8  Results

Based on what is describes in the preceding chapters, we have arrived at a concept for an indicator on the severity of trenching in Norwegian wetlands. We base the indicator on the theoretical content of the NiN variable 7GR-GI for trenching and the approach used in the 7TK variable, using the proportion of 10x10m grid cells inside 100x100m grid cells that have non-zero trenching as the metric. The reference levels (RL) are decided through expert elicitation, and we have 6 RLs, including 4 class boundaries and two end points. We use a piece-wise linear scaling to convert the variable into an indicator.

The indicator will be updated using spatial data showing the amount of trenched restored (Chapter 4), as well as updates to the ecosystem delineation maps. As the ecosystem delineation we recoment using AR5 in combination with the open wetland map (see Chapter 2).

myNorm <- function(x, x0, x20, x40, x60, x80, x100) {
  y <- case_when(
    x >x100 ~ 1,
    x >x80 ~ (x-x80)/(x100-x80)*(1 - 0.8)   + 0.8,
    x >x60 ~ (x-x60)/(x80-x60) *(0.8 - 0.6) + 0.6,
    x >x40 ~ (x-x40)/(x60-x40) *(0.6 - 0.4) +0.4,
    x >x20 ~ (x-x20)/(x40-x20) *(0.4 - 0.2) +0.2,
    x >x0  ~ (x-x0) /(x20-x0) *0.2,
    x <=x0  ~ 0
  )
  y <- y * (-1) + 1  
  return(y)
}

# Testing
#var <- seq(0, 1000, by = 5)
#ind <- myNorm(var, 100, 200, 250, 600, 700, 800)
#tibble(var, ind) |>
#  ggplot()+
#  geom_point(aes(var, ind))+
#  scale_y_continuous(breaks = seq(0,1, by = .1))

dat <- readRDS("data/grid100_freq4.rds")

dat <- dat |>
  mutate(indicator2 = myNorm(dat$freq, 0, 7, 16, 31, 52, 100))


ridge <- ggridges::geom_density_ridges_gradient(scale = 3, 
      size = 0.3)

Warning in ggridges::geom_density_ridges_gradient(scale = 3, size = 0.3):
Ignoring unknown parameters: `size`

myColour <- "black"
mySize <- 8
myAlpha <- 0.7
myShape <- 21
low <- "red"
high <- "green"

dat|>
   ggplot(aes(x = freq, y = indicator2, 
      fill = indicator2)) + geom_point(colour = myColour, 
      size = mySize, alpha = myAlpha, shape = myShape) + 
      ylab("Indicator values") + xlab("Variable values") + 
      scale_fill_gradient("Indicator values", low = low, 
        high = high) + scale_x_continuous(expand = expansion(mult = 0.2)) + 
      scale_y_continuous(expand = expansion(mult = 0.2)) + 
      guides(fill = "none")

Figure 8.1: Normalisation of the variable using piece-wise linear transformation.

trench_in_cell <- readRDS("data/trench_in_cell.RDS")
low <- "red"
high <- "green"
dat |>
  ggplot() +
  geom_sf(aes(fill = indicator2))+
  theme_bw() +
  geom_sf(data = trench_in_cell)+
  scale_fill_gradient(
    "Indicator value", 
    low = low, 
    high = high)

Figure 8.2: Indicator values representing the severity of trenching in wetlands

dat <- dat |>
  mutate(area = st_area(sf..st_make_grid.lev_crop..cellsize...100.))

ind_values <- NULL
for(i in 1:1000){
  ind_values[i] <- 
    mean(sample(dat$indicator, 
       nrow(dat),
       replace = TRUE,
       prob = dat$area))
}

med <- round(quantile(ind_values, c(0.025, 0.5, 0.975)),2)

The indicator value for the area in Figure 8.2 is 0.54 [95% CI: 0.47 - 0.6].