Calculate the consumed biomass in [t] of prey j by predator i. — calculate_consumed

Consumption data is extracted from output[...]PROD.nc. Age based groups are stored as "Eat_" non age based groups as "Grazing_". Units are mg N m^-3 d^-1. Factors are species, time, box and agecl (if present). We will refer to species as pred from here on to indicate the predator perspective. Diet contribution data is extracted from DietCheck.txt. Currently this only works for models based on the trunk code. Units are The consumed biomass is calculated as follows: - Calculate consumed biomass as Eat (or Grazing) * boxvolume per time, pred, agecl, box. - Convert to biomass in [t]. - Combine with diet contributions and calculate consumed biomass of prey species.

calculate_consumed_biomass(eat, grazing, dm, vol, bio_conv)

Arguments

eat	Dataframe with information about consumption for age-based groups. Should be generated with `load_nc`.
grazing	Dataframe with information about consumption for non-age-based groups. Should be generated with `load_nc`.
dm	Dataframe with information about diet contributions per predator. Should be generated with `load_dietcheck` using `convert_names = TRUE`.
vol	Dataframe with information about volume per polygon and layer. 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`.

Value

Dataframe with columns 'pred', 'agecl', 'polygon', 'time', 'prey'. Consumed 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")

bio_conv <- get_conv_mgnbiot(prm_biol = prm_biol)

df <- calculate_consumed_biomass(eat = ref_eat, grazing = ref_grazing, dm = ref_dm,
                                 vol = ref_vol, bio_conv = bio_conv)
#> 50% matching timesteps between PROD.nc and DietCheck.txt
#> Warning: `rename_()` was deprecated in dplyr 0.7.0.
#> Please use `rename()` instead.
#> Warning: `filter_()` was deprecated in dplyr 0.7.0.
#> Please use `filter()` instead.
#> See vignette('programming') for more help
#> 0.21% data is lost due to missing diet data despite available eat data.
#> 21.97% data is lost due to missing eat data despite available diet data.

# 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")
nc_prod <- file.path(d, "outputSETASPROD.nc")
prm_run <- file.path(d, "VMPA_setas_run_fishing_F_Trunk.prm")
fgs <- file.path(d, "SETasGroupsDem_NoCep.csv")
init <- file.path(d, "INIT_VMPA_Jan2015.nc")

bps <- load_bps(fgs = fgs, init = init)

groups_age <- c("Planktiv_S_Fish", "Pisciv_S_Fish")
groups_rest <- c("Cephalopod", "Megazoobenthos", "Diatom", "Lab_Det", "Ref_Det")

df_eat <- load_nc(nc = nc_prod, bps = bps, fgs = fgs,
               select_groups = groups_age, select_variable = "Eat",
               prm_run = prm_run, bboxes = bboxes)
df_grz <- load_nc(nc = nc_prod, bps = bps, fgs = fgs,
               select_groups = groups_rest, select_variable = "Grazing",
               prm_run = prm_run, bboxes = bboxes)
df_dm <- load_dietcheck(dietcheck = file.path(d, "outputSETASDietCheck.txt"),
                        fgs = fgs, prm_run = prm_run, version_flag = 2, convert_names = TRUE)
vol <- load_nc_physics(nc = nc_gen, select_physics = "volume",
                       prm_run = prm_run, bboxes = bboxes, aggregate_layers = FALSE)

df <- calculate_consumed_biomass(eat = df_eat, grazing = df_grz, dm = df_dm,
                                 vol = vol, bio_conv = bio_conv)
#> 50% matching timesteps between PROD.nc and DietCheck.txt
#> 11.61% data is lost due to missing diet data despite available eat data.
#> 21.97% data is lost due to missing eat data despite available diet data.