Influence of topography, vegetation, weather, and climate on Big-cone Douglas-Fir fire refugia and high fire-induced mortality after two large mixed-severity wildfires

Abstract

Big-cone Douglas-Fir (Pseudotsuga macrocarpa, hereafter BCDF) is an endemic, fire-adapted conifer found throughout the mountains of southern California. Because recent large high intensity wildfires have resulted in loss of BCDF, understanding how environmental factors, such as topography, fuels, climate, and weather, impact BCDF survivorship is important for informing restoration and conservation efforts. Here, we used randomForest (RF) and accumulated local effects (ALE) plots to examine how environmental variables contribute to the occurrence of both fire refugia and high fire-induced mortality of BCDF stands during two large wildfires. Additionally, we explored how the influence of these variables changed between the use of two different response variables: (1) visually-assessed mortality evaluated through estimation of canopy survival using Google Earth imagery and (2) RdNBR. This comparison allows us to evaluate the potential that RdNBR overestimates BCDF mortality because it is highly indicative of understory conditions post-fire, rather than direct changes to BCDF trees. We found that pre-fire fuel was one of the most influential variables contributing to both fire refugia and high mortality; sparse and oak dominant understories contributed to fire refugia, while chaparral contributed to high mortality. We also found that the role of certain variables was not consistent across the two fires. For example, areas of the landscape with hotter temperature and higher vapor pressure deficit (VPD) during the fire experienced high BCDF mortality in the Zaca Fire, but had the inverse effect in the Thomas Fire. Lastly, we found that our two metrics of response resulted in significantly different classification of BCDF stands: RdNBR resulted in more stands being classified as high intensity and fewer low severity/unburned areas, supporting our concern that it can overestimate high severity impact in some ecosystems. However, the two model types resulted in relatively similar explanatory environmental variable selections, although different rankings.