Keywords: Adaptive evolution, conifers, climate change, gene expression, hybridization, quantitative genetics
INTRODUCTION
Spatial variation in selection pressures leads to ecological specialisation and genetic differentiation among populations. Such ecological specialisation, often referred to as local adaptation, contributes to biodiversity. Common garden experiments and provenance trials of numerous species have demonstrated local adaptation via a fitness reduction when populations are translocated to abiotic and biotic conditions that diverge from those of their home environment (Clausen et al. , 1948; Savolainen et al. , 2013; Pickleset al. , 2015). Common garden studies have also revealed among-population variation for phenotypic plasticity (i.e., environment-dependent expression of a trait value; Dayan et al ., 2015), which itself is a phenotypic trait that can be adaptive (Reed et al. 2011). As climate change brings with it drastic mismatches and interannual fluctuations, variation in phenotypic plasticity among genotypes – referred to here as genotype-environment-interactions (GEI) – will be a major determinant of survival in the long term (Chevin, Collins & Lefevre, 2013; Franks, Weber & Aitken, 2014). Despite the abundance of studies investigating local adaptation and demonstrating GEI, few empirical studies have evaluated the architecture underlying GEI under novel selection pressures (Etterson & Shaw, 2001). Experimentally, novel selection pressures can be imposed through space-for-time substitution designs (Pickett, 1989) conducted by using common gardens located beyond or at the climate margin of a species range (Geber & Eckhart, 2005). Contrary to the larger contribution of small effect loci towards adaptive evolution in polygenic selection models (Fisher, 1935; Barghi et al ., 2020), novel selective pressures may disproportionately favor architectures of large effect loci. Some of these larger effect loci might have pleiotropic consequences, impacting several correlated traits which may not directly be the target of selection (Orr, 1998). The fitness benefits of pleiotropic architecture are likely to be transient and dominant only in the early phases of adapting to a new optima, specifically in species with high migration rates (Battlay et al ., 2023; Hamala et al ., 2021).
Populations of forest trees are often locally adapted (Lind et al ., 2018) with both intra- and inter-specific variants as key sources contributing towards the architecture of adaptive evolution, specifically under novel selective pressures (Aitken et al ., 2008; Taylor & Larson, 2019; Bolte & Eckert, 2020). This is likely because inter-specific gene flow via hybridization can increase additive genetic variation, which influences trait responses to selection (Falconer & McKay, 1996). Studies in spruce, pine and poplar assessing fitness-related traits such as height, volume, bud set and disease resistance have demonstrated higher heritability and phenotypic variance in hybrid populations as compared to non-hybrid populations, as well as increased hybrid performance in novel environmental conditions (Dungey, 2001; De La Torre et al ., 2014; Suarez-Gonzalez et al ., 2016, 2018). Investigations of the architecture underlying GEI in novel environments and the contribution of hybrid ancestry to the evolution of this architecture, however, have lagged behind traditional investigations of adaptive evolution in forest trees. This is partially because of the difficulty in evaluating total lifetime fitness due to the longevity of trees.
Combining gene expression with survival – a key fitness component in trees – can help overcome the challenge posed by the longevity of tree lifecycles. Regulatory elements affecting gene expression disproportionately drive signals of adaptive evolution, especially for polygenic traits (Mei et al ., 2018). As such, it is expected that gene expression, which often also shows high heritability and responses to selection (Whitehead & Crawford, 2006; Eckert et al ., 2013), should be informative about architectures of adaptive evolution in natural populations. Even before the availability of genome-wide transcriptomic datasets, studies in systems biology demonstrated that metabolites, proteins, and gene expressions operate in the context of functional modules and are related to each other through a complex network of interactions (Hartwell et al ., 1999; Tohge et al ., 2005; Civelek & Lusis, 2014). The modular nature of biological networks permits environmental cues to target specific functional modules, limiting impact on other modules. Leveraging the modular nature of gene expression patterns (Hartwell et al ., 1999) and treating gene expression itself as a quantitative trait (Roberge et al ., 2007) can aid a better understanding of the multivariate architecture underlying adaptive evolution (Fagny & Austerlitz, 2021). This is enabled by estimation of co-expression networks (Barabási & Oltvai, 2004) using genetic values of expression levels that are treated as quantitative traits (i.e., one trait per locus). Genetic value is widely used in quantitative genetics as it represents the combined effect of all the alleles underlying a trait that an individual carries (Falconer & Mackay, 1996). Variation in genetic value is likely reflective of heterogeneity in selection pressures across the studied genotypes and is key for facilitating heritable responses to selection. The patterns and strength of connections among traits in co-expression networks, moreover, is often reflective of differing selection pressures. For example, strongly connected expression traits located at the core of networks often experience strong selective constraints, while those with lower connectivity are located at the periphery and often involved in GEI (Cork & Purugganan, 2004; Josephs et al ., 2017). When co-expression networks are constructed using genetic values rather than raw expression values, the connectivity patterns can be indicative of genetic covariances which are important components of the genetic architecture (Lande, 1980). We can thus use co-expression networks connectivity as a surrogate for understanding the relative role of two components of the genetic architecture - pleiotropy and linkage disequilibrium (LD). Genetic covariances between traits is key in determining the directionality of response to selection but has received limited attention in climate change studies, partly due to the extensive and rigorous experimental designs needed (Shaw & Etterson, 2012). Due to the rapid decay of LD in forest trees (Neale & Savolainen, 2004), high connectivity observed in networks is more likely to be indicative of strong pleiotropy as opposed to LD.
Our study uses the natural hybrid zone formed between two ecologically divergent species of pine, Pinus strobiformis Engelm. andP. flexilis E. James, to evaluate signatures of GEI and its contribution towards adaptive evolution. Both species have broad geographic distributions across western North America, with hybrid populations inhabiting sky-island ecosystems of New Mexico, Arizona, Texas and southern Colorado (Bisbee, 2014; Menon et al ., 2018) where they experience ongoing gene flow from P. flexilis(Critchfield, 1975; Menon et al ., 2018). Species distributed in fragmented populations are often vulnerable to extreme environmental fluctuations due to limited standing genetic diversity preventing adaptive responses (Aguilar et al ., 2008; Willi et al ., 2006). Previous work in this system has demonstrated no reduction in genetic diversity despite the high degree of fragmentation (Menon et al ., 2020) which may be the result of adaptive introgression (Menon et al ., 2021). These findings set the stage for evaluating the effect of interactions between interspecific gene flow and novel selective pressures as experienced under changing climatic conditions on the genomic architecture of GEI in long-lived species.
We were specifically interested in evaluating the performance of the hybrid seedlings to climatic conditions that diverge from the historical norms of temperature and moisture availability and are similar to those expected under climate change models. To simulate these novel selective pressures, we utilized a space-for-time substitution design wherein we planted hybrid seedlings across two common gardens that represented warm to cool mean annual temperatures on an elevational gradient. Using this design, we tested the following four hypotheses about the role of GEI towards adaptive evolution employing transcriptome-wide expression traits at the per-transcript and co-expression module levels:
H1: Sampled populations will demonstrate strong signals of local adaptation at both the per-transcript and the module level. These will be reflective of heterogeneity in source populations’ environmental conditions as well as novel selective pressures to which seedlings were exposed at the common gardens.
H2: Environmental differences (see Fig. S1) between common gardens will result in garden-specific patterns of trait differentiation (i.e., GEI) at both the per-transcript and the module level.
H3: Based on previous work on adaptive introgression in this system (Menon et al ., 2021) hybrid genomic ancestry will impact GEI at both the per-transcript and the module level.
H4: Traits with low connectivity within the co-expression network will dominate the architecture of adaptive evolution because such traits likely experience weak selective constraints and are more amenable to physiological fine tuning.
Overall, we demonstrate the prevalence of GEI across the transcriptome of our focal species and the key role it plays in driving adaptive evolution towards novel climatic conditions. By leveraging the connectivity patterns of gene expression traits within a quantitative genetic framework, we suggest the initial steps toward tracking novel climate optima disproportionately involve pleiotropic genetic architectures.