Explore Herring Larvae Indices
Sarah Gaichas
2024-09-12
Introduction
Indices of various copeopod groups have been developed: https://noaa-edab.github.io/zooplanktonindex/CopeModResults.html
Now the question is, are the zooplankton available to herring larvae? We will explore data available on herring larvae in the EcoMon (and previous zooplankton) data.
Methods
Herring larvae data were added to the input dataset in the updated script https://github.com/NOAA-EDAB/zooplanktonindex/blob/main/data/VASTzoopindex_processinputs.R and all stations were re-mapped to OISST data to fill missing temperature values if necessary.
Where are herring larvae in each of our seasons?
herringfood_stn <- readRDS(here::here("data/herringfood_stn_all_OISST.rds"))
# make SST column that uses surftemp unless missing or 0
herringfood_stn <- herringfood_stn %>%
dplyr::mutate(sstfill = ifelse((is.na(sfc_temp)|sfc_temp==0), oisst, sfc_temp),
season_larv = month %in% c(1:2, 9:12))
herringlarvae_stn_fall <- herringfood_stn %>%
#ungroup() %>%
filter(season_ng == "FALL",
year > 1981) %>%
mutate(AreaSwept_km2 = 1, #Elizabeth's code
#declon = -declon already done before neamap merge
Vessel = 1,
Dayofyear = lubridate::yday(date) #as.numeric(as.factor(vessel))-1
) %>%
dplyr::select(Catch_g = cluhar_100m3, #use megabenwt for individuals input in example
Year = year,
Month = month,
Dayofyear,
Vessel,
AreaSwept_km2,
Lat = lat,
Lon = lon,
#btm_temp, #this leaves out many stations
#sfc_temp, #this leaves out many stations
#oisst,
sstfill
) %>%
na.omit() %>%
as.data.frame()
herringlarvae_stn_sepfeb <- herringfood_stn %>%
#ungroup() %>%
dplyr::filter(season_larv == TRUE) %>%
dplyr::mutate(AreaSwept_km2 = 1, #Elizabeth's code
#declon = -declon already done before neamap merge
Vessel = 1,
Dayofyear = lubridate::yday(date),
yearshift = ifelse(month < 3, year-1, year)#as.numeric(as.factor(vessel))-1
) %>%
dplyr::filter(yearshift>1981) |>
dplyr::select(Catch_g = cluhar_100m3, #use megabenwt for individuals input in example
Year = yearshift,
Month = month,
Dayofyear,
Vessel,
AreaSwept_km2,
Lat = lat,
Lon = lon,
#btm_temp, #this leaves out many stations
#sfc_temp, #this leaves out many stations
#oisst,
sstfill
) %>%
na.omit() %>%
as.data.frame()
herringlarvae_stn_spring <- herringfood_stn %>%
#ungroup() %>%
filter(season_ng == "SPRING",
year > 1981) %>%
mutate(AreaSwept_km2 = 1, #Elizabeth's code
#declon = -declon already done before neamap merge
Vessel = 1,
Dayofyear = lubridate::yday(date) #as.numeric(as.factor(vessel))-1
) %>%
dplyr::select(Catch_g = cluhar_100m3, #use megabenwt for individuals input in example
Year = year,
Month = month,
Dayofyear,
Vessel,
AreaSwept_km2,
Lat = lat,
Lon = lon,
#btm_temp, #this leaves out many stations
#sfc_temp, #this leaves out many stations
#oisst,
sstfill
) %>%
na.omit() %>%
as.data.frame()
nonzerofall <- herringlarvae_stn_fall |>
dplyr::filter(Catch_g > 0) #,
#Year > 1981)
nonzerosepfeb <- herringlarvae_stn_sepfeb |>
dplyr::filter(Catch_g > 0)
nonzerospring <- herringlarvae_stn_spring |>
dplyr::filter(Catch_g > 0) #,
# Year > 1981)
Fall <- ggplot(data = ecodata::coast) +
geom_sf() +
geom_point(data = FishStatsUtils::northwest_atlantic_grid, aes(x = Lon, y = Lat), colour = "coral4", size=0.05, alpha=0.1) +
geom_point(data = nonzerofall, aes(x = Lon, y = Lat), colour = "blue", size=0.5, alpha=1) +
coord_sf(xlim =c(-78.5, -65.5), ylim = c(33, 45)) + #zoomed to Hatteras and N
xlab("") +
ylab("") +
ggtitle("Fall herring larvae 1982-2022")+
theme(plot.margin = margin(0, 0, 0, 0, "cm"))
SepFeb <- ggplot(data = ecodata::coast) +
geom_sf() +
geom_point(data = FishStatsUtils::northwest_atlantic_grid, aes(x = Lon, y = Lat), colour = "coral4", size=0.05, alpha=0.1) +
geom_point(data = nonzerosepfeb, aes(x = Lon, y = Lat), colour = "blue", size=0.5, alpha=1) +
coord_sf(xlim =c(-78.5, -65.5), ylim = c(33, 45)) + #zoomed to Hatteras and N
xlab("") +
ylab("") +
ggtitle("Sept-Feb herring larvae 1982-2022")+
theme(plot.margin = margin(0, 0, 0, 0, "cm"))
Spring <- ggplot(data = ecodata::coast) +
geom_sf() +
geom_point(data = FishStatsUtils::northwest_atlantic_grid, aes(x = Lon, y = Lat), colour = "coral4", size=0.05, alpha=0.1) +
geom_point(data = nonzerospring, aes(x = Lon, y = Lat), colour = "blue", size=0.5, alpha=1) +
coord_sf(xlim =c(-78.5, -65.5), ylim = c(33, 45)) + #zoomed to Hatteras and N
xlab("") +
ylab("") +
ggtitle("Spring herring larvae 1982-2022")+
theme(plot.margin = margin(0, 0, 0, 0, "cm"))
Spring + Fall + SepFeb
Day of year and months herring larvae found (present, not abundance)
herringlarvae_stn_all <- dplyr::bind_rows(herringlarvae_stn_spring, herringlarvae_stn_fall)
hist(herringlarvae_stn_all$Dayofyear, xlim=c(0,365), breaks=366)
hist(herringlarvae_stn_all$Month, xlim=c(0,12), breaks=13)
Amount of herring larvae found (sum station volume over all years, not an abundance)
sumlarvae <- herringlarvae_stn_all |>
dplyr::group_by(Month) |>
dplyr::summarise(totlarvae = sum(Catch_g, na.rm = TRUE))
ggplot2::ggplot(sumlarvae, ggplot2::aes(x=Month, y=totlarvae)) +
ggplot2::geom_bar(stat = "identity")
So fall larvae, could be used to weight fall small copepods.
What years? Also just summing stations, not an abundance estimate
sumlarvaeyr <- herringlarvae_stn_all |>
dplyr::group_by(Year) |>
dplyr::summarise(totlarvae = sum(Catch_g, na.rm = TRUE))
ggplot2::ggplot(sumlarvaeyr, ggplot2::aes(x=Year, y=totlarvae)) +
ggplot2::geom_bar(stat = "identity")
Initial herring larvae model results
# from each output folder in pyindex,
outdir <- here::here("pyindex")
moddirs <- list.dirs(outdir)
moddirs <- moddirs[-1]
# keep folder name
modnames <- list.dirs(outdir, full.names = FALSE)
# function to apply extracting info
getmodinfo <- function(d.name){
# read settings
modpath <- stringr::str_split(d.name, "/", simplify = TRUE)
modname <- modpath[length(modpath)]
settings <- read.table(file.path(d.name, "settings.txt"), comment.char = "",
fill = TRUE, header = FALSE)
n_x <- as.numeric(as.character(settings[(which(settings[,1]=="$n_x")+1),2]))
grid_size_km <- as.numeric(as.character(settings[(which(settings[,1]=="$grid_size_km")+1),2]))
max_cells <- as.numeric(as.character(settings[(which(settings[,1]=="$max_cells")+1),2]))
use_anisotropy <- as.character(settings[(which(settings[,1]=="$use_anisotropy")+1),2])
fine_scale <- as.character(settings[(which(settings[,1]=="$fine_scale")+1),2])
bias.correct <- as.character(settings[(which(settings[,1]=="$bias.correct")+1),2])
#FieldConfig
if(settings[(which(settings[,1]=="$FieldConfig")+1),1]=="Component_1"){
omega1 <- as.character(settings[(which(settings[,1]=="$FieldConfig")+2),2])
omega2 <- as.character(settings[(which(settings[,1]=="$FieldConfig")+3),1])
epsilon1 <- as.character(settings[(which(settings[,1]=="$FieldConfig")+4),2])
epsilon2 <- as.character(settings[(which(settings[,1]=="$FieldConfig")+5),1])
beta1 <- as.character(settings[(which(settings[,1]=="$FieldConfig")+6),2])
beta2 <- as.character(settings[(which(settings[,1]=="$FieldConfig")+7),1])
}
if(settings[(which(settings[,1]=="$FieldConfig")+1),1]=="Omega1"){
omega1 <- as.character(settings[(which(settings[,1]=="$FieldConfig")+3),1])
omega2 <- as.character(settings[(which(settings[,1]=="$FieldConfig")+4),1])
epsilon1 <- as.character(settings[(which(settings[,1]=="$FieldConfig")+3),2])
epsilon2 <- as.character(settings[(which(settings[,1]=="$FieldConfig")+4),2])
beta1 <- "IID"
beta2 <- "IID"
}
#RhoConfig
rho_beta1 <- as.numeric(as.character(settings[(which(settings[,1]=="$RhoConfig")+3),1]))
rho_beta2 <- as.numeric(as.character(settings[(which(settings[,1]=="$RhoConfig")+3),2]))
rho_epsilon1 <- as.numeric(as.character(settings[(which(settings[,1]=="$RhoConfig")+4),1]))
rho_epsilon2 <- as.numeric(as.character(settings[(which(settings[,1]=="$RhoConfig")+4),2]))
# read parameter estimates, object is called parameter_Estimates
if(file.exists(file.path(d.name, "parameter_estimates.RData"))) {
load(file.path(d.name, "parameter_estimates.RData"))
AIC <- parameter_estimates$AIC[1]
converged <- parameter_estimates$Convergence_check[1]
fixedcoeff <- unname(parameter_estimates$number_of_coefficients[2])
randomcoeff <- unname(parameter_estimates$number_of_coefficients[3])
}else if(file.exists(file.path(d.name, "parameter_estimates.txt"))){
reptext <- readLines(file.path(d.name, "parameter_estimates.txt"))
AIC <- as.double(reptext[grep(reptext, pattern = "AIC")+1])
converged <- reptext[grep(reptext, pattern = "Convergence_check")+1]
fixedcoeff <- as.integer(stringr::str_split(reptext[grep(reptext, pattern = "number_of_coefficients")+2],
boundary("word"))[[1]][2])
randomcoeff <- as.integer(stringr::str_split(reptext[grep(reptext, pattern = "number_of_coefficients")+2],
boundary("word"))[[1]][3])
}else{
AIC <- NA_real_
converged <- NA_character_
fixedcoeff <- NA_integer_
randomcoeff <- NA_integer_
}
#index <- read.csv(file.path(d.name, "Index.csv"))
# return model attributes as a dataframe
out <- data.frame(modname = modname,
n_x = n_x,
grid_size_km = grid_size_km,
max_cells = max_cells,
use_anisotropy = use_anisotropy,
fine_scale = fine_scale,
bias.correct = bias.correct,
omega1 = omega1,
omega2 = omega2,
epsilon1 = epsilon1,
epsilon2 = epsilon2,
beta1 = beta1,
beta2 = beta2,
rho_epsilon1 = rho_epsilon1,
rho_epsilon2 = rho_epsilon2,
rho_beta1 = rho_beta1,
rho_beta2 = rho_beta2,
AIC = AIC,
converged = converged,
fixedcoeff = fixedcoeff,
randomcoeff = randomcoeff#,
#index = index
)
return(out)
}
modcompare <- purrr::map_dfr(moddirs, getmodinfo)
modselect <- modcompare |>
dplyr::mutate(season = dplyr::case_when(stringr::str_detect(modname, "_fall_") ~ "Fall",
stringr::str_detect(modname, "spring") ~ "Spring",
stringr::str_detect(modname, "_all_") ~ "Annual",
TRUE ~ as.character(NA))) |>
dplyr::mutate(converged2 = dplyr::case_when(stringr::str_detect(converged, "no evidence") ~ "likely",
stringr::str_detect(converged, "is likely not") ~ "unlikely",
TRUE ~ as.character(NA))) |>
dplyr::mutate(copegroup = stringr::str_extract(modname, "[^_]+")) |>
#dplyr::mutate(modname = str_extract(modname, '(?<=allagg_).*')) |>
dplyr::group_by(copegroup, season) |>
dplyr::mutate(deltaAIC = AIC-min(AIC)) |>
dplyr::select(copegroup, modname, season, deltaAIC, fixedcoeff,
randomcoeff, use_anisotropy,
omega1, omega2, epsilon1, epsilon2,
beta1, beta2, AIC, converged2) |>
dplyr::arrange(copegroup, season, AIC)
# DT::datatable(modselect, rownames = FALSE,
# options= list(pageLength = 25, scrollX = TRUE),
# caption = "Comparison of delta AIC values using Restricted Maxiumum Likelihood (REML) for alternative fixed and random effects model structures. See text for model descriptions.")
# flextable::flextable(modselect) %>%
# #dplyr::select(-c(use_anisotropy,
# #omega1, omega2, epsilon1, epsilon2,
# #beta1, beta2))
# #) %>%
# flextable::set_header_labels(modname = "Model name",
# season = "Season",
# #deltaAIC = "dAIC",
# fixedcoeff = "N fixed",
# randomcoeff = "N random",
# converged2 = "Convergence") |>
# #flextable::set_caption("Comparison of delta AIC (dAIC) values using Restricted Maxiumum Likelihood (REML) for alternative fixed and random effects model structures, with number of fixed (N fixed) and random (N random) coefficients. See text for model descriptions associated with each model name.") %>%
# flextable::fontsize(size = 9, part = "all") %>%
# flextable::colformat_double(digits = 2) |>
# flextable::set_table_properties(layout = "autofit", width = 1)Stations by season
Fall sampling for herring larvae was completed in most years aside from GLOBEC. In our definition of spring, herring larvae primarily occur in January and February. We now include a shifted season to better match larval herring availability throughout the time series: September - February. Year corresponds to September-December, and the following January and February are aligned with the previous year (hatch year) in these analyses.
for(d.name in moddirs[str_detect(moddirs, "herring")]){
modpath <- unlist(str_split(d.name, pattern = "/"))
modname <- modpath[length(modpath)]
cat(modname, "\n")
cat(paste0("![](",d.name, "/Data_by_year.png)"))
cat("\n\n")
}herringlarvae_fall_500_biascorrect 
herringlarvae_sepfeb_yrshift_500_biascorrect 
herringlarvae_spring_500_biascorrect 
Indices by group, season, and region
stratlook <- data.frame(Stratum = c("Stratum_1",
"Stratum_2",
"Stratum_3",
"Stratum_4",
"Stratum_5",
"Stratum_6",
"Stratum_7"),
Region = c("AllEPU",
"her_sp",
"her_fa",
"MAB",
"GB",
"GOM",
"SS"))
# function to apply extracting info
getmodindex <- function(d.name){
# read settings
modpath <- stringr::str_split(d.name, "/", simplify = TRUE)
modname <- modpath[length(modpath)]
if(file.exists(file.path(d.name,"Index.csv"))){
index <- read.csv(file.path(d.name, "Index.csv"))
}else{
stopifnot()
}
# return model indices as a dataframe
out <- data.frame(modname = modname,
index
)
return(out)
}
modcompareindex <- purrr::map_dfr(moddirs, purrr::possibly(getmodindex, otherwise = NULL))
splitoutput <- modcompareindex %>%
dplyr::mutate(Season = modname |> map(str_split, pattern = "_") |> map_chr(c(1,2))) %>%
dplyr::mutate(Data = modname |> map(str_split, pattern = "_") |> map_chr(c(1,1))) %>%
dplyr::mutate(Estimate = ifelse(Estimate == 0, NA, Estimate)) |>
dplyr::left_join(stratlook) #%>%
#dplyr::filter(Region %in% c(GOM", "GB", "MAB","SS", "AllEPU")) use all regions
zoomax <- max(splitoutput$Estimate, na.rm=T)
zootsmean <- splitoutput %>%
dplyr::group_by(modname, Region) %>%
dplyr::mutate(fmean = mean(Estimate, na.rm=T)) Seasonal indices
plot_zooindices <- function(splitoutput, plotdata, plotregions, plottitle){
filterEPUs <- plotregions #c("her_sp", "her_fa", "MAB", "GB", "GOM", "SS", "AllEPU")
seasons <- splitoutput |> dplyr::filter(Data==plotdata) |> dplyr::select(Season) |> dplyr::distinct()
ncols <- dim(seasons)[1]
currentMonth <- lubridate::month(Sys.Date())
currentYear <- lubridate::year(Sys.Date())
if (currentMonth > 4) {
endShade <- currentYear
} else {
endShade <- currentYear - 1
}
shadedRegion <- c(endShade-9,endShade)
shade.alpha <- 0.3
shade.fill <- "lightgrey"
lwd <- 1
pcex <- 2
trend.alpha <- 0.5
trend.size <- 2
hline.size <- 1
line.size <- 2
hline.alpha <- 0.35
hline.lty <- "dashed"
label.size <- 5
hjust.label <- 1.5
letter_size <- 4
errorbar.width <- 0.25
x.shade.min <- shadedRegion[1]
x.shade.max <- shadedRegion[2]
setup <- list(
shade.alpha = shade.alpha,
shade.fill =shade.fill,
lwd = lwd,
pcex = pcex,
trend.alpha = trend.alpha,
trend.size = trend.size,
line.size = line.size,
hline.size = hline.size,
hline.alpha = hline.alpha,
hline.lty = hline.lty,
errorbar.width = errorbar.width,
label.size = label.size,
hjust.label = hjust.label,
letter_size = letter_size,
x.shade.min = x.shade.min,
x.shade.max = x.shade.max
)
fix<- splitoutput |>
dplyr::filter(Data %in% plotdata, #c("calfin"),
Region %in% filterEPUs) |>
dplyr::group_by(Region, Season) |>
dplyr::summarise(max = max(Estimate, na.rm=T))
p <- splitoutput |>
dplyr::filter(Data %in% plotdata, #c("calfin"),
Region %in% filterEPUs) |>
dplyr::group_by(Region, Season) |>
dplyr::left_join(fix) |>
dplyr::mutate(#Value = Value/resca,
Mean = as.numeric(Estimate),
SE = Std..Error.for.Estimate,
Mean = Mean/max,
SE = SE/max,
Upper = Mean + SE,
Lower = Mean - SE) |>
ggplot2::ggplot(ggplot2::aes(x = Time, y = Mean, linetype = modname, group = modname))+
ggplot2::annotate("rect", fill = setup$shade.fill, alpha = setup$shade.alpha,
xmin = setup$x.shade.min , xmax = setup$x.shade.max,
ymin = -Inf, ymax = Inf) +
ggplot2::geom_ribbon(ggplot2::aes(ymin = Lower, ymax = Upper, fill = Season), alpha = 0.5)+
ggplot2::geom_point()+
ggplot2::geom_line()+
ggplot2::ggtitle(plottitle)+
ggplot2::ylab(expression("Relative abundance"))+
ggplot2::xlab(ggplot2::element_blank())+
ggplot2::facet_wrap(Region~Season, ncol = ncols,
labeller = label_wrap_gen(multi_line=FALSE))+
ecodata::geom_gls()+
ecodata::theme_ts()+
ecodata::theme_facet()+
ecodata::theme_title() +
ggplot2::theme(legend.position = "bottom")
return(p)
}
plot_zooindices(splitoutput = splitoutput,
plotdata = "herringlarvae",
plotregions = c("her_sp", "her_fa", "MAB", "GB", "GOM", "SS", "AllEPU"),
plottitle = "Herring larvae") 
Relative density by area.
We now see the spike in 2000 that was observed by Richardson et al. (2010).
plotdata <- c("herringlarvae")
plottitle <- "Herring larvae"
fix<- splitoutput |>
dplyr::filter(Data %in% plotdata #,
#Region %in% filterEPUs
) |>
dplyr::group_by(Season) |> #Region,
dplyr::summarise(max = max(Estimate, na.rm=T))
p <- splitoutput |>
dplyr::filter(Data %in% plotdata #, #c("calfin"),
#Region %in% filterEPUs
) |>
dplyr::group_by(Season) |> #Region,
dplyr::left_join(fix) |>
dplyr::mutate(#Value = Value/resca,
Mean = as.numeric(Estimate),
SE = Std..Error.for.Estimate,
Mean = Mean/max,
SE = SE/max,
Upper = Mean + SE,
Lower = Mean - SE) |>
ggplot2::ggplot(ggplot2::aes(x = Time, y = Mean, linetype = Region, group = Region))+
#ggplot2::annotate("rect", fill = setup$shade.fill, alpha = setup$shade.alpha,
# xmin = setup$x.shade.min , xmax = setup$x.shade.max,
# ymin = -Inf, ymax = Inf) +
ggplot2::geom_ribbon(ggplot2::aes(ymin = Lower, ymax = Upper, fill = Region), alpha = 0.5)+
ggplot2::geom_point()+
ggplot2::geom_line()+
ggplot2::ggtitle(plottitle)+
ggplot2::ylab(expression("Relative abundance"))+
ggplot2::xlab(ggplot2::element_blank())+
ggplot2::facet_wrap(~Season, #Region~ ncol = ncols,
labeller = label_wrap_gen(multi_line=FALSE))+
#ecodata::geom_gls()+
ecodata::theme_ts()+
ecodata::theme_facet()+
ecodata::theme_title() +
ggplot2::theme(legend.position = "bottom")
p
Density estimates
for(d.name in moddirs[str_detect(moddirs, "herring")]){
modpath <- unlist(str_split(d.name, pattern = "/"))
modname <- modpath[length(modpath)]
cat(modname, "\n")
if(file.exists(paste0(d.name, "/ln_density-predicted.png"))){
cat(paste0("![](",d.name, "/ln_density-predicted.png)"))
}
cat("\n\n")
}herringlarvae_fall_500_biascorrect 
herringlarvae_sepfeb_yrshift_500_biascorrect 
herringlarvae_spring_500_biascorrect 
Extract the herring larvae data by year
We want to define areas of most dense larvae each year and pull our small copepod index from there.
Maybe quantiles of herring larval density by year?
Plot the data (based on https://github.com/James-Thorson-NOAA/VAST/wiki/Plots-using-ggplot):
Two low years and two high years. Is distribution different?
d.name <- moddirs[str_detect(moddirs, "herringlarvae_sepfeb")]
fit <- readRDS(paste0(d.name, "/fit.rds"))
#fit <- VAST::reload_model(fit) #added to try to make work after restart, no previous VAST run
years <- unique(fit$data_frame$t_i)
years <- c(min(years):max(years))
mdl <- FishStatsUtils::make_map_info(Region = fit$settings$Region,
spatial_list = fit$spatial_list,
Extrapolation_List = fit$extrapolation_list#,
#added to try to make work after restart, no previous VAST run
#Include = fit$extrapolation_list[["Area_km2_x"]] > 0 & rowSums(fit$extrapolation_list[["a_el"]]) > 0
)
gmap <- ggplot(data = ecodata::coast) +
geom_sf() +
#aes(x = lon, y = lat, group = group) +
#geom_polygon(fill="black", colour = "white") +
scale_color_viridis_c(option = "magma") + # now make this quantiles...
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
panel.spacing.x=unit(0, "lines"),
panel.spacing.y=unit(0, "lines"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank() ) +
coord_sf(xlim=mdl$Xlim, ylim=mdl$Ylim)
## Below shows to you get the model estimate of density, D_gct,
## for each grid (g), category (c; not used here single
## univariate); and year (t); and link it spatially to a lat/lon
## extrapolation point. You can do this for any _gct or _gc
## variable in the Report.
names(fit$Report)[grepl('_gc|_gct', x=names(fit$Report))]## [1] "Xi1_gcp" "Omega2_gc" "D_gct" "Epsilon1_gct" "R1_gct"
## [6] "Xi2_gcp" "Omega1_gc" "R2_gct" "eta1_gct" "P1_gct"
## [11] "P2_gct" "eta2_gct" "Epsilon2_gct" "Index_gctl"D_gt <- fit$Report$D_gct[,1,] # drop the category
dimnames(D_gt) <- list(cell=1:nrow(D_gt), year=years)
## tidy way of doing this, reshape2::melt() does
## it cleanly but is deprecated
D_gt <- D_gt %>% as.data.frame() %>%
tibble::rownames_to_column(var = "cell") %>%
pivot_longer(-cell, names_to = "Year", values_to='D')
D <- merge(D_gt, mdl$PlotDF, by.x='cell', by.y='x2i')
saveRDS(D, "Dherr_sepfeb.rds")
g <- gmap +
geom_point(data=D, aes(Lon, Lat, color=log(as.vector(D)), group=NULL),
## These settings are necessary to avoid
## overlplotting which is a problem here. May need
## to be tweaked further.
size=.3, stroke=0,shape=16) + facet_wrap('Year')
#g
highyears <- c(1992, 2000)
lowyears <- c(1983, 2019)
Dsub <- D |> dplyr::filter(Year %in% c(lowyears, highyears))
g <- gmap +
geom_point(data=Dsub, aes(Lon, Lat, color=log(as.vector(D)), group=NULL)
#,
## These settings are necessary to avoid
## overlplotting which is a problem here. May need
## to be tweaked further.
#size=.3, stroke=0,shape=16
) + facet_wrap('Year')
g
Calculate quantiles of distribution across the time series. First sum all cells over time, then these are the quantiles of summed log density:
# one option, sum D_gct over all years then take quantiles in space
D <- readRDS("Dherr_sepfeb.rds")
Dtot <- D |>
dplyr::group_by(cell) |>
dplyr::mutate(Dsum = sum(D, na.rm=TRUE),
logD = log(as.vector(Dsum))) |>
dplyr::select(!c(D, Year)) |>
dplyr::distinct()
Dvec <- terra::vect(Dtot, geom=c("Lon", "Lat"))
q <- quantile(log(as.vector(Dtot$Dsum)), probs=seq(0,1,0.1))
q2 <- quantile(Dtot$logD, probs=seq(0,1,0.1))
qvec <- terra::quantile(Dvec, probs=c(0.6, 0.65, 0.7, 0.75, 0.8))
quants <- classInt::classIntervals(log(as.vector(Dtot$Dsum)),
style = "quantile", n = 10)
#quants$brks
D60pct <- Dtot |>
dplyr::filter(log(as.vector(Dsum))>qvec["60%","logD"]) |>
dplyr::rename("60th" = "Include")
D65pct <- Dtot |>
dplyr::filter(log(as.vector(Dsum))>qvec["65%","logD"]) |>
dplyr::rename("65th" = "Include")
D70pct <- Dtot |>
dplyr::filter(log(as.vector(Dsum))>qvec["70%","logD"]) |>
dplyr::rename("70th" = "Include")
D75pct <- Dtot |>
dplyr::filter(log(as.vector(Dsum))>qvec["75%","logD"]) |>
dplyr::rename("75th" = "Include")
D80pct <- Dtot |>
dplyr::filter(log(as.vector(Dsum))>qvec["80%","logD"]) |>
dplyr::rename("80th" = "Include")
qvec## Include logD
## 60% 1 3.274264
## 65% 1 3.828164
## 70% 1 4.554015
## 75% 1 5.215182
## 80% 1 5.668452Mapping the summed density below.
g <- gmap +
geom_point(data=Dtot, aes(Lon, Lat, color=log(as.vector(Dsum)), group=NULL)
,
## These settings are necessary to avoid
## overlplotting which is a problem here. May need
## to be tweaked further.
size=1.5, stroke=0,shape=16
)
g
Mapping the quantiles from 60% (white) to 80% (light blue).
Quantiles below 70% include densities south of Long Island and off Cape Hatteras. The WG thinks the larvae found to the south may be lost to the population so we likely don’t want to use a quantile below the 70th percentile.
The 80th percentile starts to show a gap on Georges Bank and along the southwest coast of Maine. This may be slicing things too finely to cover general herring larval habitat.
I think this leaves us with using either the 70th (light green) or 75th (dark green) percentile of summed density over all years to define herring larvae relevant habitat for the small copepods index.
gq <- gmap +
geom_point(data=D60pct,
aes(x=Lon, y=Lat, color = "60th"),
# color="white"
#)
# ,
## These settings are necessary to avoid
## overlplotting which is a problem here. May need
## to be tweaked further.
size=1.5, stroke=0,shape=16
) +
geom_point(data=D65pct,
aes(x=Lon, y=Lat, color = "65th"),
#color="yellow"
#)
#,
## These settings are necessary to avoid
## overlplotting which is a problem here. May need
## to be tweaked further.
size=1.5, stroke=0,shape=16
) +
geom_point(data=D70pct,
aes(x=Lon, y=Lat, color = "70th"),
#color="green"
#)
#,
## These settings are necessary to avoid
## overlplotting which is a problem here. May need
## to be tweaked further.
size=1.5, stroke=0,shape=16
) +
geom_point(data=D75pct,
aes(x=Lon, y=Lat, color = "75th"),
#color="darkgreen"
#)
#,
## These settings are necessary to avoid
## overlplotting which is a problem here. May need
## to be tweaked further.
size=1.5, stroke=0,shape=16
) +
geom_point(data=D80pct,
aes(x=Lon, y=Lat, color = "80th"),
#color="lightblue"
#)
#,
## These settings are necessary to avoid
## overlplotting which is a problem here. May need
## to be tweaked further.
size=1.5, stroke=0,shape=16
) +
ggplot2::scale_color_manual(name = "Quantile",
breaks = c("60th", "65th", "70th", "75th", "80th"),
values = c("60th" = "white", "65th" = "yellow",
"70th" = "green", "75th" = "darkgreen",
"80th" = "lightblue") )
gq
# another option, pull from fit$report Omega 1 and 2?Make a new extrapolation grid using herring larvae density 70th percentile
These methods are similar to those used in the bluefish RTA for the forage index nearshore strata 3 miles from shore.
First make the 70th percentile+ points into an sf
object, then intersect that object with the built in
FishStatsUtils::northwest_atlantic_grid:
# methods from https://stackoverflow.com/questions/78335772/find-outer-edge-of-polygon-in-r
# after much trial and error, concave_hull is what we want, only available in newer sf
# dataframe to sf object
D70pct_sf <- sf::st_as_sf(D70pct, coords = c("Lon", "Lat"))
# concave hull in newest sf only works with GEOS>3.11
D70pct_ls <- D70pct_sf |>
sf::st_union() |>
sf::st_concave_hull(ratio=0.1) |>
sf::st_cast(to ="LINESTRING") |>
sf::st_cast(to ="POLYGON") |>
sf::st_set_crs(sf::st_crs(ecodata::coast))
# just in case 75th too
D75pct_sf <- sf::st_as_sf(D75pct, coords = c("Lon", "Lat"))
D75pct_ls <- D75pct_sf |>
sf::st_union() |>
sf::st_concave_hull(ratio=0.1) |>
sf::st_cast(to ="LINESTRING") |>
sf::st_cast(to ="POLYGON") |>
sf::st_set_crs(sf::st_crs(ecodata::coast))
# Dont need this? set crs from ecodata::coast
# # set bounding boxes
# neus.xmin=-77
# neus.xmax=-65
# neus.ymin=35
# neus.ymax=45
#
# neus.bbox1 <- sf::st_set_crs(sf::st_as_sf(as(raster::extent(neus.xmin, neus.xmax, neus.ymin, neus.ymax), "SpatialPolygons")), sf::st_crs(ecodata::coast))
#
# neus.bbox2 <- sf::st_set_crs(sf::st_as_sf(as(raster::extent(-78, -74, 42, 45), "SpatialPolygons")), sf::st_crs(ecodata::coast)) # smaller bounding box to get rid of extra lines on the map
#
# neuscoast <- ecodata::coast |>
# sf::st_intersection(neus.bbox1) |>
# sf::st_difference(neus.bbox2) # gets rid of extra non coastal line
# intersect buffer with the current FishStatsUtils::northwest_atlantic_grid
# make northwest atlantic grid into sf object
nwagrid_sf <- sf::st_as_sf(FishStatsUtils::northwest_atlantic_grid, coords = c("Lon","Lat")) %>%
sf::st_set_crs(sf::st_crs(ecodata::coast))
# intersect, rearrange in same format as nwatl grid, and save
D70pct_nwa <- sf::st_intersection(nwagrid_sf,D70pct_ls) %>% #native pipe wont do dots
dplyr::mutate(Lon = as.numeric(sf::st_coordinates(.)[,1]),
Lat = as.numeric(sf::st_coordinates(.)[,2])) |>
sf::st_set_geometry(NULL) |>
#dplyr::select(-featurecla) |>
dplyr::select(stratum_number, Lon, Lat, everything())
write_rds(D70pct_nwa, here("spatialdat","D70pct_nwa.rds"))
# intersect, rearrange in same format as nwatl grid, and save
D75pct_nwa <- sf::st_intersection(nwagrid_sf,D75pct_ls) %>% #native pipe wont do dots
dplyr::mutate(Lon = as.numeric(sf::st_coordinates(.)[,1]),
Lat = as.numeric(sf::st_coordinates(.)[,2])) |>
sf::st_set_geometry(NULL) |>
#dplyr::select(-featurecla) |>
dplyr::select(stratum_number, Lon, Lat, everything())
write_rds(D75pct_nwa, here("spatialdat","D75pct_nwa.rds"))The portions of nortwest_atlantic_grid intersecting with
the 70th and 75th percentile of herring larval density (1982-2022) were
saved in the spatialdat folder. Next, we define new strata based on that
intersection and make a new extrapolation grid. Then we can use this
grid and call the new strata as strata.limits when running the small
copepods model.
Right now just make a grid for the 70th percentile; we can make one for 75th if needed later.
D70pct_nwa <- readRDS(here("spatialdat/D70pct_nwa.rds"))
D70pct_nwast <- D70pct_nwa %>%
dplyr::mutate(strat2 = 1) %>% #herring larvae = 1
dplyr::right_join(FishStatsUtils::northwest_atlantic_grid) %>%
dplyr::mutate(strat2 = replace_na(strat2, 2)) %>% #replace NA with 2 for non-larval
dplyr::mutate(stratum_number2 = as.numeric(paste0(stratum_number, strat2))) %>%
dplyr::select(-strat2)
saveRDS(D70pct_nwast, here("spatialdat","D70pct_nwa_strat2.rds"))
# new lookups
herring_spring <- c(01010, 01020, 01030, 01040, 01050, 01060, 01070, 01080, 01090,
01100, 01110, 01120, 01130, 01140, 01150, 01160, 01170, 01180,
01190, 01200, 01210, 01220, 01230, 01240, 01250, 01260, 01270,
01280, 01290, 01300, 01360, 01370, 01380, 01390, 01400, 01610,
01620, 01630, 01640, 01650, 01660, 01670, 01680, 01690, 01700,
01710, 01720, 01730, 01740, 01750, 01760)
herring_fall <- c(01050, 01060, 01070, 01080, 01090, 01100, 01110, 01120, 01130,
01140, 01150, 01160, 01170, 01180, 01190, 01200, 01210, 01220,
01230, 01240, 01250, 01260, 01270, 01280, 01290, 01300, 01360,
01370, 01380, 01390, 01400)
MAB <- c(1010:1080, 1100:1120, 1600:1750, 3010:3450, 3470, 3500, 3510)
GB <- c(1090, 1130:1210, 1230, 1250, 3460, 3480, 3490, 3520:3550)
GOM <- c(1220, 1240, 1260:1290, 1360:1400, 3560:3830)
SS <- c(1300:1352, 3840:3990)
# MAB EPU
MAB2 <- D70pct_nwast %>%
dplyr::filter(stratum_number %in% MAB) %>%
dplyr::select(stratum_number2) %>%
dplyr::distinct()
# MAB herring larvae area
MAB2herr <- MAB2 %>%
dplyr::filter(stratum_number2 %% 10 == 1)
# MAB outside larval area
MAB2out <- MAB2 %>%
dplyr::filter(stratum_number2 %% 10 == 2)
# Georges Bank EPU
GB2 <- D70pct_nwast %>%
dplyr::filter(stratum_number %in% GB) %>%
dplyr::select(stratum_number2) %>%
dplyr::distinct()
# GB herring larvae
GB2herr <- GB2 %>%
dplyr::filter(stratum_number2 %% 10 == 1)
#GB outside larval area
GB2out <- GB2 %>%
dplyr::filter(stratum_number2 %% 10 == 2)
# gulf of maine EPU
GOM2 <- D70pct_nwast %>%
dplyr::filter(stratum_number %in% GOM) %>%
dplyr::select(stratum_number2) %>%
dplyr::distinct()
# GOM herring larvae
GOM2herr <- GOM2 %>%
dplyr::filter(stratum_number2 %% 10 == 1)
#GOM outside larval area
GOM2out <- GOM2 %>%
dplyr::filter(stratum_number2 %% 10 == 2)
# scotian shelf EPU
SS2 <- D70pct_nwast %>%
dplyr::filter(stratum_number %in% SS) %>%
dplyr::select(stratum_number2) %>%
dplyr::distinct()
# SS herring larvae
SS2herr <- SS2 %>%
dplyr::filter(stratum_number2 %% 10 == 1)
#SS outside larval area
SS2out <- SS2 %>%
dplyr::filter(stratum_number2 %% 10 == 2)
# whole herring larval area
herrlarv <- dplyr::bind_rows(MAB2herr, GB2herr, GOM2herr, SS2herr)
# outside herring larval area
nolarv <- dplyr::bind_rows(MAB2out, GB2out, GOM2out, SS2out)
# spring herring NEFSC BTS
her_spr2 <- D70pct_nwast %>%
dplyr::filter(stratum_number %in% herring_spring) %>%
dplyr::select(stratum_number2) %>%
dplyr::distinct()
# fall herring NEFSC BTS
her_fall2 <- D70pct_nwast %>%
dplyr::filter(stratum_number %in% herring_fall) %>%
dplyr::select(stratum_number2) %>%
dplyr::distinct()
# all epus
allEPU2 <- D70pct_nwast %>%
dplyr::filter(stratum_number %in% c(MAB, GB, GOM, SS)) %>%
dplyr::select(stratum_number2) %>%
dplyr::distinct()Modify the function from
FishStatsUtils::Prepare_NWA_Extrapolation_Data_Fn to make a
new grid with updated strata
Prepare_NWA_Extrapolation_Data_Fn_skg <- function (strata.limits = NULL,
epu_to_use = c("All", "Georges_Bank", "Mid_Atlantic_Bight", "Scotian_Shelf", "Gulf_of_Maine", "Other")[1],
projargs = NA, zone = NA, flip_around_dateline = FALSE, ...)
{
if (is.null(strata.limits)) {
strata.limits = list(All_areas = 1:1e+05)
}
message("Using strata ", strata.limits)
if (any(tolower(epu_to_use) %in% "all")) {
epu_to_use <- c("Georges_Bank", "Mid_Atlantic_Bight",
"Scotian_Shelf", "Gulf_of_Maine", "Other")
}
utils::data(northwest_atlantic_grid, package = "FishStatsUtils")
Data_Extrap <- D70pct_nwast
Tmp = cbind(BEST_DEPTH_M = 0, BEST_LAT_DD = Data_Extrap[,
"Lat"], BEST_LON_DD = Data_Extrap[, "Lon"])
if (length(strata.limits) == 1 && strata.limits[1] == "EPU") {
Data_Extrap <- Data_Extrap[Data_Extrap$EPU %in% epu_to_use,
]
Data_Extrap$EPU <- droplevels(Data_Extrap$EPU)
a_el = matrix(NA, nrow = nrow(Data_Extrap), ncol = length(epu_to_use),
dimnames = list(NULL, epu_to_use))
Area_km2_x = Data_Extrap[, "Area_in_survey_km2"]
for (l in 1:ncol(a_el)) {
a_el[, l] = ifelse(Data_Extrap[, "EPU"] %in% epu_to_use[[l]],
Area_km2_x, 0)
}
}
else {
a_el = as.data.frame(matrix(NA, nrow = nrow(Data_Extrap),
ncol = length(strata.limits), dimnames = list(NULL,
names(strata.limits))))
Area_km2_x = Data_Extrap[, "Area_in_survey_km2"]
for (l in 1:ncol(a_el)) {
a_el[, l] = ifelse(Data_Extrap[, "stratum_number2"] %in%
strata.limits[[l]], Area_km2_x, 0)
}
}
tmpUTM = project_coordinates(X = Data_Extrap[, "Lon"], Y = Data_Extrap[,
"Lat"], projargs = projargs, zone = zone, flip_around_dateline = flip_around_dateline)
Data_Extrap = cbind(Data_Extrap, Include = 1)
Data_Extrap[, c("E_km", "N_km")] = tmpUTM[, c("X", "Y")]
Return = list(a_el = a_el, Data_Extrap = Data_Extrap, zone = attr(tmpUTM,
"zone"), projargs = attr(tmpUTM, "projargs"), flip_around_dateline = flip_around_dateline,
Area_km2_x = Area_km2_x)
return(Return)
}Now define new strata.limits. Needed? We do this at
runtime
strata.limits <- as.list(c("AllEPU" = allEPU2,
"her_sp" = her_spr2,
"her_fa" = her_fall2,
"her_larv" = herrlarv,
"no_larv" = nolarv,
"MAB" = MAB2,
"GB" = GB2,
"GOM" = GOM2,
"SS" = SS2
))Make the new extrapolation list:
Extrapolation_List <- Prepare_NWA_Extrapolation_Data_Fn_skg( strata.limits=strata.limits)
saveRDS(Extrapolation_List, file = here("spatialdat/CustomExtrapolationList.rds"))Did it work? Plot
newstrat <- readRDS(here("spatialdat/D70pct_nwa_strat2.rds"))
herrlarv_area <- newstrat |>
dplyr::filter(stratum_number2 %% 10 == 1)
ggplot2::ggplot(data = ecodata::coast) +
ggplot2::geom_sf() +
ggplot2::geom_point(data = FishStatsUtils::northwest_atlantic_grid, ggplot2::aes(x = Lon, y = Lat), size=0.05, colour = "brown", alpha=0.1) +
ggplot2::geom_point(data = herrlarv_area, ggplot2::aes(x = Lon, y = Lat), size=0.05, colour = "green", alpha=0.5) +
ggplot2::coord_sf(xlim = c(-78, -65.5), ylim = c(35, 45))+
ggplot2::ggtitle("Herring larvae area: northwest_atlantic_grid")
Results: Small copepods in the herring larval area
Using the new grid, run the smallcopeALL_sepfeb dataset in VAST to get an index in the herring larvae area as well as in the others.
Script for doing this is https://github.com/NOAA-EDAB/zooplanktonindex/blob/main/VASTscripts/VASTunivariate_zoopindex_smcopeALL_herrlarvarea.R
The model converged, diagnostics look fine.
Visualize these small copepod model indices:
# add the new herring larval stratum
stratlook2 <- data.frame(Stratum = c("Stratum_1",
"Stratum_2",
"Stratum_3",
"Stratum_4",
"Stratum_5",
"Stratum_6",
"Stratum_7",
"Stratum_8",
"Stratum_9"),
Region = c("AllEPU",
"her_sp",
"her_fa",
"her_larv",
"no_larv",
"MAB",
"GB",
"GOM",
"SS"))
# only larvarea models have this strata set
splitoutput2 <- modcompareindex %>%
dplyr::filter(str_detect(modname, "larvarea")) |>
dplyr::mutate(Season = modname |> map(str_split, pattern = "_") |> map_chr(c(1,2))) %>%
dplyr::mutate(Data = modname |> map(str_split, pattern = "_") |> map_chr(c(1,1))) %>%
dplyr::mutate(Estimate = ifelse(Estimate == 0, NA, Estimate)) |>
dplyr::left_join(stratlook2)
# tack on what should be the same model in the original extrapolation grid to make sure nothing went wrong
# the same strata should have the same trends
smcopesepfeb <- splitoutput |>
dplyr::filter(modname == "smallcopeALL_sepfeb_yrshift_500_biascorrect")
splitoutput2 <- dplyr::bind_rows(splitoutput2, smcopesepfeb)
plot_zooindices(splitoutput = splitoutput2,
plotdata = "smallcopeALL",
plotregions = c("her_larv", "no_larv", "her_sp", "her_fa", "MAB", "GB", "GOM", "SS", "AllEPU"),
plottitle = "Small copepods Sept-Feb") 
We get the same time series for the comparable areas using the original and new extrapolation grid, as expected. (All time series overlap.)
Compare small copepods inside and outside herring larval area:
plot_zooindices(splitoutput = splitoutput2,
plotdata = "smallcopeALL",
plotregions = c("her_larv", "no_larv"),
plottitle = "Small copepods Sept-Feb") 
On the same plot to compare scale:
plotdata <- c("smallcopeALL")
plottitle <- "Small copepods Sept-Feb inside (herr_larv) and outside (no_larv) herring larval area"
fix<- splitoutput2 |>
dplyr::filter(Data %in% plotdata,
Region %in% c("her_larv", "no_larv")
) |>
dplyr::group_by(Season) |> #Region,
dplyr::summarise(max = max(Estimate, na.rm=T))
p <- splitoutput2 |>
dplyr::filter(Data %in% plotdata, #c("calfin"),
Region %in% c("her_larv", "no_larv")
) |>
dplyr::group_by(Season) |> #Region,
dplyr::left_join(fix) |>
dplyr::mutate(#Value = Value/resca,
Mean = as.numeric(Estimate),
SE = Std..Error.for.Estimate,
Mean = Mean/max,
SE = SE/max,
Upper = Mean + SE,
Lower = Mean - SE) |>
ggplot2::ggplot(ggplot2::aes(x = Time, y = Mean, linetype = Region, group = Region))+
#ggplot2::annotate("rect", fill = setup$shade.fill, alpha = setup$shade.alpha,
# xmin = setup$x.shade.min , xmax = setup$x.shade.max,
# ymin = -Inf, ymax = Inf) +
ggplot2::geom_ribbon(ggplot2::aes(ymin = Lower, ymax = Upper, fill = Region), alpha = 0.5)+
ggplot2::geom_point()+
ggplot2::geom_line()+
ggplot2::ggtitle(plottitle)+
ggplot2::ylab(expression("Relative abundance"))+
ggplot2::xlab(ggplot2::element_blank())+
ggplot2::facet_wrap(~Season, #Region~ ncol = ncols,
labeller = label_wrap_gen(multi_line=FALSE))+
#ecodata::geom_gls()+
ecodata::theme_ts()+
ecodata::theme_facet()+
ecodata::theme_title() +
ggplot2::theme(legend.position = "bottom")
p
The small copepods inside the herring larval area have been about the same or more than those outside recently.
We’ll see if any of this helps as a covariate on recruitment…
References
Richardson, D.E., Hare, J.A., Overholtz, W.J., and Johnson, D.L. 2010.
Development of long-term larval indices for Atlantic
herring (Clupea harengus) on the northeast US
continental shelf. ICES Journal of Marine Science
67(4): 617–627. doi:10.1093/icesjms/fsp276.