4.5 Article

Bistability, Spatial Interaction, and the Distribution of Tropical Forests and Savannas

期刊

ECOSYSTEMS
卷 19, 期 6, 页码 1080-1091

出版社

SPRINGER
DOI: 10.1007/s10021-016-0011-1

关键词

catastrophe theory; climate change; critical transition; ecotone; wildfire; Maxwell point; reaction-diffusion system; regime shift; remote sensing; tipping point

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资金

  1. SENSE Research School
  2. National Natural Science Foundation of China [41271197]
  3. ERC

向作者/读者索取更多资源

Recent work has indicated that tropical forest and savanna can be alternative stable states under a range of climatic conditions. However, dynamical systems theory suggests that in case of strong spatial interactions between patches of forest and savanna, a boundary between both states is only possible at conditions in which forest and savanna are equally stable, called the 'Maxwell point.' Frequency distributions of MODIS tree-cover data at 250 m resolution were used to estimate such Maxwell points with respect to the amount and seasonality of rainfall in both South America and Africa. We tested on a 0.5 degrees scale whether there is a larger probability of local coexistence of forests and savannas near the estimated Maxwell points. Maxwell points for South America and Africa were estimated at 1760 and 1580 mm mean annual precipitation and at Markham's Seasonality Index values of 50 and 24 %. Although the probability of local coexistence was indeed highest around these Maxwell points, local coexistence was not limited to the Maxwell points. We conclude that critical transitions between forest and savanna may occur when climatic changes exceed a critical value. However, we also conclude that spatial interactions between patches of forest and savanna may reduce the hysteresis that can be observed in isolated patches, causing more predictable forest-savanna boundaries than continental-scale analyses of tree cover indicate. This effect could be less pronounced in Africa than in South America, where the forest-savanna boundary is substantially affected by rainfall seasonality.

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