Ecological Informatics最新文献

{"title":"Unraveling the complex dynamics of benthic algae in the Red River Basin: A comparative study","authors":"Xuekai Feng ,&nbsp;Kejian He ,&nbsp;Changming Chen ,&nbsp;Yu Han ,&nbsp;Yuan He ,&nbsp;Xingcan Chen ,&nbsp;Liqin Yan ,&nbsp;Yuelian Xu","doi":"10.1016/j.ecoinf.2025.103128","DOIUrl":"10.1016/j.ecoinf.2025.103128","url":null,"abstract":"<div><div>Benthic algae, as critical primary producers in fluvial ecosystems, exhibit distinct responses to environmental gradients across heterogeneous river systems. This comparative study analyzed three tributaries in the Red River Basin—Lixian River (LXR, pristine), Yuanjiang River (YR, anthropogenically disturbed), and Panlong River (PLR, karst-influenced)—to identify key drivers of algal community structure. Results revealed nitrogen (NH₄⁺-N) as the primary density regulator in LXR, while substrate heterogeneity and hydrological stability governed diversity (H′) and evenness (J'). In nutrient-enriched YR, total phosphorus (TP) dominated algal density, with diversity suppressed by eutrophication indicators (TP, Chl-a) and physical factors (depth, DO). PLR's calcium-rich karst environment promoted filamentous algal dominance, where density correlated with NH₄⁺-N and current velocity, serving as a proxy for benthic diversity (H′-J': R² &gt; 0.75). Basin-wide analysis demonstrated nitrogen's outsized influence over phosphorus, with geochemical factors (e.g., Ca²⁺) emerging as critical modulators of algal resilience in karst systems. These findings highlight the spatial variability of algal-environment interactions, emphasizing the need for basin-specific management strategies that account for both anthropogenic pressures and geomorphic contexts.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"88 ","pages":"Article 103128"},"PeriodicalIF":5.8,"publicationDate":"2025-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825873","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":"Forecasting urban expansion: A dynamic urban growth model using DS-ConvLSTM to simulate multi-land regulation scenarios","authors":"Juyeong Nam,&nbsp;Changyeon Lee","doi":"10.1016/j.ecoinf.2025.103136","DOIUrl":"10.1016/j.ecoinf.2025.103136","url":null,"abstract":"<div><div>This research addresses the computational inefficiency problem in deep learning-based urban growth modeling. This study proposes a novel Depthwise Separable Convolutional Long Short-Term Memory (DS-ConvLSTM) model to predict the urban growth patterns in Hanam City South Korea by 2030. The model incorporates six scenarios that reflect diverse land demands and urbanization patterns. Integrating 40 years of data, DS-ConvLSTM demonstrated superior performance compared to existing models, such as Convolutional Long Short-Term Memory (ConvLSTM), achieving an accuracy, F1-score, and Figure of Merit of 0.9801, 0.9510, and 0.8092, respectively. Notably, its efficient design reduces the network parameters by more than half compared to the ConvLSTM model, thereby decreasing model complexity. The study further explores potential land demand based on population and economic growth projections, ranging from 27.15 km² to 29.31 km². The analysis reveals trade-offs between development approaches. Business-as-usual scenarios lead to agricultural and forestland loss, while ecologically-focused development prioritizes forest preservation but increases development pressure on agricultural land. Sustainable compact development reduces land loss due to urban expansion through high-density redevelopment. However, high-density areas can lead to concentrated traffic congestion and environmental pollution. These findings provide valuable insights for urban planners, enabling them to make data-driven decisions regarding future land use policies.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"88 ","pages":"Article 103136"},"PeriodicalIF":5.8,"publicationDate":"2025-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800290","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 comparison of PhenoCam and satellite indices with in-situ observations for black spruce in the boreal forest of Quebec, Canada","authors":"Akash Kumar ,&nbsp;Ganesh Sai Sivani Noolu ,&nbsp;Siddhartha Khare ,&nbsp;Sergio Rossi","doi":"10.1016/j.ecoinf.2025.103137","DOIUrl":"10.1016/j.ecoinf.2025.103137","url":null,"abstract":"<div><div>Vegetation phenology plays a key role in regulating ecosystem processes, serving as a sensitive indicator of climate change impacts on ecosystems. Monitoring bud and leaf development is crucial for understanding ecosystem responses to environmental changes. This study compares PhenoCam with phenological observations in evergreen forests. We focused on black spruce [<em>Picea mariana</em> (Mill.) B.S.P] stands at the Simoncouche Research Station in Laurentides Wildlife Reserve, Quebec, Canada, for 2017–2020. By analyzing bud phenology from time series color indices (GCC, RCC, VCI, and ExG) and comparing them with ground observations, we aim to elucidate the effectiveness of these indices in tracking the growing season. Our results show that GCC is the most effective index for SOS with a mean difference of 13.9 days and both RCC and GCC for tracking the EOS with 10.5 and 11.1 days respectively. ExG also showed a good correlation with field observations, while VCI performed lower in comparison. The integration of a white reflectance panel in the PhenoCam setup proved crucial for normalizing images under varying illumination conditions, enhancing the accuracy of phenological assessments. Further GCC estimates improved to 0.9 day for SOS and 4.2 days for EOS with the inclusion of a reflectance panel. Field observations demonstrated closer alignment with GCC estimates than EVI, emphasizing the potential of combining ground-based and remote sensing technologies for precise phenological monitoring. The research aims to contribute to the broader understanding of how specific PhenoCam indices and calibration of data influence the reliability of phenological studies in evergreen forest ecosystems.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"88 ","pages":"Article 103137"},"PeriodicalIF":5.8,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792388","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":"Recent developments of artificial intelligence methods for sea ice concentration monitoring using high-resolution imaging datasets","authors":"Marzuraikah Mohd Stofa ,&nbsp;Siti Raihanah Abdani ,&nbsp;Asraf Mohamed Moubark ,&nbsp;Muhammad Ammirrul Atiqi Mohd Zainuri ,&nbsp;Ahmad Asrul Ibrahim ,&nbsp;Nor Azwan Mohamed Kamari ,&nbsp;Mohd Asyraf Zulkifley","doi":"10.1016/j.ecoinf.2025.103132","DOIUrl":"10.1016/j.ecoinf.2025.103132","url":null,"abstract":"<div><div>The negative effect of sea ice shrinkage on the world's climate has been widely studied. The shrinkage of sea ice affects the heat transfer balance between the ocean and atmosphere, especially in high-latitude regions. Given the influence of global warming, the number of sea ice areas has considerably decreased over the previous decades. Hence, precise, dependable measurement of sea ice concentration is necessary to ensure the security and effectiveness of marine operations in polar areas. Monitoring of sea ice concentration through remote sensing technology is critical in predicting sea ice-related hazards, facilitating safe ship navigation and enabling polar environmental research. Furthermore, a few imaging technologies, such as synthetic aperture radar (SAR), have become the predominant tools utilised for regional ice observation because of their capability to deliver high-resolution images all year round in nearly all locations so that ice charts can be mapped effectively by national ice organisations. This study examines the utilisation of artificial intelligence (AI), particularly deep learning, in monitoring sea ice concentration through high-resolution imaging datasets obtained from several resources. These datasets include comprehensive spatial information, which is essential for accurate sea ice mapping and categorisation. Some of them focus on several main obstacles, including speckle noise in SAR images, data sparsity and amalgamation of multi-modal datasets whilst suggesting AI-based ways to mitigate these problems. This study concentrates on high-resolution imaging datasets, but it also recognises the potential use of AI in other remote sensing modalities, including scatterometry, altimetry and passive microwave sensors. This review explicitly examines research articles published from 2019 to the present, particularly those that emphasise deep learning methods. It also investigates readily available sea ice datasets, such as SAR-, optical- and drone-based datasets. Subsequently, relevant sea ice mapping techniques, including conventional algorithms, machine learning-based approaches and deep learning-based methods, are assessed. This work focuses on three main applications, namely, weather prediction, marine security and environmental preservation, and their future opportunities. Comprehensive discussions of the aforementioned topics, including the main findings and patterns, are provided to stimulate future research.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103132"},"PeriodicalIF":5.8,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791055","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":"DiffusionAAE: Enhancing hyperspectral image classification with conditional diffusion model and Adversarial Autoencoder","authors":"Zeyu Cao ,&nbsp;Jinhui Li ,&nbsp;Xiangrui Xu","doi":"10.1016/j.ecoinf.2025.103118","DOIUrl":"10.1016/j.ecoinf.2025.103118","url":null,"abstract":"<div><div>Hyperspectral image (HSI) classification is essential for ecological monitoring, but faces significant challenges due to high dimensionality, complex spectral–spatial relationships, and limited labeled data. This study introduces DiffusionAAE, a novel framework that uniquely combines Adversarial Autoencoders (AAE) with conditional diffusion models to address these challenges. Unlike existing approaches, DiffusionAAE incorporates spectral similarity constraints and class label guidance into the diffusion process, ensuring the generation of physically realistic synthetic samples. Our framework’s key innovation lies in its two-stage architecture: first, an AAE extracts robust latent features capturing intricate spectral–spatial relationships; second, a conditional diffusion model refines these features through progressive denoising, enabling class-specific feature generation while maintaining physical constraints inherent to hyperspectral data. Comprehensive experiments on three benchmark datasets demonstrate DiffusionAAE’s superior performance: compared to state-of-the-art methods, our approach achieves significant improvements with an overall accuracy (OA) of 96.77% on Indian Pines (3.21% higher than CNN-based methods), 99.56% on University of Pavia (1.24% improvement over Transformer-based approaches), and 99.62% on Salinas (0.98% better than the best competing method). Notably, DiffusionAAE shows remarkable performance on minority classes, with an average 7.35% accuracy improvement across underrepresented classes in the Indian Pines dataset. The framework demonstrates particular strength in scenarios with limited training data, maintaining 95.3% accuracy even when using only 5% of available labeled samples. These results establish DiffusionAAE as a significant advancement for ecological informatics applications, especially for biodiversity monitoring and land cover classification where labeled data scarcity and class imbalance are prevalent challenges.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103118"},"PeriodicalIF":5.8,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777256","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":"Validation of sentinel 2 based machine learning models for Czech National Forest Inventory","authors":"Richard Kovárník,&nbsp;Jitka Janová","doi":"10.1016/j.ecoinf.2025.103133","DOIUrl":"10.1016/j.ecoinf.2025.103133","url":null,"abstract":"<div><div>The National Forest Inventory (NFI) of the Czech Republic provides essential data for forest management but requires significant time and resources. This study highlights the critical role of validating Sentinel-2-based machine learning models against real NFI data to ensure their reliability for forest monitoring. While satellite-based models offer a cost-effective alternative, their practical applicability depends on rigorous validation. We applied four commonly used machine learning models—Classification and Regression Trees, Random Forest, Support Vector Machine, and Naive Bayes—to Sentinel-2 imagery to estimate forest cover conditions. The Random Forest model achieved the highest overall accuracy (98.3 %). By systematically comparing model predictions with official NFI data, we address a key gap in remote sensing applications: the need for real-world validation beyond training datasets. Our findings demonstrate that properly validated Sentinel-2-based models can enhance large-scale forest monitoring, reducing the financial and labor burdens of traditional field surveys while ensuring data accuracy for sustainable forest management.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103133"},"PeriodicalIF":5.8,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783483","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":"Efficient tuna detection and counting with improved YOLOv8 and ByteTrack in pelagic fisheries","authors":"Yuanchen Cheng,&nbsp;Zichen Zhang,&nbsp;Yuqing Liu,&nbsp;Jie Li,&nbsp;Zhou Fu","doi":"10.1016/j.ecoinf.2025.103116","DOIUrl":"10.1016/j.ecoinf.2025.103116","url":null,"abstract":"<div><div>Accurate estimation of tuna catch is crucial for effective pelagic fishery management and resource conservation. However, existing manual counting methods suffer from issues such as low accuracy and poor timeliness, highlighting the urgent need for an efficient and automated solution. This paper proposes an automatic tuna counting method based on the YOLOv8n-DMTNet target detection algorithm combined with the improved ByteTrack tracking algorithm. The method uses YOLOv8n as the base model, enhanced with detail-enhanced convolution and a multi-scale feature fusion pyramid network, which significantly improves detection accuracy in complex marine environments. Additionally, a dynamic, task-aligned detection head is introduced to optimize the synergy between classification and localization tasks. To further improve counting accuracy, the ByteTrack algorithm is employed for target tracking, and a region-specific counting method is designed to prevent double counting and omission due to occlusion and motion irregularities. Experimental results show that the improved YOLOv8n-DMTNet model achieves a 9.2% increase in [email protected] and a 6.4% increase in [email protected]:0.95 compared to YOLOv8n in the tuna detection task, while reducing the number of parameters by 42.3% and computational complexity by 33.3%. The counting accuracy reaches 93.5%, and the method demonstrates superior performance in terms of accuracy, robustness, and computational resource efficiency, making it well-suited for resource-constrained fishing vessel environments. This approach provides reliable technical support for automated catch counting in pelagic fisheries.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103116"},"PeriodicalIF":5.8,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786281","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":"An improved graph neural network integrating indicator attention and spatio-temporal correlation for dissolved oxygen prediction","authors":"Fei Ding ,&nbsp;Shilong Hao ,&nbsp;Mingcen Jiang ,&nbsp;Hongfei Liu ,&nbsp;Jingjie Wang ,&nbsp;Bing Hao ,&nbsp;Haobin Yuan ,&nbsp;Hanjie Mao ,&nbsp;Yang Hu ,&nbsp;Wenpan Li ,&nbsp;Xin Xie ,&nbsp;Yong Zhang","doi":"10.1016/j.ecoinf.2025.103126","DOIUrl":"10.1016/j.ecoinf.2025.103126","url":null,"abstract":"<div><div>Accurately predicting dissolved oxygen (DO) is essential for water environment protection and management. The spatiotemporal dependencies of water quality and the interactions between indicators are neglected in existing prediction models. To improve the DO prediction accuracy, a graph neural network based on indicator attention mechanism and bayesian optimization (BO-AM-MTGNN) was proposed in this study. Hourly water quality data at 20 sampling sites in the Chaohu Lake basin from January 2022 to February 2024 were used as the research dataset. The effectiveness of the BO-AM-MTGNN model was validated through comparisons with baseline models (XGBoost, LightGBM, LSTM, GRU, Informer) and ablation experiment (BO-AM-MTGNN, AM-MTGNN, MTGNN). The results demonstrated that the BO-AM-MTGNN model effectively captured the temporal and spatial information of water quality data. Correlations between indicators can be fully extracted by the indicator attention mechanism. Compared with the MTGNN model, the MAE, RMSE, and MAPE of the BO-AM-MTGNN model decreased by 12.16 %, 5.50 %, and 12.13 %, respectively. The prediction accuracy of MTGNN outperformed the baseline models, with the performance ranking as follows: MTGNN &gt; Informer &gt; LSTM &gt; GRU &gt; LightGBM &gt; XGBoost. The BO-AM-MTGNN model proposed in this study effectively improves DO prediction accuracy. In future studies, the BO-AM-MTGNN model holds potential for water quality early warning and pollution source tracking.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103126"},"PeriodicalIF":5.8,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791056","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":"Multi-species insect recognition method based on computer visions: Sustainable agricultural development","authors":"Lijuan Zhang ,&nbsp;Shanshan Sun ,&nbsp;Hui Zhao ,&nbsp;Zhiyi Li ,&nbsp;Dongming Li","doi":"10.1016/j.ecoinf.2025.103125","DOIUrl":"10.1016/j.ecoinf.2025.103125","url":null,"abstract":"","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"88 ","pages":"Article 103125"},"PeriodicalIF":5.8,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792389","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":"Innovative soil classification approach for achieving global biodiversity framework utilizing integrated data fusion of EMIT and multispectral satellite observations: Case study of Imam Turki bin Abdullah Royal Reserve, Kingdom of Saudi Arabia","authors":"Hesham Morgan ,&nbsp;Ali Elgendy ,&nbsp;Surendra Maharjan ,&nbsp;Wenzhao Li ,&nbsp;Tamer Ismail ,&nbsp;Yehya Kh. Shehadeh ,&nbsp;Ahmed ElGharib ,&nbsp;Ahmed Abdullah Al-Dughairi ,&nbsp;Ali El Mubarak ,&nbsp;Khaled Allam Harhash ,&nbsp;Hesham El-Askary","doi":"10.1016/j.ecoinf.2025.103123","DOIUrl":"10.1016/j.ecoinf.2025.103123","url":null,"abstract":"<div><div>Soil classification is essential for sustainable land management, ecological conservation, and combating desertification, particularly in arid and semi-arid regions. This study integrates hyperspectral data from the Earth Surface Mineral Dust Source Investigation (EMIT) and multispectral imagery from Sentinel-2 to achieve accurate soil classification for the Imam Turki bin Abdullah Royal Reserve (ITBA) in Saudi Arabia. Using advanced Machine Learning (ML) techniques, including Extreme Gradient Boosting (XGBoost), the study highlights the power of data fusion in addressing the limitations of standalone remote sensing methods. The integration of hyperspectral and multispectral data combines the spectral richness of hyperspectral imaging with the spatial resolution of multispectral data, providing detailed insights into the region's heterogeneous soil types. The Gram-Schmidt fusion technique enhanced spatial resolution, enabling precise identification of inter-dune soils, linear dunes, and rocky outcrops. The resulting soil classification map achieved an accuracy of 93 %, outperforming traditional methods and existing maps. Inter-dune soils, characterized by their loamy-skeletal texture and superior moisture retention, were identified as critical for supporting vegetation and afforestation efforts. This research also developed a suitability map for afforestation by incorporating weighted overlays of soil fertility, moisture retention, and vegetation indices. These findings directly contribute to global biodiversity priorities, supporting the Convention on Biological Diversity (CBD) and the associated Global Biodiversity Framework (GBF) targets such as reducing biodiversity loss (Target 1), restoring ecosystems effectively (Target 2), minimizing the impacts of climate change (Target 8), and enhancing sustainable agriculture (Target 10). Furthermore, the study utilizes these advancements in addressing land degradation and achieving the United Nations Sustainable Development Goals (SDGs), including Zero Hunger (SDG 2), Climate Action (SDG 13), and Life on Land (SDG 15). By integrating soil classification with afforestation strategies through remote sensing and advanced data sciences approaches, this research demonstrates a robust, scalable and precise solution to support biodiversity conservation, land management, and climate resilience in arid environments.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103123"},"PeriodicalIF":5.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761023","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}