Scaling effects of stand structure characteristics and carbon stock estimation in subtropical vegetation: An example from Zhejiang Province, China

• Compared stand structure and carbon stock metrics across four subtropical forest types (EBF, DBF, CBMF, CF) using 400 m², 225 m², and 100 m² plots • Demonstrated a significant positive correlation between forest mixing degree and tree carbon stock, with smaller plots underestimating this relationship’s strength. • In coniferous forest, the use of 100 m² plots can markedly improve survey efficiency while maintaining acceptable precision In subtropical forest resource inventories, the plot-based sampling method is the primary approach for obtaining stand-structure and carbon-stock information. However, for different forest types, there is still no clear consensus on how to select an optimal plot size that balances survey effort with data precision. This study addressed this gap by establishing fixed sample plots of 400 m², 225 m², and 100 m² within four representative forest types in Zhejiang Province, China: evergreen broadleaved forest (EBF), deciduous broadleaved forest (DBF), conifer-broadleaf mixed forest (CBMF), and coniferous forest (CF). We conducted field measurements of structural metrics including stand volume, mixing degree, aggregation index and competition index, as well as tree carbon stock and soil organic carbon stock. The coefficients of variation of each index and the minimum number of sample plots were calculated at different land scales. Our results indicate that different types of subtropical forests exhibit distinct community structural characteristics, leading to significant differences in their sensitivity to reductions in plot size. In CF, a reduced plot size of 100 m² effectively lowers survey costs while maintaining acceptable estimation accuracy for tree carbon stocks. As forest types approach the climax subtropical community, a significant positive correlation emerges between stand mixing degree and tree carbon stock; however, reducing plot size tends to underestimate the strength of this relationship. The results of this study provide a scientific basis for optimizing plot size selection in forest resource surveys of different forest types in subtropical regions.