Ecological Informatics最新文献

{"title":"Tracking global large lake surface temperature variation from space using MODIS land surface temperature product","authors":"Boyu Zhao ,&nbsp;Zhongqiu Sun ,&nbsp;Min Wang ,&nbsp;Jia Du ,&nbsp;Kaishan Song","doi":"10.1016/j.ecoinf.2025.103184","DOIUrl":"10.1016/j.ecoinf.2025.103184","url":null,"abstract":"<div><div>Water temperature monitoring plays a crucial role in the ecological functioning and biogeochemical cycling of aquatic ecosystems. Compared to conventional methods, satellite remote sensing provides a more efficient way to assess lake surface water temperature (LSWT) variations, particularly for large, remote water bodies. In this study, MODIS Land Surface Temperature (LST) product Level 3 (MOD11A2) is employed to analyze the spatiotemporal changes in LST for global inland water bodies with areas exceeding 25 km². This research aims to understand LSWT variations and identify the contributing factors. The findings indicate that during the nighttime, LSWT in different lakes ranges from −11 °C to 26 °C, while diurnal temperature differences (DTDs) range from 1.3 °C to 16.9 °C. Factors such as lake depth, surface area (or volume), altitude, geographical location, and water supply sources are shown to influence LSWT variations. This study addresses the gap in long-term LSWT research for lakes larger than 25 km² worldwide, providing valuable insights into the mechanisms driving LSWT changes in similar lake systems.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103184"},"PeriodicalIF":5.8,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143905937","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":"The spatial risk of cyclone wave damage across the Great Barrier Reef","authors":"Mandy W.M. Cheung ,&nbsp;Milani Chaloupka ,&nbsp;Peter J. Mumby ,&nbsp;David P. Callaghan","doi":"10.1016/j.ecoinf.2025.103175","DOIUrl":"10.1016/j.ecoinf.2025.103175","url":null,"abstract":"<div><div>Tropical cyclones generate destructive waves that cause large-scale yet patchy structural damage to corals through dislodgement and breakage. Such damage can impede the effectiveness of active management and interventions. Here, we used a process-based spectral wave model combined with over 1500 synthetic cyclone tracks to estimate high-resolution (20–200 m) near-bottom wave velocity on more than 3000 reefs across the Great Barrier Reef (GBR). We then applied a statistical model with likelihood inference to predict damage given cyclone strength and reef spatial arrangement, and calibrated the model using field observations from five cyclones. This enabled us to define effective model-based velocity thresholds of 2.5 m/s for nearshore reefs and 3.1 m/s for offshore reefs to predict coral damage. These thresholds exceed the mechanical strength of branching and tabular corals to withstand wave energy. Reef vulnerabilities to cyclone damage vary across the GBR shelf. Although offshore reefs are more wave-tolerant compared to nearshore reefs, the central outer-shelf reefs have a higher predicted probability of damage given a cyclone (11 %), potentially because these small and sparse reefs are less effective in dissipating wave energy. Across the GBR, we identified the top 10 % most exposed cyclone hotspots as well as the top 10 % least exposed refugia with relatively high probabilities of experiencing high and low cyclonic wave velocities, respectively. Our model provides a predictive tool and risk maps to assess reef vulnerability to cyclones, highlighting natural disturbance refugia to inform management strategies for reef resilience.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103175"},"PeriodicalIF":5.8,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924166","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 AI performance in wildlife monitoring through species and environment-specific training: A case study on desert Bighorn sheep","authors":"Owen S. Okuley ,&nbsp;Christina M. Aiello ,&nbsp;Will Glad ,&nbsp;Kyle Perkins ,&nbsp;Richard Ianniello ,&nbsp;Neal Darby ,&nbsp;Clinton W. Epps","doi":"10.1016/j.ecoinf.2025.103179","DOIUrl":"10.1016/j.ecoinf.2025.103179","url":null,"abstract":"<div><div>Motion-activated cameras are widely used to monitor wildlife, offering a non-intrusive and cost-effective means to collect high volumes of data. Artificial intelligence (AI) models can expedite image processing, but automated species classifications can be too inaccurate to meet end-users' needs. This study evaluates how selection of data for model training influences AI detection of a focal species (desert bighorn sheep; <em>Ovis canadensis nelsoni</em>) across similar and novel locations. We compared two AI models: a species-specialist (deep_sheep) and a species-generalist (CameraTrapDetectoR), identified sources of bias, and retrained the specialist model using two datasets targeted toward biases associated with classification failure. Testing on 95,547 images from 36 water sources (5 novel) in the Mojave and Sonoran Deserts revealed the specialist model outperformed the generalist by 21.44 % in accuracy and reduced false negatives by 45.18 %. The specialist model was retrained first on site-representative data, then on both site-representative and extreme image-condition data. Retraining iterations consecutively reduced the false negative rate (36.94 % → 6.23 % → 4.67 %) and improved reliability across sites at the cost of a reciprocal increase in false positive rate (2.87 % → 15.22 % → 23.97 %) and variation. The site-representative retraining had the highest overall accuracy. Accuracy at out-of-sample sites remained comparable to the full dataset, though minor performance declines were observed. We found that specifying an AI's training to single-species classification and conditions within specific environments produced robust classification accuracy at minimal data requirements. By narrowing objectives while ensuring adequate training data variety, we achieved 89.33 % accuracy with a small fraction of the training data required by similar performing models.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103179"},"PeriodicalIF":5.8,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143911986","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":"Sampling biases across interaction types affect the robustness of ecological multilayer networks","authors":"Sandra Hervías-Parejo ,&nbsp;Anna Traveset ,&nbsp;Manuel Nogales ,&nbsp;Ruben Heleno ,&nbsp;John Llewelyn ,&nbsp;Giovanni Strona","doi":"10.1016/j.ecoinf.2025.103183","DOIUrl":"10.1016/j.ecoinf.2025.103183","url":null,"abstract":"<div><div>Ecological communities rely on complex networks of species interactions. While traditional studies often focus on single interaction types (e.g. plant-pollinator or host-pathogen), there is growing recognition of the need to consider multiple interaction types to accurately model community dynamics. Multilayer networks can be used to model multiple interaction types simultaneously, but building them poses challenges due to the different sampling techniques and expertise needed for documenting different interaction types. This can introduce biases that affect the completeness of data across layers (interaction types). The extent to which such biases affect multilayer network properties remain unclear. Here, we explored this issue using empirical interaction data collected through standardized field sampling in three archipelagos along a latitudinal gradient (the Balearic, Canary, and Galapagos islands). Based on these observations, we compiled three multilayer networks, each incorporating three types of plant-animal interactions: plant-pollinator, plant-herbivore, and plant-seed disperser. We then enhanced these networks by adding interactions from the literature. The observed and enhanced multilayer networks were compared to evaluate how the quantity and bias of missing information affected network properties. In the enhanced networks, the number of herbivore, pollinator and seed disperser interactions exceeded those from the observed networks by, on average, 82 %, 62 % and 96 %, respectively. The species present in the enhanced networks but missing in the observed networks exhibited distinct structural properties. These sampling biases affected both static and dynamic network properties, and the effects varied notably across archipelagos. Observed networks from the Balearic and Canary Islands were less robust to plant removal than their enhanced counterparts, while the opposite was true for the Galapagos. This study, the first to examine the effects of sampling bias on inferred robustness of ecological multilayer networks, reveals that missing data can have complex, hidden effects on modelled network dynamics. Missing data could, therefore, have important implications for predicting and mitigating species loss.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103183"},"PeriodicalIF":5.8,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918609","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 decision support tool for predicting water yield in North Florida forests","authors":"Katie Glodzik,&nbsp;Matthew J. Cohen","doi":"10.1016/j.ecoinf.2025.103180","DOIUrl":"10.1016/j.ecoinf.2025.103180","url":null,"abstract":"<div><div>We present a geospatial decision support tool for predicting water yield (precipitation minus evapotranspiration) impacts of forest management strategies across North Florida. Our tool assembles geographic data layers on forest structure, hydrogeology, and hydroclimate, the inputs for an established empirical model to predict stand-level water yield. Users input areas of interest into the ArcGIS interface, and the tool collates information on satellite image-derived leaf area index (LAI), water table depth, and climate aridity to make predictions of baseline (current) water yield, as well as changes in response to user-specified LAI scenarios. Across the study domain, forest LAI was 2.72 ± 0.86 (mean ± standard deviation), while water yield was 33.4 ± 13.4 cm yr⁻¹. The tool successfully predicts baseline water yield observations at the 30 original field site locations (<em>r</em> = +0.860). It is simple to use, enabling participatory exploration of the role of forest management in regional hydrologic processes.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103180"},"PeriodicalIF":5.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143934853","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":"Two-stage models improve machine learning classifiers in wildlife research: A case study in identifying false positive detections of Ruffed Grouse","authors":"Laurence A. Clarfeld ,&nbsp;Katherina D. Gieder ,&nbsp;Robert Abrams ,&nbsp;Christopher Bernier ,&nbsp;Joseph Cahill ,&nbsp;Susan Staats ,&nbsp;Scott Wixsom ,&nbsp;Therese M. Donovan","doi":"10.1016/j.ecoinf.2025.103166","DOIUrl":"10.1016/j.ecoinf.2025.103166","url":null,"abstract":"<div><div>Autonomous recording units are increasingly being used to monitor wildlife on large geographic and temporal scales, paired with machine learning (ML) to automate detection of wildlife. However, false positive detections from ML classifiers can result in erroneous ecological models that can lead to misguided management and conservation actions. We used a two-stage general approach to understand and reduce false positive detections, a technique in which outputs of the primary classification model are passed to a secondary classification model to yield the probability that a detection from the primary model is a true positive detection. This approach is demonstrated on two open-source models that detect Ruffed Grouse (<em>Bonasa umbellus</em>). We analyzed over 9500 h of acoustic data collected in 2022–2023 from the Green Mountain National Forest in Vermont, USA, and found the two models detected different types of acoustic signals associated with differing life history traits. The first model yielded 4106 detections (71.5 % true positives) while the second model yielded 524 detections (17.0 % true positives). Secondary logistic regression models separated true positives and false positives with high accuracy (84.5 % and 89.8 % respectively). Our findings go beyond improving Ruffed Grouse monitoring and conservation efforts to, more broadly, illustrate how two-stage ML approaches can improve the use of model-derived detections in wildlife research.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103166"},"PeriodicalIF":5.8,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924257","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":"Remote sensing algorithm for dissolved organic carbon in the Laptev Sea: Correction of photobleaching effect using spectral slope","authors":"Ruiwu Zhang ,&nbsp;Ruru Deng ,&nbsp;Jun Ying ,&nbsp;Cong Lei ,&nbsp;Jiayi Li ,&nbsp;Yu Guo ,&nbsp;Tongtong Zhao","doi":"10.1016/j.ecoinf.2025.103177","DOIUrl":"10.1016/j.ecoinf.2025.103177","url":null,"abstract":"<div><div>The absorption coefficient of colored dissolved organic matter (<span><math><msub><mi>α</mi><mi>CDOM</mi></msub></math></span>) is a critical optical parameter for quantifying dissolved organic carbon (DOC). However, photobleaching significantly reduces <span><math><msub><mi>α</mi><mi>CDOM</mi></msub></math></span>, leading to uncertainties in DOC concentration estimation, an issue that has not received widespread attention. Drawing on in situ measurements from the Laptev Sea, this study proposes a method to correct for photobleaching using the spectral slope (S_275–295). Setting a threshold for S_275–295 identifies areas that are significantly affected by photobleaching. To assess the applicability of this method, a stratified estimation model analyses the relationship between <span><math><msub><mi>α</mi><mi>CDOM</mi></msub></math></span> and DOC concentration before and after correction at different water depths. A remote sensing inversion algorithm for DOC was also developed based on <span><math><msub><mi>α</mi><mi>CDOM</mi></msub></math></span> and remote sensing reflectance data. Results indicate that <span><math><msub><mi>α</mi><mi>CDOM</mi></msub><mfenced><mn>443</mn></mfenced></math></span> effectively characterises DOC concentration across different water depths. After correction, the photobleaching-induced error decreases by approximately 8.04 %, significantly improving the non-linear fitting accuracy of <span><math><msub><mi>α</mi><mi>CDOM</mi></msub><mfenced><mn>443</mn></mfenced></math></span> with DOC concentration in the surface water layer (0-20 m). Results for depths greater than 20 m remain essentially unchanged, although incorporating temperature and salinity improves the linear correlation with DOC, with some uncertainties persisting. The correction method is therefore most applicable to surface waters. Remote sensing results show that this method reduces DOC overestimation in coastal areas by 12 %, improving fitting accuracy and minimising error distribution. This study highlights the impact of photobleaching on DOC estimation and introduces a correction model that enhances the accuracy of remote sensing-based DOC retrieval, thereby supporting marine carbon cycle monitoring</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103177"},"PeriodicalIF":5.8,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902479","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":"Temporal and spatial pattern analysis and forecasting of methane: Satellite image processing","authors":"Fatima Elshukri ,&nbsp;Noor Hussam Abusirriya ,&nbsp;Nathan Joseph Braganza ,&nbsp;Abdulkarim Ahmed ,&nbsp;Odi Fawwaz Alrebei","doi":"10.1016/j.ecoinf.2025.103176","DOIUrl":"10.1016/j.ecoinf.2025.103176","url":null,"abstract":"<div><div>Atmospheric dispersion modeling is a critical tool in environmental research, offering insights into spatial and temporal patterns of pollutants. This study introduces an innovative approach leveraging remote sensing technology to analyze and predict methane (CH₄) levels, specifically focusing on Qatar. Utilizing data from the Sentinel-5P satellite, captured through the Tropospheric Monitoring Instrument (TROPOMI), this research presents a detailed examination of methane concentrations. The methodology includes generating daily, monthly, and yearly average images, alongside Sobel gradient images to enhance the analysis of daily and monthly variations. A thresholding technique is applied to each month's data to identify critical methane concentration levels. Moreover, the study extends to forecasting methane levels for the latter half of 2024 and the entirety of 2025. These predictions are rigorously validated by comparing the predicted methane concentrations with observed data, resulting in a Root Mean Square Error (RMSE) that underscores the model's predictive accuracy. The R-squared (R²) value further demonstrates the model's robustness, particularly in scenarios where conventional prediction methods would be hampered by incomplete datasets. This research not only advances the understanding of methane dynamics in arid regions but also illustrates the potential of remote sensing as a cost-effective alternative to traditional data-intensive approaches. The accompanying Python code, detailed in the Appendix, is made publicly available to facilitate further research and application in similar environmental studies.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103176"},"PeriodicalIF":5.8,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143911987","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":"ECO(VI)2SE: Expert-computer vision integration for visual coral status exploration","authors":"Daniel Langenkämper ,&nbsp;Aksel Alstad Mogstad ,&nbsp;Ingunn Nilssen ,&nbsp;Tim W. Nattkemper","doi":"10.1016/j.ecoinf.2025.103162","DOIUrl":"10.1016/j.ecoinf.2025.103162","url":null,"abstract":"<div><div>Cold-water coral reefs and associated habitats are of high ecological relevance and are subject to a diverse spectrum of anthropogenic stressors. Consequently, being able to evaluate the biodiversity and health status of cold-water coral reefs is of high importance. A web application for large-scale assessment of multiple cold-water coral reefs would improve our understanding of the effects of these stressors, and provide an important knowledge base for future planning of human activities in relation to these reefs. In this work, we present a new computational approach to the bottleneck problem of analyzing 77 h of ROV video from cold-water coral reef health status assessments. By combining domain expert knowledge, state-of-the-art deep learning image segmentation and information visualization, we have developed an efficient and sustainable workflow for analyzing visual cold-water coral monitoring data on a continuous basis. The deep learning segmentation network detected and segmented <em>Desmophyllum pertusum</em>, <em>Paragorgia arborea</em>, other gorgonians and sponges from the background in the test set with an intersection over union values of (81.77%, 85.64%, 63.64%, 40.5%, 96.13%) despite fluctuations in water quality and marine snow. Comparisons with manual ROV video evaluations from field personnel showed that the results from the computational approach correlated with the expert-based assessment.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103162"},"PeriodicalIF":5.8,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143932087","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 multi-level, multi-scale comparison of LiDAR- and LANDSAT-based habitat selection models of Mexican spotted owls in a post-fire landscape","authors":"Ho Yi Wan ,&nbsp;Michael A. Lommler ,&nbsp;Samuel A. Cushman ,&nbsp;Jamie S. Sanderlin ,&nbsp;Joseph L. Ganey ,&nbsp;Andrew J. Sánchez Meador ,&nbsp;Paul Beier","doi":"10.1016/j.ecoinf.2025.103168","DOIUrl":"10.1016/j.ecoinf.2025.103168","url":null,"abstract":"<div><div>The increasing frequency and severity of wildfires pose significant challenges for habitat conservation, particularly in post-fire landscapes. This study evaluates the habitat selection of the Mexican spotted owl (<em>Strix occidentalis lucida</em>) in a post-fire environment using multi-level and multi-scale models derived from LANDSAT and LiDAR data. By focusing on 2nd order (home range selection) and 3rd order (microhabitat selection) habitat use, we assessed the predictive performance and ecological relevance of these datasets. Optimizing predictors across spatial scales revealed that large trees, high canopy cover, and mixed-conifer forests were consistently critical for habitat selection, regardless of the data source. When optimized for spatial scale, LANDSAT- and LiDAR-based models exhibited comparable predictive accuracy (AUC = 0.976 and 0.975, respectively), emphasizing the critical role of scale in model performance. Both models had low out-of-bag (OOB) error rates (0.037 for LANDSAT and 0.050 for LiDAR), indicating high classification reliability. High-severity fire burned 36.6 % of the study area, negatively impacting owl habitat at fine scales around nest and roost sites, whereas a mosaic of burned and unburned patches provided foraging opportunities. Spatial disagreement analysis revealed notable differences in predicted habitat suitability between LANDSAT and LiDAR models, particularly in areas with complex topography and forest composition. These findings underscore the complementary strengths of both datasets, with LiDAR excelling in fine-scale structural detail and LANDSAT providing broad-scale compositional insights. Integrating these technologies offers a scalable and cost-effective framework for monitoring habitat recovery and guiding conservation strategies in fire-affected landscapes.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103168"},"PeriodicalIF":5.8,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143905938","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}