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dc.contributor.authorMaurano, Stephen Paul
dc.date.accessioned2016-07-07T04:30:18Z
dc.date.available2016-07-07T04:30:18Z
dc.date.issued2015-12
dc.identifier.othermaurano_stephen_p_201512_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/maurano_stephen_p_201512_ms
dc.identifier.urihttp://hdl.handle.net/10724/35423",
dc.description.abstractNatural resource managers commonly monitor fish to assess the water quality condition of streams. I have evaluated a biological assessment metric, based on observed and expected fish species richness, for application in Georgia. A multivariate species distribution model was built using a Random Forest machine-learning algorithm to predict expected fish taxa based on a stream’s environmental characteristics, such as elevation, slope, and flow. The ratio of observed to expected taxa richness was then used to estimate whether a stream was close to, or far from, a least-disturbed condition. The model was useful in the species-rich northern half of the state above the Fall Line, but inadequate in the less speciose southern half. This research analyzed the biogeography of Georgia’s fishes, demonstrated a tradeoff when including rare species in bioassessment, exhibited the sensitivity of fluvial taxa to human disturbance, and provided an additional method for assessing stream conditions in the state.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectStream
dc.subjectFish
dc.subjectWater Quality
dc.subjectBioassessment
dc.subjectBiogeography
dc.subjectBiocriteria
dc.subjectGeorgia
dc.subjectMachine-Learning
dc.subjectRandom Forest
dc.subjectSpecies Distribution Model
dc.titleModeling stream fish biogeography to assess water quality in a species-rich region
dc.typeThesis
dc.description.degreeMS
dc.description.departmentInstitute of Ecology
dc.description.majorEcology
dc.description.advisorMary Freeman
dc.description.committeeMary Freeman
dc.description.committeeSeth Wenger
dc.description.committeeC. Rhett Jackson


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