Current Versions

Launch PostCRPT v.0.22

The pre-processed version of PostCRPT allows users to simply select a fire to see mapped predictions and download spatial data. Includes maps of probability of conifer regeneration, burn severity, high-severity patch size, and vegetation type. Includes all fires that burned from 2017-2021 in California and from 2017-2020 in western Oregon. Uses the latest version of the conifer regeneration prediction model (0.2b), which was trained on a larger postfire regeneration dataset, uses updated environmental predictors, and has improved predictive accuracy compared to previous versions (see the "About" tab of the tool for details). Includes a preliminary version of a customizable postfire reforestation prioritization user interface.

Launch PostCRPT v.0.123

The custom-input version of PostCRPT allows users to make predictions within weeks after a fire by uploading fire-perimeter and burn-severity data (e.g., from RAVG or created using Google Earth Engine). Includes postfire seed-production and weather scenarios and taxon-specific predictions for fir and pine. Uses an older version of the conifer regeneration prediction model (0.123). Accounts for effects of seed production only from species included in the Stewart et al. 2021 dataset.

Overview

The Postfire Conifer Reforestation Planning Tool (PostCRPT) seeks to answer the questions of where conifers will regenerate on their own after wildfire and, conversely, where conifer reforestation efforts might be targeted. The app is designed to simplify the process of creating predictive maps of postfire conifer regeneration.

PostCRPT predicts the probability of post-fire conifer regeneration under varying postfire precipitation and seed production scenarios. The predictive model was fit using data from 1,234 plots, spanning 19 wildfires, each measured five years after wildfire. PostCRPT predicts the probability that regeneration will occur within 4.4-m radius (60 m²) plots across the burn footprint. Refer to Stewart et al. (2021) for details.

The app requires the user to input a zip file containing a fire perimeter and a raster of burn severity (RdNBR). The RAVG website has both RdNBR and fire perimeter data sets available for all fires with at least 1,000 acres of National Forest land from 2007 to the present. A sample dataset is also available in the about tab of the tool.

Presentations and Briefs

• Stewart J.A.E., Young D.J.N. (2024) Evidence-Based Tools for Improved Reforestation Outcomes. Quarterly Meeting of the California Climate Hub. Virtual.

• Stewart J.A.E. (2023) Thirty-year trends in postfire conifer regeneration in California and western Oregon. 10th International Fire Ecology and Management Congress. Monterey, CA.

• van Mantgem P.J., Stewart J.A.E., Wright M.C. (2021) Rebuilding Forests After Fire. Southwest Climate Adaptation Science Center Webinar Series.

• Shive K.L., Coppoletta M. (2020) Predicting Spatial Patterns of Conifer Regeneration After Severe Wildfire: Implications for Restoration. California Fire Science Seminar Virtual Series.

• Brodie E.G., Stewart J.A.E., Winsemius S., Miller J.E.D., Latimer A.M., Safford H.D. (2022) Post-fire regeneration in California’s high elevation forests. California Fire Science Seminar Virtual Series.

• Coppoletta M. (2018) Spatial predictions of conifer regeneration after wildfire may help managers prioritize reforestation efforts. California Fire Science Consortium.

Accuracy

Confidence intervals for the PostCRPT statistical models are visualized using reliability diagrams (below). The reliability diagrams depict the performance of PostCRPT predictions when applied to new data, and were created using leave-one-fire-out cross-validation. Predictive performance is high in the all-Conifer and fir (Abies) models. The pine (Pinus) model has much room for improvement. See Stewart et al. (2021) for a full discussion.

Statistical Models

Seeking to follow the principle of Ockham’s razor, PostCRPT uses just six input variables to predict the probability of regeneration. The variables are seed availability, burn severity, historical precipitation, postfire precipitation anomaly, slope, and aspect. Other candidate variables either did not improve out-of-sample model performance or resulted in response functions that appeared to be the result of overfitting.

Citation

PostCRPT is a user interface for models developed by:

Stewart JAE, van Mantgem PJ, Young DJN, Shive KL, Preisler HK, Das AJ, Stephenson NL, Keeley JE, Safford HD, Wright MC, Welch KR & Thorne JH. (2021) Effects of postfire climate and seed availability on postfire conifer regeneration. Ecological Applications, 31(3).

PostCRPT was adapted for the web from the poscrptR package, developed in a collaboration between the US Geological Survey Western Ecological Research Center and the US Forest Service:

Wright MC, Stewart JAE, van Mantgem PJ, Young DJN, Shive KL, Preisler HK, Das AJ, Stephenson NL, Keeley JE, Safford HD, Welch KR, Thorne JH. 2020. poscrptR. R package version 1.0.0.

The models developed by Stewart et al. (2021) build on an earlier spatial model developed by Shive et al. (2018), analyses of sensitivity to postfire climate presented by Young et al. (2019), and an earlier field-based model developed by Welch (2016).

Shive KL, Preisler HK, Welch KR, Safford HD, Butz RJ, O’Hara KL, Stephens SL, 2018. From the stand scale to the landscape scale: predicting the spatial patterns of forest regeneration after disturbance. Ecological Applications, 28(6), pp.1626-1639.

Young DJN, Werner CM, Welch KR, Young TP, Safford HD, Latimer AM. 2019. Post-fire forest regeneration shows limited climate tracking and potential for drought-induced type conversion. Ecology 100:e02571.

Welch KR, Safford HD, Young TP (2016). Predicting conifer establishment post wildfire in mixed conifer forests of the North American Mediterranean‐climate zone. Ecosphere , 7(12), e01609.

Version History

• PostCRPT version 0.22 (pre-processed) – Released March, 2024. Includes a preliminary interface for postfire reforestation prioritization and planning, loosely based on methods from Coppoletta et al. (2022). Does not yet account for vegetation type in reforestation prioritization and planning tab; for the time being please see Coppoletta et al. (2022) for vegetation-type filtering methods.

• PostCRPT version 0.2 (pre-processed) – Released December, 2023. We’re working on updates to the underlying statistical models (including an expanded postfire-regen dataset) and batch processed PostCRPT predictions for all fires. This interface displays progress toward these objectives. The current coverage includes all fires that burned from 2017-2021 in California and from 2017-2020 in western Oregon. Unlike version 0.123, this version accounts for seed availability estimates from all conifer species mapped in CA and OR, not just the ones that are represented in the Stewart et al. (2021) dataset. Does not yet include taxon specific models for firs and pines (for the time being please see PostCRPT version 0.123 for taxon specific models).

• PostCRPT version 0.123 (custom input) – Released January, 2022. Allows users to incorporate the effects of multiple sequential fires. Uses the 2020 version of LEMMA GNN forest structure maps. Updates the Stewart et al (2021) regeneration model to use 2020 version of GNN structure maps. Adds useful warnings. Expands geographic coverage. Refines the spatial domain.

• PostCRPT version 0.1 (custom input) – Released January, 2021. This initial release of the app cannot account for multiple sequential burns occurring after 2012. It uses the original Stewart et al (2021) regeneration models and the 2012 version of GNN basal area maps. It was adapted for the web from poscrptR and incorporates only small modifications of the poscrptR code.

• The orginal version of the POstfire Spatial Conifer Regeneration Prediction Tool (POSCRPT) was developed by Kristen Shive and Haiganoush Preisler and was conceived of by Hugh Safford. This version lacked a graphical user interface and was composed of R and Python scripts and GIS files. It did not include taxon-specific predictions or postfire scenarios.