A Framework for Exploring How Density Dependence Early in the Life History Can Affect Louisiana’s Brown Shrimp Fishery

Abstract

The dynamics underlying fisheries stock–recruitment relationships are often obscure, especially with relatively short-lived invertebrate species, such as the brown shrimp Farfantepenaeus aztecus. Nonetheless, disentangling these dynamics can help to reveal optimal management strategies for long-term sustainability. We developed a matrix model to link early shrimp life history with the fishable stock and nested the degree of density-dependent settler (i.e., juvenile) survival in a Beverton–Holt framework. Density dependence in the settler stage was assumed to have an inverse relationship with marsh habitat availability. Thus, we could determine the level of potential compensation in the settler stage and compare how changes in habitat versus fishing pressure could ultimately affect Louisiana’s brown shrimp population. A simplified Bayesian state-space (i.e., hierarchical) model provided a theoretical framework for estimating density dependence and exploring how substantial increases in catch or density-dependent settler survival affected long-term abundance in all stages. At the baseline degree of density-dependent settler survival, which was estimated from observed CPUE data, the brown shrimp population was largely resilient to even a twofold increase in catch. However, a 50% loss of habitat had deleterious effects on brown shrimp abundances in all stages. Results highlight the importance of protecting and restoring important nursery habitat to maintain and enhance resiliency of the brown shrimp fishery.

Received February 23, 2017; accepted July 12, 2017

Acknowledgments

We thank the LDWF, NOAA Fisheries, and Gulf States Marine Fisheries Commission for providing the data that were incorporated into our models. We also appreciate the thoughtful feedback provided by Edward Camp, Marisa Litz, and Andi Stephens during the revision process. This work was undertaken as part of an interdisciplinary research effort supported by the National Science Foundation (NSF)-sponsored Quantitative Spatial Ecology, Evolution, and Environment Integrative Graduate Education and Research Traineeship program at the University of Florida (NSF Grant DGE-0801544).