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Endangered Species Research

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ESR 10:341-353 (2010)  -  DOI:

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

Jean-Baptiste Thiebot*, David Pinaud

Centre d’Etudes Biologiques de Chizé, CNRS UPR 1934, 79360 Villiers en Bois, France

ABSTRACT: 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 far-ranging 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.

KEY WORDS: Light-based geolocation · Habitat use · Method · Kalman filter · State-space model · Sea surface temperature · Large-scale movements

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Cite this article as: Thiebot JB, Pinaud D (2010) Quantitative method to estimate species habitat use from light-based geolocation data. Endang Species Res 10:341-353.

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