Abstract
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• The density and identity of tree neighbourhood is a key factor to explain tree mortality in forests, especially during the stem exclusion phase.
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• To understand this process, we built a logistic model for mortality in a spatially explicit context, including tree and neighbourhood predictors. Additionally, we used this model to build mortality risk frequency distributions. Finally, we tested this model against a random mortality model to predict the spatial pattern of the forest.
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• Annual mortality rate was high for pedunculate oak (Quercus robur, 6.99%), moderate for birch (Betula celtiberica, 2.19%) and Pyrenean oak (Q. pyrenaica, 1.58%) and low for beech (Fagus sylvatica, 0.26%). Mortality risk models for pedunculate oak and birch included stem diameter, tree height, canopy position and neighbourhood. Mortality was affected by the specific nature of the neighbourhood showing a clear competitive hierarchy: beech > pedunculate oak > birch. Models based on random mortality and logistic regression model were able to predict the spatial pattern of survivors although logistic regression predictions were more accurate.
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• Our study highlights how simple models such as the random mortality one may obscure much more complex spatial interactions.
Résumé
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• La densité et l’identité des arbres du voisinage sont un facteur clé pour expliquer la mortalité d’arbres dans les peuplements forestiers, surtout pendant la phase d’exclusion du tronc.
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• Pour comprendre ce processus, nous avons construit un modèle logistique pour la mortalité dans un contexte spatialement explicite, en incluant l’arbre et des prédicteurs de voisinage. De plus, nous avons utilisé ce modèle pour construire des distributions de fréquence de risque de mortalité. Finalement, nous avons évalué ce modèle par rapport à un modèle de mortalité aléatoire pour prédire la structure spatiale de la forêt.
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• Le taux de mortalité annuelle était élevé pour le chêne pédonculé (Quercus robur, 6.99 %), modéré pour le bouleau (Betula celtiberica, 2.19 %) et le chêne tauzin (Q. pyrenaica, 1.58 %) et faible pour le hêtre (Fagus sylvatica, 0,26 %). Les modèles de risque de mortalité pour le chêne pédonculé et le bouleau intégraient le diamètre du tronc, la hauteur de l’arbre, la position du houppier et le voisinage. La mortalité a été affectée par la nature spécifique du voisinage montrant une hiérarchie claire dans l’aptitude compétitive : hêtre > chêne pédonculé > bouleau. Les modèles basés sur une mortalité aléatoire et le modèle logistique ont été capables de prédire la répartition spatiale des survivants bien que les prédictions du modèle logistique étaient plus précises.
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• Notre étude montre comment des modèles simples basés sur une mortalité aléatoire peuvent obscurcir des interactions spatiales beaucoup plus complexes.
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Olano, J.M., Laskurain, N.A., Escudero, A. et al. Why and where do adult trees die in a young secondary temperate forest? The role of neighbourhood. Ann. For. Sci. 66, 105 (2009). https://doi.org/10.1051/forest:2008074
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Keywords
- inhomogeneous Ripley’s K
- logistic model
- temperate mixed forest
- succession
- tree mortality
Mots-clés
- Ripley’s K non homogènes
- modèle logistique
- forêt tempérée mixte
- succession
- mortalité des arbres