This study compared the soil microbial communities of three vegetation types in the coastal plain of Georgia: 1. crop land actively in use for agricultural production, 2. transitional grassland in early stages of secondary succession, and 3. pristine unmanaged forest land. Microbial species diversity and quantities of microbial DNA were determined from each of these vegetation types at three separate locations near Statesboro, Georgia. Length heterogeneity PCR(LH-PCR) methods and subsequent analysis of fungal, bacterial, and metazoan communities by analysis of similarity (ANOSIM) revealed high within-group similarity by vegetation type, indicating land management intensity and vegetation cover is a strong determining factor in community similarity. Further analysis of fragments obtained by LH-PCR revealed that fungal and metazoan communities in crop soil included the highest number of common operational taxonomic units (OTUs) represented in all treatments, while forest soils contained the least number of common OTUs of animal and fungi. This trend is not observed in bacterial communities, and may be a function of organism size. Quantitative PCR (qPCR) detection of fungal and bacterial DNA revealed significantly higher concentrations of both fungal and bacterial DNA in forest soils than concentrations in both crop and transitional soils. Despite differences in microbial communities and DNA concentrations, these soils exhibited no significant difference in their suppression of the soil-borne pathogen Sclerotium rolfsii in the context of a greenhouse experiment. S. rolfsii inoculum was successfully detected through qPCR based methods, however, S. rolsfii DNA concentrations lack correlation with Southern Blight disease incidence. In conclusion, vegetation and land management intensity significantly affect soil microbial communities. Forest soils host a fewer number of common animal and fungal OTUs than crop soils. Bacteria had no difference in the occurrence of common OTUs between vegetation types. qPCR methods were successfully employed to detect S. rolfsii inoculum. Although S. rolfsii DNA concentrations lacked correlation with disease severity, these methods are capable of detecting the potential of soil-borne disease development.
","abstract_html":"<p>This study compared the soil microbial communities of three vegetation types in the coastal plain of Georgia: 1. crop land actively in use for agricultural production, 2. transitional grassland in early stages of secondary succession, and 3. pristine unmanaged forest land. Microbial species diversity and quantities of microbial DNA were determined from each of these vegetation types at three separate locations near Statesboro, Georgia. Length heterogeneity PCR(LH-PCR) methods and subsequent analysis of fungal, bacterial, and metazoan communities by analysis of similarity (ANOSIM) revealed high within-group similarity by vegetation type, indicating land management intensity and vegetation cover is a strong determining factor in community similarity. Further analysis of fragments obtained by LH-PCR revealed that fungal and metazoan communities in crop soil included the highest number of common operational taxonomic units (OTUs) represented in all treatments, while forest soils contained the least number of common OTUs of animal and fungi. This trend is not observed in bacterial communities, and may be a function of organism size. Quantitative PCR (qPCR) detection of fungal and bacterial DNA revealed significantly higher concentrations of both fungal and bacterial DNA in forest soils than concentrations in both crop and transitional soils. Despite differences in microbial communities and DNA concentrations, these soils exhibited no significant difference in their suppression of the soil-borne pathogen <em>Sclerotium rolfsii</em> in the context of a greenhouse experiment. <em>S. rolfsii</em> inoculum was successfully detected through qPCR based methods, however, <em>S. rolsfii</em> DNA concentrations lack correlation with Southern Blight disease incidence. In conclusion, vegetation and land management intensity significantly affect soil microbial communities. Forest soils host a fewer number of common animal and fungal OTUs than crop soils. Bacteria had no difference in the occurrence of common OTUs between vegetation types. qPCR methods were successfully employed to detect <em>S. rolfsii</em> inoculum. Although <em>S. rolfsii</em> DNA concentrations lacked correlation with disease severity, these methods are capable of detecting the potential of soil-borne disease development.</p>","abstract_has_math":false,"creators":["Sabula, Michael J"],"institution":null,"degree_name":"Master of Science in Biology (M.S.)","degree_level":"Thesis (open access)","degree_discipline":"Department of Biology","degree_department":null,"school":null,"contributors":["John Scott Harrison","Subhrajit Saha"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-01-01T08:00:00Z","date_published":"2014-01-01T08:00:00Z","updated_at":"2026-07-24T02:28:15Z","subjects":["ETD","Agriculture","Microbial communities","Sclerotium rolfsii","Quantitative PCR","Length heterogeneity PCR","Biodiversity","Environmental Microbiology and Microbial Ecology","Life Sciences","Microbiology","Molecular Genetics","Plant Pathology"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.georgiasouthern.edu/etd/1212","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["John Scott Harrison","Subhrajit Saha"]},{"key":"dc:creator","label":"Author","values":["Sabula, Michael J"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2014-11-18T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Department of Biology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis (open access)"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Biology (M.S.)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["ETD","Agriculture","Microbial communities","Sclerotium rolfsii","Quantitative PCR","Length heterogeneity PCR","Biodiversity","Environmental Microbiology and Microbial Ecology","Life Sciences","Microbiology","Molecular Genetics","Plant Pathology"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.georgiasouthern.edu/etd/1212"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This study compared the soil microbial communities of three vegetation types in the coastal plain of Georgia: 1. crop land actively in use for agricultural production, 2. transitional grassland in early stages of secondary succession, and 3. pristine unmanaged forest land. Microbial species diversity and quantities of microbial DNA were determined from each of these vegetation types at three separate locations near Statesboro, Georgia. Length heterogeneity PCR(LH-PCR) methods and subsequent analysis of fungal, bacterial, and metazoan communities by analysis of similarity (ANOSIM) revealed high within-group similarity by vegetation type, indicating land management intensity and vegetation cover is a strong determining factor in community similarity. Further analysis of fragments obtained by LH-PCR revealed that fungal and metazoan communities in crop soil included the highest number of common operational taxonomic units (OTUs) represented in all treatments, while forest soils contained the least number of common OTUs of animal and fungi. This trend is not observed in bacterial communities, and may be a function of organism size. Quantitative PCR (qPCR) detection of fungal and bacterial DNA revealed significantly higher concentrations of both fungal and bacterial DNA in forest soils than concentrations in both crop and transitional soils. Despite differences in microbial communities and DNA concentrations, these soils exhibited no significant difference in their suppression of the soil-borne pathogen Sclerotium rolfsii in the context of a greenhouse experiment. S. rolfsii inoculum was successfully detected through qPCR based methods, however, S. rolsfii DNA concentrations lack correlation with Southern Blight disease incidence. In conclusion, vegetation and land management intensity significantly affect soil microbial communities. Forest soils host a fewer number of common animal and fungal OTUs than crop soils. Bacteria had no difference in the occurrence of common OTUs between vegetation types. qPCR methods were successfully employed to detect S. rolfsii inoculum. Although S. rolfsii DNA concentrations lacked correlation with disease severity, these methods are capable of detecting the potential of soil-borne disease development.
"]},{"key":"dc:title","label":"Title","values":["Soil Microbial Community Distributions And Disease Suppressiveness In The Coastal Plain Of Georgia"]}]}],"canonical_facts":{"dc:contributor":["John Scott Harrison","Subhrajit Saha"],"dc:creator":["Sabula, Michael J"],"dc:date.available":["2014-11-18T08:00:00Z"],"dc:description.abstract":["This study compared the soil microbial communities of three vegetation types in the coastal plain of Georgia: 1. crop land actively in use for agricultural production, 2. transitional grassland in early stages of secondary succession, and 3. pristine unmanaged forest land. Microbial species diversity and quantities of microbial DNA were determined from each of these vegetation types at three separate locations near Statesboro, Georgia. Length heterogeneity PCR(LH-PCR) methods and subsequent analysis of fungal, bacterial, and metazoan communities by analysis of similarity (ANOSIM) revealed high within-group similarity by vegetation type, indicating land management intensity and vegetation cover is a strong determining factor in community similarity. Further analysis of fragments obtained by LH-PCR revealed that fungal and metazoan communities in crop soil included the highest number of common operational taxonomic units (OTUs) represented in all treatments, while forest soils contained the least number of common OTUs of animal and fungi. This trend is not observed in bacterial communities, and may be a function of organism size. Quantitative PCR (qPCR) detection of fungal and bacterial DNA revealed significantly higher concentrations of both fungal and bacterial DNA in forest soils than concentrations in both crop and transitional soils. Despite differences in microbial communities and DNA concentrations, these soils exhibited no significant difference in their suppression of the soil-borne pathogen Sclerotium rolfsii in the context of a greenhouse experiment. S. rolfsii inoculum was successfully detected through qPCR based methods, however, S. rolsfii DNA concentrations lack correlation with Southern Blight disease incidence. In conclusion, vegetation and land management intensity significantly affect soil microbial communities. Forest soils host a fewer number of common animal and fungal OTUs than crop soils. Bacteria had no difference in the occurrence of common OTUs between vegetation types. qPCR methods were successfully employed to detect S. rolfsii inoculum. Although S. rolfsii DNA concentrations lacked correlation with disease severity, these methods are capable of detecting the potential of soil-borne disease development.
"],"dc:identifier":["https://digitalcommons.georgiasouthern.edu/etd/1212"],"dc:subject":["ETD","Agriculture","Microbial communities","Sclerotium rolfsii","Quantitative PCR","Length heterogeneity PCR","Biodiversity","Environmental Microbiology and Microbial Ecology","Life Sciences","Microbiology","Molecular Genetics","Plant Pathology"],"dc:title":["Soil Microbial Community Distributions And Disease Suppressiveness In The Coastal Plain Of Georgia"],"thesis:degree_discipline":["Department of Biology"],"thesis:degree_level":["Thesis (open access)"],"thesis:degree_name":["Master of Science in Biology (M.S.)"]},"updated_at":"2026-07-24T02:28:15Z"}