Data Spotlight: Predicting Conifer Regeneration After Fire and Drought

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Regrowth and Rebirth: How will conifers recover after extreme weather?

The Southwest's many distinct landscapes, ranging from arid deserts to high-precipitation conifer forests, provide habitat to the region’s many species of plants and animals. However, large, severe fires are becoming more frequent in the southwestern U.S., events which can kill trees over thousands of acres. It can be difficult for forests to recover after these fires because seeds need to travel long distances to recolonize burned areas. Further, the tree seedlings that emerge following fire may encounter unfavorably hot, dry conditions as droughts become more common in the region. This may allow for other forest or grassland types to emerge, affecting the plants, animals, and people who depend on the ecosystem services that conifer forests provide.

A Southwest CASC-funded project, led by Phillip van Mantgem and colleagues at the USGS Western Ecological Research Center (WERC) and co-investigators from the U.S. Forest Service, studied post-fire conifer regeneration to identify when and where forests might best recover, and to highlight areas where forest recovery might be aided by planting tree seeds and/or seedlings. The project collected seed count and post-fire conifer seedling observational data, matching these observations with available data on climate, topography, and burn severity, over multi-year periods in the Sierra Nevada of California to create a model predicting post-fire regeneration (Figure 1).

About the Data:

There are two datasets associated with this project:   

1. Seed source, not drought, determines patterns of seed production in Sierra Nevada conifers, contains annual counts of conifer seeds (firs, pines, and cedars) from 26 plots across two national parks over a 20-year period in addition to corresponding data regarding plot area, climate metrics, and parent tree density. The study region for these data can be found in Figure 2, which depicts the plot areas and National Weather Service Cooperative Observer Programs sites within Sequoia and Yosemite National Parks from which data were collected. Several plots experienced fire since they were established, totaling 9 post-fire observations.
2. Post-fire conifer regeneration observations for National Forest land in California (2009 – 2017) contain observations of conifer seedling presence collected 5 years following fire in study plots from 19 wildfires that burned from 2004–2012 in low elevation forests of California. In addition, this dataset contains estimates of plot-level topography (slope, aspect), relativized differenced normalized burn ratio (RdNBR) (or burn severity), post-fire climate, and live basal area (or tree density).

Access the Data:

• Download the data here and learn more about the project here.

• The dataset, "Seed source, not drought, determines patterns of seed production in Sierra Nevada conifers", was highlighted in a publication in Forest Ecology and Management which you can read here.
• The dataset, "Post-fire conifer regeneration observations for National Forest land in California (2009 - 2017)", was highlighted in a publication in Ecological Applications which you can read here.

Potential Data Application:

Seed source, not drought, determines patterns of seed production in Sierra Nevada conifers, were used to estimate the range of variability for conifer seed production in low elevation forests in the Sierra Nevada, finding that seed production was not greatly affected by a recent drought.

Post-fire conifer regeneration observations for National Forest land in California (2009 – 2017), were used to estimate post-fire recruitment for conifers across alternate climate and seed production conditions (Figure 1). The research team found that postfire climate and historical climate could similarly predict conifer regeneration. Statistically, was difficult to determine which climate metrics, postfire or historical, were most predictive.

The data were aggregated and analyzed to create a spatially explicit predictive model of post-fire conifer regeneration. The research team used the model to create the Postfire Spatial Conifer Restoration Planning Tool (POSCRPT). This tool predicts the probability of post-fire conifer regeneration for fire data supplied by the user (Figure 3). Access the tool here.