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...
Published in: | Methods in Ecology and Evolution |
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Online Access: | https://doi.org/10.1111/2041-210X.14208 https://doaj.org/article/fa6bf29f719748fda7cbf2cb4190836b |
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ftdoajarticles:oai:doaj.org/article:fa6bf29f719748fda7cbf2cb4190836b 2023-11-05T03:42:25+01:00 Accounting for unobserved spatial variation in step selection analyses of animal movement via spatial random effects Rafael Arce Guillen Finn Lindgren Stefanie Muff Thomas W. Glass Greg A. Breed Ulrike E. Schlägel 2023-10-01T00:00:00Z https://doi.org/10.1111/2041-210X.14208 https://doaj.org/article/fa6bf29f719748fda7cbf2cb4190836b EN eng Wiley https://doi.org/10.1111/2041-210X.14208 https://doaj.org/toc/2041-210X 2041-210X doi:10.1111/2041-210X.14208 https://doaj.org/article/fa6bf29f719748fda7cbf2cb4190836b Methods in Ecology and Evolution, Vol 14, Iss 10, Pp 2639-2653 (2023) animal movement habitat selection inlabru spatial statistics step selection analysis telemetry data Ecology QH540-549.5 Evolution QH359-425 article 2023 ftdoajarticles https://doi.org/10.1111/2041-210X.14208 2023-10-08T00:37:11Z 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 Directory of Open Access Journals: DOAJ Articles Methods in Ecology and Evolution 14 10 2639 2653 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
animal movement habitat selection inlabru spatial statistics step selection analysis telemetry data Ecology QH540-549.5 Evolution QH359-425 |
spellingShingle |
animal movement habitat selection inlabru spatial statistics step selection analysis telemetry data Ecology QH540-549.5 Evolution QH359-425 Rafael Arce Guillen Finn Lindgren Stefanie Muff Thomas W. Glass Greg A. Breed Ulrike E. Schlägel Accounting for unobserved spatial variation in step selection analyses of animal movement via spatial random effects |
topic_facet |
animal movement habitat selection inlabru spatial statistics step selection analysis telemetry data Ecology QH540-549.5 Evolution QH359-425 |
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. |
format |
Article in Journal/Newspaper |
author |
Rafael Arce Guillen Finn Lindgren Stefanie Muff Thomas W. Glass Greg A. Breed Ulrike E. Schlägel |
author_facet |
Rafael Arce Guillen Finn Lindgren Stefanie Muff Thomas W. Glass Greg A. Breed Ulrike E. Schlägel |
author_sort |
Rafael Arce Guillen |
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 |
https://doi.org/10.1111/2041-210X.14208 https://doaj.org/article/fa6bf29f719748fda7cbf2cb4190836b |
genre |
Gulo gulo wolverine |
genre_facet |
Gulo gulo wolverine |
op_source |
Methods in Ecology and Evolution, Vol 14, Iss 10, Pp 2639-2653 (2023) |
op_relation |
https://doi.org/10.1111/2041-210X.14208 https://doaj.org/toc/2041-210X 2041-210X doi:10.1111/2041-210X.14208 https://doaj.org/article/fa6bf29f719748fda7cbf2cb4190836b |
op_doi |
https://doi.org/10.1111/2041-210X.14208 |
container_title |
Methods in Ecology and Evolution |
container_volume |
14 |
container_issue |
10 |
container_start_page |
2639 |
op_container_end_page |
2653 |
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1781699528093597696 |