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Estimating ancient biogeographic patterns with statistical model discrimination.

Terry A Gates1,2, Hengrui Cai3, Yifei Hu4

  • 1Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina, USA.

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Summary
This summary is machine-generated.

This study introduces a new statistical method to analyze fossil records and determine the best biogeographical model for species distribution. The method successfully identified distinct plant communities in the Campanian period.

Keywords:
biogeographydinosaurecologylinear regressionphylogenetics

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Area of Science:

  • Paleontology
  • Biogeography
  • Ecology

Background:

  • Species geographic ranges are shaped by ecological and evolutionary factors.
  • Analyzing distributions of multiple species reveals large-scale evolutionary and ecological constraints.
  • Fossil records offer insights into historical biogeographical patterns.

Purpose of the Study:

  • To develop a statistical method for discriminating between hypothetical biogeographical models using fossil data.
  • To assess the fit of different models to observed species distribution patterns.
  • To provide a quantitative approach for understanding past species distributions.

Main Methods:

  • A linear regression method was developed to discriminate biogeographical models.
  • The method uses geodesic distances between sampling sites as the independent variable.
  • Dependent variables include Dice/Sorensen similarity, Euclidean distance, and phylogenetic community dissimilarity.

Main Results:

  • The method was applied to Campanian pollen data, revealing evidence for two distinct plant communities separated by a transitional zone.
  • A case study with ceratopsid dinosaurs showed no clear biogeographical pattern, possibly due to data limitations or temporal mixing.
  • Linear regression residual error was used to provide relative support for tested biogeographical models.

Conclusions:

  • The developed statistical method offers a robust approach for reconstructing past species distributions.
  • It allows researchers to quantitatively evaluate and compare different biogeographical models.
  • Future studies can utilize this method to better understand landscape-level ecological and evolutionary processes.