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You are here: Home / Resources / Teaching Materials / Phenotypic plasticity and predation / About

Phenotypic plasticity and predation

By Jeremy M Wojdak1, Justin Touchon2

1. Radford University 2. Vassar College

Students predict changes to tadpole morphology and coloration after considering characteristics of the predator species and the prey themselves then test their own hypotheses (typically with t-tests or ANOVA) by collecting novel data via image...

Listed in Teaching Materials | resource by group AIMS: Analyzing Images to learn Mathematics and Statistics

Version 2.0 - published on 29 Aug 2018 doi:10.25334/Q4XX4J - cite this

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

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TadpolePicture.jpg

Description

The variation among individuals within the same environment is substantial and of primary interest - many times phenotypic plasticity is described in textbooks (or viewed by students) as a light switch, yes or no, affair, whereas professional biologists are much more keenly aware and interested in the variation among individuals. This provides a nice opportunity for students to literally see, measure, and describe variation, a concept that many find opaque. This module can be used to teach elementary statistical concepts, but features enough real-world complexity to allow for much more sophisticated analyses, depending on the course and preparation of the students. Because the context for study comes from a published research article, we also have a great opportunity for students to practice reading the primary literature.

Potential Learning Objectives:
Basic

  • Students will be able to define phenotypic plasticity.
  • Students will practice extracting meaning from published methods section.
  • Students will generate meaningful hypotheses, given a context for investigation.
  • Students will use image analysis software to generate data from an image set.
  • Students will be able to measure, describe, and interpret variation among individuals within and across treatments.
  • Students will be able to construct bar charts with standard error bars and frequency histograms.
  • Students will calculate and interpret significance tests with categorical treatments.

Advanced/Extensions

  • Students will interpret covariation among correlated traits (e.g., body size and tail depth).

Contents

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Researchers should cite this work as follows:

Tags

  1. Activity Length
  2. Advanced
  3. ANOVA
  4. Audience Level
  5. data visualization
  6. Excel
  7. frequency histograms
  8. graphing
  9. High School
  10. hypothesis formulation
  11. image analysis
  12. Instructional Setting
  13. introductory
  14. Lab
  15. More than 1 hour
  16. phenotypic plasticity
  17. predator-prey interactions
  18. R
  19. Teaching material
  20. t-tests
  21. Undergraduate

Notes

This version includes an updated image database. 

AIMS: Analyzing Images to learn Mathematics and Statistics

AIMS: Analyzing Images to learn Mathematics and Statistics group image

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