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)

Abstract

Abstract-Yield-per-recruit and spawn ing-biomass-per-recruit models, are commonly used for evaluating the status of a fishery. In practice, model parameters are themselves usually estimates that are subject to both bias (uncertainty in the mean) and imprecision (uncertainty in the standard deviation). Using Monte Carlo simulation with data for female Japanese eel (Anguilla japonica) from the Kao-Ping River in Taiwan, we examined the sensitivity of such models to different degrees of bias and imprecision in the life history parameters. Positive biases in natural mortality and the von Bertalanffy growth coefficient led to larger relative changes in the mean and standard deviation of estimated fishing-mortality-based biological reference points (F BRPs ) than did changes under negative biases. Higher degrees of imprecision in parameters did not affect the means of F BRPs, but their standard deviations increased. Composite risks of overfishing depended mainly on the changes in the means of F BRPs rather than on their standard deviations. Therefore, reducing the biases in key life history parameters, as well as the bias and imprecision in the current rate of fishing mortality, may be the most relevant approach for obtaining correct estimates of the risks of overfishing.Yield-per-recruit (YPR) and spawning-biomass-per-recruit (SPR) models, in which the total yield or spawning biomass of a cohort is standardized for the numbers of recruits, are commonly used in fisheries assessment (Beverton and Holt, 1957;Quinn and Deriso, 1999). They can be used to infer the total yield and spawning biomass of an entire population composed of different cohorts with an assumption of a steady state and with knowledge of equilibrium recruitment (King, 2007). Several fishing-mortality-based biological reference points (F BRPs ) derived from YPR and SPR models can be used to evaluate whether the yield per recruit is optimal or the spawning biomass per recruit is sufficient for the population to persist under current fishing pressure.Uncertainties in the parameters of such models are inevitable and result from observation and process error (Charles, 1998). 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).Estimation of natural mortality (M) is challenging (Vetter, 1988) because both its mean and variance are prone to considerable uncertainty. For example, the estimates of M have varied among different empirical methods (Pascual and Iribarne, 1993;Lin and Sun, 2013). The variances of M estimates also have differed among approaches (e.g., Cubillos et al., 1999, versus Hall et al., 2004Lin et al., 201...

Life history traits and temporal trends of abundance of the orange‐spotted trevally (Carangoides bajad) from Saudi waters of the Gulf

“…In the case of the E. coioides we observed that differences in reproductive tactics between Saudi and Emirati waters went as far as to imply a reversal of sex change (Lin et al ., ). Therefore, conservation and management plans for C. bajad in The Gulf should take observed spatial differences in reproductive traits into consideration, especially when using biological reference points related to spawning biomass (Lin et al ., ).…”

Stock Assessment of Small Yellow Croaker (Larimichthys polyactis) Off the Coast of China Using Per-Recruit Analysis Based on Bayesian Inference

“…Incorrect estimates of biological reference points may be derived if uncertainty in parameters is ignored. Consequently, conclusions about the status of fish stock will be inaccurate (Helser et al, 2001;Chen and Wilson, 2002;Lin et al, 2015). Quantifying uncertainty in assessment results can enhance decision making for fishery resource management and help determine the best harvest strategies (Gavaris, 1993;Doll et al, 2017).…”

“…A coefficient of variation (CV) or standard deviation (SD) is often specified for a subset model according to the available knowledge of parameter uncertainty (e.g., Chen and Wilson, 2002;Grabowski and Chen, 2004;Jiao et al, 2005;Chang et al, 2009). The estimated SDs from the observed data can also be used to introduce random errors (Lin et al, 2015). For correlated parameters, random errors can be introduced using the estimated standard errors and the correlation (Hart, 2013), and the covariance matrix constructed by the estimated SDs and the correlation (Lin et al, 2010).…”

An assessment of European bream Abramis brama (Linnaeus, 1758) fishery in the downstream of the Irtysh River in China

“…Several fishing mortality rate (F)-based biological reference points (BRPs) derived from per-recruit analysis have been used to evaluate whether the YPR is optimum or the SSB/R is sufficient for the population to persist. It also provides fisheries managers with the ability to develop harvest policies (Aprahamian et al 2006;Lin et al 2015;Huo et al 2015). The BRPs usually appear in three forms: targets (target reference points, TRPs), thresholds (threshold reference points, ThRPs) and limits (limit reference points, LRPs).…”

“…However, point estimates present only the most possible values, whereas a wide range of values and alternative models may exist. Ignoring uncertainties in biological parameters in per-recruit analysis may yield an incorrect estimation of BPRs, and consequently, the estimation of the fish stock status could be misleading (Chen and Wilson 2002;Lin et al 2015). Caddy (1993) suggested that the imprecision or uncertainty should be taken into consideration when a per-recruit analysis is developed.…”