Meta-models as a straightforward approach to the sensitivity analysis of complex models

Complex simulation models are important tools in applied ecological and conservation research. However sensitivity analysis of this important class of models can be difficult to conduct. High level interactions and non-linear responses are common in complex simulations, and this necessitates a global sensitivity analysis, where each parameter is tested at a range of values, and in combination with changes in many other parameters. We reviewed the literature, searching for population viability analyses that used simulation models. We found only 9 out of the 122 simulation population viability analysis used global sensitivity analysis. This result is typical of other simulation models in applied ecology, where global sensitivity analysis is rare. We then demonstrate how to conduct a meta-modeling sensitivity analysis, where a simpler statistically fit function (the meta-model, also known as the surrogate model or emulator) is used to approximate the behavior of the complicated simulation. This simpler meta-model is interrogated to inform on the behavior of simulation model. We fit two example meta-models, a generalized linear model and a boosted regression tree, to exemplify the approach. Our hope is that by going through these techniques thoroughly they will become more widely adopted.