Jinhao Shi , Peng Zhang , Yang Liu , Le Tian , Yazhuo Cao , Yue Guo , Ji Li , Yunhan Wang , Junhan Huang , Ri Jin , Weihong Zhu
{"title":"基于 PLS-SEM 和 PLUS 模型的湿地时空变化研究:以三江平原为例","authors":"Jinhao Shi , Peng Zhang , Yang Liu , Le Tian , Yazhuo Cao , Yue Guo , Ji Li , Yunhan Wang , Junhan Huang , Ri Jin , Weihong Zhu","doi":"10.1016/j.ecolind.2024.112812","DOIUrl":null,"url":null,"abstract":"<div><div>Wetlands are among the most productive ecosystems and play crucial roles in relation to biodiversity conservation and various ecosystem services. However, rapid urbanization and environmental changes have led to the loss of a significant number of wetlands, making it imperative to understand the driving forces behind wetland changes. This study employed Partial Least Squares Structural Equation Modeling (PLS-SEM) and the Patch-generating Land Use Simulation (PLUS) model to investigate the influences of natural factors and urbanization on wetland distribution. Based on the driving factors, simulations were conducted for three scenarios—Natural Increase Scenario (NIS), Economic Development Scenario (EDS), and Wetland Protection Scenario (WPS)—projecting the wetland distribution in the Sanjiang Plain until 2050. Results indicate that from 1990 to 2020, the wetland area increased by 9,548.58 km<sup>2</sup>, with paddy fields increasing by 12,995.73 km<sup>2</sup> and marsh wetlands decreasing by 1,031.9 km<sup>2</sup>. The factors driving wetland distribution varied across different periods. Between 1990 and 2000, topography and urbanization significantly influenced wetland distribution, whereas climate factors became gradually more significant between 2010 and 2020. Furthermore, in addition to exerting direct impacts on wetland distribution, urbanization and climate factors can indirectly affect wetland distribution by influencing topography and soil. Future development scenarios indicate an inevitable increase in paddy field areas and decrease in wetland areas. This framework provides an effective approach for exploring regional wetland changes and supporting regional wetland conservation and future sustainable development.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"169 ","pages":"Article 112812"},"PeriodicalIF":7.0000,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Study on spatiotemporal changes of wetlands based on PLS-SEM and PLUS model: The case of the Sanjiang Plain\",\"authors\":\"Jinhao Shi , Peng Zhang , Yang Liu , Le Tian , Yazhuo Cao , Yue Guo , Ji Li , Yunhan Wang , Junhan Huang , Ri Jin , Weihong Zhu\",\"doi\":\"10.1016/j.ecolind.2024.112812\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Wetlands are among the most productive ecosystems and play crucial roles in relation to biodiversity conservation and various ecosystem services. However, rapid urbanization and environmental changes have led to the loss of a significant number of wetlands, making it imperative to understand the driving forces behind wetland changes. This study employed Partial Least Squares Structural Equation Modeling (PLS-SEM) and the Patch-generating Land Use Simulation (PLUS) model to investigate the influences of natural factors and urbanization on wetland distribution. Based on the driving factors, simulations were conducted for three scenarios—Natural Increase Scenario (NIS), Economic Development Scenario (EDS), and Wetland Protection Scenario (WPS)—projecting the wetland distribution in the Sanjiang Plain until 2050. Results indicate that from 1990 to 2020, the wetland area increased by 9,548.58 km<sup>2</sup>, with paddy fields increasing by 12,995.73 km<sup>2</sup> and marsh wetlands decreasing by 1,031.9 km<sup>2</sup>. The factors driving wetland distribution varied across different periods. Between 1990 and 2000, topography and urbanization significantly influenced wetland distribution, whereas climate factors became gradually more significant between 2010 and 2020. Furthermore, in addition to exerting direct impacts on wetland distribution, urbanization and climate factors can indirectly affect wetland distribution by influencing topography and soil. Future development scenarios indicate an inevitable increase in paddy field areas and decrease in wetland areas. This framework provides an effective approach for exploring regional wetland changes and supporting regional wetland conservation and future sustainable development.</div></div>\",\"PeriodicalId\":11459,\"journal\":{\"name\":\"Ecological Indicators\",\"volume\":\"169 \",\"pages\":\"Article 112812\"},\"PeriodicalIF\":7.0000,\"publicationDate\":\"2024-11-09\",\"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/S1470160X2401269X\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecological Indicators","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1470160X2401269X","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}
Study on spatiotemporal changes of wetlands based on PLS-SEM and PLUS model: The case of the Sanjiang Plain
Wetlands are among the most productive ecosystems and play crucial roles in relation to biodiversity conservation and various ecosystem services. However, rapid urbanization and environmental changes have led to the loss of a significant number of wetlands, making it imperative to understand the driving forces behind wetland changes. This study employed Partial Least Squares Structural Equation Modeling (PLS-SEM) and the Patch-generating Land Use Simulation (PLUS) model to investigate the influences of natural factors and urbanization on wetland distribution. Based on the driving factors, simulations were conducted for three scenarios—Natural Increase Scenario (NIS), Economic Development Scenario (EDS), and Wetland Protection Scenario (WPS)—projecting the wetland distribution in the Sanjiang Plain until 2050. Results indicate that from 1990 to 2020, the wetland area increased by 9,548.58 km2, with paddy fields increasing by 12,995.73 km2 and marsh wetlands decreasing by 1,031.9 km2. The factors driving wetland distribution varied across different periods. Between 1990 and 2000, topography and urbanization significantly influenced wetland distribution, whereas climate factors became gradually more significant between 2010 and 2020. Furthermore, in addition to exerting direct impacts on wetland distribution, urbanization and climate factors can indirectly affect wetland distribution by influencing topography and soil. Future development scenarios indicate an inevitable increase in paddy field areas and decrease in wetland areas. This framework provides an effective approach for exploring regional wetland changes and supporting regional wetland conservation and future sustainable development.
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.