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3rd March 2026<\/strong><\/p>\n\n\n\nBryony Hobden<\/strong><\/p>\n<\/div><\/div>\n\n\n\nEarly warnings of critical transitions through vector autoregression: lessons from multiscale systems<\/h2>\n\n\n\n
In a multiscale\u00a0nonlinear dynamical\u00a0system, generic critical transitions (tipping points) are not limited to folds on a slow manifold but can also include other\u00a0generic\u00a0bifurcations, such as a subcritical Hopf or even a singular Hopf bifurcation. In systems such as these, generic early warning signals associated with critical slowing down, such as observing an increase in variance and lag-1 autocorrelation, can be obscured by oscillatory behaviour. Here, we present a method using vector autoregression (VAR) that can identify multiple eigenvalues of the system from multiple time series. With this, we are not only able to deduce the system’s stability but also identify the type of bifurcation we are approaching and, therefore, the underlying dynamics, which gives us insight into the system’s future state post-tipping.\u00a0In practice, this would be beneficial for mitigating risk if the tipping point is unavoidable. We look at three bifurcations in particular: the fold, the subcritical Hopf bifurcation, and the\u00a0singular Hopf bifurcations with time-scale separation. We hope to show that VAR\u00a0is potentially a powerful\u00a0tool in an EWS toolkit and can be more enlightening than simply looking at AR1 of individual time series\u00a0if there is more than one timescale present in the system,\u00a0and especially\u00a0if more than one time series is available.<\/p>\n\n\n\n
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17th<\/strong> February 2026<\/strong><\/p>\n\n\n\nPeter Cox<\/strong><\/p>\n<\/div><\/div>\n\n\n\nAdvanTip Webinar: Tipping Points, Overshoots and Early Warning<\/h2>\n\n\n\n\n
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10th<\/strong> February 2026<\/strong><\/p>\n\n\n\nPaul Ritchie<\/strong><\/p>\n<\/div><\/div>\n\n\n\nThe role of coupling and timescales for interacting tipping elements<\/h2>\n\n\n\n
Sudden and abrupt changes can occur in a nonlinear system within many fields of science when such a system crosses a tipping point and rapid changes of the system occur in response to slow changes in an external forcing. These can occur when time-varying inputs cross a bifurcation. If an “upstream” system loses stability in this way it may cause a “downstream” system influenced by it to tip, especially if the downstream system evolves on a much faster timescale, in what we call an accelerating cascade of tipping elements. In this talk, I will identify the conditions on the coupling and timescales of the systems resulting in various types of tipping sequence. I will also present a prototypical example of a unidirectionally coupled pair of simple tipping elements with hysteresis. This allows us to map out the various types of response as a function of system parameters and to link it to bifurcations of the underlying system that may have multiple timescales.<\/p>\n\n\n\n
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13th January 2026<\/strong><\/p>\n\n\n\nHatty Wylder-Hopkinson<\/strong><\/p>\n<\/div><\/div>\n\n\n\nPositive Tipping Points for Regenerative Agriculture<\/h2>\n\n\n\n
Industrial agriculture undermines the natural systems upon which farmers depend, making rapid transition to a new agrifood system urgently needed. The literature on positive tipping points (PTPs) provides insight into how rapid, self-perpetuating, and desirable change can be intentionally triggered in socio-ecological systems. Systems thinking perspectives enable us to understand how such transitions occur by making systems visible, highlighting feedbacks, and identifying leverage points, including paradigm shifts.<\/p>\n\n\n\n
This PhD explores how positive tipping points for regenerative agriculture might be triggered. I conceptualise regenerative farming not simply as a set of practices or outcomes, but as the practical application of a paradigm shift from a linear, extractive model towards reciprocal socio-ecological systems:<\/p>\n\n\n\n
\n- The first part of the research makes the farm system visible by mapping and making explicit its elements, relationships, and feedbacks. In particular, this work illuminates the reciprocal relationships between human and nonhuman actors.<\/li>\n\n\n\n
- Building on this foundation, the second part explores the extent to which these reciprocal relationships foster stability and resilience. Understanding the stability and resilience of systems is crucial in the context of positive tipping points \u2013 rapid, self-perpetuating and irreversible shifts from one stable<\/em> state to another. Resilience is conceptualised as an emergent property of reciprocity, arising when the needs of both human and nonhuman actors are balanced. This frames resilience through an equity and justice lens and highlights the relational foundations of socio-ecological stability.<\/li>\n\n\n\n
- The final part of the research explicitly examines transitions towards regenerative, reciprocal systems by mapping the balancing and reinforcing feedbacks of the current agrifood system through the lens of the Behaviour Change Wheel.<\/li>\n<\/ol>\n\n\n\n
Overall, this research provides a foundation for understanding how farmers may act as agents of regenerative change to trigger positive tipping points, offering insights for future interventions and strategies to support regenerative transitions.<\/p>\n","protected":false},"excerpt":{"rendered":"
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