Brill et a! Horizontal and vertical movements of luvenlle Thunnus thynnus 



165 



at fronts result from cues other than SST, such as changes 

 in the photic environment associated with phytoplankton 

 distribution, changes in prey abundance, or enhanced for- 

 age opportunities. 



Aerial survey techniques and population 

 assessments of juvenile bluefin tuna 



Techniques for interpretation of aerial survey data with 

 respect to population assessments are complex (e.g. Lo et 

 al.. 1999; Newlands and Lutcavage, 2001), and a thorough 

 discussion is beyond the scope of our present study. We 

 can, however, use our data on juvenile bluefin tuna's verti- 

 cal movements and distribution patterns to provide some 

 inferences as to how often they are likely to be visible at 

 the ocean's surface or detectable at a specific depth. Juve- 

 nile bluefin tunas spent less than 13'7c of daylight hours 

 at depths of 0-3 m (Fig. 7), where visual or photographic 

 detection is possible. The depth distribution of juvenile 

 fish was similar to that of adult bluefin tuna tracked in the 

 Gulf of Maine (12*^ of daylight hours at depths of 0-4 m; 

 Lutcavage et al.. 2000). Abundance estimates based solely 

 on photographic data will, therefore, have to be corrected 

 to account for the significant number offish that maybe be 

 present, but that are beyond detection range. Fish detec- 

 tion systems that use lasers (the so called "light detection 

 and range" or "LIDAR" systems) are expected to have a 

 depth detection zone of up to 60 m (Oliver et al., 1994). 

 This detection zone encompasses almost the entire water 

 column over the sections of continental shelf where juve- 

 nile bluefin tuna are likely to be found. Moreover, if the 

 behavior of the fish that moved into deeper water off 

 the continental shelf is assumed typical, then juvenile 

 bluefin tuna would be detected by LIDAR systems even in 

 deep water The relatively small net displacement distance 

 (i.e. distance between start and end points. Table 1) may 

 require the development of filtering algorithms to reduce 

 errors caused by double counting if parallel transects are 

 flown less than =50 km apart, or if the same area is resur- 

 veyed weekly or more often. Conversely, significant fish 

 aggregations could be missed if parallel transects are too 

 widely spaced. 



Acknowledgments 



This project was funded by a grant from the National 

 Marine Fisheries Service to the Edgerton Research Labo- 

 ratory. New England Aquarium. RWB's participation was 

 funded through cooperative agreements NA37R.J0199 and 

 NA67RJ0154 from the National Oceanic and Atmospheric 

 Administration with the Joint Institute for Marine and 

 Atmospheric Research, University of Hawaii. We grate- 

 fully acknowledge Mark Luckenbeck and the staff and 

 students of the Virginia Institute of Marine Science's East- 

 ern Shore Laboratory for their gracious hospitality and 

 extraordinary efforts to make this project a success. We 

 also thank Jim Hannon and Sippican Inc. (Marion, MA) 

 for use of their Mark 12 system and generous donation 



of XBT probes, and Mitch Roffer for access to historical 

 bluefin tuna data. We acknowledge the DAAC at NASA's 

 Goddard Space Flight Center and Orbimage Inc. for ocean 

 color data and the NOAA Coast Watch Program and the 

 National Oceanographic Data Center for SST data. We 

 especially thank Captain Jack Stallings of the FV Grumpy 

 (Virginia Beach, Virginia) for his unflagging enthusiasm 

 and his significant contributions to making our project a 

 success. 



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