Modelling Asparagopsis nitrogen bioremediation efficiency in Australian coastal environments
culture_depths <- tar_read(culture_depths, store = spec_store)
factors <- tar_read(factors, store = spec_store)
state_input_timeseries <- envi_data %>%
list.files(full.names = T) %>%
str_subset("state_input_timeseries") %>%
purrr::map_dfr(arrow::read_parquet) %>%
filter(yday <= 365)
sens_cond <- file.path(spec_data, "sensitivity_conditions.parquet") %>%
arrow::read_parquet()Culture depths tested were 0.5, 2.5, 5, 10 m below the surface, with a
Sensitivity runs started on the first of every month to cover annual variability, for 12 runs per year, with the same mean annual conditions looping from December to January. Sensitivity runs were conducted at culture depths of 0.5 m, 2 m, 3.5 m, and 5 m below the surface with a light attenuation coefficient of 0.65 (clear water). The canopy was always 2.5 m in height, and the total water column depth was always 50 m. Curves for temperature, surface irradiance, salinity, water velocity, nitrate and ammonium were constructed to capture “typical” conditions based on daily means of each state from 2019-2023. This resulted in two sensitivity curves for temperature and a single sensitivity curve for irradiance, water velocity, salinity, nitrate and ammonium (fig-sens-altogether).
prettyplot2 <- prettyplot + theme(legend.position = "none", aspect.ratio = 0.75)
rm.x <- function(){theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(), axis.title.x = element_blank())}
rm.y <- function(){theme(axis.text.y = element_blank(), axis.ticks.y = element_blank(), axis.title.y = element_blank())}
p1 <- sens_cond %>%
dplyr::filter(t <= 365) %>%
ggplot(aes(x = t, y = T_input, linetype = as.factor(T_level))) +
geom_line(linewidth = 0.75) +
geom_line(data = state_input_timeseries, aes(x = yday, y = T_input_mean, colour = state), linetype = "dotted", inherit.aes = F) +
scale_y_continuous(breaks = seq(10, 35, 5), limits = c(10, 33.5)) +
labs(y = expression("Temperature ("*degree*"C)")) +
prettyplot2
p2 <- sens_cond %>%
dplyr::filter(t <= 365 & T_level == 1) %>%
ggplot(aes(x = t, y = I_input)) +
geom_line(linewidth = 0.75) +
geom_line(data = state_input_timeseries, aes(x = yday, y = I_input_mean, colour = state), linetype = "dotted", inherit.aes = F) +
scale_y_continuous(breaks = seq(0, 700, 100), limits = c(0, 660)) +
labs(y = expression("Irradiance ("*mu*"mol m"^-2*" s"^-1*")")) +
prettyplot2
p3 <- sens_cond %>%
dplyr::filter(t <= 365 & T_level == 1) %>%
ggplot(aes(x = t, y = U_input)) +
geom_line(linewidth = 0.75) +
geom_line(data = state_input_timeseries, aes(x = yday, y = UV_input_mean, colour = state), linetype = "dotted", inherit.aes = F) +
scale_y_continuous(breaks = seq(0, 0.6, 0.1), limits = c(0, 0.575)) +
labs(y = expression("Water velocity (m"^-1*" s"^-1*")")) +
prettyplot2
p4 <- sens_cond %>%
dplyr::filter(t <= 365 & T_level == 1) %>%
ggplot(aes(x = t, y = S_input)) +
geom_line(linewidth = 0.75) +
geom_line(data = state_input_timeseries, aes(x = yday, y = S_input_mean, colour = state), linetype = "dotted", inherit.aes = F) +
scale_y_continuous(breaks = seq(33.1, 36.8, 0.3), limits = c(34, 36.11)) +
labs(y = expression("Salinity (g L"^-1*")")) +
prettyplot2
pdata1 <- sens_cond %>%
dplyr::filter(t <= 365 & T_level == 1) %>%
mutate(Ni_input = set_units(Ni_input, "mg m-3") %>% set_units("umol L-1") %>% drop_units(),
Am_input = set_units(Am_input, "mg m-3") %>% set_units("umol L-1") %>% drop_units())
pdata2 <- state_input_timeseries %>%
mutate(Ni_input_mean = set_units(Ni_input_mean, "mg m-3") %>% set_units("umol L-1") %>% drop_units(),
Am_input_mean = set_units(Am_input_mean, "mg m-3") %>% set_units("umol L-1") %>% drop_units())
p5 <- pdata1 %>%
ggplot(aes(x = t, y = Ni_input)) +
geom_line(linewidth = 0.75) +
geom_line(data = pdata2, aes(x = yday, y = Ni_input_mean, colour = state), linetype = "dotted", inherit.aes = F) +
scale_y_continuous(breaks = seq(0, 5, 0.5), limits = c(0, 3)) +
labs(y = nitrate_lab) +
prettyplot2
p6 <- pdata1 %>%
ggplot(aes(x = t, y = Am_input)) +
geom_line(linewidth = 0.75) +
geom_line(data = pdata2, aes(x = yday, y = Am_input_mean, colour = state), linetype = "dotted", inherit.aes = F) +
scale_y_continuous(breaks = seq(0, 10, 0.1), limits = c(0, 0.5)) +
labs(y = ammonium_lab) +
prettyplot2
plot_grid(
plot_grid(
p1 + rm.x() + annotate("text", x = Inf, y = Inf, label = "A", hjust = 1.5, vjust = 1, size = 4, fontface = "plain"),
p2 + rm.x() + annotate("text", x = Inf, y = Inf, label = "B", hjust = 1.5, vjust = 1, size = 4, fontface = "plain"),
p3 + rm.x() + annotate("text", x = Inf, y = Inf, label = "C", hjust = 1.5, vjust = 1, size = 4, fontface = "plain"),
p4 + rm.x() + annotate("text", x = Inf, y = Inf, label = "D", hjust = 1.5, vjust = 1, size = 4, fontface = "plain"),
p5 + theme(axis.title.x = element_blank()) +
annotate("text", x = Inf, y = Inf, label = "E", hjust = 1.5, vjust = 1, size = 4, fontface = "plain"),
p6 + theme(axis.title.x = element_blank()) +
annotate("text", x = Inf, y = Inf, label = "F", hjust = 1.5, vjust = 1, size = 4, fontface = "plain"),
ncol = 2, align = "hv"
),
ggdraw() + draw_label("Day of the year", size = 12),
ncol = 1,
rel_heights = c(0.95, 0.05)
)
N_uptake <- file.path(spec_data, "N_uptake.parquet") %>%
read_parquet()
S_response <- file.path(spec_data, "S_response.parquet") %>%
read_parquet()
I_response <- file.path(spec_data, "I_response.parquet") %>%
read_parquet() %>%
mutate(depth_m = as.factor(depth_m))
T_response <- file.path(spec_data, "T_response.parquet") %>%
read_parquet() %>%
mutate(species = factor(species,
levels = c("taxiformis", "armata"),
labels = c("taxi", "arma")))
p1 <- ggplot(N_uptake, aes(x = uM, y = uptake_uM, linetype = form)) +
geom_line(linewidth = 0.75) +
labs(x = expression("Ambient concentration ("*mu*"M)"), y = expression("Uptake rate ("*mu*"mol gDW"^-1*"d"^-1*")")) +
scale_y_continuous(breaks = seq(0, 3.5, 0.05), limits = c(0, 0.30)) +
scale_x_continuous(breaks = seq(0, 30, 5), limits = c(0,25)) +
prettyplot() +
theme(aspect.ratio = 0.75)
p2 <- ggplot(T_response, aes(x = temp, y = Tlim, color = species)) +
geom_line(linewidth = 0.75) +
labs(x = expression("Temperature ("*degree*"C)"), y = "Relative growth") +
scale_y_continuous(breaks = seq(0, 1, 0.2), limits = c(0,1)) +
scale_x_continuous(breaks = seq(0, 40, 5), limits = c(0,35)) +
prettyplot() +
theme(aspect.ratio = 0.75)
p3 <- ggplot(I_response, aes(x = light, y = Ilim, linetype = as.factor(depth_m))) +
geom_line(linewidth = 0.75) +
facet_wrap(facets = vars(species)) +
labs(x = expression("Surface light (PPFD)"), y = "Relative growth") +
scale_y_continuous(breaks = seq(0, 1, 0.2), limits = c(0,1)) +
scale_linetype_manual(values = c("solid", "dashed", "dotdash", "dotted")) +
prettyplot() +
theme(aspect.ratio = 0.75, strip.text = element_blank())
p4 <- ggplot(S_response, aes(x = sali, y = Slim)) +
geom_line(linewidth = 0.75) +
labs(x = expression("Salinity (g L"^-1*")"), y = "Relative growth") +
scale_y_continuous(breaks = seq(0, 1, 0.2), limits = c(0,1)) +
scale_x_continuous(breaks = seq(0, 60, 5), limits = c(19,45)) +
prettyplot() +
theme(aspect.ratio = 0.75)
p1 <- p1 + annotate("text", x = Inf, y = Inf, label = "A", hjust = 1.5, vjust = 1.5, size = 4)
p2 <- p2 + annotate("text", x = Inf, y = Inf, label = "B", hjust = 1.5, vjust = 1.5, size = 4)
p3 <- p3 + annotate("text", x = Inf, y = Inf, label = "C", hjust = 1.5, vjust = 1.5, size = 4)
p4 <- p4 + annotate("text", x = Inf, y = Inf, label = "D", hjust = 1.5, vjust = 1.5, size = 4)
plot_grid(
plot_grid(p1, p2, p3, p4, ncol = 2, align = "hv"),
ncol = 1,
rel_heights = c(0.95, 0.05) # Adjust these values to control spacing
)Warning: Removed 24 rows containing missing values or values outside the scale range
(`geom_line()`).Warning: Removed 312 rows containing missing values or values outside the scale range
(`geom_line()`).Warning: Graphs cannot be horizontally aligned unless the axis parameter is
set. Placing graphs unaligned.
sens_arma <- file.path(spec_data, "sensitivity_arma_calc_combTU.parquet") %>%
read_parquet() %>%
dplyr::filter(!param %in% c("M_am", "C_am", "M_ot", "C_ot")) %>%
mutate(cult_dep = as.factor(cult_dep))
exlude <- unique(sens_arma$param[abs(sens_arma$sens) < 0.01]) %>% as.character
sens_arma <- sens_arma %>%
dplyr::filter(!param %in% c(exlude, "M_am", "C_am", "M_ot", "C_ot"))
sens_arma$param <- droplevels(sens_arma$param)
levels(sens_arma$param) [1] "V_am" "K_am" "C_ni" "Q_min" "Q_max" "mu" "D_ve" "T_opt" "T_min"
[10] "T_max"exlude [1] "M_ni" "K_c" "a_cs" "S_opt" "S_min" "S_max" "h_a" "h_b" "h_c"
[10] "h_max" "DWWW" "I_o" # Add axis labels
param.labs.arma <- c(
bquote("V"["Am"]),
bquote("K"["Am"]),
# bquote("M"["Ni"]),
bquote("C"["Ni"]),
bquote("Q"["min"]),
bquote("Q"["max"]),
# bquote("K"["c"]),
bquote(mu),
bquote("D"["ve"]),
# bquote("a"["cs"]),
# bquote("I"["o"]),
bquote("T"["opt"]),
bquote("T"["min"]),
bquote("T"["max"])
# bquote("S"["opt"]),
# bquote("S"["min"]),
# bquote("S"["max"]),
# bquote("h"["a"]),
# bquote("h"["b"]),
# bquote("h"["c"]),
# bquote("h"["max"]),
# "DW:WW"
)
sens_taxi <- file.path(spec_data, "sensitivity_taxi_calc_combTU.parquet") %>%
read_parquet() %>%
mutate(cult_dep = as.factor(cult_dep))
exlude <- unique(sens_taxi$param[abs(sens_taxi$sens) < 0.01]) %>% as.character
sens_taxi <- sens_taxi %>%
dplyr::filter(!param %in% c(exlude, "M_am", "C_am", "M_ot", "C_ot"))
sens_taxi$param <- droplevels(sens_taxi$param)
levels(sens_taxi$param) [1] "V_am" "K_am" "C_ni" "Q_min" "Q_max" "K_c" "mu" "D_ve" "T_opt"
[10] "T_min" "T_max"exlude [1] "M_ni" "a_cs" "S_opt" "S_min" "S_max" "h_a" "h_b" "h_c" "h_max"
[10] "DWWW" "I_o" # Add axis labels
param.labs.taxi <- param.labs.armap1 <- ggplot(dplyr::filter(sens_arma, !is.na(sens) & cult_dep != "10"),
aes(x = rev(param), y = sens, fill = as.factor(cult_dep), ymin = sens-sd, ymax = sens+sd),
alpha = 0.75) +
geom_col(position = position_dodge(), width = 0.95, colour = "black") +
geom_errorbar(position = position_dodge(width = 0.95), width = 0.5) +
coord_flip() +
scale_y_continuous(limits = c(-1.1, 1.1), breaks = seq(-1.5, 1.5, 0.2)) +
scale_x_discrete(limits = levels(sens_arma$param), labels = rev(param.labs.arma)) +
scale_fill_manual(values = depths_pal) +
labs(y = "Total N sensitivity", x = "Parameter name") +
prettyplot() +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
aspect.ratio = 0.75)Warning in fortify(data, ...): Arguments in `...` must be used.
✖ Problematic argument:
• alpha = 0.75
ℹ Did you misspell an argument name?p2 <- ggplot(dplyr::filter(sens_taxi, !is.na(sens) & cult_dep != "10"),
aes(x = rev(param), y = sens, fill = as.factor(cult_dep), ymin = sens-sd, ymax = sens+sd),
alpha = 0.75) +
geom_col(position = position_dodge(), width = 0.95, colour = "black") +
geom_errorbar(position = position_dodge(width = 0.95), width = 0.5) +
coord_flip() +
scale_y_continuous(limits = c(-1.1, 1.1), breaks = seq(-1.5, 1.5, 0.2)) +
scale_x_discrete(limits = levels(sens_arma$param), labels = rev(param.labs.taxi)) +
scale_fill_manual(values = depths_pal) +
labs(y = "Total N sensitivity", x = "Parameter name") +
prettyplot() +
theme(aspect.ratio = 0.75)Warning in fortify(data, ...): Arguments in `...` must be used.
✖ Problematic argument:
• alpha = 0.75
ℹ Did you misspell an argument name?plot_grid(p1, p2, nrow = 2, align = "hv")Warning: Removed 3 rows containing missing values or values outside the scale range
(`geom_col()`).