Extending the use of ecological models without sacrificing details: a generic and parsimonious meta-modelling approach
Résumé
Difficulties in accounting for the fine scale nature of ecological processes in large-scale simulations constitute an important issue in ecology. Among existing methods, meta-modelling, that is creating a statistical emulator of a model, has seen very few applications in ecology. Yet, meta-modelling methods are well advanced in the field of engineering. We adapted and applied a meta-modelling approach to a case study typical of the complexity found in ecosystems. It involved a highly detailed, individual-based and spatially explicit biophysical model (noTG). The model was parameterized for a multi-specific, spatially heterogeneous forest. Our goal was to increase its temporal domain of applicability by obtaining a meta-model of its light interception module many times faster. The meta-model was constructed from a series of simulations with noTG, following a latin hypercube design. Several meta-modelling techniques were compared, with neural networks providing the best results. The meta-model accurately reproduced the behaviour of noTG across a range of variables and organization levels. It was also 62 times faster. These result show that meta-modelling can be a practical tool in ecology and represents a highly powerful way to change the scope of a model while still accounting for fine details.