximum likelihood for both Cormack-Jolly-Seber and Jolly-Seber models, with and without activity center movement. The method is applied to a 12-year survey of male jaguars (Panthera onca) in the Cockscomb Basin Wildlife Sanctuary, Belize, to estimate survival probability and population abundance over time. For this application, inference is shown to be biased when assuming activity centers are fixed over time, while including a model for activity center movement provides negligible bias and nominal confidence interval coverage, as demonstrated by a simulation study. The HMM approach is compared with Bayesian data augmentation and closed population models for this application. The method is substantially more computationally efficient than the Bayesian approach and provides a lower root-mean-square error in predicting population density compared to closed population models.

Range declines, habitat connectivity, and trapping have created conservation concern for wolverines throughout their range in North America. Previous researchers used population models and observed estimates of survival and reproduction to infer that current trapping rates limit population growth, except perhaps in the far north where trapping rates are lower. Assessing the sustainability of trapping requires demographic and abundance data that are expensive to acquire and are therefore usually only achievable for small populations, which makes generalization risky. We surveyed wolverines over a large area of southern British Columbia and Alberta, Canada, used spatial capture-recapture models to estimate density, and calculated trapping kill rates using provincial fur harvest data. Wolverine density averaged 2 wolverines/1,000 km² and was positively related to spring snow cover and negatively related to road density. Observed annual trapping mortality was >8.4%/year. This level of mortality is unlikely to be sustainable except in rare cases where movement rates are high among sub-populations and sizable untrapped refuges exist. Our results suggest wolverine trapping is not sustainable because our study area was fragmented by human and natural barriers and few large refuges existed. We recommend future wolverine trapping mortality be reduced by ≥50% throughout southern British Columbia and Alberta to promote population recovery.

Estimating demographic parameters for wide‐ranging and elusive species living at low density is challenging, especially at the scale of an entire country. To produce wolf distribution and abundance estimates for the whole south‐central portion of the Italian wolf population, we developed an integrated spatial model, based on the data collected during a 7‐month sampling campaign in 2020–2021. Data collection comprised an extensive survey of wolf presence signs, and an intensive survey in 13 sampling areas, aimed at collecting non‐invasive genetic samples (NGS). The model comprised (i) a single‐season, multiple data‐source, multi‐event occupancy model and (ii) a spatially explicit capture‐recapture model. The information about species' absence was used to inform local density estimates. We also performed a simulation‐based assessment, to estimate the best conditions for optimizing sub‐sampling and population modelling in the future. The integrated spatial model estimated that 74.2% of the study area in south‐central Italy (95% CIs = 70.5% to 77.9%) was occupied by wolves, for a total extent of the wolf distribution of 108,534 km2 (95% CIs = 103,200 to 114,000). The estimate of total population size for the Apennine wolf population was of 2557 individuals (SD = 171.5; 95% CIs = 2127 to 2844). Simulations suggested that the integrated spatial model was associated with an average tendency to slightly underestimate population size. Also, the main contribution of the integrated approach was to increase precision in the abundance estimates, whereas it did not affect accuracy significantly. In the future, the area subject to NGS should be increased to at least 30%, while at least a similar proportion should be sampled for presence‐absence data, to further improve the accuracy of population size estimates and avoid the risk of underestimation. This approach could be applied to other wide‐ranging species and in other geographical areas, but specific a priori evaluations of model requirements and expected performance should be made. To produce wolf distribution and abundance estimates for the whole south‐central portion of the Italian wolf population, we developed an integrated spatial model, based on an occupancy and a spatially explicit capture‐recapture model. The estimate of total population size for the Apennine wolf population was of 2557 individuals (SD = 171.5; 95% CIs = 2127 to 2844). This approach could be applied to other wide‐ranging species and in other geographical areas, but specific a priori evaluations of model requirements and expected performance should be made.

Recovering small populations of threatened species is an important global conservation strategy. Monitoring the anticipated recovery, however, often relies on uncertain abundance indices rather than on rigorous demographic estimates. To counter the severe threat from poaching of wild tigers (Panthera tigris), the Government of Thailand established an intensive patrolling system in 2005 to protect and recover its largest source population in Huai Kha Khaeng Wildlife Sanctuary. Concurrently, we assessed the dynamics of this tiger population over the next 8 years with rigorous photographic capture-recapture methods. From 2006 to 2012, we sampled across 624-1026 km² with 137-200 camera traps. Cameras deployed for 21,359 trap days yielded photographic records of 90 distinct individuals. We used closed model Bayesian spatial capture-recapture methods to estimate tiger abundances annually. Abundance estimates were integrated with likelihood-based open model analyses to estimate rates of annual and overall rates of survival, recruitment, and changes in abundance. Estimates of demographic parameters fluctuated widely: annual density ranged from 1.25 to 2.01 tigers/100 km², abundance from 35 to 58 tigers, survival from 79.6% to 95.5%, and annual recruitment from 0 to 25 tigers. The number of distinct individuals photographed demonstrates the value of photographic capture-recapture methods for assessments of population dynamics in rare and elusive species that are identifiable from natural markings. Possibly because of poaching pressure, overall tiger densities at Huai Kha Khaeng were 82-90% lower than in ecologically comparable sites in India. However, intensified patrolling after 2006 appeared to reduce poaching and was correlated with marginal improvement in tiger survival and recruitment. Our results suggest that population recovery of low-density tiger populations may be slower than anticipated by current global strategies aimed at doubling the number of wild tigers in a decade. Recuperar las poblaciones pequeñas de las especies amenazadas es una importante estrategia global de conservación. Sin embargo, monitorear la recuperación esperada generalmente depende de índices inciertos de abundancia en lugar de estimados demográficos rigurosos. Para contrarrestar la gran amenaza causada por la cacería furtiva de tigres (Panthera tigris), el Gobierno de Tailandia estableció un sistema intensivo de patrullaje en 2005 para proteger y recuperar la población fuente más grande en el Santuario Huai Kha Khaeng. Simultáneamente, evaluamos las dinámicas de esta población de tigres durante los siguientes ocho años con rigurosos métodos fotográficos de captura-recaptura. De 2006 a 2012 muestreamos a lo largo de 624-1026 km² con 137-200 trampas cámara. Las cámaras desplegadas durante 21,359 días de trampa produjeron registros fotográficos de 90 individuos distinguibles. Usamos métodos espaciales de capturarecaptura y modelo bayesiano cerrado para estimar anualmente la abundancia de los tigres. Los estimados de abundancia estuvieron integrados por análisis de modelo abierto basados en la probabilidad para estimar la tasa anual y las tasas generales de supervivencia, reclutamiento y cambios en la abundancia. Los estimados de los parámetros demográficos fluctuaron ampliamente: la densidad anual varió entre 1.25 y 2.01 tigres/100 km², la abundancia entre 35 a 58 tigres, la supervivencia entre 79-6 y 95.5% y el reclutamiento anual de 0 a 25 tigres. El número de individuos distinguibles que fue fotografiado demuestra el valor de los métodos de captura-recaptura para la evaluación de las dinámicas poblacionales de especies raras y elusivas que son identificables gracias a marcas naturales. Posiblemente por causa de la presión ejercida por la caza furtiva, la densidad general de los tigres en Huai Kha Khaeng fue 82-90% más baja que en sitios ecológicamente comparables de India. Sin embargo, el patrullaje intensivo después de 2006 pareció reducir la caza furtiva y estuvo correlacionado con el mejoramiento marginal de la supervivencia y reclutamiento de los tigres. Nuestros resultados sugieren que la recuperación de las poblaciones de tigres con baja densidad puede ser más lenta de lo esperado por las estrategias globales actuales enfocadas en la duplicación del número de tigres en una década.