Dispersal-Vicariance Analysis: A New Approach to the Quantification of Historical Biogeography

Quantification in historical biogeography has usually been based on the search for a single branching relationship among areas of endemism. Unlike organisms, however, areas rarely have a unique hierarchical history. Dispersal barriers appear and disappear and may have different effects on different...

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Bibliographic Details
Published in:Systematic biology Vol. 46; no. 1; pp. 195 - 203
Main Author: Ronquist, Fredrik
Format: Journal Article
Language:English
Published: Society of Systematic Biologists 01-03-1997
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Summary:Quantification in historical biogeography has usually been based on the search for a single branching relationship among areas of endemism. Unlike organisms, however, areas rarely have a unique hierarchical history. Dispersal barriers appear and disappear and may have different effects on different species. As a result, the biota of an area may consist of several components with separate histories, each of which may be reticulate rather than branching. In an attempt to address these problems, I present a new biogeographic method, dispersal–vicariance analysis, which reconstructs the ancestral distributions in a given phylogeny without any prior assumptions about the form of area relationships. A three-dimensional step matrix based on a simple biogeographic model is used in the reconstruction. Speciation is assumed to subdivide the ranges of widespread species into vicariant components; the optimal ancestral distributions are those that minimize the number of implied dispersal and extinction events. Exact algorithms that find the optimal reconstruction(s) are described. In addition to their use in taxon biogeography, the inferred distribution histories of individual groups serve as a basis for the study of general patterns in historical biogeography, particularly if the relative age of the nodes in the source cladograms is known.
Bibliography:istex:EF6B7FD665548F7B0E5475069636F9ECD520B833
ark:/67375/HXZ-56V1XLM4-L
ISSN:1063-5157
1076-836X
DOI:10.1093/sysbio/46.1.195