In ericmkeen/bangarang: Bangarang data tools for the Kitimat Fjord System

knitr::opts_chunk$set(echo = TRUE)

The bangarang package is a bundle of datasets and functions that help analysts with research in the Kitimat Fjord System (KFS) in the north coast of mainland British Columbia. Nearly all of the content is catered specifically to analysis of datasets from the RV Bangarang expedition of 2013 - 2015, which was focused on the abundance, distribution, and foraging ecology of whales, seabirds, salmon, and their prey during the months of summer and early fall. The research involved line-transect sampling, active acoustic (echosounder) surveys, and oceanographic sampling (both while underway and at a grid of stations).

Methodological details can be found here. More info on the Bangarang project can be found here. The Bangarang project was carried out as a doctoral thesis at Scripps Institution of Oceanography in close collaboration with the Gitga'at First Nation, BC Whales, Fisheries & Oceans Canada, and the NOAA Southwest Fisheries Science Center.

Installation

The bangarang package can be downloaded directly from GitHub:

# Install devtools if needed
if (!require('devtools')) install.packages('devtools')

# Install package
devtools::install_github('ericmkeen/bangarang')

Load into your R session:

library(bangarang)

This vignette was made with bangarang version r utils::packageVersion('LTabundR'), and will make use of a few other packages:

library(tidyverse)
library(ggplot2)

Maps

Produce a map of the Bangarang study area in the KFS using the ggplot and sf packages:

gg_kfs()

As with all the bangarang functions, see this function's documentation for changing the geographic range, color, and transparency settings.

?gg_kfs

Datasets

Land

data(kfs_land)

kfs_land %>% class

kfs_land %>% glimpse

Snapshot of dataset:

par(mar=c(.1,.1,.1,.1))
kfs_land %>% plot

Seafloor

data(kfs_seafloor)

kfs_seafloor %>% glimpse

Plot it:

ggplot(kfs_seafloor,
       aes(x=x, y=y, color=layer)) + 
  geom_point(size=.1) + 
  xlab(NULL) + ylab(NULL) + labs(color = 'Depth (m)') + 
  theme_minimal()

Proposed LNG shipping routes

data(shiplane)

shiplane %>% glimpse

Plot it:

gg_kfs() + 
  geom_path(data=shiplane,
            mapping=aes(x=X, y=Y, group=PID)) + 
  xlab(NULL) + ylab(NULL)

Geostrata

Four main "provinces" referenced in study

data(provinces)

provinces %>% glimpse

Check it out:

gg_kfs() + 
  geom_polygon(data=provinces,
            mapping=aes(x=X, 
                        y=Y,
                        group = factor(province),
                        fill = factor(province),
                        color = factor(province)),
            alpha=.4) + 
  xlab(NULL) + ylab(NULL) + labs(fill='Province', color='Province')

Eight main channels referenced in study

data(channels)

channels %>% glimpse

Check it out:

gg_kfs() + 
  geom_polygon(data=channels,
            mapping=aes(x=X, 
                        y=Y,
                        fill = province,
                        color = province),
            alpha=.4) + 
  xlab(NULL) + ylab(NULL)

Rectangular blocks (medium size).

data(kfs_blocks_bbox)

kfs_blocks_bbox %>% glimpse

Check it out:

gg_kfs() + 
  geom_rect(data=kfs_blocks_bbox,
            mapping=aes(xmin=left, 
                        xmax=right,
                        ymin=bottom,
                        ymax=top,
                        group=id),
            fill=NA,
            color='black') + 
  xlab(NULL) + ylab(NULL)

Rectangular blocks (small size).

data(blocks)

blocks %>% glimpse

Check it out:

gg_kfs() + 
  geom_rect(data=blocks,
            mapping=aes(xmin=left, 
                        xmax=right,
                        ymin=bottom,
                        ymax=top,
                        group=name),
            fill=NA,
            color='black') + 
  xlab(NULL) + ylab(NULL)

Oceanographic stations

data(stations)

stations %>% glimpse

Check it out:

gg_kfs() + 
  geom_point(data=stations,
            mapping=aes(x=long, 
                        y=lat,
                        group = block),
            alpha=.8) + 
  xlab(NULL) + ylab(NULL) + labs(group='Waterway')

Effort

All survey effort aboard the Bangarang:

data(effort)

effort %>% glimpse

Map overview:

gg_kfs() + 
  geom_point(data=effort,
            mapping=aes(x=lon, 
                        y=lat,
                        color = effort),
            alpha=.4,
            size=.2) + 
  xlab(NULL) + ylab(NULL) 

Show each year separately:

gg_kfs() + 
  geom_point(data=effort,
            mapping=aes(x=lon, 
                        y=lat,
                        color = effort),
            alpha=.4,
            size=.2) + 
  facet_wrap(~lubridate::year(date)) + 
  xlab(NULL) + ylab(NULL) + 
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank())

Show each circuit in 2015 separately, systematic transect effort only:

# Filter to transect effort only
transects <- 
  effort %>% 
  filter(lubridate::year(date) == 2015,
         effort == 'transect') %>% 
  mutate(group = paste0(lubridate::year(date), ' circuit ', circuit))

# plot it
gg_kfs() + 
  geom_point(data= transects,
             mapping=aes(x=lon, 
                        y=lat),
            alpha=.4,
            size=.2) + 
  facet_wrap(~group) + 
  xlab(NULL) + ylab(NULL) + 
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank())

Seabirds

data(seabirds)

seabirds %>% glimpse

Map it:

gg_kfs() + 
  geom_point(data=seabirds,
            mapping=aes(x=lon, 
                        y=lat,
                        color = sp1,
                        size = best),
            alpha=.4) + 
  xlab(NULL) + ylab(NULL) 

Salmon

data(salmon)

salmon %>% glimpse

Map it;

gg_kfs() + 
  geom_point(data=salmon,
            mapping=aes(x=lon, 
                        y=lat,
                        size = jumps),
            alpha=.4) + 
  xlab(NULL) + ylab(NULL) 

ggplot(salmon %>% 
         filter(zone < 3) %>% 
         mutate(Zone = factor(zone)),
       aes(x=Zone)) + 
  geom_bar(stat='count')

Whales

data(whale_sightings)

whale_sightings %>% glimpse

gg_kfs() + 
  geom_point(data=whale_sightings,
            mapping=aes(x=x, 
                        y=y,
                        color = spp,
                        size = size),
            alpha=.4) + 
  xlab(NULL) + ylab(NULL) 

Oceanography

This dataset is an interpolated grid of oceanographic values for each circuit in each year. Some variables are from the thermosalinograph we had running at the surface during transects, some are from the echosounder we used while underway, and others are from the Seabird Electronics CTD we used at the grid of oceanographic stations.

data(ocean)

ocean %>% glimpse

Here are the variables included:

ocean$metric %>% unique %>% sort

As an example, say we wanted a map of the sea surface salinity (SSS) for each circuit of the 2015 season.

# Filter the dataset
sss <- 
  ocean %>% 
  filter(metric == 'SSS', 
         year==2015)

# Map it
gg_kfs() + 
  geom_point(data=sss,
             mapping=aes(x=lon, 
                         y=lat,
                         color=value),
             size=.05) + 
  facet_wrap(~circuit) +
  xlab(NULL) + ylab(NULL) + 
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank()) + 
  scale_color_gradientn(colours = rev(rainbow(5)))

Helper functions

in_block()

A way to see which geostratum a pair of coordinates is located within.

in_block(x= -129.4, y=53.2)

in_block(x= -129.2, y=52.9)

in_kfs()

A quick test to see if coordinates properly within the water within the boundaries of the KFS (as defined by the Bangarang project). The function returns the dataset with a new column, inkfs:

test <- in_kfs(seabirds %>% rename(x=lon, y=lat),
                 toplot = TRUE)

test$inkfs %>% table

in_water()

A quick test to see if coordinates properly within the water within the water (and not on land by mistake). The function returns the dataset with a new column, valid:

test <- in_water(seabirds %>% rename(x=lon, y=lat),
                 toplot = TRUE)

test$valid %>% table

whale_map()

Calculates the true position of a sighting within the KFS, in offshore or confined coastal waters, accounting for whether or not the observer is using the horizon or a shoreline as the basis for the reticle reading.

The X and Y you supply is the observer's location (either a boat or a stationary field station).

whalemap(X= -129.2, 
         Y=52.9,
         bearing = 313,
         reticle = 0.2,
         eye.height = 2.1,
         vessel.hdg = 172,
         toplot=TRUE)

ericmkeen/bangarang documentation built on Feb. 10, 2024, 2:58 p.m.