TITLE:
Effects of Forest Disturbance on Vegetation Structure and Above-Ground Carbon in Three Isolated Forest Patches of Taita Hills
AUTHORS:
Chemuku Wekesa, Nereoh Leley, Elias Maranga, Bernard Kirui, Gabriel Muturi, Musingo Mbuvi, Ben Chikamai
KEYWORDS:
Carbon Stocks, Forest Disturbance, Height, Indigenous Forest, REDD+, Wood Density
JOURNAL NAME:
Open Journal of Forestry,
Vol.6 No.2,
April
29,
2016
ABSTRACT: The
structure and species composition of undisturbed natural forests serve as
benchmarks for understanding forest carbon storage potential for reduced carbon
emissions. Even though Kenya is seeking to stabilize forest cover, reverse
degradation and increase forest cover through mechanisms such as REDD+, there
is relatively little information on inherent forest carbon storage potential or
its response to disturbance. Comparative studies were undertaken in three
remnant fragments of indigenous forests in Taita Hills, Kenya to characterize
the structure and forest carbon storage potential of undisturbed, moderately
and heavily disturbed sites within these forests. The sensitivity of forest
carbon storage estimates to different methods of tree biomass estimation were
also examined, including estimates which used DBH, tree height and wood density
from extracted tree cores. Disturbance altered the forest structure, reduced
species diversity and decreased the capacity of the forests to sequester
carbon. The forests’ capacity to sequester carbon reduced by between 9.2% and
70.7% depending on the site (forest fragment) and level of disturbance. Models
with DBH and wood density gave higher quantities of carbon of between 0.9% and
44.4% for sites exhibiting different levels of disturbance. The present results
suggest that disturbance had strong influence on forest structure, species
diversity and carbon stocks and therefore maintaining the forests’ ecological
integrity over the long-term may prove difficult if the frequency and intensity
of disturbance increases. Moreover, development and implementation of effective
mitigation strategies to reduce carbon emissions will require the use of local
biomass models since they are accurate.