| title | subtitle | author | date | bibliography | link-citations | always_allow_html | output | ||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Role of BRC in arid rock weathering |
Data analysis and plotting for publication |
Roey Angel (<roey.angel@bc.cas.cz>) |
2019-01-04 |
references.bib |
true |
true |
|
This script reproduces all sequence analysis steps and plots included in the paper plus some additional exploratory analyses. The analysis is heavily based on the phyloseq package [@mcmurdie_phyloseq_2013], but also on many other R packages.
set.seed(123456789)
bootstraps <- 1000
min_lib_size <- 1000Load data
read.csv("Data/Rock_weathering_new2_otuTab.txt", header = TRUE, row.names = 1, sep = "\t") %>%
t() %>%
as.data.frame() ->
Rock_weathering_OTUmat
sort_order <- as.numeric(gsub("OTU([0-9]+)", "\\1", colnames(Rock_weathering_OTUmat)))
Rock_weathering_OTUmat <- Rock_weathering_OTUmat[, order(sort_order)]
row.names(Rock_weathering_OTUmat) <- gsub("(.*)Nimrod[0-9]+|Osnat[0-9]+", "\\1", row.names(Rock_weathering_OTUmat))
Metadata <- read.csv("Data/Rock_weathering_metadata_RA.csv", row.names = 1, header = TRUE)
# Order abundance_mat samples according to the metadata
sample_order <- match(row.names(Rock_weathering_OTUmat), row.names(Metadata))
Rock_weathering_OTUmat %<>% arrange(., sample_order)
Metadata$sample_names <- row.names(Metadata)
Metadata$Uni.Source <- fct_collapse(Metadata$Source, Rock = c("Dolomite", "Limestone"))
Metadata$Climate.Source <-
factor(
paste(
Metadata$Climate,
Metadata$Source
),
levels = c(
"Arid Limestone",
"Arid Dust",
"Arid Loess soil",
"Hyperarid Dolomite",
"Hyperarid Dust",
"Hyperarid Loess soil"
),
labels = c(
"Arid limestone",
"Arid dust",
"Arid loess soil",
"Hyperarid dolomite",
"Hyperarid dust",
"Hyperarid loess soil"
)
)
Metadata$Climate.UniSource <-
factor(
paste(
Metadata$Climate,
Metadata$Uni.Source
),
levels = c(
"Arid Rock",
"Arid Dust",
"Arid Loess soil",
"Hyperarid Rock",
"Hyperarid Dust",
"Hyperarid Loess soil"
),
labels = c(
"Arid rock",
"Arid dust",
"Arid loess soil",
"Hyperarid rock",
"Hyperarid dust",
"Hyperarid loess soil"
)
)
# calculate sample size
Metadata$Lib.size = rowSums(Rock_weathering_OTUmat)
row.names(Rock_weathering_OTUmat) <- row.names(Metadata)
# Load taxonomy data
tax.file <- "Data/Rock_weathering_new2_silva.nrv119.taxonomy"
Taxonomy <- read.table(tax.file, stringsAsFactors = FALSE) # read taxonomy file
# count how many ';' in each cell and add up to 6
for (i in 1:nrow(Taxonomy)) {
semicolons <- length(gregexpr(";", Taxonomy$V2[i])[[1]])
if (semicolons < 6) {
x <- paste0(rep("Unclassified;", 6 - semicolons), collapse = "")
Taxonomy$V2[i] <- paste0(Taxonomy$V2[i], x, sep = "")
}
}
do.call( "rbind", strsplit( Taxonomy$V1, ";", fixed = TRUE)) %>%
gsub( "size=([0-9]+)", "\\1", .) %>%
data.frame( ., do.call( "rbind", strsplit( Taxonomy$V2, ";", fixed = TRUE)), stringsAsFactors = F) %>%
apply(., 2, function(x) gsub( "\\(.*\\)", "", x)) %>%
replace(., . == "unclassified", "Unclassified") ->
Taxonomy
colnames( Taxonomy ) <- c( "OTU", "Frequency", "Domain", "Phylum", "Class", "Order", "Family", "Genus" )
# rownames(Taxonomy) <- colnames(Rock_weathering_OTUmat)
rownames(Taxonomy) <- Taxonomy[, 1]
# generate phyloseq object
Rock_dust <- phyloseq(otu_table(Rock_weathering_OTUmat, taxa_are_rows = FALSE),
tax_table(Taxonomy[, -c(1, 2)]),
sample_data(Metadata)
)
# Reorder factors for plotting
sample_data(Rock_dust)$Source %<>% fct_relevel("Limestone", "Dolomite", "Dust", "Loess soil")Remove samples not for analysis
samples2remove <- c(2, 3, 4, 5, 6, 7, 8, 10, 12)
Rock_dust <- subset_samples(Rock_dust, !grepl(paste(c(sample_names(Rock_dust)[samples2remove]), collapse = "|"), sample_names(Rock_dust)))
Rock_dust <- filter_taxa(Rock_dust, function(x) sum(x) > 0, TRUE)
domains2remove <- c("", "Eukaryota", "Unclassified")
classes2remove <- c("Chloroplast")
families2remove <- c("Mitochondria")
Rock_weathering_filt <- subset_taxa(Rock_dust, !is.na(Phylum) &
!Domain %in% domains2remove &
!Class %in% classes2remove &
!Family %in% families2remove)First let's explore the prevalence of different taxa in the database.
prevdf <- apply(X = otu_table(Rock_weathering_filt),
MARGIN = ifelse(taxa_are_rows(Rock_weathering_filt), yes = 1, no = 2),
FUN = function(x){sum(x > 0)})
# Add taxonomy and total read counts to this data.frame
prevdf <- data.frame(Prevalence = prevdf,
TotalAbundance = taxa_sums(Rock_weathering_filt),
tax_table(Rock_weathering_filt))
prevdf %>%
group_by(Phylum) %>%
summarise(`Mean prevalence` = mean(Prevalence),
`Sum prevalence` = sum(Prevalence)) ->
Prevalence_phylum_summary
Prevalence_phylum_summary %>%
kable(., digits = c(0, 1, 0)) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = F)| Phylum | Mean prevalence | Sum prevalence |
|---|---|---|
| Acidobacteria | 16.9 | 1281 |
| Actinobacteria | 20.1 | 5660 |
| Aquificae | 14.3 | 43 |
| Armatimonadetes | 15.8 | 79 |
| Bacteroidetes | 16.2 | 2337 |
| Caldiserica | 2.0 | 2 |
| Candidate_division_BRC1 | 14.0 | 28 |
| Candidate_division_OD1 | 20.2 | 81 |
| Candidate_division_OP11 | 9.0 | 9 |
| Candidate_division_TM7 | 18.3 | 440 |
| Chlorobi | 15.5 | 62 |
| Chloroflexi | 14.9 | 2753 |
| Cyanobacteria | 19.4 | 874 |
| Deinococcus-Thermus | 19.6 | 274 |
| Elusimicrobia | 11.0 | 22 |
| Fibrobacteres | 16.2 | 65 |
| Firmicutes | 16.2 | 1164 |
| Fusobacteria | 14.0 | 28 |
| Gemmatimonadetes | 15.4 | 848 |
| Nitrospirae | 22.0 | 44 |
| NPL-UPA2 | 6.0 | 6 |
| Planctomycetes | 12.7 | 420 |
| Proteobacteria | 19.1 | 4979 |
| SBYG-2791 | 9.0 | 9 |
| SM2F11 | 20.0 | 20 |
| Spirochaetae | 15.0 | 15 |
| Synergistetes | 11.0 | 11 |
| Tenericutes | 3.0 | 3 |
| Thaumarchaeota | 21.0 | 21 |
| Verrucomicrobia | 13.2 | 383 |
| WCHB1-60 | 19.0 | 57 |
prevdf %>%
group_by(Order) %>%
summarise(`Mean prevalence` = mean(Prevalence),
`Sum prevalence` = sum(Prevalence)) ->
Prevalence_Order_summary
Prevalence_Order_summary %>%
kable(., digits = c(0, 1, 0)) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = F)| Order | Mean prevalence | Sum prevalence |
|---|---|---|
| 11B-2 | 9.2 | 55 |
| Acidimicrobiales | 18.2 | 709 |
| Acidithiobacillales | 21.0 | 21 |
| Actinomycetales | 12.0 | 12 |
| Aeromonadales | 21.0 | 21 |
| AKIW781 | 11.0 | 592 |
| AKYG1722 | 17.4 | 157 |
| Alteromonadales | 14.5 | 29 |
| Anaerolineales | 21.0 | 63 |
| Aquificales | 14.3 | 43 |
| Ardenticatenales | 9.8 | 98 |
| AT425-EubC11_terrestrial_group | 18.2 | 328 |
| Bacillales | 18.3 | 475 |
| Bacteroidales | 10.7 | 192 |
| BD2-11_terrestrial_group | 26.0 | 26 |
| BD72BR169 | 14.0 | 14 |
| Bdellovibrionales | 14.5 | 58 |
| BG.g7 | 26.0 | 26 |
| Bifidobacteriales | 9.0 | 18 |
| Brocadiales | 15.5 | 31 |
| Burkholderiales | 19.3 | 559 |
| C0119 | 12.0 | 120 |
| Caenarcaniphilales | 23.0 | 23 |
| Caldilineales | 17.0 | 102 |
| Caldisericales | 2.0 | 2 |
| Campylobacterales | 14.6 | 73 |
| Caulobacterales | 23.4 | 257 |
| Chlorobiales | 15.5 | 62 |
| Chloroflexales | 7.4 | 52 |
| Chromatiales | 16.8 | 84 |
| Chthoniobacterales | 14.1 | 226 |
| Clostridiales | 15.4 | 401 |
| Corynebacteriales | 16.9 | 203 |
| Cytophagales | 18.6 | 1113 |
| Dehalococcoidales | 8.0 | 16 |
| Deinococcales | 19.6 | 255 |
| Desulfobacterales | 8.4 | 42 |
| Desulfovibrionales | 8.0 | 8 |
| Desulfurellales | 11.0 | 11 |
| Desulfuromonadales | 22.0 | 22 |
| Elev-16S-976 | 23.5 | 47 |
| EMP-G18 | 3.0 | 3 |
| Enterobacteriales | 27.0 | 135 |
| Erysipelotrichales | 11.0 | 33 |
| Euzebyales | 19.4 | 252 |
| Fibrobacterales | 16.2 | 65 |
| Flavobacteriales | 16.4 | 164 |
| Frankiales | 28.2 | 367 |
| Fusobacteriales | 14.0 | 28 |
| Gaiellales | 18.4 | 386 |
| Gammaproteobacteria_Incertae_Sedis | 8.0 | 8 |
| Gemmatimonadales | 14.4 | 446 |
| GR-WP33-30 | 23.0 | 46 |
| HOC36 | 8.0 | 8 |
| JG30-KF-CM45 | 20.8 | 604 |
| Kineosporiales | 24.2 | 145 |
| Lactobacillales | 14.4 | 158 |
| Legionellales | 9.0 | 27 |
| Lineage_IIb | 9.0 | 9 |
| Lineage_IV | 13.0 | 13 |
| LNR_A2-18 | 15.0 | 15 |
| Methylophilales | 11.5 | 23 |
| Micrococcales | 24.3 | 365 |
| Micromonosporales | 16.2 | 130 |
| Myxococcales | 15.4 | 200 |
| Neisseriales | 14.5 | 58 |
| Nitriliruptorales | 21.3 | 64 |
| Nitrosomonadales | 28.0 | 28 |
| Nitrospirales | 22.0 | 44 |
| NKB5 | 21.0 | 21 |
| Obscuribacterales | 12.5 | 50 |
| Oceanospirillales | 15.0 | 15 |
| Opitutales | 10.0 | 30 |
| Order_II | 16.0 | 32 |
| Order_III | 16.0 | 48 |
| Order_IV | 26.0 | 26 |
| Pasteurellales | 14.0 | 14 |
| Phycisphaerales | 19.0 | 19 |
| Planctomycetales | 12.3 | 344 |
| Propionibacteriales | 19.6 | 352 |
| Pseudomonadales | 19.6 | 372 |
| Pseudonocardiales | 20.7 | 352 |
| PYR10d3 | 22.0 | 22 |
| Rhizobiales | 20.8 | 955 |
| Rhodobacterales | 21.9 | 219 |
| Rhodocyclales | 19.0 | 57 |
| Rhodospirillales | 18.0 | 450 |
| Rickettsiales | 19.2 | 115 |
| Rubrobacterales | 28.5 | 484 |
| S0134_terrestrial_group | 9.6 | 48 |
| SC-I-84 | 15.0 | 15 |
| Selenomonadales | 19.0 | 19 |
| Solirubrobacterales | 21.0 | 989 |
| Sphaerobacterales | 14.3 | 43 |
| Sphingobacteriales | 15.3 | 751 |
| Sphingomonadales | 24.0 | 552 |
| Streptomycetales | 23.0 | 69 |
| Streptosporangiales | 22.5 | 45 |
| Subgroup_10 | 15.0 | 30 |
| Subgroup_2 | 13.0 | 13 |
| Subgroup_3 | 16.1 | 129 |
| Subgroup_4 | 16.9 | 523 |
| Subgroup_6 | 19.2 | 441 |
| Subgroup_7 | 18.0 | 90 |
| SubsectionI | 20.7 | 62 |
| SubsectionII | 23.8 | 285 |
| SubsectionIII | 18.5 | 351 |
| SubsectionIV | 19.0 | 76 |
| Synergistales | 11.0 | 11 |
| Thermales | 19.0 | 19 |
| Thermoanaerobacterales | 13.2 | 53 |
| Thermogemmatisporales | 14.5 | 29 |
| Thermophilales | 22.8 | 91 |
| Thiotrichales | 13.0 | 39 |
| TRA3-20 | 20.2 | 101 |
| Unclassified | 16.7 | 2355 |
| Unknown_Order | 14.6 | 73 |
| Vampirovibrionales | 12.0 | 12 |
| Verrucomicrobiales | 8.0 | 24 |
| Xanthomonadales | 17.9 | 233 |
Based on that we'll remove all phyla with a prevalence of under 7
Prevalence_phylum_summary %>%
filter(`Sum prevalence` < 7) %>%
select(Phylum) %>%
map(as.character) %>%
unlist() ->
filterPhyla
Rock_weathering_filt2 <- subset_taxa(Rock_weathering_filt, !Phylum %in% filterPhyla)
sample_data(Rock_weathering_filt2)$Lib.size <- rowSums(otu_table(Rock_weathering_filt2))
print(Rock_weathering_filt)## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1259 taxa and 34 samples ]
## sample_data() Sample Data: [ 34 samples by 13 sample variables ]
## tax_table() Taxonomy Table: [ 1259 taxa by 6 taxonomic ranks ]
print(Rock_weathering_filt2)## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1256 taxa and 34 samples ]
## sample_data() Sample Data: [ 34 samples by 13 sample variables ]
## tax_table() Taxonomy Table: [ 1256 taxa by 6 taxonomic ranks ]
Plot general prevalence features of the phyla
# Subset to the remaining phyla
prevdf_phylum_filt <- subset(prevdf, Phylum %in% get_taxa_unique(Rock_weathering_filt2, "Phylum"))
ggplot(prevdf_phylum_filt,
aes(TotalAbundance, Prevalence / nsamples(Rock_weathering_filt2), color = Phylum)) +
# Include a guess for parameter
geom_hline(yintercept = 0.05,
alpha = 0.5,
linetype = 2) + geom_point(size = 2, alpha = 0.7) +
scale_x_log10() + xlab("Total Abundance") + ylab("Prevalence [Frac. Samples]") +
facet_wrap( ~ Phylum) + theme(legend.position = "none")
Plot general prevalence features of the top 20 orders
# Subset to the remaining phyla
prevdf_order_filt <- subset(prevdf, Order %in% get_taxa_unique(Rock_weathering_filt2, "Order"))
# grab the top 30 most abundant orders
prevdf_order_filt %>%
group_by(Order) %>%
summarise(Combined.abundance = sum(TotalAbundance)) %>%
arrange(desc(Combined.abundance)) %>%
.[1:30, "Order"] ->
Orders2plot
prevdf_order_filt2 <- subset(prevdf, Order %in% Orders2plot$Order)
ggplot(prevdf_order_filt2,
aes(TotalAbundance, Prevalence / nsamples(Rock_weathering_filt2), color = Order)) +
# Include a guess for parameter
geom_hline(yintercept = 0.05,
alpha = 0.5,
linetype = 2) + geom_point(size = 2, alpha = 0.7) +
scale_x_log10() + xlab("Total Abundance") + ylab("Prevalence [Frac. Samples]") +
facet_wrap( ~ Order) + theme(legend.position = "none")
We'll remove all sequences which appear in less than 10% of the samples
# Define prevalence threshold as 10% of total samples
prevalenceThreshold <- 0.1 * nsamples(Rock_weathering_filt)
prevalenceThreshold## [1] 3.4
# Execute prevalence filter, using `prune_taxa()` function
keepTaxa <-
row.names(prevdf_phylum_filt)[(prevdf_phylum_filt$Prevalence >= prevalenceThreshold)]
Rock_weathering_filt3 <- prune_taxa(keepTaxa, Rock_weathering_filt2)
sample_data(Rock_weathering_filt3)$Lib.size <- rowSums(otu_table(Rock_weathering_filt3))
print(Rock_weathering_filt2)## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1256 taxa and 34 samples ]
## sample_data() Sample Data: [ 34 samples by 13 sample variables ]
## tax_table() Taxonomy Table: [ 1256 taxa by 6 taxonomic ranks ]
print(Rock_weathering_filt3)## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1249 taxa and 34 samples ]
## sample_data() Sample Data: [ 34 samples by 13 sample variables ]
## tax_table() Taxonomy Table: [ 1249 taxa by 6 taxonomic ranks ]
This removed 7 or 0.557% of the sequences.
First let's look at the count data distribution
PlotLibDist(Rock_weathering_filt3)
sample_data(Rock_weathering_filt3) %>%
remove_rownames %>%
select(sample_title, Lib.size) %>%
as(., "data.frame") %>%
kable(.) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = F)| sample_title | Lib.size |
|---|---|
| Arid_Settled Dust_1 | 1562 |
| Hyperarid_Loess soil_8 | 6536 |
| Hyperarid_Loess soil_10 | 3921 |
| Hyperarid_Loess soil_12 | 4935 |
| Arid_Settled Dust_2 | 4421 |
| Hyperarid_Settled Dust_1 | 1001 |
| Hyperarid_Settled Dust_2 | 16095 |
| Arid_Limestone_1 | 9765 |
| Arid_Limestone_2 | 9130 |
| Arid_Limestone_3 | 11218 |
| Arid_Limestone_4 | 13838 |
| Arid_Limestone_5 | 11177 |
| Arid_Limestone_6 | 10781 |
| Arid_Limestone_7 | 15417 |
| Arid_Limestone_8 | 9721 |
| Arid_Limestone_9 | 20927 |
| Arid_Limestone_10 | 16812 |
| Arid_Limestone_11 | 14325 |
| Arid_Limestone_12 | 5112 |
| Hyperarid_Dolomite_1 | 62166 |
| Hyperarid_Dolomite_2 | 73930 |
| Hyperarid_Dolomite_3 | 123438 |
| Hyperarid_Dolomite_4 | 74161 |
| Hyperarid_Dolomite_5 | 98998 |
| Hyperarid_Dolomite_6 | 97834 |
| Hyperarid_Dolomite_7 | 160207 |
| Hyperarid_Dolomite_8 | 78535 |
| Hyperarid_Dolomite_9 | 47155 |
| Hyperarid_Dolomite_10 | 52276 |
| Hyperarid_Dolomite_11 | 63267 |
| Hyperarid_Dolomite_12 | 53859 |
| Arid_Loess soil_1 | 61130 |
| Arid_Loess soil_2 | 62204 |
| Arid_Loess soil_3 | 55724 |
(mod1 <- adonis(
otu_table(Rock_weathering_filt3) ~ Lib.size,
data = as(sample_data(Rock_weathering_filt3), "data.frame"),
method = "bray",
permutations = 9999
))##
## Call:
## adonis(formula = otu_table(Rock_weathering_filt3) ~ Lib.size, data = as(sample_data(Rock_weathering_filt3), "data.frame"), permutations = 9999, method = "bray")
##
## Permutation: free
## Number of permutations: 9999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Lib.size 1 2.5245 2.52447 8.7483 0.21469 1e-04 ***
## Residuals 32 9.2341 0.28857 0.78531
## Total 33 11.7586 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
PlotReadHist(as(otu_table(Rock_weathering_filt3), "matrix"))
notAllZero <- (rowSums(t(otu_table(Rock_weathering_filt3))) > 0)
meanSdPlot(as.matrix(log2(t(otu_table(Rock_weathering_filt3))[notAllZero, ] + 1)))
We'll use the GMPR method [@chen_gmpr:_2017]
Rock_weathering_filt3_GMPR <- Rock_weathering_filt3
Rock_weathering_filt3 %>%
otu_table(.) %>%
t() %>%
as(., "matrix") %>%
GMPR() ->
GMPR_factors## Begin GMPR size factor calculation ...
## Completed!
## Please watch for the samples with limited sharing with other samples based on NSS! They may be outliers!
Rock_weathering_filt3 %>%
otu_table(.) %>%
t() %*% diag(1 / GMPR_factors$gmpr) %>%
t() %>%
as.data.frame(., row.names = sample_names(Rock_weathering_filt3)) %>%
otu_table(., taxa_are_rows = FALSE) ->
otu_table(Rock_weathering_filt3_GMPR)
sample_data(Rock_weathering_filt3_GMPR)$Lib.size <- sample_sums(Rock_weathering_filt3_GMPR)
adonis(
otu_table(Rock_weathering_filt3_GMPR) ~ Lib.size,
data = as(sample_data(Rock_weathering_filt3_GMPR), "data.frame"),
method = "bray",
permutations = 9999
)##
## Call:
## adonis(formula = otu_table(Rock_weathering_filt3_GMPR) ~ Lib.size, data = as(sample_data(Rock_weathering_filt3_GMPR), "data.frame"), permutations = 9999, method = "bray")
##
## Permutation: free
## Number of permutations: 9999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Lib.size 1 2.5834 2.58337 9.4557 0.22809 1e-04 ***
## Residuals 32 8.7426 0.27321 0.77191
## Total 33 11.3260 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
PlotLibDist(Rock_weathering_filt3_GMPR)
PlotReadHist(as(otu_table(Rock_weathering_filt3_GMPR), "matrix"))
notAllZero <- (rowSums(t(otu_table(Rock_weathering_filt3_GMPR))) > 0)
meanSdPlot(as.matrix(log2(t(otu_table(Rock_weathering_filt3_GMPR))[notAllZero, ] + 1)))
Calculate and plot alpha diversity mertrics.
# non-parametric richness estimates
rarefaction.mat <- matrix(0, nrow = nsamples(Rock_weathering_filt3), ncol = bootstraps)
rownames(rarefaction.mat) <- sample_names(Rock_weathering_filt3)
rich.ests <- list(S.obs = rarefaction.mat, S.chao1 = rarefaction.mat, se.chao1 = rarefaction.mat,
S.ACE = rarefaction.mat, se.ACE = rarefaction.mat)
for (i in seq(bootstraps)) {
sub.OTUmat <- rrarefy(otu_table(Rock_weathering_filt3), min(rowSums(otu_table(Rock_weathering_filt3))))
for (j in seq(length(rich.ests))) {
rich.ests[[j]][, i] <- t(estimateR(sub.OTUmat))[, j]
}
}
Richness <- data.frame(row.names = row.names(rich.ests[[1]]))
for (i in c(1, seq(2, length(rich.ests), 2))) {
S <- apply(rich.ests[[i]], 1, mean)
if (i == 1) {
se <- apply(rich.ests[[i]], 1, function(x) (mean(x)/sqrt(length(x))))
} else se <- apply(rich.ests[[i + 1]], 1, mean)
Richness <- cbind(Richness, S, se)
}
colnames(Richness) <- c("S.obs", "S.obs.se", "S.chao1", "S.chao1.se", "S.ACE", "S.ACE.se")
saveRDS(Richness, file = "Results/Rock_weathering_Richness.Rds")
write.csv(Richness, file = "Results/Rock_weathering_Richness.csv")
ses <- grep("\\.se", colnames(Richness))
Richness[, ses] %>%
gather(key = "est.se") -> se.dat
Richness[, -unique(ses)] %>%
gather(key = "est") -> mean.dat
n <- length(unique(mean.dat$est))
# diversity indices
diversity.inds <- list(Shannon = rarefaction.mat, inv.simpson = rarefaction.mat, BP = rarefaction.mat)
for (i in seq(bootstraps)) {
sub.OTUmat <- rrarefy(otu_table(Rock_weathering_filt3), min(rowSums(otu_table(Rock_weathering_filt3))))
diversity.inds$Shannon[, i] <- diversityresult(sub.OTUmat, index = 'Shannon', method = 'each site', digits = 3)[, 1]
diversity.inds$inv.simpson[, i] <- diversityresult(sub.OTUmat, index = 'inverseSimpson', method = 'each site', digits = 3)[, 1]
diversity.inds$BP[, i] <- diversityresult(sub.OTUmat, index = 'Berger', method = 'each site', digits = 3)[, 1]
}
Diversity <- data.frame(row.names = row.names(diversity.inds[[1]]))
for (i in seq(length(diversity.inds))) {
S <- apply(diversity.inds[[i]], 1, mean)
se <- apply(diversity.inds[[i]], 1, function(x) (mean(x)/sqrt(length(x))))
Diversity <- cbind(Diversity, S, se)
}
colnames(Diversity) <- c("Shannon", "Shannon.se", "Inv.simpson", "Inv.simpson.se", "BP", "BP.se")
ses <- grep("\\.se", colnames(Diversity))
Diversity[, ses] %>% gather(key = "est.se") -> se.dat
Diversity[, -unique(ses)] %>% gather(key = "est") -> mean.dat
saveRDS(Diversity, file = "Results/Rock_weathering_Diversity.Rds")
write.csv(Diversity, file = "Results/Rock_weathering_Diversity.csv")Test the differences in alpha diversity.
Richness_Diversity_long[Richness_Diversity_long$Metric != "Chao1" &
Richness_Diversity_long$Metric != "Inv. Simpson" &
Richness_Diversity_long$Metric != "Berger Parker", ] %>%
droplevels() ->
Richness_Diversity_long2plot
p_alpha <- ggplot(Richness_Diversity_long2plot, aes(
x = Source,
y = Estimate
)) +
geom_violin(aes(colour = Climate, fill = Climate), alpha = 1/3) +
geom_jitter(aes(colour = Climate, fill = Climate), shape = 16, size = 2, width = 0.2, alpha = 2/3) +
scale_colour_manual(values = pom4, name = "") +
scale_fill_manual(values = pom4, name = "") +
theme_cowplot(font_size = 11, font_family = f_name) +
# geom_errorbar(alpha = 1 / 2, width = 0.3) +
xlab("") +
ylab("") +
theme(axis.text.x = element_text(
angle = 45,
vjust = 0.9,
hjust = 0.9
)) +
facet_grid(Metric ~ Climate, shrink = FALSE, scale = "free") +
background_grid(major = "y",
minor = "none") +
theme(panel.spacing = unit(2, "lines"))
dat_text <- data.frame(
label = as.character(fct_c(ph_Sobs$groups$groups, ph_ACE$groups$groups, ph_Shannon$groups$groups)),
Metric = rep(levels(Richness_Diversity_long2plot$Metric), each = 6),
Climate = str_split(rownames(ph_Sobs$groups), ":", simplify = TRUE)[, 1],
x = c("Loess soil", "Loess soil", "Limestone", "Dust", "Dolomite", "Dust"),
# x = as.factor(levels(Richness_Diversity_long2plot$Climate.Source)),
y = rep(c(460, 850, 6.5), each = 6)
# y = rep(c(40, 140, 0.5), each = 6)
)
p_alpha <- p_alpha + geom_text(
data = dat_text,
mapping = aes(x = x, y = y, label = label),
nudge_x = -0.2,
nudge_y = -0.1
)
print(p_alpha)
Richness_Diversity_long2plot %>%
group_by(Metric, Climate.Source) %>% # the grouping variable
summarise(mean_PL = mean(Estimate), # calculates the mean of each group
sd_PL = sd(Estimate), # calculates the standard deviation of each group
n_PL = n(), # calculates the sample size per group
SE_PL = sd(Estimate)/sqrt(n())) %>% # calculates the standard error of each group
kable(.) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = F)| Metric | Climate.Source | mean_PL | sd_PL | n_PL | SE_PL |
|---|---|---|---|---|---|
| S obs. | Arid limestone | 181.614750 | 51.1677757 | 12 | 14.7708645 |
| S obs. | Arid dust | 169.249500 | 109.0153596 | 2 | 77.0855000 |
| S obs. | Arid loess soil | 416.015667 | 8.2772347 | 3 | 4.7788637 |
| S obs. | Hyperarid dolomite | 128.760500 | 31.2564362 | 12 | 9.0229559 |
| S obs. | Hyperarid dust | 107.261000 | 88.7263447 | 2 | 62.7390000 |
| S obs. | Hyperarid loess soil | 220.405000 | 54.3993291 | 3 | 31.4074673 |
| ACE | Arid limestone | 353.781788 | 68.8104346 | 12 | 19.8638615 |
| ACE | Arid dust | 334.651440 | 143.6013458 | 2 | 101.5414854 |
| ACE | Arid loess soil | 746.497431 | 20.7936696 | 3 | 12.0052307 |
| ACE | Hyperarid dolomite | 314.743324 | 100.4297817 | 12 | 28.9915808 |
| ACE | Hyperarid dust | 311.050796 | 182.7650780 | 2 | 129.2344260 |
| ACE | Hyperarid loess soil | 466.260376 | 42.5794120 | 3 | 24.5832350 |
| Shannon | Arid limestone | 3.782559 | 1.0939745 | 12 | 0.3158033 |
| Shannon | Arid dust | 2.997964 | 1.5634435 | 2 | 1.1055215 |
| Shannon | Arid loess soil | 5.594932 | 0.0319481 | 3 | 0.0184452 |
| Shannon | Hyperarid dolomite | 3.328207 | 0.2683934 | 12 | 0.0774785 |
| Shannon | Hyperarid dust | 1.483307 | 0.8891069 | 2 | 0.6286935 |
| Shannon | Hyperarid loess soil | 3.779445 | 0.8513243 | 3 | 0.4915123 |
Calculate and plot beta diversity mertrics.
Is there a difference between the two sites. However, since we know that that samples are of different nature we'll have to control for rock type, source and location:
(mod1 <- adonis(
otu_table(Rock_weathering_filt3_GMPR) ~ Climate * Source * Location,
data = as(sample_data(Rock_weathering_filt3_GMPR), "data.frame"),
method = "horn",
permutations = 9999
))##
## Call:
## adonis(formula = otu_table(Rock_weathering_filt3_GMPR) ~ Climate * Source * Location, data = as(sample_data(Rock_weathering_filt3_GMPR), "data.frame"), permutations = 9999, method = "horn")
##
## Permutation: free
## Number of permutations: 9999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Climate 1 2.5299 2.52988 21.3820 0.21632 0.0001 ***
## Source 3 4.7060 1.56865 13.2579 0.40238 0.0001 ***
## Location 1 0.4870 0.48696 4.1157 0.04164 0.0033 **
## Climate:Source 1 0.4397 0.43972 3.7164 0.03760 0.0026 **
## Climate:Location 1 0.4605 0.46052 3.8922 0.03938 0.0056 **
## Source:Location 1 0.1142 0.11421 0.9653 0.00977 0.4812
## Residuals 25 2.9580 0.11832 0.25292
## Total 33 11.6952 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Rock_weathering_filt3_GMPR_Arid <- subset_samples(Rock_weathering_filt3_GMPR, Climate == "Arid")
Rock_weathering_filt3_GMPR_Arid <- filter_taxa(Rock_weathering_filt3_GMPR_Arid, function(x) sum(x) > 0, TRUE)
(mod2 <- adonis(
otu_table(Rock_weathering_filt3_GMPR_Arid) ~ Source * Location,
data = as(sample_data(Rock_weathering_filt3_GMPR_Arid), "data.frame"),
method = "horn",
permutations = 9999
))##
## Call:
## adonis(formula = otu_table(Rock_weathering_filt3_GMPR_Arid) ~ Source * Location, data = as(sample_data(Rock_weathering_filt3_GMPR_Arid), "data.frame"), permutations = 9999, method = "horn")
##
## Permutation: free
## Number of permutations: 9999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Source 2 2.3058 1.15288 7.3438 0.47881 0.0001 ***
## Location 1 0.4691 0.46905 2.9878 0.09740 0.0132 *
## Residuals 13 2.0408 0.15699 0.42379
## Total 16 4.8156 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Rock_weathering_filt3_GMPR_Hyperarid <- subset_samples(Rock_weathering_filt3_GMPR, Climate == "Hyperarid")
Rock_weathering_filt3_GMPR_Hyperarid <- filter_taxa(Rock_weathering_filt3_GMPR_Hyperarid, function(x) sum(x) > 0, TRUE)
(mod3 <- adonis(
otu_table(Rock_weathering_filt3_GMPR_Hyperarid) ~ Source * Location,
data = as(sample_data(Rock_weathering_filt3_GMPR_Hyperarid), "data.frame"),
method = "horn",
permutations = 9999
))##
## Call:
## adonis(formula = otu_table(Rock_weathering_filt3_GMPR_Hyperarid) ~ Source * Location, data = as(sample_data(Rock_weathering_filt3_GMPR_Hyperarid), "data.frame"), permutations = 9999, method = "horn")
##
## Permutation: free
## Number of permutations: 9999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Source 2 2.8454 1.42270 18.6150 0.65416 0.0001 ***
## Location 1 0.4729 0.47295 6.1882 0.10873 0.0102 *
## Source:Location 1 0.1142 0.11421 1.4944 0.02626 0.2334
## Residuals 12 0.9171 0.07643 0.21085
## Total 16 4.3497 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
According to this model we see that indeed there's an effect of site on the community (p = 0.001), and that effect accounts for about 17% of the variance. Also, considering that Location is only borderline significant and explains very little of the data, we could probably take it out of the model to make a minimal adequate model.
(mod4 <- adonis(
otu_table(Rock_weathering_filt3_GMPR) ~ Climate * Source,
data = as(sample_data(Rock_weathering_filt3_GMPR), "data.frame"),
method = "horn",
permutations = 9999
))##
## Call:
## adonis(formula = otu_table(Rock_weathering_filt3_GMPR) ~ Climate * Source, data = as(sample_data(Rock_weathering_filt3_GMPR), "data.frame"), permutations = 9999, method = "horn")
##
## Permutation: free
## Number of permutations: 9999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Climate 1 2.5299 2.52988 17.6466 0.21632 1e-04 ***
## Source 3 4.7060 1.56865 10.9418 0.40238 1e-04 ***
## Climate:Source 1 0.4452 0.44521 3.1055 0.03807 8e-03 **
## Residuals 28 4.0142 0.14336 0.34323
## Total 33 11.6952 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(mod5 <- adonis(
otu_table(Rock_weathering_filt3_GMPR_Arid) ~ Source,
data = as(sample_data(Rock_weathering_filt3_GMPR_Arid), "data.frame"),
method = "horn",
permutations = 9999
))##
## Call:
## adonis(formula = otu_table(Rock_weathering_filt3_GMPR_Arid) ~ Source, data = as(sample_data(Rock_weathering_filt3_GMPR_Arid), "data.frame"), permutations = 9999, method = "horn")
##
## Permutation: free
## Number of permutations: 9999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Source 2 2.3058 1.15288 6.4307 0.47881 2e-04 ***
## Residuals 14 2.5099 0.17928 0.52119
## Total 16 4.8156 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(mod6 <- adonis(
otu_table(Rock_weathering_filt3_GMPR_Hyperarid) ~ Source,
data = as(sample_data(Rock_weathering_filt3_GMPR_Hyperarid), "data.frame"),
method = "horn",
permutations = 9999
))##
## Call:
## adonis(formula = otu_table(Rock_weathering_filt3_GMPR_Hyperarid) ~ Source, data = as(sample_data(Rock_weathering_filt3_GMPR_Hyperarid), "data.frame"), permutations = 9999, method = "horn")
##
## Permutation: free
## Number of permutations: 9999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Source 2 2.8454 1.42270 13.241 0.65416 2e-04 ***
## Residuals 14 1.5043 0.10745 0.34584
## Total 16 4.3497 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Final model
print(mod4)##
## Call:
## adonis(formula = otu_table(Rock_weathering_filt3_GMPR) ~ Climate * Source, data = as(sample_data(Rock_weathering_filt3_GMPR), "data.frame"), permutations = 9999, method = "horn")
##
## Permutation: free
## Number of permutations: 9999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Climate 1 2.5299 2.52988 17.6466 0.21632 1e-04 ***
## Source 3 4.7060 1.56865 10.9418 0.40238 1e-04 ***
## Climate:Source 1 0.4452 0.44521 3.1055 0.03807 8e-03 **
## Residuals 28 4.0142 0.14336 0.34323
## Total 33 11.6952 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mod4_pairwise <- PairwiseAdonis(
otu_table(Rock_weathering_filt3_GMPR),
sample_data(Rock_weathering_filt3_GMPR)$Climate.Source,
sim.function = "vegdist",
sim.method = "horn",
p.adjust.m = "BH"
)
print(mod4_pairwise)## pairs total.DF F.Model R2 p.value
## 1 Arid dust vs Hyperarid loess soil 4 4.803200 0.6155424 0.1000000
## 2 Arid dust vs Hyperarid dust 3 2.992098 0.5993669 0.3333333
## 3 Arid dust vs Arid limestone 13 4.606432 0.2773884 0.0117000
## 4 Arid dust vs Hyperarid dolomite 13 6.594183 0.3546369 0.0119000
## 5 Arid dust vs Arid loess soil 4 5.363663 0.6413055 0.1000000
## 6 Hyperarid loess soil vs Hyperarid dust 4 10.866238 0.7836472 0.2000000
## 7 Hyperarid loess soil vs Arid limestone 14 5.593760 0.3008407 0.0070000
## 8 Hyperarid loess soil vs Hyperarid dolomite 14 16.022459 0.5520710 0.0026000
## 9 Hyperarid loess soil vs Arid loess soil 5 95.510242 0.9598031 0.1000000
## 10 Hyperarid dust vs Arid limestone 13 5.343419 0.3080949 0.0313000
## 11 Hyperarid dust vs Hyperarid dolomite 13 11.619149 0.4919377 0.0094000
## 12 Hyperarid dust vs Arid loess soil 4 205.847549 0.9856355 0.1000000
## 13 Arid limestone vs Hyperarid dolomite 23 21.900583 0.4988677 0.0001000
## 14 Arid limestone vs Arid loess soil 14 9.112582 0.4120994 0.0018000
## 15 Hyperarid dolomite vs Arid loess soil 14 16.841743 0.5643686 0.0032000
## p.adjusted sig
## 1 0.11538462
## 2 0.33333333
## 3 0.02231250 .
## 4 0.02231250 .
## 5 0.11538462
## 6 0.21428571
## 7 0.02100000 .
## 8 0.01200000 .
## 9 0.11538462
## 10 0.05216667
## 11 0.02231250 .
## 12 0.11538462
## 13 0.00150000 *
## 14 0.01200000 .
## 15 0.01200000 .
sig_pairs <- as.character(mod4_pairwise$pairs[mod4_pairwise$p.adjusted < 0.05])
simper(otu_table(Rock_weathering_filt3_GMPR), sample_data(Rock_weathering_filt3_GMPR)$Climate.Source, parallel = 4)## cumulative contributions of most influential species:
##
## $`Arid dust_Hyperarid loess soil`
## OTU6 OTU65 OTU838 OTU90 OTU596 OTU11 OTU187 OTU93 OTU746
## 0.2223516 0.3677866 0.4056901 0.4304991 0.4528999 0.4693262 0.4837166 0.4963321 0.5089143
## OTU167 OTU711 OTU121 OTU144 OTU99 OTU194 OTU105 OTU356 OTU340
## 0.5206772 0.5323162 0.5428037 0.5526841 0.5624404 0.5714645 0.5787230 0.5859611 0.5926902
## OTU115 OTU640 OTU55 OTU298 OTU715 OTU88 OTU197 OTU48 OTU221
## 0.5992910 0.6058761 0.6123625 0.6187386 0.6250711 0.6313974 0.6376038 0.6433882 0.6488153
## OTU301 OTU1047 OTU322 OTU16 OTU586 OTU46 OTU172 OTU386 OTU67
## 0.6541319 0.6593526 0.6645414 0.6694651 0.6739228 0.6782800 0.6825173 0.6866930 0.6908583
## OTU333 OTU854 OTU883
## 0.6945572 0.6981826 0.7016897
##
## $`Arid dust_Hyperarid dust`
## OTU6 OTU65 OTU838 OTU55
## 0.4922822 0.6517818 0.6971512 0.7299602
##
## $`Arid dust_Arid limestone`
## OTU6 OTU65 OTU20 OTU40 OTU838 OTU225 OTU422 OTU596 OTU73
## 0.1123216 0.2223095 0.2918201 0.3566264 0.3846318 0.4019503 0.4188998 0.4354418 0.4510851
## OTU46 OTU11 OTU388 OTU119 OTU29 OTU18 OTU34 OTU746 OTU711
## 0.4642743 0.4766768 0.4880677 0.4986328 0.5091612 0.5193871 0.5289518 0.5383129 0.5469937
## OTU16 OTU235 OTU854 OTU174 OTU299 OTU194 OTU68 OTU545 OTU37
## 0.5556579 0.5642893 0.5727110 0.5809073 0.5890471 0.5960083 0.6028022 0.6094612 0.6160857
## OTU925 OTU41 OTU115 OTU140 OTU356 OTU60 OTU137 OTU197 OTU166
## 0.6225697 0.6287094 0.6345215 0.6403150 0.6457386 0.6510360 0.6562074 0.6613174 0.6663109
## OTU605 OTU69 OTU55 OTU1089 OTU27 OTU218 OTU48 OTU45
## 0.6712870 0.6762240 0.6810537 0.6854239 0.6895759 0.6936273 0.6976757 0.7016868
##
## $`Arid dust_Hyperarid dolomite`
## OTU1 OTU2 OTU3 OTU7 OTU5 OTU65 OTU9 OTU18 OTU8
## 0.1527495 0.2363471 0.3165709 0.3820169 0.4147143 0.4454892 0.4704565 0.4947935 0.5170144
## OTU17 OTU41 OTU16 OTU11 OTU936 OTU20 OTU28 OTU12 OTU10
## 0.5378908 0.5586779 0.5762334 0.5933941 0.6090247 0.6239327 0.6383626 0.6526239 0.6663706
## OTU6 OTU15 OTU33 OTU14
## 0.6767124 0.6869395 0.6964878 0.7058854
##
## $`Arid dust_Arid loess soil`
## OTU65 OTU25 OTU88 OTU62 OTU11 OTU838 OTU68 OTU596
## 0.07600412 0.09862665 0.12048117 0.14055785 0.15967703 0.17826462 0.19067874 0.20145631
## OTU177 OTU78 OTU133 OTU144 OTU46 OTU115 OTU67 OTU16
## 0.21192993 0.22239646 0.23195007 0.24115456 0.25014254 0.25870758 0.26685638 0.27420872
## OTU156 OTU388 OTU116 OTU137 OTU197 OTU100 OTU91 OTU687
## 0.28151396 0.28845987 0.29497148 0.30133776 0.30763580 0.31387308 0.31978477 0.32560209
## OTU746 OTU711 OTU73 OTU412 OTU194 OTU422 OTU160 OTU218
## 0.33120586 0.33673375 0.34225822 0.34731488 0.35212805 0.35690940 0.36151216 0.36607842
## OTU152 OTU53 OTU131 OTU135 OTU356 OTU125 OTU163 OTU37
## 0.37046694 0.37485116 0.37885223 0.38274887 0.38655530 0.39035864 0.39395340 0.39753331
## OTU556 OTU311 OTU130 OTU214 OTU227 OTU240 OTU341 OTU233
## 0.40109574 0.40463803 0.40814028 0.41156987 0.41498306 0.41836142 0.42169721 0.42499571
## OTU854 OTU648 OTU101 OTU92 OTU55 OTU716 OTU220 OTU139
## 0.42827522 0.43153536 0.43476556 0.43797394 0.44115558 0.44428602 0.44740514 0.45049071
## OTU140 OTU278 OTU20 OTU103 OTU60 OTU253 OTU171 OTU1235
## 0.45356227 0.45649184 0.45941847 0.46234298 0.46512485 0.46790496 0.47063934 0.47334811
## OTU107 OTU365 OTU294 OTU301 OTU647 OTU206 OTU136 OTU231
## 0.47601073 0.47866935 0.48128416 0.48386623 0.48644557 0.48901761 0.49157714 0.49411399
## OTU479 OTU1050 OTU1288 OTU82 OTU248 OTU360 OTU181 OTU434
## 0.49659608 0.49905562 0.50151265 0.50394266 0.50636675 0.50871712 0.51105399 0.51337992
## OTU945 OTU638 OTU1102 OTU315 OTU1184 OTU457 OTU44 OTU491
## 0.51570002 0.51801894 0.52031981 0.52260630 0.52487989 0.52712302 0.52935591 0.53158523
## OTU472 OTU938 OTU184 OTU471 OTU182 OTU538 OTU352 OTU169
## 0.53380813 0.53602510 0.53823459 0.54042578 0.54253898 0.54464035 0.54673555 0.54882651
## OTU586 OTU191 OTU190 OTU751 OTU304 OTU251 OTU164 OTU667
## 0.55091733 0.55300683 0.55508810 0.55712859 0.55915785 0.56117530 0.56318056 0.56518015
## OTU69 OTU619 OTU170 OTU154 OTU992 OTU1013 OTU707 OTU302
## 0.56717714 0.56916832 0.57115385 0.57313482 0.57509789 0.57705219 0.57900145 0.58092857
## OTU725 OTU430 OTU1116 OTU132 OTU466 OTU364 OTU489 OTU166
## 0.58282302 0.58471488 0.58660087 0.58848567 0.59036511 0.59223482 0.59409227 0.59593839
## OTU33 OTU1218 OTU421 OTU1319 OTU545 OTU309 OTU456 OTU562
## 0.59778312 0.59962319 0.60146290 0.60326476 0.60505799 0.60683960 0.60859329 0.61034045
## OTU635 OTU468 OTU610 OTU347 OTU833 OTU582 OTU41 OTU333
## 0.61206287 0.61376517 0.61546266 0.61714945 0.61882254 0.62049477 0.62216179 0.62381474
## OTU6 OTU27 OTU86 OTU451 OTU162 OTU561 OTU439 OTU579
## 0.62545802 0.62705709 0.62865264 0.63024097 0.63181778 0.63338508 0.63491568 0.63644400
## OTU255 OTU653 OTU279 OTU368 OTU783 OTU93 OTU298 OTU323
## 0.63797113 0.63949107 0.64100660 0.64251744 0.64400822 0.64549236 0.64697241 0.64843615
## OTU1024 OTU1005 OTU389 OTU799 OTU348 OTU543 OTU524 OTU258
## 0.64988797 0.65133108 0.65277123 0.65418616 0.65559921 0.65699846 0.65838960 0.65977752
## OTU953 OTU929 OTU174 OTU1100 OTU999 OTU705 OTU199 OTU1049
## 0.66116274 0.66251874 0.66386908 0.66520644 0.66653398 0.66783135 0.66912435 0.67041127
## OTU1242 OTU449 OTU303 OTU232 OTU765 OTU499 OTU10 OTU1011
## 0.67169466 0.67295718 0.67420516 0.67544956 0.67668808 0.67792553 0.67913601 0.68034452
## OTU595 OTU697 OTU548 OTU353 OTU310 OTU28 OTU1170 OTU404
## 0.68154983 0.68274660 0.68394090 0.68513515 0.68632833 0.68750914 0.68868372 0.68984795
## OTU141 OTU277 OTU469 OTU883 OTU557 OTU95 OTU29 OTU1108
## 0.69101177 0.69216462 0.69330654 0.69444839 0.69558676 0.69671804 0.69784612 0.69896224
## OTU641
## 0.70007721
##
## $`Hyperarid loess soil_Hyperarid dust`
## OTU6 OTU55 OTU90 OTU11 OTU94 OTU187 OTU93 OTU167 OTU121
## 0.4547139 0.4932104 0.5179318 0.5361943 0.5506355 0.5649686 0.5775294 0.5893159 0.5997621
## OTU99 OTU144 OTU9 OTU105 OTU340 OTU298 OTU115 OTU640 OTU715
## 0.6095933 0.6193616 0.6270627 0.6343667 0.6410613 0.6476927 0.6542631 0.6608232 0.6672825
## OTU88 OTU197 OTU221 OTU322 OTU1047 OTU16 OTU484
## 0.6735780 0.6797532 0.6851539 0.6903247 0.6951534 0.6999152 0.7044646
##
## $`Hyperarid loess soil_Arid limestone`
## OTU6 OTU20 OTU40 OTU90 OTU225 OTU11 OTU73 OTU422 OTU119
## 0.2070926 0.2740292 0.3382045 0.3562460 0.3732290 0.3885619 0.4034598 0.4178979 0.4286859
## OTU29 OTU187 OTU46 OTU388 OTU18 OTU167 OTU34 OTU93 OTU299
## 0.4392449 0.4494930 0.4594962 0.4689023 0.4780078 0.4868025 0.4953853 0.5038895 0.5120122
## OTU235 OTU121 OTU174 OTU99 OTU925 OTU854 OTU545 OTU144 OTU37
## 0.5198446 0.5274965 0.5347188 0.5412268 0.5476305 0.5539799 0.5600864 0.5661491 0.5721572
## OTU197 OTU140 OTU68 OTU16 OTU340 OTU298 OTU605 OTU640 OTU69
## 0.5779404 0.5835652 0.5888852 0.5940274 0.5989837 0.6039133 0.6088284 0.6136372 0.6183414
## OTU715 OTU105 OTU41 OTU137 OTU88 OTU27 OTU1089 OTU60 OTU45
## 0.6230415 0.6277280 0.6323451 0.6367965 0.6411466 0.6453214 0.6494391 0.6534993 0.6575361
## OTU221 OTU166 OTU218 OTU261 OTU163 OTU81 OTU115 OTU322 OTU259
## 0.6615384 0.6654843 0.6693905 0.6731251 0.6767907 0.6804246 0.6836906 0.6868900 0.6900614
## OTU1047 OTU172 OTU939 OTU67
## 0.6931820 0.6962866 0.6993799 0.7024051
##
## $`Hyperarid loess soil_Hyperarid dolomite`
## OTU1 OTU2 OTU3 OTU7 OTU6 OTU5 OTU9 OTU18 OTU8
## 0.1495788 0.2321174 0.3117152 0.3744107 0.4214125 0.4537922 0.4782802 0.5014206 0.5233406
## OTU17 OTU41 OTU16 OTU936 OTU11 OTU28 OTU12 OTU20 OTU10
## 0.5440341 0.5638310 0.5798466 0.5953299 0.6102815 0.6246998 0.6387122 0.6525475 0.6660844
## OTU15 OTU14 OTU13 OTU45
## 0.6762133 0.6855007 0.6942967 0.7030461
##
## $`Hyperarid loess soil_Arid loess soil`
## OTU6 OTU25 OTU62 OTU88 OTU90 OTU11 OTU68 OTU177 OTU78
## 0.1171964 0.1409598 0.1605516 0.1798885 0.1922238 0.2043007 0.2156943 0.2264171 0.2370192
## OTU133 OTU156 OTU187 OTU46 OTU116 OTU67 OTU137 OTU100 OTU687
## 0.2468319 0.2543986 0.2615122 0.2685441 0.2751451 0.2813878 0.2874536 0.2934299 0.2993293
## OTU388 OTU115 OTU121 OTU73 OTU412 OTU91 OTU167 OTU93 OTU16
## 0.3051050 0.3105215 0.3159249 0.3213181 0.3265045 0.3316752 0.3368401 0.3418967 0.3469359
## OTU99 OTU160 OTU218 OTU144 OTU131 OTU135 OTU53 OTU556 OTU152
## 0.3517643 0.3565360 0.3611535 0.3655646 0.3697266 0.3738519 0.3776564 0.3814527 0.3852354
## OTU130 OTU125 OTU227 OTU340 OTU214 OTU233 OTU640 OTU341 OTU311
## 0.3888725 0.3924354 0.3958973 0.3993476 0.4027687 0.4061872 0.4095931 0.4129866 0.4163480
## OTU648 OTU422 OTU716 OTU105 OTU197 OTU163 OTU140 OTU44 OTU37
## 0.4196998 0.4230194 0.4263048 0.4295726 0.4328230 0.4359927 0.4391577 0.4422934 0.4454224
## OTU92 OTU240 OTU715 OTU139 OTU253 OTU221 OTU298 OTU101 OTU171
## 0.4484404 0.4514561 0.4544639 0.4573782 0.4602606 0.4631238 0.4659602 0.4687870 0.4716058
## OTU1235 OTU107 OTU365 OTU491 OTU103 OTU647 OTU479 OTU1047 OTU1288
## 0.4744165 0.4771309 0.4797889 0.4824360 0.4850823 0.4876817 0.4902569 0.4928293 0.4953810
## OTU278 OTU322 OTU136 OTU1102 OTU1050 OTU206 OTU360 OTU220 OTU248
## 0.4979283 0.5004747 0.5030146 0.5055356 0.5080079 0.5104620 0.5128571 0.5152382 0.5176119
## OTU231 OTU48 OTU638 OTU945 OTU315 OTU294 OTU181 OTU190 OTU471
## 0.5199597 0.5222842 0.5245825 0.5268765 0.5291585 0.5314373 0.5337155 0.5359310 0.5381211
## OTU172 OTU169 OTU457 OTU82 OTU191 OTU1013 OTU472 OTU182 OTU251
## 0.5403020 0.5424802 0.5446509 0.5468014 0.5489293 0.5510565 0.5531619 0.5552609 0.5573352
## OTU619 OTU184 OTU352 OTU938 OTU154 OTU707 OTU304 OTU538 OTU386
## 0.5594017 0.5614639 0.5635207 0.5655628 0.5675880 0.5696106 0.5716293 0.5736343 0.5756356
## OTU1116 OTU582 OTU751 OTU434 OTU20 OTU1319 OTU1218 OTU992 OTU309
## 0.5776195 0.5795812 0.5815142 0.5834436 0.5853509 0.5872476 0.5891360 0.5910228 0.5929026
## OTU456 OTU364 OTU302 OTU69 OTU468 OTU725 OTU86 OTU466 OTU562
## 0.5947562 0.5965751 0.5983597 0.6001340 0.6019002 0.6036514 0.6053964 0.6071295 0.6088489
## OTU1184 OTU347 OTU430 OTU164 OTU60 OTU451 OTU635 OTU489 OTU854
## 0.6105629 0.6122639 0.6139634 0.6156620 0.6173343 0.6189824 0.6206193 0.6222384 0.6238543
## OTU610 OTU132 OTU439 OTU255 OTU1046 OTU421 OTU170 OTU543 OTU579
## 0.6254567 0.6270446 0.6286319 0.6302162 0.6317761 0.6333278 0.6348784 0.6363976 0.6379151
## OTU1024 OTU27 OTU323 OTU929 OTU368 OTU953 OTU799 OTU545 OTU258
## 0.6394210 0.6409169 0.6423915 0.6438630 0.6453326 0.6467955 0.6482429 0.6496722 0.6510934
## OTU1282 OTU267 OTU28 OTU1100 OTU277 OTU279 OTU667 OTU199 OTU705
## 0.6524991 0.6538874 0.6552578 0.6566268 0.6579884 0.6593460 0.6606965 0.6620386 0.6633559
## OTU999 OTU232 OTU95 OTU303 OTU836 OTU1242 OTU653 OTU3 OTU141
## 0.6646697 0.6659821 0.6672939 0.6685879 0.6698524 0.6711151 0.6723738 0.6736310 0.6748814
## OTU697 OTU273 OTU499 OTU29 OTU1011 OTU389 OTU162 OTU1108 OTU166
## 0.6761230 0.6773607 0.6785955 0.6798301 0.6810609 0.6822766 0.6834907 0.6847033 0.6859052
## OTU595 OTU524 OTU185 OTU765 OTU41 OTU404 OTU641 OTU557 OTU1005
## 0.6871070 0.6883061 0.6894992 0.6906885 0.6918492 0.6930093 0.6941662 0.6953181 0.6964525
## OTU1049 OTU10 OTU1101 OTU449
## 0.6975821 0.6987054 0.6998284 0.7009432
##
## $`Hyperarid dust_Arid limestone`
## OTU6 OTU20 OTU40 OTU55 OTU225 OTU422 OTU73 OTU11 OTU46
## 0.3830030 0.4418277 0.4965700 0.5241168 0.5387383 0.5528407 0.5659891 0.5778580 0.5889906
## OTU94 OTU388 OTU29 OTU119 OTU18 OTU34 OTU235 OTU16 OTU854
## 0.5996827 0.6092874 0.6183609 0.6273489 0.6359934 0.6440744 0.6514858 0.6587135 0.6657503
## OTU174 OTU299 OTU68 OTU545 OTU9 OTU37
## 0.6726643 0.6795149 0.6852951 0.6910019 0.6966084 0.7021179
##
## $`Hyperarid dust_Hyperarid dolomite`
## OTU6 OTU1 OTU2 OTU3 OTU7 OTU5 OTU18 OTU9 OTU8
## 0.1443995 0.2811765 0.3569750 0.4302446 0.4872402 0.5170148 0.5383456 0.5591551 0.5792831
## OTU17 OTU41 OTU11 OTU16 OTU936 OTU28 OTU20 OTU12
## 0.5983333 0.6167381 0.6326139 0.6480207 0.6622816 0.6756378 0.6885866 0.7014489
##
## $`Hyperarid dust_Arid loess soil`
## OTU6 OTU55 OTU25 OTU88 OTU11 OTU62 OTU68 OTU177 OTU78
## 0.2717856 0.2922788 0.3102694 0.3274254 0.3433223 0.3590820 0.3688505 0.3770737 0.3852909
## OTU94 OTU133 OTU144 OTU46 OTU115 OTU67 OTU156 OTU16 OTU388
## 0.3931965 0.4006953 0.4078863 0.4149436 0.4216680 0.4280641 0.4338043 0.4395276 0.4449791
## OTU116 OTU137 OTU197 OTU100 OTU687 OTU91 OTU73 OTU412 OTU218
## 0.4500254 0.4550249 0.4599696 0.4648655 0.4695628 0.4742035 0.4785067 0.4824756 0.4864272
## OTU9 OTU422 OTU160 OTU53 OTU152 OTU125 OTU135 OTU131 OTU311
## 0.4902632 0.4938776 0.4974534 0.5009364 0.5043806 0.5077056 0.5109572 0.5141610 0.5171316
## OTU556 OTU163 OTU130 OTU484 OTU214 OTU227 OTU240 OTU37 OTU341
## 0.5200525 0.5228985 0.5256494 0.5283896 0.5310817 0.5337605 0.5364127 0.5390503 0.5416516
## OTU233 OTU854 OTU101 OTU648 OTU140 OTU716 OTU92 OTU220 OTU139
## 0.5442391 0.5467798 0.5493151 0.5518255 0.5543313 0.5568296 0.5592989 0.5617475 0.5641704
## OTU44 OTU491 OTU20 OTU278 OTU103 OTU253 OTU60 OTU1235 OTU206
## 0.5665825 0.5689943 0.5713301 0.5736308 0.5759258 0.5781072 0.5802428 0.5823693 0.5844926
## OTU171 OTU107 OTU365 OTU294 OTU647 OTU136 OTU231 OTU1050 OTU1102
## 0.5865914 0.5886821 0.5907679 0.5928210 0.5948466 0.5968391 0.5988303 0.6007964 0.6027482
## OTU479 OTU1288 OTU82 OTU248 OTU434 OTU638 OTU360 OTU945 OTU181
## 0.6046967 0.6066266 0.6085347 0.6104021 0.6122693 0.6140896 0.6158867 0.6176719 0.6194314
## OTU457 OTU472 OTU938 OTU1184 OTU184 OTU315 OTU33 OTU667 OTU471
## 0.6211887 0.6229334 0.6246741 0.6264112 0.6281455 0.6298719 0.6315934 0.6333143 0.6350343
## OTU190 OTU1013 OTU182 OTU538 OTU352 OTU191 OTU86 OTU169 OTU586
## 0.6367102 0.6383722 0.6400307 0.6416807 0.6433256 0.6449655 0.6465947 0.6482236 0.6498470
## OTU304 OTU251 OTU164 OTU619 OTU170 OTU751 OTU1116 OTU707 OTU582
## 0.6514406 0.6530247 0.6545987 0.6561620 0.6577205 0.6592749 0.6608170 0.6623473 0.6638686
## OTU302 OTU154 OTU69 OTU466 OTU725 OTU430 OTU132 OTU545 OTU489
## 0.6653819 0.6668883 0.6683903 0.6698868 0.6713743 0.6728597 0.6743389 0.6758092 0.6772674
## OTU166 OTU48 OTU992 OTU421 OTU1218 OTU1319 OTU456 OTU309 OTU364
## 0.6787234 0.6801775 0.6816228 0.6830672 0.6845111 0.6859461 0.6873638 0.6887794 0.6901816
## OTU635 OTU437 OTU610 OTU562 OTU298 OTU468 OTU347 OTU333
## 0.6915755 0.6929580 0.6943383 0.6957096 0.6970803 0.6984166 0.6997406 0.7010381
##
## $`Arid limestone_Hyperarid dolomite`
## OTU1 OTU2 OTU3 OTU6 OTU7 OTU5 OTU9 OTU8 OTU18
## 0.1440378 0.2242599 0.3019930 0.3607941 0.4191722 0.4507340 0.4744385 0.4957463 0.5163290
## OTU17 OTU41 OTU40 OTU11 OTU936 OTU16 OTU28 OTU12 OTU20
## 0.5365375 0.5544474 0.5699925 0.5851556 0.6002748 0.6145844 0.6287286 0.6422857 0.6555544
## OTU10 OTU15 OTU14 OTU13 OTU33
## 0.6687285 0.6786126 0.6876539 0.6962099 0.7045072
##
## $`Arid limestone_Arid loess soil`
## OTU6 OTU40 OTU20 OTU25 OTU88 OTU62 OTU11 OTU225
## 0.09760135 0.13750741 0.17679560 0.19728621 0.21638051 0.23340169 0.24845894 0.25799747
## OTU177 OTU78 OTU133 OTU73 OTU68 OTU119 OTU144 OTU67
## 0.26735411 0.27646905 0.28479143 0.29307840 0.30008547 0.30708372 0.31394618 0.32068762
## OTU156 OTU422 OTU18 OTU29 OTU34 OTU100 OTU116 OTU91
## 0.32725867 0.33341009 0.33935962 0.34499496 0.35056556 0.35599894 0.36127683 0.36643832
## OTU687 OTU46 OTU299 OTU388 OTU235 OTU197 OTU115 OTU218
## 0.37138684 0.37623323 0.38103236 0.38580627 0.39057213 0.39516382 0.39952480 0.40388431
## OTU412 OTU160 OTU174 OTU53 OTU152 OTU925 OTU135 OTU137
## 0.40821727 0.41229126 0.41633319 0.42024901 0.42416096 0.42806201 0.43172259 0.43528646
## OTU37 OTU854 OTU545 OTU163 OTU125 OTU130 OTU131 OTU556
## 0.43864358 0.44196239 0.44519165 0.44838886 0.45155916 0.45469790 0.45781670 0.46088453
## OTU605 OTU311 OTU69 OTU140 OTU341 OTU214 OTU27 OTU233
## 0.46394974 0.46699883 0.46996880 0.47291024 0.47584516 0.47876919 0.48167258 0.48457197
## OTU648 OTU240 OTU16 OTU92 OTU44 OTU491 OTU139 OTU220
## 0.48743615 0.49029565 0.49310940 0.49591666 0.49871621 0.50143167 0.50413985 0.50682390
## OTU227 OTU278 OTU101 OTU1089 OTU103 OTU171 OTU45 OTU253
## 0.50944639 0.51205855 0.51465647 0.51723675 0.51980481 0.52223855 0.52464256 0.52704332
## OTU107 OTU261 OTU365 OTU206 OTU231 OTU1235 OTU41 OTU479
## 0.52941636 0.53174517 0.53406670 0.53634794 0.53861594 0.54087696 0.54311887 0.54534212
## OTU1288 OTU1102 OTU81 OTU1050 OTU294 OTU181 OTU60 OTU945
## 0.54753509 0.54972106 0.55189155 0.55399097 0.55607199 0.55813344 0.56016996 0.56216653
## OTU82 OTU48 OTU360 OTU472 OTU471 OTU136 OTU169 OTU248
## 0.56416015 0.56614595 0.56813109 0.57010003 0.57205708 0.57400361 0.57593588 0.57786729
## OTU315 OTU184 OTU638 OTU182 OTU352 OTU716 OTU647 OTU191
## 0.57978907 0.58169843 0.58360105 0.58549541 0.58737150 0.58924686 0.59110929 0.59295132
## OTU349 OTU457 OTU938 OTU1013 OTU751 OTU619 OTU164 OTU166
## 0.59479300 0.59662297 0.59843435 0.60023859 0.60201582 0.60378025 0.60554228 0.60727629
## OTU251 OTU190 OTU154 OTU434 OTU707 OTU1116 OTU1218 OTU302
## 0.60900472 0.61070425 0.61239395 0.61407383 0.61574542 0.61739401 0.61904009 0.62068001
## OTU489 OTU421 OTU582 OTU286 OTU7 OTU259 OTU667 OTU364
## 0.62231663 0.62395222 0.62557826 0.62718663 0.62878593 0.63037948 0.63196953 0.63355404
## OTU456 OTU1319 OTU298 OTU992 OTU562 OTU304 OTU468 OTU610
## 0.63512882 0.63670199 0.63825851 0.63979366 0.64131334 0.64282784 0.64433016 0.64582281
## OTU170 OTU347 OTU939 OTU86 OTU430 OTU246 OTU239 OTU949
## 0.64730766 0.64879001 0.65025643 0.65166808 0.65307432 0.65445899 0.65584235 0.65720240
## OTU255 OTU439 OTU466 OTU132 OTU530 OTU579 OTU368 OTU362
## 0.65856178 0.65991755 0.66125549 0.66259309 0.66392616 0.66524781 0.66654856 0.66784058
## OTU28 OTU451 OTU653 OTU1299 OTU309 OTU333 OTU399 OTU323
## 0.66913171 0.67042259 0.67170906 0.67298919 0.67426714 0.67554482 0.67680684 0.67806486
## OTU953 OTU348 OTU162 OTU1024 OTU389 OTU1005 OTU929 OTU524
## 0.67931748 0.68056959 0.68181843 0.68303243 0.68424157 0.68543316 0.68662133 0.68779205
## OTU1100 OTU586 OTU95 OTU279 OTU799 OTU87 OTU1101 OTU232
## 0.68895982 0.69012713 0.69129203 0.69244608 0.69359440 0.69473764 0.69587999 0.69701440
## OTU725 OTU303 OTU999
## 0.69814422 0.69925971 0.70036244
##
## $`Hyperarid dolomite_Arid loess soil`
## OTU1 OTU2 OTU3 OTU7 OTU5 OTU9 OTU18 OTU8 OTU17
## 0.1319013 0.2062641 0.2786385 0.3307085 0.3600777 0.3818384 0.4015745 0.4212911 0.4401489
## OTU41 OTU936 OTU28 OTU12 OTU16 OTU10 OTU11 OTU20 OTU6
## 0.4571149 0.4711765 0.4842321 0.4967527 0.5091024 0.5210569 0.5325276 0.5434614 0.5531014
## OTU15 OTU14 OTU13 OTU24 OTU33 OTU45 OTU88 OTU25 OTU54
## 0.5622927 0.5706666 0.5785943 0.5859354 0.5932440 0.6005279 0.6077454 0.6147879 0.6216491
## OTU62 OTU34 OTU39 OTU47 OTU19 OTU22 OTU35 OTU68 OTU32
## 0.6280701 0.6340900 0.6399058 0.6455094 0.6510462 0.6560153 0.6601594 0.6640414 0.6677063
## OTU29 OTU756 OTU23 OTU37 OTU78 OTU26 OTU177 OTU30 OTU133
## 0.6713117 0.6748203 0.6783113 0.6817969 0.6852336 0.6886605 0.6920868 0.6953067 0.6984351
## OTU46
## 0.7015026
GMPR_ord <- ordinate(Rock_weathering_filt3_GMPR, "CAP", "horn", formula = Rock_weathering_filt3_GMPR ~ Climate * Source)
explained <- eigenvals(GMPR_ord)/sum( eigenvals(GMPR_ord)) * 100
explained <- as.numeric(format(round(explained, 1), nsmall = 1))
data2plot <- cbind(scores(GMPR_ord, display = "sites"), sample_data(Rock_weathering_filt3_GMPR))
p_ord <- ggplot(data2plot) +
geom_point(aes(x = CAP1, y = CAP2, colour = Source, shape = Climate), size = 3, alpha = 2/3 ) +
scale_colour_manual(values = pom4) +
stat_ellipse(aes(x = CAP1, y = CAP2, group = Climate), color = "black", alpha = 0.5, type = "norm", level = 0.95, linetype = 2) +
xlab(label = paste0("CAP1", " (", explained[1],"%)")) +
ylab(label = paste0("CAP2", " (", explained[2],"%)")) +
coord_fixed(sqrt(explained[2] / explained[1]))
print(p_ord)
Explore and plot the taxonomic distribution of the sequences
Rock_weathering_filt3_GMPR_rel <- transform_sample_counts(Rock_weathering_filt3_GMPR, function(x) x / sum(x) )
Rock_weathering_filt3_GMPR_rel %>%
sample_data() %>%
arrange(Climate, Source) %>%
.$sample_names ->
Sample_order
Rock_weathering_filt3_100 <-
prune_taxa(names(sort(taxa_sums(Rock_weathering_filt3_GMPR_rel), TRUE)[1:100]), Rock_weathering_filt3_GMPR_rel)
plot_heatmap(
Rock_weathering_filt3_100,
method = NULL,
distance = NULL,
sample.label = "sample_title",
taxa.label = "Order",
sample_order = Sample_order,
low = "#000033",
high = "#FF3300"
) #+ theme_bw(base_size = 20) + theme(axis.text.x = element_text(hjust = 0, angle = -90.0))
Let's look at the agglomerated taxa
Rock_weathering_filt3_glom <- tax_glom(Rock_weathering_filt3_GMPR,
"Phylum",
NArm = TRUE)
Rock_weathering_filt3_glom_rel <- transform_sample_counts(Rock_weathering_filt3_glom, function(x) x / sum(x))
Rock_weathering_filt3_glom_rel_DF <- psmelt(Rock_weathering_filt3_glom_rel)
Rock_weathering_filt3_glom_rel_DF$Phylum %<>% as.character()
# group dataframe by Phylum, calculate median rel. abundance
Rock_weathering_filt3_glom_rel_DF %>%
group_by(Phylum) %>%
summarise(median = median(Abundance)) ->
medians
# find Phyla whose rel. abund. is less than 0.5%
Rare_phyla <- medians[medians$median <= 0.005, ]$Phylum
# change their name to "Rare"
Rock_weathering_filt3_glom_rel_DF[Rock_weathering_filt3_glom_rel_DF$Phylum %in% Rare_phyla, ]$Phylum <- 'Rare'
# re-group
Rock_weathering_filt3_glom_rel_DF %>%
group_by(Sample, Climate, Phylum, Rock.type, Source) %>%
summarise(Abundance = sum(Abundance)) ->
Rock_weathering_filt3_glom_rel_DF_2plot
# ab.taxonomy$Freq <- sqrt(ab.taxonomy$Freq)
Rock_weathering_filt3_glom_rel_DF_2plot$Phylum %<>% sub("unclassified", "Unclassified", .)
Rock_weathering_filt3_glom_rel_DF_2plot$Phylum %<>% sub("uncultured", "Unclassified", .)
Rock_weathering_filt3_glom_rel_DF_2plot %>%
group_by(Sample) %>%
filter(Phylum == "Rare") %>%
summarise(`Rares (%)` = sum(Abundance * 100)) ->
Rares
# Percentage of reads classified as rare
Rares %>%
kable(., digits = 2, caption = "Percentage of reads per sample type classified as rare:") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = F)| Sample | Rares (%) |
|---|---|
| SbDust1S14 | 2.05 |
| SbDust2S31 | 0.43 |
| SbSlp1SNW49 | 0.82 |
| SbSlp2SNW50 | 0.91 |
| SbSlp3SNW51 | 0.85 |
| SbSlp4SNW52 | 1.99 |
| SbSlp5SNW53 | 0.72 |
| SbSlp6SNW54 | 1.59 |
| SbSoil1SA10 | 9.80 |
| SbSoil2SA11 | 10.41 |
| SbSoil3SA12 | 9.62 |
| SbWad1SNW55 | 2.90 |
| SbWad2SNW56 | 0.48 |
| SbWad3SNW57 | 0.42 |
| SbWad4SNW58 | 0.20 |
| SbWad5SNW59 | 0.47 |
| SbWad6SNW60 | 1.47 |
| UvDust1S32 | 1.60 |
| UvDust2S33 | 0.12 |
| UvSlp1GS70 | 1.09 |
| UvSlp2GS71 | 0.42 |
| UvSlp3CS25 | 0.77 |
| UvSlp3GS72 | 0.85 |
| UvSlp4GS73 | 0.64 |
| UvSlp5GS74 | 0.72 |
| UvSlp6GS75 | 0.34 |
| UvWad1GS76 | 9.37 |
| UvWad2CS23 | 2.17 |
| UvWad2GS77 | 0.75 |
| UvWad3CS27 | 2.96 |
| UvWad3GS78 | 0.16 |
| UvWad4GS79 | 0.77 |
| UvWad5GS80 | 0.30 |
| UvWad6GS81 | 0.38 |
sample_order <- match(Rares$Sample, row.names(sample_data(Rock_weathering_filt3_glom)))
Rares %<>% arrange(., sample_order)
Rares %>%
cbind(., sample_data(Rock_weathering_filt3_glom)) %>%
group_by(Climate.Source) %>%
summarise(`Rares (%)` = mean(`Rares (%)`)) ->
Rares_merged
# Percentage of reads classified as rare
Rares %>%
kable(., digits = 2, caption = "Percentage of reads per sample type classified as rare:") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = F)| Sample | Rares (%) |
|---|---|
| SbDust1S14 | 2.05 |
| UvWad2CS23 | 2.17 |
| UvSlp3CS25 | 0.77 |
| UvWad3CS27 | 2.96 |
| SbDust2S31 | 0.43 |
| UvDust1S32 | 1.60 |
| UvDust2S33 | 0.12 |
| SbSlp1SNW49 | 0.82 |
| SbSlp2SNW50 | 0.91 |
| SbSlp3SNW51 | 0.85 |
| SbSlp4SNW52 | 1.99 |
| SbSlp5SNW53 | 0.72 |
| SbSlp6SNW54 | 1.59 |
| SbWad1SNW55 | 2.90 |
| SbWad2SNW56 | 0.48 |
| SbWad3SNW57 | 0.42 |
| SbWad4SNW58 | 0.20 |
| SbWad5SNW59 | 0.47 |
| SbWad6SNW60 | 1.47 |
| UvSlp1GS70 | 1.09 |
| UvSlp2GS71 | 0.42 |
| UvSlp3GS72 | 0.85 |
| UvSlp4GS73 | 0.64 |
| UvSlp5GS74 | 0.72 |
| UvSlp6GS75 | 0.34 |
| UvWad1GS76 | 9.37 |
| UvWad2GS77 | 0.75 |
| UvWad3GS78 | 0.16 |
| UvWad4GS79 | 0.77 |
| UvWad5GS80 | 0.30 |
| UvWad6GS81 | 0.38 |
| SbSoil1SA10 | 9.80 |
| SbSoil2SA11 | 10.41 |
| SbSoil3SA12 | 9.62 |
Rock_weathering_filt3_glom_rel_DF_2plot %>%
group_by(Phylum) %>%
summarise(sum.Taxa = sum(Abundance)) %>%
arrange(desc(sum.Taxa)) -> Taxa_rank
Rock_weathering_filt3_glom_rel_DF_2plot$Phylum %<>%
factor(., levels = Taxa_rank$Phylum) %>%
fct_relevel(., "Rare", after = Inf)
p_taxa_box <-
ggplot(Rock_weathering_filt3_glom_rel_DF_2plot, aes(x = Phylum, y = (Abundance * 100))) +
geom_boxplot(aes(group = interaction(Phylum, Source)), position = position_dodge(width = 0.9), fatten = 1) +
geom_point(
aes(colour = Source),
position = position_jitterdodge(dodge.width = 1),
alpha = 1 / 2,
stroke = 0,
size = 2
) +
scale_colour_manual(values = pom4, name = "") +
theme_cowplot(font_size = 11, font_family = f_name) +
labs(x = NULL, y = "Relative abundance (%)") +
guides(colour = guide_legend(override.aes = list(size = 5))) +
facet_grid(Climate ~ .) +
background_grid(major = "xy",
minor = "none") +
theme(axis.text.x = element_text(
angle = 45,
vjust = 0.9,
hjust = 0.9
))
print(p_taxa_box)
Taxa_tests_phylum <- STAMPR(Rock_weathering_filt3_GMPR, "Phylum", sig_pairs)## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Phylum
## OTU1 "Proteobacteria"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 1118.38 11.2778 0.00078
## Source 3 412.13 4.1560 0.24511
## Climate:Source 1 2.40 0.0242 0.87637
## Residuals 28 1739.58
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 5, p-value = 0.2353
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -57.31870 77.10514
## sample estimates:
## difference in location
## -8.837742
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 13, p-value = 0.9273
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -45.05308 61.68040
## sample estimates:
## difference in location
## 3.711094
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 3, p-value = 0.03636
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -44.64033 -10.50860
## sample estimates:
## difference in location
## -24.33348
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 22, p-value = 0.6134
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -31.61913 23.25576
## sample estimates:
## difference in location
## 2.734424
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.03576
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -92.69994 -22.88429
## sample estimates:
## difference in location
## -41.7985
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 24, p-value = 0.006099
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -43.71818 -14.50227
## sample estimates:
## difference in location
## -31.94182
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 12, p-value = 0.4273
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -9.149286 9.089127
## sample estimates:
## difference in location
## -3.911288
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 3, p-value = 0.03636
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -43.181793 -5.626402
## sample estimates:
## difference in location
## -35.79785
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Phylum
## OTU2 "Deinococcus-Thermus"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 212.5 2.1429 0.14323
## Source 3 1474.7 14.8713 0.00193
## Climate:Source 1 72.6 0.7321 0.39220
## Residuals 28 1512.7
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 24, p-value = 0.03576
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.1159525 28.8971507
## sample estimates:
## difference in location
## 3.763569
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.03576
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -16.468193 -6.626912
## sample estimates:
## difference in location
## -12.42899
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 18, p-value = 1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -8.035956 20.819838
## sample estimates:
## difference in location
## 0.1319845
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 31, p-value = 0.0712
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.559324 12.947540
## sample estimates:
## difference in location
## 6.279289
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 24, p-value = 0.03576
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 6.553372 16.590053
## sample estimates:
## difference in location
## 11.95774
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 53, p-value = 0.2855
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -11.37397 7.94874
## sample estimates:
## difference in location
## -5.992007
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 35, p-value = 0.01724
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.1726432 24.9922558
## sample estimates:
## difference in location
## 2.545225
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -16.14293 -6.64311
## sample estimates:
## difference in location
## -11.97071
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Phylum
## OTU5 "Bacteroidetes"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 12.97 0.1308 0.71761
## Source 3 821.88 8.2879 0.04042
## Climate:Source 1 74.82 0.7545 0.38507
## Residuals 28 2362.83
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 5, p-value = 0.2353
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -68.34270 13.97676
## sample estimates:
## difference in location
## -27.18296
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 16, p-value = 0.5228
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -10.44161 67.94132
## sample estimates:
## difference in location
## 29.64229
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 26, p-value = 0.279
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.354092 4.988486
## sample estimates:
## difference in location
## 2.066666
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 28, p-value = 0.1703
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.9948177 10.0975854
## sample estimates:
## difference in location
## 5.62787
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 22, p-value = 0.08284
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.8221862 15.0872946
## sample estimates:
## difference in location
## 7.651679
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 51, p-value = 0.2366
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -6.951583 1.649500
## sample estimates:
## difference in location
## -3.570295
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 10, p-value = 0.279
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -5.569969 2.833986
## sample estimates:
## difference in location
## -1.641379
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 15, p-value = 0.7182
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -7.943049 5.141081
## sample estimates:
## difference in location
## -2.618944
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Phylum
## OTU7 "Cyanobacteria"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 95.56 0.9636 0.32628
## Source 3 853.37 8.6055 0.03502
## Climate:Source 1 45.07 0.4545 0.50023
## Residuals 28 2278.50
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 19, p-value = 0.2353
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -2.455407 18.180308
## sample estimates:
## difference in location
## 2.957942
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 6, p-value = 0.3153
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -37.738611 2.344579
## sample estimates:
## difference in location
## -3.324833
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 33, p-value = 0.03636
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.6713462 18.0874183
## sample estimates:
## difference in location
## 3.080276
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 34, p-value = 0.02527
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.06861458 24.43779425
## sample estimates:
## difference in location
## 5.272126
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 20, p-value = 0.1709
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.5906178 37.8005207
## sample estimates:
## difference in location
## 4.208215
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 73, p-value = 0.977
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -7.311598 5.103880
## sample estimates:
## difference in location
## 0.2627632
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 21, p-value = 0.7182
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -3.692326 14.614652
## sample estimates:
## difference in location
## 1.12547
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 16, p-value = 0.8286
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -23.702686 4.781333
## sample estimates:
## difference in location
## -1.638922
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Phylum
## OTU10 "Acidobacteria"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 2.38 0.0240 0.87682
## Source 3 1299.20 13.1012 0.00442
## Climate:Source 1 70.42 0.7101 0.39942
## Residuals 28 1900.50
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 10, p-value = 0.7842
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.875998 6.843540
## sample estimates:
## difference in location
## -0.2000774
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 5, p-value = 0.2353
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -3.891498 1.987482
## sample estimates:
## difference in location
## -1.607997
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 18, p-value = 1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.440454 1.139749
## sample estimates:
## difference in location
## -0.007451021
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 28, p-value = 0.1703
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.8000388 2.8098831
## sample estimates:
## difference in location
## 1.66951
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 21, p-value = 0.1207
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.8738157 4.2049748
## sample estimates:
## difference in location
## 2.407387
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 40, p-value = 0.06896
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -2.5382471 0.1114779
## sample estimates:
## difference in location
## -1.540419
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -10.418316 -7.394329
## sample estimates:
## difference in location
## -8.59669
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 36, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 5.710241 9.481740
## sample estimates:
## difference in location
## 7.038468
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Phylum
## OTU20 "Actinobacteria"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 584.74 5.8965 0.01517
## Source 3 761.45 7.6785 0.05315
## Climate:Source 1 0.82 0.0082 0.92769
## Residuals 28 1925.50
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 22, p-value = 0.08284
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -10.12738 56.93452
## sample estimates:
## difference in location
## 36.90999
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 7, p-value = 0.4113
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -44.932741 9.863263
## sample estimates:
## difference in location
## -6.636044
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 29, p-value = 0.1296
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -6.174561 41.738766
## sample estimates:
## difference in location
## 20.55627
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 14, p-value = 0.6134
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -23.05721 22.19200
## sample estimates:
## difference in location
## -6.063935
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 23, p-value = 0.05523
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.954275 46.692652
## sample estimates:
## difference in location
## 12.84274
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 115, p-value = 0.01414
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 7.16780 40.31334
## sample estimates:
## difference in location
## 21.9016
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 27, p-value = 0.2199
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -11.03421 33.22758
## sample estimates:
## difference in location
## 17.11401
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 25, p-value = 0.3481
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -19.10688 25.75876
## sample estimates:
## difference in location
## 15.87242
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Phylum
## OTU44 "Firmicutes"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 362.38 3.6543 0.05593
## Source 3 1569.80 15.8299 0.00123
## Climate:Source 1 1.07 0.0108 0.91740
## Residuals 28 1339.25
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.03576
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -8.618072 -4.812207
## sample estimates:
## difference in location
## -6.671831
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 24, p-value = 0.03576
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.2551915 8.7211006
## sample estimates:
## difference in location
## 5.343429
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 2, p-value = 0.02527
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.01263008 -0.03357744
## sample estimates:
## difference in location
## -0.3462271
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 23, p-value = 0.516
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.6665798 2.1292269
## sample estimates:
## difference in location
## 0.1278182
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 8, p-value = 0.5228
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -2.274515 4.521171
## sample estimates:
## difference in location
## -0.2052955
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 20, p-value = 0.002946
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.6614044 -0.2036041
## sample estimates:
## difference in location
## -0.5325774
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.2671454 -0.7882357
## sample estimates:
## difference in location
## -0.9980437
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 22, p-value = 0.6134
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.505982 1.119910
## sample estimates:
## difference in location
## 0.5223647
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Phylum
## OTU116 "Gemmatimonadetes"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 536.03 5.4053 0.02008
## Source 3 1335.07 13.4629 0.00374
## Climate:Source 1 29.40 0.2965 0.58610
## Residuals 28 1372.00
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 13, p-value = 0.9273
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.563363 2.368097
## sample estimates:
## difference in location
## 0.1096169
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 14, p-value = 0.7842
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.5939111 1.5794065
## sample estimates:
## difference in location
## 0.4927523
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 5, p-value = 0.0712
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -2.6300118 0.1465505
## sample estimates:
## difference in location
## -1.280392
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -2.8652168 -0.9657726
## sample estimates:
## difference in location
## -1.923801
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 21, p-value = 0.1207
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.1146488 0.6986154
## sample estimates:
## difference in location
## 0.2262944
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 115, p-value = 0.01414
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.06797809 1.12543652
## sample estimates:
## difference in location
## 0.575367
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -5.196120 -3.074545
## sample estimates:
## difference in location
## -4.256295
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 36, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 4.371457 5.332202
## sample estimates:
## difference in location
## 4.958494
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Phylum
## OTU225 "Chloroflexi"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 724.97 7.3106 0.00685
## Source 3 1423.13 14.3509 0.00246
## Climate:Source 1 17.07 0.1721 0.67825
## Residuals 28 1107.33
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 22, p-value = 0.08284
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.888273 27.556451
## sample estimates:
## difference in location
## 8.68754
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 13, p-value = 0.9273
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -2.388231 2.129751
## sample estimates:
## difference in location
## 0.2617992
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 6, p-value = 0.09694
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -19.842294 5.726156
## sample estimates:
## difference in location
## -8.397385
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -23.17677 -10.76493
## sample estimates:
## difference in location
## -21.05372
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 14, p-value = 0.7842
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.822953 2.765635
## sample estimates:
## difference in location
## 0.1344354
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 133, p-value = 0.0004777
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 4.219769 15.572571
## sample estimates:
## difference in location
## 8.960243
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 10, p-value = 0.279
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -15.090981 6.626764
## sample estimates:
## difference in location
## -6.713824
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 36, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 13.68642 17.40189
## sample estimates:
## difference in location
## 16.2042
Taxa_tests_order <- STAMPR(Rock_weathering_filt3_GMPR, "Order", sig_pairs)## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Order
## OTU1 "Burkholderiales"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 900.74 9.0830 0.00258
## Source 3 1292.08 13.0294 0.00457
## Climate:Source 1 79.35 0.8002 0.37104
## Residuals 28 1000.33
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.03576
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -41.194215 -3.356289
## sample estimates:
## difference in location
## -22.27526
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 12, p-value = 1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -40.18183 41.15491
## sample estimates:
## difference in location
## -0.3563909
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 2, p-value = 0.02527
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.4548998 -0.1261025
## sample estimates:
## difference in location
## -0.2969459
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 33, p-value = 0.03636
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 12.65335 40.77171
## sample estimates:
## difference in location
## 34.15781
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 22, p-value = 0.08284
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.325322 43.909135
## sample estimates:
## difference in location
## 34.7954
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 9, p-value = 0.000308
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -36.54463 -15.27047
## sample estimates:
## difference in location
## -34.67256
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.1343499 -0.3934692
## sample estimates:
## difference in location
## -0.74279
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 3, p-value = 0.03636
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -40.52361 -11.97382
## sample estimates:
## difference in location
## -33.78455
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Order
## OTU5 "Sphingobacteriales"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 183.56 1.8510 0.17367
## Source 3 1245.17 12.5564 0.00570
## Climate:Source 1 11.27 0.1136 0.73607
## Residuals 28 1832.50
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 8, p-value = 0.5228
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.1104079 0.8423098
## sample estimates:
## difference in location
## -0.3089557
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 5, p-value = 0.2353
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -13.082829 1.131093
## sample estimates:
## difference in location
## -6.123185
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 21, p-value = 0.7182
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.6640484 0.7639180
## sample estimates:
## difference in location
## 0.01919754
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 30, p-value = 0.09694
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.5096519 10.2461215
## sample estimates:
## difference in location
## 6.491376
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 21, p-value = 0.1207
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.4782018 13.6953205
## sample estimates:
## difference in location
## 6.46166
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 23, p-value = 0.005108
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -8.5172328 -0.8528489
## sample estimates:
## difference in location
## -6.135539
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -4.329414 -1.612209
## sample estimates:
## difference in location
## -3.002078
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 14, p-value = 0.6134
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -8.039024 3.572766
## sample estimates:
## difference in location
## -3.718278
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Order
## OTU6 "Enterobacteriales"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 70.62 0.7121 0.39874
## Source 3 2314.23 23.3368 0.00003
## Climate:Source 1 64.07 0.6461 0.42153
## Residuals 28 823.58
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 20, p-value = 0.1709
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.5713626 91.1323680
## sample estimates:
## difference in location
## 3.553661
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 22, p-value = 0.08284
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -10.437379 1.912076
## sample estimates:
## difference in location
## 1.69055
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 3, p-value = 0.03636
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -49.501489 -5.472717
## sample estimates:
## difference in location
## -21.46237
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -51.68982 -22.42206
## sample estimates:
## difference in location
## -26.58309
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.03576
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -84.18527 -55.63994
## sample estimates:
## difference in location
## -69.91261
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 134, p-value = 0.0003842
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 1.932528 8.058776
## sample estimates:
## difference in location
## 5.11499
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 36, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 1.298489 17.365156
## sample estimates:
## difference in location
## 5.334665
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 8, p-value = 0.1703
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.58435850 0.06542455
## sample estimates:
## difference in location
## -0.1028499
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Order
## OTU7 "SubsectionII"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 174.38 1.7585 0.18481
## Source 3 716.55 7.2257 0.06504
## Climate:Source 1 2.40 0.0242 0.87637
## Residuals 28 2379.17
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 14, p-value = 0.7842
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.04641 14.86629
## sample estimates:
## difference in location
## 0.1945796
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 4, p-value = 0.1709
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -33.5566632 0.8541497
## sample estimates:
## difference in location
## -3.536299
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 22, p-value = 0.6134
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.2696644 9.1526642
## sample estimates:
## difference in location
## 0.5183234
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 34, p-value = 0.02527
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.1576628 23.9114605
## sample estimates:
## difference in location
## 4.181034
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 21, p-value = 0.1207
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.3651775 33.6630338
## sample estimates:
## difference in location
## 4.549962
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 41, p-value = 0.07825
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -8.940779 1.057636
## sample estimates:
## difference in location
## -1.222737
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 20, p-value = 0.8286
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.5775934 9.0335277
## sample estimates:
## difference in location
## 0.2127829
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 5, p-value = 0.0712
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -23.7924121 0.1227166
## sample estimates:
## difference in location
## -3.891536
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Order
## OTU9 "Rhizobiales"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 880.26 8.8766 0.002888
## Source 3 546.82 5.5141 0.137795
## Climate:Source 1 400.42 4.0378 0.044491
## Residuals 28 1445.00
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 9, p-value = 0.6481
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -5.881060 2.947064
## sample estimates:
## difference in location
## -1.466998
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 6, p-value = 0.3153
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -11.662705 5.901289
## sample estimates:
## difference in location
## -3.357776
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 16, p-value = 0.8286
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.990814 1.211501
## sample estimates:
## difference in location
## -0.5011302
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 33, p-value = 0.03636
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.9757943 10.1285233
## sample estimates:
## difference in location
## 4.438603
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 8, p-value = 0.5228
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -7.848487 4.603742
## sample estimates:
## difference in location
## -1.200014
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 12, p-value = 0.000592
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -7.323014 -2.448342
## sample estimates:
## difference in location
## -4.6125
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -4.588206 -2.234018
## sample estimates:
## difference in location
## -2.956213
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 12, p-value = 0.4273
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -7.265095 1.276736
## sample estimates:
## difference in location
## -1.607365
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Order
## OTU10 "Subgroup_4"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 49.44 0.4986 0.48013
## Source 3 1314.88 13.2592 0.00411
## Climate:Source 1 72.60 0.7321 0.39220
## Residuals 28 1835.58
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 10, p-value = 0.7842
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.355819 6.528750
## sample estimates:
## difference in location
## -0.1126584
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 4, p-value = 0.1709
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -4.075922 1.321435
## sample estimates:
## difference in location
## -1.911203
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 14, p-value = 0.6134
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.9403130 0.8227086
## sample estimates:
## difference in location
## -0.265193
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 30, p-value = 0.09694
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.5559751 3.2464937
## sample estimates:
## difference in location
## 1.832547
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 22, p-value = 0.08284
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.6122869 4.2773329
## sample estimates:
## difference in location
## 2.400129
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 31, p-value = 0.01937
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -2.8472840 -0.3238103
## sample estimates:
## difference in location
## -1.969475
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 3, p-value = 0.03636
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -6.169976 -4.029602
## sample estimates:
## difference in location
## -5.295554
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 36, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 1.716829 5.439081
## sample estimates:
## difference in location
## 3.039978
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Order
## OTU11 "Sphingomonadales"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 4.97 0.0501 0.82285
## Source 3 595.88 6.0089 0.11118
## Climate:Source 1 66.15 0.6671 0.41408
## Residuals 28 2605.50
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 13, p-value = 0.9273
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -5.903441 12.844720
## sample estimates:
## difference in location
## 0.13997
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 9, p-value = 0.6481
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -8.326224 4.103880
## sample estimates:
## difference in location
## -0.7099819
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 20, p-value = 0.8286
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -4.128869 5.956528
## sample estimates:
## difference in location
## 0.3258721
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 22, p-value = 0.6134
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -3.226862 5.505896
## sample estimates:
## difference in location
## 1.482761
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 24, p-value = 0.03576
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 1.378665 9.581825
## sample estimates:
## difference in location
## 5.117695
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 52, p-value = 0.2602
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -3.503731 1.094646
## sample estimates:
## difference in location
## -1.199836
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 12, p-value = 0.4273
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -3.995816 3.069399
## sample estimates:
## difference in location
## -1.0588
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 15, p-value = 0.7182
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -2.890330 2.811984
## sample estimates:
## difference in location
## -0.5360087
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Order
## OTU14 "Caulobacterales"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 430.62 4.3424 0.03718
## Source 3 1247.53 12.5802 0.00564
## Climate:Source 1 40.02 0.4035 0.52527
## Residuals 28 1554.33
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 9, p-value = 0.6481
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.0704490 0.9475039
## sample estimates:
## difference in location
## -0.03852294
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 5, p-value = 0.2353
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -2.9373695 0.5232694
## sample estimates:
## difference in location
## -0.8493462
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 24, p-value = 0.4273
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.4313298 0.7733929
## sample estimates:
## difference in location
## 0.218237
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 35, p-value = 0.01724
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.2813935 2.4611436
## sample estimates:
## difference in location
## 1.096272
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 24, p-value = 0.03576
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.3652594 3.0358987
## sample estimates:
## difference in location
## 1.288226
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 17, p-value = 0.001652
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.6797914 -0.4360266
## sample estimates:
## difference in location
## -0.9214106
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 18, p-value = 1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.4322340 0.3639032
## sample estimates:
## difference in location
## 0.01551463
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -2.043705 -0.096489
## sample estimates:
## difference in location
## -0.9607206
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Order
## OTU20 "Rubrobacterales"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 362.38 3.6543 0.05593
## Source 3 1283.60 12.9439 0.00476
## Climate:Source 1 7.35 0.0741 0.78543
## Residuals 28 1619.17
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 24, p-value = 0.03576
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 1.343966 35.280345
## sample estimates:
## difference in location
## 23.05921
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.03576
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -34.39409979 -0.07048124
## sample estimates:
## difference in location
## -4.199694
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 33, p-value = 0.03636
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 5.206833 27.553233
## sample estimates:
## difference in location
## 17.11954
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 19, p-value = 0.9425
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -6.01319 27.58079
## sample estimates:
## difference in location
## 0.3061655
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 23, p-value = 0.05523
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.1729386 34.3585874
## sample estimates:
## difference in location
## 4.639865
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 110, p-value = 0.03038
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 1.007057 21.596004
## sample estimates:
## difference in location
## 13.50276
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 33, p-value = 0.03636
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 5.377442 24.819725
## sample estimates:
## difference in location
## 16.82808
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 20, p-value = 0.8286
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -26.414743 5.855238
## sample estimates:
## difference in location
## 1.69516
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Order
## OTU22 "Cytophagales"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 1164.74 11.7452 0.00061
## Source 3 44.85 0.4522 0.92925
## Climate:Source 1 281.67 2.8403 0.09192
## Residuals 28 1781.25
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 7, p-value = 0.4113
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -67.49137 13.79442
## sample estimates:
## difference in location
## -27.69087
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 24, p-value = 0.03576
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.9995852 67.2950902
## sample estimates:
## difference in location
## 34.90214
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 26, p-value = 0.279
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.069587 5.465177
## sample estimates:
## difference in location
## 1.337481
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 10, p-value = 0.279
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.8392488 0.3526159
## sample estimates:
## difference in location
## -0.2786225
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 23, p-value = 0.05523
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.1948212 1.8870346
## sample estimates:
## difference in location
## 0.5415788
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 115, p-value = 0.01414
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.416069 3.919566
## sample estimates:
## difference in location
## 2.374847
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 23, p-value = 0.516
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.866692 5.145857
## sample estimates:
## difference in location
## 1.171801
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 34, p-value = 0.02527
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.1545652 1.8629482
## sample estimates:
## difference in location
## 1.157305
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Order
## OTU30 "Micrococcales"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 265.44 2.6767 0.10183
## Source 3 749.89 7.5619 0.05599
## Climate:Source 1 33.75 0.3403 0.55964
## Residuals 28 2223.42
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 2, p-value = 0.08284
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -5.206538 4.628937
## sample estimates:
## difference in location
## -2.182672
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 22, p-value = 0.08284
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -3.828908 5.114544
## sample estimates:
## difference in location
## 2.137151
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 25, p-value = 0.3481
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.5320493 1.4765078
## sample estimates:
## difference in location
## 0.2706085
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 26, p-value = 0.279
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.5122759 0.7866787
## sample estimates:
## difference in location
## 0.2027858
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 7, p-value = 0.4113
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.5000857 5.5661994
## sample estimates:
## difference in location
## -0.2281722
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 79, p-value = 0.7075
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.2204756 0.3690566
## sample estimates:
## difference in location
## 0.0729464
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 4, p-value = 0.05135
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.5258004 0.2675506
## sample estimates:
## difference in location
## -1.065072
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 33, p-value = 0.03636
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.4223112 1.4948001
## sample estimates:
## difference in location
## 1.103329
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Order
## OTU33 "Frankiales"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 64.97 0.65517 0.41827
## Source 3 262.68 2.64887 0.44899
## Climate:Source 1 9.60 0.09681 0.75570
## Residuals 28 2935.25
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 16, p-value = 0.5228
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -2.724773 10.232076
## sample estimates:
## difference in location
## 1.105793
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 8, p-value = 0.5228
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -6.780411 2.832335
## sample estimates:
## difference in location
## -0.4845265
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 19, p-value = 0.9425
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.568094 4.262904
## sample estimates:
## difference in location
## 0.2980444
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 16, p-value = 0.8286
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -2.577901 4.274808
## sample estimates:
## difference in location
## -0.1588814
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 18, p-value = 0.3153
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.7863939 6.5607849
## sample estimates:
## difference in location
## 1.092154
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 82, p-value = 0.5834
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.373038 1.650491
## sample estimates:
## difference in location
## 0.5690406
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 17, p-value = 0.9425
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.015699 2.983868
## sample estimates:
## difference in location
## -0.1357949
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 21, p-value = 0.7182
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -4.105227 1.803981
## sample estimates:
## difference in location
## 0.4714685
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Order
## OTU40 "Deinococcales"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 24.74 0.2494 0.61748
## Source 3 1445.20 14.5734 0.00222
## Climate:Source 1 190.82 1.9242 0.16539
## Residuals 28 1611.75
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 24, p-value = 0.03576
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.1159525 28.8971507
## sample estimates:
## difference in location
## 3.763569
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.03576
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -7.3472144 -0.5879807
## sample estimates:
## difference in location
## -2.500071
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 18, p-value = 1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -8.04070 20.81984
## sample estimates:
## difference in location
## 0.1319845
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 11, p-value = 0.3481
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -7.129548 2.026183
## sample estimates:
## difference in location
## -2.583399
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 24, p-value = 0.03576
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.5144736 7.4691341
## sample estimates:
## difference in location
## 2.253139
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 82, p-value = 0.5834
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.344459 18.286424
## sample estimates:
## difference in location
## 0.9522018
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 36, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.3237722 25.0161232
## sample estimates:
## difference in location
## 2.685594
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -4.2047144 -0.6847059
## sample estimates:
## difference in location
## -2.525927
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Order
## OTU73 "Solirubrobacterales"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 670.62 6.7625 0.00931
## Source 3 1220.47 12.3072 0.00640
## Climate:Source 1 3.75 0.0378 0.84581
## Residuals 28 1377.67
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 22, p-value = 0.08284
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.2353533 27.6409333
## sample estimates:
## difference in location
## 4.612546
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 5, p-value = 0.2353
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -2.9351537 0.2608993
## sample estimates:
## difference in location
## -0.4958395
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 18, p-value = 1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -4.212403 9.958079
## sample estimates:
## difference in location
## -0.1090627
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -5.293649 -2.537352
## sample estimates:
## difference in location
## -4.224644
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 21, p-value = 0.1207
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.1764773 3.0664060
## sample estimates:
## difference in location
## 0.5754338
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 125, p-value = 0.002437
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 1.196491 11.780432
## sample estimates:
## difference in location
## 3.693183
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 17, p-value = 0.9425
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -4.307577 10.065980
## sample estimates:
## difference in location
## -0.4198441
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 36, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 2.036055 5.953411
## sample estimates:
## difference in location
## 3.962095
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Order
## OTU144 "Acidimicrobiales"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 402.62 4.0600 0.04391
## Source 3 1958.32 19.7477 0.00019
## Climate:Source 1 36.82 0.3713 0.54232
## Residuals 28 874.75
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 22, p-value = 0.08284
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.2770006 1.8753928
## sample estimates:
## difference in location
## 0.9466836
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 8, p-value = 0.5228
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.6952939 0.2939760
## sample estimates:
## difference in location
## -0.07693071
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -8.055194 -2.733487
## sample estimates:
## difference in location
## -4.376145
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -8.811553 -3.722903
## sample estimates:
## difference in location
## -5.306655
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 23, p-value = 0.05523
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.009727731 0.707286393
## sample estimates:
## difference in location
## 0.2041089
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 125, p-value = 0.002437
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.3522632 1.1649116
## sample estimates:
## difference in location
## 0.8707461
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -3.351919 -1.703828
## sample estimates:
## difference in location
## -2.347733
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 36, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 2.648734 4.108222
## sample estimates:
## difference in location
## 3.124562
##
## Taxonomy Table: [1 taxa by 1 taxonomic ranks]:
## Order
## OTU225 "JG30-KF-CM45"
##
## DV: Abundance
## Observations: 34
## D: 1
## MS total: 99.16667
##
## Df Sum Sq H p.value
## Climate 1 1050.62 10.5945 0.00113
## Source 3 1025.72 10.3434 0.01586
## Climate:Source 1 20.42 0.2059 0.65001
## Residuals 28 1175.75
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 22, p-value = 0.08284
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.3350587 20.3127324
## sample estimates:
## difference in location
## 4.143156
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 14, p-value = 0.7842
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.4073917 0.4038865
## sample estimates:
## difference in location
## 0.007441521
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 19, p-value = 0.9425
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -3.843441 11.810661
## sample estimates:
## difference in location
## 0.6013048
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 0, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -4.795687 -2.220769
## sample estimates:
## difference in location
## -4.477859
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 18, p-value = 0.3153
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.1554704 0.5143634
## sample estimates:
## difference in location
## 0.1156305
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 135, p-value = 0.000308
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 2.579639 6.320156
## sample estimates:
## difference in location
## 4.241255
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 17, p-value = 0.9425
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -3.807166 10.334104
## sample estimates:
## difference in location
## -0.2087371
##
##
## Wilcoxon rank sum test with continuity correction
##
## data: Abundance by Climate.Source
## W = 36, p-value = 0.01154
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 3.697211 4.574490
## sample estimates:
## difference in location
## 4.299239
For the arid samples
Rock_weathering_filt3_GMPR_Arid_rel <- transform_sample_counts(Rock_weathering_filt3_GMPR_Arid, function(x) x / sum(x) ) # rel abundance
Rock_weathering_filt3_GMPR_Arid_merged <- merge_samples(Rock_weathering_filt3_GMPR_Arid_rel, "Source", fun = mean) # merge by source
Rock_weathering_filt3_GMPR_Arid_merged_rel <- transform_sample_counts(Rock_weathering_filt3_GMPR_Arid_merged, function(x) x / sum(x) ) # rel abundance per source
meandf <- as(otu_table(Rock_weathering_filt3_GMPR_Arid_merged_rel), "matrix")
if (!taxa_are_rows(Rock_weathering_filt3_GMPR_Arid_merged_rel)) { meandf <- t(meandf) }
abundance <- rowSums(meandf) / sum(meandf) * 100
Arid4Ternary <- data.frame(
meandf,
Abundance = abundance,
Phylum = tax_table(Rock_weathering_filt3_GMPR_Arid_merged_rel)[, "Phylum"]
)
# Arid4Ternary <- dplyr::rename(Arid4Ternary, Loess_soil = Loess.soil)
Arid4Ternary$Phylum <-
factor(Arid4Ternary$Phylum, levels = c(levels(Arid4Ternary$Phylum), 'Rare'))
Arid4Ternary$Phylum[Arid4Ternary$Phylum %in% Rare_phyla] <- "Rare"
Arid4Ternary$Phylum %<>%
factor(., levels = Taxa_rank$Phylum) %>%
fct_relevel(., "Rare", after = Inf)
p_ternary_arid <-
ggtern(data = Arid4Ternary,
aes(
x = Loess.soil,
y = Dust,
z = Limestone,
size = Abundance,
colour = Phylum
)) +
geom_point(alpha = 1 / 2) +
scale_size(
range = c(1, 5),
name = "Abundance (%)"
) +
theme_arrownormal() +
scale_color_manual(values = pal("d3js")) +
guides(colour = guide_legend(override.aes = list(size = 3))) +
labs(x = "Loess soil") +
theme(axis.title = element_blank())
print(p_ternary_arid)
For the hyperarid samples
Rock_weathering_filt3_GMPR_Hyperarid_rel <- transform_sample_counts(Rock_weathering_filt3_GMPR_Hyperarid, function(x) x / sum(x) ) # rel abundance
Rock_weathering_filt3_GMPR_Hyperarid_merged <- merge_samples(Rock_weathering_filt3_GMPR_Hyperarid_rel, "Source", fun = mean) # merge by source
Rock_weathering_filt3_GMPR_Hyperarid_merged_rel <- transform_sample_counts(Rock_weathering_filt3_GMPR_Hyperarid_merged, function(x) x / sum(x) ) # rel abundance per source
meandf <- as(otu_table(Rock_weathering_filt3_GMPR_Hyperarid_merged_rel), "matrix")
if (!taxa_are_rows(Rock_weathering_filt3_GMPR_Hyperarid_merged_rel)) { meandf <- t(meandf) }
abundance <- rowSums(meandf) / sum(meandf) * 100
Hyperarid4Ternary <- data.frame(
meandf,
Abundance = abundance,
Phylum = tax_table(Rock_weathering_filt3_GMPR_Hyperarid_merged_rel)[, "Phylum"]
)
Hyperarid4Ternary$Phylum <-
factor(Hyperarid4Ternary$Phylum, levels = c(levels(Hyperarid4Ternary$Phylum), 'Rare'))
Hyperarid4Ternary$Phylum[Hyperarid4Ternary$Phylum %in% Rare_phyla] <- "Rare"
Hyperarid4Ternary$Phylum %<>%
factor(., levels = Taxa_rank$Phylum) %>%
fct_relevel(., "Rare", after = Inf)
p_ternary_hyperarid <-
ggtern(data = Hyperarid4Ternary,
aes(
x = Loess.soil,
y = Dust,
z = Dolomite,
size = Abundance,
colour = Phylum
)) +
geom_point(alpha = 1 / 2) +
scale_size(
range = c(1, 5),
name = "Abundance (%)"
) +
theme_arrownormal() +
scale_color_manual(values = pal("d3js")) +
guides(colour = guide_legend(override.aes = list(size = 3))) +
labs(x = "Loess soil") +
theme(axis.title = element_blank())
print(p_ternary_hyperarid)
Combine all sequence analysis plots to make Fig. 3
ternary_legend <-
get_legend(p_ternary_arid)# + theme(legend.direction = "horizontal"))
ord_legend <- get_legend(p_ord)
top_row <-
plot_grid(
p_alpha + theme(
legend.position = "none",
panel.spacing = unit(0.5, "lines")
),
p_ord + theme(axis.title.y = element_text(vjust = -3)) ,
labels = c('A', 'B'),
label_size = 12,
align = 'v',
axis = "tl",
nrow = 1,
ncol = 2
)
bottom_l <-
plot_grid(
p_taxa_box + theme(legend.position = "none"),
labels = c('C'),
label_size = 12,
ncol = 1
)
bottom_r <-
plot_grid(
p_ternary_arid +
theme(legend.position = "none",
plot.margin = unit(c(-0.1, -0.1, -0.1, -0.1), "cm"),
axis.title = element_blank()),
p_ternary_hyperarid +
theme(legend.position = "none",
plot.margin = unit(c(-0.1, -0.1, -0.1, -0.1), "cm"),
axis.title = element_blank()),
labels = c('D'),
label_size = 12,
align = 'hv',
axis = "t",
# rel_widths = c(1, 1, 0.1),
scale = c(1.2, 1.2),
nrow = 2,
ncol = 1
)
bottom_rows <- plot_grid(bottom_l,
bottom_r,
ternary_legend,
align = 'h',
axis = "l",
scale = c(1, 1, 0.08),
rel_widths = c(0.5, 0.35, 0.15),
nrow = 1,
ncol = 3)
p_all <- plot_grid(top_row, bottom_rows, align = 'v', axis = 'l', nrow = 2, rel_heights = c(0.43, 0.6)) # aligning vertically along the left axis
print(p_all)
Detect differentially abundant OTUs using ALDEx2 [@fernandes_anova-like_2013]
# Rock_weathering_filt3_s <- prune_taxa(names(sort(taxa_sums(Rock_weathering_filt3), TRUE))[1:100], Rock_weathering_filt3)
# run full model
data2test <- t(otu_table(Rock_weathering_filt3))
comparison <- as.character(unlist(sample_data(Rock_weathering_filt3)[, "Climate.Source"]))
ALDEx_full <- aldex.clr(data2test, comparison, mc.samples = 128, denom = "iqlr", verbose = TRUE, useMC = TRUE) # iqlr for slight assymetry in composition## [1] "multicore environment is is OK -- using the BiocParallel package"
## [1] "removed rows with sums equal to zero"
## [1] "computing iqlr centering"
## [1] "data format is OK"
## [1] "dirichlet samples complete"
## [1] "clr transformation complete"
ALDEx_full_glm <- aldex.glm(ALDEx_full, comparison, useMC = TRUE) # for more than two conditions## [1] "multicore environment is OK -- using the BiocParallel package"
## [1] "running tests for each MC instance:"
## |------------(25%)----------(50%)----------(75%)----------|
sig_taxa <- rownames(ALDEx_full_glm)[ALDEx_full_glm$glm.eBH < 0.05] # save names of taxa that are significant under the full model
# Pairwise comparisons
#
# dolomite - limestone
Rock_weathering_filt3_Rocks <- subset_samples(Rock_weathering_filt3, Uni.Source == "Rock")
ALDEx2plot_Rocks <- CalcALDEx(Rock_weathering_filt3_Rocks, sig_level = 0.1, LFC = 0)## [1] "multicore environment is is OK -- using the BiocParallel package"
## [1] "removed rows with sums equal to zero"
## [1] "computing iqlr centering"
## [1] "data format is OK"
## [1] "dirichlet samples complete"
## [1] "clr transformation complete"
## [1] "running tests for each MC instance:"
## |------------(25%)----------(50%)----------(75%)----------|
## [1] "multicore environment is OK -- using the BiocParallel package"
## [1] "sanity check complete"
## [1] "rab.all complete"
## [1] "rab.win complete"
## [1] "rab of samples complete"
## [1] "within sample difference calculated"
## [1] "between group difference calculated"
## [1] "group summaries calculated"
## [1] "effect size calculated"
## [1] "summarizing output"
GGPlotALDExTax(ALDEx2plot_Rocks) +
ggtitle("Hyperarid dolomite vs. Arid limestone")
# dolomite - soil
Rock_weathering_filt3_DolSoil <- subset_samples(Rock_weathering_filt3, Climate == "Hyperarid" & Source != "Dust")
ALDEx2plot_DolSoil <- CalcALDEx(Rock_weathering_filt3_DolSoil, sig_level = 0.1, LFC = 0)## [1] "multicore environment is is OK -- using the BiocParallel package"
## [1] "removed rows with sums equal to zero"
## [1] "computing iqlr centering"
## [1] "data format is OK"
## [1] "dirichlet samples complete"
## [1] "clr transformation complete"
## [1] "running tests for each MC instance:"
## |------------(25%)----------(50%)----------(75%)----------|
## [1] "multicore environment is OK -- using the BiocParallel package"
## [1] "sanity check complete"
## [1] "rab.all complete"
## [1] "rab.win complete"
## [1] "rab of samples complete"
## [1] "within sample difference calculated"
## [1] "between group difference calculated"
## [1] "group summaries calculated"
## [1] "effect size calculated"
## [1] "summarizing output"
GGPlotALDExTax(ALDEx2plot_DolSoil) +
ggtitle("Hyperarid dolomite vs. Hyperarid soil")
# dolomite - dust
Rock_weathering_filt3_DolDust <- subset_samples(Rock_weathering_filt3, Climate == "Hyperarid" & Source != "Loess soil")
ALDEx2plot_DolDust <- CalcALDEx(Rock_weathering_filt3_DolDust, sig_level = 0.3, LFC = 0)## [1] "multicore environment is is OK -- using the BiocParallel package"
## [1] "removed rows with sums equal to zero"
## [1] "computing iqlr centering"
## [1] "data format is OK"
## [1] "dirichlet samples complete"
## [1] "clr transformation complete"
## [1] "running tests for each MC instance:"
## |------------(25%)----------(50%)----------(75%)----------|
## [1] "multicore environment is OK -- using the BiocParallel package"
## [1] "sanity check complete"
## [1] "rab.all complete"
## [1] "rab.win complete"
## [1] "rab of samples complete"
## [1] "within sample difference calculated"
## [1] "between group difference calculated"
## [1] "group summaries calculated"
## [1] "effect size calculated"
## [1] "summarizing output"
GGPlotALDExTax(ALDEx2plot_DolDust) +
ggtitle("Hyperarid dolomite vs. Hyperarid dust")
# limestone - soil
Rock_weathering_filt3_LimeSoil <- subset_samples(Rock_weathering_filt3, Climate == "Arid" & Source != "Dust")
ALDEx2plot_LimeSoil <- CalcALDEx(Rock_weathering_filt3_LimeSoil, sig_level = 0.1, LFC = 0)## [1] "multicore environment is is OK -- using the BiocParallel package"
## [1] "removed rows with sums equal to zero"
## [1] "computing iqlr centering"
## [1] "data format is OK"
## [1] "dirichlet samples complete"
## [1] "clr transformation complete"
## [1] "running tests for each MC instance:"
## |------------(25%)----------(50%)----------(75%)----------|
## [1] "multicore environment is OK -- using the BiocParallel package"
## [1] "sanity check complete"
## [1] "rab.all complete"
## [1] "rab.win complete"
## [1] "rab of samples complete"
## [1] "within sample difference calculated"
## [1] "between group difference calculated"
## [1] "group summaries calculated"
## [1] "effect size calculated"
## [1] "summarizing output"
GGPlotALDExTax(ALDEx2plot_LimeSoil) +
ggtitle("Arid limestone vs. Arid soil")
# limestone - dust
Rock_weathering_filt3_LimeDust <- subset_samples(Rock_weathering_filt3, Climate == "Arid" & Source != "Loess soil")
ALDEx2plot_LimeDust <- CalcALDEx(Rock_weathering_filt3_LimeDust, sig_level = 0.3, LFC = 0)## [1] "multicore environment is is OK -- using the BiocParallel package"
## [1] "removed rows with sums equal to zero"
## [1] "computing iqlr centering"
## [1] "data format is OK"
## [1] "dirichlet samples complete"
## [1] "clr transformation complete"
## [1] "running tests for each MC instance:"
## |------------(25%)----------(50%)----------(75%)----------|
## [1] "multicore environment is OK -- using the BiocParallel package"
## [1] "sanity check complete"
## [1] "rab.all complete"
## [1] "rab.win complete"
## [1] "rab of samples complete"
## [1] "within sample difference calculated"
## [1] "between group difference calculated"
## [1] "group summaries calculated"
## [1] "effect size calculated"
## [1] "summarizing output"
GGPlotALDExTax(ALDEx2plot_LimeDust) +
ggtitle("Arid limestone vs. Arid dust")
ALDEx2plot_Rocks %<>% cbind(., Var1 = "Dolomite", Var2 = "Limestone")
ALDEx2plot_DolSoil %<>% cbind(., Var1 = "Dolomite", Var2 = "Loess soil")
ALDEx2plot_DolDust %<>% cbind(., Var1 = "Dolomite", Var2 = "Dust")
ALDEx2plot_LimeSoil %<>% cbind(., Var1 = "Limestone", Var2 = "Loess soil")
ALDEx2plot_LimeDust %<>% cbind(., Var1 = "Limestone", Var2 = "Dust")
ALDEx2plot_all <- bind_rows(ALDEx2plot_Rocks, ALDEx2plot_DolSoil, ALDEx2plot_DolDust, ALDEx2plot_LimeSoil, ALDEx2plot_LimeDust)
ALDEx2plot_all$Var2 %<>%
factor() %>% # Taxa_rank is calcuted for the taxa box plots
fct_relevel(., "Limestone")
# paste0(percent(sum(ALDEx2plot_Rocks$effect > 0 & ALDEx2plot_Rocks$Significance == "Pass")/nrow(ALDEx2plot_Rocks)), "/", percent(sum(ALDEx2plot_Rocks$effect < 0 & ALDEx2plot_Rocks$Significance == "Pass")/nrow(ALDEx2plot_Rocks)))
Labels <- c(
paste0("⬆", sum(ALDEx2plot_Rocks$effect > 0 & ALDEx2plot_Rocks$Significance == "Pass"), " ⬇", sum(ALDEx2plot_Rocks$effect < 0 & ALDEx2plot_Rocks$Significance == "Pass"), " (", nrow(ALDEx2plot_Rocks), ")"),
paste0("⬆", sum(ALDEx2plot_DolSoil$effect > 0 & ALDEx2plot_DolSoil$Significance == "Pass"), " ⬇", sum(ALDEx2plot_DolSoil$effect < 0 & ALDEx2plot_DolSoil$Significance == "Pass"), " (", nrow(ALDEx2plot_DolSoil), ")"),
paste0("⬆", sum(ALDEx2plot_DolDust$effect > 0 & ALDEx2plot_DolDust$Significance == "Pass"), " ⬇", sum(ALDEx2plot_DolDust$effect < 0 & ALDEx2plot_DolDust$Significance == "Pass"), " (", nrow(ALDEx2plot_DolDust), ")"),
paste0("⬆", sum(ALDEx2plot_LimeSoil$effect > 0 & ALDEx2plot_LimeSoil$Significance == "Pass"), " ⬇", sum(ALDEx2plot_LimeSoil$effect < 0 & ALDEx2plot_LimeSoil$Significance == "Pass"), " (", nrow(ALDEx2plot_LimeSoil), ")"),
paste0("⬆", sum(ALDEx2plot_LimeDust$effect > 0 & ALDEx2plot_LimeDust$Significance == "Pass"), " ⬇", sum(ALDEx2plot_LimeDust$effect < 0 & ALDEx2plot_LimeDust$Significance == "Pass"), " (", nrow(ALDEx2plot_LimeDust), ")")
)
Label_text <- bind_cols(
unique(ALDEx2plot_all[c("Var1", "Var2")]),
Label = Labels
)p_aldex2_all <- GGPlotALDExTax(ALDEx2plot_all) +
facet_grid(Var2 ~ Var1, scales = "free_y") +
# theme(strip.background = element_blank(), strip.placement = "outside") +
geom_text(
data = Label_text,
mapping = aes(x = Inf, y = Inf, label = Label),
hjust = 1.1,
vjust = 1.6
)
print(p_aldex2_all)
Other plots in the paper which are not based on sequence data
Isotopes <-
read_csv(
"Data/Isotopes_data.csv"
)
Isotopes %<>%
mutate(Mean.Arid = (`Limestone Shivta Fm. NWSH1` + `Limestone Shivta Fm. NWSH2`) / 2)
Isotopes %<>%
mutate(Mean.Hyperarid = (`Dolomite Gerofit Fm.UVSL5` + `Dolomite Gerofit Fm.UVSL6` ) / 2)
Isotopes2plot <- data.frame(
Rock = factor(c(rep("Limestone", 10), rep("Dolomite", 10)),
levels = c("Limestone", "Dolomite")),
Depth = rep(Isotopes$`Depth (mm)`, 2),
Isotope = rep(Isotopes$Isotope, 2),
min = c(
pmin(
Isotopes$`Limestone Shivta Fm. NWSH1`,
Isotopes$`Limestone Shivta Fm. NWSH2`
),
pmin(
Isotopes$`Dolomite Gerofit Fm.UVSL5`,
Isotopes$`Dolomite Gerofit Fm.UVSL6`
)
),
max = c(
pmax(
Isotopes$`Limestone Shivta Fm. NWSH1`,
Isotopes$`Limestone Shivta Fm. NWSH2`
),
pmax(
Isotopes$`Dolomite Gerofit Fm.UVSL5`,
Isotopes$`Dolomite Gerofit Fm.UVSL6`
)
),
mean = c(Isotopes$Mean.Arid, Isotopes$Mean.Hyperarid)
)
p_isotopes <-
ggplot(Isotopes2plot, aes(y = mean, x = Depth, colour = Isotope)) +
geom_point(size = 4, alpha = 1 / 2) +
geom_errorbar(aes(ymin = min, ymax = max), alpha = 1/2, width = 0.2) +
geom_line(alpha = 1 / 2) +
coord_flip() +
theme_cowplot(font_size = 18, font_family = f_name) +
background_grid(major = "xy",
minor = "none") +
scale_x_reverse(limits = c(4.1, -0.1), expand = c(0.01, 0.01)) +
# scale_x_continuous(limits = c(0, 50), expand = c(0.01, 0.01)) +
facet_grid(Rock ~ . , scales = "free_x", labeller = label_parsed) +
scale_color_manual(values = pom4[c(2,1)],
labels = c(expression(paste(delta ^ {13}, "C")),
expression(paste(delta ^ {18}, "O")))) +
ylab(expression(paste(delta ^ {13}, "C / ",
delta ^ {18}, "O", " (", "\u2030", "VPDB",")"
)))
p_isotopes <- plot_grid(p_isotopes, labels = "b", label_size = 20)
print(p_isotopes)
read_csv("Data/Drying_data_full.csv") ->
Drying_long
Drying_long$Rock %<>% fct_relevel(., "Limestone")
Drying_long$BRC %<>%
fct_relevel(., "Present")
Drying_long$Sample <- with(Drying_long, paste(Rock, BRC))
Drying_mods <- tibble(Sample = character(), Intercept = numeric(), b = numeric(), a = numeric(), P = numeric(), R2 = numeric())
mods <- list()
j <- 1
for (i in unique(Drying_long$Sample)) {
data2model <- Drying_long[Drying_long$Sample == i, ]
colnames(data2model) <- c("Time", "Replicate", "BRC", "Rock", "RWC", "Sample")
(mod <- lme(RWC ~ poly(Time, 2, raw = TRUE), random = ~0 + Time|Replicate, data = data2model))
intervals(mod)
# mod <- lm(`Residual water content (%)` ~ sqrt(1/(`Time (h)` + 1)), data = data2model)
mods[[j]] <- mod
Drying_mods[j, "Sample"] <- i
Drying_mods[j, "Intercept"] <- mod$coefficients$fixed[1]
Drying_mods[j, "b"] <- mod$coefficients$fixed[2]
Drying_mods[j, "a"] <- mod$coefficients$fixed[3]
Drying_mods[j, "P"] <- anova(mod)$`p-value`[2]
Drying_mods[j, "R2"] <- r.squaredGLMM(mod)[, "R2c"]
j <- j + 1
}
Drying_mods %>%
kable(., digits = c(1, 1, 2, 2, 3, 2)) %>%
kable_styling(bootstrap_options = c("hover", "condensed", "responsive"), full_width = F)| Sample | Intercept | b | a | P | R2 |
|---|---|---|---|---|---|
| Dolomite Present | 97.2 | -0.85 | 0.01 | 0.002 | 0.86 |
| Dolomite Removed | 91.9 | -2.42 | 0.03 | 0.004 | 0.77 |
| Limestone Present | 97.4 | -0.99 | 0.01 | 0.000 | 0.96 |
| Limestone Removed | 92.5 | -3.83 | 0.05 | 0.000 | 0.92 |
# comapre with and without crust
data2model <- Drying_long
colnames(data2model) <- c("Time", "Replicate", "BRC", "Rock", "RWC", "Sample")
mod_all <- lme(RWC ~ poly(Time, 2, raw = TRUE), random = ~0 + Time|Replicate, data = data2model)
mod_treatment <- lme(RWC ~ poly(Time, 2, raw = TRUE) * BRC, random = ~0 + Time|Replicate, data = data2model)
anova(mod_all, mod_treatment)| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| mod_all | lme.formula(fixed = RWC ~ poly(Time, 2, raw = TRUE), data = data2model, random = ~0 + Time | Replicate) | 1 | 5 | 1108.742 | 1122.884 | -549.3711 | NA | NA | |
| mod_treatment | lme.formula(fixed = RWC ~ poly(Time, 2, raw = TRUE) * BRC, data = data2model, random = ~0 + Time | Replicate) | 2 | 8 | 1024.040 | 1046.472 | -504.0198 | 1 vs 2 | 90.70266 | 0 |
# comapre limestone vs dolomite - with crust
mod_all <- lme(RWC ~ poly(Time, 2, raw = TRUE), random = ~0 + Time|Replicate, data = data2model[data2model$BRC == "Present", ])
mod_treatment <- lme(RWC ~ poly(Time, 2, raw = TRUE) * Rock, random = ~0 + Time|Replicate, data = data2model[data2model$BRC == "Present", ])
anova(mod_all, mod_treatment)| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| mod_all | lme.formula(fixed = RWC ~ poly(Time, 2, raw = TRUE), data = data2model[data2model$BRC == "Present", ], random = ~0 + Time | Replicate) | 1 | 5 | 409.0114 | 419.5658 | -199.5057 | NA | NA | |
| mod_treatment | lme.formula(fixed = RWC ~ poly(Time, 2, raw = TRUE) * Rock, data = data2model[data2model$BRC == "Present", ], random = ~0 + Time | Replicate) | 2 | 8 | 409.0676 | 425.5512 | -196.5338 | 1 vs 2 | 5.943792 | 0.1143771 |
# comapre limestone vs dolomite - without crust
mod_all <- lme(RWC ~ poly(Time, 2, raw = TRUE), random = ~0 + Time|Replicate, data = data2model[data2model$BRC == "Removed", ])
mod_treatment <- lme(RWC ~ poly(Time, 2, raw = TRUE) * Rock, random = ~0 + Time|Replicate, data = data2model[data2model$BRC == "Removed", ])
anova(mod_all, mod_treatment)| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| mod_all | lme.formula(fixed = RWC ~ poly(Time, 2, raw = TRUE), data = data2model[data2model$BRC == "Removed", ], random = ~0 + Time | Replicate) | 1 | 5 | 530.3667 | 540.9211 | -260.1834 | NA | NA | |
| mod_treatment | lme.formula(fixed = RWC ~ poly(Time, 2, raw = TRUE) * Rock, data = data2model[data2model$BRC == "Removed", ], random = ~0 + Time | Replicate) | 2 | 8 | 525.9703 | 542.4538 | -254.9851 | 1 vs 2 | 10.39642 | 0.0154802 |
# comapre with and without crust - limestone
mod_all <- lme(RWC ~ poly(Time, 2, raw = TRUE), random = ~0 + Time|Replicate, data = data2model[data2model$Rock == "Limestone", ])
mod_treatment <- lme(RWC ~ poly(Time, 2, raw = TRUE) * BRC, random = ~0 + Time|Replicate, data = data2model[data2model$Rock == "Limestone", ])
anova(mod_all, mod_treatment)| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| mod_all | lme.formula(fixed = RWC ~ poly(Time, 2, raw = TRUE), data = data2model[data2model$Rock == "Limestone", ], random = ~0 + Time | Replicate) | 1 | 5 | 562.9452 | 573.4996 | -276.4726 | NA | NA | |
| mod_treatment | lme.formula(fixed = RWC ~ poly(Time, 2, raw = TRUE) * BRC, data = data2model[data2model$Rock == "Limestone", ], random = ~0 + Time | Replicate) | 2 | 8 | 497.8492 | 514.3328 | -240.9246 | 1 vs 2 | 71.09599 | 0 |
mod_all <- lme(RWC ~ poly(Time, 2, raw = TRUE), random = ~0 + Time|Replicate, data = data2model[data2model$Rock == "Dolomite", ])
mod_treatment <- lme(RWC ~ poly(Time, 2, raw = TRUE) * BRC, random = ~0 + Time|Replicate, data = data2model[data2model$Rock == "Dolomite", ])
anova(mod_all, mod_treatment)| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| mod_all | lme.formula(fixed = RWC ~ poly(Time, 2, raw = TRUE), data = data2model[data2model$Rock == "Dolomite", ], random = ~0 + Time | Replicate) | 1 | 5 | 555.4483 | 566.0027 | -272.7241 | NA | NA | |
| mod_treatment | lme.formula(fixed = RWC ~ poly(Time, 2, raw = TRUE) * BRC, data = data2model[data2model$Rock == "Dolomite", ], random = ~0 + Time | Replicate) | 2 | 8 | 529.4140 | 545.8975 | -256.7070 | 1 vs 2 | 32.03428 | 5e-07 |
p_drying <-
ggplot(
Drying_long,
aes(
x = `Time (h)`,
y = `Residual water content (%)`,
colour = Rock,
# fill = Rock,
shape = BRC,
linetype = BRC
)
) +
geom_point(size = 2, alpha = 2/3) +
# geom_smooth(method = "lm", se = FALSE, alpha = 1/2, formula = (y ~ sqrt(1/(x+1)))) +
geom_smooth(method = "lm", se = TRUE, alpha = 1/3, formula = (y ~ poly(x, 2)), size = 1) +
# geom_line(alpha = 1/2) +
scale_y_continuous(limits = c(0, 100), expand = c(0.01, 0.01)) +
scale_x_continuous(limits = c(0, 50), expand = c(0.01, 0.01)) +
# scale_fill_manual(values = pom4) +
scale_color_manual(values = pom4)
print(p_drying)
devtools::session_info()## ─ Session info ─────────────────────────────────────────────────────────────────────────
## setting value
## version R version 3.4.4 (2018-03-15)
## os KDE neon User Edition 5.14
## system x86_64, linux-gnu
## ui X11
## language en_GB
## collate en_DK.UTF-8
## ctype en_DK.UTF-8
## tz Europe/Vienna
## date 2019-01-04
##
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