Predicting the potential distribution of invasive exotic species using GIS and information-theoretic approaches:A case of ragweed(Ambrosia artemisiifolia L.)distribution in China  

作　　者：CHEN Hao CHEN LiJun Thomas P. ALBRIGHT 

机构地区：[1]School of Remote Sensing Information Engineering, Wuhan University, Wuhan 430079, China [2]National Geomatics Center of China, Beijing 100080, China [3]SAIC, USGS Center for EROS, Sioux Falls, SD 57198, USA [4]Department of Zoology, University of Wisconsin-Madison, WI, USA

出　　处：《Chinese Science Bulletin》2007年第9期1223-1230,共8页

基　　金：Supported by the National Natural Science Foundation of China (Grant No. 40371084);U.S. Geological Survey (Grant No. 03CRCN0001);UW-Madison funded under U.S. Geological Survey cooperative agreement (Grant No. 03CRAG0016)

摘　　要：Invasive exotic species pose a growing threat to the economy,public health,and ecological integrity of nations worldwide. Explaining and predicting the spatial distribution of invasive exotic species is of great importance to prevention and early warning efforts. We are investigating the potential distribution of invasive exotic species,the environmental factors that influence these distributions,and the ability to predict them using statistical and information-theoretic approaches. For some species,detailed presence/absence occurrence data are available,allowing the use of a variety of standard statistical techniques. However,for most species,absence data are not available. Presented with the challenge of developing a model based on presence-only information,we developed an improved logistic regres-sion approach using Information Theory and Frequency Statistics to produce a relative suitability map. This paper generated a variety of distributions of ragweed(Ambrosia artemisiifolia L.) from logistic regression models applied to herbarium specimen location data and a suite of GIS layers including climatic,topographic,and land cover information. Our logistic regression model was based on Akaike's Information Criterion(AIC) from a suite of ecologically reasonable predictor variables. Based on the results we provided a new Frequency Statistical method to compartmentalize habitat-suitability in the native range. Finally,we used the model and the compartmentalized criterion developed in native ranges to “project” a potential distribution onto the exotic ranges to build habitat-suitability maps.Invasive exotic species pose a growing threat to the economy, public health, and ecological integrity of nations worldwide. Explaining and predicting the spatial distribution of invasive exotic species is of great importance to prevention and early warning efforts. We are investigating the potential distribution of invasive exotic species, the environmental factors that influence these distributions, and the ability to predict them using statistical and information-theoretic approaches. For some species, detailed presence/absence occurrence data are available, allowing the use of a variety of standard statistical techniques. However, for most species, absence data are not available. Presented with the challenge of developing a model based on presence-only information, we developed an improved logistic regression approach using Information Theory and Frequency Statistics to produce a relative suitability map. This paper generated a variety of distributions of ragweed （Ambrosia artemisiifolia L.） from logistic regression models applied to herbarium specimen location data and a suite of GIS layers including climatic, topographic, and land cover information. Our logistic regression model was based on Akaike＇s Information Criterion （AIC） from a suite of ecologically reasonable predictor variables. Based on the results we provided a new Frequency Statistical method to compartmentalize habitat-suitability in the native range. Finally, we used the model and the compartmentalized criterion developed in native ranges to ＂project＂ a potential distribution onto the exotic ranges to build habitat-suitability maps.

关 键 词：外来入侵物种 潜在分布 预测 信息论 GIS 豚草属 

分 类 号：S451[农业科学—植物保护] Q948.122[生物学—植物学]