Accounting for unobserved spatial variation in step selection analyses of animal movement via spatial random effects
Abstract Step selection analysis (SSA) is a common framework for understanding animal movement and resource selection using telemetry data. Such data are, however, inherently autocorrelated in space, a complication that could impact SSA‐based inference if left unaddressed. Accounting for spatial cor...
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crwiley:10.1111/2041-210x.14208 2024-09-15T18:10:30+00:00 Accounting for unobserved spatial variation in step selection analyses of animal movement via spatial random effects Arce Guillen, Rafael Lindgren, Finn Muff, Stefanie Glass, Thomas W. Breed, Greg A. Schlägel, Ulrike E. Deutsche Forschungsgemeinschaft Deutsche Forschungsgemeinschaft 2023 http://dx.doi.org/10.1111/2041-210x.14208 https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.14208 en eng Wiley http://creativecommons.org/licenses/by-nc/4.0/ Methods in Ecology and Evolution volume 14, issue 10, page 2639-2653 ISSN 2041-210X 2041-210X journal-article 2023 crwiley https://doi.org/10.1111/2041-210x.14208 2024-08-09T04:29:20Z Abstract Step selection analysis (SSA) is a common framework for understanding animal movement and resource selection using telemetry data. Such data are, however, inherently autocorrelated in space, a complication that could impact SSA‐based inference if left unaddressed. Accounting for spatial correlation is standard statistical practice when analysing spatial data, and its importance is increasingly recognized in ecological models (e.g. species distribution models). Nonetheless, no framework yet exists to account for such correlation when analysing animal movement using SSA. Here, we extend the popular method integrated step selection analysis (iSSA) by including a Gaussian field (GF) in the linear predictor to account for spatial correlation. For this, we use the Bayesian framework R‐INLA and the stochastic partial differential equations (SPDE) technique. We show through a simulation study that our method provides accurate fixed effects estimates, quantifies their uncertainty well and improves the predictions. In addition, we demonstrate the practical utility of our method by applying it to three wolverine ( Gulo gulo ) tracks. Our method solves the problems of assuming spatially independent residuals in the SSA framework. In addition, it offers new possibilities for making long‐term predictions of habitat usage. Article in Journal/Newspaper Gulo gulo wolverine Wiley Online Library Methods in Ecology and Evolution 14 10 2639 2653 |
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English |
description |
Abstract Step selection analysis (SSA) is a common framework for understanding animal movement and resource selection using telemetry data. Such data are, however, inherently autocorrelated in space, a complication that could impact SSA‐based inference if left unaddressed. Accounting for spatial correlation is standard statistical practice when analysing spatial data, and its importance is increasingly recognized in ecological models (e.g. species distribution models). Nonetheless, no framework yet exists to account for such correlation when analysing animal movement using SSA. Here, we extend the popular method integrated step selection analysis (iSSA) by including a Gaussian field (GF) in the linear predictor to account for spatial correlation. For this, we use the Bayesian framework R‐INLA and the stochastic partial differential equations (SPDE) technique. We show through a simulation study that our method provides accurate fixed effects estimates, quantifies their uncertainty well and improves the predictions. In addition, we demonstrate the practical utility of our method by applying it to three wolverine ( Gulo gulo ) tracks. Our method solves the problems of assuming spatially independent residuals in the SSA framework. In addition, it offers new possibilities for making long‐term predictions of habitat usage. |
author2 |
Deutsche Forschungsgemeinschaft Deutsche Forschungsgemeinschaft |
format |
Article in Journal/Newspaper |
author |
Arce Guillen, Rafael Lindgren, Finn Muff, Stefanie Glass, Thomas W. Breed, Greg A. Schlägel, Ulrike E. |
spellingShingle |
Arce Guillen, Rafael Lindgren, Finn Muff, Stefanie Glass, Thomas W. Breed, Greg A. Schlägel, Ulrike E. Accounting for unobserved spatial variation in step selection analyses of animal movement via spatial random effects |
author_facet |
Arce Guillen, Rafael Lindgren, Finn Muff, Stefanie Glass, Thomas W. Breed, Greg A. Schlägel, Ulrike E. |
author_sort |
Arce Guillen, Rafael |
title |
Accounting for unobserved spatial variation in step selection analyses of animal movement via spatial random effects |
title_short |
Accounting for unobserved spatial variation in step selection analyses of animal movement via spatial random effects |
title_full |
Accounting for unobserved spatial variation in step selection analyses of animal movement via spatial random effects |
title_fullStr |
Accounting for unobserved spatial variation in step selection analyses of animal movement via spatial random effects |
title_full_unstemmed |
Accounting for unobserved spatial variation in step selection analyses of animal movement via spatial random effects |
title_sort |
accounting for unobserved spatial variation in step selection analyses of animal movement via spatial random effects |
publisher |
Wiley |
publishDate |
2023 |
url |
http://dx.doi.org/10.1111/2041-210x.14208 https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.14208 |
genre |
Gulo gulo wolverine |
genre_facet |
Gulo gulo wolverine |
op_source |
Methods in Ecology and Evolution volume 14, issue 10, page 2639-2653 ISSN 2041-210X 2041-210X |
op_rights |
http://creativecommons.org/licenses/by-nc/4.0/ |
op_doi |
https://doi.org/10.1111/2041-210x.14208 |
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Methods in Ecology and Evolution |
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14 |
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10 |
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2639 |
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2653 |
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1810448094248042496 |