Abstract
Mangroves are globally important carbon stores and as such have potential for inclusion in future forest-based climate change mitigation strategies such as Reduced Emissions from Deforestation and Degradation (REDD+). Participation in REDD+will require developing countries to produce robust estimates of forest above-ground biomass (AGB) accompanied by an appropriate measure of uncertainty. Final estimates of AGB should account for known sources of uncertainty (measurement and predictive) particularly when estimating AGB at large spatial scales. In this study, mixed-effects models were used to account for variability in the allometric relationship of Kenyan mangroves due to species and site effects. A generic biomass equation for Kenyan mangroves was produced in addition to a set of species-site specific equations. The generic equation has potential for broad application as it can be used to predict the AGB of new trees where there is no pre-existing knowledge of the specific species-site allometric relationship: the most commonly encountered scenario in practical biomass studies. Predictions of AGB using the mixed-effects model showed good correspondence with the original observed values of AGB although displayed a poorer fit at higher AGB values, suggesting caution in extrapolation. A strong relationship was found between the observed and predicted values of AGB using an independent validation dataset from the Zambezi Delta, Mozambique (R2=0.96, p= <0.001). The simulation based approach to uncertainty propagation employed in the current study produced estimates of AGB at different spatial scales (tree – landscape level) accompanied by a realistic measure of the total uncertainty. Estimates of mangrove AGB in Kenya are presented at the plot, regional and landscape level accompanied by 95% prediction intervals. The 95% prediction intervals for landscape level estimates of total AGB stocks suggest that between 5.4 and 7.2 megatonnes of AGB is currently held in Kenyan mangrove forests.
Highlights
Mangrove forests are widely recognised as globally important carbon (C) stores (Bouillon et al, 2008; Chmura et al, 2003; Donato et al, 2011; McKee et al, 2007)
To the best of our knowledge such methodologies have never been applied for the purpose of propagating uncertainty to biomass estimates in mangroves. With this in mind and in the context of future REDD+ requirements for biomass/carbon accounting this study focused on: (1) the development of new allometric equations to estimate the above-ground biomass of Kenyan mangroves using linear mixed-effects models and based on a meta-analysis of all the available harvest data for Kenyan mangrove species (2) demonstrating a simulation based methodology for propagating uncertainty during the biomass estimation process and (3) demonstrating the practical application of said equations and simulations to a large forest inventory dataset spanning the entire Kenyan coastline for the purpose of producing estimates of above-ground biomass at different spatial scales with an appropriate measure of uncertainty
Conversion factors were applied to convert the fresh weight of each tree component to dry weight in kilograms and summed giving total above-ground biomass in kg dry weight (kg DW)
Summary
Mangrove forests are widely recognised as globally important carbon (C) stores (Bouillon et al, 2008; Chmura et al, 2003; Donato et al, 2011; McKee et al, 2007). Such services include but are not limited to; coastal defence (Zhang et al, 2012), fisheries production (Aburto-Oropeza et al, 2008), habitat provision for terrestrial and aquatic fauna (Kathiresan and Bingham, 2001), timber and fuelwood production (Dahdouh-Guebas et al, 2000), pollution abatement (Wickramasinghe et al, 2009) and regulation of sediment exchange between land and sea (Duarte et al, 2005)
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