This function applies the (length-converted) linearised catch curve to age composition and length-frequency data, respectively. It allows to estimate the instantaneous total mortality rate (Z). Optionally, the gear selectivity can be estimated and the cumulative catch curve cna be applied.
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a list consisting of following parameters:
numerical; indicating the column of the catch matrix which should be used for the analysis.
logical; if TRUE the cumulative catch curve is applied (Jones and van Zalinge method)
logical; if TRUE the selection ogive is additionally
calculated from the catch curve (only if
instead of using the identity method a range can be determined, which is to be used for the regression analysis. If equal to NULL identity method is applied (default). For multiple regression lines provide list with the two points for the regression line in each element of the list.
integer indicating how many separate regression lines should be applied to the data. Default 1.
logical; no interactive functions used instead regression line is chosen automatically. Default = FALSE
logical; should a plot be displayed? Default = TRUE
This function includes the identify function, which asks you to
choose two points from a graph manually. The two points which you choose by clicking
on the plot in the graphical device represent the start and end of the data points,
which should be used for the analysis. Based on these points the regression line
When the selection ogive
is calculated by means of the catch curve the assumption is made, that Z is constant
for all year classes or length groups, respectively. Accoring to Sparre and Venema
(1998) this assumption might be true, because F is smaller for young fish
(Selectivity) while M is higher for young fish (high natural mortality). The selectivity
for not fully exploited old fish (e.g. due to gillnet fishery) can not be calculated yet
by use of the catch curve.
Based on the format of the list argument
catch and whether the argument
catch_columns is defined, the function automatically
distinguishes between the catch curve with variable parameter system (if catch is a
vector) and the one with constant parameter system (if catch is a matrix or a
catch_columns = NA). In the case of the variable parameter
system the catches of one year are
assumed to represent the catches during the entire life span of a so called
The cumulative catch curve does not allow for the estimation of the selectivity
A list with the input parameters and following list objects:
classes.num, tplusdt_2, t_midL, or ln_Linf_L: age, relative age or subsitute depending on input and method,
lnC or lnC_dt: logarithm of (rearranged) catches,
reg_int: the interval used for the regression analysis,
linear_mod: linear model used for the regression analysis,
Z: instantaneous total mortality rate, confidenceInt
se: standard error of the total mortality;
confidenceInt: confidence interval of the total mortality;
in case calc_ogive == TRUE, additionally:
intercept: intercept of regression analysis,
linear_mod_sel: linear model used for the selectivity analysis,
Sobs: observed selection ogive,
ln_1_S_1: dependent variable of regression analysis for selectivity parameters,
Sest: estimated selection ogive,
t50: age at first capture (age at which fish have a 50 probability to be caught),
t75: age at which fish have a 75
L50: length at first capture (length at which fish have a 50 probability to be caught),
L75: length at which fish have a 75
Baranov, F.I., 1926. On the question of the dynamics of the fishing industry. Nauchn. Byull. Rybn. Khoz, 8 (1925), 7-11
Beverton, R.J.H. and S.J. Holt, 1956. A review of methods for estimating mortality rates in exploited fish populations, with special reference to sources of bias in catch sampling. Rapports et Proces verbaux des Reunions, Conseil Table3
Chapman, D., and D.S Robson, 1960. The analysis of a catch curve. Biometrics, 354-368
Edser, T., 1908. Note on the number of plaice at each length, in certain samples from the southern part of the North Sea, 1906. Journal of the Royal Statistical Society, 686-690
Heincke, F., 1913. Investigations on the plaice. General report. 1. The plaice fishery and protective regulations. Part I. Rapp.P.-v.Reun.CIEM, 17A:1-153 + Annexes
ICES, 1981. Report of the Ad hoc working group on the use of effort data in assessment, Copenhagen, 2-6 March 1981. ICES C.M. 1981/G:5 (mimeo)
Jones, R., and N.P. Van Zalinge, 1981. Estimates of mortality rate and population size for shrimp in Kuwait waters. Kuwait Bull. Mar. Sci, 2, 273-288
Pauly, D., 1983. Length-converted catch curves: a powerful tool for fisheries research in the tropics (part I). ICLARM Fishbyte, 1(2), 9-13
Pauly, D., 1984. Length-converted catch curves: a powerful tool for fisheries research in the tropics (part II). ICLARM Fishbyte, 2(1), 17-19
Pauly, D., 1984. Length-converted catch curves: a powerful tool for fisheries research in the tropics (III: Conclusion). ICLARM Fishbyte, 2(3), 9-10
Ricker, W.E., 1987. Computation and interpretation of biological statistics of fish populations. Bull.Fish.Res.Board Can., (191):382 p.
Robson, D.S., and D.G. Chapman, 1961. Catch curves and mortality rates. Trans.Am.Fish.Soc., 90(2):181-189
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.
Van Sickle, J. 1977. Mortality rates from size distributions: the application of a conservation law. Oecologia, Berl., 27(4):311-318
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#_______________________________________________ # Variable paramter system (with catch vector) # based on length frequency data data(goatfish) output <- catchCurve(goatfish) summary(output$linear_mod) # based on age composition data data(whiting) catchCurve(whiting, catch_columns = 1) #_______________________________________________ # Constant parameter system based on age composition data (with catch matrix) catchCurve(whiting) #_______________________________________________ # Cumulative Catch Curve # based on length frequency data data(goatfish) catchCurve(goatfish, cumulative = TRUE) # based on age composition data data(synCAA2) catchCurve(synCAA2, cumulative = TRUE) #_______________________________________________ # Catch Curve with estimation of selection ogive data(synLFQ3) output <- catchCurve(synLFQ3, calc_ogive = TRUE) summary(output$linear_mod_sel) # the same with predefined selection for regression line: output <- catchCurve(synLFQ3, calc_ogive = TRUE, reg_int = c(9,21)) plot(output, plot_selec = TRUE)
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