Package {BioIndex}

ALK (Age Length Key)

Description

Computes the Age-Length Key (ALK) from biological samples, a crucial parameter for converting length distributions into age classes and analyzing stock growth dynamics.

Usage

ALK(
  ta,
  te,
  sp,
  GSA,
  country = "all",
  nyears = NA,
  wd = NA,
  save = TRUE,
  verbose = FALSE
)

Arguments

Value

A data.frame representing the Age-Length Key.

Estimation of ALK

Description

Estimation of ALK

Usage

ALKf(te, sp, GEAR, GSA, country = NA, years = 5, wd = NA, save = TRUE)

Arguments

Value

A data.frame representing the Age-Length Key for females.

Main function to perform BioIndex analysis

Description

BioIndex is an R package designed to support the standardized analysis of MEDITS trawl survey data and the calculation of biological indicators for selected species and population components.

Usage

BioIndex(
  ta,
  tb,
  tc,
  sspp,
  rec_threshold,
  spaw_threshold,
  haul_threshold = 30,
  sexes = "all",
  depth,
  GSA,
  country = "all",
  map_lim,
  depth_lines = c(10, 200, 800),
  strata = BioIndex::strata_scheme,
  stratification_tab = BioIndex::stratification,
  resolution = NA,
  buffer = 0.1,
  wd = NA,
  zip = TRUE,
  save = TRUE,
  verbose = TRUE
)

Arguments

Value

A list containing the results of the BioIndex workflow, including data frames and plot objects.

Author(s)

Walter Zupa zupa@fondazionecoispa.org

Examples

BioIndex(ta=TA[TA$YEAR %in% c(2015, 2016), ], tb=TB[TB$YEAR %in% c(2015, 2016), ],
tc=TC[TC$YEAR %in% c(2015, 2016), ], sspp="MERLMER",rec_threshold=200,
spaw_threshold=210,sexes="all", depth=c(10,800), GSA=10, country="all",
map_lim=c(13.3,15.2,39.9,41.3),depth_lines=c(50,200,800),
strata=BioIndex::strata_scheme, stratification_tab =
BioIndex::stratification, resolution=NA, buffer=0.1, wd=tempdir(),
zip=FALSE, save=TRUE, verbose=TRUE)

Interception Union Tets

Description

Interception Union Tets

Usage

IUT(abundance, biomass, species, lastn = 5, GSA = 10, wd = NA, save = TRUE)

Arguments

Value

A data.frame containing the Indicator of Unfished Trends (IUT) results.

Length Frequency Distribution (LFD) Estimation and Plotting

Description

Input parameters include biological and haul data, stratification schemes, reference filters (e.g., sex, GSA, country, depth), and output control options (working directory, saving, verbosity).

Usage

LFD(
  mTATC,
  sex = "all",
  GSA,
  country = "all",
  depth_range,
  strata_scheme,
  stratification,
  wd = NA,
  save = TRUE,
  verbose = TRUE
)

Arguments

Details

This function estimates and plots the length frequency distribution (LFD) by year and by stratum, based on merged biological and haul data from MEDITS surveys. It computes raised numbers, applies stratification weights, and generates publication-ready plots and CSV outputs. Stratification weighting is based on swept area and surface area of the strata.

The function:

• Filters hauls by depth and country.

• Computes raised numbers per haul.

• Applies stratified weights based on the area per stratum.

• Outputs and/or plots the length frequency distribution by year and stratum.

• Handles sex-specific or combined-sex analyses.

The plot outputs include:

• Combined LFD by year across all strata.

• LFD by stratum (faceted), stratified by year.

Output files are saved as CSV and high-resolution JPEGs if 'save = TRUE' and 'wd' is defined.

Value

A named list with two elements:

'LFD'

A data frame of total LFD by year across all strata.

'LFD by stratum'

A data frame of LFD by year and by stratum.

A list of ggplot objects representing the length-frequency distributions.

Examples

data(TA)
data(TC)
# Create a merged dataset for GSA 10
mTATC <- merge_TATC(TA[TA$AREA==10,], TC[TC$AREA==10,], "MERLMER")
LFD(mTATC, sex="all", GSA=10, country="all", depth_range=c(10,800),
    strata_scheme=BioIndex::strata_scheme,
    stratification=BioIndex::stratification, wd=tempdir(), save=FALSE)

LW (Length-Weight relationship)

Description

Estimates the length-weight relationship for the selected species, a fundamental biological indicator to assess fish condition and convert numerical length estimates into biomass equivalents.

Usage

LW(
  ta,
  te,
  sp,
  GSA,
  country = "all",
  nyears = NA,
  wd = NA,
  save = TRUE,
  verbose = FALSE
)

Arguments

Value

A data.frame containing the length-weight relationship parameters and statistics.

Estimation of LW relationship

Description

Estimation of LW relationship

Usage

LWf(TE, sp, GEAR, GSA, country = NA, n_records = 10, wd = NA, save = TRUE)

Arguments

Value

A data.frame containing the length-weight relationship parameters specifically for females.

Author(s)

Walter Zupa zupa@fondazionecoispa.org

Estimation of L50 and L95

Description

Estimation of L50 and L95

Usage

Lquant(lfd, wd = NA, sspp, GSA, save = TRUE, verbose = TRUE)

Arguments

Value

A data.frame containing the length quantiles (e.g., L95) time series.

Estimation of haul distance

Description

Estimation of haul distance

Usage

MEDITS.distance(data, unit = "m", verbose = TRUE)

Arguments

Value

The function returns the vector of the distances expressed in the selected measure unit.

Examples

data(TA)
MEDITS.distance(TA, unit = "m", verbose = FALSE)

Conversion of MEDITS format coordinates in decimal degrees format

Description

Conversion of MEDITS format coordinates in decimal degrees format

Usage

MEDITS.to.dd(data)

Arguments

Value

The function returns the data frame of the TA table with the coordinates expressed as decimal degrees

Examples

data(TA)
MEDITS.to.dd(TA)

Estimation of Mean Individual Weight (MIW) time series

Description

Calculates the Mean Individual Weight (MIW) time series, providing a summary indicator of the average size within the captured population and potential shifts due to fishing pressure.

Usage

MIW(
  mTATB,
  GSA,
  country = "all",
  depth_range,
  strata_scheme,
  stratification,
  wd = NA,
  save = TRUE,
  verbose = TRUE
)

Arguments

Value

A data.frame containing the Mean Individual Weight (MIW) time series.

Examples

data(TA)
data(TB)
# Create a merged dataset for GSA 10
mTATB <- merge_TATB(TA[TA$AREA==10,], TB[TB$AREA==10,], "MERLMER")
MIW(mTATB, GSA=10, country="all", depth_range=c(10,800),
    strata_scheme=BioIndex::strata_scheme,
    stratification=BioIndex::stratification, wd=tempdir(), save=FALSE)

TA table example

Description

TA table example

Usage

TA

Format

An object of class data.frame with 100 rows and 43 columns.

TA table headings

Description

TA table headings

Usage

TA_cols

Format

An object of class character of length 22.

Author(s)

Walter Zupa zupa@fondazionecoispa.org

TB table example

Description

TB table example

Usage

TB

Format

An object of class data.frame with 3059 rows and 19 columns.

TB table headings

Description

TB table headings

Usage

TB_cols

Format

An object of class character of length 8.

Author(s)

Walter Zupa zupa@fondazionecoispa.org

TC table example

Description

TC table example

Usage

TC

Format

An object of class data.frame with 11185 rows and 22 columns.

TC table headings

Description

TC table headings

Usage

TC_cols

Format

An object of class character of length 11.

Author(s)

Walter Zupa zupa@fondazionecoispa.org

Aggregate MEDITS data across multiple GSAs

Description

Aggregate TA, TB, and TC MEDITS tables across a user-defined set of Geographical Sub-Areas (GSAs), and update the associated stratification objects accordingly.

Usage

aggregate_gsas(
  ta,
  tb,
  tc,
  gsas,
  strata_scheme = BioIndex::strata_scheme,
  stratification = BioIndex::stratification
)

Arguments

Details

This function filters the input TA, TB, and TC tables to retain only the selected GSAs, concatenates the original AREA and HAUL_NUMBER values to create unique haul identifiers across GSAs, and replaces the AREA field with a single aggregated GSA code obtained by collapsing the selected GSA values into one string. The same aggregation is applied to the stratification and strata_scheme tables.

The function performs three main operations:

1. Checks that all requested GSAs are present in the AREA column of the input ta, tb, and tc tables.

2. Filters the three MEDITS tables to the selected GSAs and updates HAUL_NUMBER by prefixing it with the original AREA value, ensuring uniqueness after aggregation.

3. Filters and updates the stratification and strata_scheme tables so that all selected GSAs are represented under a single aggregated GSA code.

The aggregated GSA code is created using paste(gsas, collapse = ""). For example, c(17, 18) becomes "1718".

Value

A list with five elements:

[[1]]

The filtered and aggregated TA table.

[[2]]

The filtered and aggregated TB table.

[[3]]

The filtered and aggregated TC table.

[[4]]

The filtered and updated stratification table.

[[5]]

The filtered and updated strata_scheme table.

See Also

strata_scheme, stratification

Examples

# Use internal data
data("strata_scheme", package = "BioIndex")
data("stratification", package = "BioIndex")
data("TA", package = "BioIndex")
data("TB", package = "BioIndex")
data("TC", package = "BioIndex")

d <- aggregate_gsas(
  ta = TA,
  tb = TB,
  tc = TC,
  gsas = 10,
  strata_scheme = BioIndex::strata_scheme,
  stratification = BioIndex::stratification
)

ta_agg <- d[[1]]
tb_agg <- d[[2]]
tc_agg <- d[[3]]
stratification_agg <- d[[4]]
strata_scheme_agg <- d[[5]]

Bubble plot of abundance and biomass indices by haul

Description

The function generates bubble plot of abundance and biomass indices by haul. If no resolution is specified (res = NA), the function works offline and uses an internal bathymetry dataset (med_bathy) covering the Mediterranean and Black Sea, reducing the computational time.

Usage

bubble_plot_by_haul_indexes(
  mTATB,
  map_lim,
  depth_lines,
  buffer = 0,
  res = NA,
  wd = NA,
  save = TRUE,
  verbose = TRUE
)

Arguments

Value

A ggplot object representing the bubble plot of abundance or biomass by hauls.

Author(s)

Walter Zupa zupa@fondazionecoispa.org

Examples

# Use internal data
data(TA)
data(TB)
# Create a merged dataset for GSA 10 and year 2016
mTATB <- merge_TATB(TA[TA$AREA == 10 & TA$YEAR == 2016, ],
                    TB[TB$AREA == 10 & TB$YEAR == 2016, ],
                    "MERLMER")
bubble_plot_by_haul_indexes(mTATB, map_lim = NA, depth_lines = c(200, 500, 800), save = FALSE)

Bubbleplot of abundance indices for recruits and spawners

Description

The function generates bubbleplots of abundance indices for recruits and spawners If no resolution is specified (res = NA), the function works offline and uses an internal bathymetry dataset (med_bathy) covering the Mediterranean and Black Sea, reducing the computational time.

Usage

bubbleplot_RS_by_hauls(
  mTATC,
  map_range,
  thresh_rec,
  thresh_spaw,
  depths = c(50, 200, 800),
  res = NA,
  buffer = 0.1,
  wd = NA,
  save = TRUE,
  verbose = FALSE
)

Arguments

Value

A list containing two ggplot objects: the bubble plot of recruits (pr) and spawners (ps) abundance indices.

Author(s)

Walter Zupa zupa@fondazionecoispa.org

Examples

# Create a merged dataset for GSA 10
mTATC <- merge_TATC(TA[TA$AREA==10,], TC[TC$AREA==10,], "MERLMER")
bubbleplot_RS_by_hauls(mTATC, map_range=c(15,21,39,43), thresh_rec=200, thresh_spaw=250)

centroidi

Description

centroidi

Usage

centroidi

Format

An object of class PackedSpatVector of length 1.

cgpmgrid

Description

cgpmgrid

Usage

cgpmgrid

Format

An object of class PackedSpatVector of length 1.

Check date haul (RoME)

Description

Check if in TB, TC or TE the date by haul is the same of the one reported in TA.

Usage

check_date_haul(DataTA, Data, year, wd = NA, suffix = NA)

Arguments

Value

TRUE if the validation passes, FALSE otherwise.

Note

This function is an internal routine based on RoME version 0.2.3. It is provided within BioIndex to ensure the package remains functional and self-sufficient for data validation.

Examples

data(TA)
data(TB)
check_date_haul(TA, TB, year = 2007)

Check dictionary (RoME)

Description

The function checks whether the values contained in specific fields are consistent with the allowed values of the dictionaries.

Usage

check_dictionary(
  ResultData,
  Field,
  Values,
  year,
  wd = NA,
  suffix = NA,
  verbose = FALSE
)

Arguments

Value

TRUE if the validation passes, FALSE otherwise.

Note

This function is an internal routine based on RoME version 0.2.3. It is provided within BioIndex to ensure the package remains functional and self-sufficient for data validation.

Examples

data(TA)
check_dictionary(TA, Field = "COUNTRY", Values = c("ITA"), year = 2007)

Check hauls TB TA (RoME)

Description

Check if all the hauls in TB are in TA.

Usage

check_hauls_TBTA(DataTA, DataTB, year, wd = NA, suffix = NA)

Arguments

Value

TRUE if the validation passes, FALSE otherwise.

Note

This function is an internal routine based on RoME version 0.2.3. It is provided within BioIndex to ensure the package remains functional and self-sufficient for data validation.

Examples

data(TA)
data(TB)
check_hauls_TBTA(TA, TB, year = 2007)

Check numeric range (RoME)

Description

The function checks whether the values contained in specific fields are consistent with the allowed ranges.

Usage

check_numeric_range(
  ResultData,
  Field,
  Values,
  year,
  wd = NA,
  suffix = NA,
  verbose = FALSE
)

Arguments

Value

TRUE if the validation passes, FALSE otherwise.

Note

This function is an internal routine based on RoME version 0.2.3. It is provided within BioIndex to ensure the package remains functional and self-sufficient for data validation.

Examples

data(TA)
check_numeric_range(TA, Field = "HAUL_DURATION", Values = c(10, 120), year = 2007)

continent

Description

continent

Usage

continent

Format

An object of class PackedSpatVector of length 1.

MEDITS coordinates in decimal degrees

Description

The function returns the data frame of the TA table with the coordinates expressed as decimal degrees.

Usage

convert_coordinates(Data)

Arguments

Value

the function return the same data frame with the coordinates converted in the decimal degrees format

Examples

data(TA)
convert_coordinates(TA)

Estimate hauls distances (decimal degrees)

Description

Function to estimate the hauls length using TA (table A, hauls data) with coordinates in the decimal degrees format (dd.ddd). The distances could be returned expressed in meters, kilometers and nautical miles.

Usage

dd.distance(data, unit = "m", verbose = TRUE)

Arguments

Value

The function returns the vector of the distances expressed in the selected measure unit.

Examples

data(TA)
ta_dd <- MEDITS.to.dd(TA)
dd.distance(ta_dd, unit = "km", verbose = FALSE)

Conversion of decimal degrees coordinates in MEDITS format

Description

Conversion of decimal degrees coordinates in MEDITS format

Usage

dd.to.MEDITS(data)

Arguments

Value

The function returns the data frame of the TA (table A) reporting the coordinates in MEDITS format.

Examples

data(TA)
ta_dd <- MEDITS.to.dd(TA)
dd.to.MEDITS(ta_dd)

Plot of hauls time series

Description

This function generates a spatial plot of haul positions over time, displaying haul labels and bathymetric lines over a customizable map extent. If no resolution is specified (res = NA), the function works offline and uses an internal bathymetry dataset (med_bathy) covering the Mediterranean and Black Sea, reducing the computational time.

Usage

hauls_position(
  mTATB,
  country = "all",
  map_lim,
  depth_lines,
  buffer = 0,
  res = NA,
  wd = NA,
  save = TRUE,
  verbose = TRUE
)

Arguments

Value

A ggplot object representing the spatial distribution of hauls.

Author(s)

Walter Zupa zupa@fondazionecoispa.org

Examples

# Use internal data
data(TA)
data(TB)
# Create a merged dataset for GSA 10 and year 2016
mTATB <- merge_TATB(TA[TA$AREA == 10 & TA$YEAR == 2016, ],
                    TB[TB$AREA == 10 & TB$YEAR == 2016, ],
                    "MERLMER")
hauls_position(mTATB, country = "all", map_lim = NA, depth_lines = c(200, 500, 800), save = FALSE)

Generating maps of indexes

Description

Generates density and biomass maps based on the spatial grid, allowing for the visual identification of resource concentration areas or potential biological hotspots.

Usage

index_on_grid(
  mTATBsp,
  stratum,
  wd = NA,
  map_range,
  threshold = 30,
  verbose = FALSE,
  save = TRUE
)

Arguments

Value

A ggplot object displaying the generated map of indices on the spatial grid.

Examples

data(TA)
data(TB)
data(TC)
m <- merge_TATBTC(TA[TA$AREA == 10, ], TB[TB$AREA == 10, ], TC[TC$AREA == 10, ],
                  species = "MERLMER", country = "all", verbose = FALSE)
mTATBsp <- overlayGrid(m[[1]], m[[2]], GSA = 10, save = FALSE, verbose = FALSE)[[1]]
map_range <- c(9, 15, 39, 42)
index_on_grid(mTATBsp, stratum = "10,800", map_range = map_range, threshold = 5,
              save = FALSE, verbose = FALSE)

Estimation of abundance indices for recruits

Description

Estimates specific abundance indices for the recruitment phase (using the user-defined cutoff value), allowing for the assessment of reproductive success and the prediction of future stock productivity.

Usage

index_recr(
  mTATB,
  mTATC,
  GSA,
  country,
  depth_range,
  cutoff,
  stratification,
  wd = NA,
  save = TRUE
)

Arguments

Value

A data.frame containing the abundance indices and density for the recruitment fraction.

Examples

data(TA)
data(TB)
data(TC)
data(stratification)
m <- merge_TATBTC(TA[TA$AREA == 10, ], TB[TB$AREA == 10, ], TC[TC$AREA == 10, ],
                  species = "MERLMER", country = "all", verbose = FALSE)
index_recr(m[[1]], m[[2]], GSA = 10, country = "all", depth_range = c(10, 800),
           cutoff = 200, stratification = stratification, save = FALSE)

Estimation of abundance indices for spawners (females)

Description

Calculates abundance and biomass indices focused on the adult fraction (spawners, using the user-defined cutoff value), an essential indicator for evaluating the self-renewal capacity of the population.

Usage

index_spawn(
  mTATB,
  mTATC,
  GSA,
  country,
  depth_range,
  cutoff,
  stratification,
  wd = NA,
  save = TRUE
)

Arguments

Value

A data.frame containing the abundance and biomass indices for the spawner fraction.

Examples

data(TA)
data(TB)
data(TC)
data(stratification)
m <- merge_TATBTC(TA[TA$AREA == 10, ], TB[TB$AREA == 10, ], TC[TC$AREA == 10, ],
                  species = "MERLMER", country = "all", verbose = FALSE)
index_spawn(m[[1]], m[[2]], GSA = 10, country = "all", depth_range = c(10, 800),
            cutoff = 200, stratification = stratification, save = FALSE)

Estimation of abundance indices for females

Description

Estimation of abundance indices for females

Usage

index_ts_F(
  mTATB,
  GSA,
  country_analysis,
  depth_range,
  strata_scheme,
  stratification,
  wd = NA,
  save = TRUE
)

Arguments

Value

A data.frame containing the time series of indices specifically for females.

Examples

data(TA)
data(TB)
data(TC)
data(strata_scheme)
data(stratification)
m <- merge_TATBTC(TA[TA$AREA == 10, ], TB[TB$AREA == 10, ], TC[TC$AREA == 10, ],
                  species = "MERLMER", country = "all", verbose = FALSE)
strata_scheme_g10 <- strata_scheme[strata_scheme$GSA == 10 & strata_scheme$COUNTRY == "ITA", ]
index_ts_F(m[[1]], GSA = 10, country_analysis = "all", depth_range = c(10, 800),
           strata_scheme = strata_scheme_g10, stratification = stratification, save = FALSE)

Estimation of abundance indices for males

Description

Estimation of abundance indices for males

Usage

index_ts_M(
  mTATB,
  GSA,
  country_analysis,
  depth_range,
  strata_scheme,
  stratification,
  wd = NA,
  save = TRUE
)

Arguments

Value

A data.frame containing the time series of indices specifically for males.

Examples

data(TA)
data(TB)
data(TC)
data(strata_scheme)
data(stratification)
m <- merge_TATBTC(TA[TA$AREA == 10, ], TB[TB$AREA == 10, ], TC[TC$AREA == 10, ],
                  species = "MERLMER", country = "all", verbose = FALSE)
strata_scheme_g10 <- strata_scheme[strata_scheme$GSA == 10 & strata_scheme$COUNTRY == "ITA", ]
index_ts_M(m[[1]], GSA = 10, country_analysis = "all", depth_range = c(10, 800),
           strata_scheme = strata_scheme_g10, stratification = stratification, save = FALSE)

Estimation of abundance and biomass indices

Description

Computes time series of stratified abundance and biomass indices, serving as the primary tool for monitoring temporal trends in stock status.

Usage

indices_ts(
  mTATB,
  GSA,
  country = "all",
  depth_range,
  strata_scheme,
  stratification,
  wd = NA,
  save = TRUE
)

Arguments

Value

A list of ggplot objects representing the time series of calculated indices.

Author(s)

Walter Zupa zupa@fondazionecoispa.org

Examples

data(TA)
data(TB)
# Create a merged dataset for GSA 10
mTATB <- merge_TATB(TA[TA$AREA==10,], TB[TB$AREA==10,], "MERLMER")
# Run indices_ts
indices_ts(mTATB, GSA=10, country="all", depth_range=c(10,800),
           strata_scheme=BioIndex::strata_scheme,
           stratification=BioIndex::stratification, wd=tempdir(), save=FALSE)

Mediterranean and Black Sea bathymetry (0–1000 m, bathy object)

Description

A precomputed bathy object containing bathymetric data for the Mediterranean Sea and the Black Sea. Depth values are restricted between 0 and -1000 meters. This dataset was downloaded using getNOAA.bathy with a resolution of 1 arc-minute, and filtered to remove deeper areas.

Usage

data(med_bathy)

Format

An object of class bathy (a matrix with longitude and latitude as axes, and depth in meters)

Details

The object can be used directly with functions from the marmap package, such as plot.bathy() and get.depth().

The spatial extent includes:

• Longitude: from -6° to 42°

• Latitude: from 30° to 47°

• Depth: from 0 to -1000 meters

Only marine cells within this depth range are retained. Land and deeper areas are set to NA.

Source

NOAA ETOPO1 via marmap::getNOAA.bathy()

See Also

getNOAA.bathy, plot.bathy, bubbleplot_RS_by_hauls

Merge TA and TB tables (haul-level catches)

Description

Combines haul-level operational data (TA) with species catch data (TB), providing the essential foundation for calculating stratified abundance (N) and biomass (kg) indices. The function keeps only valid hauls, performs quality checks (times, positions, wing opening, haul/date consistency) using internal validation functions (based on RoME version 0.2.3), computes spatial means, swept area, depth stratum, biomass/density indicators, and—optionally—writes the merged data to disk.

Usage

merge_TATB(
  ta,
  tb,
  species,
  country = "all",
  strata = BioIndex::strata_scheme,
  wd = NA,
  save = FALSE,
  verbose = TRUE
)

Arguments

Details

The implementation mirrors the original BioIndex routine.

Data Validation: The function performs syntactic data validation using internal implementations of the validation routines (based on RoME v0.2.3).

Optimisations: Includes two speed-ups: (1) vectorised replacement of missing '"NA NA"' records, (2) a single loop over depth strata instead of a nested haul × stratum loop. Results are identical to the reference routine.

Value

A 'data.frame' with one row per haul and the following groups of variables: * **TA** metadata (haul position, times, depths, etc.) * **TB** catch totals (numbers and weight) * Calculated fields: mean lat/lon, swept area, depth stratum, density ('N_h', 'N_km2') and biomass ('kg_h', 'kg_km2')

Note

This version of BioIndex uses internal validation functions based on RoME version 0.2.3. The package is fully self-sufficient and does not require external validation packages to be installed.

See Also

[BioIndex] package documentation.

Examples

# Use internal data
data("TA", package = "BioIndex")
data("TB", package = "BioIndex")
m_tb <- merge_TATB(
  ta       = TA,
  tb       = TB,
  species  = "ARISFOL",
  country  = "ITA",
  wd       = tempdir(),
  save     = FALSE
)
head(m_tb)

Merge TA–TB and TA–TC tables (MEDITS protocol)

Description

This function is the primary entry point for integrating MEDITS survey data (e.g. TA, TB, and TC tables), producing unified datasets essential for population analysis and the estimation of demographic indicators. The routine:

• filters hauls by validity and by country;

• performs a full set of quality checks (times, positions, wing opening, haul/date consistency) using internal validation functions (based on RoME version 0.2.3);

• merges TA with TB and TA with TC, respectively;

• computes swept area, mean positions, depth stratum, raising factors, densities and biomasses;

• (optionally) writes the two merged tables to file.path(wd, "output").

Usage

merge_TATBTC(
  ta,
  tb,
  tc,
  species,
  country = "all",
  strata = BioIndex::strata_scheme,
  wd = NA,
  save = TRUE,
  verbose = TRUE
)

Arguments

Details

The implementation reproduces the official BioIndex workflow.

Data Validation: The function automatically performs syntactic data validation using internal routines based on RoME version 0.2.3 logic to ensure data integrity and standardisation.

Optimisations: Two micro-optimisations are included:

1. vectorised replacement of missing "NA NA" entries in both merges;

2. a single loop over depth strata (instead of haul × stratum) to assign STRATUM_CODE.

Numerical outputs are identical to the reference routine.

Value

A list of two data.frames:

merge_TA_TB

One row per haul with TA metadata, TB catch totals, depth stratum, densities and biomasses.

merge_TA_TC

One row per haul/length-class/sex/maturity with TA metadata, TC counts, depth stratum, raising factor, densities and biomasses.

A list containing two data frames: the first with merged TA-TB-TC data at the haul level, and the second with the length-frequency distribution.

Note

This version of BioIndex uses internal validation functions based on RoME version 0.2.3. The package is fully self-sufficient and does not require external validation packages to be installed.

Author(s)

Walter Zupa zupa@fondazionecoispa.org

Examples

# Use internal data

data("TA", package = "BioIndex")
data("TB", package = "BioIndex")
data("TC", package = "BioIndex")
m <- merge_TATBTC(
  ta      = TA,
  tb      = TB,
  tc      = TC,
  species = "MERLMER",
  country = "all",
  wd      = tempdir(),
  verbose = TRUE
)
mTATB <- m[[1]]   # TA–TB merged table
mTATC <- m[[2]]   # TA–TC merged table

Merge TA and TC tables (haul-level length–frequency data)

Description

Links haul metadata (TA) with length-frequency samples (TC), enabling the analysis of population demographic structure and the reconstruction of length-frequency distributions (LFD). The function keeps only valid hauls, runs all relevant quality checks (times, positions, wing opening, haul/date consistency) using internal validation functions (based on RoME version 0.2.3), computes swept area, depth stratum, raising factors, abundance/biomass indicators, and—optionally—writes the merged data.

Usage

merge_TATC(
  ta,
  tc,
  species,
  country = "all",
  strata = BioIndex::strata_scheme,
  wd = NA,
  save = TRUE,
  verbose = TRUE
)

Arguments

Details

The code is identical to the official BioIndex routine.

Data Validation: The function performs syntactic data validation using internal implementations of the validation routines (based on RoME v0.2.3).

Optimisations: Includes two micro-optimisations: vectorised handling of missing '"NA NA"' entries and a single depth-stratum loop. Numerical results remain unchanged.

Value

A 'data.frame' in which each row represents one length-class (and sex/maturity) sampled in a haul, including: * **TA** metadata * **TC** length-frequency counts and weights * Calculated fields: mean lat/lon, swept area, depth stratum, raising factor, density ('N_h', 'N_km2') and biomass ('kg_h', 'kg_km2')

Note

This version of BioIndex uses internal validation functions based on RoME version 0.2.3. The package is fully self-sufficient and does not require external validation packages to be installed.

See Also

[BioIndex] package documentation.

Examples

# Use internal data
data("TA", package = "BioIndex")
data("TC", package = "BioIndex")
m_tc <- merge_TATC(
  ta       = TA,
  tc       = TC,
  species  = "ARISFOL",
  country  = "ESP",
  wd       = tempdir(),
  save=FALSE
)
head(m_tc)

Overlay mTATB and mTATC on GFCM spatial grid

Description

Overlays survey haul data onto a standard spatial grid (e.g., GFCM grid), harmonizing biological information with a consistent geographic structure for standardized spatial analysis.

Usage

overlayGrid(
  mTATB,
  mTATC,
  GSA = NA,
  country = "all",
  wd = NA,
  save = TRUE,
  verbose = FALSE
)

Arguments

Value

A list or data.frame containing the merged biological data mapped to the spatial grid.

Examples

# Use internal data
data(TA)
data(TB)
data(TC)
# Create a merged dataset for GSA 10
m <- merge_TATBTC(TA[TA$AREA==10,], TB[TB$AREA==10,], TC[TC$AREA==10,], species="MERLMER")
mTATB <- m[[1]]
mTATC <- m[[2]]
overlayGrid(mTATB, mTATC, GSA=10, save=FALSE)

Quantile estimation

Description

Quantile estimation

Usage

quant(weighted, qlin = 0.95)

Arguments

Value

A numeric value or vector containing the requested quantiles.

launches the embedded Shiny application included in the package.

Description

launches the embedded Shiny application included in the package.

Usage

run_BioIndex_app()

Value

No return value, called for side effects (launches the Shiny application).

Author(s)

Walter Zupa zupa@fondazionecoispa.org

Examples

if (interactive()) {
  run_BioIndex_app()
}

Sex ratio

Description

Assesses the proportion of sexes within the population, providing critical information on demographic structure, reproductive potential, and stock resilience.

Usage

sex_ratio(
  mTATB,
  GSA,
  country,
  depth_range,
  stratas,
  stratification,
  wd = NA,
  save = TRUE,
  verbose = FALSE
)

Arguments

Value

A data.frame containing the sex ratio statistics by year and stratum.

Author(s)

Walter Zupa zupa@fondazionecoispa.org

Examples

data(TA)
data(TB)
# Create a merged dataset for GSA 10
mTATB <- merge_TATB(TA[TA$AREA==10,], TB[TB$AREA==10,], "MERLMER")
sex_ratio(mTATB, GSA=10, country="all", depth_range=c(10,800),
          stratas=BioIndex::strata_scheme,
          stratification=BioIndex::stratification, wd=tempdir(), save=FALSE)

Plot sex ratio spatial distribution

Description

This function calculates and plots the spatial distribution of sex ratio (F / (F + M)) over the GFCM grid for a given species. It uses data from merged MEDITS datasets (TA, TB, TC), previously spatialized with 'overlayGrid()'. The function filters hauls by total abundance threshold and depth range, calculates sex ratio per grid cell, and plots the output using color-coded categories based on quantiles. The result is a choropleth map of sex ratio patterns, useful to highlight spatial differences in sex structure. Raw sex ratio estimates per grid cell can also be exported.

Usage

sex_ratio_on_grid(
  mTATBsp,
  depth,
  wd = NA,
  map_range,
  threshold = 30,
  verbose = FALSE,
  save = TRUE
)

Arguments

Value

A 'data.frame' containing sex ratio ('ratio'), standard deviation ('sd'), and coefficient of variation ('CV') for each GFCM grid cell.

Examples

data(TA)
data(TB)
data(TC)
m <- merge_TATBTC(TA[TA$AREA == 10, ], TB[TB$AREA == 10, ], TC[TC$AREA == 10, ],
                  species = "MERLMER", country = "all", verbose = FALSE)
mTATBsp <- overlayGrid(m[[1]], m[[2]], GSA = 10, save = FALSE, verbose = FALSE)[[1]]
map_range <- c(9, 15, 39, 42)
sex_ratio_on_grid(mTATBsp, depth = "10,800", map_range = map_range, threshold = 5,
                  save = FALSE, verbose = FALSE)

Spearman test for timeseries

Description

Spearman test for timeseries

Usage

spear(x)

Arguments

Value

A data.frame representing the Spearman correlation coefficient and related statistics.

Examples

x <- runif(10, 1, 100)
spear(x)

Spearman test

Description

Spearman test

Usage

spearman(
  abundance = NA,
  biomass = NA,
  years,
  sspp = NA,
  wd = NA,
  save = TRUE,
  verbose = FALSE
)

Arguments

Value

A data.frame or list with the results of the Spearman rank correlation test.

Examples

abundance <- data.frame(year = 2010:2020, index = runif(11, 1, 10))
biomass <- data.frame(year = 2010:2020, index = runif(11, 5, 20))
spearman(abundance=abundance, biomass=biomass, years=c(2010,2020), 
         sspp="MERLMER", wd=tempdir(), save=FALSE)

stratification scheme

Description

Stratification scheme adopted in the bottom trawl demersal survey (e.g. MEDITS survey).

Usage

strata_scheme

Format

An object of class data.frame with 126 rows and 5 columns.

Author(s)

Walter Zupa zupa@fondazionecoispa.org

stratification scheme (rapa whelk)

Description

Stratification scheme adopted in the rapa whelk survey (e.g. Black Sea beam trawl survey).

Usage

strata_scheme_rapana

Format

An object of class data.frame with 7 rows and 5 columns.

Author(s)

Walter Zupa zupa@fondazionecoispa.org

stratification

Description

Data frame containing the surface area relative to the depth strata adopted in the stratification scheme (strata_scheme) of the demersal surveys (e.g. MEDITS survey).

Usage

stratification

Format

An object of class data.frame with 276 rows and 6 columns.

stratification (rapa whelk)

Description

Data frame containing the surface area relative to the depth strata adopted in the stratification scheme (strata_scheme) of the rapa whelk surveys (e.g. Black Sea beam trawl survey).

Usage

stratification_rapana

Format

An object of class data.frame with 7 rows and 6 columns.

stratum_0_125

Description

stratum_0_125

Usage

stratum_0_125

Format

An object of class PackedSpatVector of length 1.

stratum_0_200

Description

stratum_0_200

Usage

stratum_0_200

Format

An object of class PackedSpatVector of length 1.

stratum_0_35

Description

stratum_0_35

Usage

stratum_0_35

Format

An object of class PackedSpatVector of length 1.

stratum_0_45

Description

stratum_0_45

Usage

stratum_0_45

Format

An object of class PackedSpatVector of length 1.

stratum_0_800

Description

stratum_0_800

Usage

stratum_0_800

Format

An object of class PackedSpatVector of length 1.

stratum_200_800

Description

stratum_200_800

Usage

stratum_200_800

Format

An object of class PackedSpatVector of length 1.