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Using seasonal landscape models to predict space use and migratory patterns of an arctic ungulate

BACKGROUND: Caribou in the Western Arctic Herd undertake one of the longest, remaining intact migrations of terrestrial mammals in the world. They are also the most important subsistence resource for many northern rural residents, who rely on the caribou’s migratory movements to bring them near for...

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Autores principales: Baltensperger, A. P., Joly, K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6551874/
https://www.ncbi.nlm.nih.gov/pubmed/31183112
http://dx.doi.org/10.1186/s40462-019-0162-8
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author Baltensperger, A. P.
Joly, K.
author_facet Baltensperger, A. P.
Joly, K.
author_sort Baltensperger, A. P.
collection PubMed
description BACKGROUND: Caribou in the Western Arctic Herd undertake one of the longest, remaining intact migrations of terrestrial mammals in the world. They are also the most important subsistence resource for many northern rural residents, who rely on the caribou’s migratory movements to bring them near for harvest. Migratory geography has never been static, but subsistence harvesters have reported recent shifts in migration away from areas where they traditionally occurred. The reasons behind these changes are not well-understood, but may be related to rapid climate change and anthropogenic disturbances. METHODS: To predict changes in distribution and shifting migratory areas over the past decade, we used GPS telemetry data from adult females to develop predictive ecological niche models of caribou across northwestern Alaska. We employed the machine-learning algorithm, TreeNet, to analyze interactive, multivariate relationships between telemetry locations and 37 spatial environmental layers and to predict the distributions of caribou during spring, calving season, insect-harassment season, late summer, fall, and winter from 2009 to 2017. Model results were analyzed to identify regions of repeated predicted use, quantify mean longitude, predict land cover selection, and track migratory changes over time. RESULTS: Distribution models accurately predicted caribou at a spatially-explicit, 500-m scale. Model analyses identified migratory areas that shifted annually across the region, but which predicted 4 main areas of repeated use. Niche models were defined largely by non-linear relationships with coastally-influenced, climatic variables, especially snow-free date, potential evapo-transpiration, growing season length, proximity to sea ice, winter precipitation and fall temperature. Proximity to roads and communities were also important and we predicted caribou to generally occur more than 20–100 km from these features. CONCLUSIONS: Western Arctic Herd caribou were predicted to occur in warmer, snow-free and treeless areas that may provide conditions conducive for efficient travel and foraging. Rapidly changing seasonal climates and coastal influences that determine forage availability, and human impediments that slow or divert movements are related to geographically and phenologically dynamic migration patterns that may periodically shift caribou away from traditional harvest areas. An enhanced understanding of the geographic behavior of caribou over time could inform traditional harvests and help conserve important Western Arctic caribou migratory areas. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40462-019-0162-8) contains supplementary material, which is available to authorized users.
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spelling pubmed-65518742019-06-10 Using seasonal landscape models to predict space use and migratory patterns of an arctic ungulate Baltensperger, A. P. Joly, K. Mov Ecol Research BACKGROUND: Caribou in the Western Arctic Herd undertake one of the longest, remaining intact migrations of terrestrial mammals in the world. They are also the most important subsistence resource for many northern rural residents, who rely on the caribou’s migratory movements to bring them near for harvest. Migratory geography has never been static, but subsistence harvesters have reported recent shifts in migration away from areas where they traditionally occurred. The reasons behind these changes are not well-understood, but may be related to rapid climate change and anthropogenic disturbances. METHODS: To predict changes in distribution and shifting migratory areas over the past decade, we used GPS telemetry data from adult females to develop predictive ecological niche models of caribou across northwestern Alaska. We employed the machine-learning algorithm, TreeNet, to analyze interactive, multivariate relationships between telemetry locations and 37 spatial environmental layers and to predict the distributions of caribou during spring, calving season, insect-harassment season, late summer, fall, and winter from 2009 to 2017. Model results were analyzed to identify regions of repeated predicted use, quantify mean longitude, predict land cover selection, and track migratory changes over time. RESULTS: Distribution models accurately predicted caribou at a spatially-explicit, 500-m scale. Model analyses identified migratory areas that shifted annually across the region, but which predicted 4 main areas of repeated use. Niche models were defined largely by non-linear relationships with coastally-influenced, climatic variables, especially snow-free date, potential evapo-transpiration, growing season length, proximity to sea ice, winter precipitation and fall temperature. Proximity to roads and communities were also important and we predicted caribou to generally occur more than 20–100 km from these features. CONCLUSIONS: Western Arctic Herd caribou were predicted to occur in warmer, snow-free and treeless areas that may provide conditions conducive for efficient travel and foraging. Rapidly changing seasonal climates and coastal influences that determine forage availability, and human impediments that slow or divert movements are related to geographically and phenologically dynamic migration patterns that may periodically shift caribou away from traditional harvest areas. An enhanced understanding of the geographic behavior of caribou over time could inform traditional harvests and help conserve important Western Arctic caribou migratory areas. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40462-019-0162-8) contains supplementary material, which is available to authorized users. BioMed Central 2019-06-06 /pmc/articles/PMC6551874/ /pubmed/31183112 http://dx.doi.org/10.1186/s40462-019-0162-8 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
Baltensperger, A. P.
Joly, K.
Using seasonal landscape models to predict space use and migratory patterns of an arctic ungulate
title Using seasonal landscape models to predict space use and migratory patterns of an arctic ungulate
title_full Using seasonal landscape models to predict space use and migratory patterns of an arctic ungulate
title_fullStr Using seasonal landscape models to predict space use and migratory patterns of an arctic ungulate
title_full_unstemmed Using seasonal landscape models to predict space use and migratory patterns of an arctic ungulate
title_short Using seasonal landscape models to predict space use and migratory patterns of an arctic ungulate
title_sort using seasonal landscape models to predict space use and migratory patterns of an arctic ungulate
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6551874/
https://www.ncbi.nlm.nih.gov/pubmed/31183112
http://dx.doi.org/10.1186/s40462-019-0162-8
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