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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