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Genetic diversity and origin of the rare, narrow endemic Asperula crassifolia (Rubiaceae)

  • Roberta GargiuloEmail author
  • Olga De Castro
  • Emanuele Del Guacchio
  • Paolo Caputo
Open Access
Original Article

Abstract

We examined the patterns of genetic variation in the narrow endemic Asperula crassifolia (Campania, southern Italy), taking into account the schizoendemic distribution of the Mediterranean members of Asperula sect. Cynanchicae. We obtained plastid DNA sequences of the rps16 intron and the trnC-petN intergenic spacer for several members of A. sect. Cynanchicae, for three living populations (48 individuals) and ten herbarium specimens of A. crassifolia. We also analysed nSSR data for A. crassifolia, to infer population diversity and differentiation. Our results suggest that the centre of diversity of A. crassifolia is the island of Capri, where A. crassifolia harbours four different ptDNA haplotypes, two of which are shared with other species of sect. Cynanchicae. Microsatellite analyses revealed low levels of genetic diversity for the mainland population (Nerano, Sorrentine Peninsula) and the neighbouring Sirenusae islets. Diversity in A. crassifolia is mainly explained by ancestral variation and recent divergence. Rarity in A. crassifolia is a natural condition; however, we express concern for the small census population size as it might trigger further rarefaction.

Keywords

Ancestral polymorphisms Narrow endemics nrSSRs Relict distribution Western Mediterranean 

Introduction

The endemic and disjunct distribution of closely related species in the Mediterranean flora is often thought to have arisen from range fragmentation (Favarger and Contandriopoulos 1961; Thompson et al. 2005; Mayol et al. 2012), mostly due to the spatial isolation of tectonic microplates that occurred during the Tertiary (Cardona and Contandriopoulos 1979; Verlaque et al. 1991; Médail and Quézel 1997). Present-day distribution patterns and speciation dynamics have also been heavily affected by successive palaeogeographical events (climate oscillations and eustatic variations), which fostered dispersal, recolonisation and secondary contacts (e.g. hybridisation) (Médail and Diadema 2009). This results in a more heterogeneous evolutionary history than the one expected from simple range fragmentation and allopatric speciation (Taberlet et al. 1998; Thompson 2005; Weiss and Ferrand 2007; Médail and Diadema 2009). The taxonomic units generated by these processes are not always well separated; the Mediterranean flora is rich in recently diverged and poorly defined taxa with scattered patterns (e.g. Celtis L., De Castro and Maugeri 2006Anchusa L., Coppi et al. 2008; Nigella L., Bittkau and Comes 2009; Senecio L., Molins et al. 2009; Genista L., De Castro et al. 2015) rich in congeneric endemic/widespread pairs (Lavergne et al. 2004; Matesanz et al. 2009). Conservation and decision making for these kinds of group are therefore complex, but essential to guarantee that diversity be maintained together with the main features of Mediterranean vegetation (Quézel 1985; Fréville et al. 1998; Gitzendanner and Soltis 2000). This observation can be easily adapted to other biodiversity hotspots (Myers et al. 2000).

One of the recently diverged groups of plants whose current distribution has been influenced by range fragmentation is Asperula sect. Cynanchicae (DC.) Boiss. (Rubiaceae) (Gargiulo et al. 2015). Among the species of this section, the present study is focused on A. crassifolia L., a narrow endemic to southern Italy (Campania region). This species is one of the several endemic members of the section Cynanchicae and it was previously included in the informal “ser. Palaeomediterraneae”; the series grouped schizoendemic species (i.e. derived from range fragmentation and speciation of a more widespread ancestor; Favarger and Contandriopoulos 1961) distributed in different sites (mostly islands) of the eastern, central and western Mediterranean (Gutermann and Ehrendorfer 2000). However, the monophyly of this group has not been confirmed because of the concurrence of several biological processes affecting the phylogenetic boundaries within sect. Cynanchicae (Gargiulo et al. 2015).

As for many other allied Asperula endemics, the occurrence in stable habitats (steep rocky slopes) is suggestive of poor competitiveness and avoidance of human-affected sites (Polunin 1980; Lavergne et al. 2004; Thompson et al. 2005). Although a direct count for the species has not been carried out, A. crassifolia might be considered as an “extremely narrow endemic” (ENE, López-Pujol et al. 2013; Fernández-Mazuecos et al. 2014) with a very restricted number of populations and small census sizes (< 500). The inclusion of A. crassifolia in the IUCN Red List as a vulnerable species is recommended (Santangelo, pers. comm.)

In the present study, we assess genetic diversity and divergence of A. crassifolia populations, by employing plastid markers and nuclear microsatellites (nrSSRs), to investigate: (1) the relationships among the haplotypes detected in A. crassifolia and in other allied species of A. sect. Cynanchicae; (2) the population history, by assessing genetic variation within this species (and among its populations). Moreover, (3) we discuss the conservation implications of population diversity in A. crassifolia.

Materials and methods

Study species

Asperula crassifolia (Online Resource 1), recently typified by Peruzzi et al. (2013), is a caespitose perennial, with four leaves per node (leafy stipules are here counted as leaves) at the base of the stem, in pairs from middle of the stem upwards. Inflorescences are pyramidal with hermaphroditic flowers; corollas are hypocrateriform, yellowish or reddish, densely patent-hairy as the whole plant, with tube 2–3 times as long as lobes (Guadagno 1913; Ehrendorfer and Krendl 1976). Fruits are dry and indehiscent (two mericarps). Studies on the reproductive and spreading strategies of A. crassifolia are lacking, but self-incompatibility through mechanisms of dimorphic heterostyly is reported for the perennial members of the tribe Rubieae (Bahadur 1968; Anderson 1973; Bremer and Eriksson 1992; Tao and Ehrendorfer 2011). Asperula crassifolia is morphologically similar to its widespread and sympatric congener A. aristata subsp. aristata (e.g. Tenore 1811; Parlatore 1848), especially in the case of individuals with reduced hairiness (in the past indicated as A. commutata C.Presl; Roemer and Schultes 1818; Béguinot 1907; Fiori 1923). However, we comply with the traditional taxonomic treatment, which describes A. crassifolia as a separate species, especially on the basis of the following unique morphological traits: yellowish/reddish, hairy corollas and fleshy leaves (Ehrendorfer and Krendl 1976; Ehrendorfer 1982; Del Guacchio et al. 2016). A presumed close relationship with A. aristata subsp. calabra (Fiori) Del Guacchio & Caputo has been recently disproved (Del Guacchio et al. 2016). Limited morphological divergence, however, is common in A. sect. Cynanchicae (e.g. Ehrendorfer and Krendl 1976; Del Guacchio and Caputo 2005, 2013).

Asperula crassifolia grows on rocky calcareous substrates of the southern Italian island of Capri, in the neighbouring Sirenusae archipelago, and in a single locality of the Sorrentine Peninsula (Nerano). Another locality in inland Campania, i.e. Mount Fellino (668 m a.s.l., Moraldo and La Valva 1989), is not confirmed (Santangelo, pers. comm.) and appears doubtful for geobotanical and ecological reasons.

Sampling

The rarity of A. crassifolia prevented the collection of a conspicuous number of individuals. Non-destructive sampling was conducted during the flowering season of the species and one voucher specimen was collected per population (NAP); sampling sites are shown in Fig. 1. The island of Capri was explored in each of the sites reported in the literature (Boccone 1697; Tenore 1811; Guadagno 1922; Ricciardi 1996); at Nerano, the population was discovered in the 1990s (Caputo et al. 1989–1990) and it is restricted to a rocky slope. At Sirenusae, the only accessible island of the archipelago, Gallo Lungo, was explored.
Fig. 1

Sampling sites with haplotype distribution for Asperula crassifolia populations. Asterisks indicate sampling sites at Capri (from left to right: Monte Solaro, Via Krupp and Pizzolungo-Via Tragara)

Forty-eight individuals were sampled, representative of the populations of A. crassifolia (Table 1). The sampling strategy adopted was aimed at avoiding clonal individuals. Additionally, 10 historical herbarium specimens preserved in NAP and B herbaria (acronyms according to Thiers 2016) and collected in 1808–1933 from the island of Capri were included in the analysis (Online Resource 2). In order to assess the relationships between A. crassifolia haplotypes and those detected in closely related species, we included in the analysis the following members of A. sect. Cynanchicae: A. aristata subsp. aristata, A. aristata subsp. calabra, A. aristata subsp. oreophila (Briq.) Hayek (= A. aristata subsp. scabra Nyman), A. borbasiana (Korica) Korica, A. cynanchica subsp. cynanchica L., A. cynanchica subsp. neglecta (Guss.) Arcang., A. deficiens Viv., A. garganica Huter, Porta & Rigo ex Ehrend. & Krendl, A. gussonei Boiss., A. lutea Sm., A. naufraga Ehrend. & Gutermann, A. paui subsp. paui Font Quer, A. peloritana C.Brullo, Brullo, Giusso & Scuderi, A. pumila Moris, A. staliana subsp. diomedea Korica, Lausi and Ehrend., A. visianii Korica (see Online Resource 2). This selection was carried out according to the results of the phylogenetic investigation on A. sect. Cynanchicae, choosing some of the taxa most closely related to A. crassifolia and the geographically contiguous subspecies of the two most widespread species of the section, i.e. A. aristata and A. cynanchica (Gargiulo et al. 2015).
Table 1

Genetic diversity parameters computed on the microsatellite dataset for Asperula crassifolia populations

Populations

n

N (SE)

HO (SE)

uHE (SE)

PL (SE)

F IS

AR(1)

AR(4)

Capri (Solaro)

5

4.0 (0.33)

0.51 (0.12)

0.46 (0.09)

88.9%

− 0.19

1.46

 

Capri (Pizzolungo-Tragara)

4

2.11 (0.39)

0.43 (0.13)

0.40 (0.13)

66.7%

− 0.16

1.40

 

Capri (Krupp)

10

7.11 (0.93)

0.47 (0.12)

0.41 (0.09)

88.9%

− 0.20

1.41

 

Capri (global)

19

13.22 (1.42)

0.467 (0.11)

0.46 (0.07)

100%

− 0.02

 

2.46

Nerano

19

10.00 (1.55)

0.22 (0.11)

0.36 (0.11)

66.7%

0.34

1.36

2.34

Sirenusae

10

7.11 (0.48)

0.19 (0.09)

0.31 (0.13)

44.4%

0.39

1.31

2.20

Overall

48

10.11 (0.85)

0.29 (0.06)

0.38 (0.06)

70.4% (16.1%)

   

n number of individuals, N mean number of alleles; HO observed heterozygosity; uHE unbiased expected heterozygosity; PL percentage of polymorphic loci; FIS inbreeding coefficient; AR(1) allelic richness based on the minimum sample size (1 diploid individuals), averaged over loci; AR(4) allelic richness based on the minimum sample size (4 diploid individuals, considering Capri as a single population) averaged over loci. (SE) standard error for the average values

Total genomic DNA was extracted after lysis in liquid nitrogen with CTAB method (Doyle and Doyle 1987); for recalcitrant material, the extraction was followed by purification using GENECLEAN® II Kit (MP Biomedicals).

Plastid markers

Several plastid markers (trnC-petN, psbA-trnH and trnQ-rps16 intergenic spacers, matK gene and rps16 intron) were screened for variability, by analysing the most divergent species of the dataset (following the phylogenetic outline of Gargiulo et al. 2015); eventually, rps16 intron and trnC-petN intergenic spacer were selected. For rps16 amplifications, we used the primers described by Oxelman et al. (1997) and an internal primer designed ad hoc for Asperula; primer pairs are listed in Online Resource 3. Polymerase chain amplifications were carried out in a final volume of 25 μL, 1 U DreamTaq DNA Polymerase (Fermentas, Thermo Scientific), 1X DreamTaq Buffer, 0.5–2.5 mM MgCl2, 0.2 mM dNTPs, 0.5 μM primers, DNA at various concentrations, water to the final volume and the following thermal programme: denaturation at 94 °C for 3 min, 30 cycles at 94 °C for 30 s, annealing at 55 °C for 30 s, extension for 45 s at 72 °C, followed by 5 min of final extension. PCR products were purified using PEG 8000 precipitation (http://www.mcdb.lsa.umich.edu/labs/olsen/files/PCR.pdf, last accessed January 2018). Sequencing was conducted using BigDye Terminator Cycle Sequencing Kit v3.1 (Applied Biosystems, Life Technologies), with the following conditions: 96 °C for 10 s, 50 °C for 5 s and 60 °C for 4 min, for 25 cycles. Sequence purification was conducted according to Di Maio and De Castro (2013). Sequences were electrophoresed on an ABI 3130 Genetic Analyzer (Applied Biosystems, Life Technologies). The resulting sequences have been submitted to GenBank (see Online Resource 2 for the accession numbers).

Plastid sequences were aligned using Muscle (Edgar 2004), as implemented in Mesquite v3.01 (Maddison and Maddison 2014); rps16 alignment required further manual editing, because of the presence of insertions and deletions. Two distinct matrices were generated: one with the sequences of the entire A. crassifolia sampling (A. crassifolia dataset) and the other including the detected A. crassifolia haplotypes plus the sequences of the other Asperula accessions (total dataset). Accessions in the total dataset were assigned to different haplotypes using DnaSP v5.10 (Librado and Rozas 2009). Relationships among A. crassifolia haplotypes were presented as a statistical parsimony network, generated using the program TCS v1.21 (Clement et al. 2000). Gaps were treated as fifth state, by converting each indel to a one-nucleotide state, with a 94%-connection limit, as it gave more resolution than the default value (95%). Asperula aristata subsp. aristata from Sorrentine Peninsula (i.e. DEI) was used as an outgroup.

The relationships among all the Asperula accessions were investigated in MrBayes v3.2.6 (Huelsenbeck and Ronquist 2001; Ronquist and Huelsenbeck 2003) using the online facility CIPRES Science Gateway (Miller et al. 2010). Asperula lutea was chosen as outgroup. The model GTR + I was selected as best-fitting according to jModeltest v3.7 (Posada and Crandall 1998), using the Akaike information criterion (Akaike 1974); indels were treated as missing data. Markov chain Monte Carlo sampling was conducted for 1 × 107 number of generations, with a sampling frequency of 1000 generations and a burn-in of 25%.

Plastid DNA haplotype diversity for each population (hS) and for the total range (hT), and two coefficients of gene differentiation (GST, NST) were estimated according to Pons and Petit (1996), as implemented in PermutCpSSR v2, with 1000 permutations.

Nuclear microsatellite genotyping

Nine loci were selected among the microsatellite markers developed for A. crassifolia: GA_1, GA_11, GA_17, GA_5A, GA_55A, GA_30C, GA_51C, GTT_1, GTT_ 22 (Gargiulo and De Castro 2015) and amplified in sampled populations; herbarium specimens were not genotyped as they could not be considered as discrete populations. Amplifications were carried out in a final volume of 25 μL, with 50 ng genomic DNA, 4 pmol reverse primer, 4 pmol labelled-M13 fluorescent dye (6-FAM, PET, VIC, NED), 1 pmol M13-tailed forward primer, 1X Taq Buffer B (Solis BioDyne), 0.8 mM dNTP mix (Promega), 2 mM MgCl2, 1U FIREPol DNA Polymerase (Solis BioDyne), on a 2700 thermal cycler (Applied Biosystems). Thermal programme is described in Gargiulo and De Castro (2015). One millilitre of diluted PCR product was added to 12 μL of formamide/GeneTrace 500 LIZ size standard (Carolina Biosystems) and run on the ABI 3130 Genetic Analyzer (Applied Biosystems). Fragments sizing was conducted in PeakScanner v1.0 (Applied Biosystems).

Presence of null alleles and genotyping errors (large allele dropout or stuttering) was assessed with MicroChecker v2.2.3 (van Oosterhout et al. 2004) using 1000 randomisations; anomalies were rectified using the Brookfield 2 algorithm (Brookfield 1996). We assessed the occurrence of clonal genotypes in the R (R Development Core Team 2008) package Poppr v2.8 (Kamvar et al. 2014). In order to assess the effect of null alleles and to correct the bias induced by their presence on the FST estimation, we used the Excluding Null Alleles (ENA) method as implemented in the program FreeNA (Chapuis and Estoup 2007), using 10,000 bootstrap replications. The software estimates the null allele frequencies and the global and pairwise FST values (Weir 1996). We conducted this assessment on the uncorrected dataset and after Brookfield correction, to verify the congruence of all the pairwise FST outcomes.

Departures from Hardy–Weinberg equilibrium (HWE), linkage disequilibrium (LD) and FIS values were evaluated with the exact tests implemented in GenePop v4.2.2 (Raymond and Rousset 1995). Significance level was adjusted using sequential Bonferroni correction for multiple comparisons (Rice 1989). GenAlEx v6.5 (Peakall and Smouse 2006) was used to calculate genetic diversity parameters: i.e. observed and (unbiased) expected heterozygosities (respectively HO, HE), percentage of polymorphic loci and total number of alleles. Allelic richness was calculated by using the rarefaction approach (Petit et al. 1998), as implemented in FSTAT v2.9.3.2 (Goudet 2002).

Analysis of molecular variance (AMOVA), as implemented in Arlequin v3.5 (Excoffier and Lischer 2010) Locus by Locus Amova using the number of different alleles—FST), was carried out to verify partitioning of total genetic variation at the infra- and interpopulation levels, considering population subdivision at Capri based on the locality of sampling (Fig. 1). Statistical significance was tested with 10,000 permutations.

Population structure was assessed by using the software MavericK v1.0 (Verity and Nichols 2016), using a procedure known as Thermodynamic Integration (TI), which is considered as more accurate in determining the real value of genetic clusters (K) (Verity and Nichols 2016). Both admixture and without-admixture models were tested, with K ranging from 1 to 8, 15 × 104 MCMC iterations, a burn-in of 5000 samples, repeating the MCMC procedure 5 times. TI parameters were: 50 rungs, 5000 burn-in and 55 × 103 thermodynamic samples. Bar plots were obtained by employing Distruct v1.1 (Rosenberg 2004). Results obtained in MavericK were compared to the ones obtained in Structure v2.3 (Pritchard et al. 2000; see “Results” section).

Demographic history of the populations, with particular emphasis on recent (i.e. last 2Ne–4Ne generations; Luikart et al. 1998a) reduction in effective population size was explored with Bottleneck v1.2 (Piry et al. 1999). Tests were performed with 10,000 replications, under the step-wise mutation model (SMM) and the two-phase model (TPM), which are believed to be appropriate for microsatellite evolution (Luikart and England 1999; Balloux and Lugon-Moulin 2002; Williamson-Natesan 2005). TPM model included a proportion of SMM set to 70% and the variance to 30% (multiple step mutation). Sign test and the one-tailed Wilcoxon signed rank were employed to verify significant deviations in observed heterozygosity, as predicted in the cases of recent bottleneck. Bottleneck method is based on the assumption that allele frequency is the result of equilibrium between mutation and genetic drift (Piry et al. 1999); consequently, we also tested the departure (Mode-shift) from the L-shaped distribution of allelic frequencies, which is typical of an equilibrium situation (Luikart et al. 1998b). One deme at Capri (Pizzolungo-Tragara) was excluded from the Bottleneck computations, as the number of individuals (n = 4) was not sufficient to perform the analysis.

Results

Plastid data

The A. crassifolia dataset consisted of 521 positions for the rps16 intron and 729 positions for trnC-petN, whereas for the total dataset, rps16 alignment resulted in 574 positions and trnC-petN in 685 positions (as a result of removing the initial portion because it was incomplete for some of the accessions). Total alignment is shown in Online Resource 4. The rps16 region turned out to be the most variable, consisting of different indel polymorphisms, each one considered as a different state in the TCS analysis (see Online Resource 5 for the polymorphic positions in A. crassifolia). On the basis of these polymorphisms, 17 plastid haplotypes were recognised for the Asperula accessions included in the analysis, four of them detected in A. crassifolia. TCS network is shown in Fig. 2. Haplotype A was the only one occurring at Nerano and Sirenusae; at Capri, it was present in a single accession, the herbarium specimen PAS2 collected in 1868 (see Online Resource 2). Noteworthily, haplotype B was the most represented in the historical herbarium specimens (8 out of 10 specimens), whereas it is less represented in the present-day collection at Capri (3 out of 19 specimens). Haplotype C was the most abundant, whereas haplotype D was private and present in a single individual at Capri (Online Resource 2).
Fig. 2

TCS network, showing the relationship among the haplotypes of Asperula crassifolia. Asperula aristata subsp. aristata DEI was chosen as outgroup

The Bayesian analysis on the total dataset showed that Haplotype C is clustered with A. cynanchica accessions (A. cynanchica subsp. cynanchica AN1, A. cynanchica subsp. neglecta) and the Adriatic taxa A. borbasiana and A. staliana subsp. diomedea in a strongly supported group (posterior probability—PP = 0.99), whereas the other haplotypes are shared with several other taxa (Figs. 3, 4).
Fig. 3

Bayesian phylogram showing the relationships among haplotypes in Asperula crassifolia and other members of Asperula, based on plastid regions. Note that gaps were treated as missing data

Fig. 4

Map showing the distribution of the Asperula accessions analysed in the present study. Taxa sharing the same haplotype as Asperula crassifolia are marked with boxes indicating the haplotype name. ARI2 A. aristata 2H; ARI8 A. aristata 8H; BOR A. borbasiana; CAL A. calabra; CRA A. crassifolia; CYN A. cynanchica subsp. cynanchica AN1; DIO A. staliana subsp. diomedea; DEI A. aristata subsp. aristata DEI; DEF A. deficiens; GAR A. garganica; GUS A. gussonei; LUT A. lutea; NAU A. naufraga; NEG A.cynanchica subsp. neglecta; ORE1 A. aristata subsp. oreophila 131; ORET A. aristata subsp. oreophila TRM11; PAU A. paui; PUM A. pumila; PEL A. peloritana; VIS A. visianii. For explanations about the accessions, see Online Resource 2

Genetic diversity indices are shown in Online Resource 6; hT and hS were respectively 0.70 and 0.20. The coefficients of differentiation measured over the three populations were GST = 0.705 and NST = 0.720.

Nuclear microsatellite data

Among the nine microsatellite loci, no evidence of large allele dropout or stuttering was detected; the presence of null alleles was suggested by the homozygote excess for the loci GA_17, GA_5A, GA_55A, GA_30C, differentially displayed in each population (Online Resource 7). Multilocus genotypes were variable in 47 out of 48 individuals, as two individuals at Nerano displayed the same genotype (although with a large amount of missing data). Null alleles did not affect significantly pairwise FST values (Online Resource 8).

Deviation from HWE was found for the locus GA_1 at Capri and Nerano, and for GA_17 and GA_5A at Nerano and Sirenusae (see Online Resource 9 for the Bonferroni correction); there was no evidence for linkage disequilibrium. FIS values significantly departed from random mating for almost all loci (Online Resource 10); with a mixture of negative and positive FIS values at Capri and predominantly positive values at Nerano and Sirenusae. Mean values of the genetic diversity indices for each population and total are summarised in Table 1 (details for each locus are shown in Online Resource 7). Globally, Nerano and Sirenusae populations exhibit less genetic diversity than Capri population in terms of heterozygosity. Only at Capri, HO was comparable to HE (respectively 0.468 and 0.459), whereas for Nerano and Sirenusae populations, HO was lower than expected (see Table 1). Allelic richness computed on the minimum sample size of one diploid individual was highest in Capri’s locations (values averaged over loci > 1.40, Table 1; detailed values for each locus are shown in Online Resource 11). AMOVA analysis showed moderate genetic differentiation among populations (21%), with most of the variation partitioned within individuals (67%; Online Resource 12).

Analysis of genetic structure as implemented in MavericK revealed a higher posterior probability associated with the without-admixture model, with the most likely K being equal to 2 (Fig. 5). Admixture levels were higher in Nerano, where individuals displayed a mixed proportion of the two genetic clusters. The most homogeneous populations in terms of genetic structure are Sirenusae and Capri-Via Krupp (Fig. 5). A comparison with the results obtained in the software Structure is shown in Online Resource 13.
Fig. 5

Results of the analysis of genetic differentiation in Asperula crassifolia, as implemented in MavericK v1.0. a Posterior probabilities associated with the without-admixture model and the admixture model. b Posterior probabilities associated with the number of genetic clusters, K. c Bar plot for K = 2

Bottleneck analysis did not indicate excess of heterozygosity associated with recent bottlenecks at Capri (considered as a single population) and at Nerano, either under SMM or TPM models. However, there was evidence of a shift in the normal L-shaped distribution at each locality at Capri and at Sirenusae; no evidence of bottleneck was found according to the other two tests (Online Resource 14).

Discussion

Genetic diversity and structure of the Asperula crassifolia populations

Both plastid and nuclear markers revealed a limited genetic diversity in Nerano and Sirenusae populations. One plastid haplotype was detected, and heterozygosity values computed on the nSSRs were lower than the values reported for narrow/endemic populations by Nybom (2004, i.e. HO = 0.52/0.32 and HE = 0.56/0.42, with N < 16). Moreover, inbreeding coefficients per locus were mainly positive. If we exclude the presence of clonal individuals, either the occurrence of null alleles, non-random mating or unrecognised internal structure producing an apparent heterozygote deficit (Wahlund effect), may be the causes of such values of FIS. Although null alleles are likely for GA_17 and GA_5A, as the homozygote excess seems to be locus-specific, the population-specific trend at Nerano and Sirenusae suggests that limited gene flow occurs among the populations. This is also supported by pairwise FST values (Online Resource 8).

At Capri, instead, three different haplotypes were detected (apart from the haplotype detected in a single herbarium specimen) and variation at nuclear microsatellites was expressed by higher heterozygosity values (Table 1) and comparable allelic richness, when alleles were normalised to the minimum sample size (Table 1; Online Resource 11). However, allelic richness may be underestimated because of the constraints imposed by the rarefaction method. The analysis of genetic differentiation revealed that the most homogeneous populations in terms of genetic clusters were Capri-Krupp and Sirenusae (Fig. 5), where almost all individuals have proportions of ancestry close to 100%. At Nerano, some individuals displayed a shared ancestry with individuals at Sirenusae; however, most of them were members of the main cluster detected in Capri, in line with the FST values (0.09 for Capri/Nerano vs. 0.21 Nerano/Sirenusae; Online Resource 8). These results were also in line with the AMOVA results, which defined a moderate partition of genetic variation among populations (ca. 20%; Online Resource 12).

The Bottleneck analysis showed contrasting results, in supporting a recent reduction in population size at Sirenusae and in the demes at Capri through the allele frequency distribution analysis, but not through the other tests (Online Resource 14). Moreover, the small population sizes at Solaro and Krupp and the fact that interbreeding among demes might occur in the island of Capri may have biased the outcome of the analysis, by determining spurious signals of bottlenecks (Wakeley 1999; Chikhi et al. 2010). At Sirenusae, on the other hand, a recent bottleneck may be supported by the observation of a particularly abundant population at the beginning of the last century (Guadagno 1913). We believe that Sirenusae is more threatened with genetic drift, as the observed levels of genetic variation are lower (Table 1, Online Resource 11). The evidence obtained from the nSSRs and, especially, from the plastid haplotypes suggests that genetic diversity at Nerano and Sirenusae is lower but not distinct from genetic diversity in Capri. However, our results do not clarify whether the lower genetic variation at Nerano and Sirenusae is either a consequence of a bottleneck or of a founder event from Capri.

Phylogeographic history and origin of Asperula crassifolia

Asperula crassifolia shares haplotypes with other members of the sect. Cynanchicae. In particular, Haplotype C is shared by the single island endemic A. borbasiana (Krk, Croatia) and Haplotype B is shared by various species of Asperula, including two individuals of A. aristata s.l. from southern Italy. The remaining two haplotypes are exclusive to A. crassifolia (Fig. 4; Online Resource 5). Haplotype sharing among different species may be explained as a consequence of (1) persistence of ancestral haplotypes (i.e. incomplete lineage sorting; Pamilo and Nei 1988; Maddison 1997; Avise 2000) or (2) hybridisation (and introgression) with the only sympatric taxon of the section, i.e. A. aristata subsp. aristata.

It is noteworthy that the two accessions of A. aristata subsp. aristata sampled in the Sorrentine Peninsula bear two distinct haplotypes, whereas the Haplotype B of A. crassifolia is identical to the haplotypes found in two individuals from other locations of southern Italy (subsp. aristata 2H and subsp. oreophila TRM11) (Fig. 4). However, our recent search for A. aristata at Capri, in the sites indicated under various synonyms by Tenore (1811), Guadagno (1922) and, even more recently, by Ricciardi (1996) has been unsuccessful. Consequently, it was not possible to confirm hybridisation between A. aristata and A. crassifolia. In other parts of A. aristata’s range, however, hybridisation and/or secondary contacts are suggested (Gutermann and Ehrendorfer 2000; Gargiulo et al. 2015), and the occurrence of individuals with intermediate features are reported (e.g. Tenore 1831, sub A. tomentosa). Nevertheless, we presume that ptDNA variation in A. crassifolia at Capri is also attributable to the persistence of ancestral haplotypes due to insufficient divergence time (Avise 2000). Hence, genetic variation in A. crassifolia is arguably part of the genetic variation of the ancestor of different species within sect. Cynanchicae. Population genetics theory predicts that, in small populations, polymorphisms will be lost more rapidly by stochastic events (i.e. genetic drift) and thus ancestral polymorphisms are more likely to occur in situation of recent and ongoing diversification (Ellstrand and Elam 1993). Recent diversification is hypothesised for other Mediterranean schizoendemic groups, such as Senecio sect. Senecio (Comes and Abbott 2001), Digitalis minor L. (Sales et al. 2001) and Crepis triasii (Mayol et al. 2012), where ancestral polymorphism persists by virtue of recent diversification.

Our data suggest incomplete lineage sorting related to recent and ongoing diversification in the genus Asperula, as previously reported; however, our results do not allow us to determine when A. crassifolia speciated. Capri Island and Sorrentine Peninsula were intensely shaped by eustatic and tectonic variations until the upper Pleistocene. During the middle Pleistocene (700,000–120,000 ya), a tectonic depression determined the insularity of Capri. Afterwards, an alternation of sea rises and regressions shaped the island profile and made repeatedly possible a connection via land bridges of Capri, Sirenusae, and Sorrentine Peninsula, up to 12,000 ya (Barattolo et al. 1992; Guida and Vallario 2003). Consequently, it is possible that the splitting of A. crassifolia occurred at any point during a period of isolation. Higher genetic diversity at Capri may suggest that the origin of the species occurred in the island. However, we cannot exclude that speciation occurred in Sorrentine Peninsula, where individuals and genetic variation were subsequently lost, according to the current diversity observed at Nerano and Sirenusae. Further population genetic research is necessary to clarify variation and relationships in the A. aristata/A. crassifolia species pair. However, we support the current recognition of A. crassifolia as a separate species, on the basis of morphological traits (Ehrendorfer and Krendl 1976) and of the exclusive haplotypes found in the present study.

Conclusions and conservation remarks

The Quaternary period is responsible for the genetic diversification and speciation of many endemic species (Hewitt 2000, 2004; Willis and Niklas 2004), including A. crassifolia (Gargiulo et al. 2015). Rarity in most of these species is mainly an innate condition, not descending from genetic erosion, but from local adaptation and species divergence in a peculiar ecological context (Rabinowitz et al. 1986; Gaston 1994). However, rarity triggers further rarefaction (O’Grady et al. 2004), especially in a human-affected and changing landscape. Despite the problems with taxonomic circumscription due to the “speciation in action” (Gargiulo et al. 2015), these taxa deserve protection as they are extremely valuable not only as part of the endemic/rare flora, but also as a present record of the intricate pattern of phenomena which lead to speciation in highly dynamic ecosystems. In A. crassifolia, we have observed small populations with some possible size reductions compared to past observations, and low genetic diversity in Nerano and Sirenusae. Although the number of individuals growing in inaccessible sites has not been estimated, it is quite improbable that population sizes will be sufficient to counterbalance the effects of genetic drift, inbreeding depression and stochastic oscillations of the habitat. These processes may be exacerbated by small population sizes. From a conservation perspective, human activity may contribute to further rarefaction of the species. Risk factors for the A. crassifolia are especially connected to the management systems against the natural erosion of the rocky slopes where the species grows (i.e. containing nets), and to the maintenance of tourist paths, especially at Capri and Nerano. In the Sirenusae archipelago, on the contrary, the habitat is relatively protected as not easily accessible; however, the population at Sirenusae was previously reported as more abundant than in Capri (Guadagno 1913, 1922). Given our findings, we recommend that the diversity in Capri be prioritised for conservation protection. Little is known about the actual number of individuals, which is presumably very low (R. Gargiulo, pers. obs.), but some effort should be made in order to detect individuals growing in inaccessible coastal slopes, both in Capri and in the Sirenusae archipelago. As the species is considered as Vulnerable according to IUCN (2012) [VU B1 ab (I, II, III, IV); B2 ab (I, II, III, IV), Santangelo, pers. comm.], ex situ preservation of the germplasm might be beneficial for eventual reintroductions, together with research aimed at investigating germination conditions and pollination mechanisms and constraints.

Notes

Acknowledgements

We are grateful to Annalisa Santangelo for her information about IUCN status and the botanical survey on Mount Fellino. The staff of the “Area Marina Protetta di Punta Campanella”, made the sampling at Gallo Lungo (Sirenusae) possible: the director, Antonino Miccio, and his collaborators, Carmela Guidone, Rosalinda Testa, Nicola Guida and Luca Urro are therefore gratefully acknowledged here; we are also greatly indebted towards Giovanni Russo, owner of the island, who granted access to his property. The staff of the following herbaria: ATHU, B, BC, CP, FI, G, L, MWG, UPA (in the person of the curator Elena Suslova), E (in the person of the curator Suzanne Cubey) is gratefully acknowledged. We also express our gratitude to Fabrizio Bartolucci, Liliana Bernardo, Fabio Conti, Gianpietro Giusso Del Galdo, Duilio Iamonico, Tamara Kirin, Panayiotis Trigas and Robert P. Wagensommer, who kindly provided specimens or herbarium information. Roberta Vallariello (NAP) and Luca Paino are gratefully acknowledged for their help and assistance with herbarium and laboratory procedures.

Author contribution

The authors comply will the COPE guidelines, according to the journal rules.  R.G. and O.D.C. conceived the study. R.G. collected field samples. R.G. and O.D.C. conducted laboratory work. R.G. performed bioinformatic analyses. E.D.G. supervised the analyses of herbarium specimens and provided details about morphology and taxonomic descriptions. R.G. produced maps and graphical artworks. R.G. wrote the manuscript with significant contributions from all the authors. P.C. coordinated the work of all authors and specifically contributed to the Discussion. This study was supported by O.D.C.'s research funds.

Compliance with ethical standards

Conflict of interest

The authors declare that there are no conflicts of interest.

Supplementary material

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References

  1. Akaike H (1974) A new look at the statistical model identification. IEEE Trans Automatic Control 19:716–723.  https://doi.org/10.1109/TAC.1974.1100705 CrossRefGoogle Scholar
  2. Anderson WR (1973) A morphological hypothesis for the origin of heterostyly in the Rubiaceae. Taxon 22:537–542.  https://doi.org/10.2307/1218628 CrossRefGoogle Scholar
  3. Avise JC (2000) Phylogeography: the history and formation of species. Harvard University Press, HarvardGoogle Scholar
  4. Bahadur B (1968) Heterostyly in Rubiaceae: a review. J Osmania Univ (Sci) 4(Golden Jubilee Special Volume):207–238Google Scholar
  5. Balloux F, Lugon-Moulin N (2002) The estimation of population differentiation with microsatellite markers. Molec Ecol 11:155–165.  https://doi.org/10.1046/j.0962-1083.2001.01436.x CrossRefGoogle Scholar
  6. Barattolo F, Cinque A, D’Alessandro E, Guida M, Romano P, Russo Ermolli E (1992) Geomorfologia ed evoluzione tettonica quaternaria dell’Isola di Capri. Stud Geol Camerti 1:221–229Google Scholar
  7. Béguinot A (1907) Flora Italica Exsiccata. Nuovo Giorn Bot Ital ns 14:275–276Google Scholar
  8. Bittkau C, Comes HP (2009) Molecular inference of a Late Pleistocene diversification shift in Nigella s. lat. (Ranunculaceae) resulting from increased speciation in the Aegean archipelago. J Biogeog 36:1346–1360.  https://doi.org/10.1111/j.1365-2699.2008.02003.x CrossRefGoogle Scholar
  9. Boccone P (1697) Museo di piante rare della Sicilia, Malta, Corsica, Italia, Piemonte, e Germania. Per Io. Baptista Zuccato, VenetiaGoogle Scholar
  10. Bremer B, Eriksson O (1992) Evolution of fruit characters and dispersal modes in the tropical family Rubiaceae. Biol J Linn Soc 47:79–95.  https://doi.org/10.1111/j.1095-8312.1992.tb00657.x CrossRefGoogle Scholar
  11. Brookfield J (1996) A simple new method for estimating null allele frequency from heterozygote deficiency. Molec Ecol 5:453–455.  https://doi.org/10.1046/j.1365-294X.1996.00098.x CrossRefGoogle Scholar
  12. Caputo G, La Valva V, Nazzaro R, Ricciardi M (1989–1990) La flora della penisola Sorrentina (Campania). Delpinoa ns 31/32:3–97Google Scholar
  13. Cardona MA, Contandriopoulos J (1979) Endemism and evolution in the islands of the Western Mediterranean. Plants and Islands. London Academic Press, LondonGoogle Scholar
  14. Chapuis MP, Estoup A (2007) Microsatellite null alleles and estimation of population differentiation. Molec Biol Evol 24:621–631.  https://doi.org/10.1093/molbev/msl191 CrossRefPubMedGoogle Scholar
  15. Chikhi L, Sousa VC, Luisi P, Goossens B, Beaumont MA (2010) The confounding effects of population structure, genetic diversity and the sampling scheme on the detection and quantification of population size changes. Genetics 186:983–995.  https://doi.org/10.1534/genetics.110.118661 CrossRefPubMedPubMedCentralGoogle Scholar
  16. Clement M, Posada D, Crandall K (2000) TCS: a computer program to estimate gene genealogies. Molec Ecol 9:1657–1660.  https://doi.org/10.1046/j.1365-294x.2000.01020.x CrossRefGoogle Scholar
  17. Comes HP, Abbott RJ (2001) Molecular phylogeography, reticulation, and lineage sorting in Mediterranean Senecio sect. Senecio (Asteraceae). Evolution 55:1943–1962.  https://doi.org/10.1554/0014-3820(2001)055%5b1943:MPRALS%5d2.0.CO;2 CrossRefPubMedGoogle Scholar
  18. Coppi A, Mengoni A, Selvi F (2008) AFLP fingerprinting of Anchusa (Boraginaceae) in the Corso-Sardinian system: genetic diversity, population differentiation and conservation priorities in an insular endemic group threatened with extinction. Biol Conservation 141:2000–2011.  https://doi.org/10.1016/j.biocon.2008.05.015 CrossRefGoogle Scholar
  19. De Castro O, Maugeri G (2006). Molecular notes on the Mediterranean species of the genus Celtis L. (Celtidaceae). Pl Biosyst 140:171–175CrossRefGoogle Scholar
  20. De Castro O, Véla E, Vendramin GG, Gargiulo R, Caputo P (2015) Genetic structure in the Genista ephedroides complex (Fabaceae) and implications for its present distribution. Bot J Linn Soc 177:607–618.  https://doi.org/10.1111/boj.12263 CrossRefGoogle Scholar
  21. Del Guacchio E, Caputo P (2005) Taxonomic notes on Asperula neglecta Guss. and Asperula nitens Guss. (Rubiaceae) and typification of their names. Webbia 60:569–576.  https://doi.org/10.1080/00837792.2005.10670787 CrossRefGoogle Scholar
  22. Del Guacchio E, Caputo P (2013) Taxonomic notes on Asperula sect. Cynanchicae (Rubiaceae). Taxon 62:394–400.  https://doi.org/10.12705/622.9 CrossRefGoogle Scholar
  23. Del Guacchio E, Gargiulo R, Caputo P (2016) Asperula calabra (Rubiaceae) and allied taxa in southern Apennines, Italy. Pl Biosyst 151:352–360.  https://doi.org/10.1080/11263504.2016.1174175 CrossRefGoogle Scholar
  24. Di Maio A, De Castro O (2013) SSR-patchwork: an optimized protocol to obtain a rapid and inexpensive SSR library using first-generation sequencing technology. Appl Pl Sci 1:1200158.  https://doi.org/10.3732/apps.1200158 CrossRefGoogle Scholar
  25. Doyle JJ, Doyle JL (1987) A rapid DNA isolation method for small quantities of fresh tissues. Phytochem Bull 19:11–15Google Scholar
  26. Edgar RC (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucl Acids Res 32:1792–1797.  https://doi.org/10.1093/nar/gkh340 CrossRefPubMedGoogle Scholar
  27. Ehrendorfer F (1982) Asperula L. In: Pignatti S (ed) Flora d’Italia, vol. 2. Edagricole, Bologna, pp 355–359Google Scholar
  28. Ehrendorfer F, Krendl F (1976) Asperula L. In: Tutin TG, Burges NA, Chater AO, Edmondson JR, Heywood VH, Moore DM, Valentine DH, Walters SM, Webb DA (eds) Flora Europaea, vol. 3. Cambridge University Press, Cambridge, pp 4–14Google Scholar
  29. Ellstrand NC, Elam DR (1993) Population genetic consequences of small population size: implications for plant conservation. Annual Rev Ecol Syst 24:217–242.  https://doi.org/10.1146/annurev.es.24.110193.001245 CrossRefGoogle Scholar
  30. Excoffier L, Lischer HEL (2010) Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Molec Ecol Res 10:564–567.  https://doi.org/10.1111/j.1755-0998.2010.02847.x CrossRefGoogle Scholar
  31. Favarger C, Contandriopoulos J (1961) Essai sur l’endémisme. Soc Bot Suisse 71:384–408Google Scholar
  32. Fernández-Mazuecos M, Jiménez-Mejías P, Rotllan-Puig X, Vargas P (2014) Narrow endemics to Mediterranean islands: moderate genetic diversity but narrow climatic niche of the ancient, critically endangered Naufraga (Apiaceae). Perspect Pl Ecol Evol Syst 16:190–202.  https://doi.org/10.1016/j.ppees.2014.05.003 CrossRefGoogle Scholar
  33. Fiori A (1923) Nuova flora analitica d’Italia, contenente la descrizione delle piante vascolari indigene inselvatichite e largamente coltivate in Italia, del dott. Adriano Fiori vol. 3. Tipografia di M. Ricci, FlorenceGoogle Scholar
  34. Fréville H, Colas B, Ronfort J, Riba M, Olivieri I (1998) Predicting endemism from population structure of a widespread species: case study in Centaurea maculosa Lam. (Asteraceae). Conservation Biol 12:1269–1278.  https://doi.org/10.1111/j.1523-1739.1998.96483.x CrossRefGoogle Scholar
  35. Gargiulo R, De Castro O (2015) Isolation and characterization of nuclear microsatellite loci for the short-range endemic Asperula crassifolia L. (Rubiaceae). Conservation Genet Resources 7:187–189.  https://doi.org/10.1007/s12686-014-0328-z CrossRefGoogle Scholar
  36. Gargiulo R, Del Guacchio E, Caputo P (2015) Phylogenetic reconstruction of Asperula sect. Cynanchicae (Rubiaceae) reveals a mosaic of evolutionary histories. Taxon 64:754–769.  https://doi.org/10.12705/644.7 CrossRefGoogle Scholar
  37. Gaston KJ (1994) What is rarity? In: Gaston KJ (ed) Rarity. Population and community biology series, vol. 13. Springer, Dordrecht, pp 1–21Google Scholar
  38. Gitzendanner MA, Soltis PS (2000) Patterns of genetic variation in rare and widespread plant congeners. Amer J Bot 87:783–792CrossRefGoogle Scholar
  39. Goudet J (2002) FSTAT, a program to estimate and test gene diversities and fixation indices, version 2.9.3. Available at: https://www2.unil.ch/popgen/softwares/fstat.htm. Accessed Aug 2017
  40. Guadagno M (1913) Prime notizie sulla vegetazione delle isole Sirenuse. Bull Orto Bot Regia Univ Napoli 3:75–91Google Scholar
  41. Guadagno M (1922) Note ed aggiunte alla flora dell’isola di Capri. Nuovo Giorn Bot Ital ns 29:44–66Google Scholar
  42. Guida M, Vallario A (2003) Muri sorrentini. Geologia, storia, tecnica e rischio ambientale. Franco Di Mauro Editore, NapoliGoogle Scholar
  43. Gutermann W, Ehrendorfer F (2000) Asperula naufraga (Rubiaceae), a new species from Zakinthos (Ionian Islands, Greece), with notes on its ecology, karyology and relationship (material towards a Flora Ionica, 1). Bot Chron 13:61–70Google Scholar
  44. Hewitt G (2000) The genetic legacy of Quaternary ice ages. Nature 405:907–913.  https://doi.org/10.1038/35016000 CrossRefPubMedGoogle Scholar
  45. Hewitt GM (2004) Genetic consequences of climatic oscillations in the Quaternary. Philos Trans Roy Soc B Biol Sci 359:183–195.  https://doi.org/10.1098/rstb.2003.1388 CrossRefGoogle Scholar
  46. Huelsenbeck JP, Ronquist F (2001) MRBAYES: Bayesian inference of phylogeny. Bioinformatics 17:754–755.  https://doi.org/10.1093/bioinformatics/17.8.754 CrossRefGoogle Scholar
  47. IUCN (2012) IUCN red list categories and criteria: v3.1, 2nd edn. IUCN, GlandGoogle Scholar
  48. Kamvar ZN, Tabima JF, Grünwald NJ (2014) Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2:e281CrossRefGoogle Scholar
  49. Lavergne S, Thompson JD, Garnier E, Debussche M (2004) The biology and ecology of narrow endemic and widespread plants: a comparative study of trait variation in 20 congeneric pairs. Oikos 107:505–518.  https://doi.org/10.1111/j.0030-1299.2004.13423.x CrossRefGoogle Scholar
  50. Librado P, Rozas J (2009) DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25:1451–1452.  https://doi.org/10.1093/bioinformatics/btp187 CrossRefGoogle Scholar
  51. López-Pujol J, Martinell MC, Massó S, Blanché C, Sáez L (2013) The ‘paradigm of extremes’: extremely low genetic diversity in an extremely narrow endemic species, Coristospermum huteri (Umbelliferae). Pl Syst Evol 299:439–446.  https://doi.org/10.1007/s00606-012-0732-3 CrossRefGoogle Scholar
  52. Luikart G, England PR (1999) Statistical analysis of microsatellite DNA data. Trends Ecol Evol 14:253–256.  https://doi.org/10.1016/S0169-5347(99)01632-8 CrossRefPubMedGoogle Scholar
  53. Luikart G, Allendorf FW, Cornuet JM, Sherwin WB (1998a) Distortion of allele frequency distributions provides a test for recent population bottlenecks. J Heredity 89:238–247.  https://doi.org/10.1093/jhered/89.3.238 CrossRefGoogle Scholar
  54. Luikart G, Sherwin WB, Steele BM, Allendorf FW (1998b) Usefulness of molecular markers for detecting population bottlenecks via monitoring genetic change. Molec Ecol 7:963–974.  https://doi.org/10.1046/j.1365-294x.1998.00414.x CrossRefGoogle Scholar
  55. Maddison WP (1997) Gene trees in species trees. Syst Biol 46:523–536.  https://doi.org/10.1093/sysbio/46.3.523 CrossRefGoogle Scholar
  56. Maddison WP, Maddison DR (2014) Mesquite: a modular system for evolutionary analysis. Available at: http://mesquiteproject.org. Accessed Aug 2017
  57. Matesanz S, Valladares F, Escudero A (2009) Functional ecology of a narrow endemic plant and a widespread congener from semiarid Spain. J Arid Environm 73:784–794.  https://doi.org/10.1016/j.jaridenv.2009.03.009 CrossRefGoogle Scholar
  58. Mayol M, Palau C, Rosselló JA, González-Martínez SC, Molins A, Riba M (2012) Patterns of genetic variability and habitat occupancy in Crepis triasii (Asteraceae) at different spatial scales: insights on evolutionary processes leading to diversification in continental islands. Ann Bot (Oxford) 109:429–441.  https://doi.org/10.1093/aob/mcr298 CrossRefGoogle Scholar
  59. Médail F, Diadema K (2009) Glacial refugia influence plant diversity patterns in the Mediterranean Basin. J Biogeog 36:1333–1345.  https://doi.org/10.1111/j.1365-2699.2008.02051.x CrossRefGoogle Scholar
  60. Médail F, Quézel P (1997) Hot-Spots analysis for conservation of plant biodiversity in the Mediterranean Basin. Ann Missouri Bot Gard 84:112–127.  https://doi.org/10.2307/2399957 CrossRefGoogle Scholar
  61. Miller MA, Pfeiffer W, Schwartz T (2010) Creating the CIPRES Science Gateway for inference of large phylogenetic trees. In: Proceedings of the gateway computing environments workshop (GCE), 14 Nov 2010, New Orleans, LA, pp 1–8. Available at: https://www.phylo.org/. Accessed Jan 2018
  62. Molins A, Mayol M, Rosselló JA (2009) Phylogeographical structure in the coastal species Senecio rodriguezii (Asteraceae), a narrowly distributed endemic Mediterranean plant. J Biogeog 36:1372–1383.  https://doi.org/10.1111/j.1365-2699.2009.02108.x CrossRefGoogle Scholar
  63. Moraldo B, La Valva V (1989) La flora dei Monti del Partenio (Campania, Comunità Montana del Vallo di Lauro e del Baianese). Atti Circ Cult BG Duns Scoto 14/15:75–217Google Scholar
  64. Myers N, Mittermeier RA, Mittermeier CG, Da Fonseca GA, Kent J et al (2000) Biodiversity hotspots for conservation priorities. Nature 403:853–858.  https://doi.org/10.1038/35002501 CrossRefPubMedPubMedCentralGoogle Scholar
  65. Nybom H (2004) Comparison of different nuclear DNA markers for estimating intraspecific genetic diversity in plants. Molec Ecol 13:1143–1155.  https://doi.org/10.1111/j.1365-294X.2004.02141.x CrossRefGoogle Scholar
  66. O’Grady JJ, Reed DH, Brook BW, Frankham R et al (2004) What are the best correlates of predicted extinction risk? Biol Conservation 118:513–520.  https://doi.org/10.1016/j.biocon.2003.10.002 CrossRefGoogle Scholar
  67. Oxelman B, Lidén M, Berglund D (1997) Chloroplast rps16 intron phylogeny of the tribe Sileneae (Caryophyllaceae). Pl Syst Evol 206:393–410CrossRefGoogle Scholar
  68. Pamilo P, Nei M (1988) Relationships between gene trees and species trees. Molec Biol Evol 5:568–583.  https://doi.org/10.1093/oxfordjournals.molbev.a040517 CrossRefPubMedGoogle Scholar
  69. Parlatore F (1848) Flora Italiana, vol. 7. Tipografia dei Successori Le Monnier, FlorenceGoogle Scholar
  70. Peakall R, Smouse PE (2006) GenAlEx 6: genetic analysis in excel. Population genetic software for teaching and research. Molec Ecol Notes 6:288–295.  https://doi.org/10.1111/j.1471-8286.2005.01155.x CrossRefGoogle Scholar
  71. Peruzzi L, Santangelo A, Jarvis CE (2013) Lectotypification of Linnaean names in the Italian endemic flora. Taxon 62:827–829CrossRefGoogle Scholar
  72. Petit RJ, El Mousadik A, Pons O (1998) Identifying populations for conservation on the basis of genetic markers. Conservation Biol 12:844–855CrossRefGoogle Scholar
  73. Piry S, Luikart G, Cornuet JM (1999) BOTTLENECK: a program for detecting recent effective population size reductions from allele data frequencies. Available at: http://www1.montpellier.inra.fr/CBGP/software/Bottleneck/. Accessed Aug 2017
  74. Polunin O (1980) Flowers of Greece and the Balkans: a field guide. Oxford University Press, OxfordGoogle Scholar
  75. Pons O, Petit RJ (1996) Measuring and testing genetic differentiation with ordered versus unordered alleles. Genetics 144:1237–1245PubMedPubMedCentralGoogle Scholar
  76. Posada D, Crandall KA (1998) Modeltest: testing the model of DNA substitution. Bioinformatics 14:817–818CrossRefGoogle Scholar
  77. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959PubMedPubMedCentralGoogle Scholar
  78. Quézel P (1985) Definition of the Mediterranean region and the origin of its flora. In: Gómez-Campo C (ed) Plant conservation in the Mediterranean area. W. Junk, Dordrecht, pp 9–24Google Scholar
  79. R Development Core Team (2008) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. Available at: https://www.R-project.org/ Google Scholar
  80. Rabinowitz D, Cairns S, Dillon T (1986) Seven forms of rarity and their frequency in the flora of the British Isles. In: Soule ME (ed) Conservation biology: the science of scarcity and diversity. Sinauer, Sunderland, pp 182–204Google Scholar
  81. Raymond M, Rousset F (1995) GENEPOP (Version 1.2): population genetics software for exact tests and ecumenicism. Heredity 86:248–249.  https://doi.org/10.1093/oxfordjournals.jhered.a111573 CrossRefGoogle Scholar
  82. Ricciardi M (1996) Flora di Capri (Golfo di Napoli). Ann Bot (Oxford) 54:7–169Google Scholar
  83. Rice WR (1989) Analyzing tables of statistical tests. Evolution 43:223–225.  https://doi.org/10.2307/2409177 CrossRefPubMedPubMedCentralGoogle Scholar
  84. Roemer JJ, Schultes JA (1818) Tetrandria Monogynia. Caroli a Linné equitis Systema vegetabilium: secundum classes ordines genera species cum characteribus et differentiis, vol. 3. JC Cotta, Stuttgart, pp 267–268. Available at: http://bibdigital.rjb.csic.es/ing/Libro.php?Libro=1700&Pagina=273. Accessed Aug 2017
  85. Ronquist F, Huelsenbeck JP (2003) MRBAYES 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19:1572–1574CrossRefGoogle Scholar
  86. Rosenberg NA (2004) Distruct: a program for the graphical display of population structure. Molec Ecol Notes 4:137–138.  https://doi.org/10.1046/j.1471-8286.2003.00566.x CrossRefGoogle Scholar
  87. Sales E, Nebauer SG, Mus M, Segura J (2001) Population genetic study in the Balearic endemic plant species Digitalis minor (Scrophulariaceae) using RAPD markers. Amer J Bot 88:1750–1759CrossRefGoogle Scholar
  88. Taberlet P, Fumagalli L, Wust-Saucy AG, Cosson JF (1998) Comparative phylogeography and postglacial colonization routes in Europe. Molec Ecol 7:453–464.  https://doi.org/10.1046/j.1365-294x.1998.00289.x CrossRefPubMedGoogle Scholar
  89. Tao C, Ehrendorfer F (2011) 28. Galium L., Sp. Pl. 1: 105. In: Zhengyi W, Raven PH, Deyuan H (eds) Flora of China, vol. 19 (Cucurbitaceae through Valerianaceae, with Annonaceae and Berberidaceae). Missouri Botanical Gardens Press, St. Louis, pp 104–141Google Scholar
  90. Tenore M (1811) Flora napolitana, vol. 1. Stamperia Reale, NapoliGoogle Scholar
  91. Tenore M (1831) Sylloge plantarum vascularium florae Neapolitanae. Ex Typ. Fibreni, NapoliGoogle Scholar
  92. Thiers B (2016) Index Herbariorum: a global directory of public herbaria and associated staff. New York Botanical Garden’s Virtual Herbarium. Available at: http://sweetgum.nybg.org/science/ih/. Accessed Aug 2017
  93. Thompson JD (2005) Plant evolution in the Mediterranean. Oxford University Press, New YorkCrossRefGoogle Scholar
  94. Thompson JD, Lavergne S, Affre L, Gaudeul M, Debussche M (2005) Ecological differentiation of Mediterranean endemic plants. Taxon 54:967–976.  https://doi.org/10.2307/25065481 CrossRefGoogle Scholar
  95. van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P (2004) MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Molec Ecol Notes 4:535–538.  https://doi.org/10.1111/j.1471-8286.2004.00684.x CrossRefGoogle Scholar
  96. Verity R, Nichols RA (2016) Estimating the number of subpopulations (K) in structured populations. Genetics 203:1827–1839.  https://doi.org/10.1534/genetics.115.180992 CrossRefPubMedPubMedCentralGoogle Scholar
  97. Verlaque R, Aboucaya A, Cardona MA, Contandriopoulos J (1991) Quelques exemples de speciation insulaire en Méditerranée occidentale. Bot Chron 10:137–153Google Scholar
  98. Wakeley J (1999) Nonequilibrium migration in human history. Genetics 153:1863–1871PubMedPubMedCentralGoogle Scholar
  99. Weir BS (1996) Genetic data analysis II. Sinauer Associates, SunderlandGoogle Scholar
  100. Weiss S, Ferrand N (2007) Phylogeography of southern European refugia. Springer, DordrechtCrossRefGoogle Scholar
  101. Williamson-Natesan EG (2005) Comparison of methods for detecting bottlenecks from microsatellite loci. Conservation Genet 6:551–562.  https://doi.org/10.1007/s10592-005-9009-5 CrossRefGoogle Scholar
  102. Willis KJ, Niklas KJ (2004) The role of Quaternary environmental change in plant macroevolution: the exception or the rule? Philos Trans Roy Soc London B Biol Sci 359:159–172.  https://doi.org/10.1098/rstb.2003.1387 CrossRefGoogle Scholar

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Department of Biology, Botanical Garden of NaplesUniversity of Naples “Federico II”NaplesItaly
  2. 2.Jodrell LaboratoryRoyal Botanic Gardens, KewRichmondUK
  3. 3.Botanical Garden of NaplesNaplesItaly

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