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Spatio‐temporal trends of sailfish,Istiophorus platypterus catch rates in relation to spawning ground and environmental factors in the equatorial and southwestern Atlantic Ocean
Authors:Bruno L Mourato  Fábio Hazin  Keith Bigelow  Michael Musyl  Felipe Carvalho  Humberto Hazin
Institution:1. Departamento de Pesca e Aqüicultura, Laboratório de Oceanografia Pesqueira. R. Dom Manoel de Medeiros, Universidade Federal Rural de Pernambuco, , 52171‐900 Recife, PE, Brazil;2. Departamento de Oceanografia, Universidade Federal de Pernambuco, Cidade Universitária, , 50670‐901 Recife, PE, Brazil;3. NOAA Fisheries Pacific Islands Fisheries Science Center, , Honolulu, HI, 96822 U.S.A;4. Pelagic Research Group LLC, , Honolulu, HI, 96816 U.S.A;5. University of Florida, Program of Fisheries and Aquatic Sciences, School of Forest Resources and Conservation, , Gainesville, FL, 32653 U.S.A
Abstract:Spatial and temporal trends of sailfish catch rates in the southwestern and equatorial Atlantic Ocean in relation to environmental variables were investigated using generalized additive models and fishery‐dependent data. Two generalized additive models were fit: (i) ‘spatio‐temporal’, including only latitude, longitude, month, and year; and (ii) ‘oceanographic’, including sea surface temperature (SST), chlorophyll‐a concentration, wind velocity, bottom depth, and depth of mixed layer and year. The spatio‐temporal model explained more (average ~40%) of the variability in catch rates than the oceanographic model (average ~30%). Modeled catch rate predictions showed that sailfish tend to aggregate off the southeast coast of Brazil during the peak of the spawning season (November to February). Sailfish also seem to aggregate for feeding in two different areas, one located in the mid‐west Atlantic to the south of ~15°S and another area off the north coast of Brazil. The oceanographic model revealed that wind velocity and chlorophyll‐a concentration were the most important variables describing catch rate variability. The results presented herein may help to understand sailfish movements in the Atlantic Ocean and the relationship of these movements with environmental effects.
Keywords:Atlantic Ocean  environmental effects  generalized additive models  sailfish  spatial prediction  spawning ground
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