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Annals of Forest Science

, Volume 64, Issue 7, pp 733–742 | Cite as

Predicting stand damage and tree survival in burned forests in Catalonia (North-East Spain)

  • José Ramón GonzálezEmail author
  • Antoni Trasobares
  • Marc Palahí
  • Timo Pukkala
Original Article

Abstract

The study developed models for predicting the post-fire tree survival in Catalonia. The models are appropriate for forest planning purposes. Two types of models were developed: a stand-level model to predict the degree of damage caused by a forest fire, and tree-level models to predict the probability of a tree to survive a forest fire. The models were based on forest inventory and fire data. The inventory data on forest stands were obtained from the second (1989–1990) and third (2000–2001) Spanish national forest inventories, and the fire data consisted of the perimeters of forest fires larger than 20 ha that occurred in Catalonia between the 2nd and 3rd measurement of the inventory plots. The models were based on easily measurable forest characteristics, and they permit the forest manager to predict the effect of stand structure and species composition on the expected damage. According to the stand level fire damage model, the relative damage decreases when the stand basal area or mean tree diameter increases. Conversely, the relative stand damage increases when there is a large variation in tree size, when the stand is located on a steep slope, and when it is dominated by pine. According to the tree level survival models, trees in stands with a high basal area, a large mean tree size and a small variability in tree diameters have a high survival probability. Large trees in dominant positions have the highest probability of surviving a fire. Another result of the study is the exceptionally good post-fire survival ability of Pinus pinea and Quercus suber.

damage model fire management logistic function tree mortality survival model 

Prédiction des dommages au peuplement et de la survie des arbres dans les forêts brûlées en Catalogne

Résumé

L’étude développe des modèles pour prédire la survie des arbres après feu en Catalogne. Les modèles sont appropriés à des objectifs de planification en forêt. Deux types de modèles ont été développés : un modèle au niveau des peuplements pour prédire le niveau des dommages causés par les feux de forêts, et des modèles arbre-centrés pour prédire la probabilité de survie à un feu de forêt. Les modèles sont basés sur les données de l’inventaire des forêts et des feux. Les données de l’inventaire des peuplements forestiers ont été obtenues à partir du deuxième (1989–1990) et du troisième (2000–2001) inventaire forestier espagnol, et les données sur les feux proviennent de périmètres de feux de forêts supérieurs à 20 ha qui se sont produits en Catalogne entre les deuxièmes et troisièmes mesures dans les placettes d’inventaire. Les modèles sont basés sur des caractéristiques facilement mesurables, et permettent au praticien forestier de prédire l’effet de la structure du peuplement et de la composition en espèces sur les dégâts. D’après le modèle de dommage au niveau peuplement, les dégâts diminuent lorsque la surface terrière ou le diamètre moyen des arbres augmente. Inversement, les dégãts augmentent en cas de forte variabilité de dimension des arbres, quand le peuplement est localisé sur une pente forte ou quand il est principalement composé de pins. Selon les modèles de survie arbre-centrés, les arbres de peuplements à forte surface terrière, forte dimension moyenne des arbres et faible variabilité des diamètres, présente la plus forte probabilité de survie au feu. Les grands arbres dominants présentent la plus forte probabilité de survivre au feu. Un autre résultat de cette étude est l’exceptionnelle capacité de survie après feu de Pinus pinea et Quercus suber.

modèle de dommage gestion du feu fonction logistique mortalité des arbres modèle de survie 

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Copyright information

© Springer S+B Media B.V. 2007

Authors and Affiliations

  • José Ramón González
    • 1
    Email author
  • Antoni Trasobares
    • 2
  • Marc Palahí
    • 1
  • Timo Pukkala
    • 2
  1. 1.Centre Tecnològic Forestal de CatalunyaSolsonaSpain
  2. 2.ForEcoTechnologiesBarcelonaSpain

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