Bayesian three-dimensional reconstruction of toothed whale trajectories: Passive acoustics assisted with visual and tagging measurements

Laplanche, Christophe

doi:10.1121/1.4757740

Cited by 3 publications

(2 citation statements)

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“…Acceleration (three coordinates altogether) also differs across states: descent (0.000 ±0.091 m s

^{- 2}

), at depth (0.001 ± 0.200 m s

^{- 2}

) and ascent (0.000 ± 0.081 m s

^{- 2}

). The latter values could also be used to smooth animal tracks computed from acoustic surveys, as described by Laplanche ().…”

Section: Discussionmentioning

confidence: 99%

“…One of the advantages of implementing the model in a Bayesian framework is that incorporation of additional data sources and propagating corresponding observation errors is conceptually straightforward. Acoustic‐based localization could be used as direct observations or provide time of arrival differences (TDOA) data instead of computed localization, by combining our model with that of Laplanche (), which would deal with propagating TDOA errors to localization estimates.…”

Section: Discussionmentioning

confidence: 99%

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Tracking marine mammals in 3D using electronic tag data

Marques

Thomas

2015

Methods Ecol Evol

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Summary1. Information about at-depth behaviour of marine mammals is fundamental yet very hard to obtain from direct visual observation. Animal-borne multisensor electronic tags provide a unique window of observation into such behaviours. 2. Electronic tag sensors allow the estimation of the animal's 3-dimensional (3D) orientation, depth and speed. Using tag flow noise level to provide an estimate of animal speed, we extend existing approaches of 3D track reconstruction by allowing the direction of movement to differ from that of the animal's longitudinal axis. 3. Data are processed by a hierarchical Bayesian model that allows processing of multisource data, accounting for measurement errors and testing hypotheses about animal movement by comparing models. 4. We illustrate the approach by reconstructing the 3D track of a 52-min deep dive of a Blainville's beaked whale Mesoplodon densirostris adult male fit with a digital tag (DTAG) in the Bahamas. At depth, the whale alternated regular movements at large speed (>1Á5 m s À1 ) and more complex movements at lower speed (<1Á5m s À1 ) with differences between movement and longitudinal axis directions of up to 28 . The reconstructed 3D track agrees closely with independent acoustic-based localizations. 5. The approach is potentially applicable to study the underwater behaviour (e.g. response to anthropogenic disturbances) of a wide variety of species of marine mammals fitted with triaxial magnetometer and accelerometer tags.

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“…Acceleration (three coordinates altogether) also differs across states: descent (0.000 ±0.091 m s

^{- 2}

), at depth (0.001 ± 0.200 m s

^{- 2}

) and ascent (0.000 ± 0.081 m s

^{- 2}

). The latter values could also be used to smooth animal tracks computed from acoustic surveys, as described by Laplanche ().…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Tracking marine mammals in 3D using electronic tag data

Marques

Thomas

2015

Methods Ecol Evol

Self Cite

View full text Add to dashboard Cite

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Crack Identification by Digital Image Correlation Method Using Crack Shape as Prior Information

Hana

Umeda

Akiyoshi

et al. 2023

Journal of Pressure Vessel Technology

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A new crack identification method that estimates the cracks in invisible locations based on the surface deformation measured by digital image correlation (DIC) is developed. An inverse problem is set up to estimate such invisible cracks from surface deformations. The inverse problem has an ill-condition because of noise contained in surface deformations. Our proposed regularization method uses prior information and Expectation a Posteriori (EAP) estimation. Prior information includes candidate crack shapes and surface deformations due to cracks. The candidate crack shapes are created by determining a crack's starting point and propagating it based on the force at its perimeter (ligament). A prior distribution is the surface deformations due to the candidate crack shapes. The likelihood distribution is a surface deformation measured by the DIC method. A posterior distribution is defined from the prior and likelihood distributions. In this study, the estimated result is the expected value of the posterior distribution. The validation test was performed, and the result shows that the proposed method superior to conventional L1-norm regularization method.

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A path reconstruction method integrating dead-reckoning and position fixes applied to humpback whales

2015

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BackgroundDetailed 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.ResultsHigh-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. ConclusionsBy 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s40462-015-0061-6) contains supplementary material, which is available to authorized users.

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Bayesian three-dimensional reconstruction of toothed whale trajectories: Passive acoustics assisted with visual and tagging measurements

Cited by 3 publications

References 39 publications

Tracking marine mammals in 3D using electronic tag data

Tracking marine mammals in 3D using electronic tag data

Crack Identification by Digital Image Correlation Method Using Crack Shape as Prior Information

A path reconstruction method integrating dead-reckoning and position fixes applied to humpback whales

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