Ecological Informatics最新文献

{"title":"Modelling the impact of ecosystem fragmentation on ecosystem services in the degraded Ethiopian highlands","authors":"Tegegne Molla Sitotaw ,&nbsp;Louise Willemen ,&nbsp;Derege Tsegaye Meshesha ,&nbsp;Martha Weldemichael ,&nbsp;Andrew Nelson","doi":"10.1016/j.ecoinf.2025.103100","DOIUrl":"10.1016/j.ecoinf.2025.103100","url":null,"abstract":"<div><div>Humans shape landscapes to optimise food, fibre, and fuel production. These modifications often fragment ecosystems and degrade ecological functions over time, particularly regulating and cultural ecosystem services (ES). Understanding how ecosystem fragmentation influences the temporal dynamics of ES is critical for biodiversity conservation and sustainable management under global environmental and climate change. Despite its importance, the role of fragmentation patterns in shaping ES over time remains underexplored. This study addresses this gap by assessing how fragmentation metrics—ecosystem area, perimeter-area ratio, and patch proximity—impact four key ES (wetland grass biomass, microclimate heat stress regulation, crop pollination, and nature-based tourism) in the degraded Ethiopian highlands. Using spatial generalized additive models (GAMs), we combined fragmentation metrics with relevant biophysical variables to model ES patterns for 2020 and extrapolated back to 2000 with year-specific remote sensing-based predictors. Our results reveal substantial temporal declines in all four ES driven by both linear and non-linear effects of ecosystem fragmentation. Over two decades, reductions in ecosystem area (25 %), increases in the perimeter-area ratio (15 %), and declines in patch proximity (30 %) were strongly associated with significant losses in all four ES. Ecosystem fragmentation not only reduces ES supply but also alters their spatial and temporal distribution. Therefore, incorporating fragmentation dynamics into ES modelling is crucial for accurate and comprehensive assessments of ES distribution. By demonstrating a novel temporal perspective on the relationship between landscape configuration and ES, our findings provide robust, data-driven insights for landscape planning and the development of sustainable conservation strategies in fragmented landscapes.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103100"},"PeriodicalIF":5.8,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601602","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":"Research on Atlantic surface pCO2 reconstruction based on machine learning","authors":"Jiaming Liu,&nbsp;Jie Wang,&nbsp;Xun Wang,&nbsp;Yixuan Zhou,&nbsp;Runbin Hu,&nbsp;Haiyang Zhang","doi":"10.1016/j.ecoinf.2025.103094","DOIUrl":"10.1016/j.ecoinf.2025.103094","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Ocean acidification is transforming marine ecosystems at an unprecedented rate, which in turn requires the estimation of sea surface carbon dioxide partial pressure (pCO&lt;sub&gt;2&lt;/sub&gt;) as a crucial metric to gauge acidification. This has substantial implications for marine resource assessment and management, marine ecosystems, and global climate change research. This study utilizes SOCAT cruise survey data to assess the accuracy of global sea surface pCO&lt;sub&gt;2&lt;/sub&gt; products offered by Copernicus Marine Service and the Chinese Academy of Sciences Ocean Science Research Center. Through the application of a geographic information analysis method—geographical detector—the study quantitatively reveals the significance of environmental influencing factors, such as longitude, latitude, sea surface 10 m wind speed (U&lt;sub&gt;10&lt;/sub&gt;), total precipitation (TP), evaporation (E), and significant height of combined wind waves and swell (SHWW), in the reconstruction of sea surface pCO&lt;sub&gt;2&lt;/sub&gt;. Subsequently, various machine learning models, which include convolutional neural network (CNN), back propagation neural network (BP), long short-term memory network (LSTM), extreme learning machine (ELM), support vector regression (SVR), and extreme gradient boosting tree (XGBoost), are used to reconstruct the monthly sea surface pCO&lt;sub&gt;2&lt;/sub&gt; data for the Atlantic Ocean from 2001 to 2020 to investigate the potential and suitability of high-precision reconstruction of the sea surface pCO&lt;sub&gt;2&lt;/sub&gt; dataset for this sea area. The findings indicate that: (1) The geographical detector effectively quantifies the contribution of various environmental factors used in sea surface pCO&lt;sub&gt;2&lt;/sub&gt; reconstruction. Notably, the Copernicus pCO&lt;sub&gt;2&lt;/sub&gt; and CODC-GOSD pCO&lt;sub&gt;2&lt;/sub&gt; contribute the most, with both contributing ∼0.72. These are followed by TP, latitude, longitude, SHWW, U&lt;sub&gt;10&lt;/sub&gt;, and E. (2) After comprehensive data testing, the six machine learning models select the optimal hyperparameters for reconstruction. Among these, the XGBoost model notably improved the quality of the original dataset when using Copernicus pCO&lt;sub&gt;2&lt;/sub&gt; and CODC-GOSD pCO&lt;sub&gt;2&lt;/sub&gt; products in conjunction with SHWW, U&lt;sub&gt;10&lt;/sub&gt;, and TP environmental variable data. Compared with SOCAT data, the overall reconstruction accuracy in the Atlantic Ocean reached an impressive 94 %, outperforming the standalone use of either Copernicus pCO&lt;sub&gt;2&lt;/sub&gt; or CODC-GOSD pCO&lt;sub&gt;2&lt;/sub&gt; products. Furthermore, the XGBoost model demonstrated strong applicability in regions with numerous outliers, maintaining a reconstruction accuracy of ≥95 %. (3) Stability test results reveal that the XGBoost model exhibits low sensitivity to uncertainties in all input variables. This indicates that the model can accommodate environmental data errors induced by abrupt changes in marine environments. Such robustness enhances its reliability in sea surface pCO&lt;sub&gt;2&lt;/sub&gt; reconstruction. The reconstru","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103094"},"PeriodicalIF":5.8,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143577600","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":"Metric learning unveiling disparities: A novel approach to recognize false trigger images in wildlife monitoring","authors":"Rui Zhu ,&nbsp;Enting Zhao ,&nbsp;Chunhe Hu ,&nbsp;Jiangjian Xie ,&nbsp;Junguo Zhang ,&nbsp;Huijian Hu","doi":"10.1016/j.ecoinf.2025.103091","DOIUrl":"10.1016/j.ecoinf.2025.103091","url":null,"abstract":"<div><div>Wildlife monitoring using camera traps is a vital tool for ecosystem health assessment. However, camera traps often face high rates of false-triggered images (empty shots), significantly impacting data processing efficiency. This study proposes a metric learning-based method for false-triggered image recognition. By integrating K-means clustering for sample selection and a triplet loss function for model optimization, the approach effectively distinguishes subtle feature differences in false-triggered images. Experiments demonstrate that the proposed method achieves 80.17% Accuracy, 79.79% Recall, and a reduced false positive rate (FPR) of 19.48% on test datasets collected from various regions. Compared to traditional models, it improves Accuracy and Recall by 5.5% and 5.96%, respectively, while reducing the FPR by 5%. On embedded device Jetson Nano, the method achieves a single-image inference time of just 0.076 s, showcasing its potential for deployment in resource-constrained environments. This research addresses challenges related to high intra-class diversity and inter-class similarity in false-triggered images, offering a novel solution to enhance wildlife monitoring efficiency. The code is available at <span><span>https://github.com/hzl-bjfu/AIPL/tree/master/RFTI</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103091"},"PeriodicalIF":5.8,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563159","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":"A novel algal bloom risk assessment framework by integrating environmental factors based on explainable machine learning","authors":"Lingfang Gao ,&nbsp;Yulin Shangguan ,&nbsp;Zhong Sun ,&nbsp;Qiaohui Shen ,&nbsp;Lianqing Zhou","doi":"10.1016/j.ecoinf.2025.103098","DOIUrl":"10.1016/j.ecoinf.2025.103098","url":null,"abstract":"<div><div>In recent years, the algal blooms have intensified, posing mounting threats to aquatic ecosystems and water security. However, most previous studies merely detected algal blooms according to the characteristics of the water body at the time of algal bloom occurrence, overlooking the influence of environmental factors on algae proliferation. This study proposes a novel algal bloom risk assessment framework that integrates explainable machine learning with multivariate environmental analysis. Specifically, the Shapley Additive Explanations (SHAP) effect values were used to separately explore the relationship between chlorophyll <em>a</em> (Chla) and six factors, namely the total phosphorus (TP), total nitrogen (TN), TN: TP ratio (RNP), dissolved oxygen (DO), temperature, and precipitation, across riverine and lacustrine ecosystems. Results identified TP and temperature as dominant regulators, accounting for the first two in lakes and the second and third positions in rivers. The thermal effect varies between different ecosystems: Chla decreases after reaching a peak in lakes, while Chla increases linearly with temperature in rivers. In addition, DO played an important role in rivers. The Chla concentration was estimated using Random Forest and thresholds for bloom identification were adjusted (25 μg/L for lakes and 40 μg/L for rivers), reflecting hydrodynamic and optical disparities. The risk framework was applied to the Qiantang River Basin (2020−2022), and results showed low annual risk (mean Algal Bloom Risk Index &lt;0.5) but identified spring susceptibility related to nutrient resuspension and thermal stratification. By quantifying the impact of environmental factors on algal blooms, this study improves algal bloom risk assessment in rivers and lakes, which advances proactive bloom management in mixed river-lake basins under intensifying anthropogenic and climatic pressures.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103098"},"PeriodicalIF":5.8,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143577597","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":"Early detection of Wheat Stripe Mosaic Virus using multispectral imaging with deep-learning","authors":"Malithi De Silva,&nbsp;Dane Brown","doi":"10.1016/j.ecoinf.2025.103088","DOIUrl":"10.1016/j.ecoinf.2025.103088","url":null,"abstract":"<div><div>Wheat Stripe Mosaic Virus (WhSMV) is a soilborne virus that threatens wheat yields in South Africa. Traditional WhSMV diagnosis methods rely on visual inspection, which is labor-intensive, time-consuming and prone to errors. This study explores the application of deep-learning algorithms employing various spectral filters for the early identification of WhSMV. The models were tested for classifying healthy, early, and diseased stages, where early-stage specifically included images taken before any visible disease symptoms appeared. DenseNet121 demonstrated the highest accuracy of 91.23% with the K590 filter, which can capture 590-1000 nm, including parts of the visible and near-infrared spectrum. Further, the K590 filter showed the most significant precision values with most of the tested Convolutional Neural Networks, Vision Transformers, and hybrid and Swin Transformer models. This result suggests filters that capture visible and near-infrared spectrum ranges perform better in identifying WhSMV. These findings show that multispectral images combined with deep-learning models are viable for WhSMV detection in wheat fields, especially for identifying early-stage infections.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103088"},"PeriodicalIF":5.8,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143577599","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":"Integration of national demographic-disturbance relationships and local data can improve caribou population viability projections and inform monitoring decisions","authors":"Josie Hughes,&nbsp;Sarah Endicott,&nbsp;Anna M. Calvert,&nbsp;Cheryl A. Johnson","doi":"10.1016/j.ecoinf.2025.103095","DOIUrl":"10.1016/j.ecoinf.2025.103095","url":null,"abstract":"<div><div>Across fifty-eight boreal caribou study areas in Canada, survival and recruitment decrease with the percentage of the study area that is disturbed. There is variation in demographic rates among study areas, particularly where anthropogenic disturbance is low, but no populations inhabiting areas with high anthropogenic disturbance are considered viable. Demographic projections derived from local population-specific data are uncertain for populations with limited monitoring. We propose a simple Bayesian population model that integrates prior information from a national analysis of demographic-disturbance relationships with available local demographic data to improve population viability projections, and to reduce the risk that a lack of local data will be used as a reason to delay conservation action. The model also acknowledges additional uncertainty and potential bias due to misidentification of sex or missing calves, through a term derived from a simple model of the recruitment survey observation process. We combine this Bayesian model with simulations of plausible population trajectories in a value of information analysis framework to show how the need for local monitoring varies with landscape condition, and to assess the ability of alternative monitoring scenarios to reduce the risk of errors in population viability projections. Where anthropogenic disturbance is high, reasonably accurate status projections can be made using only national demographic-disturbance relationships. At lower disturbance levels where initial uncertainty is high local data improve accuracy but each additional year of monitoring provides less new information. The estimated probability of viability indicates whether more information is needed to improve accuracy of population viability projections.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103095"},"PeriodicalIF":5.8,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143577598","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":"Estimating vegetation aboveground biomass in Yellow River Delta coastal wetlands using Sentinel-1, Sentinel-2 and Landsat-8 imagery","authors":"Yiming Xu ,&nbsp;Yunmeng Qin ,&nbsp;Bin Li ,&nbsp;Jiahan Li","doi":"10.1016/j.ecoinf.2025.103096","DOIUrl":"10.1016/j.ecoinf.2025.103096","url":null,"abstract":"<div><div>Accurate analyzing the spatial pattern and spatial uncertainty of vegetation aboveground biomass (AGB) in coastal wetland is critical for addressing sustainable blue carbon management goals. Eight models based on Extreme Gradient Boosting (XGBoost) method were established to analyze the capability of Sentinel-1 (S1), Sentine-2 (S2) and Landsat-8 (L8) data for predicting AGB in coastal wetlands of the Yellow River Delta (YRD), China. Spatial uncertainty of AGB was quantified by Quantile Regression Forest (QRF) method. The results showed that AGB model based on S2 achieved higher model performance (R²: 0.74, RMSE: 171.23 g/m²) compared with those based on L8 (R²: 0.59, RMSE: 198.84 g/m²) and S1 (R²: 0.43, RMSE: 219.60 g/m²). The AGB model based on S1, S2, L8 and other predictive variables including the terrain and biophysical factors (S1S2L8plus) achieved the highest model performance (R²: 0.80, RMSE: 154.98 g/m²) among all the models. Red-edge related-spectral indices derived from S2 were proved to be important predictors in AGB modelling. The spatial uncertainty quantified by QRF showed the spatial prediction uncertainties of AGB models based on S2S2L8plus and S2 were lower than AGB model based on S1L8. The results of this study demonstrate the suitability of optical remote sensing data especially S2 and the weak capability of S1 in modelling AGB in coastal wetlands of the YRD. The regularly modelling, mapping and uncertainty estimations of AGB could help guide the sustainable blue carbon management in coastal wetlands.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103096"},"PeriodicalIF":5.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563158","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":"Integrating circuit theory and network modeling to identify ecosystem carbon sequestration service flow networks","authors":"Chen Qu ,&nbsp;Jia Xu ,&nbsp;Wen Li ,&nbsp;Yucen Zhai ,&nbsp;Yiting Wang ,&nbsp;Baozhu Liu ,&nbsp;Shaoning Yan","doi":"10.1016/j.ecoinf.2025.103077","DOIUrl":"10.1016/j.ecoinf.2025.103077","url":null,"abstract":"<div><div>Current methods for mapping ecosystem service flows often fail to accurately capture the diverse biogeographical environments associated with these service flows when visualizing the structural features of ecosystem service flow networks. Taking the provinces of Liaoning, Jilin, and Heilongjiang in China as examples, we combined circuit theory and network model approaches to map ecosystem carbon sequestration service flow networks. We examined how network structure influences differences in the supply and demand for carbon sequestration services. Combining circuit theory and network models can be used to effectively map the flow of carbon sequestration services in ecosystems, showcasing its ability to represent these processes. From 2000 to 2020, the disparity between the supply and demand of carbon sequestration services has consistently grown, accompanied by a growing spatial imbalance in the distribution of supply and demand areas. The supply sources of carbon sequestration services have significantly declined while the demand sources have steadily increased. The length of the carbon flow corridors decreased sharply before stabilizing. There has been a continuous increase in the number of deficit nodes and disrupted edges within carbon sequestration service flow networks. The response of carbon sequestration services to landscape patterns and network topology indicators showed a nonlinear relationship, exhibiting a threshold effect. The findings have provided strategic insights for allocating and managing carbon resources at the regional level.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103077"},"PeriodicalIF":5.8,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143520518","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":"Divergent dynamics of surface water patterns and structures in Europe's coastal-zone basins","authors":"Liumeng Chen ,&nbsp;Yongchao Liu ,&nbsp;Jialin Li ,&nbsp;Chao Ying ,&nbsp;Peng Tian ,&nbsp;Wenfei Kuang ,&nbsp;Qiyu Huang ,&nbsp;Tian Zheng","doi":"10.1016/j.ecoinf.2025.103089","DOIUrl":"10.1016/j.ecoinf.2025.103089","url":null,"abstract":"<div><div>Surface water bodies are essential components of terrestrial ecosystems and play critical roles in sustainable development. Coastal-zone basins (CZBs), shaped by human activity and land–sea interactions, have undergone rapid urban expansion, landscape fragmentation, wetland degradation, and increased flood risk. However, comprehensive analyses of surface water dynamics in coastal zones on a continental scale are limited. This study examines the spatiotemporal evolution of surface water in Europe's CZBs, a region with a long history of development. The water bodies were classified into five types: weak seasonal water bodies (WSWBs), seasonal water bodies (SWBs), weak permanent water bodies (WPWBs), strong permanent water bodies (SPWBs), and permanent water bodies (PWBs). Using Landsat remote-sensing imagery from 1984 to 2023, we assessed the intensity of water-body transformations. WSWBs dominated the surface water composition, accounting for 76.94 % of the total, with concentrated patches in the eastern CZBs and sporadic distributions in the west. PWBs, comprising only 1.35 %, were primarily located in the Balkan, Apennine, and Iberian Peninsulas, and the western European plains. Between 1984 and 2023, the total surface water area exhibited a fluctuating decline, with PWBs, SWBs, and WPWBs decreasing by 97.2, 96.1, and 87 %, respectively. Conversely, SPWBs and WSWBs increased by 54.8 and 67.7 %, respectively. The degree of fragmentation varied over time, with higher fragmentation observed on the northern Iberian Peninsula, northwestern European plains, central Apennine Peninsula, and southern Balkan Peninsula. This study provides essential data for supporting biodiversity conservation, water resource management, and sustainable coastal development.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103089"},"PeriodicalIF":5.8,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534119","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 diversity of coupled synergistic paths of rural settlements and sloping cultivated land utilization in karst mountain areas of Southwest China: A case study of Huajiang Canyon","authors":"Linyu Yang,&nbsp;Yangbing Li,&nbsp;Yiyi Zhang,&nbsp;Xue Ren","doi":"10.1016/j.ecoinf.2025.103092","DOIUrl":"10.1016/j.ecoinf.2025.103092","url":null,"abstract":"<div><div>With the rapid development of urban-rural integration and socioeconomic, the utilization of rural settlements (RS) and sloping cultivated land (SCL) in the karst mountain areas (KMA) of Southwest China is gradually changing. Clarifying the evolving relationship between RS and SCL is crucial for coordinating human-land relations and promoting rural development. Therefore, this study attempts to reveal the spatial coupling relationship between the two and analyze the transformation of the human-land relationship reflected by these changes. Firstly, this study constructs a theoretical framework for the coupled evolution of RS and SCL in the KMA of Southwest China and the change in human-land relations reflected by the two. Then, based on typical cases, we conduct empirical analysis. The findings of this study are as follows: (1) The dynamics of RS and SCL have changed significantly, with the area of RS showing an increasing trend over time, while that of SCL initially increased and then decreased. (2) RS are predominantly distributed in middle elevation zones, whereas SCL is mainly distributed in middle and high elevation zones, both concentrated in areas with steeper slopes. (3) The distribution structure of coupled types of RS and SCL is different and is influenced by socioeconomic, natural environment, and policy factors. (4) Under different geomorphic units, the coupling relationship between RS and SCL is diverse, reflecting variations in human-land relations. This study provides a theoretical basis for regulating the human-land relationship and offers empirical cases for addressing the challenges of sustainable rural development in the KMA.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103092"},"PeriodicalIF":5.8,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143520516","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}