An Individual-Based Simulation Approach to the Evolution of Locomotor Performance

Maximal whole-organism performance traits measured in the laboratory and the levels of performance expressed in the field often exhibit a mismatch, complicating our understanding of the selection pressures influencing the evolution of performance traits. To better understand the evolution of locomotor performance, we built an individual-based simulation to test hypotheses about selection on locomotor performance. Starting with a population of individuals with two correlated but variable performance traits, we simulated these individuals surviving and reproducing in a complex environment, presenting each individual with successive ecological challenges requiring specific performance capabilities over their lifespan. While most challenges require sub-maximal speeds, intermittent bouts requiring increased performance, such as escape from predators, introduce strong, but infrequent, selection for maximal performance. By comparing the results of simulations run with individuals that only perform at their maximum levels versus those that adjust this effort, we show that intra-individual variation in speed confers a selective advantage, regardless of the extent of that variation. We also show that the direction and strength of the correlation between the two performance traits affects the evolutionary trajectory of phenotypic change. Ultimately, this model allows us to simulate the evolution of movement speeds over a range of selective contexts, offering insight into the factors affecting the evolutionary relationship between optimal and maximal performance.