2022 ESA Annual Meeting (August 14 - 19)

OOS 31-1 Early warning signals predict transitions away from bifurcations in rate-dependent community dynamics

1:30 PM-1:45 PM
520E
Ramesh Arumugam, Indian Institute of Science Education and Research;Frederic Guichard,McGill University;Frithjof Lutscher,Department of Mathematics and Statistics, University of Ottawa;
Background/Question/Methods

Ecological communities can show complex and abrupt responses to environmental change that include community collapse, a decline in populations, or sudden shifts between regimes. Such catastrophic changes are often described by the theory of critical transitions using a gradual environmental change. An advanced theory of early warning indicators was developed to detect critical points of the system and also to predict the upcoming transition. Despite a broad literature on early warning indicators, the ecological theory that addresses how the rate of environmental forcing can affect early warning indicators of imminent transitions is only at the beginning. As climate change can cause large unexpected changes, our ability to predict dynamic transitions in advance is of great importance for ecosystem management. In this study, we address how a rate of environmental change can affect the dynamical responses of an ecological community, the expected transitions at critical points and the prediction of upcoming transitions.

Results/Conclusions

Our results show that the rate of change in habitat quality (i.e, carrying capacity) and in spatial connectivity (i.e. dispersal rate) of a predator-prey metacommunity determines different regime shifts that take place away from bifurcation points, but with a tracking of unstable states. Such dynamical transitions include a shift between two persistence states, a shift from a steady state to an oscillatory state, a shift from asynchronized to synchronized state, and even an expected extinction state. The respective early warning indicators away from the bifurcation point reveals a better prediction of upcoming transition than those at bifurcation points. This study presents rate-dependent transitions through tracking unstable states and a better prediction of preceding transitions away from critical points.