# Propagating above ground biomass (AGB) or carbon (AGC) errors to the stand level Propagation of the errors throughout the steps needed to compute AGB or AGC. ```r AGBmonteCarlo( D, WD = NULL, errWD = NULL, H = NULL, errH = NULL, HDmodel = NULL, coord = NULL, Dpropag = NULL, n = 1000, Carbon = FALSE, Dlim = NULL, plot = NULL ) ``` ## Arguments - `D`: Vector of tree diameters (in cm) - `WD`: Vector of wood density estimates (in g/cm3) - `errWD`: Vector of error associated to the wood density estimates (should be of the same size as `WD`) - `H`: (option 1) Vector of tree heights (in m). If set, `errH` must be set too. - `errH`: (if `H`) Residual standard error (RSE) of a model or vector of errors (sd values) associated to tree height values (in the latter case the vector should be of the same length as `H`). - `HDmodel`: (option 2) Model used to estimate tree height from tree diameter (output from `modelHD()`, see example). - `coord`: (option 3) Coordinates of the site(s), either a vector giving a single site (e.g. c(longitude, latitude)) or a matrix/dataframe with two columns (e.g. cbind(longitude, latitude)). The coordinates are used to predict height-diameter allometry with bioclimatic variables. - `Dpropag`: This variable can take three kind of values, indicating how to propagate the errors on diameter measurements: a single numerical value or a vector of the same size as `D`, both representing the standard deviation associated with the diameter measurements or `"chave2004"` (an important error on 5 percent of the measures, a smaller error on 95 percent of the trees). - `n`: Number of iterations. Cannot be smaller than 50 or larger than 1000. By default `n = 1000` - `Carbon`: (logical) Whether or not the propagation should be done up to the carbon value (FALSE by default). - `Dlim`: (optional) Minimum diameter (in cm) for which above ground biomass should be calculated (all diameter below `Dlim` will have a 0 value in the output). - `plot`: (optional) Plot ID, must be either one value, or a vector of the same length as D. This argument is used to build stand-specific HD models. ## Returns Returns a list with (if Carbon is FALSE): * `meanAGB`: Mean stand AGB value following the error propagation * `medAGB`: Median stand AGB value following the error propagation * `sdAGB`: Standard deviation of the stand AGB value following the error propagation * `credibilityAGB`: Credibility interval at 95\ * `AGB_simu`: Matrix with the AGB of the trees (rows) times the n iterations (columns) ## Details See Rejou-Mechain et al. (2017) for all details on the error propagation procedure. ## Examples ```r # Load a database data(NouraguesHD) data(NouraguesTrees) # Modelling height-diameter relationship HDmodel <- modelHD(D = NouraguesHD$D, H = NouraguesHD$H, method = "log2") # Retrieving wood density values NouraguesWD <- getWoodDensity(NouraguesTrees$Genus, NouraguesTrees$Species, stand = NouraguesTrees$Plot ) # Propagating errors with a standard error for Wood density resultMC <- AGBmonteCarlo( D = NouraguesTrees$D, WD = NouraguesWD$meanWD, errWD = NouraguesWD$sdWD, HDmodel = HDmodel ) # If only the coordinates are available coord <- c(-52.683213,4.083024 ) resultMC <- AGBmonteCarlo( D = NouraguesTrees$D, WD = NouraguesWD$meanWD, errWD = NouraguesWD$sdWD, coord = coord ) # Propagating errors with a standard error in wood density in all plots at once NouraguesTrees$meanWD <- NouraguesWD$meanWD NouraguesTrees$sdWD <- NouraguesWD$sdWD resultMC <- by( NouraguesTrees, NouraguesTrees$Plot, function(x) AGBmonteCarlo( D = x$D, WD = x$meanWD, errWD = x$sdWD, HDmodel = HDmodel, Dpropag = "chave2004" ) ) meanAGBperplot <- unlist(sapply(resultMC, "[", 1)) credperplot <- sapply(resultMC, "[", 4) ``` ## References Chave, J. et al. (2004). **Error propagation and scaling for tropical forest biomass estimates**. Philosophical Transactions of the Royal Society B: Biological Sciences, 359(1443), 409-420. Rejou-Mechain et al. (2017). **BIOMASS: An R Package for estimating above-ground biomass and its uncertainty in tropical forests**. Methods in Ecology and Evolution, 8 (9), 1163-1167. ## Author(s) Maxime REJOU-MECHAIN, Bruno HERAULT, Camille PIPONIOT, Ariane TANGUY, Arthur PERE

AGBmonteCarlo function