Assessing the relative impact of historical divergence and inter-group transmission on cultural patterns: a method from evolutionary ecology

Abstract

In the study of cultural evolution, observed among-group affinity patterns reflect the effects of processes such as mutation (e.g. innovation and copying error), between-group interaction (culture flow), drift and selection. As in biology, cultural affinity patterns are often spatially correlated, making it difficult to distinguish between the opposing geographically mediated forces of divergence and interaction, which cause groups to become more distinct or similar over time, respectively. Analogous difficulties are faced by evolutionary biologists examining the relationship between biological affinity and geography, particularly at lower taxonomic levels where the potential for gene flow between lineages is greatest. Tree models are generally used to assess the fit between affinity patterns and models of historical divergence. However, factors driving lineage divergence are often spatially mediated, resulting in tree models that are themselves geographically structured. Here, we showcase a simple method drawn from evolutionary ecology for assessing the relative impact of both geographically mediated processes simultaneously. We illustrate the method using global human craniometric diversity and material culture from the northern coast of New Guinea as example case studies. This method can be employed to quantify the relative importance of history (divergence) and geographically mediated between-group interaction (culture flow) in explaining observed cultural affinity patterns.

This article is part of the theme issue ‘Bridging cultural gaps: interdisciplinary studies in human cultural evolution’.

1. Introduction

In the study of cultural evolution, patterns of among-group cultural differences and similarities are often analysed in the context of time (i.e. history) and space (i.e. geography) in an attempt to infer the processes that have generated such patterns (e.g. [1–8]). Under the umbrella of a Darwinian evolutionary model of ‘descent with modification’ [2,6,9–14], processes such as innovation, copying errors, cultural drift, selection, dispersal, population splitting and population contact all play potentially important roles in generating observed patterns of cultural diversity. Hence, despite differences in the specifics of the mechanisms, the processes of cultural differentiation, change and assimilation are, for all intents and purposes, analogous to the processes of micro- and macroevolutionary change observed in biology. As such, analytical and theoretical approaches developed in the study of evolutionary biology have been widely adopted and assimilated into the study of cultural evolutionary change (e.g. [6,7,11,14–18]).

Owing to the similarity of mechanisms and process in the operation of both cases, researchers examining cultural variation in modern human populations face many of the same issues as those studying patterns of human biological diversity. Unlike other primate species (and mammals in general), Homo sapiens is characterized by being an extremely geographically widespread (yet comparatively young) species, dispersing across all major continental landmasses and island systems in a relatively short timeframe (e.g. [19]). As a consequence, the specific histories of biological and linguistic diversification have played out over relatively vast geographical areas, resulting in an intimate correlation between global patterns of genetic, morphological and linguistic diversity [20–24]. Therefore, linguistic affinities are often used as a means of modelling the historical relationships between populations (e.g. [1,2,5,8]), when detailed genetic or other genealogical data are not available.

Several studies of human material culture variation have sought to understand the connection between spatial, historical and cultural patterns by statistically assessing the correlations between linguistic, geographical and cultural distance matrices (e.g. [2,3,25]). Such studies have sometimes been situated within the context of debates regarding the relative importance of population splitting (branching) versus assimilation or borrowing (blending) in the generation of observed cultural affinity patterns [2,25–29]. Bifurcating phylogenetic (tree-like) models are widely applied in the study of biological and cultural evolution under the assumption that groups evolve via divergence from existing groups and via the accumulation of novel-derived traits or attributes [28,30,31]. While phylogenetic tree approaches are often deemed appropriate in the study of biological speciation at macroevolutionary levels, their application to the study of biological diversity at lower taxonomic levels may be problematic on the grounds of gene flow, introgressive hybridization and horizontal gene transfer between geographically proximate lineages [32]. This is likely to be particularly true at the intraspecific level, such as the relationships among human populations, where the potential for gene flow between contiguous populations is constant [33–36]. Similar criticisms have been levelled at the use of tree models in the study of cultural diversity [26,37,38], given the potential for extensive borrowing, convergence and exchange (i.e. ‘culture flow’ [39]) among geographically contiguous groups [40]. Such criticisms may not be warranted in cases where cultural lineages are more akin to interspecific (or higher taxonomic) comparisons, although it is not always possible to judge a priori what the potential for borrowing and exchange between different groups actually is. Accordingly, some researchers argue for a ‘tangled web’ model to represent the evolutionary relationships between populations, whereby channels (lineages) split and flow into each other as a function of their historical relatedness and geographical propinquity.

Broadly speaking, recent evolutionary analyses of human material culture patterns have taken two major (but not mutually exclusive) methodological approaches to the question of historical divergence and geographical patterning (see also [29]). One approach has been to fit bifurcating tree models representing the phylogenetic (branching) history of individual groups to material culture datasets (e.g. [28,30,31,41,42]). For example, Collard et al. [28] showed that the goodness of fit of various material culture datasets to a phylogenetic tree model was approximately the same as for biological datasets representing a range of different animal taxa. They concluded that while both ‘branching’ and ‘blending’ are important processes in generating cultural diversity patterns, there was little evidence that the effects of ‘blending’ processes were so strong as to obscure the underlying signal of historical divergence among groups.

The other major analytical approach applied to such questions involves the statistical comparison of affinity matrices representing cultural, geographical or linguistic affiliations using Mantel tests [43] (e.g. [2,5,18,25,44,45]). This approach mirrors that often taken by biological anthropologists interested in understanding the relationship between intraspecific biological distances and various explanatory factors such as geographical distance, time and climate (e.g. [46–49]). As noted above, tree-building (phylogenetic) methods are often deemed appropriate when analysing biological datasets at the supra-specific level, under the assumption that evolving lineages do not exchange genetic material once they have diverged from a common ancestor. Therefore, in the case of modern human population history, where the potential for gene flow and reticulation between population lineages is high, anthropologists have often called upon the analysis of among-group affinity matrices instead of tree-building methods as an alternative method. However, affinity matrices violate the basic statistical assumptions of traditional correlation and regression techniques due to the non-independence of their elements. As a result, Mantel tests [43] and their derivatives are generally employed to compare matrices, whereby significance values are assigned via random permutation of the matrix rows and columns to produce a distribution of values against which the observed correlation can be compared. Extensions of the Mantel test include partial tests, whereby the effects of a third matrix (or more) are held constant, as well as the multiple Mantel test, whereby multiple independent matrices can be regressed against a single dependent matrix [50–52].

While intuitively simple to implement and interpret, Mantel tests have been criticized for having low statistical power and elevated type I error rates, particularly when one or more matrices are spatially (or phylogenetically) autocorrelated [53,54]. However, others have defended the use of Mantel tests [52] by pointing out that elevated type I errors can, to some extent, be corrected by adjusting the α-levels and by implementing suitable research design, such as using partial Mantel tests when testing specific hypotheses. An alternative, more statistically powerful, approach to Mantel tests involves the use of Procrustes rotation of two matrices to compare their structure and assess congruence (e.g. [48,55,56]). Here, statistical significance is assigned using a permutation test and sum-of-squares differences between the configurations of two matrices. The advantage of this method is that the effect of each individual population can be assessed independently of the whole dataset, while the main disadvantage is the lack of control over additional matrices, the effects of which may actually be driving a spurious correlation between two matrices being rotated.

(a). Hierarchical Mantel test

Here, we showcase an alternative method–the hierarchical Mantel test (HMT)—that quantifies the independent effects of multiple matrices explicitly, thereby taking spatial autocorrelation into consideration. To illustrate the problem of spatial autocorrelation, assume that we have three matched affinity matrices for a sample of human populations: (i) genetic distance, (ii) geographical distance and (iii) climatic differences. Using simple Mantel tests, we learn that all three matrices are significantly and strongly correlated with each other; i.e. genetics correlates with both geography and climate, and geography correlates with climate. So what can we conclude from these results? Is climate driving genetic differentiation? Or does climate correlate with genetic distance because climate and genetics are both spatially autocorrelated? To disentangle what is going on, we could perform a partial Mantel test and learn that the correlation between genetics and climate disappears once we account for geography. So, spatial autocorrelation has been accounted for. However, what partial Mantel tests do not allow us to do is quantify the extent to which geography (or indeed climate) potentially explain genetic distance. It could well be the case that geography is the major determinant of among-group genetic distances, but that climate also plays a minor but additive role in generating diversity. This subtlety would not be picked up by the partial Mantel test alone. A similar problem is faced in cases where biological variation correlates with geographical distance, but there is also a strong correlation between geography and population history. In such cases, it is impossible to tell whether groups have differentiated under a classic model of ‘isolation-by-distance’ [57], or whether their differentiation is primarily due to a hierarchical history of shared common ancestry, which also happens to be spatially autocorrelated [21,34,58].

Disentangling the effects of these two geographically mediated processes (i.e. phylogenetic divergence versus gene flow) in biology mirrors the stumbling blocks faced in resolving the ‘branching–blending’ debate in cultural evolution. Hence, the HMT method has wide applicability across biological and cultural datasets, where there is a strong co-correlation between geographical distance and a bifurcating tree model representing population history. The HMT was adapted from evolutionary ecology [51] by de Campos Telles & Diniz-Filho [59] to partition the effects of contemporary gene flow and historical effects of population divergence among 10 populations of the Brazilian tree species Eugenia dysenterica. They found that while all three matrices (biology, geography and history) were significantly correlated with each other, when they applied the hierarchical version of the Mantel test, almost 22% of biological variation was explained by ‘history’ alone with only an additional 1.5% explained by geographically mediated gene flow. Thus, it was possible to conclude that historical divergence (i.e. branching) was the more potent force in generating among-population genetic differences, with recent gene flow (i.e. blending) playing a relatively minor role.

The HMT works as follows: among-group biological/cultural variation is assumed to be partitioned into the effects of (a) pure ‘history’, (b) pure ‘geography’, (c) interaction between history and geography and (d) residual variation. This latter residual variation reflects imperfections in the models of ‘history’ and ‘geography’ used, as well as additional explanatory factors not considered in the HMT model. First, in order to qualify for the HMT, all three matrices (biology/culture distance (D), history (H) and geography (G)) must correlate with each other. Thereafter, two simple (I, II) and one multiple Mantel tests (III) are performed: (I) D × H, (II) D × G and (III) D × (H + G). The resultant correlation coefficients are converted into coefficients of determination (R²) and the partitions are calculated as follows:

Here, we illustrate the potential utility of the HMT method using two human datasets, one biological and one cultural. The biological case study employs a recently published global craniometric dataset [60] representing populations from all major continents. The cultural case study employs material culture data collated by Welsch et al. [1] for coastal communities from northern New Guinea. Notably, in the biological case, the geographical scale is global and large-scale historical factors due to the out-of-Africa dispersal are likely to be strong in structuring among-group patterns (e.g. [21,34]). Conversely, in the case of the cultural dataset, which operates at a far more local level geographically, patterns of inter-group cultural exchange have been proposed by some [1] to be the mostpertinent factor in constructing among-group affinity patterns.

2. Material and methods

In the case of both the biological and the cultural case studies, three different types of among-group distance or dissimilarity matrices were required, representing (i) ‘geography’, (ii) ‘history’ and (iii) the among-group affinity patterns of interest (i.e. biological distance or cultural similarity).

(a). Biological case study

The biological case study was based on modern human craniometric affinity patterns (figure 1) for 17 globally distributed populations [60].

Figure 1.

(a) Global map showing geographical location of each cranial population. Stars represent the following waypoints used to calculate geographical distances among groups: Cairo, Egypt (30.0, 31.0); Istanbul, Turkey (41.0, 28.0); Phnom Penh, Cambodia (11.0, 104.0); Anadyr, Russia (64.0, 177.0); Prince Rupert, Canada (54.0, −130.0) and Panama City, Panama (9.1, −79.4). (b) Tree topology representing a hierarchical model of historical divergence among populations based on genetic information. ‘History’ distances simply reflect the number of nodes connecting pairs of populations and branch lengths are not shown to scale.

(i) The geographical distance matrix comprised between-population great-circle distances in kilometres based on the geographical coordinates provided in table 1. Distances were calculated via the waypoints shown in figure 1 using the Geographic Distance Matrix Generator v. 1.2.3 [61].

Table 1.

Human population craniometric samples employed.

population	museuma	N	latitude, longitude
1. San	NHM, MH, AMNH, NHMW, DC	31	−21.0, 20.0
2. Biaka	NHM, MH	21	4.0, 17.0
3. Ibo	NHM	30	7.5, 5.0
4. Zulu	NHM	30	−28.0, 31.0
5. Berber	MH	30	32.0, 3.0
6. Italian	NHMW	30	46.0,10.0
7. Basque	MH	30	43.0, 0.0
8. Russian	NHMW	30	61.0, 40.0
9. Australian	DC	30	−22.0, 126.0
10. Andaman	NHM	28	12.4, 92.8
11. Mongolian	MH	30	45.0,111.0
12. Chinese	NHMW	30	32.5,114.0
13. Japanese	MH	30	38.0,138.0
14. Alaskan	AMNH	30	69.0, −158.0
15. Greenland	SNMNH	30	70.5, −53.0
16. Hawikuh	SNMNH	30	33.5, −109.0
17. Chubut	MLP	30	−43.7, −68.7

^aNHM, Natural History Museum (London, UK); MH, Museé de l'Homme (Paris, France); AMNH, American Museum of Natural History (NY, USA); NHMW, Das Naturhistorische Museum, Wien (Vienna, Austria); DC, Duckworth Collection (Cambridge, UK); SNMNH, Smithsonian National Museum of Natural History (Washington DC, USA); MLP, Museo de la Plata (La Plata, Argentina).

(ii) Among-group distances due to historical divergence were constructed based on the branching topology of a consensus genetic tree of population relatedness (figure 1b). This tree was informed primarily by the neighbour-joining analysis of 246 neutral microsatellites published by Pemberton et al. [62], with additional information regarding the branching relationships of the four New World populations derived from Reich et al. [63], and the relationship between the Australians and Andamanese inferred from Rasmussen and co-workers [47,64]. Once the fully resolved genetic tree was constructed, a distance matrix reflecting these hierarchical branching relationships was compiled, whereby each node separating two populations was counted as one unit. Hence, if two populations were separated by four nodes, their ‘history’ distance was coded as 4, and so forth. The resultant history distance matrix was analysed using a neighbour-joining [65] algorithm to confirm the hierarchical branching relationship shown in figure 1b.

(iii) Craniometric distance was calculated as the pairwise Procrustes distances among populations, following a geometric morphometric analysis of cranial landmark configurations. Table 1 provides the sample sizes and museum repositories for each cranial population sampled. Only adults were measured and sex was ascertained using standard osteological protocols [66]. One hundred and thirty-five cranial landmarks were digitized from each cranium by N.v.C.-T. (see electronic supplementary material, table S1 for anatomical descriptions) using a Microscribe 3DX^™ digitizer. The full cranial landmark configuration was also subdivided into three standard functional–developmental modules (face, cranial vault and basicranium), each of which were also analysed separately. Each cranial landmark configuration was aligned using Generalized Procrustes Analysis and tangent space projection in MorphoJ 1.06 [67], and the resultant scaled Procrustes shape variables were employed to calculate the Procrustes distance matrices representing biological distances between groups. This resulted in four biological distance matrices representing the shape of the entire cranium, the face, cranial vault and the basicranium.

(b). Cultural case study

The cultural case study was based on material culture affinity patterns (figure 2) for 10 Austronesian-speaking groups distributed along the northern coast of New Guinea [1]. These data were collated from a larger dataset compiled by Welsch et al. [1] of material culture variability across 31 Austronesian and Papuan communities, speaking languages from as many as seven different language families. We chose to focus solely on the communities speaking Austronesian languages primarily because this allowed us, in the absence of genetic data, to use linguistic information within a single language family to construct a hypothesis of historical divergence. The other Papuan language families each contained fewer community samples within families and, given the uncertainty of how language families are related to one another [68], constructing a higher-level phylogeny for all 31 communities was not feasible. Moreover, the Austronesian communities differed from the Papuan ones in being geographically widespread across the study area (figure 2) with communities in the far west and east end of the sampled region. There were three additional communities in the original dataset that were reported to speak both an Austronesian and a Papuan language, which we excluded from the present dataset, given uncertainties as to their primary linguistic affiliation. This resulted in a final dataset compiled for 10 material culture samples from Austronesian-speaking villages (table 2).

Figure 2.

(a) Map showing geographical location of each of the Austronesian communities sampled along the northern coast of New Guinea. (b) Tree topology representing the hierarchical model of historical divergence among populations based on linguistic information. Community names are those given by Welsch et al. [1] as taken from museum records at the time of sampling and do not necessarily reflect local village names used today (table 2).

Table 2.

Austronesian villages sampled for cultural traits.

communitya	language spokenc	latitude, longituded
1. Humboldt (Yos Sudarso) Bay	Yotafa (Tobati)	−2.56, 140.71
2. Sissano	Sissano	−3.00, 142.06
3. Malol	Sissanoe	−3.10, 142.24
4. Tumleob	Tumleo	−3.12, 142.40
5. Alib	Ali (Kap)f	−3.13, 142.47
6. Seleob	Ali (Kap)	−3.14, 142.49
7. Angelb	Ali (Kap)	−3.16, 142.49
8. Wogeo (Vokeo)b	Wogeo	−3.22, 144.10
9. Koilb	Wogeo	−3.36, 144.21
10. Kadowar (Kadovar)b	Bam (Biem)	−3.61, 144.59

^aAlternative modern name for village/community in parentheses.

^bIsland communities.

^cWhere different, the names of languages as given in Glottolog are provided in parentheses.

^dGeographical coordinates are approximated using Google Earth. Geographical distances (km) used in analyses are coastal distances provided by Welsch et al. [1].

^eAlthough Malol is also a recognized language, we follow Welsch et al. [1] in assigning the Malol community to the same language as Sissano.

^fAli is recognized as a dialect of the Kap language, which is closely related to Tumleo.

(i) The geographical distance matrix was constructed from the distances (in kilometres) between communities published by Welsch et al. [1]. We provide estimates of the geographical coordinates for each community based on village names given by Welsch et al. [1] in table 2. Owing to their coastal position (figure 2), the geographical distances provided by Welsch et al. [1] assume that interaction between communities was by water (via canoe) rather than overland. Therefore, the fact that some communities were island-based and some from the New Guinea mainland (table 2) should not affect the results of our analyses as it assumed that all communication between groups was via ‘water distances’.

(ii) Distances representing historical divergence between groups were based on the linguistic similarity measures published by Welsch et al. [1], as shown in figure 2b. The coding system used for language similarities also follows Welsch et al. [1], and the resultant linguistic similarity matrix used is shown in the electronic supplementary material, table S2. Use of this particular linguistic taxonomy allowed for a more direct comparison to be made between the results obtained here and those of the original study by Welsch et al. [1]. To convert the language similarity matrix to a distance matrix, we inverted the coding (i.e. 95% becomes 5%, 30% becomes 70% etc.). The resultant history distance matrix based on language was analysed using a neighbour-joining algorithm [65] to confirm the hierarchical branching relationship shown in figure 2b.

(iii) Cultural similarity between communities was quantified based on the presence or absence of 44 classes of material culture in the 10 communities (see electronic supplementary material, table S3). Of the original 47 traits coded by Welsch et al. [1], three were found to be absent in all 10 Austronesian communities and were therefore removed from the dataset as uninformative. A matrix representing the pairwise similarities among groups based on the remaining 44 cultural traits was calculated in PAST v. 3.07 [69] as Jaccard distances, which are particularly appropriate for binary (presence/absence) data [2,5,44].

(c). Hierarchical Mantel test

In both the biological and cultural datasets, the matrices representing ‘geography’ and ‘history’ were found to be significantly correlated using a Mantel test [43]: biology: r = 0.592, p < 0.001; culture: r = 0.811, p = 0.001. This confirms the strong association between the geographical positions of individual groups and their patterns of relatedness as shown in figures 1 and 2. Therefore, in both datasets, the patterns of historical divergence among groups are also strongly spatially correlated. As noted earlier, the HMT partitions out the unique contribution of two geographically mediated processes (historical divergence (VarHist) and gene/culture flow (VarGeo)), as well as quantifying the contribution of the interaction between these two processes (VarHist × Geo). Assuming three distance matrices (biology, history and geography), the test uses standard and multiple Mantel regressions to partition the effects of history, geography and the interaction between history/geography as follows:

Standard and multiple mantel tests were conducted in R v. 3.1 using the multi.mantel and the mantel functions from the phytools [70] and the vegan [71] packages, respectively. An R function to implement the HMT is provided in the electronic supplementary material.

3. Results

(a). Biological case study

Table 3 presents the results of the HMT for the biological datasets. For all four cranial matrices tested, historical divergence explains a much higher proportion of the among-group biological variation than pure geographically mediated gene flow. Depending on the cranial region under investigation, history explains between 15 and 22% of the overall variation, compared with less than 2% explained by pure geography. It should be noted that the interaction between history and geography also explained a proportion of overall variation in most cases, although this varied substantially between different regions of the skull (approx. 1% for the basicranium to approximately 15% for the vault). Contrasting the pattern of results for the basicranium and the face reveals some interesting differences in the extent to which different cranial features track phylogenetic signals compared to reflecting the past action of gene flow. The strongest historical signal was found in the basicranium (approx. 22%), with virtually no additional variation explained by geography or the interaction between history and geography. Conversely, for the face, the pure historical signal was a little weaker (19%), but with the same amount of biological variance (19%) explained by the interaction between history and geography. This may reflect the additional effect of environmental factors such as climate or diet in creating among-group facial differentiation [72] that are currently not being accounted for by the HMT.

Table 3.

Results of HMT for global cranial data. Data are proportions of among-group biological distance explained by the models of historical divergence, geographical distance and the interaction between these two factors (p-values in parentheses).

	history	geography	history × geography	total (%)
cranium	0.1979 (<0.001)	0.0002 (0.007)	0.1194 (<0.001)	31.75
vault	0.1515 (<0.001)	0.0089 (0.013)	0.1533 (0.002)	31.37
face	0.1930 (<0.001)	0.0101 (<0.001)	0.1900 (0.001)	39.31
basicranium	0.2268 (<0.001)	0.0198 (0.080)	0.0109 (<0.001)	25.75

(b). Cultural case study

Table 4 presents the analogous HMT results for the cultural dataset. By contrast with the biological case study, by far the strongest predictor of among-group cultural similarity was geography (35%), with history explaining only an additional 8% of cultural variation. Notably, the effect of the interaction between history and geography was not particularly strong for the cultural case study (approx. 1.7%), indicating that the patterns of cultural similarities represented in the Austronesian dataset are primarily driven by geographical distance rather than by the historical relatedness of these groups, as measured by linguistic similarity.

Table 4.

Results of HMT for Austronesian cultural data. Data are proportion of among-group cultural similarity explained by the models of historical divergence, geographical distance and the interaction between these two factors (p-values in parentheses).

	history	geography	history × geography	total (%)
culture	0.0866 (0.049)	0.3547 (0.010)	0.0173 (0.006)	45.86

4. Discussion

The results for the craniometric and cultural case studies differed primarily in terms of the relative contributions of historical divergence and recent gene/culture flow to explaining observed patterns of among-group affinity, despite a strong correlation between patterns of geographical distance and historical divergence in both cases. In the case of the craniometric case study, the tree-like model of historical divergence was found to explain a much larger proportion of population diversity, compared with geographically mediated gene flow. It is worth noting, however, that the relative proportions of variation explained by history and geography differed depending on the cranial region under investigation. This highlights how different areas of morphology can track phylogenetic signals of population divergence to a more accurate extent, while some regions may be influenced by additional (e.g. environmental) factors not considered as part of the hierarchical Mantel model [72]. The fact that the cranial data were better explained by a tree model of successive common ancestors is not surprising given that the dataset considered here is global rather than regional in focus. It is well established that global patterns of human genetic and morphological variation were largely established as a result of the relatively rapid migration out-of-Africa at least 70 kya [19], explaining the close correspondence between biological structure and global geography [21,34,36,47]. While population history is still important at a more local/regional level, population structure is relatively more likely to be driven by recent gene flow between geographically proximate populations as well as harbour the signals of more ancient population structure resulting from large-scale dispersals or recent migrations [34,36]. It is worth noting that there are other ways in which this ‘history’ matrix could have been constructed, including using actual genetic distance data which would reflect both the hierarchical relationships (numbers of nodes) and the relative genetic distances (branch lengths) between populations. In this case, the history model was informed by genetic data, but the lack of a single integrated genetic database for all populations considered precluded the use of primary genetic distance data.

In the case of the cultural case study, the opposite pattern was observed, whereby geographically mediated culture flow was the more prominent factor explaining among-group affinity patterns, while ‘history’, as measured by linguistic affiliation, played a relatively minor role. Our results support Welsch et al.'s [1] original conclusions that geography played a strong role in generating the observed patterns of cultural variation, at least among the Austronesian communities in their dataset. Welsch et al. [1] downplayed the role of linguistic affiliation, stating that material culture diversity appeared to be ‘unrelated to the linguistic relationships of these communities'. This assessment was later challenged by analyses carried out by Moore and co-workers [73,74], all of which demonstrated that both geography and linguistic affiliation were important for explaining cultural patterning. It is difficult to directly compare our results to those generated by the aforementioned studies, as we focused only on the Austronesian rather than the complete dataset which included communities from several Papuan language families. However, a subsequent study by Shennan & Collard [25] sought to better understand the impact of population history (branching) and geographical propinquity (blending) on this dataset by comparing pairs of Austronesian-speaking communities against neighbouring Papuan groups. The rationale was that Austronesian-speaking peoples are relatively recent migrants to New Guinea, arriving approximately 3000 years ago as part of a rapid demographic expansion across the Pacific region, originating in Taiwan approximately 5500 years ago [75,76]. Therefore, Austronesian-speaking communities form a coherent group with a particular linguistic and genetic history [77] that might also be reflected in their cultural affinity patterns. While our results concur with those of Shennan & Collard [25] in highlighting that the Austronesian communities form a coherent phylogenetic grouping, our findings also emphasize the strong role played by geographically mediated processes such as borrowing, exchange and cultural assimilation with neighbouring Papuan-speaking communities. This history of interaction is also reflected in genetic studies, which show that rates of language borrowing and exchange between Papuan and Austronesian speakers were fast and pervasive, compared with relatively low rates of genetic admixture [75]. However, it should also be noted that shore-dwelling populations in Oceania are generally more admixed than inland populations, as dispersal along shorelines via watercraft makes movement easier [75]. Therefore, it is likely that the effects of cultural ‘blending’ were relatively important in generating the among-group cultural patterns observed in the coastal Austronesian communities analysed here. One future avenue of inquiry in this regard may be to use linguistic data such as phoneme variation to reconstruct a ‘history’ matrix that compares across language families (Austronesian and Papuan), given the close association found between phonemic and genetic diversity patterns at a global level [22].

Our results suggest that, at least in the case of the material cultural datasets analysed here, ‘blending’ forces such as cultural contact, borrowing and exchange were more potent in generating among-group affinity patterns when compared with phylogenetic ‘branching’. However, there are a couple of issues that need to be considered when interpreting the results in the context of debates regarding the relative importance of ‘branching’ and ‘blending’ in driving patterns of material culture evolution. It should be made clear that the hypothesis of phylogenetic history inputted into our model uses linguistic affinities to model the population history of the people not the phylogenetic histories of the cultural attributes themselves, which may or may not amount to the same histories. Indeed, it has been shown that even different classes of artefacts from the same populations can be subject to differing cultural evolutionary forces, leading to contrasting affinity patterns in their attributes among those same communities [18]. In this case, however, we are asking whether the presence/absence patterns of multiple artefacts exhibited across different groups of people potentially tell us something about the cultural history of those communities played out across time and space. When phylogenetic methods are applied to material culture attributes directly [41], a strong tree-like signal may well be recovered, but this may or may not reflect the phylogenetic history of the particular populations of people that created the material culture patterns, as would be measured using genetic or linguistic data. Accordingly, it is possible that material culture patterns follow a strong tree-like model of successive bifurcations, yet this model does not match patterns of genetic or linguistic affiliation. Hence, it is worth noting that our results do not negate the presence of a phylogenetic history in the cultural dataset, or more specifically sub-elements of it, but rather suggest that due to geographically mediated cultural exchange among groups, the historical signal of population splitting has been overridden by a more recent signal of culture flow [39]. In some instances, it can be shown at the outset that language and geography are uncorrelated (e.g. [18]), which means that the HMT method would not be required to disentangle the effects of isolation-by-distance from historical branching under such conditions. However, as shown here, such fortuitous circumstances are not always the case.

Regardless of the precise details of these two case studies, the results serve to illustrate the usefulness of the HMT for disentangling and explicitly quantifying the relative impact of two distinct geographically mediated processes: historical divergence and recent gene/culture flow. Recent studies have acknowledged that conceptualizing these two processes as dichotomous is problematic [7], due to the complexities of inferring instances of ‘branching’ and ‘blending’ in morphological [60] or cultural (e.g. [29]) datasets. Hence, recent studies have employed other methods such as network analyses (e.g. [78–81]) to explore among-group affinities or agent-based simulations [29] to better understand the effects of fission (splitting), innovation (mutation) and horizontal transfer on observed affinity patterns. We advocate that the HMT provides a simple yet effective adjunct to existing methodological approaches for examining the spatial and phylogenetic causes of cultural diversity patterns, especially when several data matrices are correlated with each other, meaning that an independent measure of history is elusive. It is also important to note that ‘history’ can be modelled in several different ways, depending on the research questions at hand. Here, we chose to employ the same linguistic taxonomy used by Welsch et al. [1] as a means of maintaining continuity with the original study, but historical relationships could also be modelled using actual genetic distances or linguistic data such as phonemic variation. As mentioned earlier, the HMT method is likely to be most effective where both the cultural and phylogenetic patterns are spatially autocorrelated. Its primary value lies in the explicit quantification of the effects of phylogeny (history) and geographical propinquity in causing observed patterns, and assumes a priori that historical and geographical factors are related. In that sense, it avoids the pitfall of false dichotomy in assuming that affinity patterns must be caused by either branching or blending, rather than acknowledging that both processes are likely conspiring to create observed among-group differences under such conditions.

Extensions of the HMT method to include additional causal factors, such as ecological data, are also possible, although each additional factor would increase the number of interaction terms quantified. However, such an extension may prove useful in the cases of particular material culture datasets where there are clear environmental or climatic correlates (i.e. food preparation, clothing and shelters). Another possible extension of the HMT to human cultural datasets could involve testing the goodness of fit of alternative phylogenetic models for explaining particular cultural patterns. For example, von Cramon-Taubadel et al. [60] recently used the HMT to test all possible phylogenetic positions for the prehistoric Paleoamerican population from Lagoa Santa (Brazil) within the context of modern human craniometric diversity. By using the HMT to hold the effects of gene flow constant, it was possible to test alternative migration scenarios into the New World. Hence, extrapolating this method to cultural datasets may be useful in cases where the relative phylogenetic position of an individual population is uncertain within the context of a ‘known’ phylogeny, yet allows one to control for the effects of geographically mediated culture flow.

5. Conclusion

Here, we showcased a simple extension of standard matrix correlation methods, termed the HMT, to explicitly quantify the relative impact of population history (phylogeny) and among-group contact (gene/culture flow) in generating observed patterns of population affinities, using both a biological and material cultural case study. In both case studies, the among-group biological/cultural distances and the phylogenetic model employed were spatially autocorrelated, illustrating the often intimate interconnections between space (geography) and time (history) in generating human diversity patterns. In the biological case study, the results showed that historical factors (phylogeny) played the more potent role in generating observed patterns of global among-population craniometric diversity. Conversely, in the cultural case study, geographically mediated culture flow between contiguous populations explained a greater proportion of the differences in the presence or absence of material culture attributes among 10 Austronesian communities from the northern coast of New Guinea. These case studies serve to illustrate the utility of this simple and intuitive method for disentangling the relative effects of history (i.e. branching) and geography (i.e. blending) in explaining observed cultural affinity patterns.

Data accessibility

This article has no additional data.

Author contributions

N.v.C.-T. and S.J.L. performed research conception and design. N.v.C.-T. carried out data acquisition and analysis. N.v.C.-T. and S.J.L. wrote the paper. All authors approved the final manuscript for submission.

Competing interests

We have no competing interests.

Funding

We are grateful to the Research Foundation of the State University of New York for funding support.