Ecological Indicators最新文献_第4页

Corrigendum to “A fuzzy logic approach within the DPSIR framework to address the inherent uncertainty and complexity of water security assessments” [Ecol. Indic. 170 (2025) 112984]

IF 7 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecological Indicators

Pub Date : 2025-03-01 DOI: 10.1016/j.ecolind.2025.113278

Liang Yuan , Zhijie Zhou , Weijun He , Xia Wu , Dagmawi Mulugeta Degefu , Juan Cheng , Lin Chai , Thomas Stephen Ramsey

引用次数: 0

Random effects and environmental sensitivity improve the compatible biomass model systems of moso bamboo forests in Southern China

IF 7 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecological Indicators

Pub Date : 2025-03-01 DOI: 10.1016/j.ecolind.2025.113332

Xiao Zhou , Xuan Zhang , Ram P. Sharma , Fengying Guan

Accurate estimates of bamboo forest biomass can help assess the impact of climate change and help achieve the vision of carbon neutrality. Estimate of component-biomass and total-biomass of the moso bamboo (Phyllostachys edulis), which is one of the major bamboo species, is also crucial for assessing the productivity of bamboo forest ecosystem. This study is based on the measured biomass data from 368 bamboo individuals, collected across the nine provinces (growth regions) of China (Jiangsu, Anhui, Zhejiang, Jiangxi, Fujian, Sichuan, Guangxi, Hunan, and Hubei). This study applied three important modeling approaches, namely seemingly unrelated regression (SUR), two-stage error-in-variable modeling (TSEM) and seemingly unrelated mixed-effects modeling (SURM) to develop a compatible biomass model system, which is an aggregate form of a set of component-biomass models. Only the important factors describing effects of the individual bamboo characteristics, soil properties, and climate features were considered as predictor variables in a model system. The introduction of bamboo individuals (height to crown base), soil properties (soil bulk density and soil organic carbon), and climate factors (de Martonne aridity index) increased the R² of each component-biomass model by 0.7% − 4.6%. Introduction of the growth regional-level (province-level) random effects significantly improved the component models in a model system (R² increased by 1.2% − 12.4%). The model validation against the partitioned data showed a better prediction performance of SURM compared to TSUM and SUR models. A response calibration (localization) of SURM showed an increased the prediction accuracy with increased number of samples used in calibration of the random effects. Considering the investigation time and cost, we recommend using six randomly selected bamboo individuals per growth region for optimal prediction accuracy. Our models can provide a promising basis for estimation and evaluation of carbon storage in moso bamboo forests and formulating effective management strategies in southern China.

{"title":"Random effects and environmental sensitivity improve the compatible biomass model systems of moso bamboo forests in Southern China","authors":"Xiao Zhou , Xuan Zhang , Ram P. Sharma , Fengying Guan","doi":"10.1016/j.ecolind.2025.113332","DOIUrl":"10.1016/j.ecolind.2025.113332","url":null,"abstract":"<div><div>Accurate estimates of bamboo forest biomass can help assess the impact of climate change and help achieve the vision of carbon neutrality. Estimate of component-biomass and total-biomass of the moso bamboo (<em>Phyllostachys edulis</em>), which is one of the major bamboo species, is also crucial for assessing the productivity of bamboo forest ecosystem. This study is based on the measured biomass data from 368 bamboo individuals, collected across the nine provinces (growth regions) of China (Jiangsu, Anhui, Zhejiang, Jiangxi, Fujian, Sichuan, Guangxi, Hunan, and Hubei). This study applied three important modeling approaches, namely seemingly unrelated regression (SUR), two-stage error-in-variable modeling (TSEM) and seemingly unrelated mixed-effects modeling (SURM) to develop a compatible biomass model system, which is an aggregate form of a set of component-biomass models. Only the important factors describing effects of the individual bamboo characteristics, soil properties, and climate features were considered as predictor variables in a model system. The introduction of bamboo individuals (height to crown base), soil properties (soil bulk density and soil organic carbon), and climate factors (de Martonne aridity index) increased the R<sup>2</sup> of each component-biomass model by 0.7% − 4.6%. Introduction of the growth regional-level (province-level) random effects significantly improved the component models in a model system (R<sup>2</sup> increased by 1.2% − 12.4%). The model validation against the partitioned data showed a better prediction performance of SURM compared to TSUM and SUR models. A response calibration (localization) of SURM showed an increased the prediction accuracy with increased number of samples used in calibration of the random effects. Considering the investigation time and cost, we recommend using six randomly selected bamboo individuals per growth region for optimal prediction accuracy. Our models can provide a promising basis for estimation and evaluation of carbon storage in moso bamboo forests and formulating effective management strategies in southern China.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"172 ","pages":"Article 113332"},"PeriodicalIF":7.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628524","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}

引用次数: 0

Using multiple machine learning algorithms to optimize the water quality index model and their applicability

IF 7 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecological Indicators

Pub Date : 2025-03-01 DOI: 10.1016/j.ecolind.2025.113299

Fei Ding , Shilong Hao , Wenjie Zhang , Mingcen Jiang , Liangyao Chen , Haobin Yuan , Nan Wang , Wenpan Li , Xin Xie

Water quality assessment model and spatiotemporal heterogeneity pose challenges to the uncertainty of water quality assessment. To improve the accuracy of the water quality index (WQI) model, multiple machine learning algorithms (CatBoost, SVM, LR, XGBoost, LightGBM) and entropy weight method (EWM) were introduced to determine the objective weight. Six combined weights were determined by game theory combining objective and subjective weights (AHP). Three aggregation functions were established, including a new function proposed based on the sigmoid function and two existing functions. Based on the six combined weights and three aggregation functions, eighteen WQI models were developed. To reduce the influence of spatiotemporal heterogeneity, the assessment models for the different water quality characteristics were proposed respectively. To validate the performance of improved model, the monthly water quality monitoring data of 16 sampling sites in Chaohu Lake during 2016–2020 was used. Among them, totally 10 water quality indicators were selected, including TN, TP, etc. The results showed high accuracy and reliability of the improved WQI assessment models. The model improved by CatBoost and EWM had low uncertainty (0.559–0.903) than SVM and LR (0.576–1.034). The sensitivity of the models improved by six combined weights is ranked as W_AE > W_AC > W_AS > W_AX > W_ALGB > W_AL. The uncertainty of the models improved by the three aggregation functions were ranked as SGM > SWM > WQM and the sensitivity were ranked as WQM > SWM > SGM. Compared with WQM and SWM, SGM could reflect the water quality spatiotemporal heterogeneity more accurately. The WQM_AE, SGM_AC and SWM_AC models were recommended for assessing water bodies with good quality, poor quality and heterogeneity respectively. Chaohu Lake was mainly Class II and Class III water. East had better water quality than the west. Water quality in summer and autumn was better than in spring and winter. This study can provide theoretical support for related water quality assessment work.

{"title":"Using multiple machine learning algorithms to optimize the water quality index model and their applicability","authors":"Fei Ding , Shilong Hao , Wenjie Zhang , Mingcen Jiang , Liangyao Chen , Haobin Yuan , Nan Wang , Wenpan Li , Xin Xie","doi":"10.1016/j.ecolind.2025.113299","DOIUrl":"10.1016/j.ecolind.2025.113299","url":null,"abstract":"<div><div>Water quality assessment model and spatiotemporal heterogeneity pose challenges to the uncertainty of water quality assessment. To improve the accuracy of the water quality index (WQI) model, multiple machine learning algorithms (CatBoost, SVM, LR, XGBoost, LightGBM) and entropy weight method (EWM) were introduced to determine the objective weight. Six combined weights were determined by game theory combining objective and subjective weights (AHP). Three aggregation functions were established, including a new function proposed based on the sigmoid function and two existing functions. Based on the six combined weights and three aggregation functions, eighteen WQI models were developed. To reduce the influence of spatiotemporal heterogeneity, the assessment models for the different water quality characteristics were proposed respectively. To validate the performance of improved model, the monthly water quality monitoring data of 16 sampling sites in Chaohu Lake during 2016–2020 was used. Among them, totally 10 water quality indicators were selected, including TN, TP, etc. The results showed high accuracy and reliability of the improved WQI assessment models. The model improved by CatBoost and EWM had low uncertainty (0.559–0.903) than SVM and LR (0.576–1.034). The sensitivity of the models improved by six combined weights is ranked as W<sub>AE</sub> > W<sub>AC</sub> > W<sub>AS</sub> > W<sub>AX</sub> > W<sub>ALGB</sub> > W<sub>AL</sub>. The uncertainty of the models improved by the three aggregation functions were ranked as SGM > SWM > WQM and the sensitivity were ranked as WQM > SWM > SGM. Compared with WQM and SWM, SGM could reflect the water quality spatiotemporal heterogeneity more accurately. The WQM<sub>AE</sub>, SGM<sub>AC</sub> and SWM<sub>AC</sub> models were recommended for assessing water bodies with good quality, poor quality and heterogeneity respectively. Chaohu Lake was mainly Class II and Class III water. East had better water quality than the west. Water quality in summer and autumn was better than in spring and winter. This study can provide theoretical support for related water quality assessment work.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"172 ","pages":"Article 113299"},"PeriodicalIF":7.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143547947","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}

引用次数: 0

Reshaping Agriculture Eco-efficiency in China: From Greenhouse Gas Perspective

IF 7 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecological Indicators

Pub Date : 2025-03-01 DOI: 10.1016/j.ecolind.2025.113268

Guofeng Wang , Mengqi Zhao , Baohui Zhao , Xiuli Liu , Yu Wang

Incorporating emissions of greenhouse gases other than carbon dioxide into the metrics for agricultural eco-efficiency is critical for addressing climate change, given the significant role of agriculture in global emissions. Existing studies more focus on single carbon emission indicators and overlooks emissions of other significant greenhouse gases in measuring agricultural eco-efficiency, such as methane and nitrous oxide, leading to a potential underestimation of the true ecological impact of agricultural practices. Utilizing a dataset spanning 2000 to 2021 from China, this study for the first time includes methane and nitrous oxide in the assessment of agricultural eco-efficiency with SBM-DEA model, which defines a more standardized and accurate indicator system of agricultural eco-efficiency, and provides a more comprehensive framework for assessing the impact of agriculture on the environment. Furthermore, it rigorously assesses the variations in China’s agricultural eco-efficiency before and after accounting for multiple greenhouse gases. The study found that, first, before and after embedding a wide range of GHGs, China’s agricultural eco-efficiency overalltrends are basically the same, both showing slow growth in fluctuation, but the ecological impacts of non-CO₂ emissions are underestimated. Second, the agricultural eco-efficiency among China’s provinces is characterized by “high in the northwest and low in the southeast”, with spatial heterogeneity in the efficiency level. Last, the underestimation level of the balance of production and marketing area is the highest.This study proposes a novel approach by recommending the integration of various greenhouse gas emissions into the evaluation of agricultural eco-efficiency. It explores cooperative strategies for reducing emissions across several metrics. Such an approach offers quantitative insights that inform the development of green agricultural policies tailored to various regions.

{"title":"Reshaping Agriculture Eco-efficiency in China: From Greenhouse Gas Perspective","authors":"Guofeng Wang , Mengqi Zhao , Baohui Zhao , Xiuli Liu , Yu Wang","doi":"10.1016/j.ecolind.2025.113268","DOIUrl":"10.1016/j.ecolind.2025.113268","url":null,"abstract":"<div><div>Incorporating emissions of greenhouse gases other than carbon dioxide into the metrics for agricultural eco-efficiency is critical for addressing climate change, given the significant role of agriculture in global emissions. Existing studies more focus on single carbon emission indicators and overlooks emissions of other significant greenhouse gases in measuring agricultural eco-efficiency, such as methane and nitrous oxide, leading to a potential underestimation of the true ecological impact of agricultural practices. Utilizing a dataset spanning 2000 to 2021 from China, this study for the first time includes methane and nitrous oxide in the assessment of agricultural eco-efficiency with SBM-DEA model, which defines a more standardized and accurate indicator system of agricultural eco-efficiency, and provides a more comprehensive framework for assessing the impact of agriculture on the environment. Furthermore, it rigorously assesses the variations in China’s agricultural eco-efficiency before and after accounting for multiple greenhouse gases. The study found that, first, before and after embedding a wide range of GHGs, China’s agricultural eco-efficiency overalltrends are basically the same, both showing slow growth in fluctuation, but the ecological impacts of non-CO<sub>2</sub> emissions are underestimated. Second, the agricultural eco-efficiency among China’s provinces is characterized by “high in the northwest and low in the southeast”, with spatial heterogeneity in the efficiency level. Last, the underestimation level of the balance of production and marketing area is the highest.This study proposes a novel approach by recommending the integration of various greenhouse gas emissions into the evaluation of agricultural eco-efficiency. It explores cooperative strategies for reducing emissions across several metrics. Such an approach offers quantitative insights that inform the development of green agricultural policies tailored to various regions.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"172 ","pages":"Article 113268"},"PeriodicalIF":7.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512342","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}

引用次数: 0

Unraveling the response of forests to drought with explainable artificial intelligence (XAI)

IF 7 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecological Indicators

Pub Date : 2025-03-01 DOI: 10.1016/j.ecolind.2025.113308

Stenka Vulova , Katharina Horn , Alby Duarte Rocha , Fabio Brill , Márk Somogyvári , Akpona Okujeni , Michael Förster , Birgit Kleinschmit

Increases in the frequency and intensity of droughts and heat waves are threatening forests around the world. Climate-driven tree dieback and mortality is associated with devastating ecological and societal consequences, including the loss of carbon sequestration, habitat provisioning, and water filtration services. A spatially fine-grained understanding of the site characteristics making forests more susceptible to drought is still lacking. Furthermore, the complexity of drought effects on forests, which can be cumulative and delayed, demands investigation of the most appropriate meteorological indicators. To address this research gap, we investigated the drivers of drought-induced forest damage in a particularly drought-affected region of Central Europe using SHapley Additive exPlanations (SHAP) values, an explainable artificial intelligence (XAI) method which allows for the relevance of predictors to be quantified spatially. To develop a reproducible approach that facilitates transferability to other regions, open-source data was used to characterize the meteorological, vegetation, topographical, and soil drivers of tree vulnerability, representing 41 predictors in total. The forest drought response was characterized as a binary variable (“damaged” or “unchanged”) at a 30-m resolution based on the Normalized Difference Moisture Index (NDMI) anomaly (%) between a baseline period (2013–2017) and recent years (2018–2022). We revealed critical tipping points beyond which the forest ecosystem shifted towards a damaged state: <81 % tree cover density, <4 % of broadleaf trees, and < 24 m canopy height. Our study provides an enhanced understanding of trees’ response to drought, which can support forest managers aiming to make forests more climate-resilient, and serves as a prototype for interpretable early-warning systems.

{"title":"Unraveling the response of forests to drought with explainable artificial intelligence (XAI)","authors":"Stenka Vulova , Katharina Horn , Alby Duarte Rocha , Fabio Brill , Márk Somogyvári , Akpona Okujeni , Michael Förster , Birgit Kleinschmit","doi":"10.1016/j.ecolind.2025.113308","DOIUrl":"10.1016/j.ecolind.2025.113308","url":null,"abstract":"<div><div>Increases in the frequency and intensity of droughts and heat waves are threatening forests around the world. Climate-driven tree dieback and mortality is associated with devastating ecological and societal consequences, including the loss of carbon sequestration, habitat provisioning, and water filtration services. A spatially fine-grained understanding of the site characteristics making forests more susceptible to drought is still lacking. Furthermore, the complexity of drought effects on forests, which can be cumulative and delayed, demands investigation of the most appropriate meteorological indicators. To address this research gap, we investigated the drivers of drought-induced forest damage in a particularly drought-affected region of Central Europe using SHapley Additive exPlanations (SHAP) values, an explainable artificial intelligence (XAI) method which allows for the relevance of predictors to be quantified spatially. To develop a reproducible approach that facilitates transferability to other regions, open-source data was used to characterize the meteorological, vegetation, topographical, and soil drivers of tree vulnerability, representing 41 predictors in total. The forest drought response was characterized as a binary variable (“damaged” or “unchanged”) at a 30-m resolution based on the Normalized Difference Moisture Index (NDMI) anomaly (%) between a baseline period (2013–2017) and recent years (2018–2022). We revealed critical tipping points beyond which the forest ecosystem shifted towards a damaged state: <81 % tree cover density, <4 % of broadleaf trees, and < 24 m canopy height. Our study provides an enhanced understanding of trees’ response to drought, which can support forest managers aiming to make forests more climate-resilient, and serves as a prototype for interpretable early-warning systems.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"172 ","pages":"Article 113308"},"PeriodicalIF":7.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562390","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}

引用次数: 0

Disproportionate increase of flood-exposed population in Chinese cities under urban expansion and climate variation

IF 7 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecological Indicators

Pub Date : 2025-03-01 DOI: 10.1016/j.ecolind.2025.113314

Zhenyan She , Zhiyong Liu , Huayang Cai , Liyan Huang , Xin Lan , Tiewen Fu , Yu Yan

Coastal cities in China are confronting increasingly severe flood risks, which lead to significant losses of life and property. Here, this study estimate the population exposure to flooding under compounding impacts of climate variation and urban expansion over 1985–2014 and 2021–2100. It finds that the population exposure to flooding is expected to increase by approximately 1.2–4.7 times under such compound impacts. Under the most severe greenhouse gas emission scenario in the future, our projections show that most of China’s coastal provinces, except Shandong Province, will have the largest flood-exposed population. The future flood-exposed population is projected to increase by 2.0 times in Guangdong Province, and by 1.3 times in Liaoning Province compared to the historical period. Additionally, controlling urban expansion will reduce the future flood-exposed population, with the most significant reductions observed in eastern China like Zhejiang and Jiangsu provinces (by 36.2–86.5 %), followed by southern areas like Fujian and Hainan provinces (by 9.8–17.8 %). In contrast, intensified compound extreme heat and precipitation events increase the average flood-exposed population by 21–27 % in Guangdong, Liaoning, and Shandong provinces. Our findings could provide valuable insights to help develop sustainable flood mitigation measures for coastal cities confronting flood risks.

{"title":"Disproportionate increase of flood-exposed population in Chinese cities under urban expansion and climate variation","authors":"Zhenyan She , Zhiyong Liu , Huayang Cai , Liyan Huang , Xin Lan , Tiewen Fu , Yu Yan","doi":"10.1016/j.ecolind.2025.113314","DOIUrl":"10.1016/j.ecolind.2025.113314","url":null,"abstract":"<div><div>Coastal cities in China are confronting increasingly severe flood risks, which lead to significant losses of life and property. Here, this study estimate the population exposure to flooding under compounding impacts of climate variation and urban expansion over 1985–2014 and 2021–2100. It finds that the population exposure to flooding is expected to increase by approximately 1.2–4.7 times under such compound impacts. Under the most severe greenhouse gas emission scenario in the future, our projections show that most of China’s coastal provinces, except Shandong Province, will have the largest flood-exposed population. The future flood-exposed population is projected to increase by 2.0 times in Guangdong Province, and by 1.3 times in Liaoning Province compared to the historical period. Additionally, controlling urban expansion will reduce the future flood-exposed population, with the most significant reductions observed in eastern China like Zhejiang and Jiangsu provinces (by 36.2–86.5 %), followed by southern areas like Fujian and Hainan provinces (by 9.8–17.8 %). In contrast, intensified compound extreme heat and precipitation events increase the average flood-exposed population by 21–27 % in Guangdong, Liaoning, and Shandong provinces. Our findings could provide valuable insights to help develop sustainable flood mitigation measures for coastal cities confronting flood risks.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"172 ","pages":"Article 113314"},"PeriodicalIF":7.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143577169","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}

引用次数: 0

Wetland inventory, key drivers of change and their socioeconomic and environmental implications in Ethiopia

IF 7 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecological Indicators

Pub Date : 2025-03-01 DOI: 10.1016/j.ecolind.2025.113312

Workiyie Worie Assefa , Belachew Getnet Eneyew

Efforts have been made to estimate the total areas of wetlands coverage and their distribution in Ethiopia. However, their abundance and distribution throughout the country remain unclear. This study, thus, aimed to assess the status and dynamics of wetlands coverage in Ethiopia. Besides, the drivers of wetland degradation and its implications on water supply, local people’s livelihoods, climate change, and biodiversity. Global Land Analysis and Discovery (GLAD), and Global Wetland Database were used to analyze abundance distribution and dynamics of wetlands in Ethiopia. The documentary sources (journal articles and books) were used to assess the driving factors of wetland degradation and the implications of wetland loss. The global wetland dataset analysis findings indicated that the total area coverage of the country’s wetlands in 2020 was 26,424.72 km² representing 2.02 % of the country’s total area coverage. Of which, 16,501.96 km² was covered with wetlands and 9,922.76 km² was covered with open-water (lakes and artificial reservoirs). The analysis of the LULC pattern indicated that the land coverage of open-water had increased by 20 % within 20 years (2000 to 2020), which was contradictory to the decline of 9 % of the wetland coverage. The construction of dams or reservoirs for hydroelectric supply, irrigation, and water supply for large towns or cities is the principal factor for increasing open-water coverage. On the contrary, cultivated land expansion, increasing the application of agrochemicals, overgrazing, lack of standalone wetland policy, sedimentation, climate change, excessive extraction of water for irrigation and other purposes, and urban expansion contribute to the degradation of wetlands. Considerable loss of wetlands will have implications on water supply for various uses, local level livelihoods, local climate regulation, carbon emission, and aquatic biodiversity.

{"title":"Wetland inventory, key drivers of change and their socioeconomic and environmental implications in Ethiopia","authors":"Workiyie Worie Assefa , Belachew Getnet Eneyew","doi":"10.1016/j.ecolind.2025.113312","DOIUrl":"10.1016/j.ecolind.2025.113312","url":null,"abstract":"<div><div>Efforts have been made to estimate the total areas of wetlands coverage and their distribution in Ethiopia. However, their abundance and distribution throughout the country remain unclear. This study, thus, aimed to assess the status and dynamics of wetlands coverage in Ethiopia. Besides, the drivers of wetland degradation and its implications on water supply, local people’s livelihoods, climate change, and biodiversity. Global Land Analysis and Discovery (GLAD), and Global Wetland Database were used to analyze abundance distribution and dynamics of wetlands in Ethiopia. The documentary sources (journal articles and books) were used to assess the driving factors of wetland degradation and the implications of wetland loss. The global wetland dataset analysis findings indicated that the total area coverage of the country’s wetlands in 2020 was 26,424.72 km<sup>2</sup> representing 2.02 % of the country’s total area coverage. Of which, 16,501.96 km<sup>2</sup> was covered with wetlands and 9,922.76 km<sup>2</sup> was covered with open-water (lakes and artificial reservoirs). The analysis of theLULC pattern indicated that the land coverage of open-water had increased by 20 % within 20 years (2000 to 2020), which was contradictory to the decline of 9 % of the wetland coverage. The construction of dams or reservoirs for hydroelectric supply, irrigation, and water supply for large towns or cities is the principal factor for increasing open-water coverage. On the contrary, cultivated land expansion, increasing the application of agrochemicals, overgrazing, lack of standalone wetland policy, sedimentation, climate change, excessive extraction of water for irrigation and other purposes, and urban expansion contribute to the degradation of wetlands. Considerable loss of wetlands will have implications on water supply for various uses, local level livelihoods, local climate regulation, carbon emission, and aquatic biodiversity<em>.</em></div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"172 ","pages":"Article 113312"},"PeriodicalIF":7.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143577179","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}

引用次数: 0

Spatio-Temporal evolution and scenario-based optimization of urban ecosystem services supply and Demand: A block-scale study in Xiamen, China

IF 7 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecological Indicators

Pub Date : 2025-03-01 DOI: 10.1016/j.ecolind.2025.113289

Yaling Gao , Danling Fu , He Huang , Jinwen Jiang , Qunyue Liu , Liying Zhu , Guochang Ding

The imbalance between the supply and demand of urban ecosystem services significantly impacts land resource utilization and residents’ quality of life. This study innovatively examines the spatio-temporal evolution of these services at the block scale in Xiamen, China, from 2012 to 2022, addressing a gap in current research that often focuses on larger scales like watersheds. Using multi-source data, six ecosystem services, including water conservation, carbon sequestration, and habitat quality, were assessed, revealing notable deficiencies. The study also employs the GMOP-PLUS model to simulate land use and ecosystem service changes under three scenarios—Natural development (ND), Economic development (ED), and Ecological low-carbon development (EL)—projected to 2027. Results highlight a significant spatial imbalance with a “North Supply, South Demand” pattern, particularly in southern urban areas. While all scenarios show a decline in green space and ecosystem services, the economic growth scenario improves economic benefits, and the ecological conservation scenario enhances low-carbon and ecological services. This research provides novel insights and optimization strategies for urban land use planning, aiming to enhance ecosystem services and support sustainable urban development.

{"title":"Spatio-Temporal evolution and scenario-based optimization of urban ecosystem services supply and Demand: A block-scale study in Xiamen, China","authors":"Yaling Gao , Danling Fu , He Huang , Jinwen Jiang , Qunyue Liu , Liying Zhu , Guochang Ding","doi":"10.1016/j.ecolind.2025.113289","DOIUrl":"10.1016/j.ecolind.2025.113289","url":null,"abstract":"<div><div>The imbalance between the supply and demand of urban ecosystem services significantly impacts land resource utilization and residents’ quality of life. This study innovatively examines the spatio-temporal evolution of these services at the block scale in Xiamen, China, from 2012 to 2022, addressing a gap in current research that often focuses on larger scales like watersheds. Using multi-source data, six ecosystem services, including water conservation, carbon sequestration, and habitat quality, were assessed, revealing notable deficiencies. The study also employs the GMOP-PLUS model to simulate land use and ecosystem service changes under three scenarios—Natural development (ND), Economic development (ED), and Ecological low-carbon development (EL)—projected to 2027. Results highlight a significant spatial imbalance with a “North Supply, South Demand” pattern, particularly in southern urban areas. While all scenarios show a decline in green space and ecosystem services, the economic growth scenario improves economic benefits, and the ecological conservation scenario enhances low-carbon and ecological services. This research provides novel insights and optimization strategies for urban land use planning, aiming to enhance ecosystem services and support sustainable urban development.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"172 ","pages":"Article 113289"},"PeriodicalIF":7.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143593881","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}

引用次数: 0

Coastal sediments record decades of cultural eutrophication in Tampa Bay, Florida, USA

IF 7 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecological Indicators

Pub Date : 2025-03-01 DOI: 10.1016/j.ecolind.2025.113329

Amanda R. Chappel , William F. Kenney , Matthew N. Waters , Caroline Buchanan Fisher , João H.F. Amaral , Edward J. Phlips , Elise S. Morrison

Phosphorus (P) sustainability is a complex problem – it is a limited resource critical for agricultural productivity, but fertilizer production generates extensive phosphogypsum waste and can impair downstream water quality. Industrial, urban, and agricultural activities contribute to cultural eutrophication, thereby degrading both coastal and inland ecosystems and storing legacy nutrients in sediments. This study investigated the long-term effects of phosphogypsum wastewater discharges on legacy nutrient accumulation, an unintended impact of the fertilizer industry that is often overlooked and understudied. Sediment cores were collected to reconstruct the depositional history of two sites in Tampa Bay, Florida, USA that experienced past wastewater releases; the most recent in 2021. Sediments had high concentrations of stored or legacy nutrients (total P: 0.11 – 15.01 mg g⁻¹; total nitrogen: 0.04 – 0.37 %) particularly during discharge timeframes, as assessed by short-lived radioisotopes, and were predominantly in bioavailable forms, as assessed by bulk pools and ³¹P nuclear magnetic resonance spectroscopy. These values are comparable to hypereutrophic lakes impacted by agriculture and urbanization. Sediment accumulation rates were elevated relative to other Florida estuaries (Bishop Harbor: 13,092 – 46,706 g m⁻² yr⁻¹; Piney Point Creek: 3,064 – 23,990 g m⁻² yr⁻¹), which can alter biogeochemical cycling and the fate of nutrient loading. Phosphorus accumulation rates and other proxies had downcore peaks corresponding to discharge events from 2001 to 2004, 2011, and 2021 with P accumulation rates ranging from 0.5 – 559 g m⁻² yr⁻¹. These findings indicate that estuarine nutrient budgets need to incorporate stored sedimentary nutrient pools and internal benthic fluxes and highlight the need for a more sustainable P supply chain.

{"title":"Coastal sediments record decades of cultural eutrophication in Tampa Bay, Florida, USA","authors":"Amanda R. Chappel , William F. Kenney , Matthew N. Waters , Caroline Buchanan Fisher , João H.F. Amaral , Edward J. Phlips , Elise S. Morrison","doi":"10.1016/j.ecolind.2025.113329","DOIUrl":"10.1016/j.ecolind.2025.113329","url":null,"abstract":"<div><div>Phosphorus (P) sustainability is a complex problem – it is a limited resource critical for agricultural productivity, but fertilizer production generates extensive phosphogypsum waste and can impair downstream water quality. Industrial, urban, and agricultural activities contribute to cultural eutrophication, thereby degrading both coastal and inland ecosystems and storing legacy nutrients in sediments. This study investigated the long-term effects of phosphogypsum wastewater discharges on legacy nutrient accumulation, an unintended impact of the fertilizer industry that is often overlooked and understudied. Sediment cores were collected to reconstruct the depositional history of two sites in Tampa Bay, Florida, USA that experienced past wastewater releases; the most recent in 2021. Sediments had high concentrations of stored or legacy nutrients (total P: 0.11 – 15.01 mg g<sup>−1</sup>; total nitrogen: 0.04 – 0.37 %) particularly during discharge timeframes, as assessed by short-lived radioisotopes, and were predominantly in bioavailable forms, as assessed by bulk pools and <sup>31</sup>P nuclear magnetic resonance spectroscopy. These values are comparable to hypereutrophic lakes impacted by agriculture and urbanization. Sediment accumulation rates were elevated relative to other Florida estuaries (Bishop Harbor: 13,092 – 46,706 g m<sup>−2</sup> yr<sup>−1</sup>; Piney Point Creek: 3,064 – 23,990 g m<sup>−2</sup> yr<sup>−1</sup>), which can alter biogeochemical cycling and the fate of nutrient loading. Phosphorus accumulation rates and other proxies had downcore peaks corresponding to discharge events from 2001 to 2004, 2011, and 2021 with P accumulation rates ranging from 0.5 – 559 g m<sup>−2</sup> yr<sup>−1</sup>. These findings indicate that estuarine nutrient budgets need to incorporate stored sedimentary nutrient pools and internal benthic fluxes and highlight the need for a more sustainable P supply chain.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"172 ","pages":"Article 113329"},"PeriodicalIF":7.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601859","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}

引用次数: 0

Reference points for assessing significant adverse impacts on deep sea vulnerable marine ecosystems

IF 7 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecological Indicators

Pub Date : 2025-03-01 DOI: 10.1016/j.ecolind.2025.113296

Andrew J. Kenny , Pierre Pepin , James Bell , Anna Downie , Ellen Kenchington , Mariano Koen-Alonso , Camille Lirette , Christopher Barrio Froján , Neil Ollerhead , F. Javier Murillo , Mar Sacau , Susanna Fuller , Daniela Diz

Biodiversity loss due to human activities is a critical issue, particularly in the High Seas where bottom-contact fishing poses a significant threat to Vulnerable Marine Ecosystems (VMEs). Deep sea VMEs, tend to be composed of slow-growing, long-lived benthic organisms such as deep-sea corals and sponges. The United Nations Food and Agriculture Organization (FAO) has developed guidelines to protect these ecosystems from Significant Adverse Impacts (SAI) caused by bottom trawling activities.

This study focuses on the Northwest Atlantic Fisheries Organization (NAFO) Regulatory Area, utilizing fishery-independent surveys and fishing Vessel Monitoring System (VMS) data to map fishing intensity and VME functional type biomass. Seven VME types have been assessed, e.g., large-sized sponges, sea pens, sea-squirts, bryozoans, black corals, large and small gorgonian corals, to determine the risk of impact. Results indicate that sponges, black corals, and large gorgonians are the most sensitive VME types to bottom trawling activities, with significant biomass loss occurring at very low fishing intensities. The study defines bottom trawling biomass impact thresholds for each VME type in the range of 0.12–9.43 km·km⁻²·yr⁻¹ and 0.01–0.11 km·km⁻²·yr⁻¹ for upper and lower impact thresholds, respectively. The study determines that rapid losses in VME biomass occurs at bottom trawling intensities of about 0.10 km·km⁻²·y^-1 for fisheries operating in the NAFO Regulatory Area. The study concludes that modest reductions in fishing effort in sensitive areas could substantially mitigate SAI whilst having little or no impact on fishing opportunities. The findings also support the target of protecting at least 60 % to 70 % of VME biomass to likely ensure good seabed status; and the importance of implementing spatial fisheries management measures, such as defining a fishing footprint and establishing fishery closed areas, to protect VMEs.

{"title":"Reference points for assessing significant adverse impacts on deep sea vulnerable marine ecosystems","authors":"Andrew J. Kenny , Pierre Pepin , James Bell , Anna Downie , Ellen Kenchington , Mariano Koen-Alonso , Camille Lirette , Christopher Barrio Froján , Neil Ollerhead , F. Javier Murillo , Mar Sacau , Susanna Fuller , Daniela Diz","doi":"10.1016/j.ecolind.2025.113296","DOIUrl":"10.1016/j.ecolind.2025.113296","url":null,"abstract":"<div><div>Biodiversity loss due to human activities is a critical issue, particularly in the High Seas where bottom-contact fishing poses a significant threat to Vulnerable Marine Ecosystems (VMEs). Deep sea VMEs, tend to be composed of slow-growing, long-lived benthic organisms such as deep-sea corals and sponges. The United Nations Food and Agriculture Organization (FAO) has developed guidelines to protect these ecosystems from Significant Adverse Impacts (SAI) caused by bottom trawling activities.</div><div>This study focuses on the Northwest Atlantic Fisheries Organization (NAFO) Regulatory Area, utilizing fishery-independent surveys and fishing Vessel Monitoring System (VMS) data to map fishing intensity and VME functional type biomass. Seven VME types have been assessed, e.g., large-sized sponges, sea pens, sea-squirts, bryozoans, black corals, large and small gorgonian corals, to determine the risk of impact. Results indicate that sponges, black corals, and large gorgonians are the most sensitive VME types to bottom trawling activities, with significant biomass loss occurring at very low fishing intensities. The study defines bottom trawling biomass impact thresholds for each VME type in the range of 0.12–9.43 km·km<sup>−2</sup>·yr<sup>−1</sup> and 0.01–0.11 km·km<sup>−2</sup>·yr<sup>−1</sup> for upper and lower impact thresholds, respectively. The study determines that rapid losses in VME biomass occurs at bottom trawling intensities of about 0.10 km·km<sup>−2</sup>·y<sup>-1</sup> for fisheries operating in the NAFO Regulatory Area. The study concludes that modest reductions in fishing effort in sensitive areas could substantially mitigate SAI whilst having little or no impact on fishing opportunities. The findings also support the target of protecting at least 60 % to 70 % of VME biomass to likely ensure good seabed status; and the importance of implementing spatial fisheries management measures, such as defining a fishing footprint and establishing fishery closed areas, to protect VMEs.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"172 ","pages":"Article 113296"},"PeriodicalIF":7.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143520504","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}

引用次数: 0