7 Overlap between fisheries catch and sea snake diet

require(tidyverse)

# importing catch data

catch <- read.csv("./Data/catch.csv")

# importing gut content data

gutcontent <- read.csv("./Data/Sea_snakes_gut_content_2018-19.csv")

# Standarising sea snake gut content data

gutcontent = gutcontent%>%
  # removing specimens collected from fish landing centers
  filter(Snake.Species == "Hydrophis schistosus" | Snake.Species == "Hydrophis curtus",
         # removing unidentified specimens
         Prey.Family != "Unidentified", Prey.Family != "")%>%
  group_by(Snake.Species)%>%
  # caluclating abundance
  mutate(n = n())%>%
  group_by(Snake.Species, Prey.Family)%>%
  # calculating relative abunance from each snakes species
  summarise(Abundance = n(), n = last(n), Rel.prop = last(Abundance/n))

Note: Catch data was combined from multiple sources (Sharma et al. unpublished data and Gupta et al. unpublished data). The same was standardised for this analysis. Kindly refer to Functions/clean catch data.R for further details.

7.1 Sampling Adequacy for fisherie catch data

# calculating number trips and fishing effort

catch %>%
    group_by(Gear.Type, Sample) %>%
    # Number hauls and haul duration for each trip
summarise(Haul.time = last(Haul.time), No.hauls = last(No.hauls)) %>%
    group_by(Gear.Type) %>%
    # Number trips and effort sampled by gear type
summarise(N = length(unique(Sample)), Haul.Hours = sum(No.hauls * Haul.time, na.rm = T)/60)

Gear.Type	N	Haul.Hours
Gill Net	38	35.41667
Trawler	140	434.65000

7.2 Difference in catch landed per trip by gear

# importing catch weight data

tonnage <- read.csv("./Data/catch tonnage.csv")

## Summary

tonnage%>%
  skimr::skim(Total.Catch..kg.)%>%
  skimr::yank("numeric")

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
Total.Catch..kg.	0	1	252.94	265.59	5	85.25	163.88	314.82	1187.5	▇▂▁▁▁

# Testing assumption of normality

broom::tidy(shapiro.test(log(tonnage$Total.Catch..kg.)))

statistic	p.value	method
0.966559	0.0203415	Shapiro-Wilk normality test

# Test

broom::tidy(t.test(log(Total.Catch..kg.) ~ Gear.Type, data = tonnage))%>%
  mutate(d = lsr::cohensD(log(Total.Catch..kg.) ~ Gear.Type, data = tonnage))%>%# effect size
  dplyr::select(estimate1:p.value, d)%>%
  # inverse log
  mutate(estimate1 = exp(estimate1),
         estimate2 = exp(estimate2))%>%
  rename(`Gill net` = estimate1,
         Trawler = estimate2)

Gill net	Trawler	statistic	p.value	d
98.09173	226.0161	-3.896286	0.0002104	0.8447173

Trawlers landed significantly higher catches per trip than gill nets.

Note: Total catch landed per trip in kgs was log transformed for normality.

7.3 Richness of fish families found in fisheries catch

# Richness

catch %>%
    group_by(Gear.Type) %>%
    filter(Family != "") %>%
    summarise(Family.Richness = length(unique(Family)))

Gear.Type	Family.Richness
Gill Net	15
Trawler	48

7.4 No. of Sea snake prey families found in fish catch and overlap

# Creating separate prey data frame for each species

gc.hs <- filter(gutcontent, Snake.Species == "Hydrophis schistosus")

gc.hc <- filter(gutcontent, Snake.Species == "Hydrophis curtus")

# Creating data martix for fisheries catch

catch_fam <- catch%>%
  # removing unidentified speciemens
  filter(Family != "")%>%
  group_by(Gear.Type, Sample)%>%
  # Calculating total weight per sample
  mutate(Sample.Wt = sum(Weight.g, na.rm = T))%>%
  group_by(Gear.Type, Sample, Family)%>%
  # Weight of each fish family in catch
  summarise(Biomass = sum(Weight.g, na.rm = T),
            # Marking sea snake prey families
            Prey = last(ifelse(Family%in%gutcontent$Prey.Family, "Yes", "No")),
            Sample.Wt = last(Sample.Wt))%>%
  # Caluclating relative proportion in catch
  mutate(Rel.biomass = Biomass/Sample.Wt)#%>%

## Adding sea snake species

catch_fam = catch_fam%>%
  mutate(HS = if_else(Family%in%gc.hs$Prey.Family, "Yes", "No"),
         HC= if_else(Family%in%gc.hc$Prey.Family, "Yes", "No"))

# number of prey families caught by each gear

catch_fam%>%
  gather(c("HC", "HS"), key = "Snake species", value = "Prey")%>%
  filter(Prey == "Yes")%>%
  group_by(Gear.Type, `Snake species`)%>%
  summarise(`Prey Family Overlap` = length(unique(Family)))%>%
  mutate(`Snake species` = ifelse(`Snake species` == "HC", "Hydrophis curtus", "Hydrophis schistosus"))%>%
  spread(Gear.Type, `Prey Family Overlap`)

Snake species	Gill Net	Trawler
Hydrophis curtus	7	9
Hydrophis schistosus	7	10

Trawlers caught more sea snake prey families than gillnets.

7.5 Relative proportion of sea snake prey in fisheries catch

# prey of each species as relative proportion in fisheries catch

catch_fam %>%
    gather(c("HC", "HS"), key = snake, value = Prey) %>%
    filter(Prey == "Yes") %>%
    group_by(Gear.Type, Sample, snake) %>%
    summarise(N = length(unique(Family)), Biomass = sum(Biomass), Sample.Wt = last(Sample.Wt), rel.prop = Biomass/Sample.Wt) %>%
    group_by(Gear.Type, snake) %>%
    summarise(Mean.prop = mean(rel.prop)) %>%
    spread(Gear.Type, Mean.prop)

snake	Gill Net	Trawler
HC	0.8204191	0.7356892
HS	0.8188879	0.4842906

Sea snake prey consisted of a greater proportion of gillnet catch than trawler catch on average.

7.6 Is the proportion of H. curtus prey greater than that of H. schistosus in catch?

catch_fam %>%
    gather(c("HC", "HS"), key = snake, value = Prey) %>%
    filter(Prey == "Yes") %>%
    group_by(Gear.Type, Sample, snake) %>%
    summarise(Biomass = sum(Biomass), Sample.Wt = last(Sample.Wt), rel.prop = Biomass/Sample.Wt) %>%
    group_by(Gear.Type) %>%
    nest() %>%
    mutate(ttest = map(data, ~t.test(rel.prop ~ snake, data = .)), sumry = map(ttest, broom::tidy), d = map(data, ~lsr::cohensD(rel.prop ~ snake, data = .))) %>%
    dplyr::select(sumry, d) %>%
    unnest() %>%
    dplyr::select(estimate1:parameter, d, -estimate) %>%
    rename(`H. curtus` = estimate1, `H. schistosus` = estimate2)

Gear.Type	H. curtus	H. schistosus	statistic	p.value	parameter	d
Gill Net	0.8204191	0.8188879	0.0412568	0.9672023	73.99035	0.0094650
Trawler	0.7356892	0.4842906	7.0245510	0.0000000	233.02771	0.8885814

Yes, for trawlers.

7.7 Sea snake prey species in fisheries catch

Species constituting >10% of the catch on average are represented.

catch_fam %>%
    gather(c("HC", "HS"), key = "snake", value = "Prey") %>%
    filter(Prey == "Yes") %>%
    mutate(snake = ifelse(snake == "HC", "Hydrophis curtus", "Hydrophis schistosus")) %>%
    group_by(Gear.Type, snake) %>%
    summarise(N = length(unique(Family)), p = mean(Rel.biomass, na.rm = T), sd = sd(Rel.biomass, na.rm = T)/sqrt(n())) %>%
    ggplot(aes(snake, p, fill = Gear.Type)) + geom_col(width = 0.5, position = position_dodge(), col = "black") + geom_errorbar(aes(ymin = p - sd, ymax = p + sd), position = position_dodge(width = 0.5), 
    width = 0.25) + scale_fill_brewer(palette = "Greys", name = "Gear Type") + scale_y_continuous(expand = c(0, 0), limits = c(0, 0.3)) + labs(x = "Snake Species", y = "Average proportion \n in catch") + 
    theme(axis.text.x = element_text(face = "italic"))

ggsave(last_plot(), filename = "./Figures and Tables/figure5.png", height = 8, width = 8)