Spatial (Mis)Matches Between Biodiversity and Habitat Quality Under Multi-Scenarios: A Case Study in Shandong Province, Eastern China

Xiaoyin Sun (), Ruifeng Shan, Qingxin Luan, Yuee Zhang and Zhicong Chen
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Xiaoyin Sun: Key Laboratory of Nansihu Lake Wetland Ecological Conservation & Environmental Protection (Shandong Province), College of Geography and Tourism, Qufu Normal University, Rizhao 276826, China
Ruifeng Shan: Key Laboratory of Nansihu Lake Wetland Ecological Conservation & Environmental Protection (Shandong Province), College of Geography and Tourism, Qufu Normal University, Rizhao 276826, China
Qingxin Luan: Key Laboratory of Nansihu Lake Wetland Ecological Conservation & Environmental Protection (Shandong Province), College of Geography and Tourism, Qufu Normal University, Rizhao 276826, China
Yuee Zhang: Key Laboratory of Nansihu Lake Wetland Ecological Conservation & Environmental Protection (Shandong Province), College of Geography and Tourism, Qufu Normal University, Rizhao 276826, China
Zhicong Chen: Jining Planning and Design Institute, Jining 272100, China

Land, 2024, vol. 13, issue 12, 1-13

Abstract: Despite declines in biodiversity and habitat quality (HQ) at a global scale, our understanding of the HQ and matches between HQ and biodiversity under management scenarios is incomplete. To address this deficiency, the study examined trends in HQ and (mis)matches between biodiversity and HQ over four decades in Shandong province, China, identified the key drivers, and assessed the effectiveness of ecological policies, including Ecological Redlines (ERLs) and the Grain for Green (GG) program. During the 40-year period, HQ and matching degrees (indicated by related coefficients) between biodiversity and HQ decreased obviously. Correlation analysis showed that related coefficients between HQ and four biodiversity indices (vertebrate, vascular plant, and vegetation formation type richness, and comprehensive biodiversity index) were all significant ( p < 0.01), and coefficients were highest for the biodiversity composite index. An analysis of relative importance by the random forest algorithm indicated significant variation in driving factors for spatial distribution of HQ, biodiversity, and matches between them. The key determinants of biodiversity distribution were biophysical factors, such as NDVI (normalized difference vegetation index), DEM (digital elevation model), and temperature. However, the main drivers of HQ distribution were social factors, such as the accessibility of anthropogenic activities, urbanization, and population density. Ecological policy scenarios, ERLs and GG, are clearly effective and could improve HQ and the matching degree between HQ and biodiversity significantly. Furthermore, the improvement in HQ under ERLs was less than that under GG, while the increase in the matching degree was opposite. The results of this study can be integrated by ecological managers and planners for biodiversity conservation.

Keywords: habitat quality; InVEST model; biodiversity; ecosystem services; ecological redlines; Grain for Green (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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