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Social selection parapatry in an Afrotropical sunbird
McEntee et al.
Supplementary Materials
Methods:
Sequencing details
We made use of PCR to amplify and sequence five loci: the mitochondrial NADH2 subunit 2
(Sorenson et al. 1999), three Z-linked loci (BRM intron-15 – Goodwin 1997, CHDZ intron-15 –
Griffiths & Korn 1997, MUSK intron-3 – Kimball et al. 2009) and two anonymous autosomal
loci 11836 and 18142 – Backström et al. 2008). The thermocycling conditions included a hotstart
at 94°C, an initial denaturation at 94°C for 3 min, followed by 35-40 cycles at 94°C for 40s, 5260°C for 30-45s, 72°C for 30-45s, and was completed with a final extension step at 72°C for 10
min. PCR products were purified using shrimp phosphatase and exonuclease (exoSAPit,
Amersham, Foster City, CA) and cycle-sequenced in both directions using Big Dye terminator
chemistry (ABI, Applied Biosystems, Inc., Foster City, CA), and then run on an automated
AB3100 DNA sequencer. Single insertions or deletions of aligned nuclear DNA sequences were
treated as a fifth base.
Backström, N, Fagerberg, S and Ellegren, H (2008) Genomics of natural bird populations: a
gene-based set of reference markers evenly spread across the avian genome. Molecular Ecology,
17, 964-980.
Goodwin GH (1997) Isolation of cDNAs encoding chicken homologues of the yeast SNF2 and
Drosophila Brahma proteins. Gene, 184, 27–32.
Griffiths R, Korn RM (1997) CHD1 gene is Z chromosome linked in the chicken Gallus
domesticus. Gene, 197, 225–229.
Kimball RT, Braun EL, Barker FK et al. (2009) A well-tested set of primers to amplify regions
spread across the avian genome. Molecular Phylogenetics and Evolution, 50, 654–660.
Sorenson MD, Ast, JC, Dimcheff, DE, Yuri, T and Mindell, DP. (1999). Primers for a PCR-based
approach to mitochondrial genome sequencing in birds and other vertebrates. Molecular
Phylogenetics and Evolution, 12, 105-114.
Sound recording and song analyses
Recording efforts determined that both taxa have extensive vocal repertoires, consisting of
~11 unique vocal signal types (McEntee, unpublished data, McEntee 2013). The analyzed male
songs represent one of these unique signal types. They are interpreted as homologous
vocalizations in the two species because they share structural similarities and are used in similar
contexts. This structural similarity is further conserved across more distantly related taxa,
including N. mediocris, N. usambarica, and N. loveridgei (McEntee 2013), which improves
confidence in the inference of homology. Territorial males sing bouts of these songs from
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exposed perches throughout the day. It is the only vocal signal type of duration >1s that is not
exclusively associated with close-proximity conspecific interactions. Instead, it tends to be sung
at a substantial distance (usually >5m) from conspecifics.
Before analysis, recordings were standardized at 44.1 kHz sampling rate using the software
GoldWave 5.25 (Goldwave Inc. 2005). High quality song recordings were selected for analysis,
then bandpass filtered between 2 and 10 kHz in Raven Pro (Bioacoustics Research Program
2011). Subsequent sonogram production and sound analysis was performed in Luscinia [35],
which enables the extraction of data from individual component elements of songs. Signals were
extracted from files automatically, checked by eye and ear (JPM), with recordings slowed for
playback to 1/8 speed during quality checks. Some recordings were discarded at this step because
of insufficient signal:noise ratio. Minor problems in automated signal detection from high-quality
recordings were corrected using the ’brush’ tool (see additional explanation in Supplementary
Materials). Luscinia sonograms were created with the following settings: Max. frequency: 10,000
Hz; Frame length: 5 ms; Time step: 1 ms; Spectrograph points: 240; Spectrograph overlap: 80%;
Echo removal: 100%; Echo range: 100; Windowing function: Hann; and High Pass Threshold:
2000 Hz. JPM adjusted sonogram contrast with the “Dynamic range” and “Dynamic
equalization” settings to maximize the visual signal:noise ratio. After visual and auditory checks
following automatic signal detection, JPM used Luscinia’s “brush” tool to circumscribe song
elements that had been poorly circumscribed or were not circumscribed during automatic signal
detection. This tool effectively allows fine-scale automatic sound detection for the portions of the
sound where broad-brush automatic signal detection does not perform as well.
From the extracted values for elements within songs as defined by Luscinia, we measured 14
variables for each individual by calculating summary statistics over all elements (and intervals),
for each song. Means of the summary statistics for each song were taken for the set of songs
analyzed for each individual. Individual means were taken for the following per-song
measurements: mean interval duration (ms), coefficient of variation (CV) of interval duration,
median peak frequency of elements, CV peak frequency of elements, maximum peak frequency,
minimum peak frequency, range of peak frequency over elements, number of elements, median
bandwidth (Hz), CV bandwidth, per-element median frequency change (Hz), CV per-element
frequency change (Hz), song duration (ms), and element duration (ms). Peak frequency is defined
in Luscinia as the frequency with the highest amplitude for a given portion of the sonogram.
McEntee, J. P. 2013 Social selection, song evolution, and the ecology of parapatry in sunbirds.
PhD. Thesis, University of California, Berkeley.
Ecological niche analysis
The selection of an appropriate area as the buffer from which to randomly sample points for
this test is a matter of debate. For this test we used a buffer zone of 100 km around the set of
distribution points for each species. Occurrence samples included 19 occurrence points for
moreaui and 25 occurrence points for N. fuelleborni. Correspondingly, as recommended by [45],
2
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randomly sampled background points were grouped into 19 observations from the 100 km buffers
around N. fuelleborni occurrences and 25 observations from the 100 km buffers around N.
moreaui occurrences. For each point from the actual occurrence data and the pseudorandomly
sampled background sets, seven BIOCLIM variables (BIO1: annual mean temperature, BIO2:
mean diurnal temperature range, BIO5: maximum temperature of warmest month, BIO6:
minimum temperature of coldest month, BIO12: annual precipitation, BIO13: precipitation of
wettest month, BIO14: precipitation of driest month) were extracted at a resolution of 2.5 arcminutes (Hijmans et al. 2005). These seven BIOCLIM variables were chosen because they
capture a large proportion of the climatic variation across space, and their levels of correlation are
low over large areas of the globe (Peterson et al. 2009).
For both species, response curves for Maxent models developed independently for individual
predictor variables generally indicate increasing suitability with decreasing mean annual
temperature, maximum temperature of warmest month, and minimum temperature of coldest
month. Suitability for both species is highest at intermediate diurnal temperature ranges relative
to the background. For moreaui, suitability was highest at intermediate values of annual
precipitation and precipitation of the wettest month, and for low values of precipitation of the
driest month. For fuelleborni, suitability increased with annual precipitation and precipitation of
the wettest month, and was flat beyond minimal values of precipitation for the driest month.
Cline analyses
When fitting quantitative trait clines using likelihood, ’parental’ trait distributions should be
approximately normally distributed. To meet this assumption for the molecular hybrid index, we
added a small amount of Gaussian noise (SD=0.05) to q-score values, which were insufficiently
variable within parental populations to be approximated by normal distributions. Song PC1 score
and culmen length distributions for parental populations were checked for normality by
examining histograms.
While including mtDNA haplotypes as single known haplotypes for each individual in
STRUCTURE analysis violates model assumptions, we present cline fitting results using q-scores
from such analyses (Table S4) as a way to account for individuals with cytonuclear discordance.
In this study, STRUCTURE q-scores from analyses that only include nuclear variation might
underestimate the molecular cline width by excluding molecular evidence from the mitochondria.
Results were similar using results from either STRUCTURE analysis.
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Table S1: Summary statistics from DNA sequences at 5 nuclear loci and the mtDNA gene
ND2. iTwo nested indels, lengths 6bp and 31 bp, occur within BRM sequences. These
variants are not included in calculating segregating site or haplotype diversity values. *
denotes significance at p<.05 with Bonferroni correction. n = number of sequences. bp = base
pairs. R = minimum number recombination events. LISB = longest independently segregating
linkage block. S = number of variable sites. H = number haplotypes. Hd = haplotype
diversity. π = nucleotide diversity.
Marker
Locus(n)
bp
R LISB S H
Hd
π
Kst
type
moreaui
mtDNA
ND2 (53)
882
66 17 0.844 .008
-.003
autosomal 11836 (106) 461
3 106 5 7
0.694 .009
.018
18142 (108) 365
1 142 3 4
0.483 .004
.021
Z-linked
BRM (95)
241
0 241 3i 4i
0.233 .001
.047
CHDZ (97) 416
0 416 12 7
0.773 .003
.015
MUSK (97) 499
0 499 8 7
0.64
.003
.073*
fuelleborni
mtDNA
ND2 (63)
882
71 19 0.845 .005
.066*
autosomal 11836 (126) 461
0 312 7 9
0.689 .004
.028
18142 (126) 365
1 365 4 5
0.399 .002
.027
i
i
Z-linked
BRM (107) 241
0 241 2 3
0.073 .000
.001
CHDZ (105) 416
0 416 8 6
0.147 .001
.023
MUSK (107) 499
0 499 5 6
0.624 .002
.110*
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
4
161
162
163
164
Table S2. Song variables by species, with statistical test p-values from individual ANOVAs
(significant differences in bold at p<.05 following Bonferroni correction).
fuelleborni
moreaui
p-value
mean
SD
mean
SD
Mean interval
duration (s)
60.230
14.239
17.943
8.000
2.2x10-16
CV interval
duration
96.045
34.349
191.793
50.582
4.1x10-15
Mean peak
frequency (hz)
5370.897
193.292
5276.281
199.592
.03574
CV peak frequency
Max peak
frequency (hz)
Min peak
frequency (hz)
Range peak
frequency (hz)
Log mean #
elements
Log mean
bandwidth (hz)
CV bandwidth
Log mean freq
change (hz)
CV frequency
change
Log duration (s)
Median element
duration (s)
21.025
3.014
10.638
1.869
2.2x10-16
7615.83
338.7123
6491.505
346.619
2.2x10-16
3020.911
238.838
3621.423
450.148
2.0x10-10
4594.919
456.291
2870.082
505.342
2.2x10-16
4.391
0.388
4.510
0.362
.1629
6.551
105.561
0.418
25.747
6.427
100.056
0.449
20.676
.21
.2963
-2.551
0.245
-2.322
0.303
4.4x10-4
68.145
9.085
10.399
0.363
79.932
8.168
10.244
0.311
2.6x10-6
2.2x10-16
44.621
11.017
16.331
6.410
2.2x10-16
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
5
180
181
182
183
184
185
Table S3: Loadings on Principal Components from PCA on song variables, n = 101
individuals (variables are identical to those in Table S2), and cumulative variance explained.
Note match between variables with heavy loadings on PC1 and variables with significant
species differences from MANOVA/ANOVA in Table S2.
Variable/PC
Mean interval
duration
CV interval
duration
Mean peak freq
CV peak freq
Max peak freq
Min peak freq
Range peak freq
Log element #
Log mean BW
CV BW
Log mean freq Δ
CV freq Δ
Log duration
Med element
duration
Cumulative %
variance
explained
1
2
3
4
5
6
7
8
9
10
11
12
13
.887
-.053
-.176
-.021
-.100
-.101
.104
-.164
.323
.048
-.027
-.109
.054
-.763
.276
-.187
.015
.267
.163
.179
-.359
-.070
.196
.061
.026
.003
.148
-.256
.344
.852
.043
.015
.081
-.152
.045
-.170
.039
.058
.001
.873
.233
-.219
-.029
.004
.166
-.149
.071
.068
.022
.261
.086
.010
.835
.075
.157
.396
.117
.123
-.084
.094
-.130
.214
-.037
-.083
.004
-.661
-.506
.184
.260
-.133
-.300
-.028
.142
.069
.262
.069
.000
.001
.891
.299
.016
.141
.144
.230
-.043
-.006
-.122
.017
-.059
-.056
.002
-.060
.621
.634
-.170
.263
-.312
.040
.045
-.043
-.022
.030
.023
.078
.307
-.639
.288
-.266
.237
.354
.363
.170
.022
.007
.036
.005
.004
.024
.702
-.392
.294
-.286
-.109
.356
.209
-.027
.011
-.012
.031
-.003
-.466
.503
.434
.010
-.243
.452
-.085
.035
.221
.084
-.086
.076
-.008
-.481
.099
-.397
.211
.694
-.014
-.082
.176
.179
-.019
-.061
.022
-.001
.801
.285
.304
-.150
.181
-.310
.077
-.066
.099
.047
.004
.018
-.107
.868
-.346
-.167
-.073
-.007
-.129
-.007
-.056
-.048
.094
-.134
.211
.030
43.0
59.4
69.4
78.2
84.7
90.3
92.8
95.2
97.0
98.4
99.2
99.8
100.0
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
6
210
211
212
213
Table S4: Cline center and width estimates for preferred model architectures for each trait. To
account for mitonuclear discordance in the molecular index, q-scores in this analysis come from a
STRUCTURE analysis where mtDNA haplotypes (moreaui-type or fuelleborni-type) have been
included as the single known haplotype for a diploid locus (see Figure S1).
Trait
Center
Width
ΔAIC
Tails
Parameters
Song PC1
3.88
none
7
Song PC1
163.0 (161.3-165.0)
5.9 (0.8-9.5)
0
left
9
Song PC1
0.31
right
9
Song PC1
0.14
mirror
9
Song PC1
5.07
both
11
Molecular index
15.95
none
7
Molecular index
17.30
left
9
Molecular index 163.1 (160.3-164.2)
6.1 (3.7-7.0)
0
right
9
Molecular index
11.57 mirror
9
Molecular index
5.21
both
11
Culmen length
155.1 (140.4-158.8) 21.4 (8.6-54.0)
0
none
7
Culmen length
4.54
left
9
Culmen length
5.24
right
9
Culmen length
9.78
mirror
9
Culmen length
9.80
both
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214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
7
241
242
243
244
Table S5: Samples used for molecular analyses with localities and museum voucher numbers
indicated. Sample identities with a ‘JPMxxx/year’ form are blood samples.
Sample/Catalog Number
FM439496
FM439501
FM439499
FM439505
FM439504
FM439500
FM439503
FM439497
FM439502
JK8-150302
JK1-150302
JK5-030402
JK1-030402
JK2-290302
JK7-020402
JK7-030402
JK4-310302
JPM 060
JPM 061
JPM 062
JPM 063
JPM 064
JPM 065
JPM 066
JPM 067
JPM 068
JPM050/2008
JPM8
JPM9
JPM015
JPM17/2008
JPM20/2008
JPM21/2008
JPM22/2008
JPM26/2008
JPM32/2008
JPM33/2008
JPM34/2008
JPM36/2008
Locality
Misuku Hills, Malawi
Misuku Hills, Malawi
Misuku Hills, Malawi
Misuku Hills, Malawi
Misuku Hills, Malawi
Misuku Hills, Malawi
Misuku Hills, Malawi
Misuku Hills, Malawi
Misuku Hills, Malawi
Livingstone Mountains, Tanzania
Livingstone Mountains, Tanzania
Mt. Rungwe, Tanzania
Mt. Rungwe, Tanzania
Mt. Rungwe, Tanzania
Mt. Rungwe, Tanzania
Mt. Rungwe, Tanzania
Mt. Rungwe, Tanzania
Mt. Rungwe, Tanzania
Mt. Rungwe, Tanzania
Mt. Rungwe, Tanzania
Mt. Rungwe, Tanzania
Mt. Rungwe, Tanzania
Mt. Rungwe, Tanzania
Mt. Rungwe, Tanzania
Mt. Rungwe, Tanzania
Mt. Rungwe, Tanzania
Mt. Rungwe, Tanzania
Mt. Rungwe, Tanzania
Mt. Rungwe, Tanzania
Mt. Rungwe, Tanzania
Mufindi, Tanzania
Mufindi, Tanzania
Mufindi, Tanzania
Mufindi, Tanzania
Mufindi, Tanzania
Mufindi, Tanzania
Mufindi, Tanzania
Mufindi, Tanzania
Mufindi, Tanzania
SOURCE
FMNH
FMNH
FMNH
FMNH
FMNH
FMNH
FMNH
FMNH
FMNH
ZMUC
ZMUC
ZMUC
ZMUC
ZMUC
ZMUC
ZMUC
ZMUC
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
8
JPM37/2008
JPM38/2008
JPM39/2008
JPM40/2008
JPM42/2008
JPM43/2008
JPM44/2008
JPM021
JPM022
JPM023
JPM024
JPM025
JPM026
JPM027
JPM028
JPM029
JPM030
JPM031
JPMB013/2009
JPMB017/2009
JPMB018/2009
JPMB019/2009
JPM034
JPM035
RCKB1549
RCKB1554
RCKB1572
RCKB1583
RCKB1587
JPM052
JPM053
JPM055
JPM056
JPM057
JPM058
JPM059
JPM003/2010
JPM054
RCKB1580
JPM037
JPM039
JPM040
JPM041
JPM042
JPM001/2008
Mufindi, Tanzania
Mufindi, Tanzania
Mufindi, Tanzania
Mufindi, Tanzania
Mufindi, Tanzania
Mufindi, Tanzania
Mufindi, Tanzania
Ikokoto, Tanzania
Ikokoto, Tanzania
Ikokoto, Tanzania
Ikokoto, Tanzania
Ikokoto, Tanzania
Ikokoto, Tanzania
Ikokoto, Tanzania
Ikokoto, Tanzania
Ikokoto, Tanzania
Ikokoto, Tanzania
Ikokoto, Tanzania
Ikokoto, Tanzania
Ikokoto, Tanzania
Ikokoto, Tanzania
Ikokoto, Tanzania
Kihulula, Tanzania
Kihulula, Tanzania
Nyumbanitu, Tanzania
Nyumbanitu, Tanzania
Nyumbanitu, Tanzania
Nyumbanitu, Tanzania
Nyumbanitu, Tanzania
Nyumbanitu, Tanzania
Nyumbanitu, Tanzania
Nyumbanitu, Tanzania
Nyumbanitu, Tanzania
Nyumbanitu, Tanzania
Nyumbanitu, Tanzania
Nyumbanitu, Tanzania
Nyumbanitu, Tanzania
Nyumbanitu, Tanzania
Nyumbanitu, Tanzania
Selebu Mountain, Tanzania
Selebu Mountain, Tanzania
Selebu Mountain, Tanzania
Selebu Mountain, Tanzania
Selebu Mountain, Tanzania
Image, Tanzania
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
9
JPM002
JPM002/2008
JPM003
JPM003/2008
JPM004
JPM006/2008
JPM007/2008
JPM009/2008
JPM010/2008
JPM011/2008
JPM012/2008
JPM013/2008
JPM014/2008
JPM016/2008
DCM2-251197
JK5-191299
JK6-271200
JK5-160101
JK2-110101
JK3-110101
JK4-191200
JK1-040101
JK1-271200
JK6-191001
JK2-211001
JK06-151001
138653
138660
138665
138666
138669
138713
138720
138721
138722
138725
138753
138788
139006
139068
139079
139080
139089
139161
139169
Image, Tanzania
Image, Tanzania
Image, Tanzania
Image, Tanzania
Image, Tanzania
Image, Tanzania
Image, Tanzania
Image, Tanzania
Image, Tanzania
Image, Tanzania
Image, Tanzania
Image, Tanzania
Image, Tanzania
Image, Tanzania
Uvidunda Mountains, Tanzania
Mang'alisa, Tanzania
Mafwemiro, Tanzania
Mafwemiro, Tanzania
Mafwemiro, Tanzania
Mafwemiro, Tanzania
Mafwemiro, Tanzania
Mafwemiro, Tanzania
Mafwemiro, Tanzania
Wota, Tanzania
Wota, Tanzania
Wota, Tanzania
Ndundulu, Tanzania
Ndundulu, Tanzania
Ndundulu, Tanzania
Ndundulu, Tanzania
Ndundulu, Tanzania
Ndundulu, Tanzania
Ndundulu, Tanzania
Ndundulu, Tanzania
Ndundulu, Tanzania
Ndundulu, Tanzania
Ndundulu, Tanzania
Ndundulu, Tanzania
Ndundulu, Tanzania
Ndundulu, Tanzania
Ndundulu, Tanzania
Ndundulu, Tanzania
Ndundulu, Tanzania
Ndundulu, Tanzania
Ndundulu, Tanzania
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
MVZ
ZMUC
ZMUC
ZMUC
ZMUC
ZMUC
ZMUC
ZMUC
ZMUC
ZMUC
ZMUC
ZMUC
ZMUC
ZMUC
ZMUC
ZMUC
ZMUC
ZMUC
ZMUC
ZMUC
ZMUC
ZMUC
ZMUC
ZMUC
ZMUC
ZMUC
ZMUC
ZMUC
ZMUC
ZMUC
ZMUC
ZMUC
10
139179
139229
140436
245
246
247
248
249
Ndundulu, Tanzania
Ndundulu, Tanzania
Ndundulu, Tanzania
ZMUC
ZMUC
ZMUC
Figure S1. Probability of assignment to Nectarinia moreaui (green) and N. fuelleborni (purple)
from a structure analysis where ND2 haplotypes are scored as moreaui or fuelleborni in origin
and included as a diploid marker with one known and one unknown state. These values were used
as a molecular hybrid index for cline analyses using HZAR (Table S4).
250
251
252
253
254
255
256
11