MEES Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/19655
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Item IMPACTS OF WEIGHTING SCHEMES AND TRANSFORMED ENVIRONMENTAL VARIABLES ON BIODIVERSITY MODELING WITH PRESENCE-ONLY DATA(2017) Pradhan, Kavya; Fitzpatrick, Matthew C; Marine-Estuarine-Environmental Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Biodiversity modeling techniques at the community- and species-level can be used to address questions in ecology, management, and conservation. I addressed aspects of community-level and specie-level models using virtual and inventoried species in North and South America. Firstly, I assessed the effectiveness of two weighting schemes in reducing impacts (if any) of five sampling routines (simulating unrepresentative sampling in presence-only data) on the model performance of Generalized dissimilarity model (GDM). Unrepresentative sampling lowers model performance, but weighting species can reduce this negative impact to a certain extent. However, PO data severely impacts GDM’s ability to detect the relative contribution of environmental gradients. Secondly, I examined the potential of (GDM) transformed environmental variables in improving the performance of Maxent models (presence-only) along with the influence of range size, sample size, and species dependence type. Transformed environmental variables improved model performance, especially when used with small-ranged species and/or low sample sizes.Item Field-measured versus derived: What are the most effective predictor variables in stream biodiversity models?(2014) Johnston, Miriam Rebecca; Fitzpatrick, Matthew C.; Marine-Estuarine-Environmental Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Statistical 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.