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Extending density surface models to include multiple and double-observer survey data

Spatial models of density and abundance are widely used in both ecological research (e.g., to study habitat use) and wildlife management (e.g., for population monitoring and environmental impact assessment). Increasingly, modellers are tasked with integrating data from multiple sources, collected vi...

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Autores principales: Miller, David L., Fifield, David, Wakefield, Ewan, Sigourney, Douglas B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8418794/
https://www.ncbi.nlm.nih.gov/pubmed/34557355
http://dx.doi.org/10.7717/peerj.12113
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author Miller, David L.
Fifield, David
Wakefield, Ewan
Sigourney, Douglas B.
author_facet Miller, David L.
Fifield, David
Wakefield, Ewan
Sigourney, Douglas B.
author_sort Miller, David L.
collection PubMed
description Spatial models of density and abundance are widely used in both ecological research (e.g., to study habitat use) and wildlife management (e.g., for population monitoring and environmental impact assessment). Increasingly, modellers are tasked with integrating data from multiple sources, collected via different observation processes. Distance sampling is an efficient and widely used survey and analysis technique. Within this framework, observation processes are modelled via detection functions. We seek to take multiple data sources and fit them in a single spatial model. Density surface models (DSMs) are a two-stage approach: first accounting for detectability via distance sampling methods, then modelling distribution via a generalized additive model. However, current software and theory does not address the issue of multiple data sources. We extend the DSM approach to accommodate data from multiple surveys, collected via conventional distance sampling, double-observer distance sampling (used to account for incomplete detection at zero distance) and strip transects. Variance propagation ensures that uncertainty is correctly accounted for in final estimates of abundance. Methods described here are implemented in the dsm R package. We briefly analyse two datasets to illustrate these new developments. Our new methodology enables data from multiple distance sampling surveys of different types to be treated in a single spatial model, enabling more robust abundance estimation, potentially over wider geographical or temporal domains.
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spelling pubmed-84187942021-09-22 Extending density surface models to include multiple and double-observer survey data Miller, David L. Fifield, David Wakefield, Ewan Sigourney, Douglas B. PeerJ Statistics Spatial models of density and abundance are widely used in both ecological research (e.g., to study habitat use) and wildlife management (e.g., for population monitoring and environmental impact assessment). Increasingly, modellers are tasked with integrating data from multiple sources, collected via different observation processes. Distance sampling is an efficient and widely used survey and analysis technique. Within this framework, observation processes are modelled via detection functions. We seek to take multiple data sources and fit them in a single spatial model. Density surface models (DSMs) are a two-stage approach: first accounting for detectability via distance sampling methods, then modelling distribution via a generalized additive model. However, current software and theory does not address the issue of multiple data sources. We extend the DSM approach to accommodate data from multiple surveys, collected via conventional distance sampling, double-observer distance sampling (used to account for incomplete detection at zero distance) and strip transects. Variance propagation ensures that uncertainty is correctly accounted for in final estimates of abundance. Methods described here are implemented in the dsm R package. We briefly analyse two datasets to illustrate these new developments. Our new methodology enables data from multiple distance sampling surveys of different types to be treated in a single spatial model, enabling more robust abundance estimation, potentially over wider geographical or temporal domains. PeerJ Inc. 2021-09-02 /pmc/articles/PMC8418794/ /pubmed/34557355 http://dx.doi.org/10.7717/peerj.12113 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 Statistics
Miller, David L.
Fifield, David
Wakefield, Ewan
Sigourney, Douglas B.
Extending density surface models to include multiple and double-observer survey data
title Extending density surface models to include multiple and double-observer survey data
title_full Extending density surface models to include multiple and double-observer survey data
title_fullStr Extending density surface models to include multiple and double-observer survey data
title_full_unstemmed Extending density surface models to include multiple and double-observer survey data
title_short Extending density surface models to include multiple and double-observer survey data
title_sort extending density surface models to include multiple and double-observer survey data
topic Statistics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8418794/
https://www.ncbi.nlm.nih.gov/pubmed/34557355
http://dx.doi.org/10.7717/peerj.12113
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