Bass Rock Gannet Count - Scottish Seabird Centre
New technologies help researchers to quantify the impact of avian flu on the world’s largest northern gannet colony.
Researchers have found that the Bass Rock gannet colony, the largest Northern gannet colony on the Earth, has reduced by 25-30% since the last major count in 2014. The latest findings were a result of a partnership between the Scottish Seabird Centre, The University of Edinburgh’s School of Geosciences, and the UK Centre for Ecology & Hydrology. The group collected imagery from a state-of-the-art drone, implemented automated counts and combined this data with traditional seabird counting methods to help them understand the impact Avian Flu had on the island’s gannet population.
Highly Pathogenic Avian Influenza (Avian Flu) has been spreading through seabird colonies around Scotland since 2021, causing widespread mortalities. The disease was confirmed on the Bass Rock in June 2022, at the height of the gannet breeding season. Following this discovery thousands of seabirds died on the island, resulting in an extremely disrupted breeding season. A colony count undertaken in June this year indicates that the size of the gannet population has decreased from 75,000 sites to around 55,000 sites. A ‘site’ in the colony is an area occupied by a single bird or pair. Despite this significant and concerning decline, the 2023 breeding season has shown some hopeful signs of recovery, with no evidence of widespread mortality this summer.
Advances in the technology now available to monitor breeding gannets and interpret survey results have brought with it opportunities to better understand the colony in the wake of the disease. The research on Bass Rock this year has included drone surveys and machine learning trials, led by the University of Edinburgh’s Airborne Research and Innovation Facility.
“We have been delighted with the performance of the drone in the gannet colony. The data quality surpassed our expectations and we were able to operate the drone without any disturbance to the colony. The implementation of the machine learning methods allowed a fast assessment of the colony, and identified live, dead, nesting and flying gannets. Going forward we plan to publish these early findings, with our partners at the Scottish Seabird Centre and UKCEH, and continue to develop and refine the machine learning methods in wild bird colonies.” Dr Amy Tyndall & Tom Wade, School of Geosciences & Airborne Research and Innovation Facility, University of Edinburgh