Unveiling Chaos in Ecosystems: A Paradigm Shift in Ecological Understanding
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For nearly three decades, ecologists considered chaos in the living world to be surprisingly rare…[However], analyzing more than 170 sets of time-dependent ecosystem data, Tanya Rogers, a research ecologist at the University of California, Santa Cruz, and her colleagues found that chaos was present in a third of them — nearly three times more than the estimates in previous studies… Chaos reflects predictability over time. A system is said to be stable if it changes very little over a long timescale, and random if its fluctuations are unpredictable. But a chaotic system — one ruled by nonlinear responses to events — may be predictable over short periods but is subject to increasingly dramatic shifts the further out you go…
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Ecologists began flirting with the concept of chaos in the 1970s, when the mathematical biologist Robert May developed a revolutionary tool called the logistic map. This branching diagram shows how chaos creeps into simple models of population growth and other systems over time… By the early ’90s, ecologists had amassed enough time-series data sets on species populations and enough computing power to test their ideas. There was just one problem: The chaos didn’t seem to be there. Only about 10% of the examined populations seemed to change chaotically; the rest either cycled stably or fluctuated randomly… The new results, however, suggest that the older work missed where the chaos was hiding. To detect chaos, the earlier studies used models with a single dimension — the population size of one species over time. They didn’t consider corresponding changes in messy real-world factors like temperature, sunlight, rainfall and interactions with other species that might affect populations. Their one-dimensional models captured how the populations changed, but not why they changed.
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But, […] [Rogers and team] analyzed 172 time series of different organisms’ populations as models with as many as six dimensions rather than just one. In this way, they could check whether unnoticed chaotic patterns might be embedded within the one-dimensional representation of the population shifts. For example, more rainfall might be chaotically linked to population increases or decreases, but only after a delay of several years. In the population data for about 34% of the species, the signatures of nonlinear interactions were indeed present, which was significantly more chaos than was previously detected… The researchers also uncovered an inverse relationship between an organism’s body size and how chaotic its population dynamics tend to be. This may be due to differences in generation time, with small organisms that breed more often also being more affected by outside variables more often…
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Improved models with the right element of chaos could do a better job of forecasting toxic algal blooms, for example, or tracking fishery populations to prevent overfishing… However, [some ecologists caution against] placing too much faith in these chaos-conscious models. The classical concept of chaos is fundamentally a stationary concept. It is built on the assumption that chaotic fluctuations represent a departure from some predictable, stable norm. But as climate change progresses, most real-world ecosystems are becoming increasingly unstable even in the short term. Even taking many dimensions into account, scientists will have to be conscious of this ever-shifting baseline…