Seedling passage times in gaps and closed canopies reveal decades of understory persistence in a New England forest

The duration of tree seedling persistence in the understory varies greatly between forests and across environmental conditions within a forest ecosystem. To examine species-level variation in understory persistence and passage to the sapling life stage, we followed 5236 seedlings in single-tree canopy gaps and closed canopy conditions over three years and simulated seedling passage times and the number of seedlings required to produce one 1.5-m tall sapling of five common tree species in a hemlock–hardwood forest of Massachusetts, USA. Averaged across species, it took 26 years in gaps and 31 years under closed canopies to go from a first-year seedling to a 1.5-m sapling. Across species, the average number of seedlings needed for one sapling was 294 in gaps and 2674 in closed canopy environments. We observed high interspecific variation in passage times and number required for one sapling. Betula congeners and Pinus strobus took less time and significantly fewer individuals than Acer rubrum and Tsuga canadensis, which are generally regarded as more tolerant of understory conditions. The largest intraspecific difference in gaps versus closed canopy environments was for Quercus rubra, where we estimated the number of seedlings required to produce one sapling in closed canopies to be 172 times higher than in gaps. Stem breakage also increased the number of seedlings needed per sapling, especially in closed canopy environments. We evaluated our estimates in the lab by aging cross-sections obtained from seedlings in gap and closed canopy conditions. Compared to our empirical age-to-height relationships, most simulations tended to underpredict seedling age for a given height, suggesting that passage times may be even longer than our simulations indicated. Our study shows that trees can persist for decades in the seedling life stage, highlighting a need for better-parameterized recruitment processes in demographic forecasting.