Department of Genetics, Rutgers the State University of New Jersey, Piscataway, New Jersey
Correspondence: E-mail: hey{at}biology.rutgers.edu.
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Abstract |
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Key Words: Chimpanzee Bonobo Markov chain Monte Carlo speciation gene flow
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Introduction |
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The chimpanzees of Africa, including the subspecies of the common chimpanzee (Pan troglodytes) and their sister species, the bonobo or pigmy chimpanzee (P. paniscus), are of great interest because they are the closest living species to our own. In recent years, they have been the subject of many studies on polymorphism and divergence (Morin et al. 1994; Kaessmann, Wiebe, and Paabo 1999; Deinard and Kidd 2000; Stone et al. 2002; Fischer et al. 2004). Morphological and genetic data strongly support the distinct species status of the bonobo (Shea and Coolidge 1988; Ruvolo et al. 1994; Kaessmann, Wiebe, and Paabo 1999; Stone et al. 2002; Yu et al. 2003). In the case of the common chimpanzee, three geographically defined subspecies have been recognized: Pan troglodytes verus in western Africa, P. t. troglodytes in central Africa, and P. t. schweinfurthii in eastern Africa (Schwartz 1934; Hill 1969; Morin et al. 1994). Recently, additional populations between the lower Niger River and Cameroon were proposed as another separate subspecies, P. t. vellerosus (Gonder et al. 1997; Gonder 2000). The subspecies designations of Pan troglodytes also have some support from molecular data, as nuclear and mitochondrial loci reveal some divergence between the subspecies (Gagneux et al. 1999; Kaessmann, Wiebe, and Paabo 1999; Deinard and Kidd 2000). However, only in the case of the mtDNA of P. t. verus was a subspecies found to have a monophyletic gene tree estimate (Morin et al. 1994; Gagneux et al. 1999; Gonder 2000). A morphometric study, using cranial measurements, also found some divergence between the subspecies, but there was a large amount of overlap between the subspecies (Shea and Coolidge 1988).
Comparative DNA sequence data can be used to study divergence, but the relationship between DNA sequence differences and the timing of population splitting and the processes associated with population splitting can be complex. Even under the simplest models, in which an ancestral population splits into two descendant populations with no gene exchange thereafter, the amount of divergence in DNA sequences between the two populations is a complex function of the time since the split and the relative sizes of the three populations (the two descendants and the ancestral population) (Wakeley and Hey 1997; Wang, Wakeley, and Hey 1997). For histories that include gene flow between diverging populations, the situation is even more complex because gene flow can create the appearance of recent divergence even if the actual splitting events occurred long ago. Whether or not gene flow has been occurring among chimpanzee species and subspecies is a question of considerable interest (Morin et al. 1994; Gagneux et al. 1999; Gonder 2000).
Here, we adapt recently developed methods for fitting the "isolation with migration" (or IM) model to the question of how and when chimpanzee species and subspecies diverged. This Markov chain Monte Carlo method yields estimates of multiple demographic parameters (including divergence time, migration rate, effective population sizes of two current populations and an ancestral population) (Nielsen and Wakeley 2001). Hey and Nielsen (2004) enhanced this method so that a large number of unlinked loci can be studied jointly to yield a posterior probability distribution for each of the demographic parameters in the IM model. We have applied the IM model to multilocus comparative DNA sequence data from two of the subspecies of the common chimpanzee (P. t. troglodytes and P. t. verus) and the bonobo (P. paniscus). We used the large data set of Yu et al. (2003) for 50 autosomal loci together with four other independent DNA data sets from other regions of the genome.
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Materials and Methods |
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The DNA sequences from the NRY, cytb, HoxB6, and Xq13.3 regions were collected in separate studies using mostly different individuals, spanning a range of sample sizes from five to 77 individuals per subspecies. Yu et al.'s (2003) DNA sequences for 50 loci were consistently obtained from the same set of individuals. The method of analysis assumes that populations are effectively panmictic. If this is approximately true, then the different sample sizes and the use of two sequences per individual in the study of Yu et al. (2003) should not introduce any biases.
The IM analysis requires sequence data from individual loci that show polymorphic variation within or between two populations and that do not show evidence of recombination (Nielsen and Wakeley 2001). Thus, we excluded those loci that showed no variation within or between the two taxa being compared in each pairwise analysis. Yu et al.'s (2003) 50 DNA segments reside in GenBank as diploid sequence (using IUPAC nucleotide ambiguity codes) without phase separation among heterozygous nucleotide sites. Because each region is short, it is unlikely that a given segment would show evidence of recombination had the data originally been obtained in haplotype form. Haplotypes were reconstructed using Clark's (1990) method, and then examined for evidence of recombination by the four-gamete test (Hudson and Kaplan 1985). Under Clark's method, haplotypes are first identified in homozygous individuals, after which those haplotypes already identified are subtracted from relevant heterozygous individuals to reveal remaining haplotypes. Approximately half of the loci in each of the analyses had no individuals who were heterozygous for more than one base position, in which case haplotype inference is straightforward. For a small number of loci (either two or three, depending on the species pair being considered), the reconstructed haplotypes showed evidence of recombination. These loci were excluded from the analysis. Among the remaining loci, there were two cases in which Clark's method leads to two configurations, one of which was consistent with recombination. In these cases, we used the haplotype configuration that did not show evidence of recombination.
The haplotype reconstruction protocol assumes that recombination over such short regions has been rare, and it also assumes that inferred haplotypes offer an unbiased view of history, relative to what would be found if true haplotype data had been available. For loci with several polymorphic sites, there can be multiple configurations of reconstructed haplotypes (Clark 1990). To check the effect of using alternative configuration, we also constructed data sets using alternative configurations for those loci that showed multiple possible haplotype configurations by Clark's method. Analyses with these alternative configurations were nearly identical to those for the primary data set and are available upon request. The similar results found for alternative haplotype configurations suggests that with these short loci, which have few polymorphic sites and little opportunity for recombination, the analyses are not highly sensitive to the method of haplotype inference. However, the larger question of how IM model analyses are affected by assumptions of low recombination, or of accurate haplotype inference, is an important one that has not been directly addressed by this comparison.
The NRY data, consisting of 10 concatenated loci (2,784 bp in total consisting of sequence tagged site [STSs], in order, sY15, sY19, sY65, sY67, sY74, sY84, sY85, sY123, sY126, and sMCY) (Stone et al. 2002) showed no evidence of recombination by the four-gamete criterion. For the mtDNA data used in the analysis of P. t. verus and P. t. troglodytes, one polymorphic site at the 3' end of the sequence was not congruent with the remainder of the sequence, by the four-gamete criterion. This site was simply dropped from the analysis. For the HoxB6 and Xq13.3 data, applications of the four-gamete criterion revealed evidence of some recombination events. In each case, we selected the largest fragment of the data that showed no evidence of recombination. This action represents a tradeoff between the need to have a large number of loci, from different portions of the genome, and the concern that selecting large nonrecombinant blocks may bias the results. The reason for potential bias is that the analytical methods assume that loci have been sampled randomly with respect to their genealogical histories and that loci not having had recombination are expected to have shorter gene trees, on average, than other loci (i.e., those loci with shorter genealogical histories have had less time for recombination). This effect is probably quite subtle, particularly in these cases where the selected fragments of HoxB6 and Xq13.3 were not less polymorphic than those regions that were excluded. The data sets are summarized in table 1.
IM Model Computations
The posterior probability densities of the parameters of the IM model are generated by simulating a Markov chain having a stationary distribution that is proportional to that density. The basic procedure is to begin a simulation with a burn-in period (100,000 steps in our analyses), so that the state of the chain becomes independent of the starting point, and then to continue the simulation for a long time while measuring the parameter values repeatedly over the course of the run. Convergence by the Markov chain simulations, upon the true stationary distribution, is assessed by monitoring multiple independent chains started at different starting points and by assessing the autocorrelation of parameter values over the course of the run. We also used a procedure for swapping among multiple heated chains (Metropolis coupling) to further ensure that the distributions we obtained actually reflected the stationary distributions (Geyer 1992). Each locus was assigned an inheritance scalar, to adjust for its relative expected effective population size: 1.0 for autosomes, 0.25 for mtDNA and NRY, and 0.75 for X-linked loci. Individual simulations were run for 60 million updates or more. Metropolis-coupling runs used 10 coupled chains that varied over a range of heating values. The settings for the prior distributions were empirically obtained after preliminary running with larger parameter intervals. This approach violates the spirit of a Bayesian analysis (in which available prior information is included). However, we wished to exclude other information from these analyses and so selected uninformative prior distributions that would not contribute to the posterior distributions. When this is done, the posterior probability distributions are proportional to likelihood distributions, and the parameter values associated with the highest likelihoods are maximum-likelihood estimates (Nielsen and Wakeley 2001).
The IM model has six demographic parameters, each scaled by the overall neutral mutation rate (fig. 2). With multiple loci, each locus has a mutation rate scalar parameter such that the product of all mutation rate scalars is equal to 1. Thus, with multiple loci, the overall neutral mutation rate represented in the demographic parameters is the geometric mean of all of the individual locus mutation rates (Hey and Nielsen 2004). For each of the six demographic parameters in each analysis, we recorded the marginal density (as a histogram with 1,000 equally sized bins) over the course of multiple long simulations. The peaks of the resulting distributions were taken as estimates of the parameters (Nielsen and Wakeley 2001). For credibility intervals, we assessed for each parameter the 90% highest posterior density (HPD) interval, which are the boundaries of the shortest span that includes 90% of the probability density of a parameter.
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To convert the estimates of the population mutation rate parameters (1,
2, and
A ) to estimates of effective population size (N1, N2, and NA, respectively), we need a measure of mutation rate on a scale of generations. We assumed 15 years per generation for the chimpanzees then multiplied the estimated mutation rate per year (based on human/chimpanzee divergence) by 15 years per generation. These calculations yielded values for the geometric mean number of mutations per generation per locus of 7.350 x 106 for the P. paniscusP. t. verus pair, 7.493 x 106 for the P. paniscusP. t. troglodytes pair, and 7.808 x 106 for the P. t. troglodytesP. t. verus pair. These mutation rate values were then used to convert individual
estimates to effective population size estimates (i.e.,
= 4Nu and N =
/(4u)).
Migration parameters in the model can be used to obtain population migration rate estimates (i.e., M = 2Nm, the product of the effective number of gene copies and the per gene copy migration rate) using an estimate of the population mutation rate ( = 4Nu). Thus, M =
x m/2 = (4Nu x m/u)/2 = 2Nm (Hey and Nielsen 2004).
One of the benefits of a method that explicitly incorporates a changing genealogy for each locus over the course of the analysis is that the posterior densities of other quantities that are associated with the genealogy can also be recorded. We took this approach for migration events for those cases where the method reveals nonzero migration rate estimates. For each locus, we measured over the course of the simulation the distribution of the number of migration events and the distribution of the average time of migration.
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Results |
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Repeated runs of the IM program revealed unambiguous marginal posterior probability distributions of the parameters for all three species comparisons. The peaks of the primary six parameter values were confined to fairly narrow ranges with corresponding credibility intervals illustrated in figure 3.
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Pan troglodytes troglodytes and P. t. verus
The effective population sizes of P. t. troglodytes, P. t. verus, and their ancestral population were estimated to be 27,900 (90% HPD interval: 19,600 to 40,700), 7,600 (5,300 to 10,700), and 5,300 (200 to 11,300), respectively (table 2). These sizes are similar to the estimates from the comparisons with P. paniscus, with P. t. troglodytes having the largest effective population size (fig. 3g). The divergence time was estimated to be 0.422 MYA (90% HPD interval: 0.255 to 0.629 MYA), with a sharply peaked marginal posterior distribution (fig. 3i). A comparisons of divergence times among the three taxa clearly suggests that the common chimpanzee populations (P. t. troglodytes and P. t. verus) were descended from an ancestor that had earlier separated from the lineage leading to P. paniscus, consistent with other phylogenetic studies (Morin et al. 1994; Ruvolo et al. 1994; Gagneux et al. 1999; Stone et al. 2002). Migration rate distributions suggest a moderate level of gene flow from P. t. verus to P. t. troglodytes (2Nm = 0.514 [table 2]). Also, because the estimate of the probability that the migration rate is zero is itself zero (fig. 3h), we can reject a gene-flow rate of zero in the direction of P. t. verus to P. t. troglodytes. The estimate of migration in the opposite direction was estimated to be very near zero (fig. 3h and table 2), with a peak height that is very near to that at zero.
The finding of gene flow could be caused if one of the individuals identified as P. t. troglodytes in the study of Yu et al. (2003) was actually a hybrid or backcross hybrid between this subspecies and P. t. verus. To check this possibility, we examined the pairwise sequence divergence across all 50 loci within and between the subspecies of P. t. verus and P. t. troglodytes. None of the P. t. troglodytes individuals were appreciably closer than others to the P. t. verus individuals (results not shown), arguing against recent hybridization as the cause of the apparent gene flow.
To take a closer look at gene flow from P. t. verus to P. t. troglodytes, the distribution of the number and mean time of migration events were recorded over the course of the simulations, for each of the loci (table 3). The modal number of migration events per locus was one, and the mean time of migration rates was 0.098, which corresponds to 0.186 Myr, roughly half of the divergence time between P. t. verus and P. t. troglodytes. As shown in table 3, most loci (37 out of 48) had a modal number of migration events of one, with a few loci having a mode of zero (loci T2012, T2266, T2988, T812, T946, cytb, and NRY), two (loci T2019 and T2984), and three migrations (locus XQ13) during the simulations. It is interesting that both of the sex-limited loci (cytb of the mtDNA, and NRY) showed less evidence of gene flow than most other loci, although an absence of gene flow was expected for the mtDNA, given previous findings (Morin et al. 1994).
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Discussion |
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Effective Population Size Estimates
Using data from 50 loci, Yu et al. (2003) estimated the effective population size of P. paniscus, P. t. troglodytes, and P. t. verus to be 12,400, 20,100, and 13,000, respectively. Our analyses, based primarily on these same data, also found a larger size for P. t. troglodytes and smaller sizes for P. paniscus and P. t. verus (table 2). However, our values for the latter two species are lower than estimated by Yu et al. (2003), who based their estimate on the average pairwise divergence between sequences. This estimator is known to have a large variance (Tajima 1983) and to be sensitive to the site frequency distribution of polymorphic sites (Tajima 1989). Our effective population size estimate of 27,900 for P. t. troglodytes, obtained in the analysis with P. t. verus, is larger than the values estimated by Yu et al. (2003), possibly because the IM method explicitly estimates the effective size since speciation. Given that the estimated size for P. t. troglodytes is considerably larger than the estimated size of the ancestral population for this and P. t. verus (5,300 [table 2]), it appears that the population size of P. t. troglodytes has grown since this divergence began. Interestingly, using nine unlinked loci representing 19,000 bp from 14 unrelated individuals, Fischer et al. (2004) reported that chimpanzees in central Africa have a larger effective population size (25,000 or 35,000 according to two different methods) than chimpanzees in western Africa. Also, a demographic change (population growth or fine-scale population structure) was inferred from the allele frequency spectrum (Fischer et al. 2004).
Divergence Times
In our study, the estimates of divergence time between P. paniscus and the two subspecies of common chimpanzees (P. t. troglodytes and P. t. verus) tend to be smaller than those of previous studies, which used different methods. The method applied here explicitly accounts for ancestral population size by assessing the divergence time parameter jointly with other demographic parameters. Like other studies, we used a calibration point based on estimates of Homo-Pan splitting time of 6 MYA (Chen and Li 2001; Brunet et al. 2002; Vignaud et al. 2002; Glazko and Nei 2003; Wildman et al. 2003).
According to the IM analysis, the most probable divergence time between P. paniscus and chimpanzees (P. t. troglodytes and P. t. verus) was estimated to be 0.862 to 0.896 MYA (table 2). In contrast, Yu et al. (2003) estimated the divergence time between chimpanzees and bonobos to be 1.8 MYA. These authors used the Homo-Pan split at 6 MYA as a calibration point, together with an average sequence divergence (1.22%) of the 50 loci between human and chimpanzee. However, their date is simply the estimated average time of the ancestral sequence for pairs of sampled sequences and takes no account of the variation between species that is caused by variation in the ancestral population. Similar analyses in studies of individual loci also lead to high values for divergence between bonobos and common chimpanzees. The study on the nonrecombining portion of the Y chromosome (NRY) had an estimated divergence time of 1.8 MYA (Stone et al. 2002). In the case of the mtDNA, the divergence time was estimated to be 2.5 MYA (Gagneux et al. 1999). For the X chromosome locus, Xq13.3, Kaessmann, Wiebe, and Paabo (1999) estimated the ancestor to be 0.9 MYA, which is similar to our estimate.
In the case of P. t. troglodytes and P. t. verus, the method of Yu et al. (2003) converts the observed average sequence divergence between P. t. troglodytes and P. t. verus (0.125%) to a divergence time estimate of 0.62 MYA. In other single-locus studies, the divergence time for these chimpanzee subspecies was estimated to be 0.61 MYA for the NRY (excluding the uncertain haplotypes that we also excluded in our analysis) (Stone et al. 2002); 0.6 MYA for the hypervariable mtDNA region (Gonder 2000), and 2.1 MYA for Xq13.3 (Kaessmann et al. 2001). In this last case, the divergence time between subspecies of chimpanzees was estimated to be older than that of the divergence time between bonobo and chimpanzees because of a large distance between some chimpanzee subspecies and the bonobo (Kaessmann, Wiebe, and Paabo 1999). In our analysis, the divergence time of the chimpanzees in western and central Africa (P. t. verus and P. t. troglodytes) was estimated to be 0.422 MYA (90% HPD interval: 0.255 to 0.629). Fischer et al. (2004) estimated divergence times using multilocus DNA sequences (including the data of Yu et al. [2003]) and the moment estimator method of Wakeley and Hey (1997). This approach assumes a four-parameter isolation model (the same model as in figure 1 but without migration) and, thus, explicitly accounts for that component of variation caused by common ancestry. When the authors calculated divergence times using only the Yu et al.'s (2003) data, their divergence time estimates were very similar to those reported here: 0.8 MYA for bonobos and chimpanzees and 0.43 MYA for chimpanzees in western and central Africa.
In summary, we estimate that the separation of common chimpanzees and bonobos began about 900,000 years ago and was followed later by a separation at about 400,000 years between the chimpanzees in western and central Africa. These events occurred during the Pleistocene (1.8 to 0.01 MYA) epoch, which has been characterized by repeated glaciations accompanying climatic change and shift in geography of tropical forests in Africa (Hamilton 1988). Particularly, since the beginning of major glaciations around 2.4 MYA, climatic changes in Africa both before and after 1 MYA were considered to be more severe than before (Stein and Sarnthein 1984).
Isolation Since Population Splitting
A major benefit of being able to consider a full isolation with migration model is access to two-directional gene-flow rates (fig. 2). From our analyses, bonobos and common chimpanzees appear to have been isolated without gene flow since they began to diverge. Put another way, the divergence of these species appears consistent with a speciation model in which geographic isolation prevented gene flow during the separation of these species.
In contrast to the analyses involving P. paniscus, we found a clear signal of unidirectional gene flow from P. t. verus to P. t. troglodytes (fig. 3h). This finding is perhaps our most surprising, in part because of the current disjunct distributions of P. t. verus and P. t. troglodytes (fig. 1) (see figure 1 in Kortlandt [1983]). According to Kortlandt, an area (>1,000 km) from west of Ghana to the eastern side of the lower Niger River, except for one islandlike habitat region on the western side of the lower Niger River, appears to be nearly devoid of chimpanzees (the Dohomey Gap in figure 1), although some places were marked as exterminated habitats during recent years or since around 1940. Thus, evidence of gene flow suggests that the geographic distributions of these populations have changed over time.
A related puzzle is why migration between these populations might have occurred in only one direction. Although there may be many possible explanations, given the several hundred thousand years since these populations began to diverge, we consider the possibility that another population, that we have not sampled, may play a role in our findings. Recently, Gonder et al. (1997) studied a chimpanzee population that inhabits a region of Nigeria and Cameroon that lies between the lower Niger River and the Sanaga River. Mitochondrial DNA sequences from this region revealed a monophyletic lineage, having sister relationship to sequences from P. t. verus (Gonder et al. 1997; Bradley and Vigilant 2002). On this phylogeographic basis, these chimpanzees were recognized as a separate subspecies, P. t. vellerosus (Gray 1862; Gonder 2000). However, whereas this group is genetically close to P. t. verus (based on mtDNA), it is geographically close to P. t. troglodytes of central Africa, from which it is separated by the Sanaga River in Cameroon (Gagneux et al. 2001). Gonder investigated this border region with fine-scale sampling from both sides of the Sanaga River and found a few mtDNA haplotypes that clustered with samples from the southern side of the Sanaga River, suggesting that the river might not be a strict barrier for migration. Furthermore, microsatellite data revealed a high effective number of migrants per generation (Nm = 11) across the Sanaga River (Gonder 2000). Notwithstanding the possible inflation of this Nm estimate because of homoplasy in microsatellite markers, both these and the mtDNA suggest intermixing between these populations. In addition, cranial features of P. t. vellerosus more closely resemble those of P. t. troglodytes than P. t. verus (Groves 2001).
This information on the P. t. vellerosus population suggests an explanation for the observation of unidirectional gene flow from P. t. verus to P. t. troglodytes. The scenario begins with a separation of populations that today we recognize as P. t. verus and P. t. troglodytes. If, as suggested by mtDNA data, the population identified as P. t. vellerosus is indeed most closely related to P. t. verus, then it seems likely that this population formed sometime after the original split that gave rise to the central Africa and western Africa subspecies. However, today P. t. vellerosus occurs geographically near populations of P. t. troglodytes and appears to be exchanging genes with the central Africa subspecies. Thus, P. t. vellerosus may be a channel for genes into P. t. troglodytes that otherwise are closely related to genes of P. t. verus. It is possible that this is the mechanism whereby nonzero levels of gene flow appear to have occurred between P. t. troglodytes and P. t. verus, which today have quite geographically disjunct distributions. Clearly, testing this hypothesis would require inclusion of the P. t. vellerosus population in a multilocus study of the IM model. This scenario also reveals a limitation of the IM model as currently implemented. At present, we can only study populations in pairs, but in reality, many closely related populations occur in more complex geographic and demographic contexts.
The Population Status of Chimpanzee subspecies
Subspecies of the common chimpanzee have been designated on the basis of geographic ranges, with populations that are separated by large regions or by large rivers sometimes being assigned a subspecies designation (Groves 2001). These designations have found some support in phylogeographic studies of individual loci, at least in so far as some degree of genetic differentiation is repeatedly observed (see review Bradley and Vigilant [2002]). Their support on morphological grounds is less clear (Groves 2001). Certainly the current study, which includes just two subspecies, finds strong evidence of considerable (although not complete) isolation over several hundred thousands of years.
It might be argued that the designation of chimpanzee subspecies, which are based largely on current geographic distributions together with limited genetic data, are not well justified. In this study, we have taken the approach of treating previously identified taxa as hypotheses of the presence of distinct and isolated populations (Baum 1998; Hey et al. 2003). Notwithstanding the evidence of gene flow from P. t. verus to P. t. troglodytes, our analyses strongly affirm the use of these taxonomic designations, as the populations that include representatives of these taxa appear to have long been largely evolutionarily separated from one another.
It is also important to note that studies of gene flow within subspecies of common chimpanzees suggest that these taxa occur as intermingled populations. Goldberg and Ruvolo (1997) examined mtDNA migration rates for the eastern Africa chimpanzee P. t. schweinfurthii, with extensive sampling at 19 locations encompassing most of the known range of the subspecies. They estimated that the population migration rate was 3.38 among the locations and that the maximal distance of any haplotype sharing was 583 km. Despite the high gene-flow rate, a pattern of long-distance differentiation was observed in that study. Similarly for the eastern Africa chimpanzee, Gagneux et al. (1999) reported a long distance (maximum 1,000 km) haplotype sharing within the western Africa chimpanzee.
Our findings of gene flow spanning subspecies ranges may be attributable to mating strategies that overcome local inbreeding effects. Chimpanzees are known to exchange genes among groups by female transfer and mating with noncommunity members (Goodall 1986; Boesch and Boesch-Achermann 2000). For example, genetic estimates of extragroup paternity (EGP) levels found a level of 1% in Taï in West Africa (Boesch and Boesch-Achermann 2000) and 7% in three contiguous communities in the same Taï National Park in West Africa (Vigilant et al. 2001). However, no evidence of EGP was detected in an eastern Africa community (Constable et al. 2001).
In contrast to the pattern of gene flow within and among common chimpanzee subspecies, the divergence of the bonobo and the common chimpanzee is consistent with a long history (approximately 900,000 years) without gene flow. One probable factor in this divergence is geographic separation caused by climatic changes that resulted in deforestation and the expansion of arid savanna. Historical fluctuation of forest and savanna ranges caused by climate changes during the Pleistocene could have split ancestral populations and confined them in multiple geographical groups (Grubb 1982). Also, rivers that are wide and that separate geographic regions have probably been major barriers to gene flow. The Congo River, which currently separates bonobos from common chimpanzees probably had a continuous existence since well before the Pleistocene (Beadle 1981), although it can not be ruled out that there were historical time periods in some locations along the river that would have been permissive of a dispersal corridor for chimpanzees.
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Acknowledgements |
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Footnotes |
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References |
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