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Trait-dependent diversification in angiosperms: Patterns, models and data.

Andrew J Helmstetter¹, Rosana Zenil-Ferguson², Hervé Sauquet^3,4

¹Fondation pour la recherche sur la biodiversité-CEntre de Synthèse et d'Analyse sur la Biodiversité, Montpellier, France.

Ecology Letters

|February 25, 2023

Summary

This summary is machine-generated.

Traits influence species diversification in flowering plants, but universal drivers are elusive. Study findings depend on data set size and quality, highlighting the need for standardized methods in evolutionary biology research.

Keywords:

BiSSE extinction flowering plants macroevolution phylogenetics speciation trait evolution

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Area of Science:

Evolutionary Biology
Ecology
Botany

Background:

Species richness varies across the tree of life, prompting research into trait-based diversification.
Angiosperms (flowering plants) are a highly diverse group crucial for ecosystem functioning.
The relative importance and interactions of traits driving angiosperm diversification remain unclear.

Purpose of the Study:

To synthesize findings from studies using state-dependent speciation and extinction (SSE) models in angiosperm clades.
To investigate the influence of intrinsic traits on species diversification.
To assess the impact of data set properties on SSE model outcomes and propose best practices.

Main Methods:

Synthesized data from 152 published studies employing SSE models on angiosperm clades.
Analyzed the relationship between intrinsic traits (reproduction, morphology) and diversification rates.
Correlated SSE model results with data set characteristics (size, age, sampling completeness).

Main Results:

Intrinsic traits were frequently linked to diversification, but no universal drivers emerged across all clades.
SSE model outcomes were significantly influenced by data set properties.
Larger, older, or less well-sampled data sets tended to yield different trait-dependent diversification patterns.

Conclusions:

Trait-based diversification in angiosperms is complex and context-dependent.
Data set properties critically affect inferences from SSE models, necessitating methodological standardization.
Future studies should integrate ecological, demographic, and genetic factors with SSE model results for robust conclusions.