Mangroves store and sequester large quantities of
belowground biomass (BGB) in their root system. Given the important ecosystem
services played by mangroves, particularly biomass storage and mitigation of
climate change, interventions aimed to ensure that mangroves are safeguarded
from deforestation and degradation through REDD+ and effective forest
management initiatives are imperative. However, there are several challenges
and uncertainties in the estimation of BGB of mangroves. Therefore, this study
aimed to determine uncertainties in the estimation of BGB. The study
hypothesised that root sampling techniques that do not involve complete root
excavation are relatively inaccurate and hence tend to underestimate the BGB of
mangroves. Besides, the study intended to provide step-by-step approaches for
quantifying tree BGB. BGB data were generated using complete root excavation
for Avicenia marina, Sonneratia alba, and Rhizophora mucronata in Mainland
Tanzania to be used as reference data. The findings showed that all the local
species-specific BGB models were statistically accurate in the prediction of
tree BGB for the three species. Furthermore, results showed that all BGB models
were constructed using data generated by means of the trench, incomplete root excavation,
and pull-up methods under-estimated tree BGB by producing negative prediction
errors (PEs) which were significantly different from zero at a 5% level of
significance. The PEs ranged from -33% with relative precision (RP) of 68% (R.
mucronata) to -82% (RP of 83%) for S. alba. Based on various sources of
uncertainties in the estimation of BGB, the study also documented step-by-step
approaches for quantifying BGB. This study concludes that there are large
uncertainties in the estimation of BGB of mangroves. Models constructed using
data generated by means of root sampling methods (i.e. root excavation, trench
and pull-up) that do not entail complete root excavation are biased and hence
tend to under-estimate tree BGB of mangroves. Similar results are observed at
stand-level. It is recognised that, apart from methods for root sampling,
uncertainties in the estimation of BGB may also be attributed to other sources
of uncertainties e.g. BGB models, and the use of models beyond the data range.
However, based on observed systematic under-estimation of BGB both at tree and
stand-levels for estimates generated using data that do not entail complete
root excavation, the study concludes that uncertainties in the estimation of
BGB are largely attributable to root sampling methods applied rather than other
sources of uncertainties. Consequently, the study rejected the null hypothesis
in favour of the alternative hypothesis that root sampling that does not
involve complete root excavation is relatively inaccurate and tends to
underestimate the BGB of mangroves. The study also notes that the consequences
of under-estimation of BGB of mangroves observed in this study are large and
subsequently may lead to under-prioritization and valuation of mangroves in the
overall forest management, planning, and decision-making. The step-by-step
approaches for estimation of BGB established in this study aim to contribute to
existing efforts with the objective of minimising associated uncertainties and
improving existing knowledge on ecosystem services played by mangroves through
biomass stored in their roots. The study further contributes to the accurate
determination of belowground- and overall carbon stock of mangroves for
accurate and informed decisions and policies both at national and international
(e.g. REDD+) levels. This study recommends the step-by-step approaches for
quantifying tree BGB of mangroves proposed in this study since they ensure
complete root sampling and minimise uncertainties in the estimation of BGB of
mangroves.
Author(s)details:-
Marco Andrew Njana
Wildlife Conservation Society – Tanzania Country Program, P. O. Box 5196,
Dar es Salaam, Tanzania.
Please See the book
here :- https://doi.org/10.9734/bpi/raeges/v3/416
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