Underwater receiver networks (passive acoustic telemetry systems) are deployed to track animals in aquatic habitats all over the world, but remarkably limited attention has been given over to how we can strengthen the value of these networks through statistical and computational advances. Here, we upscale state-of-the-art methods of Bayesian inference to big animal-tracking datasets from acoustic telemetry, with the largest geolocation analysis in a sparse passive acoustic telemetry system (with non-overlapping receivers) to date. Using four years of data from 93 lake trout (Salvelinus namaycush) in North America's Lake Champlain (657,360 timesteps per individual), we formulate and fit state-space models to reconstruct animal movements through time. Uniquely, we directly embed biological expertise and detailed complementary datasets from fine-scale positioning systems, accelerometry, swim-tunnel experiments and field range tests in our analysis. Using simulated and real-world datasets, we map movement patterns and estimate residency in distinct management zones. We quantify array precision and deliver maps and residency estimates with a median error and precision (standard error) below 1 %. These results strengthen the evidence base for management. This work takes us a step towards robust inference of movement patterns at scale in acoustic telemetry systems across the world. We can, and should, build on prior scientific progress and extend the value of hard-earned data beyond individual studies to refine inferences for ecology and management.
Lavender, E., Futia, M. H., Scheidegger, A., Biber, S. W., Brodersen, J., Briers, R. A., Thorburn, J., Albert, C.
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