Browsing by Author "Uys, C."

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    Conservation and monitoring of invertebrates in terrestrial protected areas
    (AOSIS, 2011) McGeoch, M. A.; Sithole, H.; Samways, Michael J.; Simaika, J.P.; Pryke, J. S.; Picker, M.; Uys, C.; Armstrong, A.J.; Dippenaar-Schoeman, A. S.; Engelbrecht, I. A.; Braschler, B.; Hamer, M.
    Invertebrates constitute a substantial proportion of terrestrial and freshwater biodiversity and are critical to ecosystem function. However, their inclusion in biodiversity monitoring and conservation planning and management has lagged behind better-known, more widely appreciated taxa. Significant progress in invertebrate surveys, systematics and bioindication, both globally and locally, means that their use in biodiversity monitoring and conservation is becoming increasingly feasible. Here we outline challenges and solutions to the integration of invertebrates into biodiversity management objectives and monitoring in protected areas in South Africa. We show that such integration is relevant and possible, and assess the relative suitability of seven key taxa in this context. Finally, we outline a series of recommendations for mainstreaming invertebrates in conservation planning, surveys and monitoring in and around protected areas. Conservation implications: Invertebrates constitute a substantial and functionally significant component of terrestrial biodiversity and are valuable indicators of environmental condition. Although consideration of invertebrates has historically been neglected in conservation planning and management, substantial progress with surveys, systematics and bioindication means that it is now both feasible and advisable to incorporate them into protected area monitoring activities. © 2011.
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    Predicting outcome in severe traumatic brain injury using a simple prognostic model
    (Health & Medical Publishing Group, 2014-07) Sobuwa, S.; Hartzenberg, H. B.; Geduld, H.; Uys, C.
    Background. Several studies have made it possible to predict outcome in severe traumatic brain injury (TBI) making it beneficial as an aid for clinical decision-making in the emergency setting. However, reliable predictive models are lacking for resource-limited prehospital settings such as those in developing countries like South Africa. Objective. To develop a simple predictive model for severe TBI using clinical variables in a South African prehospital setting. Methods. All consecutive patients admitted at two level-one centres in Cape Town, South Africa, for severe TBI were included. A binary logistic regression model was used, which included three predictor variables: oxygen saturation (SpO2), Glasgow Coma Scale (GCS) and pupil reactivity. The Glasgow Outcome Scale was used to assess outcome on hospital discharge. Results. A total of 74.4% of the outcomes were correctly predicted by the logistic regression model. The model demonstrated SpO2 (p=0.019), GCS (p=0.001) and pupil reactivity (p=0.002) as independently significant predictors of outcome in severe TBI. Odds ratios of a good outcome were 3.148 (SpO2 ≥90%), 5.108 (GCS 6 - 8) and 4.405 (pupils bilaterally reactive). Conclusion. This model is potentially useful for effective predictions of outcome in severe TBI.