Action plans for the conservation of biodiversity rely on forecasts of how species and natural systems will change in the future, and how they will respond to alternative management strategies. In an ideal world we – scientists and ecologists – would be able to provide those forecasts with great accuracy. The problem about making such predictions is that they cannot be validated without the ability to travel in time. Lacking this possibility, decision makers have to rely on current model predictions for making decisions that will have consequences on the species in the long term. But how do we know if our current predictions about the future distributions of species are accurate?
In a new study published in Global Ecology and Biogeography we evaluated what aspects of the data used to fit models, the model settings and the species traits determine robust predictions of species distributions into the future.
In the absence of data from the future, we looked at a set of 318 Australian terrestrial mammals, birds, amphibians and reptiles using a historical species dataset covering the period 1950–2013. For example, we put ourselves in the shoes of a modeler in the 1980s, made predictions about the 1990s and 2000s, and then checked how good those predictions were. We found that in general models that predict well for the current time period are also likely to provide good predictions into the future time periods, that is, they are likely to transfer well into the future. However, this transferability depends on many factors.
The most influential are the breadth of a species’ range (models for species with broad geographical ranges tended to perform worse over time than models for locally restricted species) and the environmental coverage of occupancy data (how well all environmental gradients suitable for the species have been sampled). Factors related to the species traits (e.g. taxonomic group, preference for a given habitat type or species body size) were not highly influential in describing the variation in predictive performance over time.
The key message of our study is that we can make more robust predictions by ensuring we use data that sample well the environmental and geographical space in which the species is known to exist. We should also strive to identify and map drivers of widespread and generalist species.