CAFA Publications

Author(s): Kearney, K., A. Hermann, W. Cheng, I. Ortiz, and K. Aydin.

Project PI: Hollowed
DOI: http://https://doi.org/10.5194/gmd-13-597-2020

The Bering Sea is a highly productive ecosystem, supporting a variety of fish, seabird, and marine mammal populations, as well as large commercial fisheries. Due to its unique shelf geometry and the presence of seasonal sea ice, the processes controlling productivity in the Bering Sea ecosystem span the pelagic water column, the benthic sea floor, and the sympagic sea ice environments. The Bering Ecosystem Study Nutrient-Phytoplankton-Zooplankton (BESTNPZ) model has been developed to simulate the lower-trophic-level processes throughout this region. Here, we present a version of this lower-trophic-level model coupled to a three-dimensional regional ocean model for the Bering Sea. We quantify the model's ability to reproduce key physical features of biological importance as well as its skill in capturing the seasonal and interannual variations in primary and secondary productivity over the past several decades. We find that the ocean model demonstrates considerable skill in replicating observed horizontal and vertical patterns of water movement, mixing, and stratification, as well as the temperature and salinity signatures of various water masses throughout the Bering Sea. Along the data-rich central portions of the southeastern Bering Sea shelf, it is also able to capture the mean seasonal cycle of primary production. However, its ability to replicate domain-wide patterns in nutrient cycling, primary production, and zooplankton community composition, particularly with respect to the interannual variations that are important when linking variation in productivity to changes in longer-lived upper-trophic-level species, remains limited. We therefore suggest that near-term application of this model should focus on the physical model outputs, while model development continues to elucidate potential mechanisms controlling nutrient cycling, bloom processes, and trophic dynamics.

Author(s): McHuron, E. A., K. Luxa, N. A. Pelland, K. Holsman, R. Ream, T. Zeppelin, and J. T. Sterling.

Project PI: Hollowed
DOI: http://doi.org/10.3389/fmars.2020.597973

Food availability is a key concern for the conservation of marine top predators, particularly during a time when they face a rapidly changing environment and continued pressure from commercial fishing activities. Northern fur seals (Callorhinus ursinus) breeding on the Pribilof Islands in the eastern Bering Sea have experienced an unexplained population decline since the late-1990s. Dietary overlap with a large U.S. fishery for walleye pollock (Gadus chalcogrammus) in combination with changes in maternal foraging behavior and pup growth has led to the hypothesis that food limitation may be contributing to the population decline. We developed age- and sex-specific bioenergetic models to estimate fur seal energy intake from May–December in six target years, which were combined with diet data to quantify prey consumption. There was considerable sex- and age-specific variation in energy intake because of differences in body size, energetic costs, and behavior; net energy intake was lowest for juveniles (18.9 MJ sea-day^–1, 1,409.4 MJ season^–1) and highest for adult males (66.0 MJ sea-day^–1, 7,651.7 MJ season^–1). Population-level prey consumption ranged from 255,232 t (222,159 – 350,755 t, 95% CI) in 2006 to 500,039 t (453,720 – 555,205 t) in 1996, with pollock comprising between 41.4 and 76.5% of this biomass. Interannual variation in size-specific pollock consumption appeared largely driven by the availability of juvenile fish, with up to 81.6% of pollock biomass coming from mature pollock in years of poor age-1 recruitment. Relationships among metabolic rates, trip durations, pup growth rates, and energy intake of lactating females suggest the most feasible mechanism to increase pup growth rates is by increasing foraging efficiency through reductions in maternal foraging effort, which is unlikely to occur without increases in localized prey density. By quantifying year-specific fur seal consumption of pollock, our study provides a pathway to incorporate fur seals into multispecies pollock stock assessment models, which is critical for fur seal and fishery management given they were a significant source of mortality for both juvenile and mature pollock.

Author(s): Reum, J., J. L. Blanchard, K. K. Holsman, K. Aydin, A. B. Hollowed, A. Hermann, W. Cheng, A. Faig, A. Haynie, A. E. Punt.

Project PI: Hollowed
DOI: http://doi.org/10.3389/fmars.2020.00124

Characterization of uncertainty (variance) in ecosystem projections under climate change is still rare despite its importance for informing decision-making and prioritizing research. We developed an ensemble modeling framework to evaluate the relative importance of different uncertainty sources for food web projections of the eastern Bering Sea (EBS). Specifically, dynamically downscaled projections from Earth System Models (ESM) under different greenhouse gas emission scenarios (GHG) were used to force a multispecies size spectrum model (MSSM) of the EBS food web. In addition to ESM and GHG uncertainty, we incorporated uncertainty from different plausible fisheries management scenarios reflecting shifts in the total allowable catch of flatfish and gadids and different assumptions regarding temperature-dependencies on biological rates in the MSSM. Relative to historical averages (1994–2014), end-of-century (2080–2100 average) ensemble projections of community spawner stock biomass, catches, and mean body size (±standard deviation) decreased by 36% (±21%), 61% (±27%), and 38% (±25%), respectively. Long-term trends were, on average, also negative for the majority of species, but the level of trend consistency between ensemble projections was low for most species. Projection uncertainty for model outputs from ∼2020 to 2040 was driven by inter-annual climate variability for 85% of species and the community as a whole. Thereafter, structural uncertainty (different ESMs, temperature-dependency assumptions) dominated projection uncertainty. Fishery management and GHG emissions scenarios contributed little (<10%) to projection uncertainty, with the exception of catches for a subset of flatfishes which were dominated by fishery management scenarios. Long-term outcomes were improved in most cases under a moderate “mitigation” relative to a high “business-as-usual” GHG emissions scenario and we show how inclusion of temperature-dependencies on processes related to body growth and intrinsic (non-predation) natural mortality can strongly influence projections in potentially non-additive ways. Narrowing the spread of long-term projections in future ensemble simulations will depend primarily on whether the set of ESMs and food web models considered behave more or less similarly to one another relative to the present models sets. Further model skill assessment and data integration are needed to aid in the reduction and quantification of uncertainties if we are to advance predictive ecology.

Author(s): Thorson, J. T., W. Cheng, A. J. Hermann, J. N. Ianelli, M. A. Litzow, C. A. O’Leary, G. G. Thompson.

Project PI: Hollowed
DOI: http://doi.org/10.1111/gcb.15149

Ecologists and oceanographers inform population and ecosystem management by identifying the physical drivers of ecological dynamics. However, different research communities use different analytical tools where, for example, physical oceanographers often apply rank-reduction techniques (a.k.a. empirical orthogonal functions [EOF]) to identify indicators that represent dominant modes of physical variability, whereas population ecologists use dynamical models that incorporate physical indicators as covariates. Simultaneously modeling physical and biological processes would have several benefits, including improved communication across sub-fields; more efficient use of limited data; and the ability to compare importance of physical and biological drivers for population dynamics. Here, we develop a new statistical technique, EOF regression, which jointly models population-scale dynamics and spatially distributed physical dynamics. EOF regression is fitted using maximum-likelihood techniques and applies a generalized EOF analysis to environmental measurements, estimates one or more time series representing modes of environmental variability, and simultaneously estimates the association of this time series with biological measurements. By doing so, it identifies a spatial map of environmental conditions that are best correlated with annual variability in the biological process. We demonstrate this method using a linear (Ricker) model for early-life survival (“recruitment”) of three groundfish species in the eastern Bering Sea from 1982 to 2016, combined with measurements and end-of-century projections for bottom and sea surface temperature. Results suggest that (a) we can forecast biological dynamics while applying delta-correction and statistical downscaling to calibrate measurements and projected physical variables, (b) physical drivers are statistically significant for Pacific cod and walleye pollock recruitment, (c) separately analyzing physical and biological variables fails to identify the significant association for walleye pollock, and (d) cod and pollock will likely have reduced recruitment given forecasted temperatures over future decades.

Author(s): Whitehouse, G. A., K. Y. Aydin.

Project PI: Hollowed
DOI: http://doi.org/10.1016/j.ecolmodel.2020.109074

Ecosystem modelling is a useful tool for exploring the potential outcomes of policy options and conducting experiments that would otherwise be impractical in the real world. However, ecosystem models have been limited in their ability to engage in the management of living marine resources due in part to high levels of uncertainty in model parameters and model outputs. Additionally, for multispecies or food web models, there is uncertainty about the predator-prey functional response, which can have implications for population dynamics. In this study, we evaluate the sensitivity of large marine food webs in Alaska to parameter uncertainty, including parameters that govern the predator-prey functional response. We use Rpath, an R implementation of the food web modeling program Ecopath with Ecosim (EwE), to conduct a series of mortality-based perturbations to examine the sensitivity and recovery time of higher trophic level groups in the eastern Chukchi Sea, eastern Bering Sea, and Gulf of Alaska. We use a Monte Carlo approach to generate thousands of plausible ecosystems by drawing parameter sets from the range of uncertainty around the base model parameters. We subjected the ecosystem ensembles to a series of mortality-based perturbations to identify which functional groups the higher trophic level groups are most sensitive to when their mortality was increased, whether the food webs returned to their unperturbed configurations following a perturbation, and how long it took to return to that state. In all three ecosystems, we found that the number of disrupted ensemble food webs was positively related to the biomass and the number of trophic links of the perturbed functional group, and negatively related to trophic level. The eastern Chukchi Sea was most sensitive to perturbations to benthic invertebrate groups, the eastern Bering Sea was most sensitive to shrimp and walleye pollock, and the Gulf of Alaska was most sensitive to shrimps, pelagic forage fish, and zooplankton. Recovery time to perturbations were generally less than 5 years in all three ecosystems. The recovery times when fish groups were perturbed were generally longer than when benthic invertebrates were perturbed, and recovery times were shortest when it was pelagic invertebrates. The single model ensemble approach produced simulation results that described a range of possible outcomes to the prescribed perturbations and provided a sense for how robust the results are to parameter uncertainty.

Author(s): McClenachan L, M Marra, N Record, J Grabowski, B Neal, SB Scyphers.

Project PI: Scyphers
DOI: https://doi.org/10.1111/faf.12400

Climate-driven warming has both social and ecological effects on marine fisheries. While recent changes due to anthropogenic global warming have been documented, similar basin-wide changes have occurred in the past due to natural temperature fluctuations. Here, we document the effects of rapidly changing water temperatures along the United States’ east coast using observations from fisheries newspapers during a warming phase (1945–1951) and subsequent cooling phase (1952–1960) of the Atlantic Multidecadal Oscillation, which we compared to similar recent observations of warming waters (1998–2017). Historical warming and cooling events affected the abundance of species targeted by fishing, the prevalence of novel and invasive species, and physical access to targeted species. Fishing communities viewed historical cooling waters twice as negatively as they did warming waters (72% vs. 35% of observations). Colder waters were associated with a decrease in fishing opportunity due to storminess, while warming waters were associated with the potential for new fisheries. In contrast, recent warming waters were viewed as strongly negative by fishing communities (72% of observations), associated with disease, reductions in abundances of target species, and shifts in distributions across jurisdictional lines. This increasing perception that warming negatively affects local fisheries may be due to an overall reduction of opportunity in fisheries over the past half century, an awareness of the relative severity of warming today, larger changes in American culture, or a combination of these factors. Negative perceptions of recent warming waters’ effects on fisheries suggest that fishing communities are currently finding the prospect of climate adaptation difficult.

Author(s): Pershing, A. J., N. R. Record, B. S. Franklin, B. T. Kennedy, L. McClenachan, K. E. Mills, J. D. Scott, A. C. Thomas, and N. H. Wolff.

Project PI: Mills
DOI: https://doi.org/10.1073/pnas.1901084116

The community of species, human institutions, and human activities at a given location have been shaped by historical conditions (both mean and variability) at that location. Anthropogenic climate change is now adding strong trends on top of existing natural variability. These trends elevate the frequency of “surprises”—conditions that are unexpected based on recent history. Here, we show that the frequency of surprising ocean temperatures has increased even faster than expected based on recent temperature trends. Using a simple model of human adaptation, we show that these surprises will increasingly challenge natural modes of adaptation that rely on historical experience. We also show that warming rates are likely to shift natural communities toward generalist species, reducing their productivity and diversity. Our work demonstrates increasing benefits for individuals and institutions from betting that trends will continue, but this strategy represents a radical shift that will be difficult for many to make.

Author(s): Tableau, A., J.S. Collie, R. Bell, and C. Minto

Project PI: Collie
DOI: https://doi.org/10.1139/cjfas-2018-0255

The Northwest Atlantic continental shelf is a large ecosystem undergoing rapid environmental changes, which are expected to modify the productivity of natural marine resources. Current management of most fished species assumes stationary production relationships or time-invariant recruitment rates. With linear state-space models, we examined the evidence of dynamic productivity for 25 stocks of the Northeast US shelf. We expanded the suite of options available within the state-space approach to produce robust estimates. Fifteen of the stocks exhibited time-varying productivity or changes in their maximum reproductive rate. Few productivity time series are related across the whole region, though adjacent stocks of the same species exhibited similar trends. Some links to region-wide environmental variables were observed. We demonstrate that fish recruitment can often be better predicted over a short-term horizon by accounting for dynamic productivity, which could be valuable for fisheries management. Improving predictions by incorporating environmental covariates or covariance among the stocks must be considered case by case and with caution, as their relationships may change over time.

Author(s): J. Schmidt, S. Bograd, H. Arrizabalaga, et al.

Project PI: Jacox
DOI: https://doi.org/10.3389/fmars.2019.00550

Advances in ocean observing technologies and modeling provide the capacity to revolutionize the management of living marine resources. While traditional fisheries management approaches like single-species stock assessments are still common, a global effort is underway to adopt ecosystem-based fisheries management (EBFM) approaches. These approaches consider changes in the physical environment and interactions between ecosystem elements, including human uses, holistically. For example, integrated ecosystem assessments aim to synthesize a suite of observations (physical, biological, socioeconomic) and modeling platforms [ocean circulation models, ecological models, short-term forecasts, management strategy evaluations (MSEs)] to assess the current status and recent and future trends of ecosystem components. This information provides guidance for better management strategies. A common thread in EBFM approaches is the need for high-quality observations of ocean conditions, at scales that resolve critical physical-biological processes and are timely for management needs. Here we explore options for a future observing system that meets the needs of EBFM by (i) identifying observing needs for different user groups, (ii) reviewing relevant datasets and existing technologies, (iii) showcasing regional case studies, and (iv) recommending observational approaches required to implement EBFM. We recommend linking ocean observing within the context of Global Ocean Observing System (GOOS) and other regional ocean observing efforts with fisheries observations, new forecasting methods, and capacity development, in a comprehensive ocean observing framework.

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.