Qinqin Shi , Hai Chen , Xiaoying Liang , Di Liu , Tianwei Geng , Hang Zhang
{"title":"Combination of participatory mapping and Maxent model to visualize the cultural ecosystem services at county scale","authors":"Qinqin Shi , Hai Chen , Xiaoying Liang , Di Liu , Tianwei Geng , Hang Zhang","doi":"10.1016/j.ecoser.2025.101710","DOIUrl":null,"url":null,"abstract":"<div><div>Cultural ecosystem services (CESs) evaluation is the key to comprehensively understand and evaluate ecosystem services. Spatial mapping of CESs can provide visual information for ecosystem management. The combination of participatory mapping and Maxent model helps to extend place-based spatial assessment of CESs to the regional scale. In this study, representative samples were obtained through participation mapping method, and the Maxent model was used to realize the spatial mapping of six types of CESs in Mizhi County based on environmental variables, and the influence of environmental variables on the spatial distribution of CESs was analyzed. The results showed that the Maxent model was robust for CESs spatial mapping at county scale. The spatial distributions of six types of CESs in Mizhi County had similar regularity, the high-values were concentrated along the Wuding River and its east–west tributaries, the median and low-values spread to the periphery along the high-value areas successively, but the area proportions of different grades of different types of CESs were different. Moreover, the spatial distribution of different CESs had overlapping areas, 62.62% of the areas had two or more types of CESs. In terms of the impact of environmental variables on the spatial distribution of CESs, the distance to residential areas had the largest contribution rate to all CESs, ranging from 35.88% to 67.37%. The remaining variables had different contribution rates to different CESs, and the distribution probabilities of CESs changed with the change of environmental variable parameters. This study can provide a reference for the spatial mapping of CESs at the regional scale, and promote the incorporation of CESs research into decision-making.</div></div>","PeriodicalId":51312,"journal":{"name":"Ecosystem Services","volume":"72 ","pages":"Article 101710"},"PeriodicalIF":6.1000,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecosystem Services","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2212041625000142","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Cultural ecosystem services (CESs) evaluation is the key to comprehensively understand and evaluate ecosystem services. Spatial mapping of CESs can provide visual information for ecosystem management. The combination of participatory mapping and Maxent model helps to extend place-based spatial assessment of CESs to the regional scale. In this study, representative samples were obtained through participation mapping method, and the Maxent model was used to realize the spatial mapping of six types of CESs in Mizhi County based on environmental variables, and the influence of environmental variables on the spatial distribution of CESs was analyzed. The results showed that the Maxent model was robust for CESs spatial mapping at county scale. The spatial distributions of six types of CESs in Mizhi County had similar regularity, the high-values were concentrated along the Wuding River and its east–west tributaries, the median and low-values spread to the periphery along the high-value areas successively, but the area proportions of different grades of different types of CESs were different. Moreover, the spatial distribution of different CESs had overlapping areas, 62.62% of the areas had two or more types of CESs. In terms of the impact of environmental variables on the spatial distribution of CESs, the distance to residential areas had the largest contribution rate to all CESs, ranging from 35.88% to 67.37%. The remaining variables had different contribution rates to different CESs, and the distribution probabilities of CESs changed with the change of environmental variable parameters. This study can provide a reference for the spatial mapping of CESs at the regional scale, and promote the incorporation of CESs research into decision-making.
期刊介绍:
Ecosystem Services is an international, interdisciplinary journal that is associated with the Ecosystem Services Partnership (ESP). The journal is dedicated to exploring the science, policy, and practice related to ecosystem services, which are the various ways in which ecosystems contribute to human well-being, both directly and indirectly.
Ecosystem Services contributes to the broader goal of ensuring that the benefits of ecosystems are recognized, valued, and sustainably managed for the well-being of current and future generations. The journal serves as a platform for scholars, practitioners, policymakers, and other stakeholders to share their findings and insights, fostering collaboration and innovation in the field of ecosystem services.