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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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This data consists of presence/absence observations for post-fire conifer regeneration. The data also includes estimates of plot-level topography (slope, aspect), relativized differenced normalized burn ratio (RdNBR), post-fire climate, live basal area, and seed rain. These data support the following publication: Stewart, J.A.E., van Mantgem, P.J, Young, D.J.N., Shive, K.L., Preisler, H.K., Das, A.J., Stephenson, N.L., Keeley, J.E., Safford, H.D., Wright, M.C., Welch, K.R., Thorne, J.H. 2020. Influence of variable postfire climate and seed production on postfire conifer regeneration. Ecological Applications. https://doi.org/10.1002/eap.2280


    map background search result map search result map Data for Use in poscrptR Post-fire Conifer Regeneration Prediction Model Post-fire conifer regeneration observations for National Forest land in California (2009 - 2017) Post-fire conifer regeneration observations for National Forest land in California (2009 - 2017) Data for Use in poscrptR Post-fire Conifer Regeneration Prediction Model