In sean-rohan-NOAA/akfishcondition: Groundfish morphometric condition indicator
# Load packages
list_rpackage <- c("knitr", "ggplot2", "devtools", "reader", "viridis", "akfishcondition")
which_not_installed <- which(list_rpackage %in% rownames(installed.packages()) == FALSE)
if(length(which_not_installed) > 1){
install.packages(list_rpackage[which_not_installed], dep = TRUE)
# install akfishcondition from S.Rohan repo
if ("akfishcondition" %in% list_rpackage[which_not_installed]){
devtools::install_github("sean-rohan-NOAA/akfishcondition")
}
}
# load the packages from the list
lapply(list_rpackage, require, character.only = TRUE)
# pkg_version <- packageVersion("akfishcondition")
# Unzip packaged csv and map files
unzip(system.file("data/2020_ESR.zip", package = "akfishcondition"))
# Load data
goa_dat <- readr::read_csv(file = "2020_08_20_goa_condition_data_2019_ESR.csv")
# Convert 10-25 cm lengths to age-1
goa_dat$SPECIES_CODE[goa_dat$SPECIES_CODE == 21740 & goa_dat$LENGTH >= 100 & goa_dat$LENGTH <= 250] <- 21741
goa_dat$COMMON_NAME[goa_dat$SPECIES_CODE == 21740] <- "walleye pollock (>250 mm)"
goa_dat$COMMON_NAME[goa_dat$SPECIES_CODE == 21741] <- "walleye pollock (100–250 mm)"
# Load previous year's indicator and change names for plotting
old_indicator_dat <- readr::read_csv(file = "2019_GOA_ESR_lwdata_by_year.csv") %>%
dplyr::rename(SPECIES_CODE = GOA.species.i.,
YEAR = yrs) %>%
dplyr::inner_join(goa_dat %>% dplyr::select(COMMON_NAME, SPECIES_CODE)) %>%
dplyr::mutate(DISPLAY_NAME = COMMON_NAME)
old_indicator_dat$DISPLAY_NAME[!grepl("Alaska", x = old_indicator_dat$DISPLAY_NAME) & !grepl("Pacific", x = old_indicator_dat$DISPLAY_NAME)] <- tolower(old_indicator_dat$DISPLAY_NAME[!grepl("Alaska", x = old_indicator_dat$DISPLAY_NAME) & !grepl("Pacific", x = old_indicator_dat$DISPLAY_NAME)])
old_indicator_dat$DISPLAY_NAME[old_indicator_dat$DISPLAY_NAME == "age 1 walleye pollock"] <- "walleye pollock (100–250 mm)"
old_indicator_dat$DISPLAY_NAME[old_indicator_dat$DISPLAY_NAME == "walleye pollock"] <- "walleye pollock (>250 mm)"
Contributed by Ned Laman^1^, Sean Rohan^1^
^1^ Resource Assessment and Conservation Engineering Division, Groundfish Assessment Program, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA, Seattle, WA
Contact: ned.laman@noaa.gov
Last updated: September 2020
Description of Indicator: Residual body condition computed from a long-term average of length-weight-based body condition is an indicator of variability in somatic growth (Brodeur et al., 2004) and represents how heavy a fish is per unit body length. As such, it can be considered an indicator of ecosystem productivity. Positive residual body condition is interpreted to indicate fish in better condition (heavier per unit length) than those with negative residual body condition indicating poorer condition (lighter per unit length). Overall body condition of fishes likely reflects fish growth which can have implications for their subsequent survival (Paul and Paul, 1999; Boldt and Haldorson, 2004).
```rFigure 1. National Marine Fisheries Service (NMFS) Alaska Fisheries Science Center (AFSC) Resource Assessment and Conservation Engineering Groundfish Assessment Program (RACE-GAP) Gulf of Alaska summer bottom trawl survey area with International North Pacific Fisheries Commission (INPFC) statistical fishing strata delineated by the red lines.", echo = FALSE}
include_graphics("MapGOA.png")
Paired lengths and weights of individual fishes were examined from the Alaska Fisheries Science Center biennial Resource Assessment and Conservation Engineering (AFSC/RACE) - Groundfish Assessment Program’s (GAP) bottom trawl survey of the Gulf of Alaska (GOA). Analyses focused on walleye pollock (*Gadus chalcogrammus*), Pacific cod (*Gadus macrocephalus*), arrowtooth flounder (*Atheresthes stomias*), southern rock sole (*Lepidopsetta bilineata*), northern rockfish (*Sebastes polyspinis*), Pacific ocean perch (*Sebastes alutus*), and dusky rockfish (*Sebastes variabilis*) collected in trawls with satisfactory performance at standard survey stations. Data were combined in the International North Pacific Fisheries Commission (INPFC) strata; Shumagin, Chirikof, Kodiak, Yakutat and Southeast (Figure 1).
Length-weight relationships for each of the seven species were estimated within each stratum across all years where data were available (1984–2019) from a linear regression of log-transformed exponential growth, W = aLb, where W is weight (g) and L is fork length (mm). A different slope was estimated for each stratum to account for spatial-temporal variation in growth and bottom trawl survey sampling. Length-weight relationships for 100--250 mm fork length (1--2 year old) walleye pollock were established independent of the adult life history stages caught. Bias-corrected weights-at-length (log scale) were estimated from the model and subtracted from observed weights to compute individual residuals per fish. Length-weight residuals were averaged for each stratum and weighted in proportion to INPFC stratum biomass based on stratified area-swept expansion of summer bottom trawl survey catch per unit effort (CPUE). Average length-weight residuals were compared by stratum and year to evaluate spatial variation in fish condition. Combinations of stratum and year with <10 samples were used for length-weight relationships but excluded from indicator calculations.
**Methodological Changes**: The method used to calculate groundfish condition this year (2020) differs from previous years in that: 1) different regression slopes were estimated for each stratum, 2) a bias-correction was applied when predicting weights prior to calculating residuals, 3) stratum mean residuals were weighted in proportion to stratum biomass, and 4) stratum-year combinations with sample size <10 were not used in indicator calculations. As in previous years, confidence intervals for the condition indicator reflect uncertainty based on length-weight residuals, but are larger due to differences in sample sizes and stratum biomasses among years. Confidence intervals do not account for uncertainty in stratum biomass estimates.
**Status and Trends**: Residual body condition varied amongst survey years for all species considered (Figure 2). The updated computational methods used to calculate this year’s residual body condition indexes returned different values than those reported last year (Laman 2019). The patterns of above or below average residual condition observed in 2019 largely match those generated here from the updated computations, but with a notable reduction in magnitude for most years. The lower magnitude results come from using stratum-specific regression coefficients and samples weighted in proportion to biomass which reduces the influence of spatio-temporal variation in sampling intensity on the residuals. Some exceptions are 2009 southern rock sole, reported to have above average condition in 2019, shifted to neutral or slightly negative here and, for 2003 northern rockfish, residual condition calculated with the updated method here was higher above the long-term condition average than was reported in 2019. Based on these new methods, body condition is still below average for most species since 2015 (e.g., large walleye pollock, arrowtooth flounder, dusky rockfish) with some species trending downward over that time period (e.g., northern rockfish and possibly Pacific ocean perch). Residual body condition of Pacific cod and southern rock sole is trending upward over the same time, although southern rock sole remain below average. Prior to 2011, residual body condition indexes of these GOA species vary from survey to survey, cycling between negative and positive residuals with no clear temporal trends. Residual body condition of 100–250 mm walleye pollock in the GOA is strikingly positive during early years in the time series, but has remained mostly neutral or slightly negative since the early 1990s.
```r
## Calculations and figures
goa_spp_vec <- unique(goa_dat$SPECIES_CODE)
# Calculate length weight residuals
for(i in 1:length(goa_spp_vec)) {
# Separate slope for each stratum. Bias correction according to Brodziak, no outlier detection.
goa_df <- akfishcondition::calc_lw_residuals(len = goa_dat$LENGTH[goa_dat$SPECIES_CODE == goa_spp_vec[i]],
wt = goa_dat$WEIGHT[goa_dat$SPECIES_CODE == goa_spp_vec[i]],
year = goa_dat$YEAR[goa_dat$SPECIES_CODE == goa_spp_vec[i]],
stratum = goa_dat$INPFC_STRATUM[goa_dat$SPECIES_CODE == goa_spp_vec[i]],
make_diagnostics = TRUE, # Make diagnostics
bias.correction = TRUE, # Bias correction turned on
outlier.rm = FALSE, # Outlier removal turned off
region = "GOA",
species_code = goa_dat$SPECIES_CODE[goa_dat$SPECIES_CODE == goa_spp_vec[i]])
goa_dat$resid_mean[goa_dat$SPECIES_CODE == goa_spp_vec[i]] <- goa_df$lw.res_mean
goa_dat$resid_lwr[goa_dat$SPECIES_CODE == goa_spp_vec[i]] <- goa_df$lw.res_lwr
goa_dat$resid_upr[goa_dat$SPECIES_CODE == goa_spp_vec[i]] <- goa_df$lw.res_upr
}
# Estimate mean and std. err for each stratum, filter out strata with less than 10 samples
goa_stratum_resids <- goa_dat %>%
dplyr::group_by(COMMON_NAME, SPECIES_CODE, YEAR, INPFC_STRATUM, AREA_BIOMASS) %>%
dplyr::summarise(stratum_resid_mean = mean(resid_mean),
stratum_resid_sd = sd(resid_mean),
n = n()) %>%
dplyr::filter(n >= 10) %>%
dplyr::mutate(stratum_resid_se = stratum_resid_sd/sqrt(n))
# Weight strata by biomass
for(i in 1:length(goa_spp_vec)) {
goa_stratum_resids$weighted_resid_mean[goa_stratum_resids$SPECIES_CODE == goa_spp_vec[i]] <-
akfishcondition::weight_lw_residuals(residuals = goa_stratum_resids$stratum_resid_mean[goa_stratum_resids$SPECIES_CODE == goa_spp_vec[i]],
year = goa_stratum_resids$YEAR[goa_stratum_resids$SPECIES_CODE == goa_spp_vec[i]],
stratum = goa_stratum_resids$INPFC_STRATUM[goa_stratum_resids$SPECIES_CODE == goa_spp_vec[i]],
stratum_biomass = goa_stratum_resids$AREA_BIOMASS[goa_stratum_resids$SPECIES_CODE == goa_spp_vec[i]])
goa_stratum_resids$weighted_resid_se[goa_stratum_resids$SPECIES_CODE == goa_spp_vec[i]] <-
akfishcondition::weight_lw_residuals(residuals = goa_stratum_resids$stratum_resid_se[goa_stratum_resids$SPECIES_CODE == goa_spp_vec[i]],
year = goa_stratum_resids$YEAR[goa_stratum_resids$SPECIES_CODE == goa_spp_vec[i]],
stratum = goa_stratum_resids$INPFC_STRATUM[goa_stratum_resids$SPECIES_CODE == goa_spp_vec[i]],
stratum_biomass = goa_stratum_resids$AREA_BIOMASS[goa_stratum_resids$SPECIES_CODE == goa_spp_vec[i]])
}
# Biomass-weighted residual and SE by year
goa_ann_mean_resid_df <- goa_stratum_resids %>%
dplyr::group_by(YEAR, COMMON_NAME) %>%
dplyr::summarise(mean_wt_resid = mean(weighted_resid_mean),
se_wt_resid = mean(weighted_resid_se))
write.csv(goa_ann_mean_resid_df, "GOA_annual_resids.csv", row.names = FALSE)
write.csv(goa_stratum_resids, "GOA_stratum_resids.csv", row.names = FALSE)
```rFigure 2. Biomass-weighted residual body condition index across survey years (1984-2019) for seven Gulf of Alaska groundfish species collected on the National Marine Fisheries Service (NMFS) Alaska Fisheries Science Center (AFSC) Resource Assessment and Conservation Engineering Groundfish Assessment Program (RACE-GAP) standard summer bottom trawl survey. Filled bars denote weighted length-weight residuals using this year's indicator calculation, error bars denote two standard errors, points denote the mean of the unweighted length-weight residual from the previous year's (2019) ESR.", message = FALSE, warning = FALSE}
goa_ann_mean_resid_df$DISPLAY_NAME <- set_plot_order(goa_ann_mean_resid_df$COMMON_NAME, REGION = "GOA")
old_indicator_dat$DISPLAY_NAME <- set_plot_order(old_indicator_dat$DISPLAY_NAME, REGION = "GOA")
fig2 <- ggplot() +
geom_bar(data = goa_ann_mean_resid_df,
aes(x = YEAR,
y = mean_wt_resid),
stat = "identity",
fill = "plum",
color = "black") +
geom_errorbar(data = goa_ann_mean_resid_df,
aes(x = YEAR,
ymax = mean_wt_resid + 2se_wt_resid,
ymin = mean_wt_resid - 2se_wt_resid)) +
geom_hline(yintercept = 0) +
geom_point(data = old_indicator_dat,
aes(x = YEAR,
y = ymeans)) +
facet_wrap(~DISPLAY_NAME, ncol = 2, scales = "free_y") +
scale_x_continuous(name = "Year") +
scale_y_continuous(name = "Length-weight residual (ln(g))") +
theme_condition_index()
print(fig2)
```r
png("GOAbyyear.png",width=6,height=7,units="in",res=300)
print(fig2 + theme_pngs())
dev.off()
# png("AIbyyear.png", width = 6, height = 7, units = "in", res = 300)
# grid.arrange(grobs = myplot, ncol = 2)
# dev.off()
# write.csv(lwdata_by_year, "goa_lwdata_by_year.csv", row.names = FALSE)
The general patterns of above and below average residual body condition index across recent survey years for the Gulf of Alaska as described above was also apparent in the spatial condition indicators across INPFC strata (Figure 3). The relative contribution of stratum-specific residual body condition to the overall trends (indicated by the height of each colored bar segment) does not demonstrate a clear pattern. Although, for many species, the direction of residual body condition (positive or negative) was synchronous amongst strata within years. Patterns of fish distribution are also apparent in the stratum condition indexes. For example, Northern rockfish have primarily been collected from the Shumagin and Chirikof strata in recent surveys. The trend of increasingly positive Pacific cod residuals appears to be largely driven by a shift in residual body condition in the Kodiak and Shumagin strata.
```rFigure 3. Residual body condition index for seven Gulf of Alaska groundfish species collected on the National Marine Fisheries Service (NMFS) Alaska Fisheries Science Center (AFSC) Resource Assessment and Conservation Engineering Groundfish Assessment Program (RACE-GAP) standard summer bottom trawl survey (1984--2019) grouped by International North Pacific Fisheries Commission (INPFC) statistical sampling strata.", message = FALSE, warning = FALSE}
goa_stratum_resids$DISPLAY_NAME <- set_plot_order(goa_stratum_resids$COMMON_NAME, REGION = "GOA")
fig3 <- ggplot(data = goa_stratum_resids,
aes(x = YEAR,
y = stratum_resid_mean,
fill = set_stratum_order(INPFC_STRATUM, REGION = "GOA"))) +
geom_hline(yintercept = 0) +
geom_bar(stat = "identity", color = "black", position = "stack") +
facet_wrap(~DISPLAY_NAME, ncol = 2, scales = "free_y") +
scale_x_continuous(name = "Year") +
scale_y_continuous(name = "Length-weight residual (ln(g))") +
scale_fill_brewer(name = "Stratum", palette = "BrBG") +
theme_condition_index()
print(fig3)
```r
png("goa_condition_by_stratum.png",width=6,height=7,units="in",res=300)
print(fig3 + theme_pngs())
dev.off()
for(i in 1:length(unique(goa_stratum_resids$DISPLAY_NAME))) {
png(paste0("./output/GOA_condition_", gsub(">", "gt", unique(goa_stratum_resids$DISPLAY_NAME)[i]), ".png"), width = 6, height = 7, units = "in", res = 300)
print(
ggplot(data = goa_stratum_resids %>%
dplyr::filter(DISPLAY_NAME == unique(goa_stratum_resids$DISPLAY_NAME)[i]),
aes(x = YEAR,
y = stratum_resid_mean,
fill = set_stratum_order(INPFC_STRATUM, REGION = "GOA"),
ymin = stratum_resid_mean - 2*stratum_resid_se,
ymax = stratum_resid_mean + 2*stratum_resid_se)) +
geom_hline(yintercept = 0) +
geom_bar(stat = "identity", color = "black", position = "stack", width = 0.7) +
geom_errorbar(width = 0.7) +
facet_wrap(~set_stratum_order(INPFC_STRATUM, REGION = "GOA"),
ncol = 2,
scales = "free_y") +
ggtitle(unique(goa_stratum_resids$DISPLAY_NAME)[i]) +
scale_x_continuous(name = "Year") +
scale_y_continuous(name = "Length-weight residual (ln(g))") +
scale_fill_brewer(name = "Stratum", palette = "BrBG", drop = FALSE) +
theme_pngs() +
theme(legend.position = "none",
title = element_text(hjust = 0.5)))
dev.off()
}
Factors causing observed trends: Factors that could affect residual fish body condition presented here include temperature, trawl survey timing, stomach fullness, movement in or out of the survey area, or variable somatic growth. Since the Warm Blob in 2014 (Bond et al., 2015; Stabeno et al., 2019), there has been a general trend of warming ocean temperatures in the survey area through 2018 that could be affecting fish growth conditions there. Changing ocean conditions along with normal patterns of movement can cause the proportion of the population resident in the sampling area during the annual bottom trawl survey to vary. The date that the first length-weight data are collected is generally in late May and the bottom trawl survey is conducted throughout the summer months moving from west to east so that spatial and temporal trends in fish growth over the season become confounded with survey progress. In addition, spatial variability in residual condition may also reflect local environmental features which can influence growth and prey availability in the areas surveyed (e.g., warm core eddies in the central Gulf of Alaska; Atwood et al., 2010). The updated condition analyses presented here begin to, but do not wholly account for spatio-temporal variability in the underlying populations sampled.
Implications: Variations in body condition likely have implications for fish survival. In Prince William Sound, the condition of herring prior to the winter may influence their survival (Paul and Paul, 1999). The condition of Gulf of Alaska groundfish may similarly contribute to survival and recruitment. As future years are added to the time series, the relationship between length-weight residuals and subsequent survival will be examined further. It is important to consider that residual body condition for most species in these analyses was computed for all sizes and sexes combined. Requirements for growth and survivorship differ for different fish life stages and some species have sexually dimorphic growth patterns. It may be more informative to examine life-stage (e.g., early juvenile, subadult, and adult phases) and sex-specific body condition in the future.
The trend toward lowered body condition for many Gulf of Alaska species over the last 3--4 RACE/AFSC GAP bottom trawl surveys is a potential cause for concern. It could indicate poor overwinter survival or may reflect the influence of locally changing environmental conditions depressing fish growth, local production, or survivorship. Indications are that the Warm Blob (Bond et al., 2015; Stabeno et al., 2019) has been followed by subsequent years with elevated water temperatures (e.g., Barbeaux et al., 2018; Laman, 2018) which may be related to changes in fish condition in the species examined. As we continue to add years of fish condition to the record and expand on our knowledge of the relationships between condition, growth, production, and survival, we hope to gain more insight into the overall health of fish populations in the Gulf of Alaska.
Research priorities: Efforts are underway to redevelop the groundfish condition indicator for next year's (2021) ESR, using a spatio-temporal model with spatial random effects (VAST). The change is expected to allow more precise biomass expansion, improve estimates of uncertainty, and better account for spatial-temporal variation in length-weight samples from bottom trawl surveys due to methodological changes in sampling (e.g. transition from sex-and-length stratified sampling to random sampling). For 2021, revised indicators will be presented alongside a retrospective analysis that compares the historical and revised condition indicator. Currently, research is being planned across multiple AFSC programs to explore standardization of statistical methods for calculating condition indicators, and to examine relationships among morphometric condition indicators, bioenergetic indicators, and physiological measures of fish condition.
sean-rohan-NOAA/akfishcondition documentation built on June 9, 2025, 3:54 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
# Load packages list_rpackage <- c("knitr", "ggplot2", "devtools", "reader", "viridis", "akfishcondition") which_not_installed <- which(list_rpackage %in% rownames(installed.packages()) == FALSE) if(length(which_not_installed) > 1){ install.packages(list_rpackage[which_not_installed], dep = TRUE) # install akfishcondition from S.Rohan repo if ("akfishcondition" %in% list_rpackage[which_not_installed]){ devtools::install_github("sean-rohan-NOAA/akfishcondition") } } # load the packages from the list lapply(list_rpackage, require, character.only = TRUE) # pkg_version <- packageVersion("akfishcondition") # Unzip packaged csv and map files unzip(system.file("data/2020_ESR.zip", package = "akfishcondition")) # Load data goa_dat <- readr::read_csv(file = "2020_08_20_goa_condition_data_2019_ESR.csv") # Convert 10-25 cm lengths to age-1 goa_dat$SPECIES_CODE[goa_dat$SPECIES_CODE == 21740 & goa_dat$LENGTH >= 100 & goa_dat$LENGTH <= 250] <- 21741 goa_dat$COMMON_NAME[goa_dat$SPECIES_CODE == 21740] <- "walleye pollock (>250 mm)" goa_dat$COMMON_NAME[goa_dat$SPECIES_CODE == 21741] <- "walleye pollock (100–250 mm)" # Load previous year's indicator and change names for plotting old_indicator_dat <- readr::read_csv(file = "2019_GOA_ESR_lwdata_by_year.csv") %>% dplyr::rename(SPECIES_CODE = GOA.species.i., YEAR = yrs) %>% dplyr::inner_join(goa_dat %>% dplyr::select(COMMON_NAME, SPECIES_CODE)) %>% dplyr::mutate(DISPLAY_NAME = COMMON_NAME) old_indicator_dat$DISPLAY_NAME[!grepl("Alaska", x = old_indicator_dat$DISPLAY_NAME) & !grepl("Pacific", x = old_indicator_dat$DISPLAY_NAME)] <- tolower(old_indicator_dat$DISPLAY_NAME[!grepl("Alaska", x = old_indicator_dat$DISPLAY_NAME) & !grepl("Pacific", x = old_indicator_dat$DISPLAY_NAME)]) old_indicator_dat$DISPLAY_NAME[old_indicator_dat$DISPLAY_NAME == "age 1 walleye pollock"] <- "walleye pollock (100–250 mm)" old_indicator_dat$DISPLAY_NAME[old_indicator_dat$DISPLAY_NAME == "walleye pollock"] <- "walleye pollock (>250 mm)"
Contributed by Ned Laman^1^, Sean Rohan^1^
^1^ Resource Assessment and Conservation Engineering Division, Groundfish Assessment Program, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA, Seattle, WA
Contact: ned.laman@noaa.gov
Last updated: September 2020
Description of Indicator: Residual body condition computed from a long-term average of length-weight-based body condition is an indicator of variability in somatic growth (Brodeur et al., 2004) and represents how heavy a fish is per unit body length. As such, it can be considered an indicator of ecosystem productivity. Positive residual body condition is interpreted to indicate fish in better condition (heavier per unit length) than those with negative residual body condition indicating poorer condition (lighter per unit length). Overall body condition of fishes likely reflects fish growth which can have implications for their subsequent survival (Paul and Paul, 1999; Boldt and Haldorson, 2004).
```rFigure 1. National Marine Fisheries Service (NMFS) Alaska Fisheries Science Center (AFSC) Resource Assessment and Conservation Engineering Groundfish Assessment Program (RACE-GAP) Gulf of Alaska summer bottom trawl survey area with International North Pacific Fisheries Commission (INPFC) statistical fishing strata delineated by the red lines.", echo = FALSE} include_graphics("MapGOA.png")
Paired lengths and weights of individual fishes were examined from the Alaska Fisheries Science Center biennial Resource Assessment and Conservation Engineering (AFSC/RACE) - Groundfish Assessment Program’s (GAP) bottom trawl survey of the Gulf of Alaska (GOA). Analyses focused on walleye pollock (*Gadus chalcogrammus*), Pacific cod (*Gadus macrocephalus*), arrowtooth flounder (*Atheresthes stomias*), southern rock sole (*Lepidopsetta bilineata*), northern rockfish (*Sebastes polyspinis*), Pacific ocean perch (*Sebastes alutus*), and dusky rockfish (*Sebastes variabilis*) collected in trawls with satisfactory performance at standard survey stations. Data were combined in the International North Pacific Fisheries Commission (INPFC) strata; Shumagin, Chirikof, Kodiak, Yakutat and Southeast (Figure 1). Length-weight relationships for each of the seven species were estimated within each stratum across all years where data were available (1984–2019) from a linear regression of log-transformed exponential growth, W = aLb, where W is weight (g) and L is fork length (mm). A different slope was estimated for each stratum to account for spatial-temporal variation in growth and bottom trawl survey sampling. Length-weight relationships for 100--250 mm fork length (1--2 year old) walleye pollock were established independent of the adult life history stages caught. Bias-corrected weights-at-length (log scale) were estimated from the model and subtracted from observed weights to compute individual residuals per fish. Length-weight residuals were averaged for each stratum and weighted in proportion to INPFC stratum biomass based on stratified area-swept expansion of summer bottom trawl survey catch per unit effort (CPUE). Average length-weight residuals were compared by stratum and year to evaluate spatial variation in fish condition. Combinations of stratum and year with <10 samples were used for length-weight relationships but excluded from indicator calculations. **Methodological Changes**: The method used to calculate groundfish condition this year (2020) differs from previous years in that: 1) different regression slopes were estimated for each stratum, 2) a bias-correction was applied when predicting weights prior to calculating residuals, 3) stratum mean residuals were weighted in proportion to stratum biomass, and 4) stratum-year combinations with sample size <10 were not used in indicator calculations. As in previous years, confidence intervals for the condition indicator reflect uncertainty based on length-weight residuals, but are larger due to differences in sample sizes and stratum biomasses among years. Confidence intervals do not account for uncertainty in stratum biomass estimates. **Status and Trends**: Residual body condition varied amongst survey years for all species considered (Figure 2). The updated computational methods used to calculate this year’s residual body condition indexes returned different values than those reported last year (Laman 2019). The patterns of above or below average residual condition observed in 2019 largely match those generated here from the updated computations, but with a notable reduction in magnitude for most years. The lower magnitude results come from using stratum-specific regression coefficients and samples weighted in proportion to biomass which reduces the influence of spatio-temporal variation in sampling intensity on the residuals. Some exceptions are 2009 southern rock sole, reported to have above average condition in 2019, shifted to neutral or slightly negative here and, for 2003 northern rockfish, residual condition calculated with the updated method here was higher above the long-term condition average than was reported in 2019. Based on these new methods, body condition is still below average for most species since 2015 (e.g., large walleye pollock, arrowtooth flounder, dusky rockfish) with some species trending downward over that time period (e.g., northern rockfish and possibly Pacific ocean perch). Residual body condition of Pacific cod and southern rock sole is trending upward over the same time, although southern rock sole remain below average. Prior to 2011, residual body condition indexes of these GOA species vary from survey to survey, cycling between negative and positive residuals with no clear temporal trends. Residual body condition of 100–250 mm walleye pollock in the GOA is strikingly positive during early years in the time series, but has remained mostly neutral or slightly negative since the early 1990s. ```r ## Calculations and figures goa_spp_vec <- unique(goa_dat$SPECIES_CODE) # Calculate length weight residuals for(i in 1:length(goa_spp_vec)) { # Separate slope for each stratum. Bias correction according to Brodziak, no outlier detection. goa_df <- akfishcondition::calc_lw_residuals(len = goa_dat$LENGTH[goa_dat$SPECIES_CODE == goa_spp_vec[i]], wt = goa_dat$WEIGHT[goa_dat$SPECIES_CODE == goa_spp_vec[i]], year = goa_dat$YEAR[goa_dat$SPECIES_CODE == goa_spp_vec[i]], stratum = goa_dat$INPFC_STRATUM[goa_dat$SPECIES_CODE == goa_spp_vec[i]], make_diagnostics = TRUE, # Make diagnostics bias.correction = TRUE, # Bias correction turned on outlier.rm = FALSE, # Outlier removal turned off region = "GOA", species_code = goa_dat$SPECIES_CODE[goa_dat$SPECIES_CODE == goa_spp_vec[i]]) goa_dat$resid_mean[goa_dat$SPECIES_CODE == goa_spp_vec[i]] <- goa_df$lw.res_mean goa_dat$resid_lwr[goa_dat$SPECIES_CODE == goa_spp_vec[i]] <- goa_df$lw.res_lwr goa_dat$resid_upr[goa_dat$SPECIES_CODE == goa_spp_vec[i]] <- goa_df$lw.res_upr } # Estimate mean and std. err for each stratum, filter out strata with less than 10 samples goa_stratum_resids <- goa_dat %>% dplyr::group_by(COMMON_NAME, SPECIES_CODE, YEAR, INPFC_STRATUM, AREA_BIOMASS) %>% dplyr::summarise(stratum_resid_mean = mean(resid_mean), stratum_resid_sd = sd(resid_mean), n = n()) %>% dplyr::filter(n >= 10) %>% dplyr::mutate(stratum_resid_se = stratum_resid_sd/sqrt(n)) # Weight strata by biomass for(i in 1:length(goa_spp_vec)) { goa_stratum_resids$weighted_resid_mean[goa_stratum_resids$SPECIES_CODE == goa_spp_vec[i]] <- akfishcondition::weight_lw_residuals(residuals = goa_stratum_resids$stratum_resid_mean[goa_stratum_resids$SPECIES_CODE == goa_spp_vec[i]], year = goa_stratum_resids$YEAR[goa_stratum_resids$SPECIES_CODE == goa_spp_vec[i]], stratum = goa_stratum_resids$INPFC_STRATUM[goa_stratum_resids$SPECIES_CODE == goa_spp_vec[i]], stratum_biomass = goa_stratum_resids$AREA_BIOMASS[goa_stratum_resids$SPECIES_CODE == goa_spp_vec[i]]) goa_stratum_resids$weighted_resid_se[goa_stratum_resids$SPECIES_CODE == goa_spp_vec[i]] <- akfishcondition::weight_lw_residuals(residuals = goa_stratum_resids$stratum_resid_se[goa_stratum_resids$SPECIES_CODE == goa_spp_vec[i]], year = goa_stratum_resids$YEAR[goa_stratum_resids$SPECIES_CODE == goa_spp_vec[i]], stratum = goa_stratum_resids$INPFC_STRATUM[goa_stratum_resids$SPECIES_CODE == goa_spp_vec[i]], stratum_biomass = goa_stratum_resids$AREA_BIOMASS[goa_stratum_resids$SPECIES_CODE == goa_spp_vec[i]]) } # Biomass-weighted residual and SE by year goa_ann_mean_resid_df <- goa_stratum_resids %>% dplyr::group_by(YEAR, COMMON_NAME) %>% dplyr::summarise(mean_wt_resid = mean(weighted_resid_mean), se_wt_resid = mean(weighted_resid_se)) write.csv(goa_ann_mean_resid_df, "GOA_annual_resids.csv", row.names = FALSE) write.csv(goa_stratum_resids, "GOA_stratum_resids.csv", row.names = FALSE)
```rFigure 2. Biomass-weighted residual body condition index across survey years (1984-2019) for seven Gulf of Alaska groundfish species collected on the National Marine Fisheries Service (NMFS) Alaska Fisheries Science Center (AFSC) Resource Assessment and Conservation Engineering Groundfish Assessment Program (RACE-GAP) standard summer bottom trawl survey. Filled bars denote weighted length-weight residuals using this year's indicator calculation, error bars denote two standard errors, points denote the mean of the unweighted length-weight residual from the previous year's (2019) ESR.", message = FALSE, warning = FALSE} goa_ann_mean_resid_df$DISPLAY_NAME <- set_plot_order(goa_ann_mean_resid_df$COMMON_NAME, REGION = "GOA") old_indicator_dat$DISPLAY_NAME <- set_plot_order(old_indicator_dat$DISPLAY_NAME, REGION = "GOA")
fig2 <- ggplot() + geom_bar(data = goa_ann_mean_resid_df, aes(x = YEAR, y = mean_wt_resid), stat = "identity", fill = "plum", color = "black") + geom_errorbar(data = goa_ann_mean_resid_df, aes(x = YEAR, ymax = mean_wt_resid + 2se_wt_resid, ymin = mean_wt_resid - 2se_wt_resid)) + geom_hline(yintercept = 0) + geom_point(data = old_indicator_dat, aes(x = YEAR, y = ymeans)) + facet_wrap(~DISPLAY_NAME, ncol = 2, scales = "free_y") + scale_x_continuous(name = "Year") + scale_y_continuous(name = "Length-weight residual (ln(g))") + theme_condition_index() print(fig2)
```r
png("GOAbyyear.png",width=6,height=7,units="in",res=300)
print(fig2 + theme_pngs())
dev.off()
# png("AIbyyear.png", width = 6, height = 7, units = "in", res = 300)
# grid.arrange(grobs = myplot, ncol = 2)
# dev.off()
# write.csv(lwdata_by_year, "goa_lwdata_by_year.csv", row.names = FALSE)
The general patterns of above and below average residual body condition index across recent survey years for the Gulf of Alaska as described above was also apparent in the spatial condition indicators across INPFC strata (Figure 3). The relative contribution of stratum-specific residual body condition to the overall trends (indicated by the height of each colored bar segment) does not demonstrate a clear pattern. Although, for many species, the direction of residual body condition (positive or negative) was synchronous amongst strata within years. Patterns of fish distribution are also apparent in the stratum condition indexes. For example, Northern rockfish have primarily been collected from the Shumagin and Chirikof strata in recent surveys. The trend of increasingly positive Pacific cod residuals appears to be largely driven by a shift in residual body condition in the Kodiak and Shumagin strata.
```rFigure 3. Residual body condition index for seven Gulf of Alaska groundfish species collected on the National Marine Fisheries Service (NMFS) Alaska Fisheries Science Center (AFSC) Resource Assessment and Conservation Engineering Groundfish Assessment Program (RACE-GAP) standard summer bottom trawl survey (1984--2019) grouped by International North Pacific Fisheries Commission (INPFC) statistical sampling strata.", message = FALSE, warning = FALSE} goa_stratum_resids$DISPLAY_NAME <- set_plot_order(goa_stratum_resids$COMMON_NAME, REGION = "GOA")
fig3 <- ggplot(data = goa_stratum_resids, aes(x = YEAR, y = stratum_resid_mean, fill = set_stratum_order(INPFC_STRATUM, REGION = "GOA"))) + geom_hline(yintercept = 0) + geom_bar(stat = "identity", color = "black", position = "stack") + facet_wrap(~DISPLAY_NAME, ncol = 2, scales = "free_y") + scale_x_continuous(name = "Year") + scale_y_continuous(name = "Length-weight residual (ln(g))") + scale_fill_brewer(name = "Stratum", palette = "BrBG") + theme_condition_index() print(fig3)
```r
png("goa_condition_by_stratum.png",width=6,height=7,units="in",res=300)
print(fig3 + theme_pngs())
dev.off()
for(i in 1:length(unique(goa_stratum_resids$DISPLAY_NAME))) {
png(paste0("./output/GOA_condition_", gsub(">", "gt", unique(goa_stratum_resids$DISPLAY_NAME)[i]), ".png"), width = 6, height = 7, units = "in", res = 300)
print(
ggplot(data = goa_stratum_resids %>%
dplyr::filter(DISPLAY_NAME == unique(goa_stratum_resids$DISPLAY_NAME)[i]),
aes(x = YEAR,
y = stratum_resid_mean,
fill = set_stratum_order(INPFC_STRATUM, REGION = "GOA"),
ymin = stratum_resid_mean - 2*stratum_resid_se,
ymax = stratum_resid_mean + 2*stratum_resid_se)) +
geom_hline(yintercept = 0) +
geom_bar(stat = "identity", color = "black", position = "stack", width = 0.7) +
geom_errorbar(width = 0.7) +
facet_wrap(~set_stratum_order(INPFC_STRATUM, REGION = "GOA"),
ncol = 2,
scales = "free_y") +
ggtitle(unique(goa_stratum_resids$DISPLAY_NAME)[i]) +
scale_x_continuous(name = "Year") +
scale_y_continuous(name = "Length-weight residual (ln(g))") +
scale_fill_brewer(name = "Stratum", palette = "BrBG", drop = FALSE) +
theme_pngs() +
theme(legend.position = "none",
title = element_text(hjust = 0.5)))
dev.off()
}
Factors causing observed trends: Factors that could affect residual fish body condition presented here include temperature, trawl survey timing, stomach fullness, movement in or out of the survey area, or variable somatic growth. Since the Warm Blob in 2014 (Bond et al., 2015; Stabeno et al., 2019), there has been a general trend of warming ocean temperatures in the survey area through 2018 that could be affecting fish growth conditions there. Changing ocean conditions along with normal patterns of movement can cause the proportion of the population resident in the sampling area during the annual bottom trawl survey to vary. The date that the first length-weight data are collected is generally in late May and the bottom trawl survey is conducted throughout the summer months moving from west to east so that spatial and temporal trends in fish growth over the season become confounded with survey progress. In addition, spatial variability in residual condition may also reflect local environmental features which can influence growth and prey availability in the areas surveyed (e.g., warm core eddies in the central Gulf of Alaska; Atwood et al., 2010). The updated condition analyses presented here begin to, but do not wholly account for spatio-temporal variability in the underlying populations sampled.
Implications: Variations in body condition likely have implications for fish survival. In Prince William Sound, the condition of herring prior to the winter may influence their survival (Paul and Paul, 1999). The condition of Gulf of Alaska groundfish may similarly contribute to survival and recruitment. As future years are added to the time series, the relationship between length-weight residuals and subsequent survival will be examined further. It is important to consider that residual body condition for most species in these analyses was computed for all sizes and sexes combined. Requirements for growth and survivorship differ for different fish life stages and some species have sexually dimorphic growth patterns. It may be more informative to examine life-stage (e.g., early juvenile, subadult, and adult phases) and sex-specific body condition in the future.
The trend toward lowered body condition for many Gulf of Alaska species over the last 3--4 RACE/AFSC GAP bottom trawl surveys is a potential cause for concern. It could indicate poor overwinter survival or may reflect the influence of locally changing environmental conditions depressing fish growth, local production, or survivorship. Indications are that the Warm Blob (Bond et al., 2015; Stabeno et al., 2019) has been followed by subsequent years with elevated water temperatures (e.g., Barbeaux et al., 2018; Laman, 2018) which may be related to changes in fish condition in the species examined. As we continue to add years of fish condition to the record and expand on our knowledge of the relationships between condition, growth, production, and survival, we hope to gain more insight into the overall health of fish populations in the Gulf of Alaska.
Research priorities: Efforts are underway to redevelop the groundfish condition indicator for next year's (2021) ESR, using a spatio-temporal model with spatial random effects (VAST). The change is expected to allow more precise biomass expansion, improve estimates of uncertainty, and better account for spatial-temporal variation in length-weight samples from bottom trawl surveys due to methodological changes in sampling (e.g. transition from sex-and-length stratified sampling to random sampling). For 2021, revised indicators will be presented alongside a retrospective analysis that compares the historical and revised condition indicator. Currently, research is being planned across multiple AFSC programs to explore standardization of statistical methods for calculating condition indicators, and to examine relationships among morphometric condition indicators, bioenergetic indicators, and physiological measures of fish condition.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.