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Course Criteria
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3.00 Credits
Resources allocated to conservation and ecosystem management are limited. In this course, students learn how to prioritize resources allocated to environmental management, and are provided with the tools necessary to conduct their own landscape analyses using Marxan.
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1.00 Credits
Students teach and apply principles, theory, and skills required for aquatic ecosystem management and restoration projects. Students learn leadership and followership skills as they mentor, provide guidance, and offer feedback for students developing capstone restoration projects in WATS 5350. Repeatable for credit: No Grade Mode: Pass/Fail only
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1.00 Credits
This course will help improve student's abilities to communicate scientific ideas and results to peers and the public. The course is designed for new graduate students who have not yet prepared their theses proposals or presented their pre-project seminars. Pass/Fail only
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3.00 Credits
Linear, logistic, and general additive analysis of biological and/or environmental data hierarchically structured (nested) within individuals or locations or time. This course includes foundational principles, mixed effects statistics, case studies, and students analyze their own data with instructor guidance. Prerequisite: WILD 6500 or equivalent General statistical knowledge and profiency with R Cran statistical software
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3.00 Credits
Students will learn how to design and fit models describing population dynamics (e.g., abundance, age-structure, state-transition), and then use these models in simulations to compare the outcomes of alternative management options (e.g., changes to harvest, connectivity, habitat). Additional coursework is required for those enrolled in the graduate-level course Prerequisite(s): WILD 6580 Prerequisite Recommendation(s): Students must be familiar and comfortable with the R Statistical Computing Environment. Registration Restriction(s): Graduate standing or instructor permission Registration Restriction Special Approval: Senior undergraduates wishing to take this course must meet with and receive approval from the instructor prior to registering. Dual-listed as: WATS 7460 Repeatable for credit: N Grade Mode: Standard
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4.00 Credits
Detailed exploration of concepts of hydrologic processes in small, wildland watersheds. Concentrates on recent research findings concerning examining key hydrological processes. Particular attention paid to study of partitioning of water in the hydrologic cycle, sources for runoff generation, snow and snowmelt, and erosion. Features process modeling and parameter estimation techniques as related to wildland systems. Additional oral and written assignments required for graduate students. Cross-listed as: WATS 4490
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1.00 Credits
This course will provide theory and applications for analyzing streamflow data, focusing on the scale of small wildland watershed. We will use conceptual and mathematical models to understand hydrologic processes and quantitatively predict stocks and fluxes of water.
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3.00 Credits
This course covers ecosystem analysis of physical, chemical, and biological interactions in lakes and streams. It includes the application of these concepts for managing aquatic systems. Graduate students write an additional research paper and present a lecture. Additional coursework is required for those enrolled in the graduate-level course. Prerequisite(s): CHEM 1210, CHEM 1215, CHEM 1220 and CHEM 1225, and one of the following: WATS 2220 or WATS 3700 Repeatable for credit: No Grade Mode: Standard
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3.00 Credits
This course covers sources, transport and transformation processes, impacts, prevention, and mitigation of the major categories of water pollution. It includes policy and management approaches required by the Clean Water Act and state law. Additional work is required for students registered in the graduate-level course. Dual-listed as: WATS 4530 Repeatable for credit: No Grade Mode: Standard
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1.00 Credits
This course provides instruction on the underpinnings of the R computing and statistical environment, as well as how to manage and manipulate data in the R environment. Additional coursework is required for those enrolled in the graduate-level course. Dual-listed as: WATS 4580 Repeatable for credit: No Grade Mode: Standard
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