1 Introduction

This document visualizes existing datasets, MOST OF WHICH ARE NOT SPECIFIC TO HERRING.

That said, herring survey strata in the fall look a lot like the combined Gulf of Maine (GOM) and Georges Bank (GB).

Herring strata in the spring look like the full shelf, GOM, GB, and the Mid Atlantic Bight (MAB).

# from Jon
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)

herring_springgrid <-  FishStatsUtils::northwest_atlantic_grid %>%
  filter(stratum_number %in% herring_spring)

herring_fallgrid <-  FishStatsUtils::northwest_atlantic_grid %>%
  filter(stratum_number %in% herring_fall)

survdat_herring_tows <- readRDS(here::here("herringpyindex/survdat_herring_tows.rds"))

surv_herr_fall <- survdat_herring_tows |>
  dplyr::filter(SEASON == "FALL",
                YEAR > 1981)
  
surv_herr_spring <- survdat_herring_tows |>
  dplyr::filter(SEASON == "SPRING",
                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 = herring_fallgrid, aes(x = Lon, y = Lat),  colour = "green", size=0.05, alpha=0.1) +
  geom_point(data = surv_herr_fall, aes(x = LON, y = LAT), colour = "blue", size=0.5, alpha=.3) +
  coord_sf(xlim =c(-78.5, -65.5), ylim = c(33, 45)) + #zoomed to Hatteras and N
  xlab("") +
  ylab("") +
  ggtitle("Fall herring NEFSC BTS 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 = herring_springgrid, aes(x = Lon, y = Lat),  colour = "green", size=0.05, alpha=0.1) +
  geom_point(data = surv_herr_spring, aes(x = LON, y = LAT), colour = "blue", size=0.5, alpha=.3) +
  coord_sf(xlim =c(-78.5, -65.5), ylim = c(33, 45)) + #zoomed to Hatteras and N
  xlab("") +
  ylab("") +
  ggtitle("Spring herring NEFSC BTS 1982-2022")+
  theme(plot.margin = margin(0, 0, 0, 0, "cm"))
 
Spring + Fall

The goal is to identify which are most promising for further refinement.

Refinement can include evaluating different spatial footprints or seasons or both.

2 Recruitment indicators in ecodata or elsewhere

2.1 Upper TL:

2.1.1 Egg/larval predators → haddock (Micah’s work)

2.1.2 Competitors → gelantinous zooplankton

ecodata::plot_zoo_abundance_anom(report = "NewEngland", varName = "euphausid")

ecodata::plot_zoo_abundance_anom(report = "MidAtlantic", varName = "euphausid")

2.1.3 Seabirds

Common tern fledgling success at GOM nesting colonies

ecodata::plot_seabird_ne(report = "NewEngland", varName = "productivity")

2.2 Lower TL:

2.2.1 Primary production (Chlorophyll)

ecodata::plot_annual_chl_pp(report = "NewEngland", EPU = "GOM", varName = "chl", plottype = "mean") + ggplot2::ggtitle("GOM Mean chl a")

ecodata::plot_annual_chl_pp(report = "NewEngland", EPU = "GB", varName = "chl", plottype = "mean") + ggplot2::ggtitle("GB Mean chl a")

ecodata::plot_annual_chl_pp(report = "MidAtlantic", EPU = "MAB", varName = "chl", plottype = "mean") + ggplot2::ggtitle("MAB Mean chl a")

2.2.2 Small zooplankton timing and availability

Relative availability from SOE

ecodata::plot_zoo_abundance_anom(report = "NewEngland", varName = "copepod")

ecodata::plot_zoo_abundance_anom(report = "MidAtlantic", varName = "copepod")

2.2.3 Large zooplankton timing and availability

In GOM Wilkinson Basin there is evidence of changing Calanus timing (Fig. \(\ref{fig:zooplankton-season}\))

magick::image_read("https://github.com/NOAA-EDAB/ecodata/blob/dev/workshop/images/WBTS_Mesozooplankton_BIomass_2005-2022_Runge_Dullaert-Jeffrey_Runge_2024.jpg?raw=true")
Dry mass of mesozooplankton captured with a 200µm ring net towed from the bottom to surface at a deep time series station in Wilkinson Basin between 2005-2022 (Runge et al. 2023).

Figure 2.1: Dry mass of mesozooplankton captured with a 200µm ring net towed from the bottom to surface at a deep time series station in Wilkinson Basin between 2005-2022 (Runge et al. 2023).

See also zooplankton page

See also preliminary Cal fin indices annual and by season for herring areas

Small copeopod indices in development

2.3 Physical:

2.3.1 Temperature (surface and bottom)

Its going up, bottom temp in all seasons

and surface temp in all seasons

BUT, I think what we want is number of days at or above a threshold by area, like this plot for scallops:

days exceeding scallop stress temperature 2022
days exceeding scallop stress temperature 2022

Or a simpler version just looking at timing of days exceeding by depth and area, we could tailor this to herring areas and temperature thresholds.

This example shows bottom temperature for the year 2023 (black line) with range observed 1993-2023 at >15 and >24 C; Thermal habitat area is calculated by identifying 1/12 degree cells within a given EPU that are greater than or equal to the temperature threshold then taking the sum of all cell areas.

We can turn into an index N days/prop area at or above a given threshold for any given spatial footprint (e.g. herring survey areas in spring and fall, inshore vs offshore areas given a definition).

ecodata::plot_thermal_habitat_area(report="NewEngland", EPU = "GOM")

ecodata::plot_thermal_habitat_area(report="NewEngland", EPU = "GB")

ecodata::plot_thermal_habitat_area(report="MidAtlantic", EPU = "MAB")

For this we need a threshold/stressful temperature for herring eggs (bottom temp), larvae, and recruits (surface temp?)

2.3.2 Heatwave index

ecodata::plot_heatwave(report = "NewEngland")

ecodata::plot_heatwave(report = "MidAtlantic")

2.3.3 Climate variability (ie NAO)

2.3.4 Larval transport processes (wind, currents)

2.4 Demographic:

2.4.1 Fecundity

2.4.2 Body condition (deviation from expected weight at length)

load("/Users/sarah.gaichas/Documents/0_Data/zooplanktonindex/data/AtlHerring_SpringCondition.RData")

ggplot2::ggplot(condNSpp, ggplot2::aes(x = YEAR, y = MeanCond)) +
  ggplot2::geom_line() + 
  ggplot2::geom_ribbon(ggplot2::aes(ymin = MeanCond-(2*StdDevCond), ymax = MeanCond+(2*StdDevCond)), alpha = 0.3) +
  ggplot2::ggtitle("Atlantic herring spring condition, courtesy Laurel Smith")

2.4.3 Regime shift?

3 Mortality

3.1 Upper:

3.1.1 Bluefin tuna

Need indicator of timing/abundance in herring region

3.1.2 Marine Mammals

Data on this may be hard to find

3.1.3 Demersal fish

See Gartland et al in review, consumption estimates

Overlap index with predators?

3.2 Lower:

3.2.1 Zooplankton community structure

Increases in diversity due to lower abundance of common species

ecodata::plot_zoo_diversity(report = "NewEngland")

ecodata::plot_zoo_diversity(report = "MidAtlantic")

3.2.2 Zooplankton abundance, phenology, and energy content

Abundance and phenology see above

3.2.3 Menhaden competition/alternative stable states

3.3 Physical:

3.3.1 Sea surface temperature

(see above)

3.4 Demographic

3.4.1 Energy density

ecodata::plot_energy_density()