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Peer reviewed: True, Funder: University of Cambridge; Id: http://dx.doi.org/10.13039/501100000735, While droughts, intensified by climate change, have been affecting forests worldwide, pest epidemics are a major source of uncertainty for assessing drought impacts on forest trees. Thus far, little information has documented the adaptability and evolvability of traits related to drought and pests simultaneously. We conducted common-garden experiments to investigate how several phenotypic traits (i.e. height growth, drought avoidance based on water-use efficiency inferred from δ13C and pest resistance based on defence traits) interact in five mature lodgepole pine populations established in four progeny trials in western Canada. The relevance of interpopulation variation in climate sensitivity highlighted that seed-source warm populations had greater adaptive capability than cold populations. In test sites, warming generated taller trees with higher δ13C and increased the evolutionary potential of height growth and δ13C across populations. We found, however, no pronounced gradient in defences and their evolutionary potential along populations or test sites. Response to selection was weak in defences across test sites, but high for height growth particularly at warm test sites. Response to the selection of δ13C varied depending on its selective strength relative to height growth. We conclude that warming could promote the adaptability and evolvability of growth response and drought avoidance with a limited evolutionary influence from pest (biotic) pressures.

Additional file 1.

While droughts, intensified by climate change, have been affecting forests worldwide, pest epidemics are a major source of uncertainty for assessing drought impacts on forest trees. Thus far, little information has documented the adaptability and evolvability of traits related to drought and pests simultaneously. We conducted common-garden experiments to investigate how several phenotypic traits (i.e. height growth, drought avoidance based on water-use efficiency inferred from δ13C and pest resistance based on defense traits) interact in five mature lodgepole pine populations established in four progeny trials in western Canada. The relevance of interpopulation variation in climate sensitivity highlighted that seed-source warm populations had greater adaptive capability than cold populations. In test sites, warming generated taller trees with higher δ13C and increased the evolutionary potential of height growth and δ13C across populations. We found, however, no pronounced gradient in defenses and their evolutionary potential along populations or test sites. Response to selection was weak in defenses across test sites, but high for height growth particularly at warm test sites. Response to the selection of δ13C varied depending on its selective strength relative to height growth. We conclude that warming could promote the adaptability and evolvability of growth response and drought avoidance with a limited evolutionary influence from pest (biotic) pressures.

Breeding conifer species for phenotypic improvement is challenging due to delayed expression of important phenotypes related to productivity and their late sexual maturity, causing long recurrent selection cycles. Genomic selection (GS) can address such shortcomings through early prediction of phenotypes based on large numbers of jointly considered genomic markers, typically, single nucleotide polymorphisms (SNPs). Additionally, current conifer breeding genetic evaluations are based on pedigree-based predictions. However, the maximization of genetic gain in breeding programs is contingent on the accuracy of the predicted breeding values and precision of the estimated genetic parameters, which can also be improved using GS. While GS has become a new paradigm in animal breeding, it is still in its infancy for tree improvement. Thus, GS requires validation before it can be operationally implemented. Collectively, this dissertation explores some of the challenges associated with the application of GS in forest tree improvement programs. Namely, the efficiency of GS compared to traditional phenotypic selection, methods to implement GS in a cost-efficient manner, and the prediction accuracy (PA) of phenotypes across generations, life-stages, and environments. To address these challenges I structured this dissertation into three analyses which use several GS methodologies, three genotyping platforms, and three conifer species. The first study explores the temporal decay and relative efficiency of GS PA for interior spruce (Picea engelmannii × glauca). The second study investigates the use of single-step GS (ssGBLUP) to improve the precision and accuracy of genetic parameter estimates for white spruce (Picea glauca). The third study focuses on the combined use of ssGBLUP and climate data to improve intra- and inter-generation PA in unobserved environments for Douglas-fir (Pseudotsuga menziesii). The results from these three studies demonstrated that: i) updating GS models requires iv phenotypic data at least mid-rotation age to accurately reflect mature growth traits, ii) the relative efficiency of GS is greater than traditional selection assuming a 25% reduction in breeding cycle length, iii) ssGBLUP is an effective tool for improvement in the genetic evaluation of openpollinated mating designs, and iv) inclusion of climate variables as environmental covariates in the GS models yields improvement in PA for unobserved environments.

Background: Genomic selection (GS) in forestry can substantially reduce the length of breeding cycle and increase gain per unit time through early selection and greater selection intensity, particularly for traits of low heritability and late expression. Affordable next-generation sequencing technologies made it possible to genotype large numbers of trees at a reasonable cost. Results Genotyping-by-sequencing was used to genotype 1,126 Interior spruce trees representing 25 open-pollinated families planted over three sites in British Columbia, Canada. Four imputation algorithms were compared (mean value (MI), singular value decomposition (SVD), expectation maximization (EM), and a newly derived, family-based k-nearest neighbor (kNN-Fam)). Trees were phenotyped for several yield and wood attributes. Single- and multi-site GS prediction models were developed using the Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) and the Generalized Ridge Regression (GRR) to test different assumption about trait architecture. Finally, using PCA, multi-trait GS prediction models were developed. The EM and kNN-Fam imputation methods were superior for 30 and 60% missing data, respectively. The RR-BLUP GS prediction model produced better accuracies than the GRR indicating that the genetic architecture for these traits is complex. GS prediction accuracies for multi-site were high and better than those of single-sites while multi-site predictability produced the lowest accuracies reflecting type-b genetic correlations and deemed unreliable. The incorporation of genomic information in quantitative genetics analyses produced more realistic heritability estimates as half-sib pedigree tended to inflate the additive genetic variance and subsequently both heritability and gain estimates. Principle component scores as representatives of multi-trait GS prediction models produced surprising results where negatively correlated traits could be concurrently selected for using PCA2 and PCA3. Conclusions The application of GS to open-pollinated family testing, the simplest form of tree improvement evaluation methods, was proven to be effective. Prediction accuracies obtained for all traits greatly support the integration of GS in tree breeding. While the within-site GS prediction accuracies were high, the results clearly indicate that single-site GS models ability to predict other sites are unreliable supporting the utilization of multi-site approach. Principle component scores provided an opportunity for the concurrent selection of traits with different phenotypic optima.

Background: Genomic selection (GS) in forestry can substantially reduce the length of breeding cycle and increase gain per unit time through early selection and greater selection intensity, particularly for traits of low heritability and late expression. Affordable next-generation sequencing technologies made it possible to genotype large numbers of trees at a reasonable cost. Results: Genotyping-by-sequencing was used to genotype 1,126 Interior spruce trees representing 25 open-pollinated families planted over three sites in British Columbia, Canada. Four imputation algorithms were compared (mean value (MI), singular value decomposition (SVD), expectation maximization (EM), and a newly derived, family-based k-nearest neighbor (kNN-Fam)). Trees were phenotyped for several yield and wood attributes. Single- and multi-site GS prediction models were developed using the Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) and the Generalized Ridge Regression (GRR) to test different assumption about trait architecture. Finally, using PCA, multi-trait GS prediction models were developed. The EM and kNN-Fam imputation methods were superior for 30 and 60% missing data, respectively. The RR-BLUP GS prediction model produced better accuracies than the GRR indicating that the genetic architecture for these traits is complex. GS prediction accuracies for multi-site were high and better than those of single-sites while multi-site predictability produced the lowest accuracies reflecting type-b genetic correlations and deemed unreliable. The incorporation of genomic information in quantitative genetics analyses produced more realistic heritability estimates as half-sib pedigree tended to inflate the additive genetic variance and subsequently both heritability and gain estimates. Principle component scores as representatives of multi-trait GS prediction models produced surprising results where negatively correlated traits could be concurrently selected for using PCA2 and PCA3. Conclusions: The application of GS to open-pollinated family testing, the simplest form of tree improvement evaluation methods, was proven to be effective. Prediction accuracies obtained for all traits greatly support the integration of GS in tree breeding. While the within-site GS prediction accuracies were high, the results clearly indicate that single-site GS models ability to predict other sites are unreliable supporting the utilization of multi-site approach. Principle component scores provided an opportunity for the concurrent selection of traits with different phenotypic optima.

Background: Genomic selection (GS) can offer unprecedented gains, in terms of cost efficiency and generation turnover, to forest tree selective breeding; especially for late expressing and low heritability traits. Here, we used: 1) exome capture as a genotyping platform for 1372 Douglas-fir trees representing 37 full-sib families growing on three sites in British Columbia, Canada and 2) height growth and wood density (EBVs), and deregressed estimated breeding values (DEBVs) as phenotypes. Representing models with (EBVs) and without (DEBVs) pedigree structure. Ridge regression best linear unbiased predictor (RR-BLUP) and generalized ridge regression (GRR) were used to assess their predictive accuracies over space (within site, cross-sites, multi-site, and multi-site to single site) and time (age-age/ trait-trait). Results: The RR-BLUP and GRR models produced similar predictive accuracies across the studied traits. Within-site GS prediction accuracies with models trained on EBVs were high (RR-BLUP: 0.79–0.91 and GRR: 0.80–0.91), and were generally similar to the multi-site (RR-BLUP: 0.83–0.91, GRR: 0.83–0.91) and multi-site to single-site predictive accuracies (RR-BLUP: 0.79–0.92, GRR: 0.79–0.92). Cross-site predictions were surprisingly high, with predictive accuracies within a similar range (RR-BLUP: 0.79–0.92, GRR: 0.78–0.91). Height at 12 years was deemed the earliest acceptable age at which accurate predictions can be made concerning future height (age-age) and wood density (trait-trait). Using DEBVs reduced the accuracies of all cross-validation procedures dramatically, indicating that the models were tracking pedigree (family means), rather than marker-QTL LD. Conclusions: While GS models’ prediction accuracies were high, the main driving force was the pedigree tracking rather than LD. It is likely that many more markers are needed to increase the chance of capturing the LD between causal genes and markers.

22 páginas.- 4 figuras.- 4 tablas.- 111 referencias.- Supporting information :S1 Fig. Annual variation in average basal area increment (BAI) of the open-pollinated white spruce families for the period 1995–2016 at each of the three test sites. The red dashed line represents the year of the drought event and the green shadowed area represents the pre-drought period considered to calculate the Resistance index. S2 Fig. Density distribution for the studied traits in white spruce in each of the three test sites. Logarithmic transformations were applied to MFA and all monoterpene compounds to improve data normality. Abbreviations used for the traits and sites are described, respectively, in the text and Table 1. S3 Fig. Pedigree and genomic relationships. Distribution of the number of pairwise additive relationships (excluding the diagonal elements) from the pedigree (after pedigree correction, left) and genomic (right) relationship matrices. Note that y-axis (Frequency) were cut at 40,000 (A-matrix, out of 2,343,490) and at 10,000 (G-matrix, out of 1,555,212) in order to more clearly visualize the differences between relationship matrices-matrix) and genomic-based (G-matrix) relationship matrices in each of the three white spruce sites. Abbreviations used for the sites are described in the Table 1. S5 Fig. Scatter plot between estimated genetic correlation between pairs of sites from the pedigree- (A-matrix) and genomic-based (G-matrix) relationship matrices in each of the 15 assessed traits in white spruce. Abbreviations used for the traits are described in the text. S1 Table. Estimated genetic correlations (and approximate standard errors) between the different traits from the multiple-trait analysis using the pedigree- (A-matrix, above diagonal) and genomic-based (G-matrix, below diagonal) relationship matrices for white spruce in each of the three test sites. Abbreviations used for the traits and sites are described, respectively, in the text and Table 1. S2 Table. Estimated genetic correlations (and approximate standard errors) between the different sites from the multiple-site analysis using the pedigree- (A-matrix, above diagonal) and genomic-based (G-matrix, below diagonal) relationship matrices for white spruce in each of the three test sites. Abbreviations used for the traits and sites are described, respectively, in the text and Table 1. S1 Text. Chemical analysis., Tree improvement programs often focus on improving productivity-related traits; however, under present climate change scenarios, climate change-related (adaptive) traits should also be incorporated into such programs. Therefore, quantifying the genetic variation and correlations among productivity and adaptability traits, and the importance of genotype by environment interactions, including defense compounds involved in biotic and abiotic resistance, is essential for selecting parents for the production of resilient and sustainable forests. Here, we estimated quantitative genetic parameters for 15 growth, wood quality, drought resilience, and monoterpene traits for Picea glauca (Moench) Voss (white spruce). We sampled 1,540 trees from three open-pollinated progeny trials, genotyped with 467,224 SNP markers using genotyping-by-sequencing (GBS). We used the pedigree and SNP information to calculate, respectively, the average numerator and genomic relationship matrices, and univariate and multivariate individual-tree models to obtain estimates of (co)variance components. With few site-specific exceptions, all traits examined were under genetic control. Overall, higher heritability estimates were derived from the genomic- than their counterpart pedigree-based relationship matrix. Selection for height, generally, improved diameter and water use efficiency, but decreased wood density, microfibril angle, and drought resistance. Genome-based correlations between traits reaffirmed the pedigree-based correlations for most trait pairs. High and positive genetic correlations between sites were observed (average 0.68), except for those pairs involving the highest elevation, warmer, and moister site, specifically for growth and microfibril angle. These results illustrate the advantage of using genomic information jointly with productivity and adaptability traits, and defense compounds to enhance tree breeding selection for changing climate., This work was funded by Genome Canada (https://www.genomecanada.ca/) RESFOR ID 10207, grants 16R75036 to YAE, RES0034654 to NE, and RES0031330 to BRT; Genome Alberta (https://genomealberta.ca/) RESFOR ID: LRF, grants RES0034664 to NE, 16R10106 to SDM, and RES0034657 to BRT; University of Alberta / Faculty ALES / Dept RR (https://www.ualberta.ca/index.html) grant RES0034569 to BRT; Alberta Innovates –BioSolutions (https://albertainnovates.ca/) grants RES0035327 to NE, 16R75221 to SDM, and RES0028979 to BRT; Genome BC (https://www. genomebc.ca/) grants 16R75421 to YAE and 16R75546 to SDM; Forest Resource Improvement Association of Alberta (FRIAA, https://friaa.ab.ca/) grants RES0037021 and RES0036845 to BRT; National Science Foundation (NSF, https://www.nsf.gov/) grants MRI-1531128, ACI-1548562, and ACI-1445606 to CC; The Extreme Science and Engineering Discovery (XSEDE, https://xras.xsede. org/public/requests/29304-XSEDE-MCB180177) grant MCB180177 to CC. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscrip

20 páginas.- 7 figuras.- 2 tablas.- 93 referencias.- The online version contains supplementary material available at https://doi.org/10.1186/s12864-022-08747-7, Background Genomic prediction (GP) and genome-wide association (GWA) analyses are currently being employed to accelerate breeding cycles and to identify alleles or genomic regions of complex traits in forest trees species. Here, 1490 interior lodgepole pine (Pinus contorta Dougl. ex. Loud. var. latifolia Engelm) trees from four open-pollinated progeny trials were genotyped with 25,099 SNPs, and phenotyped for 15 growth, wood quality, pest resistance, drought tolerance, and defense chemical (monoterpenes) traits. The main objectives of this study were to: (1) identify genetic markers associated with these traits and determine their genetic architecture, and to compare the marker detected by single- (ST) and multiple-trait (MT) GWA models; (2) evaluate and compare the accuracy and control of bias of the genomic predictions for these traits underlying different ST and MT parametric and non-parametric GP methods. GWA, ST and MT analyses were compared using a linear transformation of genomic breeding values from the respective genomic best linear unbiased prediction (GBLUP) model. GP, ST and MT parametric and non-parametric (Reproducing Kernel Hilbert Spaces, RKHS) models were compared in terms of prediction accuracy (PA) and control of bias. Results MT-GWA analyses identified more significant associations than ST. Some SNPs showed potential pleiotropic effects. Averaging across traits, PA from the studied ST-GP models did not differ significantly from each other, with generally a slight superiority of the RKHS method. MT-GP models showed significantly higher PA (and lower bias) than the ST models, being generally the PA (bias) of the RKHS approach significantly higher (lower) than the GBLUP. Conclusions The power of GWA and the accuracy of GP were improved when MT models were used in this lodgepole pine population. Given the number of GP and GWA models fitted and the traits assessed across four progeny trials, this work has produced the most comprehensive empirical genomic study across any lodgepole pine population to date., This work was funded by Genome Canada (https://www.genomecanada. ca/) RES-FOR ID 10207, grants 16R75036 to YAE, RES0034654 to NE, and RES0031330 to BRT; Genome Alberta (https://genomealberta.ca/) RES-FORID: LRF, grants RES0034664 to NE, 16R10106 to SDM, and RES0034657 to BRT; University of Alberta/Faculty ALES/Dept RR (https://www.ualberta.ca/index. html) grant RES0034569 to BRT; Alberta Innovates – BioSolutions (https://albertainnovates.ca/) grants RES0035327 to NE, 16R75221 to SDM, and RES0028979 to BRT; Genome BC (https://www.genomebc.ca/) grants 16R75421 to YAE and 16R75546 to SDM; Forest Resource Improvement Association of Alberta (FRIAA, https://friaa.ab.ca/) grants RES0037021 and RES0036845 to BRT; National Science Foundation (NSF, tps://www.nsf.gov/) grants MRI-1531128, ACI-1548562, and ACI-1445606 to CC; The Extreme Science and Engineering Discovery (XSEDE, ttps://xras.xsede.org/public/requests/29304-XSEDE-MCB180177) grant MCB180177 to CC. The funding bodies played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript

14 páginas.- 4 figuras.- 1 tabla.- 101 referencias.- Supplementary material is available online at Evolution (https://academic.oup.com/evolut/qpad004) . A correction has been published: Evolution, Volume 77, Issue 4, 1 April 2023, Page 1174, https://doi.org/10.1093/evolut/qpad030 Issue Section: Correction This is a correction to: Yang Liu, Nadir Erbilgin, Eduardo Pablo Cappa, Charles Chen, Blaise Ratcliffe, Xiaojing Wei, Jennifer G Klutsch, Aziz Ullah, Jaime Sebastian Azcona, Barb R Thomas, Yousry A El-Kassaby, Decoupling of height growth and drought or pest resistance tradeoffs is revealed through multiple common-garden experiments of lodgepole pine, Evolution, 2023; qpad004, https://doi.org/10.1093/evolut/qpad004 In the originally published version of this manuscript, the images for Figures 3 and 4 were erroneously transposed. The images are now in their correct position to align with their respective legends. The Funding section was erroneously removed, and its details situated at the end of the Acknowledgements section. Funding details are now situated within the replaced Funding section. The publisher would like to apologise for the errors introduced here. The errors have been corrected in the article online., The environment could alter growth and resistance tradeoffs in plants by affecting the ratio of resource allocation to various competing traits. Yet, how and why functional tradeoffs change over time and space is poorly understood particularly in long-lived conifer species. By establishing four common-garden test sites for five lodgepole pine populations in western Canada, combined with genomic sequencing, we revealed the decoupling pattern and genetic underpinnings of tradeoffs between height growth, drought resistance based on δ13C and dendrochronology, and metrics of pest resistance based on pest suitability ratings. Height and δ13C correlation displayed a gradient change in magnitude and/or direction along warm-to-cold test sites. All cold test sites across populations showed a positive height and δ13C relationship. However, we did not observe such a clinal correlation pattern between height or δ13C and pest suitability. Further, we found that the study populations exhibiting functional tradeoffs or synergies to various degrees in test sites were driven by non-adaptive evolutionary processes rather than adaptive evolution or plasticity. Finally, we found positive genetic relationships between height and drought or pest resistance metrics and probed five loci showing potential genetic tradeoffs between northernmost and the other populations. Our findings have implications for deciphering the ecological, evolutionary, and genetic bases of the decoupling of functional tradeoffs due to environmental change., This work was funded by Genome Canada, Genome Alberta through Alberta Economic Trade and evelopment, Genome British Columbia, the University of Alberta, the University of Calgary, the University of Cambridge, and the Australian Research Council Centre of Excellence for Plant Success in Nature and Agriculture (CE200100015).