Additional details for methods and results
Additional details for methods and results
1. ADDITIONAL DETAILS FOR METHODS
(a) Phylogenetic relationships in Disa and character coding
Two voucherspecimens in Bytebier et al.
D. atrorubens was correctly identified as D.
comosa and D. zimbabweensis should be D. rungweensis . Correspondingly, the erroneous D. atrorubens terminal was deleted, and the D. zimbabweensis terminal renamed as D. rungweensis in all trees used here.
Spur was coded as absent in all species where taxonomic treatments of Disa
the spur obsolete, and as present otherwise.
(b) Test for phylogenetic signal and identification of ideal tree type for analyses
We used two methods to determine whether the distribution of nectar production and spur presence over the phylogeny depends significantly on phylogenetic relatedness. First, we determined the magnitude of D
D values
0 indicate that traits evolved in accordance with a
Brownian motion model and exhibit significant phylogenetic conservatism. To account for phylogenetic uncertainty, D was estimated for multiple trees.
Determination of Fritz and Purvis’ (2010) D also enabled determination of whether the use of phylograms or chronograms would result in the most accurate reconstruction of ancestral states when
using maximum-likelihood methods [12]. We determined
D for 1000 phylograms from a Mr.Bayes analysis (for details for this analysis see Bytebier et al.
[1] and for 1000 chronograms, extracted by
sampling every 10 000th generation from a MCMC run in BEAST, after the initial 2.5 million generations were excluded to guarantee a conservative burn-in. For details regarding the dating of
Disa
see [13]. Comparison of the resulting
D values with an independent-sample t-test indicated that phylogenetic signal of both traits is significantly stronger when estimated from chronograms than phylograms (nectar production: t
1982.3
= 27.4, P < 0.0001, N = 2000 trees; spur: t
1966.5
= 8.42, P <
0.0001, N = 2000 trees). Consequently, we used chronograms for the ancestral state reconstruction reported here, but note that results obtained from phylograms were essentially identical to those from chronograms.
Second, we calculated the number of steps required for parsimony reconstruction of each trait over the maximum clade credibility tree of 1000 chronograms (rescaled to reflect median node heights for the contained clades) extracted from the BEAST analysis described above (hereafter referred to as MCC chronogram), and compared this number of steps to that of the same character re-
shuffled 1000 times in Mesquite [14, version 2.75], while keeping the proportion of states constant.
The null hypothesis of phylogenetically random distribution is rejected if the observed state distribution is outside the 95% confidence interval of the randomized state distribution.
(c) Ancestral state reconstruction
Maximum likelihood analyses considered two alternative models of discrete-trait evolution. The single-rate model assumes equal transition rates between states, while the two-rate model allows unequal forward (gain) and backward (loss) rates, allowing for more complex trait evolution. The more appropriate model of trait evolution was identified by comparing the likelihoods of the single- and two-rate models with asymmetry likelihood-ratio tests in Mesquite. When conducting this test for
1000 chronograms, an asymmetrical model of character evolution did not provide a significantly better fit than the symmetric-rate model for nectar production (average D
1
= 0.29, lower 95% confidence interval [LCI] = 0.08, upper 95% confidence interval [UCI] = 0.60, P = 0.59), but for spur
(average D
1
= 4.99, LCI = 4.20, UCI = 5.76, P = 0.026). We therefore fitted a single-rate Markov k-
state model to nectar production, and an asymmetrical Markov k-state 2 parameter model to spur [14,
(d) Correlation between traits
To examine whether the existence of spurs predisposes the evolution of nectar production, we tested
for a correlation between the two traits using Pagel’s correlation test [16] as implemented in the
Pagel94 module in Mesquite. The probability that a model of dependent evolution fits the data significantly better than one of independent evolution was estimated with likelihood-ratio tests involving 1000 Monte Carlo simulations of model parameters. For each simulation, maximumlikelihood estimates of model parameters were optimized using 500 iterations.
(f) Tests for character-associated rates of speciation and extinction
To test whether absence or presence of nectar are associated with differences in the rate of speciation,
extinction, and diversification, we used the BiSSE module in Mesquite [17]. We first used
unconstrained models to derive maximum-likelihood estimates for the rates of speciation (
), extinction (
), and diversification, respectively, in the absence or presence of nectar. Second, we created three models that differed in constraints, namely equal speciation rate (
0
=
1
), equal extinction rate (
0
=
1
), and equal diversification rate (
0
=
1
and
0
=
1
) in the absence or presence of nectar, and compared their fit to those of the respective unconstrained models with likelihood-ratio
tests. To account for phylogenetic uncertainty, we compared model fit over 1000 chronograms, and compared the resulting average D statistic to a
2 distribution with one degree of freedom.
2. ADDITIONAL DETAILS FOR RESULTS
(a) Phylogenetic signal
Both measures of phylogenetic signal indicated significant phylogenetic conservatism in both nectar production and occurrence of a spur. Over 1000 phylograms the D statistic for nectar production averaged (
standard error [SE]) -0.31
0.001, compared to -0.35
0.001 over 1000 chronograms.
Average D for spurs was -0.17
0.001 for phylograms and -0.18
0.001 for chronograms. The probability that state distribution of either trait was unrelated to phylogenetic structure was zero in all examined phylogenies. By comparison, the probability that state distribution was due to phylogenetic relatedness was on average 92.1
0.08% (phylograms) and 94.8
0.05% (chronograms) for nectar production, and 70.5
1.14% (phylograms) and 71.9
0.09% (chronograms) for spurs.
The null hypothesis of phylogenetically random distribution of nectar production was rejected by parsimony reconstruction of trait evolution because the observed distribution (10 steps) was outside the 95% confidence interval of the randomized state distribution (mean = 28.2, LCI = 26.1, UCI =
29). Similarly, the observed distribution of spurs (8 steps), was outside the 95% confidence intervals of the randomized state distribution (mean = 13.4, median = 14, LCI = 10.2, UCI = 14).
(b) Ancestral state reconstruction
Maximum likelihood analyses revealed low rates of trait evolution for nectar production (Mk1 rate
(mean ± SE) 2.56 ± 0.007 x 10 -3 ( n = 1000 chronograms) and occurrence of a spur (forward rate: 5.80
± 0.02 x 10 -3 ; backward rate: 1.24 ± 0.003 x 10 -3 ; n = 1000 chronograms per rate).
The results from the parsimony analysis received overall strong support from maximum-likelihood, but ML was less decisive in several nodes. Over 1000 chronograms, ML reconstructed the root node as non-rewarding (90.86 % average probability). However, ML estimated only on average 4.9 transitions from non-rewarding to nectar production (minimum = 2, maximum = 8), and on average
0.26 losses of nectar production (minimum = 0, maximum = 1) per tree. This apparent underestimate
(c.f. Figure 1) likely reflects the scarcity of data available for correct estimation of model parameters
by ML if rates of evolution are low [18-21].
(c) Tests for character-associated rates of speciation and extinction
The null hypotheses of equal speciation, extinction, and diversification rates, respectively, in rewarding and non-rewarding species could not be rejected (speciation (
): average D
1
= 3.91 x 10 -5 ,
P > 0.95; extinction(
): average D
1
= -1.58 x 10 -4 , P > 0.95; diversification: average D
2
=3.96 x 10 -5 ,
P > 0.95). Over 1000 chronograms, speciation rate (
) averaged 3.91 x 10 -5 (median = 0, LCI = -2.83 x 10 -5 , UCI = 1.36 x 10 -5 ), extinction rate (
) averaged -1.58 x 10 -4 (median=0, LCI = -2.15 x 10 -5 ,
UCI = 1.59 x 10 -5 ), and diversification rate averaged 3.96 x 10 -5 (median = 0, LCI = -1.89 x 10 -5 , UCI
= 1.46 x 10 -5 ).
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