Redescription of <i>Speocyclops orcinus</i> Kiefer, 1937 (Copepoda Cyclopoida Cyclopidae) from the type locality, Cave Iriberi, in southern France

Female and male of the subterranean cyclopid copepod Speocyclops orcinus Kiefer, 1937 are described; the female for the first time. The material used in the present description was collected at the type locality, Cave Iriberi, a vast karst complex in the Department Atlantic Pyrenees, France. The specimens are compared with the type specimens lodged in the Friedrich Kiefer copepod collection at Karlsruhe, Germany. Sp, orcinus is found to be a true representative of the cyclopine genus Speocyclops and is reallocated to itfrom Allocyclops Kiefer, 1932 to which it has been recently assigned

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