TropFishR (version 1.6.1)

ELEFAN: ELEFAN

Description

Electronic LEngth Frequency ANalysis for estimating growth parameter.

Usage

ELEFAN(x, Linf_fix = NA, Linf_range = NA, K_range = exp(seq(log(0.1),
  log(10), length.out = 100)), C = 0, ts = 0, MA = 5, addl.sqrt = FALSE,
  agemax = NULL, flagging.out = TRUE, method = "optimise",
  cross.date = NULL, cross.midLength = NULL, cross.max = FALSE,
  hide.progressbar = FALSE, plot = FALSE, contour = FALSE,
  add.values = TRUE, rsa.colors = terrain.colors(20), plot_title = TRUE)

Arguments

x

a list consisting of following parameters:

• midLengths midpoints of the length classes,

• dates dates of sampling times (class Date),

• catch matrix with catches/counts per length class (row) and sampling date (column);

Linf_fix

numeric; if used the K-Scan method is applied with a fixed Linf value (i.e. varying K only).

Linf_range

numeric vector with potential Linf values. Default is the last length class plus/minus 5 cm

K_range

K values for which the score of growth functions should be calculated (by default: exp(seq(log(0.1),log(10),length.out = 100)))

C

growth oscillation amplitude (default: 0)

ts

onset of the first oscillation relative to t0 (summer point, default: 0)

MA

number indicating over how many length classes the moving average should be performed (default: 5, for more information see lfqRestructure).

addl.sqrt

Passed to lfqRestructure. Applied an additional square-root transformation of positive values according to Brey et al. (1988). (default: FALSE, for more information see lfqRestructure).

agemax

maximum age of species; default NULL, then estimated from Linf

flagging.out

logical; should positive peaks be flagged out? (Default : TRUE)

method

Choose between the old FiSAT option to force VBGF crossing of a pre-defined bin (method = "cross"), or the more sophisticated (but computationally expensive) option to solve for t_anchor via a maximisation of reconstructed score (default: method = "optimise").

cross.date

Date. For use with method = "cross". In combination with cross.midLength, defines the date of the crossed bin.

cross.midLength

Numeric. For use with method = "cross". In combination with cross.date, defines the mid-length of the crossed bin.

cross.max

logical. For use with method = "cross". Forces growth function to cross the bin with maximum positive score.

hide.progressbar

logical; should the progress bar be hidden? (default: FALSE)

plot

logical; indicating if plot with restructured frequencies and growth curves should be displayed

contour

if used in combination with response surface analysis, contour lines are displayed rather than the score as text in each field of the score plot. Usage can be logical (e.g. TRUE) or by providing a numeric which indicates the number of levels (nlevels in contour). By default FALSE.

add.values

logical. Add values to Response Surface Analysis plot (default: TRUE). Overridden when contour = TRUE.

rsa.colors

vector of colors to be used with the Response Surface Analysis plot. (default: terrain.colors(20))

plot_title

logical; indicating whether title to score plots should be displayed

Value

A list with the input parameters and following list objects:

• rcounts: restructured frequencies,

• peaks_mat: matrix with positive peaks with distinct values,

• ASP: available sum of peaks, sum of posititve peaks which could be potential be hit by growth curves,

• score_mat: matrix with scores for each Linf (only Linf_fix) and K combination,

• t_anchor_mat: maximum age of species,

• ncohort: number of cohorts used for estimation,

• agemax: maximum age of species,

• par: a list with the parameters of the von Bertalanffy growth function:

  • Linf: length infinity in cm,

  • K: curving coefficient;

  • t_anchor: time point anchoring growth curves in year-length coordinate system, corrsponds to peak spawning month,

  • C: amplitude of growth oscillation (if seasonalised = TRUE),

  • ts: summer point of oscillation (ts = WP - 0.5) (if seasonalised = TRUE),

  • phiL: growth performance index defined as phiL = log10(K) + 2 * log10(Linf);

• Rn_max: highest score value

Details

This functions allows to perform the K-Scan and Response surface analysis to estimate growth parameters. It combines the step of restructuring length-frequency data (lfqRestructure) followed by the fitting of VBGF curves through the restructured data (lfqFitCurves). K-Scan is a method used to search for the K parameter with the best fit while keeping the Linf fixed. In contrast, with response surface analysis both parameters are estimated and the fits are displayed in a heatmap. Both methods use optimise to find the best t_anchor value for each combination of K and Linf. To find out more about t_anchor, please refer to the Details description of lfqFitCurves. The score value Rn_max is comparable with the score values of the other ELEFAN functions (ELEFAN_SA or ELEFAN_GA) when other settings are consistent (e.g. `MA`, `addl.sqrt`, `agemax`, `flagging.out`).

References

Brey, T., Soriano, M., and Pauly, D. 1988. Electronic length frequency analysis: a revised and expanded user's guide to ELEFAN 0, 1 and 2.

Pauly, D. 1981. The relationship between gill surface area and growth performance in fish: a generalization of von Bertalanffy's theory of growth. Meeresforsch. 28:205-211

Pauly, D. and N. David, 1981. ELEFAN I, a BASIC program for the objective extraction of growth parameters from length-frequency data. Meeresforschung, 28(4):205-211

Pauly, D., 1985. On improving operation and use of ELEFAN programs. Part I: Avoiding "drift" of K towards low values. ICLARM Conf. Proc., 13-14

Pauly, D., 1987. A review of the ELEFAN system for analysis of length-frequency data in fish and aquatic invertebrates. ICLARM Conf. Proc., (13):7-34

Pauly, D. and G. R. Morgan (Eds.), 1987. Length-based methods in fisheries research. (No. 13). WorldFish

Pauly, D. and G. Gaschuetz. 1979. A simple method for fitting oscillating length growth data, with a program for pocket calculators. I.C.E.S. CM 1979/6:24. Demersal Fish Cttee, 26 p.

Pauly, D. 1984. Fish population dynamics in tropical waters: a manual for use with programmable calculators (Vol. 8). WorldFish.

Quenouille, M. H., 1956. Notes on bias in estimation. Biometrika, 43:353-360

Somers, I. F., 1988. On a seasonally oscillating growth function. ICLARM Fishbyte 6(1): 8-11.

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.

Tukey, J., 1958. Bias and confidence in not quite large samples. Annals of Mathematical Statistics, 29: 614

Tukey, J., 1986. The future of processes of data analysis. In L. V. Jones (Eds.), The Collected Works of John W. Tukey-philosophy and principles of data analysis: 1965-1986 (Vol. 4, pp. 517-549). Monterey, CA, USA: Wadsworth & Brooks/Cole

Examples

# NOT RUN {
data(alba)

### Response Surface Analysis (varies Linf and K) ###

# 'cross' method (used in FiSAT)
fit1 <- ELEFAN(
   x = alba, method = "cross",
   Linf_range = seq(from = 10, to = 20, length.out = 10),
   K_range = exp(seq(from = log(0.1), to = log(2), length.out = 20)),
   cross.date = alba$dates[3], cross.midLength = alba$midLengths[4],
   contour = TRUE
)
fit1$Rn_max; unlist(fit1$par)
plot(fit1); points(alba$dates[3], alba$midLengths[4], col=2, cex=2, lwd=2)

# 'cross' method (bin with maximum score crossed)
fit2 <- ELEFAN(
   x = alba, method = "cross",
   Linf_range = seq(from = 10, to = 20, length.out = 20),
   K_range = exp(seq(from = log(0.1), to = log(2), length.out = 20)),
   cross.max = TRUE,
   contour = TRUE
)
fit2$Rn_max; unlist(fit2$par)
plot(fit2); points(alba$dates[7], alba$midLengths[9], col=2, cex=2, lwd=2)


# 'optimise' method (default)
fit3 <- ELEFAN(
   x = alba, method = "optimise",
   Linf_range = seq(from = 10, to = 20, length.out = 10),
   K_range = exp(seq(from = log(0.1), to = log(2), length.out = 20)),
   contour = TRUE
)
fit3$Rn_max; unlist(fit3$par)
plot(fit3)


### K-Scan (varies K, Linf is fixed) ###

# 'cross' method
fit4 <- ELEFAN(
   x = alba, method = "cross",
   Linf_fix = 10,
   K_range = round(exp(seq(from = log(0.1), to = log(2), length.out = 50)),2),
   cross.date = alba$dates[3], cross.midLength = alba$midLengths[4]
)
fit4$Rn_max; unlist(fit4$par)
plot(fit4); points(alba$dates[3], alba$midLengths[4], col=2, cex=2, lwd=2)

# }
# NOT RUN {
# }

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