In this research, a novel approach that utilizes Moran’s I statistical analyses to examine the spatio-temporal dispersal patterns of the White-Nose Syndrome currently affecting North American bat species is undertaken to further understand the disease transmission mechanism(s) of this emerging wildlife epidemic. White-Nose Syndrome has been responsible for in excess of five million bat deaths to date and has the potential to alter the ecological landscape significantly; however, due to a variety of factors, little research has been conducted into the patterns of infection on a national scale. Global and Local Moran’s I analyses were performed on the spatial-temporal variable of month and location from the initial outbreak site in order to address the spread of the Geomyces destructans fungus that causes White-Nose Syndrome. A comprehensive dataset of outbreak confirmation sites has been compiled and statistical analysis using ArcGIS reveals a complex pattern of disease dispersion since initial discovery of the disease, and shows important policy and management implications, in particular the need for more standardized and rigorous data collection and reporting procedures.
","abstract_html":"<p>In this research, a novel approach that utilizes Moran’s I statistical analyses to examine the spatio-temporal dispersal patterns of the White-Nose Syndrome currently affecting North American bat species is undertaken to further understand the disease transmission mechanism(s) of this emerging wildlife epidemic. White-Nose Syndrome has been responsible for in excess of five million bat deaths to date and has the potential to alter the ecological landscape significantly; however, due to a variety of factors, little research has been conducted into the patterns of infection on a national scale. Global and Local Moran’s I analyses were performed on the spatial-temporal variable of month and location from the initial outbreak site in order to address the spread of the <em>Geomyces destructans</em> fungus that causes White-Nose Syndrome. A comprehensive dataset of outbreak confirmation sites has been compiled and statistical analysis using ArcGIS reveals a complex pattern of disease dispersion since initial discovery of the disease, and shows important policy and management implications, in particular the need for more standardized and rigorous data collection and reporting procedures.</p>","abstract_has_math":false,"creators":["Davis, Celia M."],"institution":null,"degree_name":"Master of Science","degree_level":null,"degree_discipline":"Department of Geography and Geology","degree_department":null,"school":null,"contributors":["Dr. Jason Polk (Director), Dr. Chris Groves, Dr. Rickard Toomey, Dr. Jun Yan"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012-08-01T07:00:00Z","date_published":"2012-08-01T07:00:00Z","updated_at":"2026-07-24T06:08:16Z","subjects":["Epidemiology","WNS","ArcGIS","bats","Environmental Health and Protection"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.wku.edu/theses/1194","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dr. Jason Polk (Director), Dr. Chris Groves, Dr. Rickard Toomey, Dr. Jun Yan"]},{"key":"dc:creator","label":"Author","values":["Davis, Celia M."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Department of Geography and Geology"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Epidemiology","WNS","ArcGIS","bats","Environmental Health and Protection"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.wku.edu/theses/1194"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In this research, a novel approach that utilizes Moran’s I statistical analyses to examine the spatio-temporal dispersal patterns of the White-Nose Syndrome currently affecting North American bat species is undertaken to further understand the disease transmission mechanism(s) of this emerging wildlife epidemic. White-Nose Syndrome has been responsible for in excess of five million bat deaths to date and has the potential to alter the ecological landscape significantly; however, due to a variety of factors, little research has been conducted into the patterns of infection on a national scale. Global and Local Moran’s I analyses were performed on the spatial-temporal variable of month and location from the initial outbreak site in order to address the spread of the Geomyces destructans fungus that causes White-Nose Syndrome. A comprehensive dataset of outbreak confirmation sites has been compiled and statistical analysis using ArcGIS reveals a complex pattern of disease dispersion since initial discovery of the disease, and shows important policy and management implications, in particular the need for more standardized and rigorous data collection and reporting procedures.
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"],"dc:identifier":["https://digitalcommons.wku.edu/theses/1194"],"dc:subject":["Epidemiology","WNS","ArcGIS","bats","Environmental Health and Protection"],"dc:title":["An Examination of White-Nose Syndrome Occurrence and Dispersal Patterns: Utilizing Global and Local Moran's I Analysis to Evaluate an Emerging Pathogen"],"dc:type":["Thesis"],"thesis:degree_discipline":["Department of Geography and Geology"],"thesis:degree_name":["Master of Science"]},"updated_at":"2026-07-24T06:08:16Z"}