Connecting dynamic vegetation models to data — an inverse perspective
Abstract
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.
What the paper shows and why it matters (AI-generated)
Dynamic vegetation models were, and often still are, parameterised in two disconnected steps: measure processes independently in the field, then separately check whether model output resembles reality — with no formal way to combine the two into a single estimate. This paper frames closing that gap as an inverse problem and shows how Bayesian methods let direct field measurements and indirect model-data matching be combined properly, laying groundwork used throughout much of the group's later forest-model work. With over 200 citing papers, the inverse-problem framing has become a standard lens for connecting DVMs to data, from individual-based forest dynamics models to peatland carbon-flux modelling.