Samuel M. Scheiner

The genetics of phenotypic plasticity. II. Response to selection

We selected on phenotypic plasticity of thorax size in response to temperature in Drosophila melanogaster using a family selection scheme. The results were compared to those of lines selected directly on thorax size. We found that the plasticity of a character does respond to selection and this response is partially independent of the response to selection on the mean of the character. One puzzling result was that a selection limit of zero plasticity was reached in the lines selected for decreased plasticity yet additive genetic variation for plasticity still existed in the lines. We tested the predictions of three models of the genetic basis of phenotypic plasticity: overdominance, pleiotropy, and epistasis. The results mostly support the epistasis model, that the plasticity of a character is determined by separate loci from those determining the mean of the character.

The genetics of phenotypic plasticity. V. Evolution of reaction norm shape

We present a general quantitative genetic model for the evolution of reaction norms. This model goes beyond previous models by simultaneously permitting any shaped reaction norm and allowing for the imposition of genetic constraints. Earlier models are shown to be special cases of our general model; we discuss in detail models involving just two macroenvironments, linear reaction norms, and quadratic reaction norms. The model predicts that, for the case of a temporally varying environment, a population will converge on (1) the genotype with the maximum mean geometric fitness over all environments, (2) a linear reaction norm whose slope is proportional to the covariance between the environment of development and the environment of selection, and (3) a linear reaction norm even if nonlinear reaction norms are possible. An examination of experimental studies finds some limited support for these predictions. We discuss the limitations of our model and the need for more realistic gametic models and additional data on the genetic and developmental bases of plasticity.

The genetics of phenotypic plasticity I. Heritability

Methods for estimating the genetic component of phenotypic plasticity are presented. In the general case of clonal replicates or full‐sibs raised in several environments, the heritability of plasticity can be measured as the ratio of the genotype‐environment interaction variance to the total phenotypic variance. In the special case of only two environments plasticity also can be measured as the difference among environments in genotype or family means. In that case, the heritability of plasticity can be measured as either a ratio of variance components or as the slope of a parent‐offspring regression. The general measure suffers because no least‐square standard errors have been developed, although they can be calculated by maximum‐likelihood or bootstrapping techniques. For the other two methods least‐square standard errors can be calculated but require very large experiments for statistical significance to be achieved. The heritability measures are compared using data on plasticity of thorax size in response to temperature in Drosophila melanogaster. The heritability estimates are all in close agreement. Models of the evolution of phenotypic plasticity have treated it as a trait in its own right and as a cross‐environment genetic correlation. Although the first approach is the one used here, neither one is preferred.

THE COMPARISON OF PHENOTYPIC PLASTICITY AND GENETIC VARIATION IN POPULATIONS OF THE GRASS DANTHONIA SPICATA

A species can persist in a heterogeneous environment either if individuals of that species are phenotypically flexible or if there is genetic variation among individuals (Thoday, 1953; Baker, 1965; Bradshaw, 1965; Jain, 1979). Phenotypic flexibility may be defined as the extent to which an organism can grow and reproduce in a range of environments either by varying its phenotype (plasticity) or by maintaining a constant phenotype (homeostasis or stability) (sensu Thoday, 1953; Hume and Cavers, 1982). Bradshaw (1965) recognized that phenotypic plasticity could itself be under genetic control and would therefore be subject to selective pressures. Bradshaw (1965) and others (Thoday, 1953; Levins, 1963; Marshall and Jain, 1968; Jain, 1979) have postulated that selection for phenotypic flexibility and genetic variation would be antagonistic, that there would be selection for a population to be either phenotypically flexible or genetically variable. Several studies comparing congeneric species (Cumming, 1959; Marshall and Jain, 1968; Jain, 1979) have found evidence that one of the species is more genetically variable and the other more phenotypically plastic. One study (Grant, 1974) has found differences in genetic variation and phenotypic plasticity among adjacent populations of a single species. The purpose of this paper is two-fold. First, we present mathematical definitions for plastic variation and plasticity. Second, we compare plastic vari-

THE GENETICS OF PHENOTYPIC PLASTICITY VIII. THE COST OF PLASTICITY IN DAPHNIA PULEX

In a heterogeneous world, the optimal strategy for an individual is to continually change its phenotype to match the optimal type. However, in the real world, organisms do not behave in this fashion. One potential reason why is that phenotypic plasticity is costly. We measured production and maintenance costs of plasticity in the freshwater crustacean Daphnia pulex (Cladocera: Crustacea) in response to the presence of chemical signals from a predator, the insect Chaoborus americanus. We looked at three changes in juvenile body size and shape: body length, body depth, and tailspine length. Fitness costs were measured as changes in adult growth and fecundity, and summarized as the intrinsic rate of increase (r) for individuals reared in the presence or absence of Chaoborus extract. The cost of plasticity was measured as a multiple regression of mean clone fitness against trait and trait plasticity. We found scant evidence for either production or maintenance costs of plasticity. We also failed to find direct costs of these juvenile structures, which is surprising, as others have found such costs. We attribute the lack of measurable direct or plasticity costs to a decrease in metabolic rates in the presence of the Chaoborus extract. This decrease in metabolic rate may have compensated for any cost increases. We call for more extensive measures of the costs of plasticity, especially under natural conditions, and the incorporation of costs into evolutionary models.

The genetics of phenotypic plasticity. III. Genetic correlations and fluctuating asymmetries

We examined the relationship of three aspects of development, phenotypic plasticity, genetic correlations among traits, and developmental noise, for thorax length, wing length, and number of sternopleural bristles in Drosophila melanogaster. We used 14 lines which had previously been selected on either thorax length or plasticity of thorax length in response to temperature. A half‐sib mating design was used and offspring were raised at 19° C or 25° C. We found that genetic correlations were stable across temperatures despite the large levels of plasticity of these traits. Plasticities were correlated among developmentally related traits, thorax and wing length, but not among unrelated traits, lengths and bristle counts. Amount of developmental noise, measured as fluctuating asymmetry and within‐environmental variation, was positively correlated with amount of plasticity only for some traits, thorax length and bristle number, and only at one temperature, 25° C.

Global patterns of plant diversity

SummaryUsing 94 data sets from across the globe, we explored patterns of mean community species richness, landscape species richness, mean similarity among communities and mosaic diversity. Climate affected community species richness primarily through productivity while other climatic factors were secondary. Climatic equability affected species richness only in temperate regions where richness was greatest at high levels of temperature variability and low levels of precipitation variability. Landscape species richness correlated positively with community species richness. A global gradient in mean similarity existed but was uncorrelated with community species richness. Mean similarity was least and mosaic diversity was greatest between 25 and 30° latitude. The most diverse landscapes (low mean similarity) correlated with warm temperatures, high elevations, large areas and large seasonal temperature fluctuations. The most complex landscapes (high mosaic diversity) correlated with large areas, high productivity and warm winters. We compared diversity measures among continents and found only one significant difference: Australian landscapes have greater mosaic diversity than African landscapes. Based on our analyses we propose two hypotheses: (1) for plants, biotic interactions are more important in structuring landscapes in warmer climates and (2) longer isolated landscapes have more clearly differentiated ecological subunits.

The genetics of phenotypic plasticity. VI. Theoretical predictions for directional selection

We explore the effects of linear and quadratic reaction norms on heritability and directional selection. Genetic variation for reaction norm parameters can alter the heritability of traits; the magnitude of the heritability depends upon both the environment and the correlation among the parameters. Genetic variation for reaction norm parameters can alter the response to directional selection. Selection on a trait in one environment can shift both the mean of the trait measured across environments and the plasticity of the trait; the signs and magnitudes of these responses depend on the correlations among the parameters of the reaction norm. Our model is consistent with the results of ten experiments for selection on a trait in a single environment. In all experiments, selection towards the overall mean of the population always resulted in a relatively lower plasticity than selection away from the overall mean. Our model was able to predict the results of two experiments for selection on a trait index calculated over more than one environment. Predictions were good for the direct response to selection but poorer for the correlated response to selection. Our results indicate the need for more data on the effects of environment on genetic parameters, especially correlations among reaction norm parameters.

MEASURING NATURAL SELECTION ON PHENOTYPIC PLASTICITY

To understand natural selection we need to integrate its measure across environments. We present a method for measuring phenotypic selection that combines the potential for both environmental variation and phenotypic plasticity. The method uses path analysis and a measure of selection that is analogous to selection on breeding values. For individuals growing in alternative environments, paths are created that represent potential changes in the environment. The probabilities for these changes are then multiplied by the path coefficients to calculate selection coefficients. Selection on plasticity is measured as the difference in selection within each environment. We illustrate these methods using data on selection in an experimental population of Arabidopsis thaliana. Individuals from 36 families were grown in one of four environments, a factorial combination of shaded/open and early/late shading. For final height of the inflorescence, there was positive selection in both the open and shaded environments and negative selection on plasticity of height. For bolting time, there was also positive selection in both environments, but no selection on plasticity. We show how to use this information to examine how selection would change with changes in environmental frequencies and their transition probabilities. These methods can be expanded to encompass continuous traits and continuous environments as well as other complexities of natural selection.

A search for trade-offs among life history traits inDrosophila melanogaster

SummaryAre there underlying developmental and physiological properties of organisms that can be used to build a general theory of life history evolution? Much of the theoretical work on the evolution of life histories is based on the premise of negative developmental and genetic correlations among life history traits. If negative correlations do not exist as a general rule then no general theory taking them into account is possible. Negative genetic correlations among life history traits can come about by antagonistic pleiotropy. One cause of antagonistic pleiotropy is cost allocation trade-offs. Since cost allocation trade-offs are due to underlying physiological constraints they are expected to be common to closely related groups. A second form of antagonistic pleiotropy is specialization of genotypes to different niches. This type of antagonistic pleiotropy is expected to be specific to each population. We looked for trade-offs in life history traits of longevity and fecundity inDrosophila melanogaster. We used a half-sib mating design and raised the offspring at two temperatures, 19°C and 25°C. Correlations between longevity and fecundity showed some evidence of antagonistic pleiotropy at high temperature with no evidence of any trade-offs at low temperature. Correlations of early and late fecundity traits did show evidence of cost allocation trade-offs at both temperatures. Antagonistic pleiotropy was also found for cross-environmental correlations of fecundity traits. We conclude that, although life history trade-offs can not be generally assumed, they are frequently found among functionally related traits. Thus, we provide guidelines for the development of general theories of life history evolution.