Subtropical natural forests are unique due to their ecological and economic functions. However, most of these forests are highly degraded, which impairs the ability to provide ecological and economic benefits. Enrichment planting is an important approach to restore natural degraded forests. Species arrangement is of great importance to inform enrichment planting. Species association refers to the interrelationship of different species occupying a habitat and is a static description of the organic connection formed by the interaction of species. Species association, therefore, provides a scientific basis for species arrangement in enrichment planting. Additionally, because an old-growth forest is a climax community that has attained great age without significant disturbance, the species association in an old-growth forest can provide valuable information on the reference conditions for forest management. In this study, the species association between dominant tree species (including saplings and adult trees) was investigated in an old-growth forest in the Gutianshan National Nature Reserve in Zhejiang province in subtropical China. The objective of the study was to inform species arrangement for enrichment planting. The result showed that the overall species association exhibited a significant net positive association, indicating a dynamic balance of stable structure and species composition in the old-growth forest. Additionally, the pairwise species association was examined using the χ² test, the Dice index, and Spearman’s rank correlation coefficient; significant positive and negative pairwise species associations were detected. Based on the species association and the light requirements of the tree species, an optimal species arrangement was determined to support enrichment planting for restoring natural degraded forests. It is expected that the results of this study will contribute to the restoration of natural degraded forests in subtropical China.

Direct adoption of nationwide generalized biomass models often causes systematic prediction errors when applied to forest biomass estimation in Hunan Province. This study developed localized multi–component compatible biomass model systems for Chinese fir, poplar and oak based on 461 felled–tree specimens sampled from 14 administrative cities of Hunan Province during 2022–2023. Individual allometric equations were first fitted via weighted least squares, and hierarchical compatible model structures were further established by algebraic component summation and proportional scaling under simultaneous equation constraints. The final total–biomass compatible equations were: Chinese fir: $B_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}=0.065662D^{1.750420}{H}^{0.780381}+0.024760{(D}^2{H)}^{0.774387}$; Poplar: $B_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}=0.046471D^{2.171796}{H}^{0.462424}+0.013116·{(D}^2{H)}^{0.822310}$; Oak: $B_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}=0.0927573{\left(D^{2}H\right)}^{0.89013}+0.018969{\left(D^{2}H\right)}^{0.89162}$. R² values for total and aboveground biomass exceeded 0.92 and prediction accuracy exceeded 96%, with TRE within ±3% for all species. Root biomass models reached prediction accuracy higher than 78% with R² > 0.78. Stem, wood and bark sub–models achieved distinctly better fitting performance than branch and foliage sub–models, and strict hierarchical compatibility was guaranteed among total and component biomass predictions. Calibrated exclusively with local field data, these models effectively eliminate regional systematic estimation bias and only require readily measurable DBH and tree height as input variables, serving as reliable tools for regional forest biomass assessment and routine forest resource monitoring across Hunan Province.