Predicting alpha diversity of African rain forests: models based on climate and satellite-derived data do not perform better than a purely spatial model

Parmentier, I., Harrigan, R.J., Buermann, W., Mitchard, E.T.A., Saatchi, S., Malhi, Y., Bongers, F., Hawthorne, W.D., Leal, M.E., Lewis, S.L., Nusbaumer, L., Sheil, D., Sosef, M.S.M., Affum-Baffoe, K., Bakayoko, A., Chuyong, G.B., Chatelain, C., Comiskey, J.A., Dauby, G., Doucet, J.L., Fauset, S., Gautier, L., Gillet, J.F., Kenfack, D., Kouame, F.N., Kouassi, E.K., Kouka, L.A., Parren, M.P.E., Peh, K.S.H., Reitsma, J.M., Senterre, B., Sonke, B., Sunderland, T.C.H., Swaine, M.D., Tchouto, M.G.P., Thomas, D., van Valkenburg, J.L.C.H., Hardy, O.J. 2011. Predicting alpha diversity of African rain forests: models based on climate and satellite-derived data do not perform better than a purely spatial model . Journal of Biogeography 38 (6) :1164-1176. ISSN: 0305-0270.

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 in tropical rain forest, West Africa and Atlantic Central Africa.