Mapping the hotspots and coldspots of ecosystem services in conservation priority setting

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September 14, 2017 - Yingjie Li; Liwei Zhang; Junping Yan; Pengtao Wang; Ningke Hu; Wei Cheng; Bojie Fu

Journal or Book Title: Journal of Geographical Sciences

Keywords: ecosystem services mapping; soil conservation; spatial clustering; Getis-Ord Gi* statistics; Shaanxi Province

Volume/Issue: 27(6)

Page Number(s): 681–696

Year Published: 2017

Spatial-explicitly mapping of the hotspots and coldspots is a vital link in the priority setting for ecosystem services (ES) conservation. However, little research has identified and tested the compactness and efficiency of their ES hotspots and coldspots, which may weaken the effectiveness of ecological conservation. In this study, based on the RUSLE model and Getis-Ord Gi* statistics, we quantified the variation of annual soil conservation services (SC) and identified the statistically significant hotspots and coldspots in Shaanxi Province of China from 2000 to 2013. The results indicate that, 1) areas with high SC presented a significantly increasing trend as well, while areas with low SC only changed slightly; 2) SC hotspots and coldspots showed an obvious spatial differentiation—the hotspots were mainly spatially aggregated in southern Shaanxi, while the coldspots were mainly distributed in the Guanzhong Basin and Sand-windy Plateau; and 3) the identified hotspots had the highest capacity of providing SC, with 29.6% of the total area providing 59.7% of the total service. In contrast, the coldspots occupied 46.3% of the total area, but only provided 17.2% of the total SC. In addition to conserving single ES, the Getis-Ord Gi* statistics method can also help identify multi-functional priority areas for conserving multiple ES and biodiversity.

DOI: 10.1007/s11442-017-1400-x

Type of Publication: Journal Article

Publisher: Science Press

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Tag List
  • center for systems integration and sustainability,
  • ecosystem services mapping,
  • getis-ord gi* statistics,
  • shaanxi province,
  • soil conservation,
  • spatial clustering
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