abriel Popkin writes in Science that a new study promises a way to predict — and possibly head off — catastrophic ecosystem failures and the collapse of species that support those ecosystems, with just one measurement.
For years, scientists studying lakes and other ecosystems have predicted collapses before they happen, using long-term data on environmental factors and population health. But scientists want warning signals that don’t require years of expensive monitoring.
In a 2013 laboratory study, physicist Jeff Gore of the Massachusetts Institute of Technology in Cambridge took a step in that direction by predicting when yeast colonies were about to die of stress because of low population densities. The longer the distance between healthy and unhealthy colonies, the likelier they were to collapse. Dr. Gore and his colleagues named this measure “recovery length.”
To see whether the measurement would hold up in the field, Dr. Gore teamed with Lisandro Benedetti-Cecchi, an ecologist at the University of Pisa in Italy, to study algae along the shore of the small Italian island of Capraia.
They created 2-meter-long study plots with adjacent areas of two types of algae: ecosystem-supporting miniforests of greenish brown Cystoseira amentacea and turflike species that nurture far less biodiversity. The researchers then removed different fractions of C. amentacea from each plot — 0%, 25%, 50%, and 75%. The more they removed, the farther the turf algae invaded.
By measuring how far the turfs invaded, the researchers determined each plot’s recovery length. After 2 years, in the plots missing 75% of the forest algae, the ecosystem tipped over to entirely turf algae and C. amentacea never came back, showing that recovery length could predict ecosystem collapse, the researchers reported in Nature Ecology & Evolution.