Investigating human impacts on Southeastern US stream ecology using R
Adaptation of the "Investigating human impacts on stream ecology: Scaling up from Local to National with a focus on the Southeast" specifically to focus on self-paced R code instruction
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Version 1.0 - published on 01 Jan 2021 doi:10.25334/P995-7G91 - cite this
Licensed under CC Attribution-ShareAlike 4.0 International according to these terms
Adapted from: Investigating human impacts on stream ecology: Scaling up from Local to National with a focus on the Southeast v 1.0
Description
Focus: The students will analyze indicators of stream health from across the United States by plotting data and performing descriptive statistics and linear regression.
Overview: This lesson centers around analyzing the correlation between different indicators of stream health. The students will complete a lesson in R to practice creating scatter plots and performing linear regression. Additionally, students will practice summarizing data, calculating outliers, and creating boxplots. They will apply the methods from prior lessons in the final lesson of the course to analyze stream data from Region 4, which contains Atlanta, more independently.
Learning objectives:
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Contents
Stream Biodiversity and Function Lesson Plan.pdf(PDF | 19 KB)
Stream_Biodiversity_and_Function.swc(SWC | 135 KB)
- Investigating human impacts on stream ecology: locally and nationally (Abstract) | TIEE
Impact of Urban Storm Runoff Atlanta 1980 EPA report.pdf(PDF | 2 MB)
Ivan Allen Papers_AHC_Proctor Creek.pdf(PDF | 11 MB)
streamdata.csv(CSV | 84 KB)
Student Handout_v1.docx(DOCX | 2 MB)
- License terms
Cite this work
Researchers should cite this work as follows:
- Caughman, A., Weigel, E. (2021). Investigating human impacts on Southeastern US stream ecology using R. QUBES Educational Resources. doi:10.25334/P995-7G91
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Notes
R coding in this lab is supported well asynchronously through the use of a swirl module. The module may be used as a stand-alone or in conjunction with materials published in the adapted modules.
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