title: Evolution of flowering decisions in a stochastic, density-dependent environment.
abstract: Demography is central to both ecology and evolution, and characterizing the feedback between ecology and evolution is critical for understanding organisms' life histories and how these might evolve through time. Here, we show how, by combining a range of theoretical approaches with the statistical analysis of individually structured databases, accurate prediction of life history decisions is possible in natural density-regulated populations undergoing large fluctuations in demographic rates from year to year. Our predictions are remarkably accurate and statistically well defined. In addition, we show that the predicted trait values are evolutionarily and convergence stable and that protected polymorphisms are possible.
authors.affiliation: Duke Population Research Institute, Duke University, Durham, NC 27707, USA. cjm29@duke.edu
authors.forename: C J E
authors.initials: CJ
authors.lastname: Metcalf
authors.forename: K E
authors.initials: KE
authors.lastname: Rose
authors.forename: D Z
authors.initials: DZ
authors.lastname: Childs
authors.forename: A W
authors.initials: AW
authors.lastname: Sheppard
authors.forename: P J
authors.initials: PJ
authors.lastname: Grubb
authors.forename: M
authors.initials: M
authors.lastname: Rees
mesh_terms: D005075:Biological Evolution; D031177:Carduus / Q000235:genetics; D003710:Demography; D004463:Ecology; D017753:Ecosystem; D004777:Environment; D035264:Flowers / Q000235:genetics* / Q000502:physiology; D008954:Models, Biological; D015233:Models, Statistical; D010829:Physiological Phenomena; D010944:Plants / Q000235:genetics; D011156:Population Density; D011157:Population Dynamics; D012641:Selection, Genetic; D013269:Stochastic Processes