Extending the use of ecological models without sacrificing details: a generic and parsimonious meta‐modelling approach

Abstract

Summary 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.