Wildlife Comeback in Europe 2

Extract relevant species from the Article 12 reports

CHECK Falco

bird_list <- c("Anser brachyrhynchus",
               "Branta leucopsis",
               "Cygnus cygnus",
               "Oxyura leucocephala",
               "Ciconia ciconia", #"Ciconia ciconia ciconia",
               "Platalea leucorodia", #"Platalea leucorodia leucorodia",
               "Pelecanus crispus",
               "Falco naumanni",
               "Falco cherrug",
               "Falco peregrinus", #"Falco peregrinus peregrinus",
               "Falco peregrinus all others",
               "Falco peregrinus brookei",
               "Milvus milvus",
               "Haliaeetus albicilla",
               "Gypaetus barbatus",
               "Gyps fulvus",
               "Aegypius monachus",
               "Aquila adalberti",
               "Aquila heliaca",
               "Grus grus", #"Grus grus grus"
               "Sterna dougallii") #"Sterna dougallii dougallii")

#new_species_common <- c("Black stork", "Great white egret", "Osprey", "Black-winged stilt", "Bittern")
# New species binomials
new_species = c("Ciconia nigra", "Ardea alba", "Pandion haliaetus", "Himantopus himantopus", "Botaurus stellaris")

# Combine lists
bird_list = c(bird_list, new_species)

#### *** 2020 species data
bird_species <- fread("article_12_birds/Article12_2020_dataset_20210401_csv/Article12_2020_data_birds.csv", na.strings = 'NULL')
#write.csv(bird_species,"bird_species_check.csv")

intersect(bird_list, bird_species$speciesname)

##  [1] "Anser brachyrhynchus"  "Branta leucopsis"      "Cygnus cygnus"        
##  [4] "Oxyura leucocephala"   "Ciconia ciconia"       "Platalea leucorodia"  
##  [7] "Pelecanus crispus"     "Falco naumanni"        "Falco cherrug"        
## [10] "Falco peregrinus"      "Milvus milvus"         "Haliaeetus albicilla" 
## [13] "Gypaetus barbatus"     "Gyps fulvus"           "Aegypius monachus"    
## [16] "Aquila adalberti"      "Aquila heliaca"        "Grus grus"            
## [19] "Sterna dougallii"      "Ciconia nigra"         "Ardea alba"           
## [22] "Pandion haliaetus"     "Himantopus himantopus" "Botaurus stellaris"

## 24 of 26 match****
#[1] "Falco peregrinus all others" "Falco peregrinus brookei"     

# Restrict to those in list
# **
# ** Need to check matches as losing some
# **
bird_species_short <- bird_species[bird_species$speciesname %in% bird_list, ]

#names(bird_species_short)[names(bird_species_short) == "ï..specieshash"] <- "specieshash"

# Get the species codes (unique for each species)
speciescode <- unique(bird_species_short$speciescode)

# Threats info

#### *** 2020 data
bird_threats <- fread("article_12_birds/Article12_2020_dataset_20210401_csv/Article12_2020_data_bpressures_threats.csv", na.strings = 'NULL')

# Subset threats for species we care about
bird_threats <- bird_threats[bird_threats$speciescode %in% speciescode, ]

# Filter out 'no-pressure' codes 
bird_threats = subset(bird_threats, pressurecode != "Xxt") # Get rid of Xxt
# Get rid of all of those starting with 'X'??
bird_threats = subset(bird_threats, !startsWith(bird_threats$pressurecode, "Xx"))
bird_threats = subset(bird_threats, !is.na(bird_threats$pressurecode))

bird_threats_n = bird_threats %>% 
  group_by(country, season, speciesname, speciescode) %>% 
  summarise(
    n_threat = length(unique(pressurecode))
  )

## `summarise()` has grouped output by 'country', 'season', 'speciesname'. You can override using the `.groups` argument.

# Cons measure info

#### *** 2020 data
bird_measures <- fread("article_12_birds/Article12_2020_dataset_20210401_csv/Article12_2020_data_bmeasures.csv", na.strings = 'NULL')

# Subset measures for species we care about
bird_measures <- bird_measures[bird_measures$speciescode %in% speciescode, ]

# Filter out 'no-measure' codes? 
#bird_threats = subset(bird_threats, pressurecode != "Xxt") # Get rid of Xxt

bird_measures_n = bird_measures %>% 
  group_by(country, season, speciesname, speciescode) %>% 
  summarise(
    n_measure = length(unique(measurecode))
  )

## `summarise()` has grouped output by 'country', 'season', 'speciesname'. You can override using the `.groups` argument.

# Trend data - get columns from bird species table that we want to use
bird_trend_data = bird_species_short %>% select(country, speciesname, speciescode, season,
                        population_size_min, population_size_max, population_size,                 
                        population_trend_period, population_trend, population_trend_magnitude_min, 
                        population_trend_magnitude_max, population_trend_method, #population_trend_quality,
                        population_trend_long_period, population_trend_long,
                        population_trend_long_magnitude_min, population_trend_long_magnitude_max, 
                        population_trend_long_method,
                        distribution_trend_period, distribution_trend, 
                        distribution_trend_magnitude_min, distribution_trend_magnitude_max, distribution_trend_magnitude,
                        distribution_trend_long_period, distribution_trend_long, 
                        distribution_trend_long_magnitude_min, distribution_trend_long_magnitude_max,  distribution_trend_long_magnitude, 
                        distribution_trend_long_method)

# All together now!
# Combined measure data with threat data
full_dataB = merge(bird_trend_data, bird_threats_n, by = c('country', 'season', 'speciesname', 'speciescode'), all.x = TRUE)
# Combine those with trend data
full_data = merge(full_dataB, bird_measures_n, by=c('country', 'speciesname', 'speciescode', 'season'), all.x = TRUE) 

# How many species do we end up with
length(unique(full_data$speciesname))

## [1] 24

table(full_data$speciesname)

## 
##     Aegypius monachus  Anser brachyrhynchus      Aquila adalberti 
##                     6                     8                     3 
##        Aquila heliaca            Ardea alba    Botaurus stellaris 
##                    13                    43                    34 
##      Branta leucopsis       Ciconia ciconia         Ciconia nigra 
##                    23                    34                    32 
##         Cygnus cygnus         Falco cherrug        Falco naumanni 
##                    37                    15                    16 
##      Falco peregrinus             Grus grus     Gypaetus barbatus 
##                    31                    41                     5 
##           Gyps fulvus  Haliaeetus albicilla Himantopus himantopus 
##                    12                    40                    32 
##         Milvus milvus   Oxyura leucocephala     Pandion haliaetus 
##                    28                     8                    33 
##     Pelecanus crispus   Platalea leucorodia      Sterna dougallii 
##                     9                    32                     7

# Keep only breeding season stats
full_data_breeding = subset(full_data, season == "B" | season == "W") 
full_data_breeding = subset(full_data_breeding, !is.na(speciesname))

# Make model dataset by counting measures and threats and getting a single value for trend for each sp. in each country (thus the use of unique)
model_data = full_data_breeding

model_data$population_size_mean = (model_data$population_size_min + model_data$population_size_max)/2.0
model_data$population_trend_long_magnitude_mean = (model_data$population_trend_long_magnitude_min + model_data$population_trend_long_magnitude_max) / 2.0
# %>% 
#   group_by(country, speciesname, speciescode) %>% 
#   summarise(
#     n_measure = length(unique(measurecode)), 
#     n_threat = length(unique(pressurecode)), 
#     population_size_min = unique(population_size_min), 
#     population_size_max = unique(population_size_max), 
#     population_size_mean = (unique(population_size_min) + unique(population_size_max)/2), 
#     population_trend = unique(population_trend), 
#     population_trend_long = unique(population_trend_long), 
#     population_trend_long_magnitude_min = unique(population_trend_long_magnitude_min),
#     population_trend_long_magnitude_max = unique(population_trend_long_magnitude_max),
#     population_trend_long_magnitude_mean = (unique(population_trend_long_magnitude_min) + unique(population_trend_long_magnitude_max) / 2.0))
  
# Subset for Increasing/declining/stable/fluctuating
#model_data = subset(model_data, population_trend_long == "I" | population_trend_long == "D" | population_trend_long == "S" | population_trend_long == "F")

# Alter trend data to make sign correct
model_data$population_trend_long_magnitude_min = ifelse(model_data$population_trend_long == "D", -1*model_data$population_trend_long_magnitude_min, model_data$population_trend_long_magnitude_min)
model_data$population_trend_long_magnitude_max = ifelse(model_data$population_trend_long == "D", -1*model_data$population_trend_long_magnitude_max, model_data$population_trend_long_magnitude_max)

# Binomial model data
model_data_binom = model_data
model_data_binom$population_trend = factor(model_data_binom$population_trend)
# Subset trend to only those increasing/declining 
model_data_binom = subset(model_data_binom, population_trend == "D" | population_trend == "I")
model_data_binom$increasing = ifelse(model_data_binom$population_trend == "I", 1, 0)


# Binomial model data (long)
model_data_binom_long = model_data
model_data_binom_long$population_trend_long = factor(model_data_binom_long$population_trend_long)
# Subset trend to only those increasing/declining 
model_data_binom_long = subset(model_data_binom_long, population_trend_long == "D" | population_trend_long == "I")
model_data_binom_long$population_trend_long = droplevels(model_data_binom_long$population_trend_long)
model_data_binom_long$increasing_long = ifelse(model_data_binom_long$population_trend_long == "I", 1, 0)


# Binomial model data
model_data_binom_range = model_data
model_data_binom_range$distribution_trend = factor(model_data_binom_range$distribution_trend)
# Subset trend to only those increasing/declining 
model_data_binom_range = subset(model_data_binom_range, distribution_trend == "D" | distribution_trend == "I")
model_data_binom_range$expanding = ifelse(model_data_binom_range$distribution_trend == "I", 1, 0)


# Binomial model data (long)
model_data_binom_range_long = model_data
model_data_binom_range_long$distribution_trend_long = factor(model_data_binom_range_long$distribution_trend_long)
# Subset trend to only those increasing/declining 
model_data_binom_range_long = subset(model_data_binom_range_long, distribution_trend_long == "D" | distribution_trend_long == "I")
model_data_binom_range_long$distribution_trend_long = droplevels(model_data_binom_range_long$distribution_trend_long)
model_data_binom_range_long$expanding_long = ifelse(model_data_binom_range_long$distribution_trend_long == "I", 1, 0)

Species per country.

# How many do we have
table(model_data_binom$speciesname, model_data_binom$country)

##                        
##                         AT BE BG CY CZ DE DK EE ES ESIC FI FR GIB GR HR HU IE
##   Aegypius monachus      0  0  0  0  0  0  0  0  1    0  0  1   0  1  0  0  0
##   Anser brachyrhynchus   0  0  0  0  0  0  0  0  0    0  0  0   0  0  0  0  0
##   Aquila adalberti       0  0  0  0  0  0  0  0  2    0  0  0   0  0  0  0  0
##   Aquila heliaca         1  0  2  0  1  0  0  0  0    0  0  0   0  0  1  1  0
##   Ardea alba             1  2  2  0  1  2  0  1  2    0  0  2   0  2  0  0  0
##   Botaurus stellaris     1  1  2  0  1  1  0  0  0    0  1  0   0  1  1  0  0
##   Branta leucopsis       0  1  0  0  1  2  2  0  0    0  1  0   0  0  0  0  1
##   Ciconia ciconia        1  1  1  0  0  1  0  1  0    0  0  1   0  1  0  0  0
##   Ciconia nigra          0  1  0  0  0  1  0  1  1    0  0  1   0  1  0  0  0
##   Cygnus cygnus          0  0  1  0  1  2  1  1  0    0  2  1   0  1  0  0  1
##   Falco cherrug          1  0  1  0  1  0  0  0  0    0  0  0   0  0  0  1  0
##   Falco naumanni         0  0  0  0  0  0  0  0  0    0  0  1   1  0  1  0  0
##   Falco peregrinus       0  1  0  0  1  1  1  0  0    1  0  1   0  1  0  1  0
##   Grus grus              0  0  0  0  0  2  1  0  1    0  1  2   0  0  0  0  0
##   Gypaetus barbatus      1  0  0  0  0  0  0  0  1    0  0  1   0  1  0  0  0
##   Gyps fulvus            0  0  1  1  0  0  0  0  1    0  0  1   0  1  0  0  0
##   Haliaeetus albicilla   2  0  1  0  2  1  1  1  0    0  2  2   0  1  1  2  1
##   Himantopus himantopus  1  1  0  0  1  0  0  0  2    0  0  0   0  0  1  0  0
##   Milvus milvus          1  1  0  0  1  0  1  0  1    0  0  1   0  0  0  1  1
##   Oxyura leucocephala    0  0  1  0  0  0  0  0  0    0  0  0   0  1  0  0  0
##   Pandion haliaetus      0  0  0  0  0  1  1  1  1    1  1  1   0  0  0  0  0
##   Pelecanus crispus      0  0  0  0  0  0  0  0  0    0  0  0   0  1  0  0  0
##   Platalea leucorodia    1  2  0  0  0  1  1  0  1    0  0  2   0  0  1  1  0
##   Sterna dougallii       0  0  0  0  0  0  0  0  0    0  0  1   0  0  0  0  1
##                        
##                         IT LT LU LV MT NL PL PT RO SE SI SK UK
##   Aegypius monachus      0  0  0  0  0  0  0  1  0  0  0  0  0
##   Anser brachyrhynchus   0  0  0  0  0  1  0  0  0  1  0  0  1
##   Aquila adalberti       0  0  0  0  0  0  0  1  0  0  0  0  0
##   Aquila heliaca         0  0  0  0  0  0  0  0  1  0  0  1  0
##   Ardea alba             1  1  1  1  0  2  2  1  1  0  0  1  1
##   Botaurus stellaris     0  1  0  0  0  1  1  0  0  1  0  1  2
##   Branta leucopsis       0  0  0  0  0  2  0  0  0  2  0  0  3
##   Ciconia ciconia        1  1  1  1  0  1  1  0  0  1  0  1  0
##   Ciconia nigra          1  1  1  1  0  0  0  1  0  0  0  1  0
##   Cygnus cygnus          0  1  0  1  0  1  2  0  0  2  0  0  2
##   Falco cherrug          0  0  0  0  0  0  0  0  1  0  0  1  0
##   Falco naumanni         1  0  0  0  0  0  0  1  0  0  0  0  0
##   Falco peregrinus       0  0  1  0  0  2  1  0  1  1  0  1  0
##   Grus grus              1  1  0  1  0  1  1  1  0  1  0  1  1
##   Gypaetus barbatus      1  0  0  0  0  0  0  0  0  0  0  0  0
##   Gyps fulvus            1  0  0  0  0  0  0  0  0  0  0  0  0
##   Haliaeetus albicilla   0  2  0  0  0  2  2  0  0  2  1  2  1
##   Himantopus himantopus  0  0  0  0  1  1  0  1  0  0  0  0  1
##   Milvus milvus          2  1  1  1  0  0  1  1  0  1  0  0  1
##   Oxyura leucocephala    0  0  0  0  0  0  0  0  0  0  0  0  0
##   Pandion haliaetus      0  0  0  0  0  0  1  2  0  0  0  0  1
##   Pelecanus crispus      0  0  0  0  0  0  0  0  0  0  0  0  0
##   Platalea leucorodia    1  0  0  0  0  1  0  2  0  0  0  0  1
##   Sterna dougallii       0  0  0  0  0  0  0  0  0  0  0  0  1

# First model (binomial)
library(lme4)

## Loading required package: Matrix

## 
## Attaching package: 'lme4'

## The following object is masked from 'package:expss':
## 
##     dummy

library(sjPlot)

## Install package "strengejacke" from GitHub (`devtools::install_github("strengejacke/strengejacke")`) to load all sj-packages at once!

# Dont think we need unweighted model anymore

# m1 = glmer(population_trend ~ n_measure + n_threat + (1|speciesname), 
#            data = subset(model_data_binom, season = "B"),family = "binomial") 
# # Adding 'country' throws singularities due to sparse data
# summary(m1)
# tab_model(m1)
# plot_model(m1)

Probability of increase vs threat/measure

Are populations more likely to be increasing if they have more threats/measures?

m1wA = glmer(increasing ~ n_measure + n_threat +  (1|speciesname) , 
           data = subset(model_data_binom, season = "B"), weights = log10(population_size_mean + 1), family = "binomial")

## Warning in eval(family$initialize, rho): non-integer #successes in a binomial
## glm!

m1wB = glmer(increasing ~ n_measure * n_threat +  (1|speciesname) , 
           data = subset(model_data_binom, season = "B"), weights = log10(population_size_mean + 1), family = "binomial")

## Warning in eval(family$initialize, rho): non-integer #successes in a binomial
## glm!

# Adding 'country' throws singularities due to sparse data

#AIC(m1wA, m1wB)
#summary(m1wB)

tab_model(m1wA, m1wB, show.aicc = TRUE)

	increasing			increasing
Predictors	Odds Ratios	CI	p	Odds Ratios	CI	p
(Intercept)	35.25	4.15 – 299.76	0.001	62.26	6.60 – 587.08	<0.001
n_measure	1.08	0.93 – 1.26	0.311	0.93	0.72 – 1.20	0.559
n_threat	0.92	0.80 – 1.06	0.273	0.81	0.64 – 1.02	0.077
n_measure * n_threat				1.03	0.99 – 1.06	0.165
Random Effects
σ²	3.29			3.29
τ₀₀	9.44 _speciesname			8.80 _speciesname
ICC	0.74			0.73
N	22 _speciesname			22 _speciesname
Observations	161			161
Marginal R² / Conditional R²	0.003 / 0.743			0.008 / 0.730
AIC	239.583			239.760

Seems to be better support for interaction between threats and measures…

plot_model(m1wB)

plot_model(m1wB, type = "int")

Probability of increase vs threat/measure (long term trend)

m1w_longA = glmer(increasing_long ~ n_measure + n_threat +  (1|speciesname), 
           data = subset(model_data_binom_long, season = "B"), weights = log10(population_size_mean + 1),family = "binomial")

## Warning in eval(family$initialize, rho): non-integer #successes in a binomial
## glm!

m1w_longB = glmer(increasing_long ~ n_measure * n_threat +  (1|speciesname), 
           data = subset(model_data_binom_long, season = "B"), weights = log10(population_size_mean + 1),family = "binomial")

## Warning in eval(family$initialize, rho): non-integer #successes in a binomial
## glm!

#AIC(m1w_longA, m1w_longB)
#summary(m1w_longB)

#summary(m1w_longB)
tab_model(m1w_longA, m1w_longB)

	increasing_long			increasing_long
Predictors	Odds Ratios	CI	p	Odds Ratios	CI	p
(Intercept)	46.76	11.67 – 187.42	<0.001	182.99	26.00 – 1287.74	<0.001
n_measure	1.00	0.86 – 1.17	0.978	0.75	0.56 – 1.00	0.053
n_threat	0.90	0.77 – 1.04	0.152	0.71	0.55 – 0.92	0.009
n_measure * n_threat				1.04	1.00 – 1.08	0.027
Random Effects
σ²	3.29			3.29
τ₀₀	3.02 _speciesname			3.03 _speciesname
ICC	0.48			0.48
N	23 _speciesname			23 _speciesname
Observations	203			203
Marginal R² / Conditional R²	0.020 / 0.489			0.059 / 0.510

Long term model also seems to have better support for interaction between threats and measures…

plot_model(m1w_longB)

plot_model(m1w_longB, type = "int")

Probability of expansion vs threat/measure

Are species more likely to be expanding if they have more threats/measures?

m1wA_range = glmer(expanding ~ n_measure + n_threat +  (1|speciesname) , 
           data = subset(model_data_binom_range, season = "B"), weights = log10(population_size_mean + 1),family = "binomial")

## Warning in eval(family$initialize, rho): non-integer #successes in a binomial
## glm!

m1wB_range = glmer(expanding ~ n_measure * n_threat +  (1|speciesname) , 
           data = subset(model_data_binom_range, season = "B"), weights = log10(population_size_mean + 1),family = "binomial")

## Warning in eval(family$initialize, rho): non-integer #successes in a binomial
## glm!

# Adding 'country' throws singularities due to sparse data

#AIC(m1wA_range, m1wB_range)
#summary(m1wB_range)

tab_model(m1wA_range, m1wB_range, show.aicc = TRUE)

	expanding			expanding
Predictors	Odds Ratios	CI	p	Odds Ratios	CI	p
(Intercept)	72.38	8.80 – 595.23	<0.001	39.33	3.36 – 461.10	0.003
n_measure	1.32	1.06 – 1.63	0.012	1.73	0.96 – 3.13	0.067
n_threat	0.65	0.51 – 0.82	<0.001	0.74	0.53 – 1.03	0.071
n_measure * n_threat				0.97	0.90 – 1.03	0.297
Random Effects
σ²	3.29			3.29
τ₀₀	4.80 _speciesname			6.16 _speciesname
ICC	0.59			0.65
N	22 _speciesname			22 _speciesname
Observations	122			122
Marginal R² / Conditional R²	0.118 / 0.641			0.138 / 0.700
AIC	166.056			167.037

** No clear support for interaction

plot_model(m1wA_range)

plot_model(m1wA_range, type = "pred", terms = c("n_threat", "n_measure"))

Probability of increase vs threat/measure (long term trend)

Same for long term trend

m1wA_range_long = glmer(expanding_long ~ n_measure + n_threat +  (1|speciesname) , 
           data = subset(model_data_binom_range_long, season = "B"), weights = log10(population_size_mean + 1),family = "binomial")

## Warning in eval(family$initialize, rho): non-integer #successes in a binomial
## glm!

m1wB_range_long = glmer(expanding_long ~ n_measure * n_threat +  (1|speciesname) , 
           data = subset(model_data_binom_range_long, season = "B"), weights = log10(population_size_mean + 1),family = "binomial")

## Warning in eval(family$initialize, rho): non-integer #successes in a binomial
## glm!

# Adding 'country' throws singularities due to sparse data

#AIC(m1wA_range_long, m1wB_range_long)
#summary(m1wB_range_long)

tab_model(m1wA_range_long, m1wB_range_long, show.aicc = TRUE)

	expanding_long			expanding_long
Predictors	Odds Ratios	CI	p	Odds Ratios	CI	p
(Intercept)	2015.84	119.84 – 33909.00	<0.001	76590.71	202.63 – 28950028.29	<0.001
n_measure	1.12	0.88 – 1.43	0.356	0.57	0.25 – 1.31	0.183
n_threat	0.54	0.39 – 0.74	<0.001	0.33	0.16 – 0.68	0.003
n_measure * n_threat				1.09	0.99 – 1.20	0.097
Random Effects
σ²	3.29			3.29
τ₀₀	4.01 _speciesname			3.61 _speciesname
ICC	0.55			0.52
N	21 _speciesname			21 _speciesname
Observations	145			145
Marginal R² / Conditional R²	0.303 / 0.686			0.482 / 0.753
AIC	136.288			135.175

*Again, no clear support for interaction

plot_model(m1wA_range_long)

plot_model(m1wA_range_long, type = "pred", terms = c("n_threat", "n_measure"))

# tab = table(model_data$speciesname, model_data$country)
# margin.table(tab, 2)
# 
# 
# m1_mean = lmer(population_trend_long_magnitude_mean ~ n_measure + n_threat + (1|speciesname) + (1|country), 
#                data = model_data)
# m1_mean.2 = lmer(population_trend_long_magnitude_mean ~ n_measure*n_threat + (1|speciesname) + (1|country), 
#                 data = model_data)
# 
# AIC(m1_mean, m1_mean.2)
# summary(m1_mean.2)
# plot_model(m1_mean.2, show.intercept = TRUE)
# #plot_model(m1_mean.2, type="re")
# plot_model(m1_mean.2, type = "int")
# 
# # Next model is only actual value of trend
# 
# model_data = subset(model_data, !is.na(population_size_mean))
# m1_mean_w = lmer(log10(population_trend_long_magnitude_mean + 1) ~ n_measure + n_threat + (1|speciesname) + (1|country), 
#                 weights = log10(population_size_mean + 1), data = model_data)
# m1_mean_w.2 = lmer(log10(population_trend_long_magnitude_mean + 1) ~ n_measure*n_threat + (1|speciesname) + (1|country), 
#                 weights = log10(population_size_mean + 1), data = model_data)
# # INF***
# AIC(m1_mean_w, m1_mean_w.2)
# 
# summary(m1_mean)
# plot_model(m1_mean_w)
# plot_model(m1_mean_w, type = "pred", terms = c("n_measure", "n_threat"))

Notes

Use 2020 Data! (done)
Add weighting for population size… (min or max)
Add extra 5 species (done)
Try magnitude of population/range change

sessionInfo()

## R version 4.0.3 (2020-10-10)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.7
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] sjPlot_2.8.7      lme4_1.1-26       Matrix_1.2-18     data.table_1.13.6
## [5] expss_0.10.7      dplyr_1.0.7       plyr_1.8.6       
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_1.0.7         mvtnorm_1.1-1      lattice_0.20-41    tidyr_1.1.2       
##  [5] assertthat_0.2.1   digest_0.6.27      utf8_1.2.1         R6_2.5.0          
##  [9] backports_1.2.1    coda_0.19-4        evaluate_0.14      highr_0.9         
## [13] ggplot2_3.3.3      pillar_1.6.1       rlang_0.4.11       rstudioapi_0.13   
## [17] minqa_1.2.4        performance_0.7.0  nloptr_1.2.2.2     checkmate_2.0.0   
## [21] effectsize_0.4.3   rmarkdown_2.6      labeling_0.4.2     ggeffects_1.0.1   
## [25] splines_4.0.3      statmod_1.4.35     stringr_1.4.0      foreign_0.8-81    
## [29] htmlwidgets_1.5.3  munsell_0.5.0      broom_0.7.5        modelr_0.1.8      
## [33] compiler_4.0.3     xfun_0.24          pkgconfig_2.0.3    parameters_0.11.0 
## [37] htmltools_0.5.1.1  insight_0.12.0     tidyselect_1.1.1   tibble_3.1.2      
## [41] htmlTable_2.1.0    matrixStats_0.58.0 fansi_0.5.0        crayon_1.4.1      
## [45] MASS_7.3-53        sjmisc_2.8.6       grid_4.0.3         xtable_1.8-4      
## [49] nlme_3.1-149       gtable_0.3.0       lifecycle_1.0.0    DBI_1.1.1         
## [53] magrittr_2.0.1     bayestestR_0.8.2   scales_1.1.1       estimability_1.3  
## [57] stringi_1.6.2      farver_2.1.0       ellipsis_0.3.2     generics_0.1.0    
## [61] vctrs_0.3.8        boot_1.3-25        sjlabelled_1.1.7   RColorBrewer_1.1-2
## [65] tools_4.0.3        glue_1.4.2         purrr_0.3.4        sjstats_0.18.1    
## [69] emmeans_1.5.4      yaml_2.2.1         colorspace_2.0-0   knitr_1.33

Wildlife Comeback in Europe 2

Indicators and Assessments Unit (SD, RF)

07/07/2021

Extract relevant species from the Article 12 reports

Species per country.

Probability of increase vs threat/measure

Probability of increase vs threat/measure (long term trend)

Probability of expansion vs threat/measure

Probability of increase vs threat/measure (long term trend)

Notes