Evol Ecol Res 9: 1053-1076 (2007) Full PDF if your library subscribes.
Weight loss during breeding is adaptive for female macaroni penguins, Eudyptes chrysolophus
Katherine A. Cresswell,* Geraint A. Tarling and Philip N. Trathan
British Antarctic Survey, Natural Environmental Research Council, High Cross, Madingley Road, Cambridge CB3 0ET, UK
Address all correspondence to Katherine A. Cresswell, The Center of Stock Assessment Research, Mail Stop E2, Jack Baskin School of Engineering, University of California, Santa Cruz, CA 95064, USA.
Question: How does the female macaroni penguin balance her own needs with those of her chick during breeding?
Features of the model: We model the behaviour of female macaroni penguins during a sensitive life-history stage as a function of the availability of their main prey species, Antarctic krill (Euphausia superba), using stochastic dynamic programming. In the model, females maximize accumulated delivery to the chick, accounting for metabolic losses. Chick fullness is included as a state in the model.
Range of key variables: We test three scenarios for krill availability, which changes with distance from the nest. In the first, krill abundance increases with distance from the nest, with no variability in the reward at each distance. In the second, variability increases proportionally with the increasing amount of krill available at each distance from the nest. In the third, the abundance of krill at each distance from the nest is constant, but variability decreases further from the nest.
Conclusions: Natural selection should produce females that sacrifice their own condition to meet the increasing demands of their chicks. We predict a weight loss of 10–20%, which is comparable to the empirical average of 14%. We also predict that females will endure the cost of travelling further from the nest to obtain a more predictable meal of krill, even if the mean reward does not change with distance from the nest.
Keywords: behaviour, chick provisioning, Eudyptes chrysolophus, Euphausia superba, fitness, foraging, predator, prey, stochastic dynamic programming, variability.
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