New Results

Genetic basis of susceptibility to Diplodia sapinea and Armillaria ostoyae in maritime pine

Agathe Hurel, Marina de Miguel, Cyril Dutech, Marie-Laure Desprez-Loustau, Christophe Plomion, Isabel Rodríguez-Quilón, Agathe Cyrille, Thomas Guzman, Ricardo Alía, Santiago C. González-Martínez, Katharina B. Budde
doi: https://doi.org/10.1101/699389

Summary

  • Forest ecosystems are increasingly challenged by extreme events, e.g. pest and pathogen outbreaks, causing severe ecological and economical losses. Understanding the genetic basis of adaptive traits in tree species is of key importance to preserve forest ecosystems

  • Adaptive phenotypes, including susceptibility to two fungal pathogens (Diplodia sapinea and Armillaria ostoyae) and an insect pest (Thaumetopoea pityocampa), height and needle phenology were assessed in a range-wide common garden of maritime pine (Pinus pinaster Aiton), a widespread conifer in the western Mediterranean Basin and parts of the Atlantic coast.

  • Broad-sense heritability was significant for height (0.497), needle phenology (0.231-0.468) and pathogen symptoms (0.413 for D. sapinea and 0.066 for A. ostoyae) measured after inoculation under controlled conditions, but not for pine processionary moth incidence assessed in the common garden. Genetic correlations between traits revealed contrasting trends for pathogen susceptibility to D. sapinea and A. ostoyae. Maritime pine populations from areas with high summer temperatures and frequent droughts were less susceptible to D. sapinea but more susceptible to A. ostoyae. An association study using 4,227 genome-wide SNPs revealed several loci significantly associated to each trait.

  • This study provides important insights to develop genetic conservation and breeding strategies integrating tree responses to pathogens.

Introduction

Forest ecosystems are challenged worldwide by changing environmental conditions (Turner, 2010). Warmer and drier climates are expected to increase the risks of fire, droughts and insect outbreaks while warmer and wetter climates will probably increase storm and pathogen incidence on forests (Petr et al., 2017), leading to episodes of high tree mortality (Castro et al., 2009) and in consequence severe economic losses (Hanewinkel et al., 2013). Changing environmental conditions can also cause range shifts in previously locally restricted pests and pathogens or shifts to increased pathogenicity (Desprez-Loustau et al., 2006). Thus, understanding the variability in disease response and the genetic basis of adaptive traits related to biotic and abiotic factors in tree species is crucial to develop informed restoration, conservation and management strategies. Additionally, knowledge about genes underlying adaptive traits can also serve tree breeding and increase forest productivity, e.g. targeting resistance to drought or against pests and pathogens in forest plantations (Neale & Kremer, 2011).

Forest trees are long-lived organisms characterized by mainly outcrossing mating systems, high standing genetic variation, large effective population sizes, and the production of vast numbers of seeds and seedlings exposed to strong selection (Petit et al., 2004; Petit & Hampe, 2006). High genetic and phenotypic differentiation has been observed in tree species along environmental gradients (e.g. Savolainen et al., 2007, 2013) or between contrasting habitats, indicating local adaptation (e.g. Lind et al., 2017). Common garden experiments (i.e. experiments evaluating trees from a wide range of populations under the same environmental conditions) provide valuable insights in the phenotypic and genotypic variation of tree species (Morgenstern, 2011). They have revealed genetic differentiation for adaptive traits (such as flushing, senescence or growth) along latitudinal and altitudinal gradients (Mimura & Aitken, 2007; Delzon et al., 2009). Geographical variation can also be found for disease resistance against certain pests (Menéndez-Gutiérrez et al., 2017) and pathogens (e.g. Hamilton et al., 2013; Freeman et al., 2019). Phenological traits, such as flowering or leaf flushing time and autumn leaf senescence are sometimes genetically correlated with disease resistance in forest trees and can give hints about resistance or avoidance mechanisms (Elzinga et al., 2007).

Disease resistance is generally thought to be the result of selective pressures exerted by the pathogen, in areas where host and pathogen have co-existed during considerable periods of time, under the co-evolution hypothesis (e.g. Burdon and Thrall, 2000; Ennos, 2015). In this line, geographical variation in disease resistance has been interpreted in some cases as a result of past heterogeneous pathogen pressures within the range of a host species (Ennos, 2015; Perry et al., 2016). However, the past distribution of pathogen species is often unknown (Desprez-Loustau et al., 2016), therefore, other processes than co-evolution, such as “ecological fitting” or “exaptation” should not be excluded (Agosta & Klemens, 2008). These biological processes have been suggested when, for example, variability in disease resistance has been observed in tree species with no co-evolutionary history with a pathogen (Leimu & Koricheva, 2006; Freeman et al., 2019). Such resistance may have evolved in response to other pathogens but shows broad-range efficacy, even to a novel pathogen. Generic mechanisms of resistance in conifers include the production of large amounts of non-volatile compounds (resin acids) that can act as mechanical barriers to infections (Shain, 1967; Phillips & Croteau, 1999) and volatile compounds (such as monoterpenes or phenols) that can be toxic to fungi (Cobb et al., 1968; Rishbeth, 2006). The composition of secondary metabolites can show marked differences between trees with distinct geographic origins (Meijón et al., 2016). The evolution of plant defences against biotic stressors can also be shaped by differences in resource availability and environmental constraints throughout the host’s species distribution. Depending on resource availability plants have evolved distinct strategies by investing either more in growth, to increase competition ability, or more in chemical and structural defences, to better respond to herbivores and pathogens (Herms & Mattson, 2004). Typically, faster growing trees invest more in inducible defences while slow growing trees invest more in constitutive defences (Moreira et al., 2014).

Many quantitative traits in forest species, including disease resistance, show significant heritability and often stronger differentiation (QST) between populations than neutral genetic markers (FST) (Hamilton et al., 2013; Lind et al., 2018). Major resistance genes against certain forest pathogens have been identified (e.g. Kuhlman et al., 2002; Sniezko, 2010) but most adaptive traits have a highly polygenic basis of quantitative inheritance, typically involving many loci with rather small effects (Goldfarb et al., 2013; de la Torre et al., 2019). The identification of genes underlying adaptive traits in forest trees is becoming more feasible, with the increasing availability of genetic and genomic markers. Many association genetic studies in forest tree species focused on wood property and growth traits to assist tree breeding (e.g. Pot et al., 2005; Neale et al., 2006; Beaulieu et al., 2011). Also, loci associated to other ecologically important traits, such as cold hardiness (e.g. Eckert et al., 2009; Holliday et al., 2010), drought tolerance (reviewed in Moran et al., 2017) or disease resistance (e.g. Liu et al., 2014; Resende et al., 2017) have been suggested based on this approach. However, association studies addressing biotic interaction traits, including responses to pests and pathogens, are still scarce.

Our study focused on maritime pine (Pinus pinaster Aiton), a long-lived conifer with a highly fragmented natural range in the western Mediterranean Basin, the Atlantic coasts of southern France and of the Iberian Peninsula. The species has a wide ecological amplitude and grows from sea level to 2000 m altitude. Genetic diversity in natural populations of maritime pine is high (Salvador et al., 2000; Bucci et al., 2007) and strongly structured (Petit et al., 1995; Jaramillo-Correa et al., 2015). In addition, traits, such as stem form, height (González-Martínez et al., 2002), metabolite content (Meijón et al., 2016), drought (Aranda et al., 2010; Gaspar et al., 2013) and disease resistance (Desprez-Loustau & Baradat, 1991; Burban et al., 1999; Elvira-Recuenco et al., 2014), are highly variable in maritime pine and often strongly differentiated between geographic provenances. Maritime pine has also been widely planted and is currently exploited for timber and paper, covering ~0.8 million ha in the Landes region in southwestern France, one of the largest plantation forests in Europe (Labbé et al., 2015). Despite the ecological and economical importance of maritime pine, only a few genetic association studies have been developed in this species. Lepoittevin et al., (2012) identified two loci associated to growth and wood cellulose content, respectively, Cabezas et al., (2015) revealed four SNPs in korrigan (gene ortholog to an Arabidopsis degrading enzyme cellulase) also as significantly associated to growth traits (total height and polycyclism) and Bartholomé et al., (2016) reported four loci for stem straightness and three loci for height growth. Budde et al., (2014) were able to predict 29% of the phenotypic variation in a fire adaptive trait (proportion of serotinous cones) in eastern Spain based on 17 significantly associated loci. However, none of these studies targeted biotic interaction traits, such as disease resistance.

In our study, we assessed susceptibility to pests/pathogens, height and needle phenology (bud burst and duration of bud burst) in a clonal common garden (CLONAPIN, planted in Cestas, southwestern France), which allowed us to explore variation in disease response and genetic correlations with other traits in range-wide populations of maritime pine. Considering disease and growth traits together is relevant from an evolutionary and ecological perspective, and can also have important implications in terms of management, especially in breeding programs. We selected three important disease agents: two fungal pathogens, Diplodia sapinea (Botryosphaeriaceae) and Armillaria ostoyae (Physalacriaceae), as well as the pine processionary moth, Thaumetopoea pityocampa (Thaumetopoeidae), a main defoliator of pine forests.

Diplodia sapinea is the causal agent of several diseases, such as tip-blight, canker or root collar necrosis in needles, shoots, stems and roots of conifers, eventually leading to mortality in case of severe attacks (Piou et al., 1991; Luchi et al., 2014). The pathogenicity of D. sapinea is associated to environmental conditions. It can remain in an endophytic form, i.e. without causing any symptoms, until stressful environmental conditions, such as drought (Stanosz et al., 2002; Desprez-Loustau et al., 2006), hail storms (Zwolinski et al., 1990), or changes in the nitrogen concentration of the soil (Piou et al., 1991; Stanosz et al., 2004) weaken the host and trigger D. sapinea pathogenicity. Trees from all ages are affected (Chou, 1978; Georgieva & Hlebarska, 2017), though seedlings and old trees show increased susceptibility (Swart & Wingfield, 1991). The fungus can be found in many conifers, especially in the genus Pinus and P. pinaster was classified as moderately susceptible by Iturritxa et al., (2013). The species was first described in Europe in 1823 (synonym Sphaeria sapinea; Piou et al., 1991) and recent surveys showed that D. sapinea is currently very broadly distributed in all pine forests throughout the world, though its origin is unknown (Burgess et al., 2004; Brodde et al., 2019). Serious damage associated with D. sapinea in Europe has only been reported in the last decades but it may become a serious threat to pine forests, as climate change will certainly favor pathogen activity by increasing temperature and the frequency and intensity of drought events (Woolhouse et al., 2005; Desprez-Loustau et al., 2006; Boutte, 2018). Recent outbreaks associated with D. sapinea in northern Europe suggest an ongoing northward expansion (Brodde et al., 2019).

Armillaria ostoyae is a root pathogen that causes white rot and butt rot disease in conifers, leading to growth deprivation, high mortality and major losses in timber wood, hence, its economic importance (Lung-Escarmant & Guyon, 2004; Heinzelmann et al., 2018). The species can be traced back to six millions years ago, both in Eurasia and North-America (Tsykun et al., 2013; Koch et al., 2017). Armillaria ostoyae has been reported in all the coniferous forests of the Northern Hemisphere but it is replaced by A. mellea (Marxmüller & Guillaumin, 2005) in the Mediterranean as its distribution is limited by high temperatures and drought. It is likely that A. ostoyae would have co-existed for a long time with maritime pine in Europe (Labbé et al., 2017a) and consequently has been affected by the same extinction-recolonization events associated to past climatic changes. It is one of the most common fungal species in maritime pine forests, and it is particularly dangerous, as it can act as a parasite and saprophyte (Cruickshank et al., 1997; Labbé et al., 2017b), i.e. the death of its host does not prevent its spread. In maritime pine, the severity of the symptoms is related with the age of its host, with higher mortality in young trees (Lung-Escarmant et al., 2002; Lung-Escarmant & Guyon, 2004). Climate change is predicted to increase the impact of A. ostoyae on conifer forests in the coming years (Kubiak et al., 2017).

Thaumetopoea pityocampa, the pine processionary, is considered the most severe defoliator insect in pine forests in southern Europe and northern Africa (Jactel et al., 2015) and can lead to severe growth loss (Jacquet et al., 2013). The species typically reproduces in summer followed by larval development during autumn and winter. Caterpillars and moths of T. pityocampa are sensitive to climatic and environmental conditions and the pine processionary moth is expected to expand its range following events of climate warming (Battisti et al., 2006; Toïgo et al., 2017).

The specific objectives of our study are to 1) estimate genetic variability and heritability within and among range-wide populations of maritime pine for pathogen/pest-related traits, height and needle phenology, 2) test for adaptive divergence across the maritime pine range for these traits (i.e. QST vs. FST approach); 3) analyze the genetic correlations between these traits that could be useful for conservation and breeding programs; and 4) identify loci associated to disease-related, growth and phenology traits by a genotype-phenotype association approach.

Material and Methods

Plant material and common garden measurements

A clonal common garden (CLONAPIN) was planted in 2011 in Cestas, southwestern France (for details see Rodríguez-Quilón, 2017). It includes trees from 35 populations of maritime pine covering the whole species distribution (see Table S1.1, Supporting Information for number of individuals, genotypes, and population coordinates of 33 populations included in this study), representing all differentiated gene pools (French Atlantic, Iberian Atlantic, Corsica, Central Spain, Southeastern Spain, and Morocco; see Jaramillo-Correa et al., 2015). The common garden design consisted of eight randomized complete blocks, with one clonal copy (ramet) of each genotype replicated in each block.

Height, bud burst, duration of bud burst, and incidence of processionary moth were measured in all individuals from 5-8 blocks, depending on the trait (sample size of 1,440-3,330 trees, see Table S1.1, Supporting Information). Tree height was measured in 2015, four years after the establishment of the trial. Bud burst stage was evaluated using a phenological scale ranging from 0 (bud without elongation during winter) to 5 (total elongation of the needles; see Fig. S2.1, Supporting Information). The Julian day of entry in each stage was scored for each tree. Julian days were converted into accumulated degree-days (0°C basis) from the first day of the year, to take into account the between year variability in temperature. The number of degree-days between stages 1 and 4 defines the duration of bud burst. Both needle phenology phenotypes, bud burst and duration of bud burst, were assessed in 2015 and 2017. The presence or absence of pine processionary moth nests in the tree crowns was assessed in March 2018.

Experimental evaluation of susceptibility to Diplodia sapinea

For D. sapinea inoculations, we used the pathogen strain Pier4, isolated from P. nigra cones in Pierroton, France (May 2017) and maintained on malt-agar medium. The identity of this strain as D. sapinea, was confirmed by its ITS sequence (ITS1-F and ITS4 see Gardes and Bruns 1993), and blasting against the NCBI nucleotide data base (Benoît Laurent, personal communication).

We sampled a total of 453 branches, from 151 genotypes (i.e. one branch from each of three replicate trees per genotype) in the CLONAPIN common garden representing all differentiated maritime pine gene pools. On several days between June 12th and July 31st 2018, one lateral branch per tree was cut from 30 randomly selected trees and taken to the laboratory for inoculation. Inoculations were carried out on the current year leader shoot (phenological stage 3 to 5, see Supporting Information S2.1) of excised branches (for a detailed laboratory protocol see Supporting Information S3.1).

For the inoculation, we removed a needle fascicle in the middle of each shoot with a scalpel and placed an infected malt-agar plug (5 mm diameter) on the wound and wrapped it in cellophane. For control shoots, plugs of sterile rather than colonized malt-agar were used. The shoots were put in water and kept in a climatic chamber set at 20°C with a daily cycle of 12h of light and 12h of dark (Blodgett & Bonello, 2003; Iturritxa et al., 2013). Six days after the inoculation, we removed the cellophane and measured the lesion length around the inoculation point with a caliper. Needle discoloration was assessed using a scale from 0: no discoloration to 3: all needles along the necrosis showed discoloration (see Fig. S3.1, Supporting Information).

Experimental evaluation of susceptibility to Armillaria ostoyae

For the inoculation with A. ostoyae, we used the pathogen strain A4, collected from a dying maritime pine tree in La Teste (Gironde, France) in 2010 (Labbé et al., 2017b). For the experiment, mycelium culture was prepared in 180 ml plastic jars filled with a mixture of industrial vegetable soup, malted water and hazelnut wood chips and two plugs of 5 mm diameter of malt agar with A. ostoyae mycelium on top (Heinzelmann & Rigling, 2016; for a detailed laboratory protocol see Supporting Information S3.2). The lid was closed loosely enough to allow some oxygen flow. The jars were placed in a heat chamber set at 23°C and 80% humidity during three month before inoculation.

We randomly sampled 10 maritime pine genotypes for each of the six differentiated gene pools represented in the CLONAPIN common garden. Fully elongated current year shoots were selected (bud stage 4 and 5) with a minimum diameter of 250 mm and a minimum length of 10 cm. A total of 180 branches from 60 genotypes (i.e. one branch from each of three replicate trees per genotype) were measured, cut and taken to the laboratory to be inoculated, on October 3rd-4th 2018.

The basal part of the shoots (ca. 8 cm) was placed in the center of the jars with mycelial culture in the heat chamber, maintaining the same temperature and humidity settings as for the mycelium growth, but adding an additional 12h cycle of light/dark. After 3 weeks, inoculation success was evaluated visually by confirming the presence of mycelium under the bark. The length of the colonizing mycelium and length of the lesion in the sapwood (i.e. wood browning, hereafter referred to as necrosis) were measured. In the jar, we visually evaluated the level of humidity (dry, medium and very humid) and A. ostoyae growth. Controls were prepared in the exact same manner, but with plugs of sterile malt-agar as opposed to those colonized by A. ostoyae.

Climatic Data

Summary climate data for the years 1950–2000 were retrieved for 32 variables from Worldclim (Hijmans et al., 2005) and a regional climatic model (Gonzalo, 2007) for the 11 non-Spanish and the 22 Spanish populations, respectively. Climate variables included monthly mean, highest, and lowest temperatures and mean monthly precipitation. Gonzalo’s (2007) model was favored for climate data in Spain because it considers a much denser network of meteorological stations than Worldclim, which is known to underperform in this region (see Jaramillo-Correa et al., 2015).

DNA extraction and SNP genotyping

Needles were collected from one replicate per genotype (N=416, including all genotypes used for pathogen susceptibility assays) and desiccated using silica gel. Genomic DNA was extracted using the Invisorb® DNA Plant HTS 96 Kit/C kit (Invitek GmbH, Berlin, Germany). An Illumina Infinium SNP array developed by Plomion et al. (2016), comprising potentially neutral and adaptive genetic polymorphisms, was used for genotyping. After standard filtering followed by removal of SNPs with uncertain clustering patterns (visual inspection using GenomeStudio v. 2.0), we kept 5,176 polymorphic SNPs, including 4,227 SNPs with a minor allele frequency (MAF) above 0.1.

Quantitative genetic analyses

To estimate the genetic variance components of the analyzed traits, we fitted the following mixed-effect model: Embedded Image where for any trait y, μ denotes de overall phenotypic mean, blocki represents the fixed effect of experimental block i, popj is the random effect of population j, pop(genotype)jk denotes the random effect of genotype k nested within population j, ε is the residual effect and cov represents the covariates that were only implemented when modeling the presence of pine processionary moth nests (i.e. tree height in 2015) and necrosis caused by A. ostoyae (i.e. a categorical evaluation of jar humidity).

All models were fitted in a Bayesian framework using Markov chain Monte Carlo (MCMC) methods implemented in the R package MCMCglmm (Hadfield, 2010; see Supporting Information S4.1 and Table S4.1 for further details on model specifications).

Variance components were then used to compute broad-sense heritability either including the population random effect Embedded Image or not (H2): Embedded Image Embedded Image where Embedded Image is the variance among genotypes within populations, Embedded Image is the variance between populations and Embedded Image the residual variance. When appropriate, we included an extra term in the denominator to account for implicit logit and probit link function variance (π2/3 and +1, respectively; Nakagawa and Schielzeth 2010). We also estimated the evolvability, defined as the genotype plus population variances to phenotypic mean ratio for each trait, which represents the ability of a population/genetic group to respond to selection on a certain trait (Houle, 1992). Genetic differentiation among populations for the analyzed traits was calculated as (Spitze, 1993). Embedded Image

Additionally, we estimated the global FST using all available SNP genotypes in SPAGeDi 1.5 (Hardy & Vekemans, 2002). The difference between global FST and QST values for each adaptive trait was considered significant when the 95% confidence intervals (CI) did not overlap. Genetic correlations between traits were calculated with the Pearson’s coefficient of correlation using the Best Linear Unbiased Predictors (BLUPs) of the combined population and genotype effects (Henderson, 1973; Robinson, 1991) for each trait. Finally, climate and environmental correlations were performed at the population level (using population BLUPs).

Genetic association of SNPs with growth, needle phenology and susceptibility to pathogens

We used a mixed linear regression approach (MLM, Yu et al., 2006) implemented in Tassel v. 5.0 (Bradbury et al., 2007) to identify single SNPs associated to each of the phenotypes (combining population and genotype BLUPs). Ancestry proportions of each sample were computed using STRUCTURE 2.3 (Pritchard et al., 2000) for K=6 representing the six maritime pine gene pools (see Jaramillo-Correa et al., 2015). These ancestry proportions were included as covariates in the MLM. A covariance matrix accounting for relatedness between all sample pairs was estimated using Loiselle’s kinship coefficient (Loiselle et al., 1995) in SPAGeDi 1.5 and was included as random effect. Negative kinship values were set to zero following Yu et al. (2006). Only loci with a P-value below 0.005 in the Tassel analyses and with a minimum allele frequency above 0.1 were used for further analyses. We used a Bayesian mixed-effect association approach (Bayesian Association with Missing Data, BAMD; Quesada et al., 2010; Li et al., 2012) in R v.3.4.1 (R Core Team, 2017) to estimate single-locus allelic effects under three genetic models accounting for additive, over-dominance and dominance effects (as in Budde et al., 2014). The STRUCTURE ancestry proportions were used as covariates and the relatedness matrix as random factor. Mean allelic effects (γ) and 95% confidence intervals were obtained from the distribution of the last 20,000 iterations (50,000 in total). Only those SNPs with confidence intervals not overlapping zero were considered to have a significant (non-zero) effect on the trait. Functional annotations, SNP motives, linkage groups and blast results were retrieved from Plomion et al., (2016).

Results

Phenotypic variability, heritability and genetic differentiation

Most traits showed strong differences among populations, whereas the intra-population variation (genotype effect) was smaller, as indicated by lower broad-sense heritability of the genotype variance (Table 1). Thus, we will base all results and interpretations on the BLUPs that combine the population and genotype effects if not otherwise indicated. Heritability was strongest for height (H2p: 0.497, CI [0.398-0.576]) (Table 1). The highest trees were found in populations from the Atlantic French, Atlantic Iberian and Corsican gene pools, whereas the smallest trees originated from southeastern Spain and Morocco (Fig. S5.1, Supporting Information). Heritability of susceptibility to D. sapinea, assessed as the necrosis length, was not significant at the genotype level (H2: 0.096, CI [0.000-0.186]), but was higher and significant when the population effect was taken into account (H2p: 0.413 [0.248-0.675]). The trees from northern Africa and southern Spain showed shorter necrosis length than trees from Atlantic populations (Fig. 1a). Heritability of needle discoloration caused by D. sapinea was lower, but still significant (H2p: 0.175 [0.040-0.345]). Necrosis length caused by A. ostoyae was also significantly heritable (H2p: 0.066 [0.018-0.203]) and indicated more damage in southern populations, especially in Morocco and southern Spain and less damage in northern populations, especially in populations from the French Atlantic gene pool (Fig. 1b). The importance of the population effect in several traits was also highlighted by high QST values indicating strong population differentiation (Table 1). Global FST calculated using the available SNPs was 0.109 ([0.0129; 0.3247], p-value < 0.001) which is significantly lower than the QST estimates obtained for height and necrosis length caused by D. sapinea (Table 1). The evolvability was highest for height and lowest for the necrosis length caused by A. ostoyae.

Figure 1
Figure 1

Stripchart of the best linear unbiased predictors (BLUPs, including both genotype and population effect) of necrosis length caused by D. sapinea (A) and A. ostoyae (B) for each of the Pinus pinaster populations included in each experiment. Populations were assigned to one of six gene pools (see Jaramillo-Correa et al., 2015) which correspond to the six colours and ordered by latitude (north to south) within each gene pool. Black lines indicate the average necrosis length in each population.

View this table:
Table 1

Heritability of adaptive traits in Pinus pinaster. Variability refers to the standard deviation of the raw phenotypic data. H2, broad-sense heritability of the genotype effect; H2p, broad-sense heritability of the combined genotype and population effect; QST, population differentiation; bb, bud burst; dbb, duration of bud burst; disc., needle discoloration; Processionary, presence/absence of processionary moth nests; dd, degree-days; NA: not applicable. Heritability for incidence of the processionary moth was computed using height as a covariate. Values in bold are significant. Values in squared brackets indicate the 95% confidence intervals.

Correlations between traits and with environmental variables

The genetic correlation (including the population and genotype effect) between necrosis length caused by each of the two fungal pathogens was negative (−0.692, p-value<0.001; Table 2, Fig. 2). We also observed significant genetic correlations with height, negative for necrosis length caused by A. ostoyae (−0,653, p-value<0.001) and positive for necrosis length caused by D. sapinea (0.679, p-value<0.001). However, genetic correlations for height and necrosis length of the two pathogens at the genotype level were not significant (see Table S5.1, Supporting Information). Furthermore, susceptibility to D. sapinea indicated by necrosis length was positively correlated with precipitation in winter (0.741, p-value=0.028 for precipitation in January) and negatively with mean and maximum temperatures in the summer months (−0.827, p-value=0.008 for mean temperature in July and −0.780, p-value= 0.0165 for maximum temperature in July) in the population of origin (Table 3, Fig. 3). A similar effect was found for needle discoloration although the correlations were less strong.

Figure 2
Figure 2

Genetic correlation of necrosis length caused by Diplodia sapinea and Armillaria ostoyae based on best linear unbiased predictors (BLUPs, including both genotype and population effect). A linear trend line is shown.

Figure 3
Figure 3

Stripchart of necrosis length caused by Diplodia sapinea (BLUPs, including both genotype and population effect) plotted against the maximum temperature in July in each Pinus pinaster population of origin. Black lines indicate the average necrosis length in each population.

View this table:
Table 2

Pearson’s correlation coefficients for genetic correlations of the best linear unbiased predictors (BLUPs) of the combined population and genotype effects between adaptive traits in Pinus pinaster. bb, bud burst; dbb, duration of bud burst; disc., needle discoloration. Significance levels after false discovery rate (FDR) correction: *<0.05; **<0.01; ***0.001.

View this table:
Table 3

Pearson’s correlation coefficients between population effect BLUPs for adaptive traits, and climatic and environmental data in Pinus pinaster. disc., needle discoloration; meanTa, mean temperature; Tamax, maximum temperature; prec, precipitation; Jan, January; Feb, February; Aug, August; Sep, September; N, number of genotypes available for the trait. Significance levels after false discovery rate (FDR) correction: *<0.05; **<0.01; ***0.001.

Genotype-phenotype associations

Between three and 26 SNPs were significantly associated with each of the phenotypic traits evaluated under different genotype effect models (see Table S6.1, Supporting Information). Here we only report the SNPs that were significant under the additive genetic model, this model being built on three genotypic classes and therefore considered the most robust. Based on this model, seven SNPs were associated to height, 37 SNPs were associated with needle phenology (considering altogether the different phenology traits and measurement years), and eight with pathogen susceptibility (Table 4). In total, four significantly associated SNPs showed non-synonymous changes. Two non-synonymous SNPs were significantly associated to bud burst in 2017 (Fig. S6.1, Supporting Information) and one non-synonymous SNP was associated to each needle discoloration caused by D. sapinea and duration of bud burst in 2015 (Table 5, Fig. 4 and Fig. S6.2, Supporting Information). All the remaining SNPs associated under the additive model were either non-coding or the effect of the substitution was unknown (Table S6.1, Supporting Information). The allele frequency distribution of the associated SNPs was quite variable and did not reflect the population genetic structure of the species (Fig. 5).

Figure 4
Figure 4

Genotypic effects (box plots) for two exemplary single nucleotide polymorphisms (SNPs) showing significant association with needle discoloration caused by Diplodia sapinea (A) and duration of bud burst in 2015 (B) in Pinus pinaster.

Figure 5
Figure 5

Allele frequency distribution of SNP BX679001_1418 in natural populations of Pinus pinaster. This locus was significantly associated to needle discoloration caused by Diplodia sapinea (see main text and Fig. 4).

View this table:
Table 4

Single nucleotide polymorphisms (SNPs) significantly associated to height, needle phenology and pathogen susceptibility traits in Pinus pinaster under the additive genetic model as identified by a two-step approach based on mixed-effects linear models (MLMs) implemented in Tassel and the Bayesian framework in BAMD (BMLMs). Bayesian mean SNP effects and 95% confidence intervals (CIs) were obtained from the distribution of the last 20 000 iterations in BAMD. Marker codes and linkage groups as reported in Plomion et al., (2016). disc., needle discoloration.

View this table:
Table 5

Annotation for SNPs significantly associated under the additive model and coding for a non-synonymous amino acid change, as retrieved from Plomion et al., (2016) disc., needle discoloration

Discussion

In the current context of climate change, understanding the genetic basis of adaptive traits in tree species is key for an informed forest management. In this study, we assessed genetic variation in maritime pine for response to two pathogenic fungi, D. sapinea and A. ostoyae, at the range-wide scale, using trees grown in a clonal common garden and a novel inoculation protocol based on excised branches. Broad-sense heritability of pine susceptibility (necrosis length), both across and within populations, was estimated for the first time for the two diseases. We found a strong population effect for height, needle phenology and infection-related traits. Variation of susceptibility between geographical provenances, as well as height, followed a latitudinal gradient, corresponding to a climatic gradient, but in opposite direction for the two pathogens. Genetic associations revealed several loci significantly associated to pathogen susceptibility, height and phenological traits in maritime pine. The presence of pine processionary moth nests evaluated in the common garden was not heritable but future studies should consider the level of infestation or damage quantitatively.

Genetic and climate related correlations

Genetic correlations between susceptibility to D. sapinea and A. ostoyae, height and needle phenology possibly indicated similar climate factors and environmental clines driving differentiation at these traits. Especially, maximum temperatures during summer months and precipitation at the end of the summer or in winter showed significant correlations with genetic variability of phenotypic traits across maritime pine populations. Trees from populations with low winter precipitation and high maximum summer temperatures were less susceptible to D. sapinea. This result can be interpreted in different ways: 1) If we assume that D. sapinea is native in Europe, the pathogen pressure can be expected to be stronger in southern regions, with a climate more favourable to D. sapinea pathogenic outbreaks, triggered by stress in the host plant, especially by droughts (Luchi et al., 2014). Maritime pine populations growing in regions, such as Morocco and southern Spain, would then be more likely to have evolved resistance to the disease. On the contrary, trees from populations where severe drought periods have most likely not been common so far, e.g. Atlantic populations from Iberia and France, would be more susceptible. 2) In case, maritime pine and D. sapinea did not have sufficient time to co-evolve or pathogen pressure was not strong enough, differences in susceptibility among maritime pine populations might be due to exaptation or ecological fitting, i.e. traits selected for other functions (Agosta & Klemens, 2008). Populations of maritime pine strongly vary geographically in many traits related to growth and response to drought, along the gradient from North Africa to Atlantic regions of Iberia and France (Correia et al., 2008; Aranda et al., 2010; Corcuera et al., 2012; Gaspar et al., 2013; de la Mata et al., 2014). Some of these traits may indirectly influence their susceptibility to pathogens, as observed here for D. sapinea. For example, faster growing maritime pine trees from northern populations are known to invest more in inducible defences while slow growing trees from southern populations invest more in constitutive defences (López-Goldar et al., 2018). The positive genetic correlation between height and necrosis length caused by D. sapinea might indicate that constitutive defences confer better resistance to this pathogen in the southern populations. Also, Meijón et al. (2016) showed that the metabolomes in needles of maritime pine trees from populations with distinct geographic origin (notably Atlantic versus Mediterranean provenances) were quite differentiated and flavonoids showed a significant correlation with the water regime of the population of origin. However, the expression of metabolites is organ specific (de Miguel et al., 2016) and knowledge about secondary metabolites involved in resistance to D. sapinea is still lacking.

A study on the invasive pathogen Fusarium circinatum, which did certainly not co-evolve with maritime pine, also revealed a geographic cline in susceptibility, with Atlantic maritime pine populations showing less susceptibility than Moroccan populations (Elvira-Recuenco et al., 2014). A similar pattern was observed for A. ostoyae in our study. Heritability for necrosis length caused by A. ostoyae was low but significant, at both population and genotype level. Intra-population variability of susceptibility to A. ostoyae was higher than for D. sapinea where no significant variability at intra-population level was found. Our results indicated that maritime pine from southwestern France, where A. ostoyae outbreaks have been reported frequently (Labbé et al., 2015), may have developed some resistance or might show exapted resistance to the disease. Considering the absence of reports of A. ostoyae from the south of the Iberian Peninsula (Marxmüller & Guillaumin, 2005), which is in line with the species’ preference for humid forest sites (Cruickshank et al., 1997; Heinzelmann et al., 2018), trees in Morocco and southern Spain have most likely never co-evolved with this pathogen. However, a study by Guillaumin et al., (2005) on the mortality of potted maritime pine plants revealed an opposite pattern with the Landes population in Atlantic France being the most susceptible and the Moroccan population the least susceptible to A. ostoyae. Also, Zas et al., (2007) found moderate narrow-sense heritability for mortality due to A. ostoyae at the family level (h2f=0.35) in an infested progeny trial of maritime pine seedlings, which is much higher than broad-sense heritability of necrosis length in our study. Armillaria ostoyae is a root pathogen and a critical point during natural infestation is the penetration of the root that might be key for resistance mechanisms (Prospero et al., 2004; Solla et al., 2011; Labbé et al., 2017b), as the pathogen grows faster once it enters the organism and reaches the cambium (Solla et al., 2002). This step was bypassed in our inoculation protocol on excised branches. In the future, it would therefore be interesting to carry out inoculations on potted seedlings or young trees from range-wide maritime pine populations to evaluate susceptibility.

Suitable strategies to evaluate susceptibility to D. sapinea and A. ostoyae will become increasingly important as climate change increases pathogen pressure. Droughts are expected to become more frequent throughout Europe (IPCC, 2014) which will most likely trigger D. sapinea outbreaks also in regions where the pathogen has not caused severe disease symptoms so far. Recently, a northward expansion of D. sapinea outbreaks in Europe, probably driven by higher spring temperatures, has been recorded and is causing severe damage on P. sylvestris in Sweden and eastern Baltic countries (Adamson et al., 2015; Brodde et al., 2019). Our results suggested that an increase of drought events e.g. in the Landes region in France will most likely cause severe damage in these vast maritime pine forests due to high susceptibility of this population of maritime pine. In the case of A. ostoyae, the main threat resides in the condition of the host. As mentioned before, a weaker host will be more susceptible to the fungus, and future extreme weather events are bound to weaken trees, also increasing the pathogenic power of A. ostoyae (Kubiak et al., 2017). This is in line with a mathematical model that predicted a drastic northward shift of A. ostoyae in the Northwestern United States for the years 2061-2080, leading to increased mortality of stressed and maladapted trees (Hanna et al., 2016). In this study, trees maladapted to new temperatures are also expected to be more susceptible to biotic stress.

A shift in temperatures will not only affect pathogen susceptibility, but also other traits, notably growth and spring phenology (Badeck et al., 2004; Lindner et al., 2010). Height is a crucial, frequently studied trait in forest trees (e.g. Kremer & Lascoux, 1988; Cornelius, 1994) and showed a moderate-high broad-sense heritability of 0.497, the highest of all traits in our study. This is well in line with estimates in other conifer species e.g. ranging from 0.21 in Pinus taeda to 0.78 in Picea abies (reviewed in Lind et al., 2018) and from 0.148 to 0.282 in maritime pine saplings depending on the common garden site and the provenance (Rodríguez-Quilón et al., 2016). Height is known to be a highly integrative trait closely related e.g. to abiotic factors (Alía et al., 2014; Jaramillo-Correa et al., 2015) and has been used in combination with genetic markers to identify relevant conservation units in maritime pine (Rodríguez-Quilón et al., 2016). Our study showed that not only climate factors but also biotic interaction effects, such as pathogen susceptibility, were genetically correlated with height (positively for D. sapinea and negatively for A. ostoyae). Neutral genetic differentiation, i.e. FST, was moderate (FST = 0.109 [0.0129; 0.3247], p-value < 0.001) and significantly lower than QST estimates obtained for height and necrosis length caused by D. sapinea indicating that divergent selection is promoting local adaptation in these traits (Whitlock & Guillaume, 2009; Lamy et al., 2011).

Bud burst related phenological traits showed low to moderate broad-sense heritability depending on the year. Differentiation (QST) for bud burst reached from 0.191 to 0.275 which is comparable to a mean of 0.249 for bud flush averaged over several forest tree species (reviewed in Alberto et al., 2013). In our study, trees originating from northern populations flushed later than trees from southern populations. Similar clines have been observed for other conifers (reviewed in Alberto et al., 2013), which is not surprising, as spring phenology, such as flushing time, is known to be correlated with climatic factors (e.g. Zohner & Renner, 2014). Spring phenology can also play a role in resistance to or avoidance of forest tree pathogens (e.g. Swedjemark et al., 1998; Ghelardini & Santini, 2009; Nielsen et al., 2017). In line with this, we found a positive genetic correlation between needle discoloration and necrosis length caused by D. sapinea with needle phenology indicating that earlier flushing trees with faster developing needles showed less severe disease symptoms. Krokene et al., (2012) showed that the concentrations of starch and total sugars (glucose, fructose and sucrose) in twigs of Picea abies change during shoot development, which affects pathogen-related symptoms. In our study, inoculations were carried out on twigs with elongated shoots, however, the chemical composition of twigs might differ with time since bud burst.

Genotype-phenotype associations

We revealed significantly associated loci for all heritable traits. However, genotype effects were small, pointing to a highly polygenic nature of studied traits, as often reported for adaptive traits in forest trees. In addition, for susceptibility to D. sapinea and A. ostoyae, no resistance alleles with major effects were detected. We retrieved annotations from Plomion et al., (2016) and found four non-synonymous SNPs significantly associated to duration of bud burst in 2015 (one locus), bud burst in 2017 (two loci) and needle discoloration caused by D. sapinea (one locus), see Table 5. The potential function of these genes has to be interpreted with caution as this information usually derives from studies in distantly related model species. Nevertheless, the locus (BX679001_1418), which was significantly associated to needle discoloration caused by D. sapinea, possibly codes for a translation initiation factor eIF-5 that has previously been reported to be involved in pathogen-induced cell death and development of disease symptoms in Arabidopsis thaliana (Hopkins et al., 2008). Furthermore, the locus AL749768_562, significantly associated to bud burst, matched a putative 60S ribosomal protein L9 with higher expression in active buds compared to dormant buds in Cunninghamia lanceolata (Xu et al., 2016). These two genes deserve further attention in future studies addressing the genetic control of adaptive traits in conifers.

Based on a well-replicated clonal common garden and state-of-the-art genotyping technology, we were able to study key adaptive traits in maritime pine and found evidence for non-synonymous mutations underlying genetic variation for these traits. Association studies for highly polygenic traits are still challenging. Lind et al., (2017) reported an average of 236 SNPs associated to each of four fitness-related traits in Pinus albicaulis by detecting signals of significantly higher covariance of allele frequencies than would be expected to arise by chance alone. In the near future, multilocus association methods should be used to reveal genome wide loci with non-zero effects for polygenic traits in forest trees (Goldfarb et al., 2013; de la Torre et al., 2019).

Conclusions

In our study, we have shown that several adaptive traits in maritime pine were genetically correlated and also significantly correlated to climate factors. The evolution of suits of functional traits along environmental clines is a common pattern (e.g. Chapin et al., 1993; Reich et al., 1996) and populations are typically best adapted to their environment of origin (Kawecki & Ebert, 2004). Currently, locally adapted populations are challenged by changing climate conditions and emergent pests and pathogens expanding their range (Petr et al., 2017). Susceptibility to D. sapinea was highest in the northern maritime pine populations where it is expected to cause severe outbreaks due to increased incidence of drought events in the future (Brodde et al., 2019). Opposing trends in pathogen susceptibility among maritime pine populations e.g. for D. sapinea and A. ostoyae (this study), and for the invasive pathogen F. circinatum (Elvira-Recuenco et al., 2014) challenge forest tree breeding and natural forest resilience. An improved understanding of integrated phenotypes, including responses to known pests and pathogens, and their underlying genetic architecture is fundamental to assist new-generation tree breeding and the conservation of valuable genotypes. Coupled with early detection methods (see e.g. Kenis et al., 2018), knowledge on genetic responses to emerging pests and pathogens will help to ensure the health of forests in the future.

Data availability

SNP genotypes and phenotypic BLUPs will be made available at the DRYAD repository as soon as the manuscript has been accepted.

Author contributions

SCGM, RA and CP conceived the idea of the study, supervised its development, and obtained the funding; AH, AC and TG carried out the pathogen inoculation experiments supervised by CD and MLDL, who also contributed to protocol development; IRQ contributed to phenotyping and carried out the SNP genotyping; AH, MDM and KBB carried out data analyses; AH and KBB wrote the manuscript and all co-authors critically read and contributed to the final version of the manuscript.

Supporting Information

Table S1.1 Number of genotypes from the CLONAPIN clonal common garden used to study adaptive traits in Pinus pinaster.

Figure S2.1 Phenological stages of bud burst.

Protocol S3.1 Laboratory protocol for Diplodia sapinea inoculations.

Figure S3.1 Pictures of the four scales of needle discoloration found along the necrosis caused by artificial Diplodia sapinea inoculations on excised branches of maritime pine.

Protocol S3.2 Laboratory protocol for Armillaria ostoyae inoculations.

S4.1 Model parametrization of fixed-effect models

Table S4.1 MCMCglmm Bayesian model parametrization.

Figure S5.1 Box plots of the best linear unbiased predictors (BLUP) of each phenotypic trait in each provenance (corresponding to the six gene pools) of Pinus pinaster.

Table S5.2 Pearson’s correlation coefficients for genetic correlations of the best linear unbiased predictors (BLUPs) of the genotype values between adaptive traits in Pinus pinaster.

Table S6.1 (see separate pdf-file: TableS6.1.pdf) All significant allele effects (including additive, dominance and overdominance effects) of single nucleotide polymorphisms (SNPs, minor allele frequency (MAF) > 0.1) on height, needle phenology and pathogen susceptibility traits in Pinus pinaster identified by a two-step approach based on mixed-effects linear models (MLMs) implemented in Tassel and the Bayesian framework in BAMD (BMLMs). Bayesian mean SNP effects and 95% confidence intervals (CIs) were obtained from the distribution of the last 20 000 iterations in BAMD. Marker names and linkage groups (LG) as reported in Plomion et al., (2016). Site annotations: nc, non-coding (untranslated regions or introns); non-syn, non-synonymous; syn, synonymous; unk, unknown. N, number of phenotypic observations included in the analyses.

Figure S6.1 Density plots of the effect sizes based on 20,000 BAMD simulations and genotypic effects for three single nucleotide polymorphisms showing significant association with bud burst in 2017 and coding for a non-synonymous change in Pinus pinaster.

Figure S6.2 Density plots of the effect sizes based on 20,000 BAMD simulations and genotypic effects for two single nucleotide polymorphisms showing significant association with needle discoloration caused by D. sapinea and bud burst in 2015 and coding for a non-synonymous change in Pinus pinaster.

Acknowledgements

We thank Xavier Capdevielle, Olivier Fabreguettes, Martine Martin-Clotté and Gilles St Jean for field and lab assistance, and Brigitte Lung-Escarmant, Thierry Belouard, Bernard Boutte, Claude Husson, Jean-Baptiste Daubrée, Margarita Elvira-Recuenco and Rosa Raposo-Llobet for valuable discussions. We thank Juan Majada for providing the rooted cuttings used to establish the CLONAPIN collection in Cestas and the experimental unit of INRA-Pierroton for trial establishment, height and bud flush measurements. We thank Hervé Jactel and Victor Rebillard for providing the processionary moth nest data. We acknowledge funding from IdEx Bordeaux - Chaires d’installation 2015 (EcoGenPin), the Spanish National Research Plan (ClonaPin, RTA2010-00120-C02-01), and the European Union’s Horizon 2020 research and innovation program under grant agreement No 773383 (B4EST). This work is part of the PhD research of A.H. funded by the Région de Nouvelle-Aquitaine (project Athénée) and the IdEx Bordeaux (project EcoGenPin).

Footnotes

  • The main manuscript has been shortened to meet the criteria of the scientific journal where it has been submitted for review. The figures have been revised and the quality has been improved.

References

  1. ↵
    Adamson K, Klavina D, Drenkhan R, Gaitnieks T, Hanso M. 2015. Diplodia sapinea is colonizing the native Scots pine (Pinus sylvestris) in the northern Baltics. European Journal of Plant Pathology 143: 343–350.
    OpenUrl
  2. ↵
    Agosta SJ, Klemens JA. 2008. Ecological fitting by phenotypically flexible genotypes: implications for species associations, community assembly and evolution. Ecology Letters 11: 1123–1134.
    OpenUrlCrossRefPubMed
  3. ↵
    Alberto FJ, Derory J, Boury C, Frigerio J-M, Zimmermann NE, Kremer A. 2013. Imprints of natural selection along environmental gradients in phenology-related genes of Quercus petraea. Genetics 195: 495–512.
    OpenUrlAbstract/FREE Full Text
  4. ↵
    Alía R, Chambel R, Notivol E, Climent J, González-Martínez SC. 2014. Environment-dependent microevolution in a Mediterranean pine (Pinus pinaster Aiton). BMC Evolutionary Biology 14: 200.
    OpenUrl
  5. ↵
    Aranda I, Alía R, Ortega U, Dantas ÂK, Majada J. 2010. Intra-specific variability in biomass partitioning and carbon isotopic discrimination under moderate drought stress in seedlings from four Pinus pinaster populations. Tree Genetics & Genomes 6: 169–178.
    OpenUrl
  6. ↵
    Badeck F-W, Bondeau A, Bottcher K, Doktor D, Lucht W, Schaber J, Sitch S. 2004. Responses of spring phenology to climate change. New Phytologist 162: 295–309.
    OpenUrlCrossRefWeb of Science
  7. ↵
    Bartholomé J, Bink MC, van Heerwaarden J, Chancerel E, Boury C, Lesur I, Isik F, Bouffier L, Plomion C. 2016. Linkage and association mapping for two major traits used in the Maritime Pine breeding program: Height growth and stem straightness. PLoS ONE 11: e0165323.
    OpenUrl
  8. ↵
    Battisti A, Stastny M, Buffo E, Larsson S. 2006. A rapid altitudinal range expansion in the pine processionary moth produced by the 2003 climatic anomaly. Global Change Biology 12: 662–671.
    OpenUrlCrossRefWeb of Science
  9. ↵
    Beaulieu J, Doerksen T, Boyle B, Clément S, Deslauriers M, Beauseigle S, Blais S, Poulin P-L, Lenz P, Caron S, et al. 2011. Association genetics of wood physical traits in the conifer white spruce and relationships with gene expression. Genetics 188: 197–214.
    OpenUrlAbstract/FREE Full Text
  10. ↵
    Blodgett JT, Bonello P. 2003. The aggressiveness of Sphaeropsis sapinea on Austrian pine varies with isolate group and site of infection. Forest Pathology 33: 15–19.
    OpenUrl
  11. ↵
    Boutte B. 2018. Chronologie et localisation des dégâts Indice de sévérité. Note de service DSF, France
  12. ↵
    Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES. 2007. TASSEL: Software for association mapping of complex traits in diverse samples. Bioinformatics 23: 2633–2635.
    OpenUrlCrossRefPubMedWeb of Science
  13. ↵
    Brodde L, Adamson K, Julio Camarero J, Castaño C, Drenkhan R, Lehtijärvi A, Luchi N, Migliorini D, Sánchez-Miranda Á, Stenlid J, et al. 2019. Diplodia tip blight on its way to the north: drivers of disease emergence in northern Europe. Frontiers in Plant Science 9.
  14. ↵
    Bucci G, González-Martínez SC, Le Provost G, Plomion C, Ribeiro MM, Sebastiani F, Alía R, Vendramin GG. 2007. Range-wide phylogeography and gene zones in Pinus pinaster Ait. revealed by chloroplast microsatellite markers. Molecular Ecology 16: 2137–2153.
    OpenUrlCrossRefPubMedWeb of Science
  15. ↵
    Budde KB, Heuertz M, Hernández-Serrano A, Pausas JG, Vendramin GG, Verdú M, González-Martínez SC. 2014. In situ genetic association for serotiny, a fire-related trait, in Mediterranean maritime pine (Pinus pinaster). New Phytologist 201: 230–241.
    OpenUrlCrossRefPubMedWeb of Science
  16. ↵
    Burban C, Petit RJ, Carcreff E, Jactel H. 1999. Rangewide variation of the maritime pine bast scale Matsucoccus feytaudi Duc. (Homoptera: Matsucoccidae) in relation to the genetic structure of its host. Molecular Ecology 8: 1593–1602.
    OpenUrlCrossRefPubMedWeb of Science
  17. ↵
    Burdon JJ, Thrall PH. 2000. Coevolution at multiple spatial scales: Linum marginale-Melampsora lini - From the individual to the species. Evolutionary Ecology 14: 261–281.
    OpenUrl
  18. ↵
    Burgess TI, Wingfield MJ, Wingfield BD. 2004. Global distribution of Diplodia pinea genotypes revealed using simple sequence repeat (SSR) markers. Australasian Plant Pathology 33: 513.
    OpenUrl
  19. ↵
    Cabezas JA, González-Martínez SC, Collada C, Guevara MA, Boury C, de María N, Eveno E, Aranda I, Garnier-Géré PH, Brach J, et al. 2015. Nucleotide polymorphisms in a pine ortholog of the Arabidopsis degrading enzyme cellulase KORRIGAN are associated with early growth performance in Pinus pinaster. Tree Physiology 35: 1000–1006.
    OpenUrlCrossRefPubMed
  20. ↵
    Castro J, McDowell N, Bachelet D, Cobb N, Lim J-H, Running SW, Breshears DD, Gonzalez P, Vennetier M, Semerci A, et al. 2009. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest Ecology and Management 259: 660–684.
    OpenUrl
  21. ↵
    Chapin FS, Autumn K, Pugnaire F. 1993. Evolution of Suites of Traits in Response to Environmental Stress. The American Naturalist 142: S78–S92.
    OpenUrlCrossRefWeb of Science
  22. ↵
    Chou CKS. 1978. Penetration of young stems of Pinus radiata by Diplodia pinea. Physiological Plant Pathology 13: 189–192.
    OpenUrl
  23. ↵
    Cobb FW, Kristic M, Zavarin E. 1968. Inhibitory effects of volatile oleoresin components on Fomes annosus and four Ceratocystis species. Phytopathology 58: 1327–1335.
    OpenUrlWeb of Science
  24. ↵
    Corcuera L, Gil-Pelegrin E, Notivol E. 2012. Differences in hydraulic architecture between mesic and xeric Pinus pinaster populations at the seedling stage. Tree Physiology 32: 1442–1457.
    OpenUrlCrossRefPubMedWeb of Science
  25. ↵
    Cornelius J. 1994. Heritabilities and additive genetic coefficients of variation in forest trees. Canadian Journal of Forest Research 24: 372–379.
    OpenUrlCrossRefWeb of Science
  26. ↵
    Correia I, Almeida MH, Aguiar A, Alía R, David TS, Pereira JS. 2008. Variations in growth, survival and carbon isotope composition (delta13C) among Pinus pinaster populations of different geographic origins. Tree Physiology 28: 1545–1552.
    OpenUrlPubMed
  27. ↵
    Cruickshank MG, Morrison D, Punja ZK. 1997. Incidence of Armillaria species in precommercial thinning stumps and spread of Armillaria ostoyae to adjacent Douglas-fir trees. Canadian Journal of Forest Research 27: 481–490.
    OpenUrl
  28. ↵
    Delzon S, Bresson CC, Michalet R, Vitasse Y, Kremer A. 2009. Altitudinal differentiation in growth and phenology among populations of temperate-zone tree species growing in a common garden. Canadian Journal of Forest Research 39: 1259–1269.
    OpenUrlCrossRefWeb of Science
  29. ↵
    Desprez-Loustau M-L, Aguayo J, Dutech C, Hayden KJ, Husson C, Jakushkin B, Marçais B, Piou D, Robin C, Vacher C. 2016. An evolutionary ecology perspective to address forest pathology challenges of today and tomorrow. Annals of Forest Science 73: 45–67.
    OpenUrl
  30. ↵
    Desprez-Loustau M-L, Baradat PH. 1991. Variation in susceptibility to twisting rust of maritime pine. Annals of Forest Science 48: 497–511.
    OpenUrl
  31. ↵
    Desprez-Loustau M-L, Marçais B, Nageleisen L-M, Piou D, Vannini A. 2006. Interactive effects of drought and pathogens in forest trees. Annals of Forest Science 63: 597–612.
    OpenUrlCrossRefWeb of Science
  32. ↵
    Eckert AJ, Bower AD, Wegrzyn JL, Pande B, Jermstad KD, Krutovsky K V., St. Clair JB, Neale DB. 2009. Association genetics of coastal Douglas fir (Pseudotsuga menziesii var. menziesii, Pinaceae). I. Cold-hardiness related traits. Genetics 182: 1289–1302.
    OpenUrlAbstract/FREE Full Text
  33. ↵
    Elvira-Recuenco M, Iturritxa E, Majada J, Alía R, Raposo R. 2014. Adaptive potential of maritime pine (Pinus pinaster) populations to the emerging pitch canker pathogen, Fusarium circinatum. PLoS ONE 9: e114971.
    OpenUrlCrossRefPubMed
  34. ↵
    Elzinga JA, Atlan A, Biere A, Gigord L, Weis AE, Bernasconi G. 2007. Time after time: flowering phenology and biotic interactions. Trends in Ecology and Evolution 22: 432–439.
    OpenUrl
  35. ↵
    Ennos RA. 2015. Resilience of forests to pathogens: An evolutionary ecology perspective. Forestry 88: 41–52.
    OpenUrlCrossRef
  36. Eveno E, Collada C, Guevara MÁ, Léger V, Soto A, Díaz L, Léger P, González-Martínez SC, Cervera MT, Plomion C, et al. 2008. Contrasting patterns of selection at Pinus pinaster Ait. drought stress candidate genes as revealed by genetic differentiation analyses. Molecular Biology and Evolution 25: 417–437.
    OpenUrlCrossRefPubMedWeb of Science
  37. ↵
    Freeman JS, Hamilton MG, Lee DJ, Pegg GS, Brawner JT, Tilyard PA, Potts BM. 2019. Comparison of host susceptibilities to native and exotic pathogens provides evidence for pathogen-imposed selection in forest trees. New Phytologist 221: 2261–2272.
    OpenUrl
  38. ↵
    Robinson GK. 1991. That BLUP is a good thing: The estimation of random effects. Statistical Science 6: 15–32.
    OpenUrlCrossRef
  39. ↵
    Gardes M, Bruns TD. 1993. ITS primers with enhanced specificity for basidiomycetes - application to the identification of mycorrhizae and rusts. Molecular Ecology 2: 113–118.
    OpenUrlCrossRefPubMedWeb of Science
  40. ↵
    Gaspar MJ, Velasco T, Feito I, Alía R, Majada J. 2013. Genetic variation of drought tolerance in Pinus pinaster at three hierarchical levels: A comparison of induced osmotic stress and field testing. PLoS ONE 8: e79094.
    OpenUrlCrossRefPubMed
  41. ↵
    Georgieva M, Hlebarska S. 2017. A review of Sphaeropsis sapinea occurrence on Pinus species in Bulgaria. Journal of Bioscience and Biotechnology 5: 247–250.
    OpenUrl
  42. ↵
    Ghelardini L, Santini A. 2009. Avoidance by early flushing: a new perspective on Dutch elm disease research. iForest - Biogeosciences and Forestry 2: 143–153.
    OpenUrl
  43. ↵
    Goldfarb B, Neale DB, Wegrzyn JL, Davis JM, Lee JM, Liechty JD, Quesada T, Langley CH, Cumbie WP, Eckert AJ, et al. 2013. The evolutionary genetics of the genes underlying phenotypic associations for loblolly pine (Pinus taeda, Pinaceae). Genetics 195: 1353–1372.
    OpenUrlAbstract/FREE Full Text
  44. ↵
    González-Martínez SC, Alía R, Gil L. 2002. Population genetic structure in a Mediterranean pine (Pinus pinaster Ait.): A comparison of allozyme markers and quantitative traits. Heredity 89: 199–206.
    OpenUrlCrossRefPubMedWeb of Science
  45. ↵
    Gonzalo J. 2007. Phytoclimatic analysis of the Spanish Peninsula: update and geostatistical analysis. PhD thesis, University of Valladolid, Palencia, Spain.
  46. Grivet D, Sebastiani F, Alía R, Bataillon T, Torre S, Zabal-Aguirre M, Vendramin GG, González-Martínez SC. 2011. Molecular footprints of local adaptation in two mediterranean conifers. Molecular Biology and Evolution 28: 101–116.
    OpenUrlCrossRefPubMedWeb of Science
  47. ↵
    1. Guillaumin J-J
    Guillaumin J-J, Lung-Escarmant B, Legrand P. 2005. Sensibilité des hôtes ligneux à l’armillaire et sélection pour la tolérance. In: Guillaumin J-J ed. L’armillaire et le pourridié-agraric des végétaux ligneux. Paris: INRA, 377–388.
  48. ↵
    Hadfield JD. 2010. MCMC Methods for multi-response generalized linear mixed models: The MCMCglmm R Package. Journal of Statistical Software 33.
  49. ↵
    Hamilton MG, Williams DR, Tilyard PA, Pinkard EA, Wardlaw TJ, Glen M, Vaillancourt RE, Potts BM. 2013. A latitudinal cline in disease resistance of a host tree. Heredity 110: 372–379.
    OpenUrlCrossRefPubMedWeb of Science
  50. ↵
    Hanewinkel M, Cullmann DA, Schelhaas M-J, Nabuurs G-J, Zimmermann NE. 2013. Climate change may cause severe loss in the economic value of European forest land. Nature Climate Change 3: 203–207.
    OpenUrl
  51. ↵
    Hanna JW, Warwell MV, Maffei H, Fairweather ML, Blodgett JT, Zambino PJ, Worral J, Burns KS, Jacobs JJ, Ashiglar SM, et al. 2016. Bioclimatic modeling predicts potential distribution of Armillaria solipides and Pseudotsuga menziesii (Douglas-Fir) under contemporary and changing climates in the interior Western USA. In: Proceedings of the 63rd Annual Western International Forest Disease Work Conference.
  52. ↵
    Hardy OJ, Vekemans X. 2002. SPAGeDi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Molecular Ecology Notes 2: 618–620.
    OpenUrlCrossRefWeb of Science
  53. ↵
    Heinzelmann R, Dutech C, Tsykun T, Labbé F, Soularue J-P, Prospero S. 2018. Latest advances and future perspectives in Armillaria research. Canadian Journal of Plant Pathology 41: 1–23.
    OpenUrl
  54. ↵
    Heinzelmann R, Rigling D. 2016. Mycelial fan formation of three sympatric Armillaria species on excised stem segments of Picea abies. Forest Pathology 46: 187–199.
    OpenUrl
  55. ↵
    Henderson CR. 1973. Sire evaluation and genetic trends. Journal of Animal Science 1973: 10–41.
    OpenUrlCrossRef
  56. ↵
    Herms DA, Mattson WJ. 2004. The Dilemma of Plants: To Grow or Defend. The Quarterly Review of Biology 67: 283–335.
    OpenUrl
  57. ↵
    Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965–1978.
    OpenUrlCrossRefWeb of Science
  58. ↵
    Holliday J a., Ritland K, Aitken SN. 2010. Widespread, ecologically relevant genetic markers developed from association mapping of climate-related traits in Sitka spruce (Picea sitchensis). New Phytologist 188: 501–514.
    OpenUrlCrossRefPubMedWeb of Science
  59. ↵
    Hopkins MT, Lampi Y, Wang T-W, Liu Z, Thompson JE. 2008. Eukaryotic Translation Initiation Factor 5A Is Involved in Pathogen-Induced Cell Death and Development of Disease Symptoms in Arabidopsis. Plant Physiology 148: 479–489.
    OpenUrlAbstract/FREE Full Text
  60. ↵
    Houle D. 1992. Comparing evolvability and variability of quantitative traits. Genetics 130: 195–204.
    OpenUrlAbstract/FREE Full Text
  61. ↵
    1. Core Writing Team,
    2. R.K. Pachauri and
    3. L.A. Meyer
    IPCC. 2014. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp
  62. ↵
    Iturritxa E, Ganley R, Raposo R, García-Serna I, Mesanza N, Kirkpatrick SC, Gordon TR. 2013. Resistance levels of Spanish conifers against Fusarium circinatum and Diplodia pinea. Forest Pathology 43: 488–495.
    OpenUrl
  63. ↵
    Jacquet J-S, Bosc A, O’Grady AP, Jactel H. 2013. Pine growth response to processionary moth defoliation across a 40-year chronosequence. Forest Ecology and Management 293: 29–38.
    OpenUrl
  64. ↵
    1. Roque A
    Jactel H, Barbaro L, Battisti A, Bosc A, Branco M, Brockerhoff E, Castagneyrol B, Dulaurent AM, Hódar JA, Jacquet J-S, et al. 2015. Insect-tree interactions in Thaumetopoea pityocampa. In: Roque A ed. Processionary Moths and Climate Change: An Update. 265–310.
  65. ↵
    Jaramillo-Correa JP, Rodríguez-Quilón I, Grivet D, Lepoittevin C, Sebastiani F, Heuertz M, Garnier-Géré P, Alía R, Plomion C, Vendramin GG, et al. 2015. Molecular proxies for climate maladaptation in a long-lived tree (Pinus pinaster Aiton, Pinaceae). Genetics 199: 793–807.
    OpenUrlAbstract/FREE Full Text
  66. ↵
    Kawecki TJ, Ebert D. 2004. Conceptual issues in local adaptation. Ecology Letters 7: 1225–1241.
    OpenUrlCrossRefWeb of Science
  67. ↵
    Kenis M, Li H, Fan J, Courtial B, Auger-Rozenberg M-A, Yart A, Eschen R, Roques A. 2018. Sentinel nurseries to assess the phytosanitary risks from insect pests on importations of live plants. Scientific Reports 8: 11217.
    OpenUrl
  68. ↵
    Koch RA, Wilson AW, Séné O, Henkel TW, Aime MC. 2017. Resolved phylogeny and biogeography of the root pathogen Armillaria and its gasteroid relative, Guyanagaster. BMC Evolutionary Biology 17: 33.
    OpenUrl
  69. ↵
    Kremer A, Lascoux M. 1988. Genetic architecture of height growth in maritime pine (Pinus pinaster Ait.). Silvae Genetica 37: 1–8.
    OpenUrl
  70. ↵
    Krokene P, Lahr E, Dalen LS, Skrøppa T, Solheim H. 2012. Effect of phenology on susceptibility of Norway spruce (Picea abies) to fungal pathogens. Plant Pathology 61: 57–62.
    OpenUrl
  71. ↵
    Kubiak K, Żółciak A, Damszel M, Lech P, Sierota Z. 2017. Armillaria pathogenesis under climate changes. Forests 8: 100.
    OpenUrl
  72. ↵
    Kuhlman EG, Amerson H V., Wilcox PL, Liu BH, O’Malley DM, Sederoff RR. 2002. Detection of a major gene for resistance to fusiform rust disease in loblolly pine by genomic mapping. Proceedings of the National Academy of Sciences 93: 3859–3864.
    OpenUrl
  73. ↵
    de la Mata R, Merlo E, Zas R. 2014. Among-population variation and plasticity to drought of Atlantic, Mediterranean, and interprovenance hybrid populations of maritime pine. Tree Genetics & Genomes 10: 1191–1203.
    OpenUrl
  74. ↵
    de la Torre AR, Puiu D, Crepeau MW, Stevens K, Salzberg SL, Langley CH, Neale DB. 2019. Genomic architecture of complex traits in loblolly pine. New Phytologist 221: 1789–1801.
    OpenUrl
  75. ↵
    Labbé F, Fontaine MC, Robin C, Dutech C. 2017a. Genetic signatures of variation in population size in a native fungal pathogen after the recent massive plantation of its host tree. Heredity 119: 402–410.
    OpenUrl
  76. ↵
    Labbé F, Lung-Escarmant B, Fievet V, Soularue J-P, Laurent C, Robin C, Dutech C. 2017b. Variation in traits associated with parasitism and saprotrophism in a fungal root-rot pathogen invading intensive pine plantations. Fungal Ecology 26: 99–108.
    OpenUrl
  77. ↵
    Labbé F, Marçais B, Dupouey J-L, Bélouard T, Capdevielle X, Piou D, Robin C, Dutech C. 2015. Pre-existing forests as sources of pathogens? The emergence of Armillaria ostoyae in a recently planted pine forest. Forest Ecology and Management 357: 248–258.
    OpenUrl
  78. ↵
    Lamy J-B, Bouffier L, Burlett R, Plomion C, Cochard H, Delzon S. 2011. Uniform selection as a primary force reducing population genetic differentiation of cavitation resistance across a species range. PLoS ONE 6: e23476.
    OpenUrlCrossRefPubMed
  79. ↵
    Leimu R, Koricheva J. 2006. A meta-analysis of genetic correlations between plant resistances to multiple enemies. The American Naturalist 168: E15–E37.
    OpenUrlCrossRefPubMedWeb of Science
  80. ↵
    Lepoittevin C, Harvengt L, Plomion C, Garnier-Géré P. 2012. Association mapping for growth, straightness and wood chemistry traits in the Pinus pinaster Aquitaine breeding population. Tree Genetics and Genomes 8: 113–126.
    OpenUrl
  81. ↵
    Li Z, Gopal V, Li X, Davis JM, Casella G. 2012. Simultaneous SNP identification in association studies with missing data. Annals of Applied Statistics 6: 432–456.
    OpenUrl
  82. ↵
    Lind BM, Friedline CJ, Wegrzyn JL, Maloney PE, Vogler DR, Neale DB, Eckert AJ. 2017. Water availability drives signatures of local adaptation in whitebark pine (Pinus albicaulis Engelm.) across fine spatial scales of the Lake Tahoe Basin, USA. Molecular Ecology 26: 3168–3185.
    OpenUrlCrossRef
  83. ↵
    Lind BM, Menon M, Bolte CE, Faske TM, Eckert AJ. 2018. The genomics of local adaptation in trees: are we out of the woods yet? Tree Genetics & Genomes 14: 29.
    OpenUrl
  84. ↵
    Lindner M, Maroschek M, Netherer S, Kremer A, Barbati A, Garcia-Gonzalo J, Seidl R, Delzon S, Corona P, Kolström M, et al. 2010. Climate change impacts, adaptive capacity, and vulnerability of European forest ecosystems. Forest Ecology and Management 259: 698–709.
    OpenUrlCrossRefWeb of Science
  85. ↵
    Liu J-J, Sniezko RA, Sturrock RN, Chen H. 2014. Western white pine SNP discovery and high-throughput genotyping for breeding and conservation applications. BMC Plant Biology 14: 380.
    OpenUrl
  86. ↵
    Loiselle BA, Sork VL, Nason J, Graham C. 1995. Spatial genetic structure of a tropical understory shrub, Psychotria officinalis (Rubiaceae). American Journal of Botany 82: 1420–1425.
    OpenUrlCrossRefWeb of Science
  87. ↵
    López-Goldar X, Villari C, Bonello P, Borg-Karlson AK, Grivet D, Zas R, Sampedro L. 2018. Inducibility of plant secondary metabolites in the stem predicts genetic variation in resistance against a key insect herbivore in maritime pine. Frontiers in Plant Science 9.
  88. ↵
    Luchi N, Oliveira Longa CM, Danti R, Capretti P, Maresi G. 2014. Diplodia sapinea: The main fungal species involved in the colonization of pine shoots in Italy. Forest Pathology 44: 372–381.
    OpenUrl
  89. ↵
    Lung-Escarmant B, Guyon D. 2004. Temporal and spatial dynamics of primary and secondary infection by Armillaria ostoyae in a Pinus pinaster plantation. Phytopathology 94: 125–131.
    OpenUrlCrossRefPubMedWeb of Science
  90. ↵
    1. Laflamme G,
    2. Bérubé JA,
    3. Bussières G
    Lung-Escarmant B, Maugard F, Giraud A, Escrivant M, Molinier F, Merilleau F, Vida G. 2002. Infectious cycle of Armillaria ostoyae. In: Laflamme G, Bérubé JA, Bussières G, eds. Proceedings of the 10th International Conference on Root and Butt Rot. Natural Resources Canada, Canadian Forest Service. Québec, CA, 428–431.
  91. ↵
    1. Guillaumin J-J
    Marxmüller H, Guillaumin J-J. 2005. Description et distribution des armillaires européennes. In: Guillaumin J-J ed. L’armillaire et le pourridié-agraric des végétaux ligneux. Paris, France, 63–84.
  92. ↵
    Meijón M, Feito I, Oravec M, Delatorre C, Weckwerth W, Majada J, Valledor L. 2016. Exploring natural variation of Pinus pinaster Aiton using metabolomics: Is it possible to identify the region of origin of a pine from its metabolites? Molecular Ecology 25: 959–976.
    OpenUrlCrossRef
  93. ↵
    Menéndez-Gutiérrez M, Alonso M, Toval G, Díaz R. 2017. Variation in pinewood nematode susceptibility among Pinus pinaster Ait. provenances from the Iberian Peninsula and France. Annals of Forest Science 74: 76.
    OpenUrl
  94. ↵
    de Miguel M, Guevara MÁ, Sánchez-Gómez D, de María N, Díaz LM, Mancha JA, Fernández de Simón B, Cadahía E, Desai N, Aranda I, et al. 2016. Organ-specific metabolic responses to drought in Pinus pinaster Ait. Plant Physiology and Biochemistry 102: 17–26.
    OpenUrl
  95. ↵
    Mimura M, Aitken SN. 2007. Adaptive gradients and isolation-by-distance with postglacial migration in Picea sitchensis. Heredity 99: 224–232.
    OpenUrlCrossRefPubMedWeb of Science
  96. ↵
    Moran E, Lauder J, Musser C, Stathos A, Shu M. 2017. The genetics of drought tolerance in conifers. New Phytologist 216: 1034–1048.
    OpenUrl
  97. ↵
    Moreira X, Mooney KA, Rasmann S, Petry WK, Carrillo-Gavilán A, Zas R, Sampedro L. 2014. Trade-offs between constitutive and induced defences drive geographical and climatic clines in pine chemical defences. Ecology Letters 17: 537–546.
    OpenUrlCrossRefPubMed
  98. ↵
    Morgenstern M. 2011. Geographic variation in forest trees: Genetic basis and application of knowledge in silviculture. Vancouver, CA: University of British Columbia Press.
  99. ↵
    Nakagawa S, Schielzeth H. 2010. Repeatability for Gaussian and non-Gaussian data: A practical guide for biologists. Biological Reviews 85: 935–956.
    OpenUrlCrossRefPubMed
  100. ↵
    Neale DB, Ersoz E, Wheeler NC, Nelson CD, González-Martínez SC. 2006. Association Genetics in Pinus taeda L. I. Wood Property Traits. Genetics 175: 399–409.
    OpenUrl
  101. ↵
    Neale DB, Kremer A. 2011. Forest tree genomics: Growing resources and applications. Nature Reviews Genetics 12: 111–122.
    OpenUrlCrossRefPubMed
  102. ↵
    Nielsen LR, McKinney LV, Kjær ED. 2017. Host phenological stage potentially affects dieback severity after Hymenoscyphus fraxineus infection in Fraxinus excelsior seedlings. Baltic Forestry 23:229–232.
    OpenUrl
  103. ↵
    Perry A, Brown AV, Cavers S, Cottrell JE, Ennos RA. 2016. Has Scots pine (Pinus sylvestris) co-evolved with Dothistroma septosporum in Scotland? Evidence for spatial heterogeneity in the susceptibility of native provenances. Evolutionary Applications 9: 982–993.
    OpenUrl
  104. ↵
    Petit RJ, Bahrman N, Baradat PH. 1995. Comparison of genetic differentiation in maritime pine (Pinus pinaster ait.) estimated using isozyme, total protein and terpenic loci. Heredity 75: 382–389.
    OpenUrlCrossRefWeb of Science
  105. ↵
    Petit RJ, Bialozyt R, Garnier-Géré P, Hampe A. 2004. Ecology and genetics of tree invasions: from recent introductions to Quaternary migrations. Forest Ecology and Management 197: 117–137.
    OpenUrlCrossRefWeb of Science
  106. ↵
    Petit RJ, Hampe A. 2006. Some Evolutionary Consequences of Being a Tree. Annual Review of Ecology, Evolution, and Systematics 37: 187–214.
    OpenUrlCrossRefWeb of Science
  107. ↵
    Petr M, Peltoniemi M, Trotsiuk V, Martin-Benito D, Mairota P, Wild J, Svoboda M, Honkaniemi J, Seidl R, Vacchiano G, et al. 2017. Forest disturbances under climate change. Nature Climate Change 7: 395–402.
    OpenUrl
  108. ↵
    Phillips MA, Croteau RB. 1999. Resin-based defenses in conifers. Trends in Plant Science 4: 184–190.
    OpenUrlCrossRefPubMedWeb of Science
  109. ↵
    Piou D, Chandelier P, Morelet M. 1991. Sphaeropsis sapinea, un nouveau problème sanitaire des Pins en France? Revue Forestière Française 43: 203.
    OpenUrl
  110. ↵
    Plomion C, Bartholomé J, Lesur I, Boury C, Rodríguez-Quilón I, Lagraulet H, Ehrenmann F, Bouffier L, Gion J-M, Grivet D, et al. 2016. High-density SNP assay development for genetic analysis in maritime pine (Pinus pinaster). Molecular Ecology Resources 16: 574–587.
    OpenUrl
  111. ↵
    Pot D, McMillan L, Echt C, Le Provost G, Garnier-Géré P, Cato S, Plomion C. 2005. Nucleotide variation in genes involved in wood formation in two pine species. New Phytologist 167: 101–112.
    OpenUrlCrossRefPubMedWeb of Science
  112. ↵
    Pritchard JK, Stephens M, Donnelly P. 2000. Inference of population structure using multilocus genotype data. Genetics 155: 945–959.
    OpenUrlAbstract/FREE Full Text
  113. ↵
    Prospero S, Holdenrieder O, Rigling D. 2004. Comparison of the virulence of Armillaria cepistipes and Armillaria ostoyae on four Norway spruce provenances. Forest Pathology 34: 1–14.
    OpenUrl
  114. ↵
    Quesada T, Casella G, Goldfarb B, Cumbie WP, Gopal V, Eckert AJ, Huber D a., Davis JM, Wegrzyn JL, Neale DB. 2010. Association Mapping of Quantitative Disease Resistance in a Natural Population of Loblolly Pine (Pinus taeda L.). Genetics 186: 677–686.
    OpenUrlAbstract/FREE Full Text
  115. R Development Core Team. 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL: https://www.R-project.org/.
  116. ↵
    Reich PB, Oleksyn J, Tjoelker MG. 1996. Needle respiration and nitrogen concentration in scots pine populations from a broad latitudinal range: A common garden test with field-grown trees. Functional Ecology 10: 768.
    OpenUrl
  117. ↵
    Resende RT, Resende MDV, Silva FF, Azevedo CF, Takahashi EK, Silva-Junior OB, Grattapaglia D. 2017. Regional heritability mapping and genome-wide association identify loci for complex growth, wood and disease resistance traits in Eucalyptus. New Phytologist 213: 1287–1300.
    OpenUrlCrossRef
  118. ↵
    Rishbeth J. 2006. Resistance to fungal pathogens of tree roots. Proceedings of the Royal Society of London. Series B. Biological Sciences 181: 333–351.
    OpenUrl
  119. ↵
    Rodríguez-Quilón I. 2017. Ecological and association genetics in two Mediterranean pine species. PhD Thesis, Universidad Politécnica de Madrid, Spain.
  120. ↵
    Rodríguez-Quilón I, Santos-del-Blanco L, Serra-Varela MJ, Koskela J, González-Martínez SC, Alía R. 2016. Capturing neutral and adaptive genetic diversity for conservation in a highly structured tree species. Ecological Applications 26: 2254–2266.
    OpenUrl
  121. ↵
    Salvador L, Alía R, Agúndez D, Gil L. 2000. Genetic variation and migration pathways of maritime pine (Pinus pinaster Ait) in the Iberian peninsula. Theoretical and Applied Genetics 100: 89–95.
    OpenUrlCrossRefWeb of Science
  122. ↵
    Savolainen O, Lascoux M, Merilä J. 2013. Ecological genomics of local adaptation. Nature Reviews Genetics 14: 807–820.
    OpenUrlCrossRefPubMed
  123. ↵
    Savolainen O, Pyhäjärvi T, Knürr T. 2007. Gene Flow and Local Adaptation in Trees. Annual Review of Ecology, Evolution, and Systematics 38: 595–619.
    OpenUrlCrossRefWeb of Science
  124. ↵
    Shain L. 1967. Resistance of sapwood in stems of loblolly pine to infection by Fomes annosus. Phytopath. 57: 1034–1045.
    OpenUrl
  125. ↵
    Sniezko RA. 2010. Resistance breeding against nonnative pathogens in forest trees — current successes in North America. Canadian Journal of Plant Pathology 28: S270–S279.
    OpenUrl
  126. ↵
    Solla A, Aguín O, Cubera E, Sampedro L, Mansilla JP, Zas R. 2011. Survival time analysis of Pinus pinaster inoculated with Armillaria ostoyae: genetic variation and relevance of seed and root traits. European Journal of Plant Pathology 130: 477–488.
    OpenUrl
  127. ↵
    Solla A, Tomlinson F, Woodward S. 2002. Penetration of Picea sitchensis root bark by Armillaria mellea, Armillaria ostoyae and Heterobasidion annosum. Forest Pathology 32: 55–70.
    OpenUrl
  128. ↵
    Spitze K. 1993. Population structure in Daphnia obtusa: quantitative genetic and allozymic variation. Genetics 135: 367–74.
    OpenUrlAbstract/FREE Full Text
  129. ↵
    Stanosz GR, Blodgett JT, Smith DR, Kruger EL. 2002. Water stress and Sphaeropsis sapinea as a latent pathogen of red pine seedlings. New Phytologist 149: 531–538.
    OpenUrl
  130. ↵
    Stanosz GR, Trobaugh J, Guthmiller MA, Stanosz JC. 2004. Sphaeropsis shoot blight and altered nutrition in red pine plantations treated with paper mill waste sludge. Forest Pathology 34: 245–253.
    OpenUrl
  131. ↵
    Swart WJ, Wingfield MJ. 1991. Biology and Control of Sphaeropsis sapinea on Pinus Species in South Africa. Plant Disease 75: 761.
    OpenUrl
  132. ↵
    Swedjemark G, Stenlid J, Karlsson B. 1998. Genetic variation among clones of Picea abies in resistance to growth of Heterobasidion annosum. Silvae Genetica 46: 369–374.
    OpenUrl
  133. ↵
    Toïgo M, Barraquand F, Barnagaud JY, Piou D, Jactel H. 2017. Geographical variation in climatic drivers of the pine processionary moth population dynamics. Forest Ecology and Management 404: 141–155.
    OpenUrl
  134. ↵
    Tsykun T, Rigling D, Prospero S. 2013. A new multilocus approach for a reliable DNA-based identification of Armillaria species. Mycologia 105: 1059–1076.
    OpenUrlCrossRefPubMed
  135. ↵
    Turner MG. 2010. Disturbance and landscape dynamics in a changing world. Ecology 91: 2833–2849.
    OpenUrlCrossRefPubMedWeb of Science
  136. ↵
    Whitlock MC, Guillaume F. 2009. Testing for spatially divergent selection: Comparing QST to FST. Genetics 183: 1055–1063.
    OpenUrlAbstract/FREE Full Text
  137. ↵
    Woolhouse MEJ, Haydon DT, Antia R. 2005. Emerging pathogens: The epidemiology and evolution of species jumps. Trends in Ecology and Evolution 20: 238–244.
    OpenUrl
  138. ↵
    Xu H, Cao D, Chen Y, Wei D, Wang Y, Stevenson RA, Zhu Y, Lin J. 2016. Gene expression and proteomic analysis of shoot apical meristem transition from dormancy to activation in Cunninghamia lanceolata (Lamb.) Hook. Scientific Reports 6: 19938.
    OpenUrl
  139. ↵
    Yu J, Pressoir G, Briggs WH, Bi IV, Yamasaki M, Doebley JF, McMullen MD, Gaut BS, Nielsen DM, Holland JB, et al. 2006. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nature Genetics 38: 203–208.
    OpenUrlCrossRefPubMedWeb of Science
  140. ↵
    Zas R, Solla A, Sampedro L. 2007. Variography and kriging allow screening Pinus pinaster resistant to Armillaria ostoyae in field conditions. Forestry 80: 201–209.
    OpenUrlCrossRefWeb of Science
  141. ↵
    Zohner CM, Renner SS. 2014. Common garden comparison of the leaf-out phenology of woody species from different native climates, combined with herbarium records, forecasts long-term change. Ecology Letters 17: 1016–1025.
    OpenUrlCrossRefPubMed
  142. ↵
    Zwolinski JB, Swart WJ, Wingfield MJ. 1990. Intensity of dieback induced by Sphaeropsis sapinea in relation to site conditions. Forest Pathology 20: 167–174.
    OpenUrl
Back to top
Posted July 17, 2019.
Download PDF

Supplementary Material

Email
Genetic basis of susceptibility to Diplodia sapinea and Armillaria ostoyae in maritime pine
Agathe Hurel, Marina de Miguel, Cyril Dutech, Marie-Laure Desprez-Loustau, Christophe Plomion, Isabel Rodríguez-Quilón, Agathe Cyrille, Thomas Guzman, Ricardo Alía, Santiago C. González-Martínez, Katharina B. Budde
bioRxiv 699389; doi: https://doi.org/10.1101/699389
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools

Subject Area