[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122174-en":3,"doc-seo-122174-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122174,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","The Role of Machine Learning and Network Analyses in Understanding Microbial Composition in An Experimental Prairie - Thesis Abstract","Machine learning and network analyses are applied to ecological datasets to reveal connections within complex environmental communities. Using random forests, the study identifies feature importance for soil microbial community differences across four sampling years in an experimental prairie ecosystem at Morton Arboretum. Random forests are also used to compare microbial taxa between monoculture and polyculture plots, while network analyses visualize microbial interactions across years and links to plant and edaphic variables. Results show year-based separation by random forests, network hubs increasing in older samples, and network modules associated with soil organic matter. The proposed workflow can guide land managers and ecologists toward bacterial groups and prairie variables for targeted statistical analysis or manipulation.","Northern Illinois University  \nHuskie Commons  \n\n| Graduate Research Theses & Dissertations | Graduate Research & Artistry |\n| --- | --- |\n| 2023\u003Cbr>The Role of Machine Learning and Network Analyses in\u003Cbr>Understanding Microbial Composition in An Experimental Prairie\u003Cbr>Ali Eastman Oku\u003Cbr>[allysons703@gmail.com](allysons703@gmail.com)\u003Cbr>Follow this and additional works at: [https://huskiecommons.lib.niu.edu/allgraduate-thesesdissertations](https://huskiecommons.lib.niu.edu/allgraduate-thesesdissertations)[ ](https://huskiecommons.lib.niu.edu/allgraduate-thesesdissertations) Part of the Artificial Intelligence and Robotics Commons, Bioinformatics Commons, and the Ecology and Evolutionary Biology Commons |  |\n\nRecommended Citation  \nOku, Ali Eastman, \"The Role of Machine Learning and Network Analyses in Understanding Microbial Composition in An Experimental Prairie\" (2023) . Graduate Research Theses & Dissertations. 7171.  \n[https://huskiecommons.lib.niu.edu/allgraduate-thesesdissertations/7171](https://huskiecommons.lib.niu.edu/allgraduate-thesesdissertations/7171)  \nThis Dissertation/Thesis is brought to you for free and open access by the Graduate Research & Artistry at Huskie Commons. It has been accepted for inclusion in Graduate Research Theses & Dissertations by an authorized administrator of Huskie Commons. For more information, please contact [jschumacher@niu.edu](jschumacher@niu.edu).  \nABSTRACT  \nTHE ROLE OF MACHINE LEARNING AND NETWORK ANALYSES IN UNDERSTANDING MICROBIAL COMPOSITION IN AN EXPERIMENTAL PRAIRIE  \nAli Eastman Oku, MS  \nDepartment of Biological Sciences  \nNorthern Illinois University, 2023  \nDr. Wesley D. Swingley, Director  \nMachine learning and network analyses are powerful modern tools that can process and map out connections between large amounts of ecological data from complex environmental communities. Random forests, an ensemble machine learning algorithm, are particularly powerful as they can capture complex patterns in data while remaining easily interpretable. These tools are specifically useful in experimental settings where different types of data are collected. The aim of this study was to demonstrate the utility of machine learning models and network analyses at analyzing diverse ecological data from dynamic plant-soil microbial communities in a prairie ecosystem. Our experimental system is an experimental prairie maintained at Morton Arboretum located in Lisle, Illinois that provides the opportunity to understand the relationships between soil microbes, soil chemistry and prairie plants over four sampling years. Soil microbial communities shaping each individual sampling year were identified using feature importance from random forests. Similarly, random forests were also used to map out microbial taxa that differed between monoculture plots and polyculture plots. Microbial interactions across the different sampling years  \nand their interactions with plant and edaphic variables were visualized using network analyses. The results of the random forest classification were compared against the constructed microbial networks. While the random forests models were able to pick up patterns differentiating soil microbial communities across different sampling years, the models were unable to find patterns differentiating soil microbial communities in monoculture plots from those in polyculture plots. Network analysis showed that microbial networks differed across sampling years with older samples having more established network hubs. Although network analysis showed no associations between microbial networks and plant data, network modules associated with soil organic matter were found. The tools employed here, if used in early analysis, can direct land managers and ecologists to specific bacterial groups and prairie variables to target for statistical analyses or manipulation.  \nNORTHERN ILLINOIS UNIVERSITY  \nDE KALB, ILLINOIS  \nMAY 2023  \nTHE ROLE OF MACHINE LEARNING AND NETWORK ANALYSES IN  \nUN","cbCaijy8ev6xIsVc","https://ap.wps.com/l/cbCaijy8ev6xIsVc","pdf",3352491,1,92,"English","en",105,"# Abstract\n## Study system and data sources\n## Random forest modeling and comparisons\n## Network analyses and ecological interpretations\n## Practical implications","[{\"question\":\"What machine learning method is used and what does it help identify?\",\"answer\":\"Random forests are used to capture complex patterns and to derive feature importance that highlights differences in soil microbial communities across sampling years.\"},{\"question\":\"How do the microbial comparisons differ between sampling years and between monoculture vs polyculture plots?\",\"answer\":\"Random forest classification can distinguish microbial communities across different sampling years, but it cannot detect clear patterns separating monoculture plots from polyculture plots.\"},{\"question\":\"What do the network analyses reveal about microbial interactions over time?\",\"answer\":\"Network analysis shows microbial networks differ across sampling years, with older samples exhibiting more established network hubs, and modules linked to soil organic matter.\"}]","The Role of Machine Learning and Network Analyses in Understanding Microbial Composition in An Experimental Prairie - Thesis Abstract | PDF",1785809189,232,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"the-role-of-machine-learning-and-network-analyses-in-understanding-microbial-composition-in-an-experimental-prairie-thesis-abstract","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/the-role-of-machine-learning-and-network-analyses-in-understanding-microbial-composition-in-an-experimental-prairie-thesis-abstract/122174/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What machine learning method is used and what does it help identify?","Question",{"text":75,"@type":76},"Random forests are used to capture complex patterns and to derive feature importance that highlights differences in soil microbial communities across sampling years.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do the microbial comparisons differ between sampling years and between monoculture vs polyculture plots?",{"text":80,"@type":76},"Random forest classification can distinguish microbial communities across different sampling years, but it cannot detect clear patterns separating monoculture plots from polyculture plots.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the network analyses reveal about microbial interactions over time?",{"text":84,"@type":76},"Network analysis shows microbial networks differ across sampling years, with older samples exhibiting more established network hubs, and modules linked to soil organic matter.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]