Spatial patterns analysis for natural disturbances consequences within the Hyrcanian mixed-beech (Fagus orientalis Lipsky) forests, Iran

One of the most prominent issues indicating the future quality and quantity of natural forest ecosystems is spatial pattern and distribution of disturbances consequences manifested as canopy gaps (CGs). The main purpose of this study was to illustrate the CGs distribution pattern in one of the Hyrcanian mixed-beech (Fagus orientalis Lipsky) forests, so-called preserved Glandroud forests. All CGs areas were measured based on full callipering on the basis of the radius method, and the coordinate of each CG was recorded in the forest. The univariate Ripley’s L-function, mark correlation function (MCF), and density function (DC) in turns were used for analyzing the spatial patterns, size correlation, and frequency of the CGs distribution at the observation scale. Furthermore, the statistical significance of all ordination analyses was tested by the Monte Carlo permutation method. The results showed that the frequency of small gaps (0-2 R), medium gaps (2-5 R), and in turns were almost 32%, 49% in the study forest. Only the small CGs distribution was clustered at a specified distance of 14–20 meters, though the other CGs size classes were completely randomly distributed in the forest. Integrating whole CGs size classes on the basis of Ripley’s L-function showed that the CGs spatial pattern in the studied forest was clustered at a distance of 40 m. According to the mark correlation function (MCF) and density correlation (DC) analyses, there in turns were found that the location and number of size classes in each aggregation were totally significantly independent and random based on the specific distance in the forest. Pertaining to these results, it is possible to introduce mosaics consisting of forest stands which may include specified tree stands with various tree species composition, different developmental stages, and structures in the forest. Therefore, each mosaic can be a base area for monitoring disturbances consequences and implementing optimum managements.