This course is designed to offer graduate students and upper level undergraduates the opportunity to develop knowledge and skills in advanced statistical concepts. The course complements, and builds upon, existing ecological statistics courses offered in the Department of Environmental Conservation. The course will be structured around an identified text and will operate as weekly student-led discussion and practical sessions covering chapter topics read outside of class.
In this course, we will read and talk about a selection of the seminal books in the conservation canon. For example, Aldo Leopold's "Sand County Almanac" and the Conservation Ethic that it espouses have changed how we view and interact with nature, it is a well written book, and something we all should read. Other potential authors include E.O. Wilson and Rachel Carson, but the class participants will collectively choose subsequent readings.
Provides graduate students with a broad sampling of new and cutting-edge research related to environmental conservation to help foster critical thinking and provide a more expansive view of natural resources research. Seminars will be given by departmental faculty and faculty from other departments, both on campus and from other institutions. The seminars will be designed for both students who plan a research career and those who plan a more applied path. For the former, lecturers will include topics important for funding projects and publishing findings and for the latter, topics related to interpreting and applying results.
This course introduces concepts related to the management of urbanized landscapes, focusing on what comprises the urban forest, its function as a natural system and the value of urban forests as an environmental and social catalyst. Examination of what makes up the urban forest, how these components function and the importance of sustainable urban natural landscapes will be undertaken. This seminar course will focus on developing a comprehensive understanding of the natural, social, economic and political aspects of protecting, enhancing and maintaining urban forests in populated communities.
Intermediate statistics illustrated using examples from ecology. Topics include ANOVA, linear regression (simple and multiple), correlation, logistic regression, continency tables and noparametric methods. Techniques discussed in lectures and applied in laboratories.

This course will cover a number of programming methods and applications in GIS. Beginning in the (familiar) ArcGIS environment, this course will explore fundamentals of programming in Python while learning the Model Builder interface. By exploring basic automation methods of repetitive or complex tasks, this course will also introduce foundations of computer science and computational thinking. While gaining proficiency in Model Builder, this course will expand to other python scripting applications, both within ArcGIS and in other platforms. By exploring many applications of programming to advance GIS analysis and improve workflows, students will build a strong base of knowledge and capacity for future learning and flexibility with programming in GIS.

In this course, students will:

·       Learn fundamentals of computer programming;

·       Consider how GIS applications interface with programming functions;

·       Practice writing scripts in many forms, primarily using Python;

·       Examine and practice foundations of computational thinking; and

·       Create novel GIS programming solutions through independent skill building.

This seminar offers students an opportunity to explore environmental careers that are appropriate for students in environmental conservation, sustainability science, and related fields. We will meet weekly to work on the requested elements for the professional world after graduation, such as resumes, networking, mentoring, interview skills, and more. We will also examine top sectors and growing fields to help students in professional degree programs position themselves for their intended career.
This course will provide students with a deeper understanding of various ways in which scientists can effectively engage and communicate with the public. Topics covered include models of public engagement, science-society interaction, and practical communication skills building.
Spatial data provides an extra layer of information that provides an opportunity to gain powerful insights from our data. Analysis of spatial data also poses unique challenges and pitfalls. In this course, students will learn a range of techniques for analyzing data with spatial information, from both a theoretical standpoint as well as implementation of methods in R. Topics covered in the course include descriptive and inferential concepts in that students are likely to encounter in their research including (but not limited to) spatial autocorrelation, clustering, interpolation, and geographically weighted regression.