A local evaluation of the individual state‐space to scale up Bayesian spatial capture–recapture
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, S...
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crwiley:10.1002/ece3.4751 2024-09-15T18:10:30+00:00 A local evaluation of the individual state‐space to scale up Bayesian spatial capture–recapture Milleret, Cyril Dupont, Pierre Bonenfant, Christophe Brøseth, Henrik Flagstad, Øystein Sutherland, Chris Bischof, Richard Environment Agency Environmental Protection Agency 2018 http://dx.doi.org/10.1002/ece3.4751 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fece3.4751 https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.4751 en eng Wiley http://creativecommons.org/licenses/by/4.0/ Ecology and Evolution volume 9, issue 1, page 352-363 ISSN 2045-7758 2045-7758 journal-article 2018 crwiley https://doi.org/10.1002/ece3.4751 2024-08-09T04:19:48Z 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 ( σ : the scale parameter of the half‐normal detection function), and when the detector window extended beyond the edge of the AC window by 2 σ . Importantly, LESS considerably decreased the computation time needed for fitting SCR models. In our simulations, LESS increased the computation speed of SCR models up to 57‐fold. We demonstrate the power of this new approach by mapping the density of an elusive large carnivore—the wolverine ( Gulo gulo )—with an unprecedented resolution and across the species’ entire range in Norway (> 200,000 km 2 ). Our approach helps overcome a major computational obstacle to population and landscape‐level SCR analyses. The LESS implementation in a Bayesian framework makes the customization and fitting of SCR accessible for practitioners working at scales that are relevant for conservation and management. Article in Journal/Newspaper Gulo gulo wolverine Wiley Online Library Ecology and Evolution 9 1 352 363 |
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English |
description |
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 ( σ : the scale parameter of the half‐normal detection function), and when the detector window extended beyond the edge of the AC window by 2 σ . Importantly, LESS considerably decreased the computation time needed for fitting SCR models. In our simulations, LESS increased the computation speed of SCR models up to 57‐fold. We demonstrate the power of this new approach by mapping the density of an elusive large carnivore—the wolverine ( Gulo gulo )—with an unprecedented resolution and across the species’ entire range in Norway (> 200,000 km 2 ). Our approach helps overcome a major computational obstacle to population and landscape‐level SCR analyses. The LESS implementation in a Bayesian framework makes the customization and fitting of SCR accessible for practitioners working at scales that are relevant for conservation and management. |
author2 |
Environment Agency Environmental Protection Agency |
format |
Article in Journal/Newspaper |
author |
Milleret, Cyril Dupont, Pierre Bonenfant, Christophe Brøseth, Henrik Flagstad, Øystein Sutherland, Chris Bischof, Richard |
spellingShingle |
Milleret, Cyril Dupont, Pierre Bonenfant, Christophe Brøseth, Henrik Flagstad, Øystein Sutherland, Chris Bischof, Richard A local evaluation of the individual state‐space to scale up Bayesian spatial capture–recapture |
author_facet |
Milleret, Cyril Dupont, Pierre Bonenfant, Christophe Brøseth, Henrik Flagstad, Øystein Sutherland, Chris Bischof, Richard |
author_sort |
Milleret, Cyril |
title |
A local evaluation of the individual state‐space to scale up Bayesian spatial capture–recapture |
title_short |
A local evaluation of the individual state‐space to scale up Bayesian spatial capture–recapture |
title_full |
A local evaluation of the individual state‐space to scale up Bayesian spatial capture–recapture |
title_fullStr |
A local evaluation of the individual state‐space to scale up Bayesian spatial capture–recapture |
title_full_unstemmed |
A local evaluation of the individual state‐space to scale up Bayesian spatial capture–recapture |
title_sort |
local evaluation of the individual state‐space to scale up bayesian spatial capture–recapture |
publisher |
Wiley |
publishDate |
2018 |
url |
http://dx.doi.org/10.1002/ece3.4751 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fece3.4751 https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.4751 |
genre |
Gulo gulo wolverine |
genre_facet |
Gulo gulo wolverine |
op_source |
Ecology and Evolution volume 9, issue 1, page 352-363 ISSN 2045-7758 2045-7758 |
op_rights |
http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1002/ece3.4751 |
container_title |
Ecology and Evolution |
container_volume |
9 |
container_issue |
1 |
container_start_page |
352 |
op_container_end_page |
363 |
_version_ |
1810448101352144896 |