We encourage everyone to submit items for this collection.  Please use the table above to determine how to categorize and tag your item. 

In addition, please tag which learning outcome(s) your item addresses, and include the tag Mechanisms.

EXAMPLE: If you are posting a tool that addresses learning outcomes 1.2 &1. 3 you would include the following tags:   Tools, LO1.2, LO1.3, Mechanisms

*Please use this format when tagging as it will make the collection easier to search.

This is a place to contribute resources, tools, and assessments that address the following learning outcomes under the broad theme of mechanisms of evolution.  

Students are able to:

LO1.1 Demonstrate how morphological evolution results from a subset of molecular evolution

LO1.2 Compare and contrast the likelihood of phenotypic and genotypic convergence

LO1.3 Demonstrate how evolutionary changes are constrained by existing genotypic and phenotypic variation.

LO1.4 Recognize that evolution occurs in an environment that varies over space and time.

LO1.5 Evaluate the relative importance of abiotic versus biotic factors on selection

LO1.6 Recognize and compare multiple evolutionary solutions to similar environmental challenges

This is part of the Tree of Life working group.  Click here to read about our general goals and framework.

Author: Andy Hector

    " He currently convenes and teaches statistics on the Quantitative Methods for Biologsits course for undergraduates at the Universtiy of Oxford.  He has contributed to several publications on eccologal analysis."

Published: March 15, 2015 by Oxford University Press

Summary: This book provides a contemporary introduction to the classical techniques and modern ectensions of linear model analysis.  It emphasizes on estimation-based approach that accounts for recent criticisms of over-use of probability values and introduces the alternative approach using information criteria.  It is based on the use of the open-source R programming language for statistics and graphics that is rapidly becoming the lingua franca in many areas of science.  Statistics is introduced through worked analyses preformed in R using interesting data sets from ecology, evoluntionary biology, and environmental science.  Data sets and R scripts are available as supporting material.

For a preview of this book, click here

For chapter summaries, click here

For purchase through Amazon

Author: Gregg Hartvigsen

    "Taught at a workshop on network analysis using R at the national Institute for mathematical and Biological Synthesis at the University of Tennessee, Knoxville."

Published: February 18, 2014 by Columbia University Press

Summary: This book guides readers through the processes of entering data into R, working with data in R, and using R to visualize data using histograms, boxplots, barplots, scatterplots, and other common graph types.  Hartvigsen covers testing data for normality, defining and identifying outliers, and working with non-normal data.  Students are introduced to common one- and two-sample tests as well as one- and two-analysis of variance (ANOVA), correlation, and linear and non-linear regression analyses. Also included is a secion on advanced procedures and a chapter inroducing algorithms and the art of programming using R.

For a preview of this book, click here

For a  purchase through amazon

Author: Peter Dalgaard

    "Has been a key member of the R Core Team since August 1997 and is well known among R users for his activity on R"

Published: January 9, 2004 by Springer

Summary: This book provides an elementary-level introduction to R targeting both non-statistician scientists in various fields and students of statistics.    The main mode of presentation is via code examples with liberal commenting of the code and output, from the computational as well as the statistical viewpoint.  Supplementary R package can be downloaded that contains the data sets.

The book includes examples of statistical standard deviations, one- and two- sample tests with continuous data, regression analysis, one- and two- way analysis of variance, regression analysis, analysis of tabular data and sample size calculations.  In the last chapters includes  multiple linear regression analysis, linear models in general, logistic regression, survival analysis, Poisson regression and nonlinear regression.

For preview of the book, click here

To buy book or specific chapters, click here

Author: Andrew P. Beckerman, Owen L. Petchey

    "Evolutionary Ecologists with over 20 years of combined experience using R for data analysis and visualization"

Published: July 22, 2012 by Oxford University Press

This book provides a fundamental introduction for biologists new to R.  While teaching how to import, explore, graph and analyze data, it keeps readers focussed on their ultimate goals-communicating their data in oral presentations, posters, papers, and reports.  It also provides a consistent workflow for using R that is simple, efficient, reliable, accurate, and reproducible.  The material in the book reproduces the engaging and sometimes humorous nature of the three-day course on which it is based.

For a preview of this book, click here

For purchase through Amazon

If you want to take a really critical view:

http://ir.nmu.org.ua/bitstream/handle/123456789/129284/2acc6cf8d29603bd81ba8253a752e03b.pdf?sequence=1

I think you can order print copies of this from AAAS. 

Description here: http://www.aaas.org/news/new-aaas-resources-support-undergraduate-stem-education-reform

Download here: http://ccliconference.org/measuring-teaching-practices/

Much more focused on assessment.

How People Learn is a classic - I don’t think you can go wrong with it. 

Survey of ed psych and cognitive science research.

Full text available online. 

http://www.nap.edu/openbook.php?isbn=0309070368

I haven’t read this one yet but it is hot of the presses and likely to get some attention. I believe that it is an attempt to connect teachers to the Discipline-Based Education Research: Understanding and Improving Learning in Undergraduate Science and Engineering (2012) also NAS.

Full text available online.

http://www.nap.edu/catalog/18687/reaching-students-what-research-says-about-effective-instruction-in-undergraduate

National Research Council. Reaching Students: What Research Says About Effective Instruction in Undergraduate Science and Engineering. Washington, DC: The National Academies Press, 2015.

https://llk.media.mit.edu/courses/readings/Collins-Rethinking-Education.pdf

I found this to be a pretty forward looking discussion about the impact of technology on education. The link leads to a summary of the argument that might help you decide if you want to read it all.

Students calculate F-statistics using data on 73 subpopulations of the outcrossing species Phlox drummondii. They compare their findings to data that I already presented in class on the highly selfing species Phlox cuspidata.

Created by Arietta Fleming-Davies

Created by Bob Sheehy

Created by Jeremy Seto

Created by Alison Hale

This lab activity is published by ESA in Teaching Issues and Experiments in Ecology (TIEE).

Exercise on calculating fitness and selection coefficients.

Created by Bob Sheehy at Radford University

Created by Bob Sheehy at Radford University