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 meet the country’s carbon emission targets. However, new developments in the marine environment pose added risk to threatened species. The Eastern Indian Ocean pygmy blue whale (Balaenoptera musculus brevicauda) was identified as a key species by the Australian Government for understanding the potential impacts of ORE developments. This subspecies ranges from the Subtropical Convergence (~40-45°S) to Southeast Asia (~2°S) with most of its documented distribution within the Australia Exclusive Economic Zone. Pygmy blue whale distribution overlaps various anthropogenic activities across their range, which suggests that some level of exposure to pressure and threats is likely.

We compiled all available spatial data to quantify the full and foraging distribution of pygmy blue whales and quantified exposure to individual and cumulative threats across the species distribution. Threat exposure analysis included expert elicitation to gather expert input on the probability of exposure to a threat occurring from the spatial overlap between pygmy blue whale distribution and anthropogenic pressures, with a focus on areas undergoing ORE development. The cumulative exposure assessment indicated a relatively low level of exposure of pygmy blue whales to existing threats within Australian waters, particularly those that occur within declared ORE areas. However, several gaps in data and knowledge were identified that need to be addressed prior to development of the ORE industry. Our results provide a robust baseline that can be directly incorporated by industry and regulators as spatial layers into impact assessments. The study helps inform Government, and proponents of wind farms on the current state of knowledge of pygmy blue whale distribution and exposure to threats in Australian waters for use in decision-making, helping facilitate the sustainable development of the ORE industry in Australia.

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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.

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Credit
National Environmental Science Program (NESP) Marine and Coastal Hub