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Development of a multisensor biologging collar and analytical techniques to describe high‐resolution spatial behavior in free‐ranging terrestrial mammals

Michael S. Painter Orcid Logo, Václav Silovský Orcid Logo, Justin Blanco, Mark Holton Orcid Logo, Monika Faltusová Orcid Logo, Rory Wilson Orcid Logo, Luca Borger Orcid Logo, Liza Psotta, Fabian Ramos‐Almodovar Orcid Logo, Luis Estrada, Lukas Landler, Pascal Malkemper, Vlastimil Hart, Miloš Ježek

Ecology and Evolution, Volume: 14, Issue: 9

Swansea University Authors: Mark Holton Orcid Logo, Rory Wilson Orcid Logo, Luca Borger Orcid Logo

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DOI (Published version): 10.1002/ece3.70264

Abstract

Biologging has proven to be a powerful approach to investigate diverse questions related to movement ecology across a range of spatiotemporal scales and increasingly relies on multidisciplinary expertise. However, the variety of animal-borne equipment, coupled with little consensus regarding analyti...

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Published in: Ecology and Evolution
ISSN: 2045-7758 2045-7758
Published: Wiley 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa67457
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Abstract: Biologging has proven to be a powerful approach to investigate diverse questions related to movement ecology across a range of spatiotemporal scales and increasingly relies on multidisciplinary expertise. However, the variety of animal-borne equipment, coupled with little consensus regarding analytical approaches to interpret large, complex data sets presents challenges and makes comparison between studies and study species difficult. Here, we present a combined hardware and analytical approach for standardizing the collection, analysis, and interpretation of multisensor biologging data. Here, we present (i) a custom-designed integrated multisensor collar (IMSC), which was field tested on 71 free-ranging wild boar (Sus scrofa) over 2 years; (ii) a machine learning behavioral classifier capable of identifying six behaviors in free-roaming boar, validated across individuals equipped with differing collar designs; and (iii) laboratory and field-based calibration and accuracy assessments of animal magnetic heading measurements derived from raw magnetometer data. The IMSC capacity and durability exceeded expectations, with a 94% collar recovery rate and a 75% cumulative data recording success rate, with a maximum logging duration of 421 days. The behavioral classifier had an overall accuracy of 85% in identifying the six behavioral classes when tested on multiple collar designs and improved to 90% when tested on data exclusively from the IMSC. Both laboratory and field tests of magnetic compass headings were in precise agreement with expectations, with overall median magnetic headings deviating from ground truth observations by 1.7° and 0°, respectively. Although multisensor equipment and sophisticated analyses are now commonplace in biologging studies, the IMSC hardware and analytical framework presented here provide a valuable tool for biologging researchers and will facilitate standardization of biologging data across studies. In addition, we highlight the potential of additional analyses available using this framework that can be adapted for use in future studies on terrestrial mammals.
Keywords: accelerometer, behavioral classification, biologging, dead-reckoning, GPS, machine learning,magnetic compass heading, magnetometer
College: Faculty of Science and Engineering
Funders: Ministerstvo Zemědělství, Grant/AwardNumber: QK1910462; EVA4.0, Grant/Award Number: CZ.02.1.01/0.0/0.0/16_019/0000803
Issue: 9