Davalos talk at University of Miami 2013

1. The Right Answers to the Wrong Questions
Liliana M. Dávalos
Assistant Professor, Department of Ecology & Evolution
SUNY, Stony Brook
University of Miami
8 April 2013

2. Who am I?
• Evolutionary biologist
• Focus on biodiversity,
including:
• Speciation
• Diversification
• Conservation

3. Two kinds of questions
Biological
diversity
Diversificatio
n, increase decrease Habitat loss

4. The Right Answers to the Wrong Questions
• Evolution of Diversity
• What to do when models fail
• Right answers, wrong questions
• Understanding habitat loss
• A lot of cattle without much beef

5. 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
100
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. *
100
84
96
42
100
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 1217
B. longum subsp. longum BBMN68
B. longum subsp. infantis ATCC 55813
B. longum subsp. longum JDM301
B. longum subsp. infantis ATCC 15697
B. breve DSM 20213
B. dentium Bd1
B. dentium ATCC 27679
B. adolescentis ATCC 15703
B. bifidum PRL2010
B. bifidum S17 Bifidobacterium sp. *
Corynebacterium matruchotii ATCC 14266
C. efficiens YS314
C. genitalium ATCC 33030 Sca01
C. glucuronolyticum ATCC 51866
C. urealyticum DSM 7109
Arthrobacter sp. FB24
A. chlorophenolicus A6
Kocuria rhizophila DC2201
Micrococcus luteus NCTC 2665
Clavibacter michiganensis subsp. michiganensis NCP
C. michiganensis subsp. sepedonicus
Cellulomonas flavigena DSM 20109
63
63
65
55
51
70
84
74 100
Kineococcus radiotolerans SRS30216
Nakamurella multipartita DSM 44233
Saccharopolyspora erythraea NRRL 2338
Geodermatophilus obscurus DSM 43160
Amycolatopsis mediterranei U32
Intrasporangium calvum DSM 43043
Kytococcus sedentarius DSM 20547
Nocardioides sp. JS614
Streptomyces avermitilis MA4680
S. scabiei 87 22
S. coelicolor A3 2
Catenulispora acidiphila DSM 44928
Thermobifida fusca YX
Thermobispora bispora DSM 43833
Thermomonospora curvata DSM 43183
Streptosporangium roseum DSM 43021
Micromonospora aurantiaca ATCC 27029
98
92
99
74
100
100
100
75
99
100
78
43
78
100
49
20
100
99
92
32
100
92
26
50
56
6
18
14
11
37
32
66
100
51
5
38 46
78
15
99
88
pathogenic Mycobacterium complex
(avium-bovis-tuberculosis)
non-pathogenic Mycobacterium smegmatis complex
0.1 substitution/site
Evolutionary framework Corthals et al. 2012 PLoS One

6. The organisms in
question Phyllostomidae and relatives
When models fail

7. p
i
i
p p
p
p p D
D
p
p D
pi
D p D
i
p p D
p D
p D
i
i p D
p D
p
p D i
pi
i Di
DD
D
D Di
pi
p D
p p D
p D
Di
p D i D
p D i D
p D D

8. p
Di
D D
D i
D i
D i i
Dp i
p
p
p p
p i
D p
p
D
Di
p D
p
p
D
D i
•
•
Ma
r M
r M aM
r M Ma
r M aM
rM M
r
a
a
ra
M Ma
ra
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M
r
r M a
r
r r
ra
M r M
a
r r M
a
a r M
r M
r
r M
r M
r r M c
r r M rc M

9. r
Ma
r
r
r
r
Mr a
r
M
M r
M a
a
M a
M
Ma
r
M
r M

10. •
Baker et al. 2003 Occas Pap Mus TTU
Datzmann et al. 2010 BMC Evol Biol
Wetterer et al. 2000 B Am Mus Nat Hist
?
When models fail

11. Genome not always
available
• Majority of species are
extinct
• Fossils are all that
remain
• Phylogenies must use
morphology
• Can morphology be
trusted?
Morgan Czaplewski 2012 Evolutionary
History of Bats
When models fail

12. Hermsen Hendricks 2008 Ann Missouri Springer et al. 2007 Syst Biol
Bot Gard
When models fail

13. Assumptions of
phylogeny
• Homology: character
changes reflect
common descent
• IID: Independent and
Identically Distributed
When models fail

14. Dávalos, Cirranello et al. 2012 Biol Rev
When models fail

15. Saturation is not
everything
• If rates of evolution are
high, then signal
erased over time
• Results in
unresolved
phylogeny
• Other signal must
emerge to resolve
phylogeny
When models fail
Dávalos Perkins 2008 Genomics

16. d e m
A eA me eA
eAd e
d t e
dA a
d d m A d
d d m A d
m t Ae
d m AA e
AA Am A
AAA
deA
m d m Ad e
e e
e d
A eA
d m A i
eA d m A A
d m A
d d m Ae r
d m A A e

17. d e
t e
A m
me e
Am A m
m eA AemeA
m me
d A meA
d me
m ee
AA
m eA t A A
me A i
d m Am i de
m eA eA
m eA
m eA AA
d A
md eA
me
d A eA A
m t d AA d
t Ae
m d
A d
d AA eA

19. •
•

20. •
•
Dávalos, Cirranello et al. 2012 Biol Rev Dumont, Dávalos et al. 2012 P R Soc B
When models fail
d eAm
dm A

21. Morphology has a
strong signal
Mi

22. r rc
r i M
ic

23. ir cM
Mi M
r i M
r c
r

24. r r i M
rc

25. r r i M

26. ir cM
i r i M
M
i

27. r

28. r Mi cM

29. r r i M
r
r M
i cM
c
M cM

30. r
r i M
cM r i M
rc
r r i M
i i cM

31. i
r i M
r i M
r i M
r r
i r i M
MM
rcM
r i M

32. r
r i M

33. i ri M
i i cM
ic
r i M
r c
Mi M
r i M

34. r rc
cM r i M
i r i M
M
i
Mi

35. r

36. r Mi cM
r r i M
r
r M
i cM

37. c
M cM
r r
r

38. r r i M
rcM
i r i M
MM

40. • 10Kb from 7
chromosomes mt +
300 dental traits
• = signal from dental
traits
• What makes this
signal so strong?
Dávalos et al. In Review Syst Biol
When models fail

41. 19
9.5
0
3.9
2.6
1.3
0
Molecular Morphological
Frequency (percent)
A
Figure B
Pairwise dissimilarity between characters
Relative density between morphological characters
0.0 0.2 0.4 0.6 0.8 1.0
2.5
2.0
1.5
1.0
0.5
0.0
Signal is amplified by
repetition
• Measured dissimilarity
between pairs of
characters
• Most sequence
characters are
completely dissimilar
• Despite protein-coding
loci
• This is not the case for
dental characters
Dávalos et al. In Review Syst Biol
When models fail

42. Mi
r i M
ic

43. ir cM
r i M
r c
r

44. r r i M
●
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r r i M
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45. ●
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r i M

46. i r i M
M
i

47. r

48. r Mi cM
r r i M
r
r M
i cM
c
M cM
icM

49. r
r i M
r i
cM r i M
rc
M
r r i M
M cM
Mi M
i i cM
i
r i M
r

50. i icM
r i M
cM
r r
i r i M
MM
rcM
r i M

51. r
M cM
rc
r i M

52. i ri M
M
cM
i i cM
ic
r i M
r c
Mi M

53. i icM
r i

54. ir cM
r
r i M
r i M

55. r rc
i
r i M
cM r i M
icM

56. i r i M
M
i
Mi

57. r

58. r Mi cM
r r i M
r
r M
i cM

59. c
M cM
r r
r

60. r r i M
rcM
i r i M
MM

62. ï
RUQHFWDUï

63. ●
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● When models fail
Dávalos et al. In Review Syst Biol

64. r rc

65. i r i M
rrr iii M
MM

66. Models failed
• Less is more when
collecting certain kinds of
characters
• Dental data violate key
assumptions of
phylogenetic models
• Saturation, convergence,
and non-independence
• = model failure
• New models needed
Czaplewski et al. 2003 Caldasia
When models fail

67. The Right Answers to the Wrong Questions
• Evolution of Diversity
• What to do when models fail
• Right answer, wrong question
• Understanding habitat loss
• A lot of cattle without much beef

68. MacArthur Wilson 1963 Evolution Cracraft 1983 Syst Biol
Right answer, wrong question

69. Equilibrium Null Model
MacArthur Wilson 1963 Evolution Preston 1962 Ecology
Right answer, wrong question

70. Recent disequilibrium Dávalos Turvey 2012 Bones,
Clones Biomes
Right answer, wrong question

71. Buckets of Caribbean
fossils
Right answer, wrong question

72. Why they went
extinct
• Competition by other
invasive bats – Koopman
Williams 1951
• Cave flooding – Morgan
2001
• Interglacial floods –
McFarlane Lundberg
2004
• Anthropogenic habitat
destruction – Gannon et
al. 2005
Right answer, wrong question

73. Area change and species loss
Right answer, wrong question
Dávalos Russell 2012 Ecology
Evolution

74. R2
=
0.83 R2
=
0.85
Log of present/LGM area in the Greater Antilles
í
í
í
í
í
í
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í í í í í í í
● ●
● ●
●
●
Log of present/LGM area in the Bahamas
Log of present/LGM species
í
í
í
í
í
í
●
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í í í í
Dávalos Russell 2012 Ecology
Evolution
Change in area ~
change in richness
Right answer, wrong question

75. ● ●

76. Figure 4
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Room for additional
work Dávalos Russell 2012 Ecology
Evolution
Right answer, wrong question

77. How to answer the
right question
• Equilibrium null model
• Successfully
explains richness
• Short-term
disequilibrium
modeled
• Real interest is not
richness
• But composition
Drawing by A. Tejedor
Right answer, wrong question

78. The Right Answers to the Wrong Questions
• Evolution of Diversity
• What to do when models fail
• Right answers, wrong questions
• Understanding habitat loss
• A lot of cattle without much beef

79. Vaupes, Colombia 2009 Antioquia, Colombia 2010
Understanding habitat change

80. Why deforestation? Geist Lambin 2002 BioScience
Understanding habitat change

81. World Bank Statistics 2012
Population growth over
time
Understanding habitat change

82. An in-depth look
• Guaviare from
2001-2010
• One of two large foci
of Plan Colombia (the
other was Putumayo)
• Poor development
indicators
• Extractive land uses
Guaviare, Colombia 2008
Understanding habitat change

83. 50
45
40
35
30
2200
2000
1800
1600
1400
●
●
●
●
Forest
●
●
●
●
50 45 40 35 30
NP
40
35
30
2500
2000
1500
●
●
Pasture
●
●
●
●
●
●
28 30 32 34 36 38 40
3
2
400
350
300
250
200
150
●
700 ●
650
600
550
500
●
●
●
●
●
●
●
50 45 40 35 30
PA (ha)
D E ●
350
300
● ●
250
200
●
●
●
150
●
●
28 30 32 34 36 38 40
H 14
12
10
8
6
4
2
●
110
100
90
●
●
●
●
●
●
●
●
50 45 40 35 30
PLAND
ENN (m)
120
115
110
105
100
95
●
●
●
●
●
●
● ●
28 30 32 34 36 38 40
PLAND
600
500
400
300
●
A B
G
25
2001 2004 2007 2010
25
2001 2004 2007 2010
1
PLAND
Fate of forest
• Consistent year-to-year
forest decline
• Fragmentation indices
consistent with
unchecked process of
conversion to pasture
K L Understanding habitat change
Dávalos et al. In Review Global
Environ Chang

84. Hamburger! (or steak)
Kaimowitz et al. 2004 CIFOR
Three explanations
Understanding habitat change
Coca
Dávalos et al. 2011 Environ
Sci Technol
Land tenure and property
Hecht 1993 BioScience

85. Municipality
●
●
●
Calamar
El San San Jose
El Retorno
Calamar
● ●
6,000
90,000
60,000
4,000
2,200
2,000
1,800
30
20
A B
Figure 6
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●
●●
●
●
●
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●
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●
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40
30
20
30,000 60,000 90,000
Cattle
Percentage land pasture
●
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2,000
30 40 50 60
Percentage population urban
Coca cultivation (ha)
A
B
C
Figure 4
30,000
10
Year
Ranching GDP (109 pesos) Price of beef (pesos/Kg) Cattle
2000 2002 2004 2006 2008 2010
1,600
Pastures with few cows
Understanding habitat change
Dávalos et al. In Review Global
Environ Chang

86. If coca were the
cause
• Perhaps eradication is
the solution
• Great because we can
solve the problem of
coca
coca decrease Eradication
Understanding habitat change

87. 50
45
PLAND
40
35
2001 40
35
6000
30
D E 25
Calamar
2200
3
Retorno
San 2000
2
1800
1
1600
1400
Forest lost, coca declined
Understanding habitat change
●
●
●
●
Forest
●
●
●
●
2001 2004 2007 2010
50 45 40 35 30
NP
4000
2500
2000
1500
●
●
Pasture
●
●
●
●
●
●
●
●
●
28 30 32 34 36 38 40
3
2
400
350
300
250
200
150
●
400
700 ●
●
●
14
●
A B
30
2001 2004 2007 2010
25
2001 2004 2007 2010
1
●
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2000
0 3000 6000 9000
Eradication previous year (ha)
Coca cultivation (ha)
5
● 5
mpio
●
●
●
Dávalos et al. In Review Global
Environ Chang
350
●
Coca
2010
B
C

88. Why did coca
decline?
• Each municipality
started out with
different amounts of
coca
• As the municipalities
become more urban,
there is less coca
• At ~50% urban
population there is 0
coca in the smaller
municipalities Dávalos et al. In Review Global
Municipality
●
●
●
Calamar
El Retorno
San Jose
● ●
6,000
4,000
A B
Figure 6
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40
30
20
30,000 60,000 90,000
Cattle
Percentage land pasture
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●
2,000
30 40 50 60
Percentage population urban
Coca cultivation (ha)
Environ Chang
Understanding habitat change

89. San Jose
El Retorno
Calamar
A
B
C
Figure 5
2010
0.06
0.04
0.02
0.00
50
40
30
20
5
4
3
2
2000 2002 2004 2006 2008
Year
Property Tax
(106 pesos/capita)
Construction GDP
(109 pesos)
Financial GDP
(109 pesos)
What urbanization
looks like
• Urban people paying
more taxes that
finance construction
• Finance becomes
important
• Less dependence on
ranching (and
agriculture)
Dávalos et al. In Review Global
Environ Chang
Understanding habitat change

90. Urban cows!
• Cows enhance claim
to the land
• The region is rapidly
urbanizing
• Per capita taxes are
rising = property
values are rising
• Clearing the land to
sell in future urban
market
Dávalos et al. In Review Global
Environ Chang
Understanding habitat change

91. A disturbing
development model
• Development excludes
coca
• Not eradication
• Development centered
on a model of
settlement that is
destructive
• And probably not
peaceful
Guaviare, Colombia 2008

92. coca nothing More
decrease Eradication
The real drivers of
habitat loss
Urbanization
Development
Understanding habitat change
becomes
Pasture
Cows
property is

93. Models and data: a
dialogue
• Models shape the
kinds of data we
collect
• And how we
interpret those data
• Models may answer
the wrong question
• Data may violate
model assumptions

94. Thanks!
• Funding
• NSF–DEB
• CIDER—SBU
• Speciation diversification: A.
Cirranello, E. Dumont, A. Russell,
N. Simmons, P. Velazco
• Conservation policy: A.
Bejarano, A. Corthals, L. Correa, C.
Romero
• Dávalos Lab
• Phylogenetics: S. DelSerra, A.
Goldberg, O. Warsi, L. Yohe
• Land use: P. Connell, M. Hall, E.
Simola, G. Tudda