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Dynamic vegetation models provide process‐based explanations of the dynamics and the distribution of plant ecosystems. They offer significant advantages over static, correlative modelling approaches, particularly for ecosystems that are outside their equilibrium due to global change or climate change. A persistent problem, however, is their parameterization. Parameters and processes of dynamic vegetation models (DVMs) are traditionally determined independently of the model, while model outputs are compared to empirical data for validation and informal model comparison only. But field data for such independent estimates of parameters and processes are often difficult to obtain, and the desire to include better descriptions of processes such as biotic interactions, dispersal, phenotypic plasticity and evolution in future vegetation models aggravates limitations related to the current parameterization paradigm. In this paper, we discuss the use of Bayesian methods to bridge this gap. We explain how Bayesian methods allow direct estimates of parameters and processes, encoded in prior distributions, to be combined with inverse estimates, encoded in likelihood functions. The combination of direct and inverse estimation of parameters and processes allows a much wider range of vegetation data to be used simultaneously, including vegetation inventories, species traits, species distributions, remote sensing, eddy flux measurements and palaeorecords. The possible reduction of uncertainty regarding structure, parameters and predictions of DVMs may not only foster scientific progress, but will also increase the relevance of these models for policy advice.

[1] Daisyworld is a simple planetary model designed to show the long-term effects of coupling between life and its environment. Its original form was introduced by James Lovelock as a defense against criticism that his Gaia theory of the Earth as a self-regulating homeostatic system requires teleological control rather than being an emergent property. The central premise, that living organisms can have major effects on the climate system, is no longer controversial. The Daisyworld model has attracted considerable interest from the scientific community and has now established itself as a model independent of, but still related to, the Gaia theory. Used widely as both a teaching tool and as a basis for more complex studies of feedback systems, it has also become an important paradigm for the understanding of the role of biotic components when modeling the Earth system. This paper collects the accumulated knowledge from the study of Daisyworld and provides the reader with a concise account of its important properties. We emphasize the increasing amount of exact analytic work on Daisyworld and are able to bring together and summarize these results from different systems for the first time. We conclude by suggesting what a more general model of life-environment interaction should be based on.

Aim Two of the oldest observations in plant geography are the increase in plant diversity from the poles towards the tropics and the global geographic distribution of vegetation physiognomy (biomes). The objective of this paper is to use a processbased vegetation model to evaluate the relationship between modelled and observed global patterns of plant diversity and the geographic distribution of biomes. LocationThe global terrestrial biosphere. MethodsWe implemented and tested a novel vegetation model aimed at identifying strategies that enable plants to grow and reproduce within particular climatic conditions across the globe. Our model simulates plant survival according to the fundamental ecophysiological processes of water uptake, photosynthesis, reproduction and phenology. We evaluated the survival of an ensemble of 10,000 plant growth strategies across the range of global climatic conditions. For the simulated regional plant assemblages we quantified functional richness, functional diversity and functional identity.Results A strong relationship was found (correlation coefficient of 0.75) between the modelled and the observed plant diversity. Our approach demonstrates that plant functional dissimilarity increases and then saturates with increasing plant diversity. Six of the major Earth biomes were reproduced by clustering grid cells according to their functional identity (mean functional traits of a regional plant assemblage). These biome clusters were in fair agreement with two other global vegetation schemes: a satellite image classification and a biogeography model (kappa statistics around 0.4). Main conclusionsOur model reproduces the observed global patterns of plant diversity and vegetation physiognomy from the number and identity of simulated plant growth strategies. These plant growth strategies emerge from the first principles of climatic constraints and plant functional trade-offs. Our study makes important contributions to furthering the understanding of how climate affects patterns of plant diversity and vegetation physiognomy from a process-based rather than a phenomenological perspective.