For ELC mapsMauricio Parra Quijano Ecogeographic land characterization for CWR diversity and gap analysis Training workshop 26–27 February 2014, Room UG08, Learning Centre, University of Birmingham
Ecogeographic variable selection
ELC map obtaining processAll started in 2005
Characterize germplasm or territory?
Characterizing germplasm
Y
X
Punto Roads Land use Elevation
1 C-405 Forest 1110
2 A-2 Urban 294
3 NIV Swamp 562
Characterizing the territory
PublicationTo assess representativeness in ex situ CWR collections (2008)
Map obtaining and validation (2012)
Variable selection
Geophysic variables
Cluster analysis
Determiningoptimal number
of groups
Combination(N bioclimatic*N geophysic*N edaphic)
Categories
ELC MAP
Category description by statistics from input variables
Edaphic variables
Cluster analysis
Determiningoptimal number
of groups
Bioclimatic variables
Cluster analysis
Determiningoptimal number
of groups
ELC map obtaining process
What variables are included in bioclimatic component?
-Precipitation
-Temperature
-Bioclimatic indexes
-Soil type
-pH
-CIC
-% organic carbon
-Depth
-% sand, silt and clay..
What variables are included in edaphic component?
-Slope
-Aspect
-Elevation
-Latitude/Longitude
-Solar irradiation
What variables are included in geophysic component?
Types of ELC mapsAccording to the scope of the analysis, ELC maps can be
1. Generalist maps
2. Species/Genus/Genepool 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).
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.
Variable selection by type of ELC mapGeneralist 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
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
• Redundancy? Correlation? Collinearity?
• 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.
x1
x3
x2
x3
x1 x2
Statistical analysis (objective selection)
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
Summarizing
Generalist map
Species map
CorrelationCollinearity
Expert knowledge
Significance/Regression
PCA
CorrelationCollinearity
Expert knowledge
Expert knowledge
Significance/Regression
PCA
CorrelationCollinearity
CorrelationCollinearity
Expert knowledge
Ranking
Ranking
Finalselection
Finalselection
Validation
map
map
Thank you