Projecting future expansion of invasive species: comparing and improving methodologies for species distribution modeling

作者：Kumar P. Mainali, Dan L. Warren, K. Dhileepan, Andrew John McConnachie, L. W. Strathie, Gul Hassan, Debendra Karki, Bharat Babu Shrestha, Camille Parmesan · 发表于：Global Change Biology · 年份：2015 · DOI：10.1111/gcb.13038 · 被引用次数：320 · 研究领域：Species Distribution and Climate Change、Plant and animal studies、Ecology and Vegetation Dynamics Studies

Modeling the distributions of species, especially of invasive species in non-native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species-environment relationships for Parthenium hysterophorus L. (Asteraceae) with four modeling methods run with multiple scenarios of (i) sources of occurrences and geographically isolated background ranges for absences, (ii) approaches to drawing background (absence) points, and (iii) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved using a global dataset for model training, rather than restricting data input to the species' native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e., into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interaction...