Peer Review

Macroevolution

Material Detail page

Reviewed:: Mar 11, 2025 by Biology

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Ratings

Overall Rating:

Content Quality:

Effectiveness:

Ease of Use:

Overview:

This is graduate and upper-level undergraduate course on Macroevolution. Evolutionary thinking provides the underpinnings of modern biology. In recent decades, the field of macroevolution (evolution above the species level) has matured into a rich discipline with a well-developed mathematical theory for testing hypotheses of species diversification, for understanding trait evolution, and evaluating patterns of covariation across the tree of life. This course will provide a synthetic view of biology and how life on earth has changed over time.

Type of Material:

Online Course.

Recommended Uses:

This is a course (Macroevolution) which can be taken at Iowa State University or can be followed partially by the public. The course can be adopted (at least the available parts), with modification to the schedule.

In-class, as primary or homework resource in an upper division Science class.

Technical Requirements:

Ability to run R and Rstudio; a text editor such as VS Code or Sublime Text.

Identify Major Learning Goals:

Course Outcomes

Upon completion of the course, students will:

Understand patterns of diversity in the fossil record, and changes in that diversity over time
Understand macroevolutionary patterns and processes, and the difference between gradualism, stasis, and punctuated equilibrium
Become familiar with ‘tree thinking’, and understand the principles of using a phylogenetic perspective to address evolutionary questions in biology
Gain experience in applying cutting-edge phylogenetic methods for testing hypotheses in macroevolution

Course Objectives

Be able to explain the history of life on earth, including major extinction events
Know the concepts of gradualism, stasis, and punctuated equilibrium and their importance
Understand the concepts of tempo and mode as applied to species diversification and morphological evolution
Become familiar with statistical phylogeny estimation methods and be able to apply them to biological data
Know how to estimate diversification rates and how to compare them
Understand the major macroevolutionary models of trait evolution, including Brownian motion, Ornstein-Uhlenbeck, and early-burst models
Know how to implement the phylogenetic comparative method for evaluating macroevolutionary trends across a phylogeny

Target Student Population:

College Upper Division, Graduate School.

Prerequisite Knowledge or Skills:

Strong background in biology, phylogeny and evolution, computer skills, math.
Facility with several digital text editor.
Digital skills in online modeling.
Knowledge of programming using R and RStudio.

Content Quality

Rating:

Strengths:

As a graduate level/upper division course at a major university, the content is excellent in quality.
The information is a comprehensive survey of the topic.
Current and relevant information.
Materials presented in a concise and logical manner.
The course is informed by scholarship.

Concerns:

Learners who are not skilled in computer analysis of phylogeny will not be able to benefit from this course.

Potential Effectiveness as a Teaching Tool

Rating:

Strengths:

The course provides readings (articles), slide presentations, and some activities, and other resources (e.g., computer code, scripts, data) making it effective as a teaching tool.
The focus on both the conceptual (big picture) level and on the details of the analyses makes this course effective.
Educators can use individual items (articles, slides) in their teaching without adopting the entire course.
The course provides a calendar and syllabus for those who might be teaching similar courses.
The course syllabus provides a useful map as it presents the topics and activities in sequential order.
Information provided helps students understand the relatedness of the topics to other areas of biology.
Links to primary references are provided so students can further improve their understanding of the concepts.
The course provides hands-on practice on how mathematical analyses support the concepts
Information and link to basic tutorial on The R statistical program and how to install it help students understand and extrapolate the data for broader understanding.

Concerns:

Some of the course is on Canvas which can only be accessed by Iowa State University students enrolled in the course. This is not a concern about the potential effectiveness of the material for the course itself, rather it is a concern for the public using this resource.
Some of the material would be hard to follow without prior knowledge of how it is to be used, assessed, or contextualized (things no doubt explained or done in the course itself by the faculty).
Some of the activities require the learners to have the technical skills in digital programs that are highly specific to the course.

Ease of Use for Both Students and Faculty

Rating:

Strengths:

The information is well-organized and follows a logical order.
Website is accessible with most browsers.
Easy to navigate to the different major sections of the course.
The course site (on github) is consistent in layout and design.
The resources are easy to download or view online.
The course has a CC copyright license.

Concerns:

The left side menu has low contrast and there may be other disability issues in the course.

Other Issues and Comments:: This course is licensed under a Creative Commons Attribution 4.0 International License so it can be adapted or revised following standard protocols.
Creative Commons: