Discriminating models of trait evolution.
Roa Lozano J, Jangra S, DeGiorgio M, Assis R, Adams R
Summary
PubMedWhy it matters This matters because understanding how traits like disease resistance or drought tolerance evolve helps scientists predict which plants might naturally adapt to climate change or new pests, informing smarter breeding and conservation decisions.
Imagine trying to figure out *why* plants or fungi look and behave differently today by piecing together clues from their family tree — without ever being able to watch evolution happen. This new tool, EvoDA, uses a type of computer learning to make those predictions far more accurately than older methods. Tested on fungi, it revealed that most genes are kept stable by natural selection, while a few burst into rapid change — insights that could apply to understanding crop plants too.
chevron_right Technical Details
Scientists developed a new machine-learning method called EvoDA that can better identify how traits evolve across species over time, outperforming traditional statistical approaches — especially when data contains measurement errors.
Key Findings
EvoDA (Evolutionary Discriminant Analysis) significantly outperforms conventional model selection methods, especially when trait data contains measurement error — a common real-world condition.
Analysis of fungal gene expression found that stabilizing selection (keeping traits stable) acts on the majority of genes.
A small subset of genes showed bursts of rapid evolutionary change, particularly those linked to stress response, cellular transport, and transcription regulation.
Abstract Preview
A central challenge in comparative biology is linking present-day trait variation across species with unobserved evolutionary processes that occurred in the past. In this endeavor, phylogenetic com...
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