Download evolutionary potential of chamaecrista fasciculata in relation to

Evolution, 58(7), 2004, pp. 1446–1458
EVOLUTIONARY POTENTIAL OF CHAMAECRISTA FASCICULATA IN RELATION TO
CLIMATE CHANGE. I. CLINAL PATTERNS OF SELECTION ALONG AN
ENVIRONMENTAL GRADIENT IN THE GREAT PLAINS
JULIE R. ETTERSON1
Department of Ecology, Evolution and Behavior, University of Minnesota, St. Paul, Minnesota 55812-3003
Abstract. Climate change will alter natural selection on native plant populations. Little information is available to
predict how selection will change in the future and how populations will respond. Insight can be obtained by comparing
selection regimes in current environments to selection regimes in environments similar to those predicted for the
future. To mimic predicted temporal change in climate, three natural populations of the annual legume Chamaecrista
fasciculata were sampled from a climate gradient in the Great Plains and progeny of formal crosses were reciprocally
planted back into common gardens across this climate gradient. In each garden, native populations produced significantly more seed than the other populations, providing strong evidence of local adaptation. Phenotypic selection
analysis conducted by site showed that plants with slower reproductive development, more leaves, and thicker leaves
were favored in the most southern garden. Evidence of clinal variation in selection regimes was also found; selection
coefficients were ordered according to the latitude of the common gardens. The adaptive value of native traits was
indicated by selection toward the mean of local populations. Repeated clinal patterns in linear and nonlinear selection
coefficients among populations and within and between sites were found. To the extent that temporal change in climate
into the future will parallel the differences in selection across this spatial gradient, this study suggests that selection
regimes will be displaced northward and different trait values will be favored in natural populations.
Key words.
Climate gradient, clinal variation, global warming, phenotypic plasticity, phenotypic selection.
Received January 27, 2004.
Climate strongly influences patterns of natural selection
(Linhart and Grant 1996). Clines in morphology, phenology,
and physiology are commonly found among plant populations
that occupy latitudinal and elevational climatic gradients
(Clausen et al. 1940; Chapin and Chapin 1981; Lacey 1984,
1988; Reinartz 1984; Shaver et al. 1986; Ehleringer 1988;
Neuffer and Bartelheim 1989; Körner et al. 1991; Montagnes
and Vitt 1991; Aizen and Woodcock 1992; Sawada et al.
1994; Hurme et al. 1997; Clapham et al. 1998; Weber and
Schmid 1998; Gehring and Delph 1999; Jonas and Geber
1999; Clevering et al. 2001). Yet no previous studies have
directly linked estimates of clinal selection to clinal patterns
in phenotype. Genetically based clinal patterns in phenotype
provide strong evidence of adaptive differentiation in response to a cline in natural selection (Mayr 1956).
Ongoing climate change may shift clines in selection toward higher latitudes or elevations in some geographic regions. Long-term observational experiments in the field that
document temporal changes in selection on wild populations
are very rare (but see Reale et al. 2003). Likewise, experiments that compare natural selection in current environments
to selection in experimental conditions that mimic those predicted for the future are not common (but see Totland 1999).
Characterization of spatial patterns in selection along environmental gradients will illuminate future patterns of selection.
Studies of phenotypic selection on natural populations in
native environments generally show that selection is weak
(20.1 , b , 0.1, Kingsolver et al. 2001), presumably because populations are already well adapted to local conditions. Thus, it may be difficult to detect selection. The power
1 Present address: Department of Biology, University of Minnesota Duluth, 221 Life Science Building, Duluth, Minnesota
55812-2496; E-mail: [email protected].
Accepted March 10, 2004.
to detect selection can be enhanced by expanding the phenotypic distribution such that more extreme trait values are
present than in the native population (Mitchell-Olds and
Shaw 1987; Wade and Kalisz 1990; Schmitt et al. 1999).
Such broadening of the phenotypic distribution has been accomplished through the use of mutant and transgenic lines
(for a review see Schmitt et al. 1999), physiological manipulation (Dudley and Schmitt 1996), hybridization of ecotypes
(Wolff 1988; Jordan 1991; Andersson and Widén 1993; Andersson 1996; Nagy 1997), physical manipulations (Andersson 1982; Nilsson 1988; Anholt 1991; Andersson and Widén
1993; Kingsolver 1996; O’Connell and Johnston 1998; Totland et al. 1998; Sinervo 1999), artificial selection (Ågren
and Schemske 1994), and by producing artificial populations
composed of individuals from natural populations that differ
in average phenotype (Andersson and Widén 1993). Native
phenotypes, if well adapted in the home environment, would
be favored by selection over those that have been artificially
introduced into the population.
Due to plasticity, the phenotypic distribution of a population may differ when it is reared in a novel environment
and this may, in turn, influence the strength of selection (Lewontin 1974; Mitchell-Olds and Shaw 1987; Wade and Kalisz
1990). Such phenotypic plasticity may be adaptive such that
populations express favorable phenotypes that result in partial or full maintenance of fitness in a new environment (Bradshaw 1965; Schlichting 1986; Sultan 1987; van Tienderen
1991; Schlichting and Pigliucci 1998; Donohue et al. 2001).
Phenotypic plasticity may evolve in response to the range of
environmental heterogeneity that a population experiences.
Thus, to the extent that environmental cues in future climates
elicit adaptive phenotypic responses, the strength of selection
may be moderated and populations maintain a minimum of
fitness critical to population persistence, even without substantial genetic change.
1446
q 2004 The Society for the Study of Evolution. All rights reserved.
SELECTION ALONG A CLIMATE GRADIENT
1447
FIG. 1. (A) Temperature increases from north to south and precipitation decreases from east to west produce an aridity gradient across
the Great Plains of the United States as shown by isoclines of a long-term average growing season soil water index (a for evergreen
trees, 1951–1980). a is an integrated measure of seasonal growth-limiting drought stress on plants and takes into account temperature,
precipitation, and soil texture (Thompson et al. 2000). Arrows point to the locations of focal populations of Chamaecrista fasciculata in
MN, KS, and OK. (B) The 2025–2035 prediction of a growing season soil water index for the state of MN (a* for evergreen trees)
based on the Canadian Global Coupled Model (CGCM1, a coupled atmosphere-ocean general circulation model with transient greenhouse
and sulfate aerosol forcing; Canadian Climate Center) manipulated by Vegetation/Ecosystem Modeling and Analysis Project (VEMAP2).
Experimental garden plot in MN indicated with a star.
In an effort to predict how natural selection may be altered
with climate change, I conducted a study of phenotypic selection along an aridity gradient in the Great Plains of the
United States (Fig. 1A). Patterns of selection are likely to
differ spatially along this gradient such that they may serve
as a proxy for the temporal trend in selection that northern
populations will experience as climate changes. For example,
plants from Minnesota (MN) may experience a climate more
similar to the current climate of Kansas (KS) or Oklahoma
(OK) in the future depending upon the rate of climate change
and the time frame of interest. Specific expectations are based
on the predictions of global climate models; one such model
(CGCM1, Canadian Climate Center, available via http://www.
cccma.bc.ec.gc.ca/models/cgcm1.shtml) predicting that plants
at one of the focal sites of this study in southeastern MN will
experience soil moisture conditions similar to the current conditions of northeastern KS by 2025–2035 (Fig. 1B). Likewise,
a population in the northeastern KS may experience water
availability conditions similar to southern OK (IPPC 2001).
The underlying premise of this work is that differences in
selection regimes across this spatial environmental gradient
approximate the temporal differences in selection that populations will experience in the future.
Surveys of variation among natural field populations of
Chamaecrista fasciculata from MN to southern OK (Etterson,
unpubl. data) and common-garden studies demonstrated clinal variation and genetic divergence in physiology and morphology (Etterson 2001). Fitness measures from greenhouse
drought experiments suggested that populations are adapted
to different water-availability conditions (Etterson 2001).
Based on these studies, I developed adaptive hypotheses for
three traits: (1) selection favoring rapid progression through
the life cycle occurs in the short growing season of the north
but should be weaker or reversed in sites further south; (2)
selection favoring larger plant size occurs in all sites but it
should be weaker in the south because of the greater transpirational water losses suffered by plants with more leaf
surface area (Etterson 2001); (3) thicker leaves are favored
in the more southern sites because smaller but thicker leaves
are less subject to water loss but can maintain comparable
rates of photosynthesis (Givnish 1979).
I conducted a quantitative genetics study using a reciprocal
1448
JULIE R. ETTERSON
transplant design among three populations that were sampled
from the climate gradient in the Great Plains. Three distinct
aspects of evolutionary change were investigated: natural selection on phenotypes (this paper), the underlying genetic
basis of phenotypic variance and changes in genetic architecture in different environments (Etterson 2004), and the
response to selection when genetic correlations among traits
are taken into account (Etterson and Shaw 2001). Short-term
evolutionary response (Etterson and Shaw 2001) was predicted using estimates of the additive genetic covariance between traits and fitness (Robertson 1968; Price 1970), a method akin to the genetic regression methods of Rausher (1992).
Here the four objectives are: (1) to characterize phenotypic
selection in three sites along a climate gradient in the Great
Plains using a synthetic population of plants composed of
three different populations known to differ in average phenotype; (2) to determine whether patterns of selection are
clinal by examining selection on a single population reared
in a gradient of environments and by examining differences
in selection among populations reared in a single site; (3) to
test adaptive hypotheses concerning phenological and morphological traits of C. fasciculata; and (4) to examine plastic
responses in climates similar to those predicted for the future.
The phenotypic selection analyses presented here exemplify
a general approach to understanding the changes in selection
that may arise with climate change.
MATERIALS
AND
METHODS
Chamaecrista fasciculata Michx. is an annual legume of
disturbed prairie, old field, and savanna (McGregor 1986).
The northern boundary of its native range in the Great Plains
is in central Minnesota; the southern boundary in Mexico is
not recorded.
Three focal populations were sampled across an aridity
gradient from three dry tallgrass prairie fragments in the
northern half of the species range: MN (Kellog-Weaver
Dunes, Wabasha Co.), KS (Konza Prairie, Riley Co.), and
OK (Pontotoc Ridge, Pontotoc Co.). These sites are between
448N and 348N latitude and, from north to south, range 17–
238C in average temperature and 50–60 cm total precipitation
during the growing season (see Appendix 1 for other site
differences).
Crosses
Five pods from each of 200 plants in each of the three
natural populations were collected (for more details see Etterson 2004). Seeds were hand-scarified, germinated in petri
plates, inoculated with Rhizobium spp. strain EL (LiphaTech,
Inc., Milwaukee, WI), and raised under a 15:9-h photoperiod
in a climate-controlled greenhouse in Pro-Mix potting mix
(Premier Brands Inc., Rochelle, NY). Plants were crossed
within populations according to a nested paternal half-sib
crossing design with independent sets of three dams assigned
at random to each sire (North Carolina I; Comstock and Robinson 1948; for more details see Etterson 2004).
Planting Design and Measurements
Pedigreed offspring were germinated as described above
at the University of Minnesota, transplanted into 1 3 2 3 2-
in plug trays, inoculated with commercial Rhizobium spp.
(strain EL; LiphaTech, Inc.), and grown in the greenhouse
for about three weeks. Seedlings from each population were
planted into gardens in MN (N 5 3247), KS (N 5 3360),
and OK (N 5 3301) according to a randomized block design
(four blocks per site). The planting density approximated the
average density of C. fasciculata in the native sites in the
year the quantitative genetic founders were collected (; 12
plants/m2). To avoid contamination of locally adapted populations by genes from alien populations, planting sites that
lacked C. fasciculata but had populations relatively nearby
(, 5 km) were chosen. Seeds for the OK site were planted
in the greenhouse on 1 May and transplanted into a plowed
plot 30 km northwest of the natural source population on 19–
21 May (Robert S. Kerr Environmental Research Center, Ada,
OK). Seeds for the KS site were planted on 6 May and transplanted into a plowed field 5 km from the natural population
on 25–27 May (Konza Prairie Research Natural Area, Manhatten, KS). Seeds for the MN site were planted on 13 May
and transplanted into a plowed agricultural field 110 km
northwest of the natural population on 30 May–1 June (University of Minnesota, St. Paul, MN). Previous studies of C.
fasciculata have found no evidence of local adaptation at this
scale (Galloway and Fenster 2000). The MN site was chosen
because the climate was more similar to the native site than
closer alternative sites due to the warming effect of the metropolitan area (Baker et al. 1985), but the soil type was quite
different (Appendix 1).
The objective of this experiment was to assay the three
different populations in three different bands of climate
broadly defined. To the extent possible, I controlled other
aspects of the growth conditions that differed among gardens.
Seedlings were provided with the same inoculum because
poor performance of nonlocal populations due to a lack of a
compatible Rhizobium symbiont would have obscured the
climate effect. Garden plots were plowed initially to eliminate
competition and weeded as necessary thereafter to reduce the
effects of different assemblages of competing species (one
native, two agricultural). The OK site was weeded in June;
KS and MN sites were weeded in July and September. To
reduce the effect of chance differences in local weather at
planting times, seedlings at all sites were watered as necessary for a maximum of five days after transplantation at
all sites (OK watered once followed by rains, KS site watered
twice followed by rain, MN watered three times over the
course of five days). To minimize deer herbivory, which differed radically among sites, the OK field was enclosed with
a rope fence and the KS field with an electric fence (deer
damage in OK 3.1%, KS 1.2%).
Summer measurements were taken when the plants were
8–9 weeks old (OK, 20–29 July; KS, 1–10 August; MN, 13–
30 August). I collected and pressed the uppermost fully expanded leaf and recorded leaf number for all plants except
those in one block in each of KS and MN sites. Specific leaf
area (SLA) is leaf area/dry leaf weight (m2/g). The reproductive stage of the plant was also scored: 0, died before
reproduction; 1, vegetative plant; 2, flowering plant without
pods; 3, plant with developing green pods; 4, plant with ripening brown pods; 5, plant senescing following reproduction.
Autumn measurements were taken to track the natural end
1449
SELECTION ALONG A CLIMATE GRADIENT
of the growing season (MN, 23 September–2 October; KS,
5–12 October; OK, 14–21 October). Pods . 2 cm in length
were counted. Because seed number per pod differed dramatically among sites and populations, I counted the seed
number of three pods per plant and estimated fecundity as
average seed count 3 pod number. In some cases, especially
MN genotypes in the OK site, pods had dehisced and the
plants senesced; in this case I counted the number of pedicels
that had remnant pod fragments still attached. In cases where
seed counts were not available because pods had already
dehisced, I estimated fecundity using the average seed count
of the other full-sib replicate within the block or, if that was
not available, the average seed count of the full-sib family
across blocks. The use of alternative seed counts was necessary for 879 MN, 120 KS, and 59 OK plants across all
sites.
Data Analysis
To assess mean trait differences among populations and
sites, ANOVA and MANOVA analyses were performed on
all traits (JMP, ver. 3.1, SAS Institute 1995). Block is considered a random effect; population and site are considered
fixed effects. Leaf number and specific leaf area were loge
transformed. So that selection analyses could be performed
for all traits jointly, reproductive stage was analyzed as a
continuous trait even though it was discrete and the temporal
spacing between the stages may not be even. This trait was
not transformed because this did not improve the data structure.
I used the multiple regression methods of Lande and Arnold (1983) to characterize the dynamic interface between
phenotypes and the environment. The genetic relationship
between traits and fitness (e.g., Rausher 1992) and predictions
of selection response have been reported previously (Etterson
and Shaw 2001).
Phenotypic selection analyses were done at two levels. To
compare patterns of selection imposed by environmental factors at different latitudes, I pooled data from all plants reared
at a particular site to maximize the phenotypic range and
enhance detection of selection (site-specific analyses). However, because the populations are genetically divergent and
may respond differently to the different environments, selection may differ among the populations within and among
garden sites. Therefore, I also performed phenotypic selection
analyses for each population and site combination (population-specific analyses). Partial regression coefficients of linear multiple regressions of relative fitness on traits are interpreted as the measure of direct selection, the selection
gradient (bi) on trait i (Lande and Arnold 1983). The covariance between relative fitness and trait i, the selection
differential (Si), is interpreted as a composite measure of
direct linear selection on trait i and indirect linear selection
mediated by phenotypic correlations with other traits (Robertson 1966; Price 1970; Lande and Arnold 1983). Estimates
of the curvature of selection surfaces, or the adaptive landscape, were obtained from multiple regressions that included
as predictors quadratic (gii) and cross-product functions of
the traits (gij). The gii-values reflect curvature in the selection
surface and indicate stabilizing (disruptive) selection if the
peak (valley) of fitness corresponds to an intermediate phenotype (Endler 1986; Mitchell-Olds and Shaw 1987). The gijvalues reflect selection on trait i that varies depending upon
the value of trait j (Phillips and Arnold 1989; Brodie et al.
1995). Higher order interactions (. second) were not explored because of difficulty of visualizing four-dimensional
data necessary for interpretation of the results.
Regression analyses were performed on JMP (ver. 3.1, SAS
Institute 1995) and covariances were estimated using SYSTAT (ver. 7.0, SPSS 1997). Depending on the analysis, relative fitness was calculated by dividing individual fecundity
by site mean fecundity or population mean fecundity for the
site-specific and population-specific analyses, respectively.
When relative fitness was left untransformed, the assumption
of normality of the residuals was violated in all analyses as
indicated by a significant Spearman rank correlation between
the residuals and relative fitness (Neter et al. 1996). Consequently, tables report selection differentials and gradients
based on analyses that used untransformed relative fitness
but P-values are from analyses where relative fitness was loge
transformed (Mitchell-Olds and Shaw 1987). All predictor
variables were standardized to a mean of zero and standard
deviation of one. Correlations among predictor variables for
the site- and population-specific analyses are provided in Appendices 2 and 3.
To test for differences in selection corresponding to latitude in the site-specific analyses, I pooled data from all gardens and analyzed by ANCOVA with a model that included
a categorical term coding for site, the three traits, and the
site 3 trait terms (Zar 1999). The finding of significant interaction terms in all ANCOVA analyses was followed by
one degree of freedom pairwise contrasts to determine which
slopes differed. In the population-specific analyses, patterns
of selection that are associated with clines in phenotype were
investigated in two ways. First, to examine differences in
selection on a single population raised along the environmental gradient, I pooled all data for a population across sites
and analyzed with a model that included site as a factor, the
traits, and the site 3 trait terms. Secondly, to examine differences in selection among populations within a single site,
I pooled all data from a single site across populations and
analyzed with a model that included population as a factor,
the traits, and the population 3 trait terms. In all ANCOVA
analyses, homogeneity of variance was verified by visual
inspection of the distribution of residuals.
Bivariate nonlinear selection gradients were visualized by
three-dimensional mesh plots with predicted relative fitness
on the vertical axis and trait values on the horizontal axes.
Grid coordinates were calculated with the SigmaPlot interpolation function (intervals 5 15, weight 5 3, SigmaPlot
1999).
RESULTS
Mortality during the field season was generally low but
varied between sites and populations. Plants in MN had the
highest mortality rate (6.2%) followed by OK (1.8%) and KS
(1.5%). For percent mortality, the rank among the populations
was consistent across sites; MN genotypes had the highest
mortality in all sites (MN site 7.9%, KS 2%, OK 3.2%),
1450
JULIE R. ETTERSON
FIG. 2. Least squares means and standard errors (very small) of four traits (A) fecundity; (B) reproductive stage; (C) leaf number; and
(D) specific leaf area measured on three populations (MN, circles; KS, triangles; OK, squares) in three sites. Black squares show the
mean for one site averaged across populations and the arrow indicates the direction of phenotypic selection.
followed by OK genotypes (MN site 6.2%, KS 1.6%, OK
1.2%), and KS genotypes (MN site 5.0%, KS 0.8%, OK
1.1%).
On average, plants exhibited plastic responses to the different environments as indicated by significant site effects
(Fig. 2, Table 1). Fecundity was highest in KS, followed by
OK and MN (Fig. 2A). Clinal trends corresponding to the
latitude were apparent for the rate of phenological development and leaf number. Plants in OK were the fastest to
develop, had the fewest leaves, and the smallest specific leaf
area (thickest leaves; Fig. 2B–D). Plants in MN had the op-
posite response, and plants in KS were intermediate except
for specific leaf area.
Responses to the sites differed among populations as indicated by significant site 3 population effects for each trait
(Table 1). Native populations at each site had significantly
higher estimated lifetime fecundity than alien populations,
suggesting local adaptation (Fig. 2A). MN plants had the
highest seed production at home and were progressively less
fecund in the two more southern sites (31% less than KS
plants in KS, 67% less than OK plants in OK). KS plants
had the highest seed production in KS and were intermediate
TABLE 1. Univariate and multivariate analysis of variance F-test statistics for traits measured on three populations of Chamaecrista
fasciculate in a reciprocal transplant experiment in field sites in MN, KS, and OK. * P , 0.05; ** P , 0.001; *** P , 0.0001.
F-statistics and significance levels
Reproductive stage
Fecundity
Factor
Site
Block (site)
Population
Site 3 population
Population 3 block (site)
df
2,
9,
2,
4,
18,
9
18
18
18
9769
F
49.5***
3.1*
5.8*
95.7***
18.1***
df
2,
7,
2,
4,
14,
7
14
14
14
8356
Leaf number
F
32.4**
1.2
90.4***
6.16**
24.17***
df
2,
7,
2,
4,
14,
7
14
14
14
8073
Specific leaf area
F
42.9***
52.5***
289.6***
18.7***
2.9**
df
2,
9,
2,
4,
18,
9
18
18
18
9456
F
Wilk’s l
23.2**
5.5**
68.8***
15.2***
13.2***
2399.2***
57.8***
1149.9***
568.2***
17.6***
1451
SELECTION ALONG A CLIMATE GRADIENT
TABLE 2. Site-specific selection analyses. Standardized selection differentials (Si) were estimated as the covariance between relative
fitness and measured traits after blocking factors were taken into account (blocking factors not shown). Significance levels are Bonferroni
probabilities of Pearson correlation coefficients. Standardized directional selection gradients, bi (SE) were obtained from linear multiple
regressions of relative fitness on three traits (all populations within a site included). Whole-model statistics: MN, F6,2290 5 74.82***;
KS, F5,2365 5 309.92***; OK, F6,3105 5 179.98***. Variance inflation factors (VIF) , 1.8 for all traits in all analyses. gii, indicating
stabilizing or disruptive selection, and gij, indicating joint selection on pairs of traits, were obtained from separate multiple regressions
that included quadratic and cross-product terms. Whole model statistics: MN, F11,2287 5 72.89***; KS, F11,2359 5 194.24***; OK, F11,3099
5 110.02***. VIF , 2.9 for all factors except RS 3 RS in MN and KS analyses where VIF . 10. Differences in selection among the
sites are indicated by significant site 3 trait interactions from ANCOVA. Subscripts show which b-values differ according to 1-df contrasts
with a 5 0.05. Of the 21 contrasts conducted, one would except to erroneously assign significance in 1.05 cases by chance alone. RS,
reproductive stage; LN, leaf number; SLA, specific leaf area. † P , 0.10; * P , 0.05; ** P , 0.01; *** P , 0.001.
Selection analyses
Trait
RS
LN
SLA
RS 3 RS
LN 3 LN
SLA 3 SLA
RS 3 LN
RS 3 SLA
LN 3 SLA
S
b
S
b
S
b
gii
gii
gii
gij
gij
gij
ANCOVA
MN site
KS site
OK site
0.529***
0.737 (0.038)a***
0.208***
0.405 (0.038)a***
20.010
0.094 (0.037)a†
1.000 (0.118)a***
0.088 (0.023)a
20.030 (0.020)a*
0.356 (0.042)a***
20.091 (0.039)a***
20.010 (0.035)a
20.269***
20.131 (0.019)b***
0.458***
0.539 (0.019)b***
20.245***
20.127 (0.015)b***
0.062 (0.016)b***
0.173 (0.012)b***
20.003 (0.006)b
20.032 (0.018)b*
20.073 (0.017)a
20.040 (0.017)a**
20.405***
21.004 (0.026)c***
0.632***
0.599 (0.026)b***
20.335***
20.232 (0.023)c***
20.090 (0.029)c***
0.195 (0.020)c***
20.004 (0.003)b*
20.044 (0.028)b***
0.030 (0.026)b
20.063 (0.025)a†
in both MN (60% less than MN plants) and OK (11% less
than OK plants). OK plants also produced the most seed in
their home site and were progressively less fecund in KS and
MN (7% less than KS plants in KS, 96% less than MN plants
in MN). Low fecundity of all populations at the OK site was
likely attributable to the uncharacteristically hot and dry
weather during the growing season (Appendix 1).
MN plants had the most rapid development and the fewest
leaves, regardless of the site (Fig. 2B, C). Although MN
genotypes had thinner leaves than other populations overall,
they had intermediate leaf thickness in MN and had an anomalous response in KS producing very thin leaves, which could
have been a manifestation of extreme competition for light
among the relatively small MN plants at this site (Fig. 2D).
KS and OK populations developed slowly in MN but differed
less in KS or OK (Fig. 2B). These populations showed parallel plastic responses with respect to leaf number and specific
leaf area that corresponded to latitude (Fig. 2C, D).
Site-Specific Phenotypic Selection Analyses
Linear selection. Latitudinal trends in S and b were apparent for each trait in the site-specific selection analyses
with all populations pooled (Table 2). The rank order of
selection coefficients for each trait corresponds to latitude
and shows a cline in selection. Significant site 3 trait factors
in ANCOVA and post hoc contrasts show that b differed
among the sites for all traits except for leaf number in KS
and OK. In MN, plants with shorter phenologies were favored
by selection, whereas plants with longer phenologies were
favored in KS and to a greater extent in OK. In MN, there
was a tendency for thinner leaves to be favored, although
neither b (P 5 0.09) nor S was statistically significant. In
contrast, there was significant selection for thicker leaves in
both KS and OK as estimated by either the S or b. Plants
with more leaves had higher fitness in all sites, although the
Site 3 trait
F2,7763 5 274.84***
F2,7763 5 227.85***
F2,7763
F2,7763
F2,7763
F2,7763
F2,7763
F2,7763
F2,7763
5
5
5
5
5
5
5
30.60***
70.28***
42.68***
1.46
65.08***
9.25***
1.32
strength of selection was significantly weaker in MN, where
plants were relatively large.
The direction of the selection was toward the phenotypic
mean of native populations for all traits in each site except
for leaf number in MN (arrows in Fig. 2). This either suggests
that trait values of the native population are adaptive or that
traits other than those measured are under selection and confer high fitness on plants from the local population, causing
a spurious relationship between fitness and local values of
the chosen traits. To test the latter possibility, the local population, which comprises one-third of the observations, was
removed from the analyses and, in all cases, the selection
gradients did not differ in sign from the analyses with all the
data. This verifies the relationship between fitness and these
specific traits (analyses not shown).
Quadratic selection. The shape of selection surfaces
changed clinally with latitude for five of six traits according
to the rank order of gii (Table 2). gii for reproductive stage
was positive in MN and KS but negative in OK, and all
selection coefficients were significantly different. Estimates
of gii for leaf number in MN were weak and negative but
were stronger and significantly different in KS and OK. A
trend was also evident for leaf thickness for which gii was
significantly positive in MN and weaker in KS (ns) and OK,
although these coefficients were not significantly different.
With the exception of specific leaf area in OK, visualization
of these selection surfaces over the range of the observed
data did not support the interpretation of stabilizing or disruptive selection because the maximum or minimum of the
curve coincided with the edge of the phenotypic distribution
rather than with intermediate phenotypes.
Clinal patterns of joint selection that favored combinations
of traits over and above direct selection alone were found for
all pairs of traits as indicated by the rank order of gij (Table
2). In MN, early-developing plants with more and thinner
S
b
S
b
S
b
gii
gii
gii
gij
gij
gij
RS
LN
SLA
RS 3 RS
LN 3 LN
SLA 3 SLA
RS 3 LN
RS 3 SLA
LN 3 SLA
Trait
0.027
0.061
(0.045)a,1
0.365***
0.392***
(0.024)a,1
20.152***
20.085***
(0.024)a
0.191
(0.320)1
0.038
(0.015)
20.026†
(0.016)a
0.044
(0.044)a
20.020
(0.051)
20.050
(0.025)
MN site
0.027
0.025
(0.033)ab
0.329***
0.454***
(0.033)b,1
20.044
20.059*
(0.027)b
0.092†
(0.275)
0.070***
(0.022)1
20.018**
(0.011)b
0.108*
(0.036)b
0.008
(0.035)
20.027
(0.027)1
KS site
MN population
MN site
0.266***
0.451***
(0.086)a,2
0.349***
0.365***
(0.070)a,2
20.107
0.005
(0.070)
0.547***
(0.222)a,12
0.146
(0.044)
20.006
(0.027)
0.015
(0.084)
20.060
(0.079)
0.002
(0.059)
OK site
20.004
0.029†
(0.080)b
0.028***
0.328***
(0.077)c,1
20.015
20.180†
(0.070)b
20.016
(0.009)1
0.011
(0.007)
0.001
(0.001)b
0.001
(0.010)ab
20.002
(0.010)
20.017*
(0.009)1
0.001
0.364
(0.268)ab
0.321***
0.441***
(0.022)b,2
20.020
20.003
(0.018)
1.260
(2.445)ab
0.099***
(0.011)1
20.004
(0.007)
0.496
(0.275)
20.792
(0.578)
0.002
(0.017)1
KS site
KS population
Selection analyses
20.001
0.048
(0.033)b
0.375***
0.383***
(0.028)a,2
0.008
20.002
(0.029)
20.009
(0.008)b,1
0.054
(0.017)
20.005
(0.008)
0.003
(0.025)
0.050
(0.037)
0.023
(0.032)b,2
OK site
2.059***
2.700***
(0.358)a,2
0.234
0.211
(0.376)a,3
20.560
20.609*
(0.360)
2.209***
(0.419)a,2
0.011
(0.248)a
20.041
(0.254)
1.286
(0.422)
20.378
(0.394)
20.424
(0.363)
MN site
20.011
0.047*
(0.026)b
0.256***
0.394***
(0.030)b,3
20.092**
20.059**
(0.022)
0.007
(0.026)b
0.108***
(0.019)b,2
20.002
(0.008)
0.049
(0.027)
20.023
(0.022)
0.035
(0.025)2
KS site
OK population
20.024
0.029
(0.029)b
0.444***
0.458***
(0.028)c,1
0.006
0.004
(0.027)
20.078***
(0.028)b,2
0.124**
(0.020)b
0.002
(0.004)
0.048
(0.028)
0.047†
(0.023)
0.077*
(0.034)2
OK site
TABLE 3. Population-specific selection analyses. Standardized selection differentials (Si) for each population in each site estimated as the covariance between relative fitness
and measured traits after blocking factors were taken into account (blocking factors not shown). Significance levels for selection differentials are Bonferroni probabilities of
Pearson correlation coefficients. Directional selection gradients, bi (SE) were obtained from multiple regressions of relative fitness on three traits. Whole-model statistics (site/
population): MN/MN F5,748 5 78.50***; MN/KS F5,824 5 40.83***; MN/OK F5,704 5 13.61***; KS/MN F5,758 5 53.06***; KS/KS F5,857 5 103.68***; KS/OK F5,732 5
86.54***; OK/MN F6,983 5 6.37***; OK/KS F6,1115 5 32.87***; OK/OK F6,990 5 50.27***. Variance inflation factors (VIF) , 1.6 for all traits in all analyses. Univariate
nonlinear selection gradients (gii) and bivariate nonlinear selection gradients (gij) for each population in each field site obtained from separate multiple regressions including
quadratic and cross-product terms. Whole model statistics (site/population): MN/MN F11,742 5 37.83***; MN/KS F11,818 5 21.14***; MN/OK F11,698 5 10.93***; KS/MN
F11,752 5 28.86***; KS/KS F11,851 5 59.41***; KS/OK F11,726 5 44.73***; OK/MN F12,977 5 4.01***; OK/KS F12,1109 5 17.64***; OK/OK F12,894 5 30.61***. Variance
inflation factors (VIF) , 2.9 for all factors except RS 3 RS: MN site MN and KS populations (.10); OK site OK population. RS, reproductive stage (,5.5). Differences in
selection for a single population raised in different sites are indicated by significant site 3 trait interactions from ANCOVA. Subscripts letters show which b-values are
significantly different according to 1-df contrasts with a 5 0.05. Differences in selection for among populations at a single site are indicated by significant population 3 trait
interactions from ANCOVA. Subscript numbers show which b-values are significantly different according to 1-df contrasts, with a 5 0.05. Of the 20 contrasts conducted,
one would expect to erroneously assign significance in one case by change alone. LN, leaf number; SLA, specific leaf area. † P , 0.10; * P , 0.05; ** P , 0.01; *** P ,
0.001.
1452
JULIE R. ETTERSON
F2,3109 5 3.34*
F2,3109 5 1.97
F2,3109 5 1.24
F2,3109 5 0.64
F2,3109 5 1.91
F2,3109 5 8.59***
F2,3109 5 0.75
F2,3109 5 112.58***
OK site
F2,3109 5 2.33†
SELECTION ALONG A CLIMATE GRADIENT
1453
leaves were particularly favored (Fig. 3a, d), whereas in KS
and OK, later-developing plants with more and thicker leaves
were favored (Fig. 3b, c, e, f). Selection on the rate of development and leaf thickness was significantly different in
OK. In KS and to a greater extent in OK (Fig. 3e, f), there
was selection for plants with more and thicker leaves but gij
did not differ significantly among sites.
F2,371 5 6.07**
F2,298 5 0.27
F2,2815 5 0.16
F2,2518 5 0.46
LN 3 SLA
F2,2445 5 2.77†
F2,371 5 0.57
F2,298 5 0.05
F2,2815 5 0.63
F2,2518 5 0.70
RS 3 SLA
F2,2445 5 1.61
F2,2371 5 0.96
F2,298 5 0.62
F2,2815 5 2.28†
F2,2518 5 2.92*
RS 3 LN
F2,2445 5 0.49
F2,2371 5 0.68
F2,298 5 0.46
F2,2815 5 0.18
F2,2518 5 4.69**
SLA 3 SLA
F2,2445 5 0.24
F2,2371 5 3.48*
F2,298 5 2.58†
F2,2815 5 1.12
F2,2518 5 2.19
LN 3 LN
F2,2445 5 3.92*
F2,2371 5 0.65
F2,298 5 6.12**
F2,2445 5 13.64***
F2,2518 5 1.45
RS 3 RS
F2,2815 5 12.88***
F2,2371 5 0.85
F2,2284 5 1.72
F2,2815 5 0.29
F2,2518 5 5.58**
SLA
F2,2445 5 2.67†
F2,2371 5 79.13***
F2,2284 5 64.62***
F2,2445 5 90.56***
F2,2815 5 59.83***
F2,2518 5 203.25***
LN
KS site
F2,2371 5 0.62
F2,2284 5 14.26***
F2,2445 5 5.65**
F2,2815 5 25.13***
F2,2518 5 8.69***
Site 3 trait
RS
Trait
MN population
KS population
OK population
ANCOVA
TABLE 3.
Extended.
MN site
Population 3 trait
Population-Specific Phenotypic Selection Analyses
Linear selection: comparisons of single populations across
sites. Clinal trends were apparent in S and b for the rate of
phenological development for all populations across sites
(Table 2, ANCOVA site 3 trait F-statistics and letter subscripts). The pattern of selection of leaf number was clinal
for the OK population, showing progressively stronger selection favoring plants with more leaves from north to south.
However, for MN and KS populations, selection favored
plants with more leaves to the greatest extent in KS. MN
plants with thick leaves were strongly favored in OK but
more weakly in MN and KS.
Quadratic selection: comparisons of single populations
across sites. Clinal patterns in nonlinear selection coefficients were detected in three of five cases where selection
differed significantly among populations across sites (Table
2). For the OK population, strong positive gii was estimated
for reproductive stage in MN but it was significantly negative
in OK. Also for this population, weak positive gii was estimated for leaf number in the MN site that was progressively
stronger in southern sites. For the MN population, gii for leaf
thickness was significantly more negative in MN than in the
two more southerly sites. A significant but nonclinal difference in gii was detected for reproductive stage of the KS
population. Joint selection on pairs of traits, gij, differed
among sites in only one case and did not show clinal patterns
(reproductive stage 3 leaf number, MN population).
Linear selection: comparisons among populations at a single site. Clinal patterns of selection were evident when S
or b were compared among populations reared in a single
site for all nine cases where significant population 3 trait
terms were estimated by ANCOVA (Table 2, numerical subscripts). In the MN site, rapid phenology was weakly favored
among MN plants but progressively more strongly favored
among KS and OK plants. In both the MN and KS sites,
larger MN plants were strongly favored, but progressively
less so among KS and OK plants. The opposite clinal pattern
of selection on leaf number was apparent at the OK site.
For 13 of the 15 statistically significant selection gradients
estimated in these analyses, b, the measure of direct selection
was larger, and more often statistically significant than S, the
composite measure of selection that takes into account trait
correlations. This indicates that, in most cases, trait correlations are not reinforcing selection but are having antagonistic effects that diffuse the strength of selection.
Quadratic selection: comparison among populations at a
single site. The pattern among nonlinear coefficients of selection was clinal for all significant population 3 trait tests
(Table 2). In the MN site, weak positive gii was estimated
for reproductive stage among MN plants which was progressively stronger among KS and OK plants. In contrast, gii
1454
JULIE R. ETTERSON
FIG. 3. Interpolated bivariate fitness surfaces showing significant joint selection on reproductive stage and leaf number in (a) MN; (b)
KS; and (c) OK; (d) joint selection on specific leaf area and reproductive stage in MN; and joint selection on specific leaf area and leaf
number in (e) KS and (f) OK. Negative fitness values are an artifact of the interpolation function.
for reproductive stage was negative for all populations in the
OK site but significantly more so for the OK population. In
the KS site, gii for leaf number was small for MN plants and
became progressively larger among KS and OK plants. With
the exception of gii for leaf number of MN plants in KS,
examination of the curvature of these selection surfaces did
not indicate patterns of stabilizing or disruptive selection.
Patterns of gij among populations in the KS and OK sites
suggested that individuals with more and thicker leaves were
favored among MN plants, whereas individuals with fewer
and thinner leaves were favored among KS and OK plants.
DISCUSSION
This study provides strong evidence that climate change
will alter selection regimes and challenge the evolutionary
potential of C. fasciculata across its range. When northern
populations were grown in selective environments that mimicked predicted climate change, they suffered substantial
losses in fitness. Each population of this study is currently
adapted to its native environment, as indicated by significantly higher seed production of plants native to each site.
Native trait values contribute to this local adaptation as evidenced by selection toward the mean of home populations.
Clinal patterns in selection across this environmental gradient
were repeatedly found in analyses that employed an expanded
phenotypic distribution, in analyses of single populations,
and in nonlinear aspects of selection. As climate becomes
warmer and drier in the Great Plains, selection will favor
different phenotypes and maintenance of fitness and population stability will depend on evolutionary change.
In principle, populations could respond immediately to climate change, maintaining fitness by adaptive plastic responses (Bradshaw 1965; Schlichting 1986; Sultan 1987; van Tienderen 1991; Schlichting and Pigliucci 1998; Donohue et al.
2001). For leaf number and thickness, the C. fasciculata populations of this study exhibited substantial phenotypic plasticity that could be considered adaptive because it was in the
direction of trait values favored by selection. However, even
though plasticity is in an adaptive direction here, it does not
maintain fitness across environments. Nevertheless, responses to selection on genetically based plasticity may help mitigate fitness losses as climate gradually changes.
In this study I presented evidence suggesting that evolutionary response to a northerly displacement of selection regimes could occur. Clines in selection that correspond to the
pattern of standing phenotypic variation were found for all
traits along this climate gradient in the Great Plains. The
findings support the hypothesis that, in the past, environmental gradients induced clinal patterns in selection that ultimately produced genetically based clinal patterns in phenotype through adaptive evolution. A shift in the cline in
selection may elicit a similar evolutionary response in the
future. Evolution of clinal patterns in phenotypic variation
has been documented for a number of invasive species since
the time of their arrival on the continent (Lacey 1984, 1988;
Reinartz 1984; Weber and Schmid 1998; Huey et al. 2000).
1455
SELECTION ALONG A CLIMATE GRADIENT
The primary environmental gradients across the range of
this study are climate and photoperiod. Other site-specific
environmental factors such as soils, herbivory, and competition are not expected to be clinally arrayed. Consequently,
it is likely that these factors contribute to noise rather than
pattern in the data. The finding of clinal patterns despite the
noise that arises from uncontrolled site-specific factors lends
stronger support to the inference that climate and/or photoperiod govern the patterns reported here. Nevertheless, replication of this experiment either spatially or temporally
would increase confidence in the results because the current
experimental design confounds the effects of climate zone
and site. Previous studies of C. fasciculata and other species
have demonstrated that selection regimes can vary temporally
and over relatively small spatial scales (Kalisz 1986; Gibbs
and Grant 1987; Kelly 1992; Gilbert et al. 1996), although
other studies have shown temporal and spatial stability of
selection for some traits (Rice and Mack 1991).
To the extent that the spatial gradient in climate across the
Great Plains parallels temporal change in climate into the
future, this study suggests that a cline in selection will shift
north and different trait values will be favored in natural
populations. Traits associated with a longer growing season,
such as slower phenological development, may be favored
by selection in northern populations. Likewise, selection is
likely to favor traits that confer higher fitness in more
drought-prone environments, such as thicker leaves. The
adaptive hypothesis for one trait in this study was not supported; plants with fewer leaves were not favored in the hotter
and drier environment, despite a greater potential for water
conservation. Common-garden studies in the greenhouse consistently showed a cline in leaf number with southern plants
having fewer leaves (Etterson 2001). The unexpected direction of selection may be attributable to plastic responses of
plants grown in OK, where all plants had relatively few
leaves. If the phenotypic distribution could have been extended to include plants having more leaves, selection may
have favored reduced leaf number. Alternatively, small plants
in OK (primarily MN genotypes) may have had low fecundity
for reasons other than leaf number. Several other studies have
found selection in the opposite direction than expected based
on standing ecotypic variation. Bennington and McGraw
(1995) found selection for increased plant height within in a
locally adapted population of Impatiens pallida characterized
by short stature. Likewise, Andersson (1996) found a positive
relationship between plant height and fitness within a synthetic hybrid population having an extended phenotypic range
for height in a site that is typically occupied by plants of a
prostrate ecotype of Crepis tectorum.
Broadening of the phenotypic distribution provided a clearer picture of selection corresponding to latitude as illustrated
by differences between site- and population-specific analyses.
For instance, over an expanded range of phenologies, a strong
pattern emerged suggesting that rapid development was favored in the north but the slower development in the south
(site-specific analyses). However, within the range of variation expressed by each population in each environment,
plants with more rapid phenologies were consistently favored
by direct selection (population-specific analysis). Similarly,
site-specific analyses showed that selection favored thicker
leaves most strongly in the south. In contrast, consideration
of selection of only single populations in the gradient of
environments would have led to the opposite conclusion because adaptive phenotypic responses restrict the range of phenotypes within each population, thus obscuring the overall
selection. Despite substantial plastic responses of the populations in response to the different environments, the phenotypic distribution was extended in at least one direction in
all environments in the site-specific analyses. However, in
five of nine cases, the distribution was extended in only one
direction relative to the local population, which may have
reduced the opportunity to detect stabilizing selection on
traits.
This study has demonstrated a cline in selection that corresponds to a cline in phenotype across the northern extent
of the geographical range of C. fasciculata. Adaptive hypotheses concerning clinal patterns for phenology and specific leaf area were supported by observed patterns of genetic
differentiation among populations coupled with demonstration of spatial variation in selection. Further support for the
adaptive nature of trait values of native populations is indicated by selection toward the phenotypic mean of local
populations. The companion to this paper (Etterson 2004)
characterizes the genetic architecture of populations across
environments that influences the extent to which populations
may evolve in response to altered selection regimes with
climate change (Etterson and Shaw 2001).
ACKNOWLEDGMENTS
I thank D. N. Alstad, D. A. Andow, M. B. Davis, C. G.
Eckert, R. G. Shaw, J. Willis, and four anonymous reviewers
for their valuable comments on previous drafts of this manuscript. I thank the Minnesota and Oklahoma Chapters of the
Nature Conservancy for allowing seed collections at KelloggWeaver Dunes and Pontotoc Ridge and the helpful staff at
the Konza Prairie Scientific and Natural Area and the Environment Protection Agency’s Office of Research and Development and the Ground Water and Ecosystem Restoration
Division in Ada, Oklahoma, who provided essential support
throughout this research. I express my deep gratitude to the
many people who helped with the greenhouse crosses and
field work, especially M. Etterson, D. Otterson, R. Otterson,
A. Mertyl, and J. Larson, L. Kinsell, and T. Nguyen. This
work was supported by U.S. Environmental Protection Agency STAR fellowship (U 914758-01-2), summer grants from
the Minnesota Center for Community Genetics, and DaytonWilkie grants for field research.
LITERATURE CITED
Ågren, J., and D. W. Schemske. 1994. Evolution of trichome number in a naturalized population of Brassica rapa. Am. Nat. 143:
1–13.
Aizen, M. A., and H. Woodcock. 1992. Latitudinal trends in acorn
size in eastern North American species of Quercus. Can. J. Bot.
70:1218–1222.
Andersson, M. 1982. Female choice selects for extreme tail length
in a widowbird. Nature 299:818–820.
Andersson, S. 1996. Phenotypic selection on plant height in a segregating hybrid population of Crepis tectorum (Asteraceae). Int.
J. Plant Sci. 157:488–492.
Andersson, S., and B. Widén. 1993. Pollinator-mediated selection
1456
JULIE R. ETTERSON
on floral traits in synthetic populations of Senecio integrifolius
(Asteraceae). Oikos 66:72–79.
Anholt, B. R. 1991. Measuring selection on a population of damselflies with a manipulated phenotype. Evolution 45:1091–1106.
Baker, D. G., E. L. Kuehnast, and J. A. Zendlo. 1985. Climate of
Minnesota. XV. Normal temperatures (1951–1980) and their application. Available via http://climate.umn.edu/doc/historical.
htm.
Bennington, C. C., and J. B. McGraw. 1995. Natural selection and
ecotypic differentiation in Impatiens pallida. Ecol. Monogr. 65:
303–323.
Bradshaw, A. D. 1965. Evolutionary significance of phenotypic
plasticity in plants. Adv. Genet. 13:115–155.
Brodie, E. D., III, A. J. Moore, and F. J. Janzen. 1995. Visualizing
and quantifying natural selection. Trends Ecol. Evol. 10:
313–318.
Chapin, F. S., III, and M. C. Chapin. 1981. Ecotypic differences of
growth processes in Carex aquatilis along latitudinal and local
gradients. Ecology 62:1000–1009.
Clapham, D. H., I. Dormling, I. Ekberg, G. Eriksson, M. Qamaruddin, and D. Vince-Pru. 1998. Latitudinal cline of requirement
for far-red light for the photoperiodic control of budset and
extension growth in Picea abies (Norway spruce). Physiol. Plant.
102:71–78.
Claussen, J., D. D. Keck, and W. M. Hiesey. 1940. Experimental
studies on the nature of species. I. The effect of varied environment on western North American plants. Publication 520.
Carnegie Institution of Washington, Washington, D.C.
Clevering, O. A., H. Brix, and J. Lukavska. 2001. Geographic variation in growth responses in Phragmites australis. Aquat. Bot.
69:89–108.
Comstock, R. E., and H. F. Robinson. 1948. The components of
genetic variance in populations of biparental progenies and their
use in estimating the average degree of dominance. Biometrics
4:254–266.
Donohue, K., E. Hammond Pyle, D. Messiqua, M. Shane Heschel,
and J. Schmitt. 2001. Adaptive divergence in plasticity in natural
populations of Impatiens capensis and its consequences for performance in novel habitats. Evolution 55:692–702.
Dudley, S. A., and J. Schmitt. 1996. Testing the adaptive plasticity
hypothesis: density-dependent selection on manipulated stem
length in Impatiens capensis. Am. Nat. 147:445–465.
Ehleringer, J. R. 1988. Changes in leaf characteristics of a species
along elevational gradients in the Wasatch Front, Utah. Am. J.
Bot. 75:680–689.
Endler, J. A. 1986. Natural selection in the wild. Princeton Univ.
Press, Princeton, NJ.
Etterson, J. R. 2001. Evolutionary potential of the annual legume,
Chamaecrista fasciculata in relation to global warming. Ph.D.
diss., University of Minnesota, St. Paul, MN.
———. 2004. Evolutionary potential of Chamaecrista fasciculata
in relation to climate change: II. Genetic architecture of three
populations reciprocally planted along a climate gradient in the
Great Plains. Evolution 58:1459–1471.
Etterson, J. R., and R. G. Shaw. 2001. Constraint to adaptive evolution in relation to global warming. Science 294:151–154.
Galloway, L. F., and C. B. Fenster. 2000. Population differentiation
in an annual legume: local adaptation. Evolution 54:1173–1181.
Gehring, J. L., and L. F. Delph. 1999. Fine scale genetic structure
and clinal variation in Silene aucaulis despite high gene flow.
Heredity 82:628–637.
Gibbs, H. L., and P. R. Grant. 1987. Oscillating selection on Darwin’s finches. Nature 327:511–513.
Gilbert, F., P. Willmer, F. Semida, J. Ghazoul, and S. Zalat. 1996.
Spatial variation in selection in a plant-pollinator system in the
wadis of Sinai, Egypt. Oecologia 108:479–487.
Givnish, T. J. 1979. On the adaptive significance of leaf form. Pp.
375–407 in O. T. Solbrig, S. Jain, G. B. Johnson, and P. H.
Raven, eds. Topics in plant population biology. Columbia Univ.
Press, New York.
Huey, R. B., G. W. Gilchrist, M. L. Carlson, D. Berrigan, and L.
Serra. 2000. Rapid evolution of a geographic cline in size in an
introduced fly. Science 287:308–309.
Hurme, P., T. Repo, O. Savolainen, and T. Paakkonen. 1997. Climatic adaptation of bud set and frost hardiness in Scots pine
(Pinus sylvestris). Can. J. For. Res. 27:716–723.
Intergovernmental Panel on Climate Change (IPCC). 2001. Climate
change 2001: the scientific basis. Contribution of Working
Group I to the Third Asessment Report of the Intergovernmental
Panel on Climate Change. J. T. Houghton, Y. Ding, D. J. Griggs,
M. Noguer, P. van der Linden, X. Dai, and K. Maskell, eds.
Cambridge Univ. Press, Cambridge, U.K.
Jonas, C. S., and M. A. Geber. 1999. Variation among populations
of Clarkia unguiculata (Onagraceae) along altitudinal and latitudinal gradients. Am. J. Bot. 86:333–343.
Jordan, N. 1991. Multivariate analysis of selection in experimental
populations derived from hybridization of two ecotypes of the
annual plant Diodia teres W. (Rubiaceae). Evolution 45:
1760–1772.
Kalisz, S. 1986. Variable selection on the timing of germination in
Collinsia verna (Scrophulariaceae). Evolution 40:470–491.
Kelly, C. A. 1992. Spatial and temporal variation in selection on
correlated life-history traits and plant size in Chamaecrista fasciculata. Evolution 46:1658–1673.
Kingsolver, J. G. 1996. Experimental manipulation of wing pigment
pattern and survival in western white butterflies. Am. Nat. 147:
296–306.
Kingsolver, J. G., H. E. Hoekstra, J. M. Hoekstra, D. Berrigan, S.
N. Vignieri, C. E. Hill, A. Hoang, P. Gilbert, and P. Beerli. 2001.
The strength of phenotypic selection in natural populations. Am.
Nat. 157:245–261.
Körner, C., G. D. Farquhar, and S. C. Wong. 1991. Carbon isotope
discrimination by plants follows latitudinal and altitudinal
trends. Oecologia 88:30–40.
Lacey, E. P. 1984. Seed mortality in Daucus carota populations:
latitudinal effects. Am. J. Bot. 71:1175–1182.
———. 1988. Latitudinal variation in reproductive timing of a
short-lived monocarp, Daucus carota (Apiaceae). Ecology 69:
220–232.
Lande, R., and S. J. Arnold. 1983. The measurement of selection
on correlated characters. Evolution 37:1210–1226.
Lewontin, R. C. 1974. The genetic basis of evolutionary change.
Columbia Univ. Press, New York.
Linhart, Y. B., and M. C. Grant. 1996. Evolutionary significance
of local genetic differentiation in plants. Annu. Rev. Ecol. Syst.
27:237–277.
Mayr, E. 1956. Geographical character gradients and climatic adaptation. Evolution 10:105–108.
McGregor, R. L. 1986. Flora of the Great Plains. Univ. Press of
Kansas, Kansas City, KS.
Mitchell-Olds, T., and R. G. Shaw. 1987. Regression analysis of
natural selection: statistical inference and biological interpretation. Evolution 41:1149–1161.
Montagnes, R. J. S., and D. H. Vitt. 1991. Patterns of morphological
variation is Meesia triqueta (Bryopsida: Meesiaceae) over an
arctic-boreal gradient. Syst. Bot. 16:726–735.
Nagy, E. S. 1997. Selection for native characters in hybrids between
two locally adapted plant subspecies. Evolution 51:1469–1480.
Neter, J. M., H. Kutner, C. J. Nachtsheim, and W. Wasserman.
1996. Applied linear regression models. McGraw-Hill, Columbus, OH.
Neuffer, B., and S. Bartelheim. 1989. Gen-ecology of Capsella bursa-pastoris from an altitudinal transsect in the Alps. Oecologia
81:521–527.
Nilsson, L. A. 1988. The evolution of flowers with deep corolla
tubes. Nature 344:147–149.
O’Connell, L. M., and M. O. Johnston. 1998. Male and female
pollination success in a deceptive orchid, a selection study. Ecology 79:1246–1260.
Phillips, P. C., and S. J. Arnold. 1989. Visualising multivariate
selection. Evolution 43:1209–1222.
Price, G. R. 1970. Selection and covariance. Nature 227:520–521.
Rausher, M. D. 1992. The measurement of selection on quantitative
traits: biases due to environmental covariances between traits
and fitness. Evolution 46:616–626.
Reale, D., A. D. McAdam, S. Boutin, and D. Berteaux. 2003. Ge-
1457
Andropogon gerardi,
A. scoparius
42.27 (218.94)
220
gravelly sandy loam
220
gravelly sandy loam
2,900
1
Dominant grasses
Data from the National Climatic Data Center, available via http://www5.ncdc.noaa.gov/climatenormals/clim81.
49.25 (212.78)
180
silty clay loam
180
silty clay loam
8,616
Andropogon gerardi,
A. scoparius, Sorgastrum nutans
59.69 (7.19)
130
silty loam
62.03
51.16
130
sandy loam
624
Andropogon gerardi,
A. scoparius, Koeleria macrantha
61.21
23.21
25.64 (2.43)
61.21
23.21
21.25
21.98 (0.73)
62.03
21.25
17.32
17.20 (20.12)
52.50
16.89
348749 N, 978009 W
348659 N, 968539 W
398129 N, 968369 W
398129 N, 968369 W
448989 N, 938179 W
Garden
OK
Native site
Garden
KS
Native site
Garden
MN
448319 N, 928009 W
Latitude, longitude
Average temperature
(8C)1
1998 temperature (8C)
Precipitation (cm)1
1998 precipitation
(cm)1
Growing season (days)
Soils
Fragment size (acres)
Corresponding Editor: J. Willis
Native site
netic and plastic responses of a northern mammal to climate
change. Proc. R. Soc. 270:591–596.
Reinartz, J. A. 1984. Life history variation of common mullein
(Verbascum thapsus). I. Latitudinal differences in population dynamics and timing of reproduction. J. Ecol. 72:897–912.
Rice, K. J., and R. N. Mack. 1991. Ecological genetics of Bromus
tectorum. III. The demography of reciprocally sown populations.
Oecologia 88:91–101.
Robertson, A. 1966. A mathematical model of the culling process
in dairy cattle. Anim. Prod. 8:93–108.
SAS Institute. 1995. JMP statistics and graphics guide. Ver. 3.1.
SAS Institute, Inc., Cary, NC.
Sawada, S., Y. Nakajima, M. Tsukuda, K. Sasaki, Y. Hazama, M.
Futatsuya, and A. Watanabe. 1994. Ecotypic differentiation of
dry matter production processes in relation to survivorship and
reproductive potential in Plantago asiatica populations along
climatic gradient. Funct. Ecol. 8:400–409.
Schlichting, C. D. 1986. The evolution of phenotypic plasticity in
plants. Ann. Rev. Ecol. Syst. 17:667–693.
Schlichting, C. D., and M. Pigliucci. 1998. Phenotypic evolution:
a reaction norm perspective. Sinauer Associates, Sunderland,
MA.
Schmitt, J., S. A. Dudley, and M. Pigliucci. 1999. Manipulative
approaches to testing adaptive plasticity: phytochrome-mediated
shade-avoidance responses in plants. Am. Nat. 154:S43–S54.
Shaver, G. R., N. Fetcher, and F. S. Chapin III. 1986. Growth and
flowering in Eriophorum vaginatum: annual and latitudinal variation. Ecology 67:1524–1535.
Sinervo, B. 1999. Mechanistic analyses of natural selection and a
refinement of Lack’s and William’s principles. Am. Nat. 154:
S26–S42.
Sultan, S. E. 1987. Evolutionary implications of phenotypic plasticity in plants. Evol. Biol. 21:127–178.
SPSS. 1997. SYSTAT 7.0. Statistics. SPSS, Chicago, IL.
———. 1999. SigmaPlot 5.0. SPSS, Chicago, IL.
Thompson, R. S., K. H. Anderson, and P. J. Bartlein. 2000. Atlas
of relations between climatic parameters and distributions of
important trees and shrubs in North America. U.S. Geological
Survey Professional Paper, 1650 A-B.
Totland, Ø. 1999. Effects of temperature on performance and phenotypic selection on plant traits in alpine Ranunculus acris. Oecologia 120:242–251.
Totland, Ø., H. L. Andersen, T. Bjelland, V. Dahl, W. Eide, S.
Houge, T. R. Pedersen, and E. U. Vie. 1998. Variation in pollen
limitation among plants and phenotypic selection on floral traits
in an early-spring flowering herb. Oikos 82:491–501.
van Tienderen, P. 1991. Evolution of generalists and specialists in
spatially heterogeneous environments. Evolution 45:1317–1331.
Wade, M. J., and S. Kalisz. 1990. The causes of natural selection.
Evolution 44:1947–1955.
Weber, E., and B. Schmid. 1998. Latitudinal population differentiation in two species of Solidago (Asteraceae) introduced into
Europe. Am. J. Bot. 85:1110–1121.
Wolff, K. 1988. Genetic analysis of ecological relevant morphological variability in Plantago lanceolata L. 3. Natural selection
in an F2 population. Theor. Appl. Genet. 75:772–778.
Zar, J. H. 1999. Biostatistical analyses. Prentice Hall, Upper Saddle
River, NJ.
APPENDIX 1.
Latitude and longitude, long-term average (1971–2000) temperature and precipitation during the growing season (April–September), 1998 temperature and precipitation
(deviation from normal in parentheses), growing season length, soil substrate, and prairie fragment size of the three sites of population origin and experimental gardens.
Climate records for the native and garden sites in KS and OK are from the same weather station (closest).
SELECTION ALONG A CLIMATE GRADIENT
1458
JULIE R. ETTERSON
APPENDIX 2.
Pearson product-moment correlation coefficients from site-specific
phenotypic selection analyses. Correlation coefficients indicate the
strength of the linear relationships between each pair of predictor
variables for each of the three garden plots (all populations within
a site included). RS, reproductive stage; LN, leaf number; SLA,
specific leaf area; * P , 0.05; ** P , 0.01; *** P , 0.001.
Correlation of the predictor variables
MN site
ri, j
i, j
RS, LN
RS, SLA
LN, SLA
KS site
ri, j
20.186***
0.013
20.267***
OK site
ri, j
20.264***
0.215
20.066**
20.456***
0.238***
20.161***
APPENDIX 3.
Pearson product-moment correlation coefficients from population-specific analyses, which summarize the strength of the linear relationships between each pair of predictor variables for each population in each of the three garden plots (populations within sites analyzed
individually). RS, reproductive stage; LN, leaf number; SLA, specific leaf area; † P , 0.10; * P , 0.05; ** P , 0.01; *** P , 0.001.
Correlation of the predictor variables
MN site
ri, j
i, j
RS, LN
RS, SLA
LN, SLA
KS site
ri, j
OK site
ri, j
MN
population
KS
population
OK
population
MN
population
KS
population
OK
population
MN
population
KS
population
OK
population
0.048
20.063†
20.173***
0.069*
0.085*
20.329***
20.126***
0.033
20.389***
0.172***
20.041
0.023
0.035
0.076*
0.095*
20.145***
0.048
0.097**
20.205***
20.075*
0.110***
20.102***
20.053 †
0.041
20.106***
20.032
20.003