When can we reliably estimate the productivity of fish stocks?

Abstract

In modern fishery stock assessments, the productivity of exploited stocks is frequently summarized by a scale-invariant “steepness” parameter. This parameter, which describes the slope of the spawner–recruit curve, determines resilience of a stock to exploitation and is highly influential when estimating maximum sustainable yield. In this study, we examined conditions under which steepness can be estimated reliably. We applied a statistical catch-age model to data that were simulated over a broad range of stock characteristics and exploitation patterns and found that steepness is often estimated at its upper bound regardless of underlying productivity. The ability to estimate steepness reliably was most dependent on the true value of steepness, the exploitation history of the stock, natural mortality, duration of the time series, and quality of an index of abundance; this ability was relatively unaffected by levels of stochasticity in recruitment and sampling intensity of age compositions. We further explored the method of inverse prediction to improve estimates of steepness and conclude that this approach holds promise. We illustrate the utility of simulation and inverse prediction methods with two fish stocks located off the southeastern United States, greater amberjack ( Seriola dumerili ) and gag grouper ( Mycteroperca microlepsis ).

In what direction should the fishing mortality target change when natural mortality increases within an assessment?

“…Estimation of the steepness of an SR curve is difficult even when natural mortality is constant within an assessment (Conn et al 2010). Often, parameters of the SR relationship require priors from meta-analysis or other information to allow estimation (Punt and Dorn 2014).…”

Reconsidering the impossible — linking environmental drivers to growth, mortality, and recruitment of fish

“…More importantly, variations in survivorship (R/SSB) indicated that nearly 50% of variations year-class strength was within a 2-to 3-fold range of variation around the median. Taken together, these findings imply that variations in either growth or mortality rates required to generate such low levels of variability when integrated over the early life stages of development could be so subtle as to be nearly undetectable based on the levels of accuracy and precision normally associated with field-derived measurements of these vital rates (Conn et al 2010;Helbig and Pepin 1998;Maunder and Wong 2011). For example, a 5-to 10-fold variation in survivorship can be achieved by a 5% change in vital rates when approximately 99.99% of offspring are lost during the stage when recruitment is "set" (Beyer 1989).…”

Age sample sizes and their effects on growth estimation and stock assessment outputs: Three case studies from U.S. West Coast fisheries

“…They are important for estimating selectivity of fisheries and fisheryindependent surveys (Hilborn and Walters, 1992;Punt and Hilborn, 1997;Maunder and Punt, 2013;Maunder and Piner, 2015), and critical for the estimation of biological parameters such as natural mortality, recruitment variability, and growth (Chen et al, 2003;Conn et al, 2010;Szuwalski and Punt, 2012;Methot and Wetzel, 2013;Maunder and Punt, 2013;Kolody et al, 2015). Historically, many of these parameters, particularly those related to growth, were estimated externally and then used as fixed parameters in stock assessment models, due to the challenges in estimating them internally.…”