Fitness landscapes are an effective way of showing how well organisms are doing at reproducing. Traditionally, however, these models are static snapshots of processes that often take millennia to unfold. Now, computational biologists have created a video that visualizes these adaptive landscapes over time.
Evolutionary biologist Sewall Wright came up with the idea of fitness landscapes back in 1932. His maps displayed reproductive success, or fitness, of individual organisms as a function of genotype (a organism's genetic constitution) or phenotype (or organism's observable physical characteristics). Traditionally, these maps are shown in two-dimensions, looking something a bit like this:
So, an organism with "higher" fitness has a "higher" chance of reproducing, hence the topographical nature of the maps. Populations, therefore, tend to evolve towards higher ground in the fitness landscape (which isn't entirely intuitive given that topographies tend to show more difficulty attaining higher ground, but there you have it).
Now, thanks to a set of new computer visualizations from Bjørn Østman and Randy Olson, we can see these landscapes in three-dimensions over time for a more illustrative perspective.
Here are some gifs that were provided to io9 by the authors:
Their new models explore three phenomena in evolutionary dynamics that can be difficult to comprehend. As they write at BEACON:
First we show dynamic landscapes with two fluctuating peaks in which the population track the peaks as they appear at difference locations in phenotype space. We also demonstrate negative density-dependent selection, which causes the population to split into distinct subpopulations located on separate peaks, illustrating how speciation can occur in sympatry. Lastly, we show the survival of the flattest where the population prefers a tall narrow peak at low mutation rate, but moves to the lower but wider plateau at high mutation rate. These examples highlight how visualizing evolution on fitness landscapes fosters an intuitive understanding of how populations evolve.