The Royal Society
Browse
rsif20180741_si_001.pdf (220.84 kB)

Appendices to: Community dynamics and sensitivity to model structure: toward a probabilistic view of process-based model predictions. Aldebert & Stouffer, J. R. Soc. Interface, 2018 from Community dynamics and sensitivity to model structure: towards a probabilistic view of process-based model predictions

Download (220.84 kB)
journal contribution
posted on 2018-11-15, 15:46 authored by Clement Aldebert, Daniel B. Stouffer
Statistical inference and mechanistic, process-based modelling represent two philosophically different streams of research whose primary goal is to make predictions. Here, we merge elements from both approaches to keep the theoretical power of process-based models while also considering their predictive uncertainty using Bayesian statistics. In environmental and biological sciences, the predictive uncertainty of process-based models is usually reduced to parametric uncertainty. Here, we propose a practical approach to tackle the added issue of structural sensitivity, the sensitivity of predictions to the choice between quantitatively close and biologically plausible models. In contrast to earlier studies that presented alternative predictions based on alternative models, we propose a probabilistic view of these predictions that include the uncertainty in model construction and the parametric uncertainty of each model. As a proof of concept, we apply this approach to a predator-prey system described by the classical Rosenzweig–MacArthur model, and we observe that parametric sensitivity is regularly overcome by structural sensitivity. In addition to tackling theoretical questions about model sensitivity, the proposed approach can also be extended to make probabilistic predictions based on more complex models in an operational context. Both perspectives represent important steps towards providing better model predictions in biology, and beyond.

History

Usage metrics

    Journal of the Royal Society Interface

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC