Morphological Integration, Evolutionary Constraints, and Extinction: A Computer Simulation-Based Study

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Abstract

It is well known that there is a strong relationship among the environment, selection, and extinction, but the underlying role of genetics and genetic constraints in contributing to extinction is less appreciated. Integration of characters may enhance survivability for species, providing that selective pressure is parallel with the patterns of morphological integration. However, we hypothesize that, if the direction of selection shifts, integration may also prevent populations from responding quickly enough to the new directions of selection. This would lead to the inability to find a successful adaptive solution, causing downward pressure on the population, and ultimately, extinction. We test this model with a computer simulation, using an adaptive landscape model. We generate populations of varying levels of multivariate integration and generate selection pressures to test the ability of the populations to respond to selection both parallel and orthogonal to the axis of maximum variation. In these simulations, more highly integrated populations survived longer when selection was in the direction of maximum variation. However, when selection was closer to orthogonal to the axis of maximum variation, extinction was more rapid in highly integrated populations. These results suggest that integration may play a strong role in both survivability and extinction. Tightly integrated populations are highly persistent when selection pressure is close to the axis of maximum variation, which is expected to frequently be the case since integration is likely often a product of selection. However, these highly integrated taxa are more susceptible to extinction when the direction of selection shifts, and is closer to orthogonal to the axis of maximum variation. © 2012 Springer Science+Business Media, LLC.

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Villmoare, B. (2013). Morphological Integration, Evolutionary Constraints, and Extinction: A Computer Simulation-Based Study. Evolutionary Biology, 40(1), 76–83. https://doi.org/10.1007/s11692-012-9186-3

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