Foraging behavior of abyssal grenadier fish: inferences from acoustic tagging and tracking in the North Pacific Ocean

Priede, Imants George; Smith, Kenneth L.; Armstrong, J. D.

doi:10.1016/0198-0149(90)90030-y

Cited by 122 publications

(120 citation statements)

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“…Routine swimming speeds in deepliving rattail fishes are very low (approx. 0.1 m s K1 ; Priede et al 1990;Priede & Bagley 2000), while the blue hake, Antimora rostrata, and a deep-sea eel, Synaphobranchus kaupii, reportedly have high routine swimming rates and burst swimming abilities similar to shallow-living pelagic species at equivalent temperatures (Bailey et al , 2005. However, as indicated above, swimming speeds, especially those measured remotely following attraction to bait, may be a poor indicator of capacity (Childress et al 1990a).…”

Section: Predator-prey Interactions Locomotion and Metabolism (A)mentioning

confidence: 99%

“…O'Dor 1982; Bartol et al 2001), but only for a few mesopelagic crustaceans (Cowles & Childress 1988;Cowles 2001). New tagging methods are revealing swimming capacity and movement patterns of a variety of both deep-and shallow-living species (Priede et al 1990;Collins et al 1999;Block et al 2002;O'Dor 2002;Markaida et al 2005). …”

Section: Predator-prey Interactions Locomotion and Metabolism (A)mentioning

confidence: 99%

“…Oxygen consumption rates have now been measured for deep-and shallow-living representatives of many major phyla, both benthic and pelagic, and in several regions. Biochemical proxies of metabolism have provided estimates of metabolic capacity in many additional species (Torres & Somero 1988;Childress & Somero 1990;Seibel et al 1998Drazen 2002a;Seibel & Walsh 2003;Treberg et al 2003;Dalhoff 2004;Thuesen et al 2005b), while submersibles, landers and new tagging techniques have provided detailed studies of locomotion and behaviour in the deep sea (Priede et al 1990;Marshall & Diebel 1995;Villanueva et al 1997;Hunt & Seibel 2000;Bailey et al 2003Bailey et al , 2005Drazen & Robison 2004;Robison 2004;Seibel et al 2005). More than 10 years have passed since the last specific reviews of deep-sea metabolic rates (Childress 1995;Mahaut et al 1995).…”

Section: Introductionmentioning

confidence: 99%

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The rate of metabolism in marine animals: environmental constraints, ecological demands and energetic opportunities

Seibel

Drazen

2007

Phil. Trans. R. Soc. B

273

213

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The rates of metabolism in animals vary tremendously throughout the biosphere. The origins of this variation are a matter of active debate with some scientists highlighting the importance of anatomical or environmental constraints, while others emphasize the diversity of ecological roles that organisms play and the associated energy demands. Here, we analyse metabolic rates in diverse marine taxa, with special emphasis on patterns of metabolic rate across a depth gradient, in an effort to understand the extent and underlying causes of variation. The conclusion from this analysis is that low rates of metabolism, in the deep sea and elsewhere, do not result from resource (e.g. food or oxygen) limitation or from temperature or pressure constraint. While metabolic rates do decline strongly with depth in several important animal groups, for others metabolism in abyssal species proceeds as fast as in ecologically similar shallow-water species at equivalent temperatures. Rather, high metabolic demand follows strong selection for locomotory capacity among visual predators inhabiting well-lit oceanic waters. Relaxation of this selection where visual predation is limited provides an opportunity for reduced energy expenditure. Large-scale metabolic variation in the ocean results from interspecific differences in ecological energy demand.Keywords: metabolism; deep sea; scaling; oxygen consumption; locomotion; marine INTRODUCTIONThe rates of metabolic processes in animals vary tremendously throughout the biosphere (e.g. Bennett 1991; Seibel 2007). The origins and scope of this variation are a matter of active debate. Some emphasize geometric and environmental constraints or resource limitation as its source (e.g. Gillooly et al. 2001;Brown et al. 2004), while others emphasize the diversity of ecological roles that organisms play and the associated energy demands (Childress 1995;Suarez 1996;Reinhold 1999;Clarke 2004;Seibel 2007) as a primary driver of metabolic variation. With this in mind, the present paper addresses three seemingly simple questions: (i) what is the extent of variation in the rate of metabolism across diverse taxa and environments, (ii) what environmental and ecological demands act on organisms to drive selection for high metabolic capacity, and (iii) under what environmental circumstances might such selection be relaxed or capacity be constrained?These questions have been pursued vigorously for decades but the clarity of the debate has been diminished by the subtlety of differences in capacity between the taxa most thoroughly studied, that often

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Section: Predator-prey Interactions Locomotion and Metabolism (A)mentioning

confidence: 99%

Section: Predator-prey Interactions Locomotion and Metabolism (A)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The rate of metabolism in marine animals: environmental constraints, ecological demands and energetic opportunities

Seibel

Drazen

2007

Phil. Trans. R. Soc. B

273

213

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“…Telemetry systems are widely used to track fish to observe their behavior and obtain other information such as seasonal changes (e.g., preferences for water depths or temperatures), foraging behavior, habitat utilization, home range size, spawning behavior, site fidelity, and mortality (Priede et al, 1990;Metcalfe and Arnold, 1997;Roussel et al, 2000;Meyer et al, 2000;Cunjak et al, 2005;and Skalski et al, 2001). Because many fish of interest are small, methods for implantation of sources within their bodies or external attachment are an important topic of research.…”

Section: Fisheriesmentioning

confidence: 99%

Contributed Review: Source-localization algorithms and applications using time of arrival and time difference of arrival measurements

Deng

Rauchenstein

et al. 2016

Review of Scientific Instruments

117

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Locating the position of fixed or mobile sources (i.e., transmitters) based on measurements obtained from sensors (i.e., receivers) is an important research area that is attracting much interest. In this paper, we review several representative localization algorithms that use time of arrivals (TOAs) and time difference of arrivals (TDOAs) to achieve high signal source position estimation accuracy when a transmitter is in the line-of-sight of a receiver. Circular (TOA) and hyperbolic (TDOA) position estimation approaches both use nonlinear equations that relate the known locations of receivers and unknown locations of transmitters. Estimation of the location of transmitters using the standard nonlinear equations may not be very accurate because of receiver location errors, receiver measurement errors, and computational efficiency challenges that result in high computational burdens. Least squares and maximum likelihood based algorithms have become the most popular computational approaches to transmitter location estimation. In this paper, we summarize the computational characteristics and position estimation accuracies of various positioning algorithms. By improving methods for estimating the time-of-arrival of transmissions at receivers and transmitter location estimation algorithms, transmitter location estimation may be applied across a range of applications

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“…The mechanistic models outlined by 153 Priede et al, (1990) to estimate the abundance of deep-sea demersal fish from first arrival times are 154 deterministic. However, the arrival rate of fish is stochastically related to population abundance and the 155 factors governing aspects of shallow water fish movement are often assumed to be well represented by 156 random distribution (Farnsworth et al, 2007).…”

mentioning

confidence: 99%

Absolute abundance estimates from shallow water baited underwater camera surveys; a stochastic modelling approach tested against field data

Dunlop

Ruxton

Scott

et al. 2015

Journal of Experimental Marine Biology and Ecology

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98 99 137The area sampled by the active space of the odour plume is largely unknown in shallow BUC surveys. 150A model to determine the absolute measures of shallow water fish or invertebrate abundance from 151 arrival patterns at a BUC would involve developing an area based bait dispersion model using in-situ 152 measurements of current speed and direction (Heagney et al., 2007). The mechanistic models outlined by 153 Priede et al., (1990) to estimate the abundance of deep-sea demersal fish from first arrival times are 154 deterministic. However, the arrival rate of fish is stochastically related to population abundance and the 155 factors governing aspects of shallow water fish movement are often assumed to be well represented by 156 random distribution (Farnsworth et al., 2007). This means it is important to include stochastic elements to 157 mechanistic models. The physical factors, current distribution and velocity, observed around the camera 158 system also have a random distribution within a particular range. Therefore it is important to introduce this

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Foraging behavior of abyssal grenadier fish: inferences from acoustic tagging and tracking in the North Pacific Ocean

Cited by 122 publications

References 20 publications

The rate of metabolism in marine animals: environmental constraints, ecological demands and energetic opportunities

The rate of metabolism in marine animals: environmental constraints, ecological demands and energetic opportunities

Contributed Review: Source-localization algorithms and applications using time of arrival and time difference of arrival measurements

Absolute abundance estimates from shallow water baited underwater camera surveys; a stochastic modelling approach tested against field data

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