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Having a realistic representation of ecosystems in fisheries models is important in the context of ecosystem‐based fisheries management (EBFM). While different modelling approaches support EBFM, accounting for trophic interactions and uncertainty in stock dynamics is important for management advice. Multispecies models exist, but are rarely used for assessments. Most stock assessments are single species models and predation is subsumed into natural mortality, which is often an assumed known value. The use of state‐space assessment models, which account for stochasticity in unobserved processes (process errors), is increasing. However, many stocks are managed assuming deterministic processes. Little is known of how ignoring predation and process errors in stock assessment can impact the perception of the stocks and therefore fisheries management. We developed an age‐structured multispecies operating model that simulated data with errors in observations, recruitment and fish abundance. Four estimation models (EMs) that differed according to whether or not they accounted for predation or process errors were fitted to the simulated data. Relative differences between true and predicted outputs were estimated as a measure of bias. Equilibrium unfished biomass was estimated for each model as a proxy reference point. Ignoring predation had the largest impact on stock perception and resulted in large bias in parameters, derived outputs and absolute or relative reference points. Estimating unobserved processes was not sufficient in limiting the bias when natural mortality was misspecified. Ignoring process errors had limited bias but the bias increased when no contrasts existed in fishing mortality over time. Looking solely at likelihood values to choose among models is misleading and predictive ability could be used to prevent selecting models that overfit the data. Synthesis and applications. Ignoring trophic interactions that occur in marine ecosystems induces bias in stock assessment outputs and results in low model predictive ability with subsequently biased reference points. While it may be difficult to estimate natural mortality when no data exist to inform it, stock managers should remember that, if predation is large, assuming a constant mortality over time and/or age could have large consequences on stock perception and reference point estimates and affect resulting management advice. Ignoring trophic interactions that occur in marine ecosystems induces bias in stock assessment outputs and results in low model predictive ability with subsequently biased reference points. While it may be difficult to estimate natural mortality when no data exist to inform it, stock managers should remember that, if predation is large, assuming a constant mortality over time and/or age could have large consequences on stock perception and reference point estimates and affect resulting management advice.

Increasing the knowledge of approaches to estimate the status of data-limited stocks is of crucial importance since the vast majority of stocks are data-limited, i.e., there is not enough data to conduct a fully integrated statistical catch-at-age or at-length assessment. Among the different data-limited methods, surplus production models (SPMs) are usually considered the most complete data-limited assessment methods since they are the only method that provides a full stock assessment. Due to high interest in the application of SPMs for assessing data-limited stocks, our contribution focuses on providing a practical review of these models and their corresponding characteristics. Additionally, we review the use of the surplus production concept in the “known biomass production models”, highlighting their potential through examples of relevant applications. After a general introduction to the formulation of SPMs, their framework and features, this review focuses on the SPMs most frequently applied by well-known marine research organisations: ASPIC (a stock-production model incorporating covariates), SPiCT (surplus-production model in continuous time) and JABBA (just another Bayesian biomass assessment). For each model, we provide details of its formulation and main features, in addition to evaluating the quality and characteristics of the available software. Based on this information, our comparative study highlights the advantages and disadvantages of each of the three SPMs. The conclusion provides recommendations for their use in the assessment of data-limited stocks, facilitating a decision about whether SPMs constitute an appropriate tool for guidance on and assessment of specific stocks. Finally, we evaluate which of the SPMs considered in this paper should be applied.

Fishery stock assessment is the basis of fishery management. This study explored the applicability of the Length-Based Spawning Potential Ratio method (LBSPR) to assess the exploitation status of data-limited fisheries. Using data from bottom trawl surveys in Zhanjiang Bay, the study estimated the relative fishing mortality ( F/M ) of Johnius belangerii stock. The results showed that the average lengths at 50% and 95% selectivity ( L S50 and L S95 ) were 105 mm and 145 mm, respectively, both of which are smaller than the lengths at 50% and 95% sexual maturity length ( L 50 = 125 mm and L 95 = 167 mm) determined via the logistic curve. The estimated spawning potential ratio (SPR) was 0.15 significantly below the reference threshold of 0.2. The study recommended the establishment of a minimum size range (137 to 150 mm) length for harvesting Johnius belangerii to enhance their reproductive potential. It also emphasized the importance of scientific monitoring of fishery resources and the ecological environment in Zhanjiang Bay to ensure sustainable management.

Ecosystem-based fisheries management (EBFM) aims to go beyond single-species management by incorporating ecosystem considerations to guarantee the sustainable use of marine resources. Although many countries have formally committed to the implementation of EBFM, at a practical level progress has been very slow. At the analytical level, many advances have been made, developing sophisticated ecosystem models and, on the other side, rigorous stock assessment models. Yet it is still unclear how these two parallel approaches can be brought together to provide an efficient EBFM. Here we first present the two groups of models, we then discuss ways to integrate both approaches and their resulting possible implications for fisheries advice towards sustainable management, with a special focus on spatially explicit information.

Estimation of population abundances in the absence of good observational data are notoriously difficult, yet urgently needed for biodiversity conservation and sustainable use of natural resources. In the field of fisheries research, management regulations have long demanded population abundance estimates even if data available are sparse, leading to the development of a range of fish stock assessment methods designed for data-poor populations. Here, we present methods developed within the context of fisheries research that can be applied to conduct population abundance estimations when facing data-limitations. We begin the review from the less data-demanding approaches and continue with more data-intensive ones. We discuss the advantages and caveats of these approaches, the challenges and management implications associated with data-poor stock assessments, and we propose the implementation of the Bayesian hierarchical framework as the most promising avenue for future development and improvement of the current practices.

Ensuring the sustainability of fisheries worldwide requires that scientific advice remain effective even when data and capacity are limited. To address these challenges, we propose a hierarchical assessment framework (HAF) capable of integrating auxiliary information, such as empirical indicators for fishing pressure, within a Bayesian state-space biomass dynamic modelling framework. The aim is to provide risk-equivalent advice to ensure that management does not penalise data-limited fisheries with undue precaution (and loss of potential yield), nor expose them to a higher risk of overexploitation. To achieve this, we evaluated performance using classification skill metrics, such as true skill, for stock status relative to maximum sustainable yield (MSY)-based reference points. Results demonstrate that incorporating auxiliary data, particularly fishing mortality indices from periods of high exploitation, substantially improves the accuracy of stock status classification. Adoption of hierarchical assessment frameworks will support targeted data collection and evidence-based, adaptive fisheries management.

To ensure sustainability, the Ecosystem Approach to Fisheries (EAF) requires the evaluation of the impacts of fisheries beyond the main targeted species, to include those on bycaught, endangered, threatened and protected populations and keystone species. However, traditional stock assessments require extensive datasets that are often unavailable for data-limited fisheries, particularly in small-scale settings or in the Global South. This study evaluates the robustness of length-based approaches for fish stock assessment by comparing simple indicators and quantitative methods using an age-structured Operating Model. Simulations were conducted for a range of scenarios, for a range of life-history types and recruitment and natural mortality dynamics. Results reveal that while length-based approaches can effectively track trends in fishing mortality, performance varies significantly depending on species-specific life histories and assumptions about key parameters. Simple indicators often matched or outperformed complex methods, particularly when assumptions about equilibrium conditions or natural mortality were violated. The study highlights the limitations of length-based methods for classifying stock status relative to reference points, but demonstrates their utility when used with historical reference periods or as part of empirical harvest control rules. The findings provide practical guidance for applying length-based approaches in data-limited fisheries management, ensuring sustainability in data- and capacity-limited situations.

The reproductive biology of albacore tuna, Thunnus alalunga, in the South Pacific Ocean was investigated with samples collected during broad-scale sampling between 2006 and 2011. Histology was done in a single laboratory according to standard protocols and the data analysed using generalized linear mixed-effects models. The sex ratio of albacore was female biased for fish smaller than approximately 60 cm FL and between 85 and 95 cm, and progressively more male biased above 95 cm FL. Spawning activity was synchronised across the region between 10°S and 25°S during the austral spring and summer where sea surface temperatures were ≥24 °C. The average gonad index varied among regions, with fish in easterly longitudes having heavier gonads for their size than fish in westerly longitudes. Albacore, while capable of spawning daily, on average spawn every 1.3 days during the peak spawning months of October to December. Spawning occurs around midnight and the early hours of the morning. Regional variation in spawning frequency and batch fecundity were not significant. The proportion of active females and the spawning fraction increased with length and age, and mature small and young fish were less active at either end of the spawning season than larger, older fish. Batch fecundity estimates ranged from 0.26 to 2.83 million oocytes with a mean relative batch fecundity of 64.4 oocytes per gram of body weight. Predicted batch fecundity and potential annual fecundity increased with both length and age. This extensive set of reproductive parameter estimates provides many of the first quantitative estimates for this population and will substantially improve the quality of biological inputs to the stock assessment for South Pacific albacore.