Modeling ecotoxicological stressors using GIS
Graduation Date
2011
Document Type
Thesis
Program
Other
Program
Thesis (M.S.)--Humboldt State University, Natural Resources: Natural Resources Planning and Interpretation, 2011
Committee Chair Name
Steven J. Steinberg
Committee Chair Affiliation
HSU Faculty or Staff
Keywords
Humboldt State University -- Theses -- Natural Resources Planning and Interpretation, Geospatial tool development, GIS, Geospatial programming, Geographic information systems, Ecotoxicology, Ecotoxicological stressors, Geospatial analysis
Abstract
Ecotoxicology is the study of toxic chemicals, natural or synthetic, on populations and communities. Despite numerous texts and journal articles which discuss ecotoxicological principles and methods, there are relatively few studies conducted on the use of GIS technology as it applies to spatially modeling ecotoxicological systems. Modeling ecotoxicological systems using GIS allows us to account for simultaneous processes of distribution and transformation. In order to be useful for a large number of end users, a GIS tool set should be generalized, allowing users to adjust specific parameters based on the specific scenario. The objective of this study was to explore the use of GIS for ecotoxicological applications through the development of new analytical tools for future studies in ecotoxicology and environmental management and planning. The result was the CALCULATE EXPONENTIAL DECAY script tool developed using the PYTHON programming language for use in ESRI ArcMap software environment. This tool was developed while working on "Understanding the Cumulative Affects of Environmental and Psycho-social Stressors that Threaten the Pohlik-lah and Ner-er-ner Lifeway: The Yurok Tribe's Approach", an EPA Star Grant project which began in 2008.
Recommended Citation
Ramirez, Nicolas, "Modeling ecotoxicological stressors using GIS" (2011). Cal Poly Humboldt theses and projects. 2023.
https://digitalcommons.humboldt.edu/etd/2023
https://scholarworks.calstate.edu/concern/theses/9s161850v