title: Predicting catastrophic shifts.
abstract: Catastrophic shifts are known to pose a serious threat to ecology, and a reliable set of early warning indicators is desperately needed. However, the tools suggested so far have two problems. First, they cannot discriminate between a smooth transition and an imminent irreversible shift. Second, they aimed at predicting the tipping point where a state loses its stability, but in noisy spatial system the actual transition occurs when an alternative state invades. Here we suggest a cluster tracking technique that solves both problems, distinguishing between smooth and catastrophic transitions and to identify an imminent shift in both cases. Our method may allow for the prediction, and thus hopefully the prevention of such transitions, avoiding their destructive outcomes.
authors.affiliation: Department of Physics, Bar-Ilan University, Ramat-Gan IL52900, Israel. Electronic address: wchaimw@gmail.com.
authors.forename: Haim
authors.initials: H
authors.lastname: Weissmann
authors.affiliation: Department of Physics, Bar-Ilan University, Ramat-Gan IL52900, Israel.
authors.forename: Nadav M
authors.initials: NM
authors.lastname: Shnerb
mesh_terms: D000222:Adaptation, Physiological; D000818:Animals; D003198:Computer Simulation; D004189:Disaster Planning / Q000379:methods; D004190:Disasters; D004463:Ecology / Q000379:methods*; D017753:Ecosystem; D004784:Environmental Monitoring / Q000379:methods; D025461:Feedback, Physiological; D006801:Humans; D008962:Models, Theoretical; D011157:Population Dynamics
keywords: Bistability; Clusters tracking; Continuous transition; Desertification; Discontinuous transition; Early warning indicators; Negative feedback; Positive feedback; Regime shifts; Stability of ecosystems; Tipping points