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\begin{document}

\title{Rapid adaptive evolution to drought in a subset of plant traits in a
large-scale climate change experiment}



\author[1]{Johannes Metz}%
\author[2,3]{Christian Lampei}%
\author[4]{Laura Bäumler}%
\author[4]{Herve Bocherens}%
\author[5]{Hannes Dittberner}%
\author[4]{Lorenz Henneberg}%
\author[5]{Juliette de Meaux}%
\author[4]{Katja Tielbörger}%
\affil[1]{University of Hildesheim}%
\affil[2]{University of Muenster}%
\affil[3]{University of Münster}%
\affil[4]{University of Tübingen}%
\affil[5]{University of Cologne}%


\vspace{-1em}



  
  \date{June 11, 2024}


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\selectlanguage{english}
\begin{abstract}
Rapid evolution of traits and of plasticity may enable adaptation to
climate change, yet solid experimental evidence under natural conditions
is scarce. Here, we imposed rainfall manipulations (+30\%, control,
-30\%) for ten years on entire natural plant communities in two Eastern
Mediterranean sites. Additional sites along a natural rainfall gradient
and selection analyses in a greenhouse assessed whether potential
responses were adaptive. In both sites, our annual target species
Biscutella didyma consistently evolved earlier phenology and higher
reproductive allocation under drought. Multiple arguments suggest that
this response was adaptive: it aligned with theory, corresponding trait
shifts along the natural rainfall gradient, and selection analyses under
differential watering in the greenhouse. However, another seven
candidate traits did not evolve, and there was little support for
evolution of plasticity. Our results provide compelling evidence for
rapid adaptive evolution under climate change. Yet, several non-evolving
traits may indicate potential constraints to full adaptation.%
\end{abstract}%



\sloppy


\textbf{INTRODUCTION}

Rapid adaptive evolution is considered a potential pathway for species
to cope with ongoing climate change. Principles of rapid evolution have
been studied abundantly by artificial selection under controlled
conditions with short-lived, often single-cell organisms (reviewed in
Barrett \& Schluter 2007; Hoffmann \& Sgr\selectlanguage{ngerman}ó 2011). However, translating
these findings to real life is difficult: multiple interacting factors
may substantially impede rapid adaptive evolution under natural
conditions, including e.g. environmental fluctuations, various biotic
interactions, low genetic variation and trait heritability, genetic
drift, or trade-offs between selected traits (Hoffmann \& Sgró 2011;
Shaw \& Etterson 2012). Thus, there is an urgent need for studying
adaptive evolution within the `multivariate space' of natural conditions
(Hoffmann \& Sgró 2011) to assess its relevance for ongoing climate
change (Merilä \& Henry 2014; Franks \emph{et al.} 2014, 2018).

In plants, the limited number of tests under near-natural conditions for
rapid evolution under changing climate focused on two approaches. One is
the resurrection approach, where stored seeds collected years before
within a plant community are revived and compared to plants from
recently sampled seeds from the same location (reviewed in
Franks\emph{et al.} 2018, including eight climate-related cases). Yet,
observed changes can be influenced by seed storage (Weis 2018;
Franks\emph{et al.} 2019) and attributing trait changes unambiguously to
climate as the causal factor is challenging (Franks \emph{et al.} 2018).
Another approach encompasses multi-year climate manipulations imposed on
natural communities in the field. When plants are subsequently screened
for divergent evolution, changes can be causally attributed directly to
contrasting climatic treatments - a key asset of such experiments. Only
few studies followed this highly demanding approach, reporting
evolutionary change in some genetic markers (Jump \emph{et al} . 2008,
Ravenscroft \emph{et al} . 2015) or certain phenotypic traits (Grossman
\& Rice 2014, Ravenscroft \emph{et al} . 2014, Nguyen \emph{et al} .
2016).

With either approach it has remained a great challenge though to judge
whether observed changes are adaptive. This judgement is often
done\emph{a posteriori} based on `intuition' or `common sense' (Merilä
\& Hendry 2014, Franks \emph{et al} . 2014). Yet, since species can
adapt via different sets of traits to the same climatic challenge (e.g.
Bilton\emph{et al} . 2016, Bergholz \emph{et al} . 2017, Lampei \emph{et
al} . 2017 for aridity), the traits expected to evolve can likewise
differ among species. Moreover, Sandel \emph{et al.} (2010) cautioned
that initial trait responses to altered environments may differ from
those for long-term adaptation. Such complexities may render
common-sense interpretations misleading, i.e. we need much clearer
justifications to conclude adaptive responses.

Here, we addressed this challenge using several independent lines of
evidence simultaneously. Firstly, we imposed replicated \emph{in
situ}climate manipulations on entire plant communities to directly
control the causal factor, and we did so in two independent sites.
Consistent evolutionary responses in both sites would then strongly
argue against random drift effects and in favor of adaptivity. Secondly,
we combined these climate manipulations with a corresponding natural
climatic gradient in a `space-for-time' approach: Many species show
clinal trait divergence along natural gradients when grown under common
garden conditions (e.g. Kigel \emph{et al} . 2011, Petr\selectlanguage{polish}ů \selectlanguage{english}\emph{et al} .
2006, Lampei \emph{et al} . 2017). Such clines likely reflect locally
adapted ecotypes, i.e. the species-specific long-term adaptive strategy
towards the corresponding climatic factor (Kawecki \& Ebert 2004), and
hence provide clear \emph{a priori} predictions for directional trait
evolution under climate manipulations. Thirdly, we based our selection
of study traits on evolutionary theory, i.e. attested evidence for
theoretical fitness advantages under differential climatic conditions.
Lastly, we additionally estimated selection (Lande \& Arnold 1983) on
target plants grown in the greenhouse, yet under a set of contrasting
abiotic conditions that mirrored our \emph{in situ} climate
manipulations. If the covariance between trait values and fitness
changes with climatic condition, differential trait values should be
advantageous contingent on climate. By combining these multiple
approaches, we gained unprecedented strong evidence for whether
potential evolutionary changes are adaptive.

Another recent debate addresses the role of phenotypic plasticity in
climate change adaptation (Meril\selectlanguage{ngerman}ä \& Henry 2014; Fox \emph{et al} .
2019; Scheiner \emph{et al} . 2020). High plasticity in adaptive traits,
as an immediate response to altered environments, may help genotypes to
better match their phenotype to these novel conditions (Crispo 2007;
Lande 2009; Kelly 2019). It was therefore hypothesized that climate
change can target plasticity itself for evolution and favor more plastic
genotypes, even if this is a transient response that is merely `buying
time' until the occurrence of new genotypes with specific adaptations to
the new conditions (Lande 2009; Fox \emph{et al} . 2019; Scheiner
\emph{et al} . 2020). However, empirical tests in natural populations
for evolution of increased plasticity in response to changing climate
are scarce and yielded equivocal results (Franks \emph{et al.} 2018;
Arnold \emph{et al.} 2019; Kelly 2019). This knowledge gap for evolving
plasticity is unfortunate because moreover, plasticity may also interact
with genetic adaptation by buffering selection and hence slowing down
evolution and genetic adaption (Merilä \& Hendry 2014, Kelly 2019, Fox
\emph{et al} . 2019), i.e. rapid evolution may be confined to traits
with low plasticity. Yet, studies are missing that systematically
compared rapidly evolving traits with their degree of plasticity.

To address these gaps, we conducted a uniquely comprehensive test for
rapid evolution in ten target traits and their plasticity in a
large-scale, multi-site climate change experiment (Tielbörger \emph{et
al} . 2014). Experimental rainfall manipulations (+30\%, control, -30\%)
where imposed for ten years in two sites on entire resident plant
communities in the Eastern Mediterranean, and combined with a natural
rainfall gradient. Rainfall is the key abiotic factor in these
ecosystems, with a projected -20\% decline until 2050 (Smiatek \emph{et
al} . 2011; Samuels \emph{et al} . 2013). Annual species dominate these
communities, which allows for potentially rapid evolutionary responses
(Tielbörger \emph{et al} . 2014). Since migration of most species is
limited (Siewert \& Tielbörger 2010), \emph{in situ} evolution appears
crucial for climate change adaptation. We tested for evolutionary
divergence in traits and plasticity in a naturally occurring annual
plant species after ten years of climate manipulations, and used a
comprehensive combination of lines of evidence to judge adaptivity. We
hypothesized that climate manipulation had caused adaptive evolutionary
trait divergence and selection for higher plasticity, and that evolution
is less probable in highly plastic traits.

\textbf{MATERIALS AND METHODS}

\emph{Field sites and climate manipulations}

Four sites along a natural rainfall gradient from Northern to Southern
Israel were chosen because they share most abiotic characteristics
except for mean and variance in rainfall (detailed description:
Supporting Information Fig. S1, and Tielbörger \emph{et al.} 2014). They
have Mediterranean-type climate and similar mean annual temperatures
(17.7--19.1°C), yet spanned a more than eight-fold difference in mean
annual rainfall (MAP). They represent mesic-Mediterranean (MM, 780 mm
MAP), Mediterranean (M, 540 mm), semi-arid (SA, 300 mm) and arid
conditions (A, 90 mm), respectively. The average length of the rainy
season increases from the driest (Dec--March) to the wettest site
(Oct--May). Rainfall variability among years decreases from the driest
(40\% Coefficient of Variation) to the wettest site (25\% CV)
(Metz\emph{et al.} 2018). All sites are semi-natural shrublands with
mostly annual species in the inter-shrub matrix, located at south
exposures on limestone bedrock with similar elevation (470--620 m
a.s.l.).

Three rainfall manipulation treatments (dry, control, wet) were imposed
on entire resident plant communities for ten years (2002-2012) in the
two intermediate sites, M and SA. Each treatment was replicated by five
randomized 10m × 25m plots per site. The two extreme sites, A and MM,
contained only five control plots each and extended the range of the
natural rainfall gradient. For dry manipulation, rainout shelters
intercepted 30\% of each rainfall event; for wet manipulation,
additional sprinkler irrigation after every other rain event added in
total 30\% of the site's MAP; control plots received ambient rainfall
(details in Tielbörger \emph{et al.} 2014). The magnitude of the dry
manipulations realistically approximated climate change scenarios for
our study region, predicting 20\% less rainfall until 2050 (Smiatek
\emph{et al.} 2011; Samuels \emph{et al} . 2013), while the wet
manipulations extended the general range of scenarios that were
uncertain at the onset of our study.

\emph{Study species and seed material}

We focused on \emph{Biscutella didyma} L., a winter annual Brassicaceae
with Mediterranean and Irano-Turanian distribution (Feinbrun-Dothan \&
Danin 1998). This medium-sized, common and subordinate species was
selected because it is one of few species occurring reliably across the
entire rainfall gradient. As is typical for many annuals in our study
region, \emph{B. didyma} has short dispersal distances (Siewert \&
Tielbörger 2010) and low outcrossing rates (Boaz \emph{et al.} 1990),
making gene-flow among manipulated plots unlikely.

First, seeds of 100 \emph{B. didyma} individuals (genotypes) per
rainfall treatment and site were collected in spring 2012 by randomly
picking twenty individuals within each 10m × 25m plot. Keeping \selectlanguage{english}[?]1.5m
distance among genotypes and the large plot size ensured independence
and capturing a representative fraction of the intrapopulation variance.
One offspring per genotype was raised with regular watering for one
generation in Tubingen (Germany) to reduce potential parental effects
and amplify seed material. The growth period (Nov 2012 -- May 2013)
approximated the natural vegetation season and photoperiod, and
greenhouse temperatures mirrored day temperatures in the field sites
(15--20degC in winter, gradually warming to 25--30degC in spring). Wrapping
flowering plants in light, transparent fabric (organza) ensured
self-pollination and facilitated seed collection separately per mother.
Second, of the resulting second generation, we randomly picked 40
genotypes per rainfall treatment and site (3x40 in SA, M; 40 in A, MM;
altogether 320 genotypes) for the subsequent greenhouse experiment
(detailed scheme: Appendix Fig. S1).

\emph{Greenhouse experiment}

One season later (Nov 2013 -- May 2014), we raised these 320 genotypes
under conditions as above, yet under five water levels. One pot (10 x 10
x 10 cm) per genotype was included in each water level (1600 pots in
total). Per pot, 15 seeds of one genotype were sown in a 1:1 mixture of
nutrient-poor potting soil and sand, enriched with 0.1 g Osmocote(r) slow
release fertilizer (14-14-14 NPK; Scotts GmbH, Germany). On the first
and third day of the experiment, all pots were watered equally to
saturation to ensure seed germination. After germination was completed,
seedlings were randomly thinned to one per pot and five watering levels
were initiated (90 ml, 50 ml, 30 ml, 20 ml, and 15 ml per pot and
watering event, respectively). Watering was applied manually with
dispensers approx. every 2-3 days, i.e. when most plants in the lowest
water level showed strong signs of drought stress. Pots were randomized
across the greenhouse and re-randomized every three weeks.

\emph{Phenotyping}

We focused on ten traits across the entire plant life-cycle for which
theory predicts a relationship to differential water availability.

\emph{Germination fraction} was censused per pot after germination had
ceased at the beginning of the experiment. It typically decreases
towards arid populations to hedge against more frequent unfavorable
years (Tielborger \emph{et al} . 2012; ten Brink \emph{et al.} 2020). As
climate manipulations increased (dry plots) and decreased (wet plots)
the occurrence of unfavorable years, reduced germination fractions
should be favored in dry plots and \emph{vice-versa} in wet plots.

\emph{Days to flowering} (since first irrigation) were determined by
inspecting plants daily for the first open flower. Accelerated phenology
is expected by theory and repeatedly found in annual plants from drier
sites (Cohen 1976; Kigel \emph{et al.} 2011), i.e. earlier phenology is
predicted for plants descending from dry manipulated plots and more arid
sites. Moreover, the number of leaves at the day of first flowering
(\emph{leaf number at flowering} ) provided an ontogenetic phenological
measure and a non-destructive measure of plant size. It disentangled
whether phenology changed via accelerated development (days to
flowering) or shifted ontogeny (leaves at flowering) (Kigel \emph{et al}
. 2011).

\emph{Stomata density} and \emph{carbon isotopes}(\selectlanguage{greek}\emph{δ}
\selectlanguage{english}\textsuperscript{13}C) assessed gas exchange and water use efficiency.
Stomata density was quantified by automated high-throughput microscopy
(Dittberner \emph{et al.} 2018; see Supplementary Methods). As lower
stomata density may decrease maximum transpiration (Liu\emph{et al} .,
2012) we expected lower stomata density in plants descending from drier
conditions. Due to high costs, carbon isotopic ratios (\selectlanguage{greek}\emph{δ}
\selectlanguage{english}\textsuperscript{13}C, Supplementary Methods), were analyzed only for a
subset (14 genotypes per site, rainfall treatment, and four water
levels: 15ml -- 50ml). We expected that plants from drier sites and
plots exhibit higher water use efficiency, i.e. higher\selectlanguage{greek}\emph{δ}
\selectlanguage{english}\textsuperscript{13}C (Li 1999; Hartman \& Danin 2010).

\emph{Plant height} was measured on a fixed day
(12\textsuperscript{th}April) before the onset of senescence. Moreover,
aboveground\emph{vegetative biomass} was determined at the end of the
experiment (May 15\textsuperscript{th} 2014) as the dry weight (24 h,
70\selectlanguage{ngerman}°C) of all stems and leaves. We expected greater height and vegetative
biomass in plants from wetter conditions as adaptation to intensified
aboveground competition (Westoby 1998; Schiffers \& Tielbörger 2006).

\emph{Seed number} per plant quantified fitness. Moreover, we estimated
the selfing rate per plant visually as percent of flowers that developed
into viable seeds; it served as covariate in some analyses
because\emph{B. didyma} populations may differ in self-compatibility
(Gibson-Forty 2018).

\emph{Reproductive allocation} quantified the biomass allocation to
reproductions (i.e. weight of all diaspores and flower remains) relative
to the vegetative biomass. Reproductive allocation should be higher in
plants from drier conditions as they require less investment in
vegetative tissue for outgrowing neighbors (Aronson \emph{et al.} 1993).

\emph{Diaspore weight} (maternal investment per single offspring) was
measured across 30 randomly picked diaspores per plant. Diaspore weight
consists of c. 50\% of seed mass in \emph{B. didyma} and both are
strongly correlated (r²=0.88, p\textless{}0.001; determined for 15 seeds
in 32 randomly picked individuals across sites). For this complex
feature of plant life-cycles (Westoby 1998), we expected increasing
diaspore weight under wetter conditions (DeMalach \& Kadmon 2018;
DeMalach\emph{et al.} 2019).

\emph{Statistical analyses}

Analyses of the above trait values were performed in four steps with R
3.5.2 (R core team 2018).

First, we tested for divergent trait evolution in plants descending from
dry, control and wet manipulated plots in the central sites SA and M
(N=240 genotypes). For each trait separately, linear mixed models were
calculated with climate manipulation treatment (dry, control, wet), site
(SA, M), five greenhouse watering levels, and their interactions as
fixed factors, as well as genotype as random factor (accounting for five
non-independent plants across water levels). Some traits were
transformed prior to analyses to meet homoscedasticity (sqrt: stomata
density, height, reproductive allocation, seed number; log: leaf number
at flowering, vegetative biomass). Significance was assessed with Wald
F-tests with Kenward-Roger approximated df in the package car (Fox \&
Weisberg 2011). Posthoc tests identified contrasting climate
manipulations using the package `multcomp' (Hothorn \emph{et al.} 2008)
with P-values corrected for false discovery rate (FDR)
\emph{sensu}Benjamini \& Hochberg (1995). For germination fraction
(binary) we used a corresponding glm with logit link-function and
quasibinomial error structure.

Second, we tested for clinal trends in traits across the rainfall
gradient, including only plants descending from control plots in all
four sites (N=160 genotypes). We calculated linear mixed models per
trait with site and greenhouse water level as fixed factors, and
genotype as random factor (transformations as above). Posthoc tests with
FDR-correction as above identified contrasting sites. Germination
fraction was analyzed with a binomial glm as above, using only site as
main factor.

Third, we estimated selection, i.e. the covariance of traits with
relative fitness (Lande \& Arnold 1983), under low and high irrigation
in the greenhouse. This approach reveals traits that can adapt a
population to drought and is independent of other environmental factors
correlating with rainfall (Mitchell-Olds \& Schmitt 2006). We estimated
selection for all traits showing either rapid evolution (step 1) or
clines with rainfall (step 2). We included all plants from sites with
climate manipulation, computed the genotype trait-mean across low
watering (15ml, 20ml) and high watering (50ml, 90ml), followed by
standardization (zero mean, 1 SD) per population (SA and M) and watering
level. Similarly, relative fitness was computed per population and
watering. We fitted generalized least squares models (gls,
\emph{rms}package (Harrell 2019)), with relative fitness as the
dependent variable, and trait, watering (high, low) and their
interaction as predictors. A significant trait × watering interaction
indicated contrasting directional selection on that trait contingent on
water availability, computed using type III sums of squares (Anova,
\emph{car}package (Fox \& Weisberg 2019)) with FDR-correction.

Fourth, we tested whether field climate manipulations favored genotypes
with higher plasticity. In addition to assessing the climate
manipulation × water term in step 1 above, plasticity was quantified for
the above traits using the Coefficient of Variation (CV) across the five
individuals (i.e. water levels) per genotype in the greenhouse. The
intuitive, standardized CV allows comparing plasticity across different
traits (Houle 1992) and handles well outliers and non-linear responses
across several environments. Another plasticity index
(PI\textsubscript{v}, see Valladares \emph{et al.} 2006) yielded the
same results. With these CV-values per genotype, we calculated two-way
ANOVAs and FDR-post hoc tests separately for each trait, including the
factors site (SA, M) and climate change treatment (dry, control, wet).

\textbf{RESULTS}

\emph{Trait evolution under climate manipulations}

Three out of ten traits showed significant divergence among climate
manipulations and hence rapid evolution within 10 years of field
experiments. It was always the dry treatment that had diverged from
control and wet treatments (Table 1, Fig. 1). Plants descending from dry
manipulated plots flowered on average 3-4 days (c. 4\%) earlier and with
4-5 (c. 13\%) fewer leaves, and had 10-15\% higher reproductive
allocation than plants from control and wet manipulated plots (Table 1,
Fig. 1). These evolutionary responses to drought were consistent in both
sites (no site × climate manipulation and site × water × climate
manipulation interactions; Table 1).

The seven remaining traits showed no divergence among climate
manipulation treatments (Table 1, Fig. 1).

\emph{Trait divergence along the rainfall gradient}

Eight traits showed significant clinal trends across the natural
rainfall gradient, suggesting an adaptive role for these traits with
rainfall. Towards drier sites, plants decreased in germination fraction,
days to flowering, stomata density, height, and biomass, and they
increased in reproductive allocation and seed production (Table 2, Fig.
1). Furthermore, flowering started at smaller plant size (fewer leaves)
in drier sites, although at MM, plants invested less in lateral branches
and thus possessed fewer but larger leaves along the main stem (Petr\selectlanguage{polish}ů
\selectlanguage{english}\emph{et al} . 2006; pers. obs.). There was a weak, non-significant
tendency towards lower\selectlanguage{greek}\emph{δ} \selectlanguage{english}\textsuperscript{13}C in arid sites
(p=0.12, Fig. 1) and no clinal trend for diaspore weight despite
significant differences among sites (Table 2, Fig. 1).

The clinal trends across sites in flowering time, leaf number at
flowering and reproductive allocation matched the direction of their
rapid evolutionary response to ten years of climate manipulation (Fig.
1). In contrast, five traits showed clines along the natural rainfall
gradient but no rapid evolution under climate manipulations (germination
fraction, stomata density, height, biomass, seed number).

\emph{Selection analyses}

Selection on several traits differed between high and low greenhouse
watering levels (Fig. 2). In line with the field experiment, selection
for early flowering was stronger under low watering in both traits (days
to flowering \selectlanguage{ngerman}× watering: \selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2}=13.6, df=1,
p\textless{}0.001, b\textsubscript{low}=-0.3,
b\textsubscript{high}=-0.08; leaf number at flowering \selectlanguage{ngerman}× watering:
\selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2}=9.4, df=1, p=0.003, b\textsubscript{low}=-0.25,
b\textsubscript{high}=-0.1). Similarly, high reproductive allocation was
stronger selected for under low than under high watering (reproductive
allocation \selectlanguage{ngerman}× watering: \selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2}=114.6, df=1,
p\textless{}0.001, b\textsubscript{low}=0.7,
b\textsubscript{high}=0.35). Among these three traits showing rapid
evolution in the field, genetic correlations were relatively strong and
in the direction of their joint selection (Appendix Fig. S3).

Among traits without significant rapid evolution, low watering selected
stronger against high vegetative biomass (vegetative biomass \selectlanguage{ngerman}× watering:
\selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2}=17.1, df=1, p\textless{}0.001,
b\textsubscript{low}=-0.24, b\textsubscript{high}=0.1, Fig. 2). In
contrast, greater plant height was overall favored (b=0.15,
\selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2}=11.1, df=1, p=0.001) without difference between
watering levels (height \selectlanguage{ngerman}× watering: \selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2}=0.21, df=1,
p=0.65, Fig. 2). Since vegetative biomass and plant height showed a
positive genetic correlation (Appendix Fig. S3), this suggests
conflicting selection on these traits under low watering. No
differential selection was found for stomata density
(\selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2}=2.16, df=1, p=0.17, Fig. 2).

\emph{Plasticity}

The five water levels in the greenhouse significantly affected all
traits (Table 1, 2; Appendix Fig. S1), indicating plasticity in response
to water. Significant site \selectlanguage{ngerman}× water interactions for all traits except
reproductive allocation (Table 2) indicated genetic variation in plastic
responses among the four sites of the rainfall gradient.

For the two sites with climate manipulations, the magnitude of
plasticity (quantified as CV) differed substantially among traits. CV
was lowest in \selectlanguage{greek}\emph{δ} \selectlanguage{english}\textsuperscript{13}C (3-4\%) and days to
flowering (c. 10\%), intermediate for diaspore weight, height, stomata
density, leaf number at flowering and total biomass, and particularly
high in reproductive allocation (c. 80\%) and seed number (c. 100\%)
(Fig. 3). The three traits showing rapid evolution thus had low (days to
flowering), intermediate (leaf number at flowering) and high plasticity
(reproductive allocation), respectively, signifying that plasticity and
the probability of rapid evolution in the field were independent.

Climate manipulations had overall little effect on the magnitude of
plasticity. Increased plasticity (CV) in plants from dry manipulated
plots compared to controls was found in diaspore weight (p=0.01) and a
similar non-significant tendency in days to flowering (p=0.06) (Table 3,
Fig. 3). Plasticity in vegetative biomass increased in dry plots
(p=0.03), but only compared to wet plots (Table 3, Fig. 3). The lack of
any significant climate manipulation \selectlanguage{ngerman}× site interaction showed that
plasticity responded similarly to manipulations in both sites (Fig. 3,
Table 3). Moreover, plasticity (CV) was significantly higher in the
Mediterranean than the semi-arid site in four traits: days to flowering,
height, reproductive allocation, seed number (Table 3, Fig. 3). All
plasticity results were robust when using another common plasticity
index, PI\textsubscript{v} (Valladares \emph{et al.} 2006).

\textbf{DISCUSSION}

Our overall results demonstrate rapid evolution in three out of ten
traits under \emph{in situ} climate manipulations in natural plant
communities after merely 10 years, i.e. at most 10 generations of our
annual study species. This is a remarkably short time span, given that
numerous interacting factors may hamper evolution in natural communities
(Hoffmann \& Sgró 2011; Shaw \& Etterson 2012). The fact that this
evolutionary response was consistent in two independent sites renders
chance effects, e.g. genetic drift, unlikely to cause these results and
underpins that the evolutionary response was directly driven by
manipulated rainfall. Intriguingly, our multiple independent lines of
evidence corroborate that these changes were adaptive.

After 10 years of artificial drought, phenology had evolved both in
chronological (days to flowering) and ontogenetic (leaf number at
flowering) time. Theory suggests accelerated life-cycles as a drought
avoidance strategy that reduces the risk of mortality before
reproduction (Cohen 1976; Kigel \emph{et al.} 2011), albeit at the cost
of smaller plant size and hence possibly lower competitive ability
(Liancourt \& Tielbörger 2009; Kigel \emph{et al.} 2011). In line with
theory, plants from dry-manipulated plots flowered earlier and with
fewer leaves than plants from control and wet plots. Moreover, this
rapid evolutionary response paralleled the long-term evolutionary
response of \emph{B. didyma} along the natural rainfall gradient where
plants from more arid sites flowered earlier; a trend found in many
other annuals along natural rainfall gradients (Kigel \emph{et al} .
2011; Wolfe \& Tonsor 2014; Kurze \emph{et al} . 2017). Interestingly,
the observed 3-4 days acceleration in phenology corresponds to an
ecological distance of \emph{c.} 100 mm lower rainfall at origin for
annuals along our study gradient (Kigel \emph{et al} . 2011;
Kurze\emph{et al.} 2017). Given the magnitude of rainfall reduction in
dry plots (-90 mm in SA, -160 mm in M), this suggests that phenology
could actually track a substantial part of the imposed change in
rainfall. The adaptivity of accelerated phenology under drought was
furthermore corroborated by our selection analyses under controlled,
unconfounded (Mitchell-Olds \& Schmitt 2006) watering conditions in the
greenhouse. Here, earlier flowering with fewer leaves was stronger
favored under low than under high water availability. These multiple
lines of evidence -- theory, natural rainfall gradient, selection
analyses, and consistency in both sites -- provide compelling evidence
that the observed rapid evolution in phenology was adaptive.

Rapid evolution of earlier phenology under drought was previously
reported from a Californian climate manipulation site (Nguyen \emph{et
al.} 2016) and from resurrection studies (Franks \emph{et al.} 2007;
Vigouroux \emph{et al.} 2011; Nevo \emph{et al.} 2012; Hamann \emph{et
al.} 2018). If phenology was reported, no evolution occurred merely in
one perennial (Ravenscroft et al. 2014) or under elevated
CO\textsubscript{2} (Grossman \& Rice 2014). Therefore, phenology
appears a key trait for rapid drought adaptation in annuals, congruent
with similar suggestions by theory and gradient studies (Cohen 1976;
Kigel \emph{et al} . 2011; Kurze \emph{et al.} 2017). These findings may
also indicate that phenology evolves easier than other, possibly more
complex traits. However, more multi-trait studies (e.g.
Ravenscroft\emph{et al.} 2014; Nguyen \emph{et al.} 2016; Hamann
\emph{et al.}2018) assessing comparable trait-sets are required to
confirm this idea.

Here, we also observed rapid evolution in reproductive allocation. As
competition is reduced in drier sites along our gradient (Schiffers \&
Tielbörger 2006), theory suggests reduced investment in vegetative
tissue for outgrowing neighbors and increased allocation to reproduction
(Aronson \emph{et al.} 1990; 1993). In line with theory and in both
sites, plants from dry manipulated plots produced 10-15\% more seeds per
vegetative biomass than control plants. Although reproductive allocation
was rarely assessed in climate manipulation studies, a similar tendency
was reported for a perennial herb (Ravenscroft \emph{et al} . 2014).
This evolutionary response was again congruent with our selection
analyses in the greenhouse, and with the clinal trend in reproductive
allocation along our natural rainfall gradient, and parallel clines in
other species (summarized in Kurze \emph{et al.} 2017). Thus, in all
traits showing rapid evolution in the field, our independent lines of
evidence demonstrate that these changes were adaptive. Intriguingly,
parallel studies found that many plant community parameters were
remarkably resistant to our climate manipulations (Tielbörger \emph{et
al} . 2014; Bilton \emph{et al} . 2016). Though we have studied only a
single species, our current findings suggest that rapid adaptive
evolution possibly contributed to increasing population-level and
community-level resistance to climate change.

Interestingly, evolutionary changes occurred solely in the dry
manipulated plots, i.e. the treatment which increased, rather than
decreased stress for resident plants. Drought likely lead to direct,
rapid exclusion of drought-sensitive and late-flowering genotypes,
especially in dry years. In wet plots, evolution may be slower because
selection was likely driven by competition for additional resources
(Schiffers \& Tielbörger 2006) which causes smaller fitness differences,
as was shown by cross-transplants with \emph{B. didyma} (Ariza \&
Tielbörger 2011).

Despite the evidence for rapid adaptive evolution, seven further traits
did not evolve. This was surprising because five of them exhibited
clinal shifts along the rainfall gradient, suggesting that they
contribute to \emph{B. didyma'} s long-term evolutionary response to
drier climates: germination fraction, stomata density, height,
vegetative biomass and seed number. In conjunction with existing theory
we had expected corresponding evolution of these traits under climate
manipulations (Westoby 1998; Liu \emph{et al.} 2012; Tielbörger \emph{et
al.} 2012; ten Brink \emph{et al.} 2020). Selection analyses supported
this expectation for vegetative biomass, although not for stomata
density and height; no tests were possible for germination fraction (no
differential watering) and seed number (response variable in selection
analyses). While empirical studies usually focused on (few) traits
exhibiting rapid evolution, non-evolving traits have been reported
before (e.g. Franks 2011; Ravenscroft \emph{et al.} 2014; Nguyen\emph{et
al.} 2016). One possible explanation for the lack of evolution in some
candidate traits is that selection on them was weakened by adaptation of
the fast-evolving traits, i.e. evolution of further traits was
unnecessary. Alternatively, the multiple potential constraints for
evolution under natural conditions hindered adaptation in other traits,
e.g. low genetic variation, genetic covariance and trade-offs among
traits (Hoffmann \& Sgró 2011; Shaw \& Etterson 2012). In our case,
negative genetic covariance potentially hindered evolution in vegetative
biomass (Appendix Fig. S3). The observed rapid evolution in only a
subset of traits may therefore indicate incomplete adaptation to new
conditions, cautioning that climate change may imperil species despite
rapid evolution. Importantly, most evidence for rapid adaptive evolution
under natural conditions reported rather few evolving traits (e.g.
Franks 2011; Nevo \emph{et al.} 2012; Ravenscroft\emph{et al.} 2015;
Nguyen \emph{et al} . 2016). Our findings caution that focusing on few
evolving traits may overestimate the potential of rapid evolution for
climate change adaptation.

High trait plasticity may further retard adaptive evolution (Shaw \&
Etterson 2012; Merilä \& Hendry 2014; Kelly 2019), but this idea has
rarely been tested in natural populations (Arnold \emph{et al.} 2019).
Our study, where the three rapidly evolving traits showed three
contrasting magnitudes of plasticity (CV) indicates that plasticity and
evolutionary potential are not necessarily related. However, this
conclusion should be taken with caution because it is based on three
partially correlated traits (Appendix, Fig. S3) which may have evolved
in concert.

Our findings also provide little support for the idea that climate
change leads to increased plasticity as a means to rapidly adjust the
phenotype to novel conditions (Lande 2009; Arnold \emph{et al} . 2019;
Kelly 2019; Scheiner \emph{et al} . 2020). Only a single trait, diaspore
weight, had significantly increased plasticity under drought, and days
to flowering showed a similar, non-significant tendency. Both responses,
however, were opposite to the expected adaptive direction (e.g. later,
not earlier flowering under drought; Appendix, Fig. S1), indicating
non-adaptive plasticity (Acasuso-Rivero \emph{et al.} 2019). Similarly,
no clearly increased plasticity after drought was found by resurrection
studies (Franks 2011; Hamann et al. 2018) and lower plasticity after
CO\textsubscript{2} elevation (Grossman \& Rice 2014; but see
Sultan\emph{et al.} 2013 for increased plasticity during plant
invasion). Thus, our study joins an --albeit small- body of equivocal
evidence indicating that evolution of increased plasticity is no major
pathway for climate change adaptation.

Overall, our study demonstrates that rapid evolution may play an
important role for climate change adaptation in natural annual plant
populations. The novel setup of our study -- combining \emph{in situ}
climate manipulations with a natural climatic gradient and selection
analyses under controlled conditions -- provided independent, compelling
lines of evidence that observed evolutionary shifts were adaptive.
However, with rapid evolution in merely a subset of well-justified
candidate traits, our study emphasizes the importance of multi-trait
studies for assessing whether rapid\emph{in situ} evolution may
safeguard species under climate change.

\textbf{ACKNOWLEDGEMENTS}

We are grateful to a very large number of ambitious students and
colleagues who helped at various stages of this long-term experiment.
The field experiment was part of the GLOWA Jordan River project, funded
by the German Ministry of Education and Research (BMBF) and further
supported by the DFG SPP 1529 (Adaptomics) of the German Research
Foundation (TI338/11-1 and TI338/11-2 to KT, ME 2742/6-1 to JdM and INST
211/575-1 to WWU Münster).

\textbf{DATA ACCESSIBILITY STATEMENT}

The data supporting our results are publicly available at the dryad.org
data repository.



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\textbf{SUPPORTING INFORMATION}

Additional Supporting Information may be downloaded via the online
version of this article at Wiley Online Library
(www.ecologyletters.com).

\textbf{Table 1} \emph{Linear mixed models testing for diverging trait
values in N=240 genotypes originating from three Climate Change
manipulations (CC) in two sites (semi-arid, Mediterranean), when grown
under five water levels in the greenhouse. For CC, posthoc results are
provided to identify diverging treatments (dry, control, wet).}

\emph{Note that residual df are Kenward-Roger approximated and can
therefore differ among models; 13C isotopes were measured in only 84
genotypes and 4 water levels.}\selectlanguage{english}
\begin{longtable}[]{@{}llllllllllllllll@{}}
\toprule
& trait & CC treatment & CC treatment & site & site & water & water & CC
x site & CC x site & CC x water & CC x water & site x water & site x
water & CC x si x wa & CC x si x wa\tabularnewline
\midrule
\endhead
Germination & Germination & & & & & & & & & & & & & &\tabularnewline
& germination fraction & F=\textsubscript{2,234}=0.03 & &
F=\textsubscript{1,234}=143.05 & *** & NA & &
F\textsubscript{2,234}=1.12 & & NA & & NA & & NA &\tabularnewline
Phenology & Phenology & & & & & & & & & & & & & &\tabularnewline
& days to flowering & F\textsubscript{2,234}=4.73 & ** d\textless{}c,w &
F\textsubscript{1,234}=81.49 & *** & F\textsubscript{4,830}=320.61 & ***
& F\textsubscript{2,234}=0.02 & & F\textsubscript{8,830}=0.33 & &
F\textsubscript{4,830}=5.32 & *** & F\textsubscript{8,830}=0.61
&\tabularnewline
& leafs at flowering & F\textsubscript{2,234}=5.05 & ** d\textless{}c,w
& F\textsubscript{1,234}=34.34 & *** & F\textsubscript{4,830}=218.71 &
*** & F\textsubscript{2,234}=0.01 & & F\textsubscript{8,830}=0.60 & &
F\textsubscript{4,830}=4.80 & *** & F\textsubscript{8,830}=0.58
&\tabularnewline
water \& gas exchange & water \& gas exchange & & & & & & & & & & & & &
&\tabularnewline
& stomata density & F\textsubscript{2,229}=0.20 & &
F\textsubscript{1,229}=0.67 & & F\textsubscript{4,790}=39.10 & *** &
F\textsubscript{2,229}=0.10 & & F\textsubscript{8,790}=1.96 & * &
F\textsubscript{4,790}=0.05 & & F\textsubscript{8,790}=0.99
&\tabularnewline
& 13C isotopes (WUE) & F\textsubscript{2,78}=2.24 & &
F\textsubscript{1,78}=16.02 & *** & F\textsubscript{3,230}=15.71 & *** &
F\textsubscript{2,78}=2.31 & & F\textsubscript{6,230}=0.72 & &
F\textsubscript{3,78}=5.42 & ** & F\textsubscript{6,230}=0.18
&\tabularnewline
Growth & Growth & & & & & & & & & & & & & &\tabularnewline
& plant height & F\textsubscript{2,232}=2.61 & \selectlanguage{ngerman}° &
F\textsubscript{1,232}=9.69 & ** & F\textsubscript{4,837}=304.61 & *** &
F\textsubscript{2,232}=1.47 & & F\textsubscript{8,837}=0.73 & &
F\textsubscript{4,837}=6.03 & *** & F\textsubscript{8,837}=1.33
&\tabularnewline
& vegetative biomass & F\textsubscript{2,231}=0.50 & &
F\textsubscript{1,231}=31.54 & *** & F\textsubscript{4,835}=518.22 & ***
& F\textsubscript{2,231}=0.37 & & F\textsubscript{8,835}=0.85 & &
F\textsubscript{4,835}=4.00 & ** & F\textsubscript{8,835}=0.60
&\tabularnewline
Allocation & Allocation & & & & & & & & & & & & & &\tabularnewline
& reprod. Allocation \textsuperscript{a} & F\textsubscript{2,231}=5.33 &
** d\textgreater{}c,w & F\textsubscript{1,231}=50.87 & *** &
F\textsubscript{4,790}=71.63 & *** & F\textsubscript{2,231}=0.61 & &
F\textsubscript{8,775}=1.01 & & F\textsubscript{4,775}=0.96 & &
F\textsubscript{8,775}=1.16 &\tabularnewline
& diaspore weight & F\textsubscript{2,231}=2.12 & &
F\textsubscript{1,231}=18.47 & *** & F\textsubscript{4,751}=25.30 & ***
& F\textsubscript{2,231}=0.40 & & F\textsubscript{8,751}=3.06 & ** &
F\textsubscript{4,751}=3.15 & * & F\textsubscript{8,751}=1.53
&\tabularnewline
Fitness & Fitness & & & & & & & & & & & & & &\tabularnewline
& seed number \textsuperscript{b} & F\textsubscript{2,230}=1,81 & &
F\textsubscript{1,230}=12.46 & *** & F\textsubscript{4,813}=133.71 & ***
& F\textsubscript{2,230}=1.17 & & F\textsubscript{8,793}=0.76 & &
F\textsubscript{4,793}=4.96 & *** & F\textsubscript{8,793}=0.93
&\tabularnewline
& ***p\textless{}0.001, **p\textless{}0.01, *p\textless{}0.05,
\selectlanguage{ngerman}°p\textless{}0.08 & ***p\textless{}0.001, **p\textless{}0.01,
*p\textless{}0.05, \selectlanguage{ngerman}°p\textless{}0.08 & & & & & & & & & & & &
&\tabularnewline
& \textsuperscript{a} model included covariate '\% selfing'
(F\textsubscript{1,966}=535.15, p\textless{}0.0001) &
\textsuperscript{a} model included covariate '\% selfing'
(F\textsubscript{1,966}=535.15, p\textless{}0.0001) &
\textsuperscript{a} model included covariate '\% selfing'
(F\textsubscript{1,966}=535.15, p\textless{}0.0001) &
\textsuperscript{a} model included covariate '\% selfing'
(F\textsubscript{1,966}=535.15, p\textless{}0.0001) & & & & & & & & & &
&\tabularnewline
& \textsuperscript{b} model included covariate '\% selfing'
(F\textsubscript{1,979}=483.15, p\textless{}0.0001) &
\textsuperscript{b} model included covariate '\% selfing'
(F\textsubscript{1,979}=483.15, p\textless{}0.0001) &
\textsuperscript{b} model included covariate '\% selfing'
(F\textsubscript{1,979}=483.15, p\textless{}0.0001) &
\textsuperscript{b} model included covariate '\% selfing'
(F\textsubscript{1,979}=483.15, p\textless{}0.0001) & & & & & & & & & &
&\tabularnewline
\bottomrule
\end{longtable}

\emph{}

\textbf{Table 2} \emph{Linear mixed models testing for diverging trait
values in N=160 genotypes originating from four sites along a natural
rainfall gradient (including control plots only), when grown under five
water levels in the greenhouse. For site, posthoc results are provided
to identify diverging sites (arid, semi-arid, Mediterranean,
mesic-Mediterranean). Highlighted in bold are traits where trait
divergence between sites followed a cline along the rainfall gradient.
Note that residual df are Kenward-Roger approximated and can therefore
differ among models; 13C isotopes were measured in only 56 genotypes.}\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
& trait & site & site & water & water & site x water & site x
water\tabularnewline
\midrule
\endhead
Germination & Germination & & & & & &\tabularnewline
& germination fraction & F\textsubscript{3,151}=52.85 & ***
a,sa\textless{}m,mm & NA & & NA &\tabularnewline
Phenology & Phenology & & & & & &\tabularnewline
& days to flowering & F\textsubscript{3,148}=137.78 & ***
a\textless{}sa\textless{}m,mm & F\textsubscript{4,504}=193.44 & *** &
F\textsubscript{12,504}=5.99 & ***\tabularnewline
& leafs at flowering & F\textsubscript{3,148}=17.66 & ***
a\textless{}sa,mm\textless{}m & F\textsubscript{4,507}=96.62 & *** &
F\textsubscript{12,507}=3.38 & ***\tabularnewline
water \& gas exchange & water \& gas exchange & & & & & &\tabularnewline
& stomata density & F\textsubscript{3,146}=4.29 & ** a,m\textless{}mm &
F\textsubscript{4,480}=16.10 & *** & F\textsubscript{12,480}=1.96 &
*\tabularnewline
& 13C isotopes (WUE) & F\textsubscript{3,52}=2.06 & &
F\textsubscript{3,155}=7.19 & *** & F\textsubscript{9,155}=2.27 &
*\tabularnewline
Growth & Growth & & & & & &\tabularnewline
& plant height & F\textsubscript{3,148}=16.42 & *** a\textless{}sa,m,mm
& F\textsubscript{4,513}=149.36 & *** & F\textsubscript{12,513}=2.95 &
***\tabularnewline
& vegetative biomass & F\textsubscript{3,147}=7.24 & ***
a,sa\textless{}m,mm & F\textsubscript{4,513}=285.89 & *** &
F\textsubscript{12,513}=3.00 & ***\tabularnewline
Allocation & Allocation & & & & & &\tabularnewline
& reprod. allocation\textsuperscript{a} & F\textsubscript{3,150}=26.44 &
*** a\textgreater{}sa\textgreater{}m\textgreater{}mm &
F\textsubscript{4,480}=49.14 & *** & F\textsubscript{12,480}=1.55
&\tabularnewline
& diaspore weight & F\textsubscript{3,143}=5.07 & **
mm,sa\textgreater{}m & F\textsubscript{4,412}=15.07 & *** &
F\textsubscript{12,412}=2.70 & **\tabularnewline
Fitness & Fitness & & & & & &\tabularnewline
& seed number\textsuperscript{b} & F\textsubscript{3,150}=21.65 & ***
a\textgreater{}sa\textgreater{}m\textgreater{}mm &
F\textsubscript{4,500}=71.00 & *** & F\textsubscript{12,500}=3.02 &
***\tabularnewline
& ***p\textless{}0.001, **p\textless{}0.01, *p\textless{}0.05,
\selectlanguage{ngerman}°p\textless{}0.08 & ***p\textless{}0.001, **p\textless{}0.01,
*p\textless{}0.05, \selectlanguage{ngerman}°p\textless{}0.08 & ***p\textless{}0.001,
**p\textless{}0.01, *p\textless{}0.05, \selectlanguage{ngerman}°p\textless{}0.08 & & &
&\tabularnewline
& \textsuperscript{a} model included covariate '\%selfing'
(F\textsubscript{1,577}=303.73, p\textless{}0.0001) &
\textsuperscript{a} model included covariate '\%selfing'
(F\textsubscript{1,577}=303.73, p\textless{}0.0001) &
\textsuperscript{a} model included covariate '\%selfing'
(F\textsubscript{1,577}=303.73, p\textless{}0.0001) &
\textsuperscript{a} model included covariate '\%selfing'
(F\textsubscript{1,577}=303.73, p\textless{}0.0001) & & &\tabularnewline
& \textsuperscript{b} model included covariate '\%selfing'
(F\textsubscript{1,604}=300.84, p\textless{}0.0001) &
\textsuperscript{b} model included covariate '\%selfing'
(F\textsubscript{1,604}=300.84, p\textless{}0.0001) &
\textsuperscript{b} model included covariate '\%selfing'
(F\textsubscript{1,604}=300.84, p\textless{}0.0001) &
\textsuperscript{b} model included covariate '\%selfing'
(F\textsubscript{1,604}=300.84, p\textless{}0.0001) & & &\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 3} Two-way \emph{ANOVAs testing for diverging plasticity
(measured as Coefficient of Variation across five water levels in the
greenhouse) in nine traits of N=240 genotypes descending from three
Climate Change manipulation treatments (CC) in two sites (semi-arid,
Mediterranean). Posthoc results are provided to identify diverging CC
treatments (dry, control, wet) and contrasting sites.}\selectlanguage{english}
\begin{longtable}[]{@{}llllllllll@{}}
\toprule
& & CC & CC & & site & site & & CC x site & CC x site\tabularnewline
\midrule
\endhead
& trait plasticity & F\textsubscript{1,234} & & & F\textsubscript{1,234}
& & & F\textsubscript{2,234} &\tabularnewline
phenology & phenology & & & & & & & &\tabularnewline
& days to flowering & F1,234=2.84 & \selectlanguage{ngerman}° & \selectlanguage{ngerman}° & 7.53 & ** M\textgreater{}SA
& ** M\textgreater{}SA & 0.33 &\tabularnewline
& leaves at flowering & 1.38 & & & 1.87 & & & 0.06 &\tabularnewline
water \& gas exchange & water \& gas exchange & water \& gas exchange &
& & & & & &\tabularnewline
& stomata density & 1.12 & & & 0.41 & & & 0.27 &\tabularnewline
& \selectlanguage{greek}δ\selectlanguage{english}13C & 0.22 & & & 0.44 & & & 0.59 &\tabularnewline
growth & growth & & & & & & & &\tabularnewline
& height & 0.17 & & & 6.09 & * M\textgreater{}SA & * M\textgreater{}SA &
0.08 &\tabularnewline
& veg. biomass & 3.47 & * d\textgreater{}w & * d\textgreater{}w & 2.07 &
& & 0.39 &\tabularnewline
allocation & allocation & & & & & & & &\tabularnewline
& reprod. allocation & 0.98 & & & 9.41 & ** M\textgreater{}SA & **
M\textgreater{}SA & 0.49 &\tabularnewline
& diaspore weight & 4.57 & * d\textgreater{}c,w & * d\textgreater{}c,w &
0.52 & & & 1.70 &\tabularnewline
fitness & fitness & & & & & & & &\tabularnewline
& seed number & 0.02 & & & 21.61 & *** M\textgreater{}SA & ***
M\textgreater{}SA & 0.58 &\tabularnewline
& \selectlanguage{ngerman}° p\textless{}0.08, *p\textless{}0.05, **p\textless{}0.01,
***p\textless{}0.001 & \selectlanguage{ngerman}° p\textless{}0.08, *p\textless{}0.05,
**p\textless{}0.01, ***p\textless{}0.001 & \selectlanguage{ngerman}° p\textless{}0.08,
*p\textless{}0.05, **p\textless{}0.01, ***p\textless{}0.001 & & & & &
&\tabularnewline
\bottomrule
\end{longtable}



\textbf{Hosted file}

\verb`image1.emf`  available at \url{https://authorea.com/users/297665/articles/460039-rapid-adaptive-evolution-to-drought-in-a-subset-of-plant-traits-in-a-large-scale-climate-change-experiment}

\selectlanguage{ngerman}\emph{\textbf{Figure 1} Mean values (±1SE) for ten traitsafter ten years
Climate Change manipulations (CC; dry -30\% rainfall; wet +30\%; control
ambient rainfall) in two intermediate sites (SA semi-arid, M
Mediterranean) and in two additional control sites along a natural
rainfall gradient (A arid, MM mesic-Mediterranean). Significant CC
effects indicate evolutionary trait divergence due to climate
manipulations (full statistics in Table 1).} \emph{The direction of CC
responses can be compared to trait shifts along the natural rainfall
gradient (effect of site; full statistics in Table 2).Points show mean
trait values} \emph{across 5 water levels in the greenhouse for plants
from N=40 genotypes per site and CC manipulation, whilestatistical
analyses accounted for all individual water levels. *p\textless{}0.05,
**p\textless{}0.01, ***p\textless{}0.001}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\emph{\textbf{Figure 2} Directional selection under low watering (red)
and high watering (blue) in the greenhouse. The analysis is based on the
genotype trait-means and the relative fitness of genotypes, both
computed across low water levels (15ml, 20ml) and high water levels
(50ml, 90ml), excluding the intermediate water level (30 ml) and
followed by standardization (zero mean, 1 SD) per population (SA and M)
and watering level (high and low). Differential selection between water
levels is indicated by their interaction with the tested trait (*
p\textless{} 0.05, ** p\textless{} 0.01, *** p\textless{} 0.001).}

\textbf{Hosted file}

\verb`image3.emf`  available at \url{https://authorea.com/users/297665/articles/460039-rapid-adaptive-evolution-to-drought-in-a-subset-of-plant-traits-in-a-large-scale-climate-change-experiment}

\selectlanguage{ngerman}\emph{\textbf{Figure 3} Mean plasticity (±1SE) in nine traitsafter 10
years Climate Change manipulations (CC; dry -30\% rainfall; wet +30\%;
control ambient rainfall) in two sites (SA semi-arid, M Mediterranean).
Plasticity was quantified as Coefficient of Variation (CV) across plants
grown in five water levels in the greenhouse and descending from N=40
genotypes per site and CC manipulation (except d13C carbon isotopes:
N=14). Significant CC effects indicate evolutionary divergence in
plasticity due to climate manipulations (full statistics in Table 3).
Note the different y-scaling for visualization. Plasticity for
germination fraction was not assessed as differential water levels were
not yet applied during germination. (*p\textless{}0.05,
**p\textless{}0.01, ***p\textless{}0.001)}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig1-Trait-Evolution/Fig1-Trait-Evolution}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig2-Selection-Analyses/Fig2-Selection-Analyses}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig3-Plasticity/Fig3-Plasticity}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

