顾及地形起伏的InVEST模型的生物多样性重要区识别——以云南省为例

精确识别生物多样性重要区是生态学和生物多样性研究的关键问题之一,也是生态保护红线划定和国土空间规划的重要基础。本研究以中国典型高原山区云南省为研究案例,分别用净初级生产力(NPP)定量指标法、InVEST模型和顾及地形起伏的InVEST模型识别研究区生物多样性重要区。结果表明: NPP定量指标法不适用于垂直地带性发育明显的高原山区,其识别区域仅包含自然保护地的26.1%;InVEST模型较NPP定量指标法在云南省具有较高的识别精度,识别区域包含自然保护地的49.4%,但在滇西北破碎化格局明显;顾及地形起伏的InVEST模型提高了生物多样性重要区的识别精度,包含71.7%的自然保护区,同时弥补了NPP定量指标法对水域识别的缺漏,解决了InVEST模型的破碎化问题;云南省生物多样性重要区面积达119466.94 km²,是云南省国土面积的30.3%,空间分布整体呈现“三屏两带一区多点”的格局。

Identification of important biodiversity areas by InVEST model considering opographic relief: A case study of Yunnan Province,China

Accurately identifying important areas of biodiversity is one of the key issues in ecology and biodiversity research, as well as an important basis for the delineation of the red line for ecologi-cal protection and territorial spatial planning. With China’s typical plateau mountainous area (Yunnan Province) as a research case, we used the net primary productivity (NPP) quantitative index method, InVEST model and InVEST model focusing on topographic relief to identify biodiversity important areas. The results showed that NPP quantitative index method was not suitable for the plateau mountainous areas with obvious vertical zonal development. The identified area contained only 26.1% of the protected areas. The InVEST model had higher identification accuracy than the NPP quantitative index method in Yunnan Province. The identified area covered 49.4% of the protected natural areas. Fragmentation was obvious in northwest Yunnan. The InVEST model focusing on topographic relief improved the identification accuracy of important areas of biodiversity, including 71.7% of nature reserves. The deficiency of NPP quantitative index method in water area identification was made up and the fragmentation problem of InVEST model was solved. The area of biodiversity important areas was 119466.94 km², accounting for 30.3% of the total land area of Yunnan Province. The spatial distribution showed a pattern of “three barriers, two zones and one region for multi-point development”.