Using a Machine Learning Approach to Classify the Degree of Forest Management

A prerequisite for sustainable forest management is knowing the total diversity and how management affects forests. Both are poorly studied and relate to canopy diversity and comparison with primary forests. From 2001-2004, we fogged beetles from oaks in primary and disturbed, managed sites in Bialowieza (Eastern Poland) and also in distant age-class forests. Using a machine learning (ML) method (elastic net), we identified a beetle signature based on the species abundance distribution to distinguish these forest types. The beetle communities from 2001 served as training data, with 21 signature species correctly assigning the oaks to primary and different managed forests. However, the predictive quality of the signature decreased with each year due to high spatio-temporal heterogeneity and beta diversity. To improve the power of the signature, we combined the data from all years to calculate a more general model. Due to its greater complexity, this model identified 60 species that correctly classified both the studied forests and foreign forests in Central Europe, increasing the possibility of a general classification. Further research is needed to determine whether it is possible to establish a general signature-based index on a large number of samples from different years and forest types.