Abstract
Soil microbial biomass and microbial entropy are used as important indicators of soil quality. However, the effects of forest-stand types remain poorly understood. This study focused on three stands of Cryptomeria japonica var. sinensis (CJ), Liquidambar formosana (LF), and their mixed forests (CL) in Guizhou Province, China. Soil samples were collected from three depths to investigate variations in soil microbial biomass C, N, P (MBC, MBN, MBP), as well as microbial entropy C, N, P (qMBC, qMBN, qMBP) among different forest stands. Additionally, the influence of soil organic C (SOC), total N (TN), total P (TP), and their stoichiometry, along with soil microbial C:N:P stoichiometry and soil-microbial stoichiometric imbalances on soil microbial biomass and microbial entropy are analyzed. The variance analysis revealed, compared to pure stands, the mixed forest exhibited significantly higher MBC (38.84%), MBC stocks (46.72%), MBC/MBN (52.23%), MBC/MBP (52.23%), and qMBC (23.49%; p < 0.05). Pure stand LF showed approximately 30% higher soil microbial stoichiometric imbalances (C/N(imb), C/P(imb), and N/P(imb)) than the other two stand types (p < 0.05). While the pure CJ stand exhibited significantly higher qMBN and qMBP (19.62% and 17.26%, respectively; p < 0.05). MBC, MBN, MBP, and their storage decreased significantly with increasing soil depth (p < 0.05), no significant effect on microbial stoichiometric ratios or microbial entropy. Correlation and redundancy analyses demonstrated that MBC, MBN, and MBP were highly significantly positively correlated with SOC, TN, and TP contents (p < 0.01), whereas qMBC and qMBN exhibited highly significant negative correlations with SOC, TP, SOC/TP, TN/TP, C/P(imb), and N/P(imb) (p < 0.01). Soil TP and MBC/MBP were identified as the primary factors influencing soil microbial biomass variation, with explanatory rates of 42.8% and 14.8%, respectively. Furthermore, C/N(imb) and C/P(imb) emerged as key determinants affecting microbial entropy dynamics, accounting for 31.5% and 14.2% of the observed variation, respectively. This study provided valuable data and insights for developing mixed forest management strategies in karst areas.