Using marine UAVs to integrate seabird census with phenology

Using marine UAVs to integrate seabird census with phenology

Does spatial data as to the structure of colonies link with observed patterns of predation and phenology?


Seabirds worldwide are in rapid decline and yet basic breeding of breeding success and survival are lacking across large spatial scales to disentangle the threats to them. In the polar regions, we are particularly interested in how widespread species react or adapt to environmental perturbations across their ranges and in particular their strategies of tolerance, changes in survival and reproductive success or migration and range edges.

Recent advances in both Unmanned Arial Vehicles (UAVs) and time lapse cameras now mean that we can survey colonies to obtain counts and positions of nests within and between seasons, potentially on large scales. We intend to link survey data with phenology data to determine spatial patterns of microhabitat use that are key indicators of population increase or decline, and to test spatial metrics of colony formation against predation risk and nest survival rates.

Pioneering the use of a new Animal Dynamics UAV designed for use in the marine environment, the student will create a pipeline for rapid processing of survey data to the identification of nests and use these data for large-scale comparisons of seabird colonies within and between regions.

Aims of the Project

To develop machine-learning or automated parameter estimation from camera data on seabirds.

To test these across large scales. For several species, do we detect edge of range effects in the spatial structuring of colonies and is this linked to predation

Methods to be used

Take aerial survey imagery, turn these into digital elevation models and orthographic photos. Use existing or new automation methods to

Extract the location of nests within survey images and use these to test hypotheses as to the structuring of colonies and trends across species ranges.

Extended description of the project

Marine animals; from seals to seabirds and their breeding habitats, form an essential part of the marine environment that are of concern worldwide and subject to numerous treaties and agreements. They are under threat from climate change, pollution, disturbance and competition with fisheries, but monitoring these animals and the threats to them are difficult and costly. In particular, when we don’t know the appropriate scale and frequency to disentangle these overlapping threats. Our ability to monitor and detect changes in the marine environment severely lags behind our conservation goals and therefore government bodies and NGOs have been forced to adopt risk–based approaches where regions are data deficient. Even so, we have very little ability to statistically distinguish between overlapping threats to determine what is driving such declines and therefore mitigate changes (Hart et al 2015).

Population trends are usually detected using census data, which has only been done on a large scale capable of detecting national population changes every 10-15 years in the UK and Ireland (Mitchell et al. 2004). Within this context, an analysis of monitored seabird populations between 1950 and 2010 found a 70% decline globally (Paleczny et al. 2015).

Outside of the UK, programmes such as Oceanites (, Penguin Watch ( and MAPPPD ( have shown that it is possible to increase frequency and coverage of monitoring even in challenging areas like the Southern Ocean that have been pivotal in informing the creation and management of Marine Protected Areas (MPAs;

Animal Dynamics are developing a new UAV called “Shearwater”, which is a long-range all-weather surveillance UAV for the marine sector. The design specifications of this vehicle are that it is capable of vertical take-off and landing (VTOL) from vessels and the water surface with an extended 4 hour and 200 km range. Such a vehicle has the capacity to revolutionise marine monitoring for conservation, marine spatial planning and fisheries monitoring and enforcement if we can develop the tools for appropriate assessment of data.

We wish to trial this UAV to collect widespread census data from the marine environment in the UK and further afield. We will use existing UAV imagery from two polar field seasons to construct a pipeline for the rapid processing of imagery to 3D models and the automated or semi-automated identification of nests within this landscape. Methods for the automated detection of penguins exist and these may need to be expanded for other seabird species.

Specialised skills required

The student will need to be proficient in R and preferably GIS. The student will also need to be capable of carrying out fieldwork in remote areas although this will be with substantial support and training.

If you are interested in this project please contact Tom Hart in the first instance

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