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Reliable tree species diversity and carbon stock data are crucial for establishing a baseline and aiding monitoring and management decision-making to ensure ecosystem functions and services, including carbon management. However, such data are not available for many primary forests, especially in developing countries like Nepal. For this study, we generated primary baseline data on tree species diversity, biomass, and carbon stock for forests under different management regimes in one of the critical landscapes of western Nepal. We sampled 94 clusters of concentric circular sample plots placed systematically at the nodes of 3 km by 3 km square grids in biological corridors (managed by community forest user groups) and a national park's buffer zones (managed by buffer zone user groups) to study the tree species composition and estimate the carbon stock. The number of tree species and botanical families reported in different sites did not differ significantly. The basal area was found to be highest in Brahmadev Corridor where tree density per unit area was also highest. Our study sites showed a basal area ranging from 26.42 to 40.65 m2/ha, which is higher than previously reported from similar forests. The total biomass (302.12 to 496 tons/ha) and carbon stock (142 to 233.12 tons/ha), both being highest in Brahmadev Corridor, were within the range of comparable data and national averages. The number of stems in all sites showed the reverse J-shaped pattern, indicating a stable population and good species recruitment. The buffer zones had a large proportion of forest area with the highest carbon stock classes of 300–400 tons/ha. The basal area and diameter at breast height showed a significant positive correlation with carbon stock, indicating that the basal area is a key determinant of carbon stock. Our study provides disaggregated information on tree species diversity, the quality of forests in terms of stem density and size-class distribution, and carbon stock and its distribution in different carbon stock classes in two forest management regimes. In this sense, our study is the first of its kind in providing important insights into forest and carbon management because areas with different carbon stocks need different management prescriptions.

Tree height (H) and diameter at breast height (D) are key variables to calculate tree volume and biomass. We developed a height-diameter (H-D) model for Cinnamomum tamala by evaluating 18 nonlinear models. Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Square Error (RMSE), mean bias, Mean Absolute Error (MAE), graphical appearance, and biological logic were the criteria used to evaluate the predictive performance of the models. Gompertz model (M14) performed the best for predicting the total height of C. tamala trees with the least RMSE (1.742 m), mean bias (0.012 m), and MAE (1.342 m) and satisfied model assumptions and biological logic. Validation data ranked the Gompertz model as the best model with RMSE (1.546 m), mean bias (-0.106 m), and MAE (1.149 m). Despite the consistent performance of the Gompertz model, it tended to underestimate the height prediction for taller (dominant crown class) and larger trees. Further work on refitting and validation of the proposed model with data from a larger geographic area, wider-ranging sites, and stand conditions is recommended.