Life history theory suggests that animals should balance their current
investment in young against their chances to reproduce in the future.
One fundamental prediction from the theory is that long-lived species
should be restrictive in any increase of their current investment. It
has been suggested that long-lived species, therefore, have evolved a
fixed level of investment in young in order to maximize their own adu
lt survival. However, recent experimental studies have shown that long
-lived seabirds have a flexible reproductive performance and adjust th
eir effort in raising young, both according to their own body conditio
n and to the need of the chicks. In this study, we present a model of
the optimal balance between reproductive effort and adult survival for
long-lived birds breeding in a stochastic environment. During poor br
eeding conditions, maximum fitness is achieved either by not breeding
at all, or by abandoning the brood. Beyond a certain threshold in bree
ding conditions, there is a steep increase in reproductive effort and
an equally steep decrease in adult survival. The model is applied to t
wo hypothetical long-lived seabirds differing in their potential fecun
dity. For the genotype with a potentially high fecundity, the model pr
edicts a high threshold for breeding (i.e., breeding conditions need t
o be very good for the species to attempt breeding); above the thresho
ld, the value of reproduction in terms of fitness is high. For the gen
otype with potentially low fecundity, the model predicts a low thresho
ld for breeding, and the value of reproduction in terms of fitness is
low. By increasing clutch size in the model, we examine the optimal re
sponse of the two genotypes to an experimental brood size manipulation
. For both genotypes, the model predicts that the threshold for breedi
ng is lower among controls than among enlarged broods, giving a range
of possible outcomes of the experiment depending on breeding condition
s. The few studies on brood enlargements in long-lived species carried
out so far may support the predictions from the model.