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)

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.