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

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.

Empirical dynamic programming for model‐free ecosystem‐based management

“…This single‐species approximation is likely to be accurate for long‐lived species where mortality rates are low and in systems with many generalist predators where fluctuations in abundance are damped by prey switching. Thus we expect that EDP may be most valuable for relatively short‐lived species, such as, shrimp (Tsai et al., 2022), anchovy (Deyle et al., 2013) and salmonids (Ye, Deyle, et al., 2015).…”