To quantitatively characterize the overall uncertainty introduced in QSD results, Monte Carlo methods are usually applied by using stochasticity (i.e., randomness) to solve problems that are deterministic in nature. In the context of QSD, the first step is to select a subset of QSD inputs to be included as input variables in the uncertainty analysis, after which their probability distributions are defined (e.g., through probability density functions) to quantitatively represent their uncertainties. While including more QSD inputs in the Monte Carlo simulation will likely provide more accurate characterization of the overall uncertainty of QSD outputs (i.e., sustainability indicators), it will also increase the required sample size and thus computational time. To address this, sensitivity analysis can be used to identify input variables that are key drivers of system uncertainty (Section 5.3). Notably, as the selection of uncertain input variables can affect results of the sensitivity analysis, uncertainty and sensitivity analyses may be performed iteratively to narrow the input variable pool. Similarly, selection of the probability distributions should be based on the available information on the QSD inputs (e.g., abundance