Smita Deb
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View article: Optimal sampling of spatial patterns improves deep learning-based early warning signals of critical transitions
Optimal sampling of spatial patterns improves deep learning-based early warning signals of critical transitions Open
Complex spatio-temporal systems like lakes, forests and climate systems exhibit alternative stable states. In such systems, as the threshold value of the driver is crossed, the system may experience a sudden (discontinuous) transition or s…
Efficacy of dynamic eigenvalue in anticipating and distinguishing tipping points <sup>†</sup> Open
The presence of tipping points in several natural systems necessitates having improved early warning indicators to provide accurate signals of an impending transition to a contrasting state while also detecting the type of transition. Vari…
View article: EWSmethods: an R package to forecast tipping points at the community level using early warning signals, resilience measures, and machine learning models
EWSmethods: an R package to forecast tipping points at the community level using early warning signals, resilience measures, and machine learning models Open
Early warning signals (EWSs) represent a potentially universal tool for identifying whether a system is approaching a tipping point, and have been applied in fields including ecology, epidemiology, economics, and physics. This potential un…
Climate warming and selective adaptation to thermal refugia Open
The impact of climate warming on biodiversity loss is exacerbated not only by changes in mean but also by spatio-temporal variability in temperature. Access to refugia can mitigate the impact of thermal fluctuations amongst species. The ef…
Early warning signals are hampered by a lack of critical transitions in empirical lake data Open
Quantifying the potential for abrupt non-linear changes in ecological communities is a key managerial goal, leading to a significant body of research aimed at identifying indicators of approaching regime shifts. Most of this work has built…
Rising temperature drives tipping points in mutualistic networks Open
The effect of climate warming on species' physiological parameters, including growth rate, mortality rate and handling time, is well established from empirical data. However, with an alarming rise in global temperature more than ever, pred…
View article: EWSmethods: an R package to forecast tipping points at the community level using early warning signals and machine learning models
EWSmethods: an R package to forecast tipping points at the community level using early warning signals and machine learning models Open
Early warning signals (EWSs) represent a potentially universal tool for identifying whether a system is approaching a tipping point, and have been applied in fields including ecology, epidemiology, economics, and physics. This potential un…
Tipping points in spatial ecosystems driven by correlated noise Open
Complex spatial systems can experience critical transitions or tippings on crossing a threshold value in their response to stochastic perturbations. Whilst previous studies have well-characterized the impact of white noise on tipping, the …
Rising Temperature Drives Tipping Points in Mutualistic Networks <sup>†</sup> Open
The effect of climate warming on species physiological parameters, including growth rate, mortality rate, and handling time, is well established from empirical data. However, with an alarming rise in global temperature more than ever, pred…
View article: Machine learning methods trained on simple models can predict critical transitions in complex natural systems
Machine learning methods trained on simple models can predict critical transitions in complex natural systems Open
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 d…
View article: Machine learning methods trained on simple models can predict critical transitions in complex natural systems
Machine learning methods trained on simple models can predict critical transitions in complex natural systems Open
1. Sudden transitions from one stable state to a contrasting state occur in complex systems ranging from the collapse of ecological populations to climatic change, with much recent work seeking to develop methods to predict these unexpecte…