Mechanistic and statistical approaches to predicting wind damage to individual maritime pine (Pinus pinaster) trees in forests

Abstract

Maritime pine (Pinus pinaster Aiton) forests in the Aquitaine region, southwestern France, suffered catastrophic damage from storms Martin (1999) and Klaus (2009), and more damage is expected in the future due to forest structural change and climate change. Thus, developing risk assessment methods is one of the keys to finding forest management strategies to reduce future damage. In this paper, we evaluated two approaches to calculate wind damage risk to individual trees using data from different damage data sets from two storm events. Airflow models were coupled either with a mechanistic model (GALES) or a bias-reduced logistic regression model to discriminate between damaged and undamaged trees. The mechanistic approach was found to successfully discriminate the trees for different storms but only in locations with soil conditions similar to where the model parameters were obtained from previous field experiments. The statistical approach successfully discriminated the trees only when applied to similar data as that used for creating the models, but it did not work at an acceptable level for other data sets. One variable, decade of stand establishment, was a significant variable in all statistical models, suggesting that site preparation and tree establishment could be a key factor related to wind damage in this region.