Evol Ecol Res 14: 729-742 (2012) Full PDF if your library subscribes.
A new metric to calculate the opportunity for selection on quantitative characters
Fanie Pelletier1,3 and Tim Coulson2
1Centre for Population Biology, Imperial College London, Ascot, UK, 2Department of Life Sciences, Imperial College London, Ascot, UK and 3Chaire de recherche du Canada en démographie évolutive et conservation, Université de Sherbrooke, Sherbrooke, Québec, Canada
Correspondence: F. Pelletier, Chaire de recherche du Canada en démographie évolutive et conservation, Université de Sherbrooke, 2500 boulevard de l’Université, Sherbrooke, Québec J1K 2R1, Canada.
e-mail: fanie.pelletier@usherbrooke.caABSTRACT
Background: Evolutionary changes in natural populations can occur on ecological time scales. Investigators have become very interested in characterizing short-term fluctuations in selection pressure, in identifying the circumstances under which the opportunity for selection is greatest, and in determining whether this opportunity is realized.
Aims: Introduce a new metric to explore how the opportunity for selection on the mean of a phenotypic character varies with time. Using data from two long-term studies of marked individuals, examine how the opportunity for and the selection on a character mean and variance covary with population growth.
Metrics: The traditional opportunity for selection metric (OS) is defined as the variance in relative fitness or the variation in absolute fitness divided by the square of the mean absolute fitness. This metric might not be appropriate to evaluate the maximum selection acting on a quantitative character because individual variation in both quantitative characters and in fitness underpins evolution by natural selection. We therefore develop a new metric, the opportunity for selection on a quantitative character (OSM), which considers variation in both character and fitness distributions.
Methods: Determine selection, OS, and OSM, in both simulated and empirical data. Compare the results for the traditional OS metric with the new OSM.
Results: The classical measure of the OS correlates with the OSM when calculated on simulated data but their association was curvilinear for non-normally distributed fitness components. Similar results were found for empirical data but their correlations were lower. Selection is strongest in declining populations and is greatest when the OSM is large, as in harsh environments.
Conclusions: Because most fitness components are non-normally distributed, OS will only approximately capture the maximum possible selection differential on phenotypic characters over a time step. The OSM should be a more useful metric for determining how selection will alter the distribution of characters.
Keywords: fitness, opportunity for selection, population dynamics, survival, vertebrates.
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