Realized genomic selection across generations in a reciprocal recurrent selection breeding program of Eucalyptus hybrids.

Introduction: Genomic selection (GS) experiments in forest trees have largely reported estimates of predictive abilities from cross-validation among individuals in the same breeding generation. In such conditions, no effects of recombination, selection, drift, and environmental changes are accounted for. Here, we assessed the effectively realized predictive ability (RPA) for volume growth at harvest age by GS across generations in an operational reciprocal recurrent selection (RRS) program of hybrid Eucalyptus. Methods: Genomic best linear unbiased prediction with additive (GBLUP_G), additive plus dominance (GBLUP_G+D), and additive single-step (HBLUP) models were trained with different combinations of growth data of hybrids and pure species individuals (N = 17,462) of the G1 generation, 1,944 of which were genotyped with ~16,000 SNPs from SNP arrays. The hybrid G2 progeny trial (HPT267) was the GS target, with 1,400 selection candidates, 197 of which were genotyped still at the seedling stage, and genomically predicted for their breeding and genotypic values at the operational harvest age (6 years). Seedlings were then grown to harvest and measured, and their pedigree-based breeding and genotypic values were compared to their originally predicted genomic counterparts. Results: Genomic RPAs ≥0.80 were obtained as the genetic relatedness between G1 and G2 increased, especially when the direct parents of selection candidates were used in training. GBLUP_G+D reached RPAs ≥0.70 only when hybrid or pure species data of G1 were included in training. HBLUP was only marginally better than GBLUP. Correlations ≥0.80 were obtained between pedigree and genomic individual ranks. Rank coincidence of the top 2.5% selections was the highest for GBLUP_G (45% to 60%) compared to GBLUP_G+D. To advance the pure species RRS populations, GS models were best when trained on pure species than hybrid data, and HBLUP yielded ~20% higher predictive abilities than GBLUP, but was not better than ABLUP for ungenotyped trees. Discussion: We demonstrate that genomic data effectively enable accurate ranking of eucalypt hybrid seedlings for their yet-to-be observed volume growth at harvest age. Our results support a two-stage GS approach involving family selection by average genomic breeding value, followed by within-top-families individual GS, significantly increasing selection intensity, optimizing genotyping costs, and accelerating RRS breeding.

Biometric assessment of early stem growth at a commercial stand of African mahogany (Khaya grandifoliola)

African mahogany species (Khaya spp.) have proven to be promising in the Brazilian forestry scenario, replacing native mahogany owing to their medium-fast growth and relevant timber value. This study aimed to carry out forest inventory and assessments of a Khaya grandifoliola plantation in the first years after planting, test hypsometric models to describe tree growth, and identify the maximum commercial stem yield (i.e., greater than 6 m in height). The stand was located in the municipality of Piracanjuba (GO), where seedlings of seed origin were used. Twenty random plots with a 15 m radius were allocated, and the total height (HT), stem height (HS), diameter at breast height (DBH), crown area, and forest canopy were measured. Four hypsometric models were employed in this study. The best equation was selected based on determination coefficients and standard errors. Further, the models were cross-validated to evaluate predictability and bias. At four years of planting, the largest class of HS was found to range from 3.1 to 4.1 m, and most trees had a DBH ranging from 0.084 to 0.126 m. The percentage of trees with stems > 6 m was 8.35%. The linear model ensured more consistent results for estimating HT, while the quadratic and Weibull models led to more consistent results for HS. By using models, stem measurements can be measured based on DBH, ultimately aiding the selection of stem management strategies for the growth of forests with greater commercial value.

Validating the Use of Bovine Buccal Sampling as a Proxy for the Rumen Microbiota by Using a Time Course and Random Forest Classification Approach.

Analysis of the cow microbiome, as well as host genetic influences on the establishment and colonization of the rumen microbiota, is critical for development of strategies to manipulate ruminal function toward more efficient and environmentally friendly milk production. To this end, the development and validation of noninvasive methods to sample the rumen microbiota at a large scale are required. In this study, we further optimized the analysis of buccal swab samples as a proxy for direct bacterial samples of the rumen of dairy cows. To identify an optimal time for sampling, we collected buccal swab and rumen samples at six different time points relative to animal feeding. We then evaluated several biases in these samples using a machine learning classifier (random forest) to select taxa that discriminate between buccal swab and rumen samples. Differences in the inverse Simpson's diversity, Shannon's evenness, and Bray-Curtis dissimilarities between methods were significantly less apparent when sampling was performed prior to morning feeding (P < 0.05), suggesting that this time point was optimal for representative sampling. In addition, the random forest classifier was able to accurately identify nonrumen taxa, including 10 oral and putative feed-associated taxa. Two highly prevalent (>60%) taxa in buccal and rumen samples had significant variance in relative abundances between sampling methods but could be qualitatively assessed via regular buccal swab sampling. This work not only provides new insights into the oral community of ruminants but also further validates and refines buccal swabbing as a method to assess the rumen bacterial in large herds.IMPORTANCE The gastrointestinal tracts of ruminants harbor a diverse microbial community that coevolved symbiotically with the host, influencing its nutrition, health, and performance. While the influence of environmental factors on rumen microbes is well documented, the process by which host genetics influences the establishment and colonization of the rumen microbiota still needs to be elucidated. This knowledge gap is due largely to our inability to easily sample the rumen microbiota. There are three common methods for rumen sampling but all of them present at least one disadvantage, including animal welfare, sample quality, labor, and scalability. The development and validation of noninvasive methods, such as buccal swabbing, for large-scale rumen sampling is needed to support studies that require large sample sizes to generate reliable results. The validation of buccal swabbing will also support the development of molecular tools for the early diagnosis of metabolic disorders associated with microbial changes in large herds.

Spatial and temporal distribution of urban heat islands.

The formation of an urban heat island (UHI) is one of the most common impacts of the urbanization process. To mitigate the effects of UHI, the planning of urban forests (e.g., creation of parks, forests and afforestation streets) has been the major tool applied in this context. Thus, the aim of this study is to evaluate the spatial and temporal distribution of heat islands in Vila Velha, ES, Brazil using the mono-window algorithm. The study followed these methodological steps: 1) mapping of urban green areas through a photointerpretation screen; 2) application of the mono-window algorithm to obtain the spatial and temporal patterns of land surface temperature (LST); 3) correlation between LST and the normalized difference vegetation index (NDVI) and normalized difference build-up index (NDBI); 4) application of ecological evaluation index. The results showed that the mean values of LST in urban areas were at least 2.34 to 7.19°C higher than undeveloped areas. Moreover, the positive correlation between LST and NDBI showed an amplifying effect of the developed areas for UHI, while areas with a predominance of vegetation attenuated the effect of UHI. Urban centers, clustered in some parts of the city, received the worst ecological assessment index. Finally, the adoption of measures to guide the urban forest planning within urban centers is necessary to mitigate the effect of heat islands and provide thermal comfort in urban areas.

Regional heritability mapping and genome-wide association identify loci for complex growth, wood and disease resistance traits in Eucalyptus.

Although genome-wide association studies (GWAS) have provided valuable insights into the decoding of the relationships between sequence variation and complex phenotypes, they have explained little heritability. Regional heritability mapping (RHM) provides heritability estimates for genomic segments containing both common and rare allelic effects that individually contribute too little variance to be detected by GWAS. We carried out GWAS and RHM for seven growth, wood and disease resistance traits in a breeding population of 768 Eucalyptus hybrid trees using EuCHIP60K. Total genomic heritabilities accounted for large proportions (64-89%) of pedigree-based trait heritabilities, providing additional evidence that complex traits in eucalypts are controlled by many sequence variants across the frequency spectrum, each with small contributions to the phenotypic variance. RHM detected 26 quantitative trait loci (QTLs) encompassing 2191 single nucleotide polymorphisms (SNPs), whereas GWAS detected 13 single SNP-trait associations. RHM and GWAS QTLs individually explained 5-15% and 4-6% of the genomic heritability, respectively. RHM was superior to GWAS in capturing larger proportions of genomic heritability. Equated to previously mapped QTLs, our results highlighted genomic regions for further examination towards gene discovery. RHM-QTLs bearing a combination of common and rare variants could be useful enhancements to incorporate prior knowledge of the underlying genetic architecture in genomic prediction models.

Brief history of Eucalyptus breeding in Brazil under perspective of biometric advances / Breve histórico do melhoramento genético do eucalipto no Brasil sob a ótica dos avanços biométricos

ABSTRACT: Forest breeding is a science that has been developing in Brazil since 1941 being the Eucalyptus a highlighted genus in this scenario. In a global scene, Brazil is displayed prominently in productivity of Eucalyptus planting, due to favorable environmental conditions to cultivation development, and the incentive in research for improvement of traits of interest t observed in it species and hybrids. This research included a historical review of Eucalyptus breeding over the years under genetic biometric perspective in Brazil, from reports describing the pioneer planting up to the current genome wide selection (GWS) that came as a complement of forest breeding success. This review showed some of the tracks performed by researchers aiming to improve the productive and quality of phenotypic traits from Eucalyptus genus.

RESUMO: O melhoramento florestal é uma ciência que tem tido desenvolvimento no Brasil desde o ano de 1941 e um gênero que obteve grande repercussão neste cenário foi o Eucalyptus . O Brasil apresenta-se em destaque no panorama mundial quanto à produtividade dos plantios de eucalipto, em virtude das condições ambientais favoráveis ao desenvolvimento da cultura e ao incentivo em pesquisas destinadas às melhorias dos caracteres de interesse, presentes em suas espécies e híbridos. Este trabalho inclui uma revisão histórica do melhoramento do eucalipto ao longo dos anos no Brasil, sob a ótica da genética biométrica, desde o relato que descreve o plantio pioneiro de suas espécies no país, até a atual aplicação de seleção genômica ampla (SGA), que surgiu como um complemento de sucesso no melhoramento florestal. A pesquisa ilustra ainda alguns dos caminhos percorridos por pesquisadores a fim de aumentar a produtividade e a qualidade dos caracteres fenotípicos do gênero Eucalyptus.