The basis of natural resource management is decision making under uncertainty while balancing competing objectives. Within fisheries management, a process described as management strategy evaluation (MSE) is becoming increasingly requested globally to develop and test management procedures. In a fisheries or other natural resource context, a management procedure is a rule that predetermines the management response given feedback from the resource and is simulation tested to be robust to multiple uncertainties. MSEs are distinguished from other risk or simulation analyses by the explicit testing of the feedback mechanism that applies decision rule-based management advice back to the simulated population or ecosystem. Stakeholder input is frequently cited as a best practice in the MSE process, since it fosters communication and facilitates buy-in to the process. Nevertheless, due to the substantial additional cost, time requirement, and necessary scientific personnel, full stakeholder MSEs remain relatively uncommon. With this communication, we provide guidance on what constitutes an MSE, when MSEs should be undertaken or where simpler approaches may suffice, and how to prioritize the degree of stakeholder participation.

Length‐based methods provide alternatives for estimating the instantaneous total mortality rate (Z) in exploited marine populations when data are not available for age‐based methods. We compared the performance of three equilibrium length‐based methods: the length‐converted catch curve (LCCC), the Beverton–Holt equation (BHE), and the length‐based spawning potential ratio (LB‐SPR) method. The LCCC and BHE are two historically common procedures that use length as a proxy for age. From a truncated length‐frequency distribution of fully selected animals, the LCCC estimates Z with a regression of the logarithm of catch at length by the midpoint of the length‐bins, while the BHE estimates Z as a function of the mean length. The LB‐SPR method is a likelihood‐based population dynamics model, which—unlike the LCCC and BHE—does not require data truncation. Using Monte Carlo simulations across a range of scenarios with varying mortality and life history characteristics, our study showed that neither the LCCC nor the BHE was uniformly superior in terms of bias or root mean square error across simulations, but these estimators performed better than LB‐SPR, which had the largest bias in most cases. Generally, if the ratio of natural mortality (M) to the von Bertalanffy growth rate parameter (K) is low, then the BHE is most preferred, although there is likely to be high bias and low precision. If M/K is high, then the LCCC and BHE performed better and similarly to each other. Differences in performance among commonly used truncation methods for the LCCC and BHE were small. The LB‐SPR method did not perform as well as the classical methods but may still be of interest because it provides estimates of a logistic selectivity curve. The M/K ratio provided the most contrast in the performance of the three methods, suggesting that it should be considered for predicting the likely performance of length‐based mortality estimators.

Indices of relative abundance are one of the most important inputs into a stock assessment model. For many species, we must rely on several indices that routinely conflict with each other and which may result in biased and uncertain outputs. Here, we explored whether reconciled trends obtained from dynamic factor analysis (DFA) applied to conflicting indices can be used as a trend of relative abundance input into a stock assessment model. We simulated an age-structured population of two coastal shark species in the southeast United States to generate multiple disagreeing indices, reconciled the indices using DFA, and then inserted both the multiple conflicting survey indices and the simplified DFA-predicted trend into respective stock assessment models. We compared the results of each stock assessment model to simulated values to evaluate the relative performance of each approach. We found that the DFA-based assessment generally performed similarly to the conflicting index-based assessment and may be a useful assessment tool in situations where conflicting indices with different selectivities, catchabilities, variances, and missing data are present. DFA assessment results were more consistent across simulation scenarios and outperformed many conflicting index assessments when surveys underwent shifts in catchability and the underlying stock abundance exhibited contrast.

Stock-wide trends in fish relative abundance are challenging to obtain when a single, comprehensive survey is unavailable, and multiple, spatially, and/or temporally fragmented surveys must be relied upon instead. Indices of abundance from multiple surveys frequently have differing trends, resulting in obscured true abundance patterns of the resource. We use an age-structured simulation model of two coastal shark species in the southeast United States to explore the performance of dynamic factor analysis (DFA) for reconciling multiple indices of abundance that are in conflict. Survey-specific time-variation in catchability was induced to generate conflicting indices of abundance. Key simulation sensitivities included survey variability, abundance pattern in the resource, and missing years of survey data. We caution against using DFA when there is no contrast in the underlying stock abundance or when trends in catchability in all surveys result in no survey that is representative of stock abundance. When multiple representative surveys were available, DFA proved useful across species in estimating stock-wide trends from conflicting survey indices with different selectivities, catchabilities, variances, and, to a lesser extent, with missing data. Our results suggest that resolving contrasting patterns among multiple time-series of relative abundance can improve understanding of the temporal trend in stock abundance.