1932

Abstract

Evolutionary rates play a central role in connecting micro- and macroevolution. All evolutionary rate estimates, including rates of molecular evolution, trait evolution, and lineage diversification, share a similar scaling pattern with time: The highest rates are those measured over the shortest time interval. This creates a disconnect between micro- and macroevolution, although the pattern is the opposite of what some might expect: Patterns of change over short timescales predict that evolution has tremendous potential to create variation and that potential is barely tapped by macroevolution. In this review, we discuss this shared scaling pattern across evolutionary rates. We break down possible explanations for scaling into two categories, estimation error and model misspecification, and discuss how both apply to each type of rate. We also discuss the consequences of this ubiquitous pattern, which can lead to unexpected results when comparing ratesover different timescales. Finally, after addressing purely statistical concerns, we explore a few possibilities for a shared unifying explanation across the three types of rates that results from a failure to fully understand and account for how biological processes scale over time.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-ecolsys-011921-023644
2021-11-03
2024-10-12
Loading full text...

Full text loading...

/deliver/fulltext/ecolsys/52/1/annurev-ecolsys-011921-023644.html?itemId=/content/journals/10.1146/annurev-ecolsys-011921-023644&mimeType=html&fmt=ahah

Literature Cited

  1. Albà MM, Castresana J. 2005. Inverse relationship between evolutionary rate and age of mammalian genes. Mol. Biol. Evol. 22:3598–606
    [Google Scholar]
  2. Alfaro ME, Santini F, Brock C, Alamillo H, Dornburg A et al. 2009. Nine exceptional radiations plus high turnover explain species diversity in jawed vertebrates. PNAS 106:3213410–14
    [Google Scholar]
  3. Arbogast BS, Edwards SV, Wakeley J, Beerli P, Slowinski JB. 2002. Estimating divergence times from molecular data on phylogenetic and population genetic timescales. Annu. Rev. Ecol. Syst. 33:707–40
    [Google Scholar]
  4. Bandelt H-J. 2008. Clock debate: when times are a-changin’: time dependency of molecular rate estimates: tempest in a teacup. Heredity 100:11–2
    [Google Scholar]
  5. Beaulieu JM, Jhwueng D-C, Boettiger C, Brian C, O'Meara BC. 2012. Modeling stabilizing selection: expanding the Ornstein-Uhlenbeck model of adaptive evolution. Evolution 66:82369–83
    [Google Scholar]
  6. Berv JS, Field DJ. 2018. Genomic signature of an avian Lilliput effect across the K-Pg extinction. Syst. Biol. 67:11–13
    [Google Scholar]
  7. Boucher FC, Démery V, Conti E, Harmon LJ, Uyeda J. 2018. A general model for estimating macroevolutionary landscapes. Syst. Biol. 67:2304–19
    [Google Scholar]
  8. Brown JM, Thomson RC. 2018. Evaluating model performance in evolutionary biology. Annu. Rev. Ecol. Evol. Syst. 49:95–114
    [Google Scholar]
  9. Butler MA, King AA. 2004. Phylogenetic comparative analysis: a modeling approach for adaptive evolution. Am. Nat. 164:6683–95
    [Google Scholar]
  10. Cantalapiedra JL, FitzJohn RG, Kuhn TS, Fernández MH, DeMiguel D et al. 2014. Dietary innovations spurred the diversification of ruminants during the Caenozoic. Proc. R Soc. B 281:177620132746
    [Google Scholar]
  11. Charlesworth B, Lande R, Slatkin M. 1982. A neo-Darwinian commentary on macroevolution. Evolution 36:3474–98
    [Google Scholar]
  12. Condamine FL, Rolland J, Morlon H 2019. Assessing the causes of diversification slowdowns: temperature-dependent and diversity-dependent models receive equivalent support. Ecol. Lett. 22:111900–12
    [Google Scholar]
  13. Cooney CR, Thomas GH. 2021. Heterogeneous relationships between rates of speciation and body size evolution across vertebrate clades. Nat. Ecol. Evol 5:1101–10
    [Google Scholar]
  14. Cooper N, Thomas GH, Venditti C, Meade A, Freckleton RP 2016. A cautionary note on the use of Ornstein Uhlenbeck models in macroevolutionary studies. Biol. J. Linn. Soc. Lond. 118:164–77
    [Google Scholar]
  15. DeSalle R, Freedman T, Prager EM, Wilson AC. 1987. Tempo and mode of sequence evolution in mitochondrial DNA of Hawaiian Drosophila. J. Mol. Evol. 26:1–2157–64
    [Google Scholar]
  16. Drummond AJ, Ho SYW, Phillips MJ, Rambaut A. 2006. Relaxed phylogenetics and dating with confidence. PLOS Biol 4:5e88
    [Google Scholar]
  17. Dynesius M, Jansson R. 2014. Persistence of within-species lineages: a neglected control of speciation rates. Evolution 68:4923–34
    [Google Scholar]
  18. Eldredge N, Gould SJ 1972. Punctuated equilibria: an alternative to phyletic gradualism. In Models in Paleobiologyed. TJM Schopfpp. 82115 Baltimore, MD: Johns Hopkins Univ. Press
    [Google Scholar]
  19. Eldredge N, Thompson JN, Brakefield PM, Gavrilets S, Jablonski D et al. 2005. The dynamics of evolutionary stasis. Paleobiology 31:2133–45
    [Google Scholar]
  20. Elhaik E, Sabath N, Graur D. 2005. The “inverse relationship between evolutionary rate and age of mammalian genes” is an artifact of increased genetic distance with rate of evolution and time of divergence. Mol. Biol. Evol. 23:11–3
    [Google Scholar]
  21. Emerson BC. 2007. Alarm bells for the molecular clock? No support for Ho et al.’s model of time-dependent molecular rate estimates. Syst. Biol. 56:2337–45
    [Google Scholar]
  22. Emerson BC, Hickerson MJ. 2015. Lack of support for the time-dependent molecular evolution hypothesis. Mol. Ecol. 24:4702–9
    [Google Scholar]
  23. Emerson BC, Kolm N. 2005. Species diversity can drive speciation. Nature 434:70361015–17
    [Google Scholar]
  24. Erwin DH. 2000. Macroevolution is more than repeated rounds of microevolution. Evol. Dev. 2:278–84
    [Google Scholar]
  25. Estes S, Arnold SJ. 2007. Resolving the paradox of stasis: models with stabilizing selection explain evolutionary divergence on all timescales. Am. Nat. 169:2227–44
    [Google Scholar]
  26. Etienne RS, Haegeman B, Stadler T, Aze T, Pearson PN et al. 2012. Diversity-dependence brings molecular phylogenies closer to agreement with the fossil record. Proc. R Soc. B 279:17321300–9
    [Google Scholar]
  27. Etienne RS, Rosindell J. 2012. Prolonging the past counteracts the pull of the present: Protracted speciation can explain observed slowdowns in diversification. Syst. Biol. 61:2204–13
    [Google Scholar]
  28. Felsenstein J. 1973. Maximum-likelihood estimation of evolutionary trees from continuous characters. Am. J. Hum. Genet. 25:5471–92
    [Google Scholar]
  29. Felsenstein J. 1985. Phylogenies and the comparative method. Am. Nat. 125:11–15
    [Google Scholar]
  30. Felsenstein J. 1988. Phylogenies and quantitative characters. Annu. Rev. Ecol. Syst. 19:445–71
    [Google Scholar]
  31. Felsenstein J. 2008. Comparative methods with sampling error and within-species variation: contrasts revisited and revised. Am. Nat. 171:6713–25
    [Google Scholar]
  32. FitzJohn RG. 2010. Quantitative traits and diversification. Syst. Biol. 59:6619–33
    [Google Scholar]
  33. Foote M. 1994. Temporal variation in extinction risk and temporal scaling of extinction metrics. Paleobiology 20:4424–44
    [Google Scholar]
  34. Foote M. 2003. Origination and extinction through the Phanerozoic: a new approach. J. Geol. 111:125–48
    [Google Scholar]
  35. Foote M. 2005. Pulsed origination and extinction in the marine realm. Paleobiology 31:6–20
    [Google Scholar]
  36. Futuyma DJ. 1987. On the role of species in anagenesis. Am. Nat. 130:3465–73
    [Google Scholar]
  37. Futuyma DJ. 2010. Evolutionary constraint and ecological consequences. Evolution 64:71865–84
    [Google Scholar]
  38. García-Moreno J. 2004. Is there a universal mtDNA clock for birds?. J. Avian Biol. 35:6465–68
    [Google Scholar]
  39. Gillespie JH. 1986. Rates of molecular evolution. Annu. Rev. Ecol. Syst. 17:637–65
    [Google Scholar]
  40. Gingerich PD. 1983. Rates of evolution: effects of time and temporal scaling. Science 222:4620159–61
    [Google Scholar]
  41. Gingerich PD. 2009. Rates of evolution. Annu. Rev. Ecol. Evol. Syst. 40:657–75
    [Google Scholar]
  42. Gingerich PD. 2019. Rates of Evolution: A Quantitative Synthesis Cambridge, UK: Cambridge Univ. Press
    [Google Scholar]
  43. Goldberg EE, Foo J. 2020. Memory in trait macroevolution. Am. Nat. 195:2300–14
    [Google Scholar]
  44. Goldberg EE, Lancaster LT, Ree RH. 2011. Phylogenetic inference of reciprocal effects between geographic range evolution and diversification. Syst. Biol. 60:4451–65
    [Google Scholar]
  45. Gould SJ. 1984. Smooth curve of evolutionary rate: a psychological and mathematical artifact. Science 226:4677994–96
    [Google Scholar]
  46. Gould SJ. 1985. The paradox of the first tier: an agenda for paleobiology. Paleobiology 11:12–12
    [Google Scholar]
  47. Gould SJ, Eldredge N. 1977. Punctuated equilibria: the tempo and mode of evolution reconsidered. Paleobiology 3:2115–51
    [Google Scholar]
  48. Graur D, Martin W. 2004. Reading the entrails of chickens: molecular timescales of evolution and the illusion of precision. Trends Genet 20:280–86
    [Google Scholar]
  49. Haldane JBS. 1949. Suggestions as to quantitative measurement of rates of evolution. Evolution 3:151–56
    [Google Scholar]
  50. Hansen TF. 1997. Stabilizing selection and the comparative analysis of adaptation. Evolution 51:51341–51
    [Google Scholar]
  51. Hansen TF, Bartoszek K. 2012. Interpreting the evolutionary regression: the interplay between observational and biological errors in phylogenetic comparative studies. Syst. Biol. 61:3413–25
    [Google Scholar]
  52. Hansen TF, Houle D 2004. Evolvability, stabilizing selection, and the problem of stasis. Phenotypic Integration: Studying the Ecology and Evolution of Complex Phenotypes M Pigliucci, K Preston 130–50 Oxford, UK: Oxford Univ. Press
    [Google Scholar]
  53. Hansen TF, Martins EP. 1996. Translating between microevolutionary process and macroevolutionary patterns: the correlation structure of interspecific data. Evolution 50:41404–17
    [Google Scholar]
  54. Hansen TF, Orzack SH. 2005. Assessing current adaptation and phylogenetic inertia as explanations of trait evolution: the need for controlled comparisons. Evolution 59:102063–72
    [Google Scholar]
  55. Harmon LJ 2014. Macroevolutionary rates. The Princeton Guide to Evolution A Baum, DJ Futuyma, HE Hoekstra, RE Lenski, AJ Moore et al.567–72 Princeton, NJ: Princeton Univ. Press
    [Google Scholar]
  56. Harmon LJ 2019. Phylogenetic comparative methods: learning from trees. EcoEvoRxiv. https://doi.org/10.32942/osf.io/e3xnr
    [Crossref] [Google Scholar]
  57. Harmon LJ, Harrison S 2015. Species diversity is dynamic and unbounded at local and continental scales. Am. Nat. 185:5584–93
    [Google Scholar]
  58. Harmon LJ, Losos JB, Davies TJ, Gillespie RG, Gittleman JL et al. 2010. Early bursts of body size and shape evolution are rare in comparative data. Evolution 64:82385–96
    [Google Scholar]
  59. Harmon LJ, Schulte JA, Larson A, Losos JB. 2003. Tempo and mode of evolutionary radiation in iguanian lizards. Science 301:5635961–64
    [Google Scholar]
  60. Harvey MG, Singhal S, Rabosky DL. 2019. Beyond reproductive isolation: demographic controls on the speciation process. Annu. Rev. Ecol. Evol. Syst. 50:75–95
    [Google Scholar]
  61. Hawkes AG. 2018. Hawkes processes and their applications to finance: a review. Quant. Finance. 18:2193–98
    [Google Scholar]
  62. Henao Diaz LF, Harmon LJ, Sugawara MTC, Miller ET, Pennell MW 2019. Macroevolutionary diversification rates show time dependency. PNAS 116:157403–8
    [Google Scholar]
  63. Hendry AP, Kinnison MT. 1999. The pace of modern life: measuring rates of contemporary microevolution. Evolution 53:61637–53
    [Google Scholar]
  64. Hendry AP, Kinnison MT. 2001. An introduction to microevolution: rate, pattern, process. Genetica 112–113:1–8
    [Google Scholar]
  65. Ho SYW, Duchêne S, Molak M, Shapiro B. 2015. Time-dependent estimates of molecular evolutionary rates: evidence and causes. Mol. Ecol. 24:6007–12
    [Google Scholar]
  66. Ho SYW, Lanfear R, Bromham L, Phillips MJ, Soubrier J et al. 2011. Time-dependent rates of molecular evolution. Mol. Ecol. 20:153087–101
    [Google Scholar]
  67. Ho SYW, Phillips MJ, Cooper A, Drummond AJ 2005. Time dependency of molecular rate estimates and systematic overestimation of recent divergence times. Mol. Biol. Evol. 22:71561–68
    [Google Scholar]
  68. Ho SYW, Shapiro B, Phillips MJ, Cooper A, Drummond AJ 2007. Evidence for time dependency of molecular rate estimates. Syst. Biol. 56:3515–22
    [Google Scholar]
  69. Höhna S. 2014. Likelihood inference of non-constant diversification rates with incomplete taxon sampling. PLOS ONE 9:1e84184
    [Google Scholar]
  70. Höhna S, Freyman WA, Nolen Z, Huelsenbeck JP, May MR, Moore BR 2019. A Bayesian approach for estimating branch-specific speciation and extinction rates. bioRxiv 555805. https://doi.org/10.1101/555805
    [Crossref]
  71. Höhna S, Stadler T, Ronquist F, Britton T. 2011. Inferring speciation and extinction rates under different sampling schemes. Mol. Biol. Evol. 28:92577–89
    [Google Scholar]
  72. Holmes EC, Dudas G, Rambaut A, Andersen KG. 2016. The evolution of Ebola virus: insights from the 2013–2016 epidemic. Nature 538:7624193–200
    [Google Scholar]
  73. Hopkins MJ, Bapst DW, Simpson C, Warnock RCM. 2018. The inseparability of sampling and time and its influence on attempts to unify the molecular and fossil records. Paleobiology 44:4561–74
    [Google Scholar]
  74. Houle D, Pélabon C, Wagner GP, Hansen TF. 2011. Measurement and meaning in biology. Q. Rev. Biol. 86:13–34
    [Google Scholar]
  75. Hunt G. 2007. The relative importance of directional change, random walks, and stasis in the evolution of fossil lineages. PNAS 104:4718404–8
    [Google Scholar]
  76. Hunt G. 2012. Measuring rates of phenotypic evolution and the inseparability of tempo and mode. Paleobiology 38:3351–73
    [Google Scholar]
  77. Ives AR, Midford PE, Garland T Jr. 2007. Within-species variation and measurement error in phylogenetic comparative methods. Syst. Biol. 56:2252–70
    [Google Scholar]
  78. Jablonski D. 2000. Micro- and macroevolution: scale and hierarchy in evolutionary biology and paleobiology. Paleobiology 26:415–52
    [Google Scholar]
  79. Jablonski D. 2007. Scale and hierarchy in macroevolution. Palaeontology 50:187–109
    [Google Scholar]
  80. Jackson DA, Somers KM. 1991. The spectre of “spurious” correlations. Oecologia 86:1147–51
    [Google Scholar]
  81. Johnson PLF, Slatkin M. 2008. Accounting for bias from sequencing error in population genetic estimates. Mol. Biol. Evol. 25:1199–206
    [Google Scholar]
  82. Jukes TH, Cantor CR 1969. Evolution of protein molecules. Mammalian Protein Metabolism HN Munro pp. 21132 New York: Academic
    [Google Scholar]
  83. Kendall MG. 1948. Rank Correlation Methods New York: Griffin
    [Google Scholar]
  84. Kenney BC. 1982. Beware of spurious self-correlations!. Water Resour. Res. 18:41041–48
    [Google Scholar]
  85. Kinnison MT, Hendry AP. 2001. The pace of modern life II: from rates of contemporary microevolution to pattern and process. Genetica 112–113:145–64
    [Google Scholar]
  86. Kostikova A, Silvestro D, Pearman PB, Salamin N. 2016. Bridging inter- and intraspecific trait evolution with a hierarchical Bayesian approach. Syst. Biol. 65:3417–31
    [Google Scholar]
  87. Landis MJ, Schraiber JG 2017. Pulsed evolution shaped modern vertebrate body sizes. PNAS 114:5013224–29
    [Google Scholar]
  88. Lanfear R, Ho SYW, Love D, Bromham L 2010. Mutation rate is linked to diversification in birds. PNAS 107:4720423–28
    [Google Scholar]
  89. Lewis PO. 2001. A likelihood approach to estimating phylogeny from discrete morphological character data. Syst. Biol. 50:6913–25
    [Google Scholar]
  90. Li W. 1997. Molecular Evolution Sunderland, MA: Sinauer
    [Google Scholar]
  91. Linder HP. 2008. Plant species radiations: where, when, why?. Philos. Trans. R. Soc. B 363:15063097–105
    [Google Scholar]
  92. Long SB. 1980. The continuing debate over the use of ratio variables: facts and fiction. Soc. Methodol 11:3767
    [Google Scholar]
  93. Louca S, Pennell MW. 2020. Extant timetrees are consistent with a myriad of diversification histories. Nature 580:7804502–5
    [Google Scholar]
  94. MacPherson A, Louca S, McLaughlin A, Joy JB, Pennell MW. 2020. A general birth-death-sampling model for epidemiology and macroevolution. bioRxiv 334383. https://doi.org/10.1101/2020.10.10.334383
    [Crossref] [Google Scholar]
  95. Maddison WP, Midford PE, Otto SP, Oakley T. 2007. Estimating a binary character's effect on speciation and extinction. Syst. Biol. 56:5701–10
    [Google Scholar]
  96. Magallon S, Sanderson MJ. 2001. Absolute diversification rates in angiosperm clades. Evolution 55:91762–80
    [Google Scholar]
  97. Magee AF, Höhna S, Vasylyeva TI, Leaché AD, Minin VN. 2020. Locally adaptive Bayesian birth-death model successfully detects slow and rapid rate shifts. PLOS Comput. Biol. 16:10e1007999
    [Google Scholar]
  98. Marin J, Hedges SB. 2018. Undersampling genomes has biased time and rate estimates throughout the tree of life. Mol. Biol. Evol 35:82077–84 Erratum. Mol. Biol. Evol. 2018. 35(10):2595
    [Google Scholar]
  99. McPeek MA. 2008. The ecological dynamics of clade diversification and community assembly. Am. Nat. 172:6E270–84
    [Google Scholar]
  100. McPeek MA, Brown JM. 2007. Clade age and not diversification rate explains species richness among animal taxa. Am. Nat. 169:4E97–106
    [Google Scholar]
  101. Mooers AO, Heard SB. 1997. Inferring evolutionary process from phylogenetic tree shape. Q. Rev. Biol. 72:131–54
    [Google Scholar]
  102. Moreno LF, Stratton HH, Newell JC, Feustel PJ. 1986. Mathematical coupling of data: correction of a common error for linear calculations. J. Appl. Physiol. 60:1335–43
    [Google Scholar]
  103. Morlon H, Parsons TL, Plotkin JB 2011. Reconciling molecular phylogenies with the fossil record. PNAS 108:3916327–32
    [Google Scholar]
  104. Mossel E, Vigoda E. 2005. Phylogenetic MCMC algorithms are misleading on mixtures of trees. Science 309:57442207–9
    [Google Scholar]
  105. Nee S. 2006. Birth-death models in macroevolution. Annu. Rev. Ecol. Evol. Syst. 37:1–17
    [Google Scholar]
  106. Ng J, Smith SD. 2014. How traits shape trees: new approaches for detecting character state-dependent lineage diversification. J. Evol. Biol. 27:102035–45
    [Google Scholar]
  107. Ohta T. 1992. The nearly neutral theory of molecular evolution. Annu. Rev. Ecol. Syst. 23:263–86
    [Google Scholar]
  108. Omland KE. 1997. Correlated rates of molecular and morphological evolution. Evolution 51:51381–93
    [Google Scholar]
  109. Otto SP. 2018. Adaptation, speciation and extinction in the Anthropocene. Proc. R Soc. B 285:189120182047
    [Google Scholar]
  110. Pagel M, Venditti C, Meade A 2006. Large punctuational contribution of speciation to evolutionary divergence at the molecular level. Science 314:5796119–21
    [Google Scholar]
  111. Pannetier T, Martinez C, Bunnefeld L, Etienne RS. 2021. Branching patterns in phylogenies cannot distinguish diversity-dependent diversification from time-dependent diversification. Evolution 75:125–38
    [Google Scholar]
  112. Pennell MW, FitzJohn RG, Cornwell WK, Harmon LJ. 2015. Model adequacy and the macroevolution of angiosperm functional traits. Am. Nat. 186:2E33–50
    [Google Scholar]
  113. Penny D. 2005. Relativity for molecular clocks. Nature 436:7048183–84
    [Google Scholar]
  114. Prairie YT, Bird DF. 1989. Some misconceptions about the spurious correlation problem in the ecological literature. Oecologia 81:2285–88
    [Google Scholar]
  115. Rabosky DL. 2014. Automatic detection of key innovations, rate shifts, and diversity-dependence on phylogenetic trees. PLOS ONE 9:2e89543
    [Google Scholar]
  116. Rabosky DL. 2016. Reproductive isolation and the causes of speciation rate variation in nature. Biol. J. Linn. Soc. Lond. 118:113–25
    [Google Scholar]
  117. Rabosky DL, Adams DC. 2012. Rates of morphological evolution are correlated with species richness in salamanders. Evolution 66:61807–18
    [Google Scholar]
  118. Rabosky DL, Hurlbert AH. 2015. Species richness at continental scales is dominated by ecological limits. Am. Nat. 185:5572–83
    [Google Scholar]
  119. Rabosky DL, Lovette IJ. 2008. Density-dependent diversification in North American wood warblers. Proc. R Soc. B 275:16492363–71
    [Google Scholar]
  120. Rabosky DL, Santini F, Eastman J, Smith SA, Sidlauskas B et al. 2013. Rates of speciation and morphological evolution are correlated across the largest vertebrate radiation. Nat. Commun. 4:1958
    [Google Scholar]
  121. Rabosky DL, Slater GJ, Alfaro ME. 2012. Clade age and species richness are decoupled across the eukaryotic tree of life. PLOS Biol 10:8e1001381
    [Google Scholar]
  122. Raup DM. 1985. Mathematical models of cladogenesis. Paleobiology 11:42–52
    [Google Scholar]
  123. Reitan T, Liow LH. 2019. layeranalyzer: Inferring correlative and causal connections from time series data in R. Methods Ecol. Evol. 10:122183–88
    [Google Scholar]
  124. Reitan T, Schweder T, Henderiks J. 2012. Phenotypic evolution studied by layered stochastic differential equations. Ann. Appl. Stat. 6:41531–51
    [Google Scholar]
  125. Revell LJ, Harmon LJ, Glor RE. 2005. Under-parameterized model of sequence evolution leads to bias in the estimation of diversification rates from molecular phylogenies. Syst. Biol. 54:6973–83
    [Google Scholar]
  126. Reznick DN, Ricklefs RE. 2009. Darwin's bridge between microevolution and macroevolution. Nature 457:7231837–42
    [Google Scholar]
  127. Ricklefs RE. 2006. Global variation in the diversification rate of passerine birds. Ecology 87:102468–78
    [Google Scholar]
  128. Rolland J, Condamine FL, Jiguet F, Morlon H. 2014. Faster speciation and reduced extinction in the tropics contribute to the mammalian latitudinal diversity gradient. PLOS Biol 12:1e1001775
    [Google Scholar]
  129. Rolland J, Silvestro D, Litsios G, Faye L, Salamin N. 2018. Clownfishes evolution below and above the species level. Proc. R Soc. B 285:187320171796
    [Google Scholar]
  130. Rosenblum EB, Sarver BAJ, Brown JW, Des Roches S, Hardwick KM et al. 2012. Goldilocks meets Santa Rosalia: An ephemeral speciation model explains patterns of diversification across time scales. Evol. Biol. 39:2255–61
    [Google Scholar]
  131. Rosindell J, Cornell SJ, Hubbell SP, Etienne RS 2010. Protracted speciation revitalizes the neutral theory of biodiversity. Ecol. Lett. 13:6716–27
    [Google Scholar]
  132. Sadler PM. 1981. Sediment accumulation rates and the completeness of stratigraphic sections. J. Geol. 89:5569–84
    [Google Scholar]
  133. Sarver BA, Pennell MW, Brown JW, Keeble S, Hardwick KM et al. 2019. The choice of tree prior and molecular clock does not substantially affect phylogenetic inferences of diversification rates. PeerJ 7:e6334
    [Google Scholar]
  134. Schluter D. 2000. The Ecology of Adaptive Radiation Oxford, UK: Oxford Univ. Press
    [Google Scholar]
  135. Scholl JP, Wiens JJ. 2016. Diversification rates and species richness across the tree of life. Proc. R Soc. B 283:183820161334
    [Google Scholar]
  136. Schwenk K. 1994. A utilitarian approach to evolutionary constraint. Zoology 98:251–62
    [Google Scholar]
  137. Sheets HD, Mitchell CE. 2001. Uncorrelated change produces the apparent dependence of evolutionary rate on interval. Paleobiology 27:3429–45
    [Google Scholar]
  138. Silvestro D, Kostikova A, Litsios G, Pearman PB, Salamin N. 2015. Measurement errors should always be incorporated in phylogenetic comparative analysis. Methods Ecol. Evol. 6:340–46
    [Google Scholar]
  139. Slater GJ, Harmon LJ, Alfaro ME. 2012. Integrating fossils with molecular phylogenies improves inference of trait evolution. Evolution 66:123931–44
    [Google Scholar]
  140. Stadler T. 2013. How can we improve accuracy of macroevolutionary rate estimates?. Syst. Biol. 62:2321–29
    [Google Scholar]
  141. Stadler T, Rabosky DL, Ricklefs RE, Bokma F. 2014. On age and species richness of higher taxa. Am. Nat 184:444755
    [Google Scholar]
  142. Stanley SM. 1979. Macroevolution: Pattern and Process San Francisco: WH Freeman & Co.
    [Google Scholar]
  143. Uyeda JC, Caetano DS, Pennell MW. 2015. Comparative analysis of principal components can be misleading. Syst. Biol. 64:4677–89
    [Google Scholar]
  144. Uyeda JC, Hansen TF, Arnold SJ, Pienaar J 2011. The million-year wait for macroevolutionary bursts. PNAS 108:3815908–13
    [Google Scholar]
  145. Wayne RK, Van Valkenburgh B, O'Brien SJ. 1991. Molecular distance and divergence time in carnivores and primates. Mol. Biol. Evol. 8:3297–319
    [Google Scholar]
  146. Weir JT, Schluter D. 2008. Calibrating the avian molecular clock. Mol. Ecol. 17:102321–28
    [Google Scholar]
  147. Wertheim JO, Sanderson MJ. 2011. Estimating diversification rates: How useful are divergence times?. Evolution 65:2309–20
    [Google Scholar]
  148. Wiens JJ. 2011. The causes of species richness patterns across space, time, and clades and the role of “ecological limits.”. Q. Rev. Biol. 86:275–96
    [Google Scholar]
  149. Yang Z. 2006. Computational Molecular Evolution Oxford, UK: Oxford Univ. Press
    [Google Scholar]
/content/journals/10.1146/annurev-ecolsys-011921-023644
Loading
/content/journals/10.1146/annurev-ecolsys-011921-023644
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error