A spatial open‐population capture‐recapture model

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Abstract A spatial open‐population capture‐recapture model is described that extends both the non‐spatial open‐population model of Schwarz and Arnason and the spatially explicit closed‐population model of Borchers and Efford. The superpopulation of animals available for detection at some time during a study is conceived as a two‐dimensional Poisson point process. Individual probabilities of birth and death follow the conventional open‐population model. Movement between sampling times may be modeled with a dispersal kernel using a recursive Markovian algorithm. Observations arise from distance‐dependent sampling at an array of detectors. As in the closed‐population spatial model, the observed data likelihood relies on integration over the unknown animal locations; maximization of this likelihood yields estimates of the birth, death, movement, and detection parameters. The models were fitted to data from a live‐trapping study of brushtail possums (Trichosurus vulpecula) in New Zealand. Simulations confirmed that spatial modeling can greatly reduce the bias of capture‐recapture survival estimates and that there is a degree of robustness to misspecification of the dispersal kernel. An R package is available that includes various extensions.

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