Mammal inventories in tropical forests are often difficult to carry out, and many elusive species are missed or only reported from interviews with local people. Camera traps offer a new tool for conducting inventories of large- and medium-sized terrestrial mammals. We evaluated the efficiency of camera traps based on data from two surveys carried out at a single site during 2 consecutive years. The survey efforts were 1440 and 2340 camera days, and 75 and 86% of the 28 large- and medium-sized terrestrial mammal species known to occur at the site were recorded. Capture frequencies for different species were highly correlated between the surveys, and the capture probability for animals that passed in front of the cameras decreased with decreasing size of the species. Camera spacing and total survey area had little influence on the number of species recorded, with survey effort being the main factor determining the number of recorded species. Using a model we demonstrated the exponential increase in survey effort required to record the most elusive species. We evaluated the performance of different species richness estimators on this dataset and found the Jackknife estimators generally to perform best. We give recommendations on how to increase efficiency of camera trap surveys exclusively targeted at species inventories.

https://zslpublications.onlinelibrary.wiley.com/doi/epdf/10.1111/j.1469-1795.2008.00169.x

An evaluation of camera traps for inventorying large‐ and medium‐sized terrestrial rainforest mammals

Statistical Inference from Capture Data on Closed Animal Populations

Densities of elusive terrestrial mammals are commonly estimated from camera-trap data. Typically, this is a 2-step process involving 1) fitting conventional closed population capture–recapture models to estimate abundance, and 2) using ad hoc methods to determine the effective trapping area. The methodology needs to be accurate, robust, and reliable when results are used to guide wildlife management. We critically review 47 published studies and discuss the problems associated with contemporary population estimates of elusive species from camera-trap data. In particular we discuss 1) individual identification, 2) sample size and capture probability, 3) camera location and spacing, 4) the size of the study area, and 5) ad hoc density estimation from the calculation of an effective trapping area. We also discuss the recently developed spatially explicit capture–recapture (SECR) models as an alternative approach that does not require the intermediate step of estimating an effective trapping area. We recommend 1) greater transparency in study design and quality of the data, 2) greater rigor when reviewing manuscripts, and 3) that more attention is given to the survey design to ensure data are of sufficient quality for analysis. © 2011 The Wildlife Society.

A critique of density estimation from camera-trap data†

Numerous techniques have been proposed to estimate carnivore abundance and density, but few have been validated against populations of known size. We used a density estimate established by intensive monitoring of a population of radiotagged leopards (Panthera pardus) with a detection probability of 1.0 to evaluate efficacy of track counts and camera-trap surveys as population estimators. We calculated densities from track counts using 2 methods and compared performance of 10 methods for calculating the effectively sampled area for camera-trapping data. Compared to our reference density (7.33 ± 0.44 leopards/100 km2), camera-trapping generally produced more accurate but less precise estimates than did track counts. The most accurate result (6.97 ± 1.88 leopards/100 km2) came from camera-trap data with a sampled area buffered by a boundary strip representing the mean maximum distance moved by leopards outside the survey area (MMDMOSA) established by telemetry. However, contrary to recent suggestion...

Evaluating Methods for Counting Cryptic Carnivores

We develop a hierarchical capture-recapture model for demographically open populations when auxiliary spatial information about location of capture is obtained. Such spatial capture-recapture data arise from studies based on camera trapping, DNA sampling, and other situations in which a spatial array of devices records encounters of unique individuals. We integrate an individual-based formulation of a Jolly-Seber type model with recently developed spatially explicit capture-recapture models to estimate density and demographic parameters for survival and recruitment. We adopt a Bayesian framework for inference under this model using the method of data augmentation which is implemented in the software program WinBUGS. The model was motivated by a camera trapping study of Pampas cats Leopardus colocolo from Argentina, which we present as an illustration of the model in this paper. We provide estimates of density and the first quantitative assessment of vital rates for the Pampas cat in the High Andes. The precision of these estimates is poor due likely to the sparse data set. Unlike conventional inference methods which usually rely on asymptotic arguments, Bayesian inferences are valid in arbitrary sample sizes, and thus the method is ideal for the study of rare or endangered species for which small data sets are typical.

Spatially explicit inference for open populations: estimating demographic parameters from camera-trap studies.

Conservation and management of snow leopards (Uncia uncia) has largely relied on anecdotal evidence and presence–absence data due to their cryptic nature and the difficult terrain they inhabit. These methods generally lack the scientific rigor necessary to accurately estimate population size and monitor trends. We evaluated the use of photography in capture–mark–recapture (CMR) techniques for estimating snow leopard population abundance and density within Hemis National Park, Ladakh, India. We placed infrared camera traps along actively used travel paths, scent-sprayed rocks, and scrape sites within 16- to 30-km2 sampling grids in successive winters during January and March 2003–2004. We used head-on, oblique, and side-view camera configurations to obtain snow leopard photographs at varying body orientations. We calculated snow leopard abundance estimates using the program CAPTURE. We obtained a total of 66 and 49 snow leopard captures resulting in 8.91 and 5.63 individuals per 100 trap-nights du...

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Estimating Snow Leopard Population Abundance Using Photography and Capture–Recapture Techniques

olitary felids like tigers and snow leopards are notoriously difficult to enumerate, and indirect techniques like pugmark surveys often produce ambiguous in-formation that is difﬁ cult to interpret because many factors inﬂ uence mark-ing behavior and frequency (Ahlborn & Jackson 1988). Considering the snow leopard’s rugged habitat, it is not surprising then that information on its current status and occupied range is very limited.We adapted the camera-trapping techniques pioneered by Ullas Karanth and his associates for counting Bengal tigers to the census taking of snow leop-ards in the Rumbak watershed of the India’s Hemis High Altitude National Park (HNP), located in Ladakh near Leh (76° 50’ to 77° 45’ East; 33° 15’ to 34° 20’North), with the following key objectives:1) To develop a standardized ﬁ eld method and sampling strategy for ap-plying capture-recapture models to snow leopards using remotely triggered camera traps;2) To estimate snow leopard den-sity over two consecutive (winter) seasons;3) To develop a camera-trapping pro-tocol that could be applied in other parts of snow leopard range within high and low density areas in order to estimate total numbers better; and4) To develop a snow leopard identi-ﬁ cation protocol based on their distinct pelage patterns.HNP was selected because of fre-quent snow leopard sightings, the low incidence of poaching, a relatively stable ungulate prey population, and the presence of well-deﬁ ned travel corridors where remotely-triggered camera traps could be placed to achieve consistent photo capture success.MethodsCamera trap sites were located along narrow ridgelines and in valley bot-toms close to scent-sprayed rocks or scrape sites where movement is narrowly constrained by topography, boulders or vegetation. We used up to 18 TrailMaster 1550 ™ active infrared detectors since they performed best at low temperatures with signiﬁ cantly longer battery life than less expen-sive passive infra-red sensors like the CamTrakker™. Each station had two 35 mm cameras synchronized with a multi-camera trigger and then placed 2-3 meters from the infrared beam. In the ﬁ rst year, we faced each camera directly up or down the anticipated travel path in an effort to obtain close-up photographs of the leopard’s face for quick identiﬁ cation, but this proved difﬁ cult and somewhat unreliable. In the following year, cameras were oriented at 45° or 90° angles from the anticipated travel path to capture simultaneous photographs of either sides of the snow leopard’s body, which like other spotted cats has asymmetric pelage patterning. No baits or lures were used, and cam-eras were not moved during the survey because rapid, simultaneous shifts in trapping locations were precluded by the difﬁ cult terrain and lack of road ac-cess. Camera traps were checked every 2-10 days or immediately following sustained snowfall in order to prevent accumulated snow from blocking the sensors and thus disabling the cameras. We sampled between mid-January and mid-March for 65 consecutive days in 2003 and 60 days in 2004.We developed detailed guidelines for identifying individual snow leop-ards based on their pelage pattern, speciﬁ cally the spots, rosettes and ro-sette groupings, which are detailed in a manuscript submitted for publication. Camera density was about 1.5 stations per 16-25 km

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Jerry D. Roe

Papers

Estimating Snow Leopard Population Abundance Using Photography and Capture–Recapture Techniques

Camera-Trapping of Snow Leopards