This document describes how to build indicators of Invasive Alien Species based on checklist data. In particular, this document takes into account the number of new introductions of alien species per year in Belgium.

1 Setup

Load libraries:

library(tidyverse) # To do datascience
library(magrittr) # To use extract2 function
library(tidylog) # To provide feedback on dplyr functions
library(rlang) # To use Standard Evaluation in dplyr (!!sym())
library(here) # To find files
library(trias) # To use functions developed for TrIAS
library(INBOtheme) # To use INBO theme for graphs

2 Get data

data_file <- here::here(
  "data",
  "interim",
  "data_input_checklist_indicators.tsv"
)
data <- read_tsv(data_file,
  na = "",
  guess_max = 5000
)

3 Number of new introductions of alien species per year

We use function indicator_introduction_year from trias library.

3.1 Select data from Belgium

We select data at national level (no regional distributions):

data <-
  data %>%
  tidylog::filter(locationId == "ISO_3166:BE")

3.2 Grouping by kingdom

We group data by kingdom:

facet_column <- "kingdom"

3.3 Grouping by species profile

We group data by habitat:

facet_column <- "habitat"

4 Checklist indicators for Living Planet Index

In this section we make little changes to the indicator graphs for publication on the Living Planet Index.

4.1 Grouping by kingdom

We group data by kingdom:

facet_column <- "kingdom"

We don’t take into account Fungi and Chromista due to a lack of comprehensive data on those kingdoms:

4.2 Grouping by species profile

facet_column <- "habitat"

We don’t take into account combined habitats freshwater|terrestrial and marine|freshwater due to a lack of comprehensive data on those habitats: