Incorporating Shrub Neighborhood Dynamics to Predict Forest Succession Trajectories in an Altered Fire Regime

As fire regimes shift in western North America, patches of stand-replacing fire are becoming larger. Forest succession in these patches is not well understood. There is concern that competition with rapidly reestablishing shrubs, combined with dispersal limitation, may delay or impede conifer recovery and/or shift tree composition toward shade-tolerant species. However, tree–shrub interactions and shrub neighborhood dynamics have not been closely examined. To investigate the patterns and processes determining forest recovery after severe wildfire, we developed a data-driven simulation model that we use to predict conifer emergence above the shrub canopy. Our model results showed that ponderosa pine ( Pinus ponderosa ) emerged at a faster rate than white fir ( Abies concolor ) under whitethorn ceanothus ( Ceanothus cordulatus ) and deerbrush ( Ceanothus integerrimus ), and at a similar rate under greenleaf manzanita ( Arctostaphylos patula ). Across all shrub species, ponderosa pine had a relative advantage over fir in the period between conifer establishment and peak shrub competition, requiring a mean of 16 years for 50% of individuals to emerge compared to 22 years for fir. Fir emergence rates then surpassed those of pine, leading to similar overall emergence by simulation end: 83% ± 7% for pine and 82% ± 7% for fir. These results show that, on balance, shrub neighborhood dynamics do not produce an ecological filter favoring firs for the initial cohort of established seedlings, but emergence patterns are sensitive to shrub species. Further applications of this data-driven simulation framework could improve understanding of other important components of post-fire succession.