Department of Mathematics Colloquium
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Abstract |
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| The dynamic behavior of evolving biological
systems involves a vast number of factors, including environmental
variables, complex interactions among species, and numerous organismal
traits such as morphology, physiology, and behavior. This implies
a highly multidimensional space in which evolution can operate.
However, tight relationships among factors, such as strong genetic
correlations among traits, can constrain the trajectories of evolution
to a much smaller subspace. I will discuss a novel technique for
estimating the number of independent axes along which evolution can
operate – the dimensionality of evolution – based on empirical data in
the form of interaction matrices. I will illustrate the approach
for estimating the dimensionality of reproductive isolation, the
critical step in the formation of new species. Based on datasets
from several animal groups, this dimensionality appears to have a
strong upper limit, and mapping of independent axes can provide new
insight into the underlying biological processes. The method is
easily generalizable to a wide range of biological situations, and the
type of empirical data required to draw inferences is commonly gathered
in ecological and evolutionary studies.
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