Relationships between EVI, NPP and ABI with climate variables

This figure corresponds to figure 2 in the manuscript.
An high-resolution version of this figure is available here.

Code

# pkgs 
library(tidyverse)
library(ggh4x)
library(patchwork)
library(here)
library(glue)
library(broom)
source(here::here("scripts/aux.R"))


# Read data
## ABI
abi <- read_csv(here::here("data/abi.csv")) |> 
  mutate(se = NA, sd = NA, variable = "abi") |> 
  rename(mean = abi)

## annual pet
annual_pet <- read_csv(here::here("data/spei_climate.csv")) |> 
  dplyr::select(sp_elev, year, monthly_pet, monthly_tmed, monthly_prec) |> 
  group_by(sp_elev, year) |>
  summarise(pet = sum(monthly_pet), 
            prec = sum(monthly_prec),
            tmed = mean(monthly_tmed, na.rm = TRUE)) |> 
  rowwise() |> 
  mutate(water_balance = prec - pet)


## EVI Landsat 
evi_landsat <- read_csv(here::here("data/iv_landsat.csv")) |>
  filter(iv == "evi") |>
  dplyr::select(year, sp_code, elev_code, sp_elev, mean, sd, se) |>
  mutate(variable = "evi_landsat")

## NPP MODIS
npp <- read_csv(here::here("data/npp_modis.csv")) |>
  rename(mean = npp) |>
  mutate(se = NA, sd = NA, variable = "npp") |>
  dplyr::select(year, sp_code, elev_code, sp_elev, mean, sd, se, variable)


df_index <- bind_rows(
  abi, evi_landsat, npp) |> 
  mutate(elev_code = fct_relevel(elev_code, "low-Dec","low", "medium", "high")) |> 
  mutate(Specie = paste0("P. ", sp_code)) |> 
  rename(mean_y = mean, y_variable = variable)

df <- df_index |> 
  inner_join(annual_pet) |> 
  pivot_longer(pet:water_balance, values_to = "mean_climate", 
               names_to = "climate_variable")

df_plot <- df |> 
  filter(year > 1986) |> 
  mutate(y_variable2 = case_when(
    y_variable == "abi" ~ "ABI~(g~C~m^2~year^{-1})",
    y_variable == "evi_landsat" ~ "EVI[Landsat]",
    y_variable == "npp" ~"NPP[MODIS]~(g~C~m^2~year^{-1})")) |> 
  mutate(y_variable2 = fct_relevel(y_variable2, 
                                   "EVI[Landsat]", 
                                   "NPP[MODIS]~(g~C~m^2~year^{-1})",
                                   "ABI~(g~C~m^2~year^{-1})"))




# Generate tibble with y max values. Caution, check there are equal to ylimits specified below for plot
ymax_panel <- tibble(
   y_variable = c("evi_landsat", "npp", "abi"),
   y_max = c(0.5, 1250, 700))

# Generate another tibble with linear regression
lr <- df_plot |> 
  filter(climate_variable != "pet") |> 
  group_by(y_variable, climate_variable, sp_code) |> 
  group_modify(~ {
    # Ajusta el modelo
    mod <- lm(mean_y ~ mean_climate, data = .x)
    
    # Tidy con coeficientes
    tidy_df <- broom::tidy(mod)
    
    # Añade R2 a cada fila
    tidy_df$r.squared <- broom::glance(mod)$r.squared
    
    tidy_df
  }) |>
  mutate(p = case_when(
    p.value < 0.001 ~ "<0.001",
    p.value >= 0.001 ~ as.character(round(p.value, 3))
  )) |> 
  mutate(sig = case_when(
    p.value < 0.001 ~ "sig",
    p.value >= 0.001 ~ "no sig"
  ))

# Generate equations 
l <- lr |> 
  left_join(
    lr |> filter(term == "(Intercept)") |> 
      select(y_variable, climate_variable, sp_code, intercept = estimate), 
    by = c("y_variable", "climate_variable", "sp_code")
  ) |> 
  mutate(
    eq_text = if_else(
      sig == "sig" & term == "mean_climate",
      glue("y = {round(estimate, 2)}x {ifelse(intercept < 0, '-', '+')} {round(abs(intercept), 2)}; R² = {round(r.squared, 2)}"),
     NA
    )
  ) |> 
  filter(term != "(Intercept)") |> 
  mutate(Specie = paste0("P. ", sp_code)) |> 
  mutate(y_variable2 = case_when(
    y_variable == "evi_landsat" ~ "EVI[Landsat]",
    y_variable == "npp" ~ "NPP[MODIS]~(g~C~m^2~year^{-1})",
    y_variable == "abi" ~ "ABI~(g~C~m^2~year^{-1})"
  )) 

# Filter equations 
l_eq <- l |> filter(!is.na(eq_text)) |> 
  left_join(ymax_panel, by = "y_variable") |> 
  group_by(y_variable, climate_variable) |> 
  mutate(
    offset_y = row_number() * 0.05 * y_max,  # Ajusta el offset según sea necesario
    y = y_max - offset_y
  ) |> 
  ungroup()





df_plot <- 
  df_plot |> left_join(
    l |> dplyr::select(y_variable, climate_variable, sp_code, p, sig)) |> 
  mutate(sp_code = fct_relevel(sp_code, "halepensis","pinaster", "nigra", "sylvestris")) |> 
  mutate(Specie = fct_relevel(Specie, "P. halepensis","P. pinaster", "P. nigra", "P. sylvestris"))


# Plot 
## general_parameters

alpha_points <- 0.3
main_line_width <- 1
main_line_color <- "black"
partial_lines_width <- 1.1
size_points <- 1.7
stroke_points <- 0.1 

label_landsat <- "EVI[Landsat]"
label_npp <- "NPP[MODIS]~(g~C~m^2~year^{-1})"
label_abi <- "ABI~(g~C~m^2~year^{-1})"
label_wb <- "P-PET (mm)"
label_prec <- "Precipitation (mm)"

y_scales <- list(
  scale_y_continuous(limits = c(0, 0.5)),
  scale_y_continuous(limits = c(0,1250)),
  scale_y_continuous(limits = c(0, 700))
)

## Custom themes 
custom_scales <- list(
  scale_colour_manual(
    values = colours_Specie, 
    labels = expression(italic("P. halepensis"), italic("P. pinaster"), italic("P. nigra"), italic("P. sylvestris")),
    name = "Species"),
  scale_fill_manual(
    values = colours_Specie, 
    labels = expression(italic("P. halepensis"), italic("P. pinaster"), italic("P. nigra"), italic("P. sylvestris")),
    name = "Species"),
  scale_shape_manual(values = shape_elev, name = "Elevation")
)

base_theme <- list(
  theme_bw(),
  theme(text = element_text(family = "Helvetica"),
        axis.title.x = element_text(face = "bold", size = 12),
        strip.text = element_text(face = "bold", size = 12),
        axis.text = element_text(size = 10),
        legend.text = element_text(size = 8))
) 

custom_theme_figure <- list(
  theme(
    panel.grid = element_blank(), 
    strip.background = element_blank(),
    strip.placement = "outside",
    strip.text.y = element_text(vjust = 0)
  )
) 


## Letters for label 
letras <- data.frame(
  tipo = rep(c("prec", "temp", "wb"), each = 3),  
  y_variable2 = rep(
    c("EVI[Landsat]", "NPP[MODIS]~(g~C~m^2~year^{-1})", "ABI~(g~C~m^2~year^{-1})"),
    times = 3
  ),
  label = c("a", "b", "c", "d", "e", "f", "f", "g", "h"),  
  x = -Inf,  
  y = Inf    
)


# ----------

plot_prec <- 
  df_plot|> 
  filter(climate_variable == "prec") |> 
  ggplot(aes(x=mean_climate, y = mean_y, colour = Specie)) + 
  geom_point(aes(shape = elev_code, fill = Specie), 
             alpha = alpha_points, size = size_points, stroke = stroke_points) + 
  geom_smooth(aes(linetype = sig), linewidth = partial_lines_width, method = "lm", se = FALSE) +
  scale_linetype_manual(values = lines_lm, guide = "none") + 
  geom_smooth(aes(), 
              method = "nls", 
              formula=y~SSlogis(x, Asym, xmid, scal),
              se =  FALSE, # this is important 
              linewidth = main_line_width, colour = main_line_color) +
  ylab("") + 
  xlab("Precipitation (mm)") +
  custom_scales + 
  base_theme + 
  custom_theme_figure + 
  theme(
    legend.position = "top"
    # legend.box = "vertical"
  ) + 
  scale_x_continuous(limits = c(0, 1750)) +
  facet_wrap(~factor(y_variable2, c("EVI[Landsat]", 
                                    "NPP[MODIS]~(g~C~m^2~year^{-1})",
                                    "ABI~(g~C~m^2~year^{-1})")),
             scales = "free_y", 
             labeller = label_parsed, 
             strip.position = "left") +
  ggh4x::facetted_pos_scales(y = y_scales) +
  geom_text(data = (letras |> filter(tipo == "prec")), 
            aes(x = x, y = y, label = label),
            vjust = 1.2, hjust = -1.2, size = 5, 
            fontface = "bold", inherit.aes = FALSE) +
  geom_text(data = l_eq |> filter(climate_variable == "prec"), 
            aes(x = Inf, y = y, label = eq_text), 
            hjust = 1.1, vjust = 1.1, size = 2.75,
            fontface = "bold", show.legend = FALSE) +
  guides(
    colour = guide_legend(override.aes = list(size = 4)), 
    shape = guide_legend(override.aes = list(size = 4)), 
  )




# Add NLS for EVI and Temperature (not converge with nls general)

df_plot_tmed_evi <- df_plot |> filter(y_variable == "evi_landsat", climate_variable == "tmed")
nls_fit <- nls(mean_y ~ a * exp(-b * mean_climate) + c, 
               start = list(
                 a = max(df_plot_tmed_evi$mean_y) - min(df_plot_tmed_evi$mean_y),  
                 b = 0.1,  
                 c = min(df_plot_tmed_evi$mean_y)  
               ),
               data = df_plot_tmed_evi,
               control = nls.control(maxiter = 10000, tol = 1e-5, minFactor = 1e-7))

df_pred_tmed_evi <- data.frame(
  mean_climate =
    seq(min(df_plot_tmed_evi$mean_climate), max(df_plot_tmed_evi$mean_climate),
      length.out = 100
    )
) |>
  mutate(y_variable = "evi_landsat", climate_variable = "tmed") |> 
  mutate(y_variable2 = "EVI[Landsat]")

df_pred_tmed_evi$mean_y <- predict(nls_fit, newdata = df_pred_tmed_evi) 


df_pred_tmed_evi <- df_pred_tmed_evi |> 
  bind_rows(
    data.frame(
      mean_climate = c(NA,NA),
      mean_y = c(NA,NA),
      y_variable = c("abi", "npp"),
      y_variable2 = 
        c("NPP[MODIS]~(g~C~m^2~year^{-1})",
          "ABI~(g~C~m^2~year^{-1})"), 
      climate_variable = c("tmed", "tmed")
    )
  )
  

plot_tmed <- 
  df_plot |> 
  filter(climate_variable == "tmed") |> 
  ggplot(aes(x=mean_climate, y = mean_y, colour = Specie)) + 
  geom_point(aes(shape = elev_code, fill = Specie), 
             alpha = alpha_points, size = size_points, stroke = stroke_points) +
  geom_smooth(aes(linetype = sig), linewidth = partial_lines_width, method = "lm", se = FALSE) +
  scale_linetype_manual(values = lines_lm, guide = "none") +
  geom_smooth(
    data = df_plot |> filter(y_variable != "evi_landsat"),
    aes(),
              method = "nls", 
              formula = y ~ SSlogis(x, Asym, xmid, scal),
              se =  FALSE, # this is important 
              linewidth = main_line_width, colour = main_line_color) +
  ylab("") + 
  xlab("Annual Mean Temperature (ºC)") +
  custom_scales + 
  base_theme + 
  custom_theme_figure + 
  theme(
    legend.position = "none", 
  ) + 
  scale_x_continuous(limits = c(8, 16)) +
  facet_wrap(~factor(y_variable2, c("EVI[Landsat]", 
                                    "NPP[MODIS]~(g~C~m^2~year^{-1})",
                                    "ABI~(g~C~m^2~year^{-1})")),
             scales = "free_y", 
             labeller = label_parsed, 
             strip.position = "left") +
  facetted_pos_scales(y = y_scales) +
  geom_text(data = (letras |> filter(tipo == "temp")), 
            aes(x = x, y = y, label = label),
            vjust = 1.2, hjust = -1.2, size = 5, 
            fontface = "bold", inherit.aes = FALSE) +
  geom_text(data = l_eq |> filter(climate_variable == "tmed"),
            aes(x = Inf, y = y, label = eq_text, colour = Specie), 
            hjust = 1.1, vjust = 1.1, size = 2.75, inherit.aes = FALSE,
            fontface = "bold", show.legend = FALSE) + 
  geom_line(data = df_pred_tmed_evi,
            aes(x = mean_climate, y = mean_y), 
            linewidth = main_line_width, 
            colour = main_line_color)

plot_wb <- 
  df_plot|> 
  filter(climate_variable == "water_balance") |> 
  ggplot(aes(x=mean_climate, y = mean_y, colour = Specie)) + 
  geom_point(aes(shape = elev_code, fill = Specie), alpha = alpha_points, size = size_points, stroke = stroke_points) +
  geom_smooth(aes(linetype = sig), linewidth = partial_lines_width, method = "lm", se = FALSE) +
  scale_linetype_manual(values = lines_lm, guide = "none") +
  geom_smooth(aes(), 
              method = "nls", 
              formula=y~SSlogis(x, Asym, xmid, scal),
              se =  FALSE, # this is important 
              linewidth = main_line_width, colour = main_line_color) + 
  ylab("") + 
  xlab("Water balance (mm)") +
  custom_scales + 
  base_theme + 
  custom_theme_figure + 
  theme(
    legend.position = "none", 
  ) + 
  scale_x_continuous(limits = c(-900, 1200)) +
  facet_wrap(~factor(y_variable2, c("EVI[Landsat]", 
                                    "NPP[MODIS]~(g~C~m^2~year^{-1})",
                                    "ABI~(g~C~m^2~year^{-1})")),
             scales = "free_y", 
             labeller = label_parsed, 
             strip.position = "left") +
  facetted_pos_scales(y = y_scales) +
  geom_text(data = (letras |> filter(tipo == "wb")), 
            aes(x = x, y = y, label = label),
            vjust = 1.2, hjust = -1.2, size = 5, 
            fontface = "bold", inherit.aes = FALSE) +
  geom_text(data = l_eq |> filter(climate_variable == "water_balance"),
            aes(x = Inf, y = y, label = eq_text, colour = Specie), 
            hjust = 1.1, vjust = 1.1, size = 2.75, inherit.aes = FALSE,
            fontface = "bold", show.legend = FALSE) 

wb_prec_tmed <- plot_prec / plot_tmed /plot_wb  &
  guides(
    fill = guide_legend(order = 1, override.aes = list(size = 3)),
    colour = guide_legend(order = 1),
    shape = guide_legend(override.aes = list(stroke = .5, size = 3, colour = "black")), 
  )

wb_prec_tmed

Relationships between annual Enhanced Vegetation Index (EVI) (1986-2022), annual Net Primary Productivity (NPP) (2001-2022), and Aboveground Biomass Increment (ABI) (1986-2022) with precipitation (a-c), annual mean temperature (d-f), and annual water balance (f-h). The global trends for all pine species are depicted by a black line (nonlinear least squares), while color-coded lines indicate relations for individual pine species (solid line: significant (p< 0.001), dashed line: not significant).

--- title: "Relationships between EVI, NPP and ABI with climate variables" format: html: toc: false execute: message: false warning: false editor_options: chunk_output_type: console --- - This figure corresponds to figure 2 in the manuscript. - An high-resolution version of this figure is available [here](../output/plot_combined_wb_prec_tmed.png). ```{r} #| code-fold: true #| fig-cap: "Relationships between annual Enhanced Vegetation Index (EVI) (1986-2022), annual Net Primary Productivity (NPP) (2001-2022), and Aboveground Biomass Increment (ABI) (1986-2022) with precipitation (a-c), annual mean temperature (d-f), and annual water balance (f-h). The global trends for all pine species are depicted by a black line (nonlinear least squares), while color-coded lines indicate relations for individual pine species (solid line: significant (p< 0.001), dashed line: not significant)." #| fig-width: 12 #| fig-height: 12 # pkgs library(tidyverse) library(ggh4x) library(patchwork) library(here) library(glue) library(broom) source(here::here("scripts/aux.R")) # Read data ## ABI abi <- read_csv(here::here("data/abi.csv")) |> mutate(se = NA, sd = NA, variable = "abi") |> rename(mean = abi) ## annual pet annual_pet <- read_csv(here::here("data/spei_climate.csv")) |> dplyr::select(sp_elev, year, monthly_pet, monthly_tmed, monthly_prec) |> group_by(sp_elev, year) |> summarise(pet = sum(monthly_pet), prec = sum(monthly_prec), tmed = mean(monthly_tmed, na.rm = TRUE)) |> rowwise() |> mutate(water_balance = prec - pet) ## EVI Landsat evi_landsat <- read_csv(here::here("data/iv_landsat.csv")) |> filter(iv == "evi") |> dplyr::select(year, sp_code, elev_code, sp_elev, mean, sd, se) |> mutate(variable = "evi_landsat") ## NPP MODIS npp <- read_csv(here::here("data/npp_modis.csv")) |> rename(mean = npp) |> mutate(se = NA, sd = NA, variable = "npp") |> dplyr::select(year, sp_code, elev_code, sp_elev, mean, sd, se, variable) df_index <- bind_rows( abi, evi_landsat, npp) |> mutate(elev_code = fct_relevel(elev_code, "low-Dec","low", "medium", "high")) |> mutate(Specie = paste0("P. ", sp_code)) |> rename(mean_y = mean, y_variable = variable) df <- df_index |> inner_join(annual_pet) |> pivot_longer(pet:water_balance, values_to = "mean_climate", names_to = "climate_variable") df_plot <- df |> filter(year > 1986) |> mutate(y_variable2 = case_when( y_variable == "abi" ~ "ABI~(g~C~m^2~year^{-1})", y_variable == "evi_landsat" ~ "EVI[Landsat]", y_variable == "npp" ~"NPP[MODIS]~(g~C~m^2~year^{-1})")) |> mutate(y_variable2 = fct_relevel(y_variable2, "EVI[Landsat]", "NPP[MODIS]~(g~C~m^2~year^{-1})", "ABI~(g~C~m^2~year^{-1})")) # Generate tibble with y max values. Caution, check there are equal to ylimits specified below for plot ymax_panel <- tibble( y_variable = c("evi_landsat", "npp", "abi"), y_max = c(0.5, 1250, 700)) # Generate another tibble with linear regression lr <- df_plot |> filter(climate_variable != "pet") |> group_by(y_variable, climate_variable, sp_code) |> group_modify(~ { # Ajusta el modelo mod <- lm(mean_y ~ mean_climate, data = .x) # Tidy con coeficientes tidy_df <- broom::tidy(mod) # Añade R2 a cada fila tidy_df$r.squared <- broom::glance(mod)$r.squared tidy_df }) |> mutate(p = case_when( p.value < 0.001 ~ "<0.001", p.value >= 0.001 ~ as.character(round(p.value, 3)) )) |> mutate(sig = case_when( p.value < 0.001 ~ "sig", p.value >= 0.001 ~ "no sig" )) # Generate equations l <- lr |> left_join( lr |> filter(term == "(Intercept)") |> select(y_variable, climate_variable, sp_code, intercept = estimate), by = c("y_variable", "climate_variable", "sp_code") ) |> mutate( eq_text = if_else( sig == "sig" & term == "mean_climate", glue("y = {round(estimate, 2)}x {ifelse(intercept < 0, '-', '+')} {round(abs(intercept), 2)}; R² = {round(r.squared, 2)}"), NA ) ) |> filter(term != "(Intercept)") |> mutate(Specie = paste0("P. ", sp_code)) |> mutate(y_variable2 = case_when( y_variable == "evi_landsat" ~ "EVI[Landsat]", y_variable == "npp" ~ "NPP[MODIS]~(g~C~m^2~year^{-1})", y_variable == "abi" ~ "ABI~(g~C~m^2~year^{-1})" )) # Filter equations l_eq <- l |> filter(!is.na(eq_text)) |> left_join(ymax_panel, by = "y_variable") |> group_by(y_variable, climate_variable) |> mutate( offset_y = row_number() * 0.05 * y_max, # Ajusta el offset según sea necesario y = y_max - offset_y ) |> ungroup() df_plot <- df_plot |> left_join( l |> dplyr::select(y_variable, climate_variable, sp_code, p, sig)) |> mutate(sp_code = fct_relevel(sp_code, "halepensis","pinaster", "nigra", "sylvestris")) |> mutate(Specie = fct_relevel(Specie, "P. halepensis","P. pinaster", "P. nigra", "P. sylvestris")) # Plot ## general_parameters alpha_points <- 0.3 main_line_width <- 1 main_line_color <- "black" partial_lines_width <- 1.1 size_points <- 1.7 stroke_points <- 0.1 label_landsat <- "EVI[Landsat]" label_npp <- "NPP[MODIS]~(g~C~m^2~year^{-1})" label_abi <- "ABI~(g~C~m^2~year^{-1})" label_wb <- "P-PET (mm)" label_prec <- "Precipitation (mm)" y_scales <- list( scale_y_continuous(limits = c(0, 0.5)), scale_y_continuous(limits = c(0,1250)), scale_y_continuous(limits = c(0, 700)) ) ## Custom themes custom_scales <- list( scale_colour_manual( values = colours_Specie, labels = expression(italic("P. halepensis"), italic("P. pinaster"), italic("P. nigra"), italic("P. sylvestris")), name = "Species"), scale_fill_manual( values = colours_Specie, labels = expression(italic("P. halepensis"), italic("P. pinaster"), italic("P. nigra"), italic("P. sylvestris")), name = "Species"), scale_shape_manual(values = shape_elev, name = "Elevation") ) base_theme <- list( theme_bw(), theme(text = element_text(family = "Helvetica"), axis.title.x = element_text(face = "bold", size = 12), strip.text = element_text(face = "bold", size = 12), axis.text = element_text(size = 10), legend.text = element_text(size = 8)) ) custom_theme_figure <- list( theme( panel.grid = element_blank(), strip.background = element_blank(), strip.placement = "outside", strip.text.y = element_text(vjust = 0) ) ) ## Letters for label letras <- data.frame( tipo = rep(c("prec", "temp", "wb"), each = 3), y_variable2 = rep( c("EVI[Landsat]", "NPP[MODIS]~(g~C~m^2~year^{-1})", "ABI~(g~C~m^2~year^{-1})"), times = 3 ), label = c("a", "b", "c", "d", "e", "f", "f", "g", "h"), x = -Inf, y = Inf ) # ---------- plot_prec <- df_plot|> filter(climate_variable == "prec") |> ggplot(aes(x=mean_climate, y = mean_y, colour = Specie)) + geom_point(aes(shape = elev_code, fill = Specie), alpha = alpha_points, size = size_points, stroke = stroke_points) + geom_smooth(aes(linetype = sig), linewidth = partial_lines_width, method = "lm", se = FALSE) + scale_linetype_manual(values = lines_lm, guide = "none") + geom_smooth(aes(), method = "nls", formula=y~SSlogis(x, Asym, xmid, scal), se = FALSE, # this is important linewidth = main_line_width, colour = main_line_color) + ylab("") + xlab("Precipitation (mm)") + custom_scales + base_theme + custom_theme_figure + theme( legend.position = "top" # legend.box = "vertical" ) + scale_x_continuous(limits = c(0, 1750)) + facet_wrap(~factor(y_variable2, c("EVI[Landsat]", "NPP[MODIS]~(g~C~m^2~year^{-1})", "ABI~(g~C~m^2~year^{-1})")), scales = "free_y", labeller = label_parsed, strip.position = "left") + ggh4x::facetted_pos_scales(y = y_scales) + geom_text(data = (letras |> filter(tipo == "prec")), aes(x = x, y = y, label = label), vjust = 1.2, hjust = -1.2, size = 5, fontface = "bold", inherit.aes = FALSE) + geom_text(data = l_eq |> filter(climate_variable == "prec"), aes(x = Inf, y = y, label = eq_text), hjust = 1.1, vjust = 1.1, size = 2.75, fontface = "bold", show.legend = FALSE) + guides( colour = guide_legend(override.aes = list(size = 4)), shape = guide_legend(override.aes = list(size = 4)), ) # Add NLS for EVI and Temperature (not converge with nls general) df_plot_tmed_evi <- df_plot |> filter(y_variable == "evi_landsat", climate_variable == "tmed") nls_fit <- nls(mean_y ~ a * exp(-b * mean_climate) + c, start = list( a = max(df_plot_tmed_evi$mean_y) - min(df_plot_tmed_evi$mean_y), b = 0.1, c = min(df_plot_tmed_evi$mean_y) ), data = df_plot_tmed_evi, control = nls.control(maxiter = 10000, tol = 1e-5, minFactor = 1e-7)) df_pred_tmed_evi <- data.frame( mean_climate = seq(min(df_plot_tmed_evi$mean_climate), max(df_plot_tmed_evi$mean_climate), length.out = 100 ) ) |> mutate(y_variable = "evi_landsat", climate_variable = "tmed") |> mutate(y_variable2 = "EVI[Landsat]") df_pred_tmed_evi$mean_y <- predict(nls_fit, newdata = df_pred_tmed_evi) df_pred_tmed_evi <- df_pred_tmed_evi |> bind_rows( data.frame( mean_climate = c(NA,NA), mean_y = c(NA,NA), y_variable = c("abi", "npp"), y_variable2 = c("NPP[MODIS]~(g~C~m^2~year^{-1})", "ABI~(g~C~m^2~year^{-1})"), climate_variable = c("tmed", "tmed") ) ) plot_tmed <- df_plot |> filter(climate_variable == "tmed") |> ggplot(aes(x=mean_climate, y = mean_y, colour = Specie)) + geom_point(aes(shape = elev_code, fill = Specie), alpha = alpha_points, size = size_points, stroke = stroke_points) + geom_smooth(aes(linetype = sig), linewidth = partial_lines_width, method = "lm", se = FALSE) + scale_linetype_manual(values = lines_lm, guide = "none") + geom_smooth( data = df_plot |> filter(y_variable != "evi_landsat"), aes(), method = "nls", formula = y ~ SSlogis(x, Asym, xmid, scal), se = FALSE, # this is important linewidth = main_line_width, colour = main_line_color) + ylab("") + xlab("Annual Mean Temperature (ºC)") + custom_scales + base_theme + custom_theme_figure + theme( legend.position = "none", ) + scale_x_continuous(limits = c(8, 16)) + facet_wrap(~factor(y_variable2, c("EVI[Landsat]", "NPP[MODIS]~(g~C~m^2~year^{-1})", "ABI~(g~C~m^2~year^{-1})")), scales = "free_y", labeller = label_parsed, strip.position = "left") + facetted_pos_scales(y = y_scales) + geom_text(data = (letras |> filter(tipo == "temp")), aes(x = x, y = y, label = label), vjust = 1.2, hjust = -1.2, size = 5, fontface = "bold", inherit.aes = FALSE) + geom_text(data = l_eq |> filter(climate_variable == "tmed"), aes(x = Inf, y = y, label = eq_text, colour = Specie), hjust = 1.1, vjust = 1.1, size = 2.75, inherit.aes = FALSE, fontface = "bold", show.legend = FALSE) + geom_line(data = df_pred_tmed_evi, aes(x = mean_climate, y = mean_y), linewidth = main_line_width, colour = main_line_color) plot_wb <- df_plot|> filter(climate_variable == "water_balance") |> ggplot(aes(x=mean_climate, y = mean_y, colour = Specie)) + geom_point(aes(shape = elev_code, fill = Specie), alpha = alpha_points, size = size_points, stroke = stroke_points) + geom_smooth(aes(linetype = sig), linewidth = partial_lines_width, method = "lm", se = FALSE) + scale_linetype_manual(values = lines_lm, guide = "none") + geom_smooth(aes(), method = "nls", formula=y~SSlogis(x, Asym, xmid, scal), se = FALSE, # this is important linewidth = main_line_width, colour = main_line_color) + ylab("") + xlab("Water balance (mm)") + custom_scales + base_theme + custom_theme_figure + theme( legend.position = "none", ) + scale_x_continuous(limits = c(-900, 1200)) + facet_wrap(~factor(y_variable2, c("EVI[Landsat]", "NPP[MODIS]~(g~C~m^2~year^{-1})", "ABI~(g~C~m^2~year^{-1})")), scales = "free_y", labeller = label_parsed, strip.position = "left") + facetted_pos_scales(y = y_scales) + geom_text(data = (letras |> filter(tipo == "wb")), aes(x = x, y = y, label = label), vjust = 1.2, hjust = -1.2, size = 5, fontface = "bold", inherit.aes = FALSE) + geom_text(data = l_eq |> filter(climate_variable == "water_balance"), aes(x = Inf, y = y, label = eq_text, colour = Specie), hjust = 1.1, vjust = 1.1, size = 2.75, inherit.aes = FALSE, fontface = "bold", show.legend = FALSE) wb_prec_tmed <- plot_prec / plot_tmed /plot_wb & guides( fill = guide_legend(order = 1, override.aes = list(size = 3)), colour = guide_legend(order = 1), shape = guide_legend(override.aes = list(stroke = .5, size = 3, colour = "black")), ) wb_prec_tmed ``` ```{r} #| echo: false ggsave( wb_prec_tmed, file = here::here("output/plot_combined_wb_prec_tmed.png"), dpi = 400, width = 7.09*1.3, height = 7.09*1.3 ) ```