{"title":"The effects of landscape patterns on ecosystem services of urban agglomeration in semi-arid area under scenario modeling","authors":"","doi":"10.1016/j.ecolind.2024.112610","DOIUrl":null,"url":null,"abstract":"<div><p>Optimizing landscape patterns to promote ecosystem services (ESs) can alleviate human-land conflicts and contribute to achieving the United Nations Sustainable Development Goals (SDGs). However, delving deeper into the effects of landscape patterns on ESs under future scenarios remains challenging. This study combined various models, including the PLUS, InVEST, GeoDetector and MGWR models, to explore the effects of landscape patterns on ESs from current and future perspectives based on multi-scenario land use simulation and the evaluation of landscape patterns and ESs in the Ningxia Along the Yellow River Urban Agglomeration (YUA). The results show that (1) At the optimal scale of 2.7 km × 2.7 km, landscape types have become increasingly diverse and scattered, with habitat quality (HQ) and carbon sequestration (CS) declining and water yield (WY) increasing from 2005 to 2020. The change trends in landscape diversity, fragmentation under the cropland-ecological protection (CEP) and natural development (ND) scenarios during 2020–2035, are consistent with HQ trends from 2005 to 2020, while on the contrary inconsistent with WY. Notably, these trends under the CEP scenario change more slowly than those under the ND scenario. (2) Landscape indicators exert the strongest effects on HQ, followed by CS and WY. Landscape diversity emerges as the primary driver of ESs at the landscape level, while area indicators are predominant at the class level. (3) The spatial scale of landscape patterns’ influence on ESs indicates prioritization at both county and urban agglomeration scales for developing better landscape management measures. (4) The YUA should be divided into six types of regions for landscape management, and it’s necessary to formulate differentiated landscape management measures for each region. (5) This study reveals the spatial heterogeneity of the effects of landscape patterns on ESs, providing valuable partitioning management strategies for landscape planning to further optimize its patterns and thereby promote regional ESs.</p></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":null,"pages":null},"PeriodicalIF":7.0000,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1470160X24010677/pdfft?md5=ddd96158e8fea66a0000553f894c086b&pid=1-s2.0-S1470160X24010677-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecological Indicators","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1470160X24010677","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
Optimizing landscape patterns to promote ecosystem services (ESs) can alleviate human-land conflicts and contribute to achieving the United Nations Sustainable Development Goals (SDGs). However, delving deeper into the effects of landscape patterns on ESs under future scenarios remains challenging. This study combined various models, including the PLUS, InVEST, GeoDetector and MGWR models, to explore the effects of landscape patterns on ESs from current and future perspectives based on multi-scenario land use simulation and the evaluation of landscape patterns and ESs in the Ningxia Along the Yellow River Urban Agglomeration (YUA). The results show that (1) At the optimal scale of 2.7 km × 2.7 km, landscape types have become increasingly diverse and scattered, with habitat quality (HQ) and carbon sequestration (CS) declining and water yield (WY) increasing from 2005 to 2020. The change trends in landscape diversity, fragmentation under the cropland-ecological protection (CEP) and natural development (ND) scenarios during 2020–2035, are consistent with HQ trends from 2005 to 2020, while on the contrary inconsistent with WY. Notably, these trends under the CEP scenario change more slowly than those under the ND scenario. (2) Landscape indicators exert the strongest effects on HQ, followed by CS and WY. Landscape diversity emerges as the primary driver of ESs at the landscape level, while area indicators are predominant at the class level. (3) The spatial scale of landscape patterns’ influence on ESs indicates prioritization at both county and urban agglomeration scales for developing better landscape management measures. (4) The YUA should be divided into six types of regions for landscape management, and it’s necessary to formulate differentiated landscape management measures for each region. (5) This study reveals the spatial heterogeneity of the effects of landscape patterns on ESs, providing valuable partitioning management strategies for landscape planning to further optimize its patterns and thereby promote regional ESs.
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.