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Data from: A mechanistic and empirically-supported lightning risk model for forest trees

Citation

Gora, Evan et al. (2020), Data from: A mechanistic and empirically-supported lightning risk model for forest trees, Dryad, Dataset, https://doi.org/10.5061/dryad.c59zw3r48

Abstract

  1. Tree death due to lightning influences tropical forest carbon cycling and tree community dynamics.  However, the distribution of lightning damage among trees in forests remains poorly understood. 
  2. We developed models to predict direct and secondary lightning damage to trees based on tree size, crown exposure, and local forest structure.  We parameterized these models using data on the locations of lightning strikes and censuses of tree damage in strike zones, combined with drone-based maps of tree crowns and censuses of all trees within a 50-ha forest dynamics plot on Barro Colorado Island, Panama. 
  3. The likelihood of a direct strike to a tree increased with larger exposed crown area and higher relative canopy position (emergent > canopy >>> subcanopy), whereas the likelihood of secondary lightning damage increased with tree diameter and proximity to neighboring trees.  The predicted frequency of lightning damage in this mature forest was greater for tree species with larger average diameters.
  4. These patterns suggest that lightning influences forest structure and the global carbon budget by nonrandomly damaging large trees.  Moreover, these models provide a framework for investigating the ecological and evolutionary consequences of lightning disturbance in tropical forests.

Synthesis: Our findings indicate that the distribution of lightning damage is stochastic at large spatial grain and relatively deterministic at smaller spatial grain (<15 m).  Lightning is more likely to directly strike taller trees with large crowns and secondarily damage large neighboring trees that are closest to the directly struck tree.  The results provide a framework for understanding how lightning can affect forest structure, forest dynamics, and carbon cycling.  The resulting lightning risk model will facilitate informed investigations into the effects of lightning in tropical forests.