Spatial capture‐recapture models for search‐encounter data

摘要: Summary 1. Spatial capture–recapture models make use of auxiliary data on capture location to provide density estimates for animal populations. Previously, have been developed primarily fixed trap arrays which define the observable locations individuals by a set discrete points. 2. Here, we develop class ‘search-encounter’ data, i.e. detections recognizable in continuous space, not restricted locations. In our hierarchical model, detection probability is related average distance between individual and survey path. The are allowed change over time owing movements individuals, formally model describing activity or home range centre itself regarded as latent variable model. We Bayesian analysis WinBUGS, custom MCMC algorithm R language. 3. The applied simulated territory mapping Willow Tit from Swiss Breeding Bird Survey MHB. While observed was 15 territories per nominal 1 km2 plot unknown effective sample area, produced estimate 21·12 square km (95% posterior interval: 17–26). 4. Spatial relevant virtually all population studies that seek size density, yet existing proposed mainly conventional sampling using traps. Our search-encounter where spatial pattern searching can be arbitrary may occasions, greatly expands scope utility models.