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Journal of Biogeography
SUPPORTING INFORMATION
The fundamental hominin niche in late Pleistocene Central Asia:
a preliminary refugium model
Tyler A. Beeton, Michelle M. Glantz, Anna K. Trainer,
Sayat S. Temirbekov and Robin M. Reich
Appendix S1 Deriving stream networks: a geospatial modelling approach.
Various studies have used spatial analyses to derive hydrological characteristics (e.g. Jenson &
Domingue, 1988; Tarboton, 1991; Montgomery & Foufoula-Georgiou, 1993). Stream networks can
be estimated using an interpolated elevation surface, such as a digital elevation model (DEM), and
spatial hydrology tools (Jenson & Domingue, 1988). The present study uses a 30-m resolution DEM
raster surface from NASA’s Global Digital Elevation Model (GDEM) project and ARCGIS 10 (ESRI,
Redlands, CA, USA) geoprocessing tools to model water availability during the glacial-interglacial
transitions. GDEM is a continuous raster surface that depicts elevation in 30 × 30 metre resolution
cells with a vertical resolution of 10 m. One hundred and three tiles (1 arc-second by 1 arc-second)
were downloaded, mosaiced and filled (NASA LP DAAC, 2012; available at: http://lpdaac.usgs.gov/
get_data).
Delineating biomes using focal statistics
Fluvial systems are controlled by a number of factors such as contributing area, slope, climate and
soil characteristics (Hu et al., 2005). The hydrology toolkit in ARCGIS 10 relies on elevation variability and slope to model hydrological flow. Using ARCGIS to derive stream network results in a channel
network that requires the application of a threshold defined by the user, typically on the basis of
topographic maps, wherein only values greater than the threshold are considered streams. As such,
stream networks are unique to the ecological zones in which they are being derived, and so too are
the thresholds applied. To account for this, the present study defined boundaries between the
mountains, foothills, and plains.
The present study used a focal statistics sliding-window procedure placed on the DEM surface layer to assess proximate standard deviation and mean elevation gradient changes throughout
the landscape. A four-by-four cell (16 cells at 30-m resolution) was pushed across the surface to aggregate topographic variability and identify a coarse-scale discrete boundary between the major
ecological zones. This procedure assumes that the mountain zone exhibits greater elevation variability and a higher mean elevation than the foothill zones, and the foothills are assumed to be topographically distinct when compared to the arid plains of Uzbekistan and Tajikistan. The procedure
identified a narrow boundary falling roughly on a 700–1200 m elevation belt along a north/south
transect representing foothill zones. Those pixels with values less than 700 m were considered
plains, while pixels with values greater than 1200 m were considered mountainous. The edit tool
was then used to define polygons for each zone.
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Stream network derivation
A flow direction (FD) raster is the initial input surface required to carry out any of the operations
nested in the hydrology toolkit. It creates a raster depicting the direction of flow from each cell to
its steepest down-slope neighbour (Jenson & Domingue, 1988). The flow accumulation (FA) tool
provides a raster output which identifies the total number of cells that flow into any given cell (Jenson & Domingue, 1988). Derived from the FD layer, the FA output is a rudimentary stream network
representing values from 0 (no flow accumulation) to infinity, depending on the topography of the
study area. For example, points along high order rivers such as the Syr Darya, which transects Kyrgyzstan, Tajikistan and Kazakhstan, exhibit FA values in the millions. However, not all values in the
FA output represent a stream, and it is necessary at this step to apply a threshold to eliminate ephemeral streams and other ‘noise’ from the model.
The present study used georectified topographic maps (1 : 100,000 scale) and 3-D ARCGLOBE
geovisualization techniques to determine a threshold representing the minimum contributing area
needed to support a stream network in the foothills and mountains. Water availability near sites on
the plains was digitized using georectified topographic maps. Twenty FA sample units were extracted from individual biomes. These sample units were then overlain on topographic maps and satellite imagery in three-dimensional space. Two thresholds were identified for each zone to model
water access during glacial and interglacial periods.
A few issues should be mentioned in relation to the method. The contemporary climate of
Central Asia is warmer and drier than any other period during the late Pleistocene (Forster & Heller, 1994; Ding & Ding, 2003; Herzschuh, 2006); the reliance on contemporary hydrography for
model building may therefore result in an underestimate of water availability during interglacial
periods and an overestimate during glacial periods. Other lines of evidence support the notion that
contemporary ecosystems are most similar to those of the initial stages of OIS 3 (Dodonov et al.,
2000; Rupper, 2007). Therefore, the present study used contemporary topographical maps to
determine the threshold for interglacial periods.
During glacial periods water availability would have been severely diminished (Hewitt,
2004) and previous research using GIS modelling techniques has considered only third order
streams and above as accessible water resources during glacial advances (Field et al., 2007).
Strahler’s stream order index (1957) was used to determine the threshold for glacial periods. A
simple conditional statement in the raster calculator eliminated all streams below the third order.
We measured the distance (km) from each archaeological site to the stream networks using a leastcost path (LCP) analysis. LCP analyses are preferred over straight line measurements due to their
ability to incorporate costs to dispersal. In this study, slope was identified as an impediment to
dispersal.
Model evaluation
The hydrology model presented here is based solely on topographic variability. As such, it is necessary to test whether the topographic variability observed in each biome coincides with the observed variability in the sample units used to derive the model. Landform indices derived from McNab
(1993) and the curvature tool set in ARCGIS is one way to characterize landscape variability. The
landform index identifies three major classifications: convex ridges, flat slopes and concave depressions. The primary output demarcates the curvature of the surface of an individual cell while taking
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into consideration the eight nearest neighbours surrounding an individual cell (McNab, 1989). A
positive curvature indicates that the surface is upwardly convex at that cell, a negative curvature
denotes a concave surface, and a value of 0 indicates the surface is flat. Expected values range from
−4.0 to +4.0. A curvature operation was applied to the study extent, clipped to each biome, and reclassified into five classes: very concave, concave, flat, convex and very convex.
The proportion of each class was calculated using the field calculator and cell statistics in
the attribute table to characterize the variability within the mountain and foothill zones. Next, the
layer was extracted to a 2-km buffer around the sample units used to derive thresholds for the
model. The proportion of each class was compared between the sample units and their respective
biome using a chi-square test. Although both groups are significantly different due to misrepresentation of the extremes, the sample units adequately capture the other classes and we feel that the
sample units offer a robust representation of topographic variability.
REFERENCES
Ding, F. & Ding, Z. (2003) Chemical weathering history of the southern Tajikistan loess and paleoclimate implications. Science in China D: Earth Sciences, 46, 1012–1021.
Dodonov, A.E., Tchepalyga, A.L., Mihailescu, C.D.,
Zhou, L.P., Markova, A.K., Trubikhin, V.M., Simakova, A.N. & Konikov, E.G. (2000) Last-interglacial records from central Asia to the northern
Black Sea shoreline: stratigraphy and correlation.
Netherlands Journal of Geosciences, 79, 303–311.
Field, J.S., Petraglia, M. & Lahr, M.M. (2007) The southern dispersal hypothesis and the South Asian
archaeological record: examination of dispersal
routes through GIS analysis. Journal of Anthropological Archaeology, 26, 88–108.
Forster, T. & Heller, F. (1994) Loess deposits from
the Tajik depression (Central Asia): magnetic
properties and paleoclimate. Earth and Planetary
Science Letters, 128, 501–512.
Herzschuh, U. (2006) Palaeo-moisture evolution in
monsoonal Central Asia during the last 50,000
years. Quaternary Science Reviews, 25, 163–178.
Hewitt, K. (2004) Geomorphic hazards in mountain
environments. Mountain geomorphology (ed. by
P. Owens and O. Slaymaker), pp. 187–218. Hodder Scientific, London.
Hu, Q., Willson, G.D., Chen, X. & Akyuz, A. (2005) Effects of climate and landcover change on stream
discharge in the Ozark Highlands, USA. Environmental Modeling and Assessment, 10, 9–19.
Jenson, S.K. & Domingue, J.O. (1988) Extracting topographic structure from digital elevation data for
geographic information system analysis. Photogrammetric Engineering and Remote Sensing, 54,
1593–1600.
McNab, W.H. (1989) Terrain shape index: quantifying effect of minor landforms on tree height. Forest Science, 35, 91–104.
McNab, W.H. (1993) A topographic index to quantify
the effect of mesoscale landform on site productivity. Canadian Journal of Forest Research, 23,
1100–1107.
Montgomery, D.R. & Foufoula-Georgiou, E. (1993)
Channel network source representation using
digital elevation models. Water Resources Research, 29, 3925–3934.
NASA Land Processes Distributed Active Archive
Center (LP DAAC) (2012) ASTER L1B. USGS/
Earth Resources Observation and Science (EROS)
Center, Sioux Falls, SD.
Rupper, S.B. (2007) Glacier sensitivity and regional
climate: past and present. PhD Thesis, University
of Washington, Seattle, WA.
Strahler, A.N. (1957) Quantitative analysis of watershed geomorphology. Transactions of the American Geophysical Union, 38, 913–920.
Tarboton, D.G., Bras, R.L., Rodriguez-Iturbe, I. (1991)
On the extraction of channel networks from digital elevation data. Hydrological Processes, 5, 81–
100.