Course Lecturer Name(s): Wayne Smart (Course Instructor) & Jody Daniel (Lab Instructor)
Course Director Name: Patricia Rosa
Course Lecturer(s) Contact Information: wayneasmart@gmail.com; jodyndaniel@gmail.com
Course Director Contact Information: prosa@sgu.edu
Course Lecturer(s) Office Hours: By appointment
Course Director Office Hours: N/A
Course Lecturer(s) Office Location: 2nd floor Caribbean House
Course Director Office Location: N/A
Course Support: Anna Neckles-Thomas, anecklesthomas@sgu.edu, x3435
Course Management tool: To learn to use Sakai, the Course management tool, access the link https://apps.sgu.edu/members.nsf/mycoursesintro.pdf
Course Description:
This course will allow students to apply statistical principles to experimental designs and analyses of ecological data. Ecological data is often constituted and impacted by multiple variable factors and must be placed into context within the population and systems from which it was sampled. Student will learn appropriate methods to collect and analyze common types of ecological data, such as species distribution, abundance, and occupancy. Students will be expected to evaluate quantitative methods used in current literature, apply theoretical concepts to real datasets, become proficient in the use of statistical programming software and mathematical packages, and interpret ecological significance of results from statistical significance. s
Course Objectives:
The goal of this course is to provide students with a background in the core concepts of ecological quantitative methods, enabling them to correctly link essential mathematical constructs and ecological questions and learn how to apply the scientific process to ecological datasets.
Additionally, this course will allow students to characterize appropriate ecological variables, analyze and interpret data and modeling results, and present results of experimental designs using statistical programming software.
Student Learning Outcomes:
- Apply the scientific method and hypothesis testing when designing an ecological survey.
- Choose appropriate measures to best represent the ecological process being tested.
- Select statistical tests that are appropriate for the different types of ecological data.
- Use statistical software and mathematical packages to examine and analyze ecological data.
- Interpret statistical output, both from a quantitative and ecological perspective.
Technical Skills Outcomes:
TSO-BIOL313-1. Use video from camera traps and keystroke event recording software to estimate community composition and diversity.*
TSO-BIOL313-2. Use of spreadsheets to enter, collate, analyze and display data graphically. TSO-BIOL313-3. Use of R statistical software to conduct data analyses and display data graphically
*Subject to availability of field data.
Program Outcomes Met By This Course:
MWC-PLO2. APPLICABILITY: Analyze key global ecological and conservation issues to promote long-term species viability and health of marine and terrestrial environments, with an emphasis on the Caribbean.
MWC-PLO3. RESEARCH: Apply scientific method, ecological and quantitative concepts, and technical skills to design and conduct novel field and laboratory experiments, while considering ethical and regulatory implications.
MWC-PLO4. COMMUNICATION & CRITICAL THINKING: Use relevant scientific literature and demonstrate independent, critical thinking while communicating scientific knowledge effectively in different media.
SAS Grading Scale: Grades will be assigned as follows:
A = 89.5% or better
B+ = 84.5 - 89.4%
B = 79.5 - 84.4%
C+ = 74.5 - 79.4%
C = 69.5 - 74.4%
D = 64.5 - 69.4%
F = 64.4% or less
Course Materials:
- Text: Quinn, G. P., & Keough, M. J. (2002). Experimental design and data analysis for biologists. Cambridge University Press, UK.
- Software: R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.
Supplementary Readings/Resources:
- Fox, G. A., Negrete-Yankelevich, S., & Sosa, V. J. (2015). Ecological statistics: contemporary theory and application. Oxford University Press, USA.
- Rosner, B. (2015). Fundamentals of biostatistics. Nelson Education, USA.
- Beckerman, A. P., Childs, D. Z., & Petchey, O. L. (2017). Getting started with R: an introduction for biologists. Oxford University Press, UK.
Course Grading Requirement:
- Quizzes: 4 × 15% = 60%
- Assignments: 4 × 10% = 40%
Course Requirements:
Quizzes will cover material and concepts from lectures. Questions will include a combination of multiple-choice, short-answer, and problem-solving questions, and data and graphical interpretations. You should be prepared to apply your knowledge of course material and concepts to new situations and datasets. Quizzes are non-cumulative.
Assignments will consist of using R to run statistical analyses on ecological data, and display whole data sets, summarized data, and results graphically.
Course Schedule:
|
Week |
Date |
Monday |
Wednesday |
|
1 |
Jan. 17 & 19 |
1. Role of statistics in ecological studies |
Lab: Why R and guide to install |
|
2 |
Jan. 24 & 26 |
2. Research design and hypothesis testing |
Lab: Getting started in R and RStudio |
|
3 |
Jan. 31 & Feb. 2 |
3. Research design and statistical inference |
4. Descriptive statistics Assignment 1 |
|
4 |
Feb. 7 & 9 |
5. Confirmation bias (van Wilgenburg & Elgar 2013) |
Quiz 1 (Lectures 1–4) |
|
5 |
Feb. 14 & 16 |
6. Data organization and management |
Lab: Exploring data in R |
|
6 |
Feb. 21 & 23 |
7. Data acquisition and sharing policies |
8. Inferential statistics and hypothesis testing Assignment 2 |
|
7 |
Feb. 28 & Mar. 2 |
Review |
Quiz 2 (Lectures 5–8) |
|
8 |
Mar. 7–11 |
Mid-term Week |
|
|
9 |
Mar. 14 & 16 |
9. Assumptions of normality and data exploration |
Lab: Assumptions in R |
|
10 |
Mar. 21 & 23 |
10. Data transformation and outliers |
Lab: Linear Models in R |
|
11 |
Mar. 28 & 30 |
11. General linear models (1) |
12. General linear models (2) Assignment 3 |
|
12 |
Apr. 4 & 6 |
Review |
Quiz 3 (Lectures 9–12) |
|
13 |
Apr. 11 & 13 |
13. Contingency tables |
Lab: Contingency tables, GLMs, and/or occupancy models in R |
|
14 |
Apr. 18 & 20 |
14. Generalized linear models (GLM) |
15. Occupancy models to study wildlife (USGS 2005) Assignment 4 |
|
15 |
Apr. 25 & 27 |
Review |
Quiz 4 (Lectures 13–15) |
|
16 |
May 2–6 |
Finals Week |
|
School of Arts and Sciences Master Syllabi — Info for All Sections
Academic Integrity
The St. George’s University Student Manual (2019/2020) states as follows:
“Plagiarism is regarded as a cardinal offense in academia because it constitutes theft of the work of someone else, which is then purported as the original work of the plagiarist. Plagiarism draws into disrepute the credibility of the Institution, its faculty, and students; therefore, it is not tolerated” (p. 48).
Plagiarism also includes the unintentional copying or false accreditation of work, so double check your assignments BEFORE you hand them in.
Be sure to do good, honest work, credit your sources and reference accordingly and adhere to the University’s Honor Code. Plagiarism and cheating will be dealt with very seriously following the university’s policies on Plagiarism as outlined in the Student Manual.
Your work may be subject to submission to plagiarism detection software, submission to this system means that your work automatically becomes part of that database and can be compared with the work of your classmates.
The St. George’s University Student Manual (2019/2020) states as follows:
“Students are expected to attend all classes and or clinical rotations for which they have registered. Although attendance may not be recorded at every academic activity, attendance may be taken randomly. Students’ absence may adversely affect their academic status as specified in the grading policy. If absence from individual classes, examinations, and activities, or from the University itself is anticipated, or occurs spontaneously due to illness or other extenuating circumstances, proper notification procedures must be followed. A particular course may define additional policies regarding specific attendance or participation” (p. 9).
The St. George’s University Student Manual (2019/2020) states as follows:
“All matriculated students are expected to attend all assigned academic activities for each course currently registered. Medical excuses will be based on self-reporting by students. Students who feel they are too sick to take an examination or other required activity on a specific day must submit the online SAS medical excuse, which is available on Carenage. Students are only allowed two such excuses a year. Upon consultation with the Director of University Health Service, the third excuse will result in a mandatory medical leave of absence. The policies regarding make-up examinations are at the option of the Course Director” (p.46).
For additional specific examination policies and procedures, refer to the St. George’s University Student Manual (2019/2020), pages 31 through 37.
The St. George’s University Student Manual (2019/2020) states as follows:
“A student with a disability or disabling condition that affects one or more major life activities, who would like to request an accommodation, must submit a completed application form and supporting documentation to the Student Accessibility and Accommodation Services (SAAS) located in the Dean of Students Office. It is highly recommended that students applying for accommodations do so at least one month before classes begin to allow for a more efficient and timely consideration of the request. If a fully completed application is not submitted in a timely fashion, an eligibility determination may not be made, and accommodations, where applicable, may not be granted prior to the commencement of classes and/or examinations” (p. 8).
It is the responsibility of the student to read and understand the policies, laws, rules and procedures that while they could affect your grade for a course, have not been specifically outlined in the course syllabus. These are contained in the St. George’s University Student Manual.