Calculate spatially explicit biomass (in [t]) for each group and ageclass per timestep. — calculate_biomass

Calculate 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.

Usage

calculate_biomass_spatial(nums, sn, rn, n, vol_dz, bio_conv, bps)

Arguments

nums: Dataframe with information about numbers for age-based groups. Should be generated with load_nc.
sn: Dataframe with information about structural nitrogen for age-based groups. Should be generated with load_nc.
rn: Dataframe with information about reserve nitrogen for age-based groups. Should be generated with load_nc.
n: Dataframe with information about nitrogen for non-ge-based groups. Should be generated with load_nc.
vol_dz: Dataframe with information about volume and layer height per polygon. Should be generated with load_nc_physics.
bio_conv: Numeric value to transform weight in mg N to tonnes. Should be generated with get_conv_mgnbiot.
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 load_bps.

Value

Dataframe with columns 'species', 'agecl', 'polygon', 'layer', 'time'. Biomass in [t] is stored in column 'atoutput'.

Examples

# 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)

# 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)
#> Reading in the nc file: /home/runner/work/_temp/Library/atlantistools/extdata/setas-model-new-trunk/outputSETAS.nc
sn <- load_nc(nc = nc_gen, bps = bps, fgs = fgs,
              select_groups = groups_age, select_variable = "StructN",
              prm_run = prm_run, bboxes = bboxes)
#> Reading in the nc file: /home/runner/work/_temp/Library/atlantistools/extdata/setas-model-new-trunk/outputSETAS.nc
rn <- load_nc(nc = nc_gen, bps = bps, fgs = fgs,
              select_groups = groups_age, select_variable = "ResN",
              prm_run = prm_run, bboxes = bboxes)
#> Reading in the nc file: /home/runner/work/_temp/Library/atlantistools/extdata/setas-model-new-trunk/outputSETAS.nc
n <- load_nc(nc = nc_gen, bps = bps, fgs = fgs,
             select_groups = groups_rest, select_variable = "N",
             prm_run = prm_run, bboxes = bboxes)
#> Reading in the nc file: /home/runner/work/_temp/Library/atlantistools/extdata/setas-model-new-trunk/outputSETAS.nc
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))
#> Reading in the nc file: /home/runner/work/_temp/Library/atlantistools/extdata/setas-model-new-trunk/outputSETAS.nc
#> Reading in the nc file: /home/runner/work/_temp/Library/atlantistools/extdata/setas-model-new-trunk/outputSETAS.nc
#> Reading in the nc file: /home/runner/work/_temp/Library/atlantistools/extdata/setas-model-new-trunk/outputSETAS.nc
#> Reading in the nc file: /home/runner/work/_temp/Library/atlantistools/extdata/setas-model-new-trunk/outputSETAS.nc

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)