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Habitat selection may be considered a behavioural consequence of an individual actively selecting where it lives or passively persisting in a certain habitat. There are a variety of approaches to assessing habitat selection, including relating densities to predefined habitats and the characteristics of these habitats, measuring the behaviour of animals within predefined habitats in order to assess the relative benefits to the animal and comparing these between habitats, and using optimality theory to allow the animal to provide measures of its preference for particular habitats or patches. Each approach provides different perspectives on an animal’s choice and use of habitat, with some approaches working more effectively with certain species or habitats than others. There have, however, been no attempts to apply all these approaches to a single species at a single site. The objective of this study was therefore to apply the three above-mentioned approaches to assess habitat use and quality to a single species, the springbok (Antidorcas marsupialis) at the Augrabies Falls National Park (AFNP), on the northern bank of the Orange River, in order to provide the basis for further work on comparing and integrating these approaches. The relative abundance of springbok in Augrabies Falls National park was used to develop a habitat suitability model for the park, and thus determine the habitat variables that influenced habitat suitability. Isodar analysis revealed information on the mechanisms underlying habitat preference. Behavioural models were developed to improve our understanding of how springbok behaviour changes in relation to the habitat, and incorporated the variables identified in the habitat suitability model. The different spatial and temporal scales influencing springbok habitat selection were determined using optimal foraging theory and giving-up densities. Springbok preferred open habitats providing high food quality. Springbok behaviour was related to the foraging and predation risk characteristics of the habitat, and springbok varied their temporal and spatial utilisation so as to minimise their risk of predation and maximise their food intake. The habitat suitability model, behaviour models and giving-up densities were compared for ease of use and applicability. Using giving-up densities to reveal habitat suitability had greater applicability and was both efficient and objective.

Cape St Francis, Eastern Cape, has been identified as one of four potential sites for establishing a mainland-based African penguin (Spheniscus demersus) colony. This thesis comprises three main components: a verification of a preparation method for stable isotope samples from penguin feathers; a dietary analysis of the penguins on Bird Island, Algoa Bay, though stable isotope analysis of whole blood and feathers (2012 and 2013); and an estimation of available fish surplus that could potentially support a colony of penguins at Cape St Francis. Each component contributes towards the next, all building towards answering the main research question: Will there be enough food around St Francis Bay to support a colony of penguins and sustain the already established fisheries industry within the bay? Stable isotope analysis of whole blood and feathers from breeding adults and whole blood from juveniles provided insight into the variability of African penguins’ diets at different stages in their life history. Stable isotope mixing models indicated that the predicted proportions that each prey species could potentially contribute to diet conflicted with published stomach sample data. This might arise from inaccurate trophic enrichment factors used in the model, or from systematic biases in the published stomach sampling techniques, or both. Dietary sexual dimorphism was not demonstrated by the isotope signatures of breeding penguins. Based on official catch data, the fisheries activity on the south coast, and especially around the potential colony site at St Francis, is much lower than around the west coast’s penguin colonies. The model provided a first-order estimate for fish supply around the potential colony site at St Francis both at a large coastal scale and a local small scale. At both scales the estimate indicated an ample availability of fish at current fishing levels. The model in Chapter 4 can also be applied to refining the assessments of other potential colony sites on the south coast. In conclusion, the south coast is a promising area for a new colony of penguins in terms of food availability. There is relatively low fishing activity in the area and, as suggested by the large-scale model in Chapter 4, an ample fish resource. The final chapter briefly discusses factors that need to be considered before attempting to establish a mainland-based colony of African penguins.

For the west coast of North America, from northern California to southern Washington, a habitat suitability prediction framework was developed to support wave energy device siting. Concern that wave energy devices may impact the seafloor and benthos has renewed research interest in the distribution of marine benthic invertebrates and factors influencing their distribution. A Bayesian belief network approach was employed for learning species-habitat associations for Rhabdus rectius, a tusk-shaped marine infaunal Mollusk. Environmental variables describing surficial geology and water depth were found to be most influential to the distribution of R. rectius. Water property variables, such as temperature and salinity, were less influential as distribution predictors. Species-habitat associations were used to predict habitat suitability probabilities for R. rectius, which were then mapped over an area of interest along the south-central Oregon coast. Habitat suitability prediction models tested well against data withheld for crossvalidation supporting our conclusion that Bayesian learning extracts useful information available in very small, incomplete data sets and identifies which variables drive habitat suitability for R. rectius. Additionally, Bayesian belief networks are easily updated with new information, quantitative or qualitative, which provides a flexible mechanism for multiple scenario analyses. The prediction framework presented here is a practical tool informing marine spatial planning assessment through visualization of habitat suitability. / Graduation date: 2012