Bibliography: leaves 92-99. / The community structures of three shore types namely: "mixed shores" (those where rocky and sandy-shore habitats are intermixed), pure rocky shores and pure sandy beaches in False Bay, South Africa are compared in this study. Four habitats were identified - pure rock (unaffected by sand), mixed rock (rock affected by sand), mixed sand (sand between emergent rocks) and pure sand (beaches with no emergent rock) - representing a gradation from pure rock to pure sandy beaches. The specific aims of this study were to: (1) Sample quantitatively and describe macrofaunal communities on mixed shores in False Bay; (2) make direct comparisons among both the four types of habitats and three types of shores; and (3) test the hypothesis that sand inundation increases diversity at both habitat (a-diversity) and shore (diversity) level. The biological communities of mixed shores are described in terms of species composition, trophic organisation and zonation. Mixed-shore zonation patterns are different from those previously described for pure rocky shores in the region. The ability of Charomytilus meridiana/is and inability of patellid limpets and various algae, to withstand sand inundation are largely responsible for these differences.

Includes abstract. / Includes bibliographical references (p. 39-41). / Withdrawal from insurance contracts can be a significant risk for insurers. Withdrawal rates can be difficult to predict because withdrawal is influenced by a number of inter-related factors related to, inter alia, the sales process, characteristics of the insurance contract, characteristics of the contract holder, and economic variables. Existing methods used to model and predict withdrawal rates are initially reviewed. Two additional methods which have been proposed in the literature as means for modelling insurance risks are neural networks and Bayesian networks. These two methods are utilised in order to build models to compare their predictive ability with a commonly used method for modelling withdrawal rates, namely logistic regression.