No abstract

Antarctic blue whales (Balaenoptera musculus intermedia) are the largest and formerly most abundant blue whale subspecies, but were hunted to near extinction last century. Estimated whaling mortality was unsustainable from 1928 to 1972 (except during 1942–1944), depleting them from 239,000 (95% interval 202,000–311,000) to a low of 360 (150–840) in 1973. Obtaining statistical evidence for subsequent increases has proved difficult due to their scarcity. We fitted Bayesian models to three sighting series (1968–2001), constraining maximum rates of increase to 12% per annum. These models indicated that Antarctic blue whales are increasing at a mean rate of 7.3% per annum (1.4%–11.6%). Informative priors based on blue whale biology (4.3%, SD = 1.9%) and a Bayesian hierarchical meta‐analysis of increase rates in other blue whale populations (−3%, SD = 11.6%), suggest plausible increase rates are lower (although the latter has wide intervals), but a meta‐analysis of other mysticetes obtains similar rates of increase (6.7%, SD = 4.0%). Possible biases affecting the input abundance estimates are discussed. Although Antarctic blue whales appear to have been increasing since Sovier illegal whaling ended in 1972, they still need to be protected‐their estimated 1996 population size, 1,700 (860–2,900), was just 0.7% (0.3%–1.3%) of the pre‐exploitation level.

Aim Sufficient data to describe spatial distributions of rare and threatened populations are typically difficult to obtain. For example, there are minimal modern offshore sightings of the endangered southern right whale, limiting our knowledge of foraging grounds and habitat use patterns. Using historical exploitation data of southern right whales (SRW), we aim to better understand their seasonal offshore distribution patterns in relation to broad-scale oceanography, and to predict their exposure to shipping traffic and response to global climate change.Location Australasian region between 130°W and 100°E, and 30°S and 55°S.Methods We model 19th century whaling data with boosted regression trees to determine functional responses of whale distribution relative to environmental factors. Habitat suitability maps are generated and we validate these predictions with independent historical and recent sightings. We identify areas of increased risk of ship-strike by integrating predicted whale distribution maps with shipping traffic patterns. We implement predicted ocean temperatures for the 2090-2100 decade in our models to predict changes in whale distribution due to climate change.Results Temperature in the upper 200 m, distance from the subtropical front, mixed layer depth, chlorophyll concentration and distance from ridges are the most consistent and influential predictors of whale distribution. Validation tests of predicted distributions determined generally high predictive capacity. We identify two areas of increased risk of vessel strikes and predict substantial shifts in habitat suitability and availability due to climate change.Main conclusions Our results represent the first quantitative description of the offshore foraging habitat of SRW. Conservation applications include identifying areas and causes of threats to SRW, generating effective mitigation strategies, and directing population monitoring and research efforts. Our study demonstrates the benefits of incorporating unconventional datasets such as historical exploitation data into species distribution models to inform management and help combat biodiversity loss.