Ecological inferences about marine mammals from passive acoustic data.

Monitoring on the basis of sound recordings, or passive acoustic monitoring, can complement or serve as an alternative to real-time visual or aural monitoring of marine mammals and other animals by human observers. Passive acoustic data can support the estimation of common, individual-level ecological metrics, such as presence, detection-weighted occupancy, abundance and density, population viability and structure, and behaviour. Passive acoustic data also can support estimation of some community-level metrics, such as species richness and composition. The feasibility of estimation and certainty of estimates is highly context dependent, and understanding the factors that affect the reliability of measurements is useful for those considering whether to use passive acoustic data. Here, we review basic concepts and methods of passive acoustic sampling in marine systems that often are applicable to marine mammal research and conservation. Our ultimate aim is to facilitate collaboration among ecologists, bioacousticians, and data analysts. Ecological applications of passive acoustics require one to make decisions about sampling design, which in turn requires consideration of sound propagation, sampling of signals, and data storage. One also must make decisions about signal detection and classification and evaluation of the performance of algorithms for these tasks. Investment in the research and development of systems that automate detection and classification, including machine learning, are increasing. Passive acoustic monitoring is more reliable for detection of species presence than for estimation of other species-level metrics. Use of passive acoustic monitoring to distinguish among individual animals remains difficult. However, information about detection probability, vocalisation or cue rate, and relations between vocalisations and the number and behaviour of animals increases the feasibility of estimating abundance or density. Most sensor deployments are fixed in space or are sporadic, making temporal turnover in species composition more tractable to estimate than spatial turnover. Collaborations between acousticians and ecologists are most likely to be successful and rewarding when all partners critically examine and share a fundamental understanding of the target variables, sampling process, and analytical methods.

[1]  Kaitlin E. Frasier,et al.  Geographic differences in Blainville's beaked whale (Mesoplodon densirostris) echolocation clicks , 2023, Diversity and Distributions.

[2]  S. Madhusudhana,et al.  Deep learning algorithm outperforms experienced human observer at detection of blue whale D‐calls: a double‐observer analysis , 2022, Remote Sensing in Ecology and Conservation.

[3]  L. S. M. Sugai,et al.  Acoustic indices as proxies for biodiversity: a meta‐analysis , 2022, Biological reviews of the Cambridge Philosophical Society.

[4]  M. Manser,et al.  A practical guide for generating unsupervised, spectrogram-based latent space representations of animal vocalizations. , 2022, The Journal of animal ecology.

[5]  Michał Budka,et al.  Acoustic approach as an alternative to human-based survey in bird biodiversity monitoring in agricultural meadows , 2022, PloS one.

[6]  Tyler A. Helble,et al.  Performance metrics for marine mammal signal detection and classification. , 2022, The Journal of the Acoustical Society of America.

[7]  Dan Stowell,et al.  Computational bioacoustics with deep learning: a review and roadmap , 2021, PeerJ.

[8]  T. Gentner,et al.  Toward a Computational Neuroethology of Vocal Communication: From Bioacoustics to Neurophysiology, Emerging Tools and Future Directions , 2021, Frontiers in Behavioral Neuroscience.

[9]  Kaitlin E. Frasier,et al.  A machine learning pipeline for classification of cetacean echolocation clicks in large underwater acoustic datasets , 2021, PLoS Comput. Biol..

[10]  E. Nosal,et al.  Model-based localization of deep-diving cetaceans using towed line array acoustic data. , 2021, The Journal of the Acoustical Society of America.

[11]  Oliver C. Metcalf,et al.  Optimizing tropical forest bird surveys using passive acoustic monitoring and high temporal resolution sampling , 2021, Remote Sensing in Ecology and Conservation.

[12]  Tyler A. Helble,et al.  Improve automatic detection of animal call sequences with temporal context , 2021, Journal of the Royal Society Interface.

[13]  C. Pérez‐Granados,et al.  Estimating bird density using passive acoustic monitoring: a review of methods and suggestions for further research , 2021, Ibis.

[14]  Joseph F. Vignola,et al.  Use of Underwater Acoustics in Marine Conservation and Policy: Previous Advances, Current Status, and Future Needs , 2021, Journal of Marine Science and Engineering.

[15]  Masato Suzuki,et al.  A new survey method using convolutional neural networks for automatic classification of bird calls , 2020, Ecol. Informatics.

[16]  Christiaan Meijer,et al.  Deep learning for supervised classification of temporal data in ecology , 2020, Ecol. Informatics.

[17]  OUP accepted manuscript , 2021, ICES Journal of Marine Science.

[18]  M. Roch How Machine Learning Contributes to Solve Acoustical Problems , 2021, Acoustics Today.

[19]  Tyler A. Helble,et al.  Fin Whale Song Patterns Shift Over Time in the Central North Pacific , 2020, Frontiers in Marine Science.

[20]  Tim Sainburg,et al.  Finding, visualizing, and quantifying latent structure across diverse animal vocal repertoires , 2020, PLoS Comput. Biol..

[21]  William K. Oestreich,et al.  Animal-Borne Metrics Enable Acoustic Detection of Blue Whale Migration , 2020, Current Biology.

[22]  N. Kelly,et al.  A comparison of baleen whale density estimates derived from overlapping satellite imagery and a shipborne survey , 2020, Scientific Reports.

[23]  C. Clark,et al.  Exploring movement patterns and changing distributions of baleen whales in the western North Atlantic using a decade of passive acoustic data , 2020, Global change biology.

[24]  H. Hillebrand,et al.  Year-round passive acoustic data reveal spatio-temporal patterns in marine mammal community composition in the Weddell Sea, Antarctica , 2020, Marine Ecology Progress Series.

[25]  Cara F. Hotchkin,et al.  Slocum Gliders Provide Accurate Near Real-Time Estimates of Baleen Whale Presence From Human-Reviewed Passive Acoustic Detection Information , 2020, Frontiers in Marine Science.

[26]  S. Matwin,et al.  Performance of a Deep Neural Network at Detecting North Atlantic Right Whale Upcalls , 2020, The Journal of the Acoustical Society of America.

[27]  Ned Horning,et al.  Mapping of land cover with open‐source software and ultra‐high‐resolution imagery acquired with unmanned aerial vehicles , 2020, Remote Sensing in Ecology and Conservation.

[28]  K. Seger,et al.  A decade of marine mammal acoustical presence and habitat preference in the Bering Sea , 2018, Polar Biology.

[29]  K. Goetz,et al.  Acoustically estimated size distribution of sperm whales (Physeter macrocephalus) off the east coast of New Zealand , 2020 .

[30]  N. Roy,et al.  North Atlantic right whale shift to the Gulf of St. Lawrence in 2015, revealed by long-term passive acoustics , 2019 .

[31]  Philip G Brodrick,et al.  Uncovering Ecological Patterns with Convolutional Neural Networks. , 2019, Trends in ecology & evolution.

[32]  Sharon Gannot,et al.  Machine learning in acoustics: Theory and applications. , 2019, The Journal of the Acoustical Society of America.

[33]  S. B. Blackwell,et al.  Acoustic occurrence and behavior of ribbon seals (Histriophoca fasciata) in the Bering, Chukchi, and Beaufort seas , 2019, Polar Biology.

[34]  R. W. Baird,et al.  Future Directions in Research on Beaked Whales , 2019, Front. Mar. Sci..

[35]  Diego Llusia,et al.  Terrestrial Passive Acoustic Monitoring: Review and Perspectives , 2018, BioScience.

[36]  Dan Stowell,et al.  Deep Learning for Audio Event Detection and Tagging on Low-Resource Datasets , 2018, Applied Sciences.

[37]  Olaf Boebel,et al.  On the reliability of acoustic annotations and automatic detections of Antarctic blue whale calls under different acoustic conditions. , 2018, The Journal of the Acoustical Society of America.

[38]  Teja Tscharntke,et al.  Comparing the sampling performance of sound recorders versus point counts in bird surveys: A meta‐analysis , 2018, Journal of Applied Ecology.

[39]  Jian D. L. Yen,et al.  Model selection using information criteria, but is the "best" model any good? , 2018 .

[40]  M. Noad,et al.  Changes in humpback whale singing behavior with abundance: Implications for the development of acoustic surveys of cetaceans. , 2017, The Journal of the Acoustical Society of America.

[41]  Lance B. McNew,et al.  Comparison of acoustic recorders and field observers for monitoring tundra bird communities , 2017 .

[42]  Leland McInnes,et al.  hdbscan: Hierarchical density based clustering , 2017, J. Open Source Softw..

[43]  Len Thomas,et al.  Passive acoustic monitoring of the decline of Mexico's critically endangered vaquita , 2017, Conservation biology : the journal of the Society for Conservation Biology.

[44]  Holger Klinck,et al.  Using calls as an indicator for Antarctic blue whale occurrence and distribution across the southwest Pacific and southeast Indian Oceans , 2017 .

[45]  Janelle L. Morano,et al.  Building time-budgets from bioacoustic signals to measure population-level changes in behavior: a case study with sperm whales in the Gulf of Mexico , 2017 .

[46]  Samuel L. Denes,et al.  Calls of North Atlantic right whales Eubalaena glacialis contain information on individual identity and age class , 2016 .

[47]  Ana Širović,et al.  Variability in the performance of the spectrogram correlation detector for North-east Pacific blue whale calls , 2016 .

[48]  W. Au,et al.  Listening in the Ocean , 2016 .

[49]  Len Thomas,et al.  Passive acoustic monitoring of beaked whale densities in the Gulf of Mexico , 2015, Scientific Reports.

[50]  Holger Klinck,et al.  Calls reveal population structure of blue whales across the southeast Indian Ocean and the southwest Pacific Ocean , 2015, Journal of mammalogy.

[51]  Olaf Boebel,et al.  Effects of subsampling of passive acoustic recordings on acoustic metrics. , 2015, The Journal of the Acoustical Society of America.

[52]  Marie A. Roch,et al.  Seven years of blue and fin whale call abundance in the Southern California Bight , 2015 .

[53]  Hanna K. Nuuttila,et al.  Detection rates of wild harbour porpoises and bottlenose dolphins using static acoustic click loggers vary with depth , 2015 .

[54]  Lin Yan,et al.  Improved time series land cover classification by missing-observation-adaptive nonlinear dimensionality reduction , 2015 .

[55]  Simone Baumann-Pickering,et al.  Compensating for the effects of site and equipment variation on delphinid species identification from their echolocation clicks. , 2015, The Journal of the Acoustical Society of America.

[56]  Julien Bonnel,et al.  Range estimation of bowhead whale (Balaena mysticetus) calls in the Arctic using a single hydrophone. , 2014, The Journal of the Acoustical Society of America.

[57]  Sachit Butail,et al.  Analysis and classification of collective behavior using generative modeling and nonlinear manifold learning. , 2013, Journal of theoretical biology.

[58]  H. Lotze,et al.  Recovery Trends in Marine Mammal Populations , 2013, PloS one.

[59]  Jonathan Gordon,et al.  Automatic detection and classification of odontocete whistles. , 2013, The Journal of the Acoustical Society of America.

[60]  Tyler A. Helble,et al.  Site specific probability of passive acoustic detection of humpback whale calls from single fixed hydrophones. , 2013, The Journal of the Acoustical Society of America.

[61]  V. Janik,et al.  Communication in bottlenose dolphins: 50 years of signature whistle research , 2013, Journal of Comparative Physiology A.

[62]  P. Tyack,et al.  Estimating animal population density using passive acoustics , 2012, Biological reviews of the Cambridge Philosophical Society.

[63]  R. W. Baird,et al.  Near-Real-Time Acoustic Monitoring of Beaked Whales and Other Cetaceans Using a Seaglider™ , 2012, PloS one.

[64]  Mark F Baumgartner,et al.  A generalized baleen whale call detection and classification system. , 2011, The Journal of the Acoustical Society of America.

[65]  Len Thomas,et al.  An update to the methods in Endangered Species Research 2011 paper "Estimating North Pacific right whale Eubalaena japonica density using passive acoustic cue counting" , 2011 .

[66]  David Borchers,et al.  A non-technical overview of spatially explicit capture–recapture models , 2012, Journal of Ornithology.

[67]  L. Thomas,et al.  Spatially explicit capture–recapture methods to estimate minke whale density from data collected at bottom-mounted hydrophones , 2012, Journal of Ornithology.

[68]  Len Thomas,et al.  A dive counting density estimation method for Blainville’s beaked whale (Mesoplodon densirostris) using a bottom-mounted hydrophone field as applied to a Mid-Frequency Active (MFA) sonar operation , 2010 .

[69]  Sumio Watanabe,et al.  Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory , 2010, J. Mach. Learn. Res..

[70]  John A. Hildebrand,et al.  Worldwide decline in tonal frequencies of blue whale songs , 2009 .

[71]  Peter L Tyack,et al.  Passive acoustic detection of deep-diving beaked whales. , 2008, The Journal of the Acoustical Society of America.

[72]  R. Hobbs,et al.  An assessment of shore-based counts of gray whales , 2008 .

[73]  D L Borchers,et al.  Spatially Explicit Maximum Likelihood Methods for Capture–Recapture Studies , 2008, Biometrics.

[74]  Whitlow W. L. Au,et al.  Principles of marine bioacoustics , 2008 .

[75]  Haru Matsumoto,et al.  An Overview of Fixed Passive Acoustic Observation Methods for Cetaceans , 2007 .

[76]  B. L. Boeuf,et al.  ESTIMATING POPULATION SIZE IN ASYNCHRONOUS AGGREGATIONS: A BAYESIAN APPROACH AND TEST WITH ELEPHANT SEAL CENSUSES , 2007 .

[77]  John A Hildebrand,et al.  Blue and fin whale call source levels and propagation range in the Southern Ocean. , 2007, The Journal of the Acoustical Society of America.

[78]  Julie N Oswald,et al.  A tool for real-time acoustic species identification of delphinid whistles. , 2005, The Journal of the Acoustical Society of America.

[79]  Douglas L. Jones,et al.  Classification of communication signals of the little brown bat. , 2005, The Journal of the Acoustical Society of America.

[80]  P. Tyack,et al.  Biosonar performance of foraging beaked whales (Mesoplodon densirostris) , 2005, Journal of Experimental Biology.

[81]  Toshiji Kawagoe,et al.  Voice matters in a dictator game , 2008 .

[82]  John A. Hildebrand,et al.  WAVEGUIDE PROPAGATION ALLOWS RANGE ESTIMATES FOR NORTH PACIFIC RIGHT WHALES IN THE BERING SEA , 2004 .

[83]  Jonathan Gordon,et al.  Evaluation of a method for determining the length of sperm whales (Physeter catodon) from their vocalizations , 1991 .

[84]  Vijay V. Raghavan,et al.  A critical investigation of recall and precision as measures of retrieval system performance , 1989, TOIS.

[85]  J. Scott Uniform asymptotics for spherical and cylindrical nonlinear acoustic waves generated by a sinusoidal source , 1981, Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences.