Seasonal swarming behavior of Myotis bats revealed by integrated monitoring, involving passive acoustic monitoring with automated analysis, trapping, and video monitoring

Karyotypic stasis and swarming influenced the evolution of viral tolerance in a species-rich bat radiation

The emergence of COVID-19 and severe acute respiratory syndrome (SARS) has prioritized understanding bats' viral tolerance. Myotis bats are exceptionally species rich and have evolved viral tolerance. They also exhibit swarming, a cryptic behavior where large, multi-species assemblages gather for mating, which has been hypothesized to promote interspecific hybridization. To resolve the coevolution of genome architecture and their unusual antiviral tolerance, we undertook a phylogenomic analysis of 60 Old World Myotis genomes. We demonstrate an extensive history of introgressive hybridization that has replaced the species phylogeny across 17%-93% of the genome except for pericentromeric regions of macrochromosomes. Introgression tracts were enriched on microchromosome regions containing key antiviral pathway genes overexpressed during viral challenge experiments. Together, these results suggest that the unusual Myotis karyotype may have evolved to selectively position immune-related genes in high recombining genomic regions prone to introgression of divergent alleles, including a diversity of interleukin loci responsible for the release of pro-inflammatory cytokines.

An Efficient Neural Network Design Incorporating Autoencoders for the Classification of Bat Echolocation Sounds

Bats are widely distributed around the world, have adapted to many different environments and are highly sensitive to changes in their habitat, which makes them essential bioindicators of environmental changes. Passive acoustic monitoring over long durations, like months or years, accumulates large amounts of data, turning the manual identification process into a time-consuming task for human experts. Automated acoustic monitoring of bat activity is therefore an effective and necessary approach for bat conservation, especially in wind energy applications, where flying animals like bats and birds have high fatality rates. In this work, we provide a neural-network-based approach for bat echolocation pulse detection with subsequent genus classification and species classification under real-world conditions, including various types of noise. Our supervised model is supported by an unsupervised learning pipeline that uses autoencoders to compress linear spectrograms into latent feature vectors that are fed into a UMAP clustering algorithm. This pipeline offers additional insights into the data properties, aiding in model interpretation. We compare data collected from two locations over two consecutive years sampled at four heights (10 m, 35 m, 65 m and 95 m). With sufficient data for each labeled bat class, our model is able to comprehend the full echolocation soundscape of a species or genus while still being computationally efficient and simple by design. Measured classification F1 scores in a previously unknown test set range from 92.3% to 99.7% for species and from 94.6% to 99.4% for genera.