ChiroVox: a public library of bat calls

The calls of Vietnamese bats: a major step toward the acoustic characterization of Asian bats

Southeast Asia includes several global biodiversity hotspots and bats account for nearly one-third of mammal species currently known in the region. While acoustic methods have become widespread in bat research, basic information is often lacking on the echolocation calls produced by Asian bat species. Since such information can aid a wide variety of research and conservation initiatives, descriptions of the calls emitted by Asian bats are fundamental. The aim of our study was to provide a standardized analysis and description of the Vietnamese bat echolocation calls. We analyzed call recordings of 87 species arranged in eight families. This constitutes 74% of the echolocating bats presently known in Vietnam and includes the first call descriptions for five taxa. Our use of an open-source software and the deposition of recordings in the ChiroVox repository will facilitate comparative studies in Asia and the information we provide represents one of the most comprehensive bioacoustic databases for Asian bats to date.

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.

Rediscovery of the critically endangered Hill's horseshoe bat (Rhinolophushilli) and other new records of bat species in Rwanda

Background

For forty years, there has been growing uncertainty about whether Hill's horseshoe bat (Rhinolophushilli) still persists in Nyungwe National Park, Rwanda. Only known from one small area within the National Park, R.hilli is listed as Critically Endangered by the International Union for the Conservation of Nature (IUCN), based on its extremely small geographic range and presumed low number of mature individuals. Here, we present and describe bat species occurrence data contributed to the Global Biodiversity Information Facility (GBIF) that we collected as part of a long-term collaborative project to rediscover this lost species. This data paper describes the survey methods and findings resulting from cave roost surveys, capture surveys, and acoustic sampling of bat echolocation activity in Nyungwe National Park and surrounding areas in south-western Rwanda from 2013-2020 and their conservation relevance.

New information

We report the discovery of an extant population of Hill's horseshoe bat (Rhinolophushilli) in Nyungwe National Park, Rwanda, 40 years since the last reported observation of the species in 1981. We also report the first record of Lander's horseshoe bat (Rhinolophuslanderi) in Nyungwe National Park and the first record of the Damara woolly bat (Kerivoulaargentata) in Rwanda. The dataset contributed to GBIF and described in this paper includes 278 occurrence records from 10 bat species of five families detected at 71 locations in or near Nyungwe National Park, Rwanda. We include a description of the morphological descriptions of R.hilli and present the first acoustic echolocation signatures and phylogenetic information for this species.

ecoSound-web: an open-source, online platform for ecoacoustics

Passive acoustic monitoring of soundscapes and biodiversity produces vast amounts of audio recordings, but the management and analyses of these raw data present technical challenges. A multitude of software solutions exist, but none can fulfil all purposes required for the management, processing, navigation, analysis, and dissemination of acoustic data. The field of ecoacoustics needs a software tool that is free, evolving, and accessible. We take a step in that direction and present ecoSound-web: an open-source, online platform for ecoacoustics designed and built by ecologists and software engineers. ecoSound-web can be used for storing, organising, and sharing soundscape projects, manually creating and peer-reviewing annotations of soniferous animals and phonies, analysing audio in time and frequency, computing alpha acoustic indices, and providing reference sound libraries for different taxa. We present ecoSound-web's features, structure, and compare it with similar software. We describe its operation mode and the workflow for typical use cases such as the sampling of bird and bat communities, the use of a primate call library, and the analysis of phonies and acoustic indices. ecoSound-web is available from: https://github.com/ecomontec/ecoSound-web.