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Updated: Jun 12, 2026
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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
Published on: August 14, 2018
Slowdowns in diversification rates from real phylogenies may not be real
Natalie Cusimano1, Susanne S Renner
1Systematic Botany and Mycology, Department of Biology, University of Munich, D-80638 Munich, Germany. cusimano@lrz.uni-muenchen.de
Systematic Biology
|June 16, 2010
Summary
Diversification rate downturns in phylogenies may be misleading. Biased or structured species sampling, especially below 80%, can falsely suggest rate slowdowns, impacting evolutionary studies.
Area of Science:
- Evolutionary Biology
- Phylogenetics
- Macroevolution
Background:
- Studies of diversification patterns frequently observe a decrease in lineage accumulation over time.
- This pattern of rate downturns is often interpreted as evidence for adaptive radiations, density-dependent regulation, or metacommunity interactions.
- Statistical tests (gamma statistic, MCCR, birth-death models) are commonly used to assess rate changes but assume random species sampling.
Purpose of the Study:
- To investigate the impact of nonrandom sampling on the inference of diversification rate changes.
- To evaluate how biased and structured sampling in phylogenies affect commonly used statistical tests for diversification rates.
- To establish guidelines for interpreting diversification rate patterns from empirical phylogenies.
Main Methods:
- Simulated phylogenetic trees (60 and 150 species) and a complete empirical tree (58 species) were subjected to experimental pruning to mimic different sampling strategies (random, biased, structured).
- The gamma statistic/Monte Carlo constant rates (MCCR) test and birth-death likelihood models/Akaike Information Criterion (AIC) scores were applied to assess diversification rate changes in pruned trees.
- Inferences from pruned trees were compared to analyses of complete or randomly sampled trees.
Main Results:
- Random species sampling generally allowed accurate inference of the true diversification model.
- Oversampling of early-diverging lineages (deep nodes) significantly biased inferences towards detecting rate downturns.
- Simulations indicated that biased and structured sampling strongly affects rate inference when species sampling percentages fall below 80%.
Conclusions:
- Nonrandom sampling, particularly oversampling of deep nodes, can create spurious signals of diversification rate slowdowns.
- Diversification rate models are sensitive to sampling biases, potentially leading to incorrect evolutionary interpretations.
- A minimum of 80% species sampling is recommended for real phylogenies to reliably infer diversification rate downturns.
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