Modeling the Growth Dynamics of Antarctic Krill Euphausia Superba

A time-dependent, size-structured, bioenergetically based model was developed to examine the growth dynamics of Antarctic krill Euphausia superba 2 to 60 mm in size. The metabolic processes included in the model are ingestion, a baseline respiration, respiratory losses due to feeding and digestion, and an activity-based respiration factor. The total of these processes, net production, was used as the basis for determining the growth or shrinkage of individuals. Size-dependent parameterizations for the metabolic processes were constructed from field and laboratory measurements. Environmental effects were included through time series of pelagic phytoplankton concentration that were derived from data sets collected west of the Antarctic Peninsula. Simulated growth rates during the spring and summer for all brill size classes were consistent with published growth rates; however, initial results indicated that winter shrinkage rates were too large. Although the use of a seasonally varying respiration activity factor (reduced winter respiration rates) resulted in winter shrinkage rates of adults that were consistent with observations of experimentally starved individuals, the annual change in length of specific size classes was still inconsistent with observations. Subsequent simulations examined the effect of ingestion of sea ice algae by krill in the late winter and early spring. The annual growth cycle best matched observations, particularly those for larval and subadult krill (\u3c35 mm), when reduced winter respiration rates and ingestion of sea ice algae were both included. These results suggest that the ability of krill to exploit a range of food sources and reduced winter metabolism rates are the mechanisms that allow krill to successfully overwinter. The need for additional observations of krill physiological processes, especially during winter, is clearly indicated

Lagrangian Modelling Studies of Antarctic Krill (Euphausia superba) Swarm Formation

A two-dimensional Lagrangian particle model was developed to examine the spatial distribution of Antarctic krill (Euphausia superba). The time-dependent location of particles, which represent krill individuals, is determined by random diffusion, foraging activity, and movement induced by the presence of neighbours. Foraging activity is based on prescribed food conditions and is such that krill swim slower and turn more frequently in areas of high food concentration. The presence or absence of neighbours either disperses krill, if the local concentrations become too dense, or coalesces krill, if concentrations become too dilute, respectively. Predation on krill is included and affects swarm characteristics by removing individuals. Sensitivity studies indicate that the rate of krill swarm formation and the total number of swarms formed are determined primarily by foraging response and nearest neighbour sensing distance. Simulations using food distributions that are representative of those encountered at boundaries, such as fronts, mesoscale eddies, or the sea ice edge, show that foraging activity can produce rapid swarm formation. Results from other krill swarm models show that attraction between individuals is the primary mechanism producing krill swarms. However, the parameterizations for krill interactions used in those models and that used in this model differ, thereby implying different biological dynamics. Thus, parameterization of the basic interactions it) krill swarm models remains to be defined. (C) 2004 International Council for the Exploration of the Sea. Published by Elsevier Ltd. All rights reserved

Old Dominion University

This “Late Breaking Hot Topic Paper ” introduces and tracks the progress of OceanDIVER, a project to develop a tele-immersive collaboratory that integrates archived oceanographic data with simulation and real-time data gathered from autonomous underwater vehicles. Specifically this paper describes the work in building CAVE6D, a tool for collaboratively visualizing environmental data in CAVEs, ImmersaDesks and desktop workstations

TeleImmersive Virtual Environments for Collaborative Knowledge Discovery

This paper describes the design and implementation of two tele-immersive applications, CVD and Cave6D, both designed to support collaborative knowledge discovery from large multidimensional datasets. CVD integrates the capabilities of two existing VR applications, Cave5D and Virtual Director, in order to provide immersive experiences of distributed data using high performance networks and interactive hardware and software. Cave6D is similar in function yet is tightly integrated with the CAVERNSoft toolkit so as to provide access to remote computational platforms and databases