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())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
)supp_growth_end_raw <- file.path(runs_data, "cell_growth_supp_end.parquet") %>%
read_parquet(mmap = T) %>%
merge(cell_coords, by = "cell_no")
supp_growth_end <- supp_growth_end_raw %>%
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"))
)
supp_growth_total <- supp_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
)supplement_change <- left_join(
norm_growth_end %>%
select(-c("TN_start", "TN_end", "start", "value")) %>%
rename(
norm_success = success,
norm_rem = TN_rem,
norm_value_na = value_na,
norm_value_0 = value_0
),
supp_growth_end %>%
select(-c("TN_start", "TN_end", "start", "value")) %>%
rename(
supp_success = success,
supp_rem = TN_rem,
supp_value_na = value_na,
supp_value_0 = value_0
),
by = c("state", "cell_no", "longitude", "latitude", "species", "month")
) %>%
relocate(state, .before = cell_no) %>%
relocate(species, .before = state) %>%
relocate(longitude, .after = cell_no) %>%
relocate(latitude, .after = longitude) %>%
relocate(month, .after = latitude)
supplement_change_total <- left_join(
norm_growth_total %>%
rename(
norm_total = total,
norm_total_na = total_na,
norm_total_0 = total_0
),
supp_growth_total %>%
rename(
supp_total = total,
supp_total_na = total_na,
supp_total_0 = total_0
),
by = c("state", "cell_no", "longitude", "latitude", "species")
)Provide summaries of the changes in success (ie whether the modelled seaweed was able to successfully growth at all within a cell) between normal and supplemented conditions.
tc <- supplement_change %>% pull(cell_no) %>% unique() %>% length()
# Which cells changed in success (per month)
norm_success <- supplement_change %>%
filter(norm_success) %>%
group_by(species, month) %>%
reframe(
norm_success_cells = n(),
perc_total_cells = 100*n()/tc
)
success_change <- supplement_change %>%
filter(supp_success != norm_success) %>%
group_by(species, month) %>%
reframe(success_change_cells = n())
norm_success %>%
left_join(success_change, by = c("species", "month")) %>%
mutate(
percdiff_change = round(100*success_change_cells/norm_success_cells, 1),
perctotal_change = round(100*success_change_cells/tc, 1)
) %>%
select(-norm_success_cells, -success_change_cells) %>%
print(n = 24)# A tibble: 24 × 5
species month perc_total_cells percdiff_change perctotal_change
<fct> <fct> <dbl> <dbl> <dbl>
1 A. armata 1 40.100 4.1 1.7
2 A. armata 2 38.960 1.6 0.6
3 A. armata 3 38.253 2.8 1.1
4 A. armata 4 39.624 4.7 1.9
5 A. armata 5 41.403 7.3 3
6 A. armata 6 46.161 9.3 4.3
7 A. armata 7 51.362 11.9 6.1
8 A. armata 8 54.665 7.2 3.9
9 A. armata 9 54.069 4.8 2.6
10 A. armata 10 49.617 4.1 2
11 A. armata 11 48.093 3.3 1.6
12 A. armata 12 44.484 4.6 2.1
13 A. taxiformis 1 47.506 29.7 14.1
14 A. taxiformis 2 53.115 15.9 8.5
15 A. taxiformis 3 50.724 21.5 10.9
16 A. taxiformis 4 49.966 23.6 11.8
17 A. taxiformis 5 67.084 18.7 12.6
18 A. taxiformis 6 63.526 14.6 9.3
19 A. taxiformis 7 58.180 13 7.6
20 A. taxiformis 8 56.537 12 6.8
21 A. taxiformis 9 57.584 8.9 5.1
22 A. taxiformis 10 54.733 14.4 7.9
23 A. taxiformis 11 39.130 42.6 16.7
24 A. taxiformis 12 40.773 32.9 13.4# Which cells changed in success (overall)
norm_success <- supplement_change %>%
filter(norm_success) %>%
group_by(species) %>%
reframe(
norm_success_cells = n(),
perc_total_cells = 100*n()/(12*tc)
)
success_change <- supplement_change %>%
filter(supp_success != norm_success) %>%
group_by(species) %>%
reframe(success_change_cells = n())
norm_success %>%
left_join(success_change, by = c("species")) %>%
mutate(
percdiff_change = round(100*success_change_cells/norm_success_cells, 1),
perctotal_change = round(100*success_change_cells/(tc*12), 1)
) %>%
select(-norm_success_cells, -success_change_cells)# A tibble: 2 × 4
species perc_total_cells percdiff_change perctotal_change
<fct> <dbl> <dbl> <dbl>
1 A. armata 45.566 5.6 2.6
2 A. taxiformis 53.238 19.5 10.4library(terra)
rdata_norm <- norm_growth_end %>%
select(cell_no, species, success, longitude, latitude, month) %>%
group_by(species, cell_no, longitude, latitude) %>%
reframe(
success = as.numeric(any(success)),
success = case_when(success == 0 ~ NA, T ~ 1)
)
rdata_norm_arma <- rdata_norm %>% filter(species == "A. armata")
rdata_norm_taxi <- rdata_norm %>% filter(species != "A. armata")
rdata_supp <- supp_growth_end %>%
select(cell_no, species, success, longitude, latitude, month) %>%
group_by(species, cell_no, longitude, latitude) %>%
reframe(
success = as.numeric(any(success)),
success = case_when(success == 0 ~ NA, T ~ 1)
)
rdata_supp_arma <- rdata_supp %>% filter(species == "A. armata")
rdata_supp_taxi <- rdata_supp %>% filter(species != "A. armata")
r_template <- rast(
extent = ext(c(min(rdata_norm$longitude), max(rdata_norm$longitude),
min(rdata_norm$latitude), max(rdata_norm$latitude))),
resolution = 0.12
)
area_success_all <- list()
r <- rasterize(rdata_norm_arma[, c("longitude", "latitude")], r_template, values = rdata_norm_arma$success)
r_area <- cellSize(r, unit = "km")
r_area <- mask(r_area, r)
area_success_all[[1]] <- data.frame(
species = "A. armata", treatment = "norm",
area_km2 = global(r_area, fun = sumna)[[1]]
)
r <- rasterize(rdata_supp_arma[, c("longitude", "latitude")], r_template, values = rdata_supp_arma$success)
r_area <- cellSize(r, unit = "km")
r_area <- mask(r_area, r)
area_success_all[[2]] <- data.frame(
species = "A. armata", treatment = "supp",
area_km2 = global(r_area, fun = sumna)[[1]]
)
r <- rasterize(rdata_norm_taxi[, c("longitude", "latitude")], r_template, values = rdata_norm_taxi$success)
r_area <- cellSize(r, unit = "km")
r_area <- mask(r_area, r)
area_success_all[[3]] <- data.frame(
species = "A. taxiformis", treatment = "norm",
area_km2 = global(r_area, fun = sumna)[[1]]
)
r <- rasterize(rdata_supp_taxi[, c("longitude", "latitude")], r_template, values = rdata_supp_taxi$success)
r_area <- cellSize(r, unit = "km")
r_area <- mask(r_area, r)
area_success_all[[4]] <- data.frame(
species = "A. taxiformis", treatment = "supp",
area_km2 = global(r_area, fun = sumna)[[1]]
)
area_success_all %>%
bind_rows() %>%
pivot_wider(names_from = treatment, names_prefix = "km2_", values_from = area_km2) %>%
mutate(
km2_diff = km2_supp - km2_norm,
perc_diff = round(100*km2_diff/km2_norm, 1)
)# A tibble: 2 × 5
species km2_norm km2_supp km2_diff perc_diff
<chr> <dbl> <dbl> <dbl> <dbl>
1 A. armata 873183. 894025. 20842. 2.4
2 A. taxiformis 1480785. 1521979. 41195. 2.8supplement_change %>%
mutate(success_change = case_when(supp_success != norm_success ~ T, T ~ F)) %>%
filter(success_change & species == "A. armata") %>%
ggplot(aes(x = longitude, y = latitude, fill = success_change)) +
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
) +
theme_void() + theme(legend.position = "none") +
facet_wrap(facets = vars(month), nrow = 4) +
ggtitle("Cells where A. armata became successful in each month under supplementation")
supplement_change %>%
mutate(success_change = case_when(supp_success != norm_success ~ T, T ~ F)) %>%
filter(success_change & species == "A. taxiformis") %>%
ggplot(aes(x = longitude, y = latitude, fill = success_change)) +
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
) +
theme_void() + theme(legend.position = "none") +
facet_wrap(facets = vars(month), nrow = 4) +
ggtitle("Cells where A. taxiformis became successful in each month under supplementation")
supp_growth_month <- supp_growth_end %>%
group_by(species, state, month) %>%
reframe(
mean = round(meanna(value_0), 1) %>% set_units("mg m-2"),
sd = round(sdna(value_0), 1) %>% set_units("mg m-2")
)
supp_growth_month %>%
filter(species == "A. armata") %>%
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^2] [mg/m^… [mg/… [mg/… [mg/…
1 A. armata 1 0 0 71.1 762.9 800.7 795.6 0 530.9
2 A. armata 2 0 0 44.2 722.2 763.4 760.3 0 456.4
3 A. armata 3 0 0 45.5 801.9 813.9 812.5 0 468.4
4 A. armata 4 0 0.8 174.3 795.8 782.9 786.1 0 468.8
5 A. armata 5 0 3.8 403.4 806.5 759.7 769.1 0.1 524.2
6 A. armata 6 0 65.2 614.6 763 721.1 728.4 4.6 574.5
7 A. armata 7 3.7 181 746.2 768.7 711.3 714.5 39.8 676.6
8 A. armata 8 0 201.4 753.9 769.1 645.1 691.2 35.1 768.2
9 A. armata 9 0 133.3 734.1 768.8 593.1 703.2 16 772.3
10 A. armata 10 0 2 628.7 804.6 677.7 764.3 12.2 784.1
11 A. armata 11 0 0 486.1 797 733.5 774.9 0.6 731
12 A. armata 12 0 0 361.4 793.6 789.6 797.1 0 636.9supp_growth_month %>%
filter(species == "A. armata") %>%
select(-mean) %>%
pivot_wider(names_from = state, values_from = sd, names_prefix = "sd_")# A tibble: 12 × 10
species month sd_NTE sd_QLD sd_NSW sd_SAU sd_TAS sd_VIC sd_WAN sd_WAS
<fct> <fct> [mg/m^2] [mg/m^2] [mg/m^2] [mg/m… [mg/m… [mg/m… [mg/m… [mg/m…
1 A. armata 1 0 0 191 161.9 37.3 52.6 0 349.7
2 A. armata 2 0 0 157.4 162.3 36 43.6 0 358.6
3 A. armata 3 0 0 166.1 102.4 44.8 42 0 387.5
4 A. armata 4 0 19.8 274.2 40.9 71.9 72.5 0 369.9
5 A. armata 5 0 48.2 348 48 140.1 102.3 1.9 359.4
6 A. armata 6 0 192.2 260.1 92.8 148.7 159.5 52.9 310.5
7 A. armata 7 42.3 309.9 94.2 99.6 146.8 175.7 161.1 228.9
8 A. armata 8 0 334.8 68.4 78.6 163.7 171.6 145.6 74
9 A. armata 9 0 277.7 51.7 31.3 164 87.4 99.8 33.3
10 A. armata 10 0 31.2 245.8 18.1 108.2 43.6 88.3 58.2
11 A. armata 11 0 0.5 342.5 23.1 76.6 47 9.2 148.5
12 A. armata 12 0 0 354 93.4 45.5 62.4 0 274.9supp_growth_month %>%
filter(species != "A. armata") %>%
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/… [mg/… [mg/… [mg/…
1 A. taxiformis 1 2.2 327.8 671.8 644.1 235.5 563.3 194.6 748.2
2 A. taxiformis 2 2.6 278.5 646.5 653.2 340.7 606.6 163.3 726.4
3 A. taxiformis 3 2.8 336.4 718.3 663.7 340.9 582.1 107.2 780.3
4 A. taxiformis 4 3.1 494.7 726.1 584.4 75.2 173.1 133.4 757.3
5 A. taxiformis 5 528.8 763.3 727.4 293.2 0.8 50.4 560 730.6
6 A. taxiformis 6 785.8 777 663.7 26.1 0 2.3 785.4 561.9
7 A. taxiformis 7 817 792.7 559.2 0 0 0 813.5 447.3
8 A. taxiformis 8 811.5 790.1 487.2 0 0 0 810.9 371.9
9 A. taxiformis 9 784.6 771.7 435 6.9 0 0 783.8 336.8
10 A. taxiformis 10 542.7 779.3 578 13.2 0 0.6 697.1 364.2
11 A. taxiformis 11 3.7 592.5 660.1 89.2 0 31.7 434.4 444.3
12 A. taxiformis 12 0 411 694.4 334.1 10.6 132.5 302.7 685.5supp_growth_month %>%
filter(species != "A. armata") %>%
select(-mean) %>%
pivot_wider(names_from = state, values_from = sd, names_prefix = "sd_")# A tibble: 12 × 10
species month sd_NTE sd_QLD sd_NSW sd_SAU sd_TAS sd_VIC sd_WAN sd_WAS
<fct> <fct> [mg/m^2] [mg/m… [mg/m… [mg/m… [mg/m… [mg/m… [mg/m… [mg/m…
1 A. taxiformis 1 35.7 365.9 91.4 213.3 257 195.3 327.8 55.5
2 A. taxiformis 2 43.8 339.4 68.7 184.4 291.1 137.8 294.9 47.2
3 A. taxiformis 3 43.8 355 59.2 234.4 306.2 198.6 259.8 53
4 A. taxiformis 4 48.1 330 84.4 257.6 171.2 285.2 256.6 68
5 A. taxiformis 5 201.8 60.9 126.7 277.1 8.6 162.5 271.4 127.8
6 A. taxiformis 6 18.7 52.6 189.3 58.4 0 18.5 19.3 285.8
7 A. taxiformis 7 10.5 53.1 273.3 0 0 0 14.3 347.9
8 A. taxiformis 8 11.5 47.6 289.9 0 0 0 10.4 363
9 A. taxiformis 9 39.6 39 288.7 64.7 0 0 59.9 352.1
10 A. taxiformis 10 314 50.5 185.1 95.9 0 6.2 203.3 355.5
11 A. taxiformis 11 20.6 287.1 102 173.9 0.8 106.6 360.7 308.9
12 A. taxiformis 12 0 363.3 69.9 310.8 53.2 231.8 371.5 96.6supp_growth_annual <- supp_growth_end %>%
group_by(species, state) %>%
reframe(
mean = round(meanna(value_0), 1) %>% set_units("mg m-2"),
sd = round(sdna(value_0), 1) %>% set_units("mg m-2")
)
supp_growth_annual %>%
arrange(species, -mean)# A tibble: 16 × 4
species state mean sd
<fct> <fct> [mg/m^2] [mg/m^2]
1 A. armata SAU 779.5 95.3
2 A. armata VIC 758.1 108.3
3 A. armata TAS 732.7 127.7
4 A. armata WAS 616 303.1
5 A. armata NSW 422 358.4
6 A. armata QLD 48.9 180.8
7 A. armata WAN 9 76.4
8 A. armata NTE 0.3 12.3
9 A. taxiformis NSW 630.6 197.4
10 A. taxiformis QLD 592.9 316.2
11 A. taxiformis WAS 579.6 297
12 A. taxiformis WAN 482.2 370.7
13 A. taxiformis NTE 357.1 382.2
14 A. taxiformis SAU 275.7 334.1
15 A. taxiformis VIC 178.5 283.1
16 A. taxiformis TAS 83.7 201.3supp_growth_total <- supp_growth_end %>%
group_by(species, state, cell_no) %>%
reframe(total = sum(value_0)) %>%
group_by(species, state) %>%
reframe(total = round(mean(total), 1)) %>%
arrange(species, -total)
supp_growth_total# A tibble: 16 × 3
species state total
<fct> <fct> <dbl>
1 A. armata SAU 9354
2 A. armata VIC 9097.1
3 A. armata TAS 8792.1
4 A. armata WAS 7392.3
5 A. armata NSW 5063.6
6 A. armata QLD 587.4
7 A. armata WAN 108.3
8 A. armata NTE 3.7
9 A. taxiformis NSW 7567.6
10 A. taxiformis QLD 7114.9
11 A. taxiformis WAS 6954.8
12 A. taxiformis WAN 5786.3
13 A. taxiformis NTE 4285
14 A. taxiformis SAU 3308.2
15 A. taxiformis VIC 2142.6
16 A. taxiformis TAS 1003.8plot_data <- supp_growth_month %>%
mutate(
mean = drop_units(mean),
sd = drop_units(sd),
mean = case_when(is.na(mean) ~ 0, T ~ mean),
sd = case_when(sd > mean ~ mean, is.na(sd) ~ 0, T ~ sd),
state = factor(state, levels = states_ord, labels = states_lng),
month = as.integer(month)
)
plot_data %>%
ggplot(aes(
x = month, 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 *")"))
supp_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, 10000),
breaks = seq(0, 10000, 1500),
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))norm_growth_month <- norm_growth_end %>%
group_by(species, state, month) %>%
reframe(
mean = meanna(value_0) %>% set_units("mg m-2"),
sd = sdna(value_0) %>% set_units("mg m-2")
)
diff_growth_month <- left_join(
norm_growth_month %>% select(-sd) %>% rename(norm_mean = mean),
supp_growth_month %>% select(-sd) %>% rename(supp_mean = mean),
by = c("species", "state", "month")
) %>%
mutate(
perc_diff = round(100*(supp_mean-norm_mean)/norm_mean, 1),
times = round(supp_mean/norm_mean, 1)
)
diff_growth_month %>%
select(species, state, month, perc_diff) %>%
pivot_wider(names_from = state, values_from = perc_diff, names_prefix = "pd_") %>%
print(n=24)# A tibble: 24 × 10
species month pd_NTE pd_QLD pd_NSW pd_SAU pd_TAS pd_VIC pd_WAN pd_WAS
<fct> <fct> [1] [1] [1] [1] [1] [1] [1] [1]
1 A. armata 1 NaN NaN 3360.1 893.3 1147.8 1140.6 NaN 1368.6
2 A. armata 2 NaN NaN 1927.4 578 680.3 660.7 NaN 774.3
3 A. armata 3 NaN NaN 1210.5 489.2 471.9 476 NaN 1104
4 A. armata 4 NaN 1254.5 851.1 398.6 367.1 362.2 NaN 1063.2
5 A. armata 5 NaN 1402.3 774.5 454.6 438.2 461.1 Inf 1282.6
6 A. armata 6 NaN 1129.5 522.1 478.4 477.9 448.8 1049.2 1314.4
7 A. armata 7 7955.7 983.4 554.7 546.2 600.9 549.6 1615.2 1662.5
8 A. armata 8 NaN 783.5 583.6 541.9 786.5 619.6 1703.4 1494.9
9 A. armata 9 NaN 605.1 603.9 475.1 850.5 584.8 1655.3 1274.2
10 A. armata 10 NaN 518.8 937.9 435.9 686 475.3 1326.2 1494.4
11 A. armata 11 NaN NaN 812.2 396.3 478.8 397.1 761.9 1192.3
12 A. armata 12 NaN NaN 1632.8 621 686.3 666.9 NaN 1449.3
13 A. taxifo… 1 906.5 1352.8 4354.4 1090.1 4942.5 2252.8 517.9 1175.6
14 A. taxifo… 2 416 1047.2 2831.8 661.7 1321.4 1036.4 445.8 686.5
15 A. taxifo… 3 592 1942.8 2231.9 664.2 1038.2 916.4 572.5 951.8
16 A. taxifo… 4 440.6 1621.1 810.1 677 1294.1 669.6 836.6 892.6
17 A. taxifo… 5 1076.6 1197.7 726.7 1365.3 Inf 979.6 824.5 1200.3
18 A. taxifo… 6 833.7 1143.5 631.2 178986. NaN Inf 794.7 1360.5
19 A. taxifo… 7 1392.9 1397.1 871.8 NaN NaN NaN 1296.9 1791.3
20 A. taxifo… 8 1603.8 1288.5 1215 NaN NaN NaN 1482.2 1521.9
21 A. taxifo… 9 1135.3 993.6 1314 1404.3 NaN NaN 1084.4 1285
22 A. taxifo… 10 809.2 921.6 2272.9 970.4 NaN Inf 975.7 1687.9
23 A. taxifo… 11 18685. 813.1 1506.1 1676.2 NaN 98371. 646.1 1386.1
24 A. taxifo… 12 NaN 1148.5 2489 1257.1 21006. 2453.5 595.4 1537.2diff_growth_month %>%
select(species, state, month, times) %>%
pivot_wider(names_from = state, values_from = times, names_prefix = "t_") %>%
print(n=24)# A tibble: 24 × 10
species month t_NTE t_QLD t_NSW t_SAU t_TAS t_VIC t_WAN t_WAS
<fct> <fct> [1] [1] [1] [1] [1] [1] [1] [1]
1 A. armata 1 NaN NaN 34.6 9.9 12.5 12.4 NaN 14.7
2 A. armata 2 NaN NaN 20.3 6.8 7.8 7.6 NaN 8.7
3 A. armata 3 NaN NaN 13.1 5.9 5.7 5.8 NaN 12
4 A. armata 4 NaN 13.5 9.5 5 4.7 4.6 NaN 11.6
5 A. armata 5 NaN 15 8.7 5.5 5.4 5.6 Inf 13.8
6 A. armata 6 NaN 12.3 6.2 5.8 5.8 5.5 11.5 14.1
7 A. armata 7 80.6 10.8 6.5 6.5 7 6.5 17.2 17.6
8 A. armata 8 NaN 8.8 6.8 6.4 8.9 7.2 18 15.9
9 A. armata 9 NaN 7.1 7 5.8 9.5 6.8 17.6 13.7
10 A. armata 10 NaN 6.2 10.4 5.4 7.9 5.8 14.3 15.9
11 A. armata 11 NaN NaN 9.1 5 5.8 5 8.6 12.9
12 A. armata 12 NaN NaN 17.3 7.2 7.9 7.7 NaN 15.5
13 A. taxiformis 1 10.1 14.5 44.5 11.9 50.4 23.5 6.2 12.8
14 A. taxiformis 2 5.2 11.5 29.3 7.6 14.2 11.4 5.5 7.9
15 A. taxiformis 3 6.9 20.4 23.3 7.6 11.4 10.2 6.7 10.5
16 A. taxiformis 4 5.4 17.2 9.1 7.8 13.9 7.7 9.4 9.9
17 A. taxiformis 5 11.8 13 8.3 14.7 Inf 10.8 9.2 13
18 A. taxiformis 6 9.3 12.4 7.3 1790.9 NaN Inf 8.9 14.6
19 A. taxiformis 7 14.9 15 9.7 NaN NaN NaN 14 18.9
20 A. taxiformis 8 17 13.9 13.1 NaN NaN NaN 15.8 16.2
21 A. taxiformis 9 12.4 10.9 14.1 15 NaN NaN 11.8 13.9
22 A. taxiformis 10 9.1 10.2 23.7 10.7 NaN Inf 10.8 17.9
23 A. taxiformis 11 187.8 9.1 16.1 17.8 NaN 984.7 7.5 14.9
24 A. taxiformis 12 NaN 12.5 25.9 13.6 211.1 25.5 7 16.4norm_growth_annual <- norm_growth_end %>%
group_by(species, state) %>%
reframe(
mean = meanna(value_0) %>% set_units("mg m-2"),
sd = sdna(value_0) %>% set_units("mg m-2")
)
left_join(
norm_growth_annual %>% select(-sd) %>% rename(norm_mean = mean),
supp_growth_annual %>% select(-sd) %>% rename(supp_mean = mean),
by = c("species", "state")
) %>%
mutate(
perc_diff = round(100*(supp_mean-norm_mean)/norm_mean, 1),
times = round(supp_mean/norm_mean, 1)
) %>%
arrange(species, -times)# A tibble: 16 × 6
species state norm_mean supp_mean perc_diff times
<fct> <fct> [mg/m^2] [mg/m^2] [1] [1]
1 A. armata NTE 0.0038275 0.3 7738 78.4
2 A. armata WAN 0.54197 9 1560.6 16.6
3 A. armata WAS 44.641 616 1279.9 13.8
4 A. armata QLD 5.3622 48.9 811.9 9.1
5 A. armata NSW 52.855 422 698.4 8
6 A. armata TAS 107.32 732.7 582.7 6.8
7 A. armata VIC 120.54 758.1 528.9 6.3
8 A. armata SAU 129.13 779.5 503.6 6
9 A. taxiformis TAS 5.3362 83.7 1468.5 15.7
10 A. taxiformis NSW 45.342 630.6 1290.8 13.9
11 A. taxiformis VIC 13.915 178.5 1182.8 12.8
12 A. taxiformis QLD 47.322 592.9 1152.9 12.5
13 A. taxiformis WAS 46.599 579.6 1143.8 12.4
14 A. taxiformis NTE 29.698 357.1 1102.4 12
15 A. taxiformis WAN 48.514 482.2 893.9 9.9
16 A. taxiformis SAU 29.441 275.7 836.5 9.4options(pillar.sigfig = 5)
norm_growth_total <- norm_growth_end %>%
group_by(species, state, cell_no) %>%
reframe(total = sum(value_0)) %>%
group_by(species, state) %>%
reframe(total = mean(total)) %>%
arrange(species, -total)
left_join(
norm_growth_total %>% rename(norm_total = total),
supp_growth_total %>% rename(supp_total = total),
by = c("species", "state")
) %>%
mutate(
perc_diff = round(100*(supp_total-norm_total)/norm_total, 1),
times = round(supp_total/norm_total, 1)
) %>%
arrange(species, -times)# A tibble: 16 × 6
species state norm_total supp_total perc_diff times
<fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 A. armata NTE 0.045930 3.7 7955.7 80.6
2 A. armata WAN 6.5036 108.3 1565.2 16.7
3 A. armata WAS 535.70 7392.3 1279.9 13.8
4 A. armata QLD 64.346 587.4 812.9 9.1
5 A. armata NSW 634.26 5063.6 698.4 8
6 A. armata TAS 1287.9 8792.1 582.7 6.8
7 A. armata VIC 1446.4 9097.1 528.9 6.3
8 A. armata SAU 1549.6 9354 503.6 6
9 A. taxiformis TAS 64.034 1003.8 1467.6 15.7
10 A. taxiformis NSW 544.10 7567.6 1290.8 13.9
11 A. taxiformis VIC 166.98 2142.6 1183.2 12.8
12 A. taxiformis QLD 567.86 7114.9 1152.9 12.5
13 A. taxiformis WAS 559.19 6954.8 1143.7 12.4
14 A. taxiformis NTE 356.38 4285 1102.4 12
15 A. taxiformis WAN 582.17 5786.3 893.9 9.9
16 A. taxiformis SAU 353.29 3308.2 836.4 9.4all_growth_total <- rbind(
norm_growth_total %>% mutate(scenario = "norm_conditions"),
supp_growth_total %>% mutate(scenario = "supp_conditions")
)
# hist(all_growth_total$total_na[all_growth_total$scenario == "norm_conditions"])
# hist(all_growth_total$total_na[all_growth_total$scenario != "norm_conditions"])
# minna(all_growth_total$total_na)
# maxna(all_growth_total$total_na)
all_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(1, 10000),
labels = scales::label_number(),
trans = "log10",
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") +
facet_grid(rows = vars(species), cols = vars(scenario)) +
prettyplot() +
theme(legend.position = "top", strip.text = element_blank(), strip.background = element_blank())cell_filter <- norm_growth_end %>%
distinct(cell_no, species, month, success) %>%
rename(include = success)
norm_growth_month_nre <- norm_growth_end %>%
left_join(cell_filter, by = c("cell_no", "species", "month")) %>%
filter(include) %>%
group_by(species, state, month) %>%
reframe(
mean = meanna(value_0) %>% set_units("mg m-2"),
sd = sdna(value_0) %>% set_units("mg m-2")
)
supp_growth_month_nre <- supp_growth_end %>%
left_join(cell_filter, by = c("cell_no", "species", "month")) %>%
filter(include) %>%
group_by(species, state, month) %>%
reframe(
mean = meanna(value_0) %>% set_units("mg m-2"),
sd = sdna(value_0) %>% set_units("mg m-2")
)
diff_growth_month <- left_join(
norm_growth_month_nre %>% select(-sd) %>% rename(norm_mean = mean),
supp_growth_month_nre %>% select(-sd) %>% rename(supp_mean = mean),
by = c("species", "state", "month")
) %>%
mutate(
perc_diff = round(100*(supp_mean-norm_mean)/norm_mean, 1),
times = round(supp_mean/norm_mean, 1)
)
diff_growth_month %>%
select(species, state, month, perc_diff) %>%
pivot_wider(names_from = state, values_from = perc_diff, names_prefix = "pd_") %>%
print(n=24)# A tibble: 24 × 10
species month pd_NTE pd_QLD pd_NSW pd_SAU pd_TAS pd_VIC pd_WAN pd_WAS
<fct> <fct> [1] [1] [1] [1] [1] [1] [1] [1]
1 A. armata 7 2861.7 897 535.7 544.5 600.8 548.3 1495.5 1307
2 A. armata 4 NA 959 732.1 398.6 366.9 361.8 NA 971.1
3 A. armata 5 NA 1010.9 651.1 454.6 437.7 460.4 NA 1125.6
4 A. armata 6 NA 1002 484.3 477.6 477.3 448.6 765.3 1147
5 A. armata 8 NA 760.4 533 540.4 782.7 618.1 1522.8 1331.1
6 A. armata 9 NA 593.5 590.9 475.1 833.6 583.4 1388.9 1180.2
7 A. armata 10 NA 487 847 436 685.4 475.3 1227.6 1403.3
8 A. armata 1 NA NA 1661.8 892.5 1147.8 1087.9 NA 1328.1
9 A. armata 2 NA NA 1582.2 577.8 680.3 660.7 NA 772
10 A. armata 3 NA NA 1064.1 489 471.8 476 NA 1028.5
11 A. armata 11 NA NA 760 396.2 478.7 397.1 572.2 1116.3
12 A. armata 12 NA NA 1403.8 621 686.3 650.9 NA 1320.5
13 A. taxiformis 1 863.8 1116.3 1956.4 1060.7 2617.2 1781.8 513.8 1149.6
14 A. taxiformis 2 423.7 892 1329.9 652.7 1232.1 1030.9 442.4 680
15 A. taxiformis 3 595.4 1519.7 1375.1 650.4 977.3 875.7 569.7 878.5
16 A. taxiformis 4 444.6 1415 740.5 670 970.1 588.7 699.9 832.3
17 A. taxiformis 5 979.5 1092.8 698.5 1195.1 NA 875.4 776.4 1062.8
18 A. taxiformis 6 833.7 1072.4 623.2 8580.8 NA NA 792.8 1146.8
19 A. taxiformis 7 1390.9 1247.3 832.7 NA NA NA 1285.4 1374.4
20 A. taxiformis 8 1603.9 1123.4 1014.3 NA NA NA 1478.7 1337.9
21 A. taxiformis 9 1135.2 934.3 1078 1383.4 NA NA 1084.3 1181.3
22 A. taxiformis 10 806.4 853.5 1364.2 914.7 NA NA 973.9 1511.5
23 A. taxiformis 11 4848.6 737.1 1079.1 849.6 NA 11328. 642.1 1091
24 A. taxiformis 12 NA 957 1466 1133.8 2232 1658.6 594.7 1318.7diff_growth_month %>%
select(species, state, month, times) %>%
pivot_wider(names_from = state, values_from = times, names_prefix = "t_") %>%
print(n=24)# A tibble: 24 × 10
species month t_NTE t_QLD t_NSW t_SAU t_TAS t_VIC t_WAN t_WAS
<fct> <fct> [1] [1] [1] [1] [1] [1] [1] [1]
1 A. armata 7 29.6 10 6.4 6.4 7 6.5 16 14.1
2 A. armata 4 NA 10.6 8.3 5 4.7 4.6 NA 10.7
3 A. armata 5 NA 11.1 7.5 5.5 5.4 5.6 NA 12.3
4 A. armata 6 NA 11 5.8 5.8 5.8 5.5 8.7 12.5
5 A. armata 8 NA 8.6 6.3 6.4 8.8 7.2 16.2 14.3
6 A. armata 9 NA 6.9 6.9 5.8 9.3 6.8 14.9 12.8
7 A. armata 10 NA 5.9 9.5 5.4 7.9 5.8 13.3 15
8 A. armata 1 NA NA 17.6 9.9 12.5 11.9 NA 14.3
9 A. armata 2 NA NA 16.8 6.8 7.8 7.6 NA 8.7
10 A. armata 3 NA NA 11.6 5.9 5.7 5.8 NA 11.3
11 A. armata 11 NA NA 8.6 5 5.8 5 6.7 12.2
12 A. armata 12 NA NA 15 7.2 7.9 7.5 NA 14.2
13 A. taxiformis 1 9.6 12.2 20.6 11.6 27.2 18.8 6.1 12.5
14 A. taxiformis 2 5.2 9.9 14.3 7.5 13.3 11.3 5.4 7.8
15 A. taxiformis 3 7 16.2 14.8 7.5 10.8 9.8 6.7 9.8
16 A. taxiformis 4 5.4 15.1 8.4 7.7 10.7 6.9 8 9.3
17 A. taxiformis 5 10.8 11.9 8 13 NA 9.8 8.8 11.6
18 A. taxiformis 6 9.3 11.7 7.2 86.8 NA NA 8.9 12.5
19 A. taxiformis 7 14.9 13.5 9.3 NA NA NA 13.9 14.7
20 A. taxiformis 8 17 12.2 11.1 NA NA NA 15.8 14.4
21 A. taxiformis 9 12.4 10.3 11.8 14.8 NA NA 11.8 12.8
22 A. taxiformis 10 9.1 9.5 14.6 10.1 NA NA 10.7 16.1
23 A. taxiformis 11 49.5 8.4 11.8 9.5 NA 114.3 7.4 11.9
24 A. taxiformis 12 NA 10.6 15.7 12.3 23.3 17.6 6.9 14.2norm_growth_annual_nre <- norm_growth_end %>%
left_join(cell_filter, by = c("cell_no", "species", "month")) %>%
filter(include) %>%
group_by(species, state) %>%
reframe(
mean = meanna(value_0) %>% set_units("mg m-2"),
sd = sdna(value_0) %>% set_units("mg m-2")
)
supp_growth_annual_nre <- supp_growth_end %>%
left_join(cell_filter, by = c("cell_no", "species", "month")) %>%
filter(include) %>%
group_by(species, state) %>%
reframe(
mean = meanna(value_0) %>% set_units("mg m-2"),
sd = sdna(value_0) %>% set_units("mg m-2")
)
left_join(
norm_growth_annual_nre %>% select(-sd) %>% rename(norm_mean = mean),
supp_growth_annual_nre %>% select(-sd) %>% rename(supp_mean = mean),
by = c("species", "state")
) %>%
mutate(
norm_mean = round(norm_mean, 1),
supp_mean = round(supp_mean, 1),
perc_diff = round(100*(supp_mean-norm_mean)/norm_mean, 1),
times = round(supp_mean/norm_mean, 1)
) %>%
arrange(species, -times)# A tibble: 16 × 6
species state norm_mean supp_mean perc_diff times
<fct> <fct> [mg/m^2] [mg/m^2] [1] [1]
1 A. armata NTE 21.1 624.8 2861.1 29.6
2 A. armata WAN 44.3 664 1398.9 15
3 A. armata WAS 60.7 767.8 1164.9 12.6
4 A. armata QLD 82.2 712.1 766.3 8.7
5 A. armata NSW 98.4 725.9 637.7 7.4
6 A. armata TAS 109.8 748.3 581.5 6.8
7 A. armata VIC 123.4 771.3 525 6.3
8 A. armata SAU 130.9 789.5 503.1 6
9 A. taxiformis TAS 46.4 600 1193.1 12.9
10 A. taxiformis NTE 63.5 754.8 1088.7 11.9
11 A. taxiformis VIC 55.4 632.7 1042.1 11.4
12 A. taxiformis QLD 67.9 766.8 1029.3 11.3
13 A. taxiformis WAS 66.3 746 1025.2 11.3
14 A. taxiformis NSW 68.3 713.8 945.1 10.5
15 A. taxiformis WAN 76.9 756.2 883.4 9.8
16 A. taxiformis SAU 77.1 683.9 787 8.9norm_growth_total_nre <- norm_growth_end %>%
left_join(cell_filter, by = c("cell_no", "species", "month")) %>%
filter(include) %>%
group_by(species, state, cell_no) %>%
reframe(total = sum(value_0)) %>%
group_by(species, state) %>%
reframe(total = mean(total)) %>%
arrange(species, -total)
supp_growth_total_nre <- supp_growth_end %>%
left_join(cell_filter, by = c("cell_no", "species", "month")) %>%
filter(include) %>%
group_by(species, state, cell_no) %>%
reframe(total = sum(value_0)) %>%
group_by(species, state) %>%
reframe(total = mean(total)) %>%
arrange(species, -total)
left_join(
norm_growth_total_nre %>% rename(norm_total = total),
supp_growth_total_nre %>% rename(supp_total = total),
by = c("species", "state")
) %>%
mutate(
norm_total = round(norm_total, 1),
supp_total = round(supp_total, 1),
perc_diff = round(100*(supp_total-norm_total)/norm_total, 1),
times = round(supp_total/norm_total, 1)
) %>%
arrange(species, -times)# A tibble: 16 × 6
species state norm_total supp_total perc_diff times
<fct> <fct> <dbl> <dbl> <dbl> <dbl>
1 A. armata NTE 21.1 624.8 2861.1 29.6
2 A. armata WAN 83.8 1256.6 1399.5 15
3 A. armata WAS 535.7 6773.4 1164.4 12.6
4 A. armata QLD 226.5 1962.4 766.4 8.7
5 A. armata NSW 634.3 4678.5 637.6 7.4
6 A. armata TAS 1288.9 8782.7 581.4 6.8
7 A. armata VIC 1448.8 9055.6 525 6.3
8 A. armata SAU 1549.6 9347.9 503.2 6
9 A. taxiformis TAS 115 1486.7 1192.8 12.9
10 A. taxiformis NTE 356.4 4235.6 1088.4 11.9
11 A. taxiformis VIC 169.4 1936.6 1043.2 11.4
12 A. taxiformis QLD 577.2 6514.5 1028.6 11.3
13 A. taxiformis WAS 559.2 6291.9 1025.2 11.3
14 A. taxiformis NSW 544.1 5682.7 944.4 10.4
15 A. taxiformis WAN 582.2 5720.9 882.6 9.8
16 A. taxiformis SAU 381.9 3389.6 787.6 8.9N_input <- file.path(envi_data, "cell_input_supp") %>%
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 <- supp_growth_end %>%
select(-contains("TN"), -start, -longitude, -latitude) %>%
left_join(N_input, by = c("state", "cell_no", "month")) %>%
mutate(
value_na = set_units(value_na, "mg m-2"),
biorem_eff = value_na/N_input
)biorem_eff %>%
group_by(species, month) %>%
reframe(
mean_biorem_eff = round(100*meanna(biorem_eff), 1),
max_biorem_eff = round(100*maxna(biorem_eff), 1)
) %>%
print(n = 24)# A tibble: 24 × 4
species month mean_biorem_eff max_biorem_eff
<fct> <fct> [1] [1]
1 A. armata 1 1.6 1.7
2 A. armata 2 2.9 3.1
3 A. armata 3 3.2 3.4
4 A. armata 4 3 3.4
5 A. armata 5 2.8 3.4
6 A. armata 6 2.7 3.4
7 A. armata 7 2.7 3.4
8 A. armata 8 2.7 3.4
9 A. armata 9 2.8 3.4
10 A. armata 10 2.9 3.4
11 A. armata 11 3 3.4
12 A. armata 12 3.1 3.4
13 A. taxiformis 1 1.3 1.7
14 A. taxiformis 2 2.5 3.1
15 A. taxiformis 3 2.7 3.4
16 A. taxiformis 4 2.6 3.5
17 A. taxiformis 5 2.6 3.4
18 A. taxiformis 6 2.9 3.5
19 A. taxiformis 7 3.1 3.4
20 A. taxiformis 8 3.1 3.4
21 A. taxiformis 9 3.1 3.5
22 A. taxiformis 10 2.8 3.4
23 A. taxiformis 11 2.4 3.4
24 A. taxiformis 12 2.5 3.4biorem_eff %>%
mutate(biorem_eff = 100*biorem_eff) %>%
ggplot(aes(x = biorem_eff, fill = species, colour = species)) +
geom_histogram(position = "identity", alpha = 0.35, binwidth = 0.1) +
theme_classic()Warning: Removed 124407 rows containing non-finite outside the scale range
(`stat_bin()`).
biorem_eff %>%
group_by(species, state) %>%
reframe(
mean_biorem_eff = round(100*meanna(biorem_eff), 1),
max_biorem_eff = round(100*maxna(biorem_eff), 1)
) %>%
print(n = 24)# A tibble: 16 × 4
species state mean_biorem_eff max_biorem_eff
<fct> <fct> [1] [1]
1 A. armata NTE 1.4 2.8
2 A. armata QLD 2.5 3.4
3 A. armata NSW 2.6 3.3
4 A. armata SAU 2.9 3.4
5 A. armata TAS 2.6 3.3
6 A. armata VIC 2.7 3.4
7 A. armata WAN 1.9 3.4
8 A. armata WAS 3 3.4
9 A. taxiformis NTE 3 3.5
10 A. taxiformis QLD 2.9 3.5
11 A. taxiformis NSW 2.5 3.3
12 A. taxiformis SAU 2 3.4
13 A. taxiformis TAS 1.5 3.1
14 A. taxiformis VIC 1.7 3.2
15 A. taxiformis WAN 2.9 3.5
16 A. taxiformis WAS 2.6 3.4biorem_eff %>%
group_by(species, state, month) %>%
reframe(
mean_biorem_eff = round(100*meanna(biorem_eff), 1)
) %>%
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> [1] [1] [1] [1] [1] [1] [1] [1]
1 A. armata 1 NaN NaN 0.7 1.6 1.6 1.6 NaN 1.5
2 A. armata 2 NaN NaN 2 2.9 3 3 NaN 2.8
3 A. armata 3 NaN NaN 2.1 3.2 3.2 3.2 NaN 3.1
4 A. armata 4 NaN 1.5 2 3.1 3 3 NaN 3.1
5 A. armata 5 NaN 1.4 2.3 3 2.7 2.7 0.2 3
6 A. armata 6 NaN 2.1 2.5 2.8 2.6 2.6 1.3 3
7 A. armata 7 1.4 2.5 2.7 2.7 2.4 2.5 2.3 3
8 A. armata 8 NaN 2.8 2.7 2.7 2.2 2.4 1.8 3.1
9 A. armata 9 NaN 2.6 2.8 2.8 2.1 2.5 2.2 3.3
10 A. armata 10 NaN 0.7 2.6 3 2.4 2.7 2 3.2
11 A. armata 11 NaN 0.1 2.8 3.2 2.9 3 0.4 3.1
12 A. armata 12 NaN NaN 2.7 3.2 3.2 3.2 NaN 3
13 A. taxiformis 1 0.8 1.4 1.4 1.3 0.8 1.2 1.3 1.5
14 A. taxiformis 2 2.7 2.5 2.5 2.6 1.9 2.4 2.4 2.9
15 A. taxiformis 3 2.5 2.6 2.8 2.7 2 2.3 2.7 3.2
16 A. taxiformis 4 2.9 2.8 2.9 2.5 0.8 1.2 1.8 3.2
17 A. taxiformis 5 2.2 3.1 2.8 1.5 0.2 1.4 2.4 3
18 A. taxiformis 6 3.3 3.3 2.6 0.4 NaN 0.3 3.3 2.6
19 A. taxiformis 7 3.4 3.2 2.3 NaN NaN NaN 3.3 2.5
20 A. taxiformis 8 3.3 3.2 2.2 NaN NaN NaN 3.3 2.6
21 A. taxiformis 9 3.3 3.2 2.1 2.3 NaN NaN 3.3 2.5
22 A. taxiformis 10 2.5 3.1 2.3 2.4 NaN 0.1 2.9 2.5
23 A. taxiformis 11 0.3 2.8 2.7 1.1 0.1 1 2.7 2.1
24 A. taxiformis 12 NaN 2.7 2.8 1.9 0.7 1.5 2.9 2.8biorem_eff_total <- supp_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 = round(100*meanna(biorem_eff), 1),
max_biorem_eff = round(100*maxna(biorem_eff), 1)
)# A tibble: 2 × 3
species mean_biorem_eff max_biorem_eff
<fct> [1] [1]
1 A. armata 2.8 3.4
2 A. taxiformis 2.6 3.5biorem_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()