Ecological Informatics最新文献

{"title":"Practical application of artificial intelligence for ecological image analysis: Trialling different levels of taxonomic classification to promote convolutional neural network performance","authors":"Amelia E.H. Bridges ,&nbsp;Eleanor Cross ,&nbsp;Kyran P. Graves ,&nbsp;Nils Piechaud ,&nbsp;Antony Raymont ,&nbsp;Kerry L. Howell","doi":"10.1016/j.ecoinf.2025.103146","DOIUrl":"10.1016/j.ecoinf.2025.103146","url":null,"abstract":"<div><div>The integration of artificial intelligence (AI), particularly convolutional neural networks (CNNs), into ecological research presents new opportunities for the automated analysis of image-based data. This study explores the practical application of CNNs for ecological image analysis by trialling annotation to different levels of taxonomic classification to determine their impact on model performance. We systematically compare various annotation strategies, evaluating their effects on the accuracy of CNNs in ecological contexts; as well as considering the feasibility of manually annotating training data to different levels. We demonstrate that variation in annotations groupings (animal, phylum or morphology) has little impact on model performance, despite large differences in class numbers. Consequently, the decision for annotators should hinge on whether to invest effort in detailed annotation at the beginning of a project or to perform finer sorting of model predictions at the end. These findings provide practical guidance for optimizing the workflow in AI-driven ecological studies, offering flexibility without compromising model performance.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"88 ","pages":"Article 103146"},"PeriodicalIF":5.8,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834474","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 robust-weighted AMMI modeling approach with generalized weighting schemes","authors":"Marcelo B. Fonsêca ,&nbsp;Vanda M. Lourenço ,&nbsp;Paulo C. Rodrigues","doi":"10.1016/j.ecoinf.2025.103110","DOIUrl":"10.1016/j.ecoinf.2025.103110","url":null,"abstract":"<div><div>The additive main effects and multiplicative interaction (AMMI) model and its variations are widely used to identify genotypes with specific adaptability and stability under environmental conditions in crop improvement breeding programs. However, atypical data points, arising from measurement errors, genotype characteristics, diseases, or climate phenomena, can significantly impact the model’s performance, by contributing to the violation of its underlying assumptions. To address this challenge, we propose a hybrid modeling framework called robust-weighted AMMI (RW-AMMI), which combines robust and weighted algorithms to effectively model genotype-by-environment interaction (GEI) in the presence of data contamination and heteroscedasticity. We also introduce a comprehensive set of nine weighting schemes for the weighted (W-AMMI), robust (R-AMMI), and RW-AMMI models. Our extensive Monte Carlo simulations, which encompass both contaminated and uncontaminated data with and without heterogeneous error variance, demonstrate that several models within the W-AMMI, R-AMMI, and RW-AMMI classes perform competitively relative to the conventional AMMI model. Furthermore, we validate the effectiveness of the proposed approach using real crop data, where we leverage ensemble strategies to enhance genotype recommendations, providing practical evidence of its applicability. This work provides a hybrid framework for genotype selection under diverse environmental conditions, offering breeders a reliable tool for improving stability and adaptability.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"88 ","pages":"Article 103110"},"PeriodicalIF":5.8,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863837","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":"Mapping ecosystem services based on citizen science for integrated coastal zone management in the Solomon Islands","authors":"Cholho Song ,&nbsp;Chul-Hee Lim ,&nbsp;Hyun-Ah Choi ,&nbsp;Whijin Kim ,&nbsp;Donguk Han ,&nbsp;Mary Tahu Paia ,&nbsp;Hyeon Kwon Ahn ,&nbsp;Sangin Kang ,&nbsp;Woo-Kyun Lee","doi":"10.1016/j.ecoinf.2025.103142","DOIUrl":"10.1016/j.ecoinf.2025.103142","url":null,"abstract":"<div><div>The Solomon Islands requested technical assistance from the Climate Technology Centre and Network (CTCN) to plan Integrated Coastal Zone Management (ICZM). Mangrove ecosystems, which connect terrestrial and ocean ecosystems in the coastal zone, are considered key management targets within ICZM in the Solomon Islands. However, limited spatial data resources for ecological planning of mangrove ecosystems pose a significant challenge to developing effective ICZM. A citizen science approach was employed to map mangrove distribution and related ecosystem services to address this limitation. This study organized a participatory workshop to collect local knowledge about mangrove distribution, which was then compared with established global mangrove distribution datasets. The local knowledge obtained through citizen science was digitized and converted into a hexagonal spatial framework to develop a decision-support map. Each hexagon was assigned a score from 1 to 3 based on the overlap between local and global data, reflecting mangrove density and the priority of ecosystem services. Out of 17,743 hexagons, 1283 were identified as areas with mangrove distribution and ecosystem services, with 363, 569, and 351 hexagons assigned scores of 1, 2, and 3, respectively. The highest-priority areas (score 3), characterized by dense mangrove presence and essential ecosystem services, were primarily located along the coasts of Malaita and Guadalcanal, suggesting their significance for ICZM planning. This study highlights the value of citizen science in supporting ICZM, particularly in data-scarce regions. It demonstrates how integrating local perspectives with global datasets can contribute to more effective and inclusive coastal management in the Solomon Islands.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"88 ","pages":"Article 103142"},"PeriodicalIF":5.8,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830405","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":"PestDet: A unified detection framework for accurate and efficient stored-grain pest detection","authors":"Jida Tian ,&nbsp;Muyi Sun ,&nbsp;Huiling Zhou ,&nbsp;Jiangtao Li","doi":"10.1016/j.ecoinf.2025.103145","DOIUrl":"10.1016/j.ecoinf.2025.103145","url":null,"abstract":"<div><div>Integrated pest management (IPM) is essential in the agriculture industry to ensure food safety and quality. Detecting stored-grain pests on the surfaces of grain piles is important in IPM to minimize postharvest storage losses. Recently, numerous deep learning-based detection methods have been proposed. However, accurate perception of morphological features of small-size pests still suffers from various challenges. To address these issues, we propose PestDet, a unified detection framework for accurate and efficient detection of stored-grain pests. Specifically, we propose an enhanced feature extraction block (EFEB) with a large effective receptive field (ERF) and integrate it into a designed backbone network, PestBak. Thus, rather than solely focusing on texture features of a target, the model can also focus on more detailed features regarding the shape and contour of small pests, compared to networks with smaller ERFs. Meanwhile, we also present a one-to-many label assignment (OMLA) strategy for accurate feature perception to effectively mitigate the imbalance between the number of positive and negative samples by assigning more positive samples in the training phase. In addition, it adeptly handles the uncertain assignments of the samples. Furthermore, a regression loss based on normalized Gaussian Wasserstein distance (NWD) is designed to improve detection accuracy and model convergence by introducing an additional penalty for the location deviation of the predicted bounding boxes. In addition, Reparameterization is integrated to accelerate the inference speed. Extensive experiments are conducted on GrainPest, a scenario-based dataset. PestDet achieves state-of-the-art performance with a mAP of 90.6 %, precision of 85.6 %, and recall of 88.0 %, indicating that it can serve as a general pipeline for pest detection aimed at monitoring stored-grain pests in granaries. Our code and data are available at (<span><span>https://github.com/IntelligentsystemlabTian/PestDet</span><svg><path></path></svg></span>).</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"88 ","pages":"Article 103145"},"PeriodicalIF":5.8,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838153","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":"Vapor pressure deficit dominates vegetation productivity during compound drought and heatwave events in China's arid and semi-arid regions: Evidence from multiple vegetation parameters","authors":"Mi Wang,&nbsp;Youcai Wang,&nbsp;Xiangping Liu,&nbsp;Wenxing Hou,&nbsp;Junjie Wang,&nbsp;Siyuan Li,&nbsp;Li Zhao,&nbsp;Zhuowei Hu","doi":"10.1016/j.ecoinf.2025.103144","DOIUrl":"10.1016/j.ecoinf.2025.103144","url":null,"abstract":"<div><div>Compound drought and heatwave (CDHWs) events, characterized by low soil moisture (SM) and high vapor pressure deficit (VPD), pose significant threats to the stability of vegetation ecosystems. However, regional-scale assessments on vegetation responses to CDHW events and their driving factors remain limited. This study integrates multiple satellite remote sensing indices and meteorological data to analyze the spatiotemporal dynamics of climate extremes and vegetation responses across China's Arid and Semi-Arid Regions (CASR) from 2001 to 2020. To address the debate over the dominant factors influencing vegetation productivity, we employed the XGBoost model to quantify the independent contributions of factors such as SM and VPD to multiple vegetation parameters. We also examined the interactions between these factors using a percentile-based heatmap approach. The results show that the three major CDHWs during the study period significantly suppressed vegetation growth, as evidenced by pronounced negative SM anomalies and positive anomalies in temperature (TEM) and VPD. We identified a potential critical VPD threshold, below which vegetation productivity declines rapidly. Moreover, the observed negative VPD-SM coupling (e.g., low SM and high VPD) is primarily driven by land-atmosphere feedbacks. Finally, Model results based on XGBoost demonstrate that VPD predominantly drives the decline in vegetation productivity during CDHWs in this region. These findings provide new insights into vegetation responses to compound climate extremes in dryland ecosystems, with implications for forecasting vegetation dynamics and informing adaptive management under future climate change scenarios.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"88 ","pages":"Article 103144"},"PeriodicalIF":5.8,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830404","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":"Impact of LiDAR pulse density on forest fuels metrics derived using LadderFuelsR","authors":"O. Viedma ,&nbsp;J.M. Moreno","doi":"10.1016/j.ecoinf.2025.103135","DOIUrl":"10.1016/j.ecoinf.2025.103135","url":null,"abstract":"<div><div>Reliable forest canopy metrics derived from LiDAR are essential for assessing landscape fire hazard and implementing effective wildfire prevention strategies. However, nationwide LiDAR datasets typically feature low-to-moderate pulse densities, which limit their accuracy in estimating such fuel properties. In this study, we evaluated how low-resolution LiDAR impacts forest vertical structure at the individual tree level by systematically thinning high-resolution LiDAR data to simulate typical pulse densities found in nationwide surveys. The study area encompassed diverse Mediterranean forests in Spain. Key fire hazard metrics, including leaf area density (LAD), leaf area index (LAI), canopy base height (CBH), fuel layer depth, and interlayer distances, were derived using the LadderFuelsR package at the tree level. Four fuel models, each linked to standard fuel model classifications, were identified and analyzed to evaluate the classification shifts across thinning levels and to quantify the rates of change in key fuel properties.</div><div>Our results showed that thinning causes a significant bias in fire hazard estimation. The CBH and the distance between the layers increased with thinning. In contrast, the fuel layer depth, height, and total and understory LAI decreased. However, fuel models respond differently to pulse thinning depending on their forest structure. Accordingly, thinning affected trees with open crowns and high understory biomass less because of the higher pulse density in the lower crown regions than in those with closed crowns and lower biomass. For example, the understory layer remained more stable in trees with open crowns and a near-ground fuel structure than in those with compact, taller crowns (≥10 pulses/m² vs. ≥100 pulses/m²). Similarly, the crown properties exhibited higher stability in open-canopy fuel types than in dense canopies. For instance, CBH and inter-layer distances stabilized at ≥25 pulses/m² for open, low crowns but required ≥50–100 pulses/m² for dense, tall canopies. Likely, canopy depth stabilized at ≥2–5 pulses/m² in open-canopy trees but required ≥25–50 pulses/m² in denser forests. Moreover, not all fuel metrics responded uniformly to pulse thinning. Height-based metrics were less affected than crown- and distance-related metrics, whereas the LAI was the most sensitive, declining steadily with lower pulse densities. Finally, we aggregated the tree-level data by median values before estimating the rates of change-masked intra-variability, particularly in highly heterogeneous fuel models. This study highlights the need for tailored LiDAR pulse-density thresholds in nationwide surveys to ensure a balance between data costs and reliability to support forest management and wildfire risk mitigation.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"88 ","pages":"Article 103135"},"PeriodicalIF":5.8,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830393","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":"Assessing vegetation phenology dynamics in West African rangelands: Implications for livestock sustainability and transhumance","authors":"Enrique Estefania-Salazar,&nbsp;Eva Iglesias","doi":"10.1016/j.ecoinf.2025.103138","DOIUrl":"10.1016/j.ecoinf.2025.103138","url":null,"abstract":"<div><div>West African rangelands are essential for millions of people; however, they are considerably understudied. A substantial gap exists in regional investigations of livestock mobility and sustainability. Climate change, agricultural frontier expansion, and increasing herds intensify pressure on grazing lands. In this study, we aimed to assess the phenological parameters, such as the start, end, and length of the growing season, over more than 2.9 million km² in 13 countries using 250-m resolution normalized difference vegetation index (NDVI) data for the 2003–2023 period. The NDVI dynamics within the growing season were studied through seasonal trend decomposition based on loess, focusing on seasonality, trends, and volatility. The results showed that the length of the growing season was diminishing primarily because of the delayed start of the season. This trend was more significant in the most productive southern areas and could have strong implications for traditional transhumance routes and sustainable carrying capacity. These findings are consistent with the existing literature and suggest that northern areas are greening whereas southern areas are browning. Finally, volatility is significantly increasing in areas with historically lower variability. This study underscores the necessity for climate adaptation strategies to mitigate the negative impacts of climate change on West African rangelands and ensure the sustainability of livestock production.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"88 ","pages":"Article 103138"},"PeriodicalIF":5.8,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821021","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":"PhenoAI: A deep learning Python framework to process close-range time-lapse PhenoCam data","authors":"Akash Kumar ,&nbsp;Siddhartha Khare ,&nbsp;Sergio Rossi","doi":"10.1016/j.ecoinf.2025.103134","DOIUrl":"10.1016/j.ecoinf.2025.103134","url":null,"abstract":"<div><div>Close-range digital repeat photography is a powerful technique for studying phenology and the seasonal dynamics of plants. However, the processing of PhenoCam images is time-consuming and requires substantial human expertise. This paper describes <em>PhenoAI</em>, a Python framework that automates the processing of time-series PhenoCam images. The package consists of four modules: (i) image quality control, (ii) vegetation segmentation using deep learning, (iii) greenness index calculation, and (iv) parameter extraction. These modules are consistent with the standard and established methodologies used in the literature. We demonstrate the application of the <em>PhenoAI</em> package in a case study by analyzing black spruce [<em>Picea mariana</em> (Mill.) B.S.P.] phenology in Quebec, Canada, over five years (2017–2021). The result revealed that the Start of Season (SOS) of Green Chromatic Coordinate (GCC) occurred in the third week of May (DOY 144 ± 5), End of Season (EOS) occurred in the end of September (DOY 269 ± 20) and day of maximum greenness occurred in the first week of July (DOY 183 ± 5). The findings correlate with the previous studies in the same region and species, confirming the ability of the <em>PhenoAI</em> to replicate field observations accurately. <em>PhenoAI</em> is an open-source software package that can be customized to suit specific research needs, reduces significantly the processing time, and simplifies the workflow, making it accessible for use by new users for close range observations taken by PhenoCam. <em>PhenoAI</em> will enhance efficiency and accuracy of data extraction for scientists using phenological data for ecological and forestry research.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"88 ","pages":"Article 103134"},"PeriodicalIF":5.8,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825872","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":"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}