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2_plot_and_analyze_data.R
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#' @author Brian Maitner
#' @description Scripts to wrangle data for the trait shortfalls manuscript
# Load libraries
library(tidyverse)
library(lme4)
# Get TRY data
library(sf)
#tdwg <- read_sf("manual_downloads/TDWG/level3.shp")
tdwg <- read_sf("manual_downloads/TDWG/old_lv3/level3.shp")
#Load in the trait data
#traits <- arrow::open_dataset(sources = "manual_downloads/TRY/TRY_parquet/")
#examine the data structure
# traits$schema
# traits$metadata
########################################################
# Trait completeness model: 1% threshold and broadly applicable traits
#via a single model
# Load overall trait completeness
# general_traits_one_percent_threshold <- read_rds("data/focal_trait_coverage.rds")
general_traits_one_percent_threshold <- read_rds("data/cleaning_raw_names/focal_trait_coverage.rds")
# merge with predictor variables
socio_vars <- read.csv("manual_downloads/Darwinian_shortfalls/socioeco_var.csv")
#socio_vars[2:10] <- scale(socio_vars[2:10])
# combine trait completeness and predictor variables
general_traits_one_percent_threshold <-
merge(x = general_traits_one_percent_threshold,
y = socio_vars,
by.x= "area",
by.y = "LEVEL_3_CO")
# add species richness
wcvp <- readRDS("manual_downloads/WCVP/wcvp_cleaned.RDS")
wcvp %>%
group_by(area_code_l3) %>%
summarise(richness = n()) %>%
merge(x = general_traits_one_percent_threshold,
y = .,
by.x = "area",
by.y = "area_code_l3",
all.x = TRUE) ->
general_traits_one_percent_threshold
# Add mean range size
socio_vars %>%
dplyr::select(LEVEL_3_CO, AREA_SQKM) %>%
merge(x = wcvp,
y = .,
by.x = "area_code_l3",
by.y = "LEVEL_3_CO",
all.x = TRUE) %>%
group_by(taxon_name) %>%
summarise(range_size = sum(AREA_SQKM)) %>%
merge(x = wcvp,
y = .,by = "taxon_name",
all.x = TRUE) %>%
group_by(area_code_l3) %>%
summarise(mean_species_range = mean(na.omit(range_size))) %>%
merge(x = general_traits_one_percent_threshold,
y = .,
by.x="area",
by.y = "area_code_l3") -> general_traits_one_percent_threshold
# Add endemism
wcvp %>%
group_by(taxon_name) %>%
summarize(n_countries = n()) %>%
merge(x = wcvp,
y = .) %>%
group_by(area_code_l3) %>%
filter(n_countries == 1) %>%
summarise(endemics = n()) %>%
merge(x = general_traits_one_percent_threshold,
y = .,
by.x = "area",
by.y = "area_code_l3",
all.x = TRUE) %>%
mutate(endemics = replace_na(endemics,0)) %>%
mutate(endemism = endemics/richness) %>%
dplyr::select(-endemics) -> general_traits_one_percent_threshold
#rescale predictors
general_traits_one_percent_threshold[4:ncol(general_traits_one_percent_threshold)] <- scale(general_traits_one_percent_threshold[4:ncol(general_traits_one_percent_threshold)])
general_traits_one_percent_threshold <- na.omit(general_traits_one_percent_threshold)
#Simple model
all_variables <-
glmer(data = general_traits_one_percent_threshold,
formula = completeness ~ AREA_SQKM + GDP_SUM + GDP_CAPITA + ROAD_DENSITY + POP_COUNT + POP_DENSITY + SECURITY + RESEARCH_EXP + EDUCATION_EXP
+ richness + mean_species_range + endemism +
(1|trait),
family = "binomial",
control = glmerControl(optimizer="bobyqa",
optCtrl=list(maxfun=2e5)),
na.action = "na.fail")
plot(y = fitted.values(all_variables),
x = general_traits_one_percent_threshold$completeness)
plot(y = resid(all_variables),
x = general_traits_one_percent_threshold$completeness)
library(MuMIn)
summary(all_variables)
MuMIn::r.squaredGLMM(all_variables)
r2glmm::r2beta(model = all_variables)
#check residuals
resids <- residuals(all_variables)
# dredged_all_variables <-
# dredge(global.model = all_variables)
# dredged_all_variables
#Moran's I
tdwg %>%
inner_join(y = general_traits_one_percent_threshold,
by = c("LEVEL_3_CO" = "area")) -> tdwg_combined
library(spdep)
library(tidyverse)
library(gridExtra)
library(NLMR)
library(DHARMa)
#Trying to do them all at once (if this fails, iterate and record)
nb <- poly2nb(pl = st_make_valid(tdwg_combined),
queen = TRUE)
# saveRDS(object = nb,file = "data/polygon_nb.RDS")
lw <- nb2listw(nb, style="W", zero.policy=TRUE)
comp.lag <- lag.listw(lw, tdwg_combined$completeness)
plot(comp.lag ~ tdwg_combined$completeness, pch=16, asp=1)
M1 <- lm(comp.lag ~ tdwg_combined$completeness)
abline(M1, col="blue")
I <- moran(tdwg_combined$completeness, lw, length(nb), Szero(lw))[1]
moran_test_combined <- moran.test(tdwg_combined$completeness,
lw,
alternative = "greater")
#check whether the autocorrelation is present in the residuals
moran_test_residuals <- moran.test(residuals(all_variables),
lw,
alternative = "two.sided")
# Moran I statistic standard deviate = 450.37, p-value < 2.2e-16
# alternative hypothesis: two.sided
# sample estimates:
# Moran I statistic Expectation Variance
# 4.340564e-01 -5.127416e-05 9.290920e-07
MC_test_combined <- moran.mc(residuals(all_variables),
lw,
nsim = 999,
alternative = "two.sided")
# Monte-Carlo simulation of Moran I
#
# data: residuals(all_variables)
# weights: lw
# number of simulations + 1: 1000
#
# statistic = 0.43406, observed rank = 1000, p-value < 2.2e-16
# alternative hypothesis: two.sided
moran_test_combined
MC_test_combined
MC_test_combined_two_sided
plot(MC_test_combined)
# tdwg_dist <- st_distance(x = st_make_valid(tdwg_combined))
# saveRDS(object = tdwg_dist,file = "data/tdwg_dist.RDS")
tdwg_dist <- readRDS(file = "data/tdwg_dist.RDS")
sims <- simulateResiduals(all_variables)
testOutliers(simulationOutput = sims,type = "bootstrap")
testSpatialAutocorrelation(simulationOutput = sims,
distMat = tdwg_dist)
library(sf)
library(tidyverse)
tdwg_cents <- st_centroid(st_make_valid(tdwg))
tdwg_cents <- cbind(tdwg_cents,st_coordinates(tdwg_cents))
general_traits_one_percent_threshold %>%
inner_join(y = tdwg_cents,by = c("area"= "LEVEL_3_CO")) -> general_traits_w_coords
################################
#spamm to account for spatial autocorrelation
wcvp %>%
group_by(area_code_l3) %>%
summarise(richness_untf = n()) %>%
merge(x = general_traits_w_coords,
y = .,
by.x = "area",
by.y = "area_code_l3",
all.x = TRUE) ->
general_traits_w_coords
#trying spamm
# https://datascienceplus.com/spatial-regression-in-r-part-1-spamm-vs-glmmtmb/
# https://kimura.univ-montp2.fr/~rousset/spaMM/spaMMintro.pdf
library(spaMM)
m_spamm <-
general_traits_w_coords %>%
mutate(species_w_data = completeness * richness_untf) %>%
mutate(species_wo_data = (1-completeness) * richness_untf) %>%
fitme(cbind(species_w_data,species_wo_data) ~ AREA_SQKM + GDP_SUM + GDP_CAPITA + ROAD_DENSITY + POP_COUNT + POP_DENSITY + SECURITY + RESEARCH_EXP + EDUCATION_EXP
+ richness + mean_species_range + endemism +
(1|trait) + Matern(1 | X + Y),
data = .,
family = "binomial") # for spamm, binomial models need to include successes and failures (e.g. samples and no samples)
m_spamm_null <-
general_traits_w_coords %>%
mutate(species_w_data = completeness * richness_untf) %>%
mutate(species_wo_data = (1-completeness) * richness_untf) %>%
fitme(cbind(species_w_data,species_wo_data) ~ (1|trait) + Matern(1 | X + Y),
data = .,
family = "binomial") # for spamm, binomial models need to include successes and failures (e.g. samples and no samples)
#Test whether model is significant improvement over null
spaMM::LRT(object = m_spamm,
object2 = m_spamm_null) # p << 0.001
#Look at model results
summary(m_spamm)
# ------------ Fixed effects (beta) ------------
# Estimate Cond. SE t-value
# (Intercept) -1.827439 0.167811 -10.8898
# AREA_SQKM -0.009068 0.015193 -0.5969
# GDP_SUM -0.006973 0.008457 -0.8246
# GDP_CAPITA -0.010274 0.008403 -1.2226
# ROAD_DENSITY 0.007441 0.007724 0.9634
# POP_COUNT 0.004552 0.010248 0.4442
# POP_DENSITY 0.019438 0.010070 1.9303
# SECURITY -0.014420 0.010232 -1.4093
# RESEARCH_EXP 0.060051 0.014366 4.1799
# EDUCATION_EXP -0.005070 0.007721 -0.6567
# richness -0.089983 0.014143 -6.3624
# mean_species_range 0.408990 0.017557 23.2944
# endemism -0.125873 0.012587 -10.0005
sims <- DHARMa::simulateResiduals(fittedModel = m_spamm,integerResponse = TRUE,n = 1000)
?simulateResiduals
plot(sims)
DHARMa::testOutliers(simulationOutput = sims,
type = "bootstrap")
#Check confidence intervals
confint(object = m_spamm,
parm = c("(Intercept)","AREA_SQKM","GDP_SUM","GDP_CAPITA","ROAD_DENSITY",
"POP_COUNT","POP_DENSITY","SECURITY","RESEARCH_EXP",
"EDUCATION_EXP",
"richness","mean_species_range","endemism"))
#CIs
# lower (Intercept) upper (Intercept)
# -2.201892 -1.463093
# lower AREA_SQKM upper AREA_SQKM
# -0.03921223 0.02116688
# lower GDP_SUM upper GDP_SUM
# -0.023619696 0.009742648
# lower GDP_CAPITA upper GDP_CAPITA
# -0.026964859 0.006244498
# lower ROAD_DENSITY upper ROAD_DENSITY
# -0.007744537 0.022702515
# lower POP_COUNT upper POP_COUNT
# -0.01559710 0.02469701
# lower POP_DENSITY upper POP_DENSITY
# -0.0003513056 0.0393199362
# lower SECURITY upper SECURITY
# -0.034531197 0.005745719
# lower RESEARCH_EXP upper RESEARCH_EXP
# 0.03165934 0.08863481
# lower EDUCATION_EXP upper EDUCATION_EXP
# -0.02027907 0.01011657
# lower richness upper richness
# -0.11802884 -0.06189039
# lower mean_species_range upper mean_species_range
# 0.3725413 0.4456265
# lower endemism upper endemism
# -0.1514902 -0.1002516
#Also check AICs
AIC(m_spamm)
AIC(m_spamm_null)
plot(m_spamm) #documentation suggests this may be incorrect for binomials and recommends use of dharma package
# Partial Dependence Plots
plot_effects(m_spamm,"mean_species_range")
plot_effects(m_spamm,"AREA_SQKM")
#################################
#Taxonomic Biases
family_trait_coverage <- readRDS("data/focal_trait_coverage_family.rds")
trait_list <- readRDS("data/trait_list_w_coverage.RDS")
#need overall completeness for comparison
source("R/test_family_coverage.R")
family_coverage_tests <- test_family_coverage(family_trait_coverage = family_trait_coverage,
trait_list = trait_list)
family_coverage_tests %>%
mutate(significant = case_when(p_value <= 0.05 ~ 1,
p_value > 0.05 ~ 0)) %>%
mutate(sig_greater = case_when(significant == 1 & exp_completeness > completeness ~ 1,
significant == 1 & exp_completeness < completeness ~ 0,
significant == 0 ~ 0)) %>%
mutate(sig_lesser = case_when(significant == 1 & exp_completeness < completeness ~ 1,
significant == 1 & exp_completeness > completeness ~ 0,
significant == 0 ~ 0)) -> family_coverage_tests
length(which(family_coverage_tests$significant == 1))/nrow(family_coverage_tests) # 36.3% differ from expectation
family_coverage_tests %>%
group_by(family) %>%
summarize( n_different = sum(significant),
n_greater = sum(sig_greater),
n_lesser = sum(sig_lesser),
n_spp = unique(n_spp_in_fam)) %>%
arrange(-n_different,n_greater) -> family_coverage_deviations
family_coverage_tests %>%
group_by(trait) %>%
summarize( n_different = sum(significant),
n_greater = sum(sig_greater),
n_lesser = sum(sig_lesser)) %>%
arrange(-n_different,n_greater) -> trait_coverage_deviations
#############################
#Intraspecific
# Trait completeness model: 1% threshold and broadly applicable traits
#via a single model
# Load overall trait completeness
geo_traits_one_percent_threshold <- read_rds("data/focal_georeferenced_trait_coverage.rds")
# merge with predictor variables
socio_vars <- read.csv("manual_downloads/Darwinian_shortfalls/socioeco_var.csv")
#socio_vars[2:10] <- scale(socio_vars[2:10])
# combine trait completeness and predictor variables
geo_traits_one_percent_threshold <-
merge(x = geo_traits_one_percent_threshold,
y = socio_vars,
by.x= "area",
by.y = "LEVEL_3_CO")
# add species richness
wcvp <- readRDS("manual_downloads/WCVP/wcvp_cleaned.RDS")
wcvp %>%
group_by(area_code_l3) %>%
summarise(richness = n()) %>%
merge(x = geo_traits_one_percent_threshold,
y = .,
by.x = "area",
by.y = "area_code_l3",
all.x = TRUE) ->
geo_traits_one_percent_threshold
# Add mean range size
socio_vars %>%
select(LEVEL_3_CO, AREA_SQKM) %>%
merge(x = wcvp,
y = .,
by.x = "area_code_l3",
by.y = "LEVEL_3_CO",
all.x = TRUE) %>%
group_by(taxon_name) %>%
summarise(range_size = sum(AREA_SQKM)) %>%
merge(x = wcvp,
y = .,by = "taxon_name",
all.x = TRUE) %>%
group_by(area_code_l3) %>%
summarise(mean_species_range = mean(na.omit(range_size))) %>%
merge(x = geo_traits_one_percent_threshold,
y = .,
by.x="area",
by.y = "area_code_l3") -> geo_traits_one_percent_threshold
# Add endemism
wcvp %>%
group_by(taxon_name) %>%
summarize(n_countries = n()) %>%
merge(x = wcvp,
y = .) %>%
group_by(area_code_l3) %>%
filter(n_countries == 1) %>%
summarise(endemics = n()) %>%
merge(x = geo_traits_one_percent_threshold,
y = .,
by.x = "area",
by.y = "area_code_l3",
all.x = TRUE) %>%
mutate(endemics = replace_na(endemics,0)) %>%
mutate(endemism = endemics/richness) %>%
select(-endemics) -> geo_traits_one_percent_threshold
#rescale predictors
geo_traits_one_percent_threshold[4:ncol(geo_traits_one_percent_threshold)] <- scale(geo_traits_one_percent_threshold[4:ncol(geo_traits_one_percent_threshold)])
geo_traits_one_percent_threshold <- na.omit(geo_traits_one_percent_threshold) #shouldn't be any NAs, but just to be sure
#Get coordinates needed for spatial
geo_traits_one_percent_threshold %>%
inner_join(y = tdwg_cents, by = c("area"= "LEVEL_3_CO")) -> geo_traits_w_coords
#Get un-tf-ed richness
wcvp %>%
group_by(area_code_l3) %>%
summarise(richness_untf = n()) %>%
merge(x = geo_traits_w_coords,
y = .,
by.x = "area",
by.y = "area_code_l3",
all.x = TRUE) ->
geo_traits_w_coords
#Generate a list of island vs mainland polygons
shape_sf <- st_read("manual_downloads/Darwinian_shortfalls/level3.shp")
intersects <- st_intersects(shape_sf)
intmat <- as.matrix(intersects)
shape_sf$is_island <- 0
shape_sf$is_island[which(rowSums(intmat)<=1)]<-1
shape_sf %>%
select(area = LEVEL_3_CO, is_island)%>%
st_drop_geometry() %>%
inner_join(geo_traits_w_coords) -> geo_traits_w_coords
#trying spamm
library(spaMM)
geo_m_spamm <-
geo_traits_w_coords %>%
mutate(species_w_data = completeness * richness_untf) %>%
mutate(species_wo_data = (1-completeness) * richness_untf) %>%
fitme(cbind(species_w_data,species_wo_data) ~ AREA_SQKM + GDP_SUM + GDP_CAPITA + ROAD_DENSITY + POP_COUNT + POP_DENSITY + SECURITY + RESEARCH_EXP + EDUCATION_EXP
+ richness + mean_species_range + endemism +
(1|trait) + Matern(1 | X + Y),
data = .,
family = "binomial") # for spamm, binomial models need to include successes and failures (e.g. samples and no samples)
geo_m_spamm_null <-
geo_traits_w_coords %>%
mutate(species_w_data = completeness * richness_untf) %>%
mutate(species_wo_data = (1-completeness) * richness_untf) %>%
fitme(cbind(species_w_data,species_wo_data) ~ (1|trait) + Matern(1 | X + Y),
data = .,
family = "binomial") # for spamm, binomial models need to include successes and failures (e.g. samples and no samples)
#Test whether model is significant improvement over null
spaMM::LRT(object = geo_m_spamm,
object2 = geo_m_spamm_null) # p ~ 0
geo_sims <- simulateResiduals(fittedModel = geo_m_spamm,integerResponse = TRUE,n = 1000)
plot(geo_sims)
#Look at model results
summary(geo_m_spamm)
#Check confidence intervals
# confint(object = geo_m_spamm,
# parm = c("(Intercept)","AREA_SQKM","GDP_SUM","GDP_CAPITA","ROAD_DENSITY",
# "POP_COUNT","POP_DENSITY","SECURITY","RESEARCH_EXP",
# "EDUCATION_EXP",
# "richness","mean_species_range","endemism"))
#
#Check confidence intervals using bs: convergence errors are thrown when using profiling
#This is super slow, so I'm doing them individually
# confint(object = geo_m_spamm,
# parm = c("(Intercept)","AREA_SQKM","GDP_SUM","GDP_CAPITA","ROAD_DENSITY",
# "POP_COUNT","POP_DENSITY","SECURITY","RESEARCH_EXP",
# "EDUCATION_EXP",
# "richness","mean_species_range","endemism"),
# boot_args = list(nb_cores=2, nsim=199, seed=123))
# confint(object = geo_m_spamm,
# parm = c("(Intercept)"),
# boot_args = list(nb_cores=2, nsim=100, seed=123))
#(-8.548, -8.055 )
# confint(object = geo_m_spamm,
# parm = c("AREA_SQKM"),
# boot_args = list(nb_cores=2, nsim=100, seed=123))
#( 0.8464, 0.9900 )
# confint(object = geo_m_spamm,
# parm = c("GDP_SUM"),
# boot_args = list(nb_cores=2, nsim=100, seed=123))
# ( 0.3803, 0.5201 )
# confint(object = geo_m_spamm,
# parm = c("GDP_CAPITA"),
# boot_args = list(nb_cores=2, nsim=100, seed=123))
# ( 0.2308, 0.6863 )
# confint(object = geo_m_spamm,
# parm = c("ROAD_DENSITY"),
# boot_args = list(nb_cores=2, nsim=100, seed=123))
# (-0.3849, -0.0415 )
# confint(object = geo_m_spamm,
# parm = c("POP_COUNT"),
# boot_args = list(nb_cores=2, nsim=100, seed=123))
# ( 0.0161, 0.0723 )
# confint(object = geo_m_spamm,
# parm = c("POP_DENSITY"),
# boot_args = list(nb_cores=2, nsim=100, seed=123))
# (-0.6493, -0.1608 )
# confint(object = geo_m_spamm,
# parm = c("SECURITY"),
# boot_args = list(nb_cores=2, nsim=100, seed=123))
# ( 0.1544, 0.3886 )
# confint(object = geo_m_spamm,
# parm = c("RESEARCH_EXP"),
# boot_args = list(nb_cores=2, nsim=100, seed=123))
# ( 1.217, 1.623 )
# confint(object = geo_m_spamm,
# parm = c("EDUCATION_EXP"),
# boot_args = list(nb_cores=2, nsim=100, seed=123))
# ( 0.0675, 0.2363 )
# confint(object = geo_m_spamm,
# parm = c("richness"),
# boot_args = list(nb_cores=2, nsim=100, seed=123))
# ( 1.0157, 1.3320 )
# confint(object = geo_m_spamm,
# parm = c("mean_species_range"),
# boot_args = list(nb_cores=2, nsim=100, seed=123))
# (-0.3868, 0.3891 )
# confint(object = geo_m_spamm,
# parm = c("endemism"),
# boot_args = list(nb_cores=2, nsim=100, seed=123))
# (-0.2434, -0.0449 )
# ------------ Fixed effects (beta) ------------
# Estimate Cond. SE t-value
# (Intercept) -8.18861 0.1795 -45.6066
# AREA_SQKM 0.88467 0.1638 5.4002
# GDP_SUM 0.39369 0.1681 2.3420
# GDP_CAPITA 0.25658 0.2620 0.9794
# ROAD_DENSITY -0.26328 0.2883 -0.9132
# POP_COUNT 0.02963 0.1736 0.1706
# POP_DENSITY -0.25743 0.2659 -0.9682
# SECURITY 0.24383 0.2056 1.1862
# RESEARCH_EXP 1.30454 0.2289 5.6984
# EDUCATION_EXP 0.15835 0.1914 0.8273
# richness 0.97937 0.2087 4.6927
# mean_species_range -0.17278 0.2121 -0.8144
# endemism -0.07242 0.1919 -0.3773
##############################
#Check richness
general_traits_one_percent_threshold %>%
select(area,richness)%>%
unique() %>%
inner_join(x = tdwg,y=.,by = c('LEVEL_3_CO'="area")) -> tdwg_w_richness
ggplot()+
geom_sf(data = tdwg_w_richness,mapping = aes(fill = richness))
wcvp_w_unplaced %>%
group_by(area_code_l3) %>%
summarise(richness_w_unplaced = n()) %>%
inner_join(x = tdwg_w_richness,y=.,by = c('LEVEL_3_CO'="area_code_l3")) -> tdwg_w_richness
wcvp %>%
group_by(area_code_l3) %>%
summarise(richness_untf = n()) %>%
inner_join(x = tdwg_w_richness,y=.,by = c('LEVEL_3_CO'="area_code_l3")) -> tdwg_w_richness
##############################################
############################
#venn diagram
#need to know which species have any trait, dist, gene data
#Load data products from Rudbeck
gen.data <- data.table::fread("manual_downloads/Darwinian_shortfalls/genbank_entries_w_duplicates_2022.csv", header = T, quote = "", sep = NULL) # As produced in the genbank_download.R script
#gen.data <- fread("manual_downloads/Darwinian_shortfalls/genbank_entries_2022.csv", header = T, quote = "", sep = NULL) # As produced in the genbank_download.R script
load("manual_downloads/Darwinian_shortfalls/BIEN_in_WCSP_regions_sep2021.RData") # R workspace with objects "spec.list" and "res". Spec.list is a list of all L3 regions with the species recorded there in BIEN, translated to WCSP ID's. Res is a df with the lengths of the elements of spec.list.
name.id <- data.table::fread("data\\ID_and_Names_2022.csv", header = T) # As produced in the wcvp_subset_2021.R script
rm(fin)
# BIEN data from Rudbeck et al.
bien.list <-
lapply(X = names(spec.list),
FUN = function(x){
if(length(spec.list[[x]])==0){
return(data.frame(country = x,
species = NA))
}else{
return(
data.frame(country = x,
species = spec.list[[x]])
)
}
}) %>%
do.call(what = rbind) %>%
inner_join(y = name.id,
by = c("species"="plant_name_id"))
# Genetic data from Rudbeck
gen.data <-
gen.data %>%
mutate(ID = 1:nrow(gen.data))
# Pull names of species with trait data
#tnrsed_trait_sp_names <- readRDS("data/tnrsed_names.RDS")
trait_summary <- readRDS(file = "data/cleaning_raw_names/trait_summary_overall.RDS")
# Pull the wcvp (without ferns for consistency with Rudbeck et al)
wcvp_no_ferns <- readRDS("manual_downloads/WCVP/wcvp_cleaned_no_ferns.RDS") %>%
dplyr::select(family, taxon_name) %>% unique()
wcvp_no_ferns %>%
mutate(trait_data = case_when(taxon_name %in% trait_summary$Accepted_species ~ 1,
!taxon_name %in% trait_summary$Accepted_species ~ 0))%>%
mutate(distribution_data = case_when(taxon_name %in% bien.list$taxon_name ~ 1,
!taxon_name %in% bien.list$taxon_name ~ 0))%>%
mutate(genetic_data = case_when(taxon_name %in% gen.data$species ~ 1,
!taxon_name %in% gen.data$species ~ 0))-> data_availability
trait_data_vec <- data_availability$taxon_name[which(data_availability$trait_data==1)]
dist_data_vec <- data_availability$taxon_name[which(data_availability$distribution_data==1)]
genetic_data_vec <- data_availability$taxon_name[which(data_availability$genetic_data==1)]
#Euler
library(eulerr)
data_availability%>%
dplyr::select(c("trait_data","distribution_data","genetic_data"))%>%
mutate(species = 1)%>%
euler()->eu
eu %>%
plot(labels = c("Trait","Distribution","Phylogeny","Seed Plants"),
quantities =TRUE,
fills=list(fill = c( "#FF80F7","#74ee15","#00D1D0",NA), alpha = 0.5))
cor.test(x = data_availability$trait_data,y = data_availability$distribution_data)
cor.test(x = data_availability$trait_data,y = data_availability$genetic_data)
library(Cairo)
Cairo(file="plots/euler_plot.jpg",
type="jpg",
units="in",
width=10,
height=10,
dpi=300)
## Now render the plot
eu %>%
plot(labels = c("Trait","Distribution","Phylogeny","Seed Plants"),
quantities =TRUE,
fills=list(fill = c( "#FF80F7","#74ee15","#00D1D0",NA), alpha = 0.5))
## When the device is off, file writing is completed.
dev.off()
############################
#Trait and variable correlations
library(corrplot)
# Load overall trait completeness
general_traits_one_percent_threshold <- read_rds("data/cleaning_raw_names/focal_trait_coverage.rds")
# merge with predictor variables
socio_vars <- read.csv("manual_downloads/Darwinian_shortfalls/socioeco_var.csv")
#socio_vars[2:10] <- scale(socio_vars[2:10])
# combine trait completeness and predictor variables
general_traits_one_percent_threshold <-
merge(x = general_traits_one_percent_threshold,
y = socio_vars,
by.x= "area",
by.y = "LEVEL_3_CO")
# add species richness
wcvp <- readRDS("manual_downloads/WCVP/wcvp_cleaned.RDS")
wcvp %>%
group_by(area_code_l3) %>%
summarise(richness = n()) %>%
merge(x = general_traits_one_percent_threshold,
y = .,
by.x = "area",
by.y = "area_code_l3",
all.x = TRUE) ->
general_traits_one_percent_threshold
# Add mean range size
socio_vars %>%
dplyr::select(LEVEL_3_CO, AREA_SQKM) %>%
merge(x = wcvp,
y = .,
by.x = "area_code_l3",
by.y = "LEVEL_3_CO",
all.x = TRUE) %>%
group_by(taxon_name) %>%
summarise(range_size = sum(AREA_SQKM)) %>%
merge(x = wcvp,
y = .,by = "taxon_name",
all.x = TRUE) %>%
group_by(area_code_l3) %>%
summarise(mean_species_range = mean(na.omit(range_size))) %>%
merge(x = general_traits_one_percent_threshold,
y = .,
by.x="area",
by.y = "area_code_l3") -> general_traits_one_percent_threshold
# Add endemism
wcvp %>%
group_by(taxon_name) %>%
summarize(n_countries = n()) %>%
merge(x = wcvp,
y = .) %>%
group_by(area_code_l3) %>%
filter(n_countries == 1) %>%
summarise(endemics = n()) %>%
merge(x = general_traits_one_percent_threshold,
y = .,
by.x = "area",
by.y = "area_code_l3",
all.x = TRUE) %>%
mutate(endemics = replace_na(endemics,0)) %>%
mutate(endemism = endemics/richness) %>%
dplyr::select(-endemics) -> general_traits_one_percent_threshold
general_traits_wide <-
general_traits_one_percent_threshold %>%
pivot_wider(names_from = "trait", values_from = "completeness")
general_traits_wide <-
general_traits_wide %>%
mutate("Mean Completeness" = rowMeans(dplyr::select(general_traits_wide,colnames(general_traits_wide)[14:66])))
correlations <- cor(general_traits_wide[14:67],method = "pearson")
correlations_long_names <- correlations
cnc <- colnames(correlations)
cnc_short <- sapply(X = cnc,FUN = function(x){substr(x = x,start = 1,stop = 22)})
cnc_short <-
lapply(X = 1:length(cnc),FUN = function(x){
if(str_length(cnc_short)[x]!=str_length(cnc)[x]){
cnc_short[x] <- paste(cnc_short[x],"...",sep = "")
}else{
cnc_short[x]
}
})%>%
unlist()%>%
as.character()
colnames(correlations) <- cnc_short
rownames(correlations) <- cnc_short
correlation_pvals <- cor.mtest(general_traits_wide[14:67],
conf.level = 0.95,
method="pearson",alternative="two.sided",exact = TRUE)
#correlations of individual traits with overall mean
corrplot( correlations ,
addCoef.col="black",
order = "FPC",
tl.col = "black",
p.mat = correlation_pvals$p,
lowCI.mat = correlation_pvals$lowCI,
uppCI.mat = correlation_pvals$uppCI,plotCI = "circle")
#check that the names are correct
names_df <- data.frame(colnames(correlations),colnames(correlation_pvals$p))
library(Cairo)
Cairo(file="plots/focal_trait_correlations.jpg",
type="jpg",
units="in",
width=10,
height=10,
dpi=300)
## Now render the plot
corrplot( correlations ,
order = "FPC",
tl.col = "black",
#tl.cex = 0.75,
tl.cex = c(.9,rep(.75,53)),
p.mat = correlation_pvals$p,
pch.cex = .75)
## When the device is off, file writing is completed.
dev.off()
#Get full names
corrplot( correlations_long_names ,
order = "FPC",
tl.col = "black",
#tl.cex = 0.75,
tl.cex = c(.9,rep(.75,53)),
p.mat = correlation_pvals$p,
pch.cex = .75)->corrplot_long_names
colnames(corrplot_long_names$corr)%>%
writeLines(con = "plots/full_focal_correlation_names.txt",sep = ", ")
#correlations of predictor variables with overall mean
pred_corrs <- general_traits_wide[c(2:13,67)]
pred_corrs <- scale(pred_corrs)
pred_correlations <- cor(pred_corrs,method = "pearson")
pred_pvals <- cor.mtest(pred_corrs,
conf.level = 0.95,
method="pearson",alternative="two.sided",exact = TRUE)
#correlations of predictors (weak as expected)
corrplot( pred_correlations,
addCoef.col="black",
order = "FPC",
tl.col = "black",
p.mat = pred_pvals$p)
####################################
# Does trait coverage of all traits show the same pattern, or just the focal traits?
#Note that these coverage estimates are wrong since they compare all traits to the full set of species
#so, e.g. bark trait completeness doesn't accound for whether the species have bark
total_trait_coverage <- readRDS("data/cleaning_raw_names/total_trait_coverage.RDS")
total_coverage_wide <-
total_trait_coverage %>%
pivot_wider(names_from = trait,
values_from = completeness)
general_traits_one_percent_threshold <- read_rds("data/cleaning_raw_names/focal_trait_coverage.rds")
general_traits_one_percent_threshold %>%
group_by(area)%>%
summarise(mean_completeness_focal = mean(completeness))%>%
inner_join(total_coverage_wide)%>%
dplyr::select(-area)%>%
cor() -> correlations
cor_focal <- correlations[,"mean_completeness_focal",drop=FALSE]%>%
as.data.frame()%>%
rownames_to_column(var = "TraitName")
hist(cor_focal$mean_completeness_focal, breaks = 100)
cor_focal %>%
filter(mean_completeness_focal < 0) #370 traits with correlations less than zero
trait_summary <- readRDS(file = "data/cleaning_raw_names/trait_summary_overall.RDS")
species_per_trait <-
trait_summary %>%
group_by(TraitName)%>%