Effort Dataset Analysis (Global Tuna Atlas

packages <- c("qs", "dplyr", "ggplot2", "sf", "lubridate", "here", "ows4R", "patchwork", "kableExtra")
lapply(packages, function(pkg) {
  if (!require(pkg, character.only = TRUE)) install.packages(pkg)
  library(pkg, character.only = TRUE)
})

📥 Download and load the ile

qs_file <- "global_effort_tunaatlasird_level0_1950_2023.qs"
if (!file.exists(qs_file)) {
  download.file("https://zenodo.org/record/15221705/files/global_effort_tunaatlasird_level0_1950_2023.qs?download=1", qs_file, mode = "wb")
}
effort_data <- qs::qread(qs_file)

effort_data$geom <- NULL

📊 Data Overview

This dataset provides monthly records of tuna and tuna-like species effort at the global scale between 1950 and 2023. Here is a description of the columns:

kableExtra::kable(head(effort_data))

measurement_value	measurement_unit	measurement_unit_label	source_authority	gear_type	gear_type_label	fishing_fleet	fishing_fleet_label	fishing_mode	time_start	time_end	gridtype	geographic_identifier
1	SUC.SETS	Number of successful sets	ICCAT	01.1	Purse seines	ALB	Albania	UNK	2014-06-01	2014-06-30	1deg_x_1deg	5139015
1	SUC.SETS	Number of successful sets	ICCAT	01.1	Purse seines	ALB	Albania	UNK	2015-06-01	2015-06-30	1deg_x_1deg	5139015
1	SUC.SETS	Number of successful sets	ICCAT	01.1	Purse seines	ALB	Albania	UNK	2016-06-01	2016-06-30	1deg_x_1deg	5135013
2	SUC.SETS	Number of successful sets	ICCAT	01.1	Purse seines	ALB	Albania	UNK	2017-06-01	2017-06-30	1deg_x_1deg	5139015
3	SUC.SETS	Number of successful sets	ICCAT	01.1	Purse seines	ALB	Albania	UNK	2018-06-01	2018-06-30	1deg_x_1deg	5139015
1	SUC.SETS	Number of successful sets	ICCAT	01.1	Purse seines	ALB	Albania	UNK	2019-06-01	2019-06-30	1deg_x_1deg	5139015

dplyr::glimpse(effort_data)

## Rows: 353,038
## Columns: 13
## $ measurement_value      <dbl> 1, 1, 1, 2, 3, 1, 9, 26, 14, 51, 14, 6, 9, 67, …
## $ measurement_unit       <chr> "SUC.SETS", "SUC.SETS", "SUC.SETS", "SUC.SETS",…
## $ measurement_unit_label <chr> "Number of successful sets", "Number of success…
## $ source_authority       <chr> "ICCAT", "ICCAT", "ICCAT", "ICCAT", "ICCAT", "I…
## $ gear_type              <chr> "01.1", "01.1", "01.1", "01.1", "01.1", "01.1",…
## $ gear_type_label        <chr> "Purse seines", "Purse seines", "Purse seines",…
## $ fishing_fleet          <chr> "ALB", "ALB", "ALB", "ALB", "ALB", "ALB", "EUHR…
## $ fishing_fleet_label    <chr> "Albania", "Albania", "Albania", "Albania", "Al…
## $ fishing_mode           <chr> "UNK", "UNK", "UNK", "UNK", "UNK", "UNK", "UNK"…
## $ time_start             <chr> "2014-06-01", "2015-06-01", "2016-06-01", "2017…
## $ time_end               <chr> "2014-06-30", "2015-06-30", "2016-06-30", "2017…
## $ gridtype               <chr> "1deg_x_1deg", "1deg_x_1deg", "1deg_x_1deg", "1…
## $ geographic_identifier  <chr> "5139015", "5139015", "5135013", "5139015", "51…

source_authority: The source RFMO (e.g., CCSBT, IOTC)
fishing_fleet: Code for the flag or national fleet (e.g., AUS)
time_start / time_end: Temporal coverage of the observation (monthly granularity)
geographic_identifier: Unique spatial ID for each grid cell
gear_type: Code describing the type of gear used
fishing_mode: Code describing the fishing mode or school association
measurement_unit: Unit of measurement used for effort (e.g., Days, Hours)
measurement_value: Numeric value of the reported effort
GRIDTYPE: Type of grid used (e.g., 1deg_x_1deg)
gear_type_label: Human-readable label for the gear type
fishing_fleet_label: Label for the fleet or flag state
species_label: Common name of the species
species_definition: Scientific name of the species
geom: Geometry in WKT format for mapping purposes
fishing_mode_label: Human-readable label for fishing mode)

summary(effort_data$measurement_value)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##        0        4       22    42796     2146 14188790

kableExtra::kable(table(effort_data$measurement_unit))

Var1	Freq
BOATS	60
D.FISH.G	6759
DAYS	9396
FDAYS	37098
FHOURS	84385
HOOKS	95420
HOURS	2741
Hours.FAD	24919
Hours.FSC	9224
Hours.STD	32836
HRSRH	46
KM.SETS	15
LINE.DAYS	275
LINES	1
MD	83
N.POLE-D	228
NETS	488
NO.FADS.VIS	42
NO.MTZAS	2
NO.NETS	17
NO.TRAPS	173
SETS	11339
SUC.D.FI	1274
SUC.SETS	22351
TRAP D	366
TRIPS	13500

🗺️ Effort map

First let’s download the geometry.

  cwp_grid_file <- here("data/cl_areal_grid.csv")
  if (!file.exists(cwp_grid_file)) {
    message("Downloading cl_area from github repo")
    zip_url <- "https://github.com/fdiwg/fdi-codelists/raw/main/global/cwp/cl_areal_grid.zip"
    zip_path <- here("data/cwp_grid.zip")
    download.file(zip_url, zip_path, mode = "wb")
    unzip(zip_path, exdir = here("data"))
  }
  cwp_grid <- st_read(cwp_grid_file)

## Reading layer `cl_areal_grid' from data source 
##   `/home/jovyan/firms-gta/geoflow-tunaatlas/data/cl_areal_grid.csv' 
##   using driver `CSV'

  cwp_grid <- st_as_sf(
    cwp_grid,
    wkt = "geom_wkt",   
    crs = 4326         
  ) %>%
     dplyr::rename(cwp_code = CWP_CODE) %>%
    dplyr::rename(geom = geom_wkt)
  
qs_file <- "UN_CONTINENT2.qs"

if (!file.exists(qs_file)) {
  message("Downloading UN_CONTINENT2 from FAO GeoServer...")
  WFS <- WFSClient$new(
    url = "https://www.fao.org/fishery/geoserver/fifao/wfs",
    serviceVersion = "1.0.0",
    logger = "INFO"
  )
  continent <- WFS$getFeatures("fifao:UN_CONTINENT2")
  
  # Save to .qs for faster reloads later
  qs::qsave(continent, qs_file)
} else {
  message("Loading cached UN_CONTINENT2 from local file...")
  continent <- qs::qread(qs_file)
}

sf::st_crs(continent) <- 4326

This map displays the spatial distribution of effort, aggregated by geographic grid cell. The measurement_value is summed for each area, and only the top 6 values are shown.

top_units <- effort_data %>%
  dplyr::group_by(measurement_unit) %>%
  dplyr::summarise(filtertop = sum(measurement_value)) %>%
  dplyr::arrange(desc(filtertop)) %>%
  dplyr::slice_head(n = 6) %>%
  dplyr::pull(measurement_unit)


effort_summary_by_unit <- effort_data %>%
  dplyr::filter(measurement_unit %in% top_units) %>% 
  dplyr::group_by(geographic_identifier, measurement_unit) %>%
  dplyr::summarise(total = sum(measurement_value, na.rm = TRUE), .groups = "drop") 

effort_summary_geom <- effort_summary_by_unit %>%
  dplyr::inner_join(cwp_grid %>% dplyr::select(cwp_code, geom), by = c("geographic_identifier" = "cwp_code")) %>%
  sf::st_as_sf()

plots <- effort_summary_geom %>%
  dplyr::group_split(measurement_unit) %>%
  purrr::map(function(df) {
    unit <- unique(df$measurement_unit)
    
    ggplot2::ggplot() +
      ggplot2::geom_sf(data = continent, fill = "gray90", color = "gray60") +
      ggplot2::geom_sf(data = df, ggplot2::aes(fill = total), color = NA) +
      ggplot2::scale_fill_viridis_c(option = "plasma", trans = "log", na.value = "transparent") +
      ggplot2::labs(title = paste("Unit:", unit), fill = "effort Total") +
      ggplot2::theme_minimal()
  })

patchwork::wrap_plots(plots, ncol = 2)

📈 Annual effort trends

effort_data %>%
  dplyr::group_by(year = lubridate::year(time_start), measurement_unit) %>%
  dplyr::summarise(total = sum(measurement_value, na.rm = TRUE)) %>%
  ggplot2::ggplot(ggplot2::aes(x = year, y = total)) +
  ggplot2::geom_line() +
  ggplot2::facet_wrap(~measurement_unit, scales = "free_y") +
  ggplot2::labs(title = "Total effort by year and 'measurement_unit'", y = "Measurement_value", x = "Year")

Effort Dataset Analysis (Global Tuna Atlas - IRD Level 0)

📥 Download and load the ile

📊 Data Overview

🗺️ Effort map

📈 Annual effort trends