Ecological Informatics最新文献

{"title":"Filling the gap: Improving the spatio-temporal coverage of small pelagic fish surveys through modelling approaches","authors":"Stefanie Haase ,&nbsp;Clarissa Vock ,&nbsp;Beata Schmidt ,&nbsp;Ismael Núñez-Riboni ,&nbsp;Stefan Lüdtke","doi":"10.1016/j.ecoinf.2026.103634","DOIUrl":"10.1016/j.ecoinf.2026.103634","url":null,"abstract":"<div><div>Abundance indices from fisheries-independent surveys provide key information for fish stock assessments, serving as a foundation for sustainable resource management. Changes in survey effort, e.g., caused by vessel breakdowns or bad weather conditions, can lead to areas not being covered in certain years. These spatio-temporal gaps in survey coverage increase uncertainty or can even lead to discontinuation in time series. The current approach for imputing missing strata in Baltic pelagic clupeid surveys uses area-corrected abundances from higher-level subdivision units (baseline model), which does not account for stratum-specific effects and requires at least partial subdivision coverage. We tested three alternative imputation methods: Linear mixed-effects models (LMMs), Generalized additive models (GAMs), and Gradient boosted trees (XGB). Our results suggest that modelling abundance variations across strata, as implemented in the LMMs, improved abundance estimates compared to the baseline model, particularly when only few areas were not covered, i.e., under typical annual variations in survey coverage. When larger areas were not covered, LMMs, relying on explicit spatial strata, performed worse, whereas the XGB models and, in particular, the spatial GAM remained robust and could still be applied even when entire subdivisions were not surveyed. In conclusion, advanced data imputation techniques can enhance the robustness of abundance indices and should be considered as standard practice in survey groups when survey effort varies between years.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"94 ","pages":"Article 103634"},"PeriodicalIF":7.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174000","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":"A critical review of statistical, signal processing and machine learning methods for continuous and high-frequency water quality data improvement","authors":"A.T. Badrudeen ,&nbsp;D. Sahoo ,&nbsp;C.B. Sawyer ,&nbsp;J.W. Pike ,&nbsp;R.D. Harmel","doi":"10.1016/j.ecoinf.2026.103619","DOIUrl":"10.1016/j.ecoinf.2026.103619","url":null,"abstract":"<div><div>In the digital water world, high-frequency water quality monitoring from sensors is crucial for capturing rapid changes, especially during storm events or discharge fluctuations, in which important signals can occur at sub-hourly intervals. These signals are represented in a time series and can sometimes be irregular, noisy, and prone to missing values or errors due to buried conditions, sediment interference, and signal loss. The fine resolution of reporting also increases the risk of sensor errors and data loss, necessitating effective correction methods to ensure the accuracy and usability of the data. This literature review investigates the current state of time series data correction and denoising techniques in water quality monitoring. A systematic review of peer-reviewed studies was conducted to identify commonly applied methods, evaluate their effectiveness, and assess their adaptability to high-frequency, nonlinear, and non-stationary water quality datasets. The study explored techniques, including statistical methods such as moving averages, median filtering, Savitzky-Golay smoothing, wavelet transforms, and Kalman Filter, as well as machine learning models such as random forest, support vector machine and gradient boosting. While many of these methods are well established in other fields, this review collates evidence of their application and adaptation to water resources. This review serves as a comprehensive resource for researchers and water resource practitioners to implement appropriate denoising and correction techniques for continuous and high-frequency monitoring data. It highlights the potential of both statistical, signal processing, and machine learning-based methods to support accurate analysis, decision-making, and long-term water quality monitoring, management, and modeling.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"94 ","pages":"Article 103619"},"PeriodicalIF":7.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173997","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":"Benchmarking remote sensing methods to capture plant functional diversity from space","authors":"Javier Pacheco-Labrador ,&nbsp;Ulisse Gomarasca ,&nbsp;Daniel E. Pabon-Moreno ,&nbsp;Wantong Li ,&nbsp;Mirco Migliavacca ,&nbsp;Martin Jung ,&nbsp;Gregory Duveiller","doi":"10.1016/j.ecoinf.2026.103636","DOIUrl":"10.1016/j.ecoinf.2026.103636","url":null,"abstract":"<div><div>The development of remote sensing methods to estimate plant functional diversity is hindered by mismatches between ecology and remote sensing sampling schemes and the limited representativeness of local field campaigns. The Biodiversity Observing System Simulation Experiment (BOSSE) provides a modeling framework for benchmarking new methodologies. We used BOSSE to simulate 180 different synthetic “Scenes” spanning a two-year-long time series of plant trait maps and imagery of hyperspectral reflectance factors, spectral indices, sun-induced chlorophyll fluorescence, land surface temperature, and estimates of foliar and structural plant traits (optical traits). We used these simulations to answer five fundamental, yet unsolved, questions:</div><div><em>Q1. How should remote sensing characterize functional diversity in large surfaces (sites)?</em> Diversity metric values saturate with the number of pixels involved, hampering comparisons between plant traits and remote sensing estimates in large areas. The average value of metrics computed over small samples (e.g., 3-by-3 pixel windows) should be used instead.</div><div><em>Q2. Which sources of spectral information (or combinations thereof) can best capture plant functional diversity at the site scale?</em> Accounting for background effects is the key. Optical traits are the best estimators for plant functional diversity. Other variables succeed when allowed to remove soil pixels, but their combination did not yield significant advantages.</div><div><em>Q3. How should remote sensing estimates be validated/compared with plant functional diversity measurements?</em> Leaf area index (LAI) is a better proxy of abundance than the pixel for <em>Q</em>_Rao, but not for variance-based partitioning. It is more sensitive to sample size, but also more resistant to suboptimal spatial resolution.</div><div><em>Q4. When (in the phenological year) can remote sensing best capture site-scale plant functional diversity?</em> The estimation error decreased with LAI and stabilized at values above 1 m²/m².</div><div><em>Q5. Which approaches and remote sensing variables are more resistant to the effects of suboptimal spatial resolution?</em> Optical traits and fluorescence were the most robust variables. Still, field data resolution needs to be degraded to match the sensor's resolution. We found a relative spatial resolution threshold of ∼30% (where the pixel is approximately three times the size of the plants).</div><div>Simulation frameworks like BOSSE enable testing methodologies beyond local contexts and address the current shortage of suitable global datasets, supporting the application and development of methods for assessing plant functional diversity with remote sensing. In the future, BOSSE could contribute to understanding observational results, refining and pre-testing new methodologies, and supporting the development of comparable experimental datasets.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"94 ","pages":"Article 103636"},"PeriodicalIF":7.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174423","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":"Laboratory-based hyperspectral reflectance analysis for phytoplankton species identification","authors":"R. Bentivogli ,&nbsp;L. Pezzolesi ,&nbsp;N. Caputo ,&nbsp;B. Casarotto ,&nbsp;S. Silvestri","doi":"10.1016/j.ecoinf.2026.103626","DOIUrl":"10.1016/j.ecoinf.2026.103626","url":null,"abstract":"<div><div>Monitoring phytoplankton communities is essential for assessing ecosystem health and detecting harmful algal blooms (HABs). Hyperspectral imaging has emerged as a promising tool to discriminate among microalgal species based on their unique reflectance signatures. This study presents a laboratory spectral analysis of five phytoplankton species, including bloom-forming and toxin-producing taxa common in coastal waters. Reflectance spectra were measured at multiple cell concentrations and analyzed using two normalization approaches, second- and fourth-derivative transformations, and dimensionality reduction techniques including principal component analysis (PCA) and linear discriminant analysis (LDA).</div><div>Our results demonstrate that specific spectral features, particularly in the 470–500 nm and 620–680 nm ranges, enable species-level discrimination. PCA and LDA effectively enhanced separability by reducing spectral redundancy and emphasizing class features. We further applied linear spectral unmixing (LSU) to estimate fractional species abundances in synthetic mixtures. LSU performed well in simple mixtures but revealed limitations in complex communities, where nonlinear effects and spectral similarity reduced accuracy.</div><div>Beyond classification, LSU enables quantitative assessment of species contributions, providing a valuable complement to PCA and LDA for ecological interpretation and bloom dynamics investigation. This integrated approach lays the foundation for future development of operational tools that combine spectral unmixing and machine learning for automated HAB detection. The combined use of hyperspectral reflectance data and computational methods supports scalable, real-time monitoring of phytoplankton diversity and abundance, with strong potential for deployment in early-warning systems and coastal observatories.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"94 ","pages":"Article 103626"},"PeriodicalIF":7.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025990","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":"Supporting fire behavior modelling with canopy base height and canopy bulk density estimates using airborne and spaceborne lidar","authors":"Adrian Pascual ,&nbsp;Juan Guerra-Hernández ,&nbsp;Brigite Botequim ,&nbsp;Eduardo González-Ferreiro","doi":"10.1016/j.ecoinf.2026.103612","DOIUrl":"10.1016/j.ecoinf.2026.103612","url":null,"abstract":"<div><div>The next-generation of fire behavior models must integrate 3D forest structural metrics to better explain fire spread, risk and severity. Canopy base height (CBH) and canopy bulk density (CBD) can be calibrated using lidar data collocated over field plots. Where no airborne lidar scanning data (ALS) exist, GEDI spaceborne lidar can provide 25-m predictions of CBH and CBD contingent to ALS-calibrated workflows. Our research presents GEDI footprint-level estimates of CBH and CBD for Mediterranean forests and builds upon collocated ALS-GEDI crossovers. Our accuracies are based on Random Forests classification: the R2 values for CBH and CBD were &lt; 0.4 for all plant functional types evaluated. The classification of vertical continuity was satisfactory to inform on fire-prone conditions at the GEDI footprint level. Predictions for CBD were aggregated to produce a regional baseline maps, one at 1-km resolution. The usability of these coarse-scale aggregations of fuel estimates is limited because of resolution, presence of gaps and high heterogeneity of forest fuels within small steps. To inform about this heterogeneity and change estimates over time, we predict CBH and CBD over adjacent GEDI tracks collected 5-years apart (2019/24). These change estimates are relevant to show the high variability of the forest fuels that compromises the ability to depict change adding the issue of exact collocation between GEDI measurements that are adjacent, not collocated and therefore not repeated. We discuss methodological differences between our approach and recent studies on mapping fuel baselines and their approach to inform on dynamics.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"94 ","pages":"Article 103612"},"PeriodicalIF":7.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025991","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":"AEWD: A weakly observable object detection benchmark for UAV-based endangered wildlife monitoring","authors":"Menglu Ma ,&nbsp;Lili Liu ,&nbsp;Boxun Han ,&nbsp;Linzhe Yang ,&nbsp;Xujie Gao ,&nbsp;Runlong Chang ,&nbsp;Sheng Wang ,&nbsp;Fu Xu","doi":"10.1016/j.ecoinf.2026.103662","DOIUrl":"10.1016/j.ecoinf.2026.103662","url":null,"abstract":"<div><div>Biodiversity conservation hinges on the ability to monitor endangered wildlife within forest ecosystems. Traditional survey methods are often impractical due to the elusive nature of these species and the difficulty of reaching their habitats. Unmanned Aerial Vehicles (UAVs) have emerged as a powerful and non-invasive alternative. It remains a technical bottleneck that current automated detection models struggle with complex forest canopies. High-quality data are essential for effective, robust modeling. Most existing UAV datasets focus on charismatic mammals in open savannas, leaving a critical gap for species inhabiting complex forest canopies. To address this disparity, we present the Aerial Endangered Wildlife Dataset (AEWD), curated for critical monitoring in natural environments with high clutter. It includes 7483 high-resolution images, with 27,194 annotated instances of four key species: the Amur Tiger (<em>Panthera tigris altaica</em>), Giant Panda (<em>Ailuropoda melanoleuca</em>), Golden Snub-nosed Monkey (<em>Rhinopithecus roxellana</em>), and Sichuan Takin (<em>Budorcas tibetanus</em>). Unlike conventional benchmarks, AEWD assesses detection difficulty by considering five detailed attributes: bounding box size, instance scale, target density, vegetation coverage, and occlusion degree. These metrics reflect real-world challenges, such as indistinct features and low contrast. By evaluating twelve mainstream detection models, we establish a performance baseline to catalyze future research in UAV-based wildlife conservation.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"94 ","pages":"Article 103662"},"PeriodicalIF":7.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147421593","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":"Man-made boundary or natural ecotone? Reassessing vegetation dynamics and drivers in the Giant panda National Park along the Hu-line","authors":"Qinli Xiong ,&nbsp;Weihua Xu ,&nbsp;Xiao Wang ,&nbsp;Geng Sun ,&nbsp;Ning Wu","doi":"10.1016/j.ecoinf.2026.103675","DOIUrl":"10.1016/j.ecoinf.2026.103675","url":null,"abstract":"<div><div>Monitoring vegetation dynamics is fundamental to assessing the conservation efficacy of large-scale protected areas, particularly for flagship species like the giant panda. However, the Giant Panda National Park (GPNP) faces a unique challenge: it is geographically bisected by the <em>Hu-Line</em>, a profound demographic and climatic divide in China. This study integrates MODIS Enhanced Vegetation Index (EVI) time-series data (2000–2018) with redundancy analysis (RDA) to quantify spatiotemporal vegetation patterns and disentangle the driving forces of the <em>Hu-Line</em>, climatic shifts, anthropogenic interventions, and protected-area status and long-term conservation/restoration measures in areas later consolidated into the GPNP (the GPNP was formally established in January 2017). Our results reveal that the <em>Hu-Line</em> functions as a rigid biophysical barrier rather than merely a demographic boundary. Vegetation vigor in the southeastern sector was significantly higher than in the northwestern sector (<em>p</em> &lt; 0.01), a disparity primarily driven by climatic constraints which explained 32.75% of the total variation. We identified a warming-greening mechanism where rising temperatures promoted vegetation growth in this high-altitude ecosystem. Crucially, while the GPNP exhibited a consistent recovery trend post-2013 within the current park boundary, a concerning leakage effect was detected in the contiguous non-protected areas (NGPNP), where 30.12% of the land experienced vegetation decline. These findings suggest that strict protection within the core protected areas may have displaced pressure to the fragile periphery. We conclude that effective management requires spatially differentiated strategies respecting the <em>Hu-Line's</em> natural limits, and urgently recommend integrating the contiguous zones into an Other Effective Area-based Conservation Measures network to ensure landscape-level connectivity and buffer the core habitat.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"94 ","pages":"Article 103675"},"PeriodicalIF":7.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147422409","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":"A conceptual architecture for AI-assisted Digital Twins in natural resource management","authors":"Félix Iglesias ,&nbsp;Frédéric Ros ,&nbsp;Lynh Hoang Vy Thuy ,&nbsp;Laurence Gourcy ,&nbsp;Jean-Sébastien Moquet ,&nbsp;Véronique Daële ,&nbsp;Sébastien Dupraz","doi":"10.1016/j.ecoinf.2026.103635","DOIUrl":"10.1016/j.ecoinf.2026.103635","url":null,"abstract":"<div><div>The management of natural resources is increasingly critical and challenging due to complex interactions among environmental, industrial, and societal processes. Traditional approaches often fail to integrate heterogeneous data, limiting predictive and decision-support capabilities. This study presents a conceptual architecture for an Artificial Intelligence (AI)-assisted Digital Twin (DT) of the Centre-Val de Loire region, designed to unify time-dependent multi-source data. Based on the ENVRI Reference Model, it covers Science, Information, Computational, Engineering, and Technology layers, defining standardized data exchange, communication protocols, and prototype functionalities. A proof of concept FIWARE implementation supports ingestion, monitoring and analytical services for piezometric and meteorological data, exemplified through groundwater dynamics in the Beauce aquifer. It integrates daily observations from 53 piezometric stations over more than five years, managing approximately 2.8 million records in a containerized environment.</div><div>Results show that the proposed DT architecture can enhance sustainability-oriented decision making, integrating heterogeneous data and predictive analyses while enabling collaboration across scientific and technical domains. Its modular design offers a replicable template for future AI-assisted environmental DTs, scalable to larger regions. Hence, this work illustrates how DTs can improve environmental monitoring and understanding, providing a pathway toward resilient, data-driven management of natural resources.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"94 ","pages":"Article 103635"},"PeriodicalIF":7.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080074","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":"Predicting wildfires triggered by human-caused ignition: A spatial framework integrating AI models","authors":"Sujung Heo,&nbsp;Sujung Ahn","doi":"10.1016/j.ecoinf.2026.103640","DOIUrl":"10.1016/j.ecoinf.2026.103640","url":null,"abstract":"<div><div>Human-caused ignitions—including agricultural residue burning, land clearing, and various open-fire practices—are now a major contributor to wildfire occurrence in South Korea, accounting for more than 40% of recent events and concentrating along rapidly expanding wildland–urban interface (WUI) zones. To improve national-scale understanding of these ignition processes, this study develops an integrated wildfire risk assessment framework that combines Random Forest (RF), Generalized Additive Models (GAM), and Geographically Weighted Regression (GWR). Using 3703 georeferenced human-caused wildfire records (2001–2025) and high-resolution climatic, land-use, vegetation, and demographic datasets, we identify key ignition drivers, quantify nonlinear environmental thresholds, and map spatial heterogeneity in human-driven wildfire susceptibility. RF achieved the strongest predictive performance (AUC = 0.829), while GAM revealed sharp increases in ignition probability below 15% relative humidity, around 15 m/s wind speeds, and at intermediate NDVI levels (0.2–0.4). GWR showed substantial regional variability in these effects, particularly within peri-urban landscapes where human activity and fuel continuity intersect. High-risk areas (probability ≥0.70) accounted for 16.96% of the national territory and were concentrated in northern Gyeonggi-do, eastern Gangwon-do, and parts of Chungcheong and Jeolla provinces. By integrating the complementary strengths of RF, GAM, and GWR, this study provides operational ignition thresholds and high-resolution risk maps that support evidence-based land-use planning, targeted burning restrictions, and climate-adaptive fire management. The framework offers a transferable approach for regions facing similar challenges associated with human-caused wildfire ignitions and rapidly evolving socio-ecological landscapes.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"94 ","pages":"Article 103640"},"PeriodicalIF":7.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147421595","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":"Copula–information gain-based identification of GPP response thresholds under multiscale agricultural drought","authors":"Tianlong She,&nbsp;Chen Xu,&nbsp;Quanwang Chen,&nbsp;Yanan Wang,&nbsp;Yuanyuan Hong,&nbsp;Yechao Sun,&nbsp;Qiang Wang","doi":"10.1016/j.ecoinf.2026.103654","DOIUrl":"10.1016/j.ecoinf.2026.103654","url":null,"abstract":"<div><div>Scientifically determining the threshold at which gross primary productivity (GPP) enters a significantly drought-affected state is a critical prerequisite for effective drought risk mitigation and agricultural management. Previous studies have predominantly relied on empirical criteria or probability-based analyses to determine this threshold, while objective, information-driven frameworks to characterize nonlinear drought–ecosystem response thresholds across multiple time scales are lacking. To address this limitation, we propose a Copula–Information Gain (Copula–IG) framework that integrates copula-based joint dependence modeling with information-theoretic discrimination, thereby enabling probabilistic and information-driven identification of GPP drought thresholds across multiple temporal scales. The results indicate pronounced spatiotemporal heterogeneity and strong scale dependence in agricultural drought responses across China. As the drought duration extends from short-term to medium- and long-term periods, the discriminative performance of the Copula–IG model consistently improves, while the GPP drought threshold gradually shifts from a spatially dispersed pattern toward greater convergence and stability. IG values were primarily concentrated within the range of 0.05–0.10, indicating enhanced separation between drought-affected and non-affected GPP states. Meanwhile, the mechanism underlying GPP drought threshold formation transitions from a rapid, evapotranspiration-dominated short-term response to a structurally constrained suppression process governed by cumulative soil moisture deficits under prolonged drought conditions. Overall, this study presents a probabilistic, quantitative, and spatially refined framework for identifying agricultural drought–ecosystem response thresholds, thereby providing valuable scientific support for high-resolution agricultural climate monitoring and ecological early-warning systems.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"94 ","pages":"Article 103654"},"PeriodicalIF":7.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174103","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}