Analysis of Two-State Multivariate Phenotypic Change in Ecological Studies
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The Department of Ecology, Evolution, and Organismal Biology seeks to teach the studies of ecology (organisms and their environment), evolutionary theory (the origin and interrelationships of organisms), and organismal biology (the structure, function, and biodiversity of organisms). In doing this, it offers several majors which are codirected with other departments, including biology, genetics, and environmental sciences.
History
The Department of Ecology, Evolution, and Organismal Biology was founded in 2003 as a merger of the Department of Botany, the Department of Microbiology, and the Department of Zoology and Genetics.
Dates of Existence
2003–present
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- College of Agriculture and Life Sciences (parent college)
- College of Liberal Arts and Sciences (parent college)
- Department of Botany (predecessor, 2003)
- Department of Microbiology (predecessor, 2003)
- Department of Zoology and Genetics (predecessor, 2003)
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Abstract
Analyses of two-state phenotypic change are common in ecological research. Some examples include phenotypic changes due to phenotypic plasticity between two environments, changes due to predator/non-predator character shifts, character displacement via competitive interactions, and patterns of sexual dimorphism. However, methods for analyzing phenotypic change for multivariate data have not been rigorously developed. Here we outline a method for testing vectors of phenotypic change in terms of two important attributes: the magnitude of change (vector length) and the direction of change described by trait covariation (angular difference between vectors). We describe a permutation procedure for testing these attributes, which allows non-targeted sources of variation to be held constant. We provide examples that illustrate the importance of considering vector attributes of phenotypic change in biological studies, and we demonstrate how greater inference can be made than by evaluating variance components with MANOVA alone. Finally, we consider how our method may be extended to more complex data.
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This article is from Ecology 88 (2007): 683, doi:10.1890/06-0727. Posted with permission.