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Estimating uncertainty in density surface models

Providing uncertainty estimates for predictions derived from species distribution models is essential for management but there is little guidance on potential sources of uncertainty in predictions and how best to combine these. Here we show where uncertainty can arise in density surface models (a mu...

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Autores principales: Miller, David L., Becker, Elizabeth A., Forney, Karin A., Roberts, Jason J., Cañadas, Ana, Schick, Robert S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9415456/
https://www.ncbi.nlm.nih.gov/pubmed/36032955
http://dx.doi.org/10.7717/peerj.13950
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author Miller, David L.
Becker, Elizabeth A.
Forney, Karin A.
Roberts, Jason J.
Cañadas, Ana
Schick, Robert S.
author_facet Miller, David L.
Becker, Elizabeth A.
Forney, Karin A.
Roberts, Jason J.
Cañadas, Ana
Schick, Robert S.
author_sort Miller, David L.
collection PubMed
description Providing uncertainty estimates for predictions derived from species distribution models is essential for management but there is little guidance on potential sources of uncertainty in predictions and how best to combine these. Here we show where uncertainty can arise in density surface models (a multi-stage spatial modelling approach for distance sampling data), focussing on cetacean density modelling. We propose an extensible, modular, hybrid analytical-simulation approach to encapsulate these sources. We provide example analyses of fin whales Balaenoptera physalus in the California Current Ecosystem.
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spelling pubmed-94154562022-08-27 Estimating uncertainty in density surface models Miller, David L. Becker, Elizabeth A. Forney, Karin A. Roberts, Jason J. Cañadas, Ana Schick, Robert S. PeerJ Conservation Biology Providing uncertainty estimates for predictions derived from species distribution models is essential for management but there is little guidance on potential sources of uncertainty in predictions and how best to combine these. Here we show where uncertainty can arise in density surface models (a multi-stage spatial modelling approach for distance sampling data), focussing on cetacean density modelling. We propose an extensible, modular, hybrid analytical-simulation approach to encapsulate these sources. We provide example analyses of fin whales Balaenoptera physalus in the California Current Ecosystem. PeerJ Inc. 2022-08-23 /pmc/articles/PMC9415456/ /pubmed/36032955 http://dx.doi.org/10.7717/peerj.13950 Text en https://creativecommons.org/publicdomain/zero/1.0/This is an open access article, free of all copyright, made available under the Creative Commons Public Domain Dedication (https://creativecommons.org/publicdomain/zero/1.0/) . This work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
spellingShingle Conservation Biology
Miller, David L.
Becker, Elizabeth A.
Forney, Karin A.
Roberts, Jason J.
Cañadas, Ana
Schick, Robert S.
Estimating uncertainty in density surface models
title Estimating uncertainty in density surface models
title_full Estimating uncertainty in density surface models
title_fullStr Estimating uncertainty in density surface models
title_full_unstemmed Estimating uncertainty in density surface models
title_short Estimating uncertainty in density surface models
title_sort estimating uncertainty in density surface models
topic Conservation Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9415456/
https://www.ncbi.nlm.nih.gov/pubmed/36032955
http://dx.doi.org/10.7717/peerj.13950
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