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Machine Learning Biogeographic Processes from Biotic Patterns: A New Trait-Dependent Dispersal and Diversification
Jeet Sukumaran1, Evan P Economo2, L Lacey Knowles3
1University of Michigan, Ann Arbor, MI 48109-1079, USA; jeetsukumaran@gmail.com.
Systematic Biology
|December 31, 2015
Summary
This study introduces a new phylogenetic model integrating geography, ecology, and diversification. The method accurately tests macroevolutionary theories like the taxon cycle using statistical inference.
Area of Science:
- Macroevolutionary Biology
- Biogeography
- Phylogenetics
- Trait Evolution
Background:
- Existing statistical biogeographical methods conflate geography and ecology or assume uniformity, limiting the study of lineage-specific dynamics.
- This restricts the investigation of macroevolutionary theories linking geographical and species histories through ecological and evolutionary processes, such as taxon cycle theory.
Purpose of the Study:
- To develop a novel model for generating phylogenies that incorporates interacting geographical range evolution, trait evolution, and diversification processes.
- To introduce a likelihood-free inference method for this model using discriminant analysis of principal components on phylogenetic summary statistics.
- To apply the method to test for ecological constraints in dispersal regimes within the Wallacean avifaunal radiation, specifically examining the taxon cycle.
Main Methods:
- A new model was developed to simulate phylogenies under superimposed geographical range evolution, trait evolution, and diversification.
- A likelihood-free inference approach was employed, utilizing discriminant analysis of principal components (DA-PC) on summary statistics.
- Discriminant functions were trained on simulated data, and the method was validated across a wide parameter space.
Main Results:
- The DA-PC method demonstrated efficiency and robustness in model selection.
- The approach performed well across diverse parameter spaces defined by dispersal, trait evolution, and diversification rates.
- Application to Wallacean avifauna provided insights into habitat and trophic level constraints on dispersal regimes.
Conclusions:
- The developed model and inference method enable the study of complex macroevolutionary biogeographical theories previously intractable.
- This approach allows for the investigation of lineage-specific ecological dynamics influencing diversification and range evolution.
- The findings offer a powerful new tool for analyzing empirical phylogenetic data in the context of macroevolutionary hypotheses.
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