forecasts of the magnitude and longevity of the event were very good; although forecasted high-temperature records fell 1–3 °C short of the observed highs in many cases.
Meteorologists are typically reluctant to make extreme weather forecasts at forecast horizons of around a week for fear of “crying wolf” and the associated reduction in end-user trust. In this case, however, the ensemble forecast provided sufficient certainty that meteorologists were able to warn of “extreme” heat at this relatively long-lead time—a testament to ensemble forecast technology.
Whilst the consensus method we used provided the best predictions under AUC assessment – seemingly confirming its potential for reducing model-based uncertainty in SDM predictions [58], [59] – its accuracy to predict changes in occupancy was lower than most single models. As a result, we advocate great care when selecting the ensemble of models from which to derive consensus predictions; as previously discussed by Araújo et al. [21], models should be chosen based on aspects of their individual performance pertinent to the research question being addressed, and not on the assumption that more models are better.
It's interesting that the ensembles perform best overall but more poorly for predicting changes in occupancy. It seems possible that ensembling multiple methods is basically resulting in a more static prediction, i.e., something closer to a naive baseline.