Sea Monsters that Lost their Way

R
geospatial
machine learning
textual analysis
Predicting uncertain species of cetacean strandings recorded by the Natural History Museum
Author

Carl Goodwin

Published

December 4, 2021

Modified

November 22, 2022

The Natural History Museum began recording cetacean (whales, dolphins and porpoises) strandings in 1913 (Natural History Museum 2019). Let’s explore this 1913-1989 dataset.

theme_set(theme_bw())

(cols <- wes_palette(name = "Darjeeling2"))

strandings_df <- read_csv("strandings.csv", show_col_types = FALSE) |>
  clean_names() |> 
  mutate(
    date = date_parse(date, format = "%d/%m/%Y"),
    length = parse_number(length_et),
    species_lumped = fct_lump_n(species, 20),
    across(ends_with("_val"), as.integer)
  )

# glimpse(strandings_df)

Exploratory

Some of the species labels contain a question mark or forward slash. This indicates uncertainty, so it might be fun to see if a machine learning model (multi-class classification) could learn from the known species and suggest an appropriate species where it’s uncertain.

strandings_df2 <- 
  strandings_df |> 
  mutate(species_uncertainty =
      if_else(str_detect(species, "[?/]"), "Uncertain", "Known"))

strandings_df2 |> 
  filter(species_uncertainty == "Uncertain") |> 
  count(species, sort = TRUE, name = "Count") |> 
  slice_head(n = 6)
species Count
delphis/coeruleoalba 48
phocoena? 42
melaena? 20
delphis? 18
truncatus? 18
acutorostrata? 12

The date variable has many NA’s. Fortunately, the components to construct many of these are in the year_val, month_val and day_val variables. With a little wrangling and imputation, we can coalesce these variables into a new date. This will be useful since plots of sample species by year, month and week of stranding suggest a de-constructed date could be a useful predictor.

strandings_df2 |> 
  select(date, year_val, month_val, day_val) |> 
  summary()
      date               year_val      month_val         day_val     
 Min.   :1913-02-13   Min.   :   0   Min.   : 0.000   Min.   : 0.00  
 1st Qu.:1933-09-09   1st Qu.:1933   1st Qu.: 4.000   1st Qu.: 9.00  
 Median :1954-04-13   Median :1955   Median : 7.000   Median :16.00  
 Mean   :1955-01-08   Mean   :1954   Mean   : 6.766   Mean   :15.66  
 3rd Qu.:1979-03-21   3rd Qu.:1979   3rd Qu.:10.000   3rd Qu.:22.00  
 Max.   :1989-12-25   Max.   :1989   Max.   :12.000   Max.   :31.00  
 NA's   :121                                                         
strandings_df3 <- strandings_df2 |>
  group_by(species) |> 
  mutate(
    month_val = if_else(month_val == 0, mean(month_val) |> 
                          as.integer(), month_val),
    day_val = if_else(day_val == 0, mean(day_val) |> as.integer(), day_val),
    day_val = if_else(day_val == 0, 1L, day_val),
    date2 = date_build(year_val, month_val, day_val, invalid = "NA"),
  ) |> 
  ungroup() |> 
  mutate(date3 = coalesce(date, date2)) |> 
  arrange(id) |> 
  mutate(date = if_else(is.na(date), lag(date3), date3)) |> 
  select(-date2, -date3, -ends_with("_val"))

example_species <-
  c("musculus", "melas", "crassidens", "borealis", "coeruleoalba")

known_species <- strandings_df3 |> 
  filter(species_uncertainty == "Known")

plot_date_feature <- function(var) {
  known_species |>
    mutate(
      year = get_year(date),
      month = get_month(date),
      week = as_iso_year_week_day(date) |> get_week()
    ) |>
    filter(species %in% example_species) |>
    count(species, {{ var }}) |>
    ggplot(aes(species, {{ var }})) +
    geom_violin(
      alpha = 0.7,
      fill = cols[3],
      show.legend = FALSE
    ) +
    coord_flip() +
    labs(
      title = glue("Variation in {str_to_title(as.character(var))}",
                   " of Stranding for Known Species"),
      x = NULL, y = glue("{str_to_title(as.character(var))}")
    )
}

c("year", "month", "week") |> 
  map(sym) |> 
  map(plot_date_feature) |> 
  wrap_plots(ncol = 1)

Do latitude and longitude carry useful predictive information?

A geospatial visualisation of strandings shows some species do gravitate towards particular stretches of coastline, e.g. “acutus” and “albirostris” to the east, and “coeruleoalba” to the south-west.

Some species may also be more prone to mass stranding, so something that indicates whether a species has such a history (in these data) may be worth including in the mix.

uki <- map_data("world", region = c("uk", "ireland"))

labels <- c("Mass", "Single")

uki |> 
  ggplot() +
  geom_map(aes(long, lat, map_id = region), map = uki, 
           colour = "black", fill = "grey90", size = 0.1) +
  geom_jitter(aes(longitude, latitude, colour = mass_single, 
                  size = mass_single), 
              alpha = 0.5, data = known_species) +
  facet_wrap(~ species_lumped, nrow = 3) +
  coord_map("mollweide") +
  scale_size_manual(values = c(1, 0.5), labels = labels) +
  scale_colour_manual(values = cols[c(3, 2)], labels = labels) +
  theme_void() +
  theme(legend.position = "top", 
        strip.text = element_text(colour = "grey50")) +
  labs(title = "Strandings by Species", 
       colour = NULL, size = NULL)

# Add history of mass stranding
strandings_df4 <- strandings_df3 |> 
  group_by(species) |>
  mutate(mass_possible = min(mass_single, na.rm = TRUE)) |>
  ungroup()

Some records are missing the length measurement of the mammal. Nonetheless, where present, this is likely to be predictive, and may help, for example, separate species labelled as “delphis/coeruleoalba” where the length is at the extreme ends of the “delphis” range as we see below. And the range of length may differ by mammal sex.

known_species |>
  mutate(sex = case_when(
    sex == "M" ~ "Male",
    sex == "F" ~ "Female",
    TRUE       ~ "Unknown"
  )) |> 
  filter(species_lumped != "Other") |> 
  count(species_lumped, length, sex) |> 
  mutate(species_lumped = fct_reorder(species_lumped, 
                                      desc(length), min, na.rm = TRUE)) |> 
  ggplot(aes(species_lumped, length)) + 
  geom_violin(aes(fill = if_else(str_detect(species_lumped, "^de|^co"), 
                                 TRUE, FALSE)), show.legend = FALSE) +
  facet_wrap(~ sex) +
  scale_fill_manual(values = cols[c(1, 5)]) +
  coord_flip() +
  labs(title = "Variation in Species Length by Sex", 
       x = NULL, y = "Length (metres)")

With map coordinates not always available, county could be, with a little string cleaning, a useful additional predictor.

strandings_df4 |> 
  count(county) |> 
  filter(str_detect(county, "Shet|Northumberland")) |> 
  rename(County = county, Count = n)
County Count
Fair Isle, Shetland Isles 1
Northumberland 89
Northumberland. 1
Shetland Islands, Scotland 232
Shetland Isles, Scotland 35
Shetland, Scotland 1
Shetlands, Scotland 1
regex_pattern <-
  c("[,/].*$",
    "(?<!Che|Hamp|Lanca|North York)-?shire",
    " Isl.*$",
    " &.*$",
    "[-.]$")

strandings_df5 <- strandings_df4 |>
  mutate(
    county = str_remove_all(county, str_c(regex_pattern, collapse = "|")),
    county = recode(
      county,
      "Carnarvon" = "Caernarvon",
      "E.Lothian" = "East Lothian",
      "Shetlands" = "Shetland",
      "W.Glamorgan" = "West Glamorgan",
      "S.Glamorgan" = "South Glamorgan"
    )
  ) 

strandings_df4 |>
  summarise(counties_before = n_distinct(county))
counties_before
146
strandings_df5 |>
  summarise(counties_after = n_distinct(county))
counties_after
109

Whilst count also appears to hold, based on the plot pattern below, species-related information, I’m not going to use it as a predictor as we do not know enough about how it was derived, as reflected in these variable descriptions.

strandings_df5 |>
  ggplot(aes(species, count, colour = species_uncertainty)) +
  geom_jitter(alpha = 0.5, size = 2) +
  coord_flip() +
  scale_y_log10() +
  scale_colour_manual(values = cols[c(1, 5)]) +
  labs(title = "How 'Count' Relates to Species", 
       x = NULL, y = "Count (log10)", colour = "Species") +
  theme(legend.position = "top")

Modelling

So, I’ll set aside the rows where the species is uncertain (to be used later for new predictions), and I’ll train a model on 75% of known species, and test it on the remaining 25%. I’ll use the following predictors:

  • latitude and longitude
  • Mammal length and sex
  • mass_possible indicating a species history of mass strandings
  • date reported converted into decimal, week, month and year
  • county may be useful, especially where the longitude and latitude are missing
  • fam_genus which narrows the range of likely species

I’d like to also make use of the textrecipes(Hvitfeldt 2022) package. I can tokenise the textual information in comment and location to see if these add to the predictive power of the model.

I’ll tune the model using tune_race_anova(Kuhn 2022) which quickly discards hyperparameter combinations showing little early promise.

known_species <- strandings_df5 |>
  filter(species_uncertainty == "Known") |>
  mutate(across(
    c(
      "species",
      "mass_single",
      "mass_possible",
      "county",
      "location",
      "sex",
      "fam_genus"
    ),
    factor
  ))

set.seed(123)

data_split <-
  known_species |>
  mutate(species = fct_drop(species)) |> 
  initial_split(strata = species)

train <- data_split |> training()

test <- data_split |> testing()

predictors <-
  c(
    "latitude",
    "longitude",
    "length",
    "mass_single",
    "mass_possible",
    "county",
    "location",
    "comment",
    "sex",
    "fam_genus"
  )

recipe <-
  train |>
  recipe() |>
  update_role(species, new_role = "outcome") |>
  update_role(all_of(predictors), new_role = "predictor") |>
  update_role(!has_role("outcome") & !has_role("predictor"), 
              new_role = "id") |>
  step_date(date, features = c("decimal", "week", "month", "year"), 
            label = FALSE) |>
  step_tokenize(location, comment) |>
  step_stopwords(location, comment) |>
  step_tokenfilter(location, comment, max_tokens = tune()) |> #100
  step_tf(location, comment) |>
  step_zv(all_predictors()) |>
  step_dummy(all_nominal_predictors())

xgb_model <-
  boost_tree(trees = tune(), # 440
             mtry = tune(), # 0.6
             learn_rate = 0.02) |> 
  set_mode("classification") |>
  set_engine("xgboost", 
             count = FALSE,
             verbosity = 0,
             tree_method = "hist")

xgb_wflow <- workflow() |>
  add_recipe(recipe) |>
  add_model(xgb_model)

set.seed(9)

folds <- vfold_cv(train, strata = species)

set.seed(10)

tic()

tune_result <- xgb_wflow |>
  tune_race_anova(
    resamples = folds,
    grid = crossing(
      trees = seq(200, 520, 40),
      mtry = seq(0.5, 0.7, 0.1),
      max_tokens = seq(80, 120, 20)
      ),
    control = control_race(),
    metrics = metric_set(accuracy)
  )

toc()
400.4 sec elapsed
tune_result |> 
  plot_race() + 
  labs(title = "Early Elimination of Parameter Combinations")

set.seed(123)

xgb_fit <- xgb_wflow |>
  finalize_workflow(tune_result |> 
                      select_best(metric = "accuracy")) |> 
  fit(train)

Having fitted the model with the 3080 records in the training data, I’ll test its accuracy on the 1028 records of known species the model has not yet seen.

Without spending time on alternative models, we’re getting a reasonable result for the “porpoise” of this post, as reflected in both the accuracy metric and confusion matrix.

xgb_results <- xgb_fit |> 
  augment(new_data = test)

xgb_results |>
  accuracy(species, .pred_class)
.metric .estimator .estimate
accuracy multiclass 0.9931907
xgb_results |>
  conf_mat(species, .pred_class) |>
  autoplot(type = "heatmap") +
  scale_fill_gradient2(
    mid = "white",
    high = cols[1],
    midpoint = 0
  ) +
  labs(title = "Confusion Matrix") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

The top variable importance scores include fam_genus, many of the comment tokens, plus length, mass-possible, date_decimal, date_year, and latitude.

vi(xgb_fit |> extract_fit_parsnip()) |> 
  arrange(desc(Importance)) |> 
  mutate(ranking = row_number()) |> 
  slice_head(n = 40)
Variable Importance ranking
fam_genus_Phocoena 0.1215845 1
fam_genus_Globicephala 0.0816541 2
tf_comment_unidentified 0.0736307 3
fam_genus_Delphinus 0.0710860 4
fam_genus_Tursiops 0.0500141 5
tf_comment_false 0.0498405 6
fam_genus_Lagenorhynchus 0.0448997 7
tf_comment_finned 0.0341306 8
tf_comment_sided 0.0302409 9
tf_comment_long 0.0244866 10
fam_genus_Hyperoodon 0.0240353 11
length 0.0230962 12
fam_genus_Grampus 0.0227513 13
tf_comment_beaked 0.0219021 14
tf_comment_lesser 0.0215853 15
tf_comment_rorqual 0.0199182 16
tf_comment_dolphin 0.0198597 17
fam_genus_Orcinus 0.0194480 18
tf_comment_porpoise 0.0192418 19
tf_comment_bottle 0.0165892 20
fam_genus_Pseudorca 0.0159957 21
fam_genus_Physeter 0.0159090 22
tf_comment_risso’s 0.0131135 23
mass_possible_S 0.0127573 24
fam_genus_Mesoplodon 0.0123743 25
tf_comment_fin 0.0122737 26
fam_genus_Ziphius 0.0122521 27
fam_genus_odontocete 0.0109274 28
fam_genus_cetacean 0.0104412 29
tf_comment_killer 0.0099741 30
fam_genus_Stenella 0.0087018 31
date_decimal 0.0081614 32
tf_comment_nosed 0.0075943 33
date_year 0.0072361 34
tf_comment_whale 0.0071684 35
tf_comment_white 0.0067214 36
mass_single_S 0.0041988 37
tf_comment_common 0.0041767 38
tf_comment_cuvier’s 0.0036603 39
tf_comment_sowerby’s 0.0036372 40

Do the predictions look reasonable?

The class probability is spread across 27 species. I’m going to set a high threshold of 0.9, meaning the predicted species needs to be a pretty confident prediction.

xgb_preds <- xgb_fit |> 
  augment(new_data = strandings_df5 |> 
            filter(species_uncertainty == "Uncertain"))

species_levels <- xgb_preds |> 
  select(starts_with(".pred_"), -.pred_class) |> 
  names() |> 
  as.factor()

subset_df <- xgb_preds |>
  mutate(
    .class_pred = make_class_pred(
      .pred_acutorostrata,
      .pred_acutus,
      .pred_albirostris,
      .pred_ampullatus,
      .pred_bidens,
      .pred_borealis,
      .pred_breviceps,
      .pred_cavirostris,
      .pred_coeruleoalba,
      .pred_crassidens,
      .pred_delphis,
      .pred_electra,
      .pred_europaeus,
      .pred_griseus,
      .pred_leucas,
      .pred_macrocephalus,
      .pred_melaena,
      .pred_melas,
      .pred_mirus,
      .pred_monoceros,
      .pred_musculus,
      .pred_novaeangliae,
      .pred_orca,
      .pred_phocoena,
      .pred_physalus,
      .pred_sp.indet.,
      .pred_truncatus,
      levels = levels(species_levels),
      min_prob = .9
    )
  )

subset_df |>
  group_by(species, .class_pred) |> 
  summarise(n = n()) |> 
  ungroup() |> 
  arrange(species, desc(n)) |> 
  rename("Actual" = species, "Predicted" = .class_pred, "Count" = n)
Actual Predicted Count
acutorostrata? .pred_acutorostrata 12
acutorostrata/borealis .pred_acutorostrata 1
acutus? .pred_acutus 3
albirostris? .pred_albirostris 9
ampullatus? .pred_ampullatus 3
bidens? .pred_bidens 2
bidens? [EQ] 1
cavirostris? .pred_cavirostris 7
coeruleoalba? .pred_coeruleoalba 1
delphis? .pred_delphis 18
delphis/coeruleoalba [EQ] 48
griseus? .pred_griseus 2
macrocephalus? .pred_macrocephalus 2
melaena? .pred_melaena 20
orca? .pred_orca 4
phocoena? .pred_phocoena 42
physalus? .pred_physalus 4
physalus/acutorostrata [EQ] 1
truncatus? .pred_truncatus 18
truncatus/albirostris [EQ] 5

The majority of the 203 uncertain records are predicted to be as suspected in the original labelling. The remainder are classed as equivocal as they have not met the high bar of a 0.9-or-above probability for a single species.

R Toolbox

Summarising below the packages and functions used in this post enables me to separately create a toolbox visualisation summarising the usage of packages and functions across all posts.

Package Function
base as.character[2]; as.integer[3]; c[9]; comment[3]; conflicts[1]; cumsum[1]; function[2]; is.na[1]; length[2]; levels[1]; log10[1]; mean[2]; min[1]; names[1]; search[1]; seq[3]; set.seed[4]; sum[1]; summary[1]
clock as_iso_year_week_day[1]; date_build[1]; date_parse[1]; get_month[1]; get_week[1]; get_year[1]
doParallel registerDoParallel[1]
dplyr filter[12]; across[2]; arrange[5]; case_when[1]; coalesce[1]; count[4]; desc[5]; group_by[4]; id[1]; if_else[9]; mutate[18]; n[4]; n_distinct[2]; recode[1]; rename[2]; row_number[1]; select[3]; slice_head[2]; summarise[4]; ungroup[3]
finetune control_race[1]; plot_race[1]; tune_race_anova[1]
forcats fct_drop[1]; fct_lump_n[1]; fct_reorder[1]
ggplot2 aes[6]; coord_flip[3]; coord_map[1]; element_text[2]; facet_wrap[2]; geom_jitter[2]; geom_map[1]; geom_violin[2]; ggplot[4]; labs[6]; map_data[1]; scale_colour_manual[2]; scale_fill_gradient2[1]; scale_fill_manual[1]; scale_size_manual[1]; scale_y_log10[1]; theme[3]; theme_bw[1]; theme_set[1]; theme_void[1]
glue glue[2]
janitor clean_names[1]
parsnip boost_tree[1]; set_engine[1]; set_mode[1]
patchwork wrap_plots[1]
probably make_class_pred[1]
purrr map[3]; map2_dfr[1]; possibly[1]; set_names[1]
readr parse_number[1]; read_csv[1]; read_lines[1]
recipes all_nominal_predictors[1]; all_predictors[1]; has_role[2]; step_date[1]; step_dummy[1]; step_zv[1]; update_role[3]
rsample initial_split[1]; testing[1]; training[1]; vfold_cv[1]
stats var[3]
stringr str_c[6]; str_count[1]; str_detect[5]; str_remove[2]; str_remove_all[2]; str_starts[1]; str_to_title[2]
textrecipes step_stopwords[1]; step_tf[1]; step_tokenfilter[1]; step_tokenize[1]
tibble as_tibble[1]; tibble[2]; enframe[1]
tictoc tic[1]; toc[1]
tidyr crossing[1]; unnest[1]
tune finalize_workflow[1]; select_best[1]
wesanderson wes_palette[1]
workflows add_model[1]; add_recipe[1]; workflow[1]
yardstick accuracy[2]; conf_mat[1]; metric_set[1]

References

Hvitfeldt, Emil. 2022. “Textrecipes: Extra ’Recipes’ for Text Processing.” https://CRAN.R-project.org/package=textrecipes.
Kuhn, Max. 2022. “Finetune: Additional Functions for Model Tuning.” https://CRAN.R-project.org/package=finetune.
Natural History Museum. 2019. “Query on the Natural History Museum Data Portal (Data.nhm.ac.uk) (4311 Records).” Natural History Museum. https://doi.org/10.5519/QD.IWG63595.