Understanding the responses of C:N:P stoichiometry and nutrient resorption to thinning is essential to evaluate the effects of management practices on biogeochemical cycling in plantation forest ecosystems. However, nutrient resorption and C:N:P stoichiometry do not always respond in the same way to various thinning intensities, and the underlying mechanisms are not well understood. In this study, we aimed to examine the mechanisms underlying the impacts of thinning on C:N:P stoichiometry in a Pinus massoniana plantation, focusing on interactions among soils, plant tissues (leaves and litter), and soil properties. We conducted four different thinning treatments to determine the effects of thinning on the C:N:P stoichiometric ratios in leaves, litter, and soil in a Pinus massoniana plantation ecosystem. Thinning significantly increased the C, N, and P content of leaves, litter, and soil (p < 0.05). The effects of thinning on C:N:P stoichiometry varied strongly with thinning intensity. Specifically, thinning significantly decreased all C:N:P stoichiometry except leaf N:P and litter C:N (p < 0.05). The N resorption efficiency (NRE) showed no significant change, but thinning significantly decreased the P resorption efficiency (PRE, p < 0.05). This suggests that thinning has inconsistent impacts on N and P cycling in Pinus massoniana plantations. In addition, these different responses suggest that soil physicochemical processes play a crucial role in regulating the effects of thinning. Thinning intensity regulates the biogeochemical cycles of C, N, and P in Pinus massoniana plantation ecosystems by affecting nutrient resorption and soil physicochemical processes. The inconsistent results obtained can be attributed to the complexities of stand environments and the redistribution of site resources following thinning. Therefore, incorporating the effects of thinning intensity into nutrient cycling models may improve predictions related to achieving long-term forest management strategies.

A tree skeleton reflects the geometric and structural characteristics of a tree. Terrestrial laser scanning (TLS) provides an effective means to construct tree skeletons that can capture the surface and topological features of trees. However, it is difficult to construct a tree skeleton located at the geometric centre of the tree with a detailed hierarchy of branches because of the natural intricate topology of the tree and the defects of the tree point cloud scanned by TLS. In this study, we presented a tree-skeletonization method based on density-based spatial clustering of applications with noise (DBSCAN) using TLS data. First, outliers were removed using DBSCAN, and the point-traversal order of each point was recorded. Next, a tree point cloud was divided into several tree slices using contour planes, and several tree segments were obtained by applying DBSCAN to each tree slice. Tree skeleton points were retrieved from each tree segment after the point-inversion transformation. Then, the adjacent relationship between skeleton points and the flow weight of each skeleton point was calculated based on the point-traversal order. After that, the skeleton points were classified into stems and different levels of branch points using the flow weights of the skeleton points, and the branch hierarchies were identified. Finally, the tree skeleton was optimized using the angle consistency. The positional deviation $d_p$ and directivity deviation $d_d$ were presented in this study to verify the validity of the constructed tree skeleton. From the visualization results, the constructed tree skeleton effectively reflected the geometrical structure and branch hierarchies of the tree. The averages of $d_p$ and $d_d$ were 0.418 cm and 8.474 degrees, respectively. The results demonstrated the validity of the presented method. It will help improve the visualization and accurate measurement of trees.

The long-lasting outbreak of the pine shoot beetle (PSB, Tomicus spp.) threatens forest ecological security. Effective monitoring is urgently needed for the Integrated Pest Management (IPM) of this pest. UAV-based hyperspectral remote sensing (HRS) offers opportunities for the early and accurate detection of PSB attacks. However, the insufficient exploration of spectral and structural information from early-attacked crowns and the lack of suitable detection models limit UAV applications. This study developed a UAV-based framework for detecting early-stage PSB attacks by integrating hyperspectral images (HSIs), LiDAR point clouds, and structure from motion (SfM) photogrammetry data. Individual tree segmentation algorithms were utilized to extract both spectral and structural variables of damaged tree crowns. Random forest (RF) was employed to determine the optimal detection model as well as to clarify the contributions of the candidate variables. The results are as follows: (1) Point cloud segmentation using the Canopy Height Model (CHM) yielded the highest crown segmentation accuracy (F-score: 87.80%). (2) Near-infrared reflectance exhibited the greatest decrease for early-attacked crowns, while the structural variable intensity percentile (int_P50-int_P95) showed significant differences (p < 0.05). (3) In the RF model, spectral variables were predominant, with LiDAR structural variables serving as a supplement. The anthocyanin reflectance index and int_kurtosis were identified as the best indicators for early detection. (4) Combining HSI with LiDAR data obtained the best RF model accuracy (classification accuracy: 87.31%; Kappa: 0.8275; SDR estimation accuracy: R² = 0.8485; RMSE_cv = 3.728%). RF integrating HSI and SfM data exhibited similar performance. In conclusion, this study identified optimal spectral and structural variables for UAV monitoring and improved HRS model accuracy and thereby provided technical support for the IPM of PSB outbreaks.

Facilitation can drive the successional dynamics and change the restoration trajectory of degraded forests. However, the relative importance of facilitation by tree species after variable retention harvesting is unclear. We used a field experiment to evaluate the effect of two facilitator species, Castanopsis fissa (C. fissa) and Manglietia glauca (M. glauca), managed with variable retention harvesting, on the development of two target species, Castanopsis hystrix (C. hystrix) and Erythrophloeum fordii (E. fordii), in a Masson pine (Pinus massoniana) monoculture. The following variables were measured for all of the four interplanted tree species: structural growth, regeneration, aboveground biomass accumulation, leaf area index, and soil conditions. The results indicate that the abundance, growth, and aboveground biomass were relatively greater in plots planted with C. fissa compared with M. glauca and that the target species performed best with 50% retention harvesting of C. fissa, with an improved establishment of both target species indicating a positive interaction. In addition, the regeneration, leaf area index and soil conditions differed between the two facilitators in the variable retention harvesting treatments because of the different intrinsic characteristic of the facilitators. In summary, our results imply that managers have considerable flexibility to employ various types of facilitation schemes coupled with different harvesting systems for successful short-term restoration within a monoculture.

The Species-area relationship is one of the core issues in community ecology and an important basis for scale transformation of biodiversity. However, the effect of scale on this relationship, together with the selection of an optimal species-area model for different sampling methods, is still controversial. This study is based on the data from two sampling areas of 40 km² in size, one in a Korean pine (Pinus koraiensis Sieb. et Zucc) broad-leaved mixed forest in Mt. Changbai and the other in Jiaohe, Jilin Province. The logarithmic, power, and logistic model were established on a scale of 10 km², 20 km², and 30 km², respectively, using a nested sampling plot and random sampling plot. The goodness of the species-area model was tested by the Akaike information criterion (AIC). The results show that the sampling method affected the relationship between species and area, and the data were fitted better under random sampling compared with nested sampling. The construction of the relationship between species and area was closely related to the upper limit of the sampling area size. On a small scale (10 km²), the data were fitted best with the logarithmic and logistic model, whereas the logistic model was the best fit on a medium (20 km²) and large scale (30 km²). We evaluated the scale dependence of species-area relationship in two forests with nested and random sampling methods. We further showed that the logistic model based on the random sampling plot can explain most soundly the species-area relationship in Jiaohe and Mt. Changbai. More studies are needed in other regions to develop models to optimize sampling designs for different forest types under different density constraints at different spatial scales, and for a more accurate estimation of forest dynamics under long-term observations.

Accurately describing the stem curve of precious tree species and estimating the quantity of various types of wood and their volume in the tropics can provide technical support for reasonable bucking. This study utilized Erythrophleum fordii, Castanopsis hystrix and Tectona grandis as study objects. Forty replicates of each species were used for a total of 120 individual trees. Their tape equations were constructed using simple tape equations, segmented taper equations and variable form taper equations. Statistical indicators were utilized to determine the best taper equation for the three types of precious tree species. A number of methods were compared and analyzed, including the index of correlation, the residual sum of squares, the mean prediction error, the variance of prediction errors and the root mean square error. Finally, a preliminary quantitative analysis was conducted to determine the trends of these three types of tree species. The result shows that the precision of the three predictions developed for each species is high, and, in particular, the segmented taper equations with optimized algorithms is the best. The tendency of the three species to vary was shown to be the highest for T. grandis in the range of 0.0 to 0.8 for its relative height, followed by E. fordii, while the variation of C. hystrix was the smallest. However, in the range of 0.8 to 1.0 relative height, the variation of Castanopsis hystrix was the largest, and the variation of both E. fordii and T. grandis were almost the same. Therefore, the segmented taper equations with optimization algorithms was recommended to fit the three types of tree species in the tropics. These types of equations can be used to estimate the stumpage and timber quantity and as a guide reasonable bucking for these three species.