Objective Variability in somatic growth of marine fish can affect their reproductive potential and survival and, therefore, the productivity of a population. Understanding how growth might vary among species can improve predictions of population status and responses to environmental change. Our objective was to characterize the variability in growth and body condition of groundfish species along the U.S. West Coast to support their monitoring and assessment.Methods We used geostatistical models to estimate growth rate and body condition, two interrelated traits associated with somatic growth, across space and time for nine commercially important U.S. West Coast groundfish species. We fit generalized linear mixed models with Gaussian Markov random fields to biological data collected from annual bottom trawl surveys to estimate variability at a 4- x 4-km spatial resolution.Results Our models uncover spatiotemporal variability in growth rate and body condition in all nine groundfish species with limited trends shared among species with similar traits, suggesting a greater influence from niche partitioning acting on local scales. Such interspecific differences in growth rate and body condition also occurred at regional scales, with some species exhibiting positive responses while others declined.Conclusions These findings reveal the dynamic nature of somatic growth among groundfish species and provide insight into potential mechanisms of its variability that could be considered within climate-enhanced assessments of population status for marine fish. United States West Coast groundfish species exhibit fine-scale variability in their growth. Such variability is likely attributed to local habitat conditions and can affect how we assess these populations for management.

Adaptation to climate change can have trade-offs and unintended outcomes that may add to climate impacts. Identifying how these consequences arise in local contexts is an important step in climate adaptation planning, but the tools for doing so are still evolving. We demonstrate how social-ecological qualitative network models (QNMs) can be used to explore the consequences of climate adaptation in fisheries. Drawing on the dynamics of the U.S. West Coast Dungeness crab fishery, we simulate a climate-intensified harmful algal bloom in a model fishing community and compare outcomes for human well-being, with and without climate adaptation. We consider a range of climate adaptations, from coping mechanisms to transformational adaptation, based on actions identified during participatory scenario planning. We first use QNMs to identify how common trade-offs arise across adaptation strategies, specifically highlighting how diverse strategies focusing on material loss result in persistent negative outcomes for community relationships and culture. We then explore alternative configurations of model structure to understand how plausible diversity in a social-ecological system can contribute to unintended, inequitable outcomes from climate adaptation. In our QNMs, altering in-season flexibility (fishers' capacity to increase effort in alternative fisheries not affected by a harmful algal bloom) greatly influenced the degree to which climate adaptation reduced or intensified harmful algal bloom impacts on well-being. We demonstrate that QNMs are a useful tool for climate adaptation planning because they can be used to explore common trade-offs across adaptation options; highlight potentially inequitable outcomes associated with system complexity and uncertainties; and direct future research and monitoring priorities to help early identification of unintended consequences.

Effective monitoring of fish populations is critical for understanding life-history traits such as maturation or growth, which directly impact population dynamics and fisheries management. Long-term monitoring efforts around the world are faced with rising survey costs and a more uncertain future, as environmental variability is projected to increase. Together, these pressures necessitate more efficient sampling designs. In this study, we use information theory (Fisher Information) to evaluate and optimize life-history sampling, focusing on how variation in fish growth or maturation can guide more informed sampling decisions. Fisher Information is closely related to variance, in that samples with higher information result in greater precision. Using life-history parameters from eight commercially important groundfish species assessed on the U.S. West Coast, we develop three case studies illustrating how sampling strategies can be improved using Fisher Information. The first example focuses on estimating maturity ogives in the absence of spatial and temporal variability; our second case study incorporates spatial and temporal variation; and the third adds a second life-history trait (growth) to jointly optimize sampling for maturity. Our results indicate that for informing maturity ogives, Fisher Information is maximized at lengths near the inflection point, and that the range of most informative lengths can shift across space and time due to habitat or environmental variation. When both growth and maturation are considered, optimal sampling windows broaden for some species but remain narrow for others. Our simulation results also show that sampling fish in proportion to Fisher Information, rather than random sampling, can reduce sampling effort by more than 50 % while maintaining or improving the precision of parameter estimates. While the benefits of using Fisher Information are species specific, these findings suggest that adaptive sampling based on Fisher Information can substantially increase the efficiency and effectiveness of monitoring programs, especially under budgetary or logistical constraints.

As global climate change and anthropogenic activities amplify widespread environmental variability, there is a strong need for management strategies that incorporate relationships between ecosystem components. This need is especially apparent when changes in environmental drivers cause threshold responses (abrupt, nonlinear changes) in ecosystems. Such ecological thresholds can provide useful reference points for management decisions. However, methods for detecting thresholds in empirical datasets may fail to find an existing threshold, find one that does not exist, or be biased in their estimates of threshold locations. These types of threshold misspecifications can result in high conservation and socioeconomic costs. Simulation studies can mitigate these risks by providing information about method performance across different scenarios. Here, we constructed a series of simulations to evaluate the robustness of threshold detection with generalized additive models (GAMs) when exposed to a variety of common, real-world data characteristics. GAMs generally performed best when time series were long, observation error was low, thresholds were crossed fairly frequently, and covariates were accounted for. Over realistic ranges of values, observation error and frequency of threshold crossing had stronger effects on threshold detectability than time series length. Importantly, detectability was found to depend on both the shape of the threshold relationship and the statistical definition of the threshold location. As a case study, we applied this threshold detection method to an empirical dataset relating ocean temperature and the spatial distribution of Pacific hake (Merluccius productus), the largest volume fishery on the US West Coast. While the data suggest no statistical evidence for a threshold relationship, our simulations indicated approximately equal chances of true and false threshold detection given currently available data. Our results provide general guidelines for where threshold detection with GAMs is likely to be robust and are useful in the context of indicator development for ecosystem-based management in a variable world.

Climate change can impact marine ecosystems through many biological and ecological processes. Ecosystem models are one tool that can be used to simulate how the complex impacts of climate change may manifest in a warming world. In this study, we used an end-to-end Atlantis ecosystem model to compare and contrast the effects of climate-driven species redistribution and projected temperature from three separate climate models on species of key commercial importance in the California Current Ecosystem. Adopting a scenario analysis approach, we used Atlantis to measure differences in the biomass, abundance, and weight at age of pelagic and demersal species among six simulations for the years 2013-2100 and tracked the implications of those changes for spatially defined California Current fishing fleets. The simulations varied in their use of forced climate-driven species distribution shifts, time-varying projections of ocean warming, or both. In general, the abundance and biomass of coastal pelagic species like Pacific sardine (Sardinops sagax) and northern anchovy (Engraulis mordax) were more sensitive to projected climate change, while demersal groups like Dover sole (Microstomus pacificus) experienced smaller changes due to counteracting effects of spatial distribution change and metabolic effects of warming. Climate-driven species distribution shifts and the resulting changes in food web interactions were more influential than warming on end-of-century biomass and abundance patterns. Spatial projections of changes in fisheries catch did not always align with changes in abundance of their targeted species. This mismatch is likely due to species distribution shifts into or out of fishing areas and emphasizes the importance of a spatially explicit understanding of both climate change effects and fishing dynamics. We illuminate important biological and ecological pathways through which climate change acts in an ecosystem context and end with a discussion of potential management implications and future directions for climate change research using ecosystem models.

Climate change will alter ecological dynamics, affecting the relative abundance of species. A primary challenge is whether and how to modify natural resource management practices to address these changes. We explored a model of a harvested fish population experiencing climate-driven changes in demography, finding that climate impacts impose a choice between management strategies that favor fishery yield or population biomass but not both. When climate caused a population's carrying capacity to increase, or its productivity to decrease, a climate adaptive strategy relying upon this updated information maintained higher population biomass but produced similar or lower yield than fixed management pegged to historical conditions. In contrast, when climate caused a population's carrying capacity to decrease, or its productivity to increase, a climate adaptive strategy produced greater yield but maintained lower population biomass. Both strategies prevented a population from becoming overfished (too small to achieve maximum yield), but the fixed management strategy could impose more excessive annual harvest rates (overfishing). These insights suggest climate adaptive management may not always outperform a fixed strategy. Yet in U.S. fisheries we found routine assessment of population status modifies demographic parameters, implicitly shifting management reference points that affect fishery yield and population biomass. Participatory processes can illuminate these impacts, creating opportunities to co-develop weightings for conservation and harvest objectives.

In the California Current Ecosystem, the California Undercurrent (CU) is the predominate subsurface current that transports nutrient-rich water from southern California poleward. In this study, we used a large dataset of spatially explicit in situ observations of Pacific hake ( Merluccius productus) and the CU (36.5–48.3°N) to estimate relationships between northward undercurrent velocity and hake distribution and determine whether these relationships vary across space or life-history stage. We found that both hake occurrence and density had strong spatially complex relationships with the CU. In areas north of 44°N (central Oregon), the CU effect was spatially consistent and opposite for occurrence (negative) and density (positive), indicating that hake may aggregate in areas of high northward velocity in this region. In areas south of 44°N, the CU effect showed a cross-shelf gradient for both occurrence and density, indicating a more nearshore hake distribution when northward velocity is higher in this region. Together, our results suggest that future changes in the CU due to climate change are likely to impact hake differently in northern and southern areas.

Forecasting the recruitment of fish populations with skill has been a challenge in fisheries for over a century. Previous large-scale meta-analyses have suggested linkages between environmental or ecosystem drivers and recruitment; however, applying this information in a management setting remains underutilized. Here, we use a well-studied database of groundfish assessments from the West Coast of the USA to ask whether environmental variables or ecosystem indicators derived from long-term monitoring datasets offer an improvement in our ability to skilfully forecast fish recruitment. A secondary question is which types of modelling approaches (ranging from linear models to non-parametric methods) yield the best forecast skill. Third, we examine whether simultaneous forecasting of multiple species offers an advantage over generating species-specific forecasts. We find that for approximately one third of the 29 assessed stocks, ecosystem indicators from juvenile surveys yields the highest out of sample predictive skill compared to other covariates (including environmental variables from Regional Ocean Modeling System output) or null models. Across modelling approaches, our results suggest that simpler linear modelling approaches do as well or better than more complicated approaches (reducing out of sample Root Mean Square Error by similar to 40% compared to null models), and that there appears to be little benefit to performing multispecies forecasts instead of single-species forecasts. Our results provide a general framework for generating recruitment forecasts in other species and ecosystems, as well as a benchmark for future analyses to evaluate skill. The most promising applications are likely for species that are short lived, have relatively high recruitment variability, and moderate amounts of age or length data. Forecasts using our approach may be useful in identifying covariates or mechanisms to include in operational assessments but also provide qualitative advice to managers implementing ecosystem based fisheries management.

Modification of food webs is a frequent cause of shifts in ecosystem states that resist reversal when the food web is restored to its original condition. We used the restoration of the large carnivore guild including gray wolves (Canis lupis), cougars (Felis concolor), and grizzly bears (Ursus arctos horribilis) to the northern range of Yellowstone National Park as a model system to understand how ecosystems might resist reconfiguration after the restoration of apex predators to the food web. The absence of wolves, cougars, and grizzly bears for nearly a century from the northern range was the primary cause of dramatic changes in riparian plant communities. Willows (Salix spp.) were suppressed in height by intense browsing by the dominant herbivore, elk (Cervus canadensis). The loss of activity by beavers (Castor canadensis) coincided with the loss of tall willows. We hypothesized that intense elk browsing interrupted the mutualism between willow and beavers: ecosystem engineering by beavers was a critical component of willow habitat and tall willows were a critical component of habitat for beavers. This interruption made riparian communities resilient to the disturbance caused by the restoration of apex predators. We hypothesized further that reductions in elk browsing attributable to reductions in elk population size were not sufficient to prevent the suppression of willow growth. To test these hypotheses, we conducted a 20-year, factorial experiment that crossed simulated beaver dams with the exclusion of browsing. We found that willows grew to heights expected for restored communities only in the presence of dams and reduced browsing. Willows experiencing ambient conditions remained well below this expectation. We found no difference in heights or growth rates of willows in experimental controls and willows in 21 randomly chosen sites, confirming that the results of the experiment were representative of range-wide conditions. A reorganized community of large herbivores was implicated in the suppression of willow growth. We conclude that the restoration of large carnivores to the food web failed to restore riparian plant communities on Yellowstone's northern range, supporting the hypothesis that this ecosystem is in an alternative stable state caused primarily by the extirpation of apex predators during the early 20th century.

By incorporating trophic interactions and temperature-dependent bioenergetics, multi-species models such as CEATTLE (climate-enhanced age-based model with temperature-specific trophic linkages and energetics) are a step towards ecosystem-based stock assessment and management of high-value commercial species such as Pacific hake (Merluccius productus). Hake are generalist predators and previous studies in the California Current Ecosystem have determined that their diet consists of similar to 30% cannibalism. We used CEATTLE to include cannibalism in a model of hake population dynamics and re-examined hake diet data to determine the proportion by age that can attributed to cannibalism. The proportion was highly variable, ranging between 0 and 80% of stomach contents by weight. When included in the CEATTLE model, the estimated spawning biomass, total biomass, and recruitment increased by 15, 23, and 58%, on average, relative to the single-species model, due to the estimation of time- and age-varying predation mortality, primarily for age-1 hake. The effects of cannibalism varied over time, with further increases in total biomass and recruitment resulting from the age structure of the population following large cohorts in 1980 and 1984. Results from the cannibalism model could be used to inform the estimation of time- and age-varying mortality in the single-species assessment and as a pathway for including ecosystem information in management through environmental and trophic drivers of variability in mortality.

Sablefish ( Anoplopoma fimbria) of the Northeast Pacific support a highly mobile, valuable fishery resource currently managed as three separate populations. Recent work has shown sablefish to be genetically mixed; have high movement rates; and have synchronous biomass trends, including recent declines. A management strategy evaluation was developed with stakeholders and scientists from three regions to investigate whether spatially structured management paradigms might result in better conservation and economic outcomes. The management strategy evaluation includes a transboundary operating model to represent spatial population dynamics including movement and a delay–difference estimation method with varying spatial complexities and potential stratifications, and harvest control rules. Mismatches in the spatial scale of management and the underlying biological units pose a crucial risk of localized depletion in the southern U.S. West Coast. This study presents one of the first transboundary, spatially-explicit management strategy evaluations conditioned to actual data. These results underscore the importance of spatial management strategy evaluation tools and implications when regional management is conducted in isolation. Future work should incorporate additional spatial hypotheses and investigate the drivers of recruitment patterns range-wide.

Understanding environmental drivers of recruitment variability in marine fishes remains an important challenge in fish ecology and fisheries management. We developed a conceptual life-history model for Pacific hake (Merluccius productus) along the west coast of the United States and Canada to generate stage-specific and spatiotemporally-specific hypotheses regarding the oceanographic and biological variables that likely influence their recruitment. Our model included seven life stages from pre-spawning female conditioning through pelagic juvenile recruitment (age-0 fish) for the coastal Pacific hake stock. Model-estimated log recruitment deviations from the 2020 hake assessment were used as the dependent variable, with predictor variables drawn primarily from a regional ocean reanalysis for the California Current Ecosystem. Indices of prey and predator abundance were also included in our analysis, as were predictors of local- and basin-scale climate. Five variables explained 59% of the recruitment variability not accounted for by the stock-recruitment relationship in the hake assessment. Recruitment deviations were negatively correlated with May-September eddy kinetic energy between 34.5 degrees and 42.5 degrees N, the North Pacific Current Bifurcation Index, and Pacific herring (Clupea pallasii) biomass during the spawner preconditioning stage, alongshore transport during the yolk-sac larval stage, and the number of days between storm events during the first-feeding larval stage. Other important predictors included upwelling strength during the preconditioning stage, the number of calm periods during the first-feeding larval stage, and age-1 hake predation on age-0 pelagic juveniles. These findings suggest that multiple mechanisms affect Pacific hake survival across different life stages, leading to variability in population-level recruitment.

Marine heatwaves are increasingly affecting marine ecosystems, with cascading impacts on coastal economies, communities, and food systems. Studies of heatwaves provide crucial insights into potential ecosystem shifts under future climate change and put fisheries social- ecological systems through “stress tests” that expose both vulnerabilities and resilience. The 2014– 16 Northeast Pacific heatwave was the strongest and longest marine heatwave on record and resulted in profound ecological changes that impacted fisheries, fisheries management, and human livelihoods. Here, we synthesize the impacts of the 2014– 2016 marine heatwave on US and Canada West Coast fisheries and extract key lessons for preparing global fisheries science, management, and industries for the future. We set the stage with a brief review of the impacts of the heatwave on marine ecosystems and the first systematic analysis of the economic

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.

Management Strategy Evaluation (MSE) is a decision-support tool for fisheries management.MSE uses closed-loop simulation to evaluate the long-term performance of management strategies with respect to societal goals like sustainability and profits (Punt et al., 2014 ; Figure 1;Smith, 1994).Management strategies are pre-defined decision rules that can dynamically adjust management advice given an estimate of population status.In addition to specifying management actions, management strategies may specify how a stock assessment model is configured to determine the size and status of a population (Sainsbury et al., 2000).Within MSE simulations, operating models (OMs) represent the hypothesized dynamics and relevant complexity of the system.Multiple OMs are typically generated for a single MSE to reflect different uncertainties and assess management performance under uncertainty.Developing suitable OMs requires an analyst to, at a minimum, define: the life history characteristics of the population and the fishing effort and selectivity of all fisheries affecting the population; and consider: the spatial distribution of the population and any critical environmental covariates or species interactions.OMs should be calibrated (or "conditioned") on available data to ensure that model projections are consistent with historical observations (Punt et al., 2014).Due to the many considerations, developing sufficient OMs is time-intensive.Fortunately, the requirements for specifying OMs are largely the same as the requirements for developing a stock assessment.Due to the overlap in requirements and the millions of dollars invested in developing stock assessments (Methot, 2015), MSE approaches that build on previous stock assessment products can increase productivity (Maunder, 2014).Stock assessment models for federally managed species in the U.S. undergo substantial scrutiny during a peer review process (Brown et al., 2006;Lynch et al., 2018), and thus stock assessment models provide an excellent starting point for OMs used in MSE.

Potential and emerging fisheries create challenges and opportunities for fishery managers who need to decide how to sustainably manage a fishery that does not yet exist. A new Pacific hake stock off the west coast of Mexico has been identified. The Mexican government is interested in assessing the feasibility of a new commercial fishery. This work proposes and analyzes alternative potential fishery management measures for this possible new fishery under biological and market uncertainties. Results indicate that a new fishery could be biologically sustainable and economically profitable under a set of management strategies and control rules. A limited access strategy with low effort is recommended because it is most profitable per vessel and biologically cautious, considering the high uncertainty associated with the exploitation of an unfished stock. Despite the combination of high operating costs and low prices the fishery could still be profitable in the long-term, although there is risk of overexploitation if high fishing effort is allowed. Nevertheless, our results suggest low risk of fishing down the dwarf hake stock if the fishery is managed under low effort levels, allowing for sufficient opportunities for data collection and adaptive management. This work addresses the rare opportunity of assessing a fishery previous to its operation, with all the associated data limitations and uncertainty.

Using multi-species time series data has long been of interest for estimating inter-specific interactions with vector autoregressive models (VAR) and state space VAR models (VARSS); these methods are also described in the ecological literature as multivariate autoregressive models (MAR, MARSS). To date, most studies have used these approaches on relatively small food webs where the total number of interactions to be estimated is relatively small. However, as the number of species or functional groups increases, the length of the time series must also increase to provide enough degrees of freedom with which to estimate the pairwise interactions. To address this issue, we use Bayesian methods to explore the potential benefits of using regularized priors, such as Laplace and regularized horseshoe, on estimating interspecific interactions with VAR and VARSS models. We first perform a large-scale simulation study, examining the performance of alternative priors across various levels of observation error. Results from these simulations show that for sparse matrices, the regularized horseshoe prior minimizes the bias and variance across all inter-specific interactions. We then apply the Bayesian VAR model with regularized priors to a output from a large marine food web model (37 species) from the west coast of the USA. Results from this analysis indicate that regularization improves predictive performance of the VAR model, while still identifying important inter-specific interactions.

The environmental conditions that marine populations experience are being altered because of climate change. In particular, changes in temperature and increased variability can cause shifts in spatial distribution, leading to changes in local physiological rates and recruitment success. Yet, management of fish stocks rarely accounts for variable spatial dynamics or changes in movement rates when estimating management quantities such as stock abundance or maximum sustainable yield. To address this concern, a management strategy evaluation (MSE) was developed to evaluate the robustness of the international management system for Pacific hake, an economically important migratory stock, by incorporating spatio-temporal population dynamics. Alternative hypotheses about climate-induced changes in age-specific movement rates, in combination with three different harvest control rules (HCR), were evaluated using a set of simulations that coupled single-area estimation models with alternative operating models representing spatial stock complexity. Movement rates intensified by climate change caused a median decline in catches, increased annual catch variability, and lower average spawning biomass. Impacts varied by area and HCR, underscoring the importance of spatial management. Incorporating spatial dynamics and climate change effects into management procedures for fish stocks with spatial complexity is warranted to mitigate risk and uncertainty for exploited marine populations.

One of the significant challenges to using information and ideas generated through ecosystem models and analyses for ecosystem-based fisheries management is the disconnect between modeling and management needs. Here we present a case study from the U.S. West Coast, the stakeholder review of NOAA’s annual ecosystem status report for the California Current Ecosystem established by the Pacific Fisheries Management Council’s Fisheries Ecosystem Plan, showcasing a process to identify management priorities that require information from ecosystem models and analyses. We then assess potential ecosystem models and analyses that could help address the identified policy concerns. We screened stakeholder comments and found 17 comments highlighting the need for ecosystem-level synthesis. Policy needs for ecosystem science included: (1) assessment of how the environment affects productivity of target species to improve forecasts of biomass and reference points required for setting harvest limits, (2) assessment of shifts in the spatial distribution of target stocks and protected species to anticipate changes in availability and the potential for interactions between target and protected species, (3) identification of trophic interactions to better assess tradeoffs in the management of forage species between the diet needs of dependent predators, the resilience of fishing communities, and maintenance of the forage species themselves, and (4) synthesis of how the environment affects efficiency and profitability in fishing communities, either directly via extreme events (e.g., storms) or indirectly via climate-driven changes in target species availability. We conclude by exemplifying an existing management process established on the U.S. West Coast that could be used to enable the structured, iterative, and interactive communication between managers, stakeholders, and modelers that is key to refining existing ecosystem models and analyses for management use.

Management strategy evaluation (MSE) is a simulation approach that serves as a “light on the hill” ( Smith, 1994 ) to test options for marine management, monitoring, and assessment against simulated ecosystem and fishery dynamics, including uncertainty in ecological and fishery processes and observations. MSE has become a key method to evaluate trade-offs between management objectives and to communicate with decision makers. Here we describe how and why MSE is continuing to grow from a single species approach to one relevant to multi-species and ecosystem-based management. In particular, different ecosystem modeling approaches can fit within the MSE process to meet particular natural resource management needs. We present four case studies that illustrate how MSE is expanding to include ecosystem considerations and ecosystem models as ‘operating models’ (i.e., virtual test worlds), to simulate monitoring, assessment, and harvest control rules, and to evaluate tradeoffs via performance metrics. We highlight United States case studies related to fisheries regulations and climate, which support NOAA’s policy goals related to the Ecosystem Based Fishery Roadmap and Climate Science Strategy but vary in the complexity of population, ecosystem, and assessment representation. We emphasize methods, tool development, and lessons learned that are relevant beyond the United States, and the additional benefits relative to single-species MSE approaches.