Profile likelihood-based parameter and predictive interval analysis guides model choice for ecological population dynamics

• Published in: Mathematical biosciences 2023-01, Vol.355, p.108950-108950, Article 108950
• Main Authors: Simpson, Matthew J., Walker, Shannon A., Studerus, Emma N., McCue, Scott W., Murphy, Ryan J., Maclaren, Oliver J.
• Format: Article
• Language: English
• Subjects: Algorithms; Coral reef; Great Barrier Reef; Identifiability analysis; Likelihood Functions; Logistic growth; Model selection; Models, Biological; Models, Theoretical; Population dynamics; Software
• ISSN: 0025-5564; 1879-3134
• DOI: 10.1016/j.mbs.2022.108950

Calibrating mathematical models to describe ecological data provides important insight via parameter estimation that is not possible from analysing data alone. When we undertake a mathematical modelling study of ecological or biological data, we must deal with the trade-off between data availability and model complexity. Dealing with the nexus between data availability and model complexity is an ongoing challenge in mathematical modelling, particularly in mathematical biology and mathematical ecology where data collection is often not standardised, and more broad questions about model selection remain relatively open. Therefore, choosing an appropriate model almost always requires case-by-case consideration. In this work we present a straightforward approach to quantitatively explore this trade-off using a case study exploring mathematical models of coral reef regrowth after some ecological disturbance, such as damage caused by a tropical cyclone. In particular, we compare a simple single species ordinary differential equation (ODE) model approach with a more complicated two-species coupled ODE model. Univariate profile likelihood analysis suggests that the both models are practically identifiable. To provide additional insight we construct and compare approximate prediction intervals using a new parameter-wise prediction approximation, confirming both the simple and complex models perform similarly with regard to making predictions. Our approximate parameter-wise prediction interval analysis provides explicit information about how each parameter affects the predictions of each model. Comparing our approximate prediction intervals with a more rigorous and computationally expensive evaluation of the full likelihood shows that the new approximations are reasonable in this case. All algorithms and software to support this work are freely available as jupyter notebooks on GitHub so that they can be adapted to deal with any other ODE-based models. •Explore trade-off between model complexity and data availability.•Case study about hard coral regrowth on the Great Barrier Reef, Australia.•In this case both the simple and complex models are structurally and practically identifiable.•New, efficient method for parameter-wise profile predictions enable comparison of prediction intervals.•New approximate prediction intervals compare well with those obtained by evaluating the full likelihood function.