HEURISTIC OPTIMIZATION OF THE GENERAL LIFE-HISTORY PROBLEM - A NOVEL-APPROACH

The general life history problem concerns the optimal allocation of re sources to growth, survival and reproduction. We analysed this problem for a perennial model organism that decides once each year to switch from growth to reproduction. As a fitness measure we used the Malthusi an parameter r, which we calculated from the Euler-Lotka equation. Tra de-offs were incorporated by assuming that fecundity is size dependent , so that increased fecundity could only be gained by devoting more ti me to growth and less time to reproduction. To calculate numerically t he optimal r for different growth dynamics and mortality regimes, we u sed a simplified version of the simulated annealing method. The major differences among optimal life histories resulted from different accum ulation patterns of intrinsic mortalities resulting from reproductive costs. If these mortalities were accumulated throughout life, i.e. if they were senescent, a bang-bang strategy was optimal, in which there was a single switch from growth to reproduction: after the age at matu rity all resources were allocated to reproduction. If reproductive cos ts did not carry over from year to year, i.e. if they were not senesce nt, the optimal resource allocation resulted in a graded switch strate gy and growth became indeterminate. Our numerical approach brings two major advantages for solving optimization problems in life history the ory. First, its implementation is very simple, even for complex models that are analytically intractable. Such intractability emerged in our model when we introduced reproductive costs representing an intrinsic mortality. Second, it is not a backward algorithm. This means that li fespan does not have to be fixed at the begining of the computation. I nstead, lifespan itself is a trait that can evolve. We suggest that he uristic algorithms are good tools for solving complex optimality probl ems in life history theory, in particular questions concerning the evo lution of lifespan and senescence.