Natural quantitative genetic variance in plant growth differs in response to ecologically relevant temperature heterogeneity

Abstract Adaptation to large‐scale spatial heterogeneity in the environment accounts for a major proportion of genetic diversity within species. Theory predicts the erosion of adaptive genetic variation on a within‐population level, but considerable genetic diversity is often found locally. Genetic diversity could be expected to be maintained within populations in temporally or spatially variable conditions if genotypic rank orders vary across contrasting microenvironmental settings. Taking advantage of fine‐resolution environmental data, we tested the hypothesis that temperature heterogeneity among years could be one factor maintaining quantitative genetic diversity within a natural and genetically diverse plant population. We sampled maternal families of Boechera stricta, an Arabidopsis thaliana relative, at one location in the central Rocky Mountains and grew them in three treatments that, based on records from an adjacent weather station, simulated hourly temperature changes at the native site during three summers with differing mean temperatures. Treatment had a significant effect on all traits, with 2–3‐fold increase in above‐ and belowground biomass and the highest allocation to roots observed in the treatment simulating the warmest summer on record at the site. Treatment affected bivariate associations between traits, with the weakest correlation between above‐ and belowground biomass in the warmest treatment. The magnitude of quantitative genetic variation for all traits differed across treatments: Genetic variance of biomass was 0 in the warmest treatment, while highly significant diversity was found in average conditions, resulting in broad‐sense heritability of 0.31. Significant genotype × environment interactions across all treatments were found only in root‐to‐shoot ratio. Therefore, temperature variation among summers appears unlikely to account for the observed levels of local genetic variation in size in this perennial species, but may influence family rank order in growth allocation. Our results indicate that natural environmental fluctuations can have a large impact on the magnitude of within‐population quantitative genetic variance.


| INTRODUCTION
In the wild, native populations often exhibit a fitness advantage in their home environments. This phenomenon, local adaptation, is frequently documented among populations found across heterogeneous environments and is known to maintain genetic diversity in fitness and many other quantitative traits among populations (Hereford, 2009;Leimu & Fischer, 2008). On a more local within-population level, however, natural selection is expected to reduce genetic variation as nonoptimal phenotypes are removed from populations (Kawecki & Ebert, 2004). In contrast to these predictions, substantial quantitative genetic variation is frequently found within populations, even in traits closely related to fitness that are assumed to experience strong selection (Barton & Keightley, 2002;Hill, 2010;Houle, 1992;Johnson & Barton, 2005).
In spite of extensive theoretical work on the potential of different evolutionary and ecological forces to maintain genetic variation within populations, the causes of such local diversity remain for the most part poorly understood (Johnson & Barton, 2005). Mutationselection balance on its own is considered unlikely to account for the observed high levels of genetic diversity, while various forms of balancing selection have been hypothesized to play a causal role (Barton & Keightley, 2002;Charlesworth, 2015;Hill, 2010;Johnson & Barton, 2005). Further, gene flow among genetically distinct populations from divergent environments may be a source of variation within populations (Barton, 1999). Because a population's evolutionary potential in a changing environment is determined largely by the magnitude of heritable genetic diversity in adaptive traits (Falconer & Mackay, 1996), the maintenance of genetic variation is relevant not only to basic research on evolutionary processes but also to applied conservation biology and rare species' management (Hoffmann & Sgrò, 2011). Indeed, empirical evidence that at least some populations in the wild have been able to adapt to environmental changes indicates that adaptive genetic diversity existed in previous generations (Franks, Weber, & Aitken, 2014;Merilä & Hendry, 2014; see also Geerts et al., 2015;Irwin, Finkel, Müller-Karger, & Ghinaglia, 2015).
In addition to large-scale spatial variation in the environment that often leads to genetically differentiated populations, environmental conditions may also vary at much finer spatial and temporal scales (Linhart & Grant, 1996). Consequently, a population may encounter variable conditions at its home site, which could select for the potential to express a wider range of phenotypes across distinct microenvironments. Such phenotypic variation could be achieved for instance by genetic diversity such that different genotypes have the highest fitness in differing local environmental settings, that is, by genotype × environment interactions (Ellner & Hairston, 1994;Gillespie & Turelli, 1989;Hedrick, 1995), or via phenotypic plasticity of a generalist genotype (Kawecki & Ebert, 2004).
Empirical field studies have shown that performance ranks of genotypes may change as a result of variation in inter-and intraspecific competition (Baron, Richirt, Villoutreix, Amsellem, & Roux, 2015;Shaw, Platenkamp, Shaw, & Podolsky, 1995) or disturbance (McLeod, Scascitelli, & Vellend, 2012); genotype × environment interactions in fitness have even been described on a scale of just 10 cm within a single old field (Stratton, 1994). In Betula pendula Roth in Finland, however, forest ground heterogeneity on a local level affected overall growth but was not sufficient to shift genotypic ranks (Mikola et al., 2014). Genetic mapping studies on recombinant inbred lines and experimental hybrids of model systems such as Arabidopsis, Boechera, Drosophila, and Mimulus in the field and under controlled experimental conditions have provided insights into the genetic basis of genotype × environment interactions across highly divergent environmental conditions. These studies commonly show environment-specific QTL effects for numerous quantitative traits from morphology to reproductive fitness (reviewed in Mackay, 2009;Savolainen, Lascoux, & Merilä, 2013;Weinig, Ewers, & Welch, 2014). Genetically variable populations of predominantly self-fertilizing plant species like the mustard Boechera stricta (Graham) Al-Shehbaz (Song, Clauss, Pepper, & Mitchell-Olds, 2006) are suitable study systems for exploring the maintenance of local genetic diversity in varying environments because long-distance gene flow may be reasonably excluded as the source of high levels of within-population adaptive genetic diversity (cf. Yeaman & Jarvis, 2006). Previously, Salmela et al. (2016) observed considerable quantitative genetic diversity in the circadian clock, the endogenous timekeeper regulating daily oscillations in numerous traits, and various growth traits among maternal families of B. stricta sampled at a high-elevation site in the central Rocky Mountains in the USA. In this population, the range of family means in circadian period, one characteristic of the circadian clock, accounted for over 50% of the range that has previously been reported in a global set of 150 A. thaliana genotypes by Michael et al. (2003).
Quantitative variation in the clock was associated with growth so that families with longer circadian periods grew more rapidly and to a larger size, but had a lower root-to-shoot ratio. Because the circadian clock and its variability are generally thought to reflect adaptation to variable light-dark cycles (e.g., Michael et al., 2003), Salmela et al. (2016) hypothesized that local genetic diversity in the clock could be maintained by performance trade-offs of families across seasonal environments with differing photoperiods and temperatures. Yet, growth chamber environments simulating different months within a growing season resulted in genotype × environment interactions that were larger than the overall genetic effect only for root-to-shoot allocation.
Higher root-to-shoot ratio may enhance survival in stressful and lowresource environments (Poorter et al., 2012;Wilson, 1988), an adaptive hypothesis also suggested by the larger root-to-shoot ratios found in populations from higher-elevation environments in this region. Still, the exact factors enabling the maintenance of significant fine-scale genetic diversity in this population remain unknown.
Fluctuating selection driven by temporal variation in environmental factors has been proposed to be common in the wild (Siepielski, DiBattista, & Carlson, 2009). Temporally variable environmental conditions have also been hypothesized to shape the patterns of adaptive genetic diversity within populations (Salmela, 2014), but in general, theoretical studies suggest that conditions under which temporal variation in the environment can maintain genetic diversity are more restrictive than those for spatial heterogeneity (Hedrick, 2006). Some evidence supports the role of temporal environmental heterogeneity in maintaining genetic variation within populations: Different growth seasons within a year have been reported to favor divergent genotypes in Taraxacum officinale (L.) Weber ex F.H. Wigg (Vavrek, McGraw, & Yang, 1996), while annually varying moisture conditions in a desert may conserve flower color polymorphism in the annual Linanthus parryae (A.Gray) Greene (Schemske & Bierzychudek, 2001). In the fish Xiphophorus variatus, the level of temperature variability within days and years has been found to be positively associated with withinpopulation diversity in tail spot richness (Culumber & Tobler, 2016), with the fitness rank of different spot types depending for instance on the thermal environment (Culumber, Schumer, Monks, & Tobler, 2015).
In this study, we were interested in further exploring the capacity of temporal abiotic variability to sustain local quantitative genetic variation in the wild using the aforementioned B. stricta population from the central Rocky Mountains as our study system. Two features in particular make this population an interesting model for assessing the possible environmental causes of local genetic diversity: It has already been shown to be genetically diverse in multiple quantitative traits , and its home site is located close to a weather station with long-term and fine-resolution temperature records. The population experiences for instance variable summer temperature conditions; between 1995 and 2014, mean June and July temperatures at the site varied between 8.3-15°C and 13-17.2°C, respectively. Hourly recorded temperature data from multiple years allowed us to investigate the effects of heterogeneity in a single abiotic factor, providing the unique opportunity to simulate temporally fluctuating and ecologically relevant natural environments in controlled experimental settings. We grew replicates of the same set of naturally occurring maternal families across treatments with differing average temperatures, hypothesizing that if variable summer temperature regimes were capable of preserving quantitative genetic diversity in this population, not only would the treatments affect overall means but also induce genotype × environment interactions in growth and its allocation. Moreover, we expected the magnitude of these interactions to be large compared to the average effect of family and a large proportion of the interaction variance to be due to rank shifts of families among simulated growing season temperature cycles.

| Sampling
We collected seed material for the study by maternal family at a distance of about 300-500 m from the South Brush Creek SNOTEL weather station (41.333°N, 106.500°W; elevation 2,572 m) in southeastern Wyoming on 26 July 2012. The inbreeding coefficient in populations of B. stricta is high (0.74-0.98 in Song et al., 2006), suggesting that maternal progeny consist mostly of full-sibs. All except one of the sampled plants were located along a transect of approximately 300 m. Because we were interested in natural patterns of variation present in the population at the time of sampling rather than characterizing the genetic background of quantitative trait variation, we used wild-collected seeds in the experiment. Of the 21 families investigated in this study, 19 were included in the study by Salmela et al. (2016) that documented significant within-population genetic variation in the circadian clock and growth.

| Treatments
Based on records from the weather station (http://www.wcc. nrcs.usda.gov/nwcc/site?sitenum=772), we programmed growth chamber compartments (PGC-9/2 with Percival Advanced Intellus Environmental Controller, Percival Scientific, Perry, IN, USA) to track hourly temperature changes at the site during an 8-week period on June 1-July 25 in 2009, 2010, and 2012. Thus, each treatment consisted of over 1,300 temperature steps. In the following sections of the article, we will refer to these treatments by the corresponding year. We chose to focus on June and July because an earlier study indicated the most vigorous growth in a treatment simulating early growing season conditions   in all chambers before the experiment, was approximately 250 μmol photons m −2 s −1 at the plant level. We did not replicate treatments; thus, family × treatment effects cannot be unequivocally attributed to temperature. However, variation in other controllable factors is likely to have been small compared to temperature due to very similar light levels in all chamber compartments. Daily monitoring of temperature by thermometers also indicated that chamber conditions adhered to programmed settings.
We germinated and planted seeds from the 21 families, with ten replicates per family in each treatment, following the protocol in Salmela et al. (2016). We did not score timing of germination due to rapid germination on moist paper in all the families within 4 days following cold stratification. We divided the seedlings into the three treatments, with ten blocks within each and one replicate per family randomized within a block. We watered pots to field capacity every 2 days and rotated flats within the chamber compartments twice every week. The species requires vernalization in order to flower; due to difficulties in simulating ecologically relevant high-elevation autumn, winter, and spring conditions in our experimental settings (air temperature at the site can drop below −30°C), we measured the equivalent of first-year growth only. After 8 weeks, we scored the number of leaves and the length of the longest leaf in all replicates and sampled whole plants for above-and belowground biomass and root-to-shoot ratio. We dried plants in an oven at 65°C for 3 days before biomass measurements. Leaf measures were strongly correlated with aboveground biomass (the number of leaves: r ≥ .807, p < .0001 in all treatments; the longest leaf length vs. √aboveground biomass: r ≥ .923, p < .0001 in all treatments). Because qualitatively similar results were obtained with all correlated measures, we will use biomass only as a measure of aboveground plant growth. We sampled plants at 8 weeks because the oldest leaves often start to senesce at this point in chamber conditions (M.J. Salmela, personal observation) and because an 8-week period is likely to account for a large proportion of the annual growing season at the home site of the population.

| Statistical analyses
We included families with at least four replicates in two treatments in the analysis. Prior to formal statistical tests, we examined whether our data met assumptions of analysis of variance. We detected variance heterogeneity of residuals among treatments in all traits, with variances varying 6.6-fold for aboveground biomass, 9.5-fold for belowground biomass, and 4.8-fold for root-to-shoot ratio. These Therefore, we used the raw values in the analyses presented here.
We first tested for the significant effects of family and block within each treatment using general linear models. We consider variation among maternal families to represent genetic diversity within the population, and thus, proportion of total variation explained by among-family diversity in each treatment is an estimate of broadsense heritability (H 2 ). We estimated variance components for family, block, and residual variation using the REML approach. We examined associations between the traits within treatments using replicate values and Pearson's correlation. Scatterplots suggested the slope between above-and belowground biomass varies among treatments. To test for treatment differences in slope (ß 1 ), we performed a regression analysis of aboveground biomass on belowground biomass in which we included treatment as a categorical factor and the aboveground biomass × treatment interaction term.
To investigate the contributions of different sources of variation across all three treatments and between pairs of treatments, we used the model above-or belowground biomass or root-to-shoot ratio = treatment + family + family × treatment + block(treatment).
We considered treatment a fixed factor. When nonsignificant at the F I G U R E 1 Variation in daily mean temperatures in growth chamber treatments simulating three summers with differing average temperatures at South Brush Creek in southeastern Wyoming. Overall treatment means were 12.5°C in 2009, 13.4°C in 2010, and 15.7°C in 2012. Due to the hourly tracking of temperature heterogeneity at the site, each treatment consisted of over 1,300 different temperature steps 0.10 level, we removed the interaction term from the analysis. Given that among-family variation measures genetic diversity within the population, the family × treatment term defines genotype × environment interactions. We estimated variance components for the random factors using the REML approach. Genotype × environment interactions can maintain diversity if they are caused by rank shifts of genotypes rather than by differences in among-genotype variance among treatments (Mitchell-Olds, 1992). In order to separate the effects of shifts in family rank order vs. among-family variance on a statistically significant interaction variance component, we used the equation (Cockerham, 1963): where V Family × treatment is the variance component due to family × treatment interaction, σ i and σ j are the square roots of among-family variances in treatments i and j, r GE is the genetic correlation between treatments i and j, and t is the number of treatments. The first part of the equation accounts for variance due to changes in rank order among treatments, while the second part accounts for treatment differences in among-family variance. We estimated genetic correlations using family variance components within each treatment and across pairs of treatments (Windig, 1997): where V Family is the variance component due to family across the two environments, and V Family, treatment i and V Family, treatment j are the variance components due to family in treatments i and j. We carried out all statistical analyses with IBM SPSS Statistics Version 23.  The percentage shows the proportion of total variation explained by each factor. V P = variance component for phenotypic variation; V F = variance component for among-family (genetic) variation. ns = p > .10, *p < .05, **p < .01, ***p < .001, ****p < .0001. a Estimate of broad-sense heritability (H 2 ). of phenotypic and genetic diversity. In 2009, the family effect was significant for aboveground biomass and root-to-shoot ratio, an indication of genetic variation in the population. For belowground biomass, the family effect was close to significance at the 0.05 level (p = .078). In 2010, the family effect was highly significant for all traits.

| RESULTS
Although 2012 was the most phenotypically variable treatment, we found no significant genetic variation for above-or belowground biomass in this year; variation was entirely residual. For root-to-shoot ratio, however, family differences in 2012 were highly significant. In 2010, aboveground biomass showed a weak positive correlation with root-to-shoot ratio (r = .288, p < .001, 95% CI: 0.153-0.422). In 2009 and 2012, this correlation was not significantly different from zero.
Treatment had a highly significant effect on all three traits (Table 2).
Aboveground biomass was twice and belowground biomass about three times larger in 2012 than in 2009 or 2010, and thus, also root-to-shoot ratio displayed its highest treatment mean in 2012 (Table 1). For aboveand belowground biomass, the only significant random factor across all treatments was family. We found a significant family × treatment interaction term for root-to-shoot ratio, revealing variation in pheno- However, the variance component due to family was 1.73 times bigger than that due to the interaction, signaling that the significant interaction arises mainly from the treatment differences in among-family variance (Table 1). For belowground biomass in the same treatment comparison, the pattern was comparable: The family × treatment interaction

| DISCUSSION
In this study, we combined environmental data with quantitative genetic approaches to examine whether temporal environmental heterogeneity could act as one mechanism maintaining genetic variation in growth and its allocation within a plant population experiencing variable conditions at its home site. In order to carry out the experiment in as ecologically relevant chamber conditions as possible, we took advantage of fine-resolution temperature data from a weather station that was located in the vicinity of the sampled population. This enabled us to quantify the potential of natural among-year heterogeneity in growing season temperatures to induce genotype × environment interactions in first-year growth traits.
Self-fertilization is expected to reduce genetic diversity and consequently limit long-term survival prospects of populations (reviewed in Wright, Kalisz, & Slotte, 2013). Yet, populations of predominantly selffertilizing plant species like A. thaliana and B. stricta may harbor considerable levels of local quantitative and molecular genetic variation (Baron et al., 2015;Kuittinen et al., 1997;Méndez-Vigo et al., 2013;Salmela et al., 2016;Siemens, Haugen, Matzner, & Vanasma, 2009;Song et al., 2006;Stenøien et al., 2005). Because the rate at which the population mean for a quantitative trait can change is positively correlated with the amount of genetic diversity in the trait (Falconer & Mackay, 1996;Houle, 1992), understanding the maintenance of genetic variation in the wild has become one of the key questions in modern evolutionary and conservation biology (Franks et al., 2014; T A B L E 2 General linear model results for above-and belowground biomass and root-to-shoot ratio across all treatments Interaction terms were included in the models when significant at the p < .10 level. Heritabilities and genetic variances commonly vary across environments, which complicates predicting responses to selection (Charmantier & Garant, 2005;Falconer & Mackay, 1996). Lower levels of quantitative genetic diversity may be expressed in unfavorable conditions (Charmantier & Garant, 2005;Hoffmann & Merilä, 1999). their own were not detrimental to our study population. Instead, the lack of quantitative genetic diversity in biomass in this treatment may be due to the atypical high temperatures, or the novel combination of high temperature and moisture that this particular population would be unlikely to encounter in nature. Indeed, one limitation of our study is that although we programmed our chamber treatments to track closely natural temperature fluctuations that had occurred at the site, we investigated the effects of a single environmental variable only.

F I G U R E 3 (A) Reaction norms of root-to-shoot ratio for naturally occurring maternal families of
We kept plants well-watered throughout the experiment, but records from the weather station show that no precipitation occurred at the site in June 2012. The region experienced severe drought during the summer of 2012 (NOAA National Overview for Annual 2012), which would be expected to increase allocation to roots but also limit biomass accumulation in the shoot (Mokany, Raison, & Prokushkin, 2006;Poorter et al., 2012). Therefore, a differing response might have been observed in our experiment had we simulated natural variation in multiple environmental factors simultaneously.
In the wild, natural selection operates on total phenotypic variance shaped by both genetic and nongenetic factors (Falconer & Mackay, 1996). Because we were interested in the response of a natural population to the environmental heterogeneity of its native environment, we used wild-collected seeds in the experiment. As a consequence, it Interaction terms were included in the models when significant at the p < .10 level.
is possible that our estimates of genetic variance also include a component due to maternal effects. Variation in the environment may result in differential maternal provisioning during seed development, the effects of which could resemble those caused by segregating genetic diversity (e.g., Bischoff & Müller-Schärer, 2010). Further, maternal effects may be adaptive so that they enhance offspring fitness in conditions similar to those experienced by the parents (Galloway & Etterson, 2007). Drought experienced by parents has been found to enhance drought tolerance of offspring in B. stricta (Alsdurf, Ripley, Matzner, & Siemens, 2013), but the general importance of maternal effects to trait variation in this species is not known. While we cannot rule out potential transgenerational effects on the observed phenotypic variation, it is likely that the seeds used in the experiment ma- One factor that may play a role in the maintenance of multiple phenotypically distinct lineages in a population is environmental heterogeneity on a local scale (Gillespie & Turelli, 1989;Linhart & Grant, 1996;Salmela, 2014). We hypothesized that the existence of finescale genetic variation in growth in our study system could be related to among-year variation in growing season temperature conditions in the home environment. Size is a frequent fitness proxy in plants, and shoot size has been reported to correlate moderately with reproductive effort in B. stricta (Siemens & Haugen, 2013); thus, changes in rank order for biomass could be expected to influence fitness ranks, too. In comparison with 2009 and 2010, we found that average aboveground biomass doubled and belowground biomass tripled under the unusually warm conditions of 2012. Biomass increase in response to warm temperatures is common in plants, especially when growing in nonlimiting conditions (Lin, Xia, & Wan, 2010;Pregitzer, King, Burton, & Brown, 2000). However, despite the significant treatment and family effects, we observed no significant family × treatment interactions in biomass across the three temperature environments. In the pairwise comparison of 2009 and 2010, the family × treatment interaction terms were significant or close to significant, but the variance components due to family were of greater magnitude than those for the interactions. This mirrors the observed treatment differences in genetic variances with little change in family rank order across temperature regimes.
The lack of family × treatment interactions in biomass suggests that temperature variation among growing seasons alone is unlikely to account for the genetic diversity in growth in our study system (Mitchell-Olds, 1992). This is in accordance with a previous experiment that explored the possibility that photoperiodic and temperature variation within a potential growing season could maintain genetic diversity in growth traits in the same B. stricta population . In the earlier study, month within a growing season (June, August, or September) had a large effect on overall growth, but more variation was due to family than family × treatment interactions across the three different months. In these growing season treatments, both photoperiod and temperature varied and the range of variation in temperature among treatments was more extensive than in the current study. Nevertheless, although we did not detect significant family × treatment interactions for biomass, conditional neutrality at the level of maternal family may slow the erosion of genetic variation (cf. Fry, Heinsohn, & Mackay, 1996;Schnee & Thompson, 1984). Specifically, in years similar to our 2009 and 2010 treatments, genetic variation in size would be expressed; but in years similar to 2012, genetic diversity in the population could be masked, and selection might not differentiate among distinct maternal lineages (e.g., Merilä, 1997). It is also possible that fine-scale genetic diversity in growth in this perennial species is maintained by extensive temporal heterogeneity in low winter, spring, and autumn temperatures that could not be simulated in the chambers used, or by other abiotic or biotic factors, such as fine spatial heterogeneity in soil or competition (Baron et al., 2015;Shaw et al., 1995;Stratton, 1994).
Patterns of variation expressed for root-to-shoot ratio differed from the two other traits. The trait exhibited significant genetic diversity in all three treatments, and in contrast to biomass, the most ge- Similar to biomass, we detected the highest overall mean for rootto-shoot ratio in 2012. The trait is known to be sensitive to different environmental cues such as light and moisture, and limitations in above-or belowground resources often result in increased allocation to the corresponding parts of the plant (Poorter & Nagel, 2000). In our experiment, plants grew in well-watered conditions but variable temperatures. Reduced water uptake by roots in cooler temperatures may underlie slightly increased allocation to roots in 2009 (Poorter et al., 2012). Based on among-species patterns of variation, root-to-shoot ratio is expected to be negatively correlated with shoot size in herbaceous plants (Poorter et al., 2012;Wilson, 1988; see also Mokany et al., 2006); in our study focusing on a much finer level of withinspecies diversity found in the wild, increases in above-and belowground biomass in the 2012 treatment also led to higher root-to-shoot ratios. Larger allocation to roots in this treatment may be related to the patterns of covariation between temperature and precipitation in the population's native environment: During a 20-year period (1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014), average June temperature and precipitation increment during the month at the site were negatively correlated (r = −.737, p < .001, Figure 3B). During Junes of 2012 and 2013, the 2 years with the highest mean June temperatures, no precipitation was recorded at the site, conditions under which increased allocation to roots would be expected to be beneficial (Poorter et al., 2012). Thus, higher temperatures alone in a June environment might increase root-to-shoot ratios in this population.
Unlike its two components, root-to-shoot ratio exhibited significant family × treatment interactions across all treatments, revealing substantial genetic variation in phenotypic plasticity in this trait within the population (Falconer & Mackay, 1996). As indicated by the moderate family effect across treatments, genetic correlations were positive between treatments, but the variance component due to family × treatment interaction across all treatments was larger than that of family alone. Moreover, the interaction term was caused mainly by rank changes of families, a requirement for the maintenance of quantitative genetic diversity (Mitchell-Olds, 1992). While our results support the idea that temperature variability can contribute to the maintenance of genetic diversity in root-to-shoot ratio in the B. stricta population, we do not have direct evidence on how selection acts on the examined traits in the wild. Increased allocation to roots may have fitness benefits at sites that experience long and cold winters (Poorter et al., 2012), especially in perennial species like B. stricta that have to survive over the winter in order to reproduce. Alternatively, enhanced allocation to roots could be favored under drought or low-nutrient conditions (Lloret, Casanovas, & Peñuelas, 1999;Montesinos-Navarro, Wig, Pico, & Tonsor, 2011;Wilson, 1988). Overall population differences in allocation observed among four high-elevation B. stricta populations in southeastern Wyoming suggest the trait is linked to adaptation to cold in this region: In a chamber environment simulating late June conditions, two populations sampled close to 3,000 m had root-to-shoot ratios 36% higher than the other two located closer to 2,500 m, one of which was South Brush Creek . This difference arose mainly from larger root biomass in the higher-elevation populations, while shoot size did not show a similarly clear elevational grouping. Higher root-to-shoot ratios in populations from cooler home climates have been found in other perennial species such as Carex aquatilis Wahlenb. and Picea abies (L.) H.Karst (Chapin & Chapin, 1981;Oleksyn et al., 1998). Additional work is required on B. stricta to examine how biomass and its allocation are related to fitness in the spatially and temporally variable environments in the Rocky Mountains, and how environmental heterogeneity affects survival and reproductive fitness in this species. Because simulating very cold, long-lasting and temporally variable winter conditions in growth chambers is challenging, such studies will require multiyear field experiments so that families will be exposed to their natural home site conditions.
In this experiment, our aim was to measure the potential of natural temperature heterogeneity to elicit genotype × environment interactions in a genetically diverse plant population. In conclusion, our results show that variable experimental environments affect traits differently, that the amount of genetic diversity expressed in a population can vary greatly depending on environmental conditions, that among-trait associations may vary across environments, and that natural populations can contain significant genetic diversity in phenotypic plasticity of quantitative traits. Our study revealed limited potential of natural heterogeneity in summer temperature to change biomass ranks of maternal families in the B. stricta population from the Rocky Mountains. However, the magnitude of genetic variation differed across simulated years, with no genetic diversity in size in the treatment simulating the warmest summer on record at the home site of the population; such conditional neutrality of maternal families could slow the loss of genetic variation. In rootto-shoot ratio, family ranks within the population varied in response to natural temperature variability. Therefore, our findings support the role of genotype × environment interactions in maintaining fine-grained quantitative genetic diversity in growth allocation. Because estimates of quantitative genetic variance are used to forecast extant populations' adaptive responses to novel environmental conditions (Franks et al., 2014;Hoffmann & Sgrò, 2011) our observation that ecologically relevant temperature treatments affected the manifestation of genetic variation in quantitative traits is relevant to understanding the evolutionary potential within natural populations found in changing environments.