Fine roots play a prominent role in forest carbon flux, nutrient and water acquisition; however, information on fine roots remains scarce due to the limitation of measuring methods. In this study, a nested regression method was used to estimate the biomass and surface area of fine roots of individual Larix gmelinii trees that dominate northernmost China. Aboveground traits including leaf biomass, leaf area, stem volume and aboveground biomass were also investigated. In particular, the relationships between leaves and fine roots, in terms of biomass and area, were examined. The results revealed that allometric models of fine roots, total roots, and leaves consistently fit well with Adj. R² = 0.92–0.97. The root-shoot ratio at the individual tree level was approximately 0.28. There were robust positive linear correlations between absorption (fine root biomass, fine root surface area) and production (leaf biomass, leaf area) (Adj. R² = 0.95, p < 0.001). In conclusion, the close coupling between fine roots and leaves presented in this study provides support for the theory of functional equilibrium.

Accurate estimates of tree component and aboveground biomass strongly depend on robust and precise allometric equations. However, site-specific and suitable biomass equations are currently scarce for natural Larix gmelinii forests in the western Daxing’anling Mountains, northeastern China. This study aimed to evaluate the biomass allocation patterns within tree components and develop additive allometric biomass equations for species of L. gmelinii. A total of 58 trees were destructively sampled and measured for wood (inside bark), bark, branch and leaf biomass. For each component, we assessed the share of biomass allocated to different components by computing its ratio; we also tested two allometric equations based on diameter at breast height (dbh) alone, and dbh fitted with height (h) as independent variables. Seemingly unrelated regression methodology was used to fit an additive system of biomass allometric equations. We performed an independent dataset to evaluate the predictive ability of the best model system. The results revealed that wood biomass accounted for approximately 60% of the aboveground biomass. Wood and branch biomass ratios increased with increasing dbh, while a reverse trend was observed for bark and leaf biomass ratios. All models showed good fitting results with Adj.R² = 0.958–0.995. Tree dbh provided the lowest estimation errors in the regressions associated with branches and leaves, while dbh² × h generated the most precise models for stems (wood and bark). We conclude that these allometric equations will accurately predict biomass for Larix trees in the western Daxing’anling Mountains.

We developed two tree-ring width chronologies of Mongolian Scots pine (Pinus sylvestris var. mongolica) from the low elevation forest of the northern Daxing’anling Mountains of Inner Mongolia. Although the two chronologies come from different sampling sites, significant correlations existed among the chronologies (r = 0.318), and the first principal component (PC1) accounted for 65.9% of total variance over their common period 1792–2016. Climate-growth correlation analysis revealed that the previous June and July Palmer drought severity index (PDSI_p6-7) was the main climatic factor controlling tree-ring growth. Using a linear regression model, we reconstructed the PDSI_p6-7 for the past 225 years (1792–2016). The reconstruction satisfied required statistical calibration and validation tests, and represented 38.6% of the PDSI variance recorded by instruments over the period 1955–2016. Six wet and five dry periods were revealed during these 225 years. The drought of 1903–1927 was the most severe drought in the study area in the last 225 years. Comparison with other tree-ring-based moisture-sensitive sequences from nearby regions confirmed a high degree of confidence in our reconstruction. The results of a spatial climate correlation analysis with a gridded PDSI dataset revealed that our reconstructions contained strong regional drought signals for the southern Stanovoy Range and the northern Daxing’anling Mountains. The power spectrum revealed the existence of significant frequency cycles, which may be linked to large-scale atmospheric-oceanic variability, such as the El Niño-Southern Oscillation, solar activity, and the North Atlantic Oscillation.

The biodiversity of litter can regulate carbon and nutrient cycling during mixed decomposition. It is common knowledge that the decomposition rates of mixed litters frequently deviate from those predicted for these component litter species. However, the direction and magnitude of the nonadditive effects on the degradation of mixed litters remain difficult to predict. Previous studies have reported that the different carbon fractions of leaf litters responded to litter mixture differently, which may help to explain the ambiguous nonadditive effect of diversity on bulk litter decomposition. Therefore, we conducted decomposition experiments on 32 litter mixtures from seven common tree species to test the responses of different carbon fractions to litter diversity in subtropical forests. We found that the overall mass loss of the mixed litter was faster than that estimated from single species. The relative mixing effects (RMEs) of different carbon fractions exhibited different patterns to litter diversity and were driven by different aspects of litter functional dissimilarity. Soluble carbon fractions decomposed more slowly than expected from single species, while lignin fractions decayed more quickly. Moreover, we found that the RMEs of bulk litter decomposition may be determined by the lignin fraction decomposition. Our findings further support that distinguishing the response of different carbon fractions to litter diversity is important for elucidating the nonadditive effects of total litter decomposition.

Afforestation is conducive to improving ecosystem service functions and ecosystem diversity in the Mu Us Sandy Land, however, the important attribute of biomass for Mongolian pine (Pinus sylvestris var. mongolica Litv.) plantations has yet to be accurately evaluated. This study aimed to develop additive allometric biomass equations for the species and evaluate biomass partitioning patterns within tree components. A total of 131 trees were measured for stem, branch, and leaf biomass by destructively sampling and tree climbing, with the latter as a supplement. For each biomass component, we tested three equations with the diameter at breast (D) alone, height (H) as additional, and diameter in combination with height (D²H) as predictors using the weighted least squared method. Weighted nonlinear seemingly unrelated regression was adopted to fit a system of additive allometric biomass equations utilizing the selected equations. A leave-one-out cross-validation method (the jackknife procedure) was used to assess the predictive ability. The biomass partitioning pattern was evaluated by calculating the ratios. The results revealed that the diameter alone is a good predictor for branches and foliage biomass estimates, while the stem requires H included to improve estimation accuracy. Mongolian pine allocates relatively more biomass to the crown (51.4%) compared to the stem (48.6%). Branch biomass fraction increased monotonously with increasing tree size while a reverse trend was observed for foliage. In conclusion, the additive models developed in this study provide a robust biomass estimation and can be extensively used to estimate Mongolian pine forests biomass in Mu Us Sandy Land.