Xiao Zhou

Strip cutting has emerged as a new, efficient, and mechanized bamboo forest management model. To shorten the recovery period in strip cutting management, improve bamboo resource quality, prevent soil degradation, and achieve sustainable management, we selected on-year pure Moso bamboo (Phyllostachys edulis) forests for an 8-meter-wide strip cut. Three fertilization methods were applied in a complete two-factor experiment, including spreading, cave, and bamboo stump fertilization, at three fertilization dosages of 600, 900, and 1200 kg/ha (N:P:K = 3.5:1:2). We investigated the effects of different fertilization treatments on the biomass of new bamboo shoots and soil fertility to provide a reference for identifying the optimal fertilization scheme. The results showed that fertilization treatment increased the individual stand biomass of new Moso bamboo shoots, with a decreasing trend in the proportion of branches and leaves and an increasing proportion of culms in biomass allocation. Fertilization treatment significantly increased the total nitrogen, phosphorus, and potassium and available phosphorus contents in the soil. Overall, fertilization at 900 kg/ha using the spreading method showed the best results in promoting individual plant biomass recovery (5% increase in culm proportion and 4.12 kg increase in biomass per plant) and restoring soil fertility (increase the contents of TN, TP, TK, and AP in the entire soil layer) after strip cutting Moso bamboo forests, which addresses these pertinent issues in the strip cut management model.

Microclimate ecology is attracting renewed attention because of its fundamental importance in understanding how organisms respond to climate change. A number of hot issues can be investigated in desert ecosystems , including the relationship between species distribution and environmental gradients (e.g., elevation, slope, topographic convergence index, and solar insolation). Species distribution models (SDMs) can be used to understand these relationships. We used the data acquired from the important desert plant Nitraria tangutorum Bobr. communities and desert topographic factors extracted from LiDAR (Light detection and ranging) data of one square kilometers in the Inner Mongolia region of China to construct the SDMs. We evaluated the performance of the SDMs constructed with both the variants of the parametric and nonpara-metric algorithms (bioclimatic Modelling (BIOCLIM), Domain, Mahalanobi, generalized linear model (GLM), generalized additive model (GAM), random forest (RF), and support vector machine (SVM)). The area under the receiver operating characteristic curve was used to evaluate the algorithms. The SDMs constructed with RF appeared to be the best based on the area under the curve (0.7733). We also generated Nitraria tangu-torum Bobr. distribution maps with the constructed SDMs and the suitable habitat area of the Domain model. Based on the suitability map, we conclude that Nitraria tangutorum Bobr. is more suited to the southern part with a slope of 0-20 degree at an elevation of approximately 1010 m. This is the first attempt of modelling the effects of topographic heterogeneity on desert species distribution on a small scale. The presented SDMs will have important applications for predicting species distribution and will be useful for preparing conservation and management plans for desert ecosystems on a small scale.

Improved knowledge of nutrient removals from harvesting is essential for the sustainable management of Moso bamboo forests. In particular, the growth and development of bamboo forests are considered to meet the increasing demand for biomass energy. In this study, plots with an 8 m width strip cut (SC) after five years of restoration were selected as the research object and the traditional management forest (CK) as the control. The nutrient content and biomass of Moso bamboo at different ages were studied and the nutrient storage was calculated. The results showed that five years after cutting, the soil nutrient content in different soil layers and the aboveground nutrient storage reached the same level as the CK. The nutrient ranking of each organ of Moso bamboo was nitrogen > potassium > phosphorus. The phosphorus content of bamboo culm in CK was significantly higher than that in SC at different ages (p < 0.05). The biomass accumulation of each organ of Moso bamboo was culm > branch > leaf. In addition, the relationship between nutrient storage and DBH and density in SC is a simple power form, and the coefficient of determination of most computational models is above 0.9. Our results demonstrated that strip cutting is more economical than traditional management, but nutrient addition is needed to improve the quality of new bamboo trees at the initial stage of recovery in consideration of long-term productivity maintenance.

Understanding fine root characteristics in relation to soil properties of bamboo–broadleaf mixed forests may help optimize belowground production management and ecological functions in mixed-forest ecosystems. In this study, we compared four different bamboo–broadleaf mixed forests: Castanopsis chinensis (Sprengel) Hance with moso bamboo (CCB), Alniphyllum fortunei (Hemsl.) Makino with moso bamboo (AFB), Choerospondias axillaris (Roxb.) Burtt and Hill with moso bamboo (CAB), and Castanopsis fargesii Franch with moso bamboo (CFB), and analyzed their effects on the traits of fine roots of moso bamboo, soil nutrient contents, and enzyme activities. In January 2022, fine root and soil samples from four different mixed bamboo–broadleaf forests were collected from a subtropical region of Fujian Province, China. Results showed that CAB significantly increased fine root biomass (FRB) and root length density (RLD); however, specific root length (SRL) was only in the 0–20 cm soil layer. Specific surface area (SSA) was significantly reduced in the CCB in the 0–20 cm and 20–40 cm soil layers. The total phosphorous (TP) and total potassium (TK) contents of AFB and CAB were significantly increased (p < 0.05), and the alkali-hydrolyzable nitrogen (AN) content was significantly increased by CCB in the 0–20 cm soil layer (p < 0.05). Additionally, CFB increased the activities of acid phosphatase (ACP) and catalase (CAT) but decreased the activity of sucrase (SC). Principal component analysis showed that fine root traits (FRB, RLD, SRL, and SSA) were not only positively associated with soil organic carbon (SOC), total nitrogen (TN) and available potassium (AK) but also associated with urease (UE) and CAT. Therefore, belowground interactions between different species have a significant impact on the characteristics of fine roots and soil in bamboo–broadleaf mixed forests.

Degraded bamboo shoots (DBS) constitute an important variable in the carbon fixation of bamboo forests. DBS are useful for informed decision making in bamboo forests. Despite their importance, studies on DBS are limited. In this study, we aimed to develop models to describe DBS variations. By using DBS data from 64 plots of Yixing forest farm in Jiangsu Province, China, a mixed-effects model was constructed, including block-level random effects. We evaluated the potential impact of several variables on DBS. The number of bamboo shoots (NBS), mean height to crown base (MHCB), hydrolytic nitrogen (HN), and available potassium (AK) significantly contributed to the model. By introducing the block-level random effect in the logistic model, the fitting statistics were significantly improved. The model showed that there were increased DBS in bamboo stands with decreased MHCB and AK, whereas DBS decreased with decreasing NBS and HN. The application of K fertilizer reduced the number of DBS during the emergence stage. By adjusting these factors, the number of DBS in bamboo forests can be reduced, which provides a theoretical basis for increasing the biomass of bamboo forests. It can also provide an important basis for studying the carbon sink characteristics of bamboo forests and help to formulate more effective bamboo forest management plans.

Moso bamboo (Phyllostachys edulis) is a critical forest resource in subtropical China, and reasonable cutting management of moso bamboo forests is essential for improving the productivity of bamboo forests, increasing the income of farmers, and improving the ecological environment. Therefore, we set up sample plots with different cutting widths at the Yixing Forest Farm in Jiangsu Province in December 2017. Moso bamboo growth surveys and soil sampling were conducted in May 2018 to study the effects of different cutting widths on the growth and nutrient content of moso bamboo forests. Our results indicate that strip cutting had significant effects on degraded bamboo shoots, the number of new bamboos, and their ratios. Soil elements showed surface aggregation, and cutting increased the soil nutrient content. Principal component analysis showed that stand characteristics (diameter at breast height and number of new bamboo shoots) were positively associated with total phosphorus and available phosphorus but negatively correlated with available potassium, total potassium, and soil organic carbon. A cutting width of 8 m resulted in rich nutrient content, which is suitable for bamboo cultivation. These results will provide theoretical guidance for the formulation of scientific and reasonable strip cutting methods for moso bamboo forests.

Height to crown base (HCB) is an important variable used as a predictor of forest growth and yield. This study developed a nonlinear, mixed-effects HCB model through inclusion of plot-level random effects using data from 29 sample plots distributed across a state-owned Yixing forest farm in Jiangsu province, eastern China. Among several predictor variables evaluated in the analyses, bamboo height, canopy density, and total basal area of bamboo with a diameter larger than that of the subject bamboo individual contributed significantly to the HCB variations. The inclusion of random effects improved the prediction accuracy of the model significantly, indicating that the HCB variations within and across the sample plots were substantial. The model was localized using four sampling strategies, and the study identified that using two medium-sized bamboos by diameter at breast height per sample plot resulted in the smallest prediction error. This strategy, which would balance both measurement cost and potential error, may be applied to estimate the random effects and localization of the nonlinear mixed-effects HCB model for moso bamboo in eastern China.

Moso bamboo is characterized by its fast growth and high yield and is important as a carbon sink species. Therefore, understanding the biomass distribution of its components is crucial. Based on the measured individual biomass data of 66 Phyllostachys heterocycla cv. Pubescens plants in the Yixing state-owned forest in Jiangsu Province, nonlinear simultaneous equations with measurement errors were constructed using nonlinear error-in-variable models (NEIVM) (one step, two step) and nonlinear seemingly unrelated regression (NSUR). Variables affecting biomass were evaluated, including diameter at breast height (DBH), bamboo height (H), height to crown base (HCB), node length at DBH (NL), base diameter (BD), and bamboo age (A). DBH, H, and HCB had significant effects on the biomass of each component. They were used to construct a one-predictor system using DBH, a two-predictor system using DBH and H, and a three-predictor system using DBH, H, and HCB. Regardless of the number of variables used, the fitting accuracy of the NEIVM one-step method exceeded that of the two-step method, and that of NEIVM exceeded that of NSUR estimation. As a system using three predictive variables is better than other systems, we recommend using the one-step NEIVM method for Moso bamboo biomass estimation.

The forest mortality models developed so far have ignored the effects of spatial correlations and climate, which lead to the substantial bias in the mortality prediction. This study thus developed the tree mortality models for Prince Rupprecht larch (Larix gmelinii subsp. principis-rupprechtii), one of the most important tree species in northern China, by taking those effects into account. In addition to these factors, our models include both the tree—and stand—level variables, the information of which was collated from the temporary sample plots laid out across the larch forests. We applied the Bayesian modeling, which is the novel approach to build the multi-level tree mortality models. We compared the performance of the models constructed through the combination of selected predictor variables and explored their corresponding effects on the individual tree mortality. The models precisely predicted mortality at the three ecological scales (individual, stand, and region). The model at the levels of both the sample plot and stand with different site condition (block) outperformed the other model forms (model at block level alone and fixed effects model), describing significantly larger mortality variations, and accounted for multiple sources of the unobserved heterogeneities. Results showed that the sum of the squared diameter was larger than the estimated diameter, and the mean annual precipitation significantly positively correlated with tree mortality, while the ratio of the diameter to the average of the squared diameter, the stand arithmetic mean diameter, and the mean of the difference of temperature was significantly negatively correlated. Our results will have significant implications in identifying various factors, including climate, that could have large influence on tree mortality and precisely predict tree mortality at different scales.