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

Ecological 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.

Mapping Potential Distribution of Spodoptera frugiperda (Lepidoptera: Noctuidae) in Central Asia

“…It is apparent that the environmental dataset plays an important role in model construction, and may have a profound impact on its transferability [23]. Temperature and humidity are the main environmental factors that affect the growth, development and distribution of insects.…”

Predicting the effects of climate change on future freshwater fish diversity at global scale

“…A peak of instability is observed when there are important differences in the predictor comparing the bins of presence with the corresponding ones of the study area. This index outperforms other methods proposed to identify the most appropriate environmental factors (Guisande et al 2017a;Fan et al 2018). The explanatory variables with the highest percentage contributions to the Instability Index would be those that most affect the distribution of the species in the accessible area.…”

The effects of climate warming and urbanised areas on the future distribution of Cortaderia selloana, pampas grass, in France

“…We modified the regularisation coefficient values by testing values of 1, 2, 5, 8, 10 and 15 in an attempt to produce less complex and transferable models (Merow et al ., ). Models based on fine‐tuned Maxent settings generally involve better discrimination ability than those based on default settings (Fan et al ., ). All the generated models were compared and selected based on the corrected Akaike information criterion (AICc) using ENMtools (Warren et al ., ).…”