Uncertainty in ensemble modelling of large-scale species distribution: Effects from species characteristics and model techniques
Species distribution models (SDM) have been routinely used for the purpose of species conservation and biodiversity management, especially in the context of global climate change. However, there is little knowledge about the uncertainty source on the SDM for the predictions in aquatic ecosystems, especially in the large-scale research. Therefore, we contribute to the first perspective on the uncertainties of SDMs in predicting fish species distribution in lake ecosystems. In total, 92 fish species were predicted with climatic and geographical variables, respectively, using nine widely implemented species distribution models. Generally, we focused on the potential impacts from two main kinds of uncertainty sources: species characteristics (containing species prevalence, altitude range, temperature range and precipitation range) and model technique (calibration technique and evaluation technique). Finally, our results highlight that predictions from single SDM were so variable and unreliable for all species while ensemble approaches could yield more accurate predictions; we also found that there was no significant influence on the model outcomes from the evaluation measures; we emphasized that species characteristics as species prevalence, altitude range size and precipitation range size would strongly affect the outcomes of SDMs, but temperature range size didn't show a significant influence; our findings finally verified the hypothesis that species distributed with a smaller range size could be more accurately predicted than species with large range size was plausible in aquatic ecosystems. Our research would provide promising insights into the prediction of fish species in aquatic ecosystems under the impacts of global climate change, especially for the conservation of endemic fish species in China. Moreover, our results improved the understanding of uncertainties from species characteristics and modelling techniques in species distribution model. (C) 2014 Elsevier B.V. All rights reserved.