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Updated: Jan 30, 2026

Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping
Published on: November 7, 2016
Wild ungulates and environmental temperature: analysis on the possible utilization of data from sensor placed on GPS
Alessandro Messeri1,2, Valentina Becciolini3, Gianni Messeri4
1Centre of Bioclimatology, University of Florence, Piazzale delle Cascine 18, 50144, Florence, Italy. alessandro.messeri@unifi.it.
Abstract:
GPS collars for wildlife provide a large amount of spatio-temporal location data and are frequently equipped with sensors that record the animal-level environmental temperature at a schedulable sampling frequency. The simultaneous collection of environmental temperature and animal location may contribute not only to deepen the understanding of animal behavior in different climatic conditions, but also to increase the knowledge of climate features in inaccessible areas. The measurement of environmental temperature provided by the sensors, however, can be biased by several factors (e.g., surface temperature of the animal, direct solar radiation, precipitation), so in-depth studies are required to verify the correlation. The aim of this study was to identify an equation for correcting the collar-recorded temperature data, allowing to improve and refine the results obtained by the analysis of spatial data and to highlight the environmental factors having the greatest impact on the accuracy of the measures. Temperature data from GPS collars were obtained within a research on spatial behavior on 11 hinds while spatialized temperature data were obtained from LAMMA-IBIMET dataset. These data showed high correlation and an identical trend, although the GPS collar temperature data was always higher. This model could represent a tool to obtain an accurate measurement of temperatures in complex geographical areas with wild animals but low density of weather stations. The availability of corrected temperature data, recorded simultaneously with the animal location, could be useful for a more accurate comprehension of animal behavior in free-ranging conditions, both in case of forthcoming studies and to valorize existing datasets.
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