Abstract
Critical transitions, sudden responses to slow changes in environmental drivers, are inherent in many dynamic processes, prompting a search for early warning signals. We apply this framework to understanding the coffee rust disease, which experienced an unprecedented outbreak in Mesoamerica in 2012–2013, likely a critical transition. Based on monthly infection data from 128 study quadrats in a 45-ha plot in southern Mexico from 2014 to 2020, we find that the persistent seasonal epidemic following the initial outbreak collapses in an evident subsequent critical transition. Characteristic signals of “critical slowing down” precede this collapse and are correlated with reduced rainfall, as expected from climate change, and planting of rust-resistant varieties, an ongoing management intervention. Recoveries from catastrophes may themselves be experienced as a critical transition and managers should consider the larger dynamical landscape for the possibility of subsequent transitions. Early warning signals could therefore be useful when evaluating mitigation effectiveness.
Highlights
Critical transitions, sudden responses to slow changes in environmental drivers, are inherent in many dynamic processes, prompting a search for early warning signals
The study of critical transitions can shed light on sudden responses to slow changes in environmental drivers, which are inherent in many dynamic processes such as climate change and epidemiological outbreaks[1]
An examination of the ecological principles underlying the dynamics of the disease leads theoretically to the propensity of critical transition b ehavior[10], and to the infamous inevitability of surprise so well known in crop disease h istory[11]
Summary
Sudden responses to slow changes in environmental drivers, are inherent in many dynamic processes, prompting a search for early warning signals We apply this framework to understanding the coffee rust disease, which experienced an unprecedented outbreak in Mesoamerica in 2012–2013, likely a critical transition. Identifying and anticipating critical transitions helps to characterize a system’s propensity for unexpected shifts in its dynamics and sheds light on the longevity of the current regime[2,3,4] Building such an understanding is invaluable in planning for and mitigating transitions of ever-evolving complex systems, which include regional phenomena that involve multiple interconnected social, economic, ecological, or climate drivers, where dynamical mechanisms are not completely u nderstood[5,6]. With scant evidence that their interventions are effective, managers may otherwise consider premature abandonment even though long-term effectiveness is on the horizon, though still obscured
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