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RESEARCH ARTICLE
Quantitative Variation in Water-Use Efficiency across Water Regimes and Its Relationship with Circadian, Vegetative, Reproductive, and Leaf Gas-Exchange Traits Christine E. Edwardsa,b,1, Brent E. Ewersa,c, C. Robertson McClungd, Ping Loud and Cynthia Weiniga,c a b c d
Department of Botany, University of Wyoming, Laramie, WY 82071, USA Present address: USACE ERDC, Environmental Laboratory, 3909 Halls Ferry Road, Vicksburg, MS 39180, USA Program in Ecology, University of Wyoming, Laramie, WY 82071, USA Department of Biological Sciences, Dartmouth College, Hanover, NH 03755, USA
ABSTRACT Drought limits light harvesting, resulting in lower plant growth and reproduction. One trait important for plant drought response is water-use efficiency (WUE). We investigated (1) how the joint genetic architecture of WUE, reproductive characters, and vegetative traits changed across drought and well-watered conditions, (2) whether traits with distinct developmental bases (e.g. leaf gas exchange versus reproduction) differed in the environmental sensitivity of their genetic architecture, and (3) whether quantitative variation in circadian period was related to drought response in Brassica rapa. Overall, WUE increased in drought, primarily because stomatal conductance, and thus water loss, declined more than carbon fixation. Genotypes with the highest WUE in drought expressed the lowest WUE in well-watered conditions, and had the largest vegetative and floral organs in both treatments. Thus, large changes in WUE enabled some genotypes to approach vegetative and reproductive trait optima across environments. The genetic architecture differed for gas-exchange and vegetative traits across drought and well-watered conditions, but not for floral traits. Correlations between circadian and leaf gas-exchange traits were significant but did not vary across treatments, indicating that circadian period affects physiological function regardless of water availability. These results suggest that WUE is important for drought tolerance in Brassica rapa and that artificial selection for increased WUE in drought will not result in maladaptive expression of other traits that are correlated with WUE. Key words: Brassica rapa; drought; ecophysiology; floral traits; G matrix; leaf gas-exchange traits; water-use efficiency; vegetative traits.
INTRODUCTION As sessile organisms, plants must maximize their harvesting of light energy even as they are exposed to climatic variation, and drought is one of the most important climatic factors affecting plant growth and mortality (Condit et al., 1995; Allen and Breshears, 1998; Condit, 1998; Breshears et al., 2009; Allen et al., 2010). Water deficit triggers a series of ecophysiological responses; plants reduce growth and respiration rates, divert a larger percentage of carbohydrates to storage, and close their stomates, thereby reducing the rate of water loss from transpiration and minimizing the risk of hydraulic failure (McDowell et al., 2008; McDowell, 2011). The rate of photosynthesis subsequently drops, and utilization of carbohydrates for respiration, growth, and defense decreases carbohydrate concentrations, reducing the ability to regulate osmotic potential,
produce carbon-rich defense compounds, and devote resources to reproduction. Ultimately, prolonged drought has major negative effects on growth and ecophysiological processes, and may lead to lower fitness, complete reproductive failure, or mortality, making drought an important selective agent in both natural and cultivated plant populations (Dudley, 1996; Heschel and Riginos, 2005).
1 To whom correspondence should be addressed. E-mail Christine.e.edwards @usace.army.mil, tel. (601) 634-7272, fax (601) 634-4017.
ª The Author 2012. Published by the Molecular Plant Shanghai Editorial Office in association with Oxford University Press on behalf of CSPB and IPPE, SIBS, CAS. doi: 10.1093/mp/sss004 Received 18 October 2011; accepted 1 January 2012
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Edwards et al. t (FOFUJDTPG%SPVHIU3FTQPOTFJOBrassica rapa
Statistical genetic approaches can be used to predict the evolution of complex traits in response to selection (Falconer and Mackay, 1996). Specifically, the response of a trait to selection depends on the magnitude of the additive genetic variance and on co-variation among traits in a population (Falconer and Mackay, 1996). That is, evolution of any given trait results both from direct selection on that trait and indirect selection acting on correlated traits. Lande’s (1979) multivariate generalization of the breeder’s equation can be used to predict the evolution of suites of correlated traits, such as the suite of gas-exchange and mechanistic traits that likely contribute to drought response. In this model, Dz = Gb, where a change in the mean value of several traits (z1, z2, . . . zn) is equal to the product of the genetic variance-co-variance matrix (G) and a vector of selection gradients (b). G effectively summarizes the genetic architecture, including genetic variances in multiple traits and associations among traits, that may limit a response to selection (Steppan et al., 2002). Environmental heterogeneity may influence the evolutionary trajectory of traits by affecting the expression of genetic variation and the patterns of co-variation among traits. Differences have been observed, for instance, in the G-matrix of leaf gas-exchange traits in Avena barbata across drought and wellwatered treatments due to increased trait variances in the drought treatment and environmental dependence of correlations involving leaf gas-exchange traits (Sherrard et al., 2009). However, the response of G to environmental stress may vary among traits (Hoffmann and Merila, 1999); for example, in Avena, although the G-matrix of leaf gas-exchange traits varied across water regimes, the G-matrix of performance traits did not (Sherrard et al., 2009). G-matrix behavior in response to environmental stress may also be species-specific; in contrast to patterns found in Avena, lower heritability in drought treatments was found for carbon isotope discrimination in grasses (Johnson et al., 1990), for morphological and life-history traits in Impatiens pallida (Bennington and McGraw, 1996), and for fitnessrelated traits in rice (Lafitte et al., 2004). These contrasting results suggest that the effects of drought on trait (co)variance may be species-specific, or that the effects of drought may vary among traits with different developmental bases. It is of interest to further characterize the genetic architecture of drought responses and develop predictive evolutionary models, given the patchy distribution of water in natural settings and the significant fitness effects of drought. One trait that is important in plant drought response and tolerance is water-use efficiency (WUE). Intrinsic WUE (Wg) (Seibt et al., 2008) is defined as the rate of photosynthesis (A) divided by the rate of stomatal conductance to water (gs;). gs is a measure of rate of passage of water vapor and carbon dioxide through the stomata, and A quantifies the assimilation of CO2 into biomass. In response to drought, plants commonly increase WUE, which is most often carried out by decreasing gs, as variation in A is usually limited by the rate of gs. Increased guard cell control is the mechanism by which plants decrease gs, with plants adjusting stomatal aperture
via a still incompletely known set of controls in response to internal and external conditions, which include the biochemical status of photosynthetic metabolism, light availability, water potential of the leaf and guard cells, and hormonal signals (Flexas et al., 2004); however, the response of Wg to drought may vary among species and populations. Predictive understanding of Wg across water regimes is likely to be improved by investigating the genetic architecture of photosynthetic gas-exchange traits that contribute to Wg, rather than investigating Wg in isolation (Rebetzke et al., 2008). The circadian clock may also affect WUE across water regimes. Clock mutants in Arabidopsis thaliana differ from wild-type plants in the expression of photosynthesis in wellwatered conditions (Dodd et al., 2005), and we have shown that naturally segregating variation in circadian period likewise affected the expression of A and gs in Brassica rapa (Edwards et al., 2011). Furthermore, variation in circadian period was also shown to be genetically correlated with D13C, which is often used as a proxy for WUE (Edwards et al., 2011). Given that circadian period has been shown to be related to both WUE and the component traits of WUE, these results suggest that the clock could also help modulate WUE in response to variation in water regime. However, the relationship between quantitative variation in the circadian clock and leaf gas-exchange traits has only been measured in a wellwatered environment, and it is unknown whether this genetic relationship is affected by drought or helps modulate the response of WUE to drought stress. Because water resources are increasingly limiting and ongoing population growth is placing more pressure on crop production (Foley et al., 2011), improving crop yield per unit water lost and predicting how crops may be affected by global climate change are among the primary aims of current crop research (Condon et al., 2002; Rebetzke et al., 2002; Condon et al., 2004; Yang et al., 2010). Previous research in several crop species has revealed that some genotypes within a population may be more water-use efficient than others, and artificial selection on genotypes with high WUE has been investigated as a means to improve crops for increased yield in water-limited environments (Condon et al., 2002; Rebetzke et al., 2002). Many studies have shown that yield and biomass are correlated with WUE in drought (Ehdaie et al., 1991; Condon et al., 1993; Sun et al., 1996), but this relationship has been shown to vary across water regimes; for example, in wheat, a genotype with the highest WUE had the largest biomass in drought, but also had the smallest biomass in well-watered environments because it did not utilize all available water (Condon et al., 2002). As revealed by the G matrix, selection for improved WUE may also alter the expression of other traits that are themselves critical to yield, depending on the nature of trait correlations. For example, vegetative and reproductive morphology may be affected by water availability (Carroll et al., 2001; Aspelmeier and Leuschner, 2006; Caruso, 2006) and may be correlated with WUE. Nonetheless, few studies have investigated the correlations between WUE and
Edwards et al. t (FOFUJDTPG%SPVHIU3FTQPOTFJOBrassica rapa
vegetative, reproductive (floral), and other leaf gas-exchange traits across water regimes (but see Lambrecht and Dawson, 2007). Investigating the genetic relationship among these traits across water regimes will help clarify how artificial selection for increased WUE will affect yield and fitness. In this study, we investigated how patterns of trait (co)variation of leaf gas-exchange, vegetative, circadian, and reproductive traits were affected by drought/well-watered conditions in a population of 40 recombinant inbred lines (RILs) and two parental genotypes of Brassica rapa. Our goals were to investigate (1) how the genetic architecture of WUE and the photosynthetic gas-exchange traits that contribute to WUE respond to changes in water regime, (2) whether quantitative genetic variation in circadian period is related to WUE and drought response across water regimes, (3) whether differences in the expression of WUE and other leaf gas-exchange traits across drought versus well-watered conditions affect reproductive and vegetative traits critical to fitness, and (4) whether the G-matrices of traits with different developmental bases (leaf gas-exchange, vegetative, and reproductive) differed in their responses to water regime. Results of this study provide insight into the potential for natural populations of B. rapa to evolve in response to climate change and the response of B. rapa and similar crops to artificial selection for increased water-use efficiency and yield in water-limited environments.
RESULTS Partitioning of Quantitative Genetic Variation Within and Between Treatments Yield and biomass vary in response to water availability (Condon et al., 2004; Jones, 2007). Similarly, vegetative and reproductive morphology, both of which affect yield and biomass, depend significantly on water availability (Carroll et al., 2001; Chaves et al., 2003; Aspelmeier and Leuschner, 2006; Caruso, 2006). We wished to determine whether these traits co-varied and whether they were correlated with WUE across water regimes. The occurrence of correlations among traits is relevant, because artificial selection on WUE may result in correlated phenotypic evolution of traits correlated with WUE (Steppan et al., 2002). Therefore, we investigated (co)variation of photosynthetic gas-exchange (A, photosynthetic rate; Fv’/Fm’, chlorophyll fluorescence in light; gs, stomatal conductance; E, transpiration rate; Wg, intrinsic water-use efficiency (A/gs); D13C, carbon isotope discrimination, Narea, nitrogen concentration on a leaf area basis; LMA, leaf mass per area), vegetative (biomass; root:shoot ratio; height; number of branches), circadian (period), and reproductive (pistil length; long stamen length; petal length, petal area; days to flowering) traits in drought and well-watered conditions. Within both treatments, we carried out analysis of variance for each trait to test the random effects of genotype and flat (as a blocking term) in each treatment. The effect of flat was not significant for all traits and is not reported further. All traits demonstrated significant among-genotype variance
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(P , 0.05; Table 1). Overall, estimates of VG/VP, or broad-sense heritability, where VG is the among-genotype variance component in each treatment and VP is the sum of all variance components for a trait in each treatment, were moderate to large for most traits, ranging from 0.207 to 0.825. Heritability was generally higher in the well-watered treatment (range 0.208–0.825; Table 1) than in the drought treatment (range 0.207–0.636; Table 1). Twelve of 16 traits had larger VG/VP in the well-watered treatment, nine of which were due to larger genetic variance in that treatment, and three of which were attributable to proportionally smaller environmental variance in the well-watered treatment. Only four of the 16 traits (Wg, D13C, long stamen length, and petal length) had larger VG/VP in the drought treatment (Table 1), two of which were due to larger genetic variance in the drought (for Wg and D13C) and two of which were attributable to proportionally smaller environmental variance in the drought treatment. We then tested for genotypic differences in the response to drought versus well-watered treatments by carrying out a mixed-model nested ANOVA across the two treatments (PROC MIXED, SAS ver. 9.2). We evaluated the fixed effect of treatment and the random effects of genotype, flat nested within treatment, and the genotype 3 treatment interaction on each trait. Water regime significantly affected the expression of many traits (Table 2). As expected, plants in the drought treatment had higher average intrinsic WUE (Wg). These overall increases in Wg were caused by proportionally larger decreases in stomatal conductance (gs) per unit decrease in photosynthesis (Figure 1, inset). Droughted plants had lower carbon isotope discrimination (D13C), which may also be interpreted as higher WUE in drought. Droughted plants were also shorter, had fewer secondary branches, smaller floral organs, and accumulated less than half the average biomass than plants in the well-watered treatment (Table 1). Patterns of biomass allocation were also affected by drought; the average root:shoot ratio of plants in the drought treatment was double that of plants in the wellwatered treatment, indicating that plants in the drought treatment allocated proportionally more biomass to roots. Across treatments, traits demonstrated non-significant to highly significant genotype by environment interactions (GEI; Table 2), indicating that genotypic sensitivity to treatments was trait-specific. For the leaf gas-exchange traits, significant genotype 3 environment interactions (GEI) (P , 0.05) were detected for Wg and D13C (both of which are measures of WUE at different time scales) and Narea (which is strongly related to total biochemical capacity); marginally significant GEI (P , 0.1) was detected for A (a measure of the sum total of photosynthetic capacity or demand), gs (a measure of the supply of CO2 for photosynthesis), and LMA (a measure of the partitioning of resources between leaf biomass and area). For the vegetative traits, significant GEI (P , 0.05) was detected for biomass, root:shoot ratio, and height, and marginally significant GEI was detected (P , 0.1) for number of branches (Table 2). All floral size traits demonstrated significant GEI (P , 0.01) except for days to flowering (Table 2).
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Table 1. Means, Quantitative Genetic Partitioning, and Significance of Effects for Leaf Gas-Exchange, Vegetative, and Reproductive Traits for 42 Genotypes of B. rapa in Drought and Well-Watered Treatments.
Trait A (lmol m2 s1)
Well-watered treatment Mean (SE)
Drought treatment Mean (SE)
Drought treatment VG (SE)
0.608
24.3093
0.002 (0.0004)**
0.669
0.4091
0.003 (0.0006)**
0.597
gs (mol m2 s1)
0.5846
0.005 (0.002)§
0.217
0.3150
0.003 (0.001)§
0.207
50.3361
41.25 (14.957)§
136.14 (45.273)*
0.258
D C
21.936
0.208
84.2310
0.386 (0.099)**
0.510
20.425
10.912 (3.267)*
Drought treatment VG/VP
0.4341
13
19.134 (4.670)**
Well-watered treatment VG/VP
Fv’/Fm’ Wg
27.8625
Well-watered treatment VG (SE)
0.766 (0.191)**
0.329
0.570
Narea (g m2)
2.2415
0.075 (0.019)**
0.510
2.1522
0.048 (0.013)**
0.423
LMA (g m2)
29.7241
13.115 (3.312)**
0.469
29.2969
8.531 (2.183)**
0.454
1.096
0.954 (0.216)**
0.825
0.466
0.063 (0.015)**
0.671
0.00005 (0.00001)**
0.536
Biomass Root:shoot ratio Height
0.02938 25.7554
70.55 (15.982)**
0.04269
0.00007 (0.00002)*
0.224
0.823
18.6388
11.3269 (2.6698)**
0.636
Number of branches
5.3345
3.5137 (0.8233)**
0.680
4.4676
1.934 (0.489)**
0.488
Pistil length
6.7105
0.468 (0.119)**
0.438
6.0682
0.263 (0.073)*
0.361
Long stamen length
7.0777
0.377 (0.09)**
0.563
6.5489
0.241 (0.058)**
0.567
Petal length
8.4141
0.509 (0.133)**
0.381
7.5676
0.325 (0.081)**
0.471
Petal area
19.4573
16.737 (4.033)**
0.509
14.6751
7.667 (1.822)**
0.489
Days to flowering
33.2543
14.582 (3.568)**
0.551
34.0954
20.286 (5.029)**
0.509
VG, among-genotypic variance; VG/VP, among-genotypic variance divided by total phenotypic variance; A, photosynthetic rate; Fv’/Fm’, chlorophyll fluorescence in light; gs, stomatal conductance; E, transpiration rate; Wg, intrinsic water-use efficiency (A/gs); D13C, carbon isotope discrimination; Narea, nitrogen concentration on a leaf area basis; LMA, leaf mass per area; z P , 0.05, § P , 0.01, * P , 0.001, ** P , 0.0001 Pearson correlation coefficients and significance of across-treatment genotypic correlations (rGE) for each trait. NS, not significant; y P , 0.1, zP , 0.05, § P , 0.01, * P , 0.001, ** P , 0.0001. Standard errors are indicated in parentheses.
To further assess whether the genetic architecture of each trait changed across treatments, we estimated across-treatment genotypic correlations (rGE), the genotypic correlation of each trait across the two treatments (Fry et al., 1996; Gurganus et al., 1998; Vieira et al., 2000). First, we used a multivariate ANOVA approach using restricted maximum likelihood, which takes measurement error into account, to estimate correlations for each trait across the two treatments (Messina and Fry, 2003; Holland, 2006) (SAS PROC mixed). We also used point estimates of the genotypic values of each trait in each treatment (as BLUPs; Robinson, 1991; Littell et al., 2006) to estimate Pearson product-moment correlation coefficients for each trait across treatments (SAS PROC CORR), which does not take measurement error into account. Because results were highly similar for the two types of analysis, we present only Pearson product-moment correlation coefficients. rGE indicate the extent to which the same genetic loci are expressed and alleles have the same function across treatments; estimates of rGE approaching 1 suggest that expression of the trait across environments has a similar genetic basis, whereas estimates approaching 0 suggest that different genetic loci affect the trait or alleles differ in function across treatments (Fry et al., 1996; Gurganus et al., 1998; Vieira et al., 2000). rGE was significantly different from 0 and not significantly different from 1 for days to flowering and Fv#/Fm’ (Table 2, significant effect of genotype, non-significant GEI effect), indicating that the same loci affect trait variation and alleles have the
same functional effects across treatments. rGE estimates for all other traits except Wg were significantly different from both 0 and 1 (Table 2, significant genotype and GEI effects), indicating that some loci or alleles affecting these traits had variable effects across treatment pairs. Interestingly, rGE estimates for Wg were negative across treatments (rGE = –0.53; Table 2 and Figures 1 and 2), indicating that genotypes that had a large Wg in one environment (drought) had a small Wg in the other environment (well-watered), and vice versa (Figure 2), and that the genes that conferred an increase in one environment conferred a decrease in the other environment. The cause of this negative correlation across treatments is that the extent to which gs declined in response to drought varied more among genotypes than the extent to which A declined (Figure 1, inset).
Genetic Correlations and G-Matrix Analyses To understand how genetic variance-co-variance (G) matrices changed across treatments, we used Common Principal Components (CPC) analysis, as implemented in the program CPCrand (Phillips and Arnold, 1999). CPCrand compares the structure of two matrices using a ‘jump-up’ approach, testing whether the two matrices are: (1) unrelated, (2) share CPCs, (3) are proportional, or (4) are equal, each of which is tested against the hypothesis that the matrices are unrelated. The three classes of traits (leaf gas-exchange, vegetative, and reproductive) differed in the extent to which G-matrices differed
Edwards et al. t (FOFUJDTPG%SPVHIU3FTQPOTFJOBrassica rapa
657
Table 2. Quantitative Genetic Partitioning of Variation and Significance of Effects across Drought and Well-Watered Treatments, and Pearson Correlation Coefficients and Significance of Across-Treatment Genotypic Correlations (rGE) for Each Trait. Random effects Trait A (lmol m2 s1) Fv’/Fm’ gs (mol m
Fixed effect Flat (treatment)
2
1
s )
0 (0)NS 0 (0)
NS
0 (0)
NS
0.003 (0.001)
2.092 (6.093)NS 0.011 (0.014)
NS
0.014 (0.005)
§
Narea (g m ) 2
LMA (g m ) Biomass Root:shoot ratio
0.762 (0.3984)
0.051 (0.014)** z
0.020 (0.006)* 0 (0)
0.453 (0.121)**
NS
10.231 (2.573)** 0.231 (0.0895)** 0.00004 (0.00001)
§
22.100 (7.488)
§
§
F
rGE
F1,26.3
318.80**
0.97y
0.001 (0.00009)**
F1,38.8
52.79**
0.98NS
0.017 (0.001)**
F1,39.7
365.93**
0.83y
27.171 (16.417)z
269.64 (19.249)**
F1,26.3
318.80**
0.446 (0.034)**
F1,35.4
234.24**
0.001 (0.0009)
60.749 (22.817)§
Treatment df
y
0.00004 (0.00006)
§
Residual
269.64 (19.249)** NS
0.002 (0.0005)**
13
2
1.218 (0.910)y
13.805 (3.524)**
Wg D C
Genotype 3 treatment
Genotype
0.098 (0.040)
§
0.010 (0.005)
z
0.784 (0.590)
y
0.278 (0.065)
§
0.057 (0.004)**
§
0.00002 (0.000009)
0.91§
y
0.56z
NS
3.33
12.071 (0.798)**
F1,36.5
0.91
0.98y
0.098 (0.006)**
F1,50.5
24.99**
0.91§
0.0001 (0.000009)**
F1,39.8
98.74**
0.82§
Height
1.401 (0.486)
9.4852 (0.5934)**
F1,50
45.75**
0.77**
Number of branches
0.072 (0.045)y
2.461 (0.604)**
0.281 (0.117)y
1.754 (0.110)**
F1,36.7
23.59**
0.95y
Pistil length
0.036 (0.016)z
0.283 (0.086)§
0.119 (0.042)§
0.429 (0.029)**
F1,44.4
33.16**
0.79§
Long stamen length
0.043 (0.013)*
0.258 (0.070)**
0.076 (0.023)*
0.171 (0.012)**
F1,54.7
30.45**
0.84*
§
19.0141 (4.5062)**
F1,44
–0.53
Petal length
0.108 (0.034)*
0.262 (0.092)
0.188 (0.060)*
0.468 (0.031)**
F1,54.7
30.89**
0.68*
Petal area
4.201 (1.101)**
7.447 (2.627)§
6.521 (1.707)**
6.697 (0.450)**
F1,63.9
29.29**
0.62**
Days to flowering
1.513 (0.594)§
16.534 (3.954)**
0.692 (0.580)NS
13.728 (0.859)**
F1,35.1
2.60NS
0.99NS
Standard error is indicated in parenthesis. For across-treatment genotypic correlations (rGE), P-values are from tests of significance of the genotype 3 treatment interaction term in ANOVA analyses and indicate whether cross-environment correlations are significantly different from 1, with a significant result indicating some changes in the genetic architecture across treatments. All comparisons were significantly different from 0, as indicated by a significant genotype effect in ANOVA analyses across pairs of environments, indicating that all traits share some of their genetic architecture across treatments. A, photosynthetic rate; Fv’/Fm’, chlorophyll fluorescence in light; gs, stomatal conductance; E, transpiration rate; Wg, intrinsic water-use efficiency (A/gs); D13C, carbon isotope discrimination; Narea, nitrogen concentration on a leaf area basis; LMA, leaf mass per area; NS , not significant; y P , 0.1, z P , 0.05, § P , 0.01, * P , 0.001, ** P , 0.0001.
across treatments; G-matrices for leaf gas-exchange traits shared two common principal components (CPC) across treatments, G-matrices of vegetative traits shared all CPC across treatments, and G-matrices for reproductive traits were equal across treatments (Table 3). To investigate the origins of differences in G-matrices, we used Z-tests to compare bivariate correlations across treatments. We estimated correlations among traits using both a multivariate (PROC MIXED) and a bivariate approach (PROC CORR), but, because the results of these two analyses were highly similar (results not shown), we present only Pearson correlation coefficients (Table 4). Among leaf gas-exchange traits, six (of 21 possible) bivariate correlations were significantly different across treatments (Table 4), all six of which were more significant in the drought treatment. For example, in drought, Wg was correlated with D13C, LMA, and gs, whereas these traits were uncorrelated in the wellwatered treatment (Table 4). These changes in correlations across treatments involving Wg are likely due to lower among-genotype variation for Wg in the well-watered treatment than in the drought treatment (Table 1), thereby limiting the expression of genetic correlations involving Wg in the wellwatered treatment. In addition, Narea was significantly correlated with A, Fv#/Fm’, and LMA in the drought treatment but not in the well-watered treatment (Table 4). Most other
correlations among leaf gas-exchange traits were of similar magnitude across treatments; for example, in both treatments, A was significantly correlated with Fv#/Fm’, D13C, gs, and LMA (Table 4), which is consistent with the known relationship between photosynthetic CO2 demand and supply, and the role of light-dependent biochemistry and leaf structure on photosynthesis. Fv#/Fm’ was significantly correlated with D13C, Wg, and LMA (Table 4), showing that the latter processes rely on photochemistry for power. A and Wg, Fv#/Fm’ and gs, and D13C and gs were uncorrelated in both treatments (Table 4), showing that WUE was driven more by gas supply limitations rather than biochemical demand and that this supply is not necessarily directly related to photochemistry. Z-tests to investigate the cause of significant differences in G-matrices for vegetative traits across treatments revealed that only one (of six possible) bivariate correlations were significantly different across treatments (Table 4), while matrices for reproductive traits were equal across treatments for reproductive traits. In sum, the genetic architecture of leaf gas-exchange traits was more sensitive to changes in water regime than vegetative or reproductive traits. To determine how Wg affected vegetative and reproductive traits across environments, we compared G-matrices including Wg and vegetative or reproductive traits across treatments. Matrices including both Wg and vegetative traits shared only
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Edwards et al. t (FOFUJDTPG%SPVHIU3FTQPOTFJOBrassica rapa
one CPC across treatments (Table 3), and matrices including both Wg and reproductive traits shared all CPC across treatments. All correlations between Wg and vegetative/reproductive traits were negative in the well-watered treatment and positive in the drought treatment (Table 4 and Figure 3), seven (of nine) of which were significantly different across treatments according to Z-tests of bivariate correlations. Interestingly, although the direction of correlations involving Wg and vegetative/reproductive traits shifted across drought and well-watered treatments, correlations between vegetative/reproductive traits and the component traits of Wg, A, and gs were generally not significant in either treatment, nor did they differ across treatments (Table 4); these results suggest that, although A and gs are unrelated to vegetative/reproductive traits, the ratio between A and gs and their correlation (Figure 1, inset) may dramatically affect both vegetative and reproductive growth across environments. Although not tested formally as part of a priori expectations, it is worth nothing that six (of nine possible) correlations between leaf gas-exchange/vegetative/reproductive traits and Narea differed across environments. Correlations between Narea and both vegetative and reproductive traits were generally strongly positive and significant in the drought treatment and slightly negative and non-significant in the well-watered treatment (Table 4), suggesting that the ability to take up and partition nitrogen may constrain or affect the potential for adaptive evolution only in drought conditions.
Genetic correlations to investigate the relationship between circadian period and leaf gas-exchange traits revealed that circadian period was significantly correlated with both A and gs in both treatments (Table 5).
DISCUSSION Our goal was to impose drought conditions similar to those found in agricultural settings that cause reductions in seed yield (Jones, 2007). The drought conditions imposed in this study affected most of the measured leaf gas-exchange, vegetative, and reproductive traits. Many of the traits affected by water regime in the present study (e.g. biomass, height, number of stems, etc.) have been shown to be correlated with fitness across many different studies and species (Schwaegerle
Figure 2. Response of Wg (Intrinsic Water-Use Efficiency; A/gs) across Drought and Well-Watered Treatments. Each colored line shows the change in trait means across treatments for one of 20 randomly selected genotypes. Wg increased dramatically in response to drought. However, the rank order of genotypes reversed across treatments; those genotypes with the lowest WUE in well-watered conditions had the highest WUE in droughted conditions. This is consistent with the inverse relationship between Wg in drought and well-watered treatments shown in Figure 1.
Table 3. Results of Common Principal Components Analyses (as Implemented in CPCrand) to Compare G-Matrices of B. rapa RILs between Drought and Non-Drought Environments.
Figure 1. Across-Treatment Genetic Correlation for Wg across Drought and Well-Watered Treatments (r = 0.51, P , 0.001), Showing that Genotypes with the Highest Wg in the Well-Watered Treatment Had the Lowest Wg in the Drought Treatment, and Vice Versa. Inset shows the relationships between A, photosynthetic rate, and gs, stomatal conductance, in drought (black symbols) and wellwatered treatments (white symbols), illustrating that gs decreased proportionally more than A in response to drought.
Type of traits
Number of traits
Jump-up approach
Leaf gas-exchange traits
7
CPC(2)
Vegetative traits
4
Common CPC
Floral traits
5
Matrix equality
Wg and vegetative traits
5
CPC(1)
Wg and floral traits
6
Common CPC
0.10 0.15
NS
Height
Number of branches
NS
0.10 –0.03
NS
NS
Petal area
Days to flowering
NS
0.23
–0.38
– z
–0.63**
0.19NS
–0.04NS
–
–0.54*
0.36
z
0.38z
0.48
§
0.65**
Narea
0.40
0.00
0.23 §
NS
NS
–0.37
z
–0.12
NS
–0.18
NS
–0.25
–0.15
0.01 NS
NS
NS
–0.14
NS
–0.09
NS
–0.05
NS
0.07NS –0.21NS –0.13NS –0.10NS
–0.08NS –0.51*
NS
–0.15
–
–0.57**
–0.59**
–0.43
§
D13C
–0.26
NS
0.19
0.15 NS
NS
–0.32
NS
–0.20
NS
0.20NS –0.34§
0.17
NS
–0.25
NS
–0.14
–0.06 NS
NS
–0.16
NS
–0.16
NS
–0.08NS –0.17NS
–0.11
NS
0.11
NS
0.0NS
0.50*
0.54*
0.48§
0.57**
0.41§
0.54*
0.49*
0.42
§
0.60**
–
0.89**
0.86**
0.61**
0.70**
0.76**
0.68**
0.64**
0.81**
0.59**
–
0.58 **
0.56**
NS
0.40
§
–0.60** –0.15
0.46
§
0.24NS –0.14NS
0.55**
0.66**
LMA
NS
NS
0.46
0.42 §
§
0.37z
0.47
§
0.40§
0.35
NS
0.49*
–
0.34NS
0.22NS
0.21
–0.11
0.14
NS
–0.03NS
–0.11
NS
–0.08NS
z
0.67**
0.65**
0.73**
0.81**
0.79**
0.38
–
0.21
NS
0.34NS
0.31NS
0.18
NS
–0.15
NS
0.17
NS
–0.05NS
0.07
NS
0.02NS
§
NS
0.78**
0.26
NS
0.40§
0.41
§
0.16NS
–
0.16
0.29
NS
0.67**
0.43§
0.58**
–0.42
0.37
z
0.11NS
0.10
NS
0.16NS
0.12
0.49*
0.68**
0.73**
0.82**
–
0.10
NS
0.50*
0.00
NS
0.47§
0.32NS
NS
–0.29
NS
0.41
§
–0.33NS
–0.03
NS
–0.09NS
§
NS
0.20
0.68**
0.75**
0.86**
–
0.69**
NS
0.56**
0.10
NS
0.46§
0.43§
0.40
–0.27
0.28
NS
0.06NS
0.08
NS
0.15NS
0.22
0.61**
0.79**
–
0.80**
0.52*
NS
0.43
§
–0.16
NS
0.26NS
0.26NS
0.31
NS
–0.22
NS
0.29
NS
0.11NS
0.12
NS
0.19NS
NS
NS
0.44
– §
0.68**
0.46
§
–
0.06NS
0.31NS
0.48*
0.38z
0.71**
0.33NS NS
0.36z z
0.29NS
0.07
0.36
–0.13
0.20NS
0.50*
0.54*
–0.34NS
0.24NS
0.04NS
–0.09NS
–0.04NS
NS
0.07NS
0.26NS
0.27
0.00
0.24
NS
0.14NS
0.19
NS
0.29NS
Number Long Root: shoot of stamen Days Biomass ratio Height branches Pistil length length Petal length Petal area to flower
Values above the diagonal indicate genetic correlations among traits in the drought treatment and values below the diagonal indicate genetic correlations among traits in the well-watered treatment. Values in bold represent correlations that were significant after false discovery rate correction (P , 0.05) and correlations shaded in gray indicate those for which Z-tests found significant differences in correlation coefficients across treatments. NS not significant; z P , 0.05, § P , 0.01, * P , 0.001, ** P , 0.0001.
0.05
NS
0.06NS
0.09
0.09NS
NS
0.15
0.18
NS
NS
NS
0.48§
0.04
0.15
–0.21
–
0.35
NS
0.20
NS
0.62**
0.10
Wg NS
gs
–0.01NS –0.07NS –0.33NS –0.06NS –0.16NS
NS
Petal length
NS
–0.04NS
0.29
0.08
Long stamen length
Pistil length
0.01
NS
Root:shoot ratio NS
NS
NS
0.13
0.13NS
0.14NS
Biomass
0.13 0.55**
0.76**
NS
0.00
NS
–0.56** –0.74**
LMA (g m2)
2
0.32
Narea (g m )
D C
13
0.25
NS
NS
Wg
0.15NS
0.58**
–
0.84**
gs (mol m2 s1)
–
0.83**
s )
1
Fv’/Fm’
Fv’/Fm’
A (lmol m
2
A (lmol m2 s1)
Table 4. Pearson Correlation Coefficients and Significance of Bivariate Genetic Correlations among Traits.
Edwards et al. t (FOFUJDTPG%SPVHIU3FTQPOTFJOBrassica rapa
659
660
Edwards et al. t (FOFUJDTPG%SPVHIU3FTQPOTFJOBrassica rapa
and Levin, 1991; Ungerer et al., 2002; Johnson et al., 2009). The large effects of treatment on traits associated with fitness suggest that the drought conditions imposed in the present study represent a relevant agroecological stress.
The Response of Leaf Gas-Exchange Traits to Water Regime Wg increased dramatically in response to drought. These changes in Wg across environments can be understood in light of the mechanistic partitioning of the components of
Figure 3. Correlation Coefficients for Correlations between Wg (Intrinsic Water-Use Efficiency; A/gs) and Each of the Other Traits in this Study in the Well-Watered (Black Bars) and Drought Treatments (White Bars). Bars above the center line indicate positive correlation coefficients and bars below the center line indicate negative correlation coefficients. Stars above traits indicate correlations that were significantly different across the drought and well-watered treatments (P , 0.05), as summarized by the shaded cells in Table 4. A, photosynthetic rate; Fv’/Fm’, chlorophyll fluorescence in light; gs, stomatal conductance; E, transpiration rate; Wg, intrinsic water-use efficiency (A/gs); D13C, carbon isotope discrimination; Narea, nitrogen concentration per unit leaf area; LMA, leaf mass per area.
photosynthesis. However, interpretation of the components of Wg must be made carefully to ensure that relationships do not simply represent autocorrelation (i.e. to ensure that gs is correlated to Wg because of ecophysiological and genetic mechanisms and not because gs is the denominator of the Wg calculation). Evidence for this lack of autocorrelation comes from the fact that independent measurements such as D13C show similar patterns to those found for Wg, and that the relationship between gs and Wg changed across treatments. The dramatic change in Wg across treatments was caused by large decreases in gs relative to A in drought, indicating that the loss of water from transpiration during drought was a key regulator of drought response (Chaves, 1991; Yordanov et al., 2000). The negative across-environment correlation (rGE) for Wg (Table 3 and Figure 1) was caused by differences in the extent to which gs decreased for each genotype in drought relative to the amount of decrease in A, causing a shift in rank order of genotypes across environments. Genetically, this suggests that the genes or alleles that confer an increase in Wg in one environment confer a decrease in the other environment. This negative across-environment correlation indicates that the most water-use-efficient genotypes in one treatment are the least water-use-efficient in other treatment and vice versa (Figures 1 and 2) (Stinchcombe et al., 2010). More specifically, the negative correlation is likely indicative that some genotypes are achieving the optimum trait value in each environment; that is, some genotypes express high Wg in drought and low Wg in well-watered conditions (see below). Additionally, other leaf gas-exchange traits contributed to drought response. The change in the direction of correlations between Narea and Wg with drought indicates that genotypes that partition more N to leaves will likely have more biochemical capacity and can thus better regulate their metabolism in response to drought and avoid potential down-regulation of Rubisco activity and RuBP regeneration (Flexas and Medrano, 2002; Dias and Bru¨ggemann, 2007, 2010). Such findings are supported by fertilization experiments in drought conditions, which show that increased N was associated with more efficient water use under drought (Ewers et al., 2000; Mencuccini, 2003). Furthermore, LMA was more strongly correlated with Wg under drought, indicating that the partitioning of leaf
Table 5. Pearson Correlation Coefficients and Significance of Correlations between Circadian Period at 24C (Lou et al., 2011) and All Traits Measured in the Present Study. Trait
Correlation with period in drought treatment
Correlation with period in well-watered treatment
A (lmol m2 s1)
–0.32464y
–0.32498y
Fv’/Fm’
–0.16158
–0.16227
gs (mol m
2
1
s )
Wg
–0.35576
z
–0.32355y
0.11799
–0.08924
D13C
–0.05814
0.02241
Narea (g m2)
–0.15541
0.06285
LMA (g m2)
–0.27421
–0.29036
Values in bold represent are significant at P , 0.1 after false discovery rate correction.
y
P , 0.1,
z
P , 0.05.
Edwards et al. t (FOFUJDTPG%SPVHIU3FTQPOTFJOBrassica rapa
resources into mass and area becomes more important when water is limiting. Increases in LMA indicate an increase in the demand for CO2, and this increased demand coupled with a drop in gs and, potentially, mesophyll conductance (Flexas et al., 2004), indicate that CO2 concentrations at the site of photosynthetic assimilation (i.e. within the chloroplast) will decline. If N is available, the plant can continue to photosynthesize by increasing the amount of enzymes in the lightindependent reactions. The increase in Narea with drought suggests this is a potential drought-response mechanism. Future work should investigate the entire pathway of water loss and CO2 uptake because a growing body of work shows that mesophyll conductance (i.e. the conductance from the site of Rubisco to the stomata) is a major portion of gas-supply limitation (Soolanayakanahally et al., 2009).
The Genetic Architecture of Wg, Vegetative, Reproductive, and Other Leaf Gas-Exchange Traits across Treatments G-matrix analyses that included Wg and vegetative or floral traits demonstrated dramatic shifts across environments (Tables 3 and 4, and Figure 3). All correlations between Wg and vegetative/reproductive traits were positive in the drought treatment and negative in the well-watered treatment (Figure 3), most of which (72%) were significantly different across treatments. Thus, when water was limiting, genotypes that used water conservatively (i.e. had a high Wg) were able to assimilate a higher rate of carbon per unit water lost, and ultimately accumulated more biomass and had larger flowers. In contrast, when water was freely available, genotypes that used water conservatively (i.e. had a low Wg) accumulated less biomass and had smaller floral organs. In other words, in drought, genotypes that conserved water performed best, whereas in wellwatered conditions, genotypes that did not conserve water performed best. The negative across-environment genotypic correlation for Wg (Figure 1) indicates that genotypes with the highest Wg in drought also had the lowest Wg in wellwatered conditions (Figures 1 and 2; Stinchcombe et al., 2010), both of which were associated with the largest vegetative and floral organs in their respective treatments as described above (Table 3). In contrast, genotypes with the highest Wg in well-watered conditions also had the lowest Wg in drought (Figures 1 and 2), both of which were associated with the smallest vegetative and floral organs in their respective treatments. Thus, some genotypes were capable of large changes in Wg across environments that allowed them to thrive in both water regimes, whereas other genotypes were only capable of smaller changes in Wg across treatments, and these genotypes performed poorly in both water regimes. The G-matrices of some classes of traits were more sensitive to treatments than others. G-matrices of leaf gas-exchange traits exhibited the largest differences across treatments followed by vegetative traits, while no differences were found across environments for reproductive traits (Table 4). These results are similar to those found in Avena barbata, where the G-matrix containing leaf gas-exchange traits exhibited
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larger differences across drought/well-watered treatments than morphological and performance traits (Sherrard et al., 2009). The observed differences among classes of traits across water regimes in Avena were attributed to changes in the expression of trait variances and environment-dependence of correlations involving leaf gas-exchange traits. Similarly, in the current study, Wg and several leaf gas-exchange traits were uncorrelated in well-watered conditions while being strongly correlated in drought. The lack of correlation in the wellwatered treatment is due to smaller among-line variance for Wg in the well-watered treatment, thereby limiting the expression of genetic correlations in the well-watered treatment. This result may be species-specific because previous studies in Lobelia found reduced genetic variances for gs and Wg under drought (Caruso et al., 2005). The increased numbers and greater magnitude of significant correlations among leaf gas-exchange traits in drought may indicate that drought conditions more tightly constrain the relationships within leaf gas-exchange traits—a mechanism known as functional convergence, which involves biophysical limitations along strategy axes. These types of biophysical limitations have been widely reported across species with many different traits, making up the leaf economic spectrum (Reich et al., 1997; Bucci et al., 2004; Wright et al., 2004). However, much less is known about the genetic mechanisms controlling these biophysical limitations. These differences in correlations across treatments for leaf gas-exchange traits suggest that response to selection will differ across environments (Hoffmann and Merila, 1999), and the increased significance of correlations in drought suggests that the response to selection may be more constrained in drought environments. In contrast to leaf gas-exchange traits, very few G-matrix differences were detected for vegetative traits, and no differences in G-matrices were detected for floral traits across treatments, suggesting that the response to selection for vegetative and reproductive traits would not differ across drought and wellwatered conditions. One possible explanation for why Gmatrices for reproductive traits did not differ across treatments is that the relationship among reproductive traits may be canalized. Given that the singular function of flowers is reproduction, the size and shape of floral organs are frequently coordinated and populations often maintain strong genotypic correlations among floral traits (Conner and Via, 1993; Oneil and Schmitt, 1993; Carr and Fenster, 1994; Conner and Sterling, 1995; Juenger et al., 2000; van Kleunen and Ritland, 2004; Ashman and Majetic, 2006; Brock and Weinig, 2007). Strong genetic correlations may be maintained for reproductive traits, regardless of treatment, because of pleiotropy or close linkage of genes that affect variation in multiple floral organs.
Correlations between Circadian and Leaf Gas-Exchange Traits In the present study, we found that two leaf gas-exchange traits, A and gs, were both significantly negatively correlated
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Edwards et al. t (FOFUJDTPG%SPVHIU3FTQPOTFJOBrassica rapa
with circadian period at 24C. The correlations detected between these traits are in agreement with those previously detected in the same RILs in a growth-chamber environment under well-watered conditions (Edwards et al., 2011). These results are also in agreement with previous research in other species that has shown that photosynthetic carbon assimilation (Salome et al., 2002; Dodd et al., 2004), stomatal aperture (Gorton et al., 1989; Salome et al., 2002), and stomatal conductance (Somers et al., 1998; Dodd et al., 2004) are all under circadian clock control. Moreover, several recent studies in Arabidopsis and Eucalyptus (using different approaches) have found a strong relationship between ecophysiological and circadian traits (Dodd et al., 2005; Graf et al., 2010; Solomon et al., 2010). The consistency of these correlations across growth conditions in B. rapa, as well as across a diverse set of species, suggests that a strong genetic link exists between circadian and photosynthetic gas-exchange traits in plants. However, contrary to the results of this previous study, we did not find significant correlations between circadian period and D13C. These results suggest that the expression of D13C may differ across growth-chamber and greenhouse environments. The fact that correlations between circadian period and A and gs did not change across environments and that period was not correlated with D13C or Wg in either environment suggests that circadian period may not affect the response to drought in B. rapa. This is surprising, because the circadian clock modulates the transcriptomic response to drought in both Arabidopsis and poplar (Wilkins et al., 2008, 2009). Abscisic acid (ABA) signaling, known to play critical roles in the drought response, is also gated by the clock and likely contributes to this modulation (Robertson et al., 2009). Moreover, ABA signaling feeds back on the clock to influence circadian period (Hanano et al., 2006; Robertson et al., 2009). However, we note that circadian period was measured only in a wellwatered environment, and it is possible that expression of circadian period may vary across water regimes and affect drought tolerance. To fully understand how circadian period affects drought response, future research should focus on understanding how period varies across water regimes.
Conclusions and Future Directions Future climate change may radically alter the frequency and intensity of rainfall (Lobell et al., 2008). In the face of such predicted climatic variability, increasing crop yield per unit rainfall is important for efficient and sustained production of agricultural crops; thus, increasing crop yield in drought via selection on WUE is a primary focus of plant research programs. In the present study, we found that genotypes of B. rapa differed in their ability to alter their WUE across drought and wellwatered conditions, and that genotypes with the largest variation in WUE across treatments accumulated the most biomass and had the largest reproductive organs; these genotypes may be potential targets for crop improvement. However, adding detailed anatomical, hormonal, and ecophysiological measurements will help to further identify the
specific tissues/mechanisms involved in drought response. Investigation of anatomical differences among genotypes, such as differences in stomatal, mesophyll, or vascular tissue characteristics, may help elucidate the specific tissues contributing to the observed differences among genotypes in the ability to alter WUE across environments. Another mechanism to be investigated that may affect variation in drought response in these RILs is variation in ABA signaling, which has been shown to have an important role in plant drought response (Zhang et al., 2006). Possible ecophysiological mechanisms to be investigated include how gas supply limitations may be caused by the regulation of mesophyll conductance by aquaporins, which may regulate CO2 exchange into chloroplast, leaf hydraulics, and drought response (Miyazawa et al., 2008; Sadok and Sinclair, 2009). Another mechanism may arise from the demand side of photosynthesis, which is metabolically regulated under both the Rubisco and RuBP regeneration portion of biochemical demand for CO2 (Dias and Bru¨ggemann, 2010). However, the challenge will be to develop techniques that can rapidly assess these emerging ecophysiological traits so that they may be employed for quantitative genetics studies. Once we have a greater understanding of the ecophysiological mechanisms affecting the response of WUE to water regime, we will genotype these traits in the full set of RILs to map QTL that will potentially identify genetic regions that pleiotropically affect multiple traits, with the ultimate goal of identifying candidate genes that affect variation in drought response and tolerance, which can be used to potentially determine the limits of response to natural and anthropogenic selection. These experiments will provide a deeper insight into the evolutionary implications of drought stress in natural populations and crops of B. rapa.
METHODS Study Species and Plant Material Brassica rapa is an oilseed and vegetable crop species whose native range extends from the western Mediterranean to Central Asia (Gomez Campo, 1999). Crops of B. rapa, including varieties cultivated for oil (B. rapa subsp. oleifera), as root vegetables (B. rapa subsp. rapa, or turnip), and as leafy vegetables (B. rapa subsp. chinensis, or pak choi, and B. rapa subsp. pekinensis, or Chinese cabbage), are cultivated worldwide. The species also occurs commonly in naturalized populations in association with crop fields (Dorn and Mitchell-Olds, 1991). The RILs used in this study resulted from a cross between two inbred genotypes of B. rapa, R500, and IMB211 (Iniguez-Luy et al., 2009). The IMB211 genotype was derived from the Wisconsin Fast Plant population, and artificial selection for rapid generation time in IMB211 resembles that experienced by naturalized populations and agricultural weeds of this species (Dorn and Mitchell-Olds, 1991; Mitchell-Olds, 1996). The R500 genotype is a seed-oil cultivar planted in India for at least 3000 years (Prakash and Hinata, 1980). Given
Edwards et al. t (FOFUJDTPG%SPVHIU3FTQPOTFJOBrassica rapa
their divergent selection histories, genetic variation segregating in the RILs may resemble that segregating in crop 3 wild hybrids found commonly in nature (Adler et al., 1993). Furthermore, the parents of the RILs differ in life history, vegetative, reproductive, and leaf gas-exchange traits (Edwards et al., 2009; Brock et al., 2010; Edwards and Weinig, 2011; Edwards et al., 2011; Haselhorst et al., 2011), suggesting that this is an interesting population in which to investigate trait (co)variation in response to drought. The recombinant inbred lines (RILs) used in this study were produced by crossing the two parents, resulting in 150 RILs (Iniguez-Luy et al., 2009). We previously characterized variation in leaf gas-exchange, vegetative, and reproductive traits in the full set of 150 RILs of B. rapa in a well-watered environment (Edwards and Weinig, 2011; Edwards et al., 2011). To select RILs for the present experiment, we used data from these previous experiments to plot the relationship between circadian period and photosynthesis (A), and we selected 40 RILs that maximized the range of variation for leaf gas-exchange and circadian traits present in the complete set of RILs. Specifically, we selected (1) 10 RILs with the largest values of both period and A, (2) 10 RILs with the smallest values for both period and A, (3) 10 RILs that had both the largest values for period and smallest values of A, and (4) 10 RILS that had both the smallest values of period and largest values of A. These lines were randomly selected with regard to genotypic information.
Experimental Design In each treatment, we planted eight replicates of each of the 40 RILs and the two parents (n = 336 individuals per treatment). Within each treatment, replicate plants within a genotype were fully randomized and arranged into flats. The flats of the two treatments were arranged in a checkerboard array within the greenhouse and rotated weekly to minimize spatial environmental variation. For each replicate plant, three seeds were planted in 520-ml pots filled with a 50/50 combination of Metromix 200 (Sun-Gro Horticulture, Vancouver, BC, Canada) and Fafard custom mix (Conrad Fafard, Inc., Agawam, MA) soils, with 2 ml of Osmocote 18–6–12 fertilizer (Scotts Miracle Grow, Marysville, OH, USA). Seeds were cold/dark stratified at 4C for 3 d and transferred to the Williams Conservatory greenhouse at the University of Wyoming. Plants received 15-h/9-h light/dark natural light cycles in the greenhouse, with temperatures fluctuating diurnally from 18 to 28C. Developing seedlings were watered daily for the first 15 d, during which time germinants were thinned to one seedling closest to the center of the pot. Treatments were imposed after plants germinated and developed true leaves, 15 d after planting. For all plants, the volumetric water content (VWC) of the soil was monitored throughout the experiment using an EC-5 soil moisture meter with an ECH2O Check analog read-out system (Decagon Devices, Pullman, WL, USA). Plants in the wellwatered treatment were watered daily to maintain moist soil conditions. For the drought treatment, our goals were to impose drought conditions similar to those experienced in
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agricultural settings that cause losses in yield, and to impose relatively uniform levels of drought stress across genotypes, despite genotypic differences in phenology or plant size. Thus, each plant in the drought treatment was watered with 25 ml each time it wilted and its soil VWC was at or below 8.00%. The average VWC at flowering was 29.77% in the well-watered treatment and 4.42% in the drought treatment.
Trait Measurements Plants were checked daily for bolting (i.e. when buds differentiated from the apical meristem). At bolting, leaf gas exchange was measured on six individuals per genotype per treatment. Gas-exchange measurements were carried out on a young, fully expanded leaf using a steady-state gas-exchange system equipped with a leaf chamber fluorometer (LICOR-6400XT; LICOR Biosciences Inc., Lincoln, NE, USA). We measured photosynthesis (A), chlorophyll fluorescence in light (Fv#/Fm’, or maximum photosystem II efficiency in light, a key measurement of the light-dependent reactions of photosynthesis), and stomatal conductance (gs), as described in Edwards et al. (2011), except leaf temperature was maintained at 26C to more closely match daytime greenhouse temperature conditions. These measurements were used to calculate intrinsic water-use efficiency (Wg) for each individual by dividing A by gs (Seibt et al., 2008). A leaf was then removed from all replicates of each genotype at bolting and scanned, oven dried, and weighed. Leaf area was measured from the scanned leaf images using ImageJ (Abramoff et al., 2004) and used to calculate leaf mass per area (LMA; g m2), which is mechanistically related to both photosynthetic gas supply and biochemical demand (Reich et al., 1997; Wright et al., 2004). Carbon isotope (d13C) composition, a time-integrated estimate of WUE, and mass-based leaf nitrogen composition were analyzed on six individuals of each genotype in each treatment. The oven-dried leaves collected at bolting were ground and analyzed using an elemental analyzer (ECS 4010, Costech Analytical Technologies, Inc., Valencia, CA, USA) coupled to a continuous-flow inlet isotope ratio mass spectrometer (CFIRMS; Delta-plus XP, Thermo Scientific, Waltham, MA, USA). d13C values were reported in parts per thousand relative to Vienna Peedee Belemnite (VPDB). The precision of repeated measurements of laboratory standards was ,0.1%. Leaf nitrogen concentration per unit of leaf area (Narea), which is often positively related to leaf biochemical demand for CO2 (Reich et al., 1997), was calculated using mass-based leaf nitrogen concentrations and LMA. Leaf carbon isotope discrimination (D13C) was calculated for each individual as (Farquhar et al., 1989): D13 Cð%Þ = ðd13 Cair d13 Cleaf Þ=1 + ðd13 Cleaf =1000Þ: To determine d13C of the greenhouse air, we collected two air samples in septa-capped vials during each of three time intervals (9am, 12pm, and 4pm) once every 3 d, beginning when plants developed true leaves and continuing until we finished collecting leaves for isotope analysis (totaling seven
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collection days over a 3-week period). d13Cair of the samples was analyzed using a GasBench II coupled to a Delta-plus XP CF-IRMS system. The d13Cair values were corrected to the VPDB standard using CO2-in-air laboratory working standards calibrated with CO2-in-air standards obtained from NOAA-CMDL. Repeated measurements with CO2-in-air laboratory working standards had a precision of ,0.1%. All stable isotope analyses were performed at the University of Wyoming Stable Isotope Facility. The d13C of the greenhouse air showed no progressive changes over the course of the experiment, so all measurements were averaged, resulting in a d13Cair of –9.7. After plants had bolted, they were checked for flowering each day at 2pm; flowering was scored when the sepals opened and petals became visible or when a flower bud senesced prior to opening. Plants were scored for flower maturity when the anthers on the third and fourth flowers of the main stem dehisced and began to shed pollen. At flower maturity, the third and fourth flowers were removed and placed in 75% ethanol for subsequent dissection. When the third and fourth flowers were not viable, we collected the first two viable flowers (generally not later than the 10th flower). Plants were collected at flower maturity, at which time the height of the plants and number of axillary branches was recorded, and the above-ground biomass and taproot (a proxy for total belowground biomass; Edwards et al., 2009) were removed, oven dried, and weighed. Below- and above-ground biomasses were summed to estimate total biomass. We also estimated the root:shoot ratio to provide an understanding of relative biomass allocation. Preserved flowers were dissected and photographed, and floral organ sizes were measured from the digital image using ImageJ (Rasband, 1997–2007); measured floral traits included pistil length, short stamen length (i.e. the length of a representative short stamen), long stamen length (i.e. the length of a representative long stamen), petal length, and petal area. However, because short stamen length and long stamen length were highly correlated (r = 0.90, P , 0.0001) and demonstrated highly similar patterns of correlations with other traits, short stamen length is not presented further. For data analysis, measurements of the two flowers from each individual were averaged. In summary, leaf gas-exchange traits included in this study were photosynthesis (A), chlorophyll fluorescence in light (Fv#/Fm’), stomatal conductance (gs), intrinsic water-use efficiency (Wg), carbon isotope discrimination (D13C), leaf nitrogen per unit area (Narea), and leaf mass per area (LMA). Vegetative traits included in this study were biomass, the ratio of above-ground to below-ground biomass (root:shoot ratio), number of secondary branches, and plant height. To estimate reproductive traits, we included pistil length, long stamen length, petal length, petal area, and the number of days from planting to flowering (days to flowering). Circadian period values for the RILs were collected as part of an earlier experiment on temperature compensation (Lou et al., 2011).
Quantitative Genetic and Correlation Analyses SAS ver. 9.2 was used for all ANOVA and correlation analyses. In each treatment, we used restricted maximum likelihood (REML in PROC MIXED) to estimate the random effects of genotype and flat (as a blocking term) on each phenotypic trait. The table-wise significance values for these ANOVAs were corrected for multiple comparisons by controlling the false discovery rate (FDR; Benjamini and Hochberg, 1995). The variance components estimated from this analysis were used to estimate the ratio VG/VP , or broad-sense heritability, where VG is the among-genotype variance component in each treatment and VP is the sum of all variance components for a trait in each treatment. We tested for genotypic differences in response to treatment by carrying out a mixed-model nested ANOVA across the two treatments (PROC MIXED, SAS ver. 9.2). We evaluated the fixed effect of treatment and the random effects of genotype, flat nested within treatment, and the genotype 3 treatment interaction on each trait. Table-wise FDR correction was used to control for multiple comparisons. We also used this analysis to estimate the genotypic values of each trait in each treatment as best linear unbiased predictors (BLUPs) (Robinson, 1991; Littell et al., 2006). To further assess how the genetic architecture of each trait differed across treatments, we estimated rGE, the genotypic correlation of each trait across the two treatments (Fry et al., 1996; Gurganus et al., 1998; Vieira et al., 2000). First, we used a multivariate ANOVA approach using restricted maximum likelihood, which takes measurement error into account, to estimate correlations for each trait across the two treatments (Messina and Fry, 2003; Holland, 2006) (SAS PROC mixed). We also used point estimates of the genotypic values of each trait in each treatment (as BLUPs) to estimate Pearson product-moment correlation coefficients for each trait across treatments (SAS PROC CORR), which does not take measurement error into account. Estimates of rGE indicate the extent to which the same genetic loci are expressed and alleles have the same function across treatments; estimates of rGE approaching 1 suggest that expression of the trait across environments has a similar genetic basis, whereas estimates approaching 0 suggest that different genetic loci affect the trait or alleles differ in function across treatments (Fry et al., 1996; Gurganus et al., 1998; Vieira et al., 2000). ANOVA analyses for each trait across the two treatments were used to determine the significance of rGE; rGE is significantly different from 1 when the genotype 3 treatment interaction is significant, and significantly different from 0 when the among-genotype variance is significant (Gurganus et al., 1998; Vieira et al., 2000). To assess the relationship among traits within each treatment, we used the multivariate ANOVA approach described above to estimate correlations among traits. To assess whether these correlations were significantly different from 0, we used a likelihood ratio test to compare a model in which correlations were unconstrained against 1 in which correlations were constrained to 0 (Messina and Fry, 2003; Heath, 2010). We also used point estimates of the genotypic values (as BLUPs) of each
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trait in each treatment to estimate bivariate Pearson productmoment correlation coefficients among traits (SAS PROC CORR). The significance values of all bivariate correlations were corrected for multiple comparisons by controlling the FDR. Finally, to assess correlations between circadian and leaf gas-exchange traits, we estimated the correlations between circadian period (Lou et al., 2011) and all traits measured in the present study in both the drought and well-watered treatments. For these correlations, we used estimates of circadian period at 24C (Lou et al., 2011) because this temperature is the closest to that experienced by plants in the present experiment. However, many genotypes lack estimates of period at 24C because of decreased accuracy of estimates at that temperature (Lou et al., 2011), thus reducing the number of genotypes for these genetic correlations to 33. FDR correction was used to control for multiple comparisons.
G-Matrix Analyses To understand how the genetic variance–co-variance matrices changed across treatments, we used Common Principal Components (CPC) analysis, as implemented in the program CPCrand (Phillips and Arnold, 1999). CPCrand compares the structure of two matrices using a ‘jump-up’ approach, testing whether the two matrices (1) are unrelated, (2) share CPCs, (3) are proportional, or (4) are equal, each of which is tested against the hypothesis that the matrices are unrelated. We used BLUPs from each genotype in each treatment and the ‘Phenotypic analysis’ option in CPCrand to estimate G-matrices as if they were phenotypic variance–co-variance matrices, with 5000 randomizations following Stinchcombe et al. (2009). To analyze whether traits with different developmental bases differed across treatments, we tested for differences in G-matrices including (1) only leaf gas-exchange traits, (2) only vegetative traits, and (3) only reproductive traits. To measure whether differences in the expression of WUE across drought and well-watered conditions affected reproductive and vegetative traits critical to fitness, we tested for differences in G-matrices including (1) Wg and vegetative traits and (2) Wg and reproductive traits. Whereas CPCrand assesses differences in the multivariate relationship among traits, we were also interested in understanding which specific bivariate correlations among traits differed significantly across drought and non-drought treatments and thus contributed to matrix differences; we therefore used Fisher’s Z-tests to identify bivariate correlations that were significantly different across treatments.
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REFERENCES Abramoff, M.D., Magalhaes, P.J., and Ram, S.J. (2004). Image processing with ImageJ. Biophotonics International. 11, 36–42. Adler, L.S., Wikler, K., Wyndham, F.S., Linder, C.R., and Schmitt, J. (1993). Potential for persistence of genes escaped from canola: germination cues in crop, wild, and crop-wild hybrid Brassica rapa. Funct. Ecol. 7, 736–745. Allen, C.D., and Breshears, D.D. (1998). Drought-induced shift of a forest-woodland ecotone: rapid landscape response to climate variation. Proc. Natl Acad. Sci. U S A. 95, 14839–14842. Allen, C.D., et al. (2010). A global overview of drought and heatinduced tree mortality reveals emerging climate change risks for forests. For. Ecol Manage. 259, 660–684. Ashman, T.L., and Majetic, C.J. (2006). Genetic constraints on floral evolution: a review and evaluation of patterns. Heredity. 96, 343–352. Aspelmeier, S., and Leuschner, C. (2006). Genotypic variation in drought response of silver birch (Betula pendula): leaf and root morphology and carbon partitioning. Trees—Structure and Function. 20, 42–52. Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B–Methodol. 57, 289–300. Bennington, C.C., and McGraw, J.B. (1996). Environmentdependence of quantitative genetic parameters in Impatiens pallida. Evolution. 50, 1083–1097. Breshears, D.D., Myers, O.B., Meyer, C.W., Barnes, F.J., Zou, C.B., Allen, C.D., McDowell, N.G., and Pockman, W.T. (2009). Tree die-off in response to global change-type drought: mortality insights from a decade of plant water potential measurements. Front. Ecol. Environ. 7, 185–189. Brock, M.T., and Weinig, C. (2007). Plasticity and environmentspecific covariances: an investigation of floral-vegetative and within flower correlations. Evolution. 61, 2913–2924. Brock, M.T., Dechaine, J.M., Iniguez-Luy, F.L., Maloof, J.N., Stinchcombe, J.R., and Weinig, C. (2010). Floral genetic architecture: an examination of QTL architecture underlying floral (co)variation across environments. Genetics. 186, 1451–1465. Bucci, S.J., Goldstein, G., Meinzer, F.C., Scholz, F.G., Franco, A.C., and Bustamante, M. (2004). Functional convergence in hydraulic architecture and water relations of tropical savanna trees: from leaf to whole plant. Tree. Physiol. 24, 891–899. Carr, D.E., and Fenster, C.B. (1994). Levels of genetic variation and covariation for Mimulus (Scrophulariaceae) floral traits. Heredity. 72, 606–618.
This research was supported by NSF grant #DBI 0605736.
Carroll, A.B., Pallardy, S.G., and Galen, C. (2001). Drought stress, plant water status, and floral trait expression in fireweed, Epilobium angustifolium (Onagraceae). Am. J. Bot. 88, 438–446.
ACKNOWLEDGMENTS
Caruso, C.M. (2006). Plasticity of inflorescence traits in Lobelia siphilitica (Lobeliaceae) in response to soil water availability. Am. J. Bot. 93, 531–538.
We thank A. Hemenway and O. Deninno for assistance with data collection; D. Williams and M. Brock for interesting discussions and assistance with data analysis; and F. Iniguez-Luy for providing seeds. No conflict of interest declared.
Caruso, C.M., Maherali, H., Mikulyuk, A., Carlson, K., and Jackson, R.B. (2005). Genetic variance and covariance for physiological traits in Lobelia: are there constraints on adaptive evolution? Evolution. 59, 826–837.
FUNDING
666
Edwards et al. t (FOFUJDTPG%SPVHIU3FTQPOTFJOBrassica rapa
Chaves, M.M. (1991). Effects of water deficits on carbon assimilation. J. Exp. Bot. 42, 1–16. Chaves, M.M., Maroco, J.P., and Pereira, J.S. (2003). Understanding plant responses to drought—from genes to the whole plant. Funct. Plant Biol. 30, 239–264.
Edwards, C.E., Haselhorst, M.S.H., McKnite, A.M., Ewers, B.E., Williams, D.G., and Weinig, C. (2009). Genotypes of Brassica rapa respond differently to plant-induced variation in air CO2 concentration in growth chambers with standard and enhanced venting. Theor. Appl. Genet. 119, 991–1004.
Condit, R. (1998). Ecological implications of changes in drought patterns: shifts in forest composition in Panama. Clim. Change. 39, 413–427.
Ehdaie, B., Hall, A.E., Farquhar, G.D., Nguyen, H.T., and Waines, J.G. (1991). Water-use efficiency and carbon isotope discrimination in wheat. Crop Sci. 31, 1282–1288.
Condit, R., Hubbell, S.P., and Foster, R.B. (1995). Mortality rates of 205 neotropical tree and shrub species and the impact of a severe drought. Ecol. Monogr. 65, 419–439.
Ewers, B.E., Oren, R., and Sperry, J.S. (2000). Influence of nutrient versus water supply on hydraulic architecture and water balance in Pinus taeda. Plant Cell Environ. 23, 1055–1066.
Condon, A.G., Richards, R.A., and Farquhar, G.D. (1993). Relationships between carbon-isotope discrimination, water-use efficiency and transpiration efficiency for dryland wheat. Aust. J. Agric. Res. 44, 1693–1711.
Falconer, D.S., and Mackay, T.F.C. (1996). Introduction to Quantitative Genetics (New York: Longman).
Condon, A.G., Richards, R.A., Rebetzke, G., and Farquhar, G.D. (2002). Improving intrinsic water-use efficiency and crop yield. Crop Sci. 42, 122–131. Condon, A.G., Richards, R.A., Rebetzke, G.J., and Farquhar, G.D. (2004). Breeding for high water-use efficiency. J. Exp. Bot. 55, 2447–2460. Conner, J., and Via, S. (1993). Patterns of phenotypic and genetic correlations among morphological and life-history traits in wild radish, Raphanus raphanistrum. Evolution. 47, 704–711. Conner, J.K., and Sterling, A. (1995). Testing hypotheses of functional relationships: a comparative survey of correlation patterns among floral traits in 5 insect-pollinated plants. Am. J. Bot. 82, 1399–1406. Dias, M., and Bru¨ggemann, W. (2007). Differential inhibition of photosynthesis under drought stress in Flaveria species with different degrees of development of the C4 syndrome. Photosynthetica. 45, 75–84.
Farquhar, G.D., Ehleringer, J.R., and Hubick, K.T. (1989). Carbon isotope discrimination and photosynthesis. Annu. Rev. Plant Physiol. Plant Mol. Biol. 40, 503–537. Flexas, J., and Medrano, H. (2002). Drought inhibition of photosynthesis in C3 plants: stomatal and non-stomatal limitations revisited. Ann. Bot. 89, 183–189. Flexas, J., Bota, J., Loreto, F., Cornic, G., and Sharkey, T.D. (2004). Diffusive and metabolic limitations to photosynthesis under drought and salinity in C3 plants. Plant Biol. 6, 269–279. Foley, J.A., et al. (2011). Solutions for a cultivated planet. Nature. 478, 337–342. Fry, J.D., Heinsohn, S.L., and Mackay, T.F.C. (1996). The contribution of new mutations to genotype-environment interaction for fitness in Drosophila melanogaster. Evolution. 50, 2316–2327. Gomez Campo, C. (1999). Biology of Brassica Coenospecies (Amsterdam: Elsevier). Gorton, H.L., Williams, W.E., Binns, M.E., Gemmell, C.N., Leheny, E.A., and Shepherd, A.C. (1989). Circadian stomatal rhythms in epidermal peels from Vicia faba. Plant Physiol. 90, 1329–1334.
Dias, M., and Bru¨ggemann, W. (2010). Limitations of photosynthesis in Phaseolus vulgaris under drought stress: gas exchange, chlorophyll fluorescence and Calvin cycle enzymes. Photosynthetica. 48, 96–102.
Graf, A., Schlereth, A., Stitt, M., and Smith, A.M. (2010). Circadian control of carbohydrate availability for growth in Arabidopsis plants at night. Proc. Natl Acad. Sci. U S A. 107, 9458–9463.
Dodd, A.N., Parkinson, K., and Webb, A.A.R. (2004). Independent circadian regulation of assimilation and stomatal conductance in the ztl-1 mutant of Arabidopsis. New Phytol. 162, 63–70.
Gurganus, M.C., Fry, J.D., Nuzhdin, S.V., Pasyukova, E.G., Lyman, R.F., and Mackay, T.F.C. (1998). Genotype–environment interaction at quantitative trait loci affecting sensory bristle number in Drosophila melanogaster. Genetics. 149, 1883–1898.
Dodd, A.N., Salathia, N., Hall, A., Kevei, E., Toth, R., Nagy, F., Hibberd, J.M., Millar, A.J., and Webb, A.A.R. (2005). Plant circadian clocks increase photosynthesis, growth, survival, and competitive advantage. Science. 309, 630–633.
Hanano, S., Domagalska, M.A., Nagy, F., and Davis, S.J. (2006). Multiple phytohormones influence distinct parameters of the plant circadian clock. Genes Cells. 11, 1381–1392.
Dorn, L.A., and Mitchell-Olds, T. (1991). Genetics of Brassica campestris. 1. Genetic constraints on evolution of life-history characters. Evolution. 45, 371–379.
Haselhorst, M.S.H., Edwards, C.E., Rubin, M.J., and Weinig, C. (2011). Genetic architecture of life-history traits and environmental-specific tradeoffs. Mol. Ecol. 20, 4042–4058.
Dudley, S.A. (1996). Differing selection on plant physiological traits in response to environmental water availability: a test of adaptive hypotheses. Evolution. 50, 92–102.
Heath, K.D. (2010). Intergenomic epistasis and coevolutionary constraint in plants and rhizobia. Evolution. 64, 1446–1458.
Edwards, C.E., and Weinig, C. (2011). The quantitative-genetic and QTL architecture of trait integration and modularity in Brassica rapa across three simulated seasonal settings. Heredity. 106, 661–677. Edwards, C.E., Ewers, B.E., Williams, D.G., Xie, Q., Lou, P., Xu, X., McClung, C.R., and Weinig, C. (2011). The genetic architecture of ecophysiological and circadian traits in Brassica rapa. Genetics. 189, 375–390.
Heschel, M.S., and Riginos, C. (2005). Mechanisms of selection for drought stress tolerance and avoidance in Impatiens capensis (Balsaminaceae). Am. J. Bot. 92, 37–44. Hoffmann, A.A., and Merila, J. (1999). Heritable variation and evolution under favourable and unfavourable conditions. Trends Ecol. Evol. 14, 96–101. Holland, J.B. (2006). Estimating genotypic correlations and their standard errors using multivariate restricted maximum likelihood estimation with SAS Proc MIXED. Crop Sci. 46, 642–654.
Edwards et al. t (FOFUJDTPG%SPVHIU3FTQPOTFJOBrassica rapa
Iniguez-Luy, F., Lukens, L., Franham, M.W., Amasino, R.M., and Osborn, T.C. (2009). Development of public immortal mapping populations, molecular markers and linkage maps for rapid cycling Brassica rapa and B. oleracea. Theor. Appl. Genet. 120, 31–43. Johnson, D.A., Asay, K.H., Tieszen, L.L., Ehleringer, J.R., and Jefferson, P.G. (1990). Carbon isotope discrimination: potential in screening cool-season grasses for water-limited environments. Crop Sci. 30, 338–343. Johnson, M.T.J., Agrawal, A.A., Maron, J.L., and Salminen, J.P. (2009). Heritability, covariation and natural selection on 24 traits of common evening primrose (Oenothera biennis) from a field experiment. J. Evol. Biol. 22, 1295–1307. Jones, H.G. (2007). Monitoring plant and soil water status: established and novel methods revisited and their relevance to studies of drought tolerance. J. Exp. Bot. 58, 119–130.
667
conductance to CO2 diffusion in tobacco leaves grown under long-term drought. Funct. Plant Biol. 35, 553–564. Oneil, P., and Schmitt, J. (1993). Genetic constraints on the independent evolution of male and female reproductive characters in the tristylous plant Lythrum salicaria. Evolution. 47, 1457–1471. Phillips, P.C., and Arnold, S.J. (1999). Hierarchical comparison of genetic variance-covariance matrices. I. Using the Flury hierarchy. Evolution. 53, 1506–1515. Prakash, S., and Hinata, K. (1980). Taxonomy, cytogenetics and origin of crop Brassicas, a review. Opera Botanica. 55, 1–57. Rebetzke, G., Condon, A., Farquhar, G., Appels, R., and Richards, R. (2008). Quantitative trait loci for carbon isotope discrimination are repeatable across environments and wheat mapping populations. Theor. Appl. Genet. 118, 123–137.
Juenger, T., Purugganan, M., and Mackay, T.F.C. (2000). Quantitative trait loci for floral morphology in Arabidopsis thaliana. Genetics. 156, 1379–1392.
Rebetzke, G.J., Condon, A.G., Richards, R.A., and Farquhar, G.D. (2002). Selection for reduced carbon isotope discrimination increases aerial biomass and grain yield of rainfed bread wheat. Crop Sci. 42, 739–745.
Lafitte, H.R., Price, A.H., and Courtois, B. (2004). Yield response to water deficit in an upland rice mapping population: associations among traits and genetic markers. Theor. Appl. Genet. 109, 1237–1246.
Reich, P.B., Walters, M.B., and Ellsworth, D.S. (1997). From tropics to tundra: global convergence in plant functioning. Proc. Natl Acad. Sci. U S A. 94, 13730–13734.
Lambrecht, S.C., and Dawson, T.E. (2007). Correlated variation of floral and leaf traits along a moisture availability gradient. Oecologia. 151, 574–583. Lande, R. (1979). Quantitative-genetic analysis of multivariate evolution, applied to brain-body size allometry. Evolution. 33, 402–416. Littell, R.C., Milliken, G.A., Stroup, W.W., Wolfinger, R.D., and Oliver, S. (2006). SAS for Mixed Models, 2nd edn (Cary, NC: SAS Institute, Inc.).
Robertson, F., Skeffington, A., Gardner, M., and Webb, A.A.R. (2009). Interactions between circadian and hormonal signalling in plants. Plant Mol. Biol. 69, 419–427. Robinson, G. (1991). That BLUP is a good thing: the estimation of random effects. Statistical Science. 6, 15–51. Sadok, W., and Sinclair, T.R. (2009). Genetic variability of transpiration response to vapor pressure deficit among soybean (Glycine max (L.) Merr.) genotypes selected from a recombinant inbred line population. Field Crops Res. 113, 156–160.
Lobell, D.B., Burke, M.B., Tebaldi, C., Mastrandrea, M.D., Falcon, W.P., and Naylor, R.L. (2008). Prioritizing climate change adaptation needs for food security in 2030. Science. 319, 607–610.
Salome, P.A., Michael, T.P., Kearns, E.V., Fett-Neto, A.G., Sharrock, R.A., and McClung, C.R. (2002). The out of phase 1 mutant defines a role for PHYB in circadian phase control in Arabidopsis. Plant Physiol. 129, 1674–1685.
Lou, P., Xie, Q., Xu, X., Edwards, C., Brock, M., Weinig, C., and McClung, C. (2011). Genetic architecture of the circadian clock and flowering time in Brassica rapa. Theor. Appl. Genet. 123, 397–409.
Schwaegerle, K.E., and Levin, D.A. (1991). Quantitative genetics of fitness traits in a wild population of Phlox. Evolution. 45, 169–177.
McDowell, N. (2011). Mechanisms linking drought, hydraulics, carbon metabolism, and vegetation mortality. Plant Physiol. 155, 1051–1059.
Seibt, U., Rajabi, A., Griffiths, H., and Berry, J.A. (2008). Carbon isotopes and water use efficiency: sense and sensitivity. Oecologia. 155, 441–454.
McDowell, N., et al. (2008). Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought? New Phytol. 178, 719–739.
Sherrard, M.E., Maherali, H., and Latta, R.G. (2009). Water stress alters the genetic architecture of functional traits associated with drought adaptation in Avena barbata. Evolution. 63, 702–715.
Mencuccini, M. (2003). The ecological significance of long-distance water transport: short-term regulation, long-term acclimation and the hydraulic costs of stature across plant life forms. Plant Cell Environ. 26, 163–182.
Solomon, O.L., Berger, D.K., and Myburg, A.A. (2010). Diurnal and circadian patterns of gene expression in the developing xylem of Eucalyptus trees. S. Afr. J. Bot. 76, 425–439.
Messina, F.J., and Fry, J.D. (2003). Environment-dependent reversal of a life history trade-off in the seed beetle Callosobruchus maculatus. J. Evol. Biol. 16, 501–509. Mitchell-Olds, T. (1996). Pleiotropy causes long-term genetic constraints on life-history evolution in Brassica rapa. Evolution. 50, 1849–1858. Miyazawa, S.-I., Yoshimura, S., Shinzaki, Y., Maeshima, M., and Miyake, C. (2008). Deactivation of aquaporins decreases internal
Somers, D.E., Webb, A.A.R., Pearson, M., and Kay, S.A. (1998). The short-period mutant, toc1-1, alters circadian clock regulation of multiple outputs throughout development in Arabidopsis thaliana. Development. 125, 485–494. Soolanayakanahally, R.Y., Guy, R.D., Silim, S.N., Drewes, E.C., and Schroeder, W.R. (2009). Enhanced assimilation rate and water use efficiency with latitude through increased photosynthetic capacity and internal conductance in balsam poplar (Populus balsamifera L.). Plant Cell Environ. 32, 1821–1832.
668
Edwards et al. t (FOFUJDTPG%SPVHIU3FTQPOTFJOBrassica rapa
Steppan, S.J., Phillips, P.C., and Houle, D. (2002). Comparative quantitativegenetics:evolutionoftheG matrix.TrendsEcol.Evol. 17, 320–327. Stinchcombe, J.R., Izem, R., Heschel, M.S., McGoey, B.V., and Schmitt, J. (2010). Across-environment genetic correlations and the frequency of selective environments shape the evolutionary dynamics of growth rate in Impatiens capensis. Evolution. 64, 2887–2903. Stinchcombe, J.R., Weinig, C., Heath, K.D., Brock, M.T., and Schmitt, J. (2009). Polymorphic genes of major effect: consequences for variation, selection and evolution in Arabidopsis thaliana. Genetics. 182, 911–922. Sun, Z.J., Livingston, N.J., Guy, R.D., and Ethier, G.J. (1996). Stable carbon isotopes as indicators of increased water use efficiency and productivity in white spruce (Picea glauca (Moench) Voss) seedlings. Plant Cell Environ. 19, 887–894. Ungerer, M.C., Halldorsdottir, S.S., Modliszewski, J.L., Mackay, T.F.C., and Purugganan, M.D. (2002). Quantitative trait loci for inflorescence development in Arabidopsis thaliana. Genetics. 160, 1133–1151. van Kleunen, M., and Ritland, K. (2004). Predicting evolution of floral traits associated with mating system in a natural plant population. J. Evol. Biol. 17, 1389–1399.
Vieira, C., Pasyukova, E.G., Zeng, Z.B., Hackett, J.B., Lyman, R.F., and Mackay, T.F.C. (2000). Genotype–environment interaction for quantitative trait loci affecting life span in Drosophila melanogaster. Genetics. 154, 213–227. Wilkins, O., Brautigam, K., and Campbell, M.M. (2008). Time of day shapes Arabidopsis drought transcriptomes. Plant J. 63, 715–727. Wilkins, O., Waldron, L., Nahal, H., Provart, N.J., and Campbell, M.M. (2009). Genotype and time of day shape the Populus drought response. Plant J. 60, 703–715. Wright, I.J., et al. (2004). The worldwide leaf economics spectrum. Nature. 428, 821–827. Yang, S., Vanderbeld, B., Wan, J., and Huang, Y. (2010). Narrowing down the targets: towards successful genetic engineering of drought-tolerant crops. Mol. Plant. 3, 469–490. Yordanov, I., Velikova, V., and Tsonev, T. (2000). Plant responses to drought, acclimation, and stress tolerance. Photosynthetica. 38, 171–186. Zhang, J., Jia, W., Yang, J., and Ismail, A.M. (2006). Role of ABA in integrating plant responses to drought and salt stresses. Field Crops Res. 97, 111–119.
