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An in depth investigation of the socio-ecology of the vervet monkey species complex (Cercopithecus aethiops subspp.) is presented. Herein, particular emphasis has been placed on evaluating the information content of remotely sensed primary productivity with respect to inquiries into the causal network underlying the behavioural ecology of the species. The principal aim was to construct an inter-populational model of group size and range of distribution for vervet monkeys over the whole of sub-Saharan Africa. Data were collected on a habituated group of vervet monkeys over a 12 months observation period at the Lajuma Research Centre, South Africa. In addition, behavioural information from other populations was gathered in an extensive literature review. Environmental data were accrued on both a global (inter-populational) and local (intra- populational) level using a combination of remotely sensed data and more traditional field-observation based techniques. Where appropriate, variables were integrated into the powerful modelling environment of a Geographical Information System (GIS).Biologically intuitive associations between a remotely sensed index of primary productivity (the Normalised Difference Vegetation Index, or NDVI) and climatic conditions were established on the long-term global (annual average values over sub- Saharan Africa) and short-term local level (monthly values over the home range area of the study group). Local NDVI values, moreover, were strong correlates and predictors of field estimates on local phenology and food availability as well as of temporal and spatial variation in parameters of range use and time allocation by the study group. Global NDVI values proved pivotal to the eventual inter-populational time budget model of vervet monkey group size and potential range of distribution. Current results are taken to suggest that several areas of research within primatology may stand a lot to gain from a more widespread and systematic application of the powerful methodological synergism offered by remote sensing and geographical information systems.

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