Foraging behaviour of sympatrically breeding macaroni (Eudyptes chrysolophus) and chinstrap (Pygoscelis antarcticus) penguins at Bouvetøya, Southern Ocean

Autor: Audun Narvestad, Christian Lydersen, Kit M. Kovacs, Andrew D. Lowther
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Polar Research, Vol 41, Pp 1-13 (2022)
Druh dokumentu: article
ISSN: 0800-0395
1751-8369
DOI: 10.33265/polar.v41.6351
Popis: Species with similar ecological requirements that overlap in range tend to segregate their niches to minimize competition for resources. However, the niche segregation possibilities for centrally foraging predators that breed on isolated Subantarctic islands may be reduced by spatial constraints and limitations in the availability of alternative prey. In this study we examined spatial and trophic aspects of the foraging niches of two sympatrically breeding penguin species, macaroni (Eudyptes chrysolophus; MAC) and chinstrap (Pygoscelis antarcticus; CHIN) penguins, at Bouvetøya over two breeding seasons. To measure at-sea movements and diving behaviour, 90 MACs and 49 CHINs were equipped with GPS loggers and dive recorders during two austral summer breeding seasons (2014/15 and 2017/18). In addition, blood samples from tracked birds were analysed for stable isotopes to obtain dietary information. CHINs displayed marked interannual variation in foraging behaviour, diving deeper, utilizing a larger foraging area and displaying enriched values of δ15N in 2014/15 compared to the 2017/18 breeding season. In contrast, MACs dove to similar depths and showed similar δ15N values, while consistently utilizing larger foraging areas compared to CHINs. We suggest that low krill abundances in the waters around Bouvetøya during the 2014/15 season resulted in CHINs shifting toward a diet that increased their niche overlap with MACs. Our findings may partly explain the decreasing number of breeding CHINs at the world’s most remote island, Bouvetøya, while also highlighting the importance of characterizing niche overlap of species using multi-season data sets.
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