RESUMEN
Life-history theory predicts that investment in reproduction should decrease survival (the 'cost of reproduction'). It is often assumed that energy allocation drives such trade-offs, with limited energy available for both reproduction and survival. However, the underlying mechanisms remain poorly understood, maybe because survival costs of reproduction are only apparent when resources are limited. Here, we took advantage of a natural experiment created by fluctuating environmental conditions to compare energy expenditure of a seabird, the pelagic cormorant (Phalacrocorax pelagicus), between contrasting population-scale scenarios of survival costs of reproduction. We used multi-state capture-recapture modelling across 16 years to identify which breeding seasons induced high survival costs (survival ratebreeders < survival ratenon/failed breeders) and we concomitantly estimated energy expenditure of chick-rearing males using time-energy budget models across 4 years. Daily energy expenditure (DEE) of chick-rearing pelagic cormorants varied significantly among years. However, survival costs of reproduction were observed in only 1 year, and contrary to our expectations, variation in DEE was not associated with population-level survival costs. Similarly, at the individual level, DEE in 1 year did not predict the probability of being observed again at the colony in following years (apparent survival). Finally, DEE was independent of brood size and brood age, but older individuals tended to expend less energy than younger ones. Given the lack of an apparent energetic 'cost of reproduction', lower DEE in older birds could be due to improved efficiency rather than avoidance of costs in old birds. Although future studies should account for potential sex-specific energetic constraints by including data on female energy expenditure, we conclude that a direct link between the rate of energy expenditure during breeding and subsequent survival is unlikely in this system.
RESUMEN
Emperor penguins (Aptenodytes forsteri) are under increasing environmental pressure. Monitoring colony size and population trends of this Antarctic seabird relies primarily on satellite imagery recorded near the end of the breeding season, when light conditions levels are sufficient to capture images, but colony occupancy is highly variable. To correct population estimates for this variability, we develop a phenological model that can predict the number of breeding pairs and fledging chicks, as well as key phenological events such as arrival, hatching and foraging times, from as few as six data points from a single season. The ability to extrapolate occupancy from sparse data makes the model particularly useful for monitoring remotely sensed animal colonies where ground-based population estimates are rare or unavailable.
