Reliable data on animal distribution and abundance are required to advance ecological inquiry and guide wildlife management. Data must be collected at appropriately large spatial and temporal scales to capture relevant processes for wide-ranging species and regional planning. Robust models are needed to project inferences into unsampled space and time, and inherent uncertainty must be transparently acknowledged and ultimately reduced. To strengthen inferences on wildlife dynamics, research in the WildCo Lab evaluates and integrates multiple sampling methods—including camera trapping, genetic tagging, remote sensing, and telemetry—and uses comparative analysis and simulation modelling to separate ecological signals from sampling noise. We apply advanced quantitative tools—such as spatially explicit capture-recapture and machine learning models—to disentangle complexities inherent in ecological data, and we collect new data designed to test model predictions. Our lab has a strong interest in improving the effectiveness of ecological monitoring and we are working to develop and implement a framework combining broad surveillance of cumulative effects with targeted assessments of hypotheses linked to management decisions. Our research also focuses on evaluating and effectively using participatory monitoring and citizen science to expand coverage and engage the public in wildlife science.
A key focus of our methodological research focuses on the effective use of camera trapping as a non-invasive wildlife survey tool. We are launching a new camera trap network to develop, test, and share rigorous methods for improving standardization and synthesis across camera trap studies.
Stay tuned for more information on the WildCAM network (Wildlife Cameras for Adaptive Management)!