Introduction:
Understanding the processes shaping phenotypic diversification in nature
is a central objective of ecology and evolutionary biology (Schluter
2000; Bolnick et al. 2011). Trait variation within widespread
species can be extensive due to historic demographic processes and
spatially and temporally heterogeneous landscapes exerting different
selection pressures across a species’ range (Whitlock 2008). Over time,
subpopulations can become genetically and phenotypically differentiated
due to neutral processes, such as drift, gene flow, and mutation, as
well as the adaptive process of natural selection (Wright 1931; Spitze
1993; Holsinger & Weir 2009; Leinonen et al. 2013). Natural
selection acts on both phenotypes and phenotypic plasticity, defined as
the range of phenotypes a single genotype can express as a function of
environmental change (Nicotra et al. 2010). The strength and
direction of selection may vary, creating a mosaic of trait means and
differences in trait plasticity across species’ distributions (Chevin &
Lande 2011). Adaptive evolution of individual traits and associated
plasticity can therefore differentially affect a population’s
persistence on the landscape under a changing climate, as both alter the
range of phenotypes a population can express (Kelly 2019).
Phenotypic divergence is particularly evident in long-lived forest
trees, which often show strong genetic differences and local adaptation
among populations with ecological and evolutionary consequences for
associated species and communities (Savolainen et al. 2007; Leimuet al. 2008; Hereford 2009; Whitham et al. 2020). One
common hypothesis for the origins of phenotypic variation in trees is
local adaptation in response to climate. For example, studies onPopulus have evidence of adaptive differences among populations
in growth, phenology, and physiological traits (Frewen et al.2000; Fischer et al. 2017; Blasini et al . 2020), and the
evolution of regionally adapted ecotypes (Grady et al. 2011;
Evans et al. 2014; McKown et al. 2014; Ikeda et al.2017; Cooper et al. 2019; Bothwell et al . 2021). To
definitively show that phenotypic variation among populations is due to
divergent selection by their home climate, we need approaches that
integrate molecular and phenotypic assessments of replicated genotypes
across multiple common garden environments.
In addition to understanding the role of natural selection in shaping
trait differences, there has been an increasing interest in
understanding if and how selection acts on phenotypic plasticity itself
(Josephs 2018; Arnold et al. 2019). Phenotypic plasticity is
expected to evolve proportionally to the variability and predictability
of the environment, with higher plasticity correlated with more
predictable and more heterogeneous environments (Lande 2009; Lindet al. 2011). For example, De Kort et al. (2020) found
plastic responses to drought in woodland strawberry were higher in
topographically variable sites, while Leung et al. (2020)
experimentally determined that plasticity evolved to a lower degree in
populations of a microalga experiencing less predictable salinity
conditions after 500 generations. The evolution of decreased plasticity
in homogeneous environments could occur when there is a net cost to
maintaining plasticity (DeWitt et al. 1998). Plasticity is also
thought to increase in populations adapted to more benign climates
relative to harsh ones because the fitness cost of maladaptive
plasticity producing phenotype-environment mismatches will be greater
when resource availability is limited (Albert & Simms 2002). This has
been demonstrated in studies where lower elevation plants produced
stronger plastic responses to drought compared to plants from harsher,
high-elevation sites (Gugger et al. 2015; Akman et al.2021). Higher plasticity under milder (yet variable) conditions may
occur under the normal range of background environmental fluctuations,
however when extreme events occur outside of this range, theory predicts
rapid evolution of plasticity (Lande 2009). Evolution of increased
plasticity after extreme environmental shifts can allow mean phenotypes
to approach new optima by accelerating phenotypic adaptation, and may
enhance population persistence (Lande 2009; Chevin & Lande 2010).
However, this depends on the shape of the reaction norm and the genetic
variance and covariances available for selection to act upon after the
extreme event (Chevin & Hoffman 2017). Finally, although plasticity is
commonly studied on traits in isolation, species often respond to
changes in environmental with phenotypic plasticity in multiple traits,
termed multivariate plasticity (Schlichting 1989; Nielsen & Papaj
2022). Plasticity in one trait can therefore alter plasticity in another
trait, changing the optimal multivariate plastic response and fitness
outcome to the new environment (Nielsen & Papaj 2022). Correlations
among trait plasticities may constrain the evolution of plasticity,
resulting in discrete phenotypic strategies or solutions (Schlichting
1989). Together, these processes could combine to generate a
heterogeneous landscape, where selection gradients can produce marked
differences in plasticity along environmental clines.
A common test for whether natural selection is the mechanism responsible
for generating phenotypic divergence among populations is to compare
QST, the variation in quantitative traits, to
FST, the variation in neutral genes (Wright 1951; Lande
1992; Spitze 1993). QST is the quantitative genetic
analog to FST and measures the proportion of additive
genetic variance in a trait attributed to among-population differences.
If QST > FST, there is
evidence that directional selection is responsible for population-level
divergence with respect to a trait of interest. If QST ≈
FST, the null model that population differences are due
to genetic drift alone cannot be rejected. Finally, if
QST < FST, this suggests
uniform or stabilizing selection acting to constrain among-population
divergence (Spitze 1993). Selection is expected to be uniform when
populations share the same phenotypic optimum and divergent when
phenotypic optima vary, such as across heterogeneous environments (Le
Corre & Kremer 2012). The surge in both experimental and theoretical
QST-FSTstudies has revealed a major role
of natural selection shaping intraspecific variation in quantitative
traits (McKay & Latta 2002; Leinonen et al. 2008; Leinonenet al. 2013), with approximately 70% of all studies showing
QST > FST (Leinonenet al. 2008).
QST-FST comparisons can also be used to
test for selection on phenotypic plasticity (Josephs 2018). Lindet al. (2011) used QST-FST to
test for selection on plasticity in development time among island
populations of the common frog, Rana temporaria, which vary in
pool drying regimes. DeKort et al. (2016) performed a modified
Bayesian QST-FST analysis (Ovaskainenet al. 2011) to show selection on phenological plasticity inAlnus glutinosa across a latitudinal gradient. Alternatively,
selection on plasticity can be assessed by regressing a genotype’s
plasticity against overall fitness or a fitness proxy (Pigliucci &
Schlichting 1996; Arnold et al. 2019). In Fremont cottonwood, for
example, higher plasticity in bud flush is associated with higher
survival when populations experience warmer temperatures (Cooperet al. 2019). Either approach can complement the use of
environment-trait regressions (Whitlock 2008) to test whether trait
divergence among populations is systematically related to climatic
gradients as selection pressures.
The role of selection by past climatic in shaping intraspecific
variation in foundation species is especially important to quantify in
the American Southwest, where the effects of climate change are
pronounced (Garfin et al. 2013; Williams et al. 2020). The
loss of foundations species, defined as species that create locally
stable conditions, can have dramatic effects on fundamental ecosystem
processes like energy fluxes and biodiversity (Ellison et al.2005). The Southwest is described as one of the most
“climate-challenged” regions of North America, with warming
temperatures and increasing drought events already contributing to
massive forest mortality events (Breshears et al. 2005). Fremont
cottonwood is especially sensitive to drought and high temperature,
particularly in combination, as evidenced by stand-level mortality at
the Bill Williams National Wildlife Refuge on the lower Colorado River
(Fig. 1). Recent studies by Hultine et al. (2020a) and Blasiniet al. (2020) suggest that these trees are at the edge of their
thermal tolerance where water is essential for evaporative cooling. This
mortality is associated with the megadrought that Williams et al.(2020) identify as being the second worst drought in the past 1200 years
in the American Southwest. Thus, current climatic gradients will be
exacerbated by ongoing climate change, leading to new selection
pressures on functional traits that may be locally adapted to a narrower
range of environmental conditions.
In this study, we use trait data from three experimental common gardens
to quantify divergence (QST) in both genotype means
within environments and genotype plasticities across environments for
five traits of Populus fremontii . We then compare these
QST values to neutral genetic divergence
(FST). Common gardens are necessary to ensure that
among-population variance components reflect genetic differences and are
not inflated by environmental effects (Leinonen et al. 2013).
Reciprocal common gardens can reveal traits that vary across
environmental gradients as a result of phenotypic plasticity (Kawecki &
Ebert 2004; Franks et al. 2014). Plastic responses to
environmental stress or release from stress may mask or amplify
genetically determined trait differences that have emerged as a result
of divergent selection (Oke et al. 2015). It is therefore
important to assess phenotypes in multiple growing conditions to see how
the environment can modify the degree to which we can detect evidence of
selection. Our use of multiple common gardens adds to the
QST literature by examining how the detection of trait
differences depends on environmental conditions (Akman et al.2021) and by allowing for QST-FST tests
on trait plasticity across gardens.
Both the collection and garden locations span an elevation gradient of
almost 2000 m, consistent with the species’ range and including a
difference of 12°C mean annual temperature and > 500 mm in
mean annual precipitation across source locations and
~350 mm across gardens. The benefit of these
experimental gardens is enhanced by the development of genomic data
based on the identification of > 9000 single nucleotide
polymorphisms (SNPs) for all genotypes planted. SNPs are an ideal type
of marker for QST-FST analyses because
their mutation rates and the effects of drift are considered to be more
similar to loci that control quantitative traits compared to other
molecular markers, such as hypervariable microsatellites (Edelaar &
Bjorklund 2011). Thus, the only difference between quantitative trait
loci driving QST and the loci used in
FST estimates should be that only the latter conform to
neutral molecular evolution (Leinonen et al. 2013).
In order to address whether climate-driven natural selection drives
trait and trait plasticity divergences across the range of Fremont
cottonwood, we evaluated three hypotheses: 1) Genetic variation in tree
traits will be evident among populations and genotypes in each of the
three common gardens, although the magnitude of the genetic effects may
vary across environments and among traits. Likewise, populations will
differ in the magnitude of plasticity of these traits measured across
the garden environments 2) QST values will be
significantly higher than the neutral expectation of
FST, suggesting divergent selection has outweighed drift
in shaping divergence in trait means and plasticity among populations.
3) Mean population phenotypes will show strong relationships with their
climate of origin, as is expected when climate is a primary selective
force. Similarly, trait plasticity will also be correlated with
population source climate. Such plasticity-climate relationships should
emerge when population origins differ not only in mean climate
conditions but also in climatic variability across seasons and years, as
is the case in the Southwest.