When MBIO Topics classes are offered, instructors may choose to upload a syllabus/sample syllabus to STAR and/or they may have a description posted on our website (below).
MBIO 691B Seminar in Marine Biology (General Marine Biology)
Schedule: Fridays, 3:30-4:30pm in BIL 150
Description: Seminar series on marine biology topics, literature, and concepts of current interest.
MBIO 720 Topics in Marine Education, Outreach, and Policy (Title: TBA)
MBIO 612 Data Science Fundamentals in R (3 cr)
Instructor: Mahdi Belcaid
Schedule: T 7:50am-10:20am
Description: Data science has become a crucial skill graduate students, including those pursuing careers in marine biology, must master. In academia, the R language has emerged as a valuable tool data for exploring, modeling visualizing, or interpreting data at various scales.
This lab-focused course provides an introduction to data science in life sciences using tools and libraries available in R. In addition to learning the skills required to process, acquire, and clean data, the students will also be introduced to foundational statistical techniques, as well as software and data engineering best practices needed to build efficient and reliable data science libraries and reproducible analyses.
The high-level objectives of the course are:
1. Develop familiarity with the most popular concepts, libraries, and best practices used in five high-level stages of a data science project; those are 1-data collection and organization, 2- data wrangling, 3- data exploration, 4-data modeling and 5- data interpretation and visualization.
2. Introduce scientific software engineering best practices to writing libraries or complex analyses.
3. Develop familiarity with the principles underlying robust statistical, empirical, and software reproducibility in data science.
4. Introduce students to different intensive computations and the various computing environments and paradigms used to tackle them.
If you have any questions, please contact Mahdi Belcaid (firstname.lastname@example.org)
MBIO 725 Behavioral Ecology of Coral Reef Fishes (3 cr) (listed as "Topics in Marine Physiology, Behavior, and Organismal Biology")
Instructor: Jacob Johansen
Schedule: This is an intensive, in-person, 6-session course at HIMB.
Oct 7/8, 14/15, 21/22 (i.e., Th/Fr) 8:30am - 4:30pm @ HIMB
Description: This course will focus on the methods of studying marine animal behavior to gain a deeper understanding of ecological systems. Behavioral responses determine fitness of individuals and are crucial for understanding patterns through time (e.g., abundance) and impacts of environmental change. Students will be introduced to a) the processes that affect animal behavior and their significance, b) a broad range of techniques for studying the behavior of fishes, and c) given an overview of the present status of research for coral reef fishes. The main goal of this course is for students to learn how to use behavioral techniques in their own research, including how to design behavioral sampling experiments and be proficient in the collection and handling of behavioral data. The course will be taught at Hawaii Institute of Marine Biology (HIMB), in Kaneohe Bay, and integrate lectures and practical experience in the field. The lectures will introduce the conceptual and methodological background to behavioral ecology using case studies. The field component will then allow students to apply these methods to gain a better understanding of the limitations and difficulties associated with collecting behavioral data. This is an in-person course designed to give students a chance to practice techniques in a field setting. Attendance in all components of the course is compulsory, including in-water snorkeling activities. Working in pairs, the students will collect two class datasets and one dataset of their own. At the end of the course, students will make a seminar presentation of their own project as well as an individual project report. Enrollment is limited to 12 students and requires permission to enroll.
Instructor permission requirement: To enroll, contact Dr. Jacob Johansen with a short (one or two paragraph) description of your motivation for participating in the course and any relevant prior coursework or experience (including experience with snorkeling/diving at UH).
MBIO 740 Mathematical Ecology of Marine Systems (3 cr) (listed as "Advanced Topics in Quantitative Biology")
Prerequisite: Calculus I
Instructor: Lisa McManus
Schedule:This is an intensive, 12-session course with a hybrid (i.e., partially in-person and partially online) schedule. You must be able to attend both the in-person portions at HIMB and the online portions (times updated, Aug 27, 2021)
In-person classes: (Note that Friday classes end 15 min later): Oct 28 (Thurs): 8:30 am - 12:45 pm; Oct 29 (Fri): 8:30 am - 1:00 pm; Nov 4 (Thurs): 8:30 am - 12:45 pm; Nov 5 (Fri): 8:30 am - 1:00 pm; Nov 18 (Thurs): 8:30 am - 12:45 pm; Nov 19 (Fri): 8:30 am - 1:00 pm
Remote classes: Nov 1 (Mon): 2:30-4:35 pm; Nov 2 (Tues): 2:30-4:35 pm; Nov 8 (Mon): 2:30-4:35 pm; Nov 9 (Tues): 2:30-4:35 pm; Nov 15 (Mon): 2:30-4:35 pm; Nov 16 (Tues): 2:30-4:35 pm
Description: This course will cover the use of mathematical approaches to gain a deeper understanding of ecological dynamics. Students will be introduced to a broad range of theories and techniques from mathematical ecology with an emphasis on marine systems. Topics will include single-species populations, community ecology, spatial patterns and population genetics. Students will learn to interpret equations and figures in the theoretical ecology literature, develop critical thinking skills with regards to model assumptions and level of detail, construct their own simple models, and apply analytical and computational techniques to make predictions regarding system behavior. This course will be centered on 'pen and paper' mathematical analyses, coding activities and collaborative group work. There will be a programming component that will introduce computational approaches to simulate dynamical systems using Python. Students will be encouraged to use data collected from their own research or from other courses in their final modeling projects.