Using {survdat} for Bottom Trawl Indicators
Stephanie Owen
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
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) ## Rows: 235 Columns: 11
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (4): SpeciesName, Season, Gender, ln_a
## dbl (7): LW_SVSPP, N, SEa, b, SEb, SE_estimate, r2
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.## # A tibble: 6 x 7
## Species EPU Time MeanCond nCond sd variance
## <chr> <chr> <dbl> <dbl> <int> <dbl> <dbl>
## 1 Black sea bass GB 1994 0.983 4 0.0415 0.00172
## 2 Black sea bass GB 1999 0.960 15 0.0415 0.00172
## 3 Black sea bass GB 2000 0.982 4 0.0415 0.00172
## 4 Black sea bass GB 2003 0.965 8 0.0415 0.00172
## 5 Black sea bass GB 2005 0.944 13 0.0415 0.00172
## 6 Black sea bass GB 2007 0.962 7 0.0415 0.00172
plot_condition(data = bsb_condition, var = 'Black sea bass') 
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')) %>%
dplyr::rename(INDICATOR_NAME = indicator_name,
DATA_VALUE = indicator_value)
head(bsb_range)## # A tibble: 6 x 6
## YEAR SEASON INDICATOR_NAME DATA_VALUE species SVSPP
## <dbl> <chr> <chr> <dbl> <chr> <dbl>
## 1 1983 FALL range_lat 5.33 BLACK SEA BASS 141
## 2 1983 FALL range_lon 5.93 BLACK SEA BASS 141
## 3 1984 FALL range_lat 5.67 BLACK SEA BASS 141
## 4 1984 FALL range_lon 5.2 BLACK SEA BASS 141
## 5 1985 FALL range_lat 6.57 BLACK SEA BASS 141
## 6 1985 FALL range_lon 5.58 BLACK SEA BASS 141
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')
Species diet table
Uses ‘diet’ function in R/create_bottomtrawl_indicators.R
## # A tibble: 6 x 6
## # Groups: prey_name, season, region [1]
## prey_name season region year mean_proportion range_proportion
## <chr> <chr> <chr> <int> <chr> <chr>
## 1 ANIMAL_REMAINS FALL GB 1978 "0.034 +- NA (1) " 0.034 - 0.034
## 2 ANIMAL_REMAINS FALL GB 1979 "0.006 +- NA (1) " 0.006 - 0.006
## 3 ANIMAL_REMAINS FALL GB 1986 "0.05 +- NA (1) " 0.05 - 0.05
## 4 ANIMAL_REMAINS FALL GB 1989 "0.046 +- NA (1) " 0.046 - 0.046
## 5 ANIMAL_REMAINS FALL GB 1990 "0.012 +- NA (1) " 0.012 - 0.012
## 6 ANIMAL_REMAINS FALL GB 1991 "0.022 +- NA (1) " 0.022 - 0.022Swept 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")## Prepping data ...## Spherical geometry (s2) switched off## Linking to GEOS 3.10.2, GDAL 3.4.1, PROJ 8.2.1; sf_use_s2() is FALSE## Filtering data ...## [1] 3820 3880 3890 3900 3920 1352 0 3860 3840 3870 1390 1351
## [13] 1380 3850 1360 1330 1340 3760 3790 3830 3800 3730 3810 1400
## [25] 3770 3780 3740 3750 3908 3720 3710 1270 1370 1260 3700 1310
## [37] 1280 3690 3680 3670 3660 3650 3630 3640 3620 1320 1290 1300
## [49] 1240 1220 1210 3580 3610 3590 3600 1200 3560 1180 1170 1160
## [61] 3570 1190 1230 1250 3470 3500 3510 3530 3550 3520 3540 3480
## [73] 3450 3460 1050 1090 1130 3010 3020 3030 1100 3040 3050 1060
## [85] 3060 3070 3080 1140 1150 1010 3090 3100 3110 3120 3130 3140
## [97] 1020 1070 1110 1120 3150 3160 3170 1080 1730 1030 1040 3180
## [109] 3190 3200 1740 1750 3220 3230 1760 1690 3240 3250 3260 1700
## [121] 1710 3270 3280 3290 1720 3300 3310 3320 1650 1660 1670 3330
## [133] 1680 3340 3350 3360 3370 3380 3390 3400 3410 1610 1620 1630
## [145] 1640 3420 3430 3440 8510 8520 8530 8500 7500 7510 7520 7540
## [157] 7550 99999 7590 8540 7530 7570 7600 7580 8550 7610 8560 8570
## [169] 7620 7630 7640 8580 7650 7660 7670 8590 8600 8770## Calculating Stratified Mean ...## Calculating Swept Area Estimate ...
head(bsb_swept_area)## # A tibble: 6 x 7
## Year SVSPP variance se sd indicator_name indicator_value
## <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 1983 141 611006122. 24719. 1150775. swept_area_biomass 313872.
## 2 1984 141 38320078. 6190. 1150775. swept_area_biomass 2867870.
## 3 1985 141 1413388353. 37595. 1150775. swept_area_biomass 696770.
## 4 1986 141 2779662376. 52723. 1150775. swept_area_biomass 428373.
## 5 1987 141 44213084604. 210269. 1150775. swept_area_biomass 639254.
## 6 1988 141 5163593606. 71858. 1150775. swept_area_biomass 229675.
bsb_swept_area %>%
ggplot2::ggplot(ggplot2::aes(x = Year,
y = indicator_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 ...## [1] 3820 3880 3890 3900 3920 1352 0 3860 3840 3870 1390 1351
## [13] 1380 3850 1360 1330 1340 3760 3790 3830 3800 3730 3810 1400
## [25] 3770 3780 3740 3750 3908 3720 3710 1270 1370 1260 3700 1310
## [37] 1280 3690 3680 3670 3660 3650 3630 3640 3620 1320 1290 1300
## [49] 1240 1220 1210 3580 3610 3590 3600 1200 3560 1180 1170 1160
## [61] 3570 1190 1230 1250 3470 3500 3510 3530 3550 3520 3540 3480
## [73] 3450 3460 1050 1090 1130 3010 3020 3030 1100 3040 3050 1060
## [85] 3060 3070 3080 1140 1150 1010 3090 3100 3110 3120 3130 3140
## [97] 1020 1070 1110 1120 3150 3160 3170 1080 1730 1030 1040 3180
## [109] 3190 3200 1740 1750 3220 3230 1760 1690 3240 3250 3260 1700
## [121] 1710 3270 3280 3290 1720 3300 3310 3320 1650 1660 1670 3330
## [133] 1680 3340 3350 3360 3370 3380 3390 3400 3410 1610 1620 1630
## [145] 1640 3420 3430 3440 8510 8520 8530 8500 7500 7510 7520 7540
## [157] 7550 99999 7590 8540 7530 7570 7600 7580 8550 7610 8560 8570
## [169] 7620 7630 7640 8580 7650 7660 7670 8590 8600 8770## Calculating Stratified Mean ...
head(bsb_strat_mean)## # A tibble: 6 x 8
## Year SEASON SVSPP variance se sd indicator_name indicator_value
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 1983 FALL 141 0.000850 0.0292 0.845 strat_mean_biomass 0.370
## 2 1984 FALL 141 0.000104 0.0102 0.845 strat_mean_biomass 4.72
## 3 1985 FALL 141 0.00277 0.0527 0.845 strat_mean_biomass 0.976
## 4 1986 FALL 141 0.00119 0.0344 0.845 strat_mean_biomass 0.280
## 5 1987 FALL 141 0.0636 0.252 0.845 strat_mean_biomass 0.767
## 6 1988 FALL 141 0.0233 0.153 0.845 strat_mean_biomass 0.488
bsb_strat_mean %>%
ggplot2::ggplot(ggplot2::aes(x = Year,
y = indicator_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") 