The document discusses using genetic analysis to reconstruct the demographic history of populations by examining population and conservation genetics data, specifically looking at how habitat fragmentation can impact genetic diversity within species and determining events like bottlenecks that have influenced current diversity. It also addresses new methods and software for simulation that can help separate the effects of population structure versus population crashes on bottleneck signals.

1. Génétique des populations dans
l’espace et dans le temps:
reconstruire l’histoire démographique
des populations
Laboratoire Evolution et Diversité Biologique, CNRS, Toulouse
Population and Conservation Genetics Group, IGC, Portugal
Biodiversité et Informatique, Grenoble, 28 Juin 2011
Photos: E. Quéméré, F. Jan, B. Goossens

2. POPULATION AND CONSERVATION GENETICS
POPULATION GENETIC DATA
Y chromosome mitochondrial DNA
---CAGTCAGTCAGT---
CONSERVATION GENETICS HUMAN PAST DEMOGRAPHY
Habitat Fragmentation Neolithic Transition
Population Decline Admixture in Human Populations
Admixture in Domesticates
G T
G G T T G G G G G
NEW METHODS / SOFTWARE

3. Problématiques
• Impact de la fragmentation de l’habitat sur la diversité
intra-spécifique :
– Diversité génétique et tailles des fragments
– Distance géographique entre fragments sur la différenciation
génétique
– Barrières au flux géniques (routes, rivières, fleuves, villages, etc.) ?
– Déterminer, quantifier et dater d’éventuels événements
démographiques (goulots d’étranglements, expansions, mélanges)
ayant influencé la diversité actuelle
– Importance relative de la fragmentation d’origine anthropique et
des phénomènes naturels
– Importance de la structure spatiale, des expansions et contractions
spatiales, de la structure sociale

4. The Kinabatangan Sulu Sea
Floodplain
1 cm = 5
km
Agricultural lands
(mostly oil palm
plantations)
Lower Kinabatangan
Wildlife Sanctuary
Virgin Jungle Reserves
Kinabatangan River
Main road (Sandakan
– Lahad Datu)
Villages

5. The Data
– 279 samples collected with 32 twice (hair + faeces)
– 247 individuals (176 nests and 71 faeces) extracted
and amplified
– 200 individuals genotyped for 14 microsatellite loci
divided into 9 samples (S1 to S9)
– 14*200 = 2800 genotypes (7 missing = 0.25%)
– 2 di and 12 tetra loci.

6. Size Model of Beaumont 1999 and Storz et Beaumont, 2002
Linear Exponential
N1 > N0 ? Parameters:
N0 : current size
N1 : ancestral size
ta: nb of generations
since the pop started
to increase or
decrease
N0 Reparameterize:
r = N0/N1
tf = ta/ N0
 = 2 N0 
N1 < N0 ?
Sampling
ta (in generations)
Past Present Time

7. Model of Beaumont 1999
Microsatellite
Data
Storz and Beaumont, 2002
Linear Exponential
Microsatellite
Data
r = N0/N1
tf = ta/ N0 Exponential
 = 2 N0 
N 0, N 1, t a, 

8. Population size change

9. Time since the population size change
FE: Forest exploitation
F: Farmers
HG: Hunter-gatherers
thin line: S1
thick line : S2

10. Conclusions (at the time)
1. Strong signal for a bottleneck
2. The signal is robust to the mutation model
3. The signal is robust to a linear or exponential decrease
(assuming a specific mutation model)
4. The population decrease is very important
5. The population decrease is very recent: recent anthropogenic
changes

12. Habitat Fragmentation (and loss) in Daraina
44 000 ha of fragmented forest
Loky River Indian Ocean
Manambato River
Golden-crowned sifaka
Propithecus tattersalli
Erwan Quéméré

13. Habitat Fragmentation (and loss ?) in Daraina
44 000 ha of fragmented forest
Océan Indien
Pictures: E. Quéméré

14. Habitat Fragmentation (and loss) in Daraina
Faeces from 230 individuals
(105 social groups)
13 microsatellites
Questions:
1. Role of the road as a barrier to
gene flow
2. Role of savanna / grasslands
3. Role of the Manankolana river

15. Habitat Fragmentation (and loss) in Daraina
Rôle de la rivière Manakolana:
* structure la diversité
* ancienne barrière ?
* lieu de peuplements humains ?
* etc.

16. Habitat fragmentation and loss
time
time

17. GENETIC DIVERSITY
Past
NEW
MUTATIONS
POPULATION
TIME SIZE (FINITE)
LOSS OF
MUTATIONS
Present
MUTATIONS
GENETIC DIVERSITY
GENETIC DRIFT

18. GENETIC DIVERSITY
Past Ne = 1000
Ne = 100
Present
Present Ne = 100 Ne = 1000
Ne = 500
Present
GENETIC DIVERSITY
Different demographic histories can produce
similar or counter-intuitive results

19. GENETIC DIVERSITY
EQUILIBRIUM (N1 pre-bottleneck)
HETEROZYGOSITY: function(nA, freq.)
NUMBER OF ALLELES
EQUILIBRIUM (N0 post-bottleneck)
TIME
DIFFERENT TEMPORAL DYNAMICS OF THE TWO MEASURES

20. GENETIC DIVERSITY
Past Population size change :
recognizable signature
TIME A B ... N
Present
PROBLEM : STRUCTURED POPULATIONS GENERATE A SIMILAR
SIGNATURE
TRUE AND FALSE SIGNATURES: WHO SHOULD YOU BELIEVE?...

21. Habitat fragmentation and loss
time
time

22. Effect of population structure on bottleneck signals
• Models of population structure (100 demes in all simulations) Stepping-stone
– n-island model (100 islands)
– Stepping-stone (10 x 10) (toroidal) n-island
• Parameters used
– Stepwise mutation model assumed to simulate data
– FST values used { 0.01 ; 0.05 ; 0.1 ; 0.25 } Differentiation
– θ values used { 1; 10 }
– Number of loci { 5 ; 20 } Diversity
– 50 individuals sampled (100 genes) (mutation and pop size)
• Sampling schemes :
– n-islands model: samples from 1, 2, 10 and 50 demes
– Stepping stone model: samples from 1, 2 neighbouring and 2 distant demes
• 10 independent data sets for each parameter set (except 20 loci and 10
demes)

23. Effect of population structure on bottleneck signals
Can we separate population structure
from population crash?
Bottleneck signals

24. Conclusions
1. Population structure can mimic bottleneck signals
2. The signal is particularly strong when
1. Genetic differentiation is high (gene flow is limited)
2. Genetic diversity is high
3. The number of loci used is large
3. The effect is less important when more than one
population is sampled
Need to
develop methods that can separate these two kinds of
scenarios (structure versus bottleneck)
ad hoc ways to minimize the genetic structure effect is to
spread sampling (one individual per “population”)

25. Habitat fragmentation and loss
time
or population structure
time
or both ?
time

27. Vers de nouveaux outils de simulation (1)
SPLATCHE-like: L. Excoffier et Cie
R. Rasteiro
P-A Bouttier
V. Sousa

28. Layer 1
cell
K: carrying capacity
F: friction
m: migration rate
r: growth rate
γ: admixture
Genetic parameters: mutation rates,
Layer 2 sequence length, etc

29. Vers de nouveaux outils de simulation (2)
SG1 SG2
MATRIX (ngroups*ngroups)
popstructure.txt
S1 S2 S3 S4 S5
S1 0 1 0 1 1
S2 1 0 1 1 1
SG5 SG4
S3 0 1 0 1 1
S4 1 1 1 0 1
S4 1 1 1 1 0
SG3
• SG1 is connected to pops 2, 4, 5
•SG3 is connected to pops 2, 4, 5
•SG2, SG4, SG5 are connected to all other SG’s
E. Quéméré – C. Vanpé – B. Parreira

30. 1 MALE, 1 FEMALE 1 DOMINANT M/F n MALES,
nm=1; nf=1; ndm=1; ndf=1 n NON-DOMINANT F/M m FEMALES
nm=1; nf=n; ndm=1; ndf=0 nm=n; nf=m; ndm=0; ndf=0
nm=n; nf=1; ndm=0; ndf=1
Dominance = priority in reproduction

31. CONCLUSIONS
• La génétique du paysage a tendance à ignorer le temps
• Les méthodes d’inférence en génétique des populations
ont tendance à ignorer l’espace
• Comment intégrer ces deux notions ?
• Madagascar est un lieu privilégié pour cela: colonisation
humaine récente

32. JE VOUS REMERCIE POUR VOTRE ATTENTION
Anna Rozzi

33. Brigitte Crouau-Roy Univ. Paul Sabatier, Toulouse, France
Lounes Chikhi CNRS and Univ. Paul Sabatier, Toulouse, France
Instituto Gulbenkian de Ciência, Oeiras, Portugal
Bárbara Parreira, Rita Rasteiro, Vitor Sousa Inst. Gulbenkian de Ciência, Oeiras,
Portugal
Pierre Luisi, Pierre-Antoine Bouttier INSA, Toulouse, France
Benoît Goossens Cardiff Univ., UK – Sabah Wildlife Dept, Malaysia
Mark Beaumont: Reading Univ., UK
Erwan Quéméré: Univ. Paul Sabatier,Toulouse, France
Pedro Fernandes: Bioinformatics Unit, IGC