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Soil Biodiversity

What can it tell us

about the state of the

environment?

Professor Jim Harris

Challenges

• Global climate change

• Sea level rise

• Agricultural intensification

• Food and water security

• Loss of biodiversity

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)

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)

5 tonnes per hectare –

equivalent to 100 sheep

grassland – 20 times greater = 2000 sheep per hectare

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)

SOIL BIODIVERSITY

µm

cm

mm

SOIL

BIOMASS

MAMMALS

PROTOZOA

NEMATODES

INSECTS

ARACHNIDS

MOLLUSCS

WORMS

BACTERIA

FUNGI

ALGAE

PLANT ROOTS

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

20 µm

MAP OF Armillaria bulbosa in Michigan forest

CLONE A

CLONE B

N

100 m

CLONE A

CLONE B

N

100 m

MAP OF Armillaria bulbosa in Michigan forest

Blue

Whale

Here

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

Soil biota – Data rich

How might we measure this?

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

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?

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

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

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)

COMMUNITY TRAJECTORIES…

TOTAL

BIOMASS

FUNGAL

BIOMASS

GROSS ACTIVITY

Late Grass

Mid Grass

5 Year Restored

Early Grass

Scrub

Stored Soil

Pioneer

Forest

Bare

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

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

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

COMMUNITY TRAJECTORIES…

TOTAL

BIOMASS

FUNGAL

BIOMASS

GROSS ACTIVITY

Late Grass

Mid Grass

5 Year Restored

Early Grass

Scrub

Stored Soil

Pioneer

Forest

Bare

But just “more of everything” does

not necessarily mean “better”

Examples

Phenotypic profiling

Phospholipid fatty acid (PLFA) profiling

The Analytical Process

Forest Soil Sample Extraction Partition

Fractionation Analysis

GC/GC-MS

Derivatization

(methanolysis)

+ CH3OHOH

OR'

+ H2OO

OCH3

R'

PLFA Profile from a mixed woodland

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

Abbots Hall Farm Essex

Abbots’ Hall – High Tide

Abbots Hall Farm Essex

S

O

F

Fr

Y

-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

Canonical Analysis of

Principal Co-ordinates of

Multiplex-TRFs on six

sampling dates (numbers)

for four restoration sites

(coloured)

Canonical Analysis of

Principal Co-ordinates of

ITS-fungal TRFs on six

sampling dates (numbers)

for four restoration sites

(coloured)

Restored

Stuck

Recovering

Can we measure anything which doesn’t

involve having to measure partitioning

between different biological groups?

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

Multiple

Substrate

Induced

Respiration

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™

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

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

Odum’s Ecosystem Attributes

• Community energetics • Community structure • Life-History • Nutrient cycling • Selection Pressure • Overall homeostasis

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?

TAM AIR

Harris et al 2012

Examples of heat output – samples from Fors

Heat signatures at A) 2 days, B) 6 days, C) 16 days & D) 36 days

+ Glucose

Water

Metabolism + Waste heat

Biomass

Increasing stress

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

• 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.

Harris et al 2012

Another Index:

Substrate Induced Heat Production = Total Heat Output/Soil Biomass

SIHP – the lower this number the higher the efficiency is.

Substrate Induced Heat Production index

Harris et al 2012

Metabolism + Waste heat

Biomass

Mature Immature

SIHP

Thermodynamic

efficiency

Successional age/ecosystem maturity

What might we find?

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

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

Change in SIHP with age of site

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

And another

thing……

Effect of mycorrhizal diversity on plant diversity

and productivity

“Red Queen”

Hypothesis

Pathogens favour rare

genotypes by putting

their resources into

attacking common

genotypes, thereby

enhancing non-native

over native vegetation

Inderjit and van der Putten (2010)

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).

In other words…

…microbes facilitate invasion

and novelty in ecosystems by

providing an irreversible

threshold

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

Thank you!