Author(s): Zhang, S., E.N. Curchitser, D. Kang, C.A. Stock and R. Dussin

Project PI: Curchister
DOI: http:// https://doi.org/10.1002/2017JC013402

The Gulf Stream (GS) region has intense mesoscale variability that can affect the supply of nutrients to the euphotic zone (Z_eu). In this study, a recently developed high-resolution coupled physical-biological model is used to conduct a 25-year simulation in the Northwest Atlantic. The Reynolds decomposition method is applied to quantify the nitrate budget and shows that the mesoscale variability is important to the vertical nitrate supply over the GS region. The decomposition, however, cannot isolate eddy effects from those arising from other mesoscale phenomena. This limitation is addressed by analyzing a large sample of eddies detected and tracked from the 25-year simulation. The eddy composite structures indicate that positive nitrate anomalies within Z_eu exist in both cyclonic eddies (CEs) and anticyclonic eddies (ACEs) over the GS region, and are even more pronounced in the ACEs. Our analysis further indicates that positive nitrate anomalies mostly originate from enhanced vertical advective flux rather than vertical turbulent diffusion. The eddy-wind interaction-induced Ekman pumping is very likely the mechanism driving the enhanced vertical motions and vertical nitrate transport within ACEs. This study suggests that the ACEs in GS region may play an important role in modulating the oceanic biogeochemical properties by fueling local biomass production through the persistent supply of nitrate.

Author(s): Gray SA, SB Scyphers

Project PI: Scyphers
DOI: https://doi.org/10.1016/B978-0-12-805375-1.00022-2

Including stakeholders in environmental monitoring and research has been an increasingly recognized necessity for understanding the complex nature of marine social–ecological systems (SES). Stakeholder engagement and participation is often an essential ingredient for successful conservation and management. As a result, new inclusive approaches to scientific research have emerged under a broad umbrella often referred to as “citizen science.” These are collaborative research efforts that include stakeholders in the scientific process and strive to in various ways (1) decrease uncertainty of the dynamics of marine SES through collaborative data collection; (2) harness the expertise and knowledge of stakeholders that rely on marine resources to better understand these systems; and (3) provide a venue for more inclusive forms of resource and ecosystem management decision-making. Although the literature on citizen science shows that it is a popular way to collaboratively understand and collaboratively make decisions about natural resources, to date there is little information about how citizen science can specifically support social–ecological research and participatory decision-making in marine systems. In this chapter, we provide an overview of how participatory approaches to citizen science have been applied in marine research. Further, we theorize about the role that emerging online technologies may play in the future for collaborative science, decision-making, and marine policy.

Author(s): Hollowed, A. B., K. K. Holsman, A. Haynie, A. Hermann, A. Punt, K. Aydin, J. Ianelli, S. Kasperski, W. Cheng, A. Faig, K. Kearney, J. Reum, P. Spencer, I. Spies, W. Stockhausen, C. Szuwalski, G. A. Whitehouse, T. Wilderbuer.

Project PI: Hollowed
DOI: http://doi.org/10.3389/fmars.2019.00775

The Alaska Climate Integrated Modeling (ACLIM) project represents a comprehensive, multi-year, interdisciplinary effort to characterize and project climate-driven changes to the eastern Bering Sea (EBS) ecosystem, from physics to fishing communities. Results from the ACLIM project are being used to understand how different regional fisheries management approaches can help promote adaptation to climate-driven changes to sustain fish and shellfish populations and to inform managers and fishery dependent communities of the risks associated with different future climate scenarios. The project relies on iterative communications and outreaches with managers and fishery-dependent communities that have informed the selection of fishing scenarios. This iterative approach ensures that the research team focuses on policy relevant scenarios that explore realistic adaptation options for managers and communities. Within each iterative cycle, the interdisciplinary research team continues to improve: methods for downscaling climate models, climate-enhanced biological models, socio-economic modeling, and management strategy evaluation (MSE) within a common analytical framework. The evolving nature of the ACLIM framework ensures improved understanding of system responses and feedbacks are considered within the projections and that the fishing scenarios continue to reflect the management objectives of the regional fisheries management bodies. The multi-model approach used for projection of biological responses, facilitates the quantification of the relative contributions of climate forcing scenario, fishing scenario, parameter, and structural uncertainty with and between models. Ensemble means and variance within and between models inform risk assessments under different future scenarios. The first phase of projections of climate conditions to the end of the 21st century is complete, including projections of catch for core species under baseline (status quo) fishing conditions and two alternative fishing scenarios are discussed. The ACLIM modeling framework serves as a guide for multidisciplinary integrated climate impact and adaptation decision making in other large marine ecosystems.

Author(s): Brodie, S., M. G. Jacox, S. J. Bograd, H. Welch, H. Dewar, K. L. Scales, S. M. Maxwell, D. K. Briscoe, C. A. Edwards, L. B. Crowder, R. L. Lewison, E. L. Hazen

Project PI: Jacox
DOI: https://doi.org/10.3389/fmars.2018.00219

Species distribution models (SDMs) have become key tools for describing and predicting species habitats. In the marine domain, environmental data used in modeling species distributions are often remotely sensed, and as such have limited capacity for interpreting the vertical structure of the water column, or are sampled in situ, offering minimal spatial and temporal coverage. Advances in ocean models have improved our capacity to explore subsurface ocean features, yet there has been limited integration of such features in SDMs. Using output from a data-assimilative configuration of the Regional Ocean Modeling System, we examine the effect of including dynamic subsurface variables in SDMs to describe the habitats of four pelagic predators in the California Current System (swordfish Xiphias gladius, blue sharks Prionace glauca, common thresher sharks Alopias vulpinus, and shortfin mako sharks Isurus oxyrinchus). Species data were obtained from the California Drift Gillnet observer program (1997–2017). We used boosted regression trees to explore the incremental improvement enabled by dynamic subsurface variables that quantify the structure and stability of the water column: isothermal layer depth and bulk buoyancy frequency. The inclusion of these dynamic subsurface variables significantly improved model explanatory power for most species. Model predictive performance also significantly improved, but only for species that had strong affiliations with dynamic variables (swordfish and shortfin mako sharks) rather than static variables (blue sharks and common thresher sharks). Geospatial predictions for all species showed the integration of isothermal layer depth and bulk buoyancy frequency contributed value at the mesoscale level (<100 km) and varied spatially throughout the study domain. These results highlight the utility of including dynamic subsurface variables in SDM development and support the continuing ecological use of biophysical output from ocean circulation models.

Author(s): Smith, J.A., Tommasi, D., Sweeney, J., Brodie, S., Welch, H., Hazen, E.L., Muhling, B., Stohs, S.M. and Jacox, M.G.

Project PI: Jacox
DOI: https://doi.org/10.1111/1365-2664.13565

Time-area closures are an important tool for reducing fisheries bycatch, but their effectiveness and economic impact can be influenced by the changes in species distributions. For fisheries targeting highly mobile species, the economic impact of a closure may by highly dynamic, depending on the current suitability of the closed area for the target species.We present an analysis to quantify the fine-scale economic impact of time-area closures: the ‘lost economic opportunity’, which is the percentage of total potential profit for an entire fishing season that occurs within and during a time-area closure. Our analysis integrates a spatially explicit and environment-informed catch model with a utility model that quantifies fishing revenues and costs, and thus incorporates a dynamic target species distribution in the estimated economic impact of a closure. We demonstrate this approach by evaluating the economic impact of the Loggerhead Conservation Area (LCA) on California's drift gillnet swordfish Xiphias gladius fishery.The lost economic opportunity due to the LCA time-area closure ranged from 0% to 6% per season, with variation due to port location and trip duration, as well as inter-annual changes in swordfish distribution. This increased by 40%–90% when a seasonally varying swordfish price was considered. There was a clear signal in economic impact associated with a shift from warm to cool conditions in the California Current following the 1998 El Niño, with increased lost economic opportunity from 1999. This signal was due to higher swordfish catch inside the LCA during the cool phase, associated with increased water column mixing, and due to higher catches outside the LCA in the warmer phase, associated with increased sea-surface temperature.Synthesis and applications. We found small economic impact from a fishery closure, but with meaningful inter-annual variation due to environmental change and the dynamic distribution of a target species. Our approach could be used to help determine the timing of closures, simulate impacts of proposed closures and, more generally, assess some economic consequences of climate-induced shifts in species’ ranges.

Author(s): Kaplan, I.C., Gaichas, S.K., Stawitz, C.C., Lynch, P.D., Marshall, K.N., Deroba, J.J., Masi, M., Brodziak, J.K., Aydin, K.Y., Holsman, K. and Townsend, H

Project PI: Curchister
DOI: http:// https://doi.org/10.3389/fmars.2021.624355

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

Author(s): Hazen, EL, et al

Project PI: Jacox
DOI: https://doi.org/10.1002/fee.2125

The rapid pace of environmental change in the Anthropocene necessitates the development of a new suite of tools for measuring ecosystem dynamics. Sentinel species can provide insight into ecosystem function, identify hidden risks to human health, and predict future change. As sentinels, marine apex (top) predators offer a unique perspective into ocean processes, given that they can move across ocean basins and amplify trophic information across multiple spatiotemporal scales. Because use of the terms “ecosystem sentinel” and “climate sentinel” has proliferated in the scientific literature, there is a need to identify the properties that make marine predators effective sentinels. We provide a clear definition of the term “sentinel”, review the attributes of species identified as sentinels, and describe how a suite of such sentinels could strengthen our understanding and management of marine ecosystems. We contend that the use of marine predators as ecosystem sentinels will enable rapid response and adaptation to ecosystem variability and change.

Author(s): Drenkard, E., C. Stock, A. Adcroft, M. Alexander, V. Balaji, S. J. Bograd, M. Butenschön, W. Cheng, E. Curchitser, E. Di Lorenzo, K. W. Dixon, R. Dussin, A. Haynie, M. Harrison, A. Hermann, A. Hollowed, K. Holsman, J. Holt, M. G. Jacox, C. Joo Jang, K. A. Kearney, B. A. Muhling, M. Pozo Buil, A. C. Ross, V. Saba, A. Britt Sandø, D. Tommasi, M. Wang.

Project PI: Hollowed
DOI: http://doi.org/10.1093/icesjms/fsab100

Efforts to manage living marine resources (LMRs) under climate change need projections of future ocean conditions, yet most global climate models (GCMs) poorly represent critical coastal habitats. GCM utility for LMR applications will increase with higher spatial resolution but obstacles including computational and data storage costs, obstinate regional biases, and formulations prioritizing global robustness over regional skill will persist. Downscaling can help address GCM limitations, but significant improvements are needed to robustly support LMR science and management. We synthesize past ocean downscaling efforts to suggest a protocol to achieve this goal. The protocol emphasizes LMR-driven design to ensure delivery of decision-relevant information. It prioritizes ensembles of downscaled projections spanning the range of ocean futures with durations long enough to capture climate change signals. This demands judicious resolution refinement, with pragmatic consideration for LMR-essential ocean features superseding theoretical investigation. Statistical downscaling can complement dynamical approaches in building these ensembles. Inconsistent use of bias correction indicates a need for objective best practices. Application of the suggested protocol should yield regional ocean projections that, with effective dissemination and translation to decision-relevant analytics, can robustly support LMR science and management under climate change.

Author(s): Capotondi, A, MG Jacox, et al

Project PI: Jacox
DOI: https://doi.org/10.3389/fmars.2019.00623

Many coastal areas host rich marine ecosystems and are also centers of economic activities, including fishing, shipping and recreation. Due to the socioeconomic and ecological importance of these areas, predicting relevant indicators of the ecosystem state on sub-seasonal to interannual timescales is gaining increasing attention. Depending on the application, forecasts may be sought for variables and indicators spanning physics (e.g., sea level, temperature, currents), chemistry (e.g., nutrients, oxygen, pH), and biology (from viruses to top predators). Many components of the marine ecosystem are known to be influenced by leading modes of climate variability, which provide a physical basis for predictability. However, prediction capabilities remain limited by the lack of a clear understanding of the physical and biological processes involved, as well as by insufficient observations for forecast initialization and verification. The situation is further complicated by the influence of climate change on ocean conditions along coastal areas, including sea level rise, increased stratification, and shoaling of oxygen minimum zones. Observations are thus vital to all aspects of marine forecasting: statistical and/or dynamical model development, forecast initialization, and forecast validation, each of which has different observational requirements, which may be also specific to the study region. Here, we use examples from United States (U.S.) coastal applications to identify and describe the key requirements for an observational network that is needed to facilitate improved process understanding, as well as for sustaining operational ecosystem forecasting. We also describe new holistic observational approaches, e.g., approaches based on acoustics, inspired by Tara Oceans or by landscape ecology, which have the potential to support and expand ecosystem modeling and forecasting activities by bridging global and local observations.

Author(s): Kang, Dujuan, and Enrique N. Curchitser

Project PI: Curchister
DOI: http://https://doi.org/10.1175/JPO-D-17-0063.1

The seasonal cycles of the mean kinetic energy (MKE) and eddy kinetic energy (EKE) are compared in an idealized flow as well as in a realistic simulation of the Gulf Stream (GS) region based on three commonly used definitions: orthogonal, nonorthogonal, and moving-average filtered decompositions of the kinetic energy (KE). It is shown that only the orthogonal KE decomposition can define the physically consistent MKE and EKE that precisely represents the KEs of the mean flow and eddies, respectively. The nonorthogonal KE decomposition gives rise to a residual term that contributes to the seasonal variability of the eddies, and therefore the obtained EKE is not precisely defined. The residual term is shown to exhibit more significant seasonal variability than EKE in both idealized and realistic GS flows. Neglecting its influence leads to an inaccurate evaluation of the seasonal variability of both the eddies and the total flow. The decomposition using a moving-average filter also results in a nonnegligible residual term in both idealized and realistic GS flows. This type of definition does not ensure conservation of the total KE, even if taking into account the residual term. Moreover, it is shown that the annual cycles of the three types of EKEs or MKEs have different phases and amplitudes. The local differences of the EKE cycles are very prominent in the GS off-coast domain; however, because of the spatial inhomogeneity, the area-mean differences may not be significant.