Climate change impact on the seaweed Fucus serratus, a key foundational species on North Atlantic...

Introduction Paper I Paper II Paper III Overall conclusions

Climate change impact on the seaweedFucus serratus, a key foundational species

on North Atlantic rocky shores

Alexander Jü[email protected]

Faculty of Biosciences and AquacultureUniversity of Nordland

PhD Thesis, 01.06.2010–12.08.2013

1 / 56


CO2 increase since the industrial revolution

2 / 56


Recent climate change2001–2005 mean surface temperature anomaly

(Base Period = 1951–1981)Global mean = 0.54

[Hansen et al., 2006; PNAS]3 / 56


Climate change responses

..

Temperaturerise

.

Heat waves

.

Seasonalityshi

.

Oceanacidifica on

.

Migra on

.

Acclima on

.

Adapta on

.Species

4 / 56


High sensitivity of intertidal species

5 / 56


Seaweeds are key species in temperateNorth Atlantic regions

Between the 10℃ summer and the 20℃ winter isotherm

6 / 56



6 / 56


The focal species Fucus serratus

7 / 56


Fucus in the tree of life

[Cock et al., 2010; Nature]

[Coyer et al., 2006; Mol. Phylogenet. Evol.]

Hybridization

8 / 56


Life cycle and dispersal of Fucus serratus

[Braune, 2008; Meeresalgen]

♂ ♀ dioecious

Fecundity: 106 eggs per female

low genetic exchange

9 / 56




♂ ♀ dioecious

zygote dispersal: <10m



9 / 56




♂ ♀ dioecious

zygote dispersal: <10mpanmictic unit: 0.5–2km



9 / 56


Distribution of F. serratus in the North Atlantic

Occurrence records

10 / 56


Distribution of F. serratus in the North AtlanticLast Glacial Maximum 18-20,000 years ago

Occurrence records Glacial Refugia[Hoarau et al., 2007; Mol. Ecol.]

10 / 56


Distribution of F. serratus in the North Atlantic

Occurrence records Glacial Refugia[Hoarau et al., 2007; Mol. Ecol.]

10 / 56


Recent changes in southern edge populationsof F. serratus

1999

201090% abundance declineReduced reproductive capacity[Viejo et al., 2011; Ecography]

11 / 56


Objectives

..

Migra on

.

Acclima on

.

Adapta on

.Fucusserratus

Distributional shiftuntil 2200

Warm temperaturetolerance

Genetic changes over the past decadeAdaptive potential to future stress

12 / 56


Objectives

..

Migra on

.

Acclima on

.

Adapta on

.Fucusserratus




13 / 56


Paper I - Migration

14 / 56


Objectives

..

Migra on

.

Acclima on

.

Adapta on

.Fucusserratus

Predominant seaweeds in the North-Atlantic

Fucus serratus Fucusvesiculosus

Ascophyllumnodosum

Shores with biggest ecological change?

Assemblage shift?

15 / 56


Ecological Niche ModelingPresent-day conditions

Bio-ORACLE database[Tyberghein et al., 2011; Global Ecol. Biogeogr.].

Georeferenced Occurrences

DA (m−1)SST (℃)

SAT (℃)

Ecological Niche Model (Maxent [Phillips et al., 2006; Ecol. Model.])

2000 2100 ? 2200 ?16 / 56


Ecological Niche ModelingPresent-day conditions

Bio-ORACLE database[Tyberghein et al., 2011; Global Ecol. Biogeogr.].

Georeferenced Occurrences

DA (m−1)SST (℃)

SAT (℃)

Ecological Niche Model (Maxent [Phillips et al., 2006; Ecol. Model.])

2000 2100 ? 2200 ?CO2 emission scenario changes

SST (℃)SAT (℃)

SST (℃)SAT (℃)

16 / 56


Predicted Niche ShiftsBased on the intermediate IPCC scenario A1B

17 / 56


ConclusionsMigration

..

Migra on

.

Acclima on

.

Adapta on

.Fucusserratus

Shores with biggest ecologicalchange?

Warm temperate and Arctic areasAssemblage shift?

18 / 56


Predominant seaweeds shift northward as anassemblage

East-Atlantic

F. serratus F. vesiculosus A. nodosum

19 / 56



..

Migra on

.

Acclima on

.

Adapta on

.Fucusserratus


Warm temperate and Arctic areasAssemblage shift

20 / 56


Climate change impact also on subtidal kelp

[Raybaud et al., 2013; PLOS ONE]

Percentage of models forecasting adisappearance of Laminaria digitata

21 / 56



..

Migra on

.

Acclima on

.

Adapta on

.Fucusserratus


Warm temperate and Arctic areasAssemblage shift

Mitigation by acclimation or adap-tation?

22 / 56


Objectives

..

Migra on

.

Acclima on

.

Adapta on

.Fucusserratus


Warm tempera-ture tolerance


23 / 56


Paper II - Acclimation

Thermal stress resistance of the brown algaFucus serratus along the North-Atlantic coast:acclimatization potential to climate change

Alexander Jueterbock, Spyros Kollias, Irina Smolina,Jorge M.O. Fernandes, James A. Coyer, Jeanine L. Olsen, Galice Hoarau

Marine Genomics. Submitted

24 / 56


Objectives

..

Migra on

.

Acclima on

.

Adapta on

.Fucusserratus

Local thermal adaptation?

Areas under highest extinction risk?

25 / 56


Common-garden heat stress experiments

Norway

Denmark

BrittanySpain

26 / 56



Norway

Denmark

BrittanySpain

Bodø

26 / 56



Norway

Denmark

BrittanySpain

Bodø

Acclimation at 9℃

26 / 56



Norway

Denmark

BrittanySpain

Bodø

Heat stress, > 6 ind./pop

1h Stress 24h Recovery

9℃

20℃24℃28℃32℃36℃

T (°C)

Time

26 / 56


Measuring the physiological stress response

Fitness

TemperatureBenignStress StressLethal Lethal

Pho

tosynthetic

performance

Fluorescence measurements

20℃–36℃

27 / 56


Photosynthetic response

0 4 8 12 16 20 24 28 32 36 ℃

Tested temperature range

NorwayDenmarkBrittanySpain

stress without recoverystress with recovery

28 / 56



0 4 8 12 16 20 24 28 32 36 ℃




Local adaptation

28 / 56



0 4 8 12 16 20 24 28 32 36 ℃


Thermal range in 2000NorwayDenmarkBrittanySpain


Local adaptation

sst meansst range

28 / 56



0 4 8 12 16 20 24 28 32 36 ℃


Thermal range in 2000NorwayDenmarkBrittanySpain


Local adaptation

highest upper temperature highest tolerance limit

sst meansst range

28 / 56



0 4 8 12 16 20 24 28 32 36 ℃




Thermal range in 2200

sst meansst range

28 / 56



0 4 8 12 16 20 24 28 32 36 ℃




Thermal range in 2200Stress

sst meansst range

28 / 56



0 4 8 12 16 20 24 28 32 36 ℃




Thermal range in 2200Stress

Stress limit reached at the southern range

sst meansst range

28 / 56


ConclusionsAcclimation

..

Migra on

.

Acclima on

.

Adapta on

.Fucusserratus

Local thermal adaptation

Areas under highest extinction risk?

Resilience

Photosyntheticperformance

in 2200

29 / 56


Measuring the physiological stress response

Fitness

TemperatureBenignStress StressLethal Lethal

Hsp

expressio

n

Real-time qPCR Protein denaturation

Aggregation

Refolding

Resolubilization

Prevention

Stabilization

sHSP

HSP60

HSP70

HSP90

HSP100

28℃,32℃

30 / 56


sHsp gene expression

Before heat shock exposure After 24h recovery

31 / 56



Before heat shock exposure

High constitutive expression

After 24h recovery

31 / 56



Before heat shock exposure

High constitutive expression

After 24h recovery

Reduced responsiveness

31 / 56


ConclusionsAcclimation

..

Migra on

.

Acclima on

.

Adapta on

.Fucusserratus

Local thermal adaptation

Areas under highest extinction risk?(Brittany), Spain

Resilience

Photosyntheticperformance

in 2200

Heat shockresponse

32 / 56


Objectives

..

Migra on

.

Acclima on

.

Adapta on

.Fucusserratus




33 / 56


Paper III - Adaptation

A decade of climate change onNorth Atlantic rocky shores

-can the seaweed Fucus serratusadapt to rising temperatures?

Alexander Jueterbock, Spyros Kollias, James A. Coyer, Jeanine L. Olsen,Galice Hoarau

Manuscript

34 / 56


Objectives

..

Migra on

.

Acclima on

.

Adapta on

.Fucusserratus

Effective population size Ne? Genetic changes (past 10 yrs)?

35 / 56


Sampling scheme (50–75 ind./pop)

∼ 2000 ∼ 2010

Spatial

(enviro

nmental)eff

ects

Temporal changes

1 decadeof selection

36 / 56


Methods and analysis

∼ 2000 ∼ 2010

Spatial

(enviro

nmental)eff

ects

Temporal changes


Genotyping31 microsatellite markers (20 EST-linked)

AnalysisEffective population size (Ne)Allelic richness (α)Temperature associated outlier loci

37 / 56



∼ 2000 ∼ 2010

Spatial

(enviro

nmental)eff

ects

Temporal changes




38 / 56


Effective population size NeReflecting adaptive capacity

∼ 2000 ∼ 2010

18

6320723

Norway

DenmarkBrittanySpain

32

6121026

Estimates excluding outlier loci

39 / 56


ConclusionsAdapatation

..

Migra on

.

Acclima on

.

Adapta on

.Fucusserratus


Ne

40 / 56


Methods

∼ 2000 ∼ 2010

Spatial

(enviro

nmental)eff

ects

Temporal changes




41 / 56


Changes in allelic richness

∼ 2000 ∼ 2010

3.1

4.68.04.0

Norway

DenmarkBrittanySpain

3.3

4.87.94.6

Significantdecline

42 / 56


ConclusionsAdaptation

..

Migra on

.

Acclima on

.

Adapta on

.Fucusserratus


Ne α

*

significant decline

43 / 56



∼ 2000 ∼ 2010

Spatial

(enviro

nmental)eff

ects

Temporal changes

Spatial approach



44 / 56


Temperature conditions

45 / 56



∼ 2000 ∼ 2010

Spatial

(enviro

nmental)eff

ects

Temporal changes

Spatial approach



46 / 56


Spatial outlier loci

∼ 2000 ∼ 2010

1: E6, L58

2: F36, F49, L58

3: F19, L58

1: F19, L58

2: E9, F22, F49, L58

3: E9, F19, F60, L58

123

123

47 / 56


Spatial outlier loci

∼ 2000 ∼ 2010

1: E6, L58

2: F36, F49, L58

3: F19, L58

1: F19, L58

2: E9, F22, F49, L58

3: E9, F19, F60, L58

123

123

Outliers in both years

47 / 56


Local adaptation - or?Examples fo alternative reasons for spatial outliers

Genetic incompatibilities [Bierne et al., 2011; Mol. Ecol.]

Isolation-by-distance pattern increases false positive rate[Fourcade et al. 2013; Mol. Ecol., Bierne et al., 2013; Mol. Ecol.]

48 / 56



∼ 2000 2 heat waves ∼ 2010

Spatial

(enviro

nmental)eff

ects

Temporal changes

Temporal approach



49 / 56


Temporal outlier loci

∼ 2000 ∼ 2010

1:

2: F19, F36

3: B113, B128, E6, E9, F12, F72, L58

4: F19, F65, F69, F72

1

234

50 / 56



∼ 2000 ∼ 2010

1:

2: F19, F36

3: B113, B128, E6, E9, F12, F72, L58

4: F19, F65, F69, F72

1

234

Highest proportion of outliers: strongest selection

50 / 56



∼ 2000 ∼ 2010

1:

2: F19, F36

3: B113, B128, E6, E9, F12, F72, L58

4: F19, F65, F69, F72

1

234

Highest proportion of outliers: strongest selectionCongruent outlier: broad-scale selection

50 / 56


Spatio-temporal outlier lociSpatial outliers

∼ 2000 ∼ 2010

1: E6, L58

2: F36, F49, L58

3: F19, L58

1: F19, L58

2: E9, F22, F49, L58

3: E9, F19, F60, L581

23

123


Temporal outliers∼ 2000 ∼ 2010

1:

2: F19, F36

3: B113, B128, E6, E9, F12, F72, L58

4: F19, F65, F69, F72

1

234

Highest proportion of outliers: strongest selectionCongruent outlier: broad-scale selection 51 / 56


Spatio-temporal outlier lociSpatial outliers

∼ 2000 ∼ 2010

1: E6, L58

2: F36, F49, L58

3: F19, L58

1: F19, L58

2: E9, F22, F49, L58

3: E9, F19, F60, L581

23

123


Temporal outliers∼ 2000 ∼ 2010

1:

2: F19, F36

3: B113, B128, E6, E9, F12, F72, L58

4: F19, F65, F69, F72

1

234

Highest proportion of outliers: strongest selectionCongruent outlier: broad-scale selection

F19 - response to climate change?

51 / 56


ConclusionsAdapatation

..

Migra on

.

Acclima on

.

Adapta on

.Fucusserratus


Ne α

*

Temporal outliers

Locus F19 indicatesresponse to selectionsignificant decline

52 / 56


Summary and conclusions

MigrationAcclimation

Adaptation

F. serratus, 2200

53 / 56




Adaptation

F. serratus, 2200

Norway

53 / 56




Adaptation

F. serratus, 2200

Norway

Performance, 2200

Not threatened

53 / 56




Adaptation

F. serratus, 2200

Norway

Ne

Low adaptive potential?

53 / 56




Adaptation

F. serratus, 2200

Norway

Potential to colonizeArctic shores?

53 / 56




Adaptation

F. serratus, 2200

Denmark

53 / 56




Adaptation

F. serratus, 2200

Denmark

Resilience

Local adaptation

53 / 56




Adaptation

F. serratus, 2200

Denmark

Broadest thermal range

53 / 56




Adaptation

F. serratus, 2200

Denmark

Highest plasticity

53 / 56




Adaptation

F. serratus, 2200

Brittany

53 / 56




Adaptation

F. serratus, 2200

Brittany

Resilience Performance, 2200

Low plasticity

53 / 56




Adaptation

F. serratus, 2200

Brittany

Ne Temporal outliers

Highest adaptability

53 / 56




Adaptation

F. serratus, 2200

Brittany

Will the center ofdiversity persist?

53 / 56




Adaptation

F. serratus, 2200

Spain

53 / 56




Adaptation

F. serratus, 2200

Spain

Performance,2200

Heat shockresponse

Insufficient plasticity

53 / 56




Adaptation

F. serratus, 2200

Spain

Ne α

Insufficient adaptive capacity

53 / 56




Adaptation

F. serratus, 2200

Spain

Predicted extinctionsupported by

all investigations

53 / 56


Overall conclusion

Climate change drivenecosystem shift?

54 / 56


Acknowledgements

Supervisors:

Galice HoarauJorge FernandesJeanine L. Olsen

Spyros KolliasIrina SmolinaKetil EianeKurt TandeMark PowellRandi Restad SjøvikSteinar JohnsonAll lab- and wetlab techniciansAll administrative staffAll friends

Dissertation Committee:

Christine MaggsNicolas BierneKurt Tande

Olivier De ClerckKlaas Pauly

Heroen Verbruggen

Lennert Tyberghein

James A. Coyer

Havkyst projects: 196505, 203839, 216484

55 / 56


56 / 56

Brown algae Paper I Paper II Paper III

References I

Balanya, J.; Oller, J.M.; Huey, R.B.; Gilchrist, G.W.; Serra, L. (2006)Global genetic change tracks global climate warming in Drosophila subobscura.Science 313(5794):1173–1175.

Berteaux, D.; Reale, D.; McAdam, A.G.; Boutin, S. (2004)Keeping pace with fast climate change: can arctic life count on evolution?Integrative and Comparative Biology 44(2):140–151.

Bierne, N. (2010)The distinctive footprints of local hitchhiking in a varied environment and global hitchhiking in a subdividedpopulationEvolution 64(11):3254–3272.

Bierne, N.; Welch, J.; Loire E.; Bonhomme, F.; David, P. (2011)The coupling hypothesis: why genome scans may fail to map local adaptation genesMolecular Ecology 20(10):2044–2072.

Bierne, N.; Roze, D.; Welch, J. (2013)Pervasive selection or is itâĂę? why are FST outliers sometimes so frequent?Molecular Ecology 22(8):2061–2064.

Bradshaw, W. E. and Holzapfel, C. M. (2006)Climate change - Evolutionary response to rapid climate changeScience 312(5779):1477–1478.

1 / 48


References II

Braune, W. (2008)MeeresalgenKoeltz Scientific Books Königstein, Germany.

Bussotti, F.; Desotgiu, R; Pollastrini, M.; Cascio, C. (2010)The JIP test: a tool to screen the capacity of plant adaptation to climate changeScandinavian Journal of Forest Research 25(Suppl 8): 43–50.

Charlesworth, B.; Nordborg, M.; Charlesworth, D. (1997)The effects of local selection, balanced polymorphism and background selection on equilibrium patterns ofgenetic diversity in subdivided populationsGenetic Research 70:155–174.

Coyer, J. A.; Peters, A.F.; Stam, W.T.; Olsen, J.L. (2003)Post-ice age recolonization and differentiation of Fucus serratus L. (Phaeophyceae; Fucaceae) populationsin Northern EuropeMolecular Ecology 12:1817–1829.

Coyer, J. A.; Hoarau, G.; Oudot-Le Secq, M.-P.; Stam, W.T. (2006)A mtDNA-based phylogeny of the brown algal genus Fucus (Heterokontophyta; Phaeophyta)Molecular Phylogenetics and Evolution 39:209–222.

Cock, J.M.; Sterck, L.; Rouzé, P. et al. (2010)The Ectocarpus genome and the independent evolution of multicellularity in brown algaeNature 465(3):617–621.

2 / 48


References III

Duarte L.; Viejo R.M.; Martínez B.; deCastro M.; Gómez-Gesteira M.; Gallardo T.(2013)Recent and historical range shifts of two canopy-forming seaweeds in North Spain and the link with trendsin sea surface temperatureActa Oecologica 51:1–10.

Excoffier, L.; Foll, M.; Petit, R.J. (2009)Genetic Consequences of Range ExpansionsAnnual Review of Ecology, Evolution, and Systematics 40:481–501.

Excoffier, L.; Lischer, H.E.L. (2010)Arlequin suite ver 3. 5: a new series of programs to perform population genetics analyses under Linux andWindowsMolecular Ecology Resources 10(3):564–567.

Fredriksen, S.; Christie, H.; Saethre, B.A. (2005)Species richness in macroalgae and macrofauna assemblages on Fucus serratus L. (Phaeophyceae) andZostera marina L. (Angiospermae) in Skagerrak, Norway.Marine Biology Research 1(1):2–19.

Fourcade, Y.; Chaput-Bardy, A.; Secondi, J.; Fleurant, C.; Lemaire, C. (2013)Is local selection so widespread in river organisms? Fractal geometry of river networks leads to high bias inoutlier detectionMolecular Ecology 22(8):2065–2073.

3 / 48


References IV

Halpern, B.S.; Walbridge, S.; Selkoe, K.A.; Kappel, C.V.; Micheli, F.; D’Agrosa, C.; Bruno, J.F.; Casey,K.S.; Ebert, C.; Fox, H.E. and others (2010)A global map of human impact on marine ecosystemsScience 319(5856):948–952.

Hansen, J.; Sato, M.; Ruedy, R.; Lo, K.; Lea, D.W.; Medina-Elizade, M. (2006)Global temperature changeProceedings of the National Academy of Sciences 103(39):14288–14293.

Hoarau, G.; Coyer, J.A.; Veldsink, J.H.; Stam, W.T.; Olsen, J.L. (2007)Glacial refugia and recolonization pathways in the brown seaweed Fucus serratusMolecular Ecology 16(17):3606–3616.

Hofer, T.; Ray, N.; Wegmann, D.; Excoffier, L. (2009)Large Allele Frequency Differences between Human Continental Groups are more Likely to have Occurred byDrift During range Expansions than by SelectionAnnals of Human Genetics 73(1):95–108.

Jimenez-Valverde, Alberto (2012)Insights into the area under the receiver operating characteristic curve (AUC) as a discrimination measure inspecies distribution modellingGlobal Ecology and Biogeography 21:498–507.

Jueterbock, A.; Tyberghein, L.; Verbruggen, H.; Coyer, J.A.; Olsen, J.L.; Hoarau, G. (2013)Climate change impact on seaweed meadow distribution in the North Atlantic rocky intertidalEcology and Evolution 3(5):1356–1373.

4 / 48


References V

Knight, M.; Parke, M. (1950)A biological study of Fucus vesiculosus L. and F. serratus L.Journal of the Marine Biological Association of the UK 29:439–514.

Køie, M.; Kristiansen, A.; Weitemeyer, S. (2001)Der große Kosmos Strandführer.Kosmos.

Luikart, G.; England, P. R.; Tallmon, D.; Jordan, S.; Taberlet, P. (2003)The power and promise of population genomics: from genotyping to genome typingNature Reviews Genetics 4(12):981–994.

McMahon, C.R. & Hays, G.C. (2006)Thermal niche, large-scale movements and implications of climate change for a critically endangered marinevertebrate.Global Change Biology 12(7):1330–1338.

Meehl, G.A.; Stocker, T.F.; Collins, W.D.; Friedlingstein, P.; Gaye, A.T.; Gregory, JM.; Kitoh, A.; Knutti,R.; Murphy, J.M.; Noda, A.; Raper, S.C.B.; Watterson, I.G and Weaver, A.J.; Zhao, Z.-C. (2007)Global Climate Projections.Climate Change 2007: the physical science basis: contribution of Working Group I to the FourthAssessment Report of the Intergovernmental Panel on Climate Change Eds: Solomon S. et al.

Nicastro, K.R.; Zardi, G.I.; Teixeira, S.; Neiva, J.; Serrao, E.A.; Pearson, G.A. (2013)Shift happens: trailing edge contraction associated with recent warming trends threatens a distinct geneticlineage in the marine macroalga Fucus vesiculosus.BMC Biology 11(6).

5 / 48


References VI

Nolan, T.; Hands, R.E.; Bustin, S.A. (2006)Quantification of mRNA using realtime RT-PCRNature Protocols 1(3)1559–1582.

Pannell, J. R.; Charlesworth, B. (2000)Effects of metapopulation processes on measures of genetic diversityPhilosophical Transactions of the Royal Society of London B 355(1404):1851-1864.

Pearson, G.A.; Lago-Leston, A.; Mota, C. (2009)Frayed at the edges: selective pressure and adaptive response to abiotic stressors are mismatched in lowdiversity edge populations.Journal of Ecology 97(3):450–462.

Pespeni, M.H.; Sanford, E.; Gaylord, B.; Hill, T.M; Hosfelt, J.D. et al. (2013)Evolutionary change during experimental ocean acidificationProceedings of the National Academy of Sciences 110(17):6937–6924.

Phillips, S.J.; Anderson, R.P.; Schapire, R.E. (2006)Maximum entropy modeling of species geographic distributions.Ecological Modelling 190(3-4):231–259.

Pounds, J. A.; Bustamante, M.R.; Coloma, L.A.; Consuegra, J.A.; Fogden, M.P.L.; Foster, P.N.; La Marca,E.; Masters, K.L.; Merino-Viteri, A.; Puschendorf, R.; Ron, S.R.; Sanchez-Azofeifa, G.A.; Still, C.J.; Young,B.E. (2006)Widespread amphibian extinctions from epidemic disease driven by global warmingNature 7073:161–167.

6 / 48


References VII

Raybaud, V.; Beaugrand, G.; Goberville, E.; Delebecq, G.; Destombe, C.; Valero, M.; Davoult, D.; Morin,P.; Gevaert, F. (2013)Decline in Kelp in West Europe and ClimatePLOS ONE 8:e66044.

Sturm, M.; Schimel, J.; Michaelson, G.; Welker, J.M.; Oberbauer, S.F.; Liston, G.E.; Fahnestock, J.;Romanovsky, V. (2005)Winter biological processes could help convert Arctic tundra to shrubland.Bioscience55(1):17–26.

Tyberghein, L.; Verbruggen, H.; Pauly, K.; Troupin, C.; Mineur, F.; De Clerck, O. (2011)Bio-ORACLE: a global environmental dataset for marine species distribution modelling.Global Ecology and Biogeography.

van Asch, M.; Salis, L.; Holleman, L.J.M.; van Lith, B.; Visser, M.E. (2013)Evolutionary response of the egg hatching date of a herbivorous insect under climate change.Ecography 3:244–248.

Verbruggen, H. (2012)Occurrence Thinner version 1.04.http://www.phycoweb.net/software.

Verbruggen, H. (2012)Maxent Model Surveyor v1.01.http://www.phycoweb.net/software.

7 / 48


References VIII

Viejo, R.M.; Martínez, B.; Arrontes, J.; Astudillo, C.; Hernández, L. (2011)Reproductive patterns in central and marginal populations of a large brown seaweed: drastic changes at thesouthern range limit.Ecography 34(1):75–84.

Walther, G.-R.; Post, E.; Convey, P.; Menzel, A.; Parmesan, C.; Beebee, T.J.C.; Fromentin, J.-M.;Hoegh-Guldberg, O.; Bairlein, F. (2002)Ecological responses to recent climate change.Nature 416(6879):389–395.

Warren, D. L.; Seifert, S. N. (2011)Ecological niche modeling in Maxent: the importance of model complexity and the performance of modelselection criteriaEcological Applications 21(2):335–342.

Wernberg, T.; Russell, B.D.; Thomsen, M.S.; Gurgel, F.D.; Bradshaw, C.J.A.; Poloczanska, E.S., Connell,S.D. (2011)Seaweed Communities in Retreat from Ocean Warming.Current Biology 21(21):1828–1832.

8 / 48


mtDNA based Phylogeny

[Coyer et al., 2006; Mol. Phylogenet. Evol.]

9 / 48


Human introduction

10 / 48


Life cycles of algae

[Braune, 2008; Meeresalgen] 11 / 48


Recolonization of the N-Atlantic after the LGM

[Hoarau et al., 2007; Mol. Ecol.]

12 / 48


Glacial refugia identified by mtDNA haplotype diversity

[Hoarau et al., 2007; Mol. Ecol.]

13 / 48


Choices in Ecological Niche Modeling

Occurrence sites

Sampling bias correction

Background sites

Restriction

Environmentalconditions

Variable selection

ModelModel evaluation

Threshold application

14 / 48


Sampling Bias correctionCapturing the sample density with the package ’KernSmooth’

15 / 48



Removing samples from areas of highest sample density with thejava program OccurrenceThinner [Verbruggen H., 2012]

15 / 48



15 / 48



Occurrence sites


Background sites

Restriction


Variable selection



16 / 48


North Atlantic background samples

17 / 48


Habitat suitability predictions depend on thebackground area

18 / 48



Occurrence sites


Background sites

Restriction


Variable selection



19 / 48


Variable importanceMaxent model output

Contribution of variables to the gain in model performance

20 / 48



Occurrence sites


Background sites

Restriction


Variable selection



21 / 48


Variable selection and model evaluation work together

Creating models of decreasing complexityEvaluating the performance with the AUC, AIC value

22 / 48


Automatic variable selection

based on Maxent Model Surveyor (Perl program) [Verbruggen H., 2012]

Suggests suitable predictor setStepwise exclusion (fwd) or inclusion (bwd) of parametersSignificant change in AUC is the stopping point

23 / 48


The AUC valueIndicator of a model’s ability to discriminate suitable from non-suitable habitat

[Jimenez-Valverde, 2012; Global Ecol. Biogeogr.]

Sensitivity: Present, predicted as present (True positive rate)1-Specificity: Absence, predicted as present (False positive rate)

24 / 48


Issues with the AUC value

Intrinsically higher for species with narrow ranges relative tothe background areaEstimates performance to identify the realized but not thefundamental niche (equal weight on omission and comissionerrors) [Jimenez-Valverde, 2012; Global Ecol. Biogeogr.]

AIC less overpredicting and models better transferablebetween regions and times [Warren & Seifert 2011; Ecol. Appl.]

25 / 48


Importance of temperature for algal distribution

[Jueterbock et al., 2013; Ecol. Evol.]

26 / 48


Response curvesContribution of predictors on habitat suitability

27 / 48



Occurrence sites


Background sites

Restriction


Variable selection



28 / 48


Applying a threshold to the logistic output

29 / 48


Applying a threshold to the logistic outputBinary prediction of habitat suitability

29 / 48


Present-day habitat suitability and occurrence sites

[Jueterbock et al., 2013; Ecol. Evol.]30 / 48


SRES CO2 emission scenarios

[Meehl et al., 2007; Climate Change 2007]31 / 48


Ocean currents in the North Atlantic

32 / 48


OJIP curve

Fm

F0

[Bussotti et al., 2010; Scand. J. Forest Res.]

33 / 48


Heat stress effect on photosynthesis

PS IIP680

PS IP700

NADPHCalvin CycleCO2 fixation

decrease

Photochemistry

e− transportdecrease

Photochemistry

e− transportdecrease

Heat

Fluorescence

measurableincrease

Heat

Fluorescence

h ∗ V h ∗ V

34 / 48


Quantification of gene products

[Nolan et al., 2006; Nature Protocols]

Efficiency = 10−1/slope

quantity = 10Ct−bslope

35 / 48


Long-term heat wave experiment

Sampling times (days)

Temperature (℃)

8℃

12℃

16℃

20℃

24℃

0d 4d 6d 14d 18d 28d

12h:12h light:dark cycleF. serratus, (F. distichus)4/(3) populations8 inds/pop

36 / 48



Results for F. serratus

36 / 48



Results for F. distichus

36 / 48


Unsolved questions and further studies

BrittanyLow plasticity

HFC correlations?Testing for MLH - photosynthetic performance correlationTesting if patches of higher diversity have higher shootdensity, growth etc.

37 / 48



SpainConstitutive Hsp expression - low responsiveness

Correlation could be explained by non-release of HSF1Cold stress during acclimation - heat hardening

In situ hsp measurements necessary to confirmUnlikely as hsp should have decreased over acclmation timeand 9-20℃same performanceCan reduce growth and reproduction (found for F. serratus)

Local adaptationAdaptive shift in shsp performance (sequence difference?)

Constitutive stressTemperatures in Spain are getting to warm

Paralogous genesCould perform differently under stressSequencing of gene products necessary

37 / 48


No correlation between photosynthetic performance and hspexpression

Other Hsps, like cp-sHSP protect the photoynthetic apparatusProtein kinase or amino acid oxidase can protect PSIIAlterations in cell membrane involved in HSRDanish population: increased shsp expression (28℃) andrecovery from 32℃uncoupled

37 / 48


Genome scan for outlier loci

cM0 100 200

0

0.5

1

0

0.5

1FST

Heterozygosity

Genotyped loci

Outlier locus

Selective sweep Selective sweep

38 / 48


Arlequin - test for outlier loci

[Excoffier & Lischer, 2010; Mol. Ecol. Res.]

39 / 48


Identification of outlier loci

[Luikart et al., 2003; Nature Reviews Genetics]

40 / 48


Potential reasons for low Ne values

Unequal sex ratiosVariance in family size (individual gametic contribution)Fluctuations of population size (potential reason for the lowNe in SpainReduced gene flow between populationsInbreeding

41 / 48


Ne indicates

The rate of genetic change due to genetic drift is proportionalto 1

2Ne

The effectiveness of selection over genetic drift (driftdominates if selection < 1

2Ne

Nucleotide diversity 4Nu with u being the mutation rateIn a closed population, Ne can indicate MLH. Gene flowuncouples Ne from genetic stochasticity

42 / 48


Significant FIS values can not be explained by asmall-scale family structure

Genetic correlations among individuals

[Coyer et al., 2003; Mol. Ecol.]

43 / 48


Local adaptation - or?Alternative reasons for spatial outliers

Geographic distribution that creates correlations in populationstructure [Fourcade et al. 2013; Mol. Ecol., Bierne et al., 2013; Mol. Ecol.]

44 / 48



Background selection against deleterious mutations[Charlesworth et al., 1997; Genet. Res.]

44 / 48



Species-wide selective sweeps [Bierne, 2010; Evolution]

44 / 48



Coupling of endogenous with exogenous gene flow barriers[Bierne et al., 2011; Mol. Ecol.]

red: endogenous, blue: exogenous alleles

44 / 48



Stochastic effects at the wave edge of an expandingpopulation [Excoffier et al., 2009; Annu. Rev. Ecol. Evol. Syst.]

44 / 48


Recent temperature stress in N-Spain

[Duarte et al., 2013; Acta Oecologica]

45 / 48


Work in Progress - Biological replicates

∼ 2000 ∼ 2010


46 / 48


Proposal to investigate evolutionary change toexperimental temperature stress

♂ x ♀

control heat stressday 1

day 7

selection

random geneticchange

selection

De novo transcriptome assemblymRNA libraries, SNP identification

Test for:Allele frequency changes (outliers)Enrichment in AA changing SNPs

Based on [Pespeni et al., 2013; PNAS]

47 / 48



BrittanyHigh adaptive potential?

Selection experiment (is adaptive variation high?)Outliers

Checking allele frequency shifts in temporal and spatialapproachprolong mRNA transcripts to annotate gene products

48 / 48



Low Ne in F. serratus

Likely reason: Type III survivorship curveIn Spain: Variations in population size, strong selection, thus lowfitness (literature)Likely not biased by:

unequal sex ratiosSmall-scale family structureGene flow effects

48 / 48



Significant FISWahlund Effect - Unlikely as sampling range within panmicticunit (1-100m)Inbreeding - would explain low fitness of Spanish southernedge populationsNull alleles - can not be fully excludedSelective advantage of homozygotes

48 / 48



OutlierHigh proportion of temporal outliers due to different selectiveagents (eutrophication, salinity changes, chemical pollution,UV-radiation, ocean acidification)Prolongation of long 3’-UTRs with RACE (rapid amplificationof cDNA ends) for annotationF19: track allele frequency shifts under experimental selection

48 / 48

Climate change impact on the seaweed Fucus serratus, a key foundational species on North Atlantic...

Science

Transcript of Climate change impact on the seaweed Fucus serratus, a key foundational species on North Atlantic...