The white star apple (Chrysophyllum albidum G. Don), a revealed potential pan tropical forest food species in the context of climate and global changes

Abstract Chrysophyllum albidum is a forest food tree species of the Sapotaceae family bearing large berries of nutrition, sanitary, and commercial values in many African countries. It naturally grows in lowland rain forest and is widely distributed in West, Central, and East Africa. Because of its socio-economic importance, C. albidum is threatened at least by human pressure. However, we do neither know at which extent climate change can impact its distribution, nor whether it is possible to introduce the species in other tropical regions. In order to resolve our concerns we decided to model the spatial distribution of the species. We then used the SDM package for data modelling in R to compare predictive performances of algorithms among the most common used: three machine learning algorithms (MaxEnt, Boosted Regression Trees, and Random Forests) and three regression algorithms (Generalized Linear Model, Generalized Additive Models, and Multivariate Adaptive Regression Spline). We performed model transfers in tropical Asia and Latin America. At the scale of Africa, predictions with respect to Maxent, under Africlim (scenarios rcp 4.5 and rcp 8.5, horizon 2055) and MIROCES2L (scenarios SSP245 and SSP585, horizon 2060) showed that the favorable areas of C. albidum will extend mostly in West, East, Central, and Southern Africa as well as in East Madagascar. As opposed to Maxent, in Africa, the predictions of BRT and RF were unrealistic with respect to the known ecology of C. albidum. All the algorithms were consistent in predicting a successful introduction of C. albidum in Latin America, both at present and in the future. Predictions of spatial distribution of C. albidum at present and in future under MIROCES2L, scenarios SSP245 and SSP585, horizon 2060 were coherent and even complementary between BRT and RF and showed that the species can be successfully introduced in tropical Asia.