Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) is a fast-growing evergreen conifer with high-quality timber and is an important reforestation and commercial tree species in southern China. Planting density affects the productivity of Chinese fir plantations. To study the effect of five different planting densities and soil depth on soil nutrient contents of a mature C. lanceolata plantation, the soil nutrient contents (soil depths 0–100 cm) of 36-year-old mature Chinese fir plantations under five different planting densities denoted A (1667 trees·ha⁻¹), B (3333 trees·ha⁻¹), C (5000 trees·ha⁻¹), D (6667 trees·ha⁻¹), and E (10,000 trees·ha⁻¹) were measured in Pingxiang county, Guangxi province, China. Samples were collected from the soil surface down to a one meter depth from each of 45 soil profiles, and soil samples were obtained at 10 different soil depths of 0–10, 10–20, 20–30, 30–40, 40–50, 50–60, 60–70, 70–80, 80–90, and 90–100 cm. Twelve soil physical and chemical indicators were analyzed. The results showed that: (1) as planting density increased, the organic matter, organic carbon, total N and P, available N, effective Fe, and bulk density decreased. Soil pH, total K, and effective K increased with increasing planting density. Planting density did not significantly influence the exchangeable Ca and Mg. (2) Soil organic matter; organic carbon; total N and P; effective N, P, and K; exchangeable Ca and Mg; effective Fe content; and bulk density decreased with increasing soil depth. This pattern was particularly evident in the top 30 cm of the soil. (3) Excessively high planting density is not beneficial to the long-term maintenance of soil fertility in Chinese fir plantations, and the planting density of Chinese fir plantations should be maintained below 3333 stems·ha⁻¹ (density A or B) to maintain soil fertility while ensuring high yields.

Understanding changes in the distribution patterns and diversity of soil microbial communities from the perspectives of age-related changes, seasonal variations, and the interaction between the two factors can facilitate the management of plantations. In Chinese fir plantations, we collected soils from different depths in over-mature forests, mature forests, near-mature forests, middle-aged forests, and young forests in summer, autumn, and winter in China’s subtropical regions. As the forests developed, bacterial and fungal communities’ diversity changed, reached a minimum value at near-mature forests, and then increased in mature forests or over-mature forests. Near-mature forests had the lowest topological properties. The Shannon index of microbial communities varied with seasonal changes ( P < 0.05). Bacterial and fungal community composition at genus level was more closely related to temperature indicators (including daily average temperature, daily maximum temperature, and daily minimum temperature) ( P < 0.01, 0.5554 < R ² 0.05, 0.0321 < R ² <0.6773). Bacteria were clustered by season and fungi were clustered by forest age. We suggested that extending the tree cultivation time of plantations could promote microbial community recovery. In addition, we found some species worthy of attention, including Bacteroidetes in autumn in over-mature forests, and Firmicutes in summer in young forests. IMPORTANCE: Chinese fir [ Cunninghamia lanceolata (Lamb.) Hook] is an important fast-growing species with the largest artificial forest area in China, with the outstanding problems of low quality in soil. Soil microorganisms play a crucial role in soil fertility by decomposing organic matter, optimizing soil structure, and releasing essential nutrients for plant growth. In order to maintain healthy soil quality and prevent nutrient depletion and land degradation, it is crucial to understand the changes of soil microbial composition and diversity. Our study determined to reveal the change of soil microbial community from stand age, season, and the interaction between the two aspects, which is helpful to understand how interannual changes in different years and seasonal changes in one year affect soil fertility restoration and sustainable forest plantation management. It is a meaningful exploration of soil microbial communities and provides new information for further research.

Six candidate height-diameter models were used to analyze the height-diameter relationships. The common methods for estimating the height-diameter models have taken the classical (frequentist) approach based on the frequency interpretation of probability, for example, the nonlinear least squares method (NLS) and the maximum likelihood method (ML). The Bayesian method has an exclusive advantage compared with classical method that the parameters to be estimated are regarded as random variables. In this study, the classical and Bayesian methods were used to estimate six height-diameter models, respectively. Both the classical method and Bayesian method showed that the Weibull model was the “best” model using data1. In addition, based on the Weibull model, data2 was used for comparing Bayesian method with informative priors with uninformative priors and classical method. The results showed that the improvement in prediction accuracy with Bayesian method led to narrower confidence bands of predicted value in comparison to that for the classical method, and the credible bands of parameters with informative priors were also narrower than uninformative priors and classical method. The estimated posterior distributions for parameters can be set as new priors in estimating the parameters using data2.

Stand mortality models play an important role in simulating stand dynamic processes. Periodic stand mortality data from permanent plots tend to be dispersed, and frequently contain an excess of zero counts. Such data have commonly been analyzed using the Poisson distribution and Poisson mixture models, such as the zero-inflated Poisson model (ZIP), and the Hurdle Poisson model (HP). Based on mortality data obtained from sixty Chinese pine (Pinus tabulaeformis) permanent plots near Beijing, we added the random-effects to the Poisson mixture models. Results showed that the random-effects in the ZIP model was not convergent, and HP mixed-effects model performed better in modeling stand mortality than the Poisson fixed-effects model, the Poisson mixed-effects model, the ZIP fixed-effects model and the HP fixed-effects model. Moreover, the HP model accounts for two sources of dispersion, the first accounting for extra zeros and the second accounting to some extent for the dispersion due by individual heterogeneity in the positive set. We also found that stand mortality was negatively related to stand arithmetic mean diameter and positively related to dominant height.

As the dominant height of the stand at the baseline age, the site index is an important index to evaluate site quality. However, due to the variability of environmental factors, the growth process of the dominant height of the same tree species was variable in different regions which influenced the estimation results of the site index. In this study, a methodology that established site index modeling of larch plantations with site types as a random effect in northern China was proposed. Based on 394 sample plots, nine common base models were developed, and the best model (M8) was selected (R² = 0.5773) as the base model. Moreover, elevation, aspect, and slope position were the main site factors influencing stand dominant height through the random forest method. Then, the three site factors and their combinations (site types) were selected as random effects and simulated by the nonlinear mixed-effects model based on the model M8. The R² values had raised from 0.5773 to 0.8678, and the model with combinations (94 kinds) of three site factors had the best performance (R² = 0.8678). Considering the model accuracy and practical application, the 94 combinations were divided into three groups of site types (3, 5, and 8) by hierarchical clustering. Furthermore, a mixed-effects model considering the random effects of these three groups was established. All the three groups of site types got a better fitting effect (groups 3 R² = 0.8333, groups 5 R² = 0.8616, groups 8 R² = 0.8683), and a better predictive performance (groups 3 R² = 0.8157, groups 5 R² = 0.8464, groups 8 R² = 0.8479 for 20 percent of plots randomly selected per group in the calibration procedure) using the leave-one-out cross-validation approach. Therefore, groups 5 of site types had better applicability and estimation of forest productivity at the regional level and management plan design.