Simple R Package for Fish Stock Assessment

Introduction

The aLBI package provides tools for estimating length-based indicators and assessing fish stock using methods outlined by Cope and Punt (2009) and Froese (2004). These methods are particularly useful in data-limited situations and for providing simple indicators to address over-fishing. This simple package facilitates the estimation of life history parameters of fish only from the lenght frequency data.

Function Overview

The aLBI package offers three primary functions: - FrequencyTable: Creates frequency tables from length data. - FishPar: Estimates biological parameters using bootstrapping. - FishSS: Evaluates stock status based on estimated parameters.

Methods

The package includes functions to calculate various length-based indicators and visualize fish stock data. The approaches from Cope and Punt (2009) are used to establish reference points, while Froese (2004)’s indicators help to evaluate overfishing status.

Installation

To install the aLBI package from GitHub, follow these steps. Note that devtools is required to install packages from GitHub.

# You can install the package using the following commands in your R session:
# Install devtools if you haven't already
# install.packages("devtools")
# # Install dplyr if you haven't already
# install.packages("dplyr")
# # Install readxl if you haven't already
# install.packages("readxl")
# install aLBI package from CRAN
# install.packages("aLBI")
# Install the most updated version of aLBI package from GitHub
# devtools::install_github("Ataher76/aLBI")

Package Management

Ensure the required packages are loaded in your R session. The following code checks for package availability and stops execution with a message if a package is missing.

# Check if required packages are installed and load them
# Check if required packages are installed and load them
required_packages <- c("aLBI", "readxl", "dplyr", "devtools")

for (pkg in required_packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    warning(paste("Package", pkg, "is required but not installed."))
  } else {
    library(pkg, character.only = TRUE)
  }
}
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
#> Loading required package: usethis



# Load the aLBI package
library(aLBI)
library(readxl)
library(dplyr)
library(devtools)

Data Preparation for Functions

Prepare your collected data in a specific format (.xlsx or .csv) before using the functions. Here’s an example of how to load and prepare your data using the readxl package:

Function (i): FrequencyTable

This function will calculate the frequency table from the collected and also the extract the length frequency data from the frequency table with the upper length_range.

Arguments

data: A numeric vector or data frame of fish lengths. If a data frame is used, the first numeric column is selected.
bin_width: Optional. Numeric value for bin width; calculated automatically if left NULL. If not provided, the bin width is automatically calculated using Sturges’ (1926) formula.

Example with provided data

library(readxl)
# Load your length data from the system file
lenfreq_path <- system.file("exdata", "ExData.xlsx", package = "aLBI")
print(lenfreq_path)  # Check the generated path
#> [1] "C:/Users/User/AppData/Local/Temp/RtmpwlU8OG/Rinst361832cb6c1a/aLBI/exdata/ExData.xlsx"


if (lenfreq_path == "") {
  stop("The required file ExData.xlsx is missing. Please check the inst/extdata directory.")
}

# load the lenght frequency data
lenght_data <- readxl::read_excel(lenfreq_path)
print(lenght_data)  # check the 
#> # A tibble: 1,177 × 1
#>    Length
#>     <dbl>
#>  1   75  
#>  2   66.7
#>  3   66  
#>  4   64  
#>  5   63.3
#>  6   63.2
#>  7   62.3
#>  8   62.2
#>  9   62  
#> 10   61.3
#> # ℹ 1,167 more rows

# replace with your data directory

# Running the FrequencyTable function
freqTable <- FrequencyTable(data = lenght_data, bin_width = NULL)
#> Calculated ideal bin width: 6


# Viewing the results
freqTable$lfqTable  # Display the frequency table
#> # A tibble: 11 × 2
#>    Length_Range Frequency
#>    <fct>            <dbl>
#>  1 [9,15)              78
#>  2 [15,21)            272
#>  3 [21,27)            291
#>  4 [27,33)            223
#>  5 [33,39)            155
#>  6 [39,45)             75
#>  7 [45,51)             45
#>  8 [51,57)             18
#>  9 [57,63)             14
#> 10 [63,69)              5
#> 11 [75,81]              1

freqTable$lfreq     # Display the summarized frequencies with upper length ranges
#> # A tibble: 11 × 2
#>    Length Frequency
#>     <dbl>     <dbl>
#>  1     15        78
#>  2     21       272
#>  3     27       291
#>  4     33       223
#>  5     39       155
#>  6     45        75
#>  7     51        45
#>  8     57        18
#>  9     63        14
#> 10     69         5
#> 11     81         1

Function (ii): FishPar

The FishPar function estimates biological parameters such as Lmax, Linf, Lmat, and Lopt using bootstrapping resampling method.

Arguments

data: A data frame containing Length and Frequency columns.
resample: Number of bootstrap resamples. Default is 1000.
progress: Logical value indicating whether to show progress. Default is FALSE.

Example with existing data

library(readxl)
# Load your length-frequency data from the system file
lenfreq_path <- system.file("exdata", "LC.xlsx", package = "aLBI")
print(lenfreq_path)  # Check the generated path
#> [1] "C:/Users/User/AppData/Local/Temp/RtmpwlU8OG/Rinst361832cb6c1a/aLBI/exdata/LC.xlsx"


if (lenfreq_path == "") {
  stop("The required file LC.xlsx is missing. Please check the inst/extdata directory.")
}

# load the lenght frequency data
lenfreq_data <- readxl::read_excel(lenfreq_path)
print(lenfreq_data)  # check the data 
#> # A tibble: 15 × 2
#>    LengthClass Frequency
#>          <dbl>     <dbl>
#>  1          12        26
#>  2          15       166
#>  3          18       244
#>  4          21       582
#>  5          24       973
#>  6          27      1067
#>  7          30       963
#>  8          33       511
#>  9          36       472
#> 10          39       286
#> 11          42       173
#> 12          45       171
#> 13          48        83
#> 14          51        36
#> 15          54        36

# replace with your data directory

# Running the FishPar function
results <- FishPar(data = lenfreq_data, resample = 1000, progress = FALSE)


# Viewing the results
results$estimated_length_par
#>   Parameter Mean_estimate Lower_CI Upper_CI
#> 1      Lmax      52.38300 45.00000 54.00000
#> 2      Linf      55.14000 47.36842 56.84211
#> 3      Lmat      30.57859 26.68219 31.42811
#> 4      Lopt      32.18600 27.88119 33.12655
#> 5  Lopt_p10      35.40460 30.66930 36.43920
#> 6  Lopt_m10      28.96740 25.09307 29.81389

results$estimated_froese_par
#>   Parameter Estimate
#> 1      Pmat 29.91881
#> 2      Popt 25.46208
#> 3     Pmega 21.09173

results$estimated_freq_par
#>   Parameter Estimate
#> 1      sumT     5789
#> 2   sum_mat     1732
#> 3   sum_opt     1474
#> 4  sum_mega     1221

results$forese_ind_vs_target
#>   Parameters Froese_catch Froese_tar
#> 1       Pmat     29.91881        100
#> 2       Popt     25.46208        100
#> 3      Pmega     21.09173         20

results$LM_ratio
#> [1] 0.9500587

results$Pobj
#> [1] 76.47262

Explaination of Output

The function returns a list with the following components:

estimated_length_par: Data frame of estimated length parameters with confidence intervals. estimated_froese_par: Data frame of estimated Froese indicators. estimated_freq_par: Data frame of frequency parameters. forese_ind_vs_target: Data frame comparing Froese indicators with targets. LM_ratio: Length at maturity ratio. Pobj: Objective percentage combining Pmat, Popt, and Pmega.

Function (iii): FishSS

The FishSS function evaluates stock status using criteria based on the estimated parameters.

Arguments

data: A data frame of the stock status according to Cope and Punt (2009).
LM_ratio: Length at maturity ratio from FishPar function.
Pobj: Objective percentage from FishPar function.
Pmat: Percentage of mature fish.
Popt: Percentage of optimal fish.

Example with CPdata

# Load the stock status criteria data
cpdata_path <- system.file("exdata", "cpdata.xlsx", package = "aLBI")
print(cpdata_path) #check if the path exist
#> [1] "C:/Users/User/AppData/Local/Temp/RtmpwlU8OG/Rinst361832cb6c1a/aLBI/exdata/cpdata.xlsx"


if (cpdata_path == "") {
  stop("The required file cpdata.xlsx is missing. Please check the inst/extdata directory.")
}
# loading the cope and punt table
cpdata <- readxl::read_excel(cpdata_path)
print(cpdata)
#> # A tibble: 21 × 11
#>       Tx     A     B     C     D     E     F     G     H     I     J
#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1   100     0     0     0     0     0     0     0     0   100   100
#>  2    95     0     0     0     0    22     0    11     0   100    93
#>  3    90     0     0     0     0   100    44    83    22   100    74
#>  4    85     0     0     0     0   100   100   100    67   100    63
#>  5    80     0     0     0     0   100   100   100   100    89    52
#>  6    75     0     0     0     0   100   100   100   100    74    37
#>  7    70     0     0     0     0   100   100   100   100    48    30
#>  8    65     0     0     0     0   100   100   100   100    33    22
#>  9    60     0     0     0     0   100   100   100   100    22    15
#> 10    55     0     0     0     0   100   100   100   100    11     7
#> # ℹ 11 more rows


# Running the FishSS function
stock_status <- FishSS(data = cpdata,
                       LM_ratio = results$LM_ratio,
                       Pobj = results$Pobj,
                       Pmat = results$estimated_froese_par[1, 2],
                       Popt = results$estimated_froese_par[2, 2])

# Viewing the stock status
stock_status
#> $Target_Cols
#>     Tx  B  D
#> 1  100  0  0
#> 2   95  0  0
#> 3   90  0  0
#> 4   85  0  0
#> 5   80  0  0
#> 6   75  0  0
#> 7   70  0  0
#> 8   65  0  0
#> 9   60  0  0
#> 10  55  0  0
#> 11  50  0  0
#> 12  45  0  0
#> 13  40  0  0
#> 14  35  0  0
#> 15  30  7  7
#> 16  25 19 11
#> 17  20 37 22
#> 18  15 52 44
#> 19  10 67 67
#> 20   5 67 67
#> 21   0 67 67
#> 
#> $Target_value
#> [1] 29.91881
#> 
#> $Colesest_value
#> [1] 15
#> 
#> $StockStatus
#> TSB40 LSB25 
#>     7     7

Output

The function returns a named vector with TSB40 and LSB25 values indicating stock status.

Conclusion

The Fish Stock Assessment package provides a robust framework for estimating biological parameters and assessing fish stock status. By following the steps outlined in this vignette, you can effectively utilize this package for your fish stock assessment needs.

Additional Information

Contact

For any questions or issues, please contact the package maintainer: Name: Ataher Ali Email: ataher.cu.ms@gmail.com

Acknowledgements

I would like to express my heartfelt gratitude to my supervisor, Dr. Mohammed Shahidul Alam, for his unwavering guidance, support, and encouragement. I am also sincerely thankful to the contributors and community members for their valuable feedback and support.

This vignette provides a comprehensive guide to using the Assessment package. By following these instructions, users can effectively conduct fish stock assessments and contribute to sustainable fishery management practices.

References

Sturges, H. A. (1926). The Choice of a Class Interval. Journal of the American Statistical Association, 21(153), 65–66. https://doi.org/10.1080/01621459.1926.10502161
Cope, J. M., & Punt, A. E. (2009). Length‐Based Reference Points for Data‐Limited Situations: Applications and Restrictions. Marine and Coastal Fisheries, 1(1), 169–186. https://doi.org/10.1577/C08-025.1
Froese, R. (2004). Keep it simple: Three indicators to deal with overfishing. Fish and Fisheries, 5(1), 86–91. https://doi.org/10.1111/j.1467-2979.2004.00144.x

aLBI - A Simple R Package for Estimating Length-Based Indicators and Fish Stock Assessment from Length Frequency Data

Ataher Ali

Simple R Package for Fish Stock Assessment

Introduction

Function Overview

Methods

Installation

Package Management

Data Preparation for Functions

Function (i): FrequencyTable

Arguments

Example with provided data

Function (ii): FishPar

Arguments

Example with existing data

Explaination of Output

Function (iii): FishSS

Arguments

Example with CPdata

Output

Conclusion

Additional Information

Contact

Acknowledgements

References