4.3 Article

Scaling up phenotypic plasticity with hierarchical population models

期刊

EVOLUTIONARY ECOLOGY
卷 24, 期 3, 页码 585-599

出版社

SPRINGER
DOI: 10.1007/s10682-009-9340-2

关键词

Life history components; Life table response experiments; Matrix projection models; Trait-trait covariances; Vital rates

资金

  1. Netherlands Organisation for Scientific Research (NWO) [80.33.452, 863.08.006]

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Individuals respond to different environments by developing different phenotypes, which is generally seen as a mechanism through which individuals can buffer adverse environmental conditions and increase their fitness. To understand the consequences of phenotypic plasticity it is necessary to study how changing a particular trait of an individual affects either its survival, growth, reproduction or a combination of these demographic vital rates (i.e. fitness components). Integrating vital rate changes due to phenotypic plasticity into models of population dynamics allows detailed study of how phenotypic changes scale up to higher levels of integration and forms an excellent tool to distinguish those plastic trait changes that really matter at the population level. A modeling approach also facilitates studying systems that are even more complex: traits and vital rates often co-vary or trade-off with other traits that may show plastic responses over environmental gradients. Here we review recent developments in the literature on population models that attempt to include phenotypic plasticity with a range of evolutionary assumptions and modeling techniques. We present in detail a model framework in which environmental impacts on population dynamics can be followed analytically through direct and indirect pathways that importantly incorporate phenotypic plasticity, trait-trait and trait-vital rate relationships. We illustrate this framework with two case studies: the population-level consequences of phenotypic responses to nutrient enrichment of plant species occurring in nutrient-poor habitats and of responses to changes in flooding regimes due to climate change. We conclude with exciting prospects for further development of this framework: selection analyses, modeling advances and the inclusion of spatial dynamics by considering dispersal traits as well.

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