Scales of Interaction Between Antarctic Krill and the Environment

Murphy, Eugene J.; Morris, David J.; Watkins, J. L.; Priddle, J.

doi:10.1007/978-3-642-73724-4_9

Cited by 89 publications

(72 citation statements)

References 37 publications

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“…For example, the Antarctic krill Euphausia superba is aggregated within swarms at a scale of 10s of meters. Swarms are aggregated within patches at a scale of kilometers, and patches are aggregated within concentrations at a scale of 100s of kilometers (Murphy et al 1988).…”

Section: Introductionmentioning

confidence: 99%

Spatial interaction between seabirds and prey: review and synthesis

Fauchald¹

2009

Mar. Ecol. Prog. Ser.

147

142

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The Ideal Free Distribution theory predicts a close spatial match between predators and prey. Studies have shown that seabird and prey distribution seldom conforms with this prediction. In this study, I review recent theoretical advances in spatial predator-prey interactions and relate these with studies of seabirds and pelagic schooling fish and crustaceans. Studies on seabirds and prey have generally assumed that prey are nonresponsive. Predator-prey interactions should, however, be viewed as a 2-way spatial game where seabirds track concentrations of prey while prey move away from areas with high risk of predation. The outcome of the game depends on how seabirds and prey are spatially constrained. Constraints include the spatial distribution of resources, interspecific competition, the location of spawning and breeding areas, and limitations on diving depth. Although game theoretic models can explain some general aspects of the spatial interaction, the spatial distribution of seabirds and prey is generally much more aggregated and elusive than can be expected from the game theoretic equilibrium. This is because spatial pattern is formed through selforganizing behavior that includes schooling, local enhancement and area-restricted search (ARS). Schooling and local enhancement are processes with strong positive density dependence that destabilize the predator-prey interaction locally. However, the unstable local dynamics might be stabilized by spatial constraints and the effects of ARS processes at large scales.

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Section: Introductionmentioning

confidence: 99%

Spatial interaction between seabirds and prey: review and synthesis

Fauchald¹

2009

Mar. Ecol. Prog. Ser.

147

142

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“…The swarming behaviour of Antarctic krill Euphausia superba is a feature of its biology that makes this central species in the food web of the Southern Ocean of particular interest both scientifically (see reviews by Mauchline 1980, Murphy et al 1988, Miller & Hampton 1989) and commercially (Eddie 1977). Krill are usually found in dense aggregations which have been classified as patches, shoals, swarms or schools according to the density of individuals and the physical characteristics of the aggregation (Mauchline 1980, Kalinowski & Witek 1985.…”

Section: Introductionmentioning

confidence: 99%

Variation in reproductive status of Antarctic krill swarms; evidence for a size-related sorting mechanism?

Watkins¹,

Búchholz²,

Priddle³

et al. 1992

Mar. Ecol. Prog. Ser.

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Length, sex and maturity stage of Antarctic krill Euphausia superba Dana occurring in discrete swarms were measured. Over a 14 d period 38 swarms were sampled in a small area southwest of Elephant Island, South Shetland Islands. Analysis indicated that the composition of the swarms in terms of the sex ratio and maturity stage was very variable. Swarms ranged from equal mixtures of male and female krill to nearly all one sex; furthermore krill of similar maturity frequently occurred together. The mean length of each maturity stage also varied between swarms; in some swarms the size of each maturity stage was bigger than average while in others each stage was smaller than average. The range of krill sizes in a swarm was often restricted, indicative of some type of size sorting. However size sorting alone could not account for the observed variation in sex ratio and maturity stage and it is likely that in addition, differential growth and active behavioural responses contribute to the observed variation.

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“…One of the most significant ability of krills is that they can form large swarms [13,14].Yet there are number of uncertaint ies about the mechanis m that lead to distribution of krill herd [15].There are proposed conceptual models to exp lain observed formation of krill heard [16] and result obtained from those models states that krill swarms form the basic unit of organization for this species.…”

Section: Krill Swarm's Herding Behaviormentioning

confidence: 99%

“…We can express this motion in terms of maximu m diffusion speed and a random d irect ional vector. We can formulate it as follows: (16) In (16) is the maximu m d iffusion speed [8] and is the random directional vector and it includes random values in range [-1, 1]. As krills position gets better, random motion is also reduced.…”

Section: Physical Diffusionmentioning

confidence: 99%

“…The effects of the motion induced by other krill indiv iduals and foraging motion gradually decrease increase in iterations. The physical diffusion is a random vector hence it does not steadily reduces with increase in nu mber of iterations due to which another term is added to (16). This term, linearly decreases the random speed with t ime and works on the basis of a geometrical annealing schedule: (17)…”

Section: Physical Diffusionmentioning

confidence: 99%

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Comparative Study of Krill Herd, Firefly and Cuckoo Search Algorithms for Unimodal and Multimodal Optimization

Singh

2014

IJISA

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Abstract-Today, in computer science, a computational challenge exists in finding a globally optimized solution fro m an enormously large search space. Various meta-heuristic methods can be used for finding the solution in a large search space. These methods can be exp lained as iterative search processes that efficiently perform the exp loration and exp loitation in the solution space. In this context, three such nature inspired meta-heuristic algorith ms namely Krill Herd Algorith m (KH), Firefly Algorith m (FA) and Cuckoo search Algorithm (CS) can be used to find optimal solutions of various mathemat ical optimization problems. In this paper, the proposed algorithms were used to find the optimal solution of fifteen unimodal and mult imodal benchmark test functions commonly used in the field of optimization and then compare their performances on the basis of efficiency, convergence, time and conclude that for both unimodal and mu ltimodal optimization Cuckoo Search Algorith m v ia Lé vy flight has outperformed others and for mu ltimodal optimization Krill Herd algorith m is superior than Firefly algorith m but for un imodal optimization Firefly is superior than Krill Herd algorithm.

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Scales of Interaction Between Antarctic Krill and the Environment

Cited by 89 publications

References 37 publications

Spatial interaction between seabirds and prey: review and synthesis

Spatial interaction between seabirds and prey: review and synthesis

Variation in reproductive status of Antarctic krill swarms; evidence for a size-related sorting mechanism?

Comparative Study of Krill Herd, Firefly and Cuckoo Search Algorithms for Unimodal and Multimodal Optimization

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