Spatially Explicit Capture–recapture Models Research Articles

Where the small things are: Modelling edge effects on mouse lemur population density and distribution in northwestern Madagascar.

Edge effects result from the penetration to varying depths and intensities, of abiotic and biotic conditions from the surrounding non-forest matrix into the forest interior. Although 70% of the world's forests are within 1 km of a forest edge, making edge effects a dominant feature of most forest habitats, there are few empirical data on inter-site differences in edge responses in primates. We used spatially explicit capture-recapture (SECR) models to determine spatial patterns of density for two species of mouse lemurs (Microcebus murinus and Microcebus ravelobensis) in two forest landscapes in northwestern Madagascar. The goal of our study was to determine if mouse lemurs displayed spatially variable responses to edge effects. We trapped animals using Sherman live traps in the Mariarano Classified Forest (MCF) and in the Ambanjabe Forest Fragment Site (AFFS) site within Ankarafantsika National Park. We trapped 126 M. murinus and 79 M. ravelobensis at MCF and 78 M. murinus and 308 M. ravelobensis at AFFS. For M. murinus, our top model predicted a positive edge response, where density increased towards edge habitats. In M. ravelobensis, our top model predicted a negative edge response, where density was lower near the forest edges and increased towards the forest interior. At regional and landscape-specific scales, SECR models estimated different density patterns between M. murinus and M. ravelobensis as a result of variation in edge distance. The spatial variability of our results using SECR models indicate the importance of studying the population ecology of primates at varying scales that are appropriate to the processes of interest. Our results lend further support to the theory that some lemurs exhibit a form of ecological flexibility in their responses to forest loss, forest fragmentation, and associated edge effects.

Accounting for heterogeneous density and detectability in spatially explicit capture–recapture studies of carnivores

AbstractReliable estimates of population density are fundamental for managing and conserving wildlife. Spatially explicit capture–recapture (SECR) models in combination with information‐theoretic model selection criteria are frequently used to estimate population density. Variation in density and detectability is inevitable and, when unmodeled, can lead to erroneous estimates. Despite this knowledge, the performance of SECR models and information‐theoretic criteria remain relatively untested for populations with realistic levels of variation in density and detectability. We addressed this issue using simulations of American black bear (Ursus americanus) populations with variable density and detectability between sexes and across study areas. We first assessed the reliability of Akaike information criterion adjusted for small sample sizes (AICc) to correctly identify the true data generating model or a good approximating model. We then assessed the bias, accuracy, and precision of density estimates when such a model was selected or not. We demonstrated that unmodeled heterogeneity in detection and, more importantly, density can lead to pronounced bias. However, when a good approximating model is included in the candidate set, models with lower AICc included important forms of variation and yielded accurate estimates. We encourage researchers and practitioners to consider the impact of unmodeled variation in SECR models when making inferences and to strive to include covariates likely to be the most influential based on the species biology and ecology in candidate model sets. Doing so can improve the robustness of wildlife density estimation methods that can be leveraged to make more sound conservation and management decisions.

Integrating multiple datasets into spatially-explicit capture-recapture models to estimate the abundance of a locally scarce felid

The conservation of animal populations often requires the estimation of population size. Low density and secretive behaviour usually determine scarce data sources and hampers precise abundance estimations of carnivore populations. However, joint analysis of independent scarce data sources in a common modeling framework allows unbiased and precise estimates of population parameters. We aimed to estimate the density of the European wildcat (Felis silvestris) in a protected area of Spain, by combining independent datasets in a spatially-explicit capture-recapture (SCR) framework. Data from live-capture with individual identification, camera-trapping without individual identification and radio-tracking concurrently obtained were integrated in a joint SCR and count data model. Ten live captures of five wildcats were obtained with an effort of 2034 trap-days, whereas seven wildcat independent events were recorded in camera traps with 3628 camera-days. Two wildcats were radio-tagged and telemetry information on their movements was obtained. The integration of the different data sources improved the precision obtained by the standard SCR model. The mean (± SD) density estimated with the integrated model (0.038 ± 0.017 wildcats/km2, 95% highest posterior density 0.013–0.082) is among the lowest values ever reported for this species, despite corresponding to a highly protected area. Among the likely causes of such low density, low prey availability could have triggered an extinction vortex process. We postulate that the estimated low density could represent a common situation of wildcat populations in the southern Iberia, highlighting the need for further studies and urgent conservation actions in the furthermost southwestern range of this species in Europe.

Variation in feral cat density between two large adjacent islands in Australia’s monsoon tropics

Despite contributing to the ongoing collapse of native mammal populations across northern Australian savannas, we have limited understanding of the ecological constraints of feral cat population density in this system. Addressing such knowledge gaps is a crucial step towards mitigating the impacts of feral cats, and is particularly important for the large islands off northern Australia that remain as strongholds for numerous species vulnerable to cat predation. Here, we investigated cat density across Melville and Bathurst Island, two large islands in Australia’s monsoon tropics. We deployed large grids (~13 km2) of 70 camera-traps at four locations to investigate how feral cat density varies under different combinations of fire frequency, and feral herbivore presence. Using spatially-explicit capture-recapture models, we estimated feral cat density on Melville Island to be 0.15 cats km−2. We did not record any cat detections on Bathurst Island. Using simulations, we predicted that if cat density on Bathurst Island was equal to that on Melville Island, we would have expected to record 27.9 detections of 9.9 individual cats. Our results, coupled with other recent surveys, suggest that the density of cats is much lower on Bathurst Island than the adjacent Melville Island. The absence of feral herbivores on Bathurst Island may have contributed to this variation in cat density. Management that enhances understorey vegetation density, through feral herbivore control, as well as fire management, could help mitigate the impact of feral cats on northern Australian savanna biodiversity.

Combining live and lethal trapping to inform the management of alien invasive rodent populations in a tropical montane forest

On large inhabited islands where complete eradication of alien invasive rodents through the use of poison delivery is often not practical or acceptable, mechanical trapping may represent the only viable option to reduce their impact in areas of high biodiversity value. However, the feasibility of sustained rodent control by trapping remains uncertain under realistic operational constraints. This study aimed to assess the effectiveness of non-toxic rat control strategies through a combination of lethal and live-trapping experiments, and scenario modelling, using the example of a remote montane rainforest of New Caledonia. Rat densities, estimated with spatially-explicit capture-recapture models, fluctuated seasonally (9.5–33.6 ind.ha-1). Capture probability (.01–.25) and home range sizes (HR95, .23–.75 ha) varied greatly according to trapping session, age class, sex and species. Controlling rats through the use of lethal trapping allowed maintaining rat densities at ca. 8 ind.ha-1 over a seven-month period in a 5.5-ha montane forest. Simulation models based on field parameter estimates over a 200-ha pilot management area indicated that without any financial and social constraints, trapping grids with the finest mesh sizes achieved cumulative capture probabilities > .90 after 15 trapping days, but were difficult to implement and sustain with the local workforce. We evaluated the costs and effectiveness of alternative trapping strategies taking into account the prevailing set of local constraints, and identified those that were likely to be successful. Scenario modelling, informed by trapping experiments, is a flexible tool for informing the design of sustainable control programs of island-invasive rodent populations, under idiosyncratic local circumstances.

Spatially explicit capture recapture density estimates: Robustness, accuracy and precision in a long-term study of jaguars (Panthera onca).

Camera trapping is the standard field method of monitoring cryptic, low-density mammal populations. Typically, researchers run camera surveys for 60 to 90 days and estimate density using closed population spatially explicit capture-recapture (SCR) models. The SCR models estimate density, capture probability (g0), and a scale parameter (σ) that reflects ranging behaviour. We used a year of camera data from 20 camera stations to estimate the density of male jaguars (Panthera onca) in the Cockscomb Basin Wildlife Sanctuary in Belize, using closed population SCR models. We subsampled the dataset into 276 90-day sessions and 186 180-day sessions. Estimated density fluctuated from 0.51 to 5.30 male jaguars / 100 km2 between the 90-day sessions, with comparatively robust and precise estimates for the 180-day sessions (0.73 to 3.75 male jaguars / 100 km2). We explain the variation in density estimates from the 90-day sessions in terms of temporal variation in social behaviour, specifically male competition and mating events during the three-month wet season. Density estimates from the 90-day sessions varied with σ, but not with the number of individuals detected, suggesting that variation in density was almost fully attributable to changes in estimated ranging behaviour. We found that the models overestimated σ when compared to the mean ranging distance derived from GPS tracking data from two collared individuals in the camera grid. Overestimation of σ when compared to GPS collar data was more pronounced for the 180-day sessions than the 90-day sessions. We conclude that one-off ('snap-shot') short-term, small-scale camera trap surveys do not sufficiently sample wide-ranging large carnivores. When using SCR models to estimate the density from these data, we caution against the use of poor sampling designs and/or misinterpretation of scope of inference. Although the density estimates from one-off, short-term, small-scale camera trap surveys may be statistically accurate within each short-term sampling period, the variation between density estimates from multiple sessions throughout the year illustrate that the estimates obtained should be carefully interpreted and extrapolated, because different factors, such as temporal stochasticity in behaviour of a few individuals, may have strong repercussions on density estimates. Because of temporal variation in behaviour, reliable density estimates will require larger samples of individuals and spatial recaptures than those presented in this study (mean +/- SD = 14.2 +/- 1.2 individuals, 37.7 +/- 4.7 spatial recaptures, N = 276 sessions), which are representative of, or higher than published sample sizes. To satisfy the need for larger samples, camera surveys will need to be more expansive with a higher density of stations. In the absence of this, we advocate longer sampling periods and subsampling through time as a means of understanding and describing stability or variation between density estimates.

Topographic complexity potentially mediates cat predation risk for a critically endangered rodent

ContextThe niche reduction hypothesis (NRH) predicts that the realised niche of declining species is reduced by threats that are mediated by environmental, biotic and evolutionary processes, explaining why species decline in some locations but not others. The critically endangered central rock-rat (CRR) survives only in rugged mountain range habitat in central Australia and is highly vulnerable to cat predation. We predicted that cat density and ranging behaviour, and, hence, predation risk, is mediated by habitat complexity, thus explaining the mechanism maintaining the CRR refuge. AimsWe sought to determine whether cat densities were lower in the rugged CRR refuge than in an adjacent valley dominated by less complex rocky habitats and no longer occupied by CRRs. MethodsWe installed arrays of camera traps along two parallel mountain ranges in the refuge and in the intervening valley habitats. We identified uniquely patterned individual cats and compared spatially explicit capture–recapture (SECR) models to evaluate our hypothesis that cat density varies with topographic complexity. Key resultsThe dominant effect in all models was the significant negative relationship between cat detection probability and fine-scale topographic ruggedness. Two of the best three SECR models indicated lower cat densities and relative home-range sizes in the refuge than in the valley. In total, 17% of cats were detected in both habitat types. ConclusionsWe found some evidence that cat density and home-range size were mediated by habitat complexity. Further, the negative relationship between cat detection probability and topographic complexity suggests that cats spend less time foraging in CRR refuge habitat. ImplicationsCat management programs, aimed at reducing predation pressure on the CRR, must include the refuge and surrounding habitats to control cats that pose a threat to CRR subpopulations.

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

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

Combining genetic non-invasive sampling with spatially explicit capture-recapture models for density estimation of a patchily distributed small mammal

Estimating the size of animal populations is essential for understanding the demography and conservation status of species. Genetic Non-Invasive Sampling (gNIS) combined with Spatially Explicit Capture-Recapture (SECR) modelling may provide a practical tool to obtain such estimates. Here, we evaluate for the first time the potential and limitations of this approach to estimate population densities for small mammals inhabiting patchily distributed habitats, focusing on the endemic Iberian Cabrera vole (Microtus cabrerae). Using 11 highly polymorphic microsatellites and two sex-linked introns, we compared population estimates in November/December 2011 based on live-trapping and gNIS and assessed the impact of distinct consensus criteria to differentiate unique genotypes. Live-trapping over 21 days captured 31 individuals, while gNIS over 5 days recorded 65–69 individuals. SECR models indicated that individual detectability was positively affected by live-trapping capture success on the previous occasion, while for gNIS, it was mainly affected by genotyping success rates and patch size. Live-trapping produced the lowest density estimates (mean ± SE) of 16.6 ± 3.2 individuals per hectare of suitable habitat (ind/ha). Estimates based on gNIS were higher and varied slightly between 25.2 ± 4.0 and 28.8 ± 4.5 ind/ha depending on assuming one or two genotyping errors, respectively, when differentiating individual genetic profiles. Results suggest that live-trapping underestimated the vole population, while the larger number of individuals detected through gNIS allowed better estimates with lower field effort. Overall, we suggest that gNIS combined with SECR models provides an effective tool to estimate small mammal population densities in fragmented habitats.

Noninvasive genetic spatial capture‐recapture for estimating deer population abundance

ABSTRACTInformed management of wildlife populations requires the accurate estimation of abundance, sex ratio, and other population parameters. For deer (Odocoileus spp.), the use of closed‐population, capture‐recapture (CR) methods, in conjunction with noninvasive DNA sampling, has become increasingly practical, but, up to now, these methods have been used in a non‐spatial modeling framework, which has limited their utility for population‐level inferences. In particular, extrapolation of plot‐level CR abundance estimates to the population required the use of multipliers of unknown reliability and potential bias. Spatially explicit capture‐recapture (SCR) models provide an integrated framework for directly estimating density as a function of spatial and habitat variables at landscape scales. We used fecal DNA samples in conjunction with SCR to estimate density, sex ratio, and habitat correlates to density for a mule deer (O. hemionus) population across a large (∼500 km2) area in the central Sierra Nevada Range, California, USA during 2013 and 2014. We surveyed 24 random transects within 4 30‐km2 sites representative of the study area. Based on 411 samples genotyped at a sex marker and 8–10 microsatellite loci, the sex‐ratio for the study area was 62 (95% CI = 41–93) males/100 females in 2013 and 65 (95% CI = 45–94) males/100 females in 2014. Using SCR, we estimated density at 5.0 (95% CI = 2.3–7.8) deer/km2 in 2013 and 5.1 (95% CI = 3.1–7.2) deer/km2 in 2014. In comparison, non‐spatial CR analysis produced density estimates on average 60% higher, likely reflecting bias resulting from use of the commonly employed mean maximum recapture distance (MMRD) to estimate effective sampling area. The SCR models indicated that density was effectively homogeneous throughout the study area, with no strong relationship to habitat correlates. Altogether, these results demonstrate the utility of noninvasive fecal DNA methods in a SCR framework for estimation of abundance and density in deer populations at landscape scales. © 2017 The Wildlife Society

Characterizing recolonization by a reintroduced bear population using genetic spatial capture-recapture

Many large carnivores are recolonizing range as a result of improved management and conservation policy, habitat restoration, and reintroduction programs. American black bears (Ursus americanus) are projected to recolonize portions of the United States, but few studies have characterized or provided practical methods for monitoring this process. We used noninvasive hair sampling at 4 proximal study areas along the Kentucky–Virginia, USA, border during 2012–2013 to estimate demographics and population genetics, and investigate recolonization patterns of an American black bear population that was founded by 55 bears reintroduced to a fragmented mountainous landscape during the 1990s and subjected to harvest 6 years post-reintroduction. Using spatially explicit capture–recapture (SECR) models, we estimated a density of 0.26 bear/km2, or minimum abundance of 482 bears, distributed among 2 primary core areas previously identified by occupancy analysis: a southern and northern core area. The southern core area was established by a founder adult female that exhibited post-release dispersal, but moderate asymmetrical gene flow (Nm = 6 bears) from the northern core area mitigated deleterious genetic consequences typical of such founder events. Effective number of breeders (NB = 62 bears) was similar to the number of founders, suggesting that genetically, the population remains mostly the product of reintroduction. Despite limited connectivity with other populations in the region, genetic diversity (HE = 0.78) was retained because of rapid population growth during the 16 years post-reintroduction (λ = 1.14/year). This bear population exhibited demographic characteristics indicative of continued recolonization, including a significantly female-biased sex ratio (0.53M:1.00F) and female density decreasing with increasing distance from the reintroduction release areas in the northern core. Few bear detections at 2 peripheral study areas and results from SECR model detection function transformation suggested recolonization may continue to the southwest and northeast along prominent linear mountain ridges. Although the population has grown and is genetically stable, because of relatively low population density and recolonization direction, we suggest monitoring demographic vital rates to evaluate harvest sustainability and population viability. Our study demonstrates the utility of noninvasive genetic sampling in conjunction with SECR models to characterize and monitor recolonizing bear populations, which may also be useful for management of expanding populations of other large carnivores. © 2016 The Wildlife Society.

Densities and perceptions of jaguars in coastal Nayarit, Mexico

ABSTRACTConservation of large carnivores will require greater analyses of population parameters, habitat use, and distribution in multiuse landscapes as human populations increase and agriculture expands. We used Bayesian spatially explicit capture–recapture (SECR) models and completed 82 interviews in 24 localities over 4 field seasons from April 2009 to March 2013 to estimate densities and assess residents’ perceptions of jaguars (Panthera onca) in the San Blas Municipality within the southern Marismas Nacionales ecosystem in Nayarit, Mexico. Camera traps accumulated 90 detections of 9 jaguars (2 M, 5 F, and 2 individuals of unknown sex) in a 194‐km2 area during 1,575 trap‐nights from April to June 2010. We detected jaguars at 16 of the 27 camera‐trap sites and calculated a density estimate of 2.04 (SE = 0.45) individuals/100 km2 using the SECR method. This is one of the first studies to use SECR models for jaguar density estimation and 1 of only 2 studies throughout jaguar distribution to estimate population parameters of the species where human population densities reach >50 people/km2. Future studies in this unique landscape should focus on jaguar status in the northern stretches of the Marismas Nacionales and research the mechanisms that may allow this large carnivore to persist in altered landscapes among human population densities greater than previously believed to be tolerable by the species. © 2016 The Wildlife Society.

Application of Spatial and Closed Capture-Recapture Models on Known Population of the Western Derby Eland (Taurotragus derbianus derbianus) in Senegal.

Camera trapping with capture-recapture analyses has provided estimates of the abundances of elusive species over the last two decades. Closed capture-recapture models (CR) based on the recognition of individuals and incorporating natural heterogeneity in capture probabilities are considered robust tools; however, closure assumption is often questionable and the use of an Mh jackknife estimator may fail in estimations of real abundance when the heterogeneity is high and data is sparse. A novel, spatially explicit capture-recapture (SECR) approach based on the location-specific capture histories of individuals overcomes the limitations of closed models. We applied both methods on a closed population of 16 critically endangered Western Derby elands in the fenced 1,060-ha Fathala reserve, Senegal. We analyzed the data from 30 cameras operating during a 66-day sampling period deployed in two densities in grid and line arrays. We captured and identified all 16 individuals in 962 trap-days. Abundances were estimated in the programs CAPTURE (models M0, Mh and Mh Chao) and R, package secr (basic Null and Finite mixture models), and compared with the true population size. We specified 66 days as a threshold in which SECR provides an accurate estimate in all trapping designs within the 7-times divergent density from 0.004 to 0.028 camera trap/ha. Both SECR models showed uniform tendency to overestimate abundance when sampling lasted shorter with no major differences between their outputs. Unlike the closed models, SECR performed well in the line patterns, which indicates promising potential for linear sampling of properly defined habitats of non-territorial and identifiable herbivores in dense wooded savanna conditions. The CR models provided reliable estimates in the grid and we confirmed the advantage of Mh Chao estimator over Mh jackknife when data appeared sparse. We also demonstrated the pooling of trapping occasions with an increase in the capture probabilities, avoiding violation of results.

A Unifying Model for Capture–Recapture and Distance Sampling Surveys of Wildlife Populations

A fundamental problem in wildlife ecology and management is estimation of population size or density. The two dominant methods in this area are capture–recapture (CR) and distance sampling (DS), each with its own largely separate literature. We develop a class of models that synthesizes them. It accommodates a spectrum of models ranging from nonspatial CR models (with no information on animal locations) through to DS and mark-recapture distance sampling (MRDS) models, in which animal locations are observed without error. Between these lie spatially explicit capture–recapture (SECR) models that include only capture locations, and a variety of models with less location data than are typical of DS surveys but more than are normally used on SECR surveys. In addition to unifying CR and DS models, the class provides a means of improving inference from SECR models by adding supplementary location data, and a means of incorporating measurement error into DS and MRDS models. We illustrate their utility by comparing inference on acoustic surveys of gibbons and frogs using only capture locations, using estimated angles (gibbons) and combinations of received signal strength and time-of-arrival data (frogs), and on a visual MRDS survey of whales, comparing estimates with exact and estimated distances. Supplementary materials for this article are available online.

Spatially explicit capture–recapture analysis of bobcat (Lynx rufus) density: implications for mesocarnivore monitoring

Context Accurate density estimation is crucial for conservation and management of elusive species. Camera-trapping may provide an efficient method for density estimation, particularly when analysed with recently developed spatially explicit capture–recapture (SECR) models. Although camera-traps are employed extensively to estimate large carnivore density, their use for smaller carnivores has been limited. Moreover, while camera-trapping studies are typically conducted at local scales, the utility of analysing larger-scale patterns by combining multiple camera studies remains poorly known. Aims The goal of the present study was to develop a better understanding of the utility of SECR models and camera-trapping for the estimation of density of small carnivores at local and regional scales. Methods Based on data collected from camera-traps, we used SECR to examine density of bobcats (Lynx rufus) at four study sites in north-central Texas. We then combined our density estimates with previous estimates (from multiple methodologies) across the bobcat’s geographic range, and used linear regression to examine drivers of range-wide density patterns. Key results Bobcat densities averaged 13.2 per 100 km2 across all four study sites, and were lowest at the site in the most heavily modified landscape. Bobcat capture probability was positively related to forest cover around camera-trap sites. At the range-wide scale, 53% of the variation in density was explained by just two factors: temperature and longitude. Conclusions Our results demonstrate the utility of camera-traps, combined with SECR, to generate precise density estimates for mesocarnivores, and reveal the negative effects of landscape disturbance on bobcat populations. The associations revealed in our range-wide analysis, despite variability in techniques used to estimate density, demonstrate how a combination of multiple density estimates for a species can be used for large-scale inference. However, improvement in our understanding of biogeographic density patterns for mesocarnivores could be obtained from a greater number of camera-based density estimates across the range of a species, combined with meta-analytic techniques. Implications Camera-trapping and SECR should be more widely applied to generate local density estimates for many small and medium-sized carnivores, where at least a portion of the individuals are identifiable. If such estimates are more widely obtained, meta-analytic techniques could be used to test biogeographic predictions or for large-scale monitoring efforts.

LeopardPanthera pardus fuscaDensity in the Seasonally Dry, Subtropical Forest in the Bhabhar of Terai Arc, Nepal

We estimated leopard (Panthera pardus fusca) abundance and density in the Bhabhar physiographic region in Parsa Wildlife Reserve, Nepal. The camera trap grid, covering sampling area of 289 km2with 88 locations, accumulated 1,342 trap nights in 64 days in the winter season of 2008-2009 and photographed 19 individual leopards. Using models incorporating heterogeneity, we estimated 28 (±SE 6.07) and 29.58 (±SE 10.44) leopards in Programs CAPTURE and MARK. Density estimates via 1/2 MMDM methods were 5.61 (±SE 1.30) and 5.93 (±SE 2.15) leopards per 100 km2using abundance estimates from CAPTURE and MARK, respectively. Spatially explicit capture recapture (SECR) models resulted in lower density estimates, 3.78 (±SE 0.85) and 3.48 (±SE 0.83) leopards per 100 km2, in likelihood based program DENSITY and Bayesian based program SPACECAP, respectively. The 1/2 MMDM methods have been known to provide much higher density estimates than SECR modelling techniques. However, our SECR models resulted in high leopard density comparable to areas considered better habitat in Nepal indicating a potentially dense population compared to other sites. We provide the first density estimates for leopards in the Bhabhar and a baseline for long term population monitoring of leopards in Parsa Wildlife Reserve and across the Terai Arc.

A spatially explicit approach for estimating space use and density of common genets

Many species that occur at low densities are not accurately estimated using capture–recapture methods as such techniques assume that populations are well–defined in space. To solve this bias, spatially explicit capture–recapture (SECR) models have recently been developed. These models incorporate movement and can identify areas where it is more likely for individuals to concentrate their activity. In this study, we used data from camera–trap surveys of common genets (Genetta genetta) in Serra da Malcata (Portugal), designed to compare abundance estimates produced by SECR models with traditional closed–capture models. Using the SECR models, we observed spatial heterogeneity in genet distribution and density estimates were approximately two times lower than those obtained from the closed population models. The non–spatial model estimates were constrained to sampling grid size and likely underestimated movements, thereby overestimating density. Future research should consider the incorporation of cost–weighed models that can include explicit hypothesis on how environmental variables influence the distance metric.

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

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

Estimating population size by spatially explicit capture–recapture

The number of animals in a population is conventionally estimated by capture–recapture without modelling the spatial relationships between animals and detectors. Problems arise with non‐spatial estimators when individuals differ in their exposure to traps or the target population is poorly defined. Spatially explicit capture–recapture (SECR) methods devised recently to estimate population density largely avoid these problems. Some applications require estimates of population size rather than density, and population size in a defined area may be obtained as a derived parameter from SECR models. While this use of SECR has potential benefits over conventional capture–recapture, including reduced bias, it is unfamiliar to field biologists and no study has examined the precision and robustness of the estimates. We used simulation to compare the performance of SECR and conventional estimators of population size with respect to bias and confidence interval coverage for several spatial scenarios. Three possible estimators for the sampling variance of realised population size all performed well. The precision of SECR estimates was nearly the same as that of the null‐model conventional population estimator. SECR estimates of population size were nearly unbiased (relative bias 0–10%) in all scenarios, including surveys in randomly generated patchy landscapes. Confidence interval coverage was near the nominal level. We used SECR to estimate the population of a species of skinkOligosoma infrapunctatumfrom pitfall trapping. The estimated number in the area bounded by the outermost traps differed little between a homogeneous density model and models with a quadratic trend in density or a habitat effect on density, despite evidence that the latter models fitted better. Extrapolation of trend models to a larger plot may be misleading. To avoid extrapolation, a large region of interest should be sampled throughout, either with one continuous trapping grid or with clusters of traps dispersed widely according to a probability‐based and spatially representative sampling design.

Evaluating the potential biases in carnivore capture–recapture studies associated with the use of lure and varying density estimation techniques using photographic‐sampling data of the Malagasy civet

AbstractEstimating density of elusive carnivores with capture–recapture analyses is increasingly common. However, providing unbiased and precise estimates is still a challenge due to uncertainties arising from the use of (1) bait or lure to attract animals to the detection device and (2) ad hoc boundary‐strip methods to compensate for edge effects in area estimation. We used photographic‐sampling data of the Malagasy civet Fossa fossana collected with and without lure to assess the effects of lure and to compare the use of four density estimators which varied in methods of area estimation. The use of lure did not affect permanent immigration or emigration, abundance and density estimation, maximum movement distances, or temporal activity patterns of Malagasy civets, but did provide more precise population estimates by increasing the number of recaptures. The spatially‐explicit capture–recapture (SECR) model density estimates ±SE were the least precise as they incorporate spatial variation, but consistent with each other (Maximum likelihood‐SECR = 1.38 ± 0.18, Bayesian‐SECR = 1.24 ± 0.17 civets/km2), whereas estimates relying on boundary‐strip methods to estimate effective trapping area did not incorporate spatial variation, varied greatly and were generally larger than SECR model estimates. Estimating carnivore density with ad hoc boundary‐strip methods can lead to overestimation and/or increased uncertainty as they do not incorporate spatial variation. This may lead to inaction or poor management decisions which may jeopardize at‐risk populations. In contrast, SECR models free researchers from making subjective decisions associated with boundary‐strip methods and they estimate density directly, providing more comparable and valuable population estimates.