Data authenticity and approaches to data authentication are crucial issues in ancient protein research. The advent of modern mass spectrometry has enabled the detection of traces of ancient biomolecules contained in fossils, including protein sequences. However, detecting proteins in ancient samples does not equate to demonstrating their endogenous nature: instead, if the mechanisms that drive protein preservation and degradation are understood, then the extent of protein diagenesis can be used for evaluating preservational quality, which in turn may be related to the authenticity of the protein data. 

The post-mortem deamidation of asparaginyl and glutamyl residues is a key degradation reaction, which can be assessed effectively on the basis of mass spectrometry data, and which has accrued a long history of research, both in terms of describing the mechanisms governing the reactions and with regard to the best strategies for assessing and quantifying the extent of glutamine (Gln) and asparagine (Asn) deamidation in ancient samples (Pal Chowdhury et al., 2019; Ramsøe et al., 2021, 2020; Schroeter and Cleland, 2016; Simpson et al., 2016; Solazzo et al., 2014; Welker et al., 2016; Wilson et al., 2012). 

In their paper, Nair and colleagues (2022) build on this wealth of knowledge and present a tool for quantifying the extent of Gln deamidation in parchment. Parchment is a collagen-based material which can yield extraordinary insights into manuscript manufacturing practices in the past, as well as on the daily lives of the people who assembled and used them (“biocodicology”) (Fiddyment et al., 2021, 2019, 2015; Teasdale et al., 2017). Importantly, the extent of deamidation can be directly related to the quality of the parchment produced: rapid direct deamidation of Gln is induced by the liming process, therefore high extents of deamidation are linked to prolonged exposure to the high pH conditions which are typical of liming, thus implying lower-quality parchment.

Nair et al.’s approach focuses on collagen peptides which are typically detected during MALDI-TOF mass spectrometry analyses of parchment and build a simple three-step workflow able to yield an overall index of deamidation for a sample (the parchment glutamine index - PQI) 一 taking into account that different Gln residues degrade at different rates according to their micro-chemical environment. The first step involves pre-processing the MALDI spectra, since Nair et al. are specifically interested in maximising information which can be obtained by low-quality data. The second step builds on well-established methods for quantifying Q → E from MALDI-TOF data by modelling the convoluted isotope distributions (Wilson et al., 2012). Once relative rates of deamidation in selected peptides within a given sample are calculated, the third step uses a mixed effects model to combine the individual deamidation estimates and to obtain an overall estimate of the deamidation for a parchment sample (PQI). 

The PQI can be used effectively for assessing parchment quality, as the authors show for the dataset from Orval Abbey. However, PQI could also have wider applications to the study of processed collagen, which is widely used in the food and pharmaceutical industries. In general, the study by Nair et al. is a welcome addition to a growing body of research on protein diagenesis, which will ultimately improve models for the assessment of the authenticity of biomolecular data in archaeology. 

References

Chowdhury, P.M., Wogelius, R., Manning, P.L., Metz, L., Slimak, L., and Buckley, M. 2019. Collagen deamidation in archaeological bone as an assessment for relative decay rates. Archaeometry 61:1382–1398. https://doi.org/10.1111/arcm.12492

Fiddyment, S., Goodison, N.J., Brenner, E., Signorello, S., Price, K., and Collins, M.J.. 2021. Girding the loins? Direct evidence of the use of a medieval parchment birthing girdle from biomolecular analysis. bioRxiv. https://doi.org/10.1098/rsos.202055

Fiddyment,S., Holsinger, B., Ruzzier, C., Devine, A., Binois, A., Albarella, U., Fischer, R., Nichols, E., Curtis, A., Cheese, E., Teasdale, M.D., Checkley-Scott, C., Milner, S.J., Rudy, K.M., Johnson, E.J., Vnouček, J., Garrison, M., McGrory, S., Bradley, D.G., and Collins, M.J. 2015. Animal origin of 13th-century uterine vellum revealed using noninvasive peptide fingerprinting. Proc Natl Acad Sci U S A 112:15066–15071. https://doi.org/10.1073/pnas.1512264112

Fiddyment, S., Teasdale, M.D., Vnouček, J., Lévêque, É., Binois, A., and Collins, M.J. 2019. So you want to do biocodicology? A field guide to the biological analysis of parchment. Heritage Science 7:35. https://doi.org/10.1186/s40494-019-0278-6

Nair, B., Rodríguez Palomo, I., Markussen, B., Wiuf, C., Fiddyment, S., and Collins, M. Parchment Glutamine Index (PQI): A novel method to estimate glutamine deamidation levels in parchment collagen obtained from low-quality MALDI-TOF data. BiorRxiv, 2022.03.13.483627, ver. 6 peer-reviewed and recommended by Peer community in Archaeology. https://doi.org/10.1101/2022.03.13.483627 

Ramsøe, A., Crispin, M., Mackie, M., McGrath, K., Fischer, R., Demarchi, B., Collins, M.J., Hendy, J., and Speller, C. 2021. Assessing the degradation of ancient milk proteins through site-specific deamidation patterns. Sci Rep 11:7795. https://doi.org/10.1038/s41598-021-87125-x

Ramsøe, A., van Heekeren, V., Ponce, P., Fischer, R., Barnes, I., Speller, C., and Collins, M.J. 2020. DeamiDATE 1.0: Site-specific deamidation as a tool to assess authenticity of members of ancient proteomes. J Archaeol Sci 115:105080. https://doi.org/10.1016/j.jas.2020.105080

Schroeter, E.R., and Cleland, T.P. 2016. Glutamine deamidation: an indicator of antiquity, or preservational quality? Rapid Commun Mass Spectrom 30:251–255. https://doi.org/10.1002/rcm.7445

Simpson, J.P., Penkman, K.E.H., and Demarchi, B. 2016. The effects of demineralisation and sampling point variability on the measurement of glutamine deamidation in type I collagen extracted from bone. J Archaeol Sci 69: 29-38. https://doi.org/10.1016/j.jas.2016.02.002

Solazzo, C., Wilson, J., Dyer, J.M., Clerens, S., Plowman, J.E., von Holstein, I., Walton Rogers, P., Peacock, E.E., and Collins, M.J. 2014. Modeling deamidation in sheep α-keratin peptides and application to archeological wool textiles. Anal Chem 86:567–575. https://doi.org/10.1021/ac4026362

Teasdale, M.D., Fiddyment, S., Vnouček, J., Mattiangeli, V., Speller, C., Binois, A., Carver, M., Dand, C., Newfield, T.P., Webb, C.C., Bradley, D.G., and Collins M.J. 2017. The York Gospels: a 1000-year biological palimpsest. R Soc Open Sci 4:170988. https://doi.org/10.1098/rsos.170988

Welker, F., Soressi, M.A., Roussel, M., van Riemsdijk, I., Hublin, J.-J., and Collins, M.J. 2016. Variations in glutamine deamidation for a Châtelperronian bone assemblage as measured by peptide mass fingerprinting of collagen. STAR: Science & Technology of Archaeological Research 3:15–27. https://doi.org/10.1080/20548923.2016.1258825

Wilson, J., van Doorn, N.L., and Collins, M.J. 2012. Assessing the extent of bone degradation using glutamine deamidation in collagen. Anal Chem 84:9041–9048. https://doi.org/10.1021/ac301333t

The increasing reliance on digital and big data in archaeology is pushing the scientific community more and more to reconsider their storing and use [1, 2]. Furthermore, the openness and findability in the way these data are shared represent a key matter for the growth of the discipline, especially in the case of bioarchaeology and archaeological sciences [3]. 

In this paper, [4] the author presents the result of a survey targeted on UK bioarchaeologists and then extended worldwide. The paper maintains the structure of a report as it was intended for the conference it was part of (CAA 2023, Amsterdam) but it represents the first public outcome of an inquiry on the bioarchaeological scientific community. A reflection on ourselves and our own practices. Are all the disciplines adhering to the same policies? Do any bioarchaeologist use the same protocols and formats? Are there any differences in between the domains? Is the Needs Analysis fulfilling the questions?

The results, obtained through an accurate screening to avoid distortions, are creating an intriguing picture on the current state of "fairness" and highlighting how Institutions' rules and policies can and should indicate the correct workflow to follow. In the end, the wide application of the FAIR principles will contribute significantly to the growth of the disciplines and to create an environment where the users are not just contributors, but primary beneficiaries of the system. 

[1] Huggett j. (2020). Is Big Digital Data Different? Towards a New Archaeological Paradigm, Journal of Field Archaeology, 45:sup1, S8-S17. https://doi.org/10.1080/00934690.2020.1713281

[2] Nicholson C., Kansa S., Gupta N. and Fernandez R. (2023). Will It Ever Be FAIR?: Making Archaeological Data Findable, Accessible, Interoperable, and Reusable. Advances in Archaeological Practice 11 (1): 63-75. https://doi.org/10.1017/aap.2022.40

[3] Plomp E., Stantis C., James H.F., Cheung C., Snoeck C., Kootker L., Kharobi A., Borges C., Reynaga D.K.M., Pospieszny Ł., Fulminante, F., Stevens, R., Alaica, A. K., Becker, A., de Rochefort, X. and Salesse, K. (2022). The IsoArcH initiative: Working towards an open and collaborative isotope data culture in bioarchaeology. Data in brief, 45, p.108595. https://doi.org/10.1016/j.dib.2022.108595

[4] Lien-Talks, A. (2024). How FAIR is Bioarchaeological Data: with a particular emphasis on making archaeological science data Reusable. Zenodo, 8139910, ver. 6 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.8139910

The study of the evolution of the human diet has been a central theme in numerous archaeological and paleoanthropological investigations. By reconstructing diets, researchers gain deeper insights into how humans adapted to their environments. The analysis of animal bones plays a crucial role in extracting dietary information. Most studies involving ancient diets rely heavily on zooarchaeological examinations, which, due to their extensive history, have amassed a wealth of data.

During the Pleistocene–Holocene periods, testudine bones have been commonly found in a multitude of sites. The use of turtles and tortoises as food sources appears to stretch back to the Early Pleistocene [1-4]. More importantly, these small animals play a more significant role within a broader debate. The exploitation of tortoises in the Mediterranean Basin has been examined through the lens of optimal foraging theory and diet breadth models (e.g. [5-10]). According to the diet breadth model, resources are incorporated into diets based on their ranking and influenced by factors such as net return, which in turn depends on caloric value and search/handling costs [11]. Within these theoretical frameworks, tortoises hold a significant position. Their small size and sluggish movement require minimal effort and relatively simple technology for procurement and processing. This aligns with optimal foraging models in which the low handling costs of slow-moving prey compensate for their small size [5-6,9]. Tortoises also offer distinct advantages. They can be easily transported and kept alive, thereby maintaining freshness for deferred consumption [12-14]. For example, historical accounts suggest that Mexican traders recognised tortoises as portable and storable sources of protein and water [15]. Furthermore, tortoises provide non-edible resources, such as shells, which can serve as containers. This possibility has been discussed in the context of Kebara Cave [16] and noted in ethnographic and historical records (e.g. [12]). However, despite these advantages, their slow growth rate might have rendered intensive long-term predation unsustainable.

While tortoises are well-documented in the Southeast Asian archaeological record, zooarchaeological analyses in this region have been limited, particularly concerning prehistoric hunter-gatherer populations that may have relied extensively on inland chelonian taxa. With the present paper Bochaton et al. [17] aim to bridge this gap by conducting an exhaustive zooarchaeological analysis of turtle bone specimens from four Hoabinhian hunter-gatherer archaeological assemblages in Thailand and Cambodia. These assemblages span from the Late Pleistocene to the first half of the Holocene. The authors focus on bones attributed to the yellow-headed tortoise (Indotestudo elongata), which is the most prevalent taxon in the assemblages. The research include osteometric equations to estimate carapace size and explore population structures across various sites. The objective is to uncover human tortoise exploitation strategies in the region, and the results reveal consistent subsistence behaviours across diverse locations, even amidst varying environmental conditions. These final proposals suggest the possibility of cultural similarities across different periods and regions in continental Southeast Asia.

In summary, this paper [17] represents a significant advancement in the realm of zooarchaeological investigations of small prey within prehistoric communities in the region. While certain approaches and issues may require further refinement, they serve as a comprehensive and commendable foundation for assessing human hunting adaptations.

References

[1] Hartman, G., 2004. Long-term continuity of a freshwater turtle (Mauremys caspica rivulata) population in the northern Jordan Valley and its paleoenvironmental implications. In: Goren-Inbar, N., Speth, J.D. (Eds.), Human Paleoecology in the Levantine Corridor. Oxbow Books, Oxford, pp. 61-74. https://doi.org/10.2307/j.ctvh1dtct.11

[2] Alperson-Afil, N., Sharon, G., Kislev, M., Melamed, Y., Zohar, I., Ashkenazi, R., Biton, R., Werker, E., Hartman, G., Feibel, C., Goren-Inbar, N., 2009. Spatial organization of hominin activities at Gesher Benot Ya'aqov, Israel. Science 326, 1677-1680. https://doi.org/10.1126/science.1180695

[3] Archer, W., Braun, D.R., Harris, J.W., McCoy, J.T., Richmond, B.G., 2014. Early Pleistocene aquatic resource use in the Turkana Basin. J. Hum. Evol. 77, 74-87. https://doi.org/10.1016/j.jhevol.2014.02.012

[4] Blasco, R., Blain, H.A., Rosell, J., Carlos, D.J., Huguet, R., Rodríguez, J., Arsuaga, J.L., Bermúdez de Castro, J.M., Carbonell, E., 2011. Earliest evidence for human consumption of tortoises in the European Early Pleistocene from Sima del Elefante, Sierra de Atapuerca, Spain. J. Hum. Evol. 11, 265-282. https://doi.org/10.1016/j.jhevol.2011.06.002

[5] Stiner, M.C., Munro, N., Surovell, T.A., Tchernov, E., Bar-Yosef, O., 1999. Palaeolithic growth pulses evidenced by small animal exploitation. Science 283, 190-194. https://doi.org/10.1126/science.283.5399.190

[6] Stiner, M.C., Munro, N.D., Surovell, T.A., 2000. The tortoise and the hare: small-game use, the Broad-Spectrum Revolution, and paleolithic demography. Curr. Anthropol. 41, 39-73. https://doi.org/10.1086/300102

[7] Stiner, M.C., 2001. Thirty years on the “Broad Spectrum Revolution” and paleolithic demography. Proc. Natl. Acad. Sci. U. S. A. 98 (13), 6993-6996. https://doi.org/10.1073/pnas.121176198

[8] Stiner, M.C., 2005. The Faunas of Hayonim Cave (Israel): a 200,000-Year Record of Paleolithic Diet. Demography and Society. American School of Prehistoric Research, Bulletin 48. Peabody Museum Press, Harvard University, Cambridge.

[9] Stiner, M.C., Munro, N.D., 2002. Approaches to prehistoric diet breadth, demography, and prey ranking systems in time and space. J. Archaeol. Method Theory 9, 181-214. https://doi.org/10.1023/A:1016530308865

[10] Blasco, R., Cochard, D., Colonese, A.C., Laroulandie, V., Meier, J., Morin, E., Rufà, A., Tassoni, L., Thompson, J.C. 2022. Small animal use by Neanderthals. In Romagnoli, F., Rivals, F., Benazzi, S. (eds.), Updating Neanderthals: Understanding Behavioral Complexity in the Late Middle Palaeolithic. Elsevier Academic Press, pp. 123-143. ISBN 978-0-12-821428-2. https://doi.org/10.1016/C2019-0-03240-2

[11] Winterhalder, B., Smith, E.A., 2000. Analyzing adaptive strategies: human behavioural ecology at twenty-five. Evol. Anthropol. 9, 51-72. https://doi.org/10.1002/(sici)1520-6505(2000)9:2%3C51::aid-evan1%3E3.0.co;2-7

[12] Schneider, J.S., Everson, G.D., 1989. The Desert Tortoise (Xerobates agassizii) in the Prehistory of the Southwestern Great Basin and Adjacent areas. J. Calif. Gt. Basin Anthropol. 11, 175-202. http://www.jstor.org/stable/27825383

[13] Thompson, J.C., Henshilwood, C.S., 2014b. Nutritional values of tortoises relative to ungulates from the Middle Stone Age levels at Blombos Cave, South Africa: implications for foraging and social behaviour. J. Hum. Evol. 67, 33-47. https://doi.org/10.1016/j.jhevol.2013.09.010

[14] Blasco, R., Rosell, J., Smith, K.T., Maul, L.Ch., Sañudo, P., Barkai, R., Gopher, A. 2016. Tortoises as a Dietary Supplement: a view from the Middle Pleistocene site of Qesem Cave, Israel. Quat Sci Rev 133, 165-182. https://doi.org/10.1016/j.quascirev.2015.12.006

[15] Pepper, C., 1963. The truth about the tortoise. Desert Mag. 26, 10-11.

[16] Speth, J.D., Tchernov, E., 2002. Middle Paleolithic tortoise use at Kebara Cave (Israel). J. Archaeol. Sci. 29, 471-483. https://doi.org/10.1006/jasc.2001.0740

[17] Bochaton, C., Chantasri, S., Maneechote, M., Claude, J., Griggo, C., Naksri, W., Forestier, H., Sophady, H., Auertrakulvit, P., Bowonsachoti, J. and Zeitoun, V. (2023) Zooarchaeological investigation of the Hoabinhian exploitation of reptiles and amphibians in Thailand and Cambodia with a focus on the Yellow-headed Tortoise (Indotestudo elongata (Blyth, 1854)), BioRXiv, 2023.04.27.538552 , ver. 3 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.1101/2023.04.27.538552v3

Tektites, a naturally occurring glass produced by major cosmic impacts and ejected at long distances, are known from five impacts worldwide [1]. The presence of this impact-generated glass, which can be dated in the same way as a volcanic rock, has been used to date archaeological sites in several regions of the world. This paper by Marwick and colleagues [2] reviews and adds new data on the use and misuse of this specific material as a chronological marker in Australia, East and Southeast Asia, where an impact dated to 0.78 Ma created and widely distributed tektites. This material, found in archaeological excavations in China, Laos, Thaïland, Australia, Borneo, and Vietnam, has been used to date layers containing lithic artifacts, sometimes creating a strong debate about the antiquity of the occupation and lithic production in certain regions.

The review of existing data shows that geomorphological data and stratigraphic integrity can be questioned at many sites that have yielded tektites. The new data provided by this paper for five archaeological sites located in Vietnam confirm that many deposits containing tektites are indeed lag deposits and that these artifacts, thus in secondary position, cannot be considered to date the layer. This study also emphasizes the general lack of other dating methods that would allow comparison with the tektite age. In the Vietnamese archaeological sites presented here, discrepancies between methods, and the presence of historical artifacts, confirm that the layers do not share similar age with the cosmic impact that created the tektites.

Based on this review and these new results, and following previous propositions [3], Marwick and colleagues conclude that, if tektites can be used as chronological markers, one has to prove that they are in situ. They propose that geomorphological assessment of the archaeological layer as primary deposit must first be attained, in addition to several parameters of the tektites themselves (shape, size distribution, chemical composition). Large error can be made by using only tektites to date an archaeological layer, and this material should not be used solely due to risks of high overestimation of the age of the archaeological production. 

[1] Rochette, P., Beck, P., Bizzarro, M., Braucher, R., Cornec, J., Debaille, V., Devouard, B., Gattacceca, J., Jourdan, F., Moustard, F., Moynier, F., Nomade, S., Reynard, B. (2021). Impact glasses from Belize represent tektites from the Pleistocene Pantasma impact crater in Nicaragua. Communications Earth & Environment, 2(1), 1–8, https://doi.org/10.1038/s43247-021-00155-1

[2] Marwick, B., Son, P. T., Brewer, R., Wang, L.-Y. (2022). Tektite geoarchaeology in mainland Southeast Asia. SocArXiv, 93fpa, ver. 6 peer-reviewed and recommended by PCI Archaeology, https://doi.org/10.31235/osf.io/93fpa.

[3] Tada, T., Tada, R., Chansom, P., Songtham, W., Carling, P. A., Tajika, E. (2020). In Situ Occurrence of Muong Nong-Type Australasian Tektite Fragments from the Quaternary Deposits near Huai Om, Northeastern Thailand. Progress in Earth and Planetary Science 7(1), 1–15, https://doi.org/10.1186/s40645-020-00378-4

In a recent article, Fumihiro Sakahira and Hiro'omi Tsumura (2023) used social network analysis methods to analyze change in obsidian trade networks in Japan throughout the 13,000-year-long Jomon period. In the paper recommended here (Sakahira and Tsumura 2024), Social Network Analysis of Ancient Japanese Obsidian Artifacts Reflecting Sampling Bias Reduction they revisit that data and describe additional analyses that confirm the robustness of their social network analysis. The data, analysis methods, and substantive conclusions of the two papers overlap; what this new paper adds is a detailed examination of the data and methods, including use of bootstrap analysis to demonstrate the reasonableness of the methods they used to group sites into clusters.

Both papers begin with a large dataset of approximately 21,000 artifacts from more than 250 sites dating to various times throughout the Jomon period. The number of sites and artifacts, varying sample sizes from the sites, as well as the length of the Jomon period, make interpretation of the data challenging. To help make the data easier to interpret and reduce problems with small sample sizes from some sites, the authors assign each site to one of five sub-periods, then define spatial clusters of sites within each period using the DBSCAN algorithm. Sites with at least three other sites within 10 km are joined into clusters, while sites that lack enough close neighbors are left as isolates. Clusters or isolated sites with sample sizes smaller than 30 were dropped, and the remaining sites and clusters became the nodes in the networks formed for each period, using cosine similarities of obsidian assemblages to define the strength of ties between clusters and sites.

The main substantive result of Sakahira and Tsumura’s analysis is the demonstration that, during the Middle Jomon period (5500-4500 cal BP), clusters and isolated sites were much more connected than before or after that period. This is largely due to extensive distribution of obsidian from the Kozu-shima source, located on a small island off the Japanese mainland. Before the Middle Jomon period, Kozu-shima obsidian was mostly found at sites near the coast, but during the Middle Jomon, a trade network developed that took Kozu-shima obsidian far inland. This ended after the Middle Jomon period, and obsidian networks were less densely connected in the late and last Jomon periods.

The methods and conclusions are all previously published (Sakahira and Tsumura 2023). What Sakahira and Tsumura add in Social Network Analysis of Ancient Japanese Obsidian Artifacts Reflecting Sampling Bias Reduction are:

·       an examination of the distribution of cosine similarities between their clusters for each period

·       a similar evaluation of the cosine similarities within each cluster (and among the unclustered sites) for each period

·       bootstrap analyses of the mean cosine similarities and network densities for each time period

These additional analyses demonstrate that the methods used to cluster sites are reasonable, and that the use of spatially defined clusters as nodes (rather than the individual sites within the clusters) works well as a way of reducing bias from small, unrepresentative samples. An alternative way to reduce that bias would be to simply drop small assemblages, but that would mean ignoring data that could usefully contribute to the analysis.

The cosine similarities between clusters show patterns that make sense given the results of the network analysis. The Middle Jomon period has, on average, the highest cosine similarities between clusters, and most cluster pairs have high cosine similarities, consistent with the densely connected, spatially expansive network from that time period. A few cluster pairs in the Middle Jomon have low similarities, apparently representing comparisons including one of the few nodes on the margins on the network that had little or no obsidian from the Kozu-shima source. The other four time periods all show lower average inter-cluster similarities and many cluster pairs have either high or low similarities. This probably reflects the tendency for nearby clusters to have very similar obsidian assemblages to each other and for geographically distant clusters to have dissimilar obsidian assemblages. The pattern is consistent with the less densely connected networks and regionalization shown in the network graphs. Thinking about this pattern makes me want to see a plot of the geographic distances between the clusters against the cosine similarities. There must be a very strong correlation, but it would be interesting to know whether there are any cluster pairs with similarities that deviate markedly from what would be predicted by their geographic separation.

The similarities within clusters are also interesting. For each time period, almost every cluster has a higher average (mean and median) within-cluster similarity than the similarity for unclustered sites, with only two exceptions. This is partial validation of the method used for creating the spatial clusters; sites within the clusters are at least more similar to each other than unclustered sites are, suggesting that grouping them this way was reasonable.

Although Sakahira and Tsumura say little about it, most clusters show quite a wide range of similarities between the site pairs they contain; average within-cluster similarities are relatively high, but many pairs of sites in most clusters appear to have low similarities (the individual values are not reported, but the pattern is clear in boxplots for the first four periods). There may be value in further exploring the occurrence of low site-to-site similarities within clusters. How often are they caused by small sample sizes? Clusters are retained in the analysis if they have a total of at least 30 artifacts, but clusters may contain sites with even smaller sample sizes, and small samples likely account for many of the low similarity values between sites in the same cluster. But is distance between sites in a cluster also a factor? If the most distant sites within a spatially extensive cluster are dissimilar, subdividing the cluster would likely improve the results. Further exploration of these within-cluster site-to-site similarity values might be worth doing, perhaps by plotting the similarities against the size of the smallest sample included in the comparison, as well as by plotting the cosine similarity against the distance between sites. Any low similarity values not attributable to small sample sizes or geographic distance would surely be worth investigating further.

Sakahira and Tsumura also use a bootstrap analysis to simulate, for each time period, mean cosine similarities between clusters and between site pairs without clustering. They also simulate the network density for each time period before and after clustering. These analyses show that, almost always, mean simulated cosine similarities and mean simulated network density are higher after clustering than before. The simulated mean values also match the actual mean values better after clustering than before. This improved match to actual values when the sites are clustered for the bootstrap reinforces the argument that clustering the sites for the network analysis was a reasonable result.

The strength of this paper is that Sakahira and Tsumura return to reevaluate their previously published work, which demonstrated strong patterns through time in the nature and extent of Jomon obsidian trade networks. In the current paper they present further analyses demonstrating that several of their methodological decisions were reasonable and their results are robust. The specific clusters formed with the DBSCAN algorithm may or may not be optimal (which would be unreasonable to expect), but the authors present analyses showing that using spatial clusters does improve their network analysis. Clustering reduces problems with small sample sizes from individual sites and simplifies the network graphs by reducing the number of nodes, which makes the results easier to interpret.

Reference

Sakahira, F. and Tsumura, H. (2023). Tipping Points of Ancient Japanese Jomon Trade Networks from Social Network Analyses of Obsidian Artifacts. Frontiers in Physics 10:1015870. https://doi.org/10.3389/fphy.2022.1015870

Sakahira, F. and Tsumura, H. (2024). Social Network Analysis of Ancient Japanese Obsidian Artifacts Reflecting Sampling Bias Reduction, Zenodo, 10057602, ver. 7 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.7969330