The areas in need of post-fire reforestation far exceed the resources managers have for planting. It is critical to understand where tree planting is most likely to be effective at achieving restoration goals so that limited resources can be put to use most effectively. We surveyed regenerating vegetation in 5 fires that burned and were planted roughly 10 years prior to our surveys so that the planting outcomes had time to become apparent. We surveyed over 200 paired plots straddling planting unit boundaries to quantify the effect of planting – relative to natural regeneration – along environmental gradients. The data revealed, among other environmental influences, a strong effect of shrubs that varied from negative to positive depending on how soon after fire the seedlings were planted. PReSET allows managers to predict postfire reforestation outcomes and planting benefit after wildfires that occur in Sierra Nevada mixed-conifer forests. It makes predictions of reforestation outcomes by spatially mapping the predictions of a multilevel linear model fit to a dataset of 182 field plots from reforestation projects following 6 wildfires in the Sierra Nevada of California, USA. The model is fit to log-transformed seedling density. The data and model fitting procedures are described in a manuscript in preparation.
Developed by Derek Young, Quinn Sorenson, and Andrew Latimer
Latimer Lab and Young Lab, Department of Plant Sciences, UC Davis
Funded by the Joint Fire Science Program