1 Introduction

Pinus nigra J.F. Arnold is one of the leading commercial important conifers of the Mediterranean region (Bonavita et al. 2016). P. nigra has been widely used for reforestation of difficult soils with hard climatic conditions (Portoghesi et al. 2013). This Mediterranean pine has an extensive and scattered distribution through Northern Africa, Northern Mediterranean, eastwards to the Black Sea, Corsica and Sicily islands (Vidaković 1991; Gaussen et al. 1993; Afzal-Rafii and Dodd 2007; Olsson et al. 2020). The populations of P. nigra studied by various authors presented high variation at different levels (Scaltsoyiannes et al. 1994; Rafii et al. 1996; Quézel and Médail 2003; Bogunić et al. 2003, 2007, 2009; Afzal-Rafii and Dodd 2007; del Cerro et al. 2009; Rubio-Moraga et al. 2012; Bonavita et al. 2016). Due to their geographically fragmented distribution areas, a reduced intra-population genetic diversity for these P. nigra populations is expected (Scotti-Saintagne et al. 2019). Nonetheless, recent molecular studies revealed a weak genetic spatial structure originated from events that occurred in the late Pleistocene or early Holocene (Giovannelli et al. 2019; Scotti-Saintagne et al. 2019). These events fragmented one ancestral P. nigra population into six to seven distinct genetic lineages with high gene flow among them (Giovannelli et al. 2019; Scotti-Saintagne et al. 2019). The same authors proposed the revision of the infraspecific taxonomy of P. nigra species on five subspecies with Mediterranean distribution based on the length and stiffness of needles: (i) salzmannii (Dunal) Franco; (ii) nigra Arnold; (iii) dalmatica (Visiani) Franco; (iv) pallasiana (Lambert) Holmboe and (v) laricio (Poiret) Maire (Gaussen et al. 1993), and a sixth subspecies, P. nigra subsp. mauretanica (Maire and Peyerimh) Heywood assigned to the North of Africa (Bussotti 2002). Scotti-Saintagne et al. (2019) proposed to name the recently discovered six lineages of P. nigra using regionally accepted subspecies-level names. Additionally to the six main inter-fertile P. nigra subspecies agreed so far (Quézel and Médail 2003), some of those are also divided into two to four regional varieties (Christensen 1997; Barbéro et al. 1998; Price et al. 1998; Bussotti 2002). The P. nigra subsp. salzmannii can present the varieties mauretanica, hispanica, salzmannii and cebennensis; the P. nigra subsp. nigra can be subdivided into the varieties austriaca, illyrica, pindica and italica; the P. nigra subsp. pallasiana contains the varieties banatica, tatarica, caramanica and fenzlii; and the P. nigra subsp. laricio includes the varieties calabrica or corsicana (Christensen 1997; Barbéro et al. 1998; Price et al. 1998; Bussotti 2002).

Beyond the taxonomic issues, most of the European P. nigra populations have an unknown origin and/or infraspecific taxonomy. This information is of utmost importance under the scope of afforestation/reforestation programs regarding the existence of multiple inter-fertile subspecies of P. nigra (Quézel and Médail 2003; Naydenov et al. 2006; Zaghi et al. 2008). Besides, the information about intra- and inter-population, genetic diversity is also relevant for the maintenance of the stability of forest ecosystems, the adaptive potential to biotic and abiotic stresses (Muller-Starck et al. 1992; Çengel et al. 2012), and to assign high priority populations that deserve to be preserved and managed (Bonavita et al. 2016). Knowledge of genetic diversity can give insights about the adaptive potential and/or the evolutionary history of a species, being particularly crucial in trees with a long life cycle (Hamrick 2004; Giovannelli et al. 2017). The gathering of genetic diversity and structure information of forestry populations is helpful for the development of suited and effective strategies of conservation, management, germplasm use and genetic breeding (Frankham et al. 2002; Hamrick 2004; Lučić et al. 2010). Different molecular marker systems have been performed in various pine species, including P. nigra, to assess the genetic diversity, provenances, genetic structure and relationships, and/or to discriminate closely related taxa (Vendramin et al. 1996; Liber et al. 2003; Jiménez et al. 2005; Naydenov et al. 2006; Afzal-Rafii and Dodd 2007; Soto et al. 2010; Rubio-Moraga et al. 2012; Liu et al. 2012; Šarac et al. 2015; Bonavita et al. 2016; Cipriano et al. 2016; Giovannelli et al. 2017; Dias et al. 2019).

The nuclear SSRs (nSSRs) are inherited biparentally, evolve faster than the chloroplast SSRs (cpSSRs) and are more polymorphic than the Expressed Sequence Tag-SSRs (EST-SSRs) (Wolfe et al. 1987; Mogensen 1996; Cho et al. 2000). The EST-SSRs generally consist of GC-rich trinucleotide repeats within transcribed genomic regions and their polymorphism is associated with gene diversity (Varshney et al. 2005). Since the EST-SSRs have few null alleles, they can be used to discriminate closely related taxa (Ellegren 2004; Varshney et al. 2005; Hayden et al. 2008).

The wide and fragmented distribution of P. nigra, gene flow, weak spatial genetic structure and admixed origin of the populations contributed to taxonomic classification issues (Giovannelli et al. 2019). Recent studies based on simple sequence repeat (SSR) markers were developed to resolve the complex taxonomy and evolutionary history of P. nigra in Europe (Giovannelli et al. 2017, 2019). These authors developed for the first time specific nSSRs to P. nigra but also revealed that nSSRs and EST-SSRs from other pine species are transferable to P. nigra.

With this work is intended to test a set of 14 nSSRs, which include nine nSSRs specifically developed in P. nigra by Giovannelli et al. (2017), three nSSRs specific to Pinus sylvestris and Pinus taeda, and two EST-SSRs specific to Pinus halepensis (Soranzo et al. 1998; Elsik and Williams 2001; Zhou et al. 2002; Leonarduzzi et al. 2016b) in 224 P. nigra individuals from six Portuguese populations, to evaluate the (i) intra- and inter-population genetic diversity; (ii) genetic structure and relationships and (iii) to infer about the infraspecific taxonomy of the Portuguese populations by comparing with foreign samples with known taxonomy.

2 Materials and methods

2.1 Plant material and sampling

The distribution of P. nigra in Portugal is restricted to pure even-aged planted and managed stands located at the North and Centre of the country, mainly in mountain regions whose altitude ranges from 450 to 1600 m (Dias et al. 2019). The six P. nigra populations were chosen based on the species distribution in Portugal: Minho (NW), Trás-os-Montes (N) and Beira (Central). The previous dendrometric characterisation of these populations suggested an average age ranging from 57.8 to 93.3 years old (Dias et al. 2014, 2018; Table 1). The planted areas range from 5 to 40 ha. Two sample plots in each region were established.

Table 1 Portuguese populations of P. nigra: respective geographic coordinates, average age and number of individuals sampled per population
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In each population, individuals were randomly selected within plots with an average of 0.04 ha. A total of 224 individuals were sampled for needles or differentiating xylem (in the tallest trees) (Table 1).

The plant material was immediately frozen in liquid nitrogen and maintained at − 80 °C until the extraction of genomic DNA.

A total of 30 foreign samples of P. nigra (six samples per subspecies) with known infraspecific taxonomy and provenance, and previously genotyped with SSRs by Giovannelli et al. (2017), were included in this study (Table 2) for comparison with the Portuguese individuals.

Table 2 Foreign samples of P. nigra with known infraspecific taxonomy, provenance and geographic coordinates of the sampling sites
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2.2 DNA extraction and SSRs amplification

Frozen needles or differentiating xylem (250 mg) were grounded to a fine powder in the presence of liquid nitrogen. Genomic DNA extraction was performed with a CTAB-based protocol (Doyle and Doyle 1987). The genomic DNA integrity was verified after electrophoresis on 0.8% agarose gels and the DNA quantification was performed in the spectrophotometer Nanodrop ND-1000® (Thermo Scientific, Burlington, USA).

The SSR markers were amplified in the 224 P. nigra individuals using the primers and mixtures indicated in Table 3.

Table 3 nSSR and EST-SSR loci, respective primer sequences, repeat motif, reference and labelling
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The same set of 14 SSR markers of Giovannelli et al. (2017) was used, but the procedure in terms of the final volume of the reaction mixture and amplification conditions was changed. Briefly, the primer mixes 1 and 2 were performed for a final volume of 50 μL. Each multiplex PCR contained 1–2 μL of genomic DNA (10 ng/μL), 0.6 μL of primer mix, 3 μL of QIAGEN Multiplex PCR Master Mix (Qiagen, Germany) and 1.4 μL of RNase-free water, for a total volume of 6 μL. For both multiplexes, the PCR thermal profile was the following: an initial step at 95 °C for 5 min, followed by 32 cycles at 95 °C for 30 s, 57 °C for 90 s and 72 °C for 30 s, and a final extension of 60 °C for 30 min. The amplified SSR loci labelled with fluorescent dyes were separated by capillary electrophoresis using the ABI 3500 automatic sequencer (Applied Biosystems, Foster City, CA, USA), using LIZ 500 as an internal size standard. The chromatograms were analysed with the GeneMarker software version 2.7.0 (SoftGenetics, USA).

2.3 Statistical analyses of the SSRs data

Alleles were sized manually to reduce the common allele calling and binning errors reported by Guichoux et al. (2011).

The software POPGENE 1.32 (Yeh et al. 1999) was used to calculate the (i) number of observed alleles (na); (ii) number of effective alleles (ne; Kimura and Crow 1964); (iii) Shannon’s information index (I; Lewontin 1972) that measures gene diversity (Shannon and Weaver 1949); (iv) observed heterozygosity (Ho) and expected heterozygosity (He) (Levene 1949; Nei 1973); (v) Nei’s gene diversity index (Nei 1973) (h); (vi) estimation of the unbiased genetic identity (Nei 1973) and genetic distance (Nei 1978) and (vii) inter-population genetic variation (DST).

The software STRUCTURE 2.3.4 (Pritchard et al. 2000, 2003, 2009; Falush et al. 2003, 2007) was used for genetic structure evaluation using the admixture model with correlated allele frequencies among clusters (suitable for codominant markers). For the Portuguese populations, ten independent runs for each K (from 1 to 10) were performed with 100,000 generations of a burn-in period followed by 500,000 Markov Chain Monte Carlo (MCMC) iterations. Additional genetic structure analyses were performed with the foreign samples and/or the Portuguese P. nigra populations, using ten independent runs for each K (from 1 to 20) and the same values of burn-in period and MCMC iterations. The STRUCTURE outputs of the previous analyses were summarised and tested with the software STRUCTURE Harvester (Earl and vonHoldt 2012) that quickly provided the results of the Evanno method concerning the determination of the optimal number of clusters (K) for the studied individuals without considering predefined populations (Evanno et al. 2005).

The software GenAlEx v6.5 (Peakall and Smouse 2012) was used to perform the analysis of molecular variance (AMOVA) and the principal coordinate analysis (PCoA) based on Nei’s genetic distance pairwise population matrix (Nei 1978). The GenAlex also allowed the calculation of the (i) fixation index (F); (ii) Wright’s F statistics inbreeding coefficient (FIS) (Holsinger and Weir 2009); (iii) overall inbreeding of an individual relative to the total populations (FIT); (iv) proportion of total genetic differentiation (FST); (v) Gst: coefficient of gene differentiation (analogue of FST) adjusted for bias; and (vi) differentiation index Jost’s D that measures the relative degree of differentiation of allele frequencies (Jost 2009).

3 Results

3.1 SSR polymorphism

The 14 SSR loci were amplified in the 224 Portuguese P. nigra individuals. However, the SSR locus pn2153 was discarded from this study due to the production of low-quality results reflected by unspecific amplifications. Each SSR locus was polymorphic among the 224 P. nigra individuals studied (Dias et al. 2020).

The size of the amplified SSR fragments ranged from 99 to 476 bp, matching the expected size, except for the SSR loci PHA_6062, PtTX4001 and PtTX3107 (Table 4), without affecting the binning precision.

Table 4 Expected and observed range of fragment size (bp) per SSR locus
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3.2 Genetic diversity and relationships

The average number of observed alleles (na) amplified per population with the 13 selected SSR markers ranged from 8.46 to 12.46, with a total mean of 11.32 for the six populations (Table 5).

Table 5 Genetic diversity statistics assayed per Portuguese P. nigra population. Mean (± standard deviation, s.d.) values of na, ne, I, h, Ho, He and F per SSR locus are presented. Notes: na, observed number of alleles; ne, effective number of alleles (Kimura and Crow 1964); I, Shannon’s Information Index (Lewontin 1972); h, Nei’s gene diversity index (Nei 1973); Ho, observed heterozygosity; He, expected heterozygosity (Levene 1949) and F, fixation index
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The lowest na value was detected in locus SSR PHA_4783 in all populations (Table 7). On the other hand, the highest na value was verified in the SSR locus pn6175 in Vale do Zêzere (Table 7). The total mean number of na (11.32) was higher than the total mean of the effective number of alleles (ne), which was 6.00 (Table 5).

The number of alleles amplified per locus with the eight SSRs specifically developed to P. nigra ranged from 5 to 25 (Table 7). Besides, the number of alleles amplified per locus with the SSR primers that were transferable from other species ranged from 2 to 22 in the Portuguese P. nigra samples (Table 7).

The highest average values of na and ne were registered in the populations of Campeã and Paredes de Coura, respectively (Table 5).

The populations of Vale do Zêzere and Paredes de Coura presented the highest average values of Shannon’s information index (I), Nei’s gene diversity index (h) and expected heterozygosity (He). In contrast, Vila Pouca de Aguiar showed the lowest means for the same parameters (Table 5).

The highest value of observed heterozygosity (Ho) was shown by Paredes de Coura, while the lowest one was observed in Manteigas (Table 5).

The fixation index (F) revealed its maximum value in Manteigas, and its minimum in Vila Pouca de Aguiar (Table 5).

The pooled SSR data was used to calculate the Nei’s unbiased measures of genetic identity and genetic distance among the six P. nigra populations (Table 6).

Table 6 Nei’s unbiased measures of genetic identity (above diagonal) and genetic distance (below diagonal) among the Portuguese populations of P. nigra based on the pooled SSR data
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The population of Manteigas presented high values of Nei’s unbiased measures of genetic identity with all populations except with Paredes de Coura (0.7902) (Table 6). These results were partially corroborated in the PCoA since higher proximity among Vale do Zêzere and the populations of Manteigas, Campeã and Caminha was expected (Fig. 1). The PCoA demonstrated that Campeã, Manteigas and Caminha are the most genetically proximal populations (Fig. 1).

Fig. 1
figure 1

PCoA showing the projection of the six Portuguese populations of P. nigra based on the Nei’s genetic distance matrix calculated with Nei’s coefficient (Nei 1978) and the pooled SSR data

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The cumulative percentage of total variation explained by the first three coordinates of the PCoA represented in Fig. 1 was 96.46%.

3.3 Population differentiation and genetic structure

The statistical analysis of genetic variation and gene diversity estimated within and among the studied Portuguese P. nigra populations was performed with the calculation of the inter-population genetic diversity (DST), with a value of 0.122 and the proportion of total genetic differentiation (FST) with 0.040 ± 0.005.

The genetic differentiation among populations (Gst) and the differentiation index Jost’s D that measures the relative degree of differentiation of allele frequencies (Jost 2009) were estimated per SSR locus (Table 8). Overall, the 13 SSR markers estimated a reduced average genetic differentiation (Gst = 0.025) among the six Portuguese populations of P. nigra (Table 8).

The bar plot generated after the STRUCTURE analysis performed to the six Portuguese P. nigra populations evidenced a different pattern in the populations of Paredes de Coura and Vila Pouca de Aguiar relatively to the four remaining ones (Fig. 2a). This analysis also indicated that the optimal number of genetic clusters was K = 2 (Fig. 2b).

Fig. 2
figure 2

Results of STRUCTURE analyses performed with the six Portuguese P. nigra populations (a, b) and/or the foreign P. nigra samples belonging to different subspecies (c, d). a, c Proportion of the membership coefficient for each individual for the inferred clusters when K = 2, according to STRUCTURE results. Each individual is represented by a thin vertical line, which is partitioned into coloured segments that indicate the individual’s estimated membership fraction in K = 2 clusters. Different populations are separated by vertical lines. b, d Plot of the delta K value, calculated according to Evanno et al. (2005) and plotted against the number of the modelled clusters

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In the bar plot achieved with the STRUCTURE analysis that combined the Portuguese and the foreign P. nigra samples, the former presented a common pattern that distinguished them from the foreign ones (Fig. 2c). Therefore, for this genetic structure analysis, the inferred number of clusters was also K = 2 (Fig. 2d).

The average values of total genetic differentiation proportion (FST) estimated for the two clusters of the Portuguese populations were reduced (FST_1 = 0.0867 and FST_2 = 0.0711). A reduced proportion of total genetic differentiation (FST = 0.04) was also estimated with the Genalex software. Hence, the FST values obtained with these two analyses suggested a low genetic differentiation (FST ˂ 0.25) between the two clusters (K) (Fig. 2b) and among the six Portuguese populations.

3.4 Extrapolation about the infraspecific taxonomy

In order to extrapolate about the infraspecific taxonomy of the six Portuguese P. nigra populations, their SSR profiles were compared with others previously achieved by Giovannelli et al. (2017) in foreign P. nigra samples with known subspecies and provenances. After performing a PCoA analysis based on the Nei (1978) genetic distance matrix, the Portuguese P. nigra populations were clustered apart from the seven foreign samples (Fig. 3). The number of groups observed in the PCoA was corroborated by the STRUCTURE analysis of the same samples that also inferred two clusters (K = 2) (Fig. 2d). Nonetheless, these two clusters showed reduced genetic differentiation values, FST_1 = 0.099 and FST_2 = 0.2018. A reduced proportion of total genetic differentiation (FST = 0.22) among the Portuguese and the foreign samples was also inferred with the GenAlex software.

4 Discussion

Nowadays, the six populations of P. nigra located at the North and Centre of Portugal are representative of the species distribution in our country and show high adaptive potential, as demonstrated previously by their dendrometric and ecological evaluations (Dias et al. 2014, 2018).

The first molecular characterisation of the Portuguese P. nigra populations was performed with dominant molecular markers, namely, inter-simple sequence repeats (ISSRs) and Start Codon Targeted (SCoT) markers (Dias et al. 2019). The authors detected high intra-population polymorphism, good genetic differentiation and structure among populations as well as high genetic similarity with foreign samples belonging to the subspecies laricio var. calabrica (according to SCoTs) and subsp. laricio var. corsicana (based only in ISSRs) (Dias et al. 2019).

In the present work, additional molecular characterisation of the same Portuguese populations using the codominant SSR markers was performed. A set of 14 SSR markers was tested including SSRs that were developed specifically to P. nigra by Giovannelli et al. (2017) as well as SSRs that were specific to other pine species (Soranzo et al. 1998; Elsik and Williams 2001; Zhou et al. 2002; Leonarduzzi et al. 2016b). Among the tested SSR markers, 13 revealed to be polymorphic and amplified successfully in the Portuguese P. nigra samples, confirming the transferability of SSRs markers developed specifically in Pinus sylvestris C. Linnaeus, Pinus halepensis P. Miller and Pinus taeda C. Linnaeus (Soranzo et al. 1998; Elsik and Williams 2001; Zhou et al. 2002; Leonarduzzi et al. 2016b) to P. nigra, corroborating the assumption of Giovannelli et al. (2017). Šarac et al. (2015) also studied the cross-transferability of SSRs markers developed in other species to P. nigra. These authors obtained successful amplification but also reduced the level of polymorphism or unspecific loci amplification. Here, the selected 13 SSR loci demonstrated to be polymorphic among the 224 P. nigra individuals similar to the results previously achieved by Giovannelli et al. (2017).

The SSR study revealed high genetic variability in the six P. nigra populations, with average diversity values for the six populations of 1.8416 ± 0.76 (I) and 0.74 ± 0.0209 (h). The maximum values of the Shannon index and Nei’s gene diversity were found in Paredes de Coura and Vale do Zêzere populations. In contrast, the lowest ones were observed in Vila Pouca de Aguiar. Lower results were achieved by Jiménez et al. (2005) for the same species with Nei’s gene diversity of 0.425 to 0.558, and by Naydenov et al. (2006) that reported values of 0.008 to 0.195, respectively. Concerning to the Shannon index presented by other species such as Pinus pinaster W. Aiton, Naydenov et al. (2014) also referred lower results (0.756) than those shown in this work.

In respect to the total average number of observed (na) and effective alleles (ne), values of 11.32 ± 5.78 and 6.00 ± 3.60, respectively, were achieved. In P. nigra, Bonavita et al. (2016) observed a lower value of na (5.9) but a higher value of ne (13.9).

Regarding the expected heterozygosity (He), a high number of alleles were detected, estimating high genetic diversity in this species (He = 0.75 ± 0.19). Reduced values of He (0.123 to 0.242) were found in Spanish populations of P. nigra using ISSR markers (Rubio-Moraga et al. 2012). The authors explained the reduced genetic diversity as the effect of previous forest management. Similarly, a reduced He value (0.183) was achieved with EST-SSRs in Balkan populations of P. nigra likely due to the use of primers previously developed for other pine species, which revealed to be less variable and/or monomorphic in European Black Pine (Šarac et al. 2015). The EST-SSRs used in this work were developed for P. halepensis (Leonarduzzi et al. 2016b) but showed high genetic diversity in P. nigra. Bonavita et al. (2016) achieved a He value (0.67) in P. nigra populations similar to the ones reported in this work. The same was found in other conifers such as P. sylvestris (0.850, Soranzo et al. 1998), P. taeda (0.679, Al-Rabab’ah and Williams 2002), P. pinaster (0.403, Naydenov et al. 2014) and Taxus baccata C. Linnaeus (0.621, Jaramillo-Correa et al. 2010). Slightly higher He values were found in Pinus tabuliformis E. A. Carrière (0.8739) and Pinus henryi M. T. Masters (0.8829, Liu et al. 2012). Contrarily, lower genetic diversity values were detected in some populations of Pinus pinea C. Linnaeus by different authors, possibly due to the occurrence of bottleneck effect (Fallour et al. 1997; Vendramin et al. 2008; Pinzauti et al. 2012), and also in Abies alba P. Miller (0.36 to 0.40, Leonarduzzi et al. 2016a). Overall, the He values were higher than those of Ho, suggesting low heterozygosity. The He and Ho are not expected to be under the Hardy Weinberg equilibrium due to the inexistence of descendants, being the sampled trees composed only by planted individuals. This fact does not allow a precise estimation of the null alleles, which may lead to an overestimation of the genetic differentiation between populations.

Previous works reported that the fragmented distribution of P. nigra has contributed to high intra-population variation generating taxonomic issues, particularly at the infraspecific level (Afzal-Rafii and Dodd 2007). According to Giovannelli et al. (2017), such SSR markers are highly informative and valuable for population genetic studies and can be interesting to resolve the complex taxonomy of the P. nigra. Giovannelli et al. (2019) recently reported the need for revising the infraspecific taxonomy of this pine species based on the integration of molecular data with its demographic history resulting from geological events. Regarding the wide and patchy distribution of P. nigra, it was expected a strong genetic differentiation among populations and a low within-population genetic diversity (Giovannelli et al. 2019; Scotti-Saintagne et al. 2019). However, these authors reported a weak genetic spatial structure probably resulting from the fragmentation of an ancestral population at the late Pleistocene or early Holocene into six to seven genetic lineages with high gene flow among them, actively contributing for admixture (Giovannelli et al. 2019; Scotti-Saintagne et al. 2019). Therefore, these authors recommended the revision of the infraspecific taxonomy of P. nigra. This task should be based on molecular markers data but also transcriptomics and focusing in major biogeographic regions where the species is growing naturally, as recently proposed by Olsson et al. (2020).

The six allochthonous Portuguese populations revealed a reduced proportion of total genetic differentiation among them. Their inferred genetic structure corroborated this result into two clusters with reduced FST values or genetic distinction between them. Similar results were observed in the PCoA that showed high genetic similarity among Manteigas, Caminha and Campeã, and the distance of this group relative to the populations of Vila Pouca de Aguiar and Paredes de Coura (Fig. 1).

The SSR data achieved in this work reinforced our previous hypothesis about the origin of the Portuguese populations in two provenances, based on their high genetic similarity with two varieties of subsp. laricio (Dias et al. 2019). This hypothesis may also explain the low differentiation among the six Portuguese populations assayed by the SSRs since they had origin in one main genetic group, the same subspecies (laricio). Other authors reported a weak differentiation among populations of subsp. laricio based on biochemical (Fineschi 1984) and SSR data (Bonavita et al. 2016).

Giovannelli et al. (2017) genotyped P. nigra individuals of subspecies laricio from Corsica (France) and Italy. Dias et al. (2019) compared the ISSR and SCoT patterns of the Portuguese individuals with foreign samples of subsp. laricio from var. calabrica (with origin in the region of Cosenza, Italy) and var. corsicana (Corsica, France). The use of foreign samples from different geographic areas along with the high gene flow and admixture of P. nigra lineages (Giovannelli et al. 2019) may justify the genetic distance observed in this work among the Portuguese populations and the foreign samples (Fig. 3). Despite the achievement of reduced values of genetic differentiation among the Portuguese and foreign samples with the Genalex and STRUCTURE analyses, the PCoA based on the pooled SSR data projected these two groups separately and the structure analysis inferred two main groups (Figs. 2d and 3).

The hypothetic infraspecific taxonomic classification that we propose for the Portuguese planted P. nigra populations is partially supported by a phenotypic characterisation realised in the past century, by Louro (1982). This author considered that the Portuguese P. nigra populations existent at that time belong to the subspecies laricio, salzmanni and nigra. Louro (1982) pointed out that laricio was the best adapted and predominant species in Portugal. Nevertheless, and regarding the recent researches that reported high gene flow and admixture among the P. nigra lineages (Giovannelli et al. 2019; Scotti-Saintagne et al. 2019), we do not discard the realisation of further molecular studies that include subspecies and varieties of other provenances as foreign samples to compare and to pursue the extrapolation of the infraspecific taxonomy of the Portuguese populations of P. nigra.

5 Conclusion

This study constituted the first molecular characterisation of the six P. nigra populations representative of the species distribution in Portugal, using SSR markers.

The SSR molecular data demonstrated that these allochthonous P. nigra populations have high genetic diversity at the intra-population level but reduced genetic differentiation among them. Their genetic structure into two clusters with low genetic differentiation allowed us to suggest that in their plantation, plant material with two different origins was used. The reduced proportion of total genetic differentiation found among the Portuguese, and foreign samples indicated that the former ones had origin in plant material from other subspecies or provenances, not included in this work. Hence, additional molecular studies to pursue the extrapolation of the infraspecific taxonomy of the Portuguese allochthonous populations of P. nigra are required

The understanding of the genetic diversity, structure and relationships of these P. nigra populations will be highly important under the scope of forestry management, genetic improvement and/or for the definition of afforestation and conservation strategies.