So many different kinds of mistakes: Or why systematic error is the 21st century’s sampling error
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Biology Seminar at Grand Valley State University 10 April 2014
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So many different kinds of mistakes: Or why systematic error is the 21st century’s sampling error
- 1. So many different kinds of mistakes Or why systematic error is the 21st century’s sampling error Liliana M. Dávalos Assistant Professor, Department of Ecology & Evolution SUNY, Stony Brook Grand Valley State University 10 April 2014
- 2. My lab’s research mission Biological diversity Diversification Human impact
- 3. Two kinds of questions Biological diversity Diversification, speciation decrease Habitat lossincrease
- 4. So many kinds of mistakes • Sampling error vs. systematic error • In phylogenetics • How phenotypes evolve • In environmental change • Why we are losing forests?
- 5. So many kinds of mistakes • Sampling error vs. systematic error • In phylogenetics • How phenotypes evolve • In environmental change • Why we are losing forests?
- 6. Thinking about errors • Let’s say we want to answer a question: • In a finite population, what is the frequency of an allele? Sampling vs. systematic
- 7. How to answer this question • We go out, get samples, genotype different individuals • Then we count the alleles • What is the main source of error? Sampling vs. systematic
- 8. This is sampling error • We want to get a better estimate of the allele frequency • => Sample more • We could sample the entire population • => Best possible estimate of allele frequency Sampling vs. systematic
- 9. Now let’s ask a different question • We want to find out how these 3000 microbial lineages relate to one another • We get their genomes, map out each of the single-copy genes, estimate a phylogeny Lang, Darling, Eisen 2013 PLoS One Sampling vs. systematic
- 10. But our results don’t make sense • Is it sampling error? • Can we sample more than the whole genome? • We discover the model of gene evolution we are using was wrong • What kind of error is this? Lang, Darling, Eisen 2013 PLoS One Sampling vs. systematic
- 11. This is systematic error • Even sampling whole genomes won’t fix the problem • Having more data can make the problem worse! • As long as we don’t change the model, we will keep obtaining the wrong answer Lang, Darling, Eisen 2013 PLoS One Sampling vs. systematic
- 12. So many kinds of mistakes • Sampling error vs. systematic error • In phylogenetics • How phenotypes evolve • In environmental change • Why we are losing forests?
- 13. Mycobacterium bovis BCG str. Pasteur 1173P2 M. tuberculosis H37Ra M. bovis BCG str. Tokyo 172 M. bovis AF212297 M. tuberculosis CDC1551 M. tuberculosis F11 M. tuberculosis KZN 1435 M. tuberculosis H37Rv M. avium subsp. paratuberculosis K10 M. avium 104 M. vanbaalenii PYR1 M. sp. Spyr1 M. smegmatis str. MC2 155 M. sp. KMS M. sp. MCS M. sp JLS Mycobacterium sp. * Nocardia farcinica IFM 10152 Gordonia bronchialis DSM 43247 Rhodococcus opacus B4 R. equi ATCC 33707 R. equi 103S Segniliparus rotundus DSM 44985 Bifidobacterium longum NCC2705 B. longum DJO10A B. longum subsp. infantis 157F B. longum subsp. longum JCM 121 B. longum subsp. longum BBMN68 B. longum subsp. infantis ATCC 558 B. longum subsp. longum JDM301 B. longum subsp. infantis ATCC 156 B. breve DSM 20213 B. dentium Bd1 B. dentium ATCC 100 100 84 96 42 100 63 63 65 55 84 10074 51 70 98 92 99 74 100 100 75 99 100 20 88 pathogenic (avium non-pathogenic Mycobacterium sme Phylogenetics • Testing relatedness • All of comparative biology • Historical biogeography • Evolutionary aspects of community ecology • Diagnostics and similar applications Corthals...Dávalos 2012 PLoS One How phenotypes evolve
- 14. Dated trees more important than ever • Dated trees need fossils • Why use dated trees? • Trait evolution • History of assemblages in time and space • Key innovations Dumont, Dávalos et al. 2012 P R Soc B How phenotypes evolve
- 15. • We use morphological characters • How good are the models of evolution for morphological characters? • Characteristics of the data • Compare to models molecular evolution Fossils without genomes Dávalos & Russell 2012 Ecol Evol How phenotypes evolve
- 16. Species Characters These are morphological characters • They look like this —> • Discontinuous between species • Factors, not numbers • Difficult to model How phenotypes evolve
- 17. The organisms in question New World Leaf-nosed bats and relatives How phenotypes evolve
- 18. Diphylla Diaemus Desmodus Brachyphylla Erophylla Phyllonycteris Platalina Lonchophylla Lionycteris Monophyllus Glossophaga Leptonycteris Anoura Hylonycteris Lichonycteris Scleronycteris Choeroniscus Musonycteris Choeronycteris Phylloderma Phyllostomus Macrophyllum Lonchorhina Mimon crenulatum Mimon bennettii Trachops Tonatia Chrotopterus Vampyrum Trinycteris Glyphonycteris Lampronycteris Macrotus Micronycteris minuta Micronycteris hirsuta Micronycteris megalotis Rhinophylla Carollia Sturnira Enchisthenes hartii Artibeus concolor Artibeus jamaicensis Artibeus cinereus Uroderma Platyrrhinus Vampyrodes Chiroderma Vampyressa bidens Vampyressa nymphaea Vampyressa pusilla Ectophylla Mesophylla Ametrida Centurio Sphaeronycteris Pygoderma Phyllops Stenoderma Ariteus Ardops ≥ 75% < 50% ≥ 50 & < 75% MP bootstrap Macrotus Lampronycteris Micronycteris minuta Micronycteris schmidtorum Micronycteris hirsuta Microncyteris megalotis Diphylla Diaemus Desmodus Lonchorhina Macrophyllum Trachops Chrotopterus Vampyrum Lophostoma Tonatia Phylloderma Phyllostomus Mimon Anoura Hylonycteris Choeroniscus Musonycteris Choeronycteris Erophylla Brachyphylla Monophyllus Glossophaga Leptonycteris Lonchophylla Lionycteris Carollia Trinycteris Glyphonycteris daviesi Glyphonycteris sylvestris Rhinophylla Sturnira Mesophylla Vampyressa Platyrrhinus Vampyrodes Uroderma Vampyressa bidens Vampyressa brocki Chiroderma Enchisthenes Ectophylla Artibeus Dermanura Ariteus Ardops Stenoderma Centurio Pygoderma Ametrida Sphaeronycteris < 0.97 ≥ 0.97 BYS posterior probability Baker et al. 2003 Occas Pap Mus TTU Dávalos, Cirranello et al. 2012 Biol Rev Wetterer et al. 2000 B Am Mus Nat Hist How phenotypes evolve
- 19. The trouble with morphological characters • At first, only model was parsimony • Neutral Jukes-Cantor 1969 model implemented 2001 • Current model has gamma variation across characters • Applying this model does not solve conflict Dávalos, Cirranello et al. 2012 Biol Rev How phenotypes evolve
- 20. If the Jukes-Cantor model yields conflicting answer, could the model be inadequate given these data?
- 21. q p Homoplasy I: inconsistency! q pp Felsenstein 1978 Syst Biol How phenotypes evolve consistent Non consistent
- 22. A B Background selection PercentcodonsofCYTBineachcodontype 0 20 40 60 Background selection Selection shift Significantly support Significantly reject Reject Support Type of codon Amino acid position in alignment -3 -2 -1 0 1 2 3 100 200 300 400 500 Significant support or rejection Selection shift Selection shift in Glossophaginae Type of codon CYTB COX1 Homoplasy II: ecological convergence • Can bring together unrelated ecologically similar lineages • This example: mt cytochrome b gene of nectar-feeding bats • Association adaptive molecular evolution and supporting wrong node Dávalos, Cirranello et al. 2012 Biol Rev How phenotypes evolve
- 23. Homoplasy III: correlated evolution • Expected in protein- coding genes • Models in use for codons, aminoacids, ribosomal RNA secondary structure Dávalos & Perkins 2008 Genomics How phenotypes evolve
- 24. Might these affect morphological characters? Reviewer 1: I don't see the point. If the characters are good characters (meaning that they have some phylogenetic signal at some level), then there is nothing especially wrong with the fact that they are weighted a little more than other characters. How phenotypes evolve
- 25. Dávalos, Cirranello et al. 2012 Biol Rev Inconsistency! How phenotypes evolve
- 26. Dental characters ● ● Mandibular Maxillary ● Canine Incisors Molars Premolars ● Significant A −2 0 2 Supportfornectar−feedingclade ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● A Background selection Amino acid position in alignment Supportfornectar-feedingclade -3 -2 -1 0 1 2 3 100 200 300 400 500 Significant support or rejection Selection shift Selection shift in Glossophaginae Type of codon CYTB COX1 Dávalos et al. In Press Syst Biol Dávalos, Cirranello et al. 2012 Biol Rev Convergent evolution! How phenotypes evolve
- 27. Correlated evolution! How phenotypes evolve Dissimilarity between characters -> ‘
- 28. Models incur systematic error • Morphology = phenotype • Neutrality and independence wrong for models • Not neutral • Not independent Skelly et al. 2013 Genome Res How phenotypes evolve
- 29. How does morphology evolve? • Ordering: each character state gives rise to a finite range of states • There are limits to states because of • Development • Natural selection Dávalos, Cirranello et al. 2012 Biol Rev How phenotypes evolve
- 30. Modeling selection in morphology • Brownian motion vs. Ornstein-Uhlenbeck models • Continuous phenotypic traits • Might selection explain homoplasy in morphological data? How phenotypes evolve Butler & King 2004 Am Nat
- 31. A BB C D nectarivorous other frugivorous (figs) other frugivorous (figs) other nectarivorous frugivorous (figs) other nectarivorous strictly frugivorous (figs, Short-faced bats) Figur Ardops Ariteus Carollia Diphylla Mimon Tonatia Sturnira Ametrida Centurio Pygoderma Sphaeronycteris Stenoderma Lonchophylla Chrotopterus Desmodus Diaemus Lampronycteris Lophostoma Macrotus Micronycteris Phylloderma Phyllostomus Rhinophylla Trachops Vampyrum Artibeus Chiroderma Ectophylla Enchisthenes Mesophylla Platyrrhinus Uroderma Vampyressa Vampyrodes Metavampyressa Lonchophylla Platalina Anoura Choeroniscus Choeronycteris Hylonycteris Erophylla Glossophaga Leptonycteris Monophyllus Phyllonycteris Brachyphylla Dumont ... Dávalos 2014 Evolution Engineering model of performance How phenotypes evolve
- 32. 0 100 200 300 400 500 0.0 0.4 0.8 1.2 MA count diet figs figs only nectar other • Performance related to diet • Low mechanical advantage in nectar- feeding bats • Convergence on this phenotype • Analyzing function and integrating selection better than ignoring Three performance peaks How phenotypes evolve Mechanical advantage Frequency Dumont ... Dávalos 2014 Evolution
- 33. Morphology ... Aminoacids Codons How phenotypes evolve Neutral genotype Model complexity How phenotypes evolve
- 34. The trouble with systematic error • In sampling error mode • More is more • More characters • = thousands of correlated phenotypes • This will fail, we have systematic error • Improve model • Improve data • Reduce data
- 35. So many kinds of mistakes • Sampling error vs. systematic error • In phylogenetics • How phenotypes evolve • In environmental change • Why we are losing forests?
- 36. My lab’s research mission Biological diversity Diversification Human impact
- 37. Why do rainforests decline? Three hypotheses Hamburger! (or steak) Kaimowitz et al. 2004 CIFOR Coca Dávalos et al. 2011 Environ Sci Technol Land tenure and property Hecht 1993 BioScience Why lose forests?
- 38. Predictions Hamburger! (or steak) Kaimowitz et al. 2004 CIFOR Coca Dávalos et al. 2011 Environ Sci Technol Land tenure and property Hecht 1993 BioScience Why lose forests? + demand beef + beef, + cattle + cattle, + pasture + pasture, - forest + demand cocaine + cocaine, + coca + coca, - forest + demand land + pasture, + cattle + cattle, - forest
- 39. The real drivers of habitat loss Forest, coca nothing Eradicationdecrease Urbanization & Development Dávalos et al. 2014 Biol Cons becomes Pasture & Cows isproperty Why lose forests?
- 40. These systematic errors are scary • Models inform policy • Real decisions are made based on these inadequate models • Models influence what data we collect • If we focus on cattle and the problem is palm, we are missing the real story
- 41. Shifting to the present • 20th century challenge • Collecting enough data • i.e., sampling • Still relevant in many cases • New challenges • Formulating models • “Big” data • Correlated data • Otherwise biased data Fjeldsa et al. 2005 Ambio
- 42. • Funding • NSF–DEB, CIDER–SBU • Speciation & diversification: A. Cirranello, A. Russell, N. Simmons, P. Velazco • Functional evolution: E. Dumont, S. Rossiter, E. Teeling • Conservation & policy: D. Armenteras, A. Bejarano, A. Corthals, L. Correa, J. Holmes, N. Rodriguez, C. Romero • Dávalos Lab • Phylogenetics: R. Dahan, S. DelSerra, A. Goldberg, O. Warsi, L. Yohe, X. Zhang • Land use: P. Connell, M. Hall, E. Simola, G. Tudda, Y. Shah Thanks!