Sarah M. Glaser

Distinguishing random environmental fluctuations from ecological catastrophes for the North Pacific Ocean.

The prospect of rapid dynamic changes in the environment is a pressing concern that has profound management and public policy implications. Worries over sudden climate change and irreversible changes in ecosystems are rooted in the potential that nonlinear systems have for complex and ‘pathological’ behaviours. Nonlinear behaviours have been shown in model systems and in some natural systems, but their occurrence in large-scale marine environments remains controversial. Here we show that time series observations of key physical variables for the North Pacific Ocean that seem to show these behaviours are not deterministically nonlinear, and are best described as linear stochastic. In contrast, we find that time series for biological variables having similar properties exhibit a low-dimensional nonlinear signature. To our knowledge, this is the first direct test for nonlinearity in large-scale physical and biological data for the marine environment. These results address a continuing debate over the origin of rapid shifts in certain key marine observations as coming from essentially stochastic processes or from dominant nonlinear mechanisms. Our measurements suggest that large-scale marine ecosystems are dynamically nonlinear, and as such have the capacity for dramatic change in response to stochastic fluctuations in basin-scale physical states.

Equation-free mechanistic ecosystem forecasting using empirical dynamic modeling

Significance The conventional parametric approach to modeling relies on hypothesized equations to approximate mechanistic processes. Although there are known limitations in using an assumed set of equations, parametric models remain widely used to test for interactions, make predictions, and guide management decisions. Here, we show that these objectives are better addressed using an alternative equation-free approach, empirical dynamic modeling (EDM). Applied to Fraser River sockeye salmon, EDM models (i) recover the mechanistic relationship between the environment and population biology that fisheries models dismiss as insignificant, (ii) produce significantly better forecasts compared with contemporary fisheries models, and (iii) explicitly link control parameters (spawning abundance) and ecosystem objectives (future recruitment), producing models that are suitable for current management frameworks. 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.

Are exploited fish populations stable

Shelton and Mangel (1) examined patterns of variability in fish populations and concluded that the higher stock variability observed in exploited species results from heightened effects of stochastic forcing in the supposed absence of nonlinear dynamics. In contrast, Anderson et al. (2) found that higher variability in these stocks is attributable to amplified nonlinear behavior in noisy ecological systems under exploitation. Here, we reconcile these apparently conflicting views and demonstrate that stochasticity of demographic parameters directly enhances nonlinearity (2–4), thus challenging assessments of stability based on statistical fits to noise-free models.

Science Communication in a Digital Age: Social Media and the American Fisheries Society

ABSTRACT Social media platforms are effective tools used to help communicate and increase involvement in cultural, political, and scientific circles. In 2012, an ad hoc committee was established to explore online fisheries science communication and how social media platforms can be utilized by the American Fisheries Society (AFS). A survey was disseminated to all AFS units (chapters, sections, divisions) and student subunits to better understand the current use of social media within the AFS. A relatively high response rate (82%) provided some confidence in the survey results—namely, that nearly 69% or more of units and subunits used social media. Facebook was the dominant platform used (59%; all others < 15%) and almost exclusively (97%) for the purpose of communication. Education, outreach, and member recruitment were other reasons for social media use. Finally, whether units currently use social media or not at all, it was recommended that AFS-led workshops and assistance would increase the usefulness ...

Civil conflict and world fisheries, 1952–2004

While the negative economic consequences of civil conflict are well known, does civil conflict have sector-specific effects that threaten food and economic security? This article surveys the effects of civil conflict on reported marine and inland fish catch, focusing on the effects of conflict through redeployment of labor, population displacement, counter-insurgency strategy and tactics, and third-party encroachment into territorial waters. Analysis of 123 countries from 1952 to 2004 demonstrates a strong, statistically robust and negative relationship between civil conflict and fisheries, with civil wars (1000+ battle deaths) depressing catch by over 16% relative to prewar levels. The magnitude of this effect is large: the cumulative contraction in total fish catch associated with civil war onset is roughly 13 times larger than the estimated effect of an extraordinarily strong El Niño, the ocean-atmosphere phenomenon associated with global declines in fisheries. Robust evidence of a Phoenix effect is lacking: post-conflict fisheries do not quickly bounce back to prewar catch levels due to more rapid growth. Analysis of conflict episodes indicates that conflict intensity, measured by battle deaths, negatively affects fish catch, while population displacement and conflict proximity to the coast do not. While these findings contribute to the growing literature on the economic effects of civil conflict, they also are important for regional fisheries management organizations, which must increasingly pay attention to sociopolitical factors that dramatically affect the utilization of aquatic resources.

Stock assessment and end-to-end ecosystem models alter dynamics of fisheries data

Although all models are simplified approximations of reality, they remain useful tools for understanding, predicting, and managing populations and ecosystems. However, a model’s utility is contingent on its suitability for a given task. Here, we examine two model types: single-species fishery stock assessment and multispecies marine ecosystem models. Both are efforts to predict trajectories of populations and ecosystems to inform fisheries management and conceptual understanding. However, many of these ecosystems exhibit nonlinear dynamics, which may not be represented in the models. As a result, model outputs may underestimate variability and overestimate stability. Using nonlinear forecasting methods, we compare predictability and nonlinearity of model outputs against model inputs using data and models for the California Current System. Compared with model inputs, time series of model-processed outputs show more predictability but a higher prevalence of linearity, suggesting that the models misrepresent the actual predictability of the modeled systems. Thus, caution is warranted: using such models for management or scenario exploration may produce unforeseen consequences, especially in the context of unknown future impacts.

Elevated nonlinearity as an indicator of shifts in the dynamics of populations under stress

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.

Civil Conflict, Crowding-Out Effects and World Fisheries, 1952–2004

While studies have demonstrated the negative impact of World War II on the industrial fish catch of developed nations, there has been no systematic study assessing the effect of civil conflict on fisheries. This paper surveys the effects of civil conflict on marine and inland fish catch, focusing on the effects of conflict through redeployment of labor, population displacement, counterinsurgency strategy and tactics, and third-party encroachment into territorial waters. Analysis of 123 countries from 1952 to 2004 demonstrates a strong, statistically robust and negative relationship between civil conflict and fisheries, with civil wars (1000 battle deaths) depressing catch by over 17 percent relative to pre-war levels. Robust evidence of a Phoenix effect is lacking: post-conflict fisheries do not quickly bounce back to pre-war catch levels due to rapid growth. Analysis of conflict episodes indicates that conflict intensity, measured by battle deaths, negatively affects fish catch, while population displacement and conflict proximity to the coast do not. While these findings contribute to the growing literature on the economic effects of civil conflict, they also are important for regional fisheries management organizations, which must increasingly pay attention to factors that are exogenous to oceanic conditions but nevertheless dramatically affect the utilization of aquatic resources.

Elevated nonlinearity as indicator of transition to overexploitation in fish stocks

Ecosystems may experience abrupt changes such as species extinctions, reorganisations of trophic structure, or transitions from stable population 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 use a Ricker-type model to simulate the transition of a hypothetical stable fisheries population either to irregular boom-bust dynamics or to overexploitation. Our aim is to infer the risk of extinction in these two scenarios by comparing changes in variance, autocorrelation, and nonlinearity between unexploited and exploited populations. We find that changes in these statistical metrics reflect the risk of extinction but depend on the type of dynamical transition. Variance and nonlinearity increase similarly in magnitude along both transitions. In contrast, autocorrelation depends strongly on the presence of underlying oscillating dynamics. We also compare our theoretical expectations to indicators measured in long-term datasets of fish stocks from the California Cooperative Oceanic Fisheries Investigation in the Eastern Pacific and from the Northeast Shelf in the Western Atlantic. Our results suggest that elevated variance and nonlinearity could be potentially used to rank exploited fish populations according to their risk of extinction.