1
Electronic Supplementary Material –Text and Figure Captions
2
3
Bytebier et al. - Estimating the age of fire in the Cape flora of South Africa from
4
an orchid phylogeny
5
6
1
7
Expanded Materials and Methods
8
9
Phylogenetic analyses
10
Phylogenetic relationships were inferred for 7 outgroup and 136 ingroup taxa,
11
representing 70% of all recognised Disa species and infraspecific taxa. One nuclear
12
and two plastid gene regions were sequenced and compiled in a matrix with 4094
13
characters, 1096 (26.8%) of which were parsimony informative. In a parsimony
14
analysis, 87 nodes of 142 (61%) were supported with a bootstrap support values of
15
75% or higher, while the topology resulting from a Bayesian inference analysis had
16
101 (71%) nodes with a posterior probability of 0.95 or above. The phylogenetic
17
analysis is discussed in detail in (Bytebier et al. 2007).
18
19
Molecular dating
20
Several methods have been proposed for estimating absolute divergence times in
21
phylogenies, ranging from strict molecular clocks to methods that allow each lineage
22
to evolve at an own rate (Renner 2005; Rutschmann 2006). Since a robust age
23
estimation was critical to this study, we applied two widely used algorithms that make
24
different assumptions: Penalized Likelihood (Sanderson 2002) and BEAST
25
(Drummond & Rambaut 2007). In all cases phylogenetic uncertainty was taken into
26
account.
27
28
A strict molecular clock for the maximum a posteriori (MAP) tree was rejected by a
29
likelihood ratio test (Felsenstein 1981). The Penalized Likelihood analysis was run in
30
the software r8s v. 1.71 (Sanderson 2002). A cross-validation procedure was
31
conducted to identify the best-fitting smoothing value for MAP tree, by testing 14
2
32
different smoothing values ranging from -3.0 to 3.5 log10 (smooth). The optimal value
33
found (0.01) was then used for independently dating 1,000 randomly selected trees
34
from the stationary Bayesian sample, using the settings num_restarts=3,
35
num_time_guesses=3, penalty=yes, maxiter=2000. We then performed 5 independent
36
runs in BEAST v. 1.5.4 (Drummond & Rambaut 2007), with 10 million generations
37
each, sampling every 500th tree, using a Yule process of speciation as tree prior, an
38
uncorrelated lognormal molecular clock, and the same substitution and site
39
heterogeneity models (GTR+ +I) as for the MrBayes analysis (Bytebier et al. 2007).
40
Convergence of the independent runs and effective sample sizes (>100 for all
41
parameters) was assessed in Tracer v 1.5 (Rambaut & Drummond 2007). Summary
42
statistics were summarised using TreeAnnotator v 1.5.4 (Drummond & Rambaut
43
2007).
44
45
For the PL analysis, the tree with maximum a posteriori score among 45,000 trees
46
from a post burn-in Bayesian sample was provided as reference for summarizing the
47
results, whereas for the BEAST analysis it was the tree with the highest sum of clade
48
probabilities.
49
50
Coding of biomes, habitats and rainfall seasonality
51
Disa species occurring in southern Africa were coded for occurrence in biomes and
52
habitats by Linder et al. (Linder et al. 2005). The biomes (Rutherford & Westfall
53
1986; Rutherford 1997) summarize climatic, edaphic and biotic information into
54
broad descriptive units (table S3). Species occurring outside of southern Africa were
55
coded to the same biomes according to information in Linder (Linder 1981a; Linder
56
1981e; Linder 1981c; Linder 1981b; Linder 1981d), Flora of Tropical East Africa
3
57
(Summerhayes 1968), Flora Zambesiaca (la Croix & Cribb 1995), Flore d’Afrique
58
Centrale (Geerinck 1984) and Orchids of Malawi (la Croix et al. 1991), and on the
59
personal field experience of the authors.
60
61
Linder et al. (Linder et al. 2005) coded the southern African orchid species to the
62
following habitats: Grassland, Woodland, Subalpine, Marsh, Scrub, Mature Heath,
63
Postfire, Streambank and Epilithic (table S4). To this, we added Southeast Cloud
64
Zone habitat. The importance of southeast clouds as a source of water during the dry
65
summers in the CFR was documented by Marloth (Marloth 1904). Although we could
66
not trace information on the distribution of these clouds, our field experience indicates
67
that they occur frequently along the summits of mountains, within sight of the Indian
68
Ocean. As for the biomes, the habitats for the species occurring outside of southern
69
Africa were coded based on comments in monographic and floristic treatments as well
70
as our field experience.
71
72
All Disa species were scored present or absent for winter, all year, summer or
73
bimodal rainfall. For species occurring in southern Africa, the distribution maps from
74
Linder & Kurzweil (Linder & Kurzweil 1999) were superimposed on the rainfall
75
seasonality map of Schulze (Schulze 1997). For species occurring outside of southern
76
Africa, rainfall seasonality data was extracted from Linder (Linder 1981a; Linder
77
1981e; Linder 1981c; Linder 1981b; Linder 1981d).
78
79
Character state (presence/absence) for each species and each categorical variables of
80
biome, habitat and rainfall seasonality are reported in table S5.
81
4
82
Character optimisations
83
We used binary coding (absence/presence) to allow for polymorphic states at internal
84
nodes (Hardy & Linder 2005). Thus each categorical variable was coded as either
85
present or absent (e.g. each species was scored as present or absent for fynbos, etc).
86
87
Maximum likelihood optimisation of ancestral states was performed with the
88
“asymmetric Markov k-state 2 parameter model” and with “root state frequencies
89
same as equilibrium” implemented in Mesquite version 2.6 (Maddison & Maddison
90
2009). This model has one parameter for the rate of change from state 0 to 1 (the
91
"forward" rate) and another for the rate of change from 1 to 0 (the "backward" rate)
92
and thus, allows a bias in gains versus losses. We compared the lnL scores of a two-
93
rate (forward and backward rates independent) and a one-rate (forward and backward
94
rates constrained to be equal) model for each character. The accuracy of parameter
95
estimation depends on the amount of data available as well as model complexity
96
(Mooers & Schluter 1999). For several characters (but not all) the two-rate model
97
resulted in a significantly improved fit and we therefore preferred this model since it
98
makes fewer assumptions (i.e. it does not assume the forward and backward rates of
99
characters change to be equal). We are aware, however, that the one-rate model
100
handles trees with few transitions and an imbalance of character states better than the
101
two-rate model (Mooers & Schluter 1999) and we therefore also checked the
102
optimisations with this model. In most cases this did not give a significantly different
103
result and if it did, we report these differences explicitly. Initially, all characters were
104
optimised using these assumptions over the MAP tree. Then, to take into account
105
phylogenetic uncertainty, each character was optimised over a sample of 1,000
5
106
chronograms obtained from the Penalized Likelihood analysis, and average
107
frequencies across trees calculated for each node of the MAP tree.
108
109
Randomness of the distribution
110
We tested for phylogenetic conservative characters by calculating the number of steps
111
each character required for a parsimony reconstruction over the MAP tree, and
112
comparing this to the distribution of minimum steps for the same character reshuffled
113
1000 times using Mesquite (Maddison & Maddison 2009), while keeping the
114
proportions of the states constant. If the number of steps of the observed distribution
115
was outside 950 (95%) of the randomised state distributions, then the Null hypothesis
116
that the character states were phylogenetically randomly distributed was rejected.
117
118
6
119
Supporting References
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
Bytebier, B., Bellstedt, D. U. & Linder, P. H. 2007 A molecular phylogeny for the
large African orchid genus Disa. Mol Phylogenet Evol 43, 75-90.
Drummond, A. & Rambaut, A. 2007 BEAST: Bayesian evolutionary analysis by
sampling trees. BMC Evol Biol 7, 214.
Felsenstein, J. 1981 Evolutionary trees from DNA-sequences - a maximum-likelihood
approach. J Mol Evol 17, 368-376.
Geerinck, D. 1984 Orchidaceae (premiere partie). Flore d'Afrique Centrale (ZaireRwanda-Burundi). Meise, Belgium: Jardin botanique national de Belgique.
Hardy, C. R. & Linder, H. P. 2005 Intraspecific variability and timing in ancestral
ecology reconstruction: A test case from the Cape flora. Syst Biol 54, 299-316.
la Croix, I. & Cribb, P. J. 1995 163. Orchidaceae. Flora Zambesiaca. London: Flora
Zambesiaca Managing Committee.
la Croix, I. F., la Croix, E. A. S. & la Croix, T. M. 1991 Orchids of Malawi.
Rotterdam & Brookfield: A.A. Balkema.
Linder, H. P. 1981a Taxonomic Studies in the Disinae (Orchidaceae). IV. A Revision
of Disa Berg. sect. Micranthae Lindl. Bull Jard Bot Natl Belg 51, 255-346.
Linder, H. P. 1981b Taxonomic studies in the Disinae. V. A revision of the genus
Monadenia. Bothalia 13, 339-363.
Linder, H. P. 1981c Taxonomic studies in the Disinae. VI. A revision of the genus
Herschelia. Bothalia 13, 365-388.
Linder, H. P. 1981d Taxonomic studies in the Disinae: 2. A revision of the genus
Schizodium Lindl. J. S. Afr. Bot. 47, 339-371.
Linder, H. P. 1981e Taxonomic studies on the Disinae. III. A revision of Disa Berg.
excluding Sect. Micranthae Lindl. Contr Bolus Herb 9.
Linder, H. P. & Kurzweil, H. 1999 Orchids of southern Africa. Rotterdam/Brookfield:
A.A. Balkema.
Linder, H. P., Kurzweil, H. & Johnson, S. D. 2005 The Southern African orchid flora:
composition, sources and endemism. J Biogeogr 32, 29-47.
Maddison, W. P. & Maddison, D. R. 2009 Mesquite: a modular system for
evolutionary analysis
Marloth, R. 1904 Results of experiments on Table Mountain for ascertaining the
amount of moisture deposited from southeaster clouds. Trans S African Philos
Soc 14, 403-408.
Mooers, A. O. & Schluter, D. 1999 Reconstructing ancestor states with maximum
likelihood: Support for one- and two-rate models. Syst Biol 48, 623-633.
Rambaut, A. & Drummond, A. J. 2007 Tracer
Renner, S. S. 2005 Relaxed molecular clocks for dating historical plant dispersal
events. Trends Plant Sci 10, 550-558.
Rutherford, M. C. 1997 Categorisation of biomes. In Vegetation of Southern Africa
(ed. R. M. Cowling, D. M. Richardson & S. M. Pierce), pp. 91-98. Cambridge,
U.K.: Cambridge University Press.
Rutherford, M. C. & Westfall, R. 1986 Biomes of southern Africa - an objective
categorisation. Mem Bot Surv South Africa 54, 1-98.
Rutschmann, F. 2006 Molecular dating of phylogenetic trees: A brief review of
current methods that estimate divergence times. Divers Distrib 12, 35-48.
7
166
167
168
169
170
171
172
173
Sanderson, M. J. 2002 Estimating absolute rates of molecular evolution and
divergence times: a penalized likelihood approach. Mol Biol Evol 19, 101-109.
Schulze, R. E. 1997 South African atlas of agrohydrology and climatology. Pretoria,
South Africa: Water Research Commission.
Summerhayes, V. S. 1968 Orchidaceae (Part I). Flora of Tropical East Africa.
London, U.K.: Crown Agents for Oversea Governments and Administrations.
174
8
175
Figure Captions
176
177
Figure S1 Maximum a posteriori (MAP) cladogram yielded under a Bayesian
178
analysis of phylogeny among 136 taxa within the genus Disa. Dashed lines indicate
179
nodes with Bayesian posterior probability < 0.50. Nodes numbers are shown within
180
circles and referred to in Table S1. Intrageneric sections within Disa are given
181
following the species names. For a fully annotated tree see (Bytebier et al. 2007).
182
183
Figure S2 Molecular chronogram of Disa, as inferred under Penalized Likelihood
184
implemented in r8s. Green bars at node intersections indicate 95% confidence
185
intervals of ages (N = 1,000). The tree topology is the same as in Fig. S1, but with
186
branch lengths equal to mean ages. Ages in million of years from present.
187
188
Figure S3 Molecular chronogram of Disa, as inferred under a relaxed Bayesian clock
189
implemented in BEAST. Green bars at node intersections indicate 95% highest
190
posterior densities of ages (N = 16,000). Ages in million of years from present.
191
192
Figure S4 Summary of ancestral state optimisations for biome, as inferred using the
193
asymmetrical (two-rate) likelihood model implemented in Mesquite over a sample of
194
PL chronograms (N = 1,000). Habitat shifts supported by average likelihoods ≥ 0.70
195
are indicated by an asterisk; all others have average likelihoods ≥ 0.95.
196
197
Figure S5 Summary of ancestral state optimisations for rainfall, as inferred using the
198
asymmetrical (two-rate) likelihood model implemented in Mesquite over a sample of
9
199
PL chronograms (N = 1,000). Habitat shifts supported by average likelihoods ≥ 0.70
200
are indicated by an asterisk; all others have average likelihoods ≥ 0.95.
201
202
Figure S6 Summary of ancestral state optimisations for habitat, as inferred using the
203
asymmetrical (two-rate) likelihood model implemented in Mesquite over a sample of
204
PL chronograms (N = 1,000). Habitat shifts supported by average likelihoods ≥ 0.70
205
are indicated by an asterisk; all others have average likelihoods ≥ 0.95.
10