Nature is full of chaos. A new study has confirmed it and found that chaos is more common in ecosystems than previously thought.
Populations of living things change a lot in nature. Figuring out patterns in these population changes requires some fairly difficult maths.
In general, ecologists sort these fluctuations into three broad categories: “regular” (fluctuating around a stable equilibrium), “random” (impossible to predict) – or “chaotic” (predictable in the short term but not the long term, and sensitive to very small changes).
Chaos appears in other places too; weather patterns are often highly chaotic.
“Knowing whether these fluctuations are regular, chaotic, or random has major implications for how well, and how far into the future, we can predict population sizes and how they will respond to management interventions,” says Dr Tanya Rogers, an ecologist at NOAA Fisheries, US, and lead author on a paper describing the research, published in Nature Ecology & Evolution.
Older research typically found that chaos was rare in ecological populations.
“There’s a lot more data now, and how long a time series you have makes a big difference for detecting chaotic dynamics,” says co-author Stephan Munch, a NOAA Fisheries ecologist and adjunct professor at the University of California, Santa Cruz, US.
“We also showed that methodological assumptions made in prior meta-analyses were biased against detecting chaos.”
The researchers used newer algorithms to examine 172 datasets from the Global Population Dynamics Database.
They found evidence of chaos in more than 30% of the populations they assessed.
Plankton and insects were the most chaotic, while birds and mammals were the least and fish were in the middle.
“A lot of short-lived species tend to have chaotic population dynamics, and these are also species that tend to have boom-and-bust dynamics,” says Rogers.
The researchers warn against assuming regular population dynamics when making ecological predictions – especially among short-lived species.
“From the fisheries management perspective, we want to predict fish populations so we can set limits for fishery harvests,” says Rogers.
“If we don’t recognise the existence of chaos, we could be losing out on short-term forecasting possibilities using methods appropriate for chaotic systems, while being overconfident about our ability to make long-term predictions.”