Biogeographic variations in soil respiration and its basal rate across China suggest thermal adaptation, substrate limitation, and soil moisture constraint

Abstract

Background and aims

Understanding the spatial variations in ecosystem carbon (C) fluxes is essential for predicting regional C budgets under future climate and land use change. Biogeographic variations and regulating factors of soil respiration (R_s) and its basal rate (e.g., at 10 °C, R_s10) remain largely uncertain.

Methods

We synthesized 788 and 606 records of annual R_s and R_s10, respectively, from 357 published papers to examine how R_s and R_s10 vary spatially across China and as a function of environmental factors.

Results

Annual R_s ranged 51–2480 g C m⁻² yr⁻¹ across sites. It increased with increasing mean annual air temperature, potential evapotranspiration (PET), and soil temperature, and decreased with increasing latitude and altitude, whereas R_s10 exhibited the opposite trend. Negative R_s10–temperature relationships suggest higher respiratory activity in colder areas, possibly a result of the thermal adaptation of R_s. Annual R_s was significantly enhanced by increasing mean annual precipitation (MAP) and aridity index (i.e., AI, ratio of MAP to PET), but R_s10 was not correlated with MAP and was less sensitive to AI. Both annual R_s and R_s10 showed a unimodal relationship with soil moisture and increased linearly with soil organic C and total nitrogen (N). However, neither annual R_s nor R_s10 was correlated with soil C:N, suggesting that R_s was constrained by soil water, substrate, and nutrient availability, but was less affected by substrate quality. Multivariate analyses (random forest analysis, hierarchical variance partitioning, relative weight analysis, and structural equation modeling) generally supported the patterns observed from bivariate relationships.

Conclusion

Our study suggests thermal adaptation, substrate limitation, and soil moisture constraint of R_s at the regional scale. Accordingly, terrestrial C cycle models should adequately consider the dependence of the basal rate of R_s on climatic and soil factors to accurately predict regional C budgets.