TropFishR (version 1.6.1)

recruitment: Recruitment patterns

Description

This function estimates recrutiment patterns from length-frequency data.

Usage

recruitment(param, tsample, catch_column = NA, plot = FALSE)

Arguments

param

a list consisting of following parameters:

• midLengths: midpoints of the length classes (length-frequency data),

• Linf: infinite length for investigated species in cm [cm],

• K: growth coefficent for investigated species per year [1/year],

• t0: theoretical time zero, when growth curve crosses length equalling zero,

• D: optional; for generalised vBGF

• C: optional; intensity of the (sinusoid) growth oscillations of the model,

• ts: optional; onset of the positive phase of the growth oscillation (fraction of year; i.e. Jan 1 = 0, July 1 = 0.5, etc.),

• catch: catches, vector or matrix with catches of subsequent sampling times

tsample

sampling time corresponding to time when catch was sampled as fraction of year (e.g. 0.25 for 1st of April). If catch was sampled several times (catch as matrix) vector has to be provided with sampling times in equal order.

catch_column

numeric; if catch in param is a matrix, this number indicates the column of the catch matrix which should be used for the analysis.

plot

logical; indicating whether a plot should be printed

Value

A list with the input parameters and following list objects:

• ti: actual age,

• tS_frac: age at which the length was zero expressed as fraction of the year,

• cor_months: corresponding months,

• months: numeric months (relative if no t0 is not given),

• months_abb: months (only presented if t0 is given),

• all_recruits: number of recruits per month as matrix if several length-frequency data sets are provided,

• mean_recruits: (mean) number of recruits per month,

• per_recruits: precentage number of recruits per month.

Details

This function calculates recruitment patterns of a stock by backward projection onto the length axis of a set of length frequency data using the (special, generalised or seasonalised) von Bertallanfy growth curve (vBGF, Pauly 1982). The method assumes that (i) all fish in a data set grow as described by a single set of growth parameters and (ii) one month out of twelve always has zero recruitment. The second assumption is probably not met, since temperate species may contain more than one month with zero recruitment, while tropical species may have more constant recruitment without months of no recruitment. If t0 is not provided, a relative recruitment pattern will be estimated without specific month values returned in the results. However, an estimate of t0 can be obtained by the time lag between peak spawning and recruitment. Several length-frequency data sets can be used to estimate the recrutiment pattern by providing catch as a matrix and setting catch_column to NA (default). Then the fraction per time is calculated for each size class in each sample and then pooled together. For the generalised vBGF, D is required, for the seasonalised vBGF C, ts and D.

References

Brey, T., Soriano, M., Pauly, D., 1988. Electronic length frequency analysis. A revised and expanded user's guide to ELEFAN 0, 1 and 2. (Second edition). Berichte aus dem Institut f??r Meereskunde Kiel, No 177, 31p.

Moreau, J., & Cuende, F. X., 1991. On improving the resolution of the recruitment patterns of fishes. Fishbyte, 9(1), 45-46.

Pauly, D., 1982. Studying single-species dynamics in a tropical multispecies context. In Theory and management of tropical fisheries. ICLARM Conference Proceedings (Vol. 9, No. 360, pp. 33-70).

Sparre, P., Venema, S.C., 1998. Introduction to tropical fish stock assessment. Part 1. Manual. FAO Fisheries Technical Paper, (306.1, Rev. 2). 407 p.

Examples

# NOT RUN {
# one sample
dat <- list(midLengths = seq(2,98,4),
               catch = c(0.6,17.6,93,83.2,12.6,0.3,0,0,0,1,17.1,51.4,
               26.1,2.2,0.2,4.5,21.6,17.6,3.7,8.7,10.6,6.3,5.6,2.9,0.8),
               Linf = 100,
               K = 0.5,
               t0 = 0)
recruitment(param = dat, tsample = 0.25)


# several samples
data(synLFQ4)

# add growth parameters
synLFQ4$Linf <- 80
synLFQ4$K <- 0.5
synLFQ4$t0 <- 0.25

# retrieve sampling times from catch matrix
s_dates <- as.POSIXlt(synLFQ4$dates, format="%d.%m.%Y")

recruitment(param = synLFQ4, tsample = s_dates$yday/365, plot = TRUE)


plot(synLFQ4, Fname = "catch",
   par = list(Linf = 80, K = 0.5, t_anchor = 0.25, C = 0.75, ts = 0),
   ylim = c(0,80))

# }