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Aim: Existing phytogeographical frameworks for tropical Africa lack either spatial completeness, unit definitions smaller than the regional scale or a quantitative approach. We investigate whether physical environmental variables can be used to interpolate floristically defined vegetation units, presenting an interpolated, hierarchical, quantitative phytogeographical framework for tropical Africa, which is compared to previously defined regions. Location: Tropical mainland Africa 24°N to 24°S. Taxon: 31,046 vascular plant species and infraspecific taxa. Methods: We calculate a betasim dissimilarity matrix from a comprehensive whole‐flora database of plant species distributions. We investigate environmental correlates of floristic turnover with local non‐metric multidimensional scaling. We derive a hierarchical biogeographical framework by clustering the dissimilarity matrix. The framework is modelled using a classification decision tree method and 12 physical environmental variables to interpolate and increase the resolution of the framework across the study region. Results: Floristic turnover is related strongly to water availability and temperature, with smaller contributions from land cover, topographic ruggedness and lithology. Region can be predicted with 90% accuracy by the model. We define 19 regions and 99 districts. We find a novel arrangement of the arid regions. Regional subdivision within the savanna biome is supported with minor variation to borders. Within the forests of west and central Africa, our whole‐flora gridded regionalization supports the divisions identified by a previous analysis of trees only. Main conclusions: Physical environmental variables can be used to predict floristically defined vegetation units with very high accuracy, and the approach could be pursued for other incompletely sampled taxa and areas outside of tropical Africa. Geographical coherence is higher than in previous quantitative phytoregional definitions. For most tropical African vascular plant species, we provide predictions of which species will occur within each mapped district and region of tropical Africa. The framework should be useful for future studies in ecology, evolution and conservation. [ABSTRACT FROM AUTHOR]

Our aim was to evaluate the extent to which we can predict and map tree alpha diversity across broad spatial scales either by using climate and remote sensing data or by exploiting spatial autocorrelation patterns. Tropical rain forest, West Africa and Atlantic Central Africa. Alpha diversity estimates were compiled for trees with diameter at breast height ≥ 10 cm in 573 inventory plots. Linear regression (ordinary least squares, OLS) and random forest (RF) statistical techniques were used to project alpha diversity estimates at unsampled locations using climate data and remote sensing data [Moderate Resolution Imaging Spectroradiometer (MODIS), normalized difference vegetation index (NDVI), Quick Scatterometer (QSCAT), tree cover, elevation]. The prediction reliabilities of OLS and RF models were evaluated using a novel approach and compared to that of a kriging model based on geographic location alone. The predictive power of the kriging model was comparable to that of OLS and RF models based on climatic and remote sensing data. The three models provided congruent predictions of alpha diversity in well-sampled areas but not in poorly inventoried locations. The reliability of the predictions of all three models declined markedly with distance from points with inventory data, becoming very low at distances > 50 km. According to inventory data, Atlantic Central African forests display a higher mean alpha diversity than do West African forests. The lower tree alpha diversity in West Africa than in Atlantic Central Africa may reflect a richer regional species pool in the latter. Our results emphasize and illustrate the need to test model predictions in a spatially explicit manner. Good OLS or RF model predictions from inventory data at short distance largely result from the strong spatial autocorrelation displayed by both the alpha diversity and the predictive variables rather than necessarily from causal relationships. Our results suggest that alpha diversity is driven by history rather than by the contemporary environment. Given the low predictive power of models, we call for a major effort to broaden the geographical extent and intensity of forest assessments to expand our knowledge of African rain forest diversity. [ABSTRACT FROM AUTHOR]