Using habitat models to identify marine important bird and biodiversity areas for Chinstrap Penguins Pygoscelis antarcticus in the South Orkney Islands

• Published in: Polar Biology
• Main Authors: Dias, M.P., Warwick-Evans, Victoria, Carneiro, A.P.B., Harris, C., Lascelles, B.G., Clewlow, Harriet L., Manco, F., Ratcliffe, Norman, Trathan, Phil N.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Springer 2019
• Subjects: Antarctic; The Antarctic; South Orkney Islands; Antarc*; Antarctica; antarcticus; Chinstrap penguin; Polar Biology
• Online Access: http://nora.nerc.ac.uk/id/eprint/521158/; https://nora.nerc.ac.uk/id/eprint/521158/1/Dias.pdf; https://doi.org/10.1007/s00300-018-2404-4

Tracking individual marine predators can provide vital information to aid the identification of important activity (foraging, commuting, rafting, resting, etc.) hotspots and therefore also to delineate priority sites for conservation. However, in certain locations (e.g. Antarctica) many marine mammal or seabird colonies remain untracked due to logistical constraints, and the colonies that are studied may not be the most important in terms of conservation priorities. Using data for one of the most abundant seabirds in the Antarctic as a case study (the Chinstrap Penguin Pygoscelis antarcticus), we tested the use of correlative habitat models (used to predict distribution around untracked colonies) to overcome this limitation, and to enable the identification of important areas at-sea for colonies where tracking data are not available. First, marine Important Bird and Biodiversity Areas (IBA) were identified using a standardised, published approach using empirical data from birds tracked from colonies located in the South Orkney Islands. Subsequently, novel approaches using predicted distributions of Chinstrap Penguins derived from correlative habitat models were applied to identify important marine areas, and the results compared with the IBAs. Data were collected from four colonies over 4 years and during different stages of the breeding season. Results showed a high degree of overlap between the areas identified as important by observed data (IBAs) and by predicted distributions, revealing that habitat preference models can be used to identify marine IBAs for these penguins. We provide a new method for designating a network of marine IBAs for penguins in Antarctic waters, based on outputs from correlative habitat models when tracking data are not available. This can contribute to an evidence-based and precautionary approach to aid the management framework for Antarctic fisheries and for the protection of birds.