Ecological Informatics最新文献

{"title":"A continental-scale approach to ecosystem condition monitoring using satellite imagery and deep learning","authors":"Christopher J. Owers ,&nbsp;Karel Mokany ,&nbsp;Chris Ware ,&nbsp;Thomas D. Harwood ,&nbsp;Jinyan Yang ,&nbsp;Randall J. Donohue ,&nbsp;Tim R. McVicar ,&nbsp;Eric A. Lehmann ,&nbsp;Kristen J. Willams ,&nbsp;Rebecca K. Schmidt ,&nbsp;Matt Paget ,&nbsp;Simon Ferrier ,&nbsp;Charlotte Pelletier","doi":"10.1016/j.ecoinf.2026.103639","DOIUrl":"10.1016/j.ecoinf.2026.103639","url":null,"abstract":"<div><div>Substantial declines in biodiversity over the past century demonstrate an immediate need to preserve ecosystems and further mitigate habitat loss. Monitoring changes in ecosystem condition at region thru continental to global scales can provide important information about biodiversity declines and help facilitate targeted intervention. Efforts to use satellite imagery to map ecosystem condition change have experienced challenges with distinguishing observed changes from the natural variation of ecosystems. In this study we use an innovative deep learning architecture to pair time series satellite imagery with locations of known on-ground condition. Our model was developed using 209,041 on-ground records of native species present in the landscape, as a surrogate measure of ecosystem condition, coupled with Landsat time series data and topographic and climatological datasets. We predict ecosystem condition across the Australian continent for several years (2010, 2015, 2020, 2021, 2022) at 100 m. Arid regions in Australia's interior had predicted condition scores close to reference condition (1) for all years. Comparatively, highly modified landscapes in Australia's southeastern and southwestern regions had predicted condition scores closer to fully degraded (0). Mean predicted ecosystem condition across Australia was greater than 0.65 for all years, suggesting greater overall presence of native species rather than absence, however this was spatially variable. Our results demonstrate that using deep learning techniques and time series data can provide quantitative information on ecosystem condition, accounting for temporal variability of vegetation phenology and spatial variability across bioregions. Ongoing efforts to collect essential biodiversity variables from space must consider integrating with deep leaning approaches that have capacity for context driven spatial modelling. This will help ensure mapping products can support policy and inform intervention strategies.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"94 ","pages":"Article 103639"},"PeriodicalIF":7.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174092","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 prototype coupled modeling approach for predicting harmful algal blooms: A case study in Chile","authors":"Ishara Uhanie Perera ,&nbsp;So Fujiyoshi ,&nbsp;Daiki Kumakura ,&nbsp;Carolina Medel ,&nbsp;Kyoko Yarimizu ,&nbsp;Osvaldo Artal ,&nbsp;Pablo Reche ,&nbsp;Oscar Espinoza-González ,&nbsp;Leonardo Guzman ,&nbsp;Felipe Tucca ,&nbsp;Alexander Jaramillo-Torres ,&nbsp;Jacquelinne J. Acuña ,&nbsp;Milko A. Jorquera ,&nbsp;Shinji Nakaoka ,&nbsp;Satoshi Nagai ,&nbsp;Fumito Maruyama","doi":"10.1016/j.ecoinf.2026.103615","DOIUrl":"10.1016/j.ecoinf.2026.103615","url":null,"abstract":"<div><div>Predicting harmful algal blooms (HABs) remains a major challenge for coastal management and aquaculture. This study compares three forecasting approaches developed under the Monitoring of Algae in Chile (MACH) project: a particle dispersion model, an LSTM neural network, and an empirical dynamic model (EDM) to evaluate their ability to forecast bloom events. Consequently, we applied the EDM to forecast two <em>Pseudo-nitzschia</em> species groups using data collected from Metri, Quellón, and Melinka in southern Chile. The results showed that the genus <em>Ceratium</em> and <em>Leptocylindrus</em> were commonly associated with both <em>Pseudo-nitzschia</em> species groups, and the best prediction by causal species was obtained for the <em>P. seriata</em> group, with a correlation coefficient of 0.733 (<em>P</em> &lt; 0.0001) between observed and predicted values. This case study demonstrated that species interactions can be used to predict specific HAB species; however, the prediction performance may vary depending on location and species. This study provides one of the first applications of EDM for HAB forecasting using causal species in a real-world monitoring context, demonstrating the potential of hybrid modeling frameworks to improve early warning systems and mitigate aquaculture losses.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"94 ","pages":"Article 103615"},"PeriodicalIF":7.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174091","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":"Shaping the future of computational ecology and ecological data science: An editorial perspective from Ecological Informatics","authors":"George Arhonditsis ,&nbsp;Emmanuel Dufourq ,&nbsp;Malay Kishore Dutta ,&nbsp;Jiacong Huang ,&nbsp;Falk Huettmann ,&nbsp;Cheryl Ann Johnson ,&nbsp;Dong-Kyun Kim ,&nbsp;Antonino Staiano ,&nbsp;Ana Claudia Teodoro ,&nbsp;Christopher Wellen ,&nbsp;Feng Wu","doi":"10.1016/j.ecoinf.2026.103678","DOIUrl":"10.1016/j.ecoinf.2026.103678","url":null,"abstract":"","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"94 ","pages":"Article 103678"},"PeriodicalIF":7.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147538941","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 lightweight Mamba-frequency fusion algorithm for underwater image enhancement under physical model constraints","authors":"Hui Zhou ,&nbsp;Meiwei Kong ,&nbsp;Ruizhi Wang ,&nbsp;Qunhui Yang","doi":"10.1016/j.ecoinf.2025.103590","DOIUrl":"10.1016/j.ecoinf.2025.103590","url":null,"abstract":"<div><div>Underwater optical images are crucial data for ecological environment monitoring and marine biology research. However, due to the absorption and scattering of light by water, the image quality often suffers severe degradation, directly affecting the accuracy of species identification and habitat monitoring in ecological studies. The existing underwater image enhancement (UIE) methods have two major problems: non-physical methods are prone to producing unreasonable enhancements and overfitting, while physical model methods have high computational costs, making them difficult to deploy on resource-constrained underwater devices. Therefore, there is an urgent need to design a lightweight model for underwater environments that efficiently balances color correction and detail preservation. We propose a lightweight physical model-constrained UIE network, namely the lightweight Mamba-frequency fusion (LMF) algorithm. It utilizes an underwater optical imaging model to ensure the physical rationality of the imaging process. The core innovation lies in its lightweight dual-path architecture. It uses the linear complexity Mamba module to efficiently model global features, combines convolution to extract local details, and adopts a frequency-domain fusion strategy to fuse global, local, and channel features in the frequency domain, thereby alleviating detail loss caused by scattering and color distortion caused by absorption at a low computational cost. Experiments conducted on two public datasets show that LMF outperforms mainstream methods in image quality assessment metrics, while significantly reducing the number of parameters and computational complexity. This advancement provides a technically feasible approach for high-fidelity visual data acquisition in ecological monitoring applications.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"93 ","pages":"Article 103590"},"PeriodicalIF":7.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925263","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":"Image-based analysis of long-term biocrust degradation utilizing joint energy-based deep learning","authors":"Stefan Herdy ,&nbsp;Philipp Faulhammer ,&nbsp;Emilio Rodríguez-Caballero ,&nbsp;Erika Geiger ,&nbsp;Jayne Belnap ,&nbsp;Thomas Pock ,&nbsp;Bettina Weber","doi":"10.1016/j.ecoinf.2025.103551","DOIUrl":"10.1016/j.ecoinf.2025.103551","url":null,"abstract":"<div><div>The semi-arid regions of the world are populated by highly specialized groups of organisms that can cope with the harsh climatic conditions. However, the climate and, as a result, the floristic composition have changed in these regions in recent decades. Dryland regions throughout the world host biological soil crusts, which colonize the uppermost soil layer. This superficial growth facilitates the utilization of imaging methods for monitoring purposes. In this study a deep-learning model called Joint Energy-Based Semantic Segmentation that enables robust analysis of images captured over long periods of time is proposed. It is shown how biological soil crusts have changed over time in two very different areas (Succulent Karoo, South Africa and Colorado Plateau, USA) with an accuracy of 91 % and 77 %, respectively. This provides a detailed analysis of the complex interactions between the individual taxa and external climatic influences. The results show that conditions of extreme drought led to degradation and that the soil crust organisms were unable to fully recover from this during wetter periods. On both sites, biocrusts degraded within the last 1–2 decades. A detailed time series analysis of the interactions between the occurring taxa, using time lagged cross correlation and transfer entropy metrics, identified <em>Psora</em> sp. and <em>Fulgensia</em> sp. as key indicator species, as they were highly reactive to climate alterations, and therefore inform about biocrust degradation already at an early state. This study demonstrates how modern image-based deep learning methods enable a very detailed analysis of the development of the world's dryland flora.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"93 ","pages":"Article 103551"},"PeriodicalIF":7.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning approach to landmarking and measurement error analysis for gilthead seabream (Sparus aurata) origin classification in geometric morphometrics","authors":"Igor Talijančić ,&nbsp;Josip Šarić ,&nbsp;Josip Zavada ,&nbsp;Luka Žuvić ,&nbsp;Siniša Šegvić ,&nbsp;Tanja Šegvić-Bubić","doi":"10.1016/j.ecoinf.2025.103560","DOIUrl":"10.1016/j.ecoinf.2025.103560","url":null,"abstract":"<div><div>Geometric morphometrics has transformed the study of morphological structures in biological research by providing precise tools for shape analysis. However, manual digitisation of landmarks remains prone to variation and introduces measurement error that can obscure or distort the biological signal. This study addresses these challenges by proposing a novel method for automatic landmark placement in gilthead seabream, a key species in Mediterranean aquaculture. Using a dataset of 2052 specimens of wild, farm-associated and farmed origin, the model achieved sub-millimetre precision and performance comparable to that of an experienced morphometrician, while substantially outperforming a novice annotator. Measurement error components were quantified using a repeated-measures statistical framework, demonstrating that the automated workflow reduced both systematic and random digitisation errors relative to human annotation. Sliding semilandmarks reduced localised systematic bias in regions of curvature, although this came at the cost of increased random error and did not improve classification accuracy compared to configurations containing only fixed landmarks. Systematic measurement error did not align with origin-related shape structure and classification performance remained highly consistent across annotators, conforming that the detected origin differences reflected true biological signal rather than artefacts of annotation bias. These findings show that automated landmarking can support reliable and reproducible shape analyses under controlled imaging conditions. While further work is needed to evaluate model generalisation across broader geographic and imaging contexts, the proposed workflow represents a promising step towards a cost-effective solution for important challenges in aquaculture management, such as escapees detection in wild populations and preventing fraud in fish product labelling.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"93 ","pages":"Article 103560"},"PeriodicalIF":7.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925481","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":"Improving multi-species habitat identification through species weighting assignment using joint species distribution model","authors":"Yunlei Zhang ,&nbsp;Jie Yin ,&nbsp;Yupeng Ji ,&nbsp;Chongliang Zhang ,&nbsp;Binduo Xu ,&nbsp;Yiping Ren ,&nbsp;Ying Xue","doi":"10.1016/j.ecoinf.2025.103581","DOIUrl":"10.1016/j.ecoinf.2025.103581","url":null,"abstract":"<div><div>Habitat conservation serves as the cornerstone for fishery resource recovery. Traditionally, multi-species habitat predictions are typically generated by stacking individual species' habitat suitability maps using the equal-weighting strategy. However, this approach neglects interspecific differences in data quality and life-history traits, potentially resulting in biased or ecologically suboptimal identification of shared habitats. In this study, we developed a novel framework that incorporates species-specific weighting schemes to enhance multi-species habitat prediction. We applied this approach to fish communities in the central and southern Yellow Sea, China, and compared shared habitat predictions generated by the Joint Species Distribution Model (JSDM) under alternative weighting strategies. The weighting factors considered included species-specific Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) from JSDM, prevalence, and trophic level. Our results demonstrated that incorporating species-specific weights significantly improves the prediction accuracy of multi-species habitat distributions in the study area, as indicated by higher correlation coefficients and lower standard deviations between predicted and observed efficiency indices. Notably, the weighted approach would also narrow the extent of prioritized areas and improve conservation efficiency. Under the weighted scenarios, the area identified for protection was nearly 50 % smaller than that under the unweighted approach, while the protection effort directed toward shared habitats increased by 33.7 %. This study highlights the potential of incorporating species-specific weighting into JSDMs to improve multi-species habitat suitability predictions. This will support the design of marine protected areas that enable more accurate identification and protection of key shared habitats across species, while also promoting cost-effective conservation outcomes.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"93 ","pages":"Article 103581"},"PeriodicalIF":7.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925554","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":"From reef ecology to industry 4.0: Strategic, smart and sustainable framework for crown-of-thorns starfish outbreak management","authors":"Ronnie Concepcion II ,&nbsp;Chona Camille Vince Cruz-Abeledo ,&nbsp;Ira Estropia ,&nbsp;R-Jay Relano ,&nbsp;Jovie Nicolas ,&nbsp;Immanuel Jose Valencia ,&nbsp;Jonnel Alejandrino ,&nbsp;Andres Philip Mayol ,&nbsp;Elmer Dadios ,&nbsp;Bernardo Duarte","doi":"10.1016/j.ecoinf.2025.103576","DOIUrl":"10.1016/j.ecoinf.2025.103576","url":null,"abstract":"<div><div>Outbreaks of crown-of-thorns starfish (COTS) continue to be one of the biggest dangers to Indo-Pacific coral reefs, causing widespread coral mortality, a drop in biodiversity, and a decline in the bottom lines of fishing and tourism industries. Despite their value, traditional management techniques, including chemical injections, manual removal, and predator protection, are resource intensive and have limited long-term efficacy. This analysis offers a strategic, smart, and sustainable approach for managing outbreaks by fusing new Industry 4.0 technologies with ecological and biological knowledge of COTS. By systematically synthesizing articles published between 2015 and 2025, four thematic clusters were identified: biology and ecology, traditional management, algorithm-based monitoring, and advanced detection technology. Innovation in Industry 4.0, including autonomous underwater vehicles, IoT-enabled sensor networks, environmental DNA monitoring, artificial intelligence, blockchain, and swarm robotics, demonstrates paradigm shifts in scalability, accuracy, and adaptability for reef protection. However, the current research remains fragmented, often isolating biology from technological applications. This study proposed an integrated approach linking ecological triggers with data-driven tools to enable proactive detection, scalable intervention, and cross-regional coordination and named it Strategic, Smart and Sustainable (S3-COTS). This framework contributes to a conceptual blueprint for advancing coral reef conservation through digital transformation and enhancing resilience against outbreaks in the face of climate change.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"93 ","pages":"Article 103576"},"PeriodicalIF":7.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925558","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":"Impacts of subsurface chlorophyll-a distribution on remote sensing reflectance spectra","authors":"Xiaojuan Li ,&nbsp;Wei Zhang ,&nbsp;Zhihua Mao ,&nbsp;Shanchuan Guo ,&nbsp;Hongrui Zheng","doi":"10.1016/j.ecoinf.2025.103545","DOIUrl":"10.1016/j.ecoinf.2025.103545","url":null,"abstract":"<div><div>Traditional ocean color remote sensing inversion algorithms often overlook the influence of the subsurface chlorophyll maximum layer (SCML) on remote sensing reflectance (Rrs), potentially leading to errors in estimating surface chlorophyll-a (Chl-a) concentration. To address this issue, in this study, we parameterize Chl-a vertical profiles using a Gaussian model and perform spectral simulations of Rrs under varying SCML scenarios with the Hydrolight radiative transfer model. The aim is to explore the response mechanism of Rrs to the SCML for more accurate retrieval of surface Chl-a concentration. By comparing the Rrs spectra from vertically inhomogeneous (including SCML) and homogeneous Chl-a profiles, three key findings were obtained about the SCML. First, the presence of an SCML introduces considerable biases in traditional retrieval models. Second, a global sensitivity analysis reveals that SCML parameters have a dominant influence on Rrs, with a total sensitivity index exceeding 50 % within the blue bands (395–475 nm), whereas Rrs(485)/Rrs(535) exhibits the lowest sensitivity for surface Chl-a estimation. Third, an empirical retrieval model based on Rrs(488)/Rrs(531) is proposed, which outperformed the MODIS standard algorithm. Independent validation datasets from three different ocean regions (the Mediterranean, Atlantic, and Pacific) confirm its robustness in Case-1 waters with SCML, achieving a Root Mean Square Error (RMSE) of less than 0.012 mg m⁻³. This study advances current ocean color methods by systematically quantifying the impact of SCML on Rrs and providing a validated algorithm that mitigates this effect. These findings offer a pathway to improve global Chl-a products, which are critical for applications in global carbon cycle monitoring and primary productivity estimation.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"93 ","pages":"Article 103545"},"PeriodicalIF":7.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685096","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":"CNN-based wheat yield prediction using multi-source and multi-stage data integration from UAV imagery and sensors","authors":"Süreyya Betül Rufaioglu ,&nbsp;Ali Volkan Bilgili ,&nbsp;Sibel Ipekesen ,&nbsp;Amjed Mohamed Ismael ,&nbsp;Yunus Kaya ,&nbsp;João P. Matos-Carvalho","doi":"10.1016/j.ecoinf.2026.103608","DOIUrl":"10.1016/j.ecoinf.2026.103608","url":null,"abstract":"<div><div>In this study, a convolutional neural network (CNN)-based deep learning model was developed to predict durum wheat yield by integrating RGB and multispectral UAV imagery, ground-based sensor measurements (SPAD, NDVI, LAI, and plant height), and climatic parameters under semi-arid conditions. The dataset was designed as a multi-source, multi-stage, and multi-year structure, comprising measurements collected across six phenological growth stages during the 2023 and 2024 growing seasons. Principal component analysis (PCA) indicated that approximately 69% of the total variance was explained by the first two components, with SPAD, NDVI, LAI, and plant height identified as the most influential variables in explaining yield variability. The CNN model achieved high predictive accuracy in both stage-based and year-based evaluations, with R² values ranging from 0.982 to 0.994, RMSE between 0.15 and 0.24 kg ha⁻¹, and MAE between 0.11 and 0.19 kg ha⁻¹. The highest performance was obtained during the heading and grain-filling stages. Overall, the results demonstrate that integrating UAV imagery, physiological sensor indicators, and climatic variables within a multi-source, multi-stage, multi-year deep learning framework substantially improves yield prediction accuracy compared with single-source approaches. This study presents a high-performance CNN architecture for yield forecasting and provides a robust foundation for generalizable and effective decision-support systems in precision agriculture.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"93 ","pages":"Article 103608"},"PeriodicalIF":7.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977417","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}