The combination of increasing fire-caused tree mortality and warmer, drier post-fire conditions is making forests in the western United States (West) vulnerable to ecological transformation. Yet, the relative importance of and interactions between these drivers of forest change remain unresolved, particularly over upcoming decades. Here we assess how the interactive impacts of changing climate and wildfire activity influenced conifer regeneration after 334 wildfires, using a novel dataset of post-fire conifer regeneration from 10,230 field plots. Our findings highlight declining regeneration capacity across the West over the past four decades for the eight dominant conifer species studied. Post-fire regeneration is sensitive to high-severity fire, which limits seed availability, and post-fire climate, which influences seedling establishment and survival. In the near-term, projected differences in recruitment probability between low- and high-severity fire scenarios were larger than projected impacts of climate change for most species, suggesting that reductions in fire severity, and resultant impacts on seed availability, could partially offset expected climate-driven declines in post-fire regeneration. Across 40–42% of the study area, we project post-fire conifer regeneration to be likely following low-severity but not high-severity fire under future climate scenarios (2031–2050). However, increasingly warm, dry climate conditions are projected to eventually outweigh the influence of fire severity and seed availability. The percent of the study area considered unlikely to experience conifer regeneration, regardless of fire severity, increased from 5% in 1981–2000 to 26–31% by mid-century, highlighting a limited time window over which management actions that reduce fire severity may effectively support post-fire conifer regeneration.

This archive includes field data and various spatial datasets used in Davis et al. (2023). 

Individual datasets in the Dryad archive include the following:

1) Field data. This data includes post-fire regeneration density for eight conifer species from the western US that were surveyed in plots 2–30 years following fire. Predictors that were used in the manuscript "Reduced fire severity offers near-term buffer to climate-driven declines in conifer resilience across the western United States" are included with each plot including climate data, fire severity, heat insolation load index, surrounding tree cover, and distance to nearest live seed source. The dataset is a compilation of many datasets. The field methods performed to collect the data varied by dataset/study and are described in detail in each individual study. For a list of the publications that produced each individual dataset please see the supplemental information Table S1 from Davis et al. 2023, the provided .csv file called "data_dryad_contributor_key.csv" or the definitions for each of the values of the contributor id column in the metadata. 

2) Statistical models. The final models for recruitment probability for each species and for all species combined are included as .rds files. 

3) Projections of recruitment probability made with the final models for 10-years post-fire under four climate scenarios (1981–2000, 2001–2020, 2031–2050 RCP 4.5, 2031–2050 RCP 8.5) and two fire severity scenarios (low severity: 10 m to a seed source, 30% surrounding live tree cover within 300-m radius of plot, relativized burn ratio (RBR) of 100; high severity: 150 m to a seed source, 10% surrounding live tree cover within 300-m radius of plot, RBR of 400). Plot size in projections is set to 100 m² so the projections can be interpreted as the probability of at least one seedling regenerating by 10 years post-fire in a 100 m² plot under the given climate and fire severity scenario, which is equivalent to a density of 100 trees ha^-1 or around 40 trees acre^-1. Please note that the threshold probability at which recruitment is considered likely varies between species due to differences in the models. Therefore when interpreting the probabilities it is not appropriate to compare raw probabilities between species. It is best to interpret the probabilities in light of the provided threshold probabilities above which recruitment is most likely.