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Climate change impact on the seaweed Fucus serratus, a key foundational species on North Atlantic rocky shores
- 1. Introduction Paper I Paper II Paper III Overall conclusions Climate change impact on the seaweed Fucus serratus, a key foundational species on North Atlantic rocky shores Alexander Jüterbock Alexander.Juterbock@uin.no Faculty of Biosciences and Aquaculture University of Nordland PhD Thesis, 01.06.2010–12.08.2013 1 / 56
- 2. Introduction Paper I Paper II Paper III Overall conclusions CO2 increase since the industrial revolution 2 / 56
- 3. Introduction Paper I Paper II Paper III Overall conclusions Recent climate change 2001–2005 mean surface temperature anomaly (Base Period = 1951–1981) Global mean = 0.54 [Hansen et al., 2006; PNAS] 3 / 56
- 4. Introduction Paper I Paper II Paper III Overall conclusions Climate change responses .. Temperature rise . Heat waves . Seasonality shi . Ocean acidifica on . Migra on . Acclima on . Adapta on . Species 4 / 56
- 5. Introduction Paper I Paper II Paper III Overall conclusions High sensitivity of intertidal species 5 / 56
- 6. Introduction Paper I Paper II Paper III Overall conclusions Seaweeds are key species in temperate North Atlantic regions Between the 10 summer and the 20 winter isotherm 6 / 56
- 7. Introduction Paper I Paper II Paper III Overall conclusions Seaweeds are key species in temperate North Atlantic regions © Hoarau, G., 2010 6 / 56
- 8. Introduction Paper I Paper II Paper III Overall conclusions Seaweeds are key species in temperate North Atlantic regions 6 / 56
- 9. Introduction Paper I Paper II Paper III Overall conclusions The focal species Fucus serratus 7 / 56
- 10. Introduction Paper I Paper II Paper III Overall conclusions Fucus in the tree of life [Cock et al., 2010; Nature] [Coyer et al., 2006; Mol. Phylogenet. Evol.] Hybridization 8 / 56
- 11. Introduction Paper I Paper II Paper III Overall conclusions Life cycle and dispersal of Fucus serratus [Braune, 2008; Meeresalgen] ♂ ♀ dioecious Fecundity: 106 eggs per female 9 / 56
- 12. Introduction Paper I Paper II Paper III Overall conclusions Life cycle and dispersal of Fucus serratus [Braune, 2008; Meeresalgen] ♂ ♀ dioecious zygote dispersal: <10m Fecundity: 106 eggs per female 9 / 56
- 13. Introduction Paper I Paper II Paper III Overall conclusions Life cycle and dispersal of Fucus serratus [Braune, 2008; Meeresalgen] ♂ ♀ dioecious zygote dispersal: <10m panmictic unit: 0.5–2km Fecundity: 106 eggs per female low genetic exchange 9 / 56
- 14. Introduction Paper I Paper II Paper III Overall conclusions Distribution of F. serratus in the North Atlantic Occurrence records 10 / 56
- 15. Introduction Paper I Paper II Paper III Overall conclusions Distribution of F. serratus in the North Atlantic Last Glacial Maximum 18-20,000 years ago Occurrence records Glacial Refugia [Hoarau et al., 2007; Mol. Ecol.] 10 / 56
- 16. Introduction Paper I Paper II Paper III Overall conclusions Distribution of F. serratus in the North Atlantic Occurrence records Glacial Refugia [Hoarau et al., 2007; Mol. Ecol.] 10 / 56
- 17. Introduction Paper I Paper II Paper III Overall conclusions Recent changes in southern edge populations of F. serratus 1999 2010 90% abundance decline Reduced reproductive capacity [Viejo et al., 2011; Ecography] 11 / 56
- 18. Introduction Paper I Paper II Paper III Overall conclusions Objectives .. Migra on . Acclima on . Adapta on .Fucus serratus Distributional shift until 2200 Warm temperature tolerance Genetic changes over the past decade Adaptive potential to future stress 12 / 56
- 19. Introduction Paper I Paper II Paper III Overall conclusions Objectives .. Migra on . Acclima on . Adapta on .Fucus serratus Distributional shift until 2200 Warm temperature tolerance Genetic changes over the past decade Adaptive potential to future stress 13 / 56
- 20. Introduction Paper I Paper II Paper III Overall conclusions Paper I - Migration 14 / 56
- 21. Introduction Paper I Paper II Paper III Overall conclusions Objectives .. Migra on . Acclima on . Adapta on .Fucus serratus Predominant seaweeds in the North-Atlantic Fucus serratus Fucus vesiculosus Ascophyllum nodosum Shores with biggest ecological change? Assemblage shift? 15 / 56
- 22. Introduction Paper I Paper II Paper III Overall conclusions Ecological Niche Modeling Present-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
- 23. Introduction Paper I Paper II Paper III Overall conclusions Ecological Niche Modeling Present-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
- 24. Introduction Paper I Paper II Paper III Overall conclusions Predicted Niche Shifts Based on the intermediate IPCC scenario A1B 17 / 56
- 25. Introduction Paper I Paper II Paper III Overall conclusions Conclusions Migration .. Migra on . Acclima on . Adapta on .Fucus serratus Shores with biggest ecological change? Warm temperate and Arctic areas Assemblage shift? 18 / 56
- 26. Introduction Paper I Paper II Paper III Overall conclusions Predominant seaweeds shift northward as an assemblage East-Atlantic F. serratus F. vesiculosus A. nodosum 19 / 56
- 27. Introduction Paper I Paper II Paper III Overall conclusions Conclusions Migration .. Migra on . Acclima on . Adapta on .Fucus serratus Shores with biggest ecological change? Warm temperate and Arctic areas Assemblage shift 20 / 56
- 28. Introduction Paper I Paper II Paper III Overall conclusions Climate change impact also on subtidal kelp [Raybaud et al., 2013; PLOS ONE] Percentage of models forecasting a disappearance of Laminaria digitata 21 / 56
- 29. Introduction Paper I Paper II Paper III Overall conclusions Conclusions Migration .. Migra on . Acclima on . Adapta on .Fucus serratus Shores with biggest ecological change? Warm temperate and Arctic areas Assemblage shift Mitigation by acclimation or adap- tation? 22 / 56
- 30. Introduction Paper I Paper II Paper III Overall conclusions Objectives .. Migra on . Acclima on . Adapta on .Fucus serratus Distributional shift until 2200 Warm tempera- ture tolerance Genetic changes over the past decade Adaptive potential to future stress 23 / 56
- 31. Introduction Paper I Paper II Paper III Overall conclusions Paper II - Acclimation Thermal stress resistance of the brown alga Fucus 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
- 32. Introduction Paper I Paper II Paper III Overall conclusions Objectives .. Migra on . Acclima on . Adapta on .Fucus serratus Local thermal adaptation? Areas under highest extinction risk? 25 / 56
- 33. Introduction Paper I Paper II Paper III Overall conclusions Common-garden heat stress experiments Norway Denmark Brittany Spain 26 / 56
- 34. Introduction Paper I Paper II Paper III Overall conclusions Common-garden heat stress experiments Norway Denmark Brittany Spain Bodø 26 / 56
- 35. Introduction Paper I Paper II Paper III Overall conclusions Common-garden heat stress experiments Norway Denmark Brittany Spain Bodø Acclimation at 9 26 / 56
- 36. Introduction Paper I Paper II Paper III Overall conclusions Common-garden heat stress experiments Norway Denmark Brittany Spain Bodø Heat stress, 6 ind./pop 1h Stress 24h Recovery 9 20 24 28 32 36 T (°C) Time 26 / 56
- 37. Introduction Paper I Paper II Paper III Overall conclusions Measuring the physiological stress response Fitness Temperature BenignStress StressLethal Lethal Photosynthetic performance Fluorescence measurements 20 –36 27 / 56
- 38. Introduction Paper I Paper II Paper III Overall conclusions Photosynthetic response 0 4 8 12 16 20 24 28 32 36 Tested temperature range Norway Denmark Brittany Spain stress without recovery stress with recovery 28 / 56
- 39. Introduction Paper I Paper II Paper III Overall conclusions Photosynthetic response 0 4 8 12 16 20 24 28 32 36 Tested temperature range Norway Denmark Brittany Spain stress without recovery stress with recovery Local adaptation 28 / 56
- 40. Introduction Paper I Paper II Paper III Overall conclusions Photosynthetic response 0 4 8 12 16 20 24 28 32 36 Tested temperature range Thermal range in 2000 Norway Denmark Brittany Spain stress without recovery stress with recovery Local adaptation sst mean sst range 28 / 56
- 41. Introduction Paper I Paper II Paper III Overall conclusions Photosynthetic response 0 4 8 12 16 20 24 28 32 36 Tested temperature range Thermal range in 2000 Norway Denmark Brittany Spain stress without recovery stress with recovery Local adaptation highest upper temperature highest tolerance limit sst mean sst range 28 / 56
- 42. Introduction Paper I Paper II Paper III Overall conclusions Photosynthetic response 0 4 8 12 16 20 24 28 32 36 Tested temperature range Norway Denmark Brittany Spain stress without recovery stress with recovery Thermal range in 2200 sst mean sst range 28 / 56
- 43. Introduction Paper I Paper II Paper III Overall conclusions Photosynthetic response 0 4 8 12 16 20 24 28 32 36 Tested temperature range Norway Denmark Brittany Spain stress without recovery stress with recovery Thermal range in 2200 Stress sst mean sst range 28 / 56
- 44. Introduction Paper I Paper II Paper III Overall conclusions Photosynthetic response 0 4 8 12 16 20 24 28 32 36 Tested temperature range Norway Denmark Brittany Spain stress without recovery stress with recovery Thermal range in 2200 Stress Stress limit reached at the southern range sst mean sst range 28 / 56
- 45. Introduction Paper I Paper II Paper III Overall conclusions Conclusions Acclimation .. Migra on . Acclima on . Adapta on .Fucus serratus Local thermal adaptation Areas under highest extinction risk? Resilience Photosynthetic performance in 2200 29 / 56
- 46. Introduction Paper I Paper II Paper III Overall conclusions Measuring the physiological stress response Fitness Temperature BenignStress StressLethal Lethal Hspexpression Real-time qPCR Protein denaturation Aggregation Refolding Resolubilization Prevention Stabilization sHSP HSP60 HSP70 HSP90 HSP100 28 ,32 30 / 56
- 47. Introduction Paper I Paper II Paper III Overall conclusions sHsp gene expression Before heat shock exposure After 24h recovery 31 / 56
- 48. Introduction Paper I Paper II Paper III Overall conclusions sHsp gene expression Before heat shock exposure High constitutive expression After 24h recovery 31 / 56
- 49. Introduction Paper I Paper II Paper III Overall conclusions sHsp gene expression Before heat shock exposure High constitutive expression After 24h recovery Reduced responsiveness 31 / 56
- 50. Introduction Paper I Paper II Paper III Overall conclusions Conclusions Acclimation .. Migra on . Acclima on . Adapta on .Fucus serratus Local thermal adaptation Areas under highest extinction risk? (Brittany), Spain Resilience Photosynthetic performance in 2200 Heat shock response 32 / 56
- 51. Introduction Paper I Paper II Paper III Overall conclusions Objectives .. Migra on . Acclima on . Adapta on .Fucus serratus Distributional shift until 2200 Warm temperature tolerance Genetic changes over the past decade Adaptive potential to future stress 33 / 56
- 52. Introduction Paper I Paper II Paper III Overall conclusions Paper III - Adaptation A decade of climate change on North Atlantic rocky shores - can the seaweed Fucus serratus adapt to rising temperatures? Alexander Jueterbock, Spyros Kollias, James A. Coyer, Jeanine L. Olsen, Galice Hoarau Manuscript 34 / 56
- 53. Introduction Paper I Paper II Paper III Overall conclusions Objectives .. Migra on . Acclima on . Adapta on .Fucus serratus Effective population size Ne? Genetic changes (past 10 yrs)? 35 / 56
- 54. Introduction Paper I Paper II Paper III Overall conclusions Sampling scheme (50–75 ind./pop) ∼ 2000 ∼ 2010 Spatial(environmental)effects Temporal changes 1 decade of selection 36 / 56
- 55. Introduction Paper I Paper II Paper III Overall conclusions Methods and analysis ∼ 2000 ∼ 2010 Spatial(environmental)effects Temporal changes 1 decade of selection Genotyping 31 microsatellite markers (20 EST-linked) Analysis Effective population size (Ne) Allelic richness (α) Temperature associated outlier loci 37 / 56
- 56. Introduction Paper I Paper II Paper III Overall conclusions Methods and analysis ∼ 2000 ∼ 2010 Spatial(environmental)effects Temporal changes 1 decade of selection Genotyping 31 microsatellite markers (20 EST-linked) Analysis Effective population size (Ne) Allelic richness (α) Temperature associated outlier loci 38 / 56
- 57. Introduction Paper I Paper II Paper III Overall conclusions Effective population size Ne Reflecting adaptive capacity ∼ 2000 ∼ 2010 18 63 207 23 Norway Denmark Brittany Spain 32 61 210 26 Estimates excluding outlier loci 39 / 56
- 58. Introduction Paper I Paper II Paper III Overall conclusions Conclusions Adapatation .. Migra on . Acclima on . Adapta on .Fucus serratus Effective population size Ne? Genetic changes (past 10 yrs)? Ne 40 / 56
- 59. Introduction Paper I Paper II Paper III Overall conclusions Methods ∼ 2000 ∼ 2010 Spatial(environmental)effects Temporal changes 1 decade of selection Genotyping 31 microsatellite markers (20 EST-linked) Analysis Effective population size (Ne) Allelic richness (α) Temperature associated outlier loci 41 / 56
- 60. Introduction Paper I Paper II Paper III Overall conclusions Changes in allelic richness ∼ 2000 ∼ 2010 3.1 4.6 8.0 4.0 Norway Denmark Brittany Spain 3.3 4.8 7.9 4.6 Significant decline 42 / 56
- 61. Introduction Paper I Paper II Paper III Overall conclusions Conclusions Adaptation .. Migra on . Acclima on . Adapta on .Fucus serratus Effective population size Ne? Genetic changes (past 10 yrs)? Ne α * significant decline 43 / 56
- 62. Introduction Paper I Paper II Paper III Overall conclusions Methods and analysis ∼ 2000 ∼ 2010 Spatial(environmental)effects Temporal changes Spatial approach Genotyping 31 microsatellite markers (20 EST-linked) Analysis Effective population size (Ne) Allelic richness (α) Temperature associated outlier loci 44 / 56
- 63. Introduction Paper I Paper II Paper III Overall conclusions Temperature conditions 45 / 56
- 64. Introduction Paper I Paper II Paper III Overall conclusions Methods and analysis ∼ 2000 ∼ 2010 Spatial(environmental)effects Temporal changes Spatial approach Genotyping 31 microsatellite markers (20 EST-linked) Analysis Effective population size (Ne) Allelic richness (α) Temperature associated outlier loci 46 / 56
- 65. Introduction Paper I Paper II Paper III Overall conclusions 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 1 2 3 1 2 3 47 / 56
- 66. Introduction Paper I Paper II Paper III Overall conclusions 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 1 2 3 1 2 3 Outliers in both years 47 / 56
- 67. Introduction Paper I Paper II Paper III Overall conclusions 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
- 68. Introduction Paper I Paper II Paper III Overall conclusions Methods and analysis ∼ 2000 2 heat waves ∼ 2010 Spatial(environmental)effects Temporal changes Temporal approach Genotyping 31 microsatellite markers (20 EST-linked) Analysis Effective population size (Ne) Allelic richness (α) Temperature associated outlier loci 49 / 56
- 69. Introduction Paper I Paper II Paper III Overall conclusions Temporal outlier loci ∼ 2000 ∼ 2010 1: 2: F19, F36 3: B113, B128, E6, E9, F12, F72, L58 4: F19, F65, F69, F72 1 2 3 4 50 / 56
- 70. Introduction Paper I Paper II Paper III Overall conclusions Temporal outlier loci ∼ 2000 ∼ 2010 1: 2: F19, F36 3: B113, B128, E6, E9, F12, F72, L58 4: F19, F65, F69, F72 1 2 3 4 Highest proportion of outliers: strongest selection 50 / 56
- 71. Introduction Paper I Paper II Paper III Overall conclusions Temporal outlier loci ∼ 2000 ∼ 2010 1: 2: F19, F36 3: B113, B128, E6, E9, F12, F72, L58 4: F19, F65, F69, F72 1 2 3 4 Highest proportion of outliers: strongest selection Congruent outlier: broad-scale selection 50 / 56
- 72. Introduction Paper I Paper II Paper III Overall conclusions Spatio-temporal outlier loci Spatial 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, L58 1 2 3 1 2 3 Outliers in both years Temporal outliers ∼ 2000 ∼ 2010 1: 2: F19, F36 3: B113, B128, E6, E9, F12, F72, L58 4: F19, F65, F69, F72 1 2 3 4 Highest proportion of outliers: strongest selection Congruent outlier: broad-scale selection 51 / 56
- 73. Introduction Paper I Paper II Paper III Overall conclusions Spatio-temporal outlier loci Spatial 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, L58 1 2 3 1 2 3 Outliers in both years Temporal outliers ∼ 2000 ∼ 2010 1: 2: F19, F36 3: B113, B128, E6, E9, F12, F72, L58 4: F19, F65, F69, F72 1 2 3 4 Highest proportion of outliers: strongest selection Congruent outlier: broad-scale selection F19 - response to climate change? 51 / 56
- 74. Introduction Paper I Paper II Paper III Overall conclusions Conclusions Adapatation .. Migra on . Acclima on . Adapta on .Fucus serratus Effective population size Ne? Genetic changes (past 10 yrs)? Ne α * Temporal outliers Locus F19 indicates response to selection significant decline 52 / 56
- 75. Introduction Paper I Paper II Paper III Overall conclusions Summary and conclusions MigrationAcclimation Adaptation F. serratus, 2200 53 / 56
- 76. Introduction Paper I Paper II Paper III Overall conclusions Summary and conclusions MigrationAcclimation Adaptation F. serratus, 2200 Norway 53 / 56
- 77. Introduction Paper I Paper II Paper III Overall conclusions Summary and conclusions MigrationAcclimation Adaptation F. serratus, 2200 Norway Performance, 2200 Not threatened 53 / 56
- 78. Introduction Paper I Paper II Paper III Overall conclusions Summary and conclusions MigrationAcclimation Adaptation F. serratus, 2200 Norway Ne Low adaptive potential? 53 / 56
- 79. Introduction Paper I Paper II Paper III Overall conclusions Summary and conclusions MigrationAcclimation Adaptation F. serratus, 2200 Norway Potential to colonize Arctic shores? 53 / 56
- 80. Introduction Paper I Paper II Paper III Overall conclusions Summary and conclusions MigrationAcclimation Adaptation F. serratus, 2200 Denmark 53 / 56
- 81. Introduction Paper I Paper II Paper III Overall conclusions Summary and conclusions MigrationAcclimation Adaptation F. serratus, 2200 Denmark Resilience Local adaptation 53 / 56
- 82. Introduction Paper I Paper II Paper III Overall conclusions Summary and conclusions MigrationAcclimation Adaptation F. serratus, 2200 Denmark Broadest thermal range 53 / 56
- 83. Introduction Paper I Paper II Paper III Overall conclusions Summary and conclusions MigrationAcclimation Adaptation F. serratus, 2200 Denmark Highest plasticity 53 / 56
- 84. Introduction Paper I Paper II Paper III Overall conclusions Summary and conclusions MigrationAcclimation Adaptation F. serratus, 2200 Brittany 53 / 56
- 85. Introduction Paper I Paper II Paper III Overall conclusions Summary and conclusions MigrationAcclimation Adaptation F. serratus, 2200 Brittany Resilience Performance, 2200 Low plasticity 53 / 56
- 86. Introduction Paper I Paper II Paper III Overall conclusions Summary and conclusions MigrationAcclimation Adaptation F. serratus, 2200 Brittany Ne Temporal outliers Highest adaptability 53 / 56
- 87. Introduction Paper I Paper II Paper III Overall conclusions Summary and conclusions MigrationAcclimation Adaptation F. serratus, 2200 Brittany Will the center of diversity persist? 53 / 56
- 88. Introduction Paper I Paper II Paper III Overall conclusions Summary and conclusions MigrationAcclimation Adaptation F. serratus, 2200 Spain 53 / 56
- 89. Introduction Paper I Paper II Paper III Overall conclusions Summary and conclusions MigrationAcclimation Adaptation F. serratus, 2200 Spain Performance, 2200 Heat shock response Insufficient plasticity 53 / 56
- 90. Introduction Paper I Paper II Paper III Overall conclusions Summary and conclusions MigrationAcclimation Adaptation F. serratus, 2200 Spain Ne α Insufficient adaptive capacity 53 / 56
- 91. Introduction Paper I Paper II Paper III Overall conclusions Summary and conclusions MigrationAcclimation Adaptation F. serratus, 2200 Spain Predicted extinction supported by all investigations 53 / 56
- 92. Introduction Paper I Paper II Paper III Overall conclusions Overall conclusion Climate change driven ecosystem shift? 54 / 56
- 93. Introduction Paper I Paper II Paper III Overall conclusions Acknowledgements Supervisors: Galice Hoarau Jorge Fernandes Jeanine L. Olsen Spyros Kollias Irina Smolina Ketil Eiane Kurt Tande Mark Powell Randi Restad Sjøvik Steinar Johnson All lab- and wetlab technicians All administrative staff All friends Dissertation Committee: Christine Maggs Nicolas Bierne Kurt Tande Olivier De Clerck Klaas Pauly Heroen Verbruggen Lennert Tyberghein James A. Coyer Havkyst projects: 196505, 203839, 216484 55 / 56
- 94. Introduction Paper I Paper II Paper III Overall conclusions 56 / 56
- 95. 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 subdivided population Evolution 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 genes Molecular 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 change Science 312(5779):1477–1478. 1 / 48
- 96. Brown algae Paper I Paper II Paper III References II Braune, W. (2008) Meeresalgen Koeltz 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 change Scandinavian 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 of genetic diversity in subdivided populations Genetic 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) populations in Northern Europe Molecular 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 algae Nature 465(3):617–621. 2 / 48
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- 100. Brown algae Paper I Paper II Paper III References VI Nolan, T.; Hands, R.E.; Bustin, S.A. (2006) Quantification of mRNA using realtime RT-PCR Nature Protocols 1(3)1559–1582. Pannell, J. R.; Charlesworth, B. (2000) Effects of metapopulation processes on measures of genetic diversity Philosophical 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 low diversity 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 acidification Proceedings 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 warming Nature 7073:161–167. 6 / 48
- 101. Brown algae Paper I Paper II Paper III 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 Climate PLOS 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
- 102. Brown algae Paper I Paper II Paper III 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 the southern 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 model selection criteria Ecological 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
- 103. Brown algae Paper I Paper II Paper III mtDNA based Phylogeny [Coyer et al., 2006; Mol. Phylogenet. Evol.] 9 / 48
- 104. Brown algae Paper I Paper II Paper III Human introduction 10 / 48
- 105. Brown algae Paper I Paper II Paper III Life cycles of algae [Braune, 2008; Meeresalgen] 11 / 48
- 106. Brown algae Paper I Paper II Paper III Recolonization of the N-Atlantic after the LGM [Hoarau et al., 2007; Mol. Ecol.] 12 / 48
- 107. Brown algae Paper I Paper II Paper III Glacial refugia identified by mtDNA haplotype diversity [Hoarau et al., 2007; Mol. Ecol.] 13 / 48
- 108. Brown algae Paper I Paper II Paper III Choices in Ecological Niche Modeling Occurrence sites Sampling bias correction Background sites Restriction Environmental conditions Variable selection Model Model evaluation Threshold application 14 / 48
- 109. Brown algae Paper I Paper II Paper III Choices in Ecological Niche Modeling Occurrence sites Sampling bias correction Background sites Restriction Environmental conditions Variable selection Model Model evaluation Threshold application 14 / 48
- 110. Brown algae Paper I Paper II Paper III Sampling Bias correction Capturing the sample density with the package ’KernSmooth’ 15 / 48
- 111. Brown algae Paper I Paper II Paper III Sampling Bias correction Capturing the sample density with the package ’KernSmooth’ Removing samples from areas of highest sample density with the java program OccurrenceThinner [Verbruggen H., 2012] 15 / 48
- 112. Brown algae Paper I Paper II Paper III Sampling Bias correction Capturing the sample density with the package ’KernSmooth’ 15 / 48
- 113. Brown algae Paper I Paper II Paper III Sampling Bias correction Capturing the sample density with the package ’KernSmooth’ 15 / 48
- 114. Brown algae Paper I Paper II Paper III Sampling Bias correction Capturing the sample density with the package ’KernSmooth’ 15 / 48
- 115. Brown algae Paper I Paper II Paper III Choices in Ecological Niche Modeling Occurrence sites Sampling bias correction Background sites Restriction Environmental conditions Variable selection Model Model evaluation Threshold application 16 / 48
- 116. Brown algae Paper I Paper II Paper III North Atlantic background samples 17 / 48
- 117. Brown algae Paper I Paper II Paper III North Atlantic background samples 17 / 48
- 118. Brown algae Paper I Paper II Paper III North Atlantic background samples 17 / 48
- 119. Brown algae Paper I Paper II Paper III Habitat suitability predictions depend on the background area 18 / 48
- 120. Brown algae Paper I Paper II Paper III Choices in Ecological Niche Modeling Occurrence sites Sampling bias correction Background sites Restriction Environmental conditions Variable selection Model Model evaluation Threshold application 19 / 48
- 121. Brown algae Paper I Paper II Paper III Variable importance Maxent model output Contribution of variables to the gain in model performance 20 / 48
- 122. Brown algae Paper I Paper II Paper III Choices in Ecological Niche Modeling Occurrence sites Sampling bias correction Background sites Restriction Environmental conditions Variable selection Model Model evaluation Threshold application 21 / 48
- 123. Brown algae Paper I Paper II Paper III Variable selection and model evaluation work together Creating models of decreasing complexity Evaluating the performance with the AUC, AIC value 22 / 48
- 124. Brown algae Paper I Paper II Paper III Automatic variable selection based on Maxent Model Surveyor (Perl program) [Verbruggen H., 2012] Suggests suitable predictor set Stepwise exclusion (fwd) or inclusion (bwd) of parameters Significant change in AUC is the stopping point 23 / 48
- 125. Brown algae Paper I Paper II Paper III The AUC value Indicator 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
- 126. Brown algae Paper I Paper II Paper III Issues with the AUC value Intrinsically higher for species with narrow ranges relative to the background area Estimates performance to identify the realized but not the fundamental niche (equal weight on omission and comission errors) [Jimenez-Valverde, 2012; Global Ecol. Biogeogr.] AIC less overpredicting and models better transferable between regions and times [Warren & Seifert 2011; Ecol. Appl.] 25 / 48
- 127. Brown algae Paper I Paper II Paper III Importance of temperature for algal distribution [Jueterbock et al., 2013; Ecol. Evol.] 26 / 48
- 128. Brown algae Paper I Paper II Paper III Response curves Contribution of predictors on habitat suitability 27 / 48
- 129. Brown algae Paper I Paper II Paper III Choices in Ecological Niche Modeling Occurrence sites Sampling bias correction Background sites Restriction Environmental conditions Variable selection Model Model evaluation Threshold application 28 / 48
- 130. Brown algae Paper I Paper II Paper III Applying a threshold to the logistic output 29 / 48
- 131. Brown algae Paper I Paper II Paper III Applying a threshold to the logistic output Binary prediction of habitat suitability 29 / 48
- 132. Brown algae Paper I Paper II Paper III Applying a threshold to the logistic output Binary prediction of habitat suitability 29 / 48
- 133. Brown algae Paper I Paper II Paper III Present-day habitat suitability and occurrence sites [Jueterbock et al., 2013; Ecol. Evol.] 30 / 48
- 134. Brown algae Paper I Paper II Paper III SRES CO2 emission scenarios [Meehl et al., 2007; Climate Change 2007] 31 / 48
- 135. Brown algae Paper I Paper II Paper III Ocean currents in the North Atlantic 32 / 48
- 136. Brown algae Paper I Paper II Paper III OJIP curve Fm F0 [Bussotti et al., 2010; Scand. J. Forest Res.] 33 / 48
- 137. Brown algae Paper I Paper II Paper III Heat stress effect on photosynthesis PS II P680 PS I P700 NADPH Calvin Cycle CO2 fixation decrease Photochemistry e− transport decrease Photochemistry e− transport decrease Heat Fluorescence measurable increase Heat Fluorescence h ∗ V h ∗ V 34 / 48
- 138. Brown algae Paper I Paper II Paper III Quantification of gene products [Nolan et al., 2006; Nature Protocols] Efficiency = 10−1/slope quantity = 10 Ct−b slope 35 / 48
- 139. Brown algae Paper I Paper II Paper III Long-term heat wave experiment Sampling times (days) Temperature ( ) 8 12 16 20 24 0d 4d 6d 14d 18d 28d 12h:12h light:dark cycle F. serratus, (F. distichus) 4/(3) populations 8 inds/pop 36 / 48
- 140. Brown algae Paper I Paper II Paper III Long-term heat wave experiment Results for F. serratus 36 / 48
- 141. Brown algae Paper I Paper II Paper III Long-term heat wave experiment Results for F. distichus 36 / 48
- 142. Brown algae Paper I Paper II Paper III Unsolved questions and further studies Brittany Low plasticity HFC correlations? Testing for MLH - photosynthetic performance correlation Testing if patches of higher diversity have higher shoot density, growth etc. 37 / 48
- 143. Brown algae Paper I Paper II Paper III Unsolved questions and further studies Spain Constitutive Hsp expression - low responsiveness Correlation could be explained by non-release of HSF1 Cold stress during acclimation - heat hardening In situ hsp measurements necessary to confirm Unlikely as hsp should have decreased over acclmation time and 9-20 same performance Can reduce growth and reproduction (found for F. serratus) Local adaptation Adaptive shift in shsp performance (sequence difference?) Constitutive stress Temperatures in Spain are getting to warm Paralogous genes Could perform differently under stress Sequencing of gene products necessary 37 / 48
- 144. Brown algae Paper I Paper II Paper III No correlation between photosynthetic performance and hsp expression Other Hsps, like cp-sHSP protect the photoynthetic apparatus Protein kinase or amino acid oxidase can protect PSII Alterations in cell membrane involved in HSR Danish population: increased shsp expression (28 ) and recovery from 32 uncoupled 37 / 48
- 145. Brown algae Paper I Paper II Paper III Genome scan for outlier loci cM 0 100 200 0 0.5 1 0 0.5 1 FST Heterozygosity Genotyped loci Outlier locus Selective sweep Selective sweep 38 / 48
- 146. Brown algae Paper I Paper II Paper III Arlequin - test for outlier loci [Excoffier & Lischer, 2010; Mol. Ecol. Res.] 39 / 48
- 147. Brown algae Paper I Paper II Paper III Identification of outlier loci [Luikart et al., 2003; Nature Reviews Genetics] 40 / 48
- 148. Brown algae Paper I Paper II Paper III Potential reasons for low Ne values Unequal sex ratios Variance in family size (individual gametic contribution) Fluctuations of population size (potential reason for the low Ne in Spain Reduced gene flow between populations Inbreeding 41 / 48
- 149. Brown algae Paper I Paper II Paper III Ne indicates The rate of genetic change due to genetic drift is proportional to 1 2Ne The effectiveness of selection over genetic drift (drift dominates if selection < 1 2Ne Nucleotide diversity 4Nu with u being the mutation rate In a closed population, Ne can indicate MLH. Gene flow uncouples Ne from genetic stochasticity 42 / 48
- 150. Brown algae Paper I Paper II Paper III Significant FIS values can not be explained by a small-scale family structure Genetic correlations among individuals [Coyer et al., 2003; Mol. Ecol.] 43 / 48
- 151. Brown algae Paper I Paper II Paper III Local adaptation - or? Alternative reasons for spatial outliers Geographic distribution that creates correlations in population structure [Fourcade et al. 2013; Mol. Ecol., Bierne et al., 2013; Mol. Ecol.] 44 / 48
- 152. Brown algae Paper I Paper II Paper III Local adaptation - or? Alternative reasons for spatial outliers Background selection against deleterious mutations [Charlesworth et al., 1997; Genet. Res.] 44 / 48
- 153. Brown algae Paper I Paper II Paper III Local adaptation - or? Alternative reasons for spatial outliers Species-wide selective sweeps [Bierne, 2010; Evolution] 44 / 48
- 154. Brown algae Paper I Paper II Paper III Local adaptation - or? Alternative reasons for spatial outliers Coupling of endogenous with exogenous gene flow barriers [Bierne et al., 2011; Mol. Ecol.] red: endogenous, blue: exogenous alleles 44 / 48
- 155. Brown algae Paper I Paper II Paper III Local adaptation - or? Alternative reasons for spatial outliers Stochastic effects at the wave edge of an expanding population [Excoffier et al., 2009; Annu. Rev. Ecol. Evol. Syst.] 44 / 48
- 156. Brown algae Paper I Paper II Paper III Recent temperature stress in N-Spain [Duarte et al., 2013; Acta Oecologica] 45 / 48
- 157. Brown algae Paper I Paper II Paper III Recent temperature stress in N-Spain [Duarte et al., 2013; Acta Oecologica] 45 / 48
- 158. Brown algae Paper I Paper II Paper III Work in Progress - Biological replicates ∼ 2000 ∼ 2010 1 decade of selection 46 / 48
- 159. Brown algae Paper I Paper II Paper III Proposal to investigate evolutionary change to experimental temperature stress ♂ x ♀ control heat stress day 1 day 7 selection random genetic change selection De novo transcriptome assembly mRNA libraries, SNP identification Test for: Allele frequency changes (outliers) Enrichment in AA changing SNPs Based on [Pespeni et al., 2013; PNAS] 47 / 48
- 160. Brown algae Paper I Paper II Paper III Unsolved questions and further studies Brittany High adaptive potential? Selection experiment (is adaptive variation high?) Outliers Checking allele frequency shifts in temporal and spatial approach prolong mRNA transcripts to annotate gene products 48 / 48
- 161. Brown algae Paper I Paper II Paper III Unsolved questions and further studies Low Ne in F. serratus Likely reason: Type III survivorship curve In Spain: Variations in population size, strong selection, thus low fitness (literature) Likely not biased by: unequal sex ratios Small-scale family structure Gene flow effects 48 / 48
- 162. Brown algae Paper I Paper II Paper III Unsolved questions and further studies Significant FIS Wahlund Effect - Unlikely as sampling range within panmictic unit (1-100m) Inbreeding - would explain low fitness of Spanish southern edge populations Null alleles - can not be fully excluded Selective advantage of homozygotes 48 / 48
- 163. Brown algae Paper I Paper II Paper III Unsolved questions and further studies Outlier High proportion of temporal outliers due to different selective agents (eutrophication, salinity changes, chemical pollution, UV-radiation, ocean acidification) Prolongation of long 3’-UTRs with RACE (rapid amplification of cDNA ends) for annotation F19: track allele frequency shifts under experimental selection 48 / 48