There’s a longstanding challenge in biodiversity research: how can we better understand the interplay between ecological processes — things like birth, death, and migration — and evolutionary processes like speciation, extinction and long-distance dispersal? Researchers know that these two types of processes feedback on one another, but it’s hard to study because ecological processes happen locally and on short timescales while evolution often occurs across landscapes and over long periods of time.
A working group led by SFI Omidyar Fellow Andy Rominger meets November 27-30 to explore ways to tackle this problem. They’re testing a unified approach that combines principles from statistical physics with data from modern ecosystems that have evolved, geographically isolated, in a specific chronology.
Much of our understanding of evolution comes from the fossil record. But regions like the Hawaiian Islands and East Africa’s Great Rift Lakes — two examples of geographically isolated ecosystems that evolved in chronological succession — provide a living window into evolution. “You can almost treat them like a fossil record,” says Rominger.
In Hawaii’s case, as the Pacific Plate glided over volcanic hotspots 65 million years ago, the Hawaiian Islands began to form, one after the next, every million years or so. In that same sequence, they began to support life.
“These types of ecosystems are some of the best opportunities to merge the ecological with the evolutionary,” says Rominger. They could also help us understand how ecosystems move into and then back out of steady states of equilibrium. Steady states occur when rates of input and output — for instance, energy requirements and production or immigration and extinction — balance each other out. “There are ways to guess about past ecosystems and populations of extinct species, but there’s no real way to validate. Using systems along chronosequences is one way we can kind of get at that.”
As human activity rapidly pushes ecosystems into non-steady states today, we’re seeing non-stationary dynamics that we don’t understand, says Rominger. “The ultimate goal of this work is to understand non-stationarity from its biological causes and relate that to the kind of impacts humans have on evolutionary potential.”