Principles of hierarchical modeling can be applied directly to accommodate both features of ecological data. In particular, inferences are based on spatial sampling – we can only ever sample a subset of locations where species occur -and imperfect detection – species or individuals might go undetected in the sample. Survey data are always subject to a number of observation processes that induce bias and error. Research goals of this project are to develop models, statistical methods, sampling strategies and tools for inference about animal population status from survey data. Hierarchical Models of Animal Abundance and Occurrence View Media Details Survival Probability (Public domain.) Sources/Usage: Some content may have restrictions. Linking observed encounter histories of individuals to mechanisms of spatial population ecology will enable ecologists to study these processes using new technologies such as noninvasive genetics, remote cameras and bioacoustic sampling. Furthermore, capture-recapture does not invoke any spatially explicit biological processes and thus is distinctly non-spatial, accounting neither for the inherent spatial nature of the sampling nor of the spatial distribution of individual encounters. While capture-recapture has become the standard sampling and analytical framework for the study of population processes (Williams, Nichols & Conroy 2002) it has advanced independent of and remained unconnected to the spatial structure of the population or the landscape within which populations exist. Spatial Capture-Recapture Models to Estimate Abundance and Density of Animal Populationsįor decades, capture-recapture methods have been the cornerstone of ecological statistics as applied to population biology. B., Sollmann, R., and Gardner, B., 2014, Spatial capture-recapture: Amsterdam, Elsevier.
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