globalchange  > 过去全球变化的重建
DOI: 10.1186/s40462-019-0162-8
WOS记录号: WOS:000470332800001
论文题名:
Using seasonal landscape models to predict space use and migratory patterns of an arctic ungulate
作者: Baltensperger, A. P.; Joly, K.
通讯作者: Baltensperger, A. P.
刊名: MOVEMENT ECOLOGY
ISSN: 2051-3933
出版年: 2019
卷: 7
语种: 英语
英文关键词: Alaska ; Caribou ; Climate ; Ecological niche model ; Infrastructure ; Machine learning ; Potential evapotranspiration ; Rangifer tarandus ; Snow ; Stochastic gradient boosting
WOS关键词: CARIBOU RANGIFER-TARANDUS ; CLIMATE-CHANGE ; ALASKA ; MOVEMENTS ; REINDEER ; CONSEQUENCES ; SUBSISTENCE ; PROJECTIONS ; POPULATION ; SELECTION
WOS学科分类: Ecology
WOS研究方向: Environmental Sciences & Ecology
英文摘要:

BackgroundCaribou 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.MethodsTo 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.ResultsDistribution 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 20-100km from these features.ConclusionsWestern 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.


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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/140192
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作者单位: Natl Pk Serv, 4175 Geist Rd, Fairbanks, AK 99709 USA

Recommended Citation:
Baltensperger, A. P.,Joly, K.. Using seasonal landscape models to predict space use and migratory patterns of an arctic ungulate[J]. MOVEMENT ECOLOGY,2019-01-01,7
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