Standard protocols for the analysis of school based data from echo sounder surveys

Reid, David G.; Scalabrin, Carla; Petitgas, Pierre; Massé, Jacques; Aukland, Richard; Carrera, Pablo; Georgakarakos, S.

doi:10.1016/s0165-7836(00)00164-8

Cited by 68 publications

(55 citation statements)

References 15 publications

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“…Acoustic characterization of the spatial patterns of schooling species has been done for decades for descriptive and comparative analyses (Cushing 1973;Gerlotto and Paramo 2003;Gerlotto et al 2010) and more recently to classify species (e.g., Barange 1994;Reid et al 2000;Reid 2000;Lawson et al 2001;Cabreira et al 2009;Korneliussen et al 2009;Paramo et al 2010;Gong et al 2010;Jech 2011). The use of school descriptors has been successful for quantifying behavioral (see review by Fréon and Misund 1999) changes or stability, ontogenetic shifts in distributions (Nakamura and Hamano 2009), and responses to fishing pressure Wheeler et al 2009), temporally and spatially, within and among species.…”

Section: Introductionmentioning

confidence: 99%

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

Jech

Stroman

2012

Aquat. Living Resour.

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-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 prespawning 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 prespawning 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.

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Section: Introductionmentioning

confidence: 99%

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

Jech

Stroman

2012

Aquat. Living Resour.

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“…During the past two decades, growing attention has been paid to the study of school characteristics and school behaviour in relation to fishery management based on acoustic surveys in order to improve the precision of abundance estimates, survey design and species identification (e.g. Fréon et al 1992, Haralabous and Georgakarakos 1996, Petitgas and Levenez 1996, Scalabrin et al 1996, Bahri and Fréon 2000, Coetzee 2000, Reid et al 2000, Lawson et al 2001, Muiño et al 2003.…”

mentioning

confidence: 99%

Schooling Behaviour of SardineSardinops Sagaxin False Bay, South Africa

Misund¹,

Coetzee²,

Fréon³

et al. 2003

African Journal of Marine Science

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“…There were feedforward neural networks (also called Multi-Layered Perceptrons, MLPs), using as species predictors morphometric, energetic and bathymetric school descriptors. Meanwhile, modern systems were developed, utilising faster school scrutinisation algorithms (Reid et al, 2000, Fernandes et al, 2002, multi-frequency (Korneliussen & Ona, 2002, Korneliussen et al, 2009), multi-dimensional school descriptors (Paramo et al, 2007;Trygonis et al, 2009) and advanced artificial neural networks (Cabreira et al, 2009). In the following we describe an advanced species classification system based, among other tools, on Bayesian Neural Network techniques.…”

Section: Species Identificationmentioning

confidence: 99%