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Despite continued calls for the application of ecosystem‐based fisheries management, tactical fisheries management continues to be heavily reliant on single‐species stock assessments. These stock assessments rarely quantitatively integrate the effects of ecosystem processes on fish stock productivity. This lack of integration is ultimately driven by the complexity of interactions between populations, ecosystems and fisheries, which produces uncertainty when defining which processes to include and how to include them. Models developed using a structured hypothesis testing framework would allow formalizing uncertainties while underscoring the importance of incorporating different population and ecosystem processes to explain non‐stationary stock productivity. Here, we develop a conceptual framework for extending and comparing population dynamics models of increasing complexity. We illustrate the utility of the framework by investigating the population and ecosystem processes that most likely affected the differential recovery of two flatfish populations (American plaice and yellowtail flounder) on the Newfoundland Grand Banks over the past three decades. We found that yellowtail flounder population dynamics were primarily driven by recruitment variability, which was negatively affected by warmer climatological conditions, as indicated by an integrated regional climate index. Meanwhile, American plaice population dynamics were affected by a combination of temporal variability in recruitment and natural mortality, where natural mortality increased during colder than average conditions. By exploring hypotheses about the effects of population and ecosystem processes on population dynamics, this modelling framework will improve understanding about the drivers of shifts in population productivity while serving as a transparent and robust approach to support ecosystem‐based fisheries management. [ABSTRACT FROM AUTHOR]

It is challenging in fisheries stock assessment to estimate cohort dynamics from length‐based data for hard‐to‐age stocks, and existing approaches, for example, age‐structured catch‐at‐length models (ACL) are unable to account for length‐dependent processes within each cohort. Fisheries‐dependent data are usually considered the default input to stock assessment models. However, with widespread recognition of the uncertainty of fisheries‐dependent data and the increasing availability of high‐quality survey data, a new situation emerges in some fisheries where a stock assessment model based only on survey data can provide good estimation of population dynamics. We develop an age‐ and length‐structured statistical catch‐at‐length model (ALSCL) to estimate age‐based dynamics from survey catch‐at‐length data. This approach also provides a good basis to then integrate fisheries‐dependent data in the model. ALSCL can explicitly include length‐dependent mortality and growth within each cohort by simultaneously tracking the three‐dimensional dynamics across time, age, and length. We first use simulations of yellowtail flounder (Limanda ferruginea, Pleuronectidae) and bigeye tuna (Thunnus Obesus, Scombridae) to demonstrate that ALSCL outperforms ACL by providing more accurate estimates of age‐based population dynamics when length‐dependent processes are important. Next, we apply ALSCL to estimate the cohort dynamics of female yellowtail flounder on the Grand Bank off Newfoundland using survey catch‐at‐length, weight‐at‐length, and maturity‐at‐length data. We consider ALSCL as a hybrid between ACL and length‐structured stock assessment models that keeps the advantages of both, and its ability to simultaneously track age and length dynamics is an important step toward the next‐generation of fisheries stock assessment models. [ABSTRACT FROM AUTHOR]

An age-structured, spatial survey-based assessment model (SSURBA) is developed and applied to the Grand Banks stock (NAFO Divisions 3LNO) of American plaice (Hippoglossoides platessoides) in Newfoundland and Labrador. The state-space model is fit to annual spatial (i.e., three divisions) stock size-at-age research vessel (RV) survey indices that are assumed to be proportional to abundance. We model index catchability (q) as a logistic function of fish length, which varies with age, cohort, and the time of the survey; therefore, the model facilitates the estimation of q values that change spatially and temporally following changes in fish growth and survey gears. The SSURBA model produces division-level estimates of fishing mortality rates (F), stock productivity, and stock size relative to the logistic catchability assumption with q = 1 for fully selected ages. The spatial model allows us to include additional survey information compared with the space-aggregated assessment model (all of 3LNO) that is currently used to assess stock status. The model can provide estimates of relative catch, which we compare with reported catch trends to partially validate the model. [ABSTRACT FROM AUTHOR]

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The current stock assessment model for American plaice (Hippoglossoides platessoides) on the Grand Bank of Newfoundland (NAFO Divisions 3LNO) is a virtual population analysis (VPA). This model does not account for the considerable uncertainty about the landings data for this stock. Retrospective patterns have also been noted in the current assessment with overestimation of spawning stock biomass (SSB) and underestimation of fishing mortality (F). Via a thorough model selection process, we develop a state-space stock assessment model (SSM) for this stock that accounts for the uncertainties in the landings data and reduces the retrospective patterns. Our SSM fit the data well, with overall trends in SSB and average F (ages 9--14) similar to those estimated from the current VPA. The retrospective patterns for the SSM were reduced for both SSB and average F which should lead to the provision of better scientific advice for the management of this stock. An important result from our analysis suggests that the current assumption for natural mortality (M) in the stock assessment model may be too low. The lack of recovery of the stock of American plaice on the Grand Bank has often been attributed to overfishing, however fixing M within the model to be lower than is reasonable may be over-estimating the relative impact of F and subsequently over-stating the contribution of fishing mortality to the lack of recovery of the stock. [ABSTRACT FROM AUTHOR]

Spatial synchronicity in recruitment was examined for Atlantic cod ( Gadus morhua ) in the Newfoundland and southern Labrador (NL) and Flemish Cap regions. Recruitment was estimated using a cohort strength model for survey indices of abundance at pre-exploitation ages, and spatio-temporal covariation in recruitment was then examined. Patterns of recruitment synchrony were generally consistent with management boundaries. However, there were some exceptions that suggest structure at the sub-stock level and mixing across adjacent stock boundaries. In particular, we observed weak correlations between the eastern and western 3Ps RV (Research Survey) recruitment indices. We also observed strong correlations between the eastern 3Ps RV and the fall and spring 3NO RV recruitment indices, as well as fairly strong correlations between the southern 3L, central 3KL, and 3Ps sentinel GN and LT recruitment indices. Correlations between the summer 3M RV recruitment indices and all other survey recruitment indices included in our analysis were generally very weak, as was expected. We demonstrate that survey recruitment indices can be used to identify either areas of potential sub-stock structure or mixing or areas where recruitment drivers may be operating across or differentially within stock management boundaries. Hence, our results should improve the design of future stock structure research, and provide useful new information for fisheries management. [ABSTRACT FROM AUTHOR]

Stock assessment models often require an external estimate of the natural mortality rate (M) that is usually assumed to be the same for all ages and years in the model. Although the fixed M assumption can be a major oversimplification, model diagnostics (e.g. profile likelihoods) that can help provide an understanding of how the choice of M affects model fit are often not used in practice. In the state-space setting, model diagnostics are especially complicated because of the complex dependencies in the data caused by process errors. To get a better understanding of the effect of broad changes in M across all ages and years on the state-space model fit, we develop new methods that provide profile likelihoods for individual data sources (surveys, landings, age compositions) by decomposing the state-space integrated likelihood. We also use local influence diagnostics to assess the influence of age and year specific changes in M on model fit. We jointly call these methods M diagnostics and apply them to a case study for American plaice (Hippoglossoides platessoides) on the Grand Bank of Newfoundland. The M diagnostics indicate that most input data sources are fit better with a higher M in recent years. We suggest that M diagnostics should be routinely examined when formulating an assessment model. [ABSTRACT FROM AUTHOR]