4.7 Article

How to get the most bang for your buck: the evolution and physiology of nutrition-dependent resource allocation strategies

出版社

ROYAL SOC
DOI: 10.1098/rspb.2017.0445

关键词

resource availability; resource allocation; phenotypic plasticity; evolutionary theory; proximate mechanisms; ecological context

资金

  1. NIH [R01 GM117135]

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All organisms use resources to grow, survive and reproduce. The supply of these resources varies widely across landscapes and time, imposing ultimate constraints on the maximal trait values for allocation-related traits. In this review, we address three key questions fundamental to our understanding of the evolution of allocation strategies and their underlying mechanisms. First, we ask: how diverse are flexible resource allocation strategies among different organisms? We find there are many, varied, examples of flexible strategies that depend on nutrition. However, this diversity is often ignored in some of the best-known cases of resource allocation shifts, such as the commonly observed pattern of life span extension under nutrient limitation. A greater appreciation of the wide variety of flexible allocation strategies leads directly to our second major question: what conditions select for different plastic allocation strategies? Here, we highlight the need for additional models that explicitly consider the evolution of pheno typically plastic allocation strategies and empirical tests of the predictions of those models in natural populations. Finally, we consider the question: what are the underlying mechanisms determining resource allocation strategies? Although evolutionary biologists assume differential allocation of resources is a major factor limiting trait evolution, few proximate mechanisms are known that specifically support the model. We argue that an integrated framework can reconcile evolutionary models with proximate mechanisms that appear at first glance to be in conflict with these models. Overall, we encourage future studies to: (i) mimic ecological conditions in which those patterns evolve, and (ii) take advantage of the 'omic' opportunities to produce multi-level data and analytical models that effectively integrate across physiological and evolutionary theory.

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