SYMP 26-2 - Chaos and intermittent instability in ecological systems

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. To address this hypothesis, we first performed extensive simulation testing to validate several chaos detection methods under ecologically realistic data conditions. We then applied the most accurate detection methods to 172 population time series from the Global Population Dynamics Database and found evidence for chaos in >30%. In contrast, fitting traditional one-dimensional models identified <10% as chaotic. Chaos was most prevalent among plankton and insects and least among birds and mammals. Lyapunov exponents declined with generation time and scaled as the -1/6 power of body mass among chaotic populations. Following these findings, we then explored how chaotic dynamics (global instability) and temporal patterns in local instability (responsiveness to perturbations at particular points in time) varied geographically and at different taxonomic resolutions using a compilation of plankton time series from 17 lakes and 4 marine sites. Finer taxonomic groupings were more likely to be chaotic than taxonomic aggregates. We found seasonal patterns of local instability in many species (including those that were non-chaotic), that short-term predictability was related to local instability, and that local instability occurred most often in the spring, associated with periods of high growth. Across sites, higher latitude locations had higher Lyapunov exponents and greater seasonality in local instability, but only at coarser taxonomic resolution. Overall, these 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.