Visualizing evolution in action: Dynamic fitness landscapes

Fitness landscapes were invented by Sewall Wright in 1932. They map fitness, or reproductive success, of individual organisms as a function of genotype or phenotype. Organisms with higher fitness have a higher chance of reproducing, and populations therefore tend to evolve towards higher ground in the fitness landscape. Even though only two traits can be visualized this way, we can actually observe evolution in action. Building on the idea of fitness landscapes, Bjørn Østman and I decided to create some animations of simulated evolving populations to illustrate concepts of evolution that are typically difficult to comprehend.

Here we demonstrate the effect of a dynamically changing environment on an evolving population. If the environment changes slowly enough, the population can adapt and “keep up” with environmental change. But if the environment changes too quickly, evolution breaks down and the population can no longer adapt to the environment.

Warning: The GIFs on this page are large and may take some time to load.

dynamic-slow


dynamic-fast


Here’s the full video that Bjørn and I submitted to the ALife 2014 Science Visualization Competition.

Randy is a PhD candidate in Michigan State University's Computer Science program. As a member of Dr. Chris Adami's research lab, he studies biologically-inspired artificial intelligence and evolutionary processes.

Posted in data visualization, research Tagged with: , , , ,

About this blog

The data visualizations on this blog are the result of my “data tinkering” hobby, where I tackle a new data analysis problem every week. If I find something interesting, I report my findings here to share with the world.

If you would like to use one of my graphs on your website or in a publication, please email me.

Archives

Enter your email address to subscribe to this blog and receive notifications of new posts by email.