Estimating ancient biogeographic patterns with statistical model discrimination.

Autor: Gates TA; Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina, USA.; North Carolina Museum of Natural Sciences, Raleigh, North Carolina, USA., Cai H; Department of Statistics, University of California Irvine, Irvine, California, USA., Hu Y; Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA., Han X; Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA., Griffith E; Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA., Burgener L; Department of Geology, BYU-Idaho, Rexburg, Idaho, USA., Hyland E; Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, North Carolina, USA., Zanno LE; Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina, USA.; North Carolina Museum of Natural Sciences, Raleigh, North Carolina, USA.
Jazyk: angličtina
Zdroj: Anatomical record (Hoboken, N.J. : 2007) [Anat Rec (Hoboken)] 2023 Jul; Vol. 306 (7), pp. 1880-1895. Date of Electronic Publication: 2022 Sep 23.
DOI: 10.1002/ar.25067
Abstrakt: The geographic ranges in which species live is a function of many factors underlying ecological and evolutionary contingencies. Observing the geographic range of an individual species provides valuable information about these historical contingencies for a lineage, determining the distribution of many distantly related species in tandem provides information about large-scale constraints on evolutionary and ecological processes generally. We present a linear regression method that allows for the discrimination of various hypothetical biogeographical models for determining which landscape distributional pattern best matches data from the fossil record. The linear regression models used in the discrimination rely on geodesic distances between sampling sites (typically geologic formations) as the independent variable and three possible dependent variables: Dice/Sorensen similarity; Euclidean distance; and phylogenetic community dissimilarity. Both the similarity and distance measures are useful for full-community analyses without evolutionary information, whereas the phylogenetic community dissimilarity requires phylogenetic data. Importantly, the discrimination method uses linear regression residual error to provide relative measures of support for each biogeographical model tested, not absolute answers or p-values. When applied to a recently published dataset of Campanian pollen, we find evidence that supports two plant communities separated by a transitional zone of unknown size. A similar case study of ceratopsid dinosaurs using phylogenetic community dissimilarity provided no evidence of a biogeographical pattern, but this case study suffers from a lack of data to accurately discriminate and/or too much temporal mixing. Future research aiming to reconstruct the distribution of organisms across a landscape has a statistical-based method for determining what biogeographic distributional model best matches the available data.
(© 2022 The Authors. The Anatomical Record published by Wiley Periodicals LLC on behalf of American Association for Anatomy.)
Databáze: MEDLINE