New findings supply corrective to evolutionary hypothesis

Evolutionary processes appear to operate at different time scales, perhaps necessitating the need for a new theory linking microevolution and macroevolution. The larger question has tantalized scientists: why?

There are plausible explanations. A new species may inhabit a new island chain, allowing for more variation as it spreads into new niches. An asteroid may hit the earth, increasing extinction rates. Perhaps species evolve to an "optimal" trait value and then plateau.

A paper published in PLOS Computational Biology now proposes an entirely new explanation for understanding this evolutionary pattern: statistical "noise." The paper, "Noise leads to the perceived increase in evolutionary rates over short times scales," was written by Brian C. O'Meara, a professor in the Department of Ecology and Evolutionary Biology at the University of Tennessee, and Jeremy M. Beaulieu, an associate professor of in the Department of Biological Sciences at the University of Arkansas.

The authors note that "by employing a novel statistical approach, we found that this time-independent noise, often overlooked as inconsequential, creates a misleading hyperbolic pattern, making it seem like evolutionary rates increase over shorter time frames when, in fact, they do not. In other words, our findings suggest that smaller, younger clades [groups with common ancestors] appear to evolve faster not due to intrinsic properties but because of statistical noise."

The study blends math, statistics and biology to show that this long-held hyperbolic pattern is an anomaly because it doesn't account for the fact that all species on earth are defined as much by their unique traits as the variation that exists in those traits.

It's a common principle in science that the simplest possible explanation to fit the data is usually the right one. Evolution taking place on completely different time scales is far less likely than noise in the numbers.

Ultimately, the study underscores the critical importance of accounting for inherent biases and errors in interpreting biodiversity patterns across both shallow and deep time scales.

In an unpublished summary of their work, the authors note that "[o]ur results might be seen as upsetting: a pattern that could have launched a thousand papers with really interesting biological hypotheses can be explained as an artifact.

"However, this is actually progress -- we have explained a common pattern we see in the world. Biology is rich in mysteries: actually answering one lets us move on to the next. There are still many questions about biological rates, but the current paradigm of plotting rates against time should probably end."