Study on selecting sensitive environmental variables in modelling species spatial distribution

• Main Authors: WANG, Hongshuo, LIU, Desheng, MUNROE, Darla, CAO, Kai, BIERMAN, Christine
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: Environmental variables; factor analysis; redundant information; sensitivity index; spatial variations; species distribution models; Databases and Information Systems; Urban, Community and Regional Planning
• Online Access: https://ink.library.smu.edu.sg/sis_research/5453; https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=6456&context=sis_research
• Institution: Singapore Management University

This study explores the effects of different environmental variables on the accuracy of species distribution models. Forest inventory and analysis data sets were used to generate absence and pseudo-absence points of chestnut oak (Quercus prinus) in the central and southern Appalachian mountain region of the US. We simulate chestnut oak distribution using different criteria for selecting environmental variables: (1) the selection of sensitive variables using factor analysis and the calculation of a sensitivity index, (2) principal components analysis. Factor analysis to environmental variables at both occurrence and pseudo-absence points was conducted to calculate the sensitivity index for each environmental variable. The identification of sensitive variables may use the factor loadings of first one or two factors of environmental variables. Modelling with sensitive variables (mean Kappa > 0.60; mean true skill statistic (TSS) > 0.60) can enhance model accuracy more than using PCA variables or all available environmental variables (mean Kappa ranges from 0.45 to 0.65; mean TSS ranges from 0.40 to 0.70). Modelling with leading principal components (larger than 90% variations) can achieve similar or higher accuracy than modelling with all variables. The influence of redundant information on species modelling varies with the model used. Our results suggest that selecting environmental variables using a sensitivity index defined by factor analysis may improve model accuracy and reduce redundant information in species modelling. The proposed method for selecting sensitive variables is easy to implement and has strong ecological interpretability.