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Aggregative patterns of pre-spawning Atlantic herring on Georges Bank from 1999-2010

Published online by Cambridge University Press:  02 April 2012

J. Michael Jech*
Affiliation:
NOAA Fisheries, Northeast Fisheries Science Center, 166 Water St., Woods Hole, 02543 MA, USA
Francine Stroman
Affiliation:
Integrated Statistics, 166 Water St., Woods Hole, 02543 MA, USA Current Address: 2 South Ascot Ct., Newtown18940PAUSA
*
a Corresponding author: Michael.Jech@noaa.gov
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Abstract

Atlantic herring (Clupea harengus)in the offshore regions of the Gulf of Maine migrate each fall fromtheir feeding grounds to the northern portion of Georges Bank tospawn.The Northeast Fisheries Science Center’s (NEFSC) herringacoustic survey has taken advantage of this behavior by conductingsystematic surveys of the pre-spawning fish each year since 1999.Multi-frequency acoustic and midwater trawl data were collectedalong transects oriented perpendicular to bathymetric contours.Acoustic backscatter was analyzed to describe the aggregative patterns(e.g., size, location in the water column, and spatial and temporaldistribution) of Atlantic herring during these surveys and regressiontrees were used to examine the aggregation characteristics. Thepositional variables of distance to spawning grounds and verticallocation in the water column were the primary characteristics fordescribing pre-spawning aggregations. Secondary to these were thetemporal variables of diel and survey timing, and the morphological characteristicof aggregation area. Lower numbers of aggregations were observedclose to the herring spawning grounds but with higher acoustic energythan larger numbers of aggregations observed further from the spawninggrounds but smaller in size and lower in acoustic energy. Most aggregationswere in the lower portion of the water column, but those that werein the upper portion of the water column had higher acoustic energy.Consistently throughout the decade, 90% or more of herring aggregationswere located within 40 nautical miles of their spawning grounds.The regression tree method provided valuable insight to the dataseries where it highlighted spatial and temporal patterns and wasan effective way to quantitatively summarize relationships.

Type
Research Article
Copyright
© EDP Sciences, IFREMER, IRD 2012

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