{"title":"利用MaxEnt对地中海沿岸物种进行生态位建模","authors":"Jean Stephan, Melissa Korban","doi":"10.1016/j.ecolind.2025.113167","DOIUrl":null,"url":null,"abstract":"<div><div>Despite their key environmental role, riparian tree and shrub species gained little attention in ecological niche modeling (ENM), especially in semi-arid environments.</div><div>This study examines the performance of selected climatic, topographic, and geographic predictors in ENM of obligate and non-obligate riparian tree and shrub species in perennial and intermittent streams in the Mediterranean biome.</div><div>MaxEnt algorithm was used for ENM. Three models were designed with different sets of predictors with cropped and non-cropped backgrounds around the riparian zone.</div><div>The models generated different predicted distribution maps by species and compared them with the presence points of the studied species. All models showed satisfactory results, with the third model with a non-cropped background and an exhaustive list of predictors showing the highest performance and providing accurate maps, especially when compared to the first run with a cropped background around the riparian zone and the omission of distance from the riverbank and the sea from the predictors used. Predictors such as the river flow regime, the distance from the riverbank, the Emberger Quotient, and the mean of the minimal temperature of the coldest month were essential for the predicted distribution of the selected species.</div><div>The order of contribution of each predictor in the model enabled us to validate the grouping of species into obligate and non-obligate riparian and conclude which predictors to select for ENM based on the species’ nature. The results could be suggested for red listing assessment of riparian tree species and appropriate species selection for ecosystem restoration.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"171 ","pages":"Article 113167"},"PeriodicalIF":7.0000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Ecological niche modelling using MaxEnt for riparian species in a Mediterranean context\",\"authors\":\"Jean Stephan, Melissa Korban\",\"doi\":\"10.1016/j.ecolind.2025.113167\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Despite their key environmental role, riparian tree and shrub species gained little attention in ecological niche modeling (ENM), especially in semi-arid environments.</div><div>This study examines the performance of selected climatic, topographic, and geographic predictors in ENM of obligate and non-obligate riparian tree and shrub species in perennial and intermittent streams in the Mediterranean biome.</div><div>MaxEnt algorithm was used for ENM. Three models were designed with different sets of predictors with cropped and non-cropped backgrounds around the riparian zone.</div><div>The models generated different predicted distribution maps by species and compared them with the presence points of the studied species. All models showed satisfactory results, with the third model with a non-cropped background and an exhaustive list of predictors showing the highest performance and providing accurate maps, especially when compared to the first run with a cropped background around the riparian zone and the omission of distance from the riverbank and the sea from the predictors used. Predictors such as the river flow regime, the distance from the riverbank, the Emberger Quotient, and the mean of the minimal temperature of the coldest month were essential for the predicted distribution of the selected species.</div><div>The order of contribution of each predictor in the model enabled us to validate the grouping of species into obligate and non-obligate riparian and conclude which predictors to select for ENM based on the species’ nature. The results could be suggested for red listing assessment of riparian tree species and appropriate species selection for ecosystem restoration.</div></div>\",\"PeriodicalId\":11459,\"journal\":{\"name\":\"Ecological Indicators\",\"volume\":\"171 \",\"pages\":\"Article 113167\"},\"PeriodicalIF\":7.0000,\"publicationDate\":\"2025-02-01\",\"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/S1470160X25000962\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/2/8 0:00:00\",\"PubModel\":\"Epub\",\"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/S1470160X25000962","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/2/8 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Ecological niche modelling using MaxEnt for riparian species in a Mediterranean context
Despite their key environmental role, riparian tree and shrub species gained little attention in ecological niche modeling (ENM), especially in semi-arid environments.
This study examines the performance of selected climatic, topographic, and geographic predictors in ENM of obligate and non-obligate riparian tree and shrub species in perennial and intermittent streams in the Mediterranean biome.
MaxEnt algorithm was used for ENM. Three models were designed with different sets of predictors with cropped and non-cropped backgrounds around the riparian zone.
The models generated different predicted distribution maps by species and compared them with the presence points of the studied species. All models showed satisfactory results, with the third model with a non-cropped background and an exhaustive list of predictors showing the highest performance and providing accurate maps, especially when compared to the first run with a cropped background around the riparian zone and the omission of distance from the riverbank and the sea from the predictors used. Predictors such as the river flow regime, the distance from the riverbank, the Emberger Quotient, and the mean of the minimal temperature of the coldest month were essential for the predicted distribution of the selected species.
The order of contribution of each predictor in the model enabled us to validate the grouping of species into obligate and non-obligate riparian and conclude which predictors to select for ENM based on the species’ nature. The results could be suggested for red listing assessment of riparian tree species and appropriate species selection for ecosystem restoration.
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.