How animals distribute themselves in space: energy landscapes of an Antarctic predator
Final Report Abstract
The foraging efficiency of animals determines whether they will be able to raise healthy broods, maintain their own condition, avoid predators and ultimately increase their fitness. Foraging efficiency could also affect population dynamics, with increasing populations in places with minimized energetic costs and decreasing numbers in areas of increased foraging costs. Most studies of the foraging behaviour of animals have concentrated on the description of movements. However, the mechanisms underlying animal foraging behaviour remain mostly unexplained. Recent advances in animal movement tracking and data analyses have a great potential to fill this gap in our knowledge. With the use of accelerometers, features of the habitat and the way animals deal with variable conditions can be translated into energetic costs of movement, which, in turn, can be viewed as energy landscapes. In my DFG funded research, I took advantage of the newly available technology and methods and investigated how variable foraging costs in different environments affect the performance of different population. I used the energy landscapes paradigm to investigate how an Antarctic predator has successfully adapted to recent environmental change. Using Gentoo Penguins as a model species, I studied how foraging strategies differ between populations located in optimal (Antarctic Peninsula, increasing population) and suboptimal breeding sites (Falkland Islands, fluctuating populations). The energy landscapes constructed revealed that Gentoo Penguins preferentially used the areas of the energy landscape that resulted in lower foraging costs. Additionally, Gentoo Penguins from Antarctica carried out short and long foraging trips, a strategy absent in the colonies of the same species in the Falkland/Malvinas Is. The observed short and long trips clearly allowed Gentoo Penguins from the Antarctic Peninsula to use the areas of the energy landscape that resulted in lower foraging costs. Moreover, Gentoo Penguins from the Antarctic Peninsula, an optimal site with increasing population, foraged in sectors of the energy landscape where the maximum energy requirements were only half of those observed in the Falkland/Malvinas Is., a suboptimal breeding site with fluctuating populations. Our results suggest a relationship between the population trends for Gentoo Penguins in the Falkland/Malvinas Is. and in the Antarctic Peninsula that is related to the foraging energy requirements, as shown by energy landscapes. We also investigated energy landscapes in Chinstrap Penguins from Deception I., a species with currently decreasing populations. The energy landscapes corresponding to Chinstrap Penguins revealed that although these birds experienced lower foraging costs than Gentoo Penguins from Antarctica, they may be forced by competition with other penguins to forage outside of the cheapest areas of their energy landscape. Thus, our results suggest energy landscapes as the underlying mechanisms that govern how diving seabirds distribute themselves in space for foraging as well as a relationship between energy landscapes and population trends.
Publications
- (2018) Mapping marine megafauna to inform Marine Spatial Planning in the Falklands Islands. Marine Policy 92: 61-72
Augé AA, Dias M, Lascelles B, Baylis A, Black A, Boersma D, Campagna C, Catry P, Crofts S, Galimberti F, Granadeiro JP, Hedd A, Ludynia K, Masello JF et al.
(See online at https://doi.org/10.1016/j.marpol.2018.02.017) - (2018) Spatial scales of conservation management for breeding seabirds. Marine Policy 98: 37-46
Oppel S, Bolton M, Carneiro A, Dias M, Green JA, Masello JF et al.
(See online at https://doi.org/10.1016/j.marpol.2018.08.024) - (2019) Important at-sea areas of marine higher predators on the Patagonian Shelf. Scientific Reports 9: art8517
Baylis AMM, Tierney M, Orben R, Warwick-Evans V, Grecian J, Wakefield E, Reisinger R, Trathan P, Boersma D, Campioni L, Catry P, Crofts S, Croxall J, GalimbertiF, Granadeiro JP, Handley J, Hayes S, Hedd A, Masello JF et al.
(See online at https://doi.org/10.1038/s41598-019-44695-1)
