RESEARCH INTERESTS
Eluding critical transition in socio-mutualistic network
Mutualistic plant-pollinator networks are facing an increasing extinction risk in a degrading environment. Moreover, human feedback builds in additional nonlinearities in mutualistic networks, ignoring which we may miss out on information essential to detect sudden transitions. It remains to study if social norms can mitigate the potential of critical transitions in such networks envisioned as a coupled socio-ecological system. In particular, controlling species decline in real-world mutualistic plant-pollinator networks by adopting a human conservationist opinion - refraining from the use or misuse of pesticides and switching to their organic counterparts - can likely provide valuable insights. We study the dynamics of the coupled model of bipartite interaction networks and show that the application of social norms at the nodes of one partite can evade a community collapse. Further, we design a novel algorithm deploying network structural attributes that allow applying conservation at a minimum number of nodes to prevent a critical transition.
Three different strategies to prevent collapse in socio-mutualistic networks in a deteriorating environment
Machine learning methods trained on simple models can predict critical transitions in complex natural systems
Forecasting sudden changes in complex systems is a critical but challenging task, with previously developed methods varying widely in their reliability. Here we develop a novel detection method, using simple theoretical models to train a deep neural network to detect critical transitions - the Early Warning Signal Network (EWSNet). We then demonstrate that this network, trained on simulated data, can reliably predict observed real-world transitions in systems ranging from rapid climatic change to the collapse of ecological populations. Importantly, our model appears to capture latent properties in time series missed by previous warning signals approaches, allowing us to not only detect if a transition is approaching, but critically whether the collapse will be catastrophic or non-catastrophic. These novel properties mean EWSNet has the potential to serve as an indicator of transitions across a broad spectrum of complex systems, without requiring information on the structure of the system being monitored. Our work highlights the practicality of deep learning for addressing further questions pertaining to ecosystem collapse and has much broader management implications.
Schematic representation of EWSNet
Higher temperature trigger Tipping Points in Mutualistic Networks
The effect of climate warming on species biological rates, including growth rate, handling time, and mortality rate, is well established from empirical data. However, as the climate continues to warm the planet Earth more than ever, predicting the interactive influence of these changes on mutualistic communities remains uncertain. Using 141 real plant-pollinator networks sampled across the globe and a modeling approach, we study the impact of species' individual thermal responses on mutualistic communities. We show that plant-pollinator networks are at potential risk of rapid transitions at higher temperatures, which were otherwise gradual. Evidently, generalist species plays a critical role in guiding tipping points in mutualistic networks. Further, we derive stability criteria for the networks in a range of temperatures using a two-dimensional reduced model. We identify network structures that can ascertain the delay of a community collapse. Until the end of this century, many real mutualistic networks are found to be under the threat of sudden collapse, and we frame strategies to mitigate them. Together, our results indicate that knowing individual species thermal traits and network structure can improve predictions for communities facing rapid transitions.
Climate warming trigger catastrophic collapse in mutualistic networks
Network tippings and risk estimated from future projected global temperature
Identifying Critical Transitions in Complex Diseases
Mortality and the burden of diseases worldwide continue to reach a substantial number with societal development and urbanization. In the face of a decline in human health, early detection of complex diseases is indispensable, albeit challenging. In this review, we document research carried out thus far on the appearance of complex diseases marked by a critical transition or a sudden shift from a healthy state to a disease state. The theory of resilience and critical slowing down can provide practical tools to forecast the onset of various fatal and perpetuating diseases. However, critical transitions in diseases across diverse temporal and spatial scales may not always be preceded by critical slowing down. Under this backdrop, an in-depth study of the underlying molecular mechanisms provides dynamic network biomarkers that can forewarn potential critical transitions. We put together the theory of complex diseases and resilience, and discuss the need for advanced research on developing early warning signals in the field of medicine and health care.
Critical transitions in protein Cdc2-Cyclin B and associated generic EWSs}: (a) Transitions from a lower state to an upper stable state. Solid (cyan) lines indicate stable steady states, and dashed (red) lines indicate unstable steady states of the deterministic model. The black trajectory indicates stochastic time series. (b) Pre-transition stochastic time-series (segment as indicated by the yellow boxed region in (a)). (c) Residual time series after applying a Gaussian filter (the orange curve in B is the trend used for filtering). (d) and (e) Generic EWSs calculated from the filtered time series after using a rolling window of 60% of the data length: (d) variance and (e) AR(1). (f)–(i) Filtering bandwidth and rolling window chosen based on sensitivity analysis. (f) and (h) Contour plots showing the trends of generic EWSs variance and AR(1) respectively for different rolling-window sizes and filtering bandwidths as measured by the Kendall's- tau value. The triangles indicate the rolling window size and bandwidth used to calculate the EWSs. (g) and (i) Frequency distributions of Kendall's-tau values corresponding to variance and AR(1), respectively.
Stochastic potential landscapes and basin stability for feedback strength obtained from the master equation
ONGOING PROJECT
Predicting Tipping points in spatial ecosystems driven by correlated noise