Prescribed burning is a method used to mimic historical fire regimes in ecosystems where its natural occurrence has been disrupted due to fragmentation, land use change, expansion of the wildland-urban-interface, or fire suppression. In longleaf pine savannas, fire reduces tree density, increases light availability, and alters nutrient cycling. There is spatial variation in the effects of fire on vegetation that should be related multiple factors, which may differ between habitat types. The aim of the project is to test the relative importance of multiple factors on the composition of understory plant species, which should include: soil characteristics, fuel loading, fire intensity (i.e., temperature and resident time), and canopy cover. To determine these relationships, I will compare pre vs. post-burn vegetation and the effects of soil characteristics, fuel loading, fire intensity, and canopy cover in longleaf pine-dominated stands that had been long-unburned (>20 years), infrequently- (3-5 years between fires) or frequently-burned (1-2 years between fires). Two locations were chosen with each of the fire frequencies which allows for comparisons between habitat types with different soil characteristics and moistures: mesic flatwoods with mostly Spodols and dry sandhill dominated by Entisols. Within each habitat type and fire frequency, 20 trees were randomly selected to test whether fire intensity and vegetation differ between the dripline (i.e., where pine needs fall and increase fuel loads) and outside the dripline. Each tree has two 1x1m plots, in or outside of the dripline where we have been recording species abundances, fuel loading using Brown’s method, and canopy cover using a hemispherical camera since 2016, including pre-, during, and post-fire data. The plots were burned during spring of 2017 and thermocouples were installed to measure fire temperature and residence time at and below the soil surface. This project is ongoing and much of the data described herein has already been collected, including those variables listed above, although I am currently helping with collection of the final post-fire vegetation data. Because my main interest is in statistical analyses, I will be using the data to learn and develop multivariate analyses that link soil characteristics, fuel loading, fire intensity, and canopy cover. It is also likely that we will use advanced statistical techniques, such as a path analyses, to relate all factors that have been measured in this experiment. Additionally, we will develop community-based models to estimate how changes in environmental variables will affect vegetation. Analysis of these data will result in me learning: 1) new statistical and modelling techniques; 2) to program in the language R; 3) to interpret results in a restoration context; and 4) the process of writing and publishing peer-reviewed papers. Additionally, I will gain experience collecting vegetation data in the field and participate in prescribed fires to increase my understanding of how statistical results are connected to ecological processes. Results of my analyses and models will help to inform resource managers as they use prescribed fire to achieve specific objectives including promoting biodiversity and ecosystem function.