Evaluation of the Japanese eel fishery in the lower reaches of the Kao-Ping River, southwestern Taiwan using a per-recruit analysis

Sensitivity of yield-per-recruit and spawning-biomass-per-recruit models to bias and imprecision in life history parameters: an example based on life history parameters of Japanese eel (Anguilla japonica)

“…In addition, the multiplicative error in the growth curve (ε GR ) and length-weight relationship (ε LW ) were selected as well. In this study, we used information on the mean and SD values of these parameters from previous studies conducted during [1998][1999][2000][2001][2002][2003][2004][2005][2006] on the life history and fishery of the Japanese eel in the Kao-Ping River in Taiwan (Lin and Tzeng 2009;Lin et al 2010aLin et al , 2010b to examine the sensitivity of model results to effects of parameter bias and imprecision with a Monte Carlo simulation.…”

“…We used Monte Carlo simulation, which has been widely applied to other species (e.g., Chen and Wilson, 2002;Grabowski and Chen, 2004;Chang et al, 2009;Lin et al, 2010a) to incorporate the uncertainties of the parameters into the models. In each run, random errors for natural mortality (ε M ) and for coefficients in the logistic maturation curve (ε β 0 and ε β 1 ), as well as ε LW Table 1 Formulae, estimates of model parameters, and corresponding simulated random errors for the length-weight relationship (a and b are coefficients, and ε LW =the corresponding multiplicative error), N=the population size in numbers; von Bertalanffy growth function (K=the growth coefficient (/year), L ∞ =the asymptotic length (mm), t 0 =the theoretical age at length of zero (year), and ε GR =the multiplicative error of the growth curve), M=the natural mortality (/year); ε M =the standard deviation of M; F cur =current fishing mortality (/year); fishing process under gear selection (α 0 and α 1 =selection curve coefficients), and maturation process of female Japanese eel (Anguilla japonica) in the lower reach of the Kao-Ping River in southern Taiwan (β 0 and β 1 =the maturation curve coefficients, and ε β0 and ε β1 =the standard error for β 0 and β 1 , respectively).…”

“…In each run, random errors for natural mortality (ε M ) and for coefficients in the logistic maturation curve (ε β 0 and ε β 1 ), as well as ε LW Table 1 Formulae, estimates of model parameters, and corresponding simulated random errors for the length-weight relationship (a and b are coefficients, and ε LW =the corresponding multiplicative error), N=the population size in numbers; von Bertalanffy growth function (K=the growth coefficient (/year), L ∞ =the asymptotic length (mm), t 0 =the theoretical age at length of zero (year), and ε GR =the multiplicative error of the growth curve), M=the natural mortality (/year); ε M =the standard deviation of M; F cur =current fishing mortality (/year); fishing process under gear selection (α 0 and α 1 =selection curve coefficients), and maturation process of female Japanese eel (Anguilla japonica) in the lower reach of the Kao-Ping River in southern Taiwan (β 0 and β 1 =the maturation curve coefficients, and ε β0 and ε β1 =the standard error for β 0 and β 1 , respectively). The means and standard deviations of the parameters are from previous studies conducted in 1998-2006 on life history parameters and the fishery of Japanese eel in the KaoPing River, Taiwan (Lin and Tzeng 2009, Lin et al 2010a, 2010b. Lin et al: Sensitivity of models to bias and imprecision in life history parameters 305…”

“…In addition, the multiplicative error in the growth curve (ε GR ) and length-weight relationship (ε LW ) were selected as well. In this study, we used information on the mean and SD values of these parameters from previous studies conducted during [1998][1999][2000][2001][2002][2003][2004][2005][2006] on the life history and fishery of the Japanese eel in the Kao-Ping River in Taiwan (Lin and Tzeng 2009;Lin et al 2010aLin et al , 2010b to examine the sensitivity of model results to effects of parameter bias and imprecision with a Monte Carlo simulation.We used wide ranges for the parameters for 3 reasons: 1) the scale of boundaries used; a factor of 10 was applied in a classical study (figure 4 in Goodyear, 1993); 2) a wide range is more general because it can include all likely ranges; and 3) in our previous study (Lin and Sun, 2013), we found that the variation in estimates of M from several different indirect methods can vary from 0.10/year to 2.13/year. Also, the difference in K can be large, especially when different approaches are involved.…”

“…Ignoring uncertainties in parameters can lead to incorrect estimation of F BRPs , and consequently the examination of fishery status could be misleading, given the cases of the American lobster (Homarus americanus) (Chen and Wilson, 2002), green sea urchin (Strongylocentrotus droebachiensis) (Grabowski and Chen, 2004), Atlantic cod (Gadus morhua) (Jiao et al, 2005), pronghorn spiny lobster (Panulirus penicillatus) (Chang et al, 2009), and Japanese eel (Anguilla japonica) (Lin et al, 2010a).…”