Journal
PHYSIOLOGICAL AND BIOCHEMICAL ZOOLOGY
Volume 94, Issue 3, Pages 162-170Publisher
UNIV CHICAGO PRESS
DOI: 10.1086/714018
Keywords
adaptation; integrative biology; severe testing; strong inference; phylogenetic comparative methods
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In studies comparing only a few populations or species, the principle of severity can be applied to generate strong inferences about adaptation. This approach involves ensuring closely related species in the study system, formulating clear hypotheses, collecting data that avoid statistical underdetermination, and refining alternative hypotheses systematically to differentiate potential agents of selection.
Phylogenetic comparative methods represent a major advance in integrative and comparative biology and have allowed researchers to rigorously test for adaptation in a macroevolutionary framework. However, phylogenetic comparative methods require trait data for many species, which is impractical for certain taxonomic groups and trait types. We propose that the philosophical principle of severity can be implemented in an integrative framework to generate strong inference of adaptation in studies that compare only a few populations or species. This approach requires (1) ensuring that the study system contains species that are relatively closely related; (2) formulating a specific, clear, overarching hypothesis that can be subjected to integrative testing across levels of biological organization (e.g., ecology, behavior, morphology, physiology, and genetics); (3) collecting data that avoid statistical underdetermination and thus allow severe tests of hypotheses; and (4) systematically refining and refuting alternative hypotheses. Although difficult to collect for more than a few species, detailed, integrative data can be used to differentiate among several potential agents of selection. In this way, integrative studies of small numbers of closely related species can complement and even improve on broadscale phylogenetic comparative studies by revealing the specific drivers of adaptation.
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