Montane Tree Species of the Tropical Andes
In mtsta: Accessing the Red List of Montane Tree Species of the Tropical Andes

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(mtsta)

Summary of results of this regional Red List report:

summ_df <- tibble::tribble(
     ~Conservation.status, ~tag, ~Number.of.species,
  "Critically Endangered", "CR",                 1L,
             "Endangered", "EN",                42L,
             "Vulnerable", "VU",                27L,
        "Near Threatened", "NT",                20L,
          "Least Concern", "LC",                29L,
         "Data Deficient", "DD",                 8L,
          "Not Evaluated", "NE",                 0L
  )

summ_df |> 
  dplyr::select(1,3) |> 
  janitor::adorn_totals() |> 
  knitr::kable()

Number of endemic tree species by country in the tropical Andes.

(Calderón et al. 2002; IUCN 2010; León-Yánez et al. 2011; León et al. 2006; Llamozas et al. 2003; Meneses and Beck 2005).

summ_2 <- tibble::tribble(
               ~Country, ~CR,  ~EN,  ~VU, ~NT, ~LC, ~DD, ~Subtotal,  ~NE, ~Total,
              "Ecuador",  2L,  36L,  52L,  9L,  5L,  1L,      105L,  61L,   166L,
                 "Peru",  9L,  31L,  15L,  2L,  3L, 10L,       70L,  50L,   120L,
             "Colombia",  4L,   5L,   5L,  2L,  1L,  0L,       17L,  60L,    77L,
              "Bolivia",  0L,   5L,   1L,  0L,  0L,  1L,        7L,  94L,   101L,
            "Argentina",  0L,   0L,   0L,  0L,  0L,  0L,        0L,   3L,     3L,
            "Venezuela",  0L,   0L,   0L,  0L,  0L,  0L,        0L,   0L,     0L,
        "Total endemic", 15L,  77L,  73L, 13L,  9L, 12L,      199L, 268L,   467L,
  "Regional assessment",  1L,  42L,  27L, 20L, 29L,  8L,      127L,   0L,   127L,
          "Total Andes", 16L, 119L, 100L, 33L, 38L, 20L,      326L, 268L,   594L
  )

summ_2 |> 
  knitr::kable()

Number of species per country that were evaluated

summarie_3 <- mtsta::mtsta_distribution |> 
  dplyr::select(accepted_name, distribution) |> 
  dplyr::mutate(distribution = dplyr::case_when(
    stringr::str_detect(distribution, "\\(Bolivia,\\) ") ~ stringr::str_remove(distribution, "\\(Bolivia,\\) "),
    stringr::str_detect(distribution, "\\(Colombia,\\) ") ~ stringr::str_remove(distribution, "\\(Colombia,\\) "),
    stringr::str_detect(distribution, "\\(Colombia\\) \\- ") ~ stringr::str_remove(distribution, "\\(Colombia\\) \\- "),
    stringr::str_detect(distribution, " \\- \\(Peru \\- Venezuela\\)") ~ stringr::str_remove(distribution, " \\- \\(Peru \\- Venezuela\\)"),
    TRUE ~ distribution
  )) |> 
  tidyr::separate_rows(distribution, sep = " - ") |> 
  dplyr::group_by(distribution) |> 
  dplyr::summarise(n_species = dplyr::n_distinct(accepted_name))

summarie_3 |> 
  ggplot2::ggplot(ggplot2::aes(forcats::fct_reorder(distribution, n_species, .desc = TRUE),
                               n_species)) +
  ggplot2::geom_col() +
  ggplot2::labs(y = "Species per country",
                x = "Countries") +
  ggplot2::theme_bw()