Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.1002/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
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ISSN | 2045-7758 |
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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