Chaos was proposed in the 1970s as an alternative explanation for apparently noisy fluctuations in population size. Although readily demonstrated in models, the search for chaos in nature proved challenging and led many to conclude that chaos is either rare or nigh impossible to detect. However, in the intervening half-century, it has become clear that ecosystems are replete with the enabling conditions for chaos. Chaos has been repeatedly demonstrated under laboratory conditions and has been found in field data using updated detection methods. Together, these developments indicate that the apparent rarity of chaos was an artifact of data limitations and overreliance on low-dimensional population models. We invite readers to reevaluate the relevance of chaos in ecology, and we suggest that chaos is not as rare or undetectable as previously believed.

No abstract

No abstract

Predicting the dynamics of harvested species is essential for assessing stock status and establishing index-based management strategies. However, conventional approaches for short-lived species predict dynamics poorly, possibly because unobserved interactions with other species and abiotic factors are often treated as noise. Alternatively, the empirical dynamic modeling (EDM) approach, which uses the time delays of the observed states to compensate for unobserved interactions, may improve the predictions for short-lived species. We test this idea using time series data of two federally managed, short-lived penaeid shrimp species, whose abundances were surveyed over 30 years (1987–2018) across the US Gulf of Mexico. We show that ( i) abundance dynamics of these annual shrimp stocks are well-predicted by EDM, ( ii) the dynamics are spatially similar across most of the gulf, and ( iii) the stock dynamics are characterized by nonlinear density-dependent interaction and vary with temperature. Our findings suggest that EDM may be more responsive than single-species, catch-at-age models in assessing the stock dynamics for short-lived penaeid shrimp species.