Intracolony variability in winter feeding and migration strategies of Atlantic puffins and black‑legged kittiwakes

Abstract

Polar ecosystems are subjected to many stressors, including climate change, that impact their overall functioning. Seabirds are good bioindicators of these systems as they readily respond to changes in environmental conditions. To quantify how environmental changes afect their life history, data on seabird diet, spatial distribution and body condition are needed to reveal the underlying mechanisms. We explored possible drivers of the winter distribution of single-colony populations of Atlantic pufns Fratercula arctica and black-legged kittiwakes Rissa tridactyla, two of the most numerous seabird species in the North Atlantic. Based on carbon and nitrogen stable isotopic data from feathers moulted during winter migration, we identifed three groups of Atlantic pufns and two groups of black-legged kittiwakes occupying diferent isotopic niches. We then used geolocator tracking data for the same birds to determine if these groups refected parallel diferences in the location of moulting grounds rather than diferences in their diet. We found that the isotopic niches of the three Atlantic pufn groups likely resulted from their use of diferent habitats during winter moult. In contrast, the isotopic niches of the two black-legged kittiwake groups were more likely a result of diferences in their diet, as both groups were distributed in the same area. These fndings suggest that diferent winter feeding and/or migration strategies may play a role in shaping the body condition of individuals for their following breeding season. We discuss the role of environmental conditions encountered by seabirds during winter migration to further elucidate such intracolony divergence in strategies. As polar ecosystems experience rapid changes in environmental conditions, the approach presented here may provide valuable information for the development of efective conservation measures (taking both intra- and intercolony variability into account), and to better predict future impacts of climate change.