Ecosystems are an assembly of a wide range of species that interact with one another, and with their environment, at relatively local scales, and yet they exhibit self-organized patterns and maintain stability at much larger spatiotemporal scales. We are interested in understanding the patterns and dynamics of ecosystems, what causes them, how they are influenced by variations (seasonal/stochastic) in external drivers [1,2] and to device predictive tools [3,4]. We do this by employing/extending simple theoretical tools from statistical physics and applied mathematics, and by suitably testing those with empirical/laboratory data.
Ecosystems are naturally exposed to gradual changes in their external conditions and these typically results in a proportionate smooth response in their state. Occasionally, ecosystems can undergo abrupt and irreversible changes. Well studied examples include a clear water lake with low algal density which may turn turbid and infested with algal blooms for relatively small increases in nutrient loading, or a semi-arid forest that may undergo abrupt desertification under the influence of grazing. Such abrupt changes, also referred to as catastrophic regime shifts/transitions, can lead to enormous loss of ecosystem services to humans. In this context, we are interested in devising early warning signals, or leading indicators, of abrupt transitions, testing them in both mathematical models [3,4] and in the real world [in progress]. Currently, we are in the process of collecting, and applying these tools to, data available for Indian ecosystems.
 Vishwesha Guttal, C. Jayaprakash, Omar P. Tabbaa, Robustness of early warning signals of regime shifts in time-delayed ecological models, Submitted.
 Vasilis Dakos, Stephen R. Carpenter, William A. Brock, Aaron M. Ellison, Vishwesha Guttal, Anthony R. Ives, Sonia Kefi, Valerie Livina, David A. Seekell, Egbert H. van Nes, Marten Scheffer, 2012, Methods for detecting early warnings of critical transitions in time series: Illustrated using simulated ecological data. PLoS ONE. 7(7): e41010. PDF
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