Ecological models should be rooted in data derived from observation, allowing methodical model construction and clear accounts of model results with respect to the data. Unfortunately, many models are retrospectively fitted to data because in practice it is difficult to bridge the gap between concrete data and abstract models. Our research is on automated methods to support bridging this gap. The approach proposed consists of raising the data level of abstraction via an ecological metadata ontology and from that, through logic-based knowledge representation and inference, to automatically generate prototypical partial models to be further improved by the modeler. In this chapter we aim to: 1) give an overview of current automated modelling approaches applied to ecology, and relate them to our metadata-based approach under investigation; and 2) explain and demonstrate how it is realized using logic-based formalisms. We give the overview of current automated modelling approaches in the section “Ecological Modeling and Automation: Current Approaches,” focusing on compositional modelling and model induction. The contrast between these and our approach, where we adopt metadata descriptions through an ontology and logic-based modelling, is discussed in the section “Our Automated Ecological Modelling Avenue.” The next section, “Towards a System for Metadata–Supported Automated Modeling,” makes ideas more concrete, starting with further details on the Ecolingua ontology, followed by examples of automated model structuring and parameter estimation. In the concluding section, “A Look Ahead and Conclusion,” we comment briefly on the ontologies trend and on the outlook of our research.
An overview of mixture modelling for latent evolutions in longitudinal data: Modelling approaches, fit statistics and software
