Fur seal diving behaviour in relation to vertical distribution of krill

Quantitative studies of predator-prey interactions depend on a knowledge of their spatial dynamics and behaviour. Studies on marine vertebrates have hitherto been precluded by the difficulty of acquiring the relevant data. (2) Continuous records of diving depths of female Antarctic fur seals on 3-8 day feeding trips to sea from South Georgia were analysed in conjunction with data on diel changes in the abundance and distribution of their main prey, krill. (3) In 36 complete days foraging by seven seals, 75% of 4273 dives were at night. Dives then were consistently shallower (dive depth <30 m) than in daytime (mostly 40-75 m). (4) This closely matched changes in the vertical distribution of krill, nearly all of which was below a depth of 50 m from 09.00-15.00 h, with substantial quantities above 40 m only between 21.00-06.00 h. (5) Although over 40% of krill in the water column at any time of day was below 75 m, only 3% of dives exceeded this depth. We suggest that because krill migrate vertically fur seals are able to exploit them most efficiently during shallow dives at night

Model order reduction for nonlinear IC models

Model order reduction is a mathematical technique to transform nonlinear dynamical models into smaller ones, that are easier to analyze. In this paper we demonstrate how model order reduction can be applied to nonlinear electronic circuits. First we give an introduction to this important topic. For linear time-invariant systems there exist already some well-known techniques, like Truncated Balanced Realization. Afterwards we deal with some typical problems for model order reduction of electronic circuits. Because electronic circuits are highly nonlinear, it is impossible to use the methods for linear systems directly. Three reduction methods, which are suitable for nonlinear differential algebraic equation systems are summarized, the Trajectory piecewise Linear approach, Empirical Balanced Truncation, and the Proper Orthogonal Decomposition. The last two methods have the Galerkin projection in common. Because Galerkin projection does not decrease the evaluation costs of a reduced model, some interpolation techniques are discussed (Missing Point Estimation, and Adapted POD). Finally we show an application of model order reduction to a nonlinear academic model of a diode chain