Evolution, 60(12), 2006, pp. 2478–2489
THE ADAPTIVE SIGNIFICANCE OF DROUGHT ESCAPE IN AVENA BARBATA,
AN ANNUAL GRASS
MARK E. SHERRARD1,2
1 Department
AND
HAFIZ MAHERALI1,3
of Integrative Biology, University of Guelph, Guelph, Ontario N1G 2W1, Canada
2 E-mail: [email protected]
3 E-mail: [email protected]
Abstract. Drought strongly influences plant productivity, suggesting that water limitation has shaped the evolution
of many plant physiological traits. One functional strategy that plants employ to cope with decreasing water availability
is drought escape. For drought-escaping species, high metabolic activity (gas exchange) and rapid growth are hypothesized to confer a fitness advantage, because this enables a plant to complete its life cycle before the most intense
period of drought. By growing an annual grass species (Avena barbata) under well-watered or water-limited conditions
in a greenhouse, we directly tested whether high photosynthesis, increased stomatal opening, and early flowering are
adaptive under drought. We measured phenotypic selection on instantaneous gas exchange and flowering time as well
as the underlying biochemical traits that regulate photosynthesis. We found strong selection for earlier flowering in
the dry environment, but no evidence that increased photosynthesis was adaptive under drought. Photosynthetic rate
(A) and stomatal conductance (gs) were both adaptively neutral in the dry environment. Increased photosynthetic
capacity (Amax) was maladaptive in the dry environment, perhaps because of the respiratory cost associated with
maintaining excess enzyme and substrate capacity. There was no correlational selection on the combination of physiology and flowering time in the dry environment, suggesting that accelerated development and high gas exchange
may not need to be tightly linked to promote drought escape. In contrast, there was selection for both high photosynthetic
function (Amax and A) and early flowering in the well-watered environment. These combinations of traits may have
been favored because they maximize both energy and time available for reproduction. Our results suggest that the
benefit of increased photosynthesis for plant fitness may be strongest in the absence of drought stress.
Key words. Avena barbata, dehydration avoidance, drought escape, flowering time, natural selection, photosynthetic
capacity, stomatal conductance.
Received March 10, 2006.
Accepted September 12, 2006.
Physiological traits control the uptake, use, and allocation
of resources such as carbon, water, and nutrients and ultimately determine growth and reproduction (Calow 1979;
Ackerly et al. 2000). As a result, physiological traits are
expected to influence fitness and be the targets of natural
selection (Feder 1987; Ackerly et al. 2000; Arntz and Delph
2001; Kingsolver et al. 2001). Although natural selection has
been estimated for many traits related to the functioning of
organisms, few of these studies have focused on physiology
(Endler 1986; Kingsolver et al. 2001). For example, of the
more than 2500 selection estimates published from 1984 to
1997 that were reviewed by Kingsolver et al. (2001), more
than 80% were for morphological traits. Similarly, of 45 studies reviewed by Geber and Griffen (2003) that estimated natural selection on plant traits, only two focused explicitly on
physiology. Consequently, much of our understanding of how
natural selection has shaped the evolution of physiology
comes from comparative studies that correlate inter- and intraspecific physiological variation with environmental resource gradients (e.g., Feder 1987; Ehleringer and Monson
1993; Maherali et al. 2004). However, these studies provide
only indirect evidence of adaptive physiological evolution
because there is no link between a particular trait and fitness
(Lande and Arnold 1983; Feder 1987; Arntz and Delph 2001).
To demonstrate that a particular trait is adaptive, there must
be positive covariation between the trait and fitness in that
environment, and this relationship must be lessened or absent
in a contrasting environment (Wade and Kalisz 1990; Dudley
1996).
Drought strongly limits plant productivity, suggesting that
water limitation has shaped the evolution of many plant func-
tional traits (Ackerly et al. 2000). One major functional strategy that plants use to cope with decreased water availability
is dehydration avoidance (Ludlow 1989). In this case, plants
maintain low metabolic activity and growth rate to reduce
the demand for resources throughout the period of drought
(Chapin et al. 1993; Geber and Dawson 1997; Arntz and
Delph 2001; McKay et al. 2003). If dehydration avoidance
is adaptive under low moisture, natural selection should favour conservative water use through stomatal closure (Geber
and Dawson 1997; Arntz and Delph 2001). Because stomatal
closure typically reduces transpiration more than photosynthesis (Ehleringer 1993; Maherali et al. 2003), it will increase
the ratio of photosynthesis to transpirational water loss, also
known as water use efficiency (WUE; Givnish 1986; Schulze
et al. 1987). There is evidence of direct and indirect selection
for increased WUE under drought in both annual and perennial species (Farris and Lechowicz 1990; Dudley 1996; Heschel et al. 2002, 2004; Ludwig et al. 2004), but relatively
few studies have tested whether decreased stomatal conductance is itself adaptive under drought.
Another major functional strategy to cope with decreased
water availability is drought escape (Ludlow 1989). In this
case, plants complete their life cycle before the most intense
period of drought through increased metabolic activity and
rapid growth (Bazzaz 1979; Geber and Dawson 1990; Arntz
and Delph 2001; McKay et al. 2003). For example, comparative studies indicate that drought-escaping winter annuals
such as Camissonia claviformis have unusually high stomatal
conductance and photosynthetic capacity relative to other C3
plants (Mooney et al. 1976). In addition, there is a commonly
observed life-history trade-off between leaf life span and pho-
2478
䉷 2006 The Society for the Study of Evolution. All rights reserved.
2479
SELECTION ON PHYSIOLOGY IN AVENA
tosynthetic rate (Reich et al. 1999). Thus, if drought escape
is adaptive, selection should favor plants with high stomatal
conductance, high photosynthetic rate, and low WUE because
this will allow for increased growth and accelerated development. However, the majority of studies in annuals have
found selection for increased WUE in drought-stressed environments (Dudley 1996; Heschel et al. 2002, 2004; Ludwig
et al. 2004; but see Heschel and Riginos 2005), suggesting
that traits favoring drought escape are not always adaptive.
Because photosynthesis in C3 plants is CO2 limited
(Wullschlegger 1993) and photosynthesis is positively correlated with plant growth (Dudley 1996; Arntz et al. 1998),
high photosynthetic capacity could be adaptive regardless of
moisture availability. Thus, when intercellular CO2 (Ci) is
low, as would occur after stomatal closure, natural selection
may favor increased ribulose 1,5-bisphosphate (RuBP) carboxylase-oxygenase (rubisco) activity to maintain relatively
high carbon fixation rates (Mooney et al. 1976). However, it
is also possible that high photosynthetic capacity could be
maladaptive under drought because of excessive respiratory
costs associated with elevated levels of RuBP regeneration
and enzyme maintenance (Giminez et al. 1992; Sage 1994;
Lawlor 2002; Medrano et al. 2002). Indeed, reduced photosynthetic capacity is typically observed in response to
drought in many species (Lawlor 2002). Although previous
studies indicated that there is genetic variation in natural
populations for traits associated with the biochemical regulation of photosynthesis (e.g., Geber and Dawson 1997), to
our knowledge, no studies have tested whether increased or
decreased photosynthetic capacity is adaptive under drought.
Both drought escape and dehydration avoidance predict
that physiological traits function interactively with plant development to influence fitness under drought (e.g., Geber and
Dawson 1990; Ackerly et al. 2000; Arntz and Delph 2001).
Although several studies have shown that flowering time and
photosynthetic physiology are correlated at the intraspecific
level (e.g., Geber and Dawson 1997; McKay et al. 2003),
and there is evidence of pleiotropy for these traits (McKay
et al. 2003), little is known about the adaptive value of these
trait combinations. Correlational selection occurs when a
trait’s effect on fitness is dependant on an interaction with
another trait (Lande and Arnold 1983; Endler 1986; Phillips
and Arnold 1989; Brodie 1992; Conner and Hartl 2004;
McGlothlin et al. 2005). If dehydration avoidance is adaptive,
selection should favor plants with the combination of low
gas exchange and late flowering. If drought escape is adaptive, selection should favor plants with the combination of
high gas exchange and early flowering (Mooney et al. 1976;
Fox 1990; Geber and Dawson 1997). Although correlational
selection is often difficult to detect (Arntz and Delph 2001),
it may reflect the integration of functionally related traits
(McGlothlin et al. 2005) and therefore could play an important role in the evolution of photosynthesis and stomatal conductance.
To examine the adaptive significance of drought escape
and dehydration-avoidance traits in an annual plant, we grew
recombinant inbred lines (RILs) of Avena barbata in contrasting well-watered and dry environments. These lines were
developed from a cross of parental mesic and xeric ecotypes
from California (Latta et al. 2004). We focused on A. barbata
because of its history of use in the study of drought adaptation. The parental mesic and xeric Avena ecotypes are
known to differ at five allozyme loci associated with drought
adaptation (Hamrick and Allard 1972) and a suite of quantitative traits including seed size, flowering time, root depth,
competitive ability, and fecundity (Hamrick and Allard 1975;
Latta et al. 2004). We used these RILs because recombination
following the initial cross released phenotypic variation (Rieseberg et al. 1999; Burke and Arnold 2001), increasing our
power to detect selection (Geber and Griffen 2003).
We calculated phenotypic selection differentials and gradients (Lande and Arnold 1983) on photosynthetic, stomatal,
and developmental traits in each environment and assessed
whether drought affected the strength of selection on these
traits. We also calculated correlational selection gradients on
the photosynthetic and stomatal traits in combination with
plant development (Lande and Arnold 1983; Endler 1986;
Brodie 1992) and assessed whether drought affected the
strength of selection on these suites of traits. Specifically,
we tested if dehydration avoidance, with low gas exchange
and delayed flowering, or drought escape, with high gas exchange and early flowering, was adaptive in dry environments.
MATERIALS
AND
METHODS
Study Species and Seed Source
Avena barbata is a highly selfing (⬎95%; Latta et al. 2004)
European annual grass that has invaded the Mediterranean
climatic region in the southwestern United States (Garcia et
al. 1989; Cluster and Allard 1995) since its introduction over
200 years ago (Clegg and Allard 1972). The plants used for
the initial cross were collected at xeric (⬍500 mm annual
rainfall) and mesic sites (⬎500 mm annual rainfall) in California (Latta et al. 2004). To produce the RILs, a cross was
performed between a single individual from the mesic and
xeric ecotypes (for details see Latta et al. 2004). This cross
produced F1 offspring that were heterozygous at all loci that
differed between the two parents. A single F1 individual was
allowed to propagate by self-fertilization, producing a total
of 188 F2 seeds that contained a unique combination of alleles
from the two ecotypic parents. These 188 F2 seeds were selfed
for four generations through single-seed descent (for details
see Latta et al. 2004) to reduce within-line variation and
maximize between-line variation (Lynch and Walsh 1998).
These RILs were developed as part of a larger study analyzing
selection on quantitative trait loci in A. barbata (Gardner and
Latta 2006).
Experimental Design
To determine the strength of selection on physiological
traits in contrasting environments, we selected 26 lines that
were representative of the greenhouse fitness range of all 188
lines (R. G. Latta, pers. comm.). To ensure that all physiological measurements would be made on plants at the same
life stage, we employed a randomized complete block design,
consisting of four temporal blocks of 56 plants (n ⫽ 224).
We germinated seeds from each parental line by removing
the lemma and placing them in darkness on moist filter paper
2480
M. E. SHERRARD AND H. MAHERALI
for 96 h at 4⬚C. After refrigeration, the seeds were returned
to room temperature but were left in the dark for 24 h. We
planted 56 germinated seeds (two from each of the 26 family
lines and the two parental genotypes) every 12 days. A single
germinated seedling from each line was planted in a 4.1-L
pot with Pro-Mix BX (Premier Tech, Rivière-du-Loup, Quebec) soil and placed on a greenhouse bench. During the experiment, relative humidity was approximately 50% and temperature fluctuated diurnally from 20 to 30⬚C. Supplemental
light was provided to maintain incident irradiance on the
bench surface at a minimum of 300 ␮mol m⫺2 s⫺1 (16-h days).
We watered the developing seedlings daily for 21 days with
distilled water.
After the 21-day establishment period, when the seedlings had few true leaves, half the plants from each genetic
line were assigned to a drought treatment and the other
half to a well-watered treatment. Volumetric water content
(VWC) of the two treatments was monitored using a soil
moisture probe (Hydrosense CD620, Campbell Scientific
Corp., Edmonton, Alberta). Well-watered plants were watered daily to saturation (mean VWC ⫽ 31.1% ⫾ 9.9%),
whereas the plants in the dry environment were provided
with 175 ml of water per week (50 ml every 2 days), which
maintained the VWC below 5% throughout the treatment.
On days that physiological measurements were made,
plants were watered after data collection. All plants were
provided with 100 ml of 20-20-20 fertilizer (Plant Products, Inc., Brampton, Ontario) at a concentration of 2.5
g/L every 2 weeks. Our drought treatment simulated a constant dry growing season. Because the amount of watering
was held constant as plants grew, the severity of drought
increased with time. Although drought can be episodic and
unpredictable for A. barbata at the xeric site in California,
the average monthly precipitation (118.77 ⫾ 20.00 mm)
typically remains constant throughout the growing season
(http://danrrec.ucdavis.edu/sierra 㛮 foothill/resources.html#
climate). After their initial establishment, plants in the dry
treatment were provided with the equivalent of 132 mm of
precipitation throughout the drought period (5.5 mm per
week). This amount was comparable to the total rainfall occurring during driest growing season (165 mm; October 1976
to March 1977) at the xeric site since 1963 and 69 mm less
than any growing season at the mesic site since 1953 (http://
danrrec.ucdavis.edu/hopland/resources㛮climate㛮precipitation.
html). Physiological measurements began 70 days after germination (49 days after the treatments were initiated), before
plants began flowering. Plants were measured in random order
in each block.
Physiological, Phenological, and Fitness Measurements
Photosynthesis and transpiration were measured on the
youngest fully expanded leaf for all 224 plants (eight plants
in each of 26 RILs and the two parental lines) in the experiment using an open gas-exchange system (LI-6400, Li-Cor,
Inc., Lincoln, NE) at 26⬚C, a vapor pressure deficit of 1.9–
2.0 KPa and an irradiance of 1500 ␮mol m⫺2 s⫺1. We used
saturating light for all gas-exchange measurements to ensure
they would not be biased by daily light fluctuations and that
photosynthetic capacity would not be limited by suboptimal
light. Moreover, A. barbata plants growing in the field experience light levels that are similar those used in our study
(e.g., 1000–1500 ␮mol m⫺2 s⫺1; Jackson et al. 1995). Stomatal conductance (gs) was calculated from transpiration using a boundary layer conductance of 3.54–4.82 mol m⫺2 s⫺1,
which was determined based on fan speed and leaf area using
the energy balance algorithms of the LI-6400. Leaf area was
calculated from leaf dimensions.
The first set of gas-exchange measurements were made 70
days after germination, just prior to the initiation of flowering. At this time, we recorded instantaneous light saturated
photosynthetic rate (A), stomatal conductance to water vapour
(gs) at ambient CO2 concentration (400 ␮l/L). We also calculated instantaneous WUE, defined as the ratio of photosynthetic rate to transpiration rate (A/E), where E is the product of gs and the leaf-to-air vapor pressure deficit (v). We
calculated WUE as the ratio of photosynthetic rate to stomatal
conductance (A/gs), which is an appropriate proxy for A/E
when v is held constant during gas-exchange measurements
(Donovan and Ehleringer 1994). To examine differences between pre- and postreproductive plants, a second set of instantaneous gas-exchange measurements were made under
ambient CO2 concentration (Ar; gsr; WUEr) 110 days after
germination, on leaves attached to flowering stalks after the
plants had flowered. We also measured apparent chlorophyll
concentration (Chl) on the three youngest fully expanded
leaves for each plant with a portable chlorophyll meter
(SPAD 502, Minolta Inc., Ramsey, NJ). All pre- and postreproductive measurements were made between 0830 to 1230
EST from 4 May to 27 July 2004.
Photosynthesis is biochemically regulated by two enzymatic processes. When Ci is high, photosynthesis is primarily
limited by RuBP regeneration, and when Ci is low, it is primarily limited by rubisco activity (Sharkey 1985; Geber and
Dawson 1990, 1997). To determine the CO2 saturated photosynthetic rate (Amax, which represents RuBP regeneration)
and the maximum rate of carboxylation (Vcmax, which represents rubisco activity), we measured the response of light
saturated photosynthesis (A) to the manipulation of intercellular CO2 concentration (A/Ci curve). Previous studies have
shown that photosynthesis in A. barbata is CO2 limited (Jackson et al. 1995). A/Ci curves were constructed by varying the
concentration of CO2 in the cuvette chamber from 50 to 1800
␮l/L, at 100- to 200-␮l/L intervals. Data were fit to the following nonlinear model:
A ⫽ a[1 ⫺ exp(⫺bCi)] ⫹ c,
(1)
where c is the y-intercept, 1/b is the rate constant, and CO2saturated A (Amax) is calculated as a ⫹ c (Jacob et al. 1995;
Reid and Fiscus 1998). This model has been used previously
to describe the A/Ci response of herbaceous plants (Reid and
Fiscus 1998). Vcmax was calculated as the linear regression
of the A/Ci curve at intercellular concentrations of CO2 lower
than 200 ␮l/L, when photosynthesis is CO2 limited (Wullschleger 1993; Geber and Dawson 1997). A/Ci curves for each
block were completed within six days. A/Ci curves were constructed during the first set of gas-exchange measurements
(70 days after germination), prior to the initiation of flowering.
We also measured several nonphysiological traits, includ-
2481
SELECTION ON PHYSIOLOGY IN AVENA
ing day of first flower and the proportion of seeds aborted
(number of empty fruit in 100 randomly selected fruits per
individual). All nonreproductive aboveground biomass for
each individual was harvested at the end of the study (165
days after germination), dried to a constant mass (48 h at
65⬚C), and weighed. At the time of harvest, all plants had
flowered and were still producing reproductive tillers; however, to represent a realistic growing season length in the
field, the study was terminated before the plants were finished
flowering. Fitness was estimated as total seed number, which
was calculated as the product of total spikelet number ⫻ 2
⫻ proportion of aborted seeds (note that each A. barbata
spikelet produces two single-seed florets). No reproductive
tillers or glumes were lost prior to final harvest.
Statistical Analyses
We measured phenotypic selection on all traits using both
univariate and multivariate approaches. Univariate selection
differentials, which estimate both direct selection on a trait
and indirect selection via correlated traits, were calculated
for all traits as the slope of the regression between the trait,
standardized to a mean of zero and a standard deviation of
one and fitness, relativized by dividing the fitness value of
all plants by the mean fitness in the environment (Lande and
Arnold 1983; Conner 1988) using SPSS 12.0 (SPSS, Inc.,
Chicago). In contrast, selection gradients estimate only direct
selection on a trait, assuming that all traits relevant to fitness
are included in the model. We calculated selection gradients
for each trait as the partial regression coefficient from a multiple regression of all the traits against fitness (Lande and
Arnold 1983) using SPSS 12.0. WUE and WUEr were eliminated from this analysis because they were not independent
of photosynthetic rate and stomatal conductance (Sokal and
Rohlf 1995). The remaining traits in the model were tested
for multicollinearity using variance inflation factors (VIFs;
SPSS 12.0). All traits in the model had VIFs below 10.0,
indicating that multicollinearity was low (Neter et al. 1989).
For the regression analyses, the assumptions of normality
and homogeneity of variance were tested by visually inspecting the normal probability plot from the model residuals
and using Levene’s test, respectively; no significant deviations were detected. In addition to time differences, the inclusion of the temporal block in our analyses also accounted
for any differences in environmental conditions for each set
of measurements (e.g., climate and soil moisture).
In multivariate selection analyses, developmental traits
such as vegetative biomass and highly correlated traits are
sometimes removed and analyzed separately to account for
indirect effects on fitness (Dudley 1996). To account for these
possible indirect effects, we analyzed the directional selection
gradients in models with and without the correlated traits and
vegetative biomass. Although P-values differed slightly between these analyses, the statistically significant gradients
remained so, and no nonsignificant gradients became significant. Consequently, only the gradients from the model including all traits were reported.
We tested whether the selection differentials differed between the two environments and between different time
points using analyses of covariance (ANCOVA as in Conner
1989; Dudley 1996). The phenotypic data from both treatments were combined and analyzed with a model that included a continuous term for the phenotypic trait, a categorical term coding for treatment, and a phenotypic trait ⫻
treatment term. A significant interaction term indicated that
selection coefficients differed between environments or
times. Relative fitness was the dependent variable. We also
calculated phenotypic correlations among trait combinations
(Pearson product-moment correlation, SAS 8.2; SAS Institute, Inc., Cary, NC) to identify potential causes of indirect
selection. We present P-values for all differentials, gradients,
and correlations both before and after the Dunn-Sidak correction for multiple tests (Sokal and Rohlf 1995).
To determine whether natural selection acts on physiological traits in combination with plant development, we calculated correlational selection gradients for: (1) day of first
flower and A; (2) day of first flower and gs; (3) day of first
flower and Ar; (4) day of first flower and gsr; (5) day of first
flower and Amax; and (6) day of first flower and Vcmax. Correlational selection was measured as the regression of the
cross-product of two phenotypic traits against fitness, in a
multiple regression model that also included the individual
physiological and phenological traits, the quadratic term for
these two traits, and all the traits that were phenotypically
correlated with these two traits (SPSS 12.0). We present Pvalues for the correlational selection gradients both before
and after the Dunn-Sidak correction (Sokal and Rohlf 1995).
RESULTS
Effect of Drought on Plant Performance
Although all plants survived to flowering in both environments, plant performance was significantly reduced under
drought. Well-watered plants had 221% greater vegetative
biomass and 54% greater seed production than plants in the
dry treatment. Increased fitness in the well-watered environment was achieved despite 147% higher proportion of aborted
seeds and an average flowering time that was 8 days later
than the dry treatment (Table 1). For the physiological traits
measured 70 days after germination, the well-watered plants
had 39% higher photosynthetic rate (A) and 303% higher
stomatal conductance (gs), whereas plants in the dry environment had 16% higher photosynthetic capacity (Amax),
119% higher carboxylation efficiency (Vcmax), 181% higher
WUE, and 26% higher chlorophyll concentration (Chl; Table
1). For instantaneous traits measured on reproductive tissue
110 days after germination, well-watered plants had 223%
higher stomatal conductance (gsr), the dry treatment plants
had 288% higher WUE (WUEr), and photosynthetic rate (Ar)
did not differ significantly between the two treatments (Table
1).
Selection Differentials
More physiological traits were under selection in the wellwatered environment than in the dry environment. In the wellwatered environment, plants with higher photosynthetic capacity (Amax) and higher carboxylation efficiency (Vcmax) had
higher fitness (Table 2). In the dry environment, we detected
selection for decreased Amax, though Vcmax was adaptively
2482
M. E. SHERRARD AND H. MAHERALI
TABLE 1. Mean ⫾ 1 SE of traits measured for Avena barbata in the contrasting dry and well-watered environments. Preflowering
measurements of photosynthetic capacity (Amax), carboxylation efficiency (Vcmax), chlorophyll concentration (Chl), instantaneous photosynthetic rate (A), stomatal conductance (gs) and water-use efficiency (WUE) were made 70 days after germination. A second set of
measurements of photosynthetic rate (Ar), stomatal conductance (gsr) and water-use efficiency (WUEr) were made after plants flowered,
110 days after germination. All instantaneous gas-exchange measurements (A, gs, WUE, Ar, gsr, WUEr) were made at ambient CO2
concentration (400 ␮L/L). Statistical differences of traits between environments were determined using two-sample t-tests (t- and Pvalues are shown). N ⫽ 101–104 in each environment.
Trait
Dry environment
Amax
Vcmax
Chl
A
gs
WUE
Ar
gsr
WUEr
Proportion aborted seeds
Vegetative biomass
Day of first flower
No. of seeds
20.99
133.73
44.70
6.58
0.063
109.84
8.03
0.111
86.64
0.146
7.46
92.07
523.57
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
Dry vs. well-watered
(df ⫽ 100 – 103)
Well-watered environment
0.63
4.88
0.47
0.28
0.004
2.25
0.36
0.007
4.03
0.008
0.30
1.37
30.99
18.14
61.12
35.42
9.12
0.254
39.06
7.36
0.358
22.86
0.360
23.92
100
806.13
neutral (Table 2). Selection also differed between the two
environments for four of the six instantaneous gas-exchange
traits (ANCOVA: P ⬍ 0.05). Plants with higher photosynthetic rate both before (A) and after flowering (Ar) and with
higher WUE both before and after flowering (WUEr) had
higher fitness in the well-watered environment, but photosynthetic rate and WUE were not associated with fitness in
the dry environment (Table 2). There was also selection for
higher chlorophyll concentration (Chl) in the well-watered
environment but not in the dry environment (ANCOVA: P
⬍ 0.05; Table 2). In contrast, there was no association between stomatal conductance and fitness either before (gs) or
after flowering (gsr) in either environment (Table 2). In addition, selection on photosynthetic rate and WUE in the well-
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
⫾
0.56
2.55
0.54
0.38
0.011
1.39
0.38
0.015
1.17
0.016
1.23
1.53
66.1
t
t
t
t
t
t
t
t
t
t
t
t
t
⫽
⫽
⫽
⫽
⫽
⫽
⫽
⫽
⫽
⫽
⫽
⫽
⫽
3.65, P ⬍ 0.001
14.14, P ⬍ 0.001
15.10, P ⬍ 0.001
5.69, P ⬍ 0.001
16.41, P ⬍ 0.001
25.42, P ⬍ 0.001
0.88, P ⬎ 0.05
15.40, P ⬍ 0.001
14.62, P ⬍ 0.001
12.43, P ⬍ 0.001
14.38, P ⬍ 0.001
5.72, P ⬍ 0.001
4.70, P ⬍ 0.001
watered environment was stronger after flowering than before
flowering (ANCOVA: A vs. Ar, F ⫽ 3.435, P ⫽ 0.065, df ⫽
100; WUE vs. WUEr, F ⫽ 6.985, P ⬍ 0.01, df ⫽ 100),
whereas selection on stomatal conductance did not differ with
developmental stage.
The selection differentials for flowering time, vegetative
biomass, and proportion of seeds aborted also differed between the two environments. Earlier-flowering plants had
higher fitness in both environments, but the selection differential for day of first flower was stronger in the well-watered
environment (Table 2; ANCOVA, P ⬍ 0.05). Plants with a
lower proportion of aborted seeds had higher fitness in the
well-watered environment, whereas there was no selection
on seed abortion in the dry environment (Table 2; ANCOVA,
TABLE 2. Standardized linear selection differentials (S) ⫾ 1 SE measured in dry and well-watered environments. Statistically significant
linear selection differentials are indicated in bold. F-statistics for the trait ⫻ treatment interaction (ANCOVA), which tests for a difference
between the selection differentials across environments, are also presented. Preflowering measurements of photosynthetic capacity (Amax),
carboxylation efficiency (Vcmax), chlorophyll concentration (Chl), instantaneous photosynthetic rate (A), stomatal conductance (gs), and
water-use efficiency (WUE) were made 70 days after germination. A second set of measurements of photosynthetic rate (Ar), stomatal
conductance (gsr), and water-use efficiency (WUEr) were made after plants flowered, 110 days after germination. All instantaneous gasexchange measurements (A, gs, WUE, Ar, gsr, WUEr) were made at ambient CO2 concentration (400 ␮l/L). N ⫽ 101–104 in each
environment.
Linear selection differentials
Well-watered environment
Dry environment
Trait
Amax
Vcmax
A
gs
WUE
Ar
gsr
WUEr
Chl
Proportion of seeds aborted
Vegetative biomass
Day of first flower
S
SE
S
SE
F-statistic
⫺0.127**†
⫺0.041
⫺0.05
⫺0.047
0.025
⫺0.005
0.031
⫺0.062
0.087
⫺0.066
⫺0.222***†
⫺0.281***†
0.042
0.045
0.042
0.043
0.045
0.048
0.045
0.045
0.045
0.042
0.038
0.032
0.172*
0.188*
0.173*
⫺0.022
0.19**
0.29***†
0.003
0.396***†
0.340***†
⫺0.228**†
⫺0.09
⫺0.430***†
0.072
0.073
0.073
0.069
0.066
0.069
0.072
0.073
0.071
0.071
0.07
0.055
3.004‡
4.600*
3.046*
0.497
3.809*
12.264***†
0.035
11.338***†
11.352***†
7.449***†
4.941**†
64.077***†
† Remained significant after Dunn-Sidak correction.
‡ P ⬍ 0.1, * P ⬍ 0.05, ** P ⬍ 0.01, ***P ⬍ 0.001
2483
SELECTION ON PHYSIOLOGY IN AVENA
P ⬍ 0.05). In addition, plants with less vegetative biomass
had higher fitness in the dry environment, whereas there was
no selection on vegetative biomass in the well-watered environment (Table 2; ANCOVA, P ⬍ 0.05).
Variation in fitness was higher in the well-watered (SD ⫽
659.35) than in the dry (SD ⫽ 244.05) treatment, raising the
possibility that the opportunity for selection on plant physiology was higher in the well-watered environment. When
phenotypic selection is weak, as was observed in the dry
treatment, a low coefficient of determination for the regression, or r2-value, indicates that selection was not limited by
a lack of variance in fitness (Conner 1988). In our study, r2values for phenotypic selection were lower in the dry (range
⫽ 0.20–0.26) than the well-watered treatment (range ⫽ 0.34–
0.50), suggesting that the opportunity for selection was not
reduced in the dry relative to the well-watered environment.
Phenotypic Correlations
There were more statistically significant correlations between traits in the well-watered environment than the dry
environment. Forty-six of the 78 phenotypic correlations
were significant (P ⬍ 0.05) and four were marginally significant (P ⬍ 0.10) in the well-watered environment, while
only 26 phenotypic correlations were significant and two marginally significant in the dry environment (Table 3). In the
dry environment, several correlations between instantaneous
gas exchange, flowering time, and investment in vegetative
biomass were significant at both developmental time points;
however, the direction of these correlations differed before
and after reproduction. Day of first flower and vegetative
biomass were positively correlated with gs and negatively
correlated with WUE (Table 3), whereas day of first flower
and vegetative biomass were negatively correlated with gsr
and positively correlated with WUEr. In the well-watered
environment, the phenotypic correlations between instantaneous gas exchange, flowering time, and investment in vegetative biomass were fairly consistent over time. Vegetative
biomass was positively correlated with A and WUE before
flowering and also positively correlated with Ar and marginally positively correlated with WUEr after flowering (Table
3). In addition, Amax and Vcmax were positively correlated with
vegetative biomass in the well-watered environment (Table
3). Day of first flower was only correlated with one gasexchange trait (WUEr) in the well-watered environment.
Directional Selection Gradients
Fewer physiological traits were under selection in the multivariate than in the univariate selection analysis. In the dry
environment, we detected a significant negative selection gradient for Amax and a significant positive selection gradient
for Chl (Table 4). In contrast, the selection gradients for
Vcmax, A, Ar, gs, and gsr were all nonsignificant in the dry
environment. In the well-watered environment we detected
significant positive selection gradients for Ar and Chl (Table
4), whereas the selection gradients for Amax, Vcmax, A, gs and
gsr were all nonsignificant. Although selection favored higher
Chl in both environments (Table 4), selection was stronger
in the well-watered environment.
In the dry environment, we detected significant selection
gradients to decrease the proportion of seeds aborted and to
flower earlier, however, the selection gradient for investment
in vegetative biomass was not significant (Table 4). In the
well-watered environment, we detected a significant selection
gradient to flower earlier, but the selection gradients for investment in vegetative biomass and the proportion of seeds
aborted were both nonsignificant (Table 4).
Correlational Selection Gradients
There was no correlational selection on the combination
of flowering time with any of the physiological traits in the
dry environment (Table 5). In contrast, the correlational selection gradients for the combination of A with day of first
flower and Ar with day of first flower were both significant
in the well-watered environment (Table 5). In addition, the
correlational selection gradient was also significant for the
combination of Amax with day of first flower in the wellwatered environment (Table 5). Based on the fitness surfaces,
these three results suggest that plants with high photosynthetic function and early flowering had the highest fitness in
the well-watered environment (Fig. 1). There was no correlational selection on the combinations of stomatal conductance (gs or gsr) with day of first flower or Vcmax with day of
first flower in the well-watered environment (Table 5). There
was also significant correlational selection for the combination of WUE with day of first flower in the wet but not in
the dry environment (data not shown).
DISCUSSION
Photosynthetic and stomatal traits are targets of natural
selection (e.g., Farris and Lechowicz 1990; Dudley 1996;
Arntz et al. 1998; Heschel et al. 2002; Heschel and Riginos
2005), but the adaptive significance of variation in these traits
is rarely assessed in contrasting environments. For example,
natural selection should favor conservative water use if dehydration avoidance is adaptive under low moisture (Geber
and Dawson 1997; Arntz and Delph 2001). In contrast, natural selection should favor high photosynthetic capacity and
early flowering if drought escape is adaptive under low moisture. Our results did not generally support either of these
hypotheses for A. barbata growing under well-watered and
dry environments. In particular, few physiological traits were
under selection in the dry environment and instead, only early
flowering was strongly favored. In contrast, increased photosynthetic function was adaptive only in the well-watered
environment.
Although high photosynthetic rate is usually associated
with increased growth and reproduction (Arntz et al. 1998;
Arntz and Delph 2001), our results suggest that increased
biochemical capacity for photosynthesis is maladaptive in the
dry environment. For example, there was strong direct selection for decreased CO2-saturated photosynthetic capacity
(Amax) in the dry environment (Tables 2, 4). Decreased Amax
may have been favored in the dry environment because maintaining a high regeneration of RuBP is metabolically costly
(Sage 1994), especially when plants are operating at low Ci
because of stomatal closure (Flexas and Medrano 2002). Nevertheless, this hypothesis has not been tested directly. If respiratory costs of increased RuBP regeneration were high,
2484
M. E. SHERRARD AND H. MAHERALI
TABLE 3. Phenotypic correlations among traits in the well-watered (upper, off-diagonal) and dry (lower, off-diagonal) environments in
Avena barbata. Phenotypic correlations were tested against the null hypothesis of r ⫽ 0 using two-tailed, one-sample t-tests and all
statistically significant values are indicated in bold. Preflowering measurements of photosynthetic capacity (Amax), carboxylation efficiency
(Vcmax), chlorophyll concentration (Chl), instantaneous photosynthetic rate (A), stomatal conductance (gs) and water-use efficiency (WUE)
were made 70 days after germination. A second set of measurements of photosynthetic rate (Ar), stomatal conductance (gsr) and wateruse efficiency (WUEr) were made after plants flowered, 110 days after germination. All instantaneous gas-exchange measurements (A,
gs, WUE, Ar, gsr, WUEr) were made at ambient CO2 concentration (400 ␮l/L). DFF, day of first flower; abortion, proportion of seeds
aborted, and Veg. bio., vegetative biomass. N ⫽ 101–104 in each environment.
Amax
Amax
Vcmax
Chl
A
gs
WUE
Ar
gsr
WUEr
DFF
Abortion
Veg. bio.
Seed no.
Vcmax
0.849***†
0.411***†
0.121
0.607***†
0.481***†
0.116
⫺0.042
⫺0.097
0.030
0.158
0.113
0.130
⫺0.261**
0.090
0.280**
0.098
0.277**
0.108
0.026
⫺0.042
⫺0.050
0.063
0.148
0.046
Chl
A
0.553***†
0.681***†
0.203*
0.137
0.002
⫺0.031
⫺0.004
⫺0.044
0.009
⫺0.087
0.019
0.169‡
0.809***†
0.914***†
0.582***†
0.842***†
0.147
0.021
⫺0.056
0.062
0.220*
0.084
0.259**†
⫺0.105
gs
0.413***†
0.421***†
0.229*
0.637***†
⫺0.254**
⫺0.031
⫺0.120
0.135
0.248*
0.100
0.337***†
⫺0.097
WUE
0.340***†
0.465***†
0.377***†
0.239*
⫺0.502***†
0.054
0.187‡
⫺0.195*
⫺0.233*
⫺0.040
⫺0.195*
0.051
† Remained significant after Dunn-Sidak correction.
‡ P ⬍ 0.1, * P ⬍ 0.05, ** P ⬍ 0.01, *** P ⬍ 0.001
we predict that natural selection should favor decreased respiration rate in water-limited environments.
Despite the potential increase in carbon fixation associated
with high rubisco activity when Ci is low, there was also no
selection for increased carboxylation efficiency (Vcmax) in the
dry environment (Tables 2, 4). One reason that increased
rubisco activity may not be adaptive in the dry environment
is because of a strong correlation between Vcmax and RuBP
regeneration capacity (Amax; Table 3; Wullschleger 1993; Geber and Dawson 1997). Strong selection against maintaining
high RuBP regeneration (Table 4) could have constrained
selection for increased rubisco activity, because phenotypes
with low Amax would also have low Vcmax. In contrast, there
was indirect selection for increased Amax and Vcmax in the
well-watered environment (Table 2), indicating that the benefit of increased leaf-level photosynthetic capacity on plant
fitness is greatest when resources are abundant.
Our results also suggested that instantaneous photosynthetic rate was adaptively neutral in the dry environment
(Tables 2, 4). This result conflicts with work in Cakile edentula, where higher photosynthetic rate was favored under
drought (Dudley 1996). The absence of selection on A and
Ar could be caused by biochemical limitations associated with
rubisco activity and RUBP regeneration, stomatal limitations,
or both (Sharkey 1985; Geber and Dawson 1997). For example, prereproductive photosynthetic rate (A) was positively
correlated with Amax, Vcmax and stomatal conductance in the
dry environment (Table 3). The observation that Amax was
maladaptive and Vcmax was adaptively neutral under drought
was consistent with the absence of selection on instantaneous
photosynthetic rate.
The adaptive significance of variation in leaf physiology
was dependant on developmental stage. For example, we
found that total selection on photosynthetic rate in the wellwatered environment was stronger after, rather than before,
flowering. In addition, there was direct selection for increased
photosynthetic rate, but only after flowering (Ar; Table 4).
Selection on physiology may have been stronger after flowering because of increased sink carbon demands associated
with seed filling and reproduction. Annual plants allocate
between 15% and 30% of their lifetime net carbon gain to
reproduction (Harper et al. 1970; Mooney 1972), suggesting
that high carbon assimilation would be particularly important
at the time of flowering (Hirose et al. 2005). Although many
studies report estimates of phenotypic selection on gas exchange either before or after flowering (e.g., Dudley 1996),
few studies have sampled these traits at both developmental
stages (Farris and Lechowicz 1990). It is therefore possible
that measurements of natural selection on instantaneous gas
exchange at a single time point poorly represent the adaptive
value of these traits (Arntz and Delph 2001). Although other
measures of physiological performance, such as carbon isotope discrimination (Donovan and Ehleringer 1994; Ludwig
et al. 2004), can provide time-integrated information on carbon assimilation, we also suggest that measuring selection
on leaf physiology at multiple time points will highlight lifestage-dependent differences in the adaptive value of physiological function.
The use of recombinant inbred lines in this study afforded
us the opportunity to determine if there was quantitative genetic variation for each trait, and if this variation differed
between environments. Our analysis (Sherrard 2005) indicated that broad-sense heritabilities (h2b ) for several of the
physiological traits, including Vcmax, gs, Ar, and gsr in the dry
environment and Chl and Ar in the well-watered environment,
were significantly different from zero. The h2b for fitness was
similar and significant in both environments, but h2b values
for physiological traits were generally higher in the dry than
in the well-watered environment. A complete analysis of
quantitative genetic variation for physiology in A. barbata
will be reported elsewhere (M. E. Sherrard, H. Maherali, and
R. G. Latta, unpubl. ms.).
2485
SELECTION ON PHYSIOLOGY IN AVENA
TABLE 3.
Ar
0.353***†
0.560***†
0.420***†
0.479***†
0.220*
0.227*
0.629***†
0.065
0.027
0.159
⫺0.103
⫺0.009
gsr
WUEr
0.131
0.039
0.132
0.178‡
0.157
⫺0.090
0.512***†
⫺0.558***†
⫺0.222*
0.204*
⫺0.290*
0.062
Extended.
DFF
Abortion
0.253**
0.514***†
0.367***†
0.290**
⫺0.008
0.402***†
0.437***†
⫺0.399***†
⫺0.047
⫺0.126
⫺0.125
⫺0.010
0.016
⫺0.043
⫺0.096
0.107
⫺0.225*
0.295**†
⫺0.144
0.274**
⫺0.124
⫺0.135
0.787***†
⫺0.596***†
⫺0.045
⫺0.118
⫺0.198*
⫺0.048
0.094
⫺0.093
⫺0.069
⫺0.099
0.016
0.250*
Selection for Dehydration Avoidance
Although water conservation, and thus dehydration avoidance, could be adaptive for annuals under drought, there was
no selection on gs or WUE in the dry environment (Tables
2, 4), suggesting these traits were adaptively neutral. This
result conflicts with studies in other annual species (C. edentula, Dudley 1996; Polygonum persicaria, Heschel et al.
2004; Helianthus anomalis, Ludwig et al. 2004), which reported direct selection for increased WUE under drought.
Dehydration avoidance may not be adaptive for A. barbata
because this strategy is typically favored in annuals when the
growing season is long or when drought disturbances are
infrequent (Geber and Dawson 1997). In contrast, A. barbata
plants experience relatively constant low-level precipitation
throughout their short growing season (October–March) followed by a harsh drought in the summer (Hamrick and Allard
1975). Under these conditions, which we simulated in the
Veg. bio.
0.286**
0.481***†
0.276**
0.354***†
0.165
0.199*
0.258**
0.116
0.176‡
0.287**
0.100
⫺0.158
⫺0.140
⫺0.450***†
No. of Seeds
0.190‡
0.438***†
0.353***†
0.188‡
⫺0.026
0.228*
0.316**
0.003
0.387***†
⫺0.510***†
⫺0.251**
⫺0.105
greenhouse, drought escape could be a better functional strategy than dehydration avoidance (Geber and Dawson 1997;
Arntz and Delph 2001).
In contrast to the dry environment, we found selection for
increased WUE in the well-watered environment, but this
was not evidence that water conservation was adaptive under
wet conditions. Selection for increased WUE was most likely
related to a positive correlation with photosynthetic rate (Table 3), which was selected to increase in the well-watered
environment (Tables 2, 4). This interpretation is consistent
with our observation that there was no selection for water
conservation through decreased stomatal conductance (Tables 2, 4) in the well-watered environment.
Selection for Drought Escape
We found strong selection for earlier flowering in the dry
environment, suggesting that drought escape is adaptive in
TABLE 4. Standardized linear selection gradients (␤) ⫾ 1 SE measured in dry and well-watered environments. Significant and marginally
significant linear selection gradients are indicated in bold. Preflowering measurements of photosynthetic capacity (Amax), carboxylation
efficiency (Vcmax), chlorophyll concentration (Chl), instantaneous photosynthetic rate (A) and stomatal conductance (gs) were made 70
days after germination. A second set of measurements of photosynthetic rate (Ar) and stomatal conductance (gsr) were made after plants
flowered, 110 days after germination. All instantaneous gas-exchange measurements (A, gs, Ar, gsr) were made at ambient CO2 concentration
(400 ␮l/L), N ⫽ 101–104 in each environment.
Linear selection gradients
Well-watered environment
Dry environment
␤
SE
␤
SE
⫺0.130***†
⫺0.001
0.040
0.057
0.057
⫺0.062
0.081*
⫺0.093**†
⫺0.022
⫺0.295***†
0.039
0.035
0.061
0.058
0.042
0.041
0.032
0.030
0.051
0.047
⫺0.012
0.050
0.023
⫺0.097
0.238***†
⫺0.089
0.205**†
⫺0.072
⫺0.089
⫺0.342***†
0.092
0.148
0.172
0.073
0.070
0.058
0.071
0.054
0.055
0.053
Trait
Amax
Vcmax
A
gs
Ar
gsr
Chl
Proportion of seeds aborted
Vegetative biomass
Day of first flower
* P ⬍ 0.05, ** P ⬍ 0.01, ***P ⬍ 0.001
† Remained significant after Dunn-Sidak correction.
2486
M. E. SHERRARD AND H. MAHERALI
FIG. 1. Multivariate selection surface for the combination of photosynthetic capacity (Amax) and day of first flower (DFF) against
relativized fitness in both dry and well-watered environments. P-values for the selection coefficients are indicated; n.s., nonsignificant.
The fitness surfaces for A and DFF and Ar and DFF for each environment were similar in shape to those presented here.
A. barbata. This result is similar to other studies of winter
annuals in Mediterranean climates, which also found that
rapid development and earlier flowering was adaptive under
drought stress (Sinapsis arvensis, Stanton et al. 2000; Hordeum spontaneum, Volis et al. 2002, 2004; Lasthenia californica, Rajakaruna et al. 2003). Early flowering was likely
favored in these Mediterranean annuals because it increased
the reproductive period in a short growing season (Hamrick
and Allard 1975; Hirose et al. 2005). However, earlier flowering was also favored in the well-watered environments (Tables 2, 4), suggesting that, for annuals, earlier flowering
plants always have higher fitness regardless of environment
(but see Verhoeven et al. 2004).
Although it has been suggested that high metabolic activity
2487
SELECTION ON PHYSIOLOGY IN AVENA
TABLE 5. Standardized correlational selection gradients (␥) ⫾ 1
SE measured in the dry and well-watered environments. Significant
correlational selection gradients are indicated in bold. Preflowering
measurements of photosynthetic capacity (Amax), carboxylation efficiency (Vcmax), photosynthetic rate (A) and stomatal conductance
(gs) were made 70 days after germination. A second set of photosynthetic rate (Ar) and stomatal conductance (gsr) measurements
were made after plants had flowered, 110 days after germination.
All instantaneous gas exchange measurements (A, gs, Ar, gsr) were
made at ambient CO2 concentration (400 ␮L/l). DFF, day of first
flower. N ⫽ 101–104 in each environment.
Correlational selection gradients
Dry environment
Traits
A and DFF
gs and DFF
Ar and DFF
gsr and DFF
Amax and DFF
Vcmax and DFF
Well-watered environment
␥
SE
␥
SE
⫺0.045
⫺0.022
⫺0.004
⫺0.032
0.013
⫺0.018
0.032
0.041
0.033
0.039
0.039
0.036
⫺0.119**†
⫺0.073
⫺0.101*
0.012
⫺0.139**†
⫺0.071
0.045
0.042
0.049
0.047
0.046
0.053
** P ⬍ 0.01, *** P ⬍ 0.001
† Remained significant after Dunn-Sidak correction.
accelerates development (Bazzaz 1979; Calder 1984; Ludlow
1989; Hoffmann and Parsons 1991; Wullschlegger 1993; Geber and Dawson 1997; McKay et al. 2003), the combination
of high gas exchange and earlier flowering was not adaptive
under drought (Table 5). These results differ from a previous
study in Impatiens capensis, which detected correlational selection for early flowering and high stomatal conductance
under drought (Heschel and Riginos 2005). For A. barbata
growing in dry environments, the absence of selection for
high metabolic activity in combination with early flowering
is consistent with directional selection for decreased Amax.
Our results therefore suggest that the evolution of drought
escape can occur through strong selection for early flowering
time even with selection for reduced metabolism.
In contrast to the dry environment, there was selection for
several physiological traits in combination with early flowering in the well-watered environment. However, this pattern
may not represent true correlational selection. To visualize
and interpret the form of selection simultaneously acting on
two traits, it is necessary to plot the fitness surface (e.g., Fig.
1; Phillips and Arnold 1989; Brodie 1992; Conner and Hartl
2004; McGlothlin et al. 2005). When correlational selection
occurs, the fitness surface will be either ridge- or saddleshaped, because the value associated with one trait is dependent on the value of the second trait and vice versa (Brodie
1992; Conner and Hartl 2004). Therefore, our results would
be indicative of correlational selection if the following two
conditions were met. First, the combination of early flowering
and high photosynthesis and the combination of late flowering and low photosynthesis must be associated with a fitness peak. Second, the combination of early flowering and
low photosynthesis and the combination of late flowering and
high photosynthesis must be associated with fitness trough.
In contrast, our fitness surfaces for Ar with day of first flower,
A with day of first flower, and Amax with day of first flower
(e.g., Fig. 1) all showed a fitness peak at high photosynthesis
and early flowering, but there was no peak at low photosyn-
thesis and late flowering. This suggests that there was selection for the combination of high photosynthetic function and
earlier flowering but there was no selection for a correlation
between these traits.
Although the combination of early flowering with high
photosynthetic capacity was adaptive in the well-watered environment (Table 5, Fig. 1), this result was not likely to be
indicative of the adaptive value of drought escape because
plants flowered 8 days later in the well-watered than the dry
treatment (Table 1). Instead, these combinations of traits may
have been favored because they maximize both energy and
time available for reproduction. This result suggests that photosynthetic physiology has a greater influence on plant fitness
in the absence of drought stress, which was consistent with
the relatively strong positive selection differential and selection gradient on photosynthetic rate under well-watered
conditions (Tables 2, 4).
If our measurements of selection on physiology are representative of adaptation to contrasting xeric and mesic habitats, then mesic ecotypes should have higher photosynthetic
capacity and photosynthetic rate than xeric ecotypes. However, a limitation of our study is that we have no information
on the physiological differences between ecotypes in the
field. Nevertheless, previous studies have shown that the mesic ecotype has higher biomass than the xeric ecotype when
grown in common garden environments (Hamrick and Allard
1975; Johansen-Morris and Latta 2006). This difference is
consistent with higher photosynthetic capacity in mesic versus xeric ecotypes because photosynthesis is positively correlated with plant growth (e.g., Arntz et al. 1998; Arntz and
Delph 2001).
In conclusion, many of the adaptive hypotheses for physiological variation in annuals under drought were not supported in A. barbata. In particular, we found selection for
decreased Amax in the dry environment, but selection for increased Amax in the well-watered environment. This contrasts
with the expectation that high photosynthetic capacity could
be adaptive in all environments (Dudley 1996; Ackerly et al.
2000). Moreover, the absence of selection for decreased stomatal conductance in the dry environment suggests that increased water conservation, and therefore dehydration avoidance, was not adaptive under drought. Although there was
direct selection for early flowering, there was no correlational
selection for the combination of early flowering and high gas
exchange in the dry environment. Therefore, development
time and metabolism need not be tightly linked to promote
drought escape in water-limited environments. More generally, our results reinforce the necessity of testing adaptive
hypotheses about physiological variation (Kingsolver et al.
2001) and highlight the utility of the phenotypic selection
approach for such tests.
ACKNOWLEDGMENTS
We are grateful to R. G. Latta for providing us with the
RILs used in this study and for many helpful discussions and
advice during various stages of this project. We also thank
C. M. Caruso, J. K. Conner, C. Creese, B. C. Husband, J.
Kohn, R. L. Peterson, C. Shantz, A. Walden, and two anonymous reviewers for helpful discussions and/or comments on
2488
M. E. SHERRARD AND H. MAHERALI
earlier versions of this manuscript. The assistance of M. Clifford with data collection was greatly appreciated. This work
was supported by the Ontario Graduate Scholarship Program,
a Discovery grant from the Natural Sciences and Engineering
Research Council, and grants from the Canada Foundation
for Innovation and the Ontario Innovation Trust.
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Corresponding Editor: J. Kohn