Distribution of Eastern Indian Ocean pygmy blue whales and potential impacts of offshore wind developments (NESP MaC 4.8)

Full description

The offshore renewable energy (ORE) sector is rapidly developing in Australian waters to support national carbon emission targets. However, new marine infrastructure can introduce additional risks to threatened species. The eastern Indian Ocean pygmy blue whale (Balaenoptera musculus brevicauda) was identified by the Australian Government as a priority species for assessing potential ORE impacts. This subspecies ranges from the Subtropical Convergence (~40-45°S) to Southeast Asia (~2°S), with much of its documented distribution occurring within Australia’s Exclusive Economic Zone. Its distribution overlaps with multiple anthropogenic activities, indicating likely exposure to existing and emerging pressures.

This dataset compiled available spatial information to quantify the full distribution and foraging distribution of pygmy blue whales, and to assess exposure to individual and cumulative threats across the species’ range. The threat exposure analysis incorporated expert elicitation to estimate the probability of exposure arising from spatial overlap between pygmy blue whale distribution and anthropogenic pressures, with particular focus on areas undergoing ORE development.

The cumulative exposure assessment indicated relatively low exposure of pygmy blue whales to existing threats within Australian waters, including within declared ORE areas. However, several data and knowledge gaps were identified that should be addressed before further ORE development. The resulting spatial layers provide a baseline for impact assessment by industry and regulators, and support decision-making for sustainable ORE development in Australian waters.

Lineage

Maintenance and Update Frequency: notPlanned

Statement: 𝗣𝘆𝗴𝗺𝘆 𝗕𝗹𝘂𝗲 𝗪𝗵𝗮𝗹𝗲 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 ---Tracking data--- Satellite telemetry data from 60 pygmy blue whales were compiled from multiple long-term tracking programs across southern Australia and Indonesia, spanning 22 years and a range of tag types. All tracks were processed using a state-space model to account for location error and movement autocorrelation, and a time-weighting used to reduce spatial and temporal bias near tagging sites caused by tag transmission decay. A move-persistence model was used to classify behaviour relative to the directionality of movement, with 0 representing 'area restricted search' (foraging), and 1 representing fast directional movement (migration or transit). ---Habitat suitability--- Predictions of suitable habitat were developed by incorporating tracking data and passive acoustic data (predicted spatial distribution of unique whale songs) for northwest Australia. Spatial predictions were validated and normalised between 0 and 1 to generate habitat suitability maps for (1) migration; and (2) foraging behaviour. ---Combined relative distribution--- Tracking and habitat suitability maps were combined with presence data from aerial surveys in southern Australia and the Bass Strait, vessel surveys led by the offshore resource industry, the marine mammal observer (MMO) dataset hosted by the AAD, and historical pygmy blue whale catch records obtained from the International Whaling Commission. From these, relative distribution was modelled using a 10 km × 10 km grid across the Australian EEZ to create a combined-sources distribution for pygmy blue whales. Where behaviour data were available, foraging distribution was calculated in addition to an overall distribution. For each input source, whale occurrence within 10 km × 10 km grid cells was first normalised to a 0–1 scale, allowing metrics based on different units (e.g. occupancy time vs number of whales) to be comparable. These normalised layers were then summed and the resulting surface was re-normalised from 0–1 to generate a final relative-distribution index. ---Important pygmy blue whale areas--- Important pygmy blue whale areas for (1) foraging; and (2) distribution/migration were identified using the combined, normalised relative-distribution maps. Important foraging areas were defined as the top 50% of the combined foraging distribution, while overall important areas were calculated as the top 75% of the combined overall distribution. 𝐓𝐡𝐫𝐞𝐚𝐭𝐬 ---Threat distribution--- Six key threats to pygmy blue whales in Australian waters were identified: climate change, displacement, entanglement pollution, underwater noise, and vessel strike. Each threat was associated with one or more pressures linked to human activities in the marine ecosystem. Spatial data for each of the pressures were obtained from open free sources and converted into pressure intensity on a 10 km × 10 km grid. For each threat, all relevant overlapping spatial pressures were summed and normalised between 0 (no threat present) and 1 (maximum threat intensity), which allowed intensity to be compared across threats. ---Cumulative exposure to threats--- Cumulative exposure of pygmy blue whales to key threats was calculated from both their foraging and overall distributions. Expert elicitation was used to assess the probability of exposure to each threat where whale distribution and threat layers overlapped. Each threat layer was weighted by the expert-derived probabilities of exposure. and then summed on a 10 km × 10 km grid to represent the combined intensity of all threats. The combined threat intensity was then multiplied by either the overall combined relative distribution of pygmy blue whales, or the foraging-only distribution. The resulting maps show where pygmy blue whales are most at-risk from climatic and anthropogenic threats.

Notes

Credit
The data collections described in this record are funded by the Australian Government Department of Climate Change, Energy, the Environment and Water (DCCEEW) through the NESP Marine and Coastal Hub. In addition to NESP (DCCEEW) funding, this project is matched by an equivalent amount of in-kind support and co-investment from project partners and collaborators.