FIRB presentation by Simone Vincenzi in 2011 in Rome

1. Assessing rapid evolutionary
responses in natural populations
to climate change and
intensification of weather
extremes:
Ministero dell’Istruzione, Università e Ricerca
Rome 26/07/2011
F
I
R
B
extremes:
an integrated approach
combining genetics and
evolutionary modeling
Principal Investigator: Simone Vincenzi
Institution: Università di Parma
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0
1
0

2. My research interests
• Adaptive processes and traits
increasing resilience of natural
populations
• Climate change and evolution of
life-histories University of
NOOA
Santa Cruz
life-histories
Murres Northern fur seals Salmon
University of
California

3. Climate change
IPCC, 2007. Fourth Assessment Report: Climate Change.
Most likely

4. Climate change and extreme events
Catastrophes
MaxFlow
Time (yrs)
100-yr flood
Climate change
increased intensity, altered
frequency and seasonality of
catastropic events

5. Natural populations and climate change
• (i) move (ii) adapt (iii) die
• Rapid responses and adaptations
• Adaptations to altered patterns of catastropic
events?
• Lack of methods and model systems
Kittiwake Coral fishArtic Fox

6. Novel approach
• Combining:
– molecular genetics (genes under selection and
genetic variation)
– demography and life-histories
– climate predictions
– eco-evolutionary simulation framework
• Predicting:
– risk of extinction
– adaptations
– population dynamics (size, fluctuations, age and
size structure)

7. Marble trout
Trebuscica Marble trout
Salmo marmoratus
10 wild isolated populations in Slovenia
monitored since 1996
> 10,000 individuals sampled
fish farm experiments
Distribution of marble trout
Marking

8. Marble trout populations
• 3 basins
• 30-500 fish in each
population
• Isolated for 1000s of
Baca Idrijca Soca
9
10
• Isolated for 1000s of
yrs
• High among-population
genetic differentiation
• Extremely low within-
population genetic
variability
• Genetic bottlenecks at
neutral loci
1
1 Gatsnik
2 Zadlascica
3 Lipovscek
4 Huda Grapa
5 Svenica
6 Studenc
7 Trebuscica
8 Upper Idrica
9 Zakojska
10 Gorska

9. Marble trout and climate change
Major flood
Medium flood
SS Spring
AA Autumn
YEAR
STREAM BASIN 99 00 01 02 03 04 05 06 07 08 09 10
Huda AA AA AA SS
Zakojska AA AA SS
Baca
Gorska AA AA
Lipovesck Soca AA AA AA AA AA SS AA
Zadlascica AA AA AA SS AA
Trebuscica AA AA AA AA SS SS
Studenc AA AA AA AA SS SS
Idrijca
Idrijca AA AA SS SS
Gatsnick AA AA SS SS
Svenica AA AA AA SS SS
9
1
0
1

10. Main Research Questions
i. Molecular genetics
– adaptive divergence (among populations)
– adaptive evolution through time (within
populations)
ii. Demographic and statistical analysesii. Demographic and statistical analyses
– adaptive life-histories and plasticity
– present and future patterns of floods
iii. Eco-evolutionary modeling
– evolutionary history
– evolution of life-histories and genetic
composition with climate change

11. Research Unit
Parma Research Unit
Modeling Genetics
Research Unit
Research groups
Institutions
Parma PadovaNOAA SC
All leading research groups in their fields
Statistics &
Modeling
Salmonid
genetics
Adaptive
genetics

12. Work plan
Mol genetics Field data Fish farm
I year
Demographic model
Mol genetics
II year
Flood patterns
Eco-evolutionary model
Selective forces Predictions
Mol genetics
III year

13. Molecular genetics
SNP
• Adaptive genetic
differentiation
- body growth
- time of spawning
- morphology
• SNPs as molecular markers• SNPs as molecular markers
• Discovery using Next
Generation Sequencing
• SNPs discovery will be
outsourced
• Genome regions under
selection, candidate loci, QTLs
• Heritability

14. Eco-Evolutionary Model
• Complex quantitative genetic traits
• Different levels of biological organization
Past
environments
Mean traits
Climate change Novel
environment
Individual
fitness
Population
performance
• What happened? - Approximate Bayesian Computation
• What will happen? - Forward Stochastic Simulations
Evolutionary
history
Plasticity
Genetic
variation
fitness
Evolution
performance
Population
size
Persistence

15. Specific aims and expected results
• Conservation of marble trout
• Conservation of fish population
• Disentangle contributions of ecology
Local
• Disentangle contributions of ecology
and environmental factors on
evolution
• Methodology for predicting
consequences of intensification
of weather extremes
Global

16. Why fund this project?
• Model system
• Research questions of
exceptional relevance
• Novel integrated
methodology
• Interdisciplinary,
international and
outstanding expertise
Novel
environment
Individual
fitness
Evolution
Population
performance
Population
size
Persistence

17. Assessing rapid evolutionary
responses in natural populations
to climate change and
intensification of weather
extremes:
Ministero dell’Istruzione, Università e Ricerca
Rome 26/07/2011
F
I
R
B
extremes:
an integrated approach
combining genetics and
evolutionary modeling
Principal Investigator: Simone Vincenzi
Institution: Università di Parma
2
0
1
0

18. Financing
Co-financing Hiring
General
expenses
P.I. salary
Chiara
fama
Expenses
P.I. A.1.1 A.1.2 A.2 B C.1 C.2 D E F G Total
Simone
Vincenzi
€181,458 €0 €120,000 €261,275 €134,000 €0 €5,000 €15,000 €0 €19,875 €736,608
• Modeling
- statistics
- eco-informatics
• Molecular genetics
- adaptive divergence
- parentage analysis

19. Financing

20. Body growth

21. Approximate Bayesian Computation

22. Population collapses

23. Eco-Evolutionary Model
• Complex quantitative
traits
• Stochastic simulations
• Backward (Approximate
Bayesian Computation)
What are the most likely– What are the most likely
combination of
parameters?
• Forward
– Persistence? Extinction?
– Demographic or
evolutionary
– Management actions

24. Novelties in methods
• SNPs as molecular marker discovered
with NGS
– genome regions under selection (body
growth, time of spawning, morphology),
candidate loci, QTLs
– Parentage analysis with extremely low– Parentage analysis with extremely low
variability (heritability)
• Integration of genetics, field data,
experiments
• Eco-evolutionary modeling with
individual-based models

25. Costs of SNPs discovery and
genotyping

26. Costs of SNPs discovery and
genotyping