Pygmy blue and Antarctic blue whale presence, distribution and population parameters in southern Australia based on passive acoustics
01 Nov 2018-Deep-sea Research Part Ii-topical Studies in Oceanography (Pergamon)-Vol. 157, pp 154-168
TL;DR: In this article, an upwelling index of seasonally integrated seabed water temperature across seven seasons was used to predict the seasonal variance in whale presence and the strength of this correlation was reduced by population growth of whales.
Abstract: Passive acoustic recorders set around Australia since 2004 have been used to study blue whales Calls from New Zealand pygmy blue whales occur predominantly eastward of Bass Strait (1458° E), calls from Eastern Indian Ocean pygmy blue whales (EIO PB) west of Bass Strait, while Antarctic blue whale calls occur along the entire southern Australian coast The only location all calls have been detected within a short space of time in the same season is Bass Strait, where inter-species mixing commonly occurs The EIO PB shows three migratory stages: "southbound" from November to January during which whales travel down the Western Australian coast; "southern Australian" where animals spread across the Indian and Southern Oceans spanning longitudes from 74° to 146° E down to at least a latitude of 55° S searching for food and feeding; then a northern migration where they return north to Indonesian waters post April-August Along the southern Australian coastline EIO PB whales are more often detected towards the east, favouring an area where late summer to autumn upwelling occurs mostly over ~ 134-145° E longitude Receivers on the shelf break south of Portland (1412° E) showed 10–181 times greater EIO PB whale calling when integrated over their three month season than at sites located around the Great Australian Bight to the west Within a season the Portland site did not show consistency of EIO PB whale presence or number of calling individuals, but when the number of calling individuals was integrated across a season and correlated with an upwelling index of seasonally integrated seabed water temperature across seven seasons, the upwelling index predicted 83% of the seasonal variance in whale presence The strength of this correlation will be reduced by population growth of whales By correcting EIO PB whale call rates for a variety of population growth rate values, we found a 43% growth rate for the population proportion visiting the Portland area, gave the maximum regression, correlation coefficient of r2= 90% Time averaged levels of squared pressure produced at the upper chorus frequency of the Antarctic blue whale z-call and received via deep sound channel propagation at Portland showed a 126% increase rate This is indicative of population growth rate for Antarctic blue whales using waters south of Australia, although the value of using pressure squared in this fashion and the assumptions underlying the technique need to be explored
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Dissertation•
01 Jan 1964
40 citations
TL;DR: In this article, the authors review the current prevalence of anthropogenic noise and the distribution of marine mammals in the Southern Ocean, and the current research gaps that prevent us from accurately assessing noise impacts on Antarctic marine mammals.
Abstract: The Protocol on Environmental Protection of the Antarctic Treaty stipulates that the protection of the Antarctic environment and associated ecosystems be considered in the planning and conducting of all activities in the Antarctic Treaty area. One of the key pollutants created by human activities in the Antarctic is noise, primarily caused by ship traffic (from tourism, fisheries, and research), but also by geophysical research (e.g., seismic surveys) and by research station support activities (including construction). Arguably, amongst the species most vulnerable to noise are marine mammals since they specialise in using sound for communication, navigation and foraging, and therefore have evolved the highest auditory sensitivity among marine organisms. Reported effects of noise on marine mammals in lower-latitude oceans include stress, behavioural changes such as avoidance, auditory masking, hearing threshold shifts, and—in extreme cases—death. Eight mysticete species, 10 odontocete species, and six pinniped species occur south of 60OS (i.e., in the Southern Ocean). For many of these, the Southern Ocean is a key area for foraging and reproduction. Yet, little is known about how these species are affected by noise. We review the current prevalence of anthropogenic noise and the distribution of marine mammals in the Southern Ocean, and the current research gaps that prevent us from accurately assessing noise impacts on Antarctic marine mammals. A questionnaire given to 29 international experts on marine mammals revealed a variety of research needs. Those that received the highest rankings were 1) improved data on abundance and distribution of Antarctic marine mammals, 2) hearing data for Antarctic marine mammals, in particular a mysticete audiogram, and 3) an assessment of the effectiveness of various noise mitigation options. The management need with the highest score was a refinement of noise exposure criteria. Environmental evaluations are a requirement before conducting activities in the Antarctic. Because of a lack of scientific data on impacts, requirements and noise thresholds often vary between countries that conduct these evaluations, leading to different standards across countries. Addressing the identified research needs will help to implement informed and reasonable thresholds for noise production in the Antarctic and help to protect the Antarctic environment.
29 citations
TL;DR: The importance of the GSACUS as a foraging ground for pygmy blue whales inhabiting the eastern Indian Ocean is highlighted and the whales’ migratory route to proposed breeding grounds off Indonesia is indicated.
Abstract: Knowledge about the movement ecology of endangered species is needed to identify biologically important areas and the spatio-temporal scale of potential human impacts on species. Blue whales (Balaenoptera musculus) are endangered due to twentieth century whaling and currently threatened by human activities. In Australia, they feed in the Great Southern Australian Coastal Upwelling System (GSACUS) during the austral summer. We investigate their movements, occupancy, behaviour, and environmental drivers to inform conservation management. Thirteen whales were satellite tagged, biopsy sampled and photo-identified in 2015. All were genetically confirmed to be of the pygmy subspecies (B. m. brevicauda). In the GSACUS, whales spent most of their time over the continental shelf and likely foraging in association with several seascape variables (sea surface temperature variability, depth, wind speed, sea surface height anomaly, and chlorophyll a). When whales left the region, they migrated west and then north along the Australian coast until they reached West Timor and Indonesia, where their movements indicated breeding or foraging behaviour. These results highlight the importance of the GSACUS as a foraging ground for pygmy blue whales inhabiting the eastern Indian Ocean and indicate the whales' migratory route to proposed breeding grounds off Indonesia. Information about the spatio-temporal scale of potential human impacts can now be used to protect this little-known subspecies of blue whale.
20 citations
TL;DR: In this article, the authors analyzed sea noise collected over 2003 to 2017 from the Perth Canyon, Western Australia was analyzed for variation in the South Eastern Indian Ocean pygmy blue whale song structure, and the primary song types were: P3, a three unit phrase (I, II and III) repeated with an inter-song interval (ISI) of 170-194 s; P2, a phrase consisting of only units II & III repeated every 84-96 s; and P1 with a phrase consists of only unit II repeated every 45-49 s.
Abstract: Sea noise collected over 2003 to 2017 from the Perth Canyon, Western Australia was analysed for variation in the South Eastern Indian Ocean pygmy blue whale song structure. The primary song-types were: P3, a three unit phrase (I, II and III) repeated with an inter-song interval (ISI) of 170–194 s; P2, a phrase consisting of only units II & III repeated every 84–96 s; and P1 with a phrase consisting of only unit II repeated every 45–49 s. The different ISI values were approximate multiples of each other within a season. When comparing data from each season, across seasons, the ISI value for each song increased significantly through time (all fits had p << 0.001), at 0.30 s/Year (95%CI 0.217–0.383), 0.8 s/Year (95%CI 0.655–1.025) and 1.73 s/Year (95%CI 1.264–2.196) for the P1, P2 and P3 songs respectively. The proportions of each song-type averaged at 21.5, 24.2 and 56% for P1, P2 and P3 occurrence respectively and these ratios could vary by up to ± 8% (95% CI) amongst years. On some occasions animals changed the P3 ISI to be significantly shorter (120–160 s) or longer (220–280 s). Hybrid song patterns occurred where animals combined multiple phrase types into a repeated song. In recent years whales introduced further complexity by splitting song units. This variability of song-type and proportions implies abundance measure for this whale sub population based on song detection needs to factor in trends in song variability to make data comparable between seasons. Further, such variability in song production by a sub population of pygmy blue whales raises questions as to the stability of the song types that are used to delineate populations. The high level of song variability may be driven by an increasing number of background whale callers creating ‘noise’ and so forcing animals to alter song in order to ‘stand out’ amongst the crowd.
13 citations
TL;DR: In this paper, the authors analyzed a passive acoustic dataset spanning large temporal (9 years) and spatial (3-9 sites covering more than 12 million km2 of potential acoustic habitat in the southwest Indian Ocean.
Abstract: Globally, the Indian Ocean appears to have the greatest blue whale (Balaenoptera musculus ssp) acoustic diversity, with at least four acoustic populations from three defined sub-species. To understand how these different populations use this region as habitat, we first need to characterize their spatial and seasonal distributions. Here, we build on previous passive acoustic monitoring studies and analyze a passive acoustic dataset spanning large temporal (9 years) and spatial (3–9 sites covering more than 12 million km2 of potential acoustic habitat in the southwest Indian Ocean) scales. A novel detection algorithm was employed to investigate the long-term presence of Antarctic blue whale and SEIO and SWIO pygmy blue whale calls. We found that Antarctic and pygmy blue whales have completely different spatial and seasonal distribution in the southern Indian Ocean. Antarctic blue whales are heard almost year-round on the whole array, with great inter-annual variability. The two pygmy blue whales share a highly stable seasonal acoustic presence, but their geographical distributions overlap at only a few central Indian Ocean sites. However, Antarctic and pygmy blue whale acoustic co-occurrence is common, especially in sub-tropical waters. These temporal and spatial distributions strengthen our understanding of seasonal occurrence and habitat use of distinct populations of blue whales in the southern Indian Ocean. A better comprehension of the ecology of Indian Ocean blue whales will require interdisciplinary studies to examine the drivers of the variability seen from passive acoustic studies.
11 citations
References
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TL;DR: In this paper, the authors present an overview of animal density estimation using passive acoustic data, a relatively new and fast-developing field, and provide a framework for acoustics-based density estimation, illustrated with real-world case studies.
Abstract: Reliable estimation of the size or density of wild animal populations is very important for effective wildlife management, conservation and ecology. Currently, the most widely used methods for obtaining such estimates involve either sighting animals from transect lines or some form of capture-recapture on marked or uniquely identifiable individuals. However, many species are difficult to sight, and cannot be easily marked or recaptured. Some of these species produce readily identifiable sounds, providing an opportunity to use passive acoustic data to estimate animal density. In addition, even for species for which other visually based methods are feasible, passive acoustic methods offer the potential for greater detection ranges in some environments (e.g. underwater or in dense forest), and hence potentially better precision. Automated data collection means that surveys can take place at times and in places where it would be too expensive or dangerous to send human observers. Here, we present an overview of animal density estimation using passive acoustic data, a relatively new and fast-developing field. We review the types of data and methodological approaches currently available to researchers and we provide a framework for acoustics-based density estimation, illustrated with examples from real-world case studies. We mention moving sensor platforms (e.g. towed acoustics), but then focus on methods involving sensors at fixed locations, particularly hydrophones to survey marine mammals, as acoustic-based density estimation research to date has been concentrated in this area. Primary among these are methods based on distance sampling and spatially explicit capture-recapture. The methods are also applicable to other aquatic and terrestrial sound-producing taxa. We conclude that, despite being in its infancy, density estimation based on passive acoustic data likely will become an important method for surveying a number of diverse taxa, such as sea mammals, fish, birds, amphibians, and insects, especially in situations where inferences are required over long periods of time. There is considerable work ahead, with several potentially fruitful research areas, including the development of (i) hardware and software for data acquisition, (ii) efficient, calibrated, automated detection and classification systems, and (iii) statistical approaches optimized for this application. Further, survey design will need to be developed, and research is needed on the acoustic behaviour of target species. Fundamental research on vocalization rates and group sizes, and the relation between these and other factors such as season or behaviour state, is critical. Evaluation of the methods under known density scenarios will be important for empirically validating the approaches presented here.
483 citations
TL;DR: Blue whales avoid the oligotrophic central gyres of the Indian, Pacific and Atlantic Oceans, but are more common where phytoplankton densities are high, and where there are dynamic oceanographic processes like upwelling and frontal meandering.
Abstract: 1. Blue whale locations in the Southern Hemisphere and northern Indian Ocean were obtained from catches (303 239), sightings (4383 records of 8058 whales), strandings (103), Discovery marks (2191) and recoveries (95), and acoustic recordings. 2. Sighting surveys included 7 480 450 km of effort plus 14 676 days with unmeasured effort. Groups usually consisted of solitary whales (65.2%) or pairs (24.6%); larger feeding aggregations of unassociated individuals were only rarely observed. Sighting rates (groups per 1000 km from many platform types) varied by four orders of magnitude and were lowest in the waters of Brazil, South Africa, the eastern tropical Pacific, Antarctica and South Georgia; higher in the Subantarctic and Peru; and highest around Indonesia, Sri Lanka, Chile, southern Australia and south of Madagascar. 3. Blue whales avoid the oligotrophic central gyres of the Indian, Pacific and Atlantic Oceans, but are more common where phytoplankton densities are high, and where there are dynamic oceanographic processes like upwelling and frontal meandering. 4. Compared with historical catches, the Antarctic (‘true') subspecies is exceedingly rare and usually concentrated closer to the summer pack ice. In summer they are found throughout the Antarctic; in winter they migrate to southern Africa (although recent sightings there are rare) and to other northerly locations (based on acoustics), although some overwinter in the Antarctic. 5. Pygmy blue whales are found around the Indian Ocean and from southern Australia to New Zealand. At least four groupings are evident: northern Indian Ocean, from Madagascar to the Subantarctic, Indonesia to western and southern Australia, and from New Zealand northwards to the equator. Sighting rates are typically much higher than for Antarctic blue whales. 6. South-east Pacific blue whales have a discrete distribution and high sighting rates compared with the Antarctic. Further work is needed to clarify their subspecific status given their distinctive genetics, acoustics and length frequencies. 7. Antarctic blue whales numbered 1700 (95% Bayesian interval 860–2900) in 1996 (less than 1% of original levels), but are increasing at 7.3% per annum (95% Bayesian interval 1.4– 11.6%). The status of other populations in the Southern Hemisphere and northern Indian Ocean is unknown because few abundance estimates are available, but higher recent sighting rates suggest that they are less depleted than Antarctic blue whales.
257 citations
TL;DR: In this paper, a model is developed for the calculation of the spatial properties of the noise field produced in a stratified ocean by the action of wind at the surface, where random noise sources are represented by correlated monopoles distributed over an infinite plane located an arbitrary depth below the surface.
Abstract: A model is developed for the calculation of the spatial properties of the noise field produced in a stratified ocean by the action of wind at the surface. The random noise sources are represented by correlated monopoles distributed over an infinite plane located an arbitrary depth below the surface. Wave‐theoretical methods are applied to derive expressions for the intensity and spatial correlation of the noise field. A normal‐mode representation of the noise field is used to reduce these expressions to forms which allow physical interpretation and are suitable for numerical computation. Examples are given of intensity profiles and spatial correlation in the vertical for three generic sound‐speed profiles. The results show that the sound‐speed profile and the presence of the bottom can be important in determining the spatial properties of the noise field. An example is given of a calculation of the horizontal spatial correlation using the fast field program (FFP).
245 citations
01 Jan 2006
TL;DR: Blue whale songs provide a measure for characterising worldwide blue whale population structure and it is suggested that temporally stable differences in song provide another characteristic for comparison with genetic and morphological data when defining blue whale populations.
Abstract: Blue whale songs provide a measure for characterising worldwide blue whale population structure. These songs are divided into nine regional types, which maintain a stable character. Five of the nine song types have been recorded over time spans greater than 30 years showing no significant change in character. The nine song types can be divided into those containing only simple tonal components (high latitude North Pacific, North Atlantic and Southern Ocean song types), those comprised of complex pulsed units in addition to the tonal components (Pacific Ocean margin song types from California, Chile and New Zealand), and those which have the greatest complexity of all and the longest cycling times (Indian Ocean song types from Sri Lanka, Fremantle and Diego Garcia). We suggest that temporally stable differences in song provide another characteristic for comparison with genetic and morphological data when defining blue whale populations. Furthermore, as Mellinger and Barlow (2003) recommend, when there is a lack of other data or lack of clarity in other data sets, evidence of distinct differences in songs between areas should be used as a provisional hypothesis about population structure when making management decisions. Worldwide study is needed to better understand the various populations and subspecies within species like the blue whale that have large geographic distributions and have both migrating and resident populations.
206 citations
TL;DR: Blue whale songs provide a measure for characterising worldwide blue whale population structure and it is suggested that temporally stable differences in song provide another characteristic for comparison with genetic and morphological data when defining blue whale populations.
Abstract: Blue whale songs provide a measure for characterising worldwide blue whale population structure. These songs are divided into nine regional types, which maintain a stable character. Five of the nine song types have been recorded over time spans greater than 30 years showing no significant change in character. The nine song types can be divided into those containing only simple tonal components (high latitude North Pacific, North Atlantic and Southern Ocean song types), those comprised of complex pulsed units in addition to the tonal components (Pacific Ocean margin song types from California, Chile and New Zealand), and those which have the greatest complexity of all and the longest cycling times (Indian Ocean song types from Sri Lanka, Fremantle and Diego Garcia). We suggest that temporally stable differences in song provide another characteristic for comparison with genetic and morphological data when defining blue whale populations. Furthermore, as Mellinger and Barlow (2003) recommend, when there is a lack of other data or lack of clarity in other data sets, evidence of distinct differences in songs between areas should be used as a provisional hypothesis about population structure when making management decisions. Worldwide study is needed to better understand the various populations and subspecies within species like the blue whale that have large geographic distributions and have both migrating and resident populations.
200 citations