A habitat-use model to determine essential fish habitat for juvenile brown shrimp (Farfantepenaeus aztecus) in Galveston Bay, Texas

Date

2004 Apr

Authors

Clark RD
Christensen JD
Monaco ME
Caldwell PA
Matthews GA
Minello TJ

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Abstract

A density prediction model for juvenile brown shrimp (Farfan-tepenaeus aztecus) was developed by using three bottom types, five salinity zones, and four seasons to quantify patterns of habitat use in Galveston Bay, Texas. Sixteen years of quantitative density data were used. Bottom types were vegetated marsh edge, submerged aquatic vegetation, and shallow non-vegetated bottom. Multiple regression was used to develop density estimates, and the resultant formula was then coupled with a geographical information system (GIS) to provide a spatial mosaic (map) of predicted habitat use. Results indicated that juvenile brown shrimp (<100 mm) selected vegetated habitats in salinities of 15-25 ppt and that seagrasses were selected over marsh edge where they co-occurred. Our results provide a spatially resolved estimate of high-density areas that will help designate essential fish habitat (EFH) in Galveston Bay. In addition, using this modeling technique, we were able to provide an estimate of the overall population of juvenile brown shrimp (<100 mm) in shallow water habitats within the bay of approximately 1.3 billion. Furthermore, the geographic range of the model was assessed by plotting observed (actual) versus expected (model) brown shrimp densities in three other Texas bays. Similar habitat-use patterns were observed in all three bays-each having a coefficient of determination >0.50. These results indicate that this model may have a broader geographic application and is a plausible approach in refining current EFH designations for all Gulf of Mexico estuaries with similar geomorphological and hydrological characteristics

Description

264-277

Keywords

SALT-MARSH, PENAEUS-AZTECUS, DECAPOD CRUSTACEANS, NEKTON USE, NATANT MACROFAUNA, ESTUARINE, EDGE, MICROHABITAT, SETIFERUS, SELECTION

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