Learning of foraging skills by fish

This chapter outlines the relationships between a number of key factors that influence learning and memory, and illustrates them by reference to studies on the foraging behaviour of fish. Learning can lead to significant improvements in foraging performance in only a few exposures, and at least some fish species are capable of adjusting their foraging strategy as patterns of patch profitability change. There is also evidence that the memory window for prey varies between fish species, and that this may be a function of environmental predictability. Convergence between behavioural ecology and comparative psychology offers promise in terms of developing more mechanistically realistic foraging models and explaining apparently 'suboptimal' patterns of behaviour. Foraging decisions involve the interplay between several distinct systems of learning and memory, including those that relate to habitat, food patches, prey types, conspecifics and predators. Fish biologists, therefore, face an interesting challenge in developing integrated accounts of fish foraging that explain how cognitive sophistication can help individual animals to deal with the complexity of the ecological context.

Costs of learning: the dynamics of mixed-prey exploitation by silver perch, Bidyanus bidyanus (Mitchell, 1838)

Although generalist predators have been reported to forage less efficiently than specialists, there is little information on the extent to which learning can improve the efficiency of mixed-prey foraging. Repeated exposure of silver perch to mixed prey (pelagic Artemia and benthic Chironomus larvae) led to substantial fluctuations in reward rate over relatively long (20-day) timescales. When perch that were familiar with a single prey type were offered two prey types simultaneously, the rate at which they captured both familiar and unfamiliar prey dropped progressively over succeeding trials. This result was not predicted by simple learning paradigms, but could be explained in terms of an interaction between learning and attention. Between-trial patterns in overall intake were complex and differed between the two prey types, but were unaffected by previous prey specialization. However, patterns of prey priority (i.e. the prey type that was preferred at the start of a trial) did vary with previous prey training. All groups of fish converged on the most profitable prey type (chironomids), but this process took 15-20 trials. In contrast, fish offered a single prey type reached asymptotic intake rates within five trials and retained high capture abilities for at least 5 weeks. Learning and memory allow fish to maximize foraging efficiency on patches of a single prey type. However, when foragers are faced with mixed prey populations, cognitive constraints associated with divided attention may impair efficiency, and this impairment can be exacerbated by experience. (c) 2005 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.

Northern wing, General Purpose North 3, University of Queensland, St Lucia

As seen from Blair Road.

Northern wing, General Purpose North 3, University of Queensland, St Lucia

As seen from Balir Road.

West elevation, General Purpose North 3, University of Queensland, St Lucia

Formal elevation facing University of Queensland entrance roads.

Eastern edge, General Purpose North 3, University of Queensland, St Lucia

As seen from informal courtyard; Duhig Tower beyond.

West elevation, General Purpose North 3, University of Queensland, St Lucia

Formal elevation facing University of Queensland entrance roads.

West elevation, General Purpose North 3, University of Queensland, St Lucia

Formal elevation facing University of Queensland entrance roads.

West elevation, General Purpose North 3, University of Queensland, St Lucia

Formal elevation facing University of Queensland entrance roads.

XSophe-Sophe-XeprView (R). A computer simulation software suite (v. 1.1.3) for the analysis of continuous wave EPR spectra

The XSophe-Sophe-XeprView((R)) computer simulation software suite enables scientists to easily determine spin Hamiltonian parameters from isotropic, randomly oriented and single crystal continuous wave electron paramagnetic resonance (CW EPR) spectra from radicals and isolated paramagnetic metal ion centers or clusters found in metalloproteins, chemical systems and materials science. XSophe provides an X-windows graphical user interface to the Sophe programme and allows: creation of multiple input files, local and remote execution of Sophe, the display of sophelog (output from Sophe) and input parameters/files. Sophe is a sophisticated computer simulation software programme employing a number of innovative technologies including; the Sydney OPera HousE (SOPHE) partition and interpolation schemes, a field segmentation algorithm, the mosaic misorientation linewidth model, parallelization and spectral optimisation. In conjunction with the SOPHE partition scheme and the field segmentation algorithm, the SOPHE interpolation scheme and the mosaic misorientation linewidth model greatly increase the speed of simulations for most spin systems. Employing brute force matrix diagonalization in the simulation of an EPR spectrum from a high spin Cr(III) complex with the spin Hamiltonian parameters g(e) = 2.00, D = 0.10 cm(-1), E/D = 0.25, A(x) = 120.0, A(y) = 120.0, A(z) = 240.0 x 10(-4) cm(-1) requires a SOPHE grid size of N = 400 (to produce a good signal to noise ratio) and takes 229.47 s. In contrast the use of either the SOPHE interpolation scheme or the mosaic misorientation linewidth model requires a SOPHE grid size of only N = 18 and takes 44.08 and 0.79 s, respectively. Results from Sophe are transferred via the Common Object Request Broker Architecture (CORBA) to XSophe and subsequently to XeprView((R)) where the simulated CW EPR spectra (1D and 2D) can be compared to the experimental spectra. Energy level diagrams, transition roadmaps and transition surfaces aid the interpretation of complicated randomly oriented CW EPR spectra and can be viewed with a web browser and an OpenInventor scene graph viewer.