Propagating uncertainty to estimates of above-ground biomass for Kenyan mangroves: a scaling procedure from tree to landscape level
Cohen, R; Kaino, J; Okello, J.A.; Bosire, O; Kairo, J.; Huxham, M; Mencuccini, M (2013). Propagating uncertainty to estimates of above-ground biomass for Kenyan mangroves: a scaling procedure from tree to landscape level. Forest Ecol. Manag. 310: 968-982. https://dx.doi.org/10.1016/j.foreco.2013.09.047
In: Forest Ecology and Management. Elsevier/Elsevier Science: Amsterdam; Lausanne; New York; Oxford; Shannon; Tokyo. ISSN 0378-1127; e-ISSN 1872-7042, more
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.
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