"Development of time-lapse and drone imagery for population monitoring of North Atlantic seabirds"

My research will use UAV and time-lapse imagery, classified by a combination of researchers, citizen scientists and machine learning, to study threatened seabird species across the Palearctic. I will use an existing time-lapse camera network to focus on one cliff-nesting species, the Black-legged Kittiwake Rissa tridactyla, with the aim of understanding how kittiwakes may be impacted by a changing environment. I will also deploy cameras to study a burrow-nesting species, the Atlantic Puffin Fratercula arctica, to try and improve methods for measuring productivity of burrow-nesting seabirds. By developing reproducible techniques to extract data from photographs, I hope to provide a means of sustained long-term monitoring across sufficient spatial scales to inform understanding of seabird decline.
OBJECTIVES
1) To compare accuracy of citizen science, researcher and machine learning identification of kittiwake in time-lapse images.
2) To understand how abiotic (e.g. latitude, colony size, year) and biotic (e.g. weather conditions, predation, distance to fisheries, human disturbance) factors influence kittiwake phenology, breeding success and breeding site attendance across a latitudinal gradient using time-lapse imagery.
3) To develop methods to measure puffin productivity from time-lapse images and compare with productivity calculated from 'traditional' field observations.
4) To review the use of Unmanned Aerial Vehicles (UAVs) for seabird censuses and develop a guide to 'best practice' for seabird surveys with UAVs.
5) To develop efficient methods for counting ground- and cliff-nesting seabird colonies from UAV imagery, and to use these data to test for spatial effects of colony structure linked to population trends.