ToolsSelecVar, ELC mapas, ECOGEO

Mauricio Parra QuijanoInternational Treaty on Plant Genetic Resources for Food and Agriculture CAPFITOGEN Program Coordinator

http://www.capfitogen.net

INTRODUCTION TO

ELC maps

It allows the user to create eco-geographical land characterization maps (ELC), that reflect adaptive scenarios for a given species (or species groups) and a specific country or region

Characterization of a territory

Variable selection

Geophysical variables

Cluster analysis

Determination of optimal number

of groups

Combination(N bioclimatic*N geophysical*N edaphic)

Categories

MAP

Description of categories using original variables

Edaphic variables

Cluster analysis

Determination of optimal number

of groups

Bioclimatic variables

Cluster analysis

Determination of optimal number

of groups

How an ELC map is developed?

Expert opinion / knowledge

• Experts on target species are a valuable source of information

• Surveys are an efficient way to gather information from expert knowledge (internet/email, meetings, workshops, etc.).

• Variable lists are made by components, with details on the nature of the variables (explanation of codes, variable units, source, etc..). Then a value is assigned based on the importance that a given variable has regarding the adaptation of the species.

Bibliography search on major factors in the adaptation of target species

Variable selection – subjective/objective

Subjective option

• Redundancy? Correlation? Collinearity? Importance on species adaptation?

• Bivariate correlations analysis, Principal Component Analysis• Importance of each variable analysis

x1

x2

x1

x1

x1

Variable selection – subjective/objectiveObjective option:

Easiest way: Use SelecVar

What type of map you need?Depending on the approach of the analysis, the ELC map can be :

1. Generalist map

2. Map by species / gene pool / group of related Sp (Specific map)

It defines the major environments for a large number of species (related or not). For most of these species, the ELC map should discriminate different adaptive scenarios in a given target area. It is expected to find unadjusted relationships between adaptive characteristic of a smaller group of species and the resulting map (see Parra-Quijano et al., 2012).

They define in more detail the key environments for a particular species or a limited set of genetically related species. A good fit between the map and the adaptive characteristics of the target species is expected.

ELC mapas tool results

• Maps (which can be opened with DIVA-GIS) and tables describing each category.

SelecVar

It allows to select the most important and non-redundant ecogeographical variables for ELC maps from the objective point of view

SelecVar

Why this plant/population is here…

And why when you translocate this plants and provide “better conditions” they …

What underlying or obvious abiotic factors are controlling adaptation?

CAPFITOGEN tools include105 ecogeographical variables:

67 bioclimatic7 geophysic31 edaphic

Why to select a set of most important variables?

To obtain reliable maps showing different ecogeographic scenarios

To obtain accurate species distribution models

How to select a set of most important variables?

What variables are the most important to create groups which represent similar plant adaptation scenarios?

• Clustvarsel• Random Forest

Precipitation1

Temperature12

Soil3

Landscape3

Groups

1

2

3

4

5Spec

ies p

rese

nce

data

Precipitation1Tem

perature12

Soil3

Landscape3

How to select a set of most important variables?

What variables are providing different information and have the most discriminatory ability?

• Principal Component Analysis (PCA)

Precipitation1

Temperature12

Soil3

Landscape3CS2

CS3

CS1tmax11

bio1bio3

tmin2

How to select a set of most important variables?

What variables are related to others introducing redundancy?

• Bivariate correlation analysis

Precipitation1Precipitation2Precipitation3Precipitation12

Temperature1Temperature5Annual temp

Soil2Soil3

Landscape3

P12P1P3P2

T5T1AT

S2

S1

L1

PRECIPITATION

TEMPERATURE

SOIL

landscape

PRECIPITATION

TEMPERATURE

SOIL

LANDSCAPE

ECOGEO

It allows to perform eco-geographical characterization of the geo-referenced collecting sites

0 cm

5 cm

10 cm

Internodes length

= 5.56 cm

1 2 3

1 0 10 1 0

= present = 1= absent = 0

NOT of thegermplasm

but of the collecting site

ECOGEO is a characterization

Process of ecogeographical characterization

Characterizationmatrix :Rows: Germplasm identifier Columns:Ecogreographical descriptors

passportData (includingcoordinates)

GIS

Elevation

Average Annual Temp

Soil Organic Carbon

Soil pH

….….

Y

X

Point or radial extraction?

2 4 3

1 3 2

1 3 2

1

1

3

1 1 3 4

Ecogeografical variable X

NA

NA

NA

NA

1 1 3 4NA

ACCENUMB VARIABLE

a NA

b NA

c 2

2 4 3

1 3 2

1 3 2

1

1

3

1 1 3 4

NA

NA

NA

NA

1 1 3 4NA

a

bc

Distribution of passport data entries

2 4 3

1 3 2

1 3 2

1

1

3

1 1 3 4

NA

NA

NA

NA

1 1 3 4NA

GIS overlap Extraction results

ACCENUMB VARIABLE

a NA (1)

b 1

c 3

a

bc

True location

a=68

b=65

c=50

GEOQUALuncertainty

Radius

Radial extraction

ACCENUMB CAPTURED VALUES

AVERAGE

a NA,1,1 1

b NA,1,1 1

c 3,2,1,3,2,3

2.333

GIS overlap

Results of radial extraction

ACCENUMB VARIABLE

a 1

b 1

c 3

Correct extractionACCENUMB VARIABLE

a NA

b NA

c 2

Point extraction

112.333

Radial extraction

2 4 3

1 3 2

1 3 2

1

1

3

1 1 3 4

NA

NA

NA

NA

1 1 3 4NA

Characterizationmatrix

409-

0932

0-05

319

-05

318-

0531

7-05

315-

053

16-0

540

5-09

391-

073

90-0

738

6-09

385-

073

86-0

737

5-0

640

6-0

932

3-05

376-

073

21-0

540

1-08

311-

0537

2-06

377-

07

307-

0536

9-06

299

-05

368-

0653

0-0

95

28-0

952

7-09

523-

095

24-0

937

8-07

379-

075

26-0

950

4-09

-v50

4-09

503-

09-v

503-

0950

1-09

502-

0950

7-0

953

4-09

533-

0953

1-09

532-

0930

0-05

541-

0954

0-09

536-

0953

5-09

522-

095

29-0

953

9-09

537-

095

38-0

930

8-05

414-

092

76-0

527

7-05

306-

0535

7-06

365

-06

366-

0650

5-09

-v52

5-09

415-

0928

5-05

283-

0528

4-05

546-

1040

3-09

402-

0935

5-06

356-

0630

4-05

302-

0530

3-05

349-

0633

7-06

338

-06

397-

0835

3-06

396

-08

413-

0951

6-09

454

-09

455-

0941

2-09

279

-05

281-

05

287-

052

80-0

529

1-05

309-

05

389-

07

392-

0732

4-06

350-

0635

1-06

521-

09-v

521-

0952

0-09

-v51

9-09

-v51

9-09

518-

09-v

518-

0951

7-09

-v51

7-09

516-

09-v

515-

09-v

515-

0951

4-09

-v5

14-0

94

65-0

946

4-09

463-

094

62-0

946

1-09

460-

094

59-0

945

8-09

456-

094

57-0

950

6-09

-v50

5-09

506-

0951

3-09

-v51

3-09

512-

09-v

512-

0951

1-09

-v51

1-09

510-

09-v

510-

0950

9-09

-v50

9-09

508-

0950

8-09

-v26

8-05

288-

052

89-0

536

1-06

341-

063

60-0

629

2-0

554

8-10

348

-06

347-

0634

6-06

345

-06

343

-06

342-

0633

5-06

334-

0633

3-06

332-

0632

7-06

-v32

5-06

293-

0529

8-05

551-

10

297-

0529

6-05

295-

052

94-0

526

2-05

263-

0541

0-09

411-

0941

7-09

418-

09

393-

0727

5-0

539

4-0

754

9-10

552-

1055

0-10

395

-07

404-

0926

6-0

538

0-07

274-

0546

7-09

416-

0946

6-09

383-

0738

2-07

269-

0526

5-05

267-

0538

1-07

273-

0527

2-05

270-

0527

1-05

301-

0528

2-05

305-

055

07-0

9-v

453-

0945

2-09

450

--09

451

-09

02

46

8

Cluster analysis - Ecogeographic characterization

hclust (*, "average")ecogeodist

Hei

ght

d = 1

2 3

4

5

6 7

8 9 10 11 12 13 14

15 16

17

18

19

20

21 22 23

24

25 26

27 28 29 30 31 32 33

34

35

36

37

38 39 40

41

42

43

44

45 46

47 48 49 50 51 52

53 54

55

56 57 58 59 60 61

62 63

64 65 66 67 68 69 70

71

72 73

74

75 76

77

78 79

80 81

82

83

84

85

86 87

88 89

90 91 92 93

94 95 96 97

98 99

100

101

102

103

104 105

106

107 108

109

110

111

112

113

114

115

116

117

118

119

120

121

122 123 124 125

126 127 128 129 130 131 132 133 134 135 136 137

138 139

140 141 142 143 144 145

146

147

148 149

150

151

152 153 154 155 156 157 158 159 160 161 162 163

164 165 166 167 168

169 170 171 172 173 174 175 176 177 178

179

180 181

182

183 184 185

186

187

188 189 190 191

192 193 194 195 196

197 198

199

200 201 202

203 204

DECLATITUDE

alt

northness

slope

bio_18

bio_1

t_clay

t_sand

t_oc t_silt

t_ph_h2o

Eigenvalues

Data analysis