plot.lfq {TropFishR}

Plotting of length frequency data (with VBGF curves)

Description

This function plots length frequency (lfq) samples sequentially arranged in time. An object of "lfq" class is obatined by applying the lfqRestructure function. In case growth parameters are known, von Bertalanffy growth curves can be plotted through the lfq samples.

Usage

## S3 method for class 'lfq'
plot(
  x,
  Fname = "rcounts",
  par = NULL,
  agemax = NULL,
  rel = FALSE,
  y = NA,
  curve.col = 1,
  hist.sc = 0.5,
  hist.col = c("white", "black", "orange", "darkgreen"),
  image.col = NULL,
  region.col = NULL,
  zlim = NULL,
  zlimtype = "balanced",
  date.axis = "traditional",
  date.at = seq(as.Date("1500-01-01"), as.Date("2500-01-01"), by = "months"),
  date.format = "'%y-%b",
  xlab = "",
  ylab = "Length classes",
  draw = TRUE,
  ...
)

Arguments

x

a list of the class "lfq" 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);

Fname

indicating whether restructured ("rcounts") or original frequencies ("catch") should be displayed (default: "rcounts")

par

a list with following growth parameters (default NULL):

• Linf asymptotic length,

• K growth coefficient,

• t_anchor time at length zero,

• C amplitude of growth oscillation (optional),

• ts summer point (optional);

agemax

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

rel

logical; defines if relative numbers per length class should be plotted (relative to the sample size per sampling time, e.g. month). Default: FALSE.

y

an optional second list of class "lfq" consisting of same parameters as x. This allows to plot samples from different sources (e.g. different fleets) on top of eachother, classes and dates have to correspond at least partially. Default is NA.

curve.col

colour of growth curves (default: 1)

hist.sc

defines the scaling factor to use for maximum histogram extent (x-axis direction). The default setting of hist.sc=0.5 will result in a maximum distance equal to half the distance between closest sample dates (i.e. ensures no overlap and full plotting within the plot region).

hist.col

vector of 2 values defining coloring to use on negative and positive histogram bars (default: hist.col=c("white", "black", "orange", "darkgreen"))

image.col

colour of image, by default (NULL) red and blue colours are used. To remove image coloring, set image.col=NA.

region.col

colour of plotting region. Will overwrite image.col (default: region.col=NULL)

zlim

the minimum and maximum z values for which colors should be plotted (default : NULL).

zlimtype

indicating if zlim should be based on the range of the catch ("range") or based on the maximum absolute value in Fname ("balanced", default). This parameter is only considered if zlim is NULL.

date.axis

the style of the x axis. By default the "traditional" approach is used with years under the months. Alternatively, by using "modern" the date is plotted in one line according to the chosen format date.format.

date.at

the points at which tick-marks are to be drawn. Non-finite (infinite, NaN or NA) values are omitted. By default it is seq(as.Date("1500-01-01"), as.Date("2500-01-01"), by="months")

date.format

format of date (default : "%y-%b")

xlab

label of x axis (default : "")

ylab

label of y axis (default : "Length classes")

draw

logical; indicating whether growth curves should be added to lfq plot if parameters are provided (default : TRUE)

...

additional options of the plot function

Details

This function uses lfqFitCurves when growth parameters are provided to plot growth curves, this can be turned off with draw = FALSE.

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. Meeresforschung. 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 Comittee, 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

data(alba)
res <- lfqRestructure(alba)

# simple plot or reconstructed frequencies
plot(x = res, Fname = "rcounts")

# add VBGF curves
plot(res, Fname = "rcounts", par = list(Linf = 14, K = 1.1, t_anchor = 0.3))

# add soVBGF curves, adjust hist.sc and xlim
plot(res, Fname = "catch", curve.col=4,
  par = list(Linf = 14, K = 1.1, t_anchor = 0.3, C = 0.2, ts = 0.75),
  hist.sc = 0.9,
  xlim=range(res$dates)+c(-30, 0)
)

# adjust image colors
plot(res, Fname = "rcounts", image.col = NA )
plot(res, Fname = "rcounts", image.col = rev(cm.colors(21)) )
plot(res, Fname = "rcounts", image.col = colorRampPalette(c("red","grey90","green"))(21))

# solid plot region color
plot(res, xlim=range(res$dates)+c(-60, 60),
  hist.sc=0.75, image.col="grey90") # leaves gaps
plot(res, xlim=range(res$dates)+c(-60, 60),
  hist.sc=0.75, region.col="grey90") # full coverage

# low-level plot additions
plot(res)
abline(h=4, lty=2)
mtext("Restructured frequencies (MA=5)", line=0.25, side=3)