Genetics of ecological divergence during speciation


Ecological differences often evolve early in speciation as divergent natural selection drives adaptation to distinct ecological niches, leading ultimately to reproductive isolation. Although this process is a major generator of biodiversity, its genetic basis is still poorly understood. Here we investigate the genetic architecture of niche differentiation in a sympatric species pair of threespine stickleback fish by mapping the environment-dependent effects of phenotypic traits on hybrid feeding and performance under semi-natural conditions. We show that multiple, unlinked loci act largely additively to determine position along the major niche axis separating these recently diverged species. We also find that functional mismatch between phenotypic traits reduces the growth of some stickleback hybrids beyond that expected from an intermediate phenotype, suggesting a role for epistasis between the underlying genes. This functional mismatch might lead to hybrid incompatibilities that are analogous to those underlying intrinsic reproductive isolation but depend on the ecological context.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Niche use and body size.
Figure 2: Trait variation among F2 hybrid groups.
Figure 3: Genetic architecture of niche divergence.


  1. 1

    Darwin, C. The Origin of Species by Means of Natural Selection (John Murray, 1859)

    Google Scholar 

  2. 2

    Schluter, D. The Ecology of Adaptive Radiation (Oxford Univ. Press, 2000)

    Google Scholar 

  3. 3

    Coyne, J. A. & Orr, H. A. Speciation (Sinauer Associates, 2004)

    Google Scholar 

  4. 4

    Nosil, P. Ecological Speciation (Oxford Univ. Press, 2012)

    Google Scholar 

  5. 5

    Chase, J. M. & Leibold, M. A. Ecological Niches: Linking Classical and Contemporary Approaches (Univ. of Chicago Press, 2003)

    Google Scholar 

  6. 6

    Fisher, R. A. The Genetical Theory of Natural Selection (Oxford Univ. Press, 1930)

    Google Scholar 

  7. 7

    Orr, H. A. The genetic theory of adaptation: a brief history. Nature Rev. Genet. 6, 119–127 (2005)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. 8

    Gavrilets, S. Fitness Landscapes and the Origin of Species (Monographs in Population Biology Vol. 41, Princeton Univ. Press, 2004)

    Google Scholar 

  9. 9

    Yeaman, S. & Whitlock, M. C. The genetic architecture of adaptation under migration-selection balance. Evolution 65, 1897–1911 (2011)

    Article  PubMed  Google Scholar 

  10. 10

    Mackay, T. F. C., Stone, E. A. & Ayroles, J. F. The genetics of quantitative traits: challenges and prospects. Nature Rev. Genet. 10, 565–577 (2009)

    CAS  Article  PubMed  Google Scholar 

  11. 11

    Barrett, R. D. H. & Hoekstra, H. E. Molecular spandrels: tests of adaptation at the genetic level. Nature Rev. Genet. 12, 767–780 (2011)

    CAS  Article  PubMed  Google Scholar 

  12. 12

    Taylor, E. B. & McPhail, J. D. Historical contingency and ecological determinism interact to prime speciation in sticklebacks, Gasterosteus . Proc. R. Soc. Lond. B 267, 2375–2384 (2000)

    CAS  Article  Google Scholar 

  13. 13

    Schluter, D. Experimental evidence that competition promotes divergence in adaptive radiation. Science 266, 798–801 (1994)

    CAS  Article  ADS  PubMed  Google Scholar 

  14. 14

    Schluter, D. Adaptive radiation in sticklebacks: trade-offs in feeding performance and growth. Ecology 76, 82–90 (1995)

    Article  Google Scholar 

  15. 15

    Rundle, H. D., Nagel, L. N., Boughman, J. W. & Schluter, D. Natural selection and parallel speciation in sympatric sticklebacks. Science 287, 306–308 (2000)

    CAS  Article  ADS  PubMed  Google Scholar 

  16. 16

    McPhail, J. D. Ecology and evolution of sympatric sticklebacks (Gasterosteus): evidence for a species pair in Paxton Lake, Texada Island, British Columbia. Can. J. Zool. 70, 361–369 (1992)

    Article  Google Scholar 

  17. 17

    Matthews, B., Marchinko, K. B., Bolnick, D. I. & Mazumder, A. Specialization of trophic position and habitat use by sticklebacks in an adaptive radiation. Ecology 91, 1025–1034 (2010)

    Article  PubMed  Google Scholar 

  18. 18

    McGee, M. D. & Wainwright, P. C. Convergent evolution as a generator of phenotypic diversity in threespine stickleback. Evolution 67, 1204–1208 (2013)

    Article  PubMed  Google Scholar 

  19. 19

    McGee, M. D., Schluter, D. & Wainwright, P. C. Functional basis of ecological divergence in sympatric stickleback. BMC Evol. Biol. 13, 277 (2013)

    Article  PubMed  PubMed Central  Google Scholar 

  20. 20

    Gow, J. L., Peichel, C. L. & Taylor, E. B. Contrasting hybridization rates between sympatric three-spined sticklebacks highlight the fragility of reproductive barriers between evolutionarily young species. Mol. Ecol. 15, 739–752 (2006)

    Article  PubMed  Google Scholar 

  21. 21

    Hatfield, T. & Schluter, D. Ecological speciation in sticklebacks: environment-dependent hybrid fitness. Evolution 53, 866–873 (1999)

    Article  PubMed  Google Scholar 

  22. 22

    Rundle, H. D. A test of ecologically dependent postmating isolation between sympatric sticklebacks. Evolution 56, 322–329 (2002)

    Article  PubMed  Google Scholar 

  23. 23

    Gow, J. L., Peichel, C. L. & Taylor, E. B. Ecological selection against hybrids in natural populations of sympatric threespine sticklebacks. J. Evol. Biol. 20, 2173–2180 (2007)

    CAS  Article  PubMed  Google Scholar 

  24. 24

    Marchinko, K. B. Predation’s role in repeated phenotypic and genetic divergence of armor in threespine stickleback. Evolution 63, 127–138 (2009)

    Article  PubMed  Google Scholar 

  25. 25

    Carlson, S. M., Kottas, A. & Mangel, M. Bayesian analysis of size-dependent overwinter mortality from size-frequency distributions. Ecology 91, 1016–1024 (2010)

    Article  PubMed  Google Scholar 

  26. 26

    Candolin, U. & Voigt, H.-R. Correlation between male size and territory quality: consequence of male competition or predation susceptibility? Oikos 95, 225–230 (2001)

    Article  Google Scholar 

  27. 27

    MacColl, A. D. C. Parasites may contribute to ‘magic trait’ evolution in the adaptive radiation of three-spined sticklebacks, Gasterosteus aculeatus (Gasterosteiformes: Gasterosteidae). Biol. J. Linn. Soc. 96, 425–433 (2009)

    Article  Google Scholar 

  28. 28

    Rogers, S. M. et al. Genetic signature of adaptive peak shift in threespine stickleback. Evolution 66, 2439–2450 (2012)

    Article  PubMed  PubMed Central  Google Scholar 

  29. 29

    Jones, F. C. et al. The genomic basis of adaptive evolution in threespine sticklebacks. Nature 484, 55–61 (2012)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  30. 30

    Jones, F. C. et al. A genome-wide SNP genotyping array reveals patterns of global and repeated species-pair divergence in sticklebacks. Curr. Biol. 22, 83–90 (2012)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  31. 31

    Phillips, P. C. Epistasis—the essential role of gene interactions in the structure and evolution of genetic systems. Nature Rev. Genet. 9, 855–867 (2008)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  32. 32

    Mackay, T. F. C. Epistasis and quantitative traits: using model organisms to study gene–gene interactions. Nature Rev. Genet. 15, 22–33 (2014)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  33. 33

    Whitlock, M. C., Phillips, P. C., Moore, F. B.-G. & Tonsor, S. J. Multiple fitness peaks and epistasis. Annu. Rev. Ecol. Syst. 26, 601–629 (1995)

    Article  Google Scholar 

  34. 34

    Via, S. Natural selection in action during speciation. Proc. Natl Acad. Sci. USA 106, 9939–9946 (2009)

    CAS  Article  ADS  PubMed  PubMed Central  Google Scholar 

  35. 35

    Hohenlohe, P. A. et al. Population genomics of parallel adaptation in threespine stickleback using sequenced RAD tags. PLoS Genet. 6, e1000862 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. 36

    Feder, J. L., Egan, S. P. & Nosil, P. The genomics of speciation-with-gene-flow. Trends Genet. 28, 342–350 (2012)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  37. 37

    Strasburg, J. L. et al. What can patterns of differentiation across plant genomes tell us about adaptation and speciation? Phil. Trans. R. Soc. B 367, 364–373 (2012)

    Article  PubMed  PubMed Central  Google Scholar 

  38. 38

    Seehausen, O. et al. Genomics and the origin of species. Nature Rev. Genet. 15, 176–192 (2014)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  39. 39

    Rice, W. R. & Hostert, E. E. Laboratory experiments on speciation: what have we learned in 40 years? Evolution 47, 1637–1653 (1993)

    Article  Google Scholar 

  40. 40

    Schluter, D. & Conte, G. L. Genetics and ecological speciation. Proc. Natl Acad. Sci. USA 106, 9955–9962 (2009)

    CAS  Article  ADS  PubMed  Google Scholar 

  41. 41

    Egan, S. P. & Funk, D. J. Ecologically dependent postmating isolation between sympatric host forms of Neochlamisus bebbianae leaf beetles. Proc. Natl Acad. Sci. USA 106, 19426–19431 (2009)

    CAS  Article  ADS  PubMed  Google Scholar 

  42. 42

    McBride, C. S. & Singer, M. C. Field studies reveal strong postmating isolation between ecologically divergent butterfly populations. PLoS Biol. 8, e1000529 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. 43

    Rundle, H. D. & Nosil, P. Ecological speciation. Ecol. Lett. 8, 336–352 (2005)

    Article  Google Scholar 

  44. 44

    Presgraves, D. C. The molecular evolutionary basis of species formation. Nature Rev. Genet. 11, 175–180 (2010)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  45. 45

    Fry, B. Stable Isotope Ecology (Springer, 2006)

    Google Scholar 

  46. 46

    van Ooijen, J. W. & Voorrips, R. E. JoinMap® 3.0: Software for the Calculation of Genetic Linkage Maps (Plant Research International, 2001)

    Google Scholar 

  47. 47

    Broman, K. W. & Sen, S. A Guide to QTL Mapping with R/qtl (Springer Science+Business Media, 2009)

    Google Scholar 

  48. 48

    Schluter, D. Estimating the form of natural selection on a quantitative trait. Evolution 42, 849–861 (1988)

    Article  PubMed  Google Scholar 

  49. 49

    Wootton, R. J. A Functional Biology of Sticklebacks (Univ. of California Press, 1984)

    Google Scholar 

  50. 50

    Post, D. M. Using stable isotopes to estimate trophic position: models, methods, and assumptions. Ecology 83, 703–718 (2002)

    Article  Google Scholar 

  51. 51

    R. Development Core Team. R: A Language and Environment for Statistical Computing ( (R Foundation for Statistical Computing, 2011)

  52. 52

    Vander Zanden, M. J. & Vadeboncoeur, Y. Fishes as integrators of benthic and pelagic food webs in lakes. Ecology 83, 2152–2161 (2002)

    Article  Google Scholar 

  53. 53

    Bolnick, D. I. et al. The ecology of individuals: incidence and implications of individual specialization. Am. Nat. 161, 1–28 (2003)

    Article  MathSciNet  PubMed  Google Scholar 

  54. 54

    McIntyre, P. B. & Flecker, A. S. Rapid turnover of tissue nitrogen of primary consumers in tropical freshwaters. Oecologia 148, 12–21 (2006)

    Article  ADS  PubMed  Google Scholar 

  55. 55

    Harmon, L. J. et al. Evolutionary diversification in stickleback affects ecosystem functioning. Nature 458, 1167–1170 (2009)

    CAS  Article  ADS  PubMed  Google Scholar 

  56. 56

    Behm, J. E., Ives, A. R. & Boughman, J. W. Breakdown in postmating isolation and the collapse of a species pair through hybridization. Am. Nat. 175, 11–26 (2010)

    Article  PubMed  Google Scholar 

  57. 57

    Bolnick, D. I. Sympatric speciation in threespine stickleback: why not? Int. J. Ecol. 2011, 942847 (2011)

    Article  Google Scholar 

  58. 58

    Hartigan, J. A. & Hartigan, P. M. The dip test of unimodality. Ann. Stat. 13, 70–84 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  59. 59

    Maechler, M. Package ‘diptest’: Hartigan’s Dip Test Statistic for Unimodality, corrected code, v. 0.75-4 ( (CRAN: Comprehensive R Archive Network, 2011)

  60. 60

    Schluter, D. Adaptive radiation in sticklebacks: size, shape, and habitat use efficiency. Ecology 74, 699–709 (1993)

    Article  Google Scholar 

  61. 61

    Albert, A. Y. K. et al. The genetics of adaptive shape shift in stickleback: pleiotropy and effect size. Evolution 62, 76–85 (2008)

    PubMed  Google Scholar 

  62. 62

    Rohlf, F. J. TpsDig2 ( (Department of Ecology and Evolution, State Univ. of New York, 2006)

  63. 63

    Rohlf, F. J. & Slice, D. Extensions of the Procrustes method for the optimal superimposition of landmarks. Syst. Zool. 39, 40–59 (1990)

    Article  Google Scholar 

  64. 64

    Dryden, I. L. Package ‘shapes’: Statistical Shape Analysis, v. 1.1-3 ( (CRAN: Comprehensive R Archive Network, 2009)

  65. 65

    Arnegard, M. E. et al. Sexual signal evolution outpaces ecological divergence during electric fish species radiation. Am. Nat. 176, 335–356 (2010)

    Article  PubMed  Google Scholar 

  66. 66

    Peichel, C. L. et al. The genetic architecture of divergence between threespine stickleback species. Nature 414, 901–905 (2001)

    CAS  Article  ADS  PubMed  Google Scholar 

  67. 67

    Warton, D. I., Duursma, R. A., Falster, D. S. & Taskinen, S. SMATR 3—an R package for estimation and inference about allometric lines. Methods Ecol. Evol. 3, 257–259 (2012)

    Article  Google Scholar 

  68. 68

    Smith, R. J. Use and misuse of the reduced major axis for line-fitting. Am. J. Phys. Anthropol. 140, 476–486 (2009)

    Article  PubMed  Google Scholar 

  69. 69

    Miller, A. Subset Selection in Regression, 2nd edn, Vol. 95 (Chapman & Hall/CRC, 2002)

    Google Scholar 

  70. 70

    Lumley, T. Package ‘leaps’: Regression Subset Selection, v. 2.9 ( (CRAN: Comprehensive R Archive Network, 2009)

  71. 71

    Mallows, C. L. Some comments on C P . Technometrics 15, 661–675 (1973)

    MATH  Google Scholar 

  72. 72

    Akaike, H. A new look at the statistical model identification. IEEE Trans. Automat. Contr. 19, 716–723 (1974)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  73. 73

    Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, 2nd edn (Springer, 2002)

    Google Scholar 

  74. 74

    Sambrook, J. & Russell, D. W. Molecular Cloning: A Laboratory Manual, 3rd edn (Cold Spring Harbor Laboratory Press, 2001)

    Google Scholar 

  75. 75

    Hadfield, J. D., Richardson, D. S. & Burke, T. Towards unbiased parentage assignment: combining genetic, behavioural and spatial data in a Bayesian framework. Mol. Ecol. 15, 3715–3730 (2006)

    CAS  Article  Google Scholar 

  76. 76

    Hadfield, J. Package ‘MasterBayes’: ML and MCMC Methods for Pedigree Reconstruction and Analysis, v. 2.50 ( (CRAN: Comprehensive R Archive Network, 2012)

  77. 77

    Manichaikul, A., Dupuis, J., Sen, S. & Broman, K. W. Poor performance of bootstrap confidence intervals for the location of a quantitative trait locus. Genetics 174, 481–489 (2006)

    Article  PubMed  PubMed Central  Google Scholar 

  78. 78

    Kosambi, D. D. The estimation of map distance from recombination values. Ann. Eugen. 12, 172–175 (1944)

    Article  Google Scholar 

  79. 79

    Theil, H. Economic Forecasts and Policy, 2nd edn (North-Holland, 1961)

    Google Scholar 

  80. 80

    Engle, R. F. & Brown, S. J. Model selection for forecasting. Appl. Math. Comput. 20, 313–327 (1986)

    MATH  Google Scholar 

  81. 81

    Hothorn, T. et al. Package ‘lmtest’: Testing Linear Regression Models, v. 0.9-30 ( (CRAN: Comprehensive R Archive Network, 2012)

  82. 82

    Erickson, D. L., Fenster, C. B., Stenøien, H. K. & Price, D. Quantitative trait locus analyses and the study of evolutionary process. Mol. Ecol. 13, 2505–2522 (2004)

    CAS  Article  PubMed  Google Scholar 

Download references


We thank J. Perez for counting gill rakers; C. Sather for performing lab work for SNP genotyping; and K. Broman, G. Coop, I. Goodliffe, A. Greenwood, P. Wainwright, M. White and M. Wund for constructive comments. Stable isotopes were analysed at the University of California, Davis, Stable Isotope Facility. Funding was provided by grants from the US National Institutes of Health (F32 GM086125 to M.E.A., R01 GM089733 to C.L.P. and D.S., and P50 HG002568 to C.L.P. and D.M.K.), the Natural Sciences and Engineering Research Council of Canada (to D.S.) and the Canada Foundation for Innovation (to D.S.).

Author information




M.E.A., C.L.P. and D.S. designed, planned and oversaw the project. M.E.A. made the crosses, set up the experimental pond and coordinated all field and laboratory research. M.E.A., K.B.M., S.K., N.B. and S.B. conducted fieldwork and stable-isotope analysis. M.D.M. measured functional morphological traits. B.M. and M.E.A. measured and analysed gut contents. S.K., D.S. and M.E.A. performed landmark-based morphometric analyses. M.E.A. analysed relationships between all traits and trophic variation. F.C.J., Y.F.C. and D.M.K. designed the SNP genotyping array. M.E.A., G.L.C., C.L.P and D.S. analysed SNP genotypes. D.S. determined the genealogy of the mapping population on the basis of SNP genotypes. M.E.A., C.L.P. and D.S. performed linkage and QTL analysis. M.E.A., C.L.P. and D.S. tested the genetic architecture of niche divergence. M.E.A., C.L.P., D.S., M.D.M., B.M. and G.L.C. interpreted the results. M.E.A. wrote the paper with input from C.L.P. and D.S., who are co-senior authors. All other authors provided editorial comments and approved the final version of the manuscript.

Corresponding author

Correspondence to Matthew E. Arnegard.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Experimental pond used in the study.

a, Photograph of pond no. 4 at the experimental pond facility of the University of British Columbia (Vancouver, British Columbia, Canada), taken in autumn 2008, during the collection of F2 juveniles. b, Diagram of the pond profile. See Supplementary Discussion for details on pond history before this study.

Extended Data Figure 2 Feeding patterns in relation to isotope signatures.

Plots show relationships between ingested prey counts from all available F2 hybrids (n = 99) and stable-isotope data. a, Loess-smoothed surface (span = 0.75, second-degree polynomials) of predicted chironomid counts plotted on original isotope axes (δ13C, δ15N). As with all other count data plotted here, counts were transformed as ln (chironomids + 1) and mapped according to the coloured scale. PC1 (black arrow) and PC2 (white) are based on the entire isotope distribution (Fig. 1a). Individuals are plotted as points according to the presence (crosses) or absence (filled circles) of calanoid copepods in their digestive tracts. bg, Linear or logistic regression, accordingly, of ingested prey count or presence/absence data (transformed as above) on the different axes through isotope space. b, Chironomid count against δ13C, linear regression, slope estimate = 0.415, R2 = 0.199, F1,97 = 24.1, P = 3.70 × 10−6. c, Chironomid presence against niche score, logistic regression, slope coefficient = 0.504, z = 2.23, P = 0.0255. d, Collembola presence against diet deviation score, logistic regression, slope coefficient = 1.25, z = 4.26, P = 2.03 × 10−5. e, Calanoid copepod count against δ15N, linear regression, slope estimate = 0.492, R2 = 0.0608, F1,97 = 6.28, P = 0.0139. f, Calanoid copepod presence against niche score, logistic regression, slope coefficient = −0.463, z = −1.84, P = 0.0651. g, Calanoid copepod presence against diet deviation score, logistic regression, slope coefficient = −0.958, z = −2.67, P = 0.00766. Source data

Extended Data Figure 3 Linkage map showing QTLs for all traits.

All G. aculeatus chromosomes are represented by LGs in the complete linkage map for this study (LGs and chromosomes use the same numbering29; LGs with no mapped QTLs are omitted here). Map distances are indicated with a scale at the left of each LG in centimorgans (cM). Coloured bars (at the right) are 1.5-LOD confidence intervals for QTL position (red bars, component traits of niche use; blue bars, other traits; Supplementary Table 3 provides LOD scores, map positions of LOD peaks, and effect sizes). The given SNP identifiers (IDs) are only for reference to Supplementary Table 4, which provides published SNP data30. For clarity, every other ID is omitted for SNP 066–098, even though these markers are present in the map. Markers closest to candidate QTLs for genetic model comparisons are highlighted: red text, nearest to candidate QTLs for niche score; green boxes, diet deviation score. Numbered traits are the x and y coordinates of morphometric landmarks (indicated on the fish photo): 1, posterior midpoint caudal peduncle; 2, anterior insertion anal fin at first soft ray; 3, posteroventral corner ectocoracoid; 4, posterodorsal corner ectocoracoid; 5, anteriormost corner ectocoracoid; 6, anteroventral corner opercle; 7, posterodorsal corner opercle; 8, dorsal edge opercle–hyomandibular boundary; 9, dorsalmost extent preopercle; 10, posteroventral corner preopercle; 11, anteriormost extent preopercle along ventral silhouette; 12, posteroventral extent maxilla; 13, anterodorsal extent maxilla; 14, suture between nasal and frontal bones along dorsal silhouette; 15, anterior margin orbit; 16, posterior margin orbit; 17, ventral margin orbit (landmarks 15–17 placed in line with vertical or horizontal midpoint of eye); 18, posterior extent supraoccipital along dorsal silhouette; 19, anterior insertion dorsal fin at first soft ray. Source data

Extended Data Figure 4 Shape variation among F2 hybrid groups.

Each overlaid pair of wireframe diagrams compares the mean body shape of individuals in one of three groups of F2 hybrids (B, L or A; shown in dark blue) with the relative mean shape of a reference group consisting of all other F2 hybrids (group membership shown in Fig. 1a). Using data for 19 Procrustes-superimposed and unbent landmarks (Extended Data Fig. 3), the wireframe diagrams were produced and plotted in MorphoJ v.1.04a, on the basis of discriminant function analysis (Supplementary Discussion). The shape differences represented here are magnified eightfold for easier visual comparison. Group sample sizes: n = 91 (B), n = 92 (L), n = 93 (A), n = 335 (reference group). See Supplementary Discussion for a detailed description of patterns of variation in several specific features of shape that can be interpreted from these data. Source data

Extended Data Figure 5 Variation of additional traits among F2 hybrid groups.

Means ( ± 1 s.e.m.) of F2 hybrids in groups B, L and A (Fig. 1a) are shown for the following traits (using raw data for long gill rakers and size-corrected data for the other traits): a, number of long gill rakers (ANOVA, F2,279 = 1.756, P = 0.175); b, residual anterior epaxial muscle height (F2,246 = 5.219, P = 0.00603); c, residual anterior epaxial muscle width (F2,246 = 4.223, P = 0.0157); d, residual neurocranium outlever length (F2,246 = 13.36, P = 3.10 × 10−6); e, residual buccal cavity length (F2,246 = 12.26, P = 8.42 × 10−6); f, residual gape (F2,246 = 7.974, P = 4.41 × 10−4). Numbers in parentheses are values of n. Traits are illustrated in Fig. 2e–g. The data conformed reasonably well to parametric statistical assumptions; ANOVA was therefore used to test trait variation among categories. Source data

Extended Data Figure 6 Relationships between F2 hybrid functional morphology and niche score.

For key functional morphological traits known to differ between wild Paxton benthics and limnetics, trait data from all available F2 hybrids are plotted against niche score and fitted with linear regression (raw data for gill raker counts; size-corrected data for other traits): a, number of long gill rakers (R2 = 0.0146; F1,629 = 9.32; P = 0.00236); b, number of short gill rakers (R2 = 0.0253; F1,629 = 16.30; P = 6.06 × 10−5); c, residual anterior epaxial muscle height (R2 = 0.0125; F1,552 = 7.00; P = 0.00804); d, residual anterior epaxial muscle width (R2 = 0.0189; F1,552 = 10.61; P = 0.00119); e, residual upper jaw protrusion length (R2 = 0.0580; F1,552 = 34.00; P = 9.40 × 10−9); f, residual lower jaw-opening inlever length (R2 = 0.0660; F1,615 = 43.43; P = 9.45 × 10−11). Traits are illustrated in Fig. 2e–g. Directions of benthic–limnetic divergence in Paxton Lake (arrows at left of plots, here and in Fig. 2a–d) are based on previously published studies16,18,19, combined with validating counts of long and short gill rakers for this study (data not shown). Source data

Extended Data Table 1 Goodness-of-fit tests for genomic distribution of QTLs
Extended Data Table 2 MQM model of only main effects of 14 candidate morphological QTLs on niche score
Extended Data Table 3 Full MQM model of main QTL effects and effects of pairwise QTL interactions on niche score

Supplementary information

Supplementary Information

This file contains Supplementary Tables 1-4, a Supplementary Discussion and Supplementary References. (PDF 1680 kb)

PowerPoint slides

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Arnegard, M., McGee, M., Matthews, B. et al. Genetics of ecological divergence during speciation. Nature 511, 307–311 (2014).

Download citation

Further reading


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing