No 5. Beta Diversity Estimates

Script testing different distance metrics to estimate beta diversity using the whole and core fish gut communities across a range of environmental variables.

Next, we turn our attention to beta diversity estimates of fish guts. For both the whole community (i.e., all ASVs) and the core community (i.e., only core ASVs), we assess beta diversity against the following conditions:

1. Position: inside bay, outside bay
2. Zone: Inner bay, Inner bay disturbed, Outer bay
3. Reef Type: disturbed, healthy
4. Reef: ALR, CCR, PBL, PPR, PST, RNW, ROL, SCR, SIS

For each condition or combination of conditions, we perform the following beta diversity estimates: Jaccard, Modified Gower, Bray Curtis, UNIFRAC, GUNIFRAC, WUNIFRAC. For each diversity metric, we a) calculate a dissimilarity matrix, b) assess beta dispersions, c) run a PERMANOVA, and in some cases, d) look at pairwise comparisons.

Whole Community

First, we load the rarefied whole fish gut microbiome data.

ps.whole <- readRDS("rdata/p3/ps_16S_bocas_fish_final.rds")

Jaccard

Dissimilarity Matrix

All

set.seed(1911)
type.jaccard <- phyloseq::distance(ps.whole, method = "jaccard", binary = T)
sampledf <- data.frame(sample_data(ps.whole))

Inner Only

ps.whole.inner <- subset_samples(ps.whole, Zone != "Outer bay")
type.jaccard.inner <- phyloseq::distance(ps.whole.inner,
                                         method = "jaccard", binary = T)
sampledf.inner <- data.frame(sample_data(ps.whole.inner))

Beta Dispersions

By Zone

beta.jaccard1 <- betadisper(type.jaccard, sampledf$Zone,
                            type = "centroid", bias.adjust = TRUE)
permutest(beta.jaccard1, binary = TRUE, pairwise = TRUE,
          permutations = 10000)

By Position

beta.jaccard2 <- betadisper(type.jaccard, sampledf$Position,
                            type = "centroid", bias.adjust = TRUE)
permutest(beta.jaccard2, binary = TRUE, pairwise = TRUE,
          permutations = 10000)

By Reef Type (inside bay)

beta.jaccard3 <- betadisper(type.jaccard.inner, sampledf.inner$Reef_type,
                            type = "centroid", bias.adjust = TRUE)
permutest(beta.jaccard3, binary = TRUE, pairwise = TRUE,
          permutations = 10000)

PERMANOVA

By Zone

adonis.jaccard0 <- adonis(type.jaccard ~ Zone, data = sampledf,
                          permutations = 10000)

By Reef

adonis.jaccardR <- adonis(type.jaccard ~ Reef, data = sampledf,
                          permutations = 10000)

By Zone nested with Reef

adonis.jaccard1<- adonis(type.jaccard ~ Zone/Reef, data = sampledf,
                         permutations = 10000)

By Position nested with Reef

adonis.jaccard2 <- adonis(type.jaccard ~ Position/Reef, data = sampledf,
                          permutations = 10000)

By Reef Type nested with Reef (inside bay)

The variable reef type is healthy and disturbed (based on level coral cover).

adonis.jaccard3 <- adonis(type.jaccard.inner ~ Reef_type/Reef,
                          data = sampledf.inner, permutations = 10000)

Pairwise comparisons

By Zone

pairwise1 <- pairwise.adonis(type.jaccard, factors = sampledf$Zone)

By Reef

pairwise2 <- pairwise.adonis(type.jaccard, factors = sampledf$Reef)

Modified Gower

For the modified Gower, we log transform the data using the microbiome package transformation because it uses the vegan deconstand function. The distance matrix will be created from the log transformed data.

Dissimilarity Matrix

set.seed(1911)
data.log10<- microbiome::transform(ps.whole, transform = "log10p")
sampledf.log10 <- data.frame(sample_data(data.log10))
type.modGower <- phyloseq::distance(data.log10, method = "altGower")

Beta Dispersions

By Zone

beta.Gower1 <- betadisper(type.modGower, sampledf.log10$Zone,
                          type = "centroid", bias.adjust = TRUE)
permutest(beta.Gower1, pairwise = TRUE, permutations = 10000)

By Reef

beta.GowerR <- betadisper(type.modGower, sampledf.log10$Reef,
                          type = "centroid", bias.adjust = TRUE)
permutest(beta.GowerR, pairwise = TRUE, permutations = 10000)

By Position

beta.Gower2 <- betadisper(type.modGower, sampledf.log10$Position,
                          type = "centroid", bias.adjust = TRUE)
permutest(beta.Gower2, pairwise = TRUE, permutations = 10000)

By Reef Type (inside bay)

data.log10.inner <- subset_samples(data.log10, Zone != "Outer bay")
type.modGower.inner <- phyloseq::distance(data.log10.inner, method = "altGower")
sampledf.inner <- data.frame(sample_data(data.log10.inner))
beta.Gower3 <- betadisper(type.modGower.inner, sampledf.inner$Reef_type,
                          type = "centroid", bias.adjust = TRUE)
permutest(beta.Gower3, binary = TRUE, pairwise = TRUE,
          permutations = 10000)

PERMANOVA

By Zone

set.seed(1911)
adonis.modGower0 <- adonis(type.modGower ~ Zone, data = sampledf.log10,
                           permutations = 10000)

By Reef

adonis.modGowerR <- adonis(type.modGower ~ Reef, data = sampledf.log10,
                           permutations = 10000)

By Zone nested with Reef

adonis.modGower1 <- adonis(type.modGower ~ Zone/Reef, data = sampledf.log10,
                           permutations = 10000)

By Position nested with Reef

adonis.modGower2 <- adonis(type.modGower ~ Position/Reef, data = sampledf.log10,
                           permutations = 10000)

By Reef Type nested with Reef (inside bay)

data.log10.inner <- subset_samples(data.log10, Zone != "Outer bay")
type.modGower.inner <- phyloseq::distance(data.log10.inner, method = "altGower")
sampledf.inner.log10 <- data.frame(sample_data(data.log10.inner))
adonis.modGower3 <- adonis(type.modGower.inner ~ Reef_type/Reef,
                           data = sampledf.inner.log10, permutations = 10000)

Pairwise comparisons

By Zone

set.seed(1911)
pairwise.adonis(type.modGower, factors = sampledf$Zone)

By Reef

set.seed(1911)
pairwise.adonis(type.modGower, factors = sampledf$Reef)

Bray Curtis

Dissimilarity Matrix

set.seed(1911)
type.bray <- phyloseq::distance(ps.whole, method = "bray")
sampledf <- data.frame(sample_data(ps.whole))

Beta Dispersions

By Zone

beta.bray1 <- betadisper(type.bray, sampledf$Zone,
                         type = "centroid", bias.adjust = TRUE)
permutest(beta.bray1, pairwise = TRUE, permutations = 10000)

By Reef

beta.brayR <- betadisper(type.bray, sampledf$Reef,
                         type = "centroid", bias.adjust = TRUE)
permutest(beta.brayR, pairwise = TRUE, permutations = 10000)

By Position

beta.bray2 <- betadisper(type.bray, sampledf$Position,
                         type = "centroid", bias.adjust = TRUE)
permutest(beta.bray2, pairwise = TRUE, permutations = 10000)

By Reef Type (inside bay)

ps.whole.inner <- subset_samples(ps.whole, Zone != "Outer bay")
type.bray.inner <- phyloseq::distance(ps.whole.inner, method = "bray")
beta.bray3 <- betadisper(type.bray.inner, sampledf.inner$Reef_type,
                         type = "centroid", bias.adjust = TRUE)
permutest(beta.bray3, pairwise = TRUE, permutations = 10000)

PERMANOVA

By Zone

set.seed(1911)
adonis.bray0 <- adonis(type.bray ~ Zone, data = sampledf,
                            permutations = 10000)

By Reef

adonis.brayR <- adonis(type.bray ~ Reef, data = sampledf,
                               permutations = 10000)

By Zone nested with in Reef

adonis.bray1 <- adonis(type.bray ~ Zone/Reef, data = sampledf,
                            permutations = 10000)

By Position nested with Reef

adonis.bray2 <- adonis(type.bray ~ Position/Reef, data = sampledf,
                            permutations = 10000)

By Reef Type nested with Reef (inside bay)

ps.whole.inner <- subset_samples(ps.whole, Zone != "Outer bay")
type.bray.inner <- phyloseq::distance(ps.whole.inner, method = "bray")
sampledf.inner <- data.frame(sample_data(ps.whole.inner))
adonis.bray3 <- adonis(type.bray.inner ~ Reef_type/Reef, data = sampledf.inner,
                           permutations = 10000)

Pairwise comparisons

By Zone

set.seed(1911)
pairwise.adonis(type.bray, factors = sampledf$Zone)

By Reef

set.seed(1911)
pairwise.adonis(type.bray, factors = sampledf$Reef)

UNIFRAC

Dissimilarity Matrix

set.seed(1911)
type.unifrac <- phyloseq::distance(ps.whole, method = "unifrac", weighted = F)
sampledf <- data.frame(sample_data(ps.whole))

Beta Dispersions

By Zone

beta.unifrac1 <- betadisper(type.unifrac, sampledf$Zone,
                            type = "centroid", bias.adjust = TRUE)
permutest(beta.unifrac1, pairwise = TRUE, permutations = 10000)

By Reef

beta.unifracR <- betadisper(type.unifrac, sampledf$Reef,
                            type = "centroid", bias.adjust = TRUE)
permutest(beta.unifracR, pairwise = TRUE, permutations = 10000)

By Position

beta.unifrac2 <- betadisper(type.unifrac, sampledf$Position,
                            type = "centroid", bias.adjust = TRUE)
permutest(beta.unifrac2, pairwise = TRUE, permutations = 10000)

By Reef Type (inside bay)

ps.whole.inner <- subset_samples(ps.whole, Zone != "Outer bay")
type.unifrac.inner <- phyloseq::distance(ps.whole.inner, method = "unifrac")
sampledf.whole.inner <- data.frame(sample_data(ps.whole.inner))
beta.unifrac3 <- betadisper(type.unifrac.inner, sampledf.whole.inner$Reef_type,
                            type = "centroid", bias.adjust = TRUE)
permutest(beta.unifrac3, pairwise = TRUE, permutations = 10000)

PERMANOVA

By Zone

set.seed(1911)
adonis.unifrac0 <- adonis(type.unifrac ~ Zone, data = sampledf,
                               permutations = 10000)

By Reef

adonis.unifracR <- adonis(type.unifrac ~ Reef, data = sampledf,
                                  permutations = 10000)

By Zone nested with Reef

adonis.unifrac1 <- adonis(type.unifrac ~ Zone/Reef, data = sampledf,
                             permutations = 10000)

By Position nested with Reef

adonis.unifrac2 <- adonis(type.unifrac ~ Position/Reef, data = sampledf,
                               permutations = 10000)

By Reef Type nested with Reef (inside bay)

ps.whole.inner <- subset_samples(ps.whole, Zone != "Outer bay")
type.unifrac.inner <- phyloseq::distance(ps.whole.inner, method = "unifrac")
sampledf.inner <- data.frame(sample_data(ps.whole.inner))

adonis.unifrac3 <- adonis(type.unifrac.inner ~ Reef_type/Reef,
                          data = sampledf.inner, permutations = 10000)

Pairwise comparisons

By Zone

set.seed(1911)
pairwise.adonis(type.unifrac, factors = sampledf$Zone)

By Reef

set.seed(1911)
pairwise.adonis(type.unifrac, factors = sampledf$Reef)

GUNIFRAC

Dissimilarity Matrix

asv.tab<-otu_table(ps.whole)
ps.whole.inner <- subset_samples(ps.whole, Zone != "Outer bay")
asv.tab.inner <- otu_table(ps.whole.inner)

tree.fish<-phy_tree(ps.whole)
tree.fish.inner<-phy_tree(ps.whole.inner)

fish.sample<-sample_data(ps.whole)
fish.sample.inner<-sample_data(ps.whole.inner)

groups2.whole <- fish.sample$Zone
groupsR.whole <- fish.sample$Reef
groups.inner <- fish.sample.inner$Zone
position.whole <- fish.sample$Position

unifracs <- GUniFrac(asv.tab, tree.fish,
                     alpha=c(0, 0.5, 1))$unifracs
unifracs.inner <- GUniFrac(asv.tab.inner, tree.fish.inner,
                           alpha=c(0, 0.5, 1))$unifracs

d5.all <- unifracs[, , "d_0.5"]
d5.inner <- unifracs.inner[, , "d_0.5"]
type.gunifrac <- as.dist(d5.all)
type.gunifrac.inner <- as.dist(d5.inner)

Beta Dispersion

set.seed(1911)
beta.gunifrac1 <- betadisper(as.dist(d5.all), groups2.whole,
                             type = "centroid", bias.adjust = TRUE)
permutest(beta.gunifrac1, pairwise = TRUE, permutations = 10000)

By Reef

beta.gunifracR <- betadisper(as.dist(d5.all), groupsR.whole,
                             type = "centroid", bias.adjust = TRUE)
permutest(beta.gunifracR, pairwise = TRUE, permutations = 10000)

By Position

beta.gunifrac2 <- betadisper(as.dist(d5.all), position.whole,
                             type = "centroid", bias.adjust = TRUE)
permutest(beta.gunifrac2, pairwise = TRUE, permutations = 10000)

By Reef Type (inner bay only)

beta.gunifrac.inner <- betadisper(as.dist(d5.inner), groups.inner,
                                  type = "centroid", bias.adjust = TRUE)
permutest(beta.gunifrac.inner, pairwise = TRUE, permutations = 10000)

PERMANOVA

By Zone nested with Reef

sampledf <- data.frame(sample_data(ps.whole))
sampledf.inner <- data.frame(sample_data(ps.whole.inner))
adonis.gunifrac1<- adonis(type.gunifrac ~ Zone/Reef, data = sampledf,
                           permutations = 10000)

By Position nested with Reef

adonis.gunifrac2<- adonis(type.gunifrac ~ Position/Reef, data = sampledf,
                                    permutations = 10000)

By Reef Type nested with Reef (inside bay)

adonis.gunifrac3<- adonis(type.gunifrac.inner ~ Reef_type/Reef,
                          data = sampledf.whole.inner, permutations = 10000)

Pairwise comparisons

as a substitute for posthoc test of PERMANOVA

By Zone

pairwise.adonis(type.gunifrac, factors = sampledf$Zone,
                p.adjust.m = "bonferroni")

By Reef

pairwise.adonis(type.gunifrac, factors = sampledf$Reef,
                p.adjust.m = "bonferroni")

WUNIFRAC

Dissimilarity Matrix

set.seed(1911)
type.wunifrac <- phyloseq::distance(ps.whole, method = "wunifrac")
sampledf <- data.frame(sample_data(ps.whole))

Beta Dispersions

By Zone

beta.wunifrac1 <- betadisper(type.wunifrac, sampledf$Zone,
                             type = "centroid", bias.adjust = TRUE)
permutest(beta.wunifrac1, pairwise = TRUE, permutations = 10000)

By Reef

beta.wunifracR <- betadisper(type.wunifrac, sampledf$Reef,
                             type = "centroid", bias.adjust = TRUE)
permutest(beta.wunifracR, pairwise = TRUE, permutations = 10000)

By Position

beta.wunifrac2 <- betadisper(type.wunifrac, sampledf$Position,
                             type = "centroid", bias.adjust = TRUE)
permutest(beta.wunifrac2, pairwise = TRUE, permutations = 10000)

By Zone (inside bay only)

ps.whole.inner <- subset_samples(ps.whole, Zone != "Outer bay")
type.wunifrac.inner <- phyloseq::distance(ps.whole.inner, method = "wunifrac")
sampledf.inner <- data.frame(sample_data(ps.whole.inner))

By Reef Type (inside bay)

beta.wunifrac3 <- betadisper(type.wunifrac.inner, sampledf.inner$Reef_type,
                             type = "centroid", bias.adjust = TRUE)
permutest(beta.wunifrac3, pairwise = TRUE, permutations = 10000)

PERMANOVA

By Zone

set.seed(1911)
adonis.wunifrac0 <- adonis(type.wunifrac ~ Zone, data = sampledf,
                                permutations = 10000)

By Reef

adonis.wunifracR <- adonis(type.wunifrac ~ Reef, data = sampledf,
                                permutations = 10000)

By Zone nested with Reef

adonis.wunifrac1 <- adonis(type.wunifrac ~ Zone/Reef, data = sampledf,
                                permutations = 10000)

By Position nested with Reef

adonis.wunifrac2 <- adonis(type.wunifrac ~ Position/Reef, data = sampledf,
                                permutations = 10000)

Pairwise comparisons

By Zone

set.seed(1911)
pairwise.adonis(type.wunifrac, factors = sampledf$Zone)

By Reef

set.seed(1911)
pairwise.adonis(type.wunifrac, factors = sampledf$Reef)

Whole Community Summary

Copy CSV Excel

Show 51050All entries

Search:

Sample_ID	Distance_to_centroid	Zone	Metric	Reef
	0.000000000000000 1.277399370873160

Sample_ID	Distance_to_centroid	Zone	Metric	Reef
	0.000000000000000 1.277399370873160
ML2384	0.7053	Outer bay	Jaccard	CCR
ML2385	0.5653	Inner bay	Jaccard	ROL
ML2388	0.7257	Inner bay disturbed	Jaccard	RNW
ML2390	0.6182	Outer bay	Jaccard	CCR
ML2392	0.6963	Inner bay	Jaccard	ALR

Table: Beta diversity estimates of the whole fish microbiome. Use the buttons to navigate through the table or download a copy.

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Core Community

First, we load the unrarefied core fish gut microbiome data.

ps.core <- readRDS("rdata/p1/ps_indv01_core_fish.rds")
ps.core

Jaccard

Dissimilarity Matrix

All

set.seed(1911)
type.jaccard <- phyloseq::distance(ps.core, method = "jaccard", binary = T)
sampledf <- data.frame(sample_data(ps.core))

Inner Only

ps.core.inner <- subset_samples(ps.core, Zone != "Outer bay")
type.jaccard.inner <- phyloseq::distance(ps.core.inner,
                                         method = "jaccard", binary=T)
sampledf.inner <- data.frame(sample_data(ps.core.inner))

Beta Dispersions

By Zone

beta.jaccard1 <- betadisper(type.jaccard, sampledf$Zone,
                            type = "centroid", bias.adjust = TRUE)
permutest(beta.jaccard1, binary = TRUE, pairwise = TRUE, permutations = 10000)

By Position

beta.jaccard2 <- betadisper(type.jaccard, sampledf$Position,
                            type = "centroid", bias.adjust = TRUE)
permutest(beta.jaccard2,  binary = TRUE,  pairwise = TRUE, permutations = 10000)

By Reef Type (inside bay)

beta.jaccard3 <- betadisper(type.jaccard.inner, sampledf.inner$Reef_type,
                            type = "centroid", bias.adjust = TRUE)
permutest(beta.jaccard3,  binary = TRUE,  pairwise = TRUE, permutations = 10000)

PERMANOVA

By Zone

adonis.jaccard0<- adonis(type.jaccard ~ Zone, data = sampledf,
                         permutations = 10000)

By Reef

adonis.jaccardR<- adonis(type.jaccard ~ Reef, data = sampledf,
                         permutations = 10000)

#by Zone with Reef nested in Zone
adonis.jaccard1<- adonis(type.jaccard ~ Zone/Reef, data = sampledf,
                         permutations = 10000)

By Position nested with Reef

adonis.jaccard2 <- adonis(type.jaccard ~ Position/Reef, data = sampledf,
                          permutations = 10000)

By Reef Type nested with Reef (inside bay)

adonis.jaccard3 <- adonis(type.jaccard.inner ~ Reef_type/Reef,
                          data = sampledf.inner, permutations = 10000)

Pairwise comparisons

By Zone

set.seed(1911)
pairwise1 <- pairwise.adonis(type.jaccard, factors = sampledf$Zone)

By Reef

pairwise2 <- pairwise.adonis(type.jaccard, factors = sampledf$Reef)

Modified Gower

Again, for the modified Gower, we log transform the data using the microbiome package transformation because it uses the vegan deconstand function. The distance matrix will be created from the log transformed data.

Dissimilarity Matrix

set.seed(1911)
data.log10<- microbiome::transform(ps.core, transform = "log10p")
sampledf.log10 <- data.frame(sample_data(data.log10))
type.modGower <- phyloseq::distance(data.log10, method = "altGower")

Beta Dispersions

by Zone

beta.Gower1 <- betadisper(type.modGower, sampledf.log10$Zone,
                          type = "centroid", bias.adjust = TRUE)
permutest(beta.Gower1,  pairwise = TRUE, permutations = 10000)

By Reef

beta.GowerR <- betadisper(type.modGower, sampledf.log10$Reef,
                          type = "centroid", bias.adjust = TRUE)
permutest(beta.GowerR,  pairwise = TRUE, permutations = 10000)

by Position

beta.Gower2 <- betadisper(type.modGower, sampledf.log10$Position,
                          type = "centroid", bias.adjust = TRUE)
permutest(beta.Gower2,  pairwise = TRUE, permutations = 10000)

By Reef Type (inside bay)

data.log10.inner <- subset_samples(data.log10, Zone != "Outer bay")
type.modGower.inner <- phyloseq::distance(data.log10.inner,
                                          method = "altGower")
sampledf.inner <- data.frame(sample_data(data.log10.inner))
beta.Gower3 <- betadisper(type.modGower.inner, sampledf.inner$Reef_type,
                          type = "centroid", bias.adjust = TRUE)
permutest(beta.Gower3,  binary = TRUE,  pairwise = TRUE, permutations = 10000)

PERMANOVA

By Zone

set.seed(1911)
adonis.modGower0 <- adonis(type.modGower ~ Zone, data = sampledf.log10,
                           permutations = 10000)

By Reef

adonis.modGowerR <- adonis(type.modGower ~ Reef, data = sampledf.log10,
                           permutations = 10000)

By Zone nested with Reef

adonis.modGower1 <- adonis(type.modGower ~ Zone/Reef, data = sampledf.log10,
                           permutations = 10000)

By Position nested with Reef

adonis.modGower2 <- adonis(type.modGower ~ Position/Reef, data = sampledf.log10,
                           permutations = 10000)

By Reef Type nested with Reef (inside bay)

data.log10.inner <- subset_samples(data.log10, Zone != "Outer bay")
type.modGower.inner <- phyloseq::distance(data.log10.inner, method = "altGower")
sampledf.inner.log10 <- data.frame(sample_data(data.log10.inner))
adonis.modGower3 <- adonis(type.modGower.inner ~ Reef_type/Reef,
                           data = sampledf.inner.log10, permutations = 10000)

Pairwise comparisons

By Zone

pairwise.adonis(type.modGower, factors = sampledf$Zone)

By Reef

pairwise.adonis(type.modGower, factors = sampledf$Reef)

Bray Curtis

Dissimilarity Matrix

set.seed(1911)
type.bray <- phyloseq::distance(ps.core, method = "bray")
sampledf <- data.frame(sample_data(ps.core))

Beta Dispersions

By Zone

beta.bray1 <- betadisper(type.bray, sampledf$Zone,
                         type = "centroid", bias.adjust = TRUE)
permutest(beta.bray1,  pairwise = TRUE, permutations = 10000)

By Reef

beta.brayR <- betadisper(type.bray, sampledf$Reef,
                         type = "centroid", bias.adjust = TRUE)
permutest(beta.brayR,  pairwise = TRUE, permutations = 10000)

By Position

beta.bray2 <- betadisper(type.bray, sampledf$Position,
                         type = "centroid", bias.adjust = TRUE)
permutest(beta.bray2,  pairwise = TRUE, permutations = 10000)

By Reef Type (inside bay)

ps.core.inner <- subset_samples(ps.core, Zone != "Outer bay")
type.bray.inner <- phyloseq::distance(ps.core.inner, method = "bray")
beta.bray3 <- betadisper(type.bray.inner, sampledf.inner$Reef_type,
                         type = "centroid", bias.adjust = TRUE)
permutest(beta.bray3,  pairwise = TRUE, permutations = 10000)

PERMANOVA

By Zone

set.seed(1911)
adonis.bray0 <- adonis(type.bray ~ Zone, data = sampledf,
                       permutations = 10000)

By Reef

adonis.brayR <- adonis(type.bray ~ Reef, data = sampledf,
                       permutations = 10000)

By Zone nested with in Reef

adonis.brayR
adonis.bray1 <- adonis(type.bray ~ Zone/Reef, data = sampledf,
                       permutations = 10000)

By Position nested with Reef

adonis.bray2 <- adonis(type.bray ~ Position/Reef, data = sampledf,
                       permutations = 10000)

By Reef Type nested with Reef (inside bay)

ps.core.inner <- subset_samples(ps.core, Zone != "Outer bay")
type.bray.inner <- phyloseq::distance(ps.core.inner, method = "bray")
sampledf.inner <- data.frame(sample_data(ps.core.inner))
adonis.bray3 <- adonis(type.bray.inner ~ Reef_type/Reef, data = sampledf.inner,
                       permutations = 10000)

Pairwise comparisons

By Zone

set.seed(1911)
pairwise.adonis(type.bray, factors = sampledf$Zone)

By Reef

set.seed(1911)
pairwise.adonis(type.bray, factors = sampledf$Reef)

UNIFRAC

Dissimilarity Matrix

set.seed(1911)
type.unifrac <- phyloseq::distance(ps.core, method = "unifrac", weighted=F)
sampledf <- data.frame(sample_data(ps.core))

Beta Dispersions

By Zone

beta.unifrac1 <- betadisper(type.unifrac, sampledf$Zone,
                            type = "centroid", bias.adjust = TRUE)
permutest(beta.unifrac1,  pairwise = TRUE, permutations = 10000)

By Reef

beta.unifracR <- betadisper(type.unifrac, sampledf$Reef,
                            type = "centroid", bias.adjust = TRUE)
permutest(beta.unifracR,  pairwise = TRUE, permutations = 10000)

By Position

beta.unifrac2 <- betadisper(type.unifrac, sampledf$Position,
                            type = "centroid", bias.adjust = TRUE)
permutest(beta.unifrac2, pairwise = TRUE, permutations = 10000)

By Reef Type (inside bay)

ps.core.inner <- subset_samples(ps.core, Zone != "Outer bay")
type.unifrac.inner <- phyloseq::distance(ps.core.inner, method = "unifrac")
sampledf.core.inner <- data.frame(sample_data(ps.core.inner))
beta.unifrac3 <- betadisper(type.unifrac.inner, sampledf.core.inner$Reef_type,
                            type = "centroid", bias.adjust = TRUE)
permutest(beta.unifrac3,  pairwise = TRUE, permutations = 10000)

PERMANOVA

By Zone

set.seed(1911)
adonis.unifrac0 <- adonis(type.unifrac ~ Zone, data = sampledf,
                          permutations = 10000)

By Reef

adonis.unifracR <- adonis(type.unifrac ~ Reef, data = sampledf,
                          permutations = 10000)

By Zone nested with Reef

adonis.unifrac1 <- adonis(type.unifrac ~ Zone/Reef, data = sampledf,
                          permutations = 10000)

By Position nested with Reef

adonis.unifrac2 <- adonis(type.unifrac ~ Position/Reef, data = sampledf,
                          permutations = 10000)

By Reef Type nested with Reef (inside bay)

ps.core.inner <- subset_samples(ps.core, Zone != "Outer bay")
type.unifrac.inner <- phyloseq::distance(ps.core.inner, method = "unifrac")
sampledf.inner <- data.frame(sample_data(ps.core.inner))

adonis.unifrac3 <- adonis(type.unifrac.inner ~ Reef_type/Reef,
                          data = sampledf.inner,
                          permutations = 10000)

Pairwise comparisons

By Zone

set.seed(1911)
pairwise.adonis(type.unifrac, factors = sampledf$Zone)

By Reef

set.seed(1911)
pairwise.adonis(type.unifrac, factors = sampledf$Reef)

GUNIFRAC

Dissimilarity Matrix

asv.tab<-otu_table(ps.core)
ps.core.inner <- subset_samples(ps.core, Zone != "Outer bay")
asv.tab.inner <- otu_table(ps.core.inner)

tree.fish.core<-phy_tree(ps.core)
tree.fish.core.inner<-phy_tree(ps.core.inner)

fish.sample.core<-sample_data(ps.core)
fish.sample.core.inner<-sample_data(ps.core.inner)

groups2.core <- fish.sample.core$Zone
groupsR.core <- fish.sample.core$Reef
groups.inner.core <- fish.sample.core.inner$Zone
position.core <- fish.sample.core$Position

unifracs <- GUniFrac(asv.tab, tree.fish.core,
                     alpha=c(0, 0.5, 1))$unifracs
unifracs2 <- GUniFrac(asv.tab.inner, tree.fish.core.inner,
                      alpha=c(0, 0.5, 1))$unifracs

d5.all <- unifracs[, , "d_0.5"]
d5.inner <- unifracs2[, , "d_0.5"]

type.gunifrac <- as.dist(d5.all)
type.gunifrac.inner <- as.dist(d5.inner)

Beta Dispersions

By Zone

set.seed(1911)
beta.gunifrac1 <- betadisper(as.dist(d5.all), groups2.core,
                             type = "centroid", bias.adjust = TRUE)
permutest(beta.gunifrac1, pairwise = TRUE, permutations = 10000)

By Reef

beta.gunifracR <- betadisper(as.dist(d5.all), groupsR.core,
                             type = "centroid", bias.adjust = TRUE)
permutest(beta.gunifracR, pairwise = TRUE, permutations = 10000)

By Position

beta.gunifrac2 <- betadisper(as.dist(d5.all), position.core,
                             type = "centroid", bias.adjust = TRUE)
permutest(beta.gunifrac2, pairwise = TRUE, permutations = 10000)

By Zone (Position, inside bay)

beta.gunifrac.inner <- betadisper(as.dist(d5.inner), groups.inner.core,
                                  type = "centroid", bias.adjust = TRUE)
permutest(beta.gunifrac.inner, pairwise = TRUE, permutations = 10000)

PERMANOVA

By Zone nested with Reef

sampledf <- data.frame(sample_data(ps.core))
sampledf.inner <- data.frame(sample_data(ps.core.inner))

By Zone

adonis.gunifrac0<- adonis(type.gunifrac ~ Zone, data = sampledf,
                          permutations = 10000)

By Reef

adonis.gunifracR<- adonis(type.gunifrac ~ Zone, data = sampledf,
                          permutations = 10000)

By Zone nested with in Reef

adonis.gunifrac1<- adonis(type.gunifrac ~ Zone/Reef, data = sampledf,
                          permutations = 10000)

By Position nested with Reef

adonis.gunifrac2<- adonis(type.gunifrac ~ Position/Reef, data = sampledf,
                          permutations = 10000)

By Reef Type nested with Reef (inside bay)

adonis.gunifrac3<- adonis(type.gunifrac.inner ~ Reef_type/Reef,
                          data = sampledf.core.inner, permutations = 10000)

Pairwise comparisons

By Zone

pairwise.adonis(type.gunifrac, factors = sampledf$Zone,
                p.adjust.m = "bonferroni")

By Reef

pairwise.adonis(type.gunifrac, factors = sampledf$Reef,
                p.adjust.m = "bonferroni")

WUNIFRAC

Dissimilarity Matrix

set.seed(1911)
type.wunifrac <- phyloseq::distance(ps.core, method = "wunifrac")
sampledf <- data.frame(sample_data(ps.core))

Beta Dispersions

By Zone

beta.wunifrac1 <- betadisper(type.wunifrac, sampledf$Zone,
                             type = "centroid", bias.adjust = TRUE)
permutest(beta.wunifrac1,  pairwise = TRUE, permutations = 10000)

ps.core.inner <- subset_samples(ps.core, Zone != "Outer bay")
type.wunifrac.inner <- phyloseq::distance(ps.core.inner, method = "wunifrac")
sampledf.inner <- data.frame(sample_data(ps.core.inner))

By Reef

beta.wunifracR <- betadisper(type.wunifrac, sampledf$Reef,
                             type = "centroid", bias.adjust = TRUE)
permutest(beta.wunifracR,  pairwise = TRUE, permutations = 10000)

By Position

beta.wunifrac2 <- betadisper(type.wunifrac, sampledf$Position,
                             type = "centroid", bias.adjust = TRUE)
permutest(beta.wunifrac2,  pairwise = TRUE, permutations = 10000)

By Reef Type (inside bay)

beta.wunifrac3 <- betadisper(type.wunifrac.inner, sampledf.inner$Reef_type,
                             type = "centroid", bias.adjust = TRUE)
permutest(beta.wunifrac3,  pairwise = TRUE, permutations = 10000)

PERMANOVA

By Zone

set.seed(1911)
adonis.wunifrac0 <- adonis(type.wunifrac ~ Zone, data = sampledf,
                           permutations = 10000)

By Reef

adonis.wunifracR <- adonis(type.wunifrac ~ Reef, data = sampledf,
                           permutations = 10000)

By Zone nested with Reef

adonis.wunifrac1 <- adonis(type.wunifrac ~ Zone/Reef, data = sampledf,
                           permutations = 10000)

By Position nested with Reef

adonis.wunifrac2 <- adonis(type.wunifrac ~ Position/Reef, data = sampledf,
                           permutations = 10000)

By Reef Type nested with Reef (inside bay)

ps.core.inner <- subset_samples(ps.core, Zone != "Outer bay")
type.wunifrac.inner <- phyloseq::distance(ps.core.inner, method = "wunifrac")
sampledf.inner <- data.frame(sample_data(ps.core.inner))

adonis.wunifrac3 <- adonis(type.wunifrac.inner ~ Reef_type/Reef,
                           data = sampledf.inner, permutations = 10000)

Pairwise comparisons

By Zone

pairwise.adonis(type.wunifrac, factors = sampledf$Zone)

By Reef

pairwise.adonis(type.wunifrac, factors = sampledf$Reef)

Core Community Summary

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Sample_ID	Distance_to_centroid	Zone	Metric	Reef
	0.115911687073923 1.803265721924800

Sample_ID	Distance_to_centroid	Zone	Metric	Reef
	0.115911687073923 1.803265721924800
ML2384	0.3082	Outer bay	Jaccard	CCR
ML2385	0.3546	Inner bay	Jaccard	ROL
ML2388	0.5626	Inner bay disturbed	Jaccard	RNW
ML2390	0.3784	Outer bay	Jaccard	CCR
ML2392	0.5447	Inner bay	Jaccard	ALR

Table: Beta diversity estimates of the core fish microbiome. Use the buttons to navigate through the table or download a copy.

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That’s the end of Script 5. In the next Script we assess Beta Dispersion for all beta diversity estimates.

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N^o 4. Alpha Diversity Estimates

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N^o 6. Beta Dispersion