Spatially explicit models of seed availability improve predictions of conifer regeneration following the 2018 Carr Fire in northern California

Abstract

For many conifer species in dry conifer forests of North America, seeds must be present for postfire regeneration to occur, suggesting that seed dispersal from surviving trees plays a critical role in postfire forest recovery. However, the application of tree fecundity and spatial arrangement to postfire conifer recovery predictions have only recently become more common, and is often included at relatively coarse scales (i.e., 30 meters). In this study, we mapped surviving trees using lidar and created a spatially explicit estimate of seed density (seed shadows) with 10 m, 50 m, and 100 m median dispersal distances. We estimated the number of seeds produced by each tree using allometric relationships between tree size and fecundity. Along with the seed shadows, we used a suite of topographic variables as inputs to negative binomial hurdle models to predict conifer seedling abundance in 131 plots following the 2018 Carr Fire in northern California, USA. We compared models using each of the seed shadows to each other as well as to a model using the distance to the nearest surviving tree, which served as a baseline. All model formulations indicated that estimated seed availability was positively associated with conifer regeneration. Despite the importance of seed availability plays in regeneration and the substantial differences in seed availability represented by the different seed shadows in our analysis, we found surprisingly little difference in model performance regardless of which seed shadow was used. However, the models employing seed shadows outperformed the models with distance to the nearest live tree. Although we have demonstrated a modest improvement in predicting postfire conifer regeneration, the uncertainty in our results highlights the importance of tree detection and classification in future studies of this kind. Future studies may find it useful to consider other factors such as predation, site suitability, and seed mortality as potential drivers of discrepancies between total and realized dispersal kernels.