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Where was paradise? A simulation study of the spread of early modern humans in heterogeneous environments

EXCOFFIER, Laurent Georges Louis, RAY, Nicolas, CURRAT, Mathias

Abstract

Several lines of evidence argue in favour of a recent and unique origin of modern humans in sub-Saharan Africa, but no attempt has really been made at quantifying the likelihood of this model, relative to alternative hypotheses of human evolution. In this paper, we investigate the possibility of using multi-locus genetic data to correctly infer the geographic origin of modern humans, and to distinguish between a unique origin (UO) and a multiregional evolution (ME) model. We use an approach based on realistic simulations of the genetic diversity expected after an expansion process of modern humans into the Old World from different possible areas, and with different environmental scenarios, under both UO and ME models. We find that UO and ME models should produce distinctive patterns of genetic diversity in observed data. Moreover, the geographic origin of an expansion is recoverable under the UO model provided that a large number of independent markers are used, and that precise information on past demography and potential places of origin(s) are available. We also find that the successful recovery of past scenarios is [...]

EXCOFFIER, Laurent Georges Louis, RAY, Nicolas, CURRAT, Mathias. Where was paradise?

A simulation study of the spread of early modern humans in heterogeneous environments. In:

Matsumara S., Forster P., Renfrew C. Simulations, genetics and human prehistory.

Symposium (29 July-1 Aug. 2005 ; Cambridge) . 2008. p. 9-18

Available at:

http://archive-ouverte.unige.ch/unige:2170

Disclaimer: layout of this document may differ from the published version.

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Out-of-Africa: Where was Paradise?

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Chapter 1

Where was Paradise?

A Simulation Study of the Spread of Early Modern Humans in Heterogeneous Environments

evolution with current genetic data, in order to decide which scenario is the most plausible. We are now able to simulate complex demographic events such as a spatial expansion into an empty area (Ray et al. 2003) or into an already occupied area including competi- tion and admixture between the invasive and the invaded population (Currat & Excoffier 2004; 2005; see also Currat et al. this issue). For all these studies, we simulated an environmentally homogeneous world where only geographic contours (coastlines) were taken as source of environmental information.

More recently, we incorporated extra levels of environmental heterogeneity in a study aiming to find the geographical origin of early modern humans (Ray et al. 2005). Indeed, while the recent African origin

Laurent Excoffier, Nicolas Ray & Mathias Currat

Several lines of evidence argue in favour of a recent and unique origin of modern humans in sub-Saharan Africa, but no attempt has really been made at quantifying the likelihood of this model, relative to alternative hypotheses of human evolution. In this paper, we investigate the possibility of using multi-locus genetic data to correctly infer the geographic origin of modern humans, and to distinguish between a unique origin (UO) and a multiregional evolution (ME) model. We use an approach based on realistic simulations of the genetic diversity expected after an expansion process of modern humans into the Old World from different possible areas, and with different environmental scenarios, under both UO and ME models. We find that UO and ME models should produce distinctive patterns of genetic diversity in observed data. Moreover, the geographic origin of an expansion is recoverable under the UO model provided that a large number of independent markers are used, and that precise information on past demography and potential places of origin(s) are available.

We also find that the successful recovery of past scenarios is related to the degree with which environmental heterogeneity impacts on past demography and migration, suggesting that use of a realistic representation of past environment is important to make correct inferences.

Finally, the application of our simulation framework to the problem of the origin of mankind clearly rejects multiregional evolution scenarios, and points toward a unique and African

origin of modern humans.

D

eciphering the past history of humans using cur- rent genetic data is an exciting field of research as the amount of genetic data available is exponentially growing. However, the power of classical approaches to infer correctly the past demography of our ancestors is limited by the complexity of the scenarios involved.

Indeed, analytical resolution of more complex and realistic demographic models is not possible. It is how- ever necessary to model potential historical scenarios in order to observe genetic signatures under null hypotheses, against which real data can be compared.

We have thus developed a new simulation technique allowing us to model more realistic scenarios of human evolution. The goal is to confront the virtual genetic data obtained under any potential scenario of

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10 Chapter 1

Figure 1.2. Demography and timeline of different evolution models. A) 30,000 generations ago, a small population (N = 100 genes) goes through a demographic expansion after a first speciation event. 4000 generations ago, a range expansion follows a bottleneck of 10 generations to mimic a second speciation event for modern humans. The large population preceding the speciation and range expansion can be considered to be a large subdivided population of Homo erectus.

B) Like in A, a small population goes through a speciation event and instantaneously colonizes the three continents 30,000 generations ago. For 26,000 generations the continents harbour relatively large populations and exchange occasional migrants. For three multiregional scenarios, the sizes of the three continents are equal but in six other cases, the African size is twenty times larger than those of Asia and Europe. Migration rate is also varying in order to account for different intensity of gene flow among continents, see Ray et al. (2005) for further details. 4000 generations ago, three range expansions are initiated from the three different origins shown in panel C. (Adapted from Ray et al. 2005.)

Figure 1.1. Location of the 25 simulated actual origins (black dots). Open squares indicate the location of the 14 alternative ‘assumed true’ origins (see text). The four genetic regions defined in Rosenberg et al. (2002) are delineated by black lines. (Adapted from Ray et al. 2005.)

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11 (RAO) model or one of its extensions seems the most likely evolutionary model for past human populations (Excoffier 2002), alternative models could include non-African locations for a recent and unique origin (UO) of modern humans, an incomplete replacement of Homo erectus individuals by modern humans, or the multiregional evolution model (ME model: see e.g.

Wolpoff 1989; Wolpoff et al. 2000). The latter model postulates that there was a gradual and simultaneous transition from Homo erectus to modern forms on dif- ferent continents, and that this synchronized process was possible due to continuous migrations between continents. Our main aim is to see whether the pat- terns of genetic diversity allow one to distinguish between UO and ME models. We thus simulate a range expansion process under the UO model from arbitrary geographic origins or a ME model assuming various continental sizes, environmental heterogene- ity, and rates of exchange between continents, and we see how well we can recover them by an estimation procedure based on extensive simulations. Here we do not consider any models that incorporate inter- breeding.

As there is a large uncertainty about past envi- ronments and their effect on human demography, it appears important to study the impact of various levels of environmental heterogeneity on our ability to distinguish among human evolutionary scenarios.

In the present study, we extend the recent results from Ray et al. (2005) concerning the possibility of recov- ering the origin of modern humans from spatially explicit computer simulations by incorporating extra levels of environmental heterogeneity. This paper is also an attempt at evaluating the impact of various levels of environmental heterogeneity on the recovery of the geographical origin of a range expansion.

Methods

Demographic simulations

Details of the demographic simulations are reported in Ray et al. (2005) and in Currat et al. (2004), and we only summarize them in the following. We considered a simulated world subdivided into 226 demes covering the surface of the Old World, each deme occupying an area of 100 by 100 km2. Using the software SPLATCHE (Currat et al. 2004), we performed simulations of a range expansion from 25 different geographic origins, which were evenly distributed every 2000 km on the surface of the Old World, as shown in Figure 1.1. We first simulated, forward in time, a demographic and spatial expansion from an initial population of size equal to 50 diploid individuals (100 nuclear genes). We recorded, for each generation, the number of individual genes

present in a deme, as well as the number of immigrant genes coming from each of the four nearest neighbour- ing demes on the grid. This demographic and migration history was stored in a data base, which was then used to generate, backward in time, the genealogy and the diversity of genes sampled at given locations.

We assumed a generation time of 30 years for modern humans (Tremblay & Vezina 2000). Each generation, the occupied demes were subject to a growth phase followed by an emigration phase. The growth phase was logistic with a constant growth rate of r = 0.3 (Cavalli-Sforza et al. 1994; Steele et al.

1998), and a carrying capacity (K) that depended on the environment in which the deme was located (see below). The emigration phase consisted of distribut- ing a total of 0.05 × Nt emigrants among the four nearest-neighbouring demes, where Nt is the size of the deme (number of gene copies within deme) at time t. The exact number of emigrants sent to each of the neighbouring demes i (Ei) was controlled through friction values (Fi) assigned to each neighbouring deme. Friction expresses the relative difficulty of moving through a deme, and was kept within a range of 0.1 (lowest friction, easiest migration) to 1 (highest friction, most difficult migration). Ei was computed from a multinomial distribution, with directional probabilities Pi proportional to the relative frictions Fi of four neighbouring demes obtained as

.

This formula implies that the number of emigrants sent to any deme is inversely proportional to its rela- tive friction. Seas were considered as complete barriers to migration.

For the genetic simulations, we used the sample locations and sizes of a large data set of 377 STR mark- ers analysed in 52 human worldwide populations (Rosenberg et al. 2002). However, we considered only populations with a sample size of more than 20 indi- viduals, reducing this data set to 22 population sam- ples, referred to hereafter as the ‘Rosenberg22 data set’.

We simulated 25 scenarios, each with different unique origins of early modern humans (Fig. 1.1), as well as scenarios of multiregional origins (Fig.

1.2). The characteristics of each of the multiregional scenario were chosen to cover a large panel of alterna- tive propositions. In each case, multiple origins were located in Africa, Asia and Europe, the size of the African population was related to European/Asiatic population sizes, and the migration rates between continents changed accordingly (see Ray et al. 2005 for details).

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Environmental heterogeneity

We considered three levels of environmental hetero- geneity: (1) Uniform environment: all demes have the same carrying capacity of K = 250 diploid effective individuals (about 0.05 individual/km2, in agreement with average density estimates for Palaeolithic hunter- gatherers: see e.g. Alroy 2001; Steele et al. 1998), and friction was identical across the landscape; (2) Low environmental heterogeneity: carrying capacities and frictions were related to the type of vegetation asso- ciated with each deme (see Table 1.1); a digital map of present potential vegetation (around 4000 years ago: Ray & Adams 2002; Ray et al. this volume) was used in that case; (3) High environmental heterogeneity:

similar to level (2), but with major rivers and coast- lines having carrying capacity increased by a factor two, and friction decreased by the same factor. Rivers and coastlines were thus considered as migration corridors and to have a higher density of resources.

See Ray et al. (this volume) for discussion of these landscape elements. Moreover, in this third level, topography-related friction was introduced through a topographical roughness index. For each deme, this index was computed as the standard deviation of the 10,000 altitude values given by the 1-km resolution GTOPO30 data set (USGS 1996) (see Ray et al. this volume). Areas with rough topography were indeed considered more difficult to cross than flatter areas.

The final friction values was computed as the mean of the vegetation-related and the topography-related friction terms, corrected for the presence of streams and coastlines. The relative carrying capacity and the friction maps for the two levels of environmental heterogeneity are shown in Figure 1.3.

Assignment score for a given evolutionary scenario:

the R90 statistic

In order to differentiate the various demographic scenarios, we simulated in a second phase the genetic diversity of the 22 worldwide samples of the Rosen- berg 22 data set using a coalescent approach based on the demographic information stored into the data base, itself generated during the demographic simulation (see Ray et al. 2005 for details on the coalescent simulation).

If we assume that a given genetic data set is the product of a particular evolutionary scenario, one would ideally like to estimate the likelihoods of all pos- sible scenarios compatible with the observed data, and choose the scenario with maximal likelihood. Due to the spatially explicit nature of our simulation model and the complexity of these potential scenarios, it is very dif- ficult to compute their likelihoods. An approximation was envisioned, however, by computing a goodness- of-fit summary statistic (R0) as follows:

Table 1.1. Carrying capacities and frictions attributed to the present- potential vegetation map.

Vegetation

category a Description a Carrying

capacity b Friction c

1 Tropical rainforest 1433 0.

2 Monsoon or dry forest 130 0.

3 Tropical woodland 446 0.5

4 Tropical thorn scrub and

scrub woodland 232 0.5

5 Tropical semi-desert 726 0.1

6 Tropical grassland 1598 0.1

7 Tropical extreme desert 25 0.

8 Savanna 1104 0.1

Broadleaved temperate

evergreen forest 1424 0.5

10 Montane tropical forest 1715 0.5 11 Mediterranean sclerophyll

woodland or forest 1424 0.5

12 Temperate deciduous

broadleaved forest 673 0.5

13 Southern taiga 501 0.1

14 Mid taiga 501 0.1

15 Open boreal woodlands 501 0.5

16 Semi-arid temperate

woodland or scrub 673 0.5

17 Semi-arid temperate scrub 673 0.5

18 Tundra 501 0.1

1 Steppe-tundra 501 0.1

20 Polar and alpine desert 141 0.1

21 Temperate desert 141 0.1

22 Temperate semi-desert 141 0.1

23 Forest steppe 501 0.1

24 Forest tundra 501 0.5

25 Montane mosaic 501 0.5

26 Dry steppe 50 0.1

27 Temperate steppe grassland 443 0.1

28 Bog/swamp 0 1.0

2 Ice sheet and other

permanent ice 0 1.0

30 Lakes and open water 0 1.0

31 Land bridges (Japan, Sardinia, Orcadian, Australia)

10 0.1

a Vegetation categories and descriptions are those from the Present Potential vegetation map of Ray and Adams (available at: http://lgb.

unige.ch/~ray/ppveg/index.html). Category no. 31 has been added to the original map and represents artificial land bridges (see Fig. 1.1).

b Carrying capacities were derived from the population density estimates derived from present hunter-gatherer groups by Binford (2001). Links between Binford’s environment types and the vegeta- tion categories used here are given in Ray (2003, table 3.2). They are expressed in number of effective individuals per 10,000 km2.

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13 1. Compute the observed matrix Dobs of pairwise RST

among all pairs of populations.

2. For each evolutionary scenario j (j = 1 ... 34):

1. Simulate 10,000 genetic data sets (1, 20, or 377 STR loci) and for each of them compute Dsim(i). 2. Compute the Pearson correlation coefficient

between the observed and the simulated RST matrices rji = corr(Dobs, Dsim(i)).

3. From the distribution of rji , take the 0% quan- tile value (R0) as the assignment score for the j-th evolutionary scenario.

3. Select the evolutionary scenario with the largest as- signment score (largest R0 statistic), and thus giving the best fit between observed and simulated data.

The 0% quantile value of the distribution, the R0 statistic, was taken as our goodness-of-fit index based on previous experience resulting from extensive simulations (Ray 2003).

Results

Recovering the geographic origin under a UO model In Table 1.2 we show the frequency of correct assign-

ment for 1, 20 and 377 loci for each of the environ- mental scenarios. This frequency is very low (between 0.129 and 0.168) when considering a single locus, but still three to four times higher than for a completely random assignment over 25 putative origins. It sug- gests that some information on the geographic origin of an expansion can be extracted from a single locus, despite the high stochasticity of the coalescent process.

When using 20 loci, frequencies of correct assignment increase substantially (between 0.552 and 0.721), and are very high (between 0.976 and 0.989) with 377 loci. When a limited number of loci are available, frequencies of correct assignment also increase with Figure 1.3. Representation of relative carrying capacity and friction values for the two environmental

scenarios ‘low heterogeneity’ (A and B) and ‘high heterogeneity’ (C and D). Darker colours indicate relatively higher carrying capacity or friction values.

Table 1.2. Proportion of simulated cases (over a total of 125,000) in which the true origin of the range expansion is correctly assigned.

Standard deviations over the 25 origins are given within parentheses.

Environment

Uniform Low heterogeneity High heterogeneity 1 locus 0.129 (0.152) 0.149 (0.136) 0.168 (0.148) 20 loci 0.552 (0.240) 0.657 (0.230) 0.721 (0.215) 377 loci 0.76 (0.06) 0.989 (0.035) 0.988 (0.045)

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the level of environmental heterogeneity. This is most noticeable when using 20 STR loci, where correct assignment climbs from 55.2% to 72.1% if simulations are performed in a highly heterogeneous environment rather than in a uniform environment. This shows that homogeneous and heterogeneous environment do not lead to identical genetic signatures. Note that with 377 loci, the level of environmental heterogeneity does not matter much, since the assignment is excellent in all cases. In Table 1.3, we report the rate of correct assign- ment by geographic location for the combinations of environmental heterogeneity and number of loci.

It appears that there is a large heterogeneity in correct assignment across the different geographic origins when a few loci are available. For instance, with a single locus, the origins numbering 3, 7, 17, 20 and 23, have much larger probabilities of being correctly assigned than other origins, which may be related to their position on continental edges. With 20 loci, the contrast in probabilities for these different origins is stronger. The origins with lowest assignment success are mainly inland origins, with the exceptions of Australian origins, which seem particularly difficult to assign. Note that a closer look at the incorrect Australian assignments revealed that they were mostly in favour of a Southeast Asian origin (no. 23), suggesting that a potential exit from Australia would be difficult to distinguish from an Indonesian origin, possibly due to the occurrence of spatial bottleneck in the Indonesian peninsula. With 377 loci, all origins are well recovered, with the exception of the two sets in Australia for the reasons just mentioned.

Distinguishing UO from ME models

The same procedure aiming at finding the geographic origin of a range expansion can be used to distinguish between data sets generated under a UO or under a ME model. In this context, a data set is correctly assigned if the scenario chosen on the basis of the R0

statistic belongs to the same evolution- ary model as that used to generate it, regardless of the location of the origin or the type of ME scenario. In Figure 1.5, we show the assignment scores of data sets simulated under the 25 UO scenarios and those simulated under the 9 ME scenarios. 125,000 simulated data sets were generated under the UO model (over all possible geo- graphic origins), and 45,000 data sets were generated under all considered ME scenarios. It clearly appears that the evolutionary models are extremely well discriminated. With a single locus, correct assignment increases from 76.1% to 7.7% with the level of environmental heterogeneity, but there is a sharp difference between the correct assignment of data sets generated under a single or multiple origins model, with a much lower recovery rate (less than 45%) of the ME model compared to

>85% correct assignment to the UO model. With 20 loci, correct evolutionary model assignment is around

% and reaches 100% with 377 loci.

Dealing with unknown origins and inaccurate environmental heterogeneity levels

For the demographic simulations so far, the potential geographic origins and the pattern of environmental variability were known without error. We investi- gate here the consequence of assuming an incorrect geographical origin of the expansion, as well as inad- equate environmental information on the probability of recovering the source of a spatial expansion. The locations of 14 alternative and ‘assumed true’ positions for the origins of an expansion are reported in Figure 1.1 as empty squares. A series of 10,000 simulations were performed from these origins under each of the three levels of environmental heterogeneity defined earlier. The 25 potential origins defined in Figure 1.1 were simulated as actual origins and the resulting genetic data sets were compared to those generated from ‘assumed true’ origins, either under the same or under different environmental conditions. Since the ‘assumed true’ and actual origins differ, as could be the case in reality, we measured the probability of recovering the correct geographic region of origin. We partitioned the Old World into four regions according to the results of Rosenberg et al. (2002), and as reported in Figure 1.1. The frequencies of correct assignment per region between actual and ‘assumed true’ origins for different environmental conditions are shown in Table 1.4. We first note that, barring one exception, the assignment score is best when data are simulated

Table 1.3. Proportion of the simulations in which single origin or multiregional evolution models are correctly recovered by our approach.

Environment

Uniform Low heterogeneity High heterogeneity Single Mult. Total Single Mult. Total Single Mult. Total 1 locus 0.878

(0.36) 0.436 (0.423) 0.761

(0.403) 0.935 (0.438) 0.388

(0.420) 0.70 (0.433) 0.31

(0.438) 0.425 (0.427) 0.77

(0.435) 20 loci 0.987

(0.859) 0.985 (0.872) 0.987

(0.863) 0.995 (0.884) 0.2

(0.877) 0.994 (0.882) 0.994

(0.880) 0.995 (0.876) 0.994

(0.879) 377 loci 1.000

(0.987) 1.000 (0.0) 1.000

(0.988) 1.000 (0.1) 1.000

(0.1) 1.000 (0.1) 1.000

(0.0) 1.000 (0.1) 1.000

(0.0) Values within parenthesis are the mean values of the R0 statistic computed when the inferred origin is correctly assigned.

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Figure 1.4. Relative frequencies of correct assignment for 25 simulated origins under various environmental conditions and for different number of available STR loci. Level of environmental heterogeneity are uniform (A, B and C), low (D, E and F), and high (G, H and I). The black area represents the proportion of simulations for which the origin of demographic expansion was correctly recovered from the R90 statistic.

Figure 1.5. Values of the R90 statistic for 25 UO scenarios (Nos. 1–25) and 9 ME scenarios (Nos. 26–34) computed from the Rosenberg22 data set under the ascertainment bias-uncorrected (A) and ascertainment bias-corrected (B) simulated data sets. Values of the R90 statistic were spatially interpolated between the 25 tested origins to facilitate visual comparison between regions (exact values of the statistic are found in Ray et al. (2005)). The lowest and highest R90 values are indicated on the grey intensity scale.

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under the same environmental conditions for the

‘assumed true’ origin as for the actual origin (compare diagonal elements to off-diagonal elements in columns 2–4 of Table 1.4). The assignment success also usually drops when the geographical origin of the expansion is not known exactly (compare first column to diagonal elements of columns 2–4 in Table 1.4), but to a much lesser degree when the environment is assumed to be uniform. Overall we find that the assignment scores are much lower when there are uncertainties concerning the environmental conditions and the exact origins. Increasing the number of STR loci from 20 to 377 does not lead to a vast improvement as in the case of perfectly known evolutionary conditions (Table 1.4, column 1). Unless we assume no impact of environmental heterogeneity (uniform environment) on human evolution, we see that there is about a 15–20% chance of incorrectly inferring the regional location of an expansion from the matrix of pairwise genetic distances between populations.

Application to real data

The Rosenberg22 data set was used to assign scores to the 34 different scenarios of modern human evolu- tion (25 geographic origins under the UO model as well as scenarios under the ME model) simulated under low levels of environmental heterogeneity. We only evaluated the R0 statistic under this low level of heterogeneity because higher levels of environmental heterogeneity imply strong assumptions about the impact of a given environment on human density and

dispersal abilities. Such strong assumptions would not be safe given the uncertainties about past climatic and cultural changes, which have probably modified the impact of environment on human demography.

As it still seemed reasonable to take some environmental constraints into account, the low heterogeneity described above made a good compromise.

The R0 statistics computed for the 34 scenarios are reported on Figure 1.5A, and the highest is found for a unique origin in Northwest Africa (R0 = 0.265), followed by an origin in the Near East (R0 = 0.260). As more fully explained in Ray et al. (2005), this unexpected result prompted us to examine the possibility of ascertainment bias because these 377 STR loci were origi- nally assessed on a CEPH panel made up of individuals of European ancestry. We then performed a new set of simulations by constructing sets of 377 STR loci chosen for their particularly high heterozygosity levels in European populations (see Ray et al. 2005 for details). The new results are reported in Figure 1.5B, and now ranks the highest scores first for an East African origin (R0 = 0.309) followed by a Northwest African origin (R0 = 0.290). Note that ME scenarios have much lower scores in all cases (R0 = 0.153 at best), implying that the best UO scenario explains about 4 times more of the observed genetic diversity than any ME scenario.

Discussion

Our present study complemented our recent model (Ray et al. 2005) by examining the effect of various levels of physical constraints to dispersion on human genetic diversity. These constraints are typically the contours of continents, but we have also envisioned scenarios where dispersal was limited in deserts and in mountainous regions, and facilitated along coastlines and major rivers. Our simulation results show that patterns of genetic diversity after a range expansion in a heterogeneous environment do depend on the geographical origin of the expansion, which is not the case in a uniform and homogeneous environment (Ray et al. 2003). It implies that extant patterns of genetic diversity can be used to recover the place of origin of modern humans, if one assumes that they speciated at a single and precise location. However, our results show that this location can only be well recovered provided that a large number of markers are avail- able (Fig. 1.4), that the simulated origin is close to

Table 1.4. Proportion of the UO simulations in which geographic regions of origin were correctly assigned.

Environments for the ‘true’ origins b Environments

for the simulated origins

Correct assignment score at the regional

level a

Uniform Low

heterogeneity High heterogeneity

20 loci

Uniform 0.785 0. 802 0. 740 0. 743

Low heterogeneity 0.882 0. 753 0. 771 0. 808

High heterogeneity 0.918 0. 772 0. 70 0. 824

377 loci

Uniform 0.3 0. 938 0. 778 0. 752

Low heterogeneity 0. 0. 795 0.852 0.855

High heterogeneity 1.000 0. 889 0.734 0.825

a Correct assignment score is computed by assuming that the simulated origin is the true origin, as in Figure 1.1, but for the four regions defined in Figure 1.1.

b Correct assignment score obtained by comparing data sets generated from ‘true’

origins (Fig. 1.1) to data sets generated from simulated origins that are not at the same location. In columns 2–4, diagonal elements are obtained when the true and the simulated environments are similar, and off-diagonal elements represent cases where the true environment is different from the simulated environment.

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17 the true origin, and that the effect of environmental heterogeneity on past demography of the population is known to a certain extent (Table 1.4). These results underline the importance of modelling environmental data as closely as possible to reality, and of interactions between demographic and environmental parameters.

An obvious but difficult extension of our approach would be to obtain a dense grid of point covering the whole world, and for each of these points the corresponding likelihood of being the origin of early modern humans.

The application of this inferential framework on the Rosenberg22 data set shows that multiregional evolution scenarios are overall much less favoured than most UO scenarios. Once ascertainment bias is taken into account, the data support a unique and East African origin for modern humans. While the envi- ronmental scenarios we have envisioned may not be fully realistic, our approach could be extended, such as to incorporate additional information about past environments, as well as their dynamics (see Ray et al. this volume). The fact that the dispersal abilities of early modern humans were probably deeply affected by their environment should motivate further research on their impact on early human migrations, and on the reconstruction of these palaeoenvironments, in order to refine estimates of various scenarios of human evolution.

Future improvement would also be to consider potential interactions between early modern humans and former representatives of the Homo genus (Currat

& Excoffier 2004; Eswaran et al. 2005), and potentially estimate their degree of interbreeding. While mito- chondrial DNA shows no evidence for interbreeding (Currat & Excoffier 2004) nuclear markers could be more sensitive for detecting some minor contribu- tion, as suggested by Eswaran et al. (2005). The recent advance of Approximate Bayesian Computations (ABC methods: Beaumont et al. 2002), relying on massive simulations to estimate the parameters of dif- ferent scenarios by comparing simulated to observed summary statistics, should enable us to compare the relative probabilities of various models, and to anchor the study of human evolution into a more statistical framework. Another advantage of such methods is that it would allow us to incorporate more information from the available data than the matrix of pairwise genetic distances we have been using here, like some aspects of genetic diversity within populations, (see e.g. Excoffier et al. 2005). This additional information could help us resolve not only geographic origins, but it could help establishing to which degree early humans were actually influenced by environmental heterogeneity. Some recent studies have found that

nuclear heterozygosity within populations was stead- ily decreasing with geographic distance from eastern Africa (Prugnolle et al. 2005; Ramachandran et al. 2005).

These results are compatible with the occurrence of a series of founder effects during the spread of modern humans out of Africa, and do not seem to depend on known environmental heterogeneities outside Africa. But since the environment has considerably fluctuated during the late Pleistocene, time-average environmental pressures could be quite similar for different locations, while local heterogeneities could be significant at any given time.

Acknowledgements

This work was made possible thanks to a Swiss NSF grant No. 3100A0-100800 to LE.

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