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Spatial_site_index_detection_probability.R
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### Author: Samuel Ayebare
## Spatial_site_index_detection_probability.r
## This script
# generates the null model distribution and figure for assessing detection probabilities
# among species within families - Spatial site index
# Appendix S4: Figure S2
#----------------#
#-Load libraries-#
#----------------#
library(dplyr)
library(tidyr)
library(jagsUI)
library(tidyverse)
library(ggplot2)
library(ggthemes)
library(grid)
library(gridExtra)
library(extrafont)
loadfonts()
#-----------------------#
#-Set working directory-#
#-----------------------#
setwd("./Data")
#---------------------#
#-Load Files and data-#
#---------------------#
load("../Data/HCDSM63spp.RData")
#----------------------------------------------#
#-Legend vegetation types - Detection function-#
#----------------------------------------------#
# 1 = Alpine/sub-alpine i.e Reference class
# 2 = Bamboo forest
# 3 = Secondary bush/shrub
# 4 = Grassland
# 5 = Hagenia-Hypericum woodland
# 6 = Mature mixed forest
# 7 = Swamp
# 8 = Secondary mixed forest
### alpha -species specific intercept for the scale parameter
alpha <- exp(HCDSM_Virunga_63spp$mean$alpha)* 100 ## Multiplied by 100 to account for scaling of the radial distances
## Create a vector for species id
Spp.id <- c("Spp1","Spp2","Spp3","Spp4","Spp5","Spp6","Spp7","Spp8","Spp9","Spp10","Spp11","Spp12","Spp13","Spp14","Spp15","Spp16","Spp17","Spp18","Spp19",
"Spp20","Spp21","Spp22","Spp23","Spp24","Spp25","Spp26","Spp27","Spp28","Spp29","Spp30","Spp31","Spp32","Spp33","Spp34","Spp35","Spp36",
"Spp37","Spp38","Spp39","Spp40","Spp41","Spp42","Spp43","Spp44","Spp45","Spp46","Spp47","Spp48","Spp49","Spp50","Spp51","Spp52","Spp53","Spp54",
"Spp55","Spp56", "Spp57","Spp58","Spp59","Spp60","Spp61","Spp62","Spp63")
#### Create a data.frame for species and scale parameter
sigma.alpha <- data.frame(Spp.id ,alpha)
# Transpose all but the first column (name)
Sigma.alpine <- as.data.frame(t(sigma.alpha[,-1]))
colnames(Sigma.alpine) <- Spp.id
# Check the column types
str(Sigma.alpine)
## Compute mean and standard deviation between species pairs within families
## To enable estimation of the coefficient of variation
# Lybiidae - mean
Sp3n4 <- mean(c(Sigma.alpine$Spp3, Sigma.alpine$Spp4))
Sp3n5 <- mean(c(Sigma.alpine$Spp3, Sigma.alpine$Spp5))
Sp4n5 <- mean(c(Sigma.alpine$Spp4, Sigma.alpine$Spp5))
# Lybiidae - standard deviation
Sp3n4.sd <- sd(c(Sigma.alpine$Spp3, Sigma.alpine$Spp4))
Sp3n5.sd <- sd(c(Sigma.alpine$Spp3, Sigma.alpine$Spp5))
Sp4n5.sd <- sd(c(Sigma.alpine$Spp4, Sigma.alpine$Spp5))
##Columbidae - mean
Sp7n8 <- mean(c(Sigma.alpine$Spp7, Sigma.alpine$Spp8))
Sp7n9 <- mean(c(Sigma.alpine$Spp7, Sigma.alpine$Spp9))
Sp8n9 <- mean(c(Sigma.alpine$Spp8, Sigma.alpine$Spp9))
##Columbidae - standard deviation
Sp7n8.sd <- sd(c(Sigma.alpine$Spp7, Sigma.alpine$Spp8))
Sp7n9.sd <- sd(c(Sigma.alpine$Spp7, Sigma.alpine$Spp9))
Sp8n9.sd <- sd(c(Sigma.alpine$Spp8, Sigma.alpine$Spp9))
# Cuculidae - mean
Sp10n11 <- mean(c(Sigma.alpine$Spp10, Sigma.alpine$Spp11))
Sp10n12 <- mean(c(Sigma.alpine$Spp10, Sigma.alpine$Spp12))
Sp11n12 <- mean(c(Sigma.alpine$Spp11, Sigma.alpine$Spp12))
# Cuculidae - standard deviation
Sp10n11.sd <- sd(c(Sigma.alpine$Spp10, Sigma.alpine$Spp11))
Sp10n12.sd <- sd(c(Sigma.alpine$Spp10, Sigma.alpine$Spp12))
Sp11n12.sd <- sd(c(Sigma.alpine$Spp11, Sigma.alpine$Spp12))
# Estrildidae - mean
Sp13n14 <- mean(c(Sigma.alpine$Spp13, Sigma.alpine$Spp14))
# Estrildidae - standard deviation
Sp13n14.sd <- sd(c(Sigma.alpine$Spp13, Sigma.alpine$Spp14))
##Fringillidae - mean
Sp15n16 <- mean(c(Sigma.alpine$Spp15, Sigma.alpine$Spp16))
##Fringillidae - standard deviation
Sp15n16.sd <- sd(c(Sigma.alpine$Spp15, Sigma.alpine$Spp16))
## Malaconotidae - mean
Sp18n19 <- mean(c(Sigma.alpine$Spp18, Sigma.alpine$Spp19))
Sp18n20 <- mean(c(Sigma.alpine$Spp18, Sigma.alpine$Spp20))
Sp18n21 <- mean(c(Sigma.alpine$Spp18, Sigma.alpine$Spp21))
Sp18n22 <- mean(c(Sigma.alpine$Spp18, Sigma.alpine$Spp22))
Sp19n20 <- mean(c(Sigma.alpine$Spp19, Sigma.alpine$Spp20))
Sp19n21 <- mean(c(Sigma.alpine$Spp19, Sigma.alpine$Spp21))
Sp19n22 <- mean(c(Sigma.alpine$Spp19, Sigma.alpine$Spp22))
Sp20n21 <- mean(c(Sigma.alpine$Spp20, Sigma.alpine$Spp21))
Sp20n22 <- mean(c(Sigma.alpine$Spp20, Sigma.alpine$Spp22))
Sp21n22 <- mean(c(Sigma.alpine$Spp21, Sigma.alpine$Spp22))
## Malaconotidae - standard deviation
Sp18n19.sd <- sd(c(Sigma.alpine$Spp18, Sigma.alpine$Spp19))
Sp18n20.sd <- sd(c(Sigma.alpine$Spp18, Sigma.alpine$Spp20))
Sp18n21.sd <- sd(c(Sigma.alpine$Spp18, Sigma.alpine$Spp21))
Sp18n22.sd <- sd(c(Sigma.alpine$Spp18, Sigma.alpine$Spp22))
Sp19n20.sd <- sd(c(Sigma.alpine$Spp19, Sigma.alpine$Spp20))
Sp19n21.sd <- sd(c(Sigma.alpine$Spp19, Sigma.alpine$Spp21))
Sp19n22.sd <- sd(c(Sigma.alpine$Spp19, Sigma.alpine$Spp22))
Sp20n21.sd <- sd(c(Sigma.alpine$Spp20, Sigma.alpine$Spp21))
Sp20n22.sd <- sd(c(Sigma.alpine$Spp20, Sigma.alpine$Spp22))
Sp21n22.sd <- sd(c(Sigma.alpine$Spp21, Sigma.alpine$Spp22))
#Platysteiridae - mean
Sp24n25 <- mean (c(Sigma.alpine$Spp24, Sigma.alpine$Spp25))
#Platysteiridae - standard deviation
Sp24n25.sd <- sd(c(Sigma.alpine$Spp24, Sigma.alpine$Spp25))
#Muscicapidae - mean
Sp26n27 <- mean(c(Sigma.alpine$Spp26, Sigma.alpine$Spp27))
Sp26n60 <- mean(c(Sigma.alpine$Spp26, Sigma.alpine$Spp60))
Sp26n61 <- mean(c(Sigma.alpine$Spp26, Sigma.alpine$Spp61))
Sp27n60 <- mean(c(Sigma.alpine$Spp27, Sigma.alpine$Spp60))
Sp27n61 <- mean(c(Sigma.alpine$Spp27, Sigma.alpine$Spp61))
Sp60n61 <- mean(c(Sigma.alpine$Spp60, Sigma.alpine$Spp61))
#Muscicapidae - standard deviation
Sp26n27.sd <- sd(c(Sigma.alpine$Spp26, Sigma.alpine$Spp27))
Sp26n60.sd <- sd(c(Sigma.alpine$Spp26, Sigma.alpine$Spp60))
Sp26n61.sd <- sd(c(Sigma.alpine$Spp26, Sigma.alpine$Spp61))
Sp27n60.sd <- sd(c(Sigma.alpine$Spp27, Sigma.alpine$Spp60))
Sp27n61.sd <- sd(c(Sigma.alpine$Spp27, Sigma.alpine$Spp61))
Sp60n61.sd <- sd(c(Sigma.alpine$Spp60, Sigma.alpine$Spp61))
##Musophagidae - mean
Sp29n30 <- mean(c(Sigma.alpine$Spp29, Sigma.alpine$Spp30))
##Musophagidae -- standard deviation
Sp29n30.sd <- sd(c(Sigma.alpine$Spp29, Sigma.alpine$Spp30))
#Nectariniidae - mean
Sp31n32<- mean(c(Sigma.alpine$Spp31, Sigma.alpine$Spp32))
Sp31n33<- mean(c(Sigma.alpine$Spp31, Sigma.alpine$Spp33))
Sp31n34<- mean(c(Sigma.alpine$Spp31, Sigma.alpine$Spp34))
Sp31n35<- mean(c(Sigma.alpine$Spp31, Sigma.alpine$Spp35))
Sp31n36<- mean(c(Sigma.alpine$Spp31, Sigma.alpine$Spp36))
Sp32n33<- mean(c(Sigma.alpine$Spp32, Sigma.alpine$Spp33))
Sp32n34 <- mean(c(Sigma.alpine$Spp32, Sigma.alpine$Spp34))
Sp32n35 <- mean(c(Sigma.alpine$Spp32, Sigma.alpine$Spp35))
Sp32n36 <- mean(c(Sigma.alpine$Spp32, Sigma.alpine$Spp36))
Sp33n34<- mean(c(Sigma.alpine$Spp33, Sigma.alpine$Spp34))
Sp33n35 <- mean(c(Sigma.alpine$Spp33, Sigma.alpine$Spp35))
Sp33n36 <- mean(c(Sigma.alpine$Spp33, Sigma.alpine$Spp36))
Sp34n35 <- mean(c(Sigma.alpine$Spp34, Sigma.alpine$Spp35))
Sp34n36 <- mean(c(Sigma.alpine$Spp34, Sigma.alpine$Spp36))
Sp35n36 <- mean(c(Sigma.alpine$Spp35, Sigma.alpine$Spp36))
#Nectariniidae - standard deviation
Sp31n32.sd <- sd(c(Sigma.alpine$Spp31, Sigma.alpine$Spp32))
Sp31n33.sd <- sd(c(Sigma.alpine$Spp31, Sigma.alpine$Spp33))
Sp31n34.sd <- sd(c(Sigma.alpine$Spp31, Sigma.alpine$Spp34))
Sp31n35.sd <- sd(c(Sigma.alpine$Spp31, Sigma.alpine$Spp35))
Sp31n36.sd <- sd(c(Sigma.alpine$Spp31, Sigma.alpine$Spp36))
Sp32n33.sd <- sd(c(Sigma.alpine$Spp32, Sigma.alpine$Spp33))
Sp32n34.sd <- sd(c(Sigma.alpine$Spp32, Sigma.alpine$Spp34))
Sp32n35.sd <- sd(c(Sigma.alpine$Spp32, Sigma.alpine$Spp35))
Sp32n36.sd <- sd(c(Sigma.alpine$Spp32, Sigma.alpine$Spp36))
Sp33n34.sd <- sd(c(Sigma.alpine$Spp33, Sigma.alpine$Spp34))
Sp33n35.sd <- sd(c(Sigma.alpine$Spp33, Sigma.alpine$Spp35))
Sp33n36.sd <- sd(c(Sigma.alpine$Spp33, Sigma.alpine$Spp36))
Sp34n35.sd <- sd(c(Sigma.alpine$Spp34, Sigma.alpine$Spp35))
Sp34n36.sd <- sd(c(Sigma.alpine$Spp34, Sigma.alpine$Spp36))
Sp35n36.sd <- sd(c(Sigma.alpine$Spp35, Sigma.alpine$Spp36))
# Ploceidae - mean
Sp39n40 <- mean(c(Sigma.alpine$Spp39, Sigma.alpine$Spp40))
# Ploceidae - standard deviation
Sp39n40.sd <- sd(c(Sigma.alpine$Spp39, Sigma.alpine$Spp40))
#Pycnonotidae - mean
Sp42n43 <- mean(c(Sigma.alpine$Spp42, Sigma.alpine$Spp43))
Sp42n44 <- mean(c(Sigma.alpine$Spp42, Sigma.alpine$Spp44))
Sp43n44 <- mean(c(Sigma.alpine$Spp43, Sigma.alpine$Spp44))
#Pycnonotidae - standard deviation
Sp42n43.sd <- sd(c(Sigma.alpine$Spp42, Sigma.alpine$Spp43))
Sp42n44.sd <- sd(c(Sigma.alpine$Spp42, Sigma.alpine$Spp44))
Sp43n44.sd <- sd(c(Sigma.alpine$Spp43, Sigma.alpine$Spp44))
##Cisticolidae - mean
Sp45n46 <- mean(c(Sigma.alpine$Spp45, Sigma.alpine$Spp46))
Sp45n50 <- mean(c(Sigma.alpine$Spp45, Sigma.alpine$Spp50))
Sp45n51 <- mean(c(Sigma.alpine$Spp45, Sigma.alpine$Spp51))
Sp45n54 <- mean(c(Sigma.alpine$Spp45, Sigma.alpine$Spp54))
Sp46n50 <- mean(c(Sigma.alpine$Spp46, Sigma.alpine$Spp50))
Sp46n51 <- mean(c(Sigma.alpine$Spp46, Sigma.alpine$Spp51))
Sp46n54 <- mean(c(Sigma.alpine$Spp46, Sigma.alpine$Spp54))
Sp50n51 <- mean(c(Sigma.alpine$Spp50, Sigma.alpine$Spp51))
Sp50n54 <- mean(c(Sigma.alpine$Spp50, Sigma.alpine$Spp54))
Sp51n54 <- mean(c(Sigma.alpine$Spp51, Sigma.alpine$Spp54))
##Cisticolidae - standard deviation
Sp45n46.sd <- sd(c(Sigma.alpine$Spp45, Sigma.alpine$Spp46))
Sp45n50.sd <- sd(c(Sigma.alpine$Spp45, Sigma.alpine$Spp50))
Sp45n51.sd <- sd(c(Sigma.alpine$Spp45, Sigma.alpine$Spp51))
Sp45n54.sd <- sd(c(Sigma.alpine$Spp45, Sigma.alpine$Spp54))
Sp46n50.sd <- sd(c(Sigma.alpine$Spp46, Sigma.alpine$Spp50))
Sp46n51.sd <- sd(c(Sigma.alpine$Spp46, Sigma.alpine$Spp51))
Sp46n54.sd <- sd(c(Sigma.alpine$Spp46, Sigma.alpine$Spp54))
Sp50n51.sd <- sd(c(Sigma.alpine$Spp50, Sigma.alpine$Spp51))
Sp50n54.sd <- sd(c(Sigma.alpine$Spp50, Sigma.alpine$Spp54))
Sp51n54.sd <- sd(c(Sigma.alpine$Spp51, Sigma.alpine$Spp54))
#Acrocephalidae - mean
Sp48n49 <- mean(c(Sigma.alpine$Spp48, Sigma.alpine$Spp49))
#Acrocephalidae - standard deviation
Sp48n49.sd <- sd(c(Sigma.alpine$Spp48, Sigma.alpine$Spp49))
#Phylloscopidae - mean
Sp52n53 <- mean(c(Sigma.alpine$Spp52, Sigma.alpine$Spp53))
#Phylloscopidae - standard deviation
Sp52n53.sd <- sd(c(Sigma.alpine$Spp52, Sigma.alpine$Spp53))
### Create a vector of means - species pairs within the same family
All.spp.sgm.mean <- c(Sp3n4,Sp3n5,Sp4n5,Sp7n8,Sp7n9,Sp8n9,Sp10n11,Sp10n12,Sp11n12,Sp13n14,Sp15n16,Sp18n19,Sp18n20,Sp18n21,
Sp18n22,Sp19n20,Sp19n21,Sp19n22,Sp20n21,Sp20n22,Sp21n22,Sp24n25,Sp26n27,Sp26n60,Sp26n61,Sp27n60,Sp27n61,
Sp60n61,Sp29n30,Sp31n32,Sp31n33,Sp31n34,Sp31n35,Sp31n36,Sp32n33,Sp32n34,Sp32n35,Sp32n36,Sp33n34,Sp33n35,
Sp33n36,Sp34n35,Sp34n36,Sp35n36,Sp39n40,Sp42n43,Sp42n44,Sp43n44,Sp45n46,Sp45n50,Sp45n51,Sp45n54,Sp46n50,
Sp46n51,Sp46n54,Sp50n51,Sp50n54,Sp51n54,Sp48n49,Sp52n53)
### Create a vector of standard deviations - species pairs within the same family
All.spp.sgm.sd <- c(Sp3n4.sd,Sp3n5.sd,Sp4n5.sd,Sp7n8.sd,Sp7n9.sd,Sp8n9.sd,Sp10n11.sd,Sp10n12.sd,Sp11n12.sd,Sp13n14.sd,Sp15n16.sd,Sp18n19.sd,
Sp18n20.sd,Sp18n21.sd,Sp18n22.sd,Sp19n20.sd,Sp19n21.sd,Sp19n22.sd,Sp20n21.sd,Sp20n22.sd,Sp21n22.sd,Sp24n25.sd,Sp26n27.sd,
Sp26n60.sd,Sp26n61.sd,Sp27n60.sd,Sp27n61.sd,Sp60n61.sd,Sp29n30.sd,Sp31n32.sd,Sp31n33.sd,Sp31n34.sd,Sp31n35.sd,Sp31n36.sd,
Sp32n33.sd,Sp32n34.sd,Sp32n35.sd,Sp32n36.sd,Sp33n34.sd,Sp33n35.sd,Sp33n36.sd,Sp34n35.sd,Sp34n36.sd,Sp35n36.sd,Sp39n40.sd,
Sp42n43.sd,Sp42n44.sd,Sp43n44.sd,Sp45n46.sd,Sp45n50.sd,Sp45n51.sd,Sp45n54.sd,Sp46n50.sd,Sp46n51.sd,Sp46n54.sd,Sp50n51.sd,Sp50n54.sd,
Sp51n54.sd,Sp48n49.sd,Sp52n53.sd)
# Compute mean coefficient of variation for all 60 species pairs
mean(All.spp.sgm.sd /All.spp.sgm.mean)
## Number of samples (each sample = 60 species pairs) selected to generate a null model expectation
n.samples <- 1000
## Vector for storing standard deviations for 46 species (i.e) 60 species pairs
sd.sigma <- rep(NA, 60)
## Vector for storing means for 46 species (i.e) 60 species pairs
mean.sigma <- rep(NA, 60)
## Vector for storing coefficient of variation for each species pair
cv.sigma <- rep(NA, 60)
## Vector for storing mean coefficient of variation for all species pairs in the community
mean.cv.sigma <- rep(NA, n.samples)
## Computing null distribution for scale parameter (sigma)
for (i in 1:n.samples) {
for (s in 1:60) {
# 1. Choose a random species pair from the total 63 species
scale.parameter.2sp <- as.numeric(sample(Sigma.alpine, 2,F))
# 2. Compute the standard deviation
sd.sigma [s] <- sd(scale.parameter.2sp)
# 3. Compute the mean
mean.sigma [s] <- mean(scale.parameter.2sp)
# 4. Compute the coefficient of variation
cv.sigma[s] <- sd.sigma [s]/mean.sigma [s]
} # j
mean.cv.sigma [i] <- mean(cv.sigma) # 5. Calculate the mean coefficient of variation for the 60 species pairs and store in a vector.
} # i
#write.csv(mean.cv.sigma, "mean.cv.scale.parameter.csv")
setwd("./Data_spatial_site_index")
# Importing mean coefficient of variation- sigma
cv.parameter <- read.csv("spatial.site.index.mean.cv.scale.parameter.csv", header = T)
head(cv.parameter)
## Assign col names
colnames( cv.parameter) <- c("sample.id", "mean.var")
### Histogram for null distribution
Appendix.S4.Figure.S2 <- ggplot(cv.parameter, aes(mean.var)) +
geom_histogram( bins = 15) +
geom_vline(aes(xintercept= 0.242 ), colour = "black", linetype = "dashed", linewidth = 2 ) ## All species
Appendix.S4.Figure.S2 + labs (x=" Mean (Coeffiecient of variation)", y ="Frequency", color = "Legend\n",fontface= "plain") +
scale_x_continuous(breaks=seq(0,1,0.1)) +
theme_few() +
scale_y_continuous(
labels = scales::number_format(accuracy = ))+
theme(plot.margin = unit(c(1, 1, 1, 1), "cm"),
text = element_text(family = "Times New Roman", size = 30),
panel.background = element_rect(fill = "transparent", color = NA),
panel.border = element_rect(fill=NA, colour = "black", size=1),
plot.background = element_rect(fill = "transparent", color = NA),
axis.text.x = element_text( hjust = 0.5, vjust = 0.5,size=25, color="black"),
axis.text.y = element_text( hjust = 0.5, vjust = 0.5,size=25, color = "black"),
axis.title.y = element_text(size = 30, angle = 90),
axis.title.x = element_text(size = 30, angle = 00),
legend.text=element_text(size=30,face="italic"),
legend.position = c(c(0.4,0.8)), legend.title=element_text(size=30, color="black"))
ggsave(file = "det.parameter.cv.jpg", bg = NULL, dpi = 300, width = 15, height = 10)
ggsave(file = "det.parameter.cv.svg", bg = NULL, dpi = 300, width = 15, height = 10)