PK04:Methods for the analyzes of soil biodiversity data: determining soil biological quality indicators.

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

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

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  • 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 1
  • 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). 2
  • 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 3
  • 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 4
  • 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  5
  • 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 . 6
  • 8. 5/27/2010 P14 P16 Co-inercia analysis among macrofauna sp. richness in 16 Orders P8 P15 P13 and landscape metrics P7 P1 Inhabited areas P5 P3 P9 P6 P2 DERM P4 P12 P10 LHY 35 32 P11 10 34 31 Fragmented, heterogenous TERMITES PSE 7 28 Intensive garden marketing g g LCO 33 Dominance crop systems ANTS 29 P9 27 30 21 Intact OPI SCOR PUP 36 P6 P16 P15 P14 P13 F1: 47% P5 P14 Primary forest LDER 16 ARA 5 22 HEM 2623 24 P3 P4 P12 P10 P9 DPL 13 ISO 1819 P11 CH LDI 6 25 P1 20 1 P13 ORT 4 11 17 P2 P7 P8 14 12 P7 P6 P5 8 DP 2 3 P8 15 P10 9 P12 P11 P1 GAS P4 P3 Fragmented  P16 P2 OTH COLEOPTERA P15 Primary Forest F2: 18% Rv=0.17; P<0.05 Amerindian settlements Primary + exploited forest l2 Water Infiltration •Microbial decomposer activity + storage Due to the high number of soil biodiversity functions, various methods have been developed to cover soil functional diversity. Mo •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 Soil Physical structure Nutr Cycling Climate Reg. Climate Reg Comm Activation Pop Selection SOM dynamics after Lavelle et al., 2004, in D.Wall (ed) Lavelle et al., 2006 Eur.J Soil Biol.. El FB aumenta el porcentaje de estructuras biogénicas Resultados producidas por la actividad de lombrices de tierra Aumento del porcentaje de agregación de diferentes  clases en tratamientos con FBO y método convencional Proportion of different aggregates classes Physical Biogenic Root Organic 10000 0‐10 cm Proportion of different aggregates classes FBO 100% 8.2 22.3 20.5 2.1 10‐20 cm 9000 Proportional aggregates 8000 Organic  7000 residues FBO 50% + biopesticide 7.2 15.4 8.7 0.8 60000 6000 Organic residues Proportional aggregates Root aggregates 50000 5000 FBO 50% without biopesticide 8.5 21.5 16.4 1.5 4000 Biogenic aggregates Root aggregates 40000 3000 Conventional 2000 Physical aggregates 30000 1000 Biogenic aggregates 20000 0 Conventional FBO 10000 Physical aggregates 100% 50% without  0 50% +  Proportion of biogenic aggregates in Proportion of biogenic aggregates in biopesticide biopesticide Conventional C i l FBO 50% +  2004 & 2005  Proportion of Organic matter biopesticide 100% 50% without  biopesticide Treatments 3000 4000 in 2004 & 2005 Proportion of different aggregates classes Treatments 2500 3500 2000 3000 50000 20‐30 cm Aggregates 2004 1500 2005 2500 45000 Organic residues Biogenic aggregates x 12 aggregates 40000 1000 2000 Root aggregates 2004 Proportional aggregates 35000 500 2005 1500 30000 Biogenic aggregates 0 25000 1000 Conventional FBO 20000 500 15000 50% + 100% 50% without 10000 biopesticide biopesticide 0 Physical aggregates Conventional FBO 5000 0 Treatment 50% + 100% 50% without 50% +  100% 50% without  biopesticide biopesticide biopesticide biopesticide Conventional FBO Treatment Treatments 7
  • 9. Slide 40 l2 garder?? lavelle, 25/01/2005
  • 10. 5/27/2010 Objetivo Evaluar el efecto de las poblaciones de macrofauna  Materiales y Métodos sobre la estructura del suelo. 3 Fincas x 48 parcelas 10x10m Solanum nigris Benfica: Amazonia Brasilera Arachis pintoi BLAS BLAS BLA B LA Húmedo tropical •Prec. anual de 1800mm  •Temperatura 26 °C. TB A LAS BA T LS BL S BAS Paisaje •Evaluaciones: 2 Fragmentos de bosque,  Pastos (B. brizantha) y  L AS BS BLS •Macrofauna: TSBF •Morfología barbechos.  •Biomasa vegetal •Variables físicas y Oxisoles •Variables químicas Brachiaria brizantha Leucaena leucocephala 1 ‐1 1 Isoptera ‐1 ACP: Macrofauna del suelo ACP: Morfología del suelo Chi Ara Agregados Físicos F2 (18.1%) Gas Hem Fp Fg Agregados Físicos Col.l F2(15.8%) piedra Oli amm Dipl carbón S BAS raíz For LS L hojas Rm madera Rp B Col.a T Rg tallos L Iso semillas inv Bp BLS AS Bm Bg Agregados BL Bg BS de raíz de raíz BLAS Agregados Mayor abundancia y diversidad  A BS Biogénicos Te de macrofauna T F1 (34.8%) AS F1(28.5%) A BLAS B BLA LS BLA LAS S BAS BL LA BA BLS BA Te Agregados Biogénicos LA Agregados raíz P<0.01 Poca abundancia y diversidad  LAS P<0.01 Co‐inercia entre Morfología y Macrofauna Materiales y Métodos 3 Fincas x 48 parcelas 10x10m Agregados Físicos Solanum nigris Agregados Biogénicos Arachis pintoi Agregados raíz BLAS BLAS BLA B LA LA BLAS BAS Variables T TB A LAS BA Bp Bm BA T Iso Col.l BLA LAS LS BL S BAS inv Oli Chi Rg madera Rp Hem LS S F1 (67.2%) •Evaluaciones: 2 raíces carbón Bg semillas B BL L AS BS BLS •Macrofauna: TSBF Rm Isop hojas Fg TL BLS •Morfología piedras AS L •Biomasa vegetal Fp For •Variables físicas y tallos •Variables químicas Col.a BS Agregados Físicos Ara Fm Gas P<0.01 F2 (10.2%) Dipl A Brachiaria brizantha Leucaena leucocephala 8
  • 11. 5/27/2010 1 ‐1 1 Isoptera ‐1 ACP: Macrofauna del suelo ACP: Morfología del suelo Chi Ara Agregados Físicos F2 (18.1%) Gas Hem Fp Fg Agregados Físicos Col.l F2(15.8%) piedra Oli amm Dipl carbón S BAS raíz For LS L hojas Rm madera Rp B Col.a T Rg tallos L Iso semillas inv Bp BLS AS Bm Bg Agregados BL Bg BS de raíz de raíz BLAS Agregados Mayor abundancia y diversidad  A Biogénicos Te BS de macrofauna T F1 (34.8%) AS F1(28.5%) A BLAS B BLA LS BLA LAS S BAS BL LA BA BLS BA Te Agregados Biogénicos LA Agregados raíz P<0.01 Poca abundancia y diversidad  LAS P<0.01 The Synthetic Indicator of Soil Quality (ISQ) Was designed to identify any problem in the soil function and evaluate the sustainability of land use systems. The design and calculation of the indicator were based on sequences of multivariate analyses. Morphology The sub-indicators and the general indicator were designed 1 lma large mineral aggregates = >3 cm using fifty five variables describing 2 mma medium mineral aggregates = >1cm, <3cm 3 sma small mineral aggregates = <1cm 4 loa large organic aggregates = >3 cm Were measured in 20 sampling points allocated to 8 different types of landuse: 5 moa medium organic aggregates = >1cm, <3cm 6 soa small organic aggregates = <1cm 7 a.col aggregate larvae coleoptero 8 roots roots 9 10 wood inv wood invertebrate •6 pastures (PAS), p ( ) 11 12 twig leaves twig leaves Chemical •5 corn crops (MP), 13 stone stone 1 2 pH P-total pH total phosphorus •2 coffee plantations (CP), 14 seed seed 3 P-b available phosphorus •2 fallows (FW), 4 Ca calcium 5 Mg magnesium •1 secondary forests (SF), 6 K potassium 7 Na sodium •2 mixed cultures of corn and beans (MIX), •1 house yards (HY) and •1 eroded soil (ERO). 9
  • 12. 5/27/2010 Prepare a table in XL (ej. 20 sites x 16 variables) Recommendations: 1. PCA analysis for each variables groups: • Use the same number of decimals and separate with points (.) • Set sites, uses, etc.... as lines • Set variables as columns Create the table in Excel using a similar number of decimal points (use full stops, not commas). variables Sites Lombricidae Dicty Iso Diplo Chi Derm Formicidae Ter Ort Col.a Col.l Hem Dipt.l Ara Gas Hom PAS1 25 3 1 1 2 0 436 0 3 1 0 2 2 5 0 1 Land uses (Sites) MC1 64 2 4 0 4 0 108 3 0 4 2 2 3 3 0 1 PAS: pastures PAS2 59 0 1 5 0 0 35 42 0 8 4 2 4 6 0 0 MC: Maize crop ERO 4 0 0 1 1 0 10 1 0 2 0 2 2 0 0 0 ERO: Eroded soil PAS3 16 1 0 1 0 0 42 1 0 4 7 0 1 2 0 0 SF: Second. forest SF 18 0 2 4 7 0 52 24 0 16 20 6 5 12 3 1 HG: House yard MIX: Mixed crops MC2 0 0 0 16 1 0 15 6 0 15 3 0 0 3 0 1 FW: fallows Macrofauna (16 variables) ; CP: Coffee plantations HG 22 0 7 4 0 0 36 0 10 7 19 1 12 0 1 0 MIX1 8 0 0 0 1 15 9 1 0 5 9 1 0 0 0 0 PAS4 4 0 1 2 1 15 33 0 0 4 3 2 0 0 0 0 Morphology (14) ; PAS5 FW1 23 1 0 0 0 0 1 2 0 0 0 0 122 79 0 1 0 0 6 3 8 6 0 2 1 0 0 2 0 0 0 0 Physics (6) ; CP1 137 0 3 1 8 1 1124 0 0 23 31 1 3 4 4 2 MC3 25 0 0 18 1 0 28 0 0 8 8 3 2 0 0 0 PAS6 46 0 0 1 0 0 403 1 0 15 21 3 1 1 0 0 OM (12) ; MC4 MIX2 10 19 0 0 1 3 5 13 2 2 0 0 119 224 2 13 0 0 13 21 7 19 0 5 1 5 2 9 0 1 1 0 Chemical (7) MC5 14 0 0 2 3 0 101 47 0 15 12 2 3 4 0 3 CP2 52 2 3 0 3 1 153 2 1 26 29 1 11 3 0 1 FW2 97 0 1 4 11 0 17 0 0 33 15 1 3 8 15 1 *Lom, lombricidae ; Dicty, dictyoptera; Iso, isopoda; Dip, diplopoda; Chi, chilopoda; Der, dermaptera; Formic, formicidae; Ter, termita; Ort, ortoptera; Col.a, coleoptero adult ; Col.l, coleoptero grub; Hom, homoptero ; Het, heteroptero ; Dip.l, diptero larva ; Ara, aracnidae ; Lep.l, lepidoptera grub. 2. Make 4 tables in excel and save as : text (separator:tabulation) (*txt) a. data: data.txt Sub-indicators from the Nicaragua example b. individuals/sites/uses etc…: sites.txt; uses.txt c. variables: variables.txt d. landuses: usos.txt a: data.txt b: sites.txt c:variables.txt d:landuses.txt 25 3 1 1 2 0 436 0 3 1 0 2 2 5 0 1 PAS1 Lombricidae PAS 64 2 4 0 4 0 108 3 0 4 2 2 3 3 0 1 MC1 MC 59 0 1 5 0 0 35 42 0 8 4 2 4 6 0 0 PAS2 Dicty PAS 4 0 0 1 1 0 10 1 0 2 0 2 2 0 0 0 ERO Iso ERO 16 1 0 1 0 0 42 1 0 4 7 0 1 2 0 0 PAS3 Diplo PAS 18 0 2 4 7 0 52 24 0 16 20 6 5 12 3 1 SF 0 0 0 16 1 0 15 6 0 15 3 0 0 3 0 1 Chi SF MC2 MC 22 0 7 4 0 0 36 0 10 7 19 1 12 0 1 0 HG Derm HG 8 0 0 0 1 15 9 1 0 5 9 1 0 0 0 0 MIX1 Formicidae MIX 4 0 1 2 1 15 33 0 0 4 3 2 0 0 0 0 PAS4 Ter PAS 1 0 0 1 0 0 122 0 0 6 8 0 1 0 0 0 PAS5 Ort PAS 23 0 0 2 0 0 79 1 0 3 6 2 0 2 0 0 FW1 FW 137 0 3 1 8 1 1124 0 0 23 31 1 3 4 4 2 Col.a CP1 CP 25 0 0 18 1 0 28 0 0 8 8 3 2 0 0 0 Col.l MC3 MC 46 0 0 1 0 0 403 1 0 15 21 3 1 1 0 0 PAS6 Hem PAS 10 0 1 5 2 0 119 2 0 13 7 0 1 2 0 1 MC4 Dipt.l MC 19 0 3 13 2 0 224 13 0 21 19 5 5 9 1 0 MIX MIX2 Ara 14 0 0 2 3 0 101 47 0 15 12 2 3 4 0 3 MC5 MC Gas 52 2 3 0 3 1 153 2 1 26 29 1 11 3 0 1 CP CP2 97 0 1 4 11 0 17 0 0 33 15 1 3 8 15 1 Hom FW FW2 ACP results appear a. Correlation matrix Macrofauna b. Inercia (total and p) explained by each factor 6.7 F2(16.3%) a ‐6 3.7 b HG ‐3 ----------------------- Correlation matrix -------------- Num. Eigenval. R.Iner. R.Sum Num. Eigenval. R.Iner. R.Sum 1 +4.7393E+00 +0.2962 +0.2962 2 +2.6043E+00 +0.1628 +0.4590 1 1000 3 +2.0584E+00 +0.1287 +0.5876 4 +1.4747E+00 +0.0922 +0.6798 2 111 1000 3 322 201 1000 5 +1.0861E+00 +0.0679 +0.7477 6 +1.0551E+00 +0.0659 +0.8136 4 -199 -319 -77 1000 7 +9.8424E-01 +0.0615 +0.8751 8 +5.7945E-01 +0.0362 +0.9113 5 682 24 203 -96 1000 96 9 +5.5450E-01 5.5450E 01 +0.0347 0.0347 +0.9460 0.9460 10 +3.5802E-01 3.5802E 01 +0.0224 0.0224 +0.9684 0.9684 6 -216 -140 -138 -213 -129 1000 11 +2.2298E-01 +0.0139 +0.9823 12 +1.7338E-01 +0.0108 +0.9932 7 656 142 187 -205 337 -139 1000 8 -44 -198 -92 68 33 -174 -157 1000 13 +5.4128E-02 +0.0034 +0.9965 14 +3.9466E-02 +0.0025 +0.9990 9 -68 140 710 -59 -181 -107 -34 -161 1000 15 +1.1797E-02 +0.0007 +0.9997 16 +4.1669E-03 +0.0003 +1.0000 1 CP2 10 564 -164 182 170 708 -234 279 109 -157 1000 Ort -1 1 11 531 -130 450 -90 441 -145 518 -3 156 730 1000 Iso F2(16.3%) -1 12 -39 -91 116 221 170 -77 1 342 -119 97 208 1000 13 214 205 804 -51 143 -278 2 120 661 349 581 4910 1000 Dipt.l 14 300 60 157 146 625 -323 103 473 -182 536 297 591 212 1000 15 579 -167 88 -12 829 -115 83 -96 -45 668 298 -2 84 473 1000 PAS1 MC1 MP1 16 357 160 63 -128 567 -226 389 419 -123 443 278 -65 89 334 217 1000 Dicty Col.l hor CP1 PAS3 Lombri Lom F1(29.6%) F1(16.3%) PAS5 Variables contributions PAS 6 Derm MIX2 MC4 MP4 ERO PAS4 Col.a FW1 Hem PAS2 MC3 MP3 Earthw Dic Iso Diplo Chi Derm For Ter Ort Col.a Col.l Hem Dipt.l Ara Gas Hom Chi Diplo MIX1 Hom Gas Ter F1 1269 4 441 -5 1520 -264 538 60 4 1490 1196 121 508 939 955 678 Ara SF MC2 MP2 MC5 MP5 - - - FW2 F2 0 515 2301 -214 -299 -16 21 419 2868 -158 219 -135 1975 428 259 -165 10
  • 13. 5/27/2010 Macrofauna earthw Dicty Iso Diplo Chi Derm Formic Ter Ort Col.a Col.l Hem Dipt.l Ara Gas Hom 2.Identify the variables that best differentiate the F1 1269 F2 0 4 515 441 2301 -5 -214 1520 -299 -264 -16 538 21 60 -419 4 2868 1490 -158 1196 219 121 -135 508 1975 939 -428 955 -259 678 -165 1520/2= 760 2868/2= 1434 sites according to soil quality. Morphology Lma mma sma loa moa soa acol roots wood inv twig leaves stones seed 2.1. To choose the number factors that explains at least F1 F2 348 1308 -32 2152 193 2753 -1374 156 -936 2 -1194 186 674 5 -480 -12 -869 7 -1269 226 29 -653 -831 438 611 2092 -1153 1374/2= 687 3 2753/2= 1376 60% of the total variability of the data OM FOM_l FOM_i FOM_h N C M.O N-NH4 N-NO3 CO2-4 CO2-7 CO2-14 CO2-21 2.2. To choose the variables that but contribute to the F1 1104 1452 303 1380 1411 1415 672 279 434 591 589 365 1452/2= 726 F2 -135 -95 -462 -93 -405 -399 -552 -250 1912 2310 2075 1304 2310/2= 1155 construction of first factors both, for this divides the greater value between two and choose the variables Physics moi ss rd b.d rp por with equal or superior value are chosen to this. F1 1264 -108 -257 -3485 -1604 3278 3485/2= 1743 F2 1893 5102 -2336 17 616 -33 5102/2= 2551 pH P-Total P-Bray K Ca Mg Na Chemical F1 1698 2266 2479 902 1625 -33 -994 2479/2= 1240 F2 282 -1222 -193 250 3002 4660 388 4660/2= 2330 2.3. The following step is to reduce the original variables in a rank between 0,1 and 1,00 with formulate of transformation homothétique: Number of variables to be retained for analysis: 36 Y= 0.1 + (x-b)/(a-b)* 0.9 Variable Morphology mma lma boa moa loa wood inv leaves stones seed 10 where: x = variable to transform Physics RTC b.d bd Por 3 a = value maxim of the variable; b = value minim of the variable OM LL LM N C OM R1 R2 R3 R4 9 Chemical pH P-Total P-Bray Ca Mg 5 For the variables whose value increases in grounds of bad quality it use Total 36 the following formulate: Y = 1.1 – (0.1+ (x-b)/(a-b)* 0.9) The variables transformed with this formulate were: bulk density, shear strength and the mineral aggregates. Y= 0.1 + (x-b)/(a-b)* 0.9 The transformed variables Lomb Iso Chi Ort Col.a Col.l Dipt.l Ara Gas PAS1 0.26 0.23 0.26 0.37 0.10 0.10 0.25 0.48 0.10 Y= 0.1 + (25-0)/(137-0)* 0.9 MC1 0.52 0.61 0.43 0.10 0.18 0.16 0.33 0.33 0.10 Y= 0.26 PAS2 0.49 0.23 0.10 0.10 0.30 0.22 0.40 0.55 0.10 ERO 0.13 0.10 0.18 0.10 0.13 0.10 0.25 0.10 0.10 variables PAS3 0.21 0.10 0.10 0.10 0.18 0.30 0.18 0.25 0.10 x = variable to Sites Lombricidae Dicty Iso Diplo Chi Derm Formicidae Ter Ort Col.a Col.l Hem Dipt.l Ara Gas Hom SF 0.22 0.36 0.67 0.10 0.52 0.68 0.48 1.00 0.28 transform (25) PAS1 MC1 25 64 3 2 1 4 1 0 2 4 0 0 436 108 0 3 3 0 1 4 0 2 2 2 2 3 5 3 0 0 1 1 MC2 0.10 0.10 0.18 0.10 0.49 0.19 0.10 0.33 0.10 HG 0.24 1.00 0.10 1.00 0.27 0.65 1.00 0.10 0.16 PAS2 59 0 1 5 0 0 35 42 0 8 4 2 4 6 0 0 MIX1 0.15 0.10 0.18 0.10 0.21 0.36 0.10 0.10 0.10 ERO 4 0 0 1 1 0 10 1 0 2 0 2 2 0 0 0 PAS4 0.13 0 13 0.23 0 23 0.18 0 18 0.10 0 10 0.18 0 18 0.19 0 19 0.10 0 10 0.10 0 10 0.10 0 10 PAS3 16 1 0 1 0 0 42 1 0 4 7 0 1 2 0 0 PAS5 0.11 0.10 0.10 0.10 0.24 0.33 0.18 0.10 0.10 b = value minim SF 18 0 2 4 7 0 52 24 0 16 20 6 5 12 3 1 MC2 0 0 0 16 1 0 15 6 0 15 3 0 0 3 0 1 FW1 0.25 0.10 0.10 0.10 0.16 0.27 0.10 0.25 0.10 of the variable (0) HG 22 0 7 4 0 0 36 0 10 7 19 1 12 0 1 0 CP1 1.00 0.49 0.75 0.10 0.72 1.00 0.33 0.40 0.34 MIX1 8 0 0 0 1 15 9 1 0 5 9 1 0 0 0 0 MC3 0.26 0.10 0.18 0.10 0.30 0.33 0.25 0.10 0.10 PAS4 4 0 1 2 1 15 33 0 0 4 3 2 0 0 0 0 PAS6 0.40 0.10 0.10 0.10 0.49 0.71 0.18 0.18 0.10 PAS5 1 0 0 1 0 0 122 0 0 6 8 0 1 0 0 0 MC4 0.17 0.23 0.26 0.10 0.44 0.30 0.18 0.25 0.10 a = value maxim FW1 23 0 0 2 0 0 79 1 0 3 6 2 0 2 0 0 MIX2 0.22 0.49 0.26 0.10 0.66 0.65 0.48 0.78 0.16 of the variable (137) CP1 137 0 3 1 8 1 1124 0 0 23 31 1 3 4 4 2 MC3 25 0 0 18 1 0 28 0 0 8 8 3 2 0 0 0 MC5 0.19 0.10 0.35 0.10 0.49 0.45 0.33 0.40 0.10 PAS6 46 0 0 1 0 0 403 1 0 15 21 3 1 1 0 0 CP2 0.44 0.49 0.35 0.19 0.80 0.94 0.93 0.33 0.10 MC4 10 0 1 5 2 0 119 2 0 13 7 0 1 2 0 1 FW2 0.74 0.23 1.00 0.10 1.00 0.54 0.33 0.70 1.00 MIX2 19 0 3 13 2 0 224 13 0 21 19 5 5 9 1 0 *a 137 7 11 10 33 31 12 12 15 MC5 14 0 0 2 3 0 101 47 0 15 12 2 3 4 0 3 *b 0 0 0 0 1 0 0 0 0 CP2 52 2 3 0 3 1 153 2 1 26 29 1 11 3 0 1 FW2 97 0 1 4 11 0 17 0 0 33 15 1 3 8 15 1 *a y b, maximum and minimal values 11
  • 14. 5/27/2010 The transformed variables To multiply each value of the variable reduced by his contribution to formation of the factor 1 and factor 2. To add products both. 3. To create sub indicators of soil physical quality, 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 chemical fertility, organic matter, morphology and F2 0 515 2301 -214 -299 -16 21 -419 2868 -158 219 -135 1975 -428 -259 -165 2868/2= 1434 soil macrofauna with values ranging from 0.10 to 1.00. PAS1 Lomb 0.26 Iso 0.23 Chi 0.26 Ort 0.37 Col.a 0.10 Col.l 0.10 Dipt.l 0.25 Ara 0.48 Gas 0.10 Transformed variable= MC1 0.52 0.61 0.43 0.10 0.18 0.16 0.33 0.33 0.10 0.26 x 1269 = 329.9 PAS2 ERO 0.49 0.13 0.23 0.10 0.10 0.18 0.10 0.10 0.30 0.13 0.22 0.10 0.40 0.25 0.55 0.10 0.10 0.10 3.1. To multiply each value of the variable reduced by his PAS3 0.21 0.10 0.10 0.10 0.18 0.30 0.18 0.25 0.10 SF 0.22 0 22 0.36 0 36 0.67 0 67 0.10 0 10 0.52 0 52 0.68 0 68 0.48 0 48 1.00 1 00 0.28 0 28 contribution to formation of the factor 1 and factor 2. MC2 HG 0.10 0.24 0.10 1.00 0.18 0.10 0.10 1.00 0.49 0.27 0.19 0.65 0.10 1.00 0.33 0.10 0.10 0.16 To add products both. MIX1 PAS4 0.15 0.13 0.10 0.23 0.18 0.18 0.10 0.10 0.21 0.18 0.36 0.19 0.10 0.10 0.10 0.10 0.10 0.10 PAS5 0.11 0.10 0.10 0.10 0.24 0.33 0.18 0.10 0.10 FW1 0.25 0.10 0.10 0.10 0.16 0.27 0.10 0.25 0.10 CP1 1.00 0.49 0.75 0.10 0.72 1.00 0.33 0.40 0.34 MC3 0.26 0.10 0.18 0.10 0.30 0.33 0.25 0.10 0.10 PAS6 0.40 0.10 0.10 0.10 0.49 0.71 0.18 0.18 0.10 MC4 0.17 0.23 0.26 0.10 0.44 0.30 0.18 0.25 0.10 MIX2 0.22 0.49 0.26 0.10 0.66 0.65 0.48 0.78 0.16 MC5 0.19 0.10 0.35 0.10 0.49 0.45 0.33 0.40 0.10 CP2 0.44 0.49 0.35 0.19 0.80 0.94 0.93 0.33 0.10 FW2 0.74 0.23 1.00 0.10 1.00 0.54 0.33 0.70 1.00 *a 137 7 11 10 33 31 12 12 15 *b 0 0 0 0 1 0 0 0 0 12