Checklist functions

This vignette demonstrates the main checklist functions in the trias package for analyzing and visualizing alien species data.

library(trias)
library(dplyr)
library(readr)

Load data

We’ll use example data from the TrIAS indicators repository:

df <- read_tsv(
  "https://raw.githubusercontent.com/trias-project/indicators/master/data/interim/data_input_checklist_indicators.tsv",
  show_col_types = FALSE
)

# Preview the data
head(df)
#> # A tibble: 6 × 38
#>         key  nubKey scientificName datasetKey canonicalName species genus family
#>       <dbl>   <dbl> <chr>          <chr>      <chr>         <chr>   <chr> <chr> 
#> 1 152543101 5740285 Gyrodactylus … 6d9e952f-… Gyrodactylus… Gyroda… Gyro… Gyrod…
#> 2 152543101 5740285 Gyrodactylus … 6d9e952f-… Gyrodactylus… Gyroda… Gyro… Gyrod…
#> 3 152543102 3202936 Aphanomyces a… 6d9e952f-… Aphanomyces … Aphano… Apha… Lepto…
#> 4 152543110 5179027 Scutigera col… 6d9e952f-… Scutigera co… Scutig… Scut… Scuti…
#> 5 152543110 5179027 Scutigera col… 6d9e952f-… Scutigera co… Scutig… Scut… Scuti…
#> 6 152543110 5179027 Scutigera col… 6d9e952f-… Scutigera co… Scutig… Scut… Scuti…
#> # ℹ 30 more variables: order <chr>, class <chr>, phylum <chr>, kingdom <chr>,
#> #   rank <chr>, speciesKey <dbl>, genusKey <dbl>, familyKey <dbl>,
#> #   orderKey <dbl>, classKey <dbl>, phylumKey <dbl>, kingdomKey <dbl>,
#> #   taxonomicStatus <chr>, locationId <chr>, locality <chr>, country <chr>,
#> #   status <chr>, first_observed <dbl>, last_observed <dbl>,
#> #   establishmentMeans <chr>, degree_of_establishment <chr>,
#> #   native_range <chr>, native_continent <chr>, habitat <chr>, marine <lgl>, …

Time of introduction

Number of introductions per year

The indicator_introduction_year() function shows how many new alien species were introduced each year.

# Plot number of new introductions per year
result <- indicator_introduction_year(
  df = df,
  start_year_plot = 1950,
  smooth_span = 0.85
)
#> [1] "All column names present"
#> [1] "All column names present"

# Display the plot
result$plot

Cumulative number of alien species

The indicator_total_year() function displays the cumulative number of alien species over time.

# Plot cumulative number of alien species
result <- indicator_total_year(
  df = df,
  start_year_plot = 1950
)
#> [1] "All column names present"
#> [1] "All column names present"

# Display the plot
result$plot

Native range

The indicator_native_range_year() function creates an interactive visualization showing the number of alien species per native region and year of introduction.

# Plot species by native range over time
result <- indicator_native_range_year(
  df = df,
  type = "native_range",
  response_type = "absolute"
)
#> [1] "All column names present"
#> [1] "All column names present"

# Display the plot
result$plot
#> NULL

Pathways of introduction

Pathway data

The package includes the pathwayscbd dataset containing CBD standard pathways at level 1 and level 2.

# View available CBD pathways
pathwayscbd
#> # A tibble: 51 × 2
#>    pathway_level1 pathway_level2       
#>    <chr>          <chr>                
#>  1 release        biological_control   
#>  2 release        erosion_control      
#>  3 release        fishery              
#>  4 release        hunting              
#>  5 release        landscape_improvement
#>  6 release        conservation         
#>  7 release        use                  
#>  8 release        other                
#>  9 release        unknown              
#> 10 escape         agriculture          
#> # ℹ 41 more rows

Visualize pathways at level 1

The visualize_pathways_level1() function creates bar graphs showing the number of taxa introduced through different CBD pathways at level 1.

# Visualize pathways level 1
result <- visualize_pathways_level1(
  df = df,
  category = NULL,
  from = NULL
)
#> [1] "All column names present"
#> [1] "All column names present"
#> [1] "All column names present"
#> [1] "All column names present"
#> [1] "All column names present"

# Display the plot
result$plot

Visualize pathways over time at level 1

The visualize_pathways_year_level1() function shows pathway trends over time.

# Visualize pathways level 1 over time
result <- visualize_pathways_year_level1(
  df = df,
  bin = 10,
  from = 1970
)
#> [1] "All column names present"
#> [1] "All column names present"
#> [1] "All column names present"
#> [1] "All column names present"
#> [1] "All column names present"

# Display the plot
result$plot

Visualize pathways at level 2

The visualize_pathways_level2() function creates bar graphs for CBD pathways at level 2 for a specific level 1 pathway.

# Visualize pathways level 2 for "escape"
result <- visualize_pathways_level2(
  df = df,
  chosen_pathway_level1 = "escape",
  category = NULL
)
#> [1] "All column names present"
#> [1] "All column names present"
#> [1] "All column names present"
#> [1] "All column names present"
#> [1] "All column names present"
#> [1] "All column names present"

# Display the plot
result$plot

Visualize pathways over time at level 2

The visualize_pathways_year_level2() function shows level 2 pathway trends over time for a specific level 1 pathway.

# Visualize pathways level 2 over time
result <- visualize_pathways_year_level2(
  df = df,
  chosen_pathway_level1 = "escape",
  bin = 10,
  from = 1970
)
#> [1] "All column names present"
#> [1] "All column names present"
#> [1] "All column names present"
#> [1] "All column names present"
#> [1] "All column names present"
#> [1] "All column names present"

# Display the plot
result$plot

Pathway count table

The get_table_pathways() function creates a summary table with the number of taxa per pathway and example species.

# Get pathway count table
pathway_table <- get_table_pathways(
  df = df,
  category = NULL,
  from = NULL,
  n_species = 5
)
#> [1] "All column names present"
#> [1] "All column names present"

# Display first rows
head(pathway_table)
#> # A tibble: 6 × 4
#>   pathway_level1 pathway_level2       n examples                                
#>   <chr>          <chr>            <int> <chr>                                   
#> 1 contaminant    animal_parasite      3 Mastophorus, Enderleinellus kumadai, Ho…
#> 2 contaminant    food                 1 Chaetopelma olivaceum                   
#> 3 contaminant    habitat_material   102 Centaurea psammogena, Odontites jaubert…
#> 4 contaminant    nursery             24 Oenanthe pimpinelloides, Euphorbia hype…
#> 5 contaminant    on_animals         560 Juncus foliosus, Panicum antidotale, Le…
#> 6 contaminant    on_plants           35 Ferussacia folliculum, Branchiodrilus h…

Additional resources

For more information on individual functions, see the Reference page.

Damiano Oldoni