Ecological Indicators最新文献

{"title":"Satellite insights into diatom biomass dynamics and environmental drivers in the eastern China marginal seas","authors":"Shengqiang Wang,&nbsp;Wenhao Sui,&nbsp;Chen Lu,&nbsp;Hailong Zhang,&nbsp;Deyong Sun,&nbsp;Fei Tian,&nbsp;Jian Wang","doi":"10.1016/j.ecolind.2026.115576","DOIUrl":"10.1016/j.ecolind.2026.115576","url":null,"abstract":"<div><div>Diatoms are among the dominant phytoplankton groups in marginal seas and play a pivotal role in marine primary production and biogeochemical cycling. In this study, focusing on the eastern China marginal seas (ECMS), we developed a remote sensing model to retrieve diatom biomass (<em>C</em>_diat) directly from remote sensing reflectance. Multiple linear regression and machine-learning methods were evaluated using in situ observations and satellite-in situ matchup datasets. Based on its balanced performance across the available evaluation datasets, the stepwise regression model was selected as the final regional model, achieving a coefficient of determination of 0.61 and a symmetric mean absolute percentage error of 48.3% in the satellite–in situ matchup validation. Application of this model to a 23–year record (2002–2025) of Aqua–MODIS observations revealed pronounced spatial and temporal patterns of <em>C</em>_diat across the ECMS. Spatially, elevated <em>C</em>_diat values were concentrated in coastal and estuarine waters and decreased markedly offshore. Strong seasonality was observed in coastal regions, characterized by summer maxima and winter minima. SHAP-based attribution analysis further identified iron, sea surface salinity, mixed layer depth, photosynthetically active radiation, silicon, and sea surface temperature as important environmental factors associated with nonlinear variations in <em>C</em>_diat. River discharge was positively associated with <em>C</em>_diat in estuarine waters, although these relationships weakened after removal of the seasonal cycle, indicating a substantial contribution from shared seasonal variability. These findings provide satellite-based insights into the multiscale variability of diatom biomass in the ECMS and improve our understanding of coastal ecosystem responses to environmental change.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"191 ","pages":"Article 115576"},"PeriodicalIF":8.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149212040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acoustic indices differ in their sensitivity to biophony, geophony, and temporal ecological dynamics","authors":"Stijn J. Geukes,&nbsp;Corentin Bernard,&nbsp;Cynthia Barile,&nbsp;W. Daniel Kissling","doi":"10.1016/j.ecolind.2026.115563","DOIUrl":"10.1016/j.ecolind.2026.115563","url":null,"abstract":"<div><ul><li><span>•</span><span><div>Acoustic indices differ in ecological specificity across habitats and seasons.</div></span></li><li><span>•</span><span><div>BI, NDSI and TFSD best capture phenological soundscape dynamics.</div></span></li><li><span>•</span><span><div>BI strongly distinguishes presence from absence of avian vocal activity.</div></span></li><li><span>•</span><span><div>ACI differentiates insect- from avian-dominated soundscapes.</div></span></li><li><span>•</span><span><div>Combining complementary indices strengthens soundscape interpretation.</div></span></li></ul></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"191 ","pages":"Article 115563"},"PeriodicalIF":8.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149212039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving multi-factor river water quality prediction using deep learning and large language models with retrieval-augmented generation","authors":"Zuxiang Situ,&nbsp;Qiyang Ling,&nbsp;Hongwu Tang,&nbsp;Huiming Zhang,&nbsp;Hong Chen,&nbsp;Shengqi Lyu,&nbsp;Qihua Ran,&nbsp;Xiangju Cheng","doi":"10.1016/j.ecolind.2026.115572","DOIUrl":"10.1016/j.ecolind.2026.115572","url":null,"abstract":"<div><div>Water quality prediction is critical to protect river ecosystems and sustain effective water management under increasing urbanization pressures. Current methods rely on a limited number of influencing factors and lack interpretability, which presents challenges in practical applications. Emerging large language models (LLMs) have strong capabilities for understanding domain knowledge, but require most relevant information for reliability. This study proposed an automated multi-factor river water quality prediction framework by coupling deep learning with LLMs enhanced by retrieval-augmented generation (RAG). A multi-dimensional dataset was constructed by integrating water quality with meteorological, socio-economic and hydro-topographic features to fully characterize river dynamics. Then, LLM-guided hybrid deep learning model, combining convolutional neural network (CNN), long short-term memory (LSTM) and attention mechanisms, namely LCLA model, was developed to predict six water quality indicators. Hyperparameters covering model structure and training strategy were efficiently configured through a literature-based knowledge retrieval, achieving optimal MAE, MSE, RMSE, and MAPE of 0.42, 0.96, 0.54, and 0.11, respectively, with improved optimization efficiency. Model interpretability was enhanced using Shapley analysis, revealing main contributions of turbidity, domestic water use, and river discharge. Finally, a RAG-enhanced multi-agent system was established to perform water quality grade classification and improvement recommendation generation by integrating domain knowledge and expert experience with LLM reasoning. The case study of Xinwu District demonstrated actionable management guidance consistent with on-site conditions. This study offered a reliable and efficient framework, with advantages in prediction performance and knowledge decision support, for advancing intelligent river water quality prediction and management in complex environments.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"191 ","pages":"Article 115572"},"PeriodicalIF":8.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149212262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial patterns and climatic associations of pollination demand, habitat-based supply, and pollination-dependent production in South Korea","authors":"Ayun Maeng,&nbsp;Heejung Nam,&nbsp;Wanmo Kang,&nbsp;Kwangil Cheon","doi":"10.1016/j.ecolind.2026.115583","DOIUrl":"10.1016/j.ecolind.2026.115583","url":null,"abstract":"<div><div>Pollination is a critical ecosystem service for agricultural productivity, yet climate change and intensification increasingly destabilize its delivery. Despite its importance, pollination demand, pollination-dependent production, and habitat-based supply are rarely assessed through an integrated framework. This study develops spatial indicators representing these components across 17 regions in South Korea from 2016 to 2022. The pollination demand indicator and the pollination-dependent production indicator were quantified using linear mixed-effects models relating standardized cultivation area and yield, respectively, to crop-specific pollinator dependence. The habitat-based supply indicator was estimated using the InVEST Crop Pollination model. Relationships among indicators and their associations with regional climatic and geographic variables were examined using Spearman's rank correlation analysis. Results showed a strong but imperfect association between pollination-dependent production and pollination demand indicators (<em>ρ</em> = 0.87, <em>p</em> &lt; 0.001), indicating that the relative cultivation orientation toward pollinator-dependent crops was not uniformly associated with production responses across regions. Climatic variables were more strongly associated with the pollination-dependent production indicator than with the habitat-based supply indicator. The production indicator showed stronger associations with temperature-related gradients than the pollination demand indicator, with positive correlations with latitude (<em>ρ</em> = 0.76, <em>p</em> &lt; 0.001) and negative correlations with mean annual temperature (<em>ρ</em> = −0.71, <em>p</em> &lt; 0.01). Furthermore, the habitat-based supply indicator showed only partial correspondence with the pollination demand indicator (<em>ρ</em> = 0.54, <em>p</em> &lt; 0.05) and marginal correlation with the pollination-dependent production indicator (<em>ρ</em> = 0.42, <em>p</em> &lt; 0.1), suggesting that potential habitat-based supply does not fully correspond to regional variation in the pollination-dependent production indicator across latitudinal and temperature gradients. These findings suggest that habitat-based supply alone does not fully correspond to regional variation in the pollination-dependent production indicator, while climatic conditions provide additional explanatory context beyond habitat-based supply potential. This study demonstrates the importance of explicitly distinguishing among the pollination demand indicator, the habitat-based supply indicator, and the pollination-dependent production indicator, and provides an integrated indicator framework for comparative regional assessment under climatic gradients.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"191 ","pages":"Article 115583"},"PeriodicalIF":8.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149212038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing plastic-related environmental risk across EU countries of the Danube basin using a composite risk index","authors":"Puiu Lucian Georgescu,&nbsp;Nicoleta Barbuta-Misu,&nbsp;Valentin Marian Antohi,&nbsp;Monica Laura Zlati,&nbsp;Costinela Fortea","doi":"10.1016/j.ecolind.2026.115451","DOIUrl":"10.1016/j.ecolind.2026.115451","url":null,"abstract":"<div><div>Plastic-related environmental risk in transboundary river systems represents a multidimensional challenge emerging from the interaction between plastic-related pressures, the vulnerability of receiving aquatic environments, and the capacity of circular economy systems to mitigate environmental impacts. This study investigates the structural factors associated with plastic-related environmental risk and the resulting territorial configurations across seven European Union countries of the Danube basin. Using a balanced panel dataset comprising 84 country-year observations for seven European Union countries over the period 2012–2023, the study develops the Danube Plastic-related Pollution Risk Index (DPPRI), a composite indicator that operationalizes plastic-related environmental risk through the integration of plastic-related pressure, organic pollution vulnerability, and circular economy mitigation capacity. The analytical framework combines composite indicator construction, an Ordinary Least Squares benchmark, supervised machine learning, model interpretation, and unsupervised clustering. Random Forest modelling is applied to assess the relative contribution of environmental, economic, and circular-economy indicators to modelled DPPRI variability, while SHAP (SHapley Additive exPlanations) analysis identifies the magnitude and direction of each indicator's contribution to the predicted composite-index values. K-means clustering, supported by an exploratory hierarchical clustering assessment, is further employed to identify structural risk typologies among the 84 country-year observations. The results show that trade-related plastic flows, the operationalized environmental vulnerability dimension, and indicators of circular-economy intensity and decoupling are among the principal factors associated with variability in the composite risk index, while recycling and recovery performance contribute to the mitigation dimension of the framework. The clustering analysis identifies three distinct structural risk profiles among the analysed country-year observations, reflecting different combinations of plastic-related pressure, environmental vulnerability, economic activity, and mitigation capacity. This study contributes an integrated and potentially transferable risk-screening framework for examining structural conditions associated with plastic-related environmental risk in transboundary river systems. Rather than estimating measured macroplastic or microplastic concentrations, the proposed approach integrates structural indicators of anthropogenic pressure, environmental vulnerability, and mitigation capacity to support the prioritization of monitoring needs and coordinated circular-economy and river-basin policy interventions.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"191 ","pages":"Article 115451"},"PeriodicalIF":8.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149212264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ecological industry empowers rural industrial revitalization: The practical logic and driving mechanism of karst desertification control","authors":"Zhifu Luo,&nbsp;Kangning Xiong,&nbsp;Jiawang Yan,&nbsp;Haijun Tang","doi":"10.1016/j.ecolind.2026.115555","DOIUrl":"10.1016/j.ecolind.2026.115555","url":null,"abstract":"<div><div>Ecological industry (EI) for desertification control has become an important pathway for empowering rural industrial revitalization (RIR) and enhancing regional human well-being. However, few studies have explored the driving mechanisms through which EI promote RIR in the context of typical desertification control. Therefore, this study selected three comprehensive desertification control areas—Salaxi, Hongfenghu, and Huajiang—as study areas, evaluated the spatial differences in the levels of EI and RIR based on a unified criterion-layer and structural-layer framework, and revealed their coupling characteristics. Subsequently, a driving network through which EI promotes RIR was constructed based on the modified factor contribution model, and the interactive effects of factors on EI and RIR were examined using the optimal geographic detector. The results showed that: (1) the levels of EI and RIR in the three study areas exhibited consistency: Hongfenghu (EI = 0.5350, RIR = 0.5308) &gt; Salaxi (EI = 0.2375, RIR = 0.2556) &gt; Huajiang (EI = 0.2324, RIR = 0.2550). The coupling coordination level between EI and RIR followed the order of Hongfenghu (0.80) &gt; Huajiang (0.43) &gt; Salaxi (0.40), and villages such as LDS, YQ, and ML villages achieved high-level coupling. (2) The driving effect of EI on RIR was ranked as Hongfenghu (76.38%) &gt; Huajiang (46.31%) &gt; Salaxi (45.78%), with an overall driving effect of 55.48%. Resource dependence and insufficient infrastructure allocation were the key factors constraining the development of EI in Salaxi and Huajiang, whereas EI development in Hongfenghu has shifted from resource dependence to innovation-driven industrial development. (3) Factor interaction analysis indicated that EI-driven RIR is jointly influenced by internal industrial capacity and external environmental conditions, and the synergistic effects of production capacity, industrial benefits, and geographical advantages represent important pathways for promoting RIR. This study provides a scientific basis for consolidating the achievements of desertification control and promoting the healthy and sustainable development of regional ecological economies.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"191 ","pages":"Article 115555"},"PeriodicalIF":8.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149212260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unraveling multi-scale associations and nonlinear threshold relationships between urban spatial structure and water quality in cold cities based on interpretable machine learning","authors":"Bingxin Li,&nbsp;Shanyu Lou,&nbsp;Qiang Zheng,&nbsp;Jiashuang Li","doi":"10.1016/j.ecolind.2026.115574","DOIUrl":"10.1016/j.ecolind.2026.115574","url":null,"abstract":"<div><div>The tension between urban development and water quality improvement is a major challenge for river-basin management, particularly in cold regions characterized by pronounced freeze–thaw processes. Focusing on the Songhua River Basin, this study integrated water quality monitoring and road-network data (2020–2024) with space syntax and the interpretable machine learning framework XGBoost–SHAP to investigate the nonlinear and cross-scale associations between urban spatial structure and river water quality. The overall City Water Quality Index (CWQI) increased by 6.25% during the study period, indicating an overall deterioration in water quality. Water quality was generally better during the ice-covered period than during the snowmelt period. Dissolved oxygen (DO) was the dominant water-quality indicator during the ice-covered and dry periods, whereas permanganate index (COD_Mn) was more influential during the snowmelt and flood periods. Spatially, water quality was highest in the upstream region, followed by the downstream and midstream regions. Space syntax analysis showed that spatial centrality decreased by 2.3%, with the most pronounced decline occurring at the urban scale, forming a “dual-core, dual-node, and multi-point” pattern. By contrast, spatial hierarchy increased by 1.71%, with the largest increase occurring at the block scale, forming a “one-axis, two-belt, and three-corridor” pattern. XGBoost–SHAP analysis revealed nonlinear and scale-dependent associations between spatial structure and predicted CWQI. During the non-freeze–thaw phase, urban-scale NAIN exhibited relatively larger SHAP contributions and stronger associations with predicted CWQI than district-scale and block-scale indicators. During the freeze–thaw phase, these associations became more strongly cross-scale, with the direction and strength of SHAP contributions changing across different indicator-value ranges, indicating threshold-dependent and nonlinear responses.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"191 ","pages":"Article 115574"},"PeriodicalIF":8.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149212263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-objective optimization of “human-grassland-wild ungulate-livestock” system in a typical nature reserve on the Qinghai-Tibetan plateau","authors":"Yunmeng Qin,&nbsp;Yiming Xu,&nbsp;Yunling Che,&nbsp;Shikui Dong,&nbsp;Zidong Zhou,&nbsp;Zhenqiang Liu,&nbsp;Jiayou Cao","doi":"10.1016/j.ecolind.2026.115567","DOIUrl":"10.1016/j.ecolind.2026.115567","url":null,"abstract":"<div><div>Nature reserves on the Qinghai-Tibetan Plateau (QTP) play crucial roles in supporting ecological security. Achieving the balance of “human-grassland-wild ungulate-livestock” (HGWL) system is important for sustainable development in these nature reserves. This study developed an indicator-based decision-support framework based on machine learning (ML) methods and the non-dominated sorting genetic algorithm II (NSGA-II) in the Changtang Nature Reserve on the QTP. An extreme gradient boosting model was employed to quantify the imbalance between aboveground biomass (AGB) and crude protein (CP) required by livestock and wild ungulates and that supplied by grasslands. A multi-objective optimization model of HGWL system aiming to increase the ecological and economic benefits and decrease the grassland pressure was constructed using NSGA-II based on the imbalance of AGB and CP. This model was applied to analyze the optimized cultivated grasslands planting structure, livestock structure and other variables in three scenarios. Ecological benefits decreased by 0.50% in economic development priority scenario, and economic benefits decreased by 16.22% in ecological protection priority scenario. By contrast, ecological benefits and economic benefits increased, and grassland pressure decreased by 5.06%, 5.05%, and 0.74% respectively in ecological economic balance scenario, which was identified as the optimal scenario. More areas of alfalfa, higher sheep slaughter volume and lower goat and yak slaughter volume were recommended to achieve the HGWL balance. The combination of ML and NSGA-II can help achieve the multi-objective optimization of HGWL system, and this framework has potential to be promoted to other nature reserves around the world.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"191 ","pages":"Article 115567"},"PeriodicalIF":8.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149212261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ecological threshold and the mechanisms of plateau pika (Ochotona curzoniae) bioturbation regulating ecosystem multifunctionality and stability","authors":"Tao Huang,&nbsp;Yu Qin,&nbsp;Shunhua Luo,&nbsp;Xinyu Liu,&nbsp;Baoping Meng,&nbsp;Hongyan Yu,&nbsp;Xianying Wang,&nbsp;Shuhua Yi","doi":"10.1016/j.ecolind.2026.115385","DOIUrl":"10.1016/j.ecolind.2026.115385","url":null,"abstract":"<div><div>Plateau pika (<em>Ochotona curzoniae</em>, hereafter pika) is a keystone species and allogenic engineer on the Qinghai-Tibet Plateau (QTP). However, excessive pika density adversely affects alpine grasslands. Yet the critical threshold of pika density that triggers a shift in ecosystem functioning and stability remains unclear, which hinders the implementation of precise pika control and grassland restoration. To address this knowledge gap, we conducted a large-scale field survey combining aerial photography and field sampling at 91 fixed observation sites across the northeastern QTP from 2023 to 2025. We used the effective pika hole (EPH) to quantify pika disturbance intensity at plot scale, and further evaluated the responses of ecosystem multifunctionality (EMF) and ecosystem stability (ES) along disturbance levels to clarify the critical ecological threshold for pika control. Our results showed that EMF and ES exhibited divergent unimodal responses to pika disturbance. Functional niche volume peaked under low disturbance (9–150 EPH·ha⁻¹), whereas EMF peaked at moderate disturbance (150–420 EPH·ha⁻¹). Compared with undisturbed plots, functional niche overlap decreased and centroid distance increased gradually as disturbance levels rose. Below 420 EPH·ha⁻¹, pika disturbance enhanced EMF by strengthening positive synergistic regulation of plant (standardized effect, se = 0.34*) and soil functions (se = 0.02). Above this threshold (&gt;420 EPH·ha⁻¹), those positive interactions of plant (se = −0.68*) and soil (se = −0.16) were disrupted and EMF declined significantly. Notably, the drop threshold of soil EMF (420 EPH·ha⁻¹) preceded that of plant EMF (560 EPH·ha⁻¹), suggesting that soil-mediated EMF collapsed at a lower pika disturbance. In contrast, plant functional niche (community level) was more sensitive and rapidly narrowed under low disturbance (9–150 EPH·ha⁻¹) before EMF declined. Those indicates that soil function can serve as an early warning indicator for grassland degradation, while plant community restoration should be prioritized. Our results identified that EPH &gt; 420·ha⁻¹ as the ecological threshold for pika control. Meanwhile, strengthening monitoring within the range of 150–420 EPH·ha⁻¹ was proposed to prevent irreversible grassland degradation by pika disturbance. Our findings confirm the dual ecological roles of pika, and thus we call for an ecological management strategy shifting from blanket culling to threshold-based proactive regulation.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"190 ","pages":"Article 115385"},"PeriodicalIF":8.7,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148952582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the moderating effects of artificial canal construction on regional ecosystem services: a state-space-regulation framework based on multi-objective sustainability scenarios","authors":"Min Zhao,&nbsp;Weiqun Lei,&nbsp;Yu Huang,&nbsp;Wenmin Hu,&nbsp;Guo Li","doi":"10.1016/j.ecolind.2026.115339","DOIUrl":"10.1016/j.ecolind.2026.115339","url":null,"abstract":"<div><div>The construction of artificial canals severely disrupts the coordinated development of landscape cover patterns and ecological functions. Quantitatively assessing their redistribution effects on Ecosystem Service (ES) functions and the trade-off/synergy dynamics is of great significance. This study, using <em>the Pinglu Canal</em> construction as a case, integrated SSP-RCP-based PLUS scenario simulations, the “Pressure-State-Response”(PSR) evaluation framework, and InVEST and Geographically Weighted Regression (GWR) models to develop a “State-Space-Regulation” (SSR) response assessment framework for artificial canal construction. It systematically revealed the landscape pattern evolution triggered by canal construction and its impacts on ES trade-offs and synergies. The results showed that: (1) In terms of landscape disturbance, the expansion of water area (5.3%), increased land use dynamic degree (18%), and aggravated landscape fragmentation (10.5%) were significant characteristics after the canal construction; (2) Under the SSP245 (intermediate pathway) scenario, the synergistic relationship between Carbon Storage (CS) and Habitat Quality (HQ) improved significantly (correlation coefficient increased by 0.14); whereas under the SSP585 (high‑carbon pathway) scenario, intensive human intervention significantly strengthened the trade-off relationship between Water Retention (WR) and HQ (correlation coefficient decreased by 0.08). Moreover, WR and Soil Retention (SR) exhibited a notable “distance-decay effect along the canal proximity”, indicating that ecological interactions became more sensitive closer to the canal; (3) The driving mechanism of ES evolution shifted towards human activity dominance (contribution rate increased by 40% ∼ 50%). Given that the SSP245 scenario most effectively balanced economic and ecological sustainable development in the canal region, it is necessary to propose Nature-based Solutions (NbS) for coordinating the ESs in artificial canal basins based on this scenario. This study can provide important technical and theoretical support for assessing the environmental effects and sustainable development of major ecological projects such as “artificial canals”.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"190 ","pages":"Article 115339"},"PeriodicalIF":8.7,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148854898","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}