SURVDAT pull comparison

Digging into differences in the fall Bigelow survey data across different pulls of survdat

Objective is to isolate which main columns are inconsistent across the datasets and whether the problem is isolated to specific species.

For convenience we also limited the data to include years 2006-present.

Load SURVDAT Sources

SURVDAT Pulls

For the following report we looked at 7 candidate survdat datasets.

  1. A 2016 survdat dataset
  2. A 2019 survdat dataset
  3. An August 2020 survdat dataset
  4. A second 2020 survdat dataset obtained from Janet Nye to double check our 2020
  5. The most recent 2021 pull from Sean
  6. A second survdat dataset sent 02-18-2021 correcting for a RODBC/DBI issue
  7. A third survdat dataset sent 02-23-2021 correcting abundance numbers for haddock, windowpane, butterfish

To remove clutter from the following plots numbers: (1 & 4) will not be displayed as exploratory plots showed them to be “the same” as numbers: (2 & 3) respectively.

#### Group 1: 2016 & 2019 data  ####

# # 2016
# load(paste0(mills_path, "Projects/WARMEM/Old survey data/Survdat_Nye2016.RData"))
# survdat_16 <- clean_names(survdat) %>% filter(est_year >= 2006)


# 2019
load(paste0(res_path, "NMFS_trawl/Survdat_Nye_allseason.RData"))
survdat_19 <- clean_names(survdat) %>% filter(est_year >= 2006)


####  group 2: 2020 Data  ####

# 2020
load(paste0(res_path, "NMFS_trawl/Survdat_Nye_Aug 2020.RData"))
survdat_20 <- clean_names(survdat) %>% filter(est_year >=  2006)

# # Double check of 2020 from Janet on 01282021
# load(paste0(res_path, "NMFS_trawl/Survdat_Nye_Aug 2020_check01282021.RData"))
# survdat_20_check <- clean_names(survdat) %>% filter(est_year >=  2006)


####  Group 3: 2021 Survdat Pulls


# 2021
load(paste0(res_path, "NMFS_trawl/survdat_slucey_01152021.RData"))
survdat_21 <- clean_names(survdat) %>% filter(year >= 2006)


# February 2021 following DBI package fix
load(paste0(res_path,   "NMFS_trawl/Survdat_02182021.RData"))
feb_2021 <- survdat %>% clean_names() %>% 
    filter(year >= 2006)


# February 2021 following DBI package fix
load(paste0(res_path,   "NMFS_trawl/NEFSC_BTS_02242021.RData"))
feb_23_2021 <- survey$survdat %>% clean_names() %>% 
    filter(year >= 2006)


# Data we just received in March, 2021 with errors located and corrected
load(paste0(res_path, "NMFS_trawl/NEFSC_BTS_all_seasons_03032021.RData"))
mar_3_2021 <- survey$survdat %>% clean_names() %>% 
  filter(year >= 2006)



# remove survdat
rm(survdat, survey)

Run Cleanup Function

The cleanup function does a number of steps to each survdat source. This includes the removal of specific strata, the removal of non-spring/fall seasons, removal of vessels other than the Albatross or Bigelow, the dropping of NA biomass and abundance events, and the removal of certain species like shrimp.

It also matches up the stations with larger regions they fall within and the survey effort within each of those. This should not have an impact on any of the things we are tracking below.

The code for the cleanup function can be found here

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

# Clean each up using the survdat prep function
# trawldat_16 <- survdat_prep(survdat = survdat_16) %>% 
#   mutate(survdat_source = "2016")
trawldat_19 <- survdat_prep(survdat = survdat_19) %>% 
  mutate(survdat_source = "2016 - 2019")
trawldat_20 <- survdat_prep(survdat = survdat_20) %>% 
  mutate(survdat_source = "2020")
# trawldat_20_check <- survdat_prep(survdat = survdat_20_check) %>% 
#   mutate(survdat_source = "2020_check")
trawldat_21 <- survdat_prep(survdat = survdat_21) %>% 
  mutate(survdat_source = "Jan 2021")
trawldat_feb <- survdat_prep(survdat = feb_2021) %>% 
  mutate(survdat_source = "Feb 2021")
trawldat_feb23 <- survdat_prep(survdat = feb_23_2021) %>% 
  mutate(survdat_source = "Feb 23 2021")
trawldat_mar3 <- survdat_prep(survdat = mar_3_2021) %>% 
  mutate(survdat_source = "Mar 3 2021")





rm(#survdat_16, 
   survdat_19, 
   survdat_20, #survdat_20_check, 
   survdat_21, 
   feb_2021,
   feb_23_2021,
   mar_3_2021)

ABUND/sum(NUMLEN) Comparisons

The ratio of survdat$abundance and sum(survdat$numlen) represents the scale of mismatch between the abundance of a species caught at in a tow when measured using the survdat$abundance column compared to the sum of survdat$numlen across all the lengths caught.

In theory they should be equal. Numbers greater than 1 indicate that the abundance column was greater than the sum of individuals at the different lengths.

Here is a helper function that will be applied to each survdat source to pull out the ratio between numlen and abundance totals.

# This is the same code used to make numlen_adj in the build:
get_convers <- function(trawldat){
  abundance_check <- trawldat %>%
    group_by(id, comname, catchsex, abundance) %>%
    summarise(
      numlen_abund = sum(numlen),                
      n_len_class  = n_distinct(length), # number of distinct lengths caught that station
      .groups      = "keep") %>% 
    ungroup()
  
  
  # Numlen conversion factor translates each size at length to what it would need be to be
  # for them to add up to that "abundance" column
  conv_factor <- trawldat %>% 
    distinct(id, comname, catchsex, length, abundance) %>% 
    inner_join(abundance_check, by = c("id", "comname", "catchsex", "abundance")) %>% 
    mutate(convers = abundance / numlen_abund)
    
  # Merge back and convert the numlen field
  trawldat_adjusted <- trawldat %>%
    left_join(conv_factor, by = c("id", "comname", "catchsex", "length", "abundance", "n_len_class")) %>%
    mutate(numlen_adj = numlen * convers, .after = numlen) 
  
  return(trawldat_adjusted)
}

And some organization as a list to process them:

# Put all the groups together to compare abundance ratios
trawldat_list <- list(
    # "2016"          = get_convers(trawldat_16),
    "2019"           = get_convers(trawldat_19),
    "2020"           = get_convers(trawldat_20),
    # "2020 re-check" = get_convers(trawldat_20_check),
    "Jan 2021"           = get_convers(trawldat_21),
    "Feb 2021"       = get_convers(trawldat_feb),
    "Feb 23 2021"    = get_convers(trawldat_feb23),
    "Mar 3 2021"     =  get_convers(trawldat_mar3)) %>% 
  map(~ .x %>% 
  mutate(season = str_to_title(season),
         season = factor(season, levels = c("Spring", "Fall"))))


# set levels for plotting
source_levels <- c("2019", "2020", "Jan 2021", "Feb 2021", "Feb 23 2021", "Mar 3 2021")

# Big table of all of them
trawldat_combined <- bind_rows(
  trawldat_list, 
  .id = "survdat_source") %>% 
  mutate(survdat_source = factor(survdat_source, levels = source_levels))

All Species

trawldat_combined %>% 
  ggplot(aes(x = convers, 
             y  = fct_rev(survdat_source), 
             color = survdat_source)) +
  geom_boxplot() +
  facet_grid(season ~ svvessel) + 
  scale_color_gmri() +
  labs(subtitle = "numlen to abundance ratio - All Species",
       x = "(survdat$abundance / sum(numlen))",
       y = "",
       caption = "Ratios calculated at each station for each species & sex.\nMay reflect change in catch composition.")

Haddock Only

trawldat_combined %>% 
  filter(comname == "haddock") %>% 
  ggplot(aes(x = convers, 
             y  = fct_rev(survdat_source), 
             color = survdat_source)) +
  geom_boxplot() +
  facet_grid(season ~ svvessel) + 
  scale_color_gmri() +
  labs(subtitle = "numlen to abundance ratio - Haddock only",
       x = "(survdat$abundance / sum(numlen))",
       y = "",
       caption = "Ratio value calculated at each station/sex for haddock.")

Single Species Checks

The abund/numlen ratio is helpful for identifying consistency in behavior across the different pulls, but doesn’t really explain the how/why of the differences among them.

To get at the differences that contribute to them we use four commonly caught species to compare different components of the data sets.

For each of the following species we are comparing 4 main things across the different survdat sources:

  1. The number of unique station id’s each year
  2. The total number of fish, as tallied from the survdat$numlen column
  3. The total abundance of fish as the sum of survdat$abundance from distinct stations and the species caught at them
  4. The total biomass for each year as the sum of survdat$biomass from distinct stations and the species caught at them

This should tell us whether some datasets are missing stations completely, and whether the differences exist in the numlen, abundance, or biomass columns, and for what time periods.

For each of the following plots the drawing order of the lines is top to bottom as they appear in the key. If lines do not appear they likely are prefectly masked by some of the more recent pulls, which is good.

Differences in datasets are more apparent in the biomass and abundance totals during the Bigelow survey years.

NOTE: The Below code chunk details the functions used to isolate a given species and pull its information for display. Subsequent tabs will detail the differences among the different survdat pulls.

# Function to pull single species data
species_select <- function(species_choice){
  
  # Use list of sources to pull select species from each
  source_list <- trawldat_list
  
  # Use map to not repeat so much code
  select_species <- map_dfr(source_list, function(surv_source){
    surv_source %>% 
      select(survdat_source, id, est_year, season, comname, length, 
             numlen, abundance, biom_adj) %>% 
      filter(comname == species_choice)})
  
  return(select_species)
}
####  Plotting Numlen Totals and Station counts  ####
plot_stations <- function(combined_data, species_choice){
  
  # Total numlen fish summary
  species_numlens <- combined_data %>% 
    group_by(survdat_source, est_year, season) %>% 
    summarise(n_stations = n_distinct(id),
              sum_numlen = sum(numlen, na.rm = T),
              .groups = "keep")  %>% ungroup()
  
  
  # Plot for station presence
  station_plot <- ggplot(species_numlens, 
                         aes(est_year, n_stations, color = survdat_source)) +
    geom_line() +
    geom_jitter(aes(group = survdat_source, color = survdat_source, shape = survdat_source), 
                size = 1.5, width = 0.25) +
    facet_wrap(~season) +
    scale_color_gmri() +
    labs(x = "", 
         y = "Total Stations of Species Presence", 
         color = "", shape = "",
         subtitle = paste0(species_choice, " only")) +
    theme(legend.position = "none")
  
  
  # Plot for numlen
  numlen_plot <- ggplot(species_numlens, 
                        aes(est_year, sum_numlen, color = survdat_source)) +
    geom_line() +
    geom_jitter(aes(group = survdat_source, color = survdat_source, 
                  shape = survdat_source), 
                size = 1.5, width = 0.25) +
    facet_wrap(~season) +
    scale_color_gmri() +
    labs(x = "", y = "Total Abundance using survdat$numlen",
         color = "", shape = "") +
    theme(legend.position = "bottom")
  
  plot_out <- station_plot / numlen_plot
  return(plot_out)
}
####  Plotting Biomass and Abundances  ####
plot_biomass <- function(combined_data, species_choice){
  
  # Abundance and Biomass Summary
  species_abund_bio <- combined_data %>% 
    distinct(survdat_source, est_year, season, id, abundance, biom_adj) %>% 
    group_by(survdat_source, est_year, season) %>% 
    summarise(n_stations = n_distinct(id),
              abundance = sum(abundance, na.rm = T),
              biomass = sum(biom_adj, na.rm = T),
              .groups = "keep") %>%  
    ungroup()
  
  
  # Plot for abundance
  abund_plot <- ggplot(species_abund_bio, 
                       aes(est_year, abundance, color = survdat_source)) +
    geom_line() +
    geom_jitter(aes(group = survdat_source, color = survdat_source, shape = survdat_source), 
                size = 1.5, width = 0.15) +
    facet_wrap(~season) +
    scale_color_gmri() +
    labs(x = "", y = "Total survdat$abundance", 
         subtitle = paste0(species_choice, " only")) +
    theme(legend.position = "none")
  
  # plot for biomass
  bio_plot <- ggplot(species_abund_bio, aes(est_year, biomass, color = survdat_source)) +
    geom_line() +
    geom_jitter(aes(group = survdat_source, color = survdat_source, 
                    shape = survdat_source), size = 1.5, 
              width = 0.15) +
    facet_wrap(~season) +
    scale_color_gmri() +
    labs(x = "", y = "Total survdat$biomass",
         color = "", shape = "") +
    theme(legend.position = "bottom")
  
  plot_out <- abund_plot / bio_plot
  return(plot_out)
  
}

NOTE: The following plots have had their points jittered to expose situations of overlap.

Haddock

Stations and Numlen Totals

species_choice <- "haddock"
single_species <- species_select(species_choice)
plot_stations(single_species, species_choice)

Abundance and Biomass

plot_biomass(single_species, species_choice)

Spiny dogfish

Stations and Numlen Totals

species_choice <- "spiny dogfish"
single_species <- species_select(species_choice)
plot_stations(single_species, species_choice)

Abundance and Biomass

plot_biomass(single_species, species_choice)

Atlantic Cod

Stations and Numlen Totals

species_choice <- "atlantic cod"
single_species <- species_select(species_choice)
plot_stations(single_species, species_choice)

Abundance and Biomass

plot_biomass(single_species, species_choice)

Atlantic Herring

Stations and Numlen Totals

species_choice <- "atlantic herring"
single_species <- species_select(species_choice)
plot_stations(single_species, species_choice)

Abundance and Biomass

plot_biomass(single_species, species_choice)

American Lobster

Stations and Numlen Totals

species_choice <- "american lobster"
single_species <- species_select(species_choice)
plot_stations(single_species, species_choice)

Abundance and Biomass

plot_biomass(single_species, species_choice)

Dataset Contents

This section will detail how each data set compares along a handful of common metrics. How many stations there are, how much biomass there is, which strata are present etc.

These should all be about the same, but may point us in the direction of what might be causing these differences.

trawldat_combined %>% 
  rename(`Survdat Source` = survdat_source) %>% 
  group_by(`Survdat Source`) %>% 
  summarise(
    `n Years` = n_distinct(est_year),
    `n Stations` = n_distinct(id),
    `n Strata` = n_distinct(stratum),
    `n Species` = n_distinct(comname)) %>% 
  kable() %>% 
  kable_styling()
Survdat Source n Years n Stations n Strata n Species
2019 13 5885 53 311
2020 14 6608 53 320
Jan 2021 14 6576 53 312
Feb 2021 14 6576 53 312
Feb 23 2021 14 6576 53 312
Mar 3 2021 14 6576 53 318

Summary

Based on the visualizations above there appears to be some inconsistency in the different datasets centered around data collected after 2008 on the RV Henry Bigelow.

These datasets seem to fall into 3 distinct groups when looking at abundance:

  1. Pre-2020

  2. 2020 Survdat Nye Data + 2021 Data w/o DBI error

  3. 2021 Survdat Pull w/ DBI error

And two different groups when looking at biomass:

  1. 2021 Survdat Pull w/ DBI error

  2. Everything else

# Pull a single species
species_choice <- "atlantic cod"
single_species <- species_select(species_choice) 


# Label the groups that we identified as behaving similarly:
cod_sources <- single_species %>% 
  mutate(
    `survdat group` = case_when(
      survdat_source == "2016 - 2019"    ~ "Pre-2020 Data",
      str_detect(survdat_source, "2020") ~ "2020 Data",
      survdat_source == "Jan 2021"       ~ "2021 w/ DBI Error",
      survdat_source == "Feb 2021"       ~ "2021 w/o DBI Error",
      survdat_source == "Feb 23 2021"    ~ "Feb 23 2021",
      survdat_source == "Mar 3 2021"     ~ "Mar 3 2021"),
    `survdat group` = factor(
      `survdat group`, 
      levels = c("Pre-2020 Data", 
                 "2020 Data", 
                 "2021 w/ DBI Error",
                 "2021 w/o DBI Error",
                 "Feb 23 2021",
                 "Mar 3 2021"))) %>% 
  distinct(`survdat group`, survdat_source, 
           est_year, season, id, abundance, biom_adj) %>% 
  group_by(`survdat group`, survdat_source, est_year, season) %>% 
  summarise(n_stations = n_distinct(id),
            abundance = sum(abundance, na.rm = T),
            biomass = sum(biom_adj, na.rm = T),
            .groups = "keep") %>%  
    ungroup() %>% 
    mutate(season = fct_rev(season))
  

#ellipse marker
mark1 <- cod_sources %>% 
  filter(season == "Fall", survdat_source == "Jan 2021", est_year == 2009)

Abundance

# Abundance
cod_sources %>% 
  filter(season == "Fall") %>% 
  ggplot(aes(est_year, abundance)) +
  geom_line(aes(group = survdat_source, color = `survdat group`), alpha = 0.3) +
  geom_point(aes(group = survdat_source, color = `survdat group`, 
                  shape = survdat_source), size = 1.5) +
  geom_mark_ellipse(data = mark1, 
                    aes(est_year, abundance, label = "Separation of the groups"), 
                    label.fill = "transparent",
                    linetype = 2) +
  scale_color_gmri() +
  facet_wrap(~season) +
  facet_zoom(xlim = c(2008:2011)) +
  labs(x = "", 
       y = "Total survdat$abundance", 
       title = "Data Mismatches Following Vessel Switch", 
       subtitle = "Inconsistency in Bigelow data over time.", 
       caption = paste0(species_choice, " only"),
       shape = "Pull Source",
       color = "Consistent Grouping") +
  guides("shape" = guide_legend(title.position = "top", title.hjust = 0.5, ncol = 2),
         "color"= guide_legend(title.position = "top", title.hjust = 0.5, ncol = 2)) +
  theme(legend.position = "bottom")

Biomass

# Biomass
cod_sources %>% 
  filter(season == "Fall") %>% 
  ggplot(aes(est_year, biomass)) +
  geom_line(aes(group = survdat_source, color = `survdat group`), alpha = 0.3) +
  geom_point(aes(group = survdat_source, color = `survdat group`, 
                  shape = survdat_source), size = 1.5) +
  geom_mark_ellipse(data = mark1, 
                    aes(est_year, biomass, label = "Separation of the groups"), 
                    label.fill = "transparent", 
                    linetype = 2) +
  scale_color_gmri() +
  facet_wrap(~season) +
  facet_zoom(xlim = c(2007:2013)) +
  labs(x = "", 
       y = "Total survdat$biomass", 
       title = "Data Mismatches Following Vessel Switch", 
       subtitle = "Inconsistency in Bigelow data over time.", 
       caption = paste0(species_choice, " only"),
       shape = "Pull Source",
       color = "Consistent Grouping") +
  guides("shape" = guide_legend(title.position = "top", title.hjust = 0.5, ncol = 2),
         "color"= guide_legend(title.position = "top", title.hjust = 0.5, ncol = 2)) +
  theme(legend.position = "bottom")

Abundance/Biomass Data Source Ratios

For the following figures the annual totals for biomass and abundance were compared pairwise between data source using a single species to isolate if the behavior exists across them or is isolated to specific ones.

IMPORTANT Pairwise comparisons now compare to the February 23rd 2021 dataset.

####  Data Prep and Plotting Functions


# Pull a species, pivot and plot
pull_pivot_plot <- function(target_comname = "haddock", 
                            numerator = c("Feb 23 2021", "Feb 2021", "Jan 2021", "2016-2019"),
                            denominator = c("Feb 23 2021", "Feb 2021", "Jan 2021", "2016-2019")){
  
  
  # Pull annual abundance and biomass totals for haddock
  annual_totals <- trawldat_combined %>% 
    filter(survdat_source %in% c("2019", "Jan 2021", "Feb 2021", "Feb 23 2021"), 
           comname == target_comname) %>% 
      distinct(survdat_source, est_year, season, id, abundance, biom_adj) %>% 
      group_by(survdat_source, est_year, season) %>% 
      summarise(abundance = sum(abundance, na.rm = T),
                biomass = sum(biom_adj, na.rm = T),
                .groups = "keep") %>%  
     ungroup() 
  
  
  # build comparison columns for ratio
  numerator_b_col   <- sym(paste0("biomass_", numerator))
  denominator_b_col <- sym(paste0("biomass_", denominator))
  numerator_a_col   <- sym(paste0("abundance_", numerator))
  denominator_a_col <- sym(paste0("abundance_", denominator))
  
  
  # Get Ratios for between sources for abundance / biomass
  yr_ratios <- annual_totals %>% 
    pivot_wider(names_from = survdat_source, 
                values_from = c(abundance, biomass)) %>% 
    mutate(
      b_ratio = !!numerator_b_col / !!denominator_b_col,
      a_ratio = !!numerator_a_col / !!denominator_a_col)
  
  
   # Y labels for abundance and biomass
  ylab_b <- paste0("BIOM Ratio\n", numerator, " : ", denominator)
  ylab_a <- paste0("ABUND Ratio\n", numerator, " : ", denominator)
  
  
  ####  Plot the biomass

  bio_ratio <- ggplot(yr_ratios, aes(est_year)) +
  geom_line(aes(est_year, b_ratio), linetype = 2) +
  facet_wrap(~season) + 
  labs(x = "", 
       y = ylab_b,
       title = str_to_title(target_comname),
       subtitle = "Biomass Ratios")
  
  
  ####  Plot the abundance
  abund_ratio <- ggplot(yr_ratios, aes(est_year)) +
    geom_line(aes(est_year, a_ratio), linetype = 2) +
    facet_wrap(~season) + 
    labs(x = "", 
         y = ylab_a,
         subtitle = "Abundance Ratios")

  plot_out <- bio_ratio / abund_ratio
  return(plot_out)
  
  
}

Haddock

Feb 23rd : Feb 18th

pull_pivot_plot(target_comname = "haddock", numerator = "Feb 23 2021", denominator = "Feb 2021")

Feb 23rd : Jan 2021

pull_pivot_plot(target_comname = "haddock", numerator = "Feb 23 2021", denominator = "Jan 2021")

Feb 18rd : Jan 2021

pull_pivot_plot(target_comname = "haddock", numerator = "Feb 2021", denominator = "Jan 2021")

Cod

Feb 23rd : Feb 18th

pull_pivot_plot(target_comname = "atlantic cod", numerator = "Feb 23 2021", denominator = "Feb 2021")

Feb 23rd : Jan 2021

pull_pivot_plot(target_comname = "atlantic cod", numerator = "Feb 23 2021", denominator = "Jan 2021")

Feb 18rd : Jan 2021

pull_pivot_plot(target_comname = "atlantic cod", numerator = "Feb 2021", denominator = "Jan 2021")

Lobster

Feb 23rd : Feb 18th

pull_pivot_plot(target_comname = "american lobster", numerator = "Feb 23 2021", denominator = "Feb 2021")

Feb 23rd : Jan 2021

pull_pivot_plot(target_comname = "american lobster", numerator = "Feb 23 2021", denominator = "Jan 2021")

Feb 18rd : Jan 2021

pull_pivot_plot(target_comname = "american lobster", numerator = "Feb 2021", denominator = "Jan 2021")

Species Lookup Table

The following table displays the ratio of total annual abundance between data sources for each species. The conversion factors shown are the inverse of the ratios plotted above to match how they are in the Miller paper. For abundance conversion factors I expect some deviation from the values to rounding to whole fish and across years, but they should still center around the values in the report.

# Pull annual abundance and biomass totals for haddock
species_totals <- trawldat_combined %>% 
  filter(survdat_source %in% c("Jan 2021",  "Feb 23 2021"),
         est_year >= 2009,
         comname != "0") %>% 
    distinct(comname, svspp, survdat_source, est_year, season, id, abundance, biom_adj) %>% 
    group_by(comname, svspp, survdat_source, est_year, season) %>% 
    summarise(abundance = sum(abundance, na.rm = T),
              biomass = sum(biom_adj, na.rm = T),
              .groups = "keep") %>% ungroup() 


# Split out species, pivot wider, summarize comparisons
 # Get Ratios for between sources for abundance / biomass
  species_ratios <- species_totals %>% 
    split(.$comname) %>% 
    map_dfr(function(species_dat){
      species_dat %>% 
        pivot_wider(names_from = survdat_source, 
                    values_from = c(abundance, biomass)) %>% 
        mutate(feb_23_bratio = `biomass_Feb 23 2021` / `biomass_Jan 2021`,
               feb_23_aratio = `abundance_Feb 23 2021`  / `abundance_Jan 2021`) %>% 
          group_by(comname, svspp, season) %>% 
          summarise(`Biomass Conv. Factor -  Feb, 23 : Jan, 21` = round(1 / mean(feb_23_bratio), 3),
                    `Abundance Conv. Factor Feb, 23 : Jan, 21`  = round(1 / mean(feb_23_aratio), 3),
                    .groups = "keep") %>% 
          ungroup() 
      }) %>% arrange(svspp)

# Fancy table  
DT::datatable(species_ratios, 
              rownames = FALSE, 
              caption = "Conversion Factors for species with SVSPP numbers > 100 likely to not match Miller Paper.",
              options = list(
                pageLength = 10
              ))
 

A work by Adam A. Kemberling

Akemberling@gmri.org