title: The Use of Mixed Effects Models for Obtaining Low-Cost Ecosystem Carbon Stock Estimates in Mangroves of the Asia-Pacific.
abstract: 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.
authors.affiliation: School of Forestry and Environmental Studies, Yale University, New Haven, CT, United States of America.
authors.forename: Jacob J
authors.initials: JJ
authors.lastname: Bukoski
authors.affiliation: Food and Agricultural Organization, United Nations, Bangkok, Thailand.|Spatial Informatics Group, Pleasanton, CA, United States of America.
authors.forename: Jeremy S
authors.initials: JS
authors.lastname: Broadhead
authors.affiliation: Department of Natural Resources, Washington State, Olympia, WA, United States of America.
authors.forename: Daniel C
authors.initials: DC
authors.lastname: Donato
authors.affiliation: Center for International Forestry Research (CIFOR), Bogor, Indonesia.|Department of Geophysics and Meteorology, Bogor Agricultural University, Bogor, Indonesia.
authors.forename: Daniel
authors.initials: D
authors.lastname: Murdiyarso
authors.affiliation: School of Forestry and Environmental Studies, Yale University, New Haven, CT, United States of America.
authors.forename: Timothy G
authors.initials: TG
authors.lastname: Gregoire
mesh_terms: D000465:Algorithms; D001208:Asia; D018533:Biomass; D002244:Carbon / Q000032:analysis*; D017753:Ecosystem; D005843:Geography; D008962:Models, Theoretical; D010140:Pacific Ocean; D015203:Reproducibility of Results; D012987:Soil / Q000737:chemistry
chemical_list: D012987:Soil; D002244:Carbon