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Chaotic dynamics are thought to be rare in natural populations, but this may be due to methodological and data limitations, rather than the inherent stability of ecosystems. Following extensive simulation testing, we applied multiple chaos detection methods to a global database of 172 population time series and found evidence for chaos in >30%. Chaos prevalence and Lyapunov exponents displayed patterns related to taxonomic group, generation time, and body mass. We then explored how chaotic dynamics and temporal patterns in local instability varied geographically and at different taxonomic resolutions using a compilation of plankton time series from 17 lakes and 4 marine sites. In a follow up study, we found that empirically-estimated Lyapunov exponents have meaningful consequences for prediction. Overall, the results demonstrate that chaos is not rare in natural populations, indicating that there may be intrinsic limits to ecological forecasting and cautioning against the use of steady-state approaches to conservation and management. Our results also suggest that prediction accuracy, sensitivity to change, and management efficacy may be greater at certain times of year and that prediction may be more feasible for taxonomic aggregates.

Also on zoom: https://ucdavis.zoom.us/j/98969645841