Assessing the effect of cell-level uncertainty on a forest landscape model simulation in northeastern China

LANDIS is a cell-based spatially explicit forest landscape model designed to explore successional dynamics under natural and anthropogenic disturbances. Species age cohort (10-year cohort of a given tree species) information is required for each cell in LANDIS. However, providing such information for a landscape comprising millions of cells is challenging. In this study, a stand-based assignation (SBA) approach was developed to stochastically assign species age cohorts to each cell based on forest inventory data. As a probability-based approach, SBA will introduce errors in LANDIS input. In order to assess the effect of errors produced by SBA on LANDIS results, 20 Monte Carlo simulations were produced. For each species simulated in LANDIS, the recurrence frequency (RF) of the majority species age cohort (MSAC, the most frequently occurring species age cohort) from 20 Monte Carlo simulations were used to quantify the uncertainty in species age cohorts for individual cell. Average recurrence frequency (ARF) of the MSAC was used to quantify the overall uncertainty in species age cohorts at the cell level. For each species, the coefficient of variation (CV) for the percent area and an aggregation index for the 20 Monte Carlo simulations was used to quantify the uncertainty at the landscape level. Results showed that at the cell level, uncertainty was relatively low at the beginning of the simulation (ARF was larger than 10). Seed dispersal, seedling establishment, mortality, and fire disturbance caused uncertainty to increase with simulation year. The uncertainty finally reached an equilibrium state, where input errors in original species age cohorts had little effect on the simulation outcomes. At the landscape level, species percent area and their spatial patterns were not substantially affected by the uncertainties in species age structure at the cell level. Since the typical use of LANDIS is to predict the long-term landscape pattern change, SBA can be used to parameterize species age cohorts for individual cells.