Research challenges traditional methods used to analyze trees of life

Long-used approaches for reconstructing trees of life—the branches of extinction or adaptation taken by species over evolutionary time scales—are deeply flawed, according to new research in Nature.

While paleontological evidence provides insights on how and why patterns of biodiversity have changed over geological time, fossil finds for many types of organisms are too scant to say anything, said University of Oregon biologist Stilianos Louca, lead author of a paper placed online April 15 ahead of print in the journal Nature.

An alternative approach, he noted, relies on using identifiable changes in an organism's genetic makeup, but the signal in this type of data can be misleading.

“Our finding casts serious doubts over literally thousands of studies that use phylogenetic trees of extant data to reconstruct the diversification history of taxa, especially for those taxa where fossils are rare, or that found correlations between environmental factors such as changing global temperatures and species extinction rates,” said Louca, who is a member of the UO’s Institute of Ecology and Evolution.

In their paper, Louca and Matthew W. Pennell, an evolutionary biologist at the University of British Columbia in Vancouver, also offer a way forward – a mathematical model that introduces alternative variables to characterize long-term evolutionary scenarios that can be accurately identified from phylogenetic data.

"I have been working with these traditional types of models for a decade now,” Pennell said. “I am one of the lead developers of a popular software package for estimating diversification rates from phylogenetic trees. And, as such, I thought I had a really good sense of how these models worked. I was wrong."

In their paper, the researchers note that long-used methods extract information about evolution from still-living organisms, using variants of a mathematical birth-death process. These, however, cannot possibly extract information about both speciation and extinction rates, especially for a majority of taxa, such as bacteria, that have left no fossil record.

The paleontological approach estimates the number of species that have appeared and disappeared in various intervals based on discovered fossils and their estimated minimum and maximum ages. In the phylogenetic approach, information is extracted from evolutionary relationships between existing species, using mostly genetic data, and structured in phylogenetic trees known as timetrees.

This is often done by finding a speciation/extinction scenario that would have been the most likely to generate a given phylogenetic tree.

“While an impressive suite of computational methods has been developed over the past decades for extracting whatever information is left, until now we lacked a good understanding of exactly what information is left in these trees, and what information is forever lost,” Louca said.

Louca and Pennell’s mathematically driven approach clarifies precisely what information can be extracted from extant timetrees under the generalized birth-death model. The researchers introduce new identifiable and easily interpretable variables that contain all available information about past diversification dynamics and how they can be estimated.

“We suggest that measuring and modeling these identifiable variables offers a more robust way to study historical diversification dynamics,” they write in the paper. “Our findings also make clear that paleontological data will continue to be crucial for answering some macroevolutionary questions.”

"The future depends on synthesizing information from datasets of both molecules and fossils,” Pennell said.

The researchers emphasize that their results do not invalidate the theory of evolution itself, just put constraints on what type of information can be extracted from genetic data to reconstruct evolution's path.

Louca, who joined the UO last year after earning his doctorate at the University of British Columbia, was supported by a start-up grant from the UO. Pennell had funding from a discovery grant from Canada’s Natural Sciences and Engineering Research Council.