HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Abstract Full Text (PDF) Submit a response Alert me when this article is cited Alert me when eLetters are posted Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in ISI Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via ISI Web of Science (4) Citing Articles via Google Scholar Google Scholar Articles by Jenkins Klus, D. Articles by Tonsor, S. J. Search for Related Content PubMed PubMed Citation Articles by Jenkins Klus, D. Articles by Tonsor, S. J. Agricola Articles by Jenkins Klus, D. Articles by Tonsor, S. J.

Family- and population-level responses to atmospheric CO₂ concentration: gas exchange and the allocation of C, N, and biomass in Plantago lanceolata (Plantaginaceae)¹

²Michigan State University, W.K. Kellogg Biological Station, 3700 E. Gull Lake Drive, Hickory Corners, Michigan 49060 USA ³The Ohio State University, Department of Plant Biology, Columbus Ohio 43210 USA ⁴The University of Michigan Biological Station, Pellston, Michigan 49769 USA

Received for publication May 4, 2000. Accepted for publication August 15, 2000.

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
To ascertain the inheritance of responses to changing atmospheric CO₂ content, we partitioned response to elevated CO₂ in Plantago lanceolata between families and populations in 18 families in two populations. Plants were grown in 35 Pa and 71 Pa partial pressure of CO₂ (pCO₂) in open-top chambers. We measured above- and belowground mass, carbon (C), nitrogen (N), hexose sugar, and gas exchange properties in both CO₂ treatments. Families within populations differed in mass, mass allocation, root : shoot ratios, aboveground percentage N, C : N ratio, and gas exchange properties. The CO₂ x family interaction is the main indicator of potential evolutionary responses to changing CO₂. Significant CO₂ x family interactions were observed for N content, C : N ratio, and photosynthetic rate (A: instantaneous light-saturated carbon assimilation capacity), intercellular CO₂ concentration, transpiration rate (E), and water use efficiency (WUE = A/E), but not for stomatal conductance. Families differed significantly in acclimation across time. The ratio of A in elevated vs. ambient growth CO₂, when measured at a common internal CO₂ partial pressure was 0.79, indicating down-regulation of A under CO₂ enrichment. Mass, C : N ratio, percentage, C (%C), and soluble sugar all increased significantly but overall %N did not change. Increases in %C and sugar were significant and were coincident with redistribution of N aboveground. The observed variation among populations and families in response to CO₂ is evidence of genetic variation in response and therefore of the potential for novel evolutionary trajectories with rising atmospheric CO₂.

Key Words: biomass allocation • elevated CO₂ • gas exchange • genetic variation • nitrogen assimilation • photosynthesis • Plantago lanceolata • Plantaginaceae

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
Many studies have shown that whole-plant mass in C₃ species typically increases in response to elevated CO₂ (reviewed by Poorter, 1993 ; Curtis and Wang, 1998 ). However, as the variety of species studied and the length of experiments have increased, considerable interspecific variation in the magnitude and duration of this "typical" response to elevated atmospheric CO₂ has been documented (Bazzaz, Coleman, and Morse, 1990 ; Garbutt, Williams, and Bazzaz, 1990 ; Hunt et al., 1995 ). Substantial interspecific variation has also been observed in the physiological responses to elevated CO₂, in changes in nitrogen acquisition, and in the partitioning of overall mass, carbon, and nitrogen among plant parts and functions (Sage, 1994 ; Curtis and Wang, 1998 ).

While interspecific differences in responses to elevated CO₂ can have important implications for ecological interactions, they also indicate that genetic variation in responses exists, at least at a macroevolutionary level. Intraspecific differences arise and are translated into interspecific variation through the actions of drift and divergent selection. The extent of intraspecific variation indicates, at least in a qualitative way, the potential for rapid adaptation to elevated CO₂. Thus while extant interspecific differences in response to elevated CO₂ demonstrate that evolution in CO₂ responses can occur on a geologic time scale, the extent of intraspecific variation indicates the likelihood of near-term genetic adaptation to shifting atmospheric CO₂ composition on something closer to ecological time scales. Yet the presence and nature of that intraspecific variation is largely unexplored.

To understand the potential evolutionary consequences of increasing atmospheric CO₂, we need to examine the extent of genetic variation in relevant traits both at the level of populations and at that of families. In some species, genetic variation is distributed mainly among rather than within populations, largely as a function of dispersal and mating system effects (Loveless and Hamrick, 1984 ). When genetic variation is distributed mainly among populations, and the within-population heritabilities are low, the movement of genes between populations can be as important a source of genetic variance as mutation and recombination, especially in small populations (Weber, 1990, 1992 ). Variation at the family level within populations in contrast is the classic quantitative genetic measure of genetic variation, among-sibling variance being the basis of commonly used heritability estimates (Hallauer and Miranda, 1981 ). It is therefore important to assess genetic variation both within and among populations.

In the small number of studies that have examined intraspecific variation in the context of elevated CO₂ atmospheres, both growth (Wulff and Alexander, 1985 ; Poorter, Pot, and Lambers, 1988 ; Curtis et al., 1996 ) and reproductive output (Cure and Acock, 1986 ; Curtis, Snow, and Miller, 1994 ; Curtis et al., 1994 ) exhibit genetic variation. For example, Curtis, Snow, and Miller (1994) showed genotype-specific effects of elevated CO₂ on fecundity, and Curtis et al. (1996) demonstrated that both Raphanus raphanistrum and Plantago lanceolata varied physiologically among sibships in 71 Pa CO₂. However, none of these studies determined if families or genotypes respond differently when grown in a high CO₂ environment than they do when grown in a conventional CO₂ environment. This is important for understanding the evolutionary implications of the observed variation. If families or genotypes respond similarly, i.e., maintain the same relative ranks across environments, then directional selection will favor the same genotypes in both environments. Thus any resultant evolution is not CO₂-environment specific, but is a general evolutionary response to characteristics that are common to both CO₂ environments.

To demonstrate inherited differences in response to elevated CO₂, one must show a change in the rank order among genotypes across CO₂ environments. When the phenotypic ranks of a set of genotypes (or families, or populations) change across environments, then selection for high phenotypic value will select on different genotypes in the two environments. Thus any response to selection will be through increase in the frequencies of different genotypes in the two environments.

In this study we tested for genetic variation in the response to elevated CO₂ in the short-lived perennial herb Plantago lanceolata, using maternal families from two natural populations. Plantago lanceolata has been used extensively in physiological, ecological, and genetic studies (Teramura and Strain, 1979 ; Tonsor, 1985, 1989 ; Kuiper and Bos, 1992 ; Tonsor and Goodnight, 1997 ). Populations of P. lanceolata grow in a variety of habitats and have been found to exhibit both local adaptation (Teramura and Strain, 1979 ; Teramura, 1983 ; Tonsor, 1985, 1990 ) and phenotypic plasticity (Teramura and Strain, 1979 ; Antonovics and Primack, 1982 ; van Tienderen, 1990 ). The species is known to be genetically variable for a broad variety of physiological, morphological, and life history traits (Teramura and Strain, 1979 ; Teramura, 1983 ; Wolff and Van Delden, 1987 ; Tonsor and Goodnight, 1997 ) and is capable of undergoing rapid evolutionary change (Wu and Antonovics, 1976 ; Wolff and Van Delden, 1989 ). Studies of P. lanceolata grown in elevated CO₂ have revealed genetic variation in allelochemical content (Fajer, Bowers, and Bazzaz, 1992 ) and early growth parameters (Wulff and Alexander, 1985 ). However, these studies did not address genetic differences in genotype or family ranks between CO₂ environments.

The goal of the study was to determine the extent to which there is a change in rank using the genotype x CO₂ interaction in mass gain and in a number of morphological and physiological traits that are known to change in mean value in response to elevated CO₂. We measured a hierarchy of traits including total plant mass, mass allocation, tissue sugar, carbon (C) and nitrogen (N) content. Changes in mass allocation patterns provide a measure of the extent to which plants alter their investment in roots or shoots under CO₂ enrichment. Changes in sugar content can indicate the extent to which a plant is able to down-regulate its carbon assimilation machinery sufficiently to keep N and C in balance in the plant tissue (Sage, Sharkey, and Seemann, 1988 ). Since N is often the most limiting resource in terrestrial ecosystems, shifts in tissue-specific nitrogen content can be a critical factor in optimizing N economy and fitness in an elevated CO₂ environment.

This study measured the extent to which variation in responses to an elevated CO₂ environment can be partitioned among families and populations. Inherited variation is the basis for adaptive evolutionary responses to selection, and this study establishes whether inherited variation exists in traits known to exhibit plastic responses atmospheric carbon content. However, predicting the extent of a future adaptive responses also requires careful field estimation of selection on these traits, which was beyond the scope of this study.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
We studied two populations of P. lanceolata. The Ely Lake (EL) population (Allegan County, Michigan, USA) grows on exposed sandy soil on a sunny lakeshore, experiencing high irradiance at midday (and midday water stress), but partial shade from the surrounding oak–pine forest early and late in the day. The Kellogg Field (KF) population (Kalamazoo County, Michigan, USA) grows in a sandy loam in the partial shade, intermixed in a canopy of tall herbaceous vegetation, on the edge of a mown field. Seeds from 25–30 maternal plants (families) in each population were collected in the fall of 1991. Ten families from KF and eight families from EL were selected randomly for use in the experiment.

In early June 1992 all of the seeds from each of the eighteen families were planted in flats and placed in an open-top chamber at the CO₂ level in which they were to be grown (either ambient or twice-ambient CO₂). After germination, seedlings were transplanted into 10 cm diameter x 30 cm high pots with mesh screen bottoms, one seedling per pot. Pots were filled with 2.45 L of 50 : 50 mixture of native field soil (Kalamazoo loam) and sand. Low seed numbers for some families resulted in an average within-family sample size of 8.5 seedlings. All seedlings were assigned randomly to one of eight 0.5-m³ outdoor open-top chambers (Curtis and Teeri, 1992 ). The chambers were placed adjacent to the Terrestrial Field Laboratory at the Kellogg Biological Station (Kalamazoo County, Michigan, USA). The plants were watered as needed, usually twice daily. There was no fertilizer supplementation.

Pure CO₂ was dispensed via manual flow meters into input blowers and then into the four elevated CO₂ chambers to increase CO₂ concentration. The four ambient chambers received no additional CO₂. Chamber CO₂ levels were monitored continuously with an infra-red gas analyzer (LI-6251, LI-COR, Lincoln, Nebraska, USA) (see Curtis and Teeri, 1992 ). Quantum sensors and thermocouples within each chamber were used to record irradiance levels and temperature. During daytime (0700–1900) CO₂ was 72.1 ± 5.8 Pa (mean ± 1 SD) inside the elevated chambers, and 36.4 ± 2.6 Pa inside the ambient chambers. Elevated chamber CO₂ was allowed to follow diurnal fluctuations, so that both nightime and daytime CO₂ concentrations were appropriately elevated. Daytime temperatures were 1.70 ± 0.6°C higher inside the chambers than outside, with no significant difference in temperature between ambient and elevated chambers. After 3 wk of growth, plants exhibited symptoms of light stress (prostrate growth and red pigmentation of the leaf bases) and all chambers were covered with shade cloth. The shade cloth reduced incident light 68%, and plants recovered their normal phenotype. Since both populations experienced partial shade in their native habitats (although with different variances), this seemed an appropriate amendment to the treatment.

Instantaneous carbon assimilation capacity (A), transpiration (E), stomatal conductance (g_s), and leaf internal CO₂ concentration (C_i) measurements were made twice on all plants, from 9 to 13 August and from 4 to 10 September 1992. Each plant was measured at both CO₂ growth treatment concentrations at saturating light levels (1300 µmol·m^–2·s^–1 photosynthetically active radiation [PAR]). Measurements were made on the youngest fully expanded leaf. We used two infra-red gas analyzers (Model LCA3, Analytical Development Corp., Hoddeson, UK) for these measurements. The CO₂ source (35 Pa or 71 Pa CO₂) was rotated to a different instrument each day to allow us to estimate instrument effects.

Plants were harvested after 127 d growth, above- and belowground parts separated, dried at 60°C, and weighed. No plants flowered during this experiment. To determine the extent to which tissue quality was affected by the CO₂ treatments, three families were arbitrarily selected for biochemical analysis. The available funds for CHN analysis limited the total sample size for this analysis to N = 36: (6 plants per family) x (3 families) x (2 CO₂ treatments). Shoot and root tissue of each plant was analyzed separately for percentage C (%C) and percentage N (%N) with a CHN analyzer (Carlo Erba, Paramus, New Jersey, USA). Soluble carbohydrate concentration was determined enzymatically (see Hurry et al. 1995 ). Tissue was ground and the supernatent was centrifuged prior to being divided into two samples. In the first sample, hexokinase was added to convert glucose to glucose-6-phosphate (G-6-P). Phosphoglucose isomerase was then used to drive the conversion of fructose-6-phosphate to G-6-P. Glucose-6-Phosphate was oxidized to 6-phosphogluconate using glucose-6-phosphate dehydrogenase, and the concentration of the NADPH formed in the process was measured spectrophotometrically at A₃₄₀.

Statistical analysis
In evolutionary genetic studies, changes in genotypic rank across environments are typically assessed through tests for heterogeneity of slopes in a regression of genotypic responses across environments or by a test for an interaction between genotype and environment in an analysis of variance (ANOVA) or multivariate analysis of variance (MANOVA). We used ANOVAs and MANOVAs, because our environmental treatments consisted of two discrete CO₂ concentrations.

Physiological variables
The effects of block, instrument, and date of measurement were removed from the four gas exchange variables by subtracting their respective deviations from the grand mean summed over block, instrument, and measurement dates (Tonsor and Goodnight, 1997 ). Because the four gas exchange variables were expected to be correlated with each other (Tonsor and Goodnight, 1997 ), they were initially analyzed using a multivariate analysis of variance (MANOVA), to determine overall, main, and interaction effects. The MANOVA was followed by univariate mixed model ANOVAs for the four primary physiological variables plus water use efficiency (WUE), defined as A/E. The overall mixed models for the analysis of the physiological variables included four fixed effects: the CO₂ treatment at which plants were grown (growth CO₂), month of measurement, the CO₂ concentration at which gas exchange was measured (measurement CO₂), and population of origin. The models also included one random effect (family nested within population) and two-way interactions for all main effects. Interaction terms containing the random effect were also treated as random effects.

Significance levels in the multivariate analysis were interpreted using Roy's Greatest Root because of its statistical power and suitability for post hoc statistical comparisons (Scheiner, 1993 ). Sample size for each family in each CO₂ treatment ranged from 3 to 6 individuals. All variables in the analysis were normally distributed except E, which was log₁₀ transformed, and g_s, which was square-root transformed.

Since each plant was measured at two CO₂ levels in each of 2 mo, we performed two separate repeated-measures analyses: one to test the response of the physiological variables to measurement CO₂ and another to test how these responses differed between August and September. The physiological variables were initially tested together in multivariate repeated measures ANOVAs and then were considered separately for their contributions to the multivariate results. The model was identical to the one used in the MANOVA described above.

Mass and tissue biochemistry
Mass data from the main experiment and tissue biochemistry data from the subset of three families were statistically analyzed using ANOVA. The model for the analysis of the main experiment was: y_ijkl = y..._i + ß_j + _k + _l(k) + ß_jk + ß_jk + _ijkl, where y_ijkl is them measurement for the ijklth individual, y... = grand mean, _i = mean effect of block i, ß_j = mean effect of CO₂ level j, _k = the mean effect of population k, _l = mean effect of the lth family within population k, and the paired, doubly subscripted terms are the two-way interactions. The three-way interactions and the block interactions were never significant, and so they were omitted from the final analyses. The SAS MODEL statement read: variable = BLOCK CO₂ POPULATION FAMILY(POP) (CO₂ x POPULATION) (CO₂ x FAMILY) (SAS, 1989 ). Target sample size for each family in each CO₂ environment was n = 6, although this target was not always met, due to a paucity of seeds. Of the 24 families used in the main experiment, six families were excluded from analysis because two or fewer individuals were present in one or both CO₂ growth environments.

The model for the analysis of tissue content in the three families was: y_ij = y..._i + ß_j + ß_ij + _ij, where y_ij is them measurement for the ijth individual, y .. = grand mean, _i = mean effect of CO₂ treatment i, ß_j = mean effect of family j, and ß_ij = the CO₂ x family interactions. In SAS language (SAS, 1989 ), the model statement was: variable = CO₂ FAMILY (CO₂ x FAMILY). Since only one family from the EL population was included, a population effect could not be included. All variables were normally distributed except root : shoot ratio. A log transformation of root : shoot ratio to achieve normality did not change significance levels and the untransformed data were used in the overall ANOVA.

Since a comparison of family means was intended initially, all main effects and interactions in the model were considered to be fixed (Gill, 1978 ). A statistically significant POPULATION or FAMILY mean square demonstrates that these populations or families differ genetically from each other. A statistically significant interaction term ((POPULATION x CO₂) or (FAMILY x CO₂)) indicates that the populations or families respond differentially to the CO₂ growth environment.

	RESULTS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
Physiology
Population-level effects
The MANOVA showed that populations were significantly different overall (Table 1), but in the ANOVAs, no single physiological variable showed significant variation between the two populations (Table 2). There was also a significant overall population by growth CO₂ interaction detected in the MANOVA, although none of the individual physiological factors had significant population by growth CO₂ interactions in the ANOVAs.

View larger version (33K): [in this window] [in a new window]   Fig. 1. Norms of reaction for carbon assimilation rates across 18 families measured in September. Open symbols are KF population; closed symbols are EL population

Mass allocation
Population- and family-level effects
There were significant differences between populations for all mass variables except root : shoot ratio (Table 4). Population KF always had significantly larger mass measures than population EL, and there were no significant growth CO₂ x population interaction terms (Table 4). There were significant effects of family in all four mass variables (Table 4), but no growth CO₂ x family interactions were detected for the mass variables.

View this table: [in this window] [in a new window]   Table 7. Mean (SE) from the analysis of tissue biochemistry. Soluble sugar = soluble sugar concentration. CO2 = growth, CO2 partial pressure. Significance levels for means compared across growth CO2 environments: P < 0.10, * P < 0.05, ** P < 0.01, *** P < 0.001

	DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
The organization of inherited variation is a critical factor in determining the tempo and mode of evolution. The partitioning of variation among families and populations plays a critical role in determining the magnitudes of responses to selection. The environmental dependency of genetic effects on the phenotype is also critical, although much less well understood. To a large extent, genotype–environment interactions have been treated by quantitative geneticists as noise that obscures the predictive ability of standard quantitative genetic approaches to predicting responses to selection (Roff, 1997 ). In the context of changing environmental conditions, understanding genotype–environment interactions is critical to understanding the probable evolutionary responses to the new environmental regime. When genotypes produce qualitatively different phenotypes in new environments (e.g., shifted genotypic ranks), simply measuring variation in the conventional environment may not adequately describe the nature of the inherited variation which natural selection will "see" in the new environment. In this study, we were therefore interested in both the structuring of variation at the population and family levels and in the way in which that variation changed in its expression across CO₂ environments. In keeping with the conventional biometric approaches to the partioning of phenotypic variation across levels of population structure and environments, we first discuss the main effects of family- and population-level variation, then consider the population x CO₂ and family x CO₂ interactions, the true focus of this study.

Plantago lanceolata has been shown to exhibit variation among populations or among families or genotypes in a wide variety of traits. For example, Wu and Antonovics (1976) and Tonsor (1990) documented the existence of variation in lead tolerance along roadsides that received input from car exhaust when leaded gasoline was used for fuel. Wolff and Van Delden (1987, 1989) found significant broadsense heritabilities for 18 morphological and developmental traits (significant in some populations and not in others). Wulff and Alexander (1985) observed genetic variation in early life history stages in P. lanceolata. Tonsor and Goodnight (1997) detected narrowsense heritable variation in A, E, and a set of morphological traits.

Similarly, at either the population or the family level or both, this study detected variation in many of the traits measured (4 out of 10 at the population level, 9 of 18 at the family level). In some traits, particularly those associated with mass, the same population, and the same families within populations, had highest trait values regardless of the CO₂ concentration in the atmosphere of growth. Thus, population KF had greater aboveground mass, belowground mass, and total mass than population EL, and both populations responded to CO₂ enrichment by increasing mass. While population-level variation was not examined for tissue biochemistry, four of the eight biochemical traits evidenced significant family-level variation that was maintained across CO₂ environments. For physiological variables, the MANOVA indicated both population- and family-level variation, although no single physiological trait had significant variation at either the population or family level when examined in separate ANOVAs. It is not uncommon for a MANOVA to indicate overall significance for a suite of attributes with partially shared causes, such as the physiologically integrated traits measured here, without corresponding significance for the individual traits in univariate ANOVAs (Manly, 1986 ).

Thus, in about half of the traits examined, families within populations differ significantly in their average trait values, regardless of the CO₂ environment in which they are examined. There would therefore appear to be the potential for evolutionary change in the average values of these traits. This inference is further supported by the discovery of significant differences between populations in four of the ten traits for which population-level variation was examined; these two populations have evolved different mean trait values for mass and its partitioning as well as in their overall physiological properties as measured in the MANOVA.

The family ranks in mass and its partitioning did not change across CO₂ environments. The lack of family x CO₂ interaction provides no variation on which evolutionary responses to high-CO₂ environments could be based. Root : shoot ratio responses are highly dependent on pot size in elevated CO₂ (Sage, 1994 ) and should therefore be interpreted with caution.

However, this study found significant family x CO₂ interactions in belowground C : N ratio and belowground soluble sugar. Thus, there is potential for a within-population, between-family evolutionary response to CO₂ through selection on family variation in responses of %N and aboveground C : N ratio.

This study did not incorporate measures of fitness, which would have required a second season of growth. However, %N and C : N ratio are closely related to N-use efficiency. When C limitations are removed by increased atmospheric carbon content, it is quite likely that N will be the limiting factor in many ecosystems. In addition, N uptake and N-use efficiency may drive competitive interactions and therefore structure plant communities in many N-limited ecosystems (Tilman, 1993 ). Therefore, differences in N-use efficiency are likely to have fitness consequences, although this remains to be demonstrated in the context of elevated CO₂ in P. lanceolata.

Both population x CO₂ and family x CO₂ variation was detected in the MANOVA. In the univariate ANOVAs, only WUE showed a near-significant population x CO₂ interaction. At the family level, all traits except g_s showed significant CO₂ interactions. It is clear therefore that the necessary inherited variation exists for within-population, between-families evolutionary response to elevated CO₂.

Although this study made no attempt to assess the fitness functions associated with the measured traits, Tonsor and Goodnight (unpublished data) found that there was a strong positive effect of A on fitness in P. lanceolata (1 SD increase in A predicted 0.18 SD increase in reproductive dry mass), while E had a negative effect on fitness (1 SD increase in E predicted 0.11 SD decrease in reproductive dry mass), when measured in present-day atmospheres. Since plants were only grown for one season in the present study, they did not reproduce. However, if there are equally strong fitness effects in elevated CO₂ as in ambient CO₂ environments, we might expect the family x CO₂ interactions to lead to selection favoring differing genotypes in an elevated CO₂ world. This type of response has been found in other species. In Arabidopsis thaliana, Tonsor and Vandermeulen (1998) found that G x Es similar to those found in the present study do not affect vegetative mass, but have significant effects on reproductive mass. For this reason, although we have first-season mass data, we are hesitant to use mass as a surrogate for fitness.

The growth responses to elevated CO₂ we found are in accord with the general pattern of response often reported in other studies (e.g., Bazzaz, 1990 ; Stitt, 1991 ). Despite a near-doubling of A for much of the growing season, mass only increased by 15% overall. Despite the observed down-regulation of A, tissue sugar content remained 15% higher in 71-Pa-grown plants compared to those grown at 35 Pa. This is consistent with the idea that down-regulation is not always sufficient to maintain homeostasis in nonstructural carbohydrates (Sage, Sharkey, and Seemann, 1988 ; Sage, 1994 ; Cheng, Moore, and Seemann, 1998 ).

Although leaf %N often declines in elevated CO₂ growth environments (Bassirirad, 1997 ; Chu, Coleman, and Mooney, 1992 ), our plants maintained approximately constant whole-plant %N, and leaf %N content increased at 71 Pa. Therefore, C sink capacity was the likely cause of photosynthetic down-regulation. High hexose sugar content is known to be directly involved in down-regulation in some plants (Jang and Sheen, 1994 ; Jang et al., 1997 ).

Fajer, Bowers, and Bazzaz (1989) , and Fajer (1989 ; see also Fajer, Bowers, and Bazzaz, 1989, 1992 ) observed decreases in leaf %N in Plantago lanceolata grown at 70 Pa CO₂, in contrast to our observed increase at 71 Pa CO₂. Overall, the mass accumulation in Fajer et al. (1992 ; mass not presented in the 1989 papers) was remarkably similar to our results (e.g., mean mass in the elevated treatment 20.9 and 19.6, in Fajer and this study, respectively), eliminating the possibility of size-dependent differences (see Coleman, McConnaughay, and Bazzaz, 1993 ). Neither light regime nor fertilizer treatment appears to explain the difference, and a definitive cause for the differences in N response cannot be ascertained. The differences suggest that the relationship between growth CO₂ and N allocation may be very sensitive to growth conditions. The genotypes used in the two studies were of course different as well, and this may have also contributed to the observed differences in N uptake and partitioning.

Finally, the interaction term in an ANOVA or MANOVA may not adequately determine the presence of genetic variation that can lead to an adaptive response to elevated CO₂, although it remains the best assessment method available. Analysis of responses to elevated CO₂ family by family indicated that a few families showed responses significantly differing from the "typical" or average family responses (Klus, 1995 ). The families exhibiting extreme responses (either very large or in a direction opposite of expectation) were more prevalent in the elevated CO₂ environment. These rare families may provide more information about the possible evolutionary consequences of elevated atmospheric CO₂ than families that exhibit more "typical" responses. When exposed to a novel environment, the favored genotypes may be in low frequency initially. This can result in an insignificant G x E interaction in an analysis of variance, despite the fact that these rare genotypes can contribute to a response to selection when the population is placed in the new environment. This is illustrated by the rarity of metal-tolerant genotypes in normal pasture populations, yet rapid adaptation when metal is introduced to the environment (McNeilly, 1968 ; Tonsor, 1990 ). In the present study, it is possible that adaptation to elevated CO₂ could proceed even in those traits in which no family or population x CO₂ interaction was observed. Evolution would initially proceed very slowly in such traits, increasing in rate as the genes responsible for the rare interaction types increased in frequency in response to selection. This is because the favored genes contribute little to additive genetic variance when present in low frequency and increase in their contribution to the additive genetic variance and heritability as their frequency increases (see Falconer, 1981 ).

The extent to which ecological vs. evolutionary processes will determine the fate of individual species as atmospheric CO₂ rises remains unclear. This study demonstrates that significant genetic variation exists in traits that may have causal links to the ecological responses of the plant to changing carbon availability and suggests that a better understanding of within-species genetic responses is necessary before a complete picture of community and ecosystem responses can be obtained.

	FOOTNOTES

 
¹ The authors thank Jon Ervin, Pam Woodruff, Nina Consolatti, Chris Vogel, Sandy Halstead, Hal Collins, R. Kelman Wieder, Brenda Casper, Phillip Brautigam, and Kim Hollingshead for technical help and the graduate students at W.K. Kellogg Biological Station for discussions on this topic. DJK gives most special thanks to John and Nicholas Klus. Mark Vandermeulen and the Tonsor lab group at The University of Pittsburgh provided useful comments on the manuscript. This project was supported by a Grant-in-Aid of Research through Sigma Xi and an NSF Predoctoral Fellowship to DJK, an NSF Research Training Group at Kellogg Biological Station (CIR-9113598), NSF grant BSR 8906283 to SJT, a Michigan State All University Research Initiation Grant to SK, the University of Michigan Biological Station, and the University of Michigan Global Change Project.

⁵ Current address: A234 Langley Hall, The University of Pittsburgh, Department of Biological Sciences, Pittsburgh Pennsylvania 15260 USA.

⁶ Author for reprint requests (e-mail: tonsor{at}pitt.edu ).

	LITERATURE CITED

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
Analytical Development Corporation. 1992 LCA-3 Operations manual. Hoddesdon, UK

Antonovics J. andR. B. Primack 1982 Experimental ecological genetics in Plantago VI. The demography of seedling transplants in Plantago lanceolata.. Journal of Ecology 70: 55-75[CrossRef]

Bassirirad H. J. F. Reynolds R. A. Virginia andM. H. Brunelle 1997 Growth and Root NO₃^– and PO₄^3–: uptake capacity of three desert species in response to atmospheric CO₂ enrichment. Australian Journal of Plant Physiology 24: 353-358[ISI]

Bazzaz F. A. 1990 The response of natural ecosystems to the rising global CO₂ levels. Annual Review of Ecology and Systematics 21: 167-196[CrossRef][ISI]

———, J. S. Coleman andS. R. Morse 1990 Growth responses of seven major co-occurring tree species of the northeastern United States to elevated CO₂. Canadian Journal of Forest Research 20: 1479-1484[CrossRef]

Cheng S. H. B. D. Moore andJ. R. Seemann 1998 Effects of short- and long-term elevated CO₂ on the expression of ribulose-1,5-bisphosphate carboxylase/oxygenase genes and carbohydrate accumulation in leaves of Arabidopsis thaliana.. Plant Physiology 116: 715-723[Abstract/Free Full Text]

Chu C. C. J. S. Coleman andH. A. Mooney 1992 Controls of biomass partitioning between roots and shoots: atmospheric CO₂ enrichment and the acquisition and allocation of carbon and nitrogen in wild radish. Oecologia 89: 580-587[ISI]

Coleman J. R. K. D. M. McConnaughay andF. A. Bazzaz 1993 Elevated CO₂ and plant nitrogen use: is reduced tissue nitrogen concentration size-dependent?. Oecologia 93: 195-200[CrossRef][ISI]

Cure J. D. andB. Acock 1986 Crop responses to carbon dioxide doubling: a literature survey. Agriculture and Forest Meteorology 38: 127-145[CrossRef]

Curtis P. S. D. J. Klus S. Kalisz andS. J. Tonsor 1996 Intraspecific variation in CO₂ responses in Raphanus raphanistrum and Plantago lanceolata: assessing the potential for evolutionary change with rising atmospheric CO₂. In C. Korneer and F. A. Bazzaz [eds.], Carbon dioxide, populations, and communities. Academic Press, San Diego, California, USA

———, A. A. Snow andA. S. Miller 1994 Genotype-specific effects of elevated CO₂ on fecundity in wild radish Raphanus raphanistrum. Oecologia 97: 101-105

———, and J. A. Teeri 1992 Seasonal responses of leaf gas exchange to elevated carbon dioxide in Populus grandidentata.. Canadian Journal of Forest Research 22: 1320-1325

———, and X. Wang 1998 A meta-analysis of elevated CO₂ effects on woody plant mass, form, and physiology. Oecologia 113: 299-313[CrossRef][ISI]

———, D. R. Zak K. S. Pregitzer andJ. A. Teeri 1994 Above- and below ground response of Populus grandidentata to elevated atmospheric CO₂ and soil N availability. Plant and Soil 165: 45-51[CrossRef][ISI]

Fajer E. D. 1989 The effects of enriched CO₂ atmospheres on plant–insect herbivore interactions: growth responses of larvae of the specialist butterfly, Junonia coenia Lepidoptera: Nymphalidae. Oecologia 81: 514-520[CrossRef][ISI]

———, M. D. Bowers andF. A. Bazzaz 1989a The effects of enriched carbon dioxide atmospheres on plant–insect interactions. Science 243: 1198-1200[Abstract/Free Full Text]

———, ———, and ———. 1989b Performance and allocation patterns of the perennial herb, Plantago lanceolata, in response to simulated herbivory and elevated CO₂ environments. Oecologia 81: 37-42

———, ———, and ———. 1992 The effect of nutrients and enriched CO₂ environments on production of carbon-based allelochemicals in Plantago: a test of the carbon/nutrient balance hypothesis. American Naturalist 140: 707-723[CrossRef][ISI]

Falconer D. S. 1981 Introduction to quantitative genetics,. 2nd ed. Longman, London, UK

Garbutt K. W. E. Williams andF. A. Bazzaz 1990 Analysis of the differential response of five annuals to elevated CO₂ during growth. Ecology 71: 1185-1194[CrossRef][ISI]

Gill J. L. 1978 Design and analysis of experiments in the animal and medical sciences, vol. 1. Iowa State University Press, Ames, Iowa, USA

Hallauer A. R. andJ. B. Miranda 1981 Quantitative genetics in maize breeding. Iowa State University Press, Ames, Iowa, USA

Hunt R. D. W. Hand M. A. Hannah andA. M. Neal 1995 Temporal and nutritional influences on the response to elevated CO₂ in selected British grasses. Annals of Botany 75: 207-216[Abstract/Free Full Text]

Hurry V. M. A. Strand M. Tobiaeson P. Gradestrom andG. Oquist 1995 Cold hardening of spring and winter wheat and rape results in differential effects on growth, carbon metabolism, and carbohydrate content. Plant Physiology 109: 697-706[Abstract]

Jang J. P. León L. Zhou andJ. Sheen 1997 Hexokinase as a sugar sensor in higher plants. Plant Cell 9: 5-19[Abstract]

———, and J. Sheen 1994 Sugar sensing in higher plants. Plant Cell 6: 1665-1679[Abstract]

Klus D. J. 1995 Genotypic variation in response to elevated atmospheric carbon dioxide in two populations of Plantago lanceolata L. Ph.D. dissertation, Michigan State University, East Lansing, Michigan, USA

Kuiper P. J. C. andM. Bos 1992 Plantago: a multidisciplinary study. Springer-Verlag, Berlin, Germany

Loveless M. D. andJ. L. Hamrick 1984 Ecological determinants of genetic structure in plant populations. Annual Review of Ecology and Systematics 15: 65-95

Lynch M. andB. Walsh 1997 Genetics analysis of quantitative traits. Sinauer, Sunderland, Massachusetts, USA

Manly B. F. J. 1986 Multivariate statistical methods. Chapman and Hall, New York, New York, USA

McNeilly T. 1968 Evolution in closely adjacent plant populations. III. Agrostis tenuis on a small copper mine. Heredity 23: 99-108

Poorter H. 1993 Interspecific variation in growth response of plants to an elevated ambient CO₂ concentration. Vegetatio 104/105: 77-97[CrossRef][ISI]

———, S. Pot andH. Lambers 1988 The effect of an elevated atmospheric CO₂ concentration on growth, photosynthesis and respiration of Plantago major.. Physiology of Plants 73: 553-559

Roff D. A. 1997 Evolutionary quantitative genetics.. Chapman and Hall, New York, New York, USA

Sage R. F. 1994 Acclimation of photosynthesis to increasing atmospheric CO₂: the gas exchange perspective. Photosynthesis Research 39: 351-368[CrossRef]

———, T. D. Sharkey andJ. R. Seemann 1988 The in-vivo response of the ribulose-1,5-bisphosphate carboxylase activation state and the pool sizes of photosynthetic metabolites to elevated CO2 in Phaseolus vulgaris L. Planta 174: 407-416[CrossRef][ISI]

SAS. 1989 SAS/STAT user's guide, version 6, 4th ed. vol. 2. SAS Institute, Cary, North Carolina, USA

Scheiner S. M. 1993 MANOVA: multiple response variables and multispecies interactions. In S. M. Scheiner and J. Gurevitch [eds.], Design and analysis of ecological experiments. Chapman and Hall, New York, New York, USA

Sokal R. R. andF. J. Rohlf 1981 Biometry. W. H. Freeman, New York, New York, USA

Stitt S. 1991 Rising CO₂ levels and their potential significance for carbon flow in photosynthetic cells. Plant, Cell and Environment 14: 741-762[CrossRef]

Teramura A. H. 1983 Experimental ecological genetics in Plantago. IX. Differences in growth and vegetative reproduction in Plantago lanceolata Plantaginaceae from adjacent habitats. American Journal of Botany 70: 53-58[CrossRef][ISI]

———, and B. R. Strain 1979 Localized populational differences in the photosynthetic response to temperature and irradiance in Plantago lanceolata.. Canadian Journal of Botany 57: 2559-2563[CrossRef]

Tilman D. 1993 Carbon dioxidelimitation and the potential direct effects on plant communities. In P. M. Karieva, J. G. Kingsolver, and R. B. Huey [eds.], Biotic interactions and global change. Sinauer, Sunderland, Massachusetts, USA

Tonsor S. J. 1985 Intrapopulational variation in pollen-mediated gene flow in Plantago lanceolata L. Evolution 39: 775-782[CrossRef][ISI]

———. 1989 Relatedness and intraspecific competition in Plantago lanceolata.. American Naturalist 134: 897-906[CrossRef][ISI]

———. 1990 Spatial patterns of differentiation for gene flow in Plantago lanceolata.. Evolution 44: 1373-1378[CrossRef][ISI]

———, and C. J. Goodnight 1997 Evolutionary predictability in natural populations: do mating system and nonadditive genetic variance interact to affect heritabilites in Plantago lanceolata?. Evolution 51: 1771-1782

———, and M. A. VanDermeulen 1998 Responses to evolutionarily novel environments: Arabidopsis thaliana.. Ecological Society of America Special Publication: Symposium Abstracts 23

Van Tienderen P. H. 1990 Morphological variation in in Plantago lanceolata: limits of plasticity. Evolutionary Trends in Plants 4: 35-43

Ward J. K. andB. R. Strain 1997 The effects of low and elevated CO₂ partial pressure on growth and reproduction of Arabidopsis thaliana from different elevations. Plant, Cell and Environment 20: 254-260[CrossRef]

Weber K. E. 1990 Selection on wing allometry in Drosophila melanogaster.. Genetics 126: 975-989[Abstract]

———. 1992 How small are the smallest selectable domains of form?. Genetics 103: 345-353

Wolff K. andW. Van Delden 1987 Genetic analysis of ecological relevant morphological variability in Plantago lanceolata L. I. Population characteristics. Heredity 58: 183-192[ISI]

———, and ———. 1989 Genetic analysis of ecological relevant morphological variability in Plantago lanceolata L. IV. Response and correlated response to bidirectional selection for leaf angle. Heredity 62: 153-160[ISI]

Wu L. andJ. Antonovics 1976 Experimental ecological genetics in Plantago II. Lead tolerance in Plantago lanceolata and Cynodon dactylon from a roadside. Ecology 57: 205-208[CrossRef][ISI]

Wulff R. D. andH. M. Alexander 1985 Intraspecific variation in the response to CO₂ enrichment in seeds and seedlings of Plantago lanceolata L. Oecologia 66: 458-460[CrossRef][ISI]

This article has been cited by other articles:

				F. T. MAESTRE and J. F. REYNOLDS Nutrient Availability and Atmospheric CO2 Partial Pressure Modulate the Effects of Nutrient Heterogeneity on the Size Structure of Populations in Grassland Species Ann. Bot., July 1, 2006; 98(1): 227 - 235. [Abstract] [Full Text] [PDF]

This Article Abstract Full Text (PDF) Submit a response Alert me when this article is cited Alert me when eLetters are posted Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in ISI Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via ISI Web of Science (4)