Goosefish: Northern Management Region (for ML)

Description

Mean lengths and life history for goosefish.

Usage

Goosefish

Format

An object of class MLZ_data.

References

Gedamke, T. and Hoenig, J.M. 2006. Estimating mortality from mean length data in nonequilibrium situations, with application to the assessment of goosefish. Transactions of the American Fisheries Society 135:476-487.

Examples

data(Goosefish); Goosefish

Mean length-based mortality estimator

Description

Estimator of instantaneous total mortality (Z) from a time series of mean length data.

Usage

ML(MLZ_data, ncp, start = NULL, grid.search = TRUE,
  parallel = ifelse(ncp > 2, TRUE, FALSE), min.time = 3, Z.max = 5,
  figure = TRUE)

Arguments

Details

For a model with I change points, the starting values in start is a list with the following entries: Z a vector of length = I+1. yearZ a vector of length = I.

start can be NULL, in which case, the supplied starting values depend on the value of grid.search. If grid.search = TRUE, starting values will use the values for yearZ which minimize the negative log-likelihood from the grid search. Otherwise, the starting values for yearZ evenly divide the time series.

Value

An object of class MLZ_model.

References

Gedamke, T. and Hoenig, J.M. 2006. Estimating mortality from mean length data in nonequilibrium situations, with application to the assessment of goosefish. Transactions of the American Fisheries Society 135:476-487.

See Also

profile_ML

Examples

## Not run: 
data(Goosefish)
res <- ML(Goosefish, ncp = 2)
res <- ML(Goosefish, ncp = 2, start = list(Z = c(0.1, 0.3, 0.5), yearZ = c(1978, 1988)))
res <- ML(Goosefish, ncp = 2, grid.search = TRUE)

## End(Not run)

Mean length with catch rate mortality estimator

Description

Estimator of instantaneous total mortality (Z) from a time series of mean length data.

Usage

MLCR(MLZ_data, ncp, CPUE.type = c(NA, "WPUE", "NPUE"),
  loglikeCPUE = c("lognormal", "normal"), start = NULL,
  grid.search = TRUE, parallel = ifelse(ncp > 2, TRUE, FALSE),
  min.time = 3, Z.max = 5, figure = TRUE)

Arguments

Details

For a model with I change points, the starting values in start is a list with the following entries: Z a vector of length = I+1. yearZ a vector of length = I.

start can be NULL, in which case, the supplied starting values depend on the value of grid.search. If grid.search = TRUE, starting values will use the values for yearZ which minimize the negative log-likelihood from the grid search. Otherwise, the starting values for yearZ evenly divide the time series.

Value

An object of class MLZ_model.

References

Huynh, Q.C., Gedamke, T., Porch, C.E., Hoenig, J.M., Walter, J.F, Bryan, M., and Brodziak, J. In revision. Estimating Total Mortality Rates from Mean Lengths and Catch Rates in Non-equilibrium Situations. Transactions of the American Fisheries Society.

See Also

profile_MLCR

Examples

## Not run: 
data(MuttonSnapper)
MLCR(MuttonSnapper, ncp = 2, CPUE.type = "WPUE", grid.search = TRUE)

## End(Not run)

MLZ_data

Description

An S4 class for storing data and life history parameters for a single stock. Method functions summary and plot are available for this class (see examples).

Slots

Stock

Name of stock.

Year

A vector of years to be considered in the model. Missing years are permitted.

Len_bins

A vector of midpoints of length bins for Len_matrix.

Len_matrix

A matrix of size data. The i-th row corresponds to the i-th year in MLZ_data@Year. The j-th column indexes the j-th length in MLZ_data@Len_bins.

Len_df

A data frame containing individual length observations. The first column should be the Year and the second column should be the length.

vbLinf

L-infinity from the von Bertalanffy growth function.

vbK

Parameter K from the von Bertalanffy growth function.

vbt0

Parameter t0 from the von Bertalanffy growth function.

Lc

Length of full selectivity.

M

Natural mortality rate. If specified, this is also the lower limit for Z.

lwb

Exponent b from the allometric length-weight function W = aL^b.

MeanLength

Vector of mean lengths of animals larger than Lc. The i-th entry corresponds to the i-th year in MLZ_data@Year.

ss

Vector of annual sample sizes for MeanLength. The i-th entry corresponds to the i-th year in MLZ_data@Year.

CPUE

Vector of catch-per-unit-effort data. The i-th entry corresponds to the i-th year in MLZ_data@Year.

Effort

Vector of effort data. The i-th entry corresponds to the i-th year in MLZ_data@Year.

length.units

Unit of measurement for lengths, i.e. "cm" or "mm".

Examples

data(Goosefish); Goosefish
summary(Goosefish)
plot(Goosefish)

new("MLZ_data")

MLZ_model

Description

An S4 class for storing model results. Method functions summary and plot are available for this class (see examples).

Slots

Stock

Name of stock (obtained from an object of class MLZ_data).

Model

Name of model used for mortality estimation.

time.series

A data frame summarizing observed time series data and predicted values from model.

estimates

A matrix of parameter estimates and derived values and their standard errors, from sdreport.

negLL

The negative log-likelihood from the model.

n.changepoint

The number of change points in the model.

n.species

The number of species/stocks in the model.

grid.search

A data frame reporting the log-likelihood values from a grid search over change points. See profile_ML, profile_MLCR, and profile_MLmulti.

obj

A list with components from MakeADFun.

opt

A list with components from calling optim to obj.

sdrep

A class sdreport list with components from calling sdreport to obj.

length.units

Unit of measurement for lengths, i.e. "cm" or "mm".

Examples

## Not run: 
data(Goosefish)
goose.model <- ML(Goosefish, ncp = 2, grid.search = FALSE, figure = FALSE)
class(goose.model)

summary(goose.model)
plot(goose.model, residuals = FALSE)

## End(Not run)

Mean length with effort mortality estimator

Description

Estimator of fishing and natural mortality from a time series of mean length and effort data.

Usage

MLeffort(MLZ_data, start, n_age, estimate.M = TRUE, log.par = FALSE,
  eff_init = 0, n_season = 1L, obs_season = 1L, timing = 0,
  figure = TRUE)

Arguments

Value

An object of class MLZ_model.

References

Then, A.Y, Hoenig, J.M, and Huynh, Q.C. In revision. Estimating fishing and natural mortality rates, and catchability coefficient, from a series of observations on mean length and fishing effort. ICES Journal of Marine Science.

Examples

## Not run: 
data(Nephrops)
Nephrops <- calc_ML(Nephrops, sample.size = FALSE)
res <- MLeffort(Nephrops, start = list(q = 0.1, M = 0.2),
                n_age = 24, eff_init = Nephrops@Effort[1])

## End(Not run)                

Multispecies mean length mortality estimator

Description

Estimator of instantaneous total mortality (Z) from a time series of mean length data for a suite of stocks that are fished together.

Usage

MLmulti(MLZ.list, ncp, model = c("SSM", "MSM1", "MSM2", "MSM3"),
  start = NULL, grid.search = TRUE, parallel = ifelse(ncp > 2, TRUE,
  FALSE), min.time = 3, Z.max = 5, figure = TRUE)

Arguments

Details

For a model with I change points and N species, the starting values in start is a list with the following entries:

Single Species Model (SSM, independent trends in mortality among species):

Multispecies Model 1 (MSM1, common mortality change points but changes in Z are independent):

Multispecies Model 2 (MSM2, common mortality change points. Changes in F vary by estimated relative catchabilities among species):

Multispecies Model 3 (MSM3, common mortality change points and common proportional changes in F):

If ncp = 0 change points is specified, then the method simplifies to the Single Species Model. The start list should contain a single entry:

start can be NULL, in which case, the supplied starting values depend on the value of grid.search. If grid.search = TRUE, starting values will use the values for yearZ which minimize the negative log-likelihood from the grid search. Otherwise, the starting values for yearZ evenly divide the time series.

Value

An object of class MLZ_model.

References

Huynh, Q.C, Gedamke, T., Hoenig, J.M, and Porch C. 2017. Multispecies Extensions to a Nonequilibrium Length-Based Mortality Estimator. Marine and Coastal Fisheries 9:68-78.

See Also

profile_MLmulti

Examples

## Not run: 
data(PRSnapper)
res_eq <- MLmulti(PRSnapper, ncp = 0, start = list(Z = matrix(0.5, nrow = 1, ncol = 3)))
res_SSM <- MLmulti(PRSnapper, ncp = 1, model = "SSM")

MSM1.start.Z <- matrix(0.5, nrow = 2, ncol = 3)
MSM1.start.yearZ <- 1990
start.list <- list(Z = MSM1.start.Z, yearZ = MSM1.start.yearZ)
res_MSM1 <- MLmulti(PRSnapper, ncp = 1, model = "MSM1", start = start.list, grid.search = FALSE)

res_MSM2 <- MLmulti(PRSnapper, ncp = 1, model = "MSM2")

st.Z1 <- rep(0.5, 3)
st.yearZ <- 1990
st.delta <- 1
start.list <- list(Z1 = st.Z1, yearZ = st.yearZ, delta = st.delta)
resMSM3 <- MLmulti(PRSnapper, ncp = 1, model = "MSM3", start = start.list)

## End(Not run)

Puerto Rico Mutton Snapper (for ML, MLCR)

Description

Mean lengths, CPUE, and life history for Puerto Rico mutton snapper.

Usage

MuttonSnapper

Format

An object of class MLZ_data.

References

Huynh, Q.C., Gedamke, T., Porch, C.E., Hoenig, J.M., Walter, J.F, Bryan, M, and Brodziak, J. In revision. Estimating Total Mortality Rates from Mean Lengths and Catch Rates in Non-equilibrium Situations. Transactions of the American Fisheries Society.

Examples

data(MuttonSnapper)

Male Nephrops FU 28-29 (for MLeffort)

Description

An S4 object containing length and effort time series and life history parameters of male Nephrops in FU 28-29.

Usage

Nephrops

Format

An object of class MLZ_data.

References

Then, A.Y, Hoenig, J.M, and Huynh, Q.C. In revision. Estimating fishing and natural mortality rates, and catchability coefficient, from a series of observations on mean length and fishing effort. ICES Journal of Marine Science.

Examples

data(Nephrops)

Puerto Rico Snapper (for MLmulti)

Description

Mean lengths and life history for 3 species in the Puerto Rico Deepwater Snapper Complex (Unit 1): silk snapper, blackfin snapper, and vermilion snapper.

Usage

PRSnapper

Format

A list containing objects of class MLZ_data.

References

Huynh, Q.C, Gedamke, T., Hoenig, J.M, and Porch C. In press. Multispecies Extensions to a Nonequilibrium Length-Based Mortality Estimator. Marine and Coastal Fisheries.

Examples

data(PRSnapper)

Silk Snapper

Description

Length observed from the Puerto Rico Silk Snapper handline fishery.

Usage

SilkSnapper

Format

A data frame.

References

Huynh, Q.C, Gedamke, T., Hoenig, J.M, and Porch C. In press. Multispecies Extensions to a Nonequilibrium Length-Based Mortality Estimator. Marine and Coastal Fisheries.

Examples

## Not run: 
data(SilkSnapper)
new("MLZ_data", Len_df = SilkSnapper)

## End(Not run)

Bin length data

Description

A tool to bin raw length observations into a length frequency matrix.

Usage

bin_length(df, breaks = NULL)

Arguments

Details

Length frequencies from Len_df are created by using hist function.

Value

A list with length bins, years, and frequency matrix.

Examples

## Not run: 
data(SilkSnapper)
Silk.matrix <- bin_length(SilkSnapper, breaks = seq(80, 830, 10))
Silk.matrix <- bin_length(SilkSnapper)
new.dataset <- new("MLZ_data", Year = Silk.matrix$Year, Len_bins = Silk.matrix$Len_bins,
Len_matrix = Silk.matrix$Len_matrix)

## End(Not run) 

Calculate mean lengths >= Lc

Description

Calculates mean lengths from length data and Lc for class MLZ_data.

Usage

calc_ML(MLZ_data, length.slot = c("Len_df", "Len_matrix"),
  sample.size = TRUE)

Arguments

Value

An object of class MLZ_data to fill slots MeanLength, ss.

Examples

## Not run: 
data(Nephrops)
Nephrops <- calc_ML(Nephrops, sample.size = FALSE)
Nephrops@MeanLength
plot(Nephrops)

## End(Not run)

Model selection

Description

Produces a matrix of AIC for model selection.

Usage

compare_models(..., figure = TRUE, color = NULL)

Arguments

Examples

## Not run: 
data(Goosefish)
goose <- ML(Goosefish, ncp = 0)
goose1 <- ML(Goosefish, ncp = 1)
goose2 <- ML(Goosefish, ncp = 2, grid.search = TRUE, figure = FALSE)

compare_models(goose, goose1, goose2)

data(PRSnapper)
ssm <- MLmulti(PRSnapper, ncp = 1, model = "SSM")
msm1 <- MLmulti(PRSnapper, ncp = 1, model = "MSM1")
msm2 <- MLmulti(PRSnapper, ncp = 1, model = "MSM2")
msm3 <- MLmulti(PRSnapper, ncp = 1, model = "MSM3")

compare_models(ssm, msm1, msm2, msm3)

## End(Not run)

Modal length from length data

Description

Calculates the annual modal length from the length data, which can be used to select Lc. Note: Modal length can change over time for many reasons, including a change in mortality (Hordyk et al. 2015), recruitment, or selectivity (Huynh et al. 2017).

Usage

modal_length(MLZ_data, length.slot = c("Len_df", "Len_matrix"),
  breaks = NULL, figure = TRUE)

Arguments

Details

Length frequency matrix from Len_df are created by using hist function.

Value

A data frame of plotted values.

References

Hordyk, A. Ono, K., Sainsbury, K., Loneragan, N., and Prince, J. 2015. Some explorations of the life history ratios to describe length composition, spawning-per-recruit, and the spawning potential ratio. ICES Journal of Marine Science 72:204-216.

Huynh, Q.C, Gedamke, T., Hoenig, J.M, and Porch C. 2017. Multispecies Extensions to a Nonequilibrium Length-Based Mortality Estimator. Marine and Coastal Fisheries 9:68-78.

Examples

## Not run: 
data(Nephrops)
modal_length(Nephrops)

data(SilkSnapper)
new.dataset <- new("MLZ_data", Year = 1983:2013, Len_df = SilkSnapper)
modal_length(new.dataset)
modal_length(new.dataset, breaks = seq(80, 830, 10))

## End(Not run)

plot method for S4 class MLZ_data

Description

Plots annual length frequencies from slot Len_matrix or Len_df. If there are data in both slots, Len_matrix is preferentially plotted.

Usage

## S4 method for signature 'MLZ_data'
plot(x, type = c("ML", "comp"),
  ggplot_layer = NULL)

Arguments

Examples

## Not run: 
data(Nephrops)
plot(Nephrops)
plot(Nephrops, type = "ML")

## End(Not run)

plot method for S4 class MLZ_model

Description

Plots time series of observed and predicted data from an object of class MLZ_model.

Usage

## S4 method for signature 'MLZ_model'
plot(x, residuals = TRUE)

Arguments

Examples

## Not run: 
data(Goosefish)
goose.model <- ML(Goosefish, ncp = 2, grid.search = FALSE, figure = FALSE)
plot(goose.model)

## End(Not run)

Grid search for the mean length estimator

Description

A grid search is performed over the time series, which can be used to identify local and global minima. A plot of the likelihood surface is also created similar to Figure 6 of Gedamke and Hoenig (2006) or Figure 3 of Huynh et al. (2017).

Usage

profile_ML(MLZ_data, ncp, startZ = rep(0.5, ncp + 1), min.time = 3,
  parallel = ifelse(ncp > 2, TRUE, FALSE), figure = TRUE,
  color = TRUE)

Arguments

Value

A matrix of change points with the negative log-likelihood values.

References

Gedamke, T. and Hoenig, J.M. 2006. Estimating mortality from mean length data in nonequilibrium situations, with application to the assessment of goosefish. Transactions of the American Fisheries Society 135:476-487.

Huynh, Q.C, Gedamke, T., Hoenig, J.M, and Porch C. 2017. Multispecies Extensions to a Nonequilibrium Length-Based Mortality Estimator. Marine and Coastal Fisheries 9:68-78.

Examples

## Not run: 
data(Goosefish)
profile_ML(Goosefish, ncp = 1)
profile_ML(Goosefish, ncp = 2)

## End(Not run)

Grid search for the mean length with catch rate estimator

Description

A grid search is performed over the time series, which can be used to identify local and global minima. A plot of the likelihood surface is also created similar to Figure 6 of Gedamke and Hoenig (2006) or Figure 3 of Huynh et al. (2017).

Usage

profile_MLCR(MLZ_data, ncp, CPUE.type = c(NA, "NPUE", "WPUE"),
  loglikeCPUE = c("normal", "lognormal"), startZ = rep(0.5, ncp + 1),
  min.time = 3, parallel = ifelse(ncp > 2, TRUE, FALSE),
  figure = TRUE, color = TRUE)

Arguments

Value

A matrix of change points with the total negative log-likelihood values and values from the mean lengths and catch rates.

References

Gedamke, T. and Hoenig, J.M. 2006. Estimating mortality from mean length data in nonequilibrium situations, with application to the assessment of goosefish. Transactions of the American Fisheries Society 135:476-487.

Huynh, Q.C, Gedamke, T., Hoenig, J.M, and Porch C. 2017. Multispecies Extensions to a Nonequilibrium Length-Based Mortality Estimator. Marine and Coastal Fisheries 9:68-78.

Examples

## Not run: 
data(MuttonSnapper)
profile_MLCR(MuttonSnapper, ncp = 1, CPUE.type = 'WPUE')

## End(Not run)

Grid search for the multispecies mean length estimator

Description

A grid search is performed over the time series, which can be used to identify local and global minima. A plot of the likelihood surface is also created similar to Figure 6 of Gedamke and Hoenig (2006) or Figure 3 of Huynh et al. (2017).

Usage

profile_MLmulti(MLZ.list, ncp, model = c("SSM", "MSM1", "MSM2", "MSM3"),
  startZ1 = rep(0.5, length(MLZ.list)), parallel = ifelse(ncp > 2,
  TRUE, FALSE), min.time = 3, figure = TRUE, color = TRUE)

Arguments

Value

A matrix of change points with the total negative log-likelihood values and values from the each species.

References

Gedamke, T. and Hoenig, J.M. 2006. Estimating mortality from mean length data in nonequilibrium situations, with application to the assessment of goosefish. Transactions of the American Fisheries Society 135:476-487.

Huynh, Q.C, Gedamke, T., Hoenig, J.M, and Porch C. 2017. Multispecies Extensions to a Nonequilibrium Length-Based Mortality Estimator. Marine and Coastal Fisheries 9:68-78.

Examples

## Not run: 
data(PRSnapper)
profile_MLmulti(PRSnapper, ncp = 1, model = "MSM1")

## End(Not run)

Sensitivity to Lc

Description

The function re-calculates mean lengths for each alternative value of Lc and re-estimates mortality. Currently supports only the ML estimator.

Usage

sensitivity_Lc(MLZ_data, MLZ_model, Lc.vec, grid.search = FALSE,
  figure = TRUE)

Arguments

Value

A matrix of mortality and change point estimates with each value Lc.

References

Huynh, Q.C, Gedamke, T., Hoenig, J.M, and Porch C. 2017. Multispecies Extensions to a Nonequilibrium Length-Based Mortality Estimator. Marine and Coastal Fisheries 9:68-78.

See Also

ML

Examples

## Not run: 
data(SilkSnapper)
new.dataset <- new("MLZ_data", Year = 1983:2013, Len_df = SilkSnapper, length.units = "mm",
vbLinf = 794, vbK = 0.1)

new.dataset@Lc <- 310
new.dataset <- calc_ML(new.dataset)

first.MLZmodel <- ML(new.dataset, 1)
Lc.vec <- seq(240, 340, 5)

sensitivity_Lc(new.dataset, first.MLZmodel, Lc.vec)

## End(Not run)

summary method for S4 class MLZ_data

Description

summary method for S4 class MLZ_data

Usage

## S4 method for signature 'MLZ_data'
summary(object)

Arguments

Examples

data(MuttonSnapper)
summary(MuttonSnapper)

summary method for S4 class MLZ_model

Description

summary method for S4 class MLZ_model

Usage

## S4 method for signature 'MLZ_model'
summary(object)

Arguments

Examples

## Not run: 
data(Goosefish)
goose.model <- ML(Goosefish, ncp = 2, grid.search = FALSE)
summary(goose.model)

## End(Not run)