From detections to demography: methodological challenges and opportunities in survival estimation from automated telemetry networks

Survival is a fundamental demographic process influencing individual fitness and population dynamics. Automated telemetry systems offer unprecedented opportunities to estimate survival of highly mobile organisms across broad spatial and temporal scales. However, the structure of these tracking networks creates systematic biases that render standard survival models inadequate. Receiver stations are distributed unevenly across landscapes and over time, meaning that individuals taking different movement paths encounter vastly different opportunities for detection. This spatial clustering also creates problematic dependencies: animals detected in receiver-dense areas are more likely to be detected again, while those in sparse areas may disappear from view despite being alive. These observation biases can easily be mistaken for genuine differences in survival, leading to incorrect ecological or conservation inferences. Here, we review the unique challenges posed by automated telemetry data for survival estimation, highlight existing methodological solutions including spatially explicit approaches and modified capture-mark-recapture frameworks, and provide recommendations for advancing this critical application of wildlife tracking networks.