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The Society for Integrative and Comparative Biology
Using Artificial Selection to Understand Plastic Plant Phenotypes1
1 Barnard College, Columbia University, Department of Biological Sciences, 3009 Broadway, New York, New York 10027
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The plasticity of any given trait, which has a genetic basis and which may or may not be adaptive, can intensify or attenuate evolved responses, and can itself evolve in response to selection depending on the scale of spatial or temporal heterogeneity. To investigate the complex function and evolution of plastic traits, an appealing yet challenging approach is assessing responses to artificial selection. Here, I review how artificial selection has been employed to explore four botanical research themes: (1) relationships between plastic and evolved responses to multiple stresses, (2) integration of cellular, leaf-level, and whole-plant responses to altered CO2 concentrations, (3) photomorphogenic and photoperiodic development, both mediated by phytochrome photoreceptors, and (4) the evolution of the pest-induced myrosinase-glucosinolate system in cruciferous plants. These diverse topics are unified not only because they have been studied using artificial selection experiments, but also because they have considered variability in multiple traits affected by multiple factors in the external environment. Limitations of such research include a dearth of long-term studies; a surprising but often logistically necessary omission of control or replicate lines; and numerous issues relating to assessing impacts of inbreeding and drift. In addition to discussing options for circumventing such limitations, I draw attention to strategies for integrating the results of artificial selection studies with progress in functional and evolutionary genomics.
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INTRODUCTION |
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Plants are sessile, autotrophic, and grow in an indeterminate and modular manner. Because of this, physiological and ecological research has often focused on responses to shifts in resource availability (e.g., availability of light) or to changes in strongly correlated non-resource factors (e.g., photoperiod, R: FR ratio). Such factors can elicit physiological or morphological responses within individual plants: phenotypic plasticity. Such factors can also exert selective pressures, shaping evolved responses: shifts in the distribution of phenotypic variation within populations (i.e., changes in mean, variance, covariance, etc.) (Lande and Arnold, 1983
; Rausher, 1992). The plasticity of any given trait, which has a genetic basis and which may or may not be adaptive, can intensify or attenuate evolved responses, and can itself evolve in response to selection depending on the scale of spatial or temporal heterogeneity (Via and Lande, 1985; Via et al., 1995; Pigliucci, 1996, 2001). Since selection can act on both variation for traits and variation for the plasticity of traits, the evolution of plastic traits can only be understood by addressing the complex interplay between the plastic responses of individuals and the evolved responses of populations.
The tools of ecological and quantitative genetics are therefore crucial for studying the function and evolution of plastic plant traits (Ackerly et al., 2000; Ackerly and Monson, 2003), and here I will address one of the most powerful: assessment of direct and indirect responses to artificial selection. While selection studies have the potential to provide important insights, their design, execution, and interpretation can be extremely challenging. Beyond the complexities sketched above—well-researched in plants but certainly not unique to them—another challenge emerges from a similarity between plants and other organisms: they do not respond to just a single environmental variable at a time. Any given environmental factor can elicit a plastic response, exert selection, or both. This can create constraints—short- or long-term, antagonistic or synergistic—that can affect whether and how individuals respond via plasticity to other environmental factors, as well as the future evolutionary trajectory of the population (Bell and Lechowicz, 1994; Van Tienderen and Van Hinsberg, 1996).
Understanding such constraints requires sophisticated examination of quantitative genetic architecture, including (1) how a single trait may relate to one or more other traits within a single environment (i.e., genetic correlations, pleiotropy, or linkage: Fuller et al., 2005), (2) how a single trait is expressed in multiple environments (i.e., plasticity: Scheiner, 2002; Brakefield, 2003) and sometimes (3) a merger of the first two considerations: relationships among multiple traits expressed in multiple environments. This third issue is likely to be important in wild populations.
Artificial selection experiments addressing the complex evolution of phenotypically plastic traits require subjecting large experimental populations (consisting of many different plant genotypes) to variation in multiple environmental factors, often in novel factorial combinations. Such studies can simultaneously quantify genetic variance for traits and genetic variance for plasticity, allowing insights into whether and how traits and their associated plasticities evolve, potentially but not necessarily in tandem. By concentrating on artificial selection studies that investigate phenotypically plastic traits in plants, this review aims to complement recent treatments by Conner (2003), Pigliucci (2003), Brakefield (2003), Fry (2003) and Scheiner (2002).
I discuss how useful this approach has been not only in disentangling complexity, but also in unifying at least four rather disparate topics in plant eco- and evolutionary physiology. The first topic, how populations respond to diverse stresses, has been of enduring interest for plant ecologists and is increasingly important in the face of anthropogenic global change and habitat degradation (Bradshaw and McNeilly, 1991; Chapin et al., 1993). The second topic, the potential response of plants to changes in CO2 availability, is a pressing concern given past plant responses to historical fluctuations and the dramatic changes anticipated during the 21st century (Bradshaw and McNeilly, 1991; Geber and Dawson, 1993; Sage and Coleman, 2001). The third topic, the evolution of plant photomorphogenic and photoperiodic responses, illustrates some of the challenges of translating progress in functional genomics into an evolutionary ecology framework (Callahan et al., 1997; Schlichting and Smith, 2002). A fourth topic, chemical defenses induced by herbivores and pathogens, illustrates how results that are puzzling, and even in conflict with theory, may become understandable via a more sophisticated view of interspecific ecological interactions (Mitchell-Olds and Bergelson, 2000; Agrawal, 2001, 2003).
In selection studies of plastic traits, multiple environments often come into play, either during selection, during evaluation of responses to selection, or both. In addition to making such studies especially cost- and labor-intensive, this reality may strongly influence the choice of a model organism. Another important decision is whether to use "true" artificial selection for a relevant non-fitness trait, to quantify a proxy of fitness and impose "quasi-natural" selection, or to instead target viability (Scheiner, 2002; Fry, 2003). Such decisions will affect four other important choices that are not unique to botanical studies: whether selection is short- or longer-term, whether and how to replicate lines, whether to include unselected control lines, and how to manipulate or account for changes in mating system, level of inbreeding, inbreeding depression, or genetic drift. My review of several artificial selection studies will point out choices made by plant researchers. A concluding section will discuss how these choices can limit the interpretation of studies, yet sometimes lead to opportunities for complementary research.
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PREDICTABLE ADAPTATIONS TO MULTIPLE STRESSES? |
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Stanton et al. (2000)
have used artificial selection to investigate how both evolved responses and phenotypic plasticity are influenced by stress. Their work confronts Grime's (1977, 1979) controversial C-S-R system, which classifies plant life histories based on assumed trade-offs among three basic selective pressures: competitive ability (C selection), stress tolerance (S selection) or dispersal and colonizing ability (R selection, referring to ruderals, species associated with highly disturbed habitats). Since Grime defines stress very generally as "any external constraint which limits the rate of dry-matter production of all or parts of the vegetation," different forms (e.g., aridity, salinity, nutrient deficiency) are predicted to favor a common suite of adaptations to stress. Such adaptations include slow growth, delayed reproduction, and adaptive homeostastis, and are at odds with competitive strategies such as faster growth or adaptive plasticity or the ruderal habit of reproducing very early.
The "artificial evolution" strategy employed by Stanton and her colleagues (2000, 2004) is a departure from past comparative studies addressing this issue. Rather than making comparisons among species (e.g., Westoby et al., 2002), it examines adaptive differentiation within a species, specifically among selection lines shaped by different stress histories. This allows examining genetic correlations for fitness across environments, looking for positive correlations across different types of stressful environments (Stanton et al., 2000) or negative correlations across contrasting environments believed to select for "C" as compared with "S" life-history strategies (Stanton et al., 2004).
Their initial selection study, conducted in a greenhouse, used eight replicate sets of seeds, all sampled originally from a single natural population of the wild mustard Sinapis arvensis, a weedy outcrossing annual. Each replicate consisted of six sets of 48 seeds, with each set assigned to one of six environments: a "no stress" control, high salt, high boron, low light, low water, and low nutrients. In each of three successive generations, 48 progeny (seeds) were randomly sampled from each stress-by-replicate combination. In this short-term experiment, individual fecundity was quantified and used as the target for selection ("quasi-natural" selection). Fecundity was presumably influenced by combinations of genotype and environment. One limitation of this greenhouse study is that plants were grown in individual pots at low densities, minimizing belowground plant-plant interactions that would occur in nature. Also, the only correlated physiological traits quantified were integrative performance (i.e., size) estimates. Despite these drawbacks, this ambitious study has four notable strengths. First, fecundity data were used to estimate relative fitness and to conduct formal phenotypic selection analyses. Second, the study examined whether stress affects the opportunity for selection (i.e., variation in relative fitness). Third, samples from replicate lines were exposed to multiple environments in a follow-up study, allowing assessment of not only evolved responses to selection but also phenotypic plasticity and its adaptive significance. Fourth, the study used a self-incompatible species, so that all pollinations (performed within a line) resulted in outcrossing, minimizing inbreeding.
Stanton and colleagues (2000) found consistent directional selection for stress-avoiding rather than stress-tolerating strategies, with phenotypic selection for earlier flowering occurring in all five stress environments but not in the control environment. Directional phenotypic selection for faster growth in height occurred in three of five stressful environments. Variation in relative fitness tended to increase in stressful as compared to control conditions. In other words, stress increased the opportunity for selection.
In the face of selection, however, there was a mismatch between evolved and plastic responses. Evolutionary responses toward earlier flowering and greater height matched selection gradients, but plastic responses were in the opposite direction (e.g., flowering delayed, seedling height reduced). In other words, populations evolved trait means that enhanced their fitness in stressful habitats, but their plastic responses to stressful vs. non-stressful conditions were not consistent with adaptive phenotypic plasticity (e.g., Schmitt et al., 1995; Donohue et al., 2000). Other researchers have pointed out that specialist taxa may exhibit maladaptive plasticity (e.g., Taylor and Aarssen, 1988; Lortie and Aarssen, 1996).
Additional evidence for maladaptive plasticity emerged when "low light stress" selection line were compared to control lines in a factorial field experiment involving high and low density plantings combined with either full sun or partial shading (Stanton et al., 2004). In stressful full sun treatments, drought and heat negatively impacted growth and fecundity; less stressful "partial shade" conditions were more productive. Control lines had superior fitness in partial shade at both high and low density, but control and "low light" selection lines attained similar fitness in both types of full sun treatment. Trade-offs associated with adapting to tolerate limited light availability apparently compromised the ability of selected lines to take advantage of favorable conditions.
This factorial field study also provided estimates of across-environment genetic correlations for fitness, predicted to be negative across contrasting environments: e.g., the presumably most stressful and least competitive (full sun, low density) as compared with the least stressful and most competitive treatment (partial shade, high density). As in the previous "multiple stress" study, across-environment genetic correlations for fitness were always positive, and almost always significantly so.
Results of these related studies suggest that Grime's model, designed to explain patterns of ecological specialization, may extend only partially to patterns of within-species differentiation. While evolutionary responses to diverse stresses did favor a similar suite of traits, "stress specialists" did not show the expected fitness trade-offs across contrasting stressful vs. productive habitats, and also resembled ruderals rather than stress-tolerators. Stanton et al. (2000) suggest that short-lived species may respond to stress by improving the ability to avoid or escape it, while longer-lived species instead improve stress tolerance. This plausible generalization should be viewed cautiously for at least two reasons. First, as in any selection study, it is possible that the history of the progenitor population may matter: perhaps it experienced frequent disturbance, biasing all of the selection lines in the study toward rapid evolution of ruderal strategies. Second, only a short-lived annual was studied. A comparable study with a perennial could examine whether longer-lived species evolve "S" rather than "R" strategies in the face of diverse stresses. The feasibility of such a study is questionable, however, given the arduous nature of both natural and artificial selection studies with annuals (Geber and Griffen, 2003).
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VARIATION IN CO2 LEVELS AND THE EVOLUTION OF PLANT PRODUCTIVITY |
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Because net carbon assimilation is an absolute requirement for an increase in plant biomass, artificial selection targeting increased photosynthetic capacity is an attractive tool for improving crop productivity. Some reviewers have questioned the validity of this improvement technique, because scaling from leaf-level CO2 assimilation to whole-plant yield is too complex and whole plant photosynthetic capacity cannot be properly estimated using instantaneous leaf-level measurements. It has also been suggested that genetic variation may be limited for performance traits so critical to plant fitness, particularly in modern crops (reviewed in Medrano et al., 1995
). To confront these criticisms, a novel study of tobacco, Nicotiana tabacum, combined mutagenesis with artificial selection.
Selection targeted variation in CO2 compensation point, 
, a measure of the balance between carbon gained through photosynthesis and lost through photo- and maintenance respiration. Rather than estimating instantaneous, leaf-level and using estimates to rank and select genotypes, the study imposed viability selection against plants with high compensation points by reducing CO2 concentrations to 60–70 ppm (as compared to ambient levels of 
350–360 ppm). Only 5% of individuals survived to reproductive maturity, after which selected genotypes and unselected control genotypes were clonally propagated for a series of comparative studies.
Recognizing that is a complex and phenotypically plastic trait, comparisons were made for and for many different biochemical, physiological and anatomical traits (Medrano and Primomillo, 1985; Medrano et al., 1995), taking into account how traits vary diurnally or during leaf ontogeny (Delgado et al., 1994), and sometimes in glasshouse vs. field conditions. Despite this sophistication, results are difficult to interpret because they did not characterize trait plasticities to ambient vs. novel CO2 concentrations. For example, the study failed to detect differences between selected and control genotypes in photosynthetic or photorespiratory machinery (e.g., increases in Rubisco enzyme content or activity: Delgado et al., 1993; Medrano et al., 1995). Also, selected genotypes did not show a difference in instantaneous , despite experiencing strong selection on life-time . In part, this latter result validates criticisms of instantaneous methods for estimating . More generally, it calls into question the reliability of selection strategies involving extreme or novel environments. If returning to ambient conditions evokes drastic phenotypic plasticity, among-genotype differences expressed under the conditions of selection may be diminished, eliminated, or even reversed.
Overlooking these flaws, among this study's most relevant findings were differences between selected and control genotypes in productivity, including 24% greater above-ground dry matter accumulation, 21% greater leaf area per plant, and higher leaf nitrogen (Medrano et al., 1995). The investigators astutely ask how could this happen without corresponding changes in physiology? A possible answer: selected genotypes had more leaf cells per unit area and smaller mean cell volume, which can reduce maintenance respiration and increase rates of CO2 transfer from the stomata to carbon-fixation sites in photosynthetic tissues (Delgado et al., 1992a,b; Medrano et al., 1995). Although plausible, anatomical differences were subtle, and like the physiological assays described above, failed to consider possible plastic responses triggered by very low CO2 vs. ambient.
In general, efforts to understand plant productivity both functionally and evolutionarily must account for interplay between plastic and evolved responses. This was strongly emphasized in Bradshaw and McNeilley's (1991) review of potential plant responses to increasing atmospheric CO2. Their review points out that the genetic architecture of a complex phenotype may itself change as the environment changes and suggests artificial selection a potentially useful approach for testing the short-term evolutionary effects of global change.
In this vein, Potvin and Tousignant (1996) used a rapid-cycling strain of the self-incompatible annual crop Brassica juncea (canola) to create two artificial selection lines. Both were selected for an increase in total fruit biomass, but one was grown in chamber conditions simulating a "predicted" future environment (i.e., gradual increases in temperature regimes and CO2 concentrations) and the other in a chamber simulating contemporary conditions. After seven generations of selection, both lines were exposed to both environments and 14 vegetative and reproductive traits were measured. Eleven of 14 traits showed significant phenotypic plasticity to contrasting environments, but only one of those responses was consistent with adaptive phenotypic plasticity (i.e., the direction of the plastic response matching the direction of selection gradients). Seven of the 14 traits showed genetic differentiation in response to selection, but only one of those traits showed a change consistent with adaptation.
Why could these experimental populations not become better adapted to either current or future conditions? In addition to choosing a self-incompatible annual and using a random mating scheme to minimize low levels of inbreeding, Potvin and Tousignant recognized that some matings among relatives can occur within small populations, potentially limiting standing genetic variation and responses to selection. Because the study lacked replicate lines or unselected controls, they were careful to track each selection line's effective population size, and the probability of mating among relatives within lines. With these data in hand, they were able to convincingly argue that inbreeding depression, known to be environmentally dependent (Schmitt and Ehrhardt, 1990; Wolfe, 1993), limited responses to selection (Kelly, 1999), especially in the predicted high CO2 environment.
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DO PHOTOMORPHOGENIC AND PHOTOPERIODIC RESPONSES EVOLVE TOGETHER? |
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Using fluctuations in the light environment and elaborate signal transduction mechanisms, plant cells gather information about the current environment and then use that information to adaptively modify their phenotypes (Smith, 2000
; Schmitt et al., 2003). One of the best-studied photoreceptory systems, the phytochromes, uses sensitivity to the ratio of red and far-red light (R:FR) in two ways: (1) light-regulation of growth and development throughout the life cycle (termed photomorphogenesis), and (2) sensing and responding to cyclic changes in the light environment (i.e., circadian light/dark rhythms that themselves change seasonally) (Kevei and Nagy, 2003; Mockler et al., 2003; Klejnot and Lin, 2004). In serving this dual function, phytochrome-mediated plasticity is associated with many different morphological and life history traits, making this study system another example where three complex aspects of genetic architecture are relevant: (1) trait plasticities, which influence whether selection can maintain or change trait means and variances, and which may themselves evolve; (2) multiple traits showing plasticity; and (3) variation in multiple environmental factors co-occurring.
Here, I briefly summarize my own work using artificial selection to study the integrated function and evolution of photomorphogenic and photoperiod responses. The work focused on an important life history trait, bolting time, in the model annual plant species Arabidopsis thaliana (Callahan and Pigliucci, 2005). In this and many other annual and biennial species, bolting is the earliest manifestation of the transition to reproduction. This non-labile trait can be scored by checking plants daily for the appearance of 1–2 mm of an elongating inflorescence at the center of a whorl of rosette leaves. At that point, either the number of leaves or the chronological age of plants (in days), or both, are recorded. In this species, both traits are typically accelerated by nearby vegetation that reduces R: FR and by sufficiently long photoperiods. Both of these plasticity responses are, in part, phytochrome-mediated. While the direction of this plasticity is predictable, its magnitude may vary considerably among genotypes, and may be affected by daylength. The existence of this variation motivated our efforts to develop selection lines differentiated for R:FR-mediated plasticity.
Recognizing that plasticity itself can be a heritable trait (Scheiner, 1993), we first asked whether a natural population of A. thaliana could harbor heritable variation for R:FR-mediated plasticity. Second, we examined whether or not there was parallel evolution of two related bolting time trait plasticities, since the two traits are often positively correlated and may be subject to pleiotropy (Mitchell-Olds, 1996; Callahan and Pigliucci, 2002). Third, we asked whether a change in the plasticity of a target trait would predictably alter the mean of that same trait (Bradshaw, 1965; Brumpton et al., 1977; Falconer, 1990). Finally, because these two distinct light signals jointly regulate a common trait, and because the mechanisms for detecting and responding to those signals are inter-related at the cellular level (Klejnot and Lin, 2004; Valverde et al., 2004), we investigated the co-evolution of R:FR- and photoperiod-mediated plasticities.
This project used a population originally native to Kendalville, Michigan, that had already been brought into laboratory cultivation by a commercial supplier. From a bulk seed sample, a total of 68 maternal families had already been established (Camara et al., 2000), and all families were screened in a preliminary study (1) to confirm the general tendency of low R: FR conditions to accelerate both leaf number at bolting and days to bolting and (2) to quantify among-family variation for both traits and for a plasticity index (R: FR Index = 1 – [leaves at bolting in low R:FR/leaves at bolting in high R:FR]). This dimension-less index describes the extent to which low R:FR conditions accelerate the developmental stage at which bolting occurs. Another paper provides details of how two high plasticity (HP1, HP2), two low plasticity (LP1, LP2), and two control (C1, C2) lines were initially established from this base population and then subject to two additional episodes of artificial selection targeting this plasticity index (Callahan and Pigliucci, 2005). Here, I briefly summarize a follow-up experiment. Genotypes from all six lines were grown in all four combinations of high and low R:FR conditions and long and short photoperiods.
We expected to find a direct response to short-term artificial selection, because an initial screening of the base population had detected not only a significant R: FR treatment effect and significant among-genotype variation for leaf number at bolting, but also a significant treatment-by-genotype interaction. In other words, there was variation among genotypes for the R:FR-mediated plasticity, the target of our selection protocol. A direct response to artificial selection was indeed confirmed, with HP lines becoming significantly differentiated from the LP lines for the R:FR-mediated plasticity index (Fig. 1a).
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In addition, there were indirect responses to selection. For example, the initial screening study had detected a strong positive correlation between the plasticity index of leaf number and the plasticity index of days to bolting (r = +0.71, n = 68, P < 0.001). It was therefore expected that the HP and LP lines would become differentiated not only for the plasticity index targeted by artificial selection, but also for the plasticity index of this closely related bolting time trait. Parallel evolution of these two plasticity indices did occur (Fig. 1b), corroborating earlier evidence that the two traits—and presumably their associated plasticities— are regulated pleiotropically (Mitchell-Olds, 1996
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The initial screening had also detected a positive correlation between mean leaf number at bolting and the plasticity index for this trait, but expected indirect responses to selection did not occur consistently. Mean leaf number at bolting did decrease in the LP lines, but it decreased unexpectedly in the HP lines as well (Fig. 1c). This suggests that the relationship between a trait and its plasticity may be summarized rather poorly by a simple, linear parameter. It also supports the contention that the plasticity of a trait and the trait itself can evolve independently rather than in tandem (Bradshaw, 1965; Pigliucci, 2001).
Mirroring these decreases in mean leaf number in both LP and HP lines, there were significant increases in a photoperiod-mediated plasticity index (Fig. 1d). In other words, response to disruptive selection on a R:FR-mediated plasticity index resulted, indirectly, in positive directional selection on the same trait's photoperiod-mediated plasticity index. Although somewhat surprising, this makes biological sense: genotypes that bolt with very few leaves under inductive photoperiod conditions would tend to have the greatest capacity to delay bolting developmentally when those signals are absent. Again, it seems that linear parameters may not be the most appropriate way to summarize relationships among a trait's mean and its plasticity to one or more environmental variables.
Other evolutionary ecologists have also found that the roles played by phytochromes are trait specific. For example, recognizing that reduced R:FR conditions not only accelerate bolting, but also alter seedling development, at least two studies have estimated genetic correlations between bolting- and seedling-stage traits or trait plasticities (Botto and Smith, 2002; Stenoien et al., 2002). That such correlations are weak and non-significant is entirely consistent with what developmental geneticists have learned about the complex functions of multiple phytochrome photoreceptors (e.g., partially redundant and partially antagonistic function, interactions with other photoreceptory and hormonal systems, environment-specific gene action, etc.) (Borevitz et al., 2002; Kevei and Nagy, 2003; Mockler et al., 2003).
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Many plant ecologists have addressed the function and evolution of resistance traits that allow individuals to avoid or reduce damage by herbivores or pathogens as compared to individuals lacking such traits. Understanding how selection influences resistance traits is clearly challenging because of three aspects of genetic architecture: (1) the complex interplay between evolved changes in basal (i.e., constitutive) resistance traits and inducible (i.e., plastic) resistance traits combined with the potential for plasticity itself to evolve, (2) multiple resistance traits and (3) overlapping variation in multiple environmental factors (i.e., multiple enemies).
A number of conceptual and mathematical models have tried to predict how constitutive and inducible defenses should evolve. Briefly, if pests attack infrequently and defense chemicals are energetically costly, selection should favor low basal levels but high inducible levels. On the other hand, frequent pest attacks should deflate the energetic costs of producing high basal levels of defense chemicals while simultaneously rendering induction less effective. Theory therefore predicts negative phenotypic and genetic correlations between induced and basal levels of these resistance biochemicals, as well as negative correlations between defensive chemical production and fitness components, particularly when pests are absent and where plant performance is already compromised (e.g., resource-poor, competitive, or stressful environments) (Simms, 1992; Bergelson and Purrington, 1996; Karban and Baldwin, 1997; Agrawal, 1998).
Manipulative field experiments have documented pest-mediated natural selection for resistance traits, yet natural populations typically harbor significant additive genetic variation for resistance traits (e.g., Berenbaum et al., 1986; Mauricio and Rausher, 1997). Moreover, genetic correlation between basal and induced levels of defense chemicals are typically non-significant and sometimes positive rather than negative (Zangerl and Berenbaum, 1990; Karban and Baldwin, 1997). From a mechanistic viewpoint, it is plausible that constitutive and induced levels of defensive chemicals are regulated by common sets of genes exerting positive pleiotropy, which would be detectable as a positive genetic correlation (Siemens and Mitchell-Olds, 1998).
Given equivocal evidence from past lab and field experiments, it is unsurprising that artificial selection has been brought to bear on this challenging topic. Here, I review studies by Siemens and colleagues (1998, 2002), who developed the weedy annual Brassica rapa (syn. campestris) an advantageous model system. Like all members of the mustard family, B. rapa leaf cells contain myrosinase enzymes. After pests damage cells, this enzyme hydrolyzes nitrogen- and sulfur-containing glucosinolates to produce glucose, sulfate, isothiocynates, and other secondary compounds that can confer resistance to generalist and specialist insects (Chew, 1988; Agrawal and Kurashige, 2003). Total glucosinolates and myrosinase can be inferred by quantifying glucose under different conditions, and Seimens et al. (1998) developed rapid, automated assays to accommodate the large sample sizes required for their artificial selection studies.
Two separate artificial selection studies were conducted. In one (Siemens and Mitchell-Olds, 1998), disruptive selection targeted basal glucosinolate only or basal myrosinase only, creating two types of high and low lines: GH, MH, GL, ML. In another (Siemens et al., 2002), all four combinations of high and low glucosinolates were selected (GHMH, GHML, GLMH, GLML). Although only basal levels of these compounds were quantified during artificial selection, follow-up experiments to gauge responses to selection assayed not only basal but also induced levels of defense chemicals, and induction by multiple enemies. Both studies assumed that using a large number of plants and a self-incompatible annual would minimize the impact of inbreeding and inbreeding depression, and neither study included tracking of mating between relatives, unselected controls, or replicated selection lines. Both "up" and "down" selection lines were included, presumably to serve as controls for one another (Falconer and Mackay, 1996).
In the first experiment, selection on basal glucosinolates resulted in a GH selection line that had only 16.4% higher glucosinolate concentrations than the GL line. In contrast, selection on basal myrosinase generated MH and ML lines a 2-fold difference. To assess correlations between basal and induced levels of both defense-related chemicals, follow-up studies were conducted with all four lines. Myrosinase and glucosinolate levels in the leaves of undamaged plants were compared to levels in plants with leaves damaged by Peiris xylostella caterpillars or the pathogen fungal pathogen Leptosphaeria maculans (blackleg disease). This protocol was also useful for quantifying induced resistance to these two enemies.
For resistance and for levels of both chemicals, selection line x induction treatment interaction terms were generally non-significant. One exception was the MH line, which had higher pathogen-induced levels of glucosinolates than undamaged plants, as well as higher pathogen-induced herbivore resistance. This result suggests a positive correlation, rather than the expected negative correlation consistent with basal and induced defenses being alternative strategies. Costs of resistance were also detected, using field conditions where two types of herbivores occurred: leaf herbivory by lepidopterans and seedling herbivory by a flea beetle. Even though the MH line had 10.3% greater resistance to flea beetles, an estimate of subsequent seed production in that line was 15.5% lower.
In the second artificial selection experiment, in which four divergent lines were created, there was a stronger response to selection by the MHGH and MLGL lines as compared to MHGL or MLGH lines. This indicates a positive genetic correlation between basal myrosinase and glucosinolate levels (Fig. 2). To maximize their ability to detect costs of defense chemicals, Siemens et al. then grew all four lines in factorial combinations of herbivore induction and competition from Lolium perenne (a common grass species). This design aimed to elicit a very wide range of myrosinase and glucosinolate concentrations and wide variation in performance. Costs were detected in the absence of competition, but not when competitors were present, directly contradicting theoretical predictions. Although reciprocal effects of B. rapa on L. perenne were not investigated in this field experiment, Siemens and his collaborators hypothesized that these surprising results may have occurred due to an interspecific interaction other than resource competition, namely allelopathy. Allelopathic interactions, defined as the production and release of chemicals by one plant species that inhibit the performance of a co-occurring species, were explored by conducting a series of lab assays. In one of the follow-up studies presented by Siemens et al. (2002), root elongation of L. perenne seedlings was significantly suppressed when grown in the presence of myrosinase combined with glucosinolates, but not when grown in glucosinolates alone, or in the presence of glucosinolates combined with denatured myrosinase. Their paper also describes a related follow-up study that revealed significantly greater suppression of L. perenne root growth when seedlings grew in the presence of seeds from the MHGH lines as compared to grown in the presence of MHGL, MLGH, or MLGL seeds.
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This work by Siemens and colleagues provides fascinating insights into the genetic architecture, evolutionary dynamics, and ecological function of a particular system of defense chemicals. In addition, these selection lines have proven extremely valuable in exploring other ecological interactions. Strauss et al. (1999)
, for example, demonstrated that plants with weaker defenses against herbivores experience enhanced interactions with pollinators. Although it is important to recognize that these results may be rather specific to the myrosinase-glucosinolate system, it is exciting to consider the potential for conducting similar experiments with artificial selection lines that have been developed in other plant taxa (e.g., Marak et al., 2000). |
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Because plants are inexpensive and their care subject to few regulations, they resemble insects and other invertebrates commonly used for artificial selection. In the evolutionary biology literature, however, artificial selection studies with plants are seldom maintained for many generations (cf. Dudley, 1977
) and replication is limited or entirely lacking in many plant studies, including several reviewed here. This may be because their size and generation times tend to be greater than insects', even in semelparous species. Moreover, the space, light, water, and soil nutrition required by plants can be costly. Such drawbacks are especially relevant to studies of plastic plant traits, in which lines must be maintained in multiple environments, sometimes involving factorial combinations or novel conditions (e.g., manipulated lighting, altered CO2 concentrations). As in animal studies, logistical challenges escalate when selection protocols require assaying one or multiple traits in all individuals in every generation. While such challenges can be surmounted, they can strongly influence other decisions about study duration, study design, selection protocols, and even the choice of a model organism.
Of the studies reviewed here, most imposed artificial selection for only two to four generations; the longest imposed it for six (Potvin and Tousignant, 1996). Many studies omitted replication, control lines, or both. For Siemens and colleagues, for example, chose to have "up" and "down" lines serving as controls for each other (Falconer and Mackay, 1996). Their choice most likely reflects some of the reasons sketched above, particularly the labor- and cost-intensive scoring of myrosinase and glucosinolate traits. Given their focus on relating biochemical and ecological aspects of resistance, and fitness consequences thereof, extra effort in scoring phenotypes was essential and may have justified sacrificing replication. Work by Medrano and colleagues and by Potvin and Tousignant was more modest in scope, but also omitted replication, most likely because both studies involved novel manipulations of CO2.
Stanton et al. (2000), in contrast, more rigorously include replicate lines and unselected controls. Their work also entailed quantifying multiple fitness components and other performance traits. This was appropriate given their interest in whether cross-environment correlations for fitness are positive or negative. Similarly, my own work (Callahan and Pigliucci, 2005) quantified the plasticity of a non-fitness trait, which required growing lines in novel factorial combinations of light conditions and scoring hundreds of individuals in every generation for a timing trait. The decision to work with the diminutive and selfing annual A. thaliana was strongly influenced by these challenges.
Unselected control lines, replicate selection lines, or both, are crucial for gauging how genetic drift, inbreeding, and inbreeding depression limit responses to selection (Lynch and Walsh, 1998; Kelly, 1999). To minimize inbreeding, many researchers work with self-incompatible plant species and impose random mating (Siemens and Mitchell-Olds, 1998; Stanton et al., 2000; Siemens et al., 2002), but it can be difficult to eliminating inbreeding entirely since artificial selection tends to reduce effective population sizes (Ne). Small Ne both intensifies the impact of drift and increases the probability of matings among relatives. These realities motivated Potvin and Tousignant (1996) to track variation among parental plants in their contributions to the next generation, to adjust Ne accordingly, to calculate overall rates of inbreeding, and to quantitatively assess inbreeding depression. In contrast, Siemens and colleagues simply took steps to maintain large Ne in the hope of minimizing impacts of drift and inbreeding. Stanton et al. (2000), who rigorously opted for extensive replication and imposed random mating with hand-pollinations, often observed that some but not all replicates showed evolutionary responses to selection. When a replicated selection study finds limited responses to selection, a helpful technique for distinguishing the impacts of drift vs. inbreeding depression is inter-mating replicate lines (e.g., Wijngaarden and Brakefield, 2001), and this option could be pursued by Stanton and colleagues.
Some plant researchers have instead chosen selfing species, most prominently A. thaliana, for artificial selection studies. Such a choice raises a completely different set of limitations and opportunities. On the one hand, selfing species can be problematic because limited recombination means that selection proceeds primarily via line sorting, akin to what happens in clonal organisms (e.g., Bell, 1997; Kolodynska and Pigliucci, 2003; Van Kleunen and Fischer, 2003). In studies addressing constraints on genetic architecture, it can be difficult to distinguish when a correlated response to selection (or a lack of selection response) comes about due to true pleiotropy or simply due to genetic correlations that came about due to past selection and subsequently limited recombination (i.e., linkage disequilibrium: Lynch and Walsh, 1998). High levels of LD were clearly worth considering in assessing the artificially selected A. thaliana lines in my own study (Callahan and Pigliucci, 2005). Nonetheless, results were consistent with other researchers who have more rigorously distinguished pleiotropy from LD using recombinant inbred lines and QTL mapping strategies (Mitchell-Olds, 1996). Other researchers have attempted to circumvent this limitation of A. thaliana by artificially imposing recombination. Ward et al. (2000), for example, conducted random crosses among diverse ecotypes and then used F2 seeds as the base population for artificial selection. Yet Ward's more artificial approach and results may be biased relative to natural A. thaliana populations if high levels of LD are in fact the norm in the wild (Pigliucci, 2003). Two types of studies will be helpful in illuminating the role of LD as a constraint to selection: (1) combining development of artificial selection lines with mapping techniques (Ungerer et al., 2003; Ungerer and Rieseberg, 2003) and (2) developing and/or assaying artificial selection lines in field as well as lab conditions.
These observations point toward some of the advantages of working with a selfing species. First, researchers are interested in pursuing QTL mapping projects can inter-mate contrasting selection lines to create populations of recombinant inbred lines. This task may be more easily accomplished with selfing species (Conner, 2003). Second, researchers can avoid labor-intensive artificial pollination procedures, whilst simultaneously gaining confidence that reproductive output (i.e., fitness) is not dependent on the hand-pollination procedures employed in the lab or glasshouse. Third, selfing is hardly atypical among natural or crop plants; a predominantly selfing mating system is found in 20% of annuals (Barrett, 2002). Finally, an enormous wealth of functional genomics research is available for A. thaliana and for rice (Oryza sativa)—both premier model organism for genomics, both selfing species, and one of them the world's leading crop economically (Kikuchi et al., 2003). Studies with these and other crop species are likely to provide important insights, aid interpretation of other eco- and evolutionary and physiology research, and possibly make results more relevant to the applied plant science community.
Choosing to work with crops, or simply drawing the base population for selection from a single location, can introduce a constraint deriving from past selection responses. This is, of course, a general problem of quantitative genetic studies, rather than a weakness specific to artificial selection strategies. Moreover, such constraints can exist even in species with obligate outcrossing or mixed mating systems. Medrano and colleagues, who used an inbred strain of tobacco, were well aware of this possibility and chose to augment the strain's limited genetic variability with mutagenesis. In general, comparison of selected and control lines derived from mutagenized vs. unmutagenized base populations would be useful, potentially providing important insights into constraints due to past selection (Pigliucci, 2003).
Before concluding, two additional strategies employed in studies of plastic plant traits should be mentioned, even though they were not featured in the research reviewed. First, plant researchers sometimes take advantage of the extended viability of seeds, basically "storing" a control line. Second, selection for greater plasticity can be achieved by growing successive generations in alternating environments while developing control lines in a single, constant environment (Scheiner, 2002). Employing either of these approaches raises the issue of whether environmental effects carry over from one generation to the next, obscuring or amplifying responses to selection (Mousseau and Fox, 1998). Carryover effects due to maternal genotype or maternal environment can be especially pervasive in plants, with dramatic impacts due to seed dormancy or longevity (e.g., Shaw et al., 2000; Donohue et al., 2005a) potentially influencing other adult traits (Donohue et al., 2005b). To minimize, or at least homogenize, the impact of such maternal effects, some researchers routinely postpone evaluating responses to selection until all selected and control lines have been grown in a uniform environment for at least one generation. Even more desirable is combining this strategy with control lines grown alongside lines subject to a particular selection regime (e.g., Stanton et al., 2000), which permit tracking the impact of environmental effects from generation to generation.
Finally, it should be mentioned that the studies reviewed here, and many other plant studies, favor just a few taxa such as A. thaliana or Brassica species in the mustard family (Brassicaceae). Such taxonomic parochialism mirrors the intense focus on Drosophila, E. coli, and models in other kingdoms. Despite some limitations on generalizing results from model species to a broader range of taxa, there are tremendous opportunities for integrating artificial selection studies with post-genomic approaches. In plants, this can be done with Arabidopsis and relatives in the Brassicaceae, with rice and other grasses, or with tobacco and other members of the Solanaceae. Just as within-species QTL mapping data have been helpful in interpreting the results of A. thaliana studies (e.g., Mitchell-Olds, 1996; Callahan and Pigliucci, 2005), comparative QTL mapping techniques within the Brassicaceae (Osborn et al., 1997) have the potential to illuminate artificial selection studies conducted with B. rapa or S. arvense. Myrosinase, glucosinolate, and insect resistance QTLs mapped in A. thaliana (Kliebenstein et al., 2002), for example, may to be directly relevant to the results of Siemens and colleagues. Given this potential, and many other similar opportunities, there is little doubt that artificial selection will continue playing a major role in addressing eco- and evolutionary plant physiology questions, particularly questions about phenotypically plastic traits.
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ACKNOWLEDGMENTS |
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I thank Ted Garland and John Swallow for their interest in botanical points of view. Diane Byers, Nile Kurashige, Sam Scheiner, and an anonymous reviewer provided constructive comments on earlier drafts. A Barnard College Special Assistant Professor Leave provided teaching relief and NSF IBN 97-97552 and IBN 03-44518 have supported my research activities.
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FOOTNOTES |
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1 From the Symposium Selection Experiments as a Tool in Evolutionary and Comparative Physiology: Insights into Complex Traits presented at the Annual Meeting of the Society for Integrative and Comparative Biology, 5–9 January 2004, at New Orleans, Louisiana.
2 E-mail: hcallahan{at}barnard.edu
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Ackerly, D. D., S. A. Dudley, S. E. Sultan, J. Schmitt, J. S. Coleman, C. R. Linder, D. R. Sandquist, M. A. Geber, A. S. Evans, T. E. Dawson, and M. J. Lechowicz. 2000. The evolution of plant ecophysiological traits: Recent advances and future directions. Bioscience, 50:979-995.[CrossRef][Web of Science]
Ackerly, D. D., and R. K. Monson. 2003. Waking the sleeping giant: The evolutionary foundations of plant function. Internat. J. Plant Sci, 164:S1-S6.[CrossRef]
Agrawal, A. A. 1998. Induced responses to herbivory and increased plant performance. Science, 279:1201-1202.
Agrawal, A. A. 2001. Phenotypic plasticity in the interactions and evolution of species. Science, 294:321-326.
Agrawal, A. A. 2003. Community genetics: New insights into community ecology by integrating population genetics. Ecology, 84:543-544.
Agrawal, A. A., and N. S. Kurashige. 2003. A role for isothiocyanates in plant resistance against the specialist herbivore Pieris rapae. Journal of Chemical Ecology, 29:1403-1415.[CrossRef][Web of Science][Medline]
Barrett, S. C. H. 2002. The evolution of plant sexual diversity. Nature Reviews Genetics, 3:274-284.[CrossRef][Web of Science][Medline]
Bell, G., and M. J. Lechowicz. 1994. Spatial heterogeneity at small scales and how plants respond to it. In M. M. Caldwell and R. W. Pearcy (eds.), Exploitation of environmental heterogeneity by plants, pp. 391–414. Academic Press, San Diego.
Bell, G. A. 1997. Experimental evolution in Chlamydomonas. I. Short-term selection in uniform and diverse environments. Heredity, 78:490-497.[CrossRef]
Berenbaum, M. R., A. R. Zangerl, and J. K. Nitao. 1986. Constraints on chemical coevolution—wild parsnips and the parsnip webworm. Evolution, 40:1215-1228.[CrossRef][Web of Science]
Bergelson, J., and C. B. Purrington. 1996. Surveying patterns in the cost of resistance in plants. American Naturalist, 148:536-558.[CrossRef][Web of Science]
Borevitz, J. O., J. N. Maloof, J. Lutes, T. Dabi, J. L. Redfern, G. T. Trainer, J. D. Werner, T. Asami, C. C. Berry, D. Weigel, and J. Chory. 2002. Quantitative trait loci controlling light and hormone response in two accessions of Arabidopsis thaliana. Genetics, 160:683-696.
Botto, J. F., and H. Smith. 2002. Differential genetic variation in adaptive strategies to a common environmental signal in Arabidopsis accessions: Phytochrome-mediated shade avoidance. Plant Cell and Environment, 25:53-63.[CrossRef]
Bradshaw, A. D. 1965. Evolutionary significance of phenotypic plasticity in plants. Advances in Genetics, 13:115-155.[Medline]
Bradshaw, A. D., and T. McNeilly. 1991. Evolutionary response to global climatic change. Annals of Botany, 67:5-14.
Brakefield, P. M. 2003. Artificial selection and the development of ecologically relevant phenotypes. Ecology, 84:1661-1671.
Brumpton, R. J., H. Boughey, and J. L. Jinks. 1977. Joint selection for both extremes of mean performance and of sensitivity to a macroenvironmental variable 1. Family selection. Heredity, 38:219-226.
Callahan, H. S., and M. Pigliucci. 2002. Shade-induced plasticity and its ecological significance in wild populations of Arabidopsis thaliana. Ecology, 83:1965-1980.[CrossRef][Web of Science]
Callahan, H. S., and M. Pigliucci. 2005. Indirect consequences of artificial selection on plasticity to light quality in Arabidopsis thaliana. J. Evol. Biol.
Callahan, H. S., M. Pigliucci, and C. D. Schlichting. 1997. Developmental phenotypic plasticity: Where ecology and evolution meet molecular biology. Bioessays, 19:519-525.[CrossRef][Web of Science][Medline]
Camara, M. D., C. A. Ancell, and M. Pigliucci. 2000. Induced mutations: A novel tool to study phenotypic integration and evolutionary constraints in Arabidopsis thaliana. Evol. Ecol. Res, 2:1009-1029.
Chapin, F. S., K. Autumn, and F. Pugnaire. 1993. Evolution of suites of traits in response to environmental stress. Am. Nat, 142:S78-S92.[CrossRef][Web of Science]
Chew, F. S. 1988. Searching for defensive chemistry in the Cruciferae, or, do glucosinolates always control interactions of Cruciferae with their potential herbivores and symbionts? In K. S. Spencer (ed.), Chemical mediation of coevolution, pp. 81–112. Academic Press, New York.
Conner, J. K. 2003. Artificial selection: A powerful tool for ecologists. Ecology, 84:1650-1660.
Delgado, E., J. Azconbieto, X. Aranda, J. Palazon, and H. Medrano. 1992a. Leaf photosynthesis and respiration of high CO2-grown tobacco plants selected for survival under CO2 compensation point conditions. Plant Physiol, 98:949-954.
Delgado, E., M. A. J. Parry, D. W. Lawlor, A. J. Keys, and H. Medrano. 1993. Photosynthesis, ribulose-1,5-bisphosphate carboxylase and leaf characteristics of Nicotiana tabacum L. genotypes selected by survival at low CO2 concentrations. J. Exper. Bot, 44:1-7.
Delgado, E., M. A. J. Parry, J. Vadell, D. W. Lawlor, A. J. Keys, and H. Medrano. 1992b. Effect of water stress on photosynthesis, leaf characteristics and productivity of field-grown Nicotiana tabacum L. genotypes selected for survival at low CO2. J. Exper. Bot, 43:1001-1008.
Delgado, E., J. Vadell, and H. Medrano. 1994. Photosynthesis during leaf ontogeny in field-grown Nicotiana tabacum L. lines selected by survival at low CO2 concentrations. J. Exper. Bot, 45:547-552.
Donohue, K., L. Dorn, C. Griffith, E. S. Kim, A. Aguilera, C. R. Polisetty, and J. Schmitt. 2005a. Environmental and genetic influences on the germination of Arabidopsis thaliana in the field. Evolution, 59:740-757.[CrossRef][Web of Science][Medline]
Donohue, K., L. Dorn, C. Griffith, E. S. Kim, A. Aguilera, C. R. Polisetty, and J. Schmitt. 2005b. Niche construction through germination cueing: Life history responses to timing of germination in Arabidopsis thaliana. Evolution, 59:771-785.[CrossRef][Web of Science][Medline]
Donohue, K., D. Messiqua, E. H. Pyle, M. S. Heschel, and J. Schmitt. 2000. Evidence of adaptive divergence in plasticity: Density- and site-dependent selection on shade-avoidance responses in Impatiens capensis. Evolution, 54:1956-1968.[CrossRef][Web of Science][Medline]
Dudley, J. W. 1977. 76 Generations of selection for oil and protein percentage in maize. International Conference on Quantitative Genetics, Iowa State University Press.
Falconer, D. S. 1990. Selection in different environments: Effects on environmental sensitivity (reaction norm) and on mean performance. Genetical Research, 56:57-70.
Falconer, D. S., and T. F. C. Mackay. 1996. Introduction to quantitative genetics. Longman, Essex.
Fry, J. D. 2003. Detecting ecological trade-offs using selection experiments. Ecology, 84:1672-1678.
Fuller, R. C., C. F. Baer, and J. Travis. 2005. How and when selection experiments might actually be useful. Integr. Comp. Biol, 45:391-404.
Geber, M. A., and T. E. Dawson. 1993. Evolutionary responses to global change. In P. M. Kareiva, J. G. Kingsolver, and R. B. Huey (eds.), Biotic interactions and global change, pp. 165– 178. Sinauer Associates, Sunderland, Massachusetts.
Geber, M. A., and L. R. Griffen. 2003. Inheritance and natural selection on functional traits. Internat. J. Plant Sci, 164:S21-S42.[CrossRef]
Grime, J. P. 1977. Evidence for the existence of three primary strategies in plants and its relevance to ecological and evolutionary theory. Am. Nat, 111:1169-1194.[CrossRef][Web of Science]
Grime, J. P. 1979. Plant strategies and vegetation processes. John Wiley, Chichester, U.K.
Karban, R., and I. T. Baldwin. 1997. Induced responses to herbivory. University of Chicago Press, Chicago.
Kelly, J. K. 1999. Response to selection in partially self-fertilizing populations. I. Selection on a single trait. Evolution, 53:336-349.[CrossRef]
Kevei, E., and F. Nagy. 2003. Phytochrome controlled signalling cascades in higher plants. Physiologia Plantarum, 117:305-313.[CrossRef][Medline]
Kikuchi, S.et al. 2003. Collection, mapping, and annotation of over 28,000 cDNA clones from japonica rice. Science, 301:376-379.
Klejnot, J., and C. T. Lin. 2004. A CONSTANS experience brought to light. Science, 303:965-966.
Kliebenstein, D., D. Pedersen, B. Barker, and T. Mitchell-Olds. 2002. Comparative analysis of quantitative trait loci controlling glucosinolates, myrosinase and insect resistance in Arabidopsis thaliana. Genetics, 161:325-332.
Kolodynska, A., and M. Pigliucci. 2003. Multivariate responses to flooding in Arabidopsis: An experimental evolutionary investigation. Funct. Ecol, 17:131-140.[CrossRef]
Lande, R., and S. J. Arnold. 1983. The measurement of selection on correlated characters. Evolution, 37:1210-1226.[CrossRef][Web of Science]
Lortie, C., and L. W. Aarssen. 1996. The specialization hypothesis for phenotypic plasticity in plants. Internat. J. Plant Sci, 157:484-487.[CrossRef]
Lynch, M., and B. Walsh. 1998. Genetics and analysis of quantitative traits. Sinauer Associates Inc., Sunderland, Massachusetts.
Marak, H. B., A. Biere, and J. M. M. Van Damme. 2000. Direct and correlated responses to selection on iridoid glycosides in Plantago lanceolata L. J. Evol. Biol, 13:985-996.[CrossRef]
Mauricio, R., and M. D. Rausher. 1997. Experimental manipulation of putative selective agents provides evidence for the role of natural enemies in the evolution of plant defenses. Evolution, 51:1435-1444.[CrossRef]
Medrano, H., A. J. Keys, D. W. Lawlor, M. A. J. Parry, J. Azconbieto, and E. Delgado. 1995. Improving plant production by selection for survival at low CO2 concentrations. J. Exper. Bot, 46:1389-1396.
Medrano, H., and E. Primomillo. 1985. Selection of Nicotiana tabacum haploids of high photosynthetic efficiency. Plant Physiol, 79:505-508.
Mitchell-Olds, T. 1996. Genetic constraints on life-history evolution: Quantitative-trait loci influencing growth and flowering in Arabidopsis thaliana. Evolution, 50:140-145.[CrossRef][Web of Science]
Mitchell-Olds, T., and J. Bergelson. 2000. Biotic interactions—Genomics and coevolution—Editorial overview. Curr. Opin. Plant Biol, 3:273-277.[CrossRef][Web of Science][Medline]
Mockler, T., H. Yang, X. Yu, D. Parikh, Y.-C. Cheng, S. Dolan, and C. Lin. 2003. Regulation of photoperiodic flowering by Arabidopsis photoreceptors. Proc. Nat. Acad. Sci, 100:2140-2145.
Mousseau, T. A., and C. W. Fox.(eds.) 1998. Maternal effects as adaptations. Oxford University Press, New York.
Osborn, T. C., C. Kole, I. A. P. Parkin, A. G. Sharpe, M. Kuiper, D. J. Lydiate, and M. Trick. 1997. Comparison of flowering time genes in Brassica rapa, B. napus and Arabidopsis thaliana. Genetics, 146:1123-1129.[Abstract]
Pigliucci, M. 1996. How organisms respond to environmental changes: From phenotypes to molecules (and vice versa). Trends Ecol. Evol, 11:168-173.[CrossRef]
Pigliucci, M. 2001. Phenotypic plasticity; Beyond nature and nurture. The Johns Hopkins University Press, Baltimore.
Pigliucci, M. 2003. Selection in a model system: Ecological genetics of flowering time in Arabidopsis thaliana. Ecology, 84:1700-1712.[CrossRef][Web of Science]
Potvin, C., and D. Tousignant. 1996. Evolutionary consequences of simulated global change: Genetic adaptation or adaptive phenotypic plasticity. Oecologia, 108:683-693.[CrossRef][Web of Science]
Rausher, M. D. 1992. The measurement of selection on quantitative traits: Biases due to environmental covariances between traits an fitness. Evolution, 46:616-626.[CrossRef]
Sage, R. F., and J. R. Coleman. 2001. Effects of low atmospheric CO2 on plants: More than a thing of the past. Trends Plant Sci, 6:18-24.[CrossRef][Web of Science][Medline]
Scheiner, S. M. 1993. Genetics and evolution of phenotypic plasticity. Ann. Rev. Ecol. System, 24:35-68.[CrossRef][Web of Science]
Scheiner, S. M. 2002. Selection experiments and the study of phenotypic plasticity. J. Evol. Biol, 15:889-898.[CrossRef][Web of Science]
Schlichting, C. D., and H. Smith. 2002. Phenotypic plasticity: Linking molecular mechanisms with evolutionary outcomes. Evol. Ecol, 16:189-211.[CrossRef]
Schmitt, J., and D. W. Ehrhardt. 1990. Enhancement of inbreeding depression by dominance and suppression in Impatiens capensis. Evolution, 44:269-278.[CrossRef][Web of Science]
Schmitt, J., A. C. McCormac, and H. Smith. 1995. A test of the adaptive plasticity hypothesis using transgenic and mutant plants disabled in phytochrome-mediated elongation responses to neighbors. Am. Nat, 146:937-953.[CrossRef][Web of Science]
Schmitt, J., J. R. Stinchcombe, M. S. Heschel, and H. Huber. 2003. The adaptive evolution of plasticity: Phytochrome-mediated shade avoidance responses. Integr. Comp. Biol, 43:459-469.
Shaw, R. G., D. L. Byers, and E. Darmo. 2000. Spontaneous mutational effects of reproductive traits of Arabidopsis thaliana. Genetics, 155:369-378.
Siemens, D. H., S. H. Garner, T. Mitchell-Olds, and R. M. Callaway. 2002. Cost of defense in the context of plant competition: Brassica rapa may grow and defend. Ecology, 83:505-517.
Siemens, D. H., and T. Mitchell-Olds. 1998. Evolution of pest-induced defenses in Brassica plants: Tests of theory. Ecology, 79:632-646.[CrossRef]
Simms, E. L. 1992. Costs of plant resistance to herbivory. In R. S. Fritz and E. L. Simms (eds.), Plant resistance to herbivores and pathogens, pp. 392–425. University of Chicago Press, Chicago.
Smith, H. 2000. Phytochromes and light signal perception by plants—an emerging synthesis. Nature, 407:585-91.[CrossRef][Medline]
Stanton, M. L., B. A. Roy, and D. A. Thiede. 2000. Evolution in stressful environments. I. Phenotypic variability, phenotypic selection, and response to selection in five distinct environmental stresses. Evolution, 54:93-111.[CrossRef][Web of Science][Medline]
Stanton, M. L., D. A. Thiede, and B. A. Roy. 2004. Consequences of intraspecific competition and environmental variation for selection in the mustard Sinapsis arvensis: Contrasting ecological and evolutionary perspectives. Am. Nat, 164:736-752.[CrossRef]
Stenoien, H. K., C. B. Fenster, H. Juittinen, and O. Savolainen. 2002. Quantifying latitudinal clines to light responses in natural populations of Arabidopsis thaliana (Brassicaceae). Am. J. Bot, 89:1604-1608.
Strauss, S. Y., D. H. Siemens, M. B. Decher, and T. Mitchell-Olds. 1999. Ecological costs of plant resistance to herbivores in the currency of pollination. Evolution, 53:1105-1113.[CrossRef]
Taylor, D. R., and L. W. Aarssen. 1988. An interpretation of phenotypic plasticity in Agropyron repens (Graminae). Am. J. Bot, 75:401-413.[CrossRef]
Ungerer, M. C., C. R. Linder, and L. H. Rieseberg. 2003. Effects of genetic background on response to selection in experimental populations of Arabidopsis thaliana. Genetics, 163:277-286.
Ungerer, M. C., and L. H. Rieseberg. 2003. Genetic architecture of a selection response in Arabidopsis thaliana. Evolution, 57:2531-2539.[CrossRef][Web of Science][Medline]
Valverde, F., A. Mouradov, W. Soppe, D. Ravenscroft, A. Samach, and G. Coupland. 2004. Photoreceptor regulation of CONSTANS protein in photoperiodic flowering. Science, 303:1003-1006.
Van Kleunen, M., and M. Fischer. 2003. Effects of four generations of density-dependent selection on life history traits and their plasticity in a clonally propagated plant. J. Evol. Biol, 16:474-484.[CrossRef][Web of Science][Medline]
Van Tienderen, P., and A. Van Hinsberg. 1996. Phenotypic plasticity in growth habit in Plantago lanceolata: How tight is a suite of correlated characters. Plant Species Biol, 11:87-96.[CrossRef]
Via, S., R. Gomulkiewicz, G. D. Jong, S. M. Scheiner, C. D. Schlichting, and P. H. V. Tienderen. 1995. Adaptive phenotypic plasticity: Consensus and controversy. Trends Ecol. Evol, 10:212-216.[CrossRef]
Via, S., and R. Lande. 1985. Genotype-environment interaction and the evolution of phenotypic plasticity. Evolution, 39:505-522.[CrossRef][Web of Science]
Ward, J. K., J. Antonovics, and R. B. Thomas. 2000. Is atmospheric CO2 a selective agent on model C3 annuals? Oecologia, 123:330-341.[CrossRef]
Westoby, M., D. S. Falster, A. T. Moles, and I. J. Wright. 2002. Plant ecological strategies: Some leading dimensions of variation between species. Ann. Rev. Ecol. System, 33:125-159.[CrossRef][Web of Science]
Wijngaarden, P. J., and P. M. Brakefield. 2001. Lack of response to artificial selection on the slope of reaction norms for seasonal polyphenism in the butterfly Bicyclus anynana. Heredity, 87:410-420.[CrossRef][Web of Science][Medline]
Wolfe, L. M. 1993. Inbreeding depression in Hydrophyllum appendiculatum—role of maternal effects, crowding, and parental mating history. Evolution, 47:374-386.[CrossRef][Web of Science]
Zangerl, A. R., and M. R. Berenbaum. 1990. Furanocoumarin induction in wild parsnip—Genetics and populational variation. Ecology, 71:1933-1940.[CrossRef]
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