Department of Biology, McGill University
Detecting range shifts requires repeated surveys across space and long durations of time. As such, our key datasets have been opportunistic, often coarse in spatial and temporal grain, and difficult to synthesize coherently. Yet the promise of range shift detections remains that systematic observations at high spatial and temporal resolution can allow us to better inform predictive models, that incorporate the role of ecological interactions and evolutionary change. How will we achieve the quality of observational data required? Here I review the limitations in our current approaches in terms of signal, noise, and power for testing theory and building more predictive models. I highlight the qualities and potential of our best datasets, and build an argument for using new technologies and approaches for global, systematic biogeographic monitoring.