[Insert Figure 3 here]
To disentangle the contribution of the various potential drivers, we
applied structural equation models (SEM) to quantify the environmental
controls of CH4 uptake (all factors were classified into
four groups: meteorology, microbes, plant, and edaphic factors,Figs. S2 and S3 ) using the field experiment’s data
(Figs. 3a, 3b, 3c, and 3d ). Our models considered how nutrient
additions directly or indirectly affects CH4 uptake
(Figs. 3 and S3 ). The SEM results suggest that under control
(non-fertilized) conditions, the impact of meteorology on
CH4 uptake was directly (β = -0.33, standardized
coefficient) or indirectly mediated through microbes (β = -0.8,
standardized coefficient) and plants (β = -0.21, standardized
coefficient) (Fig. S3a ). N and P additions affected
CH4 uptake indirectly through edaphic factors (N
treatment: β = 0.5, standardized coefficient, Fig. S3b ; N+P
treatment: β = 0.37, standardized coefficient, Fig. S3d ). Soil
NH4+ had a direct negative effect and
plant N content had a positive effect on the CH4 uptake
under control conditions and in all nutrient addition treatments
(Figs. 3a, 3b, 3c, and 3d ). Meanwhile, added N stimulated the
accumulation of soil NH4+ (N
treatment: β = 0.45), added P had a positive impact on plant P and soil
P, but had no impact on soil NH4+;
while the N + P treatment had a negative impact on soil
NH4+ (β = -0.18). Compared to the
control, the N addition strengthened the negative effect of
NH4+ on CH4 uptake (β
ranging from -0.57 to -0.77) (Figs. 3a and 3b ); P addition did
not change the negative effect of NH4+(β = -0.57) and the positive effect of plant N (β = 0.34) (Figs.
3a and 3c ); N + P additions strengthened the suppression effect of soil
NH4+ (β changing from -0.57 to -0.72)
(Figs. 3a and 3d ). These results showed that
CH4 uptake is highly associated with soil N and P
contents in semiarid grasslands.
Global Estimation of P alleviation of
N-suppressed CH4 Sink in
Grasslands
We further developed an empirical model to quantify the N and P impacts
on soil oxidation of atmospheric CH4 across global
grasslands, using existing global datasets of soil properties and
meteorology (Methods ). Two thirds of the compiled data
were used for model fitting, while the remaining one third of the data
was used for model validation (Fig. S7 ). The best fitting
equation obtained with the stepwise regression procedure was:
FCH4 = m + a × N + b × P + c × ln(N) × ln(P) + d × ST +
e × pH + f × SOC + g × BD + h × CL (2)
where FCH4 is the annual CH4 uptake
rate; N is the nitrogen input in g ha-1y-1; P is the phosphorus input rate in g
ha-1 y-1; ln represents the natural
logarithm; ST is soil temperature (K); pH is the soil pH value; SOC is
soil organic carbon content (in %); BD is bulk density (g
cm-3); CL is clay content (in %); m is the incept of
the function; and a, b, c, d, e, f, g, and h are coefficients. The
coefficients and key parameters for the regression are listed inTable S1 . The model explained more than 37% of the variation
in CH4 uptake rate across the globe (Fig. S7 ).