From exploitation to conservation: habitat models using whaling data predict distribution patterns and threat exposure of an endangered whale

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.

Crowdsourcing Modern and Historical Data Identifies Sperm Whale (Physeter macrocephalus) Habitat Offshore of South-Western Australia

“…These data are available as downloadable GIS layers that display presence data from museum collections, citizen scientist programs, and "platforms of opportunity" (Williams et al, 2006; Table 1). They include location of a sighting from a ship, a biopsy sample taken from an animal, a location where an animal was heard using underwater hydrophones, or the location of a "take" or kill recorded during whaling operations in the study area (Torres et al, 2013).…”

“…Topographic information, including slope and Grid cell size was a consideration in how all data were plotted in ArcGIS and Maxent. To deal with the location bias in the Townsend Yankee whaling data where the mean vessel location error was estimated to be 0.22 • latitude (about 24 km) and 0.54 • longitude (about 34 km) (Smith, T. D. et al, 2012;Torres et al, 2013) point and kernel density distributions of historical data were computed in 15 nautical mile grid cells over the study area using the Spatial Analyst tool.…”

“…Species distribution models can provide quantitative predictions of geographic distributions and have been used to model historical whaling data (Gregr and Trites, 2001;Elith et al, 2006;Torres et al, 2013). Knowing where the animals are, what characteristics influence their choice of habitat, and how this choice changes with time, is important in understanding the ecology of any species as are: identification of critical habitat, assessment of any overlap with human activities and, ultimately, guiding appropriate conservation and management efforts (Redfern et al, 2006).…”

Obstacles and Opportunities of Using a Mobile App for Marine Mammal Research

“…Monitoring the impacts of these pressures on the distribution patterns of wide-ranging marine mammals requires data across large temporal and spatial scales [4,5]. Current monitoring efforts lack the spatial extent and frequency required to detect abrupt and fast marine mammal declines [6], and therefore limit efforts to manage and protect marine mammals from various pressures.…”

“…Similarly, these methods can be applied to marine mammal research as well. Examples of marine mammal citizen science research include: the American Cetacean Society surveys [22,23] annual volunteer cetacean counts in Hawaii [24], specific species projects [25,26], and analyses of historical whaling records [5,[27][28][29][30].…”