Bat detective-Deep learning tools for bat acoustic signal detection.

Passive acoustic sensing has emerged as a powerful tool for quantifying anthropogenic impacts on biodiversity, especially for echolocating bat species. To better assess bat population trends there is a critical need for accurate, reliable, and open source tools that allow the detection and classification of bat calls in large collections of audio recordings. The majority of existing tools are commercial or have focused on the species classification task, neglecting the important problem of first localizing echolocation calls in audio which is particularly problematic in noisy recordings. We developed a convolutional neural network based open-source pipeline for detecting ultrasonic, full-spectrum, search-phase calls produced by echolocating bats. Our deep learning algorithms were trained on full-spectrum ultrasonic audio collected along road-transects across Europe and labelled by citizen scientists from www.batdetective.org. When compared to other existing algorithms and commercial systems, we show significantly higher detection performance of search-phase echolocation calls with our test sets. As an example application, we ran our detection pipeline on bat monitoring data collected over five years from Jersey (UK), and compared results to a widely-used commercial system. Our detection pipeline can be used for the automatic detection and monitoring of bat populations, and further facilitates their use as indicator species on a large scale. Our proposed pipeline makes only a small number of bat specific design decisions, and with appropriate training data it could be applied to detecting other species in audio. A crucial novelty of our work is showing that with careful, non-trivial, design and implementation considerations, state-of-the-art deep learning methods can be used for accurate and efficient monitoring in audio.

Diverse replication-associated protein encoding circular DNA viruses in guano samples of Central-Eastern European bats.

Circular replication-associated protein encoding single-stranded DNA (CRESS DNA) viruses are increasingly recognized worldwide in a variety of samples. Representative members include well-described veterinary pathogens with worldwide distribution, such as porcine circoviruses or beak and feather disease virus. In addition, numerous novel viruses belonging to the family Circoviridae with unverified pathogenic roles have been discovered in different human samples. Viruses of the family Genomoviridae have also been described as being highly abundant in different faecal and environmental samples, with case reports showing them to be suspected pathogens in human infections. In order to investigate the genetic diversity of these viruses in European bat populations, we tested guano samples from Georgia, Hungary, Romania, Serbia and Ukraine. This resulted in the detection of six novel members of the family Circoviridae and two novel members of the family Genomoviridae. Interestingly, a gemini-like virus, namely niminivirus, which was originally found in raw sewage samples in Nigeria, was also detected in our samples. We analyzed the nucleotide composition of members of the family Circoviridae to determine the possible host origins of these viruses. This study provides the first dataset on CRESS DNA viruses of European bats, and members of several novel viral species were discovered.