首页|Spatiotemporal dynamics of aboveground primary productivity along a precipitation gradient in Chinese temperate grassland
Spatiotemporal dynamics of aboveground primary productivity along a precipitation gradient in Chinese temperate grassland
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Investigating the spatial and temporal variance in productivity along natural precipitation gradients is one of the most efficient approaches to improve understanding of how ecosystems respond to climate change. In this paper, by using the natural precipitation gradient of the Inner Mongolian Plateau from east to west determined by relatively long-term observations, we analyzed the temporal and spatial dynamics of aboveground net primary productivity (ANPP) of the temperate grasslands covering this region. Across this grassland transect, ANPP increased exponentially with the increase of mean annual precipitation (MAP) (ANPP=24.47e0.005MAP,R2=0.48). Values for the three vegetation types desert steppe,typical steppe, and meadow steppe were: 60.86 gm-2a-1, 167.14 gm-2a-1 and 288.73 gm-2a-1 respectively.By contrast, temperature had negative effects on ANPP. The moisture index (K), which takes into account both precipitation and temperature could explain the spatial variance of ANPP better than MAP alone (ANPP=2020.34K1.24,R2=0.57). Temporally, we found that the inter-annual variation in ANPP (calculated as the coefficient of variation, CV) got greater with the increase of aridity. However, this trend was not correlated with the inter-annual variation of precipitation. For all of the three vegetation types,ANPP had greater inter-annual variation than annual precipitation (PPT). Their difference (ANPP CV/PPT CV) was greatest in desert steppe and least in meadow steppe. Our results suggest that in more arid regions, grasslands not only have lower productivity, but also higher inter-annual variation of production. Climate change may have significant effects on the productivity through changes in precipitation pattern, vegetation growth potential, and species diversity.
Key Laboratory of Ecosystem Network Observation and Modeling, CERN Center for Synthesis Research, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Graduate University of Chinese Academy of Sciences,Beijing 100039,China
National Key Research and Development Program of China国家自然科学基金
NETL
NSTL
万方数据
维普
