Relating sardine recruitment in the Northern Benguela to satellite-derived sea surface height using a neural network pattern recognition approach

Processes of enrichment, concentration and retention are thought to be important for the successful recruitment of small pelagic fish in upwelling areas, but are difficult to measure. In this study, a novel approach is used to examine the role of spatio-temporal oceanographic variability on recruitment success of the Northern Benguela sardine Sardinops sagax. This approach applies a neural network pattern recognition technique, called a self-organising map (SOM), to a seven-year time series of satellite-derived sea level data. The Northern Benguela is characterised by quasi-perennial upwelling of cold, nutrient-rich water and is influenced by intrusions of warm, nutrient-poor Angola Current water from the north. In this paper, these processes are categorised in terms of their influence on recruitment success through the key ocean triad mechanisms of enrichment, concentration and retention. Moderate upwelling is seen as favourable for recruitment, whereas strong upwelling, weak upwelling and Angola Current intrusion appear detrimental to recruitment success. The SOM was used to identify characteristic patterns from sea level difference data and these were interpreted with the aid of sea surface temperature data. We found that the major oceanographic processes of upwelling and Angola Current intrusion dominated these patterns, allowing them to be partitioned into those representing recruitment favourable conditions and those representing adverse conditions for recruitment. A marginally significant relationship was found between the index of sardine recruitment and the frequency of recruitment favourable conditions (r super(2) = 0.61, p = 0.068, n = 6). Because larvae are vulnerable to environmental influences for a period of at least 50 days after spawning, the SOM was then used to identify windows of persistent favourable conditions lasting longer than 50 days, termed recruitment favourable periods (RFPs). The occurrence of RFPs was compared with back-calculated spawning dates for each cohort. Finally, a comparison of RFPs with the time of spawning and the index of recruitment showed that in years where there were 50 or more days of favourable conditions following spawning, good recruitment followed (Mann-Whitney U-test: p = 0.064, n = 6). These results show the value of the SOM technique for describing spatio-temporal variability in oceanographic processes. Variability in these processes appears to be an important factor influencing recruitment in the Northern Benguela sardine, although the available data time series is currently too short to be conclusive. Nonetheless, the analysis of satellite data, using a neural network pattern-recognition approach, provides a useful framework for investigating fisheries recruitment problems.

Automatic classification of field-collected dinoflagellates by artificial neural network

Automatic taxonomic categorisation of 23 species of dinoflagellates was demonstrated using field-collected specimens. These dinoflagellates have been responsible for the majority of toxic and noxious phytoplankton blooms which have occurred in the coastal waters of the European Union in recent years and make severe impact on the aquaculture industry. The performance by human 'expert' ecologists/taxonomists in identifying these species was compared to that achieved by 2 artificial neural network classifiers (multilayer perceptron and radial basis function networks) and 2 other statistical techniques, k-Nearest Neighbour and Quadratic Discriminant Analysis. The neural network classifiers outperform the classical statistical techniques. Over extended trials, the human experts averaged 85% while the radial basis network achieved a best performance of 83%, the multilayer perceptron 66%, k-Nearest Neighbour 60%, and the Quadratic Discriminant Analysis 56%.

An Electron-Microscope X-Ray-Diffraction And Amino-Acid-Analysis Study Of The Opercular Filament Cuticle Calcareous Opercular Plate And Habitation Tube Of Pomatoceros Lamarckii Quatrefages (Polychaeta Serpulidae)

The structure, X-ray diffraction and amino acid compositions of the opercular filament cuticle, calcareous opercular plate and habitation tube of the polychaete serpulid, Pomatoceros lamarckii quatrefages, are reported. The opercular filament cuticle is made up of protein and chitin. The chitin is probably in the crystallographic α form. The structure and amino acid composition of the organic components of the opercular filament cuticle and calcareous opercular plate have similarities but are distinctly different from those of the calcareous habitation tube. The opercular plate and habitation tube are composed of different polymorphs of calcium carbonate, aragonite and calcite respectively. Comparisons are made with other chitin-protein systems, structural and calcified proteins.

A neural network based tool for semi-automatic code transformation

A neural network based tool has been developed to assist in the process of code transformation. The tool offers advice on appropriate transformations within a knowledge-driven, semi-automatic parallelisation environment. We have identified the essential characteristics of codes relevant to loop transformations. A Kohonen network is used to discover structure in the characterised codes thus revealing new knowledge that may be brought to bear on the mapping between codes and transformations or transformation sequences. A transform selector based on this process has been developed and successfully applied to the parallelisation of sequential codes.