Genetic and environmental contributions to variation and population divergence in a broad-spectrum foliar defence of Eucalyptus tricarpa (2024)

Provenance/progeny trial description

Three open-pollinated E. tricarpa provenance/progeny experiments were established by Forests New South Wales and the Victorian Department of Sustainability and the Environment as part of the Australian Low Rainfall Tree Improvement Group (ALRTIG) programme (Harwood et al., 2001). Each experiment was at a different location in south-eastern Australia. One of these, located near Culcairn in New South Wales, was sampled for foliar sideroxylonal chemistry and assessed for insect damage in late 2002 and the resulting data have been presented elsewhere (Andrew et al., 2007). This experiment was sampled again the following year, along with two additional experiments. The sites differed in soil properties observed at the time of planting (D. Stackpole, unpubl. res.; I. Johnson, unpubl. res.) and climatic variables obtained from the BIOCLIM computer program (Centre for Resource and Environmental Studies, Australian National University, Australian Capital Territory, Australia; Table1). The climates at the three sites as shown in Table1 are broadly similar: year-to-year variation within sites is probably greater than mean differences between sites. Nevertheless, Lake Tyers is typically less exposed to low winter minima than the other two sites. Each experiment was designed to consist of four complete replicates, with rows and columns as incomplete blocks and families represented by five-tree plots in each replicate. The experiments shared almost all families, which were sampled from 16 populations throughout the range of the species, from central-western Victoria to southern coastal New South Wales.

Sampling

As in a previous study (Andrew et al., 2007), we focused on the progeny of a set of eight populations (Fig.1) and sampled the first two available progeny from each plot containing the selected families (5–11 per population). Sampling occurred in December 2003 (Huntly), January 2004 (Culcairn) and March 2004 (Lake Tyers) due to constraints on access to the trials. However, care was taken to sample leaves of similar ages at the different sites; new or expanding leaves and senescent leaves were avoided. Approx. 100 g of leaves were stored at −20 °C for later freeze-drying.

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Fig. 1.

Map of south-eastern Australia showing experimental common-garden sites (filled squares) and source population locations (open circles). Population codes are as follows: Bod, Bodalla; Clu, Clunes; Hea, Heathcote; Hey, Heyfield; Lor, Lorne; MtB, Mt Bealiba; MtN, Mt Nowa Nowa; MCk, Martin's Ck.

Chemical analysis

Near infra-red spectroscopy (NIRS) was used to measure sideroxylonals in all 1487 samples, based on laboratory data from a set of calibration samples that included 60 randomly selected trees from each trial site. Laboratory and NIRS measurements of sideroxylonals followed the methods described in Wallis and Foley (2005) and Andrew et al. (2007), respectively. Sideroxylonals A and C were the dominant FPCs in the samples. Trace amounts of sideroxylonal B were detected, but concentrations were too low to be quantified reliably. Sideroxylonals A and C occurred in a constant ratio in the samples, as is seen in other eucalypts (Moore et al., 2004a), and we consider their combined concentration to be an accurate measure of the total FPCs in E. tricarpa. The optimal calibration for sideroxylonals A + C (hereafter referred to as sideroxylonal) was a modified partial least squares model based on 159 samples (excluding spectral outliers) and which used the entire spectrum (408–1092 and 1108–2492 nm). Various combinations of Savitzy–Golay derivative-based spectral smoothing functions provided by the ISI software (Win ISI; Port Matilda, PA, USA) were tested to remove unnecessary spectral signal components and improve the accuracy of the prediction models. The best model employed weighted multiplicative scatter correction and used the second derivative with a gap size of 6 nm, a maximal primary smoothing function and no secondary smoothing function (American Society for Testing and Materials, 1995). This model had an R² of 0·979 and a standard error of cross-validation of 3·1 mg g⁻¹ of dry matter (dm). The predicted mean and standard deviation were 22·9 and 14·4 mg.g⁻¹ dm, respectively.

NIRS was very successful for predicting foliar sideroxylonal concentrations, which were approximately normally distributed. There were some suggestions of multimodality within populations; however, we considered our statistical analyses valid, as multimodality was not apparent in the residuals.

Statistical analysis

All statistical analysis was performed using GenStat10 (VSN International Ltd, Hemel Hempstead, UK). The experiments were row–column designs, with the nested genetic treatments (population and family within population) applied at the level of the five-tree plot. Replicates were complete blocks; however, only 64 of the approx. 110 plots in each replicate were sampled. Site was always treated as a fixed effect, as we did not consider the sites to be a random sample of the possible environments encountered by the species. Replicate within site was also treated as fixed to limit the number of random terms. Source population was treated as fixed, with families within populations random. Analyses were performed on individual-tree data and plot was included as part of the residual variance for the stratum at which the genetic treatments were applied. Since eucalypts have mixed mating systems, additive genetic variance (V_A) was estimated from family variances, assuming a mean relatedness within families of 0·4 and no maternal, dominance or epistatic effects (Williams et al., 2002, p. 103).

Single-site analyses were conducted to compare variance components and fixed effects among sites and thereby assess the stability of the genetic variance estimates. Linear mixed models of data from all three sites were compared to test for G × E interactions and to investigate the sources of variance in chemical concentrations. Mixed models were fitted using residual maximum likelihood (REML) and likelihood ratio tests were performed to assess the significance of random effects (Payne, 2005). Residuals were examined for normality. Wald tests and F statistics were used to assess the significance of fixed effects. Population- or site-specific variances were estimated when likelihood ratio tests demonstrated significantly better fit. If this was not the case, a single variance component was estimated and the significance of the effect tested by dropping it from the model. The details of the linear models tested are given in the Appendix.

Bootstrapping was used as an additional test of the differences in family-level variance among populations. Bootstrapped data sets for each population were created by randomly selecting families (with replacement) from within the population and adding each family to the new data set with new plot identities. Confidence intervals were estimated from 10 000 bootstraps and compared among populations.

The stability of relative sideroxylonal concentrations across years was examined using type A genetic correlations (i.e. correlations among or between pairs of traits, Burdon, 1977; Williams et al., 2002) between the December 2002 and January 2004 data from Culcairn (see Appendix). Additive genetic correlations of sideroxylonal concentrations among sites were estimated using an approach similar to the estimation of more typical, type A genetic correlations, but where the trait of interest is measured on different individuals (Burdon, 1977). These ‘type B’ genetic correlations between sites are expected to be 1·0 in the absence of G × E interactions. The covariances of family effects among sites and variances within sites were estimated using REML and their significance tested using likelihood ratio tests (see Appendix for details). Correlations of population effects across sites were estimated using the predicted population means from the single-site analysis. In addition, Spearman rank correlations of family means were calculated and their confidence intervals estimated by bootstrapping in the R statistical package.

The relationship between the climates of the source populations and a simple measure of their plasticity was explored using univariate regressions. The concentration range for each population was measured as the signed difference in site-specific population mean between the maximum (at Huntly) and the minimum (at Lake Tyers). These values were regressed on each of 19 bioclimatic variables obtained from WORLDCLIM bioclimatic grids (Hijmans et al., 2005) as described previously (Andrew et al., 2007). False discovery rate was used to control for multiple independent tests (Benjamini and Hochberg, 1995).

Fain's test for major gene effects was carried out on family means and variances (Lynch and Walsh, 1998, p. 356). This test is based on the fact that family variance should have a linear relationship with the mean under an infinitesimal model of gene action, whereas genes with major effects are more likely to be hom*ozygous in the families with extreme mean trait values. The presence of a significantly negative quadratic term in a quadratic regression is a conservative test for the presence of genes of major effect. This analysis was conducted on data from each site separately and on the entire data set after controlling for differences in site means.

Table2.

Genetic parameter estimates for foliar sideroxylonal concentration measured at three sites

Table3.

Summary of multiple-site analysis

Table4.

Univariate regressions of the difference in mean sideroxylonal concentration between Huntly and Lake Tyers on bioclimatic variables

Genetic improvement of E. tricarpa

Our results point to the stability of within-population genetic effects, as demonstrated by the insignificant site × family term and the high genetic correlation of foliar sideroxylonal concentrations between years at Culcairn and among sites. Thus, different genotypes from the same source population are likely to produce consistent responses to changes in environmental conditions, although populations differ in this response. In the only other multiple-site genetic study of herbivore resistance in eucalypts, the rank correlation of family effects on damage to E. grandis by chrysomelid beetles (Henery, 2006) at the two sites was significantly positive but <1 (r_B = 0·64, s.e. = 0·17). This implies a small interaction effect between family and site, but is unlikely to have resulted from G × E interactions on phytochemistry because damage by chrysomelids was unrelated to FPC concentrations. Genetic correlations for wood traits across sites range from very low to close to 1 (Tibbits and Hodge, 1998; Hamilton et al., 2009).

While limited to the three sites studied, our finding of low or negligible G × E interactions at the family level has implications both for domestication and for evolutionary research on FPCs. The stable expression of genetic variation is favourable for the genetic improvement of defence in this species. Since the production of sideroxylonal appears not to incur a strong growth cost and variation in concentrations is highly heritable, breeding for high concentrations may be successful (Andrew et al., 2007). In contrast, if genetic parameters or the rankings of genotypes were to change when this species is grown in different conditions, it would be harder to breed for stable increases in concentrations of sideroxylonal across multiple environments. Significant G × E interactions for growth and some wood traits occur in other Eucalyptus species grown on multiple sites (Tibbits and Hodge, 1998; Lima et al., 2000; Costa e Silva et al., 2006; Hamilton et al., 2009). However, our results suggest that the within-population genetic parameters measured at one experiment or plantation site will predict those at a new site reasonably well.

In contrast, there was a significant G × E interaction in foliar sideroxylonal concentrations at the population level, which has not been documented previously for foliar chemical traits in Eucalyptus. A significant population × nutrient availability interaction effect was found on herbivory by common brushtail possums (Trichosurus vulpecula) on E. globulus grown in glasshouses, but the interaction with environment was not significant for FPCs or other chemical traits (O'Reilly-Wapstra et al., 2005). The finding of population × environment interactions for chemical defence indicates that populations differ in how they react to environments and is therefore a key result of the present study.

Although a significant population-level G × E interaction indicated that some populations had more stable levels of defence than others, it should not pose difficulties for artificial selection on sideroxylonal, as changes in rankings across sites were not common and the correlations of predicted population means were strong. The Bodalla and Mt Nowa Nowa progeny were highly ranked at each site. In a previous study of the Culcairn experiment, both of these populations had high growth rates and low levels of both insect damage and Anoplognathus Christmas beetle incidence (Andrew et al., 2007). Likewise, the low level of genetic variance in sideroxylonal concentrations in some populations is unlikely to impede genetic improvement of defence. The Clunes and Mt Bealiba material, which did not display detectable V_A despite high average concentrations, may not contribute strongly to ongoing breeding programmes, but Mt Nowa Nowa and Bodalla possessed moderate V_A. However, information on long-term survival and growth at multiple sites is required to determine whether these two provenances should be recommended for general use, because both are from the higher rainfall parts of the species' range.

In seeking explanations for this pattern, we found evidence for genes of a major effect influencing sideroxylonal concentration. Major quantitative trait loci for sideroxylonal were also detected in E. nitens (Henery, 2006), suggesting that the genetic architecture of chemical variation may be consistent among different species.

Ecology and evolution of chemical defence

Eucalypts are important foundation species, providing food and habitat for diverse vertebrate, invertebrate and microbial interactors, many of which respond to foliar chemical defences (Lawler et al., 1998; Jones et al., 2002; Marsh et al., 2003; Moore et al., 2005; Andrew et al., 2007). The stability of genetic effects across environments suggests that the evolution of eucalypt phytochemicals can play a role in structuring entire communities (Wimp et al., 2007; Barbour et al., 2009). The evolution of sideroxylonal concentrations appears unlikely to be constrained by G × E interactions; however, the lack of genetic variance for this trait in some populations may prevent adaptation to changing environments.

Populations of E. tricarpa clearly differ in levels of genetic variation in foliar sideroxylonal, independently of mean-variance scaling. Variance structure modelling is increasingly used in the analysis of multisite genetic experiments, as the residual variance may differ among sites (Costa e Silva et al., 2006), and this approach proved useful in detecting differences in family-level variance among source populations. The lack of genetic variance within certain populations suggests that these populations may be less capable of responding to natural or artificial selection on sideroxylonal, although the sampling may have missed some variation present in the population. Differentiation of genetic variances and covariances has been documented previously (Riemenschneider et al., 1994; Widen et al., 2002; Yeaman and Jarvis, 2006), but we are not aware of any similar studies for herbivore defence traits. While the likelihood of differences in the heritability of frost resistance among regions was considered by Tibbits et al. (1998), differences in the heritability or V_A of quantitative traits among populations have not been formally demonstrated before in eucalypts. This is surprising because reduced genetic variation would be expected in small populations at the edges of species distributions (Butcher et al., 2009) or those that have experienced prolonged natural selection (Foster et al., 2007). The ranges of eucalypts can encompass considerable environmental variation, over which adaptive divergence can occur (Steane et al., 2006). Our results suggest that the validity of assuming hom*ogeneity of variance should be questioned more frequently, although large sample sizes would be required to detect small differences in variance among populations. The high heritability of sideroxylonal has undoubtedly made the task easier in this study.

Our finding of evidence for gene(s) of major effect is noteworthy because it may help explain the differences in genetic variance among populations. While maternal trees were sampled at a minimum distance of 100 m, which is greater than the extent of positive spatial genetic autocorrelation in the closely related woodland tree, E. melliodora, correlation of sideroxylonal with reproductive traits (e.g. phenology or pollen production) may cause underestimation of genetic variance. In some cases, eucalypts receive pollen from only a subset of the potential pollen donors (Sampson, 1998; Millar et al., 2000; Jones et al., 2008). Although variation in the mating systems of the maternal trees (Borralho and Potts, 1996; Burgess et al., 1996; Butcher and Williams, 2002) may influence differences in variance among populations, it would not explain the apparent lack of genetic variance in some populations. Similarly, the pattern of genetic variance in E. tricarpa does not match that expected if genetic variation is lower near the range limits of species (Aitken and Libby, 1994; Eckstein et al., 2006). Range expansion, genetic drift, introgression, migration and selection may cause differentiation of genetic variances among populations under an infinitesimal model (Yeaman and Jarvis, 2006); these mechanisms are more likely to alter V_A if sideroxylonal is influenced by genes of major effect. A combination of genomics and population genetics will be required to ascertain the importance of selection, genes of major effect and/or human disturbance in producing the observed differences in genetic variance among populations. The identification of a major gene affecting FPC concentrations in leaves may offer an unprecedented chance to understand the biosynthesis of this enigmatic class of phytochemicals.

Differences among populations in responses of foliar sideroxylonal concentrations to site environments also accompanied the population divergence in foliar sideroxylonal concentrations that we have documented previously (Andrew et al., 2007). There was a significant G × E interaction at the population level, which was related to the range of temperatures experienced by the source populations. In contrast to patterns of genetic divergence in morphological and life history traits in E. globulus (Dutkowski and Potts, 1999), relationships between bioclimatic variables and physiology or defence in E. tricarpa have been elusive (Andrew et al., 2007). Yet, higher concentrations of sideroxylonals have been found in populations of E. microcorys growing on sites with cold winters (Moore et al., 2004b). Our results suggest that this may be due to environmental as well as genetic effects, highlighting the need for studies in multiple common gardens to understand geographic patterns of phenotypic variation.

While it is unclear whether plasticity in sideroxylonal production is adaptive, or even whether it is active or passive (Pigliucci, 1996; Ghalambor et al., 2007), the link with source population climate suggests that the responses of foliar sideroxylonal concentrations to environmental conditions are ecologically important in this species. Adaptive divergence may be responsible for the differing responses of population means to each site and the lack of genetic variation in plasticity detected within populations, if selection on plasticity varies among source localities. For example, the trend of greater differences in sideroxylonal concentrations between Huntly and Lake Tyers exhibited by families from source populations that experience narrower temperature ranges may result from canalization (evolution of the ability to produce the same phenotype despite environmental perturbations) in inland populations or selection for greater plasticity in coastal populations. Because the inland populations experience greater temperature extremes, there may be greater selective benefit to canalization of foliar sideroxylonal concentration (Buskirk and Steiner, 2009; Lande, 2009).

Eucalypts produce a broad range of secondary chemicals, which can comprise a substantial portion of leaf biomass. FPCs appear to be constitutively expressed (Henery et al., 2008), in contrast to other eucalypt phytochemicals (Rapley et al., 2007) and many defences in other plants that are induced by herbivores or pathogens (e.g. Agrawal et al., 2002; Keeling and Bohlmann, 2006; Björkman et al., 2008). Nevertheless, ontogeny and the timing of phenotypic phase changes may differ between the sites and contribute to the observed population × environment interaction. For this reason, we must be cautious about interpreting the 2-fold difference in mean sideroxylonal concentrations between Huntly and Lake Tyers as suggesting that the strong plasticity of this trait on average has evolved in response to variable environments (Buskirk and Steiner, 2009; Lande, 2009). Strong, positive genetic and phenotypic correlations occurred between seasons, suggesting that the relative levels of defence are stable across years; however, this information was only available for one site. Further study of this species should include longitudinal measurements of growth, herbivory and sideroxylonal concentrations at multiple sites. In order to understand fully the geographic mosaic of genetic variation in foliar sideroxylonal concentrations, the costs and benefits in multiple habitats are of great importance.

In conclusion, our results suggest that geographic divergence in parameters other than population means may be more common than is typically acknowledged. Both environmental effects on phenotypes and genetic variance may differ among populations. This may affect the way in which species respond to rapid environmental changes through adaptive plasticity or evolution.

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