Pub Date : 2025-02-01DOI: 10.1016/j.ecolind.2025.113118
Chuyi Guo , Yuchi Yang
Understanding the intricate relationships between the urban built environment, public activity, and public perception is vital for effective urban planning strategies and social sustainability. This study develops a multi-modal data analysis framework, incorporating social media data, topic modeling, and spatial statistical techniques. Using Xi’an, China, as a typical case, it examines how public activities change over time and space, how they interact with urban spaces, as well as how different aspects of the built environment shape public perception.The main findings are as follows:(1) Public activity varies significantly across day and night, interacting in complex ways with urban spatial elements;(2) Public activity is driven by multiple interconnected factors;(3) The built environment affects public perception unevenly, with certain spatial elements evoking strong emotional resonance and reinforcing cultural identity;(4) A dynamic feedback loop exists between public activity and perception.This research offers novel insights into precision-oriented, human-centered, and sustainable urban development.
{"title":"A multi-modal social media data analysis framework: Exploring the complex relationships among urban environment, public activity, and public perception—A case study of Xi’an, China","authors":"Chuyi Guo , Yuchi Yang","doi":"10.1016/j.ecolind.2025.113118","DOIUrl":"10.1016/j.ecolind.2025.113118","url":null,"abstract":"<div><div>Understanding the intricate relationships between the urban built environment, public activity, and public perception is vital for effective urban planning strategies and social sustainability. This study develops a multi-modal data analysis framework, incorporating social media data, topic modeling, and spatial statistical techniques. Using Xi’an, China, as a typical case, it examines how public activities change over time and space, how they interact with urban spaces, as well as how different aspects of the built environment shape public perception.The main findings are as follows:(1) Public activity varies significantly across day and night, interacting in complex ways with urban spatial elements;(2) Public activity is driven by multiple interconnected factors;(3) The built environment affects public perception unevenly, with certain spatial elements evoking strong emotional resonance and reinforcing cultural identity;(4) A dynamic feedback loop exists between public activity and perception.This research offers novel insights into precision-oriented, human-centered, and sustainable urban development.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"171 ","pages":"Article 113118"},"PeriodicalIF":7.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143359478","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}
Pub Date : 2025-02-01DOI: 10.1016/j.ecolind.2025.113177
Rafael de Fraga , Luiz Gustavo Rodrigues Oliveira Santos , Valeria da Cunha Tavares , Leonardo Carreira Trevelin , Maurício Takashi Coutinho Watanabe , Leandro Maioli , Samir Rolim , Carolina da Silva Carvalho
Species extinction rates have surpassed background rates in the contemporary times, triggering a sixth global mass extinction event. Failure to prevent massive extinctions may be related to the lack of efficient tools to estimate local and regional population decline and to identify where declining species likely occur. We used a set of 22 plant species distributed in a globally unique ecosystem, the eastern Amazonian mosaics of forests, iron-rich open “cangas” and iron mine lands, as a model to test whether dynamic multispecies occupancy models can be used to assess population decline. Based on the metapopulation equilibrium between colonization and extinction probabilities compared across 90 mining plots and 60 control plots, we estimate that 45.4 % of the sampled species show no evidence of population decline. For 18.2 % of the species, we found negative equilibrium for both mining and control plots. For 36.4 % of the species, we found negative equilibrium only in 2.22–23.3 % of the mining plots, although equilibrium values were similar to those estimated for most control plots. Negative equilibrium was more associated with the reduced colonization probabilities than with elevated extinction probabilities. Our results indicate that for some wide-ranging species, negative metapopulation equilibrium was restricted to mining plots, indicating sensitivity of these species to environmental changes caused by mining. For some endemic species, apparent population declines are more likely to be occurring due to natural causes than to the negative effects of mining.
{"title":"Multispecies occupancy models unravel reduced colonization probabilities in plants from the unique Amazonian cangas","authors":"Rafael de Fraga , Luiz Gustavo Rodrigues Oliveira Santos , Valeria da Cunha Tavares , Leonardo Carreira Trevelin , Maurício Takashi Coutinho Watanabe , Leandro Maioli , Samir Rolim , Carolina da Silva Carvalho","doi":"10.1016/j.ecolind.2025.113177","DOIUrl":"10.1016/j.ecolind.2025.113177","url":null,"abstract":"<div><div>Species extinction rates have surpassed background rates in the contemporary times, triggering a sixth global mass extinction event. Failure to prevent massive extinctions may be related to the lack of efficient tools to estimate local and regional population decline and to identify where declining species likely occur. We used a set of 22 plant species distributed in a globally unique ecosystem, the eastern Amazonian mosaics of forests, iron-rich open “cangas” and iron mine lands, as a model to test whether dynamic multispecies occupancy models can be used to assess population decline. Based on the metapopulation equilibrium between colonization and extinction probabilities compared across 90 mining plots and 60 control plots, we estimate that 45.4 % of the sampled species show no evidence of population decline. For 18.2 % of the species, we found negative equilibrium for both mining and control plots. For 36.4 % of the species, we found negative equilibrium only in 2.22–23.3 % of the mining plots, although equilibrium values were similar to those estimated for most control plots. Negative equilibrium was more associated with the reduced colonization probabilities than with elevated extinction probabilities. Our results indicate that for some wide-ranging species, negative metapopulation equilibrium was restricted to mining plots, indicating sensitivity of these species to environmental changes caused by mining. For some endemic species, apparent population declines are more likely to be occurring due to natural causes than to the negative effects of mining.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"171 ","pages":"Article 113177"},"PeriodicalIF":7.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377567","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}
Pub Date : 2025-02-01DOI: 10.1016/j.ecolind.2025.113217
Gege Cai , Jiamei Zhang , Wanlu Li , Jiejun Zhang , Yun Liu , Shanshan Xi , Guolian Li , Haibin Li , Xing Chen , Fanhao Song , Fazhi Xie
Phosphorus contamination in rivers has received widespread attention. However, in areas with extensive sources of phosphorus and complex hydrogeology conditions, it is difficult to accurately evaluate the main influencing factors of phosphorus. In present study, the spatiotemporal variations of phosphorus were analyzed at wet and dry seasons in the Yangtze River during 2020–2023. Phosphorus concentrations in the Yangtze River decreased by 9.15 % in four years, reaching a peak in summer. In addition, absolute principal component score‐multiple linear regression (APCS‐MLR) model was proved to be suitable for exploring the contribution of main phosphorus-influencing factors in Yangtze River. The contribution rate in wet season was ranked as point source pollution (40.13 %) > agricultural pollution (32.74 %) > organic pollutants (3.78 %), while the contribution rate in dry season was ranked as point source pollution (44.88 %) > organic pollutants (13.13 %) > seasonal factor (7.60 %). Machine learning models (e.g., RidgeCV, Random Forest, XGBoost) were used to establish a connection between total phosphorus concentrations and explanatory variables defining influencing factors, aiming to predict total phosphorus concentrations in the Yangtze River. The anthropogenic and natural variables, such as domestic sewage, GDP, agricultural area, livestock, rainfall, wind speed, temperature and population were selected as predictors. The Random Forest model performed well in predicting total phosphorus concentrations, with R2 value of 0.76. This study provides useful information for optimizing phosphorus pollution management and strategies for eutrophication control in the Yangtze River as well as in other large watersheds.
{"title":"Spatiotemporal variation and influencing factors of phosphorus in Asia’s longest river based on receptor model and machine learning","authors":"Gege Cai , Jiamei Zhang , Wanlu Li , Jiejun Zhang , Yun Liu , Shanshan Xi , Guolian Li , Haibin Li , Xing Chen , Fanhao Song , Fazhi Xie","doi":"10.1016/j.ecolind.2025.113217","DOIUrl":"10.1016/j.ecolind.2025.113217","url":null,"abstract":"<div><div>Phosphorus contamination in rivers has received widespread attention. However, in areas with extensive sources of phosphorus and complex hydrogeology conditions, it is difficult to accurately evaluate the main influencing factors of phosphorus. In present study, the spatiotemporal variations of phosphorus were analyzed at wet and dry seasons in the Yangtze River during 2020–2023. Phosphorus concentrations in the Yangtze River decreased by 9.15 % in four years, reaching a peak in summer. In addition, absolute principal component score‐multiple linear regression (APCS‐MLR) model was proved to be suitable for exploring the contribution of main phosphorus-influencing factors in Yangtze River. The contribution rate in wet season was ranked as point source pollution (40.13 %) > agricultural pollution (32.74 %) > organic pollutants (3.78 %), while the contribution rate in dry season was ranked as point source pollution (44.88 %) > organic pollutants (13.13 %) > seasonal factor (7.60 %). Machine learning models (e.g., RidgeCV, Random Forest, XGBoost) were used to establish a connection between total phosphorus concentrations and explanatory variables defining influencing factors, aiming to predict total phosphorus concentrations in the Yangtze River. The anthropogenic and natural variables, such as domestic sewage, GDP, agricultural area, livestock, rainfall, wind speed, temperature and population were selected as predictors. The Random Forest model performed well in predicting total phosphorus concentrations, with R<sup>2</sup> value of 0.76. This study provides useful information for optimizing phosphorus pollution management and strategies for eutrophication control in the Yangtze River as well as in other large watersheds.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"171 ","pages":"Article 113217"},"PeriodicalIF":7.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379179","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}
Pub Date : 2025-02-01DOI: 10.1016/j.ecolind.2025.113189
Jun Li , Chao Wang , Xin-Ying Tuo , Ram Proshad , Jun-Zhuo Liu , Zhan-Dong Gao , Fa-Yuan Zhou , Fei Zang
Heavy metal(loid)s (HMs) pollution in urban rivers is an urgent environmental concern, but addressing source-specific risks within complex urban environments remains a critical challenge. This study introduces a machine learning-driven framework designed to characterize pollution risks, delineate specific sources, and assess the source-oriented probabilistic ecological risks of HMs accumulating in surface sediments from the Kongtong section of the Jinghe River, Northwest China. Results indicated that Cd and Hg levels (average concentrations of 0.19 mg kg−1 and 0.03 mg kg−1, respectively) in the Kongtong section were significantly above multiple background values, with hotspots associated with intense human activity. The integration of the geological accumulation index (Igeo), enrichment factor (EF), pollution load index (PLI), and modified Nemerow integrated ecological risk index (mNIRI) confirmed Cd and Hg as dominant ecological risk drivers. Based on mNIRI values, 47.25 % of sites were at moderate risk, while 16.49 % posed higher risk levels. By integrating correlation analysis, super-clustering of self-organizing maps (SOM), and positive matrix factorization (PMF), five major pollution sources were identified: industrial and traffic activities (33.33 %), agriculture (27.21 %), metal manufacturing (15.49 %), natural sources (12.95 %), and smelting/electroplating (11.02 %). The source-oriented probabilistic risk assessment using Monte Carlo simulation identified industrial and traffic activities as the primary contributors to ecological hazards. This study provides a robust framework for accurately tracing multiple pollution sources, offering scientific guidance for targeted risk management and pollution control in urban river systems.
{"title":"Machine learning-driven multi-technique source tracing and source-specific probabilistic ecological risk assessment of heavy metal(loid)s in urban river sediments","authors":"Jun Li , Chao Wang , Xin-Ying Tuo , Ram Proshad , Jun-Zhuo Liu , Zhan-Dong Gao , Fa-Yuan Zhou , Fei Zang","doi":"10.1016/j.ecolind.2025.113189","DOIUrl":"10.1016/j.ecolind.2025.113189","url":null,"abstract":"<div><div>Heavy metal(loid)s (HMs) pollution in urban rivers is an urgent environmental concern, but addressing source-specific risks within complex urban environments remains a critical challenge. This study introduces a machine learning-driven framework designed to characterize pollution risks, delineate specific sources, and assess the source-oriented probabilistic ecological risks of HMs accumulating in surface sediments from the Kongtong section of the Jinghe River, Northwest China. Results indicated that Cd and Hg levels (average concentrations of 0.19 mg kg<sup>−1</sup> and 0.03 mg kg<sup>−1</sup>, respectively) in the Kongtong section were significantly above multiple background values, with hotspots associated with intense human activity. The integration of the geological accumulation index (<em>I</em><sub>geo</sub>), enrichment factor (EF), pollution load index (PLI), and modified Nemerow integrated ecological risk index (<em>mNIRI</em>) confirmed Cd and Hg as dominant ecological risk drivers. Based on <em>mNIRI</em> values, 47.25 % of sites were at moderate risk, while 16.49 % posed higher risk levels. By integrating correlation analysis, super-clustering of self-organizing maps (SOM), and positive matrix factorization (PMF), five major pollution sources were identified: industrial and traffic activities (33.33 %), agriculture (27.21 %), metal manufacturing (15.49 %), natural sources (12.95 %), and smelting/electroplating (11.02 %). The source-oriented probabilistic risk assessment using Monte Carlo simulation identified industrial and traffic activities as the primary contributors to ecological hazards. This study provides a robust framework for accurately tracing multiple pollution sources, offering scientific guidance for targeted risk management and pollution control in urban river systems.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"171 ","pages":"Article 113189"},"PeriodicalIF":7.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420005","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}
Plant leaf functional traits significantly influence carbon cycling in tropical forests, though the relationships between these traits and carbon stocks are complex. The present study investigates the role of leaf functional traits, i.e., specific leaf area (SLA), leaf dry matter content (LDMC), leaf width, and leaf thickness—on above-ground tree carbon (AGTC) stocks in two forest protected areas (PA) in northeast Bangladesh: Khadimnagar National Park (KNP) and Rema Kalenga Wildlife Sanctuary (RKWS). Data were collected from 110 plots, comprising 60 in RKWS and 50 in KNP. We observed that the community-weighted mean (CWM) leaf trait values were predominantly higher in the southwestern regions of KNP, while in RKWS, they were primarily distributed in the northern or southern regions. The results revealed that, at the landscape level, CWM-leaf width (R2 = 0.10, P < 0.01) had a significant effect on AGTC. In site-specific analyses, CWM-leaf thickness (R2 = 0.25), CWM-leaf width (R2 = 0.10), and CWM-SLA (R2 = 0.17) had significant (p < 0.05) negative effects on AGTC in KNP. However, in RKWS, only CWM-leaf width (R2 = 0.015, P < 0.01) significantly affected AGTC, while other CWM-leaf traits showed no significant impact. Additionally, the effects of two common environmental variables—solar radiation and mean annual temperature (MAT)—were significant (p < 0.05) predictors of AGTC at the landscape level but not at the site level. The total carbon stock in RKWS was 1.98 % higher than in KNP per hectare, with species-specific carbon content varying across the landscape. Notably, Chukrasia tabularis showed the highest carbon content (31.57 t ha−1). These findings highlight significant spatial variability in leaf functional traits and AGTC distribution across the two forests. This study enhances our understanding of how leaf functional traits influence AGTC stocks, underscoring the importance of localized investigations for global climate change mitigation efforts and supporting sustainable forest management in Bangladesh.
{"title":"How do leaf functional traits influence above-ground tree carbon in tropical hill forests of Bangladesh?","authors":"Ariful Khan , Md Rezaul Karim , Mohammed A.S. Arfin-Khan , Md. Shamim Reza Saimun , Fahmida Sultana , Sharif A. Mukul","doi":"10.1016/j.ecolind.2025.113131","DOIUrl":"10.1016/j.ecolind.2025.113131","url":null,"abstract":"<div><div>Plant leaf functional traits significantly influence carbon cycling in tropical forests, though the relationships between these traits and carbon stocks are complex. The present study investigates the role of leaf functional traits, i.e., specific leaf area (SLA), leaf dry matter content (LDMC), leaf width, and leaf thickness—on above-ground tree carbon (AGTC) stocks in two forest protected areas (PA) in northeast Bangladesh: Khadimnagar National Park (KNP) and Rema Kalenga Wildlife Sanctuary (RKWS). Data were collected from 110 plots, comprising 60 in RKWS and 50 in KNP. We observed that the community-weighted mean (CWM) leaf trait values were predominantly higher in the southwestern regions of KNP, while in RKWS, they were primarily distributed in the northern or southern regions. The results revealed that, at the landscape level, CWM-leaf width (R<sup>2</sup> = 0.10, P < 0.01) had a significant effect on AGTC. In site-specific analyses, CWM-leaf thickness (R<sup>2</sup> = 0.25), CWM-leaf width (R<sup>2</sup> = 0.10), and CWM-SLA (R<sup>2</sup> = 0.17) had significant (p < 0.05) negative effects on AGTC in KNP. However, in RKWS, only CWM-leaf width (R<sup>2</sup> = 0.015, P < 0.01) significantly affected AGTC, while other CWM-leaf traits showed no significant impact. Additionally, the effects of two common environmental variables—solar radiation and mean annual temperature (MAT)—were significant (p < 0.05) predictors of AGTC at the landscape level but not at the site level. The total carbon stock in RKWS was 1.98 % higher than in KNP per hectare, with species-specific carbon content varying across the landscape. Notably, <em>Chukrasia tabularis</em> showed the highest carbon content (31.57 t ha<sup>−1</sup>). These findings highlight significant spatial variability in leaf functional traits and AGTC distribution across the two forests. This study enhances our understanding of how leaf functional traits influence AGTC stocks, underscoring the importance of localized investigations for global climate change mitigation efforts and supporting sustainable forest management in Bangladesh.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"171 ","pages":"Article 113131"},"PeriodicalIF":7.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143359479","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}
Pub Date : 2025-02-01DOI: 10.1016/j.ecolind.2025.113183
Yanqin Wang , Huaju Xue , Aiqing Li , Xiaofan Ma , Ailing Sun , Jinhe Zhang
Ensuring and maintaining ecosystem health is the goal of ecological protection and environmental governance. Exploring the spatial–temporal differentiation and influencing factors of ecosystem health in national parks is a crucial foundation for promoting the construction of ecological civilization and the high-quality development of national parks. Taking Three-River-Source National Park (TNP) as a case study, this paper adopts multi-source data to quantitatively evaluate the spatial and temporal variations of ecosystem health within TNP from 2000 to 2020 by constructing the measurement model of “Vigor-Service-Resilience”. It reveals the independent and interactive effects of its influencing factors. We found that: (1) Despite the overall positive trend observed in TNP’s ecosystem health, the trend remains relatively weak, and the health level predominantly dominated by the fragile level during the study period. (2) The spatial variation in ecosystem health within TNP was pronounced, with a noticeable increase moving from the northwest to the southeast area. It also showed significant “high-high” and “low-low” clustering effects in the eastern and northwestern regions. (3) Among all the indicators assessed, ecosystem vigor was the indicator that contributed the most to the growth of the ecosystem health index. (4) Variations in ecosystem health of TNP were mainly shaped by land use intensity and precipitation, and interactions between natural environmental factors and human activity factors enhanced the explanation of these changes. This study emphasizes the importance of ecosystem health in national parks and provides practical management recommendations for sustainable ecosystem development in national parks.
{"title":"Spatial-temporal differentiation and influencing factors of ecosystem health in Three-River-Source national Park","authors":"Yanqin Wang , Huaju Xue , Aiqing Li , Xiaofan Ma , Ailing Sun , Jinhe Zhang","doi":"10.1016/j.ecolind.2025.113183","DOIUrl":"10.1016/j.ecolind.2025.113183","url":null,"abstract":"<div><div>Ensuring and maintaining ecosystem health is the goal of ecological protection and environmental governance. Exploring the spatial–temporal differentiation and influencing factors of ecosystem health in national parks is a crucial foundation for promoting the construction of ecological civilization and the high-quality development of national parks. Taking Three-River-Source National Park (TNP) as a case study, this paper adopts multi-source data to quantitatively evaluate the spatial and temporal variations of ecosystem health within TNP from 2000 to 2020 by constructing the measurement model of “Vigor-Service-Resilience”. It reveals the independent and interactive effects of its influencing factors. We found that: (1) Despite the overall positive trend observed in TNP’s ecosystem health, the trend remains relatively weak, and the health level predominantly dominated by the fragile level during the study period. (2) The spatial variation in ecosystem health within TNP was pronounced, with a noticeable increase moving from the northwest to the southeast area. It also showed significant “high-high” and “low-low” clustering effects in the eastern and northwestern regions. (3) Among all the indicators assessed, ecosystem vigor was the indicator that contributed the most to the growth of the ecosystem health index. (4) Variations in ecosystem health of TNP were mainly shaped by land use intensity and precipitation, and interactions between natural environmental factors and human activity factors enhanced the explanation of these changes. This study emphasizes the importance of ecosystem health in national parks and provides practical management recommendations for sustainable ecosystem development in national parks.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"171 ","pages":"Article 113183"},"PeriodicalIF":7.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143333437","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}
Pub Date : 2025-02-01DOI: 10.1016/j.ecolind.2025.113174
Zhiyuan Ma , Xuejun Duan , Lei Wang , Yazhu Wang , Wei Yan , Haiyue Wang , Xiayi Ju
Effectively evaluating the carrying capacity of rural resources and the environment, and identifying key factors for its enhancement, are critical for rural sustainability. To meet this objective, a coupling model for rural resource and environmental carrying capacity (RRECC) was constructed using multi-source spatiotemporal data. The model incorporates the zonal division index of resources and environment (ZI), the categorization index of socioeconomic development (CI), and the grading index of population concentration (GI). Spatial autocorrelation and factor contribution analysis were employed to simulate the RRECC of China and identify key factors for enhancing this capacity. The results show that the overall carrying capacity status of China’s rural resources and environment is favorable. Out of 30,055 rural study units, 91.076% are in balance and surplus states, encompassing 90.044% of the population and 66.451% of the land. The carrying capacity exhibits a spatial pattern of higher capacity in the east and south, with significant spatial clustering. The contributions of socioeconomic development and ecological environment protection to carrying capacity are 28.018% and 27.625%, respectively. Therefore, enhancing carrying capacity requires synergistic advancement in both areas. This study advances the evaluation methods for resource and environmental carrying capacity (RECC) in rural areas, providing scientific guidance and support for the realization of the great goal of the Rural Revitalization Strategy.
{"title":"Evaluation of China’s rural resource and environmental carrying capacity based on the ZI–CI–GI framework","authors":"Zhiyuan Ma , Xuejun Duan , Lei Wang , Yazhu Wang , Wei Yan , Haiyue Wang , Xiayi Ju","doi":"10.1016/j.ecolind.2025.113174","DOIUrl":"10.1016/j.ecolind.2025.113174","url":null,"abstract":"<div><div>Effectively evaluating the carrying capacity of rural resources and the environment, and identifying key factors for its enhancement, are critical for rural sustainability. To meet this objective, a coupling model for rural resource and environmental carrying capacity (RRECC) was constructed using multi-source spatiotemporal data. The model incorporates the zonal division index of resources and environment (ZI), the categorization index of socioeconomic development (CI), and the grading index of population concentration (GI). Spatial autocorrelation and factor contribution analysis were employed to simulate the RRECC of China and identify key factors for enhancing this capacity. The results show that the overall carrying capacity status of China’s rural resources and environment is favorable. Out of 30,055 rural study units, 91.076% are in balance and surplus states, encompassing 90.044% of the population and 66.451% of the land. The carrying capacity exhibits a spatial pattern of higher capacity in the east and south, with significant spatial clustering. The contributions of socioeconomic development and ecological environment protection to carrying capacity are 28.018% and 27.625%, respectively. Therefore, enhancing carrying capacity requires synergistic advancement in both areas. This study advances the evaluation methods for resource and environmental carrying capacity (RECC) in rural areas, providing scientific guidance and support for the realization of the great goal of the Rural Revitalization Strategy.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"171 ","pages":"Article 113174"},"PeriodicalIF":7.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143333440","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}
Pub Date : 2025-02-01DOI: 10.1016/j.ecolind.2025.113168
Xiaohan Wang , Yu Zhang , Yanhong Yu , Yunmei Li , Heng Lyu , Junda Li , Xiaolan Cai , Xianzhang Dong , Gaolun Wang , Jianzhong Li , Mengmeng Song , Lanlan Chen
Aquatic vegetation plays a crucial role as a primary producer and ecological regulator, serving as an indispensable purifier in inland lakes. However, weakened optical information caused by water absorption and reflection presents challenges in remote sensing identification and classification of submerged vegetation (SAV). In this study, Sentinel-2 satellite data was employed to examine and compare the spectral attributes of three preeminent species, Potamogeton maackianus, Vallisneria natans, and Ceratophyllum demersum, within the confines of the Lake Erhai study area. Using the disparities in the red-edge band, red-edge chlorophyll index (CIedge), and red-edge normalized vegetation index (NDVIedge), a carefully designed decision tree model was instituted for remote sensing-driven identification of SAV. The ensuing species identification exhibited a commendable overall accuracy rate of 96.8%, underscored by a Kappa coefficient of 0.95. Geospatial distribution revealed the prevalence of P. maackianus in the northern lake bay, counterbalanced by the dominance of V. natans in the southern lake bay. Furthermore, an incisive inquiry into the nexus between diverse water quality variables and aquatic vegetation was conducted. Notably, water temperature (WT) emerged as a the most influential factor, exhibiting a highly significant positive correlation with SAV. The correlation coefficient reached 0.98, indicating a pronounced influence, with WT contributing substantively to the observed variance in SAV, accounting for 63.3%. The proposed method highlights the potential of Sentinel-2 red-edge bands for ecological monitoring and management of SAV, with implications for broader environmental applications.
{"title":"Identification of dominant species of submerged vegetation based on Sentinel-2 red-edge band: A case study of Lake Erhai, China","authors":"Xiaohan Wang , Yu Zhang , Yanhong Yu , Yunmei Li , Heng Lyu , Junda Li , Xiaolan Cai , Xianzhang Dong , Gaolun Wang , Jianzhong Li , Mengmeng Song , Lanlan Chen","doi":"10.1016/j.ecolind.2025.113168","DOIUrl":"10.1016/j.ecolind.2025.113168","url":null,"abstract":"<div><div>Aquatic vegetation plays a crucial role as a primary producer and ecological regulator, serving as an indispensable purifier in inland lakes. However, weakened optical information caused by water absorption and reflection presents challenges in remote sensing identification and classification of submerged vegetation (SAV). In this study, Sentinel-2 satellite data was employed to examine and compare the spectral attributes of three preeminent species, <em>Potamogeton maackianus</em>, <em>Vallisneria natans</em>, and <em>Ceratophyllum demersum</em>, within the confines of the Lake Erhai study area. Using the disparities in the red-edge band, red-edge chlorophyll index (CI<sub>edge</sub>), and red-edge normalized vegetation index (NDVI<sub>edge</sub>), a carefully designed decision tree model was instituted for remote sensing-driven identification of SAV. The ensuing species identification exhibited a commendable overall accuracy rate of 96.8%, underscored by a Kappa coefficient of 0.95. Geospatial distribution revealed the prevalence of <em>P. maackianus</em> in the northern lake bay, counterbalanced by the dominance of <em>V. natans</em> in the southern lake bay. Furthermore, an incisive inquiry into the nexus between diverse water quality variables and aquatic vegetation was conducted. Notably, water temperature (WT) emerged as a the most influential factor, exhibiting a highly significant positive correlation with SAV. The correlation coefficient reached 0.98, indicating a pronounced influence, with WT contributing substantively to the observed variance in SAV, accounting for 63.3%. The proposed method highlights the potential of Sentinel-2 red-edge bands for ecological monitoring and management of SAV, with implications for broader environmental applications.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"171 ","pages":"Article 113168"},"PeriodicalIF":7.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143333978","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}
Pub Date : 2025-02-01DOI: 10.1016/j.ecolind.2025.113150
Xiaoyun Li , Chunsheng Wu
Ecosystem services are essential for human life, but they have undergone significant degradation in recent decades, mainly due to human-induced land use change. This research focuses on Xinjiang, the largest and most typical arid region in China, where ecosystem services are particularly sensitive to land use change. An improved framework for assessing and simulating ecosystem services combining a modified equivalent factor method, a future land use simulation (FLUS) model, and a cross-sensitivity analysis is proposed. The main results are obtained as follows: (1) During 1980 and 2020, construction land and cultivated land expanded by 115.66% and 47.18%, respectively, primarily owing to intensive socio-economic activities. The expense was the losses of forest land and grassland, with reductions of 5.84% and 4.15%, respectively. Besides, the water body expanded slightly, primarily owing to climate change. (2) The total ecosystem service value (ESV), despite a 3.2% rise during the research period, exhibited a fluctuating pattern as a result of frequent changes in land use. Given the current development momentum, the total ESV will present a downward trend by 2030 according to the simulation. (3) The ESV change revealed a notable spatial difference, wherein the loss of ESV was concentrated primarily in two major economic belts, while the rise was primarily observed in areas where the natural ecosystem and biological diversity were effectively protected. (4) The cross-sensitivity analysis demonstrated an extremely high sensitivity of ESV towards the change in water body and a relatively low sensitivity towards the change in construction land and cultivated land. (5) Enhancing intensive use of construction land, promoting yield and quality of cultivated land, prioritizing developing unused land, and strengthening water conservation are crucial for ecologically fragile arid regions. This research will enhance our understanding of the human-environment interaction in arid regions and ultimately support their sustainable development from the perspective of effective land use management.
{"title":"Sensitivity assessment and simulation of ecosystem services in response to land use change in arid regions: Empirical evidence from Xinjiang, China","authors":"Xiaoyun Li , Chunsheng Wu","doi":"10.1016/j.ecolind.2025.113150","DOIUrl":"10.1016/j.ecolind.2025.113150","url":null,"abstract":"<div><div>Ecosystem services are essential for human life, but they have undergone significant degradation in recent decades, mainly due to human-induced land use change. This research focuses on Xinjiang, the largest and most typical arid region in China, where ecosystem services are particularly sensitive to land use change. An improved framework for assessing and simulating ecosystem services combining a modified equivalent factor method, a future land use simulation (FLUS) model, and a cross-sensitivity analysis is proposed. The main results are obtained as follows: (1) During 1980 and 2020, construction land and cultivated land expanded by 115.66% and 47.18%, respectively, primarily owing to intensive socio-economic activities. The expense was the losses of forest land and grassland, with reductions of 5.84% and 4.15%, respectively. Besides, the water body expanded slightly, primarily owing to climate change. (2) The total ecosystem service value (ESV), despite a 3.2% rise during the research period, exhibited a fluctuating pattern as a result of frequent changes in land use. Given the current development momentum, the total ESV will present a downward trend by 2030 according to the simulation. (3) The ESV change revealed a notable spatial difference, wherein the loss of ESV was concentrated primarily in two major economic belts, while the rise was primarily observed in areas where the natural ecosystem and biological diversity were effectively protected. (4) The cross-sensitivity analysis demonstrated an extremely high sensitivity of ESV towards the change in water body and a relatively low sensitivity towards the change in construction land and cultivated land. (5) Enhancing intensive use of construction land, promoting yield and quality of cultivated land, prioritizing developing unused land, and strengthening water conservation are crucial for ecologically fragile arid regions. This research will enhance our understanding of the human-environment interaction in arid regions and ultimately support their sustainable development from the perspective of effective land use management.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"171 ","pages":"Article 113150"},"PeriodicalIF":7.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143334156","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}
Pub Date : 2025-02-01DOI: 10.1016/j.ecolind.2025.113126
Yan-Tin Lai, Sheng-Shan Lu, Ming-Tang Shiao
Long-term biological and phenological monitoring has become essential for conservation in the face of rapid climate change. In this study, we utilized long-term passive acoustic recording data. We employed a combination of 14 features extracted from acoustic indices and unsupervised clustering methods to classify the soundscapes of Taiwan’s subtropical rainforests. Our results demonstrated that in environments with complex soundscapes, this approach effectively distinguished predominant acoustic elements, including cicadas, orthopterans, rain, and frogs, constituting more than 10–20% of the total audio recordings, and identified smaller yet significant groups, such as avian dawn choruses, accounting for approximately 2% of the recordings. The clustering results enabled the description of dynamic changes in the soundscape throughout the year. In the subtropical rainforest, rain and wind affected the soundscape from October to March, whereas bird songs were prominent only in the early mornings from February to May, which were subsequently replaced by cicada calls that continued until late August. The nocturnal soundscape was dominated by frog calls and orthopteran stridulations in the aquatic and forest habitats. Correlations among the vocal activities of several representative groups, temperature, and rainfall were found. Our study confirms that acoustic indices can extract meaningful ecological features, and unsupervised algorithms offer valuable insights into biodiversity exploration data-scarce regions. The combination of these methods has led to the development of non-species-specific soundscape classification, which not only facilitates the monitoring of phenological dynamics across multiple biological groups in the face of climate change but also lays the foundation for further exploration of key taxa.
{"title":"Characterization of soundscapes with acoustic indices and clustering reveals phenology patterns in a subtropical rainforest","authors":"Yan-Tin Lai, Sheng-Shan Lu, Ming-Tang Shiao","doi":"10.1016/j.ecolind.2025.113126","DOIUrl":"10.1016/j.ecolind.2025.113126","url":null,"abstract":"<div><div>Long-term biological and phenological monitoring has become essential for conservation in the face of rapid climate change. In this study, we utilized long-term passive acoustic recording data. We employed a combination of 14 features extracted from acoustic indices and unsupervised clustering methods to classify the soundscapes of Taiwan’s subtropical rainforests. Our results demonstrated that in environments with complex soundscapes, this approach effectively distinguished predominant acoustic elements, including cicadas, orthopterans, rain, and frogs, constituting more than 10–20% of the total audio recordings, and identified smaller yet significant groups, such as avian dawn choruses, accounting for approximately 2% of the recordings. The clustering results enabled the description of dynamic changes in the soundscape throughout the year. In the subtropical rainforest, rain and wind affected the soundscape from October to March, whereas bird songs were prominent only in the early mornings from February to May, which were subsequently replaced by cicada calls that continued until late August. The nocturnal soundscape was dominated by frog calls and orthopteran stridulations in the aquatic and forest habitats. Correlations among the vocal activities of several representative groups, temperature, and rainfall were found. Our study confirms that acoustic indices can extract meaningful ecological features, and unsupervised algorithms offer valuable insights into biodiversity exploration data-scarce regions. The combination of these methods has led to the development of non-species-specific soundscape classification, which not only facilitates the monitoring of phenological dynamics across multiple biological groups in the face of climate change but also lays the foundation for further exploration of key taxa.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"171 ","pages":"Article 113126"},"PeriodicalIF":7.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143334157","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}
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