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macrofauna.R
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macrofauna.R
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# 01 load libraries ----------------
suppressWarnings(suppressPackageStartupMessages({
library(tidyverse)
library(readxl)
library(vegan)
library(ggord)
library(ggpubr)
library(ggvegan)
library(ggrepel)
library(emmeans)
}))
source('theme_javier.R')
theme_set(theme_javier())
set.seed(1234)
# rm(list = ls())
# 02 read and prep data -----------------------------------
macrofauna_dat <-
read_excel('data/macrofauna_data.xlsx', sheet = 'R') %>%
dplyr::filter(Sampling == 2 & Treatment != "Ambient")# excluding ambient
macrofauna_dat[is.na(macrofauna_dat)] <- 0
# read large macrofaunal data
# large_fauna <-
# read_excel('data/sabella_large_macrofauna_data.xlsx', sheet = 'R') %>%
# dplyr::select(-c(code, Treatment:Density))
#
# large_fauna[is.na(large_fauna)] <- 0
# filter taxa with zero abundance
present_taxa <-
macrofauna_dat %>%
gather(taxa, value, 7:100) %>%
group_by(taxa) %>%
summarise_at(vars(value), funs(sum), na.rm =T) %>%
arrange(desc(value)) %>%
dplyr::filter(value>0) %>%
dplyr::select(taxa) %>%
as.list()
# treat_dat <-
# macrofauna_dat %>%
# dplyr::select(code:Density)
treat_dat <-
read_csv('data/treatments.csv') %>%
dplyr::select(-weight)
# impute missing value for mimic final_density usinbg the mean ----
treat_dat %>%
filter(Treatment=='Mimic') %>%
group_by(Density) %>%
summarise(mean(final_density, na.rm = T))
# use 12.5 as estimate of missing value------------
treat_dat <-
treat_dat %>%
mutate(final_density = ifelse(is.na(final_density), 12.5, final_density))
fauna_dat <-
macrofauna_dat %>%
dplyr::select(sample, present_taxa$taxa) %>%
full_join(treat_dat, by = 'sample') %>%
dplyr::select(sample, Treatment, Density, final_density, everything()) %>%
arrange(sample) %>%
write_csv('data/fauna_dat.csv')
# transform data----------------
log_fauna <- log10(fauna_dat[,-c(1:4)]+1)
fauna_4th_root <- sqrt(sqrt(fauna_dat[,-c(1:4)]))
# calculate dissimmilarity matrices--------
d_4th_root_fauna<- vegdist(fauna_4th_root , method = 'bray')
d_log_fauna<- vegdist(log_fauna, method = 'bray')
# 03 mean + CI plots of dominant taxa-----------
long_fauna_dat <-
fauna_dat %>%
gather(taxa, abun,`Theora lubrica`:`Taeniogyrus dendyi`)
taxa_bar_plot <-
long_fauna_dat %>%
group_by(taxa) %>%
filter(sum(abun, na.rm = T) > 8) %>%
ungroup() %>%
ggplot(aes(x = Treatment, y = abun, fill = Treatment)) +
# stat_summary(fun.data = mean_se, size = .5) +
stat_summary(fun.y = mean,
geom = "bar",
color = 1) +
stat_summary(fun.data = mean_se,
geom = "errorbar",
width = 0.4) +
facet_wrap(~ taxa, scales = 'free_y') +
scale_fill_viridis_d() +
theme_javier() +
labs('x = Treatment', y = 'Abundance')
ggsave(
taxa_bar_plot,
filename = 'figures/bar_plot_macrofauna_taxa.tif',
device = 'tiff',
compression = 'lzw',
dpi = 600,
height = 6,
width = 10
)
# 04 MDS ordination -------------
mds <- metaMDS(fauna_4th_root, distance = 'bray')
mds_dat <-
fortify(mds) %>%
rename(sample = Label) %>%
dplyr::filter(Score == 'sites') %>%
bind_cols(treat_dat)
# get taxa with higher scores
vec_taxa <-
mds$species[, 1:2] %>%
data.frame %>%
mutate(Taxa = rownames(.)) %>% dplyr::filter(abs(MDS1) >.8 | abs(MDS2) >.8) %>%
dplyr::select(Taxa) %>%
as.list()
## MDS biplot
ggord(
mds,
grp_in = fauna_dat$Treatment,
poly = F,
alpha = 1,
ellipse = T,
# arrow = .3,
arrow = 0,
repel = T,
text = .01,
vec_ext = 1,
# size = fauna_dat$Density
var_sub = vec_taxa$Taxa
) +
theme_javier() +
scale_shape_manual(values = 15:19)
# 05 PCO ordination --------------------------------------------------
pco <-
capscale(
log10(fauna_dat[, -c(1:4)] + 1) ~ 1 ,
data = fauna_dat,
metaMDSdist = TRUE,
sqrt.dist = T,
add = T,
distance = "bray",
permutations = 1e6,
na.action = na.omit
)
pco_dat <- fortify(pco)
pco_ordination <-
pco_dat %>%
dplyr::filter(Score == 'sites') %>%
ggplot(.,
aes(
x = MDS1,
y = MDS2,
color = fauna_dat$Treatment,
# shape = fauna_dat$Treatment,
size = fauna_dat$Density
)) +
geom_point(alpha = .7) +
# scale_color_discrete(name = "Treatment") +
scale_shape_manual(values = 15:18, name = "Treatment") +
labs(x = "PCO1", y = "PCO2")+
scale_radius(limits = c(0, 50),
range = c(3, 7),
name = 'Density') +
scale_color_viridis_d(name = NULL,
option = 'D')
pco_ordination
# 06 CAPscale-----------------------
cap <-
capscale(
log(fauna_dat[, -c(1:4)]+1) ~ Treatment + final_density,
# sqrt(sqrt(fauna_dat[, -c(1:4)])) ~ Treatment * final_density,
data = fauna_dat,
sqrt.dist = T,
add = F,
distance = "bray",
permutations = 1e6,
na.action = na.omit
)
# anova table Cap
cap_anova_macrofauna <- anova(cap, permutations = 999, by = 'terms')
cap_anova_macrofauna
macrofauna_cap_table <-
data.frame(cap_anova_macrofauna) %>%
rownames_to_column() %>%
rename(Terms = rowname,
df = Df,
SS = SumOfSqs,
P = Pr..F.)
# get data for plots
cap_dat <- fortify(cap)
cap_sites <-
cap_dat %>%
dplyr::filter(Score == 'sites') %>%
bind_cols(treat_dat)
cap_taxa <-
cap_dat %>%
dplyr::filter(Score == 'species') %>%
dplyr::filter(abs(CAP1) > .15 | abs(CAP2) > .15) %>%
mutate(plot_label = if_else(
condition = str_detect(Label, " "),
true = paste0('italic(', Label, ')'),
false = as.character(Label)
)) %>%
mutate(plot_label = str_replace(plot_label," ","~"))
# CAP biplot-------
cap_plot <-
ggplot(cap_sites,
aes(
x = CAP1,
y = CAP2
)) +
geom_point(alpha = .7, aes(color = Treatment,
# shape = fauna_dat$Treatment,
size = Density)) +
# scale_color_discrete(name = "Treatment") +
# scale_shape_manual(values = 15:17, name = "Treatment") +
scale_radius(limits = c(0, 50),
range = c(3, 7),
name = 'Density') +
scale_color_viridis_d(name = "Treatment",
option = 'D')
cap_plot
# biplot with taxa scores
cap_biplot <-
cap_plot +
geom_text_repel(
data = cap_taxa,
aes(label = plot_label, x = CAP1 * 4, y = CAP2 * 4),
size = 4,
parse = TRUE
)
cap_biplot
ggsave(
cap_biplot,
filename = 'figures/CAP_biplot.tif',
device = 'tiff',
compression = 'lzw',
height = 4,
width = 5
)
# Both plot in one
ggarrange(pco_ordination, cap_plot, common.legend = T, labels = "auto")
ggsave(
last_plot(),
filename = 'figures/PCO_CAP.tif',
device = 'tiff',
compression = 'lzw',
height = 4,
width = 8
)
# 07 PERMANOVA--------------------------------------
permanova <-
adonis(
log(fauna_dat[, -c(1:4)]+1) ~ Treatment + final_density,
data = fauna_dat,
method = "bray",
permutations = 9999
)
permanova
macrofauna_permanova_table <-
data.frame(permanova$aov.tab) %>%
rownames_to_column() %>%
rename(Terms = rowname,
df = Df,
SS = SumsOfSqs,
MS = MeanSqs,
"Pseudo-F" = F.Model,
P = Pr..F.)
# 08 PERMANOVA pairwise comparisons-------------
source('pairwise.adonis.R')
set.seed(99)
# .Random.seed
pairwise.adonis(
log10(fauna_dat[, -c(1:4)]+1),
# sqrt(sqrt(fauna_dat[, -c(1:4)] )),
fauna_dat$Treatment,
sim.method = 'bray')
# pairs F.Model R2 p.value p.adjusted
# 1 Control vs Sabella 1.484889 0.11011501 0.091 0.273
# 2 Control vs Mimic 1.234271 0.09326323 0.204 0.612
# 3 Sabella vs Mimic 1.486769 0.07629632 0.046 0.138
# 09 SIMPER ----
simp <- simper(fauna_dat[, -c(1:4)], fauna_dat$Treatment, permutations = 999)
# simp <- simper(fauna_dat[,-c(1:4)], fauna_dat$Treatment, permutations = 999)
summary(simp, digits = 2, ordered = T)
simper_macrofauna <-
bind_rows("Control vs. Sabella" = simp$Control_Sabella %>% data.frame(),
"Control vs.Mimic" = simp$Control_Mimic %>% data.frame(),
"Sabella vs. Mimic" = simp$Sabella_Mimic %>% data.frame(),
.id = "Groups") %>%
dplyr::select(-overall, -ord) %>%
dplyr::filter(cusum<.7) %>%
rename(Taxa = "species") %>%
print(digits = 2)
# 10 PERMDISP ----------------------
dis <- vegdist(sqrt(sqrt(fauna_dat[,-c(1:3)] )))
beta_disp <-
betadisper(
dis, fauna_dat$Treatment
)
beta_disp
anova(beta_disp)
permutest(beta_disp, pairwise = TRUE, permutations = 999)
plot(beta_disp)
boxplot(beta_disp)
# 11 Univariate diversity index -------------------
indices <-
fauna_dat %>%
transmute(N = rowSums(fauna_dat[, -c(1:4)]),
H = diversity(fauna_dat[, -c(1:4)]),
S = specnumber(fauna_dat[, -c(1:4)]),
J = H/log(S),
F = fisher.alpha(fauna_dat[, -c(1:4)]),
ES = rarefy(fauna_dat[, -c(1:4)], min(N))) %>%
bind_cols(treat_dat) %>%
write_csv('data/macro_indices.csv')
boxplots <-
indices %>%
gather(index, value, c("N", "S", "J")) %>%
mutate(index = fct_relevel(index, c("N", "S", "J"))) %>%
ggplot(., aes(x = Treatment, y = value, fill = Treatment)) +
geom_boxplot(alpha = .8) +
facet_wrap(~index,scales = 'free') +
scale_fill_viridis_d(guide = F)
boxplots
# ancova indices------
indices_dat <-
indices %>%
mutate(N = log(N +1)) %>%
gather(index, value, c("N", "S", "J")) %>%
group_by(index) %>%
nest() %>%
mutate(
ancova = map(.x = data, ~ aov(value ~ Treatment + final_density, data = .x)),
anova_tab = map(ancova, ~car::Anova(., type = 2)),
ancova_table = map(anova_tab, broom::tidy),
residuals = map(ancova, broom::augment),
emm = map(ancova,~emmeans(., ~Treatment)),
posthoc = map(emm,pairs))
ancova_table_macro <-
indices_dat %>%
select(index,ancova_table) %>%
unnest()
indices_dat %>%
select(index,posthoc) %>%
flatten()
## model validation----
res <-
indices_dat %>%
select(index, residuals) %>%
unnest(residuals, .drop = TRUE)
# Plot of fitted vs. residual values by index to check the assumptions heteroscedasticity or any other pattern,
# e.g. non-linearity or missing covariates
ggplot(res) +
geom_point(aes(x = .fitted, y = .resid), alpha = .3) +
facet_wrap( ~ index, scale = 'free') +
geom_hline(yintercept = 0,
lty = 2,
col = 2)
# boxplot by type
ggplot(res) +
geom_boxplot(aes(x = Treatment, y = .resid), alpha = .3) +
facet_wrap( ~ index, scale = 'free') +
geom_hline(yintercept = 0,
lty = 2,
col = 2)
# qqplot of the normalised residuals to check the assumption of normality------------
ggplot(res) +
stat_qq(aes(sample = .std.resid), alpha = .3) +
facet_wrap( ~ index, scale = 'free') +
geom_abline(
intercept = 0,
slope = 1,
lty = 2,
col = 2
)