The ECAT dataset: expert-validated distribution data of endemic and sub-endemic trees of Central Africa (Dem. Rep. Congo, Rwanda, Burundi)

Details

Ressource 1Download: Tack et al. 2022 (828.68 [Ko])
State: Public
Version: Final published version
License: CC BY 4.0
Serval ID
serval:BIB_6828442FFB50
Type
Article: article from journal or magazin.
Collection
Publications
Title
The ECAT dataset: expert-validated distribution data of endemic and sub-endemic trees of Central Africa (Dem. Rep. Congo, Rwanda, Burundi)
Journal
PhytoKeys
Author(s)
Tack Wesley, Engledow Henry, Veríssimo Pereira Nuno, Amani Christian, Bachman Steven P., Barberá Patricia, Beentje Henk J., Bouka Gaël U. D., Cheek Martin, Cosiaux Ariane, Dauby Gilles, De Block Petra, Ewango Corneille E. N., Fischer Eberhard, Gereau Roy E., Hargreaves Serene, Harvey-Brown Yvette, Ikabanga Davy U., Ilunga wa Ilunga Edouard, Kalema James, Kamau Peris, Lachenaud Olivier, Luke Quentin, Mwanga Mwanga Ithe, Ndolo Ebika Sydney T., Nkengurutse Jacques, Nsanzurwimo Aimable, Ntore Salvator, Richards Sophie L., Shutsha Ehata Reddy, Simo-Droissart Murielle, Stévart Tariq, Sosef Marc S. M.
ISSN
1314-2003
1314-2011
Publication state
Published
Issued date
16/09/2022
Peer-reviewed
Oui
Volume
206
Pages
137-151
Language
english
Abstract
In this data paper, we present a specimen-based occurrence dataset compiled in the framework of the Conservation of Endemic Central African Trees (ECAT) project with the aim of producing global conservation assessments for the IUCN Red List. The project targets all tree species endemic or sub-endemic to the Central African region comprising the Democratic Republic of the Congo (DR Congo), Rwanda, and Burundi. The dataset contains 6361 plant collection records with occurrences of 8910 specimens from 337 taxa belonging to 153 genera in 52 families. Many of these tree taxa have restricted geographic ranges and are only known from a small number of herbarium specimens. As assessments for such taxa can be compromised by inadequate data, we transcribed and geo-referenced specimen label information to obtain a more accurate and complete locality dataset. All specimen data were manually cleaned and verified by botanical experts, resulting in improved data quality and consistency.
Keywords
Africa, conservation, data capture, data cleaning, endemics, flora, flowering plants, geographic range, herbarium, IUCN Red List, threatened
Open Access
Yes
Create date
17/09/2022 22:27
Last modification date
17/09/2022 22:30
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