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The application of systematic conservation decision-making methodologies requires data on the spatial distribution of the elements of biodiversity. When a decision on where to put a protected area to conserve biodiversity must be made at a given time, the decision must be based on the best data available. But, adequate data are often lacking. This thesis examines the use of surrogate measures of biodiversity in conservation decision-making in Guyana, South America. The study looks at different surrogate measures and their influence on the selection of priority biodiversity sites for conservation. Surrogate measures at the ecosystem and species level are examined. The research shows that measures from different hierarchical levels produce different outcomes on the location of sites, however measures at the ecosystem-level appear to capture most of the known species distributions. The thesis examines cross-taxon congruency and shows that the spatial scale of analysis influences patterns of congruency for different taxonomic groups. The influence of spatial scale is also examined for various measures of biodiversity and it is shown that variability of species richness decreases with increased selection unit size. Finally, an index of vulnerability is used to prioritise conservation of sites in Guyana based on urgency, which is defined by two different threats: agriculture and forestry. This thesis adopts a conceptual framework based on data-driven, efficient, flexible and transparent methodologies and uses it to demonstrate how a network of protected areas might be established in Guyana that uses the most comprehensive data available on biodiversity. The thesis concludes by presenting a protocol for conservation decision-making that incorporates some of the theoretical principles identified by this work as important for measuring biodiversity and planning a protected area network.