8 Calculated metrics

The assessment results from Ferrer-Paris et al. (2019) are available in a dataset in figshare (Ferrer-Paris 2018). See Chapter 3 for instructions for data download.

Here we will use R to read the data and generate summary tables.

8.1 Load libraries

library(dplyr)
library(knitr)
library(tidyr)
library(stringr)
opts <- options(knitr.kable.NA = "")

8.2 Load the data

here::here("overview-2019/assessment-results.qmd")

[1] "/Users/z3529065/proyectos/Forests-Americas/RLE-example-dry-forest-guajira/overview-2019/assessment-results.qmd"

(load(here::here("downloaded-data/20181123_MacrogroupsCountry.rda")))

[1] "Macrogroups.Global"         "Macrogroups.Country"       
[3] "SpatialCriteria.Global"     "SpatialCriteria.Country"   
[5] "FunctionalCriteria.Global"  "FunctionalCriteria.Country"

mg_key <- "M563"
bind_rows(
    {SpatialCriteria.Global |> 
    filter(IVC.macrogroup_key %in% mg_key) |>
    select(Country,best.estimate.decline.2000.2050:A2b.bounds)},
    {SpatialCriteria.Country |> 
    filter(IVC.macrogroup_key %in% mg_key) |>
    select(Country,best.estimate.decline.2000.2050:A2b.bounds)}
) |>
    t() |>
    kable()

Country	global	Colombia	Trinidad and Tobago	Venezuela
best.estimate.decline.2000.2050	0.0	3.4	18.0	0.0
bounds.estimate.decline.2000.2050	0.0 – 7.5	0.0 – 42.2	0.0 – 43.1	0.0 – 0.0
A2b	LC	LC	LC	LC
A2b.bounds	LC – LC	LC – VU	LC – VU	LC – LC