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())The point of this section is to show the most common limiting factor - overall, between species, temporally, geographically.
# These have cell_no, lat, lon, month and lims (for maps)
armata_lims <- map(1:12, function(m) {
file.path(runs_data, str_c("armata_dom_lims_raster_", fixnum(m,2), ".tif")) %>%
terra::rast() %>%
as.data.frame(xy=TRUE) %>%
rename(longitude = x, latitude = y) %>%
mutate(lim = factor(lim, levels = 1:4, labels = c("T_lim", "I_lim", "Q_lim", "S_lim")))
})
taxiformis_lims <- map(1:12, function(m) {
file.path(runs_data, str_c("taxiformis_dom_lims_raster_", fixnum(m,2), ".tif")) %>%
terra::rast() %>%
as.data.frame(xy=TRUE) %>%
rename(longitude = x, latitude = y) %>%
mutate(lim = factor(lim, levels = 1:4, labels = c("T_lim", "I_lim", "Q_lim", "S_lim")))
})
# These have only latitude, month and lim (for histograms)
forhist_armata <- find_read(runs_data, "forhist_armata.parquet") %>% split(.$month)
forhist_taxiformis <- find_read(runs_data, "forhist_taxiformis.parquet") %>% split(.$month)p_dom_hist.1 <- forhist_armata[[1]] %>%
ggplot(aes(y = latitude, x = after_stat(100*count/(13207 * 30)), fill = lim, colour = lim)) +
geom_histogram(position = "stack", alpha = 0.75, binwidth = 0.75) +
scale_fill_manual(values = lims_pal) +
scale_colour_manual(values = lims_pal) +
scale_y_continuous(limits = c(-44.5, -8.5), expand = c(0,0)) +
scale_x_continuous(limits = c(0, 5.5), breaks = seq(0, 5, 1), expand = c(0.02,0)) +
labs(x = expression("% of growing days in all cells"), y = "Latitude") +
prettyplot() +
rm.y() +
theme(plot.margin = margin(0, 0, 0, 0, "pt"), panel.spacing = unit(1, "lines"))
p_dom_map.1 <- armata_lims[[1]] %>%
ggplot(aes(x = longitude, y = latitude, fill = lim)) +
geom_raster() +
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(112.5, 160, 10)) +
scale_y_continuous(breaks = seq(-2.5, -45, -5)) +
scale_color_brewer(palette = "Dark2") +
scale_fill_manual(values = lims_pal) +
labs(x = "Longitude", y = "Latitude") +
prettyplot() +
theme(plot.margin = margin(0, 0, 0, 0, "pt"), axis.title.x = element_text(vjust = 1))stat1 <- armata_lims %>%
filter(latitude > -30)
stat1 <- stat1 %>%
group_by(cell_no, lim) %>%
reframe(perc_days = n()/nrow(stat1))
stat1_T <- sum(stat1$perc_days[stat1$lim == "T_lim"])
stat1_Q <- sum(stat1$perc_days[stat1$lim == "Q_lim"])
stat1_I <- sum(stat1$perc_days[stat1$lim == "I_lim"])
stat2 <- armata_lims %>%
filter(latitude <= -30)
stat1 <- stat1 %>%
group_by(cell_no, lim) %>%
reframe(perc_days = n()/nrow(stat1))
stat2_T <- sum(stat2$perc_days[stat2$lim == "T_lim"])
stat2_Q <- sum(stat2$perc_days[stat2$lim == "Q_lim"])
stat2_I <- sum(stat2$perc_days[stat2$lim == "I_lim"])
# For _A. armata_ grown at latitudes > -30$^\circ$, a mean of
# `r round(100*stat1_T,1)`% of growing days were limited by temperature,
# `r round(100*stat1_Q,1)`% were limited by nutrients, and
# `r round(100*stat1_I,1)`% were limited by light.
#
# For _A. armata_ grown at latitudes < -30$^\circ$, a mean of
# `r round(100*stat2_T,1)`% of growing days were limited by temperature,
# `r round(100*stat2_Q,1)`% were limited by nutrients, and
# `r round(100*stat2_I,1)`% were limited by light.Factors limiting growth of A. armata in February (A), May (B), August (C) and November (D). Colours indicate limiting factors as temperature (pink), internal nutrient quotient (green) or light availability (gold). Maps show the dominant limiting factor in each cell within the month, while historgram shows the frequency of limiting factors across all days of the month in all cells.
plot_grid(
p_dom_map.1 %+% armata_lims[[2]],
p_dom_hist.1 %+% forhist_armata[[2]],
ncol = 2,
align = "h",
rel_widths = c(1, 0.625)
) Warning: <ggplot> %+% x was deprecated in ggplot2 4.0.0.
ℹ Please use <ggplot> + x instead.Warning: Removed 6 rows containing missing values or values outside the scale range
(`geom_bar()`).plot_grid(
p_dom_map.1 %+% armata_lims[[5]],
p_dom_hist.1 %+% forhist_armata[[5]],
ncol = 2,
align = "h",
rel_widths = c(1, 0.625)
)Warning: Removed 7 rows containing missing values or values outside the scale range
(`geom_bar()`).plot_grid(
p_dom_map.1 %+% armata_lims[[8]],
p_dom_hist.1 %+% forhist_armata[[8]],
ncol = 2,
align = "h",
rel_widths = c(1, 0.625)
)Warning: Removed 7 rows containing missing values or values outside the scale range
(`geom_bar()`).plot_grid(
p_dom_map.1 %+% armata_lims[[11]],
p_dom_hist.1 %+% forhist_armata[[11]],
ncol = 2,
align = "h",
rel_widths = c(1, 0.625)
)Warning: Removed 6 rows containing missing values or values outside the scale range
(`geom_bar()`).
stat1 <- taxiformis_lims %>%
filter(latitude > -30)
stat1 <- stat1 %>%
group_by(cell_no, lim) %>%
reframe(perc_days = n()/nrow(stat1))
stat1_T <- sum(stat1$perc_days[stat1$lim == "T_lim"])
stat1_Q <- sum(stat1$perc_days[stat1$lim == "Q_lim"])
stat1_I <- sum(stat1$perc_days[stat1$lim == "I_lim"])
stat2 <- taxiformis_lims %>%
filter(latitude <= -30)
stat1 <- stat1 %>%
group_by(cell_no, lim) %>%
reframe(perc_days = n()/nrow(stat1))
stat2_T <- sum(stat2$perc_days[stat2$lim == "T_lim"])
stat2_Q <- sum(stat2$perc_days[stat2$lim == "Q_lim"])
stat2_I <- sum(stat2$perc_days[stat2$lim == "I_lim"])
# For _A. taxiformis_ grown at latitudes > -30$^\circ$, a mean of
# `r round(100*stat1_T,1)`% of growing days were limited by temperature,
# `r round(100*stat1_Q,1)`% were limited by nutrients, and
# `r round(100*stat1_I,1)`% were limited by light.
#
# For _A. taxiformis_ grown at latitudes < -30$^\circ$, a mean of
# `r round(100*stat2_T,1)`% of growing days were limited by temperature,
# `r round(100*stat2_Q,1)`% were limited by nutrients, and
# `r round(100*stat2_I,1)`% were limited by light.Factors limiting growth of A. taxiformis in February (A), May (B), August (C) and November (D). Colours indicate limiting factors as temperature (pink), internal nutrient quotient (green) or light availability (gold). Maps show the dominant limiting factor in each cell within the month, while historgram shows the frequency of limiting factors across all days of the month in all cells.
plot_grid(
p_dom_map.1 %+% taxiformis_lims[[2]],
p_dom_hist.1 %+% forhist_taxiformis[[2]],
ncol = 2,
align = "h",
rel_widths = c(1, 0.625)
) Warning: Removed 6 rows containing missing values or values outside the scale range
(`geom_bar()`).plot_grid(
p_dom_map.1 %+% taxiformis_lims[[5]],
p_dom_hist.1 %+% forhist_taxiformis[[5]],
ncol = 2,
align = "h",
rel_widths = c(1, 0.625)
) Warning: Removed 7 rows containing missing values or values outside the scale range
(`geom_bar()`).plot_grid(
p_dom_map.1 %+% taxiformis_lims[[8]],
p_dom_hist.1 %+% forhist_taxiformis[[8]],
ncol = 2,
align = "h",
rel_widths = c(1, 0.625)
) Warning: Removed 7 rows containing missing values or values outside the scale range
(`geom_bar()`).plot_grid(
p_dom_map.1 %+% taxiformis_lims[[11]],
p_dom_hist.1 %+% forhist_taxiformis[[11]],
ncol = 2,
align = "h",
rel_widths = c(1, 0.625)
) Warning: Removed 6 rows containing missing values or values outside the scale range
(`geom_bar()`).
