Qiang Xue , Yang Zhang , Qingmin Zhang , Qingxia Wu , Xiaojing Zhang , Lu Lu , Changbo Qin
{"title":"Towards ecological security: Two-thirds of China’s ecoregions experienced a decline in habitat quality from 1992 to 2020","authors":"Qiang Xue , Yang Zhang , Qingmin Zhang , Qingxia Wu , Xiaojing Zhang , Lu Lu , Changbo Qin","doi":"10.1016/j.ecolind.2025.113275","DOIUrl":null,"url":null,"abstract":"<div><div>China’s six major ecoregions are cross-administrative regions that coordinate ecological security and socioeconomic development, making the preservation of regional ecological security critically prominent. However, differences in the economic development modes between administrative regions threaten the maintenance of biodiversity in these ecoregions. Currently, the spatiotemporal evolution of habitat quality (HQ) in China’s ecoregions and its drivers have not been fully analyzed. In this study, the InVEST model, trend analysis methods, and the optimal parameter-based geographic detector (OPGD) model were used to reveal the spatiotemporal distribution of HQ in China’s six ecoregions over the past 30 years and its driving mechanism. The findings indicated that since 1992, land use alterations and conflict patterns differed throughout the ecological zones, and construction land exhibited an increasing trend in all six zones. The national HQ initially decreased and then increased, with a multi-year average of 0.6304. The Qinghai–Tibet Plateau Ecological Region (QTPER), Northeast Ecological Region (NEER), and Southeast Ecological Region (SER) had higher HQ values, while the Northwest Ecological Region (NWER) had the lowest value. The HQ in the NEER continuously declined. The longitudinal distribution of the HQ exhibited a weak W-shaped pattern from west to east. The HQ demonstrated an upward tendency in the NWER and QTPER and a downward trend in the SER, NEER, and Yangtze River and Sichuan–Yunnan Key Ecological Region (YRSYKER). Except for the SER, the Moran’s <em>I</em> values in the other ecoregions were greater than 0.80. The HQ was mainly high in the QTPER and NEER and low in the NWER and the Yellow River Key Ecological Region (YRKER). The main factors influencing the spatiotemporal heterogeneity of HQ differed between ecoregions. Among them, natural factors including the annual average vegetation index, elevation, and slope were the long-term dominant driving factors. The integrated HQ comprehensive analytical framework offers effective methodologies and empirical data for HQ evaluation and monitoring at different scales in China.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"172 ","pages":"Article 113275"},"PeriodicalIF":7.0000,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecological Indicators","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1470160X25002043","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
China’s six major ecoregions are cross-administrative regions that coordinate ecological security and socioeconomic development, making the preservation of regional ecological security critically prominent. However, differences in the economic development modes between administrative regions threaten the maintenance of biodiversity in these ecoregions. Currently, the spatiotemporal evolution of habitat quality (HQ) in China’s ecoregions and its drivers have not been fully analyzed. In this study, the InVEST model, trend analysis methods, and the optimal parameter-based geographic detector (OPGD) model were used to reveal the spatiotemporal distribution of HQ in China’s six ecoregions over the past 30 years and its driving mechanism. The findings indicated that since 1992, land use alterations and conflict patterns differed throughout the ecological zones, and construction land exhibited an increasing trend in all six zones. The national HQ initially decreased and then increased, with a multi-year average of 0.6304. The Qinghai–Tibet Plateau Ecological Region (QTPER), Northeast Ecological Region (NEER), and Southeast Ecological Region (SER) had higher HQ values, while the Northwest Ecological Region (NWER) had the lowest value. The HQ in the NEER continuously declined. The longitudinal distribution of the HQ exhibited a weak W-shaped pattern from west to east. The HQ demonstrated an upward tendency in the NWER and QTPER and a downward trend in the SER, NEER, and Yangtze River and Sichuan–Yunnan Key Ecological Region (YRSYKER). Except for the SER, the Moran’s I values in the other ecoregions were greater than 0.80. The HQ was mainly high in the QTPER and NEER and low in the NWER and the Yellow River Key Ecological Region (YRKER). The main factors influencing the spatiotemporal heterogeneity of HQ differed between ecoregions. Among them, natural factors including the annual average vegetation index, elevation, and slope were the long-term dominant driving factors. The integrated HQ comprehensive analytical framework offers effective methodologies and empirical data for HQ evaluation and monitoring at different scales in China.
期刊介绍:
The ultimate aim of Ecological Indicators is to integrate the monitoring and assessment of ecological and environmental indicators with management practices. The journal provides a forum for the discussion of the applied scientific development and review of traditional indicator approaches as well as for theoretical, modelling and quantitative applications such as index development. Research into the following areas will be published.
• All aspects of ecological and environmental indicators and indices.
• New indicators, and new approaches and methods for indicator development, testing and use.
• Development and modelling of indices, e.g. application of indicator suites across multiple scales and resources.
• Analysis and research of resource, system- and scale-specific indicators.
• Methods for integration of social and other valuation metrics for the production of scientifically rigorous and politically-relevant assessments using indicator-based monitoring and assessment programs.
• How research indicators can be transformed into direct application for management purposes.
• Broader assessment objectives and methods, e.g. biodiversity, biological integrity, and sustainability, through the use of indicators.
• Resource-specific indicators such as landscape, agroecosystems, forests, wetlands, etc.