Norway’s Farmed Salmon Mortality Shows Little Improvement Since 2020

National trend, regional gaps, and loss composition in Atlantic salmon farming, 2020–2025

A three-panel analysis of Norwegian farmed salmon mortality data (2020–2025) from the Norwegian Veterinary Institute. Explores whether national mortality rates have improved over time, how much variation exists across farming counties, and what types of losses — beyond death — characterize the industry.

Figure 1: A three-panel chart titled “Norway’s Farmed Salmon Mortality Shows Little Improvement Since 2020.” Panel A shows national median monthly mortality for Atlantic salmon from 2020 to 2025 with an interquartile range ribbon. The trend fluctuates seasonally but hovers near the 2020 baseline of roughly 0.5%, with no sustained downward movement. Panel B is a horizontal lollipop chart of average median mortality by Norwegian county. Vestland, Agder & Rogaland, and Møre og Romsdal sit above the national average in burgundy; Nordland, Troms, and Finnmark fall well below in slate blue; Trøndelag sits near the national average in gray. Panel C shows stacked area charts of monthly loss composition for Atlantic Salmon and Rainbow Trout from 2020 to 2025. Dead fish in burgundy dominate both species, accounting for roughly 75–90% of losses, with discarded, escaped, and other losses forming a smaller but persistent share at the base.

Steps to Create this Graphic

1. Load Packages & Setup

```{r}
#| label: load
#| warning: false
#| message: false      
#| results: "hide"     

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
if (!require("pacman")) install.packages("pacman")
pacman::p_load(
    tidyverse, ggtext, showtext, janitor,      
    scales, glue, skimr, patchwork, lubridate    
    )
})

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  = 12,
  height = 10,
  units  = "in",
  dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false

tt <- tidytuesdayR::tt_load(2026, week = 11)
monthly_losses_data <- tt$monthly_losses_data |> clean_names()
monthly_mortality_data <- tt$monthly_mortality_data |> clean_names()
rm(tt)
```

3. Examine the Data

```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(monthly_losses_data)
glimpse(monthly_mortality_data)

skim_without_charts(monthly_losses_data)
skim_without_charts(monthly_mortality_data)
```

4. Tidy Data

```{r}
#| label: tidy
#| warning: false

### |- species label lookup ----
species_labels <- c(
  "salmon"       = "Atlantic Salmon",
  "rainbowtrout" = "Rainbow Trout"
)

### |- Panel A: national trend (Atlantic Salmon only) ----
panel_a_data <- monthly_mortality_data |>
  filter(
    geo_group == "country",
    species == "salmon"
  ) |>
  arrange(date)

baseline_2020 <- panel_a_data |>
  filter(year(date) == 2020) |>
  summarise(baseline = mean(median, na.rm = TRUE)) |>
  pull(baseline)

### |- Panel B: county-level regional comparison (Atlantic Salmon) ----
panel_b_data <- monthly_mortality_data |>
  filter(
    geo_group == "county",
    species == "salmon"
  ) |>
  group_by(region) |>
  summarise(
    avg_median = mean(median, na.rm = TRUE),
    avg_q1     = mean(q1, na.rm = TRUE),
    avg_q3     = mean(q3, na.rm = TRUE),
    .groups    = "drop"
  )

# Tolerance band around national average to handle near-average regions
national_avg_b <- mean(panel_b_data$avg_median, na.rm = TRUE)
tol <- 0.015

panel_b_data <- panel_b_data |>
  mutate(
    rank_high = min_rank(desc(avg_median)),
    rank_low = min_rank(avg_median),
    region_group = case_when(
      abs(avg_median - national_avg_b) <= tol ~ "Near average",
      avg_median > national_avg_b & rank_high <= 3 ~ "Highest",
      avg_median < national_avg_b & rank_low <= 3 ~ "Lowest",
      TRUE ~ "Middle"
    ),
    region = fct_reorder(region, avg_median)
  )

### |- Panel C: national loss composition (country level, both species) ----
panel_c_data <- monthly_losses_data |>
  filter(
    geo_group == "country",
    losses > 0
  ) |>
  mutate(
    species_label = factor(
      species_labels[species],
      levels = c("Atlantic Salmon", "Rainbow Trout")
    ),
    pct_dead = dead / losses,
    pct_discarded = discarded / losses,
    pct_escaped = escaped / losses,
    pct_other = other / losses
  ) |>
  select(
    date, species_label,
    pct_dead, pct_discarded, pct_escaped, pct_other
  ) |>
  pivot_longer(
    cols      = starts_with("pct_"),
    names_to  = "loss_type",
    values_to = "proportion"
  ) |>
  mutate(
    loss_type = recode(
      loss_type,
      "pct_dead"      = "Dead",
      "pct_discarded" = "Discarded",
      "pct_escaped"   = "Escaped",
      "pct_other"     = "Other"
    ),
    # Keep minority categories at the bottom for visibility
    loss_type = factor(
      loss_type,
      levels = c("Other", "Escaped", "Discarded", "Dead")
    )
  )
```

5. Visualization Parameters

```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
colors <- get_theme_colors(
    palette = list(
        col_salmon    = "#7A1E3A",  
        col_discarded = "#B86B4B",  
        col_escaped   = "#7C93B6",  
        col_other     = "#A7B0AB",   
        col_high      = "#7A1E3A",   
        col_low       = "#6F7D8C",   
        col_mid       = "#C9CDD3",  
        col_ribbon    = "#C9A3AE",   
        col_ref       = "#6B6B6B",   
        col_dark      = "#1F2430",   
        col_gray      = "gray50"
    )
)

### |- titles and caption ----
title_text <- "Norway's Farmed Salmon Mortality Shows Little Improvement Since 2020"

subtitle_text <- paste(
    "National trend, regional gaps, and loss composition",
    "in Atlantic salmon farming, 2020–2025"
)

caption_text <- create_social_caption(
    tt_year     = 2026,
    tt_week     = 11,
    source_text = "Norwegian Veterinary Institute · Laksetap Shiny App & API"
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----
# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # Text styling
    plot.title = element_text(
      face = "bold", family = fonts$title, size = rel(1.1),
      color = colors$title, margin = margin(b = 10), hjust = 0
    ),
    plot.subtitle = element_text(
      face = "italic", family = fonts$subtitle, lineheight = 1.2,
      color = colors$subtitle, size = rel(0.7), margin = margin(b = 20), hjust = 0
    ),

    # Grid
    panel.grid.major.x = element_line(color = "gray90", linewidth = 0.3),
    panel.grid.major.y = element_blank(),
    panel.grid.minor   = element_blank(),
    
    # Axes
    axis.title = element_text(size = rel(0.6), color = "gray30"),
    axis.text = element_text(color = "gray30"),
    axis.text.y = element_text(size = rel(0.6)),
    axis.ticks = element_blank(),
    axis.title.y = element_blank(),

    # Facets
    strip.background = element_rect(fill = "gray95", color = NA),
    strip.text = element_text(
      face = "bold",
      color = "gray20",
      size = rel(0.9),
      margin = margin(t = 6, b = 4)
    ),
    panel.spacing = unit(1.5, "lines"),

    # Legend elements
    legend.position = "plot",
    legend.title = element_text(
      family = fonts$subtitle,
      color = colors$text, size = rel(0.8), face = "bold"
    ),
    legend.text = element_text(
      family = fonts$tsubtitle,
      color = colors$text, size = rel(0.7)
    ),
    legend.margin = margin(t = 15),

    # Plot margin
    plot.margin = margin(10, 20, 10, 20),
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

```{r}
#| label: plot
#| warning: false

### |- Panel A: National Trend ----
pa <- panel_a_data |>
  ggplot(aes(x = date)) +
  # Geoms
  geom_ribbon(
    aes(ymin = q1, ymax = q3),
    fill = colors$palette$col_ribbon,
    alpha = 0.28
  ) +
  geom_line(
    aes(y = median),
    color = colors$palette$col_salmon,
    linewidth = 1.0
  ) +
  geom_hline(
    yintercept = baseline_2020 * 1.06,
    linetype   = "dashed",
    color      = colors$palette$col_ref,
    linewidth  = 0.5,
    alpha      = 0.75
  ) +
  # Annotate
  annotate(
    "text",
    x      = as.Date("2020-03-30"),
    y      = baseline_2020 * 1.35,
    label  = "2020 baseline",
    size   = 2.8,
    color  = colors$palette$col_ref,
    hjust  = 0,
    family = fonts$text
  ) +
  # Scales
  scale_x_date(
    date_breaks = "1 year",
    date_labels = "%Y",
    expand      = c(0.01, 0)
  ) +
  scale_y_continuous(
    labels = label_number(suffix = "%", accuracy = 0.1),
    expand = expansion(mult = c(0.03, 0.12))
  ) +
  # Labs
  labs(
    title    = "A. Has Mortality Improved?",
    subtitle = "National median monthly mortality with IQR · Atlantic salmon",
    x        = NULL,
    y        = "Median monthly\nmortality rate (%)"
  ) +
  # Theme
  theme(
    axis.title.y = element_text(
      size       = rel(0.78),
      lineheight = 1.2,
      angle      = 90,
      margin     = margin(r = 6)
    )
  )

### |- Panel B: Regional Gaps ----
pb <- panel_b_data |>
  ggplot(aes(y = region)) +
  # Geoms
  geom_vline(
    xintercept = national_avg_b,
    linetype   = "dashed",
    color      = colors$palette$col_ref,
    linewidth  = 0.5,
    alpha      = 0.8
  ) +
  geom_segment(
    aes(
      x    = avg_q1,
      xend = avg_q3,
      yend = region
    ),
    color = "gray80",
    linewidth = 0.5,
    alpha = 0.9
  ) +
  geom_segment(
    aes(
      x     = national_avg_b,
      xend  = avg_median,
      yend  = region,
      color = region_group
    ),
    linewidth = 0.9,
    alpha = 0.95
  ) +
  geom_point(
    aes(x = avg_median, color = region_group),
    size = 2.8
  ) +
  # Annotate
  annotate(
    "text",
    x          = national_avg_b + 0.02,
    y          = 6.5,
    label      = "National\naverage",
    size       = 2.5,
    hjust      = 0,
    lineheight = 1.05,
    color      = colors$palette$col_ref,
    family     = fonts$text
  ) +
  # Scales
  scale_color_manual(
    values = c(
      "Highest"      = colors$palette$col_high,
      "Lowest"       = colors$palette$col_low,
      "Near average" = "#8A8F99",
      "Middle"       = colors$palette$col_mid
    )
  ) +
  scale_x_continuous(
    limits = c(0.25, 1.0),
    labels = label_number(suffix = "%", accuracy = 0.1),
    expand = expansion(mult = c(0.01, 0.02))
  ) +
  # Labs
  labs(
    title    = "B. How Wide Are Regional Gaps?",
    subtitle = "Average median mortality by county · Atlantic salmon",
    x        = "Average median mortality (%)",
    y        = NULL
  ) +
  # Theme
  theme(
    axis.text.y = element_text(size = rel(0.75)),
    panel.grid.major.x = element_line(color = "gray93", linewidth = 0.3),
    panel.grid.major.y = element_blank(),
    legend.position = "none",
  )

### |- Panel C: Loss Composition ----
pc <- panel_c_data |>
  ggplot(aes(x = date, y = proportion, fill = loss_type)) +
  # Geoms
  geom_area(
    position = "fill",
    alpha    = 0.88
  ) +
  geom_hline(
    yintercept = 0.75,
    linetype   = "dotted",
    color      = "white",
    linewidth  = 0.45,
    alpha      = 0.55
  ) +
  # Scales
  scale_y_continuous(
    labels = percent_format(accuracy = 1),
    breaks = c(0, 0.25, 0.50, 0.75, 1.00),
    expand = c(0, 0)
  ) +
  scale_x_date(
    breaks = as.Date(c("2020-01-01", "2023-01-01", "2025-01-01")),
    labels = c("2020", "2023", "2025"),
    expand = c(0.01, 0)
  ) +
  scale_fill_manual(
    values = c(
      "Dead"      = colors$palette$col_salmon,
      "Discarded" = colors$palette$col_discarded,
      "Escaped"   = colors$palette$col_escaped,
      "Other"     = colors$palette$col_other
    ),
    breaks = c("Dead", "Discarded", "Escaped", "Other")
  ) +
  # Faceta
  facet_wrap(~species_label, ncol = 2) +
  # Labs
  labs(
    title    = "C. Death Dominates — But Not Entirely",
    subtitle = "Monthly composition of total losses · Country level",
    x        = NULL,
    y        = "Share of\nmonthly losses",
    fill     = "Loss type"
  ) +
  # Theme
  theme(
    legend.position = "top",
    legend.direction = "horizontal",
    legend.justification = "left",
    legend.key.size = unit(0.55, "lines"),
    legend.text = element_text(size = rel(0.75), family = fonts$text),
    legend.title = element_text(
      size   = rel(0.78),
      family = fonts$text,
      face   = "bold"
    ),
    legend.margin = margin(b = 2),
    axis.title.y = element_text(
      size       = rel(0.78),
      lineheight = 1.2,
      angle      = 90,
      margin     = margin(r = 6)
    ),
    panel.grid.major.x = element_blank(),
    panel.grid.major.y = element_line(color = "gray93", linewidth = 0.3),
    plot.tag = element_text(
      face   = "bold",
      size   = rel(1.05),
      family = fonts$title,
      color  = colors$palette$col_dark
    )
  )

### |- Combined Plots ----
layout <- "
AAAA
BBCC
"

comnined_plot <- pa + pb + pc +
  plot_layout(
    design  = layout,
    heights = c(1.45, 1)
  ) +
  plot_annotation(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    theme = theme(
      plot.title = element_text(
        face       = "bold",
        size       = rel(1.22),
        family     = fonts$title,
        color      = colors$palette$col_dark,
        margin     = margin(b = 4),
        lineheight = 1.15
      ),
      plot.subtitle = element_text(
        size       = rel(0.88),
        family     = fonts$text,
        color      = "gray40",
        lineheight = 1.35,
        margin     = margin(b = 12)
      ),
      plot.caption = element_markdown(
        size   = rel(0.68),
        family = fonts$text,
        color  = "gray50",
        hjust  = 0,
        margin = margin(t = 10)
      ),
      plot.margin = margin(t = 20, r = 20, b = 12, l = 20)
    )
  )
```

7. Save

```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot_patchwork(
  plot = comnined_plot, 
  type = "tidytuesday", 
  year = 2026, 
  week = 11, 
  width  = 12,
  height = 10,
  )
```

8. Session Info

TipExpand for Session Info

R version 4.3.1 (2023-06-16 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 11 x64 (build 26100)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] here_1.0.2      patchwork_1.3.2 skimr_2.2.2     glue_1.8.0     
 [5] scales_1.4.0    janitor_2.2.1   showtext_0.9-7  showtextdb_3.0 
 [9] sysfonts_0.8.9  ggtext_0.1.2    lubridate_1.9.5 forcats_1.0.1  
[13] stringr_1.6.0   dplyr_1.2.0     purrr_1.2.1     readr_2.2.0    
[17] tidyr_1.3.2     tibble_3.2.1    ggplot2_4.0.2   tidyverse_2.0.0
[21] pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       httr2_1.2.2        xfun_0.56          htmlwidgets_1.6.4 
 [5] gh_1.4.1           tzdb_0.5.0         yulab.utils_0.2.4  vctrs_0.7.1       
 [9] tools_4.3.1        generics_0.1.4     parallel_4.3.1     curl_7.0.0        
[13] gifski_1.32.0-2    pkgconfig_2.0.3    ggplotify_0.1.3    RColorBrewer_1.1-3
[17] S7_0.2.0           lifecycle_1.0.5    compiler_4.3.1     farver_2.1.2      
[21] repr_1.1.7         codetools_0.2-19   snakecase_0.11.1   litedown_0.9      
[25] htmltools_0.5.9    yaml_2.3.10        crayon_1.5.3       pillar_1.10.2     
[29] camcorder_0.1.0    magick_2.8.6       commonmark_2.0.0   tidyselect_1.2.1  
[33] digest_0.6.37      stringi_1.8.7      labeling_0.4.3     rsvg_2.6.2        
[37] rprojroot_2.1.1    fastmap_1.2.0      grid_4.3.1         cli_3.6.4         
[41] magrittr_2.0.3     base64enc_0.1-6    withr_3.0.2        rappdirs_0.3.4    
[45] bit64_4.6.0-1      timechange_0.4.0   rmarkdown_2.30     tidytuesdayR_1.2.1
[49] gitcreds_0.1.2     bit_4.6.0          otel_0.2.0         hms_1.1.4         
[53] evaluate_1.0.5     knitr_1.51         markdown_2.0       gridGraphics_0.5-1
[57] rlang_1.1.7        gridtext_0.1.6     Rcpp_1.1.1         xml2_1.5.2        
[61] vroom_1.7.0        svglite_2.1.3      rstudioapi_0.18.0  jsonlite_2.0.0    
[65] R6_2.6.1           fs_1.6.6           systemfonts_1.3.2 

9. GitHub Repository

TipExpand for GitHub Repo

The complete code for this analysis is available in tt_2026_11.qmd.

For the full repository, click here.

10. References

TipExpand for References

1. Data Source:
   • TidyTuesday 2026 Week 11: Salmonid Mortality Data

11. Custom Functions Documentation

Note📦 Custom Helper Functions

This analysis uses custom functions from my personal module library for efficiency and consistency across projects.

Functions Used:

• fonts.R: setup_fonts(), get_font_families() - Font management with showtext
• social_icons.R: create_social_caption() - Generates formatted social media captions
• image_utils.R: save_plot() - Consistent plot saving with naming conventions
• base_theme.R: create_base_theme(), extend_weekly_theme(), get_theme_colors() - Custom ggplot2 themes

Why custom functions?
These utilities standardize theming, fonts, and output across all my data visualizations. The core analysis (data tidying and visualization logic) uses only standard tidyverse packages.

Source Code:
View all custom functions → GitHub: R/utils