Abstract:
Peatlands, covering approximately one-third of global wetlands, provide various ecological functions but are highly vulnerable to climate change, with their changes in space and time requiring monitoring. The sub-Antarctic Prince Edward Islands (PEIs) are a key conservation area for South Africa, as well as for the preservation of terrestrial ecosystems in the region. Peatlands (mires) found here are threatened by climate change, yet their distribution factors are poorly understood. This study attempted to predict mire distribution on the PEIs using species distribution models (SDMs) employing multiple regression-based and machine-learning models. The random forest model performed best. Key influencing factors were the Normalized Difference Water Index and slope, with low annual mean temperature, with low annual mean temperature, precipitation seasonality and distance from the coast being less influential. Despite moderate predictive ability, the model could only identify general areas of mires, not specific ones. Therefore, this study showed limited support for the use of SDMs in predicting mire distributions on the sub-Antarctic PEIs. It is recommended to refine the criteria used to select environmental factors and enhance the geospatial resolution of the data to improve the predictive accuracy of the models.
