# load data
complexity <- read_csv("Data/Cloudbreak_complexity_reefzones.csv")
coral <- read_csv("Data/annotations.csv")
surfers <- read_csv("Data/Cloudbreak_data2.csv")

# subset by resolution
complexity_1cm <- complexity %>% filter(fac == "1") 
complexity_4cm <- complexity %>% filter(fac == "4") 
complexity_8cm <- complexity %>% filter(fac == "8") 
complexity_32cm <- complexity %>% filter(fac == "32") 

# calculate coral cover
coral$Coral <- coral$Label
coral$Coral[coral$Label != "LICO"] <- "Other"

coral <- coral %>% 
  group_by(Name, Coral) %>% 
  summarise(count = n()) %>% 
  ungroup()

percent_cover <- coral %>% 
  group_by(Name) %>% 
  mutate(perc_cover = count / sum(count) * 100) %>% 
  filter(Coral != "Other") %>% 
  summarise(perc_coral_cover = sum(perc_cover))

# adjust plot name in coral dataframe to make sure it matches with surfer plots
percent_cover <- percent_cover %>%
  mutate(Plot_num = sub("CoralNet_Shapefiles_", "", sub("\\.jpg$", "", Name))) %>%
  select(-Name) %>% mutate(Plot_num = factor(Plot_num))

# change variable type
surfers <- surfers %>%
  mutate(Plot_num = factor(Plot_num))

# join surfers and corals by matching plot number 
surfer_coral <- dplyr::inner_join(percent_cover, surfers, by="Plot_num")

# add reef zone categories for each plot
left <- c(4, 19, 20, 22, 30, 32, 33, 35, 37, 40, 42,28, 44, 45, 53, 54, 56, 58, 62, 63,
          64, 67, 68, 70, 71, 73, 76, 79, 81, 85, 89, 91, 93, 95, 98, 99)
mid <- c(1, 2, 3, 5, 6, 8, 9, 11, 12, 14, 15, 18, 24, 25, 26, 29, 34, 41, 43, 47,
         49, 51, 52, 55, 59, 65, 72, 74, 77, 78, 82, 83, 84, 92, 97)
right <- c(7, 10, 13, 16, 17, 21, 23, 27, 36, 39, 46, 50, 57, 60, 61, 66, 69, 80, 
           86, 88, 90, 94, 96, 100)

surfer_coral <- surfer_coral %>%
  mutate(Reef_Zone = case_when(
    Plot_num %in% left ~ "Cloudbreak_Left",
    Plot_num %in% mid ~ "Cloudbreak_Mid",
    Plot_num %in% right ~ "Cloudbreak_Right",
    TRUE ~ NA_character_
  ))

# subset complexity metrics 
complexity_1cm <- complexity_1cm %>% select(c("surf_complex","mean_profile_curvature",
                                              "mean_slope","mean_vrm","site","height_diff"))
complexity_8cm <- complexity_8cm %>% select(c("surf_complex","mean_profile_curvature",
                                              "mean_slope","mean_vrm","site","height_diff"))
complexity_32cm <- complexity_32cm %>% select(c("surf_complex","mean_profile_curvature",
                                              "mean_slope","mean_vrm","site","height_diff"))


# summarize coral and surfer data by reef zone
surfer_coral_summ <- surfer_coral %>%
  group_by(Reef_Zone) %>%
  summarise(
    Mean_perc_coral_cover = mean(perc_coral_cover),
    SD_perc_coral_cover = sd(perc_coral_cover),
    Mean_Track_Counts = mean(Track_Counts),
    SD_Track_Counts = sd(Track_Counts),
    Mean_Sum_Track_Length_meters = mean(Sum_Track_Length_meters),
    SD_Sum_Track_Length_meters = sd(Sum_Track_Length_meters)
  )

# combine with complexity
cloudbreak <- surfer_coral_summ %>% full_join(complexity_1cm, by = c("Reef_Zone" = "site"))
cloudbreak_4 <- surfer_coral_summ %>% full_join(complexity_4cm, by = c("Reef_Zone" = "site"))
cloudbreak_8 <- surfer_coral_summ %>% full_join(complexity_8cm, by = c("Reef_Zone" = "site"))
cloudbreak_32 <- surfer_coral_summ %>% full_join(complexity_32cm, by = c("Reef_Zone" = "site"))


# complexity variance - 3 zones
complex_var3zones <- read_csv("complexity_3zones_variance.csv")
# 1cm
complex_3_1cm <- complex_var3zones %>%
  rename(zone = file) %>%
 filter(fac == "1") 
complex_3_1cm$zone <- sub(".*/([^/]+)$", "\\1", complex_3_1cm$zone)
complex_3_1cm$zone <- sub("\\.tif$", "", complex_3_1cm$zone)
# 4cm
complex_3_4cm <- complex_var3zones %>%
  rename(zone = file) %>%
 filter(fac == "4") 
complex_3_4cm$zone <- sub(".*/([^/]+)$", "\\1", complex_3_4cm$zone)
complex_3_4cm$zone <- sub("\\.tif$", "", complex_3_4cm$zone)

# complexity variance - 6 zones
complex_var <- read_csv("complexity_1cm_variance.csv")
# 1cm
complex_var1 <- complex_var %>%
  rename(zone = file) %>%
 filter(fac == "1") 
complex_var1$zone <- sub(".*/([^/]+)$", "\\1", complex_var1$zone)
complex_var1$zone <- sub("\\.tif$", "", complex_var1$zone)

# 4cm
complex_var_4 <- complex_var %>%
  rename(zone = file) %>%
 filter(fac == "4") 
complex_var_4$zone <- sub(".*/([^/]+)$", "\\1", complex_var_4$zone)
complex_var_4$zone <- sub("\\.tif$", "", complex_var_4$zone)

3 REEF ZONES

Distribution & Descriptive Plots

ggplot(surfer_coral, aes(x = perc_coral_cover, fill = Reef_Zone)) +
  geom_density(alpha = 0.5) + theme_classic()

ggplot(surfer_coral, aes(x = Reef_Zone, y = perc_coral_cover, fill = Reef_Zone)) +
  geom_boxplot() + theme_classic() + theme(legend.position="none")

ggplot(surfer_coral_summ, aes(x = Reef_Zone, y = Mean_perc_coral_cover, fill = Reef_Zone)) +
  geom_bar(stat = "identity", position = "dodge") +
  geom_errorbar(aes(ymin = Mean_perc_coral_cover - SD_perc_coral_cover,
                    ymax = Mean_perc_coral_cover + SD_perc_coral_cover),
                position = position_dodge(width = 0.9), width = 0.25) +
  labs(title = "Mean Perc Coral Cover Across Reef Zones",
       x = "Reef Zone",
       y = "Mean Perc Coral Cover") +
  theme_minimal()

ggplot(surfer_coral, aes(x = Track_Counts, fill = Reef_Zone)) +
  geom_density(alpha = 0.5) + theme_classic()

ggplot(surfer_coral, aes(x = Reef_Zone, y = Track_Counts, fill = Reef_Zone)) +
  geom_boxplot() + theme_classic() + theme(legend.position="none")

ggplot(surfer_coral_summ, aes(x = Reef_Zone, y = Mean_Track_Counts, fill = Reef_Zone)) +
  geom_bar(stat = "identity", position = "dodge") +
  geom_errorbar(aes(ymin = Mean_Track_Counts  - SD_Track_Counts ,
                    ymax = Mean_Track_Counts + SD_Track_Counts),
                position = position_dodge(width = 0.9), width = 0.25) +
  labs(title = "Mean Track Counts Across Reef Zones",
       x = "Reef Zone",
       y = "Mean Perc Coral Cover") + theme_minimal()

ggplot(surfer_coral, aes(x = Sum_Track_Length_meters, fill = Reef_Zone)) +
  geom_density(alpha = 0.5) + theme_classic()

ggplot(surfer_coral, aes(x = Reef_Zone, y = Sum_Track_Length_meters, fill = Reef_Zone)) +
  geom_boxplot() + theme_classic() + theme(legend.position="none")

ggplot(surfer_coral_summ, aes(x = Reef_Zone, y = Mean_Sum_Track_Length_meters, fill = Reef_Zone)) +
  geom_bar(stat = "identity", position = "dodge") +
  geom_errorbar(aes(ymin = Mean_Sum_Track_Length_meters - SD_Sum_Track_Length_meters,
                    ymax = Mean_Sum_Track_Length_meters + SD_Sum_Track_Length_meters),
                position = position_dodge(width = 0.9), width = 0.25) +
  labs(title = "Mean Sum Track Length Across Reef Zones",
       x = "Reef Zone",
       y = "Mean Perc Coral Cover") +
  theme_minimal()

Comparison tests

# % Coral cover by reef zone = NOT-SIGNIFICANT (P = 0.5919)
kruskal.test(perc_coral_cover ~ Reef_Zone, data = surfer_coral)

## 
##  Kruskal-Wallis rank sum test
## 
## data:  perc_coral_cover by Reef_Zone
## Kruskal-Wallis chi-squared = 1.049, df = 2, p-value = 0.5919

# Surfer tracks by reef zone = SIGNIFICANT (P = 6.951e-12)
kruskal.test(Track_Counts ~ Reef_Zone, data = surfer_coral)

## 
##  Kruskal-Wallis rank sum test
## 
## data:  Track_Counts by Reef_Zone
## Kruskal-Wallis chi-squared = 51.384, df = 2, p-value = 6.951e-12

# Surfer track length by reef zone = SIGNIFICANT (P = 7.244e-12)
kruskal.test(Sum_Track_Length_meters ~ Reef_Zone, data = surfer_coral)

## 
##  Kruskal-Wallis rank sum test
## 
## data:  Sum_Track_Length_meters by Reef_Zone
## Kruskal-Wallis chi-squared = 51.302, df = 2, p-value = 7.244e-12

Scatter Plots - 1cm

ggplot(cloudbreak, aes(x = mean_slope, y = Mean_Track_Counts , color = Reef_Zone)) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linetype = "solid", col = "grey") +  
  labs(title = "Avg. Surfer Tracks & Slope") +
  theme_classic()

ggplot(cloudbreak, aes(x = mean_profile_curvature, y =Mean_Track_Counts , color = Reef_Zone)) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linetype = "solid", col = "grey") +  
  labs(title = "Avg. Surfer Tracks & Curvature") +
  theme_classic()

ggplot(cloudbreak, aes(x = Mean_perc_coral_cover, y = Mean_Track_Counts, color = Reef_Zone)) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linetype = "solid", col = "grey") + 
  labs(title = "Avg. Surfer Tracks & Coral Cover") +
  theme_classic()

ggplot(cloudbreak, aes(x = mean_vrm, y = Mean_Track_Counts, color = Reef_Zone)) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linetype = "solid", col = "grey") +  
  labs(title = "Avg. Surfer Tracks & VRM") +
  theme_classic()

Scatter Plots - 4cm

ggplot(cloudbreak_4, aes(x = mean_slope, y = Mean_Track_Counts , color = Reef_Zone)) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linetype = "solid", col = "grey") +  
  labs(title = "Avg. Surfer Tracks & Slope") +
  theme_classic()

ggplot(cloudbreak_4, aes(x = mean_profile_curvature, y =Mean_Track_Counts , color = Reef_Zone)) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linetype = "solid", col = "grey") +  
  labs(title = "Avg. Surfer Tracks & Curvature") +
  theme_classic()

ggplot(cloudbreak_4, aes(x = Mean_perc_coral_cover, y = Mean_Track_Counts, color = Reef_Zone)) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linetype = "solid", col = "grey") + 
  labs(title = "Avg. Surfer Tracks & Coral Cover") +
  theme_classic()

ggplot(cloudbreak_4, aes(x = mean_vrm, y = Mean_Track_Counts, color = Reef_Zone)) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linetype = "solid", col = "grey") +  
  labs(title = "Avg. Surfer Tracks & VRM") +
  theme_classic()

Scatter Plots - 8cm

ggplot(cloudbreak_8, aes(x = mean_slope, y = Mean_Track_Counts , color = Reef_Zone)) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linetype = "solid", col = "grey") +  
  labs(title = "Avg. Surfer Tracks & Slope") +
  theme_classic()

ggplot(cloudbreak_8, aes(x = mean_profile_curvature, y =Mean_Track_Counts , color = Reef_Zone)) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linetype = "solid", col = "grey") +  
  labs(title = "Avg. Surfer Tracks & Curvature") +
  theme_classic()

ggplot(cloudbreak_8, aes(x = Mean_perc_coral_cover, y = Mean_Track_Counts, color = Reef_Zone)) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linetype = "solid", col = "grey") + 
  labs(title = "Avg. Surfer Tracks & Coral Cover") +
  theme_classic()

ggplot(cloudbreak_8, aes(x = mean_vrm, y = Mean_Track_Counts, color = Reef_Zone)) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linetype = "solid", col = "grey") +  
  labs(title = "Avg. Surfer Tracks & VRM") +
  theme_classic()

Correlations - 1 cm

# Mean Track Counts & Slope
cor.test(cloudbreak$Mean_Track_Counts, cloudbreak$mean_slope)

## 
##  Pearson's product-moment correlation
## 
## data:  cloudbreak$Mean_Track_Counts and cloudbreak$mean_slope
## t = 2.1095, df = 1, p-value = 0.2818
## alternative hypothesis: true correlation is not equal to 0
## sample estimates:
##       cor 
## 0.9036125

# Mean Track Counts & Curvature
cor.test(cloudbreak$Mean_Track_Counts, cloudbreak$mean_profile_curvature)

## 
##  Pearson's product-moment correlation
## 
## data:  cloudbreak$Mean_Track_Counts and cloudbreak$mean_profile_curvature
## t = -2.2683, df = 1, p-value = 0.2643
## alternative hypothesis: true correlation is not equal to 0
## sample estimates:
##        cor 
## -0.9150277

# Mean Track Counts & surf_complex
cor.test(cloudbreak$Mean_Track_Counts, cloudbreak$surf_complex)

## 
##  Pearson's product-moment correlation
## 
## data:  cloudbreak$Mean_Track_Counts and cloudbreak$surf_complex
## t = 3.9836, df = 1, p-value = 0.1566
## alternative hypothesis: true correlation is not equal to 0
## sample estimates:
##       cor 
## 0.9699077

# Mean Track Counts & Coral Cover
cor.test(cloudbreak$Mean_Track_Counts, cloudbreak$Mean_perc_coral_cover)

## 
##  Pearson's product-moment correlation
## 
## data:  cloudbreak$Mean_Track_Counts and cloudbreak$Mean_perc_coral_cover
## t = 0.36455, df = 1, p-value = 0.7775
## alternative hypothesis: true correlation is not equal to 0
## sample estimates:
##       cor 
## 0.3425028

# Mean Track Counts & Coral Cover
cor.test(cloudbreak$Mean_Track_Counts, cloudbreak$mean_vrm)

## 
##  Pearson's product-moment correlation
## 
## data:  cloudbreak$Mean_Track_Counts and cloudbreak$mean_vrm
## t = 1.421, df = 1, p-value = 0.3904
## alternative hypothesis: true correlation is not equal to 0
## sample estimates:
##       cor 
## 0.8177988

Correlations - 8 cm

# Mean Track Counts & Slope
cor.test(cloudbreak_8$Mean_Track_Counts, cloudbreak_8$mean_slope)

## 
##  Pearson's product-moment correlation
## 
## data:  cloudbreak_8$Mean_Track_Counts and cloudbreak_8$mean_slope
## t = 0.64456, df = 1, p-value = 0.6355
## alternative hypothesis: true correlation is not equal to 0
## sample estimates:
##       cor 
## 0.5417674

# Mean Track Counts & Curvature
cor.test(cloudbreak_8$Mean_Track_Counts, cloudbreak_8$mean_profile_curvature)

## 
##  Pearson's product-moment correlation
## 
## data:  cloudbreak_8$Mean_Track_Counts and cloudbreak_8$mean_profile_curvature
## t = -6.1087, df = 1, p-value = 0.1033
## alternative hypothesis: true correlation is not equal to 0
## sample estimates:
##        cor 
## -0.9868643

# Mean Track Counts & surf_complex
cor.test(cloudbreak_8$Mean_Track_Counts, cloudbreak_8$surf_complex)

## 
##  Pearson's product-moment correlation
## 
## data:  cloudbreak_8$Mean_Track_Counts and cloudbreak_8$surf_complex
## t = 2.7391, df = 1, p-value = 0.2228
## alternative hypothesis: true correlation is not equal to 0
## sample estimates:
##       cor 
## 0.9393558

# Mean Track Counts & Coral Cover
cor.test(cloudbreak_8$Mean_Track_Counts, cloudbreak_8$Mean_perc_coral_cover)

## 
##  Pearson's product-moment correlation
## 
## data:  cloudbreak_8$Mean_Track_Counts and cloudbreak_8$Mean_perc_coral_cover
## t = 0.36455, df = 1, p-value = 0.7775
## alternative hypothesis: true correlation is not equal to 0
## sample estimates:
##       cor 
## 0.3425028

# Mean Track Counts & Coral Cover
cor.test(cloudbreak_8$Mean_Track_Counts, cloudbreak_8$mean_vrm)

## 
##  Pearson's product-moment correlation
## 
## data:  cloudbreak_8$Mean_Track_Counts and cloudbreak_8$mean_vrm
## t = 5.1729, df = 1, p-value = 0.1216
## alternative hypothesis: true correlation is not equal to 0
## sample estimates:
##       cor 
## 0.9818228

Correlations - 32 cm

# Mean Track Counts & Slope
cor.test(cloudbreak_32$Mean_Track_Counts, cloudbreak_32$mean_slope)

## 
##  Pearson's product-moment correlation
## 
## data:  cloudbreak_32$Mean_Track_Counts and cloudbreak_32$mean_slope
## t = 0.81079, df = 1, p-value = 0.5663
## alternative hypothesis: true correlation is not equal to 0
## sample estimates:
##       cor 
## 0.6297909

# Mean Track Counts & Curvature
cor.test(cloudbreak_32$Mean_Track_Counts, cloudbreak_32$mean_profile_curvature)

## 
##  Pearson's product-moment correlation
## 
## data:  cloudbreak_32$Mean_Track_Counts and cloudbreak_32$mean_profile_curvature
## t = -4.2401, df = 1, p-value = 0.1474
## alternative hypothesis: true correlation is not equal to 0
## sample estimates:
##        cor 
## -0.9732983

# Mean Track Counts & surf_complex
cor.test(cloudbreak_32$Mean_Track_Counts, cloudbreak_32$surf_complex)

## 
##  Pearson's product-moment correlation
## 
## data:  cloudbreak_32$Mean_Track_Counts and cloudbreak_32$surf_complex
## t = 1.292, df = 1, p-value = 0.4193
## alternative hypothesis: true correlation is not equal to 0
## sample estimates:
##       cor 
## 0.7908023

# Mean Track Counts & Coral Cover
cor.test(cloudbreak_32$Mean_Track_Counts, cloudbreak_32$Mean_perc_coral_cover)

## 
##  Pearson's product-moment correlation
## 
## data:  cloudbreak_32$Mean_Track_Counts and cloudbreak_32$Mean_perc_coral_cover
## t = 0.36455, df = 1, p-value = 0.7775
## alternative hypothesis: true correlation is not equal to 0
## sample estimates:
##       cor 
## 0.3425028

# Mean Track Counts & Coral Cover
cor.test(cloudbreak_32$Mean_Track_Counts, cloudbreak_32$mean_vrm)

## 
##  Pearson's product-moment correlation
## 
## data:  cloudbreak_32$Mean_Track_Counts and cloudbreak_32$mean_vrm
## t = 1.1931, df = 1, p-value = 0.4441
## alternative hypothesis: true correlation is not equal to 0
## sample estimates:
##       cor 
## 0.7664078

Complexity - 3 reef zones - 1cm - WITH ERROR BARS

ggplot(complex_3_1cm, aes(x = mean_slope, y = zone)) +
  geom_point(aes(color = zone), size = 3) +
  geom_errorbarh(aes(xmin = mean_slope - sd_slope,
                     xmax = mean_slope + sd_slope), height = 0.2) +
  theme_minimal() + theme(legend.position = "bottom") + labs(title = "1 cm")

ggplot(complex_3_1cm, aes(x = mean_vrm, y = zone)) +
  geom_point(aes(color = zone), size = 3) +
  geom_errorbarh(aes(xmin = mean_vrm - sd_vrm,
                     xmax = mean_vrm + sd_vrm), height = 0.2) +
  theme_minimal() + theme(legend.position = "bottom") + labs(title = "1 cm")

ggplot(complex_3_1cm, aes(x = mean_profile_curvature, y = zone)) +
  geom_point(aes(color = zone), size = 3) +
  geom_errorbarh(aes(xmin = mean_profile_curvature - sd_profile_curvature,
                     xmax = mean_profile_curvature + sd_profile_curvature), height = 0.2) +
  theme_minimal() + theme(legend.position = "bottom") + labs(title = "1 cm")

Complexity - 3 reef zones - 4cm - WITH ERROR BARS

ggplot(complex_3_4cm, aes(x = mean_slope, y = zone)) +
  geom_point(aes(color = zone), size = 3) +
  geom_errorbarh(aes(xmin = mean_slope - sd_slope,
                     xmax = mean_slope + sd_slope), height = 0.2) +
  theme_minimal() + theme(legend.position = "bottom") + labs(title = "4 cm")

ggplot(complex_3_4cm, aes(x = mean_vrm, y = zone)) +
  geom_point(aes(color = zone), size = 3) +
  geom_errorbarh(aes(xmin = mean_vrm - sd_vrm,
                     xmax = mean_vrm + sd_vrm), height = 0.2) +
  theme_minimal() + theme(legend.position = "bottom") + labs(title = "4 cm")

ggplot(complex_3_4cm, aes(x = mean_profile_curvature, y = zone)) +
  geom_point(aes(color = zone), size = 3) +
  geom_errorbarh(aes(xmin = mean_profile_curvature - sd_profile_curvature,
                     xmax = mean_profile_curvature + sd_profile_curvature), height = 0.2) +
  theme_minimal() + theme(legend.position = "bottom") + labs(title = "4 cm")

6 FEEZ ZONES

# load data
complexity_6zones <- read_csv("Data/Cloudbreak_complexity_6_zones.csv")

# subset by resolution
complex_6zones_1cm <- complexity_6zones %>% filter(fac == "1") 
complex_6zones_8cm <- complexity_6zones %>% filter(fac == "8") 
complex_6zones_32cm <- complexity_6zones %>% filter(fac == "32") 

# 6 reef zone categories 
left_1 <- c(4, 19, 22, 28, 31, 38, 42, 44, 48, 54, 67, 68, 71, 75, 76, 79, 81, 87, 95)
left_2 <- c(20, 30, 32, 33, 35, 37, 40, 45, 53, 56, 58, 62, 63, 64, 70, 73, 85, 89, 91, 93, 98, 99)
mid_1 <- c(1, 3, 5, 25, 29, 41, 43, 52, 78, 82, 84)
mid_2 <- c(2, 6, 8, 9, 11, 12, 14, 15, 18, 24, 26, 34, 47, 49, 51, 55, 59, 65, 72, 74, 77, 83, 92, 97)
right_1 <- c(7, 10, 13, 17, 36, 57, 61, 69, 80, 94)
right_2 <- c(16, 21, 23, 27, 39, 46, 50, 60, 66, 86, 88, 90, 96, 100)

# add reef zones variable to dataframe 
surfer_coral <- surfer_coral %>%
  mutate(Reef_6_Zones = case_when(
    Plot_num %in% left_1 ~ "Left_1",
    Plot_num %in% left_2 ~ "Left_2",
    Plot_num %in% mid_1 ~ "Mid_1",
        Plot_num %in% mid_2 ~ "Mid_2",
    Plot_num %in% right_1 ~ "Right_1",
    Plot_num %in% right_2 ~ "Right_2",
    TRUE ~ NA_character_
  ))

# subset complexity metrics 
complex_6zones_1cm <- complex_6zones_1cm %>% dplyr::select(c("surf_complex","mean_profile_curvature",
                                              "mean_slope","mean_vrm","site","height_diff"))
complex_6zones_8cm <- complex_6zones_8cm %>% dplyr::select(c("surf_complex","mean_profile_curvature",
                                              "mean_slope","mean_vrm","site","height_diff"))
complex_6zones_32cm <- complex_6zones_32cm %>% dplyr::select(c("surf_complex","mean_profile_curvature",
                                              "mean_slope","mean_vrm","site","height_diff"))


# summarize coral and surfer data by reef zone
surfer_coral_summ_6 <- surfer_coral %>%
  group_by(Reef_6_Zones) %>%
  summarise(
    Mean_perc_coral_cover = mean(perc_coral_cover),
    SD_perc_coral_cover = sd(perc_coral_cover),
    Mean_Track_Counts = mean(Track_Counts),
    SD_Track_Counts = sd(Track_Counts),
    Mean_Sum_Track_Length_meters = mean(Sum_Track_Length_meters),
    SD_Sum_Track_Length_meters = sd(Sum_Track_Length_meters)
  )

# combine with complexity
zones_6_1cm <- surfer_coral_summ_6 %>% full_join(complex_6zones_1cm, by = c("Reef_6_Zones" = "site"))
zones_6_8cm <- surfer_coral_summ_6 %>% full_join(complex_6zones_8cm, by = c("Reef_6_Zones" = "site"))
zones_6_32cm <- surfer_coral_summ_6 %>% full_join(complex_6zones_32cm, by = c("Reef_6_Zones" = "site"))

Distributions & Descriptive Plots

ggplot(surfer_coral, aes(x = perc_coral_cover, fill = Reef_6_Zones)) +
  geom_density(alpha = 0.5) + theme_classic()

ggplot(surfer_coral, aes(x = Reef_6_Zones, y = perc_coral_cover, fill = Reef_6_Zones)) +
  geom_boxplot() + theme_classic() + theme(legend.position="none")

ggplot(surfer_coral_summ_6, aes(x = Reef_6_Zones, y = Mean_perc_coral_cover, fill = Reef_6_Zones)) +
  geom_bar(stat = "identity", position = "dodge") +
  geom_errorbar(aes(ymin = Mean_perc_coral_cover - SD_perc_coral_cover,
                    ymax = Mean_perc_coral_cover + SD_perc_coral_cover),
                position = position_dodge(width = 0.9), width = 0.25) +
  labs(title = "Mean Perc Coral Cover Across Reef Zones",
       x = "Reef Zone",
       y = "Mean Perc Coral Cover") +
  theme_minimal()

ggplot(surfer_coral, aes(x = Track_Counts, fill = Reef_6_Zones)) +
  geom_density(alpha = 0.5) + theme_classic()

ggplot(surfer_coral, aes(x = Reef_6_Zones, y = Track_Counts, fill = Reef_6_Zones)) +
  geom_boxplot() + theme_classic() + theme(legend.position="none")

ggplot(surfer_coral_summ_6, aes(x = Reef_6_Zones, y = Mean_Track_Counts, fill = Reef_6_Zones)) +
  geom_bar(stat = "identity", position = "dodge") +
  geom_errorbar(aes(ymin = Mean_Track_Counts  - SD_Track_Counts ,
                    ymax = Mean_Track_Counts + SD_Track_Counts),
                position = position_dodge(width = 0.9), width = 0.25) +
  labs(title = "Mean Track Counts Across Reef Zones",
       x = "Reef Zone",
       y = "Mean Perc Coral Cover") + theme_minimal()

ggplot(surfer_coral, aes(x = Sum_Track_Length_meters, fill = Reef_6_Zones)) +
  geom_density(alpha = 0.5) + theme_classic()

ggplot(surfer_coral, aes(x = Reef_6_Zones, y = Sum_Track_Length_meters, fill = Reef_6_Zones)) +
  geom_boxplot() + theme_classic() + theme(legend.position="none")

ggplot(surfer_coral_summ_6, aes(x = Reef_6_Zones, y = Mean_Sum_Track_Length_meters, fill = Reef_6_Zones)) +
  geom_bar(stat = "identity", position = "dodge") +
  geom_errorbar(aes(ymin = Mean_Sum_Track_Length_meters - SD_Sum_Track_Length_meters,
                    ymax = Mean_Sum_Track_Length_meters + SD_Sum_Track_Length_meters),
                position = position_dodge(width = 0.9), width = 0.25) +
  labs(title = "Mean Sum Track Length Across Reef Zones",
       x = "Reef Zone",
       y = "Mean Perc Coral Cover") +
  theme_minimal()

Comparison tests

# % Coral cover by reef zone = NOT-SIGNIFICANT (P = 0.5919)
kruskal.test(perc_coral_cover ~ Reef_6_Zones, data = surfer_coral)

## 
##  Kruskal-Wallis rank sum test
## 
## data:  perc_coral_cover by Reef_6_Zones
## Kruskal-Wallis chi-squared = 4.4811, df = 5, p-value = 0.4824

# Surfer tracks by reef zone = SIGNIFICANT (P = 6.951e-12)
kruskal.test(Track_Counts ~ Reef_6_Zones, data = surfer_coral)

## 
##  Kruskal-Wallis rank sum test
## 
## data:  Track_Counts by Reef_6_Zones
## Kruskal-Wallis chi-squared = 65.007, df = 5, p-value = 1.117e-12

# Surfer track length by reef zone = SIGNIFICANT (P = 7.244e-12)
kruskal.test(Sum_Track_Length_meters ~ Reef_6_Zones, data = surfer_coral)

## 
##  Kruskal-Wallis rank sum test
## 
## data:  Sum_Track_Length_meters by Reef_6_Zones
## Kruskal-Wallis chi-squared = 63.616, df = 5, p-value = 2.17e-12

Scatter Plots - 1cm

ggplot(zones_6_1cm, aes(x = mean_slope, y = Mean_Track_Counts , color = Reef_6_Zones)) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linetype = "solid", col = "grey") +  
  labs(title = "Avg. Surfer Tracks & Slope") +
  theme_classic()

ggplot(zones_6_1cm, aes(x = mean_profile_curvature, y =Mean_Track_Counts , color = Reef_6_Zones)) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linetype = "solid", col = "grey") +  
  labs(title = "Avg. Surfer Tracks & Curvature") +
  theme_classic()

ggplot(zones_6_1cm, aes(x = Mean_perc_coral_cover, y = Mean_Track_Counts, color = Reef_6_Zones)) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linetype = "solid", col = "grey") + 
  labs(title = "Avg. Surfer Tracks & Coral Cover") +
  theme_classic()

ggplot(zones_6_1cm, aes(x = mean_vrm, y = Mean_Track_Counts, color = Reef_6_Zones)) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linetype = "solid", col = "grey") +  
  labs(title = "Avg. Surfer Tracks & VRM") +
  theme_classic()

Scatter Plots - 8cm

ggplot(zones_6_8cm, aes(x = mean_slope, y = Mean_Track_Counts , color = Reef_6_Zones)) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linetype = "solid", col = "grey") +  
  labs(title = "Avg. Surfer Tracks & Slope") +
  theme_classic()

ggplot(zones_6_8cm, aes(x = mean_profile_curvature, y =Mean_Track_Counts , color = Reef_6_Zones)) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linetype = "solid", col = "grey") +  
  labs(title = "Avg. Surfer Tracks & Curvature") +
  theme_classic()

ggplot(zones_6_8cm, aes(x = Mean_perc_coral_cover, y = Mean_Track_Counts, color = Reef_6_Zones)) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linetype = "solid", col = "grey") + 
  labs(title = "Avg. Surfer Tracks & Coral Cover") +
  theme_classic()

ggplot(zones_6_8cm, aes(x = mean_vrm, y = Mean_Track_Counts, color = Reef_6_Zones)) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linetype = "solid", col = "grey") +  
  labs(title = "Avg. Surfer Tracks & VRM") +
  theme_classic()

Scatter Plots - 32cm

ggplot(zones_6_32cm, aes(x = mean_slope, y = Mean_Track_Counts , color = Reef_6_Zones)) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linetype = "solid", col = "grey") +  
  labs(title = "Avg. Surfer Tracks & Slope") +
  theme_classic()

ggplot(zones_6_32cm, aes(x = mean_profile_curvature, y =Mean_Track_Counts , color = Reef_6_Zones)) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linetype = "solid", col = "grey") +  
  labs(title = "Avg. Surfer Tracks & Curvature") +
  theme_classic()

ggplot(zones_6_32cm, aes(x = Mean_perc_coral_cover, y = Mean_Track_Counts, color = Reef_6_Zones)) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linetype = "solid", col = "grey") + 
  labs(title = "Avg. Surfer Tracks & Coral Cover") +
  theme_classic()

ggplot(zones_6_32cm, aes(x = mean_vrm, y = Mean_Track_Counts, color = Reef_6_Zones)) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linetype = "solid", col = "grey") +  
  labs(title = "Avg. Surfer Tracks & VRM") +
  theme_classic()

Complexity - 6 reef zones - 1cm - WITH ERROR BARS

ggplot(complex_var1, aes(x = mean_slope, y = zone)) +
  geom_point(aes(color = zone), size = 3) +
  geom_errorbarh(aes(xmin = mean_slope - sd_slope,
                     xmax = mean_slope + sd_slope), height = 0.2) +
  theme_minimal() + theme(legend.position = "bottom") + labs(title = "1 cm")

ggplot(complex_var1, aes(x = mean_vrm, y = zone)) +
  geom_point(aes(color = zone), size = 3) +
  geom_errorbarh(aes(xmin = mean_vrm - sd_vrm,
                     xmax = mean_vrm + sd_vrm), height = 0.2) +
  theme_minimal() + theme(legend.position = "bottom") + labs(title = "1 cm")

ggplot(complex_var1, aes(x = mean_profile_curvature, y = zone)) +
  geom_point(aes(color = zone), size = 3) +
  geom_errorbarh(aes(xmin = mean_profile_curvature - sd_profile_curvature,
                     xmax = mean_profile_curvature + sd_profile_curvature), height = 0.2) +
  theme_minimal() + theme(legend.position = "bottom") + labs(title = "1 cm")

Complexity - 6 reef zones - 4cm - WITH ERROR BARS

ggplot(complex_var_4, aes(x = mean_slope, y = zone)) +
  geom_point(aes(color = zone), size = 3) +
  geom_errorbarh(aes(xmin = mean_slope - sd_slope,
                     xmax = mean_slope + sd_slope), height = 0.2) +
  theme_minimal() + theme(legend.position = "bottom") + labs(title = "4 cm")

ggplot(complex_var_4, aes(x = mean_vrm, y = zone)) +
  geom_point(aes(color = zone), size = 3) +
  geom_errorbarh(aes(xmin = mean_vrm - sd_vrm,
                     xmax = mean_vrm + sd_vrm), height = 0.2) +
  theme_minimal() + theme(legend.position = "bottom") + labs(title = "4 cm")

ggplot(complex_var_4, aes(x = mean_profile_curvature, y = zone)) +
  geom_point(aes(color = zone), size = 3) +
  geom_errorbarh(aes(xmin = mean_profile_curvature - sd_profile_curvature,
                     xmax = mean_profile_curvature + sd_profile_curvature), height = 0.2) +
  theme_minimal() + theme(legend.position = "bottom") + labs(title = "4 cm")

Correlations - 1 cm

# Mean Track Counts & Slope
cor.test(zones_6_1cm$Mean_Track_Counts, zones_6_1cm$mean_slope)

## 
##  Pearson's product-moment correlation
## 
## data:  zones_6_1cm$Mean_Track_Counts and zones_6_1cm$mean_slope
## t = 0.74792, df = 4, p-value = 0.4961
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.6445011  0.9046658
## sample estimates:
##       cor 
## 0.3502693

# Mean Track Counts & Curvature
cor.test(zones_6_1cm$Mean_Track_Counts, zones_6_1cm$mean_profile_curvature)

## 
##  Pearson's product-moment correlation
## 
## data:  zones_6_1cm$Mean_Track_Counts and zones_6_1cm$mean_profile_curvature
## t = -0.81921, df = 4, p-value = 0.4587
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.9105140  0.6246832
## sample estimates:
##       cor 
## -0.379039

# Mean Track Counts & surf_complex
cor.test(zones_6_1cm$Mean_Track_Counts, zones_6_1cm$surf_complex)

## 
##  Pearson's product-moment correlation
## 
## data:  zones_6_1cm$Mean_Track_Counts and zones_6_1cm$surf_complex
## t = 1.6542, df = 4, p-value = 0.1734
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.3608628  0.9549606
## sample estimates:
##      cor 
## 0.637356

# Mean Track Counts & Coral Cover
cor.test(zones_6_1cm$Mean_Track_Counts, zones_6_1cm$Mean_perc_coral_cover)

## 
##  Pearson's product-moment correlation
## 
## data:  zones_6_1cm$Mean_Track_Counts and zones_6_1cm$Mean_perc_coral_cover
## t = -0.47357, df = 4, p-value = 0.6605
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.8778261  0.7148140
## sample estimates:
##        cor 
## -0.2304139

# Mean Track Counts & Coral Cover
cor.test(zones_6_1cm$Mean_Track_Counts, zones_6_1cm$mean_vrm)

## 
##  Pearson's product-moment correlation
## 
## data:  zones_6_1cm$Mean_Track_Counts and zones_6_1cm$mean_vrm
## t = 2.9681, df = 4, p-value = 0.04122
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.05424508 0.98077324
## sample estimates:
##      cor 
## 0.829298

Correlations - 8 cm

# Mean Track Counts & Slope
cor.test(zones_6_8cm$Mean_Track_Counts, zones_6_8cm$mean_slope)

## 
##  Pearson's product-moment correlation
## 
## data:  zones_6_8cm$Mean_Track_Counts and zones_6_8cm$mean_slope
## t = 0.38243, df = 4, p-value = 0.7216
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.7359189  0.8671945
## sample estimates:
##       cor 
## 0.1878116

# Mean Track Counts & Curvature
cor.test(zones_6_8cm$Mean_Track_Counts, zones_6_8cm$mean_profile_curvature)

## 
##  Pearson's product-moment correlation
## 
## data:  zones_6_8cm$Mean_Track_Counts and zones_6_8cm$mean_profile_curvature
## t = -0.15269, df = 4, p-value = 0.886
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.8360351  0.7838645
## sample estimates:
##         cor 
## -0.07612235

# Mean Track Counts & surf_complex
cor.test(zones_6_8cm$Mean_Track_Counts, zones_6_8cm$surf_complex)

## 
##  Pearson's product-moment correlation
## 
## data:  zones_6_8cm$Mean_Track_Counts and zones_6_8cm$surf_complex
## t = 0.77823, df = 4, p-value = 0.4799
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.6361474  0.9072042
## sample estimates:
##       cor 
## 0.3626296

# Mean Track Counts & Coral Cover
cor.test(zones_6_8cm$Mean_Track_Counts, zones_6_8cm$Mean_perc_coral_cover)

## 
##  Pearson's product-moment correlation
## 
## data:  zones_6_8cm$Mean_Track_Counts and zones_6_8cm$Mean_perc_coral_cover
## t = -0.47357, df = 4, p-value = 0.6605
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.8778261  0.7148140
## sample estimates:
##        cor 
## -0.2304139

# Mean Track Counts & Coral Cover
cor.test(zones_6_8cm$Mean_Track_Counts, zones_6_8cm$mean_vrm)

## 
##  Pearson's product-moment correlation
## 
## data:  zones_6_8cm$Mean_Track_Counts and zones_6_8cm$mean_vrm
## t = 0.64925, df = 4, p-value = 0.5516
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.6709156  0.8958441
## sample estimates:
##       cor 
## 0.3087647

Correlations - 32 cm

# Mean Track Counts & Slope
cor.test(zones_6_32cm$Mean_Track_Counts, zones_6_32cm$mean_slope)

## 
##  Pearson's product-moment correlation
## 
## data:  zones_6_32cm$Mean_Track_Counts and zones_6_32cm$mean_slope
## t = 0.59186, df = 4, p-value = 0.5858
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.6857101  0.8902974
## sample estimates:
##       cor 
## 0.2837654

# Mean Track Counts & Curvature
cor.test(zones_6_32cm$Mean_Track_Counts, zones_6_32cm$mean_profile_curvature)

## 
##  Pearson's product-moment correlation
## 
## data:  zones_6_32cm$Mean_Track_Counts and zones_6_32cm$mean_profile_curvature
## t = -1.698, df = 4, p-value = 0.1647
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.9564122  0.3462060
## sample estimates:
##        cor 
## -0.6471963

# Mean Track Counts & surf_complex
cor.test(zones_6_32cm$Mean_Track_Counts, zones_6_32cm$surf_complex)

## 
##  Pearson's product-moment correlation
## 
## data:  zones_6_32cm$Mean_Track_Counts and zones_6_32cm$surf_complex
## t = 0.65847, df = 4, p-value = 0.5462
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.6685004  0.8967054
## sample estimates:
##       cor 
## 0.3127211

# Mean Track Counts & Coral Cover
cor.test(zones_6_32cm$Mean_Track_Counts, zones_6_32cm$Mean_perc_coral_cover)

## 
##  Pearson's product-moment correlation
## 
## data:  zones_6_32cm$Mean_Track_Counts and zones_6_32cm$Mean_perc_coral_cover
## t = -0.47357, df = 4, p-value = 0.6605
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.8778261  0.7148140
## sample estimates:
##        cor 
## -0.2304139

# Mean Track Counts & Coral Cover
cor.test(zones_6_32cm$Mean_Track_Counts, zones_6_32cm$mean_vrm)

## 
##  Pearson's product-moment correlation
## 
## data:  zones_6_32cm$Mean_Track_Counts and zones_6_32cm$mean_vrm
## t = 0.67794, df = 4, p-value = 0.535
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.6633607  0.8984999
## sample estimates:
##       cor 
## 0.3210291

Datasets Overview

knitr::kable(cloudbreak, caption = "3 reef zones at 1 cm")

3 reef zones at 1 cm
Reef_Zone	Mean_perc_coral_cover	SD_perc_coral_cover	Mean_Track_Counts	SD_Track_Counts	Mean_Sum_Track_Length_meters	SD_Sum_Track_Length_meters	surf_complex	mean_profile_curvature	mean_slope	mean_vrm	height_diff
Cloudbreak_Left	45.69444	23.12771	11.55556	9.471956	22.42963	20.00457	1.355083	-0.1884038	28.03568	0.0263064	12.045302
Cloudbreak_Mid	46.00000	25.40489	96.77419	45.914202	127.59355	46.51200	1.463742	-0.5295614	30.18576	0.0326319	7.933768
Cloudbreak_Right	40.21875	18.88603	31.12500	22.727003	41.28125	28.21175	1.351907	-0.3923977	29.37325	0.0223371	8.862193

knitr::kable(cloudbreak_8, caption = "3 reef zones at 8 cm")

3 reef zones at 8 cm
Reef_Zone	Mean_perc_coral_cover	SD_perc_coral_cover	Mean_Track_Counts	SD_Track_Counts	Mean_Sum_Track_Length_meters	SD_Sum_Track_Length_meters	surf_complex	mean_profile_curvature	mean_slope	mean_vrm	height_diff
Cloudbreak_Left	45.69444	23.12771	11.55556	9.471956	22.42963	20.00457	1.169165	-0.1375071	20.16389	0.0188030	7.220032
Cloudbreak_Mid	46.00000	25.40489	96.77419	45.914202	127.59355	46.51200	1.215381	-0.1535148	22.76270	0.0207441	5.355778
Cloudbreak_Right	40.21875	18.88603	31.12500	22.727003	41.28125	28.21175	1.194174	-0.1435526	23.34233	0.0188692	5.263051

knitr::kable(cloudbreak_32, caption = "3 reef zones at 32 cm")

3 reef zones at 32 cm
Reef_Zone	Mean_perc_coral_cover	SD_perc_coral_cover	Mean_Track_Counts	SD_Track_Counts	Mean_Sum_Track_Length_meters	SD_Sum_Track_Length_meters	surf_complex	mean_profile_curvature	mean_slope	mean_vrm	height_diff
Cloudbreak_Left	45.69444	23.12771	11.55556	9.471956	22.42963	20.00457	1.092908	-0.0035139	14.47076	0.0168532	6.993179
Cloudbreak_Mid	46.00000	25.40489	96.77419	45.914202	127.59355	46.51200	1.126731	-0.0167661	17.75815	0.0263487	3.922493
Cloudbreak_Right	40.21875	18.88603	31.12500	22.727003	41.28125	28.21175	1.120714	-0.0093239	18.00795	0.0250290	4.700855

knitr::kable(zones_6_1cm, caption = "6 reef zones at 1 cm")

6 reef zones at 1 cm
Reef_6_Zones	Mean_perc_coral_cover	SD_perc_coral_cover	Mean_Track_Counts	SD_Track_Counts	Mean_Sum_Track_Length_meters	SD_Sum_Track_Length_meters	surf_complex	mean_profile_curvature	mean_slope	mean_vrm	height_diff
Left_1	45.67500	24.39605	2.80000	2.201010	6.06000	5.938238	1.296294	-0.3762478	26.89240	0.0232524	8.646105
Left_2	45.70588	23.11744	16.70588	8.183556	32.05882	19.091034	1.411375	-0.0470713	29.10751	0.0288984	12.045302
Mid_1	55.61364	20.35447	52.09091	12.918626	83.26364	20.339728	1.371444	0.1107141	28.58869	0.0287408	5.260808
Mid_2	40.71250	26.79691	121.35000	38.066804	151.97500	37.876723	1.526769	-0.8902330	31.27473	0.0359213	7.432290
Right_1	35.71875	17.28926	48.62500	19.631880	61.01250	26.799277	1.304968	-0.0520542	27.60620	0.0236267	6.599904
Right_2	44.71875	20.47230	13.62500	4.627171	21.55000	9.870446	1.434262	-0.7006153	32.15261	0.0248532	6.902994

knitr::kable(zones_6_8cm, caption = "6 reef zones at 8 cm")

6 reef zones at 8 cm
Reef_6_Zones	Mean_perc_coral_cover	SD_perc_coral_cover	Mean_Track_Counts	SD_Track_Counts	Mean_Sum_Track_Length_meters	SD_Sum_Track_Length_meters	surf_complex	mean_profile_curvature	mean_slope	mean_vrm	height_diff
Left_1	45.67500	24.39605	2.80000	2.201010	6.06000	5.938238	1.139692	-0.1573747	19.16479	0.0177662	5.088412
Left_2	45.70588	23.11744	16.70588	8.183556	32.05882	19.091034	1.198822	-0.1273519	21.13203	0.0198370	4.428964
Mid_1	55.61364	20.35447	52.09091	12.918626	83.26364	20.339728	1.173374	-0.1663724	21.07429	0.0177473	4.683019
Mid_2	40.71250	26.79691	121.35000	38.066804	151.97500	37.876723	1.239949	-0.1465244	23.79908	0.0225599	4.168036
Right_1	35.71875	17.28926	48.62500	19.631880	61.01250	26.799277	1.146712	-0.1299227	20.41709	0.0157859	4.126961
Right_2	44.71875	20.47230	13.62500	4.627171	21.55000	9.870446	1.247642	-0.1431191	26.95479	0.0222696	3.834919

knitr::kable(zones_6_32cm, caption = "6 reef zones at 32 cm")

6 reef zones at 32 cm
Reef_6_Zones	Mean_perc_coral_cover	SD_perc_coral_cover	Mean_Track_Counts	SD_Track_Counts	Mean_Sum_Track_Length_meters	SD_Sum_Track_Length_meters	surf_complex	mean_profile_curvature	mean_slope	mean_vrm	height_diff
Left_1	45.67500	24.39605	2.80000	2.201010	6.06000	5.938238	1.071572	-0.0102504	13.36996	0.0127206	4.923750
Left_2	45.70588	23.11744	16.70588	8.183556	32.05882	19.091034	1.114816	0.0067685	15.56199	0.0212235	4.324342
Mid_1	55.61364	20.35447	52.09091	12.918626	83.26364	20.339728	1.101611	-0.0224543	16.00891	0.0213687	3.387987
Mid_2	40.71250	26.79691	121.35000	38.066804	151.97500	37.876723	1.141501	-0.0219774	18.81221	0.0292357	3.092263
Right_1	35.71875	17.28926	48.62500	19.631880	61.01250	26.799277	1.084936	-0.0003363	15.15744	0.0162748	3.595753
Right_2	44.71875	20.47230	13.62500	4.627171	21.55000	9.870446	1.158419	0.0004206	21.36416	0.0343705	2.886472

Cloudbreak_Analysis_24

Sofia Ferreira

2024-01-11

3 REEF ZONES

Distribution & Descriptive Plots

Comparison tests

Scatter Plots - 1cm

Scatter Plots - 4cm

Scatter Plots - 8cm

Correlations - 1 cm

Correlations - 8 cm

Correlations - 32 cm

Complexity - 3 reef zones - 1cm - WITH ERROR BARS

Complexity - 3 reef zones - 4cm - WITH ERROR BARS

6 FEEZ ZONES

Distributions & Descriptive Plots

Comparison tests

Scatter Plots - 1cm

Scatter Plots - 8cm

Scatter Plots - 32cm

Complexity - 6 reef zones - 1cm - WITH ERROR BARS

Complexity - 6 reef zones - 4cm - WITH ERROR BARS

Correlations - 1 cm

Correlations - 8 cm

Correlations - 32 cm

Datasets Overview