Letter | Published:

An inverse latitudinal gradient in speciation rate for marine fishes

Naturevolume 559pages392395 (2018) | Download Citation


Far more species of organisms are found in the tropics than in temperate and polar regions, but the evolutionary and ecological causes of this pattern remain controversial1,2. Tropical marine fish communities are much more diverse than cold-water fish communities found at higher latitudes3,4, and several explanations for this latitudinal diversity gradient propose that warm reef environments serve as evolutionary ‘hotspots’ for species formation5,6,7,8. Here we test the relationship between latitude, species richness and speciation rate across marine fishes. We assembled a time-calibrated phylogeny of all ray-finned fishes (31,526 tips, of which 11,638 had genetic data) and used this framework to describe the spatial dynamics of speciation in the marine realm. We show that the fastest rates of speciation occur in species-poor regions outside the tropics, and that high-latitude fish lineages form new species at much faster rates than their tropical counterparts. High rates of speciation occur in geographical regions that are characterized by low surface temperatures and high endemism. Our results reject a broad class of mechanisms under which the tropics serve as an evolutionary cradle for marine fish diversity and raise new questions about why the coldest oceans on Earth are present-day hotspots of species formation.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


  1. 1.

    Mittelbach, G. G. et al. Evolution and the latitudinal diversity gradient: speciation, extinction and biogeography. Ecol. Lett. 10, 315–331 (2007).

  2. 2.

    Schluter, D. & Pennell, M. W. Speciation gradients and the distribution of biodiversity. Nature 546, 48–55 (2017).

  3. 3.

    Tittensor, D. P. et al. Global patterns and predictors of marine biodiversity across taxa. Nature 466, 1098–1101 (2010).

  4. 4.

    Stuart-Smith, R. D. et al. Integrating abundance and functional traits reveals new global hotspots of fish diversity. Nature 501, 539–542 (2013).

  5. 5.

    Kiessling, W., Simpson, C. & Foote, M. Reefs as cradles of evolution and sources of biodiversity in the Phanerozoic. Science 327, 196–198 (2010).

  6. 6.

    Alfaro, M. E., Santini, F. & Brock, C. D. Do reefs drive diversification in marine teleosts? Evidence from the pufferfish and their allies (Order Tetraodontiformes). Evolution 61, 2104–2126 (2007).

  7. 7.

    Cowman, P. F. & Bellwood, D. R. Coral reefs as drivers of cladogenesis: expanding coral reefs, cryptic extinction events, and the development of biodiversity hotspots. J. Evol. Biol. 24, 2543–2562 (2011).

  8. 8.

    Siqueira, A. C., Oliveira-Santos, L. G. R., Cowman, P. F. & Floeter, S. R. Evolutionary processes underlying latitudinal differences in reef fish biodiversity. Glob. Ecol. Biogeogr. 25, 1466–1476 (2016).

  9. 9.

    Hillebrand, H. On the generality of the latitudinal diversity gradient. Am. Nat. 163, 192–211 (2004).

  10. 10.

    MacArthur, R. H. Geographical Ecology (Princeton Univ. Press, Princeton, 1972).

  11. 11.

    Mannion, P. D., Upchurch, P., Benson, R. B. J. & Goswami, A. The latitudinal biodiversity gradient through deep time. Trends Ecol. Evol. 29, 42–50 (2014).

  12. 12.

    Jablonski, D., Roy, K. & Valentine, J. W. Out of the tropics: evolutionary dynamics of the latitudinal diversity gradient. Science 314, 102–106 (2006).

  13. 13.

    Allen, A. P. & Gillooly, J. F. Assessing latitudinal gradients in speciation rates and biodiversity at the global scale. Ecol. Lett. 9, 947–954 (2006).

  14. 14.

    Weir, J. T. & Schluter, D. The latitudinal gradient in recent speciation and extinction rates of birds and mammals. Science 315, 1574–1576 (2007).

  15. 15.

    Rabosky, D. L., Title, P. O. & Huang, H. Minimal effects of latitude on present-day speciation rates in New World birds. Proc. R. Soc. Lond. B 282, 20142889 (2015).

  16. 16.

    Liow, L. H., Quental, T. B. & Marshall, C. R. When can decreasing diversification rates be detected with molecular phylogenies and the fossil record? Syst. Biol. 59, 646–659 (2010).

  17. 17.

    Davis, M. P., Midford, P. E. & Maddison, W. Exploring power and parameter estimation of the BiSSE method for analyzing species diversification. BMC Evol. Biol. 13, 38 (2013).

  18. 18.

    Rohde, K. Latitudinal gradients in species diversity: the search for the primary cause. Oikos 65, 514–527 (1992).

  19. 19.

    Allen, A. P., Brown, J. H. & Gillooly, J. F. Global biodiversity, biochemical kinetics, and the energetic-equivalence rule. Science 297, 1545–1548 (2002).

  20. 20.

    Rabosky, D. L., Mitchell, J. S. & Chang, J. Is BAMM flawed? Theoretical and practical concerns in the analysis of multi-rate diversification models. Syst. Biol. 66, 477–498 (2017).

  21. 21.

    Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).

  22. 22.

    Freckleton, R. P., Phillimore, A. B. & Pagel, M. Relating traits to diversification: a simple test. Am. Nat. 172, 102–115 (2008).

  23. 23.

    Near, T. J. et al. Ancient climate change, antifreeze, and the evolutionary diversification of Antarctic fishes. Proc. Natl Acad. Sci. USA 109, 3434–3439 (2012).

  24. 24.

    Eastman, J. T. Comparison of the Antarctic and Arctic fish faunas. Cybium 21, 335–352 (1997).

  25. 25.

    Harvey, M. G. & Rabosky, D. L. Continuous traits and speciation rates: alternatives to state-dependent diversification models. Methods Ecol. Evol. 9, 984–993 (2018).

  26. 26.

    Rabosky, D. L. & Huang, H. A robust semi-parametric test for detecting trait-dependent diversification. Syst. Biol. 65, 181–193 (2016).

  27. 27.

    Brown, J. H. Why are there so many species in the tropics? J. Biogeogr. 41, 8–22 (2014).

  28. 28.

    Wagner, C. E., Harmon, L. J. & Seehausen, O. Cichlid species–area relationships are shaped by adaptive radiations that scale with area. Ecol. Lett. 17, 583–592 (2014).

  29. 29.

    Quintero, I. & Jetz, W. Global elevational diversity and diversification of birds. Nature 555, 246–250 (2018).

  30. 30.

    Smith, S. A., Beaulieu, J. M. & Donoghue, M. J. Mega-phylogeny approach for comparative biology: an alternative to supertree and supermatrix approaches. BMC Evol. Biol. 9, 37 (2009).

  31. 31.

    Betancur-R, R. et al. The tree of life and a new classification of bony fishes. PLoS Curr. Tree of Life https://doi.org/10.1371/currents.tol.53ba26640df0ccaee75bb165c8c26288 (2013).

  32. 32.

    Near, T. J. et al. Phylogeny and tempo of diversification in the superradiation of spiny-rayed fishes. Proc. Natl Acad. Sci. USA 110, 12738–12743 (2013).

  33. 33.

    Aberer, A. J., Krompass, D. & Stamatakis, A. Pruning rogue taxa improves phylogenetic accuracy: an efficient algorithm and webservice. Syst. Biol. 62, 162–166 (2013).

  34. 34.

    Lanfear, R., Frandsen, P. B., Wright, A. M., Senfeld, T. & Calcott, B. PartitionFinder 2: new methods for selecting partitioned models of evolution for molecular and morphological phylogenetic analyses. Mol. Biol. Evol. 34, 772–773 (2017).

  35. 35.

    Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).

  36. 36.

    Hedman, M. M. Constraints on clade ages from fossil outgroups. Paleobiology 36, 16–31 (2010).

  37. 37.

    Lloyd, G. T., Bapst, D. W., Friedman, M. & Davis, K. E. Probabilistic divergence time estimation without branch lengths: dating the origins of dinosaurs, avian flight and crown birds. Biol. Lett. 12, 20160609 (2016).

  38. 38.

    Smith, S. A. & O’Meara, B. C. treePL: divergence time estimation using penalized likelihood for large phylogenies. Bioinformatics 28, 2689–2690 (2012).

  39. 39.

    Stadler, T. On incomplete sampling under birth–death models and connections to the sampling-based coalescent. J. Theor. Biol. 261, 58–66 (2009).

  40. 40.

    Stadler, T. Mammalian phylogeny reveals recent diversification rate shifts. Proc. Natl Acad. Sci. USA 108, 6187–6192 (2011).

  41. 41.

    Stadler, T. Simulating trees with a fixed number of extant species. Syst. Biol. 60, 676–684 (2011).

  42. 42.

    Thomas, G. H. et al. PASTIS: an R package to facilitate phylogenetic assembly with soft taxonomic inferences. Methods Ecol. Evol. 4, 1011–1017 (2013).

  43. 43.

    Ready, J. et al. Predicting the distribution of marine organisms at the global scale. Ecol. Modell. 221, 467–478 (2010).

  44. 44.

    Kaschner, K. et al. AquaMaps: predicted range maps for aquatic species. version 08/2016 http://www.aquamaps.org (2016).

  45. 45.

    Kaschner, K. et al. AquaMaps environmental dataset: half-degree cells authority file (HCAF). version 6, 08/2016 https://www.aquamaps.org/main/envt_data.php (2016).

  46. 46.

    Mecklenburg, C. W., Mecklenburg, T. A., Sheiko, B. A. & Steinke, D. Pacific Arctic Marine Fishes (CAFF, Akureyri, 2016).

  47. 47.

    Coll, M. et al. The biodiversity of the Mediterranean Sea: estimates, patterns, and threats. PLoS ONE 5, e11842 (2010).

  48. 48.

    IUCN. The IUCN Red List of Threatened Species. version 2016-1 http://www.iucnredlist.org, downloaded on 8 February 2018 (2016).

  49. 49.

    Mora, C., Tittensor, D. P. & Myers, R. A. The completeness of taxonomic inventories for describing the global diversity and distribution of marine fishes. Proc. R. Soc. B 275, 149–155 (2008).

  50. 50.

    Belmaker, J. & Jetz, W. Relative roles of ecological and energetic constraints, diversification rates and region history on global species richness gradients. Ecol. Lett. 18, 563–571 (2015).

  51. 51.

    Rabosky, D. L. & Goldberg, E. E. FiSSE: a simple nonparametric test for the effects of a binary character on lineage diversification rates. Evolution 71, 1432–1442 (2017).

  52. 52.

    Rabosky, D. L. Automatic detection of key innovations, rate shifts, and diversity-dependence on phylogenetic trees. PLoS ONE 9, e89543 (2014).

  53. 53.

    Rabosky, D. L., Donnellan, S. C., Grundler, M. & Lovette, I. J. Analysis and visualization of complex macroevolutionary dynamics: an example from Australian scincid lizards. Syst. Biol. 63, 610–627 (2014).

  54. 54.

    Rabosky, D. L. No substitute for real data: a cautionary note on the use of phylogenies from birth–death polytomy resolvers for downstream comparative analyses. Evolution 69, 3207–3216 (2015).

  55. 55.

    Mitchell, J. S. & Rabosky, D. L. Bayesian model selection with BAMM: effects of the model prior on the inferred number of diversification shifts. Methods Ecol. Evol. 8, 37–46 (2017).

  56. 56.

    Spalding, M. D. et al. Marine ecoregions of the world: a bioregionalization of coastal and shelf areas. Bioscience 57, 573–583 (2007).

  57. 57.

    Cowman, P. F., Parravicini, V., Kulbicki, M. & Floeter, S. R. The biogeography of tropical reef fishes: endemism and provinciality through time. Biol. Rev. Camb. Philos. Soc. 92, 2112–2130 (2017).

  58. 58.

    Pebesma, E. J. & Bivand, R. S. Classes and methods for spatial data in R. R News 5, 9–13 (2005).

  59. 59.

    Bivand, R., Hauke, J. & Kossowski, T. Computing the Jacobian in Gaussian spatial autoregressive models: an illustrated comparison of available methods. Geogr. Anal. 45, 150–179 (2013).

  60. 60.

    Bivand, R. & Piras, G. Comparing implementations of estimation methods for spatial econometrics. J. Stat. Softw. 63, 1–36 (2015).

  61. 61.

    Rabosky, D. L. & Goldberg, E. E. Model inadequacy and mistaken inferences of trait-dependent speciation. Syst. Biol. 64, 340–355 (2015).

  62. 62.

    Maddison, W. P. & FitzJohn, R. G. The unsolved challenge to phylogenetic correlation tests for categorical characters. Syst. Biol. 64, 127–136 (2015).

Download references


We thank M. Grundler for statistical and coding advice, and M. Venzon and A. Noonan for assistance with dataset assembly. We are grateful to the many institutions that curate the primary biodiversity data that underlie several of our analyses (see Supplementary Table 6). This research was carried out using computational resources and services provided by Advanced Research Computing at the University of Michigan, Ann Arbor. This work was supported in part by NSF grant DEB-1256330 (D.L.R.), an NSF DDIG grant to J.C. (DEB-1601830), an Encyclopedia of Life Rubenstein Fellowship to J.C. (EOL-33066-13) and by a Fellowship from the David and Lucile Packard Foundation (D.L.R.). P.F.C. was funded by a Gaylord Donnelley Postdoctoral Environment Fellowship (Yale) and through the ARC Centre of Excellence for Coral Reef Studies. We thank J. Johnson for creating the fish images in Fig. 3 and Extended Data Fig. 7.

Reviewer information

Nature thanks O. Bininda-Emonds, O. Seehausen and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Author notes

  1. These authors contributed equally: Daniel L. Rabosky, Jonathan Chang, Pascal O. Title, Michael E. Alfaro.


  1. Museum of Zoology, Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA

    • Daniel L. Rabosky
    •  & Pascal O. Title
  2. Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA, USA

    • Jonathan Chang
    •  & Michael E. Alfaro
  3. Peabody Museum of Natural History and Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA

    • Peter F. Cowman
    •  & Thomas J. Near
  4. ARC Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, Queensland, Australia

    • Peter F. Cowman
  5. Department of Earth and Environmental Science, University of Pennsylvania, Philadelphia, PA, USA

    • Lauren Sallan
  6. Museum of Paleontology and Department of Earth and Environmental Sciences, University of Michigan, Ann Arbor, MI, USA

    • Matt Friedman
  7. Department of Biometry and Environmental System Analysis, Albert-Ludwigs-University of Freiburg, Freiburg, Germany

    • Kristin Kaschner
  8. GEOMAR Helmholtz-Zentrum für Ozeanforschung, Kiel, Germany

    • Cristina Garilao
  9. Instituto de Ciencias de Mar, Spanish National Research Council (ICM-CSIC), Barcelona, Spain

    • Marta Coll


  1. Search for Daniel L. Rabosky in:

  2. Search for Jonathan Chang in:

  3. Search for Pascal O. Title in:

  4. Search for Peter F. Cowman in:

  5. Search for Lauren Sallan in:

  6. Search for Matt Friedman in:

  7. Search for Kristin Kaschner in:

  8. Search for Cristina Garilao in:

  9. Search for Thomas J. Near in:

  10. Search for Marta Coll in:

  11. Search for Michael E. Alfaro in:


D.L.R. and M.E.A. designed the study. D.L.R. drafted the paper with substantial input from P.O.T., M.E.A. and J.C. J.C., P.O.T., M.E.A., P.F.C., L.S., M.F., K.K., C.G., T.J.N., M.C. and D.L.R. contributed data. J.C., P.O.T. and D.L.R. developed methods, and P.O.T. and J.C. developed pipelines for data processing and analysis. D.L.R., P.O.T., J.C. and M.E.A. analysed data. All authors contributed to interpretation and discussion of results. Authorship order for P.O.T. and J.C. was determined by coin toss.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Daniel L. Rabosky.

Extended data figures and tables

  1. Extended Data Fig. 1 Phylogenetic placement of fossil calibrations in major fish lineages.

    Major lineages are broken into subclades (top) to visualize fossil calibrations and are coloured by taxonomic order. Numbered nodes are described in the calibration report in the Dryad data repository. The same calibrations are red circles in the full phylogeny (bottom). A + E + L + P: Acipenseriformes, Elopiformes, Lepisosteiformes, Polypteriformes; A + E + S: Argentiniformes, Esociformes, Salmoniformes; B + H: Beryciformes, Holocentriformes; C + S + P: Centrarchiformes, Scombriformes, Perciformes; C + U: Chaetodontiformes, Uranoscopiformes; G + G: Gonorynchiformes, Gymnotiformes; G + O + S: Galaxiiformes, Osmeriformes, Stomiatiformes; P + Z: Percopsiformes, Zeiformes.

  2. Extended Data Fig. 2 Relationships between mean speciation rates and latitude for 262 marine ecoregions using alternative methods for the computation of the cell rates.

    ac, λBAMM versus latitude. df, λDR versus latitude. Ecoregion rates are mean rates across all cells assigned to each biogeographical region. Arithmetic mean is the mean rate across all taxa inferred to occur in the cell; weighted arithmetic and weighted geometric means assign proportionately greater weight to species with small geographical ranges. Weighting schemes for speciation metrics are described in the Methods. g, Simultaneous autoregressive (SAR) spatial error models for the effects of absolute latitude on mean speciation rates for ecoregions. AIC1 gives the Akaike information criterion (AIC) for a linear model with a single slope and intercept term; AIC2 is the corresponding AIC for a breakpoint model that assumes no relationship (slope = 0) between absolute latitude and speciation rate for all values below some threshold, and a linear relationship for latitudes that exceed the threshold. SAR.I and SAR.IP are global Moran’s I estimates and associated P values for assessing the presence of residual spatial autocorrelation in the model residuals; OLS.I and OLS.IP are the corresponding values for ordinary least squares (OLS) regression that ignores spatial autocorrelation. All SAR models show highly significant effects of latitude on speciation rate, and breakpoint models provided a consistently better fit than models without a breakpoint. h, OLS and SAR models for the effects of absolute latitude on speciation rate for low-latitude grid ecoregions only. The slope ratio term gives the ratio of slopes for low-latitude ecoregions (below the corresponding breakpoint; g) to the slope for ecoregions with latitude above the breakpoint. Overall, there is a marginal effect of latitude on speciation rate for low-latitude ecoregions.

  3. Extended Data Fig. 3 Relationships between speciation rate and latitude for alternative speciation rate metrics and for endemic taxa only.

    a, b, Global maps of λDR and λBAMM-TC, as in Fig. 1. c, Global map of endemic species richness, by grid cell. ‘Endemic’ taxa are those that are restricted to a single MEOW realm; an endemic taxon can occur in multiple grid cells provided all grid cells are contained within a single realm. d, Relationship between speciation rates (λDR) and latitude for ecoregions (n = 232), computed using realm endemics only. e, Relationship between speciation rates (λBAMM-TC) and latitude for ecoregions, computed using realm endemics only. f, SAR spatial error models for the relationship between ecoregion speciation rates and absolute latitude, for which ecoregion means are computed from single-realm endemics only. Weighting schemes for assemblages are described in the Methods. SAR modelling results are presented as in Extended Data Fig. 2g and show a strong correlation between latitude and speciation rate.

  4. Extended Data Fig. 4 Speciation rate, species richness, temperature and endemism.

    a, Negative relationship between species richness and mean speciation rate (λDR) for individual grid cells. b, Negative relationship between mean annual sea surface temperature and mean speciation rate. c, d, Same as a, b, but for BAMM with time-constant rate regimes (λBAMM-TC). Grid cells as in Fig. 1 (n = 16,150). See Fig. 2 for comparison. e, Correlation between mean speciation rate for MEOW biogeographical provinces and two measurements of regional endemism. ‘Occupancy (provinces)’ measures endemism as the inverse of the mean number of provinces occupied by each taxon that occurs in a particular province. ‘Range size’ is the inverse mean range size across all taxa occurring in a given province. High values of endemism indicate that a given region consists of species that are found in fewer additional provinces, or of species with smaller geographical ranges. The bottom two rows show the correlations between the endemism parameters and latitude.

  5. Extended Data Fig. 5 Speciation rates for individual taxa as a function of latitudinal midpoint.

    a, λDR for all marine species with genetic data (n = 5,229) as a function of the latitudinal (centroid) midpoint of their geographical range. Non-phylogenetic OLS regression with quadratic term is overlaid on points to denote trend in mean rates. b, λBAMM for the same taxon set. c, Sliding window analysis of λDR distributional quantiles in speciation rates by individual taxa with respect to latitudinal midpoint. Contours denote quantiles from 0.10 to 0.90, in 0.10 increments, with a sliding window size of 6°. Dark red line is the median rate. d, Distributional quantiles of λBAMM for all species with respect to latitudinal midpoint; dark red line is median rate.

  6. Extended Data Fig. 6 Temporal dimension of speciation rate variation as a function of latitude.

    a, Mean speciation rates for taxa from low latitudes (<30°), intermediate latitudes (30–60°) and high latitudes (>60°) computed using the interval method. Per-taxon interval-based rates were computed for time intervals between 0.25 and 50 million years before present. Time-averaged speciation rates for high-latitude fishes are much higher than those inferred for low-latitude fishes, even across timescales that exceed 20 million years. b, Rate differential between high-latitude and low-latitude taxa as a function of interval duration. c, Speciation-over-time curves reconstructed using the time-varying rates model in BAMM for 14 clades of high-latitude (blue) and low-latitude (red) fishes. Inset numbers for each panel give the numbers of low-, intermediate- and high-latitude (from left to right) taxa from each clade for which geographical range data are available. Low-latitude clades were selected to represent high-diversity and iconic reef-associated clades that contribute substantially to the tropical diversity peak in marine fishes. With the possible exception of gobies, there is no signal of early, rapid speciation in low-latitude or tropical shallow-water clades.

  7. Extended Data Figure 7 Speciation rates in deep-sea fishes and the phylogenetic structure of high-latitude fish diversity.

    a, Formal test of the relationship between speciation rate and depth classification for tropical fishes. ‘Classification’ is the criterion used to define fishes as deep sea versus shallow water; mean depth (200 m) thus classifies all fishes with mean depth greater than 200 m as deep sea. Among tropical fishes, there is no effect of depth state on speciation rate. b, Phylogenetic composition of high-latitude fish diversity by taxonomic order, across all marine fishes (top) and for the subset of species with genetic data (bottom). High latitude is defined as having a centroid midpoint greater than 45° north or south. Only the three most species-rich high-latitude orders are labelled. Most high-latitude marine fishes are Perciformes. c, Phylogenetic and geographical structure of the diversity of Perciformes. The latitudinal range of each perciform species in the phylogenetic dataset is shown, along with the corresponding speciation rate (λBAMM). Latitudinal ranges from species with speciation rates that are faster and slower than the median rate are shown in red and blue, respectively. High-latitude and rapidly speciating clades are nested within slowly speciating tropical lineages, and speciation rates for high-latitude taxa of Perciformes are higher than those observed in tropical lineages. Mean speciation rates for high-latitude species (>45°, n = 376) are faster than those observed for tropical (<25°, n = 287) species (tropical: λDR = 0.16, λBAMM = 0.15; high latitude: λDR = 0.30, λBAMM = 0.23). For polar species (>60°, n = 105), these rate differentials are even more extreme, with mean λDR = 0.38 and λBAMM = 0.31.

  8. Extended Data Figure 8 Latitudinal gradient in speciation rate for cell assemblages inferred from occurrence data.

    Cell assemblages (n = 843) and species latitudinal midpoints were inferred from a non-redundant merge of four primary occurrence-based biodiversity databases (GBIF, OBIS, Fishnet2 and VertNet). a, λBAMM for cell assemblages as a function of latitude. b, λDR as a function of latitude. c, SAR spatial error models for the effects of absolute latitude on mean speciation rates for grid cells. AIC1 is a linear model with a single slope and intercept term; AIC2 is the corresponding AIC for a breakpoint model that assumes no relationship (slope = 0) between absolute latitude and speciation rate for all values below some threshold, and a linear relationship for latitudes that exceed the threshold. All other column headings as in Extended Data Fig. 2g. Results indicate a strong effect of latitude on speciation rate and are nearly identical to results obtained using the dataset of the primary map. d, Effects of absolute latitudinal midpoint for individual taxa on corresponding tip speciation rates, as assessed using FiSSE. Each row gives the results of FiSSE using a different threshold for classifying lineages as tropical and temperate. λ0 and λ1 denote estimated speciation rates (similar to λDR) for tropical and temperate lineages, respectively. All column headings are identical to those shown in Extended Data Table 1. Results are nearly identical to those obtained using explicit range reconstructions and reveal a pervasive effect of latitude on lineage-level speciation rates, regardless of the threshold used to classify species.

  9. Extended Data Fig. 9 Additional checks of statistical robustness.

    a, Relationship between terminal branch lengths and absolute latitudinal midpoint; means are shown for all species falling into a given bin (±2.5° from the focal value, n = 15). Mean branch lengths decrease with increasing latitude, reflecting faster speciation at high latitudes. b, Relationship between the estimated speciation rate for each taxon (λDR, n = 5,155) and the sampling fraction for the corresponding family-level clade to which the taxon belongs; the sampling fraction is simply the percentage of known taxa from the family that were represented in the phylogenetic dataset with genetic data. There is no clear relationship between the sampling fraction and the estimated speciation rates. c, Multiple regression analysis (OLS) of the relationship between taxon-specific speciation rate (λBAMM or λDR) and two predictors (latitude and family-level sampling fraction) in a multiple regression framework (n = 5,155). If the relationship between speciation rate and latitude is driven by progressively greater (or lower) genetic taxon sampling as a function of latitude, the sampling fraction term should explain a large fraction of the overall sums of squares. Even when sampling fraction is included as a covariate, the overwhelming fraction of variance is explained by latitude. For both λDR and λBAMM, more than 98% of the total sums of squares is explained by latitude and not sampling. df, Test for the effects of molecular evolutionary rate variation and latitudinal bias in speciation rate. d, Relationship between root-to-tip branch length sum for uncalibrated (non-ultrametric) RAxML phylogeny and midpoint latitude for each marine taxon (n = 5,149). e, f, Relationship between root-to-tip distance and λDR. There is effectively no relationship between the total path length for individual tips and their absolute latitudinal midpoint (Pearson r = 0.020). Plots in e and f emphasize tropical (midpoint latitude <25°; n = 3,481; red) and temperate–polar (midpoint latitude >45°; n = 567; blue) taxa, respectively, all other taxa are shown in grey. Overall relationship between (log)λDR and the rate of molecular evolution (root-to-tip sum) is weak but positive (Pearson r = 0.130) and inconsistent with the hypothesis that slow rates of molecular evolution at high latitudes results in fast but spurious estimates of speciation rate.

  10. Extended Data Table 1 Effects of absolute latitudinal midpoint on speciation rates

Supplementary information

  1. Supplementary Information

    This file contains information on a Matrix Assembly, Taxonomic Reconciliation, Rogue Searching, Tree Searching, Fossil Calibrations, Placing Unsampled Species, Computing Tip-Specific Speciation Rates and Occurrence Dataset.

  2. Reporting Summary

About this article

Publication history




Issue Date



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.