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Modified home range kernel density estimators that take environmental interactions into account

BACKGROUND: Kernel density estimation (KDE) is a major tool in the movement ecologist toolbox that is used to delineate where geo-tracked animals spend their time. Because KDE bandwidth optimizers are sensitive to temporal autocorrelation, statistically-robust alternatives have been advocated, first...

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Autor principal: Péron, Guillaume
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6530033/
https://www.ncbi.nlm.nih.gov/pubmed/31139416
http://dx.doi.org/10.1186/s40462-019-0161-9
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author Péron, Guillaume
author_facet Péron, Guillaume
author_sort Péron, Guillaume
collection PubMed
description BACKGROUND: Kernel density estimation (KDE) is a major tool in the movement ecologist toolbox that is used to delineate where geo-tracked animals spend their time. Because KDE bandwidth optimizers are sensitive to temporal autocorrelation, statistically-robust alternatives have been advocated, first, data-thinning procedures, and more recently, autocorrelated kernel density estimation (AKDE). These yield asymptotically consistent, but very smoothed distributions, which may feature biologically unrealistic aspects such as spilling beyond impassable borders. METHOD: I introduce a semi-parametric variant of AKDE designed to extrapolate more realistic home range shapes by incorporating movement mechanisms into the bandwidth optimizer and into the base kernels. I implement a first approximative version based on the step selection framework. This method allows accommodating land cover selection, permeability of linear features, and attraction for select landscape features when delineating home ranges. RESULTS: In a plains zebra (Equus quagga), the reluctance to cross a railway, the avoidance of dense woodland, and the preference for grassland when foraging created significant differences between the estimated home range contours by the new and by previous methods. CONCLUSION: There is a tradeoff to find between fully parametric density estimators, which can be very realistic but need to be provided with a good model and adequate environmental data, and non-parametric density estimators, which are more widely applicable and asymptotically consistent, but whose details are bandwidth-limited. The proposed semi-parametric approach attempts to strike this balance, but I outline a few areas of future improvement. I expect the approach to find its use in studies that compare extrapolated resource availability and interpolated resource use, in order to discover the movement mechanisms that we need to improve the extrapolations. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40462-019-0161-9) contains supplementary material, which is available to authorized users.
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spelling pubmed-65300332019-05-28 Modified home range kernel density estimators that take environmental interactions into account Péron, Guillaume Mov Ecol Research BACKGROUND: Kernel density estimation (KDE) is a major tool in the movement ecologist toolbox that is used to delineate where geo-tracked animals spend their time. Because KDE bandwidth optimizers are sensitive to temporal autocorrelation, statistically-robust alternatives have been advocated, first, data-thinning procedures, and more recently, autocorrelated kernel density estimation (AKDE). These yield asymptotically consistent, but very smoothed distributions, which may feature biologically unrealistic aspects such as spilling beyond impassable borders. METHOD: I introduce a semi-parametric variant of AKDE designed to extrapolate more realistic home range shapes by incorporating movement mechanisms into the bandwidth optimizer and into the base kernels. I implement a first approximative version based on the step selection framework. This method allows accommodating land cover selection, permeability of linear features, and attraction for select landscape features when delineating home ranges. RESULTS: In a plains zebra (Equus quagga), the reluctance to cross a railway, the avoidance of dense woodland, and the preference for grassland when foraging created significant differences between the estimated home range contours by the new and by previous methods. CONCLUSION: There is a tradeoff to find between fully parametric density estimators, which can be very realistic but need to be provided with a good model and adequate environmental data, and non-parametric density estimators, which are more widely applicable and asymptotically consistent, but whose details are bandwidth-limited. The proposed semi-parametric approach attempts to strike this balance, but I outline a few areas of future improvement. I expect the approach to find its use in studies that compare extrapolated resource availability and interpolated resource use, in order to discover the movement mechanisms that we need to improve the extrapolations. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40462-019-0161-9) contains supplementary material, which is available to authorized users. BioMed Central 2019-05-21 /pmc/articles/PMC6530033/ /pubmed/31139416 http://dx.doi.org/10.1186/s40462-019-0161-9 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Péron, Guillaume
Modified home range kernel density estimators that take environmental interactions into account
title Modified home range kernel density estimators that take environmental interactions into account
title_full Modified home range kernel density estimators that take environmental interactions into account
title_fullStr Modified home range kernel density estimators that take environmental interactions into account
title_full_unstemmed Modified home range kernel density estimators that take environmental interactions into account
title_short Modified home range kernel density estimators that take environmental interactions into account
title_sort modified home range kernel density estimators that take environmental interactions into account
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6530033/
https://www.ncbi.nlm.nih.gov/pubmed/31139416
http://dx.doi.org/10.1186/s40462-019-0161-9
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