Capacity building for sustaining biodiversity in DR Congo

Abstract

Despite the country’s significantly diverse flora and fauna, very little data are available on the biodiversity of the Democratic Republic of Congo (DR Congo). To remedy this while facing the urgent need for better knowledge and management of biodiversity, some universities in the country are engaged in the implementation and the publishing of the results of work on biodiversity conducted by researchers. To date UPN, the National Pedagogical University at Kinshasa has published five databases in collaboration with the Royal Museum for Central Africa and GBIF: (i) a database of edible insects in DR Congo, including sixty different species belonging respectively and by degree of importance to the Lepidoptera (46.7%), Isoptera (18.6%), Orthoptera (17.6%), Coleoptera (9.7%) and Hymenoptera (3.7%) orders of insects; (ii) a database on farmed animals, including about 13 species of birds, 29 mammals, 5 rodents and 3 mollusks; (iii) a database on forage plants, including about fifty species distributed in 15 orders of plants and 20 families. For each of these species, taxonomic information, common names in French and local languages, and chemical composition are provided; (iv) a database on phytosanitary plants used as pesticides or medicines, with special taxonomic coverage in the Phylum Spermaphyta, Class Magnoliopsida, and Subclass Rosidae; and (v) a botanical database of select families of plants (e.g. Moringaceae, Asteraceae, Poaceae, Euphorbiaceae, Fabaceae, Burceraceae, Malvaceae) represented by data in the herbarium of the university. Although many Congolese scientists still retain their data in Excel spreadsheets, some of them are aware of the urgency of sharing bio-data even though they face technology and training gaps that make sustained collaboration a challenge. To increase their motivation to do proper data sharing, there is an urgent need for a better access to digitization tools (and methods) and to standards for mobilizing and integrating sample-based data