PARENT SESSION
Oral Session #69: Conservation: Biodiversity assessment and reserve design.
Presiding: T. Norton
Wednesday, August 7. 1:00 PM to 4:45 PM. Grand Ballroom Central, Radisson.
A GIS-based analysis of African phytodiversity patterns using two complementary approaches.
Kueper, Wolfgang*,1, Mutke, Jens1, Kier, Gerold1, Barthlott, Wilhelm1, 1 Botanical Institute, University of Bonn, Bonn, Germany
ABSTRACT- Conservation of biodiversity requires intensive knowledge on its spatial distribution and dependency on biotic and abiotic parameters. The Biota-Biomaps-Project of the Botanical Institute Bonn conducts a GIS-based analysis of the continental phytodiversity patterns of African vascular plants. Based on modelled distributions of more than 1000 representatively selected species, we generate maps of species richness and endemism for continental Africa and analyse their correlation with geodiversity. Until now, our knowledge on the African phytodiversity patterns is based on a handful of maps, which are derived from relatively coarse summarized floristic data of geographical units (inventory-based approach) or from mostly unprocessed collection data of plant species (taxon-based approach). The selection of species was often constrained by data availability. The validation of those maps was based on intuitive comparisons with general phytogeographical aspects. For the first time, we have conducted a quantitative comparison of the results of the two approaches in order to evaluate the accuracy of the maps. On the basis of the new maps, we could confirm a clear correlation between, e.g., species richness and precipitation or more complex hydrographic paramters, but also show the relevance of topodiversity. Our analyses also show that our image of African plant diversity is biased by historical collection intensity which is linked to human transport logistics in the respective geographical areas. Our research is part of the new German BIOTA AFRICA research programme (www.biota-africa.de)
KEY WORDS: Plant Biodiversity and Endemism, Africa, GIS, Distribution modelling