caa a22 4500 477090931 CHVBK 20180405111515.0 cr unu---uuuuu 170330e19960101xx s 000 0 eng 10.1007/BF01239349 doi (NATIONALLICENCE)springer-10.1007/BF01239349 Heuristic optimization of the general life history problem: A novel approach [Elektronische Daten] [Albert Blarer, Michael Doebeli] Summary: The general life history problem concerns the optimal allocation of resources 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 Malthusian parameterr, which we calculated from the Euler-Lotka equation. Trade-offs were incorporated by assuming that fecundity is size dependent, so that increased fecundity could only be gained by devoting more time to growth and less time to reproduction. To calculate numerically the optimalr for different growth dynamics and mortality regimes, we used a simplified version of the simulated annealing method. The major differences among optimal life histories resulted from different accumulation patterns of intrinsic mortalities resulting from reproductive costs. If these mortalities were accumulated throughout life, i.e. if they were senescent, a bangbang strategy was optimal, in which there was a single switch from growth to reproduction: after the age at maturity all resources were allocated to reproduction. If reproductive costs did not carry over from year to year, i.e. if they were not senescent, the optimal resource allocation resulted in a graded switch strategy and growth became indeterminate. Our numerical approach brings two major advantages for solving optimization problems in life history theory. 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 lifespan does not have to be fixed at the begining of the computation. Instead, lifespan itself is a trait that can evolve. We suggest that heuristic algorithms are good tools for solving complex optimality problems in life history theory, in particular questions concerning the evolution of lifespan and senescence. Chapman & Hall, 1996 heuristic optimization nationallicence resource allocation nationallicence reproductive effort nationallicence maturation nationallicence lifespan nationallicence simulated annealing nationallicence Blarer Albert Zoology Institute, Rheinsprung 9, CH-4051, Basel, Switzerland aut Doebeli Michael Zoology Institute, Rheinsprung 9, CH-4051, Basel, Switzerland aut Evolutionary Ecology Kluwer Academic Publishers 10/1(1996-01-01), 81-96 0269-7653 10:1<81 1996 10 10682 https://doi.org/10.1007/BF01239349 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/BF01239349 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Blarer Albert Zoology Institute, Rheinsprung 9, CH-4051, Basel, Switzerland aut NATIONALLICENCE 700 1- Doebeli Michael Zoology Institute, Rheinsprung 9, CH-4051, Basel, Switzerland aut NATIONALLICENCE 773 0- Evolutionary Ecology Kluwer Academic Publishers 10/1(1996-01-01), 81-96 0269-7653 10:1<81 1996 10 10682 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer