Body size information in large-scale acoustic bat databases

• Main Authors: Penone, Caterina, Kerbiriou, Christian, Julien, Jean-François, Marmet, Julie, Le Viol, Isabelle
• Format: Text
• Language: English
• Published: PeerJ, Ltd 2018
• Subjects: 580 Plants Botany; Pipistrellus pipistrellus
• Online Access: https://dx.doi.org/10.7892/boris.119906; https://boris.unibe.ch/119906/

Background Citizen monitoring programs using acoustic data have been useful for detecting population and community patterns. However, they have rarely been used to study broad scale patterns of species traits. We assessed the potential of acoustic data to detect broad scale patterns in body size. We compared geographical patterns in body size with acoustic signals in the bat species Pipistrellus pipistrellus. Given the correlation between body size and acoustic characteristics, we expected to see similar results when analyzing the relationships of body size and acoustic signals with climatic variables. Methods We assessed body size using forearm length measurements of 1,359 bats, captured by mist nets in France. For acoustic analyses, we used an extensive dataset collected through the French citizen bat survey. We isolated each bat echolocation call (n = 4,783) and performed automatic measures of signals, including the frequency of the flattest part of the calls (characteristic frequency). We then examined the relationship between forearm length, characteristic frequencies, and two components resulting from principal component analysis for geographic (latitude, longitude) and climatic variables. Results Forearm length was positively correlated with higher precipitation, lower seasonality, and lower temperatures. Lower characteristic frequencies (i.e., larger body size) were mostly related to lower temperatures and northern latitudes. While conducted on different datasets, the two analyses provided congruent results. Discussion Acoustic data from citizen science programs can thus be useful for the detection of large-scale patterns in body size. This first analysis offers a new perspective for the use of large acoustic databases to explore biological patterns and to address both theoretical and applied questions.