[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126930-en":3,"doc-seo-126930-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},126930,2336474459895,"Aria","https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916",8,"Research & Report","Triatominae in North America - A Comparison of Statistical Machine Learning Approaches for Habitat Suitability Modelling","Modeling historical habitat suitability for Triatominae in North America uses bio-climatic and land cover data combined with observations across three statistical machine learning approaches. The comparison includes MaxEnt for single-species presence-only modeling, a Bayesian Linear Regression model for joint species distributions, and a Bayesian Neural Network adapted for both joint and single-species cases. Single-species models generalize well beyond observed predictor ranges. Performance metrics report MaxEnt AUC greater than 0.9 and test accuracy with BNN fraction correct above 0.7, and future projections under CMIP6 scenarios for 2070–2100 indicate range shifts.","The Thesis Committee for Rebecca Dorothy Langdon certifies that this is the approved version of the following thesis:  \nTriatominae in North America: A Comparison of Statistical Machine Learning Approaches for Habitat Suitability Modelling.  \nAPPROVED BY  \nSUPERVISING COMMITTEE:  \nKatherine Brown, Co-Supervisor  \nClint Dawson, Co-Supervisor  \nTriatominae in North America: A Comparison of Statistical Machine Learning Approaches for Habitat Suitability Modelling.  \nby  \nRebecca Dorothy Langdon  \nTHESIS  \nPresented to the Faculty of the Graduate School of The University of Texas at Austin in Partial Fulfillment  \nof the Requirements  \nfor the Degree of  \nMASTER OF SCIENCE IN ENGINEERING, COMPUTER SCIENCE, AND MATHEMATICS  \nTHE UNIVERSITY OF TEXAS AT AUSTIN  \nMay 2023  \nAcknowledgments  \nI would like to thank my supervisor Dr. Katherine Brown for her continued support of my work and her guidance on this project. Professor Clint Dawson and the rest of the FloDisMod team have been invaluable and their knowledge into the subject matter has guided me throughout. Special thanks to my colleague Liting Huang, without whom I would not have been able to complete this work. Finally, I would like to thank Professor Eduardo A. Rebollar Tellez from the Universidad Aut´onoma de Nuevo Le´on and Professor Teresa Feria from the University of Texas Rio Grande Valley for their guidance on species distribution modelling and the data they kindly provided.  \nTriatominae in North America: A Comparison of Statistical Machine Learning Approaches for Habitat Suitability Modelling.  \nRebecca Dorothy Langdon, M.S CSEM.  \nThe University of Texas at Austin, 2023  \nSupervisors: Katherine Brown Clint Dawson  \nBio-climatic and land cover data were used alongside observations often species of Triatominae in three statistical machine learning models to determine the historical habitat suitability. The three models selected were Maximum Entropy [MaxEnt], a single species model, a joint species Bayesian Linear Regression model [BLR], and a Bayesian Neural Network [BNN] adapted to model both joint, and single species distributions. It was found that the single species models perform well outside of the observed range of predictor variables, MaxEnt AUC > 0.9 and BNN had a fraction of correct predictionson test data f > 0.7. Future projections for 2070-2100 were produced under four Coupled Model Intercomparison Project (CMIP6) scenarios; SSP1-2.6, SSP2-4 .5, SSP3-7 .0, and SSP5-8 .5. The results project a southward expansion of Triatoma gerstaeckei and a northwards expansion of Triatoma sanguisuga.  \nTable of Contents  \nAcknowledgments 3  \nAbstract 4  \nChapter 1 . Introduction 7  \n1.1 Chagas Disease .......................... 7  \nChapter 2 . Implementation 9  \n2.1 Sources of Data .......................... 9  \n2.1.1 Historical Observation Data ................ 9  \n2.1.2 Bio-Climatic Data ..................... 11  \n2.1.2.1 Land Cover Data ................. 11  \n2.2 Model Selection .......................... 12  \n2.2.1 Maximum Entropy ..................... 12  \n2.2.2 Bayesian Linear Regression ................ 13  \n2.2.3 Bayesian Neural Network ................. 15  \nChapter 3 . Results 17  \n3.1 Historic Spatial Extent ...................... 17  \n3.1.1 Maximum Entropy ..................... 17  \n3.1.2 Bayesian Linear Regression ................ 20  \n3.1.3 Bayesian Linear Neural Network ............. 22  \n3.2 Future Projections ......................... 24  \nChapter 4 . Analysis 32  \n4.1 Discussion ............................. 32  \n4.2 Future Work ............................ 34  \nAppendices 35  \nAppendix A. Glossary of Input Variables 36  \nAppendix B. Historic Habitat S 39  \nAppendix C. Future Projections of Habitat Suitability 42  \nBibliography 52  \nVita 57  \nChapter 1  \nIntroduction  \nClimate change is already impacting vector-borne disease [1] . It is established that over the next century the planet will experience more damaging and more frequent extreme weather event","cbCaipa2wLY6M53A","https://ap.wps.com/l/cbCaipa2wLY6M53A","pdf",21840440,1,57,"English","en",105,"# Acknowledgments\n# Abstract\n# Chapter 1. Introduction\n## Chagas Disease\n# Chapter 2. Implementation\n## Sources of Data\n## Model Selection\n### Maximum Entropy\n### Bayesian Linear Regression\n### Bayesian Neural Network\n# Chapter 3. Results\n## Historic Spatial Extent\n## Future Projections\n# Chapter 4. Analysis\n## Discussion\n## Future Work\n# Appendices\n## Glossary of Input Variables\n## Historic Habitat\n## Future Projections of Habitat Suitability\n# Bibliography\n# Vita","[{\"question\":\"Which machine learning models are compared for habitat suitability of Triatominae?\",\"answer\":\"The work compares Maximum Entropy (MaxEnt), a joint-species Bayesian Linear Regression (BLR), and a Bayesian Neural Network (BNN) adapted for both joint and single-species distributions.\"},{\"question\":\"How is future habitat suitability projected, and to which time period?\",\"answer\":\"Future projections are produced for 2070–2100 under four CMIP6 scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5.\"},{\"question\":\"What range changes are projected for specific Triatominae species?\",\"answer\":\"The results project a southward expansion of Triatoma gerstaeckei and a northward expansion of Triatoma sanguisuga.\"}]","Triatominae in North America - A Comparison of Statistical Machine Learning Approaches for Habitat Suitability Modelling | PDF",1785935732,144,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"triatominae-in-north-america-a-comparison-of-statistical-machine-learning-approaches-for-habitat-suitability-modelling","",{"@graph":36,"@context":86},[37,54,69],{"@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/triatominae-in-north-america-a-comparison-of-statistical-machine-learning-approaches-for-habitat-suitability-modelling/126930/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Which machine learning models are compared for habitat suitability of Triatominae?","Question",{"text":76,"@type":77},"The work compares Maximum Entropy (MaxEnt), a joint-species Bayesian Linear Regression (BLR), and a Bayesian Neural Network (BNN) adapted for both joint and single-species distributions.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is future habitat suitability projected, and to which time period?",{"text":81,"@type":77},"Future projections are produced for 2070–2100 under four CMIP6 scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5.",{"name":83,"@type":74,"acceptedAnswer":84},"What range changes are projected for specific Triatominae species?",{"text":85,"@type":77},"The results project a southward expansion of Triatoma gerstaeckei and a northward expansion of Triatoma sanguisuga.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]