ArticleProceedings of the National Academy of Sciences of the United States of America2026
Tree growth after a major hurricane reflects predisturbance vigor rather than canopy damage.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Tree crown damage from disturbance events strongly influences forest demography, yet its effect on stem growth remains poorly quantified, with both positive and negative impacts reported. Hurricanes provide a powerful natural experiment to examine these dynamics, generating a broad range of structural damage across individuals and forest stands. Here we assess how crown damage from Hurricane María (2017) affected poststorm stem growth in a wet subtropical forest in Puerto Rico by combining airborne LiDAR with field measurements for 1,082 trees. Unlike previous studies, paired pre- and posthurricane LiDAR assessment enabled us to quantify crown damage as a continuous, objective variable across the canopy. Using a causal inference framework, we separated individual- from neighborhood-level effects, defined as the damage that occurred within a 5 m radius of each tree. Repeated stem growth censuses allowed direct comparison of individual growth responses before and after the hurricane. Across the community, posthurricane stem growth rates were similar to prehurricane values. Larger and more heavily damaged trees exhibited moderately reduced growth, while neighborhood crown damage and mortality had no detectable effect. However, these damage effects were smaller than the influence of prehurricane growth rates, indicating that prehurricane individual vigor outweighed biomass loss and competitive release in shaping growth responses. These findings highlight the resilience of surviving trees in sustaining carbon uptake after a severe disturbance and challenge the assumption of strong postdamage growth suppression that is embedded in dynamic vegetation models.
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