Spatial Variation in Basal Resources and Trophic Position of Selected Fishes of the North-Central Gulf of Mexico

The North-central Gulf of Mexico is a complex hydrologic environment with freshwater influx that varies on spatial and temporal scales. Freshwater input exerts influence on the isotope values of organisms living in coastal ecosystems. The objectives of this study were to determine relationships betw...

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Bibliographic Details
Main Author: Fleming, Christopher
Format: Text
Language:unknown
Published: The Aquila Digital Community 2018
Subjects:
Online Access:https://aquila.usm.edu/masters_theses/361
https://aquila.usm.edu/context/masters_theses/article/1407/viewcontent/auto_convert.pdf
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Summary:The North-central Gulf of Mexico is a complex hydrologic environment with freshwater influx that varies on spatial and temporal scales. Freshwater input exerts influence on the isotope values of organisms living in coastal ecosystems. The objectives of this study were to determine relationships between total length and isotope value, estimate basal resource usage and trophic position of target species from Mississippi Sound, reef, and pelagic environments, and identify differences and similarities in spatial patterns of collection. Muscle tissue samples were collected from October 2014 through September 2015. Stable isotope analysis identified a trophic gradient extending from nearshore to offshore, with d13C values becoming enriched as distance from shore increased, while d15N values decreased. Species from the Mississippi Sound exhibited varying degrees of habitat usage, with Red Drum being the most diverse, while Gafftopsail Catfish and Atlantic Sharpnose shark had more habitat specificity. This study presented evidence that freshwater inputs influenced the isotope values of reef fish species. d15N values of Vermilion Snapper, Red Porgy, Red Snapper, and Tomtate were statistically higher near sources of freshwater input. Stable isotope data identified variable habitat usage in Cobia. Application of this knowledge when developing statistical models may help increase efficacy of management decisions.