Improving the robustness of phylogenetic independent contrasts: Addressing abrupt evolutionary shifts with outlier- and distribution-guided correlation

This dataset provides a comprehensive resource for evaluating phylogenetic comparative methods under diverse evolutionary scenarios. The dataset includes: simulated phylogenetic trees (fixed-full-balanced and randomly generated), trait data for 16, 128, and 256 species, incorporating both gradual and abrupt evolutionary shifts, statistical outputs from multiple phylogenetic comparative methods, including PIC-OGC, PIC-MM, and other robust regression models, and benchmark results for detecting trait correlations under varying degrees of phylogenetic autocorrelation and noise. This dataset enables researchers to explore the impact of evolutionary shifts on trait correlation analysis, compare the performance of phylogenetic methods, and validate novel approaches for handling outliers and non-normal data distributions.
, Phylogenetic tree simulation: Two types of phylogenetic trees were simulated: balanced trees with fixed topologies and randomly generated trees using a coalescent model. The random trees introduced variability in branching rates to reflect diverse phylogenetic scenarios. Tree sizes included 16,128 and 256 species.
Trait data simulation: Trait data were generated under both Brownian motion (BM) and abrupt evolutionary shift scenarios. For abrupt shifts, two traits (X1 and X2) were simulated with independent evolution except for a significant shift at the root branch. Gradual evolution data were simulated under BM with varying levels of noise.
Statistical analysis: Multiple phylogenetic comparative methods were applied to the datasets, including:

PIC-OGC: A hybrid framework integrating Pearson and Spearman correlations to handle outliers and non-normal data.
Robust regression methods (PIC-MM, PIC-L1, etc.).
PGLS models optimized across evolutionary scenarios (BM, λ, OU fix..., , # Data from: Improving the robustness of phylogenetic independent contrasts: Addressing abrupt evolutionary shifts with outlier- and distribution-guided correlation

[https://doi.org/10.5061/dryad.8w9ghx3xp](https://doi.org/10.5061/dryad.8w9ghx3xp)

## Description of the data and file structure

This dataset was generated to evaluate and enhance phylogenetic comparative methods, particularly in handling abrupt evolutionary shifts in trait data. The study aimed to develop and test the Outlier- and Distribution-Guided Correlation (OGC) method, comparing its performance with other phylogenetic methods such as PIC-MM and PGLS under diverse evolutionary scenarios. Simulated phylogenetic trees and trait data were created to reflect varying levels of noise, phylogenetic autocorrelation, and evolutionary shifts. This dataset supports robust statistical analyses and benchmarks for detecting true trait correlations and minimizing false positives in comparative studies.

### Files and variables

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