Trends in Ecology & Evolution
Volume 17, Issue 12, 1 December 2002, Pages 551-557
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Fighting change with change: adaptive variation in an uncertain world

https://doi.org/10.1016/S0169-5347(02)02633-2Get rights and content

Abstract

Organisms live in an ever-changing world. Most of evolutionary theory considers one solution to this problem: population-level adaptation. In fact, empirical studies have revealed an enormous variety of mechanisms to cope with environmental fluctuations. Some organisms use behavioral or physiological modifications that leave no permanent trace in the genes of future generations. Others withstand environmental change through the regular production of diverse offspring, in which the diversity can be either genetic or nongenetic. Evolutionary theorists now have the opportunity to catch up with the empirical evolutionary biology, and to integrate the diverse forms of ‘adaptive variation’ into a single conceptual framework. Here, we propose a classification according to the level at which the adaptive variation occurs and discuss some of the mechanisms underlying the variation. This perspective unites independent lines of research in molecular biology, microbiology, macroevolution, ecology, immunology and neurobiology, and suggests directions for a more comprehensive theory of adaptive variation.

Section snippets

Scope of the problem

The topic of evolution in fluctuating environments encompasses a potentially wide range of phenomena. The basic problem is that the environment is heterogeneous in many dimensions, and that organisms themselves alter the world around them (Box 1). Although feedback from a population to its own environment will eventually be an important component of any comprehensive theory of adaptive variation, we begin with the simplest scenario, environmental fluctuations that are exogenous to the evolving

Adaptive variation

Rather than stand steadfast in the face of environmental change (Box 2), populations sometimes confront the fluctuations through phenotypic variation either: (1) within single individuals; (2) among individuals in the population at one time; or (3) in future generations. We extend this basic tripartite distinction into a hierarchy according to the biological units that manifest adaptive variation, and call it the ‘levels of adaptive variation’ (Table 1).

We illustrate each class of adaptive

Variation derived from a single genome

In the four models that follow, variation is favored only when it stems from a single genome. The variation occurs either within individuals bearing the genome (A and B), or among the immediate descendents of any such individual (C and D).

Population-level variation

In population-level responses to a fluctuating environment, adaptive variation is achieved through long-term maintenance of multiple lineages 34., 35., 36.. In 1955, Dempster demonstrated that the persistence of a genotype in a fluctuating environment requires high geometric mean fitness [37]. Haldane and Jayakar also argued mathematically that polymorphism for a recessive allele is maintained when the arithmetic mean fitness of the recessive allele is >1 but its geometric mean fitness is <1

When populations are their own changing environment

Our discussion considers primarily variation that is maintained in the presence of environmental fluctuations that are exogenous to the population. In fact, the environmental perturbations that organisms confront are often much more complex. As a population evolves, the frequency of phenotypes in the population changes. When those phenotypes are themselves selective forces acting on the population, or when they feedback to such selective forces, then evolution necessarily occurs in a

One organism, multiple mechanisms

A single organism can display multiple strategies, each coping with fluctuations in a different facet of the environment. For example, on the within-individual level, E. coli has multiple systems for transcription regulation in response to environmental stimuli. They can sense and respond to changes in temperature, osmolarity, pH, noxious chemicals, DNA-damaging agents, mineral abundance, energy sources, electron acceptors, metabolites, chemical signals from other bacteria, and parasites. Also,

Acknowledgements

We thank Curt Lively, Michael Turelli, Michael Lachmann, Ben Kerr, Mark Tanaka and David Pfennig for their insightful comments. This work was supported, in part, by a National Science Foundation Postdoctoral Fellowship in Biological Informatics to L.A.M. and the Miescher Regents Professorship and NIH grant GM 57756 to J.J.B.

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