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Runge, J, Bathiany S, Bollt E, Camps-Valls G, Coumou D, Deyle E, Glymour C, Kretschmer M, Mahecha MD, Munoz-Mari J, van Nes EH, Peters J, Quax R, Reichstein M, Scheffer M, Scholkopf B, Spirtes P, Sugihara G, Sun J, Zhang K, Zscheischler J.  2019.  Inferring causation from time series in Earth system sciences. Nature Communications. 10   10.1038/s41467-019-10105-3   AbstractWebsite

The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal methods beyond the commonly adopted correlation techniques. Here, we give an overview of causal inference frameworks and identify promising generic application cases common in Earth system sciences and beyond. We discuss challenges and initiate the benchmark platform causeme. net to close the gap between method users and developers.

Rypdal, M, Sugihara G.  2019.  Inter-outbreak stability reflects the size of the susceptible pool and forecasts magnitudes of seasonal epidemics. Nature Communications. 10   10.1038/s41467-019-10099-y2374   AbstractWebsite

For dengue fever and other seasonal epidemics we show how the stability of the preceding inter-outbreak period can predict subsequent total outbreak magnitude, and that a feasible stability metric can be computed from incidence data alone. As an observable of a dynamical system, incidence data contains information about the underlying mechanisms: climatic drivers, changing serotype pools, the ecology of the vector populations, and evolving viral strains. We present mathematical arguments to suggest a connection between stability measured in incidence data during the inter-outbreak period and the size of the effective susceptible population. The method is illustrated with an analysis of dengue incidence in San Juan, Puerto Rico, where forecasts can be made as early as three to four months ahead of an outbreak. These results have immediate significance for public health planning, and can be used in combination with existing forecasting methods and more comprehensive dengue models.

Cenci, S, Sugihara G, Saavedra S.  2019.  Regularized S-map for inference and forecasting with noisy ecological time series. Methods in Ecology and Evolution. 10:650-660.   10.1111/2041-210x.13150   AbstractWebsite

It is well known that fluctuations of species abundances observed in ecological time series emerge from an interplay between deterministic nonlinear dynamics and stochastic forces. Importantly, nonlinearity and stochasticity introduce significant challenges to the analysis of ecological time series, such as the inference of the effect of species interactions on community dynamics and forecasting of species abundances. Local linear fits with state-space-dependent kernel functions, known as S-maps, provide an efficient method to infer Jacobian coefficients (a proxy for the local effect of species interactions) and to make reliable forecasts from nonlinear time series. Yet, while it has been shown that the S-map outperforms existing methods for nonparametric inference and forecasting, the methodology is sensitive to process noise. To overcome this limitation, we integrate the S-map with different regularization schemes. To validate our approach, we test our methodology against different levels of noise and nonlinearity using three standard population dynamics models. We show that an appropriate choice of the regularization scheme, alongside an accurate choice of the kernel functions, can significantly improve the in-sample inference of Jacobian coefficients and the out-of-sample forecast of species abundances in the presence of process noise. We further validate our methodology using two empirical time series of marine microbial communities. Our results illustrate that the regularized S-map is an efficient method for nonparametric inference and forecasting from noisy, nonlinear, ecological time series. Yet, attention must be paid on the regularization scheme and the structure of the kernel for whether inference or forecasting is the ultimate goal of a research study.

Munch, SB, Giron-Nava A, Sugihara G.  2018.  Nonlinear dynamics and noise in fisheries recruitment: A global meta-analysis. Fish and Fisheries. 19:964-973.   10.1111/faf.12304   AbstractWebsite

The relative importance of environmental and intrinsic controls on recruitment in fishes has been studied for over a century. Despite this, we are not much closer to predicting recruitment. Rather, recent analyses suggest that recruitment is virtually independent of stock size and, instead, seems to occur in distinct environmental regimes. This issue of whether or not recruitment and subsequent production are coupled to stock size is highly relevant to management. Here, we apply empirical dynamical modelling (EDM) to a global database of 185 fish populations to address the questions of whether or not variation in recruitment is (a) predictable and (b) coupled to stock size. We find that a substantial fraction of recruitment variation is predictable using only the observed history of fluctuations (similar to 40% on average). In addition, although recruitment is often coupled to stock size (107 of 185 stocks), stock size alone explains very little of the variation in recruitment; In similar to 90% of the stocks analysed, EDM forecasts have substantially lower prediction error than models based solely on stock size. We find that predictability varies across taxa and improves with the number of generations that have been sampled. In the light of these results, we suggest that EDM will be of greatest use in managing relatively short-lived species.

Deyle, E, Schueller AM, Ye H, Pao GM, Sugihara G.  2018.  Ecosystem-based forecasts of recruitment in two menhaden species. Fish and Fisheries. 19:769-781.   10.1111/faf.12287   AbstractWebsite

Gulf (Brevoortia patronus, Clupeidae) and Atlantic menhaden (Brevoortia tyrannus, Clupeidae) support large fisheries that have shown substantial variability over several decades, in part, due to dependence on annual recruitment. Nevertheless, traditional stock-recruitment relationships lack predictive power for these stocks. Current management of Atlantic menhaden explicitly treats recruitment as a random process. However, traditional methods for understanding recruitment variability carry the very specific hypothesis that the effect of adult biomass on subsequent recruitment occurs independently of other ecosystem factors such as food availability and predation. Here, we evaluate the predictability of menhaden recruitment using a model-free approach that is not restricted by these strong assumptions. We find that menhaden recruitment is predictable, but only when allowing for interdependence of stock with other ecological factors. Moreover, while the analysis confirms the presence of environmental effects, the environment alone does not readily account for the complexity of menhaden recruitment dynamics. The findings set the stage for revisiting recruitment prediction in management and serve as an instructive example in the ongoing debate about how to best treat and understand recruitment variability across species and fisheries.

Sugihara, G, Criddle KR, McQuown M, Giron-Nava A, Deyle E, James C, Lee A, Pao G, Saberski E, Ye H.  2018.  Comprehensive incentives for reducing Chinook salmon bycatch in the Bering Sea walleye Pollock fishery: Individual tradable encounter credits. Regional Studies in Marine Science. 22:70-81.   10.1016/j.rsma.2018.06.002   AbstractWebsite

After record salmon bycatch in 2007 by the Eastern Bering Sea and Aleutian Islands fishery for walleye Pollock, the North Pacific Fishery Management Council (NPFMC) concluded that additional management strategies were necessary to further control salmon bycatch. The Preliminary Preferred Alternative (PPA) was selected in April 2009 and implemented in January 2011 as Amendment 91. In this paper, we present the original comprehensive bycatch credits allocation and trading plan as designed by the first author as commissioned by the Alaskan Pollock Fleet for Chinook salmon, the Comprehensive Incentive Plan (CIP). The CIP, which uses individual (vessel-level) tradable encounter credits (ITEC), included incentives that make up the backbone of Amendment 91/PPA. While salmon bycatch has been reduced since the implementation of the PPA, the current amendment does not have individual vessel incentives that vary with the vulnerability of salmon populations. The CIP approach presented here provides robust vessel-level incentives to reduce Chinook salmon bycatch under all levels of salmon abundance, but particularly when salmon populations are at their lowest levels and are most vulnerable. The specific financial incentive structure in the full plan, with trading of by-catch liabilities among vessels, can be applied well in other fisheries where bycatch threatens both sustainability and profitability. (C) 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (

Ushio, M, Hsieh CH, Masuda R, Deyle ER, Ye H, Chang CW, Sugihara G, Kondoh M.  2018.  Fluctuating interaction network and time-varying stability of a natural fish community. Nature. 554:360-+.   10.1038/nature25504   AbstractWebsite

Ecological theory suggests that large-scale patterns such as community stability can be influenced by changes in interspecific interactions that arise from the behavioural and/or physiological responses of individual species varying over time(1-3). Although this theory has experimental support(2,4,5),evidence from natural ecosystems is lacking owing to the challenges of tracking rapid changes in interspecific interactions (known to occur on timescales much shorter than a generation time)6 and then identifying the effect of such changes on large-scale community dynamics. Here, using tools for analysing nonlinear time series(6-9) and a 12-year-long dataset of fortnightly collected observations on a natural marine fish community in Maizuru Bay, Japan, we show that short-term changes in interaction networks influence overall community dynamics. Among the 15 dominant species, we identify 14 interspecific interactions to construct a dynamic interaction network. We show that the strengths, and even types, of interactions change with time; we also develop a time-varying stability measure based on local Lyapunov stability for attractor dynamics in non-equilibrium nonlinear systems. We use this dynamic stability measure to examine the link between the time-varying interaction network and community stability. We find seasonal patterns in dynamic stability for this fish community that broadly support expectations of current ecological theory. Specifically, the dominance of weak interactions and higher species diversity during summer months are associated with higher dynamic stability and smaller population fluctuations. We suggest that interspecific interactions, community network structure and community stability are dynamic properties, and that linking fluctuating interaction networks to community-level dynamic properties is key to understanding the maintenance of ecological communities in nature.

Telschow, A, Grziwotz F, Crain P, Miki T, Mains JW, Sugihara G, Dobson SL, Hsieh C-H.  2017.  Infections of Wolbachia may destabilise mosquito population dynamics. Journal of Theoretical Biology. 428:98-105.  
Giron-Nava, A, James CC, Johnson AF, Dannecker D, Kolody B, Lee A, Nagarkar M, Pao GM, Johns DG, Sugihara G.  2017.  Quantitative argument for long-term ecological monitoring. Marine Ecology Progress Series. 572:269-274.   10.3354/meps12149  
Dakos, V, Glaser SM, Hsieh CH, Sugihara G.  2017.  Elevated nonlinearity as an indicator of shifts in the dynamics of populations under stress. Journal of the Royal Society Interface. 14   10.1098/risf.2016.0845   AbstractWebsite

Populations occasionally experience abrupt changes, such as local extinctions, strong declines in abundance or transitions from stable dynamics to strongly irregular fluctuations. Although most of these changes have important ecological and at times economic implications, they remain notoriously difficult to detect in advance. Here, we study changes in the stability of populations under stress across a variety of transitions. Using a Ricker- type model, we simulate shifts from stable point equilibrium dynamics to cyclic and irregular boom- bust oscillations as well as abrupt shifts between alternative attractors. Our aim is to infer the loss of population stability before such shifts based on changes in nonlinearity of population dynamics. We measure nonlinearity by comparing forecast performance between linear and nonlinear models fitted on reconstructed attractors directly from observed time series. We compare nonlinearity to other suggested leading indicators of instability (variance and autocorrelation). We find that nonlinearity and variance increase in a similar way prior to the shifts. By contrast, autocorrelation is strongly affected by oscillations. Finally, we test these theoretical patterns in datasets of fisheries populations. Our results suggest that elevated nonlinearity could be used as an additional indicator to infer changes in the dynamics of populations under stress.

Sugihara, G.  2017.  Niche Hierarchy: Structure, Organization, and Assembly in Natural Systems. :BOOK193pgs., Plantation, Florida: J. Ross Publishing
McGowan, JA, Deyle ER, Ye H, Carter ML, Perretti CT, Seger KD, de Verneil A, Sugihara G.  2017.  Predicting coastal algal blooms in Southern California. Ecology.   10.1002/ecy.1804   AbstractWebsite

The irregular appearance of planktonic algae blooms off the coast of southern California has been a source of wonder for over a century. Although large algal blooms can have significant negative impacts on ecosystems and human health, a predictive understanding of these events has eluded science, and many have come to regard them as ultimately random phenomena. However, the highly nonlinear nature of ecological dynamics can give the appearance of randomness and stress traditional methods—such as model fitting or analysis of variance—to the point of breaking. The intractability of this problem from a classical linear standpoint can thus give the impression that algal blooms are fundamentally unpredictable. Here, we use an exceptional time series study of coastal phytoplankton dynamics at La Jolla, CA, with an equation-free modeling approach, to show that these phenomena are not random, but can be understood as nonlinear population dynamics forced by external stochastic drivers (so-called “stochastic chaos”). The combination of this modeling approach with an extensive dataset allows us to not only describe historical behavior and clarify existing hypotheses about the mechanisms, but also make out-of-sample predictions of recent algal blooms at La Jolla that were not included in the model development. This article is protected by copyright. All rights reserved.

Deyle, ER, Maher MC, Hernandez RD, Basu S, Sugihara G.  2016.  Global environmental drivers of influenza. Proceedings of the National Academy of Sciences of the United States of America. 113:13081-13086.   10.1073/pnas.1607747113   AbstractWebsite

In temperate countries, influenza outbreaks are well correlated to seasonal changes in temperature and absolute humidity. However, tropical countries have much weaker annual climate cycles, and outbreaks show less seasonality and are more difficult to explain with environmental correlations. Here, we use convergent cross mapping, a robust test for causality that does not require correlation, to test alternative hypotheses about the global environmental drivers of influenza outbreaks from country-level epidemic time series. By moving beyond correlation, we show that despite the apparent differences in outbreak patterns between temperate and tropical countries, absolute humidity and, to a lesser extent, temperature drive influenza outbreaks globally. We also find a hypothesized U-shaped relationship between absolute humidity and influenza that is predicted by theory and experiment, but hitherto has not been documented at the population level. The balance between positive and negative effects of absolute humidity appears to be mediated by temperature, and the analysis reveals a key threshold around 75 degrees F. The results indicate a unified explanation for environmental drivers of influenza that applies globally.

Ye, H, Sugihara G.  2016.  Information leverage in interconnected ecosystems: Overcoming the curse of dimensionality. Science. 353:922-925.   10.1126/science.aag0863   AbstractWebsite

In ecological analysis, complexity has been regarded as an obstacle to overcome. Here we present a straightforward approach for addressing complexity in dynamic interconnected systems. We show that complexity, in the form of multiple interacting components, can actually be an asset for studying natural systems from temporal data. The central idea is that multidimensional time series enable system dynamics to be reconstructed from multiple viewpoints, and these viewpoints can be combined into a single model. We show how our approach, multiview embedding (MVE), can improve forecasts for simulated ecosystems and a mesocosm experiment. By leveraging complexity, MVE is particularly effective for overcoming the limitations of short and noisy time series and should be highly relevant for many areas of science.

Rikkert, M, Dakos V, Buchman TG, de Boer R, Glass L, Cramer AOJ, Levin S, van Nes E, Sugihara G, Ferrari MD, Tolner EA, van de Leemput I, Lagro J, Melis R, Scheffer M.  2016.  Slowing down of recovery as generic risk marker for acute severity transitions in chronic diseases. Critical Care Medicine. 44:601-606.   10.1097/ccm.0000000000001564   AbstractWebsite

Objective: We propose a novel paradigm to predict acute attacks and exacerbations in chronic episodic disorders such as asthma, cardiac arrhythmias, migraine, epilepsy, and depression. A better generic understanding of acute transitions in chronic dynamic diseases is increasingly important in critical care medicine because of the higher prevalence and incidence of these chronic diseases in our aging societies. Data Sources: PubMed, Medline, and Web of Science. Study Selection: We selected studies from biology and medicine providing evidence of slowing down after a perturbation as a warning signal for critical transitions. Data Extraction: Recent work in ecology, climate, and systems biology has shown that slowing down of recovery upon perturbations can indicate loss of resilience across complex, nonlinear biologic systems that are approaching a tipping point. This observation is supported by the empiric studies in pathophysiology and controlled laboratory experiments with other living systems, which can flip from one state of clinical balance to a contrasting one. We discuss examples of such evidence in bodily functions such as blood pressure, heart rate, mood, and respiratory regulation when a tipping point for a transition is near. Conclusions: We hypothesize that in a range of chronic episodic diseases, indicators of critical slowing down, such as rising variance and temporal correlation, may be used to assess the risk of attacks, exacerbations, and even mortality. Identification of such early warning signals over a range of diseases will enhance the understanding of why, how, and when attacks and exacerbations will strike and may thus improve disease management in critical care medicine.

Deyle, ER, May RM, Munch SB, Sugihara G.  2016.  Tracking and forecasting ecosystem interactions in real time. Proceedings of the Royal Society B-Biological Sciences. 283   10.1098/rspb.2015.2258   AbstractWebsite

Evidence shows that species interactions are not constant but change as the ecosystem shifts to new states. Although controlled experiments and model investigations demonstrate how nonlinear interactions can arise in principle, empirical tools to track and predict them in nature are lacking. Here we present a practical method, using available time-series data, to measure and forecast changing interactions in real systems, and identify the underlying mechanisms. The method is illustrated with model data from a marine mesocosm experiment and limnologic field data from Sparkling Lake, WI, USA. From simple to complex, these examples demonstrate the feasibility of quantifying, predicting and understanding state-dependent, nonlinear interactions as they occur in situ and in real time-a requirement for managing resources in a nonlinear, non-equilibrium world.

Ye, H, Deyle ER, Gilarranz LJ, Sugihara G.  2015.  Distinguishing time-delayed causal interactions using convergent cross mapping. Scientific Reports. 5   10.1038/srep14750   AbstractWebsite

An important problem across many scientific fields is the identification of causal effects from observational data alone. Recent methods (convergent cross mapping, CCM) have made substantial progress on this problem by applying the idea of nonlinear attractor reconstruction to time series data. Here, we expand upon the technique of CCM by explicitly considering time lags. Applying this extended method to representative examples (model simulations, a laboratory predator-prey experiment, temperature and greenhouse gas reconstructions from the Vostok ice core, and long-term ecological time series collected in the Southern California Bight), we demonstrate the ability to identify different time-delayed interactions, distinguish between synchrony induced by strong unidirectional-forcing and true bidirectional causality, and resolve transitive causal chains.

van Nes, EH, Scheffer M, Brovkin V, Lenton TM, Ye H, Deyle E, Sugihara G.  2015.  Causal feedbacks in climate change. Nature Climate Change. 5:445-448.   10.1038/nclimate2568   AbstractWebsite

The statistical association between temperature and greenhouse gases over glacial cycles is well documented(1), but causality behind this correlation remains difficult to extract directly from the data. A time lag of CO2 behind Antarctic temperature-originally thought to hint at a driving role for temperature(2,3)-is absent(4,5) at the last deglaciation, but recently confirmed at the last ice age inception(6) and the end of the earlier termination II (ref. 7). We show that such variable time lags are typical for complex nonlinear systems such as the climate, prohibiting straightforward use of correlation lags to infer causation. However, an insight from dynamical systems theory(8) now allows us to circumvent the classical challenges of unravelling causation from multivariate time series. We build on this insight to demonstrate directly from ice-core data that, over glacial-interglacial timescales, climate dynamics are largely driven by internal Earth system mechanisms, including a marked positive feedback effect from temperature variability on greenhouse-gas concentrations.

Clark, AT, Ye H, Isbell F, Deyle ER, Cowles J, Tilman GD, Sugihara G.  2015.  Spatial convergent cross mapping to detect causal relationships from short time series. Ecology. 96:1174-1181.   10.1890/   AbstractWebsite

Recent developments in complex systems analysis have led to new techniques for detecting causal relationships using relatively short time series, on the order of 30 sequential observations. Although many ecological observation series are even shorter, perhaps fewer than ten sequential observations, these shorter time series are often highly replicated in space (i.e., plot replication). Here, we combine the existing techniques of convergent cross mapping (CCM) and dewdrop regression to build a novel test of causal relations that leverages spatial replication, which we call multispatial CCM. Using examples from simulated and real-world ecological data, we test the ability of multispatial CCM to detect causal relationships between processes. We find that multispatial CCM successfully detects causal relationships with as few as five sequential observations, even in the presence of process noise and observation error. Our results suggest that this technique may constitute a useful test for causality in systems where experiments are difficult to perform and long time series are not available. This new technique is available in the multispatialCCM package for the R programming language.

Tsonis, AA, Deyle ER, May RM, Sugihara G, Swanson K, Verbeten JD, Wang GL.  2015.  Dynamical evidence for causality between galactic cosmic rays and interannual variation in global temperature. Proceedings of the National Academy of Sciences of the United States of America. 112:3253-3256.   10.1073/pnas.1420291112   AbstractWebsite

As early as 1959, it was hypothesized that an indirect link between solar activity and climate could be mediated by mechanisms controlling the flux of galactic cosmic rays (CR) [Ney ER (1959) Nature 183:451-452]. Although the connection between CR and climate remains controversial, a significant body of laboratory evidence has emerged at the European Organization for Nuclear Research [Duplissy J, et al. (2010) Atmos Chem Phys 10:1635-1647; Kirkby J, et al. (2011) Nature 476(7361):429-433] and elsewhere [Svensmark H, Pedersen JOP, Marsh ND, Enghoff MB, Uggerhoj Ul (2007) Proc R Soc A 463:385-396; Enghoff MB, Pedersen JOP, Uggerhoj Ul, Paling SM, Svensmark H (2011) Geophys Res Lett 38:L09805], demonstrating the theoretical mechanism of this link. In this article, we present an analysis based on convergent cross mapping, which uses observational time series data to directly examine the causal link between CR and year-to-year changes in global temperature. Despite a gross correlation, we find no measurable evidence of a causal effect linking CR to the overall 20th-century warming trend. However, on short interannual timescales, we find a significant, although modest, causal effect between CR and short-term, year-to-year variability in global temperature that is consistent with the presence of nonlinearities internal to the system. Thus, although CR do not contribute measurably to the 20th-century global warming trend, they do appear as a nontraditional forcing in the climate system on short interannual timescales.

Ye, H, Beamish RJ, Glaser SM, Grant SCH, Hsieh CH, Richards LJ, Schnute JT, Sugihara G.  2015.  Equation-free mechanistic ecosystem forecasting using empirical dynamic modeling. Proceedings of the National Academy of Sciences of the United States of America. 112:E1569-E1576.   10.1073/pnas.1417063112   AbstractWebsite

It is well known that current equilibrium-based models fall short as predictive descriptions of natural ecosystems, and particularly of fisheries systems that exhibit nonlinear dynamics. For example, model parameters assumed to be fixed constants may actually vary in time, models may fit well to existing data but lack out-of-sample predictive skill, and key driving variables may be misidentified due to transient (mirage) correlations that are common in nonlinear systems. With these frailties, it is somewhat surprising that static equilibrium models continue to be widely used. Here, we examine empirical dynamic modeling (EDM) as an alternative to imposed model equations and that accommodates both nonequilibrium dynamics and nonlinearity. Using time series from nine stocks of sockeye salmon (Oncorhynchus nerka) from the Fraser River system in British Columbia, Canada, we perform, for the the first time to our knowledge, real-data comparison of contemporary fisheries models with equivalent EDM formulations that explicitly use spawning stock and environmental variables to forecast recruitment. We find that EDM models produce more accurate and precise forecasts, and unlike extensions of the classic Ricker spawner-recruit equation, they show significant improvements when environmental factors are included. Our analysis demonstrates the strategic utility of EDM for incorporating environmental influences into fisheries forecasts and, more generally, for providing insight into how environmental factors can operate in forecast models, thus paving the way for equation-free mechanistic forecasting to be applied in management contexts.

Liu, H, Fogarty MJ, Hare JA, Hsieh CH, Glaser SM, Ye H, Deyle E, Sugihara G.  2014.  Modeling dynamic interactions and coherence between marine zooplankton and fishes linked to environmental variability. Journal of Marine Systems. 131:120-129.   10.1016/j.jmarsys.2013.12.003   AbstractWebsite

The dynamics of marine fishes are closely related to lower trophic levels and the environment. Quantitatively understanding ecosystem dynamics linking environmental variability and prey resources to exploited fishes is crucial for ecosystem-based management of marine living resources. However, standard statistical models typically grounded in the concept of linear system may fail to capture the complexity of ecological processes. We have attempted to model ecosystem dynamics using a flexible, nonparametric class of nonlinear forecasting models. We analyzed annual time series of four environmental indices, 22 marine copepod taxa, and four ecologically and commercially important fish species during 1977 to 2009 on Georges Bank, a highly productive and intensively studied area of the northeast U.S. continental shelf ecosystem. We examined the underlying dynamic features of environmental indices and copepods, quantified the dynamic interactions and coherence with fishes, and explored the potential control mechanisms of ecosystem dynamics from a nonlinear perspective. We found: (I) the dynamics of marine copepods and environmental indices exhibiting clear nonlinearity; (2) little evidence of complex dynamics across taxonomic levels of copepods; (3) strong dynamic interactions and coherence between copepods and fishes; and (4) the bottom-up forcing of fishes and top-down control of copepods coexisting as target trophic levels vary. These findings highlight the nonlinear interactions among ecosystem components and the importance of marine zooplankton to fish populations which point to two forcing mechanisms likely interactively regulating the ecosystem dynamics on Georges Bank under a changing environment. (C) 2013 Elsevier B.V. All rights reserved.

Glaser, S, Fogarty MJ, Liu H, Altman I, Hsieh C-H, Kaufman L, McCall AD, Rosenberg A, Ye H, Sugihara G.  2014.  Complex dynamics may limit prediction in marine fisheries. Fish and Fisheries. 15(4):616-633.   10.1111/faf.12037  
Glaser, SM, Ye H, Sugihara G.  2014.  A nonlinear, low data requirement model for producing spatially explicit fishery forecasts. Fisheries Oceanography. 23:45-53.   10.1111/fog.12042   AbstractWebsite

Spatial variability can confound accurate estimates of catch per unit effort (CPUE), especially in highly migratory species. The incorporation of spatial structure into fishery stock assessment models should ultimately improve forecasts of stock biomass. Here, we describe a nonlinear time series model for producing spatially explicit forecasts of CPUE that does not require ancillary environmental or demographic data, or specification of a model functional form. We demonstrate this method using spatially resolved (1 degrees x1 degrees cells) CPUE time series of North Pacific albacore in the California Current System. The spatial model is highly significant (P<0.00001) and outperforms two spatial null models. We then create a spatial forecast map for years beyond the range of data. Such approaches can guide spatial management of resources and provide a complement to more data-intensive, highly parameterized population dynamics and ecosystem models currently in use.

National Research Council (Ed.).  2014.  Evaluating the Effectiveness of Fish Stock Rebuilding Plans in the United States. :154., Washington, DC: The National Academies Press AbstractWebsite

In the United States (U.S.), the Fishery Conservation and Management Act of 1976, now known as the Magnuson-Stevens Fishery Conservation and Management Act (MSFCMA), was the first major legislation to regulate federal fisheries in the U.S. Fishery Conservation Zone (later designated as the U.S. exclusive economic zone). The re-authorization of the MSFCMA passed by Congress in 2006 included additional mandates for conserving and rebuilding fish stocks and strengthening the role of scientific advice in fisheries management. Approximately 20% of the fisheries that have been assessed are considered overfished according to the September 2012 stock status Report to Congress prepared by the U.S. National Oceanic and Atmospheric Administration (NOAA). Overfished refers to a stock that is below the minimum stock size threshold, commonly set to half the stock size at which maximum sustainable yield (MSY) is achieved. Under the provisions of the MSFCMA, rebuilding plans for overfished stocks should take no more than 10 years, except when certain provisions apply. Rebuilding mandates have led to substantial reductions in catch and effort for many fisheries, raising concerns about the consequent social and economic impacts to the fishing communities and industry. Evaluating the Effectiveness of Fish Stock Rebuilding Plans in the United States reviews the technical specifications that underlie current federally-implemented rebuilding plans, and the outcomes of those plans. According to this report, fisheries management has evolved substantially since 1977 when the U.S. extended its jurisdiction to 8 200 miles, in the direction of being more prescriptive and precautionary in terms of preventing overfishing and rebuilding overfished fisheries. However, the trade-offs between precaution and yield have not been fully evaluated. Evaluating the Effectiveness of Fish Stock Rebuilding Plans in the United States discusses the methods and criteria used to set target fishing mortality and biomass levels for rebuilding overfished stocks, and to determine the probability that a particular stock will rebuild by a certain date. This report will be of interest to the fishing industry, ecology professionals, and members of Congress as they debate the renewal of the Magnuson-Stevens Fishery Conservation and Management Act.