Uncertainty quantification for ecological models with random parameters

• Published in: Ecology Letters
• Main Authors: Reimer, Jody R., Adler, Frederick R., Golden, Kenneth M., Narayan, Akil
• Other Authors: Division of Mathematical Sciences, Office of Naval Research, National Science Foundation of Sri Lanka, National Institutes of Health, National Institute of Biomedical Imaging and Bioengineering
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2022
• Subjects: Sea ice
• Online Access: http://dx.doi.org/10.1111/ele.14095; https://onlinelibrary.wiley.com/doi/pdf/10.1111/ele.14095; https://onlinelibrary.wiley.com/doi/full-xml/10.1111/ele.14095; https://onlinelibrary.wiley.com/doi/am-pdf/10.1111/ele.14095

Abstract 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.