Improving ecological connectivity assessments with transfer learning and function approximation

Protecting and restoring ecological connectivity is essential to climate change adaptation, and necessary if species are to shift their geographic distributions to track their suitable climatic conditions over the coming century. Despite the increasing availability of near real-time and high resolution data for landcover change, current connectivity planning projects are hindered by the computational time required to run connectivity analyses at realistic geographic scales with realistic models of movement. This bottleneck precludes application of optimization algorithms to prioritize ecological restoration to maintain and improve connectivity. Here we propose we can make progress toward overcoming these challenges using machine-learning methods. Our proposed methods will enable rapid optimization of connectivity prioritization and extend its application to many more species than is currently possible. We conclude by illustrating how this project will contribute to efforts to apply connectivity conservation using an example of ongoing restoration in southern Québec.