Diversity of ecotypes of five species of ryegrass from Northwestern Spain by phenotypic traits and microsatellites

Abstract

Background

The Agricultural Research Centre of Mabegondo (Xunta de Galicia, A Coruña, Spain) conserves one of the most important collections of phytogenetic resources of ecotypes and natural populations of grassland species from northwestern Spain, among them populations of ryegrass (Lolium spp.), one of the most cultivated forage grasses in the world. The objective of the present study was to evaluate the diversity among commercial cultivars and natural ryegrass populations with phenotypic traits and molecular markers.

Results

Eleven polymorphic microsatellites loci were used to analyze 58 ecotypes and 10 cultivars (680 DNA samples in total) differentiating 673 genotypes. Two main groups were detected by the Structure analysis, one related to Lolium perenne and a second to Lolium multiflorum. The first group showed two subgroups and the second three. The cluster of L. multiflorum showed two subgroups not related with the third cluster including commercial varieties, one from the Canary Islands (with Lolium rigidum included) and a second one from northwestern Spain, which presented specific agromorphological characteristics, such as lower FES (number of days from 1 January, when three heads per plant were flowering per plot), CRE (growth in flowering, in g of dry matter), and AIN (number of inflorescences per plant).

Conclusions

This is the first time that a large amount of data on ryegrass from the Iberian Peninsula has been analyzed, obtaining a clear genetic differentiation of the autochthonous varieties from the commercial varieties analyzed. In addition, the genetic structure found in the ecotypes was related to the phenotypic variation analyzed. Being of interest in the conservation of biodiversity and in obtaining better adapted varieties of ryegrasses, due to their specific phenotypic traits, such as a lower FES, CRE and AIN.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12870-024-05440-7.

Background

Ryegrasses (Lolium spp.) are herbaceous plants used in pastures for forage, silage, and for lawns, producing large quantities of biomass with good nutritional quality and palatable to livestock. Some of these species also contribute to control soil erosion and the establishment of plant covers. Italian ryegrass (L. multiflorum Lam.), the most widely grown grass used as forage in Spain, is a therophyte/hemicryptophyte whose cultivation is widely distributed across the world in temperate and subtropical regions, and was domesticated in the region of Lombardy (Italy) in the 13th century. English ryegrass (L. perenne L.) is a perennial species, which forms dense root balls, that shows greater resistance to trampling, less growth in height, and great capacity for tillering; it is the most widely used grass in temperate zones for the establishment of long-term grasslands, being ideal for use in grazing. Along with Italian ryegrass, it is the main crop for grazing and silage in areas with mild temperate-humid climates such as Western Europe.

Galicia is a community where the agricultural and livestock industry is an important pillar in the economy, production, and transformation of products at the local level. In Galicia, herbaceous pastures and forage crops exceed 350,000 hectares, of which nearly 300,000 are cultivated forage corn, multispecies forage crops and Italian ryegrass [1]. Of the 6,000 tons of multispecies forage crops and Italian ryegrass seeds, 5,000 tons correspond to ryegrass (English and Italian ryegrass), being the most cultivated varieties in northern Spain for the establishment of multispecies forage crops. Almost all grass seeds cultivated in Spain are imported, except for Italian ryegrass, of which more than half of the seed is produced in Spain. In recent years the fall in milk prices and the rise in the cost of fertilizers and fuels have prompted many farms to adopt a more efficient use of their own resources to ensure agricultural profitability, including the use of genetic material that is well adapted to local conditions. Therefore, breeders and germplasm banks play a fundamental role in contributing to the stability of agricultural incomes and ensuring the competitiveness of farms.

Ryegrass seeds conserved in the Germplasm Bank of the Agricultural Research Centre of Mabegondo (CIAM), have been continually requested by farmers and researchers for production and breeding purposes, making it necessary to characterize the natural populations of ryegrass both to know their agronomic potential and for their utilization in plant breeding, as well as for the possible obtention of commercial varieties with an autochthonous genetic base more adapted to the Galician edaphoclimatic conditions. Between 1985 and 1997, a collection of English ryegrass (Lolium perenne L.) and Italian ryegrass (Lolium multiflorum Lam.) was carried out [2, 3]. For more than 40 years, different research works have been carried out at CIAM on ecotypes of Lolium spp. [4–8]. Thus, several Lolium cultivars were obtained from previous evaluations of Galician ecotypes conserved at CIAM, such as the L. perenne ‘Brigantia’ and ‘Ciami’ and the L. mutiflorum ‘Pomba’ cultivars [9]. Agromorphological studies, based on the determination of agronomic characters previously described [1, 6, 8, 10], and morphological analyses following UPVO descriptors for ryegrass [11], were carried out to characterize the germplasm to study for possible agricultural, industrial, or environmental use.

Previous studies have used microsatellite markers (SSRs) to analyse the genetic variability of ryegrass populations form different regions of the world, including the USA, Iran, Europe, Australasia, Japan and Saudi Arabia [12–18]. Moreover, genetic maps [12, 19, 20] provided a high number of polymorphic SSRs to evaluate the genetic diversity in inbreeding and outbreeding Lolium species and relationships between species, genome mapping, quantitative trait loci analysis, the ryegrass germplasm management and cultivar identification.

The extensive Lolium collection at CIAM raised the objective of this study, which was the agronomic and genetic evaluation of 58 ecotypes with 10 commercial cultivars using 9 agromorphological traits and 11 SSR molecular markers.

Methods

Plant material

We evaluated 68 ecotypes and cultivars of Lolium spp.: 23 ecotypes and 5 commercial cvs. of L. multiflorum Lam.; 29 ecotypes and 5 commercial cvs. of L. perenne L.; 2 ecotypes of L. canariense Steud.; 3 ecotypes of L. rigidum Gaudin; and 1 of L. temulentum L. Ecotypes were collected at the Agricultural Research Centre of Mabegondo between 1985 and 2000. Commercial cvs. were ‘Campivert’ ‘Litonio’ ‘Pomba’ ‘Promenade’ and ‘Vallivert’ of L. multiflorum; and ‘Barsintra’ ‘Brigantia’ ‘Barforma’ ‘Ciami’ and ‘Maraeko’ of L. perenne. Ten seedlings for each ecotype and ten for a commercial cultivar were evaluated independently by sampling their leaves. A total of 673 plant DNA samples were tested (amplification failed for 7 samples).

Phenotypic traits

A field trial was established at Centro de Investigacións Agrarias de Mabegondo (Xunta de Galicia) in Mabegondo, A Coruña, in northwestern Spain (43º 14’ 31.88”N, 8º 16’ 03.59”W). Soil preparation consisted on ploughing, milling, and the installation of anti-weed mesh, without any compost or fertilizer or liming added. In July 2018, 58 ecotypes and 10 commercial varieties (‘Brigantia’ was not included for prioritizing conservation in the bank until their multiplication) were grown in seed trays in greenhouse. In September the plants were transplanted into the field trial in plots of 40 × 40 cm randomized complete blocks design (Fig. 1).

Fig. 1.

Ryegrass growing in the greenhouse (A) and 40 × 40 cm plots in the experimental field (B)

For phenotypic characterization, the evaluation protocol was based on the International Union for the Protection of New Varieties of Plants (UPOV) [11]. For each population and commercial cultivar, the following descriptors were evaluated (three samples per plot) for 67 ecotypes/cultivars (data not available for ‘Brigantia’ for prioritizing seed conservation in the bank until their multiplication): FES: number of days from 1 January 2019, when three heads per plant were flowering per plot; CRI: growth at the end of the winter in g of dried matter (DM); CRP: growth at the end of the spring in g of DM; CRE: growth in flowering, in g of DM; FLW: flag leaf width in flowering (cm); FLL: flag leaf length in flowering (cm); AIN: number of inflorescences; HAB: growth habit in early spring before flowering, on a visual scale from 1 to 9, with 1 = prostrate to 9 = erect; ENF: tolerance to pests and diseases on a visual scale from 1 to 9, with 1 = sensitive to 9 = resistant; and APH, average plant height in flowering (cm). Additionally, altitude (ALT) was recorded for the origin of the samples.

For statistical analyses, a fixed-effects ANOVA was performed for each variable according to the following models: a) to test the differences between L. perenne and multiflorum, Xim = µ + RPPi + ∈im; where Xim is the observation of the species (L. perenne and multiflorum) i (i = 1,2) and the ecotype/cultivar m (m = 1 to 67); µ is the mean of all the observations; RPPi, and ∈im are the effects of the RPPi, and the error associated to the ecotype/cultivar m in the observation im, respectively.

b) to test the phenotypic differences between ecotypes/cultivars grouped in RRPs: Xim = µ + RPPi + ∈im; where Xim is the observation of the RPP and admixed genotypes i (i = 1 to 3 for K = 2 and i = 1 to 6 with substructure) and the ecotype/cultivar m (m = 1 to 67); µ is the mean of all the observations; RPPi, and ∈im are the effects of the RPPi, and the error associated to the ecotype/cultivar m in the observation im, respectively.

Factorial Component Analysis (FCA) was performed using the method of Principal Components in SPSS V.22. Principal components (PCs) were estimated on the correlation matrix of the standardized variables and the first three PCs were used to capture the most variation in the data.

Microsatellites

DNA extraction was carried out using 0.5–0.75 g of young leaves and using the “DNeasy^® Plant Mini Kit” (Qiagen, Hilden, Germany) for Phase I (280 DNA samples) and “E.Z.N.A.^®Plant DNA Kit” (OMEGA Bio-Tek Inc., Norcross, GA, USA) for Phase II (400 DNA samples). Genomic DNA was quantified by Nanodrop^™ ND-1000 Spectrophotometer (Thermo Scientific, Wilmington, DE, USA) and diluted to 20 ng/µL.

Eleven microsatellites from previous studies [20] were selected for these analyses (Table S1). The eleven simple sequence repeats (SSRs), were amplified in three multiplexed PCR using one of the FAM, NED, PET, VIC fluorophore-labelled primers (PE Applied Biosystems, Warrington, UK).

The amplification conditions were 94 °C for 5 min, followed by 35 cycles at 95 °C for 30 s, annealing at a specific temperature depending on the multiplex set, for 90 s, and 1 min at 72 °C, and final extension at 60 °C for 30 min.

Amplification products were diluted with water, and 2 µL of the diluted amplification product was added to 0.12 µL of 600LIZ size standard (Applied Biosystems, Foster City, CA, USA) and 9.88 µL of formamide. The allele sizes were detected using Peak Scanner TM software (Applied Biosystems).

A Bayesian analysis was performed with the Structure software [21, 22] by using the admixture model with unlinked loci and correlated allele frequencies, as defined in Fernández-Otero et al. [23] and Porras-Hurtado et al. [24], recommending a minimum of 20 iterations (30 in this study) to estimate the ancestry membership proportions of a population. We computed K = 1 to 15 unknown reconstructed panmictic populations (RPPs) of genotypes, with the options use popinfo = 0, popflag = 0, which considers that the sampled genotypes were of unidentified origin, assigning them probabilistically to RPPs based on a qI (probability of membership) of 80%, while a lower probability meant an admixed genotype. The second order change of the likelihood function, divided by the SD of the likelihood (ΔK), was also estimated to find the best K value supported by the data [25] by using Structure Harvester [26]. The inbreeding coefficient (Fis) [27] was calculated in the program GenoDive [28].

Similarity relationships among the samples were studied using multivariate analysis techniques. For each population and commercial cv. (a minimum of 8 samples were evaluated, but mostly were 10 samples), the frequency of each allele was assigned to a variable, with values 1, and 0 for presence and absence of the allele, respectively. Factorial Component Analysis (FCA) was performed using the method of Principal Components in SPSS V.22. Principal components (PCs) were estimated on the variance–covariance matrix of the allele frequencies [29–31]. Graphical representations of the PCs included the genetic structure and substructure based on SSRs.

Results

Phenotypic variation

Phenotypic variation was as follows (average and range): FES, 119.0 days from 1 January (71.4–151.8); CRI, 16.5 of growth at the end of the winter in g of DM (5.3–31.2); CRE, 102.1 of growth in flowering after taking the annotation of flowering date (19.0–316.0); HAB, 3.0 of growth habit (visual scale from 1 to 9) in early spring before flowering erect (1.9–4.2); ENF, 4.7 of tolerance (visual scale from 1 to 9) to pests and diseases (2.0–8.4); and APH, 79.9 of average plant height (cm) in flowering (47.9-115.8) (Table 1). Average value of the altitude for the 58 ecotypes was 414.22, with a minimum of 8 and a maximum of 1300 msal.

Table 1.

Phenotypic traits for 67 (N) ecotypes and commercial cvs. (data not available for ‘Brigantia’), average values for the main two species (L. Perenne and L. multiflorum) and RPPs obtained by the structure software for K = 2 (1 and 2). Significance (P) for the 2 main species and the 2 RRPs by ANOVA. Ns, non significant

Trait	N	Minimum	Maximum	Average	Average, L. perenne	Average, L. multiflorum	P for the 2 species	Average, RPP1	Average, RPP2	P for RPPs
FES	67	71.4	151.8	119.0	130.3	110.4	0.001	128.7	110.3	0.01
CRI	67	5.3	31.2	16.5	16.2	16.6	ns	16.0	17.2	ns
CRP	67	2.5	81.2	31.3	36.8	26.0	0.05	36.2	24.2	ns
CRE	67	19.0	316.0	102.1	122.7	89.2	0.05	118.5	79.9	ns
FLW	67	0.3	0.9	0.6	0.5	0.7	0.001	0.53	0.67	0.01
FLL	67	6.3	20.2	13.7	13.0	14.6	0.05	12.9	14.3	0.05
AIN	67	37.6	155.6	86.1	95.0	79.9	0.05	93.1	75.2	0.01
HAB	67	1.9	4.2	3.0	3.0	3.0	ns	3.0	3.0	ns
ENF	67	2.0	8.4	4.7	4.8	4.8	ns	4.8	4.5	ns
APH	67	47.9	115.8	79.9	78.8	80.2	ns	84.2	78.5	ns
ALT	58	8	1300	414.22	78.5	84.2	--	78.8	80.2	--

Phenotypic traits FES, number of days from 1 January 2019 when three heads per plant were flowering per plot; CRI, growth at the end of the winter in g of DM; CRP, growth at the end of the spring in g of DM; CRE, growth in flowering in g of dried matter (DM); FLW, flag leaf width in flowering (cm); FLL, flag leaf length in flowering (cm); AIN, number of inflorescences per plant (average of 3 samples taking at random); HAB, growth habit in early spring before flowering on a visual scale from 1 to 9, with 1 = prostrate to 9 = erect; ENF, tolerance to pests and diseases on a visual scale from 1 to 9, with 1 = sensitive to 9 = resistant; APH, average plant height in flowering (cm); ALT, altitude (masl)

L. perenne presented significantly higher values of FES (130.3 vs. 110.4), CRP (36.8 vs. 26.0), CRE (122.7 vs. 89.2), and AIN (95 vs. 79.9), and lower of FLL (13.0 vs. 14.6) than L. multiflorum (Table 1).

When a Factorial Component Analysis (FCA) was performed using the method of Principal Components, the first three PCs represent 66% of the accumulated variance. In PC1 (34.3% of the variance), CRE had a strongly positive influence, followed by AIN, CRP, and FES (Table S2); all of them related with the growth and the appearance of the inflorescence. In PC2 (19.2 of the variance), FLL had the strongest positive influence, and HAB and ENF had negative effects. In PC3 (12.4% of the variance), FLW had a strong positive load, and CRE and CRI a negative one. Significant correlations between phenotypic traits were AIN with FES (0.562, P < 0.001), CRE (0.707, P < 0.001), and CRP (0.625, P < 0.001); and CRE with CRP (0.731, P < 0.001).

L. perenne slightly differentiated from L. multiflorum in the positive part of the PC1 due to higher values of CRE, AIN, CRP, and FES (Table 1; Fig. 2). In the positive side of the PC2, L. multiflorum slightly differentiated due to higher values of FLL.

Fig. 2.

Factorial Component Analysis, performed using the method of Principal Components, over the ten phenotypic traits for 67 (N) ecotypes and commercial cvs. classified by the species evaluated in this study

Diversity by SSRs

A total of 673 unique genotypes were identified, 274 of L. multiflorum; 339 of L. perenne; 30 of L. rigidum; 20 of L. canariense; and 10 of L. temulentum. All SSRs used were polymorphic and 238 alleles were detected (Table S3). 184 alleles out of 238 were rare (p < 0.05), 77.3%. G03_049 was the most polymorphic SSR with 34 alleles and G01_080 the lowest with 11 alleles. The average number of alleles per locus was 22.

76 genotypes of L. multiflorum showed were putative diploids (2 alleles), 128 genotypes putative triploids (3 alleles), and 70 genotypes putative tetraploids (4 alleles); L. perenne ecotypes included 126 diploids, 156 triploids, and 57 tetraploids; L. rigidum 17 diploids, 10 triploids and 3 tetraploids; L. canariense 18 genotypes diploids and 2 triploids; and finally, L. temulentum had 10 diploids.

Genetic and geographic structure

A Bayesian analysis using the Structure software was conducted using 11 SSRs to determine the genetic structure among 673 unique genotypes. The ΔK criterion values increased until K = 2 (Fig. 3) estimated by using Structure Harvester in a group of 566 genotypes out of 673, with a qI > 80% (84.1% of all genotypes).

Fig. 3.

qI (probability of membership in %) for the reconstructed populations (RPP), obtained by the Structure software conducted using 11 SSRs, when for K = 2 (most likely) with RPP1 clustering L. perenne and L. canariense and RPP2 grouping L. multiflorum, and L. temulentum classified as Admixed. Substructure for RPP1 separated L. canariense in RPP1a other genotypes of L. perenne from RPP1b (L. perenne). Substructure for RPP2 grouped L. rigidum with other genotypes of L. multiflorum in RPP2a, RPP2b clustered commercial cvs. and RPP2c grouped only local ecotypes

Two main groups were detected by the Structure analysis (Fig. 3, Table S4), separating the two main species included in the study, L. multiflorum and L. perenne. Most of the genotypes of L. perenne were grouped in RPP1 (275 out of 292), 19 in RPP2, and 45 were admixed; meanwhile the majority of the genotypes of L. multiflorum were grouped in RPP2 (230 out of 284),, and 44 were admixed. Most of the genotypes of L. canariense (17 out of 20) were grouped in RPP1 while the remaining three were admixed. 25 genotypes out of 30 of L. rigidum were grouped in RPP2 and 5 were admixed. The 10 genotypes of L. temulentum were assigned as admixed. Fst value for K2 was 0.14 (P < 0.01) and between RPP1 and RPP2 with admixed were 0.054 and 0.04 (P < 0.01), respectively. RPP2 showed a higher number of alleles (132) than RPP1 (109), of specific alleles (61 vs. 44) and rare alleles (122 vs. 112) (Table S3).

When genotypes from RPP1 with a qI > 80% (mainly L. perenne) were analyzed separately, two clusters were obtained (K = 2) according to the ΔK criterion values (Figure S1) estimated by using Structure Harvester. The first one named RPP1a clustered 141 genotypes with a qI > 80% from Galicia, Asturias, Castilla-León, and Cataluña; 124 ecotypes of L. perenne and 17 ecotypes of L. canariense; and 4 commercial cvs. (‘Bariforma’ ‘Barsintra’ ‘CIAMI’ and ‘Maraeko’) were grouped in RPP1a. The second cluster, RPP1b, included a total of 141 genotypes with a qI > 80%, all ecotypes were L. perenne. 10 genotypes were admixed.

When genotypes from RPP2 with a qI > 80% (mainly L. multiflorum) were evaluated separately by Structure analysis, three clusters were obtained (K = 3) as the more likely according to the ΔK criterion values (Figure S1). RPP2a included 51 genotypes of ecotypes, 26 of them belonged to L. multiflorum (Canary Islands), 18 to L. rigidum (from Canary Islands), and 7 to L. perenne (Castille-Leon). RPP2b grouped 113 genotypes, all of them belonged to L. multiflorum except for 8 to L. perenne and 3 to L. rigidum; RPP2b grouped ecotypes mainly from Asturias and Cantabria, but also commercial cvs. (‘Pomba’, ‘Promenade’, ‘Campivert’, ‘Litonio’, and ‘Vallivert’). RPP2c grouped 67 genotypes of L. multiflorum and 1 of L. perenne, all ecotypes from Asturias and Galicia.

Admixed genotypes clustered in four groups, including ecotypes of L. multiflorum, L. perenne, L. temulentum, and L. rigidum.

Intra-specific variability

All ecotypes/cultivars showed intra-specific variation, showing more than genotype in each ecotype/cultivar.

22 ecotypes and 6 commercial cvs. grouped all genotypes in the same RRP, such as 11, 12, 15, 19, 53, 282, 320, 321, 325, 319, 330, 333, 337, 359, 361, 1204, 1206, 1210, 1240, 1326, 1328, 1330, ‘Barforma,’ ‘Barsintra,’ ‘Brigantia,’ ‘CIAMI,’ ‘Maraeko,’ and ‘Pomba’ (Table S4, Fig. 3). The ecotypes 7, 22, 66, and 1207 grouped some genotypes in a different RPP for the main one with a qI > 80% or as admixed.

Finally, 6 ecotypes could not be grouped in a specific RPP such as 30, 31, 47, 1212, 1228, and 1853 (L. temulentum).

Factorial component analysis (FCA) with SSRs

The FCA results, performed using the method of Principal Components, aligned with those produced by the Bayesian method Two RPPs were differentiated on the first axis, with RPP1 on the negative PC1 and RPP2 on the positive PC1, and the mixed accessions falling in between (see Fig. 4). This genetic differentiation corresponded mostly (some genotypes clustered in the other RPP, see Fig. 3) with the species L. perenne and L. canariense on the RRP1 and L. multiflorum and L. rigidum in the RPP2. L. temulentum was admixed. FCA also differentiated the substructure in RPP1 and RPP2 (Fig. 5), with RPP1a in the most negative part of the PC1 with respect to RPP1b. Moreover, RPP2a and c, both grouping only local ecotypes, grouped in the positive part of PC1, but in the negative part of PC2.

Fig. 4.

Factorial Component Analysis (FCA), performed using the method of Principal Components, based on the allele frequencies of 673 genotypes of raygrass with 11 SSRs by considering the reconstructed populations (RPPs) obtained by the Structure software when K = 2 (most likely). On the left, RPP1 in blue, RPP2 in green, admixed in red. On the right, species evaluated in this study (same FCA)

Fig. 5.

Factorial Component Analysis (FCA), performed using the method of Principal Components, based on the allele frequencies of 673 genotypes of raygrass with 11 SSRs by considering the reconstructed populations (RPPs) obtained by the Structure software analysing the substructure for RPP1 and RPP2. RPP1 (a and b, mainly L. perenne) and RPP2 (a, b and c, mainly L. multiflorum), and admixed genotypes (mainly P, L. perenne; mainly M, L. multiflorum)

Genetic and geographical origin

Most of the samples from different provinces were grouped in both RPP1 and RPP2, like those from Santa Cruz de Tenerife and Cantabria (mostly RPP2), and those from Guipuzcoa and Girona (mostly RPP1) (Figure S2). Genotypes from RPP1 (mostly L. perenne) were from locations up to 1500 masl (Fig. 6), meanwhile genotypes from RPP2 (mostly L. multiflorum) were found at altitudes lower than 1000 masl.

Fig. 6.

Factorial Component Analysis (FCA), performed using the method of Principal Components, based on the allele frequencies of 673 genotypes of raygrass with 11 SSRs by considering the reconstructed populations (RPPs) obtained by the Structure software when K = 2 (most likely). RPP1 (blue), RPP2 (green) and Admixed (red) by altitude (masl) and species evaluated in this study in the PC1 of the FCA

Genetic and hpenotypic variation

The Factorial Component Analysis over the nine phenotypic traits classified by reconstructed populations (RPPs), obtained with the Structure software for K = 2 and substructure for RPP1 and RPP2 (Table 2; Fig. 7), showed significant differences between L. perenne (mainly RPP1) and L. multiflorum (mainly RPP2). When substructure was analyzed, RRP2a and RPP2c were the most distinct subgroup of ecotypes, RPP2a clustered genotypes of L. rigidum and L. multiflorum from Canary Islands and of L. multiflorum from Asturias and Galicia; both RRP2a and RPP2c had the most negative PC1, due to significantly lower values of FES (114.0 and 93.8), of CRE (43.0 and 33.4), of AIN (64.6 and 58.4); and, with a lower eigenvector for the PC1, a lower value of APH (73.8 and 64.5), respectively (Table 2). The main difference between them, was a higher susceptibility to diseases of RPP2a (2.6) vs. RPP2c (3.7).

Table 2.

Phenotypic traits for 67 ecotypes/cultivars classified by the reconstructed populations (RPPs) of the substructure obtained by structure software (RPP1a and b; RPP2a, b and c) and admixed genotypes. Significance (P) for the 5 RPPs and admixed genotypes by ANOVA. NS, non significant

Phenotypic traits	RPP1a	RPP1b	RPP2a	RPP2b	RPP2c	Admixed	P
Number of ecotypes/cultivars	15	16	6	11	7	12
FES	133.5b	125.2b	114.0ab	116.6b	93.8a	112.0ab	***
CRI	15.3	16.8	20.2	14.7	15.8	17.9	ns
CRP	31.4b	40.6b	15.6a	34.3b	14.8a	33.7b	***
CRE	105.1b	131.1b	43.0a	122.2b	33.4a	111.2b	****
FLW	0.57a	0.49a	0.61ab	0.64ab	0.75b	0.6ab	**
FLL	11.5a	14.2ab	14.2ab	13.8ab	14.2ab	15.3b	*
AIN	93.1b	91.3b	64.6a	93.0b	58.4a	90.7b	***
HAB	3.1	2.9	2.7	3.2	3.0	3.3	ns
ENF	6.4c	3.5ab	2.6a	5.5c	3.7ab	5.0bc	***
APH	77.5b	78.8b	73.8ab	90.6b	64.5a	87.8b	****

FES number of days from 1 January 2019, when three heads per plant were flowering per plot; CRI: growth at the end of the winter in g of DM; CRP: growth at the end of the spring in g of DM; CRE: growth in flowering, in g of dried matter (DM); FLW: flag leaf width in flowering (cm); FLL: flag leaf length in flowering (cm); AIN: number of inflorescences per plant (average of 3 samples taking at random); HAB: growth habit in early spring before flowering, on a visual scale from 1 to 9, with 1 = prostrate to 9 = erect; ENF: tolerance to pests and diseases on a visual scale from 1 to 9, with 1 = sensitive to 9 = resistant; APH, Average Plant Height in flowering (cm). *,**,*** P < 0.05, 0.01, and 0.001, respectively. NS, Not Significant

Fig. 7.

Factorial Component Analysis, performed using the method of Principal Components, over the ten phenotypic traits for 67 ecotypes/cultivars classified by reconstructed populations (RPPs) obtained with the Structure software for K = 2 (most likely) and substructure for RPP1 and RPP2. Left, RPP1 (1), mainly L. perenne; RPP2 (2), mainly L. multiflorum; and admixed genotypes (3). Right, substructure: RPP1a (1) and b (2); RPP2a (3), b (4) and c (5) and admixed genotypes (6)

Discussion

There is a high rate of transferability of SSR loci across species within a genus, and a lower transferability of SSR loci across genera and beyond [32–34] as occurred in our study, in which 5 species were evaluated.

Some of the species included in this study are obligate outbreeders with a genetically determined gametophytic self-incompatibility system, which can explain the high levels of genetic variability of the ecotypes and cultivars evaluated. In our case, all SSRs were polymorphic and more than 200 alleles were detected.

SSRs used in this study showed higher genetic variation than other studies on Lolium spp., with almost triple the number of alleles per locus (22), compared to 8.19 of Lolium accessions provided by three German breeding societies and the IPK Genebank [35], or almost twice as many (13.26) as the average number of alleles per locus (A) obtained from plant breeding material mainly from Europe [36].

Several studies carried out during the first decade of the 2000s using microsatellite markers, showed a lower genetic variation. Wang et al. (2009) [15] explored the molecular diversity and relationships between Australasian perennial ryegrass (L. perenne L.) populations; for which 8 forage perennial ryegrass populations comprising 48 individual plants per population were genotyped with 29 SSR marker loci, obtaining a range of the number of alleles per locus of 3.72–6.72.

Studer et al. (2006) [37] evaluated twelve microsatellite markers from L. multiflorum on 16 individuals of each of the three grassland species (L. multiflorum, L. perenne, and Festuca pratensis) to testcross-species amplification, which was 100% for L. perenne and 83% for F. pratensis. The number of alleles detected ranged from 1 to 14 with an average of 3.4.

Jensen et al. (2005) [19] reported on the characterization and mapping of 76 SSR markers for perennial ryegrass. In 2007, Jensen et al. [34] tested the amplification of 105 perennial ryegrass SSR markers in 23 grass species representing 7 tribes from 3 subfamilies of Poaceae. 8 SSR markers were evaluated for polymorphism across 9 of the 23 grass species: 4 to 7 of the markers were polymorphic within each species, with an average detection rate of 2.4 alleles per species.

Jones et al. (2001) [38] developed SSR primers from 2 SSR-enriched libraries from perennial ryegrass. 100 selected SSR primer pairs were evaluated showed a high efficiency of amplification (81%) and a relatively high level of polymorphism (67%) with a range of 2 to 7 alleles per locus. In addition, cross-species amplification was detected in a number of related pasture and turfgrass species, with high levels of transfer to Lolium species and members of the related genus Festuca.

In the work carried out by Kubit et al. (1999) [39], the number of alleles per locus ranges from 3 in inbreeding (L. temulentum, L. remotum, L. persicum, and L. subulatum) to 10 in outbreeding Lolium species (L perenne, L. multiflorum, L. rigidum, and L. canariese), but a reduced number of accessions and SSRs per species were included in the study.

Our study agrees with that carried out by McGrath et al. (2007) [40] with chloroplast microsatellite markers because no separation of allogamous and autogamous Lolium species was found, unlike other studies by Catalan et al. (1997, 2004) [41, 42] by using chloroplast genes and ribosomal ITS; by Charmet et al. (1997) [43] with RAPD, RFLPs, and ribosomal ITS; and by Torrecilla et al. (2004) [44] by using chloroplast genes and ribosomal ITS. Thus, in the study undertaken by Catalan et al. (2004) [42], two autogamous species, L. canariense and L. rigidum grouped together, while the allogamous species L. perenne grouped with a second allogamous species, L. multiflorum. In contrast, in our study with SSRs, most of the genotypes of L. perenne and L. canariense were grouped in RPP1 while the majority of the genotypes of L. multiflorum and L. rigidum were grouped in RPP2. McGrath et al. (2007) [40] explained these unusual groupings by high homoplasy in the data set caused by rapid molecular evolution at the SSR loci studied.

Success of the grass varieties is partly determined by seed yield. A complex trait, consisting of several components, all of which are determined by the interplay of plant genetic background and environmental factors [45]. Number of inflorescences as well as their length are among the main morphological characters defining seed yield [46]. After a winter period, temperature and day length increase, resulting in greater plant growth and in the development of the number of inflorescences. This is reflected in the average data obtained for us in the Lolium analyzed for the following morphological traits: Growth at the end of the winter (CRI) with 16.5 g of DM vs. Growth at the end of spring (CRP) with 31.3 g of DM, and Number of inflorescences per plant (AIN) with a value of 86.1.

Another factor in seed production is flag-leaf size [47] which reduces the rate of seed abortion in meadow fescue by reallocating assimilates via the stems to the inflorescence during anthesis [48]. Jaskune et al. (2022) [45] considered this phenomenon very important in turfgrasses, given that the vegetative tiller, forming biomass, competes with the generative organs, although the seed head may also function in seed filling. They found high variation for flag-leaf length (FLL) and flag-leaf width (FLW) between the studied groups of ecotypes and cultivars of perennial ryegrass, with values for FLL ranging from 3.0 to 21.5 cm. In our study, FLL values were between 6.3 and 20.2 cm, with a higher average value in L. multiflorum (14.6 cm) than in L. perenne (13.0 cm).

In previous works, genetic diversity among and within seven perennial ryegrass cultivars using SRR markers showed Fst values based on AMOVA ranged from 0.065 to 0.197, with 14.6% of among-cultivar genetic variation [41]. A similar value of genetic differentiation was obtained in our study between the two RPPs, which corresponded mainly to the two main species, L. perenne and L. multiflorum, and slightly higher than in the study by Brazauskasa et al. (2011) [36] with a value of Fst of 0.12. McGrath et al. (2007) [40] estimated by AMOVA the within-population variance in 61% for L. perenne European ecotype diversity, likely caused and maintained by high levels of natural and anthropogenic seed dispersal.

When all cultivars were considered simultaneously in PCA, the first three components comprised 29% of the variance [49]; in this study the three first PCs showed higher values, with 66% of the accumulated variance, again surely related with the different species included in our study; which has also been detected with other markers. Thereby, an analysis of molecular variance (AMOVA) using RAPD markers revealed a much larger variation within populations (71%) than among them (29%) of perennial ryegrass [50].

As seen in previous studies, allelic and haplotypic variation was extremely high within and between Lolium spp. ecotypes, which had already been detected in studies carried out on Irish and European L. perenne ecotypes by McGrath et al. (2007) [40].

Migration of seed material by natural or anthropogenic means, including plant breeding, could contribute to this high level of variability [40]. Previously evidence was found, using AMOVA, of a likely migration route of L. perenne from Southern regions of Europe northwards and a pathway of migration from Southern Europe to Northwest Europe including Ireland [40].

Implementation of molecular markers in breeding programs will permit efficient early selection of desirable genotypes, which is particularly important due to the high levels of genotype x environment interaction found for agronomic characters in forage grasses [51]. Molecular markers may be used for DNA profiling in order to provide supporting information for varietal identification and seed-purity certification [52], a relevant aspect when in our study only 22 ecotypes out of 68 were classified in the same RPP (32.4%), as occurred with commercial cultivars, and 6 more could not be assigned to a specific RPP (L. temulentum included). This fact may be due to L. temulentum being a mimic weed associated with wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.), and that it is closer to other Lolium species, like L. remotum.

L. temulentum and L. remotum share very similar characteristics. Primer pairs from L. temulentum and L. perenne [38, 39, 49] amplified microsatellite fragments for both L. temulentum and L. persicum accessions [53].

Mian et al. (2005) [54] described Lolium temulentum as a potential model species for cool-season forage grasses, which displayed a close relation with the major Festuca-Lolium species. The phylogenetic trees obtained from their studies showed a clear distance between L. temulentum vs. L. multiflorum and L. perenne.

Due to the likely genetic complexity of mapping crosses and population structures in this outbreeding species, the ideal marker system for both MAS and DNA profiling is simple sequence repeat polymorphism (SSRP) [38]. We have chosen SSR markers because they are also highly reproducible, genetically co-dominant, and multiallelic.

SSR markers used in this study may be useful for ryegrass germplasm management issues such as cultivar identification and could be useful tools for synteny and evolutionary studies.

Moreover, SSR marker profiles can be used to assign individuals for outbreeding populations such as perennial ryegrass.

Conclusions

SSRs identified clearly the two main species evaluated (L. perenne and L. multiflorum) in two main genetic groups, with a slight difference related to their distribution in altitude, higher in L. perenne. The substructure identified two groups of ecotypes not related with commercial cultivars, one of L. rigidum and L. multiflorum, mainly from the Canary Islands, and a second one of L. multiflorum, mainly from Galicia and Asturias, which could be useful for the purposes of breeding local cultivars. Moreover, SSRs were useful in identifying introgressants, which will be of interest for traceability purposes in the quality control of the seeds, the derived products, and as a fast tool to remove them to homogenize the natural populations found in northern Spain. In addition, genetic structure was also related with phenotypic variation, identifying this group of local ecotypes not related with commercial cultivars with lower FES, CRE, and AIN. These can be used in the obtention of new varieties due to the manifestation of certain intrinsic characteristics that they possess, and the conservation of biodiversity. Finally, also being useful for other alternative uses such as low-input agriculture and the recovery of degraded areas.

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Abbreviations

A

Average

Ae

Average Effective

AGACAL

Agencia Gallega de la Calidad Alimentaria

AIN

Number of Inflorescences

ALTIT

Altitude

AMOVA

Analysis of Molecular Variance

ANOVA

Analysis of Variance

APC

Article Processing Charges

APH

Average plant height in flowering

°C

Degrees Celsius

CCAA

Comunidades Autónomas

CIAM

Agricultural Research Centre of Mabegondo

Cm

Centimeter

CRE

Growth in Flowering

CRI

Growth at the end of the Winter

CRP

Growth at the end of the Spring

Cvs

Cultivars

DM

Dry Matter

DNA

Deoxyribonucleic Acid

DOC

Doctor

DOI

Digital Object Identifier

ENF

Tolerance to pests and diseases

et al.

And others

FEADER

Fondo Europeo Agrario de Desarrollo Rural

FCA

Factorial Component Analysis

FES

Number of days when three heads per plant were flowering per plot

Fis

Inbreeding coefficient

FLW

Flag Leaf Width in Flowering

FLL

Flag Leaf Length in Flowering

Fst

Fixation Index

G

Gram

HA

Hectare

HAB

Growth Habit in Early Spring before Flowering

IBPGR

International Board for Plant Genetic Resources

INIA

Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria

IPK

Leibniz Institute of Plant Genetics and Crop Plant Research

ITS

Internal Transcribed Spacer

L

Lolium

Ng

Nanograms

ng/µL

Nanograms/Microliter

NS

Not Significant

MAS

Marker Assisted Selection

Masl

Metres Above Sea Level

MIN

Minute

Org

Organization

P

Probability

PC

Principal Component

PCR

Polymerase Chain Reaction

qI

Probability of membership

RAPD

Random Amplified Polymorphic DNA

RFLP

Restriction Fragment Length Polymorphism

RPP

Reconstructed Panmictic Population

RRFF

Recursos Fitogenéticos Agrarios

s

Second

SD

Standard Deviation

spp.

Species

SPSS

Statistical Package for the Social Sciences

SRR

Simple Sequence Repeat

SRRP

Simple Sequence Repeat Polymorphism

UPOV

International Union for the Protection of New Varieties of Plants

µL

Microliter

V

Version

vs.

Versus

Author contributions

CIF-O, AMR-C and SP-L Methodology; AMR-C and SP-L Software; CIF-O Resources; CIF-O, AMR-C and SP-L Data Curation; CIF-O, AMR-C and SP-L Writing-Original Draft Preparation; CIF-O, AMR-C and SP-L Writing-Review & Editing; CIF-O, AMR-C and SP-L Supervision; CIF-O Project Administration; CIF-O Funding Acquisition. All authors have read and approved the manuscript.

Funding

This research was funded by FEADER-Xunta de Galicia SubMeasure M10.22 del PLAN MARCO (RRFF 2019 12/15/11/250319/02 and RRFF 2020 12/15/11/120320/11). And the Article Processing Charges (APC) was funded by FEADER-Xunta de Galicia. CIF-O was the beneficiary of a DOC-INIA-CCAA contract co-financed by the European Social Fund (CONV. 2015). JEL-D was hired through a collaboration agreement between the Fundación Juana de Vega and AGACAL (Xunta de Galicia).

Data availability

Data is provided within the manuscript and in supplementary information file.

Declarations

Ethics approval and conset to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.