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State transitions are changes in ecosystem structure and self-reinforcing feedbacks that are initiated when an exogenous driver variable crosses a threshold. Reversing state transitions is difficult and costly. While some state transitions are relatively rapid, many take years to decades. Outside of theoretical models, very little is known about slower state transitions and how they unfold in time and space. We quantified changes in spatial variance as a mesic grassland ecosystem shifts to a shrub-dominated state, using long-term experiments and simulations that maintain grasslands with annual fires or initiate a state transition to shrub dominance by decreasing fire frequency. In the experiments, the susceptibility to state transitions varied substantially in space. In the less frequent fire treatment, some plots became shrub-dominated around year 20 and grass extirpations began in year 25, but a third of the plots were still grass-dominated in year 37. Variable rates of state transition resulted in increasing spatial variance of grass cover over time, whereas shrub cover variance decreased. In the annually burned treatment, grasses remained dominant and the spatial variance of grass cover declined. In a separate experiment, less frequent fires were maintained for 23 years and then switched to annual fires. The switch to annual fires occurred shortly after grass variance started to increase and a majority of these plots quickly returned to a grass dominated state. In simulations, spatial variance remained low and average grass cover was high under frequent fires. If fire frequency decreased below a threshold, the ecosystem transitioned to shrubland, with a transient increase in the spatial variance of grass cover during the transition between states. Synthesis. Spatial variability in the rate and susceptibility to state transitions is indicative of a system with a patchy spatial structure, high spatial heterogeneity and low connectivity between patches. Increases in spatial variance can serve as an indication that some patches have begun a state transition and that management interventions are needed to avoid widespread transitions. This is one of the first empirical examples where altering management after an increase in spatial variance prevented state transitions.

Questions: Climate extremes are predicted to become more common in many ecosystems. Climate extremes can promote and interact with disturbances, but the combined effects of climate extremes and disturbances have not been quantified in many ecosystems. In this study, we ask whether the dual impact of a climate extreme and concomitant disturbance (wildfire) has a greater affect than a climate extreme alone. Location: Tallgrass prairie in the Konza Prairie Biological Station, northeastern Kansas, USA. Methods: We quantified the response of a tallgrass prairie plant community to a 2‐year climate extreme of low growing‐season precipitation and high temperatures. In the first year of the climate extreme, a subset of plots was burned by a growing‐season wildfire. This natural experiment allowed us to compare community responses to a climate extreme with and without wildfire. Results: In plots exposed to the climate extreme but not wildfire, community structure, diversity, and composition showed minor to insignificant changes, such as a 20% reduction in grass cover and a slight increase in species diversity. Plots exposed to both the climate extreme and wildfire underwent larger changes, including an 80% reduction in grass cover, 50% increase in forb cover, and increased plant diversity. Two years after the climate extreme, structural shifts in burned plots showed little sign of recovery, indicating a potentially lasting shift in plant community structure. Conclusions: Our results suggest that community responses to climate extremes need to account for climate‐related disturbances — in this case, high temperatures, drought and wildfire. This response diverged from our expectation that heat, drought, and an additional fire would favor grasses. Although growing‐season wildfires in tallgrass prairie have been rare in recent decades, they will likely become more common with climate change, potentially leading to changes in grassland structure. [ABSTRACT FROM AUTHOR]

Abstract Many ecosystems have abruptly changed in the past and may again in the future, yet prediction and inference of mechanisms causing abrupt changes remains challenging. Critical transitions are one such mechanism, occurring when systems with alternative states cross a threshold. Such transitions are associated with a loss of resilience, often signaled by increasing variability or autocorrelation over time. However, critical transitions are difficult to distinguish from other causal mechanisms, and detection of resilience loss in sedimentary archives can be confounded by time-averaging and discontinuous sampling. Here, we simulate woodland-grassland regime shifts resulting from critical transitions and other mechanisms. We then test the diagnostic ability of two widely-used resilience indicators, standard deviation and autocorrelation time, after alterations common in sedimentary records: time averaging, discontinuous sampling, and varying sedimentation rates. Standard deviation—but not autocorrelation time—still distinguishes gradually forced critical transitions from other regime shifts when sedimentation rates are constant, and can be robust to abrupt changes in sedimentation rate. Unfortunately, shifts in standard deviation alone are rarely definitive evidence of critical transitions. Under exponential sedimentation regimes, which are common in younger upper-column sediments, neither resilience indicator is effective. Discontinuous sampling weakened the strength of resilience indicators. A demonstrative analysis of abrupt early Holocene deforestation recorded at Steel Lake, Minnesota showed signals consistent with resilience loss during early Holocene aridification. Hence, signals of resilience loss can be recovered from sedimentary archives, but efficacy varies among indicators and sedimentation regime. High-resolution and multi-proxy records remain essential to inferring causes, while process-based time series modeling such as this can be calibrated to systems of interest to explicitly test hypotheses about abrupt change causes. Highlights • Sedimentation and sampling can obscure signals of resilience loss in paleoecological records. • Standard deviation is a robust resilience indicator under most sedimentation and sampling scenarios. • Autocorrelation time is not an effective resilience indicator for sedimentary records. • Neither resilience indicator is effective when sedimentation is exponential. • Discontinuous sampling weakens or eliminates indicators of resilience loss. [ABSTRACT FROM AUTHOR]

Abrupt ecological changes are, by definition, those that occur over short periods of time relative to typical rates of change for a given ecosystem. The potential for such changes is growing due to anthropogenic pressures, which challenges the resilience of societies and ecosystems. Abrupt ecological changes are difficult to diagnose because they can arise from a variety of circumstances, including rapid changes in external drivers (e.g., climate, or resource extraction), nonlinear responses to gradual changes in drivers, and interactions among multiple drivers and disturbances. We synthesize strategies for identifying causes of abrupt ecological change and highlight instances where abrupt changes are likely. Diagnosing abrupt changes and inferring causation are increasingly important as society seek to adapt to rapid, multifaceted environmental changes. [ABSTRACT FROM AUTHOR]