6 Does fishing intensity affect the distribution of H. curtus and H. schistosus?

# loading required libraries

library(sp)
library(raster)

# importing data

dep_int <- read.csv("./Data/Fishing intensity_dep.csv") # geocoded fishing effort

snakes_den <- read.csv("./Data/snakes-density.csv") # sea snake occurence in bycatch

ext <- raster::raster("./Data/sampling_extent.tif") # sampling grid

# importing required scripts

source("./Functions/intensity extract.R")
source("./Functions/raster to df.R")

# Calculating fishing intensity

fi <- dep_int%>%
  group_by(Gear.Type)%>%
  nest()%>%
  mutate(m = map(data, ~map.extract(df = ., var = "effort", func = 'sum')),
         mdf = map(m, map.df))%>%
  dplyr::select(mdf)%>%
  unnest()%>%
  spread(Gear.Type, layer)

# Calculating sea snake CPUE

den <- snakes_den%>%
  group_by(Species)%>%
  nest()%>%
  mutate(m = map(data, ~map.extract(df = ., var = "n", func = 'sum')),
         mdf = map(m, map.df))%>%
  dplyr::select(mdf)%>%
  unnest()%>%
  spread(Species, layer)

# Calculating mean depth in cell

library(marmap)

depth <- readGEBCO.bathy("./Data/gebco_2020_n16.7_s15.5_w72.0_e73.9.nc")

depth = fortify.bathy(depth)%>% # converting to usable data frame
  dplyr::rename(lon = x, lat = y, depth = z)%>%
  filter(depth < 1)


dep <- depth%>%
  nest()%>%
  mutate(m = map(data, ~map.extract(df = ., var = "depth", func = mean)),
         mdf = map(m, map.df))%>%
  dplyr::select(mdf)%>%
  unnest()%>%
  rename(mean.depth = layer)

# Joining creating combined data frame 

fi_den<- inner_join(fi, den, by = c("x", "y"))%>%
  inner_join(dep, by = c("x", "y"))%>%
  # calculating relative abaundance of sea snakes in each grid cell
  mutate(rel.prop = HC/(HC+HS))

# saving fi_den for analysis

write.csv(fi, "./Data/Fishing intensity_grid.csv", row.names = F)

6.1 Spatial overlap between fisheries and sea snakes

# Calculating spatial overlap between sea snakes and fisheries

sp.ovlp <- fi_den %>%
    gather(c("HS", "HC"), key = Species, value = CPUE) %>%
    gather(c("GillNet", "Trawler"), key = Gear, value = intensity) %>%
    group_by(Gear, Species) %>%
    summarise(overlap = sum(CPUE > 0 & intensity > 0, na.rm = T), extent.sp = sum(CPUE > 0), rel.ovlp = overlap/extent.sp)

# Creating pretty table

sp.ovlp %>%
    dplyr::select(-extent.sp) %>%
    mutate(rel.ovlp = 100 * rel.ovlp, Species = ifelse(Species == "HC", "Hydrophis curtus", "Hydrophis schistosus")) %>%
    rename(`Relative Overlap %` = rel.ovlp, `Spatial Overlap` = overlap)

Gear	Species	Spatial Overlap	Relative Overlap %
GillNet	Hydrophis curtus	38	70.37037
GillNet	Hydrophis schistosus	49	79.03226
Trawler	Hydrophis curtus	52	96.29630
Trawler	Hydrophis schistosus	45	72.58065

6.1.1 Testing spatial overlap between sea snakes and fisheries

We tested the spatial overlap between sea snakes and fisheries along the Sindhudurg coast as the difference in relative proportion of overlap among species and gear.

sp.ovlp%>%
  mutate(Species = ifelse(Species == "HC", "Hydrophis curtus", "Hydrophis schistosus"))%>%
  group_by(Species)%>%
  nest()%>%
  mutate(ptest = map(data, ~prop.test(.$overlap, .$extent.sp)), # Z - test of proportion
         sumry = map(ptest, broom::tidy),
         h = map(sumry, ~pwr::ES.h(.$estimate2, .$estimate1)))%>% # effect size
  dplyr::select(sumry, h)%>%
  unnest()%>%
  dplyr::select(Species:p.value, h)%>%
  rename(`Gill Net` = estimate1,
         Trawler = estimate2)

Species	Gill Net	Trawler	statistic	p.value	h
Hydrophis curtus	0.7037037	0.9629630	11.2666667	0.0007891	0.7638663
Hydrophis schistosus	0.7903226	0.7258065	0.3957447	0.5292951	-0.1509486

Trawlers had significantly higher spatial overlap with both species. H. curtus overlaped more with trawlers than H. schistosus and vice - a - versa for H. shcistosus in gill nets.