Authors: Dobson, Rachel; Jennings, Stewart;Willis, Stephen G.;Cheke, Robert A.;Challinor, Andrew;Dallimer, Martin;

Mobile species pose a unique challenge to conservation management globally. Established techniques are inherently static and hence are inadequate for monitoring and protecting mobile species with dynamic distributions in space and time. State-of-the-art remote sensing datasets offer high spatiotemporal resolution data covering a breadth of ecoclimatic variables. Here, we demonstrate statistical modelling methods that incorporate these variables, using an example of an African nomadic bird species. The models accurately project both inter- and intra- annual distributions and abundances at high resolution across large spatial extents. We explore the applications of these projections for mobile species conservation, such as assessing species vulnerability by monitoring range and abundance changes over time, or targeting priority sites and timing for action. Moreover, utilising the Google Earth Engine cloud platform, we demonstrate that these dynamic remote sensing variables can be easily incorporated into statistical models with minimal computing power, time and storage demands. We suggest that the generalised methods presented should be readily applied to other taxa and systems, with the potential to advance the conservation of mobile species worldwide.