Modelling potential distribution of bramble (rubus cuneifolius) using topographic, bioclimatic and remotely sensed data in the KwaZulu-Natal Drakensberg, South Africa. (October 2018)

• Record Type: Journal Article
• Authors: Ndlovu, Phindile; Mutanga, Onisimo; Sibanda, Mbulisi; Odindi, John; Rushworth, Ian
• Is Part Of: Applied geography. Volume 99(2018)
• Page Start: 54
• Page End: 62
• Publication Date: 2018-10
• Subjects: Invasive alien plant species -- American bramble -- Remote sensing -- Maximum entropy (maxent); Geography -- Periodicals; Human geography -- Periodicals; Human ecology -- Periodicals; 910
• DOI: 10.1016/j.apgeog.2018.07.025
• Languages: English
• ISSNs: 0143-6228

Abstract

Abstract: The American bramble ( Rubus cuneifolius ), a woody perennial invasive shrub, presents serious ecological and economic impacts, particularly in ecologically rich and protected landscapes. Since the ecological factors determining its geographic distribution are poorly understood, a comprehensive analysis and understanding of its potential distribution are essential to understand probable impacts and plan control interventions. Hence, this study sought to explore the use of Maximum Entropy (Maxent) modelling approach to determine the potential distribution of American bramble in the uKhahlamba Drakensberg Park (UDP), South Africa. Four sets of model scenarios based on topographic data, topographic and remotely sensed data, topographic and bioclimatic data and a composite of all variables were generated using 73 occurrence points. Model performance was evaluated using Area Under the Curve (AUC), True Skill Statistic (TSS) and Kappa statistic. The model built using a composite of all variables yielded the highest accuracies, AUC score (0.957), indicating the best prediction of suitable and unsuitable areas for bramble. The inclusion of remotely sensed data improved model performance with bramble reflecting highly on the red edge bands. Elevation and rainfall of driest quarter were the most important variables associated with bramble distribution. The models predicted low elevation, warm and moist eastern parts as most suitable for bramble establishment and growth. Overall, all the models matched in terms of the geographic extent predicted as probable bramble distribution. Our results demonstrate that an integration of topographic, bioclimatic and remotely sensed variables are useful in determining landscape vulnerability to bramble invasion and provide a valuable tool for planning control strategies.