Semi-pelagic longline selectivity for two demersal species from the Azores: the black spot sea bream (Pagellus bogaraveo) and the bluemouth rockfish (Helicolenus dactylopterus dactylopterus)

The selectivity of four hook sizes (STELL brand(1), Quality 2335, numbers 12, 9, 6 and 4) used in a semi-pelagic longline fishery was studied in the Azores. Two species were caught in sufficient numbers for modelling of selectivity: the black spot sea bream (Pagellus bogaraveo) and the bluemouth rockfish (Helicolenus dactylopterus dactylopterus). A maximum likelihood method was used to fit a versatile model which can be used to describe a wide range of selectivity curves; from bell-shaped to asymptotic. Significant differences in size selectivity between hooks were found for both species. In the case of Pagellus bogaraveo, the smallest hook (number 12) had the lowest catch rates and all hooks were characterised by logistic-type selectivity curves, with sizes at 50% selectivity of: 27.9, 30.4, and 32.8 cm for hooks numbers 12, 9 and 6, respectively. The number 9 hook was the most efficient for Helicolenus d. dactylopterus, with selectivity curves varying from strongly skewed to the right for the number 12 hook to logistic-type for the numbers 6 and 4 hooks. Sizes at 50% selectivity for this species were 16.8, 18.7, 20.7, and 22.0 cm. respectively. (C) 1999 Elsevier Science B.V. All rights reserved.

Cross-correlations between weather variables in Australia

The information on climate variations is essential for the research of many subjects, such as the performance of buildings and agricultural production. However, recorded meteorological data are often incomplete. There may be a limited number of locations recorded, while the number of recorded climatic variables and the time intervals can also be inadequate. Therefore, the hourly data of key weather parameters as required by many building simulation programmes are typically not readily available. To overcome this gap in measured information, several empirical methods and weather data generators have been developed. They generally employ statistical analysis techniques to model the variations of individual climatic variables, while the possible interactions between different weather parameters are largely ignored. Based on a statistical analysis of 10 years historical hourly climatic data over all capital cities in Australia, this paper reports on the finding of strong correlations between several specific weather variables. It is found that there are strong linear correlations between the hourly variations of global solar irradiation (GSI) and dry bulb temperature (DBT), and between the hourly variations of DBT and relative humidity (RH). With an increase in GSI, DBT would generally increase, while the RH tends to decrease. However, no such a clear correlation can be found between the DBT and atmospheric pressure (P), and between the DBT and wind speed. These findings will be useful for the research and practice in building performance simulation.