Abstract Different aspects of ecological systems, biotic or abiotic, often fluctuate in coordinated patterns over space and time. Such high concordance between ecological processes is often referred to as ecological synchrony. Human activities, including and beyond climate change, have the potential to alter ecological synchrony by disrupting or enhancing existing synchrony. However, most studies have focused on single scales, limiting our understanding of how human activities alter ecological synchrony across spatial, temporal and organizational scales. With a social‐ecological macrosystems framework, we review how human activities, particularly beyond climate change, alter ecological synchrony from the ecosystem level to the population level. For each level, we present a case study that characterizes the roles of human agents in synchrony using data from large‐scale observations. We found that human activities alter ecological synchrony through interactions among drivers on multiple scales, often disrupting synchrony, but that adaptive management can maintain or restore synchrony. Human activities potentially modify cascades of synchrony through cross‐scale interactions and cross‐scale emergence. Finally, we recommend a set of questions to facilitate the explicit consideration of ecological synchrony as a target in sustainable management. Read the free Plain Language Summary for this article on the Journal blog.

Ecological populations fluctuate for reasons that are often ascribed-separately-to historical legacies, intrinsic nonlinear dynamics, or stochastic noise. Yet these forces rarely act in isolation. We assembled a global database of 302 abundance time series spanning birds, mammals, fishes, insects, and plankton to ask how memory, nonlinearity, and dynamical stability interact to shape predictability. We used empirical dynamic modeling to estimate the memory length and classified time series stability via effective Lyapunov exponents. We also examined the degree to which incorporating memory and nonlinearity reduce apparent noise and improve prediction. We found that memory length was greatest in neutrally stable series and proportional to the Lyapunov horizon in chaotic series. Both memory and nonlinearity improved forecasts, but gains were largest in series exhibiting positive Lyapunov exponents (oscillations and chaos). By contrast, populations with strongly stable dynamics are well captured by low dimensional linear models. Hence, nonlinearity and memory are both important components of ecological dynamics and the effective Lyapunov exponent is a useful prognosticator of predictability.

Understanding responses of ecological communities to shocks that displace species abundances is of paramount importance given the increasing frequency of extreme climatic events. However, current theory on responses to such pulse perturbations focuses on equilibrium points and we lack a unified framework that accommodates other common, but more complicated, population fluctuations such as transients and cycles. Here we introduce this framework by deriving metrics that quantify the minimum, typical and maximum amplification of perturbed abundances for nonequilibrium population dynamics. By simulating models under several nonequilibrium scenarios, we demonstrate that these metrics accurately characterise the full range of amplification of perturbed abundances in the short and long terms. Notably, we show that perturbation amplification depends strongly on community state in the short term, but this state dependency vanishes in the long term. We illustrate how our framework can provide insights about models and data for communities that are not at equilibrium.

Insect pests pose a threat to humans by jeopardizing food security in agricultural systems, acting as vectors for infectious diseases, and damaging forests and other ecosystems. Despite decades of research, effective pest management remains challenging. Incomplete understanding of the mechanisms behind pest population dynamics limits our ability to anticipate outbreaks. Hence, pest management is often reactive, meaning control actions are taken once outbreaks have already begun, allowing for damage to occur. Here we show that a datadriven model can effectively predict outbreaks, allowing us to optimize control strategies, targeting pests before outbreaks occur. Specifically, we explore empirical dynamic modeling paired with stochastic dynamic programming to keep insect populations within acceptable bounds. We show that this framework reduces outbreaks in several simulated and empirical scenarios. Our study provides a promising framework to reduce losses from pests.

Climate change is increasing the frequency of large-scale, extreme environmental events and flattening environmental gradients. Whether such changes will cause spatially synchronous, large-scale population declines depends on mechanisms that limit metapopulation synchrony, thereby promoting rescue effects and stability. Using long-term data and empirical dynamic models, we quantified spatial heterogeneity in density dependence, spatial heterogeneity in environmental responses, and environmental gradients to assess their role in inhibiting synchrony across 36 marine fish and invertebrate species. Overall, spatial heterogeneity in population dynamics was as important as environmental drivers in explaining population variation. This heterogeneity leads to weak synchrony in the California Current Ecosystem, where populations exhibit diverse responses to shared, large-scale environmental change. In contrast, in the Northeast U.S. Shelf Ecosystem, gradients in average environmental conditions among locations, filtered through nonlinear environmental response curves, limit synchrony. Simulations predict that environmental gradients and response diversity will continue to inhibit synchrony even if large-scale environmental extremes become common. However, if environmental gradients weaken, synchrony and periods of large-scale population decline may rise sharply among commercially important species on the Northeast Shelf. Our approach thus allows ecologists to 1) quantify how differences among local communities underpin landscape-scale resilience and 2) identify the kinds of future climatic changes most likely to amplify synchrony and erode species stability.

Ocean ecological time series grow more useful with longevity, but practical constraints hinder consistency, and evolving scientific priorities necessitate sampling adjustments. We leveraged 30 years of zooplankton observations (five species, 1993-2024) from the western Antarctic Peninsula to quantify how shifts in sampling intensity, timing, and survey frequency influenced accuracy, precision, and predictability. A 67% decline in sample size aliased a long-term trend in ice krill and increased uncertainty by 47% in log10 space across species. Moving survey dates by two weeks influenced the density of salps, pteropods, and Antarctic krill by 48-83%. The ability to predict krill species density declined 47% in a simulated shift from annual to biennial survey frequency. Reduced spatial resolution and slight changes in timing can be accounted for statistically, but temporal gaps represent a challenge. Autonomous approaches are complementary to (rather than substitutable for) net sampling, necessitating ship operations to maintain valuable zooplankton time series.

Many dynamical systems can exist in alternative regimes for which small changes in an environmental driver can cause sudden jumps between regimes. In ecology, predicting the regime of population fluctuations under unobserved levels of an environmental driver has remained an unsolved challenge with important implications for conservation and management. Here, we show that integrating time-series data and information on a putative driver into a Gaussian Process regression model for the system's dynamics allows us to predict dynamical regimes without the need to specify the equations of motion of the system. As a proof of concept, we demonstrate that we can accurately predict fixed-point, cyclic, or chaotic dynamics under unseen levels of a control parameter for a range of simulated population dynamics models. For a model with an abrupt population collapse, we show that our approach goes beyond an early warning signal by characterizing the regime that follows the tipping point. We then apply our approach to data from an experimental microbial food web and from a lake planktonic food web. We find that we can reconstruct transitions away from chaos in the microbial food web and anticipate the dynamics of the oligotrophic regime in the planktonic food web. These results lay the groundwork for making rational decisions about preventing, or preparing for, regime shifts in natural ecosystems and other dynamical systems.

Purpose: Prediction of athlete wellness is difficult—or, many sports-medicine practitioners and scientists would argue, impossible. Instead, one settles for correlational relationships of variables gathered at fixed moments in time. The issue may be an inherent mismatch between usual methods of data collection and analysis and the complex nature of the variables governing athlete wellness. Variables such as external load, stress, muscle soreness, and sleep quality may affect each other and wellness in a dynamic, nonlinear, way over time. In such an environment, traditional data-collection methods and statistics will fail to capture causal effects. If we are to move this area of sport science forward, a different approach is required. Methods: We analyzed data from 2 different soccer teams that showed no significance between player load and wellness or among individual measures of wellness. Our analysis used methods of attractor reconstruction to examine possible causal relationships between GPS/accelerometer-measured external training load and wellness variables. Results: Our analysis showed that player self-rated stress, a component of wellness, seems a fundamental driving variable. The influence of stress is so great that stress can predict other components of athlete wellness, and, in turn, self-rated stress can be predicted by observing a player’s load data. Conclusion: We demonstrate the ability of nonlinear methods to identify interactions between and among variables to predict future athlete stress. These relationships are indicative of the causal relationships playing out in athlete wellness over the course of a soccer season.

Different aspects of ecological systems, biotic or abiotic, often fluctuate in coordinated patterns over space and time. Such high concordance between ecological processes is often referred to as ecological synchrony. Anthropogenic activities, including and beyond climate change, have the potential to alter ecological synchrony by disrupting or enhancing existing synchrony. Despite many local studies, we have a limited systematic understanding of how ecological synchrony is shaped by management in human-dominated landscapes at regional to continental scales. From a macrosystems perspective, we review how anthropogenic activities, particularly beyond climate change, alter ecological synchrony across levels of ecological organization, from the ecosystem level to the population level. For each level, we use a large-scale case study to demonstrate ways to quantify the impacts of human modifications on synchrony using big data from remote sensing, surveys, and observatory networks. ​​For example, we detected possible homogenization of population dynamics of bird species in North America. These changes in ecological synchrony, although in different forms, often represent challenges to ecological and social systems. Collaborative research efforts that integrate emerging open data streams moving forward will be able to provide insights into the effects of different anthropogenic drivers and the consequences of changes in synchrony. ### Competing Interest Statement The authors have declared no competing interest.

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.

The abundance dynamics of short-lived marine species often exhibit large-amplitude fluctuations, potentially driven by unknown but important species interactions and environmental effects. These complex dynamics pose challenges in forecasting and establishing robust reference points. Here, we introduce an empirical dynamic modeling (EDM) framework using time-delay embeddings to recover unspecified species interactions and environmental effects, and use walk-forward simulations with varying harvest rates to estimate maximum sustainable yield (MSY). Firstly, we apply our framework to simulated data under various dynamics scenarios and demonstrate the statistical robustness of EDM-based MSY. Secondly, we apply our framework to abundance and catch time series (>30 years) of federally managed brown shrimp stocks in the US Gulf of Mexico. We identify nonlinear signals and achieve high prediction accuracy in the empirical dynamics of brown shrimp. Lastly, based on the EDM of brown shrimp dynamics, we obtain MSY for timely and effective management. Our results highlight the utility of EDM in deriving reference points for short-lived species, particularly in situations where stock abundance and catch dynamics are influenced by unobserved specie interactions and environmental effects in a complex ecosystem.

Abstract 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.

AbstractIrreversibility—the asymmetry of population dynamics when played forward versus backward in time—is a fundamental property of ecological dynamics. Despite its early recognition in ecology, irreversibility has remained a high-level and unquantifiable concept. Here, we introduce a quantitative framework rooted in non-equilibrium statistical physics to measure irreversibility in general ecological systems. Through theoretical analyses, we demonstrate that irreversibility quantifies the degree to which a system is out of equilibrium, a property not captured by traditional ecological metrics. We validate this prediction empirically across diverse ecological systems structured by different forces, such as rapid evolution, nutrient availability, and temperature. In sum, our study provides a rigorous formalism for quantifying irreversibility in ecological systems, with the potential to integrate dynamical, energetic, and informational perspectives in ecology.

Accurate models are important to predict how global climate change will continue to alter plant phenology and near-term ecological forecasts can be used to iteratively improve models and evaluate predictions that are made a priori. The Ecological Forecasting Initiative's National Ecological Observatory Network (NEON) Forecasting Challenge, is an open challenge to the community to forecast daily greenness values, measured through digital images collected by the PhenoCam Network at NEON sites before the data are collected. For the first round of the challenge, which is presented here, we forecasted canopy greenness throughout the spring at eight deciduous broadleaf sites to investigate when, where, and for what model type phenology forecast skill is highest. A total of 192,536 predictions were submitted, representing eighteen models, including a persistence and a day of year mean null models. We found that overall forecast skill was highest when forecasting earlier in the greenup curve compared to the end, for shorter lead times, for sites that greened up earlier, and when submitting forecasts during times other than near budburst. The models based on day of year historical mean had the highest predictive skill across the challenge period. In this first round of the challenge, by synthesizing across forecasts, we started to elucidate what factors affect the predictive skill of near-term phenology forecasts.

It is well established that environmental signals can modify the expression of traits during development and induce phenotypic shifts across multiple generations. Theory predicts that the induction of such 'within-generation' and 'transgenerational' plasticity is dependent upon the integration of information provided by genes, parents, and the environment. Here we tested theoretical predictions for the expression of plasticity using resurrected populations of waterfleas (Daphnia pulicaria) from lakes in Wisconsin, USA (mostly) prior to the proliferation of a novel predator (spiny waterflea, Bythotrephes longimanus). We reared Daphnia in the presence and absence of Bythotrephes cues for five generations and assessed within- and transgenerational plasticity (anti-predator behaviour) in multiple time periods. Our results show that initial exposure to Bythotrephes cues induced strong behavioural responses in Daphnia; however, parental and offspring reaction norms were in opposite directions. We then show that continued exposure to predator cues and, in turn, increased reliability of information provided to parental Daphnia facilitated a quick alignment between parent and offspring reaction norms and the erosion of plasticity over time. Our results provide new insights into the factors that influence the induction, strength, and direction of transgenerational responses to environmental signals. These results contribute to a broader understanding of non-genetic inheritance and its evolutionary implications in coping with environmental changes.

Nonstationarity due to climate change, human impacts, and invasive species presents a major challenge for ecosystem forecasting, as past behavior cannot necessarily predict future behavior. While time-varying linear models may adequately detect nonstationarity in some cases, such models are often poor at forecasting and fail to correctly identify nonstationarity in nonlinear systems. Here we propose a nonlinear generalization of existing models that improves nonstationarity quantification and forecast skill. We demonstrate the effectiveness of the method in simulated datasets and experimental and field time series known to be stationary or nonstationary. Evaluating nonstationarity over subsets of the empirical time series shows that apparent nonstationarity strongly depends on the length and starting time of the series analyzed. Thus, any evaluation of nonstationarity should be conditional on a certain time window. This method could aid both in quantifying the intensity of nonstationarity in ecosystems and producing better forecasts in a changing world.

Context Climate change is driving phenological shifts across landscapes, but uncoordinated shifts might cause a potential “phenological mismatch.” There has been little consensus on the existence and magnitude of such a mismatch. The lack of agreement among studies can be attributed to the wide variety of definitions for the term “phenological mismatch,” as well as the methods used to measure it. The lack of comparability among measures of phenological mismatch creates a challenge for conservation. Objectives We proposed a novel theoretical framework to generalize existing measures of phenological mismatch and an approach to quantify the decoupling between phenology and the environment using the loss in predictive skill over time. We aimed to estimate the magnitude of phenological mismatch on large spatial scales and test the proposed predictive approach’s ability to detect multiple types of phenological mismatch. Methods We modeled historical climate-phenology coupling and quantified phenological mismatch as the deviation between observed and predicted phenology under climate change. First, we used two large empirical spatiotemporal datasets to estimate phenological mismatch in plant flowering phenology in the eastern United States and bird reproductive phenology in Finland. Historical climate-phenology coupling was modeled with spatial linear regression. Second, we conducted four simulation experiments representing different types of mismatch during climate change. We recovered simulated phenological mismatch by fitting a data-driven nonlinear model (Gaussian Process Empirical Dynamic Modeling) and predicting phenology. Results In the eastern US, we found that advancing plant flowering phenology generally matched spring warming from 1895 to 2015, with seven out of the 19 species studied having significant phenological mismatches, with observed flowering time earlier than predictions even considering warming. A similar phenological mismatch was found in birds in Finland from 1975 to 2017, with the bird breeding season advancing more than expected in 21 out of the 36 species studied. In four simulation experiments, we were able to accurately recover the simulated phenological mismatches in the timing of events, pace of development, and intensity of activities, although with greater challenges in quantifying a mismatch in life history. Conclusions Overall, these case studies show that our prediction-based measure effectively quantifies multiple types of phenological mismatch, providing a more generalizable and comparable measure of phenological mismatch across study systems and scales. This study will enable the investigation of phenological mismatch at large scales, improving understanding of the patterns and consequences of climate-change-induced phenological changes.

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.

Chaotic dynamics appear to be prevalent in short-lived organisms including plankton and may limit long-term predictability. However, few studies have explored how dynamical stability varies through time, across space and at different taxonomic resolutions. Using plankton time series data from 17 lakes and 4 marine sites, we found seasonal patterns of local instability in many species, that short-term predictability was related to local instability, and that local instability occurred most often in the spring, associated with periods of high growth. Taxonomic aggregates were more stable and more predictable than finer groupings. Across sites, higher latitude locations had higher Lyapunov exponents and greater seasonality in local instability, but only at coarser taxonomic resolution. Overall, these results suggest that prediction accuracy, sensitivity to change and management efficacy may be greater at certain times of year and that prediction will be more feasible for taxonomic aggregates.