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The last two decades have witnessed record-breaking drought and heat extremes during summer months across Europe, with wide-ranging consequences for biodiversity, public health, and the amount of carbon taken up by the land. Climate change is expected to increase the frequency, magnitude, and duration of future droughts and heat extremes. Yet, the associated timescale needed for affected ecosystems to recover from extreme events is largely unknown and rarely considered in evaluations of future climate change impacts. Without this critical knowledge of recovery timescales, we are likely to underestimate ecosystem and associated services sensitivity to compound events (heatwaves coincident with drought), and/or repeated climate extremes. To robustly predict future impacts, we need new theories to represent our understanding of the timescales (months-to-years) over which the ecological legacy to meteorological extremes persists. This project will determine legacy timescales at multiple spatial scales by applying state-of-the-art machine learning techniques to both manipulation experiments (rainfall/warming), field data, and satellite data covering recent, record-breaking European summer extremes. These insights into the scale (both time and spatial extent) of legacy persistence will be used to test four hypotheses that govern the vegetation's response to droughts and heat extremes. This combination of statistical machine learning, alongside hypothesis-driven model development, will unlock critical new insights into the role of the past in dictating vegetation responses to future environmental extremes, reducing associated risk, and facilitating mitigation planning. Our ultimate impact will be the modification of the land surface component of UKESM - the UK's new Earth System Model - facilitating a transformative improvement in our capacity to assess the future impact of climate extremes both across Europe and globally, critical to forecasting the future terrestrial carbon sink.

Space is fundamental to physical and perceptual reality, but physical and perceptual space are not the same. Perceptual space is created by the brain and plastically formed by the sensorimotor interactions of our body with physical reality. In the digital future, these two spaces are joined by novel spaces experienced in virtual (VR) and extended (XR) reality as these new technologies massively expand in work, pleasure and social interaction. The first aim of PLACES is to understand how sensorimotor interactions in virtual environments shape perceptual space and how this interacts with virtual (VS) and real (RS) space. Secondly, deep and improved knowledge of perceptual mechanisms is essential for the future development of VR as a key digital technology for Europe. To work for the people, VR and XR need to be effective, comfortable, transparent and fair. These aims can only be reached by understanding and accounting for perception in a human-centric manner. Based on these premises, the highly interdisciplinary consortium of PLACES pursues five key objectives: to (1) use cutting-edge VR technology to advance scientific knowledge of the mechanisms of sensorimotor perception and plasticity; (2) use our understanding about spatial perception, gaze control and sensorimotor plasticity to advance VR technology and enhance VR applicability; (3) predict action intentions of users in VR and employ these predictions in advanced user interfaces; (4) understand how long-term usage of VR interacts with perceptual and sensorimotor states in real space and in virtual space; and (5) translate research findings into applied fields in vision aids and social telepresence. Reaching these objectives will put the EU on the map as a leader in perception research and its application in VR. PLACES aims for new frontiers in perception science and its applications and for a significant impact on the people of the EU.