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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.

Understanding interspecific interactions between tigers (Panthera tigris) and leopards (Panthera pardus) is crucial for effective conservation planning. However, most studies have been conducted only in well‐known protected areas, leaving knowledge gaps in other parts of their overlapping range. This study investigates the spatial‐temporal interactions between sympatric carnivores (tigers and leopards) in Parsa National Park (PNP), Nepal. Camera trap data obtained from 157 sampling sites (2 × 2 km grid cells) were used to assess daily temporal activity patterns, single‐species occupancy, and density of tigers and leopards using spatially explicit capture‐recapture models (SECR) to further examine their coexistence mechanism in light of the recent recovery of tiger populations in PNP. In general, our findings indicate that both species co‐detected at 44 camera locations, demonstrating that they spatially share habitats inside the park. However, leopards avoid peak tiger activity periods, which is likely to reduce competitive interactions. The SECR model estimated a leopard density of 3.09 individuals per 100 km2 whereas tiger density was 1.25 individuals per 100 km2 within the study area. The model‐averaged occupancy probability of leopards in PNP was 0.45 (CI: 0.30, 0.64). The normalized difference vegetation index (NDVI) had a strong correlation with leopard occupancy, while the tiger relative abundance index (RAI) had minimal impact, reflecting the importance of high‐quality habitats in protected areas for conserving both species. Conservation initiatives targeting to strengthen the tiger recovery plans should incorporate thorough studies of interspecific interactions between sympatric large carnivores like tigers, leopards, and their prey base on a fine‐grain scale to ensure effective management strategies.

The small (2- to 7-kg) leopard cat (Prionailurus bengalensis) is the most common cat species in Asia. Although it occurs in a wide range of habitats and seems to adapt well to anthropogenic habitat changes, surprisingly little is known about this species in the wild. All studies have focused on protected areas, although a large proportion of Southeast Asian forests are timber concessions. During this study, we used large camera-trapping data sets (783 records of 124 individuals) from 3 commercially used forests to investigate consequences of different logging regimes on density and habitat associations of the leopard cat. We applied spatial capture–recapture models accounting for the location of camera-traps (on or off road) to obtain estimates of leopard cat density. Density was higher in the 2 more disturbed forest reserves (X̄ = 12.4 individuals/100 km2 ± 1.6 SE and 16.5 ± 2 individuals/100 km2) than in the sustainably managed forest (9.6 ± 1.7 individuals/100 km2). Encounter rates with off-road traps were only 3.6–9.1% of those for on-road traps. Occupancy models, which accounted for spatial autocorrelation between sampling sites by using a conditional autoregressive model, revealed that canopy closure and ratio of climax to pioneer trees had a significantly negative impact on leopard cat occurrence. Our results confirm that the leopard cat is doing well in modified landscapes and even seems to benefit from the opening of forests. With such flexibility the leopard cat is an exception among tropical rain-forest carnivores.

Population size is one of the basic demographic parameters for species management and conservation. Among different estimation methods, spatially explicit capture–recapture (SCR) models allow the estimation of population density in a framework that has been greatly developed in recent years. The use of automated detection devices, such as camera traps, has impressively extended SCR studies for individually identifiable species. However, its application to unmarked/partially marked species remains challenging, and no specific method has been widely used. We fitted an SCR‐integrated model (SCR‐IM) to stone marten Martes foina data, a species for which only some individuals are individually recognizable by natural marks, and estimate population size based on integration of three submodels: (1) individual capture histories from live capture and transponder tagging; (2) detection/nondetection or “occupancy” data using camera traps in a bigger area to extend the geographic scope of capture–recapture data; and (3) telemetry data from a set of tagged individuals. We estimated a stone marten density of 0.352 (SD: 0.081) individuals/km2. We simulated four dilution scenarios of occupancy data to study the variation in the coefficient of variation in population size estimates. We also used simulations with similar characteristics as the stone marten case study, comparing the accuracy and precision obtained from SCR‐IM and SCR, to understand how submodels' integration affects the posterior distributions of estimated parameters. Based on our simulations, we found that population size estimates using SCR‐IM are more accurate and precise. In our stone marten case study, the SCR‐IM density estimation increased the precision by 37% when compared to the standard SCR model as regards to the coefficient of variation. This model has high potential to be used for species in which individual recognition by natural markings is not possible, therefore limiting the need to rely on invasive sampling procedures. We fitted a spatial capture–recapture integrated model (SCR‐IM) to stone marten (Martes foina) data, and estimate population size based on integration of three submodels: (1) individual capture histories from live capture and transponder tagging; (2) detection/nondetection or “occupancy” data using camera traps in a bigger area to extend the geographic scope of capture–recapture data; and (3) telemetry data from a set of tagged individuals. The SCR‐IM density estimation improved the precision by 37% when compared to the standard SCR model.

Camera-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 km². Our modeled bobcat density estimates are considerably below previously reported densities (30.5 individuals/100 km²) 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 fragmentedMidwestern landscapes.

Spatial capture–recapture (SCR) methods have become widely applied in ecology. The immediate adoption of SCR is due to the fact that it resolves some major criticisms of traditional capture–recapture methods related to heterogeneity in detectabililty, and the emergence of new technologies (e.g. camera traps, non‐invasive genetics) that have vastly improved our ability to collection spatially explicit observation data on individuals. However, the utility of SCR methods reaches far beyond simply convenience and data availability. SCR presents a formal statistical framework that can be used to test explicit hypotheses about core elements of population and landscape ecology, and has profound implications for how we study animal populations. In this software note, we describe the technical basis and analytical workflow of oSCR, an R package for analyzing spatial encounter history data using a multi‐session sex‐structured likelihood. The impetus for developing oSCR was to create an accessible and transparent analysis tool that allows users to conveniently and intuitively formulate statistical models that map directly to fundamental processes of interest in spatial population ecology (e.g. space use, resource selection, density and connectivity). We have placed an emphasis on creating a transparent and accessible code base that is coupled with a logical workflow that we hope stimulates active participation in further technical developments.

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.

Capture-recapture methods for estimating wildlife population sizes almost always require their users to identify every detected animal. Many modern-day wildlife surveys detect animals without physical capture—visual detection by cameras is one such example. However, for every pair of detections, the surveyor faces a decision that is often fraught with uncertainty: are they linked to the same individual? An inability to resolve every such decision to a high degree of certainty prevents the use of standard capture-recapture methods, impeding the estimation of animal density. Here, we develop an estimator for aerial surveys, on which two planes or unmanned vehicles (drones) fly a transect over the survey region, detecting individuals via high-definition cameras. Identities remain unknown, so one cannot discern if two detections match to the same animal; however, detections in close proximity are more likely to match. By modeling detection locations as a clustered point process, we extend recently developed methodology and propose a precise and computationally efficient estimator of animal density that does not require individual identification. We illustrate the method with an aerial survey of porpoise, on which cameras detect individuals at the surface of the sea, and we need to take account of the fact that they are not always at the surface.

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.

Ecologists and conservation biologists increasingly rely on spatial capture–recapture (SCR) and movement modeling to study animal populations. Historically, SCR has focused on population-level processes (e.g., vital rates, abundance, density, and distribution), whereas animal movement modeling has focused on the behavior of individuals (e.g., activity budgets, resource selection, migration). Even though animal movement is clearly a driver of population-level patterns and dynamics, technical and conceptual developments to date have not forged a firm link between the two fields. Instead, movement modeling has typically focused on the individual level without providing a coherent scaling from individual- to population-level processes, whereas SCR has typically focused on the population level while greatly simplifying the movement processes that give rise to the observations underlying these models. In our view, the integration of SCR and animal movement modeling has tremendous potential for allowing ecologists to scale up from individuals to populations and advancing the types of inferences that can be made at the intersection of population, movement, and landscape ecology. Properly accounting for complex animal movement processes can also potentially reduce bias in estimators of population-level parameters, thereby improving inferences that are critical for species conservation and management. This introductory article to the Special Feature reviews recent advances in SCR and animal movement modeling, establishes a common notation, highlights potential advantages of linking individual-level (Lagrangian) movements to population-level (Eulerian) processes, and outlines a general conceptual framework for the integration of movement and SCR models. We then identify important avenues for future research, including key challenges and potential pitfalls in the developments and applications that lie ahead.