Reconstructing harvesting diameter distribution from aggregate data

Abstract

Distribution of removed trees among species and diameter classes is usually used to characterize selection harvesting. This information is, however, rarely available when analysing past time series. The challenge is then to determine the minimal level of information required to characterize harvests. We tested in this work whether an algorithm based on the total number of trees and volume to be removed enabled the reconstruction of harvesting diameter distributions, when combined with stand diameter distribution before harvest. We tested the algorithm against empirical data in the case of selection system, comparing distributions by χ² tests, and extended its evaluation to more diversified theoretical situations. Observed harvesting distributions were well-reconstructed in most empirical cases, with better results when considering mean simulated distributions. The algorithm was also effective for other thinning and harvesting strategies: low thinning, thinning of dominants, and mechanical thinning, whatever the structure of the stand before being cut. Total number of trees and volume harvested appeared thus sufficient to reconstruct DBH distribution of removed trees in diverse situations, provided that the distribution before harvest was known. This algorithm, therefore, enables the simulation of complex harvesting operations with minimal information.