Projecting the impacts of climate change on tree biomass in Arabuko-Sokoke Forest, Kenya
Tarus George K.*, Nzioka John Muthama**, John.M .Githaiga*** and Richard Onwonga****
*Kenya Forest Service, Climate Change Programme
**Wangari Maathai Institute for Peace and Environmental Studies, University of Nairobi
***Department of Biological Sciences, University of Nairobi
****Department of Land Resource Management and Agricultural Technology, University of Nairobi
https://doi.org/10.20987/jccs.3.04.2018
Corresponding author
George K. Tarus
Climate Change Response Program, Kenya Forest Service
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(Received 10 May 2017, received in revised form 11 April 2018, Accepted 17 April 2018)
Abstract
Scientific concerns regarding tropical deforestation and global climate change have prompted efforts towards quantification of forests as terrestrial carbon stores. The Arabuko-Sokoke forest ecosystem within Kilifi County is faced with forest degrading pressures from natural and community livelihood related drivers. Very limited studies have been done in this regard and this paper seeks to project how climate change will impact on tree biomass through empirical research by using Maximum Entropy (MaxEnt) Model. The prediction model based on RCP4.5 and 8.5 indicated that MaxEnt model can be used to predict geographical distribution of brachystegia and mixed forest at 2050 and 2070 (Area under Curve (AUC) = 0.80-0.90), while cynometra forest had a poor model fit (AUC=0.60-0.70). The jack knife test indicated that variables associated with annual trends, seasonality and extremities of temperature and rainfall parameters contributed to the all predictive model in this study. The study recommended that; the forest manager develops strategies to mitigate on shifting and fundamental niche reduction for key species. Communities are advised to diversify their sources of livelihoods. Carbon accounting systems and Greenhouse Gas (GHG) systems should consider impacts of climate change on tree biomass in Arabuko-Sokoke Forest.
Key words: Climate change, Tree Biomass, Representative Concentration Pathways, Maxent and Arabuko Sokoke
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