Adaptation to fine-grained environmental variation: an analysis of within-individual leaf variation in an annual plant.
Emphasis on coarse-grained environmental variation is appropriate for the study of morphological plasticity in unitary (nonmodular) organisms. The morphological phenotype is largely determined during a brief period of development early in the life cycle in unitary organisms. Changes in the environment after development is complete, which would constitute fine-grained variation, cannot alter the morphological phenotype. In contrast, modular organisms develop continuously throughout much of their lives and therefore can potentially alter their morphological phenotypes to accommodate within-generation changes in their environments. Although a single module may experience the environment as coarse grained, the whole organism is a collection of integrated modules (e.g. leaves or ramets), each of which may develop in a different local environment. For modular organisms, fine-grained environmental variation could result in within-individual variation in module phenotype, which could constitute an adaptive evolutionary response to a variable environment. Thus fine-grained environmental variation may be an important source of selection for adaptive variation in morphological traits of modular organisms.
At least two mechanisms could contribute to phenotypic variation among modules within an individual. One mechanism is plastic response to an environmental factor that varies spatially and/or temporally as successive modules develop. I will refer to this mechanism as within-individual plasticity. Within-individual plasticity has been documented, for example, in plants in which individuals produce leaves with different morphology and physiology depending on the temperature they experience during leaf development (Ku and Hunt 1973; Fukai and Silsbury 1976; Chabot and Chabot 1977; Huner 1985; Evans et al. 1986; Korner et al. 1989). In addition, the optimum leaf phenotype has been shown to vary with temperature. Typically, larger, thicker leaves with a higher chlorophyll concentration, higher ratio of chlorophyll a to chlorophyll b, and fewer stomata are favored at lower temperatures (Huner 1985; Korner et al. 1989). Thus an individual plant may be able to produce leaves with one set of phenotypic traits during periods of low temperature and leaves with a different set of traits when the temperature is higher. In short, adaptive within-individual plasticity of leaf traits is plausible.
In addition to plasticity, programmed developmental change may cause within-individual variation in module form. Programmed developmental change, or heteroblastic development, is characterized by development of morphological differences between successively produced organs (Allsopp 1967). This mechanism can cause, for example, phenotypic differences among leaves of a single individual without the influence of external cues (e.g. Allsopp 1967; Kaplan 1980; Guerrant 1988). Programmed developmental change and within-individual plasticity may represent either alternative or complementary mechanisms for generating within-individual variation in fine-grained environments.
For adaptive within-individual variation to evolve, selection must favor different phenotypes in different environments, and genetic variation for the magnitude and/or pattern of within-individual variation must be present. In addition, the rate of evolution of within-individual variation can be influenced by genetic correlations between the expression of a trait in one environment or at one developmental stage and the expression of that same trait in a different environment or at a different developmental stage. Correlations between the expressions of two different traits in different environments or at different stages can also affect the rate of evolutionary change (Via 1984; Via and Lande 1985; Stearns and Koella 1986). Genetic correlations among traits can either retard or accelerate responses to selection for within-individual variation, depending on the sign of the correlation relative to the direction of selection on each trait in each environment or at each stage.
The purpose of this paper is to document the pattern of within-individual variation in leaf traits of an annual plant and to determine the potential for this variation to respond to natural selection. I have examined the magnitude and pattern of within-individual variation in five leaf traits in an annual plant that, in its natural habitat, produces leaves over a range of temperature from 16 to 32 [degrees] C. In this paper I describe the pattern of variation among leaves of single individuals and report results of analyses of genetic variation for within-individual variability in leaf traits and of genetic correlations that could influence the rate of further evolution of within-individual variation of these traits.
MATERIALS AND METHODS
Dicerandra linearifolia (Lamiaceae) is an annual plant that grows in well-drained sandy soils in sandhills and roadsides of northern Florida and southern Georgia (Huck 1987). Seeds germinate and plants begin producing leaves in December and January, when the mean monthly maximum temperature in northern Florida can be as low as 16 [degrees] C. Growing plants produce opposite leaves along an upright stem through the summer months, when mean monthly maximum temperatures can be greater than 30 [degrees] C. Beginning in late September, the plants produce showy, pink, perfect flowers. The species is self-compatible but not autogamous (Huck 1987; pers. obs.).
Characterization of Within-Individual Trait Variation
I collected 150 seedlings at 50-cm intervals along a randomly placed transect in a natural population of D. linearifolia in Wakulla County in northern Florida. The seedlings were raised in the greenhouse at Florida State University in Tallahassee. Twenty-five individuals were randomly selected to serve as sires in a paternal half-sib breeding design. The remaining plants served as dams only. Each of the 25 sires was hand crossed to at least four randomly selected dams to create 25 paternal half-sib seed families, each comprising at least four full-sib families. Flowers receiving pollen were emasculated and pollinated before anthesis to prevent accidental selfing.
I germinated 10 seeds from each of four randomly chosen full-sib families per sire in commercial potting mix in greenhouse flats. Four seedlings per full-sib family were randomly selected and transplanted singly to pots filled with a mixture of all-purpose sand and commercial potting mix (2:1 by volume). Seedlings of each full-sib family were divided equally between two growth chambers.
The range of within-individual variation in leaf traits was determined from measurements of two pairs of leaves produced by each individual. The first leaves were measured after the plants had grown for three months in a thermal regime with a maximum daytime temperature of 18 [degrees] C and a nighttime minimum of 13 [degrees] C. The photoperiod was 12/12 light/dark. I took measurements (see below) from the most recent fully expanded leaf pair of each individual (between the 6th and 9th leaf pair). After the first leaves were measured, plants were returned to the chambers and gradually acclimated to a thermal regime with a maximum daytime temperature of 30 [degrees] C and a nighttime minimum of 25 [degrees] C. Photoperiod remained 12/12 light/dark. When the chamber temperature first reached 30 [degrees] C, I marked the smallest visible leaf primordia of each individual with a dot of acrylic paint. The second leaves were measured two months later, after all individuals had produced at least two leaf pairs above the paint-marked pair (between nodes 17 and 22). This sampling scheme allowed both programmed developmental change and cued responses to temperature to influence leaf traits. Shoot developmental stage and temperature were allowed to covary as they do in the natural environment (e.g. leaves at lower nodes develop in the winter at lower temperatures).
At each sampling time, I removed both leaves of the most recent fully expanded leaf pair of each individual. For one leaf, I measured the area of the leaf and removed a thin cross section from the center. The remainder of the leaf was weighed and then extracted in dimethyl sulfoxide for spectrophotometric determination of chlorophyll concentration and the ratio of the concentration of chlorophyll a to chlorophyll b (Hiscox and Israelstam 1979). Leaf sections were preserved in 70% ethyl alcohol, and leaf thickness was determined from the sections under a dissecting microscope fitted with an ocular micrometer.
The second leaf at the same node from which the first leaf was taken was painted across the middle of the adaxial surface with a 0.5-cm-wide band of clear nail polish and allowed to dry overnight. The dry polish was peeled off and bore an impression of the features of the leaf surface, including stomata. The peel was mounted in distilled water on a microscope slide and examined under a compound 250x microscope. I counted the number of stomata in a single field of view (0.38 [mm.sup.2]) positioned directly adjacent to the midvein of the leaf for each peel.
Trait means for the same leaf trait measured at different sampling times were compared by means of the effect of [TABULAR DATA FOR TABLE 1 OMITTED] sampling time in repeated-measures ANOVA for each trait including the effects of chamber, sire, and dam nested within sire (plus interactions) as main effects and sampling time as a repeated factor.
The presence of genetic variation for each leaf trait at each sampling time was determined from ANOVA including main effects of chamber, sire, and dam nested within sire and the interactions between chamber and sire and between chamber and dam within sire. The existence of genetic variation for within-individual variation was detected from similar ANOVA in which the dependent variable was the difference in the value of a leaf trait between the two leaves taken from the same individual at different times (cf. Scheiner and Lyman 1989; Scheiner et al. 1991; Ebert et al. 1993). The main effects of sire and of dam nested within sire were of primary interest in these analyses. A significant sire effect indicates the presence of additive genetic variation for a trait or trait difference, and the dam-within-sire effect estimates the combination of nonadditive genetic variance and maternal effects. To maximize my power to detect significant main effects, and sire effects in particular, I dropped terms from ANOVA models according to the following criteria. The interaction between chamber and dam-within-sire was dropped if it was not significant as the last term in the full ANOVA model. If the chamber-by-dam-within-sire interaction was dropped, the chamber-by-sire interaction was dropped if it was not significant as the last term in the model without the chamber-by-dam-within-sire interaction. The dam-within-sire term was dropped if it was not significant as the last term in the model from which the interaction terms had been dropped. No term was dropped if the F-ratios associated with it exceeded 1.5. All ANOVA were conducted with the GLM procedure of SAS. The RANDOM option was invoked to generate expected mean squares and appropriate error terms for hypothesis tests in a fully random model. Because full-sib families were of unequal size, Type III sums of squares were used for significance tests.
I chose not to estimate heritabilities because the modest size of my breeding design and the artificiality of the growth-chamber environment call into question the accuracy and meaning of a point estimate of heritability. However, a significant sire effect in an ANOVA suggests that the heritability of the dependent variable would be significantly different from zero.
Additive genetic correlations among traits within each sampling time and between traits across sampling times were estimated by product-moment correlations between half-sib family means. For the analysis of relationships across sampling times, correlations were calculated among family means for the same trait at the two sampling times and between each trait expressed at one time and each other trait expressed at the other time. Product-moment correlations among half-sib family means have been shown to approximate true additive genetic correlations (Via 1984; Emerson et al. 1988; Blouin 1992). Confidence intervals were calculated for z-transformed correlation coefficients. A correlation coefficient was considered to be significantly different from zero if its 95% confidence interval did not include zero (Sokal and Rohlf 1981).
The morphology of leaves at lower nodes produced at 18 [degrees] C differed considerably from those produced by the same plants at higher nodes at 30 [degrees] C (Table 1). Leaves produced at the first sampling time were more than twice as large in area and were 22% thicker than leaves produced later at higher temperature. In addition, leaves produced at the first sampling time had significantly higher chlorophyll a:b and significantly lower density of stomata than leaves produced at the second sampling time, although these differences were less dramatic than for leaf area and thickness. Chlorophyll concentration was the only trait that did not differ significantly between leaves produced at different times (Table 1). The directional differences in leaf traits between sampling times were consistent with expectations for an adaptive response to temperature as described above.
One half-sib family was dropped from the genetic analysis because of low seed germination. Examination of residuals from ANOVA revealed apparent heteroscedasticity for leaf area only. Log transformation of leaf area successfully eliminated variance heterogeneity. With two exceptions, the interaction terms were not significant for any ANOVA. The chamber-by-sire interaction was significant for leaf area and for leaf thickness at the first sampling time.
Hypothesis tests, based on reduced ANOVA models where appropriate, revealed a significant sire effect for chlorophyll a:b and marginally significant sire effects (P [less than] 0.07) for leaf thickness and density of stomata at the first sampling time (Table 2). Sire effects were significant for leaf thickness, density of stomata, and chlorophyll a:b at the second sampling time. The dam-within-sire effect was significant for all traits except chlorophyll a:b at the first time and for leaf area, density of stomata, and chlorophyll concentration at the second sampling time (Table 2). There was a significant effect of sire on the difference between sampling times (which measures [TABULAR DATA FOR TABLE 2 OMITTED] within-individual variation) for leaf area, chlorophyll concentration, and chlorophyll a:b ratio (Table 3). In the analysis of trait differences between sampling times, the dam-within-sire effect was significant only for leaf area and density of stomata (Table 3). The frequently significant effects of chamber (Tables 2-3) reflect differences between the environments of two growth chambers of the same model, age, and manufacturer.
Pairwise correlations between family means for different traits can be divided into three subsets (Table 4). There are two subsets of correlations between traits measured at the same sampling time (subsets A and B in Table 4) and a subset of correlations measured across sampling times (subset C in Table 4). Correlations within a sampling time would affect the rate of response to selection on the value of traits expressed at that time. The correlations across sampling times would influence the rate of response to selection on the pattern of within-individual variation across sampling times.
At the first sampling time, three of the nine possible pairwise correlation coefficients were significantly different from zero (Table 4). The positive relationship between family mean leaf thickness and leaf area would facilitate response to the expected pattern of selection for simultaneous increase in these two traits at low temperature. The significant negative relationship between leaf area and density of stomata would also be facilitative. The last correlation would inhibit response to selection for adaptive change in either leaf thickness or chlorophyll a:b because adaptive change in one of these traits would cause a correlated nonadaptive change in the other. In contrast to the pattern of correlations at the first sampling time, none of the correlations between traits was significantly different from zero at the second sampling time.
Correlations between family means for the same trait and for different traits across sampling times were mostly low and nonsignificant (Table 4). Values along the diagonal of the submatrix of across-sampling-time correlations (underlined in Table 4) are correlations between the family mean values of the same trait expressed at different times. The significant positive correlation between family mean stomate density at high and low temperature shows that families that produced leaves with low (or high) stomate densities at the first sampling time also produced leaves with low (or high) stomate densities at the second sampling time. This correlation would act as a constraining force if selection favors different stomate densities at different temperatures as is thought to be the case. The generally low correlation coefficients along the diagonal of the across-sampling-time sub-matrix indicate that the value of a given trait expressed at one time is not closely related to its value expressed at another time. As a consequence, this submatrix is not symmetrical.
The off-diagonal elements of the across-sampling-time correlation matrix are correlations between a given trait at one time and a different trait expressed at a different time. Among the correlations of the expression of different traits across sampling times, only two were significantly different from zero. Families that produced leaves with high chlorophyll a:b at the first sampling time produced leaves with a large area at the second sampling time, and thickness of first leaves was negatively correlated with area of second leaves. The positive correlation between chlorophyll a:b at time one [TABULAR DATA FOR TABLE 3 OMITTED] [TABULAR DATA FOR TABLE 4 OMITTED] and leaf area at the second would tend to slow response to the expected pattern of selection for increased chlorophyll a:b at low temperatures and decreased leaf area at high temperatures. In contrast, the negative correlation between leaf thickness at time one and leaf area at time two could speed the response to selection, because a response in either trait would lead to a correlated response in the other in the direction favored by selection.
Individual plants of D. linearifolia can produce leaves that differ substantially in size, morphology, and pigment composition. Knowledge of the ecophysiology of leaf function at different temperatures suggests that the differences I observed in traits of leaves that developed at different times are consistent with an adaptive response to seasonal variation in temperature (Huner 1985; Korner et al. 1989). However, the conclusion that these within-individual changes in leaf traits are adaptive is premature in the absence of an analysis of natural selection relating the pattern and magnitude of within-individual variation to fitness in the field (cf. Van Tienderen 1991). Such an analysis is in progress.
Significant sire effects for some traits expressed at each sampling time indicate the presence of additive genetic variation for these leaf traits (Table 2). Additive genetic variation for within-individual variability is also indicated for several traits by significant sire effects in the analyses of the difference between the values of the same trait expressed at different times (Table 3). This variation for within-individual variability constitutes the raw material from which natural selection could further mold the pattern of within-individual variability in this population.
The rate at which the pattern of within-individual variation could respond to selection depends on the structure of genetic correlation among traits as well as the amount of genetic variation for within-individual variation. My measures of family mean correlations between traits within a sampling time suggest that genetic correlations would influence the response to selection on leaf traits at a given sampling time only for those traits expressed at the first sampling time. Others have shown that genetic correlations between traits depend on environmental conditions (Service and Rose 1985; Riska et el. 1989) and the stage of development at which they are measured (Atchley 1984; Atchley et el. 1985; Roach 1986), so the contrast between correlations within different sampling times reported here is perhaps not surprising.
My estimates of family mean correlations of leaf traits across environments for D. linearifolia identified few relationships that would influence the rate at which selection could change the pattern of within-individual trait variation. The small number of significant correlations may be due to the small sample of half-sib families (n = 24), but the average absolute value of the 25 correlation coefficients (x = 0.23) is also low. The absence of strong correlations of the same trait across environments suggests that, for example, the genetic control of leaf area for leaves from the first sampling time is more or less independent of the control of leaf area for leaves produced later on.
Genetic correlations less than + 1 are consistent with detection of significant genotype-by-"environment" (here sampling time) interaction (Via 1987). Thus the relatively low family mean correlations for leaf area, chlorophyll concentration, and chlorophyll a:b between sampling times are consistent with significant genetic effects detected in the analyses of trait differences. Likewise, the strong positive family mean correlation for density of stomata is consistent with the lack of evidence for genetic variation for within-individual variation in this trait. The family mean correlation for leaf thickness is low despite the lack of a significant effect of sire in the ANOVA for differences in leaf thickness, but the sire effect did approach significance (P = 0.072).
Others who have estimated genetic correlations between the expression of the same or different traits across environments have also observed a mixture of significant and non-significant correlations, Via (1984) found a significant positive full-sib family mean correlation of pupal weight for a polyphagous leaf miner raised on two plant hosts. Correlations for developmental time across host environments and for both across-trait, across-environment correlations were not significantly different from zero. Via (1991) reports that estimates of genetic correlation of performance traits of herbivorous insects across host plants for many species are low. Boulding and Hay (1993) examined full-sib family mean correlations of five shell-form traits in an intertidal snail across two rearing environments (low and high density). They observed significant positive genetic correlations for two traits across environments, but did not calculate correlations between different traits across the two environments.
Although few estimates are available, it appears that genetic correlations of the same or different traits across environments are frequently low and nonsignificant and may therefore not have a strong influence on the short-term evolution of plastic responses. However, the absence of antagonistic genetic correlations need not imply that response to selection will be unimpeded (Charlesworth 1990). For example, the five traits examined in this study may be genetically correlated with traits I did not measure, and these unmeasured correlations could also retard the response to selection.
My findings show that there is substantial within-individual variation in leaf size, morphology, and physiology and that there is additive genetic variation for the pattern of with-in-individual variability in D. linearifolia. Estimates of genetic correlations suggest that there are few relationships between traits expressed at different times that influence how fast selection changes the pattern of variability of leaf traits. Although the genetic parameters reported here may not accurately reflect genetic variation or correlations that would be expressed in a large, natural population, they do support the need for field experiments to obtain more accurate estimates of the genetic parameters that influence the evolution of within-individual variation.
The mechanistic basis for the within-individual variation described here may be programmed developmental change, plastic response to temperature, or a combination of these mechanisms. The goal of the work described here is not to separate the relative roles of these mechanisms. Rather it is to confirm that there is within-individual variation in leaf traits and genetic variation among individuals for the pattern of within-individual variation and thus to motivate further investigation of its evolutionary significance.
Adaptive phenotypic variation that accommodates fine-grained environmental variation may be common in plants and other modular organisms. Adaptive variation in leaf form and function in response to light intensity has been studied extensively (e.g. Clough et al. 1979; Bjorkman 1981; Winn and Evans 1991; Sultan and Bazzaz 1993), although not explicitly in the context of within-individual adaptation to environmental heterogeneity. Seasonal changes in light availability under a deciduous canopy as well as small-scale spatial variation in the distribution of light could select for with-in-individual variation in leaf traits that affect physiological performance in sun and shade.
Documentation of variation in morphology among connected ramets of a genet (Evans 1992; Dong 1993; de Kroon and Hutchings 1995) and the extensive literature on heterophylly (e.g. Allsopp 1967; Kaplan 1980; Guerrant 1988) also support the existence of potentially adaptive phenotypic variation among modules within individuals. Genetic variation for among-tamer variation or for heterophylly has not been investigated, and these phenomena have not been explicitly related to current theory on the evolution of phenotypic flexibility (e.g. Via and Lande 1985; Lively 1986; Van Tienderen 1991; Gomulkiewicz and Kirkpatrick 1992).
This paper benefitted significantly from comments and suggestions by C. Galen, A. S. Evans, F. C. James, T. E. Miller, J. Travis, D. Stratton, S. Via, and several anonymous reviewers. K. Chodyla, D. Porter, and F. Prado provided technical assistance. I am grateful to the staff of the St. Marks National Wildlife Refuge for allowing me to conduct research there. This work was supported by a grant from the National Science Foundation (DEB-9221107).
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|Author:||Winn, Alice A.|
|Date:||Jun 1, 1996|
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