What are the best predictors for invasive potential of weeds? Transferability evaluations of model predictions based on diverse environmental data sets for Flaveria bidentis

Abstract

SummaryEcological niche models are widely used in the study of weed invasions, yet best approaches for selecting ecologically relevant environmental predictors for weeds remain unresolved. Here, we evaluate niche model transferability based on diverse environmental data sets for an invasive herb, Flaveria bidentis. This species is native to South America, but has established populations in China that pose a threat to agriculture and animal husbandry. Relevant environmental data sets were selected via five statistical approaches: permutation importance (PI) and jackknife test (JK) in Maxent, variable importance identified by boosting regression trees (BRT), ecological niche factor analysis (ENFA) and a newly released algorithm based on a fluctuation index (FI). Climate spaces occupied by native South American and introduced Chinese populations were compared based on these environmental data sets. Native niche model predictions in China were compared across environmental data sets and model settings (i.e. default versus fine‐tuned Maxent settings). Results suggest that native and introduced populations occupy two distinct climate spaces, but that this divergence likely results from background effects. Niche models based on fine‐tuned Maxent settings generally showed better discrimination ability than those based on default settings. The best model discrimination in China was attained in the FI model using fine‐tuned settings, followed by the BRT model on default settings. The best models suggest that highly suitable areas at risk of invasion in China are to the west and north‐east of present distributional areas. Results presented here provide predictions for F. bidentis in particular, but also shed light on procedures for selecting ecologically relevant predictors for invasive species distributional predictions more generally.