Predicting nonlinear dynamics of short-lived penaeid shrimp species in the Gulf of Mexico

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.

Automated summary

The empirical dynamic modeling (EDM) approach, which compensates for unobserved interactions by using time delays of observed states, can improve the predictions for short-lived penaeid shrimp species. The abundance dynamics of these shrimp species were well-predicted by EDM, showed spatial similarity across the US Gulf of Mexico, and were characterized by nonlinear density-dependent interaction and temperature variability.