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Original research article, a case study in connecting fisheries management challenges with models and analysis to support ecosystem-based management in the california current ecosystem.

case study fish climate change

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.

Introduction

Fish stocks do not live isolated from, but exist as part of an ecosystem, and their dynamics are intrinsically related to those of their habitat, prey, and predators, from environmental conditions to humans. In recognition of the need to assess the cumulative of effects and trade-offs of fisheries management actions considering these ecological interactions there has been a longstanding worldwide push for ecosystem-based fisheries management (EBFM, May et al., 1979 ; Pikitch et al., 2004 ; Link, 2010 ; Fogarty, 2014 ; Holsman et al., 2017 ; Skern-Mauritzen et al., 2018 ; Fulton, 2021 ). In the United States, scientists have been exploring and coordinating the use of ecosystem models to address ocean ecosystem science and management questions for over a decade ( Townsend et al., 2008 , 2014 , 2017 ; Link et al., 2010 ). The National Oceanic and Atmospheric Administration (NOAA), the U.S. federal agency responsible for marine ecosystem science and ecosystem-based fisheries management in Federal waters, has prioritized ecosystem modeling as necessary to better assess the trade-offs we make to maintain resilient and productive ecosystems, and to respond to climate, habitat, and ecological change ( National Marine Fisheries Service, 2016a , b ). Nevertheless, progress, in the United States and elsewhere, in using ecosystem models and analysis to guide fishery decision-making has been slow ( Skern-Mauritzen et al., 2016 ; Townsend et al., 2019 ).

One of the significant challenges to using information and ideas generated through ecosystem modeling is a lack of connection between modeling and management priorities ( Link et al., 2012 ). Ecosystem modelers are not necessarily asking the same questions of their models as those asked by legal mandates or by managers implementing those mandates. This disconnect between scientific interest and management needs may contribute to the perceived slow pace in the uptake and implementation of ecosystem-based management ( Hilborn, 2011 ; Cowan et al., 2012 ; Marshall et al., 2018 ). Townsend et al. (2019) suggests that scientists can better understand and tune models to address management priorities by working more closely with managers, within existing processes to implement legal mandates.

Indeed, establishment of an effective scientists-decision makers knowledge exchange has been recognized as a major challenge to successful science-based management of complex socio-ecological systems ( Cvitanovic et al., 2015 ). Frameworks for facilitating the uptake of scientific research in natural resources management, such as the System Approach Framework (SAF, Hopkins et al., 2011 ), structured decision making approaches ( Gregory et al., 2012 ), and integrative assessments (see review by Mach and Field, 2017 ) stress that ongoing two-way exchange of information between scientists and decision makers and participatory communication methods are key to facilitate uptake of scientific analysis for management of complex systems ( Lidström and Johnson, 2020 ). Use of scientific knowledge in support of decision-making is dependent on such knowledge being perceived as salient to the decision-makers ( Cvitanovic et al., 2015 ; Mach and Field, 2017 ). Iterative dialogue between scientists, managers and stakeholders can ensure scientific analysis and models are relevant to the decision-making process ( Hopkins et al., 2011 ; Cvitanovic et al., 2015 ; Mach and Field, 2017 ).

There are also technical issues that can limit use of ecosystem models in decision making. These have been widely discussed elsewhere ( Skern-Mauritzen et al., 2016 , 2018 ; Holsman et al., 2017 ; Schuwirth et al., 2019 ), but we synthesize them here. There needs to be sufficient data to develop a basic mechanistic understanding of the system ( Skern-Mauritzen et al., 2016 ; Schuwirth et al., 2019 ) and few research programs exist to empirically quantify processes at this level of complexity ( Wells et al., 2020 ). Such data requirements become more difficult to meet with increasing complexity of the approach being considered ( Holsman et al., 2017 ; Skern-Mauritzen et al., 2018 ), which in turn comes at the cost of greater estimation uncertainty ( Link et al., 2011 ). This uncertainty needs to be quantifiable and factored into management decisions ( Holsman et al., 2017 ; Skern-Mauritzen et al., 2018 ; Schuwirth et al., 2019 ). For tactical management applications, predictive performance of ecosystem models also needs to be sufficient for the model to be useful ( Skern-Mauritzen et al., 2018 ; Schuwirth et al., 2019 ). Thus, increases in estimation uncertainty need to be balanced by reductions in process uncertainty to maintain adequate predictive performance ( Link et al., 2011 ). Model output also needs to be at an appropriate temporal and spatial resolution to inform management ( Schuwirth et al., 2019 ). These issues, however, should not prevent the use of ecosystem-based approaches to improve the status quo and meet the needs of decision-makers for scientific information that considers feedback and interactions between multiple ecosystem components ( Patrick and Link, 2015 ; Skern-Mauritzen et al., 2018 ). The most appropriate ecosystem model will necessarily vary in complexity depending on the policy issue and data availability, and guidelines exist to inform choice of analytical tool (e.g., Weijerman et al., 2015 ; Holsman et al., 2017 ).

In this paper, we demonstrate a practical process, based within the framework of national laws and on the practices identified by Townsend et al. (2019) , to better connect ecosystem models and analyses with fisheries management ( Figure 1 ). We define ecosystem models and analyses as a broad suite of analytical tools which incorporate interactions between physical, biological, and/or human components of the ecosystem, ranging from empirical approaches to end-to-end ecosystem models. Fisheries in the exclusive economic zone off the U.S. West Coast are managed under the advice of the Pacific Fishery Management Council (PFMC). The PFMC established a regular process through which new ecosystem initiatives are co-developed to address ideas and issues that affect multiple species and fisheries ( Pacific Fishery Management Council (PFMC), 2013 ). In doing so, this process provided an avenue for managers, stakeholders, and scientists to work together to find solutions to policy issues, the type of forum identified as necessary by Townsend et al. (2019) . Here, we use the second PFMC ecosystem initiative, the PFMC’s stakeholder review of ecosystem status indicators, to identify emerging fisheries policy issues in the U.S. West Coast that require ecosystem information. This process echoes the Issue Identification step in the SAF framework, in which a policy issue is identified in collaboration with stakeholders so that the analytical tool can be developed for the specific decision context defined with stakeholders ( Dinesen et al., 2019 ). We then connect the management questions to existing ecosystem models by specifying how their output could address some of the concerns raised by stakeholders and decision-makers about future trade-offs expected for living marine resource management in the California Current Ecosystem (CCE).

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Figure 1. Overview of the process to facilitate integration of ecosystem models and analyses into fisheries management proposed by Townsend et al. (2019) on left and adaptation of that process to address multiple issues as presented in this paper on right.

Materials and Methods

California current ecosystem status reports.

California Current ESRs are developed annually by the NOAA California Current Integrated Ecosystem Assessment (CCIEA) team. These reports focus on biophysical, economic and social indicators related to attributes such as abundance and population condition of key species, community composition and energy/material flows, extent and condition of habitat, and fisheries engagement and social vulnerability in coastal communities. In 2017, the PFMC formalized a process for technical review of individual indicators and analyses ( Box 1 ) so that new topics for in-depth technical assessment are identified annually in March and then reviewed in detail in September ( Pacific Fishery Management Council (PFMC), 2017b ; Box 1 ).

BOX 1. Glossary of terms and acronyms related to the United States West Coast approach to Ecosystem Based Fisheries Management.

Pacific Fishery Management Council (PFMC, or Council) – Management entity established under the Magnuson-Stevens Fishery Conservation and Management Act (MSA) responsible for advising the federal government on managing fisheries within the exclusive economic zone (EEZ) off the United States West Coast. Develops fishery management plans (FMPs) and fishery regulations to implement the FMPs. Advised by stakeholders (U.S. states and tribes, commercial and recreational fisheries participants, environmental and other non-governmental organizations, and the public) through Advisory Subpanels ( https://www.pcouncil.org/documents/2019/09/cop-2.pdf/ ), assisted with monitoring and analyses by Technical/Management Teams ( https://www.pcouncil.org/documents/2019/09/cop-3.pdf/ ) and Workgroups ( https://www.pcouncil.org/documents/2019/09/cop-8.pdf/ ) (including the Ecosystem Workgroup, EWG), and provided scientific advice by the Scientific and Statistical Committee (SSC) ( https://www.pcouncil.org/documents/2019/09/cop-4.pdf/ .

Fishery Ecosystem Plan (FEP) ( https://www.pcouncil.org/documents/2013/07/fep_final.pdf/ ) – PFMC’s formalized approach to Ecosystem Based Fisheries Management (EBFM) . Includes a process through which the PFMC takes up ecosystem initiatives to address ideas and issues that affect multiple species and fisheries.

California Current Ecosystem Status Report (ESR) (E.g., https://www.pcouncil.org/documents/2020/02/g-1-a-iea-team-report-1.pdf/ ) – Annual report to the PFMC providing an ecosystem overview outside of focal resource stocks and populations, considering how outside factors influence focal resources, identifying linkages between different ecosystem components. Prepared by the NOAA California Current Integrated Ecosystem Assessment (CCIEA) ( https://www. integratedecosystemassessment.noaa.gov/regions/california-current-region/index.html ).

Stakeholder review – Review of policy, regulatory, or scientific product by stakeholders and members of the public. Process followed under the second ecosystem initiative ( https://www.pcouncil.org/actions/initiative-2-coordinated-ecosystem-indicator-review/ ) to review the reliability and utility of existing ESR indicators, and identify desirable additions. Involved Council, its advisory bodies, the SSC, and public comment. Technical review – Review of scientific or analytic product by the SSC or its subcommittees. For the ESR, technical review involves an annual process of topic selection by the Council in conjunction with its advisory bodies and the CCIEA, followed by reviews by the SSC’s Ecosystem Based Management Subcommittee.

Methodology review – In-depth technical reviews of methods that are held periodically and as needed. Reviewers include members of the SSC and often outside experts, and reviews follow specific Terms of Reference (TOR) (E.g., https://www.pcouncil.org/documents/2018/06/terms-of-reference-for-the-methodology-review-process-for-groundfish-and-coastal-pelagic-species-for-2019-2020-june-2018.pdf/ ) that may also reflect established Council Operating Procedures (COP) ( https://www.pcouncil.org/documents/2019/09/cop-15.pdf/ ). Required for changes to assessment methods or forecasts, and used for other complex topics as warranted.

Management Strategy Evaluation (MSE) – A process and modeling framework used to assess performance of management strategies given uncertainty relative to a set of predefined management objectives.

PFMC Initiative to Review Indicators

While technical reviews of the statistical analyses and models are useful, they do not provide incentives for broad stakeholder and manager participation in ESR development and refinement. In 2015, the PFMC addressed this shortcoming by proposing a new ecosystem initiative, the “Coordinated Ecosystem Indicator Review” ( Figure 2 ). This initiative outlined a stakeholder review process ( Box 1 ) to address four questions ( Pacific Fishery Management Council (PFMC), 2017a ): (1) What can the PFMC reasonably expect to learn from, or monitor with, the existing indicators in the ESR? (2) How well do the existing indicators accomplish their intent, and are any redundant? (3) Are there alternate indicators, information, or analyses that may perform better in context? and (4) Are there additional ecosystem indicators that could help inform PFMC decision-making?

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Figure 2. Overview of opportunities for stakeholder feedback (orange boxes) on ESR indicators and ecosystem models and analyses in the PFMC management process.

In early 2016, the PFMC hosted a series of webinars to present the ESR indicators and discuss the four questions detailed above. Webinars were open to the public and were widely advertised by the PFMC in advance during their meetings, on their website, through their ∼1500 address email list, and through notice in participating government publications. The PFMC compiled all comments and recommendations raised during the discussion portions of the webinars. From March to September 2016, the PFMC also directly solicited feedback on the initiative’s four focal questions from its scientific and technical advisors and stakeholders. Between the live webinars and the solicitation to review the recordings of the webinars, the PFMC received 88 comments and recommendations from stakeholders and the public.

Using Public Process Results to Refine Ecosystem Modeling Planning

In this paper, we consider how the ideas generated in the initiative’s public review process might be used in ecosystem modeling planning. From the 88 comments and recommendations ( Pacific Fishery Management Council (PFMC), 2016a , b ) we selected only those that emphasize the need for ecosystem-level understanding , which acknowledges that ESRs should include not only status and trends of different indicators, but also a synthesis of how indicators interact and affect one another. Comments were characterized as belonging to the ecosystem-level understanding theme if they related to interactions between ecosystem components. The interactions considered were (1) interactions between species, (2) interactions between fishers and species, (3) impacts of abiotic components on species, and (4) impacts of abiotic components on fisheries. The authors found 17 comments that matched these criteria and thus were salient to the ecosystem-level understanding theme and could be addressed through greater inclusion of ecosystem model outputs in the ESR or in other reports to or conversations with the PFMC. These are reported in Table 1 . Per Townsend et al. (2019) , for each comment, we identified the relevant policy issue, management objectives, and the existing management process that would be used to address the problem ( Table 1 ). In the Results and Discussion section, we describe in more detail the ecosystem-information needs highlighted in the 17 comments and assess which EBFM modeling activities could contribute to resolving the management concerns. We connect the policy issues highlighted in the stakeholder comments to specific models and analyses in Table 2 . In Table 2 we present existing modeling products, but also highlight the additional modeling needs required to improve management utility. Here, EBFM modeling activities are defined broadly as those models and analyses used to assess interactions between physical, biological, and/or human components of the ecosystem. These tools include a variety of empirical approaches, species distribution models, biophysical models, climate-informed population dynamic models, multispecies models, food web models, and end-to-end ecosystem models.

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Table 1. Comments from the stakeholder review of ESR Indicators that could be informed by ecosystem models and analyses with relevant policy issues, management objectives, and existing management processes to address them.

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Table 2. Overview of existing or potential modeling products that could be developed to address the specified comments from managers and stakeholders.

Environmental Drivers of Biological Productivity

The first set of ecosystem-level understanding comments ( Table 1 , Comments 1–8) highlighted the need for improved scientific advice on how climate, physical oceanography and biogeochemistry indicators are related to biological productivity (i.e., recruitment, mortality, or growth of target and protected species). Comments 2 to 5 emphasized the requirement for improved quantification of how oceanographic processes, and in particular upwelling, affect species of management concern, such as salmon or groundfish. Comment 6 suggested, given the cumulative and potentially synergistic impacts of a variety of climate drivers on a species’ productivity, a need for a more in-depth synthesis of how environmental conditions interact to affect biological components. Comments 7 and 8 stress the need to also assess the utility of seabirds as indicators of forage or salmon productivity. Ultimately, as reflected in Comment 1, stakeholders are interested in anticipating the risk of an undesirable outcome and minimizing its impact, and thus need relevant indicators for forecasting and risk assessment.

This topic was associated with the highest number of comments ( Table 1 ), perhaps because productivity indicators can inform the setting of species-specific harvest levels, one of the main management measures used by PMFC. Harvest levels are often dependent on a forecast of stock biomass and on reference points (fishing intensity or biomass thresholds that should not be crossed or targets to be achieved) derived from stock assessments. Use of climate-linked natural mortality in stock assessment can generate less variable reference points on which to base catch advice ( O’Leary et al., 2019 ). If predictive skill is sufficient, the integration of environmental indicators of recruitment can also improve estimates of reference points ( Basson, 1999 ). In addition, short-term recruitment forecasts can enable managers to alert fishing communities of potential changes in harvest levels, allowing for development of potential remediation strategies ( Tommasi et al., 2017b ). However, the added benefit of including environmental indicators into the estimation of stock-status depends on the species’ life-history type ( Haltuch et al., 2019b ). Environmentally informed short-term recruitment forecasts are particularly important for semelparous species like salmon, as there is no direct carryover of spawning biomass across years, or for forage species whose fishable biomass consists in large part of young age classes ( Tommasi et al., 2017c ). Catch advice for long-lived stocks may instead be more responsive to changes in natural mortality ( Bax, 1998 ).

Usefulness of stock productivity indicators to management decisions is also dependent on their availability relative to the timing of council decision making. Some environmentally based forecasts of salmon returns are dependent on ocean conditions during first ocean entry 2 or 3 years prior (e.g., Rupp et al., 2012 ; Burke et al., 2013 ), and thus rely on past, observed environmental covariates. This has facilitated their inclusion in some management-relevant salmon forecasts ( Burke et al., 2013 ; Litz and Hughes, 2020 ). However, for other species and salmon stocks (e.g., Satterthwaite et al., 2020 ) key indicators may need to be forecasted months to a year in advance to improve catch advice or even longer to inform stock status projections (e.g., for groundfish). Thanks to recent advancements in global climate prediction systems, forecasts of biologically relevant variables in coastal regions months to years in advance can be skillful in some regions ( Stock et al., 2015 ; Tommasi et al., 2017a , b ; Hervieux et al., 2019 ; Jacox et al., 2019a ; Park et al., 2019 ; Jacox et al., 2020 ). Integration of such forecasts with environmentally informed single species population dynamics models can enable managers to set more effective catch limits, but their utility will be dependent on how well management needs align with the regions and times with adequate forecast skill. For example, sea surface temperature (SST) forecast skill in the CCE is variable in space (with highest skill in more northern latitudes) and time (with highest skill for late winter and early spring forecasts) ( Jacox et al., 2019a ). In an evaluation of management performance for sardine, ecological and economic metrics were improved by SST forecasts only up to 4 months in advance, as forecast skill degraded at longer lead times ( Tommasi et al., 2017c ). This 4-month lead time may not be sufficient for inclusion of such approaches into current CPS management timelines ( Tommasi et al., 2017c ), but other applications may be able to leverage greater predictability for different seasons, lead times, regions, or environmental variables.

Use of environmental indicators of stock productivity to inform tactical decisions (e.g., catch advice) require adequate process understanding of the environment-species response, long time series of both biological and environmental variables at appropriate spatial and temporal scales, a strong effect of the environmental covariate on stock dynamics, and the ability to monitor and skillfully forecast the indicator ( Skern-Mauritzen et al., 2018 ; Haltuch et al., 2019b ). Improvements of management performance with the use of climate-enhanced stock assessments and environmental covariate-based harvest control rules (HCRs, e.g., Howell et al., 2021 ) as compared to other methods needs to be carefully evaluated with management strategy evaluation ( Haltuch et al., 2019b ). In some cases, application of survey-derived recruitment indicators may be more appropriate ( Walters and Collie, 1988 ). Nevertheless, since the stakeholder review of ESR indicators, some analyses and models that speak to the needs highlighted in Comments 1–8 have been developed and used to inform PFMC management decisions ( Table 2 ). We highlight those examples below and then discuss future research avenues.

For salmon, in 2017, the Council’s advisory bodies expressed concern about increasing variability in salmon escapements and worsening performance of forecasts ( Pacific Fishery Management Council (PFMC), 2017c ). This, along with earlier calls to investigate potential threshold values in indicators reported in the ESR ( Pacific Fishery Management Council (PFMC), 2015b ), prompted Satterthwaite et al. (2020) to investigate non-linear relationships between environmental covariates and forecast performance for Chinook salmon stocks of particular management concern. While mechanistic drivers of salmon demographic rates need to be investigated further before direct inclusion into pre-season forecast models, the work demonstrates that environmental indicators could be used indirectly to alert managers that forecast performance may be poor and that a precautionary approach may be warranted ( Satterthwaite et al., 2020 ).

Similar correlative approaches also inform the PFMC’s environmentally driven exploitation rates in the HCR for Pacific sardine ( Sardinops sagax , Pacific Fishery Management Council (PFMC), 2020 ), based on the recognition that the spawner-recruit relationship barely extended above the replacement line during cool periods, while indicating substantial compensation (surplus production) during warm periods ( Jacobson and MacCall, 1995 ). In this case, rather than the environmental indicator being included directly in the stock assessment to inform a short-term forecast of fishable biomass, an age-structured population dynamics model with an environment-recruitment link was first used to determine how the fishing mortality reference point depends on a temperature indicator, and then a management strategy evaluation (MSE) was employed to compare performance of different types of harvest control rules and potential environmental indicators ( Hurtado-Ferro and Punt, 2014 ). Temperature-dependent fishing mortality target reference points are also utilized for tactical management of cod ( Gadus morhua ) and whiting ( Merlangius merlangus ) in the Celtic Sea ( Howell et al., 2021 ).

A relationship between sea level and recruitment has also been identified and included in the 2019 assessment for sablefish ( Anoplopoma fimbria , Haltuch et al., 2019c ). However, inclusion of the environmental indicator in the stock assessment did not influence assessment output as it was consistent with survey length and compositions ( Haltuch et al., 2019c ). For long-lived species like groundfish that recruit into the fishery at older ages and for which recent recruits make up a smaller fraction of the biomass, a short-term forecast (sub-annual to annual) of fishable biomass is largely informed by the observed fishery and survey data. A short-term recruitment forecast may therefore not substantially improve short-term biomass forecast skill and derived management measures. For this life history type, environmentally informed recruitment forecasts may inform longer-term (2 years onward) projections of stock biomass or reference points. To date, projections have not considered environmental conditions; however, it may be beneficial to do so in cases like sablefish, for which an environment-recruitment relationship has been established. However, the environmental covariate would need to be forecasted with adequate skill.

At PFMC, efforts to improve our understanding of drivers of species productivity and the performance of biomass forecasts and projections will continue in the future and are of interest to managers and stakeholders ( Table 1 , comments 1–8). There are numerous avenues by which ecosystem science could contribute, which are highlighted below and in Table 2 . Improvements to ecosystem indicator development, such as the use of multivariate statistical techniques that reduce the dimensionality of a large set of covariates with minimal information loss, could refine inputs to existing environmentally driven forecasts (e.g., Rupp et al., 2012 ; Burke et al., 2013 ; Muhling et al., 2018 ). Exploratory statistical analyses based on improved ecological understanding of species interactions (e.g., trophic relationships) and employing a variety of data sources can also inform development of new productivity indicators (e.g., Tolimieri et al., 2018 ). Wells et al. (2017) , examining seabird diet and forage survey data, demonstrated that salmon survival decreases when common murre ( Uria aalge ) switch from foraging juvenile rockfish ( Sebastes spp. ) offshore to anchovy inshore following changes in upwelling. Where seabird data exists, such an analysis could be extended, as suggested by Comment 8, to inform development of indicators for salmon stocks in the Northern CCE. Similar statistical models could be used to identify ecosystem indicators, such as seabird abundance or reproductive success, that relate to forage fish abundance (Comment 7).

Ecosystem models capturing the mechanistic processes leading to changes in demographic rates are also a promising tool to develop indicators to inform forecasts. For instance, processes occurring during the critical early ocean entry period have long been thought to be a major driver of overall cohort abundance for salmon ( Pearcy, 1992 ; Beamish and Mahnken, 2001 ). Fiechter et al. (2015) developed a spatially explicit bioenergetics model of salmon linked to a configuration of the Regional Ocean Modeling System (ROMS) with biogeochemistry, and an index of juvenile salmon growth potential derived from this model was capable of describing a large proportion of variation in cohort strength ( Henderson et al., 2019 ). The ROMS-informed bioenergetic model enabled synthesis of how oceanographic indicators (including krill concentration) affect juvenile growth potential ( Fiechter et al., 2015 ). Then, a multivariate statistical technique was used to summarize the spatial variation in growth across years to inform a regression model of Central Valley Chinook salmon survival ( Henderson et al., 2019 ). This model could now provide projections of juvenile survival informing pre-season forecasts of Central Valley Chinook returns, and a similar approach could be expanded to other stocks and species.

It could also be fruitful for ecosystem modelers to turn their attention to factors operating later in the life cycle that could influence growth, maturation rates or mortality. For salmon, improved estimation of maturation rates and mortality can inform forecasts based on sibling regressions where the returns of younger age classes in the previous year are used to forecast returns of older ages from the same cohort in forecast years ( Peterman, 1982 ), as well as projections of future fishable biomass. Indeed, integration of an environmentally informed mortality parameterization in a population dynamics model of summer flounder ( Paralichthys dentatus ) in the U.S. East Coast resulted in improved biomass estimates ( O’Leary et al., 2018 ).

Development of the Henderson et al., 2019 ecosystem model and derived salmon productivity indicators were facilitated by advancements in ocean modeling of the CCE. Assimilation of observational oceanographic data with ROMS has now enabled the development of a fine-scale reconstruction of physical ocean conditions going back to 1980 ( Neveu et al., 2016 ) 1 . These capabilities were also essential for the development of key indicators of rockfish recruitment ( Schroeder et al., 2018 ), sablefish recruitment ( Tolimieri et al., 2018 ), petrale sole ( Eopsetta jordani ) recruitment ( Haltuch et al., 2020 ), and new indices of upwelling ( Jacox et al., 2018 ) or upwelling habitat compression ( Santora et al., 2020 ) that may be relevant to target and protected species.

However, as evidenced by Pacific sardine ( McClatchie et al., 2010 ; Jacobson and McClatchie, 2013 ; Zwolinski and Demer, 2019 ), correlative relationships can break down over time ( Myers, 1998 ). Thus, an adaptive process enabling regular re-evaluation of the relationships between environmental indicators and fish productivity needs to be in place if they are to inform management ( Skern-Mauritzen et al., 2016 ). For salmon, the PFMC has established processes for annual technical review of proposed changes to forecast methodology ( Pacific Fishery Management Council (PFMC), 2008 ). The existing ESR indicator technical review process could enable an annual re-evaluation of the correlative relationships between stocks and their environment and allow for periodic refinements to the oceanographic, ecosystem and statistical models used to estimate species responses to the environment. Predictions derived from ecosystem-based models (e.g., Henderson et al., 2019 ) might be considered as competing models of existing approaches, and the ESR process could also provide a platform where different approaches are discussed, compared, and potentially integrated in a forecast ensemble, as is regularly done in weather and climate forecasting ( Kirtman et al., 2014 ; Bauer et al., 2015 ).

Species Distributions and Their Overlap

The second set of ecosystem-level understanding comments (9 and 10), reflects the management need for more spatial distribution information to minimize the risk of interactions between fisheries and protected species, thereby increasing opportunities to fish for the target species ( Table 1 ). This information need has become particularly critical in recent years, as populations of protected predators (e.g., sea lions) in the CCE recover, increasing the potential for overlap with fisheries ( McClatchie et al., 2018 ). In addition, Comment 10 highlights the need to assess the links between changes in prey availability over space and predator distribution ( Table 1 ). Understanding spatiotemporal overlap between predators and potential prey species is particularly important for the development of a more ecosystem-focused approach to fisheries management ( Carroll et al., 2019 ; Link et al., 2020 ). A number of negative ecological and economic events occurring within the CCE in recent years, including, but not limited to, unusual mortality events for sea lions and seabirds ( Wells et al., 2013 ), and unprecedented whale entanglements ( Santora et al., 2020 ), were the result of changes in predator distribution linked to changes in forage availability and unprecedented environmental conditions. These incidents served to highlight the need for spatial tools mapping changes in species overlap in response to changes in environmental conditions.

Species Distribution Models (SDMs) are a common tool used to describe the distribution of species, often in relation to their environment, or in relation to space and time covariates that act as proxies for unobserved processes. These geostatistical models allow for the inclusion of multiple predictors and are flexible enough to capture complex or non-linear relationships between a species and its environment ( Guisan and Zimmermann, 2000 ; Elith and Leathwick, 2009 ; Norberg et al., 2019 ). SDMs developed using long observational time series can be used to describe the typical distributions of species. As such they have the potential to highlight anomalous changes in species distributions as a function of environmental change and to examine or anticipate how environmental conditions cause variability in species associations ( Carroll et al., 2019 ; Table 2 ), making them useful tools to address Comments 9 and 10. Indeed, SDMs have been applied in various management contexts worldwide although predominantly in terrestrial systems. SDMs can be used to assess historical or climatological distributions ( Valinia et al., 2014 ), dynamic distributions ( Stanton et al., 2012 ), or predict how species distributions will change over multiple forecast horizons, from short-term forecasts ( Payne et al., 2017 ) to climate change projections ( Briscoe et al., 2016 ). A spatiotemporal mixed-effects model (vector autoregressive spatiotemporal model) has become an important SDM for fisheries scientists who seek to develop accurate historical indices of abundance for use in stock assessment ( Thorson, 2019b ). SDMs have also been used to produce climatological prediction maps of marine mammals to assess risk from sonar operations ( Forney et al., 2012 ; Roberts et al., 2016 ; Robinson et al., 2017 ), to describe temperature-driven interannual variability in the distribution of Pacific hake ( Merluccius productus ) in the context of its joint management by the United States and Canada ( Malick et al., 2020a ), for spatial management planning (e.g., Leathwick et al., 2008 ; Valavanis et al., 2008 ; Esselman and Allan, 2011 ; Smith et al., 2020 ), and climate change impact assessments (e.g., Hazen et al., 2013 ; Kleisner et al., 2017 ). Of particular interest to managers is the use of SDMs to minimize interactions between fisheries and protected species or vulnerable life stages (e.g., Hobday et al., 2011 ; Howell et al., 2015 ; Lewison et al., 2015 and references therein, Druon et al., 2015 ; Little et al., 2015 ; Hazen et al., 2018 ).

Most SDMs have been applied in a historical context, to describe and understand drivers of past changes in species distribution and their overlap. An increasing number of studies are also using SDMs for climate change applications (e.g., Shelton et al., 2020 ), but use of SDMs to anticipate short- to medium-term (days to years) changes in species availability has only recently begun to receive attention (e.g., Kaplan et al., 2016 ; Thorson, 2019a ) despite the need for such products (Comment 9). Model-based distribution forecasts have been used to reduce unintended catch of southern bluefin tuna in the East Australia Current ( Hobday et al., 2010 ) and to explore reducing seabird interactions in the North Pacific Transition Zone ( Žydelis et al., 2011 ). Since the stakeholder review of ESR indicators, pioneering applications have also been developed for the CCE that address Comments 9 and 10, as outlined below and in Table 2 .

SDMs and satellite data or ocean model output are providing near-real time likelihoods of ship strike risk for blue whales ( Balaenoptera musculus ) in the California Current ( Hazen et al., 2017 ; Abrahms et al., 2019 ), and the ratio of catch to bycatch of protected species in the California swordfish fishery ( Brodie et al., 2018 ; Hazen et al., 2018 ; Welch et al., 2019 ; Table 2 ). The latter example, termed EcoCast, integrates predictions of habitat suitability for a target species (swordfish, Xiphias gladius ) and multiple bycatch species (blue sharks, Prionace glauca ; leatherback turtles, Dermochelys coriacea ; and California sea lions, Zalophus californianus ) to provide an integrated map of opportunity and risk. This tool is now fully operational ( Welch et al., 2019 ), providing daily predictions for use by fishery managers and fishers when deciding where to fish or adjust management regulations. (Operational, here, and throughout the paper is defined as in Welch et al. (2019) , “self-contained workflows that run automatically at a prescribed temporal frequency”). In the northern CCE, the J-SCOPE project uses a ROMS model with biogeochemistry and provides twice-annual seasonal forecasts that have shown skill for physical and biochemical conditions, including hypoxia, at lead times up to ∼4 months ( Siedlecki et al., 2016 ), and these are being used to forecast Pacific hake and sardine distributions and migration ( Kaplan et al., 2016 ; Malick et al., 2020b ) and inform the ESR ( Harvey et al., 2019 ).

Several recent advancements may allow for further development of SDMs to anticipate changes in species distributions and their overlap in the CCE at longer lead times (1–12 months), and thus expand their relevance for management applications (Comments 9 and 10). Advancements include improvements in the availability of output from global climate prediction systems at lead times up to a year, the configuration of regional ocean models to downscale such predictions for the CCE, and the implementation of SDMs that use ocean model fields as input (e.g., Brodie et al., 2018 ). Indeed, decision support tools at these longer lead times have been used to model the distribution of target and bycatch species in Australian fisheries up to 4 months in advance using output from global climate prediction systems ( Hobday et al., 2011 ; Eveson et al., 2015 ).

Continued development of such products would require further interactions between PFMC managers, stakeholders, and scientists to determine species, regions, and timeframes of interest, and to ensure that physical and ecological forecast skill aligns with management needs. The ESR technical review that has created opportunities to begin those discussions may continue to provide a forum moving forward. Predictions of extreme events may be of particular interest to managers, and several steps must be taken to evaluate whether such predictions can be useful. For example, temperature anomalies were predictable for some but not all periods of the persistent 2014–2016 CCE heatwave ( Jacox et al., 2019b ), and the ability of SDMs to capture species distribution shifts under these novel conditions, even with perfect environmental data, differs by SDM model type and species ( Becker et al., 2020 ; Muhling et al., 2020 ). Thus, more work is required to assess whether SDM forecast skill is adequate for management applications, as skillful forecasting of species distribution changes requires that both environmental conditions and species responses to those environmental changes are accurately predicted. This research will include the determination of which SDM architectures are best suited to anticipate changes in species distributions over the timescales most relevant to managers.

Trophic Interactions and Management Trade-Offs

Comments 11 and 12 highlight the need for ecosystem synthesis to examine the tradeoffs between protection of dependent predators, sustainability of fish populations, including both forage and the higher trophic level target species feeding on them, and the resilience of fishing communities ( Table 1 ). It is becoming apparent that trophic cascades resulting from variability in forage can have substantial and surprising consequences on coastal communities on the U.S. West Coast ( Wells et al., 2017 ; Santora et al., 2020 ). Therefore, in addressing Comments 11 and 12 ( Table 1 ), modeling frameworks enabling a broad approach to evaluating tradeoffs should be considered. Below, we focus on tools well suited to address these tradeoffs: end-to-end models, management strategy evaluation, and spatial modeling, and highlight specific examples of their application in the CCE to inform management issues. These examples are also reported in Table 2 and avenues for further research are discussed.

In the CCE, fishery managers must weigh the provision of adequate forage for dependent species against the importance of the CPS (sardine, squid, anchovy and mackerel) fishery to West Coast communities, while also safeguarding the forage species themselves. This balancing act is not unique to this region, and tradeoffs between forage fish harvest and predators have long been the focus of global analyses, modeling, and task forces (e.g., Cury et al., 2011 ; Smith et al., 2011 ; Pikitch et al., 2012 ). The need for consideration of trophic interactions in the management of CCE CPS fisheries was recognized early on by the PFMC, with the earliest information on trophic interactions informing management advice being derived from simple correlative relationships. For example, the first FMP passed by the PFMC, the 1978 Northern Anchovy FMP, included a cutoff parameter below which large-scale harvest was not allowed to provide adequate forage for brown pelicans ( Anderson et al., 1980 ). Information about similar trophic relationships were instrumental in the PFMC’s decision in the early 2000s to reduce the Allowable Biological Catch of shortbelly rockfish, a previously non-targeted species, based on the significance of pelagic juvenile shortbelly rockfish to seabirds, salmon and other higher trophic level predators.

End-to-end ecosystem models like Atlantis ( Fulton et al., 2011 ) and Ecopath with Ecosim ( Christensen and Walters, 2004 ), which model the entire food-web from plankton to top predators, can be used to assess the bottom-up effects of increased removals of forage fish on piscivorous fish species and protected species, such as marine mammals and seabirds, that depend on forage fish as prey, as well as the top-down impacts of increasing predator biomass on forage fish ( Table 2 ). Given the long-standing objective of ensuring adequate forage for predators in the CPS FMP, and consistent with the 1998 Ecosystem Principles Advisory Panel ( Ecosystem Principles Advisory Panel (EPAP), 1999 ), end-to-end ecosystem models became increasingly important to PFMC CPS management efforts during the early 2000s. For example, later amendments to the CPS FMP addressing krill management in the CCE were informed by both empirical data and insights from mass balance ecosystem models ( Field et al., 2006 ). Confronting the management needs with the limitations of both the data and the models was helpful in this effort, as a key outcome was the recognition that the apparent high consumption of krill by key predators was often inconsistent with (considerably greater than) the estimates of krill abundance and productivity ( Pacific Fishery Management Council (PFMC), 2009 ). Although the reasons for this inconsistency remain unknown, this limitation informed the decision to ultimately prohibit a directed CCE krill fishery in the absence of improved information for management.

Recognizing that models that include ecosystem processes and interactions are key to better informing the tradeoffs between forage needs and fisheries, among other things, the PFMC asked for a methodology review ( Box 1 ) of the California Current Atlantis model in June 2014 ( Pacific Fishery Management Council (PFMC), 2014 ; Kaplan and Marshall, 2016 ). The review process served as a platform to provide feedback on improvements required to increase utility of the model to management. For instance, the review noted that many of the management scenarios integrated in the CCE Atlantis model up to that point in time were not well aligned with specific PFMC management needs. Following the Atlantis methodology review and stakeholder review of ESR indicators, end-to-end ecosystem models for the CCE continue to be refined and have been used to evaluate long-term trophic impacts of U.S. West Coast groundfish fisheries ( Pacific Fishery Management Council (PFMC), 2015a ) and to assess the impacts of depleted forage species on predators ( Koehn et al., 2016 ; Kaplan et al., 2017 , 2019 ). The CCE Atlantis model can also be used to simulate the risks of climate-driven changes in the ocean environment, such as upwelling (Comments 3 and 4) and ocean acidification (Comment 13) on the CCE food-web, and it was linked to downscaled global climate projections to evaluate how the impacts of ocean acidification on benthic invertebrates may propagate through the CCE food-web and its fisheries ( Marshall et al., 2017 ; Table 2 ). Clearly, end-to-end ecosystem models including species interactions and environmental drivers can potentially be further applied to assess tradeoffs between the ecosystem and economic impacts of management decisions ( Table 2 ), but continued dialogue between managers and modelers is required to further tailor ecosystem models to answer management needs.

MSE has been widely used in fisheries management to highlight tradeoffs associated with alternative management actions, and to identify procedures that are robust to uncertainty ( Punt et al., 2016a ; ICES, 2021 ). MSE is therefore also an important tool to address Comments 11 and 12, related to how harvest rules for forage species may affect dependent predators, and vice versa. In simpler “one way” or bottom-up cases, an ecosystem model can be used to trace impacts of harvest rules on forage fish populations and fishery yields, and subsequently on predators. This was done in a recent herring MSE on the U.S. East Coast ( Deroba et al., 2019 ; Feeney et al., 2019 ). Complex “two way” cases trace top-down impacts of predators on forage fish as well as bottom-up impacts on predators and are of interest when investigating multispecies harvest control rules or ecosystem reference points need to be tested. Kaplan et al. (2021) discuss additional applications of ecosystem models within MSE, including as operating models, and to simulate monitoring, assessment, and harvest control rules.

In one MSE example from the CCE, Punt et al. (2016b) applied a models of intermediate complexity for ecosystem assessments (MICE, Plagányi et al., 2014 ) rather than an end-to-end ecosystem model to assess the impact of CPS harvest rules on dependent predators. MICE typically simulate prey-predator interactions, but on a smaller set of ecosystem components. These simpler multispecies models are useful for answering targeted management questions relative to a specific policy concern. Their lower complexity allows, as in stock assessment, for parameter estimation based on fits to data, and uncertainty quantification, making them well suited for MSEs, and more readily understood by management bodies familiar with stock assessment models. The Punt et al. (2016a) MSE examined the links between the forage base and higher trophic level species (Comment 11), specifically the links between the population dynamics of sardine and anchovy and of two protected predator species, brown pelican ( Pelecanus occidentalis ) and California sea lion. The MSE was able to assess the tradeoffs between fishing on CPS and protection of predators (Comment 12, Table 2 ) by testing performance of current harvest policies for sardine with respect to both fishery and conservation goals. The MICE was developed in parallel with the Atlantis and Ecopath models mentioned above, facilitating model development, comparison and engagement with managers ( Francis et al., 2018 ; Kaplan et al., 2019 ). Unlike the Ecopath or Atlantis applications, this MICE was able to quantify the performance of realistic management measures (including reproductive success and survival of protected species) while considering uncertainty in environmentally driven recruitment scenarios for sardine and anchovies as well as structural uncertainty regarding predator dependence on forage ( Punt et al., 2016b ).

Linking changes in the availability of forage species to higher trophic levels within particular geographic areas, the need highlighted by Comment 11, requires spatially explicit modeling for population dynamics of the species of interest. Both the CCE Atlantis and MICE model described above are spatial and can address Comment 11. However, the models have so far assumed a spatial distribution of forage species that remains constant over time. Considering the evidence for environmentally driven spatio-temporal variability in forage species ( Muhling et al., 2020 ), with impacts on predator demographic rates, particularly for central place foragers such as sea lions ( Fiechter et al., 2016 ), and on port-level availability to fishers ( Smith et al., 2021 ), a valuable goal for future research is the refinement of existing ecosystem models in the CCE to include environmentally driven changes in forage distribution, as has been done elsewhere (e.g., Coll et al., 2019 ; Moullec et al., 2019 ). Given their fine-scale representation of spatial movement processes, individual based models (IBMs) are also suited to evaluate impacts of varying prey dynamics on central-place predator distribution and foraging behavior. For example, a multi-species IBM model of sardine, anchovy, and sea lions coupled to a regional ocean model with biogeochemistry was used to examine the impacts of environmental variability and prey availability on sea lion feeding success in the central CCE ( Fiechter et al., 2016 ).

Interactions Between the Environment and Fishing Communities

The final set of comments (14–17) underscores the need for understanding how changes in climate variability, mediated via ecosystem processes, affect fishing communities ( Table 1 ). Climate change is expected to alter fish abundance and distribution ( Cheung et al., 2010 ; Morley et al., 2018 ) and PFMC advisory bodies are interested in evaluating the potential risk of shifting species availability to coastal communities (Comments 14–15). Fine scale oceanographic data from remote sensing and ocean models, in combination with spatially explicit survey, tagging or logbook data, has enabled development of SDMs for a variety of PFMC-managed species (e.g., Thorson et al., 2016 ; Shelton et al., 2020 ). When data on fisher behavior (e.g., trip distance) is available from logbooks, port-specific fishing grounds can be identified and target species availability from SDMs over the fishing grounds can be computed ( Rogers et al., 2019 ). Below, and in Table 2 we provide CCE-specific examples of how environmentally informed ecosystem models have been integrated with economic analyses to address comments 14–17, and what gaps remain.

With regards to the groundfish fishery in the CCE, indices of groundfish availability over distinct fishing grounds have been computed over the historical period ( Selden et al., 2019 ; Table 2 ) and integrated into the ESRs ( Harvey et al., 2019 ). To address comments 14–15, future work could develop such indices using environmentally informed SDMs to assess climate-induced shifts in economic opportunity (e.g., Smith et al., 2020 ), and project such changes into the future to assess the vulnerability of coastal communities to risk from climate change, as has been done for New England and Mid Atlantic fishing communities ( Rogers et al., 2019 ). When spatially explicit other ecosystem models can also inform port-level socio-economic indices. In the CCE, the spatially explicit structure of the Atlantis model allowed translation of the results assessing climate impacts on the CCE food-web and its fisheries ( Marshall et al., 2017 ) to port-based fishing communities and fleet-level economic effects ( Hodgson et al., 2018 ). These model results have been presented to the PFMC’s to inform an ongoing strategic initiative on the effects of climate variability and change on fish stocks and fishing communities ( Kaplan et al., 2018 ). By the explicit consideration of biological processes, end-to-end ecosystem models and MICE also have high potential to assess the cumulative effects of multiple environmental drivers (Comment 6, Table 2 ), e.g., under long-term climate change ( Ainsworth et al., 2011 ; Koenigstein et al., 2016 ).

There is also a need to assess how extreme weather events directly affect safety of fishers (Comment 16, Table 1 ). Climate-change driven shifts in the frequency and strength of extreme weather events have the potential to directly affect the safety of commercial, recreational, and subsistence fishers. An active area of atmospheric research is concerned with how climate change may drive changes in storminess ( Knutson et al., 2010 ; Dominguez et al., 2012 ; Kossin et al., 2016 ; Mölter et al., 2016 ; Ornes, 2018 ; Swain et al., 2018 ; Teich et al., 2018 ). Fishers and boaters are among the most sophisticated consumers of weather forecast information ( Savelli and Joslyn, 2012 ; Finnis et al., 2019 ; Kuonen et al., 2019 ). Understanding how fishers respond to extreme weather events such as storms is essential to assessing the vulnerability of fishers and fishing communities to potential changes in storminess ( Sainsbury et al., 2018 ), as well as consideration of how fishery-specific management and regulatory incentives affect fishers’ safety by influencing the level of risk fishers take to catch and land their fish ( Pfeiffer and Gratz, 2016 ). Indeed, modeling work has shown that catch shares and other types of management that eliminate a race for fish and allow flexibility in the timing of trips decrease the propensity to take trips in hazardous weather ( Petursdottir et al., 2001 ; Pfeiffer and Gratz, 2016 ; Petesch and Pfeiffer, 2019 ; Pfeiffer, 2020 ). Hidden Markov Models provide a tool to uncover underlying fisher behavior from vessel tracking data, such as from vessel monitoring systems or automatic identification systems. Such models and data sources are being increasingly used to determine simple behavioral states of fishers, e.g., ‘fishing’ or ‘searching’ ( Joo et al., 2015 ), as well as to identify environmental factors that influence their behavior ( Watson et al., 2018 ). Future work may employ behavioral models informed by environmental conditions to examine how fisher behavior changes in response to adverse weather conditions, produce estimates of fishers’ risk tolerance, and help promote safety at sea by evaluating the change in risk from fishery policies and climate change ( Table 2 ).

Integration of environmental indicators with socio-economic models can also enable quantification of the impact of extreme events on fishing communities (Comment 17, Table 2 ). For instance, the 2014–2016 marine heatwave in the CCE triggered an unprecedented harmful algal bloom (HAB) ( McCabe et al., 2016 ; Ryan et al., 2017 ), leading to considerable economic losses in fisheries for Dungeness crab ( Metacarcinus magister ) ( Moore et al., 2019 ). To better alert communities of potential fisheries closures during HABs and mitigate their effects via adaptive actions, advisory bodies requested development of a HAB index at a localized scale and for a quantification of the economic impacts of HABs on fisheries participants (Comment 17). Moore et al. (2019) have developed a localized, community-specific index of lost fishing opportunity from HABs by computing the proportion of the Dungeness crab fishing season lost to HAB closures, which may be of interest to managers. In a follow-up study, Moore et al. (2020) , using regression models built from fishers’ survey data, found that individuals who were exposed to longer fisheries closures, as measured by the HAB index, suffered greater income losses. Moore et al. (2020) also identified potential adaptive actions to reduce the impact of HABs on Dungeness crab fishery participants. These actions include income diversification and fishing for alternate species or in alternate areas. In addition, Anderson et al. (2016) developed a model to provide nowcasts and 1–3 day forecasts of HABs for the California coast 2 by linking ROMS and satellite output to a statistical model of the likelihood of a toxic algal bloom. To better assess Comment 17 and assess the socioeconomic impacts of future shifts in HAB dynamics, future work could focus on developing more holistic models linking the socioeconomic analyses identifying the effects of HAB on fisheries described above to predictive HAB models.

For scientific information and analyses to directly support or affect public policies and regulations, the policymaking process should promote opportunities for scientists to engage with policymakers ( Hopkins et al., 2011 ; Cvitanovic et al., 2015 ). Although ecosystem modeling is relatively new to fisheries management, it has entered a policymaking space where the ongoing examination of the best scientific information available to analyze management questions is both expected by fisheries managers and required by law [16 U.S.C. §1851(a)(2), see also 16 U.S.C. §1362(2), §1386(a), and §1536(a)(2)]. U.S. federal fisheries management has a 40 + year history of discussing, debating, and improving fisheries science by bringing that science into the public arena and testing it through application to ongoing fisheries. Engaging in the existing policy making space of the fishery management council process allows ecosystem modelers to make that needed connection between modeling and management priorities.

In assessing the responses of managers and stakeholders to the review of ESR indicators, we demonstrated that policy needs for ecosystem science go beyond the setting and use of environmental indicators to improve forecasts of biomass and reference points required for the setting of harvest limits. Other uses of ecosystem models and analysis identified included: (1) assessment of shifts in the spatial distribution of target stocks and protected species to anticipate changes in availability and the potential for interactions between fisheries and protected species, (2) identification of trophic interactions to better assess tradeoffs between protection of dependent predators and resilience of fishing communities in the management of forage species and to holistically assess the impact of climate change on PFMC-managed species, and (3) synthesis of how the environment affects fishing communities, either via extreme events such as HABs or storms or via climate-driven changes in target species availability, to promote efficiency and profitability of fisheries. The identified policy needs largely reflect the broad aims of EBFM ( National Marine Fisheries Service, 2016a ) but were brought forward directly by managers and stakeholders operating in the CCE and thus are relevant to their experience and specific requirements and are more regionally actionable. By including a stakeholder review of ESR indicators into an existing policy discussion process, other regions could replicate our work to ensure that their ecosystem modeling complements legally mandated avenues for using best available science in management and for setting research priorities ( Box 1 , Pacific Fishery Management Council (PFMC), 2018 ). Given limited resources, the process here outlined could then be followed by an ecosystem risk assessment ( Holsman et al., 2017 ) to prioritize analyses and model development to focus on initially, as was done successfully by the U.S. Mid-Atlantic Fishery Management Council ( Gaichas et al., 2018 ).

While existing ecosystem modeling capabilities in the region can address many of the policy needs identified by the ESR comments ( Table 2 ), for some applications, improvements in ecosystem modeling capabilities are required to further the utility of ecosystem models and analyses to management needs ( Table 2 ). Comments 1 and 10 stressed the need to anticipate future changes in productivity or species interactions. While ecosystem models and analyses have shown skill for some species in predicting changes in productivity and distribution over the historical period using observed data or data assimilative ocean model output (e.g., Brodie et al., 2018 ; Tolimieri et al., 2018 ) and have in some cases been used to assess impacts of climate change ( Hazen et al., 2013 ; Haltuch et al., 2019a ), the skill of near term (months to years in advance) ecological forecasts needs to be tested to assess their utility to the setting of catch limits, biomass projections, or spatial management measures at the spatiotemporal scales that are relevant to managers. Development of forecasting capabilities for fish productivity or distribution changes would also benefit from expansion of the use of ecosystem models and analyses linked to oceanographic models to improve mechanistic understanding and to develop indicators with high explanatory power in modeling changes in species responses to environmental variability (e.g., Brodie et al., 2018 ; Tolimieri et al., 2018 ; Henderson et al., 2019 ). Utility of such methods should be assessed relative to current approaches and as part of a forecasting ensemble.

The ESR comments also show a clear desire on the part of managers and stakeholders to better assess the broader ecosystem impacts of management actions, particularly with regards to the tradeoffs between the forage needs of predators, fisheries for prey and predator species, and protections for non-target predator stocks. In light of the stakeholders’ and managers’ comments, ecosystem models have the potential to be used more routinely to assess the impact of changes in forage to dependent predators when linked to stock assessments (e.g., Drew et al., 2021 ) or MSE model output (e.g., Deroba et al., 2019 ), and to develop multispecies harvest control rules (HCRs) or ecosystem-level reference points ( Link, 2018 ; Fulton et al., 2019 ; Holsman et al., 2020 ). This is in addition to their demonstrated utility in addressing specific strategic questions, such as the role of krill in the ecosystem (e.g., Pacific Fishery Management Council (PFMC), 2009 ) or the impact of climate change on PFMC-managed species (e.g., Marshall et al., 2017 ). However, in some cases, model refinements to include more realistic fishing scenarios based on current harvest rules or more realistic responses to environmental variability, particularly with regards to changes in species distribution, may be required before implementation ( Table 2 ).

Many comments also acknowledged the need to better integrate human dimension considerations when assessing impacts of management policies on port-level socioeconomic metrics, particularly within the context of climate variability and change. While case studies for specific regions and fisheries have shown promising approaches (e.g., Plagányi et al., 2013 ; Rogers et al., 2019 ; Selden et al., 2019 ), further development of methods linking spatially explicit biological models to socioeconomic outcomes, as well as improved consideration of the diversity of harvesting portfolios ( Frawley et al., 2021 ), is required. In particular, links to on-shore community impacts, many of which are qualitative socio-cultural measures, have been neglected and may require direct consultation with communities rather than quantitative modeling ( Okamoto et al., 2020 ). This will necessitate further communication not only between ecosystem modelers and managers, but also between ecosystem modelers, managers, and (non-economic) social scientists. While the findings presented here can, in collaboration with managers and stakeholders, help refine ecosystem modeling planning, ecosystem model development for improved management applicability also needs to be balanced with research and development innovations to identify emerging information needs.

As ecosystem modeling insights evolve to more explicitly inform both tactical and strategic management, the means to better quantify and present uncertainty in such model outputs or scenarios will become more critical ( Link et al., 2012 ; Weijerman et al., 2015 ; Jacobsen et al., 2016 ; Haltuch et al., 2019b ). Combining information across approaches via model averaging or ensembles ( Marmion et al., 2009 ; Ianelli et al., 2016 ; Karp et al., 2019 ), or using Bayesian updating ( Staton and Catalano, 2019 ) or state-space models ( Fleischman et al., 2013 ) to formally integrate observations and modeled effects of drivers from multiple stages of a species life cycle may provide more reliable model output, and improved characterization of forecast uncertainty (i.e., model spread) on which to base decisions ( Ianelli et al., 2016 ). Agreement in the predictions of an ensemble of structurally different ecosystem models can also increase stakeholder confidence in the model results ( Jacobsen et al., 2016 ). MSE frameworks, which assess robustness of alternative management strategies to a range of uncertainties captured by a set of diverse operating models ( Punt et al., 2016a ), may be useful to both characterize uncertainty and communicate to stakeholders its impact on management performance.

Despite the growing need for ecosystem information and existing ecosystem modeling capabilities in the region potentially useful to the identified policy needs, only a few of these models or analyses have been implemented in management frameworks ( Table 2 ). With regards to stock assessment science, there is a well-established routine review process that has enabled continued feedback between managers and modelers, and model refinement aimed at improving utility to management issues. Indeed, our work demonstrates that most implementations of ecosystem analysis in the PFMC have been via the development of indices for single-species climate informed population dynamics models (i.e., salmon forecasts, sardine HCR, sablefish stock assessment) aimed at deriving better estimates of biomass and reference points on which to base harvest decisions. These models are embedded in the PFMC process: council advisory bodies are familiar with them, they are regularly used to set catch limits, and their limitations and potential improvements are routinely discussed during their review process. This has facilitated faster uptake of ecosystem consideration in the PFMC via this type of vetted models. However, examples from other regions have demonstrated that regular dialogue between ecosystem modelers and advisory bodies via existing management council processes can foster, gradually, adoption of new management approaches (e.g., Holsman et al., 2016 , 2019 ; Ianelli et al., 2019 ; Drew et al., 2021 ).

We suggest that in the PFMC and elsewhere, uses of ecosystem models and analyses could similarly be vetted and refined within the existing technical review, methodology review, stock assessment, and harvest setting process, or addressed in a more targeted review process such as for the Atlantis model in the CCE [ Kaplan and Marshall, 2016 , or as ‘key runs’ in ICES (2021) ]. As for stock assessment, such interactions between managers and ecosystem modelers should be iterative. As highlighted in Figure 3 , we propose that, for the PFMC, the annual technical review of ESR indicators, coupled with more in-depth methodology reviews when warranted, could serve as a forum for routine, iterative dialogue between managers and ecosystem modelers. This forum would enable discussion of ecosystem models and analyses showing potential utility but requiring further discussion on key details (e.g., species, timescales, and spatial scales of interest) with managers and stakeholders for implementation ( Figure 3 and Table 2 ). For those analyses and models that have already been reviewed or implemented, this forum would provide a platform for periodic review of model refinements or new applications. The manager-modelers idea sharing process here presented ( Figure 3 ) could 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.

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Figure 3. Overview of the PFMC forum for routine discussion of ecosystem models and analyses and the management process and policy issues informed by those models and analyses.

This paper explicitly looks at the comments on ESR indicators that pertained to an ecosystem-level understanding of fish stocks and fisheries. However, the comments that PFMC received on ESR indicators also ranged into questions about spatial management and links between climate variability and shifting stock distribution, extreme climate events, forecasting future risk, and about better understanding fishing community dependence on fishery resources and vulnerability to shifting stock availability. For natural resource managers, discussions of these wide-ranging questions and ideas are possible when working in an open, public process that involves stakeholders with diverse and sometimes competing goals. For ecosystem modelers, being open to the ideas that drive management processes and being willing to listen for how management processes communicate those ideas is key to successful connections between their models and management needs.

Several key aspects of our case study are present in other management systems for public trust resources, and this study may serve as a blueprint for matching models to management needs in a variety of policy making processes worldwide. To facilitate adoption of scientific knowledge in support of management decisions, existing natural resources management frameworks (e.g., Hopkins et al., 2011 ; Gregory et al., 2012 ; Mach and Field, 2017 ; Francis et al., 2018 ) highlight the need for continued, iterative engagement between scientists and decision makers. Here we find that both ecosystem scientists and managers have pre-existing tools in place, but nexus points between the science and management communities need to be present to foster information sharing and support the development of ecosystem models of interest and use to resource managers and the public. Development and use of ecosystem models should be guided by established best practices for model use (e.g., Collie et al., 2016 ; Punt et al., 2016a ), forums like ecosystem modeling workshops that focus on model improvements and information sharing (e.g., Weijerman et al., 2016 ; Townsend et al., 2017 ), and science integration templates like integrated ecosystem assessment (e.g., Levin et al., 2009 ; Harvey et al., 2020 ). Resource management processes that require regular assessments of key resources (stocks, habitats, protected species) and activities (fishing, conservation actions) foster the scientific data collection that supports ecosystem modeling. Management processes, like fishery management councils, that maintain space in their processes for discussing ecosystem science and EBFM signal their openness to considering and using new ecosystem information as it arises and can serve as forums to facilitate matchmaking between models and management needs (see Figures 2 , 3 ).

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author/s.

Author Contributions

DT, YR, HT, and CH developed the initial idea for this manuscript. DT, CH, SB, and SK reviewed and categorized comments from the Coordinated Ecosystem Indicators Review. All authors have contributed writing and editing.

This work was supported by the NOAA Climate Program Office’s Coastal and Ocean Climate Applications Program (NA17OAR4310268), Modeling, Analysis, Predictions, and Projections Program (NA17OAR4310108), and the NOAA Fisheries Office of Science and Technology.

The scientific results and conclusions, as well as any views or opinions expressed herein, are those of the author(s) and do not necessarily reflect the views of NOAA or the Department of Commerce.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We thank all the stakeholders, managers, and scientists who participated in the PFMC stakeholder review of ESR indicators as well as the participants of the NOAA NEMoW 5 workshop for useful suggestions on an early idea for the paper. We also thank J. Samhouri for helpful comments on an earlier draft of the manuscript and the two journal reviewers for insightful comments that helped improve the manuscript.

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Keywords : ecosystem-based fisheries management, ecosystem modeling, fisheries science, fisheries management, natural resource management

Citation: Tommasi D, deReynier Y, Townsend H, Harvey CJ, Satterthwaite WH, Marshall KN, Kaplan IC, Brodie S, Field JC, Hazen EL, Koenigstein S, Lindsay J, Moore K, Muhling B, Pfeiffer L, Smith JA, Sweeney J, Wells B and Jacox MG (2021) A Case Study in Connecting Fisheries Management Challenges With Models and Analysis to Support Ecosystem-Based Management in the California Current Ecosystem. Front. Mar. Sci. 8:624161. doi: 10.3389/fmars.2021.624161

Received: 30 October 2020; Accepted: 31 May 2021; Published: 30 June 2021.

Reviewed by:

Copyright © 2021 Tommasi, deReynier, Townsend, Harvey, Satterthwaite, Marshall, Kaplan, Brodie, Field, Hazen, Koenigstein, Lindsay, Moore, Muhling, Pfeiffer, Smith, Sweeney, Wells and Jacox. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Desiree Tommasi, [email protected]

This article is part of the Research Topic

Using Ecological Models to Support and Shape Environmental Policy Decisions

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Journal of Environmental Management

Research article climate change and community fisheries in the arctic: a case study from pangnirtung, canada.

Assesses adaptation to climate change in fisheries communities.

Examines how indigenous fishers experience and respond to change.

Climate change creates multiple changes in Arctic fisheries systems.

Diversification, technology, and co-management are three adaptive strategies.

Worldviews, institutions, culture, and knowledge systems can shape adaptation.

Coastal fishery systems in the Arctic are undergoing rapid change. This paper examines the ways in which Inuit fishers experience and respond to such change, using a case study from Pangnirtung, Canada. The work is based on over two years of fieldwork, during which semi-structured interviews (n = 62), focus group discussions (n = 6, 31 participants) and key informant interviews (n = 25) were conducted. The changes that most Inuit fishers experience are: changes in sea-ice conditions, Inuit people themselves, the landscape and the seascape, fish-related changes, and changes in weather conditions, markets and fish selling prices. Inuit fishers respond to change individually as well as collectively. Fishers’ responses were examined using the characteristics of a resilience-based conceptual framework focusing on place, human agency, collective action and collaboration, institutions, indigenous and local knowledge systems, and learning. Based on results, this paper identified three community-level adaptive strategies, which are diversification, technology use and fisheries governance that employs a co-management approach. Further, this work recognised four place-specific attributes that can shape community adaptations, which are Inuit worldviews, Inuit-owned institutions, a culture of sharing and collaborating, and indigenous and local knowledge systems. An examination of the ways in which Inuit fishers experience and respond to change is essential to better understand adaptations to climate change. This study delivers new insights to communities, scientists, and policymakers to work together to foster community adaptation.

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Climate risks and opportunities of the marine fishery industry: a case study in taiwan.

case study fish climate change

1. Introduction

2. integrated framework for climate change risk management in the marine fishery industry, 2.1. defining the scope of the study, 2.2. evaluation method and process, 2.2.1. step 1: defining the risk issues and setting goals, 2.2.2. step 2: risk identification, 2.2.3. step 3: choice and exploration of policy responses, 2.3. stakeholder participatory processes, 2.3.1. strategy and composition for identifying stakeholders, 2.3.2. in-depth interview questionnaire and the un sdgs, 3. climate-related risks, challenges, adaptation policies, and policy directions for the marine fishery industry, 3.1. types of climate-related risks, issues, and challenges, 3.2. policy trends, path choices, and specific adaptation actions of operators, 3.2.1. climate adaptation policies and regulations for the taiwan fishery administration, 3.2.2. specific adaptation actions of operators, 3.3. potential gaps in existing adaptation policies and actions for industry under future climate change, 4. future adaptation options and new opportunities for the marine fishery industry in the face of climate change, 4.1. adaptation plans for short-term implementation, 4.1.1. developing an integrated environmental monitoring system to help producers obtain scientific data and information, 4.1.2. financial services and loan support, 4.2. adaptation plans for medium-term and long-term implementation, 4.2.1. technology development and innovation of fishing operations, 4.2.2. development of a more diversified transport and sales system by entering new markets, 4.2.3. climate education for stakeholders and investment in and development of disaster response capacity, 4.2.4. development of and opportunities for restorative post-disaster adaptation capacity, 4.2.5. establishment of a cooperative mechanism for multi-party in the fishery, institutional review board statement, data availability statement, acknowledgments, conflicts of interest.

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Ho, C.-H. Climate Risks and Opportunities of the Marine Fishery Industry: A Case Study in Taiwan. Fishes 2022 , 7 , 116. https://doi.org/10.3390/fishes7030116

Ho C-H. Climate Risks and Opportunities of the Marine Fishery Industry: A Case Study in Taiwan. Fishes . 2022; 7(3):116. https://doi.org/10.3390/fishes7030116

Ho, Ching-Hsien. 2022. "Climate Risks and Opportunities of the Marine Fishery Industry: A Case Study in Taiwan" Fishes 7, no. 3: 116. https://doi.org/10.3390/fishes7030116

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Peer-reviewed

Research Article

Social-ecological vulnerability of fishing communities to climate change: A U.S. West Coast case study

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Current address: West Coast Regional Office, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, WA, United States of America

Affiliation School of Environmental and Forest Sciences, University of Washington, Seattle, WA, United States of America

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Roles Conceptualization, Data curation, Methodology, Visualization, Writing – original draft, Writing – review & editing

Roles Conceptualization, Data curation, Formal analysis, Methodology, Software, Validation, Writing – original draft, Writing – review & editing

Affiliation Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, WA, United States of America

Roles Data curation, Methodology, Validation, Writing – original draft, Writing – review & editing

Roles Data curation, Formal analysis, Investigation, Methodology, Software, Writing – review & editing

Affiliation Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Monterey, CA, United States of America

Roles Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing – review & editing

Affiliation Evans School of Public Policy and Governance, University of Washington, Seattle, WA, United States of America

Affiliation Ocean Sciences Department, University of California at Santa Cruz, Santa Cruz, CA, United States of America

Affiliations Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Monterey, CA, United States of America, Institute of Marine Sciences, University of California Santa Cruz, Santa Cruz, CA, United States of America

Roles Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Visualization, Writing – original draft, Writing – review & editing

Affiliations School of Environmental and Forest Sciences, University of Washington, Seattle, WA, United States of America, The Nature Conservancy in Washington, Seattle, WA, United States of America

PLOS

Fig 1

Climate change is already impacting coastal communities, and ongoing and future shifts in fisheries species productivity from climate change have implications for the livelihoods and cultures of coastal communities. Harvested marine species in the California Current Large Marine Ecosystem support U.S. West Coast communities economically, socially, and culturally. Ecological vulnerability assessments exist for individual species in the California Current but ecological and human vulnerability are linked and vulnerability is expected to vary by community. Here, we present automatable, reproducible methods for assessing the vulnerability of U.S. West Coast fishing dependent communities to climate change within a social-ecological vulnerability framework. We first assessed the ecological risk of marine resources, on which fishing communities rely, to 50 years of climate change projections. We then combined this with the adaptive capacity of fishing communities, based on social indicators, to assess the potential ability of communities to cope with future changes. Specific communities (particularly in Washington state) were determined to be at risk to climate change mainly due to economic reliance on at risk marine fisheries species, like salmon, hake, or sea urchins. But, due to higher social adaptive capacity, these communities were often not found to be the most vulnerable overall. Conversely, certain communities that were not the most at risk, ecologically and economically, ranked in the category of highly vulnerable communities due to low adaptive capacity based on social indicators (particularly in Southern California). Certain communities were both ecologically at risk due to catch composition and socially vulnerable (low adaptive capacity) leading to the highest tier of vulnerability. The integration of climatic, ecological, economic, and societal data reveals that factors underlying vulnerability are variable across fishing communities on the U.S West Coast, and suggests the need to develop a variety of well-aligned strategies to adapt to the ecological impacts of climate change.

Citation: Koehn LE, Nelson LK, Samhouri JF, Norman KC, Jacox MG, Cullen AC, et al. (2022) Social-ecological vulnerability of fishing communities to climate change: A U.S. West Coast case study. PLoS ONE 17(8): e0272120. https://doi.org/10.1371/journal.pone.0272120

Editor: Tarsila Seara, University of New Haven, UNITED STATES

Received: November 11, 2021; Accepted: July 12, 2022; Published: August 17, 2022

This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Data Availability: Data for the analysis presented in this manuscript came from a variety of sources. Much of the relevant data are within the manuscript and its Supporting Information files, including values calculated in this paper such as ecological sensitivity, exposure, risk, and community exposure, sensitivity, adaptive capacity, and vulnerability. Additionally, summarized data needed to calculate community exposure, sensitivity, adaptive capacity, and vulnerability are provided along with R code at the time of publication at https://github.com/koehnl/CommunityVuln_PlosOne (for code) or on Dryad at https://doi.org/10.5061/dryad.547d7wm9d (for data). Certain data underlying the above values presented in the study are publicly available. Specifically, through Aquamaps: https://www.aquamaps.org/ for species ranges to determine ecological risk or by contacting Aquamaps at [email protected] . Additional raster files needed to construct species range files are available here through the github page cited in this paper: O'hara CC, Afflerbach JC, Scarborough C, Kaschner K, Halpern BS. Aligning marine species range data to better serve science and conservation. PLoS One. 2017 May 3;12(5):e0175739. https://doi.org/10.1371/journal.pone.0175739 and at the associated git repository https://github.com/OHI-Science/IUCN-AquaMaps (and as part of the code to rasterize species range data available at the time of publication on github here: https://github.com/koehnl/CommunityVuln_PlosOne ). Social metric data for calculating adaptive capacity for communities are available through the CDC here: https://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.html The summarized tables of climate variables experienced by species in their ranges (output from the climate models), needed to calculate ecological exposure, sensitivity, and risk, will be available via Dryad ( https://datadryad.org/stash ) at the time of acceptance and publication (here: https://doi.org/10.5061/dryad.547d7wm9d ). The underlying physical and biogeochemical variables from the downscaled projections are available upon request from authors Mike Jacox at NOAA ( [email protected] ) or Jerome Fiechter at UC Santa Cruz ( [email protected] ). Confidential vessel-level landings data may be acquired by direct request from the Pacific Fisheries Information Network (PacFIN) ( https://pacfin.psmfc.org/ ) or the Departments of Fish and Wildlife in California, Oregon, and Washington, subject to a non-disclosure agreement. Aggregated data used to determine top species landed for each community and percent revenue from each species for each community, and all associated R code is publicly available at https://github.com/koehnl/CommunityVuln_PlosOne for R code and https://doi.org/10.5061/dryad.547d7wm9d for aggregated data (aggregated landings by ports can also be found here https://reports.psmfc.org/pacfin/f?p=501:1000:::::: and go to “All species by port group”. Values for community reliance (from NOAA California Current Integrated Ecosystem Assessment) are provided in the same Dryad repository. Also, data on PacFIN ports and species used are in the Dryad repository (here: https://doi.org/10.5061/dryad.547d7wm9d ) formulated from https://pacfin.psmfc.org/pacfin_pub/codes.php . All code used in analysis presented in this manuscript are available on github at https://github.com/koehnl/CommunityVuln_PlosOne .

Funding: 1. L.E.K., L.K.N., P.S.L, and A.C.C. were funded by the Lenfest Ocean Program. URL: https://www.lenfestocean.org/ . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 2. J.F.S. was funded by the Packard Foundation [grant numbers 2019-69817]. URL: https://www.packard.org/ . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Coastal communities are on the front line of climate change. Sea level rise is already threatening shoreline infrastructure [ 1 ], and extreme weather events are resulting in the destruction of the built environment along coastlines [ 2 – 4 ]. Climate change will also likely cause changes in fisheries resource availability [ 5 ] as a result of distributional shifts in fisheries species, declines in productivity, direct impacts on species due to warming, acidification, or declines in oxygen, and indirect loss through food-web disturbances [ 6 – 8 ]. Potential subsequent reductions in fisheries have significant implications for food security [ 9 ], culture [ 10 ] and livelihoods in coastal communities [ 11 ]. The impacts of such changes will vary among communities, households and individuals (e.g.,[ 12 ]), and may exacerbate existing inequalities within and among coastal communities [ 13 ]. The acute impacts of climate change on social-ecological systems have created an urgent need to strengthen the ability of coastal communities to adapt.

Along the West coast of the United States, marine fish and invertebrates are already experiencing substantial impacts from climate variability and change. Marine heat waves, such as the “warm blob”, have likely led to losses of commercial harvest in the California Current Ecosystem, including drastic declines in salmon abundance and landings, loss of revenue from closures in the Dungeness crab ( Metacarcinus magister ) fishery due to harmful algal blooms, and severe reduction in squid landings [ 14 – 19 ].

In total, fisheries in the U.S. portion of the California Current support over 220,000 jobs in communities in the states of Washington, Oregon, and California, and lead to total sales from commercial and recreational fishing in the region near $35 billion annually [ 20 ]. Community connection to fisheries resources is not just economic; the California Current Ecosystem supports community well-being by sustaining cultural values and practices, connections to nature, and social connections [ 21 , 22 ]. These non-economic connections are particularly evident in tribal communities like Neah Bay, Washington, where residents catch and consume an average of 125 pounds per person of seafood annually and rely on subsistence catch to support culturally important household sharing networks and traditional knowledge [ 23 ]. Additionally, in non-tribal communities along the U.S. West Coast, core components of individual and community well-being reside in the marine environment and in human interactions with it [ 22 , 24 ], and resident fishers derive non-monetary benefits from fishing practices that support expressions of identity and social capital [ 25 ].

Because communities rely upon fisheries economically and socially, the ecological impacts of climate change on fisheries resources are likely to affect livelihoods and well-being in the region. One way to assess these impacts is by describing vulnerability—the degree to which a system is likely to experience harm due to exposure to a hazard [ 26 ]. Following the Intergovernmental Panel on Climate Change (IPCC) [ 27 ], vulnerability to climate change can be conceptualized as a combination of exposure to climate change, the degree to which a system is affected by climate change (i.e., sensitivity), and the capacity to adapt to that change. In the context of environmental management, vulnerability assessments are useful for building an understanding of patterns of vulnerability (e.g., [ 28 ]), improving adaptation planning (e.g.,[ 29 ]), and revealing patterns of inequity [ 30 ].

Ecological approaches to vulnerability assessment in fisheries systems typically focus on target species [ 31 ]. For instance, Crozier and colleagues [ 32 ] evaluated the vulnerability of Pacific salmon to climate change by assessing the magnitude of the projected change in conditions resulting from climate shifts (exposure), the sensitivity of salmon to such changes, and the ability of salmon to modify phenotypes to cope with new climate conditions (adaptive capacity). In contrast, approaches to vulnerability assessment rooted in social-ecological systems theory (e.g., [ 33 ]), recognize that ecological and human vulnerability are linked [ 26 ] and thus integrate biophysical and social exposure, sensitivity and adaptive capacity [ 34 – 39 ]. Cinner and colleagues [ 35 ], for example, combined ecological exposure and sensitivity with social sensitivity and adaptive capacity to assess social-ecological vulnerability in 12 Kenyan coastal communities. Fully-integrated social-ecological vulnerability assessments developed at scales that align geographically with existing governance systems allow for more frictionless uptake and implementation of policies intended to reduce coastal community vulnerability (e.g., national level: [ 40 , 41 ]; regional level: [ 42 , 43 ]). Because of the fast-changing ecological and social contexts of these assessments, those which can be easily updated with new data and that are scalable are less likely to become quickly outdated (see, for example, [ 44 , 45 ] and references therein).

Here, we develop and deploy an assessment that is grounded in both ecological and social-ecological vulnerability approaches. Our goal is to identify communities reliant on the U.S. California Current Ecosystem that are likely to become most imperiled by the impacts of climate change on fisheries. We first assess the ecological risk of the California Current major fisheries species to climate change into the medium-term future (50 years). We next estimate the degree to which fishing communities are at risk from climate-impacted fisheries species. Finally, we integrate the climate risk faced by California Current fishing communities with the adaptive capacity of these communities to assess the overall vulnerability of these coastal populations. Using readily available public data, our overall aim is to produce an assessment that is easily reproducible, automatable, and scalable and in this case aligned with the scale of the federal fisheries governing body, the Pacific Fisheries Management Council (PFMC). In doing so, we strive to provide needed information that can guide management interventions which inform more equitable climate adaptation.

We adapted the vulnerability assessment framework outlined by Marshall and colleagues [ 46 ] and Thiault and colleagues [ 47 ] to investigate the vulnerability of fishing communities ( Fig 1 ). We first determine ecological risk, a combination of the ecological exposure and ecological sensitivity of target species to changing climate conditions ( Table 1 , Fig 1 ). Ecological risk directly informs community exposure, such that ecological risk of each target species is weighted by economic importance of those species for each community ( Table 1 ). Community sensitivity is determined by the economic reliance of communities on the fishing industry, which when combined with community exposure, gives community risk to climate change ( Table 1 ). We then consider the adaptive capacity of fishing communities, or the ability to adapt, absorb, and recover from climate change impacts, which is influenced by demographic and social factors [ 48 ]. Combining community risk with community adaptive capacity generates the overall extent that communities are vulnerable to climate change, or “community vulnerability” ( Table 1 ). We calculate ecological risk for approximately 50 years into the future so that community vulnerability reflects medium-term future conditions. This time-frame is similar to that considered by the PFMC Climate and Community Initiative [ 49 , 50 ], and is 1) a period where climate models and emissions are more certain (compared to further out, see [ 51 ]); 2) long-enough in the future where climate trends have emerged; and 3), near-term enough to still be management relevant. We focus our analysis on U.S. fishing communities in the California Current Ecosystem and define a fishing community as a geographic location that is a specified census designated place, with at least some level of commercial fishing activity associated with commercial fisheries along the continental U.S. West Coast (as defined by the National Oceanic and Atmospheric Administration and California Current Integrated Ecosystem Assessment, IEA [ 52 ]).

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Framework used to determine the coupled social-ecological vulnerability of fishing communities to climate change on the U.S. West Coast. The initial components of the framework are the ecological sensitivity and exposure to climate change of marine resources that fishing communities depend on. Ecological sensitivity and exposure are determined for four climate change variables–temperature, pH, oxygen, and chlorophyll–and are combined to determine ecological risk. Community exposure is derived by weighting ecological risk of species by the economic importance to each community. When community exposure is combined with community sensitivity, this forms community risk to climate change. When community risk is combined with adaptive capacity, which is made up of 15 social indicators, this produces overall community vulnerability which is made up of ecological (yellow), economic (green), and social indicators (blue). Design by SJ Bowden.

https://doi.org/10.1371/journal.pone.0272120.g001

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https://doi.org/10.1371/journal.pone.0272120.t001

As defined above, ecological risk is the extent to which fishery target species are predicted to be affected by climate impacts. For ecological risk, we adapted the approach of Samhouri et al [ 56 ], where ecological risk is a combination of the expected change in environmental conditions a species will face in their range (ecological exposure) and the present-day breadth of environmental conditions a species experiences in its range (ecological sensitivity, where greater breadth implies greater tolerance). To estimate ecological risk, we compiled data and model output on commercial species ranges/distributions and environmental/climate conditions in those ranges for species that are the top landed species for communities. These quantitative methods are automatable through readily available data and scalable depending on species and geographic range.

Ecological data.

To determine the commercial fishery species that communities are most dependent on, we relied upon landings receipts derived from the Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/ ) database for the years 2009–2018 (10-year range to capture potential variability from year to year). Fish ticket data for 2009–2016 and 2017–2018 were downloaded in April 2017 and June 2019, respectively (the final quarter of 2016 was not complete at the time of the initial data query and not included in analysis, specifically 9/9/2016 through 12/31/2016). To assess landings, we identified fishing ports recognized by the Pacific Fisheries Management Council and state fish and wildlife agencies. Data on species landings are provided by PacFIN; however, PacFIN data aggregate multiple ports to higher level PacFIN “port groups”. As a consequence, we assumed landings data for each individual port are the same as the PacFIN port group that the individual port is a member of (see https://pacfin.psmfc.org/pacfin_pub/codes.php for all port groupings). We excluded ports where a high percent of landings data were confidential (see below). Because the PacFIN database is updated as information is released from California, Oregon, and Washington Departments of Fish and Wildlife to Pacific States Marine Fisheries Commission, it is possible that the top species by landings and/or assignments of landings to ports may change over time (even potentially becoming not confidential). The value of the method and framework introduced in this paper is that the analysis can be updated in parallel as data is updated.

For each port, we calculated the total landed weight over the 10-year span, found the percent of total landed weight each species comprised, and only considered the species that contributed to the top 90% of landed weight for the remainder of our analyses. We focused on the top 90% of landed weight, not revenue, to focus on the majority of what is caught in the community (versus a rare but highly valued species caught). Communities were removed if >20% of landings were listed as “confidential” (species landed by <3 vessels) as the majority of data either had <20% or more than 20% (break in the data) and then less then 75% of the landings were accounted for if more then 20% was confidential. Catch was removed for landings listed as “other” or “miscellaneous” and that made up less than 5% of catch. We determined species level for “unidentified” catches of taxonomic families (unidentified urchins, unidentified hagfish, etc.) based on information from state fish and wildlife agencies (see S1 Table for which species make up each group). Additional specifics on the preparation of landings data for analysis are provided in the S1 Appendix .

Our approach to ecological risk required historic and future climate data within species distributions. We chose four climate variables that may impact species and are major climate drivers in the California Current System [ 57 ]; temperature, pH, oxygen, and chlorophyll (see S1 Appendix for justification for each variable). We used multiple variables as recent studies have indicated that using temperature alone to specifically predict range shifts can underestimate or hide vulnerability [ 58 ] and these four variables have been used in other assessments [ 28 , 32 , 59 , 60 ].

Species specific distributions for all species in the top 90% of landings were collated from Aquamaps [ 53 ] and we rasterized species range data using methods presented in O’Hara et al [ 61 ]. Final data include species distributions for the U.S. West Coast area of 180-100W, 20-60N, that are constrained further to the ocean model domain for climate variables (see below). We developed extent of occurrence [ 62 ] for each species based on probability of occurrence of ≥ 0.4 across 50 km by 50 km grid cells. We only included areas with species probability of occurrence ≥ 0.4 because areas of low probability of occurrence may not actually be within a species “core range” or relative suitable habitat and may only represent vagrant or extralimital occurrences. Other studies have suggested a cut-off of 0.6 for relative habitat suitability (specifically [ 63 ], but this study focused on marine mammals). A threshold of 0.4 is also often used or tested (for example [ 56 , 64 , 65 ]) and is more precautionary; some species suitable habitat ranges may be overestimated, but gives more confidence that we have captured the entire suitable habitat of a species.

A few species within the top 90% of landings by port had missing distribution data or distributions primarily outside of our range. These species were either removed from analysis or information from a closely related species was used depending on how prominent the species were in the landings data (see S1 Appendix ). Of the 72 species in the top 90% of landings (for ports with <20% confidential and including species that make up unspecified species groups), we were able to estimate climate risk for 68 of them, and use a surrogate species for 1, for a total of 69 species ( S1 Table ). For each species, the metric used for the climate experienced by the species, for each climate variable (pH, temperature, oxygen, and chlorophyll), depended on the species primary habitat (benthic or pelagic); e.g. bottom temperature was used for benthic species and surface temperature for pelagic (see S1 Appendix on how primary habitat was determined and metrics used for bottom versus surface, and see S1 Table for info for each species).

We used a California Current configuration of the Regional Ocean Modeling System (ROMS) coupled with a biogeochemical model (NEMUCSC) to simulate historical and future climate conditions for temperature, pH, chlorophyll, or oxygen within species geographic ranges [ 66 , 67 ]. The ROMS-NEMUCSC model domain spans 30–48˚N and from the coast to 134˚W with a horizontal resolution of 0.1 degrees (~10 km) and 42 terrain-following vertical levels. The coupled physical-biogeochemical model was forced by output from three global earth system models (ESMs) via dynamical regional downscaling, using a “time-varying delta” method for 1980–2100 under the CMIP5 high emissions scenario (RCP 8.5). The three global ESMs were GFDL-ESM2M [ 68 , 69 ]), HadGEM2-ES [ 70 ]), and IPSL-CM5A-MR [ 71 ]. These three models were chosen because they capture the CMIP5 range of potential future physical and biogeochemical change in the California Current Ecosystem. Hereafter, we refer to the different downscaled climate projections as GFDL, IPSL, and HAD.

Calculations for ecological exposure and sensitivity were run with output from each of the three ESMs downscaled climate projections. “Historical” climate experienced by species in their ranges was calculated for the reference period 1980–2010, while future conditions were calculated for 2030–2060 (using an approximately 30 year time period to capture decadal variability in climate change). To exclude numerical artifacts near the model open boundaries, we only extracted data from 31–47˚N and from the coast to 133˚W, omitting ~100 km next to the open boundaries. While some of the species ranges extend beyond the model boundaries, we focus on the California Current domain that is proximate to the U.S. West Coast communities of interest and for which the higher resolution downscaled climate projections can resolve important finer-scale processes associated with coastal upwelling dynamics.

Ecological sensitivity and exposure.

Ecological exposure of a species to a climate variable is the expected change (future-historical) in that climate variable within the species’ California Current range. Here, exposure is defined by the overlap between the historic and future distributions of climate conditions a species experiences. Specifically, for each species, ESM, and environmental variable (temperature, pH, etc.), we found the percent of the distribution of future values that falls in the 5 th -95 th percentile range of the historical distribution, and then subtracted it from 100%. Greater overlap between historic and future climate experienced implies lower exposure to climate change, while lower overlap suggests higher exposure to shifts in climate conditions. This method varies from Samhouri et al. [ 56 ] where exposure was calculated as the change in the mean climate experienced between historic and future. We used the percent of future values that fall within the historical distribution to capture both magnitude of change in the mean as well as variability around the mean.

Ecological sensitivity was defined as the inverse of the current breadth of climate conditions experienced by a species within its current spatial range. Following Dickinson et al. [ 72 ], the climatic breadth is the percentile range (5 th -95 th ) experienced by each species in its historical spatial distribution (calculated as the 95 th percentile value minus the 5 th percentile value and inversed). Species with greater climatic breadth are assumed to be less sensitive.

case study fish climate change

Ecological risk.

case study fish climate change

Community risk

Community exposure..

Community exposure is the degree to which communities are subject to the impact of climate change. Therefore, the exposure of a community is directly related to ecological risk of the species they depend on. To estimate community exposure, we weighted ecological risk for each species by the percent of total revenue for each species for each community, and then summed across the weighted values. Since we have removed confidential catch and species with missing information, the percent of revenue is not out of the total revenue for that community, but the percent revenue of the total revenue for species in the top 90% of landings for that community left after removing missing and confidential data.

case study fish climate change

Community sensitivity.

Community sensitivity reflects the conditions determining how likely a community will be affected by climate change. Since we are primarily concerned with changes in fisheries, we defined the sensitivity of a community to climate change as its economic reliance on the commercial fishing industry. For each community, we used an index of commercial fishery reliance that includes landings, revenue, permits and processing from the California Current Integrated Ecosystem Assessment [ 52 ] for the year 2017 (most recent year available). To scale commercial reliance between 0 and 1, we found the percentile rank for each community across reliance for all communities (which we denote S c ), where multiple communities had the lowest reliance and have sensitivity scores of 0.

Community risk.

We combined community exposure and community sensitivity, using Euclidean distance to get community risk ( R c ) for each community (where minimum exposure and sensitivity are both 0).

case study fish climate change

In calculations of community sensitivity, exposure, and risk, we used the species in the top 90% of landings (by weight) in current and recent years. We recognize that this species composition of landings may not be the same for future landings; however, absent other information it was the best available information.

Community adaptive capacity

We next estimated the adaptive capacity of each fishing community. Here, adaptive capacity refers to the ability of a community to cope with a hazard or pressure such as climate change [ 74 ]. We based our analysis of adaptive capacity on Flanagan and colleagues’ [ 48 ] work, following approaches similar to those used by Davies et al. [ 30 ] and Messager et al. [ 75 ] to assess the vulnerability of communities to wildfire and flooding, respectively.

Like Flanagan et al. [ 48 ], we used an index of adaptive capacity based on metrics (n = 15) nested within four themes: 1) socioeconomic—persons below poverty, unemployed civilians, per capita income, persons with no high school diploma; 2) household composition/disability—persons age 65+, persons age 17 and less, noninstitutionalized population with a disability, number of single parent households; 3) minority status/language—persons of minority, persons older than 5 that don’t speak English well; and 4) housing/transportation—housing structures with 10+ units, estimates of mobile homes, households with more people than rooms (crowding), households with no vehicles, and persons in institutionalized group quarters. These data are compiled by the Centers for Disease Control and Prevention (CDC) from the American Community Survey and are presented as an index (called the CDC Social Vulnerability index) averaged over years 2014–2018 [ 55 ]. Hereafter, we refer to this index as the “CDC Index”.

The metrics used in the CDC Index are estimated at a census tract level; therefore, for each port community, we found the census tracts that intersect with each community using the R packages acs [ 76 ], sf [ 77 ], tidycensus [ 78 ], and tigris [ 79 ]. We first found the geographic boundary of each community by state, and then found the census tracts that intersected at least partially with each community boundary. We matched data from the CDC Index to the census tracts in each community and averaged values across census tracts to find an overall score for each community for each of the 15 metrics for 2018 CDC data. We percentile ranked each of the 15 metrics across communities so that each metric is on the same scale (0 to 1), then summed across metrics for each community, and percentile ranked again across communities so that adaptive capacity is on the same scale as community exposure and sensitivity (cf [ 55 ]). This gives an index value between 0 and 1.

Increasing values of the CDC Index indicate worse conditions (e.g., more unemployment, more households without vehicles, etc), so for the per capita income metric, we reversed the percentile ranking (1 minus original ranking) so that higher values equal worse conditions in line with the other metrics (cf. [ 55 ]). As larger values equate to worse conditions, we present adaptive capacity as the CDC Index but where 0 (smallest value of the CDC Index) represents the greatest capacity to adapt and 1 (highest value of CDC Index) reflects the lowest adaptive capacity. From this, all axes of vulnerability—sensitivity, exposure, and adaptive capacity–are such that greater values equal greater vulnerability.

NOAA also calculates an annual social index using similar data [ 52 ], using methods described in [ 37 , 54 ]. The adaptive capacity generated using the NOAA index is highly correlated to that generated using the CDC index (R = 0.94, see S1 Appendix for more information on the NOAA index and S3 Fig for correlation). Because the CDC Index encompasses more communities and the two indices are highly correlated, we show the results based solely on the CDC Index.

Overall community vulnerability

case study fish climate change

Note that across all methods of calculating ecological risk, community risk, and community vulnerability, scaling values at various times in relation to averaging or calculating Euclidean distance only minimally changes the results for species risk or final community vulnerability. For example, re-scaling ecological sensitivity and exposure before calculating ecological risk using Euclidean distance or re-scaling ecological risk by climate model before averaging, does not qualitatively change the final result—communities with the highest vulnerability remain the same, and rank order of communities with lower vulnerability shifts only slightly. As presented throughout the methods, estimates of each component (community sensitivity, exposure, and adaptive capacity) were correlated across data and models showing that our results are robust to certain sources of uncertainty (see supporting information figures). We do not present further analysis of uncertainty for community vulnerability because, though we present results across multiple sources of uncertainty for species risk and associated community exposure (such as climate model used, weighting by revenue vs. landings, see supporting information figures and tables), we do not have the equivalent ability to represent uncertainty for the other components (only one measure of community sensitivity, only two adaptive capacity sources). Therefore, we present analyses of uncertainty for the individual components where we can, but not for the final community vulnerability scores. All analysis was completed using R [ 80 ] in Rstudio [ 81 ].

Ecological risk

Overall, across climate models and climate variables (pH, oxygen, chlorophyll, and temperature), the species most at risk from projected climate change on the US West Coast include Pacific hake ( Merluccius productus ), smelts ( Atherinopsis californiensis , Spirinchus starksi , Hypomesus pretiosus ), salmon ( Oncorhynchus spp.), Pacific herring ( Clupea pallasii ), spiny lobster ( Panulirus interruptus ), sablefish ( Anoplopoma fimbria ), sharks ( Alopias vulpinus , Isurus oxyrinchus ), albacore ( Thunnus alalunga ), bluefin tuna ( Thunnus orientalis ), and red sea urchins ( Strongylocentrotus franciscanus ) ( Fig 2 , S2 Table ). Some of these species such as Pacific hake and sea urchins are at risk because of their high exposure to climate change, while others (e.g, salmon, smelt, and Pacific herring) have high sensitivity to climate change because they experience a relatively narrow range of environmental conditions within the California Current ecosystem. Many of the top at risk species had high exposure and/or sensitivity to temperature (most smelt, hake, salmon) ( S4 Table ). The top 10 most at risk species also had high sensitivity to oxygen changes (except hake, but hake had high values for exposure and sensitivity to pH). A few most at risk species had high exposure to chlorophyl changes or sensitivity to pH changes (hake, sablefish and jack smelt), but no species in the top 10 at risk had high sensitivity to chlorophyl changes or high exposure to pH (but see spiny lobster and red sea urchin in the top 20).

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Ecological risk to climate change (changes in pH, temperature, chlorophyll, and oxygen) which is the Euclidean distance between ecological exposure and ecological sensitivity. Ecological exposure and sensitivity are averaged across the four climate variables (each ranging from 0 to 1) for each climate model and then averaged across three climate models for species in top 90% of landings (by weight) for US West Coast fishing communities. See S2 Table for individual species risk. Transparency of the name corresponds to standard deviation in exposure (more transparent equals higher deviation/uncertainty) across the three climate models (relative to the other species groups).

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Communities such as Neah Bay WA, Everett WA, Longview WA, Westport WA, Point Arena CA, Albion CA, ( Fig 3 , S5 Table ) are highly at risk to climate change because they are reliant on species that are at high ecological risk such as salmon, red sea urchins, or Pacific hake. However, other communities with economies that depend heavily upon fisheries are highly sensitive to climate impacts even though their catch of species at high ecological risk is relatively low (e.g., Tomales CA, La Push WA, Chinook WA/Ilwaco port; S5 Table ). The majority of communities with 25% or more revenue from a single or multiple salmon species are in the top ten percent of at-risk communities ( Fig 3 , S5 Table ). Most communities with greater than or approximately 20% of revenue from Pacific hake or sablefish were also in the top ten percent of at-risk communities. Communities with high percent revenue from red sea urchins are also highly at-risk when revenue from sea urchins is very high (near 100%, Point Arena and Albion, CA), or the communities’ catch is made up of a high percent revenue from multiple species with high ecological risk (for example, Santa Barbara–California spiny lobster, red sea urchin, and sablefish; Gold Beach, OR–kelp greenling [ Hexagrammos decagrammus ] and red sea urchins). Large catch of species somewhat at-risk ecologically, combined with high community sensitivity (i.e., economic dependence on fisheries), also leads to high community risk (Chinook, WA, albacore catch). The only other community in the top 10% for community risk, Tomales, CA, has reliance on a few species at moderate ecological risk but has high community sensitivity (see S5 Table ).

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Major fishery landings by community by state, where transparency of red is based on percent revenue for that species/catch group (solid red = greater percent revenue). Percent revenue is out of the total revenue for that community, for the species that were in the top 90% of landings for that community. Communities are ordered from highest risk (community exposure combined with community sensitivity [reliance]) to lowest (“Risk” on figure). For communities with the same landings composition (part of the same port group), a random community was picked and plotted (190 communities), to specifically show landings compositions that give high risk. Depending on the random community in each port group, risk will change due to variation in sensitivity but landings composition does not vary. Port group name abbreviations are in “()” and see S5 Table for full port group names. Species are plotted from highest to lowest ecological risk. Communities above the red dotted line are in the top 10 percentile for risk. Overall there are different combinations of species landings that lead to high community risk.

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Washington and Oregon have disproportionately higher numbers of at-risk communities compared to California. Communities in Washington and Oregon receive much of their revenue from species with high ecological risk; thus, a higher percent of communities in Washington and Oregon (46% and 29%) ranked at higher risk (i.e., top 10 percentile, risk > 1.077) to climate change than in California (5%) ( S5 Table ). This is especially true for Washington as 6 of the top 10 communities by risk are in Washington (other 4 in California). Revenue of greater ~20% from the highest risk species like salmon, hake, and/or sablefish (all in the top 10 species for ecological risk) leads to higher community risk, but most California communities have low percent revenue from these species. Communities in Northern California exhibit the greatest range and variability in community risk, with several communities in the lowest 10 percentile (Pleasant Hill, Oakland, Fortuna) and others in the highest (e.g., Point Arena, Albion, Fort Bragg), and still with many others in the middle (e.g., Arroyo Grande, Klamath, Fieldbrook) but with lower community risk than any of the communities in Oregon and Washington.

Adaptive capacity in fishing communities was greatly influenced by socioeconomic factors overall, but other social themes that contributed most to adaptive capacity varied by region ( Fig 4 ). Communities with the lowest adaptive capacity had the highest values for indicators such as high percent in poverty, low per capita income, high unemployment, and high percent with no high school diploma ( S4A and S4B Fig , S6 Table ). Adaptive capacity was most correlated with socioeconomics overall ( Fig 4B ). In Southern California communities with low adaptive capacity were also associated with minority status/language indicators, specifically percent minority and percent of the population that does not speak English well, as well as housing and transportation indicators ( Fig 4C ). Overall, our estimates of adaptive capacity were lowest for Southern California fishing communities ( Fig 4 , S5 Table ). In addition to socioeconomic factors, Washington, Oregon, and Northern California fishing communities with low adaptive capacity also had relatively high values for household composition/disability indicators ( Fig 4 ).

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(A) the four themes of adaptive capacity and individual indicators that make up each. Theme 1 is socioeconomic indicators (orange), theme 2 (green) is household composition and disability, theme 3 (yellow) is minority status and language, and theme 4 (blue) is housing/transportation. (B) The correlation between adaptive capacity and each individual indicator colored by theme. (C) The density distribution of scores for each theme and overall adaptive capacity (where greater values = lower adaptive capacity) for each geographic region.

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Community vulnerability

Integrating community risk and adaptive capacity to estimate community vulnerability to climate change reveals that communities from each state rank in the highest quadrant of vulnerability but communities in Washington and California are the most vulnerable (Figs 5 and 6 ). The inclusion of adaptive capacity produces a much different picture of vulnerability compared to estimates of community risk that omit adaptive capacity ( Fig 6 ).

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Quadrants represent high, medium, or low community vulnerability where communities can have medium vulnerability either be having low adaptive capacity (and low risk) or high adaptive capacity but high risk. Points are color coordinated by state. All states have communities with high vulnerability but the most vulnerable communities are disproportionately represented in Washington and California.

https://doi.org/10.1371/journal.pone.0272120.g005

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Communities ranked by risk (top left) and adaptive capacity (top right), community vulnerability (bottom left) across the U.S. West Coast. Communities labeled are those in top 5 percentile of risk, adaptive capacity, or vulnerability. Considering social information (adaptive capacity) compared to solely ecological/economic data (risk) changes which communities are in the top for most imperiled, though others are ranked high across the board. Also, the “difference” (bottom right) is the rank position of the community based on vulnerability (#1 rank is most vulnerable) minus it’s rank position from risk.

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In some cases, not including adaptive capacity masks communities that are imperiled. In California, for instance, National City ranks 27 th (out of 259) in risk, but when adaptive capacity is included, it rises to 3 rd ( Fig 6 , see S5 Table for other examples). Indeed, the inclusion of adaptive capacity exposes a number of California fishing communities that may be less resilient to climate change than expected when considering only community exposure and sensitivity ( Fig 6d , S5 Table ). Many communities in Southern California ranked lowest for adaptive capacity and had the highest values for social indicator Theme 3—minority status/language.

In other instances, the relatively high adaptive capacity of some communities appears to mitigate the risk inherent in their reliance on species at ecological risk from climate change. For example, a number of Washington communities along the Columbia River from the port group “Other Columbia River [OCR]” (e.g., Longview, Cathlamet, Puget Island, Washougal, Camas, and Ridgefield) are reliant on species such as salmon that are threatened by climate change. Thus, these communities were among the highest ranked communities for risk (Figs 3 and 6a ), but their relatively high adaptive capacity mitigates some of the ecological risk, lowering their overall vulnerability ( Fig 6 , S5 Table ).

Some communities ranked high no matter if we considered community risk or community vulnerability. These include cases where the community relies heavily on species at high ecological risk from climate change and adaptive capacity is low (e.g., Neah Bay, WA; Point Arena, CA amongst others; Fig 6 , S5 Table ). Alternatively, many communities that make up the “Other San Francisco” port group focus on fishery species that are at lower ecological risk due to climate change, and they have relatively high adaptive capacity, and ranked the lowest for both community risk and vulnerability ( S5 Table ).

Note, there are two communities where we miss high valued species in the landings composition because we looked at the top 90% of landings by weight instead of revenue. These are Westport, WA where using species composition by revenue instead would likely lower the vulnerability of the community because the missing species are less at risk species, and Fields Landing, CA where vulnerability would likely remain the same since the missing species are of similar risk as those used.

Vulnerability is a boundary concept (one that translates and is understood across disciplines) [ 82 ] grounded in theory that spans the social and biophysical sciences [ 83 ], and is a critical element of ecosystem-based management [ 84 – 86 ]. Our social-ecological vulnerability assessment of fishing communities of the California Current revealed a number of fishery species at risk from climate change, pinpointed communities that are highly dependent on these species, and highlighted communities with varying capacity to adapt to disruptions in fisheries species and fisheries economies. Our results emphasize that focusing solely on either the ecological risk to fishery species, the economic dependence of communities on fishing or the adaptive capacity of communities provides an incomplete evaluation of the potential vulnerability of fishing communities to climate change. Therefore, similar to recent conclusions by Payne et al [ 39 ], there is likely no single solution that can be applied to address vulnerability moving forward. Indeed, our work highlights that what we observe is not climate vulnerability in itself, but vulnerability revealed through our method of questioning (cf. [ 87 ]). Reducing the vulnerability actually experienced by the 259 U.S. West Coast communities we investigated requires that the breadth of an assessment aligns with the full set of factors that contribute to vulnerability.

Our work reveals that fishing communities across all states of the West Coast of the U.S. are highly vulnerable to climate change. These include towns such as Neah Bay and Longview in Washington State and National City, Oxnard, and Imperial Beach in California where relatively low adaptive capacity contributes to increased vulnerability. Thus, increasing adaptive capacity and economic diversification of fisheries could be beneficial to such communities (see also [ 88 ]). Our results suggested that adaptive capacity rankings were mainly driven overall by socio-economic metrics (adaptive capacity correlated most with socioeconomics than other social indicators), and, indeed, economic assets are often cited as a key component of adaptive capacity (e.g., [ 30 , 89 , 90 ]). As such, management strategies that build financial assets as well as foster economic flexibility can improve adaptive capacity. For instance, quota swapping/interchangeable quota, and side payments have been discussed as potential opportunities to increase flexibility [ 91 , 92 ]. Additionally, Barnes and colleagues [ 93 ] show that beyond building financial assets, attention to social networks, education, risk perception and fostering agency can greatly enhance adaptive capacity of fishing communities.

Our work also shows that certain communities in California are moderately vulnerable to climate change because, although they exhibit low community risk, they have low adaptive capacity. Thiault et al. [ 88 ] suggest that such communities have high latent vulnerability. For instance, our analysis suggests that Santa Paula, California has relatively low vulnerability because of a high percent of landings of California market squid ( Doryteuthis opalescens )—a species with relatively low exposure to the ecological impacts of climate change (within this study). However, the relatively low adaptive capacity of Santa Paula means that predicted declines in squid populations (e.g., [ 94 ]) or a focus on more at-risk species (e.g. sea urchins), could reveal an underlying vulnerability resulting from the relatively low adaptive capacity of the community. In cases such as these, investments in adaptive capacity will reduce latent vulnerability, allow communities to become better prepared for future impacts, and improve the resilience of these communities (see [ 95 ]).

In contrast, other communities are moderately vulnerable to climate change because they exhibit high levels of community risk, but have high adaptive capacity. The high community risk mainly results from the dependence of these communities on species that are at high risk from climate change. These communities are “potential adapters” [ 88 ]. Although these communities are vulnerable, they have the capacity to diversify the species they target (e.g., [ 96 ]), invest in emerging fisheries [ 97 ], or expand into other maritime ventures [ 98 ]. As species’ distributions shift [ 5 , 6 , 99 ], communities may be able to adapt and target species that were previously not easily accessible [ 100 ]. For example, predicted shifts in Pacific sardine ( Sardinops sagax ) distribution could lead to increased catch of this species in the northernly portion of the California Current system [ 66 , 101 ], a potentially adaptative response as other highly at risk species that these communities target become less accessible. However, as noted, certain socioeconomic factors (see [ 102 ]) and/or management regulations and flexibility [ 100 , 103 ] may influence fisher ability to take advantage of such shifts.

Our analysis suggests that Indigenous communities may be particularly vulnerable to future climate change (see also, [ 104 ])—some of the communities that we scored among the most vulnerable have large Indigenous populations (e.g., Neah Bay and La Push, home of the Makah Tribe and Quileute Nation, respectively). Cultures of coastal Indigenous people are closely tied to particular marine species [ 105 , 106 ]), and food security of these communities depends on access to seafood [ 107 ]. For example, our results showed high ecological risk for salmon species—a major food source for many West Coast tribes and species that play significant cultural and social roles in Indigenous communities [ 108 , 109 ]. We did show that adaptive capacity led to lower vulnerability compared to risk for the port group “Other Columbia River ports” that likely includes many Indigenous communities and tribal landings on salmon, but this may illustrate the challenges with using broad geographic census tract information to represent community adaptive capacity (see discussion below), where multiple cultural communities may be represented.

Importantly, our assessment of adaptive capacity relied on census data and thus may provide an incomplete portrayal for Indigenous communities. Measures of adaptive capacity based on census data directly or indirectly emphasize economic assets. In particular, our use of a standard, empirical data-based index for adaptive capacity is certainly imperfect for Indigenous communities where locally value-based, cultural indicators may more accurately represent Indigenous community resilience and adaptability (see [ 110 ]). Additionally, domains of adaptive capacity such as flexibility, social organization and learning [ 93 ] are not captured by our approach. Also, because communities are socially-constructed based on both shared meaning and geography, using census data as a proxy for communities is imperfect. Nonetheless, we chose to use census data because these data are widely used due to their coverage and availability, and because they are available for the entire U.S. West Coast at a scale relevant to policy and management.

Indeed, our work both follows from and utilizes some of the national, cross-regional community social indicators, developed from census and fisheries data nationally, and provided to the public and to fishery management councils (FMCs) by National Marine Fisheries Service social scientists as part of a broad move toward enhancing and expanding social metrics available for integrated social-ecological approaches [54, https://www.fisheries.noaa.gov/national/socioeconomics/social-indicators-coastal-communities ]. In both our reproducible use of census data and our decision to operationalize our approach at the scale of the census-designated place, the same geographic unit in use for the NMFS’ community social indicators approach, we have sought to augment the NMFS community indicators with additional layers of analysis appropriate to more specific interests in climate vulnerabilities and impacts. Accordingly, while our work is distinct from similar climate-oriented innovations for NMFS social indicators in other fishery management regions [ 37 ], it is generally in keeping with the aim of developing the community social vulnerability indicators and information available for a framework that accounts for climate exposure, sensitivity and risk. In effect, our approach presents a replicable means of expanding the utility of NMFS social indicators as potential tools for management, with particular attention to climate change. Additionally, by using the more universal CDC index, future work could look at vulnerability of geographic communities across multiple climate stressors (see for instance [ 30 ] and use of the CDC index to determine community vulnerability to wildfires).

A caveat to developing a high-level, quantitative, automatable, tool for assessing vulnerability for communities across ecological, economic, and social dimensions is that no particular focus is put on any one dimension and detailed specifics may be overlooked when working at the scale of this study. On the ecological side, our high-level analysis did not account for indirect impacts of climate change on species or impacts across species life stages which is illustrated by Dungeness crab results. Specifically, Dungeness crab larvae are at risk to climate change [ 111 , 112 ], but our analysis focused on adult life stages and showed low risk for Dungeness crab. Moreover, studies that incorporated food web effects found risk to Dungeness crabs due to effects on prey from pH changes [ 113 ]; but such indirect effects were not accounted for here. An additional caveat is that there is widespread concern about shifts in species distributions due to climate change [ 114 ], which we did not model explicitly here (though is indirectly captured in changes in climate variables experienced by a species); such work remains an important area of future research. Our assessment also does not include species in Alaska targeted by West Coast fishermen which is outside the scope of our study. Finally, our method for calculating ecological risk does not take into account seasonal shifts in distributions for migratory species; we used full ranges (with probability of occurrence >0.4), potentially resulting in an overestimate or underestimate of risk for these species.

For adaptive capacity, the social indicators we used are at a geographic community scale and therefore, may not capture specific qualities of particular persons engaged in fishing. Fishers themselves likely have varying adaptive capacity at an individual level or fleet level, outside of the geographic community as a whole, and future work should assess more specified adaptability. Moving forward, assessments of vulnerability could incorporate information from surveys aimed at eliciting individual fishers’ perceptions on adaptive capacity, since information that captures personal experiences likely better captures an individual person’s ability to adapt [ 115 ] (and see [ 116 ]), compared to broad community assessments such as the census. Though the scope of our analysis may exaggerate or understate vulnerabilities or risk for certain communities or species, our results were robust across multiple sources of uncertainty, data (three climate models, two adaptive capacity indicators, revenue versus landings data) and methods of calculations (see S1 – S3 Figs) and therefore, represents an overall picture of communities likely to be vulnerable to future climate change.

Confidentiality requirements for specific landings data led to the need to remove certain ports where a higher percent of landings were confidential, including the removal of certain communities that are potentially highly vulnerable. Approximately 16% of communities were removed because of a high percent of confidential landings data. Though we do not have all landings (and therefore can’t calculate exposure), we can calculate sensitivity and adaptive capacity for the removed communities. Of those communities, four and seven had high sensitivity and low adaptive capacity respectively (90th percentile of all communities) and thus have a higher likelihood of having high vulnerability, though it is ultimately dependent on the unknown exposure (see S7 Table ). In particular, Taholah, WA, a community with a large Indigenous population, scored high for sensitivity and low for adaptive capacity, consistent with our findings about the climate change vulnerability of coastal Indigenous communities. One of our objectives of this work was to create a reproducible, automatable assessment that is generally aligned with the scale of the federal fisheries governing body, the PFMC, so that the assessment may be used by the Council. Should the PFMC choose to use this assessment methodology, they may want to work with state Fish and Wildlife agencies to access certain confidential fisheries data under their jurisdiction in order to gain a broader understanding of fishing community vulnerability. Additionally, further work could focus specifically on communities with majority confidential landings but high sensitivity and low adaptive capacity.

The recent release of the sixth assessment from the IPCC [ 117 ] emphasizes the urgent need to strengthen the ability of coastal communities to adapt to our changing oceans. Climate change is disrupting fisheries across the world, impacting individual livelihoods and testing the resilience of coastal communities. Our results reveal that not all fishing communities are equally threatened by climate change, and mechanisms underlying disparities in climate vulnerability differ: ecological, economic and social factors each influence vulnerability but their contribution varies among communities. Recognition that the foundation of climate vulnerability varies among communities highlights the need to consider a diversity of solutions that have the potential to reduce the exposure and sensitivity while increasing the adaptive capacity of communities. Also, which communities are vulnerable and how they experience that vulnerability may change through time, or may change as we acquire more knowledge on climate change impacts. Importantly, climate change disproportionately burdens under-resourced and marginalized communities and can exacerbate existing inequities; however, existing approaches to climate risk in fisheries do not always adequately reflect reality. The methods we develop here provide a systematic, rigorous and scalable means to identify those fishing communities that are excessively affected by climate change. With analyses such as the one we present here, our hope is that we can move swiftly to chart a course to a more resilient and just future for all those who depend on fisheries and healthy oceans.

Supporting information

S1 appendix. supporting methods..

https://doi.org/10.1371/journal.pone.0272120.s001

S1 Fig. Correlations between ecological risk derived from three different climate models.

Correlations between ranked estimates of species ecological risk to climate change derived from three different downscaled climate projection models—Geophysical Fluid Dynamics Laboratory Earth System’s Model GFDL-ESM2M ([ 68 , 69 ]; referred to as “GFDL”), the Met Office Hadley Centre Earth Systems Model HadGEM2-ES ([ 70 ]; “HAD”), and the Institut Pierre Simon Laplace Model IPSL-CM5A-MR ([ 71 ]; “IPSL”). Correlation coefficient is given in red.

https://doi.org/10.1371/journal.pone.0272120.s002

S2 Fig. Correlations between community exposure derived from three different climate models.

Correlations between ranked estimates of community exposure to climate change derived from three estimates of species risk from three different downscaled climate projection models—Geophysical Fluid Dynamics Laboratory Earth System’s Model GFDL-ESM2M ([ 68 , 69 ]; referred to as “GFDL”), the Met Office Hadley Centre Earth Systems Model HadGEM2-ES ([ 70 ]; “HAD”), and the Institut Pierre Simon Laplace Model IPSL-CM5A-MR ([ 71 ]; “IPSL”). And correlation between ranked community exposure estimates when species risk is weighted by percent landings by species versus percent revenue. Correlation coefficient is given in red.

https://doi.org/10.1371/journal.pone.0272120.s003

S3 Fig. Correlations between indices of adaptive capacity.

Correlation between two ranked estimates of adaptive capacity via social indicators–the CDC index [ 55 ] and the NOAA index from the Integrated Ecosystem Assessment for the California Current [ 52 ] for U.S. West coast communities (correlation coefficient in red).

https://doi.org/10.1371/journal.pone.0272120.s004

A. Social indicator ranked scores for communities in Washington and Oregon. Percent rank scores for each social indicator theme from the CDC that make up adaptive capacity for each fishing community in Washington and Oregon, ordered from least adaptive (top) to most adaptive community (bottom). Top 10 percent of least adaptive communities are labeled in red. Theme 1 is socioeconomic indicators, Theme 2 is made up of household composition/disability indicators, Theme 3 consists of minority status/language indicators and Theme 4 is community housing and transportation indicators. Percent ranks are not rescaled by state so still comparable across state. For every state, all communities with low adaptability rank high for theme 1, but Southern California is the only location where least adaptable rank high for theme 3, and these communities have lowest adaptability overall. B. Social indicator ranked scores for communities in Northern and Southern California. Percent rank scores for each social indicator theme from the CDC that make up adaptive capacity for each fishing community in Northern and Southern California, ordered from least adaptive (top) to most adaptive community (bottom). Top 10 percent of least adaptive communities are labeled in red. Theme 1 is socioeconomic indicators, Theme 2 is made up of household composition/disability indicators, Theme 3 consists of minority status/language indicators and Theme 4 is community housing and transportation indicators. Percent ranks are not rescaled by state so still comparable across state. For every state, all communities with low adaptability rank high for theme 1, but Southern California is the only location where least adaptable rank high for theme 3, and these communities have lowest adaptability overall.

https://doi.org/10.1371/journal.pone.0272120.s005

S1 Table. Species that make-up the top 90% of landings for West Coast fishing communities.

Species (common name, scientific name, and pacFIN code) in the top 90% of landings for fishing communities on the US West Coast, for ports where <20% of catch is confidential. For catch that was labeled as an “unspecified” group (see PacFIN code column, https://pacfin.psmfc.org/pacfin_pub/data_rpts_pub/code_lists/sp_tree.txt ), species that make up all unspecified species groups are listed and numbers indicate which are grouped together. Primary habitat (benthic [or demersal] vs. pelagic) determines which climate variables (for example surface temperature versus bottom temperature) are used when calculating ecological risk for each species (see supplemental information).

https://doi.org/10.1371/journal.pone.0272120.s006

S2 Table. Species-specific ecological exposure, sensitivity, and risk derived from three climate models.

Ecological exposure, sensitivity, and risk (Euclidean distance between exposure and sensitivity) of species to climate change, averaged across exposure and sensitivity to temperature, pH, oxygen, and chlorophyll, using three different climate models (GFDL, HAD, and IPSL), and average exposure, sensitivity, and risk across the three models for all species (in the top 90% of landings for communities and species that were not removed due to missing data or other reason, see supplemental information). Ordered from most to least ecologically at risk (Risk rank).

https://doi.org/10.1371/journal.pone.0272120.s007

S3 Table. Species-specific ecological exposure, sensitivity, and risk derived from three climate models for additional species.

Ecological exposure, sensitivity, and risk of species to climate change, averaged across exposure and sensitivity to temperature, pH, oxygen, and chlorophyll, using three different climate models (GFDL, HAD, and IPSL) for a subset of species in the California Current including species caught by Indigenous populations. Average risk is the average across the three models.

https://doi.org/10.1371/journal.pone.0272120.s008

S4 Table. Species-specific ecological exposure and sensitivity to each climate variable.

Average exposure and sensitivity (across the three climate models, HAD, IPSL, GFDL) to each climate variable (temperature, pH, chlorophyll, and oxygen) for each species in the risk assessment. Species are order from most at risk (overall) to least.

https://doi.org/10.1371/journal.pone.0272120.s009

S5 Table. Community exposure, sensitivity, adaptive capacity, and vulnerability for West Coast fishing communities.

Fishing communities included in the analysis, pacFIN port group/name ( https://pacfin.psmfc.org/pacfin_pub/data_rpts_pub/code_lists/pc_tree.txt ) that each community is part of, axes of community vulnerability, and overall community vulnerability scores and rankings for each community. Community exposure is the average ecological risk (across climate models) weighted by percent revenue of species for each community. Adaptive capacity, based on social indicators, is calculated so that smaller values (closer to 0) equal greater adaptive capacity and greater values (close to 1) equal lower adaptive capacity. Risk is calculated as the Euclidean distance between exposure and sensitivity and vulnerability is the Euclidean distance between exposure, sensitivity, and adaptive capacity. Rank values are from most vulnerable, most at risk, or least adaptive, to least vulnerable, least at risk, or most adaptive, respectively.

https://doi.org/10.1371/journal.pone.0272120.s010

S6 Table. Social indicator scores that make-up adaptive capacity for each West Coast fishing community.

For each community, social indicators, percentile ranked, that make up adaptive capacity within four themes: 1) socioeconomic—persons below poverty, unemployed civilians, per capita income, persons with no high school diploma; 2) household composition/disability—persons age 65+, persons age 17 and less, noninstitutionalized population with a disability, number of single parent households; 3) minority status/language—persons of minority, persons older than 5 that don’t speak English well; and 4) housing/transportation—housing structures with 10+ units, estimates of mobile homes, households with more people than rooms, households with no vehicles, and persons in institutionalized group quarters. Communities are listed from lowest adaptive capacity to highest.

https://doi.org/10.1371/journal.pone.0272120.s011

S7 Table. Community sensitivity and adaptive capacity for West Coast fishing communities removed from analysis because of confidential data (unable to calculate exposure).

Fishing communities removed from analysis because of confidential landings data. Adaptive capacity, based on social indicators, is calculated so that smaller values (closer to 0) equal greater adaptive capacity and greater values (close to 1) equal lower adaptive capacity. Sensitivity is community economic reliance on commercial fishing. Values are from percentile ranks across all communities (included and removed).

https://doi.org/10.1371/journal.pone.0272120.s012

https://doi.org/10.1371/journal.pone.0272120.s013

Acknowledgments

We would like to thank Kristin Kaschner, Kathleen Kesner-Reyes, Cristina Garilao, and Elizabeth David for all their help with providing Aquamaps data. We also thank Donald E. Velasquez, Bob Sizemore, Timothy Zepplin, Corey Niles, Todd Neahr, Lorna Wargo, and others with Washington or California Fish and Wildlife for help with identification of “unidentified” catch, as well as Gregory Jensen for help with clam range distribution. Thank you to Casey O’Hara for help with coding for species’ range maps. We greatly acknowledge Brad Stenberg for help with PacFIN data questions. Finally, we thank SJ Bowden for the design of Fig 1 .

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