Ecological Informatics最新文献

{"title":"Improving the prediction of streamflow in large watersheds based on seasonal trend decomposition and vectorized deep learning models","authors":"Ningchang Kang,&nbsp;Zhaocai Wang,&nbsp;Anbin Zhang,&nbsp;Hang Chen","doi":"10.1016/j.ecoinf.2025.103291","DOIUrl":"10.1016/j.ecoinf.2025.103291","url":null,"abstract":"<div><div>Accurate streamflow prediction is essential for water resource management and ecological conservation. With climate change and human activities intensifying extreme weather events, the risks associated with floods have grown, threatening both socioeconomic robustness and ecological integrity. Conventional prediction methods, such as physical and statistical models, often struggle to capture the complex nonlinear and nonstationary characteristics of streamflow. To address this challenge, this study presents a vectorized hybrid STL-LSTM-GRU-Transformer model designed to enhance prediction accuracy and stability. The approach begins by applying seasonal-trend decomposition using loess (STL) to separate streamflow data into trend, seasonal, and residual components. These components are then modeled independently: long short-term memory (LSTM) and convolutional neural networks (CNN) handle trend and seasonal patterns, while gated recurrent units (GRU) and Transformer process residual fluctuations. Furthermore, the model incorporates the Runoff process vectorization (RPV) method alongside vectorization techniques to improve sensitivity to extreme events. Evaluated on 2010–2022 data from six Jialing River stations, the model achieves 0.9991 (NSE), outperforming 12 benchmarks. SHAP analysis identifies dew point temperature (26.7 % contribution) and solar radiation (15.7 %) as key drivers, while kernel density estimation provides reliable probabilistic forecasts (PICP = 0.90 at 95 % CI). Demonstrating robust performance in flood-drought transition prediction (NSE &gt; 0.9983), this approach contributes valuable insights for advancing flood early warning systems and hydro-ecological security.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103291"},"PeriodicalIF":5.8,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144480466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Fast-Greedy algorithm reveals hourly fluctuations and associated risks of shark communities in a South Pacific city","authors":"Ibtissam Chafia ,&nbsp;Jihad Zahir ,&nbsp;Christophe Lett ,&nbsp;Tarik Agouti ,&nbsp;Hajar Mousannif ,&nbsp;Laurent Vigliola","doi":"10.1016/j.ecoinf.2025.103263","DOIUrl":"10.1016/j.ecoinf.2025.103263","url":null,"abstract":"<div><div>Unprovoked shark bites are increasing globally. Regional hotspots like Nouméa show rising incidents involving bull sharks (<em>Carcharhinus leucas</em>) and tiger sharks (<em>Galeocerdo cuvier</em>), leading to the culling of these protected species. Identifying high-risk areas and times is key to balancing human safety and shark conservation. Here, we collected five years of acoustic telemetry data for both shark species in the lagoon of Nouméa. The data were categorized by species, divided into 24 hourly subsets, and modeled as bipartite graphs. The Fast-Greedy algorithm was applied to identify distinct communities of sharks and stations. Normalized mutual information was used to cluster communities and detect spatiotemporal patterns. The study revealed up to 9 hourly communities for bull sharks and 21 for tiger sharks, each grouping into 3 clusters. Several high-risk areas and times were identified. Bull sharks formed schools, and a cluster was observed in the harbor between 6:00 and 13:00, increasing bite risk on nearby beaches in the morning. Tiger sharks were more solitary and were present day and night at most stations except those in relatively turbid areas. Both species showed fission–fusion dynamics, with communities merging at dusk, indicating increased movement and a higher risk during this low-light period. A key innovation of our modeling framework was its ability to handle temporal variability in community detection algorithms applied to bipartite networks. The model identified key overlap periods of shark–human activity, highlighting the need for real-time monitoring, safety measures, and public awareness to reduce bite risk and promote coexistence.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103263"},"PeriodicalIF":5.8,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144471658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Postprocessing of convection permitting precipitation forecast using UNets","authors":"Marcos Esquivel-González,&nbsp;Albano González,&nbsp;Juan Carlos Pérez,&nbsp;Juan Pedro Díaz","doi":"10.1016/j.ecoinf.2025.103255","DOIUrl":"10.1016/j.ecoinf.2025.103255","url":null,"abstract":"<div><div>Reliable precipitation forecasting is crucial in sectors like public safety, agriculture and water management. Although Numerical Weather Prediction (NWP) models form the backbone of modern forecasting, their inherent limitations and the chaotic behavior of atmospheric equations often result in errors, requiring postprocessing to improve accuracy and quantify uncertainties. This study assesses hourly precipitation probabilistic postprocessing models tailored for the Canary Islands, aiming to improve ensemble forecasting accuracy. UNet-based models were explored using two strategies: one incorporating all 25 km-scale convection-permitting ensemble forecast simulations as input, and another applying dimensionality reduction techniques to reduce input complexity. These models were compared to traditional benchmarks like the Censored Shifted Gamma Distribution (CSGD) with Ensemble Model Output Statistics (EMOS), the Analog Ensemble method and deep learning strategies through MLP architectures. In the analysis of the results, not only the reliability of the predictions for the set of available meteorological stations was considered, but also the generalization capacity of the UNet models to obtain precipitation predictions for the whole region.</div><div>UNet models generally improved the raw WRF ensemble and performed comparably or better than traditional approaches. Probabilistic (CRPSS, BSS) and deterministic (MAESS, RMSESS, Relative Bias) skill scores demonstrated improvements, particularly in forecasting light-to-moderate rainfall. The Integrated Gradients technique revealed that incorporating a height terrain map significantly influenced UNet’s outputs, emphasizing the critical role of topographic data in rainfall forecasting. Furthermore, UNet models demonstrated strong spatial generalization, showcasing their potential for operational forecasting in areas with limited observational networks.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103255"},"PeriodicalIF":5.8,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The effect of collinearity between observed and model derived training variables on estuarine algal species distribution models","authors":"Dante M.L. Horemans ,&nbsp;Jennifer C. Lin ,&nbsp;Marjorie A.M. Friedrichs ,&nbsp;Pierre St-Laurent ,&nbsp;Raleigh R. Hood ,&nbsp;Christopher W. Brown","doi":"10.1016/j.ecoinf.2025.103225","DOIUrl":"10.1016/j.ecoinf.2025.103225","url":null,"abstract":"<div><div>Forecasts of organism distributions in time and space are needed to mitigate risks associated with changes in environmental conditions. These forecasts are often generated using correlative species distribution models (SDMs) that relate environmental variables to species presence or abundance. Biological complexity makes the construction of SDMs challenging because the collinearity between the environmental variables used to train the SDM may increase model parameter uncertainty. To analyze the effect of collinearity on SDMs, we (1) train SDMs for seven estuarine algal species commonly observed in the Chesapeake Bay (U.S.A.) using different levels of collinearity in the training information, (2) identify the environmental predictors, and (3) study their association with species presence using two statistical techniques (generalized linear models and regression trees). The novelty of our contribution is that our analysis uses both environmental <em>in situ</em> observations and environmental information generated by a mechanistic model. The environmental variables show strong collinearities in both the <em>in situ</em> observations (32 out of the total of 165 correlations) and mechanistic model output (12 out of the total of 120 correlations). To determine how collinearity between these variables affect our SDM results, we remove environmental variables that surpass a specific correlation threshold. We find that using these two different types of training information (i.e., observed vs. modeled) affects (1) the optimal set of predictors, (2) the associations between environmental variables and algal presence, and (3) the model’s predictive skill. Water temperature is generally selected as an important predictor. Strong positive or negative associations between environmental variables and algal presence are not substantially impacted by the type of training information used. Although removing collinearities may result in the detection of new important predictors, it may also result in a slight decrease (<span><math><mo>∼</mo></math></span> 5 %) of the SDM prediction skill, depending on the species of interest and type of training information. Our findings suggest that the main environmental predictors rely on both the species characteristics and training information type used in SDM construction, and highlight the challenge of interpreting the associations between environmental conditions and species presence predicted by these SDMs. These insights help us to better understand the environmental conditions of importance to these algal species and hence optimize monitoring efforts by revealing which <em>in situ</em> observations are vital to accurately forecast blooms of these estuarine algae.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103225"},"PeriodicalIF":5.8,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning based mapping of bee-friendly trees through remote sensing: A novel approach to enhance pollinator conservation","authors":"Robbe Neyns ,&nbsp;Hanna Gardein ,&nbsp;Markus Münzinger ,&nbsp;Robert Hecht ,&nbsp;Henri Greil ,&nbsp;Frank Canters","doi":"10.1016/j.ecoinf.2025.103288","DOIUrl":"10.1016/j.ecoinf.2025.103288","url":null,"abstract":"<div><div>The global decline in wild bee populations poses significant risks to ecosystem stability given bees' essential role as pollinators. Conserving bee-friendly habitats is critical for the promotion of wild bees and prevention of further losses, which requires a good understanding of the bee's ecology. This study explores the relationship between nesting sites of the ground-nesting bee <em>Andrena vaga</em> and the distribution of <em>Salix</em> trees, an essential pollen source for this and other bee species, within the city of Braunschweig, Germany. Our approach integrates multi-temporal PlanetScope imagery, a tabular transformer deep learning model, and a LiDAR-derived 3D tree model to automate the mapping of <em>Salix</em> trees. The mapping achieved an F1 score of 0.73 (precision: 0.69, recall: 0.78). Field surveys were conducted, documenting <em>Andrena vaga</em> nest aggregations and aggregation sizes. On average, the nearest <em>Salix</em> tree was located approximately 150 m from an aggregation, while the nearest five trees were within 300 m. Literature-guided estimates of the required <em>Salix</em> density for a given aggregation size showed that, on average, the theoretically necessary number of trees was found within 300 m, though for one aggregation the distance exceeded 1000 m. While overall the number of <em>Salix</em> trees around nest aggregations seems to increase with aggregation size, the relationship did not prove to be statistically significant. Our study illustrates the potential of remote sensed based mapping of tree species to enhance our understanding of floral resource availability in pollinator habitats, thereby supporting informed conservation of essential resources for bees and other insects. It also highlights how advances in remote sensing can play an important role in ecological research and habitat conservation.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103288"},"PeriodicalIF":5.8,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144480470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AlgAlert: A two-level approach for algae bloom prediction using deep learning","authors":"Areej Alsini ,&nbsp;Amina Saeed ,&nbsp;Dawood Amin","doi":"10.1016/j.ecoinf.2025.103260","DOIUrl":"10.1016/j.ecoinf.2025.103260","url":null,"abstract":"<div><div>Chlorophyll-a (Chl-a) is essential to detect harmful algae blooms that can damage aquatic ecosystems and cause economic losses. Consequently, governmental agencies and research institutions invest significant effort into monitoring water quality and developing management strategies for aquatic systems. With the increasing availability of real-time water quality, meteorological and tidal sensor data, there is growing potential to harness this information through data-driven approaches such as machine learning to support aquatic systems management. This study presents a comprehensive data-driven framework named AlgaAlert that integrates a regression model to forecast Chl-a concentrations and a classification model to predict the occurrence of blooms in a temperate estuarine system. The framework was developed by benchmarking multiple algorithms and selecting the best-performing regression and classification models for integration. The model evaluation was based on hourly water quality and meteorological data collected from early December 2019 to mid-January 2020 from the Kwilena monitoring site, the South Perth meteorological station, and a tidal gauge on Barrack Street, Perth, Australia. The AlgAlert framework combines K-Nearest-Neighbours Regression (KNN) regression to predict Chl-a levels with a custom classifier to determine bloom or no-bloom conditions based on labelled time-series data. KNN demonstrated superior regression performance, achieving 0.25 MAE, outperforming other models like random forest (RF). Classification results revealed nearly perfect F1-scores, indicating that the model accurately identified bloom events with few missed or false alarms (0.99 for no-bloom and 0.98 for bloom). This demonstrates AlgAlert’s robust predictive capabilities, offering a reliable tool to support timely decision-making in water quality management.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103260"},"PeriodicalIF":5.8,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic simulation and key influencing factors of carbon storage in the water-depleted zones of an arid Inland River Basin: Insights from the Tarim River mainstream","authors":"Kun Liu ,&nbsp;Bin Wu ,&nbsp;Fan Gao ,&nbsp;Yunfei Chen ,&nbsp;Bing He ,&nbsp;Abdul Waheed ,&nbsp;Aishajiang Aili ,&nbsp;Zhiyuan Xu ,&nbsp;Fanghong Han ,&nbsp;Hailiang Xu","doi":"10.1016/j.ecoinf.2025.103286","DOIUrl":"10.1016/j.ecoinf.2025.103286","url":null,"abstract":"<div><div>Arid inland river basins exhibit pronounced uncertainty and spatial heterogeneity in carbon storage dynamics due to extreme climate conditions, water scarcity and ecosystem vulnerability. In particular, water-depleted zones still lack systematic research on the evolution mechanism of carbon storage. To assess the evolution characteristics of carbon storage in such regions, this study developed an integrated framework combining the Patch-generating Land Use Simulation (PLUS) model, the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and a Structural Equation Model (SEM), taking the Tarim River mainstream as a representative case. It systematically analyzed the spatial and temporal evolution of land use/cover change (LUCC) and carbon storage from 1990 to 2020, simulated future trends under three scenarios: natural development (ND), cultivated land protection (CP) and ecological protection (EP), covering from 2030 to 2050, and quantitatively identified the direct and indirect drivers of spatial differentiation in carbon storage. The results revealed that over the past three decades, the most significant land transitions in the Tarim River mainstream occurred in cultivated and build-up land. Among the three scenarios, within the EP scenario, the reduction in carbon storage by 2030, 2040, and 2050 was significantly alleviated, with an additional 56 × 10⁵ tons of carbon stored compared to the cultivated land protection scenario. LUCC emerged as the dominant directly driver of regional carbon storage changes. Additionally, carbon storage in the upper, middle, and lower reaches was indirectly influenced by socio-economic and natural geographical factors, with the dominant factor varying by region. These differences modified water resource supply patterns, influenced vegetation dynamics, and ultimately indirectly affected the spatial and temporal evolution of carbon storage. This study enriches the understanding of carbon storage evolution mechanisms in arid regions and underscores the importance of region-specific carbon management strategies tailored to local conditions.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103286"},"PeriodicalIF":5.8,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Post-fire evapotranspiration estimates in ground truth limited environments","authors":"Kyra Liu ,&nbsp;Sonya R. Lopez","doi":"10.1016/j.ecoinf.2025.103275","DOIUrl":"10.1016/j.ecoinf.2025.103275","url":null,"abstract":"<div><div>Wildfires are catastrophic events with increasing incidence and severity, especially in Mediterranean climates like California. Wildfires can drastically change the hydrologic components of a watershed and require consistent and reliable data to monitor recovery. With the upcoming retirement of the Moderate Resolution Imaging Spectroradiometer (MODIS), a reliable evapotranspiration (ET) product at the subcatchment scale is necessary to monitor longitudinal ET, particularly post-fire. We evaluate NASA JPL's ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), MODIS, and the North American Land Data Assimilation System phase 2 (NLDAS-2) to assess performance in longitudinal evapotranspiration (ET) estimates and post-fire ET change detection using correlation analysis and linear regression for twelve subwatersheds located within the August Complex Fire (August 2020) in Trinity County, the Delta Fire (September 2018) in Shasta and Trinity Counties, and the Creek Fire (September 2020) in Fresno County for a four-year period. We found that MODIS and ECOSTRESS showed the strongest correlation in daily ET estimates (mean <em>r</em> = 0.56), while correlations between ECOSTRESS-NLDAS-2 (<em>r</em> = 0.06) and MODIS-NLDAS-2 (<em>r</em> = 0.30) were notably weaker. However, ECOSTRESS data availability constrained post-fire change detection and annual ET sum assessments. Its higher spatial resolution and correlation with MODIS have potential for finer-scale model parameterization of post-fire landcover changes, particularly vegetation loss in place of MODIS. NLDAS-2 can be useful in monthly and annual patterns at larger scales. By comparing ECOSTRESS with established platforms of ET estimation, we validate ECOSTRESS as a tool for post-fire ET monitoring, especially in areas where ground truth data is unavailable. These findings can allow for more informed and effective land management decisions, particularly in wildfire and vegetation monitoring.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103275"},"PeriodicalIF":5.8,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144364970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Land-cover classification in Addo Elephant National Park: Analyzing the impact of variables, classifiers, and object-based approach","authors":"Mohammad Safaei ,&nbsp;Jane Southworth ,&nbsp;Cerian Gibbes ,&nbsp;Hannah V. Herrero ,&nbsp;Mashoukur Rahaman ,&nbsp;Bewuket B. Tefera ,&nbsp;Jason K. Blackburn","doi":"10.1016/j.ecoinf.2025.103279","DOIUrl":"10.1016/j.ecoinf.2025.103279","url":null,"abstract":"<div><div>A comprehensive understanding of land-use and land-cover (LULC) dynamics is vital in steering effective conservation and management efforts, especially in ecologically rich regions like Addo Elephant National Park (AENP). Despite its importance, up-to-date LULC maps of AENP remain scarce, needing an in-depth investigation to aid conservation planning. Using Landsat time-series data, this study produced 30-m resolution LULC maps for the years 2002, 2014, and 2022, and examined changes within six LULC categories. Object-based classification was compared with a pixel-based approach, revealing the superior performance of the pixel-based approach. Two machine learning (ML) techniques, Random Forest (RF) and Support Vector Machines (SVM), were compared with a deep learning (DL) technique, UNet++. The land-cover classification process using ML algorithms involved experimentation with various predictor variables, including spectral bands, spectral indices, time-series data, and textural information. Spectral mixture analysis was performed, and the resulting fraction layers were used as independent variables in the models. The study identified RF as the preferred classification algorithm using the optimal combination of these variables, achieving high accuracy of 89.1 %, 91.2 %, and 91.9 % for the years 2002, 2014, and 2022, respectively. Variable importance analysis highlighted the consistent significance of elevation, slope, and the time-series of normalized difference indices. The final land-cover maps revealed grass as the predominant class both inside and outside AENP, followed by thicket within the park, and agriculture outside it. Land-cover change analysis indicated small changes (&lt;3 %), primarily involving transitions between thicket and grass classes inside the park, and grass and agriculture outside.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103279"},"PeriodicalIF":5.8,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resilience evolution and optimization strategies of ecological networks in the Three Gorges Reservoir Area: A scenario-based simulation approach","authors":"Haohua Wang ,&nbsp;Lulu Zhou ,&nbsp;Kangchuan Su ,&nbsp;Yun Zhou ,&nbsp;Qingyuan Yang","doi":"10.1016/j.ecoinf.2025.103285","DOIUrl":"10.1016/j.ecoinf.2025.103285","url":null,"abstract":"<div><div>With the intensification of human activities such as urbanisation, industrialization, and agricultural expansion, global ecosystems face habitat fragmentation and degradation, resulting in functional loss and decreased ecological connectivity, which compromises ecosystem service stability and sustainability. In response to these challenges, ecological networks (ENs) have gained widespread attention as comprehensive landscape conservation strategies. This study focuses on the Three Gorges Reservoir Area (TGRA), addressing human-land relationship conflicts by constructing an EN system and analyzing its spatiotemporal evolution from 2001 to 2023. To evaluate network resilience, this study employs node attack simulation methods, we dynamically assessed EN resilience through four functional and structural indicators. The results indicate that EN resilience in the TGRA varied according to the operational phases of the Three Gorges Project while maintaining relatively stable conditions between 2001 and 2023, thereby revealing the impact of different operational phases on regional EN resilience. This study establishes an EN evaluation framework for regions with complex human-land relationships, contributing by: (1) providing empirical evidence for ecological governance and sustainable development in the TGRA; (2) offering a scientific reference for optimizing ecological security patterns; (3) developing dynamic assessment methods with transferable value, thereby providing methodological guidance for EN construction and resilience assessment in similar regions.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103285"},"PeriodicalIF":5.8,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307995","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}