Simone Vincenzi's talk at the 2014 meeting of the American Society of Naturalists in Asilomar, CA.

1. ASN meeting, Asilomar 2014
Combining demography with quantitative
genetics to infer the adaptive potential of
small populations
Simone Vincenzi
EU Marie Curie Fellow
University of California Santa Cruz, US
Polytechnic of Milan, Italy

2. People
Marc Mangel
Gianluigi Rossi
Alain Crivelli
Giulio De Leo
Dusan Jesensek
Hans Skaug
Slovenian field crew

3. Extreme events
100-yr flood
Climate change 
Time (yrs)

4. Small populations
• Peculiarities
– Bottleneck effect
– Less evolutionary potential
– Persistence dependent on year effects
– High risker of extinction following extremes
– Generalities are difficult
• Can the (potential) evolution of traits increase
persistence of small populations in an increasing
extreme world?
• Eco-evolutionary feedbacks?

5. Model system
Marble trout
Slovenia
100 km

6. Marble trout
Stream-living
Size is highly plastic
Max age ~ 10 yo
α ~ 3-4 yo
Longitudinal data

7. Marble trout populations
3 basins
Baca
Idrijca
Soca
30-1000 fish in each
population
Isolated for 1000s of
yrs
High amongpopulation genetic
differentiation
Extremely low
within-population
genetic variability
10 km

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

9. DD growth and resilience
Low density
L = α D−β
High density

10. Variability of growth

11. Role of growth variation
• Evolution of growth rates may increase
persistence probability of marble trout
1.Need to tease apart shared and individual
contribution to growth
2.Life-history model
• Growth
• Other life-history traits
• Occurrence of floods
1.Run simulations of scenarios with or without
evolution of growth rates

12. Random-effect vB growth model
L(t ) = L∞ (1 − e
− k ( t − t0 )
)
L∞
L∞ = f ( x, u )
k = f ( x, v )
k
t0
x = covariates or groups
u,v = individual random effects

13. Relationship between REs
L(t ) = L∞ (1 − e − k (t −t0 ) )

14. Differences between year classes
Zakojska
Gacnik

15. Heritability
Carlson SM, Seamons TR (2008) A review of quantitative genetic components of fitness in
salmonids: implications for adaptation to future change. Evol Appl 1:222–238

16. Growth-mortality trade-off
1.0
Survival
0.6
0.4
σ
200
100
0.0
0.2
0.5
1
1.5
2.5
0
Length (mm)
b
0.8
a
300
400
Growth
5
10
15
20
25
Age (year)
0
5
10
15
20
25

17. Stream discharge
10 km

18. Simulations
EVO
Simulation year
FIX
1
Evolution of growth rates
FL-FIX
FL-EVO
No evolution
Evolution
No floods
150
Floods
300

19. Realized length
10-y moving average
EVO
FIX

20. Proportion of juveniles
10-y moving average
EVO
FIX

21. Population size
EVO
FIX
10-y moving average

22. Same risk of extinction

23. Why
•
•
•
•
Faster life histories, less BOFF
Faster life histories mainly after collapses
Realized length is only slightly different
Variation in egg production due to variation in
intrinsic growth is not crucial (given densitydependent growth and early survival)
≈

24. Conclusions
Growth rates
– may evolve
– have effects on population traits
through eco-evolutionary feedbacks
– without affecting persistence
• Further insights from parentage
analysis
• Non-linearities of densitydependence