Uncertainty quantification for ecological models with random parameters

• Published in: Ecology letters 2022-10, Vol.25 (10), p.2232-2244
• Main Authors: Reimer, Jody R., Adler, Frederick R., Golden, Kenneth M., Narayan, Akil
• Format: Article
• Language: English
• Subjects: aleatory uncertainty; Algae; Algal blooms; bloom dynamics; Computer applications; Ecological models; Ecology; epistemic uncertainty; Eutrophication; global sensitivity; Jensen's inequality; Mathematical models; Monte Carlo simulation; Parameter uncertainty; polynomial chaos; Polynomials; random parameters; Random variables; Sea ice; sea ice algae; uncertainty quantification
• ISSN: 1461-023X; 1461-0248
• DOI: 10.1111/ele.14095

There is often considerable uncertainty in parameters in ecological models. This uncertainty can be incorporated into models by treating parameters as random variables with distributions, rather than fixed quantities. Recent advances in uncertainty quantification methods, such as polynomial chaos approaches, allow for the analysis of models with random parameters. We introduce these methods with a motivating case study of sea ice algal blooms in heterogeneous environments. We compare Monte Carlo methods with polynomial chaos techniques to help understand the dynamics of an algal bloom model with random parameters. Modelling key parameters in the algal bloom model as random variables changes the timing, intensity and overall productivity of the modelled bloom. The computational efficiency of polynomial chaos methods provides a promising avenue for the broader inclusion of parametric uncertainty in ecological models, leading to improved model predictions and synthesis between models and data. Heterogeneity or parametric uncertainty can both be represented as random parameters in ecological models. We introduce and demonstrate how uncertainty quantification methods can help understand models with random parameters. We demonstrate these methods on a sea ice case study, where spatial heterogeneity leads to landscape‐scale dynamics that differ from a model parameterized with mean parameter values.