Ecological niche models are frequently used in ensembles for forecasting range shifts for species under scenarios of climate change or biological invasion. In such applications, maximizing predictive power of model transfers across temporal and spatial dimensions is crucial. Among methods used to produce ensemble models, weighted averages are most widely used, with weights usually based on metrics of interpolative performance of models. Yet model extrapolative ability is not related directly to interpolative ability. Here, we assess and evaluate this often-overlooked aspect of ensemble forecasting. We designed two sets of virtual species distributed across continents. This allowed us to assess model transferability across global geographic spaces, as opposed to simple expansion into adjacent new environments or shifts into suitable conditions within the same general area. Individual niche models were calibrated on each continent and transferred to the other continents for evaluation. Performance of consensus and individual models, together with the methods (mean, median, weight average, and PCAm) that were used to produce consensus models, were compared using AUC metrics and commission and omission errors across the spectrum of model thresholds.
Results/Conclusions
Consensus models reflected the central tendency of the individual model but did not outperform individual models. Among methods used to generate consensus models, PCAm generally ranked higher than weighted averages, whereas mean and median were impacted by individual models. We highlight pitfalls in weighting individual models for ensemble models produced for model transfers. Regardless of whether models are to be transferred, we recommend using PCAm rather than weighted average for producing consensus models, as it outperformed other approaches and inherently reflects the constituent models’ central tendency sought in ensemble forecasting. These findings will have implications for how ensemble models are used in forecasting species’ range shifts under scenarios of climate change or biological invasion.