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.    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.    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.    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.    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.    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.    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.    ppa   1
Fragmentation (Df)
Fractal
(Db)
Cowpea distribution pattern
Taxon 1 Taxon 12
Taxon 10 Taxon 11
Space-filling
Fragmented
Area
Patches

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.    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.    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.    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.    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.    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.    ppa   1
Predicted probability of occurrence Russian Wheat Aphid:
Projected climate data - 2020
Modelling/predictions
Capturing the shift induced by climate
Longitude
Latitude

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.    ppa   1
Results – Model predictions
0 50 100 150
0204060
Longitude
Latitude

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.    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.    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.    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.    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.    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