Improved models of harvest-induced bark damage

Abstract

We provided a precise quantitative analysis of the factors at the origin of bark damage during harvesting operations and developed a model able to predict them accurately. The major factors were the distance of trees to skid trails, the intensity of removals, the harvesting system as well as the interactions between the distance of trees to skid trails with harvesting systems, the average skidding distance, the tree species and tree height. During timber harvesting, trees in the remaining stand may suffer bark damage resulting from tree-felling or log manipulation. Although a multitude of case studies and empirical observations provide qualitative and quantitative information with respect to the potential causal factors, the basic quantitative relationship between major factors of influence and the resulting degree of bark damage remains largely unclear. The objective was to provide a precise quantitative analysis of impact factors explaining the occurrence of bark damage during harvesting operations. Three different modelling approaches were tested: boosted regression tree (BRT), a generalised linear mixed effects model (GLMM) and Bayesian Markov chain Monte Carlo generalised linear mixed models (MCMCglmm). The major factors with a significant impact on the occurrence of bark damage were the distance of trees to skid trails, the intensity of removals, the harvesting system and the interaction term between the distance of trees to skid trails with harvesting systems, average skidding distance, tree species and tree height. The final model includes the relevant major factors impacting on the infliction of bark damage during practical harvesting operations. Furthermore, it discriminates well with respect to the occurrence of bark damage, and it provides managers with a rational and conclusive tool for optimising harvesting operations.