Data from: A local evaluation of the individual state-space to scale up Bayesian spatial capture recapture ...
1. 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 non-spatial methods. Bayesian approaches in particular offer tremendous flexibility for SCR modelling. Increasingly, SCR d...
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ftdatacite:10.5061/dryad.42m96c8 2024-02-04T10:00:58+01:00 Data from: 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 2018 https://dx.doi.org/10.5061/dryad.42m96c8 https://datadryad.org/stash/dataset/doi:10.5061/dryad.42m96c8 en eng Dryad https://dx.doi.org/10.1002/ece3.4751 Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode cc0-1.0 Gulo gulo SCR wolverine local evaluation of state space Dataset dataset 2018 ftdatacite https://doi.org/10.5061/dryad.42m96c810.1002/ece3.4751 2024-01-05T01:14:15Z 1. 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 non-spatial methods. Bayesian approaches in particular offer tremendous flexibility for SCR modelling. Increasingly, SCR data are being collected over very large spatial extents making analysis computational intensive, sometimes prohibitively so. 2. 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. 3. LESS produced unbiased estimates of SCR ... : RData and Rscript for the wolverine exampleWolverineData.RData is the RData file necessary to perform the analysis of the wolverine data. Script.pdf is a Rmarkdown document with the steps necessary to reproduce the analysis.DataScript.zip ... Dataset Gulo gulo wolverine DataCite Metadata Store (German National Library of Science and Technology) |
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Open Polar |
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DataCite Metadata Store (German National Library of Science and Technology) |
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ftdatacite |
language |
English |
topic |
Gulo gulo SCR wolverine local evaluation of state space |
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Gulo gulo SCR wolverine local evaluation of state space Milleret, Cyril Dupont, Pierre Bonenfant, Christophe Brøseth, Henrik Flagstad, Øystein Sutherland, Chris Bischof, Richard Data from: A local evaluation of the individual state-space to scale up Bayesian spatial capture recapture ... |
topic_facet |
Gulo gulo SCR wolverine local evaluation of state space |
description |
1. 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 non-spatial methods. Bayesian approaches in particular offer tremendous flexibility for SCR modelling. Increasingly, SCR data are being collected over very large spatial extents making analysis computational intensive, sometimes prohibitively so. 2. 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. 3. LESS produced unbiased estimates of SCR ... : RData and Rscript for the wolverine exampleWolverineData.RData is the RData file necessary to perform the analysis of the wolverine data. Script.pdf is a Rmarkdown document with the steps necessary to reproduce the analysis.DataScript.zip ... |
format |
Dataset |
author |
Milleret, Cyril Dupont, Pierre Bonenfant, Christophe Brøseth, Henrik Flagstad, Øystein Sutherland, Chris Bischof, Richard |
author_facet |
Milleret, Cyril Dupont, Pierre Bonenfant, Christophe Brøseth, Henrik Flagstad, Øystein Sutherland, Chris Bischof, Richard |
author_sort |
Milleret, Cyril |
title |
Data from: A local evaluation of the individual state-space to scale up Bayesian spatial capture recapture ... |
title_short |
Data from: A local evaluation of the individual state-space to scale up Bayesian spatial capture recapture ... |
title_full |
Data from: A local evaluation of the individual state-space to scale up Bayesian spatial capture recapture ... |
title_fullStr |
Data from: A local evaluation of the individual state-space to scale up Bayesian spatial capture recapture ... |
title_full_unstemmed |
Data from: A local evaluation of the individual state-space to scale up Bayesian spatial capture recapture ... |
title_sort |
data from: a local evaluation of the individual state-space to scale up bayesian spatial capture recapture ... |
publisher |
Dryad |
publishDate |
2018 |
url |
https://dx.doi.org/10.5061/dryad.42m96c8 https://datadryad.org/stash/dataset/doi:10.5061/dryad.42m96c8 |
genre |
Gulo gulo wolverine |
genre_facet |
Gulo gulo wolverine |
op_relation |
https://dx.doi.org/10.1002/ece3.4751 |
op_rights |
Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode cc0-1.0 |
op_doi |
https://doi.org/10.5061/dryad.42m96c810.1002/ece3.4751 |
_version_ |
1789966556881485824 |