Ecological Informatics最新文献

{"title":"Automated waterbird detection, classification, and mapping from UAV imagery using a two-stage deep learning pipeline","authors":"Miel Bogaert,&nbsp;Andrée De Cock,&nbsp;Xingzhen Liu,&nbsp;Long Ho,&nbsp;Wouter H. Maes,&nbsp;Peter L.M. Goethals","doi":"10.1016/j.ecoinf.2026.103949","DOIUrl":"10.1016/j.ecoinf.2026.103949","url":null,"abstract":"<div><div>Waterbirds function as sensitive indicators of ecological change in freshwater ecosystems. Conventional monitoring is labour-intensive, spatially limited, prone to observer bias and disturbance. Unmanned Aerial Vehicle (UAV) imagery combined with deep learning (DL) offers a promising alternative for efficient and scalable monitoring.</div><div>This study evaluates a fully automated UAV-based workflow to detect, classify, and map waterbirds along a 12 km canal in Belgium. A two-stage pipeline combining YOLO-based detection and convolutional neural network (CNN) classification was developed and evaluated across varying flight altitudes and ground sampling distances (GSD). Model performance was further assessed using an independent seasonal dataset and analysed in relation to object size and habitat complexity. The detection model achieved a high precision of 0.91 and a recall of 0.81, while the classifier reached a weighted F1-score of 0.90. Detection performance was strongly dependent on ground sampling distance (GSD), with optimal results at ≤6.83 mm px⁻¹ (≤25 m altitude) and marked declines in detection performance at higher GSD values. Additional analyses showed that smaller birds were more likely to be missed, while high-entropy environments such as reed beds may further reduce detection reliability. Comparisons between automated and manual counts revealed discrepancies, influenced by environmental conditions and survey timing. Georeferenced detections projected onto orthomosaics enabled spatially explicit mapping of waterbird distribution.</div><div>Overall, UAV-based deep learning provides a promising and scalable approach for waterbird monitoring, while highlighting the importance of spatial resolution and habitat complexity in determining detection performance.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"97 ","pages":"Article 103949"},"PeriodicalIF":8.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148657709","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":"Erratum to < A roadmap for designing a virtual interface to explore a digital twin of the oceans for marine ecosystems monitoring> <Ecological Informatics Volume 92, December 2025, 103483>","authors":"Morane Clavel-Henry,&nbsp;Sascha Flögel,&nbsp;Daniel M. Toma,&nbsp;Gerard Llorach-Tó,&nbsp;Nixon Bahamon,&nbsp;Giacomo Picardi,&nbsp;Marco Francescangeli,&nbsp;Juliana Quevedo,&nbsp;Nathan J. Robinson,&nbsp;Michele Ferrari,&nbsp;Damianos Chatzievangelou,&nbsp;Antoni Sánchez-Márquez,&nbsp;Paula Espina,&nbsp;Jordi Ribera-Altimir,&nbsp;Luca Mirimin,&nbsp;Seonock Woo,&nbsp;Tim W. Nattkemper,&nbsp;Emanuela Fanelli,&nbsp;Cristina Gambi,&nbsp;Silvia Gallegati,&nbsp;Jacopo Aguzzi","doi":"10.1016/j.ecoinf.2026.103880","DOIUrl":"10.1016/j.ecoinf.2026.103880","url":null,"abstract":"","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"97 ","pages":"Article 103880"},"PeriodicalIF":8.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148738377","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":"Terrain-driven wildfire susceptibility assessment with explainable AI: A case study from Alberta","authors":"Alex Rodriguez,&nbsp;Isa Ebtehaj,&nbsp;Afshin Amiri,&nbsp;Silvio Jose Gumiere,&nbsp;Hossein Bonakdari","doi":"10.1016/j.ecoinf.2026.103948","DOIUrl":"10.1016/j.ecoinf.2026.103948","url":null,"abstract":"<div><div>Wildfires are becoming increasingly frequent and severe across boreal landscapes, posing significant threats to environmental stability, public health, and economic security, where climate change has intensified both their frequency and scale. While recent advances in remote sensing and predictive modeling have improved wildfire susceptibility assessment, existing approaches differ in how environmental interactions are represented across spatial scales and modeling frameworks. In this context, the present study applies a scalable and interpretable wildfire susceptibility analysis framework to the boreal forests of Alberta, Canada. The objective of this study is to evaluate predictive performance and model transparency using established ensemble tree-based machine learning algorithms (LightGBM, XGBoost, Random Forest, Gradient Boosting, and AdaBoost), combined with exhaustive feature selection and Bayesian optimization. Using a dataset comprising sixteen environmental, meteorological, topographic, and human-related variables, 65,451 model configurations were evaluated to identify optimal input subsets for predicting wildfire occurrence. Model performance was validated through confusion matrices and various classification-based indices, and feature importance was quantified using SHAP values. Results show that LightGBM achieved the highest predictive performance among the tested models, with overall accuracy exceeding 98% usingonly eight input variables, which include latitude, longitude, plan curvature, profile curvature, temperature, precipitation, wind direction, and distance to roads, with the two curvature metrics jointly accounting for more than 76% of the total normalized mean absolute SHAP attribution. These findings highlight the dominant influence of terrain shape on wildfire susceptibility in Alberta's boreal region and demonstrate that high-resolution susceptibility assessment can be achieved using a reduced targeted set of predictors. The results provide region-specific, interpretable insights that may support wildfire risk assessment and decision-making in applications such as early warning, zoning, and adaptive climate resilience strategies.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"97 ","pages":"Article 103948"},"PeriodicalIF":8.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148657293","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":"Neural-parameterized cellular automata for wildfire spread","authors":"Maksym Zhenirovskyy,&nbsp;Ion Matei,&nbsp;Rohit Vuppala,&nbsp;Takuya Kurihana,&nbsp;Hon Yung Wong","doi":"10.1016/j.ecoinf.2026.103928","DOIUrl":"10.1016/j.ecoinf.2026.103928","url":null,"abstract":"<div><div>Traditional wildfire models rely on rigid, low-dimensional parameters and static fuel maps, frequently underpredicting fire spread. To address this weakness, we introduce a hybrid deep-learning parameterized Probabilistic Cellular Automata (CA) framework implemented in JAX. Our approach employs a Multi-Scale Convolutional Neural Network to dynamically generate spatially varying parameters that govern fire-spread probability, wind alignment, and slope influence. This hybrid design captures complex, nonlinear environmental interactions while preserving the physical interpretability of the underlying three-state CA. The JAX implementation enables hardware acceleration and gradient-based parameter calibration. Evaluated on six large-scale wildfires in the western United States, the model maintains <span><math><mrow><mi>I</mi><mi>o</mi><mi>U</mi><mo>&gt;</mo><mn>0.6</mn></mrow></math></span> over 72-h forecast horizons after a 10-day data assimilation window during which the model is fitted incrementally to observed perimeters; the resulting forecast is a conditional projection of fire growth under the suppression regime already encoded in those observations.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"97 ","pages":"Article 103928"},"PeriodicalIF":8.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148657355","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":"Advanced optical-based water stress detection in lettuce: A novel wavelength selection technique","authors":"Nicola Dilillo,&nbsp;Gica Stefanescu Miralles,&nbsp;Lorenzo Comba,&nbsp;Massimo Pugliese,&nbsp;Maurizio Rebaudengo,&nbsp;Renato Ferrero","doi":"10.1016/j.ecoinf.2026.103944","DOIUrl":"10.1016/j.ecoinf.2026.103944","url":null,"abstract":"<div><div>Water stress is a critical factor limiting agricultural productivity, particularly in controlled environments such as greenhouses, where precision irrigation is essential. Accurate and timely detection of crop water status is vital to prevent yield losses and enhance resource use efficiency. Visible–near infrared spectral reflectance data can be acquired through non-destructive leaf-clip measurements and used to monitoring water stress in lettuce-grown plants. However, given the high dimensionality of hyperspectral data, which often includes redundant or non-informative variables that can lead to collinearity, several wavelength selection algorithms have been proposed to enhance classification performance. In this study, a new method for spectral features selection, called <em>iGA-BOSS</em>, is developed, specifically conceived for water stress detection. Its effectiveness was proven on lettuce plants under different water stress conditions induced through controlled irrigation deficits relative to optimal watering regimes. Spectral reflectance measurements were collected in vivo at two growth stages and analyzed using advanced machine learning techniques. For comparison, the reflectance data were processed using the most relevant algorithm available in the literature, and the <em>iGA-BOSS</em> achieved the best performance, with an accuracy of 0.95 and an F1 score of 0.95. The selected features were used to train Partial Least Squares Discriminant Analysis (PLS-DA) for binary classification of water stress. Notably, the proposed method outperforms models trained on the complete wavelength set and competing algorithms, offering the best trade-off between classification accuracy, spectral compactness, and computational efficiency. These results demonstrate the effectiveness of combining robust preprocessing, targeted wavelength selection, and supervised classification models for monitoring spectral water stress. The identified key wavelengths may inform the development of tailored multispectral sensors, enabling scalable and efficient irrigation management in precision agriculture.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"97 ","pages":"Article 103944"},"PeriodicalIF":8.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148657792","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":"Advancing spatial predictions of microbial communities with stacked species distribution models","authors":"Valentin Verdon,&nbsp;Daniel Scherrer,&nbsp;Lucie Malard,&nbsp;Heidi Mod,&nbsp;Erika Yashiro,&nbsp;Antoine Guisan","doi":"10.1016/j.ecoinf.2026.103947","DOIUrl":"10.1016/j.ecoinf.2026.103947","url":null,"abstract":"<div><div>Predicting microbial community composition is essential for belowground biodiversity conservation and management yet remains methodologically challenging. Stacked species distribution models (S-SDM) offer a promising framework by allowing to model large numbers of individual microbial Amplicon Sequence Variants (ASVs) along environmental gradients and assembling them into community-level predictions. However, the consequences of key modelling choices on community-level predictions remain poorly understood, particularly for microbial data. Using bacterial sequence data from 250 topsoil samples collected in the western Swiss Alps through environmentally stratified random sampling, we evaluated how cross-validation and binarisation options influence S-SDM community-level predictions. We assessed prediction accuracy relative to observed communities, differences among predicted communities, and performance along environmental gradients. Stacked ASV model performance in predicting community composition were higher at both extremes of the pH gradient. Predicted communities tended to underestimate compositional dissimilarity but preserved the overall structure of observed beta-diversity. Cross-validation community constraints had negligible effects on predictions. In contrast, binarisation methods strongly influenced outcomes: better community composition predictions were obtained when the sum of predicted probabilities was used to determine threshold value between predicted presence and absence at ASV-level. These results demonstrate that S-SDM can be successfully applied to microbial communities and that the thresholding approach based on the ranking of predicted probabilities yields the most reliable community predictions. Our findings highlight the potential of S-SDMs to support the spatial predictions of microbial biodiversity and their integration into conservation assessments in line with recent IUCN initiatives.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"97 ","pages":"Article 103947"},"PeriodicalIF":8.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148657291","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":"Short-term forecasting of Chlorophyll-a dynamics in Lake Villarrica using foundation time-series models","authors":"Lien Rodríguez-López,&nbsp;David Francisco Bustos Usta,&nbsp;Lisandra Bravo Alvarez,&nbsp;Luc Bourrel,&nbsp;Frederic Frappart,&nbsp;Roberto Urrutia","doi":"10.1016/j.ecoinf.2026.103938","DOIUrl":"10.1016/j.ecoinf.2026.103938","url":null,"abstract":"<div><div>This study evaluates short-term forecasting of chlorophyll-a (Chl-a) dynamics at two monitoring stations in Lake Villarrica, northern Patagonia, Chile, using reproducible station-level Chl-a time series and foundation time-series models. The forecasting experiments used univariate Chl-a context windows as inputs to TimesFM and Chronos Large and evaluated 1–30 day horizons under a rolling-origin design. Short-horizon skill was highest at the 1-day horizon: at La Poza, D1 MAE was 0.099 μg/L for TimesFM and 0.106 μg/L for Chronos Large; at Pucón, D1 MAE was 0.063 μg/L for TimesFM and 0.071 μg/L for Chronos Large. Forecast error increased with lead time, with D28 MAE ranging from 0.606 to 0.757 μg/L across stations and models. Lag diagnostics showed that most D7 forecast cases had maximum correlation at lag 0. However, Pucón Chronos Large showed a non-zero best lag, indicating that peak timing remains uncertain for warning applications. We also evaluated q10/q50/q90 predictive intervals and observed/gap-stratified performance. The results support the use of foundation time-series models for short-term forecasting of reconstructed Chl-a products, while highlighting limitations due to imputation, missing exogenous drivers, and the absence of validated satellite-feature ingestion in the current forecast architecture.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"97 ","pages":"Article 103938"},"PeriodicalIF":8.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148657353","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":"How many phage species remain undiscovered? Species sampling approaches to inform phage discovery","authors":"Massimo Cavallaro,&nbsp;Andrew Kinsella,&nbsp;Spyridon Megremis,&nbsp;Andrew Morozov,&nbsp;Andrew Millard,&nbsp;Fabian Freund","doi":"10.1016/j.ecoinf.2026.103915","DOIUrl":"10.1016/j.ecoinf.2026.103915","url":null,"abstract":"<div><div>The emergence of antimicrobial resistant bacteria has been identified as one of the most serious public health and development threats for the near future. The use of bacteriophages (phages) is a promising solution for the sustainable control of these pathogens. Phages are natural viral predators of bacterial pathogens. However, due to the variability and adaptability of bacteria, developing effective and sustainable phage treatments requires drawing from a wide variety of different phage species. This study applies specialised mathematical and computational estimation approaches to the problem of sampling and discovering species of phages in microbiological communities. Our goal is to predict how many new phage species will be discovered in future samples based on species recorded in a phage sequence database for eight common bacterial hosts. While internal validation shows that current estimators yield low prediction errors for how many new species can be found when sampling more phages, these critically assume that the underlying mechanisms of phage sampling and database inclusion remain similar across time — an assumption put into question by our analysis for all but the host genus <em>Mycobacterium</em>.</div><div>From this, we discuss how our results can still inform future sampling strategies — both through directly predicting the number of additional species, or by detecting changes in sampling and database inclusion.</div><div>Our results have the potential to inform and optimise the hunt for and isolation of novel of phages from the natural environment.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"97 ","pages":"Article 103915"},"PeriodicalIF":8.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148657710","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":"The mossing link: Building a trait-based database as a foundation for bryophyte integration in ecosystem models","authors":"Casey Schine,&nbsp;Robin Andresen,&nbsp;Alex Lyford,&nbsp;Benjamin N. Sulman,&nbsp;Sasha C. Reed,&nbsp;Kirsten K. Coe","doi":"10.1016/j.ecoinf.2026.103935","DOIUrl":"10.1016/j.ecoinf.2026.103935","url":null,"abstract":"<div><div>Bryophytes—mosses, liverworts, and hornworts—play an important role in carbon storage, nutrient cycling, and hydrological processes across terrestrial ecosystems, yet they remain largely absent from the land surface components of Earth System Models (ESMs). Their exclusion limits the accuracy of terrestrial biosphere simulations, particularly in high-latitude and moisture-limited regions where bryophytes can dominate primary production and mediate critical biogeochemical fluxes. The Bryophyte Functional Integration for Terrestrial Ecosystem models (BryoFITE) project has been developed to address this omission. The objective of this study is to establish a systematic, reproducible workflow for synthesizing bryophyte trait data relevant to terrestrial ecosystem models. This workflow integrates automation with expert curation and relies on tools already widely used in ecological research. We identified relevant literature by constructing a comprehensive matrix of subject-parameter search terms. We then used these search terms to execute a total of 720 targeted searches across Google Scholar and Scopus, retrieving over 84,000 records. We applied a combination of fuzzy string matching and supervised machine learning to deduplicate and classify the resulting literature, yielding a refined group of ∼35,000 unique entries. A Random Forest model, trained on a manually flagged subset of records, excluded over 50% of records as unlikely to contain relevant data based on manual and model-based classification. The remaining records were ranked by likelihood of containing usable data, enabling us to prioritize data entry toward the literature with the highest probable value for this research. We then developed a modular, cloud-based database using Google Sheets with custom JavaScript automation for collaborative, flexible data entry. The BryoFITE database captures ecophysiological parameters, including photosynthetic capacity, desiccation tolerance, and nutrient-use traits, critical for modeling bryophyte function. Our preliminary results demonstrate the utility of this workflow for synthesizing heterogenous trait data and highlight its potential to accelerate the development of bryophyte-specific plant functional types for integration into land models, to advance our capacity to simulate plant–climate interactions in ESMs, and to use probability-based workflows for integrating diverse data for model parameterization, evaluation, and development.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"97 ","pages":"Article 103935"},"PeriodicalIF":8.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148657713","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":"Noise, bias and data limitations: Virtual benchmarking of species distribution models' robustness for prediction and inference","authors":"Emma Chollet Ramampiandra,&nbsp;Gaspard Fragnière,&nbsp;Andreas Scheidegger,&nbsp;Nele Schuwirth","doi":"10.1016/j.ecoinf.2026.103941","DOIUrl":"10.1016/j.ecoinf.2026.103941","url":null,"abstract":"<div><div>Species distribution models (SDMs) are widely used to describe and predict species occurrences from environmental data, but calibration datasets are typically affected by noise and bias. We test the robustness of SDMs to common data limitations by generating ecologically realistic virtual benchmark data with a mechanistic food-web model and then systematically degrading the data quality. These datasets are used to train SDMs of different complexities, which we evaluate not only on their predictive performance, but also on their degree of overfitting and their ability to recover the true environmental response shapes.</div><div>While SDMs explicitly only capture environmental responses, species occurrences in nature are also affected by dispersal limitation and biotic interactions. To account for this, we generated virtual benchmark data with the model <em>Streambugs</em>, which explicitly includes biotic interactions and dispersal limitation at the catchment scale, to simulate presence absence data and responses of 129 stream macroinvertebrate taxa to eight environmental factors. We then systematically applied four common types of data-limitation scenarios: reduced sample size, missing predictors, random noise added to one predictor, and bias in the species detection process. Each type was assessed at four levels of degradation, and we additionally evaluated combined scenarios including all limitations.</div><div>We compared statistical and machine-learning SDMs covering a range of model complexities for 35 taxa. More flexible models achieved the highest predictive performance under the best-case scenario but were sensitive to data degradation, exhibiting strong overfitting that leads to irregular response shapes, which did not reflect the data generating processes. In contrast, simpler models produced more consistent predictions and response shapes across scenarios. We illustrate how SDMs differ in their robustness to common data limitations and reveal trade-offs between predictive performance, overfitting and ecological interpretability, enabling informed decisions about model complexity according to data quality and study objectives.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"97 ","pages":"Article 103941"},"PeriodicalIF":8.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148657292","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}