This is one of a series of posts on evolvability. It is based off my undergraduate thesis, which I wrote at the University of Puget Sound under advisors Dr. America Chambers and Dr. Adam Smith. The original thesis is available here. Blog Series on Evolvability I like digital evolution because it necessitates the examination of fundamental assumptions of what is necessary for evolution. Building a digital evolution system, a researcher must work out how the phenotypes that are being evolved should be genetically encoded. This decision raises an interesting question: how do genetic encodings for digital evolution systems influence the evolutionary process within these systems? I think this question is really interesting! Better understanding this question has practical implications for digital evolution, as well. So, I picked it up as the topic for my undergraduate thesis. […]
If mutation is the ultimate source of genetic novelty, can a bias of mutation inflow alter the evolutionary trajectory of a population? On the surface, it would appear the answer should be yes. Intuitively, the path through genotype space should be influenced by the manner in which mutations are introduced into the population. For an easy to digest analogy, consider a rephrased thought experiment proposed by Stoltzfus and Yampolsky called “Climbing Mount Probable” [Stoltzfus, A., & Yampolsky, L. Y. (2009). Climbing mount probable: Mutation as a cause of nonrandomness in evolution. Journal of Heredity, 100(5), 637–647]. Beginning with the 80 year old fitness-landscape as a mountain analogy, we can envision a population of haploid organisms clustered about the face of a mountain with the organisms’ elevation representing their absolute fitnesses. Genetic novelty introduced through mutation during […]
Laboratory components are often integral parts of both K-12 and college science courses. I certainly had a lot over the course of my science education; 5 courses with labs in high school, 8 in college. But for the overwhelming majority of them, I was essentially following a recipe and doing by rote things which had already been done and where the answers were already known. It was only in science-fair-style projects that I typically had any control over the questions I was asking, or how I would go about trying to answer them. But science education doesn’t have to be like that. Inquiry-based science practice is a growing part of the recommendations for science education1 2. Thankfully, computational tools are making these practices more accessible. NGSS Lead States. (2013). Next Generation Science Standards: For States, By […]
Stommel plots are a popular tool in ecology for thinking about the spatial and temporal scales at which processes and patterns occur. Named for Henry Stommel, who created the first such plot (shown below), these plots generally have spatial scale on the x axis, temporal scale on the y axis, and some variable indicating strength of effect on the z axis. More recently, the z axis has often been replaced with colored circles, but the concept remains the same. For more information about the history of Stommel plots, see this post. These plots are often helpful in figuring out appropriate scales for data collection and analysis, as well as for conceptualizing how processes and patterns interact. But I have yet to see one that incorporates evolution.
Previously, I talked about what fitness means. Now, it’s worth taking a moment to talk about how we calculate fitness in a few of our experimental systems, both biological and computational. Because what exactly we choose to measure can result in different outcomes, the way in which we measure something is important and therefore valuable to think critically about.