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Forecasting the response of ecological systems to environmental change is a critical challenge for sustainable management. The metabolic theory of ecology (MTE) posits scaling of biological rates with temperature, but it has had limited application to population dynamic forecasting. Here we use the temperature dependence of the MTE to constrain empirical dynamic modeling (EDM), an equation-free nonlinear machine learning approach for forecasting. By rescaling time with temperature and modeling dynamics on a "metabolic time step," our method (MTE-EDM) improved forecast accuracy in 18 of 19 empirical ectotherm time series (by 19% on average), with the largest gains in more seasonal environments. MTE-EDM assumes that temperature affects only the rate, rather than the form, of population dynamics, and that interacting species have approximately similar temperature dependence. A review of laboratory studies suggests these assumptions are reasonable, at least approximately, though not for all ecological systems. Our approach highlights how to combine modern data-driven forecasting techniques with ecological theory and mechanistic understanding to predict the response of complex ecosystems to temperature variability and trends.

Chaotic dynamics are thought to be rare in natural populations but this may be due to methodological and data limitations, rather than the inherent stability of ecosystems. Following extensive simulation testing, we applied multiple chaos detection methods to a global database of 172 population time series and found evidence for chaos in >30%. In contrast, fitting traditional one-dimensional models identified <10% as chaotic. Chaos was most prevalent among plankton and insects and least among birds and mammals. Lyapunov exponents declined with generation time and scaled as the -1/6 power of body mass among chaotic populations. These results demonstrate that chaos is not rare in natural populations, indicating that there may be intrinsic limits to ecological forecasting and cautioning against the use of steady-state approaches to conservation and management., (© 2022. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.)

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., Competing Interests: The authors declare no competing interest.

Niche-based approaches to community analysis often involve estimating a matrix of pairwise interactions among species (the "community matrix"), but this task becomes infeasible using observational data as the number of modeled species increases. As an alternative, neutral theories achieve parsimony by assuming that species within a trophic level are exchangeable, but generally cannot incorporate stabilizing interactions even when they are evident in field data. Finally, both regulated (niche) and unregulated (neutral) approaches have rarely been fitted directly to survey data using spatiotemporal statistical methods. We therefore propose a spatiotemporal and model-based approach to estimate community dynamics that are partially regulated. Specifically, we start with a neutral spatiotemporal model where all species follow ecological drift, which precludes estimating pairwise interactions. We then add regulatory relations until model selection favors stopping, where the "rank" of the interaction matrix may range from zero to the number of species. A simulation experiment shows that model selection can accurately identify the rank of the interaction matrix, and that the identified spatiotemporal model can estimate the magnitude of species interactions. A 40-yr case study for the Gulf of St. Lawrence marine community shows that recovering grey seals have an unregulated and negative relationship with demersal fishes. We therefore conclude that partial regulation is a plausible approximation to community dynamics using field data and hypothesize that estimating partial regulation will be expedient in future analyses of spatiotemporal community dynamics given limited field data. We conclude by recommending ongoing research to add explicit models for movement, so that meta-community theory can be confronted with data in a spatiotemporal statistical framework., (© 2017 by the Ecological Society of America.)

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., (© 2016 The Author(s).)

For many marine species and habitats, climate change and overfishing present a double threat. To manage marine resources effectively, it is necessary to adapt management to changes in the physical environment. Simple relationships between environmental conditions and fish abundance have long been used in both fisheries and fishery management. In many cases, however, physical, biological, and human variables feed back on each other. For these systems, associations between variables can change as the system evolves in time. This can obscure relationships between population dynamics and environmental variability, undermining our ability to forecast changes in populations tied to physical processes. Here we present a methodology for identifying physical forcing variables based on nonlinear forecasting and show how the method provides a predictive understanding of the influence of physical forcing on Pacific sardine.

Accurate predictions of species abundance remain one of the most vexing challenges in ecology. This observation is perhaps unsurprising, because population dynamics are often strongly forced and highly nonlinear. Recently, however, numerous statistical techniques have been proposed for fitting highly parameterized mechanistic models to complex time series, potentially providing the machinery necessary for generating useful predictions. Alternatively, there is a wide variety of comparatively simple model-free forecasting methods that could be used to predict abundance. Here we pose a rather conservative challenge and ask whether a correctly specified mechanistic model, fit with commonly used statistical techniques, can provide better forecasts than simple model-free methods for ecological systems with noisy nonlinear dynamics. Using four different control models and seven experimental time series of flour beetles, we found that Markov chain Monte Carlo procedures for fitting mechanistic models often converged on best-fit parameterizations far different from the known parameters. As a result, the correctly specified models provided inaccurate forecasts and incorrect inferences. In contrast, a model-free method based on state-space reconstruction gave the most accurate short-term forecasts, even while using only a single time series from the multivariate system. Considering the recent push for ecosystem-based management and the increasing call for ecological predictions, our results suggest that a flexible model-free approach may be the most promising way forward.

Ecological regime shifts are rapid, potentially devastating changes in ecosystem state that last for extended periods of time. Previous theoretical work has generated numerous early-warning indicators of regime shifts, some of which have been empirically demonstrated in closed ecological systems. Here we evaluated a suite of indicators using a previously studied three-species model under conditions likely to be observed in field studies of open ecological systems. Simulations included large correlated fluctuations in extrinsic noise and a rapidly changing driving variable, while indicators were calculated using sparsely sampled time series. All indicators performed poorly under these conditions, particularly during the beginning of the regime shift. Overall, the best performing indicator was a rise in variance. Future research should focus on methods for setting benchmark values of early warning indicators and for identifying indicators that work for sparsely sampled data sets.

Maternal effects are increasingly recognized as important drivers of population dynamics and determinants of evolutionary trajectories. Recently, there has been a proliferation of studies finding or citing a positive relationship between maternal size/age and offspring size or offspring quality. The relationship between maternal phenotype and offspring size is intriguing in that it is unclear why young mothers should produce offspring of inferior quality or fitness. Here we evaluate the underlying evolutionary pressures that may lead to a maternal size/age-offspring size correlation and consider the likelihood that such a correlation results in a positive relationship between the age or size of mothers and the fitness of their offspring. We find that, while there are a number of reasons why selection may favor the production of larger offspring by larger mothers, this change in size is more likely due to associated changes in the maternal phenotype that affect the offspring size-performance relationship. We did not find evidence that the offspring of older females should have intrinsically higher fitness. When we explored this issue theoretically, the only instance in which smaller mothers produce suboptimal offspring sizes is when a (largely unsupported) constraint on maximum offspring size is introduced into the model. It is clear that larger offspring fare better than smaller offspring when reared in the same environment, but this misses a critical point: different environments elicit selection for different optimal sizes of young. We suggest that caution should be exercised when interpreting the outcome of offspring-size experiments when offspring from different mothers are reared in a common environment, because this approach may remove the source of selection (e.g., reproducing in different context) that induced a shift in offspring size in the first place. It has been suggested that fish stocks should be managed to preserve these older age classes because larger mothers produce offspring with a greater chance of survival and subsequent recruitment. Overall, we suggest that, while there are clear and compelling reasons for preserving older females in exploited populations, there is little theoretical justification or evidence that older mothers produce offspring with higher per capita fitness than do younger mothers.

Several recent studies have demonstrated that rapid growth early in life leads to decreased physiological performance. Nearly all involved experiments over short time periods (<1 day) to control for potentially confounding effects of size. This approach, however, neglects the benefits an individual accrues by growing. The net effect of growth can only be evaluated over a longer interval in which rapidly growing individuals are allowed the time required to attain the expected benefits of large size. We used two populations of Menidia menidia with disparate intrinsic growth rates to address this issue. We compared growth and survivorship among populations subject to predation in mesocosms under ambient light and temperature conditions for a period of up to 30 days to address two questions: Do the growth rates of fish in these populations respond differently to the presence of predators? Is the previously demonstrated survival cost of growth counterbalanced by the benefits of increased size? We found that growth was insensitive to predation risk: neither population appeared to modify growth rates in response to predation levels. Moreover, the fast-growing population suffered significantly higher mortality throughout the trials despite being 40% larger than the slow-growing population at the experiment's end. These results confirm that the costs of rapid growth extend over prolonged intervals and are not ameliorated merely by the attainment of large size.