Journal
JOURNAL OF THE ROYAL SOCIETY INTERFACE
Volume 15, Issue 149, Pages -Publisher
ROYAL SOC
DOI: 10.1098/rsif.2018.0741
Keywords
model predictions; Bayesian statistics; structural sensitivity; community dynamics; predation
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Funding
- European FEDER Fund [1166-39417]
- Rutherford Discovery Fellowship [RDF-13-UOC-003, 16-UOC-008]
- Marsden Fund Council from New Zealand Government [RDF-13-UOC-003, 16-UOC-008]
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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.
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