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. 2021 May 5;128(3):357–369. doi: 10.1093/aob/mcab057

Inter-annual and spatial climatic variability have led to a balance between local fluctuating selection and wide-range directional selection in a perennial grass species

T Keep 1, S Rouet 1, J L Blanco-Pastor 1, P Barre 1, T Ruttink 2, K J Dehmer 3, M Hegarty 4, T Ledauphin 1, I Litrico 1, H Muylle 2, I Roldán-Ruiz 2, F Surault 1, R Veron 1, E Willner 3, J P Sampoux 1,✉
PMCID: PMC8389464  PMID: 33949648

Abstract

Background and Aims

The persistence of a plant population under a specific local climatic regime requires phenotypic adaptation with underlying particular combinations of alleles at adaptive loci. The level of allele diversity at adaptive loci within a natural plant population conditions its potential to evolve, notably towards adaptation to a change in climate. Investigating the environmental factors that contribute to the maintenance of adaptive diversity in populations is thus worthwhile. Within-population allele diversity at adaptive loci can be partly driven by the mean climate at the population site but also by its temporal variability.

Methods

The effects of climate temporal mean and variability on within-population allele diversity at putatively adaptive quantitative trait loci (QTLs) were evaluated using 385 natural populations of Lolium perenne (perennial ryegrass) collected right across Europe. For seven adaptive traits related to reproductive phenology and vegetative potential growth seasonality, the average within-population allele diversity at major QTLs (HeA) was computed.

Key Results

Significant relationships were found between HeA of these traits and the temporal mean and variability of the local climate. These relationships were consistent with functional ecology theory.

Conclusions

Results indicated that temporal variability of local climate has likely led to fluctuating directional selection, which has contributed to the maintenance of allele diversity at adaptive loci and thus potential for further adaptation.

Keywords: Allele diversity, climatic adaptation, adaptive diversity, fluctuating selection, genome-wide genotyping, grassland, Lolium perenne, natural genetic diversity, perennial ryegrass, intra-specific variability

Introduction

The natural diversity of a plant species present over a wide geographical range with diverse environments includes populations that are submitted to various environmental selection pressures. Local adaption to specific environmental conditions can lead to divergent genetic evolution of populations (Sober and Wilson, 2011). Phenotypic polymorphism appears when the different habitats are large and stable enough to induce habitat specialization and fitness optimization; otherwise a monomorphic compromise phenotype endures (Rosenzweig, 1987). When phenotypic polymorphism is observed, it is likely that fitness has been improved in one habitat by relinquishing some ability in another. Indeed, the intra-specific diversity of many widespread plant species includes local adaptations to a range of environments rather than just an adaptable all-purpose phenotype (Van Tienderen, 1990; Balfourier and Charmet, 1991a; Weyl and Coetzee, 2016). Local adaptations have been shown to emerge in reaction to small-scale and large-scale environmental variations (Clausen et al., 1940; Burdon, 1980; Linhart and Grant, 1996; Macel et al., 2007). At large spatial scales, climate is an important source of environmental variation and largely determines the composition of plant communities (Woodward, 1987). Relations between mean climatic conditions and population genetic or phenotypic characteristics have been widely studied for plant species (Balfourier and Charmet, 1991a; Casler, 1995; Hancock et al., 2011; Bessega et al., 2015). It is considered that a few traits are exposed to stronger selection than most others and play a greater role in the adaptation to various environments (Rieseberg et al., 2004; Carnicer et al., 2012). Functional traits related to seasonal vegetative growth potential and reproductive phenology are regarded as the most important components of species fitness (Chuine, 2010; da Silveira Pontes et al., 2015). The inter-population variability of such traits strongly contributes to local adaptation across broad environmental gradients and is possibly mainly determined by allele variability at a few major-effect genes (Castède et al., 2014; Hill and Li, 2016). Indeed, loci with a large effect on adaptive traits are likely those subject to the strongest selection pressures as their allele frequencies are expected to vary the fastest and to pull population phenotypic differentiation (Rieseberg et al., 2004; Storz, 2005).

Local climatic adaptation is notably facilitated by temporally stable constraining local climatic conditions (Kawecki and Ebert, 2004). Accordingly, the probability of fixation of adaptive alleles, and hence the allele diversity at adaptive loci, notably depends on the selection intensity imposed by the local climatic constraints and on the stability of the selection pressure through time. If the selection intensity varies along a climatic gradient, the range of possible phenotypic values that allow sufficient adaptation is expected to decrease as selection intensity increases (Dostál et al., 2016; Barghi et al., 2020) and the allele diversity at adaptive loci is also expected to follow the same trend. Furthermore, at a given position along a mean climate gradient, climatic conditions can fluctuate over time and lead to fluctuating selection pressures (Bell, 2010). In that case, the fixation of alleles adapted to mean climatic conditions is impeded. Allele frequencies at large-effect adaptive loci are likely to be the most affected by fluctuating selection pressures (Bell, 2010). The greater the magnitude and frequency of the local climatic fluctuation, the greater the local allele diversity at these loci is likely to be. Consequently, inter-annual climatic variability could maintain local genetic variance for adaptive traits. Inter-annual fluctuating selection has already been observed in various plant species (Scopece et al., 2017; Zahn, 2018; Busoms et al., 2018). For instance, an investigation into a natural shrub population performed in a Mexican forest found that directional selection gradients were opposite in sign between two consecutive years for flowering initiation date due to inter-annual differences in rainfall patterns (Domínguez and Dirzo, 1995). Within a plant species population, high levels of genetic diversity maintained by weak selection intensity and/or local climate variability could improve the population’s chances of adaptation to climate change and reduce the risk of local disappearance of the species (Herben et al., 2003; Schierenbeck, 2017).

Our study aimed to assess the relative impact of mean climatic conditions and of fluctuating inter-annual climatic conditions on the adaptive diversity within natural populations of Lolium perenne (perennial ryegrass) across Europe. This C3 diploid perennial grass is a highly outcrossing species whose natural range covers the entire European continent, the Near East and northern Africa. It has been adopted as a model species for the genetics of temperate forage grasses, and accordingly advances in genotyping technologies have been made in this species, including the release of a whole genome reference sequence (Byrne et al., 2015). Our study was based on a sample of 385 perennial ryegrass populations that originated from a large biogeographical range across Europe and the Near East and was representative of the area of primary expansion of the species (Blanco-Pastor et al., 2019). These populations were grown in three common gardens in which various traits related to agronomic value and environmental adaptation were recorded at population level. Genotyping based on next-generation sequencing technologies was applied to population DNA pools and provided allele frequencies per population for a high number of nuclear genome SNP markers. The climate at sites of origin of populations was documented with norms and inter-annual standard deviations of various climatic variables. Based on this experimental design, Keep et al. (2020) demonstrated that genome-wide association study (GWAS) models can be efficiently applied at population level to discover major quantitative trait loci (QTLs) in the natural diversity of perennial ryegrass. Blanco-Pastor et al. (2020) highlighted loci and phenotypes involved in adaptation to mean climate at sites of origin of populations. Keep et al. (2021) pointed out the major functional trade-offs and more specifically the crucial role of vegetative growth seasonality and reproductive phenology in the adaptation of perennial ryegrass to mean climate. This new study investigated the relationships between within-population allele diversity at loci strongly associated with adaptive traits and both mean climate and inter-annual climatic variability at sites of origin of populations. It focused on traits that were already evidenced as of primary importance in the climate adaptation of perennial ryegrass, i.e. traits related to vegetative growth seasonality and reproductive phenology. The conceptual framework of functional ecology was used to analyse the results in terms of the relative impacts of mean and fluctuating climatic conditions on the adaptive diversity within populations.

MATERIALS AND METHODS

Plant material

This study used 385 gene bank accessions sampling the diversity of 385 natural populations of perennial ryegrass. These accessions were provided by gene banks of European countries and the USDA and were chosen as to represent the intra-specific diversity of the species across Europe and the Near East. Information about collection sites and sampling dates is reported in Supplementary Data Table S1. Collections were undertaken by scientists and plant breeders from a number of agronomic research institutes in Europe and from the USDA between 1960 and 2013. The sampling protocol in a collection site was usually the same as the one described in Charmet et al. (1990) for populations collected in France. At the period of seed maturity, a balanced amount of seeds was collected from at least 50 plants across a homogeneous area of 100–1000 m2. Seeds were afterwards bulked (a single seed lot per collection site) and stored in cold rooms of the gene bank of the research institute that undertook collection. Perennial ryegrass is a self-incompatible cross-pollinated species and it is therefore expected that seeds sampled at a given collection site are from a panmictic population. Seed lots were regenerated and increased once soon after collection and then once or twice again (at intervals of round 15 years) for the oldest accessions. Regeneration was performed in field facilities of research institutes following a standard protocol for grassland grasses agreed in the frame of the ECPGR network promoting collaboration between gene banks of European agronomic research institutes (http://www.ecpgr.cgiar.org). Each seed regeneration step typically uses 50–100 (up to 200) plants (according to gene bank) that are grown in isolation to avoid pollination from external sources and are expected to intercross in panmixia. Seeds are harvested on each plant and a balanced seed bulk is made for each population. For the needs of the genotyping implemented in this study, 300 seeds were drawn from the most recent bulk of seeds available for each of the selected natural populations in order to grow 300 seedlings for a pooled DNA extraction. Another seed sample was drawn for each population to sow it as replicated micro-swards to be used for in-field phenotyping.

Genotyping

Genotyping was performed at the population level by estimating the allele frequencies of nuclear genome SNP markers through sequencing DNA pools extracted from bulked leaf material of 300 individuals per population, according to Byrne et al. (2013). Two methods were used to sequence a reduced, yet consistent, fraction of the genome: (1) genotyping by sequencing (GBS) as described by Elshire et al. (2011) in order to provide genome-wide coverage, and (2) highly multiplexed amplicon sequencing (Hi-Plex), developed by Floodlight Genomics (Knoxville, TN, USA), to target candidate genes putatively involved in the determinism of various traits (Veeckman et al., 2019). We performed GBS with the PstI restriction enzyme using population DNA pools following a protocol similar to Byrne et al. (2013). The sequencing and bioinformatics protocols are described in detail in Blanco-Pastor et al. (2019), who used the same genetic material and the same GBS assay. For the Hi-Plex sequencing, primers were designed with Primer3 (Untergasser et al., 2012) in candidate genes using previous knowledge of their sequence polymorphisms (Barre et al., 2014; Veeckman et al., 2019). A set of 185 amplicons of 80–140 bp was used (Keep et al., 2020). Population alternative allele frequencies of GBS and Hi-Plex bi-allelic SNPs were estimated using the Bayesian approach of the SNAPE-pooled script from Raineri et al. (2012). Only SNP markers with minor allele frequency >5 % in at least ten populations were retained. Eventually, variant calling delivered population alternative allele frequencies for 189 781 SNP markers distributed over 10 335 scaffolds of the perennial ryegrass reference genome sequence (Byrne et al., 2015), including 524 SNP markers in 42 candidate gene loci from the Hi-Plex sequencing. The raw genetic data (sequenced tags) are available in the NCBI Short Read Archive (SRA) database through accession SRP136600. The alternative allele frequencies per SNP locus and population are available in Keep et al. (2020).

Experimental design and protocol for in-field phenotyping

The 385 natural populations of perennial ryegrass were sown in experimental gardens at three locations: Poel Island (PO) in Germany (53.990N, 11.468E) on 8 April 2015, Melle (ME) in Belgium (50.976N, 3.780E) on 2 October 2015 and Lusignan (LU) in France (46.402N, 0.082E) on 9 April 2015. In each location, each population was sown in three 1-m2 micro-swards with 2, 4 or 6 g of seed according to whether the previously checked germination rate of the seed lot was >80 %, between 80 and 60 %, or <60 %, respectively. The seed density commonly used to sow dense perennial ryegrass meadows for forage usage is 2 g m−2 seeds of good germination quality (>80 %). Population micro-swards were arranged in three complete blocks in each location. The trials were monitored until the end of 2017 in PO and ME and until the end of 2019 in LU. Micro-swards were cut (all aerial biomass >7 cm above ground surface) regularly to simulate the common defoliation regime of meadows used for green forage production. Cutting dates (dd/mm/yy) were 16/06/15, 06/08/15, 04/09/15, 12/10/15, 04/03/16, 01/06/16, 13/07/16, 31/08/16, 26/10/16, 10/03/17, 07/06/17, 19/07/17, 01/09/17 and 13/10/17 at PO, 13/05/16, 08/07/16, 29/08/16, 13/10/16, 18/04/17, 31/05/17, 13/07/17, 24/08/17 and 04/10/17 at ME and 30/06/15, 03/09/15, 30/10/15, 04/02/16, 08/06/16, 26/07/16, 01/02/17, 13/06/17, 07/09/17, 07/06/18 and 27/08/18 at LU. Anti-dicotyledon herbicide was applied once in 2015 in each location and once again in each subsequent year if necessary. In each location, nitrogen fertilization was applied with 60 kg N ha−1 2 months after sowing and after each aerial biomass cut, and with 80 kg N ha−1 after winters 2015–16 and 2016–17 (LU, ME, PO) and winters 2017–18 and 2018–19 (LU) at the start of spring growth. The weather conditions experienced at each trial location for each season of each year are detailed in Keep et al. (2020).

Phenotypic traits

Traits related to seasonal vegetative growth potential and reproductive phenology have been pointed out as important for plant species fitness (Chuine, 2010; da Silveira Pontes et al., 2015) and climate adaptation (for perennial ryegrass; Blanco-Pastor et al., 2020; Keep et al., 2021). Such traits were recorded overall at the level of 1-m2 micro-swards (i.e. without scoring or measuring individual plants within micro-swards) and are described below.

Vegetative growth in non-stressful conditions

The first three traits described here were based on measurements of micro-sward canopy heights. Canopy height measurement is indeed commonly used as a proxy of grass sward standing biomass, with a correlation between canopy height and biomass nearing 0.8 (Viljanen et al., 2018; Borra-Serrano et al., 2019).

Spring canopy height.

The 2016 spring at ME was considered favourable for growth as soil water content remained well above wilting point and no frost day occurred during this period. Spring canopy height (SPH) was the estimated canopy height at 500 degree-days (base temperature of 0 °C) measured at ME in 2016 after the beginning of spring growth, which was the date when daily minimum temperature and incident shortwave global radiation did not fall again below 0 °C and 60 W m−2, respectively (Laboisse et al., 2018). At the chosen thermal time, vegetative growth was largely in progress for all populations but spike emergence had yet to occur. This canopy height was considered to indicate the vegetative spring growth potential of populations. When climatic conditions and soil nutrient resources are relatively non-limiting for growth, competitive capacity for light and space in the canopy, provided by early and strong spring vegetative growth, is expected to be a major adaptive feature (Aerts, 1999; Craine and Dybzinski, 2013; Keep et al., 2021).

Summer canopy height.

The 2016 summer at ME was fairly wet, with an average soil water content >50 % of field capacity, and relatively cool, with a daily mean temperature of 18.6 °C and an average daily maximum temperature of 23.7 °C. Summer canopy height (SMH) was the measure of the canopy height of micro-swards just before the cut undertaken in late summer of 2016 at ME after a 2-month growth period. This canopy height was considered to be typical of growth during a summer period with little water and thermal stresses and to indicate the growth potential of populations in summer. Low summer growth potential is a known adaptation to high summer temperatures, notably for perennial grasses (Balfourier and Charmet, 1991b; Volaire et al., 2014; Bristiel et al., 2018, Blanco-Pastor et al., 2020; Keep et al., 2021).

Yearly cumulated canopy height.

The year 2016 at ME was deemed relevant for the evaluation of biomass production potential in favourable conditions as no significant water or thermal stress occurred from spring to late autumn. The canopy height measurements preceding each cut were summed over the year 2016 and this sum was used to indicate the annual aerial biomass production potential of populations. High annual growth potential indicates high resource acquisition capacity, which is considered to be an adaptation to productive environments (Grace, 1993, Keep et al., 2021).

Winter growth score

The 2018–2019 winter at LU was the least stressful winter period encountered in the three trial sites over the duration of the experiments, with a daily mean temperature of 6.6 °C and only 12 frost days. Winter growth score (WGS) was visually scored on a scale of 1 (poorest growth) to 9 (strongest growth) at the end of this winter period at LU and was considered to be a relevant indicator of winter growth potential during a mild winter. High winter growth potential has been recognized as adaptive in mild winter climate conditions with relatively frequent summer drought for perennial ryegrass and cocksfoot (Cooper, 1964, Blanco-Pastor et al., 2020; Keep et al., 2021).

Reproductive phenology

Aftermath heading.

After the cut of the first spring wave of elongated fertile stems, the intensity of subsequently recurring fertile stem elongation was visually scored from 1 (no fertile stem) to 9 (100 % plants with fertile stems). This score was recorded in 2016 at LU. High aftermath heading (AHD) is typically found in Mediterranean populations of perennial ryegrass and reflects high investment in sexual reproduction (Balfourier and Charmet, 1991b; Barre et al., 2018). High investment in sexual reproduction agrees with a dehydration escape strategy relevant to drought adaptation, which may, however, have a negative counterpart in terms of plant persistency (Volaire, 2018; Blanco-Pastor et al., 2020; Keep et al., 2021).

Heading date.

After a vernalization period, heading date (HDT) is the date in spring when at least 20 spikes are arising at the top of tiller sheaths in a micro-sward. This date was converted into growing degree-days with a base temperature of 0 °C counting from the start of vegetative spring growth. Heading date was the adjusted mean of population heading dates in 2016 and 2017 at LU and PO. In perennial ryegrass, early spike emergence is partly physiologically and genetically correlated to early and strong spring vegetative growth, which enables competitive capacity for light and space in the canopy (Thiele et al., 2009; Barre et al., 2018; Blanco-Pastor et al., 2020; Keep et al., 2021).

Heading in first year.

This is a visual score indicating the density of fertile (spike-bearing) stems elongated during the year of sowing (i.e. without vernalization) on a scale of 1 (no fertile stem) to 9 (100 % plants with fertile stems). Heading in first year (HFY) was the adjusted mean of population scores recorded in 2015 at LU and PO. High first-year heading results from low vernalization requirements and has been found in perennial ryegrass populations from areas with mild winter and high annual rainfall (Charmet et al., 1990; Keep et al., 2021).

More details about scoring or measurement of the preceding traits can be found in Supplementary Data Methods S1. The mean values of populations for the different traits are displayed in Supplementary Data Table S2.

Climatic variables

We used a set of climatic variables inspired by the ETCCDI variables (http://etccdi.pacificclimate.org/list_27_indices.shtml). We set fine-resolution grids (0.05° longitude and latitude) over Europe and the surroundings of norms and inter-annual standard deviations of these variables for the 1989–2010 period using EURO4M-MESAN and EUMETSAT CM SAF data (Supplementary Data Methods S2). Values of norms and standard deviations at sites of origin of the studied populations were set as the values of grid cells containing these sites and are reported in Supplementary Data Table S3.

Data analyses

Preliminary analyses of variance.

An ANOVA model was applied to each phenotypic trait to assess the significance of the population effect and to compute adjusted means of populations over replicates within trial location and in some cases over trial locations (for more details see Supplementary Data Methods S1). ANOVAs were performed using the functions ‘lm’ and ‘Anova’ of the R (R Core Team, 2018) ‘car’ library. Pearson correlations between adjusted means of traits were calculated using the R ‘cor’ function.

Association between phenotypic traits and genomic markers.

The ‘GWAS’ function of the R ‘rrBLUP’ package (Endelman, 2011) was used to implement GWAS in order to assess the effect of each SNP locus on each phenotypic trait. This function uses the following mixed linear model:

y = μ + Xβ + Zu + e

In this model, y is the n length vector of values of a phenotypic trait for n populations (adjusted means from ANOVA models), µ is the intercept vector, X is the n length vector of the alternative allele frequencies of a given SNP for the n populations, β is the SNP fixed effect, Z is an incidence matrix, u is a vector of random effects with var(u) = σ2a × A [σ2a being the additive genetic variance estimated by restricted maximum likelihood (REML) and A the genomic relationship matrix] and e is the vector of residuals with σ2e, also estimated by REML. The genetic relationship matrix A was computed using the formula given by Feng et al. (2020). The genetic structure was implicitly taken into account in the mixed linear models since kinship (genetic relatedness between populations) encompasses fine and broad genetic structure. P-values were adjusted to q-values using a procedure to control the false discovery rate (Benjamini and Hochberg, 1995).

Estimates of within-population genetic diversity

The allele diversity of a genotyped SNP locus within a given population was estimated as HeSNP = 2 × p × q, where p and q are the alternative and reference allele frequencies, respectively, within the population. Perennial ryegrass being an obligate outbreeding species, HeSNP could be considered to be an estimate of the within-population frequency of heterozygotes at the genotyped locus (heterozygosity).

To estimate the average diversity of SNP loci significantly associated with a phenotypic trait in a given population, we computed a within-population trait-associated diversity criterion (HeA) as the average of HeSNP values over a set of non-redundant SNP loci found to be associated with this trait by GWAS. To define this set, we selected SNPs whose q-value was ≤0.05 and for which allele frequency information was available for at least 75 % of populations. Then, to avoid redundancy, pairs of SNP markers whose alternative allele frequency correlation was >0.4 were identified and only the SNP found to be most significantly associated with the trait by GWAS was kept. HeA was not computed for populations for which the alternative allele frequency information was missing for at least one of the SNPs of the defined set.

Establishing representative climatic indicators

A principal component analysis (PCA) was performed on norms of climatic variables and another one on their inter-annual standard deviations. PCAs were performed using the ‘PCA’ function from the R ‘FactoMineR’ package with the scale variables option set to true. The first four principal components of the PCAs on norms and on inter-annual standard deviations were used as climate indicators reporting the main spatial variations across Europe of the mean climate and of its inter-annual variability, respectively. The first four principal components of the PCA on climate norms are hereafter referred to as meanPC1 to meanPC4 (mean climate indicators) and those of the PCA on inter-annual standard deviations as stdPC1 to stdPC4 (climate variability indicators).

Modelling within-population trait-associated genetic diversity using climatic indicators

The following procedure was implemented to identify a limited number of mean climate indicators (meanPC1 to meanPC4) and climate variability indicators (stdPC1 to stdPC4) that best predicted the within-population trait-associated diversity criterion (HeA) of each phenotypic trait. We implemented the multivariate linear model of the ‘cv.glmnet’ function from the ‘glmnet’ R package, in which HeA was the dependant variable and the climate indicators were the potential explanatory variables. Quadratic effects of climate indicators were also included as potential explanatory variables in order to report non-linear relationships. The elastic net mixing parameter was set to 1 (lasso penalty) and 10-fold cross validation was applied with mean squared error (MSE) minimization as the criterion for model evaluation. The explanatory variables retained were those from the model with the highest possible value of λ (penalty multiplier) and such that the MSE was within one standard MSE from the MSE of the best tested model. This procedure was repeated 1000 times and the explanatory variables finally kept were those retained in the selected model for at least 500 of the iterations. This implementation of the ‘cv.glmnet’ function aimed to test a large amount of cross-validation sets in order to identify stable explanatory variables. Then, for each phenotypic trait, a linear regression predicting the HeA criterion from the selected climate indicators was implemented using the ‘lm’ R function. A stepwise option (‘step’ R function with option direction set to ‘both’) was included to remove redundancy in climate indicators according to the Bayesian information criterion (BIC).

RESULTS

Representative climate indicators

The percentages of inertia taken up by the first four principal components of the PCA of climatic norms (mean climate indicators) were 35, 25, 16 and 4, respectively. Components meanPC1, meanPC2, meanPC3 and meanPC4 were most correlated to norms of climatic variables related to maximum daily temperature in summer, cumulated precipitation throughout the year, daily temperature range throughout the year and daily minimum temperature in spring, respectively (Table 1, Supplementary Data Table S4).

Table 1.

Mean climate indicators and variability climate indicators. (A) Mean climate indicators (meanPC1 to meanPC4) are the first four principal components of a PCA of norms of climatic variables at sites of origin of populations, and (B) climate variability indicators (stdPC1 to stdPC4) are the first four principal components of a PCA of inter-annual standard deviations of climatic variables. ‘% inertia’ is the percentage of inertia explained by the principal component corresponding to a climate indicator. ‘Main climatic variable’ indicates the climatic variable for which the inter-annual norm (alternatively standard deviation) is most correlated to a given mean climate indicator (alternatively climate variability indicator)

(A) Mean climate indicator
meanPC1 meanPC2 meanPC3 meanPC4
% inertia 35 25 16 4
Main climatic variable (norm) Summer maximum daily temperature Cumulated precipitation throughout the year Annual daily temperature range Spring daily minimum temperature
(B) Climate variability indicator
stdPC1 stdPC2 stdPC3 stdPC4
% inertia 25 18 8 5
Main climatic variable (standard deviation) Cumulated precipitation autumn–winter Summer daily maximum temperature and evapotranspiration Number of frost days in spring Number of frost days in winter
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The percentages of inertia taken up by the first four principal components of the PCA of inter-annual standard deviations of climatic variables (climate variability indicators) were 25, 18, 8 and 5, respectively. Components stdPC1, stdPC2, stdPC3 and stdPC4 were most correlated to the standard deviation across years of climatic variables related to cumulated precipitation throughout autumn and winter periods, daily maximum temperature and evapotranspiration in summer, number of frost days in spring and number of frost days in winter, respectively (Table 1, Supplementary Data Table S4).

GWAS and estimation of within-population trait-associated diversity

Alternative allele frequency information was missing for 7 % of genotyped SNP loci per population on average with a maximum of 37%. Using all genotyped SNPs, the average correlation between allele frequencies of pairs of SNPs whose distance was ≤10 000, 1000, 100 and 10 bp equalled 0.19, 0.22, 0.26 and 0.32, respectively. When using only pairs of SNPs from a scaffold in which significant SNPs were detected by GWAS, it equalled 0.3, 0.35, 0.42 and 0.48, respectively.

The number of SNP loci detected by GWAS that were used to estimate the within-population trait-associated diversity criterion (HeA) varied from 2 for yearly cumulated canopy height to 17 for aftermath heading. The number of populations for which HeA could be computed varied from 125 for summer canopy height to 316 for yearly cumulated canopy height (Table 2).

Table 2.

Computation of the within-population trait-associated diversity criterion (HeA) for the studied traits. ‘Number of SNPs’ is the number of uncorrelated SNP loci found to be associated with a trait by GWAS and used to compute the HeA criterion of this trait. ‘Number of populations’ is the number of populations for which the HeA criterion of a trait could be computed (HeA could not be computed for a population if the alternative allele frequency information was missing for at least one SNP locus involved in the computation)

Trait Abbreviation Number of SNPs Number of populations
Aftermath heading AHD 17 148
Heading date HDT 16 150
Heading in first year HFY 11 183
Spring canopy height SPH 7 217
Summer canopy height SMH 9 125
Winter growth score WGS 9 131
Yearly cumulated canopy height YCH 2 316
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Association of trait means and trait-associated diversity criteria with climate indicators

The values of correlations between population trait means and mean climate indicators are displayed in Table 3 and the values of correlations of within-population trait-associated diversity criteria (HeA) with mean climate indicators and climate variability indicators are displayed in Table 4. Regarding the correlations between population trait means and mean climate indicators, aftermath heading, heading in first year, summer canopy height and winter growth were most correlated to meanPC1 and heading date and spring canopy height to meanPC2, and yearly cumulated canopy height was most correlated to meanPC3. Regarding the correlations between HeA criteria and mean climate indicators, the HeA criteria of aftermath heading, heading in first year and summer canopy height were most correlated to meanPC1 and those of heading date, spring canopy height and yearly cumulated canopy height to meanPC2, and that of winter growth score was most correlated to meanPC3. Regarding the correlations between HeA criteria and climate variability indicators, the HeA criteria of heading in first year, spring canopy height and yearly cumulated canopy height were most correlated to stdPC1 and those of summer canopy height and aftermath heading to stdPC2, that of winter growth score was most correlated to both stdPC1 and stdPC2, and that of heading date to stdPC3. Figure 1 displays the spatial distribution of the HeA criterion of heading in first year across Europe and shows that this criterion has its lowest values in north-eastern Europe, where inter-annual precipitation variability is relatively low.

Table 3.

(A) Correlations between population trait means and mean climate indicators at sites of origin of populations. Trait abbreviations are explained in Table 2. (B) 90, 95, 99 and 100 % quantiles of the absolute values of the correlations between population alternative allele frequencies at the 189 781 genotyped SNP loci and mean climate indicators. Since most genotyped SNP loci are expected to be neutral, the quantile values are expected to report the distribution of correlations of alternative allele frequencies at neutral loci with mean climate indicators

Mean climate indicator
Trait/quantile meanPC1 meanPC2 meanPC3 meanPC4
(A) AHD 0.47 −0.11 0.44 0.1
HDT −0.12 −0.34 −0.23 0.05
HFY 0.27 0.19 0.11 0.06
SPH 0.14 0.23 0.13 0.09
SMH −0.27 0.21 −0.27 −0.01
WGS 0.52 −0.06 0.44 0.06
YCH −0.12 0.24 −0.24 0.06
(B) 90 % 0.24 0.26 0.23 0.18
95 % 0.29 0.31 0.28 0.22
99 % 0.37 0.4 0.36 0.28
100 % 0.58 0.56 0.54 0.43
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Table 4.

(A) Correlations of within-population trait-associated diversity criterion (HeA) with mean climate and climate variability indicators at sites of origin of populations. Trait abbreviations are explained in Table 2. (B) 90, 95, 99 and 100 % quantiles of the absolute values of the correlations between allele diversity (HeSNP) at the 189 781 genotyped SNP loci and the mean climate and climate variability indicators. Since most genotyped SNP loci are expected to be neutral, the quantile values are expected to report the distribution of correlations of allele diversity at neutral loci with mean climate indicators

Climate indicator
Mean climate indicator Climate variability indicator
HeA/quantile meanPC1 meanPC2 meanPC3 meanPC4 stdPC1 stdPC2 stdPC3 stdPC4
(A) HeA_AHD 0.38 0.08 0.34 0 0.24 0.37 −0.13 0.04
HeA_HDT −0.19 0.2 −0.05 −0.06 0.06 −0.14 0.31 0.21
HeA_HFY 0.46 0.29 0.17 −0.13 0.54 0.18 −0.15 −0.1
HeA_SPH 0.11 0.27 0.07 0 0.31 0.01 0.12 0.12
HeA_SMH 0.39 −0.04 0.34 0.01 0.22 0.44 −0.26 −0.23
HeA_WGS 0.33 0.25 0.41 0.02 0.41 0.44 −0.01 −0.03
HeA_YCH 0.08 0.48 −0.06 −0.24 0.47 −0.14 0.17 0.11
(B) 90 % 0.23 0.24 0.22 0.18 0.28 0.26 0.16 0.18
95 % 0.27 0.29 0.27 0.21 0.34 0.31 0.2 0.21
99 % 0.35 0.37 0.35 0.27 0.45 0.4 0.27 0.28
100 % 0.56 0.57 0.54 0.43 0.68 0.63 0.44 0.41
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For a genotyped SNP locus and a population, HeSNP = 2 × p × q, where p and q are the alternative and reference allele frequencies, respectively, within the population.

Fig. 1.

Fig. 1.

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Spatial distribution across Europe of the within-population trait-associated diversity criterion of heading in first year (i.e. heading without vernalization requirements) (HeA_HFY) computed for 183 natural populations of perennial ryegrass. The pairing of dot colours and HeA_HFY ranges is given in the inset.

Neutral evolutionary forces (migration, drift, mutation) may generate a spatial distribution of neutral diversity that results in a large number of significant correlations between allele frequency (or diversity) at neutral SNP loci and climatic indicators. Taking into account that most genotyped SNP loci are expected to be neutral, a relatively high correlation between a population trait mean (alternatively an HeA criterion) and a climatic indicator was assumed to reveal natural selection only if the absolute value of this correlation was higher than the highest absolute values of correlations found between the allele frequency (alternatively diversity) at most genotyped SNP loci and this climatic indicator. All population trait means (except that of spring canopy height) were better correlated (in absolute value) to at least one climate mean indicator than the allele frequency at 90 % of genotyped SNPs (Table 3). The HeA criterion of each trait was better correlated (in absolute value) to at least one climate mean indicator and one climate variability indicator than the allele diversity (HeSNP) at 90 % of genotyped SNPs (Table 4).

Prediction of within-population trait-associated diversity criteria (HeA) from mean climate and climate variability indicators

Models predicting HeA criteria of studied traits from mean climate and climate variability indicators are described in Table 5. The coefficient of determination of models varied from 0.09 for HeA of spring canopy height to 0.38 for HeA of winter growth score. According to traits, the set of predictors included one or two climate variability indicators and from zero to two mean climate indicators. Scatterplots between HeA criteria and the most significant climate indicators predicting them are displayed in Fig. 2. The correlation between an HeA criterion and the most significant climate indicator predicting it varied from 0.31 (between HeA of spring canopy height and stdPC1) to 0.54 (between HeA of heading in first year and stdPC1). The most significant explanatory variable of the HeA criterion of aftermath heading, summer canopy height and yearly cumulated canopy height was a mean climate indicator; nevertheless, at least one climate variability indicator was also found among the explanatory variables of each of these HeA criteria (Table 5). For heading in the first year, spring canopy height, heading date and winter growth score, the most significant explanatory variable of the HeA criterion was a climate variability indicator; nevertheless, at least one mean climate indicator was also found among the explanatory variables of the HeA criterion of heading date and heading in the first year (Table 5). The mean climate indicator meanPC1 was a predictor of the HeA criterion of aftermath heading, heading in first year and summer canopy height, and meanPC2 was a predictor of the HeA criterion of heading date and yearly cumulated canopy height. The climate variability indicator stdPC1 was a predictor (positive effect) of the HeA criterion of heading in first year, spring canopy height, yearly cumulated canopy height and winter growth score; stdPC2 was a predictor (positive effect) of the HeA criterion of aftermath heading, summer canopy height and winter growth score; and stdPC3 was a predictor (positive effect) of the HeA of heading date.

Table 5.

Linear models that best predict the within-population trait-associated diversity criterion (HeA) of studied traits from mean and climate variability indicators. The climate indicators included in each model are displayed as well as their estimated effect in the model (Estimate) and the P-value of their t-test (Pr > |t|). The coefficient of determination (R2) of the model is also displayed. Trait abbreviations are explained in Table 2

HeA criterion Regression model Model R2
HeA_AHD Intercept meanPC1 stdPC2 0.22
Estimate 1.2e−01 7.0e−04 7.8e−04
Pr > |t| 1.5e−112 9.4e−05 1.8e−04
HeA_HDT Intercept stdPC3 meanPC2 0.15
Estimate 1.5e−01 1.7e−03 −6.1e−05
Pr > |t| 2.1e−99 1.4e−04 1.4e−03
HeA_HFY Intercept stdPC1 meanPC1 0.34
Estimate 7.0e−02 1.5e−03 9.3e−04
Pr > |t| 8.9e−76 2.4e−08 1.4e−04
HeA_SPH Intercept stdPC1 0.09
Estimate 7.7e−02 1.2e−03
Pr > |t| 2.2e−79 1.0e−05
HeA_SMH Intercept meanPC1 stdPC2 meanPC12 0.28
Estimate 7.9e−02 5.8e−04 8.9e−04 2.4e−05
Pr > |t| 1.1e−61 1.5e−03 2.2e−03 2.4e−02
HeA_WGS Intercept stdPC2 stdPC1 0.38
Estimate 1.2e−01 1.6e−03 1.3e−03
Pr > |t| 9.8e−98 8.0e−10 5.3e−09
HeA_YCH Intercept meanPC2 stdPC1 0.25
Estimate 8.2e−02 1.9e−03 1.6e−03
Pr > |t| 1.0e−75 1.5e−03 5.3e−03
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Fig. 2.

Fig. 2.

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Within-population trait-associated diversity criterion (HeA) of each studied trait (ordinate) plotted against the most significant climate indicator (abscissa) in the best multiple regression model predicting this criterion. (A) Aftermath heading (AHD), (B) heading date (HDT), (C) heading in first year (HFY), (D) summer canopy height (SMH), (E) spring canopy height (SPH), (F) winter growth score (WGS) and (G) yearly cumulated canopy height (YCH). meanPCi and stdPCi are the ith principal components of the PCAs of norms of climatic variables and of inter-annual standard deviations of climatic variables, respectively. The Pearson correlation coefficient (r) between the HeA criterion and the climate indicator is displayed as well as the percentage of variance explained by the climate indicator (partial R2 estimated by subtracting the coefficient of determination of the complete model without the targeted climate indicator from the one of complete model including the targeted climate indicator). The coefficient of determination of the complete model (with all retained climate indicators) is also displayed (model R2). The red diamonds represent the regression line or curve (polynomial of degree 2) of the within-population trait-associated diversity criterion (HeA) on the climate indicator when all other climate indicators in the multiple regression model are set to their mean values.

Discussion

Oligogenic adaptation

Application of GWAS to mean phenotypes and allele frequencies of populations has already been successfully implemented to discover QTLs, notably in perennial ryegrass (Byrne et al., 2013; Fè et al., 2015; Ashraf et al., 2016; Keep et al., 2020). This approach has been made possible by next-generation sequencing (NGS) technologies, which enable the sequencing of pools of DNA from a number of individuals per population, thus obtaining allele frequencies at numerous SNP loci (Byrne et al., 2013). Keep et al. (2020) showed that GWAS accounting for kinship was able to detect major QTLs when applied to mean phenotypes and allele frequencies of a set of natural populations of perennial ryegrass. With the GWAS significance level threshold we applied, no more than 17 uncorrelated SNP markers were found to be associated with a given phenotypic trait. These significant markers were assumed to be in linkage disequilibrium with as many different QTLs. The slower linkage disequilibrium decay in scaffolds containing SNPs detected by GWAS compared with that observed on average across all scaffolds suggests that genetic hitchhiking and selective sweep may have been at play in the neighbourhood of adaptive QTLs (Fay and Wu, 2000; Storz, 2005). The small number of SNPs found significantly associated with any trait could be expected under the assumption that these traits are likely of some importance for climatic adaptation. This assumption is supported by the correlations between population trait means and mean climate indicators reported in Table 3 which are consistent with adaptive trends already known in perennial ryegrass (see Material and methods, Phenotypic traits). It has been pointed out that, under directional selection, only a few large-effect adaptive QTLs can present patterns of allelic differentiation that contrast with the genome-wide stochastic background (Storz, 2005). Thus, GWAS based on natural diversity which correct for genome-wide genetic relatedness can be expected to detect loci that are in linkage disequilibrium with large-effect adaptive QTLs. The theory of adaptation based on oligogenic variation (Bell, 2010) states that adaptation to a changing environment is primarily led by change in allele frequency at a limited number of large-effect adaptive loci, despite the strong likelihood that quantitative traits are affected by many loci. It can thus be expected that beneficial alleles of large-effect QTLs are those that can experience the fastest increase in frequency under environmental selection pressure and thus are likely to spearhead species adaptation. The population frequencies of such alleles are thus also the most likely to be affected by fluctuating directional selection (Bell, 2010). On the other hand, small-effect alleles that are weakly selected are expected to increase in frequency more slowly and are vulnerable to disappearance through drift (Rieseberg et al., 2004).

Influence of local mean climatic conditions and inter-annual climate variability on within-population allele diversity at adaptive loci

The most significant predictor of within-population trait-associated diversity (HeA) was a mean climate indicator for three traits (aftermath heading, summer canopy height, yearly cumulated canopy height), whereas it was a climate variability indicator for the four remaining traits (heading date, heading in first year, spring canopy height, winter growth score) (Table 5). The HeA criterion of a putatively adaptive trait can be primarily correlated to a mean climate gradient if the range of phenotypic values (and of allele diversity of underlying QTLs) that allows for sufficient adaptation is wider at one end of the gradient than at the other end. Meanwhile, climatic conditions at a given site can fluctuate over different time scales, i.e. inter-seasonal variations, relatively stochastic inter-annual variations and climate change over longer time spans. The intensity and direction of selection imposed by climatic constraints can thus fluctuate accordingly over time. Given that selection is expected to primarily act on oligogenic diversity, which enables rapid adaptive evolution, fluctuating selection over time may lead to fluctuating allele frequencies at main adaptive QTLs (Bell, 2010). Thus, the probability of fixation of adaptive alleles may be inversely related to the level of temporal variability of the climatic constraint. This can explain why a climate variability indicator was the most significant predictor of within-population trait-associated diversity for some of the studied putatively adaptive traits (heading date, heading in first year, spring canopy height, winter growth score) and was also one of its significant predictors for the three other traits (aftermath heading, summer canopy height, yearly cumulated canopy height).

Ecological concepts could be called upon to explain the relationships between HeA trait-associated diversity criteria and climate variability indicators (Tables 3 and 4). The negative correlation of summer canopy height with meanPC1 (maximum daily temperature in summer) and the positive correlation of its within-population trait-associated diversity criterion with both meanPC1 and stdPC2 (standard deviation across years of daily maximum temperature in summer) suggest that fluctuating selection driven by ecological trade-offs has been at play. In areas where summer maximum temperatures are high on average but variable across years, individuals with a limited summer growth potential are likely best adapted to summers during which heat waves occur (protection of sensitive vegetative tissues), whereas other individuals with greater summer growth potential are best adapted to cooler summers due to their greater ability to intercept light and occupy canopy space. However, the impact of heat waves on the fitness of the second kind of individuals may be limited if seed production is sufficiently advanced before the stress period. Conversely, populations from areas where summers are consistently cool enough for substantial growth can include only light-competitive plants with great summer growth potential. A similar result was reported in a study of inter-specific diversity in a Californian grassland, in which the smaller species gained a competitive advantage during heat wave periods (Hallett et al., 2019). The positive correlation of aftermath heading with meanPC1 and the remarkably positive correlation of its HeA criterion with both meanPC1 and stdPC2 can similarly be explained by fluctuating selection. During particularly hot summers when vegetative growth is near null, recurrent production of seeds favours fitness with the counterpart of reduced persistency. On the other hand, during cool summers, plants that produce relatively few reproductive tillers can accumulate resources that contribute to the preservation of vegetative tillers and thus give competitive advantage and better fitness. The high allele diversity at isozyme loci found in perennial ryegrass populations from southern Europe (with likely high meanPC1) by Balfourier et al. (1998) could also possibly be explained by fluctuating selection mechanisms.

The within-population trait-associated diversity of vegetative growth in winter (WGS), spring (SPH) and throughout the year [yearly cumulated canopy height (YCH)] in non-stressful conditions was positively correlated to stdPC1 (standard deviation across years of spring and autumn precipitation) (Tables 3 and 4). When water availability is not limiting for canopy growth, features that improve competitive ability for light capture, such as early and strong growth (e.g. substantial winter growth and high spring growth) may increase the ability to gain canopy space (Craine and Dybzinski, 2013). On the other hand, when water availability is limiting for canopy growth, features providing greater tolerance to water deficit and greater competitive ability to extract water can improve adaptation at the expense of aerial vegetative growth (Grace, 1990, 1993).

Variability of ability to flower without vernalization requirements (HFY), of earliness of spike emergence (HDT), of AHD and of vegetative growth seasonality and potential (WGS, YCH, SPH, SMH) provides means to tune the timing of sexual reproduction, seed germination, juvenile development and vegetative organ growth in order to optimize phenology and size differences among competitors. Such strategies can be assigned to size-mediated priority effects, or early-arriver advantages, and play an essential role in the outcome of competitive interactions (Rasmussen et al., 2014; Rudolf and McCrory, 2018; Rudolf, 2019). Chase (2010) stated that local biodiversity should be large in environments with high production potential, such as areas with high annual rainfall, because of stochastic community assembly and interactions between competitors. Our results appear to corroborate this assumption since HeA was positively correlated to meanPC2 (cumulated precipitation throughout the year) for heading in first year, spring canopy height, winter growth score and yearly cumulated canopy height. Comparable results were reported by an investigation of relationships between rice diversity and environmental factors in a Chinese province (Cheng-yun, 2010). Moreover, fluctuating climatic conditions, such as variable inter-annual precipitation, can differently affect the demographic rates of various competitors and thus can lead to variable seasonal timing of their interactions and thus to fluctuation of competition pressures (Chesson, 1994; Hallett et al., 2019). This can result in fluctuating selection for phenological traits and for timing of vegetative growth and thus lead to local conservation of causal genetic diversity of these features (Domínguez and Dirzo, 1995; Bell, 2010). Such mechanisms could, for instance, contribute to the significant positive contribution of stdPC1 (spring and autumn precipitation variability) in the prediction of HeA for heading in the first year, spring canopy height, yearly cumulated canopy height and winter growth score.

Perennial ryegrass was shown to originate from the Mediterranean and Balkan areas (Blanco-Pastor et al., 2019). It has likely experienced more extreme and fluctuating climate conditions in these areas than in others due to its longer time of presence. Fluctuating directional selection has thus likely occurred there in this species at long and short time scales (Bell, 2010). Blanco-Pastor et al. (2019) also pointed out that perennial ryegrass diversity from these areas experienced introgressions from more northern regions in the late stages of the species’ expansion. Both long-term fluctuating selection and introgression events may have contributed to the relatively high within-population trait-associated diversity found in populations from southern Europe (where meanPC1 is highest) for aftermath heading, heading in first year and summer canopy height. Other studies of perennial ryegrass natural populations from various areas also found that within-population genetic (isozyme) diversity (Balfourier et al., 1998) and regional phenotypic variability (Casler, 1995) were substantially high around the Mediterranean Basin.

Local natural populations of grassland species are threatened by climate change and will likely need to evolve quickly so as to remain adapted to their environment (Henkin et al., 2010; Schierenbeck, 2017). Substantial adaptive genetic diversity present in these populations could improve their chances of future adaptation. The capacity of a grassland species population to adapt to the changing climate may notably depend on the level of past inter-annual stochastic local climatic variability. The results presented here corroborate the hypothesis that such variability induces fluctuating directional selection which contributes to the maintenance of a reserve of adaptive diversity (Herben et al., 2003).

SUPPLEMENTARY DATA

Supplementary data are available online at https://academic.oup.com/aob and consist of the following. Table S1: list of the perennial ryegrass natural populations in the study, including information on collection sites and gene banks maintaining seed samples of these populations. Table S2: trait means of perennial ryegrass natural populations. Table S3: values of climatic variables at sites of origin of perennial ryegrass natural populations. Table S4: correlations between climatic variables and the first four principal components of PCAs performed on the norms and on standard deviations of climatic variables. Methods S1: description of traits characterizing populations, computation of population trait means and trait summary statistics. Methods S2: description of climatic variables characterizing sites of origin of natural populations of perennial ryegrass.

mcab057_suppl_Supplementary_Materials_S1
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mcab057_suppl_Supplementary_Materials_S2
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mcab057_suppl_Supplementary_Materials_S3
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mcab057_suppl_Supplementary_Materials_S4
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mcab057_suppl_Supplementary_Materials_S5
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mcab057_suppl_Supplementary_Materials_S6
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Funding

This work was supported by grants awarded to the project GrassLandscape (2014 FACCE-JPI ERA-NET+ call Climate Smart Agriculture) from the European Community (grant agreement number 618105), the Agence Nationale de la Recherche (ANR) and the Institut National de la Recherche Agronomique (metaprogramme ACCAF) in France, the Biotechnology and Biological Sciences Research Council (BBSRC) in the UK and the Bundesantalt für Landwirtschaft und Ernährung (BLE) in Germany, and by a grant awarded to T.K. from the French administrative region Nouvelle-Aquitaine.

Acknowledgments

The authors thank the curators of the gene banks that provided perennial ryegrass seed samples for the needs of the project and staff from European agronomic research institutes who contributed to in situ collections in 2015. Perennial ryegrass is one of the plant species covered under the Multilateral System of the International Treaty on Plant Genetic Resources for Food and Agriculture. All genetic materials used in this study were made available to the authors after signature of a Standard Material Transfer Agreement (SMTA) by the provider and the recipient. Implementation and signature of an SMTA provides compliance with the provisions of the Nagoya Protocol for parties wishing to provide and receive genetic material under the Multilateral System. The authors thank the technical staff of IBERS, ILVO, INRAE and IPK involved in the project. Climate data were processed by Milka Radojevik and Christian Pagé (CECI, Université de Toulouse, CNRS, CERFACS, https://cerfacs.fr) from EURO4M-MESAN and EUMETSAT CM SAF grids. The authors declare no conflict of interest. T.K. with support from J.P.S. conceived the reported investigation and wrote the manuscript. S.R. and J.L.B.-P. contributed to the main conceptual ideas and to the interpretation of the results. F.S. and R.V. recorded trait data. J.P.S., T.R., P.B., K.J.D., M.H., I.L., H.M., I.R-R. and E.W. were involved in planning and supervising the project. T.L., J.L.B-.P., T.R. and T.K. processed the experimental data. All authors provided critical feedback and helped shape the research and manuscript.

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