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Brown shrimp (Farfantepenaeus aztecus) density distribution in the Northern Gulf of Mexico: an approach using boosted regression trees

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Abstract
  • The estuarine-dependent brown shrimp, Farfantepenaeus aztecus, is a significant commercial fishery and important species in the Gulf of Mexico (GOM) ecosystem as well as being a key component in energy transfer between benthic and pelagic food web systems. Because of the economical and ecological importance of brown shrimp, we developed a spatial population model to identify places of high shrimp density under a set of spatial, environmental and temporal variables in the Northern Gulf of Mexico (NGOM). We used fisheries-independent data collected by the Southeast Area Monitoring and Assessment Program (SEAMAP) from 1992 to 2007 (summer and fall seasons). The relationship between the predictor variables and shrimp density was modeled using Boosted Regression Trees (BRT). Within the environmental variables included in the model, bottom type and depth of the water column were the most important predictors of shrimp density in theNGOM. Spatial predictions performed using the trained BRT model for summer and fall seasons showed a spatial segregation of shrimp density. During the summer, higher densities were predicted near the Texas and Louisiana coast and during the fall, higher densities were predicted further offshore. The model performed well and allowed successful prediction of brown shrimp hot spots in the NGOM. Model results allow fisheries managers to evaluate the potential impact from fisheries on the resource and to develop future fisheries management strategies, understand the biology of brown shrimp as well as assess the potential impacts of oil spills or climate change.
  • Keywords: boosted regression trees, spatial prediction, brown shrimp, Gulf of Mexico
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  • Montero, J. T., Chesney, T. A., Bauer, J. R., Froeschke, J. T., & Graham, J. (2016). Brown shrimp (Farfantepenaeus aztecus) density distribution in the Northern Gulf of Mexico: an approach using boosted regression trees. Fisheries Oceanography, 25(3), 337-348. doi:10.1111/fog.12156
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  • 25
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  • 3
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  • Datasets utilized for our analyses were collected in part of a collaborative effort between Oregon State University and the National Energy Technology Laboratory in support of the RES contract DE-FE0004000.
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