Energetics of underwater swimming in the great cormorant (Phalacrocorax carbo sinensis)

Resting metabolic rate (RMR), energy requirements and body core temperature were measured during underwater swimming in great cormorants (Phalacrocorax carbo sinensis) at the zoological garden in Neumünster, Germany, using gas respirometry and stomach temperature loggers. We used a 13 m long still water canal equipped with a respiration chamber at each end. Birds swam voluntarily in the canal at a mean speed of 1.51 ms^-1. Power input during underwater swimming averaged 31.4 W kg^-1. Minimal costs of transport of 19.1 J kg^-1 m^-1 were observed at a speed of 1.92 m s^-1. Body core temperature was stable in all birds within the first 60 min spent in the canal. After that, body temperature dropped at a rate of 0.14°C min^-1 until the birds voluntarily left the water. Our data indicate that great cormorants spend 2.7 times more energy than Adélie penguins (Pygoscelis adeliae) during underwater swimming. This can be essentially attributed to their poor insulation, their mode of locomotion underwater and differences in streamlining. RMR on land was related to body mass via VO₂=0.691 M^0.755 (where VO₂ is O₂-consumption in litre h^-1 and M is body mass in kg). In order to quantify the effects of external devices on energy consumption during underwater swimming, we tested a dummy data logger attached to the back of the cormorants as well as a ring on the leg. The ring had no apparent influence on the swimming energetics of the cormorants. In birds equipped with dummy loggers, swimming speed was not significantly influenced, but both power input and costs of transport increased by a mean of 19% for swimming speeds between 1.4 and 1.8 m s^-1.     

Ansatz zur Optimierung biologischer Reinigungsstufen durch das Ph?nomen der ‘biologischen Resonanz’

Ohne Zusammenfassung Siehe UWSF12 (6) 350 (2000)     

Geoadditive regression modeling of stream biological condition

Indices of biotic integrity have become an established tool to quantify the condition of small non-tidal streams and their watersheds. To investigate the effects of watershed characteristics on stream biological condition, we present a new technique for regressing IBIs on watershed-specific explanatory variables. Since IBIs are typically evaluated on an ordinal scale, our method is based on the proportional odds model for ordinal outcomes. To avoid overfitting, we do not use classical maximum likelihood estimation but a component-wise functional gradient boosting approach. Because component-wise gradient boosting has an intrinsic mechanism for variable selection and model choice, determinants of biotic integrity can be identified. In addition, the method offers a relatively simple way to account for spatial correlation in ecological data. An analysis of the Maryland Biological Streams Survey shows that nonlinear effects of predictor variables on stream condition can be quantified while, in addition, accurate predictions of biological condition at unsurveyed locations are obtained.