Author and date: Hanno Sandvik, 24 May 2023
This R code can be used to run the analyses of the Norwegian Red Lists for species described in the paper “Metrics for quantifying how much different threats contribute to red lists of species and ecosystems” (Sandvik & Pedersen 2023).
Contents:
- Variables
- Constants
- Preliminaries
- Read and check the data
- Prepare the data frame for analyses
- Summarise the data
- Analysis of threat factors
- Figures
- Analysis with DD species excluded
- Analysis with unknown threats inferred
- Sensitivity analysis
The following variables can be used to adjust the output.
(1) Name of the data file. The default downloads the Norwegian Red
List data for species from
doi:10.5281/zenodo.7893216. To
analyse other Red Lists, use url = "" and provide the file name of the
dataset as file (including file path, if needed).
url <- "https://zenodo.org/record/7893216/files/species.csv"
file <- "species.csv"
(2) Handling of DD species. Decides whether Data Deficient species are excluded (if FALSE) or randomly assigned to other Red List Categories (if TRUE).
includeDD <- TRUE
(3) Handling of unknown threats. Decides whether (if TRUE) or not (if FALSE) unknown threat factors should be inferred from the distribution of the known threat factors.
inferThreats <- FALSE
(4) Weighting schemes. Defines the weighting scheme for the Red List Index and the Expected Loss of Species. (Defaults to “equal-steps” for RLI and the thresholds of the IUCN Red List Criterion E for ELS; other options are the IUCN Red List Criteria “A1”, “A2”, “B1”, “B2”, “C” and “D” as well as “Ev2”, “Ev3”.)
weightingRLI <- "equal-steps"
weightingELS <- "E"
(5) Column names. Column names in the dataset which contain Red List Categories, threat factors, reasons for category change, and generation time, respectively. The three former ones need to be followed by the year of assessment (for change, the year of the second of the two relevant assessments). So if the column name containing Red List Categories is not named something like “Categ21” or “Categ2021”, this needs to be adjusted here!
Categ <- "Categ"
Threat <- "Threat"
Change <- "Change"
GTime <- "GenTime"
Note the following formatting requirements of these columns:
- Red List Categories in the “Categ” column must match with the constants specified (see next section).
- Threat columns must contain text strings specifying threats. Each threat must be described as a sequence of (abbreviations for) (i) threat factor, (ii) timing, (iii) scope and (iv) severity, which are separated by colons; different threats to the same species are separated by commas.
- Change columns are needed only if the dataset contains results from more than one Red List. It must contain no more than one reason for change in Red List Category per species.
- Generation time, measured in years, must be a numerical variable.
(6) Abbreviations used for threats. What are the abbreviations used
for unknown threats? They can occur in the Threat column(s), see
previous item. (Defaults to the abbreviations used in the dataset
analysed in the paper. May need to be adjusted for other datasets.)
unknownThreat <- "unknownf"
unknownTiming <- "unknownt"
unknownScope <- "unknownp"
unknownSeverity <- "unknownd"
(7) Abbreviation used for real change. What is the abbreviations
used for real population changes? This is only needed if Red List
Categories are to be “back-cast” to earlier Red List assessments. It
must occur in the Change column(s), see item (5). (Defaults to the
abbreviation used in the dataset analysed in the paper. May need to be
adjusted for other datasets.)
realChange <- "realpopu"
(8) Timings to include. What is (are) the abbreviation(s) of the timing categories that should be considered (defaults to “ongoing”).
inclTiming <- "ongoingt"
If all threats are to be included, irrespective of timing, this would
need to be replaced (in terms of the abbreviations used in this dataset)
by
inclTiming <- c("onlypast", "suspendd", "ongoing", "onlyfutu", "unknownt").
(9) Calculation of threat scores. Decides whether threat scores are based on the product of scope and severity (if TRUE) or on severity alone (if FALSE).
useScope <- FALSE
IUCN now
states
that severities should describe the population decline within the
scope of a particular threat. If this definition has been followed,
useScope should be TRUE. Previously, however, the definition
of severity was ambiguous. In the Norwegian Red Lists analysed here,
severity was used to describe the decline of the entire population.
The default value (FALSE) assumes the latter situation, in which
severity should not be multiplied with scope. (See here
for some more detail.)
(10) Number of simulations. NB: the default takes several hours! For
exploration purposes, nsim <- 1000 will suffice. For pure
illustration, nsim <- 100 is enough.
nsim <- 100000
(11) Re-create published estimates? Decides whether the estimation of confidence intervals should be re-created exactly as published (if TRUE) or be based on novel random numbers (if FALSE).
re.create <- TRUE
(12) File names of figures. If you want to display the figures on screen, keep the default. If you want to create PNG files, specify the file names (including paths).
fig1 <- ""
fig2 <- ""
fig3 <- ""
figS1 <- ""
figS2 <- ""
figS3 <- ""
Constants should not normally need to be changed. Changing them entails modifying some underlying assumptions.
(1) Red List Categories and their weights, extinction probabilities etc. This data frame needs to contain all Red List Categories used in the Red List analysed of species that have been evaluated:
RLcateg <- data.frame(
name = c( "LC", "NT", "VU", "EN", "CR", "RE", "EW", "EX"),
LC = c( TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE),
EX = c( FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE),
wt = c( 0, 1, 2, 3, 4, 5, 5, 5),
lowP = c( 0.00, 0.05, 0.10, 0.20, 0.50, 1.00, 1.00, 1.00),
uppP = c( 0.00, 0.10, 0.20, 0.50, 1.00, 1.00, 1.00, 1.00),
lowT = c( 100, 100, 100, 20, 10, 10, 10, 10),
uppT = c( 100, 100, 20, 10, 10, 10, 10, 10),
lowG = c( 0, 0, 0, 5, 3, 1, 1, 1),
uppG = c( 0, 0, 5, 3, 1, 1, 1, 1),
lowA1 = c( 0.00, 0.25, 0.50, 0.70, 0.90, 1.00, 1.00, 1.00),
uppA1 = c( 0.00, 0.50, 0.70, 0.90, 1.00, 1.00, 1.00, 1.00),
lowA2 = c( 0.00, 0.15, 0.30, 0.50, 0.80, 1.00, 1.00, 1.00),
uppA2 = c( 0.00, 0.30, 0.50, 0.80, 1.00, 1.00, 1.00, 1.00),
lowB1 = c( 40000, 40000, 20000, 5000, 100, 0, 0, 0),
uppB1 = c( 40000, 20000, 5000, 100, 0, 0, 0, 0),
lowB2 = c( 4000, 4000, 2000, 500, 10, 0, 0, 0),
uppB2 = c( 4000, 2000, 500, 10, 0, 0, 0, 0),
lowC = c( 20000, 20000, 10000, 2500, 250, 0, 0, 0),
uppC = c( 20000, 10000, 2500, 250, 0, 0, 0, 0),
lowD = c( 2000, 2000, 1000, 250, 50, 0, 0, 0),
uppD = c( 2000, 1000, 250, 50, 0, 0, 0, 0),
distr = c("unif", "unif", "unif", "unif", "decr", "unif", "unif", "unif"),
beta = c( NA, NA, NA, NA, NA, NA, NA, NA),
stringsAsFactors = FALSE
)
The values in the dataframe are based on IUCN (2012a), IUCN (2012b), IUCN (2022) and the Norwegian guidance document (Artsdatabanken 2020). The columns have the following meanings:
- The column “LC” identifies the Red List Category “Least Concern” (defaults to IUCN’s abbreviation).
- The column “EX” identified the Red List Categories for extinction (defaults to IUCN’s abbreviations).
- The column “wt” provides the Red List Weight of the Category (defaults to equal-steps weighting).
- The columns “lowP” and “uppP” provide the lower and upper threshold values for extinction probability according to IUCN Red List Criterion E.
- The columns “lowT” and “uppT” provide the lower and upper threshold values for extinction time frames in years according to IUCN Red List Criterion E.
- The columns “lowG” and “uppG” provide the lower and upper threshold values for extinction time in generations according to IUCN Red List Criterion E.
- The columns “lowA1” and “uppA1” provide the lower and upper threshold values for population reduction according to IUCN Red List Criterion A1.
- The columns “lowA2” and “uppA2” provide the lower and upper threshold values for population reduction according to IUCN Red List Criterion A2.
- The columns “lowB1” and “uppB1” provide the lower and upper threshold values for extents of occurrence (EOO) according to IUCN Red List Criterion B1.
- The columns “lowB2” and “uppB2” provide the lower and upper threshold values for areas of occupancy (AOO) according to IUCN Red List Criterion B2.
- The columns “lowC” and “uppC” provide the lower and upper threshold values for population size according to IUCN Red List Criterion C.
- The columns “lowD” and “uppD” provide the lower and upper threshold values for population size according to IUCN Red List Criterion D.
- The column “distr” provides the distribution of extinction probabilities within the interval.
- The column “beta” is not currently needed (but may be needed if “distr” is changed).
(2) Special Red List Categories. What are the abbreviations used for data-deficient species and for species that have not been evaluated? (Defaults to IUCN’s abbreviations.)
DD <- "DD"
notEval <- c("NA", "NE")
(3) Downlisting. What is added to a Red List Category to indicate downlisting? (Defaults to the degree symbol.) If a Red List Category is followed by this symbol, it is assumed to have been downlisted by one Red List Category.
downlistSymbol <- "\u00b0" # degree symbol in unicode
downlistSymbol <- iconv(downlistSymbol, Encoding(downlistSymbol), "latin1")
(4) Scopes and their threshold values. This data frame needs to contain all scope classes of threats used in the Red List analysed. Two versions of the data frames are provided, one for analysis of the Norwegian Red List data (the default) and one with the scope classes defined by IUCN (2023). The default is to use the Norwegian scope classes.
ScopeNorway <- data.frame(
name = c("neglprop", "minority", "majority", "wholepop", "unknownp"),
lower = c( 0.00, 0.05, 0.50, 0.90, 0.00),
upper = c( 0.05, 0.50, 0.90, 1.00, 1.00),
distr = c( "unif", "unif", "unif", "unif", "beta"),
beta = c( NA, NA, NA, NA, 2),
stringsAsFactors = FALSE
)
ScopeIUCN <- data.frame(
name = c("minority", "majority", "wholepop", "unknownp"),
lower = c( 0.00, 0.50, 0.90, 0.00),
upper = c( 0.50, 0.90, 1.00, 1.00),
distr = c( "unif", "unif", "unif", "beta"),
beta = c( NA, NA, NA, 2),
stringsAsFactors = FALSE
)
Scope <- ScopeNorway
Values are the proportions of the total population affected by a threat (Artsdatabanken 2020; cf. IUCN 2023). The columns have the following meanings:
- The column “name” contains the abbreviations used for the scope classes.
- The column “lower” contains the lower limit of the respective interval.
- The column “upper” contains the upper limit of the respective interval.
- The column “distr” contains the distribution of values within the respective interval (possible values: “unif”, “incr”, “decr”, “beta”).
- The column “beta” contains the beta parameter of a Beta distribution
(a numeric values if
distr == "beta", andNAotherwise).
(5) Severities and their threshold values. This data frame needs to contain all severity classes of threats used in the Red List analysed. Two versions of the data frame are provided, one for analysis of the Norwegian Red List data (the default) and one with the severity classes defined by IUCN (2023). The default is to use the Norwegian severity classes.
SeverityNorway <- data.frame(
name = c("negldecl", "slowdecl", "rapidecl", "unknownd"),
lower = c( 0.00, 0.02, 0.20, 0.00),
upper = c( 0.02, 0.20, 1.00, 1.00),
distr = c( "incr", "unif", "decr", "beta"),
beta = c( NA, NA, NA, 20),
stringsAsFactors = FALSE
)
SeverityIUCN <- data.frame(
name = c("nodeclin", "negldecl", "slowdecl", "rapidecl", "veryrapd", "fluctuat", "unknownd"),
lower = c( 0.00, 0.00, 0.02, 0.20, 0.30, 0.02, 0.00),
upper = c( 0.00, 0.02, 0.20, 0.30, 1.00, 0.20, 1.00),
distr = c( "unif", "unif", "unif", "unif", "decr", "unif", "beta"),
beta = c( NA, NA, NA, NA, NA, NA, 20),
stringsAsFactors = FALSE
)
Severity <- SeverityNorway
Values correspond to the declines in population size over 10 years or 3 generations (whichever is largest) caused by a threat (Artsdatabanken 2020; cf. IUCN 2023). The columns have the following meanings:
- The column “name” contains the abbreviations used for the severity classes.
- The column “lower” contains the lower limit of the respective interval.
- The column “upper” contains the upper limit of the respective interval.
- The column “distr” contains the distribution of values within the respective interval (possible values: “unif”, “incr”, “decr”, “beta”; see here for the rationale).
- The column “beta” contains the beta parameter of a Beta distribution
(a numeric values if
distr == "beta", andNAotherwise).
(6) Time frame for the Expected Loss of Species, in years (defaults to 50 years).
TimeFrame <- 50
Load the set of functions belonging to this repository:
eval(parse(text = readLines("function.R")))
Define further required variables, based on the variables and constants specified above:
LC <- RLcateg$name[RLcateg$LC] # abbreviation(s) for species of Least Concern
extinct <- RLcateg$name[RLcateg$EX] # abbreviation(s) for extinct species
LC.EX <- RLcateg$name # Red List Categories of evaluated species
RedListCat <- c(LC.EX, DD, notEval) # all Red List Categories
Read the dataset “Norwegian Red List for species”:
{
foundFile <- FALSE
if (file.exists(file)) {
foundFile <- TRUE
} else {
if (nchar(url)) {
downl <- try(download.file(url, file))
foundFile <- !inherits(downl, "try-error")
}
}
if (foundFile) {
RL <- read.csv2(file, as.is=TRUE, dec=".", na.strings="n/a", encoding="latin1")
} else {
cat("The datafile was not found.\n")
}
}
Check whether the data are as expected:
{
usedCategories <- checkRL(RL)
years <- identifyYears(RL)
cat("\nYears included in this dataset:\n")
print(years)
threats <- identifyThreats(RL)
cat("\nThreat factors reported in this dataset:\n")
print(threats)
}
## Red List Categories are OK.
## Threat columns are OK.
## Change columns are OK.
## Generation time was found.
##
## Everything looks fine so far!
##
## Years included in this dataset:
## [1] 21 15 10
##
## Threat factors reported in this dataset:
## [1] "alienspe" "bycatchc" "climatec" "disturba" "huntgath" "landusec" "natcatas" "nativesp" "nothreat" "otherthr" "outsiden"
## [12] "pollutio" "unknownf"
Ensure that RedListCat and LC.EX only contain categories that are
actually used:
RedListCat <- RedListCat %A% usedCategories
LC.EX <- LC.EX %A% usedCategories
(1) Reverse downlisting:
# Take a backup of the downlisted RL Categories in 2012
RL$Cat21orig <- downlist(RL)$Categ21
# Then reverse downlisting
RL <- uplist(RL)
This step is only needed if the dataset contains species that have been downlisted due to rescue effects in other countries. Undoing this downlisting is motivated by the wish to quantify threats that can be addressed by management authorities in a given country.
If you want to analyse the data with downlisting retained, use instead
the command RL <- downlist(RL).
(2) Back-cast knowledge from the most recent Red List to earlier ones:
RL <- backCast(RL)
This step is only needed if the dataset contains Red List Categories from different Red List Assessments. It corrects earlier assessments for the best available (i.e. most recent) information.
(3) Calculate extinction probabilites for all species:
RL <- calcLoss(RL)
(4) Add columns for all threat factors:
RL <- addThreats(RL)
Summarise the Red Lists:
tab <- summariseRL(RL)
## N LC NT VU EN CR RE DD RLI Cum.ELS50
## RL10 21267 16611 1294 1260 898 289 126 789 0.9189960 1185.416
## RL10.15 21160 16656 1256 1245 910 251 118 724 0.9210217 1126.659
## RL10.21 23744 18675 1395 1519 1001 299 114 741 0.9199930 1247.429
## RL15 21160 16665 1246 1252 903 252 118 724 0.9211490 1123.817
## RL15.21 23744 18682 1389 1517 999 302 114 741 0.9200278 1246.852
## RL21 23744 18683 1392 1514 997 305 112 741 0.9200887 1245.979
In the specific case of the three Norwegian Red Lists analysed, the dataset only contains the species included in the current Red List (2021). For different reasons (such as taxonomic change), the earlier Red Lists contained species (names) that are not included in the most recent one. Therefore, the above summary table is not entirely correct for the earlier Red Lists. This has to be corrected manually by adding data for the Red Lists 2010 and 2015 (prior to back-casting). The sources for these data are:
# Create a new table for the results:
Table3 <- matrix(as.numeric(NA), 9, length(RedListCat) + 3, dimnames=list(
"RL" %+% c("2010" %+% downlistSymbol, "2010", "2010(15)", "2010(21)",
"2015" %+% downlistSymbol, "2015", "2015(21)",
"2021" %+% downlistSymbol, "2021"),
c("N", RedListCat, "RLI", "Cum.ELS50")
))
alphabetic <- sort(RedListCat)
# Manually add the figures for the earlier Red Lists:
Table3[1, match(alphabetic, colnames(Table3))] <-
c(284, 809, 890, 16762, 2580, 6528, 1310, 127, 1265)
Table3[2, match(alphabetic, colnames(Table3))] <-
c(290, 809, 908, 16745, NA, NA, 1302, 127, 1266)
Table3[5, match(alphabetic, colnames(Table3))] <-
c(247, 755, 901, 17594, 3018, 6095, 1302, 119, 1294)
Table3[6, match(alphabetic, colnames(Table3))] <-
c(252, 755, 916, 17579, NA, NA, 1297, 119, 1294)
tb <- table(RL$Cat21orig)
Table3[8, match(names(tb), colnames(Table3))] <- tb
Loss21o <- LoS(RL$Cat21orig, RL$GenTime)
# Insert the previous summary into this table:
Table3[c(4, 7, 9), colnames(tab)] <- tab[c(3, 5, 6), ]
Table3[3, ] <- Table3[4, ] - Table3[7, ] + Table3[6, ]
Table3[, "N"] <- apply(Table3[, RedListCat], 1, sum, na.rm=T)
Table3[, "RLI"] <- 1 -
apply(t(Table3[, LC.EX]) * RLW(LC.EX), 2, sum, na.rm=T) /
apply( Table3[, LC.EX], 1, sum, na.rm=T) / max(RLW(LC.EX))
Table3[c(4, 7, 9), colnames(tab)] <- tab[c(3, 5, 6), ]
Table3 <- Table3[, !is.na(apply(Table3 > 0, 2, any))]
RLI21 <- RLI(RL$Categ21.21, RL$GenTime)
RLI15 <- RLI(RL$Categ15.21, RL$GenTime)
RLI10 <- RLI(RL$Categ10.21, RL$GenTime)
# Calculate means per Red List Category
# (needed to approximate species loss for data
# that are not based on the 2021 Red List):
mn10 <- mn15 <- rep(0, length(RedListCat))
names(mn10) <- names(mn15) <- RedListCat
for (i in RedListCat) {
mn10[i] <- mean(RL$Loss10.21[which(RL$Categ10.21 == i)])
mn15[i] <- mean(RL$Loss15.21[which(RL$Categ15.21 == i)])
}
Table3[1, "Cum.ELS50"] <- sum(mn10 * Table3[1, RedListCat], na.rm=T)
Table3[2, "Cum.ELS50"] <- sum(mn10 * Table3[2, RedListCat], na.rm=T)
Table3[3, "Cum.ELS50"] <- sum(mn15 * Table3[3, RedListCat], na.rm=T)
Table3[5, "Cum.ELS50"] <- sum(mn15 * Table3[5, RedListCat], na.rm=T)
Table3[6, "Cum.ELS50"] <- sum(mn15 * Table3[6, RedListCat], na.rm=T)
Loss21o[which(isDD(RL$Cat21orig))] <- RL$Loss21[which(isDD(RL$Cat21orig))]
Table3[8, "Cum.ELS50"] <- sum(Loss21o, na.rm=T)
rm(alphabetic, tb, tab, mn10, mn15, Loss21o)
Print the corrected table (which underlies Table 3 of the paper):
print(Table3)
## N LC NT VU EN CR RE DD NA NE RLI Cum.ELS50
## RL2010° 30555 16762 1310 1265 890 284 127 809 2580 6528 0.9197500 1145.935
## RL2010 21447 16745 1302 1266 908 290 127 809 NA NA 0.9190522 1160.139
## RL2010(15) 22212 17572 1303 1296 918 249 119 755 NA NA 0.9231952 1125.832
## RL2010(21) 23744 18675 1395 1519 1001 299 114 741 NA NA 0.9199930 1247.429
## RL2015° 31325 17594 1302 1294 901 247 119 755 3018 6095 0.9237918 1114.840
## RL2015 22212 17579 1297 1294 916 252 119 755 NA NA 0.9232325 1126.575
## RL2015(21) 23744 18682 1389 1517 999 302 114 741 NA NA 0.9200278 1246.852
## RL2021° 33042 18696 1391 1528 977 299 112 741 3256 6042 0.9205843 1231.374
## RL2021 23744 18683 1392 1514 997 305 112 741 NA NA 0.9200887 1245.979
Estimate ΔRLI:
DRLI <- DeltaRLI(RL)
print(DRLI)
## RL15_21 RL10_15 RL10_21
## alienspe -1.646534e-05 -2.618685e-05 -4.265218e-05
## bycatchc 7.749949e-06 1.318314e-05 2.093309e-05
## climatec -4.591176e-06 -6.916442e-05 -7.375560e-05
## disturba -2.662081e-05 -5.771666e-06 -3.239247e-05
## huntgath -1.213312e-05 2.433856e-05 1.220543e-05
## landusec 5.594912e-05 1.295811e-04 1.855302e-04
## natcatas -8.656468e-07 1.738904e-05 1.652339e-05
## nativesp -6.302269e-05 -5.541674e-05 -1.184394e-04
## nothreat 0.000000e+00 0.000000e+00 0.000000e+00
## otherthr -4.035430e-06 0.000000e+00 -4.035430e-06
## outsiden 6.228195e-05 1.903962e-05 8.132157e-05
## pollutio -4.011468e-05 2.009000e-05 -2.002468e-05
## unknownf 1.027295e-04 -3.230370e-05 7.042581e-05
Estimate δRLI and ELS50:
drli <- dRLI(RL)
print(drli)
## $dRLI
## RL10 RL15 RL21
## alienspe 1.668608e-04 5.135959e-04 1.400196e-03
## bycatchc 1.914541e-04 4.492090e-04 4.996245e-04
## climatec 1.073670e-03 1.793189e-03 3.368249e-03
## disturba 5.466413e-04 7.152149e-04 1.533166e-03
## huntgath 2.425421e-04 2.129549e-04 3.336523e-04
## landusec 4.779402e-02 4.725921e-02 5.957715e-02
## natcatas 2.055839e-04 1.528677e-04 2.787773e-04
## nativesp 7.327134e-04 1.096337e-03 2.728609e-03
## nothreat 9.359090e-07 0.000000e+00 4.211590e-05
## otherthr 0.000000e+00 6.742164e-05 8.889181e-05
## outsiden 7.787451e-04 5.098744e-04 2.780800e-04
## pollutio 2.252088e-03 2.175072e-03 3.186530e-03
## unknownf 2.602170e-02 2.502723e-02 6.596271e-03
##
## $ELS50
## RL10 RL15 RL21
## alienspe 2.355630e+00 7.590234 19.860701
## bycatchc 2.659176e+00 6.866327 7.644216
## climatec 1.229574e+01 22.088555 33.384750
## disturba 9.173446e+00 12.314133 27.107788
## huntgath 4.009447e+00 3.969917 5.388558
## landusec 7.815321e+02 738.752575 933.621740
## natcatas 4.295266e+00 3.744886 5.290871
## nativesp 1.081129e+01 14.824032 35.077282
## nothreat 3.888889e-03 0.000000 0.892500
## otherthr 0.000000e+00 1.000578 1.227322
## outsiden 1.560218e+01 11.249315 5.514580
## pollutio 3.854984e+01 37.621407 53.329631
## unknownf 3.661415e+02 386.830041 117.639559
Confidence intervals on RLI:
print(confidenceRLI(RL, nsim, "Categ21"))
## 2.5% 25% 50% 75% 97.5%
## 0.9196513 0.9199461 0.9200893 0.9202325 0.9205020
Confidence intervals on ΔRLI, δRLI and ELS50:
results <- simulateDRLI(RL, nsim)
##
##
## Confidence intervals for DeltaRLI from 15 to 21:
## alienspe bycatchc climatec disturba huntgath landusec natcatas nativesp nothreat
## 2.5% -2.758554e-05 -7.421091e-06 -2.373302e-05 -4.250712e-05 -2.192477e-05 2.778752e-05 -2.029758e-06 -8.136700e-05 0
## 25% -1.978893e-05 2.907054e-06 -1.128957e-05 -3.100759e-05 -1.509215e-05 4.499132e-05 -1.125384e-06 -6.814067e-05 0
## 50% -1.584435e-05 8.038740e-06 -4.614244e-06 -2.515867e-05 -1.162350e-05 5.423361e-05 -7.405215e-07 -6.114062e-05 0
## 75% -1.202603e-05 1.299485e-05 2.019173e-06 -1.958080e-05 -8.235564e-06 6.355362e-05 -4.413957e-07 -5.422068e-05 0
## 97.5% -5.214519e-06 2.173912e-05 1.448183e-05 -9.957976e-06 -2.040439e-06 8.122583e-05 -1.164665e-07 -4.116538e-05 0
## otherthr outsiden pollutio unknownf
## 2.5% -7.802676e-06 3.971322e-05 -5.275180e-05 9.200229e-05
## 25% -5.237295e-06 5.286826e-05 -4.324559e-05 9.687621e-05
## 50% -3.915717e-06 5.986960e-05 -3.855195e-05 9.957235e-05
## 75% -2.558729e-06 6.685664e-05 -3.412755e-05 1.022178e-04
## 97.5% -9.954627e-07 8.031390e-05 -2.635541e-05 1.069302e-04
##
##
## Confidence intervals for DeltaRLI from 10 to 15:
## alienspe bycatchc climatec disturba huntgath landusec natcatas nativesp nothreat
## 2.5% -3.282773e-05 9.843417e-07 -7.618064e-05 -8.702975e-06 1.283346e-05 0.0001006732 1.684636e-05 -6.863247e-05 0
## 25% -2.814251e-05 8.684326e-06 -7.013827e-05 -6.674633e-06 1.967747e-05 0.0001169818 1.684636e-05 -5.892648e-05 0
## 50% -2.557454e-05 1.293225e-05 -6.688305e-05 -5.503525e-06 2.356152e-05 0.0001257049 1.684636e-05 -5.359718e-05 0
## 75% -2.292633e-05 1.725499e-05 -6.366442e-05 -4.120830e-06 2.751622e-05 0.0001344283 1.684636e-05 -4.829702e-05 0
## 97.5% -1.761208e-05 2.550015e-05 -5.728415e-05 -8.179038e-07 3.529626e-05 0.0001509216 1.684636e-05 -3.900240e-05 0
## otherthr outsiden pollutio unknownf
## 2.5% 0 6.437577e-06 1.348325e-06 -4.299881e-05
## 25% 0 1.405019e-05 1.329316e-05 -3.554056e-05
## 50% 0 1.772575e-05 1.944128e-05 -3.127023e-05
## 75% 0 2.107672e-05 2.557229e-05 -2.674915e-05
## 97.5% 0 2.652298e-05 3.717693e-05 -1.810152e-05
##
##
## Confidence intervals for DeltaRLI from 10 to 21:
## alienspe bycatchc climatec disturba huntgath landusec natcatas nativesp nothreat
## 2.5% -5.410913e-05 1.946344e-06 -9.173819e-05 -4.823333e-05 -1.839358e-06 0.0001451193 1.481660e-05 -1.391091e-04 0
## 25% -4.570030e-05 1.452778e-05 -7.855743e-05 -3.643005e-05 7.151070e-06 0.0001679223 1.572098e-05 -1.231947e-04 0
## 50% -4.139191e-05 2.093078e-05 -7.149905e-05 -3.047644e-05 1.192182e-05 0.0001799711 1.610584e-05 -1.148026e-04 0
## 75% -3.712836e-05 2.726511e-05 -6.449487e-05 -2.474219e-05 1.675213e-05 0.0001920937 1.640497e-05 -1.064025e-04 0
## 97.5% -2.924697e-05 3.898557e-05 -5.119619e-05 -1.468224e-05 2.614990e-05 0.0002155333 1.672989e-05 -9.087909e-05 0
## otherthr outsiden pollutio unknownf
## 2.5% -7.802676e-06 5.734709e-05 -4.151969e-05 5.496134e-05
## 25% -5.237295e-06 7.036890e-05 -2.680678e-05 6.355976e-05
## 50% -3.915717e-06 7.729382e-05 -1.927356e-05 6.832580e-05
## 75% -2.558729e-06 8.419113e-05 -1.188016e-05 7.323331e-05
## 97.5% -9.954627e-07 9.701202e-05 2.008100e-06 8.274663e-05
##
##
## Confidence intervals for the cumulative dRLI in 10:
## 2.5% 25% 50% 75% 97.5%
## 0.07959063 0.07986018 0.08000337 0.08014656 0.08044137
##
##
## Confidence intervals for the threat-wise dRLIs in 10:
## alienspe bycatchc climatec disturba huntgath landusec natcatas nativesp nothreat
## 2.5% 0.0001426945 0.0001668548 0.001030792 0.0004961728 0.0002127255 0.04760604 0.0001791993 0.0006847039 1.306473e-07
## 25% 0.0001579847 0.0001839374 0.001056869 0.0005305554 0.0002322180 0.04773790 0.0001963124 0.0007148973 4.994905e-07
## 50% 0.0001662953 0.0001928923 0.001071383 0.0005491434 0.0002429416 0.04780989 0.0002058424 0.0007311389 8.384147e-07
## 75% 0.0001748889 0.0002017238 0.001086568 0.0005683874 0.0002538713 0.04788406 0.0002154320 0.0007476703 1.275377e-06
## 97.5% 0.0001912517 0.0002184636 0.001118294 0.0006069659 0.0002749241 0.04803025 0.0002328631 0.0007797846 2.306426e-06
## otherthr outsiden pollutio unknownf
## 2.5% 0 0.0007253337 0.002151481 0.02564591
## 25% 0 0.0007499919 0.002213917 0.02588579
## 50% 0 0.0007626733 0.002247125 0.02601612
## 75% 0 0.0007751986 0.002281128 0.02614898
## 97.5% 0 0.0007987181 0.002345430 0.02641055
##
##
## Confidence intervals for the cumulative dRLI in 15:
## 2.5% 25% 50% 75% 97.5%
## 0.07955694 0.07982648 0.07996968 0.08011287 0.08040768
##
##
## Confidence intervals for the threat-wise dRLIs in 15:
## alienspe bycatchc climatec disturba huntgath landusec natcatas nativesp nothreat
## 2.5% 0.0004654485 0.0004058008 0.001726858 0.0006577528 0.0001830036 0.04704259 0.0001290436 0.001029409 0
## 25% 0.0004960362 0.0004340037 0.001768134 0.0006946349 0.0002023833 0.04718712 0.0001443514 0.001071179 0
## 50% 0.0005122505 0.0004489106 0.001791132 0.0007145710 0.0002130874 0.04726472 0.0001527685 0.001093675 0
## 75% 0.0005283537 0.0004641115 0.001815423 0.0007346618 0.0002241106 0.04734521 0.0001612560 0.001116644 0
## 97.5% 0.0005596139 0.0004928468 0.001864325 0.0007746213 0.0002455143 0.04750369 0.0001769820 0.001160990 0
## otherthr outsiden pollutio unknownf
## 2.5% 6.209688e-05 0.0004794150 0.002076458 0.02465761
## 25% 6.528467e-05 0.0004977467 0.002140048 0.02489357
## 50% 6.720246e-05 0.0005077713 0.002173702 0.02502169
## 75% 6.914778e-05 0.0005178658 0.002207578 0.02515309
## 97.5% 7.273932e-05 0.0005371932 0.002272940 0.02541082
##
##
## Confidence intervals for the cumulative dRLI in 21:
## 2.5% 25% 50% 75% 97.5%
## 0.07949798 0.07976752 0.07991071 0.08005391 0.08034872
##
##
## Confidence intervals for the threat-wise dRLIs in 21:
## alienspe bycatchc climatec disturba huntgath landusec natcatas nativesp nothreat
## 2.5% 0.001320317 0.0004509342 0.003271669 0.001439563 0.0003027424 0.05930110 0.0002409912 0.002620797 4.21159e-05
## 25% 0.001368545 0.0004810663 0.003332628 0.001497888 0.0003221677 0.05949384 0.0002651078 0.002689503 4.21159e-05
## 50% 0.001393922 0.0004970019 0.003365490 0.001528925 0.0003329112 0.05959713 0.0002781345 0.002726200 4.21159e-05
## 75% 0.001419573 0.0005131894 0.003399099 0.001560184 0.0003439041 0.05970240 0.0002912058 0.002762421 4.21159e-05
## 97.5% 0.001468751 0.0005440783 0.003465365 0.001621395 0.0003656166 0.05990923 0.0003169533 0.002832002 4.21159e-05
## otherthr outsiden pollutio unknownf
## 2.5% 7.353650e-05 0.0002452594 0.003067352 0.006265697
## 25% 8.323033e-05 0.0002659077 0.003143160 0.006477857
## 50% 8.869756e-05 0.0002770768 0.003183559 0.006592891
## 75% 9.419064e-05 0.0002883152 0.003224167 0.006710418
## 97.5% 1.036887e-04 0.0003105598 0.003301730 0.006943918
##
##
## Confidence intervals for the cumulative ELS50 in 10:
## 2.5% 25% 50% 75% 97.5%
## 1232.575 1242.207 1247.435 1252.680 1262.737
##
##
## Confidence intervals for the threat-wise ELS50 in 10:
## alienspe bycatchc climatec disturba huntgath landusec natcatas nativesp nothreat otherthr outsiden pollutio
## 2.5% 1.713921 2.160339 11.03325 7.835271 3.344055 771.8913 3.563334 9.497606 0.000494524 0 13.87316 35.84644
## 25% 2.114467 2.494310 11.83299 8.715928 3.765894 778.4862 4.027070 10.322642 0.001931488 0 14.77775 37.51722
## 50% 2.341902 2.679842 12.27027 9.205291 3.996742 781.9936 4.288849 10.766185 0.003325577 0 15.25393 38.40848
## 75% 2.575577 2.871803 12.71730 9.709378 4.231785 785.4870 4.560873 11.219823 0.005256977 0 15.73369 39.31298
## 97.5% 3.031599 3.250796 13.61011 10.767979 4.685093 792.1704 5.088289 12.107799 0.010524343 0 16.65851 41.07687
## unknownf
## 2.5% 355.4854
## 25% 362.3810
## 50% 366.0903
## 75% 369.8969
## 97.5% 377.3296
##
##
## Confidence intervals for the cumulative ELS50 in 15:
## 2.5% 25% 50% 75% 97.5%
## 1231.784 1241.624 1246.833 1252.089 1262.241
##
##
## Confidence intervals for the threat-wise ELS50 in 15:
## alienspe bycatchc climatec disturba huntgath landusec natcatas nativesp nothreat otherthr outsiden pollutio unknownf
## 2.5% 6.429392 5.852798 20.16523 10.75171 3.339128 728.5759 3.063295 13.19706 0 0.6998283 10.10069 34.88497 376.5288
## 25% 7.160534 6.498025 21.38156 11.74717 3.745344 735.3276 3.494316 14.21944 0 0.8642567 10.81997 36.63138 383.1682
## 50% 7.559791 6.851345 22.04545 12.28401 3.970534 738.9185 3.736478 14.77800 0 1.0003201 11.20678 37.55544 386.7761
## 75% 7.964910 7.217245 22.73950 12.83566 4.206740 742.4894 3.986534 15.34300 0 1.1355247 11.59655 38.49165 390.4636
## 97.5% 8.762859 7.936168 24.14536 13.94629 4.677196 749.4285 4.469384 16.45867 0 1.2992195 12.33046 40.31063 397.6805
##
##
## Confidence intervals for the cumulative ELS50 in 21:
## 2.5% 25% 50% 75% 97.5%
## 1231.064 1240.774 1245.968 1251.233 1261.333
##
##
## Confidence intervals for the threat-wise ELS50 in 21:
## alienspe bycatchc climatec disturba huntgath landusec natcatas nativesp nothreat otherthr outsiden pollutio unknownf
## 2.5% 18.01563 6.619278 31.21453 24.71861 4.609584 922.3794 4.239145 32.73401 0.8398355 0.8854666 4.644973 50.13332 109.1790
## 25% 19.14020 7.259777 32.58599 26.21910 5.102536 929.9284 4.906173 34.22506 0.8628706 1.0930390 5.190771 52.16730 114.5751
## 50% 19.74498 7.609586 33.34501 27.02456 5.371875 933.9718 5.269059 35.01811 0.8848130 1.2188477 5.492353 53.25436 117.5645
## 75% 20.36097 7.969205 34.13131 27.85145 5.647965 938.0420 5.640209 35.82016 0.9163174 1.3448933 5.801821 54.35730 120.6107
## 97.5% 21.56187 8.681329 35.72520 29.46728 6.188712 945.7978 6.366258 37.37600 0.9810815 1.5758903 6.406989 56.50830 126.8124
The following script recreates Figure 1.
Simplify the table by collapsing minor threats:
DRLI. <- DRLI
DRLI.[, 1] <- 0
DRLI.[10, ] <- apply(DRLI.[c(1, 2, 5, 7, 9, 10, 11, 13), ], 2, sum)
DRLI. <- DRLI.[c(6, 10, 3, 12, 8, 4), ]
DRLI. <- DRLI.[, c(1:3, 3)]
DRLI. <- rbind(0, DRLI.)
DRLI. <- rbind(DRLI., 0)
Plot a graph for ΔRLI:
{
xl <- c(2009.5, 2028)
yl <- c(0.9198, 0.92023)
if (nchar(fig1)) {
png(fig1, 1500, 1200, res = 180)
xl <- c(2009.5, 2027.5)
yl <- c(0.9198, 0.92022)
}
par(mai = c(0.96, 0.96, 0.12, 0.06), family = "sans")
plot(0, 0, xlim = xl, ylim = yl,
xaxs = "i", yaxs = "i", xaxt = "n", yaxt = "n", xlab = "",
ylab = "Red List Index",
bty = "n", cex.axis = 1.2, cex.lab = 1.8)
axis(1, c(2009, 2021.5), F, T, tcl = 0, lwd = 1.5, lend = 1)
axis(1, 2009:2021, F, T, lwd = 1.5, lend = 1)
axis(1, c(2010, 2015, 2021), T, T, cex.axis = 1.2, lwd = 1.5, lend = 1)
axis(2, seq(0.9198, 0.9202, 0.00010), T, T, cex.axis = 1.2, lwd = 1.5, lend = 1)
axis(2, seq(0.9198, 0.9202, 0.00001), F, T, tcl = -0.25, lwd = 1.5, lend = 1)
mtext("Year of Red List assessment", 1, 3, F, 2015.5, cex = 1.8)
x <- c(2010, 2015, 2021, 2023)
DRLI.[, 4] <- DRLI.[, 3] + c(0, 0, 0, 0, 0, 0, -0.00000887, 0)
lines(x[4:1], rep(RLI10, 4), lty = "12", lwd = 9.6, col = grey(0.84))
lines(x[1:4], c(RLI10, RLI15, RLI21, RLI21), lwd = 9.6, col = grey(0.84))
for (i in 2:7) {
lines(x, RLI10 + DRLI.[i, ], lty = i - 1, lwd = 2.4)
points(x[2:3], RLI10 + DRLI.[i, 2:3], pch = c(1, 4, 21, 24, 22, 25, 23)[i],
cex = 1.8, bg = "black", lwd = 2.4, ljoin = 1)
text(2023, RLI10 + DRLI.[i, 4],
c("", "land-use change", "other/unknown", "climate change", "pollution",
"native species", "disturbance")[i],
pos = 4, cex = 1.2)
}
text(2023, RLI21, "RLI", pos = 4, cex = 1.2)
text(2023, RLI10, "no change", pos = 4, cex = 1.2)
text(2015.5, 0.92021, expression(bold(Delta*RLI)), cex = 1.8)
if (nchar(fig1)) {
dev.off()
}
}
The following script recreates Figure 2.
Simplify the table by collapsing minor threats:
drli. <- drli$dRLI
ELS. <- drli$ELS50
for (i in 1:3) {
drli.["otherthr", i] <- sum(drli.[c("otherthr", "unknownf", "alienspe",
"huntgath", "outsiden", "natcatas", "bycatchc", "nothreat"), i])
ELS. ["otherthr", i] <- sum(ELS. [c("otherthr", "unknownf", "alienspe",
"huntgath", "outsiden", "natcatas", "bycatchc", "nothreat"), i])
}
drli. <- drli.[-which(rownames(drli.) %in%
c("unknownf","alienspe","huntgath","outsiden","natcatas","bycatchc","nothreat")),]
ELS. <- ELS. [-which(rownames(ELS.) %in%
c("unknownf","alienspe","huntgath","outsiden","natcatas","bycatchc","nothreat")),]
ELS. <- ELS. [order(drli.[, 3], decreasing=T),]
ELS. <- ELS. [, c(1:3, 3)]
drli. <- drli.[order(drli.[, 3], decreasing=T),]
drli. <- drli.[, 3:1]
drli. <- rbind(0, drli.)
drli. <- rbind(0, drli.)
Plot a graph for δRLI:
{
xl <- c(2009.5, 2028)
yl <- c(0.91, 1.008)
if (nchar(fig2)) {
png(fig2, 1500, 1200, res=180)
xl <- c(2009.5, 2027.5)
yl <- c(0.91, 1.002)
}
par(mai=c(0.96, 0.96, 0.06, 0.06), family="sans")
plot(0, 0, xlim = xl, ylim = yl, xaxs = "i", yaxs = "i", xaxt = "n", yaxt = "n",
xlab="", ylab = "Red List Index", bty = "n", cex.axis = 1.2, cex.lab = 1.8)
axis(1, c(2009, 2021.5), F, T, tcl=0, lwd=1.5, lend=1)
axis(1, 2009:2021, F, T, lwd=1.5, lend=1)
axis(1, c(2010, 2015, 2021), T, T, cex.axis=1.2, lwd=1.5, lend=1)
axis(2, seq(0.8, 1, 0.01), F, T, lwd=1.5, lend=1)
axis(2, seq(0.8, 1, 0.02), T, T, cex.axis=1.2, lwd=1.5, lend=1)
mtext("Year of Red List assessment", 1, 3, F, 2015.5, cex=1.8)
x <- c(2021, 2015, 2010)
for (i in 3:nrow(drli.)) {
polygon(c(x, rev(x)),
1 - c(apply(drli.[1:(i-1),], 2, sum), rev(apply(drli.[1:i,], 2, sum))),
border=NA, col=grey(1.32 - i * 0.12))
}
y0 <- rep(RLI21, 3)
y2 <- 1 - (sum(drli.[1:8,1]) + sum(drli.[1:7,1])) / 2
#y2 <- RLI21 + 0.005
for (i in 7:3) {
y1 <- 1 - apply(drli.[1:i,], 2, sum)
lines(x, y1, lwd=2.4)
lines(c(2021, 2023), c(mean(c(y0[1], y1[1])), y2), lwd=1.8)
text(2023, y2,
c("", "land-use change", "other/unknown", "climate change",
"pollution", "native species", "disturbance")[i],
pos=4, cex=1.2)
y0 <- y1
y2 <- y2 + 0.005
}
lines(c(2021, 2023), rep(y2, 2), lwd=1.8)
text(2023, y2, "land-use change", pos=4, cex=1.2)
lines(x, rep(1, 3), lwd=4.8)
lines(c(2021, 2023), rep(1, 2), lwd=1.8)
text(2023, 1, "reference value", font=2, pos=4, cex=1.2)
lines(x, c(RLI21, RLI15, RLI10), lwd=4.8)
lines(c(2021, 2023), c(RLI21, y2 - 0.03), lwd=1.8)
text(2023, y2 - 0.03, "RLI", font=2, pos=4, cex=1.2)
text(2015.5, 1.005, expression(bold(delta*RLI)), cex = 1.8)
if (nchar(fig2)) {
dev.off()
}
}
The following script recreates Figure 3.
Plot a graph for ELS50:
{
xl <- c(2009.5, 2028)
yl <- c(lg(8), 3.1)
if (nchar(fig3)) {
png(fig3, 1500, 1200, res = 180)
xl <- c(2009.5, 2027.5)
yl <- c(lg(8), 3)
}
par(mai = c(0.96, 0.96, 0.06, 0.06), family = "sans")
plot(0, 0, xlim = xl, ylim = yl,
xaxs = "i", yaxs = "i", xaxt = "n", yaxt = "n", xlab = "",
ylab = "Expected loss of species",
bty = "n", cex.axis = 1.2, cex.lab = 1.8)
axis(1, c(2009, 2021.5), F, T, tcl = 0, lwd = 1.5, lend = 1)
axis(1, 2009:2021, F, T, lwd = 1.5, lend = 1)
axis(1, c(2010, 2015, 2021), T, T, cex.axis = 1.2, lwd = 1.5, lend = 1)
axis(2, 0:3, c(1, 10, 100, 1000), T, cex.axis = 1.2, lwd = 1.5, lend = 1)
axis(2, lg(c(2:9, seq(20, 90, 10), seq(200, 900, 100))),
F, T, tcl = -0.25, lwd = 1.5, lend = 1)
mtext("Year of Red List assessment", 1, 3, F, 2015.5, cex = 1.8)
x <- c(2010, 2015, 2021, 2023)
ELS.[3, 4] <- 10^(1/3 * lg(ELS.[4, 4]) + 2/3 * lg(ELS.[6, 4]))
ELS.[5, 4] <- 10^(2/3 * lg(ELS.[4, 4]) + 1/3 * lg(ELS.[6, 4]))
for (i in 1:6) {
lines (x, lg(ELS.[i, ]), lty = i, lwd = 2.4)
points(x[1:3], lg(ELS.[i, 1:3]), pch = c(4, 21, 24, 22, 25, 23)[i],
cex = 1.8, bg = "black", lwd = 2.4, ljoin = 1)
text(2023, lg(ELS.[i, 4]),
c("land-use change", "other/unknown", "climate change",
"pollution", "native species", "disturbance")[i],
pos = 4, cex = 1.2)
}
text(2015.5, 3.01, expression(bold(ELS[50])), cex = 1.8)
if (nchar(fig3)) {
dev.off()
}
}
If the above analyses have been run with Data Deficient species included, they can be re-run with Data Deficient species excluded:
{
includeDD <- FALSE
RL. <- calcLoss(RL)
RL. <- addThreats(RL.)
cat("\nDeltaRLI excluding DD species:\n")
DRLI. <- DeltaRLI(RL.)
print(DRLI.)
drli. <- dRLI(RL.)
cat("\ndRLI excluding DD species:\n")
print(drli.$dRLI)
cat("\nELS50 excluding DD species:\n")
print(drli.$ELS50)
includeDD <- TRUE
}
##
## DeltaRLI excluding DD species:
## RL15_21 RL10_15 RL10_21
## alienspe -1.646534e-05 -2.618685e-05 -4.265218e-05
## bycatchc 7.749949e-06 1.318314e-05 2.093309e-05
## climatec -4.591176e-06 -6.916442e-05 -7.375560e-05
## disturba -2.662081e-05 -5.771666e-06 -3.239247e-05
## huntgath -1.213312e-05 2.433856e-05 1.220543e-05
## landusec 5.594912e-05 1.295811e-04 1.855302e-04
## natcatas -8.656468e-07 1.738904e-05 1.652339e-05
## nativesp -6.302269e-05 -5.541674e-05 -1.184394e-04
## nothreat 0.000000e+00 0.000000e+00 0.000000e+00
## otherthr -4.035430e-06 0.000000e+00 -4.035430e-06
## outsiden 6.228195e-05 1.903962e-05 8.132157e-05
## pollutio -4.011468e-05 2.009000e-05 -2.002468e-05
## unknownf 1.027295e-04 -3.230370e-05 7.042581e-05
##
## dRLI excluding DD species:
## RL10 RL15 RL21
## alienspe 1.712182e-04 0.0005291232 1.439336e-03
## bycatchc 1.976214e-04 0.0004636794 5.157190e-04
## climatec 1.098692e-03 0.0018248788 3.445498e-03
## disturba 5.513350e-04 0.0007304319 1.562918e-03
## huntgath 2.503551e-04 0.0002198149 3.444003e-04
## landusec 4.888584e-02 0.0482949987 6.064514e-02
## natcatas 2.122064e-04 0.0001577921 2.877576e-04
## nativesp 7.525025e-04 0.0011199129 2.804189e-03
## nothreat 9.660576e-07 0.0000000000 4.347259e-05
## otherthr 0.000000e+00 0.0000695935 9.175530e-05
## outsiden 8.038310e-04 0.0005262991 2.870379e-04
## pollutio 2.313781e-03 0.0022325508 3.269086e-03
## unknownf 2.476861e-02 0.0238031022 5.175007e-03
##
## ELS50 excluding DD species:
## RL10 RL15 RL21
## alienspe 2.336318e+00 7.570922 19.755750
## bycatchc 2.659176e+00 6.866327 7.644216
## climatec 1.214949e+01 21.629055 32.791328
## disturba 8.928366e+00 12.165633 26.734742
## huntgath 4.009447e+00 3.969917 5.388558
## landusec 7.732140e+02 730.097235 918.409787
## natcatas 4.295266e+00 3.744886 5.290871
## nativesp 1.073892e+01 14.623140 34.863913
## nothreat 3.888889e-03 0.000000 0.892500
## otherthr 0.000000e+00 1.000578 1.227322
## outsiden 1.560218e+01 11.249315 5.514580
## pollutio 3.834388e+01 37.382952 52.948254
## unknownf 3.277966e+02 349.200041 87.165679
If the above analyses have been run with unknown threats as a separate category, they can be re-run with unknown threats distributed over known threats:
{
inferThreats <- TRUE
RL. <- calcLoss(RL)
RL. <- addThreats(RL.)
cat("\nDeltaRLI with unknown threats inferred:\n")
DRLI. <- DeltaRLI(RL.)
print(DRLI.)
drli. <- dRLI(RL.)
cat("\ndRLI with unknown threats inferred:\n")
print(drli.$dRLI)
cat("\nELS50 with unknown threats inferred:\n")
print(drli.$ELS50)
inferThreats <- FALSE
}
##
## DeltaRLI with unknown threats inferred:
## RL15_21 RL10_15 RL10_21
## alienspe -1.541714e-05 -3.498722e-05 -5.040437e-05
## bycatchc 1.297978e-05 1.561721e-05 2.859699e-05
## climatec 2.165216e-06 -9.251208e-05 -9.034686e-05
## disturba -2.627743e-05 -9.221474e-06 -3.549890e-05
## huntgath -7.641481e-06 2.860047e-05 2.095899e-05
## landusec 1.223201e-04 1.374795e-04 2.597996e-04
## natcatas -9.199257e-07 2.097236e-05 2.005243e-05
## nativesp -6.296426e-05 -7.404014e-05 -1.370044e-04
## nothreat 0.000000e+00 0.000000e+00 0.000000e+00
## otherthr -4.288465e-06 0.000000e+00 -4.288465e-06
## outsiden 8.353522e-05 1.921717e-05 1.027524e-04
## pollutio -4.263000e-05 2.365228e-05 -1.897772e-05
## unknownf 0.000000e+00 0.000000e+00 0.000000e+00
##
## dRLI with unknown threats inferred:
## RL10 RL15 RL21
## alienspe 2.472902e-04 7.475371e-04 1.526174e-03
## bycatchc 2.837378e-04 6.538221e-04 5.445765e-04
## climatec 1.591196e-03 2.609980e-03 3.671295e-03
## disturba 8.101306e-04 1.040993e-03 1.671107e-03
## huntgath 3.594510e-04 3.099551e-04 3.636716e-04
## landusec 7.083145e-02 6.878562e-02 6.493741e-02
## natcatas 3.046784e-04 2.224984e-04 3.038593e-04
## nativesp 1.085892e-03 1.595715e-03 2.974106e-03
## nothreat 1.387031e-06 0.000000e+00 4.590514e-05
## otherthr 0.000000e+00 9.813195e-05 9.688955e-05
## outsiden 1.154112e-03 7.421204e-04 3.030993e-04
## pollutio 3.337628e-03 3.165809e-03 3.473227e-03
## unknownf 0.000000e+00 0.000000e+00 0.000000e+00
##
## ELS50 with unknown threats inferred:
## RL10 RL15 RL21
## alienspe 3.334304e+00 11.004252 21.9313575
## bycatchc 3.763962e+00 9.954738 8.4411939
## climatec 1.740415e+01 32.023785 36.8654100
## disturba 1.298466e+01 17.852917 29.9340185
## huntgath 5.675219e+00 5.755550 5.9503635
## landusec 1.106229e+03 1071.036752 1030.9601802
## natcatas 6.079786e+00 5.429302 5.8424922
## nativesp 1.530297e+01 21.491747 38.7344035
## nothreat 5.504574e-03 0.000000 0.9855511
## otherthr 0.000000e+00 1.450629 1.3552811
## outsiden 2.208428e+01 16.309155 6.0895246
## pollutio 5.456583e+01 54.543173 58.8897239
## unknownf 0.000000e+00 0.000000 0.0000000
The following script recreates Appendix S7 (Figure S1).
Simplify the table by collapsing minor threats:
DRLI.[, 1] <- 0
DRLI.[10, ] <- apply(DRLI.[c(1, 2, 5, 7, 9, 10, 11, 13), ], 2, sum)
DRLI. <- DRLI.[c(6, 10, 3, 12, 8, 4), ]
DRLI. <- DRLI.[, c(1:3, 3)]
DRLI. <- rbind(0, DRLI.)
DRLI. <- rbind(DRLI., 0)
Plot a graph for ΔRLI:
{
xl <- c(2009.5, 2028)
yl <- c(0.9198, 0.92034)
if (nchar(figS1)) {
png(figS1, 1500, 1200, res = 180)
xl <- c(2009.5, 2027.5)
yl <- c(0.9198, 0.92033)
}
par(mai = c(0.96, 0.96, 0.12, 0.06), family = "sans") # ylim=c(0.9199, 0.92015)
plot(0, 0, xlim = xl, ylim = yl,
xaxs = "i", yaxs = "i", xaxt = "n", yaxt = "n", xlab = "",
ylab = "Red List Index",
bty = "n", cex.axis = 1.2, cex.lab = 1.8)
axis(1, c(2009, 2021.5), F, T, tcl = 0, lwd = 1.5, lend = 1)
axis(1, 2009:2021, F, T, lwd = 1.5, lend = 1)
axis(1, c(2010, 2015, 2021), T, T, cex.axis = 1.2, lwd = 1.5, lend = 1)
axis(2, seq(0.9198, 0.9203, 0.00010), T, T, cex.axis = 1.2, lwd = 1.5, lend = 1)
axis(2, seq(0.9198, 0.9203, 0.00001), F, T, tcl = -0.25, lwd = 1.5, lend = 1)
mtext("Year of Red List assessment", 1, 3, F, 2015.5, cex = 1.8)
x <- c(2010, 2015, 2021, 2023)
DRLI.[,4] <- DRLI.[,3] + c(0, 0, 0, 0, 0, 0, -0.00000876, 0)
lines(x[3:1], rep(RLI10, 3), lty = "12", lwd = 9.6, col = grey(0.84))
lines(x[1:3], c(RLI10, RLI15, RLI21), lwd = 9.6, col = grey(0.84))
for (i in 2:7) {
lines(x, RLI10 + DRLI.[i, ], lty = i - 1, lwd = 2.4)
points(x[2:3], RLI10 + DRLI.[i, 2:3], pch=c(1, 4, 21, 24, 22, 25, 23)[i],
cex = 1.8, bg = "black", lwd = 2.4, ljoin = 1)
text(2023, RLI10 + DRLI.[i, 4],
c("", "land-use change", "other/unknown", "climate change", "pollution",
"native species", "disturbance")[i],
pos = 4, cex = 1.2)
}
text(2015.5, 0.92032, expression(bold(Delta*RLI)), cex = 1.8)
if (nchar(figS1)) {
dev.off()
}
}
The following script recreates Appendix S8 (Figure S2).
Simplify the table by collapsing minor threats:
ELS. <- drli.$ELS50
drli. <- drli.$dRLI
for (i in 1:3) {
drli.["otherthr", i] <- sum(drli.[c("otherthr", "unknownf", "alienspe",
"huntgath", "outsiden", "natcatas", "bycatchc", "nothreat"), i])
ELS. ["otherthr", i] <- sum(ELS. [c("otherthr", "unknownf", "alienspe",
"huntgath", "outsiden", "natcatas", "bycatchc", "nothreat"), i])
}
drli. <- drli.[-which(rownames(drli.) %in%
c("unknownf", "alienspe", "huntgath", "outsiden",
"natcatas", "bycatchc", "nothreat")),]
ELS. <- ELS. [-which(rownames(ELS.) %in%
c("unknownf", "alienspe", "huntgath", "outsiden",
"natcatas", "bycatchc", "nothreat")),]
ELS. <- ELS.[c("landusec", "otherthr", "climatec",
"pollutio", "nativesp", "disturba"), ]
ELS. <- ELS. [, c(1:3, 3)]
drli. <- drli.[order(drli.[, 3], decreasing=T), ]
drli. <- drli.[, 3:1]
drli. <- rbind(0, drli.)
drli. <- rbind(0, drli.)
Plot a graph for δRLI:
{
xl <- c(2009.5, 2028)
yl <- c(0.91, 1.008)
if (nchar(figS2)) {
png(figS2, 1500, 1200, res = 180)
xl <- c(2009.5, 2027.5)
yl <- c(0.91, 1.008)
}
par(mai = c(0.96, 0.96, 0.06, 0.06), family = "sans")
plot(0, 0, xlim = xl, ylim = yl,
xaxs = "i", yaxs = "i", xaxt = "n", yaxt = "n", xlab = "",
ylab = "Red List Index",
bty = "n", cex.axis = 1.2, cex.lab = 1.8)
axis(1, c(2009, 2021.5), F, T, tcl = 0, lwd = 1.5, lend = 1)
axis(1, 2009:2021, F, T, lwd = 1.5, lend = 1)
axis(1, c(2010, 2015, 2021), T, T, cex.axis = 1.2, lwd = 1.5, lend = 1)
axis(2, seq(0.8, 1, 0.01), F, T, lwd = 1.5, lend = 1)
axis(2, seq(0.8, 1, 0.02), T, T, cex.axis = 1.2, lwd = 1.5, lend = 1)
mtext("Year of Red List assessment", 1, 3, F, 2015.5, cex = 1.8)
x <- c(2021, 2015, 2010)
for (i in 3:nrow(drli.)) {
polygon(c(x, rev(x)),
1 - c(apply(drli.[1:(i-1),], 2, sum), rev(apply(drli.[1:i,], 2, sum))),
border = NA, col = grey(1.32 - i * 0.12))
}
y0 <- rep(RLI21, 3)
y2 <- 1 - (sum(drli.[1:8, 1]) + sum(drli.[1:7, 1])) / 2
for (i in 7:3) {
y1 <- 1 - apply(drli.[1:i, ], 2, sum)
lines(x, y1, lwd = 2.4)
lines(c(2021, 2023), c(mean(c(y0[1], y1[1])), y2), lwd = 1.8)
text(2023, y2,
c("", "land-use change", "other/unknown", "climate change",
"pollution", "native species", "disturbance")[i],
pos = 4, cex = 1.2)
y0 <- y1
y2 <- y2 + 0.005
}
lines(c(2021, 2023), rep(y2, 2), lwd = 1.8)
text(2023, y2, "land-use change", pos = 4, cex = 1.2)
lines(x, rep(1,3), lwd = 4.8)
lines(x, c(RLI21, RLI15, RLI10), lwd = 4.8)
text(2015.5, 1.005, expression(bold(delta*RLI)), cex = 1.8)
if (nchar(figS2)) {
dev.off()
}
}
The following script recreates Appendix S9 (Figure S3).
Plot a graph for ELS50:
{
xl <- c(2009.5, 2028)
yl <- c(lg(8), 3.25)
if (nchar(figS3)) {
png(figS3, 1500, 1200, res = 180)
xl <- c(2009.5, 2027.5)
yl <- c(lg(8), 3.1)
}
par(mai = c(0.96, 0.96, 0.06, 0.06), family = "sans")
plot(0, 0, xlim = xl, ylim = yl,
xaxs = "i", yaxs = "i", xaxt = "n", yaxt = "n", xlab = "",
ylab="Expected loss of species",
bty = "n", cex.axis = 1.2, cex.lab = 1.8)
axis(1, c(2009, 2021.5), F, T, tcl = 0, lwd = 1.5, lend = 1)
axis(1, 2009:2021, F, T, lwd = 1.5, lend = 1)
axis(1, c(2010, 2015, 2021), T, T, cex.axis = 1.2, lwd = 1.5, lend = 1)
axis(2, c(1, lg(1200)), F, T, tcl = 0, lwd = 1.5, lend = 1)
axis(2, 0:3, c(1, 10, 100, 1000), T, cex.axis = 1.2, lwd = 1.5, lend = 1)
axis(2, lg(c(2:9, seq(20, 90, 10), seq(200, 900, 100))),
F, T, tcl = -0.25, lwd = 1.5, lend = 1)
mtext("Year of Red List assessment", 1, 3, F, 2015.5, cex = 1.8)
x <- c(2010, 2015, 2021, 2023)
ELS.[2, 4] <- 10^(3/4 * lg(ELS.[4, 4]) + 1/4 * lg(ELS.[6, 4]))
ELS.[5, 4] <- 10^(2/4 * lg(ELS.[4, 4]) + 2/4 * lg(ELS.[6, 4]))
ELS.[3, 4] <- 10^(1/4 * lg(ELS.[4, 4]) + 3/4 * lg(ELS.[6, 4]))
for (i in 1:6) {
lines (x, lg(ELS.[i, ]), lty = i, lwd = 2.4)
points(x[1:3], lg(ELS.[i, 1:3]), pch = c(4, 21, 24, 22, 25, 23)[i],
cex = 1.8, bg = "black", lwd = 2.4, ljoin = 1)
text(2023, lg(ELS.[i, 4]),
c("land-use change", "other/unknown", "climate change",
"pollution", "native species", "disturbance")[i],
pos = 4, cex = 1.2)
}
text(2015.5, 3.12, expression(bold(ELS[50])), cex = 1.8)
if (nchar(figS3)) {
dev.off()
}
}
In a kind of sensitivity analysis it is possible to check how important the weighting scheme chosen is for the (ranking of the) estimates obtained. This is here tested for the Expected Loss of Species. The most relevant measure is the relative importance of threats, so in addition to ELS values themselves we should look at the fraction of the total loss of species attributable to the different threats. These fractions are directly comparable across weightings.
for (meth in c("E", "Ev2", "Ev3", "equal-steps", "A1", "A2", "B", "C", "D")) {
weightingELS <- meth
RL. <- calcLoss(RL)
RL. <- addThreats(RL.)
drli <- sort(dRLI(RL.)$ELS50[, 3], decreasing = TRUE)
drli <- cbind(drli, drli / sum(drli))
colnames(drli) <- c("ELS" %+% TimeFrame, "fraction")
drli <- rbind(drli, Cumulative = apply(drli, 2, sum))
cat("\n\nWeighting scheme " %+% meth %+% ":\n")
print(drli)
}
##
##
## Weighting scheme E:
## ELS50 fraction
## landusec 933.621740 0.7493074643
## unknownf 117.639559 0.0944153250
## pollutio 53.329631 0.0428013715
## nativesp 35.077282 0.0281523751
## climatec 33.384750 0.0267939804
## disturba 27.107788 0.0217562074
## alienspe 19.860701 0.0159398298
## bycatchc 7.644216 0.0061351056
## outsiden 5.514580 0.0044258996
## huntgath 5.388558 0.0043247565
## natcatas 5.290871 0.0042463551
## otherthr 1.227322 0.0009850257
## nothreat 0.892500 0.0007163039
## Cumulative 1245.979500 1.0000000000
##
##
## Weighting scheme Ev2:
## ELS50 fraction
## landusec 1010.480713 0.7488284470
## unknownf 125.144201 0.0927395608
## pollutio 57.077557 0.0422979853
## nativesp 39.155638 0.0290167391
## climatec 37.605759 0.0278681834
## disturba 29.310813 0.0217211174
## alienspe 22.017742 0.0163165027
## bycatchc 8.831695 0.0065448300
## huntgath 5.912539 0.0043815554
## outsiden 5.809632 0.0043052952
## natcatas 5.697062 0.0042218740
## otherthr 1.437151 0.0010650171
## nothreat 0.935000 0.0006928926
## Cumulative 1349.415500 1.0000000000
##
##
## Weighting scheme Ev3:
## ELS50 fraction
## landusec 789.7188629 0.7477821199
## unknownf 103.9801213 0.0984584252
## pollutio 45.7262220 0.0432980049
## nativesp 28.5623017 0.0270455469
## climatec 26.2429860 0.0248493946
## disturba 23.0441769 0.0218204532
## alienspe 16.6340366 0.0157507130
## bycatchc 6.4591100 0.0061161094
## outsiden 4.8739841 0.0046151591
## huntgath 4.5429454 0.0043016996
## natcatas 4.4976605 0.0042588195
## otherthr 0.9840927 0.0009318341
## nothreat 0.8150000 0.0007717207
## Cumulative 1056.0815000 1.0000000000
##
##
## Weighting scheme equal-steps:
## ELS50 fraction
## landusec 1414.621672 0.7455265259
## unknownf 156.663571 0.0825640169
## climatec 79.976493 0.0421487938
## pollutio 75.661475 0.0398747153
## nativesp 64.788403 0.0341444457
## disturba 36.403990 0.0191854406
## alienspe 33.246404 0.0175213461
## bycatchc 11.863084 0.0062520204
## huntgath 7.922241 0.0041751381
## natcatas 6.619288 0.0034884627
## outsiden 6.602732 0.0034797375
## otherthr 2.110647 0.0011123422
## nothreat 1.000000 0.0005270148
## Cumulative 1897.480000 1.0000000000
##
##
## Weighting scheme A1:
## ELS50 fraction
## landusec 1483.316289 0.7606494700
## unknownf 174.796267 0.0896361004
## pollutio 87.296556 0.0447659607
## climatec 49.207858 0.0252339515
## nativesp 48.712853 0.0249801109
## disturba 42.614569 0.0218528910
## alienspe 29.642981 0.0152010183
## bycatchc 9.081307 0.0046569242
## natcatas 8.122716 0.0041653553
## outsiden 7.886598 0.0040442736
## huntgath 6.874503 0.0035252678
## otherthr 1.593003 0.0008168972
## nothreat 0.920000 0.0004717790
## Cumulative 1950.065500 1.0000000000
##
##
## Weighting scheme A2:
## ELS50 fraction
## landusec 1032.429641 0.7611966317
## unknownf 130.280641 0.0960541821
## pollutio 62.102811 0.0457875762
## disturba 30.372850 0.0223934981
## nativesp 30.055238 0.0221593271
## climatec 27.419678 0.0202161637
## alienspe 19.704591 0.0145279323
## bycatchc 6.022574 0.0044403638
## natcatas 5.914806 0.0043609075
## outsiden 5.703598 0.0042051869
## huntgath 4.578900 0.0033759623
## otherthr 1.001672 0.0007385195
## nothreat 0.737500 0.0005437489
## Cumulative 1356.324500 1.0000000000
##
##
## Weighting scheme B:
## ELS50 fraction
## landusec 2110.519053 0.7496893383
## unknownf 218.431277 0.0775902020
## climatec 120.083690 0.0426556028
## pollutio 112.190240 0.0398517259
## nativesp 97.477974 0.0346256991
## disturba 54.981918 0.0195304362
## alienspe 48.914040 0.0173750312
## bycatchc 16.643131 0.0059119000
## huntgath 11.829632 0.0042020701
## natcatas 10.286953 0.0036540866
## outsiden 9.553232 0.0033934574
## otherthr 3.037859 0.0010790950
## nothreat 1.242500 0.0004413554
## Cumulative 2815.191500 1.0000000000
##
##
## Weighting scheme C:
## ELS50 fraction
## landusec 2104.815662 0.7497977193
## unknownf 217.532659 0.0774915800
## climatec 119.809297 0.0426796225
## pollutio 111.888623 0.0398580436
## nativesp 97.199425 0.0346253158
## disturba 54.805678 0.0195234068
## alienspe 48.744183 0.0173641227
## bycatchc 16.556846 0.0058980393
## huntgath 11.791411 0.0042004500
## natcatas 10.258609 0.0036544207
## outsiden 9.516130 0.0033899275
## otherthr 3.024477 0.0010774083
## nothreat 1.235000 0.0004399436
## Cumulative 2807.178000 1.0000000000
##
##
## Weighting scheme D:
## ELS50 fraction
## landusec 2099.357291 0.7497957667
## unknownf 217.104326 0.0775398763
## climatec 119.543900 0.0426956911
## pollutio 111.592790 0.0398559130
## nativesp 96.924421 0.0346170330
## disturba 54.635090 0.0195131908
## alienspe 48.576043 0.0173491723
## bycatchc 16.470562 0.0058825420
## huntgath 11.753189 0.0041977093
## natcatas 10.230265 0.0036537895
## outsiden 9.479027 0.0033854811
## otherthr 3.011094 0.0010754271
## nothreat 1.227500 0.0004384077
## Cumulative 2799.905500 1.0000000000