Relationships between Species Diversity and Biomass in Mountainous Habitats in Zagros Rangeland (Case Study: Baneh, Kurdistan, Iran)

Abstract

Species diversity, richness and biomasses (aboveground biomass) and their relationships are the key variables of ecosystems. This study was conducted to determine the relationship of Species Diversity (SD) and Species Richness (SR) with Above-Ground Biomass (AGB) at a local scale at 5 different habitats (shrubland, forbland, grassland, shrub-forbland and forb-shrubland) in Zagros mountains in west of Iran (2015). For each habitat, 50 plots (2m²) were determined. SD was estimated by Shannon–Wiener's index, SR was defined as the number of species per plot and AGB was detected per unit of area (gm^-2). Results indicated that all of relationship patterns existed in these habitats. The unimodal relationship was found in shrub-forbland and forb-shrubland whereas the relationship of SD/SR vs.AGB in shrubland was linear and negative, and in forbland and grassland, it was positive. There were many complex and variable mechanisms dealing with the SD/SR vs. AGB relationship. The unimodal relationship indicates that SR and SD peaked at intermediate levels of AGB, and it is an inherent attribute of the spatially heterogeneous habitats affected by life forms, micro-sites, facilitation and competition. The positive linear was related to positive response of SD, SR and AGB to environmental factors or because of any reason (grazing, disturbance level…), the AGB may not reach to the highest possible level; thus, it may only show a positive relationship. When SD and AGB are affected in the opposite directions by environmental factors such as soil fertility, negative patterns may be caused. The negative pattern represents short gradients indicating a similar species composition among plots. Greater R² in SD vs. AGB than SR vs. AGB at all habitats showed that evenness and richness (components of SD) are more important than just SR; thus, SD vs. AGB relationship is better and more reliable to predict the variations (SD and AGB).