Stephen C. Stearns

Life-History Tactics: A Review of the Ideas

This review organizes ideas on the evolution of life histories. The key life-history traits are brood size, size of young, the age distribution of reproductive effort, the interaction of reproductive effort with adutl mortality, and the variation in these traits among an individuals progeny. The general theoretical problem is to predict which combinations of traits will evolve in organisms living in specified circumstances. First consider single traits. Theorists have made the following predictions: (1) Where adult exceeds juvenile mortality, the organism should reproduce only once in its lifetime. Where juvenile exceeds adult mortality, the organism should reproduce several times. (2) Brood size should maximize the number of young surviving to maturity, summed over the lifetime of the parent. But when optimum brood-size varies unpredictably in time, smaller broods should be favored because they decrease the chances of total failure on a given attempt. (3) In expanding populations, selection should minimize age at maturity. In stable populations, when reproductive success depends on size, age, or social status, or when adult exceeds juvenile mortality, then maturation should be delayed, as it should be in declining populations. (4) Young should increase in size at birth with increased predation risk, and decrease in size with increased resource availability. Theorists have also predicted that only particular combinations of traits should occur in specified circumstances. (5) In growing populations, age at maturity should be minimized, reproductive effort concentrated early in life, and brood size increased. (6) One view holds that in stable environments, late maturity, multiple broods, a few, large young, parental care, and small reproductive efforts should be favored (K-selection). In fluctuating environments, early maturity, many small young, reduced parental care, and large reproductive efforts should be favored (r-selection). (7) But another view holds that when juvenile mortality fluctuates more than adult mortality, the traits associated with stable and fluctuating environments should be reversed. We need experiments that test the assumptions and predictions reviewed here, more comprehensive theory that makes more readily falsifiable predictions, and examination of different definitions of fitness.

Trade-offs in life-history evolution

Trade-offs represent the costs paid in the currency of fitness when a beneficial change in one trait is linked to a detrimental change in another. If there were no trade-offs, then selection would drive all traits correlated with fitness to limits imposed by history and design. However, we find that many life-history traits are maintained well within those limits. Therefore, trade-offs must exist. Trade-offs have played a central role in the development of life-history theory, from Gadgil & Bossert (1970), Charnov & Krebs (1973), Schaffer (1972, 1974a, b) and Bell (1980) on to the present. They have been measured through field observations (e.g. Clutton-Brock, Guinness & Albon, 1982, 1983), through experimental manipulations in laboratory (e.g. Partridge & Farquhar, 1981) and field (e.g. Askenmo, 1979), through -phenotypic correlations in the laboratory (e.g. Bell, 1984a, b) and through genetic correlations (e.g. Rose & Charlesworth, 1981a, b), to mention only a few of the more prominent studies. They have been reviewed by Stearns (1976, 1977), Bell (1980), Charlesworth (1980), Warner (1984), Reznick (1985), Partridge & Harvey (1985, 1988) and most thoroughly by Bell & Koufopanou (1986). In addition, the methods used to measure trade-offs have been the subject of criticism (Tuomi, Hakala & Haukioja, 1983; Partridge, 1987) and controversy (Reznick, Perry & Travis, 1986; Bell, 1986). The most prominent life-history trade-off involves the cost of reproduction. It has two major components, costs paid in survival and costs paid in future reproduction. Two approaches to analysing those costs were suggested by Williams: genetic costs represented by antagonistic pleiotropy (Williams, 1957) and phenotypic costs represented by negative correlations between current reproductive effort and future survival and reproduction (Williams, 1966a, b). A third, physiological approach to trade-offs has been developed by Hirshfield & Tinkle (1974) and Calow (1979), among many others (cf. Townsend & Calow, 1981). In this extensive discussion, a few points have not always received the attention they deserve: (1) That trade-offs can be measured and analysed at the level of the genotype, the phenotype and what lies between (intermediate structure) is well known and uncontroversial but it has not always been emphasized that each of those levels makes an essential contribution to our understanding. It is not a question of either genetic correlations or phenotypic correlations or physiological trade-offs but of how such measurements combine to deliver information about potential evolutionary responses. A study conducted at just one of these levels is likely to be of as little use as the information on the nature of the elephant delivered by one blind man holding its tail. (2) One can draw a useful distinction between intraindividual trade-offs for example, between the reproductive effort made by a female in one season and the probability that she will survive to the next season and intergenerational trade-offs for example, between a females reproductive effort and the probability that her offspring will survive to the next season. Intraindividual tradeoffs (and only some of them) have received most attention but intergenerational trade-offs, which are arguably just as important, have been relatively ignored. They deserve more attention. (3) The genetic structure of a population, in particular the genetic variance-covariance matrix for a set of important life-history traits, reflects the very recent past, describes the present and predicts the near-term future. There is no logical or direct way to use the current genetic structure of a population to infer the trade-offs that constrained the past approach to the current state even if they occurred as recently as a few tens of generations ago (J. Travis, personal communication). (4) Our understanding of a trade-off can be described as first order (slope known), second order (curvature known) or third order (all details, including interaction effects, known). In a few cases we have reliable information about firstorder effects. In no case known to me do we have reliable information on second-order effects, which are important in the theory (e.g. Schaffer, 1974a). Measurement of third-order effects, however desirable (Pease & Bull, 1988), remains a matter for future research.

THE EVOLUTION OF PHENOTYPIC PLASTICITY IN LIFE‐HISTORY TRAITS: PREDICTIONS OF REACTION NORMS FOR AGE AND SIZE AT MATURITY

We used life‐history theory to predict reaction norms for age and size at maturation. We assumed that fecundity increases with size and that juvenile mortality rates of offspring decrease as ages‐at‐maturity of parents increase, then calculated the reaction norm by varying growth rate and calculating an optimal age at maturity for each growth rate. The reaction norm for maturation should take one of at least four shapes that depend on specific relations between changes in growth rates and changes in adult mortality rates, juvenile mortality rates, or both. Most organisms should mature neither at a fixed size nor at a fixed age, but along an age‐size trajectory. The model makes possible a clear distinction between the genetic and phenotypic components of variation. The evolved response to selection is reflected in the shape and position of the reaction norm. The phenotypic response of a single organism to rapid or slow growth is defined by the location of its maturation event as a point on the reaction norm.

The Evolutionary Significance of Phenotypic PlasticityPhenotypic sources of variation among organisms can be described by developmental switches and reaction norms

Variation, the fuel that feeds evolutionary change, originates at the levels of both the genotype and the phenotype. Genetically identical organisms reared under different conditions may display quite distinct characteristics. Until recently, the types and sources of such phenotypic variation have been given little consideration in evolutionary theory. But a knowledge of the mechanisms and developmental patterns underlying phenotypic variation is crucial to the understanding of important evolutionary phenomena. Therefore some biologists are predicting an increasing focus on this variation leading to a Renaissance of the Phenotype.

Genome-Wide Transcript Profiles in Aging and Calorically Restricted Drosophila melanogaster

BACKGROUND We characterized RNA transcript levels for the whole Drosophila genome during normal aging. We compared age-dependent profiles from animals aged under full-nutrient conditions with profiles obtained from animals maintained on a low-calorie medium to determine if caloric restriction slows the aging process. Specific biological functions impacted by caloric restriction were identified using the Gene Ontology annotation. We used the global patterns of expression profiles to test if particular genomic regions contribute differentially to changes in transcript profiles with age and if global disregulation of gene expression occurs during aging. RESULTS Whole-genome transcript profiles contained a statistically powerful genetic signature of normal aging. Nearly 23% of the genome changed in transcript representation with age. Caloric restriction was accompanied by a slowing of the progression of normal, age-related changes in transcript levels. Many genes, including those associated with stress response and oogenesis, showed age-dependent transcript representation. Caloric restriction resulted in the downregulation of genes primarily involved in cell growth, metabolism, and reproduction. We found no evidence that age-dependent changes in transcription level were confined to genes localized to specific regions of the genome and found no support for widespread disregulation of gene expression with age. CONCLUSIONS Aging is characterized by highly dynamic changes in the expression of many genes, which provides a powerful molecular description of the normal aging process. Caloric restriction extends life span by slowing down the rate of normal aging. Transcription levels of genes from a wide variety of biological functions and processes are impacted by age and dietary conditions.

Life history evolution: successes, limitations, and prospects.

Abstract Life history theory tries to explain how evolution designs organisms to achieve reproductive success. The design is a solution to an ecological problem posed by the environment and subject to constraints intrinsic to the organism. Work on life histories has expanded the role of phenotypes in evolutionary theory, extending the range of predictions from genetic patterns to whole-organism traits directly connected to fitness. Among the questions answered are the following: Why are organisms small or large? Why do they mature early or late? Why do they have few or many offspring? Why do they have a short or a long life? Why must they grow old and die? The classical approach to life histories was optimization; it has had some convincing empirical success. Recently non-equilibrium approaches involving frequency-dependence, density-dependence, evolutionary game theory, adaptive dynamics, and explicit population dynamics have supplanted optimization as the preferred approach. They have not yet had as much empirical success, but there are logical reasons to prefer them, and they may soon extend the impact of life history theory into population dynamics and interspecific interactions in coevolving communities.

A new view of life-history evolution

In this paper I explore two lines of thought. First, do life-history tactics exist at the intra-specific level? Four arguments are examined: (1) biological constraints violate the assumptions of the Euler-Lotka equation; (2) experimental evidence on mosquito fish indicates that physiological problems can overwhelm the expected coadaptations of life-history traits; (3) the pattern of heritabilities of life-history traits indicates that they have not responded to the same selection forces; (4) authors of review articles perceive tactics more readily at higher taxonomic levels than within species. Tactics may not exist in the expected form. Second, when might optimality models work, and why? (1) Some optimality models contain a hidden genetic component; (2) polygenic traits are not as tightly constrained as few-locus systems; and (3) the evolution of the developmental system should uncouple the phenotype from the constraints of the genetic mechanism. Implicit in these thoughts is a more general point: training in quantitative genetics, development, and physiology is just as necessary for the study of life-history evolution as is training in demography and population genetics. Finally, four new research programs are suggested as extensions and criticisms of the arguments raised here.

On inference in ecology and evolutionary biology: the problem of multiple causes

SummaryIf one investigates a process that has several causes but assumes that it has only one cause, one risks ruling out important causal factors. Three mechanisms account for this mistake: either the significance of the single cause under test is masked by noise contributed by the unsuspected and uncontrolled factors, or the process appears only when two or more causes interact, or the process appears when there are present any of a number of sufficient causes which are not mutally exclusive. In ecology and evolutionary biology, experiments usually test single factor hypotheses, and many scientists apparently believe that hypotheses incorporating several factors are so much more difficult to test that to do so would not be practical. We discuss several areas in ecology and evolutionary biology in which the presupposition of simple causation has apparently impeded progress. We also examine a more mature field, the study of atherosclerosis, in which single factor studies did significantly delay progress towards understanding what now appears to be a multifactor process. The problem has three solutions: either factorial experiments, dynamic models that make quantitative predictions, response-surface methods, or all three. In choosing a definition for ‘cause’, we make a presupposition that profoundly influences subsequent observations and experimental designs. Alternative definitions of causation should be considered as contributing to potential cures for research problems.

The evolution of life histories in spatially heterogeneous environments: Optimal reaction norms revisited

SummaryNatural populations live in heterogeneous environments, where habitat variation drives the evolution of phenotypic plasticity. The key feature of population structure addressed in this paper is the net flow of individuals from source (good) to sink (poor) habitats. These movements make it necessary to calculate fitness across the full range of habitats encountered by the population, rather than independently for each habitat. As a consequence, the optimal phenotype in a given habitat not only depends on conditions there but is linked to the performance of individuals in other habitats. We generalize the Euler-Lotka equation to define fitness in a spatially heterogeneous environment in which individuals disperse among habitats as newborn and then stay in a given habitat for life. In this case, maximizing fitness (the rate of increase over all habitats) is equivalent to maximizing the reproductive value of newborn in each habitat but not to maximizing the rate of increase that would result if individuals in each habitat were an isolated population. The new equation can be used to find optimal reaction norms for life history traits, and examples are calculated for age at maturity and clutch size. In contrast to previous results, the optimal reaction norm differs from the line connecting local adaptations of isolated populations each living in only one habitat. Selection pressure is higher in good and frequent habitats than in poor and rare ones. A formula for the relative importance of these two factors allows predictions of the habitat in which the genetic variance about the optimal reaction norm should be smallest.

The great opportunity: Evolutionary applications to medicine and public health

Evolutionary biology is an essential basic science for medicine, but few doctors and medical researchers are familiar with its most relevant principles. Most medical schools have geneticists who understand evolution, but few have even one evolutionary biologist to suggest other possible applications. The canyon between evolutionary biology and medicine is wide. The question is whether they offer each other enough to make bridge building worthwhile. What benefits could be expected if evolution were brought fully to bear on the problems of medicine? How would studying medical problems advance evolutionary research? Do doctors need to learn evolution, or is it valuable mainly for researchers? What practical steps will promote the application of evolutionary biology in the areas of medicine where it offers the most? To address these questions, we review current and potential applications of evolutionary biology to medicine and public health. Some evolutionary technologies, such as population genetics, serial transfer production of live vaccines, and phylogenetic analysis, have been widely applied. Other areas, such as infectious disease and aging research, illustrate the dramatic recent progress made possible by evolutionary insights. In still other areas, such as epidemiology, psychiatry, and understanding the regulation of bodily defenses, applying evolutionary principles remains an open opportunity. In addition to the utility of specific applications, an evolutionary perspective fundamentally challenges the prevalent but fundamentally incorrect metaphor of the body as a machine designed by an engineer. Bodies are vulnerable to disease – and remarkably resilient – precisely because they are not machines built from a plan. They are, instead, bundles of compromises shaped by natural selection in small increments to maximize reproduction, not health. Understanding the body as a product of natural selection, not design, offers new research questions and a framework for making medical education more coherent. We conclude with recommendations for actions that would better connect evolutionary biology and medicine in ways that will benefit public health. It is our hope that faculty and students will send this article to their undergraduate and medical school Deans, and that this will initiate discussions about the gap, the great opportunity, and action plans to bring the full power of evolutionary biology to bear on human health problems.