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.