Variability and predictability of Antarctic krill swarm structure

Tarling, Geraint A.; Klevjer, Thor A.; Fielding, Sophie; Watkins, J. L.; Atkinson, Angus; Murphy, Eugene J.; Korb, Rebecca E.; Whitehouse, M. J.; Leaper, Russell

doi:10.1016/j.dsr.2009.07.004

Cited by 100 publications

(112 citation statements)

References 54 publications

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“…The sensitivity analysis showed that the number of schools detected was relatively stable to variations in most parameters, although the analysis was sensitive to changes in values for school height (Appendix Figure 1). Halving or doubling the minimum school height parameter values increased and decreased the number of detected schools by ~34-46% in each instance, in agreement with the level of variation described by and Cox et al (2011) and Tarling et al (2009).…”

Section: Fish School Detectionsupporting

confidence: 64%

“…These values were selected partially to match the resolution of our acoustic data and to faciliate comparisons with other studies in the region Klevjer et al, 2010;Tarling et al, 2009). A sensitivity analysis was conducted to examine the impacts of variations in these parameters on the number of schools detected with the SHAPES algorithm following Cox et al (2011).…”

Section: Fish School Detectionmentioning

confidence: 99%

“…Schools were identified as a group of cells that met minimum criteria for length and height, based on the sampling rate, echosounder operation parameters and ships speed, following Tarling et al(2009) and. The software was configured to detect schools with a minimum length of 15 m and height of 2 m, and the detection theshold was set to -70 dB.…”

Section: Fish School Detectionmentioning

confidence: 99%

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School characteristics of mesopelagic fish at South Georgia

Saunders

Fielding

Thorpe

et al. 2013

Deep Sea Research Part I: Oceanographic Research Papers

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In this paper, we describe the variation in myctophid schools characteristics at South Georgia using multi-frequency acoustic data collected annually between November 2007 and January 2012. We studied the relationship between the proximity of land and the distribution and schooling characteristics of myctophid fish. We also examined patterns in schooling behaviour in relation to oceanographic data. Fish schools were identified using a dualfrequency dB identification method (S v120-38 kHz ), where negative S v120-38 kHz is indicative of gas-bearing organisms, such as swimbladdered fish. Available net data were used to provide information on the meosopelagic fish community in the region. School morphometrics (e.g. length, thickness, area) data were extracted and pooled according to their distance from the shore (0-30 km, 30-60 km, 60-90 km, 90-120 km). A total of 578 schools were detected in the survey region and there was high variation in school backscattering strength (NASC), length, height, perimeter, depth and horizontal distribution between surveys, including distinct inter-annual variation in these parameters when surveys were conducted in the same season (2009)(2010)(2011)(2012). Schools were distributed predominantly on-shelf (0-500 m isobaths) during some summer surveys (2007 and 2012) and predominantly off-shelf on others (>500 2 m isobaths; 2009, 2010 and 2011). There was also evidence of bathymetric separation in the horizontal distribution of schools. Schools observed in the late-season in 2008 had the greatest NASC, largest height and the deepest depth distribution. Most schools occurred at depths between ~60-300 m, but they did not occupy the same water masses during each survey. Schools became progressively thinner, shallower and acoustically weaker with increasing distance to land, whilst school length and perimeter increased correspondingly.The change in fish schooling behaviour between environments could be a response to a combination of local predatory threats over short spatial/temporal scales and differences in oceanographic conditions, such as current velocity. The trend could also be evidence of spatial habitat partitioning of myctophids, with different schooling species, or different lifestages, orientating along a bathymetric gradient at South Georgia.

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Section: Fish School Detectionsupporting

confidence: 64%

Section: Fish School Detectionmentioning

confidence: 99%

Section: Fish School Detectionmentioning

confidence: 99%

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School characteristics of mesopelagic fish at South Georgia

Saunders

Fielding

Thorpe

et al. 2013

Deep Sea Research Part I: Oceanographic Research Papers

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“…Our study shows that fin whales are likely cueing on aggregations of large (> 44 mm) mature krill associated with hydrographic features such as the shelf-slope front and/or resulting from krill reproductive behavior (Siegel & Loeb 1995). In contrast, humpback whales may rely on the dense schooling of juvenile (< 34 mm) krill over the deep basins of Bransfield Strait (Tarling et al 2009). However, we did not examine feeding behavior of whales in relation to krill patch structure or behavior, and more study is needed to understand how foraging whales select krill patches at finer scales (<1 km) and how they influence krill distribution and swarm structure.…”

Section: Discussionmentioning

confidence: 86%

Spatial association between hotspots of baleen whales and demographic patterns of Antarctic krill Euphausia superba suggests size-dependent predation

Santora¹,

Reiss²,

Loeb³

et al. 2010

Mar. Ecol. Prog. Ser.

115

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“…This density of adult krill within patches is much higher than densities <10 and often <1 krill m -3 measured by towing nets that integrate densities over hundreds to thousands of meters (Simard & Mackas 1989, Coyle & Pinchuk 2002, Marinovic et al 2002. Such extended tows may include no krill patches of high density, or else multiple patches ranging from a few meters to hundreds of meters across (O'Brien 1988, Tarling et al 2009). Functional responses (Fig.…”

Section: Model Of Intake Ratesmentioning

confidence: 99%

Modeling profitability for the smallest marine endotherms: auklets foraging within pelagic prey patches

Lovvorn¹

2010

Aquat. Biol.

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Auklets (Alcidae) can be very abundant in north-temperate to arctic seas. Their numbers and trophic impacts in a given area depend on their ability to forage profitably as a function of the dispersion, depth, and density of prey patches. Thus, modeling these relationships is important when predicting the auklets' response to environmental change. This paper presents a simulation model of the foraging costs and intake rates of Cassin's auklets Ptychoramphus aleuticus (~170 g) and least auklets Aethia pusilla (~80 g) once they have located a patch of zooplankton prey. In the model, water temperature and dive depth (max. 20 m) have important effects on dive costs, mainly by affecting the duration and magnitude of costs during passive ascent. Within a prey patch, modeled intake rates are limited at relatively low prey densities by pursuit and handling time after a prey item is detected. Because intake rate is limited by capture time and not prey visibility, the model indicates that changes in light conditions over these depths have little direct effect on intake rates of zooplankton prey. However, vertical migration of prey in response to diel light cycles can strongly affect profitability (energy gain minus cost) by altering the depth of dives to prey patches. Because pursuit and handling time limit modeled intake rate, profitability cannot be increased further by finding patches of higher density, but rather by extending time in patches by swimming farther or slower. The model suggests that auklet dispersion should be insensitive to variations in patch density above a threshold that is relatively low compared to the very high densities that can occur. However, auklets may be attracted to higher-density patches because the patches themselves are more visible or predictable, or because other predators -from seabirds to whales -may gather in such patches and increase their visibility.

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Variability and predictability of Antarctic krill swarm structure

Cited by 100 publications

References 54 publications

School characteristics of mesopelagic fish at South Georgia

School characteristics of mesopelagic fish at South Georgia

Spatial association between hotspots of baleen whales and demographic patterns of Antarctic krill Euphausia superba suggests size-dependent predation

Modeling profitability for the smallest marine endotherms: auklets foraging within pelagic prey patches

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