Presentation 4 - SelecVar, ELCmapas and ECOGEO tools
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This document describes tools and methods for eco-geographical land characterization (ELC) mapping and characterization of plant genetic resource collecting sites. It discusses how ELC maps are developed by selecting important bioclimatic, geophysical, and edaphic variables through statistical analysis and clustering. It also describes how the Ecogeo tool can be used to characterize collecting sites based on geographic and environmental variables extracted from GIS data to build a characterization matrix for the sites.
Presentation 4 - SelecVar, ELCmapas and ECOGEO tools
- 1. Mauricio Parra Quijano International Treaty on Plant Genetic Resources for Food and Agriculture CAPFITOGEN Program Coordinator http://www.capfitogen.net
- 2. 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
- 3. Characterization of a territory
- 4. 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?
- 5. 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
- 6. • 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/objective Objective option: Easiest way: Use SelecVar
- 7. 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.
- 8. ELC mapas tool results • Maps (which can be opened with DIVA-GIS) and tables describing each category.
- 9. SelecVar It allows to select the most important and non- redundant ecogeographical variables for ELC maps from the objective point of view SelecVar
- 10. Why this plant/population is here… And why when you translocate this plants and provide “better conditions” they …
- 11. What underlying or obvious abiotic factors are controlling adaptation? CAPFITOGEN tools include 105 ecogeographical variables: 67 bioclimatic 7 geophysic 31 edaphic
- 12. Why to select a set of most important variables? To obtain reliable maps showing different ecogeographic scenarios To obtain accurate species distribution models
- 13. 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 5 Speciespresencedata
- 14. 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 Landscape3 CS2 CS3 CS1 tmax11 bio1 bio3 tmin2
- 15. How to select a set of most important variables? What variables are related to others introducing redundancy? • Bivariate correlation analysis Precipitation1 Precipitation2 Precipitation3 Precipitation12 Temperature1 Temperature5 Annual temp Soil2 Soil3 Landscape3 P12 P1 P3 P2 S2 S1 L1 PRECIPITATION TEMPERATURE SOIL landscape PRECIPITATION TEMPERATURE SOIL LANDSCAPE
- 16. ECOGEO It allows to perform eco-geographical characterization of the geo-referenced collecting sites
- 17. 0 cm 5 cm 10 cm Internodes length = 5.56 cm 1 2 3 1 0 1 0 1 0 = present = 1 = absent = 0 NOT of the germplasm but of the collecting site ECOGEO is a characterization
- 18. Process of ecogeographical characterization Characterization matrix : Rows: Germplasm identifier Columns: Ecogreographical descriptors passport Data (including coordinates) GIS Elevation Average Annual Temp Soil Organic Carbon Soil pH …. …. Y X
- 19. 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 b c 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 b c True location a=68 b=65 c=50 GEOQUAL uncertainty Radius Radial extraction
- 20. 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 extraction ACCENUMB VARIABLE a NA b NA c 2 Point extraction 1 1 2.333 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
- 21. Characterization matrix 409-09 320-05319-05 318-05317-05 315-05316-05 405-09 391-07390-07 386-09385-07 386-07375-06 406-09323-05 376-07321-05 401-08311-05 372-06 377-07307-05 369-06299-05 368-06530-09 528-09527-09 523-09524-09 378-07379-07 526-09 504-09-v504-09 503-09-v503-09 501-09502-09 507-09534-09 533-09531-09 532-09 300-05541-09 540-09536-09 535-09522-09 529-09539-09 537-09538-09 308-05414-09 276-05 277-05306-05 357-06365-06 366-06505-09-v 525-09415-09 285-05283-05 284-05546-10 403-09 402-09355-06 356-06304-05 302-05303-05 349-06337-06 338-06397-08 353-06396-08 413-09 516-09454-09 455-09412-09 279-05281-05 287-05280-05 291-05309-05 389-07392-07 324-06 350-06351-06 521-09-v521-09 520-09-v519-09-v 519-09518-09-v 518-09517-09-v 517-09516-09-v 515-09-v 515-09514-09-v 514-09465-09 464-09463-09 462-09461-09 460-09459-09 458-09456-09 457-09 506-09-v505-09 506-09513-09-v 513-09512-09-v 512-09511-09-v 511-09510-09-v 510-09509-09-v 509-09 508-09508-09-v 268-05288-05 289-05361-06 341-06360-06 292-05548-10 348-06 347-06346-06 345-06343-06 342-06335-06 334-06333-06 332-06327-06-v 325-06293-05 298-05 551-10297-05 296-05295-05 294-05262-05 263-05410-09 411-09417-09 418-09393-07 275-05 394-07549-10 552-10550-10 395-07404-09 266-05380-07 274-05467-09 416-09466-09 383-07 382-07269-05 265-05267-05 381-07273-05 272-05270-05 271-05301-05 282-05305-05 507-09-v 453-09452-09 450--09451-09 02468 Cluster analysis - Ecogeographic characterization hclust (*, "average") ecogeodist Height d = 1 23 4 5 6 7 8 9101112 1314 1516 17 18 19 20 212223 24 2526 27 2829 30313233 34 35 36 37 383940 41 42 43 44 45 46 474849505152 53 54 55 565758596061 6263 64 656667686970 71 7273 74 7576 77 7879 8081 82 83 84 85 86 87 8889 90 91 9293 949596 97 9899 100 101 102 103 104105 106 107108 109 110 111 112 113 114 115 116 117 118 119 120 121 122123124125 126127 128129130131132133134135136137 138139 140141142143144145 146 147 148149 150 151 152153154155156157158159160161162163 164165166167 168 169170171172173174175176177178 179 180181 182 183 184185 186 187 188189190191 192193 194195196 197198 199 200 201202 203 204 DECLATITUDE alt northness slope bio_18 bio_1 t_clay t_sand t_oc t_silt t_ph_h2o Eigenvalues Data analysis