RESEARCH ARTICLE| EVOLUTION

A large-­effect fitness trade-­off across environments is explainedby a single mutation affecting cold acclimation
Gwonjin Leea,b,1, Brian J. Sandersona,b,2,3, Thomas J. Ellisc,4, Brian P. Dilkesb,d, John K. McKaye, Jon Ågrenc, and Christopher G. Oakleya,b,5

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Edited by Nils Stenseth, Universitetet i Oslo, Oslo, Norway; received October 10, 2023; accepted December 26, 2023

Identifying the genetic basis of local adaptation and fitness trade-­offs across environments is a central goal of evolutionary biology. Cold acclimation is an adaptive
plastic response for surviving seasonal freezing, and costs of acclimation may be a
general mechanism for fitness trade-­offs across environments in temperate zone species. Starting with locally adapted ecotypes of Arabidopsis thaliana from Italy andSweden, we examined the fitness consequences of a naturally occurring functional
polymorphism in CBF2. This gene encodes a transcription factor that is a major
regulator of cold-­acclimated freezing tolerance and resides within a locus responsible
for a genetic trade-­off for long-­term mean fitness. We estimated the consequences ofalternate genotypes of CBF2 on 5-­y mean fitness and fitness components at the native
field sites by comparing near-­isogenic lines with alternate genotypes of CBF2 to their
genetic background ecotypes. The effects of CBF2 were validated at the nucleotide
level using gene-­edited lines in the native genetic backgrounds grown in simulatedparental environments. The foreign CBF2 genotype in the local genetic background
reduced long-­term mean fitness in Sweden by more than 10%, primarily via effects
on survival. In Italy, fitness was reduced by more than 20%, primarily via effects on
fecundity. At both sites, the effects were temporally variable and much stronger insome years. The gene-­edited lines confirmed that CBF2 encodes the causal variant
underlying this genetic trade-­off. Additionally, we demonstrated a substantial fitness
cost of cold acclimation, which has broad implications for potential maladaptive
responses to climate change.

SignificanceAn important goal in evolutionary
biology is to understand the fullcausal chain explaining how
naturally occurring sequencepolymorphisms affect traits andfitness in the naturalenvironments in which theorganisms have evolved. A 5-­yreciprocal transplant experimentdemonstrated that a genomic
segment including a major
regulator of cold acclimation, theCBF2 transcription factor, results
in a large-­effect genetic trade-­offfor long-­term mean fitness. We
recapitulated the trade-­off insimulated parental environments
and functionally validated that
CBF2 is the causal locus using
replicated gene-­edited lines in
the native genetic backgrounds.
Our results provide a uniqueconfirmation of a causal
polymorphism underlying local
adaptation and demonstrate asubstantial cost of cold
acclimation with implications forclimate change–induced
maladaptation.

antagonistic pleiotropy | cold acclimation | genetic trade-­off | local adaptation | plasticity

Adaptive differentiation among natural populations is driven by divergent selection. This
may lead to local adaptation, where the local ecotype outperforms foreign ecotypes (1, 2).
Reciprocal local adaptation in transplant experiments is direct evidence of fitness trade-­offs
across environments, i.e., that adaptation to one environment reduces fitness relative to
local ecotypes in other environments (2). Such fitness trade-­offs have long been thought
to be important drivers of biological diversification across scales (3–5). Despite numerous
studies demonstrating local adaptation (2, 6–8), the genetic and physiological mechanisms
underlying local adaptation and fitness trade-­offs across environments remain poorly
understood (9–12), as does the mechanistic basis of adaptation more generally (13–15).
The identification of causal variants for adaptation bears on long-­standing questions with
important consequences for the rate and predictability of adaptive differentiation, including the role of large-­effect alleles in adaptation (16–19) and the contribution of individualloci to fitness trade-­offs across environments (20, 21).
Despite a large literature on the genetic basis of adaptation, understanding of the fullcausal chain connecting naturally occurring sequence polymorphism to traits (molecular
or organismal) and ultimately to fitness in the natural environments in which the organisms have evolved remains an important and elusive goal. Studies have mapped genetic
loci for local adaptation (22–29) but rarely identify and functionally validate causal polymorphisms. The genetic basis of natural variation in traits that are ecologically importantunder some contexts has been discovered and functionally validated (30–33). However,pervasive genotype by environment interactions for both traits and fitness (21, 31, 34)means that interpreting these results in the context of local adaptation and fitness trade-­offs
requires testing the natural alleles in the genetic backgrounds in which they occur and in
the native environments in which the organism evolved. Indeed, recent papers on thegenetics of adaptation highlight the need to follow up mapping studies with functional
validation of candidate genes (15) and the need to explicitly test for fitness effects (19).
Answering these calls requires integration of field study of adaptation with experiments
in realistic conditions on natural ecotypes with experimentally manipulated alleles. Thisis currently practical in only a few study systems.
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The authors declare no competing interest.
This article is a PNAS Direct Submission.
Copyright © 2024 the Author(s). Published by PNAS.
This article is distributed under Creative Commons
Attribution-­NonCommercial-­NoDerivatives License 4.0(CC BY-­NC-­ND).
1

Present address: Department of Microbiology and Cell
Science, University of Florida, Gainesville, FL 32611.

2

Present address: Department of Molecular Biosciences,
University of Kansas, Lawrence, KS 66045.

3

Present address: Center for Genomics, University of
Kansas, Lawrence, KS 66045.

4

Present address: Gregor Mendel Institute of Molecular
Plant Sciences, 1030 Vienna, Austria.

5

To whom correspondence may be addressed. Email:oakleyc@purdue.edu.
This article contains supporting information online athttps://www.pnas.org/lookup/suppl/doi:10.1073/pnas.​2317461121/-­/DCSupplemental.
Published January 30, 2024.

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One potential mechanism for fitness trade-­offs across environments in broadly distributed temperate zone species is cold-­
acclimated freezing tolerance. Cold acclimation is a plastic response
to low temperatures and increases survival in climates with freezing
winter temperatures in both animals and plants (35–39). Coldacclimation involves dramatic changes in transcription, proteintranslation, and metabolism in response to cool temperatures inanimals (40–44) as well as plants (37, 38, 45). Cold acclimationis thought to be energetically costly (46) and could lead tobroad-­scale trade-­offs when cool temperatures that induce this
response are more geographically widespread than the occurrenceof freezing temperatures. Producing the wrong phenotype for agiven environment is one possible cost of plasticity, though additional costs of plasticity such as maintaining the machinery to sense
and respond to variation have also been proposed (47). In animals,
a cost of cold acclimation has been demonstrated in Drosophila(48–50). Cold acclimation may also lead to maladaptive responses
if cues earlier in the season become poorer predictors of the severityof subsequent winter because of climate change and climate
variability.
In winter annuals and other plants, cold acclimation can
increase freezing tolerance through the accumulation of soluble
sugars such as raffinose (51–53) and other compounds that help
decrease the freezing point of the cell, resist desiccation, and stabilize and protect cell membranes (37, 38, 45, 54). In Arabidopsis
thaliana, C-­repeat/DRE binding factor 2 (CBF2) is a member ofthe Apetala2 family of transcription factors and is a necessary
regulator of cold acclimation in laboratory studies (45, 55).
Natural variation in CBF2 contributes to differences in freezing
tolerance across A. thaliana ecotypes (56–59). Cold responsivenessof CBF orthologs appears to be broadly conserved in plants (37,38) and contributes to freezing tolerance in diverse species suchas canola (60), wheat (61), and poplar (62), so it seems likely that
this specific mechanism of regulating cold acclimation is of generalimportance. Cold acclimation is common in temperate zone
plants, and more information on the costs of cold acclimation cancontribute to our understanding of genetic trade-­offs across environments and the potential negative fitness consequences of climate change.
Testing for costs of cold acclimation in plants is challenging
and results are inconclusive thus far. Experimental tests on tree
ecotypes from different geographic origins under different experimental conditions found that it is difficult to tease apart acclimation responses from phenological adaptations that are also cuedby temperature and photoperiod (63). Studies in A. thaliana haveshown that overexpression of CBF2 leads to a stunted phenotype
and reduced fitness (64, 65), providing indirect evidence of a costof CBF2-­mediated cold acclimation. Clinal and correlational associations also suggest a potential cost of cold acclimation (66–69).
While the main benefit of cold acclimation in freezing environments is increased survival, it is unknown whether the costs ofacclimation will be expressed as reductions in survival, fecundity,
or both. One direct test of the costs of cold acclimation in plants
reported no cost for short-­term acclimation of A. thaliana in thelab (65). However, the costs of cold acclimation may be expressed
only under ecologically relevant conditions (c.f., refs. 48 and 49).
In our study system of locally adapted ecotypes of A. thaliana
from Sweden and Italy, cold acclimation likely plays a major rolein fitness trade-­offs across environments (29, 57, 59, 70). A
correlation between minimum winter temperature and relative
survival of the Italian ecotype in Sweden indicated that subfreezing temperature is a primary selective agent in Sweden (57, 70).
At the field site in Italy, autumn and winter temperatures arecool but typically nonfreezing (57, 71), so the cost of acclimation
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will be expressed without the benefit. Furthermore, major freezing tolerance quantitative trait loci (QTL) colocalize with fitness
trade-­off QTL detected over multiple years at the native fieldsites (29, 57). This strongly suggests that cold acclimation mediated by these freezing tolerance loci is a mechanistic basis ofgenetic trade-­offs. The causal variant for the largest effect freezing
tolerance QTL is a loss of function mutation in the Italian alleleof CBF2 (58), which can explain 1/3 of the difference in freezing
tolerance between the Swedish (SW) and Italian (IT) ecotypes(59).
The final step in conclusively demonstrating a role of CBF2 ina fitness trade-­off is to link the effects of this naturally occurring
CBF2 polymorphism to lifetime fitness in both environments. We
predicted that the functional Swedish genotype of CBF2 will be
adaptive in Sweden, but that cold acclimation induced by a functional CBF2 in Italy will incur a fitness cost. As EU regulations
prohibit planting GMO lines in the field, we took a two-­step
approach. We used near-­isogenic lines (NILs) in field experiments
and gene-­edited lines with manipulated alleles of CBF2 in thenative genetic backgrounds in growth chambers programmed to
mimic temperature and photoperiod at the native sites. We
addressed the following questions: 1) What are the effects of introgression segments containing alternate alleles of CBF2 on estimates of long-­term mean fitness when grown at the native sites?
2) What are the effects of this single gene on fitness and fitness
trade-­offs across environments? 3) Are the benefits and costs of
CBF2 in alternate environments mediated by viability selection,
fecundity selection, or both?
Results
Reciprocal Transplant Experiments at the Swedish and ItalianField Sites—Overall Fitness. There was a strong and statisticallysignificant fitness tradeoff across environments between the
ecotypes for estimates of mean lifetime fitness (total fruit
production including zeros for plants that died without having
produced any fruits) over 5 y (Fig. 1 and SI Appendix, Table S1).
This result is consistent with previous findings in this system(29, 70) and is an important prerequisite for investigating thecontribution of any polymorphism to local adaptation and fitness
trade-­offs across environments. In Sweden, 5-­y mean fitness of the
Italian ecotype (IT) was 65% less than that of the local Swedish

A

B

Fig. 1. Least squares mean estimates of fitness (number of fruits per
seedling planted) of the SW and IT ecotypes and NILs over five individual
1-­y experiments at the Swedish (A) and Italian (B) field sites. Error bars are 1
SE. The predominant color indicates the genetic background (SW ecotype =blue; IT ecotype = red). Hatching indicates the genotype of the introgression
segment containing CBF2 in the NILs. Asterisks represent statistically significant
contrasts between a NIL and its genetic background (***P < 0.001; **P < 0.01;
ns, not significant).

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(SW) ecotype (Fig. 1A), and in Italy, 5-­y mean fitness of the SW
ecotype was 82% less than that of the local IT ecotype (Fig. 1B).
Thus, there is a pattern of strong overall selection against theforeign ecotype at both sites, though the strength of selection was
somewhat stronger in Italy than in Sweden.
To quantify the effect of CBF2 at each site, we used NILs withintrogression segments containing alternate genotypes of CBF2 ineach genetic background (SI Appendix, Fig. S1) and compared their
5-­y mean fitness to that of their genetic background ecotype. There
was a strong and statistically significant signal of a genetic trade-­offacross environments for the foreign genotypes of CBF2 in thenative genetic backgrounds (Fig. 1 and SI Appendix, Table S2). At
the Swedish site, the NIL containing the IT cbf2 loss-­of-­function(LOF) genotype in the SW genetic background had 12% lower
fitness than the SW ecotype. At the Italian site, the NIL containing
the functional SW CBF2 genotype in the IT genetic backgroundhad 21% lower fitness than the IT ecotype. NILs with the local
CBF2 genotype in the foreign genetic background had a 4 and
14% increase in fitness in Sweden and Italy, respectively, thoughthese effects were not statistically significant.
Within individual years there were consistent differences
between ecotypes reflecting local adaptation, but the effects of theintrogression segments containing CBF2 were temporally variable
at both sites (Fig. 2 and SI Appendix, Tables S1 and S2). Focusing
on the foreign CBF2 genotype in the local genetic background,
we found a statistically significant effect in two out of 5 y in
Sweden, with reductions in fitness of up to 71%. In Italy, there

were significant effects in 3 y (with a suggestive effect in a fourth
year), with reductions in fitness of up to 40% (SI Appendix,
Table S2). Effect sizes in the remaining nonsignificant contrasts
were mostly, but not always, weak.
Single Gene Effects of CBF2 in Simulated Italian and Swedish
Conditions—Overall Fitness. To test the effects of CBF2 at thesingle gene level required us to use growth chamber experiments
simulating key environmental features at the native sites(SI Appendix, Fig. S2). The growth chamber programs (SI Appendix,
Fig. S3 and Supporting Text) were constructed based on long-­term
climate data collected at the native sites. These programs were thentested and refined to recapitulate differences in relative fitness
between ecotypes observed at these sites (SI Appendix, Fig. S4).
The growth chamber experiments included the parental ecotypes,
two independent CRISPR-­induced LOF cbf2 lines in the SW
genetic background, and two transgenic lines containing SW
CBF2 and native promoter in the IT background. We omitted
the IT background lines from the Swedish chamber experimentbecause the very low fitness of IT background lines in the field
experiment (Fig. 1) and results of freezing tolerance assays withthese lines (59) suggested that it would not be possible to detect
effects in this background in the Swedish environment.
There was a strong signature of a fitness trade-­off across environments between the ecotypes in the growth chambers, and single
gene estimates of the effects of CBF2 corroborate the causality ofthis gene for the NIL effects estimated in the field. We found very

A

B

Fig. 2. Least squares mean estimates of fitness (number of fruits per seedling planted) of the ecotypes and NILs in each individual year at the Swedish (A)and Italian (B) field sites. Error bars are 1 SE. The predominant color indicates the genetic background (SW ecotype = blue; IT ecotype = red). Hatching indicatesthe genotype of the introgression segment containing CBF2 in the NILs as shown in Fig. 1. Asterisks denote significant contrasts between a NIL and its genetic
background (***P < 0.001; **P < 0.01; †0.05 < P < 0.1; ns, not significant).

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strong and statistically significant selection against the IT ecotype
in the Swedish chamber (92%) and the SW ecotype in the Italianchamber (62%; Fig. 3 and SI Appendix, Fig. S4 and Table S3).
Moreover, in the Swedish chamber, both independent LOF cbf2
lines in the SW background had significantly reduced fitness (23and 22%) compared to the SW ecotype (Fig. 3A and SI Appendix,
Table S2). In the Italian chamber, both independent transgenic
SW CBF2 lines in the IT background had significantly reduced
fitness (12 and 21%) compared to the IT ecotype, and the lines
with IT cbf2 in the SW background had significantly greater fitness (23 and 16%) than the SW ecotype (Fig. 3B and SI Appendix,
Table S2).

A

B

Reciprocal Transplants at the Swedish and Italian Field Sites—
Fitness Components. To determine whether the costs (Italy) andCD

benefits (Sweden) of functional CBF2 are mediated through viability
selection, fecundity selection, or both, we compared survival andfecundity of the lines in the field experiment. The ecotypes weresignificantly locally adapted for both fitness components in Italy
and for survival in Sweden (Fig. 4 and SI Appendix, Table S1).
Note that because of very low survival of the Italian ecotype at theSwedish site, it was only possible to compare the fecundities of thetwo ecotypes in one of 5 y (SI Appendix, Fig. S5 and Table S2).
The IT ecotype had 67% lower survival than SW in Sweden, andSW had 33% lower survival and 73% lower fecundity than IT
in Italy. The contribution of CBF2 to fitness trade-­offs was mostpronounced for survival in Sweden and fecundity in Italy (Figs. 1
and 4 and SI Appendix, Table S2). For survival in Sweden, the NIL
with IT cbf2 in the SW background had 13% reduced survival
compared to SW, and SW CBF2 in IT had 15% increased survival
compared to IT, though the latter difference was only suggestive(Fig. 4A and SI Appendix, Table S2). Effects of IT cbf2 in SW on
fecundity in Sweden were weak and nonsignificant. Functional
CBF2 in IT decreased fecundity by 11% (Fig. 4C and SI Appendix,
Table S2), but both the sign and magnitude of this effect must be
interpreted cautiously because of extremely high mortality of IT
in all years except 2020. In Italy, effects of CBF2 on survival wereweak and not statistically significant in both backgrounds (Fig. 4B
and SI Appendix, Table S2). For fecundity in Italy, functional
CBF2 in IT reduced fecundity by 17% compared to IT and
nonfunctional cbf2 in SW increased fecundity by 11%, thoughA

B

Fig. 4. Fitness components of the SW and IT ecotypes and NILs over fiveindividual one-­year experiments at the Swedish and Italian field sites. Mean
proportion survival (averaged over years) at the Swedish (A) and Italian (B)field sites. Least squares mean fecundity at the same sites (C and D). Colors
and hatching as in other figures. Error bars for fecundity are 1 SE; no bars are
given for survival because it was analyzed with a binomial error distribution.
Asterisks represent statistically significant contrasts between a NIL and its
genetic background (***P < 0.001; **P < 0.01; *P < 0.05; †0.05 < P <0.1; ns,
not significant).

only the former contrast was statistically significant (Fig. 4D and
SI Appendix, Table S2).
Within individual years there were consistent differences
between ecotypes for fitness components reflecting local adaptation except for fecundity in Sweden (SI Appendix, Fig. S5 andTable S1), where in many years too few IT individuals survived
to make comparisons of fruit production to other genotypes meaningful. For survival in Sweden and both fitness components in
Italy, the effects of CBF2 varied across years. We focus on theeffects of the foreign CBF2 genotype in the local genetic background compared to the local ecotype. For survival, there were
statistically significant reductions (up to 45%) in Sweden in 2 ywith a suggestive effect in a third year; in Italy, survival was significantly reduced in one year (SI Appendix, Fig. S5 and Table S2).
For fecundity in Italy, there were significant decreases (up to 31%)in 2 y (SI Appendix, Fig. S5 and Table S2).
Single Gene Effects of CBF2 in Simulated Conditions—Fitness
Components. Patterns of relative survival and fecundity of theFig. 3. Least squares mean fitness (number of fruits per seedling planted)of the ecotypes and gene-­edited lines in the “Swedish” (A) and “Italian” (B)growth chambers. Error bars are 1 SE. The predominant color indicates thegenetic background (SW ecotype = blue; IT ecotype = red). Hatching indicatesthe genotype of CBF2 (capitals = functional copy, lower case = loss of function)in gene-­edited lines. Asterisks represent statistically significant contrasts
between a gene-­edited line and its genetic background (***P < 0.001; **P <0.01). Note that the scale of the y axis of fitness for the Italian growth chamberis double that of the Swedish growth chamber.

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ecotypes, and the effects of CBF2 on those fitness components
in the growth chamber experiments were consistent with thefield results. In the Swedish environment, effects were recorded
primarily on survival, whereas in the Italian environment, effects
were recorded primarily on fecundity (Fig. 3 and SI Appendix,
Fig. S6 and Table S3). In the Swedish chamber, the survival of
the IT ecotype was 91% lower than that of the SW ecotype,whereas in the Italian chamber, SW had 62% lower fecundity
pnas.orgthan IT. In the Swedish chamber, both independent LOF cbf2
lines in the SW background had strongly and significantly reduced
survival compared to the SW ecotype (SI Appendix, Fig. S6A and
Table S2). In the Italian chamber, both independent transgenic
SW CBF2 lines in the IT background had significantly reduced
fecundity compared to the IT ecotype, and the lines with IT cbf2
in a SW background had significantly greater fecundity than the
SW ecotype (SI Appendix, Fig. S6D and Table S2).

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Discussion
Identifying the genetic basis of local adaptation and fitness trade-­offs across environments is a central, but often unrealized goal inevolutionary biology. We demonstrated that reciprocal introgressions of a genomic segment containing CBF2 strongly affected
the long-­term mean fitness of two locally adapted ecotypes of
A. thaliana when grown in a 5-­y reciprocal transplant experimentconducted at the native sites. We then functionally validated that
CBF2 is the causal locus for this variation using replicated single
gene-­edited lines in the native genetic backgrounds grown in simulated parental environments. Taken together, the results provide
a unique confirmation of a causal polymorphism underlying local
adaptation and demonstrate a substantial cost of cold acclimation
with broad implications for potential climate change–induced
maladaptation of temperate zone plants.
At both sites, we found strong effects of introgression segments
containing CBF2 on fitness, but these effects were temporally
variable and depended on genetic background. These results confirm the strong and temporally variable effect of the genomic
region around CBF2 previously reported from QTL mapping overmultiple years at the same sites (26, 29). Despite the effort of
replicating the present field experiment for 5 y, correlations
between effect sizes and climate variables with five data points persite would be tenuous. However, the 2 y with significant effectsof CBF2 on fitness in Sweden had the coldest winter minimum
soil temperatures, consistent with a role of CBF2 in freezing tolerance (57, 59). In Italy, where freezing soil temperatures are
extremely rare, the 2 y without significant effects of CBF2 on
fitness had warmer mean soil temperatures during the coldest
period. This suggests greater costs of inducing cold acclimation
during cooler winters in Italy. There are at least two possible explanations for the tendency for CBF2 effects to be statistically significant only in the local genetic background. It may simply be thatthe very poor fitness of the foreign ecotype makes it more difficult
to detect a significant effect. Alternatively, there may be epistatic
effects between CBF2 and downstream target genes, but to test
this hypothesis requires additional data. Together, these results
indicate that the effects of a genetic trade-­off can be context
dependent, and the magnitude and ability to detect an effect at a
locus will depend on the genetic background, as well as spatial
and temporal environmental variation.
Comparison of effect sizes between the current NIL experimentand previous QTL experiments is difficult, both because the experiments took place mostly in different years and because the effectsizes represent different contrasts. Here, effect sizes represent theeffect of a single segment tested against an isogenic ecotypic background, but effect sizes in the QTL study represent the effect ofdifferent alleles at a SNP averaged over heterogenous RIL backgrounds and tested as a deviation from the mean fitness of all RILs.
That said, the effects of this genomic region in the NIL experiment
seem to be greater than those in the QTL experiment for long-­term
mean fitness in Italy and for maximum annual effects at both sites.
This suggests that estimates of effect sizes previously reported forthis QTL are not inflated as might be expected by the “Beavis
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effect” (72), and our results are among a growing number of examples of large-­effect loci involved in adaptation and adaptive traits(18, 19, 73, 74). Transcription factors may be particularly likely
candidates for large-­effect loci involved in broad-­scale adaptation
because they have downstream effects on the expression level ofmany other genes (75, 76). While the field experiments in thenative environments replicated over multiple years represent themost natural test of the fitness consequences of alternate alleles of
CBF2, an important caveat about these results is that the introgression segments in the NILs may well include polymorphisms
in additional genes affecting fitness.
Raising ecotypes and gene-­edited lines in growth chamber programs that mimic temperature and photoperiod changes at thetwo sites was therefore a critical next step in functional validationof the CBF2 polymorphism. Estimates of the relative fitness ofthe two ecotypes in the growth chamber experiments were qualitatively similar to those in the field experiments, demonstratingthat the growth chambers replicated some essential features of the
field environments (Figs. 1 and 3 and SI Appendix, Fig. S4).
Moreover, the effects of single gene edits of CBF2 in the local
genetic background on both cumulative fitness and fitness components (Fig. 3 and SI Appendix, Fig. S6) were qualitatively similar
to mean effects observed in the 5-­year field experiment using NILs
(Figs. 1 and 4) and well within the range of effect sizes observedin individual years in the field (Fig. 2 and SI Appendix, Fig. S5).
The results conclusively demonstrate a causal role for CBF2 in thefitness trade-­off observed across the growth chamber environments
and are consistent with a similar role for the trade-­off across thetwo native sites.
Elucidating the genetic basis of local adaptation is challenging.
Few systems allow for direct functional validation of the fitness
consequences of naturally occurring sequence polymorphisms in
the native genetic backgrounds grown in their native environments. Studies mapping QTL for local adaptation in field reciprocal transplants (reviewed in ref. 21) (28, 29) identify naturally
occurring polymorphisms in regions of the genome associatedwith fitness, but these QTL often contain many genes that differ
between the founder parents. Genome-­wide association mapping(GWAS) of the genetic basis of regional adaptation can provide
better, but often still incomplete, resolution of candidate polymorphisms (77–79). Both approaches test genetic hypotheses, but
the molecular identities of the causal genes need to be tested byfunctional validation (11, 13, 15). Such validation is limited tosystems in which genetic manipulation is feasible, or where one
can indirectly test the effect of a gene from the focal species in amodel organism (80, 81). Functional genetic studies on putatively
adaptive traits provide a clear link between causal variant andphenotype (30–33). Other work has evaluated the fitness effects
of mutations in field experiments (75, 82). We extended thisapproach to a reciprocal transplant in which the alleles affecting
a fitness trade-­off were evaluated in the native genetic backgrounds
and their environments. The relationships between genotype,phenotype, and fitness depend on the context of the environmentand genetic background (19, 21, 31, 34). Our approach puts thegenetic effects in such context, though legal constraints necessitated evaluating the single gene edits in simulated rather thanactual parental environments. The concordance between field andgrowth chamber results demonstrates that we captured some essential differences observed in nature.
The mechanistic basis of the fitness effects of this functional
polymorphism in the Swedish environment stems from the majorrole of the CBF2 transcription factor in the regulation of cold
acclimation. We hypothesize that investment in acclimation in
response to cool, but nonfreezing temperatures, results in the fitness
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cost in the Italian environment. This polymorphism in CBF2 haspreviously been shown to explain about 1/3 of the difference
between the SW and IT ecotypes in cold-­acclimated freezing tolerance in the lab (57, 59), and the effects of natural variation in
CBF2 on freezing tolerance have been demonstrated in otherecotypes as well (56). In our system, a short list of candidate genesthat are partially regulated by CBF2 in response to short-­term cold
acclimation have been identified using RNAseq (59). These genes
have likely roles in sugar biosynthesis, desiccation resistance, andmembrane stabilization. The strongest of these candidates was
Galactinol synthase 3 (GolS3), which is an important enzyme in thesynthesis of galactinol and raffinose. Accumulation of raffinose andother soluble sugars has previously been correlated with increased
freezing tolerance (51, 52). In A. thaliana, accessions from higher
latitudes have greater cold-­acclimated freezing tolerance (67, 83).
Accessions from colder climates also accumulate more raffinose in
response to cold, and this is negatively correlated with vegetative
growth rates (53). Additional work is needed to characterize therole of the CBF2 polymorphism on transcriptional and metabolic
responses across the cold periods in both environments to bettercharacterize the contribution of raffinose and other compounds tofitness trade-­offs across environments.
Regardless of the specific mechanisms, our fitness results in the
Italian environment provide direct evidence for a cost of cold
acclimation, in agreement with several prior lines of indirect evidence. We hypothesize that such a cost is borne even in the
Swedish environment, but that in most years the fitness benefitsof freezing tolerance outweigh the costs of cold acclimation. This
could have profound implications for maladaptive responses inmany temperate zone plants with climate change. A decoupling
between cold acclimation cues and the severity of subsequent winter could lead to reductions in fitness if autumn temperatures are
insufficient to acclimate for an anomalously severe winter or if
cool autumn temperatures that induce cold acclimation are followed by a milder winter. Direct evidence for a cost of cold acclimation in plants has until now proven difficult. This is likely bothbecause manipulating the acclimation environment (and/or using
a broad geographic sample of ecotypes) also affects phenologicalpatterns in species where cold serves to cue phenological transitions (63) and because costs of acclimation are context dependent.
Accurate quantification of the potential costs of cold acclimation
will likely require manipulating major regulators of cold acclimation as we have done here. Orthologs of CBF genes are cold
responsive in many plant lineages (37). Our approach could beapplied more broadly as transformation becomes feasible in more
species. In the meantime, we suggest that predictions about organismal resilience in the face of climate change should carefully
consider potential costs of cold acclimation.
Materials and Methods
Study System. A. thaliana is an annual, predominantly selfing plant native

to habitats in Eurasia and Africa (84, 85). Our source ecotypes are from locally
adapted populations from Rödåsen (hereafter “SW”) in north-­central Sweden(62°48′N, 18°12′E) and Castelnuovo di Porto (hereafter “IT”) in central Italy(42°07′N, 12°29′E), near the northern and southern edge of the native Eurasian
range (70). At both sites, plants exhibit a winter annual life history and overwinteras vegetative rosettes. At the Italian site (42°07′N, 12°29′E), seeds germinate
in autumn, and plants overwinter as rosettes under cool conditions followedby flowering in March and April. At the Swedish site (62°48′N, 18°12′E), seeds
germinate in late summer, and plants overwinter as rosettes and flower in
May and June. These ecotypes have been used extensively to map the genetic
basis of local adaptation (26, 27, 29, 86, 87) and ecologically important traits(27, 57, 88, 89), as well as to study physiological responses that are infeasible
to map (90, 91).
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Development of Genetic Resources. To estimate lifetime fitness effects of CBF2

in both native environments, we used a combination of NILs and gene-­editedlines. Details of NIL construction can be found in SI Appendix, Fig. S1 and ref.
59. We used two NILs containing alternate introgression segments surrounding
CBF2 in each genetic background. The SW background NIL contains a segmentwith the IT LOF cbf2 genotype. The IT background NIL contains a segment withthe functional SW CBF2 genotype (SI Appendix, Fig. S1). These segments containmany genes in addition to CBF2 and cannot provide single gene resolution but
were used to quantify fitness effects of this genomic region in field experiments
at the native sites where European Union regulations prohibit the planting of
GMO organisms.
To obtain single gene resolution of the fitness effects of CBF2, we usedgene-­edited lines and the parental ecotypes in growth chamber experiments.
To mimic the IT cbf2 LOF ecotype in the SW genetic background, we used two
independent cbf2 LOF lines (produced using CRISPR/Cas9). These lines, as wellas NILs, have previously been shown to explain over 1/3 of ecotypic differences incold-­acclimated freezing tolerance (59). For IT background lines with a functional
copy of CBF2, we used two independent transgenic lines with SW CBF2 and native
promoter inserted into the IT background. These lines were previously used inelectrolyte leakage assays of freezing tolerance (58).
Reciprocal Transplant Experiments at the Swedish and Italian Field Sites.

To estimate the effects of introgression segments containing alternate genotypesof CBF2 in both genetic backgrounds, we quantified fitness and fitness components in field reciprocal transplant experiments. We transplanted seedlings ofthe two ecotypes and two NILs (R_38 and C_19; SI Appendix, Fig. S1) at boththe native Swedish and Italian field sites in each of 5 y. Seed germination, transplanting, and field planting protocols closely follow previous field studies of theseecotypes (26, 29). In brief, in each individual year, surface-­sterilized seeds weresown on agar in petri dishes and cold stratified in the dark at 4 °C for 1 wk to
break seed dormancy and synchronize germination. The petri dishes were thentransferred to a growth room set to 22 °C, 16-­h d (16L:8D) with photosynthetically
active radiation (PAR) of 150 µmol photons m−2 s−1 for 8 to 10 d for germination. Seedlings were transplanted into a randomized design in plug trays withindividual cells of 20 × 20 × 40 mm filled with an equal mixture of local sand,gravel, and unfertilized peat in Sweden and with local soil in Italy. In each of
5 y, trays with seedlings were then set into the ground in early September at theSwedish site and in early November at the Italian site. Sample sizes per genotype per year were variable across years but ranged from 400 to 600 seedlings
each for IT (mean = 438) and SW (mean = 440), 100 to 150 seedlings for C_19
(mean = 109), and 100 to 200 seedlings for R_38 (mean = 150). For each individual plant, we estimated cumulative fitness as the total number of fruits produced
per seedling planted, which incorporates both survival and fecundity (fruit number per surviving plant). Logistical constraints prevented us from quantifying seednumber per fruit to obtain a more complete estimate of cumulative fitness, but
previous work in this system has shown that estimating fitness as fruits per seedling and seeds per seedling yield qualitatively similar patterns (87). Data fromthe present experiment is distinct from a recent reciprocal transplant at the samesites (29), though parts of both experiments were conducted in 2016 and 2017.
Within each site, we examined the effects of genotype, year and the genotype × year interaction on cumulative fitness, survival, and fecundity using
ANOVA. Both genotype (two ecotypes and two NILs) and year were treated as
fixed effects. Survival was analyzed with a binomial error distribution. Inspectionof residuals for fecundity indicated that a normal error distribution was suitable.
For cumulative fitness, there were some departures from normality and equal variances, however overall results using a normal error distribution were qualitatively
similar to nonparametric models and models with alternative error distributions(including zero-­inflated Poisson and zero-­inflated negative binomial), so we present the normal model results for simplicity. For any model with a significant effectof genotype or genotype × year, we used a priori linear contrasts to compare theIT and SW ecotypes. We also tested for the effect of the CBF2 region on differential
adaptation by contrasting NILs to their genetic background ecotypes.
Single Gene Effects of CBF2 in Simulated Italian and Swedish Conditions. To

quantify the fitness effects of the single CBF2 polymorphism, we developed growthchamber programs to simulate seasonal changes in temperature and photoperiod
at the Swedish and Italian sites. For details of field climate data, see SI Appendix,
Fig. S2 and ref. 71. Daily mean high-­and low-­temperature values across years were
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used to guide construction and optimization of the growth chamber programs(SI Appendix, Fig. S3 and Supporting Text). Although it is impossible to completely
mimic field conditions in a growth chamber, the general concordance in ecotypic
differences in relative fitness between field and growth chamber experiments(SI Appendix, Fig. S4) gives us confidence that we were able to recreate some ofthe essential features driving local adaptation in this system.
The optimized growth chamber programs were used to estimate fitness, survival,
and fecundity for the IT and SW ecotypes and gene-­edited lines with manipulated
CBF2 alleles in the native genetic backgrounds. In both environments, we included
two independent cbf2 loss-­of-­function lines in a SW background. In the Italianenvironment, we included two additional transgenic lines containing the SW CBF2
allele and native promoter in an IT background. The transgenic lines were omitted
from the Swedish growth chamber assay because preliminary trials indicated that
fitness of IT was so low as to preclude detection of an effect in an IT background line.
Methods for seed sterilization and germination closely follow those of previous
freezing tolerance assays in this system (57, 59). In brief, surface-­sterilized seeds
were sown on agar in petri dishes. The seeds were cold-­stratified in the dark at 4 °C
for 5 d to synchronize germination. The petri dishes were then transferred to a growthchamber set to 22 °C, 16-­h d (16L:8D) with PAR of 125 µmol photons m−2 s−1 forgermination for 12 d. Seedlings were then transplanted into a randomized designin plug trays with individual cells of 20 × 20 × 40 mm filled with propagation mix
soil. The trays were then transferred to a growth chamber, LTCB-­19 (BioChambers,
Winnipeg, MB, Canada) programmed as described above for either the “Italian” or“Swedish” environment (SI Appendix, Fig. S3). The entire experiment consisted ofeight trays, arranged side to side on two shelves within the growth chamber. On agiven shelf, two border rows around the perimeter of each shelf were planted with“extra” plants (no data collected) to reduce edge effects within the experiment. In
the Swedish growth chamber, we added a thin layer of shaved ice onto the plants
as soon as chamber temperatures first dropped to freezing conditions (from 4 °C to−2 °C) to facilitate ice nucleation (58). In each chamber environment, we estimatedcumulative fitness for each individual plant as the total number of fruits produced,
including zeros for plants that did not survive to reproduce.

1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.

T. J. Kawecki, D. Ebert, Conceptual issues in local adaptation. Ecol. Lett. 7, 1225–1241 (2004).
J. Hereford, A quantitative survey of local adaptation and fitness trade-­offs. Am. Nat. 173, 579–588(2009).
R. H. MacArthur, Geographical Ecology: Patterns in the Distribution of Species (Princeton University
Press, Princeton, New Jersey, 1972).
D. J. Futuyma, G. Moreno, The evolution of ecological specialization. Ann. Rev. Ecol. Syst. 19,
207–233 (1988).
A. A. Agrawal, A scale-­dependent framework for trade-­offs, syndromes, and specialization inorganismal biology. Ecology 101, e02924 (2020).
R. Leimu, M. Fischer, A meta-­analysis of local adaptation in plants. PLoS One 3, e4010 (2008),
10.1371/journal.pone.0004010.
L. C. Johnson et al., Reciprocal transplant gardens as gold standard to detect local adaptation in
grassland species: New opportunities moving into the 21st century. J. Ecol. 110, 1054–1071 (2022).
A. VanWallendael, D. B. Lowry, J. A. Hamilton, One hundred years into the study of ecotypes, newadvances are being made through large-­scale field experiments in perennial plant systems. Curr.
Opin. Plant Biol. 66, 102152 (2022).
O. Savolainen, M. Lascoux, J. Merilä, Ecological genomics of local adaptation. Nat. Rev. Genet. 14,
807–820 (2013).
P. Tiffin, J. Ross-­Ibarra, Advances and limits of using population genetics to understand local
adaptation. Trends Ecol. Evol. 29, 673–680 (2014).
A. VanWallendael et al., A molecular view of plant local adaptation: Incorporating stress-­response
networks. Ann. Rev. Plant Biol. 70, 14.11–14.25 (2019).
S. M. Wadgymar, M. L. DeMarche, E. B. Josephs, S. N. Sheth, J. T. Anderson, Local adaptation: Causal
agents of selection and adaptive trait divergence. Ann. Rev. Ecol. Evol. Syst. 53, 87–111 (2022).
J. R. Stinchcombe, H. E. Hoekstra, Combining population genomics and quantitative genetics:
Finding the genes underlying ecologically important traits. Heredity 100, 158–170 (2008).
R. D. H. Barrett, H. E. Hoekstra, Molecular spandrels: Tests of adaptation at the genetic level. Nat. Rev.
Genet. 12, 767–780 (2011).
K. Bomblies, C. L. Peichel, Genetics of adaptation. Proc. Natl. Acad. Sci. U.S.A. 119, e2122152119(2022).
M. V. Rockman, The QTN program and the alleles that matter for evolution: all that’s gold does not
glitter. Evolution 66, 1–17 (2012).
M. D. Rausher, L. F. Delph, Commentary: When does understanding phenotypic evolution require
identification of the underlying genes? Evolution 69, 1655–1664 (2015).
E. L. Dittmar, C. G. Oakley, J. K. Conner, B. A. Gould, D. W. Schemske, Factors influencing the effectsize distribution of adaptive substitutions. Proc. R Soc. B Biol. Sci. 283, 20153065 (2016).
D. Schluter et al., Fitness maps to a large-­effect locus in introduced stickleback populations. Proc.
Natl. Acad. Sci. U.S.A. 118, e1914889118 (2021).
J. T. Anderson, J. H. Willis, T. Mitchell-­Olds, Evolutionary genetics of plant adaptation. Trends Genet.
27, 258–266 (2011).
S. M. Wadgymar et al., Identifying targets and agents of selection: Innovative methods to evaluate
the processes that contribute to local adaptation. Methods Ecol. Evol. 8, 738–749 (2017).

PNAS

2024

Vol. 121No. 6

e2317461121

Statistical analyses of the growth chamber experiments closely follow that of
the field experiments. In the models for the chamber analyses, we also included
the effect of tray to account for microspatial variation within the chamber. This
was treated as a fixed effect because of the small number of trays. Error distributions for fitness, survival, and fecundity were the same as for the analyses of the
field data. In models with a significant effect of genotype, we examined a priori
contrasts of the IT and SW ecotypes and contrasted all gene-­edited lines to their
genetic background ecotypes. All statistical analyses for both field and growthchamber experiments were conducted using JMP version 16.
Data, Materials, and Software Availability. Field and growth chamber fitness

data, field temperature data, and summaries of growth chamber programs are
publicly available at the Purdue University Research Repository (92).
ACKNOWLEDGMENTS. We thank dozens of people for assistance with the fieldand growth chamber experiments including C. Amman, E. Amman, P. Gómez-­
Zapata, M.I. Jameel, J. Kraft, L. Molina, K. Palacio-­López, D. Schemske, F. Spada,
M. Vass, L. Vikström, and G. Zacchello. We also thank M. Thomashow for providing
seeds of transgenic and CRISPR lines, P. Falzini and Y. Jonsson for permission to
conduct experiments on their land, and the Orto Botanico di Roma for allowing us
to use their greenhouse facilities. This study was financially supported by grants
from the NSF (DEB-­1743273 to C.G.O. and J.K.M. and IOS-­2246545 to C.G.O. and
B.P.D.) and the Swedish Research Council (2016-­05435 and 2020-­04434 to J.Å.).
Author affiliations: aDepartment of Botany and Plant Pathology, Purdue University, West
Lafayette, IN 47907; bCenter for Plant Biology, Purdue University, West Lafayette, IN 47907;
c
Plant Ecology and Evolution, Department of Ecology and Genetics, Evolutionary Biology
Centre, Uppsala University, Uppsala SE-­752 36, Sweden; dDepartment of Biochemistry,
Purdue University, West Lafayette, IN 47907; and eDepartment of Soil and Crop Sciences,
Colorado State University, Fort Collins, CO 80523
Author contributions: J.Å. and C.G.O. designed research; G.L., B.J.S., T.J.E., J.Å., and C.G.O.
performed research; G.L. and C.G.O. analyzed data; B.P.D., J.K.M., J.Å., and C.G.O. revised
the paper; and G.L. and C.G.O. wrote the paper.

22. D. B. Lowry, M. C. Hall, D. E. Salt, J. H. Willis, Genetic and physiological basis of adaptive salt
tolerance divergence between coastal and inland Mimulus guttatus. New Phytol. 183, 776–788(2009).
23. M. C. Hall, D. B. Lowry, J. H. Willis, Is local adaptation in Mimulus guttatus caused by trade-­offs at
individual loci? Mol. Ecol. 19, 2739–2753 (2010).
24. J. T. Anderson, C.-­R. Lee, C. A. Rushworth, R. I. Colautti, T. Mitchell-­Olds, Genetic trade-­offs andconditional neutrality contribute to local adaptation. Mol. Ecol. 22, 699–708 (2013).
25. P. H. Leinonen, D. L. Remmington, J. Leppälä, O. Savolainen, Genetic basis of local adaptation andflowering time variation in Arabidopsis lyrata. Mol. Ecol. 22, 709–723 (2013).
26. J. Ågren, C. G. Oakley, J. K. McKay, J. T. Lovell, D. W. Schemske, Genetic mapping of adaptation
reveals fitness tradeoffs in Arabidopsis thaliana. Proc. Natl. Acad. Sci. U.S.A. 110, 21077–21082(2013).
27. F. M. Postma, J. Ågren, Early life stages contribute strongly to local adaptation in Arabidopsis
thaliana. Proc. Natl. Acad. Sci. U.S.A. 113, 7590–7595 (2016).
28. S. J. Wright, D. M. Goad, B. L. Gross, P. R. Muñoz, K. M. Olsen, Genetic trade-­offs underlie divergent
life history strategies for local adaptation in white clover. Mol. Ecol. 31, 3742–3760 (2021),
10.1111/mec.16180.
29. C. G. Oakley, D. W. Schemske, J. K. McKay, J. Ågren, Ecological genetics of local adaptation in
Arabidopsis: An 8-­year field experiment. Mol. Ecol. 32, 4570–4583 (2023).
30. T. F. C. Mackay, E. A. Stone, J. F. Ayroles, The genetics of quantitative traits: Challenges and prospects.
Nat. Rev. Genet. 10, 565–577 (2009).
31. D. L. Des Marais, K. M. Hernandez, T. E. Juenger, Genotype-­by-­environment interaction andplasticity: Exploring genomic responses of plants to the abiotic environment. Ann. Rev. Ecol. Evol.
Syst. 44, 5–29 (2013).
32. C. Alonso-­Blanco, B. Méndez-­Vigo, Genetic architecture of naturally occuring quantitative traits inplants: An updated synthesis. Curr. Opin. Plant Biol. 18, 37–43 (2014).
33. D. L. Des Marais et al., Variation in MPK12 affects water use efficiency in Arabidopsis and reveals
a pleiotropic link between guard cell size and ABA response. Proc. Natl. Acad. Sci. U.S.A. 111,
2836–2841 (2014).
34. E. B. Josephs, Determining the evolutionary forces shaping G x E. New Phytol. 219, 31–36 (2018).
35. K. B. Storey, J. M. Storey, Natural freezing survival in animals. Ann. Rev. Ecol. Evol. Syst. 27, 365–386(1996).
36. A. Addo-­Bediako, S. L. Chown, K. J. Gaston, Thermal tolerance, climatic variability and latitude. Proc.
R Soc. B Biol. Sci. 267, 739–745 (2000).
37. J. C. Preston, S. R. Sandve, Adaptation to seasonality and the winter freeze. Front. Plant Sci. 4, 167(2013), 10.3389/fpls.2013.00167.
38. C. Y. Chang, K. Bräutigam, N. P. A. Hüner, I. Ensminger, Champions of winter survival: Coldacclimation and molecular regulation of cold hardiness in evergreen conifers. New Phytol. 229,
675–691 (2021).
39. J. Levitt, Chilling, Freezing, and High Temperature Stresses (Academic Press, New York, 1980).
40. H. Colinet, V. Larvor, M. Laparie, D. Renault, Exploring the plastic response to cold acclimation
through metabolomics. Funct. Ecol. 26, 711–722 (2012).

https://doi.org/10.1073/pnas.2317461121

7 of 8

Downloaded from https://www.pnas.org by "UPPSALA UNIVERSITETSBIBLIOTEK , BMC-BIBLIOTEKET" on March 26, 2024 from IP address 130.238.172.43.

41. Y. Long et al., Transcriptomic characterization of cold acclimation in larval zebrafish. BMC Genomics
14, 612 (2013).
42. T. N. Kristensen, H. Kjeldal, M. F. Schou, J. L. Nielsen, Proteomic data reveal a physiological basis forcosts and benefits associated with thermal acclimation. J. Exp. Biol. 219, 969–976 (2016).
43. H. A. MacMillan et al., Cold acclimation wholly reorganizes the Drosophila melanogastertranscriptome and metabolome. Sci. Rep. 6, 28999 (2016).
44. N. K. Noer et al., Rapid adjustments in thermal tolerance and the metabolome to daily
environmental changes–A field study on the arctic seed bug Nysius groenlandicus. Front. Physiol.
13, 818485 (2022).
45. M. F. Thomashow, Molecular basis of plant cold acclimation: Insights gained from studying the CBF
cold response pathway. Plant Physiol. 154, 571–577 (2010).
46. C. Loehle, Height growth tradeoffs determine northern and southern range limits for trees. J.
Biogeography 25, 735–742 (1998).
47. J. R. Auld, A. A. Agrawal, R. A. Reylea, Re-­evauluating the costs and limits of adaptive pehotypic
plasticity. Proc. R. Soc. B Biol. Sci. 277, 503–511 (2010).
48. T. N. Kristensen et al., Costs and benefits of cold acclimation in field-­released Drosophila. Proc. Natl.
Acad. Sci. U.S.A. 105, 216–221 (2008).
49. M. F. Schou, V. Loeschcke, T. N. Kristensen, Strong costs and benefits of winter acclimatization in
Drosophila melanogaster. PLoS One 10, e0130307 (2015).
50. E. R. Everman, J. L. Delzeit, F. K. Hunter, J. M. Gleason, T. J. Morgan, Costs of cold acclimation on
survival and reproductive behavior in Drosophila melanogaster. PLoS One 13, e0197822 (2018).
51. T. Taji et al., Important roles of drought-­and cold-­inducible genes for galactinol synthase in stress
tolerance in Arabidopsis thaliana. Plant J. 29, 417–426 (2002).
52. F. Kaplan et al., Transcript and metabolite profiling during cold acclimation of Arabidopsis reveals an
intricate relationship of cold-­regulated gene expression with modifications in metabolite content.
Plant J. 50, 967–981 (2007).
53. J. Weiszmann et al., Metabolome plasticity in 241 Arabisopsis thaliana accessions reveals evolutionary
cold adaptation process. Plant Physiol. 193, 980–1000 (2023), 10.1093/plphys/kiad1298.
54. J. Barrero-­Gil, J. Salinas, "Gene regulatory networks mediating cold acclimation: The CBF pathway"
in Survival Strageies in Extreme Cold and Desiccation, M. Iwaya-­Inoue, M. Sakurai, M. Uemura, Eds.
(Springer Singapore, Singapore), 2018).
55. S. Park et al., Regulation of the Arabidopsis CBF regulon by a complex low-­temperature regulatory
network. Plant J. 82, 193–207 (2015).
56. C. Alonso-­Blanco et al., Genetic and molecular analyses of natural variation indicate CBF2 as a
candidate gene for underlying a freezing tolerance quantitative trait locus in Arabidopsis. Plant
Physiol. 139, 1304–1312 (2005).
57. C. G. Oakley, J. Ågren, R. A. Atchison, D. W. Schemske, QTL mapping of freezing tolerance: Links tofitness and adaptive trade-­offs. Mol. Ecol. 23, 4304–4315 (2014).
58. M. A. Gehan et al., Natural variation in the C-­repeat binding factor cold response pathway correlateswith local adaptation of Arabidopsis ecotypes. Plant J. 84, 682–693 (2015).
59. B. J. Sanderson et al., Genetic and physiological mechanisms of freezing tolerance in locally
adapted populations of a winter annual. Am. J. Botany 107, 250–261 (2020).
60. L. V. Savitch et al., The effect of overexpression of two Brassica CBF/DREB1-­like transcription factors
on photosynthetic capacity and freezing tolerance in Brassica napus. Plant Cell Physiolol. 46,
1525–1539 (2005).
61. A. Vágújfalvi, G. Galiba, L. Cattivelli, J. Dubcovsky, The cold-­regulated transcriptional activator Cbf3
is linked to the frost-­tolerance locus Fr-­A2 on wheat chromosome 5A. Mol. Genet. Genomics 269,
60–67 (2003).
62. C. Benedict et al., The CBF1-­dependent low temperature signalling pathway, regulon and increasein freeze tolerance are conserved in Populus spp. Plant, Cell Environ. 29, 1259–1272 (2006).
63. J. A. Savage, J. Cavender-­Bares, Phenological cues drive an apparent trade-­off between freezing
tolerance and growth in the family Salicaceae. Ecology 94, 1708–1717 (2013).
64. M. W. Jackson, J. R. Stinchcombe, T. M. Korves, J. Schmitt, Costs and benefits of cold tolerance intransgenic Arabidopsis thaliana. Mol. Ecol. 13, 3609–3615 (2004).
65. Y. Zhen, P. Dhakal, M. C. Ungerer, Fitness benefits and costs of cold acclimation in Arabidopsis
thaliana. Am. Nat. 178, 44–52 (2011).
66. Y. Zhen, M. C. Ungerer, Relaxed selection on the CBF/DREB1 regulatory genes and reduced freezing
tolerance in the southern range of Arabidopsis thaliana. Mol. Biol. Evol. 25, 2547–2555 (2008).

8 of 8https://doi.org/10.1073/pnas.2317461121

67. E. Zuther, E. Schulz, L. H. Childs, D. K. Hincha, Clinal variation in the non-­acclimated and cold-­acclimatedfreezing tolerance of Arabidopsis thaliana accessions. Plant Cell Environ. 35, 1860–1878 (2012).
68. J. G. Monroe et al., Adaptation to warmer climates by parallel functional evolution of CBF genes in
Arabidopsis thaliana. Mol. Ecol. 25, 3632–3644 (2016).
69. M. Boinot, E. Karakas, K. Koehl, M. Pagter, E. Zuther, Cold stress and freezing tolerance negatively
affect the fitness of Arabidopsis thaliana accessions under field and controlled conditions. Planta
255, 39 (2022).
70. J. Ågren, D. W. Schemske, Reciprocal transplants demonstrate strong adaptive differentiation of themodel organism Arabidopsis thaliana in its native range. New Phytol. 194, 1112–1122 (2012).
71. G. Zacchello, M. Vinyeta, J. Ågren, Strong stabilizing selection on timing of germination in a
Mediterranean population of Arabidopsis thaliana. Am. J. Botany 107, 1518–1526 (2020).
72. W. D. Beavis, "The power and deceit of QTL experiments: Lessons from comparative QTL studies"in Proceedings of the Corn and Sorghum Industry Research Conference, American Seed Trade
Association, Washington DC (1994), pp. 250–266.
73. A. Martínez-­Berdeja et al., Functional variants of DOG1 control seed chilling responses andvariation in seasonal life-­history strategies in Arabidopsis thaliana. Proc. Natl. Acad. Sci. U.S.A. 117,
2526–2534 (2020).
74. J. Wang et al., A major locus controls local adaptation and adaptive life history variation in aperennial plant. Genome Biol. 19, 72 (2018).
75. M. A. Taylor et al., Large-­effect flowering time mutations reveal conditionally adaptive paths
through fitness landscapes in Arabidopsis thaliana. Proc. Natl. Acad. Sci. U.S.A. 116, 17890–17899(2019).
76. W. A. Lopez-­Arboleda, S. Reinert, M. Nordborg, A. Korte, Global genetic heterogeneity in adaptive
traits. Mol. Biol. Evo. 38, 4822–4831 (2021).
77. A. Fournier-­Level et al., A map of local adaptation in Arabidopsis thaliana. Science 334, 86–89 (2011).
78. A. M. Hancock et al., Adaptation to climate across the Arabidopsis thaliana genome. Science 334,
83–86 (2011).
79. M. Exposito-­Alonso et al., Natural selection on the Arabidopsis thaliana genome in present andfuture climates. Nature 573, 126–129 (2019).
80. K. V. Prasad et al., A gain-­of-­function polymorphism controlling complex traits and fitness in nature.
Science 337, 1081–1084 (2012).
81. R. D. H. Barrett et al., Linking a mutation to survival in wild mice. Science 363, 499–504 (2019).
82. R. Kerwin et al., Natural genetic variation in Arabidopsis thaliana defense metabolism genes
modulates field fitness. Elife 4, e05604 (2015), 10.7554/eLife.05604.
83. Y. Zhen, M. C. Ungerer, Clinal variation in freezing tolerance among natural accessions ofArabidopsis thaliana. New Phytol. 177, 419–427 (2008).
84. M. Koornneef, C. Alonso-­Blanco, D. Vreugdenhil, Naturally occurring genetic variation in Arabidopsis
thaliana. Annu. Rev. Plant Biol. 55, 141–172 (2004).
85. A. Durvasula et al., African genomes illuminate the early history and transition to selfing in
Arabidopsis thaliana. Proc. Natl. Acad. Sci. U.S.A. 114, 5213–5218 (2017).
86. N. Price et al., Combining population genomics and fitness QTLs to identify the genetics of local
adaptation in Arabidopsis thaliana. Proc. Natl. Acad. Sci. U.S.A. 115, 5028–5033 (2018).
87. T. J. Ellis, F. M. Postma, C. G. Oakley, J. Ågren, Life-­history trade-­offs and the genetic basis of fitness
in Arabidopsis thaliana. Mol. Ecol. 30, 2846–2858 (2021).
88. J. Ågren, C. G. Oakley, S. Lundemo, D. W. Schemske, Adaptive divergence in flowering time among
natural populations of Arabidopsis thaliana: Estimates of selection and QTL mapping. Evolution 71,
550–564 (2017).
89. C. G. Oakley et al., Genetic basis of photosynthetic responses to cold in two locally adapted
populations of Arabidopsis thaliana. J. Exp. Botany 69, 699–709 (2018).
90. C. M. Cohu, O. Muller, J. J. Stewart, B. Demmig-­Adams, W. W. I. Adams, Association between minor
loading vein architecture and light-­and CO2-­saturated rates of photosynthetic oxygen evolution
among Arabidopsis thaliana ecotypes from different latitudes. Front. Plant Sci. 4, 240 (2013).
91. J. J. Stewart et al., Differences in light-­harvesting, acclimation to growth-­light environment, and leaf
structural development between Swedish and Italian ecotypes of Arabidopsis thaliana. Planta 242,
1277–1290 (2015).
92. G. Lee et al., Data for: A large effect fitness trade-­off across environments is explained by a single
mutation affecting cold acclimation. Purdue University Research Repository. https://purr.purdue.
edu/publications/4439/1. Deposited 10 January 2024.

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