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• Machine learning was applied to quantify the effects of stand, climate and soil on biomass of mixed forests with good accuracy. • Quantifying the importance of various environmental attribute factor to forest biomass depends on the combination of environmental variables and modeling methods. • Environmental variables including degree-days above 18 °C, bulk density and soil pH were most important predictors. Forest biomass is influenced by multiple environmental factors. Multi-source data on site-specific soil, climate, and stand factors are essential for stand biomass prediction. While previous studies concerning planted forest biomass at stand level highlighted the importance of environmental factors, few have been conducted on the effects of climate and soil on stand biomass estimation, especially for natural mixed forest with complicated stand structure. Machine learning (ML) algorithms provide new tools for examining the effects, but the applications are still limited. Therefore, the objectives of this paper were to develop ML models for stand biomass estimation with stand and multiple environmental predictors, and quantify the relative importance of environmental variables. Tree above- and below-ground biomass at stand level was set as output variable with the random forest (RF) and boosted regression tree (BRT) regression methods. The data were from 286 sample plots from the National Forest Inventory (NFI) in Jilin Province, northeast China. The results showed that models with the inclusion of stand, soil, and climate variables explained > 89% of forest biomass variations. BA (stand basal area) and H (stand average height) were the most important stand variables, DD18 (degree-days above 18 °C) was the most important climate variable, and bdod (bulk density) and pH were the most important soil variables. Relative importance of climate and soil factors was 0.5% to 5.1% and 1.2% to 4.1%, respectively, which were weak but important predictors of stand biomass. In the RF model, climate variables were more important than soil variables, but the opposite in the BRT models. We concluded that above forest stand, climate and soil factors were significant input variables for regional forest biomass mapping using ML. [ABSTRACT FROM AUTHOR]

• Mass ratio effect, ecological niche complementarity and green soup hypotheses simultaneously contributed to biodiversity-multifunctionality relationship, but dominant one varied with the threshold (multifunctionality level). • Generally, green soup hypothesis was the dominant mechanism behind biodiversity-multifunctionality relationship for natural temperate forests in northeast China. • Attention should be paid to maintaining high functional and structural diversity and large biomass to maximize the multifunctionality. Biodiversity plays a key role in maintaining and regulating ecosystem multifunctionality (EMF). However, there remains a knowledge gap on the ecological mechanisms behind the relationship between biodiversity and EMF (BEMF), and how these mechanisms vary with EMF gradients, especially at large scale. We used the dataset of 2224 permanent sample plots in Jilin Province, northeast China to test three BEMF mechanisms and their variations with EMF, including mass ratio, ecological niche complementarity and the green soup hypotheses. Biodiversity metrics were taxonomic, structural, phylogenetic, functional diversity and functional identity. EMF was composed of five services: water retention, soil conservation, carbon storage, stand productivity and soil fertility maintenance. EMF was measured using both the averaging (as the average of the standardized values of all services) and threshold (as the number of services passed a threshold) approach. Totally 23 piecewise structural equation models (pSEMs) were developed for EMF with the threshold level ranging from 5% to 95% by an interval of 5%. Results showed that stand biomass had strong positive effect on EMF. Stand structural diversity had significant direct and indirect effects on EMF, but taxonomic, functional diversity and functional identity only had significant direct and indirect effects on EMF at low thresholds. Stand age had significant direct effects on EMF and indirectly enhanced EMF through stand structural diversity and biomass. Our results provided strong evidence that EMF was driven simultaneously by mass ratio, niche complementarity and green soup hypotheses, but the dominant mechanism was dependent on EMF level. Generally, the green soup hypothesis played more important role in explaining EMF. Recommendations for maintaining high functional and structural diversity and large biomass for maximizing EMF were discussed for forest management. [ABSTRACT FROM AUTHOR]