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

Onderzoeksoutput: Bijdrage aan tijdschriftA1: Web of Science-artikelpeer review

  • Wesley Tack
  • Henry Engledow
  • Nuno Verissimo Pereira
  • Christian Amani
  • Steven P. Bachman
  • Patricia Barberá
  • Henk Beentje
  • Gaël Bouka
  • Martin Cheek
  • Ariane Cosiaux
  • Gilles Dauby
  • Petra De Block
  • Corneille E.N. Ewango
  • Eberhard Fischer
  • Roy Gereau
  • Serene Hargreaves
  • Yvette Harvey-Brown
  • Davy U. Ikabanga
  • Edouard Ilunga wa Ilunga
  • James Kalema
  • Peris Kamau
  • Olivier Lachenaud
  • Quentin Luke
  • Ithe Jean-Claude Mwanga Mwanga
  • Sydney Ndolo Ebika
  • Jacques Nkengurutse
  • Aimable Nsanzurwimo
  • Salvator Ntore
  • Sophie L. Richards
  • Reddy Shutsha Ehata
  • Murielle Simo-Droissart
  • Tariq Stevart
  • Marc[us] Simon Maria Sosef
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.
Originele taal-2Engels
Pagina's (van-tot)137-151
Aantal pagina's15
StatusGepubliceerd - 16-sep.-2022


  • B290-taxonomie-van-planten



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