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Kristen L Shive

Postfire Spatial Conifer Restoration Planning Tool (POSCRPT) R package (and web version) predicts the probability of post-fire conifer regeneration for fire data supplied by the user. The predictive model was fit using presence/absence data collected five years after wildfire, from 1,234 4.4m radius plots (60m2), spanning 19 wildfires in California. Please refer to Stewart et al. (2020) for more details. The poscrptR tool is designed to simplify the process of predicting post-fire conifer regeneration under different precipitation and seed production scenarios. The app was designed to use Rapid Assessment of Vegetative Condition (RAVG) data inputs. The RAVG website has both RdNBR and fire perimeter data sets...
Large, severe fires are becoming more frequent in many forest types across the western United States and have resulted in tree mortality across tens of thousands of hectares. Conifer regeneration in these areas is limited because seeds must travel long distances to reach the interior of large burned patches and establishment is jeopardized by increasingly hot and dry conditions. To better inform postfire management in low elevation forests of California, USA, we collected 5‐yr postfire recovery data from 1,234 study plots in 19 wildfires that burned from 2004–2012 and 18 yrs of seed production data from 216 seed fall traps (1999–2017). We used these data in conjunction with spatially extensive climate, topography,...
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These data support poscrptR (Wright et al. 2021). poscrptR is a shiny app that predicts the probability of post-fire conifer regeneration for fire data supplied by the user. The predictive model was fit using presence/absence data collected in 4.4m radius plots (60 square meters). Please refer to Stewart et al. (2020) for more details concerning field data collection, the model fitting process, and limitations. Learn more about shiny apps at https://shiny.rstudio.com. The app is designed to simplify the process of predicting post-fire conifer regeneration under different precipitation and seed production scenarios. The app requires the user to upload two input data sets: 1. a raster of Relativized differenced Normalized...
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