Representing prescribed fire and mechanical thinning in a demographic vegetation model: mechanisms driving forest structure, fuel, and demographic rates in California mixed-conifer forests

Abstract. Prescribed fire and mechanical thinning are common fuel management and forest restoration practices in fire-prone ecosystems, particularly where long-term fire suppression has led to significant fuel accumulation and altered stand structure. Existing field evidence shows varied outcomes depending on environmental and stand conditions, with long-term, landscape-scale management effects remaining poorly understood. The majority of large-scale models that project long-term effects lack mechanistic management processes and are rarely validated for the ecological processes driving outcomes. To address this, we implemented a new prescribed fire model and adapted a pre-existing wood harvest model to represent restoration thinning in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), a dynamic vegetation demography model. We developed a model validation framework emphasizing the key ecological mediators and expected feedback between management, vegetation, and fire. Running simulations with varying weather conditions under which prescribed fire can occur (burn window) and with the forest thinning model across three sites in California’s mixed conifer forests, we found that FATES successfully captured the observed relative changes in stand structure, tree size distribution, fuel load, and demographic rates following forest health treatments. Additionally, model results aligned with general ecological expectations and reproduced the key differences between thinning and prescribed fire: logging immediately and significantly reduces stem density and basal area but not surface fuel load, while prescribed fire results in nonsignificant to moderate reduction in stem density and basal area but an immediate decrease in surface fuel load. Notably, simulations with a wide, inclusive burn window resulted in cooler fires and caused less change in stem density and basal area than those with a narrow, exclusive burn window, but only at the warmest and driest modeled study site. Our work advances the integration of management activities into Earth system models by introducing a mechanistic, interactive prescribed fire model, and a validation framework grounded in empirical data to assist model evaluations and applications. The model provides a promising tool for projecting the complex climate-vegetation-fire-human interactions in dry conifer forests of the Western U.S. and other fire-adapted ecosystems with similar management history.