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Phyloseq.R
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#if you need to reinstall phyloseq
# if(!requireNamespace("BiocManager")){
# install.packages("BiocManager")
# }
#BiocManager::install("phyloseq")
#devtools::install_github('schuyler-smith/phylosmith')
#remotes::install_github("taowenmicro/ggClusterNet")
#devtools::install_github("jfq3/QsRutils", build_vignettes = TRUE)
#BiocManager::install("microbiome")
# Load Packages, Themes, etc... ####
pacman::p_load(phyloseq,
BiodiversityR,
tidyverse,
plyr,
geosphere,
phylosmith,
ggClusterNet,
microbiome,
multtest,
data.table,
indicspecies,
bipartite,
simplecolors,
ggnetwork,
igraph,
metagMisc,
QsRutils,
plotly,
FSA,
microbiome,
DirichletMultinomial,
magrittr,
reshape2,
ggalluvial,
wesanderson,
tmap,
alluvial)
source("~/Desktop/ggplot_themes/ggplot_themes.R")
source("~/Desktop/R_color_palettes/Gadek_custom_colors.R")
theme_set(theme_arial_clean())
#Set constants ####
merged = F #to run merged analysis where all species are merged or separate
mergeby <- "sci_name"
#Read in physeq object processed by Phyloseq_wrangler.R file####
load("data/physeq_all.Rdata")
load("data/physeq_rarefied.Rdata")
load("data/physeq_merged_spp.Rdata")
load("data/physeq_rarefied_merged_spp.Rdata")
#read in processed sample data and taxonomy file
all_meta <- read_csv("data/Bird_predictors.csv")
taxonomy <- read.table("data/zotus_tax_v3.txt", header = TRUE, sep = '\t', row.names = 1)
#remove white space
taxonomy <-taxonomy %>%
mutate(across(where(is.character), str_trim))
# Estimate sample coverage ####
phyloseq_coverage(physeq, correct_singletons = FALSE, add_attr = T)%>%
dplyr::summarise(mean = mean(SampleCoverage, na.rm=T),
sd = sd(SampleCoverage, na.rm=T),
min = min(SampleCoverage, na.rm=T),
max = max(SampleCoverage, na.rm=T))
# calculate Good's coverage (only uses singletons)
#summary(goods(otu_table(physeq)))
# calculate extrapolated species richness and plot against observed
estimate_richness(physeq, measures=c('Observed', 'Chao1'))%>%
ggplot( aes(x=Observed, y=Chao1)) +
geom_point(color="gray55")+
geom_abline(slope=1, intercept = 0)+
theme(aspect.ratio = 1)
# check whether extrapolated species richness appears to be sensitive to sequencing depth
cbind(estimate_richness(physeq, measures=c('Observed', 'Chao1')), readcounts=sample_sums(physeq))%>%
ggplot( aes(x=readcounts, y=Chao1)) +
geom_point(color="gray55")+
geom_smooth(method="lm")
#linear model to get significance stats, probably violating assumptions here
summary(lm(Chao1 ~ readcounts, data = cbind(estimate_richness(physeq, measures=c('Observed', 'Chao1')), readcounts=sample_sums(physeq))))
#extrapolated species richness appears to be sensitive to readcounts, but that's okay because using rarefied dataset to conduct community analyses
#Get summary statistics on samples sizes ####
all_meta%>%
group_by(Order1)%>%
dplyr::summarise(n=n())
all_meta%>%
group_by(Order1, sci_name)%>%
dplyr::summarise(n=n())%>%
#View()%>%
write.csv(., "data/taxon_table.csv")
all_meta%>%
group_by(sci_name)%>%
#View()%>%
dplyr::summarise(n=n())
# Confirm physeq object is correct NOTE this is before rarefaction
ntaxa(physeq) #526 zOTUs in samples
phyloseq::nsamples(physeq) #183 seq but 167 once <2000 reads were removed and 160 if we remove suspicious cranes were removed
sample_names(physeq)
taxa_names(physeq)
rank_names(physeq)
sample_variables(physeq)
##Animal symbiont summary stats####
anm.sym<-read_csv("data/animal_symb_FUNGuild_v2.csv")
anm.sym%>%
dplyr::select(-c(1))%>%
filter(zotu %in% rownames(tax_table(physeq)))%>%
group_by(animal.trophic)%>%
dplyr::summarise(n =n()/526)
anm.s<-psmelt(physeq)%>%
as.data.frame()%>%
dplyr::select(OTU, sci_name, Sample, family, Abundance)%>%
#dplyr::rename(sci_name = Sample)%>%
left_join(anm.sym%>%dplyr::rename(OTU=zotu))%>%
filter(Abundance >0)%>%
group_by(Sample)%>%
dplyr::mutate(n_total = n())%>%
group_by(Sample, animal.trophic)%>%
mutate(n_trophic = n(),
perc_trophic = n_trophic/n_total)
t.test(anm.s%>%filter(animal.trophic=="y")%>%pull(Abundance), anm.s%>%filter(animal.trophic=="n")%>%pull(Abundance))
###Cranes
psmelt(physeq)%>%
as.data.frame()%>%
filter(bird == "crane",
Abundance >0)%>%
dplyr::select(OTU, sci_name, bird)%>%
left_join(anm.sym%>%dplyr::rename(OTU=zotu))%>%
distinct()%>%
group_by(sci_name)%>%
mutate(n_total=n())%>%
group_by(sci_name, animal.trophic)%>%
mutate(n_trophic =n(),
perc_trophic =n_trophic/n_total)%>%
dplyr::select(sci_name, perc_trophic)%>%
distinct()
#Core microbiome####
# Calculate compositional version of the data
# (relative abundances)
pseq.rel <- microbiome::transform(physeq, "compositional")
#calculate core microbiome fungi that wer present in >50% of samples and greater than 0.01 relative abundance
pseq.core <- microbiome::core(pseq.rel, detection = 0.5, prevalence = 0.01) #make phyloseq object
core.taxa <- taxa_names(pseq.core)
core.taxa
# Composition plot
d<-psmelt(physeq)%>%
#filter(OTU %in% core.taxa)%>%
mutate(all_reads=sum(Abundance))%>%
group_by(family, all_reads)%>%
dplyr::summarise(reads.family = sum(Abundance),
perc_family = reads.family/all_reads)%>%
filter(family !="")%>%
distinct()%>%
arrange(perc_family)
ggplot(d, aes(x=all_reads, y=perc_family, fill=family))+
geom_bar(color="black", stat="identity", position="stack")+
labs(x=NULL, y="Percentage of core reads")+
theme(aspect.ratio = 1.5,
axis.text.x = element_blank(),
axis.ticks.x = element_blank())
# Assess host-fungi specifics through summary stats####
#Remove taxa not seen more than 3 times in at least 20% of the samples. This protects against an OTU with small mean & trivially large C.V.
f.summs = filter_taxa(physeq, function(x) sum(x > 3) > (0.2*length(x)), TRUE)
f.summs.mat<-
physeq@otu_table%>%
as.data.frame()%>%
mutate_if(is.character, str_trim)%>%
rownames_to_column(var="guid")%>%
pivot_longer(cols = contains("otu"), names_to = c("OTU"), values_to = "Abundance") %>% #make it long form
filter(Abundance > 0) %>%
left_join(taxonomy%>%rownames_to_column(var="OTU"), by = "OTU") %>%
mutate(order = replace_na(order, "Unknown"),
order = if_else(order=="", "Unknown", order),
order = if_else(order==" ", "Unknown", order)) %>%
group_by(guid, order) %>%
summarise(Abundance = sum(Abundance)) %>%
ungroup() %>%
pivot_wider(names_from = c(order), values_from = Abundance, values_fill = list(Abundance = 0))%>%
left_join(all_meta%>%dplyr::select(guid, sci_name, Family1), by="guid")
physeq@otu_table%>%
as.data.frame()%>%
rownames_to_column(var="guid")%>%
pivot_longer(2:length(.))%>%
left_join(taxonomy%>%rownames_to_column(var="name"))%>%
mutate_if(is.character, str_trim)%>%
left_join(all_meta%>%dplyr::select(Family1, sci_name, guid))%>%
group_by(class)%>%
mutate(sumorder = sum(value))%>%
ungroup()%>%
filter(value >0)%>%
group_by(Family1,sci_name, class)%>%
dplyr::summarise(n=n(),
sum =sum(value))%>%
filter(Family1 =="Gruidae",
!class %in% c("Atractiellomycetes", "Exobasidiomycetes", "Ustilaginomycotina_cls_Incertae_sedis", ""))%>%
mutate(class = factor(class, levels = c("Dothideomycetes",
"Eurotiomycetes",
"Sordariomycetes",
"Saccharomycetes",
"Pezizomycetes",
"Leotiomycetes",
"Orbiliomycetes",
"Arthoniomycetes",
"Lecanoromycetes",
"Tremellomycetes",
"Malasseziomycetes",
"Agaricomycetes",
"Microbotryomycetes",
"Agaricostilbomycetes",
"Wallemiomycetes",
"Ustilaginomycetes",
"Tritirachiomycetes",
"Mucoromycetes",
"Mortierellomycetes",
"Chytridiomycetes")))%>%
ggplot(aes(x= class, y = log10(sum), fill = sci_name))+
geom_col(position = position_dodge())+
scale_fill_manual(values=c("grey33", "grey65"))+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
legend.position = c(0.8, 0.9))+
scale_fill_manual(values =c("#803B31", "#B2AAA2"))+
labs(y = "zOTUs", x = NULL)
f.summs.mat%>%mutate(sum = rowSums(across(where(is.numeric)), na.rm=TRUE))%>%
dplyr::select(guid, Family1, sum)%>%
group_by(Family1)%>%
mutate(n = n())%>%
ungroup()%>%
group_by(Family1, n)%>%
dplyr::summarise(sum = sum(sum))%>%
arrange(.,sum)%>%
#filter(Family1 !="Gruidae")%>%
ggplot2::ggplot(.,aes(y= sum, x = reorder(Family1, -sum), label = n))+
geom_col()+
geom_text(vjust = -0.5)+
scale_y_continuous(labels = function(x) format(x, scientific = TRUE), limits = c(0, 400000))+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
labs(x = NULL, y = "Reads")
#Rarified zOTUs ####
ntaxa(physeq.r) #508
#Find taxa unique to cranes and other birds####
orders<-taxonomy%>%
filter(order != "")%>%
select(order)%>%
distinct()%>%
pull()
N <- length(orders)
##Unique to cranes####
unique.to.cranes <- data.frame(order = character(N),
unique = character(N),
number_zotu = numeric(N),
number_reads = numeric(N))
for(i in orders){
s<-physeq.r@otu_table%>%
as.data.frame()%>%
rownames_to_column("guid")%>%
mutate(guid = gsub("^([0-9]+)", "S\\1", guid))%>%
pivot_longer(cols=2:509, names_to = "zOTU", values_to = "reads")%>%
left_join(., all_meta%>%dplyr::select(guid, sci_name, bird))%>%
left_join(., taxonomy%>%rownames_to_column("zOTU"))%>%
filter(order !="",
reads >0,
order == i)
crane_not <-unique(s$bird)
if(length(crane_not)==1 && crane_not =="crane"){
unique <- "unique"
}else{
unique <- "not unique"
}
number_zotu <- s%>%
select(zOTU)%>%
distinct()%>%
pull()%>%
length()
number_zotu <- s%>%
select(zOTU)%>%
distinct()%>%
pull()%>%
length()
number_reads <-s%>%
ungroup()%>%
select(reads)%>%
dplyr::summarise(sum = sum(reads))%>%
pull()
unique.to.cranes[i, "order"] <- i
unique.to.cranes[i, "unique"] <- unique
unique.to.cranes[i, "number_zotu"] <- number_zotu
unique.to.cranes[i, "number_reads"] <- number_reads
unique.to.cranes <- unique.to.cranes%>%
filter(number_reads>0)
}
##Unique to other birds####
unique.to.others <- data.frame(order = character(N),
unique = character(N),
number_zotu = numeric(N),
number_reads = numeric(N))
for(i in orders){
s<-physeq.r@otu_table%>%
as.data.frame()%>%
rownames_to_column("guid")%>%
mutate(guid = gsub("^([0-9]+)", "S\\1", guid))%>%
pivot_longer(cols=2:509, names_to = "zOTU", values_to = "reads")%>%
left_join(., all_meta%>%dplyr::select(guid, sci_name, bird))%>%
left_join(., taxonomy%>%rownames_to_column("zOTU"))%>%
filter(order !="",
reads >0,
order == i)
crane_not <-unique(s$bird)
if(length(crane_not)==1 && crane_not =="other"){
unique <- "unique"
}else{
unique <- "not unique"
}
number_zotu <- s%>%
select(zOTU)%>%
distinct()%>%
pull()%>%
length()
number_zotu <- s%>%
select(zOTU)%>%
distinct()%>%
pull()%>%
length()
number_reads <-s%>%
ungroup()%>%
select(reads)%>%
dplyr::summarise(sum = sum(reads))%>%
pull()
unique.to.others[i, "order"] <- i
unique.to.others[i, "unique"] <- unique
unique.to.others[i, "number_zotu"] <- number_zotu
unique.to.others[i, "number_reads"] <- number_reads
unique.to.others <- unique.to.others%>%
filter(number_reads>0)
}
#Distance Decay####
library(patchwork)
library(vegan)
#vegan object
veg <- read.table("data/all_lungs_v2.txt", header = TRUE, sep = '\t') %>%
mutate(guid = paste0("S", guid))%>%
left_join(as.data.frame(physeq.r@otu_table)%>%rownames_to_column("guid"), by = "guid") %>%
drop_na()
veg_meta <-veg[,1:13] #metadata columns
veg_otus <-veg[,14:521] #otus
veg_rel<-decostand(veg_otus, "total")
#non-cranes
veg.nc <- read.table("data/all_lungs_v2.txt", header = TRUE, sep = '\t') %>%
mutate(guid = paste0("S", guid))%>%
filter(bird == "other")%>%
left_join(as.data.frame(physeq.r@otu_table)%>%rownames_to_column("guid"), by = "guid") %>%
drop_na()
veg_meta.nc <-veg.nc[,1:13] #metadata columns
veg_otus.nc <-veg.nc[,14:521] #otus
veg_rel.nc<-decostand(veg_otus.nc, "total")
# Spatial Diversity
## Mantel test
### All birds
latlon <-data.frame(veg_meta$long, veg_meta$lat) #extract geographic coordinates
spat<-distm(latlon) # create geographic distance matrix
dismat<-vegan::vegdist(vegan::decostand(veg_otus, "total"), "bray")
mc<-mantel.correlog(dismat, as.dist(spat, upper=FALSE, diag=FALSE),cutoff = FALSE) #mantel correlogram
plot(mc)
mantel(dismat, as.dist(spat, upper=FALSE, diag=FALSE))
#r=0.03357 p=0.156 not significant
mc #describe stats
###samples collected within 58 km tend to be more similar to each other
## Distance decay
plot(as.dist(spat,upper=FALSE, diag=FALSE), (1-dismat), xlab = "Distance (m)", ylab = "Bray-Curtis Similarity")
mod<-lm(as.vector((1-dismat))~as.vector(as.dist(spat,upper=FALSE, diag=FALSE)))
mod #y-int =6.264e-02 m = -2.981e-08 (very little slope)
abline(mod, col="#b21f80", lwd=3)
###very low decline in similarity of two samples as the distance between them increases
patchwork::mc + mod + plot_layout(nrow = 1)
# Spatial Diversity
## Mantel test
### Non-crane birds
latlon <-data.frame(veg_meta.nc$long, veg_meta.nc$lat) #extract geographic coordinates
spat<-distm(latlon) # create geographic distance matrix
dismat<-vegan::vegdist(vegan::decostand(veg_otus.nc, "total"), "bray")
mc<-mantel.correlog(dismat, as.dist(spat, upper=FALSE, diag=FALSE),cutoff = FALSE) #mantel correlogram
plot(mc)
mantel(dismat, as.dist(spat, upper=FALSE, diag=FALSE))
#r=0.03357 p=0.156 not significant
mc #describe stats
###samples collected within 58 km tend to be more similar to each other
## Distance decay
plot(as.dist(spat,upper=FALSE, diag=FALSE), (1-dismat), xlab = "Distance (m)", ylab = "Bray-Curtis Similarity")
mod<-lm(as.vector((1-dismat))~as.vector(as.dist(spat,upper=FALSE, diag=FALSE)))
mod #y-int =6.264e-02 m = -2.981e-08 (very little slope)
abline(mod, col="#b21f80", lwd=3)
###very low decline in similarity of two samples as the distance between them increases
patchwork::mc + mod + plot_layout(nrow = 1)
# Alpha Diversity ####
#NOTE according to function documentation this must be used with untrimmed data
er<-estimate_richness(physeq.r, measures=c("Observed", "InvSimpson", "Shannon", "Chao1", "ACE"))%>%
rownames_to_column(var = "guid")%>%
left_join(all_meta)
##Summary stats####
#mean and sd
er%>%
dplyr::summarise(mean.r = mean(Observed, na.rm=T),
sd.r = sd(Observed, na.rm=T),
mean.sh = mean(Shannon, na.rm=T),
sd.sh = sd(Shannon, na.rm=T),
mean.is = mean(InvSimpson, na.rm=T),
sd.is = sd(InvSimpson, na.rm=T),
mean.ch = mean(Chao1, na.rm=T),
sd.ch = sd(Chao1, na.rm=T))
#ranges
er%>%
dplyr::summarise(min.r = min(Observed, na.rm=T),
max.r = max(Observed, na.rm=T),
min.sh = min(Shannon, na.rm=T),
max.sh = max(Shannon, na.rm=T),
min.is = min(InvSimpson, na.rm=T),
max.is = max(InvSimpson, na.rm=T),
min.ch = min(Chao1, na.rm=T),
max.ch = max(Chao1, na.rm=T))
##Box plots####
p <-er%>%
ggplot(aes(x=Order1, y= Chao1))+
geom_boxplot(aes(), fill="grey")+
theme(axis.text.x = element_text(angle=45, vjust=1.1, hjust = 1, size=10),
aspect.ratio = 0.7)+
labs(x=NULL)
ggsave(p, file = "figures/Chao1_Order.pdf", width=5, height=3, device = cairo_pdf)
p <-er%>%
ggplot(aes(x=Order1, y= Shannon))+
geom_boxplot(aes(), fill="grey")+
theme(axis.text.x = element_text(angle=45, vjust=1.1, hjust = 1, size=10),
aspect.ratio = 0.7)+
labs(x=NULL)
p
ggsave(p, file = "figures/Shannnon_Order.pdf", width=5, height=3, device = cairo_pdf)
p <-er%>%
ggplot(aes(x=Order1, y= Observed))+
geom_boxplot(aes(), fill="grey")+
theme(axis.text.x = element_text(angle=45, vjust=1.1, hjust = 1, size=10),
aspect.ratio = 0.7)+
labs(x=NULL)
p
ggsave(p, file = "figures/Observed_Order.pdf", width=5, height=3, device = cairo_pdf)
p <-er%>%
ggplot(aes(x=Order1, y= InvSimpson))+
geom_boxplot(aes(), fill="grey")+
theme(axis.text.x = element_text(angle=45, vjust=1.1, hjust = 1, size=10),
aspect.ratio = 0.7)+
labs(x=NULL)
p
ggsave(p, file = "figures/InvSimp_Order.pdf", width=5, height=3, device = cairo_pdf)
#Beta Diversity####
#Permanova/ANOSIM ####
### All birds####
# For all ANOSIM and PERMANOVA we will use rarefied datasets
metadata <- as(sample_data(physeq.r), "data.frame")
metadata <-
set.seed(1982)
vegan::adonis2(phyloseq::distance(physeq.r, method="bray") ~ + Order1,
by = "margin", data = metadata, permutations = 10000)
# Df SumOfSqs R2 F Pr(>F)
# Order1 11 5.635 0.07582 1.156 0.005099 **
# Residual 155 68.688 0.92418
# Total 166 74.323 1.00000
# Calculate multivariate dispersions
#check for homogeneity of variances between groups with betadisp
set.seed(1982)
permutest(betadisper(phyloseq::distance(physeq.r, method="bray"), physeq.r@sam_data$Order1), permutations = 10000)
# betadispersion significant!! -> variances different USE ANOSIM for Order
set.seed(1982)
anosim(phyloseq::distance(physeq.r, method="bray"), physeq.r@sam_data$Order1, permutations = 10000)
# ANOSIM statistic R: 0.1648
# Significance: 0.0015998
set.seed(1982)
vegan::adonis2(phyloseq::distance(physeq.r, method="bray") ~ + Migratory.status,
by = "margin", data = metadata, permutations = 10000)
# Df SumOfSqs R2 F Pr(>F)
# Migratory.status 2 1.450 0.01951 1.632 0.0011 **
# Residual 164 72.873 0.98049
# Total 166 74.323 1.00000
# Calculate multivariate dispersions
#check for homogeneity of variances between groups with betadisp
set.seed(1982)
permutest(betadisper(phyloseq::distance(physeq.r, method="bray"), physeq.r@sam_data$Migratory.status), permutations = 10000)
# betadispersion significant!! -> variances different USE ANOSIM for Migration
set.seed(1982)
anosim(phyloseq::distance(physeq.r, method="bray"), physeq.r@sam_data$Migratory.status, permutations = 10000)
#ANOSIM statistic R: 0.1753
#Significance: 0.0015998
#looks good with ANOSIM so ok
set.seed(1982)
vegan::adonis2(phyloseq::distance(physeq.r, method="bray") ~ + Trophic.Level,
by = "margin", data = metadata, permutations = 10000)
# Df SumOfSqs R2 F Pr(>F)
# Trophic.Level 2 1.716 0.02308 1.9377 9.999e-05 ***
# Residual 164 72.608 0.97692
# Total 166 74.323 1.00000
# Trophic level highly significant
## Calculate multivariate dispersion
set.seed(1982)
permutest(betadisper(phyloseq::distance(physeq.r, method="bray"), physeq.r@sam_data$Trophic.Level), permutations = 10000)
# betadispersion significant!! -> variances different USE ANOSIM for Trophic.Level
set.seed(1982)
anosim(phyloseq::distance(physeq.r, method="bray"), physeq.r@sam_data$Trophic.Level, permutations = 10000)
# ANOSIM statistic R: 0.1129
# Significance: 0.018798
#Year
set.seed(1982)
vegan::adonis2(phyloseq::distance(physeq.r, method="bray") ~ + year,
by = "margin", data = metadata, permutations = 10000)
# Df SumOfSqs R2 F Pr(>F)
# year 1 0.997 0.01342 2.2444 5e-04 ***
# Residual 165 73.326 0.98658
# Total 166 74.323 1.00000
# year highly significant
## Calculate multivariate dispersion
set.seed(1982)
permutest(betadisper(phyloseq::distance(physeq.r, method="bray"), physeq.r@sam_data$year), permutations = 10000)
# betadispersion significant!! -> variances different USE ANOSIM for Trophic.Level
set.seed(1982)
anosim(phyloseq::distance(physeq.r, method="bray"), physeq.r@sam_data$year, permutations = 10000)
#ANOSIM statistic R: 0.1745
#Significance: 9.999e-05
##Highly significant for year!!
### Just Cranes ####
metadata <- as(sample_data(subset_samples(physeq.r, bird == "crane")), "data.frame")
#subspecies
sus.nk <-c(281742, 281737, 281745, 281794, 281809, 281811, 281821)
metadata <- as(sample_data(subset_samples(physeq.r, c(bird == "crane", !NK %in% sus.nk))), "data.frame")
phseq.crane.sub <- subset_samples(physeq.r, c(bird == "crane"))
set.seed(1982)
vegan::adonis2(phyloseq::distance(phseq.crane.sub, method="bray") ~ + sci_name,
by = "margin", data = metadata, permutations = 10000)
# Df SumOfSqs R2 F Pr(>F)
# sci_name 1 0.544 0.01015 1.2307 0.1316
# Residual 120 53.049 0.98985
# Total 121 53.593 1.00000
## Calculate multivariate dispersions
permutest(betadisper(phyloseq::distance(phseq.crane.sub, method="bray"), phseq.crane.sub@sam_data$sci_name), permutations = 10000)
#year
metadata <- as(sample_data(subset_samples(physeq.r, c(bird == "crane", sex %in% c("male", "female")))), "data.frame")%>%
filter(!is.na(sex))
set.seed(1982)
vegan::adonis2(phyloseq::distance(phseq.crane.sex, method="bray") ~ + factor(year),
by = "margin", data = metadata, permutations = 10000)
# Df SumOfSqs R2 F Pr(>F)
# factor(year) 1 1.063 0.02195 2.4465 2e-04 ***
# Residual 109 47.380 0.97805
# Total 110 48.444 1.00000
## Calculate multivariate dispersions
permutest(betadisper(phyloseq::distance(phseq.crane.sex, method="bray"), phseq.crane.sex@sam_data$year), permutations = 10000)
# Df Sum Sq Mean Sq F N.Perm Pr(>F)
# Groups 1 0.009626 0.0096257 4.0814 10000 0.0469 *
# Residuals 109 0.257069 0.0023584
#not significant use adonis
set.seed(1982)
anosim(phyloseq::distance(phseq.crane.sex, method="bray"), subset_samples(phseq.crane.sex)@sam_data$year, permutations = 10000)
# ANOSIM statistic R: 0.1058
# Significance: 9.999e-05
### Non-cranes ####
metadata <- as(sample_data(subset_samples(physeq.r, bird == "other")), "data.frame")
set.seed(1982)
vegan::adonis2(phyloseq::distance(subset_samples(physeq.r, bird == "other"), method="bray") ~ + Order1,
by = "margin", data = metadata, permutations = 10000)
# Df SumOfSqs R2 F Pr(>F)
# Order1 10 4.4658 0.2283 1.0059 0.4338
# Residual 34 15.0948 0.7717
# Total 44 19.5606 1.0000
## Calculate multivariate dispersions
permutest(betadisper(phyloseq::distance(subset_samples(physeq.r, bird == "other"), method="bray"), subset_samples(physeq.r, bird == "other")@sam_data$Order1), permutations = 10000)
# Df Sum Sq Mean Sq F N.Perm Pr(>F)
# Groups 10 1.45186 0.145186 27.465 10000 9.999e-05 ***
# Residuals 34 0.17973 0.005286
#Very Significant use ANOSIM
set.seed(1982)
anosim(phyloseq::distance(subset_samples(physeq.r, bird == "other"), method="bray"), subset_samples(physeq.r, bird == "other")@sam_data$Order1, permutations = 10000)
#ANOSIM statistic R: -0.03049
#Significance: 0.63834
# betadispersion not significant!!
set.seed(1982)
vegan::adonis2(phyloseq::distance(subset_samples(physeq.r, bird == "other"), method="bray") ~ + Trophic.Level,
by = "margin", data = metadata, permutations = 10000)
# Df SumOfSqs R2 F Pr(>F)
# Trophic.Level 2 1.2082 0.06177 1.3825 0.027 *
# Residual 42 18.3523 0.93823
# Total 44 19.5606 1.00000
## Calculate multivariate dispersions
permutest(betadisper(phyloseq::distance(subset_samples(physeq.r, bird == "other"), method="bray"), subset_samples(physeq.r, bird == "other")@sam_data$Trophic.Level), permutations = 10000)
# betadispersion significant!! -> variances different USE ANOSIM for Migration
set.seed(1982)
anosim(phyloseq::distance(subset_samples(physeq.r, bird == "other"), method="bray"), subset_samples(physeq.r, bird == "other")@sam_data$Trophic.Level, permutations = 10000)
#ANOSIM statistic R: 0.2392
#Significance: 0.00039996
#Migration
set.seed(1982)
vegan::adonis2(phyloseq::distance(subset_samples(physeq.r, bird == "other"), method="bray") ~ + Migratory.status,
by = "margin", data = metadata, permutations = 10000)
# Df SumOfSqs R2 F Pr(>F)
# Migratory.status 2 1.1755 0.0601 1.3427 0.0416 *
# Residual 42 18.3850 0.9399
# Total 44 19.5606 1.0000
## Calculate multivariate dispersions
permutest(betadisper(phyloseq::distance(subset_samples(physeq.r, bird == "other"), method="bray"), subset_samples(physeq.r, bird == "other")@sam_data$Migratory.status), permutations = 10000)
# Not significant use PERMANOVA
#year
set.seed(1982)
vegan::adonis2(phyloseq::distance(subset_samples(physeq.r, bird == "other"), method="bray") ~ + year,
by = "margin", data = metadata, permutations = 10000)
# Df SumOfSqs R2 F Pr(>F)
# year 1 0.4307 0.02202 0.9682 0.507
# Residual 43 19.1298 0.97798
# Total 44 19.5606 1.00000
## Calculate multivariate dispersions
permutest(betadisper(phyloseq::distance(subset_samples(physeq.r, bird == "other"), method="bray"), subset_samples(physeq.r, bird == "other")@sam_data$year), permutations = 10000)
# Df Sum Sq Mean Sq F N.Perm Pr(>F)
# Groups 10 1.41168 0.141168 22.042 10000 9.999e-05 ***
# Residuals 34 0.21775 0.006405
#Very Significant use ANOSIM
set.seed(1982)
anosim(phyloseq::distance(subset_samples(physeq.r, bird == "other"), method="bray"), subset_samples(physeq.r, bird == "other")@sam_data$year, permutations = 10000)
#ANOSIM statistic R: -0.06973
#Significance: 0.90541
### Pathogens only####
metadata <- as(sample_data(anm.sym.physeq.r), "data.frame")
set.seed(1982)
vegan::adonis2(phyloseq::distance(anm.sym.physeq.r, method="bray") ~ + Order1,
by = "margin", data = metadata, permutations = 10000)
# Df SumOfSqs R2 F Pr(>F)
# Order1 11 5.292 0.07677 1.1717 0.0249 *
# Residual 155 63.642 0.92323
# Total 166 68.934 1.00000
## Calculate multivariate dispersions
permutest(betadisper(phyloseq::distance(anm.sym.physeq.r, method="bray"), anm.sym.physeq.r@sam_data$Order1), permutations = 10000)
# Df Sum Sq Mean Sq F N.Perm Pr(>F)
# Groups 11 1.8906 0.171872 11.616 10000 9.999e-05 ***
# Residuals 155 2.2934 0.014796
# betadispersion significant!! -> variances different USE ANOSIM for Order
set.seed(1982)
anosim(phyloseq::distance(anm.sym.physeq.r, method="bray"), anm.sym.physeq.r@sam_data$Order1, permutations = 10000)
#ANOSIM statistic R: 0.1112
#Significance: 0.020698
#Trophic Level
set.seed(1982)
vegan::adonis2(phyloseq::distance(anm.sym.physeq.r, method="bray") ~ + Trophic.Level,
by = "margin", data = metadata, permutations = 10000)
# Df SumOfSqs R2 F Pr(>F)
# Trophic.Level 2 1.582 0.02296 1.9266 0.002 **
# Residual 164 67.352 0.97704
# Total 166 68.934 1.00000
## Calculate multivariate dispersions
permutest(betadisper(phyloseq::distance(anm.sym.physeq.r, method="bray"), anm.sym.physeq.r@sam_data$Trophic.Level), permutations = 10000)
# betadispersion significant!! -> variances different USE ANOSIM for Migration
set.seed(1982)
anosim(phyloseq::distance(anm.sym.physeq.r, method="bray"), anm.sym.physeq.r@sam_data$Trophic.Level, permutations = 10000)
#ANOSIM statistic R: 0.0911
#Significance: 0.046395
set.seed(1982)
vegan::adonis2(phyloseq::distance(anm.sym.physeq.r, method="bray") ~ + Migratory.status,
by = "margin", data = metadata, permutations = 10000)
# Df SumOfSqs R2 F Pr(>F)
# Migratory.status 2 1.432 0.02078 1.7401 0.0031 **
# Residual 164 67.502 0.97922
# Total 166 68.934 1.00000
## Calculate multivariate dispersions
permutest(betadisper(phyloseq::distance(anm.sym.physeq.r, method="bray"), anm.sym.physeq.r@sam_data$Migratory.status), permutations = 10000)
# betadispersion significant Use ANOSIM
set.seed(1982)
anosim(phyloseq::distance(anm.sym.physeq.r, method="bray"), anm.sym.physeq.r@sam_data$Migratory.status, permutations = 10000)
# ANOSIM statistic R: 0.06585
# Significance: 0.14249
#Year
set.seed(1982)
vegan::adonis2(phyloseq::distance(anm.sym.physeq.r, method="bray") ~ + year,
by = "margin", data = metadata, permutations = 10000)
# Df SumOfSqs R2 F Pr(>F)
# year 1 0.964 0.01398 2.3391 0.0016 **
# Residual 165 67.971 0.98602
# Total 166 68.934 1.00000
## Calculate multivariate dispersions
permutest(betadisper(phyloseq::distance(anm.sym.physeq.r, method="bray"), anm.sym.physeq.r@sam_data$year), permutations = 10000)
# betadispersion significant!! -> variances different USE ANOSIM for Order
set.seed(1982)
anosim(phyloseq::distance(anm.sym.physeq.r, method="bray"), anm.sym.physeq.r@sam_data$year, permutations = 10000)
#ANOSIM statistic R: 0.1243
#Significance: 9.999e-05
#Year non-cranes
metadata <- as(sample_data(subset_samples(physeq.r, bird == "other")), "data.frame")
set.seed(1982)
vegan::adonis2(phyloseq::distance(subset_samples(anm.sym.physeq.r, bird == "other"), method="bray") ~ + year,
by = "margin", data = metadata, permutations = 10000)
# Df SumOfSqs R2 F Pr(>F)
# year 1 0.298 0.01665 0.728 0.7915
# Residual 43 17.602 0.98335
# Total 44 17.900 1.00000
## Calculate multivariate dispersions
permutest(betadisper(phyloseq::distance(subset_samples(anm.sym.physeq.r, bird == "other"), method="bray"), subset_samples(anm.sym.physeq.r, bird == "other")@sam_data$year), permutations = 10000)
# significant use PERMANOVA
set.seed(1982)
anosim(phyloseq::distance(subset_samples(anm.sym.physeq.r, bird == "other"), method="bray"), subset_samples(anm.sym.physeq.r, bird == "other")@sam_data$Trophic.Level, permutations = 10000)
#NMDS ####
##All birds####
psprn.ord <- ordinate(physeq.r, "NMDS", "bray")
all_meta_ordin <- as_tibble(all_meta)
nmds_points <-
as_tibble(psprn.ord$points, rownames = "guid") %>%
left_join(y = all_meta_ordin, by = "guid")
## Plot NMDS ####
{
c25 <- c(
"skyblue2",
# lt pink
"maroon",
"darkorange4",
"palegreen3",
"darkorange2",
"purple3",
# lt purple
"grey66",
"gold2",
"burlywood2",
# lt orange
"steelblue4",
"pink2",
"bisque3",
"yellow4"
)
hull <- nmds_points %>%
group_by(Family1)%>%
dplyr::mutate(n = dplyr::n())%>%
filter(#!Family1 == "Gruidae",
n >2)%>%
ungroup()%>%
group_by(Family1) %>%
dplyr::slice(chull(MDS1, MDS2))
p<-nmds_points %>%
group_by(Family1)%>%
dplyr::mutate(n = dplyr::n())%>%
filter(#!Family1 == "Gruidae",
n >2)%>%
dplyr::select(Family1, MDS1, MDS2, n)%>%
mutate(Family1=fct_relevel(Family1,c("Corvidae","Fringillidae", "Strigidae", "Caprimulgidae", "Accipitridae", "Turdidae", "Tytonidae", "Gruidae")))%>%
ggplot(aes(x = MDS1, y = MDS2, color = Family1, fill=Family1)) +
geom_point(size = 1)+
# geom_polygon(data = hull,
# alpha = 0.2,
# color = "transparent") +
stat_ellipse(linetype=2)+
scale_color_manual(values = c25) +
scale_fill_manual(values = c25)+
labs(x="NMDS 1", y = "NMDS 2")+
theme(aspect.ratio=1)
ggsave(
p,
filename = paste(getwd(), "/figures/NMDS_with_crane_family.pdf", sep = ""),
width = 5,
height = 3,
units = "in",
device = cairo_pdf
)
}
p
{
p<-nmds_points %>%
group_by(Order1)%>%
dplyr::mutate(n = dplyr::n())%>%
#filter(!Family1 == "Gruidae")%>%
ggplot(aes(x = MDS1, y = MDS2, color = Trophic.Level, fill = Trophic.Level)) +
geom_point(size = 0.5)+
# geom_polygon(data = hull,
# alpha = 0.2,
# color = "transparent") +
stat_ellipse(linetype=2)+
scale_color_manual(values=c("red4","#018571", "#CD9600"))+
scale_fill_manual(values=c("red4","#018571", "#CD9600"))+
labs(x="NMDS 1", y = "NMDS 2")+
theme(aspect.ratio=1)
ggsave(
p,
filename = paste(getwd(), "/figures/NMDS_with_crane_trophic_level_ellispe.pdf", sep = ""),
width = 5,
height = 3,
units = "in",
device = cairo_pdf
)
}
p
{
cmig <- c(
# lt purple
"#077551",
"#362392",
"#AF700B",
"#AF370B"
)
p<-nmds_points %>%
group_by(Order1)%>%
dplyr::mutate(n = dplyr::n(),
Migration = factor(Migration, levels = c(3,2,1)) )%>%
#filter(!Family1 == "Gruidae")%>%
ggplot(aes(x = MDS1, y = MDS2, color = as.factor(Migration), fill = as.factor(Migration))) +
geom_point(size = 0.5)+
# geom_polygon(data = hull,
# alpha = 0.2,
# color = "transparent") +
stat_ellipse(linetype=2)+
scale_color_manual(values = cmig) +