FKNMS Stephanocoenia intersepta Population Genetics and Algal Symbionts

version: 12 June, 2024

GitHub repository

About this document

---

All analyses preformed with R version 4.2.2.

Basic setup of R environment

---

Loading required packages

For the following analyses we will require the use of a number of different R packages. We can use the following code to quickly load in the packages and install any packages not previously installed in the R console.

if (!require("pacman")) install.packages("pacman")

pacman::p_load_gh("pmartinezarbizu/pairwiseAdonis/pairwiseAdonis", "ropensci/rnaturalearthhires", "KarstensLab/microshades")

pacman::p_load("cowplot", "car", "ggrepel", "ggspatial", "paletteer", "patchwork", "rgdal", "rnaturalearth", "sf", "Hmisc", "MCMC.OTU", "pairwiseAdonis", "RColorBrewer", "Redmonder", "flextable", "lubridate", "officer", "adegenet", "dendextend", "gdata", "ggdendro", "hierfstat", "kableExtra", "poppr", "reshape2", "StAMPP", "vcfR", "vegan", "boa", "magick", "rgeos", "sdmpredictors", "ggcorrplot", "tidyverse", "TeachingDemos", "LaplacesDemon", "adespatial", "ggnewscale", "ggbeeswarm", "multcomp", "rstatix", "R.utils", "graph4lg")

options("scipen" = 10)

# load("fknmsSint.RData")

Making color palettes to use throughout all plots

# flPal = c(paletteer_d("vapoRwave::jazzCup")[c(1, 3:4)], "#4B31B3")
# flPal = c("#D72000", "#FFAD0A", "#1BB6AF", "#132157")
# flPal = c("#E73F74", "#F2B701", "#3969AC", "#7F3C8D")

flPal = paletteer_c("viridis::turbo", n = 9, direction = -1)[c(6:9)]

boundPal = c("gray30", paletteer_d("vapoRwave::vapoRwave")[10])

pink = "#FF6A8BFF"

purple = paletteer_d("vapoRwave::vapoRwave")[10]

kColPal = c(paletteer_d("rcartocolor::BluYl")[c(7, 5, 3)], "#f5e97a", "azure3")

profPal = rev(c(microshades_palette("micro_green", 5), microshades_palette("micro_cvd_turquoise", 5),  microshades_palette("micro_cvd_orange", 3),microshades_palette("micro_cvd_purple", 1, lightest = F), microshades_palette("micro_purple", 5)))

# colPalZoox = c("#807dba", "#F09163", "#48C9B0", "#FEEDA0")

# colPalZoox = c("#6A4C93",  "#0091AD", "#ACF39D", "#FF007F")
colPalZoox = c("#6A4C93",  "#1BE7FF", "#c6fca2", "#FF7C96")

Sampling info

---

Map of study sites

fknmsSites = read.csv("../data/stephanocoeniaMetaData.csv", header = TRUE)
fknmsSites$depthZone = factor(fknmsSites$depthZone)
fknmsSites$depthZone = factor(fknmsSites$depthZone, levels = levels(fknmsSites$depthZone)[c(2,1)])

fknmsSites$site = factor(fknmsSites$site)
fknmsSites$site = factor(fknmsSites$site, levels = levels(fknmsSites$site)[c(4, 1, 3, 2)])
fknmsSites$date = mdy(fknmsSites$date) %>% format("%d %b %Y")

fknmsPops = fknmsSites %>% group_by(site) %>% summarise(latDD = mean(latDD), longDD = mean(longDD), n = n()) %>% droplevels()

fknmsSampleSites = fknmsSites %>% group_by(site, siteID, depthZone) %>% summarise(latDD = min(latDD), longDD = min(longDD))

## `summarise()` has grouped output by 'site', 'siteID'. You can override using the
## `.groups` argument.

fknmsBounds = read.csv("../data/shp/fknmsSPA.csv", header = TRUE)

states = st_as_sf(ne_states(country = c("United States of America")), scale = "count",  crs = 4326) %>% filter(name_en %in% c("Florida", "Georgia", "Alabama"))
countries = st_as_sf(ne_countries(country = c("Cuba", "Mexico", "The Bahamas", "Bermuda"), scale = "Large"), crs = 4326)
bahamas = read_sf("../data/shp/bahamasShoreline.shp") %>% st_transform(crs = 4326)
cuba = read_sf("../data/shp/cubaShoreline.shp") %>% st_transform(crs = 4326)
florida = read_sf("../data/shp/floridaShoreline.shp") %>% st_transform(crs = 4326)
bathy = read_sf("../data/shp/flBathy.shp") %>% st_transform(crs = 4326) %>% subset(subset = DATASET %in% c("fl_shelf", "fl_coast"))
tortugasBathy = read_sf("../data/shp/tortugasBathy.shp") %>% st_transform(crs = 4326)

Next we build a hi-res polygon of FL with the study site marked and a zoomed in map of the colony locations. We use ggspatial to add a north arrow and scale bar to the main map.

floridaMap = ggplot() +
  geom_polygon(data = fknmsBounds[fknmsBounds$type == "Sanctuary",], aes(x = long, y = lat, group = location), alpha = 0.1, fill = "black", color =  "black") +
  geom_polygon(data = fknmsBounds[fknmsBounds$location == "FKNMS2",], aes(x = long, y = lat), fill = "aliceblue", color = NA) +
  # geom_polygon(data = fknmsBounds, aes(x = long, y = lat, color = type, group = location), fill = NA, linewidth = 0) +
  scale_fill_manual(values = flPal, name = "Site") +
  scale_color_manual(values = boundPal, name = "Boundaries", labels = c("FKNMS", "SPA")) +
  geom_point(data = fknmsSites, aes(x = longDD, y = latDD, shape = depthZone), color = NA, fill = NA) +
  scale_shape_manual(values = c(21, 23), name = "Depth") +
  geom_sf(data = florida, fill = "white", linewidth = 0.15) +
  geom_sf(data = cuba, fill = "white", linewidth = 0.15) +
  geom_sf(data = bahamas, fill = "white", linewidth = 0.15) +
  geom_segment(aes(x = -80.1, y = 25.3, xend = -78.825, yend = 24.44), linewidth = 0.25) +
  geom_segment(aes(x = -80.4, y = 25, xend = -80.27, yend = 23), linewidth = 0.25) +
  geom_segment(aes(x = -81.75, y = 24.7, xend = -82.22, yend = 24.28), linewidth = 0.25) +
  geom_segment(aes(x = -81.45, y = 24.7, xend = -80.78, yend = 24.28), linewidth = 0.25) +
  geom_segment(aes(x = -83.25, y = 24.75, xend = -84.183, yend = 24.28), linewidth = 0.25) +
  geom_segment(aes(x = -82.95, y = 24.75, xend = -82.74, yend = 24.28), linewidth = 0.25) +
  geom_rect(aes(xmin = -80.4, xmax = -80.1, ymin = 25, ymax = 25.3), fill = NA, color = "black", linewidth = 0.25, alpha = 0.5) +
  geom_rect(aes(xmin = -81.75, xmax = -81.45, ymin = 24.4, ymax = 24.7), fill = NA, color = "black", linewidth = 0.25, alpha = 0.5) +
  geom_rect(aes(xmin = -83.25, xmax = -82.95, ymin = 24.45, ymax = 24.75), fill = NA, color = "black", linewidth = 0.25, alpha = 0.5) +
  geom_point(data = fknmsPops, aes(x = longDD, y = latDD, fill = site), shape = 22, size = 2) +
  coord_sf(xlim = c(-84, -79), ylim = c(23, 27)) +
  scale_x_continuous(breaks = c(seq(-84, -79, by = 1))) +
  scale_y_continuous(breaks = c(seq(23, 27, by = 1))) +
  annotation_scale(location = "br", pad_x = unit(1.35, "cm"), text_pad = unit(-4, "cm")) +
  guides(fill = guide_legend(override.aes = list(shape = 22, color = "black", size = 2, stroke = 0.25), order = 1), shape = guide_legend(override.aes = list(size = c(2.25, 2), stroke = 0.25, color = "black"), order = 2), color = "none") +
  theme_bw() +
  theme(panel.background = element_rect(fill = "aliceblue"),

        plot.background = element_blank(),
        panel.border = element_rect(color = "black", size = 1, fill = NA),
        axis.title = element_blank(),
        axis.ticks = element_line(color = "black"),
        axis.text = element_text(color = "black"),
        legend.position = c(0.905, 0.875),
        legend.box.background = element_rect(linewidth = 0.35, fill = "white"),
        legend.title = element_text(color = "black", size = 8),
        legend.text = element_text(color = "black", size = 8),
        legend.spacing = unit(-5, "pt"),
        legend.key.size = unit(5, "pt"),
        legend.background = element_blank()
        )

floridaMap

largeMap = inset = ggplot() +
  geom_sf(data = states, fill = "white", linewidth = 0.3) +
  geom_sf(data = countries, fill = "white", linewidth = 0.3) +
  geom_rect(aes(xmin = -84, xmax = -79, ymin = 23, ymax = 27), color = "black", fill = NA, alpha = 0.25, linewidth = 0.5) +
  geom_rect(aes(xmin = -78.8, xmax = -77, ymin = 22.2, ymax = 22.6), fill = "aliceblue", color = NA) +
  annotation_scale(location = "bl", pad_x = unit(2.25, "cm")) +
  annotation_north_arrow(location = "tr", style = north_arrow_minimal(), pad_x = unit(-0.3, "cm")) +
  coord_sf(xlim = c(-87, -76), ylim = c(22, 31)) +
  theme_bw() +
  theme(legend.title = element_text(size = 9, face = "bold"),
        axis.ticks = element_blank(),
        axis.text = element_blank(),
        axis.title = element_blank(),
        panel.background = element_rect(fill = "aliceblue"),
        panel.border = element_rect(color = "black", size = 1, fill = NA),
        legend.position = "none",
        plot.background = element_blank())

# largeMap

inset = ggplot() +
  geom_polygon(data = fknmsBounds[fknmsBounds$type == "Sanctuary",], aes(x = long, y = lat, group = location), alpha = 0.1, fill = "black", color = NA) +
   geom_polygon(data = fknmsBounds[fknmsBounds$location == "FKNMS2",], aes(x = long, y = lat), fill = "aliceblue", color = NA) +
  geom_segment(aes(x = -82.9645, xend = -82.4, y = 24.6, yend = 24.6), color = "gray92", size = .55) +
  geom_sf(data = bathy, color = "gray75", size = 0.25) +
  geom_polygon(data = fknmsBounds, aes(x = long, y = lat, color = type, group = location), fill = NA) +
  scale_fill_manual(values = flPal, name = "Site") +
  scale_color_manual(values = boundPal, name = "Boundaries", labels = c("FKNMS", "SPA")) +
  geom_point(data = fknmsSampleSites, aes(x = longDD, y = latDD, fill = site, shape = depthZone, size = depthZone)) +
  geom_sf(data = florida, fill = "white", size = 0.15) +
  scale_shape_manual(values = c(21, 23), name = "Depth") +
  scale_size_manual(values = c(1.625, 1.5)) +
  theme_bw() +
  theme(legend.title = element_text(size = 9, face = "bold"),
        axis.ticks = element_blank(),
        axis.text = element_blank(),
        axis.title = element_blank(),
        panel.background = element_rect(fill = "aliceblue"),
        panel.border = element_rect(color = "black", size = 1, fill = NA),
        legend.position = "none",
        plot.background = element_blank())

# inset

upperKeys = inset +
  annotation_scale(location = "bl", pad_x = unit(1.9, "cm")) +
  coord_sf(xlim = c(-80.4, -80.1), ylim = c(25.0, 25.3)) +
  scale_x_continuous(breaks = c(seq(-80.4, -80.0, by = .1))) +
  scale_y_continuous(breaks = c(seq(25.0, 25.3, by = .1)))

lowerKeys = inset +
  annotation_scale(location = "bl", pad_x = unit(1.9, "cm")) +
  coord_sf(xlim = c(-81.75, -81.45), ylim = c(24.4, 24.7)) +
  scale_x_continuous(breaks = c(seq(-81.7, -81.3, by = .1))) +
  scale_y_continuous(breaks = c(seq(24.4, 24.7, by = .1)))

dryTortugas = ggplot() +
  geom_polygon(data = fknmsBounds[fknmsBounds$type == "Sanctuary",], aes(x = long, y = lat, group = location), alpha = 0.1, fill = "black", color = NA) +
   geom_polygon(data = fknmsBounds[fknmsBounds$location == "FKNMS2",], aes(x = long, y = lat), fill = "aliceblue", color = NA) +
  geom_segment(aes(x = -82.9645, xend = -82.4, y = 24.6, yend = 24.6), color = "gray92", size = .55) +
  geom_sf(data = tortugasBathy, color = "gray75", size = 0.25) +
  geom_polygon(data = fknmsBounds, aes(x = long, y = lat, color = type, group = location), fill = NA) +
  scale_fill_manual(values = flPal, name = "Site") +
  scale_color_manual(values = boundPal, name = "Boundaries", labels = c("FKNMS", "SPA")) +
  geom_point(data = fknmsSites, aes(x = longDD, y = latDD, fill = site, shape = depthZone, size = depthZone)) +
  geom_sf(data = florida, fill = "white", size = 0.25) +
  scale_shape_manual(values = c(21, 23), name = "Depth") +
  scale_size_manual(values = c(1.625, 1.5)) +
  annotation_scale(location = "bl", pad_x = unit(1.9, "cm")) +
  coord_sf(xlim = c(-83.25, -82.95), ylim = c(24.45, 24.75)) +
  scale_x_continuous(breaks = c(seq(-83.2, -82.9, by = .1))) +
  scale_y_continuous(breaks = c(seq(24.4, 24.7, by = .1))) +
  theme_bw() +
  theme(legend.title = element_text(size = 9, face = "bold"),
        axis.ticks = element_blank(),
        axis.text = element_blank(),
        axis.title = element_blank(),
        panel.background = element_rect(fill = "aliceblue"),
        panel.border = element_rect(color = "black", size = 1, fill = NA),
        legend.position = "none",
        plot.background = element_blank())

popData = read.csv("../data/stephanocoeniaMetaData.csv")[-c(66, 68, 164, 166, 209, 211),] %>% dplyr::select("sample" = tubeID, "pop" = site, "depth" = depthZone, "depthm" = depthM)

popData$popdepth = as.factor(paste(popData$pop, popData$depth, sep = ""))

popData$popdepth = factor(popData$popdepth, levels(popData$popdepth)[c(4, 3, 6, 5, 2, 1, 8, 7)])

pcadmix = read.table("../data/snps/k/Clumpp3xK4.output") %>%dplyr::select(V6, V7, V8, V9)
pcadmix %>% summarise(sum(V6),sum(V7), sum(V8), sum(V9)) 

##    sum(V6) sum(V7) sum(V8) sum(V9)
## 1 128.7501 43.4095 31.2726 16.5678

pcadmix = popData %>% cbind(pcadmix) %>% rename("cluster1" = "V6", "cluster2" = "V7", "cluster3" = "V8", "cluster4" = "V9") %>%dplyr::select(order(colnames(.))) 

pcadmixClust = pcadmix %>% mutate(cluster = ifelse(cluster1 < 0.75 & cluster2 < 0.75  & cluster3 < 0.75 & cluster4 < 0.75, "NA", ifelse(cluster1 >=0.75, 1, ifelse(cluster2 >= 0.75, 2, ifelse(cluster3 >= 0.75, 3,ifelse(cluster4 >= 0.75, 4, 0))))))

pcadmix = pcadmix %>% mutate(pcadmixClust)

pcadmix$cluster = as.factor(pcadmix$cluster)
levels(pcadmix$cluster) = c("Blue", "Teal", "Green", "Yellow", "Admixed")

siteLineages = pcadmix %>% dplyr::select(popdepth, cluster) %>% 
group_by(popdepth) %>% count(cluster) %>% mutate(Freq = n/sum(n)) %>% apply(2, function(x) gsub("\\s+", "", x)) %>% as.data.frame()

pieCol = c("Blue" = kColPal[1], Teal = kColPal[2], "Green" = kColPal[3], "Yellow" = kColPal[4], "Admixed" = kColPal[5])

pieDf = siteLineages %>% group_by(popdepth) %>% mutate("ymax" = cumsum(Freq)) %>% mutate("ymin" = c(0, head(ymax, n=-1)))

ukMeso = ggplot() +
    geom_rect(aes(ymax=1, ymin = 0, xmax = 4, xmin = 2), fill = flPal[1], alpha = 1) +
  geom_rect(data = pieDf%>% filter(popdepth == "UpperKeysMesophotic"), aes(ymax=ymax, ymin = ymin, xmax = 4, xmin = 3, fill = cluster), color = "black", size = 0.25) +
  annotate(geom = "text", x = 2, y = 0.75, label = "43.9", size = 2.5, fontface = "bold", color = "black") +
  scale_fill_manual(values = pieCol)+
  coord_polar(theta="y") +
  xlim(c(2, 4)) +
  theme_void() +
  theme(legend.position = "none", panel.background = element_blank())

lkMeso = ggplot() +
    geom_rect(aes(ymax=1, ymin = 0, xmax = 4, xmin = 2), fill = flPal[2], alpha = 1) +
  geom_rect(data = pieDf%>% filter(popdepth == "LowerKeysMesophotic"), aes(ymax=ymax, ymin = ymin, xmax = 4, xmin = 3, fill = cluster), color = "black", size = 0.25) +
  annotate(geom = "text", x = 2, y = 0.75, label = "32.8", size = 2.5, fontface = "bold") +
  scale_fill_manual(values = pieCol)+
  coord_polar(theta="y") +
  xlim(c(2, 4)) +
  theme_void() +
  theme(legend.position = "none", panel.background = element_blank())

tbMeso = ggplot() +
    geom_rect(aes(ymax=1, ymin = 0, xmax = 4, xmin = 2), fill = flPal[3], alpha = 1) +
  geom_rect(data = pieDf%>% filter(popdepth == "TortugasBankMesophotic"), aes(ymax=ymax, ymin = ymin, xmax = 4, xmin = 3, fill = cluster), color = "black", size = 0.25) +
  annotate(geom = "text", x = 2, y = 0.75, label = "32.0", size = 2.5, fontface = "bold", color = "white") +
  scale_fill_manual(values = pieCol)+
  coord_polar(theta="y") +
  xlim(c(2, 4)) +
  theme_void() +
  theme(legend.position = "none", panel.background = element_blank())

rhMeso = ggplot() +
    geom_rect(aes(ymax=1, ymin = 0, xmax = 4, xmin = 2), fill = flPal[4], alpha = 1) +
  geom_rect(data = pieDf %>% filter(popdepth == "Riley'sHumpMesophotic"), aes(ymax=ymax, ymin = ymin, xmax = 4, xmin = 3, fill = cluster), color = "black", size = 0.25) +
  annotate(geom = "text", x = 2, y = 0.75, label = "33.2", size = 2.5, fontface = "bold", color = "white") +
  scale_fill_manual(values = pieCol)+
  coord_polar(theta="y") +
  xlim(c(2, 4)) +
  theme_void() +
  theme(legend.position = "none", panel.background = element_blank())

ukShal = ggplot() +
    geom_rect(aes(ymax=1, ymin = 0, xmax = 4, xmin = 2), fill = flPal[1]) +
  geom_rect(data = pieDf%>% filter(popdepth == "UpperKeysShallow"), aes(ymax=ymax, ymin = ymin, xmax = 4, xmin = 3, fill = cluster), color = "black", size = 0.25) +
  annotate(geom = "text", x = 2, y = 0.75, label = "23.6", size = 2.5, fontface = "bold", color = "black") +
  scale_fill_manual(values = pieCol)+
  coord_polar(theta="y") +
  xlim(c(2, 4)) +
  theme_void() +
  theme(legend.position = "none", panel.background = element_blank())

lkShal = ggplot() +
    geom_rect(aes(ymax=1, ymin = 0, xmax = 4, xmin = 2), fill = flPal[2]) +
  geom_rect(data = pieDf%>% filter(popdepth == "LowerKeysShallow"), aes(ymax=ymax, ymin = ymin, xmax = 4, xmin = 3, fill = cluster), color = "black", , size = 0.25) +
  annotate(geom = "text", x = 2, y = 0.75, label = "18.0", size = 2.5, fontface = "bold") +
  scale_fill_manual(values = pieCol)+
  coord_polar(theta="y") +
  xlim(c(2, 4)) +
  theme_void() +
  theme(legend.position = "none", panel.background = element_blank())

tbShal = ggplot() +
    geom_rect(aes(ymax=1, ymin = 0, xmax = 4, xmin = 2), fill = flPal[3]) +
  geom_rect(data = pieDf%>% filter(popdepth == "TortugasBankShallow"), aes(ymax=ymax, ymin = ymin, xmax = 4, xmin = 3, fill = cluster), color = "black", size = 0.25) +
  annotate(geom = "text", x = 2, y = 0.75, label = "21.1", size = 2.5, fontface = "bold", color = "white") +
  scale_fill_manual(values = pieCol)+
  coord_polar(theta="y") +
  xlim(c(2, 4)) +
  theme_void() +
  theme(legend.position = "none", panel.background = element_blank())

rhShal = ggplot() +
  geom_rect(aes(ymax=1, ymin = 0, xmax = 4, xmin = 2), fill = flPal[4]) +
  geom_rect(data = pieDf %>% filter(popdepth == "Riley'sHumpShallow"), aes(ymax=ymax, ymin = ymin, xmax = 4, xmin = 3, fill = cluster), color = "black", size = 0.25) +
  annotate(geom = "text", x = 2, y = 0.75, label = "26.4", size = 2.5, fontface = "bold", color = "white") +
  scale_fill_manual(values = pieCol)+
  coord_polar(theta = "y") +
  xlim(c(2, 4)) +
  theme_void() +
  theme(legend.position = "none", panel.background = element_blank())

map = (floridaMap + 
  inset_element(largeMap, top = 1.01, right = 0.33, bottom = 0.63, left = -0.005, ignore_tag = TRUE) +
  inset_element(dryTortugas, top = 0.36, right = 0.2875, bottom = -0.01, left = -0.0075, ignore_tag = TRUE) +
  inset_element(lowerKeys, top = 0.36, right = 0.645, bottom = -0.01, left = 0.35, ignore_tag = TRUE) +
  inset_element(upperKeys, top = 0.395, right = 1.00, bottom = 0.025, left = 0.705, ignore_tag = TRUE) +
  inset_element(ukShal, top = 0.374, right = 0.99, bottom = 0.274, left = 0.89, ignore_tag = TRUE) +
  inset_element(ukMeso, top = 0.284, right = 0.99, bottom = 0.184, left = 0.89, ignore_tag = TRUE) +
  inset_element(lkShal, top = 0.209, right = 0.466, bottom = 0.109, left = 0.366, ignore_tag = TRUE) +
  inset_element(lkMeso, top = 0.119, right = 0.466, bottom = 0.019, left = 0.366, ignore_tag = TRUE) +  
  inset_element(rhShal, top = 0.209, right = 0.11, bottom = 0.109, left = 0.01, ignore_tag = TRUE) +
  inset_element(rhMeso, top = 0.119, right = 0.11, bottom = 0.019, left = 0.01, ignore_tag = TRUE) +
  inset_element(tbShal, top = 0.338, right = 0.278, bottom = 0.238, left = 0.178, ignore_tag = TRUE) +
  inset_element(tbMeso, top = 0.248, right = 0.278, bottom = 0.148, left = 0.178, ignore_tag = TRUE)
  )

ggsave("../figures/figure1.png", plot = map, height = 7, width = 7, units = "in", dpi = 300)

ggsave("../figures/figure1.svg", plot = map, height = 7, width = 7, units = "in", dpi = 300)

S. intersepta population genetics from SNPs

---

Analyzing 2bRAD generated SNPs (24,670 loci) for population structure//genetic connectivity across sites and depth zones in FKNMS

How many reads?

rawSintReads = read.delim("../data/snps/sintRawReadCounts", header = FALSE)
colnames(rawSintReads) = c("sample", "reads")

head(rawSintReads)

##    sample    reads
## 1 FKSi1-1 42167284
## 2 FKSi1-2 54651139
## 3 FKSi1-3 41635251
## 4 FKSi1-4 37754282
## 5 FKSi1-5 39973126
## 6 FKSi1-6 45580831

#total reads
sum(rawSintReads$reads)

## [1] 796139328

#average reads/sample
(sum(rawSintReads$reads)/226)

## [1] 3522740

Identifiying clonal multi-locus genotypes

Dendrogram with clones

Identification of any natural clones using technical replicates as a baseline for clonality between samples.

cloneBams = read.csv("../data/stephanocoeniaMetaData.csv") # list of bam files

# cloneMa = as.matrix(read.table("../data/snps/clones/sintClones.ibsMat")) # reads in IBS matrix produced by ANGSD 
cloneMa = as.matrix(read.table("../data/snps/clones/sintClones3x.ibsMat")) # reads in IBS matrix produced by ANGSD 

dimnames(cloneMa) = list(cloneBams[,1],cloneBams[,1])

clonePops = cloneBams$site
cloneDepth = cloneBams$depthZone

cloneDend = cloneMa %>% as.dist() %>% hclust(.,"ave") %>% as.dendrogram()
cloneDData = cloneDend %>% dendro_data()

# Making the branches hang shorter so we can easily see clonal groups
cloneDData$segments$yend2 = cloneDData$segments$yend
for(i in 1:nrow(cloneDData$segments)) {
  if (cloneDData$segments$yend2[i] == 0) {
    cloneDData$segments$yend2[i] = (cloneDData$segments$y[i] - 0.01)}}

cloneDendPoints = cloneDData$labels
cloneDendPoints$pop = clonePops[order.dendrogram(cloneDend)]
cloneDendPoints$depth=cloneDepth[order.dendrogram(cloneDend)]
rownames(cloneDendPoints) = cloneDendPoints$label

# Making points at the leaves to place symbols for populations
point = as.vector(NA)
for(i in 1:nrow(cloneDData$segments)) {
  if (cloneDData$segments$yend[i] == 0) {
    point[i] = cloneDData$segments$y[i] - 0.01
  } else {
    point[i] = NA}}

cloneDendPoints$y = point[!is.na(point)]

techReps = c("SFK066.1", "SFK066.2", "SFK066.3", "SFK162.1", "SFK162.2", "SFK162.3", "SFK205.1", "SFK205.2", "SFK205.3")

cloneDendPoints$depth = factor(cloneDendPoints$depth)
cloneDendPoints$depth = factor(cloneDendPoints$depth, levels(cloneDendPoints$depth)[c(2,1)])

cloneDendPoints$pop = factor(cloneDendPoints$pop)
cloneDendPoints$pop = factor(cloneDendPoints$pop,levels(cloneDendPoints$pop)[c(4, 1, 3, 2)])

cloneDendA = ggplot() +
  geom_rect(aes(xmin = 47.25, xmax = 50.75, ymin = 0.03, ymax = 0.085), fill = pink, alpha = 0.4) +
  geom_rect(aes(xmin = 164.25, xmax = 167.75, ymin = 0.065, ymax = 0.12), fill = pink, alpha = 0.4) +
  geom_rect(aes(xmin = 219.25, xmax = 222.75, ymin = 0.065, ymax = 0.12), fill = pink, alpha = 0.4) +
  geom_segment(data = segment(cloneDData), aes(x = x, y = y, xend = xend, yend = yend2), size = 0.5) +
  geom_point(data = cloneDendPoints, aes(x = x, y = y, fill = pop, shape = depth), size = 4, stroke = 0.25) +
  scale_fill_manual(values = flPal, name= "Site:") +
  scale_shape_manual(values = c(21, 23), name = "Depth Zone:") +
  geom_hline(yintercept = 0.12, color = pink, lty = 5, size = 1) + # creating a dashed line to indicate a clonal distance threshold
  geom_text(data = subset(cloneDendPoints, subset = label %in% techReps), aes(x = x, y = (y - .02), label = label), angle = 90) + # spacing technical replicates further from leaf
  geom_text(data = subset(cloneDendPoints, subset = !label %in% techReps), aes(x = x, y = (y - .015), label = label), angle = 90) +
  labs(y = "Genetic distance (1 - IBS)") +
  guides(fill = guide_legend(override.aes = list(shape = 22, size = 10), ncol = 2), shape = guide_legend(override.aes = list(size = 8), ncol = 1, order = 1)) +
  coord_cartesian(xlim = c(5, 218), ylim = c(0.03, 0.25)) +
  theme_classic()

cloneDend = cloneDendA + theme(
  axis.title.x = element_blank(),
  axis.text.x = element_blank(),
  axis.line.x = element_blank(),
  axis.ticks.x = element_blank(),
  axis.title.y = element_text(size = 24, color = "black", angle = 90),
  axis.text.y = element_text(size = 20, color = "black"),
  axis.line.y = element_line(),
  axis.ticks.y = element_line(),
  panel.grid = element_blank(),
  panel.border = element_blank(),
  panel.background = element_blank(),
  legend.key = element_blank(),
  legend.title = element_text(size = 24),
  legend.text = element_text(size = 20),
  legend.position = "bottom")

# cloneDend

ggsave("../figures/rmd/cloneDend3x.png", plot = cloneDend, height = 8, width = 35, units = "in", dpi = 300)

We removed the technical replicates/clones and re-ran ANGSD for all the following pop-gen analyses.

Dendrogram without clones

bams = read.csv("../data/stephanocoeniaMetaData.csv")[-c(66,68,164,166,209,211),] # list of bams files and their populations (tech reps removed)

ma = as.matrix(read.table("../data/snps/sintNoClones3x.ibsMat")) # reads in IBS matrix produced by ANGSD
# ma = as.matrix(read.table("../data/snps/sintNoClones.ibsMat")) # reads in IBS matrix produced by ANGSD

dimnames(ma) = list(bams[,1],bams[,1])

pops = bams$site
depth = bams$depthZone
cluster = pcadmix$cluster

dend = ma %>% as.dist() %>% hclust(.,"ave") %>% as.dendrogram()
dData = dend %>% dendro_data()

# Making the branches hang shorter so we can easily see clonal groups
dData$segments$yend2 = dData$segments$yend
for(i in 1:nrow(dData$segments)) {
  if (dData$segments$yend2[i] == 0) {
    dData$segments$yend2[i] = (dData$segments$y[i] - 0.01)}}

dendPoints = dData$labels
dendPoints$pop = pops[order.dendrogram(dend)]
dendPoints$depth = depth[order.dendrogram(dend)]
dendPoints$cluster = cluster[order.dendrogram(dend)]
rownames(dendPoints) = dendPoints$label

# Making points at the leaves to place symbols for populations
point = as.vector(NA)
for(i in 1:nrow(dData$segments)) {
  if (dData$segments$yend[i] == 0) {
    point[i] = dData$segments$y[i] - 0.01
  } else {
    point[i] = NA}}

dendPoints$y = point[!is.na(point)]

dendPoints$depth = factor(dendPoints$depth)
dendPoints$depth = factor(dendPoints$depth, levels(dendPoints$depth)[c(2,1)])

dendPoints$pop = factor(dendPoints$pop)
dendPoints$pop = factor(dendPoints$pop, levels(dendPoints$pop)[c(4, 1, 3, 2)])

dendPoints$cluster = factor(dendPoints$cluster)

dendNoCloneA = ggplot() +
  geom_segment(data = segment(dData), aes(x = x, y = y, xend = xend, yend = yend2), size = 0.5) +
  geom_point(data = dendPoints, aes(x = x, y = y, fill = pop, shape = depth), size = 4, stroke = 0.25) +
  scale_fill_manual(values = flPal, name= "Site:")+
  scale_shape_manual(values = c(21, 23), name = "Depth Zone:")+
 # spacing technical replicates further from leaf
  labs(y = "Genetic distance (1 - IBS)") +
  guides(fill = guide_legend(override.aes = list(shape = 22, size = 10), ncol = 2), shape = guide_legend(override.aes = list(size = 8), ncol = 1, order = 1)) +
  coord_cartesian(xlim = c(5, 218)) +
  theme_classic()

dendNoClone = dendNoCloneA + theme(
  axis.title.x = element_blank(),
  axis.text.x = element_blank(),
  axis.line.x = element_blank(),
  axis.ticks.x = element_blank(),
  axis.title.y = element_text(size = 24, color = "black", angle = 90),
  axis.text.y = element_text(size = 20, color = "black"),
  axis.line.y = element_line(),
  axis.ticks.y = element_line(),
  panel.grid = element_blank(),
  panel.border = element_blank(),
  panel.background = element_blank(),
  legend.key = element_blank(),
  legend.title = element_text(size = 24),
  legend.text = element_text(size = 20),
  legend.position = "bottom")

# dendNoClone

dendLA = ggplot() +
  geom_segment(data = segment(dData), aes(x = x, y = y, xend = xend, yend = yend2), size = 0.5) +
  geom_point(data = dendPoints, aes(x = x, y = y, fill = cluster, shape = depth), size = 4, stroke = 0.25) +
  scale_fill_manual(values = kColPal, name= "Lineage:")+
  scale_shape_manual(values = c(21, 23), name = "Depth Zone:")+
 # spacing technical replicates further from leaf
  labs(y = "Genetic distance (1 - IBS)") +
  guides(fill = guide_legend(override.aes = list(shape = 22, size = 10), ncol = 3), shape = guide_legend(override.aes = list(size = 8), ncol = 1, order = 1)) +
  coord_cartesian(xlim = c(5, 218)) +
  theme_classic()

dendL = dendLA + theme(
  axis.title.x = element_blank(),
  axis.text.x = element_blank(),
  axis.line.x = element_blank(),
  axis.ticks.x = element_blank(),
  axis.title.y = element_text(size = 24, color = "black", angle = 90),
  axis.text.y = element_text(size = 20, color = "black"),
  axis.line.y = element_line(),
  axis.ticks.y = element_line(),
  panel.grid = element_blank(),
  panel.border = element_blank(),
  panel.background = element_blank(),
  legend.key = element_blank(),
  legend.title = element_text(size = 24),
  legend.text = element_text(size = 20),
  legend.position = "bottom")

# dendL

Dendrogram plots

dendPlots = (cloneDend / dendNoClone / dendL) +
  plot_annotation(tag_levels = 'A') +
  plot_layout(guides = "collect") & 
  theme(plot.tag = element_text(size = 32),
        legend.position = "bottom")

ggsave("../figures/figureS1.png", plot = dendPlots, height = 19.5, width = 35, units = "in", dpi = 300)

ggsave("../figures/figureS1.svg", plot = dendPlots, height = 19.5, width = 35, units = "in", dpi = 300)

Population structure

PCA

cov = read.table("../data/snps/pcangsd/fkSintPcangsd3x.cov") %>% as.matrix()

pcAdmix = read.table("../data/snps/k/Clumpp3xK4.output") %>% dplyr::select(V6, V7, V8, V9)
pcAdmix %>% summarise(sum(V6),sum(V7), sum(V8), sum(V9)) 

##    sum(V6) sum(V7) sum(V8) sum(V9)
## 1 128.7501 43.4095 31.2726 16.5678

pcAdmix = pcAdmix %>% rename("cluster1" = "V6", "cluster2" = "V7", "cluster3" = "V8", "cluster4" = "V9") %>%dplyr::select(order(colnames(.)))
  
pcaEig = eigen(cov)
sintPcaVar = pcaEig$values/sum(pcaEig$values)*100
head(sintPcaVar)

## [1] 8.8597260 3.9773927 3.4518340 0.6623574 0.5049635 0.4945946

pcangsd = read.csv("../data/stephanocoeniaMetaData.csv")[-c(66, 68, 164, 166, 209, 211),] %>% dplyr::select("sample" = tubeID, "pop" = site, "depth" = depthZone, "depthm" = depthM)

pcangsd$popdepth = as.factor(paste(pcangsd$pop, pcangsd$depth, sep = " "))

pcangsd$popdepth = factor(pcangsd$popdepth, levels(pcangsd$popdepth)[c(4, 3, 6, 5, 2, 1, 8, 7)])

pcangsd$pop = factor(pcangsd$pop)
pcangsd$pop = factor(pcangsd$pop, levels(pcangsd$pop)[c( 4, 1, 3, 2)])

pcangsd$depth = factor(pcangsd$depth)
pcangsd$depth = factor(pcangsd$depth, levels(pcangsd$depth)[c(2, 1)])

pcangsd$PC1 = pcaEig$vectors[,1]
pcangsd$PC2 = pcaEig$vectors[,2]
pcangsd$PC3 = pcaEig$vectors[,3]
pcangsd$PC4 = pcaEig$vectors[,4]

pcangsdClust = pcAdmix %>% mutate(cluster = ifelse(cluster1 < 0.75 & cluster2 < 0.75  & cluster3 < 0.75 & cluster4 < 0.75, "NA", ifelse(cluster1 >=0.75, 1, ifelse(cluster2 >= 0.75, 2, ifelse(cluster3 >= 0.75, 3,ifelse(cluster4 >= 0.75, 4, 0))))))

# pcangsdClust$clusterX = as.vector(d_clust$classification)

pcangsd = pcangsd %>% mutate(pcangsdClust)

pcangsd$cluster = as.factor(pcangsd$cluster)
levels(pcangsd$cluster) = c("Blue", "Teal", "Green", "Yellow", "Admixed")

bamsClusters = pcangsd %>% dplyr::select(sample, cluster) %>% dplyr::arrange(sample) 
bamsSamples = read.delim("../data/snps/bamsNoClones", header = FALSE)
bamsClusters$sample = bamsSamples$V1

# bamsClusters = as.data.frame(bamsClusters)

write.table(x = bamsClusters, file = "../data/snps/bamsClusters", sep = "\t", row.names = FALSE, col.names = FALSE, quote = FALSE)

pcangsd = merge(pcangsd, aggregate(cbind(mean.x = PC1, mean.y = PC2)~popdepth, pcangsd, mean), by="popdepth")

adonis2(pcangsd[,c(10:13)]~pop*depth, data = pcangsd, method = "euclidean")

## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
## 
## adonis2(formula = pcangsd[, c(10:13)] ~ pop * depth, data = pcangsd, method = "euclidean")
##            Df SumOfSqs      R2       F Pr(>F)    
## pop         3    3.780 0.03119  2.6192  0.015 *  
## depth       1   11.227 0.09263 23.3361  0.001 ***
## pop:depth   3    4.206 0.03470  2.9143  0.008 ** 
## Residual  212  101.989 0.84148                   
## Total     219  121.202 1.00000                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

pcangsd %>% group_by(depth,cluster) %>% summarise(n = n())

## `summarise()` has grouped output by 'depth'. You can override using the `.groups`
## argument.

## # A tibble: 9 × 3
## # Groups:   depth [2]
##   depth      cluster     n
##   <fct>      <fct>   <int>
## 1 Shallow    Blue       50
## 2 Shallow    Teal       25
## 3 Shallow    Green      29
## 4 Shallow    Yellow     15
## 5 Shallow    Admixed     1
## 6 Mesophotic Blue       81
## 7 Mesophotic Teal       15
## 8 Mesophotic Green       2
## 9 Mesophotic Admixed     2

Plot PCA

pcaTheme = theme(axis.title.x = element_text(color = "black", size = 10),
        axis.text.x = element_blank(),
        axis.ticks.x = element_blank(),
        axis.line.x = element_blank(),
        axis.title.y = element_text(color = "black", size = 10),
        axis.text.y = element_blank(),
        axis.ticks.y = element_blank(),
        axis.line.y = element_blank(),
        legend.position = "none",
        legend.title = element_text(size = 8),
        legend.text = element_text(size = 8),
        legend.key.size = unit(5, "pt"),
        panel.border = element_rect(color = "black", size = 1),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank())

pcaPlot12SA = ggplot() +
  geom_hline(yintercept = 0, color = "gray90", size = 0.25) +
  geom_vline(xintercept = 0, color = "gray90", size = 0.25) +
  geom_point(data = pcangsd, aes(x = PC1, y = PC2, fill = pop, shape = depth, color = pop), stroke = 0, size = 2.5, alpha = 0.5, show.legend = FALSE) +
  geom_point(data = pcangsd, aes(x = mean.x, y = mean.y, fill = pop, shape = depth), color = "black", size = 2.75, alpha = 1, stroke = 0.25) +
  scale_shape_manual(values = c(21, 23), name = "Depth Zone") +
  scale_fill_manual(values = flPal, name = "Site") +
  scale_color_manual(values = flPal, name = "Site") +
  labs(x = paste0("PC 1 (", format(round(sintPcaVar[1], 1), nsmall = 1)," %)"), y = paste0("PC 2 (", format(round(sintPcaVar[2], 1), nsmall = 1), " %)")) +
  guides(shape = guide_legend(override.aes = list(size = 2, stroke = 0.25, alpha = NA), order = 2, ncol = 1), fill = guide_legend(override.aes = list(shape = 22, size = 2, fill = flPal, alpha = NA), order = 1, ncol = 1)) +
  theme_bw()

pcaPlot12S = pcaPlot12SA +
  pcaTheme +
  theme(legend.position = c(0.17, 0.23))

pcaPlot12S

pcaPlot12LA = ggplot() +
  geom_hline(yintercept = 0, color = "gray90", size = 0.5) +
  geom_vline(xintercept = 0, color = "gray90", size = 0.5) +
  geom_point(data = pcangsd, aes(x = PC1, y = PC2, fill = cluster, shape = depth), color = "black", size = 2, alpha = 1, show.legend = TRUE) +
  scale_shape_manual(values = c(21, 23), name = "Depth Zone") +
  scale_fill_manual(values = kColPal, name = "Lineage") +
  labs(x = paste0("PC 1 (", format(round(sintPcaVar[1], 1), nsmall = 1)," %)"), y = paste0("PC 2 (", format(round(sintPcaVar[2], 1), nsmall = 1), " %)")) +
  guides(shape = "none", fill = guide_legend(override.aes = list(shape = 22, size = 2, fill = kColPal, alpha = NA), order = 1, ncol = 1))+
  theme_bw()

pcaPlot12L = pcaPlot12LA +
  pcaTheme +
  theme(legend.position = c(0.12,0.15))

pcaPlot23LA = ggplot() +
  geom_hline(yintercept = 0, color = "gray90", size = 0.5) +
  geom_vline(xintercept = 0, color = "gray90", size = 0.5) +
  geom_point(data = pcangsd, aes(x = PC3, y = PC2, fill = cluster, shape = depth), color = "black", size = 2, alpha = 1, show.legend = TRUE) +
  scale_shape_manual(values = c(21, 23), name = "Depth Zone") +
  scale_fill_manual(values = kColPal, name = "Lineage") +
  labs(x = paste0("PC 3 (", format(round(sintPcaVar[3], 1), nsmall = 1)," %)"), y = paste0("PC 2 (", format(round(sintPcaVar[2], 1), nsmall = 1), " %)")) +
  guides(shape = guide_legend(override.aes = list(size = 2, stroke = 0.5, alpha = NA), order = 2, ncol = 1), fill = guide_legend(override.aes = list(shape = 22, size = 2, fill = kColPal, alpha = NA), order = 1, ncol = 1, byrow = TRUE))+
  theme_bw()

pcaPlot23L = pcaPlot23LA +
  pcaTheme

Put all plots together

pcaPlots = ((pcaPlot12S + theme(axis.title.y = element_text(margin = ggplot2::margin(r = -20, unit = "pt")))) | pcaPlot12L | pcaPlot23L) +
  plot_annotation(tag_levels = 'A') &
  theme(plot.tag = element_text(size = 18),
        panel.background = element_rect(fill = "white"),
        legend.spacing = unit(-5, "pt"),
        legend.key = element_blank(),
        legend.background = element_blank())

pcaPlots

Admixture

Prepare admixture outputs for plotting

fkSintAdmix = pcangsd %>%dplyr::select(-PC1, -PC2, -PC3, -PC4, -cluster, -depthm, -mean.x, -mean.y)
fkSintAdmix$pop = factor(fkSintAdmix$pop, levels(fkSintAdmix$pop)[c( 4, 3, 2, 1)])

fkSintAdmix = arrange(fkSintAdmix, pop, depth, -cluster1, -cluster2, cluster4)
popCounts = fkSintAdmix %>% group_by(pop, depth) %>% summarise(n = n())

## `summarise()` has grouped output by 'pop'. You can override using the `.groups`
## argument.

popCounts

## # A tibble: 8 × 3
## # Groups:   pop [4]
##   pop           depth          n
##   <fct>         <fct>      <int>
## 1 Riley's Hump  Shallow       30
## 2 Riley's Hump  Mesophotic    15
## 3 Tortugas Bank Shallow       30
## 4 Tortugas Bank Mesophotic    25
## 5 Lower Keys    Shallow       30
## 6 Lower Keys    Mesophotic    30
## 7 Upper Keys    Shallow       30
## 8 Upper Keys    Mesophotic    30

popCountList = reshape2::melt(lapply(popCounts$n,function(x){c(1:x)}))
fkSintAdmix$ord = popCountList$value

fkSintAdmixMelt = melt(fkSintAdmix, id.vars=c("sample", "pop", "depth", "popdepth", "ord"), variable.name="Ancestry", value.name="Fraction")

fkSintAdmixMelt$Ancestry = factor(fkSintAdmixMelt$Ancestry)
fkSintAdmixMelt$Ancestry = factor(fkSintAdmixMelt$Ancestry, levels = rev(levels(fkSintAdmixMelt$Ancestry)))

popAnno = data.frame(x1 = c(0.5, 0.5, 0.5, 0.5), x2 = c(30.5, 30.5, 30.5, 30.5),
                     y1 = -0.1, y2 = -0.1, sample = NA, Ancestry = NA, depth = "Mesophotic", 
                     ord  = NA, Fraction = NA,
                     pop = c("Riley's Hump", "Tortugas Bank", 
                                  "Lower Keys", "Upper Keys"))
popAnno$pop = factor(popAnno$pop)
popAnno$pop = factor(popAnno$pop, levels = levels(popAnno$pop)[c(4, 1, 3, 2)])

Make admixture plots

admixPlotA = ggplot(data = fkSintAdmixMelt, aes(x = ord, y = Fraction, fill = Ancestry, order = sample)) +
  geom_segment(data = popAnno, aes(x = x1, xend = x2, y = -.12, yend = -.12, color = pop), size = 7) +
  geom_bar(stat = "identity", position = "fill", width = 1, colour = "grey25", size = 0.2) +
  facet_grid(factor(depth) ~ pop, switch = "both") +
  geom_text(data = (fkSintAdmixMelt %>% filter(depth == "Mesophotic", pop %in% c("Riley's Hump", "Tortugas Bank"), sample %in% c(   
"SFK001", "SFK100"), Ancestry == "cluster1")), x = 15.5, y = -.1, aes(label = pop), size = 4, color = "#FFFFFF") +
  geom_text(data = (fkSintAdmixMelt %>% filter(depth == "Mesophotic", pop %in% c("Lower Keys", "Upper Keys"), sample %in% c(    
"SFK101", "SFK201"), Ancestry == "cluster1")), x = 15.5, y = -.1, aes(label = pop), size = 3.5, color = "#000000") +
  scale_fill_manual(values = kColPal) +
  scale_color_manual(values = flPal) +
  scale_x_discrete(expand = c(0.005, 0.005)) +
  scale_y_continuous(expand = c(0.001, 0.001)) +
  coord_cartesian(ylim = c(0.0, 1.0), clip = "off") +
theme_bw()
  
admixPlot = admixPlotA + 
  theme_bw()+
  theme(
  panel.grid = element_blank(),
  panel.background = element_rect(fill = "gray70"),
  plot.background = element_blank(),
  panel.border = element_rect(fill = NA, color = "black", size = 0.75, linetype = "solid"),
  panel.spacing.x = grid:::unit(0.05, "lines"),
  panel.spacing.y = grid:::unit(0.05, "lines"),
  axis.text.x = element_blank(),
  axis.text.y = element_blank(),
  axis.ticks.x = element_blank(),
  axis.ticks.y = element_blank(),
  axis.title = element_blank(),
  strip.background.x = element_blank(),
  strip.background.y = element_blank(),
  strip.text = element_text(size = 8),
  strip.text.y.left = element_text(size = 10, angle = 90),
  strip.text.x.bottom = element_text(vjust = 1, color = NA),
  legend.key = element_blank(),
  legend.position = "none",
  legend.title = element_text(size = 8))



admixPlot

Lineage demographics

Depth distribution of lineages

leveneTest(lm(depthm ~ cluster, data = subset(pcangsd, subset = pcangsd$cluster!="Admixed")))

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value   Pr(>F)    
## group   3  6.9537 0.000174 ***
##       213                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

depthAov = welch_anova_test(depthm ~ cluster, data = subset(pcangsd, subset = pcangsd$cluster!="Admixed"))

dF = depthAov$statistic[[1]]

depthPH = games_howell_test(depthm ~ cluster, data = subset(pcangsd, subset = pcangsd$cluster!="Admixed"), conf.level = 0.95) %>% as.data.frame()

depthLetters = data.frame(x = factor(c("Blue", "Teal", "Green", "Yellow")), y = c(2.5, 2.5, 2.5, 2.5),  grp = c("a", "b", "bc", "c"))

lineageViolinA = ggplot(data = subset(pcangsd, subset = pcangsd$cluster!="Admixed"), aes(x = cluster, y = depthm)) +
  annotate(geom = "rect", xmin = -Inf, xmax = Inf, ymin = 30, ymax = Inf,  fill = "black", alpha = 0.15, color = NA) +
  geom_beeswarm(aes(fill = cluster), shape = 21, size = 2, cex = 1.5, alpha = 0.5) +
  geom_violin(aes(fill = cluster),adjust = 1, linewidth = 0, color = "black", alpha = 0.35, width = 0.9, trim = F, scale = "width") +
  geom_violin(adjust = 1, linewidth = 0.4, color = "black", alpha = 1, width = 0.9, trim = F, fill = NA, scale = "width") +
  geom_boxplot(width = 0.2, color = "black", fill = "white", outlier.colour = NA, linewidth = 0.6, alpha = 0.5) +
  geom_text(data = depthLetters, aes(x = x, y = y, label = grp), size = 4) +
  annotate(geom = "text", x = 3.75, y =50, label = bquote(italic("F")~" = "~.(dF)*"; "~italic("p")~" < 0.001"), size = 3) +
  scale_fill_discrete(type = kColPal, name = "Lineage") +
  scale_color_discrete(type = kColPal, name = "Lineage") +
  xlab("Lineage") +
  ylab("Depth (m)") +
  scale_y_reverse(breaks = seq(5, 50, 5)) +
  theme_bw()

lineageViolin = lineageViolinA + theme(
        axis.title = element_text(color = "black", size = 12),
        axis.text = element_text(color = "black", size = 10),
        legend.position = "none",
        legend.key.size = unit(0.3, 'cm'),
        panel.border = element_rect(color = "black", size = 1),
        panel.background = element_blank(),
        plot.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank())

lineageViolin

Lineage differentiation

Measuring with global weighted FST calculated from SFS

First prepare and sort FST for plotting

pop.order = c("Blue", "Teal", "Green", "Yellow")

# reads in fst 
fstMa1 <- read.delim("../data/snps/sintKFst3x.out") %>% dplyr::select(-fst) %>% df_to_pw_mat(., "pop1", "pop2", "weightedFst")

fstMa1

##            Blue     Teal    Green   Yellow
## Blue   0.000000 0.047964 0.185205 0.162474
## Teal   0.047964 0.000000 0.210652 0.184036
## Green  0.185205 0.210652 0.000000 0.209234
## Yellow 0.162474 0.184036 0.209234 0.000000

fstMa = fstMa1

upperTriangle(fstMa, byrow = TRUE) <- lowerTriangle(fstMa)
fstMa <- fstMa[,pop.order] %>%
  .[pop.order,]
fstMa[upper.tri(fstMa)] <- NA
fstMa <- as.data.frame(fstMa)

# rearrange and reformat matrix
fstMa$Pop = factor(row.names(fstMa), levels = unique(pop.order))



# melt matrix data (turn from matrix into long dataframe)
fst = melt(fstMa, id.vars = "Pop", value.name = "Fst", variable.name = "Pop2", na.rm = FALSE)

fst$Fst = round(fst$Fst, 3)

fst$site = fst$Pop
fst$site = factor(gsub("\\n.*", "", fst$site))
fst$site = factor(fst$site, levels = levels(fst$site)[c(1, 3, 2, 4)])

fst$site2 = fst$Pop2
fst$site2 = factor(gsub("\\n.*", "", fst$site2))
fst$site2 = factor(fst$site2, levels = levels(fst$site2)[c(1, 3, 2, 4)])

fst$Pop2 = factor(fst$Pop2, levels = levels(fst$Pop2)[c(4, 3, 2, 1)])

Construct a heatmap of FST values

fstHeatmapA = ggplot(data = fst %>% filter(Fst != 0), aes(Pop, Pop2, fill = as.numeric(as.character(Fst)))) +
  geom_tile(color = "white") +
  geom_segment(data = fst, aes(x = 0.475, xend = 0.25, y = Pop2, yend = Pop2, color = site2), size = 21.25) + #colored boxes for Y-axis labels
  geom_segment(data = fst, aes(x = Pop, xend = Pop, y = 0.2, yend = 0.475, color = site), size = 41) + #colored boxes for X-axis labels
  scale_color_manual(values = kColPal, guide = NULL) +
  scale_fill_gradient(low = "white", high = "#EA526F", limit = c(0, 0.22), space = "Lab", name = expression(paste(italic("F")[ST])), na.value = NA,  guide = "colourbar") +

  geom_text(data = fst %>% filter(Fst != 0), aes(Pop, Pop2, label = Fst), color = "black", size = 3.5, fontface = "bold") +
  guides(fill = guide_colorbar(barwidth = 7.5, barheight = 0.75, title.position = "top", title.hjust = 0.5, direction = "horizontal", ticks.colour = "black", frame.colour = "black")) +
  scale_y_discrete(position = "left", limits = rev(levels(fst$Pop2))) +
  scale_x_discrete(limits = levels(fst$Pop2)[c(1:4)]) +
  coord_cartesian(xlim = c(1, 4), ylim = c(1, 4), clip = "off") +
  theme_minimal()

fstHeatmap = fstHeatmapA + theme(
  axis.text.x = element_text(vjust = 3.5, size = 10, hjust = 0.5, color = "black"),
  axis.text.y = element_text(size = 10, color = "black", angle = 90, hjust = 0.5, vjust = -1.5),
  axis.title.x = element_blank(),
  axis.title.y = element_blank(),
  panel.grid.major = element_blank(),
  panel.border = element_blank(),
  axis.ticks = element_blank(),
  legend.title = element_text(size = 8, color = "black"),
  legend.text = element_text(size = 8, color = "black"),
  legend.position = c(0.6, 0.9),
  plot.background = element_blank(),
  panel.background = element_blank(),
)

fstHeatmap

Lineage demographics through time

Making stairway plot of effective population sizes of each lineage throughout time

bl = read.table("../data/snps/sintBlue.final.summary", header = TRUE) %>% mutate("Lineage" = "Blue")
tl = read.table("../data/snps/sintTeal.final.summary", header = TRUE) %>% mutate("Lineage" = "Teal")
gn = read.table("../data/snps/sintGreen.final.summary", header = TRUE) %>% mutate("Lineage" = "Green")
yl = read.table("../data/snps/sintYellow.final.summary", header = TRUE) %>% mutate("Lineage" = "Yellow")

swData = rbind(bl, tl, gn, yl)
swData$Lineage = factor(swData$Lineage)
swData$Lineage = factor(swData$Lineage, levels = levels(swData$Lineage)[c(1,3,2,4)])

Constuct plot

swPlotA = ggplot(data = swData, aes(x = year, y = Ne_median, ymin = Ne_12.5., ymax = Ne_87.5., color = Lineage, fill = Lineage)) +
  geom_ribbon(color = NA, aes(alpha = Lineage)) +
  # geom_line(size = 0.6) +
  geom_line(linewidth = 1.15) +
  scale_fill_manual(values = kColPal[c(1:4)]) +
  scale_color_manual(values = kColPal[c(1:4)]) +
  scale_alpha_manual(values = c(0.25, 0.25, 0.35, 0.4)) +
  scale_x_continuous(name = "KYA", limits = c(0,5.25e5), breaks = c(1e5,2e5,3e5,4e5,5e5), labels = c("100","200", "300", "400", "500")) +
  scale_y_continuous(name = bquote(italic(N[e])~"(x10"^"3"*")"), limits = c(0,14e5), breaks = c(2.5e5,5e5,7.5e5,10e5,12.5e5), labels = c("250","500", "750", "1000", "1250"))+

  coord_cartesian(xlim = c(5.25e5, 0), expand = FALSE) +
  theme_bw()

swPlot = swPlotA + theme(
    axis.title = element_text(color = "black", size = 12),
    axis.text = element_text(color = "black", size = 10),
    legend.key.size = unit(0.3, 'cm'),
    legend.title = element_text(color = "black", size = 12),
    legend.text = element_text(color = "black", size = 12),
    legend.position = "none",
    # legend.position = c(0.85, 0.82),
    plot.background = element_blank(),
    panel.background = element_blank(),
    panel.border = element_rect(size = 1),
    panel.grid = element_blank()
)

swPlot

Heterozygosity and Inbreeding

popData = read.csv("../data/stephanocoeniaMetaData.csv")[-c(66, 68, 164, 166, 209, 211),] %>% dplyr::select("sample" = tubeID, "Site" = site, "Depth" = depthZone, "depthm" = depthM) # Reads in population data
popData$a = c(0:219)

popData$Site = factor(popData$Site)
popData$Site = factor(popData$Site, levels = levels(popData$Site)[c(2,3,1,4)]) 
popData$Depth = factor(popData$Depth)
popData$Depth = factor(popData$Depth, levels = levels(popData$Depth)[c(2,1)]) 

sampleData = fknmsSites[-c(66,68,164,166,209,211),] %>% group_by(site, depthZone)%>% summarise(depthZone = (first(depthZone)), depthRange = paste(min(depthM), "--", max(depthM), sep = ""), meanDepth = round(mean(depthM),1), n = n())%>% droplevels() %>% as.data.frame()

## `summarise()` has grouped output by 'site'. You can override using the `.groups`
## argument.

# Average depth of populations
fkPopDepths = fknmsSites[-c(66,68,164,166,209,211),] %>%  group_by(site, depthZone) %>% summarise(avgDepthM = mean(depthM), n = n())

## `summarise()` has grouped output by 'site'. You can override using the `.groups`
## argument.

fkPopDepths

## # A tibble: 8 × 4
## # Groups:   site [4]
##   site          depthZone  avgDepthM     n
##   <fct>         <fct>          <dbl> <int>
## 1 Upper Keys    Shallow         23.6    30
## 2 Upper Keys    Mesophotic      43.8    30
## 3 Lower Keys    Shallow         18.0    30
## 4 Lower Keys    Mesophotic      32.8    30
## 5 Tortugas Bank Shallow         21.1    30
## 6 Tortugas Bank Mesophotic      32.0    25
## 7 Riley's Hump  Shallow         26.4    30
## 8 Riley's Hump  Mesophotic      33.2    15

sampleTab = sampleData
colnames(sampleTab) = c("Site", "Depth zone", "Sampling \ndepth (m)", "Average \ndepth (m)", "n")

sampleTab$Site

## [1] Upper Keys    Upper Keys    Lower Keys    Lower Keys    Tortugas Bank Tortugas Bank
## [7] Riley's Hump  Riley's Hump 
## Levels: Upper Keys Lower Keys Tortugas Bank Riley's Hump

finalTabSite = c("Upper Keys", "", "Lower Keys","", "Tortugas Bank", "", "Riley's Hump", "")

sampleTab$Site = finalTabSite

hetAll = read.table("../data/snps/sintHet3x") 
colnames(hetAll) = c("sample", "He")
hetAll$sample = str_pad(hetAll$sample, 3, pad = "0")
hetAll$sample = paste("SFK",hetAll$sample, sep ="")

sintBreed = read.delim("../data/snps/fkSintF.indF", header = FALSE)

sintRelate = read.delim("../data/snps/fkSintFiltRelate3x")
sintRelate2 = sintRelate %>% group_by(a, b) %>% dplyr::select("Rab" = rab, "theta" = theta)

## Adding missing grouping variables: `a`, `b`

sintRelate2 = sintRelate2 %>% left_join(popData, by = "a") %>% left_join(popData, by = c("b" = "a"), suffix = c(".a", ".b")) 

sintRelate2$popDepth.a = paste(sintRelate2$Site.a, sintRelate2$Depth.a, sep = " ")
sintRelate2$popDepth.b = paste(sintRelate2$Site.b, sintRelate2$Depth.b, sep = " ")

sintRelate2 = sintRelate2 %>% left_join((pcangsd %>%dplyr::select(sample, cluster)) , by = c("sample.a" = "sample")) %>% left_join((pcangsd %>%dplyr::select(sample, cluster)) , by = c("sample.b" = "sample"))

sintRelate = sintRelate2 %>% filter(cluster.x != "Admixed",cluster.x == cluster.y) %>% rename(Depth = Depth.a, Site = Site.a, cluster = cluster.x)

sintRelateMean = sintRelate %>% group_by(Site, Depth) %>% dplyr::summarize(N = n(), meanRab = mean(Rab), seRab = sd(Rab)/sqrt(N), meanTheta = mean(theta), seTheta = sd(theta)/sqrt(N)) %>% dplyr::select(-N)

## `summarise()` has grouped output by 'Site'. You can override using the `.groups`
## argument.

het = left_join(popData, hetAll, by = "sample") %>% mutate("inbreed" = sintBreed$V1) %>% left_join((pcangsd %>% dplyr::select(sample, cluster)) , by = "sample") %>% dplyr::select(-a)

hetStats = het %>% group_by(cluster) %>% summarise(N = n(), meanAll = mean(He), sdAll = sd(He), seAll = sd(He)/sqrt(N), meanInbreed = mean(inbreed), sdInbreed = sd(inbreed), seInbreed = sd(inbreed)/sqrt(N))

hetStats

## # A tibble: 5 × 8
##   cluster     N meanAll     sdAll     seAll meanInbreed sdInbreed seInbreed
##   <fct>   <int>   <dbl>     <dbl>     <dbl>       <dbl>     <dbl>     <dbl>
## 1 Blue      131 0.00261 0.000157  0.0000137      0.0924    0.0315   0.00275
## 2 Teal       40 0.00235 0.000120  0.0000189      0.126     0.0302   0.00478
## 3 Green      31 0.00225 0.000126  0.0000227      0.224     0.0271   0.00486
## 4 Yellow     15 0.00239 0.0000640 0.0000165      0.214     0.0259   0.00669
## 5 Admixed     3 0.0036  0.00173   0.00100        0.119     0.126    0.0728

Heterozygosity, diversity, and inbreeding plots

Heterozygosity across all RAD loci by lineage

leveneTest(lm(He ~ cluster, data = subset(het, subset = pcangsd$cluster!="Admixed")))

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value             Pr(>F)    
## group   4  18.093 0.0000000000008437 ***
##       212                               
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

hetAov = welch_anova_test(He ~ cluster, data = subset(het, subset = het$cluster!="Admixed"))

hF = hetAov$statistic[[1]]

hetPH = games_howell_test(He ~ cluster, data = subset(het, subset = het$cluster!="Admixed"), conf.level = 0.95) %>% as.data.frame()


# hetLetters = data.frame(x = factor(c("Blue", "Teal", "Green", "Yellow")), y = c(0.0039, 0.0039, 0.0039, 0.0039),  grp = c("a", "bc", "b", "c"))

hetLetters = data.frame(x = factor(c("Blue", "Teal", "Green", "Yellow")), y = c(0.0039, 0.0039, 0.0039, 0.0039),  grp = c("a", "b", "c", "b"))

hetPlotKA = ggplot(data = het %>% filter(cluster != "Admixed"), aes(x = cluster, y = He)) +
  geom_beeswarm(aes(fill = cluster), shape = 21, size = 2, cex = 0.75, alpha = 0.5) + 
  geom_violin(aes(fill = cluster, group = cluster), adjust = 1, linewidth = 0, color = "black", alpha = 0.35, width = 0.9, trim = F, scale = "width") +
  geom_violin(aes(fill = cluster, group = cluster), adjust = 1, linewidth = 0.4, color = "black", alpha = 1, width = 0.9, trim = F, fill = NA, scale = "width") +
  geom_boxplot(aes(fill = cluster, group = cluster), width = 0.2, color = "black", fill = "white", outlier.colour = NA, linewidth = 0.6, alpha = 0.5) +  xlab("Lineage") +
  geom_text(data = hetLetters, aes(x = x, y = y, label = grp), size = 4) +
   annotate(geom = "text", x = 3.65, y =0.0036, label = bquote(italic("F")~" = "~.(hF)*"; "~italic("p")~" < 0.001"), size = 3) +
  scale_fill_discrete(type = kColPal, name = "Lineage") +
  xlab("Lineage") +
  ylab("Heterozygosity") +
  scale_y_continuous(breaks = seq(0.0022, 0.0038, 0.0004)) +
  coord_cartesian(expand = TRUE, xlim = c(0.85, 4)) +
  theme_bw()

 hetPlotK = hetPlotKA + theme(
        axis.title = element_text(color = "black", size = 12),
        axis.text = element_text(color = "black", size = 10),
        legend.position = "none",
        legend.key.size = unit(0.3, 'cm'),
        panel.border = element_rect(color = "black", size = 1),
        panel.background = element_blank(),
        plot.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank())

# hetPlotK

Mean inbreeding plot

leveneTest(lm(inbreed ~ cluster, data = subset(het, subset = pcangsd$cluster!="Admixed")))

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value     Pr(>F)    
## group   4  6.3025 0.00008245 ***
##       212                       
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

ibAov = welch_anova_test(inbreed ~ cluster, data = subset(het, subset = het$cluster!="Admixed"))

iF = ibAov$statistic[[1]]

ibPH = games_howell_test(inbreed ~ cluster, data = subset(het, subset = het$cluster!="Admixed"), conf.level = 0.95) %>% as.data.frame()


inbreedLetters = data.frame(x = factor(c("Blue", "Teal", "Green", "Yellow")), y = c(0.38, 0.38, 0.38, 0.38),  grp = c("a", "b", "c", "c"))

inbreedingPlot = ggplot(data = het %>% filter(cluster!="Admixed"), aes(x = cluster, y = inbreed)) +
  geom_beeswarm(aes(fill = cluster), shape = 21, size = 2, cex = 1.5, alpha = 0.5) +
  geom_violin(aes(fill = cluster), adjust = 1, linewidth = 0, color = "black", alpha = 0.35, width = 0.9, trim = F, scale = "width") +
  geom_violin(adjust = 1, linewidth = 0.4, color = "black", alpha = 1, width = 0.9, trim = F, fill = NA, scale = "width") +
  geom_boxplot(aes(fill = cluster),width = 0.2, color = "black", fill = "white", outlier.colour = NA, linewidth = 0.6, alpha = 0.5) + 
  geom_text(data = inbreedLetters, aes(x = x, y = y, label = grp), size = 4) +
  annotate(geom = "text", x = 3.6, y =0.032, label = bquote(italic("F")~" = "~.(iF)*"; "~italic("p")~" < 0.001"), size = 3) +
  xlab("Lineage") +
  ylab(bquote(~"Inbreeding coefficient ("*italic(F)*")")) +
  scale_fill_manual(values = kColPal) +  
  scale_color_manual(values = kColPal) +
  scale_y_continuous(breaks=seq(0, 0.4, by = .05)) +
  coord_cartesian(expand = TRUE, xlim = c(0.78, 4)) +
  theme_bw() +
  theme(legend.position = "none",
        axis.text = element_text(size = 10, color = "black"),
        axis.title = element_text(size = 12, color = "black"),
        panel.border = element_rect(color = "black", size = 1),
        plot.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank())

inbreedingPlot

Nucleotide diversity plot

# mean depth for lineages
subset(pcangsd, subset = pcangsd$cluster!="Admixed") %>% group_by(cluster) %>% summarise(depth = mean(depthm))

## # A tibble: 4 × 2
##   cluster depth
##   <fct>   <dbl>
## 1 Blue     31.4
## 2 Teal     26.3
## 3 Green    22.5
## 4 Yellow   20.2

npgList = list(read_tsv("../data/snps/blue3x.thetas.idx.pestPG") %>% mutate(lineage = "Blue", depth = 31.4),
               read_tsv("../data/snps/teal3x.thetas.idx.pestPG") %>% mutate(lineage = "Teal", depth = 26.3),
               read_tsv("../data/snps/green3x.thetas.idx.pestPG") %>% mutate(lineage = "Green", depth = 22.5),
               read_tsv("../data/snps/yellow3x.thetas.idx.pestPG")%>% mutate(lineage = "Yellow", depth = 20.2))

## Rows: 30 Columns: 14
## ── Column specification ────────────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr  (2): #(indexStart,indexStop)(firstPos_withData,lastPos_withData)(WinStart,WinSt...
## dbl (12): WinCenter, tW, tP, tF, tH, tL, Tajima, fuf, fud, fayh, zeng, nSites
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 30 Columns: 14
## ── Column specification ────────────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr  (2): #(indexStart,indexStop)(firstPos_withData,lastPos_withData)(WinStart,WinSt...
## dbl (12): WinCenter, tW, tP, tF, tH, tL, Tajima, fuf, fud, fayh, zeng, nSites
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 30 Columns: 14
## ── Column specification ────────────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr  (2): #(indexStart,indexStop)(firstPos_withData,lastPos_withData)(WinStart,WinSt...
## dbl (12): WinCenter, tW, tP, tF, tH, tL, Tajima, fuf, fud, fayh, zeng, nSites
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 30 Columns: 14
## ── Column specification ────────────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr  (2): #(indexStart,indexStop)(firstPos_withData,lastPos_withData)(WinStart,WinSt...
## dbl (12): WinCenter, tW, tP, tF, tH, tL, Tajima, fuf, fud, fayh, zeng, nSites
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

piAll = purrr::reduce(npgList, rbind) %>% 
  group_by(lineage) %>% 
  mutate(tPps = tP/nSites) %>%
  dplyr::summarize(tPps = mean(tPps), depth = max(depth))


piAll$lineage = as.factor(piAll$lineage)
piAll$lineage = factor(piAll$lineage, levels(piAll$lineage)[c(1, 3, 2, 4)])

lmpi = lm(tPps~depth, data=piAll)
summary(lmpi)

## 
## Call:
## lm(formula = tPps ~ depth, data = piAll)
## 
## Residuals:
##           1           2           3           4 
##  0.00009393 -0.00028006 -0.00006686  0.00025299 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept) 0.00355804 0.00083771   4.247   0.0512 .
## depth       0.00006938 0.00003291   2.108   0.1696  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.000279 on 2 degrees of freedom
## Multiple R-squared:  0.6896, Adjusted R-squared:  0.5345 
## F-statistic: 4.444 on 1 and 2 DF,  p-value: 0.1696

r2 = round(summary(lmpi)$r.squared, 3)

nuclDivPlot = ggplot(piAll, aes(x = depth, y = tPps)) +
  geom_smooth(se = F, color = 'black', method='lm', linewidth = 0.75) +
  geom_point(aes(fill = lineage),shape = 21, size = 3) +
  scale_color_manual(values = kColPal) +
  scale_fill_manual(values = kColPal) +
  labs(x='Depth (m)', y = bquote("Nucleotide diversity ("*pi*")"), shape = 'Lineage') +
  annotate(geom = "text", x = 30, y = 0.0048, label = bquote(italic(R^2)~"="~.(r2)), size = 3) + 
  theme_bw() +
  theme(axis.title.y = element_text(color = "black", size = 12),
        axis.title.x = element_text(color = "black", size = 12),
        axis.text = element_text(color = "black", size = 10),
        legend.position = "none",
        legend.key.size = unit(0.3, 'cm'),
        panel.border = element_rect(color = "black", size = 1),
        panel.background = element_blank(),
        plot.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank())

nuclDivPlot

## `geom_smooth()` using formula = 'y ~ x'

piAll$Ne = (piAll$tPps)/(4*2e-8)
piAll

## # A tibble: 4 × 4
##   lineage    tPps depth     Ne
##   <fct>     <dbl> <dbl>  <dbl>
## 1 Blue    0.00583  31.4 72879.
## 2 Green   0.00484  22.5 60487.
## 3 Teal    0.00532  26.3 66447.
## 4 Yellow  0.00521  20.2 65155.

subset(pcangsd, subset = pcangsd$cluster!="Admixed") %>% group_by(cluster) %>% summarise(range(depthm))

## `summarise()` has grouped output by 'cluster'. You can override using the `.groups`
## argument.

## # A tibble: 8 × 2
## # Groups:   cluster [4]
##   cluster `range(depthm)`
##   <fct>             <dbl>
## 1 Blue               16.8
## 2 Blue               45.4
## 3 Teal               14.6
## 4 Teal               44.5
## 5 Green              17.1
## 6 Green              32  
## 7 Yellow             17.4
## 8 Yellow             25.9

Lineage plots

lineagePlots = (lineageViolin | hetPlotK | inbreedingPlot) / ( swPlot | fstHeatmap) +
  plot_annotation(tag_levels = "A") &
  theme(plot.tag = element_text(size = 14))

# lineagePlots

ggsave("../figures/figure3.png", plot = lineagePlots, height = 7, width = 12, units = "in", dpi = 300)

ggsave("../figures/figure3.svg", plot = lineagePlots, height = 7, width = 12, units = "in", dpi = 300)

Isolation by distance

S. intersepta isolation by distance

Mantel’s test

# Isolation by distance
library(geosphere)

#Get the geographic distances in km
coords  = read.csv("../data/stephanocoeniaMetaData.csv")[-c(66, 68, 164, 166, 209, 211),] %>% 
dplyr::select(longDD, latDD)

dGeo = as.dist((distm(coords, fun = distGeo)/1000), diag = TRUE)
snpDist = as.dist(read.table("../data/snps/sintFiltSnps.ibsMat"), diag = TRUE)

# Test IBD
set.seed(694)
snpIBD = mantel.randtest(dGeo, snpDist, nrepet = 9999)
snpIBD

## Monte-Carlo test
## Call: mantel.randtest(m1 = dGeo, m2 = snpDist, nrepet = 9999)
## 
## Observation: -0.01026129 
## 
## Based on 9999 replicates
## Simulated p-value: 0.7143 
## Alternative hypothesis: greater 
## 
##         Std.Obs     Expectation        Variance 
## -0.597377856407  0.000007439758  0.000295484722

SNP Mantel plot

snpGenDist =  melt(as.matrix(snpDist), varnames = c("row", "col"), value.name = "dist")
snpGenDist = snpGenDist[snpGenDist$row != snpGenDist$col,]

geo = melt(as.matrix(dGeo), varnames = c("row", "col"), value.name = "geo")
geo = geo[geo$row != geo$col,]

snpMantelDF = data.frame(cbind(snpGenDist$dist, geo$geo))
colnames(snpMantelDF) = c("dist", "geo")

snpMantelA = ggplot(data = snpMantelDF, aes(x = geo, y = dist)) +
  scale_fill_gradientn(colors = paletteer_d("wesanderson::Zissou1")) +
  stat_density_2d(aes(fill = stat(density)), n = 300, contour = FALSE, geom = "raster") +
  geom_smooth(method = lm, col = "black", fill = "gray40", fullrange = TRUE) +
  geom_point(shape = 21, fill = "gray40", alpha = 0.25) +
  scale_x_continuous(limits = c(0,300), expand = c(0,0)) +
  scale_y_continuous(limits = c(0.25,0.5), breaks = seq(0.25,0.5, by = 0.05), expand = c(0,0)) +
  annotate("label", x = 225, y = 0.46, 
           label = paste("r = ", round(snpIBD$obs, 3), "; p = ", snpIBD$pvalue), 
           size = 4, alpha = 0.6) +             
  labs(x = "Geographic distance (km)", y = expression(paste("Genetic distance "))) +
  ggtitle("SNP") +
  theme_bw()

snpMantel = snpMantelA + theme(
  axis.title.x = element_blank(),
  axis.text.x = element_text(size = 12, color = "black"),
  axis.ticks.x = element_line(color = "black"),
  axis.line.x = element_blank(),
  axis.title.y = element_text(color = "black"),
  axis.text.y = element_text(size = 12, color = "black"),
  axis.ticks.y = element_line(color = "black"),
  axis.line.y = element_blank(),
  panel.border = element_rect(size = 1.2, color = "black"),
  plot.margin = margin(0.2,0.5,0.1,0.1, unit = "cm"),
  legend.position = "none")

snpMantel

## `geom_smooth()` using formula = 'y ~ x'

Genetic connectivity

Checking deviance among model runs from BayesAss we ran on HPC

# fileList = substr(list.files("../data/snps/bayesAss/", "BA3trace.*.txt$"),1,10)
fileList = substr(list.files("../data/snps/BA3/", "BA3trace.*.txt$"),1,11)

bayesian_deviance <- function(trace, burnin = 0, sampling.interval = 0){
  if(burnin == 0) stop('No burnin specified')
  if(sampling.interval == 0) stop('No sampling interval specified')
  range <- (trace$State > burnin & trace$State %% sampling.interval == 0)
  D <- -2*mean(trace$LogProb[range])
  return(D)
}

for(i in 1:length(fileList)){
  assign(fileList[i], read.delim(paste("../data/snps/BA3/", fileList[i], ".txt", sep = ""))) %>% dplyr::select(-last_col())
  print(paste(fileList[i], bayesian_deviance(get(fileList[i]), burnin = 10000000, sampling.interval = 100)))
}

## [1] "BA3trace.01 1078700.5909"
## [1] "BA3trace.02 1078407.0012"
## [1] "BA3trace.03 1078727.4004"
## [1] "BA3trace.04 1078794.1071"
## [1] "BA3trace.05 1078778.0004"
## [1] "BA3trace.06 1078681.4417"
## [1] "BA3trace.07 1078710.4509"
## [1] "BA3trace.08 1078817.9814"
## [1] "BA3trace.09 1078717.2801"
## [1] "BA3trace.10 1078433.2838"

# [1] "BA3trace.01 1078700.5909"
# [1] "BA3trace.02 1078407.0012"
# [1] "BA3trace.03 1078727.4004"
# [1] "BA3trace.04 1078794.1071"
# [1] "BA3trace.05 1078778.0004"
# [1] "BA3trace.06 1078681.4417"
# [1] "BA3trace.07 1078710.4509"
# [1] "BA3trace.08 1078817.9814"
# [1] "BA3trace.09 1078717.2801"
# [1] "BA3trace.10 1078433.2838"

All traces have similar deviance (this is good!). Using the trace with the lowest deviance (BA3trace.02.txt, in this case)

bayesAss = read.delim("../data/snps/BA3/BA3trace.02.txt") %>% filter(State > 10000000) %>% dplyr::select(-State, -LogProb, -X)

baMean = bayesAss %>% summarise(across(everything(), list(mean))) %>% t() %>% as_tibble() %>% rename(., mean=V1) %>% mutate(pops = colnames(bayesAss))

baSumm = bayesAss %>% summarise(across(everything(), list(median))) %>% t() %>% as.data.frame() %>% rename(., median=V1) %>% mutate(pops = baMean$pops, mean = round(baMean$mean, 3)) %>% relocate(median, .after = mean)

baSumm$median = round(baSumm$median, 3)

baHpd =as.data.frame(t(sapply(bayesAss, emp.hpd)))
colnames(baHpd) = c("hpdLow", "hpdHigh")
baHpd$pops = rownames(baHpd)

ESS = as.data.frame(sapply(bayesAss, ESS))
colnames(ESS) = "ESS"

baSumm = baSumm %>% left_join(baHpd)

## Joining with `by = join_by(pops)`

baSumm$hpdLow = round(baSumm$hpdLow, 3)
baSumm$hpdHigh = round(baSumm$hpdHigh, 3)
baSumm$ESS = ESS$ESS

### FROM BAYESASS: ###
## Population Index -> Population Label:
## 0->TortugasBank_Mesophotic 1->TortugasBank_Shallow
## 2->RileysHump_Mesophotic 3->RileysHump_Shallow
## 4->LowerKeys_Mesophotic 5->LowerKeys_Shallow
## 6->UpperKeys_Shallow 7->UpperKeys_Mesophotic

popi = rep(c("Tortugas Bank\nMesophotic", "Tortugas Bank\nShallow", "Riley's Hump\nMesophotic", "Riley's Hump\nShallow", "Lower Keys\nMesophotic", "Lower Keys\nShallow", "Upper Keys\nShallow", "Upper Keys\nMesophotic"), each = 8)

popj = rep(c("Tortugas Bank\nMesophotic", "Tortugas Bank\nShallow", "Riley's Hump\nMesophotic", "Riley's Hump\nShallow", "Lower Keys\nMesophotic", "Lower Keys\nShallow", "Upper Keys\nShallow", "Upper Keys\nMesophotic"), times = 8)

baSumm = baSumm %>% mutate(pop.i = popi, pop.j = popj) %>% relocate(c(pop.i, pop.j), .after = pops) %>% dplyr::select(-pops)

baSumm$pop.i = factor(baSumm$pop.i)
baSumm$pop.i = factor(baSumm$pop.i, levels = levels(baSumm$pop.i)[c(8, 2, 6, 4, 7, 1, 5, 3)])

baSumm$pop.j = factor(baSumm$pop.j)
baSumm$pop.j = factor(baSumm$pop.j, levels = levels(baSumm$pop.j)[c(8, 2, 6, 4, 7, 1, 5, 3)])

baSumm$site.i = word(baSumm$pop.i, 1, sep = "\n")
baSumm$site.i = factor(baSumm$site.i)
baSumm$site.i = factor(baSumm$site.i, levels = levels(baSumm$site.i)[c(4, 1, 3, 2)])

baSumm$site.j = word(baSumm$pop.j, 1, sep = "\n")
baSumm$site.j = factor(baSumm$site.j)
baSumm$site.j = factor(baSumm$site.j, levels = levels(baSumm$site.j)[c(4, 1, 3, 2)])

baSumm$depth.i = word(baSumm$pop.i, 2, sep = "\n")
baSumm$depth.i = factor(baSumm$depth.i)
baSumm$depth.i = factor(baSumm$depth.i, levels = levels(baSumm$depth.i)[c(2, 1)])

baSumm$depth.j = word(baSumm$pop.j, 2, sep = "\n")
baSumm$depth.j = factor(baSumm$depth.j)
baSumm$depth.j = factor(baSumm$depth.j, levels = levels(baSumm$depth.j)[c(2, 1)])

#All sites (excluding self retention)
baMeans = baSumm %>% filter(pop.i != pop.j) %>% summarise(mean = mean(mean), sd = sd(.$mean), se = sd(.$mean)/sqrt(nrow(.))) %>% mutate(dataset = "Global")

#mesophotic sources
baMeans = baSumm %>% filter(pop.i != pop.j, depth.j == "Mesophotic") %>% summarise(mean = mean(mean), sd = sd(.$mean), se = sd(.$mean)/sqrt(nrow(.))) %>% mutate(dataset = "Mesophotic Source") %>% bind_rows(baMeans, .)

#shallow sources
baMeans = baSumm %>% filter(pop.i != pop.j, depth.j == "Shallow") %>% summarise(mean = mean(mean), sd = sd(.$mean), se = sd(.$mean)/sqrt(nrow(.))) %>% mutate(dataset = "Shallow Source") %>% bind_rows(baMeans, .)

#mesophotic sinks
baMeans = baSumm %>% filter(pop.i != pop.j, depth.i == "Mesophotic") %>% summarise(mean = mean(mean), sd = sd(.$mean), se = sd(.$mean)/sqrt(nrow(.)))  %>% mutate(dataset = "Mesophotic Sink") %>% bind_rows(baMeans, .)

#shallow sinks
baMeans = baSumm %>% filter(pop.i != pop.j, depth.i == "Shallow") %>% summarise(mean = mean(mean), sd = sd(.$mean), se = sd(.$mean)/sqrt(nrow(.)))  %>% mutate(dataset = "Shallow Sink") %>% bind_rows(baMeans, .)

#mesophotic -> shallow
baMeans = baSumm %>% filter(pop.i != pop.j, depth.j == "Mesophotic", depth.i == "Shallow") %>% summarise(mean = mean(mean), sd = sd(.$mean), se = sd(.$mean)/sqrt(nrow(.))) %>% mutate(dataset = "Mesophotic -> Shallow") %>% bind_rows(baMeans, .)

#mesophotic -> mesophotic
baMeans = baSumm %>% filter(pop.i != pop.j, depth.j == "Mesophotic", depth.i == "Mesophotic") %>% summarise(mean = mean(mean), sd = sd(.$mean), se = sd(.$mean)/sqrt(nrow(.))) %>% mutate(dataset = "Mesophotic -> Mesophotic") %>% bind_rows(baMeans, .)

#shallow -> mesophotic
baMeans = baSumm %>% filter(pop.i != pop.j, depth.j == "Shallow", depth.i == "Mesophotic") %>% summarise(mean = mean(.$mean), sd = sd(.$mean), se = sd(.$mean)/sqrt(nrow(.))) %>% mutate(dataset = "Shallow -> Mesophotic") %>% bind_rows(baMeans, .)

#shallow -> shallow
baMeans = baSumm %>% filter(pop.i != pop.j, depth.j == "Shallow", depth.i == "Shallow") %>% summarise(mean = round(mean(.$mean), 5), sd = round(sd(.$mean), 5), se = round(sd(.$mean)/sqrt(nrow(.)), 3)) %>% mutate(dataset = paste("Shallow -> Shallow")) %>% bind_rows(baMeans, .) %>% relocate(dataset, .before = mean) %>% as.data.frame()

baMeans[,c(2:4)] = baMeans[,c(2:4)] %>% round(4)

baMeans

##                    dataset   mean     sd     se
## 1                   Global 0.0410 0.0572 0.0076
## 2        Mesophotic Source 0.0670 0.0725 0.0137
## 3           Shallow Source 0.0151 0.0042 0.0008
## 4          Mesophotic Sink 0.0378 0.0648 0.0122
## 5             Shallow Sink 0.0443 0.0495 0.0094
## 6    Mesophotic -> Shallow 0.0649 0.0579 0.0145
## 7 Mesophotic -> Mesophotic 0.0698 0.0912 0.0263
## 8    Shallow -> Mesophotic 0.0138 0.0049 0.0012
## 9       Shallow -> Shallow 0.0168 0.0025 0.0010

baMeansTabPub = baMeans %>%
  flextable() %>%
  flextable::compose(part = "header", j = "dataset", value = as_paragraph("Dataset")) %>%
  flextable::compose(part = "header", j = "mean", value = as_paragraph(as_i("m"))) %>%
  flextable::compose(part = "header", j = "sd", value = as_paragraph("SD")) %>%
  flextable::compose(part = "header", j = "se", value = as_paragraph("SEM")) %>%
  flextable::font(fontname = "Times New Roman", part = "all") %>%
  flextable::fontsize(size = 10, part = "all") %>%
  flextable::bold(part = "header") %>%
  flextable::align(align = "left", part = "all") %>%
  flextable::autofit()

table3 = read_docx()
table3 = body_add_flextable(table3, value = baMeansTabPub)
print(table3, target = "../tables/table3.docx")

baMeansTabPub

baSumm$mean = sprintf('%.3f', baSumm$mean)
baSumm$mean2 = baSumm$mean
baSumm$hpdLow = sprintf('%.3f', baSumm$hpdLow)
baSumm$hpdHigh = sprintf('%.3f', baSumm$hpdHigh)

baLabs = tibble(pop.i = unique(baSumm$pop.i), pop.j = unique(baSumm$pop.j))

migrateA = ggplot(data = baSumm, aes(pop.i, pop.j))+
  geom_tile(data = subset(baSumm, subset = baSumm$mean2>0.65), fill = "gray35", color = "white") +
  geom_segment(data = baSumm, aes(x = 0.4755, xend = -0.55, y = pop.j, yend = pop.j, color = pop.j), size = 14) +
  geom_segment(data = baSumm, aes(x = pop.i, xend = pop.i, y = 0.45, yend = -0.425, color = pop.i), size = 32) +
  scale_color_manual(values = flPal[c(1:4, 1:4)], guide = NULL) +

  guides(fill = guide_colorbar(ticks.colour = "black", barwidth = 1, barheight = 10, frame.colour = "black")) +
  # new_scale("fill") +
  geom_tile(data = subset(baSumm, subset = baSumm$mean<0.65), aes(fill = as.numeric(as.character(mean))), color = "white") +
  scale_fill_gradientn(colours = paletteer_c("viridis::mako", n = 10, direction = -1)[c(1:7)], limit = c(0,0.27), space = "Lab", name = expression(paste(italic("m"))), na.value = "transparent",  guide = "colourbar", values = c(0, 0.05, 0.1, 0.15, 0.2,0.5,0.75,1)) +
  # scale_fill_gradientn(colours = paletteer_d("khroma::smoothrainbow"), limit = c(0,0.27), space = "Lab", name = expression(paste(italic("m"))), na.value = "transparent",  guide = "colourbar", values = c(0, 0.05, 0.1, 0.15, 0.2,0.5,0.75,1)) +
  geom_text(data = baSumm, aes(x = pop.i, y = pop.j, label = paste(mean, "\n", sep = "")), color = ifelse(baSumm$mean > 0.6, "white", "gray5"), fontface = ifelse(as.numeric(baSumm$hpdLow)>0, "bold", "plain"), size = ifelse(as.numeric(baSumm$hpdLow)>0, 4.75, 4)) +
  geom_text(data = baSumm, aes(x = pop.i, y = pop.j, label = paste("\n(",hpdLow,"–",hpdHigh, ")", sep = "")), color = ifelse(baSumm$mean > 0.6, "white", "gray5"), size = 3.25) +
  
  geom_text(data = (baLabs %>% filter(pop.j %in% c("Tortugas Bank\nMesophotic", "Tortugas Bank\nShallow", "Riley's Hump\nMesophotic", "Riley's Hump\nShallow"))), x = -.02, aes(y = pop.j, label = pop.j), size = 3.75, color = "#FFFFFF", family = "sans") +
  geom_text(data = (baLabs %>% filter(!pop.j %in% c("Tortugas Bank\nMesophotic", "Tortugas Bank\nShallow", "Riley's Hump\nMesophotic", "Riley's Hump\nShallow"))), x = -.02, aes(y = pop.j, label = pop.j), size = 3.75, color = "#000000", family = "sans") +
  geom_text(data = (baLabs %>% filter(pop.i %in% c("Tortugas Bank\nMesophotic", "Tortugas Bank\nShallow", "Riley's Hump\nMesophotic", "Riley's Hump\nShallow"))), y = -.01, aes(x = pop.i, label = pop.i), size = 3.75, color = "#FFFFFF", family = "sans") +
  geom_text(data = (baLabs %>% filter(!pop.i %in% c("Tortugas Bank\nMesophotic", "Tortugas Bank\nShallow", "Riley's Hump\nMesophotic", "Riley's Hump\nShallow"))), y = -.01, aes(x = pop.i, label = pop.i), size = 3.75, color = "#000000", family = "sans") +
  
  labs(x = "Sink", y = "Source") +
  scale_y_discrete(limits = rev(levels(baSumm$pop.i))[c(1:8)], position = "left") +
  coord_cartesian(xlim = c(1, 8), ylim = c(1, 8), clip = "off") +
  theme_minimal()

migrate = migrateA + theme(
  axis.text.x = element_text(vjust = 1, size = 12, hjust = 0.5, color = NA),
  axis.text.y = element_text(size = 10, color = NA),
  axis.title.x = element_text(size = 14),
  axis.title.y = element_text(size = 14),
  panel.grid.major = element_blank(),
  axis.ticks = element_blank(),
  # legend.position = c(1.055, 0.5),
  legend.direction = "vertical",
  legend.title = element_text(size = 12, face = "bold")
)

migrate

ggsave("../figures/figureS2.png", plot = migrate, width = 26, height = 12, units = "cm", dpi = 300)

ggsave("../figures/figureS2.svg", plot = migrate, width = 26, height = 12, units = "cm", dpi = 300)

baSumm$mean = as.numeric(baSumm$mean)
baSumm$hpdLow = as.numeric(baSumm$hpdLow)
baSumm$hpdHigh = as.numeric(baSumm$hpdHigh)

baSummSelf = baSumm %>% filter(pop.i == pop.j) %>% mutate(popdepth = paste(site.i, depth.i)) %>% mutate(retention = mean) %>% dplyr::select(-mean)

fknmsPopsMigrate2 = fknmsSites %>% group_by(site, depthZone, siteID) %>% dplyr::summarise(latDD = first(latDD), longDD = first(longDD)) %>% dplyr::filter(siteID %in% c("Ian's Lumps Site 52", "Site 48", "Site 47", "Site 45", "Site 35/36", "Site 37", "Site 39", "Site 19")) %>% dplyr::select(-site) %>% droplevels() %>% mutate(popdepth = paste(site, depthZone)) %>% as.data.frame() %>% slice(-5) %>% left_join(dplyr::select(baSummSelf, popdepth, retention))

## `summarise()` has grouped output by 'site', 'depthZone'. You can override using the
## `.groups` argument.
## Adding missing grouping variables: `site`
## Joining with `by = join_by(popdepth)`

fknmsPopsMigrate = fknmsPopsMigrate2[c(1,2,4,3,5:8),]

migratePal = c("Upper Keys" = flPal[1], "Lower Keys" = flPal[2], "Tortugas Bank" = flPal[3], "Riley's Hump" = flPal[4])

lines = c("Shallow" = 5, "Mesophotic" = 1)

baMapData = dplyr::select(baSumm, -mean2) %>% left_join(dplyr::select(fknmsPopsMigrate,-retention,-popdepth), by = c("site.i" = "site", "depth.i" = "depthZone")) %>% left_join(dplyr::select(fknmsPopsMigrate,-retention,-popdepth),, by = c("site.j" = "site", "depth.j" = "depthZone"), suffix = c(".i", ".j")) %>% filter(mean >= 0.02)

for(x in 1:nrow(baMapData)) {
  if (baMapData$pop.i[x] == baMapData$pop.j[x]) {
    baMapData$latDD.i[x] = NA;
    baMapData$latDD.j[x] = NA;
    baMapData$longDD.i[x] = NA;
    baMapData$longDD.j[x] = NA;
    baMapData$mean[x] = NA;
    baMapData$median[x] = NA
  }
}

migrateMap = ggplot() +
  geom_sf(data = florida, fill = "white", size = 0.25) +

# SHALLOW SOURCES
geom_curve(data = baMapData[6,], aes(x = longDD.j, y = latDD.j, xend = longDD.i-0.02, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = -1.5) +

geom_curve(data = baMapData[8,], aes(x = longDD.j, y = latDD.j, xend = longDD.i+0.01, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = -0.2) +

geom_curve(data = baMapData[9,], aes(x = longDD.j, y = latDD.j, xend = longDD.i+0.02, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = 0.2) +

geom_curve(data = baMapData[12,], aes(x = longDD.j, y = latDD.j, xend = longDD.i-0.03, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = 1.1) +  

geom_curve(data = baMapData[14,], aes(x = longDD.j, y = latDD.j, xend = longDD.i, yend = latDD.i-0.02, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = -4) + 

geom_curve(data = baMapData[16,], aes(x = longDD.j, y = latDD.j, xend = longDD.i+0.01, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = -0.1) + 

geom_curve(data = baMapData[17,], aes(x = longDD.j, y = latDD.j, xend = longDD.i+0.01, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = 0.2) + 

  
# MESO SOURCES  
geom_curve(data = baMapData[2,], aes(x = longDD.j, y = latDD.j, xend = longDD.i+0.01, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = 0.2) +     

geom_curve(data = baMapData[3,], aes(x = longDD.j, y = latDD.j, xend = longDD.i+0.01, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = -0.2) +       

geom_curve(data = baMapData[5,], aes(x = longDD.j, y = latDD.j, xend = longDD.i-0.01, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = -1) +    

geom_curve(data = baMapData[7,], aes(x = longDD.j, y = latDD.j, xend = longDD.i+0.01, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = 0.4) + 

geom_curve(data = baMapData[10,], aes(x = longDD.j, y = latDD.j, xend = longDD.i+0.01, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = -0.3) + 

geom_curve(data = baMapData[11,], aes(x = longDD.j, y = latDD.j, xend = longDD.i-0.01, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = 1.4) +  

geom_curve(data = baMapData[15,], aes(x = longDD.j, y = latDD.j, xend = longDD.i+0.02, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = 0.2) +  
  
geom_curve(data = baMapData[18,], aes(x = longDD.j, y = latDD.j, xend = longDD.i+0.01, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = -0.2) + 
  
geom_curve(data = baMapData[20,], aes(x = longDD.j, y = latDD.j, xend = longDD.i+0.01, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = 0.3) +    
  
geom_curve(data = baMapData[21,], aes(x = longDD.j, y = latDD.j, xend = longDD.i+0.01, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = -0.2) +

geom_curve(data = baMapData[24,], aes(x = longDD.j, y = latDD.j, xend = longDD.i-0.01, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = 0.2) +

geom_curve(data = baMapData[25,], aes(x = longDD.j, y = latDD.j, xend = longDD.i-0.01, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = -6) +

geom_curve(data = baMapData[27,], aes(x = longDD.j, y = latDD.j, xend = longDD.i+0.01, yend = latDD.i-0.01, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = -0.4) +

geom_curve(data = baMapData[28,], aes(x = longDD.j, y = latDD.j, xend = longDD.i-0.01, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = 0.2) +

geom_curve(data = baMapData[30,], aes(x = longDD.j, y = latDD.j, xend = longDD.i+0.02, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = 2) +

geom_curve(data = baMapData[31,], aes(x = longDD.j, y = latDD.j, xend = longDD.i-0.01, yend = latDD.i-0.01, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = 0.3) +
  
geom_curve(data = baMapData[23,], aes(x = longDD.j, y = latDD.j, xend = longDD.i+0.01, yend = latDD.i, color = site.j, linetype = depth.j, size = mean), alpha = 0.7, arrow = arrow(type = "open", length = unit(0.03, "npc")), curvature = -0.1) +

  scale_fill_manual(values = migratePal, name = "Source site") +
  scale_color_manual(values = migratePal, guide = NULL) +
  scale_shape_manual(values = c(21, 23), name = "Depth") +
  scale_size(range = c(0.5, 2), breaks = c(0.02,0.06,0.1,0.14,0.18,0.22,0.26),name = expression(paste("Migration (", italic("m"), ")", sep = "")), guide = guide_legend(ncol = 1, order = 5)) +
  geom_point(data = fknmsPopsMigrate, aes(x = longDD, y = latDD, fill = site, shape = depthZone), size = 3.5) +
  scale_linetype_manual(values = lines, name = "Source depth") +
  coord_sf(xlim = c(-83.1, -80.25), ylim = c(24.3, 25.3)) +
  scale_x_continuous(breaks = c(seq(-84, -80, by = .5))) +
  scale_y_continuous(breaks = c(seq(24, 26, by = .2))) +
  annotation_scale(location = "br") +
  annotation_north_arrow(location = "br", which_north = "true", style = north_arrow_minimal(), pad_x = unit(-0.25, "cm") , pad_y = unit(0.75, "cm")) +
  guides(fill = guide_legend(override.aes = list(shape = 22, color = NA, size = 4),ncol = 2, order = 1, reverse = TRUE), shape = guide_legend(override.aes = list(size = 3), order = 2), size = guide_legend(ncol = 4), order = 3) +
  theme_bw() +
  theme(panel.background = element_rect(fill = "aliceblue"),
        panel.border = element_rect(color = "black", size = 0.75, fill = NA),
        plot.background = element_blank(),
        axis.title = element_blank(),
        axis.ticks = element_line(color = "black"),
        axis.text = element_text(color = "black"),
        plot.title = element_blank(),
        legend.key.size = unit(15, "pt"),
        legend.spacing = unit(10, "pt"),
        legend.position = "bottom",
        legend.direction = "vertical",
        legend.box = "horizontal",
        legend.background = element_blank()
  )

migrateMap

ggsave("../figures/figure4.png", plot = migrateMap, width = 28, height = 12, units = "cm", dpi = 300)

ggsave("../figures/figure4.svg", plot = migrateMap, width = 28, height = 12, units = "cm", dpi = 300)

S. intersepta algal symbionts

---

Now let’s examine algal endosymbionts with the results of SymPortal analysis of Symbiodiniaceae ITS2 sequences.

ITS2 Data

How many raw reads?

rawItsReads = read.delim("../data/ITS2/sintItsReadCounts", header = FALSE)
colnames(rawItsReads) = c("sample", "reads")

rawItsReads$sample = gsub("_S.*", "", rawItsReads$sample)
rawItsReads = rawItsReads %>% group_by(sample) %>% summarise(reads = first(reads))

head(rawItsReads)

## # A tibble: 6 × 2
##   sample reads
##   <chr>  <int>
## 1 SFK001 34806
## 2 SFK002 38328
## 3 SFK003 18122
## 4 SFK004 52311
## 5 SFK005 30791
## 6 SFK006 40545

#total reads
sum(rawItsReads$reads)

## [1] 7385411

#average reads/sample
(sum(rawItsReads$reads)/nrow(rawItsReads))

## [1] 33723.34

its2Seqs = read.delim("../data/ITS2/148_20210301_DBV_20210401T112728.seqs.absolute.abund_CLEAN.txt", header = TRUE)
its2Profs = read.csv("../data/ITS2/148_20210301_DBV_20210401T112728.profiles.absolute.abund_CLEAN.csv", header = TRUE, check.names = FALSE) 

head(its2Seqs)

##   Sample Symbiodinium  A3 A3b A3at A3ax X43947_A X34778_A X495083_A X36534_A X34149_A
## 1 SFK115            0  12   0    0    0        0        0         0        0        0
## 2 SFK022            0   7   0    0    0        0        0         0        0        0
## 3 SFK025           28 242   7    7    0        6        0         0        7        0
## 4 SFK095            0  14   0    0    0        0        0         0        0        0
## 5 SFK170            0  26   0    0    0        0        0         0        0        0
## 6 SFK175            0   5   0    0    0        0        0         0        0        0
##   X50854_A A3av A3s A3q X33981_A X1402229_A A3au A3aw X50835_A X34175_A X33953_A A3r
## 1        0    0   0   0        0          0    0    0        0        0        0   0
## 2        0    0   0   0        0          0    0    0        0        0        0   0
## 3        0    0  17  23        5          0    0    0        0        0        0   6
## 4        0    0   0   0        0          0    0    0        0        0        0   0
## 5        0    0   0   0        0          0    0    0        0        0        0   0
## 6        0    0   0   0        0          0    0    0        0        0        0   0
##   X1402205_A X364481_A X363583_A A4 X1402230_A X50833_A X50842_A X363143_A X72388_A
## 1          0         0         0  0          0        0        0         0        0
## 2          0         0         0  0          0        0        0         0        0
## 3          0         0         0  0          0        0        0        10        0
## 4          0         0         0  0          0        0        0         0        0
## 5          0         0         0  0          0        0        0         0        0
## 6          0         0         0  7          0        0        0         0        0
##   X363606_A X797686_A X22386_A X529468_A X34696_A X363636_A X1402231_A X34151_A
## 1         0         0        0         0        0         0          0        0
## 2         0         0        0         0        0         0          0        0
## 3         0         0        0         0        0         0          0        0
## 4         0         0        0         0        0         0          0        0
## 5         0         0        0         0        0         0          0        0
## 6         0         0        0         0        0         0          0        0
##   X364459_A X363570_A X363645_A X363578_A X1402232_A X364267_A X363625_A X50845_A
## 1         0         0         0         0          0         0         0        0
## 2         0         0         0         0          0         0         0        0
## 3         0         0         0         0          0         0         0        0
## 4         0         0         0         0          0         0         0        0
## 5         0         0         0         0          0         0         0        0
## 6         0         0         0         0          0         0         0        0
##   X363142_A X1402267_A X22415_A X363639_A X905679_A X363598_A X363706_A X363685_A
## 1         0          0        0         0         0         0         0         0
## 2         0          0        0         0         0         0         0         0
## 3         0          0        0         0         0         0         0         0
## 4         0          0        0         0         0         0         0         0
## 5         0          0        0         0         0         0         0         0
## 6         0          0        0         0         0         0         0         0
##   X1402206_A X1402233_A X1402235_A X43753_A X500385_A X1402234_A A3d X1402202_A
## 1          0          0          0        0         0          0   0          0
## 2          0          0          0        0         0          0   0          0
## 3          0          0          0        0         0          0   0          0
## 4          0          0          0        0         0          0   0          0
## 5          0          0          0        0         0          0   0          0
## 6          0          0          0        0         0          0   0          0
##   X1402236_A X364620_A X363593_A X367833_A X22400_A X50850_A X22463_A X363687_A
## 1          0         0         0         0        0        0        0         0
## 2          0         0         0         0        0        0        0         0
## 3          0         0         0         0        0        0        0         0
## 4          0         0         0         0        0        0        0         0
## 5          0         0         0         0        0        0        0         0
## 6          0         0         0         0        0        0        0         0
##   X693524_A X37988_A X66961_A X22436_A X363590_A X22426_A X45527_A A6b X36953_A
## 1         0        0        0        0         0        0        0   0        0
## 2         0        0        0        0         0        0        0   0        0
## 3         0        0        0        0         0        0        0   0        0
## 4         0        0        0        0         0        0        0   0        0
## 5         0        0        0        0         0        0        0   0        0
## 6         0        0        0        0         0        0        0   0        0
##   X363563_A X37985_A X693526_A X22451_A X33927_A X1402203_A A4.3 X35200_A X22392_A
## 1         0        0         0        0        0          0    0        0        0
## 2         0        0         0        0        0          0    0        0        0
## 3         0        0         0        0        0          0    0        0        0
## 4         0        0         0        0        0          0    0        0        0
## 5         0        0         0        0        0          0    0        0        0
## 6         0        0         0        0        0          0    0        0        0
##   X65140_A X1402245_A X364567_A X364639_A X1402216_A X22464_A X22429_A X49905_A
## 1        0          0         0         0          0        0        0        0
## 2        0          0         0         0          0        0        0        0
## 3        0          0         0         0          0        0        0        0
## 4        0          0         0         0          0        0        0        0
## 5        0          0         0         0          0        0        0        0
## 6        0          0         0         0          0        0        0        0
##   X49571_A X29211_A X36825_A X364218_A X1402237_A X363617_A X73521_A X364172_A
## 1        0        0        0         0          0         0        0         0
## 2        0        0        0         0          0         0        0         0
## 3        0        0        0         0          0         0        0         0
## 4        0        0        0         0          0         0        0         0
## 5        0        0        0         0          0         0        0         0
## 6        0        0        0         0          0         0        0         0
##   X363654_A X1402274_A X49906_A X37990_A X50843_A X363674_A X363646_A X33878_A
## 1         0          0        0        0        0         0         0        0
## 2         0          0        0        0        0         0         0        0
## 3         0          0        0        0        0         0         0        0
## 4         0          0        0        0        0         0         0        0
## 5         0          0        0        0        0         0         0        0
## 6         0          0        0        0        0         0         0        0
##   X1402268_A X1402282_A X22444_A X373280_A X1402238_A X366219_A X69439_A Breviolum B5
## 1          0          0        0         0          0         0        0         0  0
## 2          0          0        0         0          0         0        0         0  0
## 3          0          0        0         0          0         0        0         0  0
## 4          0          0        0         0          0         0        0         0  0
## 5          0          0        0         0          0         0        0         0  0
## 6          0          0        0         0          0         0        0         0  0
##   B18c B18b X1402208_B X1402209_B X45548_B X1402210_B X43411_B X37534_B X1402211_B
## 1    0    0          0          0        0          0        0        0          0
## 2    0    0          0          0        0          0        0        0          0
## 3    0    0          0          0        0          0        0        0          0
## 4    0    0          0          0        0          0        0        0          0
## 5    0    0          0          0        0          0        0        0          0
## 6    0    0          0          0        0          0        0        0          0
##   X1402212_B X1402213_B X1160454_B X1402214_B X1402215_B X37591_B B5ai X1402254_B
## 1          0          0          0          0          0        0    0          0
## 2          0          0          0          0          0        0    0          0
## 3          0          0          0          0          0        0    0          0
## 4          0          0          0          0          0        0    0          0
## 5          0          0          0          0          0        0    0          0
## 6          0          0          0          0          0        0    0          0
##   X71511_B X1402256_B X71527_B X1402255_B X1402257_B X71517_B X71509_B X71508_B
## 1        0          0        0          0          0        0        0        0
## 2        0          0        0          0          0        0        0        0
## 3        0          0        0          0          0        0        0        0
## 4        0          0        0          0          0        0        0        0
## 5        0          0        0          0          0        0        0        0
## 6        0          0        0          0          0        0        0        0
##   X1402258_B X71518_B X71510_B X1402259_B X368876_B X1402260_B X50427_B X1402262_B
## 1          0        0        0          0         0          0        0          0
## 2          0        0        0          0         0          0        0          0
## 3          0        0        0          0         0          0        0          0
## 4          0        0        0          0         0          0        0          0
## 5          0        0        0          0         0          0        0          0
## 6          0        0        0          0         0          0        0          0
##   X71525_B X71523_B X71515_B X368004_B X1402261_B X71526_B X71524_B X1402266_B
## 1        0        0        0         0          0        0        0          0
## 2        0        0        0         0          0        0        0          0
## 3        0        0        0         0          0        0        0          0
## 4        0        0        0         0          0        0        0          0
## 5        0        0        0         0          0        0        0          0
## 6        0        0        0         0          0        0        0          0
##   X1402265_B X1402264_B X1402263_B X43545_B X1402252_B X900132_B X1390171_B X1402253_B
## 1          0          0          0        0          0         0          0          0
## 2          0          0          0        0          0         0          0          0
## 3          0          0          0        0          0         0          0          0
## 4          0          0          0        0          0         0          0          0
## 5          0          0          0        0          0         0          0          0
## 6          0          0          0        0          0         0          0          0
##   B1 X1402276_B X1402280_B X38112_B Cladocopium    C3 C1 C16 C3go C3.10 C42.2 C1dl C3gm
## 1  0          0          0        0         761 10037  0   0    0   123     0    0    0
## 2  0          0          0        0        1273 15896  0   0    0   260     0    0    0
## 3  0          0          0        0         661 11333  0   0    0   115     0    0    0
## 4  0          0          0        0        1019 13663  0   0    0   717     0    0    0
## 5  0          0          0        0        1077 12007  0   0    0   619     0    0    0
## 6  0          0          0        0        1975 21478  0   0    0   385     0    0  110
##   C3gl C3hb C3gr C16b X110271_C X334025_C C3gk C1dk X22330_C X11408_C X18596_C X21897_C
## 1    0    0    0    0         0         0    0    0       58        0        0       90
## 2    0    0    0    0       164       158    0    0      150        0        0      157
## 3    0    0    0    0        67        68    0    0       85        0        0       91
## 4    0    0    0    0         0         0    0    0        0        0        0      227
## 5    0    0    0    0       102       120    0    0       83        0        0       93
## 6    0    0    0    0       282       260    0    0      210        0        0      274
##   C6c C3gq C3gp X65808_C C3gn C15hx C3dw C1cy X1402187_C X20795_C C93.1 X65703_C
## 1   0    0    0       54    0     0    0    0        181        0     0      144
## 2   0    0    0        0    0     0    0    0        120        0     0        0
## 3   0    0    0        0    0     0    0    0          0        0     0       68
## 4   0    0    0       88    0     0    0    0        133        0   229       96
## 5   0    0    0      106    0     0    0    0        131        0    81      120
## 6   0    0    0      166    0     0    0    0        303        0     0        0
##   X385070_C C3ge X1402188_C X1372_C X3238_C X95094_C C3hc X24879_C X91373_C X3699_C
## 1         0    0        107       0       0        0    0        0        0       0
## 2         0    0         93       0     135        0    0      188        0     108
## 3         0    0         76       0       0        0    0      100        0       0
## 4         0    0        120     185       0        0    0        0        0       0
## 5         0    0         89      82       0        0    0        0        0       0
## 6         0    0        128       0       0        0    0      120        0       0
##   C3gt C3dz X20934_C C1af C3gs X25557_C X40208_C X470358_C X40209_C X1402193_C X2239_C
## 1    0    0        0    0    0        0        0         0        0          0       0
## 2    0    0        0    0    0        0        0         0        0          0       0
## 3    0    0        0    0    0        0        0         0        0          0       0
## 4    0    0        0    0    0      170        0         0        0          0       0
## 5    0    0        0    0    0        0        0         0        0          0       0
## 6    0    0      232    0    0        0        0         0        0          0       0
##   X1402195_C C16a X17495_C X17534_C X2097_C X40211_C X93722_C C1v X40207_C X40212_C
## 1          0    0        0        0       0        0        0   0        0        0
## 2          0    0        0        0       0        0        0   0        0        0
## 3         55    0        0        0       0        0        0   0        0        0
## 4          0    0        0        0       0        0        0   0        0        0
## 5          0    0        0        0       0        0        0   0        0        0
## 6          0    0        0        0       0        0        0   0        0        0
##   X1402196_C X1402197_C X9944_C X1402198_C X1402219_C X470998_C X54162_C X22574_C
## 1          0          0       0          0          0         0        0        0
## 2          0          0       0          0          0         0        0        0
## 3          0          0       0          0          0         0        0        0
## 4          0          0       0          0          0         0        0        0
## 5          0          0      94          0          0         0        0        0
## 6          0          0       0          0          0         0        0        0
##   X20921_C X33343_C X1402200_C X25492_C X1402218_C X3240_C X2037_C X85729_C X5371_C
## 1        0        0          0        0          0       0       0        0       0
## 2        0        0          0        0          0       0       0        0       0
## 3        0        0          0       51          0       0       0        0       0
## 4        0        0          0        0          0       0       0        0       0
## 5        0        0          0       57          0       0       0        0       0
## 6        0        0          0        0          0       0       0        0       0
##   X1402225_C X909389_C X1402207_C C3ag X2428_C X1402220_C X4062_C X103828_C X1402199_C
## 1          0         0          0    0       0          0       0       127          0
## 2          0         0          0    0       0          0       0         0          0
## 3          0         0          0    0       0          0       0         0          0
## 4          0         0          0    0       0          0       0         0          0
## 5          0         0          0    0       0          0       0         0          0
## 6          0         0          0    0       0          0       0         0          0
##   X1398518_C X90670_C X1402204_C X1402227_C X1402248_C C6b X1402247_C X1402194_C X870_C
## 1          0        0          0          0          0   0          0          0      0
## 2          0        0          0          0          0   0          0          0      0
## 3          0        0          0          0          0   0          0          0      0
## 4          0        0          0          0          0   0          0          0      0
## 5          0        0          0          0          0   0          0          0      0
## 6          0        0          0          0          0   0          0          0      0
##   X71029_C C3ga X91285_C X1402192_C X1402244_C C1bz X18746_C X1402228_C X694_C C3i
## 1        0    0        0          0          0    0        0          0      0   0
## 2        0    0        0          0          0    0        0          0      0   0
## 3        0    0        0          0          0    0        0          0      0   0
## 4        0    0        0          0          0    0        0          0      0   0
## 5        0    0        0          0          0    0        0          0      0   0
## 6        0    0        0          0          0    0        0          0      0   0
##   X1402250_C X1402243_C C21 X1402281_C C3ca X9108_C X11201_C X11191_C X7821_C
## 1          0          0   0          0    0       0        0        0       0
## 2          0          0   0          0    0       0        0        0       0
## 3          0          0   0          0    0       0        0        0       0
## 4          0          0   0          0    0       0        0        0       0
## 5          0          0   0          0    0       0        0        0       0
## 6          0          0   0          0    0       0        0        0       0
##   X1402273_C C3ck X1402240_C X1402249_C X99988_C X1356_C X69324_C X24193_C C3bb C40f
## 1          0    0          0          0        0       0        0        0    0    0
## 2          0    0          0          0        0       0        0        0    0    0
## 3          0    0          0          0        0       0        0        0    0    0
## 4          0    0          0          0        0       0        0        0    0    0
## 5          0    0          0          0        0       0        0        0    0    0
## 6          0    0          0          0        0       0        0        0    0    0
##   X1401572_C X47282_C X16815_C X5726_C X1402270_C X1402221_C C1ap X1402275_C X21673_C
## 1          0        0        0       0          0          0    0          0        0
## 2          0        0        0       0          0          0    0          0        0
## 3          0        0        0       0          0          0    0          0        0
## 4          0        0        0       0          0          0    0          0        0
## 5          0        0        0       0          0          0    0          0        0
## 6          0        0        0       0          0          0    0          0        0
##   X69758_C C1bt X1402269_C X2943_C C70 X1402271_C X42218_C X1402277_C X9807_C C1ai C3t
## 1        0    0          0       0   0          0        0          0       0    0   0
## 2        0    0          0       0   0          0        0          0       0    0   0
## 3        0    0          0       0   0          0        0          0       0    0   0
## 4        0    0          0       0   0          0        0          0       0    0   0
## 5        0    0          0       0   0          0        0          0       0    0   0
## 6        0    0          0       0   0          0        0          0       0    0   0
##   X54249_C X26258_C X1402278_C X1402272_C C1x X40218_C X21804_C X1402279_C C3de
## 1        0        0          0          0   0        0        0          0    0
## 2        0        0          0          0   0        0        0          0    0
## 3        0        0          0          0   0        0        0          0    0
## 4        0        0          0          0   0        0        0          0    0
## 5        0        0          0          0   0        0        0          0    0
## 6        0        0          0          0   0        0        0          0    0
##   X13929_C X23354_C X1402223_C X99010_C X983542_C X3366_C X1402226_C X1402222_C
## 1        0        0          0        0         0       0          0          0
## 2        0        0          0        0         0       0          0          0
## 3        0        0          0        0         0       0          0          0
## 4        0        0          0        0         0       0          0          0
## 5        0        0          0        0         0       0          0          0
## 6        0        0          0        0         0       0          0          0
##   X42529_C X2152_C X62532_C X4558_C X2427_C X1829_C C3fo X18793_C X11809_C X31248_C
## 1        0       0        0       0       0       0    0        0        0        0
## 2        0       0        0       0       0       0    0        0        0        0
## 3        0       0        0       0       0       0    0        0        0        0
## 4        0       0        0       0       0       0    0        0        0        0
## 5        0       0        0       0       0       0    0        0        0        0
## 6        0       0        0       0       0       0    0        0        0        0
##   X1402241_C X816_C X921460_C C3ao C3an C3cn X3241_C X103581_C X21093_C X1402224_C
## 1          0      0         0    0    0    0       0         0        0          0
## 2          0      0         0    0    0    0       0         0        0          0
## 3          0      0         0    0    0    0       0         0        0          0
## 4          0      0         0    0    0    0       0         0        0          0
## 5          0      0         0    0    0    0       0         0        0          0
## 6          0      0         0    0    0    0       0         0        0          0
##   X18813_C X1402201_C X22869_C X23865_C C6a C1j X1402239_C X3434_C X22178_C X17016_C
## 1        0          0        0        0   0   0          0       0        0        0
## 2        0          0        0        0   0   0          0       0        0        0
## 3        0          0        0        0   0   0          0       0        0        0
## 4        0          0        0        0   0   0          0       0        0        0
## 5        0          0        0        0   0   0          0       0        0        0
## 6        0          0        0        0   0   0          0       0        0        0
##   X1402242_C X42518_C X54160_C X873_C C50f X1402246_C X1390080_C X113247_C X99987_C
## 1          0        0        0      0    0          0          0         0        0
## 2          0        0        0      0    0          0          0         0        0
## 3          0        0        0      0    0          0          0         0        0
## 4          0        0        0      0    0          0          0         0        0
## 5          0        0        0      0    0          0          0         0        0
## 6          0        0        0      0    0          0          0         0        0
##   X3601_C X1866_C X1402251_C X2895_C X9153_C C3fn X866_C X864_C X18159_C X990_C
## 1       0       0          0       0       0    0      0      0        0      0
## 2       0       0          0       0       0    0      0      0        0      0
## 3       0       0          0       0       0    0      0      0        0      0
## 4       0       0          0       0       0    0      0      0        0      0
## 5       0       0          0       0       0    0      0      0        0      0
## 6       0       0          0       0       0    0      0      0        0      0
##   X1402189_C X21205_C C3fc X871_C X1402190_C X8117_C X55844_C X54218_C X51874_C
## 1          0        0    0      0          0       0        0        0        0
## 2          0        0    0      0          0       0        0        0        0
## 3          0        0    0      0          0       0        0        0        0
## 4          0        0    0      0          0       0        0        0        0
## 5          0        0    0      0          0       0        0        0        0
## 6          0        0    0      0          0       0        0        0        0
##   X874099_C X27927_C C65b X46049_C X37410_C X28411_C D1 G3l G3b
## 1         0        0    0        0        0        0  0   0   0
## 2         0        0    0        0        0        0  0   0   0
## 3         0        0    0        0        0        0  0   0   0
## 4         0        0    0        0        0        0  0   0   0
## 5         0        0    0        0        0        0  0   0   0
## 6         0        0    0        0        0        0  0   0   0

sum(its2Seqs[,c(2:ncol(its2Seqs))])

## [1] 3280586

sum(its2Profs[,c(2:ncol(its2Profs))])

## [1] 2728837

its2SeqsGen = its2Seqs %>% rowwise() %>%  summarise(sample = Sample, symbiodinium = sum(c_across(2:121)), breviolum = sum(c_across(122:176)), cladocopium = sum(c_across(177:405)), durusdinium = sum(c_across(406)), gerakladium = sum(c_across(406:407)))

round(sum(its2SeqsGen$symbiodinium)/sum(its2SeqsGen[,-1])*100, 2)

## [1] 19.67

round(sum(its2SeqsGen$breviolum)/sum(its2SeqsGen[,-1])*100, 2)

## [1] 0.29

round(sum(its2SeqsGen$cladocopium)/sum(its2SeqsGen[,-1])*100, 2)

## [1] 80.03

round(sum(its2SeqsGen$durusdinium)/sum(its2SeqsGen[,-1])*100, 4)

## [1] 0.0002

round(sum(its2SeqsGen$gerakladium)/sum(its2SeqsGen[,-1])*100, 4) 

## [1] 0.002

its2ProfsGen = its2Profs %>% rowwise() %>%  summarise(sample = Sample, symbiodinium = sum(c_across(2:7)), breviolum = sum(c_across(8:10)), cladocopium = sum(c_across(11:20)))

round(sum(its2ProfsGen$symbiodinium)/sum(its2ProfsGen[,-1])*100, 2)

## [1] 21.33

round(sum(its2ProfsGen$breviolum)/sum(its2ProfsGen[,-1])*100, 2)

## [1] 0.22

round(sum(its2ProfsGen$cladocopium)/sum(its2ProfsGen[,-1])*100, 2)

## [1] 78.45

Read in SymPortal outputs for ITS2 type profiles

stephanocoeniaMetaData = read.csv("../data/stephanocoeniaMetaData.csv", header = TRUE, check.names = FALSE)[-c(66, 68, 164, 166, 209, 211),] %>% dplyr::select(c(sample = tubeID, site, depthM, depthZone))

its2Profs = read.csv("../data/ITS2/148_20210301_DBV_20210401T112728.profiles.absolute.abund_CLEAN.csv", header = TRUE, check.names = FALSE) %>% rename(sample = Sample)

its2Profs = stephanocoeniaMetaData %>% right_join(its2Profs) %>% arrange(sample) 

## Joining with `by = join_by(sample)`

its2Profs$site = factor(its2Profs$site)
its2Profs$site = factor(its2Profs$site, levels(its2Profs$site)[c( 2, 3, 1, 4)])
its2Profs$depthZone = factor(its2Profs$depthZone)
its2Profs$depthZone = factor(its2Profs$depthZone, levels(its2Profs$depthZone)[c(2, 1)])

its2Profs = its2Profs %>% arrange(site, depthZone,   desc(`C3/C3.10`),  desc(`C1/C3-C42.2-C1dl-C3gl-C3gm-C3gk`), desc(`C3-C1-C3.10`), desc(`C3-C1dk-C15hx`), desc(`C3-C3go-C6c-C3gq-C3gp-C3gn-C3dw`), desc(`C16/C3-C16b`), desc(`C3-C3hb-C3ge-C3hc-C1dk`), desc(`C3-C3gr-C3gt-C3gs-C3.10`), desc(`C3/C1`),desc(`A3-A3b-A3at-A3ax`), desc(`A3-A3at-A3b-A3q-A3s`), desc(`A3-A3s-A3q`), desc(`A3`), desc(`A3-A3b-A3av-A3au-A3aw`),desc(`A4`), desc(`C3`), desc(`B18b`), desc(`B18c`), desc(`B5`))

sampleCounts = plyr::count(its2Profs, c('site','depthZone'))
meltedList = reshape2::melt(lapply(sampleCounts$freq,function(x){c(1:x)}))
its2Profs$barPlotOrder = meltedList$value
its2Profs = its2Profs[c(1,ncol(its2Profs),2:(ncol(its2Profs)-1))]

head(its2Profs)

ITS2 type profiles

Preparing ITS2 type profiles for plotting

its2ProfsPerc = its2Profs
its2ProfsPerc$sum = apply(its2ProfsPerc[, c(6:length(its2ProfsPerc[1,]))], 1, function(x) {
sum(x, na.rm = T)
})

its2ProfsPerc = cbind(its2ProfsPerc[, c(1:5)], (its2ProfsPerc[, c(6:(ncol(its2ProfsPerc)-1))]
/ its2ProfsPerc$sum))
head(its2ProfsPerc)

##   sample barPlotOrder         site depthM depthZone A3-A3b-A3at-A3ax
## 1 SFK068            1 Riley's Hump   27.4   Shallow                0
## 2 SFK091            2 Riley's Hump   26.2   Shallow                0
## 3 SFK073            3 Riley's Hump   26.2   Shallow                0
## 4 SFK084            4 Riley's Hump   26.2   Shallow                0
## 5 SFK083            5 Riley's Hump   26.2   Shallow                0
## 6 SFK082            6 Riley's Hump   26.5   Shallow                0
##   A3-A3at-A3b-A3q-A3s A3-A3s-A3q A3 A3-A3b-A3av-A3au-A3aw A4 B18b B18c B5 C3/C3.10
## 1                   0          0  0                     0  0    0    0  0        0
## 2                   0          0  0                     0  0    0    0  0        0
## 3                   0          0  0                     0  0    0    0  0        0
## 4                   0          0  0                     0  0    0    0  0        0
## 5                   0          0  0                     0  0    0    0  0        0
## 6                   0          0  0                     0  0    0    0  0        0
##   C1/C3-C42.2-C1dl-C3gl-C3gm-C3gk C3-C1-C3.10 C3-C1dk-C15hx
## 1                               1           0             0
## 2                               1           0             0
## 3                               1           0             0
## 4                               1           0             0
## 5                               1           0             0
## 6                               1           0             0
##   C3-C3go-C6c-C3gq-C3gp-C3gn-C3dw C16/C3-C16b C3-C3hb-C3ge-C3hc-C1dk
## 1                               0           0                      0
## 2                               0           0                      0
## 3                               0           0                      0
## 4                               0           0                      0
## 5                               0           0                      0
## 6                               0           0                      0
##   C3-C3gr-C3gt-C3gs-C3.10 C3/C1 C3
## 1                       0     0  0
## 2                       0     0  0
## 3                       0     0  0
## 4                       0     0  0
## 5                       0     0  0
## 6                       0     0  0

# check that all proportions add up to 1
apply(its2ProfsPerc[, c(6:(ncol(its2ProfsPerc)))], 1, function(x) {
sum(x, na.rm = T)
})

##   [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [42] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [83] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [124] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [165] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [206] 1 1 1 1 1 1 1 1 1 1 1 1 1 1

admixOrd = fkSintAdmix %>% dplyr::select(sample, ord) 
pcangsdITS = pcangsd %>% dplyr::select(sample, cluster)

its2ProfsPerc = its2ProfsPerc %>% left_join(admixOrd) %>% relocate(ord,.after = barPlotOrder) %>% left_join(pcangsdITS) %>% relocate(cluster, .after = depthZone)

## Joining with `by = join_by(sample)`
## Joining with `by = join_by(sample)`

Everything looks good and is ready to plot

gssProf = otuStack(its2ProfsPerc, count.columns = c(8:length(its2ProfsPerc[1, ])),
 condition.columns = c(1:7)) %>% filter(otu != "summ") %>% droplevels() # remove summ rows

levels(gssProf$otu)
levels(gssProf$depthZone)
levels(gssProf$site)

Consruct ITS2 type profile barplot

zooxAnno = data.frame(x1 = c(0.5, 0.5, 0.5, 0.5), x2 = c(30.5, 30.5, 30.5, 30.5),
                     y1 = -0.22, y2 = -0.22, site = c("Riley's Hump", "Tortugas Bank", "Lower Keys", "Upper Keys"))

zooxAnno$site = factor(zooxAnno$site)
zooxAnno$site = factor(zooxAnno$site, levels = levels(zooxAnno$site)[c(2, 3, 1, 4)])


gssProfPlot = gssProf %>% left_join(zooxAnno, by = "site")
gssProfPlot$ord = as.numeric(gssProfPlot$ord)
  
its2ProfsPlotA = ggplot(gssProfPlot, aes(x = ord, y = count, fill = otu)) +
  geom_bar(position = "stack", stat = "identity", color = "gray25", size = 0.25) + 
  scale_fill_manual(values = profPal, name = expression(paste(italic("ITS2"), " type profile"))) +
 
  geom_segment(data = gssProfPlot, aes(x = ord-0.5, xend = ord+0.5, color = cluster), y = -.07, yend = -.07, linewidth = 4) +
  scale_color_manual(values = kColPal, guide = "none") +
  ggnewscale::new_scale_color() +
  
  geom_segment(data = gssProfPlot %>% filter(depthZone == "Mesophotic"), aes(x = x1, xend = x2, y = y1, yend = y2, color = site), linewidth = 7) +
  scale_color_manual(values = rev(flPal)) +
  
   geom_text(data = (gssProfPlot %>% filter(depthZone == "Mesophotic", site %in% c("Riley's Hump", "Tortugas Bank"), sample %in% c("SFK001", "SFK100"), otu == "A4")), x = 15.5, y = -.205, aes(label = site), size = 4, color = "#FFFFFF") +
  geom_text(data = (gssProfPlot %>% filter(depthZone == "Mesophotic", site %in% c("Lower Keys", "Upper Keys"), sample %in% c("SFK101", "SFK201"), otu == "A4")), x = 15.5, y = -.205, aes(label = site), size = 4, color = "#000000") +
  labs(title = expression(italic("SymPortal")), fill = expression(paste(italic("ITS2"), " type profile"))) +
  
  guides(color = "none", fill = guide_legend(ncol = 3, reverse = FALSE)) +
  facet_grid(factor(depthZone) ~ site, scales = "free", switch = "both", space = "free") + # faceting plots by Depth and Site
  
  coord_cartesian(ylim = c(0, 1), xlim = c(0.5, 30.5), clip = "off") +
  scale_x_discrete(expand = c(0.005, 0.005)) +
  scale_y_continuous(expand = c(0.001, 0.001)) +
theme_bw()

its2ProfsPlot = its2ProfsPlotA +
theme(plot.title = element_text(),
  panel.grid = element_blank(),
  # panel.background = element_blank(),
  panel.background = element_rect(fill = "gray70"),
  panel.border = element_rect(fill = NA, color = "black", size = 0.75, linetype = "solid"),
  plot.background = element_blank(),
  legend.background = element_blank(),
  panel.spacing.x = grid:::unit(0.05, "lines"),
  panel.spacing.y = grid:::unit(0.78, "lines"),
  axis.text.x = element_blank(),
  axis.text.y = element_blank(),
  axis.ticks.x = element_blank(),
  axis.ticks.y = element_blank(),
  axis.title = element_blank(),
  strip.background.x = element_blank(),
  strip.background.y = element_blank(),
  strip.text = element_text(size = 12),
  strip.text.y.left = element_text(angle = 90),
  strip.text.x.bottom = element_text(vjust = .75, color = NA),
  legend.key.size = unit(0.75, "line"),
  legend.key = element_blank(),
  legend.title = element_text(size = 10, angle = 90),
  legend.text = element_text(size = 8),
  legend.position = "right")

# its2ProfsPlot

Structure plots

structurePlots = ((pcaPlot12S + theme(axis.title.y = element_text(margin = ggplot2::margin(r = -20, unit = "pt")))) | pcaPlot12L | pcaPlot23L)/((admixPlot + labs(title = expression(paste(italic("S. intersepta"))))) | (its2ProfsPlot + guides(color = "none", fill = guide_legend(ncol = 1, reverse = FALSE)) + labs(title = "Symbiodiniaceae", fill = expression(paste(italic("ITS2"), " type profile"))) + theme(legend.title = element_text(size = 8, angle = 0), plot.title = element_text(size = 10), legend.text = element_text(size = 6), strip.text.y.left = element_text(angle = 90, size = 10),strip.text.x = element_text(size = 8)))) +
plot_annotation(tag_levels = 'A') &
  theme(plot.tag = element_text(size = 16),
        plot.title = element_text(size = 10),
        plot.title.position =  "panel",
        legend.spacing = unit(-5, "pt"),
        legend.key = element_blank(),
        legend.background = element_blank())



ggsave("../figures/figure2.png", plot = structurePlots, height = 6.6, width = 12, units = "in", dpi = 300)

ggsave("../figures/figure2.svg", plot = structurePlots, height = 6.6, width = 12, units = "in", dpi = 300)

SNP vs ITS2 genera

Pulling genera of Symbiodiniaceae from SNPS and comparing to genera of ITS2 profiles from SymPortal

popData = read.csv("../data/stephanocoeniaMetaData.csv")[-c(66, 68, 164, 166, 209, 211),] %>% dplyr::select("sample" = tubeID, "site" = site, "depth" = depthZone)

zoox = read.delim("../data/snps/symbionts/zooxReads", header = FALSE, check.names = FALSE)

head(zoox)

##                               V1 V2
## 1 fk_S001.trim.zoox.zoox.bt2.bam NA
## 2                           chr1 77
## 3                           chr2 78
## 4                           chr3 87
## 5                           chr4 80
## 6                           chr5  2

# Reconstruct read mapping output into dataframe usable for analysis
zoox$V2[is.na(zoox$V2)] <- as.character(zoox$V1[is.na(zoox$V2)])
zoox$V1 = gsub(pattern = "fk_*", "chr", zoox$V1)
zoox$V2 = gsub(".trim.*", "", zoox$V2)
zoox = zoox %>% filter(zoox$V1 != "*")
zooxLst = split(zoox$V2, as.integer(gl(length(zoox$V2), 20, length(zoox$V2))))

zooxMaps = NULL

for(i in zooxLst){
  zooxMaps = rbind(zooxMaps, data.frame(t(i)))
}

# remove tech reps
zooxMaps = zooxMaps[-c(66, 68, 164, 166, 209, 211),]

# rename columns and samples to match other ITS2 dataframe
zooxMaps$X1 = gsub("fk_S", "SFK", zooxMaps$X1)
zooxMaps$X1 = gsub("\\.[1-3]", "", zooxMaps$X1)
colnames(zooxMaps) = c("sample",zoox$V1[c(2:20)])

# convert characters to numeric
str(zooxMaps)

## 'data.frame':    220 obs. of  20 variables:
##  $ sample: chr  "SFK001" "SFK002" "SFK003" "SFK004" ...
##  $ chr1  : chr  "77" "59" "37" "953" ...
##  $ chr2  : chr  "78" "82" "22" "1069" ...
##  $ chr3  : chr  "87" "79" "30" "1383" ...
##  $ chr4  : chr  "80" "118" "19" "1360" ...
##  $ chr5  : chr  "2" "2" "0" "18" ...
##  $ chr6  : chr  "24" "17" "40" "18" ...
##  $ chr7  : chr  "42" "67" "35" "13" ...
##  $ chr8  : chr  "63" "85" "57" "29" ...
##  $ chr9  : chr  "61" "58" "51" "30" ...
##  $ chr10 : chr  "3065" "3920" "3611" "2820" ...
##  $ chr11 : chr  "4388" "5206" "5077" "3605" ...
##  $ chr12 : chr  "4606" "5437" "5442" "3855" ...
##  $ chr13 : chr  "4294" "4919" "4995" "3485" ...
##  $ chr14 : chr  "3538" "4217" "4013" "3072" ...
##  $ chr15 : chr  "518" "566" "533" "437" ...
##  $ chr16 : chr  "29" "105" "13" "82" ...
##  $ chr17 : chr  "19" "59" "17" "28" ...
##  $ chr18 : chr  "13" "44" "4" "26" ...
##  $ chr19 : chr  "3" "6" "1" "5" ...

for(i in c(2:20)){
  zooxMaps[,i] = as.numeric(zooxMaps[,i])
  }

str(zooxMaps)

## 'data.frame':    220 obs. of  20 variables:
##  $ sample: chr  "SFK001" "SFK002" "SFK003" "SFK004" ...
##  $ chr1  : num  77 59 37 953 44 ...
##  $ chr2  : num  78 82 22 1069 68 ...
##  $ chr3  : num  87 79 30 1383 80 ...
##  $ chr4  : num  80 118 19 1360 53 ...
##  $ chr5  : num  2 2 0 18 4 105 0 0 0 0 ...
##  $ chr6  : num  24 17 40 18 9 34 19 26 43 20 ...
##  $ chr7  : num  42 67 35 13 22 35 21 57 71 50 ...
##  $ chr8  : num  63 85 57 29 32 33 40 60 80 86 ...
##  $ chr9  : num  61 58 51 30 39 32 30 84 72 43 ...
##  $ chr10 : num  3065 3920 3611 2820 1501 ...
##  $ chr11 : num  4388 5206 5077 3605 1807 ...
##  $ chr12 : num  4606 5437 5442 3855 2061 ...
##  $ chr13 : num  4294 4919 4995 3485 1736 ...
##  $ chr14 : num  3538 4217 4013 3072 1564 ...
##  $ chr15 : num  518 566 533 437 250 190 473 769 559 821 ...
##  $ chr16 : num  29 105 13 82 71 93 20 11 59 19 ...
##  $ chr17 : num  19 59 17 28 24 51 22 40 40 38 ...
##  $ chr18 : num  13 44 4 26 40 55 16 16 33 13 ...
##  $ chr19 : num  3 6 1 5 2 13 3 0 0 7 ...

# collapse fake chromosomes into representative genera
zooxMaps$Symbiodinium = rowSums(zooxMaps[2:6])
zooxMaps$Breviolum = rowSums(zooxMaps[7:10])
zooxMaps$Cladocopium = rowSums(zooxMaps[11:16])
zooxMaps$Durusdinium = rowSums(zooxMaps[17:20])

# keep genera totals and turn into proportions for barplot
zooxMaps = zooxMaps[,c(1, 21:24)]
zooxProp = zooxMaps
zooxProp$sum = apply(zooxProp[, c(2:length(zooxProp[1,]))], 1, function(x) {
sum(x, na.rm = T)
})
zooxProp = cbind(zooxProp$sample, (zooxProp[, c(2:(ncol(zooxProp)-1))]
/ zooxProp$sum))

colnames(zooxProp)[1] = "sample"

head(zooxProp)

##   sample Symbiodinium   Breviolum Cladocopium Durusdinium
## 1 SFK001  0.015438128 0.009053223   0.9724591 0.003049507
## 2 SFK002  0.013575022 0.009063323   0.9688174 0.008544279
## 3 SFK003  0.004500563 0.007625953   0.9864150 0.001458516
## 4 SFK004  0.214599785 0.004038047   0.7750359 0.006326274
## 5 SFK005  0.026469650 0.010842989   0.9481237 0.014563623
## 6 SFK006  0.718347383 0.005328456   0.2678941 0.008430094

# Check that all samples total to 1
apply(zooxProp[, c(2:(ncol(zooxProp)))], 1, function(x) {
sum(x, na.rm = T)
})

##   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20  21  22 
##   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1 
##  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44 
##   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1 
##  45  46  47  48  49  50  51  52  53  54  55  56  57  58  59  60  61  62  63  64  65  67 
##   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1 
##  69  70  71  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90 
##   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1 
##  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107 108 109 110 111 112 
##   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1 
## 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 
##   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1 
## 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 
##   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1 
## 157 158 159 160 161 162 163 165 167 168 169 170 171 172 173 174 175 176 177 178 179 180 
##   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1 
## 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 
##   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1 
## 203 204 205 206 207 208 210 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 
##   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1

# add sample metadata to proportions
snpSym = popData %>% left_join(zooxProp)

## Joining with `by = join_by(sample)`

Combining SNP and ITS2 data for comparison of Symbiodiniaceae genera This will allow us to plot individuals in the same order across methods

#sum profiles into genera
symGenera = its2Profs
symGenera$itsSymbiodinium = rowSums(symGenera[6:11])
symGenera$itsBreviolum = rowSums(symGenera[12:14])
symGenera$itsCladocopium = rowSums(symGenera[15:24])
symGenera$itsDurusdinium = 0  

symGenera = symGenera %>% dplyr::select(sample, barPlotOrder, itsSymbiodinium, itsBreviolum, itsCladocopium, itsDurusdinium) %>% left_join(admixOrd) %>% relocate(ord, .after = barPlotOrder)

## Joining with `by = join_by(sample)`

#convert to proportions
symGenera$sum = apply(symGenera[, c(4:length(symGenera[1,]))], 1, function(x) {
sum(x, na.rm = T)
})

symGeneraProp = cbind(symGenera$sample, symGenera[, c(4:(ncol(symGenera)-1))]
/ symGenera$sum)

colnames(symGeneraProp)[1] = "sample"

#Check that all samples total to 1
apply(symGeneraProp[,c(2:5)], 1, function(x) {
sum(x, na.rm = T)
})

##   [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [42] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [83] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [124] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [165] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [206] 1 1 1 1 1 1 1 1 1 1 1 1 1 1

#construct combined dataframe
symGenera = symGenera %>% dplyr::select(sample, ord) %>% left_join(snpSym) %>%  left_join(symGeneraProp) 

## Joining with `by = join_by(sample)`
## Joining with `by = join_by(sample)`

symGenera$depth = factor(symGenera$depth)
symGenera$depth = factor(symGenera$depth, levels = levels(symGenera$depth)[c(2, 1)])

symGenera$site = factor(symGenera$site)
symGenera$site = factor(symGenera$site, levels = levels(symGenera$site)[c(2, 3, 1, 4)])

#turn into melted dataframe with otustack() and remove "summ" rows
gssSym = otuStack(symGenera, count.columns = c(5:length(symGenera[1, ])),
 condition.columns = c(1:4)) %>% filter(otu != "summ") %>% droplevels()

#check that levels are correct/ordered
levels(gssSym$otu)

## [1] "Symbiodinium"    "Breviolum"       "Cladocopium"     "Durusdinium"    
## [5] "itsSymbiodinium" "itsBreviolum"    "itsCladocopium"  "itsDurusdinium"

levels(gssSym$depth)

## [1] "Shallow"    "Mesophotic"

levels(gssSym$site)

## [1] "Riley's Hump"  "Tortugas Bank" "Lower Keys"    "Upper Keys"

Creating Symbiodiniaceae genera relative proportion barplots

SNPs:

gssSymPlot = gssSym %>% left_join(zooxAnno, by = "site") %>% left_join(pcangsdITS)

## Joining with `by = join_by(sample)`

gssSymPlot$ord = as.numeric(gssSymPlot$ord)

zooxSNPA = ggplot(data = subset(gssSymPlot, subset = otu %in% c("Symbiodinium", "Breviolum", "Cladocopium", "Durusdinium" )), aes(x = ord, y = count, fill = otu, order = ord)) +
  geom_point(aes(x=1, y=0.5, fill = otu), shape = 22, size = 0) +
  geom_bar(stat = "identity", position = "stack", colour = "grey25", width = 1, size = 0.2, show.legend = FALSE) +
  xlab("Population") +
  
  scale_fill_manual(values = colPalZoox, name = "Symbiodiniaceae genus") +
   geom_segment(data = (subset(gssSymPlot, subset = otu %in% c("Symbiodinium", "Breviolum", "Cladocopium", "Durusdinium" )) %>% filter(depth == "Mesophotic")), aes(x = x1, xend = x2, y = y1, yend = y2, color = site), size = 7) +
  scale_color_manual(values = rev(flPal), guide = "none") +
  ggnewscale::new_scale_color() +
  
  geom_segment(data = (subset(gssSymPlot, subset = otu %in% c("Symbiodinium", "Breviolum", "Cladocopium", "Durusdinium" ))), aes(x = ord-0.5, xend = ord+0.5, color = cluster), y = -.07, yend = -.07, linewidth = 4) +
  scale_color_manual(values = kColPal, name = "Lineage") +
  
  coord_cartesian(ylim = c(0, 1), xlim = c(0.5, 30.5), clip = "off") +

  scale_x_discrete(expand = c(0.005, 0.005)) +
  scale_y_continuous(expand = c(0.001, 0.001)) +
  facet_grid(factor(depth) ~ site, drop = TRUE, scales = "free", switch = "both", space = "free") +

    geom_text(data = subset(gssSymPlot, subset = otu == "Symbiodinium") %>% filter(sample %in% c("SFK095", "SFK015")), x = 15.5, y = -.205, aes(label = site), size = 4.4, color = "#FFFFFF") +
  geom_text(data = subset(gssSymPlot, subset = otu == "Symbiodinium") %>% filter(sample %in% c("SFK195", "SFK156")), x = 15.5, y = -.205, aes(label = site), size = 4.4, color = "#000000") +

  ggtitle("2bRAD") +
  guides(color = guide_legend(override.aes = list(size = 4), ncol = 1), fill = "none")  +
  theme_bw()

zooxSNP = zooxSNPA + theme(plot.title = element_text(),
  panel.grid = element_blank(),
  # panel.background = element_blank(),
  panel.background = element_rect(fill = "gray70"),
  panel.border = element_rect(fill = NA, color = "black", size = 0.75, linetype = "solid"),
  panel.spacing.x = grid:::unit(0.05, "lines"),
  panel.spacing.y = grid:::unit(0.82, "lines"),
  axis.text.x = element_blank(),
  axis.text.y = element_blank(),
  axis.ticks.x = element_blank(),
  axis.ticks.y = element_blank(),
  axis.title = element_blank(),
  strip.background.x = element_blank(),
  strip.background.y = element_blank(),
  strip.text = element_text(size = 12),
  strip.text.y.left = element_text(size = 12, angle = 90),
  strip.text.x.bottom = element_text(vjust = -.1, color = NA),
  legend.title = element_text(size = 10),
  legend.text = element_text(size = 8),
  legend.key.size = unit(0.75, "line"),
  legend.key = element_blank(),
  legend.position = "bottom",
  legend.direction = "vertical",
  legend.box = "horizontal")

# zooxSNP

ITS2:

zooxITSA = ggplot(data = subset(gssSymPlot, subset = !(otu %in% c("Symbiodinium", "Breviolum", "Cladocopium", "Durusdinium" ))), aes(x = ord, y = count, fill = otu, order = ord)) +
  geom_point(aes(x=1, y=0.5, fill = otu), shape = 22, size = 0) +
  geom_bar(stat = "identity", position = "stack", colour = "grey25", width = 1, size = 0.2, show.legend = FALSE) +
  xlab("Population") +
  scale_fill_manual(values = colPalZoox, name = "Symbiodiniaceae genus", labels = c("Symbiodinium", "Breviolum", "Cladocopium", "Durusdinium")) +

    geom_segment(data = (subset(gssSymPlot, subset = !otu %in% c("Symbiodinium", "Breviolum", "Cladocopium", "Durusdinium" ))), aes(x = ord-0.5, xend = ord+0.5, color = cluster), y = -.07, yend = -.07, linewidth = 4) +
  scale_color_manual(values = kColPal, guide = "none") +
  ggnewscale::new_scale_color() +
    
  geom_segment(data = (subset(gssSymPlot, subset = !otu %in% c("Symbiodinium", "Breviolum", "Cladocopium", "Durusdinium" )) %>% filter(depth == "Mesophotic")), aes(x = x1, xend = x2, y = y1, yend = y2, color = site), size = 7) +
  scale_color_manual(values = rev(flPal)) +
  
  coord_cartesian(ylim = c(0, 1), xlim = c(0.5, 30.5), clip = "off") +

  scale_x_discrete(expand = c(0.005, 0.005)) +
  scale_y_continuous(expand = c(0.001, 0.001)) +
  facet_grid(factor(depth) ~ site, drop = TRUE, scales = "free", switch = "both", space = "free") +

  geom_text(data = subset(gssSymPlot, subset = otu == "itsSymbiodinium") %>% filter(sample %in% c("SFK095", "SFK015")), x = 15.5, y = -.205, aes(label = site), size = 4.4, color = "#FFFFFF") +
  geom_text(data = subset(gssSymPlot, subset = otu == "itsSymbiodinium") %>% filter(sample %in% c("SFK195", "SFK156")), x = 15.5, y = -.205, aes(label = site), size = 4.4, color = "#000000") +
  
  guides(fill = guide_legend(override.aes = list(size = 4), ncol = 1), color = "none")  +
  labs(title = expression(italic("ITS2"))) +
  theme_bw()

zooxITS = zooxITSA + theme(plot.title = element_text(),
  panel.grid = element_blank(),
  # panel.background = element_blank(),
  panel.background = element_rect(fill = "gray70"),
  panel.border = element_rect(fill = NA, color = "black", size = 0.75, linetype = "solid"),
  panel.spacing.x = grid:::unit(0.05, "lines"),
  panel.spacing.y = grid:::unit(0.82, "lines"),
  axis.text.x = element_blank(),
  axis.text.y = element_blank(),
  axis.ticks.x = element_blank(),
  axis.ticks.y = element_blank(),
  axis.title = element_blank(),
  strip.background.x = element_blank(),
  strip.background.y = element_blank(),
  strip.text = element_text(size = 12),
  strip.text.y.left = element_text(size = 12, angle = 90),
  strip.text.x.bottom = element_text(vjust = -.1, color = NA),
  legend.title = element_text(size = 10),
  legend.text = element_text(size = 8, face = "italic"),
  legend.key = element_blank(),
  legend.key.size = unit(0.5, "line"),
  legend.position = "bottom",
  legend.direction = "vertical",
  legend.box = "horizontal")
 
# zooxITS

Symbiodiniaceae barplots

# its2Plots = (its2ProfsPlot + theme(legend.position = "right",  legend.title = element_text(angle = 0)) & guides(color = "none", fill = guide_legend(ncol = 1, reverse = FALSE)))/zooxITS/zooxSNP +

its2Plots = zooxITS/zooxSNP +
  plot_annotation(tag_levels = "A") &
  theme(plot.tag = element_text(size = 16), 
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        legend.position = "right",
        legend.title = element_text(color = "black", size = 10),
        legend.text = element_text(color = "black", size = 8))

ggsave("../figures/figure5.png", plot = its2Plots, height = 18, width = 20, units = "cm", dpi = 300)

ggsave("../figures/figure5.svg", plot = its2Plots, height = 18, width = 20, units = "cm", dpi = 300)

Procrustes analysis

Comparing the two outputs with procrustes analysis

popData = read.csv("../data/stephanocoeniaMetaData.csv")[-c(66, 68, 164, 166, 209, 211),] %>% dplyr::select("sample" = tubeID, "site" = site, "depth" = depthZone)

symSnpDf = zooxMaps %>% left_join(popData) %>% relocate(c(site, depth), .after = sample) %>% filter(!row_number()==131) %>% mutate(dataSet = "SNPs") %>% relocate(dataSet, .after = sample)

## Joining with `by = join_by(sample)`

rownames(symSnpDf) = symSnpDf$sample

symITS2 = its2Profs
symITS2$Symbiodinium = rowSums(symITS2[6:11])
symITS2$Breviolum = rowSums(symITS2[12:14])
symITS2$Cladocopium = rowSums(symITS2[15:24])
symITS2$Durusdinium = 0  

symITS2Df = symITS2 %>% dplyr::select(sample, Symbiodinium, Breviolum, Cladocopium, Durusdinium) %>% left_join(popData) %>% relocate(c(site, depth), .after = sample) %>% arrange(sample) %>% mutate(dataSet = "ITS2") %>% relocate(dataSet, .after = sample)

## Joining with `by = join_by(sample)`

rownames(symITS2Df) = symITS2Df$sample

#create distance matrices
symSnpdist = vegdist(decostand(symSnpDf[c(5:ncol(symSnpDf))], method = "normalize"), method = "bray")
    
symITS2dist = vegdist(decostand(symITS2Df[c(5:ncol(symITS2Df))], method = "normalize"), method = "bray")

snpPcOA = cmdscale(symSnpdist, eig = TRUE, x.ret = TRUE)
its2PcOA = cmdscale(symITS2dist, eig = TRUE, x.ret = TRUE)

#procrustes analysis
its2GeneraProcrustes = protest(Y = its2PcOA, X = snpPcOA, choices = c(1, 2), 
permutations = 9999, symmetric = FALSE)

its2GeneraProcrustes

## 
## Call:
## protest(X = snpPcOA, Y = its2PcOA, permutations = 9999, choices = c(1,      2), symmetric = FALSE) 
## 
## Procrustes Sum of Squares (m12 squared):        0.1561 
## Correlation in a symmetric Procrustes rotation: 0.9187 
## Significance:  0.0001 
## 
## Permutation: free
## Number of permutations: 9999

plot(its2GeneraProcrustes, kind = 1)

plot(its2GeneraProcrustes, kind = 2)

popData = read.csv("../data/stephanocoeniaMetaData.csv")[-c(66, 68, 164, 166, 209, 211),] %>% dplyr::select("sample" = tubeID, "site" = site, "depth" = depthZone)

symGenProcPlot = procrustes(X = snpPcOA, Y = its2PcOA, choices = c(1, 2), symmetric = FALSE)

symGenProcPlotData = cbind(symGenProcPlot$X, symGenProcPlot$Yrot) %>% as.data.frame()
rownames(symGenProcPlotData) = rownames(symGenProcPlot$X)
colnames(symGenProcPlotData) = c("x1", "y1", "x2", "y2")
symGenProcPlotData$sample = row.names(symGenProcPlotData)
symGenProcPlotData$sample = gsub(pattern = "\\.2", "", symGenProcPlotData$sample)
symGenProcPlotData = symGenProcPlotData %>% left_join(popData) %>% relocate(sample, .before = x1)

## Joining with `by = join_by(sample)`

symGenProcPlotData$depth = factor(symGenProcPlotData$depth)
symGenProcPlotData$depth = factor(symGenProcPlotData$depth, levels(symGenProcPlotData$depth)[c(2,1)])
symGenProcPlotData$site = factor(symGenProcPlotData$site)
symGenProcPlotData$site = factor(symGenProcPlotData$site, levels(symGenProcPlotData$site)[c(4, 1, 3, 2)])
  
head(symGenProcPlotData)

##   sample          x1            y1         x2            y2          site      depth
## 1 SFK001 -0.12000280  0.0086944453 -0.1479462 -0.0009289205 Tortugas Bank Mesophotic
## 2 SFK002 -0.11999040  0.0070304820 -0.1310021  0.0069938380 Tortugas Bank Mesophotic
## 3 SFK003 -0.13226702  0.0005281082 -0.1479462 -0.0009289205 Tortugas Bank Mesophotic
## 4 SFK004  0.06906114 -0.0593680913  0.1380109 -0.0344824197 Tortugas Bank Mesophotic
## 5 SFK005 -0.10683940  0.0156151506 -0.1479462 -0.0009289205 Tortugas Bank Mesophotic
## 6 SFK006  0.57063159 -0.0790486422  0.4430768 -0.0616296036 Tortugas Bank Mesophotic

#Calculate the angle of rotation, and then the slope of the rotated axis
theta = acos(symGenProcPlot$rotation[1,1]) 
slope = tan(theta)

#Calculate the y-intercepts for rotated axes
symGenProcInt = symGenProcPlot$translation[2] - (slope*symGenProcPlot$translation[1])
symGenProcInt2 = symGenProcPlot$translation[2] - (-1/slope*symGenProcPlot$translation[1])

sintSymGenProcPlotA = ggplot() +
  geom_hline(yintercept = 0, color = "gray90", linetype = 1) +
  geom_vline(xintercept = 0, color = "gray90", linetype = 1) +
  geom_abline(intercept = symGenProcInt, slope = slope, color = "gray75", linetype = 2) +
  geom_abline(intercept = symGenProcInt2, slope = -(1/slope), color = "gray75", linetype = 2) +
    geom_segment(data = symGenProcPlotData, aes(x = x2*(1-symGenProcPlot$scale), y = y2*(1-symGenProcPlot$scale), xend = x1*(1-symGenProcPlot$scale), yend = y1*(1-symGenProcPlot$scale), color = site), alpha = 0.5) +
  geom_point(data = symGenProcPlotData, aes(x = x2*(1-symGenProcPlot$scale), y = y2*(1-symGenProcPlot$scale), fill = site, shape = depth), alpha = 0.5)+
  geom_point(data = symGenProcPlotData, aes(x = x1*(1-symGenProcPlot$scale), y = y1*(1-symGenProcPlot$scale), fill = site, shape = depth), size = 2) +
  annotate(geom = "label", x = 0.172, y = 0.172, label = "           ", size = 10) +
  annotate(geom = "text", x = 0.172, y = 0.1825, label = "Procrustes analysis:", size = 3) +
  annotate(geom = "text", x = 0.172, y = 0.165, label = "italic(t[0]) == 0.919 *','~italic(p) < 0.0001", parse = TRUE, size = 3) +
  scale_color_manual(values = flPal) +
  scale_fill_manual(values = flPal, name = "Site") +
  scale_shape_manual(values = c(21, 23), name = "Depth zone") +
  guides(color = "none", fill = guide_legend(override.aes = list(shape = 15, color = flPal, size = 3), ncol =2, order = 1), shape = guide_legend(ncol = 1)) +
  theme_bw()

sintSymGenProcPlot = sintSymGenProcPlotA +
  theme(panel.grid = element_blank(),
        panel.border = element_rect(color = "black", size = 0.75, fill = NA),
        axis.title = element_blank(),
        axis.ticks = element_line(color = "black"),
        axis.text = element_text(color = "black", size = 8),
        legend.position = "bottom",
        legend.direction = "vertical",
        legend.box = "horizontal",
        legend.key = element_blank(),
        legend.background = element_blank(),
        legend.title = element_text(color = "black", size = 8),
        legend.text = element_text(color = "black", size = 8)
        )

sintSymGenProcPlot

S. intersepta genetic distance vs Symbiodiniaceae B-C distance

its2DistA = its2Profs %>% arrange(sample)
its2Dist = its2DistA[c(6:ncol(its2Profs))] %>% decostand("normalize") %>% vegdist(method = "bray")
its2PCoA = cmdscale(its2Dist, eig = TRUE, x.ret = TRUE)

sintIBS = read.table("../data/snps/sintFiltSnps.ibsMat")[-131,-131] %>% as.matrix() %>% as.dist(diag = FALSE)
sintPCoA = cmdscale(sintIBS, eig = TRUE, x.ret = TRUE)

set.seed(981) 
its2IBSProcrustes = protest(X = sintPCoA, Y = its2PCoA, permutations = 9999)
its2IBSProcrustes

## 
## Call:
## protest(X = sintPCoA, Y = its2PCoA, permutations = 9999) 
## 
## Procrustes Sum of Squares (m12 squared):        0.9519 
## Correlation in a symmetric Procrustes rotation: 0.2194 
## Significance:  0.0001 
## 
## Permutation: free
## Number of permutations: 9999

plot(its2IBSProcrustes)

plot(its2IBSProcrustes, kind = 2)

admixpops = read.csv("../data/stephanocoeniaMetaData.csv")[-c(66, 68, 164, 166, 209, 211),] %>% dplyr::select("sample" = tubeID, "pop" = site, "depth" = depthZone)
admixpops$popdepth = as.factor(paste(admixpops$pop, admixpops$depth, sep = " "))

clumpp4 = read.table("../data/snps/k/ClumppK4.output", header = FALSE)
clumpp4$V1 = admixpops$sample

sintAdmix = admixpops[-131,] %>% left_join(clumpp4[,c(1, 6:9)], by = c("sample" = "V1"))

admixDist = sintAdmix[c(5:ncol(sintAdmix))] %>% vegdist(method = "euclidean")

admixPCoA = cmdscale(admixDist, eig = TRUE, x.ret = TRUE)

set.seed(981) 
its2AdmixProcrustes = protest(X = admixPCoA, Y = its2PCoA, permutations = 9999)

its2AdmixProcrustes

## 
## Call:
## protest(X = admixPCoA, Y = its2PCoA, permutations = 9999) 
## 
## Procrustes Sum of Squares (m12 squared):        0.9359 
## Correlation in a symmetric Procrustes rotation: 0.2531 
## Significance:  0.0001 
## 
## Permutation: free
## Number of permutations: 9999

plot(its2AdmixProcrustes, kind = 1)

plot(its2AdmixProcrustes, kind = 2)

Cheking dispersion with PERMDISP

Using vegan::betadisper() to look at multivariate homogeneity of dispersion (PERMDISP) between sites and depths. This is using Bray-Curtis dissimilarity.

alpha = with(its2Profs, tapply(specnumber(its2Profs[, c(6:ncol(its2Profs))]), site, mean))
alpha

##  Riley's Hump Tortugas Bank    Lower Keys    Upper Keys 
##      1.177778      1.290909      1.237288      1.200000

gamma = with(its2Profs, specnumber(its2Profs[, c(6:ncol(its2Profs))], site))
gamma

##  Riley's Hump Tortugas Bank    Lower Keys    Upper Keys 
##            13            11            11             9

gamma/alpha

##  Riley's Hump Tortugas Bank    Lower Keys    Upper Keys 
##     11.037736      8.521127      8.890411      7.500000

set.seed(694) 
its2dispS = betadisper(vegdist(decostand(its2Profs[, c(6:ncol(its2Profs))], "normalize")), its2Profs$site)

anova(its2dispS)

## Analysis of Variance Table
## 
## Response: Distances
##            Df Sum Sq  Mean Sq F value Pr(>F)
## Groups      3 0.0892 0.029731  0.7719 0.5109
## Residuals 215 8.2815 0.038518

No significant effect of Site on betadiversity.

alpha = with(its2Profs, tapply(specnumber(its2Profs[, c(6:ncol(its2Profs))]), depthZone, mean))
alpha

##    Shallow Mesophotic 
##   1.310924   1.130000

gamma = with(its2Profs, specnumber(its2Profs[, c(6:ncol(its2Profs))], depthZone))
gamma

##    Shallow Mesophotic 
##         18         11

gamma/alpha

##    Shallow Mesophotic 
##  13.730769   9.734513

set.seed(694)
its2dispD = betadisper(vegdist(decostand(its2Profs[, c(6:ncol(its2Profs))], "normalize")), its2Profs$depthZone)

its2dispD

## 
##  Homogeneity of multivariate dispersions
## 
## Call: betadisper(d = vegdist(decostand(its2Profs[, c(6:ncol(its2Profs))],
## "normalize")), group = its2Profs$depthZone)
## 
## No. of Positive Eigenvalues: 28
## No. of Negative Eigenvalues: 27
## 
## Average distance to median:
##    Shallow Mesophotic 
##     0.6525     0.5770 
## 
## Eigenvalues for PCoA axes:
## (Showing 8 of 55 eigenvalues)
##  PCoA1  PCoA2  PCoA3  PCoA4  PCoA5  PCoA6  PCoA7  PCoA8 
## 26.118 18.773 13.961  9.281  6.898  6.102  3.901  3.190

anova(its2dispD)

## Analysis of Variance Table
## 
## Response: Distances
##            Df Sum Sq  Mean Sq F value   Pr(>F)   
## Groups      1 0.3104 0.310449  10.815 0.001175 **
## Residuals 217 6.2290 0.028705                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Depth does significantly affect beta diversity.

its2ProfsLineage = its2Profs %>% left_join(dplyr::select(pcangsd,sample,cluster)) %>% filter(cluster != "Admixed") %>% droplevels()

## Joining with `by = join_by(sample)`

alpha = with(its2ProfsLineage, tapply(specnumber(its2ProfsLineage[, c(6:24)]), cluster, mean))
alpha

##     Blue     Teal    Green   Yellow 
## 1.167939 1.230769 1.354839 1.466667

gamma = with(its2ProfsLineage, specnumber(its2ProfsLineage[, c(6:24)], cluster))
gamma

##   Blue   Teal  Green Yellow 
##     15     13     12      6

gamma/alpha

##      Blue      Teal     Green    Yellow 
## 12.843137 10.562500  8.857143  4.090909

set.seed(694)
its2dispL = betadisper(vegdist(decostand(its2ProfsLineage[, c(6:(ncol(its2ProfsLineage)-1))], "normalize")), its2ProfsLineage$cluster)

anova(its2dispL)

## Analysis of Variance Table
## 
## Response: Distances
##            Df Sum Sq  Mean Sq F value Pr(>F)
## Groups      3 0.2486 0.082868   2.021  0.112
## Residuals 212 8.6928 0.041004

No significant effect of Lineage on betadiversity.

Running PERMANOVA in R

Now let’s see how different Symbiodiniaceae are from each other with PERMANOVA. We will utilize the vegan::adonis() function. We will use Bray-Curtis similarity for our distance matrix and run a total 0f 9,999 permutations, and test the effects of Site, Depth, and the interaction between Site and Depth.

its2ProfsLineage = its2Profs %>% left_join(dplyr::select(pcangsd,sample,cluster)) %>% filter(cluster != "Admixed") %>% droplevels()

## Joining with `by = join_by(sample)`

set.seed(694)
its2Adonis = adonis2(decostand(its2ProfsLineage[, c(6:((ncol(its2ProfsLineage)-1)))], "normalize") ~ cluster*depthZone*site, data = its2ProfsLineage, permutations = 9999, method = "bray")

its2Adonis 

## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 9999
## 
## adonis2(formula = decostand(its2ProfsLineage[, c(6:((ncol(its2ProfsLineage) - 1)))], "normalize") ~ cluster * depthZone * site, data = its2ProfsLineage, permutations = 9999, method = "bray")
##                         Df SumOfSqs      R2       F Pr(>F)    
## cluster                  3    8.969 0.09906 10.4777 0.0001 ***
## depthZone                1    2.237 0.02471  7.8398 0.0001 ***
## site                     3    7.841 0.08661  9.1605 0.0001 ***
## cluster:depthZone        2    0.750 0.00829  1.3150 0.1809    
## cluster:site             8    7.948 0.08779  3.4818 0.0001 ***
## depthZone:site           3    6.631 0.07325  7.7473 0.0001 ***
## cluster:depthZone:site   3    1.376 0.01520  1.6079 0.0400 *  
## Residual               192   54.782 0.60510                   
## Total                  215   90.535 1.00000                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

set.seed(694)
its2PWAdonis = pairwise.adonis(decostand(its2Profs[,c(6:(ncol(its2Profs)))], "normalize"), factors = its2Profs$site, sim.method = "bray", p.adjust.m = "fdr", perm = 9999)

its2PWAdonis

##                           pairs Df SumsOfSqs   F.Model         R2 p.value p.adjusted
## 1 Riley's Hump vs Tortugas Bank  1 3.0054485  7.606522 0.07202701  0.0001    0.00015
## 2    Riley's Hump vs Lower Keys  1 4.5770266 11.695628 0.10286788  0.0001    0.00015
## 3    Riley's Hump vs Upper Keys  1 3.4373577  9.258215 0.08247250  0.0001    0.00015
## 4   Tortugas Bank vs Lower Keys  1 0.9811441  2.446466 0.02137651  0.0334    0.03340
## 5   Tortugas Bank vs Upper Keys  1 1.6940679  4.427003 0.03770004  0.0014    0.00168
## 6      Lower Keys vs Upper Keys  1 3.4239083  9.014882 0.07153823  0.0001    0.00015
##   sig
## 1  **
## 2  **
## 3  **
## 4   .
## 5   *
## 6  **

its2profsRxD = paste(its2Profs$site, its2Profs$depthZone, sep = " ")

set.seed(694)
its2PWAdonis2 = pairwise.adonis(decostand(its2Profs[, c(6:ncol(its2Profs))], "normalize"), factors = its2profsRxD, sim.method = "bray", p.adjust.m = "fdr", perm = 9999)

its2PWAdonis2

##                                                  pairs Df SumsOfSqs   F.Model
## 1      Riley's Hump Shallow vs Riley's Hump Mesophotic  1 0.6958548  1.851042
## 2        Riley's Hump Shallow vs Tortugas Bank Shallow  1 2.8030339  7.429995
## 3     Riley's Hump Shallow vs Tortugas Bank Mesophotic  1 3.0884300  8.640125
## 4           Riley's Hump Shallow vs Lower Keys Shallow  1 5.0368955 15.488866
## 5        Riley's Hump Shallow vs Lower Keys Mesophotic  1 4.6194798 13.467087
## 6           Riley's Hump Shallow vs Upper Keys Shallow  1 4.7577989 13.584574
## 7        Riley's Hump Shallow vs Upper Keys Mesophotic  1 2.2928012  6.845341
## 8     Riley's Hump Mesophotic vs Tortugas Bank Shallow  1 1.7078494  4.482170
## 9  Riley's Hump Mesophotic vs Tortugas Bank Mesophotic  1 1.1310176  3.195846
## 10       Riley's Hump Mesophotic vs Lower Keys Shallow  1 3.2155336 10.357235
## 11    Riley's Hump Mesophotic vs Lower Keys Mesophotic  1 1.8698060  5.584033
## 12       Riley's Hump Mesophotic vs Upper Keys Shallow  1 2.4146387  7.007461
## 13    Riley's Hump Mesophotic vs Upper Keys Mesophotic  1 0.4347921  1.342139
## 14   Tortugas Bank Shallow vs Tortugas Bank Mesophotic  1 2.6960612  7.456037
## 15         Tortugas Bank Shallow vs Lower Keys Shallow  1 1.9880105  6.041735
## 16      Tortugas Bank Shallow vs Lower Keys Mesophotic  1 3.3741943  9.729362
## 17         Tortugas Bank Shallow vs Upper Keys Shallow  1 3.5069150  9.905960
## 18      Tortugas Bank Shallow vs Upper Keys Mesophotic  1 3.2100015  9.476618
## 19      Tortugas Bank Mesophotic vs Lower Keys Shallow  1 5.1052811 16.781413
## 20   Tortugas Bank Mesophotic vs Lower Keys Mesophotic  1 0.3865517  1.192600
## 21      Tortugas Bank Mesophotic vs Upper Keys Shallow  1 1.2421221  3.741093
## 22   Tortugas Bank Mesophotic vs Upper Keys Mesophotic  1 1.2155556  3.855401
## 23         Lower Keys Shallow vs Lower Keys Mesophotic  1 6.2867594 21.368532
## 24            Lower Keys Shallow vs Upper Keys Shallow  1 6.0655965 20.114834
## 25         Lower Keys Shallow vs Upper Keys Mesophotic  1 6.1026797 21.338933
## 26         Lower Keys Mesophotic vs Upper Keys Shallow  1 1.5730255  4.919063
## 27      Lower Keys Mesophotic vs Upper Keys Mesophotic  1 2.7859051  9.149432
## 28         Upper Keys Shallow vs Upper Keys Mesophotic  1 3.3019234 10.593110
##            R2 p.value   p.adjusted sig
## 1  0.04127087  0.1058 0.1139384615    
## 2  0.11355640  0.0001 0.0002000000  **
## 3  0.14017047  0.0001 0.0002000000  **
## 4  0.21367234  0.0001 0.0002000000  **
## 5  0.18843760  0.0001 0.0002000000  **
## 6  0.18976958  0.0001 0.0002000000  **
## 7  0.10556412  0.0003 0.0004941176  **
## 8  0.09439691  0.0008 0.0011200000   *
## 9  0.07757691  0.0243 0.0272160000   .
## 10 0.19781860  0.0001 0.0002000000  **
## 11 0.11493555  0.0025 0.0033333333   *
## 12 0.14012831  0.0003 0.0004941176  **
## 13 0.03026780  0.2359 0.2446370370    
## 14 0.12332989  0.0002 0.0003733333  **
## 15 0.09583706  0.0008 0.0011200000   *
## 16 0.14365058  0.0001 0.0002000000  **
## 17 0.14587762  0.0001 0.0002000000  **
## 18 0.14044299  0.0001 0.0002000000  **
## 19 0.24398180  0.0001 0.0002000000  **
## 20 0.02200670  0.2878 0.2878000000    
## 21 0.06593269  0.0121 0.0147304348   .
## 22 0.06781063  0.0221 0.0257833333   .
## 23 0.27266725  0.0001 0.0002000000  **
## 24 0.26084260  0.0001 0.0002000000  **
## 25 0.27239244  0.0001 0.0002000000  **
## 26 0.07818081  0.0047 0.0059818182   *
## 27 0.13625479  0.0004 0.0006222222  **
## 28 0.15443402  0.0001 0.0002000000  **

its2PWAdonis2 %>% filter(p.adjusted > 0.05)

##                                               pairs Df SumsOfSqs  F.Model         R2
## 1   Riley's Hump Shallow vs Riley's Hump Mesophotic  1 0.6958548 1.851042 0.04127087
## 2  Riley's Hump Mesophotic vs Upper Keys Mesophotic  1 0.4347921 1.342139 0.03026780
## 3 Tortugas Bank Mesophotic vs Lower Keys Mesophotic  1 0.3865517 1.192600 0.02200670
##   p.value p.adjusted sig
## 1  0.1058  0.1139385    
## 2  0.2359  0.2446370    
## 3  0.2878  0.2878000

its2PWAdonis2Tab = its2PWAdonis2 %>% mutate(pairs = pairs, F.Model = round(F.Model, 3), R2 = round(R2,3), p.adjusted =  round(p.adjusted, 4)) %>%dplyr::select(-p.value, -sig, -SumsOfSqs, -Df) %>%
  flextable() %>%
  flextable::compose(part = "header", j = "pairs", value = as_paragraph("Comparison")) %>%
  flextable::compose(part = "header", j = "F.Model", value = as_paragraph(as_i("Pseudo-F"))) %>%
  flextable::compose(part = "header", j = "R2", value = as_paragraph(as_i("R2"))) %>%
  flextable::compose(part = "header", j = "p.adjusted", value = as_paragraph("p-value")) %>%
  flextable::font(fontname = "Times New Roman", part = "all") %>%
  flextable::fontsize(size = 10, part = "all") %>%
  flextable::bold(part = "header") %>%
  flextable::align(align = "left", part = "all") %>%
  flextable::autofit()

table4 = read_docx()
table4 = body_add_flextable(table4, value = its2PWAdonis2Tab)
print(table4, target = "../tables/table4.docx")


set.seed(694)
its2PWAdonis3 = pairwise.adonis(decostand(its2ProfsLineage[, c(6:(ncol(its2ProfsLineage)-1))], "normalize"), factors = its2ProfsLineage$cluster, sim.method = "bray", p.adjust.m = "fdr", perm = 9999)

its2PWAdonis3

##             pairs Df SumsOfSqs   F.Model         R2 p.value p.adjusted sig
## 1    Blue vs Teal  1  1.069739  2.736448 0.01602732  0.0266    0.02660   .
## 2  Blue vs Yellow  1  3.863104 10.345806 0.06703004  0.0001    0.00012  **
## 3   Blue vs Green  1  4.895495 12.811659 0.07413654  0.0001    0.00012  **
## 4  Teal vs Yellow  1  1.972402  5.020780 0.08805176  0.0001    0.00012  **
## 5   Teal vs Green  1  2.596461  6.351794 0.08542893  0.0001    0.00012  **
## 6 Yellow vs Green  1  2.391874  6.622737 0.13082535  0.0001    0.00012  **

save.image("fknmsSint.RData")