Spectroscopy-based analysis of

SOM-soil fauna relationships

Juan J. Jiménez

22 January 2019

Tartu (Estonia)

2

Soil ecosystem engineering – Non-trophic based interactions

Ecosystem engineers(sensu Jones et al. 1994) OM

Aggregates

(Decaëns et al. 2008)

??

3

Soil aggregation

iPOM

Fresh

residues

Macro-

aggregate

<250 µm

Micro-aggregate

<250 µm

Binding agents

+ bacteria and

fungi

Aggregate size (µm)

Cementing agents

Process

Abiotic Biotic

0.2 Clay plates, Oxides, Amorphous alluminosillicates

Organic polymers sorbed onto clay surfaces

Electrostatic bonding; flocculation

2-20 Clay particles (packages)

Microbial, fungal and plant debris

Encrustation

200 Particles or aggregates

Roots, hyphae

Binding

2,000 Aggregates Soil fauna Selection of particlesFormation of biogenic structures

Aggregation processes in soil

Grouping of primary soil particles into larger units = aggregates, surrounded by pores (diff. size)

Physical or biological origin: angular-blocky, prismatic forms, rounded shapes.

(Jiménez and Lal 2006)

4

Soil aggregate’s turnover affecting

SOM dynamics (soil fauna)

Soil structure

iPOM

Free

aggregates

Macro-

aggregates

(>250 μm)

Micro-

aggregates

(<250 μm)

Fresh residues, litter

Macro-aggregate

Soil ecosystem engineers

(compact vs. decompact)

Micro-aggregate

Increased

mineralization

CO2 flux

Reduced

mineralization

CO2 flux

Larger macro-aggregates (casts, fecal pellets)

iPOM-clay

interaction

Fresh residues-micro-

organisms’ interaction +

biogenic structures

Conceptual model of aggregate formation

by soil ecosystem engineering activities

Jiménez and Lal (2006)

5

LitterBiogenic

structures

CO2 CO2 CO2

Aggregatedetachment Free macro-

and micro-aggregates

Wind, watererosion

[DOC]L

[DOC]BS

[DOC]F

Bel

ow

-gro

un

dA

bo

ve-g

rou

nd

Litter and soil organisms

StreamsAquatic systems

Anthropic practices

C-Clay interaction

Aggregate formation

Bioturbation

Incorporation to soil matrix

Macro-aggregates

Micro-aggregates

Tillage

ASML ASMBS ASMF

BioturbationSpatial and temporal

scales

Physical protection of C

DOC = Dissolved organic C; L = litter, BS = biogenic structure; F = free aggregates; ASM = Activation of soil micro-organisms (Jiménez and Lal, 2006)

Conceptual framework of the role of soil ecosystem engineering activities on C accumulation at different

spatio-temporal scales

Water run-off

6

Glass fiber net

Metal plate

4O cm

3O cm2

4Chemical control area

7O cm

15 cm

1

Sampling(0, 6 and 18 months):

• Vegetation• Soil• Earthworms

3

15 cm

12 unitsw/wt

Loss of beneficial functions when soil ecosystem engineers are absent

(Decaëns et al. 1999)

Soil organism-driven impacts are measurable

6m

18m

A

B+

-

0m

bSamplingdate

cGrasslandtype d

Ecosystemengineerpresence

Axis1.

Axis2

Axis1.Axis1.

Axis2

GB LB

WB

%L

LB RB

BD

MWD

pH

%C

%N

TP

Al

CaMg

K

PR

STermAnt

Gs

Ay

Mc

Oc

Axis 1.(29.3%)

Axis 2.(16.5%)

A

a

Axis2

> soil compaction< plant biomass> weed incidence< C in soil

Soil degradation

PCA of soil and plant properties with and without earthworms

7

Each biogenic structure has its own

NIRS signal

Case 1

8

Near Infrared (region) Spectroscopy – a region of the electromagnetic

spectrum from 700 to 2,500 nm;

Most equipments also obtain the visible región (VNIRS);

Efficient, rapid and non-destructive analytical technique to analyse soil

samples to estimate soil properties, i.e. C and N contents;

Useful in SOM studies linking species responsible for the production of

biogenic aggregates in the soil matrix;

Also MIRS (mid-infrared spectroscopy), more sensitive;

What is NIRS?

9

Granular casts produced by Prosellodriluspsammophilus Qiu & Bouché, 1998 from “Los Monegros” at the Ebro basin (Spain)

10

Soil habitat transformation

Indirect modification of resources via

production of physical structures

Autogenic engineers "change the environment via

their own physical structures, i.e. their living and dead

tissues."

Allogenic engineers "change the environment by

transforming living or nonliving materials from one

physical state to another, via mechanical or other

means."

• In soil: earthworms, ants, termites,

and roots

Pheidole sp1

Atta laevigata

Trachymyrmex sp.

Atta cephalotes

Velocitermes sp.

Nasutitermes sp.

Microcerotermes sp.

Termitinae

Ruptitermes sp.

Spinitermes sp.

Globular

Martiodrilus sp.

Hyperiodrilus africanus

Millsonia anomala

Colonization andRoot biomass

Granular

Prosellodrilus sp.

earthworms

ants

termites

Soil ecosystem engineers

11

Pasteboard-like

termite mounds

Earthworm

casts

Organo-mineral

termite moundsTermite

sheathings

Ant

deposits

Control

soil

F1 (64.1%)

F2 (

12.8

%)

(Decaëns et al. 2001; Hedde et al. 2005;

Velasquez et al. 2007; Zhang et al., 2009;

Zangerlé et al. 2011)

Near Infrarred Spectroscopy (NIRS) spectra of

14 biogenic structures (Colombian «Llanos»)

species-specific organic

fingerprints in their respective

above-ground biostructures

1. Accumulators of protected OM

2. Soil compactors

3. Soil decompactors

12

(Jiménez et al. 2006)

Biogenic

structure 50

100

20

40

60

80

Spatial C and N dynamics in biogenic structures

1 m

0-5

5-10

Sample location

1 2 3 4 5 6

NH

4 (

g d

ry s

oil

-1)

0

100

200

300

400

500

600

700

800

b b b b

b

b

a aa a

7(0-5 cm)

8 7 8(5-10 cm)

Sample location

1 2 3 4 5 6

Co

rg (

g k

g-1

)

0

10

20

30

40

50

60

70

ns

7(0-5 cm)

8 7 8(5-10 cm)

13

45 m

45

m

Earthwormsampling

Soil sampling (Metal cylinders):

1 Size-class aggregates

2 Bulk density, hydraulicconductivity and soilcompaction

3 C, N and P determinations

4 Root length and biomass (fine and coarse)

1

10

91

100 50

Attalea maripa

Nectandra membranacea

Cecropia sp.

Didymopanax morototoni

Virola sp.

Tree species, > 5cm DBH

Sampling protocol

Study site

SouthAmerica

Eastern Plains of ColombiaCIAT Carimagua researchstation (22,000 ha.)

Well drained isohyperthermic savannasOxisols of low fertility, low pH and high Al saturation (>90%)

14

Species pool hypothesis –

species filtering process

Decaëns et al. (2006)

Anthropogenic changes

Global change, N2

deposition

Soil erosion

Land use changes

Agricultural practices

Exotic introduction (invasive species)

Pictures © Pavel Krasensky; Tree of life web

Spatial scale

REGION

PATCH

LANDSCAPE

ECOSYSTEM

GLOBAL

COMMUNITY

Climate

Landscape structure

Spatial competitive mechanisms

Soil abiotic factors

Land use practices

Biotic interactions

15

Sem

i-v

aria

nce

Lag distance (m)

0 5 10 15 20

0.005

0

0.015

0.010

0.020

0.030

0.025

0.035

335224

251268447161

178

0.0106067 Nug(0) + 0.0220092 Sph(8.11863)1

20

30

40

50

60

70

45

0 4515 30

15

30

C concentration (g kg-1)

Spatial distribution of selectedsoil variables

(Jiménez et al. 2011)

Lag distance (m)

0 5 10 15 20

Sem

i-v

aria

nce

1.46103 Nug(0) + 4.41099 Sph(721.71)

1

178

161447

335

224

251

268

0

1

0.2

0.4

0.6

0.8

1.2

1.4

1.6

1.8

0

20

40

60

80

100

120

140

160

180

0 4515 30

45

15

30

Soil penetration resistance (kg cm-3)

16

Interestructure(dates)

0

1.4

0 4

F1=44.9%

Compromise(species)

0

0.35

0 7

Axis I Axis II

F1=28.1%

F1=2

2.5

%

Spatio-temporal stability (PTA)

(Jiménez et al. 2006)

17

(Jiménez et al. 2012)

Use of trophic and space resources

Number of inds. found in each variable (range)

C0-5

0-25 25-50 >50 N

Glo 9 819 18 846

Aym 0 630 18 648

And 18 288 0 306

Epi 0 477 477 954

Anr 0 9 0 9

Ocn 36 2313 54 2403

Mrt 9 972 45 1026

C5-10

0-15 15-30 30-45 >45 >60

0 819 27 0 0

0 585 18 45 0

9 297 0 0 0

477 477 0 0 0

0 9 0 0 0

36 2331 18 18 0

9 963 45 9 0

BD

0.7-1.0 1.0-1.2 1.2-1.4 >1.4

0 225 549 72

0 315 333 0

0 45 216 45

513 27 414 0

0 9 0 0

18 513 1827 45

9 225 783 9

PR2.5

0-1 1-3 3-5 5-7 >7

315 18 9 261 243

495 0 18 108 27

72 72 18 90 54

180 9 36 720 9

9 0 0 0 0

936 153 81 783 450

594 36 9 288 99

Trophic (nutrients) Space (physical)

Also for assemblages CA1+, CA1-, CA2+, CA2- ……

Pianka Ojk index

18

ESSEM COST Action ES1406

Glossodrilus

AymaraAndiodrilus

Andiorrhinus

OcnerodrilidaeMartiodrilus

Litter

N0-5

N5-10

P0-5

P5-10

C0-5

C5-10

C:N0-5

C:N5-10

FiRL

CoRL

FiRW

CoRW

PR5-20

0.053-0.125

0.125-0.250.25-0.5

0.5-1

1-2

2-5

5-10

>10Agg

BulkDensity

Soil compaction

Cond

Hum(%)

-0.43

0.36-0.36 0.3

F I (64.1%)

F II

(1

7.7

%)

p<0.0001

MedAgg

SmallAgg

LargeAgg

VLargeAgg

VSmallAggRoot Length

Root Biomass

Montecarlo rand.

Co-Inertia analysis (fauna$tab and vars$tab)

Ew effect

Nutrient resources

Species 1(Jiménez et al. 2012)

Challenges on modelling SOM – soil fauna

A huge quantity of empirical data needed?

Many mechanisms are involved (comminution, bioturbation, “priming effect”, …. )

Several spatial and temporal scales are involved (processes may vary)

Many organisms are involved

Many OM compartments are involved

The impact of soil fauna on the dynamics of dead organic matter has been studied for ages,

Yes but …

19

Correlogram with the factorial

coordinates of axis 1 (square)

and axis 2 (triangle) extracted

in the CoIA

Mo

ran

's I

2 7 11 16 20 25 30 34 39 43 48 52-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

Lag distance

CA1+

CA1-

CA2+

CA2-

CA3+

CA3-

Species

Species

assemblages

Heterogeneity of soil resources allow

co-occurrence of competing species

(Jiménez et al. 2012, 2014)

PCNM1PCNM3PCNM5PCNM8PCNM12PCNM33PCNM51

10 20 30 40 50 60

0.0

02

0.0

06

0.0

10

0.0

14

Distance (m)

Sem

ivari

an

cePrincipal Component of Neighbouring Matrices (Dray et al. 2006)

Spatial analysis of biotic interactions (competition)

20

(Jiménez et al. 2014)

Environmental contribution to observed spatial pattern

(>30 m) (10-20 m) (<10 m)

21

NIRS signals differ with the age of the

in-soil biogenic structure… until a

certain time-lag!

Case 2

22

Lab protocol

EW removal 1d

Soil and ew casts Incubation

1 – 32 (64) days

3 reps /3 species

casts retrieval

sieved at <200 µm

NIR spectral readings

QualitySpec® spectrophotometer

C and N determinations with dry combustion method (Variomax CN Analyzer, Germany)

Diffuse reflectance(Stenberg et al. 2010)

<2 mm sieved soil

+

23

Background

Endogeic earthworm Aporrectodea caliginosa (Savigny 1826)

(Zangerlé et al. 2014)

NIRS and age of aggregates

24

Background

2. Is it possible to identify the origin and age of a single macroaggregatefrom its NIR spectral signature?

3. Explore, in natural field conditions of mountain ecosystems, the capacity of NIRS to indicate which ecosystem engineer was responsible for the formation of a given macroaggregate.

1. Changes in SOM quantity and quality that occur in biostructures produced by ecosystem engineers result in species-specific OM fingerprints.

25

Study site

Surroundings of Ordesa and Monte PerdidoNational Park, UNESCO Heritage and LTER network site.

Alpine grasslands grazed by domestic cattleduring summer.

Climate alpine, 5 ◦C and 1,720 mm

Earthworm species:

Aporrectodea rosea Savigny 1826

Lumbricus friendi Cognetti, 1904

Prosellodrilus pyrenaicus (Cognetti, 1904)

(Bueno and Jiménez 2014)

26

Sampling2

0 m

20 m

Eartworm hand-sorting area for

lab rearing and NIRS analysis.

Soil invertebrates hand-sorting

(25x25x20 cm)

Nine soil blocks (10x10 cm)

Morphology analysis

Morphology analysis10x10 cm and 10 cm depth soil blocks were takenbiogenic aggregates, root-aggregates, and non-aggregated soil in order to compare the signals withthe temporal reference signatures of earthwormcasts from the lab experiment.

Elena Velásquez Patrick Lavelle

Yamileth Domínguez

Silvia Gutiérrez

Mountain Team

27

A =

log(

1/R

)

(Unscrambler X 10.2, CAMO software)

Data pretreatment needed

Reflectance (R) is converted to absorbance (A) 10 scans per sample

NIR spectra from 1,100 to2,400 nm (10 nm intervals)further transformed toSavitzky-Golay 2nd deriv(noise reduction).(Savitzky and Golay 1964; Guthrie and Walsh, 1997; Cécillon et al. 2010)

PCAs and Partial Least SquareRegression are normally used

As H2O has a strong absorbance in the NIRS due to complicated hydrogen-bonding interactions

Dry samples

28

Savitzky-Golay 2nd deriv

Derivatives are used to:

– Correct for various baseline effects

– Reveal “hidden” spectral features in

overlapping peaks

29

Data analysis

NIRS raw data

NIRS y’’ 2nd deriv

30

2nd Derivative of NIR-data

31

Data pretreatment

Noise reduction

WinISI software (Shenk and Westerhaus, 1991), MixOmics, Ade4, Prospectr package in R

Matlab, Unscrambler (CAMO software),

d1 <- t(diff(t(NIRsoil$spc), differences = 1)) # first

derivative

d2 <- t(diff(t(NIRsoil$spc), differences = 2)) #

second derivative

1200 1400 1600 1800 2000 2200 2400

-0.0

010.

000

0.00

10.

002

0.00

30.

004

0.00

5

Wavelength

1st der

gap-segment 1st der

Prospectr package in R

32

ResultsFor the correlation circle, variables that had respective weights on axes 1 and 2 higher than 50% of total variance explained are selected.

In total 89 columns, i.e. number of variables = 2nd deriv. absorbance and 130 rows, i.e. number of objects = samples.

33

A. rosea

RMSECV: 52.6%

d = 5

s1_1

s1_16 s1_2

s1_32

s1_4

s1_8

d = 5

s2_1

s2_16

s2_2

s2_32

s2_4

s2_8

L. friendi

RMSECV: 57.3%

Root mean square error of cross validation = prediction ability calculated by the model based on the NIR data of the reference library (Shenk and Westerhaus 1993)

d = 2

s3_1

s3_16

s3_2

s3_32 s3_4

s3_8

P. pyrenaicus

RMSECV: 49.8%

35

RMSECV: 43.7%Spectral data of fresh casts for 3 species (reference library data)

A. rosea

1 day-old

L. friendi

1 day-old

P. pyrenaicus

1 day-old

PLS-DA modelPLS factors are determined with the root

mean squared error of prediction (RMSEP) d = 5

s1_1

s2_1

s3_1

Not identified: 44.4% (4 casts) !!

A. rosea

L. friendi

P. pyrenaicus

11.1% identified (1 cast)

33.3% identified (3 casts)

11.1% identified (1 cast)

Projection of field signals

36

In a highly heterogenous soil (i.e. mine)

NIRS is able to distinguish biogenic and

non-biogenic structures

Case 3

37

La Guajira, Colombia

Restored sites at Cerrejón coal mine

38

6-yr

39

Biogenic aggregates (BA) produced by macroinvertebrates (mainly earthworms, ants, termites, Coleopteran larvae, and Diplopoda)

Rhizosphere aggregates (RHIZ) are made of soil stuck to the roots

Physical aggregates (PHYS) produced by physical processes (e.g., drying and rewetting of the soil

Non-macroaggregated fraction (NON): this corresponds to smaller soil aggregates that are difficult to identify

90 – 120 minutes/sample

40

Projection of barycenters of NIRS spectra grouped by site and type of aggregates in the plane defined by the first two factors of PCA – between. BA = Biogenic aggregates, NAS= non-agreggrate soil and PA = Physical aggregates. 1, 5, 8, 9, 16, 20-y = Age of rehabilitation. F = Forest.

g

BA

NAS

PA

Axis

II

= 3

1%

Axis I = 68%

P < 0.01

Projection of NIRS spectra of macroaggregates type from forests and rehabilitated areas in the plane defined

by the first two factors of PCA.

(Domínguez et al. 2018)

Between-class PCA , 39%, p < 0.01

BA-16-y

BA-20-y

BA-5-y

BA-8-y BA-9-y

BA-F-1 BA-F-2

NAS-1-y

NAS-16-y

NAS-20-y NAS-5-y

NAS-8-y NAS-9-y

NAS-F-1 NAS-F-2

PA-1-y

Axis 1 = 29%

Axis 2 = 24%

41

KEYSOM protocol 5

Case 4

42

Germany (Hind Khalili)

Italy (A. Sofo)

Croatia (D. Hackenberger)

Poland (Lidia Sas)

Poland, site 2 (Lidia Oktaba)

Finland (V. Nuutinen)

Portugal (J. Nunes)

Romania (M. Iamandei)

Russia (A. Tiunov)

Switzerland (A. Frossaard)

UK (D. Jones)

KEYSOM protocol 5

List of countries

43P<0.001

Montecarlo rand

DE_Meadow

HR_Meadow

Olive

Forest

Histogram of sim

sim

Fre

quency

0.02 0.06 0.10

0100

300

Axis I = 76.5%

Ax

is I

I =

13

.7%

NE Spain

SE Italy

Germany

Croatia

Preliminary results

44

Preliminary results

Second derivative

NIR spectra

1,000 – 2,500 nm

CROATIA FINLAND

GERMANY GERMANY2

ITALY2 POLAND2

PORTUGAL

ROMANIA RUSSIA

SWITZERLAND

TURKEY UK

Histogram of sim

sim

Fre

qu

en

cy

0.02 0.08 0.14

05

01

50

F1: 45.3%

F2:

17.1

%

P<0.001

Montecarlo rand

45

P<0.001

Montecarlo rand

Preliminary results

Axis I = 78.0%

Ax

is I

I =

17

.5%

Biogenic

NON

Rhizospheric

Histogram of sim

sim

Fre

quency

0.05 0.15 0.25

0100

200

300

Physicogenic

Russia – mixed forest

Aggregate types

46

Physical aggregates (18.69 g)Biogenic aggregates (15.60 g)Non-macroaggregated fraction (19.4 g)Rhizosphere aggregates (16.70 g)

Thanks Alexei Tiunov!!

47

The likelihood to distinguish spectral values of aggregates from different specieswas demonstrated, at least during the first 30 days after being produced.

Spectroscopy analyses are useful to predict and distinguish aggregates andstructures produced by large invertebrates from many sites and ecosystems.

Take-home message

Many systems are inherently non-linear, which limits our ability to make ecological predictions. NIRS offers the potential for a shift towards whole-system empirical modeling in several areas of ecology.

NIRS is not just a tool to build an empirical model but indicated an underlying mechanism that could be further investigated.

These analyses are necessary to help implement the role of soil fauna into SOM models for land use and natural resource management.

Models were not robust enough to predict soil aggregates age. Only fresh casts were be predicted.

Thanks!!

Spectroscopy-based analysis of

SOM-soil fauna relationships

Juan J. Jiménez

22 January 2019

Tartu (Estonia)

1

2

3

Soil monolith

extraction25x25 x 30 cm3

30 c

mHand-sorting- Litter

- 0-10 cm

- 10-20 cm

- 20-30 cm

Laboratory:

- Identification (S)

- Abundance (N)

- Biomass (B)

Biogenic structures

identificacion

25 cm

Método TSBFISO 23611-5:2011

(Anderson e Ingram 1993)