The shaping of social and symbolic capital during the transition to farming in the Western Mediterranean: Archaeological network analyses of pottery decorations and personal ornaments

Daniel Pereira; Claire Manen; Solange Rigaud

doi:10.1371/journal.pone.0294111

. 2023 Nov 8;18(11):e0294111. doi: 10.1371/journal.pone.0294111

The shaping of social and symbolic capital during the transition to farming in the Western Mediterranean: Archaeological network analyses of pottery decorations and personal ornaments

Daniel Pereira ^1,^*, Claire Manen ², Solange Rigaud ¹

Editor: John P Hart³

PMCID: PMC10631656 PMID: 37939080

Abstract

Storing information and circulating it between individuals and groups is a critical behaviour that signals a tipping point in our evolutionary history. Such practices enabled the preservation and consolidation of knowledge over extended periods, facilitating the accumulation of cultural innovations across generations. In this study, we used Social Network Analysis methods to explore how knowledge circulated during the transition to agriculture in the Western Mediterranean region. Previous studies have shown that specific elements of the material culture reveal distinct patterns of cultural interaction among early farming communities. Here, we investigated if two archaeological proxies, personal ornaments and pottery decorations, both with an exclusively symbolic function, reveal different network structures, and if the different degree of connexions acted equally on the transmission of styles, symbols, and network changes over time. Our results relied on cultural data recorded from 77 archaeological occupations covering Italy, France, and Spain, spanning over 1,500 years (ca. 7950~6450 cal BP). By utilizing a chronological dataset comprising 114 radiocarbon dates, we revealed that pottery decorative techniques networks exhibited stronger connexions over space and time, with nodes organized in clear cluster, when compared to personal ornaments networks. The findings highlight the regionalization and fragmentation of cultural networks during the Early Neolithic, and that the transmission of cultural traits within each category of artefact operated through varying cultural and social mechanisms. Pottery expressed a dynamic regional identity, continuously shaped by geographical and chronological proximity, while bead-type associations contributed to enduring identities shared across vast geographical scales. These networks shed light on the multifaceted shaping of social and symbolic capital among the Mediterranean’s early farmers, emphasizing the strength and quality of social ties that existed between communities and the level of reciprocity and cooperation required to foster these diverse social, economic, and cultural development strategies.

Introduction

A key characteristic unique to the human lineage is our ability to materialize and store information outside the brain. Recently, this external storage of information has taken the form of writing, mass media, and the internet [1]. In the archaeological record, mobile and rock art, and personal ornaments–some of the material productions that attest to such an ability, whatever the symbolic message behind those productions–are attested as far back as 140 ky ago [2–7]. Our ability to store information and circulate it between individuals and groups is a critical behaviour that signals a tipping point in our evolutionary history. By doing so, we have been able to maintain and consolidate knowledge and cumulate cultural innovations over time [8–10]. But how did information and knowledge circulate during the past? How did past people structure their networks? And what mechanisms contributed to this network shaping?

The transfer of cultural traits within and between past communities has long been investigated. Contact between populations can be seen through the circulation of raw materials from their source to regions located several hundred kilometers away [11–15]. The sharing of common stylistic traits in pottery design [16–20], bone tool shaping and decoration [21,22], as well as flint weaponry production [23–25], are also commonly used to track interactions between communities. Maps describing connexions, circulation routes, and exchange networks have been produced for many periods of prehistory across many regions [26–28].

For the last three decades, Social Network Analysis (SNA) has been regularly used within various geographical and chronological contexts [29,30], especially over the past ten years, to explore various scenarios, such as the impact of demographic events on network structures [31,32], the mechanisms responsible for their long persistence [33], the social fabric of early villages [34], and multigenerational changes in network structure [35]. Here, we applied SNA to explore how pottery decorations and personal ornaments can be used as proxies for tracking the production, transmission, and accumulation of social [36,37] and symbolic capital during the transition to farming, whilst also emphasizing the strength and quality of social ties that existed between communities and their level of reciprocity and cooperation, all of which may have aided the successful diffusion of farming technologies throughout Europe.

This period represents the process by which human groups switched from hunting and gathering wild resources to a reliance on systems of food production based on domesticated plants and animals. In the Fertile Crescent, sedentism, farming, and herding progressively took place 15,000/12,000 years (y) ago, then spread across Europe from 8,800 until 5,500 y ago [38–40]. Increasingly refined archaeological [41–43], anthropological [44–47], and chronological data [48] have identified a succession of profound cultural, technical, and economic changes between the last indigenous hunter-gatherers and the first Early Neolithic farmers in Europe. This revolution played a pivotal role in establishing the economic and social underpinnings upon which many present-day societies rely, including diverse techniques for food production and storage, the emergence of surpluses, the shift towards sedentism, the specialization of labor, the growth of social complexity, and, ultimately, the formation of state institutions.

Previous studies have shown that a cultural substrate made up of ultra-connected European foraging communities may have represented favorable conditions for enhancing the rapid dispersion of the Neolithic way of life in Europe [28,49,50]. The vast networks documented between farming communities, and between local foraging communities and farmers, indicate that maintaining and reinforcing connexions between neighboring communities represented an efficient strategy for emerging farming societies seeking to spread and access new territory [49].

Our primary goal is to document the interactions and contact networks during the Neolithic transition in the Western Mediterranean region, in order to observe how past cultural diversity was shaped by repeated interactions and how it may have changed through time. We postulate that the network structure between archaeological occupations may have impacted past mobility and the diffusion of knowledge, information, and innovations during the Early Neolithic.

Research objectives, material, and methods

Research objectives

Previous studies have shown that during the Early Neolithic human communities were highly connected and mobile, with well-developed large-scale circulation networks [51–54] that favoured both cultural and demic diffusions. Additional studies have evidenced that each mechanism acted to a different extent across Europe [55,56], although these interactions did not uniformly affect the various elements of the first farmers’ material culture. Nevertheless, by comparing two proxies with an exclusively symbolic function, pottery decorations and personal ornaments, distinct patterns of interaction among early farming communities can be identified. Pottery decoration diversity indicates local circulation and exchange, resulting in the emergence and persistence of stylistic and symbolic distinctions between groups. In contrast, personal ornaments reflect extensive networks and the mobility of Early Neolithic farmers [57]. This previous comparative study did not, however, specifically explore the shape, structure, and density of the networks, preventing an exploration of how information flowed between groups. By applying various SNA methods, and by specifically applying this approach to both pottery decorative techniques and personal ornaments, we propose to investigate if the two archaeological proxies can reveal different network structures, if the communities who spread to the Western Mediterranean area developed tight or loose inter-connexions, if those connexions acted similarly on the transmissions of personal ornaments and pottery styles, and if networks were reshaped through time.

By assessing the extent of shared cultural traits among archaeological occupations, we can infer the degree of connexion between them, based on the premise that a greater cultural resemblance indicates stronger past interactions between communities. Our objective in exploring these networks of interactions is to identify communities of practice that shared common social and symbolic engagement.

We explored these questions using a large chronological scope covering the 1,500 years of Neolithic diffusion in the region through a time-sequential perspective. We explored how networks of interactions were reshaped through time, the potential lagged effects on the networks as communities moved to new locations, and the outcome of different communities separating or coalescing while spreading to new territories.

Material

Our sample is constituted of two updated previously published datasets (for detailed information see Rigaud et al. 2018, Supplementary Material text B), including cultural and chronological data recorded from 77 archaeological occupations covering Italy, France, and Spain, spanning over 1,500 years (ca. 7950~6450 cal BP; Fig 1; worksheets A and C in S1 Dataset) [57]. The chronological dataset derives from 114 radiocarbon dates (worksheet J in S1 Dataset) and the cultural datasets from a typological classification system that we developed for the study of pottery decorative techniques [19,20,58] and personal ornaments [50,59].

Fig 1 — (A) archaeological occupations recorded for the pottery decorative techniques. (B) archaeological occupations recorded for the personal ornaments. Marker colours: red–IMP; golden–TyC; blue–RPC; green–LCC; cyan–VC; pink–LCE; purple–VE; black–La Balma Margineda. Marker shapes: square– 1^st period; round– 2^nd period; triangle– 3^rd period. Due to geographical coordinates overlapping, not all archaeological locations markers are visible. Maps were made by D. P. using the software QGIS 3.22.16 [60] and Natural Earth raster maps [61].

The pottery dataset included 11 pottery decoration traits recorded within 44 Early Neolithic occupations (worksheet A, B and F in S1 Dataset). Because pottery fragment recovery is not as sensitive to field methods as beads, each variable was counted in each occupation included in the database. The visual aspect of decorative attributes may vary depending on the tools used to decorate the artefacts, the gestures used to apply the tools to the surface of the pottery, and eventually the morphology of the decoration itself. Each archaeological assemblage included in the database is described with quantitative variables regarding these decorative attributes.

The personal ornaments dataset includes 88 mutually exclusive bead-types, recorded within 46 occupations and coded as presence/absence in order to avoid any bias due to field methods that may have impacted small items recovery [50,57] (worksheet C, D and H in S1 Dataset). Discrete bead types were created with reference to raw material, morphology, system of suspension (e.g., perforation or groove), size, section, and profile. In the case of animal teeth, we also considered anatomical and species identification.

Geographic coordinates, cultural affiliation, and the corresponding time span of existence (worksheet J in S1 Dataset) are documented for each archaeological occupation of the two datasets. Our sample covers seven archaeological cultures attributed to early farming communities.

Methods

Cultural diversity

Each archaeological occupation was assigned to one of the three main cultural units (Impressa, Cardial, or Epicardial) encompassing the seven Early Neolithic Mediterranean archaeological cultures, as defined in the literature according to lithic technology, settlement patterns, and ceramic production: (Impressa (IMP), Tyrrhenian Cardial (TyC), Rhodanian-Provençal Cardial (RPC), Languedocian-Provençal Cardial (LCC), Valencian Cardial (VC), Languedocian-Provençal Epicardial (LCE), and Valencian Epicardial (VE) (for detailed information see Rigaud et al. 2018, Supplementary Material text A). First, we quantified how the archaeological occupations differed in pottery and personal ornament attributes by using two different similarity indices. The Brainerd-Robinson similarity index [62,63] is appropriate for count data and has been used for calculating pairwise site differences according to their pottery attributes (worksheet A in S2 File). The Jaccard similarity index [64,65] is appropriate for presence/absence data and has been used for calculating pairwise site differences according to their bead-type diversity (worksheet G in S2 File). Both indices are on the scale [0, 1], with a coefficient value of 1 indicating the two assemblages are perfectly similar, while a 0 value indicates that there is no similarity. For more information on the two indexes see Supplementary Text A in S1 File.

Time-sequential analysis

In order to explore if, how, and why the network structure may have changed through time we performed a time-sequential analysis. The datasets cover a period of 1,500 years, with secure radiocarbon dates for most of the archaeological occupations or, alternatively, a widely accepted relative chronology [57,66]. We divided the datasets into three separate time sequences: 1) the emergence of farming societies in the region between 8000 BP and 7600 BP years [66]; 2) the period corresponding to an increase in site numbers, a wider spatial distribution of farming occupations in diversified ecosystems, and ceramic style diversification between 7650 BP and 6800 BP years [66]; 3) the end of the Early Neolithic, between 7000 BP and 6500 BP years, corresponding to durable cultural territories and the gradual loss of pottery decoration diversity counterbalanced by an increase in personal ornament diversity [66,67]. This period represents the final phase of the Neolithisation process. We constructed networks for each two consecutive time-sequences, i.e., sequences 1–2 and sequences 2–3.

Archaeological Similarity Network

We used Archaeological Similarity Networks (ASN) to explore the degree of connexion between the different archaeological occupations based on their level of similarity [29,68]). In ASN, each node represents an archaeological occupation, the edge drawn between nodes represents the level of similarity between two occupations, and the edge weight (thickness) is a direct reflection of the similarity value [68–70]. In ASN, a higher level of similarity between two archaeological occupations is represented by a thicker edge between the two nodes; conversely, the lower the similarity between two occupations, the thinner the edge.

Due to the expansive chronology considered in this study, many sites were not contemporaneous, even though some belong to the same archaeological culture, and because of the age range inherent to each absolute date, we have no evidence of their strict contemporaneity. Therefore, actual contact between groups is not granted and common cultural traits should mostly be understood as a reflection of transmission through time.

The presence of geographically and chronologically ubiquitous personal ornaments and pottery traits led to a large number of connections, resulting in an overcrowded and dense network which was barely readable. We therefore imposed a threshold to limit the amount of edges plotted on the network, allowing the underlying network structure to become more apparent and to specifically highlight the node connections of high value [31,32,35]. The threshold chosen was the minimum value that allowed the network to remain as a single component, i.e., the minimum value needed to keep all nodes in the network connected with the exception of a few extreme outliers. Consequently, only the edges with equal or higher value than the threshold are displayed in the drawn networks and, unless otherwise specified, used for all extracted network statistics.

Our network design was performed within R [71] using adapted and extended codes from Brughmans and Peeples [69,72,73] (R protocol in S1 Code). Networks were plotted using two layout methodologies: the Fruchterman-Reingold [74] forced-directed graph layout for a best-fit, and a manual layout using the geographical coordinates of each archaeological occupation.

Different characteristics and information from an archaeological occupation, such as cultural affinity, time-sequence, and geographical area, can be used to inform and help the network’s interpretation. For this study, both colour and shape coded each node in the network according to two levels of information: their archaeological culture (colour code), and their time-sequence (symbol code) (worksheets B and D in S1 Dataset).

Descriptive network statistics

Multiple statistics can be calculated to infer on network connectivity, graph structure, and node connections (S1 Table in S1 File). To explore the graph structure and investigate the significance of each node within the network, concerning information retention and transmission, we calculated network density, cluster coefficient, network interval statistics, weighted centralities, and weighted centralization statistics (normalized) for degree, eigenvector, and betweenness centralities [72,73,75–77]. Degree and eigenvector centralities are classical metrics that assess a node’s connectivity within a network. Betweenness centrality examines nodes connecting different parts of the network, enabling one to observe how distant segments of the network are interconnected and how information flows between them. The definition and application of each metric is described in S1 Table in S1 File. For each main cultural unit, we also computed its similarity radius, network density, cluster coefficient, and network interval statistics (presented in S4 and S5 Table in S1 File).

Isolation by distance

Understanding how isolation may have impacted cultural flow requires the incorporating of chronological and geographical distances into the analyses of cultural transmission and differentiation [50,78,79]. Both our datasets cover a wide geographical and temporal range. To account for the effect of geographical and chronological distances on cultural diversity, we performed correlation tests between cultural, geographical, and chronological distances using Mantel and partial Mantel tests [80]. Great-circle (spatial) distance was calculated from the latitude and longitude data (R command published by Shennan et al., 2015, worksheets B and H in S2 File). We used the Euclidean distance between the earliest and latest dates for each archaeological culture (worksheets C and I in S2 File). The correlation tests were implemented using Mantel tests (code provided in the S1 Code) and performed using distance matrices that included all the archaeological occupations present in the datasets (full matrices), and the matrices that included only pair of nodes connected in the network built with a threshold (threshold matrices).

Results

Pottery decorative techniques

Archaeological Similarity Networks

The pottery decorative techniques networks were built with a threshold of 0.44. The networks (Fig 2) encompass 44 nodes connected by 231 edges, have a density of 0.2442, and a cluster coefficient of 0.7169 (S2 Table in S1 File).

The network plotted using the Fruchterman-Reingold layout (Fig 2A) shows two main clusters, each including tightly interconnected nodes. One cluster groups archaeological occupations attributed to the various regional expressions of the Cardial archaeological culture (RPC, LCC and VC), while the other cluster groups archaeological occupations attributed to the different regional expressions of the Epicardial archaeological culture (LCE and VE). Archaeological occupations belonging to the Impressa culture are spread in the peripheral areas of the network, linked to either of the two main clusters. Cardial archaeological occupations are connected by edges showing the highest weight, indicating their high level of similarity. More generally, edges with a high weight mostly connect archaeological occupations belonging to the same archaeological culture.

The network plotted using the geographical coordinates of each archaeological occupation (Fig 2B) shows a smaller number of nodes, due to the various archaeological occupations sharing similar geographical coordinates, documented within the same archaeological site. The network also highlights a geographic gradient with the archaeological occupations attributed to the Impressa, documented within the most eastward sites of the dataset, spread mostly on the right side of the network, and those attributed to the Cardial and the Epicardial, documented within the most westward sites of the dataset, clustering on the left side of the network. The right portion of the network shows few Impressa nodes, connected by few and thin edges. Comparatively, the Cardial and Epicardial occupations, on the left portion of the network, are connected by more numerous and thicker edges.

Edge weights (similarity values between pairs of archaeological occupations) for the pottery decorative techniques are distributed within the range of 0.4414 (between occupations SC9-SC37) and 0.9446 (SC29-SC30), with a mean value of 0.6091. The plotted edges range from 0 to 1425 km (SC34-[SC40, SC41]), with a mean geographical distance of 348 km. The chronological distance between the connected occupations ranges from 0 to 775 years (SC34-SC40), with a mean temporal distance of 211 years. All network interval statistics can be found on S3 Table in S1 File.

Archaeological occupations belonging to the Cardial archaeological cultures show the highest degree and eigenvector centrality scores. The highest betweenness centrality scores do not correspond to a specific archaeological culture (Table 1). All graph centralization measures present low values (bellow 0.2). A complete table of node centrality scores is presented in worksheet D in S2 File.

Table 1. Pottery decorative techniques and personal ornaments ASN’s node weighted centralities and graph centralization scores.

	Pottery decorative techniques			Personal ornaments
	deg	eigen	bet	deg	eigen	bet
node level	SC5:12.0034	SC35: 1.9054	SC36: 194	S42: 6.0722	S29: 2.1549	S42: 133.333
	SC4: 11.3801	SC4: 1.8995	SC25: 120	S29: 5.7833	S42: 2.0839	S34: 109.333
	SC35: 11.1161	SC32: 1.8886	SC5: 103	S40: 5.3409	S16: 2.0367	S48: 103.333
	SC32: 10.9880	SC5: 1.8437	SC31: 87	S34: 5.0857	S41: 2.0367	S21: 87.333
	SC10: 10.5550	SC2: 1.8124	SC34: 79	S48: 4.8199	S40: 1.8983	S40: 84.833
graph level	0.13663	0.18736	4.44836E-07	0.08402	0.21139	4.14E-07

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Only the highest scoring nodes from each node centrality are presented. For a complete node score list, see worksheets D and J in S2 File.

The Cardial cultural unit has both the highest network density and the highest cluster coefficient (0.7333 and 0.8938, respectively), followed by the Epicardial cultural unit (S4 Table in S1 File). These two cultural units correspond to the tightest and most densely organized node clusters. The low values obtained for the Impressa indicate a looser and less dense group of nodes.

The similarity radius wielded similar results when calculated over full and threshold matrices (Table 2). In both cases, the Impressa presented the highest mean similarity radius. The various regional expressions of the Epicardial (LCE and VE), show a higher mean similarity radius than the archaeological cultures belonging to the Cardial unit.

Table 2. Pottery decorative techniques and personal ornaments intra-cultural similarity radius.

Pottery decorative techniques
	Full matrices			Threshold matrices
	Max	Mean	Min	Max	Mean	Min
IMP	7930.5250	2164.9173	0	2452.8539	776.1145	0
RPC	571.9154	218.3470	12.8483	360.5481	195.8913	12.8483
LCC	1101.6831	297.2573	4.3940	373.3214	138.2737	4.3940
VC	57.6515	57.6515	57.6515	57.6515	57.6515	57.6515
LCE	1465.7943	314.2967	0	680.4047	219.9777	0
VE	77.8382	77.8382	77.8382	77.8382	77.8382	77.8382
Personal ornaments
	Full matrices			Threshold matrices
	Max	Mean	Min	Max	Mean	Min
IMP	Inf	Inf	0	3856.3917	2306.8235	0
TyC	0	0	0	0	0	0
RPC	Inf	Inf	44.0010	1027.8844	390.9746	44.0010
LCC	Inf	Inf	0	972.0296	294.2533	0
VC	155.407	155.407	155.407	155.4070	155.4070	155.4070
LCE	Inf	Inf	0	979.8394	447.0258	0
VE	-Inf	NaN	Inf	-Inf	NA	Inf

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Isolation by distance

Correlations calculated between pottery decorative techniques diversity and the spatial and chronological distance matrices show different outcomes if calculated with the full or threshold matrices (Table 3). Using the full matrices, variation in pottery decorative techniques diversity suggests a statistically significant correlation with the geographic distance matrix (p < 0.05), with approximately 16.6% of the variance explained by geography. The chronological distance between archaeological sites significantly contributes to approximately 38.7 to 41.6% of the variance. Mantel tests performed on the threshold matrices do not show statistically significant correlations between the pottery attributes or the geographic and chronological distance.

Table 3. Pottery decorative techniques and personal ornaments Mantel and Partial Mantel tests.

	Pottery decor. techn.		Personal ornaments
Test	Mantel R	P-value	Mantel R	P-value
Cultural dist v Geographic dist	0.165826	0.004995	0.150148	0.003996
Cultural dist v Geographical dist (*t)	0.121685	0.211788	0.173578	0.128871
Cultural dist vs Geo. dist ~ Chrono. Dist	-0.007375	0.545455	0.118599	0.028971
Cultural dist vs Geo. dist ~ Chrono. dist (*t)	0.118424	0.219780	0.179971	0.137862
Cultural dist vs Chronological dist	0.4161334	0.000999	0.106843	0.011988
Cultural dist vs Chronological dist (*t)	0.028177	0.451548	0.008415	0.505495
Cultural dist vs Chrono. dist ~ Geo. Dist	0.387084	0.000999	0.053294	0.115884
Cultural dist vs Chrono.dist ~ Geo. dist (*t)	0.000115	0.495504	-0.049000	0.643357

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(*t) Tests performed over threshold matrices.

Personal ornaments

Archaeological Similarity Network

The personal ornaments networks were built with a threshold of 0.16. The networks (Fig 3) encompass 46 nodes connected by 217 edges, have a network density of 0.2097, and a cluster coefficient of 0.5470 (S2 Table in S1 File).

The network plotted using the Fruchterman-Reingold layout (Fig 3A) does not identify any clear cluster, with nodes connected by evenly weighted edges. Archaeological occupations mostly present connections with broadly contemporaneous occupations.

The network plotted using the geographical coordinates of each archaeological occupation (Fig 3B) shows a smaller number of nodes, due to various archaeological occupations sharing similar geographical coordinates, documented within the same archaeological site. The top-left portion of the network shows the majority of nodes and ties. The Impressa archaeological occupations, documented as the most eastward sites of the dataset, are located in the lower-right portion of the network and are loosely connected to the other section of the network. The Valencian archaeological occupations, documented has the most south-westward sites of the dataset, are located in the lower-left portion of the network and are also loosely connected to the other section of the network.

All archaeological occupation edge weights for the personal ornament ASN’s are distributed within the range 0.1613 (between occupations S30-S47) and 1 (S16-S41), with a mean value of 0.2603. Only four edges had a weight above 0.5. The plotted edges range from 0 to 1541 km (S27-S54), with a mean geographical distance of 262 km. Chronological distance between connected occupations range from 0 to 950 years (S37-S56), with a mean temporal distance of 310 years. All network interval statistics can be found in S3 Table in S1 File.

The highest centrality values correspond to archaeological occupations belonging to various archaeological cultures (Table 1). With the exception of eigenvector, all graph centralization measures present low values (bellow 0.2). A complete table of node centrality scores is presented in worksheet J in S2 File.

The Impressa cultural unit has both the highest network density and cluster coefficient (0.4 and 0.6, respectively). The Cardial cultural unit has the second highest network density and the lowest cluster coefficient and the Epicardial shows the lowest network density (S4 Table in S1 File).

The similarity radius for the threshold networks (Table 2) shows that the Impressa presents the highest mean values, and the Languedocian-Catalonian Epicardial has a higher mean similarity radius than the Cardial archaeological cultures. Due to sampling biases, Tyrrhenian Cardial and Valencian Epicardial presented null results: the former because all occupations have the same geographical location, and the latter as it is only represented by a single occupation.

Isolation by distance

Correlations calculated between bead-type diversity and the spatial and chronological distance matrices showed different outcomes if calculated with the full or threshold matrices (Table 3). When using the full matrices, variation in bead-type diversity showed a statistically significant correlation with the geographic distance matrix (p < 0.05), with approximately 11.9 to 15% of the variance explained by geography. The chronological distance between archaeological sites significantly contributes to 10.7% of the variance. Mantel tests performed on the threshold matrices do not show statistically significant correlations between personal ornament diversity or geographic and chronological distances.

Pottery decorative techniques time-sequential analysis

Archaeological Similarity Networks

The pottery decorative techniques sequences 1–2 network (Fig 4A) was built using an imposed threshold of 0.44. The network has 37 nodes connected by 183 edges, a network density of 0.2748, and a cluster coefficient of 0.7302 (S2 Table in S1 File). Archaeological occupations attributed to the various regional expressions of the Cardial almost exclusively cluster together in the central area of the sequences 1–2 network and are connected by heavy weighted edges. Archaeological occupations attributed to the Epicardial are more loosely connected, and the set of Impressa archaeological occupations is fragmented into three separate clusters, loosely connected to the other archaeological occupations.

The pottery decorative techniques sequences 2–3 network (Fig 4B) was built using an imposed threshold of 0.58. The network has 32 nodes connected by 96 edges, a network density of 0.1935, and a cluster coefficient of 0.7830 (S2 Table in S1 File). Archaeological occupations are distributed into two loosely connected main clusters linked by a single Valencian Epicardial occupation with low weight edges to each cluster. One of the two main clusters includes almost all of the occupations attributed to the various expressions of the Cardial, while the other cluster includes almost exclusively occupations attributed to the Epicardial. Archaeological occupations are tightly connected within each of the two main clusters. Heavy edges are observed within each of the two clusters, with the Cardial cluster presenting the thicker links.

Archaeological occupation edge weights for the pottery decorative techniques sequences 1–2 ASN are distributed within the range of 0.4414 (between occupations SC9-SC37) and 0.9446 (SC29-SC30), with a mean value of 0.6068. The plotted edges range from 0 to 1425 km (SC34-[SC40, SC41]), with a mean geographical distance of 385 km. Chronological distance between connected occupations ranges from 0 years and 775 years (SC34-SC40), with a mean temporal distance of 210 years. All network interval statistics can be found in S6 Table in S1 File.

Archaeological occupation edge weights for the pottery decorative techniques sequences 2–3 ASN are distributed within the range of 0.5805 (SC9-SC37) and 0.9080 (SC29-SC30), with a mean value of 0.7037. The plotted edges range from 0 (SC12-SC17 & SC21-SC23) to 811 km (SC10-SC35), with a mean geographical distance of 282 km. The chronological distance between occupations ranges from 0 to 500 years (SC12-SC18 & SC31-SC34), with a mean distance of 170 years. All network interval statistics can be found in S6 Table in S1 File.

With a single exception, the highest degree and eigenvector centrality values correspond to archaeological occupations belonging to the various regional expressions of the Cardial (Table 4). The sequences 1–2 network highest betweenness centrality scores belong to various archaeological cultures, while for the sequences 2–3 network the highest values belong, with a single exception, to the Epicardial. All graph centralization measures present low values (below 0.2) for the two pottery decorative techniques time-sequential networks, with the exception of the eigenvector calculated for the sequences 2–3 network. A complete table of node centrality scores is presented in the worksheets E and F in S2 File.

Table 4. Pottery decorative techniques and personal ornaments time-sequential analysis ASN’s node weighted centralities and graph centralization scores.

	Pottery decorative techniques
	*TS 1–2*			*TS 2–3*
	degree	eigen	bet	degree	eigen	bet
node level	SC5: 12.0034	SC35: 1.7530	SC36: 136	SC32: 8.1966	SC35: 1.8693	SC36: 244
	SC4: 11.3800	SC4: 1.7478	SC25: 99	SC38: 7.9980	SC32: 1.8677	SC12: 244
	SC35: 11.1161	SC32: 1.7378	SC5: 95	SC35: 7.7032	SC2: 1.8443	SC33: 225
	SC32: 10.9880	SC5: 1.6966	SC31: 86	SC2: 7.5677	SC4: 1.8253	SC15: 208
	SC10: 10.5550	SC2: 1.6695	SC6: 55	SC4: 7.4861	SC38: 1.8179	SC13: 111
graph level	0.17622	0.17338	8,68E-07	0.13675	0.23062	1.60E-06
	Personal ornaments
	*TS 1–2*			*TS 2–3*
	deg	eigen	bet	deg	eigen	bet
node level	S42: 5.0722	S42: 2.3109	S42: 125.4167	S29: 5.1167	S16: 2.0181	S30: 96.0000
	S29: 4.3833	S29: 2.2109	S40: 97.9167	S34: 5.0857	S41: 2.0181	S48: 95.3333
	S40: 4.3052	S40: 2.0947	S9: 92.3333	S42: 4.6167	S29: 1.9159	S34: 92.3333
	S48: 3.6381	S48: 1.6683	S21: 84.6667	S16: 4.6000	S42: 1.7588	S45: 92.3333
	S21: 3.4024	S41: 1.5717	S34: 63.5000	S41: 4.6000	S34: 1.7303	S33: 67.0000
graph level	0.09639	0.26245	9.90E-07	0.08557	0.21597	9.78E-07

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Only the highest scoring nodes for each node centrality are presented. For a complete node score list, see worksheets E, F, K and L in S2 File.

Isolation by distance

Correlations calculated between pottery decorative techniques and the spatial and chronological matrices show different outcomes if calculated with the full or threshold matrices (Table 5).

Table 5. Pottery decorative techniques and personal ornaments time-sequential analysis Mantel and Partial Mantel tests.

	Pottery decorative techniques
	*TS 1–2*		*TS 2–3*
Test	Mantel R	P-value	Mantel R	P-value
Cultural dist v Geographic dist	0.187233	0.009990	-0.072005	0.921079
Cultural dist v Geographical dist (*t)	0.111553	0.238761	0.096484	0.364635
Cultural dist vs Geo. dist ~ Chrono. Dist	0.051056	0.227772	-0.077251	0.947053
Cultural dist vs Geo. dist ~ Chrono. dist (*t)	0.098865	0.304695	0.107089	0.355644
Cultural dist vs Chronological dist	0.317534	0.000999	0.251832	0.000999
Cultural dist vs Chronological dist (*t)	0.058371	0.370629	-0.186109	0.749251
Cultural dist vs Chrono. dist ~ Geo. Dist	0.265688	0.000999	0.253291	0.000999
Cultural dist vs Chrono.dist ~ Geo. dist (*t)	0.026703	0.436563	-0.191678	0.778222
	Personal ornaments
	*TS 1–2*		*TS 2–3*
Test	Mantel R	P-value	Mantel R	P-value
Cultural dist v Geographic dist	0.132137	0.026973	0.181606	0.004995
Cultural dist v Geographical dist (*t)	0.178398	0.239760	0.125572	0.232767
Cultural dist vs Geo. dist ~ Chrono. Dist	0.121619	0.047952	0.182949	0.001998
Cultural dist vs Geo. dist ~ Chrono. dist (*t)	0.145400	0.312687	0.116618	0.274725
Cultural dist vs Chronological dist	0.052471	0.149850	-0.043506	0.726274
Cultural dist vs Chronological dist (*t)	0.108724	0.338661	-0.142061	0.805195
Cultural dist vs Chrono. dist ~ Geo. Dist	-0.006666	0.512488	-0.048969	0.765235
Cultural dist vs Chrono.dist ~ Geo. dist (*t)	0.030257	0.464535	-0.134248	0.766234

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(*t) Tests performed using threshold matrices.

Using the full matrices for the sequences 1–2 network, variation in pottery decorative techniques diversity shows a significant correlation with the geographical matrix (p<0.05), with approximately 18.7% of the variance being explained by geography. The chronological distance between the archaeological occupations significantly contributes to approximately 26.6 to 31.7% of the variance.

Using the full matrices for the sequence 2–3 network, the variation in pottery decorative techniques diversity shows a significant correlation with the chronological matrix, (p<0.05), with approximately 25.2 to 25.3% of the variance being explained by temporal distance.

Mantel tests performed on the threshold matrices of both time-sequential datasets do not show statistically significant correlations between the pottery attributes or the geographic and chronological distances.

Personal ornaments time-sequential analysis

Archaeological Similarity Networks

The personal ornaments sequences 1–2 network (Fig 5A) was built using an imposed threshold of 0.19. The network has 37 nodes connected by 117 edges, a network density of 0.1757, and a cluster coefficient of 0.5134 (S2 Table in S1 File).

The personal ornaments sequences 2–3 network (Fig 5B) was built using an imposed threshold of 0.16 The network has 37 nodes connected by 151 edges, a network density of 0.2267, and a cluster coefficient of 0.6207 (S2 Table in S1 File).

The time-sequential networks did not identify any clear cluster, with nodes being connected by evenly weighted edges. Archaeological occupations mostly presented connections with broadly contemporaneous occupations. The archaeological occupations attributed to the Valencian Cardial and Epicardial appear almost isolated in both networks.

Archaeological occupation edge weights for the personal ornaments sequences 1–2 ASN are distributed within the range 0.1905 (between occupations S30-S54) and 0.6667 (S8-S9), with a mean value of 0.2830. The plotted edges range from 0 to 1401 km (S40-S54]), with a mean geographical distance of 294 km. The chronological distance between connected occupations ranges from 0 years and 950 years (S37-S56), with a mean temporal distance of 260 years. All network interval statistics can be found in S6 Table in S1 File.

Archaeological occupation edge weights for the personal ornaments sequences 2–3 ASN are distributed within the range 0.1613 (S30-S47) and 1 (S16-S41), with a mean value of 0.2699. The plotted edges range from 0 to 763 km (S33-S46), with a mean geographical distance of 146 km. The chronological distance between connected occupations range from 0 years to 850 years (S34-[S15, S16]), with a mean temporal distance of 280 years. All network interval statistics can be found in S6 Table in S1 File.

With only two exceptions, the highest personal ornaments time-sequential network centrality values correspond to archaeological occupations from various archaeological cultures attributed to the 2^nd period (Table 4). The personal ornaments sequences 2–3 network highest betweenness centrality values correspond to archaeological occupations belonging to various regional Cardial expressions.

For both personal ornaments time-sequential networks, all graph centralization measures presented low values (bellow 0.2), with the exception of eigenvectors. A complete table of node centrality scores is presented in worksheets K and L in S2 File.

Isolation by distance

Correlations between personal ornaments diversity and the spatial and chronological distance matrices show different outcomes when considering full or threshold matrices (Table 5).

Using the full matrices for the sequences 1–2 network, variation in personal ornament diversity shows a significant correlation with the geographic distance matrix (p<0.05), with approximately 12.2 to 13.2% of the variance explained by geography.

Using the full matrices for the sequences 2–3 network, variation in personal ornament diversity shows a significant correlation with the geographic distance matrix, (p<0.05), with approximately 18.2 to 18.3% of the variance explained by geography.

Mantel tests performed on the threshold matrices of both time-sequential datasets do not show statistically significant correlations between the personal ornaments or the geographic and chronological distances.

Discussion

The networks built with pottery decoration techniques data revealed a higher density score, cluster coefficient, and edge weights compared to the ornament network, indicating that the transmission of pottery decorative techniques was characterized by tighter and stronger connections over time and space. Further study of the pottery network shows that the Impressa nodes are loosely connected and separated into three small clusters. Archaeological data and radiocarbon dates available in the region suggest that approximately 8,000 years ago the first small pioneer Neolithic communities sporadically settled in Liguria and Languedoc [81,82]. The loosely connected Impressa nodes may reflect this pioneer, fragmentary, and small-scale peopling event. The low degree of transformation observed during this spatially fragmented pioneer phase is seen as resulting from the process of long-distance maritime displacement [56,83–85] and exploratory behavior [86]. This long-distance connection, highlighted by the higher Impressa mean similarity radius, may reflect these maritime displacements, compared to later periods when the smaller-scale inland dispersions of bigger communities is more likely [83,87]. In contrast, the Cardial archaeological culture nodes are strongly connected and characterized by the highest degree and eigenvector centrality scores, network density, and cluster coefficient, suggesting a significant role in the transmission of pottery decorative techniques over time and space. The lower similarity radius of the Cardial (and inherently smaller geographical distance between connected occupations) show that cultural traits circulated at shorter distances in a denser network of sites. The higher mean edge weights observed in time sequences 2–3 also show that more cultural traits were shared between groups compared to previous time sequences. Absolute chronology evidences a fairly continuous and rapid dispersion of the Neolithic economy during this period (around 7500 cal BP) [19], with the multiplication of sites been seen as a phenomenon of regular demographic expansion [86]. The high level of clustering observed during the Cardial period suggests that it was a time of cultural consolidation, as people settled more densely and permanently over a large territory, fostering intense and regular contacts. Within the following Epicardial, nodes are divided into two main tightly connected clusters, with the Valencian Epicardial in a peripheral position. The post-Cardial network fragmentation (characterized by a lower network density, cluster coefficient, and edge weights calculated for the Epicardial) show changes in the way information flowed between communities, favoring a more regional pattern in the cultural trait circulation. The post-Cardial similarity radius is another indicator of this cultural fragmentation, associated with large amounts of small-scale contacts. This late network fragmentation episode has been previously documented at a smaller geographic scale in Iberia [51].

The time-sequential analysis confirms this pattern. The Cardial is characterized by many overlapping internal connections with only a few external connections to nodes belonging to other archaeological cultures. The Impressa appears loosely connected to the Cardial in time sequences 1–2 and 2–3; the Epicardial is divided into two portions, barely connected to the Cardial. Mean edge weights slightly increase between the two time-sequential networks indicating that connections become stronger through time as communities spread and densify. The cluster coefficient increases between the two time sequences while the network density decrease. Similarly, the decrease in the mean geographical distance connecting archaeological occupations in sequences 2–3 reflects a regionalization process. The high geographical distance between the LCE occupations is due to the network structure and analysis constrains and is not a contradictory result. Contrary to the Cardial cluster, whose archaeological cultures are all tightly interconnected in the network, the LCE occupations are divided into two groups; however, its network interval statistics are calculated as a single group, which might be the reason for this seemingly dissonant result.

The bead-type similarity network shows a lower cluster coefficient and network density than the pottery decorative technique network, as well as lower mean edge weights. Further analysis suggests that all the nodes appear to be loosely connected with no specific cluster. Despite the increase in both the network density and cluster coefficient between time sequences, no change in the network structure was observed from the Neolithic pioneer phase up to the later phases, corresponding to a demographic boom and Neolithic dispersion [53]. The interval statistics, however, echo results observed for the pottery decorative techniques. The Cardial similarity radius is drastically lower when compared with the previous Impressa period, indicative of the network’s densification and regionalization associated to shorter distance connections. The similarity radius in the post-Cardial period also reflects the process of network fragmentation, with the persistence of intense small-scale contacts.

The bead-type similarity network, visibly more open than the pottery network, suggests that the symbolic messages carried by personal ornaments were not particularly impacted by a structured network, compared to pottery decorative techniques, with no particular culture having higher centrality scores than any other. The propensity of personal ornaments to easily circulate in space and time is also evidenced by Mantel tests and interval statistics, which confirm, as previously shown [57], that bead-type associations were spatially more homogeneous than pottery decorative techniques and more persistent over time.

This framework suggests that early farmers’ social and symbolic capital was built via complex social networks between groups of people who actively expressed their categorical affiliation through ornaments and pottery decorations.

Conclusion

Observed differences in the network structures and centralities between pottery and personal ornaments highlight that the two cultural proxies functioned as cultural signals at different social and symbolic levels, and that the transmission of each category of cultural traits was not ruled by similar cultural and social mechanisms [88]. Pottery echoes a regional identity, constantly renegotiated and reinvented by geographical and chronological proximity. Contrastingly, ornaments refer to a common cultural background that developed in large parts of the Mediterranean. Bead-type association contributed to build identities shared at large geographic scales and persisted through time, while pottery styles evolved quickly and were used to constantly reassert more local identities. Relationships built through the transmission of pottery decorative techniques and bead-types during the transition to farming reveal that the shaping of the social and symbolic capital of the first villagers who settled in the Mediterranean was multifaceted, and probably contributed to multiple successful strategies for developing new social, economic, and cultural opportunities during their dispersion. The adaptation of Near Eastern farming technologies to the European environment [89] most likely resulted from the accumulation and consolidation of new knowledge, facilitated by efficient cooperation and the strong, high-quality social ties that existed among farming communities.

Supporting information

S1 Code. Analyses code protocol.

(TXT)

Click here for additional data file.^{(36KB, txt)}

S1 Dataset. Archaeological site dataset.

Database of the archaeological sites, layers, variables, and radiocarbon dates used in the analysis.

(XLSX)

Click here for additional data file.^{(80.6KB, xlsx)}

S1 File. Method and results.

Supplementary information for cultural diversity calculation; descriptive statistics definition and application; extra supporting results.

(PDF)

Click here for additional data file.^{(692.4KB, pdf)}

S2 File. Results.

Cultural similarity, geographical and chronological distance matrices; complete node centralities score tables.

(XLSX)

Click here for additional data file.^{(113.2KB, xlsx)}

Acknowledgments

We would like to acknowledge Alain Queffelec and Mathew Peeples for their help in solving code issues. We thank Jill Cucchi for her help in editing this article.

Data Availability

All relevant data are within the manuscript and its Supporting information files.

Funding Statement

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work was supported by the French National Research Agency under the IDEX Bordeaux NETAWA Emergence project № ANR-10-IDEX-03-02 ‘Out of the Core: Exploring social NETworks at the dawn of Agriculture in Western Asia 10 000 years ago’ [SR], the CNRS Momentum project ‘Symboling and neighboring at the dawn of Agriculture’ [SR] and the Grand Programme de Recherche ‘Human Past’ of the University of Bordeaux [SR].

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PLoS One. doi: 10.1371/journal.pone.0294111.r001

Decision Letter 0

John P Hart

25 Aug 2023

PONE-D-23-22913The shaping of social and symbolic capital during the transition to farming in the Western Mediterranean: archaeological network analyses of pottery decorations and personal ornamentsPLOS ONE

Dear Dr. Pereira,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Both reviews are positive. The reviewers offer suggestions for improving your manuscript. Please address these comments while making your revisions. Also, please add an explanation for your use of the three centrality indices. Why were these three (out of the many available) chosen? Each of of the centrality indices measures different network properties. How do the results of each centrality index affect your interpretations of the networks?

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This work was supported by the French National Research Agency under the IDEX Bordeaux NETAWA Emergence project № ANR-10-IDEX-03-02 ‘Out of the Core: Exploring social NETworks at the dawn of Agriculture in Western Asia 10 000 years ago’ [SR], the CNRS Momentum project ‘Symboling and neighboring at the dawn of Agriculture’ [SR] and the Grand Programme de Recherche ‘Human Past’ of the University of Bordeaux [SR].”

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Comments to the Author

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Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

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Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #1: No

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This paper addresses important questions regarding the nature of Early Neolithic society in western Europe. It presents the existing literature coherently and appears to be very much up to date (though I am not closely familiar with the details). The datasets appear to be well curated and appropriately structured for network analysis. The choice of similarity measures in the latter is appropriate, and draws on their successful use in recent archaeological network analysis. Clearly the authors are familiar with the latest methods and make extensive use of the latest and best work in the field, that by Brughmans and Peeples in particular.

I find the interpretation of the contrasting patterns observed in pottery vs ornaments quite convincing. Here we have an excellent example of how network analysis can help draw out and visualise patterns in complex datasets that might otherwise be elusive. I do have one observation about the interpretation though, particularly as the authors place emphasis on the identities engendered by bead types (at a wide geographic scale) versus those embodied in ceramic decorative techniques (more localised). In his book ‘Connected Communities’, Peeples makes good use of a sociological distinction between relational and categorical identities. This has also been taken up in a recent review on network science and island archaeology by Helen Dawson. Do the authors think there may be such a distinction in operation here, with beads connected to a kind of categorical identity, while pottery decoration is more linked to relational identity? It seems worth exploring, particularly given the expression of this difference in these recent network approaches in archaeology.

In summary, I feel this will be a great case study for those interested in archaeological network applications, that is to say at a methodological level, as well as contributing to a more nuanced understanding of processes of cultural identity and transmission in the European Early Neolithic. I would accept with some minor revisions – a careful read through by a native speaker, to iron out various infelicities in language, for example, lines 66-68:

correct to “Our ability to store information and make it circulate between individuals and groups is a critical behaviour that signals a tipping point in our evolutionary history”

Line 70, correct to:

How did information and knowledge circulate during the past?

Lines 71-2 also need attention;

Line 79: circulation routes, not roads

Line 124: preventing to explore… correct to ‘hindering an exploration of’, or similar

ETC…

Reviewer #2: Here are some thoughts on the main strengths and areas for improvement in this manuscript:

Strengths:

The study addresses an interesting and important research question about how different types of material culture reflect social networks and knowledge transmission in the past.

The methods are sound, using social network analysis and statistical tests to analyze patterns in two distinct archaeological datasets. The large sample size spanning 1500 years seems appropriate for the addressed topic.

The findings reveal distinct structures and transmission dynamics for pottery vs. personal ornaments, highlighting how each operated differently as cultural proxies. This is a novel contribution.

The discussion links the results back to the broader context of cultural transmission and diffusion during the Neolithic transition.

Areas for improvement:

• The abstract provides a nice overview of the study and highlights the key objectives, methods, and findings, but it is way too long. Some suggestions:

- The first sentence refers to the "storage and circulation of information" - consider rephrasing to be more specific about the types of information being studied here (e.g. symbolic, cultural).

- When introducing the two types of data analyzed (pottery decorations and personal ornaments), it may help to briefly explain why these are useful proxies.

- The summary of findings could highlight more clearly that the two types of data revealed different network structures and transmission patterns (the pottery data showed tighter, more regional networks while the ornament data revealed more widespread, persistent associations). There is no need for a full explanation here.

- The last sentence refers to "social and symbolic capital" - this concept could perhaps be introduced more clearly earlier in the abstract so the connection is evident.

- Make sure the tense is consistent throughout (currently it switches between past and present). Using past tense since this is reporting completed research findings may flow better.

• The introduction could provide more background on why the Neolithic transition is significant and how cultural transmission relates to the research questions.

• The description of the methods/analysis is quite brief - more detail on the network measures, statistical tests, etc. would help readers better evaluate the approach.

• The conclusions could connect back more directly to the original research aims and highlight the wider theoretical implications of the findings.

The structure jumps around a bit - the results section intersperses the main findings with more detailed statistics. Consider reorganizing for clarity.

Careful editing could improve clarity and readability throughout - some sections are dense with archaeological terminology.

Overall the study tackles an important research question and provides novel evidence using social network analysis. However, strengthening the background framing, methods reporting, results presentation, and discussion would improve the clarity and impact of the work. With minor changes, this work can make a valuable contribution to the literature on cultural transmission and Neolithic social dynamics.

**********

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Reviewer #1: No

Reviewer #2: No

**********

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PLoS One. 2023 Nov 8;18(11):e0294111. doi: 10.1371/journal.pone.0294111.r002

Author response to Decision Letter 0

11 Oct 2023

1. We note that Figure 1 in your submission contain [map/satellite] images which may be copyrighted. All PLOS content is published under the Creative Commons Attribution License (CC BY 4.0), which means that the manuscript, images, and Supporting Information files will be freely available online, and any third party is permitted to access, download, copy, distribute, and use these materials in any way, even commercially, with proper attribution. For these reasons, we cannot publish previously copyrighted maps or satellite images created using proprietary data, such as Google software (Google Maps, Street View, and Earth). For more information, see our copyright guidelines: http://journals.plos.org/plosone/s/licenses-and-copyright.

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Editors comment:

Both reviews are positive. The reviewers offer suggestions for improving your manuscript. Please address these comments while making your revisions. Also, please add an explanation for your use of the three centrality indices. Why were these three (out of the many available) chosen? Each of the centrality indices measures different network properties. How do the results of each centrality index affect your interpretations of the networks?

R: We would like to express our gratitude to the editor for handling our manuscript and for providing valuable comments. In our study, we have employed three network centralities, namely degree, eigenvector, and betweenness centralities, measured both at the node and graph levels. Our primary objective was to investigate the significance of each node within the network concerning information retention and transmission.

Two fundamental ways to assess these aspects of network information flow are by examining the level of connectivity of each node within the network and their positioning in critical points of the transmission chain. Degree and eigenvector centralities are classical metrics that assess a node's connectivity within a network. Eigenvector centrality, in particular, not only identifies highly connected nodes but also highlights those with the highest social importance, as it accounts for connections to highly connected nodes. These two metrics effectively address both facets of information flow mentioned above.

However, given the nature of our networks, which exhibit various distinct groups or clusters, we have chosen to include a third metric: betweenness centrality. The incorporation of betweenness centrality, a metric that examines nodes connecting different parts of the system, enables us to explore how distant and sometimes diverse segments of the network are interconnected and how information traverses between them.

To enhance the clarity of our manuscript for readers, we have included a brief explanation in the Methods section that outlines the rationale behind our selection of each centrality metric.

Reviewer #1 comments:

This paper addresses important questions regarding the nature of Early Neolithic society in western Europe. It presents the existing literature coherently and appears to be very much up to date (though I am not closely familiar with the details). The datasets appear to be well curated and appropriately structured for network analysis. The choice of similarity measures in the latter is appropriate, and draws on their successful use in recent archaeological network analysis. Clearly the authors are familiar with the latest methods and make extensive use of the latest and best work in the field, that by Brughmans and Peeples in particular.

R: We would like to thank the reviewer for the time taken to review our manuscript and for the comments and suggestions made for its improvement. Because our dataset relies exclusively on symbolic productions—pottery decorations and personal ornaments—our results primarily reflect categorical identities. If we had also considered the compositional data of pottery, we might have been able to uncover relational identities, as suggested by Peeple in his work titled 'Identity and Social Transformation

in the Prehispanic Cibola World: A.D. 1150-1325.' Furthermore, we believe that personal ornaments could potentially provide insights into relational identities at a smaller scale. However, achieving this level of information would require more refined contextual and chronological data, potentially including primary deposits such as burials, in order to extend our analysis to the intra-group social composition. However, we agree that categorical identity is a key notion in our work and we briefly introduce it at the end of the discussion in the new version of the manuscript. We thank the reviewer and we will likely dig deeper this topic in the future.

correct to “Our ability to store information and make it circulate between individuals and groups is a critical behaviour that signals a tipping point in our evolutionary history”

Line 70, correct to:

How did information and knowledge circulate during the past?

Lines 71-2 also need attention;

Line 79: circulation routes, not roads

Line 124: preventing to explore… correct to ‘hindering an exploration of’, or similar

ETC…

R: We thank the reviewer for pointing out these problems. We have reviewed the manuscript and made the suitable changes. We have also submitted our revised manuscript for proofing by a native English speaker. We hope the changes addressed helped in making our revised manuscript clearer.

Reviewer #2 comments:

Here are some thoughts on the main strengths and areas for improvement in this manuscript:

Strengths:

The study addresses an interesting and important research question about how different types of material culture reflect social networks and knowledge transmission in the past.

The findings reveal distinct structures and transmission dynamics for pottery vs. personal ornaments, highlighting how each operated differently as cultural proxies. This is a novel contribution.

The discussion links the results back to the broader context of cultural transmission and diffusion during the Neolithic transition.

Areas for improvement:

• The abstract provides a nice overview of the study and highlights the key objectives, methods, and

findings, but it is way too long. Some suggestions:

- The first sentence refers to the "storage and circulation of information" - consider rephrasing to be more

specific about the types of information being studied here (e.g. symbolic, cultural).

- When introducing the two types of data analyzed (pottery decorations and personal ornaments), it may help to briefly explain why these are useful proxies.

- The last sentence refers to "social and symbolic capital" - this concept could perhaps be introduced more clearly earlier in the abstract so the connection is evident.

- Make sure the tense is consistent throughout (currently it switches between past and present). Using past tense since this is reporting completed research findings may flow better.

R: We would like to thank the reviewer for the time taken to review our manuscript and for the various comments and suggestions made to improve its quality. We have reviewed the abstract to make it more concise, clearer and keep the tense consistent following his advices.

• The introduction could provide more background on why the Neolithic transition is significant and how cultural transmission relates to the research questions.

R: We have detailed in the introduction that the Neolithic revolution played a pivotal role in establishing the economic and social underpinnings upon which many present-day societies rely. This includes diverse technics for food production and storage, the emergence of surpluses, the shift towards sedentism, the specialization of labor, the growth of social complexity, and, ultimately, the formation of state institutions.

We also added that previous studies have shown that circulation networks favoured both cultural and demic diffusions and that each mechanisms acted at different extent in the various region of Europe (Fort 2012, Fort 2022).

• The description of the methods/analysis is quite brief - more detail on the network measures, statistical tests, etc. would help readers better evaluate the approach.

R: In our methods section we tried to give a clear and detailed description of our network construction method, including threshold level decision process, and the detailing of other tests performed along with the reasoning for such. For the sake of clarity and brevity, where we saw this would not hinder a readers understanding of the methods, we decided to leave the description/ definition of some of the metrics, for the supplementary materials. In Supplementary Text A (S1 Supplementary Materials) we further clarify our choice of similarity indices. In Supplementary Table 1 (S1 Supplementary Materials) we present for each of the various metrics used, a definition and application. We feel that including these information in the text would make the methods section saturated and cumbersome for the reader, while its presentation

in the supplementary materials is a clearer and viable form. In order to improve our manuscript and make it clearer to the readers, we included in the Methods section of the main text a short explanation on the reasons for each metric choice, and indicated in a clearer way that further details can be found in the S1 Supplementary Information’s document.

• The conclusions could connect back more directly to the original research aims and highlight the wider theoretical implications of the findings.

R: We have added to the conclusion a short sentence that recalls the very first themes introduced at the very beginning of the paper, by stating that cooperation, accumulation and consolidation of knowledge’s likely allowed communities to adapt near eastern farming technologies to the European environment.

The structure jumps around a bit - the results section intersperses the main findings with more detailed statistics. Consider reorganizing for clarity.

Careful editing could improve clarity and readability throughout - some sections are dense with archaeological terminology.

R: The deep editing of the text by a profession English editor has allowed to fix those issues.

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(515.8KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0294111.r003

Decision Letter 1

John P Hart

25 Oct 2023

The shaping of social and symbolic capital during the transition to farming in the Western Mediterranean: archaeological network analyses of pottery decorations and personal ornaments

PONE-D-23-22913R1

Dear Dr. Pereira,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0294111.r004

Acceptance letter

John P Hart

31 Oct 2023

PONE-D-23-22913R1

The shaping of social and symbolic capital during the transition to farming in the Western Mediterranean: archaeological network analyses of pottery decorations and personal ornaments

Dear Dr. Pereira:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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on behalf of

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Code. Analyses code protocol.

(TXT)

Click here for additional data file.^{(36KB, txt)}

S1 Dataset. Archaeological site dataset.

Database of the archaeological sites, layers, variables, and radiocarbon dates used in the analysis.

(XLSX)

Click here for additional data file.^{(80.6KB, xlsx)}

S1 File. Method and results.

Supplementary information for cultural diversity calculation; descriptive statistics definition and application; extra supporting results.

(PDF)

Click here for additional data file.^{(692.4KB, pdf)}

S2 File. Results.

Cultural similarity, geographical and chronological distance matrices; complete node centralities score tables.

(XLSX)

Click here for additional data file.^{(113.2KB, xlsx)}

Attachment

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Data Availability Statement

All relevant data are within the manuscript and its Supporting information files.

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PERMALINK

The shaping of social and symbolic capital during the transition to farming in the Western Mediterranean: Archaeological network analyses of pottery decorations and personal ornaments

Daniel Pereira

Claire Manen

Solange Rigaud

Roles

Abstract

Introduction

Research objectives, material, and methods

Research objectives

Material

Fig 1. Location of the archaeological occupations recorded in the database.

Methods

Cultural diversity

Time-sequential analysis

Archaeological Similarity Network

Descriptive network statistics

Isolation by distance

Results

Pottery decorative techniques

Archaeological Similarity Networks

Fig 2. Archaeological Similarity Network for pottery decorative techniques.

Table 1. Pottery decorative techniques and personal ornaments ASN’s node weighted centralities and graph centralization scores.

Table 2. Pottery decorative techniques and personal ornaments intra-cultural similarity radius.

Isolation by distance

Table 3. Pottery decorative techniques and personal ornaments Mantel and Partial Mantel tests.

Personal ornaments

Archaeological Similarity Network

Fig 3. Archaeological Similarity Network for personal ornaments.

Isolation by distance

Pottery decorative techniques time-sequential analysis

Archaeological Similarity Networks

Fig 4. Archaeological Similarity Network for pottery decorative techniques time-sequential analysis.

Table 4. Pottery decorative techniques and personal ornaments time-sequential analysis ASN’s node weighted centralities and graph centralization scores.

Isolation by distance

Table 5. Pottery decorative techniques and personal ornaments time-sequential analysis Mantel and Partial Mantel tests.

Personal ornaments time-sequential analysis

Archaeological Similarity Networks

Fig 5. Archaeological Similarity Network for personal ornaments time-sequential analysis.

Isolation by distance

Discussion

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

John P Hart

Roles

Author response to Decision Letter 0

Decision Letter 1

John P Hart

Roles

Acceptance letter

John P Hart

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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