[en] Central Africa ; Large spatial scale ; Communities ; Tropical forest
[en] In this paper we seek to identify the ﬂoristic determination biases contained in large-scale commercial inventories conducted by logging companies and to determine whether this impacts on the observed patterns of alpha and beta diversity. The study focused on ﬂoristic data recently collected by industrial timber companies in the tropical forests of the Central African Republic (28,229 0.5-ha plots spread over 14,000km2). A subset of these plots (n = 1107) was later re-sampled for controlling purposes by experienced botanists. The proportion of agreement between the two samplings was assessed for each species and independently for small and large trees, and at genus and family resolutions. Unsurprisingly, large trees and common species were more accurately identiﬁed than small trees and rare species. We found that the quality of the ﬂoristic determination increased slightly from species to families. We also detected a signiﬁcant variation between concessions in the quality of the ﬂoristic determination that was more dependent on working conditions during forest inventories than on ﬁeld workers. Contrary to a widespread belief, we did not ﬁnd a strong bias toward commercial species, showing that commercial inventory data could also be valid for non-commercial species in ecological studies. Finally, we found that both alpha and beta diversity patterns in commercial inventories were highly consistent with those of the re-sampled inventory. This latter result shows that commercial inventories are well suited to detect large-scale patterns of ﬂoristic variation. Large-scale commercial inventories could thus play an important role in the identiﬁcation of large-scale patterns in tropical tree diversity. This could enhance our ability to manage tropical forests by designing representative reserve networks and developing management plans that integrate diversity patterns at the landscape scale.