Using spatiotemporal statistical models to estimate animal abundance and infer ecological dynamics from survey counts
Ecologists often fit models to survey data to estimate and explain variation in animal abundance. Such models typically require that animal density remains constant across the landscape where sampling is being conducted, a potentially problematic assumption for animals inhabiting dynamic landscapes...
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crwiley:10.1890/14-0959.1 2024-06-23T07:51:46+00:00 Using spatiotemporal statistical models to estimate animal abundance and infer ecological dynamics from survey counts Conn, Paul B. Johnson, Devin S. Hoef, Jay M. Ver Hooten, Mevin B. London, Joshua M. Boveng, Peter L. 2015 http://dx.doi.org/10.1890/14-0959.1 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1890%2F14-0959.1 https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1890/14-0959.1 en eng Wiley http://onlinelibrary.wiley.com/termsAndConditions#vor Ecological Monographs volume 85, issue 2, page 235-252 ISSN 0012-9615 1557-7015 journal-article 2015 crwiley https://doi.org/10.1890/14-0959.1 2024-06-06T04:24:17Z Ecologists often fit models to survey data to estimate and explain variation in animal abundance. Such models typically require that animal density remains constant across the landscape where sampling is being conducted, a potentially problematic assumption for animals inhabiting dynamic landscapes or otherwise exhibiting considerable spatiotemporal variation in density. We review several concepts from the burgeoning literature on spatiotemporal statistical models, including the nature of the temporal structure (i.e., descriptive or dynamical) and strategies for dimension reduction to promote computational tractability. We also review several features as they specifically relate to abundance estimation, including boundary conditions, population closure, choice of link function, and extrapolation of predicted relationships to unsampled areas. We then compare a suite of novel and existing spatiotemporal hierarchical models for animal count data that permit animal density to vary over space and time, including formulations motivated by resource selection and allowing for closed populations. We gauge the relative performance (bias, precision, computational demands) of alternative spatiotemporal models when confronted with simulated and real data sets from dynamic animal populations. For the latter, we analyze spotted seal ( Phoca largha ) counts from an aerial survey of the Bering Sea where the quantity and quality of suitable habitat (sea ice) changed dramatically while surveys were being conducted. Simulation analyses suggested that multiple types of spatiotemporal models provide reasonable inference (low positive bias, high precision) about animal abundance, but have potential for overestimating precision. Analysis of spotted seal data indicated that several model formulations, including those based on a log‐Gaussian Cox process, had a tendency to overestimate abundance. By contrast, a model that included a population closure assumption and a scale prior on total abundance produced estimates that largely ... Article in Journal/Newspaper Bering Sea Sea ice Wiley Online Library Bering Sea Ecological Monographs 85 2 235 252 |
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Wiley Online Library |
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English |
description |
Ecologists often fit models to survey data to estimate and explain variation in animal abundance. Such models typically require that animal density remains constant across the landscape where sampling is being conducted, a potentially problematic assumption for animals inhabiting dynamic landscapes or otherwise exhibiting considerable spatiotemporal variation in density. We review several concepts from the burgeoning literature on spatiotemporal statistical models, including the nature of the temporal structure (i.e., descriptive or dynamical) and strategies for dimension reduction to promote computational tractability. We also review several features as they specifically relate to abundance estimation, including boundary conditions, population closure, choice of link function, and extrapolation of predicted relationships to unsampled areas. We then compare a suite of novel and existing spatiotemporal hierarchical models for animal count data that permit animal density to vary over space and time, including formulations motivated by resource selection and allowing for closed populations. We gauge the relative performance (bias, precision, computational demands) of alternative spatiotemporal models when confronted with simulated and real data sets from dynamic animal populations. For the latter, we analyze spotted seal ( Phoca largha ) counts from an aerial survey of the Bering Sea where the quantity and quality of suitable habitat (sea ice) changed dramatically while surveys were being conducted. Simulation analyses suggested that multiple types of spatiotemporal models provide reasonable inference (low positive bias, high precision) about animal abundance, but have potential for overestimating precision. Analysis of spotted seal data indicated that several model formulations, including those based on a log‐Gaussian Cox process, had a tendency to overestimate abundance. By contrast, a model that included a population closure assumption and a scale prior on total abundance produced estimates that largely ... |
format |
Article in Journal/Newspaper |
author |
Conn, Paul B. Johnson, Devin S. Hoef, Jay M. Ver Hooten, Mevin B. London, Joshua M. Boveng, Peter L. |
spellingShingle |
Conn, Paul B. Johnson, Devin S. Hoef, Jay M. Ver Hooten, Mevin B. London, Joshua M. Boveng, Peter L. Using spatiotemporal statistical models to estimate animal abundance and infer ecological dynamics from survey counts |
author_facet |
Conn, Paul B. Johnson, Devin S. Hoef, Jay M. Ver Hooten, Mevin B. London, Joshua M. Boveng, Peter L. |
author_sort |
Conn, Paul B. |
title |
Using spatiotemporal statistical models to estimate animal abundance and infer ecological dynamics from survey counts |
title_short |
Using spatiotemporal statistical models to estimate animal abundance and infer ecological dynamics from survey counts |
title_full |
Using spatiotemporal statistical models to estimate animal abundance and infer ecological dynamics from survey counts |
title_fullStr |
Using spatiotemporal statistical models to estimate animal abundance and infer ecological dynamics from survey counts |
title_full_unstemmed |
Using spatiotemporal statistical models to estimate animal abundance and infer ecological dynamics from survey counts |
title_sort |
using spatiotemporal statistical models to estimate animal abundance and infer ecological dynamics from survey counts |
publisher |
Wiley |
publishDate |
2015 |
url |
http://dx.doi.org/10.1890/14-0959.1 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1890%2F14-0959.1 https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1890/14-0959.1 |
geographic |
Bering Sea |
geographic_facet |
Bering Sea |
genre |
Bering Sea Sea ice |
genre_facet |
Bering Sea Sea ice |
op_source |
Ecological Monographs volume 85, issue 2, page 235-252 ISSN 0012-9615 1557-7015 |
op_rights |
http://onlinelibrary.wiley.com/termsAndConditions#vor |
op_doi |
https://doi.org/10.1890/14-0959.1 |
container_title |
Ecological Monographs |
container_volume |
85 |
container_issue |
2 |
container_start_page |
235 |
op_container_end_page |
252 |
_version_ |
1802642902826876928 |