Description Usage Arguments Details Value Author(s) References Examples
A new approach for estimating stock status from lengthfrequency data
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lfq 
A list of the class "lfq" consisting of following parameters:

startYear 
Start year of assessment. If NA (default), the first year in the

endYear 
Final year of assessment. If NA (default), the last year in the

years 
Manual selection of years for assessment. If NA (default), all years
in the 
binSize 
Optional; determines bin size (class width) for lengthfrequency data.
If NA (default) bin size remains unchanged (as in 
LinfUser 
Optional; user defined asymptotic length. Any length observation larger than this value will be removed from the data. If NA (default), Linf is estimated by the model. 
LcutUser 
Optional, user defined minimum length. Any length observation smaller than
this value will be removed form the data. By default all length obervations are used
( 
LcUser 
Optional, user defined selectivity parameter. If NA (default) L10 and L90 are used to estimate a proxy for Lc. 
LstartUser 
Optional, user defined length at which selectivity is 0.95. If NA (default) Lstart (L95) is estimated by the model. 
MKUser 
Optional; user defined MK ratio. If NA (default) MK ratio is set to 1.5. 
mmUser 
Logical; indicating the unit of length measurements, where TRUE indicates that lengths are in mm and FALSE (default) indicate that lengths are in cm. 
GausSel 
Logical; indicating the selectivity pattern. If FALSE (default) trawllike, if TRUE gaussian selectivity is assumed. 
MergeLF 
Logical; indicating if the data of subsequent years should be merged with data of preceeding years. (Default: FALSE). 
n.chains 
Number of Markov chains (default: 3). 
n.cluster 
Number of clusters to use to run parallel chains (default: 3). 
plot 
Logical; should the individual year plot be displayed? (Default: FALSE). 
mfrow 
A vector of the form 'c(nr, nc)'. Subsequent figures will be drawn in an 'nr'by'nc' array on the device by _rows_ ('mfrow'). If NA (default), a panel with 3 columns and several rows (dependent on number of years) is used. 
Requires the Gibbs sampler JAGS to be installed on your computer, available for your Operating System from the following web site: http://sourceforge.net/projects/mcmcjags/files/JAGS/4.x/. LBB is a new method for the analysis of length frequency data from the commercial fishery. It works for species that grow throughout their lives, such as most commercial fish and invertebrates, and requires no input in addition to length frequency data. It estimates asymptotic length (Linf), length at first capture (Lc), relative natural mortality (M/K) and relative fishing mortality (F/M) as means over the age range represented in the lengthfrequency sample. With these parameters as input, standard fisheries equations can be used to estimate depletion or current exploited biomass relative to unexploited biomass (B/B0). In addition, these parameters allow the estimation of the length at first capture that would maximize catch and biomass for the given fishing effort (Lc_opt), and estimation of a proxy for the relative biomass capable of producing maximum sustainable yields (Bmsy/B0). Relative biomass estimates of LBB were not significantly different from the "true" values in simulated data and similar to independent estimates from full stock assessments.
A list with the input parameters and following list objects:
GausSel: indicating if gaussian Selection was used,
priors: priors,
refLev: matrix with all reference levels for all years,
medianRefLev: median reference levels (plus 95% confidence intervals),
lastRefLev: reference levels in the last year,
LFall: matrix with lengths and relative frequencies.
Rainer Froese, ([email protected])
R. Froese, H. Winker, G. Coro, N. Demirel, A.C. Tsikliras, D. Dimarchopoulou, G. Scarcella, W.N. Probst, M. Dureuil, and D. Pauly (2018) A new approach for estimating stock status from length frequency data. ICES Journal of Marine Science. DOI: 10.1093/icesjms/fsy078
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23  ## load data
data(synLFQ8)
## arrange lfq data
lfq < lfqModify(synLFQ8, aggregate = "year")
## plot lfq data traditionally
plot(lfq)
## plot data in LBB manner
plotLBB.data(lfq)
## add length at maturity to lfq data
lfq$Lm50 < 36
## run LBB model
res < LBB(lfq, plot = TRUE)
## plot results
plotLBB(res)
## plot time series
plotLBB.ts(res)

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