Ecological Indicators最新文献

{"title":"A social-ecological-technological vulnerability approach for assessing urban hydrological risks","authors":"S. Khromova ,&nbsp;G. Villalba Méndez ,&nbsp;M.J. Eckelman ,&nbsp;P. Herreros-Cantis ,&nbsp;J. Langemeyer","doi":"10.1016/j.ecolind.2025.113334","DOIUrl":"10.1016/j.ecolind.2025.113334","url":null,"abstract":"<div><div>In the context of urban population growth and climate change, and ever greater number of people are anticipated to face severe risks associated with extreme climate events; major ones are due to stormwater-related hazards. This study offers novel understanding of the complex nature of water-related risks in urban geographies by employing a Social-Ecological-Technological Systems (SETS) framework to assess vulnerabilities. Hydrology-informed urban risk index was developed, quantifying seventeen indicators from historical and modeled data on sewer overflow and flood events. The spatially explicit SETS-based approach identifies high-risk communities and hotspots where multiple vulnerabilities intersect and can serve as a valuable tool for guiding policy and decision-making to support more resilient urban futures. Our findings reveal that social vulnerability plays a critical role in determining the overall risk (R = 0.4), with the greatest impacts imposed on socially vulnerable communities. However, insights from the ecological (R = 0.2) and technological (R = 0.1) domains provide essential guidance for future risk reduction strategies—such as upgrading outdated sewer infrastructure and exploring green space potential for run-off mitigation. The framework proposed is generalizable to other cities facing similar environmental challenges, highlighting its potential as a foundational tool for policymaking to reduce risks associated with extreme climate events.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"173 ","pages":"Article 113334"},"PeriodicalIF":7.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Alien fish shift the biomass distribution towards a bottom-heavy pyramid in the Pearl River Basin, China","authors":"Yuguo Xia ,&nbsp;Yuefei Li ,&nbsp;Shuli Zhu ,&nbsp;Xinhui Li ,&nbsp;Yuxin Zhang ,&nbsp;Jie Li","doi":"10.1016/j.ecolind.2025.113430","DOIUrl":"10.1016/j.ecolind.2025.113430","url":null,"abstract":"<div><div>The effects of biological invasion on aquatic ecosystems have been widely evaluated, with varying effects at the population and food web levels. However, the effects of invasive alien fish on trophic cascades, biomass distribution, and population dynamics are poorly understood, leading to poor ecosystem management. In this study, we quantified the trophic disruptions attributable to alien fish by investigating changes in the biomass structure of a food web pyramid. Using a 3-year catch dataset from the Pearl River Basin in China, we employed Bayesian mixed-effects models, a piecewise structural equation model, and Pearson’s correlation analysis to explore alien fish effects and trophic interactions in food webs. Our results indicated that invasive alien fish lead to a downward shift in the food web. The alien fish did not affect the total catch per unit effort (CPUE) but significantly decreased the mean trophic level. Trophic interactions were primarily controlled by bottom-up forces in the invaded ecosystem. Moreover, biomass compensation effects were observed between the alien and native species. Invasion did not considerably affect the biomass structure but shifted the biomass distribution towards a more traditional bottom-heavy pyramid. Our results highlight the importance of managing invasive alien species at the ecosystem level and that biomass compensation could be considered for controlling invasive populations.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"173 ","pages":"Article 113430"},"PeriodicalIF":7.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decreasing soil erosion in South China with uncertainties driven by NDVI estimates","authors":"Xinqing Lu ,&nbsp;Yulian Liang ,&nbsp;Tongtiegang Zhao ,&nbsp;Xudong Zhu ,&nbsp;Zhangcai Qin","doi":"10.1016/j.ecolind.2025.113422","DOIUrl":"10.1016/j.ecolind.2025.113422","url":null,"abstract":"<div><div>Investigating soil erosion in South China, one of China’s most populous coastal regions, is crucial for understanding regional soil and water conservation, preventing soil degradation, and safeguarding food security. However, significant discrepancies persist among existing estimates of soil erosion, calling for further evaluation of long-term trends in its magnitude and spatial–temporal dynamics. This study utilized the Revised Universal Soil Loss Equation (RUSLE) model to assess soil erosion dynamics over 35 years, and further evaluated the influence of the Normalized Difference Vegetation Index (NDVI). Our findings revealed a general decline in soil erosion across South China, dominated by slight and mild erosion. However, the spatiotemporal patterns exhibited marked variations depending on NDVI datasets, particularly in interannual fluctuations and spatial discrepancies. The soil erosion modulus estimated from AVHRR NDVI demonstrated higher values and greater variability than those based on GIMMS NDVI. Spatially, three out of five datasets indicated a consistent reduction in erosion intensity, while two AVHRR datasets showed an initial decline followed by a resurgence over the past decade. Variations in NDVI data can lead to order-of-magnitude differences in soil erosion estimates, highlighting the need for careful dataset selection for soil erosion analysis. A comprehensive analysis and understanding of these differences are needed to provide valuable insights into the applicability of various NDVI datasets in future soil erosion modeling and risk assessment.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"173 ","pages":"Article 113422"},"PeriodicalIF":7.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial scale effects on the response strength of water resources supply-demand balance to driving factors","authors":"Xiangping Liu,&nbsp;Zhuowei Hu,&nbsp;Yongcai Wang,&nbsp;Mi Wang,&nbsp;Wenxing Hou","doi":"10.1016/j.ecolind.2025.113431","DOIUrl":"10.1016/j.ecolind.2025.113431","url":null,"abstract":"<div><div>Amidst escalating global climate change and intensifying anthropogenic activities, the imbalance between water supply and demand has become a critical sustainability challenge. Nevertheless, our understanding of the spatiotemporal heterogeneity in the water resources supply–demand balance is limited, particularly regarding scale-dependent responses of driving factors. Therefore, this study adopts a geographical perspective, focusing on the Haihe River Basin — an internationally recognized water-scarce region. By integrating machine learning and GIS spatial analysis techniques, it reveals the multi-scale spatial relationships and mechanisms between various factors, including natural and socio-economic ones, in the water resources supply–demand system, from the aspects of feature importance, variable correlation, and spatial heterogeneity. The results show that the Haihe River Basin faces severe water shortages. Compared to localized water shortages at finer scales (grid scale), regional average water scarcity is more pronounced at coarser scales (sub-basin and city scales), suggesting that management strategies based on a single scale may have limited applicability. Additionally, the response of water supply and demand to driving factors exhibits uncertainty influenced by spatial scale. Specifically, the correlation between water supply and natural factors such as precipitation and slope is stronger at the sub-basin scale than at the city scale. As for water demand, at the city scale, factors with strong correlations become more pronounced, with human factors having a greater influence on water demand. The effect scale of influencing factors on water supply is smaller, while the effect scale on water demand is larger. Topographic factors such as elevation and slope show significant spatial heterogeneity in water demand, whereas climatic and vegetation factors exhibit pronounced spatial heterogeneity in water supply. Natural geographical factors tend to exhibit smaller effect scales.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"173 ","pages":"Article 113431"},"PeriodicalIF":7.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scale effects and threshold management in the influence of landscape patterns on non-point source pollution","authors":"Jianhong Wu ,&nbsp;Ziqing Yang ,&nbsp;Hengqin Wang ,&nbsp;Jiani Xu","doi":"10.1016/j.ecolind.2025.113439","DOIUrl":"10.1016/j.ecolind.2025.113439","url":null,"abstract":"<div><div>The migration of nonpoint source (NPS) pollution is influenced by the resistance cost distance between landscape units and rivers. Understanding the relationship between landscape proximity and riverine pollutants is crucial for optimizing landscape patterns to mitigate NPS pollution. However, given that water quality responses to landscape patterns may depend on the proximity of landscape units to rivers and exhibit nonlinear tendencies, these relationships remain poorly understood. This study applied redundancy analysis and nonlinear segmented regression analysis to evaluate the effects of landscape patterns on NPS pollution migration in a headwater watershed in eastern China, comprising 29 sub-watersheds with diverse landscape characteristics. The results revealed that landscape patterns in the high-proximity zone were most effective in explaining riverine pollutant variations during the wet season, while those in the extremely low-proximity zone were more influential during the dry season. Therefore, landscape pattern regulation should adopt a multiscale perspective. The key landscape indicators affecting water quality differed across proximity zones. In the high-proximity zone, the land-use intensity index (LI), percentage of residential area (Res), and aggregation index of residential areas (AI_res) were crucial. In the extremely low-proximity zone, LI and the aggregation index of forestland (AI_for) played dominant roles. To improve water quality, landscape planning should consider maintaining LI &lt; 183.22 and AI_res &lt; 92.66 % in the high-proximity zone, and AI_for &lt; 95.68 % in the extremely low-proximity zone. This study highlights that optimizing landscape patterns through a multiscale approach and the consideration of landscape thresholds could enhance the effectiveness of NPS pollution control and ultimately improve water quality in headwater watersheds.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"173 ","pages":"Article 113439"},"PeriodicalIF":7.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143785100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global-scale improvement of terrestrial gross primary productivity estimation by integrating optical remote sensing with meteorological data","authors":"Yao Wenyu ,&nbsp;Bie Qiang","doi":"10.1016/j.ecolind.2025.113429","DOIUrl":"10.1016/j.ecolind.2025.113429","url":null,"abstract":"<div><div>This study integrates sunlight-induced chlorophyll fluorescence (SIF), meteorological factors, and various optical factors (such as LAI, FAPAR, and NDVI) to estimate global-scale gross primary productivity (GPP) using a random forest model (RF). The results show that the random forest model can significantly improve the GPP estimation accuracy, and its overall coefficient of determination (R²) is 0.84, the consistency index (IOA) is 0.97, and the root mean square error (RMSE) is only 1.73 g C m^-² d^-1. As the core variable of the model, SIF showed significant contributions in all vegetation types, making up for the limitations of the traditional vegetation index in monitoring the dynamic changes of photosynthesis. The multi-source data fusion method effectively improves the adaptability and robustness of the model to the dynamic changes in GPP in complex ecosystems (such as wetlands and farmland). This study shows that the integration of SIF, meteorological factors and optical factors to construct a multi-source fusion model can not only improve the accuracy and spatiotemporal resolution ability of global-scale GPP estimation, but also provide scientific support for further research on the interaction between ecosystem carbon cycle and climate change.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"173 ","pages":"Article 113429"},"PeriodicalIF":7.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143767546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review on environmental DNA (eDNA) Science: An eco-friendly survey method for conservation and restoration of fragile ecosystems","authors":"Ashish Sahu ,&nbsp;Mahender Singh ,&nbsp;Adnan Amin ,&nbsp;Monisa Mehboob Malik ,&nbsp;Shariq Nazir Qadri ,&nbsp;Adnan Abubakr ,&nbsp;S.Surya Teja ,&nbsp;Shabir Ahmad Dar ,&nbsp;Ishtiyaq Ahmad","doi":"10.1016/j.ecolind.2025.113441","DOIUrl":"10.1016/j.ecolind.2025.113441","url":null,"abstract":"<div><div>The conservation and restoration of fragile ecosystems and their genetic resources depends on precisely assessing species diversity and richness, understanding spatiotemporal dynamics, and stock structure analysis. Traditional monitoring methods like visual surveys, physical captures, individual counting, sibling or cryptic species identification, and immature life stages (juveniles) of animals are often intrusive, time-consuming, and provide rough estimates. Environmental DNA (eDNA) has emerged as a novel powerful tool for detecting and quantifying the presence or absence of species through genetic traces left in the environment. It is particularly suited for vulnerable habitats that are sensitive to human disturbance. Herein, we present a temporal analysis from 2008 to 2024, which indicates a consistent increase in eDNA research across fragile ecosystems and their living microbes and microorganisms. Freshwater eDNA studies contribute the highest (33.93%), followed by brackish (26.19%) and marine waters (27.38%). There is a notable focus on invasive (25.48%) and endangered species (22.36%). This systematic review provides an overview of the standard methodological considerations for eDNA science, covering stages from sample collection to advanced bioinformatics processing. It addresses various pipelines, databases, and software tools, essential for accurate data interpretation. The areas for improvement in eDNA science and future directions are also presented that can improve sensitivity, scalability, and reliability. Further, the review highlights important worldwide facilities, organizations, and laboratories leading in eDNA research, along with societies, projects, and programs promoting knowledge exchange.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"173 ","pages":"Article 113441"},"PeriodicalIF":7.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143767544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Landscape ecological risk assessment and driving factors analysis based on optimal spatial scales in Luan River Basin, China” [Ecol. Indic. 169 (2024) 112821]","authors":"Nijuan Yang ,&nbsp;Ting Zhang ,&nbsp;Jianzhu Li ,&nbsp;Ping Feng ,&nbsp;Nina Yang","doi":"10.1016/j.ecolind.2025.113366","DOIUrl":"10.1016/j.ecolind.2025.113366","url":null,"abstract":"","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"173 ","pages":"Article 113366"},"PeriodicalIF":7.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial–temporal evolution of economic-ecological benefits and their driving factors in Yellow River irrigation areas","authors":"Chang Liu ,&nbsp;Enhui Jiang ,&nbsp;Bo Qu ,&nbsp;Lingqi Li ,&nbsp;Lingang Hao ,&nbsp;Wei Zhang","doi":"10.1016/j.ecolind.2025.113419","DOIUrl":"10.1016/j.ecolind.2025.113419","url":null,"abstract":"<div><div>Understanding the spatial–temporal variations in the economic-ecological benefits and driving factors within the Yellow River irrigation areas (YRIA) is crucial for ensuring high-quality socioeconomic development and ecological environment health of the Yellow River Basin, China, which is inherently water scarce. This study improved an accounting method for economic-ecological benefits in irrigation areas, based on the quantification methods for ecosystem service value and economic output value at constant prices, and quantitatively analyzed the different types of benefits and total benefits in the YRIA. The key driving factors of the economic-ecological benefits per unit area (<em>EEBUA</em>) in the YRIA and their spatial and temporal driving mechanisms were analyzed by the least absolute shrinkage and selection operator (LASSO) algorithm and geographically and temporally weighted regression (GTWR) model. The results revealed that the economic-ecological benefits markedly increased during 1990–2020. The YRIA in the middle and lower reaches of the Yellow River had stronger economic benefit output capabilities, while the YRIA in the upper reaches of the Yellow River had stronger ecological benefit output capabilities. The order of importance of the key factors influencing the economic-ecological benefits was precipitation (<em>PRE)</em>, industrial water use (<em>IW</em>), the proportion of the cultivated land area (<em>CL</em>), the proportion of the arable land area (<em>AL</em>), and agricultural water use (<em>AW</em>). Under the limited total water consumption assumption, the average standardized regression coefficient of <em>AW</em> with the <em>EEBUA</em> decreased from 0.034 to −0.120 in 1995–2000, indicating a shift from a promoting to an inhibiting effect. The average standardized regression coefficient of <em>IW</em> notably increased from 0.37 to 1.69, indicating a rapid rise in the importance of <em>IW</em> for the economic-ecological benefits economic-ecological benefits in 2010–2015. This inhibiting effect of <em>AW</em> on the <em>EEBUA</em> was greater in the downstream areas of the Yellow River, and the intensity gradually increased. These findings could provide a basis for water and land resource utilization planning and the yellow river water dispatch scheme.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"173 ","pages":"Article 113419"},"PeriodicalIF":7.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decadal-scale changes in fish spawning strategies: A case study of striped bass in the Hudson River","authors":"Xindong Pan ,&nbsp;Wenchao Zhang ,&nbsp;Wanqi Liu ,&nbsp;Anh Nguyen ,&nbsp;Jessica Best ,&nbsp;Richard Pendleton ,&nbsp;Liangmin Huang ,&nbsp;Yong Chen","doi":"10.1016/j.ecolind.2025.113410","DOIUrl":"10.1016/j.ecolind.2025.113410","url":null,"abstract":"<div><div>Understanding spawning behavior is critical in evaluating the productivity and vulnerability of fish populations to exploitation and climate change. Using the ichthyoplankton data collected in the Hudson River Biological Monitoring Program (HRBMP), we evaluated the spawning behavior of striped bass (<em>Morone saxatilis</em>) in the Hudson River estuary (HRE). We developed three novel spawning optimum indices: Thermal Optimum Index (THOI), Temporal Optimum Index (TEOI), and Spatial Optimum Index (SOI). Our results showed that striped bass prefer to spawn at certain temperature ranges during two specific and distinct time periods but in extensive locations in the HRE. Their spawning behavior had changed over time, with two shifts occurring in 1985 and 1998 and resulting in three distinct periods with different spawning strategies. These changes, including narrower range of optimal spawning temperatures and reduced diversity in spatial and temporal spawning behavior, may negatively impact the population’s stability and reproductive resilience. Our study demonstrates the importance of a long-term monitoring program to understand spawning strategies in striped bass and highlights the importance of considering spawning behavior in fisheries management.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"173 ","pages":"Article 113410"},"PeriodicalIF":7.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143767545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}