@article {57918, title = {Automatic standardized processing and identification of tropical bat calls using deep learning approaches}, year = {2019}, abstract = {

Consistent and comparable metrics to automatically monitor biodiversity across the landscape remain a gold-standard for biodiversity research, yet such approaches have frequently been limited to a very small selection of species for which visual approaches (e.g., camera traps) make continuous monitoring possible. Acoustic-based methods have been widely applied in the monitoring of bats and some other taxa across extended spatial scales, but are have yet to be applied to diverse tropical communities.

In this study, we developed a software program \“Waveman\” and prepared a reference library using over 880 audio-files from 36 Asian bat species. The software incorporated a novel network \“BatNet\” and a re-checking strategy (ReChk) to maximize accuracy. In Waveman, BatNet outperforms three other published networks: CNNFULL, VggNet and ResNet_v2, with over 90\% overall accuracy and 0.94 AUC on the ROC plot. The classification accuracy rates for all 36 species are at least 86\% when analysed in combination. Moreover, our library preparation and ReChk greatly improved the sensitivity and reduced the false positive rate, when tested with 15 species for which more detailed and situationally diverse records were available. Finally, BatNet was successfully used to identify Hipposideros larvatus and Rhinolophus siamensis from three different environments. We hope this pipeline is useful tool to process bioacoustic data accurately, effectively and automatically, therefore allowing for greater standardization and comparability for researchers to understand bat activities across space and time and therefore provide a consistent tool for monitoring biodiversity for management and conservation.

}, keywords = {Algorithms, Automated monitoring, Automatic processing, bats, bioacoustics, Biodiversity metrics, Calls, Deep learning, echolocation, machine learning, Monitoring protocol, Neural network}, doi = {10.1016/j.biocon.2019.108269}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0006320719308961}, author = {Chen, Xing and Zhao, Jun and Chen, Yan-hua and Zhou, Wei and Hughes, Alice C.} } @article {57912, title = {Vocalisation in the mountain hare: calls of a mostly silent species}, year = {2019}, abstract = {

While the occurrence and function of vocalisation by pikas has been studied (genus Ochotona), little is known about vocalisation by hares (genus Lepus). In this study, we investigate the vocalisation activity of mountain hare (Lepus timidus) over the course of a year to better understand its occurrence and function. We found that mountain hares have a low vocalisation rate of one vocalisation every 33 night hours. We hypothesise that this low rate is the result of a strategy to minimise detection by predators. However, the 20 recorded vocal events correspond with intraspecific communication events such as warning others about danger and defending resources. This case study identifies further research directions to help strengthen our understanding of communication between solitary hares.

}, keywords = {Alps, behaviour, Calls, Lagomorpha, predator avoidance, sonogram}, doi = {10.1007/s10344-019-1331-1}, url = {http://link.springer.com/10.1007/s10344-019-1331-1}, author = {Rehnus, Maik and Wehrle, Martin and Obrist, Martin K.} }