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.
The calling seasonality of blue (
Balaenoptera musculus) and fin (
B. physalus) whales was assessed using acoustic data recorded on seven autonomous acoustic recording packages (ARPs) deployed from ...March 2001 to February 2003 in the Western Antarctic Peninsula. Automatic detection and acoustic power analysis methods were used for determining presence and absence of whale calls. Blue whale calls were detected year round, on average 177 days per year, with peak calling in March and April, and a secondary peak in October and November. Lowest calling rates occurred between June and September, and in December. Fin whale calling rates were seasonal with calls detected between February and June (on average 51 days/year), and peak calling in May. Sea ice formed a month later and retreated a month earlier in 2001 than in 2002 over all recording sites. During the entire deployment period, detected calls of both species of whales showed negative correlation with sea ice concentrations at all sites, suggesting an absence of blue and fin whales in areas covered with sea ice. A conservative density estimate of calling whales from the acoustic data yields 0.43 calling blue whales per 1000
n
mi
2 and 1.30 calling fin whales per 1000
n
mi
2, which is about one-third higher than the density of blue whales and approximately equal to the density of fin whales estimated from the visual surveys.
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 6 kHz 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.
Links in the trophic chain Barlow, Dawn R.; Bernard, Kim S.; Escobar-Flores, Pablo ...
Marine ecology. Progress series (Halstenbek),
05/2020, Letnik:
642
Journal Article
Recenzirano
Odprti dostop
The response of marine predators to global climate change and shifting ocean conditions is tightly linked with their environment and prey. Environmental data are frequently used as proxies for prey ...availability in marine predator distribution models, as the ephemeral nature of prey makes sampling difficult. For this reason, the functional, ecological links between environment, prey, and predator are rarely described or explicitly tested. We used 3 years of vessel-based whale survey data paired with oceanographic sampling and hydroacoustic backscatter to model trophic relationships between water column structure, krill availability, and blue whale Balaenoptera musculus brevicauda distribution in New Zealand’s South Taranaki Bight region under typical (2014 and 2017) and warm (2016) austral summer oceanographic regimes. The warm regime was characterized by a shallower mixed layer, and a stronger, thicker, and warmer thermocline. Boosted regression tree models showed that krill metrics predicted blue whale distribution (typical regime = 36% versus warm regime = 64% cross-validated deviance explained) better than oceanography (typical regime = 19% versus warm regime = 31% cross-validated deviance explained). However, oceanographic features that predicted more krill aggregations (typical regime) and higher krill density (warm regime) aligned closely with the features that predicted higher probability of blue whale presence in each regime. Therefore, this study confirms that environmental drivers of prey availability can serve as suitable proxies for blue whale distribution. Considering changing ocean conditions that may influence the distribution of marine predators, these findings emphasize the need for models based on functional relationships, and calibrated across a broad range of conditions, to inform effective conservation management.
In terrestrial systems, the green wave hypothesis posits that migrating animals can enhance foraging opportunities by tracking phenological variation in high-quality forage across space (i.e., ...“resource waves”). To track resource waves, animals may rely on proximate cues and/or memory of long-term average phenologies. Although there is growing evidence of resource tracking in terrestrial migrants, such drivers remain unevaluated in migratory marine megafauna. Here we present a test of the green wave hypothesis in a marine system. We compare 10 years of blue whale movement data with the timing of the spring phytoplankton bloom resulting in increased prey availability in the California Current Ecosystem, allowing us to investigate resource tracking both contemporaneously (response to proximate cues) and based on climatological conditions (memory) during migrations. Blue whales closely tracked the long-term average phenology of the spring bloom, but did not track contemporaneous green-up. In addition, blue whale foraging locations were characterized by low long-term habitat variability and high long-term productivity compared with contemporaneous measurements. Results indicate that memory of long-term average conditions may have a previously underappreciated role in driving migratory movements of long-lived species in marine systems, and suggest that these animals may struggle to respond to rapid deviations from historical mean environmental conditions. Results further highlight that an ecological theory of migration is conserved across marine and terrestrial systems. Understanding the drivers of animal migration is critical for assessing how environmental changes will affect highly mobile fauna at a global scale.
Ecologically similar species may coexist when resource partitioning over time and space reduces interspecific competition. Understanding resource use within these species assemblages may help predict ...how species relative abundance might influence ecosystem functioning. In the Gulf of St. Lawrence, Canada, 4 species of rorqual whales (blue Balaenoptera musculus, fin B. physalus, minke B. acutorostrata and humpback Megaptera novaeangliae) coexist during the summer feeding period. They can be observed within hundreds of meters of one another, suggesting an overlap in ecological niches; yet fine-scale habitat use analyses suggest some resource partitioning. While major ecological changes have been observed in marine ecosystems, including the Gulf of St. Lawrence, we have little understanding of how the removal of predatory fish might cascade through ecosystems. Here, we take advantage of a 19 yr tissue collection subsequent to a fishery collapse (which occurred in 1992) to investigate trophic niche partitioning within a guild of rorqual whales following the loss of a key ecosystem component, groundfish. We analyzed stable isotope ratios for 626 rorqual individuals sampled between 1992 and 2010. Using Bayesian isotopic mixing models, we demonstrated that the 4 rorqual species segregated trophically by consuming different proportions of shared prey. An overall increase in delta super(15)N values over the study period (post groundfish collapse), particularly for fin and humpback whales, suggested a progressive use of higher-trophic level prey, such as small pelagic fish, whereas the stability of blue whale diet over time confirmed their specialized feeding behaviour. This study provides the first longterm assessment of trophic ecology among rorqual populations on this Northwest Atlantic feeding ground, and evidence for differential resource use among large marine predators following ecosystem change.
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.
Passive acoustic monitoring is an efficient way to provide insights on the ecology of large whales. This approach allows for long-term and species-specific monitoring over large areas. In this study, ...we examined six years (2010 to 2015) of continuous acoustic recordings at up to seven different locations in the Central and Southern Indian Basin to assess the peak periods of presence, seasonality and migration movements of Antarctic blue whales (Balaenoptera musculus intermedia). An automated method is used to detect the Antarctic blue whale stereotyped call, known as Z-call. Detection results are analyzed in terms of distribution, seasonal presence and diel pattern of emission at each site. Z-calls are detected year-round at each site, except for one located in the equatorial Indian Ocean, and display highly seasonal distribution. This seasonality is stable across years for every site, but varies between sites. Z-calls are mainly detected during autumn and spring at the subantarctic locations, suggesting that these sites are on the Antarctic blue whale migration routes, and mostly during winter at the subtropical sites. In addition to these seasonal trends, there is a significant diel pattern in Z-call emission, with more Z-calls in daytime than in nighttime. This diel pattern may be related to the blue whale feeding ecology.
•Fecal progesterone metabolites reveal reproductive state in blue whales.•Corticosterone metabolite is the predominant glucocorticoid in feces from blue whales.•Fecal corticosterone metabolite ...concentration differs by reproductive state in blue whales.•Gestation can be considered an important physiological stressor.
Steroid hormone assessment using non-invasive sample collection techniques can reveal the reproductive status of aquatic mammals and the physiological mechanisms by which they respond to changes in their environment. A portion of the eastern North Pacific blue whale (Balaenoptera musculus) population that seasonally visits the Gulf of California, Mexico has been monitored using photo-identified individuals for over 30 years. The whales use the area in winter-early spring for nursing their calves and feeding and it therefore is well suited for fecal sample collection. Using radioimmunoassays in 25 fecal samples collected between 2009 and 2012 to determine reproductive state and stress, we validated three steroid hormones (progesterone, corticosterone and cortisol) in adult female blue whales. Females that were categorized as pregnant had higher mean fecal progesterone metabolite concentrations (1292.6 ± 415.6 ng·g-1) than resting and lactating females (14.0 ± 3.7 ng·g-1; 23.0 ± 5.4 ng·g-1, respectively). Females classified as pregnant also had higher concentrations of corticosterone metabolites (37.5 ± 9.9 ng·g-1) than resting and lactating females (17.4 ± 2.0 ng·g-1; 16.8 ± 2.8 ng·g-1, respectively). In contrast, cortisol metabolite concentrations showed high variability between groups and no significant relationship to reproductive state. We successfully determined preliminary baseline parameters of key steroid hormones by reproductive state in adult female blue whales. The presence of pregnant or with luteal activity and known lactating females confirms that the Gulf of California is an important winter-spring area for the reproductive phase of these blue whales. The baseline corticosterone levels we are developing will be useful for assessing the impact of the increasing coastal development and whale-watching activities on the whales in the Gulf of California.
Species distribution models (SDMs) are a valuable statistical approach for both understanding species distributions and identifying potential impacts of environmental changes or management decisions ...to species, but multiple SDMs for the same species in a region can create confusion in decision‐making processes.
One solution is to create ensembles (i.e. combinations) of predictions from existing SDMs. However, creating ensembles can be challenging if the predictions were made at different spatial resolutions, using different data sources, or with different prediction value types (e.g. abundance and probability of occurrence).
We present esdm, an r package that allows users to create an ensemble of SDM predictions overlaid onto a single base geometry. These predictions can be evaluated (e.g. through among‐model uncertainty or AUC, TSS and RMSE metrics), mapped, and exported. esdm includes a built‐in GUI created using the r package shiny, which makes the package accessible to non‐r users.
We provide an overview of esdm functionality and use esdm to create an ensemble of predictions from three blue whale Balaenoptera musculus SDMs for the California Current Ecosystem.
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