期刊
WETLANDS ECOLOGY AND MANAGEMENT
卷 25, 期 3, 页码 313-330出版社
SPRINGER
DOI: 10.1007/s11273-016-9518-7
关键词
Conservation; Distribution; Management; Probability model; Semi-arid; Ephemeral wetland
资金
- Water Research Commission [WRC K5/2182]
- DAAD-NRF
- Nelson Mandela Metropolitan University (NMMU)
- Dormehl-Cunningham Scholarship
Until recently, little research has been conducted on the distribution and structure of ephemeral systems in semi-arid areas. This information is critical for appropriate wetland management and conservation. The Nelson Mandela Bay Municipality is a semi-arid area along the south-eastern coastline of the Eastern Cape Province of South Africa. The Municipality encapsulates a wide range of geological and geomorphological features as well as vegetation types within an area of some 1950 km(2), providing an ideal area for such research. The distribution and abundance of wetlands were defined, and a logistic regression (LR) model was used to establish whether this modelling technique is viable in semi-arid areas with highly variable rainfall patterns. Wetlands were delineated manually using geographical information systems, high-resolution aerial photographs and environmental data. More than 1700 wetland polygons were identified, with 80% of the systems being categorised as depressions, seeps and wetland flats. Unchannelled (8%) and channelled (7%) valley bottom wetlands and floodplain wetlands (5%) were also identified. The wetland database was then used to create a wetland occurrence probability model. There were 19 environmental variables used to develop the LR model, with eight variables used in the final model output. The predictive capacity of the model was good, with an area under curve value of 0.68 and an overall accuracy of 66%. This indicates that probabilistic wetland models are useful in highly variable environments with high numbers of small (< 1 ha) wetlands. Such predictive models provide a tool to assist in improving the accuracy of land cover datasets in semi-arid areas, and can be used to inform management decisions on flood risk areas and key conservation zones. In addition, abiotic variables that are significant in the model output provide an indication of the factors influencing wetland functioning in the region.
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