Pub Date : 2024-08-29DOI: 10.1016/j.ecolind.2024.112549
Shuhan Zhang, Hailing Jiang, Hailin Yu, Xinhui Feng, Mingxuan Fan
Numerous disorderly expansions of impervious surfaces have resulted from the ongoing urbanization process, eroding the ecological security pattern and exacerbating ecological risks, subsequently leading to a progressive deterioration in its integrity. The tension between the expansion of ecological space and urban spatial expansion is becoming increasingly acute. Exploring the connection between ecological risks in urban landscapes is important for effectively managing regional ecological risks and optimizing patterns of regional ecological security. However, previous studies focus on selecting ecological sources in an ecological network and ignore how ecological resilience is impacted by factors such as species diversity, ecological structure complexity, etc. Based on remote sensing images of Liaoning Province in 2000, 2010, and 2020, the study used the landscape core index as the main indicator of ecological risk assessment, constructed a landscape ecological risk assessment model, and analyzed the spatiotemporal evolution of landscape ecological risk over the past 20 years. The impact of landscape ecological risk on ecological resistance was further explored, and the landscape ecological risk results in 2020 were used as one of the resistance factors to construct a multi-indicator comprehensive resistance surface. After identifying important ecological sources, an ecological network based on the minimum cumulative resistance model (MCR) was constructed, the optimal path connecting important ecological source areas was determined, and the distribution pattern characteristics of the internal ecological network in Liaoning Province were revealed. The findings indicate a general shift in ecological risks from high to low, following a distribution pattern of “high in the east and low in the west”. Additionally, the study identified 8 ecological sources and established 28 ecological corridors, spanning a total length of roughly 8160 km. This study aims to furnish a scientific foundation for developing an ecological security pattern network and optimizing the landscape pattern within Liaoning Province.
{"title":"Construction of landscape ecological network based on MCR risk assessment Model: A case study of Liaoning Province, China","authors":"Shuhan Zhang, Hailing Jiang, Hailin Yu, Xinhui Feng, Mingxuan Fan","doi":"10.1016/j.ecolind.2024.112549","DOIUrl":"https://doi.org/10.1016/j.ecolind.2024.112549","url":null,"abstract":"Numerous disorderly expansions of impervious surfaces have resulted from the ongoing urbanization process, eroding the ecological security pattern and exacerbating ecological risks, subsequently leading to a progressive deterioration in its integrity. The tension between the expansion of ecological space and urban spatial expansion is becoming increasingly acute. Exploring the connection between ecological risks in urban landscapes is important for effectively managing regional ecological risks and optimizing patterns of regional ecological security. However, previous studies focus on selecting ecological sources in an ecological network and ignore how ecological resilience is impacted by factors such as species diversity, ecological structure complexity, etc. Based on remote sensing images of Liaoning Province in 2000, 2010, and 2020, the study used the landscape core index as the main indicator of ecological risk assessment, constructed a landscape ecological risk assessment model, and analyzed the spatiotemporal evolution of landscape ecological risk over the past 20 years. The impact of landscape ecological risk on ecological resistance was further explored, and the landscape ecological risk results in 2020 were used as one of the resistance factors to construct a multi-indicator comprehensive resistance surface. After identifying important ecological sources, an ecological network based on the minimum cumulative resistance model (MCR) was constructed, the optimal path connecting important ecological source areas was determined, and the distribution pattern characteristics of the internal ecological network in Liaoning Province were revealed. The findings indicate a general shift in ecological risks from high to low, following a distribution pattern of “high in the east and low in the west”. Additionally, the study identified 8 ecological sources and established 28 ecological corridors, spanning a total length of roughly 8160 km. This study aims to furnish a scientific foundation for developing an ecological security pattern network and optimizing the landscape pattern within Liaoning Province.","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195740","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}
Pub Date : 2024-08-29DOI: 10.1016/j.ecolind.2024.112543
Yaotao Xu, Peng Li, Minghui Zhang, Lie Xiao, Bo Wang, Xiaoming Zhang, Yunqi Wang, Peng Shi
As the pace of industrialization and urbanization accelerates, water quality management faces increasing challenges, with traditional methods for pollutant source apportionment often proving inadequate in handling complex environmental data. This study enhances the precision and reliability of pollutant source identification by integrating Positive Matrix Factorization (PMF) models with diverse machine learning techniques. Utilizing data from 17 water quality monitoring stations along the Wuding River from 2017 to 2021, we employed Random Forest (RF), Support Vector Machine (SVM), Elastic Net (EN), and Extreme Gradient Boosting (XGBoost) models to predict the Water Quality Index (WQI) during dry and wet seasons. Results indicate that the RF model exhibited optimal performance in the dry season (R = 0.93), while the SVM was superior in the wet season (R = 0.94). SHAP (SHapley Additive exPlanations) value analysis identified COD and NH-N as significant influencers on WQI in the dry season, whereas COD, BOD, and TP gained prominence during the wet season. SHAP values reveal the contribution of each feature to the model output, thereby enhancing the model’s transparency and interpretability. Additionally, feature importance identified by machine learning was utilized as weights to optimize the contribution rates predicted by the PMF model. The optimised model was able to identify the contribution of domestic and farm effluent discharges more accurately in the dry season, with a significant increase in the percentage of identification from 19.4 % to 45.4 %, and an increase in the percentage of contribution from agricultural non-point sources and domestic effluent in the rainy season. This research offers a novel perspective on the characteristics of river water pollution and holds significant implications for formulating data-driven environmental management strategies.
{"title":"Quantifying seasonal variations in pollution sources with machine learning-enhanced positive matrix factorization","authors":"Yaotao Xu, Peng Li, Minghui Zhang, Lie Xiao, Bo Wang, Xiaoming Zhang, Yunqi Wang, Peng Shi","doi":"10.1016/j.ecolind.2024.112543","DOIUrl":"https://doi.org/10.1016/j.ecolind.2024.112543","url":null,"abstract":"As the pace of industrialization and urbanization accelerates, water quality management faces increasing challenges, with traditional methods for pollutant source apportionment often proving inadequate in handling complex environmental data. This study enhances the precision and reliability of pollutant source identification by integrating Positive Matrix Factorization (PMF) models with diverse machine learning techniques. Utilizing data from 17 water quality monitoring stations along the Wuding River from 2017 to 2021, we employed Random Forest (RF), Support Vector Machine (SVM), Elastic Net (EN), and Extreme Gradient Boosting (XGBoost) models to predict the Water Quality Index (WQI) during dry and wet seasons. Results indicate that the RF model exhibited optimal performance in the dry season (R = 0.93), while the SVM was superior in the wet season (R = 0.94). SHAP (SHapley Additive exPlanations) value analysis identified COD and NH-N as significant influencers on WQI in the dry season, whereas COD, BOD, and TP gained prominence during the wet season. SHAP values reveal the contribution of each feature to the model output, thereby enhancing the model’s transparency and interpretability. Additionally, feature importance identified by machine learning was utilized as weights to optimize the contribution rates predicted by the PMF model. The optimised model was able to identify the contribution of domestic and farm effluent discharges more accurately in the dry season, with a significant increase in the percentage of identification from 19.4 % to 45.4 %, and an increase in the percentage of contribution from agricultural non-point sources and domestic effluent in the rainy season. This research offers a novel perspective on the characteristics of river water pollution and holds significant implications for formulating data-driven environmental management strategies.","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195768","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}
Pub Date : 2024-08-28DOI: 10.1016/j.ecolind.2024.112544
Albert Nkwasa, Celray James Chawanda, Maria Theresa Nakkazi, Ting Tang, Steven J. Eisenreich, Stuart Warner, Ann van Griensven
The ambition of Sustainable Development Goal (SDG) target 6.3 is to improve global water quality by 2030. SDG indicator 6.3.2 monitors progress towards this target by assessing water bodies against ‘good’ ambient water quality criteria, with nutrients (nitrogen and phosphorus) as part of the key metrics. However, large data gaps present a fundamental challenge, especially for Africa on how to assess the progress being made with respect to both the current and desired future situations. Here, a continental water quality model for Africa is presented to simulate river sediment load, Total Nitrogen (TN) and Total Phosphorus (TP) loads and concentrations. Furthermore, critical areas and hotspots of TN and TP pollution are mapped for the period 2017 – 2019, in relation to the United Nations Environment Programme (UNEP) target thresholds used for the assessment of SDG indicator 6.3.2. Utilizing the UNEP’s criteria, which designates a water body as having “good ambient water quality” if 80% or more of its monitored values meet their targets, it is estimated that 44 % and 15 % of African rivers fail to meet the set water quality thresholds for simulated TP and TN, respectively. When synthesizing data for both TP and TN, 34 % of the rivers do not qualify as having “good ambient water quality”. Geographically, the most pronounced nutrient pollution hotspots were in North Africa, Niger River Delta, Nile River basin, Congo River basin and specific zones in Southern Africa. These areas correlate with regions characterized by high inputs of fertilizers, manure and wastewater discharge.
{"title":"One third of African rivers fail to meet the ’good ambient water quality’ nutrient targets","authors":"Albert Nkwasa, Celray James Chawanda, Maria Theresa Nakkazi, Ting Tang, Steven J. Eisenreich, Stuart Warner, Ann van Griensven","doi":"10.1016/j.ecolind.2024.112544","DOIUrl":"https://doi.org/10.1016/j.ecolind.2024.112544","url":null,"abstract":"The ambition of Sustainable Development Goal (SDG) target 6.3 is to improve global water quality by 2030. SDG indicator 6.3.2 monitors progress towards this target by assessing water bodies against ‘good’ ambient water quality criteria, with nutrients (nitrogen and phosphorus) as part of the key metrics. However, large data gaps present a fundamental challenge, especially for Africa on how to assess the progress being made with respect to both the current and desired future situations. Here, a continental water quality model for Africa is presented to simulate river sediment load, Total Nitrogen (TN) and Total Phosphorus (TP) loads and concentrations. Furthermore, critical areas and hotspots of TN and TP pollution are mapped for the period 2017 – 2019, in relation to the United Nations Environment Programme (UNEP) target thresholds used for the assessment of SDG indicator 6.3.2. Utilizing the UNEP’s criteria, which designates a water body as having “good ambient water quality” if 80% or more of its monitored values meet their targets, it is estimated that 44 % and 15 % of African rivers fail to meet the set water quality thresholds for simulated TP and TN, respectively. When synthesizing data for both TP and TN, 34 % of the rivers do not qualify as having “good ambient water quality”. Geographically, the most pronounced nutrient pollution hotspots were in North Africa, Niger River Delta, Nile River basin, Congo River basin and specific zones in Southern Africa. These areas correlate with regions characterized by high inputs of fertilizers, manure and wastewater discharge.","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195770","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}
Despite providing significant assistance to human society, cascade dams can also have negative impacts on river ecosystems. As the crucial components of river ecosystem, the responses of phytoplankton and zooplankton to cascade dams have rarely been studied simultaneously, and thus, lacking the understanding of the difference in succession between them. Here, we investigated the phytoplankton and zooplankton communities in a river-way with cascade dams using an environmental DNA metabarcoding technology. Along the reservoir areas separated by dams, we found an obvious downward trend in diversity of plankton communities with significant variations in their compositions. The relative abundances of Bacillariophyta and Chlorophyta continued to decrease while Intramacronucleata increased along the river-way. Results of association analyses recognized temperature and flow rate as potential factors resiling the impacts of cascade dams. Beta diversity decomposition indicated species replacement as the main mechanism for variations in plankton communities with higher contribution for phytoplankton. Additionally, we detected wider environmental adaptation (broader environmental breadth, phylogenetic single, and niche breadth) and stronger dispersal ability in phytoplankton than in zooplankton. Environmental variables showed a stronger effect for variations in phytoplankton than zooplankton. Furthermore, we observed that community assembly of phytoplankton and zooplankton was, based on the null model, by heterogeneous selection and drift, respectively. These results suggested differences in phytoplankton and zooplankton response to cascade dams and highlighted the stronger environmental filtering in phytoplankton.
尽管梯级大坝为人类社会提供了重要帮助，但也会对河流生态系统产生负面影响。作为河流生态系统的重要组成部分，浮游植物和浮游动物对梯级水坝的反应很少被同时研究，因此缺乏对它们之间演替差异的了解。在此，我们利用环境 DNA 代谢编码技术研究了有梯级水坝的河道中的浮游植物和浮游动物群落。在大坝分隔的库区沿岸，我们发现浮游生物群落的多样性有明显的下降趋势，其组成也有显著的变化。沿河道，芽胞藻属（Bacillariophyta）和叶绿藻属（Chlorophyta）的相对丰度持续下降，而核内藻属（Intramacronucleata）的相对丰度上升。关联分析结果表明，温度和流速是抑制梯级水坝影响的潜在因素。Beta 多样性分解显示，物种替换是浮游生物群落变化的主要机制，浮游植物的贡献率更高。此外，与浮游动物相比，我们在浮游植物中发现了更广泛的环境适应性（更广泛的环境广度、系统发育单一性和生态位广度）和更强的扩散能力。环境变量对浮游植物变化的影响比浮游动物更大。此外，我们还观察到，浮游植物和浮游动物的群落组合，根据无效模型，分别是由异质选择和漂移造成的。这些结果表明浮游植物和浮游动物对梯级坝的反应存在差异，并强调了浮游植物对环境的过滤作用更强。
{"title":"Environmental DNA metabarcoding revealing the distinct responses of phytoplankton and zooplankton to cascade dams along a river-way","authors":"Yanjun Shen, Yufeng Zhang, Xinxin Zhou, Qinghua Li, Jiaming Zhang, Ruli Cheng, Qing Zuo","doi":"10.1016/j.ecolind.2024.112545","DOIUrl":"https://doi.org/10.1016/j.ecolind.2024.112545","url":null,"abstract":"Despite providing significant assistance to human society, cascade dams can also have negative impacts on river ecosystems. As the crucial components of river ecosystem, the responses of phytoplankton and zooplankton to cascade dams have rarely been studied simultaneously, and thus, lacking the understanding of the difference in succession between them. Here, we investigated the phytoplankton and zooplankton communities in a river-way with cascade dams using an environmental DNA metabarcoding technology. Along the reservoir areas separated by dams, we found an obvious downward trend in diversity of plankton communities with significant variations in their compositions. The relative abundances of Bacillariophyta and Chlorophyta continued to decrease while Intramacronucleata increased along the river-way. Results of association analyses recognized temperature and flow rate as potential factors resiling the impacts of cascade dams. Beta diversity decomposition indicated species replacement as the main mechanism for variations in plankton communities with higher contribution for phytoplankton. Additionally, we detected wider environmental adaptation (broader environmental breadth, phylogenetic single, and niche breadth) and stronger dispersal ability in phytoplankton than in zooplankton. Environmental variables showed a stronger effect for variations in phytoplankton than zooplankton. Furthermore, we observed that community assembly of phytoplankton and zooplankton was, based on the null model, by heterogeneous selection and drift, respectively. These results suggested differences in phytoplankton and zooplankton response to cascade dams and highlighted the stronger environmental filtering in phytoplankton.","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195769","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}
Pub Date : 2024-08-28DOI: 10.1016/j.ecolind.2024.112520
Ran Zhang, Lei Cao, Lei Wang, Letian Wang, Jinjin Wang, Ninghan Xu, Junjie Luo
The park environment is crucial for promoting physical activity (PA). While numerous studies show that park environments influence PA behavior, inconsistencies remain, likely due to varing research methods and parks types. This study employs a fixed spatial grid method to systematically sample four representative parks in Tianjin, China. High-precision orthophoto map (DOM) data from drones provided detailed environmental attributes (like tree canopy area, lawn area, and paved area) and PA characteristics (number of participants, intensity, diversity). The results show: 1) Cluster analysis grouped 1839 park grids into 12 environmental attribute integrations, each correlating with different PA characteristics. “Tree-lined jogging corridors” and “Large sports field areas” exhibit the highest PA intensity, while “Entrance plazas”, “Central plazas,” and “Open sports spaces” have the highest number of participants and PA diversity. 2) Correlation analysis shows that various environmental attributes, including Lawn Area, and Paved Area, are significantly correlated with PA characteristics. 3）Random Forest analysis indicates the key attributes are the paved area for the number of PA participants and PA diversity, and specialized sports facilities area for PA intensity. These findings support urban green space planning and highlight the importance of better park environments for public health.
{"title":"Assessing the relationship between urban park spatial features and physical activity levels in Residents: A spatial analysis Utilizing drone remote sensing","authors":"Ran Zhang, Lei Cao, Lei Wang, Letian Wang, Jinjin Wang, Ninghan Xu, Junjie Luo","doi":"10.1016/j.ecolind.2024.112520","DOIUrl":"https://doi.org/10.1016/j.ecolind.2024.112520","url":null,"abstract":"The park environment is crucial for promoting physical activity (PA). While numerous studies show that park environments influence PA behavior, inconsistencies remain, likely due to varing research methods and parks types. This study employs a fixed spatial grid method to systematically sample four representative parks in Tianjin, China. High-precision orthophoto map (DOM) data from drones provided detailed environmental attributes (like tree canopy area, lawn area, and paved area) and PA characteristics (number of participants, intensity, diversity). The results show: 1) Cluster analysis grouped 1839 park grids into 12 environmental attribute integrations, each correlating with different PA characteristics. “Tree-lined jogging corridors” and “Large sports field areas” exhibit the highest PA intensity, while “Entrance plazas”, “Central plazas,” and “Open sports spaces” have the highest number of participants and PA diversity. 2) Correlation analysis shows that various environmental attributes, including Lawn Area, and Paved Area, are significantly correlated with PA characteristics. 3）Random Forest analysis indicates the key attributes are the paved area for the number of PA participants and PA diversity, and specialized sports facilities area for PA intensity. These findings support urban green space planning and highlight the importance of better park environments for public health.","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195771","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}
A profound grasp of the quantitative spatial heterogeneity and distribution of the soil physicochemical attributes is crucial in understanding agricultural landscapes for ensuring the provisioning of soil ecosystem services. However, the analysis of data from remote sensing, like NDVI, can be of help in analysing the capacity of the landscape to provide supporting ecosystem services such as primary productivity. The research investigated and addressed the dispersion of important soil physico-chemical attributes in agricultural lands of the temperate Himalayan region of India using a geostatistical method and combining normalized difference vegetation index (NDVI) time-series data and the regression Kriging method. A 206 soil samples were gathered and assessed for soil parameters like pH, EC, OC, and available N, P, K, Ca, and Mg from Kishtwar district of Jammu. The coefficient of variation (CV) for pH and electrical conductivity (EC) ranged notably from 8.75 % to 118.98 %, highlighting diverse soil characteristics critical for local management practices. Mean elevation averaged 2743.32 m (m), with a moderate NDVI of 0.15, indicating dynamics in vegetation cover. Soil pH ranged from intensely acidic to marginally alkaline, with varying EC levels. Seemingly high organic carbon (OC), nitrogen (N), and potassium (K) levels, accompanied by medium phosphorus (P), calcium (Ca), and magnesium (Mg) levels were found in the region. The study employed ordinary kriging (OK) to map the spatial distribution of soil parameters, utilizing mean square error (MSE), root mean square error (RMSE), and the Moran’s I index. Exponential models were the best fit models for OC, while spherical models were fit for pH, EC, N, P, and Ca. Mathematical models were best fit for K and Mg. Spatial analysis using spherical and exponential models revealed distinct distribution patterns for pH, N, P, Ca, and Mg. The results of the degree of spatial dependence from the semi-variogram analyses indicated a strong (0.06 %) to moderate (0.51 %) to weak (2.81 %) dependence. The interpolated maps showed a distinct gradient in elevation (1053–4413 m), OC (0.13–2.80 %), NDVI (−0.16–0.54), pH (4.80–8.00), EC (0.03–9.80 dS m), N (201.15–993.19 kg ha), P (3.00–96.00 kg ha), K (124.88–1110.71 kg ha), Ca (7.00–46.00 meq 100 g soil), and Mg (2.30–21.50 meq 100 g soil) at the regional scale, indicating a wide range of spatial soil heterogeneity. The heterogeneity maps of soil parameters generated by this research can be effectively used by land planners and farm managers at a regional scale for crop nutrient management to reduce soil contamination risk. These maps serve as baseline materials and effective tools for suitable land management strategies such as conservation-effective tillage, integrated nutrient management, and organic farming based on the spatial distribution of soil properties and they can significantly enhance the long-term ecological sustainability of agro-ecosystems’ management.
{"title":"Geostatistical modelling of soil properties towards long-term ecological sustainability of agroecosystems","authors":"Owais Ali Wani, Vikas Sharma, Shamal Shasang Kumar, Ab. Raouf Malik, Aastika Pandey, Khushboo Devi, Vipin Kumar, Ananya Gairola, Devideen Yadav, Donatella Valente, Irene Petrosillo, Subhash Babu","doi":"10.1016/j.ecolind.2024.112540","DOIUrl":"https://doi.org/10.1016/j.ecolind.2024.112540","url":null,"abstract":"A profound grasp of the quantitative spatial heterogeneity and distribution of the soil physicochemical attributes is crucial in understanding agricultural landscapes for ensuring the provisioning of soil ecosystem services. However, the analysis of data from remote sensing, like NDVI, can be of help in analysing the capacity of the landscape to provide supporting ecosystem services such as primary productivity. The research investigated and addressed the dispersion of important soil physico-chemical attributes in agricultural lands of the temperate Himalayan region of India using a geostatistical method and combining normalized difference vegetation index (NDVI) time-series data and the regression Kriging method. A 206 soil samples were gathered and assessed for soil parameters like pH, EC, OC, and available N, P, K, Ca, and Mg from Kishtwar district of Jammu. The coefficient of variation (CV) for pH and electrical conductivity (EC) ranged notably from 8.75 % to 118.98 %, highlighting diverse soil characteristics critical for local management practices. Mean elevation averaged 2743.32 m (m), with a moderate NDVI of 0.15, indicating dynamics in vegetation cover. Soil pH ranged from intensely acidic to marginally alkaline, with varying EC levels. Seemingly high organic carbon (OC), nitrogen (N), and potassium (K) levels, accompanied by medium phosphorus (P), calcium (Ca), and magnesium (Mg) levels were found in the region. The study employed ordinary kriging (OK) to map the spatial distribution of soil parameters, utilizing mean square error (MSE), root mean square error (RMSE), and the Moran’s I index. Exponential models were the best fit models for OC, while spherical models were fit for pH, EC, N, P, and Ca. Mathematical models were best fit for K and Mg. Spatial analysis using spherical and exponential models revealed distinct distribution patterns for pH, N, P, Ca, and Mg. The results of the degree of spatial dependence from the semi-variogram analyses indicated a strong (0.06 %) to moderate (0.51 %) to weak (2.81 %) dependence. The interpolated maps showed a distinct gradient in elevation (1053–4413 m), OC (0.13–2.80 %), NDVI (−0.16–0.54), pH (4.80–8.00), EC (0.03–9.80 dS m), N (201.15–993.19 kg ha), P (3.00–96.00 kg ha), K (124.88–1110.71 kg ha), Ca (7.00–46.00 meq 100 g soil), and Mg (2.30–21.50 meq 100 g soil) at the regional scale, indicating a wide range of spatial soil heterogeneity. The heterogeneity maps of soil parameters generated by this research can be effectively used by land planners and farm managers at a regional scale for crop nutrient management to reduce soil contamination risk. These maps serve as baseline materials and effective tools for suitable land management strategies such as conservation-effective tillage, integrated nutrient management, and organic farming based on the spatial distribution of soil properties and they can significantly enhance the long-term ecological sustainability of agro-ecosystems’ management.","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195784","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}
Pub Date : 2024-08-27DOI: 10.1016/j.ecolind.2024.112519
Youxiao Wang, Chunsheng Wu, Zhonghe Zhao, Bowei Yu, Gaohuan Liu
China has been facing severe organic pollution and the nonpoint source export from surface soil is usually overlooked in coastal areas. In this paper, from the perspective of catchment and attenuation, we have constructed a risk assessment method for coastal nonpoint source pollution (NSP) by applying the revised universal soil loss equation (RUSLE) and geostatistical analysis. Combined with the soil and water monitoring data, we have simulated regional NSP risks originating from surface soil organic matter (SOM) in the Yellow River Delta (YRD). Field surveys have verified the significant positive correlations between watershed SOM exporting risks and estuarine chemical oxygen demand (COD) fluxes during the rainy season. There present obvious logarithmic relations between the COD fluxes and the rainfall quantities, and the surface organic NSP risks. Larger NSP contributing sources are mainly located in the areas with higher soil exposure and stronger land-sea interactions. It should focus on the agricultural areas and improve relevant fertilizing and tillage methods to reduce source-exporting risks. Additionally, the summer rainfall concentrating periods need to be controlled emphatically, and vegetation-improving strategies need to be supplemented with the spatiotemporal-specific managements. This study provides a new insight for getting early terrestrial warning information for offshore organic water pollution, and presents research references for similar regional NSP issues.
中国一直面临着严重的有机污染，而沿海地区的表层土壤非点源排放通常被忽视。本文从汇水和衰减的角度出发，运用修订的土壤流失通用方程（RUSLE）和地质统计分析方法，构建了沿海非点源污染风险评估方法。结合水土监测数据，我们模拟了黄河三角洲（YRD）地区源于表层土壤有机质（SOM）的区域非点源污染风险。实地调查证实，流域 SOM 输出风险与雨季河口化学需氧量（COD）通量之间存在显著的正相关关系。COD 通量与降雨量、地表有机物 NSP 风险之间存在明显的对数关系。较大的可吸入颗粒物污染源主要位于土壤暴露程度较高、海陆相互作用较强的地区。应重点关注农业区，改进相关施肥和耕作方法，以降低源输出风险。此外，还需要重点控制夏季降雨集中期，并在改善植被的同时辅以特定时空的管理策略。这项研究为获取近海有机水污染的陆地早期预警信息提供了新的视角，并为类似的区域性非污染源问题提供了研究参考。
{"title":"Assessing spatiotemporal heterogeneity of coastal organic nonpoint source pollution via soil erosion in Yellow River Delta, China","authors":"Youxiao Wang, Chunsheng Wu, Zhonghe Zhao, Bowei Yu, Gaohuan Liu","doi":"10.1016/j.ecolind.2024.112519","DOIUrl":"https://doi.org/10.1016/j.ecolind.2024.112519","url":null,"abstract":"China has been facing severe organic pollution and the nonpoint source export from surface soil is usually overlooked in coastal areas. In this paper, from the perspective of catchment and attenuation, we have constructed a risk assessment method for coastal nonpoint source pollution (NSP) by applying the revised universal soil loss equation (RUSLE) and geostatistical analysis. Combined with the soil and water monitoring data, we have simulated regional NSP risks originating from surface soil organic matter (SOM) in the Yellow River Delta (YRD). Field surveys have verified the significant positive correlations between watershed SOM exporting risks and estuarine chemical oxygen demand (COD) fluxes during the rainy season. There present obvious logarithmic relations between the COD fluxes and the rainfall quantities, and the surface organic NSP risks. Larger NSP contributing sources are mainly located in the areas with higher soil exposure and stronger land-sea interactions. It should focus on the agricultural areas and improve relevant fertilizing and tillage methods to reduce source-exporting risks. Additionally, the summer rainfall concentrating periods need to be controlled emphatically, and vegetation-improving strategies need to be supplemented with the spatiotemporal-specific managements. This study provides a new insight for getting early terrestrial warning information for offshore organic water pollution, and presents research references for similar regional NSP issues.","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195791","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}
Pub Date : 2024-08-27DOI: 10.1016/j.ecolind.2024.112542
Shanlin Tong, Jie Chen, Chong-Yu Xu
Water transfer and storage are two effective anthropogenic management strategies to alleviate the contradictions between water supply and demand. However, the trade-off and synergistic impacts of management strategies in alleviating water stress remain unclear at the national level. Therefore, this study proposes a framework that integrates the fraction of runoff being withdrawn (scarcity coefficient) and the variation of runoff weighted by reservoir (variability coefficient) to evaluate the multifaceted impacts of management strategies on water stress mitigation. The proposed framework evaluates the changes in both the water supply–demand balance and the historical variability of runoff by considering physical water transfers, virtual water flows, and reservoir operations. This study applied the framework to evaluate the spatiotemporal patterns of water stress and used principal component analysis to estimate the relative contributions of management strategies across ten first-order basins in China for the period of 2014–2018. Results show that water-resource scarcity coefficient varied between −37.15% and 13.28% at the basin scale (the national average varied −5%) and water-resource variability coefficient varied between −100% and −19.26% at the basin scale (the national average varied −61.11%). Management strategies, incorporating water transfer and storage strategies, shifted the distribution patterns of national water stress. The attribution analysis revealed that reservoir storage capacity was the largest contributor to the first principal component representing infrastructure element, whereas the second component representing economic element was affected by the net virtual water inflows. Overall, through exploring the outcomes of combined effects among management strategies, this proposed framework provides a comprehensive perspective for investigating how human activity alleviates regional water stress.
{"title":"A framework for evaluating the combined effects of water transfer and storage strategies on water stress alleviation","authors":"Shanlin Tong, Jie Chen, Chong-Yu Xu","doi":"10.1016/j.ecolind.2024.112542","DOIUrl":"https://doi.org/10.1016/j.ecolind.2024.112542","url":null,"abstract":"Water transfer and storage are two effective anthropogenic management strategies to alleviate the contradictions between water supply and demand. However, the trade-off and synergistic impacts of management strategies in alleviating water stress remain unclear at the national level. Therefore, this study proposes a framework that integrates the fraction of runoff being withdrawn (scarcity coefficient) and the variation of runoff weighted by reservoir (variability coefficient) to evaluate the multifaceted impacts of management strategies on water stress mitigation. The proposed framework evaluates the changes in both the water supply–demand balance and the historical variability of runoff by considering physical water transfers, virtual water flows, and reservoir operations. This study applied the framework to evaluate the spatiotemporal patterns of water stress and used principal component analysis to estimate the relative contributions of management strategies across ten first-order basins in China for the period of 2014–2018. Results show that water-resource scarcity coefficient varied between −37.15% and 13.28% at the basin scale (the national average varied −5%) and water-resource variability coefficient varied between −100% and −19.26% at the basin scale (the national average varied −61.11%). Management strategies, incorporating water transfer and storage strategies, shifted the distribution patterns of national water stress. The attribution analysis revealed that reservoir storage capacity was the largest contributor to the first principal component representing infrastructure element, whereas the second component representing economic element was affected by the net virtual water inflows. Overall, through exploring the outcomes of combined effects among management strategies, this proposed framework provides a comprehensive perspective for investigating how human activity alleviates regional water stress.","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195773","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}
Pub Date : 2024-08-27DOI: 10.1016/j.ecolind.2024.112528
Chong Zhao, Shiyu Wu, Lin Yang, Yixiao Wu, Pengnan Xiao, Jie Xu, Yujie Liu
With the development of the social economy, ecological environment damage has become increasingly serious, and how to better protect ecological security has gradually attracted people’s attention. This paper takes Hubei Province in China as the research object, using the PLUS model version 1.4 to design four development scenarios to predict land use and land cover changes (LUCC) in 2035: the natural development scenario (S1), the cultivated land protection scenario (S2), the ecological protection area restriction scenario (S3), and the ecological security pattern (ESP) restriction scenario (S4). The study evaluates the ecological effects under these four different scenarios using Conefor Sensinode 2.6 software, Fragstats v4.2.1 software, and the InVEST model. The conclusions are as follows: (1) ESP has better ecological landscape connectivity. (2) Comparing the ecological security indices of the four scenarios, they are 0.5378, 0.5288, 0.5318, and 0.5405, respectively, with the S4 scenario showing the best protection effect. (3) Comparing the habitat quality of the four scenarios, high-grade habitats degrade under S1 and S2 scenarios; homogenization occurs under S3 and S4 scenarios, but the retention rate of high-grade habitat areas is the highest under the S4 scenario. In conclusion, compared to natural progression and prioritizing cultivated land protection, implementing ecological protection policies yields better ecological effects, and a planned ESP provides more targeted policy recommendations.
{"title":"Evaluating ecological conservation effectiveness of security patterns under multiple scenarios: A case study of Hubei Province","authors":"Chong Zhao, Shiyu Wu, Lin Yang, Yixiao Wu, Pengnan Xiao, Jie Xu, Yujie Liu","doi":"10.1016/j.ecolind.2024.112528","DOIUrl":"https://doi.org/10.1016/j.ecolind.2024.112528","url":null,"abstract":"With the development of the social economy, ecological environment damage has become increasingly serious, and how to better protect ecological security has gradually attracted people’s attention. This paper takes Hubei Province in China as the research object, using the PLUS model version 1.4 to design four development scenarios to predict land use and land cover changes (LUCC) in 2035: the natural development scenario (S1), the cultivated land protection scenario (S2), the ecological protection area restriction scenario (S3), and the ecological security pattern (ESP) restriction scenario (S4). The study evaluates the ecological effects under these four different scenarios using Conefor Sensinode 2.6 software, Fragstats v4.2.1 software, and the InVEST model. The conclusions are as follows: (1) ESP has better ecological landscape connectivity. (2) Comparing the ecological security indices of the four scenarios, they are 0.5378, 0.5288, 0.5318, and 0.5405, respectively, with the S4 scenario showing the best protection effect. (3) Comparing the habitat quality of the four scenarios, high-grade habitats degrade under S1 and S2 scenarios; homogenization occurs under S3 and S4 scenarios, but the retention rate of high-grade habitat areas is the highest under the S4 scenario. In conclusion, compared to natural progression and prioritizing cultivated land protection, implementing ecological protection policies yields better ecological effects, and a planned ESP provides more targeted policy recommendations.","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195790","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}
At present, the accuracy of remote sensing estimation models of plant alpha diversity is generally low, and high-precision estimation models in deciduous broadleaved forest (DBF), deciduous coniferous forest (DCF) and evergreen coniferous forest (ECF) are still lacking. The main purpose of this study is to construct high-precision remote sensing models for plant alpha diversity in multiple ecosystems at global scale. Normalized Difference Vegetation Index (NDVI) were derived from Sentinel-2 data. NDVI and NDVI based spectral diversity/heterogeneity indices were selected as predictive variables, and alpha diversity indices were selected as response variables. Simple linear regression (SLR), partial linear regression (PLSR), and random forest (RF) models were used to evaluate the predictive ability of the predictive variables against the response variables under six ecosystems (evergreen broadleaved forest (EBF), DBF, ECF, DCF, shrub, and grassland), and to compare the estimated robustness of various spectral diversity indices. In terms of prediction accuracy, the SLR models were the worst, and the PLSR model were average. RF performed best, outperforming most current models. Especially in DBF, ECF, shrub and grassland, the determination coefficient R of RF models can be as high as 0.9. In terms of the prediction of α-diversity, the prediction effect of species richness was better than that of Shannon index, Simpson index and Pielou index. The higher the vegetation complexity, the more accurate the assessment of vegetation α-diversity tends to be, especially in DBF, shrub and grassland. According to the importance of predictive variables and model stability evaluation results, NDVI, standard deviation of NDVI (SD), and NDVI derived Shannon’s diversity index (Sha), Cumulative Residual Entropy (CRE), Pielou’s evenness index (Pie), Hill’s numbers (Hill), Berger-Parker’s diversity index (Ber), Parametric Rao’s index of quadratic entropy(paRao) are all powerful indicators for predicting plant alpha diversity. Among them, the prediction performance of NDVI and SD is better. This study is not only an exploration of the practicability of R package “rasterdiv”, but also an attempt to construct high-precision remote sensing estimation models of plant alpha diversity at global scale.
目前，植物α多样性遥感估算模型的精度普遍较低，在落叶阔叶林（DBF）、落叶针叶林（DCF）和常绿针叶林（ECF）中仍缺乏高精度的估算模型。本研究的主要目的是在全球范围内构建多种生态系统中植物阿尔法多样性的高精度遥感模型。归一化植被指数（NDVI）由哨兵-2 数据得出。选择归一化差异植被指数和基于归一化差异植被指数的光谱多样性/异质性指数作为预测变量，选择α多样性指数作为响应变量。使用简单线性回归（SLR）、部分线性回归（PLSR）和随机森林（RF）模型评估了预测变量对六个生态系统（常绿阔叶林（EBF）、DBF、ECF、DCF、灌木和草地）下响应变量的预测能力，并比较了各种光谱多样性指数的估计稳健性。在预测准确性方面，SLR 模型最差，PLSR 模型一般。RF 性能最佳，优于大多数现有模型。特别是在 DBF、ECF、灌木和草地中，RF 模型的判定系数 R 可高达 0.9。在预测α多样性方面，物种丰富度的预测效果优于香农指数、辛普森指数和皮鲁指数。植被复杂度越高，植被α-多样性的评估往往越准确，尤其是在 DBF、灌木和草地中。根据预测变量的重要性和模型稳定性评价结果，NDVI、NDVI标准差（SD）和NDVI衍生的香农多样性指数（Sha）、累积残差熵（CRE）、皮鲁均匀度指数（Pie）、希尔数（Hill）、伯杰-帕克多样性指数（Berger-Parker's diversity index，Ber）、参数拉奥二次熵指数（Parametric Rao's index of quadratic entropy，paRao）都是预测植物α多样性的有力指标。其中，NDVI 和 SD 的预测效果较好。本研究不仅是对 R 软件包 "rasterdiv "实用性的探索，也是构建全球尺度植物α多样性高精度遥感估测模型的尝试。
{"title":"High-precision estimation of plant alpha diversity in different ecosystems based on Sentinel-2 data","authors":"Jiaxun Xin, Jinning Li, Qingqiu Zeng, Yu Peng, Yan Wang, Xiaoyi Teng, Qianru Bao, Linyan Yang, Huining Tang, Yuqi Liu, Jiayao Xie, Yue Qi, Guanchen Liu, Xuyao Li, Ning Tang, Zhenyao Sun, Weiying Zeng, Ziyu Wei, Heyuan Chen, Lizheng He, Chenxi Song, Linmin Zhang, Jingting Qiu, Xianfei Wang, Xinyao Xu, Chonghao Chen","doi":"10.1016/j.ecolind.2024.112527","DOIUrl":"https://doi.org/10.1016/j.ecolind.2024.112527","url":null,"abstract":"At present, the accuracy of remote sensing estimation models of plant alpha diversity is generally low, and high-precision estimation models in deciduous broadleaved forest (DBF), deciduous coniferous forest (DCF) and evergreen coniferous forest (ECF) are still lacking. The main purpose of this study is to construct high-precision remote sensing models for plant alpha diversity in multiple ecosystems at global scale. Normalized Difference Vegetation Index (NDVI) were derived from Sentinel-2 data. NDVI and NDVI based spectral diversity/heterogeneity indices were selected as predictive variables, and alpha diversity indices were selected as response variables. Simple linear regression (SLR), partial linear regression (PLSR), and random forest (RF) models were used to evaluate the predictive ability of the predictive variables against the response variables under six ecosystems (evergreen broadleaved forest (EBF), DBF, ECF, DCF, shrub, and grassland), and to compare the estimated robustness of various spectral diversity indices. In terms of prediction accuracy, the SLR models were the worst, and the PLSR model were average. RF performed best, outperforming most current models. Especially in DBF, ECF, shrub and grassland, the determination coefficient R of RF models can be as high as 0.9. In terms of the prediction of α-diversity, the prediction effect of species richness was better than that of Shannon index, Simpson index and Pielou index. The higher the vegetation complexity, the more accurate the assessment of vegetation α-diversity tends to be, especially in DBF, shrub and grassland. According to the importance of predictive variables and model stability evaluation results, NDVI, standard deviation of NDVI (SD), and NDVI derived Shannon’s diversity index (Sha), Cumulative Residual Entropy (CRE), Pielou’s evenness index (Pie), Hill’s numbers (Hill), Berger-Parker’s diversity index (Ber), Parametric Rao’s index of quadratic entropy(paRao) are all powerful indicators for predicting plant alpha diversity. Among them, the prediction performance of NDVI and SD is better. This study is not only an exploration of the practicability of R package “rasterdiv”, but also an attempt to construct high-precision remote sensing estimation models of plant alpha diversity at global scale.","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195797","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}
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