Population genetic structure of brown crab (Cancer pagurus) in Irish waters

The brown crab (Cancer pagurus) fishery in Ireland is one of the most important financially and socio-economically, with the species worth approximately €15m per year in the first half of the decade. Only mackerel (Scomber scombrus) and Dublin Bay prawn (Nephrops norvegicus) are of greater value. Despite this, very little research has been conducted to describe the stock structure of brown crab on a national scale. In this study a country-wide assessment of genetic population structure was carried out. Sampling was conducted from commercial fishing boats from 11/06 to 04/08 at seven sample sites representing the central Irish brown crab fisheries, with one sample site from the UK also included in the study. Six microsatellite markers, specifically developed for brown crab, were used to assess genetic diversity and estimate population differentiation parameters. Significant genetic structuring was found using F-statistics (Fst = 0.007) and exact tests, but not with Bayesian methods. Samples from the UK and Wexford were found to be genetically distinct from all other populations. Three northern populations from Malm Head and Stanton Bank were genetically similar with Fst estimates suggesting connectivity between them. Also, Stanton Bank, again on the basis of Fst estimates, appeared to be connected to populations down the west coast of Ireland, as far south as Kerry. Two Galway samples, one inside and one outside of Galway Bay, were genetically differentiated despite their close geographic proximity. It is hypothesised that a persistent northerly summer current could transport pelagic larvae from populations along the southwest and west coasts of Ireland towards Stanton Bank in the North, resulting in the apparent connectivity observed in this study.

Application of multivariate statistical process control to fuel cell manufacturing

Univariate statistical control charts, such as the Shewhart chart, do not satisfy the requirements for process monitoring on a high volume automated fuel cell manufacturing line. This is because of the number of variables that require monitoring. The risk of elevated false alarms, due to the nature of the process being high volume, can present problems if univariate methods are used. Multivariate statistical methods are discussed as an alternative for process monitoring and control. The research presented is conducted on a manufacturing line which evaluates the performance of a fuel cell. It has three stages of production assembly that contribute to the final end product performance. The product performance is assessed by power and energy measurements, taken at various time points throughout the discharge testing of the fuel cell. The literature review performed on these multivariate techniques are evaluated using individual and batch observations. Modern techniques using multivariate control charts on Hotellings T2 are compared to other multivariate methods, such as Principal Components Analysis (PCA). The latter, PCA, was identified as the most suitable method. Control charts such as, scores, T2 and DModX charts, are constructed from the PCA model. Diagnostic procedures, using Contribution plots, for out of control points that are detected using these control charts, are also discussed. These plots enable the investigator to perform root cause analysis. Multivariate batch techniques are compared to individual observations typically seen on continuous processes. Recommendations, for the introduction of multivariate techniques that would be appropriate for most high volume processes, are also covered.