Dynamic modelling to predict the likelihood of plant species persistence under climate change

Background/Question/Methods

In the face of global climate change, it is important to predict the relative abilities of species to migrate with shifting conditions into new suitable areas, accounting for dynamic processes such as dispersal, maturation, mortality and reproduction, as well as landscape characteristics such as level of habitat fragmentation and connectivity. In this study, we developed a spatially-explicit individual-based model that represents the important processes likely to affect a plant species’ capacity to migrate across a landscape fast enough to keep pace with a changing climate. We also examined the effect of varying climate impact at different key points of vulnerability within a plant's lifecycle: at the juvenile establishment phase, adult phase, and reproduction phase. Finally, we investigated how the model could be used to inform management decisions regarding options such as assisted migration or the creation of large-scale corridors that increase the connectivity of fragmented landscapes in order to help species migrate naturally and find suitable environments in new climates.

Results/Conclusions

We found that even if the landscape is fully intact, only an average of 34.2% of all simulated functional groups had a good chance of successfully tracking climate change. Variation in the phase of life that climate change impacts showed no major effect on resulting persistence or extinction. However, our study highlights the power of strategic restoration as a tool for increasing species persistence.  Corridors linking fragments increased species persistence rates by up to 24%.  The lowest persistence rates were found for trees, a functional group with high dispersal but also long generation times. Our results indicate that for trees, intervention techniques such as assisted migration would be required to prevent species losses.