Bird Sightings

Author

Jo Hardin

Published

April 14, 2026

Code

library(tidyverse) # ggplot, lubridate, dplyr, stringr, readr...
library(praise)

Bird Sightings at Sea

This week we’re exploring Bird Sightings at Sea! The data this week comes from Te Papa Tongarewa, The Museum of New Zealand. It consists of log book entries of bird sightings at sea near New Zealand, from 1969 to 1990.

Thank you to David Hood for the dataset suggestion.

The data was recorded using guidelines for the Australasian Seabird Mapping Scheme and counts seabirds seen from a ship during a 10 minute period. The data includes geolocations of the sightings, bird species, numbers and behaviour, observer and ship name, and observation date and time.

The data was recorded by hand and split into standardized columns. Do the logbook entries in species_common_name in the birds dataset always match up with the split columns such as species_scientific_name, age, wan_plumage_phase, plumage_phase, and sex.

Code

beaufort_scale <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2026/2026-04-14/beaufort_scale.csv')
birds <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2026/2026-04-14/birds.csv') |>
  mutate(genus = stringr::str_extract(species_scientific_name, "\\w+"))

sea_states <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2026/2026-04-14/sea_states.csv')
ships <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2026/2026-04-14/ships.csv')

Code

birds |> 
  group_by(genus) |> 
  drop_na(genus) |>
  summarize(count_genus = n()) |>
  arrange(desc(count_genus))

# A tibble: 47 × 2
   genus        count_genus
   <chr>              <int>
 1 Diomedea           15375
 2 Puffinus            6238
 3 Pterodroma          4995
 4 Thalassarche        3231
 5 Daption             2966
 6 Macronectes         2526
 7 Pachyptila          2457
 8 Larus               2366
 9 Procellaria         1786
10 Morus               1679
# ℹ 37 more rows

Code

birds |> 
  group_by(genus) |> 
  drop_na(genus) |>
  mutate(count_genus = n()) |> 
  filter(count_genus > 1000)

# A tibble: 43,619 × 28
# Groups:   genus [10]
   bird_observation_id record_id species_common_name      species_scientific_n…¹
                 <dbl>     <dbl> <chr>                    <chr>                 
 1                   1   1083001 Royal / Wandering albat… Diomedea epomophora /…
 2                   2   1083001 Black-browed albatross … Diomedea impavida / m…
 3                   3   1083001 Cape petrel              Daption capense       
 4                   4   1083001 Fairy prion              Pachyptila turtur     
 5                   5   1083001 Sooty shearwater         Puffinus griseus      
 6                   6   1084001 Royal albatross sensu l… Diomedea epomophora /…
 7                   7   1084001 Black-browed albatross … Diomedea impavida / m…
 8                   8   1084001 Sooty shearwater         Puffinus griseus      
 9                   9   1084002 Royal albatross sensu l… Diomedea epomophora /…
10                  10   1084002 Black-browed albatross … Diomedea impavida / m…
# ℹ 43,609 more rows
# ℹ abbreviated name: ¹species_scientific_name
# ℹ 24 more variables: species_abbreviation <chr>, age <chr>,
#   wan_plumage_phase <chr>, plumage_phase <chr>, sex <lgl>, count <dbl>,
#   n_feeding <dbl>, feeding <lgl>, n_sitting_on_water <dbl>,
#   sitting_on_water <lgl>, n_sitting_on_ice <dbl>, sitting_on_ice <lgl>,
#   sitting_on_ship <lgl>, in_hand <lgl>, n_flying_past <dbl>, …

Code

birds |>
  filter(count != 99999) |>
  mutate(countTF = (!is.na(n_feeding) + 0) + 
              (!is.na(n_sitting_on_water) + 0) + 
              (!is.na(n_sitting_on_ice) + 0) +  
              (!is.na(n_flying_past) + 0) + 
              (!is.na(n_accompanying) + 0) + 
              (!is.na(n_following_ship) + 0)) |>
  filter(countTF == 6) |>
  filter(genus == "Puffinus" | genus == "Diomedea") |>
  pivot_longer(c(n_feeding, n_sitting_on_ice, n_sitting_on_water,
                 n_flying_past, n_accompanying, n_following_ship), 
               values_to = "number", names_to = "where") |>
  filter(number > 0) |>
  mutate(log10count = log10(number)) |>
  left_join(ships, by = "record_id") |>
  filter(date > "1975-01-01", date < "1990-01-01") |>
  ggplot(aes(x = date, y = where, size = log10count, color = genus)) + 
  geom_jitter(alpha = 0.5, width = 0) +
  scale_color_brewer(palette = "Set1")

Scatterplot with date, roughly 1975 to 1990, on the x-axis and location on the y-axis. The different locations include: feeding, sitting on water, sitting on ice, flying past, accompanying, and following ship. There are two types of birds, albatross and puffins. The majority of albatross are following the ship, over all time periods; the majority of puffins are flying past, over all time periods. — For genus of Diomedea (albatross) and Puffinus (puffins), the number of birds in each activity is diplayed over time. The albatross seem more likely to be following the ship; the puffins seem more likely to be flying past. More puffins seem to be observed in the late 80s and onward, but that might be more to do with counting practices than bird patterns.

Code

praise()

[1] "You are unreal!"