A path reconstruction method integrating dead-reckoning and position fixes applied to humpback whales
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BACKGROUND: Detailed information about animal location and movement is often crucial in studies of natural behaviour and how animals respond to anthropogenic activities. Dead-reckoning can be used to infer such detailed information, but without additional positional data this method results in uncertainty that grows with time. Combining dead-reckoning with new Fastloc-GPS technology should provide good opportunities for reconstructing georeferenced fine-scale tracks, and should be particularly useful for marine animals that spend most of their time under water. We developed a computationally efficient, Bayesian state-space modelling technique to estimate humpback whale locations through time, integrating dead-reckoning using on-animal sensors with measurements of whale locations using on-animal Fastloc-GPS and visual observations. Positional observation models were based upon error measurements made during calibrations. RESULTS: High-resolution 3-dimensional movement tracks were produced for 13 whales using a simple process model in which errors caused by water current movements, non-location sensor errors, and other dead-reckoning errors were accumulated into a combined error term. Positional uncertainty quantified by the track reconstruction model was much greater for tracks with visual positions and few or no GPS positions, indicating a strong benefit to using Fastloc-GPS for track reconstruction. Compared to tracks derived only from position fixes, the inclusion of dead-reckoning data greatly improved the level of detail in the reconstructed tracks of humpback whales. Using cross-validation, a clear improvement in the predictability of out-of-set Fastloc-GPS data was observed compared to more conventional track reconstruction methods. Fastloc-GPS observation errors during calibrations were found to vary by number of GPS satellites received and by orthogonal dimension analysed; visual observation errors varied most by distance to the whale. CONCLUSIONS: By systematically accounting for the observation errors in the position fixes, our model provides a quantitative estimate of location uncertainty that can be appropriately incorporated into analyses of animal movement. This generic method has potential application for a wide range of marine animal species and data recording systems.
Wensveen , P J , Thomas , L & Miller , P J O 2015 , ' A path reconstruction method integrating dead-reckoning and position fixes applied to humpback whales ' Movement Ecology , vol 3 , 31 . DOI: 10.1186/s40462-015-0061-6
© 2015 Wensveen et al. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
PW received a PhD studentship with matched funding from The Netherlands Ministry of Defence (administered by TNO) and the UK Natural Environment Research Council (NE/J500276/1). The 3S2 project was funded by the US Office of Naval Research (N00014-10-1-0355), the Norwegian Ministry of Defence, and The Netherlands Ministry of Defence. Part of this work was supported by the Multi-study Ocean acoustics Human effects Analysis (MOCHA) project funded by the US Office of Naval Research (N00014-12-1-0204).
- NERC Sea Mammal Research Unit (SMRU) Research
- Mathematics & Statistics Research
- Institute of Behavioural and Neural Sciences Research
- Biology Research
- Centre for Research into Ecological & Environmental Modelling (CREEM) Research
- Statistics Research
- Scottish Oceans Institute Research
- Centre for Social Learning and Cognitive Evolution Research
- University of St Andrews Research
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