This document discusses representativeness in genebanks and methods to assess it. It is difficult to fully evaluate representativeness due to the large number of unsampled populations and costs of extensive characterization. Ecogeography, which studies adaptive scenarios based on biotic and abiotic factors, can help map diversity and identify core collections. The Focused Identification of Germplasm Strategy uses ecogeographical data to predict traits and select a reduced number of accessions for breeders to evaluate, helping address utilization challenges with large genebank collections. Ecogeographical analyses and FIGS can boost utilization of plant genetic resources.

2. Representativeness in genebanks
 Measured in terms of diversity captured within ex situ genebanks
 Is the best guarantee to face future threats
 Is a concern for curators, not for breeders
 The reference must be nature for CWR, agroecosystems for landraces
 To assess it, comparative studies (genebank vs nature/farm) are required

3. Why is it so difficult to assess it?
REPRESENTED
GAPS!
 We conserve genetic diversity – genetic markers – pheno/genotypic
 Massive characterization involve high costs and efforts
 How to evaluate unsampled populations?

4. Ecogeography and genetics
Phenotype = Genotype + Environment + (GxE)
Incomplete,
totally absent Longitude / Latitude
or unaffordable
Genebank
External sources
Adaptation
(indirectly)

5. Ecogeography:
Study of adaptive scenarios of any individual, population or species through
the analysis of those biotic and abiotic factors which affect their survival or
are able to create new ecotypes or landraces

6. Abiotic factors
 More predictable and stable
 Easier to interpret
 Increased availability

7. Ecogeographical representativeness
If we are able to map the different adaptive scenarios for the target species, we could:
 Carry out efficient germplasm collections
 Create ecogeographical
core collections
 Support genotypic/phenotypic
studies
 Determine optimal places to
multiply/regenerate germplasm

8. Ecogeographical Land Characterization maps
GIS
Geophysic
Edaphic
E
L
C
maps
Bioclimatic

9. Ecogeographical core collections
P
E
Ecogeographical Core Collection Phenotypic Core collection
REPRESENTATIVENESS

10. Utilization
 Curators Representativeness  Breeders Traits

11. Utilization paradox
 Few funds for germplasm characterization & evaluation
 Plant breeders need information (characterization and evaluation data) about
germplasm collections in order to use them
 Curators/PGR centers focus their efforts on conservation and sometimes (when
funds are available) on germplasm characterization
 Plant breeders may expect up to 100-200 germplasm accessions to evaluate
for a particular trait as a part of their routine activities
 Frequently what they find are large gene bank
collections
 They have limited screening capacity
 Scarce evaluation data (at least, available data)
 Low level of utilization

12. Focused Identification Germplasm Strategy
 FIGS idea: Michael Mackay (1986,1990, 1995)
Phenotype = Genotype + Environment + (GxE)
Resistance/Tolerance = Genotype + Environment + (GxE)
 Identifying plant germplasm with a higher likelihood of having desired genetic
diversity for a target trait
 Using ecogeographic data for prediction of crop traits a priori, BEFORE the field trials
 With fewer or no characterization efforts, providing a reduced number of germplasm
accessions to breeders/curators to be evaluated
Boosting
 Generating FIGS subsets (≠ core collections)

13. Classic approach
Germplasm
FILTERING!! Illustration by
Mackay (1995)
based on latitude & longitude
Data layers sieve accessions
Temperature
Ecogeographical variables
Salinity score
GIS layers /
Elevation
Rainfall
Agro-climatic zone
Expert’s knowledge Disease
distribution
 Species specialists
 Breeders
 Entomologists, pathologists
FOCUSED IDENTIFICATION OF GERMPLASM STRATEGY

14. Model approach
Resistance/ Ecogeographical
Tolerance Y = b + X1 + X2 + X3 variables
Characterized germplasm Pattern Prediction on uncharacterized germplasm
Classifier method AUC Kappa Real (field)
validation
Principal Component 0.69 0.40 ?
Regression (PCR)
Partial Least Squares (PLS) 0.69 0.41 ?
Random Forest (RF) 0.70 0.42 ?
Support Vector Machines 0.71 0.44 ?
(SVM)
Artificial Neural Networks 0.71 0.44 ?
(ANN)
(Genebank: ICARDA wheat collection – Trait: Stem rust (Puccinia gramini)

15. Perspectives
 ELC maps are being used in designing CWR in situ conservation strategies in Europe
(Czech Republic and Spain cases). PGR secure project WP3.
 Ecogeographical core collections for several legume species will be created and
published by the Spanish National Program (CRF-INIA). SIERFE Project.
 FIGS method is being used to prioritize areas for in situ conservation of CWR and
landraces for Beta, Avena, Medicago and Brassica genus groups in Europe. PGR secure
project WP2.
 Field validation (field resistance evaluation) of FIGS subsets (both approaches) from
Phaseolus vulgaris Spanish collection for bacterial diseases.
 ELC maps and FIGS to collect germplasm
 FIGS to detect areas of high interest for in situ conservation of Trifolium repens and T.
pratense genepool on a global scale using evaluation data (over 20 traits).

17. More information
ELC maps:
1. M. Parra-Quijano, J.M. Iriondo , M.E. Torres. 2012. Ecogeographical land characterization maps as a tool for
assessing plant adaptation and their implications in agrobiodiversity studies. Genetic Resources and Crop
Evolution 59(2):205-217
Ecogeographical Core Collections:
2. M. Parra-Quijano, J.M. Iriondo , M.E. Torres, L. De la Rosa. 2011. Evaluation and validation of ecogeographical core
collections using phenotypic data. Crop
Science 51:694-703
FIGS “classic” approach (study case):
3. El Bouhssini, M., et al.(2011). Sources of resistance in bread wheat to Russian wheat aphid (Diuvaphis noxia) in
Syria identified using the Focused Identification of Germplasm Strategy (FIGS). Plant Breeding 130: 97-97
FIGS modelling approach:
4. Bari, A., K. Street, , M. Mackay, D.T.F. Endresen, E. De Pauw, and A. Amri (2011). Focused Identification of
Germplasm Strategy (FIGS) detects wheat stem rust resistance linked to environment variables. Genetic
Resources and Crop Evolution [online first]. doi:10.1007/s10722-011-9775-5.
Websites
http://www.figstraitmine.org/ (FIGS subsets on wheat)
Review about ecogeographical and GIS tools
http://revistas.inia.es/index.php/sjar/article/view/1859/1673