Using Count Regression to Investigate Millennial-Scale Vegetation and Fire Response from Multiple Sites Across the Northern Rocky Mountains, USA

The Northern Rocky Mountains, USA contain a vast forested landscape, managed primarily by the federal government. This region contains some of the highest elevations forests and most iconic endangered and threatened species in the contiguous United States. The influence of human impacts and climate change are evident on the landscape today, with larger and more frequent fires impacting vegetation composition and recovery. This project uses paleoecological data from six lake sediment cores to investigate what drives fire across this region over the Holocene. Count regression was used to predict charcoal influx as a function of Pinus pollen accumulation rates (PAR) and percent. The results show that fire activity increases significantly with Pinus pollen, and that baseline fire activity varies significantly across sites, largely following an elevation gradient. The results of this analysis illustrate a novel way to use paleoecological data to provide valuable information to federal agencies as they prepare for future management of these ecologically valuable areas.