   ppa   1
Pattern and Climate Change-Induced
Patterns and their Implications in the
Predictions to Search for ...
   ppa   1
Geographic patterns  historically used to trace the origin and
evolution of plant species
(Vavilov ...
   ppa   1
Plant species distribution  geographical and temporal patterns of variation
(Harlan 1975, Maurer 19...
   ppa   1
• 1920, Olof Arrhenius  proposed the mathematical
description of this relationship
• 1967, MacArthu...
   ppa   1
Cowpea is an important food legume in Africa
Cowpea distribution pattern
Taxon Variety (morphology)
...
   ppa   1
The highest diversity of
wild cowpea
-20 -10 0 10 20 30 40 50
LONGITUDE
-40
-20
0
20
LATITUDE
10
0 1...
   ppa   1
The hot spot extends over three countries and harbors two sites
of high endemism and high diversity ...
   ppa   1
Fragmentation (Df)
Fractal
(Db)
Cowpea distribution pattern
Taxon 1 Taxon 12
Taxon 10 Taxon 11
Space...
   ppa   1
Db Df MAT_D_AREA L_PATCH_N L_PATCH_S
Db 1.000
Df -0.336 1.000
Frac_D_AREA -0.790 0.427 1.000
L_PATCH...
   ppa   1
Detect presence of patterns (environment x trait)
Presence of patterns -----> quantification and pre...
   ppa   1
Accuracy metrics
The ROC curve and the resulting pdf’s of trait distribution (trait states)
1
1 
1-...
   ppa   1
Trait data set (Y)
.
.
.
.
.
Trait data
(Y as dependent variable)
Genetic Resources - ICARDA
Disease...
   ppa   1
Model AUC Sensitivity Specificity
Proportion
correct Kappa
SVM mean 0.72 0.67 0.78 0.75 0.41
RF mean...
   ppa   1
Predicted probability of occurrence/resistance to RWA
Current climate data
Modelling/predictions
Cap...
   ppa   1
Predicted probability of occurrence Russian Wheat Aphid:
Projected climate data - 2020
Modelling/pre...
   ppa   1
Predicted probability of occurrence Russian Wheat Aphid:
Projected climate data - 2050
Modelling/pre...
   ppa   1
Results – Model predictions
0 50 100 150
0204060
Longitude
Latitude
   ppa   1
Sub-Setting procedure – adjustment
based on phenology
Alignment of data based
on phenology
To reduce...
   ppa   1
Modelling/predictions
Capturing the shift induced by climate
Based on the estimation of the duration...
   ppa   1
Accuracy and agreement parameters of aligned data
Sub-Setting procedure – adjustment
based on phenol...
   ppa   1
Modelling/predictions
Capturing the shift induced by climate - verification
0 100 200 300
020406080
...
   ppa   1
Future directions
Explore the use of a
variety of applied
mathematics
approaches in relation to
phen...
   ppa   1
Beyond our tools, and through
them, it is old Mother Nature that
we reach, an experience that we
sha...
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THEME – 2 Pattern and Climate Change-Induced Patterns and their Implications in the Predictions to Search for Traits of Mitigation and Adaptation

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THEME – 2 Pattern and Climate Change-Induced Patterns and their Implications in the Predictions to Search for Traits of Mitigation and Adaptation

  1. 1.    ppa   1 Pattern and Climate Change-Induced Patterns and their Implications in the Predictions to Search for Traits of Mitigation and Adaptation 24-27 June 2014 Rabat Morocco
  2. 2.    ppa   1 Geographic patterns  historically used to trace the origin and evolution of plant species (Vavilov 1920s) Different species  different geographic patterns (Harlan 1975) Patterns boundaries  set up by ecological and evolutionary processes. (Maurer 1994) Plant diversity patterns
  3. 3.    ppa   1 Plant species distribution  geographical and temporal patterns of variation (Harlan 1975, Maurer 1994, Hadly & Maurer 2001) Plant diversity patterns VI VII IV V III I II I. The Tropical Center II. The East Asiatic III. The Southwest Asiatic IV. The Mediterranean V. Abyssinia VI. The Central American VII. The Andean Center Biodiversity unevenly distributed  Spatial structure (The structure of populations/ecosystems vary from region to region)
  4. 4.    ppa   1 • 1920, Olof Arrhenius  proposed the mathematical description of this relationship • 1967, MacArthur & Wilson  developed the island theory • Recent years, this relationship  to design of in situ conservation or reserve areas Biodiversity assessment for CC traits Common to plant distribution patterns  fundamental “law-like” processes The relationship between (species) diversity (S) and area (A) of occurance
  5. 5.    ppa   1 Cowpea is an important food legume in Africa Cowpea distribution pattern Taxon Variety (morphology) 1 dekindtiana 2 ciliolate 3 affinis 4 congolensis 5 grandiflora 6 hullensis 7 pubescens 8 protracta 9 kgalagadiensis 10 rhomboidea 11 tenuis 12 oblonga 13 parviflora Sampled/Recorde d sites of wild cowpea Number of wild cowpea relatives confined mostly to Africa
  6. 6.    ppa   1 The highest diversity of wild cowpea -20 -10 0 10 20 30 40 50 LONGITUDE -40 -20 0 20 LATITUDE 10 0 10 20 30 40 50 Distance 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Coefficient Observed Modeled Cowpea distribution pattern IITA Genebank - cowpea
  7. 7.    ppa   1 The hot spot extends over three countries and harbors two sites of high endemism and high diversity in the area: Conservation International 2005 Biodiversity Hotspot Wild cowpea distribution pattern
  8. 8.    ppa   1 Fragmentation (Df) Fractal (Db) Cowpea distribution pattern Taxon 1 Taxon 12 Taxon 10 Taxon 11 Space-filling Fragmented Area Patches
  9. 9.    ppa   1 Db Df MAT_D_AREA L_PATCH_N L_PATCH_S Db 1.000 Df -0.336 1.000 Frac_D_AREA -0.790 0.427 1.000 L_PATCH_N 0.446 0.191 0.169 1.000 L_PATCH_S 0.953 -0.468 -0.911 0.182 1.000 Correlation between the fragmentation and the patch size Cowpea distribution pattern Patch i, Taxon j aij is the area of patch i of taxon j Aj = Sum of patches ai’s of taxon j Fragmentation = Nj, number of aij Log (Nj) / Log (Aj/Nj) TAXON 1 3 7 8 9 10 11 12 152 171 225 234 116 73 167 214 0.90 1.46 1.42 1.79 1.45 1.43 1.83 1.31 Algorithms
  10. 10.    ppa   1 Detect presence of patterns (environment x trait) Presence of patterns -----> quantification and prediction MacArthur (1972) Assessing PGR/Agro-Biodiversity for rust resistance Environment (tmin, tmax, prec) Trait (T) (Resistance to stripe Rust) Bayes – Laplace approach (inverse probability) Learning based approach (risk minimization) Cherkassky & Mulier (2007) The Bayes-Laplace inverse theorem focuses on the probability of causes in relation to their effects, in contrast to the probability of effects in relation to their causes. Fisher (1922, 1930) (E)
  11. 11.    ppa   1 Accuracy metrics The ROC curve and the resulting pdf’s of trait distribution (trait states) 1 1  1-  ROC curve pdf’s of trait distribution  High AUC (area) values indication of potential trait-environment relationship Patterns present in data Predictions Frequency Truepositiverate False positive rate Environment
  12. 12.    ppa   1 Trait data set (Y) . . . . . Trait data (Y as dependent variable) Genetic Resources - ICARDA Disease Resistance (rusts) Grain filling period for entire wheat accessions data (grey colour bars) and the subsets prior to evaluation (green bars) and after evaluation (red bars). Drought tolerance (faba bean) Heat tolerance (wheat)
  13. 13.    ppa   1 Model AUC Sensitivity Specificity Proportion correct Kappa SVM mean 0.72 0.67 0.78 0.75 0.41 RF mean 0.71 0.63 0.80 0.75 0.40 NN mean 0.74 0.74 0.74 0.73 0.41 Test/unknown set – in silico evaluation vs actual evaluation Results – accuracy metrics values (Yr) -0.5 0.0 0.5 1.0 1.5 01234 Distribution by trait state False positive rate Truepositiverate 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 -0.2900.290.580.871.16 Bari et al. (2014). Predicting resistance to stripe (yellow) rust in wheat genetic resources using Focused Identification of Germplasm Strategy (FIGS). Journal of Agricultural Science ROC plots (left) and density plots class prediction (right) Frequency
  14. 14.    ppa   1 Predicted probability of occurrence/resistance to RWA Current climate data Modelling/predictions Capturing the shift induced by climate A wheat landrace from Turkey collected and conserved in a genebank in 1948 was later re-discovered (in the 1980s) to carry genes for resistance to a range of fungal diseases that are still used in crop improvement programs (Atalan-Helicke 2012, FAO 2013). Longitude Latitude
  15. 15.    ppa   1 Predicted probability of occurrence Russian Wheat Aphid: Projected climate data - 2020 Modelling/predictions Capturing the shift induced by climate Longitude Latitude
  16. 16.    ppa   1 Predicted probability of occurrence Russian Wheat Aphid: Projected climate data - 2050 Modelling/predictions Capturing the shift induced by climate Longitude Latitude
  17. 17.    ppa   1 Results – Model predictions 0 50 100 150 0204060 Longitude Latitude
  18. 18.    ppa   1 Sub-Setting procedure – adjustment based on phenology Alignment of data based on phenology To reduce: • The “out phase” differences due to different growing seasons/periods The daily data were derived from models involving the proposed model by Epstein (1991) as a sum of harmonic components.
  19. 19.    ppa   1 Modelling/predictions Capturing the shift induced by climate Based on the estimation of the duration of the period during the year in which neither moisture nor temperature are limiting to plants. Target specific phase of crop development Bari et al. (in press). Searching for climate change related traits in plant genetic resources collections using Focused Identification of Germplasm Strategy (FIGS). Options Méditerranéennes. Alignment of data based on phenology
  20. 20.    ppa   1 Accuracy and agreement parameters of aligned data Sub-Setting procedure – adjustment based on phenology - results Data type AUC Omission rate Sensitivity Specificity Correct classification Kappa monthly 0.81 0.28 0.72 0.90 0.86 0.61 daily data 0.82 0.30 0.70 0.93 0.88 0.64 aligned daily data 0.83 0.28 0.72 0.95 0.90 0.70 210 days False positive rate Truepositiverate 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 -0.2900.290.580.871.16
  21. 21.    ppa   1 Modelling/predictions Capturing the shift induced by climate - verification 0 100 200 300 020406080 x$x x$ysmth Data alignment to growing season Algorithms Separate phase variation from amplitude variation 0 100 200 300 50100150200 x$x x$ysmth Site (i) : Si(xi, yi) Site (j): Sj(xj, yj) day rainfall day http://mpe2013.org/
  22. 22.    ppa   1 Future directions Explore the use of a variety of applied mathematics approaches in relation to phenology aspects of both the pathogen and the host. host pathogen
  23. 23.    ppa   1 Beyond our tools, and through them, it is old Mother Nature that we reach, an experience that we share with gardeners, sailors, or poets. Au delà de l’outil, et à travers lui, c’est la vieille nature que nous retrouvons, celle du jardinier, du navigateur, ou du poète. Saint-Exupéry Thank you

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