RStudio project

Open the RStudio project that we created in the first session. I recommend to use this RStudio project for the entire course and within the RStudio project create separate R scripts for each session.

  • Create a new empty R script by going to the tab “File”, select “New File” and then “R script”
  • In the new R script, type # Session a5: Species threats and save the file in your folder “scripts” within your project folder, e.g. as “a5_SpeciesThreats.R”

The IUCN Red List of threatened species is a useful resource when analysing diversity changes, species range changes and population changes: www.iucnredlist.org (IUCN 2019). It provides information on the global conservation status of species, specifically of animals, fungi and plant species. The IUCN has defined a set of criteria to evaluate the extinction risk of species. According to these criteria, species are classified into nine different categories although strictly only five categories describe the conservation status - from least concern to critically endangered [Fig. 1; IUCN (2012)].

**Figure 1. The IUCN red list categories. Adapted from [@IUCN2012].**

The IUCN distinguishes five criteria that are used to classify species into one of the three threatened categories (Fig. 1; IUCN (2012)):

  • A. Population size reduction
  • B. Extent of occurrence (B1) or Area of occupancy (B2)
  • C. Small population size and decline
  • D. Very small or restricted population
  • E. Quantitative analysis (probability of extinction within next 100 years)

All of these information (the red list category, the relevant assessment criteria, the population trends, etc.) are provided by the IUCN. We can look at all these information online, e.g. for the Balearic Shearwater.

Of course, if you want to analyse your species data in light of these IUCN assessments, it can become very tedious to look up all information by hand. The IUCN red list team provides an API for this purpose, and the R package rredlist is a client to access this IUCN red list API. It requires an API key / a token to use the API. You have to indicate your research objectives for using the API.

I have got permission to use some red list information for the UK breeding birds in class, but I am not allowed to post them. Thus, course participants can download the data (UK_birds_redlist_status.csv and UK_birds_redlist_threats.csv) in the secured moodle folder (but please be aware that the IUCN terms of use apply!). External readers are advised to generate an API token:

library(rredlist)
# Generate your personal API token
rl_use_iucn()

We will work with the UK breeding bird data from practical 3 available here. If not already done so, please download the data and save them in an appropriate folder (e.g. in data folder).

# Read in the distribution dataset:
bird_dist <- read.table('data/UK_BBatlas_2008.csv',header=T, sep=',', stringsAsFactors = F)

# Species names are contained in the columns:
spp <- names(bird_dist)[-c(1:3)]

# For later usage, we need to remove the underscore in the names:
spp <- sub('_',' ',spp)

1 IUCN Red list categories

If you have your own IUCN API key, you can easily download the information on species’ red list categories:

# Download red list category for single species using your personal API token "MY_IUCN_REDLIST_KEY"
(rl_search('Gavia stellata', key= MY_IUCN_REDLIST_KEY))
## $name
## [1] "Gavia stellata"
## 
## $result
##    taxonid scientific_name  kingdom   phylum class       order   family genus
## 1 22697829  Gavia stellata ANIMALIA CHORDATA  AVES GAVIIFORMES GAVIIDAE Gavia
##    main_common_name           authority published_year assessment_date category
## 1 Red-throated Loon (Pontoppidan, 1763)           2018      2018-08-07       LC
##   criteria population_trend marine_system freshwater_system terrestrial_system
## 1       NA       Decreasing          TRUE              TRUE               TRUE
##                 assessor        reviewer aoo_km2  eoo_km2 elevation_upper
## 1 BirdLife International Westrip, J.R.S.      NA 59900000             500
##   elevation_lower depth_upper depth_lower errata_flag errata_reason
## 1              NA          NA          NA          NA            NA
##   amended_flag amended_reason
## 1           NA             NA
# Download red list categories for all species
redlist_status <- do.call(rbind,lapply(spp,FUN=function(sp){rl_search(sp, key= MY_IUCN_REDLIST_KEY)$result}))

Course participants can download the data from moodle and read it in:

redlist_status <- read.table('data/UK_birds_redlist_status.csv', header=T, sep=',')

Here is an example of the kind of information in the table. Compare this to the information given on the IUCN website, e.g. for the Balearic Shearwater.

##              authority published_year assessment_date category criteria
## 1  (Pontoppidan, 1763)           2018      2018-08-07       LC     <NA>
## 2     (Linnaeus, 1758)           2018      2018-08-07       LC     <NA>
## 3       (Pallas, 1764)           2019      2016-10-01       LC     <NA>
## 4     (Linnaeus, 1758)           2019      2019-08-11       LC     <NA>
## 5          Brehm, 1831           2018      2018-08-07       LC     <NA>
## 6     (Linnaeus, 1761)           2018      2018-08-07       LC     <NA>
## 7     (Brünnich, 1764)           2018      2018-08-17       LC     <NA>
## 8     (Linnaeus, 1758)           2018      2018-08-07       LC     <NA>
## 9     (Linnaeus, 1758)           2018      2018-08-10       LC     <NA>
## 10    (Linnaeus, 1758)           2019      2018-08-09       LC     <NA>
##    population_trend marine_system freshwater_system terrestrial_system
## 1        Decreasing          TRUE              TRUE               TRUE
## 2        Decreasing          TRUE              TRUE               TRUE
## 3        Decreasing          TRUE              TRUE               TRUE
## 4           Unknown          TRUE              TRUE               TRUE
## 5           Unknown          TRUE              TRUE               TRUE
## 6        Increasing          TRUE             FALSE               TRUE
## 7           Unknown          TRUE             FALSE               TRUE
## 8           Unknown          TRUE             FALSE               TRUE
## 9        Increasing          TRUE             FALSE               TRUE
## 10       Increasing          TRUE              TRUE               TRUE
##                  assessor                     reviewer
## 1  BirdLife International              Westrip, J.R.S.
## 2  BirdLife International                   Martin, R.
## 3  BirdLife International Butchart, S.H.M. & Symes, A.
## 4  BirdLife International                    Smith, D.
## 5  BirdLife International                   Hermes, C.
## 6  BirdLife International              Westrip, J.R.S.
## 7  BirdLife International                 Wheatley, H.
## 8  BirdLife International              Westrip, J.R.S.
## 9  BirdLife International                   Hermes, C.
## 10 BirdLife International              Westrip, J.R.S.
redlist_status[1:10,10:20]

1.2 Mapping hotspots of threatened species

Next, I would like to analyse the distribution of threatened species. We can find the relevant red list information in the data frame on the red list status. Then, we need to combine this red list information with the distribution data. To this end, we will have to extract all species that belong to a specific threat category, then compute the species richness of these species per cell and map this.

For example, we can extract all species that are classified as vulnerable:

(subset(redlist_status,category=='VU')$scientific_name)
## [1] "Branta ruficollis"   "Aythya ferina"       "Rissa tridactyla"   
## [4] "Fratercula arctica"  "Streptopelia turtur"

Using the red list information we can then map hotspots of species, meaning the species richness of species falling into different red list categories.

library(terra)
library(raster)

# Identify all vulnerable species 
vu_spp <- subset(redlist_status,category=='VU')$scientific_name
# We have to make sure that species names are written in the same way in the redlist and distribution data
vu_spp <- sub(' ','_', intersect(vu_spp,spp))

# Identify all least concern species
lc_spp <- subset(redlist_status,category=='LC')$scientific_name
lc_spp <- sub(' ','_', intersect(lc_spp,spp))

# Now, we extract the distribution data for the VU and LC species groups, make rasters, stack these and plot
spplot( c(
  terra::rast(data.frame(bird_dist[,2:3], log_VU=log(rowSums(bird_dist[,vu_spp]))), type='xyz'),
  terra::rast(data.frame(bird_dist[,2:3], log_LC=log(rowSums(bird_dist[,lc_spp]))), type='xyz')),
  main='log( species richness)')

Exercise:

Map species richness of all species belonging to the threatened categories (CR, EN, VU) and species richness of all species belonging to the non-threatened categories (NT, LC).

  • Discuss the patterns. Where are hotspots of threatened species?

2 Red list threats

The IUCN also assesses the main threats per species as you saw in the Balearic Shearwater example. The Threat Classification Scheme can be found here.

If you have your own IUCN API key, you can easily download the information on species’ red list threats:

# Download red list threats for single species
rl_threats('Gavia stellata', key= MY_IUCN_REDLIST_KEY)
## $name
## [1] "Gavia stellata"
## 
## $result
##    code                                          title  timing
## 1  11.1                  Habitat shifting & alteration  Future
## 2   3.3                               Renewable energy Ongoing
## 3   4.3                                 Shipping lanes Ongoing
## 4   5.4         Fishing & harvesting aquatic resources Ongoing
## 5 5.4.4 Unintentional effects: (large scale) [harvest] Ongoing
## 6   9.2                Industrial & military effluents Ongoing
## 7 9.2.1                                     Oil spills Ongoing
##               scope                   severity         score invasive
## 1 Majority (50-90%)        Negligible declines Low Impact: 3       NA
## 2   Minority (<50%)                    Unknown       Unknown       NA
## 3   Minority (<50%)        Negligible declines Low Impact: 4       NA
## 4   Minority (<50%)        Negligible declines Low Impact: 4       NA
## 5   Minority (<50%)        Negligible declines Low Impact: 4       NA
## 6   Minority (<50%) Slow, Significant Declines Low Impact: 5       NA
## 7   Minority (<50%) Slow, Significant Declines Low Impact: 5       NA
# Download red list threats for all species
redlist_threats <- do.call(rbind,lapply(seq_len(length(spp)),FUN=function(i){xi <- rl_threats(spp[i], key= MY_IUCN_REDLIST_KEY); if(length(xi$result)) {data.frame(species=spp[i],xi$result) }}))

Course participants can download the data from moodle and read it in:

redlist_threats <- read.table('data/UK_birds_redlist_threats.csv', header=T, sep=',')

The threats are ordered hierarchically from broad threat type to very detailed threat, e.g.:

  • 2 Agriculture & aquaculture > 2.2 Wood & pulp plantations > 2.2.1 Small-holder plantations
  • 5 Biological resource use > 5.4 Fishing & harvesting aquatic resources > 5.4.3 Unintentional effects: subsistence/small scale (species being assessed is not the target)[harvest]

Here is an example of the kind of information in the table. For more details, please have a look at the IUCN website, e.g. the Balearic Shearwater example, and at the Threat Classification Scheme.

redlist_threats[sample(nrow(redlist_threats),10),-c(1:2)]
##                                                          title
## 337                         Named species (Accipiter gentilis)
## 483 Unintentional effects: (subsistence/small scale) [harvest]
## 499                        Annual & perennial non-timber crops
## 213                 Agro-industry grazing, ranching or farming
## 815                     Hunting & trapping terrestrial animals
## 729                                          Roads & railroads
## 248                                      Agro-industry farming
## 719                          Agricultural & forestry effluents
## 772                                                   Droughts
## 290                     Hunting & trapping terrestrial animals
##                     timing             scope                   severity
## 337                Ongoing   Minority (<50%)        Negligible declines
## 483                Ongoing   Minority (<50%) Slow, Significant Declines
## 499                Ongoing      Whole (>90%) Slow, Significant Declines
## 213                Ongoing   Minority (<50%)        Negligible declines
## 815                Ongoing   Minority (<50%)             Rapid Declines
## 729                Ongoing   Minority (<50%)                    Unknown
## 248                Ongoing Majority (50-90%) Slow, Significant Declines
## 719                Ongoing   Minority (<50%)        Negligible declines
## 772 Past, Likely to Return   Minority (<50%) Slow, Significant Declines
## 290                Ongoing Majority (50-90%)        Negligible declines
##                score           invasive
## 337    Low Impact: 4 Accipiter gentilis
## 483    Low Impact: 5               <NA>
## 499 Medium Impact: 7               <NA>
## 213    Low Impact: 4               <NA>
## 815 Medium Impact: 6               <NA>
## 729          Unknown               <NA>
## 248 Medium Impact: 6               <NA>
## 719    Low Impact: 4               <NA>
## 772      Past Impact               <NA>
## 290    Low Impact: 5               <NA>

We can extract many useful information from this table, for example, when specific threats occurred.

table(redlist_threats$timing)
## 
##                   Future                  Ongoing   Past, Likely to Return 
##                       58                      750                       18 
## Past, Unlikely to Return 
##                        8

Exercise:

Explore the threats table. For example,

  • Pick a species and identify which threats are causing rapid declines and slow declines.

2.1 Mapping hotspots of threats

We can also analyse the spatial distribution of threats. To do so, we need to extract the species that are affected by a particular threat in a particular time period. Then we can extract the distribution data for these species, compute the species richness and map this in space.

# Which ongoing threats are the most common ?
sort(table(subset(redlist_threats, species %in% spp & timing=='Ongoing')$title), decreasing=T)[1:10]
## 
##         Hunting & trapping terrestrial animals 
##                                             63 
##        Intentional use (species is the target) 
##                                             40 
##                Industrial & military effluents 
##                                             26 
##              Agricultural & forestry effluents 
##                                             24 
##            Annual & perennial non-timber crops 
##                                             23 
## Unintentional effects: (large scale) [harvest] 
##                                             23 
##                          Agro-industry farming 
##                                             21 
##         Fishing & harvesting aquatic resources 
##                                             21 
##                               Renewable energy 
##                                             21 
##                      Herbicides and pesticides 
##                                             20
# Identify the species experiencing threats from hunting
spp_threat1 <- sub(' ','_',subset(redlist_threats,title=="Hunting & trapping terrestrial animals" & species %in% spp)$species)
# Identify the species experiencing threats from industry and military
spp_threat2 <- sub(' ','_',subset(redlist_threats,title=="Industrial & military effluents" & species %in% spp)$species)

# Map species experiencing threats from hunting
plot(terra::rast(data.frame(bird_dist[,2:3],rowSums(bird_dist[,spp_threat1])), type='xyz'), main="Hunting & trapping terrestrial animals")

# species experiencing threats from industry and military
plot(terra::rast(data.frame(bird_dist[,2:3],rowSums(bird_dist[,spp_threat2])), type='xyz'), main="Industrial & military effluents")

Exercise:

  • Pick two other ongoing threats and map the species richness of those breeding birds affected by these threats.
  • Pick a future and a past threat and map the species richness of those breeding birds affected by these threats.

Interpret.

References

IUCN. 2012. IUCN Red List Categories and Criteria: Version 3.1. Gland, Switzerland; Cambridge, UK: IUCN.
———. 2019. The IUCN Red List of Threatened Species. Version 2019-2. http://www.iucnredlist.org. Downloaded on 27 October 2019.