Songbird dynamics under the sea : acoustic interactions between humpback whales suggest song mediates male interactions

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Royal Society Open Science 5 (2018): 171298, doi:10.1098/rsos.171298.The function of song has been well studied in numerous taxa and plays a role in mediating both intersexual and intrasexual interactions. Humpback whales are among few mammals who sing, but the role of sexual selection on song in this species is poorly understood. While one predominant hypothesis is that song mediates male–male interactions, the mechanism by which this may occur has never been explored. We applied metrics typically used to assess songbird interactions to examine song sequences and movement patterns of humpback whale singers. We found that males altered their song presentation in the presence of other singers; focal males increased the rate at which they switched between phrase types (p = 0.005), and tended to increase the overall evenness of their song presentation (p = 0.06) after a second male began singing. Two-singer dyads overlapped their song sequences significantly more than expected by chance. Spatial analyses revealed that change in distance between singers was related to whether both males kept singing (p = 0.012), with close approaches leading to song cessation. Overall, acoustic interactions resemble known mechanisms of mediating intrasexual interactions in songbirds. Future work should focus on more precisely resolving how changes in song presentation may be used in competition between singing males.D.M.C. was supported by an EPA Science to Achieve Results (STAR) Fellowship for PhD research

Deep neural networks for automated detection of marine mammal species

Authors thank the Bureau of Ocean Energy Management for the funding of MARU deployments, Excelerate Energy Inc. for the funding of Autobuoy deployment, and Michael J. Weise of the US Office of Naval Research for support (N000141712867).Deep neural networks have advanced the field of detection and classification and allowed for effective identification of signals in challenging data sets. Numerous time-critical conservation needs may benefit from these methods. We developed and empirically studied a variety of deep neural networks to detect the vocalizations of endangered North Atlantic right whales (Eubalaena glacialis). We compared the performance of these deep architectures to that of traditional detection algorithms for the primary vocalization produced by this species, the upcall. We show that deep-learning architectures are capable of producing false-positive rates that are orders of magnitude lower than alternative algorithms while substantially increasing the ability to detect calls. We demonstrate that a deep neural network trained with recordings from a single geographic region recorded over a span of days is capable of generalizing well to data from multiple years and across the species’ range, and that the low false positives make the output of the algorithm amenable to quality control for verification. The deep neural networks we developed are relatively easy to implement with existing software, and may provide new insights applicable to the conservation of endangered species.Publisher PDFPeer reviewe

Improve automatic detection of animal call sequences with temporal context

Funding: This work was supported by the US Office of Naval Research (grant no. N00014-17-1-2867).Many animals rely on long-form communication, in the form of songs, for vital functions such as mate attraction and territorial defence. We explored the prospect of improving automatic recognition performance by using the temporal context inherent in song. The ability to accurately detect sequences of calls has implications for conservation and biological studies. We show that the performance of a convolutional neural network (CNN), designed to detect song notes (calls) in short-duration audio segments, can be improved by combining it with a recurrent network designed to process sequences of learned representations from the CNN on a longer time scale. The combined system of independently trained CNN and long short-term memory (LSTM) network models exploits the temporal patterns between song notes. We demonstrate the technique using recordings of fin whale (Balaenoptera physalus) songs, which comprise patterned sequences of characteristic notes. We evaluated several variants of the CNN + LSTM network. Relative to the baseline CNN model, the CNN + LSTM models reduced performance variance, offering a 9-17% increase in area under the precision-recall curve and a 9-18% increase in peak F1-scores. These results show that the inclusion of temporal information may offer a valuable pathway for improving the automatic recognition and transcription of wildlife recordings.Publisher PDFPeer reviewe

Learning deep models from synthetic data for extracting dolphin whistle contours

We present a learning-based method for extracting whistles of toothed whales (Odontoceti) in hydrophone recordings. Our method represents audio signals as time-frequency spectrograms and decomposes each spectrogram into a set of time-frequency patches. A deep neural network learns archetypical patterns (e.g., crossings, frequency modulated sweeps) from the spectrogram patches and predicts time-frequency peaks that are associated with whistles. We also developed a comprehensive method to synthesize training samples from background environments and train the network with minimal human annotation effort. We applied the proposed learn-from-synthesis method to a subset of the public Detection, Classification, Localization, and Density Estimation (DCLDE) 2011 workshop data to extract whistle confidence maps, which we then processed with an existing contour extractor to produce whistle annotations. The F1-score of our best synthesis method was 0.158 greater than our baseline whistle extraction algorithm (~25% improvement) when applied to common dolphin (Delphinus spp.) and bottlenose dolphin (Tursiops truncatus) whistles.Postprin

Management of acoustic metadata for bioacoustics

Recent expansion in the capabilities of passive acoustic monitoring of sound-producing animals is providing expansive data sets in many locations. These long-termdata sets will allowthe investigation of questions related to the ecology of sound-producing animals on time scales ranging fromdiel and seasonal to inter-annual and decadal. Analyses of these data often span multiple analysts from various research groups over several years of effort and, as a consequence, have begun to generate large amounts of scattered acoustic metadata. It has therefore become imperative to standardize the types of metadata being generated. A critical aspect of being able to learn from such large and varied acoustic data sets is providing consistent and transparent access that can enable the integration of various analysis efforts. This is juxtaposed with the need to include new information for specific research questions that evolve over time. Hence, a method is proposed for organizing acoustic metadata that addresses many of the problems associated with the retention of metadata from large passive acoustic data sets. A structure was developed for organizing acoustic metadata in a consistent manner, specifying required and optional terms to describe acoustic information derived from a recording. A client-server database was created to implement this data representation as a networked data service that can be accessed from several programming languages. Support for data import froma wide variety of sources such as spreadsheets and databases is provided. The implementation was extended to access Internet-available data products, permitting access to a variety of environmental information types (e.g. sea surface temperature, sunrise/sunset, etc.) fromawide range of sources as if they were part of the data service. This metadata service is in use at several institutions and has been used to track and analyze millions of acoustic detections from marine mammals, fish, elephants, and anthropogenic sound sources.Publisher PDFPeer reviewe

Exploring movement patterns and changing distributions of baleen whales in the western North Atlantic using a decade of passive acoustic data

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Davis, G. E., Baumgartner, M. F., Corkeron, P. J., Bell, J., Berchok, C., Bonnell, J. M., Thornton, J. B., Brault, S., Buchanan, G. A., Cholewiak, D. M., Clark, C. W., Delarue, J., Hatch, L. T., Klinck, H., Kraus, S. D., Martin, B., Mellinger, D. K., Moors-Murphy, H., Nieukirk, S., Nowacek, D. P., Parks, S. E., Parry, D., Pegg, N., Read, A. J., Rice, A. N., Risch, D., Scott, A., Soldevilla, M. S., Stafford, K. M., Stanistreet, J. E., Summers, E., Todd, S., & Van Parijs, S. M. Exploring movement patterns and changing distributions of baleen whales in the western North Atlantic using a decade of passive acoustic data. Global Change Biology, (2020): 1-30, doi:10.1111/gcb.15191.Six baleen whale species are found in the temperate western North Atlantic Ocean, with limited information existing on the distribution and movement patterns for most. There is mounting evidence of distributional shifts in many species, including marine mammals, likely because of climate‐driven changes in ocean temperature and circulation. Previous acoustic studies examined the occurrence of minke (Balaenoptera acutorostrata ) and North Atlantic right whales (NARW; Eubalaena glacialis ). This study assesses the acoustic presence of humpback (Megaptera novaeangliae ), sei (B. borealis ), fin (B. physalus ), and blue whales (B. musculus ) over a decade, based on daily detections of their vocalizations. Data collected from 2004 to 2014 on 281 bottom‐mounted recorders, totaling 35,033 days, were processed using automated detection software and screened for each species' presence. A published study on NARW acoustics revealed significant changes in occurrence patterns between the periods of 2004–2010 and 2011–2014; therefore, these same time periods were examined here. All four species were present from the Southeast United States to Greenland; humpback whales were also present in the Caribbean. All species occurred throughout all regions in the winter, suggesting that baleen whales are widely distributed during these months. Each of the species showed significant changes in acoustic occurrence after 2010. Similar to NARWs, sei whales had higher acoustic occurrence in mid‐Atlantic regions after 2010. Fin, blue, and sei whales were more frequently detected in the northern latitudes of the study area after 2010. Despite this general northward shift, all four species were detected less on the Scotian Shelf area after 2010, matching documented shifts in prey availability in this region. A decade of acoustic observations have shown important distributional changes over the range of baleen whales, mirroring known climatic shifts and identifying new habitats that will require further protection from anthropogenic threats like fixed fishing gear, shipping, and noise pollution.We thank Chris Pelkie, David Wiley, Michael Thompson, Chris Tessaglia‐Hymes, Eric Matzen, Chris Tremblay, Lance Garrison, Anurag Kumar, John Hildebrand, Lynne Hodge, Russell Charif, Kathleen Dudzinski, and Ann Warde for help with project planning, field work support, and data management. For all the support and advice, thanks to the NEFSC Protected Species Branch, especially the passive acoustics group, Josh Hatch, and Leah Crowe. We thank the field and crew teams on all the ships that helped in the numerous deployments and recoveries. This research was funded and supported by many organizations, specified by projects as follows: data recordings from region 1 were provided by K. Stafford (funding: National Science Foundation #NSF‐ARC 0532611). Region 2 data: D. K. Mellinger and S. Nieukirk, National Oceanic and Atmospheric Administration (NOAA) PMEL contribution #5055 (funding: NOAA and the Office of Naval Research #N00014–03–1–0099, NOAA #NA06OAR4600100, US Navy #N00244‐08‐1‐0029, N00244‐09‐1‐0079, and N00244‐10‐1‐0047). Region 3A data: D. Risch (funding: NOAA and Navy N45 programs). Region 3 data: H. Moors‐Murphy and Fisheries and Oceans Canada (2005–2014 data), and the Whitehead Lab of Dalhousie University (eastern Scotian Shelf data; logistical support by A. Cogswell, J. Bartholette, A. Hartling, and vessel CCGS Hudson crew). Emerald Basin and Roseway Basin Guardbuoy data, deployment, and funding: Akoostix Inc. Region 3 Emerald Bank and Roseway Basin 2004 data: D. K. Mellinger and S. Nieukirk, NOAA PMEL contribution #5055 (funding: NOAA). Region 4 data: S. Parks (funding: NOAA and Cornell University) and E. Summers, S. Todd, J. Bort Thornton, A. N. Rice, and C. W. Clark (funding: Maine Department of Marine Resources, NOAA #NA09NMF4520418, and #NA10NMF4520291). Region 5 data: S. M. Van Parijs, D. Cholewiak, L. Hatch, C. W. Clark, D. Risch, and D. Wiley (funding: National Oceanic Partnership Program (NOPP), NOAA, and Navy N45). Region 6 data: S. M. Van Parijs and D. Cholewiak (funding: Navy N45 and Bureau of Ocean and Energy Management (BOEM) Atlantic Marine Assessment Program for Protected Species [AMAPPS] program). Region 7 data: A. N. Rice, H. Klinck, A. Warde, B. Martin, J. Delarue, and S. Kraus (funding: New York State Department of Environmental Conservation, Massachusetts Clean Energy Center, and BOEM). Region 8 data: G. Buchanan, and K. Dudzinski (funding: New Jersey Department of Environmental Protection and the New Jersey Clean Energy Fund) and A. N. Rice, C. W. Clark, and H. Klinck (funding: Center for Conservation Bioacoustics at Cornell University and BOEM). Region 9 data: J. E. Stanistreet, J. Bell, D. P. Nowacek, A. J. Read, and S. M. Van Parijs (funding: NOAA and US Fleet Forces Command). Region 10 data: L. Garrison, M. Soldevilla, C. W. Clark, R. A. Chariff, A. N. Rice, H. Klinck, J. Bell, D. P. Nowacek, A. J. Read, J. Hildebrand, A. Kumar, L. Hodge, and J. E. Stanistreet (funding: US Fleet Forces Command, BOEM, NOAA, and NOPP). Region 11 data: C. Berchok as part of a collaborative project led by the Fundacion Dominicana de Estudios Marinos, Inc. (Dr. Idelisa Bonnelly de Calventi; funding: The Nature Conservancy [Elianny Dominguez]) and D. Risch (funding: World Wildlife Fund, NOAA, and Dutch Ministry of Economic Affairs)

EVALUATING THE ROLE OF SONG IN THE HUMPBACK WHALE (MEGAPTERA NOVAEANGLIAE) BREEDING SYSTEM WITH RESPECT TO INTRA-SEXUAL INTERACTIONS

Dr. Christopher W. Clark Dr. Sandra L. Vehrencamp Dr. H. Kern Reeve Dr. Andre DhondtEVALUATING THE ROLE OF SONG IN THE HUMPBACK WHALE (Megaptera novaeangliae) BREEDING SYSTEM WITH RESPECT TO INTRA-SEXUAL INTERACTIONS Danielle M. Cholewiak, Ph.D. Cornell University 2008 Underlying any interaction between two or more individuals is some form of communication, which can be defined as the transmission of information by one individual and the use of that information by another individual in future decision-making. While natural selection shapes signals that are important for survival, sexual selection shapes signals that are important within the context of reproduction. In some cases, sexual selection may lead to the evolution of highly exaggerated traits, such as the extravagant plumage of birds-of-paradise, or the elaborate vocal performance of the common nightingale. Humpback whales (Megaptera novaeangliae) are well-known for their rich acoustic display. Humpback song is the most complex among baleen whales with respect to both spectral and temporal characteristics. Although song features and the behavior of singing males have been studied since the 1970s, our understanding of the role of song within this breeding system remains incomplete. This study investigated the role that song plays in mediating intra-sexual interactions in humpback whales. I combined passive acoustic recording and playback experiments, and developed song metrics derived from avian literature to analyze the behavior of individual singing males with respect to one another. An analysis of diel singing activity found higher numbers of singing males during nighttime hours. Analyses of song patterns revealed that males changed their song presentation while in the presence of other singers by increasing the rate at which they switch between themes, and in some cases, thematically overlapping other singers more than expected (similar to ?song matching? in songbirds). Singing males were found to approach one another more than expected and previously believed. Singers responded acoustically to the presentation of song playback experiments by altering their song presentation similarly to the ways in which they responded in the presence of other singers. Taken together, these data provide evidence that male humpback whales do respond to and interact with one another while singing, and may alter their behavior to target particular individuals. The implications of these results are discussed with respect to hypotheses regarding the function of humpback song.Cornell University, Cornell Laboratory of Ornithology, Environmental Protection Agency, National Geographic Society, Animal Behavior Societ