Context . Commercial pine (Pinus spp.) plantations in southern Africa have been subjected to bark stripping by Chacma baboons (Papio ursinus) for many decades, resulting in severe financial losses to producers. The drivers of this behaviour are not fully understood and have been partially attributed to resource distribution and availability.
Aims . The study sought to develop a spatially explicit ecological-risk model for bark stripping by baboons to understand the environmental factors associated with the presence of damage in the pine plantations of the Mpumalanga province of South Africa.
Methods . The model was developed in Random Forests, a machine learning algorithm. Baboon damage information was collected through systematic surveys of forest plantations conducted annually. Environmental predictors included aspects of climate, topography and compartment-specific attributes. The model was applied to the pine plantations of the study area for risk evaluation.
Key results . The Random Forests classifier was successful in predicting damage occurrence (F1 score = 0.84, area under curve (AUC) = 0.96). Variable predictors that contributed most to the model classification accuracy were related to pine-stand characteristics, with the age of trees being the most important predictor, followed by species, site index and altitude. Variables pertaining to the environment surrounding a pine stand did not contribute substantially to the model performance.
Key conclusions . (1) The study suggests that bark stripping is influenced by compartment attributes; (2) predicted risk of bark stripping is higher in stands above the age of 5 years planted on high-productivity forestry sites, where site index (SI) is above 25; (3) presence of damage is not related to the proximity to natural areas; (4) further studies are required to investigate ecological and behavioural patterns associated with bark stripping.
Implications . The model provides a tool for understanding the potential extent of the risk of bark stripping by baboons within this region and it can be applied to other forestry areas in South Africa for risk evaluation. It contributes towards the assessment of natural hazards potentially affecting pine plantations and supports the development of risk-management strategies by forest managers. The model highlights opportunities for cultural interventions that may be tested for damage control.