Estimating ancient biogeographic patterns with statistical model discrimination
Estimating ancient biogeographic patterns with statistical model discrimination
复制标题
用统计模型辨别估计古代生物地理模式
DOI:
10.1002/ar.25067
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发表时间:
2023
期刊:
影响因子:
--
通讯作者:
Zanno, Lindsay E.
中科院分区:
文献类型:
--
作者:
Gates, Terry A.;Cai, Hengrui;Hu, Yifei;Han, Xu;Griffith, Emily;Burgener, Landon;Hyland, Ethan;Zanno, Lindsay E.
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 orp‐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.
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DOI:
--
发表时间:
1971
期刊:
影响因子:
--
作者:
P. Dodson
通讯作者:
P. Dodson
影响因子:
1.4
作者:
Terry A Gates;E. Gorscak;P. Makovicky
通讯作者:
P. Makovicky
影响因子:
2.6
作者:
Thomas W. Dudgeon;Zoe Landry;Wayne R. Callahan;C. Mehling;Steven Ballwanz
通讯作者:
Steven Ballwanz
DOI:
10.1016/j.palaeo.2010.03.008
发表时间:
2010-05-15
影响因子:
3
作者:
Gates, T. A.;Sampson, S. D.;Getty, M. A.
通讯作者:
Getty, M. A.
DOI:
10.1073/pnas.0606028103
发表时间:
2006-09
期刊:
Proceedings of the National Academy of Sciences
影响因子:
--
作者:
Steve C. Wang;P. Dodson
通讯作者:
Steve C. Wang;P. Dodson