Catch-Per-unit-Effort (CPUE) from the Taiwanese longline fishery shows a rapid increase following the introduction of management measures that were designed to rebuild the stock. Age-group spatiotemporal standardization was applied to investigate CPUE patterns and to evaluate whether fishery-dependent evidence was consistent with the success of these management measures. The Taiwanese longline fishery provides the only remaining fishery-dependent indicator of adult Pacific bluefin tuna used in the ISC stock assessment. A reconstructed spatiotemporal dataset for this fishery (2003-2024) was assembled by combining dockside market inspection data, with fork length measured for >95% of landed fish, and independently reconstructed fishing effort from vessel data recorder-derived fishing days. Standardized adult CPUE indices were developed and contrasted using a traditional delta-generalized linear mixed model (delta-GLMM) and a spatiotemporal Vector Autoregressive Spatio-Temporal (VAST) framework. Outputs were generated for approximate age groups using the VAST framework ("VAST-at-age") to characterize cohort-resolved patterns and associated spatial distribution metrics (effective area occupied and center of gravity) during rebuilding. Age groups were approximated using deterministic length-based assignment. Model evaluation was conducted using descriptive diagnostics (cross-validation and retrospective analyzes) to assess internal consistency. Standardized adult indices showed increasing trends after 2012, with more rapid increases in recent years. Cohort-resolved outputs indicated that recent increases were dominated by younger adult groups (6-8 and 9-11 years), while older groups (>15 years) contributed proportionally less over the same period. Together, these results provide a cohort-resolved and spatially explicit description of changes in the adult index and provide fishery-dependent evidence consistent with stock assessment conclusions that reduced juvenile fishing mortality contributed to rebuilding of the stock.

Despite their potential to inform sustainable regional harvest and climate-resilient fisheries management, spatial stock assessment models remain underused for management advice. To identify barriers that inhibit broader use of these methods, we conducted a blinded international simulation experiment mimicking real-world stock assessment development when confronting spatial complexity. Seven analyst teams built spatially aggregated and spatially explicit assessment models using data simulated from high-resolution operating models based on Indian Ocean yellowfin tuna and Ross Sea Antarctic toothfish dynamics. Each team documented how assessment software platform, data analyses, model building approach, and diagnostics influenced model complexity and realism. A consensus emerged on key assessment building approaches: (1) conduct high-resolution data analyses to identify appropriate spatial structure; (2) start with simplified models and incrementally add complexity; (3) iteratively evaluate diagnostics to determine necessary spatial complexity; and (4) maintain models with different spatial structures to aid interpretation. The experiment also revealed several valuable insights for parameterising assessments, including consideration of data pre-processing with spatiotemporal models to better inform data-sparse regions; regression trees to identify fleet and spatial structure; trade-offs in complexity between productivity and movement dynamics to achieve tractable and stable model structures; and ensemble modelling approaches to address structural uncertainty. Our findings demonstrate that international collaborations and simulation experiments are crucial for addressing challenges in implementing spatial stock assessments and for evaluating whether their added complexity is justified given management objectives. Broader collaborations are encouraged to foster innovation in fisheries management and to help recognise the practical trade-offs between model parsimony and complexity.

For large-scale tropical tuna purse-seine fisheries, it is prohibitively costly to obtain adequate sampling coverage to estimate fleet-level catch composition solely from sample data. Logbook or observer data, with complete fleet coverage, are often available but may be considered unreliable for species composition. Previous studies have developed models, trained with sample data, to predict set-level species compositions based on environmental and operational covariates. Here, models were developed to predict well-level species composition from uncorrected observer data and covariates affecting the observers’ view of the catch during loading, with port-sampling data as the response variable. The analysis used paired, well-level data from sets made on floating objects by the Eastern Pacific Ocean tuna purse-seine fleet during 2023–2024. Results indicated that, overall, observer data proportions of bigeye (BET) and yellowfin tunas tended to be greater than the model-estimated proportions, with the opposite occurring for skipjack tuna (SKJ). However, vessel effects sometimes modified these tendencies. Model complexity was greatest for BET and least for SKJ. For BET, observer data proportions and model-estimated proportions were more similar when the vessel had a hopper. They were also more similar in 2023 as compared to 2024, suggesting sample data for bias adjustments should be collected annually. The approach shows potential for predicting the species composition of unsampled wells.

The Center for the Advancement of Population Assessment Methodology (CAPAM) was established in 2013, envisioned as an institute that could conduct, organize, and communicate stock assessment research with the aim of benefiting fisheries assessment efforts internationally. CAPAM’s activities have focused on its workshop series and consequent special issues in Fisheries Research. The information generated through CAPAM and its permanent recording as journal articles has greatly benefited the stock assessment community and can potentially contribute to modelling in general. We discuss what has made CAPAM successful, its future, and what could be done to reach the ultimate goal of producing a good practices guide for fisheries stock assessment.

Integrated fisheries stock assessment models (SAMs) and integrated population models (IPMs) are used in biological and ecological systems to estimate abundance and demographic rates. The approaches are fundamentally very similar, but historically have been considered as separate endeavors, resulting in a loss of shared vision, practice and progress. We review the two approaches to identify similarities and differences, with a view to identifying key lessons that would benefit more generally the overarching topic of population ecology. We present a case study for each of SAM (snapper from the west coast of New Zealand) and IPM (woodchat shrikes from Germany) to highlight differences and similarities. The key differences between SAMs and IPMs appear to be the objectives and parameter estimates required to meet these objectives, the size and spatial scale of the populations, and the differing availability of various types of data. In addition, up to now, typical SAMs have been applied in aquatic habitats, while most IPMs stem from terrestrial habitats. SAMs generally aim to assess the level of sustainable exploitation of fish populations, so absolute abundance or biomass must be estimated, although some estimate only relative trends. Relative abundance is often sufficient to understand population dynamics and inform conservation actions, which is the main objective of IPMs. IPMs are often applied to small populations of conservation concern, where demographic uncertainty can be important, which is more conveniently implemented using Bayesian approaches. IPMs are typically applied at small to moderate spatial scales (1 to 104 km2), with the possibility of collecting detailed longitudinal individual data, whereas SAMs are typically applied to large, economically valuable fish stocks at very large spatial scales (104 to 106 km2) with limited possibility of collecting detailed individual data. There is a sense in which a SAM is more data- (or information-) hungry than an IPM because of its goal to estimate absolute biomass or abundance, and data at the individual level to inform demographic rates are more difficult to obtain in the (often marine) systems where most SAMs are applied. SAMs therefore require more 'tuning' or assumptions than IPMs, where the 'data speak for themselves', and consequently techniques such as data weighting and model evaluation are more nuanced for SAMs than for IPMs. SAMs would benefit from being fit to more disaggregated data to quantify spatial and individual variation and allow richer inference on demographic processes. IPMs would benefit from more attempts to estimate absolute abundance, for example by using unconditional models for capture-recapture data.

Indices of abundance based on fishery catch-per-unit-effort (CPUE) are important components of many stock assessments, particularly when fishery-independent surveys are unavailable. Standardizing CPUE to develop indices that better reflect the relative abundance requires the analyst to make numerous decisions, which are influenced by factors that include the biology of the study species, the structure of the fishery of interest, the nature of the available data, and the objectives of the analysis such as how standardized data will be used in a subsequent assessment model. Alternative choices can substantially change the index, and hence stock assessment outcomes and management decisions. To guide decisions, we provide advice on good practices in 16 areas, focusing on decision points: fishery definitions, exploring and preparing data, misreporting, data aggregation, density and catchability covariates, environmental variables, combining CPUE and survey data, analysis tools, spatial considerations, setting up and predicting from the model, uncertainty estimation, error distributions, model diagnostics, model selection, multispecies targeting, and using CPUE in stock assessments. Often the most influential outcome of exploring and analysing catch and effort data is that analysts better understand the population and the fishery, thereby improving the stock assessment.

Conceptual models are simplified representations of the main components and processes of a dynamic system, the mechanisms by which they are related, and the ways they are observed (i.e., the data generating processes). Constructing a conceptual model (CM) should be the first step when planning a new stock assessment or updating previous assessments, because it can improve the modelling process by guiding the workflow and “modelling what to model”. CMs should be built by summarizing information about a system while also proposing hypotheses or assumptions about the uncertainties and unknown aspects. Several steps are necessary to build a CM: 1) gather known information about the species and the fisheries that interact with it, 2) state the objectives of the stock assessment, 3) define the spatial scale, 4) define the temporal scale, and 5) outline components and processes of the system (biological, fisheries and observation processes) and what drives them. Initial draft CMs should be based on the best available science and constructed using the fundamental principles of ecology, socioecology, fisheries and other relevant sciences. CMs offer a framework for integrating knowledge across domains, and benefit from an elicitation process. The elicitation process is a set of deliberate activities (e.g., workshops) that allow other experts and relevant parties to contribute with their knowledge to enrich draft CMs. CMs are not static entities but rather dynamic constructs that can identify future research directions and evolve to incorporate new insights and knowledge. Fisheries systems for highly migratory pelagic species in the Pacific Ocean (north Pacific Albacore tuna, eastern Pacific Dorado, and south Pacific Swordfish) are used as examples to illustrate how to develop CMs, and demonstrate improvements to the subsequent assessment models following development of the CMs.

Indices of relative abundance directly inform how population abundance changes over time, providing one of the most important pieces of information for a stock assessment. Ideally, indices of abundance should be calculated based on fishery-independent survey data. Survey data are characterized by a spatially random or fixed sampling design, and consistent employment of the same fishing gear and fishing operation across time. However, the unavailability of survey data for most tuna species means that the derivation of abundance indices for these species comes solely from fishery-dependent catch-per-unit-effort (CPUE). We conduct two simulation experiments, based on real fishery-dependent longline data, to quantitatively evaluate the impacts of reduced fishing effort on the standardized longline CPUE for bigeye tuna in the eastern Pacific Ocean. The key findings of the two simulation experiments are 1) a reduced spatial coverage of CPUE data leads to increased bias in the abundance index; 2) the index bias has a minor long-term trend if the reduced spatial coverage of CPUE data is not caused by local depletion; and 3) that bias has a positive long-term trend (i.e., hyper-stable abundance index) if the reduced spatial coverage of CPUE data co-occurs with a local depletion in the abandoned area. This bias, however, can be significantly reduced if the CPUE standardization model includes a temporal correlation structure in spatiotemporal random fields. In addition, the CPUE standardization model provides more realistic estimates of the coefficient of variation of fish abundance when its spatiotemporal random fields are assumed to be correlated in time. This study underscores the necessity of accounting for the temporal correlation structure in spatiotemporal random fields in cases where local depletion and depletion-driven fishery contraction co-occur.

Stock depletion level is an important concept in the assessment and management of exploited fish stocks because it is often used in conjunction with reference points to infer stock status. Both the depletion level and reference points can be highly dependent on the stock–recruitment relationship. Here, we show how depletion level is estimated in stock assessment models, what data inform the depletion level, and how the stock–recruitment relationship influences the depletion level. There are a variety of data that provide information on abundance. In addition, to estimate the depletion level, unexploited absolute abundance needs to be determined. This often means extrapolating the abundance back in time to the start of the fishery, accounting for the removals and the productivity. Uncertainty in the depletion level arises because the model can account for the same removals by either estimating low productivity (e.g., low natural mortality) and high carrying capacity or high productivity and a low carrying capacity, and by estimating different relationships between productivity and depletion level, which are strongly controlled by the stock–recruitment relationship. Therefore, estimates of depletion are particularly sensitive to uncertainty in the biological processes related to natural mortality and the stock–recruitment relationship and to growth when length composition data are used. In addition, depletion-based reference points are highly dependent on the stock–recruitment relationship and need to account for recruitment variability, particularly autocorrelation, trends, and regime shifts. Future research needs to focus on estimating natural mortality, the stock–recruitment relationship, asymptotic length, shape of the selectivity curve, or management strategies that are robust to uncertainty in these parameters. Tagging studies, including close-kin mark-recapture, can address some of these issues. However, the stock–recruitment relationship will remain uncertain.

Spatial models enable understanding potential redistribution of marine resources associated with ecosystem drivers and climate change. Stock assessment platforms can incorporate spatial processes, but have not been widely implemented or simulation tested. To address this research gap, an international simulation experiment was organized. The study design was blinded to replicate uncertainty similar to a real-world stock assessment process, and a data-conditioned, high-resolution operating model (OM) was used to emulate the spatial dynamics and data for Indian Ocean yellowfin tuna (Thunnus albacares). Six analyst groups developed both single-region and spatial stock assessment models using an assessment platform of their choice, and then applied each model to the simulated data. Results indicated that across all spatial structures and platforms, assessments were able to adequately recreate the population trends from the OM. Additionally, spatial models were able to estimate regional population trends that generally reflected the true dynamics from the OM, particularly for the regions with higher biomass and fishing pressure. However, a consistent population biomass scaling pattern emerged, where spatial models estimated higher population scale than single-region models within a given assessment platform. Balancing parsimony and complexity trade-offs were difficult, but adequate complexity in spatial parametrizations (e.g., allowing time- and age-variation in movement and appropriate tag mixing periods) was critical to model performance. We recommend expanded use of high-resolution OMs and blinded studies, given their ability to portray realistic performance of assessment models. Moreover, increased support for international simulation experiments is warranted to facilitate dissemination of methodology across organizations.

Marine fishes are heterogeneously distributed across their ranges according to population dynamics governed by complex spatiotemporal relationships between ontogenetic habitat usage, species interactions, environmental variability, and harvest patterns. However, few stock assessments incorporate spatial population structure in the determination of population status and sustainable catch limits. A small number of generalized stock assessment software platforms are utilized worldwide to assess a large number of marine fish populations. Although each platform relies on similar underlying population dynamics, the spatial capabilities and functionality often differ among them. We catalogue spatial dynamics and capabilities across stock assessment platforms to leverage collective experiences and identify future needs for next generation assessment software packages. Despite commonalities across platforms (e.g., most models allow for a single population with spatial heterogeneity, apportionment of recruitment, and age-varying connectivity), no single platform is flexible enough to address the full breadth of spatial dynamics observed for managed marine fish species. Our review clarifies spatial assessment design and modeling ‘good practices’, while emphasizing the need for more generalizable and modular next generation assessment platforms that can account for the spatiotemporal complexity of marine resources (such as natal homing and spawning migrations, ontogenetic movement patterns, metapopulation structure, and complex fleet dynamics). Generalized, spatially-integrated assessment platforms will be key decision-tools to account for spatiotemporal species and fishery interactions, particularly as managers attempt to address climate change and implement ecosystem-based fisheries management.

Estimating growth (increase in size with age) is an integral component of fish population assessment. The use of integrated assessment models combined with the influence of misfitting size composition data on results have led to renewed interest in how growth is modeled in the assessment process. The types of data available to describe the growth process control how the length-at-age relationship will be estimated. Many factors contribute to the complexity of estimating length-at-age, including multiple sources of biological variability and difficulties in getting representative samples. The growth process in the population dynamics model is linked to all other processes and data but most directly influences the assessment model through 1) converting numbers into weight and vice versa, 2) productivity, and 3) modifying fits of size composition data. In some cases, an assessment may be insensitive to moderate levels of misspecification of the growth process, and therefore, relatively simple treatments may be adequate. However, in many cases, especially those where the fit of size composition is influential in estimating scale, a more thorough treatment of the growth process is needed. A complete treatment of growth will estimate the most important forms of biological variability, including individual, sex-specific, temporal, and spatial variability. Several types of sampling bias, including selectivity, length-stratified sampling, and spatial and measurement error, will likely also need to be addressed. When sufficient data are available, assessment authors should consider estimating the growth process as part of the integrated assessment model or consider empirical approaches for situations with high biological variability and sampling bias.

There has been substantial progress in fitting population dynamics models to data and this has greatly improved management advice in a variety of situations from exploitation to conservation. One of the major developments has been integrated analysis where multiple diverse data sets are fit simultaneously within the same model. However, issues such as model misspecification, unmodelled process variation, and data weighting make integrated analysis problematic. Here I provide a personal perspective on a framework for Model Development (FMD) based on the Center for the Advancement of Population Assessment Methodology (CAPAM) workshops and special issues, my own research, and other information. The FMD is motivated by fisheries stock assessment but is relevant to any form of population dynamics modelling or modelling in general. I provide an outline of the modeling framework and discuss the important topic of data weighting. The FMD starts with one or more conceptual models which are implemented as population dynamics models fit to data using a comprehensively researched Good Practices Guide (GPG). The models are evaluated, improved, and selected, based on a diagnostic “expert” system that has been rigorously developed using a comprehensive simulation analysis. The final models that are accepted in the ensemble are equally weighted (until the data weighting issue is fully resolved) to provide management advice. I also outline necessary future research.

In response to concerns about the stock status of bigeye tuna (BET) in the Eastern Pacific Ocean, the InterAmerican Tropical Tuna Commission adopted additional management measures for BET in 2021, including an individual-vessel catch threshold system for the purse-seine fishery. Development of an enhanced port-sampling program for estimation of trip-level BET catch was also mandated to support member countries and their purseseine vessels in their conservation efforts. To this end, a pilot study was conducted in the ports of Manta and Posorja, Ecuador, in 2022. In these ports, well unloading often takes place with small containers, which are used to transport fish from inside the well to the vessel's wet deck. Using a high-frequency systematic sampling protocol, 63 wells of 37 trips with catch exclusively from floating-object (OBJ) sets were sampled from the start to the end of unloading, at a fixed interval from a random starting point, sampling about 10 % of all the containers of fish unloaded from each well. Notable large-scale pattern in the proportion of BET per container was found for 38 of the 56 wells that had BET catch. The proportion of BET per container, which could be high at the beginning, middle or end of the well unloading, sometimes varied by 0.40 or more. The magnitude of this largescale within-well pattern had a significant increasing relationship with the number of OBJ sets associated with the well catch, consistent with variability in species composition among OBJ sets being an important factor in determining variability in species composition during catch unloading. Simulation studies demonstrated that such large-scale within-well pattern has potential implications for OBJ-set well-level sampling design, where, as expected from sampling theory, a systematic sampling protocol for containers of fish generally resulted in lower mean squared error (MSE) on the proportion of BET in the well, compared to simple random sampling of containers of fish. The reduction in MSE was largely due to a reduction in variance. Simulations also showed that the within-well variance component of the trip-level estimator for the proportion of BET can be larger than the among-well variance component, unless the within-well sampling coverage is relatively high. To minimize the negative impact of within-well variability on the precision of the estimator, results indicate that for a sampling protocol with one systematic sample per well, the within-well coverage should be at least 2 % of containers of fish unloaded from the well, and preferably more than 3 %. Finally, simulation studies were conducted with observer well plan data from 2013 to 2022 to put the well-level results in a larger context. Results indicated that the level of within-well sampling coverage may affect the variance on fleet-level estimates of the proportion of BET in the catch, with potentially substantially higher variance when the within-well sampling coverage is low. Taken together, these results suggest that both the type of within-well sampling and the level of within-well sampling coverage have important implications for the variance on BET catch estimates, from the well level to the fleet level.

A twelve-year hiatus in fishery-independent marine mammal surveys in the eastern tropical Pacific Ocean (ETP), combined with a mandate to monitor dolphin stock status under international agreements and the need for reliable stock status information to set dolphin bycatch limits in the tuna purse-seine fishery, has renewed debate about how best to assess and monitor ETP dolphin stock status. The high cost of replicating previous ship-based surveys has intensified this debate. In this review, transect methods for estimating animal abundance from dedicated research surveys are considered, with a focus on both contemporary and potential methods suitable for surveying large areas for dolphin species that can form large, multi-species aggregations. Covered in this review are potential improvements to the previous ship-based survey methodology, other ship-based methods, alternative approaches based on high-resolution imagery and passive acoustics, and combinations of ship-based and alternative approaches. It is concluded that for immediate management needs, ship-based surveys, with some suggested modifications to improve precision, are the only reliable option despite their high cost. However, it is recommended that a top research priority should be development of composite methods. Pilot studies on the use of high-resolution imagery and passive acoustics for development of indices of relative abundance to be used in composite methods should be part of any future ship-based survey efforts.

The ideal stock assessment would be able to estimate all of the key parameters related to population processes within a framework that assigns appropriate weight to the data, fits the data adequately, and captures all sources of uncertainty related to estimation, including model uncertainty, process uncertainty, and observation uncertainty. The aim of good practice guidelines is to avoid the pitfalls of earlier analysis methods, and consequently provide assessments that reflect objective scientific information on which management decisions can be based. This paper outlines a framework for the component of a stock assessment related to fitting population dynamics models to monitoring data to support decision making, which follows from what would be considered good (but not necessarily best) practice in the field. The paper identifies current good and best practices related to selecting a model structure, parameterizing growth, recruitment, natural mortality and the stock-recruitment relationship, as well as how to select among model configurations based on diagnostics and weight data and priors within assessments based on the existing literature, including past Center for the Advancement of Population Assessment Methodology (CAPAM) workshop reports and the results of simulation studies that explored the performances of different ways to configure stock assessments.

Conditionally autoregressive (CAR) space-time models have wide applicability because spatial association structure can be captured through an adjacency matrix, without expressly relying on distance information. However, in the traditional spatio-temporal CAR (STCAR), the adjacency matrix is based on adjacency relationships, which can be problematic in practical applications where those relationships are dependent on non-spatial considerations. A new formulation of a parametric structure for the adjacency structure was developed, offering a functional and flexible solution to this problem. This method is applied to estimate bigeye tuna catch for the purse-seine fishery in the eastern Pacific Ocean for 2020–2021. During the COVID-19 pandemic, collection of some data types for this fishery was severely negatively impacted, resulting in non-random loss of data. To mitigate this, a STCAR model was developed, combining multiple sources of spatio-temporal data to obtain enhanced prediction results. The objective was to produce statistically similar catch estimates to the historical time series on which current fisheries management is based, thereby minimizing bias due to a change in estimation methodology. The traditional CAR formulation assumes uniform weight in defining spatial dependence between neighboring areas when they share boundaries or corners, and no weight when the areal units do not share boundaries or corners. The new parametric approach uses expert opinions to define weighted adjacency in the formulation where the 'weight' is based on how substitutions rules apply when data is missing and areas can be 'adjacent' in terms of characteristics rather than simply spatially. The new parametric formulation of the adjacency matrix performed best, as compared to traditional formulations, was asymptotic as the parameter got smaller. This parametric specification of the adjacency matrix, along with an AR (1) structure in the STCAR, performed robustly when a sensitivity analysis was carried out by simulating non-random pandemic-like data-loss for pre-pandemic years.

The values used for natural mortality (M) are very influential in stock assessment models, affecting model outcomes and management advice. Natural mortality is one of the most difficult demographic parameters to estimate, and there is often limited information about the true levels. Here, we summarise the evidence used to estimate natural mortality at age for the four main stocks of yellowfin tuna (Indian, Western and Central Pacific, Eastern Pacific, and Atlantic Oceans), including catch curves, tagging experiments, and maximum observed age. We identify important issues for estimating M such as variation with age linked to size, maturity state or senescence, and highlight information gaps. We describe the history of natural mortality values used in stock assessments by the tuna Regional Fisheries Management Organisations responsible for managing each stock and assess the evidence supporting these values. In June 2021, an online meeting was held by the Center for the Advancement of Population Assessment Methodology (CAPAM), to provide advice and guidance on practices for modelling natural mortality in fishery assessments. Based on approaches presented and discussed at the meeting, we develop a range of yellowfin tuna natural mortality estimates for each stock. We also recommend future research to improve these estimates of natural mortality.