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About the problem

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

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).

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This version includes an updated image database. 

AIMS: Analyzing Images to learn Mathematics and Statistics

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