Mangroves provide extensive ecosystem services that support local livelihoods and international environmental goals, including coastal protection, biodiversity conservation and the sequestration of carbon (C). While voluntary C market projects seeking to preserve and enhance forest C stocks offer a potential means of generating finance for mangrove conservation, their implementation faces barriers due to the high costs of quantifying C stocks through field inventories. To streamline C quantification in mangrove conservation projects, we develop predictive models for (i) biomass-based C stocks, and (ii) soil-based C stocks for the mangroves of the Asia-Pacific. We compile datasets of mangrove biomass C (197 observations from 48 sites) and soil organic C (99 observations from 27 sites) to parameterize the predictive models, and use linear mixed effect models to model the expected C as a function of stand attributes. The most parsimonious biomass model predicts total biomass C stocks as a function of both basal area and the interaction between latitude and basal area, whereas the most parsimonious soil C model predicts soil C stocks as a function of the logarithmic transformations of both latitude and basal area. Random effects are specified by site for both models, which are found to explain a substantial proportion of variance within the estimation datasets and indicate significant heterogeneity across-sites within the region. The root mean square error (RMSE) of the biomass C model is approximated at 24.6 Mg/ha (18.4% of mean biomass C in the dataset), whereas the RMSE of the soil C model is estimated at 4.9 mg C/cm3 (14.1% of mean soil C). The results point to a need for standardization of forest metrics to facilitate meta-analyses, as well as provide important considerations for refining ecosystem C stock models in mangroves.
School of Forestry and Environmental Studies, Yale University, New Haven, CT, United States of America;Food and Agricultural Organization, United Nations, Bangkok, Thailand;Spatial Informatics Group, Pleasanton, CA, United States of America;Department of Natural Resources, Washington State, Olympia, WA, United States of America;Center for International Forestry Research (CIFOR), Bogor, Indonesia;Department of Geophysics and Meteorology, Bogor Agricultural University, Bogor, Indonesia;School of Forestry and Environmental Studies, Yale University, New Haven, CT, United States of America
Recommended Citation:
Jacob J. Bukoski,Jeremy S. Broadhead,Daniel C. Donato,et al. The Use of Mixed Effects Models for Obtaining Low-Cost Ecosystem Carbon Stock Estimates in Mangroves of the Asia-Pacific[J]. PLOS ONE,2017-01-01,12(1)