David A. Seekell

Early Warnings of Regime Shifts: A Whole-Ecosystem Experiment

High-frequency monitoring of manipulated and reference lakes enabled early detection of subsequent catastrophic regime shift. Catastrophic ecological regime shifts may be announced in advance by statistical early warning signals such as slowing return rates from perturbation and rising variance. The theoretical background for these indicators is rich, but real-world tests are rare, especially for whole ecosystems. We tested the hypothesis that these statistics would be early warning signals for an experimentally induced regime shift in an aquatic food web. We gradually added top predators to a lake over 3 years to destabilize its food web. An adjacent lake was monitored simultaneously as a reference ecosystem. Warning signals of a regime shift were evident in the manipulated lake during reorganization of the food web more than a year before the food web transition was complete, corroborating theory for leading indicators of ecological regime shifts.

A global inventory of lakes based on high‐resolution satellite imagery

An accurate description of the abundance and size distribution of lakes is critical to quantifying limnetic contributions to the global carbon cycle. However, estimates of global lake abundance are ...

Methods for Detecting Early Warnings of Critical Transitions in Time Series Illustrated Using Simulated Ecological Data

Many dynamical systems, including lakes, organisms, ocean circulation patterns, or financial markets, are now thought to have tipping points where critical transitions to a contrasting state can happen. Because critical transitions can occur unexpectedly and are difficult to manage, there is a need for methods that can be used to identify when a critical transition is approaching. Recent theory shows that we can identify the proximity of a system to a critical transition using a variety of so-called ‘early warning signals’, and successful empirical examples suggest a potential for practical applicability. However, while the range of proposed methods for predicting critical transitions is rapidly expanding, opinions on their practical use differ widely, and there is no comparative study that tests the limitations of the different methods to identify approaching critical transitions using time-series data. Here, we summarize a range of currently available early warning methods and apply them to two simulated time series that are typical of systems undergoing a critical transition. In addition to a methodological guide, our work offers a practical toolbox that may be used in a wide range of fields to help detect early warning signals of critical transitions in time series data.

Early warning signals of ecological transitions: methods for spatial patterns.

A number of ecosystems can exhibit abrupt shifts between alternative stable states. Because of their important ecological and economic consequences, recent research has focused on devising early warning signals for anticipating such abrupt ecological transitions. In particular, theoretical studies show that changes in spatial characteristics of the system could provide early warnings of approaching transitions. However, the empirical validation of these indicators lag behind their theoretical developments. Here, we summarize a range of currently available spatial early warning signals, suggest potential null models to interpret their trends, and apply them to three simulated spatial data sets of systems undergoing an abrupt transition. In addition to providing a step-by-step methodology for applying these signals to spatial data sets, we propose a statistical toolbox that may be used to help detect approaching transitions in a wide range of spatial data. We hope that our methodology together with the computer codes will stimulate the application and testing of spatial early warning signals on real spatial data.

Virtual water transfers unlikely to redress inequality in global water use

The distribution of renewable freshwater resources between countries is highly unequal and 80% of humanity lives in regions where water security is threatened. The transfer of agricultural and industrial products to areas where water is limited through global trade may have potential for redressing water imbalances. These transfers represent ‘virtual water’ used in commodity production. We evaluated the current water-use inequality between countries and the potential of virtual water transfers to equalize water use among nations using multiple statistical measures of inequality. Overall, the actual use of renewable water resources is relatively equal even though the physical distribution of renewable water resources is highly unequal. Most inequality (76%) in water use is due to agricultural production and can be attributed to climate and arable land availability, not social development status. Virtual water use is highly unequal and is almost completely explained by social development status. Virtual water transfer is unlikely to increase water-use equality primarily because agricultural water use dominates national water needs and cannot be completely compensated by virtual water transfers.

Conditional Heteroscedasticity as a Leading Indicator of Ecological Regime Shifts

Regime shifts are massive, often irreversible, rearrangements of nonlinear ecological processes that occur when systems pass critical transition points. Ecological regime shifts sometimes have severe consequences for human well-being, including eutrophication in lakes, desertification, and species extinctions. Theoretical and laboratory evidence suggests that statistical anomalies may be detectable leading indicators of regime shifts in ecological time series, making it possible to foresee and potentially avert incipient regime shifts. Conditional heteroscedasticity is persistent variance characteristic of time series with clustered volatility. Here, we analyze conditional heteroscedasticity as a potential leading indicator of regime shifts in ecological time series. We evaluate conditional heteroscedasticity by using ecological models with and without four types of critical transition. On approaching transition points, all time series contain significant conditional heteroscedasticity. This signal is detected hundreds of time steps in advance of the regime shift. Time series without regime shifts do not have significant conditional heteroscedasticity. Because probability values are easily associated with tests for conditional heteroscedasticity, detection of false positives in time series without regime shifts is minimized. This property reduces the need for a reference system to compare with the perturbed system.

Conditional Heteroskedasticity Forecasts Regime Shift in a Whole-Ecosystem Experiment

Regime shifts in stochastic ecosystem models are often preceded by early warning signals such as increased variance and increased autocorrelation in time series. There is considerable theoretical support for early warning signals, but there is a critical lack of field observations to test the efficacy of early warning signals at spatial and temporal scales relevant for ecosystem management. Conditional heteroskedasticity is persistent periods of high and low variance that may be a powerful leading indicator of regime shift. We evaluated conditional heteroskedasticity as an early warning indicator by applying moving window conditional heteroskedasticity tests to time series of chlorophyll-a and fish catches derived from a whole-lake experiment designed to create a regime shift. There was significant conditional heteroskedasticity at least a year prior to the regime shift in the manipulated lake but there was no significant conditional heteroskedasticity in an adjacent reference lake. Conditional heteroskedasticity was an effective leading indicator of regime shift for the ecosystem manipulation.

Early warnings of regime shifts: evaluation of spatial indicators from a whole-ecosystem experiment

Critical transitions between alternate ecosystem states are often preceded by increased variance and autocorrelation in time series of ecosystem properties. Analogous changes may occur in spatial statistics as ecosystems approach thresholds for critical transitions. Changes in spatial statistics near thresholds have been described using models, laboratory experiments, and remotely sensed data, but there have been no tests using deliberate manipulations of whole ecosystems in the field. We previously documented a whole-lake manipulation resulting in a transition to predator dominance, a type of critical transition. The food web of an experimental lake was forced via cascading trophic interactions from a stable state characterized by abundant prey fish, small zooplankton, and high chlorophyll concentrations to an alternative state dominated by predatory fish, large zooplankton and low chlorophyll concentrations. Time series of zooplankton and chlorophyll concentrations provided early warning of the regime shift. Here we test if similar early warning signals were present in space by applying spatial variance and the discrete Fourier transform to spatially distributed prey fish catch data from this regime shift. Prey fish spatial distributions were monitored daily using minnow traps deployed around the lake perimeter. Added predators reduced prey fish populations and altered their spatial distributions. Increases in spatial variance and shifts to low frequency spatial variance were observed up to a year in advance of the shift. There was no response in an adjacent reference lake. Our results demonstrate that spatial signals of approaching thresholds can be detected at the ecosystem scale.

Climate and landscape influence on indicators of lake carbon cycling through spatial patterns in dissolved organic carbon

Freshwater ecosystems are strongly influenced by both climate and the surrounding landscape, yet the specific pathways connecting climatic and landscape drivers to the functioning of lake ecosystems are poorly understood. Here, we hypothesize that the links that exist between spatial patterns in climate and landscape properties and the spatial variation in lake carbon (C) cycling at regional scales are at least partly mediated by the movement of terrestrial dissolved organic carbon (DOC) in the aquatic component of the landscape. We assembled a set of indicators of lake C cycling (bacterial respiration and production, chlorophyll a, production to respiration ratio, and partial pressure of CO2 ), DOC concentration and composition, and landscape and climate characteristics for 239 temperate and boreal lakes spanning large environmental and geographic gradients across seven regions. There were various degrees of spatial structure in climate and landscape features that were coherent with the regionally structured patterns observed in lake DOC and indicators of C cycling. These different regions aligned well, albeit nonlinearly along a mean annual temperature gradient; whereas there was a considerable statistical effect of climate and landscape properties on lake C cycling, the direct effect was small and the overall effect was almost entirely overlapping with that of DOC concentration and composition. Our results suggest that key climatic and landscape signals are conveyed to lakes in part via the movement of terrestrial DOC to lakes and that DOC acts both as a driver of lake C cycling and as a proxy for other external signals.

Asymmetric response of early warning indicators of phytoplankton transition to and from cycles

Phytoplankton populations often exhibit cycles associated with nuisance blooms of cyanobacteria and other algae that cause toxicity, odor problems, oxygen depletion, and fish kills. Models of phytoplankton blooms used for management and basic research often contain critical transitions from stable points to cycles, or vice-versa. It would be useful to know whether aquatic systems, especially water supplies, are close to a critical threshold for cycling blooms. Recent studies of resilience indicators have focused on alternate stable points, although theory suggests that indicators such as variance and autocorrelation should also rise prior to a transition from stable point to stable cycle. We investigated changes in variance and autocorrelation associated with transitions involving cycles using two models. Variance rose prior to the transition from a small-radius cycle (or point) to a larger radius cycle in all cases. In many but not all cases, autocorrelation increased prior to the transition. However, the transition from large-radius to small-radius cycles was not associated with discernible increases in variance or autocorrelation. Thus, indicators of changing resilience can be measured prior to the transition from stable to cyclic plankton dynamics. Such indicators are potentially useful in management. However, these same indicators do not provide useful signals of the reverse transition, which is often a goal of aquatic ecosystem restoration. Thus, the availability of resilience indicators for phytoplankton cycles is asymmetric: the indicators are seen for the transition to bloom–bust cycles but not for the reverse transition to a phytoplankton stable point.