Variation of Secondary Metabolite Profile of Zataria multiflora Boiss. Populations Linked to Geographic, Climatic, and Edaphic Factors

Geographic location and connected environmental and edaphic factors like temperature, rainfall, soil type, and composition influence the presence and the total content of specific plant compounds as well as the presence of a certain chemotype. This study evaluated whether geographic, edaphic, and climatic information can be utilized to predict the presence of specific compounds from medicinal or aromatic plants. Furthermore, we tested rapid analytical methods based on near infrared spectroscopy (NIR) coupled with gas chromatography/flame ionization (GC/FID) and gas chromatography/mass spectrometry (GC/MS) analytical methods for characterization and classification metabolite profiling of Zataria multiflora Boiss. populations. Z. multiflora is an aromatic, perennial plant with interesting pharmacological and biological properties. It is widely dispersed in Iran as well as in Pakistan and Afghanistan. Here, we studied the effect of environmental factors on essential oil (EO) content and the composition and distribution of chemotypes. Our results indicate that this species grows predominantly in areas rich in calcium, iron, potassium, and aluminum, with mean rainfall of 40.46 to 302.72 mm·year(−1) and mean annual temperature of 14.90°C to 28.80°C. EO content ranged from 2.75% to 5.89%. Carvacrol (10.56–73.31%), thymol (3.51–48.12%), linalool (0.90–55.38%), and p-cymene (1.66–13.96%) were the major constituents, which classified 14 populations into three chemotypes. Corresponding to the phytochemical cluster analysis, the hierarchical cluster analysis (HCA) based on NIR data also recognized the carvacrol, thymol, and linalool chemotypes. Hence, NIR has the potential to be applied as a useful tool to determine rapidly the chemotypes of Z. multiflora and similar herbs. EO and EO constituent content correlated with different geographic location, climate, and edaphic factors. The structural equation models (SEMs) approach revealed direct effects of soil factors (texture, phosphor, pH) and mostly indirect effects of latitude and altitude directly affecting, e.g., soil factors. Our approach of identifying environmental predictors for EO content, chemotype or presence of high amounts of specific compounds can help to select regions for sampling plant material with the desired chemical profile for direct use or for breeding.