2 Unifying Life History Analyses for Inference of Fitness and Population Growth

Department of Ecology, Evolution, and Behavior, Minnesota Center for Community Genetics, University of Minnesota, St. Paul, Minnesota 55108, USA.
The American Naturalist (Impact Factor: 3.83). 08/2008; 172(1):E35-47. DOI: 10.1086/588063
Source: PubMed


The lifetime fitnesses of individuals comprising a population determine its numerical dynamics, and genetic variation in fitness results in evolutionary change. This dual importance of individual fitness is well understood, but empirical fitness records generally violate the assumptions of standard statistical approaches. This problem has undermined comprehensive study of fitness and impeded empirical synthesis of the numerical and genetic dynamics of populations. Recently developed aster models remedy this problem by explicitly modeling the dependence of later-expressed components of fitness (e.g., fecundity) on those expressed earlier (e.g., survival to reproduce). Moreover, aster models employ different sampling distributions for different components of fitness (e.g., binomial for survival over a given interval and Poisson for fecundity). Analysis is done by maximum likelihood, and the resulting distributions for lifetime fitness closely approximate observed data. We illustrate the breadth of aster models' utility with three examples demonstrating estimation of the finite rate of increase, comparison of mean fitness among genotypic groups, and analysis of phenotypic selection. Aster models offer a unified approach to addressing the breadth of questions in evolution and ecology for which life-history data are gathered.

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    • "known generalizations for vector distributions (Gorban, 1984;Metz et al., 1992). However, there is no universal rule to measure various traits of organisms by the changes in the average reproduction coefficient, despite exerted efforts, development of special methods, and gaining some success (Haldane, 1954;Waxman and Welch, 2005;Kingsolver and Pfennig, 2007;Shaw et al., 2008;Karev and Kareva, 2014). There may be additional difficulties because the evolutionary optimality is not necessarily related to organisms, and the non-trivial question arises: 'what is optimal?' Another difficulty is caused by possible non-stationarity of the optimum: selected organisms change their environment and become non-optimal on the background of the new ecological situation (Gorban, 1984). "
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