Disentangling uncertainties from niche modeling in freshwater ecosystems

Abstract

Predictions by ecological niche models (ENM) are affected by several sources of uncertainty including the modeling methods and type of variables employed. The predictive uncertainty has been often assessed in terrestrial ecosystems, but it is still unknown how freshwater variables affect the performance of ENMs, contributing to unreliable predictions for aquatic species. Here, we used the ecologically and economically relevant Amazon giant catfish (Brachyplatystoma filamentosum) as a model species to assess uncertainties on ENM predictions in freshwater ecosystems. Specifically, we assessed uncertainty by coupling ENM predictions using five modeling methods and four sets of freshwater environmental variables. Our results indicate that the modeling methods and secondarily the variables account for significant uncertainty in predicting freshwater species distribution using ENM. Areas with high environmental suitability such as the Amazon large rivers and nearby areas presented high uncertainty for the methods component, and lower uncertainties for freshwater variables. Moreover, freshwater variables accounted also for uncertainties in metrics of models’ performance. Whereas Topographic variables better predicted presences (higher sensitivities and lower omission errors), Land cover and Soil variables better predicted pseudo-absences (higher specificities and lower commission errors). The Hydroclimatic variables had better accuracy metrics values (AUC and TSS) but also generated the greatest uncertainty for the final models. When included variables from all groups, ENMs presented low uncertainties and good accuracy. In sum, our findings suggest the importance of measuring and mapping the uncertainties of ENMs using freshwater environmental database.