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Nomadic ecology shaped the highland geography of Asia’s Silk Roads


There are many unanswered questions about the evolution of the ancient ‘Silk Roads’ across Asia. This is especially the case in their mountainous stretches, where harsh terrain is seen as an impediment to travel. Considering the ecology and mobility of inner Asian mountain pastoralists, we use ‘flow accumulation’ modelling to calculate the annual routes of nomadic societies (from 750 m to 4,000 m elevation). Aggregating 500 iterations of the model reveals a high-resolution flow network that simulates how centuries of seasonal nomadic herding could shape discrete routes of connectivity across the mountains of Asia. We then compare the locations of known high-elevation Silk Road sites with the geography of these optimized herding flows, and find a significant correspondence in mountainous regions. Thus, we argue that highland Silk Road networks (from 750 m to 4,000 m) emerged slowly in relation to long-established mobility patterns of nomadic herders in the mountains of inner Asia.

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Figure 1: Geography of inner Asian study zone (in colour) and location of main Silk Road cities across Asia.
Figure 2: Flow accumulation pathways generated by the pastoralist participation model between highland pastures (green) and winter campsite (blue) zones (750 m to 4,000 m).
Figure 3: Comparative percentages of non-zero values extracted from the pastoralist participation model using 200 cohorts of 258 randomly generated points (left) versus 258 actual Silk Road sites (arrow) within the modelled elevation ROI (750 m to 4,000 m).
Figure 4: Geographic correspondence between known highland Silk Road sites and 500 aggregated simulated flow accumulations of the pastoralist participation model.

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Funding for research at the SAIE laboratory was provided by Washington University in St. Louis (M.D.F.). C. Copp and M. Webb provided assistance with modelling and coding in the SAIE laboratory and GIS laboratory of Washington University in St. Louis. C. Chady assisted in copy editing. An early version of the pastoralist participation model was presented at the Advanced Seminar ‘New Geospatial Approaches in Anthropology’ at the School for Advanced Research, March 6–10, 2016. P. Daly, R. Pinhasi, G. Larson and D. Meltzer provided commentary on drafts of this article.

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Authors and Affiliations



M.D.F. conceptualized the study, designed, and executed the spatial model, and wrote the manuscript with editing and approval from C.E.S., T.W. and C.M.T. C.E.S. carried out spatial modelling and script coding in ArcGIS and Python. C.M.T. carried out statistical modelling and analysis of the results. T.W. provided the Silk Road site database and contributed to analysis and interpretation of the results. M.D.F. carried out spatial and statistical analysis and interpretation of the results with contributions and input from C.E.S., C.M.T. and T.W.

Corresponding author

Correspondence to Michael D. Frachetti.

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The authors declare no competing financial interests.

Additional information

Reviewer Information Nature thanks D. Rogers and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Figure 1 Flowchart of the PastPart model workflow yielding a summed aggregate of 500 runs of flow accumulation within the modelled elevation ROI (750–4,000 m).

Inputs: (1) model source directory; (2) NDVI data; (3–6) DEM images; (7–10) model parameter files: settlement_class.txt, sheep_class.txt, pasture_class.txt, vegetation_class.txt. Outputs: (1) processing geodatabase; (2) results geodatabase; (3) NDVI converted to GRID format; (4–7) DEM images converted to GRID format; (8) study area DEM; (9) study area NDVI; (10) probablity surface; (11) cost surface; (12) weight raster; (13) 5,000 random spatially balanced points; (14) cost distance raster; (15) flow direction raster; (16) flow accumulation raster. Processes: (1) create geodatabases; (2) load files to processing geodatabase; (3) mosaic DEM files and clip images to study area; (4) create model inputs: (a) classify study area DEM using settlement_class.txt to create probability surface, (b) classify NDVI using vegetation_class.txt create vegetation priority raster, (c) classify DEM using sheep_class.txt to create highland flow weight raster; (5) generate spatially balanced random settlements (n = 5,000); (6) calculate cost distance; (7) calculate flow direction; (8) calculate flow accumulation; (9) sum flow accumulation rasters. Variables: (1) number of iterations; (2) current iteration value; (3) number of output points. Unused data: (1) output backlink raster; (2) output drop raster.

Extended Data Figure 2 Distribution of Silk Road sites versus randomized points across the modelled elevation ROI.

Top, distribution of known Silk Road Sites (n = 258) in relation to the modelled elevation ROI (750–4,000 m, in grey). Bottom, distribution of a single run of randomized test points (n = 258) generated in relation to the modelled elevation ROI (in grey). 200 runs of random test point cohorts were calculated in the modelled elevation ROI.

Extended Data Figure 3 Detailed view of the seasonal highland–lowland division of the elevation ROI (750–4,000 m).

Highland ‘summer’ zones (green) are defined from 1,500 m to 4,000 m above sea level, and ‘winter’ campsite zones (blue) are defined from 600 m to 1,500 m above sea level. Prominent Silk Road cities are mapped for geographic reference.

Extended Data Figure 4 Detailed view of one run of spatially balanced random points distributed throughout in the winter campsite elevation.

In total, 5,000 points were generated (for each of the 500 runs the PastPart model) in the winter campsite settlement elevation range (from 600 m to 1,500 m) across the entire study zone (map inset). Each iteration of sites recalculates (along with the reclassified vegetative weight raster) the ‘cost distance’ map across the modelled elevation ROI (750–4,000 m).

Extended Data Figure 5 Input data classes for the pastoralist participation model.

Top, detail of reclassified vegetative weight raster (veg_priority) overlaid with one run of randomized foothill points (n = 5,000) simulating winter campsites. Bottom, detail of the cost distance raster (CD_x) calculated using 5,000 randomized ‘winter settlement’ points and the vegetative weight raster. Each of the 500 iterations of new points changes the geographic distribution of ‘low cost’ travel between weighted pasture zones (750 m to 4,000 m) and the lower elevations—effectively causing variations in the flow accumulations from pastures to hypothetical settlements in the foothills with each iteration.

Extended Data Figure 6 Comparative geography of proposed Silk Road corridors.

Calculated using least-cost (ease-of-travel) algorithms (black and blue lines) to connect known archaeological/historical sites7 and the simulated herding flow pathways from 500 aggregate runs of the PastPart model (yellow and red ‘flow’ network). Source Data is available in the online version of this paper. Notable differences between modelled ‘ease-of-travel’ corridors and ecologically derived flow accumulation pathways are visible in the inset detail maps. a, PastPart flow accumulation pathways illustrate highland Silk Road routes between Narat (61) and Karkara (18) and alternative highland passes to the east of the Turugart Pass (31) towards Kashgar (133). b, A potentially undocumented corridor of the Silk Road into the Tibetan Plateau to the south of Dunhuang (406) (China). c, A number of alternative networks of connectivity across the western Himalaya and Pamir Mountains, for example diverse routes from Kashgar (133) to Tashkurgan (345).

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Supplementary Information

This file contains the Supplementary Discussion, Supplementary References, Supplementary Table 1, a link to the executable ArcPython script to run the PastPart model in ArcGIS and the statistical report providing comprehensive data and illustration of the range of statistical analysis carried out for this study. (PDF 341 kb)

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Frachetti, M., Smith, C., Traub, C. et al. Nomadic ecology shaped the highland geography of Asia’s Silk Roads. Nature 543, 193–198 (2017).

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