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 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.

The impact of climate change and human activities on endangered plants has been a serious concern in forest ecology. Some Carpinus plants have become extinct. Thus, we need to pay more attention to the Carpinus plants that are not yet extinct but are endangered. Here, we employed the species distribution model (SDM) considering different climate change scenarios and human footprint to test the potential habitat changes of three Carpinus species (C. oblongifolia, C. tientaiensis, and C. purpurinervis) in the future. Our results showed that the mean diurnal range of temperature (MDRT), isothermality, mean temperature of wettest quarter, and human footprint were the most influential factors determining the distribution of C. oblongifolia. Precipitation seasonality (coefficient of variation), MDRT, and precipitation of driest quarter were the most important climatic factors affecting C. tientaiensis. The minimum temperature of the coldest month was the most important factor in the distribution of C. purpurinervis. Our results also showed that the three species had different adaptability and habitat change trends under the future climate change scenarios, although they belong to the same genus. The potential habitats of C. oblongifolia would expand in the future, while the potential habitats of C. tientaiensis and C. purpurinervis would decrease for the same period. The predicted changes of these three endangered species on temporal and spatial patterns could provide a theoretical basis for their conservation strategies.

Forest trees exhibit a large variation in the basal area increment (BAI), and the variation is attributed to the stand density, biodiversity, and stand spatial structure. Studying and quantifying the effect of these above variables on tree growth is vital for future forest management. However, the stand spatial structure based on neighboring trees has rarely been considered, especially in the mixed forests. This study adopted the random-forest (RF) algorithm to model and interpret BAI based on stand density, biodiversity, and spatial structure. Fourteen independent variables, including two stand density predictors, four biodiversity predictors, and eight spatial structure predictors, were evaluated. The RF model was trained for the whole stand, three tree species groups (gap, neutral, and shade_tolerant), and two tree species (spruce and fir). A 10-fold blocked cross-validation was then used to optimize the hyper-parameters and evaluate the models. The squared correlation coefficients (R²) for the six groups were 0.233 for the whole stand, 0.575 for fir, 0.609 for shade_tolerant, 0.622 for neutral, 0.722 for gap, and 0.730 for spruce. The Stand Density Index (SDI) was the most-important predictor, suggesting that BAI is primarily restricted by competition. BAI and species biodiversity were positively correlated for the whole stand. The stands were expected to be randomly distributed based on the relationship between the uniform angle index (W) and growth. The relationship between dominance (U) and BAI indicated that small trees should be planted around the light-demanding tree species and vice versa. Of note, these findings emphasize the need to consider the three types of variables in mixed forests, especially the spatial structure factors. This study may help make significant advances in species composition, spatial arrangement, and the sustainable development of mixed forests.

Increasing evidence shows that biodiversity–ecosystem functioning relationships (BEFs) become stronger as forests develop, but much of the evidence is drawn from experiments (less than 30 years). How the biodiversity effects vary with stand development stages remains largely unexplored. Using a large temperate forest dataset with 2392 permanent plots in northeastern China, we examined the relationships between biodiversity (i.e. tree species richness, functional diversity, and functional composition) and aboveground biomass (AGB) across different development stages of temperate forests (covering all stages from young to overmature forests). Specifically, the complementarity and mass‐ratio effects across different forest development stages were evaluated to elucidate emerging patterns that explain ecosystem functioning. We observed positive BEFs using both tree species richness and functional diversity, but these positive effects decreased with forest development. However, the effects of community‐weighted mean (CWM) on AGB showed two peaks in young and mature stands. Interestingly, the effects of CWM on AGB became larger than the effects of functional diversity after the forests developed to near‐mature/mature stands, indicating that BEFs are driven by mass‐ratio effects (i.e. dominant tree species) rather than niche complementarity in old stands. The high AGB in young stands was characterized by tree species with high resource acquisition ability, however, in old stands, it was associated with tree species with both high resource acquisition ability and conservative traits. Our findings indicate how the developmental stage influences the effects of biodiversity on ecosystem functioning in natural forests. The findings tentatively advocate for a mechanistic framework of BEFs covering all developmental stages of temperate forests, which could facilitate the formulation of effective strategies for enhancing ecosystem functioning at different development stages.