Bayesian Estimation of Animal Movement from Archival and Satellite Tags

Sumner, Michael; Wotherspoon, Simon; Hindell, Mark A.

doi:10.1371/journal.pone.0007324

Cited by 170 publications

(171 citation statements)

References 43 publications

(60 reference statements)

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“…This function provides a direct implementation of the MetropolisHastings algorithm (Metropolis et al 1953, Hastings 1970, to run location estimates using Markov Chain Monte Carlo (MCMC). MCMC is a Bayesian simulation approach which enables the user to make inferences about unknown states and parameters of the model by simulating values of the states and parameters conditional on previously generated values until the chain of samples converges to the posterior distribution (see Geman & Geman 1984, Gilks et al 1996, Patterson et al 2008, Sumner et al 2009). Finally, the estimation model runs over the simulations, and the maximum likelihood estimates are saved for each simulation in the 'ch' object.…”

Section: Methodsmentioning

confidence: 99%

Quantitative method to estimate species habitat use from light-based geolocation data

Thiebot¹,

Pinaud²

2010

Endang. Species. Res.

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The development of biologging techniques has been instrumental in studying the behaviour of wild animals and interpreting it with respect to the bio-physical features of their habitat. Light-based geolocation currently appears to be the only technique suitable for the study of farranging small species, particularly marine species, over long periods, but it provides locations with low precision. In this study, we sought to improve the exploitation of these data. Specifically, the goals were to (1) correct rather than reject estimates, especially during equinox periods, (2) perform reproducible time-saving routine analyses, and (3) determine the spatial likelihood of the estimations to adapt inferences on habitat use by the population. We therefore applied an existing data-processing method based on spatial template fitting, using Markov Chain Monte Carlo and state-space modelling (Kalman filter) improved by a facultative sea surface temperature-matching procedure and a land mask. The main functions used for geolocation are grouped under the R package 'TripEstimation', freely available online. We focused on a typical example of animal movement that at present can only be ethically obtained from light-based geolocation. The method made it possible to estimate realistic positions during equinox periods and to routinely process the 12 available datasets. We thus obtained the most probable location for each sunrise/sunset as well as the posterior distribution around each estimated location, allowing an intuitive habitat use investigation at a scale of 100 km. This paper describes the method used and provides the complete and comprehensively annotated commands required for its use.

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Section: Methodsmentioning

confidence: 99%

Quantitative method to estimate species habitat use from light-based geolocation data

Thiebot¹,

Pinaud²

2010

Endang. Species. Res.

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“…We used a Bayesian framework to refine the initial, rough positions estimated from the threshold method and to derive uncertainty estimates. The R package 'SGAT' (Wotherspoon et al 2013b) uses Markov Chain Monte Carlo (MCMC) simulations allowing the incorporation of a spatial probability mask, prior definition of the error distribution of twilight events (twilight model) and a flight speed distribution to refine location estimates (for detailed information see Sumner et al 2009 andLisovski et al 2016). The twilight model should reflect the expected error in detecting the real time of sunrise and sunset.…”

Section: Movement Pathway Analysismentioning

confidence: 99%

Consistent variation in individual migration strategies of brown skuas

Krietsch¹,

Hahn²,

Kopp³

et al. 2017

Mar. Ecol. Prog. Ser.

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Seabirds show remarkable variability in migration strategies among individuals and populations. In this study, we analysed 47 migrations of 28 brown skuas Catharacta antarctica lonnbergi breeding on King George Island in the Maritime Antarctic. Brown skuas from this population used a large area during the non-breeding period north of 55°S, including parts of the Patagonian Shelf, Argentine Basin and South Brazil Shelf, areas which are characterised by high levels of marine productivity. However, individual birds utilised only a subset of these areas, adopting 1 of 4 distinct migration strategies to which they were highly faithful between years, and showed high repeatability in departure and arrival dates at the breeding ground. Although they spent the majority of the non-breeding season within a particular region, almost all individuals used the same area in the late winter, exploiting its seasonal peak in productivity. Overall, these results indicate consistent individual variation in migration strategies that may reflect a combination of genetic control and individual experience, but with considerable flexibility to shift distribution in response to prevailing environmental conditions. KEY WORDS: Catharacta antarctica lonnbergi · Seabird ecology · Light-level geolocation · Non-breeding distribution · Individual consistency · Ocean primary productivity · Migratory connectivity Contribution to the Theme Section 'Individual varability in seabird foraging and migration'

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“…We used average travelling speed calculated from the GPS surveys (22.6 km h −1 ) to set up the specific movement model used to constrain location estimates. We summarized early model runs for this analysis following Thiebot & Pinaud (2010); however, proper implementation of these scripts should now involve summarizing from the posterior after running the chain for a large number of iterations (see Sumner et al 2009). The current implementation nevertheless produced valid location estimates from the GLS loggers compared with the satellite tracks of the individuals surveyed during a similar stage (Fig.…”

Section: Handling Of Tracking Datamentioning

confidence: 99%

Stage-dependent distribution of the Critically Endangered Amsterdam albatross in relation to Economic Exclusive Zones

Thiebot¹,

Delord²,

Marteau³

et al. 2014

Endang. Species. Res.

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Long-lived animals typically exhibit several stages throughout their life cycle during which their distribution may vary substantially, which may challenge the relevance of protection measures. Here we surveyed individual movements of the Critically Endangered Amsterdam albatross from Amsterdam Island, southern Indian Ocean, throughout its life cycle. Our goal was to identify, from the areas visited by the albatrosses, which coastal states share responsibility in regulating industrial fishing in their Exclusive Economic Zones (EEZs) in order to promote the preservation of this species. Using stage-relevant tracking techniques (satellite tags, GPS and GLS loggers), we surveyed 361 at-sea trips by 93 individuals over 9 yr, covering incubation, brooding, chick-rearing, sabbatical, failed-breeding, juvenile and immature stages. Our data show that Amsterdam albatrosses exhibit a wide and variable foraging radius (from 326 ± 193 km during brooding to 5519 ± 766 km for immatures) and at-sea distribution across stages, putting them beyond the French EEZ of Amsterdam Island for all or part of the trips surveyed in each stage, and even outside the Indian Ocean when breeding. In all, the breeding versus non-breeding albatrosses visited the EEZs of 1 to 3 versus 3 to 4 countries, respectively. Only breeders visited the EEZs of Madagascar and Mauritius, while only non-breeders visited the EEZs of Australia, South Africa and Namibia. This study stresses the relevance to conservation of obtaining synoptic information on the distribution of threatened species, especially regarding the breeding versus nonbreeding categories of populations.

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Bayesian Estimation of Animal Movement from Archival and Satellite Tags

Cited by 170 publications

References 43 publications

Quantitative method to estimate species habitat use from light-based geolocation data

Quantitative method to estimate species habitat use from light-based geolocation data

Consistent variation in individual migration strategies of brown skuas

Stage-dependent distribution of the Critically Endangered Amsterdam albatross in relation to Economic Exclusive Zones

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