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Phenotypic plasticity and predation

Author(s): Jeremy M Wojdak1, Justin Touchon2

1. Radford University 2. Vassar College

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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...

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

Version 1.0 - published on 24 Aug 2018 doi:10.25334/Q4GX34 - cite this Last public release: 2.0


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:

  • 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.


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

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