Estimating individual animal movement from observation networks


Correspondence author. E-mail: and


  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 (SSM) 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 SSM outperforms existing techniques in terms of estimating detailed movements and that estimates are robust towards mis-specification 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 undulatus) at Palmyra Atoll in the central Pacific Ocean. Here, detection conditions have a strong diel component, which is controlled for using detection efficiency information from a reference device.
  5. The presented approach enhances the toolbox for analysis of observation network data as collected by acoustic telemetry or potentially other aspiring methods such as camera trapping and mobile phone tagging. By explicitly modelling movement and observation processes, the model integrates all sources of uncertainty and provides a sound statistical basis for making well-informed management decisions from imperfect information.