How should functional relationships be evaluated using phylogenetic comparative methods? A case study using metabolic rate and body temperature.
Macroevolution
models/simulations
phylogenetics
physiology
Journal
Evolution; international journal of organic evolution
ISSN: 1558-5646
Titre abrégé: Evolution
Pays: United States
ID NLM: 0373224
Informations de publication
Date de publication:
05 2021
05 2021
Historique:
received:
28
05
2020
accepted:
20
01
2021
pubmed:
1
4
2021
medline:
10
11
2021
entrez:
31
3
2021
Statut:
ppublish
Résumé
Phylogenetic comparative methods are often used to test functional relationships between traits. However, million-year macroevolutionary observational datasets cannot definitively prove causal links between traits-correlation does not equal causation and experimental manipulation over such timescales is impossible. Although this caveat is widely understood, it is less appreciated that different phylogenetic approaches imply different causal assumptions about the functional relationships of traits. To make meaningful inferences, it is critical that our statistical methods make biologically reasonable assumptions. Here we illustrate the importance of causal reasoning in comparative biology by examining a recent study by Avaria-Llautureo et al (2019). that tested for the evolutionary coupling of metabolic rate and body temperature across endotherms and found that these traits were unlinked through evolutionary time and that body temperatures were, on average, higher in the early Cenozoic than they are today. We argue that the causal assumptions embedded into their models made it impossible for them to test the relevant functional and evolutionary hypotheses. We reanalyze their data using more biologically appropriate models and find support for the exact opposite conclusions, corroborating previous evidence from physiology and paleontology. We highlight the vital need for causal thinking, even when experiments are impossible.
Banques de données
Dryad
['10.5061/dryad.z612jm6bj']
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
1097-1105Informations de copyright
© 2021 The Authors. Evolution © 2021 The Society for the Study of Evolution.
Références
Avaria-Llautureo, J., C. E. Hernández, E. Rodríguez-Serrano, and C. Venditti. 2019. The decoupled nature of basal metabolic rate and body temperature in endotherm evolution. Nature 572:651-654.
Blomberg, S. P., J. G. Lefevre, J. A. Wells, and M. Waterhouse. 2012. Independent contrasts and PGLS regression estimators are equivalent. Syst. Biol. 61:382-391.
Boucher, F. C., V. Démery, E. Conti, L. J. Harmon, and J. Uyeda. 2017. A general model for estimating macroevolutionary landscapes. Syst. Biol. 67:304-319.
Brown, J. H., J. F. Gillooly, A. P. Allen, V. M. Savage, and G. B. West. 2004. Toward a metabolic theory of ecology. Ecology 85:1771-1789.
Chikina, M., J. D. Robinson, and N. L. Clark. 2016. Hundreds of genes experienced convergent shifts in selective pressure in marine mammals. Mol. Biol. Evol. 33:2182-2192.
Clarke, A., P. Rothery, and N. J. Isaac. 2010. Scaling of basal metabolic rate with body mass and temperature in mammals. J. Anim. Ecol. 79:610-619.
Fangue, N. A., J. E. Podrabsky, L. I. Crawshaw, and P. M. Schulte. 2009. Countergradient variation in temperature preference in populations of killifish Fundulus heteroclitus. Physiol. Biochem. Zool. 82:776-786.
Felsenstein, J. 1985. Phylogenies and the comparative method. Am. Nat. 125:1-15.
Garland, T., P. H. Harvey, and A. R. Ives. 1992. Procedures for the analysis of comparative data using phylogenetically independent contrasts. Syst. Biol. 41:18-32.
Gascuel, O., and M. Steel. 2019. A Darwinian uncertainty principle. Syst. Biol. 69:521-529.
Glazier, D. S. 2005. Beyond the “3/4-power law”: variation in the intra-and interspecific scaling of metabolic rate in animals. Biol. Rev. 80:611-662.
Glazier, D.S. 2010. A unifying explanation for diverse metabolic scaling in animals and plants. Biol. Rev. 85:111-138.
Green, P. J. 1995. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82:711-732.
Grigg, G. C., L. A. Beard, and M. L. Augee. 2004. The evolution of endothermy and its diversity in mammals and birds. Physiol. Biochem. Zool. 77:982-997.
Hansen, T. F. 1997. Stabilizing selection and the comparative analysis of adaptation. Evolution 51:1341-1351.
Hansen, T. F., and K. Bartoszek. 2012. Interpreting the evolutionary regression: the interplay between observational and biological errors in phylogenetic comparative studies. Syst. Biol. 61:413-425.
Hansen, T. F., and E. P. Martins. 1996. Translating between microevolutionary process and macroevolutionary patterns: the correlation structure of interspecific data. Evolution 50:1404-1417.
Ho, L. S. T., and C. Ané. 2014. Intrinsic inference difficulties for trait evolution with Ornstein-Uhlenbeck models. Meth. Ecol. Evol. 5:1133-1146.
Houle, D., C. Pélabon, G. P. Wagner, and T. F. Hansen. 2011. Measurement and meaning in biology. Q. Rev. Biol. 86:3-34.
Hu, Z., T. B. Sackton, S. V. Edwards, and J. S. Liu. 2019. Bayesian detection of convergent rate changes of conserved noncoding elements on phylogenetic trees. Mol. Biol. Evol. 36:1086-1100.
Huey, R. B., and J. G. Kingsolver. 1989. Evolution of thermal sensitivity of ectotherm performance. Trends Ecol. Evol. 4:131-135.
Kolokotrones, T., V. Savage, E. J. Deeds, and W. Fontana. 2010. Curvature in metabolic scaling. Nature 464:753-756.
Li, Y., Z. Liu, P. Shi, and J. Zhang. 2010. The hearing gene prestin unites echolocating bats and whales. Curr. Biol. 20:R55-R56.
Louca, S., and M. W. Pennell. 2020. Extant timetrees are consistent with a myriad of diversification histories. Nature 508:502-505.
Lovegrove, B.G. 2017. A phenology of the evolution of endothermy in birds and mammals. Biol. Rev. 92:1213-1240.
Martins, E. P., and T. F. Hansen. 1997. Phylogenies and the comparative method: a general approach to incorporating phylogenetic information into the analysis of interspecific data. Am. Nat. 149:646-667.
Pagel, M. 1999. Inferring the historical patterns of biological evolution. Nature 401:877-884.
Pélabon, C., C. Firmat, G. H. Bolstad, K. L. Voje, D. Houle, J. Cassara, A. L. Rouzic, and T. F. Hansen. 2014. Evolution of morphological allometry. Ann. N. Y. Acad. Sci. 1320:58-75.
Sackton, T. B., P. Grayson, A. Cloutier, Z. Hu, J. S. Liu, N. E. Wheeler, P. P. Gardner, J. A. Clarke, A. J. Baker, M. Clamp, et al. 2019. Convergent regulatory evolution and loss of flight in paleognathous birds. Science 364:74-78.
Schluter, D. 2016. Speciation, ecological opportunity, and latitude: (American Society of Naturalists Address). Am. Nat. 187:1-18.
Schluter, D., and M. W. Pennell. 2017. Speciation gradients and the distribution of biodiversity. Nature 546:48-55.
Schoolfield, R. M., P. Sharpe, and C. E. Magnuson. 1981. Non-linear regression of biological temperature-dependent rate models based on absolute reaction-rate theory. J. Theor. Biol. 88:719-731.
Schultz, E., K. Reynolds, and D. Conover. 1996. Countergradient variation in growth among newly hatched Fundulus heteroclitus: geographic differences revealed by common-environment experiments. Funct. Ecol. 10:366-374.
Smith, S. D., M. W. Pennell, C. W. Dunn, and S. V. Edwards. 2020. Phylogenetics is the new genetics (for most of biodiversity). Trends Ecol. Evol. 35:415-425.
Uyeda, J. C., D. S. Caetano, and M. W. Pennell. 2015. Comparative analysis of principal components can be misleading. Syst. Biol. 64:677-689.
Uyeda, J. C., M. W. Pennell, E. T. Miller, R. Maia, and C. R. McClain. 2017. The evolution of energetic scaling across the vertebrate tree of life. Am. Nat. 190:185-199.
Uyeda, J. C., R. Zenil-Ferguson, and M. W. Pennell. 2018. Rethinking phylogenetic comparative methods. Syst. Biol. 67:1091-1109.
Venditti, C., A. Meade, and M. Pagel. 2011. Multiple routes to mammalian diversity. Nature 479:393-396.
Voje, K. L., T. F. Hansen, C. K. Egset, G. H. Bolstad, and C. Pelabon. 2014. Allometric constraints and the evolution of allometry. Evolution 68:866-885.
Walsh, B., and M. Lynch. 2018. Evolution and selection of quantitative traits. Oxford Univ. Press, Oxford.
Weir, J. T., and D. Schluter. 2007. The latitudinal gradient in recent speciation and extinction rates of birds and mammals. Science 315:1574-1576.
West, G. B., W. H. Woodruff, and J. H. Brown. 2002. Allometric scaling of metabolic rate from molecules and mitochondria to cells and mammals. Proc. Natl. Acad. Sci. 99:2473-2478.
White, C. R., T. M. Blackburn, and R. S. Seymour. 2009. Phylogenetically informed analysis of the allometry of mammalian basal metabolic rate supports neither geometric nor quarter-power scaling. Evolution 63:2658-2667.
White, C. R., D. J. Marshall, L. A. Alton, P. A. Arnold, J. E. Beaman, C. L. Bywater, C. Condon, T. S. Crispin, A. Janetzki, E. Pirtle, et al. 2019. The origin and maintenance of metabolic allometry in animals. Nat. Ecol. Evol. 3:598-603.