Patterns of tree species richness in Southwest China.

As a region known for its high species richness, southwest China plays an important role in preserving global biodiversity and ensuring ecological security in the Yangtze, Mekong, and Salween river basins. However, relatively few studies focus on the response of tree species richness to climate change in this part of China. This study determined the main tree species in southwest China using the Vegetation Map of China and the Flora of China. From simulations of 1970 to 2000 and three forecasts of future benign, moderate, and extreme climate warming anticipated during 2061 to 2080, this study used a maximum entropy model (MaxEnt) to simulate main tree species richness in southwest China. Regions with a peak species richness at intermediate elevations were typically dominated by complex mountainous terrain, such as in the Hengduan Mountains. Likewise, regions with the smallest richness were low-elevation areas, including the Sichuan Basin, and the high-elevation Sichuan-Tibet region. Annual precipitation, minimum temperature of the coldest month, temperature seasonality, and elevation were the most critical factors in estimating tree species richness in southwest China. During future 2061 to 2080 climate scenarios, tree species tended to migrate towards higher elevations as mean temperatures increased. For climate change scenarios RCP2.6-2070 (benign) and RCP4.5-2070 (moderate), the main tree species richness in the study area changed little. During the RCP8.5-2070 extreme scenario, tree species richness decreased. This study provides useful guidance to plan and implement measures to conserve biodiversity.

Fine-Scale Quantification of Absorbed Photosynthetically Active Radiation (APAR) in Plantation Forests with 3D Radiative Transfer Modeling and LiDAR Data.

Quantifying the relationship between light and stands or individual trees is of great significance in understanding tree competition, improving forest productivity, and comprehending ecological processes. However, accurately depicting the spatiotemporal variability of light under complex forest structural conditions poses a challenge, especially for precise forest management decisions that require a quantitative study of the relationship between fine-scale individual tree structure and light. 3D RTMs (3-dimensional radiative transfer models), which accurately characterize the interaction between solar radiation and detailed forest scenes, provide a reliable means for depicting such relationships. This study employs a 3D RTM and LiDAR (light detection and ranging) data to characterize the light environment of larch plantations at a fine spatiotemporal scale, further investigating the relationship between absorbed photosynthetically active radiation (APAR) and forest structures. The impact of specific tree structural parameters, such as crown diameter, crown area, crown length, crown ratio, crown volume, tree height, leaf area index, and a distance parameter assessing tree competition, on the daily-scale cumulative APAR per tree was investigated using a partial least squares regression (PLSR) model. Furthermore, variable importance in projection (VIP) was also calculated from the PLSR. The results indicate that among the individual tree structure parameters, crown volume is the most important one in explaining individual tree APAR (VIP = 4.19), while the competition from surrounding trees still plays a role in explaining individual tree APAR to some extent (VIP = 0.15), and crown ratio contributes the least (VIP = 0.03). Regarding the spatial distribution of trees, the average cumulative APAR per tree of larch plots does not increase with an increase in canopy gap fraction. Tree density and average cumulative APAR per tree were fitted using a natural exponential equation, with a coefficient of determination (R2 = 0.89), and a small mean absolute percentage error (MAPE = 0.03). This study demonstrates the potential of combining 3D RTM with LiDAR data to quantify fine-scale APAR in plantations, providing insights for optimizing forest structure, enhancing forest quality, and implementing precise forest management practices, such as selective breeding for superior tree species.