4.5 Article

Empirical Modelling of Stem Cambium Heating Caused by Prescribed Burning in Mediterranean Pine Forest

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FIRE-SWITZERLAND
卷 6, 期 11, 页码 -

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MDPI
DOI: 10.3390/fire6110430

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wildfire; fire effects; cambium damage; forest management; vulnerability; hurdle model; Pinus nigra; Pinus pinaster

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Little is known about the interactions between variables involved in the post-fire response of Mediterranean pine species to prescribed burning. A study was conducted to develop an empirical model and assess the influence of various factors on the probability of stem cambium damage. The results showed significant differences in cambium damage between burn seasons and highlighted the importance of bark thickness and crown scorch in protecting the cambium. Prescribed burning generally resulted in a low level of cambium damage in Mediterranean pine species.
Little is known about the interactions between the variables involved in the post-fire response of Mediterranean pine species to prescribed burning (PB). Thus, it is essential to develop an empirical model in order to assess the influence of tree and stand attributes, burn season, and fire severity on the probability of stem cambium damage occurring. Prescribed burnings were conducted in different seasons and areas covering a wide climatic and ecological range. Potential explanatory variables were measured. A random effects hurdle model framework was used to evaluate the temperature duration above 60 degrees C as a proxy for stem cambium damage at tree scale. The results showed significant differences in cambium damage between the PB seasons. Pinus nigra was more resistant than other pine species. Bark thickness was critical for protecting cambium. Volume of crown scorch, percentage of stem scorch, and maximum outer bark temperature were directly related to temperature duration above 60 degrees C in the cambium. Prescribed burning conducted under tree canopy in Mediterranean pine species generally results in a low level of cambium damage. Empirical models could help managers to predict the effects of PB and thus select the most suitable prescriptions.

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