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dc.contributor.advisorFitzpatrick, Matthew C.en_US
dc.contributor.authorJohnston, Miriam Rebeccaen_US
dc.date.accessioned2014-10-11T06:00:27Z
dc.date.available2014-10-11T06:00:27Z
dc.date.issued2014en_US
dc.identifierhttps://doi.org/10.13016/M2T605
dc.identifier.urihttp://hdl.handle.net/1903/15835
dc.description.abstractStatistical models used to predict and map patterns of biodiversity require environmental variables with full coverage across an area of interest. By necessity, these variables are derived from GIS, remote sensing, or via interpolation, and may not be as physiologically relevant to biota or as representative of on-the-ground conditions as field-measured variables. This research used generalized dissimilarity modeling and occurrence data for freshwater fish and benthic invertebrates in Maryland to examine differences in explanatory power, predictive ability, and management inference yielded by derived and field-measured variables. Across the state and for both taxa, models fit with field-measured variables were superior in explanation and prediction, and nearly always more parsimonious. However, there was little difference between the variable sets in ability to predict management-related indices. Results suggest that field-measured variables are preferred over derived variables overall, but their absence from predictive models may not have a large effect on management inference.en_US
dc.language.isoenen_US
dc.titleField-measured versus derived: What are the most effective predictor variables in stream biodiversity models?en_US
dc.typeThesisen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentMarine-Estuarine-Environmental Sciencesen_US
dc.subject.pqcontrolledBiologyen_US
dc.subject.pqcontrolledEnvironmental scienceen_US
dc.subject.pquncontrolledcommunity-level modelingen_US
dc.subject.pquncontrolledgeneralized dissimilarity modelingen_US
dc.subject.pquncontrolledMaryland Biological Stream Surveyen_US
dc.subject.pquncontrolledpredictor variablesen_US
dc.subject.pquncontrolledstream biodiversityen_US


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