As one of the most important limiting factors of grassland productivity, drought is predicted to increase in intensity and frequency. Greenhouse studies suggest that arbuscular mycorrhizal fungi (AMF) can improve plant drought resistance. However, whether AMF can improve plant drought resistance in field conditions and whether the effects of AMF on drought resistance differ among plants with different photosynthetic pathways remain unclear. To evaluate the effect of indigenous AMF on plant drought resistance, an in situ rainfall exclusion experiment was conducted in a temperate meadow in northeast China. The results showed that AMF significantly reduced the negative effects of drought on plant growth. On average, AMF enhanced plant biomass, photosynthetic rate ( A ), stomatal conductance ( g s ), intrinsic water use efficiency (iWUE), and superoxide dismutase (SOD) activity of the C 3 species Leymus chinensis by 58, 63, 38, 15, and 45%, respectively, and reduced levels of malondialdehyde (MDA) by 32% under light and moderate drought (rainfall exclusion of 30 and 50%, respectively). However, under extreme drought (rainfall exclusion of 70%), AMF elevated only aboveground biomass and catalase (CAT) activities. Averagely, AMF increased the aboveground biomass, A , and CAT activity of Hemarthria altissima (C 4 ) by 37, 28, and 30%, respectively, under light and moderate droughts. The contribution of AMF to plant drought resistance was higher for the C 3 species than that for the C 4 species under both light and moderate drought conditions. The results highlight potential photosynthetic type differences in the magnitude of AMF-associated enhancement in plant drought resistance. Therefore, AMF may determine plant community structure under future climate change scenarios by affecting the drought resistance of different plant functional groups.

Characterization of the vertical distribution of soil organic carbon (C), nitrogen (N), and phosphorus (P) may improve our ability to accurately estimate soil C, N, and P storage. Based on a database of 21 354 records in 74 long-term monitoring plots from 2004 to 2013 in the Chinese Ecosystem Research Network (CERN), we built fitting functions to quantify the vertical distribution of soil C, N, and P (up to 100 cm depth) in the typical Chinese terrestrial ecosystems. The decrease of soil C, N, and P content with depth can be well fitted with various mathematical functions. The fitting functions differed greatly between artificial (agriculture) and natural (desert, forest, and grassland) ecosystems, and also differed with respect to soil C, N, and P content. In both the artificial and natural ecosystems, the best fitting functions were exponential functions for C, quadratic functions for N, and quadratic functions for P. Furthermore, the stoichiometric ratios of soil C, N, and P were ranked in descending order: grassland > forest > agriculture > desert, and were also associated with climate. This study is the first to build the fitting functions for the profile distribution of soil C, N, and P in China at a national scale. Our findings provide a scientific basis to accurately assess the storage of C, N, and P in soils at a large scale, especially for the integrative analysis of historical data.

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