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Hierarchical Structuring of Trait Data

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Title: Hierarchical Structuring of Trait Data


1
Hierarchical Structuring of Trait Data
  • Bot 940 Evidence for Evolution
  • Eric Caldera

2
Scientific Method
  1. Observation and description Organisms seem to
    have changed over time
  2. Formulation of a Hypothesis Evolution by decent
    with modification / common ancestry
  3. Predictions Biological traits should appear in a
    nested hierarchical structure groups within
    groups
  4. Experimental test of predictions Do traits
    suspected to have arisen by decent with
    modification show a greater degree of
    hierarchical structure?

3
Nested hierarchical structure groups within
groups
4
Note that we dont see overlap across groups
Example there are no fungi with vascular
tissue, insects with four limbs or amphibians
with vascular tissueetc.
5
Why do we predict nested hierarchical structure?
  • Only branching evolutionary processes are
    capable of generating nested hierarchical
    structure.
  • For example, human languages, which have common
    ancestors and are derived by descent with
    modification, generally can be classified in
    objective nested hierarchies (Pei 1949 Ringe
    1999).

6
  • Only certain things can be classified objectively
    in a consistent, unique nested hierarchy.
  • The difference drawn here is between "subjective"
    and "objective.
  • Anything can be grouped into hierarchies (for
    example, automobiles), but the importance of
    characters must be weighted subjectively.

7
Subjective grouping of automobiles
three wheels
three wheels
four wheels
A
B
four wheels
Characters
four wheels
Note that one tree is not more parsimonious over
the other. In tree A, the number of wheels is
subjectively weighted over color, and vice versa
in tree B.
three wheels
red
blue
8
  • A cladistic analysis of automobiles will not
    produce a unique, consistent, well-supported tree
    that displays nested hierarchies.
  • A cladistic analysis of automobiles (or any
    analysis of randomly assigned characters) will
    result in a phylogeny, but there will be a very
    large number of other phylogenies, many of them
    with very different topologies, that are as
    well-supported by the same data.
  • Cladistic analysis of an actual genealogical
    process will produce one or a small amount of
    trees that are much more well-supported by the
    data than the other possible trees.

9
  • The nested hierarchical organization contrasts
    with other possible biological patterns, such as
    the continuum of "the great chain of being"

10
  • Mere similarity between organisms is not enough
    to support evolution.
  • The nested classification pattern produced by a
    branching evolutionary process, such as common
    descent, is much more specific than simple
    similarity.

11
Is demonstrating that phylogenies show
hierarchical structure enough? How much nested
structuring is necessary to show that the
structure is non-random?
www.talkorigins.org
12
Testing for Hierarchical Structure
  • Plylogenetic signal (the degree to which a
    phylogeny shows a unique well supported tree) can
    be quantified.
  • We will discuss two methods the randomization
    test and the consistency index (CI). (Archie,
    1989 Klasssen et al. 1991)
  • For additional tests see Faith and Cranston
    1991 Farris, 1989, Felsenstein 1985 Hillis
    1991 Hillis and Huelsenbeck 1992, Huelsenbeck et
    al. 2001

13
Archie, 1989
  • Provides a test to determine whether the minimum
    length tree for a given dataset is significantly
    different from that expected from random data.

14
Archies Randomization test
  • 1. randomize character data and perform the
    cladistic analysis
  • 2. repeat this process to obtain a distribution
    of the minimum tree lengths for the randomized
    data
  • 3. Test whether the minimum length tree generated
    from the real data is significantly smaller than
    the trees generated from randomized data

15
Vascular tissue Chloroplasts Water-tight egg Four limbs
bacteria 0 0 0 0
amphibians 0 0 0 1
humans 0 0 1 1
mammals 0 0 1 1
birds 0 0 1 1
reptiles 0 0 1 1
fishes 0 0 0 0
insects 0 0 0 0
fungi 0 0 0 0
mosses 0 1 0 0
ferns 1 1 0 0
flowering plants 1 1 0 0
Real data
Vascular tissue Chloroplasts Water-tight egg Four limbs
bacteria 1 0 0 1
amphibians 0 0 0 0
humans 0 1 1 0
mammals 0 0 0 0
birds 0 0 1 0
reptiles 0 1 0 1
fishes 0 0 1 0
insects 0 0 0 1
fungi 0 0 0 1
mosses 1 0 0 0
ferns 0 1 1 1
flowering plants 0 0 0 0
Randomized data
16
Minimum length tree from real data
Distribution of minimum length trees from
randomized dataset
17
Min tree from real data
Distribution of randomized data
Archie, 1998
18
Klassen et al. 1991
  • Makes use of the consistency index (CI)
  • CI represents the reciprocal of the number of
    steps per character
  • The further from one, and the closer to zero
    increased homoplasy
  • A problem with CI is that is seems to decrease as
    a function of number of characters and taxa

19
  • Klassen et al generated CI distributions for
    random datasets of varying numbers of taxa and
    characters.
  • The CI values for random data can then be
    compared to real data

20
Note that most real datasets are well above the
95 confidence interval for CI values, and in no
cases are they below the 95 limit.
Klassen et al. 1991
21
Klassen et al. 1991
22
Did GOD do it?
  • How might you respond to the idea that nested
    hierarchal structure is seen because certain
    traits, by design, work better together?
  • Is testing hierarchical structure against a
    random alternative sufficient. What about those
    that argue that god did not design things
    randomly

23
  • Archie, J. W. (1989) "A randomization test for
    phylogenetic information in systematic data."
    Systematic Zoology 38 219-252.
  • Faith, D. P., and Cranston, P. S. (1991) "Could a
    cladogram this short have arisen by chance
    alone? on permutation tests for cladistic
    structure." Cladistics 7 1-28.
  • Farris, J. S. (1989) "The retention index and the
    rescaled consistency index." Cladistics
    5417-419.
  • Felsenstein, J. (1985) "Confidence limits on
    phylogenies an approach using the bootstrap."
    Evolution 39 783-791.
  • Hillis, D. M. (1991) "Discriminating between
    phylogenetic signal and random noise in DNA
    sequences." In Phylogenetic analysis of DNA
    sequences. pp. 278-294 M. M. Miyamoto and J.
    Cracraft, eds. New York Oxford University Press.
  • Hillis, D. M., and Huelsenbeck, J. P. (1992)
    "Signal, noise, and reliability in molecular
    phylogenetic analyses." Journal of Heredity 83
    189- 195.
  • Hillis, D. M., Moritz, C. and Mable, B. K. Eds.
    (1996) Molecular systematics. Sunderland, MA
    Sinauer Associates.
  • Klassen, G. J., Mooi, R. D., and Locke, A. (1991)
    "Consistency indices and random data." Syst.
    Zool. 40446-457.
  • Pei, M. (1949) The Story of Language.
    Philadelphia Lippincott.
  • Ringe, D. (1999) "Language classification
    scientific and unscientific methods." in The
    Human Inheritance, ed. B. Sykes. Oxford Oxford
    University Press, pp. 45-74.
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