CalCOFI Analysis

for CINMS/MBNMS infographics using ERDDAP

library(dplyr)
library(rlang)
library(purrr)
library(here)
library(fs)
library(glue)
library(sf)
library(ggplot2)
library(plotly)
library(scales)
library(mapview)
library(DT)
library(rerddap)
library(lubridate)
library(readr)
library(stringr)
here = here::here

q_calcofi        <- "CalCOFI Larvae Counts, Scientific Names"
dir_cache        <- here("cache") 
dir_img          <- "/Volumes/GoogleDrive/My Drive/projects/nms-web/cinms/Images"
lrvcntSBtoSC_csv <- glue("{dir_cache}/erdCalCOFIlrvcntSBtoSC.csv")
stns_cinms_csv   <- here("data/stns_cinms.csv")
plys_cinms_geo   <- here("data/plys_cinms.geojson")
yr_min           <- 1978
spp_csv          <- here("data/spp.csv")
spp_grps_csv     <- here("data/spp_grps.csv")
redo_grp_ply_csv <- F

# functions ----
convert_fig <- function(fig_beg, fig_end, redo=F){
  stopifnot(file.exists(fig_beg), dir.exists(dirname(fig_end)))
  #browser()
  if (!file.exists(fig_end) | redo){
    cmd <- glue("magick convert '{fig_beg}' -resize 800 -alpha on '{fig_end}'")
    system(cmd)
    
    paths_01 <- glue("{path_ext_remove(fig_end)}-{c(0,1)}.png")
    if (all(file.exists(paths_01))){
      file_copy(paths_01[2], fig_end, overwrite = T)
      file_delete(paths_01)
    }
  }
}  

get_nms_ply <- function(nms){
  # eg get_nms_ply("cinms")
  # get polygon for National Marine Sanctuary
  
  nms_shp <- here(glue("data/shp/{nms}_py.shp"))
  
  if (!file.exists(nms_shp)){
    # download if needed
    
    # https://sanctuaries.noaa.gov/library/imast_gis.html
    nms_url <- glue("https://sanctuaries.noaa.gov/library/imast/{nms}_py2.zip")
    nms_zip <- here(glue("data/{nms}.zip"))
    shp_dir <- here("data/shp")
    
    download.file(nms_url, nms_zip)
    unzip(nms_zip, exdir = shp_dir)
    file_delete(nms_zip)
  }
  # read and convert to standard geographic projection
  read_sf(nms_shp) %>%
    st_transform(4326)
}

Reference CINMS Condition Report figures

App.F.12.17 map of net samples

figs <- list(
  folder = "App F: LR (AppF.Q#.#)/AppendixF_Q12",
  figure = c(
    "App.F.12.17.Map_CalCOFI.tiff"))

figs_beg <- path(dir_img, figs$folder, figs$figure)
figs_end <- here(glue("docs/figures/{path_ext_remove(basename(figs_beg))}.png"))
walk2(figs_beg, figs_end, convert_fig)
knitr::include_graphics(figs_end)

Figure App.F.12.17. Location of spring season net samples by the California Cooperative Oceanic Fisheries Investigations (CalCOFI) used in analyses of abundance and trends in pelagic resources in two regions: Channel Islands National Marine Sanctuary region (red) includes CalCOFI stations inside and adjacent to CINMS; and Southern California Shelf (yellow) includes all CalCOFI stations over the shelf. Figure: A. Thompson/NOAA

App.F.12.18 small plankton volume in CINMS/SoCal

figs <- list(
  folder = "App F: LR (AppF.Q#.#)/AppendixF_Q12",
  figure = c(
    "App.F.12.18_CalCOFI_SPV.tiff"))

figs_beg <- path(dir_img, figs$folder, figs$figure)
figs_end <- here(glue("docs/figures/{path_ext_remove(basename(figs_beg))}.png"))
walk2(figs_beg, figs_end, convert_fig)
knitr::include_graphics(figs_end)

Figure App.F.12.18. Mean volume of small plankton collected in net samples during CalCOFI cruises at only sites in the Channel Islands National Marine Sanctuary region (top) and all sites over the Southern California Shelf (bottom) from 1978 to 2015 (sampling locations shown in Figure App.F.12.17). Horizontal lines show the mean (dashed line) ± 1.0 standard deviation (solid lines) over the full time series. Symbol at upper right indicates whether data over the last five years (green shaded areas) had a positive trend (), a negative trend (), or no trend (↔). Symbol at lower right indicates whether the mean over the past five years was greater than (+), less than (–), or within 1 standard deviation (●) of the mean of the full time series.. Figure App.C.4.15. Variation over time in percentage of stations from winter CalCOFI cruises with plastic micro-debris. Micro-debris was present in more than 50 percent of samples at each time period. Figure: Gilfillan et al. 2009

App.F.12.19 key forage groups in CINMS/SoCal

figs <- list(
  folder = "App F: LR (AppF.Q#.#)/AppendixF_Q12",
  figure = c(
    "App.F.12.19b.CalCOFI_Med_ichthy.tiff",
    "App.F.12.19a.CalCOFI_High_ichthy.tiff"))

figs_beg <- path(dir_img, figs$folder, figs$figure)
figs_end <- here(glue("docs/figures/{path_ext_remove(basename(figs_beg))}.png"))
walk2(figs_beg, figs_end, convert_fig)
knitr::include_graphics(figs_end)

Figure App.F.12.19. Relative abundance of key forage groups collected in net samples during spring CalCOFI cruises at sites in the Channel Islands National Marine Sanctuary (CINMS) region from 1978 to 2015. Forage is grouped by high (left panel) and medium (right panel) energy density. High energy taxa are Pacific sardines, northern anchovies, and and Myctophids. Medium- energy taxa are Pacific hake, shortbelly rockfish, and sanddabs. Although sardine were completely absent in net samples from 2011 to 2014 in the CINMS region, comparison with samples collected in the larger Southern California Shelf region reveal that sardine were at very low abundance but not completely absent from the region (sampling locations shown in Figure App.F12.17). Symbols on graph explained in the caption of Figure App.F12.18. Data source: CalCOFI; Figure: A. Thompson/NOAA

App.F.12.20 market squid in CINMS/SoCal

figs <- list(
  folder = "App F: LR (AppF.Q#.#)/AppendixF_Q12",
  figure = c(
    "App.F.12.20.CalCOFI_squid.tiff"))

figs_beg <- path(dir_img, figs$folder, figs$figure)
figs_end <- here(glue("docs/figures/{path_ext_remove(basename(figs_beg))}.png"))
walk2(figs_beg, figs_end, convert_fig)
knitr::include_graphics(figs_end)

Figure App.F.12.20. Mean abundance of market squid (Doryteuthis opalescens) collected in net samples during CalCOFI cruises at only sites in the Channel Islands National Marine Sanctuary region (top) and all sites over the Southern California Shelf (bottom) from 1997 to 2015 (sampling locations shown in Figure App.F12.17). Symbols on graph explained in the caption of Figure App.F12.18. Data source: CalCOFI; Figure: A. Thompson/NOAA

App.F.15.5 larval fish, warm- & cold-

figs <- list(
  folder = "App F: LR (AppF.Q#.#)/AppendixF_Q15",
  figure = c(
    "App.F.15.5.CalCOFI_water_associated.tiff"))

figs_beg <- path(dir_img, figs$folder, figs$figure)
figs_end <- here(glue("docs/figures/{path_ext_remove(basename(figs_beg))}.png"))

walk2(figs_beg, figs_end, convert_fig)

knitr::include_graphics(figs_end)

Figure App.F.15.5. Relative abundance of cool-water (top panels) and warm-water (bottom panels) associated ichthyoplankton (fish larvae) groups collected in net samples during spring CalCOFI cruises at sites in the Channel Islands National Marine Sanctuary (CINMS) region (left panels) and over the Southern California Shelf (right panels) from 1978 to 2015. Sampling sites are shown on Figure App.F.12.17. Symbols on the graph are explained in the caption for Figure App.F.12.18. Data source: CalCOFI; Figure: A. Thompson/NOAA

App.F.15.6 larval fish richness & diversity

figs <- list(
  folder = "App F: LR (AppF.Q#.#)/AppendixF_Q15",
  figure = c(
    "App.F.15.6.CalCOFI_diversity.tiff"))

figs_beg <- path(dir_img, figs$folder, figs$figure)
figs_end <- here(glue("docs/figures/{path_ext_remove(basename(figs_beg))}.png"))

walk2(figs_beg, figs_end, convert_fig)

knitr::include_graphics(figs_end)

Figure App.F.15.6. Mean species richness (top panels) and mean Gini-Simpson diversity (bottom panels) of fish larvae (ichthyoplankton) collected in net samples during spring CalCOFI cruises at sites in the Channel Islands National Marine Sanctuary (CINMS) region (left panels) and over the Southern California Shelf (right panels) from 1978 to 2015. Species richness is the number of species present in a net sample. Gini-Simpson diversity (1-λ form) takes into account the number of species present, as well as the relative abundance of each species. Species richness tends to increase in El Niño years due to influx of central Pacific species to the shelf. Gini-Simpson diversity is high when individuals are well-distributed among species suggesting that the 2015 spike in richness was not due to rare species. Sampling sites are shown on Figure App.F.12.17. Symbols on the graph are explained in the caption for Figure App.F.12.18. Data source: CalCOFI; Figure: A. Thompson/NOAA

Extract data from ERDDAP

• ERDDAP - Search: calcofi: 50 matching datasets
• R package rerddap
  • Using rerddap
  • Environmental drivers of small pelagic fish

Download from ERDDAP

• ERDDAP - Search: CalCOFI Larvae Counts

dir_create(dir_cache)
dataset_ids <- ed_search(
  query = q_calcofi, which = "tabledap") %>%  .$info %>% 
  arrange(dataset_id) %>% 
  pull(dataset_id)

for (i in 1:length(dataset_ids)){ # i = 12
  
  id <- dataset_ids[i]
  message(glue("{i %>% str_pad(2, '0', side='left')} of {length(dataset_ids)}: {id} - {Sys.time()}"))
  
  d_csv <- glue("{dir_cache}/{id}.csv")
  
  if (!file.exists(d_csv) & i != 12){
    d <- tabledap(id)
    write_csv(d, d_csv)
  }
  if (i == 12 & !file.exists(d_csv)){
    browser()
    tabledap(id, store=disk(path=dir_cache, overwrite = T))
    # paste0(digest::digest(ky), ".", fmt) # https://github.com/ropensci/rerddap/blob/a4fd75a79179fe5c6c56d553a11940baecc78100/R/grid.R#L309
    digest_csv <- glue("{dir_cache}/b3d5c9eb945013f5e878258049a923a3.csv")
    file_move(digest_csv, d_csv)
  }
}
# ~4.4 min ea * 31 datasets = 136.9 min

dir_info(dir_cache) %>% 
  mutate(
    file = basename(path),
    size = format(size)) %>% 
  select(file, size) %>% 
  datatable()

Prep Spatial filter

stns <- tabledap('erdCalCOFIstns') %>% 
  arrange(cruise, ship_code, order_occupied)

p_cinms <- get_nms_ply("cinms")

ymd_min = ymd(glue("{yr_min}-01-01"))

#info("erdCalCOFIlrvcntSBtoSC")
#flds <- c("calcofi_species_code", "station", "line", "longitude", "latitude", "net_location")
#d_lrvcntSBtoSC <- tabledap("erdCalCOFIlrvcntSBtoSC", fields=flds) #%>% 
#tic()

if (!file.exists(stns_cinms_csv)){
  
  tbl_stns_cinms <- read_csv(lrvcntSBtoSC_csv)

  tbl_stns_cinms <- tbl_stns_cinms %>% 
    as_tibble() %>% 
    mutate(
      stn_cruise_ship_order = glue("{cruise}-{ship_code}-{order_occupied}"),
      lon   = as.numeric(longitude), # rng: -179.82, -77.23
      lat   = as.numeric(latitude),  # rng:    0.01,  51.09
      dtime = as_datetime(time),
      line_station = glue("{line} {station}")) %>% 
    select(-longitude, -latitude, -time) %>% 
    filter(
      dtime        >= ymd_min,          # n: 2,416,384 -> 1,501,280
      # select only starboard samples from the bongo nets
      net_location == "starboard") %>%  # n: 2,416,384 -> 1,792,640
    arrange(cruise, ship_code, order_occupied) #  x 25
  
  # core stations
  linestation_core = c(
    "76.7 49", "76.7 51", "76.7 55", "76.7 60","76.7 70","76.7 80","76.7 90", "76.7 100","80 51",
    "80 55","80 60","80 70","80 80", "80 90","80 100",
    "81.8 46.9","83.3 40.6", "83.3 42", "83.3 51", "83.3 55", "83.3 60","83.3 70","83.3 80", "83.3 90", "83.3 100","83.3 110",
    "86.7 33", "86.7 35", "86.7 40", "86.7 45", "86.7 50", "86.7 55","86.7 60","86.7 70", "86.7 80", "86.7 90", "86.7 100", "86.7 110",
    "90 28", "90 30", "90 35","90 37", "90 45", "90 53", "90 60", "90 70", "90 80", "90 90", "90 100", "90 110", "90 120",
    "93.3 26.7", "93.3 28", "93.3 30", "93.3 35", "93.3 40","93.3 45", "93.3 50","93.3 60","93.3 70", "93.3 80", "93.3 90", "93.3 100","93.3 110","93.3 120")
  
  # CINMS stations
  linestation_cinms = c(
    "76.7 49", "76.7 51", "76.7 55", "76.7 60","80 51", "80 55", 
    "80 60","81.8 46.9","83.3 40.6", "83.3 42", "83.3 51", "83.3 55", "83.3 60",
    "86.7 33", "86.7 35", "86.7 40", "86.7 45", "86.7 50", "86.7 55","86.7 60","90 28", "90 30", "90 35",
    "90 37", "90 45", "90 53","90 60", "93.3 26.7", "93.3 28", "93.3 30", "93.3 35", "93.3 40",
    "93.3 45", "93.3 50","93.3 60")
  
  # SB stations
  linestation_sb <- c("80 55","80 51","81.8 46.9","83.3 55","83.3 51","83.3 42","83.3 40.6")
  
  tbl_stns_cinms <- tbl_stns_cinms %>% 
    mutate(
      is_core  = line_station %in% linestation_core,
      is_cinms = line_station %in% linestation_cinms,
      is_sb    = line_station %in% linestation_sb)
  # View(tbl_stns_cinms)
  
  write_csv(tbl_stns_cinms, stns_cinms_csv)
} 

tbl_stns_cinms <- read_csv(stns_cinms_csv)
pts_stns <- tbl_stns_cinms %>% 
    filter(is_core) %>% 
    group_by(stn_cruise_ship_order) %>% 
    summarise(
      lon          = mean(lon),
      lat          = mean(lat),
      line_station = first(line_station),
      is_core      = first(is_core),
      is_cinms     = first(is_cinms),
      is_sb        = first(is_sb)) %>% 
    st_as_sf(coords=c("lon", "lat"), crs=4326, remove=F)

if (!file.exists(plys_cinms_geo)){
  
  ply_cinms <- pts_stns %>% 
    filter(is_cinms) %>% 
    st_union() %>% st_convex_hull()
  
  ply_core <- pts_stns %>% 
    filter(is_core) %>% 
    st_union() %>% st_convex_hull()
  
  ply_sb <- pts_stns %>% 
    filter(is_sb) %>% 
    st_union() %>% st_convex_hull()
  
  plys <- tibble(
    ply_code = c("CINMS", "SoCal Shelf", "SoCal"),
    geometry = c(ply_sb, ply_cinms, ply_core)) %>% 
    st_as_sf()
  write_sf(plys, plys_cinms_geo, delete_dsn=T)
  
}
plys <- read_sf(plys_cinms_geo)

mapviewOptions(
  basemaps = c("Esri.OceanBasemap", "Stamen.TonerLite"))

mapview(plys) + 
  pts_stns

stns_shelf <- filter(pts_stns, is_cinms)
plys_shelf <- filter(plys, ply_code != "SoCal")
mapview(plys_shelf) +
  stns_shelf

Prep Taxa filter

if (!file.exists(spp_csv)){
  csvs    <- list.files(dir_cache, "erdCalCOFIlrvcnt.*\\.csv")
  
  tbl_spp <- csvs %>%
    map(function(x)
      glue("{dir_cache}/{x}") %>% 
        read_csv() %>% 
        group_by(
          scientific_name, common_name, itis_tsn, calcofi_species_code) %>% 
        summarize(
          n    = n(),
          path = x)) %>% 
    reduce(rbind)
  
  tbl_spp <- tbl_spp %>% 
    filter(!is.na(scientific_name)) %>% 
    group_by(
      scientific_name, common_name, itis_tsn, calcofi_species_code) %>% 
    summarize(
      n     = sum(n, na.rm = T),
      paths = paste(path, collapse = "|"))
  
  write_csv(tbl_spp, spp_csv)
}
spp <- read_csv(spp_csv)

table(is.na(spp$scientific_name))

## 
## FALSE  TRUE 
##   913     1

table(is.na(spp$common_name))

## 
## FALSE  TRUE 
##   623   291

datatable(spp)

Assign species to taxonomic groups manually

In data/spp_grps.csv:

read_csv(spp_grps_csv) %>% 
  datatable()

Generate time series csv’s by taxa and spatial

# taxonomic filter by groups
spp      <- read_csv(spp_csv)
spp_grps <- read_csv(spp_grps_csv)
grps     <- sort(unique(spp_grps$group))

# spatial filter by polygons
plys <- read_sf(plys_cinms_geo) %>% 
  filter(!ply_code %in% c("SoCal"))

# iterate over taxonomic groups
for (i in 1:length(grps)){ # i = 1; 
  
  grp <- grps[i]
  
  grp_sci <- spp_grps %>% 
    filter(group == !!grp) %>% 
    pull(scientific_name)
  spp_grp <- spp %>% 
    filter(scientific_name %in% grp_sci)
  grp_csvs <- spp_grp %>% 
    pull(paths) %>% str_split("\\|") %>% unlist() %>% unique()
  message(glue("
  {grp} [{i} of {length(grps)} grps]:
    scientific_names: {paste(grp_sci, collapse=', ')}
    csvs: {paste(grp_csvs, collapse=', ')}"))
  
  grp_ply_csvs <- here(glue("data/{grp}_{plys$ply_code}.csv"))
  if (all(file.exists(grp_ply_csvs)) & !redo_grp_ply_csv){
    message(glue("  All grp_ply_csvs already exist, skipping", trim = F))
    next
  }
  
  d <- grp_csvs %>%
    map(function(x)
      glue("{dir_cache}/{x}") %>% 
        read_csv() %>% 
        mutate(
          stn_cruise_ship_order = glue("{cruise}-{ship_code}-{order_occupied}"),
          lon                   = as.numeric(longitude),
          lat                   = as.numeric(latitude),
          dtime                 = as_datetime(time),
          line_station          = glue("{line} {station}")) %>% 
        select(-longitude, -latitude, -time) %>% 
        filter(
          # !is.na(larvae_count),
          # !is.na(volume_sampled),
          scientific_name %in% !! grp_sci,
          dtime            >=  ymd_min,         
          net_location     ==  "starboard")) %>% 
    reduce(rbind) %>% 
    mutate(
      larvae_count   = as.numeric(larvae_count),
      volume_sampled = as.numeric(volume_sampled)) %>% 
    filter(
      !is.na(larvae_count),
      !is.na(volume_sampled))%>% 
    mutate(
        larvae_count_per_volume_sampled = larvae_count / volume_sampled) %>% 
    st_as_sf(coords=c("lon", "lat"), crs=4326, remove=F) %>% 
    st_join(plys) %>% 
    filter(!is.na(ply_code))
  
  # iterate over spatial polygons
  for (j in 1:nrow(plys)){ # j = 1
    
    ply_code <- slice(plys, j) %>% pull(ply_code)
    grp_ply_csv <- here(glue("data/{grp}_{ply_code}.csv"))
    message(glue("
      {grp} - {ply_code} [{j} of {nrow(plys)} ply_codes]
        csv: {grp_ply_csv}"))
    
    d_j <- d %>% 
      filter(ply_code == !!ply_code) %>% 
      st_drop_geometry() %>% 
      filter(!is.na(larvae_count_per_volume_sampled)) %>% 
      group_by(year = floor_date(dtime, "year")) %>% 
      summarize(
        avg_larvae_count_per_volume_sampled = mean(larvae_count_per_volume_sampled)) %>% 
      mutate(
        spp_grp  = !!grp,
        ply_code = !!ply_code)
    
    write_csv(d_j, grp_ply_csv)
  }
}

Output interactive plots

Abundance time series by taxa & region

calcofi_plot <- function(csv, x_fld, y_fld, x_lab, y_lab, yrs_recent=5, interactive=T, title=NULL, y_trans=NULL){
  # csv     = here("data/Anchovy_CINMS.csv")
  # x_fld   = "year"
  # y_fld   = "avg_larvae_count_per_volume_sampled"
  # y_trans = "log(y + 1)"
  # x_lab   = "Year" 
  # y_lab   = "ln(mean abundance + 1)"
  # title   =  "Anchovy - CINMS Region"
  # yrs_recent = 5; interactive=T
  
  library(dplyr)

  d <- readr::read_csv(csv)
  
  if (nrow(d) == 0) return(NULL)
  
  flds <- list(x = sym(x_fld), y = sym(y_fld))
  d <- select(d, !!!flds)
  
  if (!is.null(y_trans))
    d <- mutate(d, y = !! rlang::parse_expr(y_trans))

  z <- filter(d, x < max(x) - years(yrs_recent))
  y_avg <- mean(z$y)
  y_sd  <- sd(z$y)
  y_r   <- expand_range(range(d$y), mul=0.05)
  
  g <- ggplot(d, aes(x = x, y = y)) + 
    annotate(
      "rect",
      xmin = max(d$x) - years(yrs_recent), xmax = max(d$x) + months(6),
      ymin = y_r[1], ymax = y_r[2],
      fill  = "lightblue", alpha=0.5) +
    geom_line() + 
    geom_point() + 
    geom_hline(
      yintercept = c(y_avg + y_sd, y_avg,  y_avg - y_sd), 
      linetype   = c("solid", "dashed", "solid"),
      color       = "darkblue") + 
    coord_cartesian(
      xlim = c(
        min(d$x) - months(6),
        max(d$x) + months(6)), expand = F) + 
    theme_light() +
    labs(
      x     = x_lab,
      y     = y_lab,
      title = title)
  
  if (interactive){
    p <- plotly::ggplotly(g)
    print(htmltools::tagList(p))
  } else {
    print(g)
  }
}

# iterate over taxonomic groups
for (i in 1:length(grps)){ # i = 1
  
  grp <- grps[i]
  
  # iterate over spatial polygons
  for (j in 1:nrow(plys)){ # j = 1
    
    ply_code <- plys$ply_code[j]
    
    cat(glue("\n\n\n#### {grp} in {ply_code} Region\n\n", trim=F))
    
    calcofi_plot(
      csv     = here(glue("data/{grp}_{ply_code}.csv")),
      x_fld   = "year",
      y_fld   = "avg_larvae_count_per_volume_sampled",
      y_trans = "log(y + 1)",
      x_lab   = "Year",
      y_lab   = "ln(mean abundance + 1)",
      title   =  glue("{grp} - {ply_code} Region"))
  }
}

Anchovy in CINMS Region

Anchovy in SoCal Shelf Region

Cool-water associated ichthyoplankton in CINMS Region

Cool-water associated ichthyoplankton in SoCal Shelf Region

Hake in CINMS Region

Hake in SoCal Shelf Region

Myctophids in CINMS Region

Myctophids in SoCal Shelf Region

Rockfish in CINMS Region

Rockfish in SoCal Shelf Region

Sanddab in CINMS Region

Sanddab in SoCal Shelf Region

Sardine in CINMS Region

Sardine in SoCal Shelf Region

Warm-water associated ichthyoplankton in CINMS Region

Warm-water associated ichthyoplankton in SoCal Shelf Region

library(htmltools)
library(htmlwidgets)

# attach the Dependencies
# since the do not get included with renderTags(...)$html
output <- list()
output[[1]] <- qplot(Petal.Width, Sepal.Length, data = iris, color = Species) %>% ggplotly() %>% as.widget()

deps <- lapply(
  Filter(function(x){inherits(x,"htmlwidget")}, output),
  function(hw){
    renderTags(hw)$dependencies
  }
)

attachDependencies(
  tagList(),
  unlist(deps,recursive=FALSE)
)

Questions to complete

• Code for figures to creating standard IEA time series plots? How do you do this already (eg ggplot2 in R, or Matlab/Python/etc) with green mean +/- sd, most recent 5 years, and filled segments above/below standad deviation (sd)?
• Temporal
  • time >= 1978 ok?
  • Overall Mean +/- SD. Is the overall mean for the entire time series or historical up to the last 5 years? Should we archive previous years of plots given the different mean +/- sd these would produce?
• Other Filters
  • Select only starboard samples from the bongo nets: net_location == “starboard”? Assume yes.
  • Should we limit by cruise_shipcode? If so, what’s the criteria for future years?
  • Any other criteria of concern that we’re missing?
• Spatial
  • Do “SoCal Shelf” and “CINMS” areas in * Prep Spatial filter look correct?
  • Is it OK to include all station data within these areas or were there other reasons for explicitly outlining each station?
• Taxonomy See spp.csv for all species found across ERDDAP datasets - Search: CalCOFI Larvae Counts, Scientific Names, and spp_grps.csv for species matched to taxonomic group for CINMS Condition Report.
  • ☑ Is Anchovy one or all of common_name = “Anchoveta”, “Anchovies”, “Buccaneer anchovy”, “Northern anchovy”?
  • ☐ Market Squid Doryteuthis opalescens not found in spp.csv so where is this data based on ERDDAP datasets - Search: CalCOFI?
  • Cool-water associated ichthyoplankton (McClatchie et al., 2016):
    • ☑ Mexican lampfish (Triphoturus mexicanus)
    • ☑ lightfishes (mainly Vinciguerria lucetia)
      • Panama lightfish (Vinciguerria lucetia)
      • ok to also include?…
      • Lightfishes (Phosichthyidae)
      • Oceanic lightfish (Vinciguerria nimbaria)
      • Highseas lightfish (Vinciguerria poweriae)
      • Bigeye lightfish (Woodsia nonsuchae)
      • Bulldog lightfish (Ichthyococcus irregularis)
    • ☑ snubnose blacksmelt (Bathylagoides wesethi)
    • ☑ Diogenes lanternfish (Diogenichthys laternatus)
    • ☑ fangtooth lanternfish (Ceratoscopelus townsendi), and
      • Dogtooth lampfish (Ceratoscopelus townsendi)
    • ☑ bigfin lanternfish (Symbolophorus californiensis)."
      • California lanternfish (Symbolophorus californiensis)
  • Warm-water associated ichthyoplankton (larval fishes):
    • ☑ northern lampfish (Stenobrachius leucopsarus)
    • ☑ California smoothtongue (Leuroglossus stilbius)
    • ☑ eared black-smelt (Lipolagus ochotensis)
      • Popeye blacksmelt (Lipolagus ochotensis)
    • ☑ blue lanternfish (Tarletonbeania crenularis), and
    • ☑ California flashlightfish (Protomyctophum crockeri)
  • ☐ Small Plankton species per Figure App.F.12.18? See Metrics / Volume below.
  • Figure App.F.12.19 key forage groups in CINMS/SoCal
    • Hake: despite using following taxa, don’t seem to have any data?
      • Hakes (Merlucciidae)
      • Dwarf hake (Merluccius)
      • Pacific hake or whiting (Merluccius productus)
    • Rockfish
      • Rockfishes (Sebastes)
      • Aurora rockfish (Sebastes aurora)
      • Splitnose rockfish (Sebastes diploproa)
      • Shortbelly rockfish (Sebastes jordani)
      • Cow rockfish (cowcod) (Sebastes levis)
      • Mexican rockfish (Sebastes macdonaldi)
      • Blackgill rockfish (Sebastes melanostomus)
      • Stripetail rockfish (Sebastes saxicola)
    • Sanddab
      • Sanddabs (Citharichthys)
      • Gulf sanddab (Citharichthys fragilis)
      • Mimic sanddab (Citharichthys gordae)
      • Small sanddab (Citharichthys platophrys)
      • Pacific sanddab (Citharichthys sordidus)
      • Speckled sanddab (Citharichthys stigmaeus)
      • Longfin sanddab (Citharichthys xanthostigma)
    • Myctophids
      • Family: Lanternfishes (Myctophidae)
      • many Myctoph*:
        • Golden lanternfish (Myctophum aurolaternatum)
        • Lanternfishes (Myctophidae)
        • NA (Myctophiformes)
        • NA (Myctophum asperum)
        • NA (Myctophum lychnobium)
        • NA (Myctophum obtusirostre)
        • NA (Myctophum selenops)
        • NA (Myctophum spinosum)
        • NA (Myctophum)
        • NA (Protomyctophum)
        • Northern flashlightfish (Protomyctophum thompsoni)
        • Pearly lanternfish (Myctophum nitidulum)
      • many Lanternfishes:
        • Bermuda lanternfish (Hygophum hygomii)
        • Blue lanternfish (Tarletonbeania crenularis)
        • California lanternfish (Symbolophorus californiensis)
        • Diogenes lanternfish (Diogenichthys laternatus)
        • Dwarf lanternfish (Loweina rara)
        • Evermann’s lanternfish (Symbolophorus evermanni)
        • Golden lanternfish (Myctophum aurolaternatum)
        • Lanternfish (Notoscopelus caudispinosus)
        • Lanternfishes (Myctophidae)
        • Longfin lanternfish (Diogenichthys atlanticus)
        • Panama lanternfish (Benthosema panamense)
        • Roundnose lanternfish (Centrobranchus nigroocellatus)
        • Pearly lanternfish (Myctophum nitidulum)
        • Slender lanternfish (Hygophum reinhardtii)
        • Slendertail lanternfish (Gonichthys tenuiculus)
        • Spinycheek lanternfish (Benthosema fibulatum)
        • Thickhead lanternfish (Hygophum atratum)
• Metrics
  • Abundance. For y-axis of “ln(mean abundance+1)” is ‘abundance’ actually ‘concentration’, ie mean(larvae_count / volume_sampled) grouped by year, to account for varying effort and volumes?
    • So not using fields larvae_10m2 (1% NAs) or larvae_1000m3 (100% NAs)?
    • Units of larvae_count / volume_sampled presumed to be \(n/m^3\), per metadata
  • Mean Species Richness + Mean Simpson Diversity for ichthyoplankton Figure S.LR.15.3:
    • Are these two plots using the combined species (and not genus, eg Lightfishes Phosichthyidae) for warm- and cool-water icthyoplankton species, per caption “The average species richness (left) and species diversity (right) in each net sample is shown for the entire time series”?
    • For calculating “Mean Simpson Diversity” are you using vegan::diversity like so: diversity(x, index = "invsimpson"), given caption “Gini-Simpson diversity (1-λ form) is a measure of the equitability of species in a sample.”
    • Volume per Small Plankton Volume in Figure App.F.12.18: is this from erdCalCOFItows and should I use small_plankton_volume (\(ml/1000 m^3\)) and not sorted_plankton_volume or total_plankton_volume per metadata?

References

McClatchie, S., Thompson, A. R., Alin, S. R., Siedlecki, S., Watson, W., & Bograd, S. J. (2016). The influence of Pacific Equatorial Water on fish diversity in the southern California Current System: ICHTHYOPLANKTON AND EQUATORIAL WATER. Journal of Geophysical Research: Oceans, 121(8), 6121–6136. https://doi.org/10.1002/2016JC011672