soil biodiversitysoil ecosystem maturity ‘as microbial communities develop during succession from...
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Soil Biodiversity
What can it tell us
about the state of the
environment?
Professor Jim Harris
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Challenges
• Global climate change
• Sea level rise
• Agricultural intensification
• Food and water security
• Loss of biodiversity
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Soil biology is essential
to ecosystem structure
and function
• Organic matter decomposition and nutrient
cycling – and therefore in regulating plant
productivity and community dynamics (Wardle et
al, 2004; Van der Heijden et al, 2008);
• Soil structural generation (Feeney et al, 2006);
• Successional processes “crossing barriers”
(Kardol et al 2009)
• Plant diversity, ecosystem variability, and
productivity (Van der Heijden et al 1998)
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HOW MUCH IS
THERE ? •SOIL BIOMASS
• handful of arable soil
(c. 200g)
• approximately
0.5 g of fresh biomass
(mainly ‘microbial’)
• Over 10,000 species
per gram
(conservative
estimate)
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5 tonnes per hectare –
equivalent to 100 sheep
grassland – 20 times greater = 2000 sheep per hectare
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DISTRIBUTION WITHIN SOIL PROFILE
POPLAR PLANTATION
(2-YEARS OLD)
0
5
10
15
20
25
30
35
0 - 25 25 - 60 60 -100
Depth (cm)
t h
a-1
TOTAL C
0
50
100
150
200
250
300
0 - 25 25 - 60 60 -100
Depth (cm)
MIC
RO
BE
, k
g /
ha
0
200
400
600
800
1000
1200
1400
RO
OT
, k
g /
ha
Fungal C
Bacterial C
Root C
BIOLOGICAL C
(Horwath, 1993, adapted from Paul and Clark, 1996)
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SOIL BIODIVERSITY
µm
cm
mm
SOIL
BIOMASS
MAMMALS
PROTOZOA
NEMATODES
INSECTS
ARACHNIDS
MOLLUSCS
WORMS
BACTERIA
FUNGI
ALGAE
PLANT ROOTS
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MAMMALS
PROTOZOA
NEMATODES
INSECTS
ARACHNIDS
MOLLUSCS
WORMS
BACTERIA
FUNGI
PLANT ROOTS
100,000,000,000
50,000 m
100,000
10,000
5000
0.001
500 m
# INDIVIDUALS
10,000
100
100
0.001
10
# SPECIES
SOIL BIODIVERSITY
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20 µm
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MAP OF Armillaria bulbosa in Michigan forest
CLONE A
CLONE B
N
100 m
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CLONE A
CLONE B
N
100 m
MAP OF Armillaria bulbosa in Michigan forest
Blue
Whale
Here
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Plant
shoots
Plant
roots
Organic
matter
Plant
Feeding
nematodes
Mycorrhizae
Saprophytic
fungi
Bacteria
Mesostigmatid
mites
Fungal feeding
nematodes
Bacterial feeding
nematodes
Flagellates
Amoebae
Ciliates
Fungal
Feeding mites
Predatory
Nematodes
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Soil biota – Data rich
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How might we measure this?
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Criteria for ecological
indicators
• Easily measured
• Sensitive
• Respond predictably to stress
• Anticipatory
• Allow for adaptive management intervention
• Integrative
• Have known responses to stress, disturbances and
time
• Low variability in response
Derived from Dale and Beyeler 2001
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CHARACTERISING THE SOIL BIOTA
FUNCTIONAL
• processes – the working engine
PHENOTYPIC
• expressed information – the parts
STRUCTURAL
• the physical organisation – the engine
GENOTYPIC
• fundamental information – the blueprint
SIZE
• how much is there?
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0 200 400 600 800 1000 1200 1400
0-5
5-10
10-15
15-20
20-25
25-30
Depth (cm)
DHA g/kg/d
Cut 5 Year
Grazed 5 Year
Cut 10 Year
Grazed 10 Year
Wet Reference
Dry Reference
Microbial activity decreases with depth
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Soil ecosystem maturity
‘As microbial communities develop during succession from r-dominated (principally bacterial) to K-dominated (principally fungal) communities, they become more thermodynamically efficient, manifest as producing less waste heat per unit added glucose per unit biomass.’
Harris Science 2009
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Fungal:Bacterial Ratio
Microbial
Biomass
in
Soil bulk
phase
Raw substrate/degraded site
Pioneer/
Immature
system
Late grassland
Scrub
Forest
Normal
successional
trajectory
Restoration
shortcut
Harris, Science (2009)
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COMMUNITY TRAJECTORIES…
TOTAL
BIOMASS
FUNGAL
BIOMASS
GROSS ACTIVITY
Late Grass
Mid Grass
5 Year Restored
Early Grass
Scrub
Stored Soil
Pioneer
Forest
Bare
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3D Scatterplot (Spreadsheet1 in Workbook3 4v*12c)
Floodmeadow 1
Restored Grass 5 yr
Floodmeadow 2
Restored Woodland 1
Rough Grassland
Restored Grass 10 yr
Restored Woodland 2
Breckland
Woodland 1
Chalk Grassland
Woodland 2Woodland 3
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3D Scatterplot (Spreadsheet1 in Workbook3 4v*12c)
Floodmeadow 1
Restored Grass 5 yr
Floodmeadow 2
Restored Woodland 1
Rough Grassland
Restored Grass 10 yr
Restored Woodland 2
Breckland
Woodland 1
Chalk Grassland
Woodland 2Woodland 3
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3D Scatterplot (Spreadsheet1 in Workbook3 4v*12c)
Floodmeadow 1
Restored Grass 5 yr
Floodmeadow 2
Restored Woodland 1
Rough Grassland
Restored Grass 10 yr
Restored Woodland 2
Breckland
Woodland 1
Chalk Grassland
Woodland 2Woodland 3
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COMMUNITY TRAJECTORIES…
TOTAL
BIOMASS
FUNGAL
BIOMASS
GROSS ACTIVITY
Late Grass
Mid Grass
5 Year Restored
Early Grass
Scrub
Stored Soil
Pioneer
Forest
Bare
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But just “more of everything” does
not necessarily mean “better”
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Examples
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Phenotypic profiling
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Phospholipid fatty acid (PLFA) profiling
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The Analytical Process
Forest Soil Sample Extraction Partition
Fractionation Analysis
GC/GC-MS
Derivatization
(methanolysis)
+ CH3OHOH
OR'
+ H2OO
OCH3
R'
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PLFA Profile from a mixed woodland
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PLFA PROFILES
• Provide phenotypic ‘fingerprint’
of soil community structure
• Studies consistently show there
are distinct profiles associated
with:
– ecosystems
– vegetation types
– environmental factors (pollutants)
– management effects (cropping
system, tillage, type and rate of
substrate input)
PLFA TYPE
AMOUNT
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Abbots Hall Farm Essex
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Abbots’ Hall – High Tide
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Abbots Hall Farm Essex
S
O
F
Fr
Y
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-5
-4
-3
-2
-1
0
1
2
3
-7 -5 -3 -1 1 3 5
Saltmarsh
Farmland
Farmland (former marsh)
Restored Marsh (1995 flood)
Restored Marsh (2002)
Saltmarsh
Farmland
Reclaimed Farmland (300yr)
2002 Restoration
1995 Restoration
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Canonical Analysis of
Principal Co-ordinates of
Multiplex-TRFs on six
sampling dates (numbers)
for four restoration sites
(coloured)
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Canonical Analysis of
Principal Co-ordinates of
ITS-fungal TRFs on six
sampling dates (numbers)
for four restoration sites
(coloured)
Restored
Stuck
Recovering
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Can we measure anything which doesn’t
involve having to measure partitioning
between different biological groups?
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FUNCTIONAL PROFILING
MEASUREMENT OF PROCESSES
• Example: carbon transformations
– C utilisation profiling
• simultaneously determine the ability of soil
community to utilise a range of substrates of
varying composition and properties
0
1
2
3
4
5
6
7
8
9
10
1 3 5 7 9
11
13
15
17
19
21
23
Compound type
Utilisa
tio
n
0
1
2
3
4
5
6
7
8
9
10
1 3 5 7 9
11
13
15
17
19
21
23
Compound type
Utilisa
tio
nA B
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Multiple
Substrate
Induced
Respiration
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MicroResp™ is a unique microtitre-plate based respiration system
which allows 96 whole soil, sediment, biological tissue or water
samples to be analysed simultaneously.
Community-Level Physiological Profiles (CLPP),
www.microresp.com
MicroResp™
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SIR Response Profile for IGER Pasture
Polymers Water
Respiration response
(µg CO2-C g-1 soil)
0
200
400
600
800
1000
Aromatics
Amino Acids Alcohols
Carboxylic acids Sugars
Amines Amides
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SIR Response Profile for Lake Pasture
12
48
±83
11
70
±102
14
83
±42
Respiration response
(µg CO2-C g-1 soil)
0
200
400
600
800
1000
Aromatics
Amino Acids Alcohols
Carboxylic acids Sugars
Amines Amides
Water Polymers
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Odum’s Ecosystem Attributes
• Community energetics • Community structure • Life-History • Nutrient cycling • Selection Pressure • Overall homeostasis
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Contrast of non-equilibrium vs equilibrium
Nonequilibrium • Biotic decoupling • Species independence • Unsaturated • Abiotic limitation • Density independence • Opportunism • Large stochastic effects • Loose patterns
Equilibrium • Biotic coupling • Competition • Saturated • Resource limitation • Density dependence • Optimality • Few stochastic effects • Tight patterns
redrawn from Wiens 1984
Inefficient? Efficient?
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TAM AIR
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Harris et al 2012
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Examples of heat output – samples from Fors
Heat signatures at A) 2 days, B) 6 days, C) 16 days & D) 36 days
+ Glucose
Water
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Metabolism + Waste heat
Biomass
Increasing stress
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Long-Term Soil Organic Matter Experiment
Inorganic input regimes
• Calcium nitrate • Ammonium sulphate
Organic input regimes
• Straw + calcium nitrate • Farmyard manure • Sewage sludge
x
Started in 1956
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• How effectively energy is used?
• How much input energy is converted into biomass
and how much is lost as heat?
Where: ηeff = thermodynamic efficiency; Qgluc = heat production of glucose
amended sample (J g-1 soil); Qcontrol = heat production of unamended soil (J
g-1 soil); ΔHgluc = the enthalpy change for complete combustion of glucose
The higher this number the more efficient
the system is.
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Harris et al 2012
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Another Index:
Substrate Induced Heat Production = Total Heat Output/Soil Biomass
SIHP – the lower this number the higher the efficiency is.
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Substrate Induced Heat Production index
Harris et al 2012
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Metabolism + Waste heat
Biomass
Mature Immature
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SIHP
Thermodynamic
efficiency
Successional age/ecosystem maturity
What might we find?
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Map showing the location of the Morteratsch Valley in S.E. Switzerland (Google maps), with sample sites displayed as yellow markers on a semi-transparent Google EarthTM overlay of Burga’s (1999) moraine sequence map
A post-glacial successional gradient
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Examples of samples sites from; A) site B, 2011 moraine, B) site 5, 2006 moraine, C) site 7, 1960 moraine, and D) site 12, 1857 moraine
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Change in SIHP with age of site
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In summary
We can tell us:
• If the microbiota is under stress
• The structural and functional aspects of the biotic
community following a trajectory that matches an
appropriate reference (or control) ecosystem
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And another
thing……
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Effect of mycorrhizal diversity on plant diversity
and productivity
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“Red Queen”
Hypothesis
Pathogens favour rare
genotypes by putting
their resources into
attacking common
genotypes, thereby
enhancing non-native
over native vegetation
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Inderjit and van der Putten (2010)
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Microbial controls on
vegetation
composition
Direct effects include:
• native soil communities (including soil
pathogens) which resist invasion (Nijjer et al.
2007);
• native soil biota that can create positive
feedback (e.g. Callaway et al. 2004); and ,
• complete or partial release from enemies, such
as fungi or viruses (e.g. Keane and Crawley,
2002; Knevel et.al 2004).
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In other words…
…microbes facilitate invasion
and novelty in ecosystems by
providing an irreversible
threshold
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In conclusion
• Soil function is driven by biology
• Soil contains high levels of biodiversity
• This biodiversity is exquisitely sensitive to
environmental conditions and land use
• This data richness makes soil biology an ideal
indicator of function, status and change
• Soil biodiversity is being recognised as a critical
control on plant community assemblage and
therefore ecosystem structure and function
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Thank you!