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

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

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

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)

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

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

ppa 1

The highest diversity of

wild cowpea

-20 -10 0 10 20 30 40 50

LONGITUDE

-40

-20

0

20

LA

TIT

UD

E

10

0 10 20 30 40 50 Distance

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Co

eff

icie

nt

Observed

Modeled

Cowpea distribution pattern

IITA Genebank - cowpea

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

ppa 1Fragmentation (Df)

Fractal

(Db)

Cowpea distribution pattern

Taxon 1 Taxon 12

Taxon 10 Taxon 11

Space-filling

Fragmented

Area

Patches

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

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)

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

Fre

qu

ency

Tru

e p

osi

tive

rat

e

False positive rate

Environment

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)

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

01

23

4

Distribution by trait state

False positive rate

True

pos

itive

rat

e

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

-0.2

90

0.29

0.58

0.87

1.16

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)

Fre

qu

ency

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

La

titu

de

ppa 1

Predicted probability of occurrence Russian Wheat Aphid:

Projected climate data - 2020

Modelling/predictions Capturing the shift induced by climate

Longitude

La

titu

de

ppa 1

Predicted probability of occurrence Russian Wheat Aphid:

Projected climate data - 2050

Modelling/predictions Capturing the shift induced by climate

Longitude

La

titu

de

ppa 1

Results – Model predictions

0 50 100 150

020

4060

Longitude

Latit

ude

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.

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

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

Tru

e p

ositiv

e r

ate

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

-0.2

90

0.2

90.5

80.8

71.1

6

ppa 1

Modelling/predictions Capturing the shift induced by climate - verification

0 100 200 300

020

4060

80

x$x

x$ys

mth

Data

alignment to

growing season

Algorithms

Separate phase

variation from

amplitude variation

0 100 200 300

50

100

150

200

x$x

x$ysm

th

Site (i) : Si(xi, yi) Site (j): Sj(xj, yj)

day

rain

fall

day

http://mpe2013.org/

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

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