Eucalyptus cladocalyx F. Muell is a tree species suitable for low-rainfall sites, even with annual average precipitation as low as 150 mm per year. Its wood is classified as highly durable and its permanence in soil is longer than 25 years, so it can be used for multiple applications. Given that about 41% of the world’s land area is classified as drylands, added to the impact of climate change on the availability of water resources, it becomes necessary to use plant species that can tolerate environments with low water availability. In this study, a Bayesian analysis of genetic parameters showed that wood density (WD) was moderately heritable, with a posterior mean of h² = 0.29 and a Bayesian credibility region (90%) of 0.06–0.74, while the slenderness coefficient (SC) was highly heritable, with a posterior mean of h² = 0.48 and a Bayesian credibility region (90%) of 0.11–0.87. Through Bayesian regression analysis, we identified four and three significant associations for WD and SC, respectively. Another important finding of the bi-trait Bayesian analysis was the detection of three large-effect pleiotropic QTLs located on LG4 at 52 cM, on LG2 at 125 cM, and on LG6 at 81 cM. Bayesian bi-trait regression and the posterior probability of association indicated that three QTLs presented strong evidence of association with WD and SC. This provides convincing evidence that the loci qtlWD130/qtlSC130, qtlWD195/qtlSC195, and qtlWD196/qtlSC196 have a significant pleiotropic effect. The association mapping based on multivariate Bayesian regression was useful for the identification of genomic regions with pleiotropic effects. These loci can be used in molecular marker-assisted breeding to select trees with better wood density.

Studying population structure has made an essential contribution to understanding evolutionary processes and demographic history in forest ecology research. This inference process basically involves the identification of common genetic variants among individuals, then grouping the similar individuals into subpopulations. In this study, a spectral-based classification of genetically differentiated groups was carried out using a provenance–progeny trial of Eucalyptus cladocalyx. First, the genetic structure was inferred through a Bayesian analysis using single-nucleotide polymorphisms (SNPs). Then, different machine learning models were trained with foliar spectral information to assign individual trees to subpopulations. The results revealed that spectral-based classification using the multilayer perceptron method was very successful at classifying individuals into their respective subpopulations (with an average of 87% of correct individual assignments), whereas 85% and 81% of individuals were assigned to their respective classes correctly by convolutional neural network and partial least squares discriminant analysis, respectively. Notably, 93% of individual trees were assigned correctly to the class with the smallest size using the spectral data-based multi-layer perceptron classification method. In conclusion, spectral data, along with neural network models, are able to discriminate and assign individuals to a given subpopulation, which could facilitate the implementation and application of population structure studies on a large scale.

Because of the harmful effect on the environment and the uncertainty in the future offer of the conventional source of energy, the lignocellulosic biomass is being considered as the most promising alternatives of solution. The evidence shows that an important way to get biomass turned into biofuels will be through establishing plantations with high growth rates and short rotation periods; plantations specially conceived for that. This article describes characteristics and possibilities of using hybrid poplars on energetic plantation models, which has an enormous potential to be developed in Chile.