Elbow Fish Analyses
Alexander Ilich
April 05, 2021
Study Area
The study area is marked in red, and is know as “The Elbow.” It is about 145 km west-northwest of Tampa Bay, Florida. The area is a popular offshore fishing area that contains contains hard bottom ridges and is hypothesized to be a paleoshoreline shaped by wave action approximately 12,000 years ago.
The three lines shown represent day-time video transects completed using a towed video camera system that flies about 2-4 meters above the seafloor. Along these transects, fish were annotated at the frame-level, and habitat was annotated every 15 seconds.
Figure S2.1
Read in and reformat data
Habitat data
Habitat was annotated every 15 seconds of video
hab<- read_csv("Data/derived/CBASS/hab.csv")
hab<- hab %>% select(-c(CBASSLat, CBASSLon, Frame_num, Substrate))
hab<- hab %>% filter(Transect!="T1")
hab<- hab %>% mutate(habitat=ifelse(test = Relief=="None", yes = "Sand", no = Relief))
hab<- hab %>% select(-Relief)
print(hab)Fish Counts
Fish were annotated on using C-Vision’s video annotation software at the frame level. Each row is an individual fish, and we can see exactly what frame that fish was seen.
fish_annotations<- read_csv("Data/derived/CBASS/fish_annotations.csv")
print(fish_annotations)To turn these annotations into counts, each individual fish was matched to the closest habitat observation in time, and the number of annotations for each taxa were then summed.
fish_counts<- bind_rows(mutate(match_cvision_fish2hab(merged_cvision_csv = select(filter(fish_annotations, Transect=="T3"), -Transect), video_num = filter(hab, Transect=="T3")$Vid, video_sec = filter(hab, Transect=="T3")$Sec, fps = 12, vid_length = 1, include_vid_sec = TRUE, include_framenum = FALSE), Transect="T3"),
mutate(match_cvision_fish2hab(merged_cvision_csv = select(filter(fish_annotations, Transect=="T5"), -Transect), video_num = filter(hab, Transect=="T5")$Vid, video_sec = filter(hab, Transect=="T5")$Sec, fps = 12, vid_length = 1, include_vid_sec = TRUE, include_framenum = FALSE), Transect="T5"),
mutate(match_cvision_fish2hab(merged_cvision_csv = select(filter(fish_annotations, Transect=="T6"), -Transect), video_num = filter(hab, Transect=="T6")$Vid, video_sec = filter(hab, Transect=="T6")$Sec, fps = 12, vid_length = 1, include_vid_sec = TRUE, include_framenum = FALSE), Transect="T6")) %>% select(Transect, everything())
fish_counts[is.na(fish_counts)]<- 0
fish_counts<- fish_counts %>% select(Transect, Video, Seconds, everything())
names(fish_counts)[2:3]<- c("Vid", "Sec")
#Merge taxa that are difficult to differentiate consistently
fish_counts<- fish_counts %>% mutate(AMBERJACK_SPP= rowSums(across(starts_with("amberjack_", ignore.case = FALSE)))) %>% select(-starts_with("amberjack_", ignore.case = FALSE))
fish_counts<- fish_counts %>% mutate(BUTTERFLYFISH_SPP= rowSums(across(starts_with("butterflyfish_", ignore.case = FALSE)))) %>% select(-starts_with("butterflyfish_",ignore.case = FALSE))
fish_counts<- fish_counts %>% mutate(PORGY_SPP= rowSums(across(starts_with("porgy_", ignore.case = FALSE)))) %>% select(-starts_with("porgy_",ignore.case = FALSE))
fish_counts<- fish_counts %>% mutate(TRIGGERFISH_SPP= rowSums(across(starts_with("triggerfish_",ignore.case = FALSE)))) %>% select(-starts_with("triggerfish_",ignore.case = FALSE))
fish_counts<- fish_counts %>% select(-seaturtle_spp) #NOt a fish
names(fish_counts)[4:ncol(fish_counts)]<- tolower(names(fish_counts)[4:ncol(fish_counts)])
fish_counts<- fish_counts %>% select(Transect, Vid, Sec, sort_species(names(fish_counts)[4:ncol(fish_counts)]))
fish_counts<- fish_counts %>% left_join(select(hab, timestamp, Transect, Vid, Sec), by=c("Transect", "Vid", "Sec"))
fish_counts<- fish_counts %>% select(Transect, Vid, Sec, timestamp, everything())print(fish_counts)Raw Auxillary Data
altitude<- bind_rows<- bind_rows(mutate(read_tsv("Data/raw/CBASS/sensors/T3_D1/altimeter_readings.tsv"), Transect="T3"),
mutate(read_tsv("Data/raw/CBASS/sensors/T5_D14/altimeter_readings.tsv"), Transect="T5"),
mutate(read_tsv("Data/raw/CBASS/sensors/T6_D13/altimeter_readings.tsv"), Transect="T6"))
altitude<- altitude %>% mutate(timestamp_exact= timestamp+dmicroseconds(u_second))
compass<- bind_rows<- bind_rows(mutate(read_tsv("Data/raw/CBASS/sensors/T3_D1/compass_readings.tsv"), Transect="T3"),
mutate(read_tsv("Data/raw/CBASS/sensors/T5_D14/compass_readings.tsv"), Transect="T5"),
mutate(read_tsv("Data/raw/CBASS/sensors/T6_D13/compass_readings.tsv"), Transect="T6"))
compass<- compass %>% mutate(timestamp_exact= timestamp+dmicroseconds(u_second))
Speed<- get_HypackSpeed(read_hypack(filename = list.files("Data/raw/Hypack/", pattern = "\\.RAW$", full.names = TRUE)))
Speed<- Speed %>% mutate(Speed_mps=Speed_kph*(1000/(60*60)))
names(Speed)[1]<- "timestamp_exact"Community Analysis
Calculate the area viewed for each 15 second bin
Equations from McCollough (1893) and Grasty (2014)
Horizontal Angular Field of View
Calculate the angular field of view for the camera
H_AFOV_air = 2 * atan(sensor_width/(2 * focal_length))
Use Snell’s Law to adjust for refraction between air and seawater
H_AFOV_sea = 2 * asin(sin((H_AFOV_air/2)) * (n_air/n_sea))
*n is the index of refraction for the specified medium (n_air~1; n_sea ~1.33)
Distance to Center of Frame
Calculate the camera angle relative to the seafloor
cam_angle_to_ground = mounted_camera_angle - pitch
Adjust the raw altitude measurements to the true altitude by accounting for the pitch of the system
adjusted_altitude = cos(pitch) * altitude
Calculate the distance in meters between the camera lens and the seafloor at the center of an image
centerline_distance = adjusted_altitude/sin(cam_angle_to_ground)
Width of frame
Calculate the width of the image in meters at the center of an image
Width = 2 * centerline_distance * tan(H_AFOV_water/2)
Area Covered
Calculate distance covered in the 15 second observation window
Distance = Speed * 15s
*The median rather than the mean of speed over the 15s observation window is used so that faulty or poor readings do not adversely affect the calculation
Calculate the area viewed by the camera system
Area = Width * Distance
hab<- hab %>% mutate(Area=NA_real_)
H_AFOV_air<- calc_AFOV(d = 13.3, f = 5, medium = "air", type = "horiz", pix=c(1920, 1200))
for (i in 1:nrow(hab)) {
curr_transect<- hab$Transect[i]
mid_time<- hab$timestamp[i]
st_time<- mid_time-dseconds(7.5)
end_time<- mid_time+dseconds(7.5)
curr_alt<- median(altitude$altitude[altitude$timestamp_exact>=st_time & altitude$timestamp_exact<=end_time])
curr_pitch<- median(compass$pitch[compass$timestamp_exact>=st_time & compass$timestamp_exact<=end_time])
curr_speed<- median(Speed$Speed_mps[Speed$timestamp_exact>=st_time & Speed$timestamp_exact<=end_time])
curr_width<- calc_width(alt = curr_alt, pitch = curr_pitch, cam_angle = 32.8, H_AFOV_air = H_AFOV_air)
curr_dist<- curr_speed * 15
hab$Area[i]<- curr_width*curr_dist
}Merge sequential observations of the same habitat class
Consecutive observations of habitat are collapsed into a single sample for community analysis and counts are summed among the merged observations. This is done to reduce the amount of rows with all zero counts which cannot be handled using semi-metric distances such as Bray-Curtis, and allows us to keep most information from sandy habitats while having more balance in sample sizes across habitat groups.
hab<- hab %>% mutate(samp_num=NA_real_)
hab$samp_num[1]<-1
samp_num<-1
for (i in 2:nrow(hab)) {
curr_hab_obs<- hab$habitat[i]
prev_hab_obs<- hab$habitat[i-1]
curr_obs_transect<- hab$Transect[i]
prev_obs_transect<- hab$Transect[i-1]
if((curr_hab_obs!=prev_hab_obs)|(curr_obs_transect!=prev_obs_transect)|is.na(hab$Area[i])){
samp_num<- samp_num+1
}
hab$samp_num[i]<-samp_num
}
fish_hab<- fish_counts %>% left_join(hab, by=c("Vid", "Sec", "timestamp", "Transect"))
fish_hab<- fish_hab %>% select(Transect,Vid, Sec, timestamp, samp_num, habitat, Area, everything())
fish_hab_collapsed<- fish_hab %>% group_by(samp_num) %>%
summarize(habitat=unique(habitat), Vid_min = first(Vid)+first(Sec)/60, Vid_max= last(Vid)+last(Sec)/60, across(Area:smnoid_smnoid, .fns = sum), .groups="drop")
print(fish_hab_collapsed)Transform Data
To account for unequal lengths of the samples, counts are divided by the area duration. Additionally to down-weight over abundant taxa, densities are square root transformed.
dens_hab_collaped<- fish_hab_collapsed %>% mutate(across(amberjack_spp:smnoid_smnoid)/Area)
sq_dens_hab_collaped<- dens_hab_collaped %>% mutate(across(amberjack_spp:smnoid_smnoid, .fns=sqrt))
sq_dens_hab_collaped<- sq_dens_hab_collaped %>% filter(!is.na(Area) & habitat!="No_Vis")
sq_dens_hab_collaped<- sq_dens_hab_collaped %>% select(-c(smnoid_smnoid, lgnoid_lgnoid))
sq_dens<- sq_dens_hab_collaped %>% select(-c(samp_num, habitat, Vid_min, Vid_max, Area))
comm_hab<- sq_dens_hab_collaped %>% select(habitat)
idx<- rowSums(sq_dens)>0 #Bray Curtis distance cannot calculate distance in rows with all zeros
sq_dens<- sq_dens[idx,]
comm_hab<- comm_hab[idx,]
comm_hab$habitat<- factor(comm_hab$habitat, levels= c("Sand", "Low_Relief", "Moderate_Relief", "High_Relief"))PERMANOVA
Check meet assumption of homogenous dispersion
set.seed(5)
permdisp_res<- permutest(betadisper(vegdist(sq_dens, methd="bray"), group= comm_hab$habitat, type = "median"), permutations = 9999)p = 0.1862
p > .05 so we meet the assumtion of homogeneity of multivariate dispersion among groups and can therefore procedure with the PERMANOVA
Conduct global test
set.seed(6)
PERMANOVA_res<- adonis(sq_dens~habitat, data=comm_hab, permutations=999, method = "bray")
print(PERMANOVA_res)
Call:
adonis(formula = sq_dens ~ habitat, data = comm_hab, permutations = 999, method = "bray")
Permutation: free
Number of permutations: 999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
habitat 3 4.762 1.58737 4.4454 0.09052 0.001 ***
Residuals 134 47.849 0.35708 0.90948
Total 137 52.611 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1Since p < .05 in the PERMANOVA, we conclude that there are sigificant differences in the fish communities among the different habitats
Conduct Pair-wise Test
To see which habitats have fish communities that differ from another, we follow up the global test with pairwise PERMANOVA tests
set.seed(7)
PW_comp<- adonis_PW(formula = sq_dens~habitat, data=comm_hab, permutations = 9999, method = "bray")Table of pairwise comparisons
p_holm represents a p-value adjusted forf multiple comparisons using a Holm’s sequential Bonferroni correction. The adjusted p value is less than .05 for all comparisons except for between moderate and high relief rock, indicating that moderate and high relief rock did not have significantly different fish communities from one another, but all other habitats did.
Canonical Analysis of Principal Coordinates
Figure S2.2
CAP_result<- CAPdiscrim(as.data.frame(sq_dens)~habitat, data=as.data.frame(comm_hab), dist="bray", m=0, add=FALSE)Overall classification success (m=10) : 73.9130434782609 percent
Sand (n=41) correct: 63.4146341463415 percent
Low_Relief (n=80) correct: 88.75 percent
Moderate_Relief (n=10) correct: 40 percent
High_Relief (n=7) correct: 14.2857142857143 percentCAP_Plot<- plot_CAP(CAP_result = CAP_result, Y = sq_dens, Group = comm_hab$habitat, n_spec = 15, color = c("black", "blue", "orange", "red"), legend_title = "Habitat")
plot(CAP_Plot)
Fish Abundance Estimates
Habitat Specific Densities
fish_hab2<- fish_hab %>% filter(!is.na(Area) & habitat!="No_Vis")
fish_hab2<- fish_hab2 %>% mutate(habitat=ifelse(test = habitat %in% c("Moderate_Relief", "High_Relief"), yes = "Mod/High_Relief", no = habitat))
fish_hab2<- fish_hab2 %>% select(-smnoid_smnoid) %>% mutate(All_Fish= rowSums(across(amberjack_spp:lgnoid_lgnoid))) %>% select(-lgnoid_lgnoid) #Calculate total fish include large no id's then remove large no id's
hab_types<- unique(fish_hab2$habitat)
for (i in 1:length(hab_types)) {
curr_fish_hab<- fish_hab2 %>% filter(habitat==hab_types[i])
set.seed(i)
curr_avg_dens<- avg_density(counts =select(curr_fish_hab, amberjack_spp:All_Fish), area = curr_fish_hab$Area, iter=1000, conf = .95)
curr_avg_dens<- curr_avg_dens %>% mutate(Type= c("lower_bound", "mean", "upper_bound"))
curr_avg_dens<- curr_avg_dens %>% pivot_longer(cols = -Type, names_to="Taxa", values_to="Density") #Tidy
curr_avg_dens<- curr_avg_dens %>% pivot_wider(names_from = Type, values_from= Density)
curr_avg_dens<- curr_avg_dens %>% mutate(habitat=hab_types[i])
curr_avg_dens<- curr_avg_dens %>% select(Taxa, habitat, lower_bound, mean, upper_bound)
if(i==1){
avg_dens<- curr_avg_dens}else{
avg_dens<- avg_dens %>% bind_rows(curr_avg_dens)
}
}
avg_dens$habitat<- factor(avg_dens$habitat, levels = c("Sand", "Low_Relief", "Mod/High_Relief"))
avg_dens<- avg_dens %>% arrange(Taxa, habitat)Fish densities were estimated by habitat type for 36 species or species groups, as well as for all fish combined. For most taxa (33 of the 36 species/species groups), average fish density was highest over rocky habitats, and only 3 species had their highest densities over sand. Of the 33 taxa that had highest densities over a rocky habitat, 17 had had their highest densities over moderate-high relief rock and 16 had their highest densities over low relief rock.
Table of mean fish densities per Taxa in numbers of fish per kilometer squared with the lower and upper bounds of the 95% bootstrap confidence intervals
Plot of selected Taxa from table
Figure S2.3
Habitat Map
hab_map<- raster("Products/Mapping/Elbow_Supervised_HabRelief.tif")
hab_map<- reclassify(hab_map, matrix(data=c(21, 1, 11, 2, 12, 3, 13, 3), ncol = 2, byrow = TRUE)) #Merge Moderate and High Relief
hab_map<- as.factor(hab_map)
levels(hab_map)[[1]]$habitat<- c("Sand", "LR", "MR/HR")Figure S2.4
Scale Densities up to Abundance
Multiply densities of taxa for each habitat by the corresponding area of the habitat to get abundance
abd<- avg_dens %>% left_join(hab_area, by="habitat") %>%
mutate(across(c(lower_bound, mean, upper_bound))*Area_m2) %>%
dplyr::select(Taxa, habitat, lower_bound, mean, upper_bound)
abd<- bind_rows((abd %>% group_by(Taxa) %>% summarize(habitat="Total", across(where(is.numeric), .fns=sum), .groups="drop")), abd)
abd$habitat<- factor(abd$habitat, levels = c("Total", "Sand", "Low_Relief", "Mod/High_Relief"))
abd<- abd %>% arrange(Taxa, habitat)Table of abundance estimates
There are estimated to be a total of approximately 109616 fish in total within the study area. Of these, 38893 (35%) are on sand, 25299 (23%) are on low relief rock, and 45424 (41%)` are on moderate to high relief rock.
Plot of estimated abundance for select species
Figure S2.5
---
title: "Elbow Fish Analyses"
author: "Alexander Ilich"
date: "`r format(Sys.time(), '%B %d, %Y')`"
output:
  html_notebook:
editor_options: 
  chunk_output_type: inline
---

```{r setup, include=FALSE}
knitr::opts_knit$set(root.dir = normalizePath(".."))
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE) #Default chunk options

installed_packages<- installed.packages()
needed_packages<- c("rgdal", "sp", "raster", "stars", "tmap", "devtools", "RColorBrewer",
                   "vegan", "BiodiversityR", "ggplot2", "ggrepel", "lubridate", "tidyverse", "Rcmdr")

packages_to_install<- needed_packages[!(needed_packages %in% installed_packages)]
if(length(packages_to_install)>0){
  if("Rcmdr" %in% packages_to_install){
    install.packages("Rcmdr", dependencies = TRUE)
    packages_to_install<- packages_to_install[packages_to_install!="Rcmdr"]
    }
  install.packages(packages_to_install)
  }
if(!("mytools" %in% installed_packages)){
  devtools::install_github("ailich/mytools@v0.31") 
  } 
library(rgdal) #Spatial
library(sp) #Spatial
library(raster) #Spatial
library(stars) #Spatial 
library(tmap) #Spatial plotting
library(RColorBrewer) #Color palette
library(vegan) #Stats
library(BiodiversityR) #Stats
library(ggplot2) #Plotting
library(ggrepel) #Plotting
library(lubridate) #For timestamps
library(tidyverse) #Data wrangling/tidying
library(mytools) #My functions
options(stringsAsFactors = FALSE)
Sys.setenv(TZ='UTC') #Set time to display in UTC
options(digits.secs= 6) #Time digits displayed and written to 6 places
```

```{r warning=TRUE, include=FALSE}
#mytools and GLCMTextures are packages I created. mytools is assorted functions I have made over time and GLCMTextures calculates texture metrics
if(packageVersion("mytools")!="0.3"){
  warning("mytools is a different version than the one I used to run this script and may produce different results")
}
```

```{r eval=FALSE, include=FALSE}
#Note to load the environment as it would be at the end of the script without running it, run this line of code
load("Data/R_environment/Elbow_R_Fish_Environment.RData")
```

```{r Define Functions, include=FALSE}
adonis_PW<- function(formula, data, permutations, method){
  grp_colname<- labels(terms(formula))
  grps<- unique(data[[grp_colname]])
  pw_comps<- as.data.frame(t(combn(as.character(grps), m = 2)))
  Y<- get_all_vars(formula, data = data) %>% select(-all_of(grp_colname)) 
  output<- pw_comps
  output<- output %>% mutate(F_ratio=NA_real_)
  output<- output %>% mutate(p=NA_real_)
  for (i in 1:nrow(pw_comps)) {
    idx<- data[[grp_colname]] %in% c(pw_comps$V1[i], pw_comps$V2[i])
    data2<- data[idx, ,drop=FALSE]
    Y2<- Y[idx,]
    res<- adonis(formula=as.formula(paste0("Y2~", grp_colname)), data=data2, permutations = permutations, method = method)
    output$F_ratio[i]<- res[["aov.tab"]][["F.Model"]][[1]]
    output$p[i]<-  res[["aov.tab"]][["Pr(>F)"]][[1]]
  }
  output<- output %>% mutate(p_holm=p.adjust(p, method = "holm"))
  
  return(output)
}

scaled_length <- function(ax1, ax2, w1, w2){
  return(sqrt((ax1*w1)^2 + (ax2*w2)^2))
}

gg_color_hue <- function(n) {
  hues = seq(15, 375, length = n + 1)
  hcl(h = hues, l = 65, c = 100)[1:n]
} #https://stackoverflow.com/questions/8197559/emulate-ggplot2-default-color-palette


plot_CAP<- function(CAP_result, Y, Group, color=gg_color_hue(length(unique(Group))), n_spec=ncol(Y), legend_title="Group"){
  CAP_df<- as.data.frame(scores(CAP_result, choices = 1:2, display = "sites"))
  CAP_df$Group<- Group
  CAP_vect_df<- as.data.frame(add.spec.scores(CAP_result, comm = Y, method = "cor.scores")$cproj[,1:2]) #Correlation vectors
  CAP_vect_df$Species<- rownames(CAP_vect_df)
  Canonical_eigenvals<- CAP_result$lda.other$svd^2 #Numerical Ecology with R, 2018, pg 267
  Can_Var<- 100*(Canonical_eigenvals/sum(Canonical_eigenvals)) #Canonical Variation
  CAP_vect_df<- CAP_vect_df %>% 
    mutate(SL= scaled_length(ax1 = LD1, ax2 = LD2, w1 = all_of(Can_Var[1]), w2 = all_of(Can_Var[2])))
  CAP_vect_df<- CAP_vect_df %>% arrange(desc(SL))
  scale_factor<- max(abs(c(CAP_df$LD1, CAP_df$LD2)))
  CAP_df$LD1<- CAP_df$LD1/scale_factor
  CAP_df$LD2<- CAP_df$LD2/scale_factor #Scale values from -1 to 1
  CAP_Centroids<- CAP_df %>% group_by(Group) %>% summarize(LD1=mean(LD1), LD2=mean(LD2), .groups="drop")
  
  CAP_plot<- ggplot()+
    geom_point(data= CAP_df, mapping = aes(x=LD1, y=LD2, color= Group))+
    scale_color_manual(values=color)+
    geom_segment(data=CAP_vect_df[1:n_spec,], mapping = aes(x=0, xend=LD1, y=0, yend=LD2),
               arrow=arrow(length=unit(0.25,"cm")), color="darkgrey")+
    geom_text_repel(data = CAP_vect_df[1:n_spec,], mapping=aes(x=LD1, y=LD2, label=Species), size=3, segment.color = NA)+
    scale_fill_manual(values=color)+
    geom_label(data=CAP_Centroids, mapping = aes(x=LD1, y=LD2, label= Group, fill= Group), show.legend = FALSE, color="white", fontface="bold")+
    xlab(label = paste0("Canonical Axis 1 (", round(Can_Var[1],2), "%)"))+
    ylab(label = paste0("Canonical Axis 2 (", round(Can_Var[2],2), "%)"))+
    theme_bw()+
    labs(col=legend_title)
  return(CAP_plot)
}
```

# Study Area

The study area is marked in red, and is know as "The Elbow." It is about 145 km west-northwest of Tampa Bay, Florida. The area is a popular offshore fishing area that contains contains hard bottom ridges and is hypothesized to be a paleoshoreline shaped by wave action approximately 12,000 years ago.

The three lines shown represent day-time video transects completed using a towed video camera system that flies about 2-4 meters above the seafloor. Along these transects, fish were annotated at the frame-level, and habitat was annotated every 15 seconds.

Figure S2.1
```{r include=FALSE, results='hide', fig.width=8, fig.height=6}
FL_shp<- readOGR("Data/derived/shapefiles/Florida_Shoreline__1_to_2%2C000%2C000_Scale_.shp") #Source: https://geodata.myfwc.com/datasets/florida-shoreline-1-to-2000000-scale?geometry=-102.366%2C24.356%2C-65.298%2C31.156
EL_shp<- readOGR("Data/derived/shapefiles/EL_Shp.shp")
bathy<- raster("Data/derived/Multibeam/bathy.tif")
Transect_shp<- rbind(readOGR("Data/derived/shapefiles/ELT3_Shiptrack.shp"),
readOGR("Data/derived/shapefiles/ELT5_Shiptrack.shp"),
readOGR("Data/derived/shapefiles/ELT6_Shiptrack.shp"))
bathy_pal<- colorRampPalette(brewer.pal(n = 9, name = "Blues")[3:9])
Study_Area_Plot<- tm_shape(FL_shp)+
  tm_fill()+
  tm_shape(EL_shp)+
  tm_fill(col="red")+
  tm_graticules(lines=FALSE)+
  tm_credits("https://geodata.myfwc.com/datasets", position = c("left", "bottom"))

Transect_Plot<- tm_shape(bathy, raster.downsample = FALSE)+
  tm_raster(palette = bathy_pal(100), style="cont", title = "Bathymetry (m)")+
  tm_shape(Transect_shp)+
  tm_lines()+
  tm_compass(type = "8star", position = c("right", "top"))+
  tm_scale_bar(breaks= c(0, 2.5, 5), text.size=1)
```
```{r echo=FALSE}
tmap_arrange(Study_Area_Plot, Transect_Plot)
```


# Read in and reformat data

## Habitat data

Habitat was annotated every 15 seconds of video

```{r}
hab<- read_csv("Data/derived/CBASS/hab.csv")
hab<- hab %>% select(-c(CBASSLat, CBASSLon, Frame_num, Substrate))
hab<- hab %>% filter(Transect!="T1")
hab<- hab %>% mutate(habitat=ifelse(test = Relief=="None", yes = "Sand", no = Relief))
hab<- hab %>% select(-Relief)
print(hab)
```

## Fish Counts

```{r echo=FALSE}
T3_annotation<- merge_cvision_csv(file_list = list.files(path = "Data/raw/CBASS/fish_counts/T3_D1", pattern = "\\.csv$", full.names = TRUE), frames_per_sec = 12, vid_length = 1)
T5_annotation<- merge_cvision_csv(file_list = list.files(path = "Data/raw/CBASS/fish_counts/T5_D14", pattern = "\\.csv$", full.names = TRUE), frames_per_sec = 12, vid_length = 1)
T6_annotation<- merge_cvision_csv(file_list = list.files(path = "Data/raw/CBASS/fish_counts/T6_D13", pattern = "\\.csv$", full.names = TRUE), frames_per_sec = 12, vid_length = 1)
 #T3 annotated before consistent naming for species. Reformat species names in T3.
T3_annotation$Fish_Type[T3_annotation$Fish_Type=="sheepshead"]<- "porgy_sheepshead"
T3_annotation$Fish_Type[T3_annotation$Fish_Type=="large_noid"]<- "lgnoid_lgnoid"
T3_annotation$Fish_Type[T3_annotation$Fish_Type=="gray triggerfish"]<- "triggerfish_gray"
T3_annotation$Fish_Type[T3_annotation$Fish_Type=="sand tilefish"]<- "tilefish_sand"
T3_annotation$Fish_Type[T3_annotation$Fish_Type=="blue angelfish"]<- "angelfish_blue"
T3_annotation$Fish_Type[T3_annotation$Fish_Type=="gray angelfish"]<- "angelfish_gray"
T3_annotation$Fish_Type[T3_annotation$Fish_Type=="small_noid"]<- "smnoid_smnoid"
T3_annotation$Fish_Type[T3_annotation$Fish_Type=="rainbow runner"]<- "jack_rainbowrunner"
T3_annotation$Fish_Type[T3_annotation$Fish_Type=="gray snapper"]<- "snapper_gray"
T3_annotation$Fish_Type[T3_annotation$Fish_Type=="pearly razorfish"]<- "wrasse_pearlyrazorfish"
T3_annotation$Fish_Type[T3_annotation$Fish_Type=="reef butterflyfish"]<- "butterflyfish_reef"
T3_annotation$Fish_Type[T3_annotation$Fish_Type=="red grouper"]<- "grouper_red"
T3_annotation$Fish_Type[T3_annotation$Fish_Type=="sea turtle spp"]<- "seaturtle_spp"
T3_annotation$Fish_Type<- str_replace(string = T3_annotation$Fish_Type, pattern = " ", "_")

fish_annotations<- bind_rows(mutate(T3_annotation, Transect="T3"),
                             mutate(T5_annotation, Transect="T5"),
                             mutate(T6_annotation, Transect="T6"))
write_csv(fish_annotations, "Data/derived/CBASS/fish_annotations.csv")
```

Fish were annotated on using C-Vision's video annotation software at the frame level. Each row is an individual fish, and we can see exactly what frame that fish was seen.

```{r}
fish_annotations<- read_csv("Data/derived/CBASS/fish_annotations.csv")
print(fish_annotations)
```

To turn these annotations into counts, each individual fish was matched to the closest habitat observation in time, and the number of annotations for each taxa were then summed.

```{r }
fish_counts<- bind_rows(mutate(match_cvision_fish2hab(merged_cvision_csv = select(filter(fish_annotations, Transect=="T3"), -Transect), video_num = filter(hab, Transect=="T3")$Vid, video_sec = filter(hab, Transect=="T3")$Sec, fps = 12, vid_length = 1, include_vid_sec = TRUE, include_framenum = FALSE), Transect="T3"),
          mutate(match_cvision_fish2hab(merged_cvision_csv = select(filter(fish_annotations, Transect=="T5"), -Transect), video_num = filter(hab, Transect=="T5")$Vid, video_sec = filter(hab, Transect=="T5")$Sec, fps = 12, vid_length = 1, include_vid_sec = TRUE, include_framenum = FALSE), Transect="T5"),
          mutate(match_cvision_fish2hab(merged_cvision_csv = select(filter(fish_annotations, Transect=="T6"), -Transect), video_num = filter(hab, Transect=="T6")$Vid, video_sec = filter(hab, Transect=="T6")$Sec, fps = 12, vid_length = 1, include_vid_sec = TRUE, include_framenum = FALSE), Transect="T6")) %>% select(Transect, everything())
fish_counts[is.na(fish_counts)]<- 0 
fish_counts<- fish_counts %>% select(Transect, Video, Seconds, everything())
names(fish_counts)[2:3]<- c("Vid", "Sec")

#Merge taxa that are difficult to differentiate consistently
fish_counts<- fish_counts %>% mutate(AMBERJACK_SPP= rowSums(across(starts_with("amberjack_", ignore.case = FALSE)))) %>% select(-starts_with("amberjack_", ignore.case = FALSE))

fish_counts<- fish_counts %>% mutate(BUTTERFLYFISH_SPP= rowSums(across(starts_with("butterflyfish_", ignore.case = FALSE))))  %>% select(-starts_with("butterflyfish_",ignore.case = FALSE))

fish_counts<- fish_counts %>% mutate(PORGY_SPP= rowSums(across(starts_with("porgy_", ignore.case = FALSE))))  %>% select(-starts_with("porgy_",ignore.case = FALSE))

fish_counts<- fish_counts %>% mutate(TRIGGERFISH_SPP= rowSums(across(starts_with("triggerfish_",ignore.case = FALSE))))  %>% select(-starts_with("triggerfish_",ignore.case = FALSE))

fish_counts<- fish_counts %>% select(-seaturtle_spp) #NOt a fish

names(fish_counts)[4:ncol(fish_counts)]<- tolower(names(fish_counts)[4:ncol(fish_counts)])
fish_counts<- fish_counts %>% select(Transect, Vid, Sec, sort_species(names(fish_counts)[4:ncol(fish_counts)]))
fish_counts<- fish_counts %>% left_join(select(hab, timestamp, Transect, Vid, Sec), by=c("Transect", "Vid", "Sec"))
fish_counts<- fish_counts %>% select(Transect, Vid, Sec, timestamp, everything())
```

```{r echo=FALSE}
write_csv(fish_counts, "Data/derived/CBASS/fish_counts.csv")
```

```{r}
print(fish_counts)
```

## Raw Auxillary Data

```{r}
altitude<- bind_rows<- bind_rows(mutate(read_tsv("Data/raw/CBASS/sensors/T3_D1/altimeter_readings.tsv"), Transect="T3"),
                                 mutate(read_tsv("Data/raw/CBASS/sensors/T5_D14/altimeter_readings.tsv"), Transect="T5"),
                                 mutate(read_tsv("Data/raw/CBASS/sensors/T6_D13/altimeter_readings.tsv"), Transect="T6"))
altitude<- altitude %>% mutate(timestamp_exact= timestamp+dmicroseconds(u_second))
compass<- bind_rows<- bind_rows(mutate(read_tsv("Data/raw/CBASS/sensors/T3_D1/compass_readings.tsv"), Transect="T3"),
                                 mutate(read_tsv("Data/raw/CBASS/sensors/T5_D14/compass_readings.tsv"), Transect="T5"),
                                 mutate(read_tsv("Data/raw/CBASS/sensors/T6_D13/compass_readings.tsv"), Transect="T6"))
compass<- compass %>% mutate(timestamp_exact= timestamp+dmicroseconds(u_second))

Speed<- get_HypackSpeed(read_hypack(filename = list.files("Data/raw/Hypack/", pattern = "\\.RAW$", full.names = TRUE)))
Speed<- Speed %>% mutate(Speed_mps=Speed_kph*(1000/(60*60)))
names(Speed)[1]<- "timestamp_exact"
```

# Community Analysis

## Calculate the area viewed for each 15 second bin

Equations from McCollough (1893) and Grasty (2014)

### Horizontal Angular Field of View

[Calculate the angular field of view for the camera]{.ul}

*H_AFOV_air = 2 \* atan(sensor_width/(2 \* focal_length))*

[Use Snell's Law to adjust for refraction between air and seawater]{.ul}

*H_AFOV_sea = 2 \* asin(sin((H_AFOV_air/2)) \* (n_air/n_sea))*

\*n is the index of refraction for the specified medium (n_air\~1; n_sea \~1.33)

### Distance to Center of Frame

[Calculate the camera angle relative to the seafloor]{.ul}

*cam_angle_to_ground = mounted_camera_angle - pitch*

[Adjust the raw altitude measurements to the true altitude by accounting for the pitch of the system]{.ul}

*adjusted_altitude = cos(pitch) \* altitude*

[Calculate the distance in meters between the camera lens and the seafloor at the center of an image]{.ul}

*centerline_distance = adjusted_altitude/sin(cam_angle_to_ground)*

### Width of frame

[Calculate the width of the image in meters at the center of an image]{.ul}

*Width = 2 \* centerline_distance \* tan(H_AFOV_water/2)*

### Area Covered

[Calculate distance covered in the 15 second observation window]{.ul}

*Distance = Speed \* 15s*

\*The median rather than the mean of speed over the 15s observation window is used so that faulty or poor readings do not adversely affect the calculation

[Calculate the area viewed by the camera system]{.ul}

*Area = Width \* Distance*

```{r}
hab<- hab %>% mutate(Area=NA_real_)
H_AFOV_air<- calc_AFOV(d = 13.3, f = 5, medium = "air", type = "horiz", pix=c(1920, 1200))
for (i in 1:nrow(hab)) {
  curr_transect<- hab$Transect[i]
  mid_time<- hab$timestamp[i]
  st_time<- mid_time-dseconds(7.5)
  end_time<-  mid_time+dseconds(7.5)
  
  curr_alt<- median(altitude$altitude[altitude$timestamp_exact>=st_time & altitude$timestamp_exact<=end_time])
  curr_pitch<- median(compass$pitch[compass$timestamp_exact>=st_time & compass$timestamp_exact<=end_time])
  curr_speed<- median(Speed$Speed_mps[Speed$timestamp_exact>=st_time & Speed$timestamp_exact<=end_time])
  
  curr_width<- calc_width(alt = curr_alt, pitch = curr_pitch, cam_angle = 32.8, H_AFOV_air = H_AFOV_air)
  curr_dist<- curr_speed * 15
  
  hab$Area[i]<- curr_width*curr_dist
  }
```

## Merge sequential observations of the same habitat class

Consecutive observations of habitat are collapsed into a single sample for community analysis and counts are summed among the merged observations. This is done to reduce the amount of rows with all zero counts which cannot be handled using semi-metric distances such as Bray-Curtis, and allows us to keep most information from sandy habitats while having more balance in sample sizes across habitat groups.

```{r}
hab<- hab %>% mutate(samp_num=NA_real_)
hab$samp_num[1]<-1
samp_num<-1
for (i in 2:nrow(hab)) {
  curr_hab_obs<- hab$habitat[i]
  prev_hab_obs<- hab$habitat[i-1]
  curr_obs_transect<- hab$Transect[i]
  prev_obs_transect<- hab$Transect[i-1]
  if((curr_hab_obs!=prev_hab_obs)|(curr_obs_transect!=prev_obs_transect)|is.na(hab$Area[i])){
    samp_num<- samp_num+1
  }
  hab$samp_num[i]<-samp_num
}

fish_hab<- fish_counts %>% left_join(hab, by=c("Vid", "Sec", "timestamp", "Transect"))
fish_hab<- fish_hab %>% select(Transect,Vid, Sec, timestamp, samp_num, habitat, Area, everything())

fish_hab_collapsed<- fish_hab %>% group_by(samp_num) %>% 
  summarize(habitat=unique(habitat), Vid_min = first(Vid)+first(Sec)/60, Vid_max= last(Vid)+last(Sec)/60, across(Area:smnoid_smnoid, .fns = sum), .groups="drop")

print(fish_hab_collapsed)
```

## Transform Data

To account for unequal lengths of the samples, counts are divided by the area duration. Additionally to down-weight over abundant taxa, densities are square root transformed.

```{r}
dens_hab_collaped<- fish_hab_collapsed %>% mutate(across(amberjack_spp:smnoid_smnoid)/Area)
sq_dens_hab_collaped<- dens_hab_collaped %>% mutate(across(amberjack_spp:smnoid_smnoid, .fns=sqrt))
sq_dens_hab_collaped<- sq_dens_hab_collaped %>% filter(!is.na(Area) & habitat!="No_Vis")
sq_dens_hab_collaped<- sq_dens_hab_collaped %>% select(-c(smnoid_smnoid, lgnoid_lgnoid))
sq_dens<- sq_dens_hab_collaped %>% select(-c(samp_num, habitat, Vid_min, Vid_max, Area)) 
comm_hab<- sq_dens_hab_collaped %>% select(habitat)
idx<- rowSums(sq_dens)>0 #Bray Curtis distance cannot calculate distance in rows with all zeros
sq_dens<- sq_dens[idx,]
comm_hab<- comm_hab[idx,]
comm_hab$habitat<- factor(comm_hab$habitat, levels= c("Sand", "Low_Relief", "Moderate_Relief", "High_Relief"))
```

## PERMANOVA

### Check meet assumption of homogenous dispersion

```{r}
set.seed(5)
permdisp_res<- permutest(betadisper(vegdist(sq_dens, methd="bray"), group= comm_hab$habitat, type = "median"), permutations = 9999)
```

p = `r permdisp_res$tab[["Pr(>F)"]][1]`

p \> .05 so we meet the assumtion of homogeneity of multivariate dispersion among groups and can therefore procedure with the PERMANOVA

### Conduct global test

```{r}
set.seed(6)
PERMANOVA_res<- adonis(sq_dens~habitat, data=comm_hab, permutations=999, method = "bray")
print(PERMANOVA_res)
```

Since p \< .05 in the PERMANOVA, we conclude that there are sigificant differences in the fish communities among the different habitats

### Conduct Pair-wise Test

To see which habitats have fish communities that differ from another, we follow up the global test with pairwise PERMANOVA tests

```{r}
set.seed(7)
PW_comp<- adonis_PW(formula = sq_dens~habitat, data=comm_hab, permutations = 9999, method = "bray")
```

Table of pairwise comparisons

```{r echo=FALSE}
print(mutate(dplyr::select(PW_comp, -p), F_ratio=round(F_ratio,2)))
```

p_holm represents a p-value adjusted forf multiple comparisons using a Holm's sequential Bonferroni correction. The adjusted p value is less than .05 for all comparisons except for between moderate and high relief rock, indicating that moderate and high relief rock did not have significantly different fish communities from one another, but all other habitats did.

## Canonical Analysis of Principal Coordinates

Figure S2.2
```{r fig.height=6, fig.width=10}
CAP_result<- CAPdiscrim(as.data.frame(sq_dens)~habitat, data=as.data.frame(comm_hab), dist="bray", m=0, add=FALSE)
CAP_Plot<- plot_CAP(CAP_result = CAP_result, Y = sq_dens, Group = comm_hab$habitat, n_spec = 15, color = c("black", "blue", "orange", "red"), legend_title = "Habitat")
plot(CAP_Plot)
```

# Fish Abundance Estimates

## Habitat Specific Densities

```{r}
fish_hab2<- fish_hab %>% filter(!is.na(Area) & habitat!="No_Vis")
fish_hab2<- fish_hab2 %>% mutate(habitat=ifelse(test = habitat %in% c("Moderate_Relief", "High_Relief"), yes = "Mod/High_Relief", no = habitat))
fish_hab2<- fish_hab2 %>% select(-smnoid_smnoid) %>% mutate(All_Fish= rowSums(across(amberjack_spp:lgnoid_lgnoid))) %>% select(-lgnoid_lgnoid) #Calculate total fish include large no id's then remove large no id's

hab_types<- unique(fish_hab2$habitat)

for (i in 1:length(hab_types)) {
  curr_fish_hab<- fish_hab2 %>% filter(habitat==hab_types[i])
  set.seed(i)
  curr_avg_dens<- avg_density(counts =select(curr_fish_hab, amberjack_spp:All_Fish), area = curr_fish_hab$Area, iter=1000, conf = .95)
  curr_avg_dens<- curr_avg_dens %>% mutate(Type= c("lower_bound", "mean", "upper_bound"))
  curr_avg_dens<- curr_avg_dens %>% pivot_longer(cols = -Type, names_to="Taxa", values_to="Density") #Tidy
  curr_avg_dens<- curr_avg_dens %>% pivot_wider(names_from = Type, values_from= Density)
  curr_avg_dens<- curr_avg_dens %>% mutate(habitat=hab_types[i])
  curr_avg_dens<- curr_avg_dens %>% select(Taxa, habitat, lower_bound, mean, upper_bound)
  if(i==1){
    avg_dens<- curr_avg_dens}else{
      avg_dens<- avg_dens %>% bind_rows(curr_avg_dens)
      }
  }
avg_dens$habitat<- factor(avg_dens$habitat, levels = c("Sand", "Low_Relief", "Mod/High_Relief"))
avg_dens<- avg_dens %>% arrange(Taxa, habitat)
```

```{r include=FALSE}
max_dens_hab_table<- avg_dens %>% filter(Taxa!="All_Fish") %>% group_by(Taxa) %>% filter(mean==max(mean)) %>% ungroup() %>% group_by(habitat) %>% summarize(n=n(), .groups="drop")
```
Fish densities were estimated by habitat type for `r length(unique(avg_dens$Taxa))-1` species or species groups, as well as for all fish combined. For most taxa (`r max_dens_hab_table %>% filter(habitat!="Sand") %>% pull(n) %>% sum()` of the `r length(unique(avg_dens$Taxa))-1` species/species groups), average fish density was highest over rocky habitats, and only `r max_dens_hab_table %>% filter(habitat=="Sand") %>% pull(n) %>% sum()` species had their highest densities over sand. Of the `r max_dens_hab_table %>% filter(habitat!="Sand") %>% pull(n) %>% sum()` taxa that had highest densities over a rocky habitat, `r max_dens_hab_table %>% filter(habitat=="Mod/High_Relief") %>% pull(n) %>% sum()` had had their highest densities over moderate-high relief rock and `r max_dens_hab_table %>% filter(habitat=="Low_Relief") %>% pull(n) %>% sum()` had their highest densities over low relief rock. 

Table of mean fish densities per Taxa in numbers of fish per kilometer squared with the lower and upper bounds of the 95% bootstrap confidence intervals

```{r echo=FALSE}
mutate(avg_dens, round(across(where(is.numeric))*1e6),2)
```

Plot of selected Taxa from table

Figure S2.3
```{r echo=FALSE, fig.height=8, fig.width=8}
#Plot
sel_taxa<- c("All_Fish", "jack_amberjack", "bigeye_spp", "angelfish_blue", "grouper_creolefish", "snapper_gray", "lionfish_spp", "porgy_spp", "tilefish_sand",  "squirrelfish_spp") #Taxa to plot
avg_dens_sel<- avg_dens %>% filter(Taxa %in% sel_taxa)

avg_dens_sel$Taxa[avg_dens_sel$Taxa=="All_Fish"]<- "All Fishes"
avg_dens_sel$Taxa[avg_dens_sel$Taxa=="angelfish_blue"]<- "Blue Angelfish"
avg_dens_sel$Taxa[avg_dens_sel$Taxa=="bigeye_spp"]<- "Bigeyes"
avg_dens_sel$Taxa[avg_dens_sel$Taxa=="lionfish_spp"]<- "Lionfishes"
avg_dens_sel$Taxa[avg_dens_sel$Taxa=="porgy_spp"]<- "Porgies"
avg_dens_sel$Taxa[avg_dens_sel$Taxa=="snapper_gray"]<- "Gray Snapper"
avg_dens_sel$Taxa[avg_dens_sel$Taxa=="squirrelfish_spp"]<- "Squirrelfishes"
avg_dens_sel$Taxa[avg_dens_sel$Taxa=="tilefish_sand"]<- "Sand Tilefish"
avg_dens_sel$Taxa[avg_dens_sel$Taxa=="jack_amberjack"]<- "Amberjacks"
avg_dens_sel$Taxa[avg_dens_sel$Taxa=="grouper_creolefish"]<- "Creolefish" #Clean names for plotting

dens_plot<- ggplot(data= avg_dens_sel, mapping=aes(x=habitat))+
  geom_col(mapping=aes(fill=habitat, y = mean), color="black")+
  facet_wrap(~Taxa, scales="free_y", ncol = 3)+
  scale_fill_manual(values=c("#FFEBBE", "dodgerblue", "red"))+
  geom_errorbar(mapping= aes(ymin= lower_bound, ymax=upper_bound), width=.3)+
  xlab("")+
  ylab(bquote('Density (# of Individuals / m ' ^2 ~')'))+
  theme(axis.text.x = element_blank())+
  theme(strip.text.x = element_text(size = 12))
dens_plot
```

## Habitat Map

```{r}
hab_map<- raster("Products/Mapping/Elbow_Supervised_HabRelief.tif")
hab_map<- reclassify(hab_map, matrix(data=c(21, 1, 11, 2, 12, 3, 13, 3), ncol = 2, byrow = TRUE)) #Merge Moderate and High Relief
hab_map<- as.factor(hab_map)
levels(hab_map)[[1]]$habitat<- c("Sand", "LR", "MR/HR")
```

Figure S2.4
```{r echo=FALSE, fig.height=9, fig.width=6}
tm_shape(hab_map, raster.downsample = FALSE)+
  tm_raster(title = "Habitat", palette= c("#FFEBBE", "dodgerblue", "red"))
```

```{r echo=FALSE}
hab_area<- as.data.frame(freq(hab_map, useNA="no"))
hab_area$habitat<- c("Sand", "Low_Relief", "Mod/High_Relief")
hab_area<- hab_area %>% mutate(Area_m2= prod(res(all_of(hab_map)))*count)
hab_area<- hab_area %>% mutate(Area_km2= Area_m2/1e6)
print(hab_area %>% dplyr::select(habitat, Area_km2))
```

## Scale Densities up to Abundance

Multiply densities of taxa for each habitat by the corresponding area of the habitat to get abundance

```{r}
abd<- avg_dens %>% left_join(hab_area, by="habitat") %>% 
  mutate(across(c(lower_bound, mean, upper_bound))*Area_m2) %>%
  dplyr::select(Taxa, habitat, lower_bound, mean, upper_bound)
abd<- bind_rows((abd %>% group_by(Taxa) %>% summarize(habitat="Total", across(where(is.numeric), .fns=sum), .groups="drop")), abd)
abd$habitat<- factor(abd$habitat, levels = c("Total", "Sand", "Low_Relief", "Mod/High_Relief"))
abd<- abd %>% arrange(Taxa, habitat)
```

Table of abundance estimates

```{r echo=FALSE}
mutate(abd, across(where(is.numeric), .fns = round))
```

```{r echo=FALSE}
tot_num<- abd %>% filter(Taxa=="All_Fish" & habitat=="Total") %>% pull(mean)
sand_num<- abd %>% filter(Taxa=="All_Fish" & habitat=="Sand") %>% pull(mean)
low_num<- abd %>% filter(Taxa=="All_Fish" & habitat=="Low_Relief") %>% pull(mean)
mh_num<- abd %>% filter(Taxa=="All_Fish" & habitat=="Mod/High_Relief") %>% pull(mean)
```

There are estimated to be a total of approximately `r as.integer(round(tot_num))` fish in total within the study area. Of these, `r as.integer(round(sand_num))` (`r round(100*sand_num/tot_num)`%) are on sand, `r as.integer(round(low_num))` (`r round(100*low_num/tot_num)`%) are on low relief rock, and `r as.integer(round(mh_num))` (`r round(100*mh_num/tot_num)`%)` are on moderate to high relief rock.

Plot of estimated abundance for select species

Figure S2.5
```{r echo=FALSE, fig.height=8, fig.width=8}
abd_sel<- abd %>% filter(Taxa %in% sel_taxa)

abd_sel$Taxa[abd_sel$Taxa=="All_Fish"]<- "All Fishes"
abd_sel$Taxa[abd_sel$Taxa=="angelfish_blue"]<- "Blue Angelfish"
abd_sel$Taxa[abd_sel$Taxa=="bigeye_spp"]<- "Bigeyes"
abd_sel$Taxa[abd_sel$Taxa=="lionfish_spp"]<- "Lionfishes"
abd_sel$Taxa[abd_sel$Taxa=="porgy_spp"]<- "Porgies"
abd_sel$Taxa[abd_sel$Taxa=="snapper_gray"]<- "Gray Snapper"
abd_sel$Taxa[abd_sel$Taxa=="squirrelfish_spp"]<- "Squirrelfishes"
abd_sel$Taxa[abd_sel$Taxa=="tilefish_sand"]<- "Sand Tilefish"
abd_sel$Taxa[abd_sel$Taxa=="jack_amberjack"]<- "Amberjacks"
abd_sel$Taxa[abd_sel$Taxa=="grouper_creolefish"]<- "Creolefish" #Clean names for plotting

abd_plot<- ggplot(data= abd_sel, mapping=aes(x=habitat))+
  geom_col(mapping=aes(fill=habitat, y = mean), color="black")+
  facet_wrap(~Taxa, scales="free_y", ncol = 3)+
  scale_fill_manual(values=c("purple", "#FFEBBE", "dodgerblue", "red"))+
  geom_errorbar(mapping= aes(ymin= lower_bound, ymax=upper_bound), width=.3)+
  xlab("")+
  ylab("Abundance")+
  theme(axis.text.x = element_blank())+
  theme(strip.text.x = element_text(size = 12))
abd_plot
```

```{r echo=FALSE}
ggsave(CAP_Plot, filename = "CAP_Plot.tiff", device = "tiff", width = 8, height = 6, path = "Products/figures", units = "in", dpi = 300)
ggsave(dens_plot, filename = "dens_plot.tiff", device = "tiff", width = 7, height = 9, path = "Products/figures", units = "in", dpi = 300)
ggsave(abd_plot, filename = "abd_plot.tiff", device = "tiff", width = 7, height = 9, path = "Products/figures", units = "in", dpi = 300)
write_csv(avg_dens %>% mutate(across(where(is.numeric), .fns = format, scientific=FALSE)), path = "Products/tables/fish_dens.csv")
write_csv(abd %>% mutate(across(where(is.numeric), .fns=round)), path = "Products/tables/fish_abd.csv")
```

```{r echo=FALSE}
save.image(file = 'Data/R_environment/Elbow_R_Fish_Environment.RData')
```
