  Predictions of biodiversity trajectories under climate change are crucial in
order to act effectively in maintaining the diversity of species. In many
ecological applications, future predictions are made under various global
warming scenarios as described by a range of different climate models. The
outputs of these various predictions call for a reliable interpretation. We
propose a interpretable and flexible two step methodology to measure the
similarity between predicted species range maps and cluster the future scenario
predictions utilizing a spectral clustering technique. We find that clustering
based on ecological impact (predicted species range maps) is mainly driven by
the amount of warming. We contrast this with clustering based only on predicted
climate features, which is driven mainly by climate models. The differences
between these clusterings illustrate that it is crucial to incorporate
ecological information to understand the relevant differences between climate
models. The findings of this work can be used to better synthesize forecasts of
biodiversity loss under the wide spectrum of results that emerge when
considering potential future biodiversity loss.
