population at each generation, following model parameters values, and pairing them to produce the
offspring in the admixed population at the following generation. At the end of the admixture process at time
G, we sample individuals with sample-sizes matching observed sample-sizes, either randomly or setting a
threshold of parental relatedness as is commonly done in human genetics by flagging individuals'
genealogies over the last 2 generations. Note that while we implemented a mutation model for microsatellite
data, we did not implement a mutation model for genotyped independent SNPs. Indeed, the values of a
mutation rate for such data is not consensual, and in any case low and slowly affecting population allelic
frequencies. While it is reasonable to neglect such mutation for such type of markers for recent evolutionary
processes, such as those having occurred during and after the TAST, this might be an issue when
considering, in the future, much older admixture histories. Finally, note that while only these types of
markers were readily implemented in *MetHis*, Dr. Laurent coded them as “boxes” that could in principle
easily be filled with independent genetic sequences rather than SNPs. Nevertheless, developing *MetHis* for
sequences rather than SNPs or microsatellites, will need to elaborate on sequence mutation and
recombination models which is not trivial, albeit completely feasible without changing the core architecture
of the software (Fortes-Lima, Laurent et al. 2021).

4.1.b. *Calculating summary-statistics for ABC inferences*

For our proof of concept with Dr. Fortes-Lima, we simply had resorted to a constellation of existing
software to calculate summary statistics on each simulation. This was computationally tedious and costly
as simulated data had to be read by multiple different software. We thus obviously opted for embedding
summary statistics calculation within a “Summary-Statistics calculation” tool within *MetHis*. This allowed
to substantially diminish memory costs and calculation efficiency, a code-writing task nevertheless non-
trivial and tedious that, again Dr. Laurent supervised, with the help of Antoine Cools, Ferdinand Petit, and
Maël Pretet, Licence and Master students in bio-informatics at the time, that I had hired and supervised for
these tasks over the years.

We thus chose to implement a number of classical population genetics ABC summary statistics, likely
informative about our model parameters. As detailed in Supplementary note of Fortes-Lima, Laurent et al.
2021, we computed a series of “within-population” statistics for the source populations and the admixed
population, separately, including, for SNP data, SNP-by-SNP expected heterozygosities (Nei 1978), their
means and variances, the means and variances of individual pairwise ASD within each population, and an
inbreeding coefficient F (Danecek et al. 2011). For microsatellite data, we computed the mean number of
alleles per markers within each population, the mean expected heterozygosity, the mean allele size variance
across markers, the mean and variance of individual pairwise ASD (Bowcock et al. 1994) within
populations. As per population genetics theory, and empirically shown in ABC classical approaches since
Beaumont et al. (2002), these statistics were often informative about effective sizes and drift within
populations, and also indirectly reflecting admixture across populations (detected possibly from an increase
in heterozygosities due to gene-flow for instance). We also computed several classical “between-
populations” statistics such as, for both SNP and microsatellite data, multilocus pairwise F<sub>ST</sub> (Weir and
Cockerham 1984), f3-admixture statistics (Patterson et al. 2012), as well as mean pairwise population ASD.

In addition to these classical ABC summary-statistics, and as extensively explained above and in the
previous **Chapter 3**, we developed sets of summary-statistics specifically describing the distribution of
admixture fractions from one of the two source populations (the other one being one minus this one in such
model) across individuals sampled in the admixed population. As said, while numerous methods exist for
estimating such values from genetic data, most are tedious or very tedious computationally and sometimes conceptually, rendering most methods out of reach for application in an ABC framework where they would need to be computed for tens or hundreds of thousands of simulations. We therefore implemented a new a particular estimate of individual admixture fractions based on pairwise ASD that we already calculated for other summary-statistics. Based on the statistical description of allelic frequencies in an admixed population since Bernstein (see **Chapter 3**), we expected admixed individuals to be at intermediate genetic distances from either source populations, proportionally to their average admixture fraction. Thus, projecting the ASD matrix using Multi-Dimensional Scaling could provide us with an estimate of such distance proportional to admixture fractions (**Figure F4.1.b**), very efficiently computationally. We therefore implemented such calculation to obtain the estimated distribution of admixture fractions from one source population within the admixed population. From this distribution, we used minimum, maximum, all 10% quantiles, mean, variance, kurtosis, and skewness, as 16 separate summary statistics for ABC scenario-choice and posterior parameter inferences.

<figure><img src="image_48.png" /><figcaption>**Figure F4.1.b.**
Schematic representation of ASD-MDS statistical estimation of average individual admixture fraction from one of the source populations in a two-source population admixture model implemented in *MetHis*. Schematics represent estimation in a 2-Dimension MDS projection for simplicity, but note that estimates obtained with *MetHis* consider, instead, a 3D MDS projection for such calculations. Figure originally presented in Supplementary Figure SF12 in Laurent et al. (2022), freely available on BioRxiv at https://doi.org/10.1101/2022.04.11.487833, and currently under review at *eLife*.</figcaption></figure>

Note that, by doing the calculation presented in the schematic **Figure F4.1.b**, we could obtain a priori “negative” admixture fractions for admixed individuals projected in the MDS on the other side of the centroid of a given source population. This would ultimately reflect a high resemblance of this individual with the other individuals in the source population. This was not a problem per se in ABC, as this could happen with real observed data, and our goal in ABC is to best mimic summary statistics obtained from real data, whatever their meaning.
**4.2. MetHis coupled with machine-learning ABC in practice**

4.2.a. *MetHis-Random Forest ABC scenario-choice; MetHis-Neural Network ABC*
*posterior parameters joint inferences*

*MetHis* produces reference tables ready to be used for ABC scenario-choice and parameter inference procedures using existing software implemented in R (Csilléry et al. 2012; Pudlo et al. 2016; Raynal et al. 2019). In 2016, during the initial development of *MetHis*, a novel ABC scenario-choice approach relying on Random Forest machine learning (Breiman 2001), was proposed and rapidly adopted by the community as a massive improvement from previous scenario-choice approaches relying on regression among other methods (Beaumont et al. 2002). Indeed, in Random Forest ABC (Pudlo et al. 2016), authors proposed to build a random forest of decision trees based on the summary statistics calculated from simulations, and then used this random forest for predicting which class of competing scenarios produced vectors of summary statistics closest to the observed ones, comparing, to do so, each scenario performances.

ABC scenario-choice had always been, to me, of major interest for formally testing competing scenarios from the data, compared to the conceptually and statistical difficult comparison of maximum-likelihood values obtained by fitting real data separately to scenarios comprising different sets of parameters. Random Forest ABC in this context represented a major practical, and even philosophical, improvement compared to previous approaches (Figure 4.2.a). Indeed, it is a categorization algorithm rather than a regression or MCMC algorithm, thus to my views much better suited for the categorization question at stake for scenario-choice procedures. And indeed, it proved to perform much more efficiently with equal accuracy compared to previous methods: one needs ten to a hundred folds less simulated data to perform robust scenario-choice, and, furthermore, RF-ABC is not affected by correlations among summary-statistics, a very concrete frequent issue of previous methods (see Sisson et al. 2019). Indeed, one can even formally use RF-ABC to classify the importance of each statistic on the shape of the random forest and on the prediction, to illustrate how each summary-statistics relatively contributes to the finale decision (Pudlo et al. 2016).

While Random-Forest ABC has also been developed for posterior-parameter estimation (Raynal et al. 2019), it still faces major inherent difficulties. Indeed, as we just said, RF is a classification algorithm, and therefore only provides posterior estimates of each parameter separately, rather than jointly, and for quantiles of the posterior distribution rather than the full distribution itself. While one can, *a posteriori*, reconstruct the distribution from each estimated quantile, the risk of over-fitting or, alternatively, of increased noise is high. In all cases, we prefer to estimate all parameters jointly rather than independently, as our evolutionary models incorporate possible trade-offs across the various demographic parameters which would be better accounted for with a joint estimation of vectors of parameters, rather than when considering original parameters separately (Fortes-Lima, Laurent et al. 2021). We therefore opted for machine-learning Neural Network ABC posterior estimation (Csilléry et al. 2012), as our primary ABC posterior parameter inference method, albeit note that the reference tables produced by *MetHis* are readily usable with other classical ABC software implementing other such methods.
1. Produce simulated data separately under three competing scenarios (Scenario A, Scenario B, and Scenario C)

2. Compute sets of summary-statistics for each simulation separately and obtain thus a vector of summary-statistics known to have been produced under either competing-scenario

<figure><img src="image_49.png" /><figcaption>Figure F4.2.a.</figcaption></figure>

Schematic representation of Random Forest ABC scenario choice for simulations performed under three competing scenarios

Estimate the percentage of votes obtained by the observed data for each scenario
across the Random Forest and decide on the «winning» scenario

REPAT 500-1000 times to obtain a Random Forest of 500-1000 decision trees
4.2.b. *A case study for evaluating MetHis-ABC performances in practice*

As I was interested, initially and primarily, in investigating complex admixture processes having occurred
during the TAST, we evaluated the “real-life” performances of our novel *MetHis*-ABC approach for a case
study of two separate enslaved-African descendant admixed populations in the Americas, for which
extensive data were readily available in public database: a sample from an Afro-American community from
South-West USA (Afro-American ASW), and a sample from Barbados (Barbadian ACB). Previous studies
had already shown that both populations were admixed mainly between North-Western European
populations and West Central African populations for which, in existing databases, the British GBR and
Nigerian Yoruba YRI represented good proxy source populations in the 1000 Genomes Project (2015)
database<fnref n="48" />. Beyond my personal interest in the outcome of such case-study, this was a challenging task that
could challenge the limits of our approach while explicitly showing its’ novelty compared to previous
approaches.

First, as said before, African and European populations are, in absolute, not that genetically different
from one another, a thus challenging problem for reconstructing admixture histories that rely primarily on
the level of genetic differentiation of putative source populations. Second, this was a recent (roughly 20
generations ~ 500 years) admixture history, which further challenged parameter estimation in such a small
time-frame. Finally, we were also interested in evaluating whether recent voluntary migrations since the
1960’s influenced admixture patterns, thus allowing us to evaluate empirically our power to identify such
recent events from genetic data. This latter option is not trivial with previous maximum-likelihood methods
relying on admixture LD decay (Gravel 2012; Hellenthal 2014). Indeed, very recent admixture events result
in long stretches of admixture from either source populations, associated with high LD within stretches, in
the genomes of first-generation admixed individuals. These very recent stretches prove hard to scale and fit
to the expected curves of LD decay, and they further may be confused with long stretches of admixture in
lower LD due to shorter (older) admixture stretches having recently recombined. They are thus often
excluded from analyses in practice, which *de facto* prevents the researcher to evaluate very recent admixture
events with these methods.

We considered nine competing scenarios of highly complex admixture histories, combinations of three
classes of models from either source population (Figure 4.2.b1). We considered scenarios where either
African or European source populations (or both) contributed to the gene-pool of the admixed population
in two separate independent pulses drawn randomly in the 20 generations after the original founding
admixture pulse. Alternatively, we considered scenarios where either source population (or both)
contributed in a recurring monotonically decreasing way to the gene-pool of the admixed population after
the independent initial founding admixture pulse at generation 0. Finally, we considered scenarios where
either source population (or both) contributed in a recurring monotonically increasing way to the gene-pool
of the admixed population after founding. This latter class of scenario was put into competition to explicitly
evaluate whether recent admixture processes may overwhelm previous signals of admixture, in particular
as historical demographic data demonstrated, at least for the USA, that more than ten times more people
voluntarily emigrated to the USA from Africa since the 1960’s than the total amount of enslaved-Africans
forcibly deported from Africa to the USA over the last, most intense, two hundred years of the TAST until
the 1810’s (Berlin 1998, 2010; Eltis and Richardson 2015).

<footnote n="48">⁴⁸ Which was also consistent with expectations from historical records as both regions (the USA and Barbados), had been British peopling colony during the TAST, and the Bight of Biafra being a major zone of embarkation of enslaved-Africans within the British Empire TAST commercial network.</footnote>
Note that recurring admixture scenarios were implemented minimizing the number of parameters thanks to Dr. Bruno Toupance (Université Paris Cité), who adapted a classical rectangular hyperbola function to simply control the shape of the recurring admixture process between the onset time and associated intensity and the offset time and intensity, for a period of time set with priors by the user. In between, admixture parameter values at each generation are the numerical solutions of this scaled rectangular hyperbola function for which the steepness parameter “*u*” is drawn between 0 and 0.5. In practice, for decreasing admixture scenarios, a value of *u* = 0 corresponds to a very steep recurring admixture, virtually constant until the end of the admixture period after an initial pulse at the beginning of the period; *u* = 0.5 instead corresponds to a linear process between the onset and offset intensities. Note that for equivalent onset and offset admixture intensities and a value of *u* equal to 0.5, the period of recurring admixture corresponds to a constant introgression from the source population of that intensity, thus corresponding to a classical unidirectional migration model during that period (see **Chapter 3**).

<figure><img src="image_50.png" /><figcaption>Figure F4.2.b1.</figcaption></figure>

Nine competing scenarios of complex admixture histories for the genetic patterns of the Afro-American (ASW) and Barbadian (ACB) population samples from the 1000 Genomes Project, investigated with *MetHis-ABC*. We conducted 10,000 separate simulations under each competing scenario for ABC scenario-choice with Random Forest, and 100,000 simulations under the winning models for further ABC posterior parameter estimations with Neural Network ABC. Detailed description of the models and parameter priors are presented in Fortes-Lima, Laurent et al. *Molecular Ecology Resources* (2021). Figure originally published as Figure 1 from this publication.
This competing scenario design was thus aimed at disentangling whether genetic and admixture diversity patterns observed in the Afro-American ASW and the Barbadian ACB were better explained by pulse scenarios (three possible pulses per source population), compared to recurring decreasing and increasing admixture scenarios. Indeed, demographic migrations from Europe and Africa to the Americas, whether forced or voluntary, occurred at least on a yearly basis since the 16th century, with periods during the Plantation Economy era, between the 17th and 19th century, when population movements occurred much more frequently (Eltis and Richardson 2015; Fortes-Lima and Verdu 2021). It could thus, a priori, be expected that admixture between European and African source populations also occurred in a recurring way over time during the TAST, instead of more punctual, pulse-like, events; pulses-models that had been, until now, the only investigated models from a genetics perspective.

Importantly, note that I firmly believe that *MetHis-ABC* ought to be used only if more complex models are investigated than the ones approached by other maximum likelihood methods. Otherwise, I incite the user to instead use these ML methods, more elegant mathematically and often much more powerful statistically and computationally than ABC.

### *MetHis-ABC prior checks*

Before launching any ABC inference, it is necessary to evaluate whether simulated data under the proposed models could produce sets of summary statistics encompassing the observed values calculated from the real data; otherwise, ABC inference is meaning-less. We thus conducted classical goodness-of-fit tests, visually inspected that each summary statistics observed in the real data fell within the spaces of values obtained from simulations, and verified that observed vectors of summary statistics also fell within the multidimensional space of summary-statistics calculated from simulations using PCA. All was in order, our design for *MetHis* simulations were indeed able to produce simulations whose summary-statistics values mimicked observed values separately obtained from the Afro-American (ASW) and the Barbadian (ACB) real genetic samples (Fortes-Lima, Laurent et al. 2021).

### *MetHis-ABC Random Forest scenario-choice*

With our *MetHis-ABC RF* procedure, we found that the nine competing scenarios could be distinguished from one-another accurately in a vast majority of cases: without using observed data but instead considering in turn each 90,000 simulations as pseudo-observed data for RF scenario-choice. This was rather surprising to me given the high level of scenario-nestedness in certain parts of the parameter space (Robert et al. 2010). Indeed, for instance, parameter values corresponding to a recurring admixture model with a very sharp reduction of introgression rates the generation immediately after the onset, perfectly mimic a pulse-like model of admixture. Analogously, values of the model-parameters corresponding to a very sharp increase in recurring admixture at the last generation will a priori be confused with a single recent pulse of admixture occurring close to the present.

ABC cross validation approaches are of particular interest as they easily allow to partition a *posteriori* the space of parameter values to evaluate empirically the influence of such nestedness on RF scenario-choice confusions (Fortes-Lima, Laurent et al. 2021). Indeed, we found that the scenario-choice confusions made with our approach between different scenarios were most often occurring in spaces of the parameter-values where models were highly nested and thus biologically un-discriminated. Finally, note that despite these promising scenario-choice results, in real-data inferences with ABC, the question is not so much to perform well in distinguishing scenarios in general, i.e. over the entire space of parameter-values. Instead,
we want good performances for distinguishing among scenarios in the vicinity of our observed data, which
can be the case even if scenarios are hard to distinguish overall.

Furthermore, we found that, as predicted by our analytical investigation of the general admixture
model, RF-ABC scenario-choice was massively driven by extensive information from the summary-
statistics describing the distribution of admixture fractions in the admixed population, largely before the
amount of information brought by other more classical statistics. In particular, note that higher moments of
the distribution, namely its shape as determined by variance, kurtosis and skewness, as well as
minimal/maximal admixture values and the first and last 10% quantiles, were the most informative
statistics.

Note that our cross-validation performances (before using real data as above), remained very
satisfactory when considering, respectively, 50,000 or even 10,000 SNPs instead of 100,000 **(Figure  
F4.2.b2)**. Furthermore, considering five times fewer individual samples from either source and admixed
populations lowered our power to accurately distinguish among scenarios, but only very reasonably so and
in any case, did not change patterns of correct and incorrect scenario prediction.

Altogether, this showed that our *MetHis-ABC* approach performed very satisfactorily, even with
reduced data. This was to some extent expected by the nature of ABC inferences. Indeed, they rely on
information carried by summary-statistics, rather than the absolute amount of data. Thus, as long as data is
sufficient to estimate reasonably well the statistics, and if they are informative about model parameters,
ABC will perform well, whichever the absolute amounts of data considered to calculate them.

Finally, using observed data, we found that for both the Afro-American ASW and the Barbadian ACB,
the best scenario was the “AfrDE-EurDE” scenario, which encompassed monotonically decreasing
recurring admixture from both the African and European source populations; a result that we will not detail
more extensively here and encourage interested readers to refer to Fortes-Lima, Laurent et al. 2021 for
further information and discussion.
<caption>**Figure F4.2.b2.**</caption>

Cross-validation results for the discriminatory power of *MetHis-ABC* using Random Forest scenario-choice with 1,000 decision trees in the random forest, as a function of the number of SNP markers or of the numbers of individuals sampled in the admixed and source populations. Figure built from Figure 2 and Supplementary Figure SF5 from Fortes-Lima, Laurent et al. *Molecular Ecology Resources* 2021.

<figure><img src="image_51.png" /></figure>
*MetHis-ABC Neural Network posterior parameters joint estimations*

Under the winning model, separately for each target population, we then performed additional
simulations to feed the Neural Network learning for further ABC posterior parameter inferences (Csilléry
et al. 2012; Jay et al. 2019). Note that the very tedious parts of Neural Network ABC procedures are not
the inferences using real data themselves, but rather determining the optimal basic parameters of the NN,
namely the tolerance level, which is the number of closest simulations to be used for NN inference, and the
numbers of neurons in the hidden layers of the network, using empirically simulations as pseudo-observed
data. Indeed, there are no theoretical or empirical ways of determining these parameters for specific case-
studies *a priori* (Jay et al. 2019). They thus need to be tested in turn, in order to determine *a posteriori*
which couple of NN-parameter values minimize posterior model-parameters’ error rates. Once these are
determined, a “final” Neural Network can be trained based on the observed data using the chosen tolerance
level and number of neurons in the hidden layer, to obtain posterior parameter distributions for all model-
parameters estimated jointly. Note that we empirically showed that this particular ABC posterior parameter
estimation procedure outperformed other methods such as rejection, random-forest, or NN for each
parameter separately, at least in our study-case.

We reproduced below our obtained results for each target admixed population for exemplification (Figure F4.2.b3). We can see here, in general, that older model-parameters are less distinguished from their respective priors than more recent events, as expected: more recent events erase the identifiability of previous parameters, albeit not completely in this recent admixture process, also as expected from our previous theoretical work (see Chapter 3 about Buzbas and Verdu 2018). Furthermore, we can see that certain parameters are extremely precisely inferred *a posteriori*, which we confirmed by extensive ABC posterior-parameter errors and Credibility-Intervals accuracies estimations, another interesting possibility provided by any ABC approach. Indeed, note that previous ML methods for dating admixture events do not readily provide confidence intervals or even standard errors around the point estimations. Here, not only the inference provides posterior-parameter distributions, but, in addition, simulations in the vicinity of the observed data can be re-used as pseudo-observed to further evaluate the statistical power of the inference, or the lack of satisfactory power.

Finally, we will not recapitulate here the discussions of the obtained results from an anthropological
genetics perspective. Indeed, our goal was here to propose a novel method and evaluate how well it
performed using real, complex, genetic data. The scenarios here explored are somewhat un-satisfying as
they still lack complexity if we were to reconstruct the detailed admixture histories of these populations as
the primary focus of interest; instead, they served as a proof of concept for our methods. For instance,
among many others, we considered in these analyses, for simplicity, constant reproductive sizes over
generations in the admixed population; a likely problematic oversimplification as we expected changes in
effective sizes in the admixed population to interact with admixture and genetic diversity patterns. Finally,
we did not dwell on anthropological genetics interpretations of our results, as both Afro-American (ASW)
and Barbadian (ACB) sample sets from the 1000 Genome Project were not accurately informed and
categorized, hence lacking crucial information for better detailing models to be tested, and, most
importantly, for interpreting our inference results in the light of historical information; a crucial
categorization problem detailed in **Chapter 1**.

In the following **Chapter 5**, we will use this *MetHis-ABC* framework with the explicit objective of
investigating the anthropological genetics history of admixture experienced in each Cabo Verdean island
separately, as we initially intended as the root for the whole project (**Chapters 3 and 4**) started in 2009.
<figure><img src="image_52.png" /><figcaption>Figure F4.2.b3.</figcaption></figure>

*MetHis-ABC* posterior parameter estimations under the winning model using Neural Network approaches, for the Afro-American ASW and the Barbadian ACB respectively.

Figure originally published as Figure 4 in Fortes-Lima, Laurent et al. *Molecular Ecology Resources* (2021).
**4.3. Ongoing developments and future perspectives for the inference of complex admixture histories from genetic data using Approximate Bayesian Computation**

Our *MetHis*-ABC framework released in 2021 has been used, with success, for disentangling migrations from admixture processes at play for the human peopling of Oceania in a recently published article in *Nature* (Choin et al. 2021). Beyond the anthropological genetics question of interest in this paper, we could empirically confirm here that *MetHis* simulations of autosomal independent genotyped SNP data over much older admixture histories (300 generations) than the ones previously explored, were still in good agreement with the observations, even without explicitly implementing a mutation model. Furthermore, ABC scenario-choice and posterior parameter inferences still performed similarly well as in our proof of concept. Finally, we deployed this version of our *MetHis*-ABC framework for studying Cabo Verde in the following **Chapter 5**.

4.3.a. Novel summary-statistics for *MetHis*-ABC

Since the original developments presented above, several novel features have been implemented and released publicly in *MetHis*. First, we built a novel summary-statistics for ABC inference in the hopes to better capture genetic variation patterns in the admixed populations. Indeed, the user can now consider, in addition to the distribution of admixture fractions in the admixed population, the ASD-MDS two-dimensional distribution of angles between admixed individuals and either centroid of the source populations (Figure 4.3.a). We noticed from previous experience, and further deterministic use of *MetHis* (see next), that the individuals in the admixed population departed, over time, from the trajectory between the centroids joining source populations projected on a 2D MDS based on individual pairwise ASD. Without formal proof, we think that this behavior may be due to drift and mutation in the admixed population since the last admixture event, until the admixed population forms a specific ASD-MDS cluster when sufficiently drifted from the sources after the last admixture event. We thus try to capture this information with angles as in **Figure 4.3.a**, and use the distribution of these angles similarly as previously (min, max, 10%-quantiles, mean, variance, kurtosis, skewness), as a set of possible summary statistics for ABC inference, in the hopes that it would provide further information for scenario-choice and posterior-parameter estimations. For instance, we expect the variance of this distribution to be very low in ASD-MDS projections, where admixed individuals cluster tightly, whichever their relative distance to either source populations' clusters on the 2D projection. We expect to find such pattern when the last admixture event has occurred sufficiently long ago (see **Chapter 3**). Note, however, that we do not have, to our knowledge, a formal proof of the behavior we think we may capture, at least in part, with this set of summary-statistics. Future analytical work on MDS dimensionality reduction from ASD matrices will need to be endeavored to rigorously justify the interest in this novel summary-statistics, that we can nevertheless readily play-with empirically with *MetHis*.

Finally, on the ABC summary-statistics front, as said before, admixture linkage-disequilibrium statistics are extremely informative about complex admixture histories' parameters. Using them in an ABC inference framework is nevertheless highly challenging, as such statistics have to be compiled for numerous independent simulations similarly as they are computed on real data; a task out of reach computationally as these complex statistics already require substantial time and power to be calculated only once on the real data. However, with Pr. Zachary A. Szpiech (Pennsylvania State University) and Dr. Romain Laurent, we are currently trying to develop efficient ways to calculate Runs-of-Homozygosity patterns (ROHs) and
ancestry-specific ROHs (see **Chapter 5**), for further use as summary-statistics in an ABC framework. While ROHs are not exactly admixture-LD patterns, they may capture parts of the same information for the benefit of ABC inferences performances. In case of success, we will thus further need to implement recombination and linkage disequilibrium in *MetHis* simulations rather than only considering biologically independent SNP markers as done currently (see previous sections in this chapter and also **section 4.3.b** next).

<figure><img src="image_53.png" /><figcaption>Figure F4.3.a.</figcaption></figure>

Schematic representation of ASD-MDS statistical estimation of average individual admixture fraction from one of the source populations, as well as that of the distribution of angles formed by admixed individuals from the line-joining the centroids of the source populations, in a two-source population admixture model and implemented in *MetHis*.

Figure originally presented in Supplementary Figure SF12 in Laurent et al. (2022), freely available on BioRxiv at https://doi.org/10.1101/2022.04.11.487833, and currently under review at *eLife*.
### 4.3.b. Other types of genetic data and models simulated with *MetHis*

Most importantly, with a PhD student, Marta Ciccarella, co-supervised by Jorge M. Rocha (CIBIO-University of Porto) and myself, we currently aim at developing a sex-specific version of *MetHis*, as presented in our theoretical analyses in **Chapter 3**. Further implementing sex-specific summary-statistics will allow to expand our approach for investigating complex sex-biased admixture histories from genetic data. Ms. Ciccarella is also interested in developing the parameterization of non-random matting processes in the admixed population over time, in the current version of *MetHis*. This will allow us, as explained in the **Chapter 3**'s theoretical perspectives, to empirically estimate deviations from random matting in the admixed population and possibly better understand the role of complex socio-marital stratification possibly at play in the genetic evolution of human admixed populations, for instance (Zaitlen et al. 2017; Versluys et al. 2021).

*MetHis* was designed forward-in-time in part to ease future implementation of natural selection processes in the admixed population, in particular to investigate post-admixture selection processes exemplified in our empirical findings in Central African populations (Patin et al. 2017). In principle, *MetHis* could relatively easily be tuned for such purpose by controlling the reproductive success and survival of individuals in the admixed population with a novel selection parameter, which remains yet to be fully defined and implemented. Thus, in collaboration with Dr; Patin's team, we hope to be able to further explore such possibilities provided by future significant developments of our inference framework and novel computational tools. In particular, we will need to develop *MetHis* for simulating sequences rather than SNPs or microsatellites, which, as said in previous sections of this chapter, can relatively easily be done based on the existing design of our simulator.

Finally, I am currently co-supervising the PhD project of Ms. Juliette Sauvage with Dr. Céline Bon (MNHN) and Dr. Marie-Claude Marsollier-Kergoat (CEA), which aims at reconstructing the history of the Paris Basin peopling from ancient DNA data. For this project, we are generating genomic sequences of human remains un-earthed by archaeologists in the region of Paris from different archaeological sites since the Mesolithic period, more than 5000 years ago, up-until the middle-ages. Specifically concerning *MetHis*-ABC, we aim at reconstructing the admixture history of individuals from each archaeological site over time. To do so, we will need to implement, in the *MetHis* simulator, the type of data that we expect to obtain from ancient DNA, and associated summary-statistics for ABC inferences. Rather than trying to simulate such data under the complex admixture-history models themselves, we aim at conducting the same types of simulations performed for modern DNA, and extracting then haploid partial genomic markers mimicking the observed ancient DNA data. Using then haplotypic summary-statistics classically used in paleo-genetics, this will allow us to perform ABC inferences projecting ourselves in past populations, at each point in time and for each archaeological site separately.

### 4.3.c. Using *MetHis* deterministically

I supervised, recently in 2021, a Master student in bio-informatics, Maël Pretet, for investigating, deterministically, how changes in reproductive sizes in the admixed population interacted with admixture and genetic diversity patterns, as captured by summary statistics calculated in *MetHis*, over time and as a function of admixture parameters. To do so, Mr. Pretet implemented as an option for *MetHis* users, with Dr. Laurent's help, the possibility to follow over time a given simulation, by stopping the simulation at each generation and calculating summary-statistics, before starting the simulation again. Furthermore, in
order to empirically evaluate the behavior of computationally intensive Bayesian clustering algorithms, such as STRUCTURE or ADMIXTURE, and beyond that, for any other possible software of interest, Mr. Pretet also implemented the possibility for the simulated genetic data to be written in full at any generation of *MetHis* simulations (the user should beware of usable storage space on his/her disks when choosing this option).

Beyond the results that Mr. Pretet produced and that we are currently in the process of further exploring to envision a possible future publication, these sets of tools and options are readily implemented in *MetHis*. They will be, I believe, of interest for exploring and benchmarking the behavior of numerous population genetics statistics under highly complex admixture scenarios. These tools will be of particular interest for several widely-used population genetics statistics that are lacking analytical expectations, or those statistics which have not been benchmarked thoroughly, under such complex models, such as Paterson's $f_3$-statistics (2012) for an instance. Furthermore, based on these developments of *MetHis*, we hope to be able to use our software for future predictions of genetic and admixture patterns expected in populations having undergone or still undergoing admixture, for instance for conservation biology purposes.

### 4.3.d. Ongoing projects using *MetHis-ABC* for non-human species

We sincerely hope that our *MetHis-ABC* framework will be interesting beyond the study of human populations. Therefore, we started to initiate two projects investigating the admixture history of diploid plants populations, from diverse types of genetic data including microsatellites and RAD sequencing SNPs. The first project, initiated in collaboration with Pr. Hardy (Université Libre de Bruxelles), is about the ancient admixture history of *Terminalia superba* in the Dahomey Gap in Western Africa. Pr. Hardy had previously shown that this tree species was born from hybridization between previously isolated sub-populations from West Western Africa and from Central Africa respectively, when the two forest massifs overlapped 190,000 years ago, before being re-separated 20,000 years ago and since (Demenou et al. 2018). We are thus in the process of testing different possible past admixture processes having given birth to observed genetic patterns.

The other plant-project for *MetHis-ABC*, that we have initiated with Pr. Nathalie Machon (MNHN) and a previous Master Student, Claire Stragier, that I supervised, is interested in admixture patterns in four local populations of *Arenaria grandiflora* in the Fontainebleau Forest. These populations have been the focus of a hybrid reintroduction program since 1999 from plant clones salvaged in a botanical conservatory before their extinction locally in the 1990's, and from other clones from a separate population from the region of Chinon in North-Central France. Since then, populations are followed and genotyped for a variety of markers every generation (roughly 4 years for this plant) in a genetic-rescue framework. We ought to use *MetHis* here to better understand the admixture process ongoing under our eyes, and in particular possible departures from random matting, in turn possibly indicative of hybrid depression or heterosis due, for instance, to phenetic differences as a function of individual admixture levels. In addition, *MetHis* could possibly be used to explore, predictively, the expected genetic and admixture patterns of these populations in the future, which may help managing the populations in the future. This project has been funded by the ANR in 2022, as part of a larger project (ANR FloRes) piloted by Dr. Ophélie Ronce (CNRS-University of Montpellier) aiming at evaluating plants' conservation and rescue programs in the face of climatic changes in a variety of case-studies in France.
# Chapitre 5

## The genetic and linguistic admixture histories of Cabo Verde
<figure><img src="image_54.png" /><figcaption>Alto Mira.
Santo Antão, Cabo Verde, 2016
©Paul Verdu</figcaption></figure>
**Chapter 5. The genetic and linguistic admixture histories of Cabo Verde**

In **Chapter 1.2** above, I introduced, in details, the genesis and categorization and sampling protocol at the root of our interdisciplinary project aiming at reconstructing the genetic and linguistic histories of Cabo Verde, in the context of European colonization since the 15th century and the Trans-Atlantic Slave Trade (TAST). We further generalized the fundamental population genetics questions of interest about Cabo Verde, to the investigation of the admixture history of enslaved-African descendant admixed population during the TAST in the same **Chapter 1.2**, and in the population genetics’ theoretical **Chapter 3** and methodological **Chapter 4**.

As a recapitulation from the introductions in **Chapter 1.2**, **Chapter 3**, and **Chapter 4**, the key questions of interest to the genetics side of the Cabo Verde project were:

i) Which continental European and African populations contributed to the gene-pool of Cabo Verde today?

ii) What are the patterns of genetic diversity and admixture within Cabo Verde at reduced geographical scale? Are their islands or group of islands relatively more or less reproductively isolated than others due to serial founding events and isolation triggered by political changes over the 400 years of the peopling of the archipelago? Or, instead, are genetic diversity and admixture patterns evenly distributed throughout the archipelago as suggested by numerous population movements across islands throughout history? (Figure F1.2.b)

iii) When and where did the admixture events that built genetic diversity and admixture patterns today occur? Were there more admixture events identifiable during the massive population movements triggered by the expansion of Plantation Economy between the mid-17th and the early 19th century? Did the abolition of the TAST reduced the opportunity for admixture events to shape extant genetic patterns? Did the abolition of slavery and the major sociological changes that followed increased the opportunity for admixture events to occur? Did recent 20th century work-related migrations resulted in novel, recent, admixture events?

Furthermore, remember that the origins of the project stemmed from a linguistic question brought to us by Pr. Marlyse Baptista: could population genetics help, indirectly, informing which languages may have contributed to the lexical, grammatical, and/or syntactic diversity of Cabo Verdean Kriolu? Comparative and historical linguistics already identified numerous African and European languages having contributed to the lexical, grammatical, and syntactic diversity of Cabo Verdean Kriolu. Intuitively, identifying previously unsuspected populations at the source of Cabo Verde genetic diversity may, in turn, propose novel hypotheses for the languages that may have contributed to the linguistic diversity of Kriolu.

We decided to collect linguistic utterances, i.e. individual realizations of Kriolu in a given context, that of academic semi-spontaneous interviews conducted individually (see the end of **Chapter 1.2**), rather than “Languages” as per classical linguistic definitions. We collected such data among the same individuals we sampled genetically to address the above, genetics, disciplinary questions. Indeed, while several linguists, including Pr. Baptista, had described linguistic variation across Cabo Verdean islands, we did not have systematic data representative of each Kriolu linguistic variants, such as classical word-lists, based on which we could conduct classical computational linguistics investigations whose results could then be
compared with the outcome of classical population genetics descriptive analyses (Figure F1.2.c)<fnref n="49" />.
However, by collecting anew individual Kriolu utterances as we did, we could compare at the same scale
centered on individuals, a specific type of linguistic variation, individual’s “manners of speaking Kriolu”,
with inter-individual genetic variation. Several pluri-disciplinary questions spontaneously emerged from
this interdisciplinary fieldwork setting:

i) What is the linguistic diversity of Kriolu within Cabo Verde at reduced geographical scale, estimated as inter-individual utterance variation? Are there correlations between the geographic distribution of genetic and linguistic diversity within Cabo Verde, possibly reflecting parallel historical trajectories or phenomenon having given birth to either type of biological and cultural variation?

ii) What is the linguistic admixture diversity of individual manners of speaking Kriolu throughout Cabo Verde? Are specific utterances of a particular African or European origin more often employed in individual discourses in certain parts of Cabo Verde than in others? Are linguistic and genetic admixture histories correlated or disjointed in Cabo Verde?

Altogether, why answering all these questions could be of importance?

First, Cabo Verde is the first European peopling colony in Sub-Saharan Africa of the TAST era. Furthermore, Cabo Verde was a major slave-trade platform during most of the TAST. Probably as a result, Cabo Verdean Kriolu is also the first Atlantic Creole language from the era, born from linguistic contacts between African and European languages. As such, investigating genetic and linguistic admixture histories in Cabo Verde allows to investigate the influence of the TAST on cultural and biological diversity from the very beginning of this historical era, more than 30 years before Columbus (Figure F1.2.a). Finally, while numerous American and Caribbean population have been the focus of anthropological genetics studies in the context of the TAST, the influence of this era on genetic diversity patterns in Africa have been seldom explored.

Second, numerous populations of European and African origins were forcibly deported or voluntarily migrated to Cabo Verde since the 15th century, as attested by historical records, and which are known by linguists to have left an identifiable trace in Cabo Verdean Kriolu. Which ones contributed or not to the genetic diversity of Cabo Verde is still debated among historians and geneticists (Figure F1.2.a), similarly as for all enslaved-African descendant populations having emerged during the TAST.

Third, the islands of the Cabo Verdean archipelago were peopled successively over the course of more than 300 years during the TAST (Figure F1.2.b). This represents an ideal geographic and historic framework to investigate, at a micro-geographical scale, the histories of genetic and linguistic founding events, demographic migrations, and admixture. Furthermore, Cabo Verde peopling history could reveal possible such mechanisms also at play in other enslaved-African descendant populations born from the TAST, and possibly reflects the histories and socio-demographic processes having influence the genetic and linguistic diversity of numerous populations at a macro-geographical scale across the three continents.

Finally, classical genetic-linguistic comparisons are often conducted between isolated languages and genetic populations, albeit their respective descriptions of variation stem from observations collected at very different scales and with different methods. Indeed, genetic variation stem from observed genetic data

<footnote n="49">⁴⁹ a type of genetic-linguistic comparison that we had endeavored, for instance, in the Central African project (Chapter 2.1).</footnote>
compared across individuals grouped into populations, while linguistic variation stem from comparisons
across complex meta-constructed languages obtained from specific (few) informants (see **Figure F1.2.c**).
Studying jointly genetic and linguistic variation collected in the same individuals would thus allow us, not
only to investigate genetic-linguistic correlations from a contact-language, but also to anchor genetic-
linguistic comparisons on different objects (genes and utterances), nevertheless collected at the same scale
centered on individuals.

Therefore, answering these questions could both provide invaluable knowledge about the possible
mechanisms having influenced genetic and linguistic variation in numerous other populations descending
from enslaved-Africans deported throughout the TAST since before Columbus, as well as, more directly,
about the construction of genetic and linguistic diversity in Cabo Verde at a micro-geographical scale.

Since 2009 and the beginning of this particular project, we have developed the theoretical and
methodological tools to address the population-genetics aspects of these questions using the genetic and
anthropological data collected during six interdisciplinary fieldworks between 2010 and 2018. During these
fieldworks, we also collected systematically for the same individuals, semi-spontaneous Kriolu utterances,
fully transcribed with the morpho-phonetic orthographic norm of Kriolu (the ALUPEC), which we could
describe with classical statistical tools. Finally, we further collected word-list utterances from a substantial
subset of our dataset (see **section 5.3** below). Based on both types of data, we developed a novel “population
linguistics” general framework, and further developed a novel joint genetic and linguistic inference
methodology and associated computation tools for reconstructing, from word-list utterance data and genetic
data collected in the same individuals, the genetic and linguistic histories of populations.

In this chapter, I will first (**section 5.1**) report on the results we obtained about the genetic admixture
history of Cabo Verde, finishing with ongoing work furthering these results (**section 5.2**). Then, I will focus
on the pluri-disciplinary statistical comparison of genetic and linguistic variation within the archipelago
(**section 5.3**). Finally, I will present the inter-disciplinary aspects of our ongoing project about joint
genetics-linguistics historical inferences (**section 5.4**). Note that, after 13 years piloting this project, some
of this work has already been published, some other parts are currently (while writing these lines in mid-
2022), under review and released as pre-prints in open access archives, and some are in preparation or still
on the research workbench.
**5.1. The genetic admixture histories of Cabo Verde**

First, we set out to infer the European and African populations having contributed to the gene-pool of extant Cabo Verdeans. Second, we investigated in details the distribution of genetic and admixture diversity at micro-geographical scale, within Cabo Verde. Finally, we reconstructed, using *MetHis*-ABC (see **Chapter 4**), the complex admixture histories of each Cabo Verdean island separately.

*Note: The results described and summarized in this section 5.1 are currently in pre-print format by Laurent et al. (2022), freely available on BioRxiv at https://doi.org/10.1101/2022.04.11.487833. Since April 2022, this article is under review for publication in eLife, and still in progress at the time of writing this chapter in June 2022.*

**5.1.a. European and African populations at the source of Cabo Verdean genetic diversity today.**

As detailed in **Chapter 3**, population genetics methods to infer the possible sources of genetic admixture in a target population do not necessarily rely on explicit mechanistic models of complex admixture histories. Instead, they rely on statistical methods whose goal is to estimate the proportion of the genetic material observed in the admixed population ultimately originating from putative source populations.

Intuitively, these approaches rely on the systematic comparison of allelic or haplotypic frequencies between the target admixed population and, preferably, numerous other worldwide populations. These comparisons aim at identifying, among these extant populations, which ones share most recent common ancestries with each part of admixed individuals' genomes, respectively. These extant populations are then considered as descendants from the proxy source populations for the past admixture processes, which eventually gave birth to the observed proportions of genetic admixture in the target population.

We thus had to merge the 2.5 million SNPs genotyped from the 261 Cabo Verdean individual DNA samples collected during our fieldwork, with previously published data. We decided to merge our novel data with genetic data from 2504 individuals from the 1000 Genomes Project in order to ubicate the genetic diversity patterns from Cabo Verde in the worldwide human genetic diversity. Furthermore, we wanted to compare Cabo Verdean data with extensive population samples from Africa, where enslaved-Africans had been forcibly deported in part to Cabo Verde during the TAST. Thus, we obtained access to the African Genome Variation Project data comprising genome-wide autosomal genotyping data for 1307 individuals from more than 20 Western, Eastern and South African populations (Gurdasani et al. 2015; EGA accession number EGAD00001000959); and further merged the data from Patin et al. (2017), a similar genome-wide genotyping dataset, comprising 1235 individuals mainly from Cameroon, Gabon, and Angola (EGA accession number EGAS00001002078), a later study to which we had contributed extensively in our Central African projects (see **Chapter 2**). We then conducted the quality control and merging procedures, among Cabo Verdean genotyping batches over the six fieldworks first, and then with these previously published datasets, and summarized the pipeline in the **Figure F5.1.a1** below.
<figure><img src="image_55.png" /><figcaption>Figure F5.1.a1.</figcaption></figure>

Quality control and merging procedures for the autosomal genotyping data of Cabo Verde. Figure originally presented as Supplementary Figure SF1 in Laurent et al. (2022), freely available on BioRxiv at https://doi.org/10.1101/2022.04.11.487833, and currently under review at *eLife*.
I will not detail here all the procedures described in this **Figure 5.1.a1**, but it is important for me to
remind here that those are extremely complex and tedious procedures. I believe that, unfortunately, they do
not obtain the recognition they deserve either in publications nor in the human population genetics
community in general. Indeed, these procedures require excellent knowledge of databases produced by
other independent researchers, handling efficiently massive amounts of complex data, and performing, in
principle, simple population genetics checks and calculations rendered non-trivial by nestedness among the
various steps and by the very large amounts of data. I feel that some researchers often under-estimate the
challenges of these quality-control procedures and the numerous bio-informatics skills needed to achieve
them. In turn, this results in often under-estimating the time needed for students to acquire the skills to
perform these procedures, nevertheless at the root of all the data analyses one might endeavor. To my views,
these bio-informatics quality-controls and merging procedures largely replaced, in the human population
genetics training, the place occupied previously by tedious point-by-point DNA extraction, PCR, and old-
generation sequencing molecular genetics procedures needed to generate working data. Indeed, while I
spent roughly 8 months, during my PhD, generating microsatellite variation by hand, and only a few weeks
subsequently thoroughly curating the data, it is not uncommon, today, that massive genome-wide data can
be generated (when DNA samples are available) in a few weeks' time, while curating them often require
numerous months of intense work for untrained researchers.

In this case, I conducted myself the curation of all genotyping data from the different batches, and Dr. Laurent brilliantly conducted all subsequent data merging procedures (Figure F5.1.a1), resulting in a merged working dataset for identifying sources of admixture to the Cabo Verdean gene-pool comprising more than 450,000 autosomal SNPs genotyped in more than 5,157 worldwide samples including 233 family unrelated Cabo Verdeans (see Figure F5.1.a2).

Based on this data, we first explored individual pairwise ASD patterns using MDS 3D projections (see **Chapter 4**), sub-sampling populations, in turn, and recomputing the MDS on the subsampled population set, in order to empirically explore the sets of individuals apparently more closely resembling one another. Ultimately, we found that (**Figure F5.1.a3**), Cabo Verdean individuals were on a different ASD-MDS trajectory between continental European and African individuals than other enslaved-African descendant populations from the TAST in the Americas, represented in our dataset by a sample from USA Afro-Americans (ASW), a sample of Barbadians (ACB), and a sample of Puerto Ricans (PUR). Indeed, we found Cabo Verdeans aligned on a trajectory between individuals from West Western African Senegambia and individuals from South-Western Europe. Instead, we found both Afro-American and Barbadian samples aligned on a trajectory between individuals from West Central African Nigeria and individuals from North-Western Europe. Finally, and again in contrast, we found Puerto-Rican individuals on a trajectory between individuals from Central Western Africa and individuals from South-Western Europe.

These patterns would be expected if these four populations had experienced admixture between
different European and African populations (see **Chapter 3 and 4**). However, it is important to emphasize
here that these real data analyses do not represent a proof of such admixture process. Indeed, dimensionality
reduction in an MDS projection can bias our observations by hiding in higher dimensions preferred genetic
relationships among other groups of individuals. In any case, we describe genetic dissimilarities among real
data in these procedures without formally testing the origins of the observed patterns, which could be
produced by other mechanisms than admixture.
<figure><img src="image_56.png" /><figcaption>Figure F5.1.a2.</figcaption></figure>

Population sample map and interview and birth-place location in Cabo Verde.

Figure originally presented as Figure F1 in Laurent et al. (2022), freely available on BioRxiv at https://doi.org/10.1101/2022.04.11.487833, and currently under review at eLife.
<figure><img src="image_57.png" /><figcaption>Figure F5.1.a3.</figcaption></figure>

Three-dimensional ASD-MDS projections considering the same European and African populations and, separately, Cabo Verdeans, Afro-Americans, Barbadians, and Puerto-Ricans. Symbols can be found in Figure F5.1.b.

Figure reshaped from Figure F2 in Laurent et al. (2022), freely available on BioRxiv at https://doi.org/10.1101/2022.04.11.487833, and currently under review at *eLife*.
We obtained very similar results, as expected, using the maximum-likelihood clustering algorithm implemented in ADMIXTURE (Alexander et al. 2009), for values of K-clusters ranging from 2 to 10 (Figure F5.1.a4).

Indeed, while ADMIXTURE results are more than often over-interpreted in the human population genetics literature (Lawson et al. 2018), this clustering algorithm in fact captures very similar information as the ASD-MDS decomposition and should be interpreted similarly. In fact, ADMIXTURE genotype membership clustering patterns for individuals should be interpreted as levels of genetic similarity among individuals: two individuals showing similar membership proportions patterns are more resembling one another than two individuals showing different patterns. Interpreting membership proportions from a single cluster (color) across individuals exhibiting substantial levels of several colors as “ancestries” or “admixture” is dangerous: “German flags” patterns often indicate unresolved clustering of the individual at this value of K, similarly as when short Euclidean distances in 2D ASD-MDS projections might hide substantial differentiation in higher-order dimensions. In other words, similarly to ASD-MDS, ADMIXTURE recapitulates inter-individual levels of genomic resemblance and dissimilarities, which can be interpreted as admixture levels but which does not formally test for such underlying model (despite the name of the software), and could very well be due to either unresolved clustering or, alternatively, other demographic processes than admixture which may result in similar patterns. This very common misconception was nevertheless extremely explicitly warned against by the original conceivers of these methods since Pritchard et al. (2000) with STRUCTURE, both in their papers and in the software manuals; and equations are un-ambiguous. However, these authors used the word “ancestry” to translate the statistical model they used for inferring levels of genotype membership in a given individual from, in fact, the K virtual genetic clusters built by the method and maximizing allelic frequencies differentiations among all individuals included in the analysis. It seems that numerous anthropological geneticists and human population geneticists misconceived this “virtual ancestry” for a modelized explicit “genealogical ancestry”, which is what everyone is ultimately after including me, when the methods actually do not model such process in their computations.

So, why use STRUCTURE/ADMIXTURE on top of ASD-MDS if they are redundant? In fact, ADMIXTURE allows to visually investigate numerous axes of variation (K) at the same time, which is extremely tedious and confusing when exploring combinations of 2D MDS for numerous axes. So, while MDS provides a better visual intuition of levels of inter-individual genetic differentiation, which STRUCTURE/ADMIXTURE do not provide easily as one does not readily see the “distance” between colors, this latter analysis allows to explore efficiently levels of resemblances from higher-order dimensions and is thus highly complementary for the initial statistical description of genetic diversity patterns in any population genetics data analysis approach.

In our case and in summary (Laurent et al. 2022), indeed, while ADMIXTURE results recapitulated our ASD-MDS decompositions, we can see from K=6, and on, that genetic patterns across several groups of Cabo Verdean islands are in fact substantially differentiated from one another, a pattern barely previously seen in the previous ASD-MDS analyses. At K=10, we can identify three different clusters differentiating individual genetic patterns born respectively in Santiago-Fogo-Brava, Maio-Boa Vista-Sal-São Nicolau, and Santo Antão-São Vicente patterns, thus showing high levels of genetic differentiation at reduced scale within the Cabo Verde archipelago (see **section 5.1.b**). These results and the previous ASD-MDS ones thus significantly expanded previous results obtained at much larger continental scale (Belezza et al. 2012; Verdu et al. 2017).
<figure><img src="image_58.png" /><figcaption>Figure F5.1.a4.</figcaption></figure>

ADMIXTURE analysis for K between 2 and 10, based on 1,369 individuals genotyped at 102,543 independent low-LD SNPs.
Figure originally from Figure F3 in Laurent et al. (2022), freely available on BioRxiv at https://doi.org/10.1101/2022.04.11.487833, and currently under review at *eLife*.
So, are these observed patterns indeed stemming from admixture between the specific continental African and European populations? To answer this question, we conducted local-ancestry explicit inferences implemented in the ShapeIT2-Chromopainter-Globbetrotter-SourceFind pipeline (Lawson et al. 2012; Delaneau et al. 2012; Hellenthal et al. 2014; Chacón-Duque et al. 2018). This analysis relies on inferring by a formal testing, the most recent shared haplotypic ancestries within a targeted individual phased genome, with specific populations among a set of putative sources chosen by the user. Here, we decided to consider all populations from the ADMIXTURE analysis (Figure F5.1.a4) as possible sources for the haplotypic patterns of each Cabo Verdean individual genomes, considering either 4 putative sources or six putative sources, and then summarized results for individuals within islands of birth in Cabo Verde (Laurent et al. 2022; Figure F5.1a5).

<figure><img src="image_59.png" /><figcaption>Figure F5.1.a5.

Shared haplotypic inference of putative source populations for the genomic patterns of Afro-American, Barbadian, and Cabo Verdean individuals born in different islands of the archipelago. Figure originally from Figure F3 in Laurent et al. (2022), freely available on BioRxiv at https://doi.org/10.1101/2022.04.11.487833, and currently under review at eLife.</figcaption></figure>

We thus found somewhat surprising and unexpected results to our first question: where did the genetic ancestors of Cabo Verdeans come from in continental Europe and Africa?

First (Laurent et al. 2022), despite numerous voluntary and forced migrations from Europe and Africa to the Cabo Verdean archipelago since the establishment of the colony in the 1460’s, throughout the TAST era and the Portuguese colonization, and until today, only a very reduced number of these populations contributed to the genetic diversity of Cabo Verdeans today. Indeed, Cabo Verdeans from all islands almost exclusively share ancestry with extant Iberian populations from South Western Europe, with some very limited contributions from Italians and British populations in a couple islands. On the African side, Cabo Verdeans almost exclusively share recent haplotypic ancestries with certain Senegambian populations, Mandinka and Wolof-speakers, the latter of the two proxy source populations being only found substantially in São Vicente and Santo Antão and to some extent in São Nicolau and Sal. Finally, albeit Western Central and Angolan populations are known to have been forcibly displaced to Cabo Verde during the TAST, even leaving unquestionable linguistic contributions to Caob Verdean Kriolu, these populations only left a very limited genetic signatures only in certain Cabo Verdean islands. Altogether, our results show a fascinating discrepancy between historically known extensive demographic movements to Cabo Verde from all over the Atlantic coast of Africa and from several European regions, and genetic contributions from only certain populations from the, small and nearby Cabo Verde, Senegambian region.
In contrast, our analyses for the Afro-American and Barbadian samples from the 1000 Genomes project showed, similarly to previous studies, much more substantial contributions from various populations from West Western Africa until Angola, hereby qualitatively reflecting, in the case of these two population samples, what could have been expected from extensive historical demographic migrations during the TAST (Laurent et al. 2022).

Finally, we further identified in this analysis (and other in Laurent et al. 2022), significant variation in relative admixture proportions from either source across individuals born on different Cabo Verdean islands, further echoing the different clustering patterns across islands found with the ADMIXTURE analysis.

**5.1.b. Isolation-By-Distance genetic patterns at reduced geographical scale within the archipelago.**

Together with Pr. Szpiech, we thus set out to investigate in more details genetic diversity patterns within individuals born in Cabo Verde only, using patterns of Runs-Of-Homozygosities (Szpiech et al. 2019), ASD-MDS (Figure 5.1.b), and Mantel and partial-Mantel testing. In brief (Laurent et al. 2022), we found substantial genetic differentiation across individuals from different islands, a priori irrespective of inter-island distances. Furthermore, we found ROH patterns also substantially differing across islands but all indicating substantial reproductive isolation within islands. Finally, together with Mantel testing, we found significant substantial genetic Isolation-By-Distance at very reduced geographical scale (Rousset 1997, and see the end of Chapter 2.2.b), among Cabo Verdean-born individuals across, and even within, islands of birth. The strength of this signal was also of some surprise to us, as sampled individuals reported, in our anthropological interviews, extensive exploratory mobility across Cabo Verde, mainly for familial, professional, religious, or other social reasons. Furthermore, historians had also reported extensive population movements across islands throughout the history of Cabo Verde. However, again, these demographic movements do not seem to have entirely resulted in the expected homogenization of genetic diversity across Cabo Verdean islands, as we found, instead, substantial differentiation resulting from long-term reproductive isolation strongly anchored in birth places. Finally, these patterns were also consistent, in particular ROH patterns, with possible deviations from random-mating locally, albeit we did not formally test this hypothesis, yet (see section 5.2 below).

We also investigated specifically the distribution of admixture patterns across individuals' islands of birth. While we did not find a specific geographic distribution of admixture patterns correlated with island proximity throughout Cabo Verde, we nevertheless found that, overall, two individuals born far away from one another, had higher chances of exhibiting more differentiated admixture patterns than two individuals born nearby. Furthermore, investigating ancestry-specific ROH patterns (ROH specifically within African or European admixed genomic segments respectively, or ROH spanning ancestry break-points), we found some differences across islands, indicating that, for some islands, admixture likely predated the subsequent reproductive isolation across islands.

Altogether these results were strongly indicative of a serial founder model followed by relative reproductive isolation across islands in the Cabo Verdean archipelago. Furthermore, our results were consistent with certain islands being initially peopled by individuals already admixed between African and European source populations (Laurent et al. 2022).
ASD-MDS within Cabo Verde
from 1,899,878 autosomal SNPs

<figure><img src="image_60.png" /></figure>

<figure><img src="image_61.png" /></figure>

ROH within Cabo Verde
from 455,705 autosomal SNPs

<figure><img src="image_62.png" /></figure>

<figure><img src="image_63.png" /></figure>

<figure><img src="image_64.png" /></figure>

225 Cabo Verdeans’ birth-places

<figure><img src="image_65.png" /><figcaption>Figure F5.1.b.</figcaption></figure>

3D ASD-MDS projection within Cabo Verdean born individuals and ROH patterns. Note that ASD-MDS projections have been procrusted to the actual geographical coordinates of individuals’ birth-places.

Figure adapted from Figure F4 and F5 in Laurent et al. (2022), freely available on BioRxiv at https://doi.org/10.1101/2022.04.11.487833, and currently under review at *eLife*.
5.1.c. Complex admixture histories for each Cabo Verdean islands reconstructed with *MetHis-ABC*

On these bases, we set out to reconstruct the detailed admixture histories of each Cabo Verdean island separately from independent autosomal SNPs and using the *MetHis-ABC* framework described in the previous **Chapter 4**. We found out that the European and African admixture patterns in Cabo Verde stemmed from very few populations from either continent, and that the African contributions mostly stemmed from genetically very close populations only differentiated using extensive haplotypic information rather than a more limited SNP set. Therefore, we deemed reasonable in our case with *MetHis-ABC* to simplify possible admixture histories by considering only a two-source population model with the South-Western European Iberian IBS and the Senegambian African Mandinka at the root of the admixture process for each island.

We thus conducted 9 independent *MetHis-ABC* full-blown inference procedure, each corresponding to genetic data from individuals' born on the nine Cabo Verdean islands represented in our dataset. We considered four competing scenarios represented in **Figure 5.1.c1** below, for which detailed descriptions can be found in Laurent et al. 2022 at https://doi.org/10.1101/2022.04.11.487833, following the same parameter descriptions previously detailed in **Chapter 4**.

<figure><img src="image_66.png" /><figcaption>Figure F5.1.c1.</figcaption></figure>

Four competing complex admixture models inferred with *MetHis-ABC* for each 9 Cabo Verdean island, separately.
Figure adapted from Figure F6 in Laurent et al. (2022), freely available on BioRxiv at https://doi.org/10.1101/2022.04.11.487833, and currently under review at *eLife*.

Scenario 1 considered two possible independent admixture pulses from both European and African source populations, after the founding pulse at generation 0. Scenario considered the same scenario of 3 admixture pulses from the African source, and a scenario with a period of recurring admixture of any short or long duration from the European source. Indeed, we here draw the onset and offset time for this period randomly within the 20 generations of the admixture process after the founding admixture pulse. Note that, also differing from models in Chapter 4, we here considered possible constant recurring admixture processes rather than strongly constraining a decrease in recurring admixture as we did in **Chapter 4**. Scenario 3 considered the same scenarios as the Scenario 2, but exchanging source populations of origins.
Finally, Scenario 4 considered a possible period of recurring admixture independently from either source, after the founding admixture pulse. Finally, also complexifying from previous competing scenarios in Chapter 4, we wanted here to evaluate the influence of changes in the reproductive sizes within the admixed population on our inferences. Therefore, we implemented a possibly increasing reproductive size within the admixed population with founding parameter values drawn in a Uniform [10, 1000] distribution, and in Uniform [100, 100,000] in the present, reproductive sizes in between being the numerical solution of a rectangular hyperbola function scaled between beginning and end sizes of “steepness” parameter *u* drawn in [0, 0.5].

Therefore, note that scenario nestedness was high in substantial spaces of parameter-values, more nested a priori than in our initial proof of concept (Fortes-Lima, Laurent et al. 2021). However, as we demonstrated then and explained in Chapter 4, our goal was not to test whether we were able to always discriminate among these subtle different complex models, but rather focus on the vicinity of our observed data. Furthermore, as we also demonstrated, nestedness and scenario-choice confusion with *MetHis-ABC* occurs in spaces of parameter values where scenarios are, in fact, biologically equivalent concerning the admixture mechanistic process.

Therefore, our competing scenario design aimed at identifying qualitatively whether highly complex models, with three pulses of admixture from either source, or if periods of recurring admixture, or combinations of both scenarios, best explained our observations on each Cabo Verdean islands separately. Under the winning model for each island, we would then estimate posterior parameter distributions, and contrasting the obtained genetic inference with historical data on the peopling history of each island that we compiled based on numerous historical previous publications, to propose possible explanations for the inferred admixture mechanisms reconstructed from genetic data.

Based on these scenario definitions, we conducted *MetHis* simulations and subsequent ABC Random Forest scenario-choice and ABC Neural Network posterior parameter estimations similarly as presented in **Chapter 4**, for each island of birth of Cabo Verdean individuals separately.

Our Random Forest Scenario-choice procedures favored scenarios comprising three pulses of admixture from both European and African sources for explaining genetic diversity patterns in 6 Cabo Verdean islands out of 9. Furthermore, we found that a scenario encompassing pulse-like models from the African source and, instead, a period of recurring admixture from the European source were favored in two islands out of nine. Finally, our inferences favored a scenario with pulses of admixture from Europe and a period of recurring admixture from Africa in a single island out of nine. Finally, note that Scenario 4, comprising a period of recurring admixture from both Africa and Europe, was the least favored model by Random Forest ABC inferences for all nine Cabo Verdean islands (Figure F5.1.c2).

Based on the winning scenario for each island, we then performed *MetHis* 100,000 simulations and conducted nine separate ABC Neural Network posterior parameter inferences (Figure F5.1.c3), following the same methodology as in Fortes-Lima, Laurent et al. 2021, described in Chapter 4.
<figure><img src="image_67.png" /><figcaption>Figure F5.1.c2.</figcaption></figure>

Random Forest ABC scenario-choice for the four competing complex admixture models inferred with *MetHis-ABC* for each 9 Cabo Verdean island, separately.
Figure adapted from Figure F7 in Laurent et al. (2022), freely available on BioRxiv at https://doi.org/10.1101/2022.04.11.487833, and currently under review at *eLife*.

In parallel, we explored the historical literature about the peopling history of Cabo Verde, with a
specific focus on census data on each island gathered by historians based, among others, on Royal decrees,
real estate contracts and notary legacy deeds, parish establishments and records, tax and commercial
records, and later, official demographic census. We reported our detailed findings associated with precise
references from this literature outside of my disciplinary fields, in an extensive table in Supplementary
Table ST2 from Laurent et al. 2022.

Based on these previous studies, we thus determined the historical date for the first administrative
official settlement of each island separately, and also the first historical date when records unquestionably
showed a perennial number of residents above 100 individuals until today, i.e. the first time in Cabo Verde
history when 100 individuals reside on a given island and henceforth never got below this number until
today. Finally, also based on historical records, we determined that the height of TAST, corresponding to a
sharp demographic increase of enslaved-Africans deportations started in the mid-17th century until the
abolition of the TAST in the early 19th century (see Fortes-Lima and Verdu 2021 for a review of
anthropological genetics perspectives on the TAST, and see below). This period corresponds to the
expansion of Plantation Economy, and the concomitant shift from a Society with Slaves to a Slave-Society
(Berlin 1998; Chaudenson and Mufwene 2001), in the Caribbean and the Americas as well as in Cabo
Verde and São Tomé e Principe, this latter archipelago of the Bight of Biafra being the original testing-
grounds of this economic system. Finally, we defined a transition period during the 19th century
corresponding to the period between the abolition of the TAST and that of slavery throughout the entire
Portuguese empire in 1878. Note that the independence of Cabo Verde from Portugal was enacted in 1975
(Albuquerque et al. 1991; Carrera 2000).
<figure><img src="image_68.png" /><figcaption>Figure F5.1.c3.</figcaption></figure>

Neural Network ABC posterior parameter estimates for the complex admixture history of each Cabo Verdean island inferred, separately, with *MetHis-ABC*.

Median posterior point-estimates are provided with corresponding 50% Credibility Intervals. Parameters posterior distributions departing little from their priors and that our inference thus failed to identify are reported in gray.

Historical periods stem from historical records as described in text.

Top panels correspond to Reproductive Size parameters only and bottom panels to the admixture process itself, albeit posterior estimations were conducted jointly for all model-parameters.

Figure adapted from Figure F7 in Laurent et al. (2022), freely available on BioRxiv at https://doi.org/10.1101/2022.04.11.487833, and currently under review at *eLife*.

Further refer to this publication for full-blown posterior distributions for each parameter and each island.
The interested reader should refer to Laurent et al. 2022 and Supplementary Text herein, for extended
discussions of these results for each Cabo Verdean island separately in the light of historical knowledge
about the peopling of each island. Here, we briefly synthesize and discuss these results at the scale of the
Cabo Verde archipelago.

First (Figure F5.1.c3, top panels), our *MetHis-ABC Neural Network* posterior parameter estimations showed that Cabo Verdean reproductive sizes experienced a sharp increase very recently in the 20th century for almost every island, except Santiago, the historical economic and administrative center of Cabo Verde since the establishment of the colony in the 1460’s, where reproductive size increase is estimated to have been more linear (and with the exception of São Nicolau where we largely failed to identify these parameters). These result echo historical reports of the difficult perennial peopling of Cabo Verde until as late as the 19th century on Sal, and the often large time-lapse between the first administrative establishment of an island and the first census perennially above 100 settlers until today in certain islands. Such difficult settlement and low attractiveness of the islands were due mainly to the dry Sahelian climate with overall difficult access to fresh-water surface resources<fnref n="50" />, to chronic droughts triggering massive famine outbreaks accompanied by deadly epidemic outbreaks, to shifting political dominion and changing commercial opportunities, to piracy disrupting the fragile settlements during the 17th and 18th centuries, and to the distance to continental Europe. Interestingly, historical records show the repeated strong incentives to settle Cabo Verde issued regularly by the Portuguese crown between the 15th and early 17th centuries, delivering extremely liberal commercial opportunities and freedom to emigrants to the new colony, in the hope to maintain the Portuguese dominion on the archipelago which occupied a primary geostrategic position in the commercial routes between the three continents (Albuquerque et al. 1991; Carreira 2000).

Concerning the admixture histories themselves (Figure F5.1.c3, bottom panels), note first that only Santiago, Santo Antão, and to a less clear extent, São Nicolau, show the first identifiable admixture event synchronically to the first peopling of an island. All other islands show founding admixture events occurring before, sometime long before, the historical dates indicating perennial peopling. Thus, together with ROH results presented above (section 5.1.b), this indicated that Cabo Verdean islands, apart from the three islands cited above, were initially settled by already admixed individuals coming from other Cabo Verdean islands. Second while we identify numerous admixture events between the 1460’s and the 1650’s from either Europe or Africa separately, or more synchronically from both continents, we massively fail, for all islands, to infer significant admixture events during the height of the TAST between the 1650’s and the early 19th century. This indicated very tenuous information in genetic data for identifiable admixture events from this entire period, when in fact far most enslaved-Africans were brought to the archipelago, and when European emigration waves were also substantial. Furthermore, note that during the period between the abolition of the TAST and that of slavery, we precisely estimate (with posterior estimations sharply departing from their priors, reduced 95% Credibility Intervals, and low error rates), once again, numerous admixture events from either Africa or Europe depending on the island. Finally, we identify several precise events of admixture during the 20th century, with low intensities, albeit very accurately inferred with our procedures.

Altogether, these results, together with local ancestry inferences’ results in **section 5.1.a** and ROH results in **section 5.1.b**, are consistent with a very limited contribution of the numerous deportations from all over Africa to Cabo Verde, during the height of the TAST (Figure F5.1.c4), to the genomic diversity and genetic admixture histories of the archipelago. This could be due to two, non-mutually exclusive

<footnote n="50">⁵⁰ Cabo Verde is named in reference to the proximity of “Cap Vert” in Senegal, rather than because it is “green”, which is in fact very far from reality during most of the year on the archipelago</footnote>
phenomenon. First, it is plausible that during this era, most enslaved-Africans deported to Cabo Verde only
transited via the archipelago before being re-deported to the Americas and the Caribbean mainly, and to
Europe to a much lesser extent. It has been long-debated by historians whether Cabo Verde during this era
was, indeed, only a transit platform, or whether enslaved-Africans stayed for much longer amounts of time
or indefinitely. Our results cannot conclude to either hypothesis (see below), but we can confidently assess
that, whichever the duration of the stay in Cabo Verde of these individuals, they probably did not
substantially contribute to the genetic diversity of the archipelago until today. Note however, that the
contribution of these migration waves to the cultural diversity of Cabo Verde is unquestionable as Cabo
Verde Kriolu still bears traces of the influence of numerous languages from continental Africa from
Senegambia to Angola (Quint 2000; and see also **section 5.3** below).

We cannot literally conclude to settle this demographic and historical debate, as we, in population
genetics, only witness genetic (and not genealogical) histories of extant populations, and not their life-
history of mobility. As such, another, non-mutually exclusive, socio-cultural process may very well explain
the observed genetic and admixture patterns, irrespective of the duration of the stay of enslaved-Africans
on the archipelago.

Historians often describe the shift from a “Society with Slaves” to a “Slave-Society” with the expansion of the Plantation Economic system in the mid-1650’s (Berlin 1998; Chaudenson and Mufwene 2001; Eltis and Richardson 2015). This period would be characterized by major socio-cultural changes, and, in particular, concerning marital and reproductive relationships between enslaved and non-enslaved communities, as well as within enslaved-communities between newcomers and previously established enslaved-African descendant communities. Indeed, this historical era was characterized by the emergence, everywhere throughout colonial empires, of legally enforced socio-marital segregations strongly forbidding marriages between enslaved and non-enslaved communities, as well as providing legal support to slave-owners for controlling unions among enslaved individuals. This is best exemplified by the enactment of the Code Noir in French colonies in the Americas (which represented at the time vast regions of the Caribbean and most of the North American continent) in 1685 and its strict enforcement from the early 18th century and on, whether under the French rule or not. Similar legally enforced changes also occurred in Cabo Verde, as exemplarily demonstrated in the seminal work of Carreira (2000).

This period also saw the normalization of racist discriminations and increase in brutal dehumanization and violence exerted in particular towards enslaved-Africans, notwithstanding the undeniable horrors committed during slavery prior to this era. Importantly, this Plantation Economy era also corresponds to a demographic shift with massive deportations of enslaved-Africans (**Figure F5.1.c4**), accompanied punctually and locally, by immigration waves (forced or voluntary) from Europe, resulting in global increases of non-native population censuses in European colonies on both sides of the Atlantic (Eltis 2002; Fortres-Lima and Verdu 2021).

Therefore, it is plausible that these major socio-cultural shifts regarding relationships between enslaved and non-enslaved communities triggered with success marital and reproductive isolation among communities and therefore, prevented substantial admixture to occur; despite massive demographic movements putting into contact previously genetically isolated continental European and African populations, and notwithstanding known dramatic sexual abuses exerted by non-enslaved individuals on enslaved individuals. Furthermore, the increased control of slave-owners on marital and reproductive relationships within enslaved communities might have further prevented admixture between newcomers and previously established enslaved-communities, thus also further limiting genetic admixture events. Note,
finally, that such process could have effectively limited genetic admixture, irrespective of the duration of
the stay of enslaved individuals, locally.

<figure><img src="image_69.png" /><figcaption>Figure F5.1.c4.</figcaption></figure>

Historical records for cumulated estimates of enslaved-Africans demographic forced deportation from Africa and disembarkations in the Americas and the Caribbean, based in part on Eltis and Richardson 2002. Source tables openly available at https://www.slavevoyages.org/estimates/L8KDKZCH.

The massive expansion of the Plantation Economic system, tested first in São Tome e Principé, and rapidly implanted in the Americas and the Caribbean as well as European colonies in Africa in the first half of the 17th century, triggered the massive expansion of enslaved Africans deportations until the abolition of the TAST starting in 1807. The 19th century still witnessed extensive enslaved-African deportations, often illegal, until the effective abolition of Slavery in European colonial empires in the second half of the 19th century.

Figure previously published in Fortes-Lima and Verdu *Human Molecular Genetics* 2021.
In this context, the observed genetic patterns today and detailed genetic inference results would be highly consistent with the following schematic historical scenario:

1460's-1630's: Initial settlement of European migrants in the island of Santiago, low in numbers and strongly sex-biased towards males. Deportations of enslaved-Africans to Cabo Verde, including women, forced to participate in the building of the colony. Rapidly, as witnessed by historical records (see Chapter 1.2), admixture between European males and enslaved-African females occurred and significantly contributed to the genetic make-up of the new colony. Further enslaved-African deportations, massively from the nearby Senegambia, contributed, via admixture with pre-existing populations, to the genetic diversity of the archipelago. During this period, successive settlements of other islands either readily involved admixed individuals or rapidly funded anew admixed populations via the same process as described in Santiago.

1630's-1800's: Normative and prevalent socio-cultural barriers strongly segregating enslaved and non-enslaved communities deployed with the expansion of Plantation Economy, and intensified enslaved-African deportations, would have successfully prevented admixture between communities as well as between the varied newly arrives continental African populations and pre-existing slave-communities mainly of Senegambian origins.

1800's-1875's: the abolition of the TAST and that of slavery triggered numerous illegal enslaved-African deportations to Cabo Verde (and elsewhere). In parallel, socio-cultural changes in European colonies led to these abolitions and further echo profound societal changes in the relationships between enslaved and non-enslaved communities, despite segregation and racism being durably socio-legally established in most colonies. Nevertheless, these changes could have triggered, once again, substantial admixture events as identified in our analyses.

1875's-present day: Strong demographic population expansion of Cabo Verde limited the influence of novel admixture events to strongly modify populations' genetic diversity patterns in Cabo Verde. Nevertheless, we identified significant and very accurate; albeit reduced in intensity, admixture events from either Europe and Africa, possibly related to known historical workforce and economy-related migrations among the former Lusophone empire colonies in Africa and beyond, in particular in the 20th century.
**5.2 Ongoing work and perspectives for section 5.1: the genetic admixture histories of Cabo Verde.**

Future work will need to evaluate the plausibility of this detailed socio-historical causes for explaining the observed genetic admixture processes here described. Indeed, population genetics can only infer the “how”, but not directly explain the “why”. However, such hypothetical, but likely, scenario would imply strongly sex-biased admixture processes in a strongly stratified society, possibly both mechanisms being dynamic over time. We did not evaluate such possibilities, as our approaches only consider non-sex-specific admixture processes and random matting in the admixed population (see **Chapter 4**).

We are currently, with my PhD student Marta Ciccarella, developing a sex-specific version of *MetHis* and further parameterizing non-random matting. This will soon allow us to formally test the above hypotheses, and evaluate whether the genetic expectations of this history of socio-cultural changes may match, or not, the genetic patterns observed today. Note, however, that investigating non-random matting processes in a very small census population is not conceptually trivial, as mate choices are already limited.

Importantly in this dialectical approach, finding a match would not validate that the socio-historical scenario here proposed is indeed causative of the observed genetic and admixture patterns. However, finding a mis-match between genetic expectations under such historical causative model and observed genetic patterns would allow us to formally reject this particular historical process as causative; a result which would further require building novel hypotheses for future testing.

Furthermore, Ms Ciccarella is also in the process of reconstructing the admixture histories for São Tomé e Principe, using the same type of genome-wide genotyping data that I generated anew thanks to a collaboration with Pr. Jorge M. Rocha (CIBIO-University of Porto). Indeed, Pr. Rocha sampled São Tomé e Principe populations more than twenty years ago for reconstructing the genetic peopling history of this Central African archipelago. We decided that I would genotype remaining DNA extracts from these collections and that Pr. Rocha would co-supervise Ms Ciccarella’s PhD, in order to widen our questions of interest to compare the detailed admixture histories at play separately in the two former Portuguese peopling colonies in Africa. Furthermore, this ongoing project will allow us to investigate migrations and diasporas between the two archipelagos. Indeed, there is still a substantial community of Cabo Verdean descendants in São Tomé e Principé, due to known ancient and recent workforce and economic migrations, as also witnessed in my anthropological questionnaires within Cabo Verde about individual mobility histories. This project will likely soon further our understanding of the influence of historical socio-cultural dynamics on genetic diversity and admixture patterns in human populations, in the context of the TAST.

Finally, in addition to the methodological perspectives for *MetHis*-ABC developments presented previously in **Chapter 4**, this Cabo Verdean study highlights the empirical need to evaluate the sensitivity of our inferences to the use of extant populations as proxy source populations. Indeed, a long-standing question in admixture statistical or mechanistic inferences regards the influence of how far genetically the proxy population may be from the real population at the source of admixture in the past, before strongly biasing the results of our analyses. To formally test these issues empirically, we are currently testing several cases with *MetHis* by, *i*), comparing inference results obtained using different source populations, or, *ii*), combining samples from different populations, including the Cabo Verde admixed one, in a single source; or, *iii*), by using, instead of observed genetic data, explicit sequential-coalescent simulations for producing source populations genetic data. We started to conduct this methodological-empirical project with Dr. Laurent and Dr. Toupance in the lab, and Dr. Laval and Dr Patin at the Pasteur Institute.
**5.3. Parallel trajectories of genetic and linguistic admixture histories of Cabo Verde**

Our initial project was not only to reconstruct the genetic admixture of Cabo Verde, but investigate also a
certain type of linguistic diversity: individual utterances in the context of semi-spontaneous discourses
conducted by academic researchers, across individuals for which we had sampled linguistic, genetic, and
anthropological data (see **Chapter 1.2**, and introduction to **Chapter 5**). As said, this was thus a pluri-
disciplinary project in nature as, despite having built the project and conducted sampling in a largely
interdisciplinary way, subsequent analyses and problematics were disciplinarily and conducted separately
between genetics and linguistics, and only then results would be compared between disciplines.

In this context, we had gathered extensive semi-spontaneous discourses, fully transcribed in the
morpho-phonetic orthographic norm for writing Cabo Verdean Kriolu (ALUPEC), a tedious task requiring
extensive linguistic skills, mostly conducted by Sergio S da Costa over the years. Based on this data, I
aimed at investigating linguistic utterance diversity patterns across individuals, as a function of the
anthropological co-variables such as residence location, birth-places and parental and grand-parental birth-
places, academic education duration, age, and sex.

Furthermore, I initially thought that linguistic expertise could establish a form of qualitative distance
between individual utterances and African and, respectively, European source languages. Indeed, as
creolists already referred to complex concepts such as “basilectal” or “acrolectal” Kriolu language variants,
I candidly thought that these concepts designated a form of relative distance to, respectively, African or
European languages. With such index, I could compare linguistic variation across individuals with genetic
admixture patterns, and evaluate whether linguistic and genetic patterns matched or mismatched (as, at the
population scale, in the Central African example in **Chapter 2**), and further elaborate on the different
cultural and biological histories of admixture in Cabo Verde.

In 2011, drowning in post-doctorate life juggling with multiple projects and job-applications, I was
immersed in the genetic analyses of the project and could not devote all the time required to explore the
possibilities of the linguistic data we had gathered on the field. Luckily for me, a theoretical population
genetics PhD student supervised by Pr. Rosenberg, Dr. Ethan M. Jewett, was working on a side project
about N-grams and linguistic diversity within the Anglo-American language. Discussing my Cabo Verde
project with him, he proposed to help us with parsing and describing the linguistic data we had already
gathered. His help was seminal to our project, and we published together, in 2017, the first article, to our
knowledge, about genetic-linguistic descriptive comparisons among the same individuals at the within-
population and language scale, for a preliminary sample of 44 Cabo Verdean individuals collected in the
2010 and 2011 fieldworks. To further help Dr. Jewett to better represent the pluri-disciplinary challenges
of our project and data, he came to observe our fieldwork sampling with Pr. Baptista, Sergio da Costa, my
students, and myself, for the 2014 and 2016 fieldtrips. Dr. Jewett soon-after left academic research for
private research in bio-informatics, and, several years after the initial publication, we could replicate and
expand our initial methods to the full Cabo Verdean dataset, confirming that our results were not due to
small preliminary sample sizes, and furthering them and their interpretations. I will, henceforth, synthesize
these results published preliminarily in Verdu, Jewett et al. (2017), and in full in Laurent et al. (2022), pluri-
disciplinarily alongside the genetic history of admixture detailed above in **section 5.1**, freely available on
BioRxiv at https://doi.org/10.1101/2022.04.11.487833, and currently under review at *eLife*.

We first parsed all individuals' semi-spontaneous discourses of Kriolu "spoken every day" (see
**Chapter 1.2**), by considering various types of morpho-phonetic or syntactic variation of the same lexical
root transcribed explicitly in ALUPEC as different itemized "utterances"; and excluded onomatopoeia,
names and interjections from parsing. Note that we kept as different uttered items several, very rare, English or French words that fitted our definition of utterances (Chapter 1.2).

This parsing resulted in a total of 92,432 uttered items across the 225 genetically unrelated individuals born on Cabo Verde in our dataset, out of which 4,831 were unique. We then simply counted the occurrence of each unique uttered item for each individual discourse, and then calculated the relative frequency of usage of each unique item in an individual’s discourse: his/her manners of uttering Kriolu in this context. Finally, we computed from there the individual pairwise matrix of Euclidean distances between unique utterances’ relative frequencies, a measure of inter-individual linguistic distances between individuals’ manners of speaking Kriolu (Figure F5.3.a). Note that, again (Chapter 1.2), we did not decide whether individual’s spoke expert Kriolu or no Kriolu at all: individuals just self-reported speaking Kriolu and we asked them to speak in their own Kriolu from every day, for instance, that they spoke at home. Furthermore, we did not interrupt people during the narration, and fully transcribed them even in the cases (rare, but not exceptional either) when participants drifted to speak about something completely different.

## Individual X's uttered discourse:

N ta lenbra sin. Na kel filmi, kel rapas staba xintadu si.. kel rapas panha kel masan...kel masan e ruma si... e ruma-l asi... e dura ku el ta ruma asi... dipos di kel, e staba xintadu pa si... e tene si kel otu kolega ki staba di la... aian, N odja si...

[etc.]

## Individual X's corresponding unique utterance count:

<table><thead><tr><th>N</th><th>ta</th><th>lenbra</th><th>sin</th><th>na</th><th>kel</th><th>filmi</th><th>rapas</th><th>staba</th><th>xintadu</th><th>si</th><th>panha</th><th>masan</th><th>e</th><th>ruma</th><th>ruma-l</th><th>[etc.]</th></tr></thead><tbody><tr><td>2</td><td>2</td><td>1</td><td>1</td><td>1</td><td>7</td><td>1</td><td>2</td><td>3</td><td>2</td><td>4</td><td>1</td><td>2</td><td>5</td><td>2</td><td>1</td><td>[etc.]</td></tr></tbody></table>

## Individual unique utterances' relative frequencies:

<table><thead><tr><th></th><th>a</th><th>á</th><th>abakate</th><th>abakáti</th><th>abaná</th><th>ábitu</th><th>ábitus</th><th>abizu</th><th>abô</th><th>abo</th><th>abon</th><th>aboreside</th><th>abra</th><th>abrí</th><th>abri</th><th>[etc.]</th></tr></thead><tbody><tr><td>CV2018_BOR_04</td><td>0.00214</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>CV2018_BOR_05</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>CV2018_BOR_06</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>CV2018_BOR_07</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.00146</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>CV2018_BOR_08</td><td>0.00203</td><td>0</td><td>0</td><td>0.00203</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>[etc.]</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

## Pairwise matrix of inter-individual Euclidean distances between vectors of uttered relative-frequencies:

For each pair of individuals *i* and *j*, and for *L* = 4831 unique uttered items:

$$d(f_i, f_j) = \sqrt{\sum_{l=1}^{L} (f_{i,l} - f_{j,l})^2}$$

<caption>Figure F5.3.a.</caption>

Schematic representation of the parsing of semi-spontaneous discourses collected for each sampled individual in Cabo Verde, further counting unique utterances and individual unique utterance relative frequencies from which we compute a pairwise Euclidean distance matrix of linguistic variation. Method originally presented in Verdu, Jewett et al. *Current Biology* 2017, and further described in Laurent et al. (2022), freely available on BioRxiv at https://doi.org/10.1101/2022.04.11.487833, and currently under review at *eLife*.
We described the obtained matrix of pairwise utterance distances using similar statistical-approach and tools as previously for genetics descriptions at the within Cabo Verde level, and could thus juxtapose results alongside the obtained genetics patterns for comparison.

First, we conducted MDS projections of this utterance Euclidean distance matrix (Figure F5.3.b), which showed that the first axis of variation interestingly showed a gradient of inter-individual distances separating, apparently, different manners of speaking Kriolu across individuals born on different islands. The second axis distinguished mostly across certain individuals within Cabo Verde. Furthermore, considering instead the first and third axes, further revealed clearly differentiated manners of speaking Kriolu among individuals born on different islands, or groups of islands, with some striking similarities with what we had identified in the first three axes of the genetic ASD-MDS for the same individuals. Indeed, we found substantial differentiation between Santiago-born individuals' manners of speaking Kriolu from that of Fogo-, Maio-, and Brava-born individuals, differing from Santo Antão- and São Vicente-born individuals, and with São Nicolau-, Boa Vista-, and Sal-born individuals being at intermediate distances between the three groups. To some extent, such patterns of linguistic differentiation was expected by linguists having investigated the different Kriolu linguistic variants, although they did not formally evaluate inter-individual distances in manners of realizing in utterances these different language variants, as this was not their object of study (Chapter 1.2).

Investigating in details Mantel correlations between utterance distances and the corresponding distances between individuals' anthropological co-variables, we found that differences in manners of speaking Kriolu were significantly positively correlated with age differences: as classically expected in socio-linguistics, tow individuals with major age differences speak, on average, in a more differentiated way than two individuals closer in age. Beyond this result, we found, surprisingly that differences in utterance frequencies significantly correlated with geographic distances among individual birth-places within Cabo Verde (Spearman rho = 0.2855, two-sided Partial-Mantel p<2.10⁻⁴, correcting for age differences). Furthermore, we found significant positive correlations also with paternal and maternal birth-places' distances respectively. Finally, we did not find correlations wit other co-variables such as residence locations (people do not speak Cabo Verdean variants according specifically to where they live), nor with education duration (people do not speak Cabo Verdean variants according to whether they receive longer or shorter academic education).

Altogether (Verdu, Jewett et al. 2017; Laurent et al. 2002), these results indicated, first, that, similarly to genetic differences, individual manners of speaking Kriolu as captured by differences in utterance frequencies, are for a significant part anchored in individual birth-places: two individuals born far apart in Cabo Verde are more likely to speak Kriolu in different ways than two individuals born close-by. Furthermore, correlations with parental birth-places, as well as in our 2017 publication, with grand parental birth places, demonstrated that at least a part of inter-individual variation in manners of speaking Kriolu is transmitted from one generation to the next, via anchorage in birth-places.
<figure><img src="image_70.png" /><figcaption>MDS of Euclidean distances btwn.
individual utterances' frequencies
from 4831 unique utterances</figcaption></figure>

<figure><img src="image_71.png" /></figure>

<figure><img src="image_72.png" /></figure>

<figure><img src="image_73.png" /><figcaption>225 Cabo Verdeans' birth-places</figcaption></figure>

<figure><img src="image_74.png" /><figcaption>Figure F5.3.b.</figcaption></figure>

3D ASD-MDS projection within Cabo Verdean born individuals as in Figure 5.1.b above. 3D MDS of the matrix of pairwise Euclidean distances between individual utterance frequencies as in Figure F5.3.a
Note that all MDS projections have been procrusted to the actual geographical coordinates of individuals’ birth-places.
Figure adapted from Figure F5 in Laurent et al. (2022), freely available on BioRxiv at https://doi.org/10.1101/2022.04.11.487833, and currently under review at eLife.
Based on these results, we further aimed at comparing genetic and linguistic admixture patterns. However, my initial thoughts of establishing an individual “basilectal” or “acrolectal” score that could be translated into levels of “African or European linguistic admixture levels” were long-lived... Indeed, as per linguistic paradigms (see **Chapter 1.2**), the “basilectal” or “acrolectal” qualifications apply to language variants, not to individual realizations of these linguistic variants in a given context, as what we had with our utterances. Nevertheless, we could do something else (Verdu, Jewett et al. 2017; Laurent et al. 2022).

We decided to classify each 4831 unique utterances in one of four different categories. Category A comprised uttered items identified by linguists as directly coming from a known African language. Category B comprised uttered items identified by linguists as having a dual African-European etymology. Category C comprised items identified by linguists as of a strictly European (mainly Portuguese) origin, not bearing any traces of “Africanization”, whether phonetically, morphologically, or semantically. Finally, Category D comprised words of a recognized Portuguese origin but showing possible traces of influences from African languages, whether phonetically, morphologically, syntactically, or semantically. We thus set out, Pr. Baptista, Mr. Costa, and myself, to evaluate in turn each one of 4831 the unique uttered items and classify them into each one of these classes. Note that we found 88 unique items in category A, but only uttered in total 3803 times over the 92,432 utterances, thus relatively rarely. Category B comprised 256 other items uttered 6960 times in total. Category D comprised only 26 items uttered 6762 times in total. Category C comprised all other items.

We then decided to count specifically the relative frequency of usage of items in each category specifically, and compute thus an “African linguistic score” as: for individual *i* and the set of utterances in each category denoted *Cat* (in [A; B; A&B]), $Z_{i,cat} = \sum_{l=1}^{L_{cat}} f_{i,l}$, with $L_{cat}$ the number of uttered items in the corresponding category, and $f_{i,l}$ the frequency of utterance of item *l* by individual *i*, defined as previously.

Results showed that the inter-individual differences in relative frequency of Kriolu uttered items specifically marking intense past contacts with African languages, were positively significantly correlated with individual birth-places distances (for Category A&B, Spearman rho =0.1297, two-sided Mantel $p<2 \times 10^{-4}$) and even marginally significant at very close geographical scale within each island and without considering inter-birth-island distances. In other words, two individuals born far apart in Cabo Verde were more likely to use different frequencies of African-origin utterances than two individuals born nearby.

Ultimately (**Figure F5.3.c**), we found a strong positive correlation (Spearman rho =0.2070, p = 0.0018) between individuals’ linguistic African scores, measured as the relative frequency of usage of uttered items with African origins or strong African influences, and individual levels of genetic admixture from African origin estimated as previously (see **section 5.1**).
<figure><img src="image_75.png" /><figcaption>Figure F5.3.c.</figcaption></figure>

Positive correlation between individual genetic admixture levels from Africa, and individual relative frequency of using uttered items marked by a strong influence of African languages.
Figure adapted from Figure F5 in Laurent et al. (2022), freely available on BioRxiv at https://doi.org/10.1101/2022.04.11.487833, and currently under review at eLife.

Altogether (Verdu, Jewett et al. 2017; Laurent et al. 2022), we found that a significant part of individuals' respective manners of uttering Kriolu were inherited vertically from one generation to the next, and anchored in birth-places. Furthermore, we found a positive correlation between genetic admixture levels and linguistic African admixture levels, also anchored in birth-places from one generation to the next. We therefore proposed two non-mutually exclusive scenarios for explaining the observed patterns, presented in **Figure 5.3.d** below.

First, genetic and linguistic admixture histories seem to have occurred in parallel, anchored in individual birth-places under a mechanism involving genetic and linguistic Isolation-By-Distance, including, or not, some form of non-random matting possibly based on manners of speaking of individuals.

Second, indiscriminately whether the linguistic manner of speaking Kriolu is transmitted vertically over generations, horizontally among peers within generations, and/or obliquely via other sources such as medias, individuals could self-construct their linguistic identity to use more or less frequently Africanisms in their Kriolu utterances, reflecting their genealogically known or putative ties to African or European sources, or doing so based on pre-assumed levels of African or European genetic ancestry anchored in imperfect phenotypic markers of such origins such as skin color, or else.

In all cases, our results showed that genetic and linguistic admixture histories occurred in parallel in Cabo Verde, whether mechanistically and/or whether the social construction of individual utterance of the Kriolu language followed indirectly the genetic admixture histories of the archipelago.

Importantly, we have no formal way of testing these hypotheses, as such inferences would require knowledge of an explicit linguistic model of transmission over generations with possible variation (mutations) and borrowings (migration) which, to our knowledge and that of our linguists' colleagues, simply does not exist for such complex linguistic traits as utterances...

Which brings us, finally, to the last section of this dissertation.
<figure><img src="image_76.png" /><figcaption>Figure F5.3.d.</figcaption></figure>

Schematic representation of possible mechanisms to explain the observed correlations between genetic and linguistic admixture within Cabo Verde.
Note that the first panel represents what would be expected if vertical transmission of linguistic features among individuals from one generation to the
next was marginal, as opposed to what we observed in our data.
Figure adapted from Figure 1 and 6 in Verdu, Jewett et al. *Current Biology* (2017).
**5.4. Ongoing perspectives: A novel “population linguistic” framework, and novel joint inferences of genetic and linguistic histories from observed data**

**5.4.a. ABC inference for reconstructing the linguistic history of populations**

Between 2015 and 2018, Dr. Valentin Thouzeau’s PhD, co-supervised by Dr. Frédéric Austerlitz and myself, aimed initially at comparing genetic and linguistic diversities and reconstructing the respective phylogenetic evolutionary trees using newly developed maximum likelihood approaches, focusing on extant genetic and linguistic data gathered in the lab over the years from Central Asia by Pr. Evelyne Heyer and Dr. Philippe Mennecier (see Thouzeau et al. 2017). However, these powerful methods were nevertheless limited due to difficult explicit implementation of possible migrations or admixtures among branches of the genetic or linguistic trees, as the likelihood itself of such highly complex models when considering numerous population samples is often impossible to write or, in all cases, computationally very hard to maximize in a convergent way (see **Chapter 4**).

Facing such classical issues, we decided to try to see what could be done with an ABC approach. For the genetics part, numerous methodological tools already existed and we were already versed in such analyses (see **Chapter 2 and 4**). But for linguistics data, the fundamental requirements of ABC, namely the ability to simulate efficiently data mimicking the observed data and the ability to calculate summary-statistics a priori informative about the underlying parameters of the simulated models, were virtually inexistent. As we just said above, this was mainly due to the lack of a formal synthetic model of linguistic evolution. Indeed, while certain fields of linguistics, such as glottochronology, phonetics, historical linguistics, or syntactics, indeed sometimes formalized models of linguistic transmission with variation over time, such theoretical models differed across linguistic traits and items, and, in any case, the vast majority of linguistics highly complex objects lacked explicit consensual such models. As a result, while we had extensive classical lexical linguistics data characterizing linguistic diversity across numerous Central Asian populations, we had no mechanistic models for simulating such data, and thus no possibilities of conducting ABC inferences for reconstructing the complex history of linguistic isolation, borrowing, and variation having given birth to the observed data.

In this context, Dr. Thouzeau set out to build, not a “synthetic theory of linguistic evolution”, but an empirical mechanistic model of linguistic transmission with changes and possible borrowings over time, in order to be able to simulate, not words, but linguistic variation across languages observed from a specific type of linguistic construct: lexical cognates. From lexical comparisons across languages, historical and comparative linguists classically define “lexical cognates” as sets of lexemes, or words, from different languages with a common meaning<fnref n="51" /> and a common etymological root in a putative common language of origin (Pagel 2009). For instance, the English “brother”, the German “Bruder”, and the French “frère” form a lexical cognate as they have a common etymological root, but the Spanish “hermano” forms another cognate as it has the same meaning but derives from a different etymological root.

Based on these definitions, Dr. Mennecier determined the cognates for all items in the word-lists he had established to characterize the different languages from which Pr. Heyer obtained genetic samples in joint fieldworks over the years. We thus had population genetics data from grouped individual samples and corresponding cognate diversities across languages spoken by these populations. In this context, and further

<footnote n="51">⁵¹ Same meaning is not always used by linguists for defining cognates, but was considered in the particular definition of cognates by Dr. Mennecier in the study-case of Central Asian languages.</footnote>
expanding the “common origin” definition of cognates, Dr. Thouzeau built two mechanistic models, one for classical admixture and one for classical migration (see **Figure F3.1** and **Chapter 3**), able to produce cognate diversity in a set of populations. He implemented a software package, *PopLingSim* (Thouzeau et al. 2017), to simulate efficiently lexical cognate diversity as a function of parameters drawn from prior distributions set by the user (**Figure F5.4.a1**). Under these models, a set of *N* lexemes with different meanings evolved as a vector of independent items in three-populations topologies, each lexeme possibly changing punctually with a probability “μ” (the cognate “mutation” rate) at each “linguistic generation”.

Then, in unidirectional migration models, each lexeme could be borrowed from one branch to another one with a probability “δ” (the cognate “migration” rate); while in the linguistic two-population admixture model, at a given point in time, a source population would contribute a proportion of *r* lexemes’ cognates to the lexical list of the new admixed population (and 1 – *r* lexemes contributed by the other source population). Note that these models of linguistic evolution only generate diversity among the objects defined by linguists following these rules. The objects in question are not directly observed in language practices (i.e. one does not speak in cognates, but in utterances), but are, as classically in linguistics, objects built buy linguists and relying on complex scientific knowledge and elaborations.

<figure><img src="image_77.png" /></figure>

<figure><img src="image_78.png" /><figcaption>Figure F5.4.a1.</figcaption></figure>

Schematic representations of mechanistic models of transmission with changes over (linguistic) time lexical cognate diversity among three language varieties. Such models are possibly implemented in the *PopLingSim* software published in Thouzeau et al. *Proceedings of the Royal Society B* (2017). Figure originally published in Supplementary Material of Thouzeau et al. (2017).
I would like to elaborate a bit further on the linguistic generation time implemented in these models. Indeed, we implicitly define a “linguistic time” that is scaled in units of lexical changes, analogously to the coalescent time in genetics which is scaled in units of genetic mutations. Thus, both “times” are self-sufficient in their circular disciplinary definition, and are never equivalent to absolute chronological time. This is an important fundamental and axiomatic issue: the genetic “time”, which is the only one of explicit interest to population genetics methods, can be translated to a chronological time by a number of assumptions and approximations on the biological-generation chronological time of the species of interest, and on the mutation rate, rendered possible by a synthetic theory of molecular evolution anchored in extensive experimental observations. Here, for the “linguistic time”, it might be, in principle, possible to also translate it into an absolute chronological time, but it would require to have assumptions on the linguistic generation time and on the “real” linguistic mutation rate, in a first place. To our knowledge, we do not have such assumptions yet in the lack of a synthetic theory of linguistic evolution of cognates, which probably may not even exist in reality, but this is another debate for linguists...

Nevertheless, as in genetics, it is already of major interest to compare linguistic time ratios, thus freeing
ourselves from the needed assumptions to translate linguistic times in absolute chronological times. For an
intuitive schematic example, it is of interest to know that, in the unspecified realm of linguistic changes
over time, it took twice more time to obtain a given observed cognate diversity in two given language
varieties, than the time needed to obtain another given observed cognate diversity between two other
language varieties.

Based on this modelling framework, Dr. Thouzeau thus set out to conduct simulations by drawing the
parameters of the models (split and admixture times, mutation and migration rates, etc.) in prior
distributions. He then built a number of basic summary-statistics to describe cognate diversity such as the
number of cognates and the variance of the number of cognates etc., mostly stemming from the realm of
multivariate analyses in statistics, and thus not necessarily specifically built by linguists for linguistic
purposes. He also considered a number of classical computational linguistic statistics specifically designed
for cognates and equivalent to dissimilarity statistics in genetics.

Briefly, he ultimately designed five competing scenarios for the topological and admixture or
migration history of language varieties, completely analogous to such scenarios in population genetics with
the corresponding definitions of the parameters drawn, obviously, from different priors (Figure F5.4.a2).
Based on these five scenarios and associated genetic and linguistic simulation tools, he conducted ABC
inferences to determine which scenarios best explained observed data, separately for genetic and cognate
diversity data observed in Central Asia, but, again, concerning the same groups of individuals from which
genetic and linguistic data were both sampled originally. Without entering the details of the obtained results
specific to the peopling history of Central Asia described in this article (Thouzeau et al. 2017), my bottom
line here is that it worked substantially well! Indeed, Dr. Thouzeau could identify different scenarios
underlying the genetic history of certain populations while, for the same populations, he found alternative
scenarios best explaining the lexical cognate diversity data. In other populations, both the genetic and
linguistic scenarios best explaining both data, matched. Furthermore, he could, for each data separately,
formally test competing scenarios both in terms of the topological histories and/or of whether linguistic
borrowings occurred in an admixture-like mechanism or in a migration-like mechanism (Thouzeau et al.
2017).

These promising results, the first to our knowledge to have formally tested alternative complex
scenarios to reconstruct the genetic and linguistic histories of the same set of populations, paved the way
for our on-going project specifically focusing on Cabo Verde linguistic and genetic history, at the within-
population and within-language levels.

<figure><img src="image_79.png" /><figcaption>Figure F5.4.a2.</figcaption></figure>

Five competing scenarios for the genetic or, respectively, linguistic history of three Central Asian populations or, respectively, language varieties, inferred using ABC using observed genetic data or, respectively, lexical cognate diversity.

Figure originally published in Figure 1 of Thouzeau et al. *Proceedings of the Royal Society B* (2017).
5.4.b. ABC inference for reconstructing jointly the genetic and linguistic history of groups
of individuals

Based on this initial seminal work, we worked with Dr. Thouzeau to confront the asymmetry of classical genetic-linguistic comparisons (Figure F1.2.c and Chapter 1.2), by building a novel population linguistics paradigm that would allow to conduct historical inference jointly for genetic and linguistic data gathered at the same scale and centered on individuals. We had already built the framework allowing statistical descriptions and comparisons of genetic and linguistic data at the same scale, which we presented above in section 5.3, but, as mentioned, while we had built an ABC framework to conduct genetic inferences from observed data, we did not have equivalent tools for conducting ABC linguistic inferences at the scale of individuals within languages.

Involving Dr. Thouzeau in the Cabo Verde project, we could not find nor elaborate a mechanistic model describing inter-individual changes over time of utterances from semi-spontaneous discourses... We have not completely dropped the idea, but it still seems out of reach today and we are not actively pursuing this research direction. Instead, in 2016, Dr. Thouzeau proposed to me to hijack another classical linguistic method and object that I had initially rejected with Pr. Baptista (see **Chapter 1.2.c**): Swadesh word-lists (Swadesh 1971).

Indeed, we could not use the same ABC methods based on simulation of cognate diversity between
language varieties that we had developed in PopLingSim, at the individual level within language for semi-
spontaneous discourses. However, we could perhaps modify our simulation approach to consider individual
word lists within language.

A first problem was that cognates are uninformative for individual utterances within language, as they stem from comparing lexemes across language varieties. Individual utterances are just realization of said varieties, and while two different utterances for a given lexeme may differ between individuals, they are most often classified as the same cognate as they extremely often stem from the same lexical item (and thus meaning and etymological root) within language. Therefore, Dr. Thouzeau decided to use, instead of cognate variation, utterance variation in the same way that we had previously investigated utterance variation across individuals' semi-spontaneous discourses (Verdu, Jewett et al. 2017; Laurent et al. 2022).

The goal was to present individuals with a list of pictures representing, hopefully as unambiguously
as possible, a single meaning, and record the “word” individuals would use as a given utterance of this
meaning. We would then simply consider each utterance as a fixed item, and compare “brutally” utterances
for each meaning, across individuals and within the language, whichever was the source of the difference
between uttered items; that is, we would count similarly any difference between utterances for a given
meaning, whether the difference was morphological, phonetical, or, more drastically, lexical.

Dwelling on my prior experiences conducting word-lists linguistic collections in Central Africa, and after discussing the many known issues of such sampling with Dr. Mennecier, we built a Swadesh-like list of pictures representing colors, numbers, body parts, basic natural and anthropic objects, animals, or certain basic verbs. We then set out with Dr. Thouzeau for our 2016 fieldwork in Cabo Verde and tested this Swadesh-like protocol, in addition to the genetic anthropological and linguistic protocols we continued, with Pr. Baptista, from previous fieldwork. Remember that (Chapter 1.2.c), with Pr. Baptista, we had initially rejected such protocol and instead went for semi-spontaneous discourses, because, by definition of the Swadesh word-list, these words were likely very little variable across individuals within a language.
<figure><img src="image_80.png" /><figcaption>Figure F5.4.b1.</figcaption></figure>

Utterance variation from word-lists in Cabo Verde (top 2 panels). Linguistic utterance variation correlates with individuals' island of birth (third horizontal panel). Genetic variation correlates with individuals' islands of birth (fourth horizontal panel), as previously seen for more SNP markers in Chapter 5.1. Thouzeau et al. in *Prep*
However, we discovered during our first recordings on the field, that there were really many ways of saying (phonetically and morphologically) the word “tree” in Kriolu, a word that came up systematically in the discourses we had recorded. Some individuals would say “arvi”, “arvori”, “arv’”, “spinu”, etc. Thus, while I did not have much hopes about Dr. Thouzeau’s idea to capture a form of systematic inter-individual linguistic diversity with such word-list protocol, I reckoned that there might also be unsuspected utterance variation in other very basic and usually invariable words. The fact that the goal was, ultimately, to be able to conduct explicit simulations for linguistic ABC inference among individuals within languages, further made me believe that it was worth a try. Even if it failed, Dr. Thouzeau would have acquired field experience which would unquestionably enrich his perspectives for his PhD dissertation...

But it did not fail, and thus became an additional part of our protocol for the following fieldwork. Indeed, we had identified a set of 34 meanings that performed well with individuals, in that they would very rapidly utter the corresponding item, as little ambiguous in meaning as possible. Analyzing this data for the 147 genetically unrelated individuals born in Cabo Verde, we found tremendous amounts of highly structured variation across Cabo Verde with respect to individual birth-places, even more geographically structured than the variation observed previously with semi-spontaneous uttered discourses (Figure F5.4.b1).

We thus had built a novel linguistic object: inter-individual utterance variation for a given list of meanings, collected from the very same individual for which we had collected genetic and anthropological data. Dr. Thouzeau then set out to build a mechanistic model for simulating inter-individual variation of such objects within a language, implementing, similarly as he did with PopLingSim, specific models of changes (mutation) of utterances over time drawing parameter priors from large prior distributions (as we had no idea *a priori* what such mutation rate might be). Furthermore, he implemented such simulation software to be centered on individuals forward-in-time, continuous-in-time following a Moran (1958) model, and allowing for groups of individuals to diverge from one-another. Finally, as if it was not enough, he built a software so as to simulate jointly genetic (autosomal independent SNPs) and linguistic such data for the same individuals, where genetic population divergences would be distinct from linguistic varieties divergences, thus essentially allowing the genetic and linguistic histories to diverge, or not, for the group of individuals investigated. Using this simulator, GeLiS, coupled with Random Forest ABC scenario-choice and Neural Network ABC posterior parameter estimation procedures (see Chapter 4), we could reconstruct jointly the genetic and linguistic histories of Cabo Verde, from observed genetic and linguistic data gathered in the same individuals.

Therefore, based on the historical data compiled for the census and settlement history of Cabo Verde (section 5.1), we set out to reconstruct the history of founding events, associated with bottleneck changes of population sizes, and successive settlement of each island in the archipelago; the history from a genetics and linguistic point of view, separately or jointly (Figure F5.4.b2).
## 6 Competing scenarios for the genetic and/or linguistic histories of Fogo and Brava

St-Fg-Br: Scenario 1

<figure><img src="image_81.png" /></figure>

St-Fg-Br: Scenario 2

<figure><img src="image_82.png" /></figure>

St-Fg-Br: Scenario 3

<figure><img src="image_83.png" /></figure>

St-Fg-Br: Scenario 4

<figure><img src="image_84.png" /></figure>

St-Fg-Br: Scenario 5

<figure><img src="image_85.png" /></figure>

St-Fg-Br: Scenario 6

<figure><img src="image_86.png" /></figure>

## 6 Competing scenarios for the genetic and/or linguistic histories of Santiago and Santo Antão, and either São Nicolau, or Boa Vista, or São Vicente, or Sal, separately

St-SA-SN/SV/BV/Sal: Scenario 1

<figure><img src="image_87.png" /></figure>

St-SA-SN/SV/BV/Sal: Scenario 2

<figure><img src="image_88.png" /></figure>

St-SA-SN/SV/BV/Sal: Scenario 3

<figure><img src="image_89.png" /></figure>

St-SA-SN/SV/BV/Sal: Scenario 4

<figure><img src="image_90.png" /></figure>

St-SA-SN/SV/BV/Sal: Scenario 5

<figure><img src="image_91.png" /></figure>

St-SA-SN/SV/BV/Sal: Scenario 6

<figure><img src="image_92.png" /></figure>

## 4 Competing scenarios for the genetic and/or linguistic histories of Maio

St-Ma: Scenario 1

<figure><img src="image_93.png" /></figure>

St-Ma: Scenario 2

<figure><img src="image_94.png" /><figcaption>Figure F5.4.b2.</figcaption></figure>

Competing scenarios for the genetic and linguistic peopling history of each Cabo Verdean island, based on prior historical information, simulated with GeLiS and inferred using Random Forest ABC scenario-choice and Neural Network ABC posterior parameter inference, from observed individual-centered genetic and linguistic data.

Thouzeau et al. in *Prep*
We are in the process of writing the article corresponding to this extensive and exhaustive work. As this dissertation is already long, I will thus only rapidly tease some of our not-yet peer-reviewed results, and leave the reader to follow up on this work, hopefully published in the next few months, or year.

In practice, we found out that it worked extremely satisfactorily. We could disentangle the different
possible successive settlements of each island, and found, in all cases, but that of Boa Vista, congruencies
between the linguistic and genetic histories of the founding peopling of each Cabo Verde islands: the people
who historically gave birth to the gene-pool of an island also gave birth to its linguistic utterance diversity.
The exception of Boa Vista showed, instead, that it's genetic peopling came from a different island than its
linguistic peopling. Furthermore, we found that linguistic utterances' diversity derived more rapidly than
genetic diversity everywhere in Cabo Verde: while manners of speaking Kriolu differentiated rapidly after
the establishment of a novel settlement on an island, reproductive isolation among islands took more time.
In other words, linguistic differentiation predated genetic differentiation everywhere in Cabo Verde

Therefore, and to conclude here, this work sets the bases for a novel population linguistics paradigm
analogous to the classical population genetics paradigm, anchored on inter-individual genetic and linguistic
variation from the same sets of individuals (Figure F5.4.b3). This framework allows us, for the first time
to our knowledge, to formally infer the genetic and linguistic joint or separated genetic and linguistic
admixture histories, and will allow, in the future, to further disentangle the fundamental influence of socio-
cultural behaviors on the genetic and linguistic histories of complex admixture processes having given birth
to human biological and cultural variation observed today.
<figure><img src="image_95.png" /><figcaption>Figure F5.4.b3.</figcaption></figure>

A novel paradigm for joint genetic-linguistic historical inference from inter-individual genetic and linguistic data.

Thouzeau et al. in Prep
<figure><img src="image_96.png" /><figcaption>From left to right: Sergio da Costa, Valentin Thouzeau, and Ethan Jewett (back) during word-list utterance sampling in the village of Paul</figcaption></figure>

Santo Antão, Cabo Verde, 2016

©Paul Verdu
# Conclusion

## Histories of Admixture
**Conclusion. Histories of Admixture**

I hope to have illustrated here how the past 20 years of research in anthropological genetics and human population genetics, to which I have tried to contribute, significantly elaborated on the classical synthetic theory of evolutionary genetics presented schematically in introduction (see updated figure below).

<figure><img src="image_97.png" /><figcaption>**Figure Conclusion**</figcaption></figure>

Schematic representation of the influence of evolutionary forces (mutation, demography, selection) on genetic diversity and partially associated phenotypic diversity, in the updated neutral, neo-Darwinian, synthetic theory of evolution.

While formally productive in Human Sciences disciplines, the Nature-Nurture, or Nature-Culture, separation is largely un-operational from a population genetics evolutionary perspective: “Nature” and “Culture” interact constantly in a dynamic way to influence genetic diversity patterns observed within and among populations.

In particular, I hope that I have shown how complex admixture processes have shaped and continue to shape rapidly the genetic diversity and evolution of our species, heavily determined by complex socio-cultural behaviors. In that, our work and that of numerous other groups, in my eyes, highlight that Culture can be a massive force influencing rapidly the biological diversity and evolution of our species, through genetic admixture and migration processes in particular. Based on the examples provided above and numerous others in the anthropological genetics' literature, I strongly think that the Nature-Culture schism in anthropology, mainly stemming from cultural anthropology disciplines but also often fully adopted by numerous human biologists, is profoundly un-operational for investigating human genetic diversity and evolution. Indeed, we showed how genetic diversity and evolution was profoundly shaped by cultural processes, themselves often intertwined with complex relationships with heavily anthropized ecological environments and, sometimes, rooted in genetically-determined, in part, phenotypic variation.

Furthermore, as numerous studies start to investigate ancient admixture events across *Homo sapiens* populations and with other, now extinct, hominid species, it is clear now that admixture has been pervasive
throughout *Homo sapiens* history. While we cannot directly access the socio-cultural mechanisms at play in a remote past, it is nevertheless consistent to consider, as a starting point, that socio-cultural mechanisms were indeed at play in the past to determine admixture and migration processes. Indeed, considering that our species is a social species, and that it has always been a social species since its emergence, there is no reason, in the light of the fundamental, recent or more ancient, mechanisms highlighted in this dissertation, to think that equivalent mechanisms were not also at play in very ancient times.

More generally, I thus hope to have participated in developing novel ways to slightly shift the classical paradigm for admixture and migration investigation in population genetics; from a history of isolation where admixture and migrations are secondary, often nuisance, mechanisms, to a framework where admixture and migration events can be constitutive of our diversity and evolution, separated by periods of reproductive isolation. In other words, instead of imagining human populations as being isolated and sometimes admixed, considering them as related by dynamic admixture and migration processes and, sometimes, isolated, can produce novel insights in both the cultural and biological evolution of our species. Future work investigating more or less ancient human DNA will allow us, for the first time, to literally travel back in our evolutionary history, and further decipher the evolutionary forces, including possible cultural mechanisms, having led to the genetic landscape observed in human populations today.

To further our understanding of the influence of cultural behavior in shaping our evolution, I think we need, collectively, to overcome a fundamental challenge. Population geneticists can investigate mechanistically the evolution of genetic diversity, thanks to a unified robust model of inter-generational transmission of genetic material with mutation. Of course, numerous fundamental genetics disciplines still extensively work on deciphering how mutations arise, with interesting advances from quantum genetics lately, as well as on how DNA is indeed transmitted to offspring. These may or may not lead to fundamentally revising the synthetic theory of evolution in the future. For now, this theory remains our best paradigm and hypotheses-testing dialectics to build novel knowledge and understanding of our biological evolution. In this context, it is crucial to understand that there is no equivalent of such synthetic theory for exploring the transmission of culture with possible changes over time. This is practically due to the fact that mendelian inheritance is, in the end, far much simpler than cultural transmission for virtually any cultural trait of interest.

More conceptually, genetic diversity stands only on observed inter-individual genetic variation; the object of population genetics is thus solely the marginal product of what is observed at the inter-individual level, and mutation, demographic, and selection forces act on the living individuals and the genetic diversity they carry by determining its transmission, or not, to the next generation. Conversely, whether cultural variation is born from the marginal product of individual behaviors in different groups in interactions or, alternatively, whether cultural variation answers to its own dynamics from which individuals and groups of individuals may realize certain differentiated behaviors, remains largely unclear. Therefore, while I use cultural variation as facts observed at a given point in time and influencing genetic diversity patterns, I cannot predict its mechanistic influence on biological evolution over time, as I have no formal conception of how it may change over time. One ultimate goal would thus be to determine the mechanisms, and their parameters, underlying cultural transmission, changes, and innovations, to better integrate cultural and genetic anthropology, at least to the benefit of the latter discipline.

Personally, without proof and based on my direct experiences investigating cultural variation, I have the intuition that cultural variation answers to its own mechanisms of transmission with or without changes over time and space, and that individual cultural behaviors stem from possibilities available within the
frames they have been taught by social interactions through life (and which include the transgression of the frame). I thus do not think, but again may be wrong, that cultural variation is the marginal product of individual behavioral variation. From there, it would be necessary to build anew a complete mechanistic theory of cultural evolution answering to its own forces and mechanisms, without ever even trying to hijack some aspects of the synthetic theory of genetic evolution. This may seem obvious: we all intuitively know that utterances, for instance, are not transmitted in a diploid mendelian way. Nevertheless, the task I propose is immense, in a large part due to the fact that such questions are of interest to me, but are not always the core focus of interest of the vast majority of researchers in numerous cultural anthropology fields. Advancing in this direction would thus require, from my part, a renewed intense inter-disciplinary effort, as it is not honest to simply ask cultural anthropologist to handle my problems. In all cases, this would likely produce novel objects of study, similarly to our novel population linguistics objects for historical linguistic inference, which may or may not be of interest to other researchers from diverse fields.

Altogether, as illustrated by the numerous experienced and proposed pluri-disciplinary and inter-
disciplinary challenges detailed throughout this dissertation, I believe that there is a vast need, today, to
take the time for mediating the scientific paradigms, methods, and dialectics from one discipline to the
other. It is to some extent obvious for vastly differing disciplines, such as cultural anthropology and
population genetics here illustrated, but it is also the case, I think, for more closely related disciplines.
Indeed, the “phylogenetic” proximity between evolutionary anthropologists and anthropological geneticists
often leads to an apparently common specialized lexicon, which, in fact, fundamentally differs. For
instance, drift or population sizes are vastly differing concepts between computational linguistics,
archaeology, ecology, population dynamics, and population genetics. Thinking they are actually “roughly
similar” leads, to my views, to major confusions, misunderstandings, and sterile disagreements. Another
example is the importance of explicating the scientific categories used from different disciplinary
perspectives, as detailed in the first chapter of this dissertation: “what is a population?” is probably the
major unifying question that needs to be at least thought about prior to starting any pluri-disciplinary
research across evolutionary anthropology disciplines. I reckon that taking the time for such essential, but
often not efficient, dialogue is costly. I nevertheless think that it is a necessity, unless one wants to take a
tremendous risk to mutilate others’ disciplines leading to false interpretations of one’s disciplinary results.

Finally, the diffusion and mediation of the scientific methods to the broader, non-specialized and non-
academic, audience is equally crucially necessary to my views. Since its discovery and the rapid advances
to our understanding of biology that it brought, DNA and genetics has gained incredible traction in our
societies and in our daily life. Today, molecular genetics and population genetics tools are extensively
deployed in practice for judicial and medical purposes, and even more recently for “recreational” purposes
(as explained in Chapter 1.4), de facto hiding still tremendous amounts of scientific disagreement and
debate. DNA is often seen, in my numerous repeated experiences of interactions with journalists, tribunals,
policy makers, and broad-audience public debates and conferences, as the ultimate grail, endorsed with
mathematical certainty and objectiveness, while I have shown that, in reality, neither qualificatives truly
apply in the probabilistic realm of genetics. I think that scientists in general, and geneticists in particular,
should spend more time explaining how they obtain their results rather than spending most of their “on air”
time highlighting the results themselves, without ever trying to detail the how? and the from where? I have
very often been opposed by scientists that “no one cares about these details; people are interested in the
results”. I think that this is purely and simply wrong: it is not because mediating the scientific method is
much more difficult than simply communicating the obtained, grandiose, results, that people are interested
in the latter and not the former. I think that people are interested in stories, and the process underlying
scientific research is a story, a fascinating and exciting one, both to tell and to listen to.
## Remerciements

Je tiens à remercier ici, vivement et très sincèrement, tous les participants à mes projets de recherche au Cameroun, au Gabon, en Ouganda, et au Cap Vert. Ils m'ont accordé patiemment et avec bienveillance leur temps, afin de partager avec moi des bouts de leur vie, de leur langue, et me fournir des petits échantillons de leur salive et autres mesures anthropométriques. Sans eux, je n'aurais pas pu mener les différents projets de recherche présentés ici. Merci aussi à tous ceux qui m'ont aidé sur le terrain, comme assistants, traducteurs, et/ou comme collaborateurs, et notamment Alain Fezeu, Omarou Mendjok, Nyantzi Yosam, Mugisa Ezekiel, Walina Zephanus, et Angelo « Djiño » Barbosa.

Je tiens également à remercier les étudiants de tous les niveaux avec qui j'ai eu la chance de pouvoir travailler, même brièvement, et celles et ceux que j'ai pu encadrer, ou co-encadrer, au cours de mes recherches. Vous m'avez toutes et tous très souvent bien plus appris que ce que j'ai pu vous enseigner. Merci donc (dans l'ordre chronologique de nos premières rencontres) à Héloïse Bastide, Viola Grugni, Noémie Becker, Amy Goldberg, Margeritte Lapierre, Mirian Barbosa, Claire Stragier, Antoine Cools, Alexandra Blant, Ferdinand Petit, François Mallordy, Marie Lopez, Cesar Fortes-Lima et Kazunari Matsudaira. Une mention toute spéciale pour les thésards et thésardes (du passé et du présent), Valentin Thouzeau, Gwenna Breton, Marta Ciccarella, et Juliette Sauvage : merci pour votre patience, votre enthousiasme communicatif, et la richesse de vos regards, tout comme pour m'avoir fait l'honneur, parfois, de partager avec moi vos doutes, scientifiques ou non, vos états d'âmes et vos interrogation sur l'avenir académique ou non ; dans l'espoir que nous continuions les collaborations scientifiques tous azimuts !

Un grand, un immense MERCI à mes compagnons de science, et en tout premier lieu à Marie-France Mifune, Romain Laurent, Zachary Szpiech, Bruno Toupance, Samuel Pavard, José Utgé, Erkan Buzbas et Etienne Patin, pour votre indéfectible soutien et intérêt, parfois salutairement critique, pour mes travaux et mes lubies scientifiques et, je l'espère, pour votre amitié. Sans vous, aucun de mes travaux, même ceux en solitaire, n'auraient été menés à bien. Et merci, bien sur à tous les autres compagnons, peut-être moins en première ligne, mais pourtant bien tous présents ; vous qui m'avez forgé tant scientifiquement qu'humainement au fil de ces années : Franz Manni, Laure Ségurel, Ethan Jewett, Guillaume Laval, Raphaël Leblois, Cécile Garcia, Victor Narat, et Céline Bon.

Merci aux rapporteurs de ce document et aux membres du jury d'avoir accepté ce travail, long (... désolé), de discussion de mes travaux de recherche.

Merci à Philippe Chambon et à Aline Thomas, pour leur convivialité, leurs échanges scientifiques avec moi sur le monde magique de l'archéologie, et aussi un peu pour avoir aimablement lu et corrigé un morceau de ce document, la partie de perspective sur les catégorisations archéologiques.

Merci à Sylvie Le Bomin, Serge Bahuchet, et Marie-France Mifune pour m'avoir patiemment formé sur le terrain à la méthode ethnographique, et avoir fait de mes campagnes d'échantillonnage, des succès.

Merci à Marlyse Baptista, pour m'avoir proposé de travailler au Cap Vert, pour m'avoir fait découvrir ce pays et ses habitants ; et merci à elle et à Sergio Costa, d'avoir partagé leurs savoirs et savoirs faire en linguistique.
Merci à Myriam Georges, Begoña Martinez-Cruz et Hélène Quach, de m'avoir appris la génétique moléculaire et de m'avoir toujours épaulé dans mes projets.

Merci au Plateau de Paleogénomique et Génétique Moléculaire (P2GM) du MNHN sur le site du Musée de l'Homme, et en particulier à Françoise Dessarps-Freichey et José Utgé, pour avoir permis la genèse de très nombreuses données génétiques exploitées dans le Chapitre 5 de ce document.

Merci à Arnaud Estoup, d'avoir pris le temps de m'enseigner l'ABC, et d'avoir pris le temps de continuer !

Merci à Céline Bon, José Utgé, Sophie Lafosse, Françoise Dessarps-Freichey, et Amélie Chimènes pour l'incroyable aventure P2GM que vous m'avez offert et qu'on continue à partager ; et merci d'avoir absorbé toutes les tâches et les soucis quotidiens m'ayant permis de trouver le temps d'écrire ce document ! Et en route pour de nouvelles aventures !

Merci à Taouès Lahrem et à Florence Loiseau, pour avoir encadré administrativement l'immense majorité de mes projets. Sans vous, en pratique, aucun projet de recherche ou d'encadrement n'aurait pu être mis en œuvre. Et surtout, merci de m'avoir tant aidé, avec le sourire !

Merci à Emilie Masson, Estelle Bervas-Clerc, et Iris Boh, pour nos collaborations et vos enseignements et réflexions juridiques et déontologiques sur mes projets et au-delà.

Merci au vaste peuple du Musée de l'Homme, pour m'avoir accueilli et soutenu professionnellement et pour sans cesse avoir partagé vos nombreux savoirs avec moi en cette formidable maison de science, et ce depuis 2002. Je ne peux pas tous vous citer ici, mais vous savez pouvoir vous attribuer une partie, méritée, des travaux rapportés ici.

Et merci à toutes celles et ceux qui m'ont donné à penser, et souvent plus, si généreusement au cours du temps : Frank Alvarez-Pereyre, Alain Froment, Alain Epelboin, Barry Hewlett, Bonnie Hewlett, Fernando Ramirez-Rossi, Renaud Vitalis, Marie-Claude Marsollier-Kergoat, Nathalie Machon, Mattias Jakobsson, Carina Schlebusch, et Jorge Rocha.

Merci à tous mes anciens encadrants ayant accompagnés mes premiers pas en recherche scientifique : Kenneth Kidd, Judith Kidd, et Luis Barreiro, je n'aurais surement pas continué sans votre aide et votre exemplarité.

Et merci, merci, merci à mes mentors, officiels ou non, mais à qui je dois tout scientifiquement et académiquement. Dans l'ordre chronologique donc : Lluis Quintana-Murci, le premier qui a cru que c'était possible, confiance sans cesse renouvelée depuis, et qui continuera à l'être, je l'espère, pour le futur. Frédéric Austerlitz, qui m'a littéralement forgé à la méthode scientifique et à la recherche en génétique des populations, qui m'a soutenu, guidé, et patiemment reliu au cours de ces nombreuses années, sans oublier ses enseignements essentiels à la « convivialité » en milieu académique. Serge Bahuchet, qui a eu la patience de m'enseigner l'anthropologie en générale et celle de l'Afrique Centrale en particulier, de la théorie au terrain, pour m'avoir introduit dans le monde étrange des « Pygmologues » et, bien au-delà, pour
toutes les discussions scientifiques pluridisciplinaires que nous avons eues et qui m'ont tant marquée. Noah
Rosenberg, pour sa patience infinie, sa science, et sa méthode, qui m'a guidé sur le chemin de la théorie en
génétique des populations, qui m'a soutenu et épaulé pour l'obtention de mon poste académique, et qui
continue à être un collaborateur et un relecteur essentiel à mes travaux de recherche.

Et, bien évidemment et avant toutes et tous, merci infiniment à Evelyne Heyer, ma directrice de thèse, ma collaboratrice, et ma directrice d'unité. Merci pour la science, pour toutes les opportunités incroyables que tu m'as offertes sur un plateau toutes ses années, pour ton soutien actif et inébranlable, pour ton écoute de mes (trop) nombreux états d'âmes et autres coups de gueule, pour les encouragements, pour l'accueil dans ton équipe puis dans ton laboratoire et pour ton travail immense, souvent invisible, à m'avoir fourni le cadre idéal de réalisation de mes travaux passés, présents et futurs. Merci donc, chère Evelyne.

Merci enfin à toutes celles et ceux que j'aurais bien malencontreusement oublié ici.

Plus personnellement, merci à ma famille : mes parents, Jean-Paul et Geneviève, pour m'avoir toujours soutenu et encouragé dans mon parcours, et ma sœur, Caroline, pour son enthousiasme et son optimisme sans faille. Merci aussi à tous mes amis et amies, vous saurez vous reconnaitre. Vous savez que ce travail n'existerait pas sans vous, vos conseils, vos soutiens, et vos critiques, scientifiques ou autres. Merci à #payetonconfinement, groupe ultra-moderne, réseau-socialisé mais restreint, le seul dont je fais partie en fait... qui se reconnaitra et qui m'a soutenu tout au long de cette écriture ardue.

Et merci à Marie-France, à qui se travail est dédié, et grâce à qui, je le maintiens, tout est possible.
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