Uses of GBIF in scientific research

Peer-reviewed research citing GBIF as a data source, with at least one author from Uganda.
Extracted from the Mendeley GBIF Public Library.

List of publications

  • Kindt, R., Lillesø, J., van Breugel, P., Bingham, M., Demissew, S., Dudley, C., Friis, I., Gachathi, F., Kalema, J., Mbago, F., Moshi, H., Mulumba, J., Namaganda, M., Ndangalasi, H., Ruffo, C., Minani, V., Jamnadass, R., Graudal, L.

    Correspondence in forest species composition between the Vegetation Map of Africa and higher resolution maps for seven African countries

    (Journal name unavailable from Mendeley API. To be updated soon...)

    Abstract Question How well does the forest classification system of the 1:5,000,000 vegetation map of Africa developed by Frank White correspond with classification systems and more extensive information on species assemblages of higher resolution maps developed for Ethiopia, Kenya, Malawi, Rwanda, Tanzania, Uganda and Zambia? Methods We reviewed various national and sub-national vegetation maps for their potential in increasing the resolution of the African map. Associated documentation was consulted to compile species assemblages, and to identify indicator species, for national forest vegetation types. Indicator species were identified for each regional forest type by selecting those species that, among all the species listed for the same phytochorion (regional centre of endemism), were listed only for that forest type. For each of the national forest types, we counted the number of indicator species of the anticipated regional type. Floristic relationships (expressed by four different ecological distance measures) among national forest types were investigated based on distance-based redundancy analysis, permutational multivariate analysis of variance (PERMANOVA) using distance matrices and hierarchical clustering. Results For most of the national forests, the analysis of indicator species and floristic relationships confirmed the regional classification system for the majority of national forest types, including the allocation to different phytochoria. Permutation tests confirmed allocation of national forest types to regional typologies, although the number of possible permutations limited inferences for the Zambezian and Lake Victoria phytochoria. Two forest types from Ethiopia and Kenya did not correspond to regional forest types. Conclusions Our analysis provides support that as the classification systems are compatible, the resolution and information content of the vegetation map of Africa can be directly improved by adding information from national maps, probably leading to improved liability of its application domains. We found statistical evidence for a distinct Afromontane phytochorion. We suggest expanding the regional forest classification system with ‘Afromontane moist transitional forest’. Among the various application domains of the higher resolution maps, these maps allow for an enhanced phytochoristic analysis of eastern Africa.

    Keywords: beta-sim distance, Ethiopia, Frank White, indicator species, Kenya, Kulczynski distance, Malawi, phytochorion, Rwanda, Tanzania, Uganda, Zambia