Par.min: Function used in the minimizing process to calculate...

In habeebollah/montiR: Biomass dynamic models for fisheries using time series data fitting methods

Function used in the minimizing process to calculate Schaefer's management parameters

Description

This function calculates Schaefer's management parameters using maximum likelihood minimization.

Usage

Par.min(inpars, df, OWT = FALSE, currentF = 0.7, weight = 1000)

Arguments

inpars	surplus production parameters which consist of K (carrying capacity), B0 (biomass when fishing is started), r (intrinsic growth rate), q (catchability coefficient), s.sigma (observation error)
df	dataframe containing three columns; year, catch and unit of effort. A fourth column with biomass should be added if OWT (One Way Trip) option uses "Biomass"
OWT	is CPUE plot showing One Way Trip pattern? The default is FALSE, but should be replaced with either "Biomass" or "Depletion" when the plot shows One Way Trip type of data
currentF	Current exploitation rate collected from other survey. Only being used when OWT="Depletion" is chosen
weight	weight given to the deviation between observed and predicted value in either biomass or exploitation rate.

Value

A penalized likelihood is used to fix the lack of contrast in One Way Trip type of data using Depletion or Biomass data.

The Biomass option in OWT is used when biomass time series data from acoustic or trawl survey is available and should be added as the fourth columns in the input dataframe. The default weight when Biomass level is set at 0.9 with range between 0-1 (lower accuracy with high variance as closer to 0, constrain the estimation procedure to fit the auxiliary information as closer to 1)

The Depletion option in OWT uses current harvest rate from survey or expert knowledge as penalty. Depletion range is between 0 to 1, where higher number represent higher depletion level. The default is 0.7 to say that the depletion is high and many fish were caught. The default weight for harvest rate is 1000 and can be adjusted so the predicted harvest rate reach a closest value to the current exploitation rate. Predicted harvest rate value in each optimization step will show up when optimization process is being executed.

References

Hilborn, Ray, and Carl J. Walters, eds. Quantitative fisheries stock assessment: choice, dynamics and uncertainty. Springer Science & Business Media, 1992.

Polacheck, T., Hilborn, R., and A.E. Punt. 1993. Fitting surplus production models: Comparing methods and measuring uncertainty. Canadian Journal of Fisheries and Aquatic Sciences, 50: 2597-2607.

habeebollah/montiR documentation built on Dec. 11, 2022, 7:55 p.m.