A state-space model for estimating detailed movements and home range from acoustic receiver data

We present a state-space model for acoustic receiver data to estimate detailed movement and home range of individual fish while accounting for spatial bias. An integral part of the approach is the detection function, which models the probability of logging tag transmissions as a function of distance to receiver. The same function is used to provide absence information at times where no detections occur. In a simulation study we found that the ability of the state-space model to estimate detailed movements outperform existing non-mechanistic techniques in terms of location error. We also found that the location error scales log-linearly with detection range and movement speed. This result can be used as guideline for designing network layout when species movement capacity and acoustic environment are known or can be estimated prior to network deployment. Finally, as an example, the state-space model is used to estimate home range and movement of a reef fish in the Pacific Ocean.