Evaluation of fire recurrence effect on genetic diversity in maritime pine (Pinus pinaster Ait.) stands using Inter-Simple Sequence Repeat profiles

STOTEN-19118; No of Pages 7 Science of the Total Environment xxx (2016) xxx–xxx

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Evaluation of fire recurrence effect on genetic diversity in maritime pine (Pinus pinaster Ait.) stands using Inter-Simple Sequence Repeat profiles M.E. Lucas-Borja a, O. Ahrazem b, D. Candel-Pérez a,⁎, D. Moya c, T. Fonseca d, E. Hernández Tecles c, J. De las Heras c, L. Gómez-Gómez b a

Department of Agricultural Technology and Science and Genetics, ETSIAM, University of Castilla-La Mancha, Campus Universitario s/n, Albacete E-02071, Spain Department of Agricultural Technology and Science and Genetics, Faculty of Pharmacy, Institute of Botany, University of Castilla-La Mancha, Campus Universitario s/n, Albacete, E-02071, Spain Department of Vegetal Production, ETSIAM, University of Castilla-La Mancha, Campus Universitario s/n, Albacete E-02071, Spain d Department of Forestry Sciences and Landscape Architecture, University of Trás-os-Montes e Alto Douro, 5001-801, Vila Real, Portugal b c

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Fire regime plays an important role affecting genetic diversity in the short-term. • Fire regime does not generate maritime pine genetic erosion. • ISSR are sufficiently informative for assessing genetic variability. • Twice burned maritime pine population showed higher genetic diversity.

a r t i c l e

i n f o

Article history: Received 30 November 2015 Received in revised form 17 January 2016 Accepted 17 January 2016 Available online xxxx Editor: D. Barcelo Keywords: Maritime pine Genetic diversity Forest fire recurrence Postfire natural regeneration ISSR

a b s t r a c t The management of maritime pine in fire-prone habitats is a challenging task and fine-scale population genetic analyses are necessary to check if different fire recurrences affect genetic variability. The objective of this study was to assess the effect of fire recurrence on maritime pine genetic diversity using inter-simple sequence repeat markers (ISSR). Three maritime pine (Pinus pinaster Ait.) populations from Northern Portugal were chosen to characterize the genetic variability among populations. In relation to fire recurrence, Seirós population was affected by fire both in 1990 and 2005 whereas Vila Seca-2 population was affected by fire just in 2005. The Vila Seca-1 population has been never affected by fire. Our results showed the highest Nei's genetic diversity (He = 0.320), Shannon information index (I = 0.474) and polymorphic loci (PPL = 87.79%) among samples from twice burned populations (Seirós site). Thus, fire regime plays an important role affecting genetic diversity in the short-term, although not generating maritime pine genetic erosion. © 2016 Elsevier B.V. All rights reserved.

⁎ Corresponding author. E-mail address: [email protected] (D. Candel-Pérez).

http://dx.doi.org/10.1016/j.scitotenv.2016.01.105 0048-9697/© 2016 Elsevier B.V. All rights reserved.

Please cite this article as: Lucas-Borja, M.E., et al., Evaluation of fire recurrence effect on genetic diversity in maritime pine (Pinus pinaster Ait.) stands using Inter-Simple Sequence Repeat profiles..., Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.01.105

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1. Introduction Pinus pinaster Aiton (maritime pine) is one of the main coniferous trees in western Mediterranean Basin, covering more than 1.5 million hectares from sea level to about 1500 m asl) (De las Heras et al., 2012). However, global change is inducing a direct reduction in maritime pine habitat distribution, increasing altitudinal requirements and being replaced by other coniferous or broadleaf species (Dale et al., 2001; Resco et al., 2007). In addition, fire severity increase and recurrence of fires are also contributing to reduce the maritime pine habitat distribution (Pausas and Vallejo, 1999; Pereira and Santos, 2003; Fernandes et al., 2004; Tapias et al., 2004). As Díaz-Delgado et al. (2002) showed, high recurrence of fire has been related to the alteration of the plant auto-succession, leading to a reduction of resilience even in ecosystems adapted to fire, such as maritime pine forest. Recently, a decrease in the maritime pine distribution area has been observed in Portugal (AFN, 2010; Oliveira et al., 2012). Maia et al. (2012) argued that this trend could be due to rises in fire severity or recurrence. The selection pressure resulting from disturbances, climate change and their interactions in the future of species distribution is closely related to the effects of genetic diversity (Garzón et al., 2011). High genetic variability of forest species is therefore important for the processes of adaptation to biotic and abiotic stresses in order to ensure the viability of the species. Trees are more vulnerable than annual plants to rapid climate changes since they are not able to respond by migration or genetic selection within a short period of time. Since global change and other man-induced perturbations have brought about significant losses in diversity, especially in tree species, it is essential to gain a comprehensive idea of genetic variability among populations in order to provide a basis for in situ conservation and forest management (Aitken et al., 2008). In this context, forest management should include genetic diversity and variability concepts, since seedling and sapling survival possibilities increase according to high intra-population diversity and adaptive traits

(Garzón et al., 2011). Moreover, the analysis of post-fire regenerative traits of species is a key approach in the assessment of vulnerability and resilience of plant species to fire regimes (Lloret et al., 2005), being an important input for modelling changes in species distribution and community composition in fire-prone areas (Syphard and Franklin, 2010). Budde et al. (2014) developed different genetic association models for serotiny in fire-prone areas of the Iberian Peninsula, showing a strong genetic control and phenotypic variability after forest fires. Serotiny refers to the persistence of closed mature cones in the tree canopy until seed release is triggered by high temperatures, e.g. crown fires (Lamont et al., 1991). Thus, genetic knowledge should be implemented in models of spatial distribution, including climate change and fire-related traits, to identify response of populations to changes in fire regime (Budde et al., 2014). Evolutionary adaptations and fire persistence abilities help perpetuation in fire-prone environments, so fire response simulation, combining theoretical expectations, modelling hypothesis and empirical observations would be a valuable approach for managing burned ecosystems (Vincenzi and Piotti, 2014). Different maritime pine populations have been identified in the Iberian Peninsula, with the Atlantic group stands showing a lower level of genetic diversity (Salvador et al., 2000). Genetic diversity and population structure of maritime pine showed extended phenotypes and differential selection promoted by microenvironmental variation at the population level, thus implying that genes dispersion is relevant for whole-ecosystem dynamics (González-Martínez et al., 2006). Inter-simple sequence repeat (ISSR) regions of the nuclear DNA, which are dominant molecular markers with high reproducibility, can be used to detect DNA variability at different levels, from single base changes to deletions and insertions (Rubio-Moraga et al., 2012). Furthermore, polymorphisms can be detected without any previous knowledge of a tree's DNA sequence. Some authors (Mariette et al., 2001; Gómez et al., 2005; Rubio-Moraga et al., 2012) recommend the use of molecular markers such as AFLPs and microsatellites to compare

Fig. 1. Study area.

Please cite this article as: Lucas-Borja, M.E., et al., Evaluation of fire recurrence effect on genetic diversity in maritime pine (Pinus pinaster Ait.) stands using Inter-Simple Sequence Repeat profiles..., Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.01.105

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Table 1 Experimental site and forest characteristics of the three studied populations. Experimental site

Vila Seca-1 Vila Seca-2 Seirós

Coordinates Lat: 41°20′12″N Long: 7°43′6″W Lat: 41°22′14″N Long: 7°44′7″W Lat: 41°34′40″N Long: 7°46′1″W

Fire Recurrence

Stand origin

Unburned

Afforestation stand

Burned in 2005 Burned twice in 1990 and 2005

Tree density (trees/ha)

Stand age (years)

400–500

50–60

Post-fire saplings

1200–1400

5–6

Post-fire saplings

1200–1400

5–6

pine has different mechanisms (e.g. serotiny) to promote natural regeneration after forest fire ensuring genetic diversity and high intrapopulation diversity (De las Heras et al., 2012).

Table 2 ISSR primers and their sequences used in the present study. Primer name

Sequence (5′–3′)

Tm (°C)

ISCS7 ISCS10 ISCS11 ISCS16 ISCS26 ISCS69

TCTCTCTCTCTCTCTCTCC ACACACACACACACACC CTCTCTCTCTCTCTCTT HBHGAGGAGGAGGAGGAG GTGTGTGTGTGTGTGTYG CACACACACACACACAA

52 °C 52 °C 52 °C 52 °C 52 °C 52 °C

2. Material and methods 2.1. Study site and plant material The study area was located in the district of Vila Real (Region of Trás-os-Montes e Alto Douro, North Portugal), where the mean annual rainfall was 1020 mm, the mean temperature 13.6 °C and air temperature typically ranged from 2.8 °C to 38.4 °C. The major soil types for both sites were Umbric Leptosols derived from schists, according to Agroconsultores-Coba (1991). Inside the study area, three sites were selected in maritime pine forests (Fig. 1, Table 1); Vila Seca-1 (520 m above sea level, 41°20′12″N, 7°43′6″W), Vila Seca-2 (535 m asl; 41°22′14″N, 7°44′7″W) and Seirós (600 m asl; 41°34′40″N, 7°46′1″ W). According to Portuguese Forest services and forest owners, the forest stands used in this study had similar seed origin being all the studied forest stands co-managed by the Official Forest Services (Instituto de Conservação da Natureza e das Florestas, ICNF) and the local communities. Also, all forests were managed according to typical silvicultural guidelines for the species. In relation to fire recurrence, the Seirós population was affected by fire both in 1990 and 2005 whereas Vila Seca-2 population was affected by fire just in 2005. The Vila Seca-1 population has never been affected by fire. Other characteristics such as stand age, stand composition or tree sampled origin of the three forest sites can be observed in Table 1. We used historical data and dendrochronology to obtain the forest ages exposed in Table 1. In October 2012, sixty trees were randomly selected from each population (Seirós, Vila Seca-1 and Vila Seca-2) and sampled, generating each tree one sample composed of 25 needles. These needles were randomly obtained from all the parts of the tree (higher, medium and lower parts of the tree crown).

B: G + T + C; D: G + A + T; H: A + C + T; V: G + C + A.

and determine the genetic structure of the populations and also to measure within-populations diversity with adaptive and economic traits to define a proper strategy for forest management. On a worldwide scale, it is assumed that the mapped markers reflect the whole genome, although some AFLP loci can be affected by possible differential selection (reaching values of 12.0%) (Simental-Rodríguez et al., 2014). The management of maritime pine in fire-prone habitats is a challenging task. According to Hernández-Serrano et al. (2013), fine-scale population genetic analyses should be carried out to test the hypothesis of differential spatial structure driven by different fire regimes to improve the knowledge of the demographic and selective roles of fire in shaping maritime pine populations. This species is highly variable in morphology and in attributes interpreted as evolutionary adaptations to fire, suggesting that it does not fit in a single general fire regime category (Fernandes and Rigolot, 2007). Portugal has the highest fire incidence in southern Europe, with a mean annual burnt area from 1980 to 2005 of 1.18% of the total area of the country (Oliveira et al., 2012). Maritime pine stands are located mainly in the northern half of the country. On average, 2.5% of Northern Portugal burns annually and the median fire return interval varies from 13 to 24 years (Fernandes et al., 2015). As Doblas-Miranda et al. (2015) pointed out; the relationship between genotypic diversity and fire recurrence is a research priority and has to be deeply studied, especially for species such as maritime pine, which is being highly affected by fire. The objective of this study was to assess the effect of fire recurrence on maritime pine genetic diversity using Inter-Simple Sequence Repeat Profiles (ISSR). Two forest stands were chosen, one burned in 1990 and the other one burned in both 1990 and 2005 (two times). Results were compared with an unburned population closely located. We hypothesised that no genetic erosion is generated as consequence of fire recurrence since maritime

2.2. DNA Extraction DNA was extracted from 150 to 300 mg of needle/bud material using a modified method (Doyle and Doyle, 1987). Leaf material was then ground to a fine powder in liquid nitrogen and placed in a microcentrifuge tube with 2 ml of extraction buffer (2% CTAB, 100 mM Tris–HCl pH 8.0, 20 mM EDTA, 1.4 M NaCl, and 0.01% proteinase K) plus 40 μL of 2-mercaptoethanol. Following incubation at 65 °C

Table 3 Mean and standard error of different parameters used to assess genetic diversity of P. pinaster populations in this study (number of samples in each population n = 60). Experimental site Vila Seca-1 (unburned: fire 0) Vila Seca-2 (Burned in 2005: fire 1) Seirós (burned in 1990 and 2005: fire 2) Mean

Na

Ne

I

He

uHe

% polymorphic loci

1.653(0.107)

1.496 (0.055)

0.422 (0.038)

0.269 (0.027)

0.276 (0.028)

81.63 (1.81)

1.735 (0.086)

1.464 (0.054)

0.403 (0.036)

0.284 (0.028)

0.291 (0.029)

81.63 (1.81)

1.816 (0.075)

1.564 (0.053)

0.474 (0.034)

0.320 (0.026)

0.329 (0.026)

87.79 (1.26)

1.735 (0.091)

1.508 (0.054)

0.433 (0.036)

0.291 (0.027)

0.298 (0.027)

83.68 (1.62)

Na: number of different alleles; Ne: number of effective alleles; I: Shannon's Index diversity; He: expected heterozygosity (gene diversity); uHe: unbiased expected heterozygosity; PPL: percentage of polymorphic loci.

Please cite this article as: Lucas-Borja, M.E., et al., Evaluation of fire recurrence effect on genetic diversity in maritime pine (Pinus pinaster Ait.) stands using Inter-Simple Sequence Repeat profiles..., Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.01.105

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for 30 min, 1.4 ml of chloroform:isoamyl alcohol (24:1) was added, mixed and centrifuged at 8000 rpm for 30 min; the supernatant was transferred to a new tube and then the process was repeated three times. DNA was precipitated with isopropanol (2/3 volume of supernatant), then centrifuged at 8000 rpm for 30 min, the supernatant discarded and the pellet washed in 70% ethanol containing 10 mM ammonium acetate for 20 min. The pellet was dissolved in 100 μL of TE buffer (10 mM Tris–HCl pH 7.4, 1 mM EDTA) and the DNA was reprecipitated with ½ volume of ammonium acetate 3 M and 2.5 volumes of ethanol. After centrifuging at 8000 rpm for 30 min, the pellet was redissolved in TE buffer with 10 μg/mL RNase and incubated at 30 °C for 30 min. The extracted DNA was quantified with a spectrophotometer, diluted to 30 ng/μL in TE and then stored at −20 °C for further analysis. 2.3. DNA Amplification 15 and 30 ng of genomic DNA were amplified in a volume of 25 μL containing 10 mM Tris–HCl, pH 9.0, 1.5 mM MgCl2, 200 μM each of dATP, dCTP, dGTP, and dTTP, 0.4 μM primer, and 1 unit of Taq DNA polymerase using a thermal cycler (MJ-Mini, BioRad). The cycling programme began with an initial 2 min at 94 °C followed by 40 cycles at 94 °C for 45 s, 52–62 °C for 45 s and 72 °C for 2 min plus a final 10 min at 72 °C and storage at 4 °C. Water was the negative control while the positive control showed an amplicon of expected size = 320 bp. Amplification products were separated by electrophoresis in 2% agarose gel containing 1 μg/ml ethidium bromide and TAE buffer. Ten microlitres of amplified DNA were mixed with 3 μL sample buffer (1.2 mg/ml; 125 mg/ml Ficoll) and 10 μL was applied in each well of the gel. DNA molecular weight markers (1 kb, Promega) were then added to each gel. The gels were run at a current of 50 mA until the bromophenol had migrated 10 cm from the well. The bands were then visualized under UV light and photographed. To ensure the reproducibility of the method, the procedure was repeated three times for each concentration of genomic DNA and primer. 2.4. Data Analysis Two Pinus pinaster samples were selected for preliminary experiments to determine optimal amplification reaction conditions and primer screening for ISSR. Thus, only six primers tested resulted in well-separated bands. On the basis of this data, the 180 samples included in the study were analysed with the primers listed in Table 2. Most loci were polymorphic within each population with respect to presence and absence of bands (Table 3). We analysed ISSR data based on both allele and phenotypic frequencies. Polymorphic bands were selected at the 95% level (two-tailed test) for use in further analyses. Data matrices were analysed using POPGENE version 1.32 (Popgene, version 1.32, 1997) with the assumption that the populations were in Hardy–Weinberg equilibrium. The following parameters were determined: percentage of polymorphic loci (PPL), number of alleles per locus (na), effective number of alleles per locus (ne), genetic diversity (HE = expected heterozygocity) and Shannon's index diversity (I). Genetic relationships among populations were studied via principal component analysis (PCA) using GenAlEx 6.41 (Peakall and Smouse, 2006). Bayesian assignment tests were applied to estimate the number

Table 4 Interpolation of genetic distances calculated by Nei's method. Above the diagonal are values of Nei's unbiased genetic distances, those below the diagonal are Nei's genetic distances. The underlined values are maximum or minimum genetic distances.

Vila Seca-1 Vila Seca-2 Seirós

Vila Seca-1

Vila Seca-2

Seirós

– 0.083 0.079

0.073 – 0.044

0.067 0.033 –

Fig. 2. Principal component analysis (PCA) plot of the studied populations based on the first two principal axes (first axis = 36.93% % and the second = 23.64%).

of genetic clusters and to evaluate the degree of admixture among them using Structure v2.3.3 (Pritchard et al., 2000). Structure was run with a “burn-in” setting of 100,000 followed by 20,000 MCMC iterations using the admixture model with sampling localities as prior population assignment and with allelic frequencies correlated among populations. Ten runs were performed for each value for K ranging from 1 to 5. The most likely value for K was calculated with Structure Harvester (Earl and von Holdt, 2012) using the statistic ΔK, which represents the greatest rate of change between each subsequent K value (Evanno et al., 2005). 3. Results The Seirós population, which was affected by fire in 1990 and 2005, showed the highest Nei's genetic diversity (He = 0.320). Moreover, examination of intra-population genetic diversity revealed the highest values of Shannon information index (I = 0.474) and polymorphic loci (PPL = 87.79%) among samples from twice burned populations (Seirós population). The lowest value of Nei's genetic diversity (He = 0.276) was found at the unburned area of the study (Vila Seca-1), while the lowest value of Shannon information index (I = 0.403) appeared at the population burned in 2005 (Vila Seca-2). Both Vila Seca populations showed lower Polymorphic loci values (PPL = 81.63%) than Seirós population (Table 3). Pair-wise Nei's distances (Nei 1973) were calculated for all the three populations. The highest inter-population average distance (0.083) was between Vila Seca-1 and Vila Seca-2, while the lowest distance (0.044) was between Vila Seca-2 and the Seirós populations (Table 4). Based on these distances, principal component analysis (PCA) was computed showing the presence of significant differences in Component 1 (Fig. 2) and explaining a 60.57% of variance (36.93% for Component 1 and 23.64% for Component 2). Vila Seca-1 and Seirós samples were clearly clustered along the PC1 axe whereas Vila Seca-2 showed some samples overlapped on the same side of the PCA plot. Expected heterozygosity

Table 5 Table of Component Weights for the PCA analysis.

Na Ne I He uHe % polymorphic loci

PC1

PC2

0.300 0.250 0.231 0.536 −0.538 −0.466

−0.091 0.691 0.363 −0.311 −0.623 −0.011

Na: number of different alleles; Ne: number of effective alleles; I: Shannon's Index diversity; He: expected heterozygosity (gene diversity); uHe: unbiased expected heterozygosity; PPL: percentage of polymorphic loci.

Please cite this article as: Lucas-Borja, M.E., et al., Evaluation of fire recurrence effect on genetic diversity in maritime pine (Pinus pinaster Ait.) stands using Inter-Simple Sequence Repeat profiles..., Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.01.105

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of Pritchard et al. (2000) showed that the log likelihood estimates increase progressively as K increases and start to decrease when K = 4 (Fig. 3). The plot of the average log likelihood values over 10 runs for K values ranging from 1 to 5. The optimal value of K was 3 as determined by the ΔK statistic STRUCTURE (Fig. 4). 4. Discussion

Fig. 3. The relationship between the log probability of the data and the number of clusters K using the ISSR data.

(gene diversity; He), number of different alleles (Na), number of effective alleles (Ne) and Shannon's Index diversity (I) had a positive weight in the PC1 whereas unbiased expected heterozygosity (uHe) and percentage of polymorphic loci (PPL) presented a negative weight (Table 5). Moreover, number of effective alleles (Ne) and Shannon's Index diversity (I) were the only parameters having a positive weight in the PC2 (Table 5). Based on the AMOVA analysis, only 14% of the total variation resides among the Maritime pine populations, while 86% is attributed to the differences among individuals within populations. The contributions from each of both sources indicated statistically significant genetic differentiations among and within populations (P b 0.01 in the AMOVA tests). The population structure of maritime pine inferred using the method

Total genetic diversity for maritime pine in this study (He = 0.291) was lower than other Pinus species similarly researched with ISSR markers, such as Pinus tabuleaformis (0.415) (Wang and Hao, 2010) or Pinus koraiensis (0.348) (Feng et al., 2006), and higher than Pinus squamata (0.029) (Zhang et al., 2005), Pinus sibirica (0.270) (Yang et al., 2005), and Pinus sylvestris (0.262) (Liu et al., 2005). The differences in the levels of genetic diversity among these species may be related to geographic distribution, number of population tested, population size of the species and the effect of climate changes during the last glacial maximum (Rubio-Moraga et al., 2012). The obtained results showed that the He parameter (expected heterozygosity; gene diversity) was higher for Seirós population (area affected by fire in 1990 and 2005) comparing to the Vila Seca-1 population (unburned area). These results could indicate that maritime pine is a species adapted to fire since there is no genetic variability erosion. This species has the ability to regenerate after fire disturbance and the role of fire as a disturbance that generally favours the Pinus genus is well recognized (Agee, 1998). Most pine species have the ability either to evade or resist fire, respectively, by storing seeds in serotinous cones or insulating tissues from lethal temperatures (Agee, 1998). Moreover, fire is the most significant disturbance to maritime pine forest but also an essential factor in the perpetuation of natural stands. Maritime pine is facilitated by different reproduction processes to recovery after stand replacement fire, as it is adapted to survive after low-intensity fires by the seeds stored in serotinous cones. Nevertheless the strategies of fire resistance and fire evasion vary considerably between populations as a result of evolutionary adaptations to fire (Fernandes and Rigolot, 2007).

Fig. 4. STRUCTURE analysis of populations of Pinus pinaster sampled to assess inter-simple sequence repeat markers. (A) K = 3 appeared to be the optimal number of clusters by showing the ΔK at its peak; (B) Results based on K = 3 using a Bayesian framework implemented in the STRUCTURE programme across individuals from the studied populations.

Please cite this article as: Lucas-Borja, M.E., et al., Evaluation of fire recurrence effect on genetic diversity in maritime pine (Pinus pinaster Ait.) stands using Inter-Simple Sequence Repeat profiles..., Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.01.105

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Results also showed that the genetic variability was even greater within populations (86%) but smaller among populations (14%). The distribution of the genetic variation of maritime pine in Portugal, as revealed by chloroplast microsatellites (cpSSR), indicated that there are low levels of differentiation among populations and that the diversity found is mainly within populations (Ribeiro et al., 2001). Evidences of strong anthropogenic influence associated with extensive gene flow could explain these findings, which is also a characteristic of gymnosperms (Ribeiro, 2001). The high level of observed genetic diversity within populations is in accordance with the findings of Hamrick and Godt (1989) who suggested that gymnosperms, with long lifespans, high outcrossing rates and fecundity, maintain high intra-population genetic diversity. The majority of conifer populations studied display relatively high levels of genetic diversity and low levels of interpopulational differentiation compared to other groups of plants. Generally speaking they also display genotypic frequencies consistent with Hardy–Weinberg equilibrium or an excess of heterozygotes. The extraordinary high differentiation among regions is probably due to the action of genetic drift and relatively low levels of gene flow. The same pattern was reported in other studies within conifer species (Aguirre-Planter et al., 2000). The loss of genetic diversity in forest trees has gained attention lately because of increasing forest fragmentation and impending climate change (Lefèvre, 2004; Hamrick, 2004). Perturbations, such as wildfires, can also cause reductions in genetic diversity (e.g., Buchert et al., 1997; Rajora et al., 2000). However, our results did not follow this trend and no genetic erosion was generated after fire disturbance. The highest genetic distances calculated by Nei's method were found comparing Vila Seca-1 and Vila Seca-2 sites whereas the lower values were found by comparing the two burned forest population (Table 4) suggesting different patterns of genetic exchange and genetic divergence between burned and unburned populations. Based on these distances, principal component analysis (PCA) was computed showing the presence of three major groups (Fig. 2). As seen in Fig. 2, the PCA clearly clustered the three sampled maritime pine populations, showing twicw-burned populations higher levels of genetic diversity. Thus, these different fire dynamics may have consequences on the maritime pine genetic diversity, but not diminishing the genetic variability after the succession of two wildfires. 5. Conclusion The successful adaptive management of maritime pine in fire-prone regions is undoubtedly a challenging task that should be completed by comprehensive genetic variability and fire ecology knowledge. According to the results, maritime pine population burned twice showed higher genetic diversiy. Also, fire regimes clustered the studied population, into three different groups. Thus, fire regime plays an important role affecting genetic diversity in the short-term, without generating maritime pine genetic erosion. This supports the hypothesis of being viable to manage the species from regenerated stands after forest fires occur, once or even twice, in a short period. Our results indicate that ISSR are sufficiently informative for assessing genetic variability. Because of the dominant nature of the ISSR markers, the application of codominant molecular markers should be undertaken to complement the data reported here. In this way, forest management plans and post-fire restoration programmes of maritime pine populations should take into account genetic characteristics and current changing fire regimes (e.g. increasing recurrence and severity). Acknowledgments We thank J. Argandoña Picazo for technical assistance and K.A. Walsh for the language revision. Authors also acknowledge Artur Mota and Daniela Fraga for collecting the data and J.P. Calçada Duarte for providing additional information about the study area. This work

was supported by the Junta de Comunidades de Castilla-La Mancha (JCCM) [POII10-0112-7316]. D. Candel-Pérez is funded by a postdoctoral fellowship of the Junta de Comunidades de Castilla-La Mancha (ESF 2007-2013). O. Ahrazem was funded by PCYTA through the INCRECYT Programme.

References AFN, 2010. National forest inventory of mainland Portugal (2005–2006). Autoridade Florestal Nacional, Lisbon, Portugal (in Portuguese). Agee, J.K., 1998. Fire and pine ecosystems. In: Richardson, D.M. (Ed.), Ecology and biogeography of Pinus. University Press, Cambridge, pp. 193–218. Agroconsultores-Coba, 1991. Carta dos solos, do uso actual da terra e da aptidão da terra do nordeste de Portugal. UTAD/PDRITM, Vila Real. Aguirre-Planter, E., Furnier, G.R., Eguiarte, L.E., 2000. Low levels of genetic variation within and high levels of genetic differentiation among populations of species of Abies from southern Mexico and Guatemala. Am. J. Bot. 87, 362–371. Aitken, S.N., Yeaman, S., Holliday, J.A., Wang, T., Curtis-McLane, S., 2008. Adaptation, migration or extirpation: climate change outcomes for tree populations. Evol. Appl. 1, 95–111. Buchert, G.P., Rajora, O.P., Hood, J.V., Dancik, B.P., 1997. Effects of harvesting on genetic diversity in old-growth eastern white pine in Ontario, Canada. Conserv. Biol. 11, 747–758. Budde, K.B., Heuertz, M., Hernández-Serrano, A., Pausas, J.G., Vendramin, G.G., Verdú, M., González-Martínez, S.C., 2014. In situ genetic association for serotiny, a fire-related trait, in Mediterranean maritime pine (Pinus pinaster). New Phytol. 201 (1), 230–241. Dale, V.H., Joyce, L.A., Mcnulty, S., Neilson, R.P., Ayres, M.P., Flannigan, M.D., Hanson, P.J., Irland, L.C., Lugo, A.E., Peterson, C.J., Simberloff, D., Swanson, F.J., Stocks, B.J., Wotton, M., 2001. Climate change and forest disturbances. Bioscience 51, 723–734. De las Heras, J., Moya, D., Vega, J.A., Daskalakou, E., Vallejo, R., Grigoriadis, N., Tsitsoni, T., Baeza, J., Valdecantos, A., Fernández, C., Espelta, J., Fernandes, P., 2012. Post-fire management of serotinous pine forests. In: Moreira, F., Arianotsou, M., Corona, P., De las Heras, J. (Eds.), Post-fire management and restoration of southern European forests. Manag. For. Ecosyst. 24, pp. 151–170. Díaz-Delgado, R., Lloret, F., Pons, X., Terradas, J., 2002. Satellite evidence of decreasing resilience in Mediterranean plant communities after recurrent wildfires. Ecology 83 (8), 2293–2303. Doblas-Miranda, E., Martínez-Vilalta, J., Lloret, F., Álvarez, A., Ávila, A., Bonet, F.J., Brotons, L., Castro, J., Curiel Yuste, J., Díaz, M., Ferrandis, P., García-Hurtado, E., Iriondo, J.M., Keenan, T.F., Latron, J., Llusià, J., Loepfe, L., Mayol, M., Moré, G., Moya, D., Peñuelas, J., Pons, X., Poyatos, R., Sardans, J., Sus, O., Vallejo, V.R., Vayreda, J., Retana, J., 2015. Reassessing global change research priorities in Mediterranean terrestrial ecosystems: how far have we come and where do we go from here? Glob. Ecol. Biogeogr. 24, 25–43. Doyle, J.J., Doyle, J.L., 1987. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem. Bull 19, 11–15. Earl, D.A., von Holdt, B.M., 2012. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361. Evanno, G., Reganut, E., Goudet, J., 2005. Detecting the number of clusters of individuals using STRUCTURE: a simulation study. Mol. Ecol. 14, 2611–2620. Feng, F.J., Han, S.J., Wang, H.M., 2006. Genetic diversity and genetic differentiation of natural Pinus koraiensis populations. J. For. Res. 17, 21–24. Fernandes, P.M., Rigolot, E., 2007. The fire ecology and management of maritime pine (Pinus pinaster Ait.). For. Ecol. Manag. 241 (1–3), 1–13. Fernandes, P., Loureiro, C., Botelho, H.S., 2004. Fire behaviour and severity in a maritime pine stand under differing fuel conditions. Ann. For. Sci. 61, 537–544. Fernandes, P.M., Fernandes, M.M., Loureiro, C., 2015. Post-fire live residuals of maritime pine plantations in Portugal: structure, burn severity, and fire recurrence. For. Ecol. Manag. 347, 170–179. Garzón, M.B., Alía, R., Robson, T.M., Zavala, M.A., 2011. Intra-specific variability and plasticity influence potential tree species distributions under climate change. Glob. Ecol. Biogeogr. 20 (5), 766–778. Gómez, A., Vendramin, G.G., González-Martínez, S.C., Alía, R., 2005. Genetic diversity and differentiation of two Mediterranean pines (Pinus halepensis Mill. and Pinus pinaster Ait.) along a latitudinal cline using chloroplast microsatellite markers. Divers. Distrib. 11 (3), 257–263. González-Martínez, S.C., Burczyk, J., Nathan, R., Nanos, N., Gil, L., Alía, R., 2006. Effective gene dispersal and female reproductive success in Mediterranean maritime pine (Pinus pinaster Aiton). Mol. Ecol. 15 (14), 4577–4588. Hamrick, J.L., 2004. Response of forest trees to global environmental changes. For. Ecol. Manag. 197, 323–335. Hamrick, J., Godt, M., 1989. Allozyme diversity in plant species. In: Urbanski, K. (Ed.), Differentiation patterns in higher plants. Academic Press, New York, pp. 53–67. Hernández-Serrano, A., Verdú, M., González-Martínez, S.C., Pausas, J., 2013. Fire structures pine serotiny at different scales. Am. J. Bot. 100, 2349–2356. Lamont, B.B., Le Maitre, D.C., Cowling, R.M., Enright, N.J., 1991. Canopy seed storage in woody plants. Bot. Rev. 57, 277–317. Lefèvre, F., 2004. Human impacts on forest genetic resources in the temperate zone: an updated review. For. Ecol. Manag. 197, 257–271. Liu, G.F., Dong, J.X., Jiang, Y., Lu, Y.F., Jiang, J., Zhao, G.Y., 2005. Analysis of genetic relationship in 12 species of section Strobus with ISSR markers. J. For. Res. 16, 213–215.

Please cite this article as: Lucas-Borja, M.E., et al., Evaluation of fire recurrence effect on genetic diversity in maritime pine (Pinus pinaster Ait.) stands using Inter-Simple Sequence Repeat profiles..., Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.01.105

M.E. Lucas-Borja et al. / Science of the Total Environment xxx (2016) xxx–xxx Lloret, F., Estevan, H., Vayreda, J., Terradas, J., 2005. Fire regenerative syndromes of forest woody species across fire and climatic gradients. Oecologia 146, 461–468. Maia, P., Pausas, J.G., Vasques, A., Keizer, J.J., 2012. Fire severity as a key factor in post-fire regeneration of Pinus pinaster (Ait.) in central Portugal. Ann. For. Sci. 69 (4), 489–498. Mariette, S., Chagné, D., Lézier, C., Pastuszka, P., Raffin, A., Plomion, C., Kremer, A., 2001. Genetic diversity within and among Pinus pinaster populations: comparison between AFLP and microsatellite markers. Heredity 86, 469–479. Oliveira, S.L.J., Pereira, J.M.C., Carreiras, J.M.B., 2012. Fire frequency analysis in Portugal (1975-2005), using Landsat-based burnt area maps. Int. J. Wildland Fire 21 (1), 48–60. Pausas, J.G., Vallejo, V.R., 1999. The role of fire in European Mediterranean ecosystems. In: Chuvieco, E. (Ed.), Remote sensing of large wildfires in the European Mediterranean basis. Springler-Verlag, Berlin, pp. 3–16. Peakall, R.O.D., Smouse, P.E., 2006. GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 6, 288–295. Pereira, J.M.C., Santos, M.T., 2003. Áreas queimadas e risco de incêndio em Portugal. DGF, MADRP, Lisboa (in Portuguese). Popgene, version 1.32, 1997. The user-friendly shareware for population genetic analysis. Molecular Biology and Biotechnology Center. University of Alberta, Edmonton, Canada http://www.ualberta.ca/~fyeh. Accessed 23 November 2011. Pritchard, J.K., Stephens, M., Donnelly, P., 2000. Inference of population structure using multilocus genotype data. Genetics 155, 945–959. Rajora, O.P., Rahman, M.H., Buchert, G.P., Dancik, B.P., 2000. Microsatellite DNA analysis of genetic effects of harvesting in old-growth eastern white pine (Pinus strobus) in Ontario, Canada. Mol. Ecol. 9, 339–348. Resco, V., Fischer, C., Colinas, C., 2007. Climate change effects on Mediterranean forests and preventive measures. New For. 33, 29–40. Ribeiro, M.M., 2001. Genetics of Pinus pinaster Aiton. with cytoplasmic and nuclear markers. Doctoral dissertation. Swedish University of Agricultural Sciences.

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Ribeiro, M.M., Plomion, C., Petit, R.J., Vendramin, G.G., et al., 2001. Variation of chloroplast single-sequence repeats in Portuguese maritime pine (Pinus pinaster Ait.). Theor. Appl. Genet. 102, 97–103. Rubio-Moraga, A., Candel-Pérez, D., Lucas-Borja, M.E., Tiscar, P.A., Viñegla, B., Linares, J.C., Gómez-Gómez, L., Ahrazem, O., 2012. Genetic diversity of Pinus nigra Arn. populations in southern Spain and northern Morocco revealed by inter-simple sequence repeat profiles. Int. J. Mol. Sci. 13 (5), 5645–5658. Salvador, L., Alía, R., Agúndez, D., Gil, L., 2000. Genetic variation and migration pathways of maritime pine (Pinus pinaster Ait) in the Iberian Peninsula. Theor. Appl. Genet. 100 (1), 89–95. Simental-Rodríguez, S.L., Quiñones-Pérez, C.Z., Moya, D., Hernández-Tecles, E., LópezSánchez, C.A., Wehenkel, C., 2014. The relationship between species diversity and genetic structure in the rare Picea chihuahuana tree species community, Mexico. PLoS ONE 9 (11), e111623. Syphard, A.D., Franklin, J., 2010. Species traits affect the per-formance of species distribution models for plants in south-ern California. J. Veg. Sci. 21, 177–189. Tapias, R., Climent, J., Pardos, J., Gil, L., 2004. Life histories of Mediterranean pines. Plant Ecol. 171, 53–68. Vincenzi, S., Piotti, A., 2014. Evolution of serotiny in maritime pine (Pinus pinaster) in the light of increasing frequency of fires. Plant Ecol. 215 (7), 689–701. Wang, M.B., Hao, Z.Z., 2010. Rangewide genetic diversity in natural populations of Chinese pine (Pinus tabulaeformis). Biochem. Genet. 48, 590–602. Yang, C.P., Wei, L., Jiang, J., Liu, G.F., Zhao, G.Y., 2005. Analysis of genetic diversity for nineteen populations of Pinus sibirica Du Tour with technique of ISSR. J. Northeast For Univ. 33, 1–3. Zhang, Z.Y., Chen, Y.Y., Li, D.Z., 2005. Detection of low genetic variation in a critically endangered Chinese pine, Pinus squamata, using RAPD and ISSR markers. Biochem. Genet. 43, 239–249.

Please cite this article as: Lucas-Borja, M.E., et al., Evaluation of fire recurrence effect on genetic diversity in maritime pine (Pinus pinaster Ait.) stands using Inter-Simple Sequence Repeat profiles..., Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.01.105