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Hierarchical spatial capture-recapture models: Modeling population density from stratified populations

March 24, 2014
  1. Capture–recapture studies are often conducted on populations that are stratified by space, time or other factors. In this paper, we develop a Bayesian spatial capture–recapture (SCR) modelling framework for stratified populations – when sampling occurs within multiple distinct spatial and temporal strata.
  2. We describe a hierarchical model that integrates distinct models for both the spatial encounter history data from capture–recapture sampling, and also for modelling variation in density among strata. We use an implementation of data augmentation to parameterize the model in terms of a latent categorical stratum or group membership variable, which provides a convenient implementation in popular BUGS software packages.
  3. We provide an example application to an experimental study involving small-mammal sampling on multiple trapping grids over multiple years, where the main interest is in modelling a treatment effect on population density among the trapping grids.
  4. Many capture–recapture studies involve some aspect of spatial or temporal replication that requires some attention to modelling variation among groups or strata. We propose a hierarchical model that allows explicit modelling of group or strata effects. Because the model is formulated for individual encounter histories and is easily implemented in the BUGS language and other free software, it also provides a general framework for modelling individual effects, such as are present in SCR models.

Citation Information

Publication Year 2014
Title Hierarchical spatial capture-recapture models: Modeling population density from stratified populations
DOI 10.1111/2041-210X.12135
Authors J. Andrew Royle, Sarah J. Converse
Publication Type Article
Publication Subtype Journal Article
Series Title Methods in Ecology and Evolution
Series Number
Index ID 70099277
Record Source USGS Publications Warehouse
USGS Organization Patuxent Wildlife Research Center