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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  and
Francine Stroman
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 from their feeding grounds to the northern portion of Georges Bank to spawn. The Northeast Fisheries Science Center’s (NEFSC) herring acoustic survey has taken advantage of this behavior by conducting systematic surveys of the pre-spawning fish each year since 1999. Multi-frequency acoustic and midwater trawl data were collected along 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 temporal distribution) of Atlantic herring during these surveys and regression trees were used to examine the aggregation characteristics. The positional variables of distance to spawning grounds and vertical location in the water column were the primary characteristics for describing pre-spawning aggregations. Secondary to these were the temporal variables of diel and survey timing, and the morphological characteristic of aggregation area. Lower numbers of aggregations were observed close to the herring spawning grounds but with higher acoustic energy than larger numbers of aggregations observed further from the spawning grounds but smaller in size and lower in acoustic energy. Most aggregations were in the lower portion of the water column, but those that were in the upper portion of the water column had higher acoustic energy. Consistently throughout the decade, 90% or more of herring aggregations were located within 40 nautical miles of their spawning grounds. The regression tree method provided valuable insight to the data series where it highlighted spatial and temporal patterns and was an effective way to quantitatively summarize relationships.

Keywords

Herring schoolAcoustic baskscatter surveyRegression tree
Type
Research Article
Information
Aquatic Living Resources , Volume 25 , Issue 1 , January 2012 , pp. 1 - 14
Copyright
© EDP Sciences, IFREMER, IRD 2012

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References

Andersen L.N., 2001, The new Simrad EK60 scientific echosounder system. J. Acoust. Soc. Am. 109, 2336.
Azarovitz T.R., 1981, A brief historical review of Woods Hole Laboratory trawl survey time series. In: Doubleday, W.G., Rivard, D. (eds.), Bottom Trawl Surveys. Ottawa, Dep. Fisheries and Oceans. Canadian Special Publication of the Fisheries and Aquatic Sciences 58, pp. 62–67.
Bakun, A., Babcock, E.A., Santora, C., 2009, Regulating a complex adaptive system via its wasp-waist: grappling with ecosystem-based management of the New England herring fishery. ICES J. Mar. Sci. 66, 1768–1775. CrossRefGoogle Scholar
Barange, M., 1994, Acoustic identification, classification and structure of biological patchiness on the edge of the Agulhas Bank and its relation to frontal features. S. Afr. J. Mar. Sci. 14, 333–347. CrossRefGoogle Scholar
Bodholt, H., Nes, H., Solli, H., 1989, A new echosounder system. Proc. Inst. Acoustics, 11, 123–130. Google Scholar
Cabreira, A.G., Tripode, M., Madirolas, A., 2009, Artificial neural networks for fish-species identification. ICES J. Mar. Sci. 66, 1119–1129.CrossRefGoogle Scholar
Cushing D.H., 1973, The detection of fish. Pergamon Press, NY.
De’ath, G., 2007, Boosted trees for ecological modeling and prediction. Ecology 88, 243–251. CrossRefGoogle ScholarPubMed
De’ath, G., Fabricius, K.E., 2000, Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology 81, 3178–3192. CrossRefGoogle Scholar
Diner, N., 2001, Correction on school geometry and density: approach based on acoustic image simulation. Aquat. Living Resour. 14, 211–222. CrossRefGoogle Scholar
Fablet, R., Lefort, R., Karoui, I., Berger, L., Massé, J., Scalabrin, C., Boucher, J.M., 2009, Classifying fish schools and estimating their species proportions in fishery-acoustic surveys. ICES J. Mar. Sci. 66, 1136–1142. CrossRefGoogle Scholar
Fernandes, P.G., 2009, Classification trees for species identification of fish-school echotraces. ICES J. Mar. Sci. 66, 1073–1080. CrossRefGoogle Scholar
Foote K.G., Knudsen H.P., Vestnes G., MacLennan D.N., Simmonds E.J., 1987, Calibration of acoustic instruments for fish density estimation: A practical guide. ICES Coop. Res. Rep. 44.
Fréon, P., Gerlotto, F., Soria, M., 1996, Diel variabilityof school structure with special reference to transition periods. ICES J. Mar. Sci. 53, 459–464. CrossRefGoogle Scholar
Fréon P., Misund O.A., 1999, Dynamics of pelagic fish distribution and behaviour: effects on fisheries and stock assessment. Fishing News Books, Blackwell Science Ltd., Oxford.
Gerlotto, F., Jones, E., Bez, N., Reid, D.G., 2010, When good neighbours become good friends: observing small scale structures in fish aggregations using multibeam sonar. Aquat. Living Resour. 23, 143–151. CrossRefGoogle Scholar
Gerlotto, F., Paramo, J., 2003, The three-dimensional morphology and internal structure of clupeid schools as observed using vertical scanning multibeam sonar. Aquat. Living Resour. 16, 113–122. CrossRefGoogle Scholar
Gong, Z., Andrews, M., Jagannathan, S., Patel, R., Jech, J.M., Makris, N.C., Ratilal, P., 2010, Low-frequency target strength and abundance of shoaling Atlantic herring (Clupea harengus) in the Gulf of Maine during the Ocean Acoustic Waveguide Remote Sensing 2006 Experiment. J. Acoust. Soc. Am. 127, 104–123. CrossRefGoogle ScholarPubMed
Haralabous, J., Georgakarakos, S., 1996, Artificial neural networks as a tool for species identification of fish schools. ICES J. Mar. Sci. 53, 173–180. CrossRefGoogle Scholar
Harris, B.P., Stokesbury, D.E., 2010, The spatial structure of local surficial sediment characteristics on Georges Bank, USA. Cont. Shelf Res. 30, 1840–1853. CrossRefGoogle Scholar
Jech, J.M., 2011, Interpretation of multi-frequency acoustic data: Effects of fish orientation. J. Acoust. Soc. Am. 129, 54–63. CrossRefGoogle ScholarPubMed
Jech, J.M., Michaels, W.L., 2006, A multifrequency method to classify and evaluate fisheries acoustics data. Can. J. Fish. Aquat. Sci. 63, 2225–2235. CrossRefGoogle Scholar
Jech J.M., Michaels W., Overholtz W., Gabriel W., Azarovitz T., Ma D., Dwyer K., Yetter R., 2000, Fisheries acoustic surveys in the Gulf of Maine and on Georges Bank at the Northeast Fisheries Science Center. In: Proc. 6th International Conference on Remote Sensing for Marine and Coastal Environments. 1–3 May, Charleston, South Carolina, Veridian ERIM International, Ann Arbor, Michigan, pp. 168–175.
Kelly K.H., Moring J.R., 1986, species profiles: life histories and environmental requirements of coastal fishes and invertebrates (North Atlantic)- Atlantic Herring. U.S. Fish Wildl. Serv. Biol. Rep. 82, 22.
Kieser, R., Mulligan, T.J., Richards, L.J., Leaman, B.M., 1993, Bias correction of rockfish school cross-section widths from digitized echo sounder data. Can. J. Fish. Aquat. Sci. 50, 1801–1811. CrossRefGoogle Scholar
Korneliussen, R.J., Heggelund, Y., Eliassen, I.K., Johansen, G.O., 2009, Acoustic species identification of schooling fish. ICES J. Mar. Sci. 66, 1111–1118. CrossRefGoogle Scholar
Lawson, G.L., Barange, M., Fréon, P., 2001, Species identification of pelagic fish schools on the South African continental shelf using acoustic descriptors and ancillary information. ICES J. Mar. Sci. 58, 275–287. CrossRefGoogle Scholar
Lefeuvre, P., Rose, G.A., Gosine, R., Hale, R., Pearson, W., Khan, R., 2000, Acoustic species identification in the northwest Atlantic using digital image processing. Fish. Res. 47, 137–147. CrossRefGoogle Scholar
MacLennan, D.N., Fernandes, P.G., Dalen, J., 2002, A consistent approach to definitions and symbols in fisheries acoustics. ICES J. Mar. Sci. 59, 365–369. CrossRefGoogle Scholar
Nakamura, T., Hamano, A., 2009, Seasonal differences in the vertical distribution pattern of Japanese jack mackerel, Trachurus japonicus: changes according to age? ICES J. Mar. Sci. 66, 1289–1295. Google Scholar
Nero, R.W., Magunson, J.J., 1989, Characterization of patches along transects using high-resolution 70-kHz integrated acoustic data. Can. J. Fish. Aquat. Sci. 46, 2056–2064. CrossRefGoogle Scholar
Óskarsson, G.J., Gudmundsdottir, A., Sigurdsson, T., 2009, Variation in spatial distribution and migration of Icelandic summer-spawning herring. ICES J. Mar. Sci. 66, 1762–1767. CrossRefGoogle Scholar
Óskarsson, G.J., Taggart, C.T., 2009, Spawning time variation in Icelandic summer-spawning herring (Clupea harengus). Can. J. Fish. Aquat. Sci. 66, 1666–1681. Google Scholar
Overholtz, W.J., 2002, The Gulf of Maine-Georges Bank Atlantic herring (Clupea harengus): spatial pattern analysis of the collapse and recovery of a large marine fish complex. Fish. Res. 57, 237–254. CrossRefGoogle Scholar
Overholtz, W.J., Jech, J.M., Michaels, W.L., Jacobson, L.D., Sullivan, P.J., 2006, Empirical comparisons of survey designs in acoustic surveys of Gulf of Maine-Georges Bank Atlantic herring. J. Northw. Atl. Fish. Sci. 36, 127–144. CrossRefGoogle Scholar
Paramo, J., Gerlotto, F., Oyarzun, C., 2010, Three dimensional structure and morphology of pelagic fish schools. J. Appl. Ichthyol. 26, 853–860. CrossRefGoogle Scholar
Petitgas, P., Levenez, J.J., 1996, Spatial oranisiations of pelagic fish: echogram strucure, spatio-temporal condition, and biomass in Senegalese waters. ICES J. Mar. Sci. 53, 147–153. CrossRefGoogle Scholar
Petitgas P., Massé J., Beillois P., Labarbier E., LeCann, A., 2003, Sampling variance of species identification in fisheries-acoustic surveys based on automated procedures associating acoustic images and trawls. ICES J. Mar. Sci. 60, 437–445. Google Scholar
Pitcher T.J., 2001, Fish Schooling. In: Steele J.H., Turekian K.K., Thorpe S.A. (eds.), Encyclopedia of Ocean Sciences, Academic Press, pp. 975–987.
Pitcher T.J., Parrish J.K., 1993, Functions of shoaling behaviour in teleosts. In: Pitcher T.J. (ed.), Behaviour of Teleost Fishes, Chapman and Hall, London, pp. 363–439.
R: A Language and Environment of Statistical Computing, 2009, R Foundation for Statistical Computing, Vienna, Austria, http://www.R-project.org.
Reid D.G. (ed.), 2000. Report on echo trace classification. ICES Coop. Res. Rep. 238.
Reid, D., Scalabrin, C., Petitgas, P., Massé, J., Aukland, R., Carrera, P., Georgakarakos, S., 2000, Standard protocols for the analysis of school based data from echo sounder surveys. Fish. Res. 47, 125–136. CrossRefGoogle Scholar
Reid R.N., Cargnelli L.M., Griesbach S.J., Packer D.B., Johnson D.L., Zetlin C.A., Morse W.W., Berrien P.L., 1999, Atlantic herring, Clupea harengus, life history and habitat characteristics. NOAA Technical Memorandum NMFS-NE-126.
Richards, L.J., Kieser, R., Mulligan, T.J., Candy, J.R., 1991, Classification of fish assemblages based on echo integration surveys. Can. J. Fish. Aquat. Sci. 48, 1264–1272. CrossRefGoogle Scholar
Robotham, H., Castillo, J., Bosch, P., Perez-Kallens, J., 2011, A comparison of multi-class support vector machine and classification three methods for hydroacoustic classification of fish-schools in Chile. Fish. Res. 111, 170–176. CrossRefGoogle Scholar
Secor, D.H., Kerr, L.A., Cadrin, S.X., 2009, Connectivity effects on productivity, stability, and persistence in a herring metapopulation model. ICES J. Mar. Sci. 66, 1726–1732. CrossRefGoogle Scholar
Skaret, G., Nøttestad, L., Fernö, A., Johannessen, A., Axelsen, B.E., 2003, Spawning of herring: day or night, today or tomorrow. Aquat. Living Resour. 16, 299–306. CrossRefGoogle Scholar
Stephenson, R.L., Melvin, G.D., Power, M.J., 2009, Population integrity and connectivity in northwest Atlantic herring: a review of assumptions and evidence. ICES J. Mar. Sci. 66, 1733–1739. CrossRefGoogle Scholar
Stevenson D.K., Scott M.L., 2005, Essential Fish Habitat Source Document: Atlantic herring, Clupea harengus, life history and habitat characteristics. NOAA Technical Memorandum NMFS-NE-192. U.S. Dept. Commerce, Washington, DC.
Tupper M.H., Anthony V.C., Chenoweth S.B., MacCluen H.A., 1998, Biology and assessment of Gulf of Maine herring stocks. Gulf of Maine Aquarium, Portland, Maine.
Vabø, R., Skaret, G., 2008, Emerging school structures and collective dynamics in spawning herring: a simulation study. Ecol. Model. 214, 125–140. CrossRefGoogle Scholar
Wheeler, J.P., Purchase, C.F., Macdonald, P.D.M., Fill, R., Jacks, L., Wang, H., Ye, C., 2009, Temporal changes in maturation, mean length-at-age, and condition of spring-spawning Atlantic herring (Clupea harengus) in Newfoundland waters. ICES J. Mar. Sci. 66, 1800–1807. CrossRefGoogle Scholar
Woillez, M., Poulard, J.-C., Rivoirard, J., Petitgas, P., Bez, N., 2007, Indices for capturing spatial patterns and their evolution in time, with application to European hake (Merluccius merluccius) in the Bay of Biscay. ICES J. Mar. Sci. 64, 537–550.CrossRefGoogle Scholar