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

By

Tack, Wesley
Engledow, Henry
Veríssimo Pereira, Nuno
Amani, Christian
Bachman, Steven
Barbera, Patricia
Beentje, Henk
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
Jean-Claude, Mwanga Mwanga
Ndolo Ebika, Sydney T.
Nkengurutse, Jacques
Nsanzurwimo, Aimable
Ntore, Salvator
Richards, Sophie L.
Shutsha Ehata, Reddy
Simo-Droissart, Murielle
Stévart, Tariq
Sosef, Marc

Type

Article

Date of Publication

2022

Original Publication

PhytoKeys

Volume

206

Pages

137--151

Subjects

Africa , Conservation , data capture , data cleaning , endemics , Flora , Flowering plants , geographic range , Herbarium , IUCN Red List , threatened

Contributed by

Pensoft Publishers

Language

English

Published by

Sofia, Bulgaria Pensoft Publishers [2010]-

Identifiers

DOI: https://doi.org/10.3897/phytokeys.206.77379

 

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.