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.

Climate change is expected to have significant impacts on the biology, abundance and distribution of transboundary fish stocks, not only among neighbouring countries within the jurisdictions of regional fisheries management organisations (RFMOs) but also between adjacent RFMOs. Using South Pacific albacore tuna ( Thunnus alalunga ) as a case study, we highlight how RFMOs need to understand the impacts of climate change on transboundary stocks under their purview with greater certainty. We identify four areas of research that should assist RFMOs to adapt their scientific processes—strengthened understanding of changes in the biology of target stocks; enhanced collection of data to support modelling; improved modelling of catch-per-unit of effort (CPUE) to better reflect climate change impacts on stock abundance for assessments; and ensuring that scientific advice is adaptive and robust to climate change, including through implementation of tested harvest strategies. Investments in these research areas should enable RFMOs to improve the science underpinning management measures designed to sustain transboundary stocks and increase fishery performance during climate change.

Spillover is a term commonly applied to the dispersal of fish and/or larvae from inside a closed area to areas open to fishing. The presence of spillover is often quantified by measuring gradients in attributes such as abundance or catch rates near the boundaries of closed areas or by measuring higher abundance inside closed areas compared to outside. It is commonly assumed that such gradients or ratios indicate that the closed area has benefitted the fishery and the total abundance of fish. We explore this assumption using a spatially explicit model of closed areas with different intensities of fishing and fish movement, and we find that such gradients will be expected any time there is higher abundance inside the closed area. However, such gradients do not necessarily indicate a benefit to the fishery either in terms of total catch or catch rate, and unless pre-closure fishing was intense, total abundance is not expected to rise significantly. We examine case studies that argue that spillover exists and leads to fishery benefits. We then evaluate the evidence for net benefits in these case studies and find those with evidence of net benefits all come from places where fishing pressure was intense. While most analysis come from quite small coastal closed areas, two studies of very large open-ocean closed areas are discussed, and we find that both suggest little overall impact on the tuna populations that support the main commercial fisheries affected by the closures in question.

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.

Large-scale, no-take marine protected areas (MPAs) have been established in several locations in the Pacific and expansion of such areas to reach 30% of the ocean area is actively promoted in some quarters. Justification for the establishment of large oceanic MPAs often includes the conservation benefits that they would bring for tuna stocks, which are the subject of important commercial fisheries in the Pacific. The aim of this paper was to evaluate the conservation efficacy of an existing MPA, the Phoenix Islands Protected Area (PIPA) and a series of large hypothetical MPAs each constituting approximately 33% of the western and central Pacific Ocean, for two important and contrasting tuna species, skipjack and bigeye tuna. The evaluation was conducted by comparing control and counterfactual simulations in which the estimated population and fishery dynamics of the species were modelled using a high-resolution modelling framework known as SEAPODYM (Spatial Ecosystem And Population DYnamics Model). We found that stock-wide conservation benefits of the PIPA for these species, assuming that total fishing effort is maintained, to be weak to non-existent, and only modest increases in spawning biomass of both species occur within and in the near vicinity of the PIPA itself. For the larger 33% hypothetical MPAs, changes in stock-wide spawning biomass were estimated to be -0.1% to +5.8% for skipjack tuna and +4.8% to +12.0% for bigeye tuna. Conservation efficacy of MPAs for species such as tropical tunas is limited by their wide larval dispersal and high mobility of later life stages, which spatially dissipate the protective effects of MPAs. Also, the displacement of fishing effort from MPAs to areas remaining open can have negative consequences for stocks and fisheries performance in those areas. We conclude that large oceanic MPAs are not likely to be effective frontline management tools for tropical tunas and other species having similar life history characteristics.

Climate-driven redistribution of tuna threatens to disrupt the economies of Pacific Small Island Developing States (SIDS) and sustainable management of the world’s largest tuna fishery. Here we show that by 2050, under a high greenhouse gas emissions scenario (RCP 8.5), the total biomass of three tuna species in the waters of ten Pacific SIDS could decline by an average of 13% (range = −5% to −20%) due to a greater proportion of fish occurring in the high seas. The potential implications for Pacific Island economies in 2050 include an average decline in purse-seine catch of 20% (range = −10% to −30%), an average annual loss in regional tuna-fishing access fees of US$90 million (range = −US$40 million to –US$140 million) and reductions in government revenue of up to 13% (range = −8% to −17%) for individual Pacific SIDS. Redistribution of tuna under a lower-emissions scenario (RCP 4.5) is projected to reduce the purse-seine catch from the waters of Pacific SIDS by an average of only 3% (range = −12% to +9%), indicating that even greater reductions in greenhouse gas emissions, in line with the Paris Agreement, would provide a pathway to sustainability for tuna-dependent Pacific Island economies. An additional pathway involves Pacific SIDS negotiating within the regional fisheries management organization to maintain the present-day benefits they receive from tuna, regardless of the effects of climate change on the distribution of the fish.

The impacts of climate change are expected to have profound effects on the fisheries of the Pacific Ocean, including its tuna fisheries, the largest globally. This study examined the combined effects of climate change on the yellowfin tuna population using the ecosystem model SEAPODYM. Yellowfin tuna fisheries in the Pacific contribute significantly to the economies and food security of Pacific Island Countries and Territories and Oceania. We use an ensemble of earth climate models to project yellowfin populations under a high greenhouse gas emissions (IPCC RCP8.5) scenario, which includes, the combined effects of a warming ocean, increasing acidification and changing ocean chemistry. Our results suggest that the acidification impact will be smaller in comparison to the ocean warming impact, even in the most extreme ensemble member scenario explored, but will have additional influences on yellowfin tuna population dynamics. An eastward shift in the distribution of yellowfin tuna was observed in the projections in the model ensemble in the absence of explicitly accounting for changes in acidification. The extent of this shift did not substantially differ when the three-acidification induced larval mortality scenarios were included in the ensemble; however, acidification was projected to weaken the magnitude of the increase in abundance in the eastern Pacific. Together with intensive fishing, these potential changes are likely to challenge the global fishing industry as well as the economies and food systems of many small Pacific Island Countries and Territories. The modelling framework applied in this study provides a tool for evaluating such effects and informing policy development.

The current management of the bigeye tuna (Thunnus obesus) fishery in the Western and Central Pacific Ocean (WCPO) is based on a "single-stock" paradigm. We investigated the spatial and temporal distribution of the neutral and potentially adaptive genetic diversity in samples collected from waters near Micronesia, Marshall, Phoenix and Gilbert Islands and French Polynesia using genotyping by sequence and single nucleotide polymorphisms (SNP). Analysis of 144 individuals and 2,682 SNP did not provide any substantial evidence to challenge the current assumption of panmixia in the WCPO using putatively neutral loci. However, there was a signal of potentially adaptive divergence (F-ST range 0.11-0.24) of samples collected in 2013 in the exclusive economic zone of the Phoenix Islands, possibly because of exposure to differing selective constraints. The geographic origin of these tuna remains to be determined, as the samples originate from tagging cruises that sample large schools of tuna, and some of those tuna have subsequently showed substantial movement. We highly recommend further studies involving large sample sizes of larvae and young-of-the-year across the region and over multiple years to uncover the underlying processes that are potentially driving adaptive divergence and to infer any biological significance it may have on conservation and management of tropical bigeye tuna fishery stock.

Abundance indices derived from fisheries-dependent data (catch-per-unit-effort or CPUE) are known to have potential for bias, in part because of the usual non-random nature of fisheries spatial distributions. However, given the cost and lack of availability of fisheries-independent surveys, fisheries-dependent CPUE remains a common and informative input to fisheries stock assessments. Recent research efforts have focused on the development of spatiotemporal delta-generalized linear mixed models (GLMMs) which simultaneously standardize the CPUE and predict abundance in unfished areas when estimating the abundance index. These models can include local seasonal environmental covariates (e.g. sea surface temperature) and a spatially varying response to regional annual indices (e.g. the El Niño Southern Oscillation) to interpolate into unfished areas. Spatiotemporal delta-GLMMs have been demonstrated in simulation studies to perform better than conventional, non-spatial delta-generalized linear models (GLMs). However, spatiotemporal delta-GLMMs have rarely been evaluated in situations where fisheries spatial sampling patterns change over time (e.g. fisheries expansion or spatial closures). This study develops a simulation framework to evaluate 1) how the nature of fisheries-dependent spatial sampling patterns may bias estimated abundance indices, 2) how shifts in spatial sampling over time impact our ability to estimate temporal changes in catchability, and 3) how including seasonal environmental covariates and/or regional annual indices in spatiotemporal delta-GLMMs can improve the estimation of abundance indices given shifts in spatial sampling. Spatiotemporal delta-GLMMs are then applied to a case study example where the spatial sampling pattern changed dramatically over time (contraction of the Japanese pole-and-line fishery for skipjack tuna Katsuwonus pelamis in the western and central Pacific Ocean). Results from simulations indicate that spatial sampling in proportion to the underlying biomass can produce similar abundance indices to those produced under random sampling. Though estimated abundance indices were not perfect, spatiotemporal GLMMs were generally able to disentangle shifts in spatial sampling from temporal changes in catchability when shifts in spatial sampling were not too extreme. Lastly, the inclusion of seasonal environmental covariates and/or regional oceanographic indices in spatiotemporal GLMMs did not improve abundance index estimation and in some cases resulted in degraded model performance.

Tunas are the focus of significant fisheries in the Pacific Ocean, where landings of four species - skipjack tuna (Katsuwonus pelamis), yellowfin tuna (Thunnus albacares), bigeye tuna (Thunnus obesus) and albacore tuna (Thunnus alalunga) - constitute approximately 70 % of the global tuna catch. Stock assessments for skipjack, yellowfin and bigeye tunas in the Pacific Ocean currently assume eastern and western stocks. For albacore tuna, separate North Pacific Ocean and South Pacific Ocean stocks are currently assumed. In each case, these geographic definitions reflect the historical development of fisheries management across the Pacific rather than biological considerations. There is widespread agreement that uncertainties surrounding the stock structures of these four tuna species could have important impacts on the population dynamics models used to assess their status and inform management options. Knowledge of stock structure is also essential for improved modelling of the effects of climate change on tuna distribution and abundance and associated implications for fisheries. This paper reviews current knowledge and understanding of the stock structures of skipjack, yellowfin, bigeye and South Pacific albacore tunas in the Pacific Ocean, by exploring available literature relating to their biology, movement and spatial dynamics. As a guide for future research in this area, we identify the main uncertainties in defining the stock structure of these four tunas in the Pacific, including i) spawning dynamics; ii) the degree of spawning area fidelity and localised residency; iii) the provenance of individuals in, and proportional contributions of self-replenishing populations to, fishery catches within the Pacific Ocean; iv) linkages with adjacent 'stocks', v) the effects of climate change on stock structure and proportional contributions of self-replenishing populations to fisheries; and vi) the implications of improved knowledge of tuna stock structure for stock assessment and climate change model assumptions and fisheries management. We also briefly propose some approaches that future studies could use to address these uncertainties.

Skipjack tuna (Katsuwonus pelamis) has historically been considered panmictic at the ocean basin scale because of the species’ life history and substantial absolute population size. However, recent advances in population genomics have enabled scientists to demonstrate population structure in other species that were likewise traditionally assumed to be panmictic. Accordingly, we used DArTseq, a proprietary platform of reduced-representation genome sequencing developed by Diversity Arrays Technology (DArTech) to genotype 222 skipjack tuna from 9 sample groups collected across 6 exclusive economic zones (EEZs) and over 6 years in the tropical Western and Central Pacific Ocean. This is one of the most fine-scale population genetics assessments of skipjack tuna to date. We found several statistically significant pairwise FST comparisons using purportedly neutral loci, but very little indication of structure based on observed and expected heterozygosity, effective population size, a Mantel test, AMOVA, numerous genetic clustering and population assignment tests, and exploration of correlation with oceanographic features. Significant pairwise FST values were often shallow and resulted in uncertainty about their biological significance. Furthermore, a test of the hypothesis that the geographic and temporal distribution of the potentially adaptive genetic diversity may depict cryptic fine scale structure using two different outlier detection methods, could not reject panmixia based on estimates of fixation indices and two clustering software. Our results support the presence of a single population of skipjack tuna in the tropical Western and Central Pacific Ocean, but highly recommend the expansion of the study area, in latitude, longitude, and seasonality, before drawing any global conclusions.

Integrated analysis (or integrated population modelling) methods have become the preferred approach for conducting stock assessments, and providing the basis for management advice for fish and invertebrate stocks since the publication of a seminal paper by Fournier and Archibald in 1982. Methods to assess fish stocks based on single-species, single-area, age-structured models are now standard, with the major debates associated with these models related to data choice, model configuration assumptions, and data weighting. However, the current generation of stock assessment packages is not addressing all of the needs of stock assessment analysts and managers. A major challenge for any next-generation stock assessment package is the set of extensions needed to assess stocks that do not satisfy the ‘well-mixed single-stock’ paradigm. In addition, the next-generation stock assessment package needs to: (a) be able to capture age and size/stage dynamics simultaneously yet computationally efficiently, (b) scale from data-rich to data-poor, (c) include some multi-species capability, and (d) more appropriately deal with temporal variation (e.g., random effects and state-space models). In relation to data, there is a need to ensure that the next-generation stock assessment package better handles tagging data (age-size/stage models may help in this regard), in particular, to be able to use close-kin mark-recapture data. Efficient methods are needed to share parameter priors among stocks (satisfying the promise of the ‘Robin Hood’ paradigm). The next-generation stock assessment package needs to have associated appropriate training programs and documentation. Adoption of such a package will be facilitated by a data entry system that is well-documented, does not require specification of inputs that will not be used in an application, has an expert system to configure default settings based on best practices, and has associated code to automatically produce diagnostic statistics. Some technical challenges that have plagued stock assessment for decades warrant continued attention (at the theoretical and applied level) such as automatic data weighting and tuning, how to handle spatial and stock structure, improved coding to facilitate application of state-of-the-art methods for quantifying uncertainty, and adoption of true state-space formulations to allow more parameters to be treated as random effects. Future needs for features cannot be anticipated, so the key design consideration for the next-generation stock assessment package is to be flexible and modifiable to meet the requirements of analysts and users.

We present a modelling approach to estimate and predict the dynamics of highly migratory albacore tuna populations in the South Pacific and Atlantic oceans. We use the previously developed model SEAPODYM (Spatial Ecosystem And POpulation DYnamics Model) with its parameter estimation approach based on the maximum likelihood estimation and adjoint method. The model describes temporal and spatial population dynamics using continuous advection-diffusion-reaction equations with an ageing term. We introduce a method to account for seasonal spawning migrations as movements of population density modelled in a Eulerian framework. The aim of this study is to explore the capacity of SEAPODYM to predict complex spatial dynamics of highly migratory species that are consistent with available data and existing knowledge on albacore biology. The geo-referenced fisheries dataset used for model calibration includes effort, catch and length-frequency distributions, constituting the total fishing pressure on the South Pacific albacore population over more than thirty years of exploitation. The model is then validated on the fishing data for Atlantic albacore, thus providing evidence of estimated model parameter invariance with respect to space and time. Given the robustness of the results, the quantitative approach presented here could be used to assist stock assessment and to improve management advice.

SEAPODYM is a model developed for investigating spatiotemporal dynamics of fish populations under the influence of both fishing and the environment. The model simulates age-structured population dynamics using advection–diffusion–reaction equations describing movement, recruitment, and natural and fishing mortality. The dynamic processes are constrained by environmental data and distributions of prey species. Model parameter estimation using fishing data was implemented earlier based on a maximum likelihood estimation (MLE) approach and adjoint technique. Here, we describe the integration of tagging data into the existing MLE approach with application to skipjack tuna (Katsuwonus pelamis) in the Pacific Ocean. We find that tagging data improve estimates of species habitat parameters and movement rates and hence allow better representation of spatial dynamics of fish population. Due to estimated lower diffusion and higher advection rates, the model predicts less non-observed “cryptic” biomass, which leads to the stock sizes being closer to those estimated by stock assessment models commonly used by tuna commissions.

In an effort to increase purse seine fishing efficiency for tropical tunas, over 30,000 drifting Fish Aggregating Devices (dFADs) are deployed every year by fishers in the Western and Central Pacific Ocean (WCPO). The use of dFADs also impacts ecosystems, in particular through marine pollution and dFAD beaching. This paper presents the first estimate of dFAD beaching events in the WCPO (>1300 in 2016-2017) and their distribution. Lagrangian simulations of virtual dFADs, released subject to contrasting deployment distributions, help us determine the relative importance of operational versus environmental drivers of dFADs drifting to beaching areas. The highest levels of beaching, occurring on Papua New Guinea and Solomon Islands, are likely a result of the prevailing westward oceanic circulation and subsequent local processes driving dFADs towards land. Similarly, high beaching rates in Tuvalu appear to be due to the general circulation of the WCPO. In contrast, beaching in Kiribati Gilbert Islands appear to be more strongly related to dFAD deployment strategy. These findings indicate that reducing beaching events via changes in deployment locations may be difficult. As such, management approaches combining dFAD deployment limits, the use of biodegradable dFADs, recoveries at-sea close to sensitive areas and/or beached dFAD removal should be considered.

The distribution of marine species is often modeled using Eulerian approaches, in which changes to population density or abundance are calculated at fixed locations in space. Conversely, Lagrangian, or individual-based, models simulate the movement of individual particles moving in continuous space, with broader-scale patterns such as distribution being an emergent property of many, potentially adaptive, individuals. These models offer advantages in examining dynamics across spatiotemporal scales and making comparisons with observations from individual-scale data. Here, we introduce and describe such a model, the Individual-based Kinesis, Advection and Movement of Ocean ANimAls model (Ikamoana), which we use to replicate the movement processes of an existing Eulerian model for marine predators (the Spatial Ecosystem and Population Dynamics Model, SEAPODYM). Ikamoana simulates the movement of either individual or groups of animals by physical ocean currents, habitat-dependent stochastic movements (kinesis), and taxis movements representing active searching behaviours. Applying our model to Pacific skipjack tuna (Katsuwonus pelamis), we show that it accurately replicates the evolution of density distribution simulated by SEAPODYM with low time-mean error and a spatial correlation of density that exceeds 0.96 at all times. We demonstrate how the Lagrangian approach permits easy tracking of individuals' trajectories for examining connectivity between different regions, and show how the model can provide independent estimates of transfer rates between commonly used assessment regions. In particular, we find that retention rates in most assessment regions are considerably smaller (up to a factor of 2) than those estimated by this population of skipjack's primary assessment model. Moreover, these rates are sensitive to ocean state (e.g. El Nino vs La Nina) and so assuming fixed transfer rates between regions may lead to spurious stock estimates. A novel feature of the Lagrangian approach is that individual schools can be tracked through time, and we demonstrate that movement between two assessment regions at broad temporal scales includes extended transits through other regions at finer-scales. Finally, we discuss the utility of this modeling framework for the management of marine reserves, designing effective monitoring programmes, and exploring hypotheses regarding the behaviour of hard-to-observe oceanic animals.

In PNAS, McDermott et al. (1) analyze a 2014–2016 central Pacific fishing surge, focusing on the Phoenix Islands Protected Area (PIPA) inside the Kiribati exclusive economic zone (EEZ). The authors incorrectly attribute the surge to the anticipated industrial fishing closure of PIPA and describe the phenomenon as a blue paradox (i.e., an unintended negative consequence of a conservation policy). However, a broader analysis demonstrates that this surge was unrelated to the closure of PIPA and was due to a strong El Nino event that created a fishing surge across multiple EEZs and high seas, not just PIPA (2). McDermott et al. (1) do not consider regional data that reveal confounding factors. Long-term catch … [↵][1]1To whom correspondence should be addressed. Email: hanich{at}uow.edu.au. [1]: #xref-corresp-1-1