Assessing the potential of microsatellite markers to infer origin and movements of locust swarms : The case studies of L. migratoria in Madagascar and C. terminifera in Australia

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Assessing the potential of microsatellite markers

to infer origin and movements of locust swarms:

the case studies of

L. migratoria

in Madagascar

and

C. terminifera

in Australia

Chapuis MP, Loiseau A, Popple JA, Michalakis Y, Lecoq M, Franc A, McCulloch L, Simpson S, Estoup AE, Sword G

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Introduction

Locusta migratoria

in Madagascar

1992-1996 1997 1998 Sugar Cane Outbreaks : 1006 _{individuals/swarm} Remissions : 1-100 individuals/hectare

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Introduction

-Chortoicetes terminifera

in Australia

Outbreaks

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Introduction

Preventative control strategy

Malagasy National Anti-Locust Center (CNA)

Australian Plague Locust Commission (APLC)

- monitoring locust populations (locust surveys and light trap information)

- forecasting (meteorological, habitat, and density data) - early controls (chemical and biological)

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Introduction

Need for a better understanding of the spatial population

dynamics in the early outbreak stage

- limits of direct ecological approaches because of the great dispersal ability of locusts

- population genetics may be an useful alternative

WHAT IS THE POTENTIAL OF APPLICATION OF

POPULATION GENETICS TO THE MANAGEMENT

OF SWARMING LOCUSTS?

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Introduction

The genetic approaches might lack resolution for low

genetic differentiation among populations

- Demographic effect = outbreaking populations experience demographic flushes

Æ large number of effective migrants

- Behavioural effect = outbreaking insects travel together in migratory bands of nymphs and fly large distances in swarms of adults

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M&M - Sampling C. terminifera in Australia L. migratoria in Madagascar 2006-2007 > 50 populations 10 loci 2001-2004 12 populations 14 loci

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Presence of null alleles

M&M - Microsatellite markers

L. migratoria in Madagascar Æ

from 32 loci, 47% excluded by pre-testing from 14 remaining loci, 13% of null alleles

C. terminifera in Australia Æ

from 33 loci, 59% excluded by pre-testing from 10 remaining loci, 22% of null alleles

1 2 3 4 5 6 7 8 9 10 11 A B C _D E 1 2 3 4 5 6 7 8 9 10 11 A B C _D E

Null genotypes and heterozygote deficiencies 3’TTTCATCCTCCTAAATCGGA5’ 5’TGTAAACGACGGCCAGTGAT…(GT)_n… AATGTAGGAGGATTTAGCCT3’ binding site 2 Repeat region binding site 1 primer

×

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M&M - Microsatellite markers

Presence of null alleles

– HOW DO I ANALYSE DATA?

- Null alleles bias

- Efficiency of the method for correcting genotype datasets

INA

correction Æ 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 Dempster et al. 1977

Null allele frequency ˆ

r

Allocating a single new allele state common to all

genotyped populations Estimating null allele frequency ˆr

Adjusting allele and genotype frequencies based on ˆr

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Traditionnaly used estimator of genetic variation among populations

Presence of null alleles M&M - Microsatellite markers

F

_ST

(Weir 1996)

-0.05 0.05 0.15 0.25 0.35 0.45 0.01 0.1 1 10

Number of migrants per

generation

Absence of null alleles

Null allele frequency > 0.20

Estimating

F

_STby excluding null allele state

ENA

correction -0.05 0.05 0.15 0.25 0.35 0.45 0.01 0.1 1 10 -0.05 0.05 0.15 0.25 0.35 0.45 0.01 0.1 1 10

INA

correction Æ FreeNA software Panmixia is assumed ! http://www.montpellier.inra.fr/URLB/

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M&M - Microsatellite markers Presence of null alleles

Delineating clusters of population samples:

BAPS (Corander

et al. 2003)

Number of migrants per generation

% correct

population

structures

0 20 40 60 80 100 1 2 3 4 5

Absence of null alleles

Null alleles: r ≈ 0.20 0 20 40 60 80 100 1 2 3 4 5 INA correction 0 20 40 60 80 100 1 2 3 4 5

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C. terminifera in Australia

* A single genetic cluster of populations

* Global F_ST{ENA} = 0.001 [-0.002 - 0.004] Results L. migratoria in Madagascar * Global F_ST{ENA} = 0.002 [0.000 - 0.004] * A single genetic cluster of populations

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Comparative study in

L. migratoria

Results

Æ

absence or low levels of genetic differentiation are partly

associated with outbreaks

-0.02

0

0.02

0.04

0.06

0.08

Madagascar China Western Europe

ns

P

= 0.0001

Value per locus

Value for the 14 loci

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Conclusions

Homogenizing effect of outbreaks at a wide scale

Æ Lack of resolution to detect migrants and infer their origin

Perspectives

Æ integrating other type of data, such as demographic, behavioural, or environmental data

Æ use of genetic linkage disequilibrium information (e.g. Bayesian methods of Gaggiotti et al. 2002, 2004)

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