A dynamic Brownian bridge movement model to estimate utilization distributions for heterogeneous animal movement.

摘要: 1. The recently developed Brownian bridge movement model (BBMM) has advantages over traditional methods because it quantifies the utilization distribution of an animal based on its path rather than individual points and accounts for temporal autocorrelation high data volumes. However, BBMM assumes unrealistic homogeneous behaviour across all data. 2. Accurate quantification is important identifying way animals use landscape. 3. We improve by allowing changes in behaviour, using likelihood statistics to determine change along animal's path. 4. This novel extension, outperforms current as indicated simulations examples a territorial mammal migratory bird. unique ability our work with tracks that are not sampled regularly especially GPS tags have frequent failed fixes or dynamic sampling schedules. Moreover, extension provides useful one-dimensional measure behavioural tracks. 5. new method more accurate better describes space realistic, behaviourally heterogeneous