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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ftdatacite:10.6084/m9.figshare.c.3309942 2023-05-15T15:44:00+02:00 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 Mevin B. Hooten London, Joshua M. Boveng, Peter L. 2016 https://dx.doi.org/10.6084/m9.figshare.c.3309942 https://figshare.com/collections/Using_spatiotemporal_statistical_models_to_estimate_animal_abundance_and_infer_ecological_dynamics_from_survey_counts/3309942 unknown Figshare https://dx.doi.org/10.1890/14-0959.1 CC-BY http://creativecommons.org/licenses/by/3.0/us CC-BY Environmental Science Ecology FOS Biological sciences Collection article 2016 ftdatacite https://doi.org/10.6084/m9.figshare.c.3309942 https://doi.org/10.1890/14-0959.1 2021-11-05T12:55:41Z 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 conformed to our a priori expectation. Although care must be taken to tailor models to match the study population and survey data available, we argue that hierarchical spatiotemporal statistical models represent a powerful way forward for estimating abundance and explaining variation in the distribution of dynamical populations. Article in Journal/Newspaper Bering Sea Sea ice DataCite Metadata Store (German National Library of Science and Technology) Bering Sea |
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Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
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language |
unknown |
topic |
Environmental Science Ecology FOS Biological sciences |
spellingShingle |
Environmental Science Ecology FOS Biological sciences Conn, Paul B. Johnson, Devin S. Hoef, Jay M. Ver Mevin B. Hooten London, Joshua M. Boveng, Peter L. Using spatiotemporal statistical models to estimate animal abundance and infer ecological dynamics from survey counts |
topic_facet |
Environmental Science Ecology FOS Biological sciences |
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 conformed to our a priori expectation. Although care must be taken to tailor models to match the study population and survey data available, we argue that hierarchical spatiotemporal statistical models represent a powerful way forward for estimating abundance and explaining variation in the distribution of dynamical populations. |
format |
Article in Journal/Newspaper |
author |
Conn, Paul B. Johnson, Devin S. Hoef, Jay M. Ver Mevin B. Hooten London, Joshua M. Boveng, Peter L. |
author_facet |
Conn, Paul B. Johnson, Devin S. Hoef, Jay M. Ver Mevin B. Hooten 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 |
Figshare |
publishDate |
2016 |
url |
https://dx.doi.org/10.6084/m9.figshare.c.3309942 https://figshare.com/collections/Using_spatiotemporal_statistical_models_to_estimate_animal_abundance_and_infer_ecological_dynamics_from_survey_counts/3309942 |
geographic |
Bering Sea |
geographic_facet |
Bering Sea |
genre |
Bering Sea Sea ice |
genre_facet |
Bering Sea Sea ice |
op_relation |
https://dx.doi.org/10.1890/14-0959.1 |
op_rights |
CC-BY http://creativecommons.org/licenses/by/3.0/us |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.6084/m9.figshare.c.3309942 https://doi.org/10.1890/14-0959.1 |
_version_ |
1766378230970843136 |