S
Simon Scheiter
Researcher at Technische Universität München
Publications - 55
Citations - 3146
Simon Scheiter is an academic researcher from Technische Universität München. The author has contributed to research in topics: Climate change & Vegetation. The author has an hindex of 22, co-authored 47 publications receiving 2731 citations. Previous affiliations of Simon Scheiter include American Museum of Natural History.
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Journal ArticleDOI
Effects of four decades of fire manipulation on woody vegetation structure in Savanna.
Steven I. Higgins,William J. Bond,Edmund C. February,Andries Bronn,Douglas I. W. Euston-Brown,Beukes Enslin,Navashni Govender,Louise Rademan,Sean O'Regan,A.L.F. Potgieter,Simon Scheiter,Richard Sowry,Lynn Trollope,W.S.W. Trollope +13 more
TL;DR: Evidence is provided that savannas are demographically resilient to fire, but structurally responsive, and the density of woody individuals was unresponsive to fire.
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Next‐generation dynamic global vegetation models: learning from community ecology
TL;DR: A trait- and individual-based vegetation model (aDGVM2) that allows individual plants to adopt a unique combination of trait values that may yield novel insights as to how vegetation may respond to climate change and could foster collaborations between functional plant biologists and vegetation modellers.
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Impacts of climate change on the vegetation of Africa: an adaptive dynamic vegetation modelling approach
Simon Scheiter,Steven I. Higgins +1 more
TL;DR: The adaptive dynamic global vegetation model (aDGVM) as mentioned in this paper combines established components from existing DGVMs with novel process-based and adaptive modules for phenology, carbon allocation and fire within an individual-based framework.
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Atmospheric CO2 forces abrupt vegetation shifts locally, but not globally.
Steven I. Higgins,Simon Scheiter +1 more
TL;DR: It is shown that tropical grassland, savanna and forest ecosystems, areas large enough to have powerful impacts on the Earth system, are likely to shift to alternative states, and increasing atmospheric CO2 concentration will force transitions to vegetation states characterized by higher biomass and/or woody-plant dominance.
Journal ArticleDOI
Connecting dynamic vegetation models to data – an inverse perspective
Florian Hartig,James G. Dyke,Thomas Hickler,Steven I. Higgins,Robert B. O'Hara,Simon Scheiter,Andreas Huth +6 more
TL;DR: It is explained how Bayesian methods allow direct estimates of parameters and processes, encoded in prior distributions, to be combined with inverse estimates, encodedin likelihood functions, in order to bridge the gap in parameterization of dynamic vegetation models.