Using {survdat} for Bottom Trawl Indicators
Stephanie Owen and Abigail Tyrell
Source:vignettes/using_survdat_data.Rmd
using_survdat_data.RmdBottom Trawl Indicators in NEESP2
This package contains functions for producing bottom trawl indicators from {survdat} data pulls: swept area biomass, stratified mean biomass, species condition, diet, and range. This vignette provides example output data tables and plots using these functions.
For more information on accessing {survdat} data, see vignette “Accessing Bottom Trawl Survey Data”.
Species Condition - Black sea bass
Create and plot species condition
Uses ‘species_condition’ and ‘plot_condition’ functions in R/create_condition_indicator.R and R/plot_condition.R
condition <- species_condition(
data = NEesp2::survdat_subset,
LWparams = LWparams,
species.codes = species.codes,
output = "esp"
)## Joining with `by = join_by(STRATUM)`
## Joining with `by = join_by(count)`
## Removing 16811 outliers from the data set.## # A tibble: 6 × 5
## Species EPU YEAR DATA_VALUE INDICATOR_NAME
## <chr> <chr> <dbl> <dbl> <chr>
## 1 Black sea bass MAB 1992 0.985 mean condition
## 2 Black sea bass MAB 1993 0.998 mean condition
## 3 Black sea bass GB 1994 0.983 mean condition
## 4 Black sea bass MAB 1994 1.02 mean condition
## 5 Black sea bass MAB 1995 0.997 mean condition
## 6 Black sea bass MAB 1996 0.982 mean condition
plot_condition(data = bsb_condition, var = "Black sea bass")
Data are aggregated at the EPU level by default, but the data can also be assessed as a unit.
condition <- species_condition(
data = NEesp2::survdat_subset,
LWparams = LWparams,
species.codes = species.codes,
output = "esp",
by_EPU = FALSE
)## Joining with `by = join_by(count)`
## Removing 16811 outliers from the data set.## # A tibble: 6 × 5
## Species EPU YEAR DATA_VALUE INDICATOR_NAME
## <chr> <chr> <dbl> <dbl> <chr>
## 1 Black sea bass UNIT 1992 0.986 mean condition
## 2 Black sea bass UNIT 1993 0.997 mean condition
## 3 Black sea bass UNIT 1994 1.01 mean condition
## 4 Black sea bass UNIT 1995 0.995 mean condition
## 5 Black sea bass UNIT 1996 0.981 mean condition
## 6 Black sea bass UNIT 1997 0.986 mean condition
plot_condition(data = bsb_condition, var = "Black sea bass")
Create species condition for the State of the Ecosystem report
By setting output = "soe", the
species_condition function will format the output in the
same style as ecodata::condition. Note that black sea bass
is the only species included in the sample dataset shown below, but if
additional species were present in the data, the output would be
aggregated at the species level.
condition <- species_condition(
data = NEesp2::survdat_subset,
LWparams = LWparams,
species.codes = species.codes,
output = "soe"
)## Joining with `by = join_by(STRATUM)`
## Joining with `by = join_by(count)`
## Removing 16811 outliers from the data set.
head(condition)## # A tibble: 6 × 5
## Time Var EPU Value Units
## <dbl> <chr> <chr> <dbl> <chr>
## 1 1992 Acadian redfish GB 1.02 MeanCond
## 2 1992 Acadian redfish GOM 0.978 MeanCond
## 3 1992 Acadian redfish SS 1.00 MeanCond
## 4 1993 Acadian redfish GB 1.02 MeanCond
## 5 1993 Acadian redfish GOM 0.994 MeanCond
## 6 1993 Acadian redfish SS 1.02 MeanCondAdditional condition queries
species_condition can also assess the data by sex and by
length bin. If these options are selected, the output will be a “full”
dataset that contains additional data columns identifying the grouping
parameters and data variance. For example:
condition <- species_condition(
data = NEesp2::survdat_subset,
LWparams = LWparams,
species.codes = species.codes,
by_sex = TRUE,
length_break = c(0, 20, 65)
)## You asked to group results by sex and/or length ; data will not be formatted for SOE or ESP output.## Joining with `by = join_by(STRATUM)`
## Joining with `by = join_by(count)`
## Removing 16811 outliers from the data set.
head(condition)## # A tibble: 6 × 10
## Species YEAR EPU sexMF length_group MeanCond nCond sd variance
## <chr> <dbl> <chr> <chr> <fct> <dbl> <int> <dbl> <dbl>
## 1 Acadian redfish 1992 GOM 0 [0,20] 1.09 6 0.0559 0.00312
## 2 Acadian redfish 1992 GOM 1 [0,20] 1.02 27 0.0273 0.000746
## 3 Acadian redfish 1992 GOM 1 (20,65] 0.985 99 0.0194 0.000378
## 4 Acadian redfish 1992 GOM 2 [0,20] 0.927 13 0.0267 0.000713
## 5 Acadian redfish 1992 GOM 2 (20,65] 0.958 93 0.0281 0.000790
## 6 Acadian redfish 1992 SS 1 [0,20] 1.04 11 0.0358 0.00128
## # ℹ 1 more variable: INDICATOR_NAME <chr>Species Range - Black sea bass in fall
Uses ‘species_range’ function in R/create_bottomtrawl_indicators.R
range <- species_range(data = NEesp2::survdat_subset, species = NEesp2::species)## Joining with `by = join_by(SVSPP)`
bsb_range <- range %>%
subset(SPECIES == "BLACK SEA BASS") %>%
subset(SEASON == "FALL") %>%
subset(INDICATOR_NAME == c("range_lat", "range_lon"))
head(bsb_range)## # A tibble: 6 × 7
## YEAR SEASON INDICATOR_NAME DATA_VALUE SPECIES SVSPP INDICATOR_UNITS
## <dbl> <chr> <chr> <dbl> <chr> <dbl> <chr>
## 1 1983 FALL range_lat 5.33 BLACK SEA BASS 141 degrees
## 2 1983 FALL range_lon 5.93 BLACK SEA BASS 141 degrees
## 3 1984 FALL range_lat 5.67 BLACK SEA BASS 141 degrees
## 4 1984 FALL range_lon 5.2 BLACK SEA BASS 141 degrees
## 5 1985 FALL range_lat 6.57 BLACK SEA BASS 141 degrees
## 6 1985 FALL range_lon 5.58 BLACK SEA BASS 141 degrees
bsb_range %>%
ggplot2::ggplot(ggplot2::aes(
x = YEAR,
y = DATA_VALUE,
color = INDICATOR_NAME,
)) +
ggplot2::geom_point() +
ggplot2::geom_path() +
ggplot2::theme_bw() +
ggplot2::labs(
title = "Black Sea Bass Range in Fall (Lat + Lon)",
x = "Year",
y = "Range (degrees)"
) +
ggplot2::facet_wrap("INDICATOR_NAME")
Swept area biomass - Black sea bass in fall
Uses ‘create_swept_area’ indicator in R/create_bottomtrawl_indicators.R
Modified from ‘calc_swept_area’ in {survdat} package.
bsb_swept_area <- create_swept_area(
surveyData = NEesp2::survdat_subset,
areaPolygon = "NEFSC strata",
areaDescription = "STRATA",
filterByArea = "all",
filterBySeason = "FALL",
groupDescription = "SVSPP",
filterByGroup = "all",
mergesexFlag = T,
tidy = F,
q = NULL,
a = 0.0384
) %>%
dplyr::filter(SVSPP == "141")## Warning: The `tidy` argument is deprecated and will be removed in the next
## version. `calc_swept_area()` will soon strictly return tidy output.## Prepping data ...## Spherical geometry (s2) switched off## Filtering data ...## Calculating Stratified Mean ...## Calculating Swept Area Estimate ...## Assuming a value of q = 1 for all groups
head(bsb_swept_area)## # A tibble: 6 × 8
## YEAR SVSPP variance se sd INDICATOR_UNITS INDICATOR_NAME DATA_VALUE
## <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <dbl>
## 1 1983 141 1.82e 9 4.27e4 3.43e7 numberstow-1 swept_area_bi… 173138.
## 2 1984 141 2.12e11 4.60e5 3.43e7 numberstow-1 swept_area_bi… 520280.
## 3 1985 141 7.09e 9 8.42e4 3.43e7 numberstow-1 swept_area_bi… 291244.
## 4 1986 141 6.36e 9 7.98e4 3.43e7 numberstow-1 swept_area_bi… 255195.
## 5 1987 141 3.45e10 1.86e5 3.43e7 numberstow-1 swept_area_bi… 428221.
## 6 1988 141 2.73e 9 5.22e4 3.43e7 numberstow-1 swept_area_bi… 159033.
bsb_swept_area %>%
ggplot2::ggplot(ggplot2::aes(
x = YEAR,
y = DATA_VALUE,
)) +
ggplot2::geom_point() +
ggplot2::geom_path() +
ggplot2::theme_bw() +
ggplot2::labs(
title = "Black Sea Bass Swept Area Biomass (Fall)",
x = "Year",
y = "Swept Area Biomass"
)
Stratified mean biomass - Black sea bass in fall
Uses ‘create_stratified_mean’ indicator in R/create_bottomtrawl_indicators.R
Modified from ‘calc_stratified_mean’ in {survdat} package.
bsb_strat_mean <- create_stratified_mean(
surveyData = survdat_subset,
areaPolygon = "NEFSC strata",
areaDescription = "STRATA",
filterByArea = "all",
filterBySeason = "FALL",
groupDescription = "SVSPP",
filterByGroup = "all",
mergesexFlag = T,
tidy = F,
returnPrepData = F
) %>%
dplyr::filter(SVSPP == "141")## Prepping data ...## Filtering data ...## Calculating Stratified Mean ...
head(bsb_strat_mean)## # A tibble: 6 × 9
## YEAR SEASON SVSPP variance se sd INDICATOR_UNITS INDICATOR_NAME
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
## 1 1983 FALL 141 0.0000423 0.00650 5.87 kgtow-1 strat_mean_biomass
## 2 1984 FALL 141 0.00502 0.0709 5.87 kgtow-1 strat_mean_biomass
## 3 1985 FALL 141 0.000169 0.0130 5.87 kgtow-1 strat_mean_biomass
## 4 1986 FALL 141 0.000151 0.0123 5.87 kgtow-1 strat_mean_biomass
## 5 1987 FALL 141 0.000861 0.0293 5.87 kgtow-1 strat_mean_biomass
## 6 1988 FALL 141 0.0000702 0.00838 5.87 kgtow-1 strat_mean_biomass
## # ℹ 1 more variable: DATA_VALUE <dbl>
bsb_strat_mean %>%
ggplot2::ggplot(ggplot2::aes(
x = YEAR,
y = DATA_VALUE,
)) +
ggplot2::geom_point() +
ggplot2::geom_path() +
ggplot2::theme_bw() +
ggplot2::labs(
title = "Black Sea Bass Stratified Mean Biomass (Fall)",
x = "Year",
y = "Stratified Mean Biomass"
)