Variability in Mixed Conifer Spatial Structure Changes Understory Light Environments

Abstract

In fire-adapted conifer forests of the Western U.S., changing land use has led to increased forest densities and fuel conditions partly responsible for increasing the extent of high-severity wildfires in the region. In response, land managers often use mechanical thinning treatments to reduce fuels and increase overstory structural complexity, which can help improve stand resilience and restore complex spatial patterns that once characterized these stands. The outcomes of these treatments can vary greatly, resulting in a large gradient in aggregation of residual overstory trees. However, there is limited information on how a range of spatial outcomes from restoration treatments can influence structural complexity and tree regeneration dynamics in mixed conifer stands. In this study, we model understory light levels across a range of forest density in a stem-mapped dry mixed conifer forest and apply this model to simulated stem maps that are similar in residual basal area yet vary in degree of spatial complexity. We found that light availability was best modeled by residual stand density index and that consideration of forest structure at multiple spatial scales is important for predicting light availability. Second, we found that restoration treatments differing in spatial pattern may differ markedly in their achievement of objectives such as density reduction, maintenance of horizontal and tree size complexity, and creation of microsite conditions favorable to shade-intolerant species, with several notable tradeoffs. These conditions in turn have cascading effects on regeneration dynamics, treatment longevity, fire behavior, and resilience to disturbances. In our study, treatments with high aggregation of residual trees best balanced multiple objectives typically used in ponderosa pine and dry mixed conifer forests. Simulation studies that consider a wide range of possible spatial patterns can complement field studies and provide predictions of the impacts of mechanical treatments on a large range of potential ecological effects.