vignettes/rdopa_vignette.md

In ropensci/rdopa: R client to Joint Research Centre's DOPA REST API

output: html_document: theme: flatly

Basic information and statistics

DOPA provides an array of information on species and protected area (PA) occurrence within the borders (or in some cases the Exclusive Economic Zone boundary) of a given country. country_list() can be used to list all the countries available in the DOPA database:

library(rdopa)
library(dplyr)

cl <- country_list()
head(cl)

##   cid iso2 iso3           name              minx              miny             maxx              maxy
## 1   4   AF  AFG    Afghanistan  60.4784431460001      29.377476867     74.878563529      38.483426111
## 2   8   AL  ALB        Albania  19.2767783890001      39.644562022 21.0572813000001  42.6607506710001
## 3  12   DZ  DZA        Algeria -8.67386722599994  18.9600276950001 11.9795484540001  37.0915184020001
## 4  16   AS  ASM American Samoa    -171.088394165      -14.38247776    -169.41607666 -11.0518255229999
## 5  20   AD  AND        Andorra  1.42387998100008      42.435089111 1.78038597100004      42.658706665
## 6  24   AO  AGO         Angola      11.680835724 -18.0417575839999 24.0821189880001 -4.37932348299994

Species

There are also few convience function to retrieve species information on conuntry-level. To list species included in a particular IUCN status category within a given country, use function country_species_list(). For example, if you're interested in all (globally) threatened species (i.e. those in IUCN categories CR, EN, or VU) that occur in Finland, do the following:

threatened.fin <- country_species_list('Finland', status=c("CR", "EN", "VU"))
# Select only part of the columns (using dplyr)
select(threatened.fin, iucn_species_id, taxon, class, status, commonname)

##    iucn_species_id                taxon          class status                 commonname
## 1           161817  Alisma wahlenbergii     Liliopsida     VU                       <NA>
## 2            60344    Anguilla anguilla Actinopterygii     CR                       <NA>
## 3         22679886     Anser erythropus           Aves     VU Lesser White fronted Goose
## 4             2191      Astacus astacus   Malacostraca     VU      Broad clawed Crayfish
## 5         13152906       Bombus alpinus        Insecta     VU                       <NA>
## 6         22696027        Clanga clanga           Aves     VU                       <NA>
## 7         22680427    Clangula hyemalis           Aves     VU           Long tailed Duck
## 8           135672    Coregonus maraena Actinopterygii     VU         European whitefish
## 9             5383    Coregonus trybomi Actinopterygii     CR      Spring spawning cisco
## 10        22720966     Emberiza aureola           Aves     EN    Yellow breasted Bunting
## 11           11200          Lamna nasus Chondrichthyes     VU            Beaumaris shark
## 12        22724836      Melanitta fusca           Aves     EN              Velvet Scoter
## 13          161870 Papaver laestadianum  Magnoliopsida     VU                       <NA>
## 14        22680415  Polysticta stelleri           Aves     VU            Steller s Eider
## 15           39326    Squalus acanthias Chondrichthyes     VU                   Blue dog
## 16           39332    Squatina squatina Chondrichthyes     CR                      Angel
## 17           21860      Thunnus thynnus Actinopterygii     EN      Atlantic Bluefin Tuna

Function country_species_count() returns the total number of species within a given country that belong to a specific status categories. For example, to get the number of threatened species in Brazil:

country_species_count('Brazil', rlstatus=c("CR", "EN", "VU"))

## [1] 285

This - and breakdown into individual categories - can of course be achieved by working with the complete country-specific species list:

threatened.bra <- country_species_list('Brazil', status=c("CR", "EN", "VU"))
# Total number
nrow(threatened.bra)

## [1] 380

# Per category (using dplyr)
threatened.bra %>%
  group_by(status) %>%
  summarise(count = n()) 

## Source: local data frame [3 x 2]
## 
##   status count
## 1     CR    60
## 2     EN   105
## 3     VU   215

Protected areas

Protected area (PA) statistics are available per country according to IUCN PA categories:

country_stats("Sweden")

##    category area_protected area_protected_perc area_total countryiso   name       iucn_cat
## 1         0          78753               13.02      78753        752 Sweden              0
## 2         1           5644                7.17       5644        752 Sweden             Ia
## 3         2          30974               39.33      30974        752 Sweden             Ib
## 4         3           7386                9.38       7386        752 Sweden             II
## 5         4             NA                  NA      78753        752 Sweden            III
## 6         5           1425                1.81       1425        752 Sweden             IV
## 7         6           6791                8.62       6791        752 Sweden              V
## 8         7             NA                  NA      78753        752 Sweden             VI
## 9         8          24379               30.96      24379        752 Sweden   Not Reported
## 10        9           1816                2.31       1816        752 Sweden   Not Assigned
## 11        9             NA                  NA      78753        752 Sweden Not Applicable

Within a given country, statistics for individual PAs can be retrieved using pa_country_stats(). Let's find out all the PAs within Uganda:

uganda_pa_stats <- pa_country_stats("Uganda")
nrow(uganda_pa_stats)

## [1] 58

So Uganda has 28 individual PAs in the WDPA.

ropensci/rdopa documentation built on May 18, 2022, 6:32 p.m.