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      Seasonal and Geographic Variation of Southern Blue Whale Subspecies in the Indian Ocean

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          Abstract

          Understanding the seasonal movements and distribution patterns of migratory species over ocean basin scales is vital for appropriate conservation and management measures. However, assessing populations over remote regions is challenging, particularly if they are rare. Blue whales ( Balaenoptera musculus spp) are an endangered species found in the Southern and Indian Oceans. Here two recognized subspecies of blue whales and, based on passive acoustic monitoring, four “acoustic populations” occur. Three of these are pygmy blue whale ( B.m. brevicauda) populations while the fourth is the Antarctic blue whale ( B.m. intermedia). Past whaling catches have dramatically reduced their numbers but recent acoustic recordings show that these oceans are still important habitat for blue whales. Presently little is known about the seasonal movements and degree of overlap of these four populations, particularly in the central Indian Ocean. We examined the geographic and seasonal occurrence of different blue whale acoustic populations using one year of passive acoustic recording from three sites located at different latitudes in the Indian Ocean. The vocalizations of the different blue whale subspecies and acoustic populations were recorded seasonally in different regions. For some call types and locations, there was spatial and temporal overlap, particularly between Antarctic and different pygmy blue whale acoustic populations. Except on the southernmost hydrophone, all three pygmy blue whale acoustic populations were found at different sites or during different seasons, which further suggests that these populations are generally geographically distinct. This unusual blue whale diversity in sub-Antarctic and sub-tropical waters indicates the importance of the area for blue whales in these former whaling grounds.

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          Recognizing transient low-frequency whale sounds by spectrogram correlation.

          A method is described for the automatic recognition of transient animal sounds. Automatic recognition can be used in wild animal research, including studies of behavior, population, and impact of anthropogenic noise. The method described here, spectrogram correlation, is well-suited to recognition of animal sounds consisting of tones and frequency sweeps. For a sound type of interest, a two-dimensional synthetic kernel is constructed and cross-correlated with a spectrogram of a recording, producing a recognition function--the likelihood at each point in time that the sound type was present. A threshold is applied to this function to obtain discrete detection events, instants at which the sound type of interest was likely to be present. An extension of this method handles the temporal variation commonly present in animal sounds. Spectrogram correlation was compared to three other methods that have been used for automatic call recognition: matched filters, neural networks, and hidden Markov models. The test data set consisted of bowhead whale (Balaena mysticetus) end notes from songs recorded in Alaska in 1986 and 1988. The method had a success rate of about 97.5% on this problem, and the comparison indicated that it could be especially useful for detecting a call type when relatively few (5-200) instances of the call type are known.
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            Chilean blue whales as a case study to illustrate methods to estimate abundance and evaluate conservation status of rare species.

            Often abundance of rare species cannot be estimated with conventional design-based methods, so we illustrate with a population of blue whales (Balaenoptera musculus) a spatial model-based method to estimate abundance. We analyzed data from line-transect surveys of blue whales off the coast of Chile, where the population was hunted to low levels. Field protocols allowed deviation from planned track lines to collect identification photographs and tissue samples for genetic analyses, which resulted in an ad hoc sampling design with increased effort in areas of higher densities. Thus, we used spatial modeling methods to estimate abundance. Spatial models are increasingly being used to analyze data from surveys of marine, aquatic, and terrestrial species, but estimation of uncertainty from such models is often problematic. We developed a new, broadly applicable variance estimator that showed there were likely 303 whales (95% CI 176-625) in the study area. The survey did not span the whales' entire range, so this is a minimum estimate. We estimated current minimum abundance relative to pre-exploitation abundance (i.e., status) with a population dynamics model that incorporated our minimum abundance estimate, likely population growth rates from a meta-analysis of rates of increase in large baleen whales, and two alternative assumptions about historic catches. From this model, we estimated that the population was at a minimum of 9.5% (95% CI 4.9-18.0%) of pre-exploitation levels in 1998 under one catch assumption and 7.2% (CI 3.7-13.7%) of pre-exploitation levels under the other. Thus, although Chilean blue whales are probably still at a small fraction of pre-exploitation abundance, even these minimum abundance estimates demonstrate that their status is better than that of Antarctic blue whales, which are still <1% of pre-exploitation population size. We anticipate our methods will be broadly applicable in aquatic and terrestrial surveys for rarely encountered species, especially when the surveys are intended to maximize encounter rates and estimate abundance. ©2011 Society for Conservation Biology.
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              Vocal characteristics of pygmy blue whales and their change over time.

              Vocal characteristics of pygmy blue whales of the eastern Indian Ocean population were analyzed using data from a hydroacoustic station deployed off Cape Leeuwin in Western Australia as part of the Comprehensive Nuclear-Test-Ban Treaty monitoring network, from two acoustic observatories of the Australian Integrated Marine Observing System, and from individual sea noise loggers deployed in the Perth Canyon. These data have been collected from 2002 to 2010, inclusively. It is shown that the themes of pygmy blue whale songs consist of ether three or two repeating tonal sounds with harmonics. The most intense sound of the tonal theme was estimated to correspond to a source level of 179 ± 2 dB re 1 μPa at 1 m measured for 120 calls from seven different animals. Short-duration calls of impulsive downswept sound from pygmy blue whales were weaker with the source level estimated to vary between 168 to 176 dB. A gradual decrease in the call frequency with a mean rate estimated to be 0.35 ± 0.3 Hz/year was observed over nine years in the frequency of the third harmonic of tonal sound 2 in the whale song theme, which corresponds to a negative trend of about 0.12 Hz/year in the call fundamental frequency. © 2011 Acoustical Society of America
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2013
                13 August 2013
                : 8
                : 8
                : e71561
                Affiliations
                [1 ]Observatoire PELAGIS CNRS – UMS 3462, University of La Rochelle, La Rochelle, France
                [2 ]Applied Physics Laboratory, University of Washington, Seattle, Washington, United States of America
                [3 ]School of Aquatic and Fishery Sciences, University of Washington, Seattle, Washington, United States of America
                [4 ]NOAA Ocean Acoustics Program, Office of Science and Technology National Marine Fisheries Service, Silver Spring, Maryland, United States of America
                [5 ]Laboratoire Domaines Océaniques CNRS – UMR 6538, University of Brest, Plouzané, France
                [6 ]NOAA Pacific Marine Environmental Laboratory, Newport, Oregon, United States of America
                [7 ]Centre d’Etudes Biologiques de Chizé, CNRS – UPR 1934, Villiers en Bois, France
                Ecole Normale Supérieure de Lyon, France
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: FS KMS. Performed the experiments: FS KMS CG JYR RPD. Analyzed the data: FS KMS TAB JG. Contributed reagents/materials/analysis tools: JYR RPD KMS FS JG. Wrote the paper: FS KMS TAB.

                Article
                PONE-D-13-03884
                10.1371/journal.pone.0071561
                3742792
                23967221
                4c0cabb1-509d-4ab2-81a7-47373681b08e
                Copyright @ 2013

                This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

                History
                : 25 January 2013
                : 2 July 2013
                Page count
                Pages: 10
                Funding
                Funding for deployment and recovery for the hydrophone array in the Indian Ocean was provided by the Conseil Régional de Bretagne, the Institut National des Sciences de l’Univers (a department of the Centre National de la Recherche Scientifique, France), Institut Paul Emile Victor, Total Foundation and the NOAA’s Ocean Exploration and Research program that provided support to the Pacific Marine Environmental Laboratory to build the hydrophones and moorings for the DEFLO experiment. Analysis and writing were supported by French Minister of Ecology, Terres Australes et Antarctiques Françaises, University of La Rochelle. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Ecology
                Community Ecology
                Species Interactions
                Biodiversity
                Conservation Science
                Marine Ecology
                Marine Biology
                Marine Conservation
                Marine Monitoring
                Population Biology
                Population Ecology
                Zoology
                Mammalogy
                Earth Sciences
                Marine and Aquatic Sciences
                Marine Biology
                Marine Ecology
                Marine Monitoring

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