Geographic variation in Dendrocalamus latiflorus culm physical and mechanical traits is associated with source-climate gradients
- 1College of Forestry, Fujian Agriculture and Forestry University
- 2College of Landscape Architecture and Art, Fujian Agriculture and Forestry University
Abstract
Introduction: Geographic variation in bamboo culm traits can reveal associations between source environments and biomechanical performance and can help prioritize germplasm for further testing.
Methods: We quantified 11 physical and mechanical traits in 31 geographic sources of Dendrocalamus latiflorus from nine provincial-level regions of China and related source-level means to 25 geographic and climatic variables. Analyses combined coefficients of variation, Pearson correlations with Benjamini–Hochberg false-discovery-rate correction, partial least squares regression (PLSR) with leave-one-out cross-validation and 1,000 bootstrap resamples, Moran’s I tests of residual spatial autocorrelation, principal component analysis, objectively validated Ward clustering, and a seven-trait entropy-weighted TOPSIS evaluation with equal-weight sensitivity analysis.
Results: Trait coefficients of variation ranged from 7.1% to 24.4%. After correction of 100 Pearson tests, 32 associations remained significant at q < 0.05; the strongest was between precipitation of the warmest quarter (BIO18) and compressive strength parallel to grain (CSPG; r = 0.658, q = 0.0058). PLSR showed the highest cross-validated performance for CSPG (Q ² = 0.599), moderate performance for air-dry and oven-dry density, and weak performance for modulus of rupture (Q ² = 0.076). Raw density and CSPG values were spatially autocorrelated, but PLSR residuals were not. Two validated trait classes showed no significant differences in six key source-climate variables. Entropy-weighted TOPSIS ranked P6, P4, and P19 highest; equal weighting produced strongly concordant ranks (Spearman rho = 0.911).
Discussion: These results identify robust climate-associated trait patterns and candidate sources for further testing while avoiding causal inference from source-site sampling.
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BibTeX
@article{fang2026geographic,
title = {{Geographic variation in Dendrocalamus latiflorus culm physical and mechanical traits is associated with source-climate gradients}},
author = {Ying Fang and Hang Tao and Jiannan Lin and Jiaying Liu and Yawen Yang and Yushan Zheng and Liguang Chen and Jundong Rong and Tianyou He},
journal = {Frontiers in Plant Science},
year = {2026},
month = oct,
doi = {10.3389/fpls.2026.1946733},
url = {https://www.frontiersin.org/articles/10.3389/fpls.2026.1946733/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Geographic variation in Dendrocalamus latiflorus culm physical and mechanical traits is associated with source-climate gradients
AU - Ying Fang
AU - Hang Tao
AU - Jiannan Lin
AU - Jiaying Liu
AU - Yawen Yang
AU - Yushan Zheng
AU - Liguang Chen
AU - Jundong Rong
AU - Tianyou He
PY - 2026
DA - 2026/10/14
JO - Frontiers in Plant Science
DO - 10.3389/fpls.2026.1946733
UR - https://www.frontiersin.org/articles/10.3389/fpls.2026.1946733/full
AB - Introduction: Geographic variation in bamboo culm traits can reveal associations between source environments and biomechanical performance and can help prioritize germplasm for further testing. Methods: We quantified 11 physical and mechanical traits in 31 geographic sources of Dendrocalamus latiflorus from nine provincial-level regions of China and related source-level means to 25 geographic and climatic variables. Analyses combined coefficients of variation, Pearson correlations with Benjamini–Hochberg false-discovery-rate correction, partial least squares regression (PLSR) with leave-one-out cross-validation and 1,000 bootstrap resamples, Moran’s I tests of residual spatial autocorrelation, principal component analysis, objectively validated Ward clustering, and a seven-trait entropy-weighted TOPSIS evaluation with equal-weight sensitivity analysis. Results: Trait coefficients of variation ranged from 7.1% to 24.4%. After correction of 100 Pearson tests, 32 associations remained significant at q < 0.05; the strongest was between precipitation of the warmest quarter (BIO18) and compressive strength parallel to grain (CSPG; r = 0.658, q = 0.0058). PLSR showed the highest cross-validated performance for CSPG (Q ² = 0.599), moderate performance for air-dry and oven-dry density, and weak performance for modulus of rupture (Q ² = 0.076). Raw density and CSPG values were spatially autocorrelated, but PLSR residuals were not. Two validated trait classes showed no significant differences in six key source-climate variables. Entropy-weighted TOPSIS ranked P6, P4, and P19 highest; equal weighting produced strongly concordant ranks (Spearman rho = 0.911). Discussion: These results identify robust climate-associated trait patterns and candidate sources for further testing while avoiding causal inference from source-site sampling.
SN - 1664-462X
ER -