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We evaluated the relative importance of annual rainfall, temperature and their seasonality to tree species distribution in Ghana. We used species presence/absence data from 2505 1-ha plots systematically distributed over Ghana's forests. Logistic regression was used to determine species responses to four climatic variables generated from the Worldclim database. The distribution of 95% of 20 species was significantly associated with annual rainfall, 60% with rainfall seasonality, 45% with isothermality and 40% with temperature seasonality. Annual rainfall explained on average most of the variation (17%, range = 0.5–52%) in species distribution, followed by rainfall seasonality 5% (range = 0.5–27%), isothermality 4% (range = 0.8–24%) and temperature seasonality 1% (range = 0.4–4.5%). Our results suggest that, out of the climatic variables investigated, rainfall is the main factor determining tree species distribution in Ghana; temperature also influences the distribution of a number of species, although it explains much less of the variation. The reduction in annual rainfall that prevailing climate-change scenarios predict for the region will result in a shift in the distribution of most species, whereas the predicted increase in temperature variation is likely to have little effect. [ABSTRACT FROM PUBLISHER]

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]