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.

Representation and learning of visual information for pose recognition

Recovering position from sensor information is an important problem in mobile robotics, known as localisation. Localisation requires a map or some other description of the environment to provide the robot with a context to interpret sensor data. The mobile robot system under discussion is using an artificial neural representation of position. Building a geometrical map of the environment with a single camera and artificial neural networks is difficult. Instead it would be simpler to learn position as a function of the visual input. Usually when learning images, an intermediate representation is employed. An appropriate starting point for biologically plausible image representation is the complex cells of the visual cortex, which have invariance properties that appear useful for localisation. The effectiveness for localisation of two different complex cell models are evaluated. Finally the ability of a simple neural network with single shot learning to recognise these representations and localise a robot is examined.

Assessing the seasonal influence of sewage and agricultural nutrient inputs in a subtropical river estuary

A combination of physical and chemical measurements and biological indicators identified nutrient impacts throughout an Australian subtropical river estuary. This was a balance of sewage inputs in the lower river and agricultural inputs in the mid-upper river, the combined influence being greater in the wet season due to greater agricultural surface runoff. Field sampling in the region was conducted at 6 sites within the river, over 5 surveys to encapsulate both wet and dry seasonal effects. Parameters assessed were tissue nitrogen (N) contents and delta(15)N signatures of mangroves and macroalgae, phytoplankton nutrient addition bioassays, and standard physical and chemical variables. Strong spatial (within river) and temporal (seasonal) variability was observed in all parameters. Poorest water quality was detected in the middle (agricultural) region of the river in the wet season, attributable to large diffuse inputs in this region. Water quality towards the river mouth remained constant irrespective of season due to strong oceanic flushing. Mangrove and macroalgal tissue delta(15)N and %N proved a successful combination for discerning sewage and agricultural inputs. Elevated delta(15)N and %N represented sewage inputs, whereas low delta(15)N and elevated %N was indicative of agricultural inputs. Phytoplankton bioassays found the system to be primarily responsive to nutrient additions in the warmer wet season, with negligible responses observed in the cooler dry season. These results indicate that the Tweed River is sensitive to the different anthropogenic activities in its catchment and that each activity has a unique influence on receiving water quality.

Modeling of hardening and softening processes in Mg alloys

The tensile deformation behavior of a range of supersaturated Mg-Al solid solutions and an as-cast magnesium alloy AM60 has been studied. The Mg-Al alloys were tested at room temperature while the alloy AM60 was tested in the temperature range 293-573 K. The differences in the deformation behavior of the alloys is discussed in terms of hardening and softening processes. In order to identify which processes were active, the stress dependence of the strain-hardening coefficient was assessed using Lukac and Balik's model of hardening and softening. The analysis indicates that hardening involves solid solution hardening and interaction with forest dislocations and non-dislocation obstacles such as second phase particles. Cross slip is not a significant recovery process in the temperature range 293-423 K. At temperatures between 473 and 523 K the analysis suggests that softening is controlled by cross slip and climb of dislocations. At temperatures above 523 K softening seems to be controlled by dynamic recrystallisation. (C) 2004 Elsevier B.V. All rights reserved.

delta O-18, Sr/Ca and Mg/Ca records of Porites lutea corals from Leizhou Peninsula, northern South China Sea, and their applicability as paleoclimatic indicators

Combined seasonal to monthly resolution coral skeletal delta(18)O, Sr/Ca, and Mg/Ca records are reported for one modem and two late Holocene Porites lutea corals from a fringing reef at Leizhou Peninsula, the northern coast of the South China Sea (SCS). All the profiles for the period 1989-2000 reveal annual cycles well correlated with instrumental sea surface temperatures (SST), and display broad peaks in summer and narrow troughs in winter, reflecting seasonal growth rate variations. Calibration against instrumental SST yields the following equations: delta(18)O=-0.174(+/- 0.010)xSST(degrees C)-1.02(+/- 0.27) (MSWD=5.8), Sr/Ca-(mmol/mol)=-0.0424(+/- 0.0031)xSST(degrees C)+9.836(+/- 0.082) (MSWD=8.6), and Mg/Ca-(mmol/mol)=0.110(+/- 0.009)XSST(degrees C)+ 1.32(+/- 0.23) (MSWD=55). The scatter in the Mg/Ca-SST relationship is much larger than analytical uncertainties can account for, suggesting the presence of SST-unrelated components in the Mg/Ca variation. Calculated Sr/Ca-SST values for two later Holocene Porites lutea samples (U-series ages similar to 541 BC and similar to 487 AD, respectively) from the same reef suggest that SST in the SCS at similar to 541 BC was nearly as warm as in the 1990s (the warmest decade of the last century), but at similar to 487 AD, it was significantly cooler. This observation is consistent with climatic data reported in Chinese historic documents, confirming that the Sr/Ca-SST relationship is a reliable thermometer. Removing the SST component in the delta(18)O variation based on calculated Sr/Ca-SST values, the residual delta(18)O reflects the deviation of the Holocene seawater delta(18)O from the modem value, which is also a measure of the Holocene sea surface salinity (SSS) or the summer monsoon moisture level in mainland China. Such residual delta(18)O was close to zero at similar to 541 BC and -0.3 parts per thousand at similar to 487 AD, suggesting that it was as wet as in the 1990s at similar to 541 BC but significantly drier at similar to 487 AD in mainland China, which are also consistent with independent historic records. Calculated Mg/Ca-SST values for the two late Holocene corals are significantly lower than the Sr/Ca-SST values and are also in conflict with Chinese historic records, suggesting that coral Mg/Ca is not reliable proxy for SST. At comparable Sr/Ca ranges, fossil corals always display negative Mg/Ca offsets if compared with the modem coral of the same site. We interpret this observation as due to preferential loss of Mg during meteoric dissolution of cryptic Mg-calcite-bearing microbialites in the exposed fossil corals. Microbialites (MgO up to 17%, Sr only 100-300 ppm) are ubiquitous during reef-building processes and their presence in only a trace amount will have a significant impact on coral Mg/Ca ratios without detectable influence on coral Sr/Ca ratios. (c) 2004 Elsevier B.V. All rights reserved.