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