samaneh Tajik; shamsollah Ayoubi; jahangir khajehali; shaban shataee
Abstract
Introduction: Soil microorganisms are the essential part of forest ecosystems which play a key role on soil nutrient changes. The biological activity in soil is largely concentrated in topsoil. Despite the small volume of microorganisms in soil, they have a key role on nitrogen, sulphur and phosphorous ...
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Introduction: Soil microorganisms are the essential part of forest ecosystems which play a key role on soil nutrient changes. The biological activity in soil is largely concentrated in topsoil. Despite the small volume of microorganisms in soil, they have a key role on nitrogen, sulphur and phosphorous cycles and the decomposition of organic residues. Soil microorganisms have been identified as the sensitive indicators for soil quality. The composition of microorganisms and their fractional activities in soils significantly affect biochemical cycles, carbon sequestration and soil fertility. As soil microbial communities respond differently respected to environmental conditions, it seems that variation in forest ecosystem could significantly affect microbial community. Plants are one of the important variables for assessing soil microbial communities which their effect is related to root secretions and litter decomposition. The phospholipid fatty acid (PLFA) analysis is one of the methods that can overcome the problem of selective growth of microorganisms on culture media which is a major defect in the identification of microbial diversity. The objective of this study was to investigate the effects of different tree compositions and soil properties on soil microbial community using PLFA analysis approach.
Materials and Methods: This study was conducted in ShastKalate forest, an experimental forest station of Gorgan University, located at eastern Caspian region, North of Iran (36° 43′ 27″ N ,54°24′ 57″ E). Eleven different tree compositions were selected and the surface soils collected from 0-10 cm depth of 33 plots. Soil samples were air dried and passed through a 2mm sieve. Then one portion of the sieved samples was used for physical and chemical analyses. The other portion was rewetted to 65% of field capacity and incubated at 37 °C for 3 days to analyses PLFA. Soil particle size distribution (clay, silt and sand) was determined using the hydrometer method. Soil pH in 1/ 2.5 soil to water suspension and electrical conductivity (EC) in the same extract were measured.. Calcium carbonate equivalent (CCE),soil organic carbon (OC) and total nitrogen (TN) was determined, too. Biological analyzes including soil microbial respiration determination and PLFA analysis were carried out. The PLFA detection and quantification were performed with a Hewlett-Packard 5890 Series II gas chromatograph (GC) equipped with an HP Ultra 2 capillary column and a flame ionization detector. The normalized data were employed for Pearson's correlation analysis and ANOVA to determine the effects of soil properties and different tree compositions on soil microbial community.
Results and Discussion: Gram+ and Gram- bacteria were the most microorganisms and protozoa were the least microorganisms in soil samples. The results of the correlation between soil properties and microorganisms showed that OC and TN had significant positive effects on microorganism’s communities. EC was significantly correlated with Arbuscular Mycorrhizal Fungi (AMF), actionbacterial, protozoa and total PLFA. In addition, soil microorganisms and total PLFA were significantly correlated with soil respiration. However, there was no significant correlation between TN and OC with protozoa. The correlations between pH, EC, CCE and sand with protozoa were significantly negative, but in the case of silt, this correlation was significantly positive. Different studies showed that soil organic matter is the main nutrient source for soil microorganisms and soil microorganisms are also the essential part of C and N cycles. The effects of tree compositions on 16:0 10-methyl, 18:2 w6c, 20:2 w6c, 20:3 w6c and 20:4 w6c were significant(p
S. Tajik; Sh. Ayoubi; F. Nourbakhash
Abstract
Enzymes are so crucial in the mineralization process of organic material. Information of the soil enzymes activity is used in determining of the soil microbial properties and they are also important in soil health and quality. Topographic attributes, soil properties and soil enzymes are associated together. ...
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Enzymes are so crucial in the mineralization process of organic material. Information of the soil enzymes activity is used in determining of the soil microbial properties and they are also important in soil health and quality. Topographic attributes, soil properties and soil enzymes are associated together. Hence, it is essential to know how these parameters affect on the soil enzymes activity. This study has been implemented in hilly region of Semiroum district located at southern Isfahan province, to develop a regression model between soil enzymes activity and soil and topographic characteristics. Mean annual temperature and precipitation in the studied area is 10.6°C and 350 mm, respectively. Soil sampling was done in a systematic randomly manner from the 0-10 cm surface layer. Topographic attributes were calculated by the digital elevation model with 10×10 m spatial resolution. Soil properties were determined by laboratory analysis. Multiple regression models between these parameters and soil enzymes activity were established and then the predictive models were validated using 20% of data. Results indicated soil parameters explained 33-63% of total variability of soil enzymes activity in the studied site. Topographic attributes explained 14- 15 %, and a combination of soil and topographic characteristics could explain 33-67% of total variability of soil enzymes activity. Therefore, the use of a combined data set of soil properties and topographic attributes could provide the powerful models for predicting of soil enzymes activity. These results confirmed that soil enzyme activity in the studied area is influenced by soil and topographic attributes synchronously. The results of validation ascertained that the predictors were unbiased and sufficiently accurate.