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

• Published in: Ecology (Durham) 2010-11, Vol.91 (11), p.3376-3383
• Main Authors: Gardner, Beth, Reppucci, Juan, Lucherini, Mauro, Royle, J. Andrew
• Format: Article
• Language: English
• Subjects: Andes Mountains; Animal and plant ecology; Animal populations; Animal, plant and microbial ecology; Animals; Argentina; Bayesian analysis; Biological and medical sciences; camera trapping; Cats; data augmentation; Density estimation; Deoxyribonucleic acid; DNA; Ecological modeling; Ecosystem; Felidae - physiology; Fundamental and applied biological sciences. Psychology; General aspects; hierarchical model; Inference; Jolly-Seber model; Modeling; Models, Biological; Pampas; Pampas cats; Parametric models; Population Density; Population ecology; Population estimates; spatial capture-recapture; Spatial models; Studies; Survival analysis; trapping arrays
• ISSN: 0012-9658; 1939-9170
• DOI: 10.1890/09-0804.1

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.