Genotype-by-environment interaction in coast redwood outside natural distribution - search for environmental cues

Carbon sequestration potential of plantation forests in New Zealand - no single tree species is universally best

BACKGROUND

Plantation forests are a nature-based solution to sequester atmospheric carbon and, therefore, mitigate anthropogenic climate change. The choice of tree species for afforestation is subject to debate within New Zealand. Two key issues are whether to use (1) exotic plantation species versus indigenous forest species and (2) fast growing short-rotation species versus slower growing species. In addition, there is a lack of scientific knowledge about the carbon sequestration capabilities of different plantation tree species, which hinders the choice of species for optimal carbon sequestration. We contribute to this discussion by simulating carbon sequestration of five plantation forest species, Pinus radiata, Pseudotsuga menziesii, Eucalyptus fastigata, Sequoia sempervirens and Podocarpus totara, across three sites and two silvicultural regimes by using the 3-PG an ecophysiological model.

RESULTS

The model simulations showed that carbon sequestration potential varies among the species, sites and silvicultural regimes. Indigenous Podocarpus totara or exotic Sequoia sempervirens can provide plausible options for long-term carbon sequestration. In contrast, short term rapid carbon sequestration can be obtained by planting exotic Pinus radiata, Pseudotsuga menziesii and Eucalyptus fastigata.

CONCLUSION

No single species was universally better at sequestering carbon on all sites we tested. In general, the results of this study suggest a robust framework for ranking and testing candidate afforestation species with regard to carbon sequestration potential at a given site. Hence, this study could help towards more efficient decision-making for carbon forestry.

Genomic selection for resistance to mammalian bark stripping and associated chemical compounds in radiata pine

The integration of genomic data into genetic evaluations can facilitate the rapid selection of superior genotypes and accelerate the breeding cycle in trees. In this study, 390 trees from 74 control-pollinated families were genotyped using a 36K Axiom SNP array. A total of 15,624 high-quality SNPs were used to develop genomic prediction models for mammalian bark stripping, tree height, and selected primary and secondary chemical compounds in the bark. Genetic parameters from different genomic prediction methods-single-trait best linear unbiased prediction based on a marker-based relationship matrix (genomic best linear unbiased prediction), multitrait single-step genomic best linear unbiased prediction, which integrated the marker-based and pedigree-based relationship matrices (single-step genomic best linear unbiased prediction) and the single-trait generalized ridge regression-were compared to equivalent single- or multitrait pedigree-based approaches (ABLUP). The influence of the statistical distribution of data on the genetic parameters was assessed. Results indicated that the heritability estimates were increased nearly 2-fold with genomic models compared to the equivalent pedigree-based models. Predictive accuracy of the single-step genomic best linear unbiased prediction was higher than the ABLUP for most traits. Allowing for heterogeneity in marker effects through the use of generalized ridge regression did not markedly improve predictive ability over genomic best linear unbiased prediction, arguing that most of the chemical traits are modulated by many genes with small effects. Overall, the traits with low pedigree-based heritability benefited more from genomic models compared to the traits with high pedigree-based heritability. There was no evidence that data skewness or the presence of outliers affected the genomic or pedigree-based genetic estimates.

Selective Sweeps and Polygenic Adaptation Drive Local Adaptation along Moisture and Temperature Gradients in Natural Populations of Coast Redwood and Giant Sequoia

Dissecting the genomic basis of local adaptation is a major goal in evolutionary biology and conservation science. Rapid changes in the climate pose significant challenges to the survival of natural populations, and the genomic basis of long-generation plant species is still poorly understood. Here, we investigated genome-wide climate adaptation in giant sequoia and coast redwood, two iconic and ecologically important tree species. We used a combination of univariate and multivariate genotype-environment association methods and a selective sweep analysis using non-overlapping sliding windows. We identified genomic regions of potential adaptive importance, showing strong associations to moisture variables and mean annual temperature. Our results found a complex architecture of climate adaptation in the species, with genomic regions showing signatures of selective sweeps, polygenic adaptation, or a combination of both, suggesting recent or ongoing climate adaptation along moisture and temperature gradients in giant sequoia and coast redwood. The results of this study provide a first step toward identifying genomic regions of adaptive significance in the species and will provide information to guide management and conservation strategies that seek to maximize adaptive potential in the face of climate change.

Genotype–Environment Interactions for Tree Growth and Leaf Phytochemical Content of Cyclocarya paliurus (Batal.) Iljinskaja

Cyclocarya paliurus (Batal.) Iljinskaja, as a woody tree species, has various health-promoting phytochemicals and is commonly used in tea production in China. A field trial for C. paliurus was conducted at two typical sites to evaluate genotype × environmental interaction (GEI) effects and identify the stability and adaptability of 13 families. The genotype main effects and genotype × environment interaction effects (GGE) were used to analyze the GEI effects for tree height, diameter at breast height (DBH), and leaf flavonoid and triterpenoid contents. Two-way analysis of variance indicated that the tree growth and leaf phytochemical contents were mainly affected by genotype and GEI. Based on the results of GGE, the JH1 family had high mean values and high stability in total flavonoid and total triterpenoid accumulations, while the KM4 family exhibited high means and high stability in tree height and DBH growth, as well as high means and medium stability in total triterpenoid content. Pearson’s correlation analysis indicated that the annual mean DBH was significantly and positively correlated with leaf phytochemical content in C. paliurus when all data for the 13 families at the two sites were used. Our results suggest that JH1 should be mainly extended as a superior genotype for the production of flavonoids and triterpenoids in the leaves, while KM4 could be spread for timber production or leaf triterpenoid production. This study provides useful information on the selection of C. paliurus genotypes in target sites and cultivation strategies for establishing oriented plantations.