Survdat Abundance and Biomass Check

Species Abundance and Biomass Check for 2020 Survdat

We discovered that vessel conversion factors had been applied incorrectly in past retrievals of the federal bottom trawl survey data that we obtained from the NEFSC. After resolving these problems, we set up this document to show whether any of the changes may have important implications for analyses that have recently been conducted using earlier datasets.

This report compares two datasets:

1. Survdat_Nye_Aug_2020.RData = August 2020 Survdat

2. NEFSC_BTS_all_seasons_03032021.RData = March 2021 Survdat

Load Packages Paths and Themes

###__ Packages  ####
library(here)
library(janitor)
library(gmRi)
library(patchwork)
library(tidyverse)
library(knitr)
library(kableExtra)

# Set Paths to Box Resources
box_paths  <- research_access_paths(os.use = "unix")
mills_path <- box_paths$mills
res_path   <- box_paths$res
nmfs_path  <- shared.path("unix", "RES_Data", "NMFS_Trawl")

#### Set theme  ####
theme_set(theme_bw() + theme(
  axis.text.y = element_text(size = 11),
  axis.text.x = element_text(angle = 90, vjust = 0.5)))

Load SURVDAT Sources

# Cleanup functions
source(here("R/01_nefsc_ss_build_nodrop.R"))

# 2020 data received last august
load(paste0(nmfs_path, "Survdat_Nye_Aug 2020.RData"))
survdat_20 <- clean_names(survdat) 

# Data we just received in 2021 with errors located and corrected
load(paste0(nmfs_path, "2021_survdat/NEFSC_BTS_all_seasons_03032021.RData"))
survdat_21 <- survey$survdat %>% clean_names()

Run Standard Cleanup

# Run cleanup
survdat_20 <- survdat_prep_nodrop(survdat = survdat_20) %>% 
  mutate(survdat_source = "survdat_nye2020")


# Run cleanup
survdat_21 <- survdat_prep_nodrop(survdat = survdat_21) %>% 
  mutate(survdat_source = "survdat_2021") 

rm(survdat, survey)

Load List of Published Species

####  Load the species list from Andrew
species_check <- read_csv(here("data/andrew_species/Assesmentfishspecies.csv"), 
                          col_types = cols())
species_check <- species_check %>% 
  clean_names() %>% 
  mutate(svspp = str_pad(svspp, 3, pad = "0", side = "left"),
         comname = tolower(comname),
         species = str_to_title(species)) %>% 
  arrange(svspp)


# Put in a list
source_list <- list("survdat_nye_2020" = survdat_20,
                    "survdat_2021"     = survdat_21)

Making Comparisons

In this code chunk the different sources are put together in a list, and the same cleanup steps are applied to each. The years are limited to before 2019 so there are no extra years in some not available in others. The SVSPP codes are also limited to just the ones in the list used by Andrew for Allyn et al. (2020).

After that, repeated records for abundance and biomass are dropped. These repeats exist to accommodate the length and number at length details. We don’t need them for this so they are dropped here.

# Make years comparable
# Filter species down for both

source_list <- map(source_list, function(survdat_data){
  
  # filter years
  sdat <- survdat_data %>% 
    filter(est_year >= 2008,
           est_year <= 2019,
           svspp %in% species_check$svspp)
  
  # Pull distinct station abundance and biomass for each species
  sdat <- sdat %>% 
    distinct(id, est_year, season, svvessel, 
             comname, svspp, catchsex, abundance, biom_adj)

  })


# Split each survdat set into a list of single species tables
source_splits <- map(source_list, function(source_data){
  source_split <- source_data %>% split(.$comname) })

To make comparisons between the different data sets I first check that the svspp code is present in the dataset. Then for each source I get a total annual value for biomass and abundance. Then across data sources I calculate the correlation between these values, and the percent change from one data source to the most recently revised data pull.

The table itself is retained in addition to some overall summary metrics.

# Make  Comparisons

# Vector of species and their common names, atleast the common names Andrew used
# names are the svspp codes, passed to imap below as "species_svspp"
andrew_species <- species_check$comname %>% setNames(species_check$svspp)


# Pulling out details for the species
species_comparisons <- imap(andrew_species, function(species_comname, species_svspp){
  
  # there are some name mismatches in common name, so catch those and return an error 
  # that explains which data it was not in
  in_20   <- species_comname %in% names(source_splits$survdat_nye_2020)
  in_21   <- species_comname %in% names(source_splits$survdat_2021)
  in_both <- in_20 & in_21
  
  if(in_both == FALSE){
    return(list("in_20"    = in_20, 
                "in_21"    = in_21,
                "data"    = data.frame(),
                "metrics" = data.frame())) }
  
  
  
  # Pull just the species from the 2020 data
  summ_20 <- source_list$survdat_nye_2020 %>% 
    filter(svspp == species_svspp) %>% 
    group_by(comname, est_year) %>% 
    summarise(abund_20 = sum(abundance, na.rm = T),
              biom_20  = sum(biom_adj, na.rm = T),
              .groups = "keep") %>% ungroup()
  
  
   # and 2021 data
  summ_21 <- source_list$survdat_2021 %>% 
    filter(svspp == species_svspp) %>% 
    group_by(comname, est_year) %>% 
    summarise(abund_21 = sum(abundance, na.rm = T),
              biom_21  = sum(biom_adj, na.rm = T),
              .groups = "keep") %>% ungroup()
  
  # join them for side-by-side dataframe
  yr_df <- data.frame("est_year" = c("2008":"2019"))
  
  combined_data <- yr_df %>% 
    left_join(summ_20, by = "est_year") %>% 
    left_join(summ_21, by = c("comname", "est_year")) %>% 
    mutate(
      abund_change_20to21 = ((abund_21 - abund_20) / abund_20) * 100,
      biom_change_20to21  = ((biom_21 - biom_20) / biom_20) * 100) 
  
  
  # overall abundance correlation
  abund_cor_20to21   <- cor(combined_data$abund_21, 
                            combined_data$abund_20, 
                            use = "pairwise.complete.obs")
  
  # overall biomass correlation
  biomass_cor_20to21 <- cor(combined_data$biom_21, 
                            combined_data$biom_20,   
                            use = "pairwise.complete.obs")
  
  # overall average shifts
  abund_shift_20to21 <- mean(combined_data$abund_change_20to21, na.rm = T)
  biom_shift_20to21  <- mean(combined_data$biom_change_20to21, na.rm = T)
  
  # put in list to export
  list("data" = combined_data,
       "metrics" = data.frame(
         "comname"            = species_comname,
         "svspp"              = species_svspp,
         "abund_corr_20to21"  = abund_cor_20to21,
         "biom_corr_20to21"   = biomass_cor_20to21,
         "perc_abund_20to21"  = abund_shift_20to21,
         "perc_biom_20to21"   = biom_shift_20to21)
       )
  })

For simplicity I then append the tables together so they are single data frames and not lists.

# put data and metrics into a table
comparison_data    <- map_dfr(species_comparisons, ~.x[["data"]]) 
comparison_metrics <- map_dfr(species_comparisons, ~.x[["metrics"]]) 

Abundance/Biomass Comparisons

As a quick visual reference, here is how total annual abundance and biomass compare across data sources. The species have been split into groups to make paneling less crowded.

# How many species are there, and how many can we panel well - 84
#unique(andrew_species)

# make even number groups
even_groups <- list(
  group_1 = sort(andrew_species)[1:17],
  group_2 = sort(andrew_species)[18:34],
  group_3 = sort(andrew_species)[35:51],
  group_4 = sort(andrew_species)[52:68],
  group_5 = sort(andrew_species)[69:84])

Building Plots for Actual Abundance/Biomass

# Build list of abundance and biomass observations
timeseries_plots <- map(even_groups, function(species_subset){
  
  species_subset_dat <- comparison_data %>% 
    filter(comname %in% species_subset) %>% 
    mutate(comname = str_to_title(comname),
           comname = fct_drop(comname),
           comname = fct_inorder(comname))
  
  
  # Abundance plot
  abundance_plot <- ggplot(species_subset_dat, aes(est_year)) +
   geom_jitter(aes(y = abund_20, color = "Survdat Nye - August 2020"), 
                size = 1, height = 0, width = 0.1) +
    geom_jitter(aes(y = abund_21, color = "Newest Survdat"), 
                size = 1, height = 0, width = 0.1) +
    geom_line(aes(y = abund_20, 
                  color = "Survdat Nye - August 2020",
                  linetype = "Survdat Nye - August 2020"), 
              group = 1, alpha = 0.7) +
    geom_line(aes(y = abund_21, 
                  color = "Newest Survdat",
                  linetype = "Newest Survdat"), 
              group = 1, alpha = 0.7) +
    scale_y_continuous(labels = scales::comma_format()) +
    scale_x_continuous(breaks = scales::pretty_breaks()) +
    scale_color_gmri() +
    facet_wrap(~comname, scales = "free") +
    labs(x  = "", y = "Annual Abundance", 
         color = "Survdat Retrieval", 
         linetype = "Survdat Retrieval") +
    theme(legend.position = "none")
  
  # Biomass Plot
  biomass_plot <- ggplot(species_subset_dat, aes(est_year)) +
    geom_jitter(aes(y = biom_20, color = "Survdat Nye - August 2020"), 
                size = 1, height = 0, width = 0.1) +
    geom_jitter(aes(y = biom_21, color = "Newest Survdat"), 
                size = 1, height = 0, width = 0.1) +
    geom_line(aes(y = biom_20, 
                  color = "Survdat Nye - August 2020",
                  linetype = "Survdat Nye - August 2020"), 
              group = 1, alpha = 0.7) +
    geom_line(aes(y = biom_21, 
                  color = "Newest Survdat",
                  linetype = "Newest Survdat"), 
              group = 1, alpha = 0.7) +
    scale_y_continuous(labels = scales::comma_format()) +
    scale_x_continuous(breaks = scales::pretty_breaks()) +
    scale_color_gmri() +
    facet_wrap(~comname, scales = "free") +
    labs(x  = "", y = "Annual Biomass", 
         color = "Survdat Retrieval", 
         linetype = "Survdat Retrieval") +
    theme(legend.position = "bottom")
  
  return(list(a_plot = abundance_plot, b_plot = biomass_plot,
              stacked = (abundance_plot / biomass_plot)))
  
  
})

Building Plots for Percent Change in Abundance/Biomass

# build list of percent difference plots
timeseries_diff_plots <- map(even_groups, function(species_subset){
  
  species_subset_dat <- comparison_data %>% 
    filter(comname %in% species_subset) %>% 
    mutate(comname = str_to_title(comname),
           comname = fct_drop(comname),
           comname = fct_inorder(comname))
  
  
  # Abundance plot
  abundance_plot <- ggplot(species_subset_dat, aes(est_year)) +
    geom_jitter(aes(y = abund_change_20to21, color = "Abundance Change 2020 to 2021"), 
                size = 1, height = 0, width = 0.1) +
    geom_line(aes(y = abund_change_20to21, 
                  color = "Abundance Change 2020 to 2021",
                  linetype = "Abundance Change 2020 to 2021"), 
              group = 1, alpha = 0.7) +
    scale_y_continuous(labels = scales::comma_format()) +
    scale_x_continuous(breaks = scales::pretty_breaks()) +
    scale_color_gmri() +
    facet_wrap(~comname, scales = "free") +
    labs(x  = "", 
         y = "Percent Difference in Annual Abundance", 
         color = "Survdat Retrieval Comparison", 
         linetype = "Survdat Retrieval Comparison") +
    theme(legend.position = "none")
  
  # Biomass plot
  biomass_plot <- ggplot(species_subset_dat, aes(est_year)) +
    geom_jitter(aes(y = biom_change_20to21, color = "Biomass Change 2020 to 2021"), 
                size = 1, height = 0, width = 0.1) +
    geom_line(aes(y = biom_change_20to21, 
                  color = "Biomass Change 2020 to 2021",
                  linetype = "Biomass Change 2020 to 2021"), 
              group = 1, alpha = 0.7) +
    scale_y_continuous(labels = scales::comma_format()) +
    scale_x_continuous(breaks = scales::pretty_breaks()) +
    scale_color_gmri() +
    facet_wrap(~comname, scales = "free") +
    labs(x  = "", 
         y = "Percent Difference in Annual Biomass", 
         color = "Survdat Retrieval Comparison", 
         linetype = "Survdat Retrieval Comparison") +
    theme(legend.position = "bottom")
  
  return(list(a_plot = abundance_plot, b_plot = biomass_plot,
              stacked = (abundance_plot / biomass_plot)))
  
  
})

Group 1

Observations

timeseries_plots$group_1$stacked

Percent Differences

timeseries_diff_plots$group_1$stacked

Group 2

Observations

timeseries_plots$group_2$stacked

#### Percent Differences

timeseries_diff_plots$group_2$stacked

Group 3

Observations

timeseries_plots$group_3$stacked

#### Percent Differences

timeseries_diff_plots$group_3$stacked

Group 4

Observations

timeseries_plots$group_4$stacked

#### Percent Differences

timeseries_diff_plots$group_4$stacked

Group 5

Observations

timeseries_plots$group_5$stacked

#### Percent Differences

timeseries_diff_plots$group_5$stacked

Poor Correlation and Percent Change Comparisons

Abundance

Haddock Spotlight

haddock_data <- comparison_data %>% 
  filter(comname == "haddock")


ab_abund <- haddock_data %>% 
  ggplot(aes(est_year)) +
  geom_line(aes(y = abund_20, color = "Survdat Nye - August 2020")) +
  geom_line(aes(y = abund_21, color = "Newest Survdat")) +
  scale_color_gmri() +
  scale_y_continuous(labels = scales::comma_format()) +
  scale_x_continuous(breaks = scales::pretty_breaks()) +
  labs(x = "", y = "Abundance", subtitle = "Haddock Absolute Abundance", color = "") +
  theme(legend.position = "bottom")

p_change <- haddock_data %>% 
  ggplot(aes(est_year, abund_change_20to21)) +
  geom_line() +
  scale_x_continuous(breaks = scales::pretty_breaks()) +
  labs(y = "Percent Change in Abundance\n2020 -> 2021", x = "")

ab_abund / p_change

Lobster Spotlight

lob_data <- comparison_data %>% 
  filter(comname == "american lobster")

ab_abund <- lob_data %>% 
  ggplot(aes(est_year)) +
  geom_line(aes(y = abund_20, color = "Survdat Nye - August 2020")) +
  geom_line(aes(y = abund_21, color = "Newest Survdat")) +
  scale_color_gmri() +
  scale_y_continuous(labels = scales::comma_format()) +
  scale_x_continuous(breaks = scales::pretty_breaks()) +
  labs(x = "", y = "Abundance", 
       subtitle = "American Lobster Absolute Abundance", color = "") +
  theme(legend.position = "bottom")

p_change <- lob_data %>% 
  ggplot(aes(est_year, abund_change_20to21)) +
  geom_line() +
  scale_x_continuous(breaks = scales::pretty_breaks()) +
  labs(y = "Percent Change in Abundance\n2020 -> 2021", x = "")

ab_abund / p_change

Problem Species

Poor Correlation

# correlation
corr_cutoff <- 0.8
corr_species <- comparison_metrics %>% 
  filter(abund_corr_20to21 <= corr_cutoff) %>% 
  mutate(comname = fct_drop(comname)) %>% 
  pull(comname)


# filter species
corr_sub <- comparison_data %>% 
  filter(comname %in% corr_species) 


if(length(corr_species) != 0){
  
  
  # how many species per plot
p1 <- corr_species[1:6]
#p2 <- corr_species[-c(1:6)]

# p1
corr_sub %>% 
  filter(comname %in% p1) %>% 
  mutate(comname = fct_drop(comname)) %>% 
  ggplot() +
  geom_line(aes(est_year, abund_20, color = "Survdat Nye - August 2020")) +
  geom_line(aes(est_year, abund_21, color = "Newest Survdat")) +
  facet_wrap(~comname, ncol = 1, scales = "free" ) +
  scale_y_continuous(labels = scales::comma_format()) +
  scale_x_continuous(breaks = scales::pretty_breaks()) +
  scale_color_gmri() +
  labs(x = "", 
       y = "Annual Abundance", 
       title = paste0("Species with Annual Abundance Correlation <= ", corr_cutoff),
       color = "Survdat Source",
       subtitle = "Subset 1")
  
}

Large Percent Change

# percent change
perc_cutoff <- 25
perc_species <- comparison_metrics %>% 
  filter(perc_abund_20to21 >= perc_cutoff) %>% 
  mutate(comname = fct_drop(comname)) %>% 
  pull(comname) 


comparison_data %>% 
  filter(comname %in% perc_species) %>% 
  mutate(comname = fct_drop(comname)) %>% 
  ggplot() +
  geom_line(aes(est_year, abund_20, color = "Survdat Nye - August 2020")) +
  geom_line(aes(est_year, abund_21, color = "Newest Survdat")) +
  facet_wrap(~comname, ncol = 1, scales = "free") +
  scale_y_continuous(labels = scales::comma_format()) +
  scale_x_continuous(breaks = scales::pretty_breaks()) +
  scale_color_gmri() +
  labs(x = "", 
       y = "Annual Abundance",
       title = paste0("Species with Avg. Shift in Abundance >= ", perc_cutoff, "%"),
       color = "Survdat Source")

Biomass

Haddock Spotlight

ab_biom <- haddock_data %>% 
  ggplot(aes(est_year)) +
  geom_line(aes(y = biom_20, color = "Survdat Nye - August 2020")) +
  geom_line(aes(y = biom_21, color = "Newest Survdat")) +
  scale_color_gmri() +
  scale_y_continuous(labels = scales::comma_format()) +
  labs(x = "", y = "Biomass", subtitle = "Haddock Absolute Biomass", color = "") +
  theme(legend.position = "bottom")

p_change <- haddock_data %>% 
  ggplot(aes(est_year, biom_change_20to21)) +
  geom_line() +
  labs(y = "Percent Change in Biomass\n2020 -> 2021", x = "")

ab_biom / p_change

Lobster Spotlight

ab_biom <- lob_data %>% 
  ggplot(aes(est_year)) +
  geom_line(aes(y = biom_20, color = "Survdat Nye - August 2020")) +
  geom_line(aes(y = biom_21, color = "Newest Survdat")) +
  scale_color_gmri() +
  scale_y_continuous(labels = scales::comma_format()) +
  labs(x = "", y = "Biomass", subtitle = "American Lobster Absolute Biomass", color = "") +
  theme(legend.position = "bottom")

p_change <- lob_data %>% 
  ggplot(aes(est_year, biom_change_20to21)) +
  geom_line() +
  labs(y = "Percent Change in Biomass\n2020 -> 2021", x = "")

ab_biom / p_change

Problem Species

Poor Correlation

# correlation
corr_cutoff <- 0.8
corr_species <- comparison_metrics %>% 
  filter(biom_corr_20to21 <= corr_cutoff) %>% 
   mutate(comname = fct_drop(comname)) %>% 
  pull(comname)

# plot
if(length(corr_species) > 0){
comparison_data %>% 
  filter(comname %in% corr_species) %>% 
  mutate(comname = fct_drop(comname)) %>% 
  ggplot() +
  geom_line(aes(est_year, biom_20, color = "Survdat Nye - August 2020")) +
  geom_line(aes(est_year, biom_21, color = "Newest Survdat")) +
  facet_wrap(~comname, ncol = 1, scales = "free" ) +
  scale_y_continuous(labels = scales::comma_format()) +
  scale_x_continuous(breaks = scales::pretty_breaks()) +
  scale_color_gmri() +
  labs(x = "", 
       y = "Annual Biomass", 
       title = paste0("Species with Annual Biomass Correlation <= ", corr_cutoff),
       color = "Survdat Source")
}

Large Percent Change

# percent change
perc_cutoff <- 25
perc_species <- comparison_metrics %>% 
  filter(perc_biom_20to21 >= perc_cutoff) %>% 
  mutate(comname = fct_drop(comname)) %>% 
  pull(comname) 


comparison_data %>% 
  filter(comname %in% perc_species) %>% 
  mutate(comname = fct_drop(comname)) %>% 
  ggplot() +
  geom_line(aes(est_year, biom_20, color = "Survdat Nye - August 2020")) +
  geom_line(aes(est_year, biom_21, color = "Newest Survdat")) +
  facet_wrap(~comname, ncol = 1, scales = "free") +
  scale_y_continuous(labels = scales::comma_format()) +
  scale_x_continuous(breaks = scales::pretty_breaks()) +
  scale_color_gmri() +
  labs(x = "", 
       y = "Annual Biomass",
       title = paste0("Species with Avg. Shift in Biomass >= ", perc_cutoff, "%"),
       color = "Survdat Source")

# this is legacy code from when we were digging in to the table across sources.
write_csv(comparison_data, here("data/survdat_biom_abund_compare.csv"))

 

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A work by Adam A. Kemberling

Akemberling@gmri.org