The total amount of BNPP
Averaging across the 725 NPPobs soil profiles (Fig. 1a), BNPP in the 0-200 cm soil profile is Mg ha–1 yr–1 (mean with 2.5% and 97.5% quantiles, Fig. 1b). BNPP is significantly different among biomes (P < 0.05; Fig. 1b). Mediterranean/montane shrublands have the highest BNPP ( Mg ha–1 yr–1), followed by croplands ( Mg ha–1 yr–1) and tropical/subtropical forests ( Mg ha–1 yr–1); and tundra has the lowest ( Mg ha–1 yr–1; Fig. 1b). The proportion of BNPP to total NPP (i.e., fBNPP) is , with significant difference among biomes (Fig. 1c). It is on average greater than 50% in arid or semi-arid environments such as temperate grasslands and deserts, but only ~30% in tropical/subtropical, temperate and boreal forests (Fig. 1c).
The depth distribution of BNPP
Using root profiles (i.e., the Rootobs data set) to infer the proportional depth distribution (PDD, unit in %) of BNPP in the seven soil layers, the results indicate that on average roughly 60% of BNPP is allocated to the 0-20 cm soil layer (Fig. 2a); and the top 40 cm soil layer holds around 80% of BNPP (Fig. 2a). In other deeper soil layers, BNPP allocation is relatively small and shows much less variance than in upper layers (Fig. 2a). In the top 20 cm soil layer, for example, the proportional allocation of BNPP ranges from 37% (2.5% quantile) to 78% (97.5% quantile). For proportional BNPP allocation to a typical soil layer, it is significantly (P < 0.05) different among biomes (Fig. 2c). Boreal forests and tundra relatively allocate more (~70% of BNPP) to upper layers (e.g., 0-20 cm) than other biome types (e.g., <50% in tropical/subtropical grasslands/savannas and deserts which allocate more BNPP to deeper layer depths, Fig.2c).
Because the locations of NPPobs soil profiles do not match with that of Rootobs (Fig. 1a), we develop machine learning models trained by Rootobs data set to predict PDD in NPPobs locations (Fig. S2). Multiplying the observed BNPP at NPPobs locations by PDD predictions, the absolute amount of BNPP in the seven soil layers is estimated with the consideration of prediction uncertainties by the model. As the PDD (Fig. 2a), the absolute amount of BNPP also decreases exponentially with soil depths, with greater variances in upper soil layers than in deeper layers. Across the globe, an average BNPP of 1.60 Mg ha–1 yr–1 is estimated in the top 20 cm soil (Fig. 2b). In the 20–40 cm soil layer, the average BNPP is reduced to 0.69 Mg ha–1 yr–1. In deeper layers, BNPP is relatively small and comparable (< 0.30 Mg ha–1 yr–1) with smaller variances (Fig. 2b). Among biomes, the absolute BNPP shows significant disparities (Fig. 2d). In the top layers (e.g., 0–20 cm), higher BNPP is observed in Mediterranean/Montane shrublands (2.37 Mg ha–1 yr–1) and temperate forests (2.11 Mg ha–1 yr–1) than in boreal forests (0.94 Mg ha–1 yr–1) and tundra (0.52 Mg ha–1 yr–1). In deeper soil layers, the variations in absolute BNPP among biomes are in general consistent with those in the top 0-20 cm soil (Fig. 2d).
Drivers of BNPP allocation
As might be expected, soil depth is the most important predictor for the depth distribution of BNPP (Fig. 3a and b). An exponential model using depth as the only predictor can explain 23–56% of the variance in the depth distribution of BNPP across the globe (R2 = 0.34) and in different biome types (R2 ranges from 0.23 to 0.56; Fig. 3a, Table S3). The coefficients of the exponential model indicating BNPP allocated to the top layer and decreasing rate of BNPP with soil depth are significantly different among biomes (Table S3), further demonstrating that the depth distribution of BNPP is significantly different among biomes (Fig. 2).
A random forest model taking into account soil depth, biome type and additional 55 environmental covariates (Table S2), after controlling multicollinearity among the covariates (Figs. S3, S4), can explain 92% (R2 = 0.92) of the variance in the depth distribution of BNPP in the whole 0-200 cm soil profile across the globe (Fig. 3b). Following soil depth, MAT (mean annual temperature, which is shown as BIO1 in the figure), AE (mean actual daily soil evaporation), biome type, BD (soil bulk density) and MAP (mean annual precipitation, BIO12) are the most important five predictors (Fig. 3b). Grouping the environmental predictors into climatic (temperature- and precipitation-related, a total of 7 variables in the model), edaphic (12 variables) and topographic (7 variables) ones, the result indicates that the contributions of climate, soil and topography are 26%, 28%, 13% (Table S4). Focusing on BNPP in specific soil layer depths, the fitted random forest models can explain over 80% of BNPP variances in each of the seven soil layers (Fig. S5). MAT is consistently the most important factor, followed by AE, MAP, soil carbon:nitrogen ratio (i.e., SCN) in deeper layers (Fig. 3b; Fig. S5). In terms of the overall influence of climatic, edaphic and topographic variables, climatic variables together contribute 33-40% depending on soil layer depths; and edaphic and topographic properties contribute 33-40% and 18-28%, respectively (Figs. 3b, S5; Table S4).
Besides the relative importance of various environmental variables identified by the random forest models, linear mixed-effects modelling is further conducted to identify the direction and magnitude of their first-order relationship with BNPP depth allocation (Fig. 3c). As expected, BNPP depth allocation is negatively correlated to soil depth (Fig. 3c). In general, the effects of all climate-related variables are positive; while the effects of soil-related variables are negative except the positive effects of AE and soil carbon to nitrogen ratio. In different biomes, however, the magnitude and, in some cases, the direction of the regression coefficient are substantially different (Fig. 3c). For example, MAT (i.e., BIO1) has a positive effect in most biomes, but its effect is negative in tropical/subtropical grasslands/savannas. Overall, the varying regression coefficients of the predictor variables in terms of both magnitude and direction among biome types (Fig. 3c) indicate that the effects of environmental controls are biome-dependent. However, it should be noted that the first-order effects of all variables on BNPP are relatively weak at the global scale albeit significant (Figs. 3c and S6). A partial correlation analysis controlling the effect of soil depth is also conducted to assess whether the effects of soil-, climate- and topography-related variables are modulated by soil depth. The results of the zero-order correlations between BNPP and the assessed predictors are generally consistent with the results of the mixed-effects regression (Fig. 3c vs Fig. S6a). After controlling the effect of soil depth by conducting a partial correlation analysis, however, most correlation coefficients have been changed, particularly the correlations with soil properties (Fig. S6b,c) which suggest that the effects of soil properties are depth-dependent.
Global pattern of the depth distribution of BNPP
The depth distribution of BNPP is mapped across the globe at the 0.0083° resolution (which is equal to ~1 km at the equator; Fig. 4). Using the NPPobs data set, machine learning-based models are first developed to predict BNPP (Fig. S7). Combining with models for estimating the proportional depth distribution of BNPP (PDD, Fig. S2), then BNPP in each 1 km grid and each of the seven depths is estimated (i.e., BNPP ×PDD) taking into account uncertainties in the estimation of both BNPP and PDD (Fig. 4). Across the globe, BNPP in the 0–200 cm soil profile is Mg ha–1 yr–1 (Figs. 4, 5a). The largest BNPP on average occurs in ~20° N (Fig. 5a). In all soil layers, it is general that BNPP is relatively low in desert and northern Hemisphere high latitudinal regions (Fig. 4) with an apparent decreasing trend from 40° N to 80° N (Fig. 5a). The highest BNPP is in tropical/subtropical forests, temperate forests and croplands; and the lowest in tundra, boreal forests and deserts (Fig. 4). For the proportional depth distribution, averaging across the globe, it is 57% and 77% in the top 20 and 40 cm soil layers, respectively (Fig. 5b). With increasing latitudes from 40° N to 80° N, more BNPP is allocated to upper soil layers (Fig. 5b). Fig. 6 shows the uncertainty (i.e., coefficient of variance in each 1 km grid) in predicted depth-specific BNPP. The uncertainty is greater in deeper soil layers, particularly in the northern Hemisphere high latitudinal regions such as tundra and boreal forests. Indeed, the uncertainty is markedly higher in tundra and boreal forests than in other biomes in deeper soil layers. Across the globe, the average uncertainty in the top 20 cm layer is , and increased to in the 150-200 cm layer, respectively (Fig. 6).