Ecological Indicators最新文献

{"title":"Vulnerability assessment of Social-Ecological systems in arid Regions: A Cross-Efficiency modified DEA model with entropy weight aggregation","authors":"Lingxiao Sun ,&nbsp;Yang Yu ,&nbsp;Jing He ,&nbsp;Chunlan Li ,&nbsp;Xiang Yu ,&nbsp;Lingyun Zhang ,&nbsp;Yuanbo Lu ,&nbsp;Ireneusz Malik ,&nbsp;Malgorzata Wistuba","doi":"10.1016/j.ecolind.2025.114149","DOIUrl":"10.1016/j.ecolind.2025.114149","url":null,"abstract":"<div><div>Arid social-ecological systems (SES) face escalating vulnerability from climate change and anthropogenic pressures, thus necessitating robust assessment frameworks for targeted governance. To address this need, this study extracted a typical hyper-arid area of the Tarim Basin, the Hotan Prefecture, as the research area and developed an integrated methodology combining an enhanced Data Envelopment Analysis (DEA) model and a Pressure-State-Response (PSR) indicator framework. Specifically, the DEA model was modified through cross-efficiency evaluation and entropy weight aggregation, thereby resolving traditional DEA ranking paradoxes while enabling objective, data-driven assessments. Crucially, the results revealed profound spatial-structural imbalances within the SES, as the social system (SS) exhibited significantly higher vulnerability than the ecological system (ES), contributing 68.6% of total variance. Furthermore, spatiotemporal analysis (2005–2023) indicated fluctuating but marginally declining regional vulnerability, albeit with heightened instability in the east. Regarding key drivers, pressures propelling SS vulnerability included low income, unemployment, and population crowding. ES pressures stemmed primarily from industrial emissions, inadequate wastewater treatment, and fertilizer overuse, with concentrations in northern industrial/agricultural belts. Significant SS deficiencies involved urbanization lag and income deficits, whereas agricultural resilience was observed. For ES, deficiencies centered on severe vegetation cover shortages in northern deserts, where afforestation was constrained by low precipitation and soil organic matter. Collectively, these findings demonstrate the dominance of socioeconomic drivers in arid SES vulnerability and underscore the necessity for spatially targeted governance. Key priorities therefore include: livelihood diversification, equitable economic development, ecological restoration in fragile northern corridors, and enhanced institutional capacity. Finally, future research should advance dynamic simulation, multi-scale integration, and policy coordination to effectively bridge vulnerability diagnosis with comprehensive governance.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"179 ","pages":"Article 114149"},"PeriodicalIF":7.0,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144933911","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":"Structural and compositional indicators of the conservation status of forest habitats: A case study of ravine forests – EU priority habitat type Tilio-Acerion","authors":"Lado Kutnar ,&nbsp;Janez Kermavnar ,&nbsp;Anže Martin Pintar","doi":"10.1016/j.ecolind.2025.114079","DOIUrl":"10.1016/j.ecolind.2025.114079","url":null,"abstract":"<div><div>Maintaining the conservation status of habitat types such as the ravine forests (<em>Tilio-Acerion</em>) assessed in this study is a priority of the European Natura 2000 network. Ravine forests often occur in smaller, fragmented areas, but are widely distributed throughout European forests. Reliable indicators of the conservation status of Natura 2000 habitats, which support monitoring and reporting under Article 17 of the Habitats Directive, are often not available. Therefore, we tested a set of 161 structural and compositional variables as potential indicators of the conservation status of close-to-nature managed ravine forests in a Natura 2000 site in eastern Slovenia. The studied forests ranged from <em>Acer pseudoplatanus</em>-dominated stands to those dominated by <em>Fraxinus excelsior</em> or <em>Tilia</em> species. Most forests were classified as having either a favourable or inadequate conservation status. The main pressures included game browsing and mortality of the key tree species, primarily caused by invasive alien fungi. Favourable conservation status was associated with less intensively managed <em>Tilia</em>-dominated stands on rocky ridges and steep slopes. It was also linked to higher tree layer cover, particularly of <em>Acer pseudoplatanus</em>, in well-preserved forest stands. Conversely, indicators of bad conservation status were associated with <em>Fraxinus excelsior</em>-dominated stands that had been severely affected by invasive alien fungi, resulting in increased volumes of standing and lying deadwood. The resulting tree mortality created more open stand canopies with increased light availability at the forest floor, as indicated by the higher number of plant species in the herb and shrub layer. The conservation status of ravine forests is likely to be increasingly threatened by the adverse effects of climate change, including pests and disease outbreaks and other disturbances. To ensure the continued favourable conservation status of ravine forests, it is essential to monitor key indicators and apply appropriate forest management measures.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"179 ","pages":"Article 114079"},"PeriodicalIF":7.0,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925878","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":"Development of an improved Scaled Drought Condition Index (ISDCI) incorporating soil moisture for drought monitoring in Xinjiang","authors":"Ying Cao ,&nbsp;Hao Guo ,&nbsp;Xiangchen Meng ,&nbsp;Wei Wang ,&nbsp;Chunrui Guo ,&nbsp;Weimeng Gan ,&nbsp;Anming Bao ,&nbsp;Philippe De Maeyer","doi":"10.1016/j.ecolind.2025.114115","DOIUrl":"10.1016/j.ecolind.2025.114115","url":null,"abstract":"<div><div>Various composite drought indices have been developed to quantify drought conditions. However, many challenges, such as inadequate soil moisture representation, empirical variable weights, and arbitrary drought classification, still exist. Xinjiang was selected as a typical study area, and an Improved Scaled Drought Condition Index (ISDCI) was developed by integrating precipitation, vegetation, temperature, and downscaled soil moisture with objectively determined weights. Additionally, the classification of drought severity has been refined in a relatively objective way. The results indicate the following: (1) Compared to the Scaled Drought Condition Index (SDCI) and the Vegetation Health Index (VHI), ISDCI showed a higher correlation with the Standardized Precipitation Evapotranspiration Index (SPEI) and the self-calibrating Palmer Drought Severity Index (scPDSI). (2) The optimized drought classification scheme exhibited better alignment with SPEI in terms of drought frequency and drought affected area. (3) Based on ISDCI, Xinjiang experienced an overall drying trend during the 2001–2023 growing seasons. The Ili River Basin dried the fastest (slope = − 8 × 10⁻⁴), while the Tarim River Basin showed the slowest drying trend (slope = −2 × 10⁻⁴). Oases and some mountainous areas became wetter, whereas deserts continued to dry. (4) ISDCI effectively captured major droughts, including those in Xinjiang (2008) and the Ili River Basin (2014). Overall, ISDCI offers high-resolution and near-real-time drought monitoring capabilities, providing scientific support for agricultural management and water resource regulation in arid and semi-arid regions.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"179 ","pages":"Article 114115"},"PeriodicalIF":7.0,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925879","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":"Optimized MaxEnt modeling reveals major decline and shift of giant panda habitat under CMIP6 ensemble projections","authors":"Haoyuan Xu,&nbsp;Chaoling Jiang,&nbsp;Xu Li,&nbsp;Huiran Fan,&nbsp;Jiameng Wang,&nbsp;Jinjian Li","doi":"10.1016/j.ecolind.2025.114150","DOIUrl":"10.1016/j.ecolind.2025.114150","url":null,"abstract":"<div><div>The giant panda (<em>Ailuropoda melanoleuca</em>) faces severe habitat loss and fragmentation due to climate change, necessitating predictive modeling to inform future conservation strategies. This study employed an optimized Maximum Entropy (MaxEnt) model, combined with the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model ensemble mean (MME), to project shifts in suitable giant panda habitat across all major mountain ranges for the 2030s, 2050s, 2070s, and 2090s under four Shared Socioeconomic Pathway (SSP) scenarios (SSP126, SSP245, SSP370, and SSP585). Our models demonstrated high predictive accuracy (AUC = 0.876, TSS = 0.734), with the minimum temperature of the coldest month, annual precipitation, temperature annual range, and mean diurnal range identified as the dominant environmental variables (cumulative permutation importance = 72.4 %). Projections reveal a dramatic decline in habitat area, with total suitable habitat shrinking by up to 52.49 % under the highest-emission SSP585 scenario by the 2090s. The centroid of suitable habitat is projected to shift northwestward by up to 106 km and upward in elevation by up to 2599 m, moving into regions currently outside the existing protected area network. These findings underscore the potential inadequacy of the current conservation framework in addressing future climate change impacts. We recommend establishing new protected areas in the identified northwestern climate refugia and restoring climate-resilient corridors to connect deteriorating eastern habitats with more stable western refugia. This study provides a scientific basis for revising giant panda conservation policies to proactively address the impacts of climate change.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"179 ","pages":"Article 114150"},"PeriodicalIF":7.0,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934008","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":"Urbanization, urban form, and PM2.5 concentration in China: a hybrid machine learning and semiparametric approach","authors":"Chengye Jia ,&nbsp;Shuang Feng","doi":"10.1016/j.ecolind.2025.114146","DOIUrl":"10.1016/j.ecolind.2025.114146","url":null,"abstract":"<div><div>In the process of industrialization and urbanization, PM_2.5 poses significant risks to environment and public health. Understanding the key driving factors of PM_2.5 concentration is crucial, as this enables policymakers to develop targeted and effective control measures that protect both environment and public health. In this paper, we first identify the driving factors of PM_2.5 concentration from four aspects--urban environmental infrastructure, industrial structure, economic development, and urban form--in 286 Chinese cities surveyed from 2000 to 2018 by using the eXtreme Gradient Boosting (XGBoost) and Shapley Additive exPlanations (SHAP) methods. We then quantitatively investigate the mean and quantile effects of these driving factors on PM_2.5 concentration and estimate the interaction effect of population density, the most important driving factor, using a semiparametric varying coefficient model. In addition, we conduct a mediation effect analysis to show how population density affects PM_2.5 concentration indirectly <em>via</em> urban form. Our paper shows that: (1) Population density, the industrial nitrogen oxide discharge, and the proportion of service sector in gross domestic product (GDP) are identified as the three most important driving factors. (2) The effects of PM_2.5 driving factors are <em>heterogeneous</em> at different quantiles of PM_2.5 concentration distribution, and are significantly and nonlinearly affected by population density. And (3) Population density indirectly affects PM_2.5 concentration through accelerating the process of urbanization and changing urban form, where urban form is measured by urban expansion, urban compactness, and urban complexity.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"179 ","pages":"Article 114146"},"PeriodicalIF":7.0,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925877","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":"Spatiotemporal mapping of lakes across climatic zones using a node-based random forest approach (2000–2023)","authors":"Fangrui Zhao ,&nbsp;Chunsheng Mu ,&nbsp;Kaishan Song ,&nbsp;Guangyi Mu ,&nbsp;Zhaohua Liu","doi":"10.1016/j.ecolind.2025.113935","DOIUrl":"10.1016/j.ecolind.2025.113935","url":null,"abstract":"<div><div>Wetlands are vital for biodiversity conservation and the provision of critical ecosystem services, yet lakeshore wetlands worldwide are increasingly threatened by climate change and human disturbances. Despite extensive studies on aquatic vegetation classification, limited knowledge exists regarding how its dynamics respond to hydrological variability across diverse climatic regions. This study hypothesized that the relationship between water level variations and aquatic vegetation extent differs significantly among climatic regions. To test this hypothesis, we employed a Random Forest (RF) classification model combined with Landsat imagery and Google Earth Engine to analyze aquatic vegetation dynamics from 2000 to 2023 across four representative lakes—Great Salt Lake, Poyang Lake, Tonle Sap Lake, and Ayakkum Lake—spanning semi-arid, subtropical, tropical, and cold desert climates. Model interpretability was enhanced by integrating SHAP values and feature importance metrics. Results showed high classification accuracy, with overall accuracy ranging from 89.67% to 91.22%. Subtropical (Poyang Lake) and tropical (Tonle Sap Lake) lakes exhibited strong negative correlations between aquatic vegetation area and water level (R² up to 0.6963), whereas semi-arid and cold desert lakes demonstrated weaker associations due to more stable hydrological regimes. By integrating remote sensing and interpretable machine learning, this study delivers the first cross-climatic zone analysis of aquatic vegetation dynamics, offering valuable insights into wetland ecosystem responses under diverse hydrological and climatic scenarios.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"178 ","pages":"Article 113935"},"PeriodicalIF":7.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144921119","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":"Advancing ecosystem service monitoring by mapping the current use of essential ecosystem service variables","authors":"Sebastian Theis ,&nbsp;Flavio Affinito ,&nbsp;Peter Rodriguez ,&nbsp;Marie-Josée Fortin ,&nbsp;Andrew Gonzalez","doi":"10.1016/j.ecolind.2025.113940","DOIUrl":"10.1016/j.ecolind.2025.113940","url":null,"abstract":"<div><div>Essential variables are a well-established tool to support the calculation of ecological indicators. The recently conceptualized Essential Ecosystem Service Variables (EESVs) are regrouped into six classes – <em>Ecological Supply</em>, <em>Demand</em>, <em>Use</em>, <em>Relational Value</em>, <em>Instrumental Value</em>, and <em>Anthropogenic Contribution</em> – designed to capture changes in the multiple dimensions of ecosystem services. Prior to the proposal to monitor ecosystem services using EESVs, many variables relevant to these classes were already used in ecosystem services studies. Here, we perform a systematic retrospective analysis across disciplines to determine the potential of EESV classes for monitoring ecosystem services effectively. We conducted a comprehensive keyword search across 439 studies, based on a review paper on ecosystem services. Network analyses revealed that <em>Anthropogenic Contribution</em> had the highest overall presence based on odds ratios, while <em>Relational Value</em> was the least represented, often showing interdependencies with other classes and low network connectivity and centrality. Network centrality metrics identified <em>Ecological Supply</em> as the most interconnected class, reflecting its foundational role across studies. Journal analysis across seven major journal types showed a good overall distribution of EESV classes across fields, while still emphasizing disciplinary priorities. Urban journals focused more on <em>Anthropogenic Contribution</em> and <em>Relational Value</em> while biological journals prioritized <em>Ecological Supply</em>. Agricultural journals often highlighted <em>Use</em> and <em>Demand</em> as well as <em>Instrumental Value</em> and management and policy journals emphasized <em>Instrumental Value</em>. Addressing gaps in EESV class coverage stresses that underrepresented classes like <em>Relational Value</em> are empirically grounded and measurable, yet these classes are essential for monitoring both the ecological and socio-cultural dimensions of ecosystem services.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"178 ","pages":"Article 113940"},"PeriodicalIF":7.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144921974","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":"Developing an indicator for assessing wetland degradation based on soil quality, water drainage, and human-related landscape factors","authors":"Chuma B. Géant ,&nbsp;Mushagalusa N. Gustave ,&nbsp;Serge Schmitz","doi":"10.1016/j.ecolind.2025.113987","DOIUrl":"10.1016/j.ecolind.2025.113987","url":null,"abstract":"<div><div>Due to environmental stress and anthropogenic pressures, wetlands are declining and being degraded in areas with increased settlement and road construction. Although various indicators have been developed to assess ecosystem degradation, few studies have specifically addressed wetland soil degradation and its underlying drivers. In this study, we attempted to create a Wetland Soil Degradation indicator (WSDI) and identify its driving factors in a contrasting landscape characterized by significant anthropogenic changes. We selected the eastern Democratic Republic of Congo (DRC), a region where severe wetland degradation primarily caused by agriculture and brickmaking activities has been reported. We combined Geographic Information System (GIS), remote sensing approaches and soil profile analysis. For landscape change, four concentric circles from the two wetland centers were made. A WSDI was developed and refined using the minimum data set (MDS) coupled with multivariate statistical techniques to assess the level of wetland degradation. For the case study, an overall WSDI score averaged 0.52 across the two sites. Higher degradation was observed in brickmaking (0.62) compared to agriculture zones (0.52), while intact zones had a lower score (0.28). Degradation was also more severe in completely drained areas (0.72) than in partially (0.48) and intact, non-drained areas (0.28). Significant correlations were found between the level of degradation and human-related landscapes, notably the proximity to villages, rural settlements, and roads. Wetland degradation was strongly linked to road accessibility and the distance to human-related landscapes. The indicator confirmed a gradual degradation pattern, starting from the wetland edges and moving toward the center. Overall, the WSDI is essential for diagnostic purposes before developing a restoration plan to ensure sustainability and to question these critical ecosystems’ future.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"179 ","pages":"Article 113987"},"PeriodicalIF":7.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925876","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":"Wildfire probability assessment and analysis based on multi-source data in Guangxi Province, China","authors":"Kun Xu ,&nbsp;Xin Pan ,&nbsp;Kevin Tansey ,&nbsp;Akram Abdulla ,&nbsp;Rufat Guluzade ,&nbsp;Zi Yang ,&nbsp;Min He ,&nbsp;Congbao Zhu ,&nbsp;Shile Yang ,&nbsp;Yingbao Yang","doi":"10.1016/j.ecolind.2025.114148","DOIUrl":"10.1016/j.ecolind.2025.114148","url":null,"abstract":"<div><div>Wildfires are key environmental variables in global climate monitoring systems that significantly impact ecosystems, human life, and property. This study assessed the probability of wildfires in Guangxi Province using multi-source data and the Evidence Belief Function (EBF) model. A stratified random sampling method was applied to evaluate the accuracy of the three wildfire remote sensing products (MCD64A1, Fire_CCI51, and MCD14ML). Among these, MCD64A1 was identified as the most reliable dataset for mapping wildfires in the region. By dividing the year into three wildfire occurrence periods (A, B, and C) and combining 14 wildfire factors from 2003 to 2018, the EBF model was used to map and analyze the spatial distribution of wildfire risk. The relative importance of each wildfire factor was evaluated using a Random Forest (RF) model. The validation results confirmed that the AUC values of the success rate of the EBF model were all greater than 0.7, indicating that the model performed well in wildfire risk assessments. The derived wildfire probability map showed strong spatial consistency between the assessed probability levels and observed wildfire events, with high-risk areas in the northwest, central, and northeast regions of Guangxi. Furthermore, relative humidity was identified as the most significant factor influencing wildfire probability, while land surface temperature (LST), wind speed, and a human influence indicator, the Human Footprint (HPF), also had a significant effect on wildfire probability. These findings highlight the reliability of the EBF and RF models as tools for prediction evaluation and provide insights into effective resource allocation and wildfire prevention strategies in the region.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"179 ","pages":"Article 114148"},"PeriodicalIF":7.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925924","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":"Priority conservation zoning for future karst areas based on the construction of a multi-perspective ecological security pattern","authors":"Qi Yuan ,&nbsp;Rui Li","doi":"10.1016/j.ecolind.2025.113941","DOIUrl":"10.1016/j.ecolind.2025.113941","url":null,"abstract":"<div><div>In the process of urbanization, human activities have caused significant changes in land use, posing serious threats to ecosystems. Issues such as habitat fragmentation and reduced structural connectivity hinder regional sustainability. The vulnerability of karst areas exacerbates these impacts. Effectively managing the human-environment relationship in karst regions is crucial for maintaining ecological security, currently a focal point of research. Scientific prediction and rational construction of future ecological networks are key to enhancing regional ecological security. Taking Guizhou Province, the central area of southern Chinese karst, as an example, this study utilizes CA-Markov modeling to forecast future (by 2030) land use conditions. On this basis, we constructed the ecological network of Guizhou Province from the perspectives of morphology, habitat quality analysis, and integration. We compared the differences between the networks generated from different perspectives. Finally, we delineated priority protection areas and proposed corresponding protection recommendations. Results indicate an increasing trend in arable land, water bodies, and urban residential land by 2030, with arable land showing the largest change, increasing by 2.79 %, primarily due to conversions from forest land, grassland, and urban land. Using habitat quality, 254 source points are selected, while 191 are selected using MSPA, with an overlap of 106 source points. In highly urbanized areas, source points are predominantly located on administrative boundaries. Corridor construction results indicate variations in certain characteristics between different perspectives but generally show an eastern bias. Corridors overlapped across the three perspectives total 23,578 km. In regions characterized by high human activity in central and western areas, networks constructed based on habitat quality exhibit a more fragmented and isolated spatial pattern compared to those based on morphological criteria. Based on these findings, priority ecological restoration areas are categorized into ecological protection, restoration, and auxiliary regeneration areas, each with corresponding restoration recommendations. This research contributes to understanding the future ecological security patterns in karst regions, providing a scientific basis for biodiversity conservation and ecological environment security in karst areas.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"178 ","pages":"Article 113941"},"PeriodicalIF":7.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144921623","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}