Publisher
University of Tennessee at Chattanooga
Place of Publication
Chattanooga (Tenn.)
Abstract
Biodiversity hotspots are geographic regions that contain exceptionally high levels of species richness, often under significant threat from human activities. These areas are critical for conservation because they support a large proportion of the world’s biological diversity within relatively limited land areas. Studying biodiversity hotspots is important for understanding patterns of species distribution, identifying priority areas for conservation, and guiding sustainable resource management. This study will use publicly available ecological data and data visualization techniques to quantify patterns of species distribution across selected geographic regions. By applying statistical measures and spatial visualization tools, the analysis will identify areas of high biodiversity concentration. The findings will provide a data-driven framework for understanding biodiversity patterns and supporting evidence-based conservation planning.
Document Type
posters
Language
English
Rights
http://rightsstatements.org/vocab/InC/1.0/
License
http://creativecommons.org/licenses/by/4.0/
Recommended Citation
Geick, Maya P., "Statistical Identification of Fungal Biodiversity Hotspots Using Public Ecological Data". ReSEARCH Dialogues Conference proceedings. https://scholar.utc.edu/research-dialogues/2026/posters/1.
Statistical Identification of Fungal Biodiversity Hotspots Using Public Ecological Data
Biodiversity hotspots are geographic regions that contain exceptionally high levels of species richness, often under significant threat from human activities. These areas are critical for conservation because they support a large proportion of the world’s biological diversity within relatively limited land areas. Studying biodiversity hotspots is important for understanding patterns of species distribution, identifying priority areas for conservation, and guiding sustainable resource management. This study will use publicly available ecological data and data visualization techniques to quantify patterns of species distribution across selected geographic regions. By applying statistical measures and spatial visualization tools, the analysis will identify areas of high biodiversity concentration. The findings will provide a data-driven framework for understanding biodiversity patterns and supporting evidence-based conservation planning.