17 research outputs found
Future Directions in Research on Bryde's Whales
One of the lesser known species of baleen whales, the Bryde's whale, also known as Eden's whale (Balaenoptera edeni edeni and B. edeni brydei), although hunted as part of a North Pacific Japanese research programme1, was not heavily exploited by commercial whaling and remains a data deficient species. Their taxonomic status is not fully resolved and they are often mistaken for other species leading to uncertainty about their true distribution, behavior and conservation status. Some populations are critically endangered, whilst others are small but have high genetic diversity suggesting wider connectivity. The species' unpredictable coastal and offshore global distribution throughout warm-temperate waters has led to populations with unknown genetic variation, and facing different threats. Few areas are well-studied, but each study reveals often contrasting movement patterns, foraging strategies, and vocal repertoires; there are considerable knowledge gaps for Bryde's whales. There are few Bryde's populations with abundance estimates but they typically number in the mid- to high-hundreds of individuals, with other populations small, <100 mature individuals, and exposed to high levels of anthropogenic impacts. Future research should focus on understanding the diversity within and between populations. Here, we suggest an integrative, comparative approach toward future work on Bryde's whales, including acoustic monitoring, trophic interactions, telemetry tools, understanding their novel behaviors, and resolving their species status. This will inform conservation management of this unusual species of whale vulnerable to anthropogenic impacts
Two unit analysis of Sri Lankan pygmy blue whale song over a decade
J.L.M.O. and S.L.N. were funded by the Office of Naval Research (Award No. N000141110619). D.V.H. was funded by the Office of Naval Research (Award No. N000141612364).Sri Lankan pygmy blue whale song consists of three repeated units: (1) low frequency pulsive unit, (2) frequency modulated (FM) upsweep, and (3) long tonal downsweep. The Unit 2 FM unit has up to three visible upsweeps with energy concentrated at approximately 40, 50, and 60 Hz, while the Unit 3 (∼100 Hz) tonal downsweep is the most distinct unit lasting 20–30 s. Spectral characteristics of the Units 2 and 3 song elements, along with ocean sound levels, were analyzed in the Indian Ocean from 2002 to 2013. The peak frequency of the tonal Unit 3 calls decreased from approximately 106.5 to 100.7 Hz over a decade corresponding to a 5.4% decrease. Over the same time period, the frequency content of the Unit 2 upsweeps did not change as dramatically with only a 3.1% change. Ambient sound levels in the vocalization bands did not exhibit equivalent patterns in amplitude trends. Analysis showed no increase in the ambient sound or compensated peak amplitude levels of the tonal downsweeps, eliminating the presence of a Lombard effect. Here it is proposed that each song unit may convey different information and thus may be responding to different selective pressures.PostprintPeer reviewe
Drone Up! Quantifying Whale Behavior From a New Perspective Improves Observational Capacity
During traditional boat-based surveys of marine megafauna, behavioral observations are typically limited to records of animal surfacings obtained from a horizontal perspective. Achieving an aerial perspective has been restricted to brief helicopter or airplane based observations that are costly, noisy, and risky. The emergence of commercial small unmanned aerial systems (UAS) has significantly reduced these constraints to provide a stable, relatively quiet, and inexpensive platform that enables replicate observations for prolonged periods with minimal disturbance. The potential of UAS for behavioral observation appears immense, yet quantitative proof of utility as an observational tool is required. We use UAS footage of gray whales foraging in the coastal waters of Oregon, United States to develop video behavior analysis methods, determine the change in observation time enabled by UAS, and describe unique behaviors observed via UAS. Boat-based behavioral observations from 53 gray whale sightings between May and October 2016 were compared to behavioral data extracted from video analysis of UAS flights during those sightings. We used a DJI Phantom 3 Pro or 4 Advanced, recorded video from an altitude ≥25 m, and detected no behavioral response by whales to the UAS. Two experienced whale ethologists conducted UAS video behavioral analysis, including tabulation of whale behavior states and events, and whale surface time and whale visible time (total time the whale was visible including underwater). UAS provided three times more observational capacity than boat-based observations alone (300 vs. 103 min). When observation time is accounted for, UAS data provided more and longer observations of all primary behavior states (travel, forage, social, and rest) relative to boat-based data, especially foraging. Furthermore, UAS enable documentation of multiple novel gray whale foraging tactics (e.g., headstands: n = 58; side-swimming: n = 17; jaw snapping and flexing: n = 10) and 33 social events (nursing and pair coordinated surfacings) not identified from boat-based observation. This study demonstrates the significant added value of UAS to marine megafauna behavior and ecological studies. With technological advances, robust study designs, and effective analytical tools, we foresee increased UAS applications to marine megafauna studies to elucidate foraging strategies, habitat associations, social patterns, and response to human disturbance
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Drone Up! Quantifying Whale Behavior From a New Perspective Improves Observational Capacity
During traditional boat-based surveys of marine megafauna, behavioral observations are typically limited to records of animal surfacings obtained from a horizontal perspective. Achieving an aerial perspective has been restricted to brief helicopter or airplane based observations that are costly, noisy, and risky. The emergence of commercial small unmanned aerial systems (UAS) has significantly reduced these constraints to provide a stable, relatively quiet, and inexpensive platform that enables replicate observations for prolonged periods with minimal disturbance. The potential of UAS for behavioral observation appears immense, yet quantitative proof of utility as an observational tool is required. We use UAS footage of gray whales foraging in the coastal waters of Oregon, United States to develop video behavior analysis methods, determine the change in observation time enabled by UAS, and describe unique behaviors observed via UAS. Boat-based behavioral observations from 53 gray whale sightings between May and October 2016 were compared to behavioral data extracted from video analysis of UAS flights during those sightings. We used a DJI Phantom 3 Pro or 4 Advanced, recorded video from an altitude ≥25 m, and detected no behavioral response by whales to the UAS. Two experienced whale ethologists conducted UAS video behavioral analysis, including tabulation of whale behavior states and events, and whale surface time and whale visible time (total time the whale was visible including underwater). UAS provided three times more observational capacity than boat-based observations alone (300 vs. 103 min). When observation time is accounted for, UAS data provided more and longer observations of all primary behavior states (travel, forage, social, and rest) relative to boat-based data, especially foraging. Furthermore, UAS enable documentation of multiple novel gray whale foraging tactics (e.g., headstands: n = 58; side-swimming: n = 17; jaw snapping and flexing: n = 10) and 33 social events (nursing and pair coordinated surfacings) not identified from boat-based observation. This study demonstrates the significant added value of UAS to marine megafauna behavior and ecological studies. With technological advances, robust study designs, and effective analytical tools, we foresee increased UAS applications to marine megafauna studies to elucidate foraging strategies, habitat associations, social patterns, and response to human disturbance
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Drone Up! Quantifying Whale Behavior From a New Perspective Improves Observational Capacity
During traditional boat-based surveys of marine megafauna, behavioral observations are typically limited to records of animal surfacings obtained from a horizontal perspective. Achieving an aerial perspective has been restricted to brief helicopter or airplane based observations that are costly, noisy, and risky. The emergence of commercial small unmanned aerial systems (UAS) has significantly reduced these constraints to provide a stable, relatively quiet, and inexpensive platform that enables replicate observations for prolonged periods with minimal disturbance. The potential of UAS for behavioral observation appears immense, yet quantitative proof of utility as an observational tool is required. We use UAS footage of gray whales foraging in the coastal waters of Oregon, United States to develop video behavior analysis methods, determine the change in observation time enabled by UAS, and describe unique behaviors observed via UAS. Boat-based behavioral observations from 53 gray whale sightings between May and October 2016 were compared to behavioral data extracted from video analysis of UAS flights during those sightings. We used a DJI Phantom 3 Pro or 4 Advanced, recorded video from an altitude >= 25 m, and detected no behavioral response by whales to the UAS. Two experienced whale ethologists conducted UAS video behavioral analysis, including tabulation of whale behavior states and events, and whale surface time and whale visible time (total time the whale was visible including underwater). UAS provided three times more observational capacity than boat-based observations alone (300 vs. 103 min). When observation time is accounted for, UAS data provided more and longer observations of all primary behavior states (travel, forage, social, and rest) relative to boat-based data, especially foraging. Furthermore, UAS enable documentation of multiple novel gray whale foraging tactics (e.g., headstands: n = 58; side-swimming: n = 17; jaw snapping and flexing: n = 10) and 33 social events (nursing and pair coordinated surfacings) not identified from boat-based observation. This study demonstrates the significant added value of UAS to marine megafauna behavior and ecological studies. With technological advances, robust study designs, and effective analytical tools, we foresee increased UAS applications to marine megafauna studies to elucidate foraging strategies, habitat associations, social patterns, and response to human disturbance
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Seasonal presence of cetaceans and ambient noise levels in polar waters of the North Atlantic
In 2009 two calibrated acoustic recorders were deployed in polar waters of the North Atlantic to study the seasonal occurrence of blue, fin, and sperm whales and to assess current ambient noise levels. Sounds from these cetaceans were recorded at both locations in most months of the year. During the summer months, seismic airguns associated with oil and gas exploration were audible for weeks at a time and dominated low frequency noise levels. Noise levels might further increase in the future as the receding sea ice enables extended human use of the area.Keywords: underwater sound, acoustic measurement, acoustic nois
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Sounds from airguns and fin whales recorded in the mid-Atlantic Ocean, 1999-2009
Between 1999 and 2009, autonomous hydrophones were deployed to monitor seismic activity from 16° N to 50° N along the Mid-Atlantic Ridge. These data were examined for airgun sounds produced during offshore surveys for oil and gas deposits, as well as the 20 Hz pulse sounds from fin whales, which may be masked by airgun noise. An automatic detection algorithm was used to identify airgun sound patterns, and fin whale calling levels were summarized via long-term spectral analysis. Both airgun and fin whale sounds were recorded at all sites. Fin whale calling rates were higher at sites north of 32° N, increased during the late summer and fall months at all sites, and peaked during the winter months, a time when airgun noise was often prevalent. Seismic survey vessels were acoustically located off the coasts of three major areas: Newfoundland, northeast Brazil, and Senegal and Mauritania in West Africa. In some cases, airgun sounds were recorded almost 4000 km from the survey vessel in areas that are likely occupied by fin whales, and at some locations airgun sounds were recorded more than 80% days/month for more than 12 consecutive months
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Calls reveal population structure of blue whales across the southeast Indian Ocean and the southwest Pacific Ocean
For effective species management, understanding population structure and distribution is critical. However, quantifying population structure is not always straightforward. Within the Southern Hemisphere, the blue whale (Balaenoptera musculus) complex is extremely diverse but difficult to study. Using automated detector methods, we identified “acoustic populations” of whales producing region-specific call types. We examined blue whale call types in passive acoustic data at sites spanning over 7,370 km across the southeast Indian Ocean and southwest Pacific Ocean (SWPO) from 2009 to 2012. In the absence of genetic resolution, these acoustic populations offer unique information about the blue whale population complex. We found that the Australian continent acts as a geographic boundary, separating Australia and New Zealand blue whale acoustic populations at the junction of the Indian and Pacific Ocean basins. We located blue whales in previously undocumented locations, including the far SWPO, in the Tasman Sea off the east coast of Australia, and along the Lau Basin near Tonga. Our understanding of population dynamics across this broad scale has significant implications to recovery and conservation management for this endangered species, at a regional and global scale.Keywords: vocalization, pygmy blue whale, bioacoustics, Lau Basin, New Zealand, passive acoustic monitoring, marine mammal, Australi
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Temporal segregation of the Australian and Antarctic blue whale call types (Balaenoptera musculus spp.)
We examined recordings from a 15-month (May 2009–July 2010) continuous acoustic data set collected from a bottom-mounted passive acoustic recorder at a sample frequency of 6kHz off Portland, Victoria, Australia (38°33′01″S, 141°15′13″E) off southern Australia. Analysis revealed that calls from both subspecies were recorded at this site, and general additive modeling revealed that the number of calls varied significantly across seasons. Antarctic blue whales were detected more frequently from July to October 2009 and June to July 2010, corresponding to the suspected breeding season, while Australian blue whales were recorded more frequently from March to June 2010, coinciding with the feeding season. In both subspecies, the number of calls varied with time of day; Antarctic blue whale calls were more prevalent in the night to early morning, while Australian blue whale calls were detected more often from midday to early evening. Using passive acoustic monitoring, we show that each subspecies adopts different seasonal and daily call patterns which may be related to the ecological strategies of these subspecies. This study demonstrates the importance of passive acoustics in enabling us to understand and monitor subtle differences in the behavior and ecology of cryptic sympatric marine mammals.This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by Oxford University Press on behalf of American Society of Mammalogists. The published article can be found at: http://jmammal.oxfordjournals.org/content/96/3/603Keywords: cryptic sympatric marine mammals, seasonal, diel, Australia, calls, Balaenoptera musculus intermedia, Balaenoptera musculus brevicauda, ecology, vocalizationsKeywords: cryptic sympatric marine mammals, seasonal, diel, Australia, calls, Balaenoptera musculus intermedia, Balaenoptera musculus brevicauda, ecology, vocalization
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)