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Investigating Determinants of Richness in Ephemeral and Permanent Wetlands Using Linear Models in Shiny

Author(s): Kristofor Voss

Regis University

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Summary:
This resource seeks to provide a quantitative platform by which students can be taught about interactive effects using the TIEE module created by Little (2018). Key to the adaptation is the use of a Shiny app to create figures and R output.

Licensed under CC Attribution-ShareAlike 4.0 International according to these terms

Version 1.0 - published on 21 Jan 2019 doi:10.25334/Q4RQ9T - cite this

Adapted from: Environment-Richness Relationships in Ephemeral and Permanent Wetlands: Guided Inquiry with Graph Interpretation (Abstract) | TIEE v 1.0

Keywords

  1. aquatic macroinvertebrates
  2. linear regression
  3. hypothesis testing
  4. shiny
  5. wetlands

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Contents:
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Description

This resource seeks to provide a quantitative platform by which students can be taught about interactive effects using the TIEE module created by Little (2018)a. Key to the adaptation is the use of a Shiny app to create figures and R output for a model that fits an interaction between a driver of plant and macroinvertebrate richness and wetland permanence. Included in the resources are instructor notes, a pre/post assessment, student handouts, and a link to the Shiny app. The Shiny app can be accessed at https://kristoforvoss.shinyapps.io/dig_practice/.

aThis resource is an adaptation of  Little, A.M. (2018). Environment-richness relationships in ephemeral and permanent wetlands: Guided inquiry with graph interpretation. Teaching Issues and Experiments in Ecology, 13, online at http://tiee.esa.org/vol/v13/issues/data_sets/little/abstract.html

 

Notes

While the Shiny app works well, there are a few glitches that are solely my responsibility. The main glitch is that students must enter the x-axis and y-axis labels they want on the figure. This also affects the R output. That is, even when they choose the correct dropdown menu, the axes and R output labels don’t automatically populate. Additionally, the model description at the top of the output corresponds to the R code embedded in the app rather than a readable summary of the model.

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