Teaching
FOR 474 — Forest Measurement, Modeling, and Inventory
Mathematical and statistical foundations for the analysis of natural resource data. Statistical sampling designs, implementation, and analysis of forest inventory data. Linear and nonlinear regression techniques and their applications in allometric equations. Models for quantifying growth and yield of timber and non-timber products under different management and disturbance regimes. Application of Program R for efficient forest inventory workflows.
FOR 273 — Forest System Mapping and Mensuration II
Procedures and instruments for measuring various tree and stand characteristics. Determination of stem volume and taper. Planning and implementation of forest resource samples to provide population estimates using fixed-radius and variable-radius sampling. Detailed coverage of land measurements and mapping of boundary surveys. Use of aerial photography, topographic maps, and GPS to aid in resource assessment. Incorporation of inventory data into a GIS. Statistical and mathematical concepts applied to resource measurements. Taught off campus at Hill Forest.
FOR 491 — Statistics in Natural Resources
An introduction to statistics applied to natural resources, forestry, wildlife ecology, and environmental science. Emphasis on graphical and tabular exploration of data, experimental design, survey sampling, regression, and statistical inference. Use of computers to apply statistical methods to problems in natural resources management.
FOR/STT 875 — R Programming for Data Science
R has emerged as a preferred programming language in a wide range of data intensive disciplines. This course teaches applied and theoretical aspects of R programming for data science, covering generic programming language concepts as implemented in high-level languages such as R. Course content focuses on design and implementation of R programs to meet routine and specialized data manipulation/management and analysis objectives, along with the concepts and tools necessary for implementing reproducible research.
IBIO 831 — Statistical Methods in Ecology and Evolution II
This graduate level survey course is the second semester in a two-semester sequence, focused on the fundamental elements of data analysis in the fields of ecology and evolution. Students learn how to interpret and model biological data with modern methods for estimation and inference using the R computing language. Topics include: frequentist and Bayesian inference, linear models, generalized linear models, random effects, generalized linear mixed models, multilevel models, zero-inflation models, model comparison and evaluation, and a holistic view of quantitative tools throughout the scientific process.