Estimating individual animal movement from observation networks

1. Observation network data comprise animal presences detected by observer stations at fixed spatial locations. Statistical analysis of these data is complicated by spatial bias in sampling and temporal variability in detection conditions. Advanced methods for analysis of these data are required but are currently underdeveloped. 2. We propose a state-space model for observation network data to estimate detailed movements of individual animals. The underlying movement model is an Ornstein-Uhlenbeck (OU) process, which is stationary and therefore has an inherent mechanism that models home range behaviour. An integral part of the approach is the detection function, which models the probability of logging animal presences. The detection function is also used to provide absence information when animals are undetected. Since the ability to detect an animal often depends on time varying external factors such as environmental conditions we use covariate information about detection efficiency as control variables. 3. Via simulation we found that movement estimation error scales log-linearly with network sparsity. This result can be used to indicate the number of stations necessary to achieve a desired upper bound on estimation error. Furthermore we found that the state-space model outperforms existing techniques in terms of estimating detailed movements, and that estimates are robust toward misspecification of the detection function. We also tested the importance of accounting for time varying detection conditions and found that the probability of making wrong conclusions decreases substantially when covariate information is exploited. 4. The model is used to estimate movements and home range of a humphead wrasse (Cheilinus