Assessment and spatial partitioning of ecosystem services importance in Giant Panda National Park: To provide targeted ecological protection

Abstract

Giant Panda National Park is crucial for China’s ecological security strategic pattern known as "two screens and three belts." The importance assessment and classification of ecosystem services in giant panda national parks has an important guiding role in the protection of giant panda national park ecosystems. In this study, we examined four indicators of habitat quality: carbon storage, water conservation, and soil and water conservation. Combined with data analysis were used to evaluate and classify the importance of ecosystem services in the study area. The results showed that: (1) the overall habitat quality index in the study area was relatively high, and the index was generally greater than 0.5. The total carbon storage was 60.5 × 106 t, and the highest carbon storage in the region was 16.9533 t. The area with the highest water conservation reached 715.275 mm. The total soil conservation was 2555.7 × 107 t. (2) From the perspective of spatial characteristics, the habitat quality in the study area presented a spatial distribution pattern of high–low from west to east. The carbon storage presented a spatial distribution pattern of high–low from east to west. The soil conservation presented a spatial pattern of decreasing from west to east, and the water conservation increased from west to east. (3) We divided the research into four levels of importance: The area of general importance in the study site accounted for 1017.58 km2 and was distributed in the northwest of the study site. The moderately important areas were distributed in the east of the study site, with an area of 1142.40 km2. The highly important areas were distributed in the west of the study site, totaling 2647.84 km2. Extremely important areas were distributed in the middle, with an area of 1451.32 km2. (4) The grid cell scale of the study area was used as the dataset to determine the weighting. This makes the weighting more objective and ensures that the spatial distribution of areas with different degrees of importance will be more accurate.