Biodiversity increased both productivity and its spatial stability in temperate forests in northeastern China

Emergy benefit and radiation effect of multi-dimensional service function of vegetation ecosystem

Vegetation, as a multi-type and multi-functional green energy, plays an important role in regional carbon emission reduction and carbon neutrality. This study carried out the concept of green and sustainable development in depth and constructed an emergy quantification methodology system for the multidimensional service functions of vegetation ecosystems consisting of forests and grasslands based on the theory of emergy analysis and multidisciplinary integration methods. Using the theory of spatial correlation and breakpoints, we delineated the major ecological zones and investigated the radiation effects of typical regulating functions. Taking Luoyang, China, as an example, the results showed that the annual sequence of vegetation ecosystem service function (VES) emergy in Luoyang City showed a decreasing and then increasing trend with 2015 as the cut-off point. Early-stage Forest exploitation had profound effects, while increasing cultural benefits in later stages demonstrated national emphasis on forest research and conservation. The forest's high-quality ecological zone in Luoyang City could be found in the three southern counties of Luoning (LN), Luanchuan (LC), and Song (S). The radiation effect encompassed the entire city, resulting in an obvious impact with a total radiation of approximately 4.10E+20 sej. The high-quality ecological zone of the grassland did not appear until 2020 and is located in Yiyang (YY) county in central Luoyang. It benefited only the surrounding counties and had a total radiation of 1.32E+18sej. However, the development trend is optimistic. The spatial pattern of vegetation should be suitable for natural conditions, and the development strategy of localization as the driving force of the whole should be realized through the establishment of high-quality ecological zones, so as to promote harmonious coexistence between human and nature through green development.

Factors influencing productivity of pine-dominated stands in South Korea

Stand productivity research has mainly focused on increasing yield and has recently begun to examine changes in carbon storage. The Korean government is interested in finding ways to increase forest carbon capture to meet carbon neutrality requirements because approximately 63% of the land is covered by forests. In addition, 69% of these forests are older than 30 years old and their productivity and aboveground carbon storage rates are expected to decline. The purpose of this study was to examine the effect of quadratic mean diameter (QMD), stand basal area, site index, slope, climate (MAT and MAP), stand age, stand structural diversity, and stand composition on the productivity of aging Korean red pine-dominated stands. Based on the effects of these factors, we explored how to manage pine forests with the focus of increasing their productivity. Random forest regression was used for the analysis, and periodic basal area increment (PBAI) was used as the dependent variable of stand productivity. Our results show that the most influential factor on stand productivity was QMD. PBAI dramatically decreased from approximately 0.8 to 0.53 m²/ha·year as QMD increased up to 18 cm. Since diameter (QMD) increment is closely associated with changes in tree competition; increasing tree competition with increasing QMD and stand basal area may lead to decreases in PBAI owing to decreases in growth rate due to space and resource limitations and increases in mortality. PBAI decreased when basal area increased from 22 to 51.5 m²/ha. PBAI increased for site index values between 8 and 12.5 m and decreased for stand age values up to approximately 31 years. For climate factors, PBAI generally increased with increasing MAP and slightly increased as MAT increased up to approximately 11.2 °C and then decreased at higher MAT. PBAI initially increased with increasing slope values, decreased with values lower than 15°, and remained stable at slope values in the range of 16-34°. Stand structural diversity, which ranged from 1.32 to 1.62, exhibited a similar negative influence on PBAI associated with increasing stand density. With regard to pine composition, pine stands with a large proportion of pine basal area, showed higher productivity due to the simple stand structure resulting in better growth of shade intolerant pine. This study found that stand density increases with the development of pine stands and that density increases had negative influences on stand productivity. Collectively, our results suggest that stand density management is essential for increasing stand productivity and carbon sequestration in the Korean red pine-dominated stands of South Korea.

Tropical forest strata shifts in plant structural diversity-aboveground carbon relationships along altitudinal gradients

Aboveground carbon storage in forests can be influenced by both structural and compositional diversity of plant communities. However, the relative and interactive effects of structural and compositional diversity on multilevel aboveground carbon storage across forest strata and how these relationships vary with altitude and soil nutrients remain unclear. Using data obtained from 34 tropical forest plots (total area 8.5 ha) in Hainan Island, China, we analyzed the relationships between aboveground carbon at four levels (litter, understory, overstory, and whole-community) with structural diversity (diameter and height diversity) and compositional diversity (species diversity and evenness) in the understory and overstory. The direct and indirect effects of altitude, soil nutrients (total N and total P and N/P ratio), structural diversity, and compositional diversity on aboveground carbon were explored via Bayesian structural equation modeling. The results showed that structural diversity, rather than compositional diversity, in overstory stratum determined aboveground carbon. Specifically, overstory structural diversity was negatively associated with understory carbon, while positively associated with overstory and whole-community carbon. Furthermore, diversity‑carbon relationships were slightly affected by soil nutrients but strongly by altitude. Specifically, the relationship between overstory and whole-community carbon content with overstory tree height diversity weakened with altitude, while their relationship with overstory diameter diversity strengthened. Altitude directly and indirectly affected overstory tree height and diameter diversity through overstory species diversity, thereby reducing understory and increasing overstory and whole-community carbon. Altitude directly promoted litter carbon. We provide evidence that the effects of plant diversity on aboveground carbon storage are forest strata- and altitude-dependent. As overstory structural diversity plays a crucial role in storing aboveground carbon at all altitudes, we proposed that focusing on overstory structural diversity would be promising for predicting trends in how plant diversity affects aboveground carbon in response to climate change.

Quantifying the Effects of Stand and Climate Variables on Biomass of Larch Plantations Using Random Forests and National Forest Inventory Data in North and Northeast China

The accurate estimation of forest biomass is crucial for supporting climate change mitigation efforts such as sustainable forest management. Although traditional regression models have been widely used to link stand biomass with biotic and abiotic predictors, this approach has several disadvantages, including the difficulty in dealing with data autocorrelation, model selection, and convergence. While machine learning can overcome these challenges, the application remains limited, particularly at a large scale with consideration of climate variables. This study used the random forests (RF) algorithm to estimate stand aboveground biomass (AGB) and total biomass (TB) of larch (Larix spp.) plantations in north and northeast China and quantified the contributions of different predictors. The data for modelling biomass were collected from 445 sample plots of the National Forest Inventory (NFI). A total of 22 independent variables (6 stand and 16 climate variables) were used to develop and train climate-sensitive stand biomass models. Optimization of hyper parameters was implemented using grid search and 10-fold cross-validation. The coefficient of determination (R2) and root mean square error (RMSE) of the RF models were 0.9845 and 3.8008 t ha−1 for AGB, and 0.9836 and 5.1963 t ha−1 for TB. The cumulative contributions of stand and climate factors to stand biomass were >98% and <2%, respectively. The most crucial stand and climate variables were stand volume and annual heat-moisture index (AHM), with relative importance values of >60% and ~0.25%, respectively. The partial dependence plots illustrated the complicated relationships between climate factors and stand biomass. This study illustrated the power of RF for estimating stand biomass and understanding the effects of stand and climate factors on forest biomass. The application of RF can be useful for mapping of large-scale carbon stock.