Strontium isoscape of sub-Saharan Africa allows tracing origins of victims of the transatlantic slave trade

Domain: earth_science
Published: 2024-12-30
Editorial outcome: major_revision

Abstract
Strontium isotope (87Sr/86Sr) analysis with reference to strontium isotope landscapes (Srisoscapes) allows reconstructing mobility and migration in archaeology, ecology, and forensics. However, despite the vast potential of research involving 87Sr/86Sr analysis particularly in Africa, Srisoscapes remain unavailable for the largest parts of the continent. Here, we measure the 87Sr/86Sr ratios in 778 environmental samples from 24 African countries and combine this data with published data to model a bioavailable Srisoscape for sub-Saharan Africa using random forest regression. We demonstrate the efficacy of this Srisoscape, in combination with other lines of evidence, to trace the African roots of individuals from historic slavery contexts, particularly those with highly radiogenic 87Sr/86Sr ratios uncommon in the African Diaspora. Our study provides an extensive African 87Sr/86Sr dataset which includes scientifically marginalized regions of Africa, with significant implications for the archaeology of the transatlantic slave trade, wildlife ecology, conservation, and forensics.

Introduction
In Africa, human mobility and migration played fundamental roles in the evolution of our species1 and formed the diversity of peoples, cultures, and linguistics of the continent2,3. For instance, the great expansion of Bantu-speaking agriculturists from West Africa to eastern and southern Africa starting ~4000 years ago significantly contributed to the dynamics of contemporary African populations, subsistence practices, and languages4. One of the most notable migration events in the history of the continent occurred with the transatlantic slave trade between the 15th and 19th centuries, during which at least 12.5 million Africans were abducted, enslaved, and transported to the Americas and Europe, which dramatically changed the demographics, economics, and politics of both Africa and the Americas5. Although the transatlantic slave trade is well documented and known as the largest forced migration event in human history5, archaeologists and historians alike have struggled to identify the geographic origins and individual life histories of enslaved individuals in the African Diaspora. Countless historic documents describe the journeys of at least 36,079 vessels transporting African captives and included detailed information on the African ports visited, and the total number of captive individuals embarked on a given ship5. However, the individual journeys that captive Africans endured before reaching coastal shipping ports, as well as the regions from which they were taken, remain largely unclear in the archaeology of the African Diaspora. Recently, ancient DNA (aDNA) and isotopic analyses have been successfully applied to archaeological human remains from sites associated with the slave trade in the Caribbean6–8, Brazil9, North America (e.g., refs. 10–12), St Helena13, and South Africa14, providing critical first insights into the African origins of enslaved individuals. While information from aDNA can potentially determine the population ancestry of an individual, it will not illuminate where a person was born and raised15. A growing number of such studies have employed strontium isotope (87Sr/86Sr) analysis of human remains, which demonstrates great potential for reconstructing the geographic origins and migration of individuals, particularly when local- or large-scale strontium isoscapes are available as references16–18.

The 87Sr/86Sr ratios of a location primarily relate to the underlying bedrock geology, with additional influences from atmospheric deposition geomorphological, and biochemical processes, and remain stable over archaeological time scales19. 87Sr/86Sr ratios in animal and human body tissues primarily mirror biologically available 87Sr/86Sr incorporated via the consumption of plants and water from the local substrate with minimal fractionation which is corrected during post-measurement data processing16,17 (Supplementary Note 1). In African archaeological contexts, 87Sr/86Sr analysis has allowed the tracing of the Paleolithic ostrich eggshell bead trade20 and the landscape use of early hominins21. The analysis of 87Sr/86Sr can also be applied in the study of wildlife ecology and conservation. Sub-Saharan Africa harbours some of the largest global biomass movements related to seasonal migrations of mammals, birds, and insects22. Previous 87Sr/86Sr-based studies have provided critical insights into the habitat use of African elephants23 and rhinoceroses24, as well as forensic studies of the ivory trade25. Other forensic applications could include the identification of human origins in forensic cases, such as the remains of deceased migrants26. However, the vast potential of 87Sr/86Sr analysis in African archaeology, ecology, and forensics remains largely unexplored as Srisoscapes do not yet exist for the largest parts of the continent. In particular, the absence of data from West and West-Central Africa impedes our ability to use 87Sr/86Sr analysis in the study of slavery and the identification of the specific geographic origins of the countless victims of the slave trade.

In the last two decades, strontium isoscapes have been successfully developed at the local, regional, and even global scale using environmental and/or archaeological samples and various modelling approaches (Supplementary Note 1)17,27. The current leading approach in isoscape modelling employs random forest (RF) regression, which integrates georeferenced 87Sr/86Sr data with a range of geological and environmental covariates affecting natural isotopic variation and displays high accuracy in their spatial prediction of 87Sr/86Sr17,18. Numerous strontium isoscapes using RF modelling methods are now available for Europe18,28, North America29, New Zealand30, Madagascar17, parts of Tanzania and Kenya31, Angola32, and even globally17. However, regions with very limited sample coverage continue to exhibit poor 87Sr/86Sr predictions in the global Sr isoscape, particularly in Africa. This research gap in Africa can be attributed to the presumably high cost of large-scale 87Sr/86Sr projects and the lack of the respective isotope ratio mass spectrometer infrastructure in sub-Saharan Africa, with the exception of South Africa. In addition, obtaining samples in many African regions is challenging due to considerable logistical difficulties, as well as conflicts and instability (e.g., Burkina Faso, Burundi, the Central African Republic, Mali, Sudan, South Sudan, Somalia, and parts of Nigeria33).

This study aims to draft a strontium isoscape of sub-Saharan Africa using both newly measured and previously reported bioavailable 87Sr/86Sr data. We analyze 778 environmental samples (including plants, soil leachates, and microfauna) from 24 African countries, focusing particularly on West and West-Central Africa, where 87Sr/86Sr data were previously nearly absent (Fig. 1b). We combine this dataset with the 1488 published bioavailable 87Sr/86Sr data (Supplementary Data 1) and employ a random forest modelling approach to generate a bioavailable strontium isoscape for sub-Saharan Africa. By applying this isoscape to 87Sr/86Sr data from enslaved Africans at two cemeteries in the African Diaspora, namely the Anson Street African Burial Ground in the US10 and the Pretos Novos cemetery in Brazil9, we demonstrate the predictive potential of this isoscape in the bioarchaeology of slavery. To aid in the estimation of individual origin, we combine published human tooth enamel 87Sr/86Sr data with corresponding genetic evidence for the Anson Street African Burial Ground34 or oxygen isotope data for the Pretos Novos cemetery35. From both cemeteries, we select five individuals whose 87Sr/86Sr ratios are inconsistent with their regions of captivity and burial. Finally, we discuss the potential further applications of this Srisoscape in wildlife ecology and forensics in sub-Saharan Africa.

Results
Bioavailable 87Sr/86Sr variability in sub-Saharan Africa
The bioavailable 87Sr/86Sr ratios in sub-Saharan Africa range from 0.70381 to 0.87810, with a mean of 0.71774 ± 0.01229 (1σ), reflecting the diverse geological characteristics of the African continent (Fig. 1a and Supplementary Note 2). In addition to the large isotopic variability, Africa exhibits many highly radiogenic 87Sr/86Sr compositions, with a first quartile to third quartile (Q1–Q3) range of 0.7099–0.7223. This range far exceeds that reported for any other continent, including Europe (Q1–Q3 of 0.7087–0.7120)17, the USA (Q1–Q3 of 0.7088–0.7109)17, China (Q1–Q3 of 0.7096–0.7119)36, and even worldwide (Q1–Q3 of 0.7084–0.7115)17. Notably, the most radiogenic 87Sr/86Sr ratios (>0.730) are restricted to regions dominated by underlying Archaean bedrock, such as in present-day Angola, Zimbabwe, Zambia, western Tanzania, northern South Africa, and several southern West African countries (Supplementary Note 3).

Strontium isoscape modelling results
We selected environmental variables based on extensive research from previous isoscape studies17,18 and excluded those that might lead to correlation and collinearity issues within our model. Ultimately, we used 11 predictors (dust, terrane age, soil clay content, soil cation exchange capacity, soil organic carbon content, maximum age of bedrock, sea salt deposition, mean annual precipitation, srsrq3, lithology, and elevation) to model the 87Sr/86Sr ratios of 2266 samples using a random forest algorithm tuned with both hyperparameters (number of variables randomly sampled at each split and maximum node size) set to 2. Overall, the RF model explained 80% of the out-of-bag variance, with an average root-mean-square error (RMSE) of 0.0056. Approximately 63% of the model residuals fell within the range of −0.002 to +0.002 (Supplementary Fig. 1). We detected no spatial autocorrelation in the model residuals, indicating that no unaccounted proximity effects (between samples) existed within the variance unexplained by the model (Supplementary Fig. 2). The RF model demonstrated strong predictive performance on our validation set (R2=0.84, RMSE=0.0056), enabling us to use the fitted model to predict the pattern of 87Sr/86Sr variation across sub-Saharan Africa (Fig. 2a). At least 75% of the standard predicted errors of the isoscape fell within the range of 0.0016–0.0075 (Fig. 2b).

Our Srisoscape intentionally excludes regions where at least one environmental predictor variable value falls outside the range of those represented by our 87Sr/86Sr sampling (grey regions in Fig. 2c). This conservative depiction of the isoscape accounts for the fact that RF models are not reliable when extrapolating into areas without matching training data. In addition, we provide a standard error map to represent prediction accuracy (Fig. 2b) and multivariate Mahalanobis distances to indicate regions of environmental dissimilarity (Fig. 2c).

The ranking of the relative importance of input parameters indicates that dust deposition is the most important factor influencing spatial bioavailable 87Sr/86Sr variation, followed by terrane age, soil clay content, maximum age of bedrock, soil cation exchange capacity, sea salt deposition, mean annual precipitation, elevation, soil organic carbon content, srsrq3, and lithology (Supplementary Fig. 1). The partial dependence plots (Supplementary Fig. 3) reveal more radiogenic 87Sr/86Sr ratios occur in regions with low dust deposition, while lower and relatively homogeneous 87Sr/86Sr ratios are more prevalent in regions receiving a high annual amount of aeolian dust, such as central southern Africa associated with the Kalahari Desert. Geological variables also dominate in predicting bioavailable 87Sr/86Sr across Africa, including the age of geological units and soil properties, both of which have nearly a linear effect on bioavailable 87Sr/86Sr ratios. We observe more radiogenic 87Sr/86Sr ratios in several craton regions dominated by Archaean plutonic and metamorphic rocks, which tend to form soils with low cation exchange capacity and clay content31,37. Conversely, low 87Sr/86Sr ratios are found in regions covered by Mesozoic-Cenozoic volcanic rocks in East Africa and basalts in central South Africa, where soils generally have high cation exchange capacity and clay content31,37. In addition, our 87Sr/86Sr data show a positive relationship with both mean annual precipitation and elevation, with the former possibly influenced by increased silicate weathering rates due to higher precipitation and the latter resulting from the exposure and

Methods

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Overall, I believe the authors have very carefully taken into account all my reviews and I appreciate the time and efforts they have put into improving this manuscript. I believe this manuscript is very improved and is a very interesting use of strontium isotopes that will certainly be highly cited.\nI will first reiterate why I think this study deserve publication in a high-impact journal like Nature Communications.\n1) This study is data rich with close to ~800 new bioavailable strontium isotope data from very remote area in Africa a region in which there are many interests to apply geolocation but where data were very scarce\n2) The pilot strontium isoscape for Africa has broad reaching relevance to many fields. Those include archeologists, anthropologist, forensic specialists but also ecologists, paleoecologists and environmental scientists. So I believe the product will be broadly used and will be of great interest to Nat Comm readers\n3) This study provides some advances to model strontium isotopes. This is a booming field with a growing community and the study provides some new interesting tools to assess uncertainty of the isoscape and support improvement in sampling strategy to fill data gap.\n4) The rewriting of the paper towards African slaves identification is great as well as the rewriting of the discussion combining isotopes and genomics. I think it creates a more focused story that has ethical and historical relevance for humanity that will again be of interest to Nat. Comm broad reader base.\nSo I would argue that this study should be published quickly as the community needs it and it has already been through very significant revisions. Before it is published I have some minor comments:\n1) The authors need to add a little bit more details on the geographic assignments. I have not had the time to look at the script this time but it needs to be clearer what function in assignR was used with what inputs. For the strontium isoscape, are talking about the predicted mean and pseudo-sd from the quantile random forest regression? Correct? If yes it needs to be specified. For the oxygen, the mean precipitation d18O values and its associated uncertainty +1 per mile? Correct? Please detail this a bit more, what function was used and with what tunning.\n2) I am not fully convinced about their reply on pseudoreplication. I totally agree with the nice new paragraph about how local uncertainty associated with the range of bioavailable 87Sr/86Sr values is meaningful and needs to be incorporated into the modeling one way or another. However, if you take multiple points per grid cell it creates pseudo-replication by definition even if the values are slightly different. I don’t have a clear solution to this problem but I did not fully understand their reply. So I am not asking the author to redo any modeling but I would like to understand why Zuckerberg, B. et al. suggest that resampling at one site does not involve pseudoreplication as this is the study cited to justify this statement? And how this cross-correlogram from BjØrnstad, O. N. & Falck, W. help us to see that there is no spatial-autocorrelation linked to pseudo-replication? Could you clarify those two points with a bit more details in the text?\n3) I think the author could combine the last two paragraphs (applications for wildlife/application for forensics) into one concluding paragraph describing the broader implications/applications of their isoscape. I find the last two paragraphs still distract a bit from the main message on African slave origin as they are pretty long. So I would simply have one final paragraph where the authors describe briefly all the cool applications and potential solutions that this new isoscape brings to African ecology, forensics, paleoecology… Short and exciting with a few refs.\nLine 173: refered to what? Weird sentence ending?\n(Remarks on code availability)\nI had reviewed and commented the code last time in great details. The authors have taken into account all my comments in their reviews. I did not have time to rerun fully the script this time but I believe most of the issues have been addressed.", "reviewer_2": "This version of the manuscript addresses most of the problems I identified previously. My major concern about this version is to do with the interpretation of Figs 3 and 4 (see below). I also have a number of relatively minor comments, listed below.\nFirst paragraph: I am not at all sure that the slave trade was “the most notable migration event” in Africa – the Bantu expansion was arguably as influential. Add “forced” before “migration”, as you say a couple of lines on, and no-one would argue.\nIt is untrue that “little to nothing is known about …. what regions and communities they [African slaves] had been taken from” – there is a large body of scholarship on this in African history.\nSecond paragraph: “87Sr/86Sr ratios in animal and human body tissues primarily mirror biologically available 87Sr/86 Sr incorporated via the consumption of plants and water from the local substrate with minimal fractionation which can be corrected during mass spectrometric analysis(Supplementary Note S1)” is still incorrect. This fraction IS corrected for (not “can be”), not during mass spectrometric analysis, rather during post-measurement data processing. These might seem like picky points, but science requires precise use of language.\nSecond paragraph of Results: Specify the “several soil properties” used as predictors in your isoscape.\nIt is untrue that “that RF models are unable to extrapolate into areas without matching training data”. What is true is that we can have little confidence in such extrapolations.\nThe level of “match” between 87Sr/86Sr of the skeletons and the areas they may have come from is quite uneven. Fig 3e shows very good agreement, but 3d and 3f appear to offer at best 50-60% match. Similarly, in Fig 4, 4c offers quite good agreement but 4f does not. This requires some discussion.\nDoes the DNA from Daba, Lima and Ganda specifically link them to the southern coast of West Africa, or just to West Africa in general? There were major West African slaving ports that fall outside the limits of this isoscape, especially Goree Island off Dakar. Could they have come from there?\nIn Fig 4, what about the d13C values? Do these help narrow down the possibilities?\nNote that St Helena was simply a pick-up point – slaves taken on board there could have come from many areas. The same is true of Mozambique- slaves were brought from the interior, likely spanning several contemporary countries, and shipped from Mozambique. Individuals recorded as “of Mozambique” did therefore not necessarily originate from what we call Mozambique today.\nUnder “Limitations and future work”, the phrase “regions with strict data extrapolation” needs re-wording, making it clear that these are regions lacking actual analyses."}, "author_response": "Point-by-Point Response to Reviewers – Author response in blue font throughout\n(please note: Line numbers refer to the numbering in the clean version of the manuscript)\n*******************************************************************************\nReviewers’ comments:\nReviewer #1 (Remarks to the Author):\nComment 1:\nOverall, I believe the authors have very carefully taken into account all my reviews and I appreciate\nthe time and efforts they have put into improving this manuscript. I believe this manuscript is very\nimproved and is a very interesting use of strontium isotopes that will certainly be highly cited.\nI will first reiterate why I think this study deserve publication in a high-impact journal like Nature\nCommunications.\n1) This study is data rich with close to ~800 new bioavailable strontium isotope data from very\nremote area in Africa a region in which there are many interests to apply geolocation but where data\nwere very scarce\n2) The pilot strontium isoscape for Africa has broad reaching relevance to many fields. Those\ninclude archeologists, anthropologist, forensic specialists but also ecologists, paleoecologists and\nenvironmental scientists. So I believe the product will be broadly used and will be of great interest\nto Nat Comm readers\n3) This study provides some advances to model strontium isotopes. This is a booming field with a\ngrowing community and the study provides some new interesting tools to assess uncertainty of the\nisoscape and support improvement in sampling strategy to fill data gap.\n4) The rewriting of the paper towards African slaves identification is great as well as the rewriting\nof the discussion combining isotopes and genomics. I think it creates a more focused story that has\nethical and historical relevance for humanity that will again be of interest to Nat. Comm broad\nreader base.\nSo I would argue that this study should be published quickly as the community needs it and it has\nalready been through very significant revisions. Before it is published I have some minor comments:\nResponse: Thank you for your thorough and positive feedback. We are delighted that you find our\nstudy to be of high significance and potential impact. We greatly appreciate your detailed\nendorsement of our work and the acknowledgment of our efforts in addressing your previous\nreviews. Based on your minor comments, please find our detailed responses and corresponding\nrevisions below.\nComment 2:\n1) The authors need to add a little bit more details on the geographic assignments. I have not had\nthe time to look at the script this time but it needs to be clearer what function in assignR was used\nwith what inputs. For the strontium isoscape, are talking about the predicted mean and pseudo-sd\nfrom the quantile random forest regression? Correct? If yes it needs to be specified. For the oxygen,\nthe mean precipitation d18O values and its associated uncertainty +1 per mile? Correct? Please detail\nthis a bit more, what function was used and with what tunning.\nResponse: Thank you for your suggestion. To fully address your open questions, we rewrote sections\nand added more details about the geographic assignments and the specific isoscapes, error maps and\nfunctions used in the “assignR” package (see lines 494-520).\n“We estimated the probable geographic origins of ten enslaved Africans from two slave\ncemeteries in the African Diaspora. To achieve this, we applied a predicted strontium isoscape\nof sub-Saharan Africa (mean values) and its estimated standard error (calculated using the\ninfinitesimal jackknife) resulting from the random forest regression and the continuous-\nsurface assignment framework from the R package “assignR” using the ‘pdRaster’ function70.\nThe resulting probability surfaces for each sample were normalized for comparison (each cell\nprobability was divided by the maximum probability).\nThe human enamel 87Sr/86Sr data were sourced from previous studies, including five\nindividuals from the Anson Street African Burial Ground (1760-1790 CE) in Charleston, USA10 and\nfive individuals from the Pretos Novos cemetery (1769-1830 CE) in Rio de Janeiro, Brazil9. From\nboth sites, we selected individuals with comparatively radiogenic 87Sr/86Sr ratios exceeding 0.720,\nas such ratios are certainly not found in coastal South Carolina29 and are unlikely to be present in\nthe coastal sugar plantation regions of Rio de Janeiro province.\nFor the Anson Street ancestors, we combined 87Sr/86Sr data with aDNA evidence to identify\nfirst-generation victims of the slave trade, using published genetic findings to narrow predictions of\nindividual origin to specific African regions (West Africa vs. West-Central Africa)34.\nFor the ancestors from the Pretos Novos cemetery, we employed dual-isotope (87Sr/86Sr and\nδ18O) geographic assignments due to the availability of δ18O data from human tooth enamel35. Here\nwe additionally used the modern precipitation oxygen isoscape of Africa41 in the “assign R”\nframework. We firstly adjusted human tooth carbonate δ18O values to those of drinking water\nusing established equations. Specifically, we converted enamel carbonate δ18O values from V-\nPDB to V-SMOW using the equation from Coplen et al. (1983)71 (δ18O = 1.03091 ×\ncarb-VSMOW\nδ18O + 30.91), then converted carbonate δ18O values to phosphate δ18O values using\ncarb-VPDB\nthe equation from Iacumin et al. (1996)72 (δ18O = 0.98 × δ18O - 8.5 ), and\np-VSMOW carb-VSMOW\nfinally applied the equation from Daux et al. (2008)73 to convert the resulting δ18O values to\ndrinking water δ18O values (δ18O = 1.54 × δ18O - 33.72 ). We compared the\nw-VSMOW p-VSMOW\ncalculated human drinking water δ18O values with the annual mean precipitation oxygen\nisoscape of Africa (RCWIP)41, accounting for the uncertainty of the isoscape and an additional\nuncertainty of 1‰ (considering the error ranges of the conversions above74).”\nComment 3:\n2) I am not fully convinced about their reply on pseudoreplication. I totally agree with the nice new\nparagraph about how local uncertainty associated with the range of bioavailable 87Sr/86Sr values is\nmeaningful and needs to be incorporated into the modeling one way or another. However, if you\ntake multiple points per grid cell it creates pseudo-replication by definition even if the values are\nslightly different. I don’t have a clear solution to this problem but I did not fully understand their\nreply. So I am not asking the author to redo any modeling but I would like to understand why\nZuckerberg, B. et al. suggest that resampling at one site does not involve pseudoreplication as this\nis the study cited to justify this statement? And how this cross-correlogram from BjØrnstad, O. N.\n& Falck, W. help us to see that there is no spatial-autocorrelation linked to pseudo-replication?\nCould you clarify those two points with a bit more details in the text?\nResponse: Thank you for your insightful comment. We are suggesting a resolution to the question\nof pseudoreplication v. data independence in our approach, by rigorously and transparently testing\nif the largest error source, spatial autocorrelation, is indeed introduced by repeated sampling from\nthe same grid cells. We clarified this point by elaborating more on the issue of landscape resampling\nand the question on spatial autocorrelation in the method section describing the isoscape modeling\napproach (see lines 457-479):\n“Unlike previous isoscape studies17,18, we did not apply the median 87Sr/86Sr ratios for\ndata points within the same grid cell (here 1 km2) but instead treated all data as independent.\nThe rationale for using the median is that samples in close proximity (within the same grid\ncell) have the same predictor values, i.e. landscape overlap64, and this leads to\npseudoreplication and violates the assumption of independence. Firstly, we argue that our\nsampling is sufficiently independent, that the “resampling” is caused by low predictor\nresolution, and that aggregating data leads to other types of error. In fact, our 87Sr/86Sr\nsamples were collected from various organisms and substrates, often hundreds of meters\napart. Yet, the lack of higher-resolution maps for predictor variables at the continental scale\nresults in these samples originating from the same cell and, thus the same predictor space (or\nlandscape). While data from the same grid cell (1km2) will share the same environmental\nproperties (overlapping landscapes), their 87Sr/86Sr ratios will differ due to natural variation\nof 87Sr/86Sr between sample locations within that cell, as well as among different sample types\n(e.g. various plants, soils, and animals), which are influenced by various factors affecting Sr\nsourcing17,37. Additionally, aggregating data using medians would drastically reduce the\neffective sample size, leading to increased Type I error64. Treating each data point as\nindependent allows us to integrate the natural variation in 87Sr/86Sr ratios within each grid\ncell as an important part of the algorithm training process. Secondly, we argue that the\nindependence assumption refers to the model residuals (the part of the variance not explained\nby the model) rather than the sampling65,66. Zuckerberg et al. (2012)66 demonstrated that\nreducing landscape oversampling does not necessarily reduce spatial autocorrelation (an\nindication of residual dependence in spatial models). Spatial autocorrelation in the residuals\nwould indicate that there is an effect of sample proximity effects or landscape overlap that are\nnot explained by the model. Therefore, to determine if our approach violates statistical\nindependence, we assessed potential spatial auto-correlation in the model residuals using a\ncross-correlogram67.”\nThen, per the reviewer’s request, we elaborated more on how to interpret the cross-correlogram\nand how it demonstrates the lack of auto-correlation in our dataset in the main text result section\n(see lines 221-223): “We detected no spatial autocorrelation in the model residuals, indicating\nthat there are no unaccounted proximity effects (between samples) within the variance\nunexplained by the model (Supplementary Fig. 2).”\nWe further added a sentence to the figure caption of Supplementary Figure 2 to aid the reader\nin interpreting the cross-correlogram:\nI\ns\nn’\na\nr\no\nM\nFig. S2. Cross-correlogram showing the cross-correlation at different distances (km) between the\nmodel residuals and 87Sr/86Sr ratios. The generally low Moran’s I indicate that proximity (short\ndistance) does not increase the correlation of residuals suggesting the absence of spatial\nautocorrelation in the residuals. Further, the confidence interval (grey shading) always\nincludes zero, indicating the absence of auto-correlation.\nComment 4:\n3) I think the author could combine the last two paragraphs (applications for wildlife/application for\nforensics) into one concluding paragraph describing the broader implications/applications of their\nisoscape. I find the last two paragraphs still distract a bit from the main message on African slave\norigin as they are pretty long. So I would simply have one final paragraph where the authors describe\nbriefly all the cool applications and potential solutions that this new isoscape brings to African\necology, forensics, paleoecology… Short and exciting with a few refs.\nResponse: Thank you for your valuable suggestion. We have combined the last two paragraphs\n(applications for wildlife/application for forensics) into one concise concluding paragraph to\nhighlight the broader implications and applications of our isoscape. Please see our revisions to the\ndiscussion (lines 368-387):\n“Beyond the applications in the archaeology of the transatlantic slave trade, our\nstrontium isoscape, integrated with other isotopic systems (e.g., oxygen, sulfur, hydrogen, and\ncarbon), holds transformative potential for future provenance studies across multiple\ndisciplines. In wildlife conservation, it serves as a powerful tool for identifying the origins of\ntraded wildlife, such as illegally logged timber46 and endangered species47 (e.g. elephants25,48,49\nand chimpanzees50), aiding efforts to combat poaching and wildlife trafficking by enabling\nlaw enforcement to pinpoint hotspots of illegal activity. Furthermore, it supports the study of\nwildlife dispersal (e.g. chimpanzees51) and the mobility of migratory species (e.g. bird and\ninsect species52) at large spatial scales, and provides insights into the ecology and adaptations\nof extinct animal species53 through the analysis of field, museum, and archaeological\nspecimens. In forensic science, our isoscape is crucial for tracing the geographic origins of\nunidentified human remains, particularly in the identification of deceased African migrants.\nFor instance, in the 2001 London murder case involving the unidentified child ‘Adam’, our\nstudy suggests much broader ranges of origins within Africa than previously thought54,55.\nAdditionally, 87Sr/86Sr and complementary isotope systems could provide a new, rapid, and\ncomparatively cost-effective approach to help identify the thousands of African migrants who\nperish in the Mediterranean Sea during their passage to southern Europe. Cattaneo and her\ncolleagues56 called this the “largest humanitarian disaster” in Europe since the Second World\nWar, emphasizing the rights of the victims to be identified and repatriated if possible. Overall,\nthese diverse applications highlight the usefulness of our isoscape, paving the way for rapid\nadvances in provenance applications in African archaeology, ecology, forensic science, and\npaleoecology.”\nComment 5:\nLine 173: refered to what? Weird sentence ending?\nResponse: Thank you for pointing this out. We revised the sentence to:\n“A growing number of such studies employed strontium isotope (87Sr/86Sr) analysis of human\nremains, which demonstrates great potential for reconstructing the geographical origins and\nmigration of individuals, particularly when local- or large-scale strontium isoscapes are available\nas references”.\nComment 6:\nRemarks on code availability: I had reviewed and commented the code last time in great details.\nThe authors have taken into account all my comments in their reviews. I did not have time to rerun\nfully the script this time but I believe most of the issues have been addressed.\nResponse: Thank you for your detailed review of our code in the previous round and for\nacknowledging the revisions we made. We have re-verified the code to ensure its reproducibility\nand reliability before publication.\n*********************************************************************\nReviewer #2 (Remarks to the Author):\nComment 1:\nThis version of the manuscript addresses most of the problems I identified previously. My major\nconcern about this version is to do with the interpretation of Figs 3 and 4 (see below). I also have a\nnumber of relatively minor comments, listed below.\nResponse: Thank you for your constructive feedback and for noting the improvements in our\nmanuscript. We’ve addressed your concerns about Figures 3 and 4 in detail in the revised manuscript,\nand have also revised every single minor points you mentioned.\nComment 2:\nFirst paragraph: I am not at all sure that the slave trade was “the most notable migration event” in\nAfrica – the Bantu expansion was arguably as influential. Add “forced” before “migration”, as you\nsay a couple of lines on, and no-one would argue.\nResponse: Thanks so much for your suggestion and attention to detail. We acknowledge the\nsignificance of other major migration events, such as the Bantu expansion, which we mentioned in\nthe preceding sentence of the introduction. We have revised this sentence in the revised manuscript:\n“One of the most notable migration events in the history of the continent occurred between the\n15th and 19th centuries, during which at least 12.5 million Africans were abducted, enslaved,\nand transported to the Americas and Europe, which dramatically changed the demography, economics,\nand politics of both Africa and the Americas5. Although the transatlantic slave trade is well\ndocumented and known as the largest forced migration event…”.\nComment 3:\nIt is untrue that “little to nothing is known about …. what regions and communities they [African\nslaves] had been taken from” – there is a large body of scholarship on this in African history.\nResponse: Thank you for pointing out the vague phrasing of this sentence. We are well aware of\nhow much we know about the transatlantic slave trade from historical documents, oral histories and\neven written first-hand accounts. However, we here aimed at pointing at the lack of understanding\nregarding the specific origins and journeys of individual persons whose skeletal remains are later\nfound within the African Diaspora. We reworded this sentence in the introduction accordingly:\n“However, the individual treacherous journeys captive Africans endured before reaching\ncoastal shipping ports, as well as the regions from which they were taken remained largely\nunclear in the bioarchaeology of the African Diaspora.”\nComment 4:\nSecond paragraph: “87Sr/86Sr ratios in animal and human body tissues primarily mirror\nbiologically available 87Sr/86Sr incorporated via the consumption of plants and water from the local\nsubstrate with minimal fractionation which can be corrected during mass spectrometric\nanalysis(Supplementary Note S1)” is still incorrect. This fraction IS corrected for (not “can be”),\nnot during mass spectrometric analysis, rather during post-measurement data processing. These\nmight seem like picky points, but science requires precise use of language.\nResponse: Thank you for your careful review and for emphasizing the importance of precise\nlanguage. We appreciate your guidance on accurately describing the correction process for isotope\nfractionation. We have revised the sentence both in the revised manuscript (introduction) and in\nSupplementary Note S1:\n“87Sr/86Sr ratios in animal and human body tissues primarily mirror biologically available\n87Sr/86Sr incorporated via the consumption of plants and water from the local substrate with\nminimal fractionation which is corrected during post-measurement data processing16,17\n(Supplementary Note S1).”\nComment 5:\nSecond paragraph of Results: Specify the “several soil properties” used as predictors in your\nisoscape.\nResponse: Thanks for your suggestion. We have now clearly listed these soil properties. Please refer\nto the second paragraph of the result section in the revised manuscript (lines 215-217):\n“Ultimately, we used 11 predictors (dust, terrane age, soil clay content, soil cation exchange\ncapabilities, soil organic carbon content, maximum age of bedrock, sea salt deposition, mean\nannual precipitation, srsrq3, lithology, and elevation) to model the 87Sr/86Sr ratios of 2266 samples\nusing a random forest algorithm tuned with the hyperparameter “number of variables randomly\nsampled at each split” both set to 2.”\nComment 6:\nIt is untrue that “that RF models are unable to extrapolate into areas without matching training data”.\nWhat is true is that we can have little confidence in such extrapolations.\nResponse: Thank you for correcting our statement about the extrapolation capabilities of random\nforest models. Here is our revision to this specific sentence in the result section:\n“This conservative depiction of an isoscape accounts for the fact that RF models are not\nreliable when extrapolating into areas without matching training data.”\nComment 7:\nThe level of “match” between 87Sr/86Sr of the skeletons and the areas they may have come from is\nquite uneven. Fig 3e shows very good agreement, but 3d and 3f appear to offer at best 50-60%\nmatch. Similarly, in Fig 4, 4c offers quite good agreement but 4f does not. This requires some\ndiscussion.\nResponse: Thank you for raising this important point. We apologize for the lack of clarity in the\noriginal explanation of Figs. 3 and 4, which may have led to some confusion. To clarify, the\nprobabilities shown in these figures are not the direct results from the “assignR” tool but, as we\nexplained in the method section, normalized probability surfaces for better comparison across\nindividuals. The non-normalized probabilities generated by assignR are presented in the Review\nResponse Fig. 1 below (please note that the scale maximum changes between figures).\nReview Response Fig. 1 The non-normalized probability of geographical origins for five enslaved\nindividuals excavated from the Anson Street African Burial Ground in Charleston (USA).\nIn our analysis, we normalized the resulting probability surfaces for each individual by dividing\nthe probability value of each grid cell by the maximum probability value. This means that the color\nscale does not represent absolute probabilities (e.g., 50-60% as you mentioned) but rather relative\nprobabilities. As a result, darker colors indicate a higher probability compared to other cells within\nthe same map, rather than a direct probability of origin.\nFor example, in Fig. 3e (Review Response Fig. 1e above), the dark color suggests that all the\ncells have an equal probability of being the origin, as this individual’s 87Sr/86Sr ratio (~0.720) is\nwidely distributed across various regions in Africa. Therefore, many areas are equally probable\norigins (with the ratio of many grid cells to the maximum probability being close to 1, see Review\nResponse Fig. 1e). In contrast, in Figs. 3d and 3f, fewer regions show high probability because these\nindividuals’ 87Sr/86Sr ratios (> 0.730) are less common in West Africa. This leads to a more\nconcentrated set of potential origins, where only a small number of grid cells show higher\nprobabilities. As these grid cells are limited, the dark color may appear less prominent (see Review\nResponse Fig. 1d and 1f).\nWe have modified the captions of Figs. 3 and 4 to clarify this point:\n“Fig. 3 The normalized probability of geographic origins for five enslaved individuals\nexcavated from the Anson Street African Burial Ground in Charleston (USA) using published tooth\nenamel 87Sr/86Sr data and our new strontium isoscape, in combination with aDNA data restricting\nthe possible region of origin10. (a) The boxplot (representing the median and interquartile range)\nshows the original 87Sr/86Sr data distribution from 29 individuals buried at this site. We selected five\nindividuals with good aDNA coverage and 87Sr/86Sr ratios ≥ 0.720. Lower 87Sr/86Sr ratios between\n0.709 and 0.715 are likely present in coastal South Carolina, where the Anson Street Ancestors were\nheld captive and buried10,29. (b) and (c) show the isotopic geolocation to parts of West-Central Africa\nfor the individuals named Kuto and Banza, respectively. (d-f) show the isotopic geolocation of the\nindividuals named Daba, Lima, and Ganda within West Africa. The probability surface was\nnormalized by dividing each cell’s value by the maximum probability. Consequently, darker\nregions represent areas with a higher relative probability of origin compared to other cells,\nrather than the actual probability values.”\n“Fig. 4 The normalized probability of geographic origins for five enslaved individuals\nexcavated from the Pretos Novos cemetery in Rio de Janeiro (Brazil) using published tooth enamel\n87Sr/86Sr data9 and our new strontium isoscape, in combination with published enamel δ18O data35\nand the annual mean precipitation oxygen isoscape based on RCWIP data products41. The boxplot\n(a) shows the original 87Sr/86Sr data distribution from 30 individuals buried at this site. From these,\nwe selected five individuals with 87Sr/86Sr ratios ≥ 0.720, which are very likely not present in the\ncoastal sugar plantation regions of Rio de Janeiro province, suggesting they originated in parts of\nAfrica with more radiogenic bedrock9. The isotopic geolocation suggests individual P6 (b) could\nhave originated in West or South Africa, whereas the other four individuals (c-f) with more\nradiogenic 87Sr/86Sr ratios likely came from present-day Angola or the region of South-East Africa.\nThe probability surface was normalized by dividing each cell’s value by the maximum\nprobability. Consequently, darker regions represent areas with a higher relative probability\nof origin compared to other cells, rather than the actual probability values.”\nWe hope this explanation clarifies the intent behind the figures, and we are happy to incorporate\nfurther details into the main text if needed.\nComment 8:\nDoes the DNA from Daba, Lima and Ganda specifically link them to the southern coast of West\nAfrica, or just to West Africa in general? There were major West African slaving ports that fall\noutside the limits of this isoscape, especially Goree Island off Dakar. Could they have come from\nthere?\nResponse: Thank you for your questions. According to previous genetic studies (Fleskes et al., 2023),\nthe aDNA from Daba, Lima, and Ganda links them to West Africa in general, not specifically to the\nsouthern coast. It is unlikely that they were from Gorée Island, as the coast of Senegal, including\nGorée Island, has relatively low 87Sr/86Sr ratios (< 0.711; see Fig. 2a in the revised manuscript),\nwhereas Daba, Lima, and Ganda all have 87Sr/86Sr ratios higher than 0.720. In any case, slave ports\nsuch as Gorée were only brief stations in the journey of enslaved Africans, making it highly unlikely\nany captive person would have 87Sr/86Sr ratios matching that of a slave port itself. People were\nbrought to these ports from elsewhere.\nFrom a geological perspective, Gorée Island is part of the West African Atlantic margin and\nfeatures episodic volcanic activity from the Eocene-Oligocene boundary up to the Quaternary (Cap-\nVert peninsula; Ndiaye et al., 2014). The island has a high zone in the south with basaltic cliffs from\nvolcanic activity between the Oligocene and the upper Miocene, and a low zone in the north and\ncenter consisting mainly of tertiary limestone and silts (Bakhoum et al., 2017). Both basalt (0.702-\n0.705) and limestone (~0.708) typically have very low 87Sr/86Sr values (Bentley, 2006). Additionally,\nindividuals born along the coast might also be influenced by lower marine strontium values\n(~0.7092). Therefore, we think it is unlikely that Daba, Lima, and Ganda with high 87Sr/86Sr ratios\nwere from oceanic islands along the coast of West Africa outside of our isoscape map.\nReferences\nFleskes RE, Cabana GS, Gilmore JK, et al. (2023). Community-engaged ancient DNA project\nreveals diverse origins of 18th-century African descendants in Charleston, South Carolina.\nProceedings of the National Academy of Sciences, 120(3): e2201620120.\nBentley R (2006). Strontium isotopes from the earth to the archaeological skeleton: a review. Journal\nof Archaeological Method and Theory, 13(3): 135-187.\nNdiaye A, Ngom PM (2014). The geodynamic context of the Cenozoic volcanism of the Cap-Vert\nPeninsula (Senegal). International Journal of Geosciences, 5(12): 1521.\nBakhoum PW, Ndour A, Niang I, et al. (2017). Coastline mobility of Goree Island (Senegal),\nfrom 1942 to 2011. Marine Science, 7(1): 1-9.\nComment 9:\nIn Fig 4, what about the d13C values? Do these help narrow down the possibilities?\nResponse: Thank you for raising this important point. While this paper centers on strontium isotopes\nand a strontium isoscape, we initially considered using carbon isotopes to refine our conclusions\nhere (and we do so in our other multi-isotope work on the archeology of the slave trade). However,\nseveral challenges limit their effectiveness in pinpointing the geographic origins of the individuals,\nwhich we re-evaluate in this study. The primary issue is the lack of a detailed crop distribution map\nduring the time of the transatlantic slave trade. Modern C and C plant distributions cannot serve\n3 4\nas accurate baselines, mainly because agricultural practices in the 16th and 19th centuries were\ndifferent.\nHowever, some historical records indicate the regions in which the main African C crops were\n3\ncultivated during the time of slavery, including yam, rice, and manioc, and regions in which C crops\n4\nsuch as sorghum, millet, and maize were predominant (Ogot, 2010). In the 20th century, researchers\ndocumented the traditional crop zones and subareas in West Africa in detail (Harris, 1976), revealing\nthat a variety of staple crops, including both C and C plants, were cultivated even within small\n3 4\nregions (see details Review Response Fig. 2). This information helps narrow down the origin in the\ncase of individual P6 (C signal diet). We revised the manuscript as follows (lines 308-322):\n4\n“In the case of the Brazilian slave cemetery, Pretos Novos in Rio de Janeiro, individual\nP6 may have originated from West or South Africa based on the 87Sr/86Sr and δ18O data35.\nThis includes various discrete locations within Guinea, central Nigeria, northern Cameroon,\nas well as broad regions in South Africa (Fig. 4b). The C -plant signal in this individual’s diet\n4\nmight further exclude the possibility that this person originated from regions dominated by\nC crops in West Africa, such as the “Rice Coast” - the traditional rice-growing region between\nzone in most regions of southern West Africa, which were dominated by yams, manioc, and\nother C root crops (Supplementary Fig. 7)43. Therefore, within West Africa, the origin of this\n3\nperson can be constrained to a very limited area in central Nigeria, within the sorghum-millet\nzone (C crops), or northern Cameroon, located at the boundary between sorghum-millet and\n4\nmaize-dominated zones (C crops). While it remains a strong possibility that individual P6\n4\ncame from South Africa, which has dry farming conditions suitable for C plant growing,\n4\nhistoric records suggest a much higher probability of a West African origin, as Brazil received\nat least 1,540,113 captives from the West African coasts throughout the duration of the slave\ntrade compared to 336,896 captives from South-East Africa and the Indian Ocean5. Future\naDNA analysis could help distinguish between West and South African origins for individual\nP6.”\nFor the other four individuals with a high likelihood of origin in Angola, further narrowing\ndown their origins is challenging. This difficulty arises from the widespread cultivation of both C\n3\nand C crops by agricultural societies of West and Atlantic Central Africa (including Angola),\n4\nfollowing the introduction of new crop species by European colonizers (Isichei, 1997). These four\nindividuals may also have a (less) likely origin from South-East Africa based on Sr isotopic evidence,\nwhere the tribes cultivated yam (C ), sorghum (C ), millet (C ), manioc (C ), and maize (C ) (Ogot,\n3 4 4 3 4\n2010; Bastos et al., 2016). However, apart from sporadic historical records of agricultural activities,\nwe currently don’t have detailed agricultural distributions within Africa from the 18th and 19th\ncenturies. If we refer to 2005 distribution map of main crops across sub-Saharan Africa (Review\nResponse Fig. 3), it shows that within Angola, C crops (mostly manioc) dominated central and\n3\nnorthern regions, while C crops (maize and millet) were grown in central and southern regions.\nmap to the colonial period is uncertain, which is why we did not use carbon isotopes to further\nconstrain the origins of these four individuals. Therefore, in the revised manuscript, when discussing\nthese four individuals, we can only briefly mention the agricultural activities during the colonial\nperiod, as referenced in lines 328-331:\n“Indeed, their tooth enamel δ13C values suggest that they did not share the same dietary\ncustoms in early life. “This finding is consistent with historic records indicating that the\nwidespread cultivation of both C (manioc and other C root crops) and C (maize and millet)\n3 3 4\ncrops by tribes across Atlantic Central Africa (including Angola), introduced by European\ncolonizers, became key staples for local populations44.”\nAdditionally, apart from cultivated crops, the distribution of modern natural vegetation in\nAfrica (Review Response Fig. 4) shows that C vegetation is mainly found in the drier regions of\n4\nsouthern Angola, while C plants are widely distributed in most regions of Angola. In South-East\n3 4\ndiffering from anthropogenic crop cultivation, adds complexity to the interpretation of carbon\nisotope data.\nReview Response Fig. 2 West Africa: traditional crop zones and subareas of crop dominance\n(adopted from Harris, 1976)\nReview Response Fig. 3 Distribution of main crops across sub-Saharan Africa in 2005 (adopted\nfrom Fatema and Kibriya, 2019)\nReview Response Fig. 4 (a) Stable carbon isotopic distribution for the African continent today, (b)\npercentage of vegetation that uses the C pathway, and (c) percentage of vegetation that uses the\nIsichei E (1997). A History of African Societies to 1870. Cambridge University Press, Cambridge.\nFatema N, Kibriya S (2019). Rice, Culture and conflict in sub-Saharan Africa. Available at SSRN:\nhttps://ssrn.com/abstract=3667213.\nHarris DR (1976). Traditional systems of plant food production and the origins of agriculture in\nWest Africa. In: Harlan, J, De Wet, J, Stemler, A (eds). Origins of African plant domestication.\nMouton, pp. 311-356.\nOgot BA (2010). História geral da África, V: África do século XVI ao XVIII. Unesco.\nBastos M, Santos, R, de Souza S, et al. (2016). Isotopic study of geographic origins and diet of\nenslaved Africans buried in two Brazilian cemeteries. Journal of Archaeological Science, 70: 82-90.\nStill CJ, Powell RL (2010). Continental-scale distributions of vegetation stable carbon isotope ratios.\nIn: West J, Bowen G, Dawson T, Tu K (eds). Isoscapes: understanding movement, pattern, and\nprocess on Earth through isotope mapping. Springer Netherlands, Dordrecht, pp. 179-193,\nComment 10:\nNote that St Helena was simply a pick-up point – slaves taken on board there could have come from\nmany areas. The same is true of Mozambique- slaves were brought from the interior, likely spanning\nseveral contemporary countries, and shipped from Mozambique. Individuals recorded as “of\nMozambique” did therefore not necessarily originate from what we call Mozambique today.\nResponse: Thank you for your insightful comment. We fully agree and acknowledge that St. Helena\nand Mozambique were significant transit points and embarking areas respectively, with slaves taken\non board potentially originating from various regions across Africa. This complexity underscores\nthe limitations of using historical records alone to determine individual origins, which is why our\nstrontium isotope research is crucial. As a side note, the first and last authors of this manuscript have\nbeen part of a project studying the origins of the so called “liberated” Africans from St. Helena using\nenamel Sr isotope data (Wang, Watson et al, in prep). Interestingly, we identified several individuals\nfrom St. Helena who likely originated from the hinterlands of the Mozambique coastline, a large\nregion that, as shown in the current manuscript, exhibits highly radiogenic Sr isotope ratios.\nWe have revised the corresponding sentence in the discussion (lines 362-365) to:\n“While Namibia and the Sahel, with their low population densities, were probably not the focus\nof slave traders58, the region of Mozambique and its adjacent hinterlands were heavily impacted\nduring the slave trade of the early to mid-19th century, with at least half a million enslaved people\ntaken from the South-East African region5.”\nComment 11:\nUnder “Limitations and future work”, the phrase “regions with strict data extrapolation” needs re-\nwording, making it clear that these are regions lacking actual analyses.\nResponse: Thanks for your suggestion. We have revised the phrase to “We employed a conservative\napproach by identifying and excluding predictor space from our isoscape that would have\nrequired extrapolation.”"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed all my comments.\n1) I appreciate the detailed answer regarding pseudoreplication and the cross-correlogram.\n2) I think the methods is improved with the new additions. I do think that the oxygen isoscape uncertainty could have been generated potentially in a better way by compiling oxygen data in enamel and then using calRaster() to generate a true uncertainty layer. However, I believe the author approach with a 1 per mile uncertainty addition to the precipitation layer is conservative.\n3) The implications section of the manuscript is much improved.\nSo overall I believe that manuscript should be published.\n(Remarks on code availability)\nI had reviewed the code in details previously and provided detailed comments about how to make it more reproducible. I believe this has all been addressed.", "reviewer_2": "I am satisfied that this revision deals with all my earlier questions and criticisms. Two very minor points:\nIt is inconsistent to say in line 162 that “the 87Sr/86Sr ratios of a location primarily relate to the underlying bedrock geology” and in lines 245-6 that “dust deposition is the most important factor influencing spatial bioavailable 87Sr/86Sr variation”\nLine 422: “board” should be “broad”\nOnce the authors have dealt with these, I believe the manuscript should be published."}, "author_response": "Point-by-Point Response to Reviewers – Author response in blue font throughout\n(please note: Line numbers refer to the numbering in the clean version of the manuscript)\n*******************************************************************************\nReviewers’ comments:\nReviewer #1 (Remarks to the Author):\nComment 1:\nReviewer #1 (Remarks to the Author):\nThe authors have addressed all my comments.\n1) I appreciate the detailed answer regarding pseudoreplication and the cross-correlogram.\n2) I think the methods is improved with the new additions. I do think that the oxygen isoscape\nuncertainty could have been generated potentially in a better way by compiling oxygen data in\nenamel and then using calRaster() to generate a true uncertainty layer. However, I believe the author\napproach with a 1 per mile uncertainty addition to the precipitation layer is conservative.\n3) The implications section of the manuscript is much improved.\nSo overall I believe that manuscript should be published.\nReviewer #1 (Remarks on code availability):\nI had reviewed the code in details previously and provided detailed comments about how to make\nit more reproducible. I believe this has all been addressed.\nResponse: We appreciate the reviewer’s suggestions, which greatly helped to improve the\nmanuscript. We agree that the 1‰ uncertainty added to the precipitation layer is conservative. Given\nthe many factors influencing oxygen isotopes (e.g., climate variability and changes in conversion\nformulas), it is difficult to precisely quantify the uncertainty. As a result, we adopted a conservative\nestimate of 1‰, based on Chenery et al. (2012), which accounts for the error ranges in the isotope\nconversion process.\nReferences\nChenery CA, Pashley V, Lamb AL et al. (2012). The oxygen isotope relationship between the\nphosphate and structural carbonate fractions of human bioapatite. Rapid Communications in Mass\nSpectrometry, 26: 309-319.\n*******************************************************************************\nReviewer #2 (Remarks to the Author):\nComment 1:\nI am satisfied that this revision deals with all my earlier questions and criticisms. Two very minor\npoints:\nIt is inconsistent to say in line 162 that “the 87Sr/86Sr ratios of a location primarily relate to the\nunderlying bedrock geology” and in lines 245-6 that “dust deposition is the most important factor\ninfluencing spatial bioavailable 87Sr/86Sr variation”\nResponse: Thank you for pointing out the inconsistency regarding the influence of strontium\nisotopes. As you noted, strontium isotopes are primarily known to be influenced by bedrock geology\nbut we increasingly understand that other environmental factors might play a bigger role than we\nthought even just a decade ago. We revised the respective sentences for clarity.\nIn the Introduction section, we have changed the wording to: “The 87Sr/86Sr ratios of a location\nprimarily relate to the underlying bedrock geology, with additional influences from\natmospheric deposition, geomorphological and biochemical process, and remain stable over\narchaeological time scales19.”\nIn Supplementary Note 1, we have clarified further: “While largely derived from the underlying\nbedrock geology, the isotopic compositions of bioavailable Sr and living organisms (e.g. plants\nand snail shells) can differ from the 87Sr/86Sr ratios of whole rocks and bulk soils7,8. This is\nbecause some factors can also potentially influence the biosphere 87Sr/86Sr in a given region,\nincluding differential mineral compositions and weathering rates of different rock types,\ntopographic processes, as well as non-geological sources from atmospheric deposition (e.g.,\nrainfall, dust, and coastal sea-spray) and anthropogenic pollution9-11.”\nComment 2:\nLine 422: “board” should be “broad”\nResponse: We have corrected the typo from “board” to “broad”.\nComment 3:\nOnce the authors have dealt with these, I believe the manuscript should be published.\nResponse: Thank you once again for your thorough and constructive comments."}]

Decision letter