Life histories for structured populations in fluctuating environments

This project aims at extending the Classical life history theory by merging an efficient state-dependent optimization technique that works well with structured populations (dynamic programmring) with evolutionary population dynamics (adaptive dynamics). T he resulting methodology can thus find optimal life history strategies with the following three complications included simultaneously: population structure (individuals in a population differ in e.g. age, size, and quality, and these differences may affec t interactions or vital rates), density dependence (all populations are regulated by density dependence - if they were not, they would simply swamp the earth), and environmental fluctuations (even for populations in a constant physical environment, inters pecies interactions may lead to a continuously changing biotic environment). This project first aims at developing the required methodology by merging dynamic programming and adaptive dynamics. Because the Classical life history theory largely ignores flu ctuating environments and density dependence, it is difficult to say which predictions may change, and how, when these complications are included. Therefore, the fundamental effects of fluctuating environments and density dependence on life history evolut ion will be investigated in an age and size structured energy-allocation life-history model. Finally, these results will guide the application of the methodology on a specific case: the role of density-dependent population regulation on life history evolu tion in guppies. Guppies have become the text-book example of contemporary life history evolution, and density dependence is the missing piece of the puzzle for a fuller understanding of how growth, mortality, reproduction and senescence act together in t he evolution of life history strategies.