Course Description
Provides an exposure to the R statistical package for analyzing data and models used in natural resource management. Topics include data organization, input, and analysis; models of population and forest dynamics, fitting data to models, forecasting and optimization. Course centered on R and other freely available programs.
Additional Requirements for Graduate Students:
Graduate students will be expected to apply methods to analysis
of data and construction of models relevant to their own studies
(e.g., using field data from a thesis study, obtained from
colleagues, or derived from published archives) and to present
their results in a written report and oral presentation at the
end of the semester.
Athena Title
Natural Resource Software
Non-Traditional Format
Undergraduates have the option to take the first section of the course (introduction to R and data management/analysis) for 1 credit hour.
Undergraduate Prerequisite
BIOS 2010 or BIOS 2010E or FANR 2010-2010L or MSIT 3000 or MSIT 3000H or MSIT 3000E or BUSN 3000 or BUSN 3000E or BUSN 3000H or STAT 2000 or STAT 2000E or STAT 2100H or UNIV 1108
Graduate Prerequisite
BIOS 2010 or BIOS 2010E or FANR 2010-2010L or MSIT 3000 or MSIT 3000H or MSIT 3000E or BUSN 3000 or BUSN 3000E or BUSN 3000H or STAT 2000 or STAT 2000E or STAT 2100H or UNIV 1108
Semester Course Offered
Offered spring
Grading System
A - F (Traditional)
Course Objectives
• Facility with the R programming language • Familiarity with R statistical packages for analysis of data for natural resource management • Recognition of appropriate statistical tools for specific data problems in natural resource management • Ability to record and manipulate field data for suitable input into appropriate statistical packages • Ability to use R to construct simple models of forest growth and animal population dynamics • Familiarity with standard techniques for fitting natural resource models to field data • Ability to interpret and critically evaluate output from statistical packages and provide tabular and graphical output suitable for use in reports and scientific publications
Topical Outline
• Introduction to computing using R • Using R to manage data o Organizing field data for input into R o R dataframes o Summarizing and graphing data using R o Examples including:  Wildlife counts  Forest inventory  Fish capture-recapture • Standard statistical tests using R to analyze data (above examples) o Frequency analysis/ contingency tables o t-tests and ANOVA o Simple and multiple linear regression • Introduction to programming in R o Basic elements of programming o Built in math and stat functions o User-defined functions o Examples  Deterministic and stochastic population growth  Population viability analysis  Deterministic and stochastic forest growth  Stand dynamic models • Fitting models to data using R o Least-squares fit of linearized models o Least-squares fit of nonlinear models o Maximum likelihood and generalized linear models o Examples:  Fitting population models to wildlife counts  Fitting exploitation models to fisheries catch data  Fitting forest growth and yield models to forest inventory data • Using models to guide natural resource decisions o Forecasting using models o Basic of optimization using R  Unconstrained optimization  Constrained optimization (linear programming, classical programming) o Incorporating stochastic and dynamic effects and parameter uncertainty into models o Examples  Optimizing harvest  Avoiding extinction subject to cost constraints  Maximizing forest yield subject to uncertainty • Project presentations (graduate students)
Syllabus
Public CV