Topography is a major determinant of forest–savanna distributions in coexistence landscapes in Central Africa

Forests and savannas frequently coexist as patches within tropical landscapes, yet the mechanisms controlling their spatial configuration remain unclear. The presence of both vegetation states under similar climatic conditions is often attributed to fire–vegetation feedbacks, but could also reflect variation in overlooked external drivers. In Central Africa, forest–savanna coexistence becomes more common with increasing topographic roughness, but how well topographic heterogeneity explains the forest–savanna configuration within coexistence landscapes is unknown.

Here we address this question and examine the role of individual topographic variables that may influence tree cover by, for instance, changing water availability and fire spread. We identify coexistence landscapes from remotely sensed tree cover data and derive topographic variables from a digital elevation model. We use these variables to develop machine learning algorithms predicting vegetation state within coexistence landscapes.

Models achieved an average prediction accuracy of 0.75, with local elevation (relative to the surrounding 500 m or 5000 m) emerging as the strongest predictor of vegetation state. Both model accuracy and the role of topographic predictors varied strongly among landscapes, reflecting the diverse pathways by which topography can influence tree cover. This highlights the need to consider local context when analysing the distribution and stability of tropical forest and savanna ecosystems. Overall, our findings indicate that topographic heterogeneity is a major driver of forest–savanna coexistence in Central Africa. Coexistence landscapes are more deterministic than previously assumed, suggesting that their response to disturbances and climate change will be spatially heterogeneous, thereby reducing the likelihood of abrupt large-scale shifts between forest and savanna states.