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Inferring biological soil crust successional stage using combined PLFA, DGGE, physical and biophysiological analyses

Inferring biological soil crust successional stage using combined PLFA, DGGE, physical and biophysiological analyses

Arid areas are highly sensitive to climate change and are ideal model systems to study the potential impact of climate change on species' community structure. Biological soil crust (BSC) formation plays an ecological role in a number of key processes in the development of dry ecosystems. It was hypothesized that BSC succession and function are affected by aridity level and limited by rainfall. Furthermore, it is possible to infer the direction of the BSC succession based on aridity level, and the latter can imitate future climate change scenarios. The objectives of this study were to investigate the microbial biomass and diversity of the BSC structure in three sites differing in aridity level (semiarid, arid and hyper-arid), by combining physical and biophysiological measurements with 16S rRNA gene fragment and phospholipid fatty acid (PLFA) analyses. Physical and biophysiological parameters of the BSC were significantly influenced by aridity level. Total protein and polysaccharide contents were strongly correlated with total PLFA-based microbial biomass. Gram-positive biomarkers and microbial biomass were significantly higher in the wettest (semiarid) site than in the driest (hyper-arid) one. Multivariate-analysis based ordination of the PLFA data segregated the cluster of semiarid data from that of the hyper-arid site, while data from the arid site were dispersed between the two. The phylogenetic distribution of prominent 16S rRNA bacterial gene sequences along the aridity levels was in agreement with the PLFA analysis: the hyper-arid site was dominated by the cyanobacterium Microcoleus vaginatus, while diverse populations of cyanobacteria and soil bacteria were found in the other sites. These complementary tools allowed a simple and sensitive measurement of the influence of aridity levels on BSC successional stage. The results demonstrate that different aridity levels correspond to different BSC successional stages and those differences can be used as parameters for global change scenarios.

Eric Ariel Ben-David

August 23, 2022
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  1. Predicting the successional stage in biological soil crust by physical,

    biophysiological measurements and in combination with molecular methods (DGGE 16S rRNA) and phospholipid fatty acid analysis (PLFA) Ben-David A. Eric1,Tzaady Eli1, Sher Yoni2, Tsirkin Regina2, Najidat Ali2 1Natural resources, Gilat Research Center, ARO. 2Department of Microbiology and Environmental Hydrology, Desert Research Institutes, Ben-Gurion University of the Negev.
  2. Climate Change Climate change is associated with instability in the

    amount and patterns of precipitation both in time and in space and their impact is evident in large parts of the planet. Image based on data from the Intergovernmental Panel on Climate Change (IPCC)
  3. Climate change is associated with instability in the amount and

    patterns of precipitation both in time and in space and affects large parts of the planet. Arid regions are sensitive to climate change and are an ideal system for studying the impact of climate change on soil population structure. Climate Change
  4. Climate Change Climate change is associated with instability in the

    amount and patterns of precipitation both in time and in space and affects large parts of the planet. Arid regions are sensitive to climate change and are an ideal system for studying the impact of climate change on soil population structure. Microbial soil populations in arid and semi-arid regions are mainly represented by biological soil crusts.
  5. Biological Soil Crust Biological soil crusts are an important factor

    in the ecosystem: As soil surface stabilizers
  6. Biological Soil Crust Biological soil crusts are an important factor

    in the ecosystem: As ground surface stabilizers Primary producers in food chains Enriching the soil with nitrogen and carbon Create/inhibit runoff Encourage/inhibit seed anchoring in soil and germination They can appear in different compositions of populations: cyanobacteria, soil algae, lichens and mosses Their successional stage is affected by differences in rainfall and soil moisture
  7. Stages in the development of biological soil crust B. Boeken

    Loose soil 10 mm Silt seal after wetting Cyanobacteria Crust with mosses and lichens 1 2 3 4
  8. SEMI-ARID ARID HYPER-ARID BEER SHEVA NIZZANA ANNUAL AVERAGE RAINFALL STUDY

    SITES
  9. Research hypotheses The effect of aridity level and rainfall on

    the successional phase of the biological crust will be reflected in the composition of the microbial population in the soil. The aridity level can be used as a tool to predict the direction of the biological crust's succession and future scenarios related to climate change.
  10. Study Objectives Gain insight into the biomass and microbial composition

    of the crusts in the sand dunes along the rain gradient. Can the use of the phospholipid fatty acids found in organisms and/or the examination of the sequence of bases encoded for the gene 16S rRNA serve as a means of evaluating the successional phase of the biological soil crusts?
  11. Research Methods For monitoring the stages of the succession of

    the biological crust: Physical methods: pressure necessary for the crust to break and the rate of crust permeability to water. Biophysical methods: polysaccharides, protein and chlorophyll content (A and B) For insight into the biomass and microbial composition of crusts: Analysis of phospholipid fatty acids (PLFA) and 16S rRNA - DGGE
  12. Phospholipid fatty acids (PLFA) PLFA analysis provides information regarding the

    entire microbial population in three domains: Biomass – PLFA breaks down quickly when the cell dies, so the total PLFA in the sample represents all living cells. Fingerprint of the population – certain organisms produce specific fatty acids and therefore it is possible to quantify functional microbial groups such as iron and sulfur reducers, Gram positive and negative, etc. The relative percentage of these groups of the total population creates a 'fingerprint' of the population in question. Microbial activity – Some bacteria, especially proteobacteria, respond to environmental stress by changing specific fatty acids in the cell membrane. These changes allow for insight into the metabolic state of these bacteria. Phospholipid fatty acids are the main component of the membrane of microorganisms
  13. Living cells are composed of membranes containing mainly phospholipid fatty

    acids. The fatty acids break down quickly when the cell dies and therefore whole phospholipid fatty acids can only be extracted from living cells. The composition of fatty acids varies according to the type of organism and therefore it is possible to create a 'fingerprint' of the microbial population. The extraction of fatty acids is done using solvents and identification is done using GC-MS. Phospholipid Fatty Acid Analysis
  14. DGGE Analysis DGGE is a DNA-based technique that creates a

    genetic profile or 'fingerprint' of the microbial population. The DNA sequences or 'bands' can be cut and identified according to them the dominant species in the population. Changes in microbial populations can be assessed by the similarity/difference between the DGGE profiles. The technique is based on the separation of the strands of the double helix in DNA segments along a gel containing gradient concentrations of denaturant. The higher the concentration of GC nucleotides in the DNA segments, the more difficult it is to separate the strands. Segments with a higher GC concentration will move a greater distance compared to segments with a lower GC concentration. DGGE – Profile and identity of dominant species in the population
  15. Physical variables Biophysical variables Resistance to breakage pressure increases and

    water permeability decreases as the amount of rain increases. The result of an increase in the amount of polysaccharides produced by cyanobacteria and green algae. The levels of biophysical variables are markers of changes in biomass and organic matter. There is an increase in the biophysical variables as the amount of rain increases.
  16. Fig. 1. Total PLFA-based biomass (nmol g-1 dry weight) for

    the three sites along the aridity gradient according to the method of Franzmann et al. (1996). General microbial biomass (nmol PLFA g-1) along the rain gradient a a b The microbial biomass at the rainiest site is significantly higher compared to other sites. A strong positive correlation was found between microbial biomass and crust biophysical parameters including protein level (r2 = 0.90) and polysaccharides level (r2 = 0.86).
  17. Selected relationships between fatty acid groups Gram negative/Gram positive ratio

    The ratio between the percentage of monounsaturated PLFA and the percentage of saturated fatty acids that have terminal branching. The ratio indicates a higher percentage of G+ve bacteria at the rainier site. Microbe/general population ratio Ratio of percentage of isomers of C15:0 and the percentage of C16:0 fatty acid - the most common in the animal world. The ratio indicates a higher percentage of bacteria out of the total population at the rainier site. The rate of variance in the population The ratio between the percentage of monounsaturated PLFA and the percentage of saturated fatty acids. The ratio indicates higher population variability in the rainier site.
  18. Fig. 2. PCA ordination of relative PLFA abundance data (mol%)

    for the crust samples from the three tested sites. (a) The PCA ordination plot showing average and standard deviation of the coordinates of each point from each of the three sites. (b) The loading plot of individual PLFA species. PCA coordination of phospholipid fatty acid percentage data in soil crusts from the three sites
  19. 16S rRNA Targeted DGGE Fingerprinting of Microbial Communities SEMI ARID

    ARID HYPER ARID SEMI ARID HYPER ARID ARID Dendrogram from multivariate cluster, Ward's cluster analysis of the DGGE banding patterns of the three sites. Denaturing Gradient Gel Electrophoresis (DGGE) banding pattern differences between the three sites.
  20. Closest matches of excised and sequenced 16S rRNA- derived DGGE

    bands Accession number % similarity Most similar sp. DGGE band EU861933 87 Uncultured soil bacterium 2.5 AM398777 98 Phormidium sp. (cyanobacterium) 3.3 EF651204 99 Uncultured Firmicutes sp. 6.1 AF336359 97 Beta proteobacterium 6.2 AB074509 94 Oscillatoria sp. 8.1 EF667395 99 Uncultured soil bacterium 8.2 EF667962 99 Microcoleus vaginatus 11.1 AY647893 95 Uncultured bacterium 12.1 EF654061 94 Pseudanabaenaceae, Oscillatoria sp. 14.1 EF667962 99 Microcoleus vaginatus 15.1 SEMI ARID ARID HYPER ARID
  21. Conclusions The results obtained indicated that the increase in the

    amount of rainfall did indeed significantly affect the formation and composition of the biological crust. The total biomass based on the phospholipid fatty acids of the organisms in the soil was found to be positively correlated with the physical and biophysical measurements. An examination of the sequence of bases encoded to 16S rRNA from the three sites showed that the southernmost site was dominated by the cyanobacteria M. vaginatus in contrast to the diverse composition of microorganisms found at the more northern sites. Each of the two methods or a combination of the two methods can serve as a means of assessing the successional stage of biological soil crusts, their physiological state and environmental stresses, and therefore the state of the entire ecosystem. The information received regarding the population structure in the soil can assist in sustainable rehabilitation and conservation in climate change scenarios.