June 16, 2025 — Tipping points are the death of ecosystems. So scientists watch as warning signs gradually worsen until an ecosystem reaches the point of no return, when animal populations suddenly collapse. While tipping points can sometimes be predicted, what comes next is often shrouded in mystery, stymying efforts to prevent the impending disaster or prepare for what’s to come.
A new study by a team of researchers at the University of California, Santa Cruz, and the National Oceanic and Atmospheric Administration (NOAA) introduces a method for modeling the murky future beyond a tipping point. The paper, published on June 13 in PNAS, demonstrates how this model can act as a “crystal ball” into the future of ecosystems—providing enough lead time to intervene before there’s nothing left to save.
“It gives us this fundamental insight into predicting what’s going to happen in the future,” said Eric Palkovacs, a senior author on the paper and professor of ecology and evolutionary biology at UC Santa Cruz. “That allows us to either do the things necessary to avoid that transition, or if we’re going to experience it, to plan for it and figure out the best ways to cope with it.”
Seeing the future
In healthy ecosystems, species populations fluctuate in predictable ways: Sea urchins feed on a kelp forest, otters then feed on the urchins, and the kelp regrows. But if the ecosystem loses equilibrium, disaster can suddenly strike. If warming waters drive sea urchins to kill off a kelp forest, the ecosystem suddenly crosses a tipping point that can doom all the species it supports. The result is a new regime of population fluctuations that can be hard to correct.
“You have many of these cases where the system can live in different states. You have a state with lots of kelp, and a state without kelp,” said Lucas Medeiros, the study’s lead author and a former postdoctoral scholar at UC Santa Cruz.
Currently, researchers have some methods for predicting what lies beyond an ecosystem’s tipping point, but each approach has its tradeoffs. Some existing methods make predictions using machine-learning algorithms. However, these approaches require large datasets, which often don’t exist for research on ecosystems, where data might be collected yearly or even less frequently.