Understanding minke whale occurrence to inform offshore wind environmental assessment

Addressing a complex monitoring challenge for stronger baseline evidence

Assessing the potential effects of planned offshore wind developments required a clearer understanding of when minke whales were present in the area. However, limited long-term data made it difficult to identify seasonal patterns or understand how their occurrence varied between years and monitoring locations.

The project addressed this gap by analysing ten years of passive acoustic monitoring data to establish a more robust baseline of minke whale occurrence.

The team analysed passive acoustic monitoring data collected between 2015 and 2025. The dataset combined recordings from six moorings in the ongoing Scottish Passive Acoustic Network, supported by the Scottish Government, with data from three additional monitoring units in the Southern Trench MPA. These additional units formed part of a two-year targeted monitoring campaign funded by offshore wind developers.

Analysing ten years of broadband recordings across multiple locations required an efficient way to identify minke whale vocalisations. A recently developed machine-learning tool automatically detected minke whale songs, with detections manually reviewed where necessary to validate the results. This enabled the team to examine seasonal, interannual and spatial patterns in minke whale occurrence across the study area.

In total, the analysis identified 651 minke whale detections across 3,902 monitored days at nine locations.

Cross-organisational project

• ^{Caledonia Offshore Wind Farm}
• ^{Muir Mhòr Offshore Wind Farm}
• ^{Crown Estate Scotland}
• ^NatureScot
• ^{Scottish Government Marine Directorate}
• ^{Scottish Association for Marine Science}
• ^{SAMS Enterprise}

Project Type: Marine renewable energy - offshore wind and marine environmental monitoring

Status: Ongoing

Location: Moray Firth, including the Southern Trench MPA, North Sea, Scotland, UK

 

Services Provided:

• ^{Long-term passive acoustic dataset analysis}
• ^{Marine mammal occurrence and seasonality assessment}
• ^{Automated analysis of minke whale vocalisations using a machine-learning detection tool}
• ^{Manual validation of automated detections}
• ^{Interpretation of spatial, seasonal and interannual patterns}
• ^{Scientific evidence to support offshore wind impact assessment}
• ^{Evidence to inform future monitoring and management of the Southern Trench MPA}

The results

Clear seasonal patterns were observed. Minke whale song was typically detected between May or June and November, with few detections recorded from December to April. Detection rates also varied between locations, with substantially lower daily detection rates at the three most offshore monitoring sites.

The long-term baseline provides project developers and decision-makers with clearer evidence of when vocalising minke whales are present in the area. This can strengthen assessments of potential underwater noise effects during the construction and operation of planned offshore wind developments.

The findings can also inform future monitoring and management of the Southern Trench MPA.

 

"The SAMS Minke Whale Monitoring Programme represents a benchmark for environmentally responsible offshore wind development, demonstrating how strategic collaboration and high-quality scientific evidence can unlock complex consenting challenges while safeguarding marine ecosystems. The programme is already informing project assessments and supporting ongoing consenting processes across the offshore wind projects portfolios, demonstrating early and tangible impact since its inception in 2024."

Professor Nicholas J P Owens, Director, Scottish Association for Marine Science (SAMS), UK

Turning ten years of acoustic data into environmental evidence

The project shows how long-term datasets collected through strategic monitoring networks can be combined with targeted, industry-funded monitoring to address evidence gaps relevant to offshore wind development and marine conservation.

Applying a machine-learning detection tool to ten years of broadband acoustic recordings enabled the team to analyse minke whale occurrence across multiple years and locations at a scale that would have been difficult to achieve through manual review alone./p>

This project also demonstrates the value of collaboration between offshore wind developers, public bodies, environmental organisations and marine scientists.

Similar programmes could combine shared monitoring data with specialist acoustic analysis to establish stronger environmental baselines, improve assessments of potential effects and support proportionate monitoring and management across other offshore development areas.