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DIMRS.R
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DIMRS.R
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########################################################################
#### Project: Danjugan Island Marine Reserve and Sanctuaries corals ####
#### Author: Luka Seamus Wright ####
#### Date: 5th April 2021 ####
########################################################################
#### Univariate data ####
### Load data ####
danju <- read.csv("~/PATH/DIMRS.csv")
### Data wrangling ####
## Response variables ####
HC <- danju$HC # Hard coral cover (%)
NIA <- danju$NIA # Macroalgal cover (%)
DB <- danju$DIS + danju$FBL + danju$PBL # Diseased or bleached (full + partial) coral (%)
# given as a percentage of live coral
DC <- danju$DC + danju$RKC # Dead (long + recent) coral (%)
# given as a percentage of total coral
LHC <- HC*(1-DC/100) # Live hard coral cover (%)
## Explanatory variables ####
site <- danju$site
depth <- danju$depth
### Data exploration ####
require(fitdistrplus)
## Response variables ####
par(mfrow = c(3,1), mar = c(2,2,2,1))
hist(HC)
boxplot(HC, horizontal = T)
descdist(HC, boot = 500)
# almost uniform
par(mfrow = c(3,1), mar = c(2,2,2,1))
hist(LHC)
boxplot(LHC, horizontal = T)
descdist(LHC, boot = 500)
# almost normal
hist(NIA)
boxplot(NIA, horizontal = T)
descdist(NIA, boot = 500)
# right skewed
hist(DC)
boxplot(DC, horizontal = T)
descdist(DC, boot = 500)
# close to normal
hist(DB)
boxplot(DB, horizontal = T)
descdist(DB, boot = 500)
# right skewed
par(mfrow = c(1,1), mar = c(5, 4, 4, 2) + 0.1)
## Explanatory and response variables ####
boxplot(HC ~ site) # homogenous
boxplot(LHC ~ site) # homogenous
boxplot(NIA ~ site) # heterogenous
boxplot(DC ~ site) # homogenous
boxplot(DB ~ site) # heterogenous
boxplot(HC ~ depth)
boxplot(LHC ~ depth)
boxplot(DC ~ depth) # no depth-effect on coral
boxplot(DB ~ depth)
boxplot(NIA ~ depth) # more algae and diseased coral in shallow water
plot(DB ~ NIA, pch = site)
cor.test(NIA, DB) # positive correlation
plot(LHC ~ NIA, pch = site)
cor.test(NIA, LHC) # no negative correlation
### Data analysis ####
## Total hard coral cover ####
# Build model
m1 <- lm(HC ~ site)
# Test model fit
par(mfrow = c(2,2), mar = c(2,2,2,1))
plot(m1) # quite homogenous
par(mfrow = c(1,2), mar = c(2,2,2,1))
hist(resid(m1)) # residuals are quite normally distributed
qqnorm(resid(m1))
qqline(resid(m1))
par(mfrow = c(1,1), mar = c(5, 4, 4, 2) + 0.1)
# overall m1 fits the data well
# Interpret model
require(car)
Anova(m1, type = 2) # Type II sum of squares test
# Response: HC
# Sum Sq Df F value Pr(>F)
# site 10380 3 14.184 8.267e-06 ***
# Residuals 6830 28
summary(m1) # pairwise contrasts
# Bamboo vs Manta; p < 0.001 ***
# Bamboo vs Moray; p < 0.001 ***
# Bamboo vs Tabon; 0.79
site <- factor(site, levels = c("Manta", "Moray", "Tabon", "Bamboo"))
m1 <- lm(HC ~ site)
summary(m1)
# Manta vs Moray; p = 0.54
# Manta vs Tabon; p < 0.001 ***
site <- factor(site, levels = c("Tabon", "Moray", "Manta", "Bamboo"))
m1 <- lm(HC ~ site)
summary(m1)
# Tabon vs Moray; p < 0.001 ***
site <- factor(site, levels = c("Bamboo", "Manta", "Moray", "Tabon"))
## Live hard coral cover ####
# Build model
m2 <- lm(LHC ~ site)
# Test model fit
par(mfrow = c(2,2), mar = c(2,2,2,1))
plot(m2) # quite homogenous
par(mfrow = c(1,2), mar = c(2,2,2,1))
hist(resid(m2)) # residuals are normally distributed
qqnorm(resid(m2))
qqline(resid(m2))
par(mfrow = c(1,1), mar = c(5, 4, 4, 2) + 0.1)
# Interpret model
Anova(m2, type = 2) # Type II sum of squares test
# Response: LHC
# Sum Sq Df F value Pr(>F)
# site 6011.9 3 11.098 5.654e-05 ***
# Residuals 5055.8 28
summary(m2) # pairwise contrasts
# Bamboo vs Manta; p < 0.001 ***
# Bamboo vs Moray; p < 0.001 ***
# Bamboo vs Tabon; p = 0.31
site <- factor(site, levels = c("Manta", "Moray", "Tabon", "Bamboo"))
m2 <- lm(LHC ~ site)
summary(m2)
# Manta vs Moray; p = 0.25
# Manta vs Tabon; p < 0.001 ***
site <- factor(site, levels = c("Tabon", "Moray", "Manta", "Bamboo"))
m2 <- lm(LHC ~ site)
summary(m2)
# Tabon vs Moray; p = 0.009 **
site <- factor(site, levels = c("Bamboo", "Manta", "Moray", "Tabon"))
## Macroalgal cover ####
# Build model
m3 <- lm(NIA ~ site)
# Test model fit
par(mfrow = c(2,2), mar = c(2,2,2,1))
plot(m3) # heterogenous
par(mfrow = c(1,2), mar = c(2,2,2,1))
hist(resid(m3)) # normal
qqnorm(resid(m3))
qqline(resid(m3))
par(mfrow = c(1,1), mar = c(5, 4, 4, 2) + 0.1)
# try fitting gamma distribution to data
gamma <- fitdist(NIA+1, "gamma")
norm <- fitdist(NIA+1, "norm")
cdfcomp(list(gamma, norm),
legendtext = c("Gamma", "Normal"),
fitlty = 1)
gofstat(list(gamma, norm),
fitnames = c("Gamma", "Normal"))
# gamma fits better
# Build gamma models
m4 <- glm(NIA+1 ~ site, family = Gamma(link = "log"))
# Test model fit
par(mfrow = c(2,2), mar = c(2,2,2,1))
plot(m4) # quite homogenous
par(mfrow = c(1,2), mar = c(2,2,2,1))
hist(resid(m4)) # residuals are quite normally distributed
qqnorm(resid(m4))
qqline(resid(m4))
par(mfrow = c(1,1), mar = c(5, 4, 4, 2) + 0.1)
# m4 fits the data well
# Interpret model
Anova(m4, type = 2) # Type II sum of squares test
# Response: NIA + 1
# LR Chisq Df Pr(>Chisq)
# site 27.166 3 5.435e-06 ***
summary(m4) # pairwise contrasts
# Bamboo vs Manta; p = 0.01 *
# Bamboo vs Moray; p = 0.41
# Bamboo vs Tabon; p < 0.001 ***
site <- factor(site, levels = c("Manta", "Moray", "Tabon", "Bamboo"))
m4 <- glm(NIA+1 ~ site, family = Gamma(link = "log"))
summary(m4)
# Manta vs Moray; p = 0.08
# Manta vs Tabon; p = 0.05 *
site <- factor(site, levels = c("Tabon", "Moray", "Manta", "Bamboo"))
m4 <- glm(NIA+1 ~ site, family = Gamma(link = "log"))
summary(m4)
# Tabon vs Moray; p < 0.001 ***
site <- factor(site, levels = c("Bamboo", "Manta", "Moray", "Tabon"))
## Discoloured (diseased or bleached) coral ####
# Build models
m5 <- lm(DB ~ site)
# Test model fit
par(mfrow = c(2,2), mar = c(2,2,2,1))
plot(m5) # quite homogenous
par(mfrow = c(1,2), mar = c(2,2,2,1))
hist(resid(m5)) # residuals deviate at distribution edges
qqnorm(resid(m5))
qqline(resid(m5)) # but overall quite balanced
par(mfrow = c(1,1), mar = c(5, 4, 4, 2) + 0.1)
# Interpret model
Anova(m5, type = 2) # Type II sum of squares test
# Response: DB
# Sum Sq Df F value Pr(>F)
# site 8517.3 3 9.297 0.0001983 ***
# Residuals 8550.6 28
summary(m5) # pairwise contrasts
# Bamboo vs Manta; p = 0.002 **
# Bamboo vs Moray; p = 0.99
# Bamboo vs Tabon; p < 0.001 ***
site <- factor(site, levels = c("Manta", "Moray", "Tabon", "Bamboo"))
m5 <- lm(DB ~ site)
summary(m5)
# Manta vs Moray; p = 0.002 **
# Manta vs Tabon; p = 0.5
site <- factor(site, levels = c("Tabon", "Moray", "Manta", "Bamboo"))
m5 <- lm(DB ~ site)
summary(m5)
# Tabon vs Moray; p < 0.001 ***
site <- factor(site, levels = c("Bamboo", "Manta", "Moray", "Tabon"))
## Dead coral (% of total coral) ####
# Build model
m6 <- lm(DC ~ site)
# Test model fit
par(mfrow = c(2,2), mar = c(2,2,2,1))
plot(m6) # quite homogenous
par(mfrow = c(1,2), mar = c(2,2,2,1))
hist(resid(m6)) # normality is ok
qqnorm(resid(m6))
qqline(resid(m6))
par(mfrow = c(1,1), mar = c(5, 4, 4, 2) + 0.1)
# Interpret model
Anova(m6, type = 2) # Type II sum of squares test
# Response: DC
# Sum Sq Df F value Pr(>F)
# site 7445.2 3 8.0249 0.0005162 ***
# Residuals 8659.1 28
summary(m6) # pairwise contrasts
# Bamboo vs Manta; p < 0.001 ***
# Bamboo vs Moray; p = 0.04 *
# Bamboo vs Tabon; p < 0.001 ***
site <- factor(site, levels = c("Manta", "Moray", "Tabon", "Bamboo"))
m6 <- lm(DC ~ site)
summary(m6)
# Manta vs Moray; p = 0.04 *
# Manta vs Tabon; p = 0.93
site <- factor(site, levels = c("Tabon", "Moray", "Manta", "Bamboo"))
m6 <- lm(DC ~ site)
summary(m6)
# Tabon vs Moray; p = 0.05
site <- factor(site, levels = c("Manta", "Moray", "Tabon", "Bamboo"))
### Data visualisation ####
## Calculate descriptive statistics ####
require(psych)
HCstat <- describeBy(HC, site, mat = T)
NIAstat <- describeBy(NIA, site, mat = T)
DBstat <- describeBy(DB, site, mat = T)
## Modify coral cover dataframe ####
HCstat2 <- rbind(describeBy(LHC, site, mat = T), # live coral cover
describeBy(HC*DC/100, site, mat = T)) # dead coral cover
HCstat2$group2 <- c(rep("live", 4), rep("dead", 4))
HCstat2$mean2 <- rep(HCstat$mean, 2)
HCstat2$se2 <- rep(HCstat$se, 2)
## Load historical data and combine ####
# this data was sourced from https://wedocs.unep.org/handle/20.500.11822/29101
# hdf <- data.frame(year = c(rep(2002+8/12, 4), rep(2002+11/12, 4),
# rep(2003+2/12, 4), rep(2003+5/12, 4),
# rep(2016+12/12, 4)),
# site = rep(c("Manta", "Moray", "Tabon", "Bamboo"), 5),
# LC = c(48, 43, 42, NA, 42, 59, 37, NA, 54, 48, 46, NA, 47, 52, 41, NA,
# HCstat2$mean[1:4]),
# DC = c(13, 23, 23, NA, 6, 25, 8, NA, 6, 16, 7, NA, 2, 23, 3, NA,
# HCstat2$mean[5:8]),
# HC = c(61, 66, 65, NA, 48, 84, 45, NA, 60, 64, 53, NA, 49, 75, 44, NA,
# HCstat2$mean2[1:4]),
# NIA = c(16, 9, 14, NA, 23, 5, 13, NA, 19, 12, 31, NA, 34, 7, 31, NA,
# NIAstat$mean),
# LCse = c(rep(0, 16), HCstat2$se[1:4]),
# DCse = c(rep(0, 16), HCstat2$se[5:8]),
# HCse = c(rep(0, 16), HCstat2$se2[1:4]),
# NIAse = c(rep(0, 16), NIAstat$se))
hdf <- data.frame(year = c(rep(2002, 4), rep(2003, 4), rep(2016, 4)),
site = rep(c("Manta", "Moray", "Tabon", "Bamboo"), 3),
LC = c(45, 51, 39.5, NA, 50.5, 50, 43.5, NA, HCstat2$mean[1:4]),
DC = c(9.5, 24, 15.5, NA, 4, 19.5, 5, NA, HCstat2$mean[5:8]),
HC = c(54.5, 75, 55, NA, 54.5, 69.5, 48.5, NA, HCstat2$mean2[1:4]),
NIA = c(19.5, 7, 13.5, NA, 26.5, 9.5, 31, NA, NIAstat$mean),
LCse = c(rep(0, 8), HCstat2$se[1:4]),
DCse = c(rep(0, 8), HCstat2$se[5:8]),
HCse = c(rep(0, 8), HCstat2$se2[1:4]),
NIAse = c(rep(0, 8), NIAstat$se))
# note that this study used a different control site for the east side of the island
# hence, Bamboo is not represented in the data
## Set theme ####
require(ggplot2)
mytheme <- theme(panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
axis.line = element_line(),
axis.title = element_text(size = 15),
axis.title.x = element_blank(),
axis.text = element_text(size = 12, colour = "black"),
axis.ticks.length = unit(.25, "cm"),
axis.ticks = element_line(colour = "black"),
axis.ticks.x = element_blank(),
legend.key = element_blank(),
legend.background = element_blank(),
legend.text = element_text(size = 12),
legend.text.align = 0,
legend.title = element_blank(),
text = element_text(family = "Helvetica Neue"))
## Plot ####
# Total hard coral cover (simple)
HCplot <- ggplot(HCstat, mapping = aes(factor(group1, levels = unique(group1)), mean)) +
geom_col(width = 0.7, fill = c("#7fb8d5", "#467289", "#f7b060", "#ef8407")) +
geom_errorbar(mapping = aes(ymin = mean - se, ymax = mean + se),
width = .1, lwd = .4) +
geom_text(aes(x = group1, y = mean + se + 2,
label = c("a","a","b","b"))) +
ylab("Scleractinian coral cover (%)") +
scale_y_continuous(expand = c(0,0)) +
coord_cartesian(ylim = c(0, 80)) +
mytheme
HCplot # dimensions: 4 x 4 in
# Coral cover (including split between live and dead)
HCplot2 <- ggplot(HCstat2, mapping = aes(factor(group1, levels = unique(group1)),
mean, alpha = group2)) +
geom_col(position = "stack", width = 0.7,
fill = rep(c("#7fb8d5", "#467289", "#f7b060", "#ef8407"), 2)) +
geom_errorbar(mapping = aes(ymin = mean2 - se2, ymax = mean2 + se2),
width = .1, lwd = .4) +
geom_text(aes(x = group1, y = mean2 + se2 + 2,
label = rep(c("a","a","b","b"), 2))) +
scale_alpha_manual(values = c(0.5, 1),
labels = c("Dead", "Live"),
guide = "none") +
ylab("Scleractinian coral cover (%)") +
scale_y_continuous(expand = c(0,0)) +
coord_cartesian(ylim = c(0, 80)) +
mytheme
HCplot2 # dimensions: 4 x 4 in
# Coral cover (including historical data)
hdf$site <- factor(hdf$site, levels = unique(hdf$site))
HCplot3 <- ggplot(hdf, mapping = aes(year, HC, colour = site)) +
geom_line(aes(group = site), size = 0.6, position = position_dodge(width = -3.2)) +
geom_pointrange(aes(ymin = HC - HCse, ymax = HC + HCse,
shape = site, fill = site), size = 0.6,
position = position_dodge(width = -3.2)) +
annotate("text", x = c(2018.4, 2017.1), y = c(57.01219, 21.18903),
label = c("a", "b")) +
scale_colour_manual(values = c("#7fb8d5", "#467289", "#f7b060", "#ef8407"),
labels = c("Manta", "Moray", "Tabon", "Bamboo"),
guide = guide_legend()) +
scale_fill_manual(values = c("#7fb8d5", "#467289", "#f7b060", "#ef8407"),
labels = c("Manta", "Moray", "Tabon", "Bamboo"),
guide = guide_legend()) +
scale_shape_manual(values = 21:24,
labels = c("Manta", "Moray", "Tabon", "Bamboo"),
guide = guide_legend()) +
ylab("Scleractinian coral cover (%)") +
scale_y_continuous(expand = c(0,0)) +
scale_x_continuous(expand = c(0,0), breaks = seq(2000, 2018, by = 9)) +
coord_cartesian(ylim = c(0, 100), xlim = c(2000, 2018),
clip = "off") +
mytheme +
theme(legend.position = c(0.88, 0.88),
axis.ticks.x = element_line(colour = "black"),
plot.margin = unit(c(0.2, 0.5, 0.2, 0.2),"cm"))
HCplot3 # dimensions: 4 x 4 in
# Live coral cover (including historical data)
LCplot <- ggplot(hdf, mapping = aes(year, LC, colour = site)) +
geom_pointrange(aes(ymin = LC - LCse, ymax = LC + LCse,
shape = site, fill = site), size = 0.6,
position = position_dodge(width = -3.8)) +
geom_line(aes(group = site), size = 0.6, position = position_dodge(width = -3.8)) +
annotate("text", x = c(2018.3, 2016.6), y = c(39.32926, 12.95731),
label = c("a", "b")) +
scale_colour_manual(values = c("#7fb8d5", "#467289", "#f7b060", "#ef8407"),
labels = c("Manta", "Moray", "Tabon", "Bamboo"),
guide = guide_legend()) +
scale_fill_manual(values = c("#7fb8d5", "#467289", "#f7b060", "#ef8407"),
labels = c("Manta", "Moray", "Tabon", "Bamboo"),
guide = guide_legend()) +
scale_shape_manual(values = 21:24,
labels = c("Manta", "Moray", "Tabon", "Bamboo"),
guide = guide_legend()) +
ylab("Live scleractinian coral cover (%)") +
scale_y_continuous(expand = c(0,0)) +
scale_x_continuous(expand = c(0,0), breaks = seq(2000, 2018, by = 9)) +
coord_cartesian(ylim = c(0, 100), xlim = c(2000, 2018),
clip = "off") +
mytheme +
theme(legend.position = c(0.88, 0.88),
axis.ticks.x = element_line(colour = "black"),
plot.margin = unit(c(0.2, 0.5, 0.2, 0.2),"cm"))
LCplot # dimensions: 4 x 4 in
# Macroalgal cover
NIAplot <- ggplot(NIAstat, mapping = aes(factor(group1, levels = unique(group1)), mean)) +
geom_col(width = 0.7, fill = c("#7fb8d5", "#467289", "#f7b060", "#ef8407")) +
geom_errorbar(mapping = aes(ymin = mean - se, ymax = mean + se),
width = .1, lwd = .4) +
geom_text(aes(x = group1, y = mean + se + 1.3,
label = c("a","ab","c","b"))) +
ylab("Macroalgal cover (%)") +
scale_y_continuous(expand = c(0,0)) +
coord_cartesian(ylim = c(0, 50)) +
mytheme
NIAplot # dimensions: 4 x 4 in
# Macroalgal cover (including historical data)
NIAplot2 <- ggplot(hdf, mapping = aes(year, NIA, colour = site)) +
geom_pointrange(aes(ymin = NIA - NIAse, ymax = NIA + NIAse,
shape = site, fill = site), size = 0.6,
position = position_dodge(width = -1)) +
geom_line(aes(group = site), size = 0.6, position = position_dodge(width = -1)) +
annotate("text", x = c(2017.6, 2017.6, 2017.2, 2017), y = hdf$NIA[9:12],
label = c("b", "bc", "a", "c")) +
scale_colour_manual(values = c("#7fb8d5", "#467289", "#f7b060", "#ef8407"),
labels = c("Manta", "Moray", "Tabon", "Bamboo"),
guide = guide_legend()) +
scale_fill_manual(values = c("#7fb8d5", "#467289", "#f7b060", "#ef8407"),
labels = c("Manta", "Moray", "Tabon", "Bamboo"),
guide = guide_legend()) +
scale_shape_manual(values = 21:24,
labels = c("Manta", "Moray", "Tabon", "Bamboo"),
guide = guide_legend()) +
ylab("Macroalgal cover (%)") +
scale_y_continuous(expand = c(0,0)) +
scale_x_continuous(expand = c(0,0), breaks = seq(2000, 2018, by = 9)) +
coord_cartesian(ylim = c(0, 80), xlim = c(2000, 2018),
clip = "off") +
mytheme +
theme(legend.position = c(0.88, 0.88),
axis.ticks.x = element_line(colour = "black"),
plot.margin = unit(c(0.2, 0.5, 0.2, 0.2),"cm"))
NIAplot2 # dimensions: 4 x 4 in
# Discoloured coral
DBplot <- ggplot(DBstat, mapping = aes(factor(group1, levels = unique(group1)), mean)) +
geom_col(width = 0.7, fill = c("#7fb8d5", "#467289", "#f7b060", "#ef8407")) +
geom_errorbar(mapping = aes(ymin = mean - se, ymax = mean + se),
width = .1, lwd = .4) +
geom_text(aes(x = group1, y = mean + se + 1.5,
label = c("a","b","a","b"))) +
ylab("Discoloured scleractinian coral (%)") +
scale_y_continuous(expand = c(0,0)) +
coord_cartesian(ylim = c(0, 60)) +
mytheme
DBplot # dimensions: 4 x 4 in
# "Morphospecies" richness (point range)
DBplot2 <- ggplot(DBstat, mapping = aes(factor(group1, levels = unique(group1)), mean)) +
geom_pointrange(aes(ymin = mean - se, ymax = mean + se,
colour = factor(group1, levels = unique(group1)),
fill = factor(group1, levels = unique(group1)),
shape = factor(group1, levels = unique(group1))),
size = 0.6) +
scale_colour_manual(values = c("#7fb8d5", "#467289", "#f7b060", "#ef8407"),
labels = c("Manta", "Moray", "Tabon", "Bamboo"),
guide = guide_legend()) +
scale_fill_manual(values = c("#7fb8d5", "#467289", "#f7b060", "#ef8407"),
labels = c("Manta", "Moray", "Tabon", "Bamboo"),
guide = guide_legend()) +
scale_shape_manual(values = 21:24,
labels = c("Manta", "Moray", "Tabon", "Bamboo"),
guide = guide_legend()) +
geom_text(aes(x = group1, y = mean + se + 3,
label = c("a","b","a","b"))) +
scale_y_continuous(expand = c(0,0)) +
ylab("Discoloured scleractinian coral (%)") +
coord_cartesian(ylim = c(0, 80)) +
mytheme +
theme(legend.position = "none",
axis.text.x = element_blank(),
axis.line.x = element_blank())
DBplot2 # dimensions: 4 x 4 in
#### Multivariate "morphospecies" data ####
require(vegan)
### Load data ####
morph <- danju[,c(1:6, 23:32)]
str(morph)
### Data wrangling ####
## Reorder levels of site factor ####
morph$site <- factor(morph$site, levels = c("Manta", "Moray", "Tabon", "Bamboo"))
str(morph)
## Define environmental variables ####
env <- morph[,1:5]
## Define "morphospecies" abundance data ####
coral <- morph[,6:16]
coral <- data.frame(coral, row.names = 1)
### Data analysis ####
## Calculate Bray-Curtis dissimilarity ####
coral.dist <- vegdist(coral, method = "bray")
## Test for homogeneity of dispersion ####
betad <- with(env, betadisper(coral.dist, site))
permutest(betad, permutations = 9999) # heterogenous
## Transform ####
sqrt.coral <- sqrt(coral)
## Calculate Bray-Curtis dissimilarity ####
coral.dist <- vegdist(sqrt.coral, method = "bray")
## Test for homogeneity of dispersion ####
betad <- with(env, betadisper(coral.dist, site))
permutest(betad, permutations = 9999) # homogenous
## PERMANOVA, "morphospecies" abundance data ####
adonis(sqrt.coral ~ site, env, permutations = 9999)
# Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
# site 3 1.4676 0.48919 5.4988 0.37074 1e-04 ***
# Residuals 28 2.4909 0.08896 0.62926
# Total 31 3.9585 1.00000
# site significantly affects the community composition of
# "morphospecies" and explains ~37% of the variance in
# community composition
## SIMPER, "morphospecies" influence on dissimilarity between sites ####
morphospecies <- with(env, simper(sqrt.coral, site, permutations = 9999))
summary(morphospecies, ordered = T)
# corymbose, solitary and submassive coral seem to be a major drivers of dissimilarity
# -> worth looking into some univariate analysis
## Data exploration ####
boxplot(danju$S ~ site)
boxplot(danju$SM ~ site) # looks interesting
boxplot(danju$C ~ site) # looks interesting
site <- factor(site, levels = c("Bamboo", "Manta", "Moray", "Tabon"))
## Data analysis ####
## Corymbose coral (% of total coral) ####
# Build model
C <- danju$C
m7 <- lm(C ~ site)
# Test model fit
par(mfrow = c(2,2), mar = c(2,2,2,1))
plot(m7) # quite homogenous
par(mfrow = c(1,2), mar = c(2,2,2,1))
hist(resid(m7)) # residuals are normally distributed
qqnorm(resid(m7))
qqline(resid(m7))
par(mfrow = c(1,1), mar = c(5, 4, 4, 2) + 0.1)
# Interpret model
Anova(m7, type = 2) # Type II sum of squares test
# Response: C
# Sum Sq Df F value Pr(>F)
# site 12095.9 3 16.597 2.164e-06 ***
# Residuals 6802.2 28
summary(m7)
# Bamboo vs Manta; p = 0.42
# Bamboo vs. Moray; p < 0.001 ***
# Bamboo vs. Tabon; p = 0.07
site <- factor(site, levels = c("Manta", "Moray", "Tabon", "Bamboo"))
m7 <- lm(C ~ site)
summary(m7)
# Manta vs Moray; p < 0.001 ***
# Manta vs Tabon; p = 0.01 *
site <- factor(site, levels = c("Tabon", "Moray", "Manta", "Bamboo"))
m7 <- lm(C ~ site)
summary(m7)
# Tabon vs Moray; p < 0.001 ***
site <- factor(site, levels = c("Bamboo", "Manta", "Moray", "Tabon"))
## Submassive coral (% of total coral) ####
# Build models
SM <- danju$SM
m8 <- lm(SM ~ site)
# Test model fit
par(mfrow = c(2,2), mar = c(2,2,2,1))
plot(m8) # quite homogenous
par(mfrow = c(1,2), mar = c(2,2,2,1))
hist(resid(m8)) # residuals are quite normally distributed
qqnorm(resid(m8))
qqline(resid(m8))
par(mfrow = c(1,1), mar = c(5, 4, 4, 2) + 0.1)
# Interpret model
Anova(m8, type = 2) # Type II sum of squares test
# Response: SM
# Sum Sq Df F value Pr(>F)
# site 11413 3 8.8594 0.0002736 ***
# Residuals 12023 28
summary(m8) # pairwise contrasts
# Bamboo vs Manta; p = 0.11
# Bamboo vs. Moray; p = 0.46
# Bamboo vs. Tabon; p < 0.001 ***
site <- factor(site, levels = c("Manta", "Moray", "Tabon", "Bamboo"))
m8 <- lm(SM ~ site)
summary(m8)
# Manta vs Moray; p = 0.02 *
# Manta vs Tabon; p = 0.02 *
site <- factor(site, levels = c("Tabon", "Moray", "Manta", "Bamboo"))
m8 <- lm(SM ~ site)
summary(m8)
# Tabon vs Moray; p < 0.001 ***
site <- factor(site, levels = c("Bamboo", "Manta", "Moray", "Tabon"))
## Calculate diversity indices ####
S <- specnumber(coral) # Species richness
D <- diversity(coral, index = "invsimpson", base = exp(1)) # Simpson's reciprocal index (1/D)
D2 <- diversity(coral, index = "simpson", base = exp(1)) # Simpson's diversity index (1-D)
H <- diversity(coral, index = "shannon", base = exp(1)) # Shannon-Wiener diversity index
E <- D/S # Simpson's evenness index
J <- H/log(S) # Pielou's evenness index
J # NaN produced -> proceed with E instead of J
## "Morphospecies" richness ####
# Build model
m9 <- lm(S ~ site)
# Test model fit
par(mfrow = c(2,2), mar = c(2,2,2,1))
plot(m9) # quite homogenous
par(mfrow = c(1,2), mar = c(2,2,2,1))
hist(resid(m9)) # residuals are quite normally distributed
qqnorm(resid(m9))
qqline(resid(m9))
par(mfrow = c(1,1), mar = c(5, 4, 4, 2) + 0.1)
# Interpret model
Anova(m9, type = 2) # Type II sum of squares test
# Response: S
# Sum Sq Df F value Pr(>F)
# site 23.125 3 5.2965 0.005094 **
# Residuals 40.750 28
summary(m9) # pairwise contrasts
# Bamboo vs Manta; p = 0.11
# Bamboo vs. Moray; p = 0.84
# Bamboo vs. Tabon; p = 0.03 *
site <- factor(site, levels = c("Manta", "Moray", "Tabon", "Bamboo"))
m9 <- lm(S ~ site)
summary(m9)
# Manta vs Moray; p = 0.16
# Manta vs Tabon; p < 0.001 ***
site <- factor(site, levels = c("Tabon", "Moray", "Manta", "Bamboo"))
m9 <- lm(S ~ site)
summary(m9)
# Tabon vs Moray; p = 0.02 *
site <- factor(site, levels = c("Bamboo", "Manta", "Moray", "Tabon"))
## Simpson's evenness ####
# Build model
m10 <- lm(E ~ site)
# Test model fit
par(mfrow = c(2,2), mar = c(2,2,2,1))
plot(m10) # quite homogenous
par(mfrow = c(1,2), mar = c(2,2,2,1))
hist(resid(m10)) # residuals are quite normally distributed
qqnorm(resid(m10))
qqline(resid(m10))
par(mfrow = c(1,1), mar = c(5, 4, 4, 2) + 0.1)
# Interpret model
Anova(m10, type = 2) # Type II sum of squares test
# Response: E
# Sum Sq Df F value Pr(>F)
# site 0.95375 3 22.052 1.558e-07 ***
# Residuals 0.40368 28
summary(m10) # pairwise contrasts
# Bamboo vs Manta; p < 0.001 ***
# Bamboo vs. Moray; p < 0.001 ***
# Bamboo vs. Tabon; p = 0.81
site <- factor(site, levels = c("Manta", "Moray", "Tabon", "Bamboo"))
m10 <- lm(E ~ site)
summary(m10)
# Manta vs Moray; p = 0.94
# Manta vs Tabon; p < 0.001 ***
site <- factor(site, levels = c("Tabon", "Moray", "Manta", "Bamboo"))
m10 <- lm(E ~ site)
summary(m10)
# Tabon vs Moray; p < 0.001 ***
site <- factor(site, levels = c("Bamboo", "Manta", "Moray", "Tabon"))
### Data visualisation ####
## Calculate nMDS data ####
coralmds <- metaMDS(sqrt.coral, distance = "bray", autotransform = FALSE)
coralmds$stress # stress = 0.18
# NB metaMDS function may have to be run several times before convergence
# (i.e. multidimentional space arrangement solution) is reached.
# nMDS without convergence is not reliable and will result in a markedly
# different plot and interpretation of results.
## Calculate data points and 95% confidence ellipses ####
site <- factor(site, levels = c("Manta", "Moray", "Tabon", "Bamboo"))
df <- data.frame(coralmds$points, group = site)
plot(coralmds)
ellipse <- ordiellipse(coralmds, site, display = "sites", kind = "se",
conf = 0.95, label = F)
## Make data compatible with ggplot2
# the following function was sourced from
# https://github.com/jarioksa/vegan/blob/master/R/veganCovEllipse.R
veganCovEllipse <- function (cov, center = c(0, 0), scale = 1, npoints = 100)
{
theta <- (0:npoints) * 2 * pi/npoints
Circle <- cbind(cos(theta), sin(theta))
Q <- chol(cov, pivot = TRUE)
o <- attr(Q, "pivot")
t(center + scale * t(Circle %*% Q[,o]))
}
dfell <- data.frame()
for(g in levels(df$group)) {
dfell <- rbind(dfell,
cbind(as.data.frame(with(df[df$group==g,],
veganCovEllipse(ellipse[[g]]$cov,
ellipse[[g]]$center,
ellipse[[g]]$scale))),
group = g))
}
## Define colours, shapes and factor level order ####
colours <- c("#7fb8d5", "#467289", "#f7b060", "#ef8407")
shapes <- c(21:24)
## Plot ####
morph.plot <- ggplot(data = df, aes(MDS1, MDS2)) +
geom_point(aes(colour = group, shape = group, fill = group),
size = 3) +
geom_polygon(data = dfell, aes(NMDS1, NMDS2, colour = group,
fill = group), size = 0.7, alpha = 0.3) +
scale_fill_manual(values = colours,
labels = c("Manta", "Moray", "Tabon", "Bamboo"),
guide = guide_legend(title = "Site")) +
annotate("text", label = "Stress = 0.18", x = -0.74, y = 0.54,
size = 4.2) +
scale_colour_manual(values = colours,
labels = c("Manta", "Moray", "Tabon", "Bamboo"),
guide = guide_legend(title = "Site")) +
scale_shape_manual(values = shapes,
labels = c("Manta", "Moray", "Tabon", "Bamboo"),
guide = guide_legend(title = "Site")) +
mytheme +
theme(legend.position = c(0.15, 0.86),
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
axis.line = element_blank(),
panel.border = element_rect(fill = NA, size = 1)) +
coord_cartesian(y = c(-0.8, 1.1))
morph.plot # dimensions: 4 x 4 in
## Calculate univariate descriptive statistics ####
Cstat <- describeBy(C, site, mat = T)
SMstat <- describeBy(SM, site, mat = T)
Sstat <- describeBy(S, site, mat = T)
Estat <- describeBy(E, site, mat = T)
## Plot ####
# Corymbose coral
Cplot <- ggplot(Cstat, mapping = aes(factor(group1, levels = unique(group1)), mean)) +
geom_col(width = 0.7, fill = c("#7fb8d5", "#467289", "#f7b060", "#ef8407")) +
geom_errorbar(mapping = aes(ymin = mean - se, ymax = mean + se),
width = .1, lwd = .4) +
geom_text(aes(x = group1, y = mean + se + 2,
label = c("a","b","c","ac"))) +
ylab("Corymbose scleractinian coral (%)") +
scale_y_continuous(expand = c(0,0)) +
coord_cartesian(ylim = c(0, 80)) +
mytheme
Cplot # dimensions: 4 x 4 in
# Submassive coral
SMplot <- ggplot(SMstat, mapping = aes(factor(group1, levels = unique(group1)), mean)) +
geom_col(width = 0.7, fill = c("#7fb8d5", "#467289", "#f7b060", "#ef8407")) +
geom_errorbar(mapping = aes(ymin = mean - se, ymax = mean + se),
width = .1, lwd = .4) +
geom_text(aes(x = group1, y = mean + se + 2,
label = c("a","b","c","ab"))) +
ylab("Submassive scleractinian coral (%)") +
scale_y_continuous(expand = c(0,0)) +
coord_cartesian(ylim = c(0, 80)) +
mytheme
SMplot # dimensions: 4 x 4 in
# "Morphospecies" richness
Splot <- ggplot(Sstat, mapping = aes(factor(group1, levels = unique(group1)), mean)) +
geom_col(width = 0.7, fill = c("#7fb8d5", "#467289", "#f7b060", "#ef8407")) +
geom_errorbar(mapping = aes(ymin = mean - se, ymax = mean + se),
width = .1, lwd = .4) +
geom_text(aes(x = group1, y = mean + se + 0.16,
label = c("a","a","b","a"))) +
scale_y_continuous(expand = c(0,0)) +
ylab("Number of growth morphologies") +
coord_cartesian(ylim = c(0, 6)) +
mytheme
Splot # dimensions: 4 x 4 in
# "Morphospecies" richness (point range)
Splot2 <- ggplot(Sstat, mapping = aes(factor(group1, levels = unique(group1)), mean)) +
geom_pointrange(aes(ymin = mean - se, ymax = mean + se,
colour = factor(group1, levels = unique(group1)),
fill = factor(group1, levels = unique(group1)),
shape = factor(group1, levels = unique(group1))),
size = 0.6) +
scale_colour_manual(values = c("#7fb8d5", "#467289", "#f7b060", "#ef8407"),
labels = c("Manta", "Moray", "Tabon", "Bamboo"),
guide = guide_legend()) +
scale_fill_manual(values = c("#7fb8d5", "#467289", "#f7b060", "#ef8407"),
labels = c("Manta", "Moray", "Tabon", "Bamboo"),
guide = guide_legend()) +
scale_shape_manual(values = 21:24,
labels = c("Manta", "Moray", "Tabon", "Bamboo"),
guide = guide_legend()) +
geom_text(aes(x = group1, y = mean + se + 0.3,
label = c("a","a","b","a"))) +
scale_y_continuous(expand = c(0,0)) +
ylab("Number of growth morphologies") +
coord_cartesian(ylim = c(0, 8)) +
mytheme +
theme(legend.position = c(0.88, 0.88),
axis.text.x = element_blank(),
axis.line.x = element_blank())
Splot2 # dimensions: 4 x 4 in
# Simpson's evenness
Eplot <- ggplot(Estat, mapping = aes(factor(group1, levels = unique(group1)), mean)) +
geom_col(width = 0.7, fill = c("#7fb8d5", "#467289", "#f7b060", "#ef8407")) +
geom_errorbar(mapping = aes(ymin = mean - se, ymax = mean + se),
width = .1, lwd = .4) +
geom_text(aes(x = group1, y = mean + se + 0.028,
label = c("a","a","b","b"))) +
ylab("Simpson's evenness index") +
scale_y_continuous(expand = c(0,0)) +
coord_cartesian(ylim = c(0, 1)) +
mytheme
Eplot # dimensions: 4 x 4 in
# Simpson's evenness (point range)
Eplot2 <- ggplot(Estat, mapping = aes(factor(group1, levels = unique(group1)), mean)) +
geom_pointrange(aes(ymin = mean - se, ymax = mean + se,
colour = factor(group1, levels = unique(group1)),
fill = factor(group1, levels = unique(group1)),
shape = factor(group1, levels = unique(group1))),
size = 0.6) +
scale_colour_manual(values = c("#7fb8d5", "#467289", "#f7b060", "#ef8407"),
labels = c("Manta", "Moray", "Tabon", "Bamboo"),
guide = guide_legend()) +
scale_fill_manual(values = c("#7fb8d5", "#467289", "#f7b060", "#ef8407"),
labels = c("Manta", "Moray", "Tabon", "Bamboo"),
guide = guide_legend()) +
scale_shape_manual(values = 21:24,
labels = c("Manta", "Moray", "Tabon", "Bamboo"),
guide = guide_legend()) +
geom_text(aes(x = group1, y = mean + se + 0.04,
label = c("a","a","b","b"))) +
scale_y_continuous(expand = c(0,0)) +
ylab("Simpson's evenness index") +
coord_cartesian(ylim = c(0, 1)) +
mytheme +
theme(legend.position = "none",
axis.text.x = element_blank(),
axis.line.x = element_blank())
Eplot2 # dimensions: 4 x 4 in
#### Multivariate substratum data ####
### Load data ####
sub <- danju[,1:16]
str(sub)
### Data wrangling ####
## Reorder levels of site factor ####
sub$site <- factor(sub$site, levels = c("Manta", "Moray", "Tabon", "Bamboo"))
str(sub)
## Define environmental variables ####
env <- sub[,1:5]
## Define species abundance data ####
subtyp <- sub[,6:16]
subtyp <- data.frame(subtyp, row.names = 1)