Class 'Data' — Data-class • MSEtool Functions

An object for storing fishery data for analysis

Slots

Name: The name of the Data object. Single value. Character string
Common_Name: Common name of the species. Character string
Species: Scientific name of the species. Genus and species name. Character string
Region: Name of the general geographic region of the fishery. Character string
LHYear: The last historical year of the simulation (before projection). Single value. Positive integer
MPrec: The previous recommendation of a management procedure. Vector of length nsim. Positive real numbers
Units: Units of the catch/absolute abundance estimates. Single value. Character string
MPeff: The current level of effort. Vector of length nsim. Positive real numbers
nareas: Number of fishing areas. Vector of length nsim. Non-negative integer
MaxAge: Maximum age. Vector nsim long. Positive integer
Mort: Natural mortality rate. Vector nsim long. Positive real numbers
CV_Mort: Coefficient of variation in natural mortality rate. Vector nsim long. Positive real numbers
vbLinf: Maximum length. Vector nsim long. Positive real numbers
CV_vbLinf: Coefficient of variation in maximum length. Vector nsim long. Positive real numbers
vbK: The von Bertalanffy growth coefficient K. Vector nsim long. Positive real numbers
CV_vbK: Coefficient of variation in the von Bertalanffy K parameter. Vector nsim long. Positive real numbers
vbt0: Theoretical age at length zero. Vector nsim long. Non-positive real numbers
CV_vbt0: Coefficient of variation in age at length zero. Vector nsim long. Positive real numbers
wla: Weight-Length parameter alpha. Vector nsim long. Positive real numbers
CV_wla: Coefficient of variation in weight-length parameter a. Vector nsim long. Positive real numbers
wlb: Weight-Length parameter beta. Vector nsim long. Positive real numbers
CV_wlb: Coefficient of variation in weight-length parameter b. Vector nsim long. Positive real numbers
steep: Steepness of stock-recruitment relationship. Vector nsim long. Value in the range of one-fifth to 1
CV_steep: Coefficient of variation in steepness. Vector nsim long. Positive real numbers
sigmaR: Recruitment variability. Vector nsim long. Positive real numbers
CV_sigmaR: Coefficient of variation in recruitment variability. Vector nsim long. Positive real numbers
L50: Length at 50 percent maturity. Vector nsim long. Positive real numbers
CV_L50: Coefficient of variation in length at 50 per cent maturity. Vector nsim long. Positive real numbers
L95: Length at 95 percent maturity. Vector nsim long. Positive real numbers
LenCV: Coefficient of variation of length-at-age (assumed constant for all age classes). Vector nsim long. Positive real numbers
LFC: Length at first capture. Vector nsim long. Positive real numbers
CV_LFC: Coefficient of variation in length at first capture. Vector nsim long. Positive real numbers
LFS: Shortest length at full selection. Vector nsim long. Positive real numbers
CV_LFS: Coefficient of variation in length at full selection. Vector nsim long. Positive real numbers
Vmaxlen: Vulnerability of individuals at asymptotic length. Vector nsim long. Real number between 0 and 1.
Year: Years that corresponding to catch and relative abundance data. Vector nyears long. Positive integer
Cat: Total annual catches. Matrix of nsim rows and nyears columns. Non-negative real numbers
CV_Cat: Coefficient of variation in annual catches. Matrix nsim rows and either 1 or nyear columns. Positive real numbers. Note: built-in MPs use only the first value of CV_Cat for all years.
Effort: Annual fishing effort. Matrix of nsim rows and nyears columns. Non-negative real numbers
CV_Effort: Coefficient of variation in annual effort. Matrix nsim rows and either 1 or nyear columns. Positive real numbers. Note: built-in MPs use only the first value of CV_Effort for all years.
Ind: Relative total abundance index. Matrix of nsim rows and nyears columns. Non-negative real numbers
CV_Ind: Coefficient of variation in the relative total abundance index. Matrix nsim rows and either 1 or nyear columns. Positive real numbers. Note: built-in MPs use only the first value of CV_Ind for all years
SpInd: Relative spawning abundance index. Matrix of nsim rows and nyears columns. Non-negative real numbers
CV_SpInd: Coefficient of variation in the relative spawning abundance index. Matrix nsim rows and either 1 or nyear columns. Positive real numbers.
VInd: Relative vulnerable abundance index. Matrix of nsim rows and nyears columns. Non-negative real numbers
CV_VInd: Coefficient of variation in the relative vulnerable abundance index. Matrix nsim rows and either 1 or nyear columns. Positive real numbers.
AddInd: Optional additional indices. Array of dimensions nsim, n additional indices, and nyears (length Year).
CV_AddInd: Coefficient of variation for additional indices. Array of same dimensions as AddInd
AddIndV: Vulnerability-at-age schedules for the additional indices. Array with dimensions: nsim, n additional indices, MaxAge+1.
AddIunits: Units for the additional indices - biomass (1; default) or numbers (0). Numeric vector length n.ind.
AddIndType: Index calculated from total stock (1, default), spawning stock (2), or vulnerable stock (3). Numeric vector of length n.ind
Rec: Recent recruitment strength. Matrix of nsim rows and nyears columns. Non-negative real numbers
CV_Rec: Log-normal CV for recent recruitment strength. Matrix nsim rows and either 1 or nyear columns. Positive real numbers. Note: built-in MPs use only the first value of CV_Rec for all years.
ML: Mean length time series. Matrix of nsim rows and nyears columns. Non-negative real numbers
Lc: Modal length of catches. Matrix of nsim rows and nyears columns. Positive real numbers
Lbar: Mean length of catches over Lc. Matrix of nsim rows and nyears columns. Positive real numbers
Vuln_CAA: Optional vulnerability-at-age schedule for catch-at-age samples. Used to condition OM for closed-loop simulation testing. Replaces the fleet selectivity schedule in the OM used to generate CAA samples. Matrix with dimensions nsim x MaxAge+1.
CAA: Catch at Age data (numbers). Array of dimensions nsim x nyears x MaxAge+1. Non-negative integers
Vuln_CAL: Optional vulnerability-at-length schedule for catch-at-length samples. Used to condition OM for closed-loop simulation testing. Replaces the fleet selectivity schedule in the OM used to generate CAL samples. Matrix with dimensions nsim x length(CAL_mids).
CAL_bins: The values delimiting the length bins for the catch-at-length data. Vector. Non-negative real numbers
CAL_mids: The values of the mid-points of the length bins. Optional, calculated from CAL_bins if not entered. Vector. Non-negative real numbers.
CAL: Catch-at-length data. An array with dimensions nsim x nyears x length(CAL_mids). Non-negative integers. By default the CAL data will be the retained lengths (i.e, not including discards). If OM@control$CAL =="removals" then the CAL data will include all removals (retained + discards).
Dep: Stock depletion SSB(current)/SSB(unfished). Vector nsim long. Fraction.
CV_Dep: Coefficient of variation in current stock depletion. Vector nsim long. Positive real numbers
Abun: An estimate of absolute current vulnerable abundance. Vector nsim long. Positive real numbers
CV_Abun: Coefficient of variation in estimate of absolute current stock size. Vector nsim long. Positive real numbers
SpAbun: An estimate of absolute current spawning stock abundance. Vector nsim long. Positive real numbers
CV_SpAbun: Coefficient of variation in estimate of absolute spawning current stock size. Vector nsim long. Positive real numbers
FMSY_M: An assumed ratio of FMSY to M. Vector nsim long. Positive real numbers
CV_FMSY_M: Coefficient of variation in the ratio in FMSY/M. Vector nsim long. Positive real numbers
BMSY_B0: The most productive stock size relative to unfished. Vector nsim long. Fraction
CV_BMSY_B0: Coefficient of variation in the position of the most productive stock size relative to unfished. Vector nsim long. Positive real numbers
Cref: Reference or target catch level (eg MSY). Vector of length nsim. Positive real numbers
CV_Cref: Log-normal CV for reference or target catch level. Vector of length nsim. Positive real numbers
Bref: Reference or target biomass level (eg BMSY). Vector of length nsim. Positive real numbers
CV_Bref: Log-normal CV for reference or target biomass level. Vector of length nsim. Positive real numbers
Iref: Reference or target relative abundance index level (eg BMSY / B0). Vector of length nsim. Positive real numbers
CV_Iref: Log-normalCV for reference or target relative abundance index level. Vector of length nsim. Positive real numbers
t: The number of years corresponding to AvC and Dt. Single value. Positive integer
AvC: Average catch over time t. Vector nsim long. Positive real numbers
CV_AvC: Coefficient of variation in average catches over time t. Vector nsim long. Positive real numbers
Dt: Depletion over time t SSB(now)/SSB(now-t+1). Vector nsim long. Fraction
CV_Dt: Coefficient of variation in depletion over time t. Vector nsim long. Positive real numbers
Ref: A reference management level (eg a catch limit). Single value. Positive real number
Ref_type: Type of reference management level (eg 2009 catch limit). Single value. Character string
Log: A record of events. Single value. Character string
params: A place to store estimated parameters. An object. R list
PosMPs: The methods that can be applied to these data. Vector. Character strings
TAC: The calculated catch limits (function TAC). An array with dimensions PosMPs x replicate TAC samples x nsim. Positive real numbers
Sense: The results of the sensitivity analysis (function Sense). An array with dimensions PosMPs x sensitivity increments. Positive real numbers
MPs: The methods that were applied to these data. Vector. Character strings
OM: A table of operating model conditions. R table object of nsim rows. Real numbers
Obs: A table of observation model conditions. R table object of nsim rows. Real numbers
Misc: Other information for MPs. An object. R list

Objects from the Class

Objects can be created by calls of the form new('Data', stock)

Author

T. Carruthers and A. Hordyk

Examples


newdata<-new('Data')