  It can be difficult to identify ways to reduce the complexity of large models
whilst maintaining predictive power, particularly where there are hidden
parameter interdependencies. Here, we demonstrate that the analysis of model
sloppiness can be a new invaluable tool for strategically simplifying complex
models. Such an analysis identifies parameter combinations which strongly
and/or weakly inform model behaviours, yet the approach has not previously been
used to inform model reduction. Using a case study on a coral calcification
model calibrated to experimental data, we show how the analysis of model
sloppiness can strategically inform model simplifications which maintain
predictive power. Additionally, when comparing various approaches to analysing
sloppiness, we find that Bayesian methods can be advantageous when unambiguous
identification of the best-fit model parameters is a challenge for standard
optimisation procedures.
