Assessing the state of forest health in Oriental beech (Fagus orientalis L.) dominated forests in Iran

Abstract

We assessed landscape-scale forest health in northern Iran based on the Forest Health Monitoring (FHM) method. Using five plot clusters, we collected, analyzed, and reported information on the four key selected indicators (i.e., tree biodiversity, crown condition, natural regeneration, and deadwood) in FHM. To obtain a numerical value of forest health to make the indicators operational we used an analytical networking process to assess the contribution of each indicator to forest health. The results demonstrated that tree species diversity and species evenness were high (Shannon–Wiener index= 2.11; Simpson index= 0.8; Pielou index= 0.76), but species richness was at an intermediate level in the forest (Margalef index= 2.81).  In terms of crown condition, with an average crown diameter of 7.1 m, the results of crown dieback classification showed that the most-healthy class had the highest frequency with 65.2% in the study area. The mean density of natural regeneration was 273 individuals ha^-1 and 80% of which were represented by healthy seedlings. Total deadwood was 44.12 m³ ha^-1 and was formed by 168 individuals per ha^-1. A great contribution to the total deadwood in number (89%) falling in the lower DBH class (<50 cm), representing 51% by volume. From the point of wood decay, Class 1 (least decomposed) had the most numerous and the most significant volume of deadwood. The assessment results of FHM show that the major indicator influencing forest health is tree diversity which contributes 61%. The remaining three indicators include crown condition, natural regeneration, and deadwood contributing 21.7%, 13%, and 4.4%, respectively. It must be highlighted that forest health monitoring information in temperate Hyrcanian forests is currently not available, and therefore, this paper presents the first experimental study carried out. The findings of this study are required to assess the state of current health and identify trends that will be used in the decision-making process for better management of the forests.