Ecological Informatics最新文献

{"title":"Association of IUCN-threatened Indian mangroves: A novel data-driven rule filtering approach for restoration strategy","authors":"Moumita Ghosh ,&nbsp;Sourav Mondal ,&nbsp;Rohmatul Fajriyah ,&nbsp;Kartick Chandra Mondal ,&nbsp;Anirban Roy","doi":"10.1016/j.ecoinf.2025.103164","DOIUrl":"10.1016/j.ecoinf.2025.103164","url":null,"abstract":"<div><div>Restoring biodiversity is crucial for ecological sustainability. This study introduces a novel data-driven rule-filtering framework that adopts domain knowledge of taxonomic distinctness and proposes a new metric, total taxonomic distinctness, to prioritize species selection for targeted restoration efforts. We extract and validate association rules to identify frequently co-occurring species and rank them based on total taxonomic distinctness. This structured approach ensures the selection of ecologically significant species that enhance biodiversity and ecosystem resilience. We apply this three-stage framework to Indian mangrove ecosystems, focusing on four IUCN Red List species: <em>Heritiera fomes</em>, <em>Sonneratia griffithii</em>, <em>Ceriops decandra</em>, and <em>Phoenix paludosa</em>. Our results indicate that taxonomically distinct species tend to co-occur more frequently, enhancing ecosystem resilience. Statistical validation using multiple hypothesis testing ensures the robustness of our findings. To assess the framework’s broader applicability, we extend our analysis to species presence-absence data from sacred groves in the laterite regions of eastern India. The results reinforce our previous findings, demonstrating frequent association patterns among taxonomically distinct species. This study provides actionable insights for ecological restoration, guiding species selection and co-planting strategies. The framework is adaptable across ecosystems, offering a scalable approach to biodiversity conservation.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103164"},"PeriodicalIF":5.8,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069088","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":"Bayesian networks for causal analysis in socioecological systems","authors":"Rafael Cabañas ,&nbsp;Ana D. Maldonado ,&nbsp;María Morales ,&nbsp;Pedro A. Aguilera ,&nbsp;Antonio Salmerón","doi":"10.1016/j.ecoinf.2025.103173","DOIUrl":"10.1016/j.ecoinf.2025.103173","url":null,"abstract":"<div><div>Analyzing the influence of socioeconomy on land use is an important task, as socioeconomic factors can drive changes in land use that may ultimately affect human well-being. Recognizing the key factors that induce these changes may help policymakers design more effective strategies for addressing socioeconomic alterations on land-use planning, anticipate potential challenges, and mitigate negative impacts on both the environment and society. While probabilistic graphical models have been employed for this purpose in the past, this paper proposes the application of counterfactual reasoning to enhance the analysis by quantifying the degrees of necessity and sufficiency of various socioeconomic factors influencing land uses and population growth. Specifically, we present a case study using non-experimental data from southern Spain. For this, we propose the use of structural causal models, which are kind probabilistic models for causal analysis that simplify this kind of reasoning due to their graphical representation. They can be regarded as extensions of the so-called Bayesian networks, a well known modeling tool commonly used in environmental and ecological problems. This proposed approach is particularly effective for the identification of social and ecological variables that can be used in environmental monitoring and planning, offering key advantages including enhanced interpretability, and ease of adoption by environmental researchers. Our study reveals that immigration is both necessary and sufficient for population growth. In addition, built-up areas and herbaceous crops are favored by non-mountainous terrain and by high population density, whereas natural areas and mixed crops are supported by mountainous terrain and by low population density.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103173"},"PeriodicalIF":5.8,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144083781","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":"Strategic landfill site selection for sustainable waste management in Phu Yen Province, Vietnam using geospatial technologies","authors":"Diem-My Thi Nguyen ,&nbsp;Dorian Tosi Robinson ,&nbsp;Christian Zurbrügg ,&nbsp;Thi Hanh Tien Nguyen ,&nbsp;Huu-Lieu Dang ,&nbsp;Van-Manh Pham","doi":"10.1016/j.ecoinf.2025.103198","DOIUrl":"10.1016/j.ecoinf.2025.103198","url":null,"abstract":"<div><div>Solid waste management is a growing global challenge, especially in developing countries such as Vietnam, where rapid urbanisation and inadequate infrastructure intensify environmental and public health risks. Landfilling is one of the most environmentally harmful waste disposal methods. However, it remains widely used in many countries because of its cost-effectiveness. Proper disposal of solid waste is a significant priority for reducing environmental pollution. Selecting suitable landfill sites requires consideration not only of physical and environmental aspects but also of economic and social factors. In Phu Yen Province, located in south central Vietnam, solid waste management faces growing challenges in solid waste management. Limited landfill infrastructure and poor operational standards are already impacting public health and the environment. Moreover, with existing landfills approaching the end of their usable lifespans, identifying new, appropriate sites has become an urgent priority. This study introduces a novel approach that integrates a geographic information system (GIS)-based multi-criteria decision analysis (MCDA) with a fuzzy analytic hierarchy process (Fuzzy AHP) to enhance landfill site suitability assessments. This study's approach enables a holistic evaluation of economic, environmental, topographical, and social factors, thereby ensuring a more comprehensive decision-making process. The findings reveal that 45 % of the study area is very highly or highly potential for landfill sites, 28 % is of medium potential, 27 % is of low or very low potential, and 25.7 % of the existing landfill locations pose significant environmental and human health risks. A spatial distribution map obtained from a comprehensive analysis incorporating economic, social, environmental, and topographical factors helped identify potential future sites for solid waste disposal in Phu Yen Province. The methodology demonstrated in this study is highly transferable and can be applied to other low- and middle-income countries that face similar waste management challenges.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103198"},"PeriodicalIF":5.8,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068987","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":"Integrating UAV and Landsat data: A two-scale approach to topsoil moisture mapping in coastal wetlands","authors":"Ricardo Martínez Prentice ,&nbsp;Miguel Villoslada ,&nbsp;Raymond D. Ward ,&nbsp;Kalev Sepp","doi":"10.1016/j.ecoinf.2025.103197","DOIUrl":"10.1016/j.ecoinf.2025.103197","url":null,"abstract":"<div><div>Surface soil moisture (SSM) is a key variable influencing ecosystem dynamics, particularly in wetland systems, highlighting its importance for management. This study integrates UAV-derived high-resolution SSM maps with Landsat-based predictions to enable multiscale SSM monitoring in wetland ecosystems. UAV multispectral and thermal imagery were used to estimate the Temperature Vegetation Dryness Index (TVDI), which was calibrated with in-situ measurements of volumetric water content percentage (VWC%) to produce fine-scale SSM maps. These maps were aggregated to train and test XGBoost models using Landsat-derived predictors.</div><div>While UAV data captured fine-scale SSM variability, Landsat-based predictions provided consistency at lower spatial scales (30 m of spatial resolution from Collection-2 Level-2), with RMSE values below 10 %. Among all surveyed periods, August yielded the most reliable results. During this month—the warmest and most hydrologically dynamic—TVDI and Land Surface Temperature (LST) emerged as the strongest predictors. This also demonstrates that XGBoost model to better represent the full range of moisture conditions.</div><div>This framework addresses challenges like cloud cover in high-latitude regions and offers scalable solutions for SSM monitoring. Results contribute to the understanding of essential climate variables and support the restoration and management of coastal meadows. By bridging UAV and satellite observations, this approach provides a reliable and scalable tool for SSM assessment across diverse ecosystems. Future efforts should prioritize surveys during ecologically responsive periods, such as August, and explore the methodology's applicability in other wetland systems and long-term monitoring schemes.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103197"},"PeriodicalIF":5.8,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143941296","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":"BattyCoda: A novel open-source software for bat call annotation and classification","authors":"Gabriela C. Nunez-Mir ,&nbsp;Kevin M. Boergens ,&nbsp;Jessica C. Montoya ,&nbsp;Hannah ter Hofstede ,&nbsp;Angeles Salles","doi":"10.1016/j.ecoinf.2025.103195","DOIUrl":"10.1016/j.ecoinf.2025.103195","url":null,"abstract":"<div><div>The field of acoustic communication needs tools that facilitate the annotation and labeling of animal calls. Bat acoustic libraries gathered over the past few decades have primarily focused on compiling echolocation calls, which have been leveraged to develop machine learning algorithms capable of classifying bat species. However, because these classification methods require large training datasets, they have not yet been generalized to classify types of bat communication calls. Communication call repertoires in bats are wide, and distinct syllables occur with varying frequency, with some call types being recorded only rarely. Furthermore, collecting communication calls poses greater technical challenges, making these calls more difficult to capture reliably. Here, we present BattyCoda, an open-access, customizable tool to categorize and label bat communication call types within the repertoire of a species using small training datasets (tens to hundreds of labeled calls). In this work, we compiled an initial training dataset of 11 types of big brown bat (<em>Eptesicus fuscus</em>) calls, tested the performance of various candidate classifiers, and assessed the final classifier's training sample size sensitivity. We found that the best performing classifier achieved a balanced accuracy of ∼50 %, with common call types achieving classification accuracies over 70 %. Our tool can greatly facilitate annotating bat calls in recordings by providing accurate labels for common call types, while also assisting researchers in categorizing rarer communication calls. BattyCoda has the potential to build research capacity in the field of acoustic communication by expanding the availability of libraries including a wider range of bat calls and species, thereby enabling the exploration of new hypotheses.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103195"},"PeriodicalIF":5.8,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143934854","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":"Forest aboveground biomass retrieval integrating ICESat-2, Landsat-8, and environmental factors","authors":"Sunjie Ma ,&nbsp;Jisheng Xia ,&nbsp;Chun Wang ,&nbsp;Zhifang Zhao ,&nbsp;Fuyan Zou ,&nbsp;Maolin Zhang ,&nbsp;Guize Luan ,&nbsp;Ci Li ,&nbsp;Xi Tu ,&nbsp;Letian Li","doi":"10.1016/j.ecoinf.2025.103194","DOIUrl":"10.1016/j.ecoinf.2025.103194","url":null,"abstract":"<div><div>The synergistic integration of optical imSagery and LiDAR data provides a comprehensive spatial framework for the precise estimation of aboveground biomass (AGB). However, the technical pathway for AGB estimation in complex mountainous regions using multi-source heterogeneous data, including active and passive remote sensing and environmental data, requires further validation. This study proposes a novel framework for high-resolution AGB retrieval by integrating ICESat-2 LiDAR and Landsat-8 data, along with meteorological and topographic factors. AGB estimates were derived from ICESat-2 footprints using second-class forest survey data from the Jinsha River Basin, China. Relationships between canopy metrics and AGB were analyzed across beam types using LASSO and random forest (RF) models. The optimized RF model was then used to generate wall-to-wall AGB maps incorporating Landsat-8, meteorological, and topographic variables. The Nighttime-Strong beam achieved the highest AGB retrieval accuracy (R² = 0.71), followed by the Nighttime-Weak beam (R² = 0.69), all beams combined (R² = 0.68), the Daytime-Strong beam (R² = 0.68), and the Daytime-Weak beam (R² = 0.55); the LASSO model outperformed the RF model. In the AGB retrieval model using canopy metrics, mean canopy height, relative canopy height, canopy coverage, and canopy quadratic mean were strong predictors (correlation coefficients of 0.67, 0.65, 0.63, and 0.62, respectively). Adding meteorological and topographic data substantially improved wall-to-wall AGB mapping, with topography having a greater impact than meteorology. In conclusion, AGB retrieval accuracy can be markedly improved by using ICESat-2 Nighttime-Strong beams combined with meteorological and topographic datasets. This study proposes a more precise and effective methodology for forest monitoring in complex environments.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103194"},"PeriodicalIF":5.8,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143932086","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":"Exploiting facial side similarities to improve AI-driven sea turtle photo-identification systems","authors":"Lukáš Adam ,&nbsp;Kostas Papafitsoros ,&nbsp;Claire Jean ,&nbsp;ALan F. Rees ,&nbsp;Vojtěch Čermák","doi":"10.1016/j.ecoinf.2025.103158","DOIUrl":"10.1016/j.ecoinf.2025.103158","url":null,"abstract":"<div><div>Animal photo-identification (photo-ID), the process of identifying individual animals from images, has proven to be a valuable tool for various studies on sea turtles, increasing the knowledge of their ecology and informing conservation efforts. Photo-ID in sea turtles is predominantly based on the geometric patterns of the scales of their two head sides, which are unique to every individual and different from side to side. As such, both manual and automated photo-ID techniques are traditionally performed under a side-specific setting. There, an image showing a single profile of an unknown individual is compared only to images showing the same side of previously identified individuals. In this paper, we show for the first time an inherent visual similarity between left and right facial profiles of the same individuals in three sea turtle species. We do so by employing two state-of-the-art automated neural network-based photo-ID methods, one local feature-based and one deep embedding-based, designed to rank profiles based on their similarities. Both methods rank the similarity of the left and right profiles of the same individual higher than those of different individuals. These similarities are detectable even when images are taken years apart under diverse conditions. We further show that the exploitation of this similarity results in improved accuracies when compared to the traditional side-specific photo-ID setting. Our results indicate two concrete guidelines for improving automated sea turtle photo-ID workflows. When trying to match a photo of a given profile, searches should not be restricted only to photos of the same profile. As the first method of choice, a deep embedding model finely-trained using a photo-database of the focal sea turtle population should be used. In the absence of such training database, a neural network-based local feature method is preferable, but in that case searches should be performed with both the original query image and its horizontally flipped version.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103158"},"PeriodicalIF":5.8,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935028","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":"Wild ActionFormer: Enhancing wildlife action recognition for 11 endangered species in Wolong","authors":"Dan Liu ,&nbsp;Qianqian Sun ,&nbsp;Jin Hou ,&nbsp;Bochuan Zheng ,&nbsp;Jindong Zhang ,&nbsp;Desheng Li ,&nbsp;Tomas Norton ,&nbsp;Jifeng Ning","doi":"10.1016/j.ecoinf.2025.103148","DOIUrl":"10.1016/j.ecoinf.2025.103148","url":null,"abstract":"<div><div>The video of wild animals captured by trap cameras provides conservationists with intuitive information on animal action, holding significant potential in ethology and ecology. This study focuses on 11 endangered wild animal species videos in the Wolong Nature Reserve and develops a video self-supervised learning-based animal action recognition network—Wild ActionFormer, to achieve automated analysis of wild animal action classes. We utilize UniformerV2 as the base backbone network, integrating self-supervised learning methods to enhance feature extraction capabilities. We constructed a differential dispersion regularization loss function to maintain the alignment of self-supervised learning features and improve the network’s robustness against interference. The introduction of the Focal Loss reweighting strategy optimizes the loss for long-tail classes, mitigating the bias towards head data. Experimental results on our released LoTE-Animal open-source dataset show that the proposed action recognition network achieves a Top-1 accuracy of 95.09%, approximately 4 percentage points higher than the baseline. The LoTE-Animal dataset comprises 10k videos, including endangered wild animals from the Wolong Nature Reserve in Sichuan, China, such as the giant panda, sambar, and Sichuan snub-nosed monkey.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103148"},"PeriodicalIF":5.8,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143932088","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":"Bias in transect counts of forest birds: An agent-based simulation model and an empirical assessment","authors":"Asko Lõhmus,&nbsp;Ants Kaasik","doi":"10.1016/j.ecoinf.2025.103181","DOIUrl":"10.1016/j.ecoinf.2025.103181","url":null,"abstract":"<div><div>Transect counts are often used to estimate broad-scale densities of conspicuous organisms, notably birds. However, these counts are prone to numerous biases, which are difficult to disentangle in purely empirical studies due to observer-related and contextual uncertainty. To measure how different biases combine, we constructed a model that simulates observer movement across a theoretical landscape that is inhabited by birds moving within their circular territories. The model was parameterized based on data from Estonian forests where, as an additional field test, we conducted actual transect counts of bird assemblages that had been territory-mapped based on multiple visits. The simulations revealed that biases vary significantly among bird species. In dense populations, accurately locating detections can be a key issue that can produce either over- or underestimation when combined with observer speed. Counts of sparsely distributed, poorly or only seasonally detectable species appeared most challenging. Compared with these field errors, record interpretation had smaller effect on the density estimates. The test counts confirmed variable underestimation of the territory-mapped bird densities and a resulting underestimation of local species richness. We conclude that biases of single-visit transect counts cannot be easily corrected to reveal true densities of birds and should be considered as abundance indices. The capacity to detect trends in repeated counts is profoundly affected by changes in observer persona and may be sufficient in common species only. We encourage using agent-based models to analyze the behavior of researchers who collect ecological data as a tool to inform methodological standardization and researcher training.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103181"},"PeriodicalIF":5.8,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924165","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":"Adversarial domain adaptation for deforestation detection in remote sensing imagery","authors":"José Matheus Fonseca dos Santos ,&nbsp;Pedro Juan Soto Vega ,&nbsp;Guilherme Lucio Abelha Mota ,&nbsp;Gilson Alexandre Ostwald Pedro da Costa","doi":"10.1016/j.ecoinf.2025.103124","DOIUrl":"10.1016/j.ecoinf.2025.103124","url":null,"abstract":"<div><div>Semantic segmentation models aim at classifying images at the pixel level. In general terms, training such models with the traditional supervised approach requires sufficient amount of images and corresponding class label maps. While state-of-the-art deep semantic segmentation networks offer high classification performance, producing the references for supervised training often proves to be quite laborious and costly. Additionally, the accuracy delivered by those networks is directly impacted by the quality and volume of training data. Moreover, the resulting classifiers are, in general, domain specific, what means that after being trained with specific domain data, a significant performance drop is expected when evaluating them on data from another domain, even when dealing with the exact same classification task. In the context of remote sensing applications, a domain is represented by images from different sites, related to different landscapes and/or captured at different dates, likely with different acquisitions conditions. Alike other remote sensing applications, deforestation detection tends to present a poor accuracy when evaluated in a cross-domain scenario. As solution to mitigate such a problem, this work investigates the use of unsupervised domain adaptation techniques combined in a novel method. Despite requiring source domain data alongside the respective class labels, the devised method needs no references for the target domain data during training. Our solution, specialized for deforestation detection, combines two domain adaptation strategies, namely, appearance adaptation and representation matching. In the experimental analysis, we assess the performance of different variants of the proposed method, and compare their outcomes with those delivered by state-of-the-art domain adaptation methods for deforestation detection, over forest areas in the Brazilian Amazon and Cerrado biomes.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103124"},"PeriodicalIF":5.8,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924695","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}