pp 1-48 | Cite as

Seascape Genomics: Contextualizing Adaptive and Neutral Genomic Variation in the Ocean Environment

  • Libby LigginsEmail author
  • Eric A. Treml
  • Cynthia Riginos
Part of the Population Genomics book series


Seventy-one per cent of the earth’s surface is covered by ocean which contains almost 80% of the world’s phyla – “seascape genomics” is the study of how spatial dependence and environmental features in the ocean influence the geographic structure of genomic patterns in marine organisms. The field extends from seascape genetics where the study of small numbers of neutral loci predominates, to additionally consider larger numbers of loci from throughout the genome that may be of some functional or adaptive significance and are subject to selection. Seascape genomics is conceptually similar to landscape genomics; the disciplines share theoretical underpinnings, and the genetic measures and analytical methods are often the same. However, the spatio-temporal variability of the physical ocean environment and the biological characteristics of marine organisms (e.g. large population sizes and high dispersal ability) present some characteristic challenges and opportunities for spatial population genomics studies. This chapter provides an overview of the field of seascape genomics, outlines concepts and methods to consider when conducting seascape genomics studies, and highlights future research avenues and opportunities for the application of seascape genomics to global issues affecting our marine environment.


Adaptation Genetic-environment association Genotype-by-sequencing Landscape genomics Natural selection Oceanography Outlier test Population genomics Seascape genetics SNPs 



This chapter brings together ideas developed during several collaborative projects, workshops, and research activities with many colleagues and supported by several institutions. We acknowledge and appreciate the contributions of colleagues within the Diversity of the Indo-Pacific Network (DIPnet,, Ira Moana Project (, and C. Noble for editorial help. Our collaborations have been supported by the National Evolutionary Synthesis Center (NESCent), DIPnet Research Coordination Network Grant (NSF: DEB 1457848), and a Royal Society Te Apārangi Catalyst Seeding Fund (17-MAU-309-CSG). L.L. was supported by a New Zealand Rutherford Foundation Postdoctoral Fellowship.


  1. Allendorf FW, England PR, Luikart G, Ritchie PA, Ryman N. Genetic effects of harvest on wild animal populations. Trends Ecol Evol. 2008;23:327–37.Google Scholar
  2. Allendorf FW, Hohenlohe PA, Luikart G. Genomics and the future of conservation genetics. Nat Rev Genet. 2010;11:697.Google Scholar
  3. Almany GR, Berumen ML, Thorrold SR, Planes S, Jones GP. Local replenishment of coral reef fish populations in a marine reserve. Science. 2007;316:742–4.Google Scholar
  4. Anderson CD, Epperson BK, Fortin MJ, Holderegger R, James PM, Rosenberg MS, Scribner KT, et al. Considering spatial and temporal scale in landscape-genetic studies of gene flow. Mol Ecol. 2010;19:3565–75.Google Scholar
  5. Andrews KR, Good JM, Miller MR, Luikart G, Hohenlohe PA. Harnessing the power of RADseq for ecological and evolutionary genomics. Nat Rev Genet. 2016;17:81–92.Google Scholar
  6. Angilletta MJ Jr, Angilletta MJ. Thermal adaptation: a theoretical and empirical synthesis. Oxford: Oxford University Press; 2009.Google Scholar
  7. Avise JC. Phylogeography: the history and formation of species. Cambridge: Harvard University Press; 2000.Google Scholar
  8. Ayre DJ, Minchinton TE, Perrin C. Does life history predict past and current connectivity for rocky intertidal invertebrates across a marine biogeographic barrier? Mol Ecol. 2009;18:1887–903.Google Scholar
  9. Balkenhol N, Fortin M-J. Basics of study design: sampling landscape heterogeneity and genetic variation for landscape genetic studies. In: Balkenhol N, Cushman S, Storfer A, Waits L, editors. Landscape genetics: concepts, methods, applications. West Sussex: Wiley; 2016. p. 58–75.Google Scholar
  10. Balkenhol N, Cushman S, Storfer A, Waits LP. Introduction to landscape genetics: defining, learning and applying an interdisciplinary field. In: Balkenhol N, Cushman S, Storfer A, Waits L, editors. Landscape genetics: concepts, methods, applications. West Sussex: Wiley; 2016a. p. 1–17.Google Scholar
  11. Balkenhol N, Cushman S, Storfer A, Waits LP. Landscape genetics: concepts, methods, applications. West Sussex: Wiley; 2016b.Google Scholar
  12. Balkenhol N, Dudaniec RY, Krutovsky KV, Johnson JS, Cairns DM, Segelbacher G, Selkoe KA, et al. Landscape genomics: understanding relationships between environmental heterogeneity and genomic characteristics of populations. In: Rajora OP, editor. Population genomics: concepts, approaches and applications. Cham: Springer Nature Switzerland AG; 2019. p. 261–322.Google Scholar
  13. Baltazar-Soares M, Hinrichsen HH, Eizaguirre C, Handling editor: Mikko Heino. Integrating population genomics and biophysical models towards evolutionary-based fisheries management. ICES J Mar Sci. 2018;75:1245–57.Google Scholar
  14. Barth JM, Berg PR, Jonsson PR, Bonanomi S, Corell H, Hemmer-Hansen J, Jakobsen KS, Johannesson K, Jorde PE, Knutsen H, Moksnes PO. Genome architecture enables local adaptation of Atlantic cod despite high connectivity. Mol Ecol. 2017;26:4452–66.Google Scholar
  15. Barton NH. Gene flow past a cline. Heredity. 1979;43:333.Google Scholar
  16. Bay RA, Harrigan RJ, Le Underwood V, Gibbs HL, Smith TB, Ruegg K. Genomic signals of selection predict climate-driven population declines in a migratory bird. Science. 2018;359:83–6.Google Scholar
  17. Benestan L, Quinn BK, Maaroufi H, Laporte M, Clark FK, Greenwood SJ, Rochette R, et al. Seascape genomics provides evidence for thermal adaptation and current-mediated population structure in American lobster (Homarus americanus). Mol Ecol. 2016;25:5073–92.Google Scholar
  18. Bernatchez L. On the maintenance of genetic variation and adaptation to environmental change: considerations from population genomics in fishes. J Fish Biol. 2016;89:2519–56.Google Scholar
  19. Bernatchez L, Wellenreuther M, Araneda C, Ashton DT, Barth JM, Beacham TD, Maes GE, et al. Harnessing the power of genomics to secure the future of seafood. Trends Ecol Evol. 2017;32:665–80.Google Scholar
  20. Bi K, Vanderpool D, Singhal S, Linderoth T, Moritz C, Good JM. Transcriptome-based exon capture enables highly cost-effective comparative genomic data collection at moderate evolutionary scales. BMC Genomics. 2012;13:403.Google Scholar
  21. Bierne N, Welch J, Loire E, Bonhomme F, David P. The coupling hypothesis: why genome scans may fail to map local adaptation genes. Mol Ecol. 2011;20:2044–72.Google Scholar
  22. Bierne N, Gagnaire PA, David P. The geography of introgression in a patchy environment and the thorn in the side of ecological speciation. Curr Zool. 2013;59:72–86.Google Scholar
  23. Bierne N, Bonhomme F, Arnaud-Haond S. Editorial dedicated population genomics for the silent world: the specific questions of marine population genetics. Curr Zool. 2016;62:545–50.Google Scholar
  24. Blanchet FG, Legendre P, Maranger R, Monti D, Pepin P. Modelling the effect of directional spatial ecological processes at different scales. Oecologia. 2011;166:357–68.Google Scholar
  25. Bode M, Leis JM, Mason LB, Williamson DH, Harrison HB, Choukroun S, Jones GP. Successful validation of a larval dispersal model using genetic parentage data. PLoS Biol. 2019;17:e3000380.Google Scholar
  26. Bonanomi S, Pellissier L, Therkildsen NO, Hedeholm RB, Retzel A, Meldrup D, Olsen SM, et al. Archived DNA reveals fisheries and climate induced collapse of a major fishery. Sci Rep. 2015;5:15395.Google Scholar
  27. Bongaerts P, Riginos C, Brunner R, Englebert N, Smith SR, Hoegh-Guldberg O. Deep reefs are not universal refuges: reseeding potential varies among coral species. Sci Adv. 2017;3:e1602373.Google Scholar
  28. Borcard D, Legendre P, Drapeau P. Partialling out the spatial component of ecological variation. Ecology. 1992;73:1045–55.Google Scholar
  29. Bourret V, Kent MP, Primmer CR, Vasemägi A, Karlsson S, Hindar K, et al. SNP-array reveals genome-wide patterns of geographical and potential adaptive divergence across the natural range of Atlantic salmon (Salmo salar). Mol Ecol. 2013;22:532–51.Google Scholar
  30. Bradbury IR, Hubert S, Higgins B, Bowman S, Borza T, Paterson IG, et al. Genomic islands of divergence and their consequences for the resolution of spatial structure in an exploited marine fish. Evol Appl. 2013;6:450–61.Google Scholar
  31. Campbell NR, Harmon SA, Narum SR. Genotyping-in-thousands by sequencing (GT-seq): a cost effective SNP genotyping method based on custom amplicon sequencing. Mol Ecol Resour. 2014;15(4):855–67.Google Scholar
  32. Capblancq T, Luu K, Blum MG, Bazin E. Evaluation of redundancy analysis to identify signatures of local adaptation. Mol Ecol Resour. 2018;18:1223–33.Google Scholar
  33. Case RA, Hutchinson WF, Hauser L, Van Oosterhout C, Carvalho GR. Macro-and micro-geographic variation in pantophysin (PanI) allele frequencies in NE Atlantic cod Gadus morhua. Mar Ecol Prog Ser. 2005;301:267–78.Google Scholar
  34. Colosimo PF, Hosemann KE, Balabhadra S, Villarreal G, Dickson M, Grimwood J, Schmutz J, et al. Widespread parallel evolution in sticklebacks by repeated fixation of ectodysplasin alleles. Science. 2005;307:1928–33.Google Scholar
  35. Cowen RK, Sponaugle S. Larval dispersal and marine population connectivity. Ann Rev Mar Sci. 2009;1:443–66.Google Scholar
  36. Crandall ED, Treml EA, Barber PH. Coalescent and biophysical models of stepping-stone gene flow in neritid snails. Mol Ecol. 2012;21:5579–98.Google Scholar
  37. Crandall ED, Riginos C, Bird CE, Liggins L, Treml E, Beger M, Barber PH, Connolly SR, Cowman PF, DiBattista JD, Eble JA. The molecular biogeography of the Indo-Pacific: testing hypotheses with multispecies genetic patterns. Glob Ecol Biogeogr. 2019;28:943–60.Google Scholar
  38. D’Aloia CC, Bogdanowicz SM, Francis RK, Majoris JE, Harrison RG, Buston PM. Patterns, causes, and consequences of marine larval dispersal. Proc Natl Acad Sci U S A. 2015;112:13940–5.Google Scholar
  39. Dale MR, Fortin MJ. Spatial analysis: a guide for ecologists: Cambridge University Press; 2014.Google Scholar
  40. Dalongeville A, Andrello M, Mouillot D, Lobreaux S, Fortin MJ, Lasram F, Belmaker J, Rocklin D, Manel S. Geographic isolation and larval dispersal shape seascape genetic patterns differently according to spatial scale. Evol Appl. 2018a;11:1437–47.Google Scholar
  41. Dalongeville A, Benestan L, Mouillot D, Lobreaux S, Manel S. Combining six genome scan methods to detect candidate genes to salinity in the Mediterranean striped red mullet (Mullus surmuletus). BMC Genomics. 2018b;19:217.Google Scholar
  42. Davey JW, Blaxter ML. RADSeq: next-generation population genetics. Brief Funct Genomics. 2010;9:416–23.Google Scholar
  43. Davies SW, Treml EA, Kenkel CD, Matz MV. Exploring the role of Micronesian islands in the maintenance of coral genetic diversity in the Pacific Ocean. Mol Ecol. 2015;24:70–82.Google Scholar
  44. Dawson MN, Hays CG, Grosberg RK, Raimondi PT. Dispersal potential and population genetic structure in the marine intertidal of the eastern North Pacific. Ecol Monogr. 2014;84:435–56.Google Scholar
  45. de Villemereuil P, Gaggiotti OE. A new FST-based method to uncover local adaptation using environmental variables. Methods Ecol Evol. 2015;6:1248–58.Google Scholar
  46. de Villemereuil P, Frichot É, Bazin É, François O, Gaggiotti OE. Genome scan methods against more complex models: when and how much should we trust them? Mol Ecol. 2014;23:2006–19.Google Scholar
  47. de Wit P, Palumbi SR. Transcriptome-wide polymorphisms of red abalone (Haliotis rufescens) reveal patterns of gene flow and local adaptation. Mol Ecol. 2013;22:2884–97.Google Scholar
  48. de Wit P, Pespeni MH, Ladner JT, Barshis DJ, Seneca F, Jaris H, Therkildsen NO, Morikawa M, Palumbi SR. The simple fool's guide to population genomics via RNA-Seq: an introduction to high-throughput sequencing data analysis. Mol Ecol Resour. 2012;12:1058–67.Google Scholar
  49. DeBoer TS, Naguit MR, Erdmann MV, Ablan-Lagman MC, Carpenter KE, Toha AH, Barber PH. Concordance between phylogeographic and biogeographic boundaries in the Coral Triangle: conservation implications based on comparative analyses of multiple giant clam species. Bull Mar Sci. 2014;90:277–300.Google Scholar
  50. Dennenmoser S, Vamosi SM, Nolte AW, Rogers SM. Adaptive genomic divergence under high gene flow between freshwater and brackish-water ecotypes of prickly sculpin (Cottus asper) revealed by Pool-Seq. Mol Ecol. 2017;26:25–42.Google Scholar
  51. Diopere E, Vandamme SG, Hablützel PI, Cariani A, Van Houdt J, Rijnsdorp A, Tinti F, FishPopTrace Consortium, Volckaert FA, Maes GE. Seascape genetics of a flatfish reveals local selection under high levels of gene flow. ICES J Mar Sci. 2017;75:675–89.Google Scholar
  52. Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carré G, Marquéz JR, et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography. 2013;36:27–46.Google Scholar
  53. Dray S, Legendre P, Peres-Neto PR. Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM). Ecol Model. 2006;196:483–93.Google Scholar
  54. Duforet-Frebourg N, Bazin E, Blum MG. Genome scans for detecting footprints of local adaptation using a Bayesian factor model. Mol Biol Evol. 2014;31:2483–95.Google Scholar
  55. Duranton M, Allal F, Fraïsse C, Bierne N, Bonhomme F, Gagnaire PA. The origin and remolding of genomic islands of differentiation in the European sea bass. Nat Commun. 2018;9:2518.Google Scholar
  56. Dyer RJ. Is there such a thing as landscape genetics? Mol Ecol. 2015;24:3518–28.Google Scholar
  57. Dyer RJ, Nason JD. Population graphs: the graph theoretic shape of genetic structure. Mol Ecol. 2004;13:1713–27.Google Scholar
  58. Dyer RJ, Nason JD, Garrick RC. Landscape modelling of gene flow: improved power using conditional genetic distance derived from the topology of population networks. Mol Ecol. 2010;19:3746–59.Google Scholar
  59. Eaton DA. PyRAD: assembly of de novo RADseq loci for phylogenetic analyses. Bioinformatics. 2014;30:1844–9.Google Scholar
  60. Eldon B, Riquet F, Yearsley J, Jollivet D, Broquet T. Current hypotheses to explain genetic chaos under the sea. Curr Zoo. 2016;62:551–66.Google Scholar
  61. Epling C, Dobzhansky T. Genetics of natural populations. VI. Microgeographic races in Linanthus parryae. Genetics. 1942;27:317.Google Scholar
  62. Epperson BK. Geographical genetics (MPB-38). Princeton: Princeton University Press; 2003.Google Scholar
  63. Ewers-Saucedo C, Pringle JM, Sepúlveda HH, Byers JE, Navarrete SA, Wares JP. The oceanic concordance of phylogeography and biogeography: a case study in Notochthamalus. Ecol Evol. 2016;6:4403–20.Google Scholar
  64. Excoffier L, Hofer T, Foll M. Detecting loci under selection in a hierarchically structured population. Heredity. 2009;103:285.Google Scholar
  65. Faircloth BC, McCormack JE, Crawford NG, Harvey MG, Brumfield RT, Glenn TC. Ultraconserved elements anchor thousands of genetic markers spanning multiple evolutionary timescales. Syst Biol. 2012;61:717–26.Google Scholar
  66. Faurby S, Barber PH. Theoretical limits to the correlation between pelagic larval duration and population genetic structure. Mol Ecol. 2012;21:3419–32.Google Scholar
  67. Feder ME, Mitchell-Olds T. Evolutionary and ecological functional genomics. Nat Rev Genet. 2003;4:649.Google Scholar
  68. Fitzpatrick MC, Keller SR. Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation. Ecol Lett. 2015;18:1–6.Google Scholar
  69. Flanagan SP, Forester BR, Latch EK, Aitken SN, Hoban S. Guidelines for planning genomic assessment and monitoring of locally adaptive variation to inform species conservation. Evol Appl. 2018;11:1035–52.Google Scholar
  70. Forester BR, Jones MR, Joost S, Landguth EL, Lasky JR. Detecting spatial genetic signatures of local adaptation in heterogeneous landscapes. Mol Ecol. 2016;25:104–20.Google Scholar
  71. Forester BR, Lasky JR, Wagner HH, Urban DL. Comparing methods for detecting multilocus adaptation with multivariate genotype–environment associations. Mol Ecol. 2018;27:2215–33.Google Scholar
  72. Foster NL, Paris CB, Kool JT, Baums IB, Stevens JR, Sanchez JA, Bastidas C, et al. Connectivity of Caribbean coral populations: complementary insights from empirical and modelled gene flow. Mol Ecol. 2012;21:1143–57.Google Scholar
  73. François O, Waits LP. Clustering and assignment methods in landscape genetics. In: Balkenhol N, Storfer A, Cushman SA, Waits LP, editors. Landscape genetics: concepts, methods, applications. Wiley: Chichester; 2015. p. 114–28.Google Scholar
  74. François O, Martins H, Caye K, Schoville SD. Controlling false discoveries in genome scans for selection. Mol Ecol. 2016;25:454–69.Google Scholar
  75. Frichot E, Schoville SD, Bouchard G, François O. Testing for associations between loci and environmental gradients using latent factor mixed models. Mol Biol Evol. 2013;30:1687–99.Google Scholar
  76. Frichot E, Schoville SD, de Villemereuil P, Gaggiotti OE, François O. Detecting adaptive evolution based on association with ecological gradients: orientation matters! Heredity. 2015;115:22.Google Scholar
  77. Gaggiotti OE. Metapopulations of marine species with larval dispersal: a counterpoint to Ilkka’s Glanville fritillary metapopulations. Ann Zool Fenn. 2017;54(1–4):97–113. Finnish Zoological and Botanical Publishing BoardGoogle Scholar
  78. Gagnaire PA, Gaggiotti OE. Detecting polygenic selection in marine populations by combining population genomics and quantitative genetics approaches. Curr Zoo. 2016;62:603–16.Google Scholar
  79. Gagnaire PA, Normandeau E, Côté C, Hansen MM, Bernatchez L. The genetic consequences of spatially varying selection in the panmictic American eel (Anguilla rostrata). Genetics. 2012;190:725–36.Google Scholar
  80. Gagnaire PA, Broquet T, Aurelle D, Viard F, Souissi A, Bonhomme F, Arnaud-Haond S, et al. Using neutral, selected, and hitchhiker loci to assess connectivity of marine populations in the genomic era. Evol Appl. 2015;8:769–86.Google Scholar
  81. Gaither MR, Rocha LA. Origins of species richness in the Indo-Malay-Philippine biodiversity hotspot: evidence for the centre of overlap hypothesis. J Biogeogr. 2013;40:1638–48.Google Scholar
  82. Galindo HM, Olson DB, Palumbi SR. Seascape genetics: a coupled oceanographic-genetic model predicts population structure of Caribbean corals. Curr Biol. 2006;16:1622–6.Google Scholar
  83. Gerlach G, Atema J, Kingsford MJ, Black KP, Miller-Sims V. Smelling home can prevent dispersal of reef fish larvae. Proc Natl Acad Sci U S A. 2007;104:858–63.Google Scholar
  84. Getis A, Ord JK. The analysis of spatial association by use of distance statistics. In: Perspectives on spatial data analysis. Berlin: Springer; 2010. p. 127–45.Google Scholar
  85. Gleason LU, Burton RS. Genomic evidence for ecological divergence against a background of population homogeneity in the marine snail Chlorostoma funebralis. Mol Ecol. 2016;25:3557–73.Google Scholar
  86. Gould AL, Dunlap PV. Genomic analysis of a cardinalfish with larval homing potential reveals genetic admixture in the Okinawa Islands. Mol Ecol. 2017;26:3870–82.Google Scholar
  87. Grummer JA, Beheregaray LB, Bernatchez L, Hand BK, Luikart G, Narum SR, et al. Aquatic landscape genomics and environmental effects on genetic variation. Trends Ecol Evol. 2019;34(7):641–54.Google Scholar
  88. Guillot G, Rousset F. Dismantling the Mantel tests. Methods Ecol Evol. 2013;4:336–44.Google Scholar
  89. Guillot G, Leblois R, Coulon A, Frantz AC. Statistical methods in spatial genetics. Mol Ecol. 2009;18:4734–56.Google Scholar
  90. Guillot G, Vitalis R, le Rouzic A, Gautier M. Detecting correlation between allele frequencies and environmental variables as a signature of selection. A fast computational approach for genome-wide studies. Spat Stat. 2014;8:145–55.Google Scholar
  91. Günther T, Coop G. Robust identification of local adaptation from allele frequencies. Genetics. 2013;195:205–20.Google Scholar
  92. Guo B, DeFaveri J, Sotelo G, Nair A, Merilä J. Population genomic evidence for adaptive differentiation in Baltic Sea three-spined sticklebacks. BMC Biol. 2015;13:19.Google Scholar
  93. Gutenkunst RN, Hernandez RD, Williamson SH, Bustamante CD. Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data. PLoS Genet. 2009;5:e1000695.Google Scholar
  94. Hand BK, Lowe WH, Kovach RP, Muhlfeld CC, Luikart G. Landscape community genomics: understanding eco-evolutionary processes in complex environments. Trends Ecol Evol. 2015;30:161–8.Google Scholar
  95. Hansen MM, Hemmer-Hansen J. Landscape genetics goes to sea. J Biol. 2007;6:6.Google Scholar
  96. Hansen MM, Olivieri I, Waller DM, Nielsen EE, GeM Working Group. Monitoring adaptive genetic responses to environmental change. Mol Ecol. 2012;21:1311–29.Google Scholar
  97. Hauser L, Carvalho GR. Paradigm shifts in marine fisheries genetics: ugly hypotheses slain by beautiful facts. Fish Fish. 2008;9:333–62.Google Scholar
  98. Hedgecock D. Does variance in reproductive success limit effective population sizes of marine organisms. Gen Evol Aqua Org. 1994;122:122–34.Google Scholar
  99. Hedrick PW. Perspective: highly variable loci and their interpretation in evolution and conservation. Evolution. 1999;53:313–8.Google Scholar
  100. Hellberg ME. Gene flow and isolation among populations of marine animals. Annu Rev Ecol Evol Syst. 2009;40:291–310.Google Scholar
  101. Hellberg ME, Burton RS, Neigel JE, Palumbi SR. Genetic assessment of connectivity among marine populations. Bull Mar Sci. 2002;70:273–90.Google Scholar
  102. Henriques R, von der Heyden S, Lipinski MR, du Toit N, Kainge P, Bloomer P, Matthee CA. Spatio-temporal genetic structure and the effects of long-term fishing in two partially sympatric offshore demersal fishes. Mol Ecol. 2016;25:5843–61.Google Scholar
  103. Hoban S, Bertorelle G, Gaggiotti OE. Computer simulations: tools for population and evolutionary genetics. Nat Rev Genet. 2012;13:110.Google Scholar
  104. Hoey JA, Pinsky ML. Genomic signatures of environmental selection despite near-panmixia in summer flounder. Evol App. 2018;11:1732–47.Google Scholar
  105. Hoffmann A, Griffin P, Dillon S, Catullo R, Rane R, Byrne M, Jordan R, Oakeshott J, Weeks A, Joseph L, Lockhart P. A framework for incorporating evolutionary genomics into biodiversity conservation and management. Clim Change Resp. 2015;2:1.Google Scholar
  106. Hohenlohe PA, Phillips PC, Cresko WA. Using population genomics to detect selection in natural populations: key concepts and methodological considerations. Int J Plant Sci. 2010;171:1059–71.Google Scholar
  107. Holderegger R, Wagner HH. Landscape genetics. Bioscience. 2008;58:199–207.Google Scholar
  108. Holliday JA, Hallerman EM, Haak DC. Genotyping and sequencing technologies in population genetics and genomics. In: Rajora OP, editor. Population genomics: concepts, approaches and applications. Cham: Springer Nature Switzerland AG; 2019. p. 83–126.Google Scholar
  109. Jackson TM, Roegner GC, O’Malley KG. Evidence for interannual variation in genetic structure of Dungeness crab (Cancer magister) along the California Current System. Mol Ecol. 2018;27:352–68.Google Scholar
  110. Johnson MS, Black R. Chaotic genetic patchiness in an intertidal limpet, Siphonaria sp. Mar Biol. 1982;70:157–64.Google Scholar
  111. Johnson MS, Black R. Pattern beneath the chaos: the effect of recruitment on genetic patchiness in an intertidal limpet. Evolution. 1984;38:1371–83.Google Scholar
  112. Johnston IA, Dunn JE. Temperature acclimation and metabolism in ectotherms with particular reference to teleost fish. Symp Soc Exp Biol. 1987;41:67–93.Google Scholar
  113. Jombart T, Pontier D, Dufour AB. Genetic markers in the playground of multivariate analysis. Heredity. 2009;102:330.Google Scholar
  114. Joost S, Bonin A, Bruford MW, Després L, Conord C, Erhardt G, Taberlet P. A spatial analysis method (SAM) to detect candidate loci for selection: towards a landscape genomics approach to adaptation. Mol Ecol. 2007;16:3955–69.Google Scholar
  115. Karl SA, Toonen RJ, Grant WS, Bowen BW. Common misconceptions in molecular ecology: echoes of the modern synthesis. Mol Ecol. 2012;21:4171–89.Google Scholar
  116. Kelley JL, Brown AP, Therkildsen NO, Foote AD. The life aquatic: advances in marine vertebrate genomics. Nat Rev Genet. 2016;17:523–34.Google Scholar
  117. Kingsford MJ, Leis JM, Shanks A, Lindeman KC, Morgan SG, Pineda J. Sensory environments, larval abilities and local self-recruitment. Bull Mar Sci. 2002;70:309–40.Google Scholar
  118. Kinlan BP, Gaines SD. Propagule dispersal in marine and terrestrial environments: a community perspective. Ecology. 2003;84:2007–20.Google Scholar
  119. Knutsen H, Jorde PE, Sannæs H, Rus Hoelzel A, Bergstad OA, Stefanni S, et al. Bathymetric barriers promoting genetic structure in the deepwater demersal fish tusk (Brosme brosme). Mol Ecol. 2009;18:3151–62.Google Scholar
  120. Koehn RK, Siebenaller JF. Biochemical studies of aminopeptidase polymorphism in Mytilus edulis. II. Dependence of reaction rate on physical factors and enzyme concentration. Biochem Genet. 1981;19:1143–62.Google Scholar
  121. Koehn RK, Newell RI, Immermann F. Maintenance of an aminopeptidase allele frequency cline by natural selection. Proc Natl Acad Sci U S A. 1980;77:5385–9.Google Scholar
  122. Kofler R, Orozco-terWengel P, De Maio N, Pandey RV, Nolte V, Futschik A, Kosiol C, et al. PoPoolation: a toolbox for population genetic analysis of next generation sequencing data from pooled individuals. PLoS One. 2011;6:e15925.Google Scholar
  123. Kool JT, Moilanen A, Treml EA. Population connectivity: recent advances and new perspectives. Landsc Ecol. 2013;28:165–85.Google Scholar
  124. Landguth E, Cushman SA, Balkenhol N. Simulation modeling in landscape genetics. In: Balkenhol N, Cushman S, Storfer A, Waits L, editors. Landscape genetics: concepts, methods, applications. West Sussex: Wiley; 2016. p. 99–113.Google Scholar
  125. Le Moan A, Gagnaire PA, Bonhomme F. Parallel genetic divergence among coastal–marine ecotype pairs of European anchovy explained by differential introgression after secondary contact. Mol Ecol. 2016;25:3187–202.Google Scholar
  126. Le Port A, Montgomery JC, Smith AN, Croucher AE, McLeod IM, Lavery SD. Temperate marine protected area provides recruitment subsidies to local fisheries. Proc R Soc Lond B Biol Sci. 2017;284:20171300.Google Scholar
  127. Legendre P, Anderson MJ. Distance-based redundancy analysis: testing multispecies responses in multifactorial ecological experiments. Ecol Monogr. 1999;69:1–24.Google Scholar
  128. Legendre P, Lapointe FJ, Casgrain P. Modeling brain evolution from behavior: a permutational regression approach. Evolution. 1994;48:1487–99.Google Scholar
  129. Legendre P, Fortin MJ, Borcard D. Should the Mantel test be used in spatial analysis? Methods Ecol Evol. 2015;6:1239–47.Google Scholar
  130. Leis JM, van Herwerden L, Patterson HM. Estimating connectivity in marine fish populations: what works best? Oceanogr Mar Biol. 2011;49:193–234.Google Scholar
  131. Lenormand T. Gene flow and the limits to natural selection. Trends Ecol Evol. 2002;17:183–9.Google Scholar
  132. Liggins L, Treml EA, Riginos C. Taking the plunge: an introduction to undertaking seascape genetic studies and using biophysical models. Geogr Compass. 2013;7:173–96.Google Scholar
  133. Liggins L, Treml EA, Possingham HP, Riginos C. Seascape features, rather than dispersal traits, predict spatial genetic patterns in co-distributed reef fishes. J Biogeogr. 2016;43:256–67.Google Scholar
  134. Limborg MT, Helyar SJ, De Bruyn M, Taylor MI, Nielsen EE, Ogden RO, Carvalho GR, FPT Consortium, Bekkevold D. Environmental selection on transcriptome-derived SNPs in a high gene flow marine fish, the Atlantic herring (Clupea harengus). Mol Ecol. 2012;21:3686–703.Google Scholar
  135. Lotterhos KE, Whitlock MC. Evaluation of demographic history and neutral parameterization on the performance of FST outlier tests. Mol Ecol. 2014;23:2178–92.Google Scholar
  136. Lotterhos KE, Whitlock MC. The relative power of genome scans to detect local adaptation depends on sampling design and statistical method. Mol Ecol. 2015;24:1031–46.Google Scholar
  137. Lowry DB, Willis JH. A widespread chromosomal inversion polymorphism contributes to a major life-history transition, local adaptation, and reproductive isolation. PLoS Biol. 2010;8:e1000500.Google Scholar
  138. Lowry DB, Hoban S, Kelley JL, Lotterhos KE, Reed LK, Antolin MF, Storfer A. Responsible RAD: striving for best practices in population genomic studies of adaptation. Mol Ecol Resour. 2017;17:366–9.Google Scholar
  139. Luikart G, England PR, Tallmon D, Jordan S, Taberlet P. The power and promise of population genomics: from genotyping to genome typing. Nat Rev Genet. 2003;4:981–94.Google Scholar
  140. Luikart G, Kardos M, Hand BK, Rajora OP, Aitken SN, Hohenlohe PA. Population genomics: advancing understanding of nature. In: Rajora OP, editor. Population genomics: concepts, approaches and applications. Cham: Springer Nature Switzerland AG; 2019. p. 3–79.Google Scholar
  141. Manel S, Schwartz MK, Luikart G, Taberlet P. Landscape genetics: combining landscape ecology and population genetics. Trends Ecol Evol. 2003;18:189–97.Google Scholar
  142. Manel S, Joost S, Epperson BK, Holderegger R, Storfer A, Rosenberg MS, Scribner KT, Bonin A, Fortin MJ. Perspectives on the use of landscape genetics to detect genetic adaptive variation in the field. Mol Ecol. 2010;19:3760–72.Google Scholar
  143. Manel S, Perrier C, Pratlong M, Abi-Rached L, Paganini J, Pontarotti P, Aurelle D. Genomic resources and their influence on the detection of the signal of positive selection in genome scans. Mol Ecol. 2016;25:170–84.Google Scholar
  144. Manel S, Andrello M, Henry K, Verdelet D, Darracq A, Guerin PE, Desprez B, Devaux P. Predicting genotype environmental range from genome–environment associations. Mol Ecol. 2018;27:2823–33.Google Scholar
  145. Mantel N. The detection of disease clustering and a generalized regression approach. Cancer Res. 1967;27:209–20.Google Scholar
  146. Marko PB, Hart MW. The complex analytical landscape of gene flow inference. Trends Ecol Evol. 2011;26:448–56.Google Scholar
  147. Marshall DJ, Morgan SG. Ecological and evolutionary consequences of linked life-history stages in the sea. Curr Biol. 2011;21:R718–25.Google Scholar
  148. Marshall DJ, Monro K, Bode M, Keough MJ, Swearer S. Phenotype–environment mismatches reduce connectivity in the sea. Ecol Lett. 2010;13:128–40.Google Scholar
  149. Mastretta-Yanes A, Arrigo N, Alvarez N, Jorgensen TH, Piñero D, Emerson BC. Restriction site-associated DNA sequencing, genotyping error estimation and de novo assembly optimization for population genetic inference. Mol Ecol Resour. 2015;15:28–41.Google Scholar
  150. Matz MV. Fantastic beasts and how to sequence them: ecological genomics for obscure model organisms. Trends Genet. 2018;34:121–32.Google Scholar
  151. Matz MV, Treml EA, Aglyamova GV, Bay LK. Potential and limits for rapid genetic adaptation to warming in a Great Barrier Reef coral. PLoS Genet. 2018;14:e1007220.Google Scholar
  152. McCauley DJ, Pinsky ML, Palumbi SR, Estes JA, Joyce FH, Warner RR. Marine defaunation: animal loss in the global ocean. Science. 2015;347:1255641.Google Scholar
  153. McRae BH. Isolation by resistance. Evolution. 2006;60:1551–61.Google Scholar
  154. McRae BH, Dickson BG, Keitt TH, Shah VB. Using circuit theory to model connectivity in ecology and conservation. Ecology. 2008;89(10):2712–24.Google Scholar
  155. Meirmans PG, Hedrick PW. Assessing population structure: FST and related measures. Mol Ecol Resour. 2011;11:5–18.Google Scholar
  156. Mercer TR, Gerhardt DJ, Dinger ME, Crawford J, Trapnell C, Jeddeloh JA, Mattick JS, et al. Targeted RNA sequencing reveals the deep complexity of the human transcriptome. Nat Biotechnol. 2012;30:99.Google Scholar
  157. Munguia-Vega A, Jackson A, Marinone SG, Erisman B, Moreno-Baez M, Girón-Nava A, Pfister T, Aburto-Oropeza O, Torre J. Asymmetric connectivity of spawning aggregations of a commercially important marine fish using a multidisciplinary approach. PeerJ. 2014;2:e511.Google Scholar
  158. Nei M. Genetic distance between populations. Am Nat. 1972;106:283–92.Google Scholar
  159. Nosil P, Funk DJ, Ortiz-Barrientos DA. Divergent selection and heterogeneous genomic divergence. Mol Ecol. 2009;18:375–402.Google Scholar
  160. O’Connor MI, Bruno JF, Gaines SD, Halpern BS, Lester SE, Kinlan BP, Weiss JM. Temperature control of larval dispersal and the implications for marine ecology, evolution, and conservation. Proc Natl Acad Sci. 2007;104:1266–71.Google Scholar
  161. Oleksiak MF. Adaptation without boundaries: population genomics in marine systems. In: Rajora OP, editor. Population genomics: concepts, approaches and applications. Cham: Springer Nature Switzerland AG; 2019. p. 587–612.Google Scholar
  162. Palumbi SR. Marine speciation on a small planet. Trends Ecol Evol. 1992;7:114–8.Google Scholar
  163. Palumbi SR. Molecular biogeography of the Pacific. Coral Reefs. 1997;16:S47–52.Google Scholar
  164. Palumbi SR, Pinsky ML. Marine dispersal, ecology and conservation. In: Bertness MD, Bruno JF, Silliman B, Stachowicz JJ, editors. Marine community ecology. 2nd ed. Sunderland: Sinauer; 2013. p. 57–83.Google Scholar
  165. Paris CB, Chérubin LM, Cowen RK. Surfing, spinning, or diving from reef to reef: effects on population connectivity. Mar Ecol Prog Ser. 2007;347:285–300.Google Scholar
  166. Parobek CM, Archer FI, DePrenger-Levin ME, Hoban SM, Liggins L, Strand AE. Skelesim: an extensible, general framework for population genetic simulation in R. Mol Ecol Resour. 2017;17:101–9.Google Scholar
  167. Paterno M, Schiavina M, Aglieri G, Ben Souissi J, Boscari E, Casagrandi R, Chassanite A, Chiantore M, Congiu L, Guarnieri G, Kruschel C. Population genomics meet Lagrangian simulations: oceanographic patterns and long larval duration ensure connectivity among Paracentrotus lividus populations in the Adriatic and Ionian seas. Ecol Evol. 2017;7:2463–79.Google Scholar
  168. Pelc RA, Warner RR, Gaines SD. Geographical patterns of genetic structure in marine species with contrasting life histories. J Biogeogr. 2009;36:1881–90.Google Scholar
  169. Pespeni MH, Palumbi SR. Signals of selection in outlier loci in a widely dispersing species across an environmental mosaic. Mol Ecol. 2013;22:3580–97.Google Scholar
  170. Pinsky ML, Palumbi SR. Meta-analysis reveals lower genetic diversity in overfished populations. Mol Ecol. 2014;23:29–39.Google Scholar
  171. Pörtner HO, Knust R. Climate change affects marine fishes through the oxygen limitation of thermal tolerance. Science. 2007;315:95–7.Google Scholar
  172. Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000;155:945–59.Google Scholar
  173. Prunier JG, Kaufmann B, Fenet S, Picard D, Pompanon F, Joly P, Lena JP. Optimizing the trade-off between spatial and genetic sampling efforts in patchy populations: towards a better assessment of functional connectivity using an individual-based sampling scheme. Mol Ecol. 2013;22:5516–30.Google Scholar
  174. Prunier JG, Colyn M, Legendre X, Nimon KF, Flamand MC. Multicollinearity in spatial genetics: separating the wheat from the chaff using commonality analyses. Mol Ecol. 2015;24:263–83.Google Scholar
  175. Puritz JB, Lotterhos KE. Expressed exome capture sequencing: a method for cost-effective exome sequencing for all organisms. Mol Ecol Resour. 2018;18:1209–22.Google Scholar
  176. Puritz JB, Hollenbeck CM, Gold JR. dDocent: a RADseq, variant-calling pipeline designed for population genomics of non-model organisms. PeerJ. 2014;2:e431.Google Scholar
  177. Rajora OP, Eckert AJ, Zinck JWR. Single-locus versus multilocus patterns of local adaptation to climate in eastern white pine (Pinus strobus, Pinaceae). PLoS One. 2016;11:e0158691. Scholar
  178. Ralls K, Ballou JD, Dudash MR, Eldridge MD, Fenster CB, Lacy RC, et al. Call for a paradigm shift in the genetic management of fragmented populations. Conserv Lett. 2018;11:e12412.Google Scholar
  179. Ravinet M, Westram A, Johannesson K, Butlin R, André C, Panova M. Shared and nonshared genomic divergence in parallel ecotypes of Littorina saxatilis at a local scale. Mol Ecol. 2016;25:287–305.Google Scholar
  180. Rellstab C, Gugerli F, Eckert AJ, Hancock AM, Holderegger R. A practical guide to environmental association analysis in landscape genomics. Mol Ecol. 2015;24:4348–70.Google Scholar
  181. Riginos C, Liggins L. Seascape genetics: populations, individuals, and genes marooned and adrift. Geogr Compass. 2013;7:197–216.Google Scholar
  182. Riginos C, Douglas KE, Jin Y, Shanahan DF, Treml EA. Effects of geography and life history traits on genetic differentiation in benthic marine fishes. Ecography. 2011;34:566–75.Google Scholar
  183. Riginos C, Crandall ED, Liggins L, Bongaerts P, Treml E. Navigating the currents of seascape genomics: how spatial analyses can augment population genomic studies. Curr Zoo. 2016;62:581–601.Google Scholar
  184. Riginos C, Hock K, Matias AM, Mumby PJ, van Oppen MJH, Lukoschek V. Asymmetric dispersal is a critical element of concordance between biophysical dispersal models and spatial genetic structure in Great Barrier Reef corals. Div Dist. 2019; Scholar
  185. Riquet F, Liautard-Haag C, Woodall L, Bouza C, Louisy P, Hamer B, et al. Parallel pattern of differentiation at a genomic island shared between clinal and mosaic hybrid zones in a complex of cryptic seahorse lineages. Evolution. 2019;73:817–35.Google Scholar
  186. Robinson JD, Coffman AJ, Hickerson MJ, Gutenkunst RN. Sampling strategies for frequency spectrum-based population genomic inference. BMC Evol Biol. 2014;14:254.Google Scholar
  187. Rousset F. Genetic differentiation between individuals. J Evol Biol. 2000;13:58–62.Google Scholar
  188. Roux C, Tsagkogeorga G, Bierne N, Galtier N. Crossing the species barrier: genomic hotspots of introgression between two highly divergent Ciona intestinalis species. Mol Biol Evol. 2013;30:1574–87.Google Scholar
  189. Saenz-Agudelo P, Jones GP, Thorrold SR, Planes S. Estimating connectivity in marine populations: an empirical evaluation of assignment tests and parentage analysis under different gene flow scenarios. Mol Ecol. 2009;18:1765–76.Google Scholar
  190. Saenz-Agudelo P, DiBattista JD, Piatek MJ, Gaither MR, Harrison HB, Nanninga GB, et al. Seascape genetics along environmental gradients in the Arabian Peninsula: insights from ddRAD sequencing of anemonefishes. Mol Ecol. 2015;24:6241–55.Google Scholar
  191. Saha A, Hauser L, Kent M, Planque B, Neat F, Kirubakaran TG, Huse I, Homrum EÍ, Fevolden SE, Lien S, Johansen T. Seascape genetics of saithe (Pollachius virens) across the North Atlantic using single nucleotide polymorphisms. ICES J Mar Sci. 2015;72:2732–41.Google Scholar
  192. Sandoval-Castillo J, Robinson NA, Hart AM, Strain LW, Beheregaray LB. Seascape genomics reveals adaptive divergence in a connected and commercially important mollusc, the greenlip abalone (Haliotis laevigata), along a longitudinal environmental gradient. Mol Ecol. 2018;27:1603–20.Google Scholar
  193. Savolainen O, Lascoux M, Merilä J. Ecological genomics of local adaptation. Nat Rev Genet. 2013;14:807.Google Scholar
  194. Schlötterer C, Tobler R, Kofler R, Nolte V. Sequencing pools of individuals – mining genome-wide polymorphism data without big funding. Nat Rev Genet. 2014;15:749.Google Scholar
  195. Schmidt PS, Rand DM. Intertidal microhabitat and selection at MPI: interlocus contrasts in the northern acorn barnacle, Semibalanus balanoides. Evolution. 1999;53:135–46.Google Scholar
  196. Schmidt PS, Serrao EA, Pearson GA, Riginos C, Rawson PD, Hilbish TJ, et al. Ecological genetics in the North Atlantic: environmental gradients and adaptation at specific loci. Ecology. 2008;89:S91–107.Google Scholar
  197. Schoville SD, Bonin A, François O, Lobreaux S, Melodelima C, Manel S. Adaptive genetic variation on the landscape: methods and cases. Annu Rev Ecol Evol Syst. 2012;43:23–43.Google Scholar
  198. Schunter C, Carreras-Carbonell J, Macpherson E, Tintoré J, Vidal-Vijande E, Pascual A, Guidetti P, et al. Matching genetics with oceanography: directional gene flow in a Mediterranean fish species. Mol Ecol. 2011;20:5167–81.Google Scholar
  199. Seeb JE, Carvalho G, Hauser L, Naish K, Roberts S, Seeb LW. Single-nucleotide polymorphism (SNP) discovery and applications of SNP genotyping in nonmodel organisms. Mol Ecol Resour. 2011;11:1–8.Google Scholar
  200. Selkoe KA, Toonen RJ. Marine connectivity: a new look at pelagic larval duration and genetic metrics of dispersal. Mar Ecol Prog Ser. 2011;436:291–305.Google Scholar
  201. Selkoe KA, Gaines SD, Caselle JE, Warner RR. Current shifts and kin aggregation explain genetic patchiness in fish recruits. Ecology. 2006;87:3082–94.Google Scholar
  202. Selkoe KA, Henzler CM, Gaines SD. Seascape genetics and the spatial ecology of marine populations. Fish Fish. 2008;9:363–77.Google Scholar
  203. Selkoe KA, D’Aloia CC, Crandall ED, Iacchei M, Liggins L, Puritz JB, et al. A decade of seascape genetics: contributions to basic and applied marine connectivity. Mar Ecol Prog Ser. 2016a;554:1–19.Google Scholar
  204. Selkoe KA, Scribner KT, Galindo HM. Waterscape genetics – applications of landscape genetics to rivers, lakes, and seas. In: Balkenhol N, Cushman S, Storfer A, Waits L, editors. Landscape genetics: concepts, methods, applications. West Sussex: Wiley; 2016b. p. 220–46.Google Scholar
  205. Shafer AB, Wolf JB, Alves PC, Bergström L, Bruford MW, Brännström I, Colling G, Dalén L, De Meester L, Ekblom R, Fawcett KD. Genomics and the challenging translation into conservation practice. Trends Ecol Evol. 2015;30:78–87.Google Scholar
  206. Shanks AL. Pelagic larval duration and dispersal distance revisited. Biol Bull. 2009;216:373–85.Google Scholar
  207. Shima JS, Swearer SE. Larval quality is shaped by matrix effects: implications for connectivity in a marine metapopulation. Ecology. 2009;90:1255–67.Google Scholar
  208. Slatkin M. Rare alleles as indicators of gene flow. Evolution. 1985;39:53–65.Google Scholar
  209. Slatkin M. Isolation by distance in equilibrium and non-equilibrium populations. Evolution. 1993;47:264–79.Google Scholar
  210. Smouse PE, Long JC, Sokal RR. Multiple regression and correlation extensions of the Mantel test of matrix correspondence. Syst Zool. 1986;35:627–32.Google Scholar
  211. Sodeland M, Jorde PE, Lien S, Jentoft S, Berg PR, Grove H, et al. Islands of divergence’ in the Atlantic cod represent polymorphic chromosomal rearrangements. Genome Biol Evol. 2016;8:1012–22.Google Scholar
  212. Sork VL, Aitken SN, Dyer RJ, Eckert AJ, Legendre P, Neale DB. Putting the landscape into the genomics of trees: approaches for understanding local adaptation and population responses to changing climate. Trends Gen Genom. 2013;9:901–11.Google Scholar
  213. Spear SF, Balkenhol N, Fortin MJ, McRae BH, Scribner KI. Use of resistance surfaces for landscape genetic studies: considerations for parameterization and analysis. Mol Ecol. 2010;19:3576–91.Google Scholar
  214. Stone GN, Nee S, Felsenstein J. Controlling for non-independence in comparative analysis of patterns across populations within species. Phil Trans Roy Soc B: Biol Sci. 2011;366:1410–24.Google Scholar
  215. Storfer A, Murphy MA, Evans JS, Goldberg CS, Robinson S, Spear SF, et al. Putting the ‘landscape’ in landscape genetics. Heredity. 2007;98:128.Google Scholar
  216. Storfer A, Murphy MA, Spear SF, Holderegger R, Waits LP. Landscape genetics: where are we now? Mol Ecol. 2010;19:3496–514.Google Scholar
  217. Strathmann RR. Why life histories evolve differently in the sea. Am Zool. 1990;30:197–207.Google Scholar
  218. Sylvester EV, Beiko RG, Bentzen P, Paterson I, Horne JB, Watson B, Lehnert S, et al. Environmental extremes drive population structure at the northern range limit of Atlantic salmon in North America. Mol Ecol. 2018;27:4026–40.Google Scholar
  219. Teske PR, Sandoval-Castillo J, Golla TR, Emami-Khoyi A, Tine M, von der Heyden S, Beheregaray LB. Thermal selection as a driver of marine ecological speciation. Proc R Soc B. 2019;286:20182023.Google Scholar
  220. Therkildsen NO, Palumbi SR. Practical low-coverage genome-wide sequencing of hundreds of individually barcoded samples for population and evolutionary genomics in nonmodel species. Mol Ecol Resour. 2017;17:194–208.Google Scholar
  221. Therkildsen NO, Hemmer-Hansen J, Hedeholm RB, Wisz MS, Pampoulie C, Meldrup D, et al. Spatiotemporal SNP analysis reveals pronounced biocomplexity at the northern range margin of Atlantic cod Gadus morhua. Evol Appl. 2013;6:690–705.Google Scholar
  222. Tigano A, Friesen VL. Genomics of local adaptation with gene flow. Mol Ecol. 2016;25:2144–64.Google Scholar
  223. Tine M, Kuhl H, Gagnaire P-A, Louro B, Desmarais E, Martins RST, et al. European sea bass genome and its variation provide insights into adaptation to euryhalinity and speciation. Nat Commun. 2014;5:5770.Google Scholar
  224. Tobler WR. A computer movie simulating urban growth in the Detroit region. Econ Geogr. 1970;46(sup1):234–40.Google Scholar
  225. Toonen RJ, Grosberg RK. Causes of chaos: spatial and temporal genetic heterogeneity in the intertidal anomuran crab Petrolisthes cinctipes. Phylogeogr Popul Genet Crustacea. 2011;2011:75–107.Google Scholar
  226. Toonen RJ, Andrews KR, Baums IB, Bird CE, Concepcion GT, Daly-Engel TS, et al. Defining boundaries for ecosystem-based management: a multispecies case study of marine connectivity across the Hawaiian Archipelago. J Marine Biol. 2011;2011. pii: 460173Google Scholar
  227. Treml EA, Roberts JJ, Chao Y, Halpin PN, Possingham HP, Riginos C. Reproductive output and duration of the pelagic larval stage determine seascape-wide connectivity of marine populations. Integr Compar Biol. 2012;52:525–37.Google Scholar
  228. Treml EA, Ford JR, Black KP, Swearer SE. Identifying the key biophysical drivers, connectivity outcomes, and metapopulation consequences of larval dispersal in the sea. Mov Ecol. 2015a;3:17.Google Scholar
  229. Treml EA, Roberts J, Halpin PN, Possingham HP, Riginos C. The emergent geography of biophysical dispersal barriers across the Indo-West Pacific. Div Dist. 2015b;21:465–76.Google Scholar
  230. Vandamme SG, Maes GE, Raeymaekers JA, Cottenie K, Imsland AK, Hellemans B, et al. Regional environmental pressure influences population differentiation in turbot (S cophthalmus maximus). Mol Ecol. 2014;23:618–36.Google Scholar
  231. Véliz D, Bourget E, Bernatchez L. Regional variation in the spatial scale of selection at MPI∗ and GPI∗ in the acorn barnacle Semibalanus balanoides (Crustacea). J Evol Biol. 2004;17:953–66.Google Scholar
  232. Vigliola L, Doherty PJ, Meekan MG, Drown DM, Jones ME, Barber PH. Genetic identity determines risk of post-settlement mortality of a marine fish. Ecology. 2007;88:1263–77.Google Scholar
  233. Villacorta-Rath C, Souza CA, Murphy NP, Green BS, Gardner C, Strugnell JM. Temporal genetic patterns of diversity and structure evidence chaotic genetic patchiness in a spiny lobster. Mol Ecol. 2018;27:54–65.Google Scholar
  234. von der Heyden S. Making evolutionary history count: biodiversity planning for coral reef fishes and the conservation of evolutionary processes. Coral Reefs. 2017;36:183–94.Google Scholar
  235. Vuilleumier S, Possingham HP. Does colonization asymmetry matter in metapopulations? Proc R Soc Lond B Biol Sci. 2006;273:1637–42.Google Scholar
  236. Wagner H, Fortin M-J. Basics of spatial data analysis: linking landscape and genetic data for landscape genetic studies. In: Balkenhol N, Cushman S, Storfer A, Waits L, editors. Landscape genetics: concepts, methods, applications. West Sussex: Wiley; 2016. p. 77–98.Google Scholar
  237. Wang IJ, Bradburd GS. Isolation by environment. Mol Ecol. 2014;23:5649–62.Google Scholar
  238. Waples RS. Separating the wheat from the chaff: patterns of genetic differentiation in high gene flow species. J Hered. 1998;89:438–50.Google Scholar
  239. Waples RS, Gaggiotti O. Invited review: what is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity. Mol Ecol. 2006;15:1419–39.Google Scholar
  240. Ward RD, Woodwark M, Skibinski DO. A comparison of genetic diversity levels in marine, freshwater, and anadromous fishes. J Fish Biol. 1994;44:213–32.Google Scholar
  241. Waters JM, Fraser CI, Hewitt GM. Founder takes all: density-dependent processes structure biodiversity. Trends Ecol Evol. 2013;28:78–85.Google Scholar
  242. Wellenreuther M, Bernatchez L. Eco-evolutionary genomics of chromosomal inversions. Trends Ecol Evol. 2018;33:427–40.Google Scholar
  243. Wellenreuther M, Hansson B. Detecting polygenic evolution: problems, pitfalls, and promises. Trends Genet. 2016;32:155–64.Google Scholar
  244. Westram AM, Galindo J, Alm Rosenblad M, Grahame JW, Panova M, Butlin RK. Do the same genes underlie parallel phenotypic divergence in different Littorina saxatilis populations? Mol Ecol. 2014;23:4603–16.Google Scholar
  245. White C, Selkoe KA, Watson J, Siegel DA, Zacherl DC, Toonen RJ. Ocean currents help explain population genetic structure. Proc R Soc Lond B Biol Sci. 2010;277:1685–94.Google Scholar
  246. Whitlock MC, Lotterhos KE. Reliable detection of loci responsible for local adaptation: inference of a null model through trimming the distribution of FST. Am Nat. 2015;186:S24–36.Google Scholar
  247. Whitlock MC, Mccauley DE. Indirect measures of gene flow and migration: FST≠ 1/(4Nm+ 1). Heredity. 1999;82:117–25.Google Scholar
  248. Wilkinson-Herbots HM, Ettridge R. The effect of unequal migration rates on. FST Theor Popul Biol. 2004;66:185–97.Google Scholar
  249. Willette DA, Allendorf FW, Barber PH, Barshis DJ, Carpenter KE, Crandall ED, et al. So, you want to use next-generation sequencing in marine systems? Insight from the Pan-Pacific Advanced Studies Institute. Bull Mar Sci. 2014;90:79–122.Google Scholar
  250. Wolf JB, Ellegren H. Making sense of genomic islands of differentiation in light of speciation. Nat Rev Genet. 2017;18:87.Google Scholar
  251. Wood S, Paris CB, Ridgwell A, Hendy EJ. Modelling dispersal and connectivity of broadcast spawning corals at the global scale. Global Ecol Biog. 2014;23(1):1.Google Scholar
  252. Wright S. Evolution in Mendelian populations. Genetics. 1931;16:97.Google Scholar
  253. Wright S. Isolation by distance. Genetics. 1943;28:114–38.Google Scholar
  254. Xuereb A, Benestan L, Normandeau É, Daigle RM, Curtis JM, Bernatchez L, Fortin MJ. Asymmetric oceanographic processes mediate connectivity and population genetic structure, as revealed by RAD seq, in a highly dispersive marine invertebrate (Parastichopus californicus). Mol Ecol. 2018a;27:2347–64.Google Scholar
  255. Xuereb A, Kimber CM, Curtis JM, Bernatchez L, Fortin MJ. Putatively adaptive genetic variation in the giant California sea cucumber (Parastichopus californicus) as revealed by environmental association analysis of restriction-site associated DNA sequencing data. Mol Ecol. 2018b;27:5035–48.Google Scholar
  256. Yeaman S. Local adaptation by alleles of small effect. Am Nat. 2015;186:S74–89.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Libby Liggins
    • 1
    • 2
    Email author
  • Eric A. Treml
    • 3
  • Cynthia Riginos
    • 4
  1. 1.School of Natural and Computational SciencesMassey UniversityAucklandNew Zealand
  2. 2.Auckland War Memorial Museum, Tāmaki Paenga HiraAucklandNew Zealand
  3. 3.School of Life and Environmental Sciences, and Centre for Integrative EcologyDeakin UniversityGeelongAustralia
  4. 4.School of Biological SciencesUniversity of QueenslandSt LuciaAustralia

Personalised recommendations