Too Many Males, Too Few Females

Island captures show a persistent male bias; females are in poorer condition and lay smaller clutches.

A three-panel patchwork visualization exploring 16 years of capture-recapture data from a Hermann’s tortoise population on Golem Grad island (Lake Prespa, North Macedonia). Island captures show a persistent and severe male bias — females represent fewer than 10% of annual captures — and island females exhibit lower body condition scores and smaller clutch sizes compared to mainland counterparts.

Figure 1: A three-panel data visualization titled “Too Many Males, Too Few Females” examines Hermann’s tortoise captures from Golem Grad island (Lake Prespa) over 16 years. Panel A shows a line chart of the percentage of female captures on the island from 2008 to 2023, consistently hovering near 5–10% — far below the 50% parity reference line. Panel B displays violin plots comparing the body condition index of mainland versus island females; mainland females (median: 8.1, n=976) show notably higher body condition than island females (median: 6.3, n=268). Panel C presents a dot plot of clutch size by locality; island females average 3 eggs per clutch (n=22) compared to a mainland average of 6 eggs per clutch (n=31). Together, the panels illustrate a cause-to-consequence chain: extreme male bias on the island is associated with poorer female body condition and reduced reproductive output.

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, patchwork, binom
)
})

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  = 9,
  height = 9,
  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 = 09)
clutch_size_raw <- tt$clutch_size_cleaned |> clean_names()
tortoise_body_raw <- tt$tortoise_body_condition_cleaned |> clean_names()
rm(tt)
```

3. Examine the Data

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

glimpse(clutch_size_raw)
glimpse(tortoise_body_raw)
```

4. Tidy Data

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

### |- locality label mapping ----
loc_map <- c(
  "Plateau" = "Island",
  "Konjsko" = "Mainland",
  "Beach"   = "Island (Beach)"
)

### |- clean tortoise body data ----
tortoise_body <- tortoise_body_raw |>
  mutate(
    sex          = str_to_upper(sex),
    locality     = str_to_title(locality),
    season       = str_to_title(season),
    locality_lbl = recode(locality, !!!loc_map),
    locality_lbl = factor(locality_lbl, levels = c("Mainland", "Island"))
  )

### |- clean clutch data ----
clutch_size <- clutch_size_raw |>
  mutate(
    locality     = str_to_title(locality),
    locality_lbl = recode(locality, !!!loc_map),
    locality_lbl = factor(locality_lbl, levels = c("Mainland", "Island"))
  )

## ── PANEL A: % Female Captures Over Time ----
pa_data <- tortoise_body |>
    filter(
        sex %in% c("M", "F"),
        locality == "Plateau"
    ) |> # island only — matches headline claim
    count(year, sex) |>
    pivot_wider(names_from = sex, values_from = n, values_fill = 0) |>
    mutate(
        total    = M + F,
        ci       = binom::binom.wilson(F, total),
        pct_f    = F / total,
        ci_lower = ci$lower,
        ci_upper = ci$upper
    )

## ── PANEL B: Female Body Condition Index — Island vs. Mainland ----
pb_data <- tortoise_body |>
    filter(
        sex == "F",
        locality %in% c("Plateau", "Konjsko"),
        !is.na(body_condition_index)
    )

# summary stats for annotation
pb_stats <- pb_data |>
    group_by(locality_lbl) |>
    summarise(
        med = median(body_condition_index, na.rm = TRUE),
        n = n(),
        .groups = "drop"
    )

## ── PANEL C: Clutch Size by Locality ----
pc_data <- clutch_size |>
    filter(
        !is.na(eggs),
        locality %in% c("Beach", "Konjsko", "Plateau")
    ) |>
    # consolidate island localities
    mutate(
        locality_lbl = case_when(
            locality_lbl == "Mainland" ~ "Mainland",
            TRUE ~ "Island"
        ),
        locality_lbl = factor(locality_lbl, levels = c("Mainland", "Island"))
    )

pc_stats <- pc_data |>
    group_by(locality_lbl) |>
    summarise(
        mean_eggs = mean(eggs, na.rm = TRUE),
        med_eggs  = median(eggs, na.rm = TRUE),
        n         = n(),
        .groups   = "drop"
    )
```

5. Visualization Parameters

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

### |-  plot aesthetics ----
colors <- get_theme_colors(
  palette = list(
      "accent" = "#C0392B",   
      "neutral" = "gray70",    
      "ribbon"  = "#C0392B"   
    )
)

### |- titles and caption ----
title_text    <- str_glue("Too Many Males, Too Few Females")

subtitle_text <- str_glue(
    "Island captures show a persistent male bias; ",
    "females are in poorer condition and lay smaller clutches."
)

caption_text <- create_social_caption(
    tt_year      = 2026,
    tt_week      = 09,
    source_text  = "Bertolero et al. (2026) · Ecology Letters · #TidyTuesday"
)

### |-  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 = 'sans', size = rel(1.1),
      color = colors$title, margin = margin(b = 10), hjust = 0
    ),
    plot.subtitle = element_text(
      face = "italic", family = 'sans', lineheight = 1.2,
      color = colors$subtitle, size = rel(0.7), margin = margin(b = 20), hjust = 0
    ),

    # Grid
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_blank(),
    panel.grid.major = element_line(color = "gray90", linewidth = 0.25),

    # 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(),

    # 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: % Female Captures Over Time ----
pa <- ggplot(pa_data, aes(x = year, y = pct_f)) +
  # Geom
  geom_hline(
    yintercept = 0.5, linetype = "dashed",
    color = "gray80", linewidth = 0.4
  ) +
  annotate("text",
    x = 2008, y = 0.52, label = "50% parity",
    size = 2.6, color = "gray65", hjust = 0, fontface = "italic"
  ) +
  geom_ribbon(aes(ymin = ci_lower, ymax = ci_upper),
    fill = colors$palette$ribbon, alpha = 0.15
  ) +
  geom_line(color = colors$palette$accent, linewidth = 1) +
  geom_point(
    color = colors$palette$accent, size = 2.2, shape = 21,
    fill = "white", stroke = 1.2
  ) +
  # Scales
  scale_y_continuous(
    labels = label_percent(accuracy = 1),
    limits = c(0, 0.55),
    breaks = seq(0, 0.5, 0.1)
  ) +
  scale_x_continuous(breaks = seq(2008, 2023, 3)) +
  # Labs
  labs(
    title    = "A · Persistent male bias in captures",
    subtitle = "Island (Plateau) captures only · Wilson 95% CI shaded",
    x        = NULL,
    y        = "% of captures that are female"
  )

## ── PANEL B: Female Body Condition Index — Island vs. Mainland ----
pb <- ggplot(
  pb_data,
  aes(
    x = locality_lbl, y = body_condition_index,
    fill = locality_lbl
  )
) +
  # Geoms
  geom_violin(alpha = 0.55, color = NA, width = 0.85) +
  geom_boxplot(
    width = 0.12, fill = "white", color = "gray40",
    outlier.shape = NA, linewidth = 0.5
  ) +
  geom_text(
    data = pb_stats,
    aes(
      x = locality_lbl,
      y = max(pb_data$body_condition_index, na.rm = TRUE) + 0.3,
      label = glue("median: {round(med, 1)}")
    ),
    size = 2.6, color = "gray45", fontface = "italic",
    inherit.aes = FALSE
  ) +
  geom_text(
    data = pb_stats,
    aes(
      x = locality_lbl,
      y = min(pb_data$body_condition_index, na.rm = TRUE) - 0.4,
      label = glue("n = {comma(n)}")
    ),
    size = 2.8, color = "gray50", inherit.aes = FALSE
  ) +
  # Scales
  scale_fill_manual(values = c("Mainland" = colors$palette$neutral, "Island" = colors$palette$accent)) +
  scale_y_continuous(
    n.breaks = 5,
    limits = c(
      min(pb_data$body_condition_index, na.rm = TRUE) - 0.6,
      max(pb_data$body_condition_index, na.rm = TRUE) + 0.7
    )
  ) +
  # Labs
  labs(
    title    = "B · Island females in poorer condition",
    subtitle = "Body condition index · Females only",
    x        = NULL,
    y        = "Body condition index"
  )

## ── PANEL C: Clutch Size by Locality ----
pc <- ggplot(pc_data, aes(x = eggs, y = locality_lbl, color = locality_lbl)) +
  # Geoms
  geom_jitter(
    size = 3, alpha = 0.75,
    position = position_jitter(height = 0.12, seed = 42)
  ) +
  geom_point(
    data = pc_stats,
    aes(x = mean_eggs, y = locality_lbl, color = locality_lbl),
    shape = 23, size = 5.5, fill = "white",
    stroke = 1.5, inherit.aes = FALSE
  ) +
  geom_text(
    data = pc_stats,
    aes(
      x = mean_eggs, y = locality_lbl,
      label = glue("mean: {round(mean_eggs, 1)}")
    ),
    nudge_y = 0.28, size = 2.8, color = "gray40",
    fontface = "italic", inherit.aes = FALSE
  ) +
  geom_text(
    data = pc_stats,
    aes(
      x = 13.8, y = locality_lbl,
      label = glue("n = {n}")
    ),
    size = 2.8, color = "gray50", hjust = 1, inherit.aes = FALSE
  ) +
  # Scales
  scale_color_manual(values = c("Mainland" = colors$palette$neutral, "Island" = colors$palette$accent)) +
  scale_x_continuous(breaks = 2:14, limits = c(2, 14)) +
  scale_y_discrete() +
  # Labs
  labs(
    title    = "C · Island females lay fewer eggs",
    subtitle = "Eggs per clutch · Diamond = mean per group",
    x        = "Eggs per clutch",
    y        = NULL
  )


## ── Combined Plots ----
combined_plots <- (pa / pb / pc) +
    plot_annotation(
        title    = title_text,
        subtitle = subtitle_text,
        caption  = caption_text,
        theme    = theme(
            plot.title = element_text(
                size = rel(1.4),
                family = 'sans',
                face = "bold",
                color = colors$title,
                lineheight = 1.15,
                margin = margin(t = 0, b = 5)
            ),
            plot.subtitle = element_text(
                size = rel(0.8),
                family = 'sans',
                color = alpha(colors$subtitle, 0.88),
                lineheight = 1.5,
                margin = margin(t = 5, b = 5)
            ),
            plot.caption = element_markdown(
                size = rel(0.5),
                family = fonts$subtitle,
                color = colors$caption,
                hjust = 0,
                lineheight = 1.4,
                margin = margin(t = 20, b = 5)
            )
        )
    )
```

7. Save

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

### |-  plot image ----  
save_plot_patchwork(
  plot = combined_plots, 
  type = "tidytuesday", 
  year = 2026, 
  week = 09, 
  width  = 9,
  height = 9,
  )
```

8. Session Info

TipExpand for Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
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      binom_1.1-1.1   patchwork_1.3.2 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_2.1.6    
[17] tidyr_1.3.2     tibble_3.3.1    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       xfun_0.47          httr2_1.0.1        htmlwidgets_1.6.4 
 [5] gh_1.4.1           tzdb_0.5.0         yulab.utils_0.1.7  vctrs_0.7.1       
 [9] tools_4.4.0        generics_0.1.3     parallel_4.4.0     curl_5.2.1        
[13] gifski_1.32.0-2    pkgconfig_2.0.3    ggplotify_0.1.2    RColorBrewer_1.1-3
[17] S7_0.2.1           lifecycle_1.0.5    compiler_4.4.0     farver_2.1.2      
[21] textshaping_0.3.7  codetools_0.2-20   snakecase_0.11.1   htmltools_0.5.8.1 
[25] yaml_2.3.10        crayon_1.5.2       pillar_1.11.1      camcorder_0.1.0   
[29] magick_2.9.0       commonmark_2.0.0   tidyselect_1.2.1   digest_0.6.37     
[33] stringi_1.8.3      labeling_0.4.3     rsvg_2.6.0         rprojroot_2.1.1   
[37] fastmap_1.2.0      grid_4.4.0         cli_3.6.5          magrittr_2.0.4    
[41] withr_3.0.1        rappdirs_0.3.3     bit64_4.0.5        timechange_0.4.0  
[45] rmarkdown_2.28     tidytuesdayR_1.2.1 gitcreds_0.1.2     bit_4.0.5         
[49] hms_1.1.4          evaluate_1.0.0     knitr_1.48         markdown_1.12     
[53] gridGraphics_0.5-1 rlang_1.1.7        gridtext_0.1.5     Rcpp_1.1.1        
[57] xml2_1.5.2         vroom_1.6.5        svglite_2.2.2      rstudioapi_0.18.0 
[61] jsonlite_2.0.0     R6_2.5.1           fs_1.6.4           systemfonts_1.3.1 

9. GitHub Repository

TipExpand for GitHub Repo

The complete code for this analysis is available in tt_2026_09.qmd.

For the full repository, click here.

10. References

TipExpand for References

1. Data Source:
   • TidyTuesday 2026 Week 09: Golem Grad Tortoise 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