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ISSN : 2287-8327
Biotic and spatial factors potentially explain the susceptibility of forests to direct hurricane damage
Andrew C. Millington (Flinders University College of Science and Enginee)
Charles W. Lafon (Texas A&M University College Station)
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Abstract
Background: Ecologists continue to investigate the factors that potentially affect the pattern and magnitude of tree damage during catastrophic windstorms in forests. However, there still is a paucity of research on which trees are more vulnerable to direct damage by winds rather than being knocked down by the fall of another tree. We evaluated this question in a mixed hardwood–softwood forest within the Big Thicket National Preserve (BTNP) of southeast Texas, USA, which was substantially impacted by Hurricane Rita in September 2005. Results: We showed that multiple factors, including tree height, shade-tolerance, height-to-diameter ratio, and neighborhood density (i.e., pre-Rita stem distribution) significantly explained the susceptibility of trees to direct storm damage. We also found that no single factor had pervasive importance over the others and, instead, that all factors were tightly intertwined in a complex way, such that they often complemented each other, and that they contributed simultaneously to the overall susceptibility to and patterns of windstorm damage in the BTNP. Conclusions: Directly damaged trees greatly influence the forest by causing secondary damage to other trees. We propose that directly and indirectly damaged (or susceptible) trees should be considered separately when assessing or predicting the impact of windstorms on a forest ecosystem; to better predict the pathways of community structure reorganization and guide forest management and conservation practices. Forest managers are recommended to adopt a holistic view that considers and combines various components of the forest ecosystem when establishing strategies for mitigating the impact of catastrophic winds.
- keywords
- Disturbance, Hurricane Rita, Life history traits, Neighborhood density, Spatial point pattern analysis
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