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This study provides novel insights into the ecology of the margay (Leopardus wiedii), focusing on its occupancy, density, and activity patterns in the Madre de Dios region, Peru, by utilizing both arboreal and terrestrial camera traps. Conducted across 10 km2, the research achieved 47 detections, utilizing semi-arboreal, lower canopy cameras for the first time to capture margay activities. Occupancy models revealed a mean occupancy probability of 53.82% and a detection probability of 6.57%. Among the environmental covariates, diameter at breast height (DBH) was identified as a significant predictor, negatively impacting occupancy, suggesting margays favor areas with smaller tree diameters. Contrary to expectations, the Normalized Difference Vegetation Index (NDVI) did not significantly influence occupancy. Tree density exhibited a positive, though non-significant, association with margay presence. Spatial capture-recapture (SECR) models estimated a margay density of 71.46 individuals per 100 km², with significant sex-based differences in spatial behavior. Males demonstrated larger home ranges (approximately 13.50 km²) compared to females (approximately 3.79 km²). Activity pattern analyses indicated primary nocturnal behavior with peaks at midnight to 3 am, 5 am, and 6 pm. Temporal overlap analysis revealed a low overlap coefficient with jaguarundis (Dhat1 = 0.21) and a higher overlap with ocelots (Dhat4 = 0.79), reflecting intricate interspecies dynamics. Our findings highlight important ecological aspects of margay behavior, including habitat preferences, nocturnal activity patterns, and interspecies interactions, which were effectively captured through the combined use of terrestrial and arboreal camera traps. The study emphasizes the importance of habitat preservation and the development of conservation strategies tailored to the ecological needs of margays, potentially influencing global practices for the management and protection of lesser-studied semi-arboreal wild cats. [ABSTRACT FROM AUTHOR]

Aim: Accounting 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. Location: Global (simulation) and North Carolina, United States (case study). Taxon: None (simulation) and Aves (case study). Methods: We 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. Results: A 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 Conclusions: Unlike 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. [ABSTRACT FROM AUTHOR]

Abstract 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 =

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. [ABSTRACT FROM AUTHOR]

The ongoing expansion of wolf (Canis lupus) populations in Europe has led to a growing demand for up‐to‐date abundance estimates. Non‐invasive genetic sampling (NGS) is now widely used to monitor wolves, as it allows individual identification and abundance estimation without physically capturing individuals. However, NGS is resource‐intensive, partly due to the elusive behaviour and wide distribution of wolves, as well as the cost of DNA analyses. Optimisation of sampling strategies is therefore a requirement for the long‐term sustainability of wolf monitoring programs. Using data from the 2020–2021 Italian Alpine wolf monitoring, we investigate how (i) reducing the number of samples genotyped, (ii) reducing the number of transects, and (iii) reducing the number of repetitions of each search transect impacted spatial capture‐recapture population size estimates. Our study revealed that a 25% reduction in the number of transects or, alternatively, a 50% reduction in the maximum number of repetitions yielded abundance estimates comparable to those obtained using the entire dataset. These modifications would result in a 2046 km reduction in total transect length and 19,628 km reduction in total distance searched. Further reducing the number of transects resulted in up to 15% lower and up to 17% less precise abundance estimates. Reducing only the number of genotyped samples led to higher (5%) and less precise (20%) abundance estimates. Randomly subsampling genotyped samples reduced the number of detections per individual, whereas subsampling search transects resulted in a less pronounced decrease in both the total number of detections and individuals detected. Our work shows how it is possible to optimise wolf monitoring by reducing search effort while maintaining the quality of abundance estimates, by adopting a modelling framework that uses a first survey dataset. We further provide general guidelines on how to optimise sampling effort when using spatial capture‐recapture in large‐scale monitoring programmes. [ABSTRACT FROM AUTHOR]

Understanding the spatial requirements of exploited wildlife species, including population density and home range size, is important for wildlife management and conservation. Wolverines (Gulo gulo) are hunted and trapped across the Arctic, and are vulnerable to numerous, often interrelated, threats resulting from anthropogenic changes in their environment. Previous population density estimates for wolverines in the Arctic range tenfold, from the lowest to highest available for the species, limiting their utility outside the specific areas and times they were derived. The most recent density estimate in Arctic Alaska, USA, was produced 4 decades ago and was derived from a relatively small study area. We evaluated wolverine population density and home range size across the North Slope of Alaska during 2017–2022 using global positioning system (GPS)‐based collar data and spatial capture‐recapture models. Population density estimates were 2.0 individuals/1,000 km2 (95% credible interval = 1.3–3.5) in 2018 and 2.8 individuals/1,000 km2 (95% credible interval = 1.7–3.5) in 2021. Median home range sizes modeled with autocorrelated kernel density estimators and Ornstein‐Uhlenbeck foraging movement processes were 699 km2 (range = 158–2,895 km2) among 12 females and 2,332 km2 (range = 797–4,699 km2) among 10 males. These population density estimates are nearly 10 times lower than the previous estimate for Arctic Alaska. We recommend incorporating this information into management strategies to ensure sustainable harvest, particularly as the region's remote areas are more efficiently accessed by hunters and are being considered for transportation corridors supporting new industrial development. [ABSTRACT FROM AUTHOR]

Small carnivores play an important role in ecosystems, but their often hidden lifestyle makes them generally hard to observe. Camera-trapping methodology shows great promise as a standardized monitoring tool for small carnivores. The European polecat (Mustela putorius) is a species of conservation concern, listed on the Annex V of the EU Council's Directive on the conservation of natural habitats and of wild fauna and flora. Because of this, EU member states have to report the status of their polecat populations regularly. However, standardized methods to estimate the population size of polecat populations are currently lacking. We present a tube-based camera-trapping system, the Polecam, designed to obtain photographs of the facial masks of individual polecats. We tested the system in four study areas in southern Sweden during Spring and Autumn 2021. We obtained 52 observations of polecats, out of which we were able to identify 38 (73%) to individual. From these observations, we estimated area and season specific estimates of density for five season-area combinations using a multi-session spatial capture-recapture model. Density estimates ranged from 0.84 to 2.7 individuals/1000 ha, similar to the estimates from similar landscapes in other countries in Europe. We also obtained observations of six other species of small carnivores, suggesting that the Polecam could be used for multi-species monitoring, as well as density estimation of European polecats. [ABSTRACT FROM AUTHOR]

This study provides novel insights into the ecology of the margay (Leopardus wiedii), focusing on its occupancy, density, and activity patterns in the Madre de Dios region, Peru, by utilizing both arboreal and terrestrial camera traps. Conducted across 10 km2, the research achieved 47 detections, utilizing semi-arboreal, lower canopy cameras for the first time to capture margay activities. Occupancy models revealed a mean occupancy probability of 53.82% and a detection probability of 6.57%. Among the environmental covariates, diameter at breast height (DBH) was identified as a significant predictor, negatively impacting occupancy, suggesting margays favor areas with smaller tree diameters. Contrary to expectations, the Normalized Difference Vegetation Index (NDVI) did not significantly influence occupancy. Tree density exhibited a positive, though non-significant, association with margay presence. Spatial capture-recapture (SECR) models estimated a margay density of 71.46 individuals per 100 km², with significant sex-based differences in spatial behavior. Males demonstrated larger home ranges (approximately 13.50 km²) compared to females (approximately 3.79 km²). Activity pattern analyses indicated primary nocturnal behavior with peaks at midnight to 3 am, 5 am, and 6 pm. Temporal overlap analysis revealed a low overlap coefficient with jaguarundis (Dhat1 = 0.21) and a higher overlap with ocelots (Dhat4 = 0.79), reflecting intricate interspecies dynamics. Our findings highlight important ecological aspects of margay behavior, including habitat preferences, nocturnal activity patterns, and interspecies interactions, which were effectively captured through the combined use of terrestrial and arboreal camera traps. The study emphasizes the importance of habitat preservation and the development of conservation strategies tailored to the ecological needs of margays, potentially influencing global practices for the management and protection of lesser-studied semi-arboreal wild cats.

Abstract Identifying individuals is key to estimating population sizes by spatial capture–recapture, but identification errors are sometimes made. The most common identification error is the failure to recognise a previously detected individual, thus creating a ‘ghost’ Johansson. This results in positively biased abundance estimates. Ghosts typically manifest as single detection individuals (‘singletons’) in the capture history. To deal with ghosts, we develop a spatial capture–recapture method conditioned on at least K detections. The standard spatial capture–recapture (SCR) model is the special case of K=1. Ghosts can mostly be excluded by fitting a model with K=2 (SCR‐2). We investigated the effect of ‘singleton’ ghosts on the estimation of the model parameters by simulation. The SCR method increasingly overestimated abundance with increasing percentage of ghosts, with positive bias even when only 10% of the detected individuals were ghosts, and bias between 43% and 71% when 30% were ghosts. Estimates from the SCR‐2 method showed lower bias in the presence of ghosts, at the cost of a loss of precision. The mean squared error of the estimated abundance from the SCR‐2 method was lower in all scenarios with ghosts under high encounter rates and for scenarios with 30% or more ghosts with low encounter rates. We also applied our method to capture histories from camera trap surveys of snow leopards (Panthera uncia) at two sites from Mongolia and find that the SCR method produced higher abundance estimates at both sites. Capture histories are susceptible to errors when generated from passive detectors such as camera traps and genetic samples. The SCR‐2 method can remove bias from ghost capture histories, at the cost of some loss in precision. We recommend using the SCR‐2 method in cases when there may be more than 10% ghosts or surveys with a large number of single detection capture histories, except perhaps when the sample size is very low.

Abstract The Eurasian otter Lutra lutra is a territorial semi-aquatic carnivore usually found at low densities in rivers, coastal areas, and wetlands. Its diet is based on prey associated with aquatic environments. Mediterranean rivers are highly seasonal, and suffer reduced flow during the summer, resulting in isolated river sections (pools) that sometimes can be left with a minimal amount of water, leading to concentrations of food for otters. To our knowledge, this process, which was known to field naturalists, has not been accurately described, nor have otter densities been estimated under these conditions. In this study, we describe the population size and movements of an aggregation of otters in an isolated pool in the Guadiana River in the Tablas de Daimiel National Park (central Spain), which progressively dried out during the spring–summer of 2022, in a context of low connectivity due to the absence of circulating water in the Guadiana and Gigüela rivers. Using non-invasive genetic sampling of 120 spraints collected along 79.4 km of sampling transects and spatial capture-recapture methods, we estimated the otter density at 1.71 individuals/km of river channel length (4.21 individuals/km2) in a progressively drying river pool, up to five times higher than previously described in the Iberian Peninsula. The movement patterns obtained with the spatial capture-recapture model are not quite different from those described in low density, which seems to indicate a wide home range overlap, with low signs of territoriality.

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

Identifying individuals is key to estimating population sizes by spatial capture–recapture, but identification errors are sometimes made. The most common identification error is the failure to recognise a previously detected individual, thus creating a 'ghost' Johansson. This results in positively biased abundance estimates.Ghosts typically manifest as single detection individuals ('singletons') in the capture history. To deal with ghosts, we develop a spatial capture–recapture method conditioned on at least K detections. The standard spatial capture–recapture (SCR) model is the special case of K=1. Ghosts can mostly be excluded by fitting a model with K=2 (SCR‐2).We investigated the effect of 'singleton' ghosts on the estimation of the model parameters by simulation. The SCR method increasingly overestimated abundance with increasing percentage of ghosts, with positive bias even when only 10% of the detected individuals were ghosts, and bias between 43% and 71% when 30% were ghosts. Estimates from the SCR‐2 method showed lower bias in the presence of ghosts, at the cost of a loss of precision. The mean squared error of the estimated abundance from the SCR‐2 method was lower in all scenarios with ghosts under high encounter rates and for scenarios with 30% or more ghosts with low encounter rates. We also applied our method to capture histories from camera trap surveys of snow leopards (Panthera uncia) at two sites from Mongolia and find that the SCR method produced higher abundance estimates at both sites.Capture histories are susceptible to errors when generated from passive detectors such as camera traps and genetic samples. The SCR‐2 method can remove bias from ghost capture histories, at the cost of some loss in precision. We recommend using the SCR‐2 method in cases when there may be more than 10% ghosts or surveys with a large number of single detection capture histories, except perhaps when the sample size is very low. [ABSTRACT FROM AUTHOR]

The Eurasian otter Lutra lutra is a territorial semi-aquatic carnivore usually found at low densities in rivers, coastal areas, and wetlands. Its diet is based on prey associated with aquatic environments. Mediterranean rivers are highly seasonal, and suffer reduced flow during the summer, resulting in isolated river sections (pools) that sometimes can be left with a minimal amount of water, leading to concentrations of food for otters. To our knowledge, this process, which was known to field naturalists, has not been accurately described, nor have otter densities been estimated under these conditions. In this study, we describe the population size and movements of an aggregation of otters in an isolated pool in the Guadiana River in the Tablas de Daimiel National Park (central Spain), which progressively dried out during the spring–summer of 2022, in a context of low connectivity due to the absence of circulating water in the Guadiana and Gigüela rivers. Using non-invasive genetic sampling of 120 spraints collected along 79.4 km of sampling transects and spatial capture-recapture methods, we estimated the otter density at 1.71 individuals/km of river channel length (4.21 individuals/km2) in a progressively drying river pool, up to five times higher than previously described in the Iberian Peninsula. The movement patterns obtained with the spatial capture-recapture model are not quite different from those described in low density, which seems to indicate a wide home range overlap, with low signs of territoriality. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Tigers and other large predatory carnivores have suffered population extirpations and range contractions. This is particularly true for tiger populations in southeastern Asia, which harbours one-third of their remaining habitats. In stark contrast, a sustained recovery of a wild tiger population has occurred between 2007 and 2023, in three reserves of Thailand: Huai Kha Khaeng (HKK), Thung Yai East (TYE) and Thung Yai West (TYW), which together cover 6470 km2 (36%) of the larger Western Forest Complex (WEFCOM). We quantitatively monitored this recovery employing closed and open model analyses of data from photographic capture-recapture sampling. The resulting estimates of tiger population dynamic parameters showed: mean (±SE) tiger abundance annually varied from 36 (1.0) to 79 (1.53) in HKK, 2 (0.26) to 20 (4.45) in TYE and 3 (0.26) to 44 (2.11) in TYW, driven by mean annual survival rates of 0.79 (0.02) in HKK, 0.72 (0.05) in TYE, and 0.69 (0.05) in TYW. The annual numbers of recruits fluctuated from 0 (1.69) to 33 (1.93) tigers in HKK, 0 (0.47) to 13 (0.57) in TYE and 0 (1.13) to 36 (2.28) in TYW. Overall, the mean tiger population densities/100 km2 ranged between 1.3 (0.19) and 2.9 (0.29) in HKK, 0.2 (0.08) and 1.8 (0.34) in TYE, and 0.2 (0.07) and 3.1 (0.56) in TYW. Generally, the tiger population trended upward, with reserves protected over longer periods leading the tiger recovery. Our results are further backed by ancillary records on births of 67 cubs, 47 tiger dispersal events, as well as the recovery corresponding with incremental spatio-temporal coverage by the patrols. Cumulatively, our results provide evidence that effective law enforcement should be a critical component for achieving tiger population recoveries in Asia. Alternative conservation strategies that ignore this component do not appear to be evidence-based. Our results also demonstrate the utility of the independent collaborative monitoring framework adopted by the Thailand Government.

Abstract Background Improved 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. Methods We 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. Results Model 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. Conclusions Density-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.

Spatial capture-recapture methods are often used to produce density surfaces, and these surfaces are often misinterpreted. In particular, spatial change in density is confused with spatial change in uncertainty about density. We illustrate correct and incorrect inference visually by treating a grayscale image of the Mona Lisa as an activity center intensity or density surface and simulating spatial capture-recapture survey data from it. Inferences can be drawn about the intensity of the point process generating activity centers, and about the likely locations of activity centers associated with the capture histories obtained from a single survey of a single realization of this process. We show that treating probabilistic predictions of activity center locations as estimates of the intensity of the process results in invalid and misleading ecological inferences, and that predictions are highly dependent on where the detectors are placed and how much survey effort is used. Estimates of the activity center density surface should be obtained by estimating the intensity of a point process model for activity centers. Practitioners should state explicitly whether they are estimating the intensity or making predictions of activity center location, and predictions of activity center locations should not be confused with estimates of the intensity. [ABSTRACT FROM AUTHOR]

Some dolphin species produce signature whistles, which may allow the identification of individual dolphins using passive acoustic monitoring (PAM). Identifying individuals by their sounds may enhance the opportunities for monitoring and addressing biological and ecological questions about these species. Here, we explored the potential of signature whistles to investigate ecological aspects of a resident bottlenose dolphin population. Using a limited data set, with few individuals recognized by signature whistles, combined with spatial capture‐recapture (SCR) methods, we investigated how effective such approach is describing spatial use patterns and estimating density for this population. The data were collected using 4–6 stationary bottom‐moored recorders. Since only eight signature whistles were identified, our density estimate may represent a subset of the entire population. However, even with only a few signature whistles identified, our results confirmed the center of the core area used by these dolphins as the area with the highest encounter probability. In addition, our results provided evidence that these dolphins have the same spatial use pattern at night as during the day. This study shows that SCR analysis of signature whistle data can improve our ecological knowledge and understanding of dolphin populations. [ABSTRACT FROM AUTHOR]

Abstract 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.

The wolf (Canis lupus) is among the most controversial of wildlife species. Abundance estimates are required to inform public debate and policy decisions, but obtaining them at biologically relevant scales is challenging. We developed a system for comprehensive population estimation across the Italian alpine region (100,000 km2), involving 1513 trained operators representing 160 institutions. This extensive network allowed for coordinated genetic sample collection and landscape‐level spatial capture–recapture analyses that transcended administrative boundaries to produce the first estimates of key parameters for wolf population status assessment. Wolf abundance was estimated at 952 individuals (95% credible interval 816–1120) and 135 reproductive units (i.e., packs) (95% credible interval 112–165). We also estimated that mature individuals accounted for 33–45% of the entire population. The monitoring effort was spatially estimated thereby overcoming an important limitation of citizen science data. This is an important approach for promoting wolf–human coexistence based on wolf abundance monitoring and an endorsement of large‐scale harmonized conservation practices. [ABSTRACT FROM AUTHOR]

The Eurasian lynx is a large carnivore widely distributed across Eurasia. However, our understanding of population status is heterogeneous across their range, with some populations isolated that are at risk of reduced genetic variation and a complete lack of information about others. In many European countries, Eurasian lynx are monitored through demographic studies crucial for their conservation and management. Even so, there are only rough and fragmented population assessments from Ukraine and Belarus, despite strict protection in both countries and their importance for lynx connectivity across Europe. We monitored lynx from October 2020 to March 2021 and used camera trapping in combination with spatial capture–recapture (SCR) methods in a Bayesian framework to provide the first SCR density estimation of three lynx populations across Ukraine and Belarus, including the Ukrainian Chornobyl Exclusion Zone, southern Belarus and the Ukrainian Carpathians. Our density estimates varied within our study areas ranging from 0.45 to 1.54 individuals/100 km2. This work provides a substantial scientific component to the overall understanding of lynx conservation for a region where only broad information is available and opens the doors for further large‐scale monitoring and trend assessments. The crucial information we provide can greatly enhance the range‐wide assessments of the status of this protected species. We also discuss the implications for Eurasian lynx conservation, despite the geopolitical realities impacting species monitoring in the region. Our work serves as a baseline, not only for future conservation interventions but also to evaluate the effects of disturbance and threats to these protected populations. [ABSTRACT FROM AUTHOR]

Wildlife 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. [ABSTRACT FROM AUTHOR]

The application of next‐generation sequencing (NGS) to non‐invasive samples is one of the most promising methods in conservation genomics, but these types of samples present significant challenges for NGS. The European mink (Mustela lutreola) is critically endangered throughout its range. However, important aspects such as census size and inbreeding remain still unknown in many populations, so it is crucial to develop new methods to monitor this species. In this work, we placed hair tubes along riverbanks in a border area of the Iberian population, which allowed the genetic identification of 76 European mink hair samples. We then applied a reduced representation genomic sequencing (ddRAD) technique to a subset of these samples to test whether we could extract sufficient genomic information from them. We show that several problems with the DNA, including contamination, fragmentation, oxidation, and possibly sample mixing, affected the samples. Using various bioinformatic techniques to reduce these problems, we were able to unambiguously genotype 19 hair samples belonging to six individuals. This small number of individuals showed that the demographic status of the species in this peripheral population is worse than expected. The data obtained also allowed us to perform preliminary analyses of relatedness and inbreeding. Although further improvements in sampling and analysis are needed, the application of the ddRAD technique to non‐invasively obtained hairs represents a significant advance in the genomic study of endangered species. [ABSTRACT FROM AUTHOR]

Noninvasive data collection methods are increasingly used to monitor endangered, cryptic, or otherwise difficult to observe wildlife populations. Spatial capture‐recapture and count‐based statistical methods are often used to model these data to derive population estimates, each with their own advantages. The goal of our study was to integrate these 2 data sources and model structures to maximize the use of single‐season surveys in which capture‐recapture and count‐based data are collected. We formulated a Bayesian spatial, closed integrated population model (cIPM) that leverages scat DNA‐based spatial capture‐recapture and camera‐based spatial count sub‐models to derive sex and age class‐specific population estimates. Our cIPM has the advantage of sharing the spatial scaling (σ) parameter directly between sub‐models and drawing proposed individuals from the same data‐augmented superpopulation for each sub‐model. This allows for separate estimation of sex and age‐class groups simultaneously without any assumptions of population structure or conversions between model output types. We applied our cIPM to a wild population of Roosevelt elk (Cervus canadensis roosevelti, Karuk: íshyuux) during winter 2018–2019 in their winter range within Karuk Ancestral Territory in northern California, USA. We also conducted a simulation study to test our cIPM on a known population consisting of 2 age classes and 2 sexes. The cIPM was able to estimate most population, detection, and σ parameters with good accuracy, including population size. The assignment of sex and age was biased, however, resulting in fewer juvenile males, more juvenile females, and more adult males. Our cIPM applied to the data estimated approximately 310 (95% CI = 235–411) female adults, 90 (95% CI = 57–134) male adults, 75 (95% CI = 43–149) female calves, and 23 (95% CI = 12–47) male calves. These estimates were different from those that would have been made had we relied on common assumptions such as equal calf sex ratios, though this ratio was likely biased towards females based on our simulation results. Our methods are widely applicable and maximize the amount of information available to managers and researchers from any single‐season survey in which separate data sources capturing sex and age‐class information are obtained. [ABSTRACT FROM AUTHOR]

After centuries of intense persecution, several large carnivore species in Europe and North America have experienced a rebound. Today's spatial configuration of large carnivore populations has likely arisen from the interplay between their ecological traits and current environmental conditions, but also from their history of persecution and protection. Yet, due to the challenge of studying population‐level phenomena, we are rarely able to disentangle and quantify the influence of past and present factors driving the distribution and density of these controversial species. Using spatial capture‐recapture models and a data set of 742 genetically identified wolverines Gulo gulo collected over ½ million km2 across their entire range in Norway and Sweden, we identify landscape‐level factors explaining the current population density of wolverines in the Scandinavian Peninsula. Distance from the relict range along the Swedish–Norwegian border, where the wolverine population survived a long history of persecution, remains a key determinant of wolverine density today. However, regional differences in management and environmental conditions also played an important role in shaping spatial patterns in present‐day wolverine density. Specifically, we found evidence of slower recolonization in areas that had set lower wolverine population goals in terms of the desired number of annual reproductions. Management of transboundary large carnivore populations at biologically relevant scales may be inhibited by administrative fragmentation. Yet, as our study shows, population‐level monitoring is an achievable prerequisite for a comprehensive understanding of the distribution and density of large carnivores across an increasingly anthropogenic landscape. [ABSTRACT FROM AUTHOR]

BACKGROUND: Trapping is commonly used as the primary management tool in attempts to reduce invasive wild pigs (Sus scrofa), but traditional trapping techniques are often ineffective. However, recently developed traps permit the capture of entire social groups (sounders) of wild pigs, and the strategy of whole‐sounder removal may achieve more effective control. Our objective was to experimentally compare traditional control (TC; primarily traditional trapping, but including hunting with dogs, and opportunistic shooting) and whole‐sounder removal (WSR) strategies by assessing density reduction and removal rate after 1 and 2 years of treatment. RESULTS: After 1 year of trapping, average wild pig density on WSR units declined 53% and remained stable after the second year, whereas on TC units, pig density did not differ after trapping, although it declined 33% and remained stable after the second year of trapping. The median removal rate (percentage of uniquely marked pigs present at the beginning of each year that were removed) was 42.5% for WSR units and 0.0% for TC units during 2018 and were 29.6% from WSR units and 5.3% from TC units during 2019. CONCLUSIONS: WSR removal was more effective at reducing wild pig density than TC, but factors such as previous exposure of this population to traditional traps and the lack of barriers to recolonization from surrounding areas may have reduced WSR efficacy. WSR can effectively reduce wild pig density to a greater extent than TC, but managers should recognize the additional time and expense necessary for implementation. Published 2023. This article is a U.S. Government work and is in the public domain in the USA. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Knowledge of large carnivore population abundance is essential for wildlife management and conservation, but these data are often difficult to obtain in inherently low‐density species. In particular, the snow leopard, Panthera uncia, an enigmatic cat occupying remote mountains in Central Asia, has received insufficient assessments of its population abundance because of logistical and methodological challenges. Here, we aimed to develop a robust density estimation of snow leopards based on 81 days of camera trapping within a contiguous and previously unsurveyed 1950 km2 area of habitat on the Tibetan Plateau (Mayue Township, Shenzha County, Tibet Autonomous Region, China). By applying spatially explicit capture–recapture models, we produced an estimate of 1.40 (95%CI: 1.06–1.84) individuals per 100 km2. Results also suggested sex‐specific variation in the range of movement around activity centers, with male (N = 10, σ = 4.02) movement considerably greater than female (N = 8, σ = 1.84) movement. The findings can serve as a reliable baseline reference for assessing the population trends of this endangered felid species with future estimates. This study will provide context to contribute toward a better understanding of ecological factors shaping the distribution and abundance of snow leopards and the related conservation measures needed to sustain their long‐term survival on the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

Estimating population density is critical for effective species conservation, wildlife management planning, and long‐term monitoring. Obtaining accurate estimates is especially important for the wolf (Canis lupus), a widely distributed northern hemisphere apex predator whose management and conservation are highly controversial in most of its range, and whose presence usually generates high‐profile media coverage. The peculiarities of wolf social spatial organization and behavior can violate the assumptions of capture–recapture models (uniformity and independence, respectively) to a greater or lesser extent and make it difficult to obtain precise and reliable density estimates. This paper presents a case study, which estimated the population density of the Iberian wolf in the Dorsal Gallega mountain ridge (Galicia, NW Spain) based on the identification of individual wolves from their traits and behavior using video camera traps and spatially explicit capture–recapture (SCR) analyses. The study followed three phases. Firstly, field data were collected by installing camera traps and changing their location until the entire area was sampled. Second, a complete morphological and behavioral study of the wolves recorded was performed to facilitate individual recognition. Third, overdispersion due to gregariousness and other sources of heterogeneity was modeled in the SCR analyses comparing Poisson and negative binomial observation models with different random effects on the baseline detection probability. We estimated a density of 2.88 (SD: 0.37) wolves/100 km2 in the study area. We concluded that estimating wolf population size using camera trap videos, individual identification, and SCR provides a feasible method and can be used for estimating the density in similar species. [ABSTRACT FROM AUTHOR]

The ongoing recovery of terrestrial large carnivores in North America and Europe is accompanied by intense controversy. On the one hand, reestablishment of large carnivores entails a recovery of their most important ecological role, predation. On the other hand, societies are struggling to relearn how to live with apex predators that kill livestock, compete for game species, and occasionally injure or kill people. Those responsible for managing these species and mitigating conflict often lack fundamental information due to a long-standing challenge in ecology: How do we draw robust population-level inferences for elusive animals spread over immense areas? Here we showcase the application of an effective tool for spatially explicit tracking and forecasting of wildlife population dynamics at scales that are relevant to management and conservation. We analyzed the worlds largest dataset on carnivores comprising more than 35,000 noninvasively obtained DNA samples from over 6,000 individual brown bears (Ursus arctos), gray wolves (Canis lupus), and wolverines (Gulo gulo). Our analyses took into account that not all individuals are detected and, even if detected, their fates are not always known. We show unequivocal quantitative evidence of large carnivore recovery in northern Europe, juxtaposed with the finding that humans are the single-most important factor driving the dynamics of these apex predators. We present maps and forecasts of the spatiotemporal dynamics of large carnivore populations, transcending national boundaries and management regimes.

The distribution of the jaguar has decreased by approximately 50% with its conservation highly dependent upon its persistence and mobility in anthropogenic landscapes. Consequently, understanding the effects of land use on jaguar populations and their connectivity is a necessary precursor for effective conservation of the species. We simultaneously estimated jaguar density and landscape connectivity in the ranching landscape of the Dry Chaco of western Paraguay, a deforestation hotspot, as a function of proportional forest area using spatial capture-recapture modeling. Using camera trap sampling at four sites along a deforestation gradient of 17%–51% area deforested, we estimated densities of 0.44–1.6 individuals/100 km2, whereby densities and connectivity from the more deforested sites were significantly lower than those from the less deforested sites. Our results warrant concern for the long-term viability of jaguar in the Paraguayan Dry Chaco, highlighting the need for the effective implementation of existing national laws and management plans for the conservation of the jaguar and its habitat. Furthermore, we showed the importance of accounting for landscape heterogeneity typical of anthropogenic landscapes in the conservation of the jaguar, suggesting results from protected areas may be generating unrepresentative inferences for jaguars in general, while indicating the need to place a greater research emphasis on anthropogenic landscapes to meet range-wide conservation goals for the jaguar.

1. The mesopredator release theory predicts that the density of subordinate predators will increase as dominant predators decline. Persistent debate around mesopredator release in part reflects the lack of robust, replicated experiments that test this theory, and the use of population indices that confound changes in mesopredator density and detectability. This uncertainty has immediate impacts for conservationists who are faced with managing sympatric invasive predators. 2. We used replicated experimental designs and spatially explicit models to examine whether mesopredator release of the feral cat Felis catus occurs in response to targeted control of the introduced red fox Vulpes vulpes. We surveyed three Control‐Impact paired landscapes in a region with long‐term fox control (1080 poison baiting) and conducted a Before‐After Control‐Impact Paired‐Series experiment in another region. We used fox occurrence as a simple metric of fox populations and estimated feral cat density with spatial mark–resight models. 3. Lethal fox control had varying effects on fox occurrence, consistent with variation in the duration and intensity of poison baiting. Correspondingly, responses in feral cat density ranged from negligible to a 3.7‐fold higher density in fox‐baited landscapes. At a fine spatial scale (200 m2), feral cat density was negatively associated with fox occurrence probability across both regions. These results were consistent with mesopredator release, although uncertainty was high in the region where fox control had only recently commenced. 4. Feral cat detectability also varied across the (artificially manipulated) gradients of fox occurrence probability. In one region, nonlinear models indicated that feral cats had lower detection and increased movement rates when foxes were uncommon, giving way to density suppression at high fox occurrence probabilities. 5. Synthesis and applications. Our study provides replicated, experimental evidence that dominant predator suppression can be associated with a higher mesopredator density. Mesopredator release can manifest as changes in both behaviour and density, distorting inference if these processes are not distinguished. Our results may help explain why fox control does not consistently improve native prey persistence, suggesting integrated pest management may be necessary to improve conservation outcomes. [ABSTRACT FROM AUTHOR]

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

Population parameters provide essential information for conservation efforts aimed at target species. We used the spatially explicit capture-recapture method to estimate the jaguar density and population size in the Gurupi Jaguar Conservation Unit (JCU), located in the most threatened ecoregion of the Amazon. The estimated density of 2.62 individuals/100 km2 in a continuous forest of over 10,000 km2 implies a small effective population size, underscoring the threat to the long-term viability of the Gurupi JCU's jaguar population. We recommend urgent forest restoration actions to reduce fragmentation and improve connectivity between Gurupi JCU and other forest fragments to facilitate jaguar gene flow. [ABSTRACT FROM AUTHOR]

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

In the first 2 decades of the twenty‐first century, American black bear (Ursus americanus) populations rebounded with range expansions into areas where the species was previously extirpated. While there are a number of factors that limit range expansion, habitat quality and availability are among the most important. Such factors may be particularly important in western Nevada, USA, at the transition zone of the Sierra Nevada and the Great Basin Desert. We deployed a multi‐faceted data collection system including motion‐sensitive cameras, noninvasive hair sampling and genotyping, and global positioning system (GPS) tracking. We analyzed data using spatial capture‐recapture to estimate population density and dynamic occupancy models to estimate habitat use. Black bear habitat use and density were substantially higher in the Sierra Nevada than the Great Basin Desert and had strong positive relationships with the presence of conifer land cover in the transition zone. The average black bear density was >4 times higher in the mixed‐conifer forests of the Sierra Nevada (12.4 bears/100 km2) than in desert mountain ranges with piñon (Pinus monophylla)‐juniper (Juniperus spp.) woodland (2.7 bears/100 km2). The low‐elevation shrub and grassland portions of the study area had even lower estimated black bear density (0.6 bears/100 km2) and probability of use (0.03, 95% CI = 0.00–0.09). Across these spatially variable configurations in black bear density, we estimated the population size to be 418 individuals (95% CI = 239–740). Declining density towards the range edge, coupled with a relatively stable range of black bears in Nevada observed since 2000, suggests that further species range expansion into the western Great Basin may be limited by habitat quality and availability. [ABSTRACT FROM AUTHOR]

Over the last decades, large‐scale ecological projects have emerged that require collecting ecological data over broad spatial and temporal coverage. Yet, obtaining relevant information about large‐scale population dynamics from a single monitoring programme is challenging, and often several sources of data, possibly heterogeneous, need to be integrated. In this context, integrated models combine multiple data types into a single analysis to quantify the population dynamics of a targeted population. When working at large geographical scales, integrated spatial models have the potential to produce spatialized ecological estimates that would be difficult to obtain if data were analysed separately. In this study, we illustrate how spatial integrated modelling offers a relevant framework for conducting ecological inference at large scales. Focusing on the Mediterranean bottlenose dolphins (Tursiops truncatus), we combined 21,464 km of photo‐identification boat surveys collecting spatial capture–recapture data with 24,624 km of aerial line transect following a distance sampling protocol. We analysed spatial capture–recapture data together with distance sampling data to estimate the abundance and density of bottlenose dolphins. We compared the performances of the distance sampling model and the spatial capture–recapture model fitted independently to our integrated spatial model. The outputs of our spatial integrated models inform bottlenose dolphin ecological status in the French Mediterranean Sea and provide ecological indicators that are required for regional‐scale ecological assessments like the EU Marine Strategy Framework Directive. We argue that integrated spatial models are widely applicable and relevant to conservation research and biodiversity assessment at large spatial scales. [ABSTRACT FROM AUTHOR]

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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. [ABSTRACT FROM AUTHOR]

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

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

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

Context: Leiopelma archeyi is a threatened New Zealand amphibian species translocated for conservation purposes. A disease outbreak triggered the translocation of 70 frogs to Pureora Forest in 2006 to establish a new wild population of L. archeyi. Ten years after, 60 more frogs were translocated to this site to enhance the genetic and demographic profile of L. archeyi in Pureora Forest. Here, we analysed 14 years of capture–recapture monitoring data collected for this translocated population. Aims: Our aim was to estimate population demographic parameters that allow us to assess the demographic performance of this translocated population. Methods: We used spatially explicit capture–recapture (SECR; also called spatial capture–recapture) multi strata/session models to estimate population density and derive its rate of change over time. Key results: Here we show that the density of translocated Leiopelma archeyi in Pureora (central North Island, New Zealand) remains stable for most of the study period. After the release of 70 frogs in 2006, density varied from 0.02 frogs/m2 in April 2007 to 0.06 frogs/m2 in December 2014. After the second release of 60 frogs in 2016, density in Pureora of L. archeyi varied from 0.21 frogs/m2 in November 2016 to 0.63 frogs/m2 in November 2018. Conclusions: The study species is a long-lived k-selected species, therefore long-term monitoring (>20 years) is required to corroborate demographic indicators. Nevertheless, as the current density estimates are higher than the density estimated for this population after each release (April 2007 and November 2016), we suggest progress towards the establishment of a new wild population of L. archeyi in Pureora Forest. Implications: Translocations are a useful conservation tool for many threatened species and post-release monitoring data are the main source of information needed to empirically prove their success. Population demographics are important to assess the establishment of translocated animals. We used capture–recapture monitoring data to estimate population density of a long-lived New Zealand anuran translocated for conservation purposes. Based on our results we concluded that the demographic performance observed on the translocated population is like that observed on other relict populations of the species. However, we recommend continued monitoring for another decade at least due to the longevity of the target species. Photograph by Javiera Cisternas. [ABSTRACT FROM AUTHOR]

Spatially explicit capture–recapture (SECR) models treat detection probability as a function of the distance between each animal and its notional activity centre. Open-population variants of these models (open SECR) are increasingly used to estimate the vital rates (survival and recruitment) of spatial populations subject to turnover between sampling times. If activity centres also move between sampling times then modelling the movement can reduce bias in estimates of vital rates. The usual movement model in open SECR is a random walk with step length governed by a probability kernel. Space is discretized in open SECR for computational convenience, and in some implementations this includes truncation of the probability kernel. Computations for the movement submodel are nevertheless very time-consuming owing to the repeated convolution steps and the need to manage boundary effects. A novel 'sparse' discretized kernel is proposed that greatly reduces fitting time. The sparse kernel was tested by simulation and applied to two datasets. Differences between models fitted using the sparse and full kernels were minor and unlikely to matter in practice. The sparse kernel extends the practical limits of the movement modelling in open SECR to greater dispersal distances and greater spatial resolution. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]

Capture‐recapture (CR) data and corresponding models have been used extensively to estimate the size of wildlife populations when detection probability is less than 1. When the locations of traps or cameras used to capture or detect individuals are known, spatially‐explicit CR models are used to infer the spatial pattern of the individual locations and population density. Individual locations, referred to as activity centers (ACs), are defined as the locations around which the individuals move. These ACs are typically assumed to be independent, and their spatial pattern is modeled using homogeneous Poisson processes. However, this assumption is often unrealistic, since individuals can interact with each other, either within a species or between different species. In this article, we consider a vector of point processes from the general class of interaction point processes and develop a model for CR data that can account for interactions, in particular repulsions, between and within multiple species. Interaction point processes present a challenge from an inferential perspective because of the intractability of the normalizing constant of the likelihood function, and hence standard Markov chain Monte Carlo procedures to perform Bayesian inference cannot be applied. Therefore, we adopt an inference procedure based on the Monte Carlo Metropolis Hastings algorithm, which scales well when modeling more than one species. Finally, we adopt an inference method for jointly sampling the latent ACs and the population size based on birth and death processes. This approach also allows us to adaptively tune the proposal distribution of new points, which leads to better mixing especially in the case of non‐uniformly distributed traps. We apply the model to a CR data‐set on leopards and tigers collected at the Corbett Tiger Reserve in India. Our findings suggest that between species repulsion is stronger than within species, while tiger population density is higher than leopard population density at the park. [ABSTRACT FROM AUTHOR]