- Stewart, Joseph AE;
- van Mantgem, Phillip J;
- Young, Derek JN;
- Shive, Kristen L;
- Preisler, Haiganoush K;
- Das, Adrian J;
- Stephenson, Nathan L;
- Keeley, Jon E;
- Safford, Hugh D;
- Wright, Micah C;
- Welch, Kevin R;
- Thorne, James H
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, forest composition, and burn severity surfaces to construct taxon-specific, spatially explicit models of conifer regeneration that incorporate climate conditions and seed availability during postfire recovery windows. We found that after accounting for other predictors both postfire and historical precipitation were strong predictors of regeneration, suggesting that both direct effects of postfire moisture conditions and biological inertia from historical climate may play a role in regeneration. Alternatively, postfire regeneration may simply be driven by postfire climate and apparent relationships with historical climate could be spurious. The estimated sensitivity of regeneration to postfire seed availability was strongest in firs and all conifers combined and weaker in pines. Seed production exhibited high temporal variability with seed production varying by over two orders of magnitude among years. Our models indicate that during droughts postfire conifer regeneration declines most substantially in low-to-moderate elevation forests. These findings enhance our mechanistic understanding of forecasted and historically documented shifts in the distribution of trees.