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TropFishR

Package description

TropFishR is a collection of fisheries models based on the FAO Manual "Introduction to tropical fish stock assessment" by Sparre and Venema (1998, 1999). Not only scientists working in the tropics will benefit from this new toolbox. The methods work with age-based or length-frequency data and assist in the assessment of data poor fish stocks. Overall, the package comes with 30 functions, 19 data sets and 10 s3 methods. All objects are documented and provide examples that allow reproducing the examples from the FAO manual.

News

You can find detailed descriptions of new features, bug fixes, other changes of specific package versions here.

Installation

Download the released version of TropFishR from CRAN:

install.packages(“TropFishR”)

Or the development version from GitHub:

# install.packages(devtools)
devtools::install_github(“tokami/TropFishR”)

Citation

Please use the R command citation("TropFishR") to receive information on how to cite this package.

Documentation

The tutorial demonstrates the use of the main functions of TropFishR for a single-species stock assessment with length-frequency data. The lfqDataTutorial gives a brief description of LFQ data and illustrates how files with raw length measurements (e.g. excel files) can be imported into R and trimmed for the use with TropFishR. The ELEFANTutorial demonstrates the ELEFAN functions available in TropFishR in detail and discusses best practices.

Questions / Issues

In case you have questions or find bugs, please write an email to Tobias Mildenberger or post on TropFishR/issues. If you want to be updated with the development of the package or want to discuss with TropFishR users and developers, follow the project on ResearchGate.

References

  1. Sparre, P., Venema, S.C., 1998. Introduction to tropical fish stock assessment. Part 1. Manual. FAO Fisheries Technical Paper, (306.1, Rev. 2). 407p. link
  2. Sparre, P., Venema, S.C., 1999. Introduction to tropical fish stock assessment. Part 2. Excercises. FAO Fisheries Technical Paper, (306.2, Rev. 2). 94p. link
  3. Mildenberger, T. K., Taylor, M. H. and Wolff, M., 2017. TropFishR: an R package for fisheries analysis with length-frequency data. Methods in Ecology and Evolution, 8: 1520-1527. doi:10.1111/2041-210X.12791 link
  4. Taylor, M. H., and Mildenberger, T. K., 2017. Extending electronic length frequency analysis in R. Fisheries Management and Ecology, 24:330-338. doi:10.1111/fme.12232 link

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Version

Install

install.packages('TropFishR')

Monthly Downloads

950

Version

1.6.1

License

GPL-3

Issues

Pull Requests

Stars

Forks

Last Published

January 17th, 2019

Functions in TropFishR (1.6.1)

synCAA1

Synthetic Catch-at-age data I
plot.VPA

VPA plot
plot.recruitment

Plot of recruitment patterns
plot.catchCurve

Plotting catch curve
plot.select

Selectivity plot
ELEFAN_SA

ELEFAN_SA
synCAA2

Synthetic Catch-at-age data II
trammelnet

Trammel net data
growth_tagging

Growth from tagging data
trawl_fishery_Java

Data from the trawl fishery off the North coast of Java
Z_CPUE

Estimate Z from CPUE data
ELEFAN

ELEFAN
Bhattacharya

Bhattacharya's method
goatfish

Yellowstriped goatfish data
startingPoint2tanchor

Convert FiSAT's starting point to t_anchor value
alba

Length-frequency data of the clam Abra alba
gillnetfit

Millar's original gillnet selectivity fitting function
lfqFitCurves

Fitting VBGF growth curves through lfq data
lfqModify

Modify lfq data for further analysis
gillnet

Gillnet data
predict_mod

Prediction models
haddock

Haddock data
hake

Hake data
growth_length_age

Estimation of growth parameter using length-at-age data
plot.lfq

Plotting of length frequency data (with VBGF curves)
VPA

Virtual Population Analysis (VPA)
stock_sim

Stock simulation
Z_BevertonHolt

Beverton & Holt's Z-Equations
plot.predict_mod

Plotting prediction models yield per recruit and Thompson & Bell
prod_mod

Production models
plot.prod_mod_ts

Plotting time series production models
plot.prod_mod

Plotting production models
synLFQ8

Synthetic length-frequency data VIII with variable harvest rate
synLFQ6

Synthetic length-frequency data VI (without seasonal oscillation)
bream

bream data
select

Selectivity model
select_Millar

Millar's selectivity model
synLFQ7

Synthetic length-frequency data VII with seasonal oscillation
lfqCreate

Create lfq data from length measurements
catchCurve

Catch curve
synCPUE

Synthetical catch per unit of effort (CPUE) dataset
tilapia

Tilapia data
rcurves_Millar

Predict gillnet selectivity (old Millar method)
prod_mod_ts

Production models with time series fitting
recruitment

Recruitment patterns
rtypes_Millar

Millar's selectivity types
synLFQ2

Synthetic length frequency data II
synLFQ1

Synthetic length-frequency data I
plot.select_Millar

Millar's selectivity plot
whiting

Whiting data
powell_wetherall

Powell-Wetherall method
yeardec2date

Year - Date conversion
select_ogive

Selectivity patterns
shrimps

Shrimp data
synLFQ4

Synthetic length-frequency data IV (with seasonal oscillation)
synLFQ3

Synthetic length frequency data III
synLFQ5

Synthetic length-frequency data V (without seasonal oscillation)
ypr

Yield per recruit
ypr_sel

Yield per recruit with selection ogive
M_empirical

Empirical formulas for the estimation of natural mortality
VBGF

Von Bertalanffy Growth function (VBGF)
lfqRestructure

Restructuring of length frequency data
plot.Bhattacharya

Bhattacharya plot
ELEFAN_GA

ELEFAN_GA
date2yeardec

Date - Year conversion
emperor

Emperor data