Arid
DOI10.1002/ece3.3936
A machine-learning approach for extending classical wildlife resource selection analyses
Shoemaker, Kevin T.1; Heffelfinger, Levi J.1; Jackson, Nathan J.1; Blum, Marcus E.1; Wasley, Tony2; Stewart, Kelley M.1
通讯作者Shoemaker, Kevin T.
来源期刊ECOLOGY AND EVOLUTION
ISSN2045-7758
出版年2018
卷号8期号:6页码:3556-3569
英文摘要

Resource selection functions (RSFs) are tremendously valuable for ecologists and resource managers because they quantify spatial patterns in resource utilization by wildlife, thereby facilitating identification of critical habitat areas and characterizing specific habitat features that are selected or avoided. RSFs discriminate between known-use resource units (e.g., telemetry locations) and available (or randomly selected) resource units based on an array of environmental features, and in their standard form are performed using logistic regression. As generalized linear models, standard RSFs have some notable limitations, such as difficulties in accommodating nonlinear (e.g., humped or threshold) relationships and complex interactions. Increasingly, ecologists are using flexible machine-learning methods (e.g., random forests, neural networks) to overcome these limitations. Herein, we investigate the seasonal resource selection patterns of mule deer (Odocoileus hemionus) by comparing a logistic regression framework with random forest (RF), a popular machine-learning algorithm. Random forest (RF) models detected nonlinear relationships (e.g., optimal ranges for slope and elevation) and complex interactions which would have been very challenging to discover and characterize using standard model-based approaches. Compared with standard RSF models, RF models exhibited improved predictive skill, provided novel insights about resource selection patterns of mule deer, and, when projected across a relevant geographic space, manifested notable differences in predicted habitat suitability. We recommend that wildlife researchers harness the strengths of machine-learning tools like RF in addition to classical tools (e.g., mixed-effects logistic regression) for evaluating resource selection, especially in cases where extensive telemetry data sets are available.


英文关键词habitat suitability logistic regression machine learning Odocoileus hemionus random forest resource selection function
类型Article
语种英语
国家USA
收录类别SCI-E
WOS记录号WOS:000428522100041
WOS关键词DESERT MULE DEER ; SPECIES DISTRIBUTION MODELS ; GLOBAL POSITIONING SYSTEM ; VARIABLE IMPORTANCE ; ECOLOGICAL DATA ; RANDOM FORESTS ; POPULATION-DYNAMICS ; LOGISTIC-REGRESSION ; HABITAT SELECTION ; LARGE HERBIVORES
WOS类目Ecology ; Evolutionary Biology
WOS研究方向Environmental Sciences & Ecology ; Evolutionary Biology
资源类型期刊论文
条目标识符http://119.78.100.177/qdio/handle/2XILL650/208798
作者单位1.Univ Nevada, Dept Nat Resources & Environm Sci, Reno, NV 89557 USA;
2.Nevada Dept Wildlife, Reno, NV USA
推荐引用方式
GB/T 7714
Shoemaker, Kevin T.,Heffelfinger, Levi J.,Jackson, Nathan J.,et al. A machine-learning approach for extending classical wildlife resource selection analyses[J],2018,8(6):3556-3569.
APA Shoemaker, Kevin T.,Heffelfinger, Levi J.,Jackson, Nathan J.,Blum, Marcus E.,Wasley, Tony,&Stewart, Kelley M..(2018).A machine-learning approach for extending classical wildlife resource selection analyses.ECOLOGY AND EVOLUTION,8(6),3556-3569.
MLA Shoemaker, Kevin T.,et al."A machine-learning approach for extending classical wildlife resource selection analyses".ECOLOGY AND EVOLUTION 8.6(2018):3556-3569.
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