Plant diversity enhances productivity and soil carbon storage

Abstract

Despite evidence from experimental grasslands that plant diversity increases biomass production and soil organic carbon (SOC) storage, it remains unclear whether this is true in natural ecosystems, especially under climatic variations and human disturbances. Based on field observations from 6,098 forest, shrubland, and grassland sites across China and predictions from an integrative model combining multiple theories, we systematically examined the direct effects of climate, soils, and human impacts on SOC storage versus the indirect effects mediated by species richness (SR), aboveground net primary productivity (ANPP), and belowground biomass (BB). We found that favorable climates (high temperature and precipitation) had a consistent negative effect on SOC storage in forests and shrublands, but not in grasslands. Climate favorability, particularly high precipitation, was associated with both higher SR and higher BB, which had consistent positive effects on SOC storage, thus offsetting the direct negative effect of favorable climate on SOC. The indirect effects of climate on SOC storage depended on the relationships of SR with ANPP and BB, which were consistently positive in all biome types. In addition, human disturbance and soil pH had both direct and indirect effects on SOC storage, with the indirect effects mediated by changes in SR, ANPP, and BB. High soil pH had a consistently negative effect on SOC storage. Our findings have important implications for improving global carbon cycling models and ecosystem management: Maintaining high levels of diversity can enhance soil carbon sequestration and help sustain the benefits of plant diversity and productivity.

Keywords: aboveground net primary productivity; belowground biomass; human disturbance; soil carbon storage; species richness.

Fig. 1.

Median and interquartile range of plant SR, ANPP, BB, and SOC storage in 0–30 cm soil depth across different vegetation and biome types. The boundary of the box indicates the 25th and 75th percentile. Error bars denote the 90th and 10th percentiles. Abbreviations of vegetation types: ADS, subalpine deciduous broadleaved shrubland; AM, alpine meadow; AS, alpine steppe; ASS, subalpine sclerophyllous evergreen broadleaved shrubland; CTF, cold temperate coniferous forest; LSM, lowland and saline meadow; SBF, subtropical coniferous-broadleaved forest; SCF, subtropical coniferous forest; SDE, subtropical deciduous-evergreen broadleaved forest; SDF, subtropical deciduous broadleaved forest; SDS, subtropical deciduous broadleaved shrubland; SEF, subtropical evergreen broadleaved forest; SES, subtropical evergreen broadleaved shrubland; TBF, temperate coniferous-broadleaved forest; TBS, temperate deciduous broadleaved shrubland; TCF, temperate coniferous forest; TD, temperate desert; TDF, temperate deciduous broadleaved forest; TDG, temperate desert steppe; TDS, temperate desert shrubland; TG, tropical grassland; TMF, temperate montane coniferous forest; TMM, temperate mountain meadow; TMS, temperate meadow steppe; TRF, tropical rainforest and monsoon forest; TSG, temperate sandy grassland; TTS, temperate typical steppe; WTG, warm temperate grassland.

Fig. 2.

SEMs fitted to connections among SR, ANPP, BB, and SOC storage and the effects of climate, soil, and human activity variables on SR, ANPP, BB, and SOC in forests (A), shrublands (B), and grasslands (C). Numbers adjacent to arrows represent the standardized path coefficients. Climate is PCA component 1 of mean annual precipitation, mean annual temperature, and photosynthetically active radiation. Human activities represent a composite variable including nitrogen deposition rate and road density. R² indicates the proportion of variance explained. Solid arrows represent significant paths (P < 0.10), and dashed arrows represent nonsignificant paths (P > 0.10).

Fig. 3.

Relationships of SR with ANPP, BB, and SOC storage at 0–30 cm soil depth in forests (Left), shrublands (Center), and grasslands (Right). Colored lines present regression plots of different vegetation types as indicated in Fig. 1. Bold black lines indicate SR–ANPP, SR–BB, and SR–SOC relationships at the biome-type level. The intercept and slope were the fixed effects of SR on response variables estimated by linear mixed-effects model (random slope model), with the effect of vegetation type as a random factor. All variables were natural logarithm transformed before regression analysis.