R/gistoolS3G4.R
In milee16/GIS.Tool.mkl: GIS-Tool Analisys of Marine Coastal Areas
Defines functions
gistoolS3G4
Documented in
gistoolS3G4
#' Gradient analysis of WB
#'
#' Uses Model data for WB divided into five gradient polygons. Model scenarios are "Baseline", "N-30%" and "P-30%"
#' @param WBData Tables of gradient polygons
#' @return Vertical profiles and Monod growth kinetics in various plots
#' @export
gistoolS3G4 <- function() {
if(!require(ggplot2)) install.packages("ggplot2")
if(!require(extrafont)) install.packages("extrafont")
library(ggplot2)
library(extrafont)
font_import()
loadfonts(device = "win")
fonts()
theme_pub <<- function(base_size = 14, base_family = "Times New Roman",
line_size = 0.25, ...) {
half_line <<- base_size / 2
small_rel <<- 0.8
small_size <<- small_rel * base_size
theme_bw(base_size = base_size, base_family = base_family, ...) %+replace%
theme(
rect = element_rect(fill = NA, color = "black", size = 1.5),
text = element_text(family = base_family, face = "plain",
colour = "black", size = base_size, hjust = 0.5,
vjust = 0.5, angle = 0, lineheight = 0.9,
margin = ggplot2::margin(), debug = F),
axis.text = element_text(size = base_size),
axis.text.x = element_text(margin = ggplot2::margin(t = small_size/4),
vjust = 1),
axis.text.y = element_text(margin = ggplot2::margin(r = small_size/4),
hjust = 1),
axis.title.x = element_text(margin = ggplot2::margin(t = small_size,
b = small_size)),
axis.title.y = element_text(angle = 90,
margin = ggplot2::margin(r = small_size,
l = small_size/4)),
axis.ticks = element_line(colour = "black", size = line_size+0.75),
axis.ticks.length = unit(0.25, 'lines'),
axis.line = element_line(colour = "black", size = line_size),
axis.line.x = element_line(colour = "black", size = line_size),
axis.line.y = element_line(colour = "black", size = line_size),
legend.spacing = unit(base_size/4, "pt"),
legend.key = element_blank(),
legend.key.size = unit(1.5 * base_size, "pt"),
legend.key.width = unit(1.5 * base_size, 'pt'),
legend.text = element_text(size = base_size),
legend.title = element_blank(),
legend.position = c(1,1),
legend.box = 'horizontal',
legend.justification = c(1,0.9),
plot.tag.position = c(0.18,0.93),
plot.tag = element_text(size = 22),
panel.spacing = unit(1, "lines"),
panel.background = element_blank(),
panel.border = element_rect(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
strip.text = element_text(size = base_size),
strip.background = element_rect(fill = NA, colour = "black", size = 0.125),
strip.text.x = element_text(face = 'bold', hjust = 0,
margin = ggplot2::margin(b = small_size/2,
t = small_size/4)),
strip.text.y = element_text(angle = -90, face = 'bold',
margin = ggplot2::margin(l = small_size/2,
r = small_size/4)),
plot.margin = unit(c(5,5,0,0), "pt"),
plot.background = element_blank(),
plot.title = element_text(face = "bold", size = 1.2 * base_size,
margin = ggplot2::margin(b = half_line),
hjust = 0)
)
}
if(!require(tidyverse)) install.packages("tidyverse")
library(tidyverse)
#WB_B
#Rename Columns in WB_B_G1
WB_B_G1 <<- WB_B_G1 %>%
arrange(desc(upper)) %>%
rename(
area_km2 = X_COL3,
area_m2 = Sum_area_m2_,
volume_m3 = Sum_volume_m3_,
EC = X_COL6,
EN = X_COL7,
EP = X_COL8,
BC2 = X_COL9,
BN2 = X_COL10,
BP2 = X_COL11,
BC1 = X_COL12,
BN1 = X_COL13,
BP1 = X_COL14,
BDC = X_COL15,
BDN = X_COL16,
BDP = X_COL17,
PC = X_COL18,
PN = X_COL19,
PP = X_COL20,
CH = X_COL21,
DIN = X_COL22,
DIP = X_COL23,
lo_b = X_COL24,
AvgSD = Avg_SD_,
PREC = X_COL26,
PRBC2 = X_COL27,
PRBC1 = X_COL28,
PRBDC = X_COL29,
PRPC = X_COL30,
DIN_b = X_COL31,
DIP_b = X_COL32
)
#Rename Columns in WB_B_G2
WB_B_G2 <<- WB_B_G2 %>%
arrange(desc(upper)) %>%
rename(
area_km2 = X_COL3,
area_m2 = Sum_area_m2_,
volume_m3 = Sum_volume_m3_,
EC = X_COL6,
EN = X_COL7,
EP = X_COL8,
BC2 = X_COL9,
BN2 = X_COL10,
BP2 = X_COL11,
BC1 = X_COL12,
BN1 = X_COL13,
BP1 = X_COL14,
BDC = X_COL15,
BDN = X_COL16,
BDP = X_COL17,
PC = X_COL18,
PN = X_COL19,
PP = X_COL20,
CH = X_COL21,
DIN = X_COL22,
DIP = X_COL23,
lo_b = X_COL24,
AvgSD = Avg_SD_,
PREC = X_COL26,
PRBC2 = X_COL27,
PRBC1 = X_COL28,
PRBDC = X_COL29,
PRPC = X_COL30,
DIN_b = X_COL31,
DIP_b = X_COL32
)
#Rename Columns in WB_B_G3
WB_B_G3 <<- WB_B_G3 %>%
arrange(desc(upper)) %>%
rename(
area_km2 = X_COL3,
area_m2 = Sum_area_m2_,
volume_m3 = Sum_volume_m3_,
EC = X_COL6,
EN = X_COL7,
EP = X_COL8,
BC2 = X_COL9,
BN2 = X_COL10,
BP2 = X_COL11,
BC1 = X_COL12,
BN1 = X_COL13,
BP1 = X_COL14,
BDC = X_COL15,
BDN = X_COL16,
BDP = X_COL17,
PC = X_COL18,
PN = X_COL19,
PP = X_COL20,
CH = X_COL21,
DIN = X_COL22,
DIP = X_COL23,
lo_b = X_COL24,
AvgSD = Avg_SD_,
PREC = X_COL26,
PRBC2 = X_COL27,
PRBC1 = X_COL28,
PRBDC = X_COL29,
PRPC = X_COL30,
DIN_b = X_COL31,
DIP_b = X_COL32
)
#Rename Columns in WB_B_G4
WB_B_G4 <<- WB_B_G4 %>%
arrange(desc(upper)) %>%
rename(
area_km2 = X_COL3,
area_m2 = Sum_area_m2_,
volume_m3 = Sum_volume_m3_,
EC = X_COL6,
EN = X_COL7,
EP = X_COL8,
BC2 = X_COL9,
BN2 = X_COL10,
BP2 = X_COL11,
BC1 = X_COL12,
BN1 = X_COL13,
BP1 = X_COL14,
BDC = X_COL15,
BDN = X_COL16,
BDP = X_COL17,
PC = X_COL18,
PN = X_COL19,
PP = X_COL20,
CH = X_COL21,
DIN = X_COL22,
DIP = X_COL23,
lo_b = X_COL24,
AvgSD = Avg_SD_,
PREC = X_COL26,
PRBC2 = X_COL27,
PRBC1 = X_COL28,
PRBDC = X_COL29,
PRPC = X_COL30,
DIN_b = X_COL31,
DIP_b = X_COL32
)
#WB_S1
#Rename Columns in WB_S1_G1
WB_S1_G1 <<- WB_S1_G1 %>%
arrange(desc(upper)) %>%
rename(
area_km2 = X_COL3,
area_m2 = Sum_area_m2_,
volume_m3 = Sum_volume_m3_,
EC = X_COL6,
EN = X_COL7,
EP = X_COL8,
BC2 = X_COL9,
BN2 = X_COL10,
BP2 = X_COL11,
BC1 = X_COL12,
BN1 = X_COL13,
BP1 = X_COL14,
BDC = X_COL15,
BDN = X_COL16,
BDP = X_COL17,
PC = X_COL18,
PN = X_COL19,
PP = X_COL20,
CH = X_COL21,
DIN = X_COL22,
DIP = X_COL23,
lo_b = X_COL24,
AvgSD = Avg_SD_,
PREC = X_COL26,
PRBC2 = X_COL27,
PRBC1 = X_COL28,
PRBDC = X_COL29,
PRPC = X_COL30,
DIN_b = X_COL31,
DIP_b = X_COL32
)
#Rename Columns in WB_S1_G2
WB_S1_G2 <<- WB_S1_G2 %>%
arrange(desc(upper)) %>%
rename(
area_km2 = X_COL3,
area_m2 = Sum_area_m2_,
volume_m3 = Sum_volume_m3_,
EC = X_COL6,
EN = X_COL7,
EP = X_COL8,
BC2 = X_COL9,
BN2 = X_COL10,
BP2 = X_COL11,
BC1 = X_COL12,
BN1 = X_COL13,
BP1 = X_COL14,
BDC = X_COL15,
BDN = X_COL16,
BDP = X_COL17,
PC = X_COL18,
PN = X_COL19,
PP = X_COL20,
CH = X_COL21,
DIN = X_COL22,
DIP = X_COL23,
lo_b = X_COL24,
AvgSD = Avg_SD_,
PREC = X_COL26,
PRBC2 = X_COL27,
PRBC1 = X_COL28,
PRBDC = X_COL29,
PRPC = X_COL30,
DIN_b = X_COL31,
DIP_b = X_COL32
)
#Rename Columns in WB_S1_G3
WB_S1_G3 <<- WB_S1_G3 %>%
arrange(desc(upper)) %>%
rename(
area_km2 = X_COL3,
area_m2 = Sum_area_m2_,
volume_m3 = Sum_volume_m3_,
EC = X_COL6,
EN = X_COL7,
EP = X_COL8,
BC2 = X_COL9,
BN2 = X_COL10,
BP2 = X_COL11,
BC1 = X_COL12,
BN1 = X_COL13,
BP1 = X_COL14,
BDC = X_COL15,
BDN = X_COL16,
BDP = X_COL17,
PC = X_COL18,
PN = X_COL19,
PP = X_COL20,
CH = X_COL21,
DIN = X_COL22,
DIP = X_COL23,
lo_b = X_COL24,
AvgSD = Avg_SD_,
PREC = X_COL26,
PRBC2 = X_COL27,
PRBC1 = X_COL28,
PRBDC = X_COL29,
PRPC = X_COL30,
DIN_b = X_COL31,
DIP_b = X_COL32
)
#Rename Columns in WB_S1_G4
WB_S1_G4 <<- WB_S1_G4 %>%
arrange(desc(upper)) %>%
rename(
area_km2 = X_COL3,
area_m2 = Sum_area_m2_,
volume_m3 = Sum_volume_m3_,
EC = X_COL6,
EN = X_COL7,
EP = X_COL8,
BC2 = X_COL9,
BN2 = X_COL10,
BP2 = X_COL11,
BC1 = X_COL12,
BN1 = X_COL13,
BP1 = X_COL14,
BDC = X_COL15,
BDN = X_COL16,
BDP = X_COL17,
PC = X_COL18,
PN = X_COL19,
PP = X_COL20,
CH = X_COL21,
DIN = X_COL22,
DIP = X_COL23,
lo_b = X_COL24,
AvgSD = Avg_SD_,
PREC = X_COL26,
PRBC2 = X_COL27,
PRBC1 = X_COL28,
PRBDC = X_COL29,
PRPC = X_COL30,
DIN_b = X_COL31,
DIP_b = X_COL32
)
#WB_S2
#Rename Columns in WB_S2_G1
WB_S2_G1 <<- WB_S2_G1 %>%
arrange(desc(upper)) %>%
rename(
area_km2 = X_COL3,
area_m2 = Sum_area_m2_,
volume_m3 = Sum_volume_m3_,
EC = X_COL6,
EN = X_COL7,
EP = X_COL8,
BC2 = X_COL9,
BN2 = X_COL10,
BP2 = X_COL11,
BC1 = X_COL12,
BN1 = X_COL13,
BP1 = X_COL14,
BDC = X_COL15,
BDN = X_COL16,
BDP = X_COL17,
PC = X_COL18,
PN = X_COL19,
PP = X_COL20,
CH = X_COL21,
DIN = X_COL22,
DIP = X_COL23,
lo_b = X_COL24,
AvgSD = Avg_SD_,
PREC = X_COL26,
PRBC2 = X_COL27,
PRBC1 = X_COL28,
PRBDC = X_COL29,
PRPC = X_COL30,
DIN_b = X_COL31,
DIP_b = X_COL32
)
#Rename Columns in WB_S2_G2
WB_S2_G2 <<- WB_S2_G2 %>%
arrange(desc(upper)) %>%
rename(
area_km2 = X_COL3,
area_m2 = Sum_area_m2_,
volume_m3 = Sum_volume_m3_,
EC = X_COL6,
EN = X_COL7,
EP = X_COL8,
BC2 = X_COL9,
BN2 = X_COL10,
BP2 = X_COL11,
BC1 = X_COL12,
BN1 = X_COL13,
BP1 = X_COL14,
BDC = X_COL15,
BDN = X_COL16,
BDP = X_COL17,
PC = X_COL18,
PN = X_COL19,
PP = X_COL20,
CH = X_COL21,
DIN = X_COL22,
DIP = X_COL23,
lo_b = X_COL24,
AvgSD = Avg_SD_,
PREC = X_COL26,
PRBC2 = X_COL27,
PRBC1 = X_COL28,
PRBDC = X_COL29,
PRPC = X_COL30,
DIN_b = X_COL31,
DIP_b = X_COL32
)
#Rename Columns in WB_S2_G3
WB_S2_G3 <<- WB_S2_G3 %>%
arrange(desc(upper)) %>%
rename(
area_km2 = X_COL3,
area_m2 = Sum_area_m2_,
volume_m3 = Sum_volume_m3_,
EC = X_COL6,
EN = X_COL7,
EP = X_COL8,
BC2 = X_COL9,
BN2 = X_COL10,
BP2 = X_COL11,
BC1 = X_COL12,
BN1 = X_COL13,
BP1 = X_COL14,
BDC = X_COL15,
BDN = X_COL16,
BDP = X_COL17,
PC = X_COL18,
PN = X_COL19,
PP = X_COL20,
CH = X_COL21,
DIN = X_COL22,
DIP = X_COL23,
lo_b = X_COL24,
AvgSD = Avg_SD_,
PREC = X_COL26,
PRBC2 = X_COL27,
PRBC1 = X_COL28,
PRBDC = X_COL29,
PRPC = X_COL30,
DIN_b = X_COL31,
DIP_b = X_COL32
)
#Rename Columns in WB_S2_G4
WB_S2_G4 <<- WB_S2_G4 %>%
arrange(desc(upper)) %>%
rename(
area_km2 = X_COL3,
area_m2 = Sum_area_m2_,
volume_m3 = Sum_volume_m3_,
EC = X_COL6,
EN = X_COL7,
EP = X_COL8,
BC2 = X_COL9,
BN2 = X_COL10,
BP2 = X_COL11,
BC1 = X_COL12,
BN1 = X_COL13,
BP1 = X_COL14,
BDC = X_COL15,
BDN = X_COL16,
BDP = X_COL17,
PC = X_COL18,
PN = X_COL19,
PP = X_COL20,
CH = X_COL21,
DIN = X_COL22,
DIP = X_COL23,
lo_b = X_COL24,
AvgSD = Avg_SD_,
PREC = X_COL26,
PRBC2 = X_COL27,
PRBC1 = X_COL28,
PRBDC = X_COL29,
PRPC = X_COL30,
DIN_b = X_COL31,
DIP_b = X_COL32
)
#Create WB_gradient_no entries and Avg DIN/DIP for all scenarios and gradient polygons
WB_gradient_no <<- c(1:4)
WB_B_avgDIN_AG <<- c(mean(WB_B_G1$DIN),mean(WB_B_G2$DIN),mean(WB_B_G3$DIN),mean(WB_B_G4$DIN))
WB_B_avgDIP_AG <<- c(mean(WB_B_G1$DIP),mean(WB_B_G2$DIP),mean(WB_B_G3$DIP),mean(WB_B_G4$DIP))
WB_B_avgDIN_b_AG <<- c(mean(WB_B_G1$DIN_b),mean(WB_B_G2$DIN_b),mean(WB_B_G3$DIN_b),mean(WB_B_G4$DIN_b))
WB_B_avgDIP_b_AG <<- c(mean(WB_B_G1$DIP_b),mean(WB_B_G2$DIP_b),mean(WB_B_G3$DIP_b),mean(WB_B_G4$DIP_b))
WB_S1_avgDIN_AG <<- c(mean(WB_S1_G1$DIN),mean(WB_S1_G2$DIN),mean(WB_S1_G3$DIN),mean(WB_S1_G4$DIN))
WB_S1_avgDIP_AG <<- c(mean(WB_S1_G1$DIP),mean(WB_S1_G2$DIP),mean(WB_S1_G3$DIP),mean(WB_S1_G4$DIP))
WB_S1_avgDIN_b_AG <<- c(mean(WB_S1_G1$DIN_b),mean(WB_S1_G2$DIN_b),mean(WB_S1_G3$DIN_b),mean(WB_S1_G4$DIN_b))
WB_S1_avgDIP_b_AG <<- c(mean(WB_S1_G1$DIP_b),mean(WB_S1_G2$DIP_b),mean(WB_S1_G3$DIP_b),mean(WB_S1_G4$DIP_b))
WB_S2_avgDIN_AG <<- c(mean(WB_S2_G1$DIN),mean(WB_S2_G2$DIN),mean(WB_S2_G3$DIN),mean(WB_S2_G4$DIN))
WB_S2_avgDIP_AG <<- c(mean(WB_S2_G1$DIP),mean(WB_S2_G2$DIP),mean(WB_S2_G3$DIP),mean(WB_S2_G4$DIP))
WB_S2_avgDIN_b_AG <<- c(mean(WB_S2_G1$DIN_b),mean(WB_S2_G2$DIN_b),mean(WB_S2_G3$DIN_b),mean(WB_S2_G4$DIN_b))
WB_S2_avgDIP_b_AG <<- c(mean(WB_S2_G1$DIP_b),mean(WB_S2_G2$DIP_b),mean(WB_S2_G3$DIP_b),mean(WB_S2_G4$DIP_b))
#Transmute area-specific WB tables to non area-specific tables to use in creating single values data frame for all gradient polygons later on
WB_B_G1_no_Aspec <<- WB_B_G1 %>%
transmute(
EC = EC*area_m2,
EN = EN*area_m2,
EP = EP*area_m2,
BC2 = BC2*area_m2,
BN2 = BN2*area_m2,
BP2 = BP2*area_m2,
BC1 = BC1*area_m2,
BN1 = BN1*area_m2,
BP1 = BP1*area_m2,
BDC = BDC*area_m2,
BDN = BDN*area_m2,
BDP = BDP*area_m2,
PC = PC*volume_m3,
PN = PN*volume_m3,
PP = PP*volume_m3,
CH = CH*volume_m3,
DIN = DIN*volume_m3,
DIP = DIP*volume_m3,
lo_b = lo_b*area_m2,
PREC = PREC*area_m2,
PRBC2 = PRBC2*area_m2,
PRBC1 = PRBC1*area_m2,
PRBDC = PRBDC*area_m2,
PRPC = PRPC*area_m2,
DIN_b = DIN_b*volume_m3,
DIP_b = DIP_b*volume_m3
)
WB_B_G2_no_Aspec <<- WB_B_G2 %>%
transmute(
EC = EC*area_m2,
EN = EN*area_m2,
EP = EP*area_m2,
BC2 = BC2*area_m2,
BN2 = BN2*area_m2,
BP2 = BP2*area_m2,
BC1 = BC1*area_m2,
BN1 = BN1*area_m2,
BP1 = BP1*area_m2,
BDC = BDC*area_m2,
BDN = BDN*area_m2,
BDP = BDP*area_m2,
PC = PC*volume_m3,
PN = PN*volume_m3,
PP = PP*volume_m3,
CH = CH*volume_m3,
DIN = DIN*volume_m3,
DIP = DIP*volume_m3,
lo_b = lo_b*area_m2,
PREC = PREC*area_m2,
PRBC2 = PRBC2*area_m2,
PRBC1 = PRBC1*area_m2,
PRBDC = PRBDC*area_m2,
PRPC = PRPC*area_m2,
DIN_b = DIN_b*volume_m3,
DIP_b = DIP_b*volume_m3
)
WB_B_G3_no_Aspec <<- WB_B_G3 %>%
transmute(
EC = EC*area_m2,
EN = EN*area_m2,
EP = EP*area_m2,
BC2 = BC2*area_m2,
BN2 = BN2*area_m2,
BP2 = BP2*area_m2,
BC1 = BC1*area_m2,
BN1 = BN1*area_m2,
BP1 = BP1*area_m2,
BDC = BDC*area_m2,
BDN = BDN*area_m2,
BDP = BDP*area_m2,
PC = PC*volume_m3,
PN = PN*volume_m3,
PP = PP*volume_m3,
CH = CH*volume_m3,
DIN = DIN*volume_m3,
DIP = DIP*volume_m3,
lo_b = lo_b*area_m2,
PREC = PREC*area_m2,
PRBC2 = PRBC2*area_m2,
PRBC1 = PRBC1*area_m2,
PRBDC = PRBDC*area_m2,
PRPC = PRPC*area_m2,
DIN_b = DIN_b*volume_m3,
DIP_b = DIP_b*volume_m3
)
WB_B_G4_no_Aspec <<- WB_B_G4 %>%
transmute(
EC = EC*area_m2,
EN = EN*area_m2,
EP = EP*area_m2,
BC2 = BC2*area_m2,
BN2 = BN2*area_m2,
BP2 = BP2*area_m2,
BC1 = BC1*area_m2,
BN1 = BN1*area_m2,
BP1 = BP1*area_m2,
BDC = BDC*area_m2,
BDN = BDN*area_m2,
BDP = BDP*area_m2,
PC = PC*volume_m3,
PN = PN*volume_m3,
PP = PP*volume_m3,
CH = CH*volume_m3,
DIN = DIN*volume_m3,
DIP = DIP*volume_m3,
lo_b = lo_b*area_m2,
PREC = PREC*area_m2,
PRBC2 = PRBC2*area_m2,
PRBC1 = PRBC1*area_m2,
PRBDC = PRBDC*area_m2,
PRPC = PRPC*area_m2,
DIN_b = DIN_b*volume_m3,
DIP_b = DIP_b*volume_m3
)
WB_S1_G1_no_Aspec <<- WB_S1_G1 %>%
transmute(
EC = EC*area_m2,
EN = EN*area_m2,
EP = EP*area_m2,
BC2 = BC2*area_m2,
BN2 = BN2*area_m2,
BP2 = BP2*area_m2,
BC1 = BC1*area_m2,
BN1 = BN1*area_m2,
BP1 = BP1*area_m2,
BDC = BDC*area_m2,
BDN = BDN*area_m2,
BDP = BDP*area_m2,
PC = PC*volume_m3,
PN = PN*volume_m3,
PP = PP*volume_m3,
CH = CH*volume_m3,
DIN = DIN*volume_m3,
DIP = DIP*volume_m3,
lo_b = lo_b*area_m2,
PREC = PREC*area_m2,
PRBC2 = PRBC2*area_m2,
PRBC1 = PRBC1*area_m2,
PRBDC = PRBDC*area_m2,
PRPC = PRPC*area_m2,
DIN_b = DIN_b*volume_m3,
DIP_b = DIP_b*volume_m3
)
WB_S1_G2_no_Aspec <<- WB_S1_G2 %>%
transmute(
EC = EC*area_m2,
EN = EN*area_m2,
EP = EP*area_m2,
BC2 = BC2*area_m2,
BN2 = BN2*area_m2,
BP2 = BP2*area_m2,
BC1 = BC1*area_m2,
BN1 = BN1*area_m2,
BP1 = BP1*area_m2,
BDC = BDC*area_m2,
BDN = BDN*area_m2,
BDP = BDP*area_m2,
PC = PC*volume_m3,
PN = PN*volume_m3,
PP = PP*volume_m3,
CH = CH*volume_m3,
DIN = DIN*volume_m3,
DIP = DIP*volume_m3,
lo_b = lo_b*area_m2,
PREC = PREC*area_m2,
PRBC2 = PRBC2*area_m2,
PRBC1 = PRBC1*area_m2,
PRBDC = PRBDC*area_m2,
PRPC = PRPC*area_m2,
DIN_b = DIN_b*volume_m3,
DIP_b = DIP_b*volume_m3
)
WB_S1_G3_no_Aspec <<- WB_S1_G3 %>%
transmute(
EC = EC*area_m2,
EN = EN*area_m2,
EP = EP*area_m2,
BC2 = BC2*area_m2,
BN2 = BN2*area_m2,
BP2 = BP2*area_m2,
BC1 = BC1*area_m2,
BN1 = BN1*area_m2,
BP1 = BP1*area_m2,
BDC = BDC*area_m2,
BDN = BDN*area_m2,
BDP = BDP*area_m2,
PC = PC*volume_m3,
PN = PN*volume_m3,
PP = PP*volume_m3,
CH = CH*volume_m3,
DIN = DIN*volume_m3,
DIP = DIP*volume_m3,
lo_b = lo_b*area_m2,
PREC = PREC*area_m2,
PRBC2 = PRBC2*area_m2,
PRBC1 = PRBC1*area_m2,
PRBDC = PRBDC*area_m2,
PRPC = PRPC*area_m2,
DIN_b = DIN_b*volume_m3,
DIP_b = DIP_b*volume_m3
)
WB_S1_G4_no_Aspec <<- WB_S1_G4 %>%
transmute(
EC = EC*area_m2,
EN = EN*area_m2,
EP = EP*area_m2,
BC2 = BC2*area_m2,
BN2 = BN2*area_m2,
BP2 = BP2*area_m2,
BC1 = BC1*area_m2,
BN1 = BN1*area_m2,
BP1 = BP1*area_m2,
BDC = BDC*area_m2,
BDN = BDN*area_m2,
BDP = BDP*area_m2,
PC = PC*volume_m3,
PN = PN*volume_m3,
PP = PP*volume_m3,
CH = CH*volume_m3,
DIN = DIN*volume_m3,
DIP = DIP*volume_m3,
lo_b = lo_b*area_m2,
PREC = PREC*area_m2,
PRBC2 = PRBC2*area_m2,
PRBC1 = PRBC1*area_m2,
PRBDC = PRBDC*area_m2,
PRPC = PRPC*area_m2,
DIN_b = DIN_b*volume_m3,
DIP_b = DIP_b*volume_m3
)
WB_S2_G1_no_Aspec <<- WB_S2_G1 %>%
transmute(
EC = EC*area_m2,
EN = EN*area_m2,
EP = EP*area_m2,
BC2 = BC2*area_m2,
BN2 = BN2*area_m2,
BP2 = BP2*area_m2,
BC1 = BC1*area_m2,
BN1 = BN1*area_m2,
BP1 = BP1*area_m2,
BDC = BDC*area_m2,
BDN = BDN*area_m2,
BDP = BDP*area_m2,
PC = PC*volume_m3,
PN = PN*volume_m3,
PP = PP*volume_m3,
CH = CH*volume_m3,
DIN = DIN*volume_m3,
DIP = DIP*volume_m3,
lo_b = lo_b*area_m2,
PREC = PREC*area_m2,
PRBC2 = PRBC2*area_m2,
PRBC1 = PRBC1*area_m2,
PRBDC = PRBDC*area_m2,
PRPC = PRPC*area_m2,
DIN_b = DIN_b*volume_m3,
DIP_b = DIP_b*volume_m3
)
WB_S2_G2_no_Aspec <<- WB_S2_G2 %>%
transmute(
EC = EC*area_m2,
EN = EN*area_m2,
EP = EP*area_m2,
BC2 = BC2*area_m2,
BN2 = BN2*area_m2,
BP2 = BP2*area_m2,
BC1 = BC1*area_m2,
BN1 = BN1*area_m2,
BP1 = BP1*area_m2,
BDC = BDC*area_m2,
BDN = BDN*area_m2,
BDP = BDP*area_m2,
PC = PC*volume_m3,
PN = PN*volume_m3,
PP = PP*volume_m3,
CH = CH*volume_m3,
DIN = DIN*volume_m3,
DIP = DIP*volume_m3,
lo_b = lo_b*area_m2,
PREC = PREC*area_m2,
PRBC2 = PRBC2*area_m2,
PRBC1 = PRBC1*area_m2,
PRBDC = PRBDC*area_m2,
PRPC = PRPC*area_m2,
DIN_b = DIN_b*volume_m3,
DIP_b = DIP_b*volume_m3
)
WB_S2_G3_no_Aspec <<- WB_S2_G3 %>%
transmute(
EC = EC*area_m2,
EN = EN*area_m2,
EP = EP*area_m2,
BC2 = BC2*area_m2,
BN2 = BN2*area_m2,
BP2 = BP2*area_m2,
BC1 = BC1*area_m2,
BN1 = BN1*area_m2,
BP1 = BP1*area_m2,
BDC = BDC*area_m2,
BDN = BDN*area_m2,
BDP = BDP*area_m2,
PC = PC*volume_m3,
PN = PN*volume_m3,
PP = PP*volume_m3,
CH = CH*volume_m3,
DIN = DIN*volume_m3,
DIP = DIP*volume_m3,
lo_b = lo_b*area_m2,
PREC = PREC*area_m2,
PRBC2 = PRBC2*area_m2,
PRBC1 = PRBC1*area_m2,
PRBDC = PRBDC*area_m2,
PRPC = PRPC*area_m2,
DIN_b = DIN_b*volume_m3,
DIP_b = DIP_b*volume_m3
)
WB_S2_G4_no_Aspec <<- WB_S2_G4 %>%
transmute(
EC = EC*area_m2,
EN = EN*area_m2,
EP = EP*area_m2,
BC2 = BC2*area_m2,
BN2 = BN2*area_m2,
BP2 = BP2*area_m2,
BC1 = BC1*area_m2,
BN1 = BN1*area_m2,
BP1 = BP1*area_m2,
BDC = BDC*area_m2,
BDN = BDN*area_m2,
BDP = BDP*area_m2,
PC = PC*volume_m3,
PN = PN*volume_m3,
PP = PP*volume_m3,
CH = CH*volume_m3,
DIN = DIN*volume_m3,
DIP = DIP*volume_m3,
lo_b = lo_b*area_m2,
PREC = PREC*area_m2,
PRBC2 = PRBC2*area_m2,
PRBC1 = PRBC1*area_m2,
PRBDC = PRBDC*area_m2,
PRPC = PRPC*area_m2,
DIN_b = DIN_b*volume_m3,
DIP_b = DIP_b*volume_m3
)
#Create single entries of summed area and volume for all gradient polygons used to create primary producers biomass and production single values for all gradient polygons
WB_sumarea_G1 <<- sum(WB_B_G1$area_m2);WB_sumarea_G2 <<- sum(WB_B_G2$area_m2);WB_sumarea_G3 <<- sum(WB_B_G3$area_m2);WB_sumarea_G4 <<- sum(WB_B_G4$area_m2)
WB_sumvolume_G1 <<- sum(WB_B_G1$volume_m3);WB_sumvolume_G2 <<- sum(WB_B_G2$volume_m3);WB_sumvolume_G3 <<- sum(WB_B_G3$volume_m3);WB_sumvolume_G4 <<- sum(WB_B_G4$volume_m3)
# Create summed area and volumes for all gradient polygons used later in final data frame
WB_B_sumarea_m2_AG <<- c(sum(WB_B_G1$area_m2),sum(WB_B_G2$area_m2),sum(WB_B_G3$area_m2),sum(WB_B_G4$area_m2))
WB_B_sumvolume_m3_AG <<- c(sum(WB_B_G1$volume_m3),sum(WB_B_G2$volume_m3),sum(WB_B_G3$volume_m3),sum(WB_B_G4$volume_m3))
WB_S1_sumarea_m2_AG <<- c(sum(WB_S1_G1$area_m2),sum(WB_S1_G2$area_m2),sum(WB_S1_G3$area_m2),sum(WB_S1_G4$area_m2))
WB_S1_sumvolume_m3_AG <<- c(sum(WB_S1_G1$volume_m3),sum(WB_S1_G2$volume_m3),sum(WB_S1_G3$volume_m3),sum(WB_S1_G4$volume_m3))
WB_S2_sumarea_m2_AG <<- c(sum(WB_S2_G1$area_m2),sum(WB_S2_G2$area_m2),sum(WB_S2_G3$area_m2),sum(WB_S2_G4$area_m2))
WB_S2_sumvolume_m3_AG <<- c(sum(WB_S2_G1$volume_m3),sum(WB_S2_G2$volume_m3),sum(WB_S2_G3$volume_m3),sum(WB_S2_G4$volume_m3))
#Create single value entries for all primary producers (biomass and production) for all gradient polygons and all model scenarios
WB_B_EC_AG <<- c((sum(WB_B_G1_no_Aspec$EC))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$EC))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$EC))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$EC))/WB_sumarea_G4)
WB_B_EN_AG <<- c((sum(WB_B_G1_no_Aspec$EN))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$EN))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$EN))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$EN))/WB_sumarea_G4)
WB_B_EP_AG <<- c((sum(WB_B_G1_no_Aspec$EP))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$EP))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$EP))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$EP))/WB_sumarea_G4)
WB_B_BC2_AG <<- c((sum(WB_B_G1_no_Aspec$BC2))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$BC2))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$BC2))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$BC2))/WB_sumarea_G4)
WB_B_BN2_AG <<- c((sum(WB_B_G1_no_Aspec$BN2))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$BN2))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$BN2))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$BN2))/WB_sumarea_G4)
WB_B_BP2_AG <<- c((sum(WB_B_G1_no_Aspec$BP2))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$BP2))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$BP2))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$BP2))/WB_sumarea_G4)
WB_B_BC1_AG <<- c((sum(WB_B_G1_no_Aspec$BC1))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$BC1))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$BC1))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$BC1))/WB_sumarea_G4)
WB_B_BN1_AG <<- c((sum(WB_B_G1_no_Aspec$BN1))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$BN1))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$BN1))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$BN1))/WB_sumarea_G4)
WB_B_BP1_AG <<- c((sum(WB_B_G1_no_Aspec$BP1))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$BP1))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$BP1))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$BP1))/WB_sumarea_G4)
WB_B_BDC_AG <<- c((sum(WB_B_G1_no_Aspec$BDC))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$BDC))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$BDC))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$BDC))/WB_sumarea_G4)
WB_B_BDN_AG <<- c((sum(WB_B_G1_no_Aspec$BDN))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$BDN))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$BDN))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$BDN))/WB_sumarea_G4)
WB_B_BDP_AG <<- c((sum(WB_B_G1_no_Aspec$BDP))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$BDP))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$BDP))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$BDP))/WB_sumarea_G4)
WB_B_PC_AG <<- c((sum(WB_B_G1_no_Aspec$PC))/WB_sumvolume_G1,(sum(WB_B_G2_no_Aspec$PC))/WB_sumvolume_G2,(sum(WB_B_G3_no_Aspec$PC))/WB_sumvolume_G3,(sum(WB_B_G4_no_Aspec$PC))/WB_sumvolume_G4)
WB_B_PN_AG <<- c((sum(WB_B_G1_no_Aspec$PN))/WB_sumvolume_G1,(sum(WB_B_G2_no_Aspec$PN))/WB_sumvolume_G2,(sum(WB_B_G3_no_Aspec$PN))/WB_sumvolume_G3,(sum(WB_B_G4_no_Aspec$PN))/WB_sumvolume_G4)
WB_B_PP_AG <<- c((sum(WB_B_G1_no_Aspec$PP))/WB_sumvolume_G1,(sum(WB_B_G2_no_Aspec$PP))/WB_sumvolume_G2,(sum(WB_B_G3_no_Aspec$PP))/WB_sumvolume_G3,(sum(WB_B_G4_no_Aspec$PP))/WB_sumvolume_G4)
WB_B_CH_AG <<- c((sum(WB_B_G1_no_Aspec$CH))/WB_sumvolume_G1,(sum(WB_B_G2_no_Aspec$CH))/WB_sumvolume_G2,(sum(WB_B_G3_no_Aspec$CH))/WB_sumvolume_G3,(sum(WB_B_G4_no_Aspec$CH))/WB_sumvolume_G4)
WB_B_DIN_AG <<- c((sum(WB_B_G1_no_Aspec$DIN))/WB_sumvolume_G1,(sum(WB_B_G2_no_Aspec$DIN))/WB_sumvolume_G2,(sum(WB_B_G3_no_Aspec$DIN))/WB_sumvolume_G3,(sum(WB_B_G4_no_Aspec$DIN))/WB_sumvolume_G4)
WB_B_DIP_AG <<- c((sum(WB_B_G1_no_Aspec$DIP))/WB_sumvolume_G1,(sum(WB_B_G2_no_Aspec$DIP))/WB_sumvolume_G2,(sum(WB_B_G3_no_Aspec$DIP))/WB_sumvolume_G3,(sum(WB_B_G4_no_Aspec$DIP))/WB_sumvolume_G4)
WB_B_PREC_AG <<- c((sum(WB_B_G1_no_Aspec$PREC))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$PREC))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$PREC))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$PREC))/WB_sumarea_G4)
WB_B_PRBC2_AG <<- c((sum(WB_B_G1_no_Aspec$PRBC2))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$PRBC2))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$PRBC2))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$PRBC2))/WB_sumarea_G4)
WB_B_PRBC1_AG <<- c((sum(WB_B_G1_no_Aspec$PRBC1))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$PRBC1))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$PRBC1))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$PRBC1))/WB_sumarea_G4)
WB_B_PRBDC_AG <<- c((sum(WB_B_G1_no_Aspec$PRBDC))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$PRBDC))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$PRBDC))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$PRBDC))/WB_sumarea_G4)
WB_B_PRPC_AG <<- c((sum(WB_B_G1_no_Aspec$PRPC))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$PRPC))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$PRPC))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$PRPC))/WB_sumarea_G4)
WB_B_DIN_b_AG <<- c((sum(WB_B_G1_no_Aspec$DIN_b))/WB_sumvolume_G1,(sum(WB_B_G2_no_Aspec$DIN_b))/WB_sumvolume_G2,(sum(WB_B_G3_no_Aspec$DIN_b))/WB_sumvolume_G3,(sum(WB_B_G4_no_Aspec$DIN_b))/WB_sumvolume_G4)
WB_B_DIP_b_AG <<- c((sum(WB_B_G1_no_Aspec$DIP_b))/WB_sumvolume_G1,(sum(WB_B_G2_no_Aspec$DIP_b))/WB_sumvolume_G2,(sum(WB_B_G3_no_Aspec$DIP_b))/WB_sumvolume_G3,(sum(WB_B_G4_no_Aspec$DIP_b))/WB_sumvolume_G4)
WB_S1_EC_AG <<- c((sum(WB_S1_G1_no_Aspec$EC))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$EC))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$EC))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$EC))/WB_sumarea_G4)
WB_S1_EN_AG <<- c((sum(WB_S1_G1_no_Aspec$EN))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$EN))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$EN))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$EN))/WB_sumarea_G4)
WB_S1_EP_AG <<- c((sum(WB_S1_G1_no_Aspec$EP))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$EP))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$EP))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$EP))/WB_sumarea_G4)
WB_S1_BC2_AG <<- c((sum(WB_S1_G1_no_Aspec$BC2))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$BC2))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$BC2))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$BC2))/WB_sumarea_G4)
WB_S1_BN2_AG <<- c((sum(WB_S1_G1_no_Aspec$BN2))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$BN2))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$BN2))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$BN2))/WB_sumarea_G4)
WB_S1_BP2_AG <<- c((sum(WB_S1_G1_no_Aspec$BP2))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$BP2))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$BP2))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$BP2))/WB_sumarea_G4)
WB_S1_BC1_AG <<- c((sum(WB_S1_G1_no_Aspec$BC1))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$BC1))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$BC1))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$BC1))/WB_sumarea_G4)
WB_S1_BN1_AG <<- c((sum(WB_S1_G1_no_Aspec$BN1))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$BN1))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$BN1))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$BN1))/WB_sumarea_G4)
WB_S1_BP1_AG <<- c((sum(WB_S1_G1_no_Aspec$BP1))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$BP1))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$BP1))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$BP1))/WB_sumarea_G4)
WB_S1_BDC_AG <<- c((sum(WB_S1_G1_no_Aspec$BDC))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$BDC))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$BDC))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$BDC))/WB_sumarea_G4)
WB_S1_BDN_AG <<- c((sum(WB_S1_G1_no_Aspec$BDN))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$BDN))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$BDN))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$BDN))/WB_sumarea_G4)
WB_S1_BDP_AG <<- c((sum(WB_S1_G1_no_Aspec$BDP))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$BDP))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$BDP))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$BDP))/WB_sumarea_G4)
WB_S1_PC_AG <<- c((sum(WB_S1_G1_no_Aspec$PC))/WB_sumvolume_G1,(sum(WB_S1_G2_no_Aspec$PC))/WB_sumvolume_G2,(sum(WB_S1_G3_no_Aspec$PC))/WB_sumvolume_G3,(sum(WB_S1_G4_no_Aspec$PC))/WB_sumvolume_G4)
WB_S1_PN_AG <<- c((sum(WB_S1_G1_no_Aspec$PN))/WB_sumvolume_G1,(sum(WB_S1_G2_no_Aspec$PN))/WB_sumvolume_G2,(sum(WB_S1_G3_no_Aspec$PN))/WB_sumvolume_G3,(sum(WB_S1_G4_no_Aspec$PN))/WB_sumvolume_G4)
WB_S1_PP_AG <<- c((sum(WB_S1_G1_no_Aspec$PP))/WB_sumvolume_G1,(sum(WB_S1_G2_no_Aspec$PP))/WB_sumvolume_G2,(sum(WB_S1_G3_no_Aspec$PP))/WB_sumvolume_G3,(sum(WB_S1_G4_no_Aspec$PP))/WB_sumvolume_G4)
WB_S1_CH_AG <<- c((sum(WB_S1_G1_no_Aspec$CH))/WB_sumvolume_G1,(sum(WB_S1_G2_no_Aspec$CH))/WB_sumvolume_G2,(sum(WB_S1_G3_no_Aspec$CH))/WB_sumvolume_G3,(sum(WB_S1_G4_no_Aspec$CH))/WB_sumvolume_G4)
WB_S1_DIN_AG <<- c((sum(WB_S1_G1_no_Aspec$DIN))/WB_sumvolume_G1,(sum(WB_S1_G2_no_Aspec$DIN))/WB_sumvolume_G2,(sum(WB_S1_G3_no_Aspec$DIN))/WB_sumvolume_G3,(sum(WB_S1_G4_no_Aspec$DIN))/WB_sumvolume_G4)
WB_S1_DIP_AG <<- c((sum(WB_S1_G1_no_Aspec$DIP))/WB_sumvolume_G1,(sum(WB_S1_G2_no_Aspec$DIP))/WB_sumvolume_G2,(sum(WB_S1_G3_no_Aspec$DIP))/WB_sumvolume_G3,(sum(WB_S1_G4_no_Aspec$DIP))/WB_sumvolume_G4)
WB_S1_PREC_AG <<- c((sum(WB_S1_G1_no_Aspec$PREC))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$PREC))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$PREC))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$PREC))/WB_sumarea_G4)
WB_S1_PRBC2_AG <<- c((sum(WB_S1_G1_no_Aspec$PRBC2))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$PRBC2))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$PRBC2))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$PRBC2))/WB_sumarea_G4)
WB_S1_PRBC1_AG <<- c((sum(WB_S1_G1_no_Aspec$PRBC1))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$PRBC1))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$PRBC1))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$PRBC1))/WB_sumarea_G4)
WB_S1_PRBDC_AG <<- c((sum(WB_S1_G1_no_Aspec$PRBDC))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$PRBDC))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$PRBDC))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$PRBDC))/WB_sumarea_G4)
WB_S1_PRPC_AG <<- c((sum(WB_S1_G1_no_Aspec$PRPC))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$PRPC))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$PRPC))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$PRPC))/WB_sumarea_G4)
WB_S1_DIN_b_AG <<- c((sum(WB_S1_G1_no_Aspec$DIN_b))/WB_sumvolume_G1,(sum(WB_S1_G2_no_Aspec$DIN_b))/WB_sumvolume_G2,(sum(WB_S1_G3_no_Aspec$DIN_b))/WB_sumvolume_G3,(sum(WB_S1_G4_no_Aspec$DIN_b))/WB_sumvolume_G4)
WB_S1_DIP_b_AG <<- c((sum(WB_S1_G1_no_Aspec$DIP_b))/WB_sumvolume_G1,(sum(WB_S1_G2_no_Aspec$DIP_b))/WB_sumvolume_G2,(sum(WB_S1_G3_no_Aspec$DIP_b))/WB_sumvolume_G3,(sum(WB_S1_G4_no_Aspec$DIP_b))/WB_sumvolume_G4)
WB_S2_EC_AG <<- c((sum(WB_S2_G1_no_Aspec$EC))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$EC))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$EC))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$EC))/WB_sumarea_G4)
WB_S2_EN_AG <<- c((sum(WB_S2_G1_no_Aspec$EN))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$EN))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$EN))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$EN))/WB_sumarea_G4)
WB_S2_EP_AG <<- c((sum(WB_S2_G1_no_Aspec$EP))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$EP))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$EP))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$EP))/WB_sumarea_G4)
WB_S2_BC2_AG <<- c((sum(WB_S2_G1_no_Aspec$BC2))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$BC2))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$BC2))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$BC2))/WB_sumarea_G4)
WB_S2_BN2_AG <<- c((sum(WB_S2_G1_no_Aspec$BN2))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$BN2))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$BN2))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$BN2))/WB_sumarea_G4)
WB_S2_BP2_AG <<- c((sum(WB_S2_G1_no_Aspec$BP2))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$BP2))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$BP2))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$BP2))/WB_sumarea_G4)
WB_S2_BC1_AG <<- c((sum(WB_S2_G1_no_Aspec$BC1))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$BC1))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$BC1))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$BC1))/WB_sumarea_G4)
WB_S2_BN1_AG <<- c((sum(WB_S2_G1_no_Aspec$BN1))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$BN1))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$BN1))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$BN1))/WB_sumarea_G4)
WB_S2_BP1_AG <<- c((sum(WB_S2_G1_no_Aspec$BP1))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$BP1))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$BP1))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$BP1))/WB_sumarea_G4)
WB_S2_BDC_AG <<- c((sum(WB_S2_G1_no_Aspec$BDC))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$BDC))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$BDC))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$BDC))/WB_sumarea_G4)
WB_S2_BDN_AG <<- c((sum(WB_S2_G1_no_Aspec$BDN))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$BDN))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$BDN))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$BDN))/WB_sumarea_G4)
WB_S2_BDP_AG <<- c((sum(WB_S2_G1_no_Aspec$BDP))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$BDP))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$BDP))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$BDP))/WB_sumarea_G4)
WB_S2_PC_AG <<- c((sum(WB_S2_G1_no_Aspec$PC))/WB_sumvolume_G1,(sum(WB_S2_G2_no_Aspec$PC))/WB_sumvolume_G2,(sum(WB_S2_G3_no_Aspec$PC))/WB_sumvolume_G3,(sum(WB_S2_G4_no_Aspec$PC))/WB_sumvolume_G4)
WB_S2_PN_AG <<- c((sum(WB_S2_G1_no_Aspec$PN))/WB_sumvolume_G1,(sum(WB_S2_G2_no_Aspec$PN))/WB_sumvolume_G2,(sum(WB_S2_G3_no_Aspec$PN))/WB_sumvolume_G3,(sum(WB_S2_G4_no_Aspec$PN))/WB_sumvolume_G4)
WB_S2_PP_AG <<- c((sum(WB_S2_G1_no_Aspec$PP))/WB_sumvolume_G1,(sum(WB_S2_G2_no_Aspec$PP))/WB_sumvolume_G2,(sum(WB_S2_G3_no_Aspec$PP))/WB_sumvolume_G3,(sum(WB_S2_G4_no_Aspec$PP))/WB_sumvolume_G4)
WB_S2_CH_AG <<- c((sum(WB_S2_G1_no_Aspec$CH))/WB_sumvolume_G1,(sum(WB_S2_G2_no_Aspec$CH))/WB_sumvolume_G2,(sum(WB_S2_G3_no_Aspec$CH))/WB_sumvolume_G3,(sum(WB_S2_G4_no_Aspec$CH))/WB_sumvolume_G4)
WB_S2_DIN_AG <<- c((sum(WB_S2_G1_no_Aspec$DIN))/WB_sumvolume_G1,(sum(WB_S2_G2_no_Aspec$DIN))/WB_sumvolume_G2,(sum(WB_S2_G3_no_Aspec$DIN))/WB_sumvolume_G3,(sum(WB_S2_G4_no_Aspec$DIN))/WB_sumvolume_G4)
WB_S2_DIP_AG <<- c((sum(WB_S2_G1_no_Aspec$DIP))/WB_sumvolume_G1,(sum(WB_S2_G2_no_Aspec$DIP))/WB_sumvolume_G2,(sum(WB_S2_G3_no_Aspec$DIP))/WB_sumvolume_G3,(sum(WB_S2_G4_no_Aspec$DIP))/WB_sumvolume_G4)
WB_S2_PREC_AG <<- c((sum(WB_S2_G1_no_Aspec$PREC))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$PREC))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$PREC))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$PREC))/WB_sumarea_G4)
WB_S2_PRBC2_AG <<- c((sum(WB_S2_G1_no_Aspec$PRBC2))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$PRBC2))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$PRBC2))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$PRBC2))/WB_sumarea_G4)
WB_S2_PRBC1_AG <<- c((sum(WB_S2_G1_no_Aspec$PRBC1))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$PRBC1))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$PRBC1))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$PRBC1))/WB_sumarea_G4)
WB_S2_PRBDC_AG <<- c((sum(WB_S2_G1_no_Aspec$PRBDC))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$PRBDC))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$PRBDC))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$PRBDC))/WB_sumarea_G4)
WB_S2_PRPC_AG <<- c((sum(WB_S2_G1_no_Aspec$PRPC))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$PRPC))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$PRPC))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$PRPC))/WB_sumarea_G4)
WB_S2_DIN_b_AG <<- c((sum(WB_S2_G1_no_Aspec$DIN_b))/WB_sumvolume_G1,(sum(WB_S2_G2_no_Aspec$DIN_b))/WB_sumvolume_G2,(sum(WB_S2_G3_no_Aspec$DIN_b))/WB_sumvolume_G3,(sum(WB_S2_G4_no_Aspec$DIN_b))/WB_sumvolume_G4)
WB_S2_DIP_b_AG <<- c((sum(WB_S2_G1_no_Aspec$DIP_b))/WB_sumvolume_G1,(sum(WB_S2_G2_no_Aspec$DIP_b))/WB_sumvolume_G2,(sum(WB_S2_G3_no_Aspec$DIP_b))/WB_sumvolume_G3,(sum(WB_S2_G4_no_Aspec$DIP_b))/WB_sumvolume_G4)
#Create final data frame with single values for primary producers (biomass and production) for all gradient polygons and all model scenarios. Also adds column "scenario"
WB_B_AG_ASSV <<- data.frame(WB_gradient_no,WB_B_avgDIN_AG,WB_B_avgDIP_AG,WB_B_EC_AG,WB_B_EN_AG,WB_B_EP_AG,WB_B_BC2_AG,WB_B_BN2_AG,WB_B_BP2_AG,WB_B_BC1_AG,WB_B_BN1_AG,WB_B_BP1_AG,WB_B_BDC_AG,WB_B_BDN_AG,WB_B_BDP_AG,WB_B_PC_AG,WB_B_PN_AG,WB_B_PP_AG,WB_B_CH_AG,WB_B_DIN_AG,WB_B_DIP_AG,WB_B_PREC_AG,WB_B_PRBC2_AG,WB_B_PRBC1_AG,WB_B_PRBDC_AG,WB_B_PRPC_AG,WB_B_DIN_b_AG,WB_B_DIP_b_AG) %>%
add_column(scenario = "Baseline", .before = "WB_gradient_no")
WB_S1_AG_ASSV <<- data.frame(WB_gradient_no,WB_S1_avgDIN_AG,WB_S1_avgDIP_AG,WB_S1_EC_AG,WB_S1_EN_AG,WB_S1_EP_AG,WB_S1_BC2_AG,WB_S1_BN2_AG,WB_S1_BP2_AG,WB_S1_BC1_AG,WB_S1_BN1_AG,WB_S1_BP1_AG,WB_S1_BDC_AG,WB_S1_BDN_AG,WB_S1_BDP_AG,WB_S1_PC_AG,WB_S1_PN_AG,WB_S1_PP_AG,WB_S1_CH_AG,WB_S1_DIN_AG,WB_S1_DIP_AG,WB_S1_PREC_AG,WB_S1_PRBC2_AG,WB_S1_PRBC1_AG,WB_S1_PRBDC_AG,WB_S1_PRPC_AG,WB_S1_DIN_b_AG,WB_S1_DIP_b_AG) %>%
add_column(scenario = "N-30%", .before = "WB_gradient_no")
WB_S2_AG_ASSV <<- data.frame(WB_gradient_no,WB_S2_avgDIN_AG,WB_S2_avgDIP_AG,WB_S2_EC_AG,WB_S2_EN_AG,WB_S2_EP_AG,WB_S2_BC2_AG,WB_S2_BN2_AG,WB_S2_BP2_AG,WB_S2_BC1_AG,WB_S2_BN1_AG,WB_S2_BP1_AG,WB_S2_BDC_AG,WB_S2_BDN_AG,WB_S2_BDP_AG,WB_S2_PC_AG,WB_S2_PN_AG,WB_S2_PP_AG,WB_S2_CH_AG,WB_S2_DIN_AG,WB_S2_DIP_AG,WB_S2_PREC_AG,WB_S2_PRBC2_AG,WB_S2_PRBC1_AG,WB_S2_PRBDC_AG,WB_S2_PRPC_AG,WB_S2_DIN_b_AG,WB_S2_DIP_b_AG) %>%
add_column(scenario = "P-30%", .before = "WB_gradient_no")
#Rename all column headers to enable merging of tables
WB_B <<- WB_B_AG_ASSV %>%
rename(
AvgDIN = WB_B_avgDIN_AG,
AvgDIP = WB_B_avgDIP_AG,
EC = WB_B_EC_AG,
EN = WB_B_EN_AG,
EP = WB_B_EP_AG,
BC2 = WB_B_BC2_AG,
BN2 = WB_B_BN2_AG,
BP2 = WB_B_BP2_AG,
BC1 = WB_B_BC1_AG,
BN1 = WB_B_BN1_AG,
BP1 = WB_B_BP1_AG,
BDC = WB_B_BDC_AG,
BDN = WB_B_BDN_AG,
BDP = WB_B_BDP_AG,
PC = WB_B_PC_AG,
PN = WB_B_PN_AG,
PP = WB_B_PP_AG,
CH = WB_B_CH_AG,
DIN = WB_B_DIN_AG,
DIP = WB_B_DIP_AG,
PREC = WB_B_PREC_AG,
PRBC2 = WB_B_PRBC2_AG,
PRBC1 = WB_B_PRBC1_AG,
PRBDC = WB_B_PRBDC_AG,
PRPC = WB_B_PRPC_AG,
DIN_b = WB_B_DIN_b_AG,
DIP_b = WB_B_DIP_b_AG
)
WB_S1 <<- WB_S1_AG_ASSV %>%
rename(
AvgDIN = WB_S1_avgDIN_AG,
AvgDIP = WB_S1_avgDIP_AG,
EC = WB_S1_EC_AG,
EN = WB_S1_EN_AG,
EP = WB_S1_EP_AG,
BC2 = WB_S1_BC2_AG,
BN2 = WB_S1_BN2_AG,
BP2 = WB_S1_BP2_AG,
BC1 = WB_S1_BC1_AG,
BN1 = WB_S1_BN1_AG,
BP1 = WB_S1_BP1_AG,
BDC = WB_S1_BDC_AG,
BDN = WB_S1_BDN_AG,
BDP = WB_S1_BDP_AG,
PC = WB_S1_PC_AG,
PN = WB_S1_PN_AG,
PP = WB_S1_PP_AG,
CH = WB_S1_CH_AG,
DIN = WB_S1_DIN_AG,
DIP = WB_S1_DIP_AG,
PREC = WB_S1_PREC_AG,
PRBC2 = WB_S1_PRBC2_AG,
PRBC1 = WB_S1_PRBC1_AG,
PRBDC = WB_S1_PRBDC_AG,
PRPC = WB_S1_PRPC_AG,
DIN_b = WB_S1_DIN_b_AG,
DIP_b = WB_S1_DIP_b_AG
)
WB_S2 <<- WB_S2_AG_ASSV %>%
rename(
AvgDIN = WB_S2_avgDIN_AG,
AvgDIP = WB_S2_avgDIP_AG,
EC = WB_S2_EC_AG,
EN = WB_S2_EN_AG,
EP = WB_S2_EP_AG,
BC2 = WB_S2_BC2_AG,
BN2 = WB_S2_BN2_AG,
BP2 = WB_S2_BP2_AG,
BC1 = WB_S2_BC1_AG,
BN1 = WB_S2_BN1_AG,
BP1 = WB_S2_BP1_AG,
BDC = WB_S2_BDC_AG,
BDN = WB_S2_BDN_AG,
BDP = WB_S2_BDP_AG,
PC = WB_S2_PC_AG,
PN = WB_S2_PN_AG,
PP = WB_S2_PP_AG,
CH = WB_S2_CH_AG,
DIN = WB_S2_DIN_AG,
DIP = WB_S2_DIP_AG,
PREC = WB_S2_PREC_AG,
PRBC2 = WB_S2_PRBC2_AG,
PRBC1 = WB_S2_PRBC1_AG,
PRBDC = WB_S2_PRBDC_AG,
PRPC = WB_S2_PRPC_AG,
DIN_b = WB_S2_DIN_b_AG,
DIP_b = WB_S2_DIP_b_AG
)
#Merging tables
WB_AS_AG_FINAL <<- bind_rows(WB_B,WB_S1,WB_S2)
#Install and load GGplot2, gridExtra and ggpubr packages
if(!require(ggplot2)) install.packages("ggplot2")
library(ggplot2)
if(!require(gridExtra)) install.packages("gridExtra")
library(gridExtra)
if(!require(ggpubr)) install.packages("ggpubr")
library(ggpubr)
if(!require(devtools)) install.packages("devtools")
library(devtools)
if(!require(patchwork)) install.packages("patchwork")
library(patchwork)
#Production VS. AvgDIN plots
WB_PREC_vs_AvgDIN <<- ggplot(WB_AS_AG_FINAL, aes(x = AvgDIN, y = PREC, group = scenario)) +
geom_line(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
xlab('Avg. DIN ('*'g C'~m^-2*')') +
ylab('Eelgrass production ('*' g C'~m^-2~GS^-1*')') +
scale_x_continuous(limits = c(0,0.45), breaks = seq(0,0.45, by = 0.1), labels = scales::number_format(accuracy = 0.05)) +
scale_y_continuous(limits = c(0,25), breaks = seq(0,25, by = 5), labels = scales::number_format(accuracy = 1))
WB_panel_PREC_vs_AvgDIN <<- WB_PREC_vs_AvgDIN + theme_pub()
WB_PRBC1_vs_AvgDIN <<- ggplot(WB_AS_AG_FINAL, aes(x = AvgDIN, y = PRBC1, group = scenario)) +
geom_line(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
xlab('Avg. DIN ('*'g C'~m^-2*')') +
ylab('Opp. macroalgae production ('*' g C'~m^-2~GS^-1*')') +
scale_x_continuous(limits = c(0,0.45), breaks = seq(0,0.45, by = 0.1), labels = scales::number_format(accuracy = 0.05)) +
scale_y_continuous(limits = c(0,35), breaks = seq(0,35, by = 5), labels = scales::number_format(accuracy = 1))
WB_panel_PRBC1_vs_AvgDIN <<- WB_PRBC1_vs_AvgDIN + theme_pub()
WB_PRPC_vs_AvgDIN <<- ggplot(WB_AS_AG_FINAL, aes(x = AvgDIN, y = PRPC, group = scenario)) +
geom_line(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
xlab('Avg. DIN ('*'g C'~m^-2*')') +
ylab('Phytoplankton production ('*' g C'~m^-2~GS^-1*')') +
scale_x_continuous(limits = c(0,0.45), breaks = seq(0,0.45, by = 0.1), labels = scales::number_format(accuracy = 0.05)) +
scale_y_continuous(limits = c(0,70), breaks = seq(0,70, by = 10), labels = scales::number_format(accuracy = 1))
WB_panel_PRPC_vs_AvgDIN <<- WB_PRPC_vs_AvgDIN + theme_pub()
WB_panel_PREC_vs_AvgDIN + labs(x = '', y = expression('Eelgrass production'~(g~C*~m^-2~GS^-1))) +
WB_panel_PRBC1_vs_AvgDIN + labs(x = '', y = expression('Opp. macroalgae production'~(g~C*~m^-2~GS^-1))) +
WB_panel_PRPC_vs_AvgDIN +
plot_layout(ncol = 1) +
plot_annotation(tag_levels = 'A') +
ggsave(filename = 'WB_PRXX_vs_AvgDIN_panel.tiff',
width = 15,
height = 35,
units = 'cm',
device='tiff',
dpi=300)
#Vertical profiles of primary producers and benthic light for all gradient polygons for each scenario
WB_B_G1 <<- add_column(WB_B_G1, scenario = 'Baseline', WB_gradient_no = 'no. 1', .before = 'upper')
WB_B_G2 <<- add_column(WB_B_G2, scenario = 'Baseline', WB_gradient_no = 'no. 2', .before = 'upper')
WB_B_G3 <<- add_column(WB_B_G3, scenario = 'Baseline', WB_gradient_no = 'no. 3', .before = 'upper')
WB_B_G4 <<- add_column(WB_B_G4, scenario = 'Baseline', WB_gradient_no = 'no. 4', .before = 'upper')
WB_B_AG <<- bind_rows(WB_B_G1, WB_B_G2, WB_B_G3, WB_B_G4)
WB_S1_G1 <<- add_column(WB_S1_G1, scenario = 'N-30%', WB_gradient_no = 'no. 1', .before = 'upper')
WB_S1_G2 <<- add_column(WB_S1_G2, scenario = 'N-30%', WB_gradient_no = 'no. 2', .before = 'upper')
WB_S1_G3 <<- add_column(WB_S1_G3, scenario = 'N-30%', WB_gradient_no = 'no. 3', .before = 'upper')
WB_S1_G4 <<- add_column(WB_S1_G4, scenario = 'N-30%', WB_gradient_no = 'no. 4', .before = 'upper')
WB_S1_AG <<- bind_rows(WB_S1_G1, WB_S1_G2, WB_S1_G3, WB_S1_G4)
WB_S2_G1 <<- add_column(WB_S2_G1, scenario = 'P-30%', WB_gradient_no = 'no. 1', .before = 'upper')
WB_S2_G2 <<- add_column(WB_S2_G2, scenario = 'P-30%', WB_gradient_no = 'no. 2', .before = 'upper')
WB_S2_G3 <<- add_column(WB_S2_G3, scenario = 'P-30%', WB_gradient_no = 'no. 3', .before = 'upper')
WB_S2_G4 <<- add_column(WB_S2_G4, scenario = 'P-30%', WB_gradient_no = 'no. 4', .before = 'upper')
WB_S2_AG <<- bind_rows(WB_S2_G1, WB_S2_G2, WB_S2_G3, WB_S2_G4)
#HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH WB_B VERTICAL PROFILES HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH
WB_B_PREC_vs_lower <<- ggplot(WB_B_AG, aes(x = PREC, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Eelgrass production'~(g~C*~m^-2~GS^-1)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,120), breaks = seq(0,120, by = 20), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-6,0), breaks = seq(-6,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_B_PREC_vs_lower + theme_pub()
WB_B_PRBC1_vs_lower <<- ggplot(WB_B_AG, aes(x = PRBC1, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Opp. macroalgae Production'~(g~C*~m^-2~GS^-1)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,45), breaks = seq(0,45, by = 5), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-8,0), breaks = seq(-8,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_B_PRBC1_vs_lower + theme_pub()
WB_B_PRBC2_vs_lower <<- ggplot(WB_B_AG, aes(x = PRBC2, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Perennial macroalgae Production'~(g~C*~m^-2~GS^-1)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,18), breaks = seq(0,18, by = 3), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-8,0), breaks = seq(-8,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_B_PRBC2_vs_lower + theme_pub()
WB_B_PRBDC_vs_lower <<- ggplot(WB_B_AG, aes(x = PRBDC, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Benthic diatom Production'~(g~C*~m^-2~GS^-1)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,50), breaks = seq(0,50, by = 5), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-10,0), breaks = seq(-10,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_B_PRBDC_vs_lower + theme_pub()
WB_B_PC_vs_lower <<- ggplot(WB_B_AG, aes(x = PC, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Phytoplankton biomass'~(g~C*~m^-3)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,2), breaks = seq(0,2, by = 0.3), labels = scales::number_format(accuracy = 0.1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 3), labels = scales::number_format(accuracy = 1))
WB_B_PC_vs_lower + theme_pub()
WB_B_lo_b_vs_lower <<- ggplot(WB_B_AG, aes(x = lo_b, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Benthic light'~(mu*E*~m^-2*~s^-1)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,650), breaks = seq(0,650, by = 100), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 3), labels = scales::number_format(accuracy = 1))
WB_B_lo_b_vs_lower + theme_pub()
WB_B_PREC_vs_lower+ theme_pub() + labs(x = expression('Eelgrass production'~(g~C*~m^-2~GS^-1)), y = '') + theme(legend.justification = c(1.1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_B_PRBC1_vs_lower+ theme_pub() + labs(x = expression('Opp. macroalgae production'~(g~C*~m^-2~GS^-1)), y = '') + theme(legend.justification = c(1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_B_PRBC2_vs_lower+ theme_pub() + theme(legend.justification = c(1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_B_PRBDC_vs_lower+ theme_pub() + labs(x = expression('Benthic diatom production'~(g~C*~m^-2~GS^-1)), y = '') + theme(legend.justification = c(1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_B_PC_vs_lower+ theme_pub() + labs(x = expression('Phytoplankton biomass'~(g~C*~m^-3)), y = '') + theme(legend.justification = c(1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_B_lo_b_vs_lower + theme_pub() + labs(x = expression('Benthic light'~(mu*E*~m^-2*~s^-1)), y = '') + theme(legend.justification = c(1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
plot_layout(ncol = 2) +
plot_annotation(tag_levels = 'A') +
ggsave(filename = 'WB_B_AG_production.tiff',
width = 30,
height = 30,
units = 'cm',
device='tiff',
dpi=300)
#HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH WB_S1 VERTICAL PROFILES HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH
WB_S1_PREC_vs_lower <<- ggplot(WB_S1_AG, aes(x = PREC, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Eelgrass Production'~(g~C*~m^-2~GS^-1)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,120), breaks = seq(0,120, by = 20), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-6,0), breaks = seq(-6,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_S1_PREC_vs_lower + theme_pub()
WB_S1_PRBC1_vs_lower <<- ggplot(WB_S1_AG, aes(x = PRBC1, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Opp. macroalgae Production'~(g~C*~m^-2~GS^-1)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,45), breaks = seq(0,45, by = 5), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-8,0), breaks = seq(-8,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_S1_PRBC1_vs_lower + theme_pub()
WB_S1_PRBC2_vs_lower <<- ggplot(WB_S1_AG, aes(x = PRBC2, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Perennial macroalgae Production'~(g~C*~m^-2~GS^-1)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,18), breaks = seq(0,18, by = 3), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-8,0), breaks = seq(-8,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_S1_PRBC2_vs_lower + theme_pub()
WB_S1_PRBDC_vs_lower <<- ggplot(WB_S1_AG, aes(x = PRBDC, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Benthic diatom Production'~(g~C*~m^-2~GS^-1)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,50), breaks = seq(0,50, by = 5), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-10,0), breaks = seq(-10,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_S1_PRBDC_vs_lower + theme_pub()
WB_S1_PC_vs_lower <<- ggplot(WB_S1_AG, aes(x = PC, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Phytoplankton biomass'~(g~C*~m^-3)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,2), breaks = seq(0,2, by = 0.3), labels = scales::number_format(accuracy = 0.1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 3), labels = scales::number_format(accuracy = 1))
WB_S1_PC_vs_lower + theme_pub()
WB_S1_lo_b_vs_lower <<- ggplot(WB_S1_AG, aes(x = lo_b, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Benthic light'~(mu*E*~m^-2*~s^-1)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,650), breaks = seq(0,650, by = 100), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 3), labels = scales::number_format(accuracy = 1))
WB_S1_lo_b_vs_lower + theme_pub()
WB_S1_PREC_vs_lower+ theme_pub() + labs(x = expression('Eelgrass production'~(g~C*~m^-2~GS^-1)), y = '') + theme(legend.justification = c(1.1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_S1_PRBC1_vs_lower+ theme_pub() + labs(x = expression('Opp. macroalgae production'~(g~C*~m^-2~GS^-1)), y = '') + theme(legend.justification = c(1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_S1_PRBC2_vs_lower+ theme_pub() + theme(legend.justification = c(1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_S1_PRBDC_vs_lower+ theme_pub() + labs(x = expression('Benthic diatom production'~(g~C*~m^-2~GS^-1)), y = '') + theme(legend.justification = c(1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_S1_PC_vs_lower+ theme_pub() + labs(x = expression('Phytoplankton biomass'~(g~C*~m^-3)), y = '') + theme(legend.justification = c(1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_S1_lo_b_vs_lower + theme_pub() + labs(x = expression('Benthic light'~(mu*E*~m^-2*~s^-1)), y = '') + theme(legend.justification = c(1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
plot_layout(ncol = 2) +
plot_annotation(tag_levels = 'A') +
ggsave(filename = 'WB_S1_AG_production.tiff',
width = 30,
height = 30,
units = 'cm',
device='tiff',
dpi=300)
#HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH WB_S2 VERTICAL PROFILES HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH
WB_S2_PREC_vs_lower <<- ggplot(WB_S2_AG, aes(x = PREC, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Eelgrass Production'~(g~C*~m^-2~GS^-1)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,120), breaks = seq(0,120, by = 20), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-6,0), breaks = seq(-6,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_S2_PREC_vs_lower + theme_pub()
WB_S2_PRBC1_vs_lower <<- ggplot(WB_S2_AG, aes(x = PRBC1, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Opp. macroalgae Production'~(g~C*~m^-2~GS^-1)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,45), breaks = seq(0,45, by = 5), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-8,0), breaks = seq(-8,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_S2_PRBC1_vs_lower + theme_pub()
WB_S2_PRBC2_vs_lower <<- ggplot(WB_S2_AG, aes(x = PRBC2, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Perennial macroalgae Production'~(g~C*~m^-2~GS^-1)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,18), breaks = seq(0,18, by = 3), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-8,0), breaks = seq(-8,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_S2_PRBC2_vs_lower + theme_pub()
WB_S2_PRBDC_vs_lower <<- ggplot(WB_S2_AG, aes(x = PRBDC, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Benthic diatom Production'~(g~C*~m^-2~GS^-1)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,50), breaks = seq(0,50, by = 5), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-10,0), breaks = seq(-10,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_S2_PRBDC_vs_lower + theme_pub()
WB_S2_PC_vs_lower <<- ggplot(WB_S2_AG, aes(x = PC, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Phytoplankton biomass'~(g~C*~m^-3)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,2), breaks = seq(0,2, by = 0.3), labels = scales::number_format(accuracy = 0.1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 3), labels = scales::number_format(accuracy = 1))
WB_S2_PC_vs_lower + theme_pub()
WB_S2_lo_b_vs_lower <<- ggplot(WB_S2_AG, aes(x = lo_b, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Benthic light'~(mu*E*~m^-2*~s^-1)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,650), breaks = seq(0,650, by = 100), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 3), labels = scales::number_format(accuracy = 1))
WB_S2_lo_b_vs_lower + theme_pub()
WB_S2_PREC_vs_lower+ theme_pub() + labs(x = expression('Eelgrass production'~(g~C*~m^-2~GS^-1)), y = '') + theme(legend.justification = c(1.1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_S2_PRBC1_vs_lower+ theme_pub() + labs(x = expression('Opp. macroalgae production'~(g~C*~m^-2~GS^-1)), y = '') + theme(legend.justification = c(1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_S2_PRBC2_vs_lower+ theme_pub() + theme(legend.justification = c(1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_S2_PRBDC_vs_lower+ theme_pub() + labs(x = expression('Benthic diatom production'~(g~C*~m^-2~GS^-1)), y = '') + theme(legend.justification = c(1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_S2_PC_vs_lower+ theme_pub() + labs(x = expression('Phytoplankton biomass'~(g~C*~m^-3)), y = '') + theme(legend.justification = c(1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_S2_lo_b_vs_lower + theme_pub() + labs(x = expression('Benthic light'~(mu*E*~m^-2*~s^-1)), y = '') + theme(legend.justification = c(1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
plot_layout(ncol = 2) +
plot_annotation(tag_levels = 'A') +
ggsave(filename = 'WB_S2_AG_production.tiff',
width = 30,
height = 30,
units = 'cm',
device='tiff',
dpi=300)
#HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH WB_ASAG_PREC VERTICAL PROFILES HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH
WB_G1_AS <<- bind_rows(WB_B_G1, WB_S1_G1, WB_S2_G1)
WB_G2_AS <<- bind_rows(WB_B_G2, WB_S1_G2, WB_S2_G2)
WB_G3_AS <<- bind_rows(WB_B_G3, WB_S1_G3, WB_S2_G3)
WB_G4_AS <<- bind_rows(WB_B_G4, WB_S1_G4, WB_S2_G4)
WB_AS_G1_PREC <<- ggplot(WB_G1_AS, aes(x = PREC, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = expression(' Eelgrass production'~(g~C*~m^-2*~GS^-1)), y = '') +
scale_x_continuous(limits = c(0,120), breaks = seq(0,120, by = 20), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-6,0), breaks = seq(-6,0, by = 1), labels = scales::number_format(accuracy = 1))
WB_AS_G1_PREC + theme_pub()
WB_AS_G2_PREC <<- ggplot(WB_G2_AS, aes(x = PREC, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = '') +
scale_x_continuous(limits = c(0,120), breaks = seq(0,120, by = 20), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-6,0), breaks = seq(-6,0, by = 1), labels = scales::number_format(accuracy = 1))
WB_AS_G2_PREC + theme_pub()
WB_AS_G3_PREC <<- ggplot(WB_G3_AS, aes(x = PREC, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = expression(' Depth'~(m))) +
scale_x_continuous(limits = c(0,120), breaks = seq(0,120, by = 20), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-6,0), breaks = seq(-6,0, by = 1), labels = scales::number_format(accuracy = 1))
WB_AS_G3_PREC + theme_pub()
WB_AS_G4_PREC <<- ggplot(WB_G4_AS, aes(x = PREC, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = '') +
scale_x_continuous(limits = c(0,120), breaks = seq(0,120, by = 20), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-6,0), breaks = seq(-6,0, by = 1), labels = scales::number_format(accuracy = 1))
WB_AS_G4_PREC + theme_pub()
WB_AS_G1_PREC+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G2_PREC+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G3_PREC+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G4_PREC+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
plot_layout(ncol = 2) +
plot_annotation(tag_levels = '1', tag_prefix = 'no. ') +
ggsave(filename = 'WB_ASAG_PREC_vs_lower.tiff',
width = 30,
height = 30,
units = 'cm',
device='tiff',
dpi=300)
#HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH WB_ASAG_PRBC1 VERTICAL PROFILES HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH
WB_AS_G1_PRBC1 <<- ggplot(WB_G1_AS, aes(x = PRBC1, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = expression(' Opp. macroalgae production'~(g~C*~m^-2*~GS^-1)), y = '') +
scale_x_continuous(limits = c(0,45), breaks = seq(0,45, by = 5), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-8,0), breaks = seq(-8,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_AS_G1_PRBC1 + theme_pub()
WB_AS_G2_PRBC1 <<- ggplot(WB_G2_AS, aes(x = PRBC1, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = '') +
scale_x_continuous(limits = c(0,45), breaks = seq(0,45, by = 5), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-8,0), breaks = seq(-8,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_AS_G2_PRBC1 + theme_pub()
WB_AS_G3_PRBC1 <<- ggplot(WB_G3_AS, aes(x = PRBC1, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = expression(' Depth'~(m))) +
scale_x_continuous(limits = c(0,45), breaks = seq(0,45, by = 5), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-8,0), breaks = seq(-8,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_AS_G3_PRBC1 + theme_pub()
WB_AS_G4_PRBC1 <<- ggplot(WB_G4_AS, aes(x = PRBC1, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = '') +
scale_x_continuous(limits = c(0,45), breaks = seq(0,45, by = 5), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-8,0), breaks = seq(-8,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_AS_G4_PRBC1 + theme_pub()
WB_AS_G1_PRBC1+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G2_PRBC1+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G3_PRBC1+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G4_PRBC1+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
plot_layout(ncol = 2) +
plot_annotation(tag_levels = '1', tag_prefix = 'no. ') +
ggsave(filename = 'WB_ASAG_PRBC1_vs_lower.tiff',
width = 30,
height = 30,
units = 'cm',
device='tiff',
dpi=300)
#HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH WB_ASAG_PRBC2 VERTICAL PROFILES HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH
WB_AS_G1_PRBC2 <<- ggplot(WB_G1_AS, aes(x = PRBC2, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = expression(' Perennial macroalgae production'~(g~C*~m^-2*~GS^-1)), y = '') +
scale_x_continuous(limits = c(0,18), breaks = seq(0,18, by = 3), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-8,0), breaks = seq(-8,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_AS_G1_PRBC2 + theme_pub()
WB_AS_G2_PRBC2 <<- ggplot(WB_G2_AS, aes(x = PRBC2, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = '') +
scale_x_continuous(limits = c(0,18), breaks = seq(0,18, by = 3), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-8,0), breaks = seq(-8,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_AS_G2_PRBC2 + theme_pub()
WB_AS_G3_PRBC2 <<- ggplot(WB_G3_AS, aes(x = PRBC2, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = expression(' Depth'~(m))) +
scale_x_continuous(limits = c(0,18), breaks = seq(0,18, by = 3), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-8,0), breaks = seq(-8,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_AS_G3_PRBC2 + theme_pub()
WB_AS_G4_PRBC2 <<- ggplot(WB_G4_AS, aes(x = PRBC2, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = '') +
scale_x_continuous(limits = c(0,18), breaks = seq(0,18, by = 3), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-8,0), breaks = seq(-8,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_AS_G4_PRBC2 + theme_pub()
WB_AS_G1_PRBC2+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G2_PRBC2+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G3_PRBC2+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G4_PRBC2+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
plot_layout(ncol = 2) +
plot_annotation(tag_levels = '1', tag_prefix = 'no. ') +
ggsave(filename = 'WB_ASAG_PRBC2_vs_lower.tiff',
width = 30,
height = 30,
units = 'cm',
device='tiff',
dpi=300)
#HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH WB_ASAG_PRBDC VERTICAL PROFILES HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH
WB_AS_G1_PRBDC <<- ggplot(WB_G1_AS, aes(x = PRBDC, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = expression(' Benthic diatom production'~(g~C*~m^-2*~GS^-1)), y = '') +
scale_x_continuous(limits = c(0,55), breaks = seq(0,55, by = 5), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-10,0), breaks = seq(-10,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_AS_G1_PRBDC + theme_pub()
WB_AS_G2_PRBDC <<- ggplot(WB_G2_AS, aes(x = PRBDC, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = '') +
scale_x_continuous(limits = c(0,55), breaks = seq(0,55, by = 5), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-10,0), breaks = seq(-10,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_AS_G2_PRBDC + theme_pub()
WB_AS_G3_PRBDC <<- ggplot(WB_G3_AS, aes(x = PRBDC, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = expression(' Depth'~(m))) +
scale_x_continuous(limits = c(0,55), breaks = seq(0,55, by = 5), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-10,0), breaks = seq(-10,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_AS_G3_PRBDC + theme_pub()
WB_AS_G4_PRBDC <<- ggplot(WB_G4_AS, aes(x = PRBDC, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = '') +
scale_x_continuous(limits = c(0,55), breaks = seq(0,55, by = 5), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-10,0), breaks = seq(-10,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_AS_G4_PRBDC + theme_pub()
WB_AS_G1_PRBDC+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G2_PRBDC+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G3_PRBDC+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G4_PRBDC+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
plot_layout(ncol = 2) +
plot_annotation(tag_levels = '1', tag_prefix = 'no. ') +
ggsave(filename = 'WB_ASAG_PRBDC_vs_lower.tiff',
width = 30,
height = 30,
units = 'cm',
device='tiff',
dpi=300)
#HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH WB_ASAG_PC VERTICAL PROFILES HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH
WB_AS_G1_PC <<- ggplot(WB_G1_AS, aes(x = PC, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = expression(' Phytoplakton biomass'~(g~C*~m^-3)), y = '') +
scale_x_continuous(limits = c(0,2), breaks = seq(0,2, by = 0.3), labels = scales::number_format(accuracy = 0.1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 3), labels = scales::number_format(accuracy = 1))
WB_AS_G1_PC + theme_pub()
WB_AS_G2_PC <<- ggplot(WB_G2_AS, aes(x = PC, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = '') +
scale_x_continuous(limits = c(0,2), breaks = seq(0,2, by = 0.3), labels = scales::number_format(accuracy = 0.1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 3), labels = scales::number_format(accuracy = 1))
WB_AS_G2_PC + theme_pub()
WB_AS_G3_PC <<- ggplot(WB_G3_AS, aes(x = PC, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = expression(' Depth'~(m))) +
scale_x_continuous(limits = c(0,2), breaks = seq(0,2, by = 0.3), labels = scales::number_format(accuracy = 0.1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 3), labels = scales::number_format(accuracy = 1))
WB_AS_G3_PC + theme_pub()
WB_AS_G4_PC <<- ggplot(WB_G4_AS, aes(x = PC, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = '') +
scale_x_continuous(limits = c(0,2), breaks = seq(0,2, by = 0.3), labels = scales::number_format(accuracy = 0.1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 3), labels = scales::number_format(accuracy = 1))
WB_AS_G4_PC + theme_pub()
WB_AS_G1_PC+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G2_PC+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G3_PC+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G4_PC+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
plot_layout(ncol = 2) +
plot_annotation(tag_levels = '1', tag_prefix = 'no. ') +
ggsave(filename = 'WB_ASAG_PC_vs_lower.tiff',
width = 30,
height = 30,
units = 'cm',
device='tiff',
dpi=300)
#HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH WB_ASAG_lo_b VERTICAL PROFILES HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH
WB_AS_G1_lo_b <<- ggplot(WB_G1_AS, aes(x = lo_b, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = expression(' Benthic light'~(mu*E*~m^-2*~s^-1)), y = '') +
scale_x_continuous(limits = c(0,700), breaks = seq(0,700, by = 100), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 3), labels = scales::number_format(accuracy = 1))
WB_AS_G1_lo_b + theme_pub()
WB_AS_G2_lo_b <<- ggplot(WB_G2_AS, aes(x = lo_b, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = '') +
scale_x_continuous(limits = c(0,700), breaks = seq(0,700, by = 100), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 3), labels = scales::number_format(accuracy = 1))
WB_AS_G2_lo_b + theme_pub()
WB_AS_G3_lo_b <<- ggplot(WB_G3_AS, aes(x = lo_b, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = expression(' Depth'~(m))) +
scale_x_continuous(limits = c(0,700), breaks = seq(0,700, by = 100), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 3), labels = scales::number_format(accuracy = 1))
WB_AS_G3_lo_b + theme_pub()
WB_AS_G4_lo_b <<- ggplot(WB_G4_AS, aes(x = lo_b, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = '') +
scale_x_continuous(limits = c(0,700), breaks = seq(0,700, by = 100), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 3), labels = scales::number_format(accuracy = 1))
WB_AS_G4_lo_b + theme_pub()
WB_AS_G1_lo_b+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G2_lo_b+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G3_lo_b+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G4_lo_b+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
plot_layout(ncol = 2) +
plot_annotation(tag_levels = '1', tag_prefix = 'no. ') +
ggsave(filename = 'WB_ASAG_lo_b_vs_lower.tiff',
width = 30,
height = 30,
units = 'cm',
device='tiff',
dpi=300)
#HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH Benthic Vs. WC production HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH
WB_benthic_vs_WC_prod <<- WB_AS_AG_FINAL %>%
select(scenario, WB_gradient_no, PREC, PRBC1, PRBC2, PRBDC, PRPC) %>%
mutate(benthic_prod = PREC + PRBC1 + PRBC2 + PRBDC,
WC_prod = PRPC,
prod_100_pct = benthic_prod + WC_prod,
benthic_prod_pct = benthic_prod / prod_100_pct * 100,
WC_prod_pct = WC_prod / prod_100_pct *100,
control = benthic_prod_pct + WC_prod_pct
) %>%
rename('Benthic prod.' = benthic_prod_pct,
'WC prod.' = WC_prod_pct) %>%
select(scenario, WB_gradient_no, 'Benthic prod.', 'WC prod.') %>%
gather(prod_ID, prod_pct, 'Benthic prod.':'WC prod.')
WB_benthic_vs_WC_prod_plot <<- ggplot(data = WB_benthic_vs_WC_prod, aes(x = WB_gradient_no, y = prod_pct, group = interaction(scenario, prod_ID))) +
geom_path(size = 1, aes(linetype = prod_ID)) +
geom_point(size = 3, aes(shape = scenario, color = scenario, fill = scenario)) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = 'Distance (km)', y = 'Relative growth season production\n(% of total production)') +
scale_x_discrete(limits = c('0-6', '6-8', '8-11', '11-14'), expand = c(0,0.05)) +
scale_y_continuous(limits = c(0,105), breaks = seq(0,105, by = 10), labels = scales::number_format(accuracy = 1), expand = c(0,0.05))
WB_benthic_vs_WC_prod_plot + theme_pub() +
theme(
legend.text = element_text(size = 20),
legend.background = element_blank(),
axis.title = element_text(size = 20),
axis.text = element_text(size = 20),
legend.justification = c(1.3,2),
legend.direction = 'horizontal',
legend.spacing.x = unit(0.05, 'cm'),
axis.text.x = element_text(angle = -45, vjust = 1, hjust = 0),
plot.margin = unit(c(5,50,0,0), "pt"),
) +
ggsave(filename = 'WB_Benthic_vs_WC_prod.tiff',
width = 30,
height = 20,
units = 'cm',
device='tiff',
dpi=300)
#HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH Monod tabels and monod vertical profiles HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH
EC_vmax_C <<- 0.04; EC_kmDIN_C <<- 0.09; EC_kmDIP_C <<- 0.005; EC_kmlight_C <<- 355
BC1_vmax_C <<- 0.23; BC1_kmDIN_C <<- 0.06; BC1_kmDIP_C <<- 0.015; BC1_kmlight_C <<- 200
BC2_vmax_C <<- 0.09; BC2_kmDIN_C <<- 0.02; BC2_kmDIP_C <<- 0.01; BC2_kmlight_C <<- 170
BDC_vmax_C <<- 1; BDC_kmDIN_C <<- 0.025; BDC_kmDIP_C <<- 0.005; BDC_kmlight_C <<- 30
PC_vmax_C <<- 2; PC_kmDIN_C <<- 0.03; PC_kmDIP_C <<- 0.005; PC_kmlight_C <<- 75
WB_monod_table <<- bind_rows(WB_B_AG, WB_S1_AG, WB_S2_AG) %>%
select(scenario, WB_gradient_no, lower, EC, EN, EP, BC1, BN1, BP1, BC2, BN2, BP2, BDC, BDN, BDP, PC, PN, PP, DIN, DIP, lo_b, DIN_b, DIP_b) %>%
rename(
EC_kmDIN = EC,
EC_kmDIP = EN,
EC_kmlight = EP,
BC1_kmDIN = BC1,
BC1_kmDIP = BN1,
BC1_kmlight = BP1,
BC2_kmDIN = BC2,
BC2_kmDIP = BN2,
BC2_kmlight = BP2,
BDC_kmDIN = BDC,
BDC_kmDIP = BDN,
BDC_kmlight = BDP,
PC_kmDIN = PC,
PC_kmDIP = PN,
PC_kmlight = PP
) %>%
mutate(
EC_kmDIN = DIN_b / (DIN_b + EC_kmDIN_C),
EC_kmDIP = DIP_b / (DIP_b+EC_kmDIP_C),
EC_kmlight = lo_b / (lo_b + EC_kmlight_C),
BC1_kmDIN = DIN_b / (DIN_b + BC1_kmDIN_C),
BC1_kmDIP = DIP_b / (DIP_b+BC1_kmDIP_C),
BC1_kmlight = lo_b / (lo_b + BC1_kmlight_C),
BC2_kmDIN = DIN_b / (DIN_b + BC2_kmDIN_C),
BC2_kmDIP = DIP_b / (DIP_b+BC2_kmDIP_C),
BC2_kmlight = lo_b / (lo_b + BC2_kmlight_C),
BDC_kmDIN = DIN_b / (DIN_b + BDC_kmDIN_C),
BDC_kmDIP = DIP_b / (DIP_b+BDC_kmDIP_C),
BDC_kmlight = lo_b / (lo_b + BDC_kmlight_C),
PC_kmDIN = DIN / (DIN + PC_kmDIN_C),
PC_kmDIP = DIP / (DIP+PC_kmDIP_C),
PC_kmlight = lo_b / (lo_b + PC_kmlight_C)
)
#Monod EC
WB_monod_EC_light <<- ggplot(subset(WB_monod_table, scenario %in% c('Baseline', 'N-30%')), aes(x = EC_kmlight, y = lower, group = interaction(scenario, WB_gradient_no))) +
geom_path(size = 1.5, aes(color = scenario)) +
geom_point(size = 3, aes(shape = WB_gradient_no, fill = WB_gradient_no)) +
scale_shape_manual(values = c(21, 24, 22, 22, 24)) +
scale_color_manual(values = c('black', 'orange', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white', 'white')) +
scale_x_continuous(limits = c(0,0.65), breaks = seq(0,0.65, by = 0.1), labels = scales::number_format(accuracy = 0.1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 2), labels = scales::number_format(accuracy = 1)) +
labs(x = 'Eelgrass growth limitation by benthic light\n', y = 'Depth (m)')
WB_monod_EC_light + theme_pub() +
theme(
plot.margin = unit(c(1,1,1,1), "pt"),
legend.justification = c(1,1.6),
legend.direction = 'vertical',
legend.box = 'vertical' ,
legend.text = element_text(size = 22)
)
WB_monod_EC_light + theme_pub() + theme(plot.margin = unit(c(15,1,1,1), "pt"), axis.title = element_text(size = 22), axis.text = element_text(size = 18), legend.justification = c(1.05,6),
legend.direction = 'vertical',
legend.box = 'vertical',
legend.text = element_text(size = 22),
legend.spacing.x = unit(0.05, 'cm'),) +
plot_layout(ncol = 1) +
ggsave(filename = 'WB_monod_EC_light.tiff',
width = 30,
height = 40,
units = 'cm',
device='tiff',
dpi=300)
#Monod BC1
WB_monod_BC1_DIN <<- ggplot(subset(WB_monod_table, scenario %in% c('Baseline', 'N-30%')), aes(x = BC1_kmDIN, y = lower, group = interaction(scenario, WB_gradient_no))) +
geom_path(size = 1.2, aes(color = scenario)) +
geom_point(size = 2, aes(shape = WB_gradient_no, fill = WB_gradient_no)) +
scale_shape_manual(values = c(21, 24, 22, 22, 24)) +
scale_color_manual(values = c('black', 'orange', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white', 'white')) +
scale_x_continuous(limits = c(0,1.1), breaks = seq(0,1.1, by = 0.2), labels = scales::number_format(accuracy = 0.1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 2), labels = scales::number_format(accuracy = 1)) +
labs(x = 'Opp. macroalgae growth inhibition by DIN\n', y = '')
WB_monod_BC1_DIN + theme_pub() +
theme(
plot.margin = unit(c(20,1,1,1), "pt"),
legend.justification = c(1,1.6),
legend.direction = 'vertical',
legend.box = 'vertical',
)
WB_monod_BC1_DIP <<- ggplot(subset(WB_monod_table, scenario %in% c('Baseline', 'N-30%')), aes(x = BC1_kmDIP, y = lower, group = interaction(scenario, WB_gradient_no))) +
geom_path(size = 1.2, aes(color = scenario)) +
geom_point(size = 2, aes(shape = WB_gradient_no, fill = WB_gradient_no)) +
scale_shape_manual(values = c(21, 24, 22, 22, 24)) +
scale_color_manual(values = c('black', 'orange', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white', 'white')) +
scale_x_continuous(limits = c(0.50,1.09), breaks = seq(0.50,1.09, by = 0.1), labels = scales::number_format(accuracy = 0.1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 2), labels = scales::number_format(accuracy = 1)) +
labs(x = 'Opp. macroalgae growth inhibition by DIP\n', y = 'Depth (m)')
WB_monod_BC1_DIP + theme_pub() +
theme(
plot.margin = unit(c(20,1,1,1), "pt"),
legend.justification = c(1,1.6),
legend.direction = 'vertical',
legend.box = 'vertical'
)
WB_monod_BC1_light <<- ggplot(subset(WB_monod_table, scenario %in% c('Baseline', 'N-30%')), aes(x = BC1_kmlight, y = lower, group = interaction(scenario, WB_gradient_no))) +
geom_path(size = 1.2, aes(color = scenario)) +
geom_point(size = 2, aes(shape = WB_gradient_no, fill = WB_gradient_no)) +
scale_shape_manual(values = c(21, 24, 22, 22, 24)) +
scale_color_manual(values = c('black', 'orange', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white', 'white')) +
scale_x_continuous(limits = c(0,0.8), breaks = seq(0,0.8, by = 0.1), labels = scales::number_format(accuracy = 0.1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 2), labels = scales::number_format(accuracy = 1)) +
labs(x = 'Opp. macroalgae growth inhibition by benthic light\n', y = '')
WB_monod_BC1_light + theme_pub() +
theme(
plot.margin = unit(c(20,1,1,1), "pt"),
legend.justification = c(1,1.6),
legend.direction = 'vertical',
legend.box = 'vertical'
)
WB_monod_BC1_DIN + theme_pub() + theme(plot.margin = unit(c(15,1,1,1), "pt"), axis.title = element_text(size = 18), legend.justification = c(1,1.75),
legend.direction = 'vertical',
legend.box = 'vertical') +
WB_monod_BC1_DIP + theme_pub() + theme(plot.margin = unit(c(15,1,1,1), "pt"), axis.title = element_text(size = 18), legend.justification = c(1,1.75),
legend.direction = 'vertical',
legend.box = 'vertical') +
WB_monod_BC1_light + theme_pub() + theme(plot.margin = unit(c(15,1,1,1), "pt"), axis.title = element_text(size = 18), legend.justification = c(1,1.75),
legend.direction = 'vertical',
legend.box = 'vertical') +
plot_layout(ncol = 1) +
ggsave(filename = 'WB_monod_BC1.tiff',
width = 20,
height = 40,
units = 'cm',
device='tiff',
dpi=300)
#Monod PC
WB_monod_PC_DIN <<- ggplot(subset(WB_monod_table, scenario %in% c('Baseline', 'N-30%')), aes(x = PC_kmDIN, y = lower, group = interaction(scenario, WB_gradient_no))) +
geom_path(size = 1.2, aes(color = scenario)) +
geom_point(size = 2, aes(shape = WB_gradient_no, fill = WB_gradient_no)) +
scale_shape_manual(values = c(21, 24, 22, 22, 24)) +
scale_color_manual(values = c('black', 'orange', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white', 'white')) +
scale_x_continuous(limits = c(0,1.1), breaks = seq(0,1.1, by = 0.2), labels = scales::number_format(accuracy = 0.1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 2), labels = scales::number_format(accuracy = 1)) +
labs(x = 'Phytoplankton growth inhibition by DIN\n', y = '')
WB_monod_PC_DIN + theme_pub() +
theme(
plot.margin = unit(c(20,1,1,1), "pt"),
legend.justification = c(1,1.6),
legend.direction = 'vertical',
legend.box = 'vertical',
)
WB_monod_PC_DIP <<- ggplot(subset(WB_monod_table, scenario %in% c('Baseline', 'N-30%')), aes(x = PC_kmDIP, y = lower, group = interaction(scenario, WB_gradient_no))) +
geom_path(size = 1.2, aes(color = scenario)) +
geom_point(size = 2, aes(shape = WB_gradient_no, fill = WB_gradient_no)) +
scale_shape_manual(values = c(21, 24, 22, 22, 24)) +
scale_color_manual(values = c('black', 'orange', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white', 'white')) +
scale_x_continuous(limits = c(0.75,1), breaks = seq(0.75,1, by = 0.05), labels = scales::number_format(accuracy = 0.01), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 2), labels = scales::number_format(accuracy = 1)) +
labs(x = 'Phytoplankton growth inhibition by DIP\n', y = 'Depth (m)')
WB_monod_PC_DIP + theme_pub() +
theme(
plot.margin = unit(c(20,1,1,1), "pt"),
legend.justification = c(1,1.6),
legend.direction = 'vertical',
legend.box = 'vertical'
)
WB_monod_PC_light <<- ggplot(subset(WB_monod_table, scenario %in% c('Baseline', 'N-30%')), aes(x = PC_kmlight, y = lower, group = interaction(scenario, WB_gradient_no))) +
geom_path(size = 1.2, aes(color = scenario)) +
geom_point(size = 2, aes(shape = WB_gradient_no, fill = WB_gradient_no)) +
scale_shape_manual(values = c(21, 24, 22, 22, 24)) +
scale_color_manual(values = c('black', 'orange', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white', 'white')) +
scale_x_continuous(limits = c(0,0.9), breaks = seq(0,0.9, by = 0.1), labels = scales::number_format(accuracy = 0.1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 2), labels = scales::number_format(accuracy = 1)) +
labs(x = 'Phytoplankton growth inhibition by benthic light\n', y = '')
WB_monod_PC_light + theme_pub() +
theme(
plot.margin = unit(c(20,1,1,1), "pt"),
legend.justification = c(1,1.6),
legend.direction = 'vertical',
legend.box = 'vertical'
)
WB_monod_PC_DIN + theme_pub() + theme(plot.margin = unit(c(15,1,1,1), "pt"), axis.title = element_text(size = 18), legend.justification = c(1,1.75),
legend.direction = 'vertical',
legend.box = 'vertical') +
WB_monod_PC_DIP + theme_pub() + theme(plot.margin = unit(c(15,1,1,1), "pt"), axis.title = element_text(size = 18), legend.justification = c(5.5,1.75),
legend.direction = 'vertical',
legend.box = 'vertical') +
WB_monod_PC_light + theme_pub() + theme(plot.margin = unit(c(15,1,1,1), "pt"), axis.title = element_text(size = 18), legend.justification = c(1,1.75),
legend.direction = 'vertical',
legend.box = 'vertical') +
plot_layout(ncol = 1) +
ggsave(filename = 'WB_monod_PC.tiff',
width = 20,
height = 40,
units = 'cm',
device='tiff',
dpi=300)
#Monod BC2
WB_monod_BC2_DIN <<- ggplot(subset(WB_monod_table, scenario %in% c('Baseline', 'N-30%')), aes(x = BC2_kmDIN, y = lower, group = interaction(scenario, WB_gradient_no))) +
geom_path(size = 1.2, aes(color = scenario)) +
geom_point(size = 2, aes(shape = WB_gradient_no, fill = WB_gradient_no)) +
scale_shape_manual(values = c(21, 24, 22, 22, 24)) +
scale_color_manual(values = c('black', 'orange', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white', 'white')) +
scale_x_continuous(limits = c(0.15,1), breaks = seq(0.15,1, by = 0.1), labels = scales::number_format(accuracy = 0.1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 2), labels = scales::number_format(accuracy = 1)) +
labs(x = 'Perennial macroalgae growth inhibition by DIN\n', y = '')
WB_monod_BC2_DIN + theme_pub() +
theme(
plot.margin = unit(c(20,1,1,1), "pt"),
legend.justification = c(1,1.6),
legend.direction = 'vertical',
legend.box = 'vertical',
)
WB_monod_BC2_DIP <<- ggplot(subset(WB_monod_table, scenario %in% c('Baseline', 'N-30%')), aes(x = BC2_kmDIP, y = lower, group = interaction(scenario, WB_gradient_no))) +
geom_path(size = 1.2, aes(color = scenario)) +
geom_point(size = 2, aes(shape = WB_gradient_no, fill = WB_gradient_no)) +
scale_shape_manual(values = c(21, 24, 22, 22, 24)) +
scale_color_manual(values = c('black', 'orange', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white', 'white')) +
scale_x_continuous(limits = c(0.5,1), breaks = seq(0.5,1, by = 0.1), labels = scales::number_format(accuracy = 0.1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 2), labels = scales::number_format(accuracy = 1)) +
labs(x = 'Perennial macroalgae growth inhibition by DIP\n', y = 'Depth (m)')
WB_monod_BC2_DIP + theme_pub() +
theme(
plot.margin = unit(c(20,1,1,1), "pt"),
legend.justification = c(1,1.6),
legend.direction = 'vertical',
legend.box = 'vertical'
)
WB_monod_BC2_light <<- ggplot(subset(WB_monod_table, scenario %in% c('Baseline', 'N-30%')), aes(x = BC2_kmlight, y = lower, group = interaction(scenario, WB_gradient_no))) +
geom_path(size = 1.2, aes(color = scenario)) +
geom_point(size = 2, aes(shape = WB_gradient_no, fill = WB_gradient_no)) +
scale_shape_manual(values = c(21, 24, 22, 22, 24)) +
scale_color_manual(values = c('black', 'orange', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white', 'white')) +
scale_x_continuous(limits = c(0,0.8), breaks = seq(0,0.8, by = 0.1), labels = scales::number_format(accuracy = 0.1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 2), labels = scales::number_format(accuracy = 1)) +
labs(x = 'Perennial macroalgae growth inhibition by benthic light\n', y = '')
WB_monod_BC2_light + theme_pub() +
theme(
plot.margin = unit(c(20,1,1,1), "pt"),
legend.justification = c(1,1.6),
legend.direction = 'vertical',
legend.box = 'vertical'
)
WB_monod_BC2_DIN + theme_pub() + theme(plot.margin = unit(c(15,1,1,1), "pt"), axis.title = element_text(size = 18), legend.justification = c(5.5,1.75),
legend.direction = 'vertical',
legend.box = 'vertical') +
WB_monod_BC2_DIP + theme_pub() + theme(plot.margin = unit(c(15,1,1,1), "pt"), axis.title = element_text(size = 18), legend.justification = c(5.5,1.75),
legend.direction = 'vertical',
legend.box = 'vertical') +
WB_monod_BC2_light + theme_pub() + theme(plot.margin = unit(c(15,1,1,1), "pt"), axis.title = element_text(size = 18), legend.justification = c(1,1.75),
legend.direction = 'vertical',
legend.box = 'vertical') +
plot_layout(ncol = 1) +
ggsave(filename = 'WB_monod_BC2.tiff',
width = 20,
height = 40,
units = 'cm',
device='tiff',
dpi=300)
}
milee16/GIS.Tool.mkl documentation built on Jan. 17, 2021, 1:17 a.m.
R Package Documentation
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Defines functions gistoolS3G4
Documented in gistoolS3G4
#' Gradient analysis of WB
#'
#' Uses Model data for WB divided into five gradient polygons. Model scenarios are "Baseline", "N-30%" and "P-30%"
#' @param WBData Tables of gradient polygons
#' @return Vertical profiles and Monod growth kinetics in various plots
#' @export
gistoolS3G4 <- function() {
if(!require(ggplot2)) install.packages("ggplot2")
if(!require(extrafont)) install.packages("extrafont")
library(ggplot2)
library(extrafont)
font_import()
loadfonts(device = "win")
fonts()
theme_pub <<- function(base_size = 14, base_family = "Times New Roman",
line_size = 0.25, ...) {
half_line <<- base_size / 2
small_rel <<- 0.8
small_size <<- small_rel * base_size
theme_bw(base_size = base_size, base_family = base_family, ...) %+replace%
theme(
rect = element_rect(fill = NA, color = "black", size = 1.5),
text = element_text(family = base_family, face = "plain",
colour = "black", size = base_size, hjust = 0.5,
vjust = 0.5, angle = 0, lineheight = 0.9,
margin = ggplot2::margin(), debug = F),
axis.text = element_text(size = base_size),
axis.text.x = element_text(margin = ggplot2::margin(t = small_size/4),
vjust = 1),
axis.text.y = element_text(margin = ggplot2::margin(r = small_size/4),
hjust = 1),
axis.title.x = element_text(margin = ggplot2::margin(t = small_size,
b = small_size)),
axis.title.y = element_text(angle = 90,
margin = ggplot2::margin(r = small_size,
l = small_size/4)),
axis.ticks = element_line(colour = "black", size = line_size+0.75),
axis.ticks.length = unit(0.25, 'lines'),
axis.line = element_line(colour = "black", size = line_size),
axis.line.x = element_line(colour = "black", size = line_size),
axis.line.y = element_line(colour = "black", size = line_size),
legend.spacing = unit(base_size/4, "pt"),
legend.key = element_blank(),
legend.key.size = unit(1.5 * base_size, "pt"),
legend.key.width = unit(1.5 * base_size, 'pt'),
legend.text = element_text(size = base_size),
legend.title = element_blank(),
legend.position = c(1,1),
legend.box = 'horizontal',
legend.justification = c(1,0.9),
plot.tag.position = c(0.18,0.93),
plot.tag = element_text(size = 22),
panel.spacing = unit(1, "lines"),
panel.background = element_blank(),
panel.border = element_rect(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
strip.text = element_text(size = base_size),
strip.background = element_rect(fill = NA, colour = "black", size = 0.125),
strip.text.x = element_text(face = 'bold', hjust = 0,
margin = ggplot2::margin(b = small_size/2,
t = small_size/4)),
strip.text.y = element_text(angle = -90, face = 'bold',
margin = ggplot2::margin(l = small_size/2,
r = small_size/4)),
plot.margin = unit(c(5,5,0,0), "pt"),
plot.background = element_blank(),
plot.title = element_text(face = "bold", size = 1.2 * base_size,
margin = ggplot2::margin(b = half_line),
hjust = 0)
)
}
if(!require(tidyverse)) install.packages("tidyverse")
library(tidyverse)
#WB_B
#Rename Columns in WB_B_G1
WB_B_G1 <<- WB_B_G1 %>%
arrange(desc(upper)) %>%
rename(
area_km2 = X_COL3,
area_m2 = Sum_area_m2_,
volume_m3 = Sum_volume_m3_,
EC = X_COL6,
EN = X_COL7,
EP = X_COL8,
BC2 = X_COL9,
BN2 = X_COL10,
BP2 = X_COL11,
BC1 = X_COL12,
BN1 = X_COL13,
BP1 = X_COL14,
BDC = X_COL15,
BDN = X_COL16,
BDP = X_COL17,
PC = X_COL18,
PN = X_COL19,
PP = X_COL20,
CH = X_COL21,
DIN = X_COL22,
DIP = X_COL23,
lo_b = X_COL24,
AvgSD = Avg_SD_,
PREC = X_COL26,
PRBC2 = X_COL27,
PRBC1 = X_COL28,
PRBDC = X_COL29,
PRPC = X_COL30,
DIN_b = X_COL31,
DIP_b = X_COL32
)
#Rename Columns in WB_B_G2
WB_B_G2 <<- WB_B_G2 %>%
arrange(desc(upper)) %>%
rename(
area_km2 = X_COL3,
area_m2 = Sum_area_m2_,
volume_m3 = Sum_volume_m3_,
EC = X_COL6,
EN = X_COL7,
EP = X_COL8,
BC2 = X_COL9,
BN2 = X_COL10,
BP2 = X_COL11,
BC1 = X_COL12,
BN1 = X_COL13,
BP1 = X_COL14,
BDC = X_COL15,
BDN = X_COL16,
BDP = X_COL17,
PC = X_COL18,
PN = X_COL19,
PP = X_COL20,
CH = X_COL21,
DIN = X_COL22,
DIP = X_COL23,
lo_b = X_COL24,
AvgSD = Avg_SD_,
PREC = X_COL26,
PRBC2 = X_COL27,
PRBC1 = X_COL28,
PRBDC = X_COL29,
PRPC = X_COL30,
DIN_b = X_COL31,
DIP_b = X_COL32
)
#Rename Columns in WB_B_G3
WB_B_G3 <<- WB_B_G3 %>%
arrange(desc(upper)) %>%
rename(
area_km2 = X_COL3,
area_m2 = Sum_area_m2_,
volume_m3 = Sum_volume_m3_,
EC = X_COL6,
EN = X_COL7,
EP = X_COL8,
BC2 = X_COL9,
BN2 = X_COL10,
BP2 = X_COL11,
BC1 = X_COL12,
BN1 = X_COL13,
BP1 = X_COL14,
BDC = X_COL15,
BDN = X_COL16,
BDP = X_COL17,
PC = X_COL18,
PN = X_COL19,
PP = X_COL20,
CH = X_COL21,
DIN = X_COL22,
DIP = X_COL23,
lo_b = X_COL24,
AvgSD = Avg_SD_,
PREC = X_COL26,
PRBC2 = X_COL27,
PRBC1 = X_COL28,
PRBDC = X_COL29,
PRPC = X_COL30,
DIN_b = X_COL31,
DIP_b = X_COL32
)
#Rename Columns in WB_B_G4
WB_B_G4 <<- WB_B_G4 %>%
arrange(desc(upper)) %>%
rename(
area_km2 = X_COL3,
area_m2 = Sum_area_m2_,
volume_m3 = Sum_volume_m3_,
EC = X_COL6,
EN = X_COL7,
EP = X_COL8,
BC2 = X_COL9,
BN2 = X_COL10,
BP2 = X_COL11,
BC1 = X_COL12,
BN1 = X_COL13,
BP1 = X_COL14,
BDC = X_COL15,
BDN = X_COL16,
BDP = X_COL17,
PC = X_COL18,
PN = X_COL19,
PP = X_COL20,
CH = X_COL21,
DIN = X_COL22,
DIP = X_COL23,
lo_b = X_COL24,
AvgSD = Avg_SD_,
PREC = X_COL26,
PRBC2 = X_COL27,
PRBC1 = X_COL28,
PRBDC = X_COL29,
PRPC = X_COL30,
DIN_b = X_COL31,
DIP_b = X_COL32
)
#WB_S1
#Rename Columns in WB_S1_G1
WB_S1_G1 <<- WB_S1_G1 %>%
arrange(desc(upper)) %>%
rename(
area_km2 = X_COL3,
area_m2 = Sum_area_m2_,
volume_m3 = Sum_volume_m3_,
EC = X_COL6,
EN = X_COL7,
EP = X_COL8,
BC2 = X_COL9,
BN2 = X_COL10,
BP2 = X_COL11,
BC1 = X_COL12,
BN1 = X_COL13,
BP1 = X_COL14,
BDC = X_COL15,
BDN = X_COL16,
BDP = X_COL17,
PC = X_COL18,
PN = X_COL19,
PP = X_COL20,
CH = X_COL21,
DIN = X_COL22,
DIP = X_COL23,
lo_b = X_COL24,
AvgSD = Avg_SD_,
PREC = X_COL26,
PRBC2 = X_COL27,
PRBC1 = X_COL28,
PRBDC = X_COL29,
PRPC = X_COL30,
DIN_b = X_COL31,
DIP_b = X_COL32
)
#Rename Columns in WB_S1_G2
WB_S1_G2 <<- WB_S1_G2 %>%
arrange(desc(upper)) %>%
rename(
area_km2 = X_COL3,
area_m2 = Sum_area_m2_,
volume_m3 = Sum_volume_m3_,
EC = X_COL6,
EN = X_COL7,
EP = X_COL8,
BC2 = X_COL9,
BN2 = X_COL10,
BP2 = X_COL11,
BC1 = X_COL12,
BN1 = X_COL13,
BP1 = X_COL14,
BDC = X_COL15,
BDN = X_COL16,
BDP = X_COL17,
PC = X_COL18,
PN = X_COL19,
PP = X_COL20,
CH = X_COL21,
DIN = X_COL22,
DIP = X_COL23,
lo_b = X_COL24,
AvgSD = Avg_SD_,
PREC = X_COL26,
PRBC2 = X_COL27,
PRBC1 = X_COL28,
PRBDC = X_COL29,
PRPC = X_COL30,
DIN_b = X_COL31,
DIP_b = X_COL32
)
#Rename Columns in WB_S1_G3
WB_S1_G3 <<- WB_S1_G3 %>%
arrange(desc(upper)) %>%
rename(
area_km2 = X_COL3,
area_m2 = Sum_area_m2_,
volume_m3 = Sum_volume_m3_,
EC = X_COL6,
EN = X_COL7,
EP = X_COL8,
BC2 = X_COL9,
BN2 = X_COL10,
BP2 = X_COL11,
BC1 = X_COL12,
BN1 = X_COL13,
BP1 = X_COL14,
BDC = X_COL15,
BDN = X_COL16,
BDP = X_COL17,
PC = X_COL18,
PN = X_COL19,
PP = X_COL20,
CH = X_COL21,
DIN = X_COL22,
DIP = X_COL23,
lo_b = X_COL24,
AvgSD = Avg_SD_,
PREC = X_COL26,
PRBC2 = X_COL27,
PRBC1 = X_COL28,
PRBDC = X_COL29,
PRPC = X_COL30,
DIN_b = X_COL31,
DIP_b = X_COL32
)
#Rename Columns in WB_S1_G4
WB_S1_G4 <<- WB_S1_G4 %>%
arrange(desc(upper)) %>%
rename(
area_km2 = X_COL3,
area_m2 = Sum_area_m2_,
volume_m3 = Sum_volume_m3_,
EC = X_COL6,
EN = X_COL7,
EP = X_COL8,
BC2 = X_COL9,
BN2 = X_COL10,
BP2 = X_COL11,
BC1 = X_COL12,
BN1 = X_COL13,
BP1 = X_COL14,
BDC = X_COL15,
BDN = X_COL16,
BDP = X_COL17,
PC = X_COL18,
PN = X_COL19,
PP = X_COL20,
CH = X_COL21,
DIN = X_COL22,
DIP = X_COL23,
lo_b = X_COL24,
AvgSD = Avg_SD_,
PREC = X_COL26,
PRBC2 = X_COL27,
PRBC1 = X_COL28,
PRBDC = X_COL29,
PRPC = X_COL30,
DIN_b = X_COL31,
DIP_b = X_COL32
)
#WB_S2
#Rename Columns in WB_S2_G1
WB_S2_G1 <<- WB_S2_G1 %>%
arrange(desc(upper)) %>%
rename(
area_km2 = X_COL3,
area_m2 = Sum_area_m2_,
volume_m3 = Sum_volume_m3_,
EC = X_COL6,
EN = X_COL7,
EP = X_COL8,
BC2 = X_COL9,
BN2 = X_COL10,
BP2 = X_COL11,
BC1 = X_COL12,
BN1 = X_COL13,
BP1 = X_COL14,
BDC = X_COL15,
BDN = X_COL16,
BDP = X_COL17,
PC = X_COL18,
PN = X_COL19,
PP = X_COL20,
CH = X_COL21,
DIN = X_COL22,
DIP = X_COL23,
lo_b = X_COL24,
AvgSD = Avg_SD_,
PREC = X_COL26,
PRBC2 = X_COL27,
PRBC1 = X_COL28,
PRBDC = X_COL29,
PRPC = X_COL30,
DIN_b = X_COL31,
DIP_b = X_COL32
)
#Rename Columns in WB_S2_G2
WB_S2_G2 <<- WB_S2_G2 %>%
arrange(desc(upper)) %>%
rename(
area_km2 = X_COL3,
area_m2 = Sum_area_m2_,
volume_m3 = Sum_volume_m3_,
EC = X_COL6,
EN = X_COL7,
EP = X_COL8,
BC2 = X_COL9,
BN2 = X_COL10,
BP2 = X_COL11,
BC1 = X_COL12,
BN1 = X_COL13,
BP1 = X_COL14,
BDC = X_COL15,
BDN = X_COL16,
BDP = X_COL17,
PC = X_COL18,
PN = X_COL19,
PP = X_COL20,
CH = X_COL21,
DIN = X_COL22,
DIP = X_COL23,
lo_b = X_COL24,
AvgSD = Avg_SD_,
PREC = X_COL26,
PRBC2 = X_COL27,
PRBC1 = X_COL28,
PRBDC = X_COL29,
PRPC = X_COL30,
DIN_b = X_COL31,
DIP_b = X_COL32
)
#Rename Columns in WB_S2_G3
WB_S2_G3 <<- WB_S2_G3 %>%
arrange(desc(upper)) %>%
rename(
area_km2 = X_COL3,
area_m2 = Sum_area_m2_,
volume_m3 = Sum_volume_m3_,
EC = X_COL6,
EN = X_COL7,
EP = X_COL8,
BC2 = X_COL9,
BN2 = X_COL10,
BP2 = X_COL11,
BC1 = X_COL12,
BN1 = X_COL13,
BP1 = X_COL14,
BDC = X_COL15,
BDN = X_COL16,
BDP = X_COL17,
PC = X_COL18,
PN = X_COL19,
PP = X_COL20,
CH = X_COL21,
DIN = X_COL22,
DIP = X_COL23,
lo_b = X_COL24,
AvgSD = Avg_SD_,
PREC = X_COL26,
PRBC2 = X_COL27,
PRBC1 = X_COL28,
PRBDC = X_COL29,
PRPC = X_COL30,
DIN_b = X_COL31,
DIP_b = X_COL32
)
#Rename Columns in WB_S2_G4
WB_S2_G4 <<- WB_S2_G4 %>%
arrange(desc(upper)) %>%
rename(
area_km2 = X_COL3,
area_m2 = Sum_area_m2_,
volume_m3 = Sum_volume_m3_,
EC = X_COL6,
EN = X_COL7,
EP = X_COL8,
BC2 = X_COL9,
BN2 = X_COL10,
BP2 = X_COL11,
BC1 = X_COL12,
BN1 = X_COL13,
BP1 = X_COL14,
BDC = X_COL15,
BDN = X_COL16,
BDP = X_COL17,
PC = X_COL18,
PN = X_COL19,
PP = X_COL20,
CH = X_COL21,
DIN = X_COL22,
DIP = X_COL23,
lo_b = X_COL24,
AvgSD = Avg_SD_,
PREC = X_COL26,
PRBC2 = X_COL27,
PRBC1 = X_COL28,
PRBDC = X_COL29,
PRPC = X_COL30,
DIN_b = X_COL31,
DIP_b = X_COL32
)
#Create WB_gradient_no entries and Avg DIN/DIP for all scenarios and gradient polygons
WB_gradient_no <<- c(1:4)
WB_B_avgDIN_AG <<- c(mean(WB_B_G1$DIN),mean(WB_B_G2$DIN),mean(WB_B_G3$DIN),mean(WB_B_G4$DIN))
WB_B_avgDIP_AG <<- c(mean(WB_B_G1$DIP),mean(WB_B_G2$DIP),mean(WB_B_G3$DIP),mean(WB_B_G4$DIP))
WB_B_avgDIN_b_AG <<- c(mean(WB_B_G1$DIN_b),mean(WB_B_G2$DIN_b),mean(WB_B_G3$DIN_b),mean(WB_B_G4$DIN_b))
WB_B_avgDIP_b_AG <<- c(mean(WB_B_G1$DIP_b),mean(WB_B_G2$DIP_b),mean(WB_B_G3$DIP_b),mean(WB_B_G4$DIP_b))
WB_S1_avgDIN_AG <<- c(mean(WB_S1_G1$DIN),mean(WB_S1_G2$DIN),mean(WB_S1_G3$DIN),mean(WB_S1_G4$DIN))
WB_S1_avgDIP_AG <<- c(mean(WB_S1_G1$DIP),mean(WB_S1_G2$DIP),mean(WB_S1_G3$DIP),mean(WB_S1_G4$DIP))
WB_S1_avgDIN_b_AG <<- c(mean(WB_S1_G1$DIN_b),mean(WB_S1_G2$DIN_b),mean(WB_S1_G3$DIN_b),mean(WB_S1_G4$DIN_b))
WB_S1_avgDIP_b_AG <<- c(mean(WB_S1_G1$DIP_b),mean(WB_S1_G2$DIP_b),mean(WB_S1_G3$DIP_b),mean(WB_S1_G4$DIP_b))
WB_S2_avgDIN_AG <<- c(mean(WB_S2_G1$DIN),mean(WB_S2_G2$DIN),mean(WB_S2_G3$DIN),mean(WB_S2_G4$DIN))
WB_S2_avgDIP_AG <<- c(mean(WB_S2_G1$DIP),mean(WB_S2_G2$DIP),mean(WB_S2_G3$DIP),mean(WB_S2_G4$DIP))
WB_S2_avgDIN_b_AG <<- c(mean(WB_S2_G1$DIN_b),mean(WB_S2_G2$DIN_b),mean(WB_S2_G3$DIN_b),mean(WB_S2_G4$DIN_b))
WB_S2_avgDIP_b_AG <<- c(mean(WB_S2_G1$DIP_b),mean(WB_S2_G2$DIP_b),mean(WB_S2_G3$DIP_b),mean(WB_S2_G4$DIP_b))
#Transmute area-specific WB tables to non area-specific tables to use in creating single values data frame for all gradient polygons later on
WB_B_G1_no_Aspec <<- WB_B_G1 %>%
transmute(
EC = EC*area_m2,
EN = EN*area_m2,
EP = EP*area_m2,
BC2 = BC2*area_m2,
BN2 = BN2*area_m2,
BP2 = BP2*area_m2,
BC1 = BC1*area_m2,
BN1 = BN1*area_m2,
BP1 = BP1*area_m2,
BDC = BDC*area_m2,
BDN = BDN*area_m2,
BDP = BDP*area_m2,
PC = PC*volume_m3,
PN = PN*volume_m3,
PP = PP*volume_m3,
CH = CH*volume_m3,
DIN = DIN*volume_m3,
DIP = DIP*volume_m3,
lo_b = lo_b*area_m2,
PREC = PREC*area_m2,
PRBC2 = PRBC2*area_m2,
PRBC1 = PRBC1*area_m2,
PRBDC = PRBDC*area_m2,
PRPC = PRPC*area_m2,
DIN_b = DIN_b*volume_m3,
DIP_b = DIP_b*volume_m3
)
WB_B_G2_no_Aspec <<- WB_B_G2 %>%
transmute(
EC = EC*area_m2,
EN = EN*area_m2,
EP = EP*area_m2,
BC2 = BC2*area_m2,
BN2 = BN2*area_m2,
BP2 = BP2*area_m2,
BC1 = BC1*area_m2,
BN1 = BN1*area_m2,
BP1 = BP1*area_m2,
BDC = BDC*area_m2,
BDN = BDN*area_m2,
BDP = BDP*area_m2,
PC = PC*volume_m3,
PN = PN*volume_m3,
PP = PP*volume_m3,
CH = CH*volume_m3,
DIN = DIN*volume_m3,
DIP = DIP*volume_m3,
lo_b = lo_b*area_m2,
PREC = PREC*area_m2,
PRBC2 = PRBC2*area_m2,
PRBC1 = PRBC1*area_m2,
PRBDC = PRBDC*area_m2,
PRPC = PRPC*area_m2,
DIN_b = DIN_b*volume_m3,
DIP_b = DIP_b*volume_m3
)
WB_B_G3_no_Aspec <<- WB_B_G3 %>%
transmute(
EC = EC*area_m2,
EN = EN*area_m2,
EP = EP*area_m2,
BC2 = BC2*area_m2,
BN2 = BN2*area_m2,
BP2 = BP2*area_m2,
BC1 = BC1*area_m2,
BN1 = BN1*area_m2,
BP1 = BP1*area_m2,
BDC = BDC*area_m2,
BDN = BDN*area_m2,
BDP = BDP*area_m2,
PC = PC*volume_m3,
PN = PN*volume_m3,
PP = PP*volume_m3,
CH = CH*volume_m3,
DIN = DIN*volume_m3,
DIP = DIP*volume_m3,
lo_b = lo_b*area_m2,
PREC = PREC*area_m2,
PRBC2 = PRBC2*area_m2,
PRBC1 = PRBC1*area_m2,
PRBDC = PRBDC*area_m2,
PRPC = PRPC*area_m2,
DIN_b = DIN_b*volume_m3,
DIP_b = DIP_b*volume_m3
)
WB_B_G4_no_Aspec <<- WB_B_G4 %>%
transmute(
EC = EC*area_m2,
EN = EN*area_m2,
EP = EP*area_m2,
BC2 = BC2*area_m2,
BN2 = BN2*area_m2,
BP2 = BP2*area_m2,
BC1 = BC1*area_m2,
BN1 = BN1*area_m2,
BP1 = BP1*area_m2,
BDC = BDC*area_m2,
BDN = BDN*area_m2,
BDP = BDP*area_m2,
PC = PC*volume_m3,
PN = PN*volume_m3,
PP = PP*volume_m3,
CH = CH*volume_m3,
DIN = DIN*volume_m3,
DIP = DIP*volume_m3,
lo_b = lo_b*area_m2,
PREC = PREC*area_m2,
PRBC2 = PRBC2*area_m2,
PRBC1 = PRBC1*area_m2,
PRBDC = PRBDC*area_m2,
PRPC = PRPC*area_m2,
DIN_b = DIN_b*volume_m3,
DIP_b = DIP_b*volume_m3
)
WB_S1_G1_no_Aspec <<- WB_S1_G1 %>%
transmute(
EC = EC*area_m2,
EN = EN*area_m2,
EP = EP*area_m2,
BC2 = BC2*area_m2,
BN2 = BN2*area_m2,
BP2 = BP2*area_m2,
BC1 = BC1*area_m2,
BN1 = BN1*area_m2,
BP1 = BP1*area_m2,
BDC = BDC*area_m2,
BDN = BDN*area_m2,
BDP = BDP*area_m2,
PC = PC*volume_m3,
PN = PN*volume_m3,
PP = PP*volume_m3,
CH = CH*volume_m3,
DIN = DIN*volume_m3,
DIP = DIP*volume_m3,
lo_b = lo_b*area_m2,
PREC = PREC*area_m2,
PRBC2 = PRBC2*area_m2,
PRBC1 = PRBC1*area_m2,
PRBDC = PRBDC*area_m2,
PRPC = PRPC*area_m2,
DIN_b = DIN_b*volume_m3,
DIP_b = DIP_b*volume_m3
)
WB_S1_G2_no_Aspec <<- WB_S1_G2 %>%
transmute(
EC = EC*area_m2,
EN = EN*area_m2,
EP = EP*area_m2,
BC2 = BC2*area_m2,
BN2 = BN2*area_m2,
BP2 = BP2*area_m2,
BC1 = BC1*area_m2,
BN1 = BN1*area_m2,
BP1 = BP1*area_m2,
BDC = BDC*area_m2,
BDN = BDN*area_m2,
BDP = BDP*area_m2,
PC = PC*volume_m3,
PN = PN*volume_m3,
PP = PP*volume_m3,
CH = CH*volume_m3,
DIN = DIN*volume_m3,
DIP = DIP*volume_m3,
lo_b = lo_b*area_m2,
PREC = PREC*area_m2,
PRBC2 = PRBC2*area_m2,
PRBC1 = PRBC1*area_m2,
PRBDC = PRBDC*area_m2,
PRPC = PRPC*area_m2,
DIN_b = DIN_b*volume_m3,
DIP_b = DIP_b*volume_m3
)
WB_S1_G3_no_Aspec <<- WB_S1_G3 %>%
transmute(
EC = EC*area_m2,
EN = EN*area_m2,
EP = EP*area_m2,
BC2 = BC2*area_m2,
BN2 = BN2*area_m2,
BP2 = BP2*area_m2,
BC1 = BC1*area_m2,
BN1 = BN1*area_m2,
BP1 = BP1*area_m2,
BDC = BDC*area_m2,
BDN = BDN*area_m2,
BDP = BDP*area_m2,
PC = PC*volume_m3,
PN = PN*volume_m3,
PP = PP*volume_m3,
CH = CH*volume_m3,
DIN = DIN*volume_m3,
DIP = DIP*volume_m3,
lo_b = lo_b*area_m2,
PREC = PREC*area_m2,
PRBC2 = PRBC2*area_m2,
PRBC1 = PRBC1*area_m2,
PRBDC = PRBDC*area_m2,
PRPC = PRPC*area_m2,
DIN_b = DIN_b*volume_m3,
DIP_b = DIP_b*volume_m3
)
WB_S1_G4_no_Aspec <<- WB_S1_G4 %>%
transmute(
EC = EC*area_m2,
EN = EN*area_m2,
EP = EP*area_m2,
BC2 = BC2*area_m2,
BN2 = BN2*area_m2,
BP2 = BP2*area_m2,
BC1 = BC1*area_m2,
BN1 = BN1*area_m2,
BP1 = BP1*area_m2,
BDC = BDC*area_m2,
BDN = BDN*area_m2,
BDP = BDP*area_m2,
PC = PC*volume_m3,
PN = PN*volume_m3,
PP = PP*volume_m3,
CH = CH*volume_m3,
DIN = DIN*volume_m3,
DIP = DIP*volume_m3,
lo_b = lo_b*area_m2,
PREC = PREC*area_m2,
PRBC2 = PRBC2*area_m2,
PRBC1 = PRBC1*area_m2,
PRBDC = PRBDC*area_m2,
PRPC = PRPC*area_m2,
DIN_b = DIN_b*volume_m3,
DIP_b = DIP_b*volume_m3
)
WB_S2_G1_no_Aspec <<- WB_S2_G1 %>%
transmute(
EC = EC*area_m2,
EN = EN*area_m2,
EP = EP*area_m2,
BC2 = BC2*area_m2,
BN2 = BN2*area_m2,
BP2 = BP2*area_m2,
BC1 = BC1*area_m2,
BN1 = BN1*area_m2,
BP1 = BP1*area_m2,
BDC = BDC*area_m2,
BDN = BDN*area_m2,
BDP = BDP*area_m2,
PC = PC*volume_m3,
PN = PN*volume_m3,
PP = PP*volume_m3,
CH = CH*volume_m3,
DIN = DIN*volume_m3,
DIP = DIP*volume_m3,
lo_b = lo_b*area_m2,
PREC = PREC*area_m2,
PRBC2 = PRBC2*area_m2,
PRBC1 = PRBC1*area_m2,
PRBDC = PRBDC*area_m2,
PRPC = PRPC*area_m2,
DIN_b = DIN_b*volume_m3,
DIP_b = DIP_b*volume_m3
)
WB_S2_G2_no_Aspec <<- WB_S2_G2 %>%
transmute(
EC = EC*area_m2,
EN = EN*area_m2,
EP = EP*area_m2,
BC2 = BC2*area_m2,
BN2 = BN2*area_m2,
BP2 = BP2*area_m2,
BC1 = BC1*area_m2,
BN1 = BN1*area_m2,
BP1 = BP1*area_m2,
BDC = BDC*area_m2,
BDN = BDN*area_m2,
BDP = BDP*area_m2,
PC = PC*volume_m3,
PN = PN*volume_m3,
PP = PP*volume_m3,
CH = CH*volume_m3,
DIN = DIN*volume_m3,
DIP = DIP*volume_m3,
lo_b = lo_b*area_m2,
PREC = PREC*area_m2,
PRBC2 = PRBC2*area_m2,
PRBC1 = PRBC1*area_m2,
PRBDC = PRBDC*area_m2,
PRPC = PRPC*area_m2,
DIN_b = DIN_b*volume_m3,
DIP_b = DIP_b*volume_m3
)
WB_S2_G3_no_Aspec <<- WB_S2_G3 %>%
transmute(
EC = EC*area_m2,
EN = EN*area_m2,
EP = EP*area_m2,
BC2 = BC2*area_m2,
BN2 = BN2*area_m2,
BP2 = BP2*area_m2,
BC1 = BC1*area_m2,
BN1 = BN1*area_m2,
BP1 = BP1*area_m2,
BDC = BDC*area_m2,
BDN = BDN*area_m2,
BDP = BDP*area_m2,
PC = PC*volume_m3,
PN = PN*volume_m3,
PP = PP*volume_m3,
CH = CH*volume_m3,
DIN = DIN*volume_m3,
DIP = DIP*volume_m3,
lo_b = lo_b*area_m2,
PREC = PREC*area_m2,
PRBC2 = PRBC2*area_m2,
PRBC1 = PRBC1*area_m2,
PRBDC = PRBDC*area_m2,
PRPC = PRPC*area_m2,
DIN_b = DIN_b*volume_m3,
DIP_b = DIP_b*volume_m3
)
WB_S2_G4_no_Aspec <<- WB_S2_G4 %>%
transmute(
EC = EC*area_m2,
EN = EN*area_m2,
EP = EP*area_m2,
BC2 = BC2*area_m2,
BN2 = BN2*area_m2,
BP2 = BP2*area_m2,
BC1 = BC1*area_m2,
BN1 = BN1*area_m2,
BP1 = BP1*area_m2,
BDC = BDC*area_m2,
BDN = BDN*area_m2,
BDP = BDP*area_m2,
PC = PC*volume_m3,
PN = PN*volume_m3,
PP = PP*volume_m3,
CH = CH*volume_m3,
DIN = DIN*volume_m3,
DIP = DIP*volume_m3,
lo_b = lo_b*area_m2,
PREC = PREC*area_m2,
PRBC2 = PRBC2*area_m2,
PRBC1 = PRBC1*area_m2,
PRBDC = PRBDC*area_m2,
PRPC = PRPC*area_m2,
DIN_b = DIN_b*volume_m3,
DIP_b = DIP_b*volume_m3
)
#Create single entries of summed area and volume for all gradient polygons used to create primary producers biomass and production single values for all gradient polygons
WB_sumarea_G1 <<- sum(WB_B_G1$area_m2);WB_sumarea_G2 <<- sum(WB_B_G2$area_m2);WB_sumarea_G3 <<- sum(WB_B_G3$area_m2);WB_sumarea_G4 <<- sum(WB_B_G4$area_m2)
WB_sumvolume_G1 <<- sum(WB_B_G1$volume_m3);WB_sumvolume_G2 <<- sum(WB_B_G2$volume_m3);WB_sumvolume_G3 <<- sum(WB_B_G3$volume_m3);WB_sumvolume_G4 <<- sum(WB_B_G4$volume_m3)
# Create summed area and volumes for all gradient polygons used later in final data frame
WB_B_sumarea_m2_AG <<- c(sum(WB_B_G1$area_m2),sum(WB_B_G2$area_m2),sum(WB_B_G3$area_m2),sum(WB_B_G4$area_m2))
WB_B_sumvolume_m3_AG <<- c(sum(WB_B_G1$volume_m3),sum(WB_B_G2$volume_m3),sum(WB_B_G3$volume_m3),sum(WB_B_G4$volume_m3))
WB_S1_sumarea_m2_AG <<- c(sum(WB_S1_G1$area_m2),sum(WB_S1_G2$area_m2),sum(WB_S1_G3$area_m2),sum(WB_S1_G4$area_m2))
WB_S1_sumvolume_m3_AG <<- c(sum(WB_S1_G1$volume_m3),sum(WB_S1_G2$volume_m3),sum(WB_S1_G3$volume_m3),sum(WB_S1_G4$volume_m3))
WB_S2_sumarea_m2_AG <<- c(sum(WB_S2_G1$area_m2),sum(WB_S2_G2$area_m2),sum(WB_S2_G3$area_m2),sum(WB_S2_G4$area_m2))
WB_S2_sumvolume_m3_AG <<- c(sum(WB_S2_G1$volume_m3),sum(WB_S2_G2$volume_m3),sum(WB_S2_G3$volume_m3),sum(WB_S2_G4$volume_m3))
#Create single value entries for all primary producers (biomass and production) for all gradient polygons and all model scenarios
WB_B_EC_AG <<- c((sum(WB_B_G1_no_Aspec$EC))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$EC))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$EC))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$EC))/WB_sumarea_G4)
WB_B_EN_AG <<- c((sum(WB_B_G1_no_Aspec$EN))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$EN))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$EN))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$EN))/WB_sumarea_G4)
WB_B_EP_AG <<- c((sum(WB_B_G1_no_Aspec$EP))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$EP))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$EP))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$EP))/WB_sumarea_G4)
WB_B_BC2_AG <<- c((sum(WB_B_G1_no_Aspec$BC2))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$BC2))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$BC2))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$BC2))/WB_sumarea_G4)
WB_B_BN2_AG <<- c((sum(WB_B_G1_no_Aspec$BN2))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$BN2))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$BN2))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$BN2))/WB_sumarea_G4)
WB_B_BP2_AG <<- c((sum(WB_B_G1_no_Aspec$BP2))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$BP2))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$BP2))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$BP2))/WB_sumarea_G4)
WB_B_BC1_AG <<- c((sum(WB_B_G1_no_Aspec$BC1))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$BC1))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$BC1))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$BC1))/WB_sumarea_G4)
WB_B_BN1_AG <<- c((sum(WB_B_G1_no_Aspec$BN1))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$BN1))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$BN1))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$BN1))/WB_sumarea_G4)
WB_B_BP1_AG <<- c((sum(WB_B_G1_no_Aspec$BP1))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$BP1))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$BP1))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$BP1))/WB_sumarea_G4)
WB_B_BDC_AG <<- c((sum(WB_B_G1_no_Aspec$BDC))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$BDC))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$BDC))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$BDC))/WB_sumarea_G4)
WB_B_BDN_AG <<- c((sum(WB_B_G1_no_Aspec$BDN))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$BDN))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$BDN))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$BDN))/WB_sumarea_G4)
WB_B_BDP_AG <<- c((sum(WB_B_G1_no_Aspec$BDP))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$BDP))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$BDP))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$BDP))/WB_sumarea_G4)
WB_B_PC_AG <<- c((sum(WB_B_G1_no_Aspec$PC))/WB_sumvolume_G1,(sum(WB_B_G2_no_Aspec$PC))/WB_sumvolume_G2,(sum(WB_B_G3_no_Aspec$PC))/WB_sumvolume_G3,(sum(WB_B_G4_no_Aspec$PC))/WB_sumvolume_G4)
WB_B_PN_AG <<- c((sum(WB_B_G1_no_Aspec$PN))/WB_sumvolume_G1,(sum(WB_B_G2_no_Aspec$PN))/WB_sumvolume_G2,(sum(WB_B_G3_no_Aspec$PN))/WB_sumvolume_G3,(sum(WB_B_G4_no_Aspec$PN))/WB_sumvolume_G4)
WB_B_PP_AG <<- c((sum(WB_B_G1_no_Aspec$PP))/WB_sumvolume_G1,(sum(WB_B_G2_no_Aspec$PP))/WB_sumvolume_G2,(sum(WB_B_G3_no_Aspec$PP))/WB_sumvolume_G3,(sum(WB_B_G4_no_Aspec$PP))/WB_sumvolume_G4)
WB_B_CH_AG <<- c((sum(WB_B_G1_no_Aspec$CH))/WB_sumvolume_G1,(sum(WB_B_G2_no_Aspec$CH))/WB_sumvolume_G2,(sum(WB_B_G3_no_Aspec$CH))/WB_sumvolume_G3,(sum(WB_B_G4_no_Aspec$CH))/WB_sumvolume_G4)
WB_B_DIN_AG <<- c((sum(WB_B_G1_no_Aspec$DIN))/WB_sumvolume_G1,(sum(WB_B_G2_no_Aspec$DIN))/WB_sumvolume_G2,(sum(WB_B_G3_no_Aspec$DIN))/WB_sumvolume_G3,(sum(WB_B_G4_no_Aspec$DIN))/WB_sumvolume_G4)
WB_B_DIP_AG <<- c((sum(WB_B_G1_no_Aspec$DIP))/WB_sumvolume_G1,(sum(WB_B_G2_no_Aspec$DIP))/WB_sumvolume_G2,(sum(WB_B_G3_no_Aspec$DIP))/WB_sumvolume_G3,(sum(WB_B_G4_no_Aspec$DIP))/WB_sumvolume_G4)
WB_B_PREC_AG <<- c((sum(WB_B_G1_no_Aspec$PREC))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$PREC))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$PREC))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$PREC))/WB_sumarea_G4)
WB_B_PRBC2_AG <<- c((sum(WB_B_G1_no_Aspec$PRBC2))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$PRBC2))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$PRBC2))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$PRBC2))/WB_sumarea_G4)
WB_B_PRBC1_AG <<- c((sum(WB_B_G1_no_Aspec$PRBC1))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$PRBC1))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$PRBC1))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$PRBC1))/WB_sumarea_G4)
WB_B_PRBDC_AG <<- c((sum(WB_B_G1_no_Aspec$PRBDC))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$PRBDC))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$PRBDC))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$PRBDC))/WB_sumarea_G4)
WB_B_PRPC_AG <<- c((sum(WB_B_G1_no_Aspec$PRPC))/WB_sumarea_G1,(sum(WB_B_G2_no_Aspec$PRPC))/WB_sumarea_G2,(sum(WB_B_G3_no_Aspec$PRPC))/WB_sumarea_G3,(sum(WB_B_G4_no_Aspec$PRPC))/WB_sumarea_G4)
WB_B_DIN_b_AG <<- c((sum(WB_B_G1_no_Aspec$DIN_b))/WB_sumvolume_G1,(sum(WB_B_G2_no_Aspec$DIN_b))/WB_sumvolume_G2,(sum(WB_B_G3_no_Aspec$DIN_b))/WB_sumvolume_G3,(sum(WB_B_G4_no_Aspec$DIN_b))/WB_sumvolume_G4)
WB_B_DIP_b_AG <<- c((sum(WB_B_G1_no_Aspec$DIP_b))/WB_sumvolume_G1,(sum(WB_B_G2_no_Aspec$DIP_b))/WB_sumvolume_G2,(sum(WB_B_G3_no_Aspec$DIP_b))/WB_sumvolume_G3,(sum(WB_B_G4_no_Aspec$DIP_b))/WB_sumvolume_G4)
WB_S1_EC_AG <<- c((sum(WB_S1_G1_no_Aspec$EC))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$EC))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$EC))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$EC))/WB_sumarea_G4)
WB_S1_EN_AG <<- c((sum(WB_S1_G1_no_Aspec$EN))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$EN))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$EN))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$EN))/WB_sumarea_G4)
WB_S1_EP_AG <<- c((sum(WB_S1_G1_no_Aspec$EP))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$EP))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$EP))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$EP))/WB_sumarea_G4)
WB_S1_BC2_AG <<- c((sum(WB_S1_G1_no_Aspec$BC2))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$BC2))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$BC2))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$BC2))/WB_sumarea_G4)
WB_S1_BN2_AG <<- c((sum(WB_S1_G1_no_Aspec$BN2))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$BN2))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$BN2))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$BN2))/WB_sumarea_G4)
WB_S1_BP2_AG <<- c((sum(WB_S1_G1_no_Aspec$BP2))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$BP2))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$BP2))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$BP2))/WB_sumarea_G4)
WB_S1_BC1_AG <<- c((sum(WB_S1_G1_no_Aspec$BC1))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$BC1))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$BC1))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$BC1))/WB_sumarea_G4)
WB_S1_BN1_AG <<- c((sum(WB_S1_G1_no_Aspec$BN1))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$BN1))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$BN1))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$BN1))/WB_sumarea_G4)
WB_S1_BP1_AG <<- c((sum(WB_S1_G1_no_Aspec$BP1))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$BP1))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$BP1))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$BP1))/WB_sumarea_G4)
WB_S1_BDC_AG <<- c((sum(WB_S1_G1_no_Aspec$BDC))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$BDC))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$BDC))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$BDC))/WB_sumarea_G4)
WB_S1_BDN_AG <<- c((sum(WB_S1_G1_no_Aspec$BDN))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$BDN))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$BDN))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$BDN))/WB_sumarea_G4)
WB_S1_BDP_AG <<- c((sum(WB_S1_G1_no_Aspec$BDP))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$BDP))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$BDP))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$BDP))/WB_sumarea_G4)
WB_S1_PC_AG <<- c((sum(WB_S1_G1_no_Aspec$PC))/WB_sumvolume_G1,(sum(WB_S1_G2_no_Aspec$PC))/WB_sumvolume_G2,(sum(WB_S1_G3_no_Aspec$PC))/WB_sumvolume_G3,(sum(WB_S1_G4_no_Aspec$PC))/WB_sumvolume_G4)
WB_S1_PN_AG <<- c((sum(WB_S1_G1_no_Aspec$PN))/WB_sumvolume_G1,(sum(WB_S1_G2_no_Aspec$PN))/WB_sumvolume_G2,(sum(WB_S1_G3_no_Aspec$PN))/WB_sumvolume_G3,(sum(WB_S1_G4_no_Aspec$PN))/WB_sumvolume_G4)
WB_S1_PP_AG <<- c((sum(WB_S1_G1_no_Aspec$PP))/WB_sumvolume_G1,(sum(WB_S1_G2_no_Aspec$PP))/WB_sumvolume_G2,(sum(WB_S1_G3_no_Aspec$PP))/WB_sumvolume_G3,(sum(WB_S1_G4_no_Aspec$PP))/WB_sumvolume_G4)
WB_S1_CH_AG <<- c((sum(WB_S1_G1_no_Aspec$CH))/WB_sumvolume_G1,(sum(WB_S1_G2_no_Aspec$CH))/WB_sumvolume_G2,(sum(WB_S1_G3_no_Aspec$CH))/WB_sumvolume_G3,(sum(WB_S1_G4_no_Aspec$CH))/WB_sumvolume_G4)
WB_S1_DIN_AG <<- c((sum(WB_S1_G1_no_Aspec$DIN))/WB_sumvolume_G1,(sum(WB_S1_G2_no_Aspec$DIN))/WB_sumvolume_G2,(sum(WB_S1_G3_no_Aspec$DIN))/WB_sumvolume_G3,(sum(WB_S1_G4_no_Aspec$DIN))/WB_sumvolume_G4)
WB_S1_DIP_AG <<- c((sum(WB_S1_G1_no_Aspec$DIP))/WB_sumvolume_G1,(sum(WB_S1_G2_no_Aspec$DIP))/WB_sumvolume_G2,(sum(WB_S1_G3_no_Aspec$DIP))/WB_sumvolume_G3,(sum(WB_S1_G4_no_Aspec$DIP))/WB_sumvolume_G4)
WB_S1_PREC_AG <<- c((sum(WB_S1_G1_no_Aspec$PREC))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$PREC))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$PREC))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$PREC))/WB_sumarea_G4)
WB_S1_PRBC2_AG <<- c((sum(WB_S1_G1_no_Aspec$PRBC2))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$PRBC2))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$PRBC2))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$PRBC2))/WB_sumarea_G4)
WB_S1_PRBC1_AG <<- c((sum(WB_S1_G1_no_Aspec$PRBC1))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$PRBC1))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$PRBC1))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$PRBC1))/WB_sumarea_G4)
WB_S1_PRBDC_AG <<- c((sum(WB_S1_G1_no_Aspec$PRBDC))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$PRBDC))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$PRBDC))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$PRBDC))/WB_sumarea_G4)
WB_S1_PRPC_AG <<- c((sum(WB_S1_G1_no_Aspec$PRPC))/WB_sumarea_G1,(sum(WB_S1_G2_no_Aspec$PRPC))/WB_sumarea_G2,(sum(WB_S1_G3_no_Aspec$PRPC))/WB_sumarea_G3,(sum(WB_S1_G4_no_Aspec$PRPC))/WB_sumarea_G4)
WB_S1_DIN_b_AG <<- c((sum(WB_S1_G1_no_Aspec$DIN_b))/WB_sumvolume_G1,(sum(WB_S1_G2_no_Aspec$DIN_b))/WB_sumvolume_G2,(sum(WB_S1_G3_no_Aspec$DIN_b))/WB_sumvolume_G3,(sum(WB_S1_G4_no_Aspec$DIN_b))/WB_sumvolume_G4)
WB_S1_DIP_b_AG <<- c((sum(WB_S1_G1_no_Aspec$DIP_b))/WB_sumvolume_G1,(sum(WB_S1_G2_no_Aspec$DIP_b))/WB_sumvolume_G2,(sum(WB_S1_G3_no_Aspec$DIP_b))/WB_sumvolume_G3,(sum(WB_S1_G4_no_Aspec$DIP_b))/WB_sumvolume_G4)
WB_S2_EC_AG <<- c((sum(WB_S2_G1_no_Aspec$EC))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$EC))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$EC))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$EC))/WB_sumarea_G4)
WB_S2_EN_AG <<- c((sum(WB_S2_G1_no_Aspec$EN))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$EN))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$EN))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$EN))/WB_sumarea_G4)
WB_S2_EP_AG <<- c((sum(WB_S2_G1_no_Aspec$EP))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$EP))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$EP))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$EP))/WB_sumarea_G4)
WB_S2_BC2_AG <<- c((sum(WB_S2_G1_no_Aspec$BC2))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$BC2))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$BC2))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$BC2))/WB_sumarea_G4)
WB_S2_BN2_AG <<- c((sum(WB_S2_G1_no_Aspec$BN2))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$BN2))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$BN2))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$BN2))/WB_sumarea_G4)
WB_S2_BP2_AG <<- c((sum(WB_S2_G1_no_Aspec$BP2))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$BP2))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$BP2))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$BP2))/WB_sumarea_G4)
WB_S2_BC1_AG <<- c((sum(WB_S2_G1_no_Aspec$BC1))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$BC1))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$BC1))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$BC1))/WB_sumarea_G4)
WB_S2_BN1_AG <<- c((sum(WB_S2_G1_no_Aspec$BN1))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$BN1))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$BN1))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$BN1))/WB_sumarea_G4)
WB_S2_BP1_AG <<- c((sum(WB_S2_G1_no_Aspec$BP1))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$BP1))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$BP1))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$BP1))/WB_sumarea_G4)
WB_S2_BDC_AG <<- c((sum(WB_S2_G1_no_Aspec$BDC))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$BDC))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$BDC))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$BDC))/WB_sumarea_G4)
WB_S2_BDN_AG <<- c((sum(WB_S2_G1_no_Aspec$BDN))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$BDN))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$BDN))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$BDN))/WB_sumarea_G4)
WB_S2_BDP_AG <<- c((sum(WB_S2_G1_no_Aspec$BDP))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$BDP))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$BDP))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$BDP))/WB_sumarea_G4)
WB_S2_PC_AG <<- c((sum(WB_S2_G1_no_Aspec$PC))/WB_sumvolume_G1,(sum(WB_S2_G2_no_Aspec$PC))/WB_sumvolume_G2,(sum(WB_S2_G3_no_Aspec$PC))/WB_sumvolume_G3,(sum(WB_S2_G4_no_Aspec$PC))/WB_sumvolume_G4)
WB_S2_PN_AG <<- c((sum(WB_S2_G1_no_Aspec$PN))/WB_sumvolume_G1,(sum(WB_S2_G2_no_Aspec$PN))/WB_sumvolume_G2,(sum(WB_S2_G3_no_Aspec$PN))/WB_sumvolume_G3,(sum(WB_S2_G4_no_Aspec$PN))/WB_sumvolume_G4)
WB_S2_PP_AG <<- c((sum(WB_S2_G1_no_Aspec$PP))/WB_sumvolume_G1,(sum(WB_S2_G2_no_Aspec$PP))/WB_sumvolume_G2,(sum(WB_S2_G3_no_Aspec$PP))/WB_sumvolume_G3,(sum(WB_S2_G4_no_Aspec$PP))/WB_sumvolume_G4)
WB_S2_CH_AG <<- c((sum(WB_S2_G1_no_Aspec$CH))/WB_sumvolume_G1,(sum(WB_S2_G2_no_Aspec$CH))/WB_sumvolume_G2,(sum(WB_S2_G3_no_Aspec$CH))/WB_sumvolume_G3,(sum(WB_S2_G4_no_Aspec$CH))/WB_sumvolume_G4)
WB_S2_DIN_AG <<- c((sum(WB_S2_G1_no_Aspec$DIN))/WB_sumvolume_G1,(sum(WB_S2_G2_no_Aspec$DIN))/WB_sumvolume_G2,(sum(WB_S2_G3_no_Aspec$DIN))/WB_sumvolume_G3,(sum(WB_S2_G4_no_Aspec$DIN))/WB_sumvolume_G4)
WB_S2_DIP_AG <<- c((sum(WB_S2_G1_no_Aspec$DIP))/WB_sumvolume_G1,(sum(WB_S2_G2_no_Aspec$DIP))/WB_sumvolume_G2,(sum(WB_S2_G3_no_Aspec$DIP))/WB_sumvolume_G3,(sum(WB_S2_G4_no_Aspec$DIP))/WB_sumvolume_G4)
WB_S2_PREC_AG <<- c((sum(WB_S2_G1_no_Aspec$PREC))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$PREC))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$PREC))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$PREC))/WB_sumarea_G4)
WB_S2_PRBC2_AG <<- c((sum(WB_S2_G1_no_Aspec$PRBC2))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$PRBC2))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$PRBC2))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$PRBC2))/WB_sumarea_G4)
WB_S2_PRBC1_AG <<- c((sum(WB_S2_G1_no_Aspec$PRBC1))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$PRBC1))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$PRBC1))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$PRBC1))/WB_sumarea_G4)
WB_S2_PRBDC_AG <<- c((sum(WB_S2_G1_no_Aspec$PRBDC))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$PRBDC))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$PRBDC))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$PRBDC))/WB_sumarea_G4)
WB_S2_PRPC_AG <<- c((sum(WB_S2_G1_no_Aspec$PRPC))/WB_sumarea_G1,(sum(WB_S2_G2_no_Aspec$PRPC))/WB_sumarea_G2,(sum(WB_S2_G3_no_Aspec$PRPC))/WB_sumarea_G3,(sum(WB_S2_G4_no_Aspec$PRPC))/WB_sumarea_G4)
WB_S2_DIN_b_AG <<- c((sum(WB_S2_G1_no_Aspec$DIN_b))/WB_sumvolume_G1,(sum(WB_S2_G2_no_Aspec$DIN_b))/WB_sumvolume_G2,(sum(WB_S2_G3_no_Aspec$DIN_b))/WB_sumvolume_G3,(sum(WB_S2_G4_no_Aspec$DIN_b))/WB_sumvolume_G4)
WB_S2_DIP_b_AG <<- c((sum(WB_S2_G1_no_Aspec$DIP_b))/WB_sumvolume_G1,(sum(WB_S2_G2_no_Aspec$DIP_b))/WB_sumvolume_G2,(sum(WB_S2_G3_no_Aspec$DIP_b))/WB_sumvolume_G3,(sum(WB_S2_G4_no_Aspec$DIP_b))/WB_sumvolume_G4)
#Create final data frame with single values for primary producers (biomass and production) for all gradient polygons and all model scenarios. Also adds column "scenario"
WB_B_AG_ASSV <<- data.frame(WB_gradient_no,WB_B_avgDIN_AG,WB_B_avgDIP_AG,WB_B_EC_AG,WB_B_EN_AG,WB_B_EP_AG,WB_B_BC2_AG,WB_B_BN2_AG,WB_B_BP2_AG,WB_B_BC1_AG,WB_B_BN1_AG,WB_B_BP1_AG,WB_B_BDC_AG,WB_B_BDN_AG,WB_B_BDP_AG,WB_B_PC_AG,WB_B_PN_AG,WB_B_PP_AG,WB_B_CH_AG,WB_B_DIN_AG,WB_B_DIP_AG,WB_B_PREC_AG,WB_B_PRBC2_AG,WB_B_PRBC1_AG,WB_B_PRBDC_AG,WB_B_PRPC_AG,WB_B_DIN_b_AG,WB_B_DIP_b_AG) %>%
add_column(scenario = "Baseline", .before = "WB_gradient_no")
WB_S1_AG_ASSV <<- data.frame(WB_gradient_no,WB_S1_avgDIN_AG,WB_S1_avgDIP_AG,WB_S1_EC_AG,WB_S1_EN_AG,WB_S1_EP_AG,WB_S1_BC2_AG,WB_S1_BN2_AG,WB_S1_BP2_AG,WB_S1_BC1_AG,WB_S1_BN1_AG,WB_S1_BP1_AG,WB_S1_BDC_AG,WB_S1_BDN_AG,WB_S1_BDP_AG,WB_S1_PC_AG,WB_S1_PN_AG,WB_S1_PP_AG,WB_S1_CH_AG,WB_S1_DIN_AG,WB_S1_DIP_AG,WB_S1_PREC_AG,WB_S1_PRBC2_AG,WB_S1_PRBC1_AG,WB_S1_PRBDC_AG,WB_S1_PRPC_AG,WB_S1_DIN_b_AG,WB_S1_DIP_b_AG) %>%
add_column(scenario = "N-30%", .before = "WB_gradient_no")
WB_S2_AG_ASSV <<- data.frame(WB_gradient_no,WB_S2_avgDIN_AG,WB_S2_avgDIP_AG,WB_S2_EC_AG,WB_S2_EN_AG,WB_S2_EP_AG,WB_S2_BC2_AG,WB_S2_BN2_AG,WB_S2_BP2_AG,WB_S2_BC1_AG,WB_S2_BN1_AG,WB_S2_BP1_AG,WB_S2_BDC_AG,WB_S2_BDN_AG,WB_S2_BDP_AG,WB_S2_PC_AG,WB_S2_PN_AG,WB_S2_PP_AG,WB_S2_CH_AG,WB_S2_DIN_AG,WB_S2_DIP_AG,WB_S2_PREC_AG,WB_S2_PRBC2_AG,WB_S2_PRBC1_AG,WB_S2_PRBDC_AG,WB_S2_PRPC_AG,WB_S2_DIN_b_AG,WB_S2_DIP_b_AG) %>%
add_column(scenario = "P-30%", .before = "WB_gradient_no")
#Rename all column headers to enable merging of tables
WB_B <<- WB_B_AG_ASSV %>%
rename(
AvgDIN = WB_B_avgDIN_AG,
AvgDIP = WB_B_avgDIP_AG,
EC = WB_B_EC_AG,
EN = WB_B_EN_AG,
EP = WB_B_EP_AG,
BC2 = WB_B_BC2_AG,
BN2 = WB_B_BN2_AG,
BP2 = WB_B_BP2_AG,
BC1 = WB_B_BC1_AG,
BN1 = WB_B_BN1_AG,
BP1 = WB_B_BP1_AG,
BDC = WB_B_BDC_AG,
BDN = WB_B_BDN_AG,
BDP = WB_B_BDP_AG,
PC = WB_B_PC_AG,
PN = WB_B_PN_AG,
PP = WB_B_PP_AG,
CH = WB_B_CH_AG,
DIN = WB_B_DIN_AG,
DIP = WB_B_DIP_AG,
PREC = WB_B_PREC_AG,
PRBC2 = WB_B_PRBC2_AG,
PRBC1 = WB_B_PRBC1_AG,
PRBDC = WB_B_PRBDC_AG,
PRPC = WB_B_PRPC_AG,
DIN_b = WB_B_DIN_b_AG,
DIP_b = WB_B_DIP_b_AG
)
WB_S1 <<- WB_S1_AG_ASSV %>%
rename(
AvgDIN = WB_S1_avgDIN_AG,
AvgDIP = WB_S1_avgDIP_AG,
EC = WB_S1_EC_AG,
EN = WB_S1_EN_AG,
EP = WB_S1_EP_AG,
BC2 = WB_S1_BC2_AG,
BN2 = WB_S1_BN2_AG,
BP2 = WB_S1_BP2_AG,
BC1 = WB_S1_BC1_AG,
BN1 = WB_S1_BN1_AG,
BP1 = WB_S1_BP1_AG,
BDC = WB_S1_BDC_AG,
BDN = WB_S1_BDN_AG,
BDP = WB_S1_BDP_AG,
PC = WB_S1_PC_AG,
PN = WB_S1_PN_AG,
PP = WB_S1_PP_AG,
CH = WB_S1_CH_AG,
DIN = WB_S1_DIN_AG,
DIP = WB_S1_DIP_AG,
PREC = WB_S1_PREC_AG,
PRBC2 = WB_S1_PRBC2_AG,
PRBC1 = WB_S1_PRBC1_AG,
PRBDC = WB_S1_PRBDC_AG,
PRPC = WB_S1_PRPC_AG,
DIN_b = WB_S1_DIN_b_AG,
DIP_b = WB_S1_DIP_b_AG
)
WB_S2 <<- WB_S2_AG_ASSV %>%
rename(
AvgDIN = WB_S2_avgDIN_AG,
AvgDIP = WB_S2_avgDIP_AG,
EC = WB_S2_EC_AG,
EN = WB_S2_EN_AG,
EP = WB_S2_EP_AG,
BC2 = WB_S2_BC2_AG,
BN2 = WB_S2_BN2_AG,
BP2 = WB_S2_BP2_AG,
BC1 = WB_S2_BC1_AG,
BN1 = WB_S2_BN1_AG,
BP1 = WB_S2_BP1_AG,
BDC = WB_S2_BDC_AG,
BDN = WB_S2_BDN_AG,
BDP = WB_S2_BDP_AG,
PC = WB_S2_PC_AG,
PN = WB_S2_PN_AG,
PP = WB_S2_PP_AG,
CH = WB_S2_CH_AG,
DIN = WB_S2_DIN_AG,
DIP = WB_S2_DIP_AG,
PREC = WB_S2_PREC_AG,
PRBC2 = WB_S2_PRBC2_AG,
PRBC1 = WB_S2_PRBC1_AG,
PRBDC = WB_S2_PRBDC_AG,
PRPC = WB_S2_PRPC_AG,
DIN_b = WB_S2_DIN_b_AG,
DIP_b = WB_S2_DIP_b_AG
)
#Merging tables
WB_AS_AG_FINAL <<- bind_rows(WB_B,WB_S1,WB_S2)
#Install and load GGplot2, gridExtra and ggpubr packages
if(!require(ggplot2)) install.packages("ggplot2")
library(ggplot2)
if(!require(gridExtra)) install.packages("gridExtra")
library(gridExtra)
if(!require(ggpubr)) install.packages("ggpubr")
library(ggpubr)
if(!require(devtools)) install.packages("devtools")
library(devtools)
if(!require(patchwork)) install.packages("patchwork")
library(patchwork)
#Production VS. AvgDIN plots
WB_PREC_vs_AvgDIN <<- ggplot(WB_AS_AG_FINAL, aes(x = AvgDIN, y = PREC, group = scenario)) +
geom_line(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
xlab('Avg. DIN ('*'g C'~m^-2*')') +
ylab('Eelgrass production ('*' g C'~m^-2~GS^-1*')') +
scale_x_continuous(limits = c(0,0.45), breaks = seq(0,0.45, by = 0.1), labels = scales::number_format(accuracy = 0.05)) +
scale_y_continuous(limits = c(0,25), breaks = seq(0,25, by = 5), labels = scales::number_format(accuracy = 1))
WB_panel_PREC_vs_AvgDIN <<- WB_PREC_vs_AvgDIN + theme_pub()
WB_PRBC1_vs_AvgDIN <<- ggplot(WB_AS_AG_FINAL, aes(x = AvgDIN, y = PRBC1, group = scenario)) +
geom_line(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
xlab('Avg. DIN ('*'g C'~m^-2*')') +
ylab('Opp. macroalgae production ('*' g C'~m^-2~GS^-1*')') +
scale_x_continuous(limits = c(0,0.45), breaks = seq(0,0.45, by = 0.1), labels = scales::number_format(accuracy = 0.05)) +
scale_y_continuous(limits = c(0,35), breaks = seq(0,35, by = 5), labels = scales::number_format(accuracy = 1))
WB_panel_PRBC1_vs_AvgDIN <<- WB_PRBC1_vs_AvgDIN + theme_pub()
WB_PRPC_vs_AvgDIN <<- ggplot(WB_AS_AG_FINAL, aes(x = AvgDIN, y = PRPC, group = scenario)) +
geom_line(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
xlab('Avg. DIN ('*'g C'~m^-2*')') +
ylab('Phytoplankton production ('*' g C'~m^-2~GS^-1*')') +
scale_x_continuous(limits = c(0,0.45), breaks = seq(0,0.45, by = 0.1), labels = scales::number_format(accuracy = 0.05)) +
scale_y_continuous(limits = c(0,70), breaks = seq(0,70, by = 10), labels = scales::number_format(accuracy = 1))
WB_panel_PRPC_vs_AvgDIN <<- WB_PRPC_vs_AvgDIN + theme_pub()
WB_panel_PREC_vs_AvgDIN + labs(x = '', y = expression('Eelgrass production'~(g~C*~m^-2~GS^-1))) +
WB_panel_PRBC1_vs_AvgDIN + labs(x = '', y = expression('Opp. macroalgae production'~(g~C*~m^-2~GS^-1))) +
WB_panel_PRPC_vs_AvgDIN +
plot_layout(ncol = 1) +
plot_annotation(tag_levels = 'A') +
ggsave(filename = 'WB_PRXX_vs_AvgDIN_panel.tiff',
width = 15,
height = 35,
units = 'cm',
device='tiff',
dpi=300)
#Vertical profiles of primary producers and benthic light for all gradient polygons for each scenario
WB_B_G1 <<- add_column(WB_B_G1, scenario = 'Baseline', WB_gradient_no = 'no. 1', .before = 'upper')
WB_B_G2 <<- add_column(WB_B_G2, scenario = 'Baseline', WB_gradient_no = 'no. 2', .before = 'upper')
WB_B_G3 <<- add_column(WB_B_G3, scenario = 'Baseline', WB_gradient_no = 'no. 3', .before = 'upper')
WB_B_G4 <<- add_column(WB_B_G4, scenario = 'Baseline', WB_gradient_no = 'no. 4', .before = 'upper')
WB_B_AG <<- bind_rows(WB_B_G1, WB_B_G2, WB_B_G3, WB_B_G4)
WB_S1_G1 <<- add_column(WB_S1_G1, scenario = 'N-30%', WB_gradient_no = 'no. 1', .before = 'upper')
WB_S1_G2 <<- add_column(WB_S1_G2, scenario = 'N-30%', WB_gradient_no = 'no. 2', .before = 'upper')
WB_S1_G3 <<- add_column(WB_S1_G3, scenario = 'N-30%', WB_gradient_no = 'no. 3', .before = 'upper')
WB_S1_G4 <<- add_column(WB_S1_G4, scenario = 'N-30%', WB_gradient_no = 'no. 4', .before = 'upper')
WB_S1_AG <<- bind_rows(WB_S1_G1, WB_S1_G2, WB_S1_G3, WB_S1_G4)
WB_S2_G1 <<- add_column(WB_S2_G1, scenario = 'P-30%', WB_gradient_no = 'no. 1', .before = 'upper')
WB_S2_G2 <<- add_column(WB_S2_G2, scenario = 'P-30%', WB_gradient_no = 'no. 2', .before = 'upper')
WB_S2_G3 <<- add_column(WB_S2_G3, scenario = 'P-30%', WB_gradient_no = 'no. 3', .before = 'upper')
WB_S2_G4 <<- add_column(WB_S2_G4, scenario = 'P-30%', WB_gradient_no = 'no. 4', .before = 'upper')
WB_S2_AG <<- bind_rows(WB_S2_G1, WB_S2_G2, WB_S2_G3, WB_S2_G4)
#HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH WB_B VERTICAL PROFILES HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH
WB_B_PREC_vs_lower <<- ggplot(WB_B_AG, aes(x = PREC, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Eelgrass production'~(g~C*~m^-2~GS^-1)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,120), breaks = seq(0,120, by = 20), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-6,0), breaks = seq(-6,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_B_PREC_vs_lower + theme_pub()
WB_B_PRBC1_vs_lower <<- ggplot(WB_B_AG, aes(x = PRBC1, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Opp. macroalgae Production'~(g~C*~m^-2~GS^-1)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,45), breaks = seq(0,45, by = 5), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-8,0), breaks = seq(-8,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_B_PRBC1_vs_lower + theme_pub()
WB_B_PRBC2_vs_lower <<- ggplot(WB_B_AG, aes(x = PRBC2, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Perennial macroalgae Production'~(g~C*~m^-2~GS^-1)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,18), breaks = seq(0,18, by = 3), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-8,0), breaks = seq(-8,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_B_PRBC2_vs_lower + theme_pub()
WB_B_PRBDC_vs_lower <<- ggplot(WB_B_AG, aes(x = PRBDC, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Benthic diatom Production'~(g~C*~m^-2~GS^-1)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,50), breaks = seq(0,50, by = 5), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-10,0), breaks = seq(-10,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_B_PRBDC_vs_lower + theme_pub()
WB_B_PC_vs_lower <<- ggplot(WB_B_AG, aes(x = PC, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Phytoplankton biomass'~(g~C*~m^-3)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,2), breaks = seq(0,2, by = 0.3), labels = scales::number_format(accuracy = 0.1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 3), labels = scales::number_format(accuracy = 1))
WB_B_PC_vs_lower + theme_pub()
WB_B_lo_b_vs_lower <<- ggplot(WB_B_AG, aes(x = lo_b, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Benthic light'~(mu*E*~m^-2*~s^-1)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,650), breaks = seq(0,650, by = 100), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 3), labels = scales::number_format(accuracy = 1))
WB_B_lo_b_vs_lower + theme_pub()
WB_B_PREC_vs_lower+ theme_pub() + labs(x = expression('Eelgrass production'~(g~C*~m^-2~GS^-1)), y = '') + theme(legend.justification = c(1.1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_B_PRBC1_vs_lower+ theme_pub() + labs(x = expression('Opp. macroalgae production'~(g~C*~m^-2~GS^-1)), y = '') + theme(legend.justification = c(1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_B_PRBC2_vs_lower+ theme_pub() + theme(legend.justification = c(1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_B_PRBDC_vs_lower+ theme_pub() + labs(x = expression('Benthic diatom production'~(g~C*~m^-2~GS^-1)), y = '') + theme(legend.justification = c(1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_B_PC_vs_lower+ theme_pub() + labs(x = expression('Phytoplankton biomass'~(g~C*~m^-3)), y = '') + theme(legend.justification = c(1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_B_lo_b_vs_lower + theme_pub() + labs(x = expression('Benthic light'~(mu*E*~m^-2*~s^-1)), y = '') + theme(legend.justification = c(1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
plot_layout(ncol = 2) +
plot_annotation(tag_levels = 'A') +
ggsave(filename = 'WB_B_AG_production.tiff',
width = 30,
height = 30,
units = 'cm',
device='tiff',
dpi=300)
#HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH WB_S1 VERTICAL PROFILES HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH
WB_S1_PREC_vs_lower <<- ggplot(WB_S1_AG, aes(x = PREC, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Eelgrass Production'~(g~C*~m^-2~GS^-1)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,120), breaks = seq(0,120, by = 20), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-6,0), breaks = seq(-6,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_S1_PREC_vs_lower + theme_pub()
WB_S1_PRBC1_vs_lower <<- ggplot(WB_S1_AG, aes(x = PRBC1, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Opp. macroalgae Production'~(g~C*~m^-2~GS^-1)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,45), breaks = seq(0,45, by = 5), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-8,0), breaks = seq(-8,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_S1_PRBC1_vs_lower + theme_pub()
WB_S1_PRBC2_vs_lower <<- ggplot(WB_S1_AG, aes(x = PRBC2, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Perennial macroalgae Production'~(g~C*~m^-2~GS^-1)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,18), breaks = seq(0,18, by = 3), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-8,0), breaks = seq(-8,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_S1_PRBC2_vs_lower + theme_pub()
WB_S1_PRBDC_vs_lower <<- ggplot(WB_S1_AG, aes(x = PRBDC, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Benthic diatom Production'~(g~C*~m^-2~GS^-1)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,50), breaks = seq(0,50, by = 5), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-10,0), breaks = seq(-10,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_S1_PRBDC_vs_lower + theme_pub()
WB_S1_PC_vs_lower <<- ggplot(WB_S1_AG, aes(x = PC, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Phytoplankton biomass'~(g~C*~m^-3)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,2), breaks = seq(0,2, by = 0.3), labels = scales::number_format(accuracy = 0.1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 3), labels = scales::number_format(accuracy = 1))
WB_S1_PC_vs_lower + theme_pub()
WB_S1_lo_b_vs_lower <<- ggplot(WB_S1_AG, aes(x = lo_b, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Benthic light'~(mu*E*~m^-2*~s^-1)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,650), breaks = seq(0,650, by = 100), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 3), labels = scales::number_format(accuracy = 1))
WB_S1_lo_b_vs_lower + theme_pub()
WB_S1_PREC_vs_lower+ theme_pub() + labs(x = expression('Eelgrass production'~(g~C*~m^-2~GS^-1)), y = '') + theme(legend.justification = c(1.1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_S1_PRBC1_vs_lower+ theme_pub() + labs(x = expression('Opp. macroalgae production'~(g~C*~m^-2~GS^-1)), y = '') + theme(legend.justification = c(1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_S1_PRBC2_vs_lower+ theme_pub() + theme(legend.justification = c(1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_S1_PRBDC_vs_lower+ theme_pub() + labs(x = expression('Benthic diatom production'~(g~C*~m^-2~GS^-1)), y = '') + theme(legend.justification = c(1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_S1_PC_vs_lower+ theme_pub() + labs(x = expression('Phytoplankton biomass'~(g~C*~m^-3)), y = '') + theme(legend.justification = c(1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_S1_lo_b_vs_lower + theme_pub() + labs(x = expression('Benthic light'~(mu*E*~m^-2*~s^-1)), y = '') + theme(legend.justification = c(1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
plot_layout(ncol = 2) +
plot_annotation(tag_levels = 'A') +
ggsave(filename = 'WB_S1_AG_production.tiff',
width = 30,
height = 30,
units = 'cm',
device='tiff',
dpi=300)
#HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH WB_S2 VERTICAL PROFILES HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH
WB_S2_PREC_vs_lower <<- ggplot(WB_S2_AG, aes(x = PREC, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Eelgrass Production'~(g~C*~m^-2~GS^-1)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,120), breaks = seq(0,120, by = 20), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-6,0), breaks = seq(-6,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_S2_PREC_vs_lower + theme_pub()
WB_S2_PRBC1_vs_lower <<- ggplot(WB_S2_AG, aes(x = PRBC1, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Opp. macroalgae Production'~(g~C*~m^-2~GS^-1)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,45), breaks = seq(0,45, by = 5), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-8,0), breaks = seq(-8,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_S2_PRBC1_vs_lower + theme_pub()
WB_S2_PRBC2_vs_lower <<- ggplot(WB_S2_AG, aes(x = PRBC2, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Perennial macroalgae Production'~(g~C*~m^-2~GS^-1)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,18), breaks = seq(0,18, by = 3), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-8,0), breaks = seq(-8,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_S2_PRBC2_vs_lower + theme_pub()
WB_S2_PRBDC_vs_lower <<- ggplot(WB_S2_AG, aes(x = PRBDC, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Benthic diatom Production'~(g~C*~m^-2~GS^-1)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,50), breaks = seq(0,50, by = 5), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-10,0), breaks = seq(-10,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_S2_PRBDC_vs_lower + theme_pub()
WB_S2_PC_vs_lower <<- ggplot(WB_S2_AG, aes(x = PC, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Phytoplankton biomass'~(g~C*~m^-3)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,2), breaks = seq(0,2, by = 0.3), labels = scales::number_format(accuracy = 0.1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 3), labels = scales::number_format(accuracy = 1))
WB_S2_PC_vs_lower + theme_pub()
WB_S2_lo_b_vs_lower <<- ggplot(WB_S2_AG, aes(x = lo_b, y = lower, group = WB_gradient_no)) +
geom_path(size = 1) +
geom_point(aes(shape = WB_gradient_no, color = WB_gradient_no, fill = WB_gradient_no), size = 3) +
scale_shape_manual(values = c(21, 24, 22, 22)) +
scale_color_manual(values = c('black', 'black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white')) +
labs(x = expression('Benthic light'~(mu*E*~m^-2*~s^-1)), y = expression('Depth'~(m)))+
scale_x_continuous(limits = c(0,650), breaks = seq(0,650, by = 100), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 3), labels = scales::number_format(accuracy = 1))
WB_S2_lo_b_vs_lower + theme_pub()
WB_S2_PREC_vs_lower+ theme_pub() + labs(x = expression('Eelgrass production'~(g~C*~m^-2~GS^-1)), y = '') + theme(legend.justification = c(1.1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_S2_PRBC1_vs_lower+ theme_pub() + labs(x = expression('Opp. macroalgae production'~(g~C*~m^-2~GS^-1)), y = '') + theme(legend.justification = c(1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_S2_PRBC2_vs_lower+ theme_pub() + theme(legend.justification = c(1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_S2_PRBDC_vs_lower+ theme_pub() + labs(x = expression('Benthic diatom production'~(g~C*~m^-2~GS^-1)), y = '') + theme(legend.justification = c(1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_S2_PC_vs_lower+ theme_pub() + labs(x = expression('Phytoplankton biomass'~(g~C*~m^-3)), y = '') + theme(legend.justification = c(1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_S2_lo_b_vs_lower + theme_pub() + labs(x = expression('Benthic light'~(mu*E*~m^-2*~s^-1)), y = '') + theme(legend.justification = c(1,2.15), plot.tag.position = c(0.952,0.77), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
plot_layout(ncol = 2) +
plot_annotation(tag_levels = 'A') +
ggsave(filename = 'WB_S2_AG_production.tiff',
width = 30,
height = 30,
units = 'cm',
device='tiff',
dpi=300)
#HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH WB_ASAG_PREC VERTICAL PROFILES HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH
WB_G1_AS <<- bind_rows(WB_B_G1, WB_S1_G1, WB_S2_G1)
WB_G2_AS <<- bind_rows(WB_B_G2, WB_S1_G2, WB_S2_G2)
WB_G3_AS <<- bind_rows(WB_B_G3, WB_S1_G3, WB_S2_G3)
WB_G4_AS <<- bind_rows(WB_B_G4, WB_S1_G4, WB_S2_G4)
WB_AS_G1_PREC <<- ggplot(WB_G1_AS, aes(x = PREC, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = expression(' Eelgrass production'~(g~C*~m^-2*~GS^-1)), y = '') +
scale_x_continuous(limits = c(0,120), breaks = seq(0,120, by = 20), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-6,0), breaks = seq(-6,0, by = 1), labels = scales::number_format(accuracy = 1))
WB_AS_G1_PREC + theme_pub()
WB_AS_G2_PREC <<- ggplot(WB_G2_AS, aes(x = PREC, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = '') +
scale_x_continuous(limits = c(0,120), breaks = seq(0,120, by = 20), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-6,0), breaks = seq(-6,0, by = 1), labels = scales::number_format(accuracy = 1))
WB_AS_G2_PREC + theme_pub()
WB_AS_G3_PREC <<- ggplot(WB_G3_AS, aes(x = PREC, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = expression(' Depth'~(m))) +
scale_x_continuous(limits = c(0,120), breaks = seq(0,120, by = 20), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-6,0), breaks = seq(-6,0, by = 1), labels = scales::number_format(accuracy = 1))
WB_AS_G3_PREC + theme_pub()
WB_AS_G4_PREC <<- ggplot(WB_G4_AS, aes(x = PREC, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = '') +
scale_x_continuous(limits = c(0,120), breaks = seq(0,120, by = 20), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-6,0), breaks = seq(-6,0, by = 1), labels = scales::number_format(accuracy = 1))
WB_AS_G4_PREC + theme_pub()
WB_AS_G1_PREC+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G2_PREC+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G3_PREC+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G4_PREC+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
plot_layout(ncol = 2) +
plot_annotation(tag_levels = '1', tag_prefix = 'no. ') +
ggsave(filename = 'WB_ASAG_PREC_vs_lower.tiff',
width = 30,
height = 30,
units = 'cm',
device='tiff',
dpi=300)
#HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH WB_ASAG_PRBC1 VERTICAL PROFILES HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH
WB_AS_G1_PRBC1 <<- ggplot(WB_G1_AS, aes(x = PRBC1, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = expression(' Opp. macroalgae production'~(g~C*~m^-2*~GS^-1)), y = '') +
scale_x_continuous(limits = c(0,45), breaks = seq(0,45, by = 5), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-8,0), breaks = seq(-8,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_AS_G1_PRBC1 + theme_pub()
WB_AS_G2_PRBC1 <<- ggplot(WB_G2_AS, aes(x = PRBC1, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = '') +
scale_x_continuous(limits = c(0,45), breaks = seq(0,45, by = 5), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-8,0), breaks = seq(-8,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_AS_G2_PRBC1 + theme_pub()
WB_AS_G3_PRBC1 <<- ggplot(WB_G3_AS, aes(x = PRBC1, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = expression(' Depth'~(m))) +
scale_x_continuous(limits = c(0,45), breaks = seq(0,45, by = 5), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-8,0), breaks = seq(-8,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_AS_G3_PRBC1 + theme_pub()
WB_AS_G4_PRBC1 <<- ggplot(WB_G4_AS, aes(x = PRBC1, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = '') +
scale_x_continuous(limits = c(0,45), breaks = seq(0,45, by = 5), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-8,0), breaks = seq(-8,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_AS_G4_PRBC1 + theme_pub()
WB_AS_G1_PRBC1+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G2_PRBC1+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G3_PRBC1+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G4_PRBC1+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
plot_layout(ncol = 2) +
plot_annotation(tag_levels = '1', tag_prefix = 'no. ') +
ggsave(filename = 'WB_ASAG_PRBC1_vs_lower.tiff',
width = 30,
height = 30,
units = 'cm',
device='tiff',
dpi=300)
#HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH WB_ASAG_PRBC2 VERTICAL PROFILES HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH
WB_AS_G1_PRBC2 <<- ggplot(WB_G1_AS, aes(x = PRBC2, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = expression(' Perennial macroalgae production'~(g~C*~m^-2*~GS^-1)), y = '') +
scale_x_continuous(limits = c(0,18), breaks = seq(0,18, by = 3), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-8,0), breaks = seq(-8,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_AS_G1_PRBC2 + theme_pub()
WB_AS_G2_PRBC2 <<- ggplot(WB_G2_AS, aes(x = PRBC2, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = '') +
scale_x_continuous(limits = c(0,18), breaks = seq(0,18, by = 3), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-8,0), breaks = seq(-8,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_AS_G2_PRBC2 + theme_pub()
WB_AS_G3_PRBC2 <<- ggplot(WB_G3_AS, aes(x = PRBC2, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = expression(' Depth'~(m))) +
scale_x_continuous(limits = c(0,18), breaks = seq(0,18, by = 3), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-8,0), breaks = seq(-8,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_AS_G3_PRBC2 + theme_pub()
WB_AS_G4_PRBC2 <<- ggplot(WB_G4_AS, aes(x = PRBC2, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = '') +
scale_x_continuous(limits = c(0,18), breaks = seq(0,18, by = 3), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-8,0), breaks = seq(-8,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_AS_G4_PRBC2 + theme_pub()
WB_AS_G1_PRBC2+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G2_PRBC2+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G3_PRBC2+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G4_PRBC2+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
plot_layout(ncol = 2) +
plot_annotation(tag_levels = '1', tag_prefix = 'no. ') +
ggsave(filename = 'WB_ASAG_PRBC2_vs_lower.tiff',
width = 30,
height = 30,
units = 'cm',
device='tiff',
dpi=300)
#HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH WB_ASAG_PRBDC VERTICAL PROFILES HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH
WB_AS_G1_PRBDC <<- ggplot(WB_G1_AS, aes(x = PRBDC, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = expression(' Benthic diatom production'~(g~C*~m^-2*~GS^-1)), y = '') +
scale_x_continuous(limits = c(0,55), breaks = seq(0,55, by = 5), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-10,0), breaks = seq(-10,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_AS_G1_PRBDC + theme_pub()
WB_AS_G2_PRBDC <<- ggplot(WB_G2_AS, aes(x = PRBDC, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = '') +
scale_x_continuous(limits = c(0,55), breaks = seq(0,55, by = 5), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-10,0), breaks = seq(-10,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_AS_G2_PRBDC + theme_pub()
WB_AS_G3_PRBDC <<- ggplot(WB_G3_AS, aes(x = PRBDC, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = expression(' Depth'~(m))) +
scale_x_continuous(limits = c(0,55), breaks = seq(0,55, by = 5), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-10,0), breaks = seq(-10,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_AS_G3_PRBDC + theme_pub()
WB_AS_G4_PRBDC <<- ggplot(WB_G4_AS, aes(x = PRBDC, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = '') +
scale_x_continuous(limits = c(0,55), breaks = seq(0,55, by = 5), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-10,0), breaks = seq(-10,0, by = 2), labels = scales::number_format(accuracy = 1))
WB_AS_G4_PRBDC + theme_pub()
WB_AS_G1_PRBDC+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G2_PRBDC+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G3_PRBDC+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G4_PRBDC+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
plot_layout(ncol = 2) +
plot_annotation(tag_levels = '1', tag_prefix = 'no. ') +
ggsave(filename = 'WB_ASAG_PRBDC_vs_lower.tiff',
width = 30,
height = 30,
units = 'cm',
device='tiff',
dpi=300)
#HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH WB_ASAG_PC VERTICAL PROFILES HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH
WB_AS_G1_PC <<- ggplot(WB_G1_AS, aes(x = PC, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = expression(' Phytoplakton biomass'~(g~C*~m^-3)), y = '') +
scale_x_continuous(limits = c(0,2), breaks = seq(0,2, by = 0.3), labels = scales::number_format(accuracy = 0.1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 3), labels = scales::number_format(accuracy = 1))
WB_AS_G1_PC + theme_pub()
WB_AS_G2_PC <<- ggplot(WB_G2_AS, aes(x = PC, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = '') +
scale_x_continuous(limits = c(0,2), breaks = seq(0,2, by = 0.3), labels = scales::number_format(accuracy = 0.1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 3), labels = scales::number_format(accuracy = 1))
WB_AS_G2_PC + theme_pub()
WB_AS_G3_PC <<- ggplot(WB_G3_AS, aes(x = PC, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = expression(' Depth'~(m))) +
scale_x_continuous(limits = c(0,2), breaks = seq(0,2, by = 0.3), labels = scales::number_format(accuracy = 0.1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 3), labels = scales::number_format(accuracy = 1))
WB_AS_G3_PC + theme_pub()
WB_AS_G4_PC <<- ggplot(WB_G4_AS, aes(x = PC, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = '') +
scale_x_continuous(limits = c(0,2), breaks = seq(0,2, by = 0.3), labels = scales::number_format(accuracy = 0.1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 3), labels = scales::number_format(accuracy = 1))
WB_AS_G4_PC + theme_pub()
WB_AS_G1_PC+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G2_PC+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G3_PC+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G4_PC+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
plot_layout(ncol = 2) +
plot_annotation(tag_levels = '1', tag_prefix = 'no. ') +
ggsave(filename = 'WB_ASAG_PC_vs_lower.tiff',
width = 30,
height = 30,
units = 'cm',
device='tiff',
dpi=300)
#HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH WB_ASAG_lo_b VERTICAL PROFILES HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH
WB_AS_G1_lo_b <<- ggplot(WB_G1_AS, aes(x = lo_b, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = expression(' Benthic light'~(mu*E*~m^-2*~s^-1)), y = '') +
scale_x_continuous(limits = c(0,700), breaks = seq(0,700, by = 100), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 3), labels = scales::number_format(accuracy = 1))
WB_AS_G1_lo_b + theme_pub()
WB_AS_G2_lo_b <<- ggplot(WB_G2_AS, aes(x = lo_b, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = '') +
scale_x_continuous(limits = c(0,700), breaks = seq(0,700, by = 100), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 3), labels = scales::number_format(accuracy = 1))
WB_AS_G2_lo_b + theme_pub()
WB_AS_G3_lo_b <<- ggplot(WB_G3_AS, aes(x = lo_b, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = expression(' Depth'~(m))) +
scale_x_continuous(limits = c(0,700), breaks = seq(0,700, by = 100), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 3), labels = scales::number_format(accuracy = 1))
WB_AS_G3_lo_b + theme_pub()
WB_AS_G4_lo_b <<- ggplot(WB_G4_AS, aes(x = lo_b, y = lower, group = scenario)) +
geom_path(size = 1) +
geom_point(aes(shape = scenario, color = scenario, fill = scenario), size = 3) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = '', y = '') +
scale_x_continuous(limits = c(0,700), breaks = seq(0,700, by = 100), labels = scales::number_format(accuracy = 1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 3), labels = scales::number_format(accuracy = 1))
WB_AS_G4_lo_b + theme_pub()
WB_AS_G1_lo_b+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G2_lo_b+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G3_lo_b+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
WB_AS_G4_lo_b+ theme_pub() + theme(legend.justification = c(1,4.45), plot.tag.position = c(0.93,0.82), plot.tag = element_text(size = 22), axis.title = element_text(size = 18)) +
plot_layout(ncol = 2) +
plot_annotation(tag_levels = '1', tag_prefix = 'no. ') +
ggsave(filename = 'WB_ASAG_lo_b_vs_lower.tiff',
width = 30,
height = 30,
units = 'cm',
device='tiff',
dpi=300)
#HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH Benthic Vs. WC production HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH
WB_benthic_vs_WC_prod <<- WB_AS_AG_FINAL %>%
select(scenario, WB_gradient_no, PREC, PRBC1, PRBC2, PRBDC, PRPC) %>%
mutate(benthic_prod = PREC + PRBC1 + PRBC2 + PRBDC,
WC_prod = PRPC,
prod_100_pct = benthic_prod + WC_prod,
benthic_prod_pct = benthic_prod / prod_100_pct * 100,
WC_prod_pct = WC_prod / prod_100_pct *100,
control = benthic_prod_pct + WC_prod_pct
) %>%
rename('Benthic prod.' = benthic_prod_pct,
'WC prod.' = WC_prod_pct) %>%
select(scenario, WB_gradient_no, 'Benthic prod.', 'WC prod.') %>%
gather(prod_ID, prod_pct, 'Benthic prod.':'WC prod.')
WB_benthic_vs_WC_prod_plot <<- ggplot(data = WB_benthic_vs_WC_prod, aes(x = WB_gradient_no, y = prod_pct, group = interaction(scenario, prod_ID))) +
geom_path(size = 1, aes(linetype = prod_ID)) +
geom_point(size = 3, aes(shape = scenario, color = scenario, fill = scenario)) +
scale_shape_manual(values = c(22, 22, 24)) +
scale_color_manual(values = c('black', 'black', 'black')) +
scale_fill_manual(values = c('black', 'white', 'black')) +
labs(x = 'Distance (km)', y = 'Relative growth season production\n(% of total production)') +
scale_x_discrete(limits = c('0-6', '6-8', '8-11', '11-14'), expand = c(0,0.05)) +
scale_y_continuous(limits = c(0,105), breaks = seq(0,105, by = 10), labels = scales::number_format(accuracy = 1), expand = c(0,0.05))
WB_benthic_vs_WC_prod_plot + theme_pub() +
theme(
legend.text = element_text(size = 20),
legend.background = element_blank(),
axis.title = element_text(size = 20),
axis.text = element_text(size = 20),
legend.justification = c(1.3,2),
legend.direction = 'horizontal',
legend.spacing.x = unit(0.05, 'cm'),
axis.text.x = element_text(angle = -45, vjust = 1, hjust = 0),
plot.margin = unit(c(5,50,0,0), "pt"),
) +
ggsave(filename = 'WB_Benthic_vs_WC_prod.tiff',
width = 30,
height = 20,
units = 'cm',
device='tiff',
dpi=300)
#HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH Monod tabels and monod vertical profiles HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH
EC_vmax_C <<- 0.04; EC_kmDIN_C <<- 0.09; EC_kmDIP_C <<- 0.005; EC_kmlight_C <<- 355
BC1_vmax_C <<- 0.23; BC1_kmDIN_C <<- 0.06; BC1_kmDIP_C <<- 0.015; BC1_kmlight_C <<- 200
BC2_vmax_C <<- 0.09; BC2_kmDIN_C <<- 0.02; BC2_kmDIP_C <<- 0.01; BC2_kmlight_C <<- 170
BDC_vmax_C <<- 1; BDC_kmDIN_C <<- 0.025; BDC_kmDIP_C <<- 0.005; BDC_kmlight_C <<- 30
PC_vmax_C <<- 2; PC_kmDIN_C <<- 0.03; PC_kmDIP_C <<- 0.005; PC_kmlight_C <<- 75
WB_monod_table <<- bind_rows(WB_B_AG, WB_S1_AG, WB_S2_AG) %>%
select(scenario, WB_gradient_no, lower, EC, EN, EP, BC1, BN1, BP1, BC2, BN2, BP2, BDC, BDN, BDP, PC, PN, PP, DIN, DIP, lo_b, DIN_b, DIP_b) %>%
rename(
EC_kmDIN = EC,
EC_kmDIP = EN,
EC_kmlight = EP,
BC1_kmDIN = BC1,
BC1_kmDIP = BN1,
BC1_kmlight = BP1,
BC2_kmDIN = BC2,
BC2_kmDIP = BN2,
BC2_kmlight = BP2,
BDC_kmDIN = BDC,
BDC_kmDIP = BDN,
BDC_kmlight = BDP,
PC_kmDIN = PC,
PC_kmDIP = PN,
PC_kmlight = PP
) %>%
mutate(
EC_kmDIN = DIN_b / (DIN_b + EC_kmDIN_C),
EC_kmDIP = DIP_b / (DIP_b+EC_kmDIP_C),
EC_kmlight = lo_b / (lo_b + EC_kmlight_C),
BC1_kmDIN = DIN_b / (DIN_b + BC1_kmDIN_C),
BC1_kmDIP = DIP_b / (DIP_b+BC1_kmDIP_C),
BC1_kmlight = lo_b / (lo_b + BC1_kmlight_C),
BC2_kmDIN = DIN_b / (DIN_b + BC2_kmDIN_C),
BC2_kmDIP = DIP_b / (DIP_b+BC2_kmDIP_C),
BC2_kmlight = lo_b / (lo_b + BC2_kmlight_C),
BDC_kmDIN = DIN_b / (DIN_b + BDC_kmDIN_C),
BDC_kmDIP = DIP_b / (DIP_b+BDC_kmDIP_C),
BDC_kmlight = lo_b / (lo_b + BDC_kmlight_C),
PC_kmDIN = DIN / (DIN + PC_kmDIN_C),
PC_kmDIP = DIP / (DIP+PC_kmDIP_C),
PC_kmlight = lo_b / (lo_b + PC_kmlight_C)
)
#Monod EC
WB_monod_EC_light <<- ggplot(subset(WB_monod_table, scenario %in% c('Baseline', 'N-30%')), aes(x = EC_kmlight, y = lower, group = interaction(scenario, WB_gradient_no))) +
geom_path(size = 1.5, aes(color = scenario)) +
geom_point(size = 3, aes(shape = WB_gradient_no, fill = WB_gradient_no)) +
scale_shape_manual(values = c(21, 24, 22, 22, 24)) +
scale_color_manual(values = c('black', 'orange', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white', 'white')) +
scale_x_continuous(limits = c(0,0.65), breaks = seq(0,0.65, by = 0.1), labels = scales::number_format(accuracy = 0.1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 2), labels = scales::number_format(accuracy = 1)) +
labs(x = 'Eelgrass growth limitation by benthic light\n', y = 'Depth (m)')
WB_monod_EC_light + theme_pub() +
theme(
plot.margin = unit(c(1,1,1,1), "pt"),
legend.justification = c(1,1.6),
legend.direction = 'vertical',
legend.box = 'vertical' ,
legend.text = element_text(size = 22)
)
WB_monod_EC_light + theme_pub() + theme(plot.margin = unit(c(15,1,1,1), "pt"), axis.title = element_text(size = 22), axis.text = element_text(size = 18), legend.justification = c(1.05,6),
legend.direction = 'vertical',
legend.box = 'vertical',
legend.text = element_text(size = 22),
legend.spacing.x = unit(0.05, 'cm'),) +
plot_layout(ncol = 1) +
ggsave(filename = 'WB_monod_EC_light.tiff',
width = 30,
height = 40,
units = 'cm',
device='tiff',
dpi=300)
#Monod BC1
WB_monod_BC1_DIN <<- ggplot(subset(WB_monod_table, scenario %in% c('Baseline', 'N-30%')), aes(x = BC1_kmDIN, y = lower, group = interaction(scenario, WB_gradient_no))) +
geom_path(size = 1.2, aes(color = scenario)) +
geom_point(size = 2, aes(shape = WB_gradient_no, fill = WB_gradient_no)) +
scale_shape_manual(values = c(21, 24, 22, 22, 24)) +
scale_color_manual(values = c('black', 'orange', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white', 'white')) +
scale_x_continuous(limits = c(0,1.1), breaks = seq(0,1.1, by = 0.2), labels = scales::number_format(accuracy = 0.1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 2), labels = scales::number_format(accuracy = 1)) +
labs(x = 'Opp. macroalgae growth inhibition by DIN\n', y = '')
WB_monod_BC1_DIN + theme_pub() +
theme(
plot.margin = unit(c(20,1,1,1), "pt"),
legend.justification = c(1,1.6),
legend.direction = 'vertical',
legend.box = 'vertical',
)
WB_monod_BC1_DIP <<- ggplot(subset(WB_monod_table, scenario %in% c('Baseline', 'N-30%')), aes(x = BC1_kmDIP, y = lower, group = interaction(scenario, WB_gradient_no))) +
geom_path(size = 1.2, aes(color = scenario)) +
geom_point(size = 2, aes(shape = WB_gradient_no, fill = WB_gradient_no)) +
scale_shape_manual(values = c(21, 24, 22, 22, 24)) +
scale_color_manual(values = c('black', 'orange', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white', 'white')) +
scale_x_continuous(limits = c(0.50,1.09), breaks = seq(0.50,1.09, by = 0.1), labels = scales::number_format(accuracy = 0.1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 2), labels = scales::number_format(accuracy = 1)) +
labs(x = 'Opp. macroalgae growth inhibition by DIP\n', y = 'Depth (m)')
WB_monod_BC1_DIP + theme_pub() +
theme(
plot.margin = unit(c(20,1,1,1), "pt"),
legend.justification = c(1,1.6),
legend.direction = 'vertical',
legend.box = 'vertical'
)
WB_monod_BC1_light <<- ggplot(subset(WB_monod_table, scenario %in% c('Baseline', 'N-30%')), aes(x = BC1_kmlight, y = lower, group = interaction(scenario, WB_gradient_no))) +
geom_path(size = 1.2, aes(color = scenario)) +
geom_point(size = 2, aes(shape = WB_gradient_no, fill = WB_gradient_no)) +
scale_shape_manual(values = c(21, 24, 22, 22, 24)) +
scale_color_manual(values = c('black', 'orange', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white', 'white')) +
scale_x_continuous(limits = c(0,0.8), breaks = seq(0,0.8, by = 0.1), labels = scales::number_format(accuracy = 0.1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 2), labels = scales::number_format(accuracy = 1)) +
labs(x = 'Opp. macroalgae growth inhibition by benthic light\n', y = '')
WB_monod_BC1_light + theme_pub() +
theme(
plot.margin = unit(c(20,1,1,1), "pt"),
legend.justification = c(1,1.6),
legend.direction = 'vertical',
legend.box = 'vertical'
)
WB_monod_BC1_DIN + theme_pub() + theme(plot.margin = unit(c(15,1,1,1), "pt"), axis.title = element_text(size = 18), legend.justification = c(1,1.75),
legend.direction = 'vertical',
legend.box = 'vertical') +
WB_monod_BC1_DIP + theme_pub() + theme(plot.margin = unit(c(15,1,1,1), "pt"), axis.title = element_text(size = 18), legend.justification = c(1,1.75),
legend.direction = 'vertical',
legend.box = 'vertical') +
WB_monod_BC1_light + theme_pub() + theme(plot.margin = unit(c(15,1,1,1), "pt"), axis.title = element_text(size = 18), legend.justification = c(1,1.75),
legend.direction = 'vertical',
legend.box = 'vertical') +
plot_layout(ncol = 1) +
ggsave(filename = 'WB_monod_BC1.tiff',
width = 20,
height = 40,
units = 'cm',
device='tiff',
dpi=300)
#Monod PC
WB_monod_PC_DIN <<- ggplot(subset(WB_monod_table, scenario %in% c('Baseline', 'N-30%')), aes(x = PC_kmDIN, y = lower, group = interaction(scenario, WB_gradient_no))) +
geom_path(size = 1.2, aes(color = scenario)) +
geom_point(size = 2, aes(shape = WB_gradient_no, fill = WB_gradient_no)) +
scale_shape_manual(values = c(21, 24, 22, 22, 24)) +
scale_color_manual(values = c('black', 'orange', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white', 'white')) +
scale_x_continuous(limits = c(0,1.1), breaks = seq(0,1.1, by = 0.2), labels = scales::number_format(accuracy = 0.1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 2), labels = scales::number_format(accuracy = 1)) +
labs(x = 'Phytoplankton growth inhibition by DIN\n', y = '')
WB_monod_PC_DIN + theme_pub() +
theme(
plot.margin = unit(c(20,1,1,1), "pt"),
legend.justification = c(1,1.6),
legend.direction = 'vertical',
legend.box = 'vertical',
)
WB_monod_PC_DIP <<- ggplot(subset(WB_monod_table, scenario %in% c('Baseline', 'N-30%')), aes(x = PC_kmDIP, y = lower, group = interaction(scenario, WB_gradient_no))) +
geom_path(size = 1.2, aes(color = scenario)) +
geom_point(size = 2, aes(shape = WB_gradient_no, fill = WB_gradient_no)) +
scale_shape_manual(values = c(21, 24, 22, 22, 24)) +
scale_color_manual(values = c('black', 'orange', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white', 'white')) +
scale_x_continuous(limits = c(0.75,1), breaks = seq(0.75,1, by = 0.05), labels = scales::number_format(accuracy = 0.01), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 2), labels = scales::number_format(accuracy = 1)) +
labs(x = 'Phytoplankton growth inhibition by DIP\n', y = 'Depth (m)')
WB_monod_PC_DIP + theme_pub() +
theme(
plot.margin = unit(c(20,1,1,1), "pt"),
legend.justification = c(1,1.6),
legend.direction = 'vertical',
legend.box = 'vertical'
)
WB_monod_PC_light <<- ggplot(subset(WB_monod_table, scenario %in% c('Baseline', 'N-30%')), aes(x = PC_kmlight, y = lower, group = interaction(scenario, WB_gradient_no))) +
geom_path(size = 1.2, aes(color = scenario)) +
geom_point(size = 2, aes(shape = WB_gradient_no, fill = WB_gradient_no)) +
scale_shape_manual(values = c(21, 24, 22, 22, 24)) +
scale_color_manual(values = c('black', 'orange', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white', 'white')) +
scale_x_continuous(limits = c(0,0.9), breaks = seq(0,0.9, by = 0.1), labels = scales::number_format(accuracy = 0.1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 2), labels = scales::number_format(accuracy = 1)) +
labs(x = 'Phytoplankton growth inhibition by benthic light\n', y = '')
WB_monod_PC_light + theme_pub() +
theme(
plot.margin = unit(c(20,1,1,1), "pt"),
legend.justification = c(1,1.6),
legend.direction = 'vertical',
legend.box = 'vertical'
)
WB_monod_PC_DIN + theme_pub() + theme(plot.margin = unit(c(15,1,1,1), "pt"), axis.title = element_text(size = 18), legend.justification = c(1,1.75),
legend.direction = 'vertical',
legend.box = 'vertical') +
WB_monod_PC_DIP + theme_pub() + theme(plot.margin = unit(c(15,1,1,1), "pt"), axis.title = element_text(size = 18), legend.justification = c(5.5,1.75),
legend.direction = 'vertical',
legend.box = 'vertical') +
WB_monod_PC_light + theme_pub() + theme(plot.margin = unit(c(15,1,1,1), "pt"), axis.title = element_text(size = 18), legend.justification = c(1,1.75),
legend.direction = 'vertical',
legend.box = 'vertical') +
plot_layout(ncol = 1) +
ggsave(filename = 'WB_monod_PC.tiff',
width = 20,
height = 40,
units = 'cm',
device='tiff',
dpi=300)
#Monod BC2
WB_monod_BC2_DIN <<- ggplot(subset(WB_monod_table, scenario %in% c('Baseline', 'N-30%')), aes(x = BC2_kmDIN, y = lower, group = interaction(scenario, WB_gradient_no))) +
geom_path(size = 1.2, aes(color = scenario)) +
geom_point(size = 2, aes(shape = WB_gradient_no, fill = WB_gradient_no)) +
scale_shape_manual(values = c(21, 24, 22, 22, 24)) +
scale_color_manual(values = c('black', 'orange', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white', 'white')) +
scale_x_continuous(limits = c(0.15,1), breaks = seq(0.15,1, by = 0.1), labels = scales::number_format(accuracy = 0.1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 2), labels = scales::number_format(accuracy = 1)) +
labs(x = 'Perennial macroalgae growth inhibition by DIN\n', y = '')
WB_monod_BC2_DIN + theme_pub() +
theme(
plot.margin = unit(c(20,1,1,1), "pt"),
legend.justification = c(1,1.6),
legend.direction = 'vertical',
legend.box = 'vertical',
)
WB_monod_BC2_DIP <<- ggplot(subset(WB_monod_table, scenario %in% c('Baseline', 'N-30%')), aes(x = BC2_kmDIP, y = lower, group = interaction(scenario, WB_gradient_no))) +
geom_path(size = 1.2, aes(color = scenario)) +
geom_point(size = 2, aes(shape = WB_gradient_no, fill = WB_gradient_no)) +
scale_shape_manual(values = c(21, 24, 22, 22, 24)) +
scale_color_manual(values = c('black', 'orange', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white', 'white')) +
scale_x_continuous(limits = c(0.5,1), breaks = seq(0.5,1, by = 0.1), labels = scales::number_format(accuracy = 0.1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 2), labels = scales::number_format(accuracy = 1)) +
labs(x = 'Perennial macroalgae growth inhibition by DIP\n', y = 'Depth (m)')
WB_monod_BC2_DIP + theme_pub() +
theme(
plot.margin = unit(c(20,1,1,1), "pt"),
legend.justification = c(1,1.6),
legend.direction = 'vertical',
legend.box = 'vertical'
)
WB_monod_BC2_light <<- ggplot(subset(WB_monod_table, scenario %in% c('Baseline', 'N-30%')), aes(x = BC2_kmlight, y = lower, group = interaction(scenario, WB_gradient_no))) +
geom_path(size = 1.2, aes(color = scenario)) +
geom_point(size = 2, aes(shape = WB_gradient_no, fill = WB_gradient_no)) +
scale_shape_manual(values = c(21, 24, 22, 22, 24)) +
scale_color_manual(values = c('black', 'orange', 'black')) +
scale_fill_manual(values = c('black', 'black', 'black', 'white', 'white')) +
scale_x_continuous(limits = c(0,0.8), breaks = seq(0,0.8, by = 0.1), labels = scales::number_format(accuracy = 0.1), position = 'top') +
scale_y_continuous(limits = c(-16,0), breaks = seq(-16,0, by = 2), labels = scales::number_format(accuracy = 1)) +
labs(x = 'Perennial macroalgae growth inhibition by benthic light\n', y = '')
WB_monod_BC2_light + theme_pub() +
theme(
plot.margin = unit(c(20,1,1,1), "pt"),
legend.justification = c(1,1.6),
legend.direction = 'vertical',
legend.box = 'vertical'
)
WB_monod_BC2_DIN + theme_pub() + theme(plot.margin = unit(c(15,1,1,1), "pt"), axis.title = element_text(size = 18), legend.justification = c(5.5,1.75),
legend.direction = 'vertical',
legend.box = 'vertical') +
WB_monod_BC2_DIP + theme_pub() + theme(plot.margin = unit(c(15,1,1,1), "pt"), axis.title = element_text(size = 18), legend.justification = c(5.5,1.75),
legend.direction = 'vertical',
legend.box = 'vertical') +
WB_monod_BC2_light + theme_pub() + theme(plot.margin = unit(c(15,1,1,1), "pt"), axis.title = element_text(size = 18), legend.justification = c(1,1.75),
legend.direction = 'vertical',
legend.box = 'vertical') +
plot_layout(ncol = 1) +
ggsave(filename = 'WB_monod_BC2.tiff',
width = 20,
height = 40,
units = 'cm',
device='tiff',
dpi=300)
}
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