a presentation by Prof. Patrick Lavelle on the Methods for the analyzes of soil biodiversity data: determining soil biological quality indicators.

1. 5/27/2010
Methods for the analyses of soil Organism communities and activities
biodiversity data: covary with soil characteristics
• Many examples in BGBD
Determining soil biological quality indicators
Patrick LAVELLE, Elena VELASQUEZ, Nuria RUIZ‐CAMACHO • Soil is both the habitat and a
construction of soil organisms
IRD‐BIOEMCO, Paris; CIAT, Cali, Colombia (Ecosystem engineers)
UNAL, Palmira, Colombia
UNAL Palmira Colombia
• Soil biodiversity regulates
microbial activities (Biological
regulation)
• Soil microorganisms operate
nutrient cycling (Chemical
engineers)
An example : The IFB project in Amazonia Protocole
3 Farms x 48 lots 10 x 10m
S: Solanum nigris
Evaluate the effect of changes in plant communities
on macrofauna and soil processes A: Arachis pintoi
BLAS
BLAS BLA B LA
TB A LAS BA
T
LS BL S BAS
L AS BS BLS
Benfica, Para: Brazilian Amazonia
B: Brachiaria brizantha L: Leucaena leucocephala
1
‐1 1
‐1
Isoptera
Chi Ara PCA: Soil Macrofauna PCA: Soil Morphology
Physical
Gas Hem aggregates F2 (18.1%)
Fp
Ewm
Col.l Fg
Physical
F2(15.8%) piedra
Dipl amm aggregates
carbón S
BAS
For Leucaena raíz
LS
L hojas Rm
madera Rp
Col.a B
T Rg
tallos
Iso Arachis semillas inv
L
Bp BLS
AS
Bm Bg Root BL
Bg BS
aggregates
BLAS
Biogenic
Density and diversity A
BS
aggregates Te
Increased in Arachis combinations T
F1 (34.8%) AS
F1(28.5%) A
BLAS B
BLA
LS
BLA
LAS
S BAS
BL LA
BA
BLS Root
Brachiaria BA
aggregates
Te
LA
Low abundance and diversity Biogenic
LAS
P<0.01 In Brachiaria aggregates
P<0.01
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2. 5/27/2010
Co‐inertia between Morphology and Macrofauna
What is soil biological quality ?
Coinertia analysis p < 0.01
Biogenic aggregates
Bp
• Biodiversity ?
Bm
Root aggregates Col.l
Iso
TER
Chi
inv
EWM
• Ability of soil organisms
Rg
Rp
raíces
Hem
madera
to participate in ES
Bg semillas
Isop
Rm
carbón
hojas
provision?
piedras Fg
ANTS
– Chemical engineers
Fp
tallos
– Biological regulators
Col.a
Ara Fm – Ecosystem engineers
Gas
Dipl
Physical aggregates
Source: Soil Biodiversity: functions, threats and tools for policy makers; EU, 2009
What do organisms and their activities tell
Assessing Biota link to soil quality us about soil integrity and function??
• Microbial indicators
– Enzyme activities
– Biomass
– Community composition
• Faunal indicators
– Communities
– Indicator species
– Activities (Soil
morphology)
Conceptual Model
l1
Ecosystem
Organisms in an auto organised soil system
services
Building indicators of soil quality
Soil catenas
Structures
Created • The shopping list • BISQ , Breure et al., 2003
Soil horizon approach
Biogenic • The concept of minimal
structures data set
BIODIVERSITY communities at different scales
Intermediate
Ecosystem
aggregates • The Benchmark approach
STRUCTURES Indicators of Ecosystem Services • (reference soil)
Microbial Community
aggregates of Ecos. Eng.
PROCESSES at different scales
Ecosystem ES Organisms
engineer
• The Numerical approach
Microfoodwebs
Indicators must be multidisciplinary and syntetic • (no reference)
Microorganisms
after Lavelle et al., 2004, in Wall (ed).
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3. Slide 11
l1 garder??
lavelle, 25/01/2005

4. 5/27/2010
The Synthetic Indicator of Soil Quality (GISQ)
Velasquez, E., Lavelle , P., and Andrade, M. (2007). GIQS: a multifunctional indicator of soil quality.
Principle
Soil Biol. Biochem. 39, 3066‐3080.
Evaluates from 0.1 to 1.0 :
1. SENSITIVE VARIABLES:
Physical quality Organic matter stocks Select from a non limited
list the ones that
discriminate sites
(multivariate analysis)
2. FORMULA : Build a
formula based on
Chemical fertility respective weights of the
selected variables
3. READ : variations from 0.1
to 1.0 for readibility
Aggregation and morphology Biodiversity macrofauna
Example: Macrofauna at 21 sites of Nicaragua
Velasquez et al., 2007
Past
F2(16.3%)
‐6
6.7
3.7 GISQ formula for sub indicators
HG ‐3
Macrofauna SI = F1 load * Σ(Variable load F1 * Variable value) +
F2 load Σ(Variable load F2 * Variable value)
1
Ort -1 1
Iso F2(16.3%) -1
Dipt.l Variables with loading > 50% of the highest value; reduced from 0 to 1
CP2
Macrofauna
Dicty
Col.l
Pastures
Lombri
Lom
hor
F1(29.6%) Coffee Plant
Plant. MC1
MP1 PAS1
earthw Dicty Iso Diplo Chi Derm Formic Ter Ort Col.a Col.l Hem Dipt.l Ara Gas Hom
Derm
Col.a F1 1269 4 441 -5 1520 -264 538 60 4 1490 1196 121 508 939 955 678 1520/2= 760
Hem Diplo
Chi Hom CP1 PAS3 F2 0 515 2301 -214 -299 -16 21 -419 2868 -158 219 -135 1975 -428 -259 -165 2868/2= 1434
Gas Ter
F1(16.3%) PAS5
Ara
MIX2 PAS 6 PAS4
MC4
MP4 FW1 ERO
PAS2 MC3
MP3 MIX1
SF MC2
MP2
MC5
MP5 I macrofauna i = F1%[1269 (Ewm i) + 1520(Chi i) + 1490 (Cola i) + 1196 Col l i) + 939 Ara i + 955 Gas] +F2%[ 2301 Iso i + 2868 Ort i + 1975 Dipl i]
FW2
Fallow Maize
Reduced from 0.1 to 1.0
Variable loadings
earthw Dicty Iso Diplo Chi Derm Formic Ter Ort Col.a Col.l Hem Dipt.l Ara Gas Hom
F1 1269 4 441 -5 1520 -264 538 60 4 1490 1196 121 508 939 955 678 1520/2= 760
F2 0 515 2301 -214 -299 -16 21 -419 2868 -158 219 -135 1975 -428 -259 -165 2868/2= 1434
Fenêtre 4
Mosaïque agricole mixte
GISQ – Morvan (France)
General GISQ
Fau Phy Chimi Morpho MO
0.57 0.77 0.65 0.40 401 0.38 0.53 0.47 0.80 0.27
402 0.71 0.28 0.58 0.63 0.39
403 1.00 0.90 0.57 0.68 0.27
404 0.59 0.48 0.10 0.62 0.51
405 0.29 0.86 0.54 0.61 0.31
0.34 0.74 0.71 0.67
406 0.61 0.31 0.58 0.66 0.26
407 0.40 0.31 0.60 0.78 0.40
408 0.68 0.84 0.61 0.90 0.34
409 0.54 0.54 0.68 0.53 0.29
410 0.31 0.83 0.68 0.59 0.29
411 0.33 0.93 0.40 0.63 0.26
0.59 0.41 0.27 0.34 412 0.28 0.82 0.53 0.61 0.30
Sub indicators used as variables general indicator 413 0.23 0.89 0.54 0.65 0.26
414 0.23 0.77 0.50 0.57 0.26
Nicaragua example: 415
416
0.36
0.30
0.39
0.15
0.53
0.45
0.67
0.72
0.42
0.31
0.32 0.32 0.58 0.65
IGQS= 1.2*Fauna –1.2*Morphology + 0.5*Physic +1.4* OM + 1.9*Chemical
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5. 5/27/2010
The Indicator Value Method
Indicator species of soil quality (Dufrêne et Legendre, 1997)
• Objective:
INDVAL= Aij X Bij X 100
– Identify species that are
indicator of certain
ragnes of values of SQ Specificity Fidelity
– Develop participative Aij= Nindividualsij/Nindividualsi Bij= Nsitesij/Nsitesj
approaches for
validation
– Use as indicator of ES
IndVal = 100% when species i are observed in all sites
production of only one site group.
13 SubIndicateurs
An example from MEXICO
GISQ average Gradient of soil quality Indicator earthworm species of soil qualities in
Earthworms the AMAZ Brazilian sites
Indicator Species F2(16.1%)
Maize ORGANIC SOIL
BIODIV PHYSICAL CHEMICAL
Group Indicator value Pasture EARTHWORM sp. MATTER MORPHO
average
Pontoscolex corethrurus 40.05 A C
1 0.81
2 0.49 Pontoscolex (P.) corethrurus p < 0.02 p < 0.02
3 0.31 5
4 0.55
C C
5 0.56 Andiorrhinus (Andiorrhinus) sp p < 0.08 p<0.07
6 0.69 1
7 0.29 C
8 0.62 8 p < 0.01
Ocnerodrilidae
9 0.74
C C
1 Diplothecodrilus sp2 p<0.10 p<0.07
-1 1
Sub_Colembolos -1 Rhinodrilus sp1
Sub-OM
Sub_Ants Gen. Nov 2
Diplothecodrilus sp3
Sub-chemical
F1(20.1%) 2 A
Sub-physical Kaxdrilus parcus 74.16 P. (Pontoscolex.) sp p<0.03
Kaxdrilus sylvicola 26.45
Gen. Nov. 2
Ramiellona sp. 1 45.62
Sub_Termites 6 4
Forest 3 7
Andiorrhinus sp2
Sub_earworms
Sub_Diplopodos B
Sub_Nematodes Acanthodrilidae sp p < 0.06
Sub_BFN
C
Sub-<macrofauna
Sub_HFM Enchytraeidae p<0.09
9
Sub_Chilopodos
Higher biodiversity A : 0.1‐0.4 A : 0.4‐0.7 A : 0.7‐1.0
P<0.01
THANK YOU !!!
Characteristics Microbial decomposers Biological regulators Ecosystem engineers
Protists, nematodes, mites, Ants, termites, earthworms,
Main Organisms Bacteria, fungi
springtails (Collembola) plants roots
Organic matter decomposition, Creation and maintenance of
regulation of microbial soil habitats; transformation of
Organic matter decomposition,
community dynamics, faecal physical state of both biotic
mineralisation + nutrients
Function pellet structures, and abiotic material,
release, pest control, toxic
mineralisation, nutrient accumulation of organic
compounds degradation
availability regulation matter, compaction of soil, de‐
(indirect), litter transformation compaction of soil
2‐200 µm (protists)
0.5‐5 µm (bacteria) 0.1‐5 cm (ants)
500 µm (nematodes)
Body size 2‐10 µm (fungal hyphae 0.3‐7 cm (termites)
0.5‐2 mm (mites)
diameter) 0.5‐20 cm (earthworms)
0.2‐6 mm (springtails)
9 6
10 cells/g of soil (bacteria) 10 g/soil (protists)
2 3 2
10 meters/g of soil (fungal 10‐50 g/soil (nematodes) 10 ‐10 m /soil (ants)
Density in soil
hyphae) 103‐105 per m2 /soil(mites) 10‐102 m2/soil (earthworms)
2 4 2
10 ‐10 m /soil (springtails)
cm (protists)
Tens of meters (nematodes)
T f t ( t d ) cm‐m (ants, termites,
( t t it
Scale of spatial
From 1 to 10²µ Hundred of meters (springtails, earthworms)
aggregation
termites)
From mm to hundred of
meters (protists) 1 to 100m (earthworms)
Scale of active and
µm (active); no limt (passive) From mm to m (protists) up to 1000m social insects
passive dispersion
From mm to meters (springtail
and mites)
100µ to a few mm
Scale of resources 1 to 10²µ (bacteria)
(nematodes) same scales
use µm‐ meters (fungal hyphae)
mm to cm (mites, springtails)
Intermediate (through
Ability to change
Highly restricted to micro formation of small and fragile
the physical High
environments organic biogenic structures) +
environment
litter fragmentation
Resistance to the
High (Protist, nematodes)
environmental High (cysts, spores) Low
Intermediate (meso‐fauna)
stresses
Source: Soil Biodiversity: functions, threats and tools for policy makers; EU, 2009
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6. 5/27/2010
Biotic Index of Soil Quality
Ruiz Camacho et al., 2009
n
IBQS = Σ ln (Di+1)×Si
i=1
where: Di= average abundance of the indicator taxon i
Si= indicator l
Si i di t value of th t
f the taxon i
0<IBQS<20
Organic variables: organic C, total N, C:N
THANK YOU!! Chemical variables: pH, CEC, Na+, K+, Mg++, Ca++, P2O5
Physical variables: % sand, % clay, % silt , NIRS
Biological variables: 110 macro-invertebrates taxa, respirometry
El FBO mejora significativamente la estructura del suelo
F2(17.4%) 4.1
-5.6 4.5
-3.4
MO
Rápida descomposición
Convencional
T4
Mejora la estructura Físicos
del suelo 1 F1(29.4%) T2
lP -1 1
-1
mP
T3
sP leaf
seed
T1 FBO
MO
shoot
Lenta descomposición
stone lB
lR
inv mB
sR root sB
mR
Biogénicos
Raíz
P<0.05
97
10596 82
77 6952
104 57565199 120 2.2
Biotic Index of Soil Quality 1 8658 108
22 95
76107 -2.4 1.8
-2.7
125
145 61
84 25
54 116
74 2
71 67 92 28
106 72 26
16 27
139
n 48 8
23 98
62 41 32 24 93103
121 134 144 20
IBQS = Σ ln (Di+1)×Si 123 60
135 140
124
137
138
102
i=1 122 21
42
127 133
59 79 55
147
14345
44
6 150 F1: 48%
153126 39 5 3
111 100128 65 114 4 117
14143 132
1527336 112 90 142
where: Di= average abundance of the indicator taxon i 101136
149
115
63 94
1
Si= indicator l
Si i di t value of th t
f the taxon i 34 29
70 35
11
18 40
64
91 118 146 83 38 17
30 10 19
47
0<IBQS<20 15 13
151 14 33 9
68
148
75
119
12 89 81 129
88 85 31 37
8087 130
Organic variables: organic C, total N, C:N 49 113
50
Chemical variables: pH, CEC, Na+, K+, Mg++, Ca++, P2O5 53
78
109
7
Physical variables: % sand, % clay, % silt , NIRS 110 46
66 131
Biological variables: 110 macro-invertebrates taxa, respirometry F2: 24%
Figure 1b: Distribution of soil macro‐invertebrates on co‐inertia axis F1 and F2
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7. 5/27/2010
F2 1.5
C/N 0.42 -1.8 1.5
-0.36 0.43 F5 -2.1
-0.4
Silt
C9
C1 C8 C7
C2
C5C3 C10
Clay C4
C6 F1: 48%
F4
F3 C11
F1: 48%
P5
Na
WHC H
pH C13
Organic C P6 C12
P1
CEC RV: 0.7
Total N Sand P2O5 (Olsen) Ca
Mg K
F1 p<0.001
F2: 24%
P2
Figure 1c: Distribution of soil physico‐chemical parameters on co‐inertia axis 1 and 2. WHC: Water Holding
Capacity F2: 24%
Figure 1a: Co‐inertia analysis. Sites ordination depending on soil physico‐chemical parameters (circles for fields, squares for
grasslands and triangle for forests) and on soil macro‐invertebrates populations (end of the arrows).
Landscape:
an example in French Guyana
C10
C9
C8
• Three 1 Km²landsacpe
windows
C3
C7 C12 F5
• Sp richness measured at
C4 P5
C13 F4
P2
C5 P6 F3
P1 regularly spaced points on
a grid, in 16 Orders
C6 C11 F2
C2 F1
C1
• TSBF methodology; 1
Figure 2: Typology of sites described by soil physico‐chemical parameters sample every 200m
•Microbial decomposer activity
Due to the high number of soil biodiversity functions, various methods have been developed to cover soil functional diversity. Mo
取样地描述 •Soil decomposition rates through measuring the rate of organic residue consumption
•Soil respiration rate through measuring the CO2 production
•Soil nitrification rate performed by specialised bacteria
•Soil enzymatic activity
Yingde
Guangzhou
英德 (24°N, 113°E) 为亚热带气候,土壤类型为由第四季红土进化而来的酸性粘土 .
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