calc.CI: Calculating confident interval for MSY in Schaefer's...

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

Calculating confident interval for MSY in Schaefer's management parameter

Description

This function calculates the confident interval for MSY in Schaefer's management parameters using likelihood profile. Observation error is used to increase the accuracy of data fitting. It is assumed to occur in the relationship between stock biomass and index of abundance and is estimated assuming lognormal distribution in maximum likelihood (Polacheck et al., 1993).

Since fishing effort data collection are not always conducted regularly while catch is likely have a better time series information, this function also allow for some lose of catch and effort data.

This function also consider the different quality data, for instance if the data shows a one way trip pattern which losing rate of catch increase.

Usage

calc.CI(
  MSYval,
  rval,
  df,
  OWT = FALSE,
  currentF = 0.7,
  weight = 0.5,
  plot = FALSE
)

Arguments

MSYval	management parameter representing Maximum Sustainable Yield (MSY)
rval	parameter which uses r (intrinsic growth)
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.
weight	weight given to the deviation between observed and predicted value in either biomass or exploitation rate.
plot	option to show the plot as result of likelihood profile estimation

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.

Input are kept at initial value without using log() like the other minimization inputs. If the fitted parameters resulting in minus value, use the constrained variables and "L-BFGS-B" optimization method, and produce the standard error from hessian using steps in https://stackoverflow.com/questions/27202395/how-do-i-get-standard-errors-of-maximum-likelihood-estimates-in-stan

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.

Punt, A. E., & Hilborn, R. 1996. Biomass dynamic models. FAO Computerized Information Series Fisheries, 10, 1-62.

Examples

calc.CI(MSYval= 50, rval= 0.2, df=df.goodcontrast, plot=TRUE)

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