Modelling Asparagopsis nitrogen bioremediation efficiency in Australian coastal environments
cell_coords <- find_read(envi_data, "R2_cell_coords") %>%
mutate(state = factor(state, levels = states_ord)) %>%
dplyr::select(-layer)
cells_included <- find_read(runs_data, "include")
cells_omitted <- find_read(runs_data, "omit")
totals <- cell_coords %>%
filter(!cell_no %in% cells_omitted) %>%
group_by(state) %>%
reframe(cells = n())A total of 1459 were omitted for being too shallow to accommodate the 5.5 m macroalgae canopy, leaving 11748 cells.
norm_growth_end <- file.path(runs_data, "cell_growth_end.parquet") %>%
read_parquet(mmap = T) %>%
merge(cell_coords, by = "cell_no")
# Total N removed at each harvest
norm_growth_end <- norm_growth_end %>%
mutate(
value = TN_end - TN_start,
value_na = case_when(value <= 0 ~ NA, T ~ value),
value_0 = case_when(value <= 0 ~ 0, T ~ value),
month = as.factor(month(parse_date_time(start, orders = "j"))),
species = factor(species, levels = c("armata", "taxiformis"), labels = c("A. armata", "A. taxiformis"))
)
norm_growth_total <- norm_growth_end %>%
mutate(
value_na = case_when(value <= 0 ~ NA, T ~ value),
value_0 = case_when(value <= 0 ~ 0, T ~ value)
) %>%
group_by(state, species, cell_no, latitude, longitude) %>%
reframe(
total = sumna(value), # good growth months may be outweighed by bad growth months
total_na = sumna(value_na), # bad growth months are removed
total_na = case_when(total_na == 0 ~ NA, T ~ total_na),
total_0 = sumna(value_0) # bad growth months count as zeros
)options(pillar.sigfig = 4)
df_arma_mon <- norm_growth_end %>%
filter(species == "A. armata") %>%
group_by(species, state, month) %>%
reframe(
mean = meanna(value_0) %>% set_units("mg m-2"),
sd = sdna(value_0) %>% set_units("mg m-2")
)
df_arma_mon %>%
select(-sd) %>%
pivot_wider(names_from = state, values_from = mean)# A tibble: 12 × 10
species month NTE QLD NSW SAU TAS VIC WAN WAS
<fct> <fct> [mg/m^2] [mg/m^2] [mg/m^2] [mg/m… [mg/m… [mg/m… [mg/m^… [mg/…
1 A. armata 1 0 0 2.055 76.80 64.17 64.13 0 36.15
2 A. armata 2 0 0 2.180 106.5 97.83 99.95 0 52.20
3 A. armata 3 0 0 3.472 136.1 142.3 141.1 0 38.90
4 A. armata 4 0 0.05906 18.33 159.6 167.6 170.1 0 40.30
5 A. armata 5 0 0.2529 46.13 145.4 141.2 137.1 0 37.91
6 A. armata 6 0 5.303 98.80 131.9 124.8 132.7 0.4003 40.62
7 A. armata 7 0.04593 16.71 114.0 119.0 101.5 110.0 2.320 38.39
8 A. armata 8 0 22.80 110.3 119.8 72.77 96.06 1.946 48.17
9 A. armata 9 0 18.91 104.3 133.7 62.40 102.7 0.9115 56.20
10 A. armata 10 0 0.3232 60.57 150.1 86.23 132.8 0.8554 49.18
11 A. armata 11 0 0 53.29 160.6 126.7 155.9 0.06961 56.56
12 A. armata 12 0 0 20.86 110.1 100.4 103.9 0 41.11df_arma_mon %>%
select(-mean) %>%
pivot_wider(names_from = state, values_from = sd)# A tibble: 12 × 10
species month NTE QLD NSW SAU TAS VIC WAN WAS
<fct> <fct> [mg/m^2] [mg/m^2] [mg/m^2] [mg/m^2] [mg/m… [mg/… [mg/m… [mg/…
1 A. armata 1 0 0 7.641 25.18 16.44 24.78 0 28.77
2 A. armata 2 0 0 10.65 29.96 15.77 23.42 0 43.85
3 A. armata 3 0 0 15.89 37.65 16.27 28.42 0 38.36
4 A. armata 4 0 2.111 39.89 44.32 29.90 28.32 0 41.64
5 A. armata 5 0 5.734 61.83 52.16 42.61 38.15 0 38.37
6 A. armata 6 0 20.42 68.59 51.32 39.27 37.53 6.349 36.34
7 A. armata 7 1.156 36.46 61.86 45.67 35.75 37.94 10.85 33.27
8 A. armata 8 0 44.20 64.46 39.83 35.91 37.29 9.307 29.07
9 A. armata 9 0 42.99 53.01 37.67 35.55 32.61 7.130 27.15
10 A. armata 10 0 5.185 48.68 45.49 32.64 19.37 7.166 21.82
11 A. armata 11 0 0 52.75 45.96 30.09 23.38 1.367 25.56
12 A. armata 12 0 0 29.85 33.69 24.90 29.40 0 25.75df_taxi_month <- norm_growth_end %>%
filter(species != "A. armata") %>%
group_by(species, state, month) %>%
reframe(
mean = meanna(value_0) %>% set_units("mg m-2"),
sd = sdna(value_0) %>% set_units("mg m-2")
)
df_taxi_month %>%
select(-sd) %>%
pivot_wider(names_from = state, values_from = mean)# A tibble: 12 × 10
species month NTE QLD NSW SAU TAS VIC WAN WAS
<fct> <fct> [mg/m^2] [mg/… [mg/… [mg/m^2] [mg/m^2] [mg/m^2] [mg/… [mg/…
1 A. taxifor… 1 0.2186 22.56 15.08 54.12 4.670 23.94 31.50 58.65
2 A. taxifor… 2 0.5038 24.28 22.05 85.75 23.97 53.38 29.92 92.35
3 A. taxifor… 3 0.4046 16.47 30.80 86.85 29.95 57.27 15.94 74.19
4 A. taxifor… 4 0.5735 28.74 79.78 75.21 5.394 22.49 14.24 76.30
5 A. taxifor… 5 44.94 58.82 87.99 20.01 0 4.668 60.57 56.19
6 A. taxifor… 6 84.16 62.49 90.77 0.01457 0 0 87.78 38.47
7 A. taxifor… 7 54.73 52.95 57.54 0 0 0 58.24 23.65
8 A. taxifor… 8 47.63 56.90 37.05 0 0 0 51.25 22.93
9 A. taxifor… 9 63.51 70.56 30.76 0.4587 0 0 66.18 24.32
10 A. taxifor… 10 59.69 76.28 24.36 1.233 0 0 64.80 20.37
11 A. taxifor… 11 0.01970 64.89 41.10 5.022 0 0.03219 58.22 29.90
12 A. taxifor… 12 0 32.92 26.82 24.62 0.05022 5.189 43.53 41.87df_taxi_month %>%
select(-mean) %>%
pivot_wider(names_from = state, values_from = sd)# A tibble: 12 × 10
species month NTE QLD NSW SAU TAS VIC WAN WAS
<fct> <fct> [mg/m^2] [mg/m… [mg/… [mg/m^… [mg/m… [mg/m^… [mg/m… [mg/…
1 A. taxiformis 1 4.301 32.81 25.38 32.90 11.68 23.76 54.50 29.38
2 A. taxiformis 2 8.456 37.19 31.48 39.10 28.16 28.28 56.22 33.05
3 A. taxiformis 3 7.113 26.67 35.25 46.99 35.74 39.00 41.55 39.57
4 A. taxiformis 4 9.312 33.57 55.70 50.30 18.52 46.39 38.11 40.09
5 A. taxiformis 5 33.47 41.52 54.50 27.85 0 18.66 44.69 36.17
6 A. taxiformis 6 14.27 37.81 51.81 0.2933 0 0 19.78 37.86
7 A. taxiformis 7 11.81 37.87 47.01 0 0 0 15.43 31.95
8 A. taxiformis 8 11.09 41.56 44.11 0 0 0 10.77 30.18
9 A. taxiformis 9 14.92 43.01 37.95 4.598 0 0 8.957 32.07
10 A. taxiformis 10 35.34 34.39 32.32 10.02 0 0 20.58 26.29
11 A. taxiformis 11 0.3548 46.52 38.70 20.84 0 0.4262 50.12 36.21
12 A. taxiformis 12 0 39.19 32.60 35.76 1.276 14.07 54.79 32.13df_arma_year <- norm_growth_end %>%
filter(species == "A. armata") %>%
group_by(species, state) %>%
reframe(
mean = meanna(value_0) %>% set_units("mg m-2 month-1"),
sd = sdna(value_0) %>% set_units("mg m-2 month-1")
) %>%
arrange(-mean)
df_arma_year# A tibble: 8 × 4
species state mean sd
<fct> <fct> [mg/(m^2*month)] [mg/(m^2*month)]
1 A. armata SAU 129.1 47.58
2 A. armata VIC 120.5 41.74
3 A. armata TAS 107.3 44.56
4 A. armata NSW 52.85 63.93
5 A. armata WAS 44.64 33.94
6 A. armata QLD 5.362 23.18
7 A. armata WAN 0.5420 5.446
8 A. armata NTE 0.003828 0.3338df_taxi_year <- norm_growth_end %>%
filter(species != "A. armata") %>%
group_by(species, state) %>%
reframe(
mean = meanna(value_0) %>% set_units("mg m-2 month-1"),
sd = sdna(value_0) %>% set_units("mg m-2 month-1")
) %>%
arrange(-mean)
df_taxi_year# A tibble: 8 × 4
species state mean sd
<fct> <fct> [mg/(m^2*month)] [mg/(m^2*month)]
1 A. taxiformis WAN 48.51 44.17
2 A. taxiformis QLD 47.32 42.97
3 A. taxiformis WAS 46.60 41.30
4 A. taxiformis NSW 45.34 48.97
5 A. taxiformis NTE 29.70 34.96
6 A. taxiformis SAU 29.44 44.78
7 A. taxiformis VIC 13.91 29.59
8 A. taxiformis TAS 5.336 17.63options(pillar.sigfig = 5)
df_arma_alltotal <- norm_growth_end %>%
filter(species == "A. armata") %>%
group_by(species, state, cell_no) %>%
reframe(total = sum(value_0)) %>%
group_by(species, state) %>%
reframe(total = mean(total)) %>%
arrange(-total)
df_arma_alltotal# A tibble: 8 × 3
species state total
<fct> <fct> <dbl>
1 A. armata SAU 1549.6
2 A. armata VIC 1446.4
3 A. armata TAS 1287.9
4 A. armata NSW 634.26
5 A. armata WAS 535.70
6 A. armata QLD 64.346
7 A. armata WAN 6.5036
8 A. armata NTE 0.045930df_taxi_alltotal <- norm_growth_end %>%
filter(species != "A. armata") %>%
group_by(species, state, cell_no) %>%
reframe(total = sum(value_0)) %>%
group_by(species, state) %>%
reframe(total = mean(total)) %>%
arrange(-total)
df_taxi_alltotal# A tibble: 8 × 3
species state total
<fct> <fct> <dbl>
1 A. taxiformis WAN 582.17
2 A. taxiformis QLD 567.86
3 A. taxiformis WAS 559.19
4 A. taxiformis NSW 544.10
5 A. taxiformis NTE 356.38
6 A. taxiformis SAU 353.29
7 A. taxiformis VIC 166.98
8 A. taxiformis TAS 64.034norm_growth_end %>%
mutate(value = case_when(value <= 0 ~ 0, T ~ value)) %>%
group_by(species, state, start) %>%
reframe(
mean = meanna(value),
sd = sdna(value)
) %>%
mutate(
sd = case_when(sd > mean ~ mean, T ~ sd),
state = factor(state, levels = states_ord, labels = states_lng)
) %>%
ggplot(
aes(
x = start, y = mean, ymin = mean - sd, ymax = mean + sd,
color = state, fill = state, linetype = species
)
) +
geom_line(linewidth = 0.75) +
geom_ribbon(alpha = 0.25, linewidth = 0.25) +
facet_wrap(facets = vars(state), ncol = 2) +
# scale_y_continuous(breaks = seq(0, 150, 25), limits = c(0, 175), expand = c(0,0)) +
scale_color_manual(values = states_pal) +
scale_fill_manual(values = states_pal) +
env_plot() +
labs(x = "Day of the year", y = expression("Total N removed (mg m"^-2 *")"))
# minna(norm_growth_total$total_na)
# maxna(norm_growth_total$total_na)
norm_growth_total %>%
ggplot(aes(x = longitude, y = latitude, fill = total_na)) +
geom_tile() +
geom_sf(data = ozmap_data(data = "states"), inherit.aes = F) +
coord_sf(xlim = c(111.5, 155), ylim = c(-44.5, -8.5), expand = F) +
scale_x_continuous(breaks = seq(100, 160, 10)) +
scale_y_continuous(breaks = seq(-2.5, -45, -5)) +
scale_fill_viridis_c(
limits = c(0, maxna(norm_growth_total$total_na)),
breaks = seq(0, 2250, 500),
na.value = "grey",
guide = guide_colorbar(
title = expression("Total N removed (mg m"^-2 * ")"),
barheight = 1,
barwidth = 20,
title.position = "top",
title.hjust = 0.5
)
) +
labs(x = "Longitude", y = "Latitude") +
prettyplot() +
theme(legend.position = "top", strip.text = element_blank(), strip.background = element_blank()) +
facet_grid(rows = vars(species))
# minna(norm_growth_end$value_na[norm_growth_end$species == "A. armata"])
# maxna(norm_growth_end$value_na[norm_growth_end$species == "A. armata"])
norm_growth_end %>%
filter(species == "A. armata") %>%
ggplot(aes(x = longitude, y = latitude, fill = value_na)) +
geom_tile() +
geom_sf(data = ozmap_data(data = "states"), inherit.aes = F) +
coord_sf(xlim = c(111.5, 155), ylim = c(-44.5, -8.5), expand = F) +
scale_x_continuous(breaks = seq(100, 160, 10)) +
scale_y_continuous(breaks = seq(-2.5, -45, -5)) +
scale_fill_viridis_c(
limits = c(0, maxna(norm_growth_end$value_na[norm_growth_end$species == "A. armata"])),
breaks = seq(0, 250, 50),
na.value = "grey",
guide = guide_colorbar(
title = expression("Total N removed (mg m"^-2 * ")"),
barheight = 1,
barwidth = 20,
title.position = "top",
title.hjust = 0.5
)
) +
labs(x = "Longitude", y = "Latitude") +
prettyplot() +
theme(legend.position = "bottom") +
facet_wrap(facets = vars(month)) +
ggtitle("A. armata")
# minna(norm_growth_end$value_na[norm_growth_end$species == "A. taxiformis"])
# maxna(norm_growth_end$value_na[norm_growth_end$species == "A. taxiformis"])
norm_growth_end %>%
filter(species == "A. taxiformis") %>%
ggplot(aes(x = longitude, y = latitude, fill = value_na)) +
geom_tile() +
geom_sf(data = ozmap_data(data = "states"), inherit.aes = F) +
coord_sf(xlim = c(111.5, 155), ylim = c(-44.5, -8.5), expand = F) +
scale_x_continuous(breaks = seq(100, 160, 10)) +
scale_y_continuous(breaks = seq(-2.5, -45, -5)) +
scale_fill_viridis_c(
limits = c(0, maxna(norm_growth_end$value_na[norm_growth_end$species == "A. taxiformis"])),
breaks = seq(0, 250, 50),
na.value = "grey",
guide = guide_colorbar(
title = expression("Total N removed (mg m"^-2 * ")"),
barheight = 1,
barwidth = 20,
title.position = "top",
title.hjust = 0.5
)
) +
labs(x = "Longitude", y = "Latitude") +
prettyplot() +
theme(legend.position = "bottom") +
facet_wrap(facets = vars(month)) +
ggtitle("A. taxiformis")
N_input <- file.path(envi_data, "cell_input_all") %>%
list.files(full.names = T) %>%
purrr::map(function(fnm) {
fnm %>% arrow::read_parquet(col_select = c("cell_no", "state", "yday", "Ni_input", "Am_input"))
}) %>%
bind_rows() %>%
mutate(
N_input = 5.5*(Ni_input + Am_input),
month = month(make_date(2023, 12, 31) + days(yday)) %>% as.factor()
) %>%
group_by(state, cell_no, month) %>%
reframe(N_input = sum(N_input)) %>%
mutate(N_input = set_units(N_input, "mg m-2"))
biorem_eff <- norm_growth_end %>%
select(-contains("TN"), -start) %>%
left_join(N_input, by = c("state", "cell_no", "month")) %>%
mutate(
value_na = set_units(value_na, "mg m-2"),
biorem_eff = drop_units(value_na/N_input)
)biorem_eff %>%
group_by(species, month) %>%
reframe(
mean_biorem_eff = 100*meanna(biorem_eff),
max_biorem_eff = 100*maxna(biorem_eff)
) %>%
print(n = 24)# A tibble: 24 × 4
species month mean_biorem_eff max_biorem_eff
<fct> <fct> <dbl> <dbl>
1 A. armata 1 4.7299 7.1877
2 A. armata 2 12.278 17.173
3 A. armata 3 8.8691 12.418
4 A. armata 4 6.7898 13.207
5 A. armata 5 4.6018 14.612
6 A. armata 6 4.3451 16.112
7 A. armata 7 4.1396 13.921
8 A. armata 8 4.5580 16.239
9 A. armata 9 5.4672 18.370
10 A. armata 10 5.5143 13.664
11 A. armata 11 8.2433 17.213
12 A. armata 12 8.7588 14.974
13 A. taxiformis 1 3.7772 6.9494
14 A. taxiformis 2 9.6349 18.964
15 A. taxiformis 3 7.0580 17.071
16 A. taxiformis 4 7.1777 20.670
17 A. taxiformis 5 5.9172 15.845
18 A. taxiformis 6 8.7357 16.118
19 A. taxiformis 7 8.7818 16.295
20 A. taxiformis 8 10.133 19.771
21 A. taxiformis 9 11.578 20.949
22 A. taxiformis 10 8.4605 13.544
23 A. taxiformis 11 7.9653 16.159
24 A. taxiformis 12 6.8592 14.314 biorem_eff %>%
mutate(biorem_eff = 100*biorem_eff) %>%
ggplot(aes(x = biorem_eff, fill = species, colour = species)) +
geom_histogram(position = "identity", alpha = 0.35, binwidth = 1) +
theme_classic()Warning: Removed 142662 rows containing non-finite outside the scale range
(`stat_bin()`).
options(pillar.sigfig = 3)
biorem_eff %>%
group_by(species, state) %>%
reframe(
mean_biorem_eff = 100*meanna(biorem_eff),
max_biorem_eff = 100*maxna(biorem_eff)
) %>%
print(n = 24)# A tibble: 16 × 4
species state mean_biorem_eff max_biorem_eff
<fct> <fct> <dbl> <dbl>
1 A. armata NTE 4.80 9.02
2 A. armata QLD 7.93 16.1
3 A. armata NSW 3.47 14.7
4 A. armata SAU 6.64 16.4
5 A. armata TAS 4.84 17.2
6 A. armata VIC 5.14 16.9
7 A. armata WAN 8.74 18.4
8 A. armata WAS 8.51 17.7
9 A. taxiformis NTE 11.0 19.8
10 A. taxiformis QLD 8.53 20.7
11 A. taxiformis NSW 3.31 14.7
12 A. taxiformis SAU 6.09 15.6
13 A. taxiformis TAS 4.21 14.3
14 A. taxiformis VIC 4.62 16.5
15 A. taxiformis WAN 10.0 20.9
16 A. taxiformis WAS 7.92 17.7 biorem_eff %>%
group_by(species, state, month) %>%
reframe(
mean_biorem_eff = 100*meanna(biorem_eff)
) %>%
pivot_wider(names_from = state, values_from = mean_biorem_eff) %>%
print(n = 24)# A tibble: 24 × 10
species month NTE QLD NSW SAU TAS VIC WAN WAS
<fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 A. armata 1 NaN NaN 1.64 4.84 4.87 4.96 NaN 4.55
2 A. armata 2 NaN NaN 5.43 12.2 12.6 12.6 NaN 12.1
3 A. armata 3 NaN NaN 3.75 9.17 8.79 8.61 NaN 8.91
4 A. armata 4 NaN 3.64 2.78 7.43 5.74 5.81 NaN 8.35
5 A. armata 5 NaN 8.22 2.86 5.20 3.18 3.01 NaN 6.70
6 A. armata 6 NaN 8.40 3.94 4.31 2.29 2.44 9.51 6.59
7 A. armata 7 4.80 8.31 3.28 3.65 1.61 1.79 7.49 6.34
8 A. armata 8 NaN 7.87 3.39 3.81 1.20 1.60 9.95 7.44
9 A. armata 9 NaN 7.59 3.49 5.00 1.28 1.96 11.1 9.82
10 A. armata 10 NaN 2.42 2.95 6.11 2.18 3.30 7.49 8.88
11 A. armata 11 NaN NaN 4.46 8.96 5.39 6.52 2.29 11.2
12 A. armata 12 NaN NaN 3.88 9.11 8.26 8.60 NaN 9.95
13 A. taxiformis 1 1.44 4.30 2.20 3.79 2.02 2.89 3.23 4.31
14 A. taxiformis 2 5.97 11.9 5.60 10.1 6.19 7.19 6.10 11.7
15 A. taxiformis 3 4.49 9.91 3.52 7.16 3.60 4.05 5.49 8.80
16 A. taxiformis 4 7.77 9.63 4.14 5.18 1.61 3.36 5.04 8.59
17 A. taxiformis 5 4.52 7.70 3.65 3.01 NaN 1.27 5.99 6.53
18 A. taxiformis 6 11.0 9.21 3.61 0.381 NaN NaN 10.1 6.40
19 A. taxiformis 7 12.0 8.16 2.57 NaN NaN NaN 10.4 6.36
20 A. taxiformis 8 13.5 8.53 2.13 NaN NaN NaN 12.9 7.90
21 A. taxiformis 9 14.2 9.33 2.34 6.26 NaN NaN 15.8 9.88
22 A. taxiformis 10 9.05 8.51 2.22 8.43 NaN NaN 9.98 7.52
23 A. taxiformis 11 0.314 7.78 3.34 4.67 NaN 0.218 9.43 9.69
24 A. taxiformis 12 NaN 7.42 3.97 5.60 2.02 2.94 7.40 7.68# minna(biorem_eff$biorem_eff[biorem_eff$species == "A. armata"])
# maxna(biorem_eff$biorem_eff[biorem_eff$species == "A. armata"])
biorem_eff %>%
mutate(biorem_eff = 100*biorem_eff) %>%
filter(species == "A. armata") %>%
ggplot(aes(x = longitude, y = latitude, fill = biorem_eff)) +
geom_tile() +
geom_sf(data = ozmap_data(data = "states"), inherit.aes = F) +
coord_sf(xlim = c(111.5, 155), ylim = c(-44.5, -8.5), expand = F) +
scale_x_continuous(breaks = seq(100, 160, 10)) +
scale_y_continuous(breaks = seq(-2.5, -45, -5)) +
scale_fill_viridis_c(
limits = c(0, 18.5),
breaks = seq(0, 30, 2),
na.value = "grey",
guide = guide_colorbar(
title = expression("Total N removed (mg m"^-2 * ")"),
barheight = 1,
barwidth = 20,
title.position = "top",
title.hjust = 0.5
)
) +
labs(x = "Longitude", y = "Latitude") +
prettyplot() +
theme(legend.position = "bottom") +
facet_wrap(facets = vars(month)) +
ggtitle("A. armata")
# minna(biorem_eff$biorem_eff[biorem_eff$species == "A. taxiformis"])
# maxna(biorem_eff$biorem_eff[biorem_eff$species == "A. taxiformis"])
biorem_eff %>%
mutate(biorem_eff = 100*biorem_eff) %>%
filter(species == "A. taxiformis") %>%
ggplot(aes(x = longitude, y = latitude, fill = biorem_eff)) +
geom_tile() +
geom_sf(data = ozmap_data(data = "states"), inherit.aes = F) +
coord_sf(xlim = c(111.5, 155), ylim = c(-44.5, -8.5), expand = F) +
scale_x_continuous(breaks = seq(100, 160, 10)) +
scale_y_continuous(breaks = seq(-2.5, -45, -5)) +
scale_fill_viridis_c(
limits = c(0, 21),
breaks = seq(0, 30, 2),
na.value = "grey",
guide = guide_colorbar(
title = expression("Total N removed (mg m"^-2 * ")"),
barheight = 1,
barwidth = 20,
title.position = "top",
title.hjust = 0.5
)
) +
labs(x = "Longitude", y = "Latitude") +
prettyplot() +
theme(legend.position = "bottom") +
facet_wrap(facets = vars(month)) +
ggtitle("A. taxiformis")
biorem_eff_total <- norm_growth_end %>%
select(-contains("TN"), -start, -longitude, -latitude) %>%
left_join(N_input, by = c("state", "cell_no", "month")) %>%
mutate(
N_input = drop_units(N_input)
) %>%
group_by(species, state, cell_no) %>%
reframe(
value_na = sumna(value_na),
N_input = sumna(N_input)
) %>%
mutate(
value_na = set_units(value_na, "mg m-2"),
N_input = set_units(N_input, "mg m-2"),
biorem_eff = value_na/N_input
)
biorem_eff %>%
group_by(species) %>%
reframe(
mean_biorem_eff = 100*meanna(biorem_eff),
max_biorem_eff = 100*maxna(biorem_eff)
)# A tibble: 2 × 3
species mean_biorem_eff max_biorem_eff
<fct> <dbl> <dbl>
1 A. armata 6.37 18.4
2 A. taxiformis 8.08 20.9biorem_eff_total %>%
mutate(biorem_eff = 100*biorem_eff) %>%
ggplot(aes(x = biorem_eff, fill = species, colour = species)) +
geom_histogram(position = "identity", alpha = 0.35, binwidth = 1) +
theme_classic()