Exploring the relationship between periphyton and water quality in karstic wetlands
Physicochemical properties and nutrients drive aquatic processes that sustain biota. Therefore, aquatic assessments usually investigate these variables as well as biological indicators to gain a better understanding of water quality. In this module, students will use regression analysis and online resources to investigate the relationship between periphyton and variables associated with water quality (including nutrients). Students will also examine the role of diatom indicator species. As students explore the concepts in this module, they will be introduced to and practice biostatistical skills needed to answer the over-arching question: What are the relationships between periphyton and variables associated with water quality in the Everglades and Caribbean? And, what can regional diatom species indicate about these relationships?
- Know how to perform diagnostics on a regression model to check for meeting assumptions of linearity and normality
- Perform regression analyses of transformed data
- Interpret results from regression analyses to answer over-arching question(s)
- Determine diatom indicator species and their autecology using literature and online resources
Context for Use
- This module can be completed in 3-4 hours. This module can be used after and in conjunction with the Exploring diatom biodiversity in the Everglades and Caribbean wetlands module for multiple lab classes
- Appropriate for graduate or advanced undergraduate students in Freshwater Ecology or Limnology courses
- Designed for a class or lab size of up to 20 students. For larger classes, students can work in small groups.
- Students should be familiar with R, RStudio, and the tidyverse family of R packages (primarily ggplot2)
- Students should have prior introductory statistics background (e.g., regression analysis)
- The length and concepts of this lab can be modified according to student skill level
How Instructors Have Used This Module
Using Project EDDIE modules in Freshwater Ecology
Jen Klug, Fairfield University
This module introduces students to a unique ecosystem (karstic wetlands) that they are probably not familiar with. The module teaches them how to run and interpret diagnostic tests for linear models in R without requiring much coding experience. Sharing their results with their peers allows them to practice comparing models to determine which variables are the best predictors of periphyton biomass and food quality.
Description and Teaching Materials
See the teaching materials files, provided below. For a step-by-step description for carrying out this module please refer to the instructor's PowerPoint, student handout, and student R script. An instructor's answer key (and the associated R script to generate the key) to check student progress is also provided.
Quick overview of the activities in this module
- Activity A: As a class, students will go over diagnostic techniques to check that model assumptions of linearity (residuals vs. fitted plot) and normality (e.g., Shapiro test and Q-Q plot) are met for Everglades water pH and percent periphyton organic content from the FCE LTER data set. Students will answer questions in the PowerPoint about these tests before moving to the next activity.
- Activity B: As a class, students will conduct linear regressions, check diagnostics, and transform data for Everglades periphyton ash-free-dry mass and total phosphorus from the FCE LTER data set.
- Activity C: In groups, students will choose predictor and response variables associated with water quality data from either the Everglades or Caribbean. They will model and investigate relationships through linear regressions. With regression analyses results, students will answer questions in the PowerPoint before moving to the next activity. Then, as a class, the instructor will guide students through online resources about diatom species. In groups, students will choose and research a diatom species' autecology for information about water quality indication. Students will answer questions in the PowerPoint before moving to the post-lab questions.
Workflow of this module
- Assign pre-lab questions (Optional: students should come to class having already read the scientific article).
- Start the PowerPoint presentation as a refresher for basic correlation and linear regression concepts, pausing to conduct activities after going over concepts (see PPT and instructor's R script).
- Instructor guides students through the module activities using RStudio (assistant is recommended to walk around and aid students, if available). After initial RStudio work for Activities A-B, students will continue the module independently in small groups (Activity C).
- After all activities are completed, the instructor assigns post-lab questions and homework (optional; if students were unable to complete activities A-C).
- Student handout (Microsoft Word 2007 (.docx) 198kB Aug19 22)
- Student R script (R script 17kB Aug18 22)
- Everglades and Caribbean dataset (Comma Separated Values 62kB Aug18 22)
- Zipped RStudio project ready to go with data file and student R script (Zip Archive 17kB Aug18 22)
Teaching Notes and Tips
Use Instructor's PowerPoint to introduce concepts, stopping along the way to carry out activities. The Instructor's key R output provides a number of possible calculations and plots. The student R script provides commented guidance to generate calculations and plots
- Cover concrete examples of how correlation does not equal causation, the importance of checking regression model assumptions.
- Emphasize that students should work through one output at a time rather than trying to run multiple outputs all at once.
- Students do not need to fully understand every bit of syntax, but should focus on what the code is doing overall.
- We recommend the instructor use the student RStudio script when walking students through the exercises. The instructor's RStudio script generates possible R graphs and answers for all activities, and those outputs can be found in the instructor's key Word document.
- We recommend a teaching assistant walk around and aid students as needed.
- Instructor may want to request students copy their R plots and outputs into their student handouts to submit with any other material at the end of class.
- We recommend using this module after and in conjunction with the Exploring diatom biodiversity in the Everglades and three tropical karstic wetlands module for multiple course sessions.
- Use Instructor's PowerPoint to introduce concepts, stopping along the way to carry out activities. The Instructor's key R output provides a number of possible calculations and plots. The student R script provides commented guidance to generate calculations and plots.
Assessment for this module is both formative and summative. Student success can be measured by the completion of steps throughout the lab and the quality of answers/ inferences made at the end (their interpretation of results and how they relay to the overarching question). Student success can also be measured by comparing answers to pre-lab and post-lab questions. By the end of this module students should:
- Activity A: Be able to check model assumptions of linearity and normality to assess the appropriateness of their model for a relationship.
- Activity B: Be able to transform data to satisfy model assumptions of linearity and normality and be able to interpret the relationship between variables associated with water quality and periphyton variables using linear models.
- Activity C: Be able to independently choose data and interpret the relationship between variables associated with water quality and periphyton variables using linear models (transforming data when necessary) to answer the first overarching question. With identified indicator species, students should be able to use online materials to research regional species autecology and use findings to answer the second over arching question.
References and Resources
- Pre-reading (optional): La Hée, J. M., & Gaiser, E. E. (2012). Benthic diatom assemblages as indicators of water quality in the Everglades and three tropical karstic wetlands. Freshwater Science, 31(1), 205-221.
- Linear regression concepts:
- Data sets:
- Gaiser, E. 2022. Periphyton data from LTER Caribbean Karstic Region (CKR) study in Yucatan, Belize and Jamaica (FCE LTER) during 2006, 2007, 2008. Environmental Data Initiative. https://doi.org/10.6073/pasta/6f71911870cb18e274416d0bf297cdc4. Dataset accessed 2022-05-03.
- Gaiser, E. 2012. Environmental data from FCE LTER Caribbean Karstic Region (CKR) study in Yucatan, Belize and Jamaica during Years 2006, 2007 and 2008. Environmental Data Initiative. https://doi.org/10.6073/pasta/5a01d59e5f7d73bd1f7baee2c71af765. Dataset accessed 2022-05-03.
- Gaiser, E. 2022. Periphyton and Associated Environmental Data Relative from Samples Collected from the Greater Everglades, Florida, USA from September 2005 to November 2014. Environmental Data Initiative. https://doi.org/10.6073/pasta/eadd93a36c2d935c069f3b0a4c98775b. Dataset accessed 2022-05-03.
- Conjunction module: Exploring diatom biodiversity in the Everglades and three tropical karstic wetlands
- Additional optional resources:
- Gaiser et al. (2015). New perspectives on an iconic landscape from comparative international long‐term ecological research. Ecosphere, 6(10), pp.1-18.
- Ambrose, H. W., & Ambrose, K. P. (1987). A handbook of biological investigation. Winston-Salem, N.C., Hunter Textbooks.
- Online resource about diatom species: use this site to access information on bioindicator autecology, taxonomy, and other species information