Henley, Lucy, Finch, Domhnall, Mathews, Fiona, Jones, Owen ![]() ![]() ![]() |
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Abstract
Bats play a pivotal role in pest control, pollination, and seed dispersal. Despite their ecological significance, locating bat roosts remains a challenging task for ecologists. Traditional field surveys are time-consuming, expensive, and may disturb sensitive bat populations. In this paper, we combine data from static audio detectors with a bat movement model to facilitate the detection of bat roosts. Crucially, our technique not only provides a point prediction for the most likely location of a bat roost, but, because of the algorithm's speed, it can be applied over an entire landscape, resulting in a likelihood map, which provides optimal searching regions. To illustrate the success of the algorithm and highlight limitations we apply our technique to greater horseshoe bat (Rhinolophus ferrumequinum) acoustic data acquired from six surveys from four different UK locations and over six different times in the year. Further, we investigate what happens to the accuracy of our predictions in the case that the roost is not contained within the area spanned by the detectors. This innovative approach to searching rural environments holds the potential to greatly reduce the labour required for roost finding and, hence, enhance the conservation efforts of bat populations and their habitats.
Item Type: | Article |
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Date Type: | Publication |
Status: | Published |
Schools: | Mathematics |
Subjects: | Q Science > QA Mathematics |
Publisher: | The Royal Society |
ISSN: | 2054-5703 |
Funders: | EPSRC |
Date of First Compliant Deposit: | 29 February 2024 |
Date of Acceptance: | 22 February 2024 |
Last Modified: | 17 Jun 2024 10:16 |
URI: | https://orca.cardiff.ac.uk/id/eprint/166550 |
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