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Abstract Landscape‐scale bioacoustic projects have become a popular approach to biodiversity monitoring. Combining passive acoustic monitoring recordings and automated detection provides an effective means of monitoring sound‐producing species' occupancy and phenology and can lend insight into unobserved behaviours and patterns. The availability of low‐cost recording hardware has lowered barriers to large‐scale data collection, but technological barriers in data analysis remain a bottleneck for extracting biological insight from bioacoustic datasets.We provide a robust and open‐source Python toolkit for detecting and localizing biological sounds in acoustic data.OpenSoundscape provides access to automated acoustic detection, classification and localization methods through a simple and easy‐to‐use set of tools. Extensive documentation and tutorials provide step‐by‐step instructions and examples of end‐to‐end analysis of bioacoustic data. Here, we describe the functionality of this package and provide concise examples of bioacoustic analyses with OpenSoundscape.By providing an interface for bioacoustic data and methods, we hope this package will lead to increased adoption of bioacoustics methods and ultimately to enhanced insights for ecology and conservation. 

Abstract Acoustic recordings of soundscapes are an important category of audio data that can be useful for answering a variety of questions, and an entire discipline within ecology, dubbed “soundscape ecology,” has risen to study them. Bird sound is often the focus of studies of soundscapes due to the ubiquitousness of birds in most terrestrial environments and their high vocal activity. Autonomous acoustic recorders have increased the quantity and availability of recordings of natural soundscapes while mitigating the impact of human observers on community behavior. However, such recordings are of little use without analysis of the sounds they contain. Manual analysis currently stands as the best means of processing this form of data for use in certain applications within soundscape ecology, but it is a laborious task, sometimes requiring many hours of human review to process comparatively few hours of recording. For this reason, few annotated data sets of soundscape recordings are publicly available. Further still, there are no publicly available strongly labeled soundscape recordings of bird sounds that contain information on timing, frequency, and species. Therefore, we present the first data set of strongly labeled bird sound soundscape recordings under free use license. These data were collected in the Northeastern United States at Powdermill Nature Reserve, Rector, Pennsylvania, USA. Recordings encompass 385 minutes of dawn chorus recordings collected by autonomous acoustic recorders between the months of April through July 2018. Recordings were collected in continuous bouts on four days during the study period and contain 48 species and 16,052 annotations. Applications of this data set may be numerous and include the training, validation, and testing of certain advanced machine‐learning models that detect or classify bird sounds. There are no copyright or propriety restrictions; please cite this paper when using materials within. 

Abstract A core goal of the National Ecological Observatory Network (NEON) is to measure changes in biodiversity across the 30‐yr horizon of the network. In contrast to NEON’s extensive use of automated instruments to collect environmental data, NEON’s biodiversity surveys are almost entirely conducted using traditional human‐centric field methods. We believe that the combination of instrumentation for remote data collection and machine learning models to process such data represents an important opportunity for NEON to expand the scope, scale, and usability of its biodiversity data collection while potentially reducing long‐term costs. In this manuscript, we first review the current status of instrument‐based biodiversity surveys within the NEON project and previous research at the intersection of biodiversity, instrumentation, and machine learning at NEON sites. We then survey methods that have been developed at other locations but could potentially be employed at NEON sites in future. Finally, we expand on these ideas in five case studies that we believe suggest particularly fruitful future paths for automated biodiversity measurement at NEON sites: acoustic recorders for sound‐producing taxa, camera traps for medium and large mammals, hydroacoustic and remote imagery for aquatic diversity, expanded remote and ground‐based measurements for plant biodiversity, and laboratory‐based imaging for physical specimens and samples in the NEON biorepository. Through its data science‐literate staff and user community, NEON has a unique role to play in supporting the growth of such automated biodiversity survey methods, as well as demonstrating their ability to help answer key ecological questions that cannot be answered at the more limited spatiotemporal scales of human‐driven surveys. 

The AudioMoth is a popular autonomous recording unit (ARU) that is widely used to record vocalizing species in the field. Despite its growing use, there have been few quantitative tests on the performance of this recorder. Such information is needed to design effective field surveys and to appropriately analyze recordings made by this device. Here, we report the results of two tests designed to evaluate the performance characteristics of the AudioMoth recorder. First, we performed indoor and outdoor pink noise playback experiments to evaluate how different device settings, orientations, mounting conditions, and housing options affect frequency response patterns. We found little variation in acoustic performance between devices and relatively little effect of placing recorders in a plastic bag for weather protection. The AudioMoth has a mostly flat on-axis response with a boost above 3 kHz, with a generally omnidirectional response that suffers from attenuation behind the recorder, an effect that is accentuated when it is mounted on a tree. Second, we performed battery life tests under a variety of recording frequencies, gain settings, environmental temperatures, and battery types. We found that standard alkaline batteries last for an average of 189 h at room temperature using a 32 kHz sample rate, and that lithium batteries can last for twice as long at freezing temperatures compared to alkaline batteries. This information will aid researchers in both collecting and analyzing recordings generated by the AudioMoth recorder. 

Birds singing in choruses must contend with the possibility of interfering with each other's songs, but not all species will interfere with each other to the same extent due to signal partitioning. Some evidence suggests that singing birds will avoid temporal overlap only in cases where there is overlap in the frequencies their songs occupy, but the extent to which this behaviour varies according to level of frequency overlap is not yet well understood. We investigated the hypothesis that birds will increasingly avoid heterospecific temporal overlap as their frequency overlap increases by testing for a linear correlation between frequency overlap and temporal avoidance across a community of temperate eastern North American birds. We found that there was a significant correlation across the whole community and within 12 of 15 commonly occurring individual species, which supports our hypothesis and adds to the growing body of evidence that birds adjust the timing of their songs in response to frequency overlap.