Final project
Miriam
26 February 2019
GENERAL SUMMARIZED INTRODUCTION
This assignment report intends to answer Path Number 1. The path requires to use ConR: An R package to assist large-scale multispecies preliminary conservation assessments using available distribution data. The research question that was meant to be addressed in this work was ‘What is the conservation status of a given living organism from georeferenced distribution data?’ I choose path1 where I used Con R package since it provides the necessary libraries to execute operations that calculate extents of key geographic range parameters (AOO and EOO), make approximations of location counts sensu for assessments but also generate as well preliminary assessments of multiple species Ecuador is the chosen country as it was seen to have a diversity of a class of amphibians called ‘anura’ whose distribution data were available and whose range of species is widely distributed in terms of from Least concerned to Endangered in the IUCN red list spectrum (IUCN, 2012) In this exercise the two references provided “women in the world” and “mapping world development indicators” were used to get an inspiration of code writing and manipulation.
Relevant libraries were then run to call required functions. Then a series of codes were used to upload distribution data from GBIF, the region specific distribution data (Ecuador) was obtained. The family and species data were extracted and finally mapping of distribution depending on their positions in the IUCN red list was done as shown in the following list of operations.
library(sf)## Warning: package 'sf' was built under R version 3.5.2## Linking to GEOS 3.6.1, GDAL 2.2.3, PROJ 4.9.3library(raster)## Warning: package 'raster' was built under R version 3.5.2## Loading required package: sp## Warning: package 'sp' was built under R version 3.5.2library(tidyverse)## Warning: package 'tidyverse' was built under R version 3.5.2## -- Attaching packages ---------------------------------------------------------------------- tidyverse 1.2.1 --## v ggplot2 3.1.0 v purrr 0.3.0
## v tibble 2.0.1 v dplyr 0.8.0.1
## v tidyr 0.8.2 v stringr 1.3.1
## v readr 1.3.1 v forcats 0.4.0## Warning: package 'tibble' was built under R version 3.5.2## Warning: package 'tidyr' was built under R version 3.5.2## Warning: package 'readr' was built under R version 3.5.2## Warning: package 'purrr' was built under R version 3.5.2## Warning: package 'dplyr' was built under R version 3.5.2## Warning: package 'forcats' was built under R version 3.5.2## -- Conflicts ------------------------------------------------------------------------- tidyverse_conflicts() --
## x tidyr::extract() masks raster::extract()
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## x dplyr::select() masks raster::select()#install.packages("ConR")
library(sp)
library(ConR)## Warning: package 'ConR' was built under R version 3.5.2- Downloading data from GBIF The GBIF provides access to taxonomic data about a country and can easily be downloaded using either the GBIF website or using the rgbif R package (hosted by OpenSci) After registering in the GBIF website, I looked for plants ocurrences data in the Colombian Amazonian. After some minutes, the GBIF tool informed that a dataset was ready for downloading. The dataset was identified as DOI10.15468/dl.cbzf8v. The URL for downloading the dataset is http://api.gbif.org/v1/occurrence/download/request/0045549-181108115102211.zip Now, we can proceed to downloading and unzipping this dataset from R:
#install.packages("rgbif")
library(rgbif)## Warning: package 'rgbif' was built under R version 3.5.2library(dplyr)
equ_gbif <- occ_download_get(key = "0045549-181108115102211", overwrite = TRUE) %>%
occ_download_import(equ_gbif_download, na.strings = c("", NA))## Download file size: 2.99 MB## On disk at C:\Users\Azim Baibagyssov\Documents\0045549-181108115102211.zipequ_gbif## # A tibble: 87,271 x 45
## gbifID datasetKey occurrenceID kingdom phylum class order family genus
## <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 1.99e9 ad43e954-~ MH306193 Animal~ Chord~ Amph~ Anura Strab~ Pris~
## 2 1.99e9 ad43e954-~ MH306194 Animal~ Chord~ Amph~ Anura Strab~ Pris~
## 3 1.99e9 50c9509d-~ https://www~ Animal~ Chord~ Amph~ Anura Phyll~ Call~
## 4 1.99e9 50c9509d-~ https://www~ Animal~ Chord~ Amph~ Anura Strab~ Pris~
## 5 1.99e9 50c9509d-~ https://www~ Animal~ Chord~ Amph~ Anura Lepto~ Lept~
## 6 1.99e9 50c9509d-~ https://www~ Animal~ Chord~ Amph~ Anura Hylid~ Tepu~
## 7 1.99e9 50c9509d-~ https://www~ Animal~ Chord~ Amph~ Anura Strab~ Pris~
## 8 1.99e9 50c9509d-~ https://www~ Animal~ Chord~ Amph~ Anura Hylid~ Oste~
## 9 1.99e9 50c9509d-~ https://www~ Animal~ Chord~ Amph~ Anura Dendr~ Amee~
## 10 1.99e9 50c9509d-~ https://www~ Animal~ Chord~ Amph~ Anura Strab~ Pris~
## # ... with 87,261 more rows, and 36 more variables: species <chr>,
## # infraspecificEpithet <chr>, taxonRank <chr>, scientificName <chr>,
## # countryCode <chr>, locality <chr>, publishingOrgKey <chr>,
## # decimalLatitude <dbl>, decimalLongitude <dbl>,
## # coordinateUncertaintyInMeters <dbl>, coordinatePrecision <lgl>,
## # elevation <dbl>, elevationAccuracy <dbl>, depth <dbl>,
## # depthAccuracy <dbl>, eventDate <chr>, day <int>, month <int>,
## # year <int>, taxonKey <int>, speciesKey <int>, basisOfRecord <chr>,
## # institutionCode <chr>, collectionCode <chr>, catalogNumber <chr>,
## # recordNumber <chr>, identifiedBy <chr>, dateIdentified <chr>,
## # license <chr>, rightsHolder <chr>, recordedBy <chr>, typeStatus <chr>,
## # establishmentMeans <chr>, lastInterpreted <chr>, mediaType <chr>,
## # issue <chr>library(tidyverse)
equ_gbif %>% drop_na(kingdom) %>% count(kingdom, sort = TRUE)## # A tibble: 1 x 2
## kingdom n
## <chr> <int>
## 1 Animalia 87271equ_gbif %>% filter(taxonRank == "SPECIES") %>% count(species) %>% arrange(desc(n))## # A tibble: 568 x 2
## species n
## <chr> <int>
## 1 Dendropsophus triangulum 4631
## 2 Dendropsophus carnifex 3764
## 3 Atelopus ignescens 2763
## 4 Pristimantis w-nigrum 2331
## 5 Pristimantis curtipes 1796
## 6 Pristimantis achatinus 1784
## 7 Pristimantis unistrigatus 1574
## 8 Rhinella margaritifera 1557
## 9 Epipedobates machalilla 1473
## 10 Hypsiboas punctatus 1408
## # ... with 558 more rowslibrary(tidyverse)
equ_gbif %>% count(taxonRank)## # A tibble: 6 x 2
## taxonRank n
## <chr> <int>
## 1 FAMILY 739
## 2 GENUS 8278
## 3 ORDER 102
## 4 SPECIES 78093
## 5 SUBSPECIES 57
## 6 VARIETY 2equ_gbif %>% count(family, sort = TRUE) %>% drop_na(family) %>% filter(n > 500) %>%
ggplot(aes(x = reorder(family, n), y = n, fill = family)) + geom_bar(stat = "identity",
show.legend = FALSE) + labs(x = "SPECIES", y = "Number of Occurrence Records (observations)") +
coord_flip()
equ_gbif %>% filter(species == "Phyllomedusa palliata")## # A tibble: 98 x 45
## gbifID datasetKey occurrenceID kingdom phylum class order family genus
## <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 1.98e9 fccafd83-~ http://data~ Animal~ Chord~ Amph~ Anura Hylid~ Phyl~
## 2 1.32e9 832b8188-~ FFA319BA-5F~ Animal~ Chord~ Amph~ Anura Hylid~ Phyl~
## 3 1.32e9 832b8188-~ 2A4BF902-12~ Animal~ Chord~ Amph~ Anura Hylid~ Phyl~
## 4 1.32e9 832b8188-~ 296A26FB-BF~ Animal~ Chord~ Amph~ Anura Hylid~ Phyl~
## 5 1.15e9 7f6dd0f7-~ ce7641e3-0b~ Animal~ Chord~ Amph~ Anura Hylid~ Phyl~
## 6 1.15e9 7f6dd0f7-~ 977acf19-a1~ Animal~ Chord~ Amph~ Anura Hylid~ Phyl~
## 7 1.07e9 852628c6-~ urn:catalog~ Animal~ Chord~ Amph~ Anura Hylid~ Phyl~
## 8 8.66e8 852628c6-~ urn:catalog~ Animal~ Chord~ Amph~ Anura Hylid~ Phyl~
## 9 8.66e8 852628c6-~ urn:catalog~ Animal~ Chord~ Amph~ Anura Hylid~ Phyl~
## 10 8.66e8 852628c6-~ urn:catalog~ Animal~ Chord~ Amph~ Anura Hylid~ Phyl~
## # ... with 88 more rows, and 36 more variables: species <chr>,
## # infraspecificEpithet <chr>, taxonRank <chr>, scientificName <chr>,
## # countryCode <chr>, locality <chr>, publishingOrgKey <chr>,
## # decimalLatitude <dbl>, decimalLongitude <dbl>,
## # coordinateUncertaintyInMeters <dbl>, coordinatePrecision <lgl>,
## # elevation <dbl>, elevationAccuracy <dbl>, depth <dbl>,
## # depthAccuracy <dbl>, eventDate <chr>, day <int>, month <int>,
## # year <int>, taxonKey <int>, speciesKey <int>, basisOfRecord <chr>,
## # institutionCode <chr>, collectionCode <chr>, catalogNumber <chr>,
## # recordNumber <chr>, identifiedBy <chr>, dateIdentified <chr>,
## # license <chr>, rightsHolder <chr>, recordedBy <chr>, typeStatus <chr>,
## # establishmentMeans <chr>, lastInterpreted <chr>, mediaType <chr>,
## # issue <chr>phyllomedusa <- equ_gbif %>% dplyr::filter(genus == "Phyllomedusa") %>%
select(decimalLatitude, decimalLongitude, species)
print(phyllomedusa)## # A tibble: 780 x 3
## decimalLatitude decimalLongitude species
## <dbl> <dbl> <chr>
## 1 -78.1 -78.1 <NA>
## 2 0.0856 -77.0 <NA>
## 3 -78.1 -78.1 <NA>
## 4 -0.411 -76.6 <NA>
## 5 -0.204 -77.7 <NA>
## 6 0.0856 -77.0 <NA>
## 7 0.0856 -77.0 <NA>
## 8 0.0856 -77.0 <NA>
## 9 -0.167 -77.6 <NA>
## 10 0.0856 -77.0 <NA>
## # ... with 770 more rowsEOO.results <- EOO.computing(phyllomedusa, export_shp = T)## [1] "Skipping 129 occurrences because of missing coordinates for c(\"Phyllomedusa palliata\", \"Phyllomedusa vaillantii\", \"Phyllomedusa tarsius\", \"Phyllomedusa tomopterna\", \"Phyllomedusa perinesos\", \"Phyllomedusa bicolor\", \"Phyllomedusa loris\", \"Phyllomedusa burmeisteri\", NA, \"Phyllomedusa boliviana\")"## Warning in .EOO.comp(x, Name_Sp = ifelse(ncol(XY) > 2,
## as.character(unique(x$tax)), : EOO for Phyllomedusa craspedopus is not
## computed because there is only 1 unique occurrence## Warning in .EOO.comp(x, Name_Sp = ifelse(ncol(XY) > 2,
## as.character(unique(x$tax)), : EOO for Phyllomedusa ecuatoriana is not
## computed because there is only 1 unique occurrencesummary(EOO.results)## Length Class Mode
## Phyllomedusa craspedopus 1 -none- numeric
## Phyllomedusa ecuatoriana 1 -none- numeric
## Phyllomedusa palliata 2 -none- list
## Phyllomedusa perinesos 2 -none- list
## Phyllomedusa tarsius 2 -none- list
## Phyllomedusa tomopterna 2 -none- list
## Phyllomedusa vaillantii 2 -none- listlibrary(rnaturalearth)## Warning: package 'rnaturalearth' was built under R version 3.5.2library(sp)
#world countries
sp::plot(ne_countries(country='ecuador'), border="grey",
main="EOO for Phyllomedusa palliata - Convex Hull")
sp::plot(EOO.results[[7]][[2]], col="red", add=TRUE)
#install.packages("alphahull")
library(alphahull)## Warning: package 'alphahull' was built under R version 3.5.2EOO.results2 <- EOO.computing(phyllomedusa, method.range = "alpha.hull",
export_shp = T, alpha=3)## [1] "Skipping 129 occurrences because of missing coordinates for c(\"Phyllomedusa palliata\", \"Phyllomedusa vaillantii\", \"Phyllomedusa tarsius\", \"Phyllomedusa tomopterna\", \"Phyllomedusa perinesos\", \"Phyllomedusa bicolor\", \"Phyllomedusa loris\", \"Phyllomedusa burmeisteri\", NA, \"Phyllomedusa boliviana\")"## Warning in .EOO.comp(x, Name_Sp = ifelse(ncol(XY) > 2,
## as.character(unique(x$tax)), : EOO for Phyllomedusa craspedopus is not
## computed because there is only 1 unique occurrence## Warning in .EOO.comp(x, Name_Sp = ifelse(ncol(XY) > 2,
## as.character(unique(x$tax)), : EOO for Phyllomedusa ecuatoriana is not
## computed because there is only 1 unique occurrencesummary(EOO.results2)## Length Class Mode
## Phyllomedusa craspedopus 1 -none- numeric
## Phyllomedusa ecuatoriana 1 -none- numeric
## Phyllomedusa palliata 2 -none- list
## Phyllomedusa perinesos 2 -none- list
## Phyllomedusa tarsius 2 -none- list
## Phyllomedusa tomopterna 2 -none- list
## Phyllomedusa vaillantii 2 -none- list#
sp::plot(EOO.results2[[7]][[2]], col="red", main="EOO for Phyllomedusa palliata - Alpha Hull")
sp::plot(ne_countries(country='ecuador'), border="grey", add=TRUE)
SUB <- subpop.comp(phyllomedusa, Resol_sub_pop=50)## [1] "Skipping 129 occurrences because of missing coordinates for c(\"Phyllomedusa palliata\", \"Phyllomedusa vaillantii\", \"Phyllomedusa tarsius\", \"Phyllomedusa tomopterna\", \"Phyllomedusa perinesos\", \"Phyllomedusa bicolor\", \"Phyllomedusa loris\", \"Phyllomedusa burmeisteri\", NA, \"Phyllomedusa boliviana\")"SUB## $`Phyllomedusa craspedopus`
## $`Phyllomedusa craspedopus`$`Number of subpopulation`
## [1] 1
##
## $`Phyllomedusa craspedopus`$subpop.poly
## class : SpatialPolygons
## features : 1
## extent : -77.08249, -76.18418, -2.819304, -1.914174 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
##
##
## $`Phyllomedusa ecuatoriana`
## $`Phyllomedusa ecuatoriana`$`Number of subpopulation`
## [1] 1
##
## $`Phyllomedusa ecuatoriana`$subpop.poly
## class : SpatialPolygons
## features : 1
## extent : -78.94916, -78.05084, -3.486229, -2.580615 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
##
##
## $`Phyllomedusa palliata`
## $`Phyllomedusa palliata`$`Number of subpopulation`
## [1] 1
##
## $`Phyllomedusa palliata`$subpop.poly
## class : SpatialPolygons
## features : 1
## extent : -77.41582, -76.16751, -0.8522122, 0.502191 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
##
##
## $`Phyllomedusa perinesos`
## $`Phyllomedusa perinesos`$`Number of subpopulation`
## [1] 1
##
## $`Phyllomedusa perinesos`$subpop.poly
## class : SpatialPolygons
## features : 1
## extent : -78.15416, -77.13414, -0.6530311, 0.3521869 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
##
##
## $`Phyllomedusa tarsius`
## $`Phyllomedusa tarsius`$`Number of subpopulation`
## [1] 3
##
## $`Phyllomedusa tarsius`$subpop.poly
## class : SpatialPolygons
## features : 1
## extent : -78.48246, -75.71968, -3.261894, 0.502191 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
##
##
## $`Phyllomedusa tomopterna`
## $`Phyllomedusa tomopterna`$`Number of subpopulation`
## [1] 3
##
## $`Phyllomedusa tomopterna`$subpop.poly
## class : SpatialPolygons
## features : 1
## extent : -78.96583, -75.98414, -3.886418, 0.5368924 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
##
##
## $`Phyllomedusa vaillantii`
## $`Phyllomedusa vaillantii`$`Number of subpopulation`
## [1] 1
##
## $`Phyllomedusa vaillantii`$subpop.poly
## class : SpatialPolygons
## features : 1
## extent : -78.61586, -75.61946, -3.20278, 0.5355253 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0sp::plot(SUB[["Phyllomedusa palliata"]][["subpop.poly"]], col="red")
sp::plot(ne_countries(country='ecuador'), border="grey", add=TRUE)
equ_gbif## # A tibble: 87,271 x 45
## gbifID datasetKey occurrenceID kingdom phylum class order family genus
## <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 1.99e9 ad43e954-~ MH306193 Animal~ Chord~ Amph~ Anura Strab~ Pris~
## 2 1.99e9 ad43e954-~ MH306194 Animal~ Chord~ Amph~ Anura Strab~ Pris~
## 3 1.99e9 50c9509d-~ https://www~ Animal~ Chord~ Amph~ Anura Phyll~ Call~
## 4 1.99e9 50c9509d-~ https://www~ Animal~ Chord~ Amph~ Anura Strab~ Pris~
## 5 1.99e9 50c9509d-~ https://www~ Animal~ Chord~ Amph~ Anura Lepto~ Lept~
## 6 1.99e9 50c9509d-~ https://www~ Animal~ Chord~ Amph~ Anura Hylid~ Tepu~
## 7 1.99e9 50c9509d-~ https://www~ Animal~ Chord~ Amph~ Anura Strab~ Pris~
## 8 1.99e9 50c9509d-~ https://www~ Animal~ Chord~ Amph~ Anura Hylid~ Oste~
## 9 1.99e9 50c9509d-~ https://www~ Animal~ Chord~ Amph~ Anura Dendr~ Amee~
## 10 1.99e9 50c9509d-~ https://www~ Animal~ Chord~ Amph~ Anura Strab~ Pris~
## # ... with 87,261 more rows, and 36 more variables: species <chr>,
## # infraspecificEpithet <chr>, taxonRank <chr>, scientificName <chr>,
## # countryCode <chr>, locality <chr>, publishingOrgKey <chr>,
## # decimalLatitude <dbl>, decimalLongitude <dbl>,
## # coordinateUncertaintyInMeters <dbl>, coordinatePrecision <lgl>,
## # elevation <dbl>, elevationAccuracy <dbl>, depth <dbl>,
## # depthAccuracy <dbl>, eventDate <chr>, day <int>, month <int>,
## # year <int>, taxonKey <int>, speciesKey <int>, basisOfRecord <chr>,
## # institutionCode <chr>, collectionCode <chr>, catalogNumber <chr>,
## # recordNumber <chr>, identifiedBy <chr>, dateIdentified <chr>,
## # license <chr>, rightsHolder <chr>, recordedBy <chr>, typeStatus <chr>,
## # establishmentMeans <chr>, lastInterpreted <chr>, mediaType <chr>,
## # issue <chr>tmp <- equ_gbif %>% dplyr::filter(genus == "Phyllomedusa") %>%
select(decimalLatitude, decimalLongitude, species, genus)
tmp## # A tibble: 780 x 4
## decimalLatitude decimalLongitude species genus
## <dbl> <dbl> <chr> <chr>
## 1 -78.1 -78.1 <NA> Phyllomedusa
## 2 0.0856 -77.0 <NA> Phyllomedusa
## 3 -78.1 -78.1 <NA> Phyllomedusa
## 4 -0.411 -76.6 <NA> Phyllomedusa
## 5 -0.204 -77.7 <NA> Phyllomedusa
## 6 0.0856 -77.0 <NA> Phyllomedusa
## 7 0.0856 -77.0 <NA> Phyllomedusa
## 8 0.0856 -77.0 <NA> Phyllomedusa
## 9 -0.167 -77.6 <NA> Phyllomedusa
## 10 0.0856 -77.0 <NA> Phyllomedusa
## # ... with 770 more rowsnphyllomedusa <- tmp %>%
filter(!is.na(decimalLatitude)) %>%
filter(!is.na(species))
nphyllomedusa## # A tibble: 613 x 4
## decimalLatitude decimalLongitude species genus
## <dbl> <dbl> <chr> <chr>
## 1 -1.10 -76.7 Phyllomedusa vaillantii Phyllomedusa
## 2 -1.11 -76.7 Phyllomedusa vaillantii Phyllomedusa
## 3 -1.07 -77.6 Phyllomedusa vaillantii Phyllomedusa
## 4 -1.07 -77.6 Phyllomedusa vaillantii Phyllomedusa
## 5 -0.638 -76.1 Phyllomedusa vaillantii Phyllomedusa
## 6 -0.637 -76.1 Phyllomedusa vaillantii Phyllomedusa
## 7 -0.638 -76.1 Phyllomedusa vaillantii Phyllomedusa
## 8 -0.674 -76.4 Phyllomedusa tarsius Phyllomedusa
## 9 -1.10 -76.7 Phyllomedusa vaillantii Phyllomedusa
## 10 -1.10 -76.7 Phyllomedusa tarsius Phyllomedusa
## # ... with 603 more rowsMyResults <- IUCN.eval(nphyllomedusa, Cell_size_AOO = 10)##
|
| | 0%## Warning in .IUCN.comp(x, NamesSp = as.character(unique(x$tax)), DrawMap
## = DrawMap, : EOO statistic is not computed for Phyllomedusa craspedopus
## because there is less than 3 records##
|
|========= | 14%## Warning in .IUCN.comp(x, NamesSp = as.character(unique(x$tax)), DrawMap
## = DrawMap, : EOO statistic is not computed for Phyllomedusa ecuatoriana
## because there is less than 3 records##
|
|=================== | 29%##
|
|============================ | 43%##
|
|===================================== | 57%##
|
|============================================== | 71%##
|
|======================================================== | 86%##
|
|=================================================================| 100%
## [1] "Number of species per category"
##
## EN LC or NT
## 4 3
## [1] "Ratio of species per category"
##
## EN LC or NT
## 57.1 42.9The IUCN Categories are:
LC: Least Concern
NT: Near Threatened VU: Vulnerable EN: Endangered CR: Critically Endangered
The NT category is assigned here for taxa with a number of location lower than 14 and higher than 10, with EOO higher or equal to 20,000 km2 and with AOO higher or equal to 2,000 km2.
According to the output there are 4 endangered species, 3 near threatened and 6 vulnerable. None of this species was considered as a critically endangered specie.
knitr::include_graphics("./IUCN__results_map/IUCN_Phyllomedusa_craspedopus.png")
IUCN Phyllomedusa craspedopus
knitr::include_graphics("./IUCN__results_map/IUCN_Phyllomedusa_ecuatoriana.png")
knitr::include_graphics("./IUCN__results_map/IUCN_Phyllomedusa_palliata.png")
knitr::include_graphics("./IUCN__results_map/IUCN_Phyllomedusa_perinesos.png")
knitr::include_graphics("./IUCN__results_map/IUCN_Phyllomedusa_tarsius.png")
knitr::include_graphics("./IUCN__results_map/IUCN_Phyllomedusa_tomopterna.png")
ecuador <- ne_countries(country='ecuador')library(crayon)##
## Attaching package: 'crayon'## The following object is masked from 'package:ggplot2':
##
## %+%cat(blue("map.res(Results, Occurrences, country_map=NULL, Resol=1, threshold=0,
LatMin=NULL, LatMax=NULL, LongMin=NULL,
LongMax=NULL, export_map=FALSE, file_name=NULL, export_data=FALSE"))## map.res(Results, Occurrences, country_map=NULL, Resol=1, threshold=0,
## LatMin=NULL, LatMax=NULL, LongMin=NULL,
## LongMax=NULL, export_map=FALSE, file_name=NULL, export_data=FALSEcat(green("map.res(MyResults, Occurrences=nphyllomedusa, country_map=ecuador, export_map=TRUE, threshold=0,
LatMin=-3,LatMax=3,LongMin=-78, LongMax=-68, Resol=0.1"))## map.res(MyResults, Occurrences=nphyllomedusa, country_map=ecuador, export_map=TRUE, threshold=0,
## LatMin=-3,LatMax=3,LongMin=-78, LongMax=-68, Resol=0.1cat(red("Error in ConR::map.res(MyResults2, Occurrences = as_data_frame(nphyllomedusa), : Results and Occurrences input files have different number of species"))## Error in ConR::map.res(MyResults2, Occurrences = as_data_frame(nphyllomedusa), : Results and Occurrences input files have different number of speciesMyResults %>% count(taxa) %>% arrange(desc(taxa)) ## # A tibble: 7 x 2
## taxa n
## <fct> <int>
## 1 Phyllomedusa vaillantii 1
## 2 Phyllomedusa tomopterna 1
## 3 Phyllomedusa tarsius 1
## 4 Phyllomedusa perinesos 1
## 5 Phyllomedusa palliata 1
## 6 Phyllomedusa ecuatoriana 1
## 7 Phyllomedusa craspedopus 1nphyllomedusa %>% count(species) %>% arrange(desc(species))## # A tibble: 7 x 2
## species n
## <chr> <int>
## 1 Phyllomedusa vaillantii 135
## 2 Phyllomedusa tomopterna 143
## 3 Phyllomedusa tarsius 168
## 4 Phyllomedusa perinesos 87
## 5 Phyllomedusa palliata 78
## 6 Phyllomedusa ecuatoriana 1
## 7 Phyllomedusa craspedopus 1levels(MyResults$taxa)## [1] "Phyllomedusa craspedopus" "Phyllomedusa ecuatoriana"
## [3] "Phyllomedusa palliata" "Phyllomedusa perinesos"
## [5] "Phyllomedusa tarsius" "Phyllomedusa tomopterna"
## [7] "Phyllomedusa vaillantii"MyResults2 <- MyResults %>%
mutate(taxa = as.character(taxa))
MyResults2 %>% count(taxa) %>% arrange(desc(taxa))## # A tibble: 7 x 2
## taxa n
## <chr> <int>
## 1 Phyllomedusa vaillantii 1
## 2 Phyllomedusa tomopterna 1
## 3 Phyllomedusa tarsius 1
## 4 Phyllomedusa perinesos 1
## 5 Phyllomedusa palliata 1
## 6 Phyllomedusa ecuatoriana 1
## 7 Phyllomedusa craspedopus 1cat(blue("map.res(MyResults2, Occurrences=as_data_frame(nphyllomedusa), country_map=ecuador, export_map=TRUE, threshold=0,
LatMin=-3,LatMax=3,LongMin=-78, LongMax=-68, Resol=0.1"))## map.res(MyResults2, Occurrences=as_data_frame(nphyllomedusa), country_map=ecuador, export_map=TRUE, threshold=0,
## LatMin=-3,LatMax=3,LongMin=-78, LongMax=-68, Resol=0.1REFERENCES
Dauby, G., Stévart, T., Droissart, V., Cosiaux, A., Deblauwe, V., Simo-Droissart, M., Sosef, M., Lowry, P. P., Schatz, G. E., Gereau, R. E., . Couvreur, T. (2017). ConR: An R package to assist large-scale multispecies preliminary conservation assessments using distribution data. Ecology and evolution, 7(24), 11292-11303. doi:10.1002/ece3.3704
IUCN. 2012. IUCN Red List Categories and Criteria: Version 3.1. Second edition. Gland, Switzerland and Cambridge, UK: IUCN.