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Milleret C, Dupont P, Bonenfant C, et al. A local evaluation of the individual state-space to scale up Bayesian spatial capture-recapture. Ecol Evol. 2018;9(1):352-363doi: 10.1002/ece3.4751.
Milleret, C., Dupont, P., Bonenfant, C., Brøseth, H., Flagstad, �. �., Sutherland, C., & Bischof, R. (2019). A local evaluation of the individual state-space to scale up Bayesian spatial capture-recapture. Ecology and evolution, 9(1), 352-363. https://doi.org/10.1002/ece3.4751
Milleret, Cyril, et al. "A local evaluation of the individual state-space to scale up Bayesian spatial capture-recapture." Ecology and evolution vol. 9,1 (2019): 352-363. doi: https://doi.org/10.1002/ece3.4751
Milleret C, Dupont P, Bonenfant C, Brøseth H, Flagstad Ø, Sutherland C, Bischof R. A local evaluation of the individual state-space to scale up Bayesian spatial capture-recapture. Ecol Evol. 2018 Dec 18;9(1):352-363. doi: 10.1002/ece3.4751. eCollection 2019 Jan. PMID: 30680119; PMCID: PMC6342129.
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Ecol Evol. 2018 Dec 18;9(1):352-363. doi: 10.1002/ece3.4751. eCollection 2019 Jan.

A local evaluation of the individual state-space to scale up Bayesian spatial capture-recapture.

Ecology and evolution

Cyril Milleret, Pierre Dupont, Christophe Bonenfant, Henrik Brøseth, Øystein Flagstad, Chris Sutherland, Richard Bischof

Affiliations

  1. Faculty of Environmental Sciences and Natural Resource Management Norwegian University of Life Sciences Ås Norway.
  2. Centre National de la Recherche Scientifique (CNRS), Unité Mixte de Recherche (UMR) 5558, Laboratoire de Biométrie et Biologie Évolutive Université Lyon 1 Villeurbanne France.
  3. Norwegian Institute for Nature Research Trondheim Norway.
  4. Department of Environmental Conservation University of Massachusetts Amherst Massachusetts USA.

PMID: 30680119 PMCID: PMC6342129 DOI: 10.1002/ece3.4751

Abstract

Spatial capture-recapture models (SCR) are used to estimate animal density and to investigate a range of problems in spatial ecology that cannot be addressed with traditional nonspatial methods. Bayesian approaches in particular offer tremendous flexibility for SCR modeling. Increasingly, SCR data are being collected over very large spatial extents making analysis computational intensive, sometimes prohibitively so. To mitigate the computational burden of large-scale SCR models, we developed an improved formulation of the Bayesian SCR model that uses local evaluation of the individual state-space (LESS). Based on prior knowledge about a species' home range size, we created square evaluation windows that restrict the spatial domain in which an individual's detection probability (detector window) and activity center location (AC window) are estimated. We used simulations and empirical data analyses to assess the performance and bias of SCR with LESS. LESS produced unbiased estimates of SCR parameters when the AC window width was ≥5σ (

Keywords: computation speed; local evaluation of the state-space; spatial capture–recapture; wolverines

References

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