This document discusses the process for selecting ecogeographic variables to create Ecogeographic Land Characterization (ELC) maps. There are three main components considered: bioclimatic, edaphic, and geophysic variables. For each component, relevant variables are identified and subjected to statistical analysis to avoid redundancy and ensure they are not correlated or collinear. Variables can be selected objectively based on the statistical analysis or subjectively based on expert knowledge. The final selected variables are then used to categorize ELC maps, which can be generalist maps covering broad species ranges or more specific species/genus maps. The process incorporates both statistical analysis and expert knowledge to determine the optimal variables for characterizing ecogeographic conditions

1. Ecogeographic variable selection
For ELC maps
Mauricio Parra Quijano
Ecogeographic land characterization for CWR diversity and gap analysis
Training workshop
26–27 February 2014, Room UG08, Learning Centre, University of Birmingham

2. ELC map obtaining process
All started in 2005

3. Characterize germplasm or territory?

4. Characterizing germplasm
Y
X
Punto
Roads
Land use
Elevation
1
C-405
Forest
1110
2
A-2
Urban
294
3
NIV
Swamp
562

5. Characterizing the territory

6. Publication
To assess representativeness in ex situ CWR collections (2008)
Map obtaining and validation (2012)

7. ELC map obtaining process
Variable selection
Bioclimatic variables
Geophysic variables
Edaphic variables
Cluster
analysis
Cluster
analysis
Cluster
analysis
Determining
optimal number
of groups
Determining
optimal number
of groups
Determining
optimal number
of groups
Combination
(N bioclimatic*N geophysic*N edaphic)
Categories
ELC MAP
Category description by statistics from input variables

8. What variables are included in
bioclimatic component?
-Precipitation
-Temperature
-Bioclimatic indexes

9. What variables are included in
edaphic component?
-Soil type
-pH
-CIC
-% organic carbon
-Depth
-% sand, silt and clay
.
.

10. What variables are included in
geophysic component?
-Slope
-Aspect
-Elevation
-Latitude/Longitude
-Solar irradiation

11. Types of ELC maps
According to the scope of the analysis, ELC maps can be
1. Generalist maps
Define major environments for great numbers of related/unrelated
species. For most of the species the ELC map should discriminate
different adaptive scenarios. An unadjusted relationship between ELC
category and adaptive traits in a minor group of species is expected
(see Parra-Quijano et al., 2012).
2. Species/Genus/Genepool maps
Define key environments for a particular species or a limited set of
genetically related species. An adjusted relationship between ELC
category and adaptive traits is expected.

12. Variable selection by type of ELC map
Generalist map
 Most recognizable influencing variables on plant physiology
 Variables which are known to determine vegetation zones within the work
frame
 Variables that best summarize a group of variables (annual rather than
monthly, average rather than maximum-minimum)
Species/genus/genepool map
 Most recognizable influencing variables on species/genus/genepool
distribution
 Most recognizable influencing variables related to most important
biotic/abiotic adaptation traits for the species/genus/genepool
 Particular interesting variables for the curator/breeder

13. But in all cases, there are rules to select
 Avoid correlated variables, leaving only one per group of correlation (in each
component)
 Avoid collinearity in selected variables
 Avoid homogeneous variables (same value for the workframe)
 Avoid introducing too many variables (more than ± five per component)
 Do not over-represent variables about the same aspect in a single component if
the aim is to preserve the balance. Example:
Annual Precipitation + Precipitation of Wettest Quarter + Annual Mean Temperature

14. Statistical analysis (objective selection)
• Redundancy? Correlation? Collinearity?
x2 x3
x1
x2
x1
x3
• Bivariate correlation analysis, PCA, variance inflation factor VIF
• Significance. Through multiple regression analysis using as dependent variable
(adaptive variable such as plant height, 100 seed weight).
*Collinearity: refers to an exact or approximate linear relationship between two
explanatory variables.

15. Expert knowledge (subjective selection)
2012
To take advantage of the expertise knowledge to select the most important
variables , we can use two ways to obtain this valuable information:
1. References
2. Email/internet surveys

16. Summarizing
Expert knowledge
Generalist map
Correlation
Collinearity
Correlation
Collinearity
Ranking
Final
selection
Expert knowledge
map
Expert knowledge
Validation
Correlation
Collinearity
map
Correlation
Collinearity
PCA
PCA
Significance/
Regression
Ranking
Species map
Significance/
Regression
Expert knowledge
Final
selection

17. Thank you