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, Ingrid; Harrigan, Ryan; Buermann, Wolfgang; Mitchard, Edward T.; Saatchi, Sassan; Malhi, Yadvinder; Bongers, Frans; Hawthorne, William; Leal, Miguel; Lewis, Simon; Nusbaumer, Louis; Sheil, Douglas; Sosef, Marc S.; Affum-Baffoe, Kofi; Bakayoko, Adama; Chuyong, George; Chatelain, Cyrille; Comiskey, James; Dauby, Gilles; Doucet, Jean-Louis; Fauset, Sophie; Gautier, Laurent; Gillet, Jean-Francois; Kenfack, David; Kouame, Francois; Kouassi, Edouard; Kouka, Lazare; Parren, Marc P.; Peh, Kelvin S-H; Reitsma, Jan; Senterre, Bruno; Sonke, Bonaventure; Sunderland, Terry C.; Swaine, Mike; Tchouto, Mbatchou G.; Thomas, Duncan W; Van Valkenburg, Johan L. C.; Hardy, Olivier J.

doi:doi/10.1111/j.1365-2699.2010.02467.x

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

Article révisé par les pairs

Résumé :

Aim 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. Location Tropical rain forest, West Africa and Atlantic Central Africa. Methods Alpha diversity estimates were compiled for trees with diameter at breast height >

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