Most capture-recapture studies are inherently spatial in nature, with capture probabilities depending on the location of traps relative to animals. The spatial component of the studies has until recently, however, not been incorporated in statistical capture-recapture models. This paper reviews capture-recapture models that do include an explicit spatial component. This is done in a non-technical way, omitting much of the algebraic detail and focussing on the model formulation rather than on the estimation methods (which include inverse prediction, maximum likelihood and Bayesian methods). One can view spatially explicit capture-recapture (SECR) models as an endpoint of a series of spatial sampling models, starting with circular plot survey models and moving through conventional distance sampling models, with and without measurement errors, through mark-recapture distance sampling (MRDS) models. This paper attempts a synthesis of these models in what I hope is a style accessible to non-specialists, placing SECR models in the context of other spatial sampling models.

Live-trapping capture-recapture studies of animal populations with fixed trap locations inevitably have a spatial component: animals close to traps are more likely to be caught than those far away. This is not addressed in conventional closed-population estimates of abundance and without the spatial component, rigorous estimates of density cannot be obtained. We propose new, flexible capture-recapture models that use the capture locations to estimate animal locations and spatially referenced capture probability. The models are likelihood-based and hence allow use of Akaike's information criterion or other likelihood-based methods of model selection. Density is an explicit parameter, and the evaluation of its dependence on spatial or temporal covariates is therefore straightforward. Additional (nonspatial) variation in capture probability may be modeled as in conventional capture-recapture. The method is tested by simulation, using a model in which capture probability depends only on location relative to traps. Point estimators are found to be unbiased and standard error estimators almost unbiased. The method is used to estimate the density of Red-eyed Vireos (Vireo olivaceus) from mist-netting data from the Patuxent Research Refuge, Maryland, U.S.A. Estimates agree well with those from an existing spatially explicit method based on inverse prediction. A variety of additional spatially explicit models are fitted; these include models with temporal stratification, behavioral response, and heterogeneous animal home ranges.

Until recently, large apex consumers were ubiquitous across the globe and had been for millions of years. The loss of these animals may be humankind's most pervasive influence on nature. Although such losses are widely viewed as an ethical and aesthetic problem, recent research reveals extensive cascading effects of their disappearance in marine, terrestrial, and freshwater ecosystems worldwide. This empirical work supports long-standing theory about the role of top-down forcing in ecosystems but also highlights the unanticipated impacts of trophic cascades on processes as diverse as the dynamics of disease, wildfire, carbon sequestration, invasive species, and biogeochemical cycles. These findings emphasize the urgent need for interdisciplinary research to forecast the effects of trophic downgrading on process, function, and resilience in global ecosystems.

Reliable assessments of animal abundance are key for successful conservation of endangered species. For elusive animals with individually-unique markings, camera-trap surveys are a benchmark standard for estimating local and global population abundance. Central to the reliability of resulting abundance estimates is the assumption that individuals are accurately identified from photographic captures. To quantify the risk of individual misidentification and its impact on population abundance estimates we performed an experiment under controlled conditions in which 16 captive snow leopards (Panthera uncia) were camera-trapped on 40 occasions and eight observers independently identified individuals and recaptures. Observers misclassified 12.5% of all capture occasions, resulting in systematically inflated population abundance estimates on average by one third (mean ± SD = 35 ± 21%). Our results show that identifying individually-unique individuals from camera-trap photos may not be as reliable as previously believed, implying that elusive and endangered species could be less abundant than current estimates indicate.

We develop a class of models for inference about abundance or density using spatial capture-recapture data from studies based on camera trapping and related methods. The model is a hierarchical model composed of two components: a point process model describing the distribution of individuals in space (or their home range centers) and a model describing the observation of individuals in traps. We suppose that trap- and individual-specific capture probabilities are a function of distance between individual home range centers and trap locations. We show that the models can be regarded as generalized linear mixed models, where the individual home range centers are random effects. We adopt a Bayesian framework for inference under these models using a formulation based on data augmentation. We apply the models to camera trapping data on tigers from the Nagarahole Reserve, India, collected over 48 nights in 2006. For this study, 120 camera locations were used, but cameras were only operational at 30 locations during any given sample occasion. Movement of traps is common in many camera-trapping studies and represents an important feature of the observation model that we address explicitly in our application.

The fear large carnivores inspire, independent of their direct killing of prey, may itself cause cascading effects down food webs potentially critical for conserving ecosystem function, particularly by affecting large herbivores and mesocarnivores. However, the evidence of this has been repeatedly challenged because it remains experimentally untested. Here we show that experimentally manipulating fear itself in free-living mesocarnivore (raccoon) populations using month-long playbacks of large carnivore vocalizations caused just such cascading effects, reducing mesocarnivore foraging to the benefit of the mesocarnivore's prey, which in turn affected a competitor and prey of the mesocarnivore's prey. We further report that by experimentally restoring the fear of large carnivores in our study system, where most large carnivores have been extirpated, we succeeded in reversing this mesocarnivore's impacts. We suggest that our results reinforce the need to conserve large carnivores given the significant "ecosystem service'' the fear of them provides.

Accurate assessments of the status of threatened species and their conservation planning require reliable estimation of their global populations and robust monitoring of local population trends. We assessed the adequacy and suitability of studies in reliably estimating the global snow leopard (Panthera uncia) population. We compiled a dataset of all the peer-reviewed published literature on snow leopard population estimation. Metadata analysis showed estimates of snow leopard density to be a negative exponential function of area, suggesting that study areas have generally been too small for accurate density estimation, and sampling has often been biased towards the best habitats. Published studies are restricted to six of the 12 range countries, covering only 0.3-0.9% of the presumed global range of the species. Re-sampling of camera trap data from a relatively large study site (c.1684 km(2)) showed that small-sized study areas together with a bias towards good quality habitats in existing studies may have overestimated densities by up to five times. We conclude that current information is biased and inadequate for generating a reliable global population estimate of snow leopards. To develop a rigorous and useful baseline and to avoid pitfalls, there is an urgent need for (a) refinement of sampling and analytical protocols for population estimation of snow leopards (b) agreement and coordinated use of standardized sampling protocols amongst researchers and governments across the range, and (c) sampling larger and under-represented areas of the snow leopard's global range.

Between November 2013 and March 2016, twenty Ltl-6210MC infrared-triggered camera-traps were installed in Wolong National Nature Reserve, Sichuan, China, to monitor snow leopards(Panthera uncia) and other wildlife. A total of 7,056 camera-days of data were collected, including approximately 120,000 photos and video clips. The effective number of snow leopard samples collected by the infrared cameras was 43, and the calculated relative abundance index (RAI) for this species was 6.09. During the 28 months of continuous data collection using infrared cameras, records of snow leopards, including leopard cubs, demonstrated that Wolong National Nature Reserve is a relatively hospitable environment for this species. The analytical results indicate that the annual activity peaked in January, and the daily activity peaked between the hours of 18:00-20:00, and daily activity patterns showed seasonal variations. When considering environmental factors, the preferred habitat is the alpine scree where snow leopards appeared most in the ridge areas and the range of preferred reference temperatures for snow leopard activity was found between -10℃ and -3℃. In addition, the lunar-phase relative abundance index was originally created to assess the appearance of wildlife under different night lightness levels. It was found that the snow leopard is fairly active at night, and the lunar-phase has a significant impact on its level of nocturnal activity (P < 0.01). We provided primary sources for further protection and research of snow leopards and the other wildlife. This study could be utilized to comprehensively learn the ecological characteristics and assess snow leopard habitat.

2013年11月至2016年3月, 在四川卧龙国家级自然保护区内布设20台Ltl-6210MC红外相机, 对雪豹(Panthera uncia)及其同域野生动物进行研究。相机工作日7,056个, 拍摄记录约12万条。雪豹的有效探测43次, 相对多度指数为6.09。28个月中红外相机持续记录到雪豹, 并且记录中还有幼雪豹, 说明卧龙国家级自然保护区雪豹的生存状况较好。数据分析表明, 雪豹年活动高峰是1月, 日活动高峰为18:00-20:00, 日活动节律存在季节差异。关于环境因子, 雪豹偏好生境为高山流石滩, 尤其常见于高山流石滩的山脊, 其活动最适宜的参考温度范围为-10℃到-3℃。此外, 本文提出了月相活动强度指数, 用于评价夜间不同光照强度下野生动物的活动情况。结果显示, 雪豹夜行能力较强, 月相对其夜间活动有显著影响(P &#x0003C; 0.01)。本研究为进一步开展雪豹及其同域野生动物的保护和研究提供了第一手资料, 对全面了解雪豹的生态习性及其栖息地评价有参考意义。