Date Awarded

2010

Document Type

Thesis

Degree Name

Master of Science (M.Sc.)

Department

Virginia Institute of Marine Science

Abstract

Blue marlin Makaira nigricans and white marlin Kajikia albida (formerly Tetrapturus albidus) are overfished in the Atlantic Ocean, with the vast majority of fishing mortality resulting from the pelagic longline fishery that targets tunas (Thunnus spp.) and swordfish Xiphias gladius. Time series of catch-per-unit-effort (CPUE) data have been fundamental to assessments of blue marlin and white marlin stocks, but these time series have been affected by a shift over time in pelagic longline fishing practices from shallow to deeper sets. One method for adjusting CPUE data for changes in fishing practices is a habitat-based standardization that modifies fishing effort in proportion to the vertical distribution of the species of interest and the fishing gear. For these models to be successfully applied to population assessments, the vertical habitat utilized by blue marlin and white marlin must be known. Pop-up satellite archival tags (PSATs) provide a means of collecting high resolution vertical movement and distribution data for billfishes.

In my study, 62 blue marlin and 40 white marlin were caught in recreational fisheries off the U.S. mid-Atlantic, Yucatan Peninsula, northern Caribbean, Venezuela, and Brazil, tagged with Microwave Telemetry, Inc. PTT-100 HR PSATs, and released. Data were recovered from PSATs attached to 57 surviving blue marlin and 36 surviving white marlin. PSATs successfully transmitted 18-100% of the data they recorded (mean 72%). The minimum 10-day displacements of both species averaged 242 km (range 9 to 942 km) and varied significantly between tagging locations, but not between species. Blue marlin spent a significantly higher (62%) amount of time in the upper 10 m of water than white marlin (56%). Both species spent greater than 95% of the time in water that was within 8o C of the sea surface temperature. Only 3.1% of white marlin demonstrated diel differences in the maximum depth of dives, while 29% of blue marlin dove into deeper waters during the day. Variables identified as explaining the most variation in dives were total dive duration, bottom time, ascent time, number of wiggles, wiggle depth, interdive interval, skew of ascent and descent, % time ascending, and % time descending. Using these variables, two dive types were identified through cluster analysis: simple dives representing traditional "U" and "V" shapes, and complex dives with multiple descents, plateaus, and wiggles. There were significant differences in dive variables among locations, individuals, diel periods, and dive types. There was significant overlap in range, habitat use, and vertical movement patterns, and therefore no strong evidence of niche partitioning between blue marlin and white marlin. My analyses can be used to further define the physical and physiological factors limiting marlins' vertical movements and therefore improve stock assessments based on longline CPUE data by correcting for changes in fishing practices.

DOI

https://dx.doi.org/doi:10.25773/v5-tfr1-dw77

Rights

© The Author

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