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A Cautionary Note on Phylogenetic Signal Estimation from Imputed Databases

Abstract

Given the prevalence of missing data on species’ traits – the Raunkiaeran shortfall-, several methods have been proposed to fill sparse databases. However, analyses based on these imputed databases can introduce several biases. Here, we evaluated potential estimation biases caused by the use of imputed databases. In the evaluation, we considered the estimation of descriptive statistics, regression coefficient, and phylogenetic signal for different missing and imputing scenarios. We found that percentage of missing data, missing mechanisms and imputation methods were important in determining estimation errors. Imputation errors are not linearly related to estimate errors. Adding phylogenetic information provides better estimates of the evaluated statistics, but this information should be combined with other variables such as traits correlated to the missing data variable. Using an empirical dataset, we found that even traits that are strongly correlated to each other, such as brain and body size of primates, can produce biases when estimating phylogenetic signal from missing data datasets. We advise researchers to share both their raw and imputed data as well as to consider the pattern of missing data to evaluate methods that perform better for their goals. In addition, the performance of imputation methods should be mainly based on statistical estimates instead of only in imputation error.

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Data Availability

Not applicable.

Code Availability

All scripts of simulations, analyses and graphics are archived in https://github.com/lucas-jardim/imputation_evolutionary_biology.

References

  1. Barzi, F. (2004). Imputations of missing values in practice: Results from imputations of serum cholesterol in 28 cohort studies. American Journal of Epidemiology, 160(1), 34–45. https://doi.org/10.1093/aje/kwh175

    Article  PubMed  Google Scholar 

  2. Beaulieu, J. M., Jhwueng, D. C., Boettiger, C., & O’Meara, B. C. (2012). Modeling stabilizing selection: Expanding the Ornstein-Uhlenbeck model of adaptive evolution. Evolution, 66(8), 2369–2383. https://doi.org/10.1111/j.1558-5646.2012.01619.x

    Article  PubMed  Google Scholar 

  3. Blackwell, M., Honaker, J., & King, G. (2017). A unified approach to measurement error and missing data: Overview and applications. Sociological Methods and Research, 46(3), 303–341. https://doi.org/10.1177/0049124115585360

    Article  Google Scholar 

  4. Blomberg, S. P., Garland, T., & Ives, A. R. (2003). Testing for phylogenetic signal in comparative data: Behavioral traits are more labile. Evolution, 57(4), 717–745. https://doi.org/10.1111/j.0014-3820.2003.tb00285.x

    Article  PubMed  Google Scholar 

  5. Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel inference (2nd ed.). Springer-Verlag.

    Google Scholar 

  6. Butler, M. A., & King, A. A. (2004). Phylogenetic comparative analysis: A modeling approach for adaptive evolution. The American Naturalist, 164(6), 683–695. https://doi.org/10.1086/426002

    Article  PubMed  Google Scholar 

  7. Cavender-Bares, J., Kozak, K. H., Fine, P. V. A., & Kembel, S. W. (2009). The merging of community ecology and phylogenetic biology. Ecology Letters, 12, 693–715. https://doi.org/10.1111/j.1461-0248.2009.01314.x

    Article  PubMed  Google Scholar 

  8. DeCasien, A. R., Williams, S. A., & Higham, J. P. (2017). Primate brain size is predicted by diet but not sociality. Nature Ecology & Evolution, 1, 0112. https://doi.org/10.1038/s41559-017-0112

    Article  Google Scholar 

  9. Diniz-Filho, J. A. F. (2001). Phylogenetic autocorrelation under distinct evolutionary process. Evolution, 55(6), 1104–1109.

    CAS  Article  Google Scholar 

  10. Diniz-Filho, J. A. F., Sant’Ana, C. E. R., & Bini, L. M. (1998). An eigenvector method for estimating phylogenetic inertia. Evolution, 52(5), 1247–1262.

    Article  Google Scholar 

  11. Diniz-Filho, J. A. F., Villalobos, F., & Bini, L. M. (2015). The best of both worlds: Phylogenetic eigenvector regression and mapping. Genetics and Molecular Biology, 38(3), 396–400

    Article  Google Scholar 

  12. Etard, A., Morrill, S., & Newbold, T. (2020). Global gaps in trait data for terrestrial vertebrates. Global Ecology and Biogeography, 29(12), 2143–2158. https://doi.org/10.1111/geb.13184

    Article  Google Scholar 

  13. Enders, C. K. (2010). Applied missing data analysis (1st ed.). Guilford Press.

  14. Felsenstein, J. (1985). Phylogenies and the comparative method. The American Naturalist, 125(1), 1–15.

    Article  Google Scholar 

  15. Felsenstein, J. (2008). Comparative methods with sampling error and within-species variation: Contrasts revisited and revised. American Naturalist, 171(6), 713–725. https://doi.org/10.1086/587525

    Article  Google Scholar 

  16. Freckleton, R. P., Harvey, P. H., & Pagel, M. (2002). Phylogenetic analysis and comparative data: A test and review of evidence. The American Naturalist, 160(6), 712–726.

    CAS  Article  Google Scholar 

  17. Freckleton, R. P., & Jetz, W. (2009). Space versus phylogeny: Disentangling phylogenetic and spatial signals in comparative data. Proceedings of the Royal Society B, 276(1654), 21–30. https://doi.org/10.1098/rspb.2008.0905

    Article  PubMed  Google Scholar 

  18. Gaston, K. J., & Blackburn, T. M. (1994). Are newly described bird species Small-bodied ? Biodiversity Letters, 2(1), 16–20.

    Article  Google Scholar 

  19. Gaston, K. J., Chown, S. L., & Evans, K. L. (2008). Ecogeographical rules: Elements of a synthesis. Journal of Biogeography, 35, 483–500. https://doi.org/10.1111/j.1365-2699.2007.01772.x

    Article  Google Scholar 

  20. Gillespie, D. (1996). Exact numerical simulation of the Ornstein-Uhlenbeck process and its integral. Physical Review E, 54(2), 2084–2091. https://doi.org/10.1103/PhysRevE.54.2084

    CAS  Article  Google Scholar 

  21. Gittleman, J. L., & Kot, M. (1990). Adaptation: Statistics and a null model for estimating phylogenetic effects. Systematic Zoology, 39(3), 227–241.

    Article  Google Scholar 

  22. Goldberg, E. E., Kohn, J. R., Lande, R., Robertson, K. A., Smith, S. A., & Igic, B. (2010). Species selection maintains self-incompatibility. Science, 330(493), 493–495. https://doi.org/10.1126/science.1194513

    CAS  Article  PubMed  Google Scholar 

  23. Gonzalez-Suarez, M., Lucas, P. M., & Revilla, E. (2012). Biases in comparative analyses of extinction risk: Mind the gap. The Journal of Animal Ecology, 81, 1211–1222. https://doi.org/10.1111/j.1365-2656.2012.01999.x

    Article  PubMed  Google Scholar 

  24. Goolsby, E. W., Bruggeman, J., & Ané, C. (2017). Rphylopars: Fast multivariate phylogenetic comparative methods for missing data and within-species variation. Methods in Ecology and Evolution, 8(1), 22–27. https://doi.org/10.1111/2041-210X.12612

    Article  Google Scholar 

  25. Grabowski, M., Voje, K. L., & Hansen, T. F. (2016). Evolutionary modeling and correcting for observation error support a 3/5 brain-body allometry for primates. Journal of Human Evolution, 94, 106–116. https://doi.org/10.1016/j.jhevol.2016.03.001

    Article  PubMed  Google Scholar 

  26. Graham, J. W., Olchowski, A. E., & Gilreath, T. D. (2007). How many imputations are really needed? Some practical clarifications of multiple imputation theory. Prevention Science, 8, 206–213. https://doi.org/10.1007/s11121-007-0070-9

    Article  PubMed  Google Scholar 

  27. Guénard, G., Legendre, P., & Peres-Neto, P. (2013). Phylogenetic eigenvector maps: A framework to model and predict species traits. Methods in Ecology and Evolution, 4(12), 1120–1131. https://doi.org/10.1111/2041-210X.12111

    Article  Google Scholar 

  28. Hadfield, J. D. (2008). Estimating evolutionary parameters when viability selection is operating. Proceedings of the Royal Society B: Biological Sciences, 275(1635), 723–734. https://doi.org/10.1098/rspb.2007.1013

    Article  PubMed  Google Scholar 

  29. Hagen, O., Hartmann, K., Steel, M., & Stadler, T. (2015). Age-dependent speciation can explain the shape of empirical phylogenies. Systematic Biology, 64(3), 432–440. https://doi.org/10.1093/sysbio/syv001

    Article  PubMed  PubMed Central  Google Scholar 

  30. Hansen, T. F. (1997). Stabilizing selection and the comparative analysis of adaptation. Evolution, 51(5), 1341–1351

    Article  Google Scholar 

  31. Hansen, T. F., & Martins, E. P. (1996). Translating between microevolutionary process and macroevolutionary patterns: Correlation structure of interspecific data. Evolution, 50(4), 1404–1417

    Article  Google Scholar 

  32. Hardy, O. J., & Pavoine, S. (2012). Assessing phylogenetic signal with measurement error: A comparison of mantel tests, Blomberg et al.’s K, and phylogenetic distograms. Evolution, 66(8), 2614–2621. https://doi.org/10.1111/j.1558-5646.2012.01623.x

    Article  PubMed  Google Scholar 

  33. Harmon, L. J., Losos, J. B., Jonathan Davies, T., Gillespie, R. G., Gittleman, J. L., Bryan Jennings, W., Kozak, K. H., McPeek, M. A., Moreno-Roark, F., Near, T. J., Purvis, A., Ricklefs, R. E., Schluter, D., Schulte II, J. A., Seehausen, O., Sidlauskas, B. L., Torres-Carvajal, O., Weir, J. T., & Mooers, A. Ø. (2010). Early burst of body size and shape evolution are rare in comparative data. Evolution, 64(8), 2385-2396. https://doi.org/10.1111/j.1558-5646.2010.01025.x

    Article  PubMed  Google Scholar 

  34. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning. Springer

    Book  Google Scholar 

  35. Hortal, J., de Bello, F., Diniz-Filho, J. A. F., Lewinsohn, T. M., Lobo, J. M., & Ladle, R. J. (2015). Seven shortfalls that beset large-scale knowledge of biodiversity. Annual Review of Ecology, Evolution, and Systematics, 46, 523–549. https://doi.org/10.1146/annurev-ecolsys-112414-054400

    Article  Google Scholar 

  36. Ives, A. R., Midford, P. E., & Garland, T. (2007). Within-species variation and measurement error in phylogenetic comparative methods. Systematic Biology, 56(2), 252–270. https://doi.org/10.1080/10635150701313830

    Article  PubMed  Google Scholar 

  37. Jetz, W., & Freckleton, R. P. (2015). Towards a general framework for predicting threat status of data-deficient species from phylogenetic, spatial and environmental information. Philosophical Transactions of the Royal Society B: Biological Sciences, 370(1662), 20140016. https://doi.org/10.1098/rstb.2014.0016

  38. Johnson, T. F., Isaac, N. J. B., Paviolo, A., & González-Suárez, M. (2020). Handling missing values in trait data. Global Ecology and Biogeography, 30(1), 51–62. https://doi.org/10.1111/geb.13185

    Article  Google Scholar 

  39. Jones, K. E., Bielby, J., Cardillo, M., Fritz, S. A., O’Dell, J., Orme, C. D. L., Safi, K., Sechrest, W., Boakes, E. H., Carbone, C., Connolly, C., Cutts, M. J., Foster, J. K., Grenyer, R., Habib, M., Plaster, C. A., Price, S. A., Rigby, E. A., Rist, J., … Purvis, A. (2009). PanTHERIA: a species-level database of life history, ecology, and geography of extant and recently extinct mammals. Ecology, 90(9), 2648–2648. https://doi.org/10.1890/08-1494.1

    Article  Google Scholar 

  40. Kattge, J., Ogle, K., Bönisch, G., Díaz, S., Lavorel, S., Madin, J., Nadrowski, K., Nöllert, S., Sartor, K., & Wirth, C. (2011). A generic structure for plant trait databases. Methods in Ecology and Evolution, 2, 202–213. https://doi.org/10.1111/j.2041-210X.2010.00067.x

    Article  Google Scholar 

  41. Kim, S. W., Blomberg, S. P., & Pandolfi, J. M. (2018). Transcending data gaps: A framework to reduce inferential errors in ecological analyses. Ecology Letters, 21(8), 1200–1210. https://doi.org/10.1111/ele.13089

    Article  PubMed  Google Scholar 

  42. Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data (2nd ed.). Wiley.

    Book  Google Scholar 

  43. Lukas, D., & Clutton-Brock, T. H. (2013). The evolution of social monogamy in mammals. Science, 341, 526–530. https://doi.org/10.1126/science.1238677

    CAS  Article  Google Scholar 

  44. Marcondes, R. S. (2019). Realistic scenarios of missing taxa in phylogenetic comparative methods and their effects on model selection and parameter estimation. PeerJ, 7(10), e7917. https://doi.org/10.7717/peerj.7917

    Article  PubMed  PubMed Central  Google Scholar 

  45. Meng, X.-L. (1994). Multiple-Imputation inference with uncogenial sources of input. Statistical Science, 9(4), 538–573.

    Google Scholar 

  46. Münkemüller, T., Lavergne, S., Bzeznik, B., Dray, S., Jombart, T., Schiffers, K., & Thuiller, W. (2012). How to measure and test phylogenetic signal. Methods in Ecology and Evolution, 3, 743–756. https://doi.org/10.1111/j.2041-210X.2012.00196.x

    Article  Google Scholar 

  47. Nakagawa, S. (2015). Missing data: Mechanisms, methods, and messages. In G. A. Fox, S. Negrete-Yankelevich, & V. J. Sosa (Eds.), Ecological statistics: Contemporary theory and application (1st ed., pp. 81–105). Oxford University Press.

    Chapter  Google Scholar 

  48. Nakagawa, S., & Freckleton, R. P. (2008). Missing inaction: The dangers of ignoring missing data. Trends in Ecology & Evolution, 23(11), 592–596. https://doi.org/10.1016/j.tree.2008.06.014

    Article  Google Scholar 

  49. Nakagawa, S., & Freckleton, R. P. (2010). Model averaging, missing data and multiple imputation: A case study for behavioural ecology. Behavioral Ecology and Sociobiology, 65(1), 103–116. https://doi.org/10.1007/s00265-010-1044-7

    Article  Google Scholar 

  50. Nakagawa, S., & De Villemereuil, P. (2019). A general method for simultaneously accounting for phylogenetic and species sampling uncertainty via rubin’s rules in comparative analysis. Systematic Biology, 68(4), 632–641. https://doi.org/10.1093/sysbio/syy089

    Article  PubMed  Google Scholar 

  51. Norman, K. E. A., Chamberlain, S., & Boettiger, C. (2020). Taxadb: A high-performance local taxonomic database interface. Methods in Ecology and Evolution, 11(9), 1153–1159. https://doi.org/10.1111/2041-210X.13440

    Article  Google Scholar 

  52. Oliveira, B. F., Machac, A., Costa, G. C., Brooks, T. M., Davidson, A. D., Rondinini, C., & Graham, C. H. (2016). Species and functional diversity accumulate differently in mammals. Global Ecology and Biogeography, 25(9), 1119–1130. https://doi.org/10.1111/geb.12471

    Article  Google Scholar 

  53. Paradis, E., Claude, J., & Strimmer, K. (2004). APE: Analyses of phylogenetics and evolution in R language. Bioinformatics, 20(2), 289–290. https://doi.org/10.1093/bioinformatics/btg412

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  54. Penone, C., Davidson, A. D., Shoemaker, K. T., Marco, M. D., Rondinini, C., Brooks, T. M., Young, B. E., Graham, C. H., & Costa, G. C. (2014). Imputation of missing data in life-history traits datasets: Which approach performs the best? Methods in Ecology and Evolution, 5(9), 961–970. https://doi.org/10.1111/2041-210X.12232

    Article  Google Scholar 

  55. Purvis, A., Gittleman, J. L., Cowlishaw, G., & Mace, G. M. (2000). Predicting extinction risk in declining species. Proceeding of the Royal Society B, 267, 1947–1952. https://doi.org/10.1098/rspb.2000.1234

    CAS  Article  Google Scholar 

  56. R Core Team. (2020). R: A language and environment for statistical computing. Vienna, Austria. https://www.r-project.org/

  57. Rabosky, D. L. (2015). No substitute for real data: A cautionary note on the use of phylogenies from birth – death polytomy resolvers for downstream comparative analyses. Evolution, 62(12), 3207–3216. https://doi.org/10.1111/evo.12817

    Article  Google Scholar 

  58. Reddy, S., & Dávalos, L. M. (2003). Geographical sampling bias and its implications for conservation priorities in Africa. Journal of Biogeography, 30, 1719–1727.

    Article  Google Scholar 

  59. Reichman, O. J., Jones, M. B., & Schildhauer, M. P. (2011). Challenges and opportunities of open data in ecology. Science, 331(6018), 703–705. https://doi.org/10.1126/science.1197962

    CAS  Article  PubMed  Google Scholar 

  60. Revell, L. J. (2012). phytools: An R package for phylogenetic comparative biology (and other things). Methods in Ecology and Evolution, 3(2), 217–223. https://doi.org/10.1111/j.2041-210X.2011.00169.x

    Article  Google Scholar 

  61. Revell, L. J., Harmon, L. J., & Collar, D. C. (2008). Phylogenetic signal, evolutionary process, and rate. Systematic Biology, 57(4), 591–601. https://doi.org/10.1080/10635150802302427

    Article  PubMed  Google Scholar 

  62. Rosado, B. H., Figueiredo, M. S., de Mattos, E. A., & Grelle, C. E. (2015). Eltonian shortfall due to the Grinnellian view: Functional ecology between the mismatch of niche concepts. Ecography, 39(11), 1034–1041. https://doi.org/10.1111/ecog.01678

    Article  Google Scholar 

  63. Rubin, D. (1976). Inference and missing data. Biometrika, 63(3), 581–592.

    Article  Google Scholar 

  64. Schafer, J. L., & Graham, J. W. (2002). Missing Data: Our view of the state of the art. Psychological Methods, 7(2), 147–177.

    Article  Google Scholar 

  65. Schrodt, F., Kattge, J., Shan, H., Fazayeli, F., Joswig, J., Banerjee, A., Reichstein, M., Bönisch, G., Díaz, S., Dickie, J., Gillison, A., Karpatne, A., Lavorel, S., Leadley, P., Wirth, C. B., Wright, I. J., Wright, S. J., & Reich, P. B. (2015). BHPMF a hierarchical Bayesian approach to gap-filling and trait prediction for macroecology and functional biogeography. Global Ecology and Biogeography, 24(12), 1510–1521. https://doi.org/10.1111/geb.12335

    Article  Google Scholar 

  66. Slater, G. J., Harmon, L. J., Wegmann, D., Joyce, P., Revell, L. J., & Alfaro, M. E. (2012). Fitting models of continuous trait evolution to incompletely sampled comparative data using approximate bayesian computation. Evolution, 66, 752–762. https://doi.org/10.1111/j.1558-5646.2011.01474.x

    Article  PubMed  Google Scholar 

  67. Springer, M. S., Meredith, R. W., Gatesy, J., Emerling, C. A., Park, J., Rabosky, D. L., Stadler, T., Steiner, C., Ryder, O. A., Janecka, J. E., Fisher, C. A., & Murphy, W. J. (2012). Macroevolutionary dynamics and historical biogeography of primate diversification inferred from a species supermatrix. PLoS One, 7(11), e49521. https://doi.org/10.1371/journal.pone.0049521

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  68. Swenson, N. G. (2014). Phylogenetic imputation of plant functional trait databases. Ecography, 37, 105–110. https://doi.org/10.1111/j.1600-0587.2013.00528.x

    Article  Google Scholar 

  69. Swenson, N. G., Weiser, M. D., Mao, L., Araújo, M. B., Diniz-Filho, J. A. F., Kollmann, J., Nogués-Bravo, D., Normand, S., Rodríguez, M. A., García-Valdés, R., Valladares, F., Zavala, M. A., & Svenning, J.-C. (2017). Phylogeny and the prediction of tree functional diversity across novel continental settings. Global Ecology and Biogeography, 26(5), 553–562. https://doi.org/10.1111/geb.12559

    Article  Google Scholar 

  70. Therneau, T., & Atkinson, B. (2019). rpart: Recursive partitioning and regression trees. https://cran.r-project.org/package=rpart

  71. Upham, N. S., Esselstyn, J. A., & Jetz, W. (2019). Inferring the mammal tree: Species-level sets of phylogenies for questions in ecology, evolution, and conservation. PLOS Biology, 17(12), e3000494. https://doi.org/10.1371/journal.pbio.3000494

  72. van Buuren, S. (2012). Flexible imputation of missing data (1st ed.). Chapman and Hall/CRC.

    Book  Google Scholar 

  73. van Buuren, S., & Groothuis-Oudshoorn, K. (2011). Mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3). https://doi.org/10.18637/jss.v045.i03

  74. van Buuren, S., Brands, J. P. L., Groothuis-Oudshoorn, K., & Rubin, D. B. (2006). Fully conditional specification in multivariate imputation. Journal of Statistical Computation and Simulation, 76(12), 1049–1064.

    Article  Google Scholar 

  75. Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with S (4th ed.). Springer. http://www.stats.ox.ac.uk/pub/MASS4.

  76. Vilela, B., & Villalobos, F. (2015). LetsR: A new R package for data handling and analysis in macroecology. Methods in Ecology and Evolution, 6, 1229–1234. https://doi.org/10.1111/2041-210X.12401

    Article  Google Scholar 

  77. Vilela, B., Villalobos, F., Rodríguez, M. Á., & Terribile, L. C. (2014). Body size, extinction risk and knowledge bias in New World snakes. PLoS One, 9(11), e113429. https://doi.org/10.1371/journal.pone.0113429

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  78. von Hippel, P. T. (2009). How to impute squares, interactions, and other transformed variables. Sociological Methodology, 39, 265–291. https://doi.org/10.1111/j.1467-9531.2009.01215.x

    Article  Google Scholar 

  79. von Hippel, P. T. (2018). How many imputations do you need? A two-stage calculation using a quadratic rule. Sociological Methods & Research. https://doi.org/10.1177/0049124117747303

    Article  Google Scholar 

  80. Webb, C. O., Ackerly, D. D., Mcpeek, M. A., & Donoghue, M. J. (2002). Phylogenies and community ecology. Annual Review of Ecology, Evolution, and Systematics, 33, 475–505. https://doi.org/10.1146/annurev.ecolsys.33.010802.150448

    Article  Google Scholar 

  81. Wiens, J. J., & Graham, C. H. (2005). Niche conservatism: Integrating evolution, ecology, and conservation biology. Annual Review of Ecology, Evolution, and Systematics, 36, 519–539. https://doi.org/10.1146/annurev.ecolsys.36.102803.095431

    Article  Google Scholar 

  82. Wilman, H., Belmaker, J., Simpson, J., de la Rosa, C., Rivadeneira, M. M., & Jetz, W. (2014). EltonTraits 10: Species-level foraging attributes of the world’s birds and mammals. Ecology, 95, 2027.

    Article  Google Scholar 

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Acknowledgements

We thank G. Guénard for advices on PEM and L. Revell for help on evolutionary simulations. We are also grateful to Tiago Quental, Matheus Ribeiro, Daniel Paiva, João Nabout, Cristian Dambros, Tiago Freitas, Bruno R. Ribeiro, Gracielle Higino, Pablo Silva, and Adriana Diaz for comments that improved this study. L.J. was supported by a CAPES doctoral fellowship. FV was supported by a BJT “Science without borders” CNPq grant while conducting this study and currently by CONACYT (Ciencia Básica A1-S-34563). L.M.B. and J.A.F.D.F. are continuously supported by CNPq productivity grants. This paper was developed in the context of the National Institutes for Science and Technology (INCT) project in “Ecology, Evolution and Biodiversity Conservation”, supported by MCTIC/CNPq (proc. 465610/2014-5) and FAPEG (proc. 201810267000023).

Funding

This work was supported by CAPES doctoral fellowship, BJT “Science without borders” CNPq grant, MCTIC/CNPq (proc. 465610/2014-5), FAPEG (proc. 201810267000023), and CONACYT Ciencia Básica (A1-S-34563).

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Jardim, L., Bini, L.M., Diniz-Filho, J.A.F. et al. A Cautionary Note on Phylogenetic Signal Estimation from Imputed Databases. Evol Biol 48, 246–258 (2021). https://doi.org/10.1007/s11692-021-09534-0

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Keywords

  • Statistical bias
  • Multiple Imputation
  • Trait databases
  • Phylogenetic Eigenvector Maps
  • Phylogenetic signal
  • Phylogenetic Comparative Methods