High genetic gains in wood volume and fecundity can be both achieved by direct selection in half-sib families of Pinus yunnanensis Franch.

Experiment background

This study focused on characterizing phenotypic variation among and within provenances of Pinus yunnanensis Franch. aged 16 years in a common garden, with an emphasis on key traits such as cone production, trunk straightness, and crown health, as well as their relationships with traditional growth traits like tree height, diameter at breast height, and wood volume. Specifically, the objectives were to (1) characterize the variation of each trait within and among provenances; (2) assess inter-trait relationships, exploring patterns of co-variation and potential trade-offs; and (3) evaluate the feasibility of multi-trait selection strategies that aim for simultaneous improvements in growth, trunk straightness, and fecundity, contributing valuable insights for advancing P. yunnanensis breeding efforts.

Experimental Design

This study was conducted in a common garden for P. yunnanensis located in Lufeng County, central Yunnan Province (102°12' E, 25°13' N) at an altitude of 1860 meters. The site lies in the transition zone between the subtropical humid climate of eastern Yunnan and the sub-humid climate of southwest Yunnan. The climate is characterized by warm and dry winters, humid and hot summers, with a mean annual temperature of 15.5°C and annual precipitation ranging between 900–1000 mm. The dry season extends from November to April, accounting for 6%-17% of the total annual precipitation.

The common garden was established in 2006, with progeny from 179 superior trees selected from six provenance regions, including Anning County (AN), Qujing City (QJ), Yongren County (YR), Yulong County (YL), Tengchong County (TC), and Ninglang County (NL). Each provenance includes 30 families, except for one provenance with 29 families.

The common garden has an area of about 3 ha, with a random block design, and a planting scheme of 2 m × 3 m. To minimize environmental variation across the study site, a horizontal banding method was used for land preparation prior to planting. To reduce environmental variation across the study site, a horizontal banding method was used during land preparation. In each block, six provenances were randomly arranged, and families were randomly assigned within each provenance. Five plants from each family were planted in rows, and the design was replicated four times. A total of 3467 progeny from 179 superior trees across six provenances were included in the trial.

Experimental Variables

The study measured nine phenotypic traits, which included both quantitative and qualitative traits, as outlined below:

Tree Height (H): Measured directly with a Vertex Laser instrument (DZH-30, Harbin, China) in meters (m).

Diameter at Breast Height (D): Measured using a circumference tape in centimeters (cm).

Crown Diameter (LCD, SCD): Long crown diameter (LCD) and short crown diameter (SCD), representing the maximum and minimum tree crown diameter, respectively, measured in meters (m) using a tower ruler.

Height Under the Branch (TH): Measured in meters (m) using a tower ruler.

Wood Volume (V): Estimated using the formula based on the forestry industry standard for P. yunnanensis (Agriculture and Forestry Ministry of China, 1977), with units in cubic meters (m³).

Cone Production (CP): The number of open and closed cones in the canopy, including both serotinous and non-serotinous cones, recorded in counts to assess fecundity.

Trunk Straightness (ST): A subjective visual assessment using a classification system: 1 for a highly twisted stem, 5 for a perfectly straight stem.

Crown Health (CH): Visual assessment of the tree's crown, considering damage from abiotic and biotic stresses, with a grading scale from 1 (severely damaged) to 5 (perfectly healthy).

Data Analysis Methods

Data analysis was performed using R (version 3.6.3). The following statistical methods were employed:

Variance Analysis: Nested variance analysis was used to evaluate the significance of differences and partition phenotypic variation among and within provenances.

Principal Component Analysis (PCA): PCA was performed on the standardized matrix of nine phenotypic traits to reveal the dimensional structure and patterns of the data.

Structural Equation Modeling (SEM): SEM was used to examine the direct and indirect relationships among traits, such as growth (H, D, V), crown size (LCD, SCD, TH), fecundity (CP), trunk straightness (ST), and crown health (CH). This analysis helped identify the causal pathways between the traits.

Random Forest Analysis (RF): RF analysis was conducted to assess the importance of specific traits in predicting fecundity (CP) and trunk straightness (ST). Regression and classification methods were used for these analyses, with 1000 decision trees to ensure stable importance measures.

Dataset Description

The excel file (Raw Data) includes the following sheets: 1- Variables: Details on all the variables. 2- Values of phenotypic traits. 3- Variance components of phenotypic traits among and within provenances. 4- Coefficient of variance for phenotypic traits. 5- The average membership function values (SFM) and comprehensive weight of each principal component (PCA) of phenotypic traits.