文献类型：期刊文献

英文题名：The use of classification and regression algorithms using the random forests method with presence-only data to model species' distribution

作者：Zhang, Lei[1] Huettmann, Falk[2] Zhang, Xudong[1] Liu, Shirong[3] Sun, Pengsen[3] Yu, Zhen[4] Mi, Chunrong[5]

第一作者：张雷

通信作者：Zhang, L[1]

机构：[1]Chinese Acad Forestry, Res Inst Forestry, Beijing 100091, Peoples R China;[2]Univ Alaska Fairbanks, Inst Arctic Biol, Dept Biol & Wildlife, Fairbanks, AK 99775 USA;[3]Chinese Acad Forestry, Res Inst Forest Ecol Environm & Protect, Beijing 100091, Peoples R China;[4]Iowa State Univ Sci & Technol, Dept Ecol Evolut & Organismal Biol, Ames, IA 50011 USA;[5]Chinese Acad Sci, Inst Zool, Beijing 100101, Peoples R China

年份：2019

卷号：6

起止页码：2281-2292

外文期刊名：METHODSX

收录：Scopus(收录号：2-s2.0-85073034526);WOS:【ESCI(收录号:WOS:000493729600247)】；

基金：This article was funded by the National Key R&D Program of China (2017YFC0505501, 2017YFC0505603) and the National Natural Science Foundation of China (41301056).

语种：英文

外文关键词：Binary prediction; Numerical prediction; Threshold; Machine learning; Species traits; Climate change; Forestation

摘要：Random forests (RF) is a powerful species distribution model (SDM) algorithm. This ensemble model by default can produce categorical and numerical species distribution maps based on its classification tree (CT) and regression tree (RT) algorithms, respectively. The CT algorithm can also produce numerical predictions (class probability). Here, we present a detailed procedure involving the use of the CT and RT algorithms using the RF method with presence-only data to model the distribution of species. CT and RT are used to generate numerical prediction maps, and then numerical predictions are converted to binary predictions through objective threshold-setting methods. We also applied simple methods to deal with collinearity of predictor variables and spatial autocorrelation of species occurrence data. A geographically stratified sampling method was employed for generating pseudo-absences. The detailed procedural framework is meant to be a generic method to be applied to virtually any SDM prediction question using presence-only data. How to use RF as a standard method for generic species distributions with presence-only data How to choose RF (CT or RT) methods for the distribution modeling of species A general and detailed procedure for any SDM prediction question. (C) 2019 The Authors. Published by Elsevier B.V.