The hidden chaos lurking in ecosystems

By the early 1990s, ecologists had amassed enough time series datasets of species populations and enough computing power to test these ideas. There was just one problem: the chaos didn’t seem to be there. Only about 10% of the populations examined seemed to change chaotically; the rest cycled stably or fluctuated randomly. Ecosystem chaos theories fell into scientific disuse in the mid-1990s.

The new results from Rogers, Munch and fellow Santa Cruz mathematician Bethany Johnson, however, suggest that older work missed where the chaos lay. To detect chaos, previous studies used one-dimensional models – the size of a species’ population over time. They did not take into account corresponding changes in disordered real-world factors such as temperature, sunlight, precipitation, and interactions with other species that might affect populations. Their one-dimensional models captured how populations changed, but not why they changed.

But Rogers and Munch “went to get [chaos] in a more sensible way,” said Aaron King, professor of ecology and evolutionary biology at the University of Michigan, who was not involved in the study. Using three different complex algorithms, they analyzed 172 population time series of different organisms as patterns with up to six dimensions instead of just one, leaving room for the potential influence of non-human environmental factors. specified. In this way, they could check whether unnoticed chaotic patterns could be incorporated into the one-dimensional representation of population changes. For example, more precipitation might be chaotically linked to increases or decreases in population, but only after a delay of several years.

In the demographic data of about 34% of the species, Rogers, Johnson and Munch found that the signatures of nonlinear interactions were indeed present, which was much more chaotic than previously detected. In most of these datasets, the population changes for the species did not appear chaotic at first, but the relationship between the numbers and the underlying factors did. They couldn’t say precisely what environmental factors were responsible for the chaos, but whatever they were, their fingerprints were on the data.

The researchers also found an inverse relationship between an organism’s body size and the chaotic nature of its population dynamics. This may be due to differences in generation times, with smaller organisms that reproduce more often also being more often affected by outside variables. For example, diatom populations with generations around 15 hours show much more chaos than wolf packs with generations around five years.

However, this does not necessarily mean that wolf populations are inherently stable. “One possibility is that we don’t see the chaos there because we just don’t have enough data to go back over a long enough period to see it,” Munch said. In fact, he and Rogers suspect that due to their data constraints, their models might be underestimating the amount of underlying chaos present in ecosystems.

Sugihara thinks the new findings could be important for conservation. Improved models with the right element of chaos could do a better job of predicting toxic algal blooms, for example, or tracking fish populations to prevent overfishing. Considering chaos could also help researchers and conservation managers understand how far it is possible to meaningfully predict population size. “I think it’s helpful that the issue is on people’s minds,” he said.

However, he and King caution against placing too much reliance on these chaos-aware models. “The classic concept of chaos is basically a stationary concept,” King said. It is built on the assumption that chaotic fluctuations represent a deviation from a predictable and stable norm. But as climate change progresses, most real-world ecosystems are becoming increasingly unstable, even in the short term. Even considering many dimensions, scientists will need to be aware of this ever-changing baseline.

Nevertheless, taking chaos into account is an important step towards more accurate modeling. “I think it’s really exciting,” Munch said. “It just goes against the way we currently think about ecological dynamics.”

Original story reproduced with permission from Quanta Magazine, an editorially independent publication Simons Foundation whose mission is to enhance the public understanding of science by covering developments and trends in research in mathematics and the physical and life sciences.