Plant diversity and biomass in the Kenyan coastal forest
2 Bahir Dar University, Ethiopia
3 National Museums of Kenya, Kilifi, Kenya
4 University of Salzburg, Austria
5 Pwani University, Kilifi, Kenya
6 National Museums of Kenya, Nairobi, Kenya
7 Martin-Luther-University Halle-Wittenberg, Germany
O 5.3 in Session 5: From forest dynamics to island biogeography
05.05.2023, 13:45-14:00, SWO conference room
The Kenyan coastal forests form part of the Coastal Forests of Eastern Africa Biodiversity Hotspot and are characterized by exceptionally high species diversity and high threat from human activities. Some are considered as sacred by the local Mijikenda people and were designated as World Cultural Heritage in 2008. Overall, there are more than 30 coastal forest patches that vary in size and are located within a landscape dominated by agricultural uses. The study aimed to assess plant diversity and biomass in one forest patch called Kaya Kambe using a combination of field surveys and remote sensing data. Furthermore, our objective was to use biomass data as a proxy for assessing degradation patterns in other remaining forest patches. The forest patches were digitalized using 2022 ESRI World Imagery. Biomass data were downloaded from the Global Forest Watch Aboveground Live Woody Biomass Density map (version 02/2022). At Kaya Kambe, we identified all woody species and measured diameter at breast height (DBH) and height for individuals with DBH > 5cm in 19 study plots (r = 15m). Field work was conducted in early 2022 within the framework of the BioCult project. So far, 77 woody species were identified to species level, 26 were identified to genus level and 29 remain for further identification. Vegetation structure at the forest edge was strongly affected by human uses, which also showed in the global biomass data. In fact, this pattern of forest degradation was visible in all of the studied forest patches. Further analysis will show how well modeled biomass values correspond to the field data and in how far forest biomass patterns also depend on rainfall variability and hence growth conditions along the coast. The results can contribute to adapting and bolstering existing forest conservation efforts in the region.
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