Genetic confirmation of a hybrid between two highly divergent cardinalid species: A rose-breasted grosbeak (Pheucticus ludovicianus) and a scarlet tanager (Piranga olivacea).

Using low-coverage whole-genome sequencing, analysis of vocalizations, and inferences from natural history, we document a first-generation hybrid between a rose-breasted grosbeak (Pheucticus ludovicianus) and a scarlet tanager (Piranga olivacea). These two species occur sympatrically throughout much of eastern North America, although were not previously known to interbreed. Following the field identification of a putative hybrid, we use genetic and bioacoustic data to show that a rose-breasted grosbeak was the maternal parent and a scarlet tanager was the paternal parent of the hybrid, whose song was similar to the latter species. These two species diverged >10 million years ago, and thus it is surprising to find a hybrid formed under natural conditions in the wild. Notably, the hybrid has an exceptionally heterozygous genome, with a conservative estimate of a heterozygous base every 100 bp. The observation that this hybrid of such highly divergent parental taxa has survived until adulthood serves as another example of the capacity for hybrid birds to survive with an exceptionally divergent genomic composition.

An annotated set of audio recordings of Eastern North American birds containing frequency, time, and species information.

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.

Acoustic localization of terrestrial wildlife: Current practices and future opportunities.

Autonomous acoustic recorders are an increasingly popular method for low-disturbance, large-scale monitoring of sound-producing animals, such as birds, anurans, bats, and other mammals. A specialized use of autonomous recording units (ARUs) is acoustic localization, in which a vocalizing animal is located spatially, usually by quantifying the time delay of arrival of its sound at an array of time-synchronized microphones. To describe trends in the literature, identify considerations for field biologists who wish to use these systems, and suggest advancements that will improve the field of acoustic localization, we comprehensively review published applications of wildlife localization in terrestrial environments. We describe the wide variety of methods used to complete the five steps of acoustic localization: (1) define the research question, (2) obtain or build a time-synchronizing microphone array, (3) deploy the array to record sounds in the field, (4) process recordings captured in the field, and (5) determine animal location using position estimation algorithms. We find eight general purposes in ecology and animal behavior for localization systems: assessing individual animals' positions or movements, localizing multiple individuals simultaneously to study their interactions, determining animals' individual identities, quantifying sound amplitude or directionality, selecting subsets of sounds for further acoustic analysis, calculating species abundance, inferring territory boundaries or habitat use, and separating animal sounds from background noise to improve species classification. We find that the labor-intensive steps of processing recordings and estimating animal positions have not yet been automated. In the near future, we expect that increased availability of recording hardware, development of automated and open-source localization software, and improvement of automated sound classification algorithms will broaden the use of acoustic localization. With these three advances, ecologists will be better able to embrace acoustic localization, enabling low-disturbance, large-scale collection of animal position data.