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Keywords:

  • Boosted regression trees;
  • distribution patterns;
  • global climate change;
  • habitat use;
  • historical data;
  • rare species;
  • southern right whale;
  • species distribution models

Abstract

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.