R/calculate-biomass-spatial.R
calculate_biomass_spatial.RdCalculate spatially explicit biomass time series for each group and ageclass within our model. Data is read in from 'output[...].nc'. Biomass for age-based groups is calculated as (StructN [mgN] + ResN [mgN]) * Numbers [individuals]. Biomass for non age-based groups is calculated as N [mgN] * volume [m^3] (sediment-dz [m] / volume [m^3] for epibenthic groups). mgN is converted to t based on the stettings in the biol.prm file. Simulation time steps are converted to time in years based on output timesteps given in run.prm.
calculate_biomass_spatial(nums, sn, rn, n, vol_dz, bio_conv, bps)
| nums | Dataframe with information about numbers for age-based groups.
Should be generated with |
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| sn | Dataframe with information about structural nitrogen for age-based groups.
Should be generated with |
| rn | Dataframe with information about reserve nitrogen for age-based groups.
Should be generated with |
| n | Dataframe with information about nitrogen for non-ge-based groups.
Should be generated with |
| vol_dz | Dataframe with information about volume and layer height per polygon.
Should be generated with |
| bio_conv | Numeric value to transform weight in mg N to tonnes.
Should be generated with |
| bps | Vector of character strings giving the complete list of epibenthic
functional groups (Only present in the sediment layer). The names have to match
the column 'Name' in the 'functionalGroups.csv' file.
Should be generated with |
Dataframe with columns 'species', 'agecl', 'polygon', 'layer', 'time'. Biomass in [t] is stored in column 'atoutput'.
# 1. Using built in datasets. d <- system.file("extdata", "setas-model-new-trunk", package = "atlantistools") prm_biol <- file.path(d, "VMPA_setas_biol_fishing_Trunk.prm") fgs <- file.path(d, "SETasGroupsDem_NoCep.csv") init <- file.path(d, "INIT_VMPA_Jan2015.nc") bps <- load_bps(fgs = fgs, init = init) bio_conv <- get_conv_mgnbiot(prm_biol = prm_biol) df <- calculate_biomass_spatial(nums = ref_nums, sn = ref_structn, rn = ref_resn, n = ref_n, vol_dz = ref_vol_dz, bio_conv = bio_conv, bps = bps)#> Warning: `select_()` was deprecated in dplyr 0.7.0. #> Please use `select()` instead.#> Warning: `mutate_()` was deprecated in dplyr 0.7.0. #> Please use `mutate()` instead. #> See vignette('programming') for more help# 2. Read in dataframes from existing Atlantis simulation. bboxes <- get_boundary(boxinfo = load_box(file.path(d, "VMPA_setas.bgm"))) nc_gen <- file.path(d, "outputSETAS.nc") prm_run <- file.path(d, "VMPA_setas_run_fishing_F_Trunk.prm") groups_age <- c("Planktiv_S_Fish", "Pisciv_S_Fish") groups_rest <- c("Cephalopod", "Megazoobenthos", "Diatom", "Lab_Det", "Ref_Det") nums <- load_nc(nc = nc_gen, bps = bps, fgs = fgs, select_groups = groups_age, select_variable = "Nums", prm_run = prm_run, bboxes = bboxes) sn <- load_nc(nc = nc_gen, bps = bps, fgs = fgs, select_groups = groups_age, select_variable = "StructN", prm_run = prm_run, bboxes = bboxes) rn <- load_nc(nc = nc_gen, bps = bps, fgs = fgs, select_groups = groups_age, select_variable = "ResN", prm_run = prm_run, bboxes = bboxes) n <- load_nc(nc = nc_gen, bps = bps, fgs = fgs, select_groups = groups_rest, select_variable = "N", prm_run = prm_run, bboxes = bboxes) vol <- load_nc_physics(nc = nc_gen, select_physics = c("volume", "dz"), prm_run = prm_run, bboxes = bboxes, aggregate_layers = FALSE) df <- calculate_biomass_spatial(nums = nums, sn = sn, rn = rn, n = n, vol_dz = vol, bio_conv = bio_conv, bps = bps) # 3. Read in dataframes from existing Atlantis simulation with Map(). vars <- list("Nums", "StructN", "ResN", "N") grps <- list(groups_age, groups_age, groups_age, groups_rest) dfs <- Map(load_nc, select_variable = vars, select_groups = grps, MoreArgs = list(nc = nc_gen, bps = bps, fgs = fgs, prm_run = prm_run, bboxes = bboxes)) df <- calculate_biomass_spatial(nums = dfs[[1]], sn = dfs[[2]], rn = dfs[[3]], n = dfs[[4]], vol_dz = vol, bio_conv = bio_conv, bps = bps)