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The Scientific World Journal
Volume 2014, Article ID 629791, 14 pages
http://dx.doi.org/10.1155/2014/629791
Research Article

Modeling Coral Reef Fish Home Range Movements in Dry Tortugas, Florida

1Division of Marine Biology and Fisheries, Rosenstiel School of Marine and Atmospheric Science, University of Miami, 4600 Rickenbacker Causeway, Miami, FL 33149, USA
2Sustainable Fisheries Division, Southeast Regional Office, NOAA National Marine Fisheries Service, 263 13th Avenue South, St. Petersburg, FL 33701, USA

Received 30 August 2013; Accepted 1 October 2013; Published 16 January 2014

Academic Editors: H. A. Lessios and X. Pochon

Copyright © 2014 Nicholas A. Farmer and Jerald S. Ault. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Underestimation of reef fish space use may result in marine reserves that are too small to effectively buffer a portion of the stock from fishing mortality. Commonly used statistical home range models, such as minimum convex polygon (MCP) or 95% kernel density (95% KD) methods, require the exclusion of individuals who move beyond the bounds of the tracking study. Spatially explicit individual-based models of fish home range movements parameterized from multiple years of acoustic tracking data were developed for three exploited coral reef fishes (red grouper Epinephelus morio, black grouper Mycteroperca bonaci, and mutton snapper Lutjanus analis) in Dry Tortugas, Florida. Movements were characterized as a combination of probability of movement, distance moved, and turning angle. Simulations suggested that the limited temporal and geographic scope of most movement studies may underestimate home range size, especially for fish with home range centers near the edges of the array. Simulations provided useful upper bounds for home range size (red grouper:  km2 MCP,  km2 KD; black grouper:  km2 MCP,  km2 KD; mutton snapper:  km2 MCP,  km2 KD). Simulations also suggested that MCP home ranges are more robust to artifacts of passive array acoustic detection patterns than 95% KD methods.