Published on March 5, 2014
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
ELC map obtaining process All 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
Publication To assess representativeness in ex situ CWR collections (2008) Map obtaining and validation (2012)
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
What variables are included in bioclimatic component? -Precipitation -Temperature -Bioclimatic indexes
What variables are included in edaphic component? -Soil type -pH -CIC -% organic carbon -Depth -% sand, silt and clay . .
What variables are included in geophysic component? -Slope -Aspect -Elevation -Latitude/Longitude -Solar irradiation
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.
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
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
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.
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 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
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