Shifts in the dynamics of productivity signal ecosystem state transitions at the biome‐scale |
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| Authors: | Zhongmin Hu Qun Guo Shenggong Li Shilong Piao Alan K Knapp Philippe Ciais Xinrong Li Guirui Yu |
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| Institution: | 1. School of Geography, South China Normal University, Guangzhou, China;2. Synthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China;3. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China;4. Department of Ecology, College of Urban and Environmental Science, Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing, China;5. Department of Biology and Graduate Degree Program in Ecology, Colorado State University, CO, USA;6. Laboratoire des Sciences du Climatet de l'Environnement, Gif‐sur‐Yvette, France;7. Shapotou Desert Research and Experiment Station, Northwest Institute of Eco‐Environment and Resources, Chinese Academy of Sciences, Lanzhou, China |
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| Abstract: | Understanding ecosystem dynamics and predicting directional changes in ecosystem in response to global changes are ongoing challenges in ecology. Here we present a framework that links productivity dynamics and ecosystem state transitions based on a spatially continuous dataset of aboveground net primary productivity (ANPP) from the temperate grassland of China. Across a regional precipitation gradient, we quantified spatial patterns in ANPP dynamics (variability, asymmetry and sensitivity to rainfall) and related these to transitions from desert to semi‐arid to mesic steppe. We show that these three indices of ANPP dynamics displayed distinct spatial patterns, with peaks signalling transitions between grassland types. Thus, monitoring shifts in ANPP dynamics has the potential for predicting ecosystem state transitions in the future. Current ecosystem models fail to capture these dynamics, highlighting the need to incorporate more nuanced ecological controls of productivity in models to forecast future ecosystem shifts. |
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| Keywords: | Climate change grassland resilience state transition tipping point variability |
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