Seasonal variations in prey selection by estuarine black-headed gulls (Larus ridibundus)

The number of black-headed gulls (Larus ridibundus) in the Clyde Estuary is large. In summer the average density has reached 1350 gulls km^?2 and in winter 180 gulls km^?2. This paper compares prey selection and feeding efficiency in gulls during summer and winter on tidal flats, and considers how seasonal differences may be adaptations to cope with seasonal changes in prey availability.Gross and net rates of energy intake were highest in summer because gulls captured more of the polychaete N. diversicolor than the amphipod C. volutator. In winter, gulls selected for C. volutator and therefore an energetically less profitable diet. Throughout the year gulls selected more C. volutator relative to N. diversicolor than expected on energetic grounds and so apparently did not maximize potential net rate of energy intake.Gulls used three techniques to capture prey and made most intensive use of the ‘crouch’ technique. Crouching gulls attained a much higher net rate of energy intake than ‘upright’ or ‘paddling’ gulls.A log-linear model showed that (a) season, water depth and gull density determined feeding technique and (b) feeding technique and season independently determined foraging success and prey selection. Thus gull density and water depth acted on prey selection through imposed variations in feeding technique.Reasons for gulls selecting energetically unprofitable C. volutator and for the use of several distinct feeding techniques are discussed.     

Robertson-Walker type model with conformally invariant scalar field with trace-free energy momentum tensor

A non-static exact solution of the Einstein equations corresponding to a conformally invariant scalar field with trace-free energy momentum tensor is obtained for the Robertson-Walker type metric. Some physical properties of the solution are discussed.     

First results from the Gauribidanur Radio heliograph

Observations of the Sun at two frequencies (51 and 77 MHz) using the East-West arm of the Gauribidanur Radio heliograph are presented.     

Solar-planetary cycles in Jupiter's great red spot darkness

The change in the darkness of the Great Red Spot (GRS) of Jupiter (1894–1974) has been analysed with Fourier (FFT), Maximum Entropy and Power spectrum (Blackman-Tukey window) (PSA) methods of spectrum analysis. Significance, non-randomness and stationarity tests assigned high variance to periodicities of 33 ± 4, 13–15, about 11, 9 and 3 yrs. The highest correlation between solar activity and GRS darkness was found for the 14th and 16th solar cycle. The periodicities obtained are interpreted as the combined eftects of solar activity, planetary resonances and internal jovian mechanisms.     

Brief description of bare essentials of surface transfer and results from simulations with the HAPEX-MOBILHY data

The Bare Essentials of Surface Transfer (BEST) land surface scheme is briefly described and the key physical parameterisations discussed. Results are then presented to illustrate how the model performs, with forcing data for HAPEX-MOBILHY, compared to a series of other schemes in the simulation of evaporation and sensible heat. The implications of the models performance, and some indications of the future development of the scheme are provided. The basic version of BEST was found to overestimate evaporation for the HAPEX-MOBILHY data, simulating 816 mm yr⁻¹ compared to a range of 550 to 816 mm yr⁻¹ for all models. A modification to the β parameterisation reduced the evaporation to 759 mm yr⁻¹ which, although an improvement, is still probably too high.     

Identification of physical processes inherent in artificial neural network rainfall runoff models

The emergence of artificial neural network (ANN) technology has provided many promising results in the field of hydrology and water resources simulation. However, one of the major criticisms of ANN hydrologic models is that they do not consider/explain the underlying physical processes in a watershed, resulting in them being labelled as black‐box models. This paper discusses a research study conducted in order to examine whether or not the physical processes in a watershed are inherent in a trained ANN rainfall‐runoff model. The investigation is based on analysing definite statistical measures of strength of relationship between the disintegrated hidden neuron responses of an ANN model and its input variables, as well as various deterministic components of a conceptual rainfall‐runoff model. The approach is illustrated by presenting a case study for the Kentucky River watershed. The results suggest that the distributed structure of the ANN is able to capture certain physical behaviour of the rainfall‐runoff process. The results demonstrate that the hidden neurons in the ANN rainfall‐runoff model approximate various components of the hydrologic system, such as infiltration, base flow, and delayed and quick surface flow, etc., and represent the rising limb and different portions of the falling limb of a flow hydrograph. Copyright © 2004 John Wiley & Sons, Ltd.