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Ecological forecasts are potentially of great value for managing fisheries and for stakeholders dependent on their long-term sustainability. Yet, most forecasting approaches are data-intensive, requiring information not just on the focal species but also on ecological interactions and the physical environment. Empirical dynamic modeling (EDM) is an equation-free approach to forecasting species' abundance using only data on past abundance, but the time series required for this approach must be long enough to reconstruct the dynamics of the system. This requirement is rarely met, especially for long-lived species. Here we used simulations and empirical data to demonstrate that incorporating time series from multiple age classes can improve our ability to forecast abundance compared to a single age class or index of total abundance. Including data from multiple age classes produced the greatest gains in forecast accuracy when time series were the shortest. Overall, our results show that the incorporation of age structure could allow EDM to be applied to many species for which relatively short time series would have previously been a limiting factor. Key words: forecast, age structure, ecology, population dynamics, empirical dynamic modeling, Introduction Predicting population changes is a critical component of sustainable fisheries management and conservation biology (Clark et al. 2001; Hobday et al. 2016; Payne et al. 2017; Jacox et al. [...]

Predicting the dynamics of harvested species is essential for assessing stock status and establishing index-based management strategies. However, conventional approaches for short-lived species predict dynamics poorly, possibly because unobserved interactions with other species and abiotic factors are often treated as noise. Alternatively, the empirical dynamic modeling (EDM) approach, which uses the time delays of the observed states to compensate for unobserved interactions, may improve the predictions for short-lived species. We test this idea using time series data of two federally managed, short-lived penaeid shrimp species, whose abundances were surveyed over 30 years (1987-2018) across the US Gulf of Mexico. We show that (i) abundance dynamics of these annual shrimp stocks are well-predicted by EDM, (ii) the dynamics are spatially similar across most of the gulf, and (iii) the stock dynamics are characterized by nonlinear density-dependent interaction and vary with temperature. Our findings suggest that EDM may be more responsive than single-species, catch-at-age models in assessing the stock dynamics for short-lived penaeid shrimp species. Key words: penaeid shrimp fisheries, short-lived species, nonlinear time-series forecasting, dynamic correlation, spatial synchrony, Gulf of Mexico fisheries management, Introduction Dynamics of short-lived marine species are notoriously difficult to predict, often exhibiting large fluctuations in population size and increased sensitivity to environmental variability compared with longer-lived stocks (Hsieh et [...]

Anthropogenic environmental change is altering the behavior of animals in ecosystems around the world. Although behavior typically occurs on much faster timescales than demography, it can nevertheless influence demographic processes. Here, we use detailed data on behavior and empirical estimates of demography from a coral reef ecosystem to develop a coupled behavioral-demographic ecosystem model. Analysis of the model reveals that behavior and demography feed back on one another to determine how the ecosystem responds to anthropogenic forcing. In particular, an empirically observed feedback between the density and foraging behavior of herbivorous fish leads to alternative stable ecosystem states of coral population persistence or collapse (and complete algal dominance). This feedback makes the ecosystem more prone to coral collapse under fishing pressure but also more prone to recovery as fishing is reduced. Moreover, because of the behavioral feedback, the response of the ecosystem to changes in fishing pressure depends not only on the magnitude of changes in fishing but also on the pace at which changes are imposed. For example, quickly increasing fishing to a given level can collapse an ecosystem that would persist under more gradual change. Our results reveal conditions under which the pace and not just the magnitude of external forcing can dictate the response of ecosystems to environmental change. More generally, our multiscale behavioral-demographic framework demonstrates how high-resolution behavioral data can be incorporated into ecological models to better understand how ecosystems will respond to perturbations.

Experiments have revealed much about top-down and bottom-up control in ecosystems, but manipulative experiments are limited in spatial and temporal scale. To obtain a more nuanced understanding of trophic control over large scales, we explored long-term time-series data from 13 globally distributed lakes and used empirical dynamic modelling to quantify interaction strengths between zooplankton and phytoplankton over time within and across lakes. Across all lakes, top-down effects were associated with nutrients, switching from negative in mesotrophic lakes to positive in oligotrophic lakes. This result suggests that zooplankton nutrient recycling exceeds grazing pressure in nutrient-limited systems. Within individual lakes, results were consistent with a 'seasonal reset' hypothesis in which top-down and bottom-up interactions varied seasonally and were both strongest at the beginning of the growing season. Thus, trophic control is not static, but varies with abiotic conditions - dynamics that only become evident when observing changes over large spatial and temporal scales.

The systematic substitution of direct observational data with synthesized data derived from models during the stock assessment process has emerged as a low-cost alternative to direct data collection efforts. What is not widely appreciated, however, is how the use of such synthesized data can overestimate predictive skill when forecasting recruitment is part of the assessment process. Using a global database of stock assessments, we show that Standard Fisheries Models (SFMs) can successfully predict synthesized data based on presumed stock-recruitment relationships, however, they are generally less skillful at predicting observational data that are either raw or minimally filtered (denoised without using explicit stock-recruitment models). Additionally, we find that an equation-free approach that does not presume a specific stock-recruitment relationship is better than SFMs at predicting synthesized data, and moreover it can also predict observational recruitment data very well. Thus, while synthesized datasets are cheaper in the short term, they carry costs that can limit their utility in predicting real world recruitment.

Ecosystems contain numerous species interacting with each other and the environment. However, data on all relevant state variables is rarely available, hampering inference and prediction. Empirical dynamic modelling (EDM) is a valuable tool for prediction, inference and control in such partially observed systems.However, EDM typically assumes that the available time series are observed without error. Failing to account for observation noise strongly biases estimates of Lyapunov exponents and reduces forecast accuracy. To address this limitation, we propose incorporating EDM into a hidden Markov framework and using an iterative scheme based on the expectation maximization (EM) algorithm to obtain filtered state and parameter estimates.We evaluate the performance of this approach on several simulated dynamical systems with a range of additive noise levels, as well as on insect population time series.Accounting for observation noise improved accuracy of population forecasts and estimates of Lyapunov exponents (LE) over a wide range of noise levels relevant to ecological time series. [ABSTRACT FROM AUTHOR]

Small pelagic fish support some of the largest fisheries globally, yet there is an ongoing debate about the magnitude of the impacts of environmental processes and fishing activities on target species. We use a nonparametric, nonlinear approach to quantify these effects on the Pacific sardine (Sardinops sagax) in the Gulf of California. We show that the effect of fishing pressure and environmental variability are comparable. Furthermore, when predicting total catches, the best models account for both drivers. By using empirical dynamic programming with average environmental conditions, we calculated optimal policies to ensure long-term sustainable fisheries. The first policy, the equilibrium maximum sustainable yield, suggests that the fishery could sustain an annual catch of ~2.16 * [10.sup.5] tonnes. The second policy with dynamic optimal effort, reveals that the effort from 2 to 4 years ago impacts the current maximum sustainable effort. Consecutive years of high effort require a reduction to let the stock recover. Our work highlights a new framework that embraces the complex processes that drive fisheries population dynamics yet produces simple and robust advice to ensure long-term sustainable fisheries. Si les petits poissons pelagiques soutiennent certaines des plus importantes peches du monde, un debat se poursuit quant a la magnitude des impacts de processus environnementaux et des activites de peche sur des especes visees. Nous utilisons une approche non lineaire et non parametrique pour quantifier ces effets sur la sardine du Pacifique (Sardinops sagax) dans le golfe de Californie. Nous demontrons que la pression de peche et la variabilite environnementale ont des effets comparables. En outre, pour ce qui est de predire les prises totales, les meilleurs modeles tiennent compte de ces deux facteurs. En combinant la programmation dynamique empirique aux conditions environnementales moyennes, nous etablissons les politiques optimales pour assurer la durabilite a long terme de la peche. La premiere politique, concernant le rendement equilibre maximal, indique que la peche pourrait soutenir des prises annuelles de ~2,16 * [10.sup.5] tonnes. La deuxieme politique, qui integre l'effort optimal dynamique, revele que l'effort durant les deux a quatre annees precedentes a une incidence sur l'effort equilibre maximal actuel. Des annees consecutives d'effort eleve requierent une reduction pour permettre au stock de se retablir. Nos travaux mettent en lumiere un nouveau cadre qui tient compte des processus complexes qui modulent la dynamique des populations de ressources halieutiques, tout en produisant des avis simples et robustes pour assurer la perennite de ces ressources. [Traduit par la Redaction], Introduction Small pelagic fish, such as sardines and anchovies, support the largest fisheries in the world, contributing up to 37% of global landings by weight (Essington et al. 2015), and [...]

Quantitative ecosystem‐based management typically relies on hypothetical ecosystem models that are difficult to validate for all but the best‐studied systems.Here, we develop a management scheme that is based on predictive models driven by the observed dynamics.We show that near‐optimal management policies can be constructed from time‐series data by merging empirical dynamic modelling and stochastic dynamic programming. The Empirical Dynamic Programming approach performs well in cases we examined and outperformed a commonly used single‐species alternative.We expect model‐free ecosystem‐based management to be of use wherever ecosystem dynamics are uncertain or observations of the system do not cover all relevant species. [ABSTRACT FROM AUTHOR]

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.

Many important ecological questions require accounting for spatial variation in demographic rates (e.g., survival) and population variables (e.g., abundance per unit area). However, ecologists have few spatial modelling approaches that (i) fit directly to spatially referenced data, (ii) represent population dynamics explicitly and mechanistically, and (iii) estimate parameters using rigorous statistical methods. We therefore demonstrate a new and computationally efficient approach to spatial modelling that uses random fields in place of the random variables typically used in spatially aggregated models. We adapt this approach to delay-difference dynamics to estimate the impact of fishing and natural mortality, recruitment, and individual growth on spatial population dynamics for a fish population. In particular, we develop this approach to estimate spatial variation in average production of juvenile fishes (termed recruitment), as well as annual variation in the spatial distribution of recruitment. We first use a simulation experiment to demonstrate that the spatial delay-difference model can, in some cases, explain over 50% of spatial variance in recruitment. We also apply the spatial delay-difference model to data for rex sole (Glyptocephalus zachirus) in the Gulf of Alaska and show that average recruitment (across all years) is greatest near Kodiak Island but that some years show greatest recruitment in Southeast Alaska or the western Gulf of Alaska. Using model developments and software advances presented here, we argue that future research can develop models to approximate adult movement, incorporate spatial covariates to explain annual variation in recruitment, and evaluate management procedures that use spatially explicit estimates of population abundance. De nombreuses questions ecologiques importantes necessitent la prise en consideration des variations spatiales de taux demographiques (p. ex. de survie) et de variables relatives a la population (p. ex. l'abondance par unite de surface). Les ecologistes disposent toutefois de peu d'approches de modelisation spatiale qui (i) peuvent etre calees directement sur des donnees referencees dans l'espace, (ii) representent de maniere explicite et mecaniste la dynamique des populations et (iii) emploient des methodes statistiques rigoureuses pour l'estimation de parametres. Nous presentons donc une nouvelle approche de modelisation spatiale efficace sur le plan computationnel qui fait appel a des champs aleatoires plutot qu'aux variables aleatoires normalement utilisees dans les modeles regroupes spatialement. Nous adaptons cette approche a la dynamique a differences retardees pour estimer l'incidence de la mortalite naturelle et par peche, du recrutement et de la croissance individuelle sur la dynamique spatiale d'une population de poissons. Nous developpons plus particulierement cette approche pour estimer les variations spatiales de la production moyenne de poissons juveniles (appelee le recrutement), ainsi que les variations annuelles de la repartition spatiale du recrutement. Nous utilisons d'abord une experience de simulation pour demontrer que le modele spatial a differences retardees peut, dans certains cas, expliquer plus de 50% de la variance spatiale du recrutement. Nous appliquons egalement le modele spatial a differences retardees a des donnees sur la plie royale (Glyptocephalus zachirus) dans le golfe d'Alaska et demontrons que le recrutement moyen (pour toutes les annees) est le plus grand pres de l'ile Kodiak, mais que, pour certaines annees, le recrutement maximum est observe dans le sud-est de l'Alaska ou dans l'ouest du golfe d'Alaska. A la lumiere des modeles et avancees logicielles presentes, nous arguons que des travaux futurs peuvent mener a des modeles permettant d'estimer approximativement les deplacements des adultes, d'integrer des covariables spatiales pour expliquer les variations annuelles du recrutement et d'evaluer les procedures de gestion qui font appel a des estimations spatialement explicites de l'abondance de la population. [Traduit par la Redaction], Introduction Ecologists are increasingly tasked with understanding how population and community dynamics vary spatially. Understanding spatial processes is necessary to predict many important phenomena, including the impact of global climate [...]