syllabus.RmdThe COVID-19 pandemic has forced many changes to our lives, including to the ways we teach and learn. I have attempted to make changes to the format and materials for this course to make online learning more effective and inclusive. However, I have no doubt that unforeseen issues will arise throughout the semester. For this reason, the materials and policies outlined in the syllabus are subject to change at the discretion of the instructor. Feedback about what is working and what is not working is encouraged to assist with making necessary changes. Bottom line - we’re all in this together, each doing their best. My goal is to help you learn and to that end, I promise to flexible with accommodating unique circumstances caused by the pandemic. In return, I ask the same of you.
Lecture: Tuesday and Thursday 1:30-2:45Location: ENLAB 248 (Engineering Laboratory Building) Zoom
Credits: 3
Dr. Clark Rushing
clark.rushing@usu.edu
Office: NR 146 Zoom
Office hours: Monday and Wednesday 1:00-2:30
Quantitative models play a central role in linking data to inferences about ecological processes. Although quantitative ecological modeling involves a diverse array of questions and techniques, many of the most common modeling frameworks are based on a set of general underlying principles. This course aims to provide students with a firm understanding of these principles and their application, with a particular focus on modeling the dynamics of plant and animal populations using Bayesian methods. By building from common principles to more specific procedures, students will be better equipped to tailor analyses to their specific data and questions, ultimately leading to deeper and more robust understanding of the ecological systems they study. Concepts discussed during lectures will be reinforced through lab exercises focused on implementing statistical models using modern software tools (e.g. R, JAGS).
The primary objective of this course is to give you the tools to tackle your research questions using rigorous statistical models appropriate for your data. In particular, you will leave this course with:
A secondary objective of this course is to provide you with tools and best practices for storing, manipulating, and analyzing ecological data and developing reproducible code for analyses. In my experience, most graduate students are well trained in methods for data collection and, to a degree, data analysis. However, most students do not receive formal training on the steps between data collection and reporting results from statistical models (e.g., proofing, storing, and formatting data; developing well-documented, reproducible analyses; preparing reports). Advancing the field of ecology also requires a scientific community capable of judging the quality of the code, interpretation, reporting of quantitative models. Graduate students often get few opportunities to critically review the work of their peers or to get constructive feedback on their reviews. The lab portion of this course is specifically designed to provide you with hands on experience with:
Students should have at least one semester of both basic ecology and introductory statistics. Although we will thoroughly cover the foundational principles of common statistical models, a basic understanding of ecological theory and statistical inference will be very helpful.
All lab activities for this course will rely heavily on the statistical programming language R (and associated software, including RStudio, JAGS, and git). During the first few weeks, each lab session will begin with a tutorial on these tools so students are not expected to be experts prior to the course. However, we will quickly move into activities that require some degree of R proficiency so I highly recommend that you have a basic understanding of programming in R (e.g., importing/exporting & manipulating data objects, visualizing data) prior to this course. If you find yourself struggling with any aspects of using R, please seek individual help during office hours. The earlier we can get you up to speed, the more painless the remainder of the semester will be.
This course will be taught using a combined lecture/lab structure. During each class, I will provide a brief lecture to introduce important concepts. At the beginning of the semester, lectures will likely take up the majority of class time as we go over the basics of ecological modeling and Bayesian methods. As the semester progresses and we move towards implementing these concepts to fit ecological models, class time will include more “lab” activities. These activities will be designed to reinforce and clarify lecture topics, allowing students to get hands on experience manipulating, analyzing, and visualizing data. All class materials, including lecture slides, computer code, lab documents, and data, will be posted on the course website prior to class.
Although there are many excellent texts covering various aspects of ecological modeling, the lectures for this course will closely follow two:
Hobbs, N.T. and M.B. Hooten, 2015. Bayesian Models: A Statistical Primer for Ecologists (available here)
Kéry, M. and M. Schaub, 2011. Bayesian population analysis using WinBUGS: A hierarchical perspective (available here).
Weekly readings will be assigned from these books so I highly recommend all students purchase them prior to the start of the semester. The Kéry & Schaub book also contains a wealth of useful code that, although optional, students are encouraged to implement as they read the text. Chapters from these books may occasionally be supplemented with primary journal articles. When applicable PDF’s of these articles will be posted on the course website.
Although the two text books and lab materials are sufficient for mastering the material presented in this course, a number of other excellent books are available and students may find some/all of these books helpful. The following books are 100% optional for this course but I have found helpful for learning this material:
Kéry, M. & Royle, J.A. 2020. Applied Hierarchical Modeling in Ecology: Analysis of distribution, abundance, and species richness in R and BUGS, Vol. II. Academic Press.
Kéry, M. & Royle, J.A. 2016. Applied Hierarchical Modeling in Ecology: Analysis of distribution, abundance, and species richness in R and BUGS, Vol. I. Academic Press.
Kéry, M., 2010. Introduction to WinBUGS for ecologists: Bayesian approach to regression, ANOVA, mixed models and related analyses. Academic Press.
Williams, B.K., Nichols, J.D., Conroy, M.J., 2001. Analysis and management of animal populations. Academic Press.
Bolker, B.M., 2008. Ecological models and data in R. Princeton University Press.
Materials for labs will be provided as HTML and R Markdown files on the course webpage. These materials will include step-by-step tutorials for all lab exercises as well as links to additional online resources, problem sets, and homework assignments.
All lab computers will have R and RStudio installed on them so students are not required to install software on their own computers. However, students are free to use their own laptops during lab and having R and RStudio installed will make it easier to complete lab assignments outside of class. Any students wishing to use their own computers should have R, RStudio, and JAGS installed and running prior to the first lab. Detailed instructions for installing R and RStudio can be found here. JAGS can be downloaded here.
If you plan to use your own computer, be sure you have the most recent versions of all three software programs installed. This will greatly decrease the chances of running to issue running the code I will provide in lab. Prior to the start of the semester, test that you have R and RStudio installed correctly by doing the following:
Launch RStudio
Put the cursor in the window labeled Console. Type the following code followed by enter or return: x <- 2 * 4. Next type x followed by enter or return. You should see the value 8 print to screen. If yes, you’ve succeeded in installing R and RStudio.
If you encounter any problems with the previous steps, please contact me prior to the first class. We will diagnose issues running JAGS in class but for now, make sure you have the most up-to-date R packages installed by running the following:
remove.packages(c('coda','rjags', 'devtools', 'tidyverse'))
install.packages(c('coda','rjags', 'devtools', 'tidyverse'))
library(devtools)
install_github("kenkellner/jagsUI")As graduate students who willingly signed up for this course, I presume that you are eager to learn the material and are self-motivated enough to put in the required effort. For this reason, I do not set a formal attendance policy. However, we will cover a lot of material over the course of the semester and each topic will build on concepts from previous weeks. As a result, missing even a few lectures or labs will make it difficult for you to fully master the learning outcomes described above. If you know you will be missing any lectures or labs, please contact me in advance so we can make sure you do not get too far behind on the material.
I realize that many students have field work obligations during the spring semester. If you need to take this course but know in advance that you will be in the field for a portion of the semester, please let me know ASAP so we can discuss whether field work will be a barrier to taking the course or merely an inconvenience. This distinction will mainly be a function of how long and when you will miss class. If these absences are relatively few, taking the course may still be an option. I will do my best to record lectures and make them available via Canvas. Students will still be expected to complete and turn in any assignments they miss. If you are going to miss too many classes or will be unable to complete the assignments while you’re in the field, it may be better to take the class at a time when your field commitments are smaller.
Your grade in the course will be based on a total of 200 possible points. Thirty five percent of your grade (70 points) will come from 7 homework assignments. Twenty five percent of your grade (50 points) will come from a midterm exam covering the topics we discuss in lecture during the first half of the semester. The final 40% (80 points) will be based on an individual project due at the end of the semesters.
The 7 homework assignments will build on concepts and skills that we cover in lecture and lab. Specific objectives and tasks for each assignment, along with any necessary data, will be posted on the course website. In general, students will be provided with ‘raw’ data and will need to clean/prepare (and document!) data prior to analysis. Each assignment will be due before class the week after the assignment is posted. All assignments must be prepared as R Markdown files that include all text, code, model output, and figures necessary to fully document your work and conclusions (we will spend the first several labs going over preparation of reports using R Markdown so, again, no previous experience is necessary). See here for instructions on submitting assignment
Individual projects may address an ecological question of your choosing, but must be approved by me during the first half of the semester. You are free to use any data you have access to, including data from your own projects, published datasets, or even simulated data. Once the project topic and data are approved, you are expected to develop statistical models using the tools and skill gained during the semester to gain insights into the ecological processes that generated your data. Once completed, a final report should be prepared as if you were submitting the results to a short-format scientific journal. Additionally, you will give a short oral presentation of your results during the final week of class as if you were presenting at a professional conference. Later in the semester, I will provide a rubric for how the written and oral reports will be assessed.
| GRADE | % RANGE | POINT RANGE |
|---|---|---|
| A | 93-100% | 186-200 |
| A- | 90-92.9% | 180-185 |
| B+ | 87-89.9% | 174-179 |
| B | 83-86.9% | 166-173 |
| B- | 80-82.9% | 160-165 |
| C+ | 77-79.9% | 154-159 |
| C | 73-76.9% | 146-153 |
| C- | 70-72.9% | 140-145 |
| D+ | 67-69.9% | 134-139 |
| D | 60-66.9% | 126-133 |
| F | 59.9% and below | 0-125 |
All USU students attending classes in Logan, at our Regional Campuses, or online can access all databases, e-journals, and e-books regardless of location. Additionally, the library will mail printed books to students, at no charge to them. Students can also borrow books from any Utah academic library. Take advantage of all library services and learn more at libguides.usu.edu/rc.
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