Estimating individual animal movement from observation networks
Article first published online: 9 AUG 2013
© 2013 The Authors. Methods in Ecology and Evolution © 2013 British Ecological Society
Methods in Ecology and Evolution
Volume 4, Issue 10, pages 920–929, October 2013
How to Cite
Pedersen, M. W., Weng, K. C. (2013), Estimating individual animal movement from observation networks. Methods in Ecology and Evolution, 4: 920–929. doi: 10.1111/2041-210X.12086
- Issue published online: 7 OCT 2013
- Article first published online: 9 AUG 2013
- Accepted manuscript online: 12 JUL 2013 12:52AM EST
- Manuscript Accepted: 3 JUN 2013
- Manuscript Received: 13 JAN 2013
- Pelagic Fisheries Research Program (PFRP). Grant Number: NA17RJ1230/NA09OAR4320075
- Joint Institute for Marine and Atmospheric Research (JIMAR)
- National Oceanic and Atmospheric Administration (NOAA)
- NOAA's Undersea Research Program
- Coral Reef Conservation Program. Grant Numbers: NA05OAR4301108, NA09OAR4300219
- Hawaii Undersea Research Laboratory
- acoustic telemetry;
- detection probability;
- Ornstein–Uhlenbeck process;
- state-space model
- 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.
- 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.
- 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.
- 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.
- 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.