Genetic variation of Fraxinus excelsior half-sib families in response to ash dieback disease following simulated spring frost and summer drought treatments

Abstract

Ten juvenile Fraxinus excelsior half-sib families from two Lithuanian populations have been tested in the controlled environment for their response to ash dieback disease caused by Hymenoscyphus fraxineus, detecting changes of genetic variation and heritability, as well as estimating genotype by environment (G×E) interaction and phenotypic plasticity following artificial spring frost and summer drought treatments. In 2014, a batch of 200 four-year-old ash seedlings was used for each treatment and control (no treatment). Health condition, bud flushing phenology and height were assessed for each seedling, and disease incidence and survival ratios were assessed for each family both before (at the beginning of the vegetation season) and after the treatments (at the end of the vegetation season). Disease incidence ratio increased from 0.77-0.80 up to 0.90-0.95. Tree mortality rates during one vegetation season were significantly lower in the frost treatment (21%) than in the drought treatment (25%) or control (31%). None of the tested F. excelsior families were completely resistant to ash dieback, although significant among-family differences in disease incidence and damage rates suggest an additive mode of gene action and thus a quantitative resistance to the disease. Neither disease incidence rates, nor tree health condition scores differed significantly among the applied treatments (including control) indicating in general a negligible effect of the simulated adverse conditions on health status of the ash seedlings. However, G×E interaction was found to be significant (at P > 0.05) for disease incidence, length of necrotic shoots and tree survival, implying that susceptibility of ash families to the dieback disease unequally depends on environmental conditions, and indicating a presence of genetic variation in plasticity and reaction norms of the tested families across environments (treatments). Substantially increased coefficients of additive genetic variation and heritability in health condition following both frost and drought treatments and compared to control showed that simulated stress conditions may noticeably contribute to expression of differences among the tested F. excelsior families in their resistance traits, thus enabling a better evaluation of performance of different families, an effective family selection for resistance, and achievement of a marked genetic gain.

Introduction

Since 1992, a severe dieback of common ash (Fraxinus excelsior L.) has spread from Eastern Poland to many European countries ([18] and references therein). The disease is caused by the ascomycete fungus Hymenoscyphus fraxineus (T. Kowalski) Baral et al. (=Chalara fraxinea T. Kowalski, syn. Hymenoscyphus pseudoalbidus - [36]), which in Europe is an invasive species ([20], [18]). The disease causes wilting and necroses of ash leaves and petioles, necrotic lesions on stems, branches and shoots, stem cankers and wood discolouration, followed by a gradual crown dieback, and in most severe cases - death of an entire tree ([1], [26], [27], [24], [45]). In Lithuania, dieback of F. excelsior was first observed in 1995-1996 in the north-central part of the country ([22]) and since then large areas of ash stands underwent sanitary fellings ([28]). Currently, the disease is in its chronic phase, and the health condition of the remaining ash stands continues to deteriorate; yet, no effective control measures have been offered so far ([19], [34], [28]). Consequently, the density of ash trees in a stand is often reduced to few individuals per hectare, meaning that the effective population size (N_e) has also decreased, thus compromising the genetic diversity of F. excelsior in mature stands and in regenerating offspring ([34]).

Studies in natural stands, clonal seed orchards and progeny trials in a number of European countries provide evidence of existing substantial genetic variation at individual, clonal, family and population levels in the susceptibility of F. excelsior to H. fraxineus, and that there is a significant genetic heritability in disease resistance/tolerance ([33], [30], [34], [23], [25], [47], [35], [8]). The genetically inherited resistance may provide a basis for a natural adaptation. The success of breeding programs is also determined to a great extent by the presence of sufficient genetic variation in a breeding population, as well as by sufficient heritability of resistance traits that facilitate identification and selection of truly resistant genotypes. Progeny studies show that disease incidence and severity varies among test sites, which was in general explained by the influence of different infection loads and/or environment conditions ([33], [30], [34], [35], [23], [25], [47]). It remains unknown to what extent changes in environmental conditions (e.g., from normal to stressed) can influence the disease development and spread among trees, and how this contributes to changes in genetic parameters such as coefficient of genetic variation, heritability and genetic correlations among the tree traits.

Two strategies of plant adaptation can be distinguished: (i) adaptation via genetic variation, and (ii) adaptation via phenotypic plasticity ([43]). Following environmental changes, a population that has a considerable genetic variation adapts through survival and reproduction of the most resistant genotypes, while maladapted genotypes disappear due to natural selection. When rapid adaptation is required, or when genetic variation is lacking, plants respond to changes in the environment by modifying their phenotype ([43]). This specific response to a certain range of conditions for a single or a set of traits is called phenotypic plasticity ([3]). Studies of plastic response along an environmental gradient indicate that reaction norms vary significantly among genotypes ([16], [31]). There are continuous debates over whether phenotypic plasticity shields genotypes from selection or generates novel opportunities for selection to act ([13]). One hypothesis suggests that genetic variation and plasticity represent alternative strategies for coping with environment heterogeneity ([29], [21]). Other hypotheses however suggest that genetic variation and phenotypic plasticity are positively correlated ([15], [17]). The phenotypic plasticity can be considered to be a trait in itself that is under genetic control, and which can evolve independently of the trait ([3], [41], [42], [39], [40]). Phenotypic plasticity encompasses diverse adaptive and non-adaptive responses to the environment variation ([13]).

A phenotypic response can be adaptive when it enhances plant fitness in a changed environment ([50]), or non-adaptive when it results in deterioration of fitness/condition of an individual plant which reflects an inevitable metabolic or developmental response ([48], [49]). Good adaptation to heterogeneous environments can be achieved by phenotypic plasticity or by stability (canalization). In the first case, populations may be subjected to a selection resulting in specialized genotypes of high plasticity that perform better in an indigenous (optimal) environment than in an unaccustomed environment ([51]). The second case refers to a situation when selection favors genotypes capable of buffering their phenotypes in the changed environment. Thus, the phenotypic plasticity of such genotypes is low. It has been concluded that the adaptive plasticity that places populations close enough to a new phenotypic optimum for directional selection to act is the only plasticity that predictably enhances fitness and is most likely to facilitate adaptive evolution ([13]). On the other hand, the authors pointed up that in stressful environments, the non-adaptive plasticity can result in a response being further away from the optimum or increase the variance due to the expression of cryptic genetic variation. The role of the phenotypic plasticity in plant susceptibility (degree of damage) to diseases/genetic resistance in expression of cryptic genetic variation, natural selection and adaptation processes remains unclear and definitely deserves further studies. Plasticity and plasticity-related changes in genetic variation and heritability in the incidence of the ash dieback disease under spring frosts and summer drought have never been assessed. Searching for not only resistant ash genotypes, but also for heritable adaptability traits and environment conditions under which its largest genetic variation and highest heritability can be obtained may be one of the most promising strategies in breeding for resistance to restore damaged F. excelsior stands.

The main aim of the present study was to assess the susceptibility of ten juvenile F. excelsior half-sib families to ash dieback disease caused by H. fraxineus, and to detect changes in genetic variation and heritability of disease resistance traits as well as to estimate genotype by environment (G×E) interaction and phenogenetic plasticity of selected ash families following simulated spring frost and summer drought treatments.

Materials and methods

Material

Ten half-sib families of F. excelsior originating from two northern Lithuanian populations heavily damaged by H. fraxineus (and therefore having undergone strong natural selection) were selected for the present experiment: Biržai (families no. B080, B076, B069, B078, B072 - 56^o 15′ 25″ N, 24^o 34′ 30″ E) and Zeimelis (families no. Z049, Z053, Z054, Z060, Z061 - 56^o 15′ 45’ N, 24^o 02′ 32″ E). Seeds from the respective families were collected in autumn 2008, stratified and sown in spring 2010 in a forest nursery at Dubrava Experimental-Educational State Forest Enterprise (EESFE) located in Kaunas region, central Lithuania. The seedlings were grown outdoors at the same forest nursery for two years. In spring 2012, 60 two-year-old seedlings of generally good health condition (very few with external disease symptoms) were selected from each family (in total 600 seedlings), planted in 5-liter plastic pots containing peat substrate and grown for another two years (until May 2014) under standard greenhouse conditions. In the greenhouse, the pots were arranged with 0.2 × 0.2 m spacing, and regular watering and fertilization was applied.

Treatments

In early May 2014, ash juveniles to be used for spring frost, summer drought and control treatments were randomly selected among the 600 four-year-old potted trees, according to the available number of trees in groups of different health conditions (scored from 1 - tree with dry stem and branches - to 5 - externally healthy tree, modified from [34]) within each family. The following approach was used to randomly distribute trees among the treatments: prior to the treatments, in each family, the pots with ash seedlings were grouped into five batches representing different health condition classes, then plants from each of those five batches were randomly selected to form three batches (each consisting of four plants) to be used in different treatments. As a result, a batch of 200 trees (10 families, 20 trees per family) was formed for each treatment. The trials (treatments) were established in a randomized complete block design with 2 blocks, each consisting of ten trees per family.

On May 16, 2014, a batch of 200 trees assigned for the simulated spring frost treatment was placed in a PE2422UVLX climatic chamber (Angelantoni Test Technologies, Massa Martana, Italy). The temperature in the climatic chamber was first gradually (in one hour) reduced from +20 ^oC to -5 ^oC, then held at -5 ^oC for 30 minutes, and finally was gradually (in one hour) raised back to +20 ^oC. After this cycle, trees were taken out of the climatic chamber and further grown outdoors for one vegetation season in the forest nursery of Dubrava EESFE. Watering was applied when needed depending on weather conditions. During the applied spring frost treatment, leaves of the ash seedlings became black and wilted. New leaves flushed and shoots resprouted from adventitious latent buds on almost all terminal and lateral shoots in two weeks (by the end of May 2014).

Concurrently, batches of seedlings assigned to the control and summer drought treatments were moved from the greenhouse to the outdoor nursery and placed next to the spring frost-treated seedlings. Watering was applied equally to all seedlings when needed, depending on weather conditions. On June 20, 2014, a batch of 200 trees assigned for the summer drought treatment was transported to the greenhouse, and left there for two weeks without any watering until severe wilting of leaves occurred. During this treatment, the temperature in the greenhouse varied from +25 to +35 ^oC during day time, and relative air humidity varied between 40-60%. Thereafter, trees were transported back to the outdoor nursery and watered regularly (depending on weather conditions). Leaves regained turgor and shoots regained growth as soon as the watering was applied.

Assessment of tree biometric parameters and sanitary condition

Measurement of tree biometric parameters and scoring of the extent of damage by H. fraxineus were performed in May 2012 (during outplanting of the ash seedlings into plastic pots), and repeated in August 2013, on May 5, 2014 (before the treatments), and on September 3, 2014 (at the end of vegetation season, after the treatments). The assessed traits were: (1) disease incidence ratio (the ratio between the number of symptomatic trees and the total number of trees in a family); (2) health condition of an individual tree (scored between 1 - tree with dry stem and branches - and 5 - externally healthy tree -, [34]); (3) survival ratio (the ratio between the number of living and dead trees in a family); (4) seedling height; (5) total length of necroses on leader and lateral shoots in an individual plant; and (6) total length of necrotic lesions formed on leader and lateral shoots and stem of an individual plant. Bud flushing phenology was assessed in beginning of May 2014 when all phases were present and distribution of scores was closest to normal, using a categorical scale of five degrees (points) adopted from Douglas et al. ([6]): 5 - very early; 4 - early; 3 - of moderate earliness; 2 - late; and 1 - very late flushing.

Variance analysis

The variance analysis of the data was done using the MIXED procedure of the SAS software package (SAS^® Analytics Pro 12.1 - [38]), which uses Mixed model equations (MME) and the restricted maximum likelihood (REML) method. The significance of fixed effects (of block and treatment) was tested with F-tests. The significance of the random effects was tested using Z-test (SAS^® Analytics Pro 12.1 - [38]). The analysis was performed separately for spring frost and summer drought treatments, and in both cases included the same control batch of trees. The combined linear statistic model was used for joint analysis of data from treatments’ and control batches together (eqn. 1):

(1)yijklm=μ+zi+bj+pk+fl+fl⋅zi+εijklm" role="presentation"> yijklm=μ+zi+bj+pk+fl+fl⋅zi+εijklm(1) (1)yijklm=μ+zi+bj+pk+fl+fl⋅zi+εijklm

where y_ijklm is an observation of the m^th tree from the l^th family in the k^th population in the j^th block of the i^th environment (treatment), µ is the overall mean, z_i is the fixed effect of the i^th environment, b_j is the j^th block effect, p_k is the k^th population effect, f_l is the effect of l^th family, f_l · z_i is the interaction effect of l^th family and i^th environment (treatment), ε_ijklm is the random residual. The population effect later was omitted from the model as in most cases it was non-significant. The model assumed that random effects were normally distributed with expectation zero and corresponding variances: σ_p², σ_f², σ_f·z² and σ_e². The normality of residuals’ distribution and homogeneity of variances were tested with SAS GLM and UNIVARIATE procedures (SAS^® Analytics Pro 12.1 - [38]).

The variance components of random effects of families and family by environment (treatment) interaction (G×E) were computed from corresponding variances obtained in joint ANOVA (statistical model 1; SAS^® Analytics Pro 12.1 - [38]), and expressed in percentage of the total random variation (eqn. 2, eqn. 3):

(2)vcf=σf/(σf+σf⋅z+σe)" role="presentation"> vcf=σf/(σf+σf⋅z+σe)(2) (2)vcf2=σf2/(σf2+σf⋅z2+σe2)

(3)vcf⋅z=σf⋅z/(σf+σf⋅z+σe)" role="presentation"> vcf⋅z=σf⋅z/(σf+σf⋅z+σe)(3) (3)vcf⋅z2=σf⋅z2/(σf2+σf⋅z2+σe2)

where vc_f² and vc_f·z² are the family and family by environment interaction variance components, σ_f² is the family variance, σ_f·z² is the variance of family by environment interaction, and σ_e² is the variance of random residuals.

The simplified linear model was used for variance analysis of the data from each individual environment (treatment - eqn. 4):

(4)yjlm=μ+bj+fl+εjlm" role="presentation"> yjlm=μ+bj+fl+εjlm(4) (4)yjlm=μ+bj+fl+εjlm

where y_jlm is an observation of the m^th tree from the l^th family in the j^th block, µ is the overall mean, b_j is the j^th block effect, f_l is the effect of l^th family, and ε_jlm is the random residual. The model assumed that random effects are normally distributed with expectation zero and corresponding variances σ_f² and σ_e². Means of environments (treatments) and families were computed using the SAS MEANS procedure (SAS^® Analytics Pro 12.1 - [38]).

Genetic parameters estimate

Genetic parameters: family variance components (vc_f), coefficients of additive genetic variation (CV_a), additive heritability coefficients (h_a²) and their standard errors (se) of each trait were assessed using variances and covariances obtained in the analysis of variances of the SAS MIXED procedure. The variance components of families in each environment (treatment) were derived from corresponding variances and expressed in percentage of the total random variation (eqn. 5):

(5)vcf=σf/(σf+σe)⋅" role="presentation"> vcf=σf/(σf+σe)⋅(5) (5)vcf=σf2/(σf2+σe2)⋅100

The coefficient of additive genetic variation of a trait was calculated for each individual environment (treatment) using the following formula ([10], [11] - eqn. 6):

(6)CVg=⋅σf⋅/X¯" role="presentation"> CVg=⋅σf⋅/X¯(6) (6)CVg=3⋅σf2⋅100/X¯

where X is the phenotypic mean of the trait. Coefficient 3 was used as progenies in the present experiment were considered as an admixture of half-sibs and full-sibs. The narrow sense individual heritability coefficients were calculated using a formula (eqn. 7):

(7)ha=⋅σf/(σf+σe)" role="presentation"> ha=⋅σf/(σf+σe)(7) (7)ha2=3⋅σf2/(σf2+σe2)

where h_a² is the individual additive heritability coefficient. B-type genetic correlations ([4]) between the same traits assessed on different trees from the same families in different environments (treatments) were estimated using the following formula (eqn. 8):

(8)rGxy=rxy/rTPxrTPy" role="presentation"> rGxy=rxy/rTPxrTPy(8) (8)rGxy=rxy/rTPxrTPy

where r_xy is the product-moment correlation between best linear unbiased predictor (BLUP) values derived from an individual environment (treatment) by variance analysis (SAS MIXED procedure), and r_TPx and r_TPy are the estimated relation between true and predicted family values for a trait at x and y environments (treatments), respectively; r_TPx was calculated as follows (eqn. 9):

(9)rTPx=hk+h(k−)" role="presentation"> rTPx=hk+h(k−)(9) (9)rTPx=h2k1+h2(k−1)

where h² is the individual narrow sense heritability coefficient.

To evaluate the stability of individual families across environments (treatments) and the contribution of each of the family plasticity (in percent) to the family by environment (treatment) interaction (G×E) variances, the Wricke’s ecovalence values ([52]) were calculated using families’ least-squares means obtained within each environment (treatment), using the “Lsmeans” option of the SAS MIXED procedure. The Shukla stability variances were computed and the statistical significance (P) of the ecovalences was tested using the F-test developed by Shukla ([44]). In calculating ecovalences, to better fulfill the assumptions behind the linear model thus reducing the scale effects of different environments (treatments) in the joint ANOVA, data were transformed to equal genetic variance using the method of Danell ([5]). For each environment (treatment), the assessed values for each tree were multiplied by a scaling factor, which for the i^th environment (treatment) was the mean genetic (family) standard deviations over all environments (treatments) and for the i^th environment (treatment), respectively.

Deviations of families’ treatments least-squares means were calculated by subtracting families’ least-squares means obtained in the MIXED analysis at the individual treatments from total least-squares means of treatments. Phenotypic plasticity of each family was estimated as the difference between maximum and minimum least-squares means obtained for different treatments.

Families’ least-squares means from MIXED analysis at the individual environments (treatments) were regressed on environment (treatment) least-squares means to estimate the reaction norms of individual families over environments (treatments) according to Finlay & Wilkinson ([12]) using the REG procedure of the SAS software (SAS^® Analytics Pro 12.1 - [38]). The following Finlay and Wilkinson parameters were obtained: intercept (a) and slope coefficients (b) of linear regression equation, regression residuals and coefficient of determination (R²).

Results and discussion

No families were completely resistant and free from disease (Fig. 1 and Fig. 2). This confirms the results of our previous study on resistance of 340 half-sib families from 24 European F. excelsior populations, where none of the tested families showed complete resistance to the ash dieback, and only a fraction exhibited reduced susceptibility ([34]). In clonal studies, differences among F. excelsior clones are usually pronounced: a small fraction of the clones exhibit good disease resistance (tolerance), while the majority experience high (and increasing) disease incidence rates becoming heavily damaged over time ([30], [23], [47], [34]).

Fig. 1 - Disease incidence (a-c), health condition (d-f) and tree survival (g-i) estimates for ten tested Fraxinus excelsior families (for explanation of the variables and family name codes see Materials and methods) before (May 5, 2014, light bars) and after (September 3, 2014, dark bars) spring frost, summer drought and control treatments. Bars are means ± standard error.

Fig. 2 - Length of necrotic leader shoot (a-c), total length of necrotic shoots (d-f) and total length of necrotic lesions (g-i) in ten tested Fraxinus excelsior families (for family name codes see Materials and methods) before (May 5, 2014, light bars) and after (September 3 2014, dark bars) spring frost, summer drought and control treatments. Bars are means ± standard error.

Concluding remarks

None of the tested F. excelsior families were completely resistant to H. fraxineus, although the significant among-family variation detected in disease incidence and health condition in each treatment points to the additive mode of gene action, thus to a quantitative resistance to the disease. Such a resistance might be durable in long-term as it combines different plant defense mechanisms, thereby diminishing the probability of breaking the resistance due to mutation or adaptation of a pathogen. Neither disease incidence rates, tree health condition scores, nor survival rates differed significantly among the applied treatments (including control), indicating a negligible effect of the simulated adverse conditions on health status of F. excelsior. However, the presence of significant genotype by environment (family × treatment) interactions for disease incidence, total length of necrotic shoots and seedling survival ratios implies that susceptibility of ash families to the dieback disease unequally depends on environmental conditions. This indicates the presence of genetic variation in plasticity and reaction norms across different environments (treatments). Different levels of damage among the ten tested families in two stress-induced events (spring frost and summer drought treatments) and control indicated variable adaptive potential of different families, and warrants testing of material across range of environments in tree breeding for resistance. In general, the plasticity in disease resistance traits should be considered as non-adaptive as it reflects a deterioration of plant health condition and fitness, and adaptive significance of plasticity will depend upon reaction norms and performance of families under certain environmental conditions.

In general, health condition scores and seedling survival ratio showed rather strong positive correlations with the bud flushing phenology scores, in that early-flushing clones are less susceptible to disease caused by H. fraxineus.

Simulated stress conditions may noticeably contribute to expression of the tree traits which are used to rank tested ash individuals, families or populations for their susceptibility to the dieback. Subsequently, this should enable a better evaluation of the performance of different families, effective family selection, and achievement of a marked genetic gain. High heritability coefficients obtained indicate that stressed environment conditions aid in the detection of resistant tree genotypes by their phenotype for recruiting individuals within families for crossing.

Acknowledgments

The study was financially supported by the Research Council of Lithuania (project No. MIP-040/2012 - UOSIS 2012-2014). Sincere thanks to nursery staff of the Dubrava Experimental-Educational State Forest Enterprise for assistance in raising seedlings. We are grateful to anonymous reviewers for their comments and constructive advices.

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