A Long-Term Study on the Local and Regional Genetic Population Structure of the Cooperative Breeding Placid Greenbul (Phyllastrephus Placidus)

Biology Department Research Group Terrestrial Ecology ______

A LONG-TERM STUDY ON THE LOCAL AND REGIONAL GENETIC POPULATION STRUCTURE OF THE COOPERATIVE BREEDING PLACID GREENBUL (PHYLLASTREPHUS PLACIDUS).

Annelore De Ro Studentnumber: 01403714

Supervisor: Prof. Dr. Luc Lens

Scientific tutor: Laurence Cousseau

Master’s dissertation submitted to obtain the degree of Master of Science in Biology

Academic year: 2018 - 2019

© Faculty of Sciences – research group Terrestrial Ecology All rights reserved. This thesis contains confidential information and confidential research results that are property to the UGent. The contents of this master thesis may under no circumstances be made public, nor complete or partial, without the explicit and preceding permission of the UGent representative, i.e. the supervisor. The thesis may under no circumstances be copied or duplicated in any form, unless permission granted in written form. Any violation of the confidential nature of this thesis may impose irreparable damage to the UGent. In case of a dispute that may arise within the context of this declaration, the Judicial Court of Gent only is competent to be notified.

TABLE OF CONTENT

1. INTRODUCTION ...... 3 2. OBJECTIVES ...... 6 3. MATERIAL AND METHODS ...... 6 3.1 Study area and species ...... 6 3.2 Genetic data...... 7 3.3 Genetic diversity and population structure ...... 8 3.3.1 Genetic analyses ...... 8 3.3.2 Genetic structure among fragments ...... 9 3.3.3 Dispersal between fragments ...... 9 3.3.4 Regional spatial autocorrelation ...... 9 3.4 Local genetic patterns and sex-biased dispersal ...... 10 3.4.1 Local genetic patterns ...... 10 3.4.2 Sex-biased dispersal ...... 10 4. RESULTS ...... 10 4.1 Genetic diversity and population structure ...... 10 4.1.1 Genetic diversity ...... 10 4.1.2 Genetic structure among fragments ...... 11 4.2 Fine scale genetic patterns and sex-biased dispersal ...... 16 5. DISCUSSION ...... 20 5.1 Genetic diversity ...... 20 5.2 Population structure on a regional scale ...... 21 5.3 Population structure on a local scale ...... 23 5.4 Sex-biased dispersal ...... 24 6. CONCLUSION ...... 25 7. SUMMARY ...... 26 8. SAMENVATTING ...... 29 9. TEXT FOR THE BROADER AUDIENCE ...... 32 10. ACKNOWLEDGMENTS ...... 34 11. REFERENCES ...... 35 12. APPENDICES ...... 43 Appendix 1: The potential of genetic and demographic rescue in the Taita Apalis, a critically-endangered bird of south-east Kenya...... 43 Appendix 2: Structure Harvester results ...... 45 Appendix 3: BIMr results ...... 45 Appendix 4: Local spatial autocorrelation graphs ...... 46

1. INTRODUCTION

Dispersal is an important life history trait that has an influence on several ecological scales. First, dispersal plays an important role in the dynamics of metapopulations (Hanski 1998). Colonisation of empty habitat patches depends on the dispersal capabilities of the species and can save them from (local) extinction (Kot 1996). This forms the base of several population structure theories like source-sink dynamics (Frankham et al. 2010). Dispersal is also a crucial driver of gene flow between populations which has important consequences for the population genetic structures (Van Dijk et al. 2015). There are several factors that can affect animal dispersal strategies which in turn influence population dynamics and gene flow. Habitat patch isolation will have a strong impact on the cost of dispersal between patches, making it harder for species to colonise new habitat patches (Bowler et al. 2005). Costs paid during movement between patches include factors like energy investment and exposure to predation (Travis & Dytham 1999, Bowler et al. 2005). The costs can further be increased when the matrix of the patches is inhospitable (Wiens et al. 1993). Next, temporal variation in environmental quality (e.g. decrease of food resources) may affect the carrying capacity of the patch and increase the variance of the reproductive success of individuals (Johst & Brandl 1997, Bowler et al. 2005). In such case, dispersal can be considered as a bet-hedging strategy to reduce the variance in the fitness of an individual (den Boer 1968). Next, several studies confirmed a negative correlation between patch size and emigration rate (Johst & Brandl 1997, Hill et al. 1996, Kindvall 1999, Baguette et al. 2000, Pakanen et al. 2017). A decline in patch size increases the edge to size ratio and the likelihood that an individual leaves a patch may increase when the individual is more likely to encounter the edge (Stamps et al. 1987, Kindvall & Petersson 2000). Lastly, emigration is often favoured when there is an increase in population density mainly due to an increase in competition (Bowler et al. 2005).

On a local scale, restricted dispersal can have an influence on the spatial genetic structuring within populations. Both clustering of kin and sex-biased dispersal will lead to a higher level of relatedness among individuals, resulting in a positive genetic autocorrelation between them. Clustering of kin around the natal site causes an increase in the chance of inbreeding (Bengtsson 1978, Greenwood 1980, Gandon 1999) and an increase in kin competition (Hamilton & May 1977), which is often a driver for dispersal. However, high population density and clustering of kin do not always cause an increase in dispersal. Negative density- dependent emigration might be more advantageous when the presence of kin increases the fitness of the individual (Baglione et al. 2003) and the benefits of living in a group exceed the costs of competition (Bowler et al. 2005), which is the case in cooperative breeding species.

Cooperative breeding is fairly common in birds species, as up to 13% of all birds species display this breeding strategy (Griesser et al. 2017) where a breeding pair is assisted by one or more members of a social group to take care of their offspring (Stacey & Koenig, 1990). For the majority of cooperative breeders, the ‘helpers’ consist of offspring of the breeding couple that delay dispersal and stay with their parents in their natal territory (Ekman et al. 2004, Dickinson & Hatchwell 2004, Hatchwell 2009), thereby postponing their own reproduction. A large proportion of the individuals of cooperative breeders, often the males, apply this ‘stay-and-foray’ (SAF) strategy (Brown, 1987; Walters et al., 2004). This gives them the chance to take over breeding positions in their close surroundings in the future. Females are more likely to apply the ‘depart- and-search’ (DAS) strategy, which means that individuals continuously disperse further away, in search for vacant breeding spots (Brown, 1987). The difference in dispersal strategy between sexes will have an influence on the gene flow and genetic population structure of cooperative breeders, both among as within populations (Triggs et al. 1992).

Sex-biased dispersal can evolve when each sex is under a different selective pressure. According to the resource defences hypothesis, there is often a male philopatry in birds (Greenwood 1980). They benefit from being familiar with their natal surroundings as they mainly invest in defending resources and their territory. Because of the natal philopatry, there will be a high level of relatedness among the males in the same population, which is expected to decrease with increasing geographical distance (Beck et al. 2008). This results in a positive genetic autocorrelation between the males at shorter distances (Sugg et al. 1996). Females are more likely to disperse as their main drivers are to avoid inbreeding and enhance their reproduction by choosing the best mate (Greenwood 1980). But the difference in gene flow between sexes is also scale dependent. At a larger, regional scale, there may be a strong influence of the matrix on between-fragment dispersal. The selective forces on dispersal will be equal for both sexes, which will balance dispersal rates in males and females on a regional scale (Yannic et al. 2010).

Habitat fragmentation and loss have numerous negative impacts on wildlife populations and are the key drivers of biodiversity loss (Turner 1996). Fragmentation leads to a reduction in total habitat area and creates isolated patches that usually lay in a matrix of now-inhospitable terrain (Frankham et al. 2010). This will affect dispersal and gene flow among populations as described above. Furthermore, small, isolated populations can become more vulnerable to demographic and environmental stochastic events (Lande 1988). Fragmentation leads to an overall decrease in population size (Frankham et al. 2010), which may reduce the genetic diversity of the populations due to genetic drift (Young et al. 1996). The loss of genetic diversity may in turn reduce the fitness of the population, which leads to a negative feedback towards the population size (Allendorf et al. 2013).

The impact of fragmentation on cooperative breeding species depends on several factors including fragment size, the connectivity and distance between fragments, the maximum dispersal distance of the helpers and the fraction of individuals applying the DAS strategy (Walters et al. 2004). In larger fragments, cooperative breeding helps to stabilise the size of the breeding population, since helpers applying the SAF strategy are expected to increase the recolonization rate of breeding spots that recently became vacant. This strategy can thus act as a buffer against environmental and demographic stochasticity caused by habitat fragmentation (Walters et al. 2004). Yet in small, isolated fragments, cooperative breeding species are expected to be more vulnerable to fragmentation compared to pair-forming species (Walters et al. 2004, Fischer & Lindenmayer 2007). Strong isolation of populations may increase the chance of incestuous mating and thus inbreeding depression (Koenig & Haydock 2004) since these species usually live in groups composed of close relatives (Ekman et al. 2004, Dickinson and Hatchwell 2004). Inbreeding can be reduced or avoided by sex-biased dispersal between fragments by the individuals who display the DAS strategy (Koenig & Haydock 2004, Komdeur 2004, Double et al. 2005, Temple et al. 2006, Hatchwell 2009). However, since only a small proportion of the population employs this strategy, there will be less individuals available to occupy vacant breeding spots compared to non-cooperative breeding species. This may result in restricted mating and a decrease in reproduction and population growth (Walters et al. 2004).

The Placid Greenbul (Phyllastrephus placidus) is a facultative cooperative breeding bird species living in subtropical forests of south-east Africa. The Placid Greenbul populations in the severely fragmented Taita Hills in south-east Kenya have been the subject of several studies (Callens 2012, Vangestel et al. 2013, Husemann et al. 2015, Van de Loock 2019). The area is surrounded by over 80 km of unsuitable habitat,

which makes it a particularly suitable area to study closed metapopulation systems in a context of forest fragmentation.

Previous studies by Vangestel et al. (2013) and Husemann et al. (2015) on the Placid Greenbul populations revealed an increase in admixture between the populations over a short time period, as well as a decrease in individual-based genetic autocorrelation values over time. This indicated that there is possibly an increased gene flow over the past decade. Autocorrelation analyses showed spatial genetic clustering of males, suggesting sex-biased dispersal with the females as the dispersive sex. However, these patterns were observed at a local scale but not at a regional scale.

2. OBJECTIVES

To the best of our knowledge, there is no study that compared genetic population dynamics between different time frames and on different spatial scales. Yet, there is still much research needed to evaluate the long-term meta-population viability of tropical cooperative breeders under persisting habitat change. The aim of this study is to assess the genetic population dynamics of the cooperative breeding Placid Greenbul in the Taita Hills on a long-term scale. We will use data collected over almost two decades to compare the genetic population structure between three consecutive periods both on a regional scale as on a local scale. This way we aim to get more insight on the dynamics between and within fragments that differ in size and level of degradation.

3. MATERIAL AND METHODS

3.1 Study area and species The Taita Hills is an ancient mountain massif with an area of circa 150 km² that forms the most northern border of the Eastern Arc Mountains range of southern Kenya (Nature Kenya, 2015). The Taita Hills is one of the 34 global biodiversity hotspots (Mittermeier et al., 2004), but the species living in the forest remnants of the Taita Hills are threatened by a rapidly expanding human population and deforestation. The natural forest is restricted to three larger fragments (86-220 ha) and twelve smaller, isolated fragments (<1 – 10 ha) located on two mountain isolates (Dabida and Mbololo) (Pellikka et al. 2009, Aerts et al. 2011, Van de Loock, 2019) (Figure 1). These fragments are surrounded by a landscape dominated by smallholder agriculture and villages (Pellikka et al., 2009). In this study we focused on the dispersal between and within the three largest fragments: Mbololo (220 ha), Ngangao (120 ha) and Chawia (86 ha) (Pellikka et al. 2009, Aerts et al. 2011, Van de Loock 2019).

The Placid Greenbul (Phyllastrephus placidus) is a medium sized songbird from the Pycnonotidae family, with a conservation status of least concern. Its large range spreads from east- to south-central Africa, where the species lives in subtropical forest and shrubland (BirdLife International, 2019). The Placid greenbul can be found in most of the fragments of the Taita Hills (Vangestel et al. 2013), while actively breeding pairs were found in nine of these fragments during the period of 2006 till 2016 (Van de Loock 2019). The species is a facultative cooperative breeder living in small groups with up to three helpers (Callens 2012, Van de Loock 2019). Homing experiments (Aben et al. 2012, 2014) and mark-recapture data (Lens et al. 2002) indicate that this species has low to moderate rates of mobility and dispersal between fragments.

Figure 1: Map of all the remaining indigenous forest fragments of the Taita Hills (South-east Kenya). Actively breeding pairs of the Placid Greenbul (Phyllastrephus placidus) were found in the three large fragments (Chawia, Ngangao and Mbololo) and six small fragments of which Fururu and Ndiwenyi are indicated on the map. The two smallest fragments (Macha and Mwachora) were only recently colonized. The two mountain islolates (Dabida and Mbololo) are indicated with circles (Callens, 2012).

3.2 Genetic data We used genetic data from Placid Greenbuls caught with mist nest in three fragments (Chawia (CH), Ngangao (NG) and Mbololo (MB)) spread over three periods (P1: 1997 - 1999, P2:2007 – 2009, P3: 2014 – 2016) (Table 1). Blood samples from the trapped birds were stored in ethanol and feather samples were stored dry. The DNA samples were already genotyped before the start of this thesis (Husemann et al. 2015, De Mits 2019). During this thesis, similar genotyping techniques were used to perform the first genetic study about the Taita Apalis (Apalis fuscigularis) (Appendix 1).

DNA from the blood samples was extracted using a Chelex-based DNA isolation procedure. The samples were incubated for 90 minutes at 56°C with 5% CHELEX and proteinase K, followed by boiling for 8 minutes. DNA from the feather samples was extracted using the DNeasy Blood and Tissue kit from Qiagen (Hilden, Germany). 8 microsatellite markers were used (MCY4, LS1, IND41, PFL54, PFI04, ASE18, WBSW2, PCA3 (Husemann et al. 2015)). PCR products were sequenced and the results were analysed in Geneious v11.0 (Kearse et al. 2012). For the majority of the individuals there was also sex data available. Sex was determined by PCR amplification of the CHD-W and CHD-Z genes (Griffiths et al., 1998) with primers P2/P8. A gel electrophoresis was performed, coloured with ethidium bromide. The sex of the individual was visually checked (two bands for females, one band for males) (De Mits 2019). Lastly, for the majority of the individuals there were also GPS coordinates recorded (Table 1). To avoid pseudo replication, we only kept the data from the first time that individuals were caught.

Table 1: The number of individuals per fragment per period for which genetic data was available. We made a subset of the total genetic dataset of individuals for which we also had geographic data (latitudinal and longitudinal coordinates) and a subset of individuals for which we had sexing data (F=female, M=male).

Total number of Ind. with Ind. with sex data ind. geographic data Chawia P1 79 41 F: 15 M:26 P2 123 116 F: 51 M:57 P3 25 22 F: 10 M:11 Ngangao P1 73 59 F: 19 M:39 P2 123 118 F: 39 M:66 P3 40 40 F: 13 M:26 Mbololo P1 60 / F: 9 M:7 P2 65 65 F: 20 M:41 P3 30 30 F: 7 M:21

3.3 Genetic diversity and population structure 3.3.1 Genetic analyses We used the total genetic dataset for the genetic diversity and population structure analyses. We used the package POPGENREPORT 3.0.0 (Adamack & Gruber 2014, Gruber & Adamack 2015) to test if the loci were at Hardy-Weinberg equilibrium. A Bonferroni correction for multiple testing was applied. All loci but one (Ngangao, locus Mcuy4, period two) were at Hardy-Weinberg equilibrium. The presence of null alleles was also determined with POPGENREPORT. Based on the 95% confidence intervals, null alleles were suggested at locus Ls1 in Ngangao during the second period. Linkage disequilibrium was tested in ARLEQUIN 3.5 (Excoffier & Lischer 2010). After applying a Bonferroni correction, we found linkage disequilibrium between several loci in the first two periods (P1: between Mcuy4 & Pca3 in CH and Pfi04 & Pfl54 in NG, P2: between

Ls1 & Mcuy4 and Pfi04 & Pfl54 in CH and between Ase18 & Wbsw2 and LS1 & Mcuy4 in NG). Because the deviation from Hardy-Weinberg equilibrium, linkage disequilibrium and the null alleles were not consistent over fragments or periods, we kept these loci in the analyses. We also wanted to include as much data as possible to perform meaningful analyses.

Next, we used ADEGENET v2.1.1, (Jombart 2008) to estimate the expected and observed heterozygosity, allelic richness and number of private alleles. We used a Wilcoxon rank-sum test to test differences between the estimates of heterozygosity and allelic richness both within fragments per period as among fragments per period.

3.3.2 Genetic structure among fragments

Pairwise Jost’s D was determined using the package MMOD v1.3.3 (Winter et al. 2012). We chose Jost’s D as differentiation index because it is considered as more accurate than other indices of differentiation, such as Fst, when there are highly polymorphic microsatellite markers (Jost, 2008). Bayesian admixture models from the programme STRUCTURE v2.3.4 (Pritchard et al. 2000) were used to assess the genetic population structure and level of admixture between the three fragments (CH, NG, MB) separately for each period. We ran admixture models with correlated allele frequencies. We set the parameters at a burn-in period of 100 000 MCMC’s and 100 000 MCMC iterations after burn-in. The number of genetic clusters (K) was restricted from 1 to 6 and 15 independent replications were run for each K. The optimal K was determined by STRUCTURE HARVESTER (Earl & von Holdt 2012). We also used GenAlEx (Peakall & Smouse 2006) to perform an AMOVA test to detect genetic differentiation between the fragments per period. For each AMOVA test we ran 999 permutations.

3.3.3 Gene flow between fragments We used BIMr 1.0 (Faubet & Gaggiotti, 2008) to estimate gene flow between the different fragments (CH, NG, MB) separately for each period. BIMr implements the Bayesian method developed by Faubet and Gaggiotti (2008) and makes inferences of recent proportions of immigrant genes in subdivided populations. To ensure convergence of MCMC we ran 10 replicates. To improve migration estimates, we used the F- model (model with correlated allele frequencies) which takes the possible population admixture before the last generation of migration into account (Faubet & Gaggiotti 2008). We ran 20 pilot runs of 1000 iterations and set the number of burn-in iterations at 100 000. For each replicate a total number of 50 000 samples were drawn with a thinning interval (the number of iterations between two samples) of 100 iterations. The parameter estimates of the run with the lowest Dassign (the probability of assignment given a migration rate) were selected, as this run showed the best convergence (Faubet et al. 2007). Significance of migration rates were tested by looking at the 95% highest posterior density intervals (HPDI).

3.3.4 Regional spatial autocorrelation GenAlEx v6.5 (Peakall & Smouse 2006) was used to perform analyses to detect genetic patterns among sexes at a regional scale. We first calculated the pairwise geographical and genetic distance matrices for spatial autocorrelation tests per period while combining all the fragments. For the spatial autocorrelation test of period one there was no data available for MB. We ran the analyses for 999 permutations and 999 bootstraps. Spatial autocorrelation (r) between individuals was visualized as a function of cumulative geographical distance in the correlogram by defining multiple, variable distance class sizes (option

MultipleDclass). Distance classes were chosen in such a way that they resembled distances between fragments. The autocorrelations at a shorter distance represent the autocorrelation within fragments, while autocorrelation between fragments is represented by the autocorrelation at longer distances. Autocorrelations at distances of 9 to 12 km represents the r between CH and NG, distances of 12 to 15 km the r between NG and MB and the largest distances (17 – 19 km) represent the r between CH and MB.

3.4 Local genetic patterns and sex-biased dispersal 3.4.1 Local genetic patterns For the detection of genetic patterns on a local scale and the relatedness between the individuals within the fragments, we again used GenAlEx v6.5 (Peakall & Smouse 2006). Tests of spatial genetic patterns in period one could only be performed for CH and NG. We performed an AMOVA test to detect genetic differentiation between the periods per fragment. For each AMOVA test we ran 999 permutations. We then calculated pairwise geographical and genetic distance matrices. Genetic isolation by distance was tested using a Mantel test for each fragment (CH, NG, MB) per sex during each period. We ran each analyse for 9999 permutations. We further performed spatial autocorrelation tests with equal distance class sizes of 200m (option MultipleDclass) for each fragment (CH, NG, MB) over each period per sex. In period three, MB was only sampled in one half of the fragment. We ran the analyses for 999 permutations and 999 bootstraps. Tests for statistical significance were performed following Peakall et al. (2003).

3.4.2 Sex-biased dispersal To detect sex-biased dispersal, we used the sex bias assignment tests in GenAlEx. An Assignment Index correction value (AIC) is assigned to each sex following the method of Favre et al. (1998) and extended by Mossman & Waser (1999). For each individual, a log likelihood assignment index was calculated, corresponding to the expected frequency of each individual’s genotype for the population in which it was trapped. The individual’s AIC was then calculated by subtracting the mean log likelihood of the population from the log likelihood of the individual. The sex with a negative value has the higher possibility of being an migrant or the more widely ranging sex (with the rarer genotypes). The test was performed for data within fragments (CH, NG, MB) for each period. Significant differences between males and females were tested with a U-test.

4. RESULTS

4.1 Genetic diversity and population structure 4.1.1 Genetic diversity There were no significant differences among populations or periods in levels of allelic richness and observed and expected levels of heterozygosity (Wilcoxon signed rank test: all P > 0.05) (Table 2). The number of private alleles in CH decreased in the second period, but increased again in period three. In NG there was an increase in private alleles over the three periods. In MB there was an increase in period three and a slight decrease of private alleles in period three (Table 2).

Table 2: Allelic richness (AR), observed heterozygosity (Ho), expected heterozygosity (He) and number of private alleles (PA) per period and fragment.

Site Period AR (s.d.) Ho (s.d.) He (s.d.) PA

Chawia 1 6.13 (1.32) 0.71 (0.11) 0.69 (0.12) 4 2 6.10 (1.61) 0.67 (0.15) 0.69 (0.16) 1 3 5.77 (1.87) 0.73 (0.16) 0.67 (0.16) 4

Ngangao 1 6.54 (1.45) 0.67 (0.12) 0.65 (0.12) 5 2 6.73 (1.80) 0.62 (0.15) 0.66 (0.13) 6

3 6.48 (1.40) 0.63 (0.13) 0.67 (0.10) 8 Mbololo 1 5.34 (1.95) 0.68 (0.15) 0.68 (0.13) 3 2 5.60 (1.72) 0.70 (0.17) 0.69 (0.11) 6 3 5.41 (1.53) 0.71 (0.14) 0.67 (0.11) 5

4.1.2 Genetic structure among fragments Pairwise Jost’D results showed that, despite a very slight decrease in differentiation between period one and two, values were globally similar among fragments and among periods (Table 3). The main difference between periods is a strong increase in differentiation between MB and CH in period three.

Table 3: Pairwise Jost’D between the three fragments Chawia (CH), Ngangao (NG) and Mbololo (MB) over three periods.

Jost’ D CH NG Period 1 NG 0.090 MB 0.128 0.107 Period 2 NG 0.084

MB 0.115 0.086

Period 3 NG 0.076 MB 0.228 0.129 By performing STRUCTURE analyses, we identified three genetic clusters (K=3) for both period one and period two (Figure 2 & Appendix 2). We did, however, observe an increase in admixture in CH and NG and a decrease of admixture in MB (Table 4). In period 3, we observed a strong increase in admixture, especially for CH and NG. We identified two genetic clusters (K=2) in period three, whereby the population of MB was separated from the other populations.

Period 1 Period 2

Chawia Ngangao Mbololo Chawia Ngangao Mbololo Period P33

Chawia Ngangao Chawia Ngangao Mbololo

Figure 2: STRUCTURE graphs for the three fragments per period with K ranging from 2 to 4. For both period one and period two the o ptimal number of clusters was 3. For the most recent period the optimal number of clusters was 2.

Table 4: Levels of admixture between the three fragments during each period for K=3.

K=3 Given pop Inferred clusters

P1 1 2 3

Chawia 0.77 0.13 0.10 Ngangao 0.13 0.74 0.13 Mbololo 0.10 0.12 0.78

P2 1 2 3 Chawia 0.67 0.15 0.17 Ngangao 0.10 0.72 0.18 Mbololo 0.08 0.08 0.84

P3 1 2 3 Chawia 0.48 0.44 0.07 Ngangao 0.40 0.47 0.13 Mbololo 0.19 0.10 0.71

AMOVA tests indicated that there was only a small percentage of variation among periods within fragments (Table 5) with the largest variation in MB. There was a larger percentage of variation among fragments within period with the largest percentage in the third period.

Table 5: Results of the AMOVA tests with percentage of variation among periods and among fragments.

Percentage Source of variation variation

Among periods within fragment Chawia 0.5%

Ngangao 0.4% Mbololo 2.3% Among fragments within period Period 1 4.7%

Period 2 3.7%

Period 3 6.2%

Although there was an extensive overlap of HPD intervals among the three periods, results of the BIMr analyses showed that there might be a shift in gene flow over time (Figure 3 & Appendix 3). CH seemed to mainly receive immigrants from MB in period one and immigrants from NG and MB in period two. In period three, however, there seemed to be almost no immigration. NG seemed to mainly receive immigrants from MB and CH in period one and MB in period two. In period three, there seemed to be a strong increase in immigrants from CH. Immigration rates in MB stayed low during the first two periods, while in period three, there seemed to be rather strong immigration from NG. Overall, there seemed to be more immigration in CH and NG in the first and second period, while in the third period, there seemed to be more immigration in NG and MB.

Period 1 0.12 Mbololo Period 2 0.13 Mbololo 0.88 0.95

0.07 0.02 Ngangao Ngangao 0.78 0.83

0.11 0.17 0.10 0.04 0.07 0.17 0.05 0.04

Chawia Chawia 0.83 0.66 Mbololo Period 3 0.05 0.75

Ngangao 0.19 0.65

0.004 0.31 0.01 0.06

Chawia 0.98

Figure 3: Previous generation mean proportion of immigrants in each of the three fragments per period estimated in BIMr. The thickness of the arrows is proportional to the proportion of immigrants. Distance between fragments is not in scale.

The regional correlograms per period all showed a positive genetic structure which indicates a non-random spatial distribution of genotypes and a stronger genetic similarity between individuals caught on a small geographical distance from each other (Figure 4a-c). In period two only, there was a clear indication of a difference in autocorrelation between the males and females with a stronger autocorrelation between the males (Figure 4b). In the third period there was an overall stronger correlation between the individuals (Figure 4c), which also shows on the regional correlogram where males and females were clumped together (Figure 4d).

Regional - P1 r F r M 0,140 0,120 (r) 0,100 0,080 0,060 0,040 0,020 0,000

Autocorrelation -0,020 -0,040

Distance (km)

Figure 4a: Variable distance class plot for period one on a regional scale. There is a significant positive autocorrelation for both sexes at distances up to 10km. There was no geographic data available for MB.

Regional - P2 r F r M

0,14

0,12 (r) 0,1 0,08 0,06 0,04 0,02

Autocorrelation 0 -0,02

Distance (km) Figure 4b: Variable distance class plot for period two on a regional scale. There is a significant positive autocorrelation between the males at distances up to 17km. There is a significant positive autocorrelation between the females at distances up to 11km.

Regional - P3 r F r M

0,140 0,120 0,100 0,080 0,060 0,040 0,020

0,000 Autocorrelation (r) Autocorrelation -0,020 -0,040

Distance (km) Figure 4c: Variable distance class plot for period three on a regional scale. There is a significant positive autocorrelation between the males at distances up to 14km. There is a significant positive autocorrelation between the females at distances up to 11km.

Regional r P1 r P2 r P3 0,100

0,080

0,060

0,040

0,020

Autocorrelation (r) Autocorrelation 0,000

-0,020

Distance (km) Figure 4d: Variable distance class plot on a regional scale for the three periods combined with clumping of the males and females. There is a significant positive autocorrelation between the individuals in P1 at distances up to 11 km. There is a significant positive autocorrelation between the individuals in P2 and P3 at distances up to 18 km.

4.2 Fine scale genetic patterns and sex-biased dispersal Correlograms on a local scale (within fragment) of period one and two showed the same pattern. For both sexes, there was a positives genetic structure that steadily decreased over distance, while males appeared to have a higher autocorrelation than females. However, there was a large overlap between the 95% confidence intervals and we only found significant positive autocorrelations for the males in NG in period one (Figure 5a) and in MB in period two for both sexes (Figure 5b). Non-significant autocorrelation plots of period one and two can be found in Appendix 4. There were some clear differences between fragments in the third period (Figure 5 c-e), but no significant positive autocorrelation values were found.

Local - NG P1 r F r M 0,230 0,205 0,180 0,155 0,130 0,105 0,080 0,055 0,030 0,005 Autocorrelation(r) -0,020 -0,045 -0,070

Distance (m) Figure 5a: Even distance class on a local scale of Ngangao in period one. There is a significant positive autocorrelation between males at shorter distances up to 200m.

Local - MB P2 r F r M 0,230 0,205

(r) 0,180 0,155 0,130 0,105 0,080 0,055 0,030 0,005 Autocorrelation -0,020 -0,045

Distance (m) Figure 5b: Even distance class plot on a local scale of Mbololo in period two. There is a significant positive autocorrelation between males at distances between 0m and 800m and between 1400m and 1800m. For the females there was a significant positive autocorrelation between distances of 600m and 800m

Local - CH P3 0,230 r F r M 0,200 ) 0,170 0,140 0,110 0,080 0,050 0,020 -0,010 -0,040 -0,070

Autocorrelation (r Autocorrelation -0,100 -0,130 -0,160

Distance (m) Figure 5c: Even distance class plot on a local scale of Chawia in period three. There were no significant positive autocorrelations for either of the sexes.

Local - MB P3 r F r M

0,230 0,200 0,170 (r) 0,140 0,110 0,080 0,050 0,020 -0,010 -0,040 -0,070 -0,100

Autocorrelation -0,130 -0,160 -0,190

Distance (m) Figure 5d: Even distance class plot on a local scale of Mbololo in period 3. There were no significant positive autocorrelations for either of the sexes. Only half of the fragment was sampled.

Local - NG P3 r F r M 0,230 0,205 0,180 0,155 0,130 0,105 0,080 0,055 0,030 0,005

Autocorrelation (r) Autocorrelation -0,020 -0,045 -0,070

Distance (m) Figure 5e: Even distance class plot on a local scale of Ngangao in period three. There were no significant positive autocorrelations for either of the sexes.

Mantel tests showed no evidence of isolation by distance in the genetic data (p-value > 0.05), except for two situations. In MB in the second period, there was a significant positive slope for both sexes, indicating a non-random spatial distribution of genotypes (slope F: 0.91, P-value F: 0.01; slope M: 0.69, P-value M: 0.003). The slope of the females was steeper, indicating a larger genetic isolation at a larger distance. The mantel test of NG showed that only the strong positive slope of the males was significant (slope M: 1.41, P- value M: 0.011).

Analyses of sex-biased dispersal showed that, overall, females had a negative AIC value and males a positive AIC value. Significant differences between the sexes was only observed in MB and NG in the second period (Table 6).

Table 6: Results of the sex-biased dispersal test. Significance is indicated by bold numbers.

U-test AIC (SE) Two-tailed Lower tailed Upper tailed U Female Male prob. prob. prob. CH P1 131 0.26 0.13 0.87 -0.32 (0.37) 0.18 (0.26) CH P2 1018 0.20 0.10 0.90 -0.18 (0.20) 0.17 (0.13) CH P3 24 0.28 0.14 0.86 -0.30 (0.55) 0.21 (0.35) MB P1 25 0.49 0.75 0.25 -0.03 (0.21) 0.04 (0.42) MB P2 165 0.09 0.04 0.96 -0.48 (0.38) 0.22 (0.25) MB P3 16,5 0.30 0.15 0.85 -0.58 (0.54) 0.29 (0.49) NG P1 336 0.67 0.66 0.34 0.18 (0.29) -0.09 (0.26) NG P2 578 0.03 0.02 0.99 -0.61 (0.34) 0.32 (0.20) NG P3 62 0.64 0.32 0.68 -1.17 (0.53) 0.12 (0.38)

5. DISCUSSION

Previous studies of the genetic population structure of the cooperative breeding Placid Greenbul found an increase in genetic admixture among populations and gene flow over time (between 1996 – 2000 and 2006 – 2010) (Vangestel et al. 2013, Husemann et al. 2015). Female-biased dispersal was detectible at a local scale, although local autocorrelation was only assessed in the largest fragment (MB), but not at a regional scale (Vangestel et al. 2013). In this study, we evaluated the genetic population structure on a long-term scale by including a third, more recent period (2014 – 2016). To get more insight in dispersal within fragments, we analysed the fine-scale genetic pattern in the three largest fragments of the Taita Hills (CH, MB, NG).

We found no significant difference in genetic diversity over the three periods. We did find evidence of an increase in genetic connectivity. The genetic differentiation between MB and the other fragments increased over the three periods, while there was a decrease in differentiation between CH and NG. There was an increase in genetic admixture in CH and NG over the three periods, while the levels of admixture stayed nearly constant in MB. Gene flow seemed to increase over time and sink populations seemed to shift from CH and MB to NG and MB. Next, we found evidence of genetic sub-structuring at a regional scale, where genetic correlation between the individuals gradually decreased over geographic distance, with overall higher autocorrelation values in the most recent period. At a local scale, we only found evidence of asymmetrical dispersal of the sexes in NG and MB.

5.1 Genetic diversity Genetic diversity in the three studied fragments stayed constant over the three periods. Yet we would expect a decrease in genetic diversity due to fragmentation (Young et al. 1996). The Placid Greenbul is a species with a low to moderate mobility (Lens et al. 2002, Aben et al. 2012, 2014) and a study on several woodland birds found that fragmentation has a larger effect on sedentary or low-mobility species (Amos et al. 2014). However, while there are many studies that observed decreases in genetic diversity due to fragmentation (in toad populations: Dixo et al. 2009, in butterfly populations: Krauss et al. 2004, Van Dongen et al. 1997, in ant populations: Bickel et al. 2006, in bird populations: Uimaniemi et al. 2000), there are also several studies that found none or limited effects of fragmentation on the genetic diversity (in skink populations: Sumner et al. 2004, in an endangered bird population: Lindsay et al. 2008, in orang-utan populations: Goossens et al. 2004). It is possible that there is an extinction debt which means that there is a time lag of the effect of environmental change and fragmentation on the genetic diversity and structure of the populations (Tilman et al. 1994). The constant genetic diversity in the Placid Greenbul might also be linked to its cooperative breeding strategy. In isolated but large fragments, cooperative breeding can act as a buffer against environmental and demographic stochasticity since the natal philopatric individuals are expected to increase the recolonization rate of breeding spots that recently became vacant (Walters et al. 2004). This will keep the effective breeding population constant, meaning that the genetic diversity passed on to the next generation will be kept constant (Franklin et al. 1980). Furthermore, a study by Van de Loock (2019), concerning the effect of anthropogenic habitat changes on life-history strategies of the Placid Greenbul, found no evidence of interferences of fragmentation on cooperative breeding. The proportion of cooperatively breeding pairs, the group composition and the natal dispersal strategies did not differ between fragments or years. This suggests that the degree of isolation is less severe than was initially

assumed, or that, at least for the Placid Greenbul, habitat degradation does not or only has a limited influence on the critical resources that are related to the variation in dispersal strategy (Banks et al. 2007). The effects of fragmentation may be species specific however (Callens et al. 2011).

5.2 Population structure on a regional scale Every analysis that we performed to get more insight in the population structure on a regional scale revealed overall the same pattern and indicated that the dynamics between the fragments varied over time. Regional autocorrelation graphs showed that there is a positive genetic autocorrelation over the three periods, indicating a non-random spatial genetic distribution (Vangestel et al. 2013). The relatedness between individuals was the largest within the fragments (1-3 km) and decreased with an increasing distance, which represents the autocorrelation between fragments. Comparison between periods showed that in the second period the positive autocorrelation was over longer distances significant than in period one, suggesting an increase in genetic similarity between the fragments. Next, both the genetic differentiation between fragments, estimated with Jost’ D, and the genetic population structure and levels of admixture, determined by STRUCTURE, suggested that CH and NG became more genetically similar to MB during the first two periods.

But in the most recent period, STRUCTURE results showed that CH and NG clustered together and showed high levels of admixture, while the genetic similarity between MB and NG decreased. AMOVA results also indicated that there was a higher genetic variation among the fragments in the third period, while the genetic variation between periods was the highest in MB. This shift in genetic similarity can be explained by the observed shift in the direction of gene flow as revealed by BIMr analysis. In the first two periods, there was a higher proportion of immigrants in both NG and CH that originated from MB. MB is the largest and least degraded fragment of the three fragments (Aerts et al. 2011, Thijs 2015, Chege & Bytebier 2005), and asymmetrical rates of gene flow between the fragments suggests that MB might have served as the main source in a source-sink metapopulation dynamics (Husemann et al. 2015). In the most recent period, however, there was relatively no gene flow from MB into the two other fragments. Immigrants in NG originated from CH, the most degraded fragment of the three fragments, while the immigrants in MB originated from NG.

The change in gene flow might be explained by temporal variation in habitat quality, as both temporal variation in patch carrying capacity and variation in individual demographic parameters caused by temporal environmental variation favour dispersal (Bowler et al. 2005). Another explanation might be linked to the complexity of source-sink dynamics. In source-sink populations, there is a net flow of dispersers from populations in a high quality fragment (sources) to populations in inferior fragments (sinks) (Gaggiotti 1996). A recent theoretical study indicated that the persistence or the strength of source-sink dynamics is influenced by several factors whereby strong dynamics are characterised by species that, in order of decreasing impact, have a rapid population growth, occupy habitat patches with more dissimilar qualities among patches and live in relatively stable environments. A lower dispersal ability of the species and occupancy of patches with different sizes have the weakest impacts (Heinrichs et al. 2016). A small clutch size (usually two eggs) and the longevity of the Placid Greenbul (the oldest individuals recorded in the Taita Hills was at least 19 years old) (Van de Loock 2019) indicate that this species, like many other tropical bird species (Wiersma et al. 2007), has a slow population growth. Furthermore, two of the three studied fragments are moderately (NG) and severely degraded (CH) and suffer from perturbations like cattle grazing and fire wood collection (Aerts et al. 2011, Thijs 2015). Both factors have a high impact on the strength of the dynamics and suggest weak source-sink dynamics, which makes it more difficult to detect and measure these dynamics (Heinrichs et al. 2016).

While STRUCTURE and BIMr provided evidence of an increase in gene flow over time, results of regional autocorrelation might suggest the opposite. Autocorrelation analyses revealed temporal variation, as we found a higher autocorrelation between individuals in the most recent period, both at shorter and longer distances, with a higher difference between periods at longer distances. An increase in genetic autocorrelation over time may reveal a decrease in gene flow over longer distances (Double et al. 2005). A reduction in gene flow seems contradictive with the results of the previous analyses. The difference in regional autocorrelation between periods was the largest at distances of 9 to 12 km, which represents the genetic autocorrelation between CH and NG and the largest gene flow between fragments was observed between these two fragments in previous analyses. However, a higher relatedness between these fragments might have resulted from a higher gene flow between the fragments combined with a decrease in population sizes. Studies on banner-tailed kangaroo rats, where both of the sexes show natal philopatry (Busch et al. 2009) and black and brown bears, with male-biased dispersal and negative density-dependent dispersal (Kristensen et al. 2017), found that a higher spatial autocorrelation might be linked to a lower population density. Since cooperative breeders employ a negative density-dependent dispersal strategy, there will be an increase in chances of dispersal between fragments when population densities are low. This increases the chance that individuals that are related to each other are located in two different fragments. Regional autocorrelation results may thus indicate a decrease in population sizes, but this should be further investigated.

We did, however, find some incongruencies in the results of our analyses. Jost’ D results showed a constant differentiation between CH and NG, while levels of admixture estimated in STRUCTURE showed a clear increase and there was an increase in gene flow estimated by BIMr. This might be explained by differences in assumptions. Furthermore, Jost’ D will be influenced by historical rates of gene flow (Allendorf & Luikart 2007) while STRUCTURE and BIMr are more capable of detecting recent patterns of gene flow (Pritchard et al. 2000; Faubet & Gaggiotti 2008).

It is also important to mention that indications of an increased gene flow in the most recent periods should be interpreted with caution. A high genetic admixture, as observed between CH and NG in the most recent period, does not necessarily mean that the gene flow between those fragments is very high. One reproducing immigrant per generation is already sufficient to prevent complete differentiation between populations (Wright 1931). This means that gene flow between two populations does not need to be high to have an impact on the genetic structure of the populations. Furthermore, we experienced that STRUCTURE results are sensitive to sample sizes (Box 1) and we had much less samples in the third period compared to the other two periods. Next, the percentage of estimated immigrants assigned to each fragment in BIMr is proportional to the effective population size of the fragment itself (Faubet et al. 2007). This means that for the same number of immigrants, a fragment with a large effective population size will have a smaller percentage of immigrants than a fragment with a small effective population size. Even though the previous study of Husemann et al. (2015) showed that the effective population sizes of the fragments in the first two periods remained equal or increased slightly, it is possible that stochastic environmental events caused fluctuations in the effective population sizes. Due to possible differences in population sizes per period and between fragments, it is not possible to easily compare the percentages of immigrants between periods or fragments.

Box 1: The influence of sample sizes on STRUCTURE analyses.

An increase in sample size of the MB population (4 ind. in figure above vs. 30 in figure below) showed a change in estimated admixture proportion between the three populations. In the first scenario, the number of suggested optimal clusters was 1 while the optimal number of clusters in the second period was 2. This shows the importance of equally weighing of samples among populations for the interpretation of STRUCTURE plots (Lawson et al. 2016). The study by Lawson et al. further suggests interpreting these plots as a visualisation of genetic variation between individuals instead of levels of admixture between populations. If genetic drift due to a bottleneck severely reduces the genetic variation between samples, then there is no variation between individuals for the model to explain. This would result in an underestimation of genetic clusters. Our first scenario can be interpreted as a reduction in genetic variation due to a bottleneck.

5.3 Population structure on a local scale Local autocorrelation graphs showed significant positive autocorrelation values in the largest, less degraded fragments: in MB in period two for distances up to 800 m and distances between 1400m and 1800m and in NG in period one for shorter distances up to 200m. At similar distance classes, autocorrelation values were higher for MB and NG than for CH, and we did not find any significant autocorrelation values for CH. This means that there was no clustering of related individuals at short distances. These results suggest that fragmentation may disrupt the spatial genetic patterns in smaller and/or more degraded fragments leading to more random mating. A study on cooperatively breeding acorn woodpeckers showed that individuals born in a low-quality habitat were more eager to disperse than individuals born in a high-quality habitat (Stacey et al. 1987). Territory quality has a significant effect on the survival and reproductive success of an individual. The best option for an individual is to employ the bet-hedging strategy and thus to disperse when its expectations for its future fitness is low. Next, smaller fragments might also lead to an increase in inbreeding, since relatives will live closer to each other, which increases the chance of incestuous mating. This may lead to an increase of dispersal distances (Koenig & Haydock 2004).

However, local autocorrelation graphs of the most recent period showed different patterns of autocorrelation per fragment. None of the values were significant and there were large, overlapping 95% confidence intervals. Furthermore, for NG we only observed a significant genetic population structure in period one. This may indicate that dispersal patterns within fragments are dynamic over time. Differences

between the periods might be due to temporal fluctuations in habitat quality, similar to what we observed at a regional scale.

In this study we only included the three largest fragments of the Taita Hills since there was not enough genetic data available for the smaller fragments, that may act as stepping stones between CH and NG. Further research that includes these fragments might give more insights in the factors that affect gene flow and the genetic population structures.

5.4 Sex-biased dispersal While mantel tests provided clear evidence of female-biased dispersal in NG in period three only, analyses of sex-biased dispersal, performed in GenAlEx, provided evidence of female-biased dispersal in all fragments, but it was only significant in NG and MB in period two. Next, local autocorrelation tests showed overall higher autocorrelation values between the males than between females in all of the fragments during the first two periods. These positive values for the males were significant in NG in period one for shorter distances up to 200m, and in MB in period two for distances up to 800 m and distances between 1400m and 1800m. All these results combined provides evidence for the female-biased dispersal within fragments and clustering of related males at shorter distances. This is what we would expect for cooperative breeding birds (Zack 1990).

Further evidence might be provided by the autocorrelation tests on a regional scale. In contrast to the results of Vangestel et al. (2013), we did find a significant difference between the sexes in the second period, where males had the most positive autocorrelation. The difference between both studies might be explained by the difference in fragments that were included. While the study of Vangestel et al. (2013) combined data from two other small fragments, next to CH and NG, it did not include MB. In our study, MB was included in the last two periods both on a regional as on a local scale. This suggests that there might have been a strong local pattern in spatial autocorrelation in MB that had an effect on the regional autocorrelation pattern. However, we did not observe a significant difference between the sexes in the third period, but in that period, only one half of MB was sampled.

The differences between periods in our study, and the difference in significance between the study of Vangestel et al. (2013), might be explained by a difference in sample sizes. Statistical tests indicated that the power of identifying asymmetrical autocorrelation patterns increases when the sample size increases due to larger confidence intervals (Banks et al. 2012). Compared to the sample sizes of the other periods in our study, there was a double amount, or more, of samples available in the second period compared to the third period. Compared to the samples size of Vangestel et al. (2013), there was a double amount of samples available for the second period in our study. Lastly, the lack of sex-biased dispersal in CH might be explained by a lower habitat quality, as previously discussed, since male philopatry is only expected when the lifetime fitness benefits acquired through philopatry, like eventually inheriting a high-quality territory, outweighs the benefits of immediate dispersal (Stacey et al. 1990).

6. CONCLUSIONS

The results of this long-term study on the genetic population structure of the Placid Greenbul show that there is an increase in admixture between populations and an increase in gene flow over time, as indicated in earlier studies with a shorter time frame. We did, however, also find a change in population dynamics, both between (on a regional scale) as well as within the different populations (on a local scale). We also found evidence that fragmentation may disrupt the spatial genetic patterns in smaller and/or more degraded fragments. This study thus emphasizes the importance of long term studies on the genetic population structure of species, since stochastic events can have a big influence on population dynamics. However, further research is needed to get more insight in the causes and consequences of the increase in admixture and gene flow between populations. The increase in admixture might reflect growing and strongly connected populations, but it can also mean that dispersal might be more advantageous than staying in fragments, despite its costs.

7. SUMMARY

Dispersal is an important life history trait that has an influence on many ecological scales that range from meta-population dynamics and breeding strategies to the local spatial genetic population structure. Colonisation of empty habitat patches depends on the dispersal capabilities of the species and can save species from (local) extinction. This forms the base of several population structure theories like metapopulation and source-sink dynamics. Dispersal is also a crucial driver of gene flow between populations which has important consequences for the population genetic structures. Factors like habitat patch isolation, patch size, temporal variation in habitat quality and population density can influence dispersal strategies which influences population dynamics and gene flow.

Cooperative breeding is a fairly common breeding strategy in birds species that is characterised by sex- biased dispersal. A breeding pair is assisted by one or more members of a social group to take care of their offspring. For the majority of cooperative breeders, the ‘helpers’ consist of the offspring of the breeding couple that delay dispersal and stay with their parents in their natal territory. For birds, it is common that the males are the philopatric sex, while females disperse sooner and farther than males. The difference in dispersal strategy between sexes has an influence on the gene flow and genetic population structure of cooperative breeders. The general short-distance dispersal of the philopatric male will result in close spatial associations between relatives. Consequently, local spatial genetic structures may differ between the sexes, where a more positive spatial genetic structure is expected for the males.

Habitat fragmentation and loss have numerous negative impacts on wildlife populations and are the key drivers of biodiversity loss. Consequences of fragmentation are a reduction in habitat area, isolation of habitat patches, decreases in population sizes and reduction of the genetic diversity. The impact of fragmentation on cooperative breeding species depends on the fragment size. In larger fragments, cooperative breeding helps to stabilise the size of the breeding population because of a fast colonisation of vacant breeding spots. Yet in small, isolated fragments, there may be an increase in the chance of incestuous mating and thus inbreeding depression since these species usually live in groups composed of close relatives.

This study focusses on populations of the facultative cooperative breeding Placid Greenbul (Phyllastrephus placidus) of the severely fragmented Taita Hills in south-east Kenya. Not much is known about the long- term meta-population viability of tropical cooperative breeding birds under persisting habitat change. Furthermore, there are no studies to our knowledge that compare genetic population dynamics between different time frames and on different spatial scales. The aim of this study is to get more insight in the genetic population dynamics of the cooperative breeding Placid Greenbul in the Taita Hills on a long-term scale. We will use data collected over almost two decades to compare the genetic population structure between three consecutive periods, both on a regional scale (between fragments) as well on a local scale (within fragments).

We used genetic data from Placid Greenbuls caught with mist nets in the three largest fragments of the Taita Hills: Chawia (86 ha), Ngangao (120 ha) and Mbololo (220 ha). The three fragments also differ in their level of degradation. MB is the least degraded fragment while NG and CH are respectively moderately and severely degraded. The data was collected over three periods (P1: 1997 - 1999, P2:2007 – 2009, P3: 2014 – 2016). For the majority of the individuals there was also sex data and geographic data (latitudinal and longitudinal GPS coordinates) available.

Genetic differentiation between fragments per period was calculated with the Jost’s D differentiation index. Bayesian admixture models from the programme STRUCTURE were used to assess the genetic population structure and level of admixture between the three fragments (CH, NG, MB) separately for each period. We also performed AMOVA tests to detect genetic variation between periods and between fragments. The amount of gene flow was estimated in BIMr. Next we used GenAlEx to perform spatial autocorrelation tests per period. Data from three fragments were combined to detect regional genetic patterns while local genetic patterns were analysed per fragment using smaller distance classes. To detect these patterns, we visualized the relatedness between individuals as a function of cumulative geographical distance in a correlogram. Genetic isolation by distance was tested using a Mantel test for each fragment (CH, NG, MB) during each period. Each analysis was performed per sex to reveal sex specific genetic patterns. For further evidence of sex-biased dispersal, we used the sex bias assignment test in GenAlEx.

The constant genetic diversity might be linked to the cooperative breeding strategy of the Placid Greenbul. In isolated but large fragments, cooperative breeding can act as a buffer against environmental and demographic stochasticity. Natal male philopatry increases the recolonization rate of vacant breeding spots, keeping the effective population and thus the genetic diversity passed on to the next generation constant. Furthermore, a study concerning the effect of anthropogenic habitat changes on life-history strategies of the Placid Greenbul, found no evidence of interferences of fragmentation on the cooperative breeding strategy of this species. However, it is also possible that there is an extinction debt. This means that there is a time lag of the effect of environmental change and fragmentation on the genetic diversity and structure of the populations.

Next, every analysis that we performed to get more insight in the population structure on a regional scale revealed overall the same pattern and indicated that the dynamics between the fragments varied over time. The positive autocorrelation was over longer distances significant in period two than in period one, suggesting an increase in genetic similarity between the fragments. But in the most recent period, results showed that CH and NG clustered together and showed high levels of admixture, while the genetic similarity between MB and NG decreased. An observed shift in the direction of gene flow might explain the shift in genetic similarity. While there was a higher proportion of immigrants in both NG and CH that originated from MB in the first two periods, there was relatively no gene flow from MB into the two other fragments in the most recent period, while the gene flow from CH to NG increased. The change in gene flow might be explained by temporal variation in habitat quality, as both temporal variation in patch carrying capacity and variation in individual demographic parameters caused by temporal environmental variation favour dispersal. Another explanation might be linked to the complexity of source-sink dynamics. The longevity of the species and level of degradation of the fragments suggest weak source-sink dynamics, which makes it harder to detect and measure these dynamics.

Regional autocorrelation analyses also revealed temporal variation. We found a higher autocorrelation between individuals in the most recent period, both at shorter and longer distances, with a higher difference in autocorrelation between periods at longer distances. We hypothesise that a higher autocorrelation between these fragments might have resulted from a higher gene flow between the fragments combined with a decrease in population sizes. Since cooperative breeders employ a negative density-dependent dispersal strategy, there will be an increase in chances of dispersal between fragments when population densities are low. This increases the chance that individuals that are related to each other are located in two different fragments. Regional autocorrelation results may thus indicate a decrease in population sizes, but this should be further investigated.

Local spatial autocorrelation tests suggest that fragmentation may disrupt the spatial genetic patterns in smaller and/or more degraded fragments. We found no significant population structuring in CH, where a low habitat quality may favour dispersal of the individuals. However, we only included the three largest fragments of the Taita Hills. Further research that includes smaller forest fragments might give more insights in the factors that affect gene flow and the genetic population structures. Next, local spatial autocorrelation tests, mantel tests and sex-biased assignment tests combined provide evidence for the female-biased dispersal within fragments and clustering of related males at shorter distances, as we would expect for cooperative breeding birds. However, local autocorrelation test of the most recent period showed different patterns. This might be due to temporal variations in habitat quality or due to differences in sample sizes between periods.

8. SAMENVATTING

Verspreiding is een belangrijk proces dat een invloed heeft op vele ecologische schalen die van metapopulatie dynamieken en reproductie strategieën tot de lokale ruimtelijke genetische populatie structuur reiken. De kolonisatie van vrijgekomen habitatfragmenten hang af van de verspreidingscapaciteiten van de soort, en kan soorten van (lokale) extinctie redden. Dit vormt de basis van verschillend theorieën over populatie structuren zoals source-sink dynamieken. Verspreiding is ook een belangrijke drijfveer voor de uitwisseling van genen tussen populaties en dit heeft belangrijke consequenties voor de genetische structuur van populaties. Verschillende factoren zoals isolatie van habitatfragmenten, fragmentgrootte, tijdelijke variatie in de habitatskwaliteit en populatiedensiteit, kunnen een invloed hebben op verspreidingsstrategieën. Deze kunnen op hun beurt een invloed hebben op populatiedynamieken en de uitwisseling van genen tussen populaties.

Coöperatieve reproductie is een redelijk veel voorkomende reproductiestrategie voor vogelsoorten en is gekarakteriseerd door een verschil in verspreidingsstrategieën tussen mannetjes en vrouwtjes. Een koppel wordt door één of meerdere leden van een sociale groep geassisteerd bij het opvoeden van hun jongen. Deze helpers zijn meestal oudere jongen van het koppel die hun verspreiding uitstellen en in hun geboorteplaats blijven. Bij vogelsoorten zijn het gewoonlijk de mannetjes die streekgebonden zijn, terwijl de vrouwtjes zich sneller en verder verspreiden. Dit heeft een invloed op de uitwisseling van genen en de genetische populatiestructuur van soorten met coöperatieve reproductie. De in het algemeen kortere verspreidingsafstand van de mannetjes zorgt voor een hogere concentratie van verwante individuen. Dit heeft als gevolg dat de lokale ruimtelijke genetische structuur kan verschillen tussen beide geslachten. Voor de mannetjes wordt er een positievere ruimtelijke genetische structuur verwacht.

Habitatfragmentatie en -verlies hebben verschillende negatieve impacts op dierenpopulaties en zijn de belangrijkste oorzaken van biodiversiteitsverlies. De gevolgen van fragmentatie zijn onder andere reductie van habitatgebied, isolatie van habitatfragmenten, afname van populatiegroottes en reductie in de genetische diversiteit. De impact van fragmentatie op soorten met een coöperatieve reproductie hangt onder andere af van de fragmentgrootte. In grotere fragmenten zorgt de reproductiestrategie voor een stabilisatie van het aantal reproducerende individuen door snelle kolonisatie van vrijgekomen broedplaatsen. Maar in kleine, geïsoleerde fragmenten is er een hogere kans op incest, wat kan leiden tot een inteeltdepressie, aangezien deze soorten in groepen van dichte verwanten samenleven.

Deze studie focust zich op de populaties van de Placid Greenbul (Phyllastrephus placidus) in de ernstig gefragmenteerde Taita Hills in zuidoost Kenia die een facultatieve coöperatieve reproductiestrategie heeft. Er is niet veel geweten over de lange termijn levensvatbaarheid in metapopulaties van tropische vogelsoorten die coöperatief reproduceren, onder aanhoudende veranderingen in hun habitat. Bovendien vonden we geen studies die de genetische populatiedynamieken tussen verschillende periodes en op verschillende ruimtelijke schalen vergelijken. Het doel van dit onderzoek is om meer inzicht te krijgen in de genetische populatiedynamieken van de Placid Greenbul populaties in de Taita Hills op een lange termijn. We gebruiken data die tijdens bijna twee decennia verzameld zijn om de genetisch populatiestructuur tussen drie opeenvolgende periodes te vergelijken. Dit zowel tussen fragmenten binnenin fragmenten. De genetische data zijn afkomstig van individuen die gevangen werden met mistnetten in de drie grootste fragmenten van de Taita Hills: Chawia (86 ha), Ngangao (120 ha) en Mbololo (220 ha). De drie fragmenten hebben ook een verschil in degradatie. MB is het minst gedegradeerd terwijl NG en CH respectievelijk matig en ernstig gedegradeerd zijn. De data werden verzameld over drie periodes (P1: 1997 – 1999, P2:2007 –

2009, P3: 2014 – 2016). Voor de meeste individuen waren er ook geslachts- en geografische data (GSP coördinaten) beschikbaar.

Genetische differentiatie tussen fragmenten werd per periode berekend met de Jost’s D differentiatie index. We gebruikten Bayesian admixture modellen uit het programma STRUCTURE om de genetische populatiestructuur en de genetische gelijkenissen tussen de drie fragmenten te berekenen gedurende elke periode. We voerden ook AMOVA testen uit om de genetische variatie tussen periodes en fragmenten te schatten. De uitwisseling van genen werd geschat via het programma BIMr. Vervolgens gebruikten we GenAlEx om ruimtelijke autocorrelatietesten uit te voeren. De data van de drie fragmenten werden samen genomen om regionale genetische patronen te detecteren terwijl lokale genetische patronen per fragment en met kleinere afstandsklassen werden geanalyseerd. We gebruikten Mantel testen om de genetische isolatie door afstand te analyseren, en dit voor elk fragment en tijdens iedere periode. Elke analyse werd per geslacht uitgevoerd om geslacht specifieke genetische patronen te onthullen. We gebruikten sex bias assignment tests in GenAlEx als verder bewijs voor een verschil in verspreidingsstrategieën van de geslachten.

We vonden geen significante verschillen in genetische diversiteit over de drie periodes maar vonden wel bewijs voor een toename in genetische connectiviteit. De genetische differentiatie tussen MB en de andere fragmenten nam toe over de drie perioden, terwijl er een afname in differentiatie was tussen CH en NG. Er was een toename in genetische gelijkenis tussen CH en NG over de drie perioden, terwijl de genetische samenstelling van de populatie in MB nagenoeg constant bleef. Er was een toename in genetische uitwisseling en er leek een shift te zijn in sink populaties: van CH en MB naar NG en MB. Vervolgens vonden we bewijs voor genetische sub-structurering op een regionale schaal. Hierbij was er een geleidelijke afname in genetische autocorrelatiewaarden met toenemende geografische afstanden. Er waren over het algemeen hogere autocorrelatiewaarden in de meest recente periode. Op lokale schaal vonden we enkel in NG en MB bewijs voor asymmetrische verspreiding van de geslachten.

De constante genetische diversiteit zou kunnen gelinkt worden aan de coöperatieve reproductiestrategie van de Placid Greenbul. In grote, geïsoleerde fragmenten kan de reproductiestrategie als een buffer werken tegen omgevings- en demografische stochasticiteit. Er is een hoge mate van kolonisatie van vrijgekomen broedplaatsen door de streekgebonden mannetjes, waardoor de effectieve populatie, en dus ook de genetische diversiteit die doorgegeven wordt naar de volgende generatie, constant blijft. Bovendien werd in een studie die zich focuste op het effect van antropogene habitatsveranderingen op de levensstrategieën van de Placid Greenbul, geen bewijs gevonden dat fragmentatie een effect heeft op de reproductiestrategie van deze soort. Echter, het is ook mogelijk dat er een tijdsvertraging is van het effect van de veranderende omgeving en fragmentatie op de genetische diversiteit en structuur van de populaties.

Vervolgens vertoonde elke analyse die we uitvoerden om meer inzicht te krijgen in de genetische populatiestructuur op een regionale schaal in het algemeen hetzelfde patroon. Ze toonden aan dat de dynamieken tussen de fragmenten varieerden over de periodes. De positieve autocorrelatie was in de tweede periode over langere afstanden significant dan in de eerste periode, wat een toename in de genetische gelijkenis tussen de fragmenten aanwijst. Maar in de meest recente periode toonden de resultaten aan dat CH en NG een genetische cluster vormden, terwijl de genetische gelijkenis tussen MB en NG afnam. Deze shift in genetische gelijkenis zou kunnen verklaard worden door een verandering in de richting van de genetische uitwisseling. Terwijl er in de eerste twee perioden een hogere proportie immigranten in zowel NG en CH aanwezig waren die afkomstig waren uit MB, was er in de meest recente periode geen genetische uitwisseling vanuit MB naar de twee andere fragmenten toe, terwijl de genetische

uitwisseling van CH naar NG toenam. Deze verandering in genetische uitwisseling zou verklaard kunnen worden door tijdelijke variatie in habitatskwaliteit, aangezien dit tijdelijke variatie in de draagkracht van een fragment en demografische parameters van een individu veroorzaakt en dus verspreiding aanmoedigt. Een andere verklaring kan gelinkt zijn aan de complexiteit van source-sink dynamieken. De lange levensduur van deze soort en de degradatie van de fragmenten geven aan dat er zwakke source-sink dynamieken zijn, waardoor het moeilijker is om deze dynamieken te detecteren en op te volgen.

Regionale autocorrelatie analyses toonden ook aan dat er tijdelijke variatie is. We vonden in de recente periode een hogere autocorrelatie tussen individuen, zowel op kortere als langere afstanden, met een groter verschil in autocorrelatie tussen periodes op langere afstanden. We veronderstellen dat een hogere autocorrelatie tussen deze fragmenten voort zou zijn gekomen uit een hogere genetische uitwisseling tussen de fragmenten, gecombineerd met een afname in populatiegroottes. Aangezien soorten met een coöperatief reproductiestrategie een verspreidingsstrategie hebben die negatief gecorreleerd is aan populatiedensiteit, is er meer kans dat er verspreiding is tussen fragmenten wanneer de populatiedensiteit laag is. Dit verhoogt de kans dat individuen die verwant zijn aan elkaar zich in twee verschillende fragmenten bevinden. De resultaten van regionale autocorrelatietesten geven dus aan dat er een afname in populatiegroottes kan geweest zijn, maar dit zou nog verder moeten onderzocht worden.

Lokale autocorrelatietesten wijzen aan dat fragmentatie de ruimtelijke genetische patronen in kleinere en/of meer gedegradeerde fragmenten kan verstoren. We vonden geen significante populatiestructurering in CH, waar een lage habitatskwaliteit de kans op verspreiding kan verhogen. Vervolgens leverde de combinatie van de resultaten van lokale rautocorrelatietesten, manteltesten, en sex bias assignmenttests bewijs voor asymmetrische verspreiding waarbij de vrouwtjes zich verder verspreiden in fragmenten. Ze leveren ook bewijs voor het klusteren van verwante mannetjes op kortere afstanden, zoals we zouden verwachten voor vogels met een coöperatief reproductiesysteem. Echter, de lokale autocorrelatietesten van de meest recente perioden toonden andere patronen. Dit kan te wijten zijn aan tijdelijke variatie in habitatkwaliteit of door een verschil in steekproefgroottes in de verschillende periodes. Bovendien werd er in deze studie enkel op de drie grootste fragmenten van de Taita Hills gefocust. Verder onderzoek dat ook met de kleinere fragmenten rekening houdt kan meer inzicht geven in de factoren die een effect hebben op gene flow en de genetische populatiestructuur.

De resultaten van deze lange termijn studie van de genetische populatiestructuur van de Placid Greenbul tonen aan dat er een toename is in genetische gelijkenis tussen de populaties en dat er een toename is in genetische uitwisseling. Dit werd eerder ook al aangetoond in studies over een kortere tijdspan. We namen echter wel veranderingen in populatiedynamieken waar, zowel op regionale schaal als op lokale schaal. We vonden ook bewijs voor verstoorde spatiale genetisch patronen door fragmentatie in kleinere en/of meer gedegradeerde fragmenten. Deze studie legt dus de nadruk op het belang van lange termijn studies over de genetische populatiestructuur van soorten. Er is echter verder onderzoek nodig naar de oorzaken en gevolgen van de toename in genetische gelijkenis en de genetische uitwisseling tussen populaties.

9. TEXT FOR THE BROADER AUDIENCE

The importance of long-term genetic population studies

To the best of our knowledge, there are no studies that compare genetic population dynamics between different time frames and on different spatial scales. However, climate change and anthropogenic habitat changes, caused by an increase in urbanisation and expanding agriculture, are a threat to many species and can change their habitat range and interaction between populations over time.

Changes in land use often lead to the fragmentation of the habitat area of species which has numerous consequences for the viability of the populations. Habitat fragmentation and loss are the key drivers of biodiversity loss. They create isolated habitat patches that are usually surrounded by an inhospitable environment. This leads to a decrease in population size, which may reduce the genetic diversity of the population due to genetic drift. Small, isolated populations are also more vulnerable to demographic and environmental stochastic events, like sudden droughts or the emergence of diseases, since the loss of genetic diversity can reduce the adaptability of a population to new environmental conditions. This consequently reduces the fitness of the population.

Fragmentation also has an effect on the dispersal between patches and the dynamics between populations. Metapopulation dynamics depend on the dispersal between patches. The colonisation rate of empty patches is related to the dispersal capabilities of the species and can save species from (local) extinction. Dispersal is also an important driver of gene flow between populations, which can counteract the decrease of genetic diversity in fragments. But when the cost of dispersal increases, due to an inhospitable matrix, an increase of energy investment and/or exposure to predation, it will be harder for individuals to disperse between fragments. There will be a decrease in chances of dispersal and, consequently, a decrease in gene flow. The dynamics can also be affected in the opposite way where fragmentation increases the chances of dispersal. Anthropogenic fragmentation often causes degradation of the fragments and an increase in perturbations such as cattle grazing, fire wood collection and controlled wildfires. Individuals tend to leave patches with a higher temporal variation in habitat quality, for example where food resources are unpredictable. Dispersal can in this case be considered as a bet-hedging strategy to reduce the variance in the fitness of an individual.

It is thus important to study dispersal if we want to get a better insight in how fragmentation affects the population dynamics of a certain species. Unfortunately, it is difficult to investigate dispersal and to observe changes in dynamics. It often requires intensive, large-scale and long-term demographic studies that mainly rely on capture-recapture approaches. Genetic studies can reduce the effort required to describe dispersal patterns and can address methodological limitations of capture-recapture studies, like long-distance dispersal. Powerful statistical models used in genetic studies are able to estimate rates of gene flow among populations and detect sex-biased dispersal. They provide more insight in the genetic population structure and can detect genetic patterns on both a regional scale (between fragments) as well on a local scale (within fragments). However, there are also some limitations to genetic approaches as they may fail to reveal current gene flow and non-effective dispersal movements. It is therefore recommended to combine demographic and genetic approaches to get a correct interpretation of the current situation.

Our long-term genetic study on the cooperative breeding Placid Greenbul populations of the Taita Hills in south-east Kenya gave more insight in the genetic population structure and population dynamics and emphasises the importance of long-term studies. We used data collected over almost two decades to compare the genetic population structure between three consecutive periods, both on a regional as on a local scale. Previous studies on these populations, with data from a shorter timeframe, found an increase in genetic similarity and gene flow between populations. Our study confirmed this, but in the most recent period, we found a change in the direction of gene flow. Populations that first seemed to act as sink populations now became source populations. This was reflected in the genetic population structure on both a regional scale as well on a local scale. We hypothesise that these changes might have been caused by stochastic environmental events that might have caused a decrease in population densities.

Lastly, detecting changes in dynamics can also have important implications on the conservation of populations. A short-term study that only looks at one timeframe might observe clear dynamics between populations, which might falsely lead to the decision to only take conservation actions for the most important populations, for example the source populations in source-sink dynamics. A long-term study will give more information about fluctuations in population sizes, for example, and possible changing dynamics. This can give more insight for conservation actions.

10. ACKNOWLEDGMENTS

First of all, I would like to thank Laurence Cousseau for all the advice and the many revisions. I would also like to thank prof. Dr. Carl Vangestel for his view on the results. Further, I would like to thank all the people involved in the data collection and genotyping. I would also like to thank my parents and close friends for the support, and my dog Pippa for always being her enthusiastic self. Next, I would like to thank the Terrestrial Ecology Unit of the University of Ghent for the use of their facilities. Lastly, I would like to thank prof. Dr. Luc Lens for making this master’s dissertation possible.

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12. APPENDICES

APPENDIX 1: The potential of genetic and demographic rescue in the Taita Apalis, a critically- endangered bird of south-east Kenya.

A1.1 Introduction The Taita Apalis (Apalis fuscigularis) has a conservation status of critically endangered (Birdlife International, 2018) and recent estimates indicate that the total population size only counts 100-150 individuals (Borghesio et al. 2017). The Taita Apalis is endemic to the Taita Hills, an ancient mountain massif in South-East Kenya, where it only occurs in only five of the 12 small fragments of the forest (Borghesio et al. 2015). The species is threatened by an increase in habitat loss and fragmentation (Pellikka et al. 2009). The population is stable in three fragments (Vuria, Msidunyi and Yale), but the population in Ngangao is experiencing a strong decrease. Fragmentation is often the cause of a decrease in population size and consequently in a decline of genetic diversity (Frankham et al. 2002) (Allendorf et al. 1996). To be able to conserve the species, high levels of genetic diversity have to be maintained.

Not much is known about the reasons why the populations of the Taita Apalis are declining. The initial aim of this master thesis was to perform the first genetic study on the species ever. This would provide more insight in the temporal variation of the genetic diversity and genetic population structure of the species. There were no microsatellites available for this specific species, so the first aim of this study was to find enough polymorphic microsatellites to be able to perform the genetic analyses.

A1.2 Material and Methods The DNA samples used for this study were collected from 1996 till 2017, divided in three periods, from five different forest fragments: Msidunyi (20 samples), Ngangao (63 samples), Vuria (59 samples), Yale (3 samples) and Chawia (1 sample) (table 7). These samples consist of both blood and feather samples.

Table 7: Number of DNA samples per period and per fragment.

1996-2001 2004-2011 2014-2017 Msidunyi 20 Ngangao 20 16 27 Vuria 10 49 Chawia 1 Yale 2 1

For DNA extraction from the collected blood samples we used a Chelex-based DNA isolation procedure. The blood was stored in microtubes filled with ethanol. Approximately 10µl of the blood in ethanol was taken from each sample and put in a new micro tube. The samples were centrifuged at 600 rpm at 21°C and the ethanol was removed. Then we added 200µl of 5% CHELEX and 5µl of proteinase K. The samples were incubated for 2h30 at 56°C and afterwards boiled for 8 minutes at 95°C. Then the samples were vortexed and again centrifuged for 3 minutes at 14000 rpm at 21°C. The supernatant was collected in a new micro tube and stored in a freezer until further use.

For the extraction of the DNA from the collected body feathers we followed the DNeasy Blood & Tissue Kit Quick-Start protocol (2011) from Qiagen (Hilden, Germany). From the biggest feathers we cut the tips of the calamus (closest to the body), smaller feathers were used as a whole. We used approximately 10 feathers from each sample and put them in micro tubes. After following the protocol and mixing by vortexing, the samples were incubated over night at 56°C while continuously being mixed at 250 rpm. All the other steps were followed like on the protocol. The extracted DNA was stored in a freezer until further use.

In a first exploration to find different allelic polymorphisms we used 16 different DNA samples. These samples were chosen so that samples from each fragment and each period were included. We used 80 different microsatellites specifically developed for the Taita Apalis by the company AllGenetics (A Coruña, Spain) to test for polymorphisms. The PCRs were carried out following Schuelke (2000) where the forward primers were tailed with the universal sequences M13 (5'GGA AAC AGC TAT GAC CAT), M13-38 (5’ TTT CCC AGT CAC GAC GTT G), M13ModB (5’ CAC TGC TTA GAG CGA TGC) and M13ModA (5’ TAG GAG TGC AGC AAG CAT) . A primer mix was prepared which contained 1µl of the forward primer, 4µl of the reverse primer, 4µl fluorescent colour (specific for each primer) and 91µl TE. The DNA was multiplied through a PCR using a 96- well plate. Each well was filled with 2µl Multiplex MM, 2µl of a specific primer mix and 2µl DNA from one of the 16 samples. The PCR products were analysed on an ABI 3130XL Genetic Analyzer (Applied Biosystems), and genotypes were scored with GENEIOUS 7.0.5 (Kearse et al. 2012).

A1.3 Results and discussion Out of the 80 different microsatellites, we only found 5 different polymorphic loci. Each of them had two to three different alleles. Since only 5 polymorphic loci is too few to be able to perform a strong and meaningful statistical analysis, we changed the focus of the master thesis. Further research should not be excluded however. We suggest using SNP’s (Single Nucleotide Polymorphisms) instead of microsatellites as genetic markers. SNP’s biggest source of genetic variability in most genomes (Brumfield et al. 2003, Morin et al. 2004, Wayne & Morin 2004) and can be used for genome wide screenings of selectively neutral or adaptive variation (Luikart et al. 2003, Wayne & Morin 2004). SNP’s also have a high potential for population structure analysis. In species where a large number of SNPs have been scanned, it has been shown that a only a small fraction of the SNP’s provide enough information for meaningful analyses (Rosenberg et al. 2003; Turakulov & Easteal 2003; Lao et al. 2006; Paschou et al. 2007) and sometimes even outperform microsatellites (Liu et al. 2005).

APPENDIX 2: Structure Harvester results

Table 8: Optimal numbers of genetic clusters (K) among the fragments (CH, NG, MB) for the three periods, based on 15 independent runs in STRUCTURE. The optimal number of clusters are indicated in bold.

Period K Mean LnP(K) Stdev Ln'(K) |Ln''(K)| Delta K LnP(K) 1 1 -5144.17 0.30

2 -4968.49 1.45 175.69 47.35 32.63

3 -4840.15 1.60 128.33 188.20 117.66

4 -4900.02 21.24 -59.87 73.69 3.47

5 -4886.19 26.38 13.83 206.06 7.81

6 -5078.43 86.93 -192.23

2 1 -7279.51 0.29

2 -7031.41 1.32 248.11 47.99 36.39

3 -6831.29 1.55 200.11 223.80 144.06

4 -6854.98 45.34 -23.69 55.01 1.21

5 -6823.65 37.94 31.33 29.31 0.77

6 -6821.64 93.30 2.01

3 1 -2203.79 0.64

2 -2157.63 3.65 46.17 81.49 22.30

3 -2192.95 30.23 -35.32 112.38 3.72

4 -2340.65 45.71 -147.70 81.49 1.78

5 -2406.86 69.06 -66.21 177.79 2.57

6 -2650.86 954.26 -244.00

APPENDIX 3: BIMr results

Table 9: BIMr results of the run with the highest probability, with the mean immigration rate and 95% highest posterior density intervals (HPDI) per period. Direction of migration is indicated as ‘sink population – source population’.

P1 P2 P3

mean HPDI mean HPDI mean HPDI m CH-CH 0.83 [0.543;0.974] 0.66 [0.468;0.825] 0.98 [0.343;0.961] m CH-MB 0.11 [0.00457;0.29] 0.17 [0.0423;0.365] 0.0040 [0.00673;0.258] m CH-NG 0.066 [0.0149;0.358] 0.17 [0.0253;0.336] 0.013 [0.00871;0.613] m MB-CH 0.050 [0.00902;0.253] 0.035 [0.00652;0.294] 0.061 [0.0119;0.424] m MB-MB 0.88 [0.635;0.984] 0.95 [0.605;0.981] 0.75 [0.4;0.973] m MB-NG 0.070 [0.0146;0.298] 0.018 [0.000454;0.221] 0.19 [0.00896;0.553] m NG-CH 0.10 [0.00605;0.333] 0.040 [0.00639;0.188] 0.31 [0.0195;0.696] m NG-MB 0.12 [0.0157;0.398] 0.13 [0.00257;0.333] 0.045 [0.00785;0.389] m NG-NG 0.78 [0.516;0.976] 0.83 [0.578;0.973] 0.65 [0.261;0.965]

APPENDIX 4: Local spatial autocorrelation graphs

Local - CH P1 r F r M 0,230 0,205 0,180 0,155 0,130 0,105 0,080 0,055 0,030 0,005 -0,020

Autocorrelation (r) Autocorrelation -0,045 -0,070 -0,095

Distance (m)

Figure 6a: Even distance class plot on a local scale of Chawia in period one. There were no significant positive autocorrelations for either of the sexes.

Local - CH P2 r F r M 0,230 0,205 0,180 0,155 0,130 0,105 0,080 0,055 0,030 0,005 Autocorrelation (r) Autocorrelation -0,020 -0,045

Distance (m)

Figure 6b: Even distance class plot on a local scale of Chawia in period two. There were no significant positive autocorrelations for either of the sexes.

Local - NG P2 r F r M 0,23 0,21 0,19 0,17 0,15 0,13 0,11 0,09 0,07 0,05 0,03 0,01

Autocorrelation (r) Autocorrelation -0,01 -0,03 -0,05 -0,07

Distance (m) Figure 6c: Even distance class plot on a local scale of Ngangao in period two. There were no significant positive autocorrelations for either of the sexes.