Following the steps outlined above helps to keep your instructors informed about your absences and ensures your absence or missing an assignment due date because of illness will be marked excused. You will still be responsible to complete within a week of returning to class any assignments, quizzes, or exams you miss because of illness. Links for additional info:
Student Health Services
Student Affair COVID-19
Student COVID-19 Protocol
Texas Tech Commitment

Students with disabilities: Any student who, because of a disability, may require special arrangements in order to meet the course requirements should contact me as soon as possible to make any necessary arrangements. Students should present appropriate verification from Student Disability Services. Please note that instructors are not allowed to provide classroom accommodations to a student until appropriate verification from Student Disability Services has been provided. For additional information, please contact the Student Disability Services office at 335 West Hall or (806) 742-2405.

Religious holy day statement: "Religious holy day" means a holy day observed by a religion whose places of worship are exempt from property taxation under Texas Tax Code §11.20. A student who intends to observe a religious holy day should make that intention known in writing to the instructor prior to the absence. A student who is absent from classes for the observance of a religious holy day shall be allowed to take an examination or complete an assignment scheduled for that day within a reasonable time after the absence. A student who is excused under section 2 may not be penalized for the absence; however, the instructor may respond appropriately if the student fails to complete the assignment satisfactorily.

Academic integrity: Academic integrity is taking responsibility for one’s own class and/or course work, being individually accountable, and demonstrating intellectual honesty and ethical behavior. Academic integrity is a personal choice to abide by the standards of intellectual honesty and responsibility. Because education is a shared effort to achieve learning through the exchange of ideas, students, faculty, and staff have the collective responsibility to build mutual trust and respect. Ethical behavior and independent thought are essential for the highest level of academic achievement, which then must be measured. Academic achievement includes scholarship, teaching, and learning, all of which are shared endeavors. Grades are a device used to quantify the successful accumulation of knowledge through learning. Adhering to the standards of academic integrity ensures grades are earned honestly. Academic integrity is the foundation upon which students, faculty, and staff build their educational and professional careers. [Texas Tech University (“University”) Quality Enhancement Plan, Academic Integrity Task Force, 2010]

Academic dishonesty: Academic dishonesty includes, but is not limited to, cheating, plagiarism, collusion, falsifying academic records, misrepresenting facts, and any act designed to give unfair academic advantage to the student (such as, but not limited to, submission of essentially the same assignment for two courses without the prior permission of the instructor) or the attempt to commit such an act. If a student is involved in any form of academic misconduct and is proven that the action took place, the instructor may initiate a disciplinary action. The penalties for academic dishonesty can include but are not limited to a zero or a grade of "F" on the work in question, a grade of "F" in the course, or suspension. Please make sure that you review the university’s Academic Integrity Policy (https://www.depts.ttu.edu/opmanual/OP34.12.php).

Recommended references on community ecology analyses:

• Multivariate Statistics for Wildlife and Ecology Research (McGarigal et al., 2000) – although this book is somewhat dated now, it provides a clear explanation for many multivariate stats tests that are used in wildlife biology and ecology; this book would be most useful if you have already had a basic statistics class

• Community Ecology: Analytical Methods using R and Excel (Gardener, 2014) – this book covers how to perform some basic analyses on biodiversity data in both R and Microsoft Excel

Recommended references for learning and using R:

• Getting Started with R: An Introduction for Biologists, 2^nd ed. (Beckerman et al., 2017) – this is a very good and short book that will teach you how to load data, do some simple stats tests (t-test, ANOVA), and make some simple plots/graphs

• The R Book, 2^nd ed. (Crawley, 2012) – this is the comprehensive reference for R

Important notes: This is a new course, and it is the first time that I will have taught a course entirely online. I will be teaching out of my home from my old laptop (because my work desktop does not have a camera). I will be working from a PC platform but anticipate that several of you will be using Macs. Finally, I developed and maintain this website, which is not part of TTU’s Blackboard platform. Not using Blackboard has pros and cons. I do not use it primarily because I make my teaching materials available to everyone worldwide and not just enrolled TTU students (Blackboard materials require a TTU login whereas my website and YouTube channel do not). For all of these reasons, I expect there to be quite a few technological glitches. Please inform me of any problems right away!

Moreover, there is a very large disparity in students in terms of previous experience with R and with data analysis. (That is, some of you will already be experienced with these things whereas other students will have had no experience with them.) This disparity may make live video sessions uncomfortable for those students who don’t understand something; such a student may be reluctant to speak up if he/she thinks (based on questions/discussion in the live sessions, etc.) that he/she is the only one to not “get it.” Please do not hesitate to ask questions. You can also contact me or the T.A. to set up an individual appointment on Zoom.

I recommend that you interact with the course materials in the following order each week:
1. First, read the week's online notes and work through the R scripts.
2. Then watch the lecture video.
3. Then do the assignment.

List of topics to be covered:

1. Introduction to ecological communities: what are they, why do we study them, and what kinds of data are involved?
2. Introduction to R
         R – see primers at https://rstudio.cloud/learn/primers
         RStudio
         RMarkdown – see primers at https://rmarkdown.rstudio.com/
         Loading and manipulating site x species and site x environment data
         Simple graphical summaries and basic statistical analyses of data
3. How do I describe the structure of a community?
         Richness + evenness = diversity
         Abundance
4. How do I measure other dimensions of biodiversity?
         Phylogenetic and functional diversity
5. How do I summarize complex relationships and identify the most important patterns from a near-infinite number of possible arrangements of multivariate data ?
         Ordination
6. How do I identify the most important variables driving patterns in a large dataset of correlated variables (and then use those variables in other analyses)?
         Principal Components Analysis (PCA)
         Redundancy Analysis (RDA)
7. How can I determine which environmental variables are associated with species occurrences at different sites?
         Nonmetric Multi-Dimensional Scaling (NMDS)
         Canonical Correspondence Analysis (CCA)
8. How do I determine whether my data are grouped?
         Cluster Analysis
9. If I have communities with a priori groupings, how do I determine whether those groups are valid?
         Discriminant Function Analysis (DFA) via CART (Classification and Regression Trees)