Time-dependent sensitivity of a process-based ecological model

- Many ecological models include processes that change over time.
- We conducted FAST analysis of a forest growth model 3-PG2.
- Parameter sensitivities changed substantially with stand age.
- Climatic and soil forcing data also influenced parameter sensitivities.
- Implications for sensitivity analysis of ecological models are explored.

Sensitivity analysis is useful for understanding the behaviour of process-based ecological models. Often, time influences many model processes. Hence, the sensitivity of model outputs to variation in input parameters may also change with simulation period. We assessed the time-dependence of parameter sensitivity in a well-established forest growth model 3-PG (Physiological Principles for Predicting Growth) (Landsberg and Waring, 1997) as a case study. We used a screening method to select influential parameters for two key model outputs, i.e., stand volume and foliage biomass, then applied the Fourier amplitude sensitivity test (FAST) to quantify the sensitivity of the outputs to these selected parameters. Sensitivities were assessed on an annual time-step spanning 5-50 years of forest stand age. The influence of climatic and soil variables on time-dependent sensitivities was also quantified. We found that the sensitivities of most parameters changed substantially with forest stand age. Different climate and soil data also influenced the sensitivities of some parameters. Time-dependent sensitivity analysis provided much greater insight into model structure and behaviour than previous snapshot sensitivity analyses. Failing to account for time-dependence in sensitivity analysis could lead to misguided efforts in model calibration and parameter refinement, and the mis-identification of insensitive parameters for default value allocation. We concluded that sensitivity analysis should be conducted at simulation periods compatible with the process of interest. A more comprehensive sensitivity analysis scheme is required for temporal models to explore parameter sensitivities over the full simulation period and over the full variation in forcing data.