Logistic regression models for wind and snow damage in northern Finland based on the National Forest Inventory data

The susceptibility of forest stands to wind and snow damage was predicted w ith basic logistic regression models from a large systematic sample and wit h conditional logistic regression from a smaller matched study. Data was fr om the 8th National Forest Inventory, recorded in northern Finland during 1 992-1994. The current investigation adds to previous studies by considering the role of individual explanatory variables more closely instead of only predicting the absolute risk. From both, the basic logistic and the matched models, we obtain odds ratios for explanatory variables, which estimate th e so called relative risk; i.e. increase in the damage risk by a particular factor. In our study, matching by cluster or municipality was used to cont rol the possible effects of stochastic local factors that were not quantifi ed, such as wind and snow conditions, on the odds ratios. According to the basic logistic regression model, the susceptibility of a s tand to wind damage was increased by: large mean diameter, high stand age, seed-tree cutting, special cutting (cutting for ditches, roads or power lin es, or sanitation cutting after damage), and decreasing temperature sum. Si gnificant exposure factors for snow damage were: decreasing temperature sum s, elevation >200 m a.s.l., conifer-dominance, mineral soil, undrained, unt hinned, development stage pole stand and no proximity to stand edge. The distribution of damaged stands at the landscape level could not be pred icted well with the basic logistic regression model, since it is only conce rned with the susceptibility component of the probability, and not the occu rrence of the damaging agents; i.e. unfavourable weather. Odds ratios can b e used as an alternative to probabilities for comparing the damage risks re lated to combinations of exposure factors from site, stand and forest manag ement. The matched approach showed promise as an alternative method for modelling the odds ratios. The results suggest that matching was useful for wind dama ge, but less so for snow damage. If the incidence of winds can be taken int o account in some way, as with the matched model, the effect of exposure fa ctors on damage risk can be estimated more precisely. Wind damage is a more stochastic phenomenon than snow damage: at least in northern Finland, snow damage may be more tied to more regularly occurring climatic factors at th e site than wind damage. Consequently, the basic logistic regression model where these can be included as explanatory variables was better for snow da mage than the matched model. (C) 2000 Elsevier Science B.V. All rights rese rved.