General Ecology. Ecology is the scientific study of the abundance and distribution of organisms in relation to other organisms and environmental conditions (Ricklefs & Releya 2014). It is a diverse field, and requires knowledge of biology, chemistry, geology, geography, mathematics, and physics. General Ecology will explore fundamental theories and concepts in Ecology and investigate the relationships between basic ecological science and current environmental problems. In addition to the case studies presented in the text, my course in General Ecology incorporates case studies unique to Hawai‘i and the Pacific. The primary objectives of General Ecology are as follows: 1. Gain an appreciation and understanding for Ecology; 2. Learn factual information of population, behavioral, community, and ecosystem level Ecology; 3. Demonstrate skill in written and oral interpretation, synthesis, and presentation of Ecology; 4. Describe research studies and experiments that have improved our understanding of Ecology; and 5. Relate the theories and concepts you learn in class to current environmental problems.
Advanced Insect Taxonomy & Ecology. This graduate level course provide an in-depth investigation into the taxonomy, evolution, and ecology of major endemic Hawaiian lineages of insects and introduced insects. Students taking this course will gain an appreciation and understanding of insect diversity and learn factual knowledge about the taxonomy, evolution, and ecology of major endemic Hawaiian lineages of insects and introduced insects. Students will also learn to identify insect orders and common families present in Hawai‘i and gain an understanding of the different roles insects play across ecosystems. Finally, students will gain knowledge and experience to identify and assess threats by insects to man-made and native ecosystems.
Quantitative Ecology. This graduate level course in Quantitative Ecology will cover introductory and advanced techniques in statistics using the R software environment for statistical computing and data visualization. Specific topics include descriptive statistics, parametric and non-parametric hypothesis testing, estimation and confidence intervals, basic principles of sampling and experimental design, data management, and power analysis. Advanced techniques such as ANOVA, ANCOVA, linear regression, GLMs, and diagnostic and model selection procedures will also be introduced. The goal of this course is to train students on the use of statistical techniques common in testing hypotheses important to Ecology and Conservation. The primary objectives of Quantitative Ecology are as follows: 1. Discover best practices for data management; 2. Design, analyze, and model most types of biological datasets; 3. Interpret the output of statistical analysis; 4. Concisely summarize results of a statistical analysis in a manner appropriate for a scientific paper; 5. Critically evaluate statistical analyses presented in scientific papers; and 6. Use the opens-source R software environment to analyze biological datasets.
CONSTATS101 - A crash course for the entry-level Conservation Biologist. From 2016 to 2018 I have mentored STEM students on diverse research projects examining invasive and endangered insects in Hawaii. During my tenure, I have developed curriculum that introduces students to basic concepts in hypothesis testing, experimental design, frequentist statistics, data management, statistical test interpretation, and basic scripting using the R environment for statistical computing and graphics. The following material examines a hypothetical scenario where a young investigator examined the effect of elevation, seasonality, and food availability on rodent health. Measures of rodent health are weight (g) and age class (Juvenile, Adult, and PregAdult (Pregnant Adult). An additional rodent variable measured is sex (M, F). Independent variables include season (Spring, Summer), temperature (degrees C), elevation (m), and host plant abundance (counts). Additional data includes the collection event (when the individual was collected) and Site (where the individual was collected). Here are resources that will be useful for current and future students. Script: MouseAnalysisR.R | Data: MouseData_v1.csv | Lecture: Constats.pdf