1
2
Population signatures of large-scale, long-term disjunction and small-scale, short-term
habitat fragmentation in an Afromontane forest bird
3
4 Jan Christian Habel
1*
, Ronald K. Mulwa
2
, Franz Gassert
3 , Dennis Rödder 4
, Werner Ulrich
5
,
5 Luca Borghesio
6
, Martin Husemann
1
, Luc Lens
7
6
7
8
1
Terrestrial Ecology Research Group, Department of Ecology and Ecosystem Management,
Technische Universität München, D-85350 Freising-Weihenstephan, Germany
9
2
Department of Ornithology, National Museums of Kenya, KE-00100 Nairobi, Kenya
10
3
Department of Neurobehavioral Genetics, Trier University, D-54290 Trier
11 4 Zoologisches Forschungsmuseum Alexander Koenig, Adenauerallee 160, D-53113 Bonn,
12
13
Germany
5
Nicolaus Copernicus University, Department of Animal Ecology, Pl-87100 Toruń, Poland
14
15
6
Department of Biological Sciences, U niversity of Illinois, 60607-Chicago, Illinois USA
7
Terrestrial Ecology Unit, Ghent University, B-9000 Ghent, Belgium
16
17 *Corresponding author:
18 Jan Christian Habel, Terrestrial Ecology Research Group, Department of Ecology and
19 Ecosystem Management, Technische Universität München, Hans-Carl-von-Carlowitz-Platz 2,
20 85350 Freising-Weihenstephan, Germany
21 Email: Janchristianhabel@gmx.de
22
23 Running title: Fragmentation genetics in a tropical forest bird
24
25 Keywords: Biometrics, cloud forest, habitat continuity, habitat change, population genetics,
26 microsatellites, temporal comparison, Species Distribution Modelling
1

27 ABSTRACT
28 The Eastern Afromontane cloud forests occur as geographically distinct mountain exclaves.
29 The conditions of these forests range from large to small, and from fairly intact to strongly
30 degraded. For this study we sampled individuals of the forest bird species, the Montane
31 White-eye Zosterops poliogaster from 16 sites and four mountain archipelagos. We analysed
32 12 polymorphic microsatellites and three phenotypic traits, and calculated Species
33 Distribution Models (SDMs) to project past distributions and predict potential future range
34 shifts under a scenario of climate warming. We found well supported genetic and
35 morphologic clusters that corresponded to the mountain ranges where populations were
36 sampled, with 43% of all alleles being restricted to single mountains. Genetic population
37 differentiation strongly matched each other at a regional level. Our data suggest that large-
38 scale and long-term geographic isolation on mountain islands caused genetically and
39 morphologically distinct populations clusters in Z. poliogaster . However, major genetic and
40 biometric splits were not correlated to the geographic distances among populations. This
41 heterogeneous pattern can be explained by past climatic shifts, as highlighted by our SDM-
42 projections. Anthropogenically fragmented populations showed lower genetic diversity and a
43 lower mean body mass, possibly in response to suboptimal habitat conditions. Based on these
44 findings and the results from our SDM analysis, we predict further losses of genotypic and
45 phenotypic uniqueness in the wake of climate change, due to the contraction of the species´
46 climatic niche and subsequent decline in population size.
47
2

48 INTRODUCTION
49 Fragmented populations have long been assumed to constitute valid ecological models to
50 predict population trajectories in landscapes subject to rapid anthropogenic habitat
51 fragmentation (MacDougall-Shackleton et al ., 2011). Indeed, fragmented populations often
52 have smaller effective population sizes, reduced dispersal rates, and lower genetic diversity at
53 the population level, compared to panmictic populations (Frankham, 1997; Blanchet et al .,
54 2010). However, in contrast to populations living in historically stable habitat conditions,
55 species exposed to rapid fragmentation of formerly interconnected habitats are rarely able to
56 adapt to these new environmental conditions, in particular since they are often exposed to
57 simultaneous deterioration of the remaining habitat (Stratford and Robinson, 2005;
58 Keyghobadi, 2007; Walker et al ., 2008, Habel and Zachos, 2013). While demographic and
59 genetic effects may be slowed down or counterbalanced by migration and gene flow among
60 populations (Wright, 1951), population connectivity often rapidly decreases when habitat
61 fragmentation increases (Fahrig, 2003). Ultimately, the combination of habitat loss,
62 deterioration and isolation is expected to modify the ecological conditions for populations,
63 which have been shown to be subject to stochastic demographic fluctuations, increased
64 inbreeding, loss of heterozygosity and accumulation of mildly deleterious alleles, while
65 genetic variation might additionally be lost through strong genetic drift in exceedingly small
66 populations (Frankham, 1995; Higgins and Lynch, 2001; Keller and Waller, 2002;
67 Kalinowski and Waples, 2002; Frankham, 2005; Palstra and Ruzzante, 2008). The direction
68 and strength of these effects, however, is expected to vary with both the (evolutionary) history
69 of the landscape change and with species-specific life history traits (Callens et al ., 2011,
70 MacDougall-Shackleton et al ., 2011).
71
72 The Eastern Afromontane (EAM) biodiversity hotspot offers a unique setting to study how
73 long-term natural disjunction and short-term anthropogenic fragmentation may affect
3

74 populations over different temporal and spatial scales. This biodiversity hotspot region
75 consists of widely scattered but biogeographically similar mountains, running from the Arabic
76 Peninsula to Mozambique and Zimbabwe (White, 1978). Strong isolation caused by
77 orographic heterogeneity and relatively stable climatic conditions have led to an accumulation
78 of endemic species and intraspecific lineages (Bowie et al ., 2006; Burgess et al ., 2007). Many
79 taxa in the region currently occur as geographically restricted units at single mountains, and
80 as such, comprise naturally fragmented populations with independent evolutionary trajectories
81 (Measey and Tolley, 2011). In addition to these long term evolutionary dynamics, plant and
82 animal taxa of the EAM biodiversity hotspot are currently affected by severe loss and
83 degradation of their natural habitats over very short timescales. The EAM forests, in
84 particular, have been subjected to variable anthropogenic effects over the last decades that
85 resulted in a wide range of contemporary forest types, i.e. (i) both large and small pristine
86 forests; (ii) historically fragmented forests; and (iii) anthropogenically fragmented and highly
87 degraded forests.
88
89 In this study, we analysed genetic variation based on 12 polymorphic microsatellite markers
90 and phenotypic variation based on three morphologic traits, in Zosterops poliogaster
91 populations sampled in 16 sites across four mountain blocks. Z. poliogaster requires cool and
92 moist climatic conditions and is thus largely restricted to the cloud forests on top of the East
93 African mountains. We sampled populations in each of the above mentioned forest types with
94 contrasting habitat history and current characteristics. To study genetic and phenotypic
95 population effects of rapid habitat change, we sampled and measured both museum specimens
96 and current captures, i.e. covering past and recent episodes of forest fragmentation (Pellikka
97 et al ., 2009). We further performed Species Distribution Models (SDMs) based on 19
98 bioclimatic variables to project the past (6000 and 21,000 years BP), current and future (2080)
4

99 climatic niches of Z. poliogaster . By integrating results from genetic, biometric and SDM
100 analyses, we addressed the following research questions:
101 (i) How do long-term and short-term processes of habitat isolation and fragmentation
102
103 affect the genetic and morphological structure of mountain populations of Z. poliogaster ?
104 (ii) How do habitat persistence and rapid habitat change affect the population genetic
105 structure in Z. poliogaster ?
106 (iii) How may climate change affect the intraspecific structure and uniqueness of Z. poliogaster in the near future? 107
108
5

109 MATERIAL AND METHODS
110 Study species
111 The bird genus Zosterops is known as a “great speciator”, i.e. showing strong levels of genetic
112
113 and phenotypic differentiation across various geographical ranges (Warren et al ., 2006;
Moyle et al
., 2009; Milá et al ., 2010). The East African Mountain White-eye, Zosterops
114 poliogaster is a omnivorous (mostly insectivorous - nectarivorous) flocking bird species that
115 inhabits moist and cool mountain cloud forests across Kenya, Tanzania, Uganda, Somalia,
116 Ethiopia and Eritrea at an elevation of 1500-2500m (Mulwa et al.
, 2007; Redman et al .,
117 2009). Populations at geographically-isolated mountains show distinct morphological (e.g.
118 plumage colouration; Redman et al ., 2009), genetic (Habel et al ., 2013) and bioacoustic
119 (Habel et al ., 2014) variation, which has resulted in to the recognition of different taxonomic
120 entities (Borghesio and Laiolo 2004; Mulwa et al ., 2007; Del Hoyo et al.
, 2008).
121
122 Sampling scheme
123 Between 1990 and 2011, a total of 390 individuals from 16 populations were sampled with
124 mist nets in four mountain ranges across the Kenyan section of the EAM: (i) Mt. Kulal
125 (2,69N; 36,94E; ca. 2000m) and Mt. Kasigau (-3,82N; 38,65E; ca. 1500m) still harbour
126 pristine, interconnected and intact cloud forest, in which 46 and 22 individuals were sampled,
127 respectively; (ii) In the Chyulu Hills (-2,66N; 37,87E, ca. 2200m), cloud forests constitute a
128 forest-grassland mosaic that has at least persisted since the Maasai pastoralists caused regular
129 fires many hundreds of years ago. A total of 52 individuals were sampled in two forest
130 fragments located at the northern- and southernmost edges of the mountain range; (iii) In the
131 Taita Hills (-3,41N; 38,30E; ca. 1800m), continuous cloud forest has been transformed into
132 isolated and degraded forest remnants mainly due to small-scale subsistence agriculture, with
133 a particularly dramatic loss, deterioration and isolation of the remaining forest cover since the
6

134 1960s (Pellika et al ., 2009). Here, a total of 270 individuals were sampled in 11 forest
135 fragments located along the entire higher elevational range.
136
137 Upon capture, each individual was banded with a unique aluminium ring from the East
138 African Ringing Scheme. After measuring its wing length (mm), tarsus length (mm), and
139 body mass (g), a blood sample (stored in pure ethanol and subsequently frozen at -20°C) or
140 feather sample was collected. To allow analysis of temporal variation, field samples collected
141 during 1990, 1997 and 2000 (Ngangao 1990: N = 28; Mbololo 1990: N = 27; Mbololo 2000:
142 N = 14; Mt. Kulal 1997: N = 25) were complemented with 17 museum specimens collected in
143 Chyulu Hills during 1938 (National Museums of Kenya, Nairobi). From these museum
144 specimens, DNA was extracted from blood, feathers or toe pads. The sampling location of
145 each population is shown in Fig. 1; further details on locations, sample sizes and genetic
146 parameters are provided in Table 1.
147
148 Genetic analysis
149
150
DNA was extracted using the Qiagen DNeasy TM Tissue Extraction Kit (Hilden, Germany) following the manufacturer´s protocol for blood and toe-pads or a user-developed one for
151 feathers (see De Volo et al ., 2008). Microsatellite loci were amplified using Thermozyme
152 Mastermix (Molzym, Bremen, Germany). The PCR products were visualised with an
153 automated sequencer (Beckmann, Coultier). Details of primer-specific PCR conditions and
154 multiplex-assignments are given in Habel et al . (2013). The following 12 microsatellite
155
156 primers were genotyped: Cu28, LZ44, LZ41, LZ22, LZ45, LZ14, LZ54, LZ35, Mme12,
LZ18, LZ50, and LZ2. The forward primer of each pair was 5´-labelled with the fluorescent
157 dyes BMN-6 or CY5. Allele sizes were scored against the internal standard ROX-400SD
158 using GENEMAPPER 3.5 (Applied Biosystems).
159
7

160 Statistics on genetic data
161 We used the program MICROCHECKER 2.2.3 (Van Oosterhout et al ., 2004) to test for
162 patterns indicating stutter bands, large allele dropout or null alleles (Selkoe and Toonen,
163 2006). The mean number of alleles per population ( A ), allelic richness ( AR ) and locus-specific
164 allele frequencies were calculated with FSTAT 2.9.3.2 (Goudet, 1995). Calculations of
165 observed ( H o
) and expected ( H e
) heterozygosity, tests for Hardy-Weinberg equilibrium
166 (HWE) and linkage disequilibrium (LD). Recent changes in effective population size were
167 analysed with BOTTLENECK 1.2.02 (Cornuet and Luikart, 1996).
168
169 Hierarchical and non-hierarchical genetic variance analysis (AMOVAs) was performed with
170 ARLEQUIN 3.1 (Excoffier et al ., 2005). AMOVAs were computed using the microsatellite-
171 specific R -statistics (Slatkin, 1995; Selkoe and Toonen 2006). The most probable number of
172 genetic clusters without a priori definition of groups was inferred with STRUCTURE 3.1
173 (Hubisz et al . 2009). The batch run function was applied to carry out a total of 100 runs (10
174 each for one to ten clusters), i.e. K = 1 to K = 10. Replicate runs allowed us to calculate means
175 and standard deviations for fixed K -values. For each run, burn-in and simulation lengths were
176 150,000 and 500,000, respectively. Since log probability values for K values were earlier
177 shown to be unreliable in some cases (Evanno et al . 2005), we calculated the more refined ad
178 hoc statistic ∆ K , based on the rate of change in the log probability of data between successive
179 K values. Mantel tests were applied to test for correlations between genetic and geographic
180 distances at a distributional (i.e. after merging individuals within each mountain massif and
181 creating four groups, Mt. Kasigau, Taita Hills, Chyulu Hills and Mt. Kulal) and regional (i.e.
182 across all 11 Taita populations) scale. Matrices of genetic distances between populations were
183 calculated based on Cavalli-Sforza and Edwards (1976) distances and as pairwise R st
using
184 ARLEQUIN 3.1. A total of 5000 permutations were performed to infer levels of statistical
185 significance.
8

186
187 To assess the level and direction of gene flow at the same two geographic scales, we
188 estimated the proportion of non-migrants and the source of migrants for each population by
189
190 using a Markov chain Monte Carlo (MCMC) algorithm in BAYESASS 1.3 (Wilson and
Rannala, 2003). We performed 9*10
6
iterations with 3*10
6
iterations discarded as burn-in.
191 Delta values of m = 0.30, P = 0.15, and F = 0.15 yielded an average number of changes
192 within the accepted range (Wilson and Rannala, 2003).
193
194 Phenotypic analyses
195 We measured wing length (mm), tarsus length (mm) and body mass (g) to test for potential
196 phenotypic differentiation among regional population clusters. We tested for auto-correlation
197 among the three characters using a MANCOVA. The obtained residuals were used for
198 subsequent analyses to adjust for potential allometry. As no significant auto-correlation was
199 detected, differences in morphometrics between populations were analysed by orthogonal
200 squares ANOVA in combination with post-hoc Tukey tests. We used principal component
201 analysis (PCA) to reduce the data complexity and to determine traits for which populations
202 were significantly diverged.
203
204 Species Distribution Modelling
205 Geo-referenced species records were compiled from own field data supplemented with data
206 from specimens housed in the collections of the National Museums of Kenya (NMK, Nairobi,
207 Kenya), the Zoological Research Museum Alexander Koenig (ZFMK, Bonn, Germany), the
208 Zoological Museum Kopenhagen (ZMUC, Denmark), and records from the Global
209 Biodiversity Information Facility (GBIF), as well as from various publications (Zimmerman
210 et al ., 1996; Borghesio and Laiolo, 2004; Mulwa et al ., 2007; Redman et al ., 2009).
211 Confounding effects resulting from spatial autocorrelation were minimized by randomly
9

212 selecting one species record per 10 arc min grid cell (i.e. spatially filtering 195 records).
213 Current local climatic conditions were inferred from the WORLDCLIM 1.4 database with a
214 spatial resolution of 2.5 arc min (Hijmans et al ., 2005). Expected climatic conditions for 2080
215 were derived from four global circulation models (GCMs) (CCCMA-CGCM2, CISRO-MK2,
216 HCCPR HADCM3, NIES99). A2a and B2a scenarios developed by the Inter-governmental
217 Panel on Climate Change (IPCC, 2007) were spatially downscaled to 2.5 arc min (Ramirez
218 and Jarvis, 2008). As each climate data set comprised 19 bioclimatic variables (Busby 1991),
219 we used pairwise squared Pearson’s correlation coefficients to quantify multi-colinearity
220 (Heikkinen et al ., 2006). In case of R
2
> 0.75, one variable of each pair that was considered
221 biologically most relevant was retained for species distribution modeling. This procedure
222
223
224
225
226 resulted in ten bioclimatic variables, i.e. ‘annual mean temperature’ (bio1), ‘isothermality’
(bio3), ‘temperature seasonality’ (bio4), ‘temperature annual range’ (bio7), ‘annual precipitation’ (bio12), ‘precipitation of wettest month’ (bio13), ‘precipitation of driest month’
(bio14), ‘precipitation seasonality’ (bio15), ‘precipitation of warmest quarter’ (bio18), and
‘precipitation of coldest quarter’ (bio19).
227
228
229
SDMs were computed with MAXENT 3.3.3k, applying default settings and a logistic output format (Elith et al ., 2011; Phillips et al
., 2006; Phillips and Dudík, 2008) and using a training
230 area enclosed by a 100 km buffer around the species records (see recommendation by Mateo
231 et al ., 2010). To evaluate SDMs through the area under the receiver operating characteristic
232 curve (AUC; Swets, 1988), a total of 100 SDMs were computed, each trained with 70% of the
233 species records and tested with the remaining 30%. Based on the output of these replicate
234 runs, average predictions scores were computed for past, current and future bioclimatic
235 conditions as suggested by each of the four GCMs. We selected the minimum MAXENT
236 score at a 5% sample omission rate as presence/absence threshold. Non-analogous climatic
237 conditions within projection areas exceeding those that a SDM was trained for, may reduce
10

238 the reliability of predictions (Elith et al.
, 2010; Fitzpatrick and Hargrove, 2009; Rocchini et
239 al ., 2011). This potential source of uncertainty was quantified in a spatially explicit way in
240 each scenario by using multivariate environmental similarity surfaces (MESS; Elith et al .,
241 2010). Climatic conditions projected for the mid Holocene climate optimum (MH, 6,000
242 years BP) were extracted from CCSM and MIROC simulations available through the PMIP 2
243 database (Braconnot et al ., 2007) and spatially downscaled following the same technique as
244 the 21,000 years BP data, which is described in detail in Peterson and Nyári (2007).
245
11

246 RESULTS
247 Genetic diversity
248 The following locality*loci combinations showed significant deviations from Hardy-
249 Weinberg equilibrium due to the presence of null alleles: Chyulu-1938: ZL22; Fururu: ZL44;
250 Ndiwenyi: ZL14, ZL54, Mme12; Ngangao: ZL22, ZL14; Mbololo: ZL14. Samples from Mt.
251 Kulal showed no deviations from Hardy-Weinberg equilibrium, and for Ronge we did not
252 obtain valid results due to small sample sizes. After Bonferroni correction for multiple testing,
253 linkage disequilibrium was not significant in any pair of loci. Locus- and locality-specific
254
255 allele frequencies are listed in Appendix S1. Microsatellite DNA polymorphisms ranged between 2 (ZL41, Mme12, Cu28) and 12 (Zl49) alleles per locus, with a mean of 4.7 ± 2.3
256 alleles/locus and a total of 61 alleles across all loci. Allele sizes did not significantly differ
257 between recent and historic samples (sampling years are given in the list of allele frequencies
258 in Appendix S1). A total of 26 out of 61 alleles (43%) were restricted to single mountains.
259 The proportions of private alleles per population are listed in Table 1.
260
261 Mean numbers of alleles ( A ), allelic richness ( AR ), proportions of private alleles ( AP ), and
262 percentages of expected ( H e
) and observed ( H o
) heterozygosity did not significantly differ at
263 regional level among the mountain populations (Kruskal-Wallis ANOVA, all p > 0.05).
264 Current and historic estimates (i.e. inferred from museum specimens collected in the same
265 forests) did not significantly differ for the following pairwise comparisons in the Taita Hills
266 (Ngangao 1990-2009, Mbololo 1990-2009, Mbololo 2000-2009), Chyulu Hills (1938-
267 current), and Mt. Kulal (1997-2010) (paired comparisons per fragment including time
268 intervals as co-variable, population-wise U-tests: all p>0.05). The following populations from
269 the Taita Hills showed a significant excess of homozygotes (tested over all loci): Vuria,
270 Ngangao, Ndiwenyi, Chawia, Fururu, Macha (all p < 0.05). Population- and mountain-
271 specific values of genetic diversity are listed in Table 1.
12

272
273 Genetic population structuring
274 The best supported model assigned individuals to two genetic clusters ( K = 2 ): a first cluster
275 comprising individuals from the Taita Hills and Mt. Kasigau, and a second one comprising
276 individuals from Chyulu Hills and Mt. Kulal. While this model effectively yielded the
277 strongest statistical support (Fig. 2), Hausdorf and Hennig (2010) caution against clustering
278 genotypes in few groups. The second best models assigned individuals to seven genetic
279 clusters, thereby (i) splitting individuals from the Taita Hills and Mt. Kasigau, (ii) lumping
280
281 individuals from the Chyulu Hills and Mt. Kulal, and (iii) assigning individuals from the Taita
Hills to a very heterogeneous cluster (Fig. 2). ∆K values of all different models are listed in
282 Appendix S2.
283
284 At a regional scale, the level of genetic variance among Z. poliogaster populations from Mt.
285 Kasigau, Taita Hills, Chyulu Hills and Mt. Kulal was 11.1878 ( R
ST
: 0.5488, p < 0.001).
286 Hierarchical variance analysis assigned the strongest genetic split between the Chyulu Hills
287 and the neighbouring Taita Hills (<100 km geographic distance) with a genetic variance of
288 14.6971 ( R
CT
: 0.6245, p < 0.001), while the remote Mt. Kulal and Chyulu populations (>600
289 km geographic distance) showed comparatively poor genetic differentiation (10.9803, R
CT
:
290 0.5079, p < 0.001). Within the Taita Hills, the 11 remnant populations showed significant
291 genetic differentiation (0.6885, R
ST
: 0.0757, p < 0.001), partitioned over two spatial scales,
292 i.e. between the two main mountain isolates Dabida and Mbololo (0.7642, R
CT
: 0.0795, p <
293 0.05) and within Dabida (0.4684, R
ST
: 0.0504, p < 0.001). In contrast, the southern- and
294 northernmost populations of the Chyulu Hills were not genetically differentiated (-0.0179,
295 R
ST
: -0.0026, p > 0.05) despite comparable geographic distances (see Fig. 1). The level of
296 genetic differentiation between Dabida and Mbololo increased over a 19 year period from
13

297 non-significant in 1990 to highly significant in 2009 (1990: 0.0044, R
CT
: 0.0006, p > 0.05;
298 2009: 0.6871, R
CT
: 0.0930, p < 0.001). All values are listed in Table 2.
299
300 Mantel tests did not yield significant correlations between genetic and geographic distances at
301 regional (among four mountain groups, Mt. Kasigau, Taita Hills, Chyulu Hills and Mt. Kulal;
302 r = 0.32, p = 0.13) and local (11 Taita Hills forest fragments; r = -0.20, p = 0.90) scales. While
303 the overall rate of genetic exchange among the four Z. poliogaster mountain groups was low,
304 there was weak gene flow detected among the 11 Taita Hills populations. The small
305 Mwachora and Ndiwenyi populations thereby seemed to act as sources (see Table 3B).
306
307 Phenotypic structures
308 Principal component analysis revealed a morphological split between populations from the
309 Taita Hills and those from the Chyulu Hills and Mt. Kulal (Fig. 3A), while individuals from
310 Mt. Kasigau took an intermediate position. Taita individuals were significantly smaller and
311 lighter than individuals from Mt Kulal and the Chyulu Hills (ANOVA: Tukey post-hoc
312 comparisons p < 0.01), while individuals from Mt Kasigau showed intermediate values.
313 Populations from different mountains did not significantly differ in morphology (t-test: p >
314 0.1). A PCA bi-plot showed a strong congruence between morphological and genetic
315 differentiation at a regional level, i.e. separating the Chyulu Hills and Mt. Kulal populations
316 from those of the Taita Hills and Mt. Kasigau (Fig. 3B). Population means and standard
317 deviations of all phenotypic data are listed in Appendix S3.
318
319 Species Distribution Modelling
320 Based on 100 replicates, average training and test AUC scores were 0.932 and 0.871,
321 respectively. Variable bio1 (`annual mean temperature’) (35.0%) contributed most strongly to
322 the SDMs, followed by bio18 (‘precipitation of warmest quarter’) (16.9%), bio19
14

323 ‘precipitation of coldest quarter’ (10.1%), bio14 (8.3%), bio3 (8.2%), bio15 (5.6%), and bio7
324 (5.3%). All other variables contributed less than 5% on average.
325
326 The current potential distribution of Z. poliogaster covers major parts of the East African
327 highlands such as Pare Mts. Mt. Meru, Kilimajaro, Taita Hills, Chyulu Hills, Central Kenyan
328 Highlands, Cherangani Hills, and the northern Kenyan Highlands (Mt. Kulal and Mt. Nyiru)
329 (Fig. 4). The projected past distribution of Z. poliogaster showed a strong differentiation into
330 three areas with suitable climatic conditions for 6,000 years BP, covering (i) parts of the
331 Eastern Arc Mountains, (ii) Central Kenyan Highlands, and (iii) Ethiopian Highlands. The
332 distribution range in Southern Kenya (Chyulu Hills, Taita Hills and Mt. Kasigau), however,
333 did not reflect the species´ specific climatic niche (Fig. 4). When projecting the SDMs to
334
335 future climate change scenarios A2a and B2a as proposed by IPCC (2007) for 2080, a severe retraction in the distribution of the species´ climatic niche is projected. Particularly strong
336 effects are expected near the southern distribution edge (Eastern Arc Mountains), but also
337 across parts of the Central Kenyan Highlands and northern Kenya. Suitable climatic exclaves
338 might remain in some areas of the Pare and Usambara Mountains, parts of the Central Kenyan
339 Highlands and restricted areas in the Ethiopian highlands (Fig. 4).
340
15

341 DISCUSSION
342 Populations of Z. poliogaster are genetically and phenotypically differentiated over both,
343 regional and local scales. Morphological and genetic data indicate that distinct mountain
344 specific clusters evolved independently from each other. We found no correlation between
345 intraspecific and geographic distance suggesting that geographic isolation is not a main driver
346 of differentiation in this species. On a regional level (here the Taita Hills forest archipelago)
347 we found significant genetic differentiation even among neighbouring populations (Dabida
348 and Mbololo mountains). Temporal analyses point towards an increase in genetic
349 differentiation over time between the Taita Hills populations, while the adjoining Chyulu
350 Hills populations do not show such pattern.
351
352 Effects of long-term disjunction
353 Based on our analyses, we identify two major genetic and morphometric clusters within the
354 studied region (Fig. 4). A first cluster comprises the two southernmost mountain populations,
355 Mt. Kasigau and the Taita Hills that are separated by 60 km of dry savannah. A second cluster
356 comprises both populations of the Chyulu Hills (60 km distant from the Taita Hills) and the
357 one from Mt. Kulal, located 600km north. This intraspecific clustering may reflect the
358 geological history of the mountain massifs: Mt. Kasigau and the Taita Hills represent the
359 northernmost edge of the Eastern Arc Mts. which evolved very early during the Precambrian
360 (White, 1978). In contrast, the other two mountain massifs, Chyulu Hills and Mt. Kulal are
361 geologically much younger (Dimitrov et al ., 2012). The influence of these contrasting
362 geological ages on evolutionary processes have been demonstrated for other species, with
363 deep inter- and intraspecific splits detected in taxa inhabiting the Eastern Arc Mts. (Bowie et
364 al ., 2006; Dimitrov et al . 2012; Fuchs et al ., 2011; Tolley et al ., 2011). Yet, to formally test
365 this hypothesis in the genus Zosterops , phylogenetic analyses and robust estimates of
366 divergence times between mountain-specific lineages are required.
16

367
368 An alternative explanation for the observed within-taxon differentiation is based on climatic
369 fluctuations of the last thousands of years (including the glacial-interglacial cycles). The last
370 glaciation ended about 10,000 years ago and caused major forest contractions. During such
371 glaciation many forest species most probably expanded their ranges and colonize isolated
372 forest massifs (Hamilton, 1982). As indicated by the results from SDMs, the past distribution
373 of Z. poliogaster (6000 years BP) was strongly divided into a southern (Eastern Arc) and
374 northern (Central Kenyan Highlands) distribution range. This could be still reflected by our
375 data-sets. The obtained differentiation pattern and the lack of an isolation-by-distance further
376 indicate that the geographic distance is not the most important force driving the genetic and
377 morphological differentiation of these populations.
378
379 In addition to this major genetic split, we detected significant genetic differentiation within
380 mountain massifs, e.g. between populations from two Taita Hills isolates (Mbololo and
381 Dabida) that are separated by a small valley only (cf. Callens et al ., 2011). These findings
382 underline the specific environmental demands (moist and cool climatic conditions, see SDM
383 results) and resulting strong geographical restriction of Z. poliogaster to higher elevations
384 (Mulwa et al.
, 2007; Redman et al ., 2009).
385
386 Habitat persistence and habitat change
387 The restriction of gene flow among remnant populations after the break down of landscape
388 connectivity is a commonly observed coherence (Knutsen et al ., 2000). However, only few
389 studies used historical data to empirically assess the impact of rapid landscape changes on the
390 intraspecific structure of populations. Temporal comparisons of the genetic structure of
391 populations from individuals sampled in the Taita Hills over a 19 years period indicate a
392 significant temporal increase in genetic differentiation. This is likely the result of severe
17

393 habitat fragmentation which has taken place in the Taita Hills during the past decades (Pellika
394 et al ., 2009).
395
396 Several studies have addressed the negative effects of rapid ecosystem change on the viability
397 of populations (Fahrig, 2003), also in the EAM. For instance, Lens et al . (1999) showed that
398 levels of fluctuating asymmetry in Z. poliogaster and six sympatric forest bird species were
399 four to seven times higher for birds in the smallest, most degraded fragments. Increased levels
400
401 of FA have been correlated with reductions in the growth rates and competitive ability of a range of organisms (see reviews by Møller, 1997; Møller & Thornhill, 1998), as well as with
402 reduced survival probabilities in the critically-threatened Taita thrush ( Turdus helleri ) (Lens
403 et al., 2002). In the latter species, Galbusera et al . (2000) also reported severe levels of genetic
404 drift in small, degraded populations. A comparable pattern as found for the Mountain White-
405 eye was observed in the Spanish imperial eagle ( Aquila adalberti ) of which the historic
406 panmictic population network recently collapsed into a scatter of isolated remnant populations
407 (Martinez-Cruz et al ., 2007). The fact that Z. poliogaster individuals from the
408 anthropogenically fragmented forest patches of the Taita Hills have a lighter weight which
409 might be a plastic response to the lower habitat quality. In contrast, populations from more
410 undisturbed fragments, i.e. where habitat conditions remained fairly stable during the recent
411 past, such as in the Chylu Hills, showed higher levels of genetic diversity and weaker genetic
412 differentiation.
413
414 Such contrasting intraspecific signatures of habitat subdivision in two adjoining mountain
415 massifs might be explained by two different scenarios. First, while the Taita Hills forest
416 experienced rapid habitat fragmentation and degradation, the Chyulu Hills are covered by
417 forest-grassland mosaics that persisted during the past hundreds of years. While the current
418 degree of forest fragmentation in the Chyulu and Taita Hills is roughly comparable, both
18

419 areas hence underwent fundamentally different habitat histories. The coinciding patterns of
420 genetic differentiation and habitat history suggest that populations affected from fast habitat
421 change (e.g. the Taita Hills) might suffer severely, whereas populations living in (e.g. the
422 Chyulu Hills) are less affected. Thus, fragmented habitats must not always affect species
423 negatively (Vucetich et al ., 2001; Habel and Schmitt, 2012; Habel and Zachos, 2013).
424
425 Climate warming threats intraspecific diversity
426 Our biometric and genetic analyses suggest a high level of intraspecific uniqueness for single
427 mountain areas. 43% of all detected alleles are geographically restricted to single mountain
428 massifs. This high level of variability and intraspecific endemicity is threatened by a
429 combination of direct (see above) and indirect factors. Strong demographic pressure across
430 major parts of Africa and the increasing need for agricultural products and thus agricultural
431 land lead to increased deforestation (Pellika et al ., 2009). In addition, our SDM projections
432 highlight the relevance of cool and moist climatic conditions for this species (the factor
433 `annual mean temperature’ contributes 35.0%, ‘precipitation of warmest quarter’ contributes
434 16.9%, and ‘precipitation of coldest quarter’ contributes 10.1% to the explanatory power of
435 the suitability of habitats for Z. poliogaster ). These models further suggest that climate
436 change will cause warmer and drier conditions leading to a decline of suitable habitats for Z.
437 poliogaster (Fig. 4). This might lead to extinction of local populations and result in the loss of
438 intraspecific uniqueness.
19

439 Acknowledgements
440 The research was financed by the German Academic Exchange Service (DAAD) and the
441
442
Natural History Museum Luxembourg (MNHN Luxembourg). We thank Titus Imboma,
Onesmus Kioko (Nairobi, Kenya) and Dirk Louy, Thomas Schmitt and Sönke Twietmeyer
443 (Trier, Germany) for field assistance. We thank Hans Matheve (Ghent, Belgium) and two
444 anonymous referees for improving a draft version of this article.
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25

610
604 Figure 1: Overview of the 16 sampling locations of Zosterops poliogaster . The inset (B)
605 details the Taita Hills sampling sites. 1: Mt. Kasigau, 2: TH-Chawia, 3: TH-Fururu, 4: TH-
606 Macha, 5: TH-Mwachora, 6: TH-Ndiwenyi, 7: TH-Ngangao, 8: TH-Vuria, 9: TH-Wundanyi,
607 10: TH-Yale; 11: TH-Ronge, 12: TH-MBololo, 13: CH-Satellite, 14: CH-Simba valley, 15:
608 Chyulu Hills-1938; 16: Mt. Kulal. Numbers of sampling sites and names of localities coincide
609 with Table 1. Abbreviations: TH = Taita Hills, CH = Chyulu Hills.
26

611 Figure 2: Bayesian structure analyses of populations from Zosterops poliogaster performed
612 with STRUCTURE (Hubisz et al ., 2009), for K = 1-10. Results supported by highest Δ K
613 values ( K = 2 and K = 7) are presented, distinguishing the mountain population of Mt.
614 Kasigau and Taita Hills (TH), while Chyulu Hills (CH) clusters together with Mt. Kulal.
615 Names of populations coincide with other figures and tables.
616
27

617 Figure 3: A: Principal component analysis (PCA) based on three biometric characters
618 segregates the individuals from the Taita Hills (open triangles) from Chyulu Hills (Simba and
619 Satellite) (open dots) and the individuals from Mt. Kulal (open squares). Individuals from Mt.
620
621
Kaisgau (black dots) scores intermediate between both groups. Axis 1 correlates highly with body mass (r
2
= 0.54, p < 0.01). B: A PCA biplot for average morphological (black dots) and
622 genetic R
ST
distances (open circles) segregates populations from Chyulu Hills (Simba,
623 Satellite) and Mt Kulal from the populations from Taita Hills. and Mt. Kasigau.
624
0.1
0
-0.1
-0.2
A
0
PCA 1
0.2
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
B
Simba
Simba
Satellite
Satellite
Mt. Kulal
Mt. Kulal
Mt. Kasigau
Taita
-0.6 -0.4 -0.2
0 0.2
0.4
PCA 1
28

625 Figure 4: Projections of the distribution of Zoterops poliogaster from past, present to future.
626 Warmer colours indicated a higher environmental suitability for the species, unsuitable areas
627 are indicated in light grey. Darker areas indicate areas with non-analogues climatic conditions
628 relative to the training area of the SDMs as identified by MESS analyses. A) Currently
629 realized and potential distribution of Zosterops poliogaster in East Africa, as well as average
630 projections of its currently realized niche and future anthropogenic climate change scenarios
631 provided by the 4 th
IPCC Assessment (A2a and B2a) for 2080. Species records are indicated
632 by black crosses; light grey areas are suggested to be unsuitable; dark grey shading indicates
633 extrapolation of the SDM outside the environmental training range as quantified by MESS. B)
634 Potential distribution assuming under two palaeoclimatic models (CCSM – A&C; MIROC –
635 B&D) under environmental conditions as can be expected for around 6,000 (A&B) and
636 21,000 years BP (C&D).A)
29

637
30

638 B)
639
31

640 Table 1: Parameters of genetic diversity for Zosterops poliogaster (averaged across loci). Given are site number, locality, forest type, number of
641 sampled individuals, mean number of alleles ( A ), allelic richness ( AR ), mountain specific private alleles ( AP ), percentage of expected heterozygosity
642 ( H e
), and percentage of observed heterozygostiy ( H o
). Allelic richness was calculated based on the lowest number of individuals available for a
643 population, which were 9 individuals from forest patch Yale (as we excluded populations from Furia and Ronge from this analysis). Abbreviations:
644 TH = Taita Hills, CH = Chyulu Hills, P = Pristine forest, HC = habitat change, S = stable conditions.
Site Locality-Years
1 Mt. Kasigau
2 TH-Chawia
3 TH-Fururu
4 TH-Macha
5 TH-Mwachora
6 TH-Ndiwenyi
7
TH-Ngangao-1990
TH-Ngangao-2009
8 TH-Vuria
9 TH-Wundanyi
10 TH-Yale
11 TH-Ronge
TH-MBololo-1990
12 TH-MBololo-2000
TH-MBololo-2009
Mean (±sd)
13 CH-Satellite
14 CH-Simba valley
15 Chyulu Hills-1938
Mean (±sd)
History N A
P
HC
HC
HC
HC
HC
HC
HC
HC
HC
HC
HC
HC
HC
HC
S
S
S
22 1.7
28 2.4
25 2.3
27 2.5
13 1.6
31 2.5
28 1.8
21 1.9
6 1.9
16 1.7
9 2.4
4 1.9
27 2.2
14 1.7
21 1.5
AR AP
1.5 0/22
AP[%]
0
1.9 4/31 12.9
1.9 4/30 13.3
1.9 5/33 15.2
1.6 1/21 4.8
1.9
1.9
1.5
-
1.8
1.8
-
1.8
1.8
1.7
8/32
3/22
2/25
2/25
2/23
4/31
2/26
6/28
1/22
1/20
25.0
13.3
8.0
8.0
8.9
12.9
7.8
21.4
4.5
5.0
-
2.0
(±0.4)
20 1.8
1.8
(±0.2)
-
10.7
(±6.6)
1.7 2/24 8.3
15
17
1.9
1.8
1.8 2/25 8.0
1.8 2/24 8.3
1.9 1.8 - 8.2
H o
[%]
34.3
25.0
21.4
21.2
34.9
16.9
29.4
26.5
26.7
45.4
36.7
48.2
46.9
33.9
31.3
31.9
(±9.5)
31.7
33.9
27.2
H e
[%]
35.0
27.2
28.3
31.2
35.2
23.3
30.9
25.2
39.9
40.7
39.5
25.0
32.9
24.9
23.5
30.9
(±6.1)
41.0
45.2
43.2
32.8 43.1
32

16
Mt. Kulal-1997
Mt. Kulal-2010
Mean (±sd)
P
P
(±0.1) (±0.0)
25 2.4 1.9 6/31
(±0.2)
19.4
(±1.6)
38.6
(±2.9)
34.1
21 2.2 2.1 7/28 25.0 40.8 34.8
-
2.3
(±0.1)
2.0
(±0.1)
-
22.2
(±3.9)
39.7
(±1.6)
34.5
(±0.5)
33

645 Table 2: Non-hierarchical and hierarchical analyses on molecular variance (AMOVA) to analyse genetic differentiation among/between mountain
646 groups e.g. species, among individuals within populations, and within individuals. Variance values (top line) with the respective R statistics (in
647 parenthesis below). Groupings were created following Structure analyses (Fig. 2). Abbreviations: *: p < 0.05, **: p < 0.01, ***: p < 0.0001.
648
649 Non-hierarchical variance analyses
650
651
Group
All populations
Taita Hills
Chyulu Hills
Ngangao
Ngangao vs vs
Mbololo past
Mbololo recent
Mt. Kasigau vs Mt. Kulal
Hierarchical variance analyses among populations
(R
ST
)
6.2668
(0.4219***)
0.6885
(0.0757***)
-0.0179
(-0.0026)
0.0044
(0.0006)
0.6871
(0.0930**)
14.6219
(0.5846***)
Among individuals within populations (R
IS
)
-0.1439
(-0.0168)
-0.5478
(-0.0652)
0.8502
( 0.1208)
-0.1963
(-0.0245)
-0.6952
(-0.1038)
0.8331
(0.0802)
Within individuals
8.7299
8.9505
6.1857
8.2143
7.3942
9.5589
Group
Mt. Kasigau vs Taita Hills vs
Chyulu Hills vs Mt. Kulal
Taita Hills,
Among groups or species (R
CT
)
11.1878
(0.5488***)
0.7642
Among populations within groups (R
SC
)
0.6137
(0.0667***)
0.4507
Within individuals
8.7299
8.9505
34

Ngangao vs Mbololo
Mt. Kasigau vs Taita Hills
Mt. Kasigau vs Chyulu Hills
Taita Hills vs Chyulu Hills
Taita Hills vs Mt. Kulal
Chyulu Hills vs Mt. Kulal
(0.0795*)
1.4441
(0.1428*)
20.7425
(0.7818***)
14.6971
( 0.6245***)
12.6453
( 0.5609***)
10.9803
( 0.5079***)
(0.0509***)
0.6998
(0.0807***)
0.0341
(0.0059)
0.6294
( 0.0712***)
0.6585
(0.0665***)
-0.1393
(-0.0131)
8.5150
5.4107
8.5587
9.4979
9.5000
35

652 Table 3: Results of five independent runs of a non-equilibrium Bayesian assessment of
653 migration proportion by population calculated with the programme BAYESASS (Wilson and
654 Ranalla, 2003) at A) the distributional scale and B) the regional scale (i.e. within the Taita
655 Hills). Bolded terms = values >10% of the proportion of non-migrants within a population.
656 Values in columns (populations of origin in first column) represent migrant genes donated to
657 other populations. Calculations were performed for the four mountain groups Mt. Kasigau,
658 Taita Hills, Chyulu Hills and Mt. Kulal.
659
660
661
A)
Mt. Kasigau
Taita Hills
Chyulu Hills
Mt. Kulal
Mt. Kasigau Taita Hills Chyulu Hills Mt. Kulal
0.9751
0.0133
0.0057
0.0011
0.9979
0.0005
0.0031
0.0029
0.9911
0.0025
0.0023
0.0027
0.0059 0.0005 0.0029
0.9925
36

662
663
B)
TH-Chawia
TH-Fururu
TH-Macha
TH-Yale
Chawia Fururu Macha Mwachora Ndiwenyi Vuria Wundanyi Yale Ngangao Mbololo Ronge
0.6859
0.0053 0.0043 0.0029 0.0035 0.0146 0.0074 0.0117 0.0081 0.0110 0.0178
0.0039
0.0039
0.6783
0.0043
0.003
0.6789
0.0024
0.0028
TH-Mwachora 0.0556 0.0470 0.0120
0.9611
TH-Ndiwenyi 0.2074 0.1819 0.2638
0.0068
TH-Vuria 0.0038 0.0041 0.0032 0.0021
TH-Wundanyi 0.0041 0.0048 0.0034 0.0024
0.0040 0.0042 0.0035 0.0019
0.0015 0.0136 0.0068 0.0113 0.0031 0.0102 0.0147
0.0018 0.0150 0.0078 0.0098 0.0034 0.0093 0.0149
0.0021 0.0214
0.1335 0.1229
0.0079 0.0237 0.0239
0.9734
0.0763 0.0931 0.0500
0.2470
0.0141 0.0458
0.0019
0.7080
0.0065 0.0116 0.0030 0.0095 0.0153
0.0017
0.0021
0.0134
0.0125
0.6856
0.0069
0.0109
0.6975
0.0035
0.0028
0.0104
0.0093
0.0158
0.0147
TH-Ngangao 0.0065 0.0047 0.0038 0.0024
TH-MBololo 0.0036 0.0042 0.0036 0.0024
TH-Ronge 0.0037 0.0040 0.0034 0.0021
0.0021 0.0144 0.0071 0.0111
0.6858
0.0114 0.0159
0.0020 0.0128 0.0067 0.0111 0.0034
0.8837
0.0161
0.0022 0.0123 0.0069 0.0103 0.0034 0.0095
0.8215
37

664 Appendix S1: Allele-frequencies calculated for all populations and loci used for this study. Private alleles restricted to one population or mountain
665 area are marked in bold. Population numbers coincide with Table 1 and Figure 1, historic samples are marked with respective sampling years.
666
Zl54
Zl54
Zl35
Zl35
Zl35
Zl35
Zl35
Zl35
Zl35
Zl14
Zl14
Zl14
Zl14
Zl14
Zl14
Zl14
Zl54
Zl41
Zl41
Zl22
Zl22
Zl22
Zl22
Zl22
Zl45
Zl45
Zl45
Zl45
Zl45
Zl45
Zl45
Locus
Cu_28
Cu_28
Zl44
Zl44
Zl44
3
4
5
6
7
2
3
1
2
5
6
7
1
1
2
3
4
2
3
4
5
6
7
3
4
5
1
1
2
1
2
Allele Size
1
2
162
164
1
2
3
216
220
224
122
124
121
123
125
127
129
131
133
134
140
142
144
146
148
150
120
112
116
118
120
122
124
82
86
143
145
149
155
157
106
Abbreviations: MtKa = Mt. Kasigau, TH = Taita Hills, CH = Chyulu Hills, MtKu = Mt. Kulal.
MtKa
-
TH
-
TH
-
TH
-
TH
-
TH
-
TH
1990
TH
-
TH
-
TH
-
TH
-
TH
-
TH
1990
TH
2000
TH
-
CH
1938
CH
-
CH
-
MtKu
1990
MtKu
-
1 2 3 4 5 6 7 7 8 9 10 11 12 12 12 13 14 15 16 16
0.4524 0.0179 0.0800 0.0179 0.0000 0.0000 0.0000 0.0000 0.0000 0.0238 0.0862 0.0161 0.0250 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.5476 0.9821 0.9200 0.9821 1.0000 1.0000 1.0000 1.0000 1.0000 0.9762 0.9138 0.9839 0.9750 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0323 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
1.0000 1.0000 0.9600 1.0000 1.0000 0.9677 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.8667 0.9286
0.0000 0.0000 0.0400 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.1333 0.0714
0.9524 0.9643 1.0000 0.9821 1.0000 0.9839 1.0000 1.0000 0.9444 0.9762 0.9483 0.9839 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
0.0476 0.0357 0.0000 0.0179 0.0000 0.0161 0.0000 0.0000 0.0556 0.0238 0.0517 0.0161 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0179 0.0000 0.0000 0.0000 0.0484 0.0000 0.0000 0.0000 0.0000 0.0185 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.9762 0.9107 0.9400 1.0000 0.9615 0.9516 1.0000 0.8750 0.8889 1.0000 0.9444 0.9677 1.0000 0.8889 1.0000 0.1500 0.0667 0.1304 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.1724 0.0714
0.0238 0.0714 0.0600 0.0000 0.0385 0.0000 0.0000 0.1250 0.1111 0.0000 0.0370 0.0323 0.0000 0.1111 0.0000 0.6250 0.8667 0.7609 0.8276 0.9286
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.1250 0.0667 0.1087 0.0000 0.0000
0.0000 0.0800 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0238 0.2400 0.1905 0.2400 0.3333 0.2759 0.2500 0.4667 0.5000 0.2500 0.2586 0.3571 0.3421 0.3889 0.1667 0.1000 0.1000 0.1087 0.1500 0.1786
0.0000 0.0000 0.0000 0.0600 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0357 0.0000 0.0000 0.0000 0.2250 0.2667 0.2391 0.2167 0.0357
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0526 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.9762 0.6800 0.8095 0.6600 0.6667 0.7241 0.7500 0.5333 0.5000 0.7500 0.7414 0.6071 0.6053 0.6111 0.8333 0.6750 0.5667 0.6522 0.3667 0.6071
0.0000 0.0000 0.0000 0.0400 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0667 0.0000 0.2667 0.1786
0.0000 0.0192 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.1154 0.0000 0.0200 0.1818 0.0000 0.0000 0.1333 0.1667 0.0000 0.0536 0.0000 0.0263 0.0000 0.0000 0.9000 0.9643 0.9348 0.1786 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0385 0.2000 0.0000 0.0000 0.0000 0.0714 0.0000 0.0526 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
1.0000 0.8654 1.0000 0.9600 0.8182 0.9615 0.8000 0.8667 0.8333 1.0000 0.8750 1.0000 0.8947 1.0000 1.0000 0.1000 0.0357 0.0652 0.4464 0.7308
0.0000 0.0000 0.0000 0.0200 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0263 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.2321 0.1538
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.1429 0.1154
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.1000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0263 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
1.0000 0.9815 0.9783 1.0000 1.0000 0.9655 0.9000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9737 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
0.0000 0.0185 0.0217 0.0000 0.0000 0.0345 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0556 0.0000 0.0000 0.0000 0.0000 0.6842 0.6154 0.6591 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0192 0.0000 0.0000 0.0000 0.0000 0.0000 0.0484 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.6190 0.8636 0.9500 0.9630 1.0000 0.9423 1.0000 1.0000 1.0000 1.0000 0.9444 0.9516 1.0000 1.0000 1.0000 0.3158 0.3846 0.3409 0.0000 0.0000
0.0476 0.0000 0.0000 0.0185 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.4821 0.6667
0.3333 0.1364 0.0500 0.0185 0.0000 0.0385 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0417
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.3571 0.2500
38

Zl35
Mme12
Mme12
Zl18
Zl18
Zl18
Zl18
Zl18
Zl50
Zl50
Zl50
Zl50
Zl49
Zl49
Zl49
Zl49
Zl49
Zl49
Zl49
Zl49
Alleles
AP
1
2
3
4
5
1
2
3
4
8
1
2
1
2
3
4
5
6
7
8
135
142
146
128
130
134
136
140
127
130
133
136
100
102
104
106
108
110
112
114
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.1607 0.0417
1.0000 1.0000 1.0000 1.0000 1.0000 0.9655 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0345 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.2000 0.0625 0.1538 0.2500 0.0962 0.0833 0.0625 0.0625 0.1190 0.1379 0.0323 0.0263 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0172 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0690 0.0357
1.0000 0.8000 0.8750 0.7885 0.7500 0.8654 0.9167 0.8438 0.9375 0.8571 0.8276 0.9677 0.9474 0.9444 0.8750 1.0000 1.0000 1.0000 0.9310 0.9643
0.0000 0.0000 0.0625 0.0577 0.0000 0.0385 0.0000 0.0938 0.0000 0.0238 0.0172 0.0000 0.0263 0.0556 0.1250 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0400 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0417 0.0000 0.0200 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000 1.0000 1.0000 0.9231 0.9643
0.2250 0.8542 0.7174 0.7600 0.9500 0.8393 0.7500 0.8182 0.6429 0.7105 0.6852 0.5968 0.5278 0.4000 0.7500 0.0000 0.0000 0.0000 0.0000 0.0000
0.7750 0.1042 0.2826 0.1800 0.0500 0.1607 0.2500 0.1818 0.3571 0.2895 0.3148 0.4032 0.4722 0.6000 0.2500 0.0000 0.0000 0.0000 0.0769 0.0357
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0526 0.3000 0.0435 0.0000 0.0000
0.0000 0.1400 0.0682 0.0000 0.3636 0.0179 0.0000 0.2500 0.3333 0.0000 0.0000 0.0000 0.0789 0.1500 0.0000 0.6316 0.4333 0.6739 0.4333 0.2143
0.0714 0.1800 0.3409 0.1667 0.4545 0.2679 0.2500 0.2500 0.4444 0.2381 0.1458 0.0345 0.1053 0.0500 0.1667 0.1053 0.1000 0.0870 0.0500 0.0000
0.0000 0.0200 0.0227 0.0417 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0417 0.0000 0.0000 0.0000 0.0000 0.2105 0.1667 0.1957 0.0167 0.0714
0.2857 0.3600 0.1591 0.3125 0.1818 0.3750 0.0833 0.1071 0.0556 0.3571 0.1875 0.2931 0.2632 0.0500 0.3333 0.0000 0.0000 0.0000 0.0500 0.0714
0.6429 0.3000 0.2955 0.4167 0.0000 0.2321 0.5000 0.3571 0.1111 0.4048 0.5208 0.2241 0.2105 0.3000 0.1667 0.0000 0.0000 0.0000 0.4333 0.6429
0.0000 0.0000 0.1136 0.0625 0.0000 0.0893 0.1667 0.0357 0.0000 0.0000 0.1042 0.4483 0.3421 0.4500 0.3333 0.0000 0.0000 0.0000 0.0167 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0179 0.0000 0.0000 0.0556 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
22
0
31
4
30
4
33
5
21
1
32
8
22
3
25
2
25
2
23
2
31
4
26
2
28
6
22
1
20
1
24
2
25
2
24
2
31
6
28
7
39

667 Appendix S2: Results of the Structure analysis for different numbers of given groups ( K = 1-
668 10) analysed based on all individuals. Ln(Pr) is the natural logarithm of the probability
669 calculated using the Structure software that K is the correct number of populations. SD is the
670 standard deviation calculated from 10 independent runs. The ad hoc statistic Delta K is not
671 applicable for K = 1, and from the equation given in the methods section it is obvious that it
672 cannot be calculated for the highest K number either (because data for K = 1 are needed), and
673 is not proper for K = 2 (cf. Hausdorf and Hennig, 2010).
K
1
Ln(Pr) ± SD
-6881.01
± 0.03
∆K
-
2 -5310.51±0.32 4198.93
3 -5089.05
±
147.29 0.15
4 -4845.65
± 176.52
5 -4684.34
±
192.06
6 -4517.69
±
174.03
0.47
0.03
0.38
7 -4416.84±2.92
8 -4425.38
±
42.12
9 -4363.23
±
50.12
10 -4348.24
±
36.38
37.46
1.68
0.94
-
40

674 Appendix S3: Biometric measures for each population. Given are number of measured
675 individuals, length of the wing (cm), length of tarsus (cm), and body mass (g).
676
Locality + No
Mt. Kasigau-1
TH-Chawia-2
TH-Fururu-3
TH-Macha-4
N Wing Tarsus Weight
20
59,0 (±0,9)
21,0 (±0,5)
10,6 (±0,9)
25 59,0 (±1,2) 20,0 (±0,5) 10,9 (±0,6)
25 58,9 (±0,9) 19,8 (±0,5) 10,9 (±0,8)
31 58,4 (±1,7) 19,37 (±0,7) 11,01 (±0,8)
TH-Mwachora-5
TH-Ndiwenyi-6
13 58,9 (±1,8) 19,9 (±0,5) 10,6 (±0,6)
30 58,9 (±1,7) 19,7 (±0,6) 10,9 (±0,8)
TH-Ngangao 1990-7 27 58,7 (±1,3) 20,0 (±0,7) 10,5 (±0,8)
TH-Ngangao2010-7 20 58,5 (±2,0) 20,1 (±0,5) 9,8 (±1,0)
TH-Vuria-8
TH-Wundanyi-9
TH-Yale-10
6 59,0 (±1,3 19,4 (±0,7) 11,0 (±0,6)
16 58,8 (±1,4) 19,8 (±0,7) 10,5 (±0,6)
9 59,1 (
±
1,7) 20,3 (
±
0,26) 11,2 (± 0,4)
TH-Ronge-11 4 58,5 (±1,3) 19,1 (±0,9) 10,4 (±0,3)
TH-MBololo-1990-12 20 58,3 (±1,3) 19,9 (±0,5) 10,7 (±0,4)
TH-MBololo-2009-12 30 58,4 (±1,2) 20,2 (±0,6) 9,5 (±0,7)
CH-Satellite-13 25
62,9 (±1,6) 20,9 (±0,3)
12,7 (±1,6)
CH-Simba valley-14 10 61,1 (±1,7) 21,2 (±0,8) 11,4 (±0,6)
Mt. Kulal-1990-16 32 63,4 (±1,2) 21,7 (±0,6) 13,6 (±0,9)
41