Ecological Informatics最新文献

{"title":"Senecio inaequidens DC. will thrive in future climate: A case study in a Mediterranean biodiversity hotspot","authors":"Erika Bazzato, Giacomo Calvia, Michela Marignani, Alessandro Ruggero, Vanessa Lozano","doi":"10.1016/j.ecoinf.2024.102783","DOIUrl":"https://doi.org/10.1016/j.ecoinf.2024.102783","url":null,"abstract":"Monitoring the expansion of invasive non-native plants under current and future climatic conditions is crucial for understanding biodiversity threats, addressing the ecological impact, and developing effective management strategies. This study focuses on modelling the expansion and distribution of DC. on the island of Sardinia (Italy) to address these environmental challenges. The objectives were to identify bio-climatically suitable areas under current conditions, project potential future distribution, and evaluate invasion dynamics on the island to localize suitable areas for effective management strategies.","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194321","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":"Coupled space-time scale analysis for obtaining enhanced accuracy in species dynamics","authors":"Zhangqi Ding, Yuxin Zhang, Zhenqing Li, Huijie Qiao, Zhanfeng Liu","doi":"10.1016/j.ecoinf.2024.102776","DOIUrl":"https://doi.org/10.1016/j.ecoinf.2024.102776","url":null,"abstract":"Scale effects significantly affect the rationality and accuracy of ecological models, so temporal and spatial scales are included in the construction and application of ecological studies. However, few ecological pattern analyses focus on both of these scales simultaneously, which is especially important in terms of synchrony. In this study, based on fishery catch data and simulated data, we propose a framework for coupled space-time scale analysis and reveal possible deficiencies in conventional scale studies. We verified the feasibility and reliability of the scheme using simulated data with different combinations of temporal and spatial scales. The results showed that our scheme can simultaneously identify and localize scale features in the variation in species spatial-temporal patterns, specifically regarding the synchrony and amplitude of temporal dynamics (or spatial patterns) of multiple sites (or times). Our scheme revealed that spatial scales had an important effect on the intrinsic 13-year scale features, which significantly improved our understanding of global Rajiformes fishery dynamics. This showed that the spatial-temporal patterns and scale features obtained based on the coupled space-time scale analysis were complete and more accurate. At the same time, our scheme correctly identified complex pattern structures, such as data stratification and multiple combinations of time and space scales, which can reduce possible errors in practical applications. This scheme can be applied to the identification and prediction of the spatial-temporal patterns in biodiversity in the future and will help in formulating effective policies promoting sustainable fishery resources management and comprehensive conservation of endangered marine species.","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194322","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":"Normalized Difference Red-NIR-SWIR: A new Sentinel-2 three-band spectral index for mapping freshly-opened swiddens in the tropics","authors":"Peng Li, Wenyu Li, Dong Shi, Arun Jyoti Nath","doi":"10.1016/j.ecoinf.2024.102775","DOIUrl":"https://doi.org/10.1016/j.ecoinf.2024.102775","url":null,"abstract":"Swidden agriculture is undergoing a rapid but overlooked transition and transformation in the tropics, complicating global carbon budgeting and sustainable livelihood assessment of swiddeners. Remotely sensed algorithms for accurately detecting swiddening practices have been slowly developed to generate annual updates on their dynamics. This is primarily because, using medium spatial resolution imagery (≥30 m), it is challenging to identify the exact boundary of swidden patches. Spectral-based approaches have by far dominated the detection and mapping of swidden agriculture, but the potential of Sentinel-2 has not been examined. To reconstruct annual information of swidden agriculture, a new Sentinel-2 three-band spectral index, i.e., the Normalized Difference Red, Near-infrared (NIR), and Shortwave-infrared (SWIR), or NDRII, has been developed to map freshly-opened swiddens in tropical regions. As Red (visible), NIR, and SWIR spectral band combinations (i.e., the VNIR-SWIR spectroscopy) are sensitive to vegetation-moisture variations and thermal anomalies caused by slash and burn in tropical uplands during the dry season, NDRII delineates exact patches of freshly opened swiddens. The latest 20-m map facilitates probing into the landscape patterns of newly opened swiddens and underlines their prevalence in Laos for the first time. Established within the VNIR-SWIR spectroscopy of Sentinel-2 and Landsat-8 Operational Land Imager, the NDRII algorithm contributes to reconstructing historical datasets of tropical swiddens via integrating state-of-the-art approaches that use temporally stacked observations with available VNIR/SWIR satellite imagery and further understanding the dynamics of landscape pattern and disturbance due to rapid transition and transformation.","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194323","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":"Traits.build: A data model, workflow and R package for building harmonised ecological trait databases","authors":"Elizabeth Wenk ,&nbsp;Payal Bal ,&nbsp;David Coleman ,&nbsp;Rachael Gallagher ,&nbsp;Sophie Yang ,&nbsp;Daniel Falster","doi":"10.1016/j.ecoinf.2024.102773","DOIUrl":"10.1016/j.ecoinf.2024.102773","url":null,"abstract":"<div><p>Trait databases have proliferated over the past decades, facilitating research on the ecology, evolution, and conservation of taxa across the Tree of Life. Typically, teams of independent researchers build these databases, and each must develop their own workflow and output structure. This divests research hours from downstream tasks such as trait-based analysis and interpretation and the resultant datasets are often difficult to integrate due to disparate database structures. Here we introduce the {traits.build} R-package, which offers a generalised workflow for building trait databases. {traits.build} contains bespoke functions for propagating metadata files, extensive tutorials, and sample configuration files, allowing researchers to efficiently build a new trait database using open-source tools. In addition, the {traits.build} output structure is fully documented by a data model, ensuring the meaning of each variable and semantic relationship between variables is transparent and consistent. The data standard links to terms in previously published data standards, drawing strongly on DarwinCore and the Ecological Trait-data Standard, but also includes the ability to fully map location and context properties absent from these vocabularies. It is the first published database-building workflow that adheres to the Extensible Observation Ontology. Simultaneously developing a generalised workflow and publishing a data standard for the workflow provides {traits.build} users a straightforward pathway to build a new trait database that achieves the FAIR principles. The meaning of all variables in a {traits.build} database are already documented, allowing further integration with either other {traits.build} databases or indeed any other database with a documented data model. This follows the vision of the Open Traits Network to build trait databases whose data can be easily integrated for further analysis.</p></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574954124003157/pdfft?md5=e03eb11edbb5b2108e0764240bfb8ac5&pid=1-s2.0-S1574954124003157-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142122467","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":"Transforming crocodile traceability: Deep metric learning for identifying Siamese crocodiles","authors":"Kriengsak Treeprapin, Kantapon Kaewtip, Worapong Singchat, Nattakan Ariyaraphong, Thitipong Panthum, Prateep Duengkae, Yosapong Temsiripong, Kornsorn Srikulnath, Suchin Trirongjitmoah","doi":"10.1016/j.ecoinf.2024.102771","DOIUrl":"https://doi.org/10.1016/j.ecoinf.2024.102771","url":null,"abstract":"This study introduces a novel method for identifying individual Siamese crocodiles (), which is a crucial requirement for conservation and sustainable industry practices. Although deep metric learning (DML) has improved identification model robustness and reduced dependency on large datasets, comprehensive field studies and long-term deployments are lacking. To address this, DML combined with convolutional neural network (CNN) was applied for enhancing accuracy using a limited and imbalanced number of images per class and distinguishing dissimilar scale patterns of the head and ventral regions. Individual crocodiles were identified using the k-nearest neighbor (KNN) and support vector machine (SVM) classifiers based on the extracted features. Data were collected from 30 individuals on a crocodile farm using photographs taken over two consecutive years. Two identification types, Type 1, based on a model trained on images collected over two years; and Type 2, based on a model trained exclusively on images from the first year, were implemented. Type 1 identification, which used a CNN combined with the KNN and SVM classifiers, exhibited an accuracy exceeding 99.75 and 92.93% for the ventral and head regions, respectively. Type 2 identification exhibited a reduced accuracy because of a comparatively smaller amount of learning information; the proposed CNN achieved 83.99% accuracy for ventral identification and 67.14 and 65.61% for head identification with KNN and SVM, respectively. This study underscores the efficacy of DML and CNN for handling small, imbalanced datasets in identifying individual crocodiles, and has significant implications for traceability and conservation initiatives in the crocodile industry.","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194457","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":"Numerical simulation of layout and landscape elements on the thermal environment of urban squares","authors":"Zetian Chu, Sheng Li, Tao Li, Huijuan Qian, Chuan Liu, Zihan Yan","doi":"10.1016/j.ecoinf.2024.102770","DOIUrl":"https://doi.org/10.1016/j.ecoinf.2024.102770","url":null,"abstract":"Extreme urban heat poses significant challenges to the resilience and livability of cities. Urban squares are important open public spaces in cities, serving as main locations for residents' leisure and recreational activities and reflect the identity and cultural background of the cities. Improvement in thermal comfort in urban squares can improve the quality of the urban environment and help mitigate the urban heat island effect. This study developed 18 layout and landscape combination scenarios based on statistical analysis of data from 100 urban squares in regions with hot summers and cold winters. Further, the effectiveness of the proposed design solutions in improving the thermal environment was comprehensively analyzed. The results show that: 1) The block array layout with vegetation, water bodies, and permeable tiles is the most effective scenario, with a PET of 31.84 °C, approaching a comfortable level of heat stress; 2) Among the three layouts, the block array layout optimizes thermal comfort better than the main view entrance layout and main view center layout, and reduces physiological equivalent temperature by 10.51–10.77 °C compared with that of the other two layouts; 3) Scenarios incorporating water bodies provide better optimization effects than those with only trees, even when the total area covered by greenery remains unchanged; and 4) Permeable paving materials with high albedo more effectively improve the thermal comfort. Our results highlight a great potential of comprehensively using multiple landscape strategies to improve outdoor thermal comfort, and provides design basis for enhancing thermal comfort in similar sized urban squares in hot summer and cold winter areas.","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194324","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":"Hyperspectral characterization of vegetation in hemiboreal, boreal and Arctic peatlands using a geographically extensive field dataset","authors":"Sini-Selina Salko, Aarne Hovi, Iuliia Burdun, Jussi Juola, Miina Rautiainen","doi":"10.1016/j.ecoinf.2024.102772","DOIUrl":"https://doi.org/10.1016/j.ecoinf.2024.102772","url":null,"abstract":"Northern peatlands store up to 25% of global soil organic carbon and function as important hotspots for biodiversity. However, they are facing degradation from climate change driven by human activities as well as anthropogenic land use changes, up to the point of endangering the ecosystems' functioning and the storage of soil organic carbon entailed within them. The surface vegetation of northern peatlands is an important indicator of the ecosystem's functioning and ecohydrology, highlighting the importance of its large-scale, continuous monitoring. Approaches utilizing hyperspectral data for monitoring vegetation health and species composition can also be applied to peatland vegetation. To support the development of methods for interpreting hyperspectral satellite data from peatlands, we conducted a comprehensive in situ study of hemiboreal, boreal, sub-Arctic and Arctic peatland vegetation. We measured the reflectance spectra (350–2500 nm), soil moisture, and various vegetation-related attributes from a total of 446 vegetation plots in Estonia and Finland, from a 1500 km south-north interval. We then investigated (i) the spectral variation in surface vegetation of hemiboreal, boreal, sub-Arctic and Arctic peatlands and (ii) explored its connection to plant functional types (PFTs) and soil moisture, as well as evaluated the potential of hyperspectral data in estimating PFT cover using simple vegetation indices and partial least square (PLS) regression. Our results indicate that (i) the best spectral regions to retrieve information regarding the PFT vary greatly especially between vascular plants and bryophytes, (ii) the reflectance at an individual wavelength as well as simple vegetational index can, to some extent, predict the PFT, and that (iii) the PLS regression can predict the PFT with good accuracy. Overall, our findings demonstrate the potential of using hyperspectral data in monitoring PFTs in northern peatlands. The spectral library and the ancillary data from the peatland sites collected for this study are available as open data.","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194388","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":"Spatial-temporal distributions of phytoplankton shifting, chlorophyll-a, and their influencing factors in shallow lakes using remote sensing","authors":"Ke Rao, Xia Cao, Yafei Wang, Yuqin Zhang, Hesi Huang, Yongliang Ma, Jing Xu","doi":"10.1016/j.ecoinf.2024.102765","DOIUrl":"https://doi.org/10.1016/j.ecoinf.2024.102765","url":null,"abstract":"Understanding phytoplankton dynamics is crucial for assessing water ecosystem health. However, traditional in-situ investigations often fall short of capturing the spatial-temporal variations of phytoplankton community structure and biomass on large scales. This study introduces a novel approach that harnesses the power of machine learning models coupled with Sentinel-2 satellite data to predict phytoplankton shifting and chlorophyll-a (Chla) in three large shallow lakes in central China from 2016 to 2021. We employed an array of machine learning algorithms, including the extreme gradient boosting method (XGBoost), random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN) to retrieve Chla and phytoplankton groups clustered by hierarchical cluster analysis based on the Bray-Curtis similarity index (HCA). The influences of environmental factors, including meteorology and nutrients, on phytoplankton dynamics were then explored. Our results identified four distinct phytoplankton groups, each exhibiting unique structural characteristics according to the HCA. The RF and XGBoost, utilizing top-of-atmosphere reflectance, provided the most accurate estimations of phytoplankton shifting and Chla, respectively. The red edge-red ratio emerged as a key variable in both models, underlining the significance of red edge bands in phytoplankton monitoring. We mapped spatial-temporal patterns of phytoplankton group occurrence and average Chla concentration across the three lakes. Our findings indicated that eutrophication extended periods of cyanobacteria domination and algal blooms, particularly in bays and nearshore areas. Redundancy analysis and classification and regression tree model highlighted temperature as a significant driver of phytoplankton shifting, while nutrient levels exhibited stronger influences on Chla. Our study underscores the potential of integrating machine learning models with Sentinel-2 data to enhance the monitoring and prediction of phytoplankton dynamics in shallow lakes. This approach offers valuable insights for the early detection of algal blooms and informed management strategies, contributing to the preservation and sustainable management of aquatic ecosystems.","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194389","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":"Driving forces and prediction of urban open spaces morphology: The case of Shanghai, China using geodetector and CA-Markov model","authors":"Yaoyao Zhu, Gabriel Hoh Teck Ling","doi":"10.1016/j.ecoinf.2024.102763","DOIUrl":"https://doi.org/10.1016/j.ecoinf.2024.102763","url":null,"abstract":"Urban open spaces offer both environmental and social benefits. However, comprehensive studies that integrate both quantitative and qualitative evaluations of the factors driving change in these spaces and their long-term predictions are lacking. Most existing studies concentrate on land-use development rather than conducting empirical research specific to urban open spaces in Shanghai. This study addresses this gap by employing a geographic detector (geodetector) to analyze the influence of various driving factors on open-space changes. These factors were then used as weight values in a multicriteria CA-Markov model to simulate and predict change in Shanghai's urban open spaces by 2050. The advantage of analyzing driving forces lies in their ability to capture the multifactor synergy influencing change in urban open spaces, aligning with the aim of this study to quantitatively evaluate the interaction between natural, climatic, and socioeconomic factors. Additionally, semi-structured interviews were conducted with 10 policymakers and planners to assess the reliability of the quantitative predictions. The results indicate that socioeconomic factors are the primary drivers of change in urban open spaces. Specifically, the interaction between the normalized difference vegetation index (NDVI) and population density (PD) emerged as the most influential variables. For prediction outcomes, the unconstrained scenario predicts a decrease in open-space area from 5610.94 km in 2020 to 5124.36 km in 2050. The planning intervention scenario anticipates minimal changes in Shanghai's total urban open-space area with almost no floating changes. However, the economic development scenario predicts a rapid decline in open spaces. Experts and planners evaluated these three scenarios and confirmed the reliability and accuracy of the prediction models. The methods and findings of this study can support zoning planning for urban open-space systems in other cities and regions.","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194390","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":"Desertification in northern China from 2000 to 2020: The spatial–temporal processes and driving mechanisms","authors":"Junfang Wang, Yuanqing Wang, Duanyang Xu","doi":"10.1016/j.ecoinf.2024.102769","DOIUrl":"https://doi.org/10.1016/j.ecoinf.2024.102769","url":null,"abstract":"Desertification is one of the most significant environmental and social challenges globally. Monitoring desertification dynamics and quantitatively identifying the contributions of its driving factors are crucial for land restoration and sustainable development. This study develops a standardized methodological framework that combines desertification dynamics with driving mechanisms at the pixel level, applied to northern China from 2000 to 2020. Using multisource data and employing the Time Series Segmentation and Residual Trend analysis (TSS-RESTREND) method alongside geographical detector, we quantitatively assessed desertification reversion, expansion, and abrupt change processes, along with the impacts and interactions of natural and human factors were quantitatively assessed. Over the past two decades, the proportion of desertified land decreased by 5.60%. Notably, 32.88% of the study area experienced significant desertification reversion, while only 5.86% underwent expansion. Abrupt changes in both reversed and expanding areas were observed, primarily in the central and western regions, with these changes concentrated in the periods of 2009–2011 and 2014–2016. The impacts of various factors in different sub-regions exhibited significant spatial heterogeneity. Increased precipitation, temperature, and evapotranspiration contributed to reversion in the western area, while decreased wind speed influenced the eastern area. Additionally, decreased population density and afforestation activities also promoted desertification reversion. In contrast, decreased precipitation and increased temperature contributed to expansion in the western and eastern areas, respectively, with increased population density exacerbating this process. Overall, the interactions between natural and human factors were enhanced. Future desertification control and ecological engineering planning should focus on the coupling effects of different driving factors and relevant abrupt vegetation changes.","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194325","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}