Vincenzi - Wildlife seminar series, UC Berkeley, October 2014

Simone Vincenzi

EU Marie Curie Fellow University of California Santa Cruz, US Polytechnic of Milan, Italy simonevincenzi.com

UC Berkeley, October 24th 2014

Genetic and life-history variation in small populations

living in stochastic environments

Collaborators

UCSC/SWFSC

Stanford

U. of Bergen

Slovenia

Marc  Mangel  

Hans  Skaug  

Giulio  De  Leo  

Carlos  Garza  

Slovenian  field  crew   Alain  Crivelli   Dusan  Jesensek  

David  Vendrami  

Ecology in the 21st century

Past environments

Evolutionary history

Pheno traits

Genetic variation

Climate change Novel environment

Individual fitness

Evolution

Population performance

Population size

Persistence

Time

Marble trout Salmo marmoratus

Marble trout

•  Resident stream-living salmonid endemic in: –  Adriatic basin of Slovenia and ex-

Yugoslavia –  Po river basin in Northern Italy

•  High plasticity of body size, up to 20-25 kg

•  Spawning in November •  Emergence in June •  Maximum age 10 to 15 yo •  Low movement

Phylogeny  

Crête-Lafrenière, A., Weir, L. K., & Bernatchez, L. (2012). Framing the Salmonidae family phylogenetic portrait: a more complete picture from increased taxon sampling. PloS One, 7(10), e46662

Conservation project in Slovenia

Why –  At risk of extinction, major risk

hybridization with S. trutta and displacement by rainbow trout O. mykiss

Goal –  Conservation and “genetic

rehabilitation” Where

–  Soca River –  Streams protected –  Fly-fishing

When –  1993

1993

Slovenia

Only surviving pure marble trout populations

Soca

Gacnik

TrebuscicaIdrijca

Studenc

Sevnica

Zakojska

Huda

Gorska

Lipovscek

Zadlascica 30-1000 fish in each population

Isolated

High among-population genetic differentiation

Low within-population genetic variability

Fumagalli, et al. (2002). Extreme genetic differentiation among the remnant populations of marble trout (Salmo marmoratus) in Slovenia. Molecular Ecology, 11(12), 2711–2716

Gacnik

TrebuscicaIdrijca

Studenc

Sevnica

Zakojska

Huda

Gorska

Lipovscek

Zadlascica Once a year Zadlascica Trebuscica Studenc Svenica Zakojska* Gacnik*

Twice a year Huda* Lipovscek Lower Idrijca Upper Idrijca

* Whole population sampled

Huda 0

1000

2000

2000 2005 2010Year

Fish/ha

0

1000

2000

2000 2005 2010Year

Fish/ha

L Idrijca

0

1000

2000

2000 2005 2010Year

Fish/haU Idrijca

0

1000

2000

2000 2005 2010Year

Fish/haLipovesck

0

1000

2000

2000 2005 2010Year

Fish/ha

Zadlascica

0

1000

2000

2000 2005 2010Year

Fish/ha

Trebuscica

0

1000

2000

2000 2005 2010Year

Fish/ha

Zakojska

0

1000

2000

2000 2005 2010Year

Fish/haGacnik

Gacnik

TrebuscicaIdrijca

Studenc

Sevnica

Zakojska

Huda

Gorska

Lipovscek

Zadlascica

30-70 fish

10-300 fish

>1000 fish

Past environments

Evolutionary history

Pheno traits

Genetic variation

Climate change Novel environment

Individual fitness

Evolution

Population performance

Population size

Persistence

Genome •  By  sequencing  the  genome  we  may  inves3gate  

– how  genotype  leads  to  phenotype  – pressures  and  processes  that  shape  diversity  in  popula3ons  

Peterson, et al. (2012). Double digest RADseq: an inexpensive method for de novo SNP discovery and genotyping in model and non-model species. PloS One, 7(5), e37135.

Genetic structure of marble trout

Fumagalli, et al. (2002). Extreme genetic differentiation among the remnant populations of marble trout (Salmo marmoratus) in Slovenia. Molecular Ecology, 11(12), 2711–2716.

Idrijca drainage

All pairwise Fst 0.31-0.88

Zadla

Huda

Lipo

Prede

Gacnik

TrebuscicaIdrijca

Studenc

Sevnica

Zakojska

Huda

Gorska

Lipovscek

Zadlascica

Genetic structure

Pustovrh, G., Sušnik Bajec, S., & Snoj, A. (2011). Evolutionary relationship between marble trout of the northern and the southern Adriatic basin. Molecular Phylogenetics and Evolution, 59(3), 761–6.

18 nuclear loci

New technology

$100,000,000

10,000,000

1,000,000

100,000

10,000

1,000

200320052007200920112013

Year

Cos

t per

gen

ome

Next-gen sequencing parallelizes the sequencing process, producing thousands or millions of sequences concurrently

Sequencing of marble trout

•  13 fish from Huda •  8 from

–  Lower Idrijca –  Upper Idrijca –  Trebuscica –  Zadlascica –  Lipovscek

Huda

Zadla Prede

Lipo

Idrijca drainage

Pipeline  •  Illumina MiSeq •  ddRad sequencing •  Size selection ~ 500 bp •  Stacks for de novo assembly and

genotyping •  Finding SNPs à variation in a single

DNA base within a sequence Davey, J. W. et al. (2011). Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nature Reviews Genetics, 12(7), 499–510.

Catchen, J. M. et al. (2011). Stacks: building and genotyping loci de novo from short-read sequences. G3, 1(3), 171–82.

Genetic structure

MDS plot

~ 5 000 SNPs 14 msats

18 nuclear loci

MDS plot

Huda

Zadla Pred

Lipo

Idri drainage

Structure

K = 3

K = 6

Huda Lipo U Idri L Idri Zadla Trebu

Pritchard, J. K., Stephens, M., & Donnelly, P. (2000). Inference of population structure using multilocus genotype data. Genetics, 155, 945–959.

Pair-­‐wise  gene1c  differences  (Fst)  Huda   Lipo   U  Idri   Zadla   Treb  

Lipo   0.71    

U  Idri   0.75   0.57  

Zadla   0.77   0.62   0.59  

Trebu   0.74   0.55   0.40   0.59  

L  Idri   0.84   0.65   0.00   0.68   0.49  

0.31 – 0.88 (Fumagalli et al. 2002)

Inbreeding •  Occurs with the mating of individuals that are

genetically related •  Increased homozygosity and more likely occurrence

of recessive traits à possible inbreeding depression •  Individual inbreeding coefficients estimated from

genomic data (based on the observed vs. expected number of homozygous genotypes)

Inbreeding

Mean ± sd

0.4

0.6

0.8

Huda Lipo U Idri Zadla Trebu L Idri

Inbreeding

Points

•  Strong genetic divergence •  Little shared polymorphism •  High to very high inbreeding •  Adaptive divergence vs. drift?

Past environments

Evolutionary history

Pheno traits

Genetic variation

Climate change Novel environment

Individual fitness

Evolution

Population performance

Population size

Persistence

Phenotypic traits

•  Survival

•  Growth

•  Morphology

•  Reproductive traits –  age at maturity, iteroparity, size dependency

100

300

500

1 3 5 7 9

Age

Leng

th (m

m)

Gacnik

100

300

500

1 3 5 7 9

Age

Leng

th (m

m)

Zakojska

100

300

500

1 3 5 7 9

AgeLe

ngth

(mm

)

Huda

100

300

500

1 3 5 7 9

Age

Leng

th (m

m)

Lipo

100

300

500

1 3 5 7 9

Age

Leng

th (m

m)

L Idri

100

300

500

1 3 5 7 9

Age

Leng

th (m

m)

U Idri

100

300

500

1 3 5 7 9

Age

Leng

th (m

m)

Zadla

100

300

500

1 3 5 7 9

Age

Leng

th (m

m)

Trebu

Survival

ϕ(x) p(y) model npar AIC DeltaAIC Phi(~time)p(~Flood) 20 4698.23 0.00 Phi(~time)p(~Age) 20 4698.75 0.52 Phi(~time)p(~1) 19 4701.14 2.91 Phi(~time)p(~Coh) 20 4701.91 3.67

Laake, J. L., Johnson, D. S., & Conn, P. B. (2013). marked: An R package for maximum-likelihood and MCMC analysis of capture-recapture data. Methods in Ecology and Evolution, 4, 885–890

Survival Capture

Survival

Mean ± 95% CI

0.2

0.3

0.4

0.5

0.6

Gac Huda L Idri Lipo Stu Sve Trebu U Idri Zadla Zak

Ann

ual s

urvi

val

Growth

100

300

500

1 3 5 7 9

Age

Leng

th (m

m)

Gacnik

100

300

500

1 3 5 7 9

Age

Leng

th (m

m)

Zakojska

100

300

500

1 3 5 7 9

Age

Leng

th (m

m)

Huda

100

300

500

1 3 5 7 9

Age

Leng

th (m

m)

Lipo

100

300

500

1 3 5 7 9

Age

Leng

th (m

m)

L Idri

100

300

500

1 3 5 7 9

Age

Leng

th (m

m)

U Idri

100

300

500

1 3 5 7 9

Age

Leng

th (m

m)

Zadla

100

300

500

1 3 5 7 9

Age

Leng

th (m

m)

Trebu

Growth model

0( )( ) (1 )k t tL t L e− −∞= −

Vincenzi, S. et al. (2014). Determining individual variation in growth and its implication for life-history and population processes using the Empirical Bayes method. PLoS Computational Biology, 10, e1003828.

L∞

k

0t

Expected asymptotic size

Mean ± 95% sd

300

350

400

450

500

Gac Huda L Idri Lipo Stu Sve TrebuU IdriZadla Zak

L∞ (m

m)

Survival-Growth among populations

r = -0.79 p<0.05

Gac

ZakHuda

L IdriU Idri

Zadla

Trebu

StuSve

280

320

360

400

0.2 0.4 0.6 0.8σ

L∞ (m

m)

Example for Zakojska

Survival-Growth within populations

Points

•  High variability in survival and growth among populations

•  Likely trade-off between growth and survival at the population level, not within population

•  Selective forces?

Past environments

Evolutionary history

Mean traits

Plasticity

Genetic variation

Climate change Novel environment

Individual fitness

Evolution

Population performance

Population size

Persistence

risk

0

500

1000

1500

'00 '04 '08 '12

Year

Fish

/ ha

Gorska

risk

0

500

1000

1500

'00 '04 '08 '12

Year

Fish

/ ha

Gorska

risk

0

500

1000

1500

'00 '04 '08 '12

Year

Fish

/ ha

Lipovscek

risk

0

500

1000

1500

'00 '04 '08 '12

Year

Fish

/ ha

Lipovscek

risk

0

500

1000

1500

'00 '04 '08 '12

Year

Fish

/ ha

Zakojska

risk

0

500

1000

1500

'00 '04 '08 '12

Year

Fish

/ ha

Zakojska

Marble

Slovenia

0 1000 2000Rainfall (mm)

Extreme events are increasingly relevant

Montpellier – Late September

Parma – 10 days ago

Extinct

0

500

1000

1500

'00 '04 '08 '12

Year

Fish

/ ha

?

0

500

1000

1500

'00 '04 '08 '12

Year

Fish

/ ha

Safe

0

500

1000

1500

'00 '04 '08 '12

Year

Fish

/ ha

Zakojska

Lipovscek

Gorska

Time

risk

0

50

100

5 10 15 20

Year

Pop

ulat

ion

size

Population bottleneck

Time

risk

0

50

100

5 10 15 20

Year

Pop

ulat

ion

size

Time

risk

0

50

100

5 10 15 20

Year

Pop

ulat

ion

size

Extinct

0

500

1000

1500

'00 '04 '08 '12

Year

Fish

/ ha

Time

risk

0

50

100

5 10 15 20

Year

Pop

ulat

ion

size

Time

risk

0

50

100

5 10 15 20

Year

Pop

ulat

ion

size

Safe

0

500

1000

1500

'00 '04 '08 '12

Year

Fish

/ ha

Who’s passing through?

Does it matter?

Time

risk

0

50

100

5 10 15 20

Year

Pop

ulat

ion

size

Evolution following floods

Vincenzi, S., Crivelli, A. J., Satterthwaite, W. H., & Mangel, M. (2014). Eco-evolutionary dynamics induced by massive mortality events. Journal of Fish Biology, 85, 8–30.

Gac

ZakHuda

L IdriU Idri

Zadla

Trebu

StuSve

280

320

360

400

0.2 0.4 0.6 0.8σ

L∞ (m

m)

Trade-off related to flood events?

Lipovscek

Safe

0

500

1000

1500

'00 '04 '08 '12

Year

Fish

/ ha

Lipovscek

risk

0

500

1000

1500

'00 '04 '08 '12

Year

Fish

/ ha

Downstream

Upstream

Downstream

Downstream (2010) 16 adults

Upstream (2011) 15 adults

Cohort 2011 (as of June 2014) 215 downstream 65 upstream

Questions

•  Who reproduced after the 2009 flood? •  Where were the fish in 2011 cohort born? •  Were there any family-related consequences

in terms of fitness?

How parentage works •  Molecular markers for parentage inference are

highly polymorphic –  Microsatellites

•  SNPs –  abundant –  low genotyping error rates –  scoring SNP genotypes is easy

•  Assignment of trios (mother, father, offspring) is probabilistic (genotyping error, chance)

•  ~80-100 SNPs, reliable reconstruction of the pedigree

Anderson, E. C., & Garza, J. C. (2006). The power of single-nucleotide polymorphisms for large-scale parentage inference. Genetics, 172, 2567–82

Steps for parentage

•  Discover a panel of molecular markers using Next-Gen data

- 77 polymorphic loci •  Genotyping

- ~ 400 fish •  Pedigree reconstruction

- using FRANz

Markus Riester, Peter F Stadler, Konstantin Klemm 2009. FRANz: Reconstruction of wild multi-generation pedigrees.. Bioinformatics. 25:2134-2139.

Parentage

•  FRANz    – Single  parent  – Mul3-­‐genera3on  – Fast    

Markus Riester, Peter F Stadler, Konstantin Klemm 2009. FRANz: Reconstruction of wild multi-generation pedigrees. Bioinformatics. 25:2134-2139.

Offspring Parent_1 Parent_2 Posterior 2511 11676 <NA> 0.9831 2514 62197 11103 0.9416 2516 G1994 <NA> 1.0000 2517 G2071 G1991 0.9934 2519 G2071 G1991 1.0000

Statistics Downstream Upstream

0

100

200

Offspring Assigned

# of

fspr

ing

0

35

70

Offspring Assigned

# of

fspr

ing

215 197 63 63

0

2

4

6

8

0 10 20 30 40 50Number of offspring

Num

ber

of p

aren

ts

0

2

0 10 20 30 40 50Number of offspring

Num

ber

of p

aren

ts

Who Mark Sex yob 11676  M 2005 13269 F 2007 G1991 M 2006 G2071 F 2006

Mark Sex yob G1994 M 2006 G1991 M 2006 G2071 F 2006

Downstream

Upstream

~45% of downstream offspring born upstream

0

2

0 10 20 30 40 50Number of offspring

Num

ber

of p

aren

ts

42 offspring

31 offspring

0

2

4

6

8

0 10 20 30 40 50Number of offspring

Num

ber

of p

aren

ts

Survival up to 2014 •  Es3mated  survival  using  3me,  season,  family,  and  internal  relatedness  as  predictors  

•  Internal  relatedness  is  a  measure  of  inbreeding,  very  posi3ve  values  à  highly  inbred  

0

40

80

120

-0.5 0.0 0.5 1.0Internal Relatedness

# of

fish

No differences •  Linear and non-linear models with internal

relatedness poorly supported (ΔAIC = 50) •  No difference in survival (and growth) between big

family and others

0.4

0.5

0.6

Fam Other

Ann

ual s

urvi

val

Past environments

Evolutionary history

Pheno traits

Genetic variation

Climate change Novel environment

Individual fitness

Evolution

Population performance

Population size

Persistence

Ecology in the 21st century

Extreme events