Community dynamics and sensitivity to model structure: towards a probabilistic view of process-based model predictions.

ABSTRACT: 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 proba

SUBMITTER: Aldebert C 

PROVIDER: S-EPMC6303789 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature