Methodology: Reconstruction of the OIS5a/c
ecosystems of the Deba and Urola valleys.
The topographic data for the analysis was
acquired as a digitised version of the Servicio de Información
Territorial’s 1:5000 series of maps of the
1. 25 metre interval
contour lines
2. natural bodies of water
3. rivers
4. streams
5. stream/river beds
This data was then cleaned and georeferenced[2]
using AutoCAD Map 2000 and imported into GRASS 5.0.2. as a vector file[3]
and the lines reconstructed[4]
before labelling[5].
Once contour labelling was complete, contour heights were checked against the
original paper maps and found to be highly accurate.
The downloaded data did not, however, include
the bathymetric elevation data necessary to model rising and falling
Pleistocene sea levels. Bathymetric contour lines were thus digitised by hand
from georeferenced .tiff files[6].
Once the bathymetric and terrestrial contour
maps were joined[7]
and transformed to a raster format[8],
a raster mask of ‘null’ values was layered over the resulting map[9]
to maintain its original boundaries[10]. One issue of concern was the difference
between the contour intervals in the terrestrial (at regular 25m intervals) and
bathymetric data (at uneven intervals: -5, -10, -20, -50, -100 and -200m). As
distances between the bathymetric contour lines increase, so the spatial
interpolation module produces a ‘stepped’ effect in the raster map,
whereby the interpolated values do not result in a smooth surface (a common
problem, see e.g. Wheatley & Gillings 2002 for discussion of potential
solutions). However, the affected regions were only really a factor during
periods of maximum sea regression, as for example at the Last Glacial Maximum,
and so impact on only a small subset of the analysis, and proved insignificant
at the scale of the analysis, as described below.
The first part of the process of
‘placing’ animals into the landscape involves its classification in
terms relevant to the animal species’ distributions. A number of systems
of classification have been presented in the literature, including Sturdy et al.’s comparable work in Epirus,
(1997), Butzer and Clark’s work in Palaeolithic Cantabria (Butzer 1981,
1986; Clark 1983), and in the Holocene, van Hove’s work in Calabria,
Southern Italy (2003) and Hammond’s ‘classes of land-surface
form’ for the Holocene USA (1964) as well as other systems designed for
non-archaeological uses, such as agricultural potential and land-use maps
produced by many agricultural agencies around the world such as the Soil Survey
of England and Wales’ Land Use
Capability Classification (Bibby and Mackney, 1977).
The two major axes, corresponding to the major
kinds of data required, remain reasonably constant:
1. topographic: data on
elevation, with its associated temperature and vegetational variation, as well
as slope and drainage systems and what Sturdy et al. (1997) call
general ‘ruggedness’ of terrain, which essentially describes the
ease of access of parts of the landscape by different animal species.
2. edaphic: ‘the
underlying soil and subsoil characteristics which make a piece of ground more
or less attractive to animal species in terms of their nutritional needs’
(ibid.: 593)
Topographic factors are of course covered by
the downloaded data; edaphic factors, however, are more difficult to examine.
Sturdy et al. (1997) use a
combination of geological and chemical analyses of the major soil types of the
area, but although it was originally hoped that a geological element would be
incorporated into this reconstruction, geological data for the region proved
extremely difficult to source as the 1:25,000 maps produced by the Instituto
Geologico y Minero de Espana (IGME) were unavailable for digitising.
IGME’s 1:50,000 Mapa Geologico de Espana sheet 5/12 (Bermeo/Bilbao) and
that published by Galan (1988) served to supplement the topographically-based
reconstructions as necessary. Chemical analysis is beyond the scope of this
project, and no relevant work has been done in the region to date. Modern soil
distribution maps are of course available, but modern soils have been subject
to various processes of change throughout the course of prehistory and
particularly in the modern era with the adoption of intensive farming
practices.
However, the development of particular soils is
in any case a highly context-specific process dependent on a multiplicity of
factors including the duration of development, climatic, chemical and physical
characteristics of the immediate environment and particularly the parent
material, the local bedrock (Buol, et al.
1973, 108-9; Wild, 1993, 49). Thus there are no one-to-one linkages between
rock and soil types, and this study draws on geological and edaphic data only
in a very general way to supplement the data from palynology and ecology used
for reconstruction of the ‘timeslices’.
Development of these ‘timeslices’
maps required ‘translation’ of the palaeoenvironmental data into
essentially topographically-based categories that would make sense in terms of
a GIS model: such descriptive terms as ‘sheltered valley bottoms’
therefore needed to be broken down in terms of variables handled by the
computer model, i.e. altitude, slope, aspect, distance to water. Such variables
describe the environment in terms which are hugely significant for the movement
of embodied entities through it.
Specific details for individual timeslices (and how they relate to the
habitats preferred by various animal species.
Changing Pleistocene sea levels are perhaps the
most obvious altitudinal consideration. Although it is important to consider
the potential roles of marine and littoral species, this analysis focuses
mainly on the terrestrial animal species with which hominid populations
interacted and thus areas of land below the sea level estimated for each
timeslice are simply assigned to the category of ‘sea’ and treated
as functionally impassable (by being assigned a high ‘movement’
cost). At the opposite extreme, areas at altitudes above the snowline can also
be considered out of bounds (Bailey 1983)
Other altitudinal effects are of course not so
binary but nevertheless significant in terms of the experience of the landscape
to people moving through it: altitude is associated with differing climatic and
geological and thus vegetational regimes and potential views. Perhaps the most
obvious example of this is the treeline; in Vasco-Cantabria today beech forests
grow at altitudes of around 1600m, and deciduous oaks to 1100m (Butzer 1986,
212). However, such altitudinal associations are of course hugely affected by
climatic regime and would have varied considerably over the course of the
Middle and
Similarly, Clark’s altitudinal and slope
categorisation system for early Holocene/Boreal Vasco-Cantabrian
The gradient of a slope is also highly
significant in terms of the way a landscape is perceived and experienced. It
has a significant effect on the vegetation able to grow (for example, steeper
slopes in the coldest palaeoenvironmental phases are likely to have been highly
unstable, with minimal soil development and thus largely bare; e.g. Butzer
1986), and is therefore indirectly as well as directly (in terms of access)
related to the animal species that might be associated with parts of the
landscape.
For the purposes of this analysis I have
projected that slope gradients would not have changed significantly over the
course of the Pleistocene, and have used the same map layer for all timeslices.
Of course this is certainly overly simplistic – slope gradients,
particularly those of river valleys, will have changed almost constantly and
sometimes dramatically throughout the various palaeoenvironmental phases, with
changing moisture regimes and drainage patterns in particular altering the
landscape through slope movement processes, erosion and the deposition of
colluvium and alluvium. However, such change would be virtually impossible to
model within the scope of this analysis, and in any case it seems likely that
the overall structure of the landscape has not changed significantly. Provided
the influence of slope gradient is not taken as an exact reconstruction of the
palaeoenvironmental phase in question, but rather a guide to large-scale
landscape patterns, this will not be a significant problem for the analysis.
Although the model can provide a more or less
precise measurement of slope in terms of either degree or percentage, for the
reasons discussed above, using these raw figures would provide a spurious
accuracy to the analysis. In any case, human movement around the landscape is
based less on calculation of exact slope gradients than on their mental
categorisation of them as perhaps ‘steep’ or ‘gentle’.
Exactly how slope gradient may be divided in terms of human judgement is a
matter of some debate. Clark, for example, considers any gradient of greater
than 40 degrees as ‘steep’ (1983: table 9.2.), while Hammond (1964)
prefers to term any slope of less than 8% or 5 degrees ‘gentle’
(see also Butzer 1986). Van Hove (2003) defines slopes of gradients 0-10% as
‘low’, 10-90% as ‘high’, and slopes of 100%
‘cliffs’. Munier et al.
suggest categories of <5, 5-10, 10-20 and >20 degrees (2001, table 2),
and Vogt et al. (2003, table 1) used
5 categories of 0-2%, 3-13%, 14-20%, 21-55% and >56%, while the Scottish
Avalanche Information Service’s Avalanche Hazard Scale defines
‘steep’ slopes as being greater than 30% (c25 degrees)[11].
Consideration can also be given, of course, to
the energy costs and changing experience of bodily movement over slopes of
varying gradients; energy costs of moving uphill appear to increase
monotonically with slope much as would be expected, but the energy costs
associated with walking downhill decrease until -10% (c5 degrees), and then
begin to increase again. When running downhill, the decrease lasts until the
slope reaches -20% (c11 degrees; Susta et al. 2000). Working from these
examples, I have settled on a figure of 0-10%/0-5 degrees as a
‘gentle’ slope, 11-30%/6-c25 degrees as ‘moderate’ and
anything greater than 30%/c25 degrees as ‘steep’.
Another potentially significant topographic
factor is aspect. More ‘sheltered’ south-facing locations were more
likely to provide suitable conditions for acting as refugia for vegetational
species which could not survive on more exposed north, coast-facing slopes, and
the exposure or shelter of an area to weather or sunlight, for example, would
have been a factor in the perception and experience of the landscape.
An aspect map was automatically generated by
GRASS at the same time as the slope map[12],
and was further reclassified into three categories: Exposed or north-facing[13],
‘sheltered’ or South/East/West facing[14]
and ‘flat’ (land with a gradient of <5 degrees), given a
‘null’ value as it cannot sensibly be said to have an
‘aspect’ (Munier, et al.
2001). The resulting map could of course be used for all timeslices, albeit
with the same caveats as discussed in the section above.
The topic of changing sea-levels was considered
above; however, proximity to open coastline also has a significant effect on
vegetational patterns. Although Butzer defines the ‘coastal plain’
of full glacial Vasco-Cantabria solely in terms of altitude and slope (1986,
204),
Proximity to rivers and streams is also a
factor, particularly in ‘warmer’, more humid palaeoenvironments.
Van Hove (2003) used a category of ‘river channel’, defined as
being within 50m of a river and at an altitude of between 15m and 1000m. The
maximum width of the modern flood plain around both modern rivers is c200m; in
this analysis, therefore, the GRASS module r.buffer was used to create a
landscape category of ‘watercourse’ <200m from each river. This
category was used unaltered for all timeslices, subject, of course, to the
caveats discussed above.
In Cantabria, sea level changes would have had
an important effect on the extent of the presently narrow coastal plain. Full
glacial sea levels would have been between 100m and 130m lower than today,
although the narrowness of the continental shelf just off the northern coast of
Spain meant that even in full glacial conditions only an extra 4-12km of
coastal plain was exposed (Straus 1992).
At the opposite altitudinal extreme, snowlines
probably marked the upper limit of human and animal activity:
Although browsing is possible under all but the
heaviest snowfalls, feeding would not be easy above the snowlines, even if
there was not much snow. Vegetation would have been ice-encrusted and there
would have been relatively little browseable scrub (Turner & Sanchez Goni,
1997; see also Gilbert and Beckinsale 1941, cited Bailey 1983, 150).
In the present climatic conditions there is no
permanent snowline in the region (Bailey 1983, 150), although further west in
the higher Picos de Europa there are some year-round snowfields at heights of c.
2400m-2600m, with patchy snow still lying at heights of above 2200m as late as
June. Current permanent snowlines in the French Pyrenees are around 2800m, and
(theoretical) permanent snowlines in the Sierra de Aralar c. 10km to the
southeast of the head of the Urola valley are calculated at 2400m (Kopp 1965,
14). Sturdy et al. cite a general
modern permanent snowline of 2400m, with a pleniglacial snowline depression of
around 700m (Sturdy, et al. 1997,
591).
Estimates of the permanent snowline during cold
phases of the Pleistocene range between 1,650m and 1,025m above sea level see
references in (Straus 1992, 21). At the Last Glacial Maximum (LGM), glaciers
existed in the Sierra de Aralar, with terminal moraines found at 825m above
(current) sea level, 25km from the coast (ibid.) and perhaps even lower,
down to 460m on the northeast slopes (Kopp, 1965: 14). Glacier tongues of the
Atlantic catchments extended as low as 500m in the west and 900m in the North,
while Regional Climatic Snowlines (RCS) appear to have been at 1100-1700m along
the Atlantic-Duero watershed, with the higher mountains snowbound year-round
(Butzer 1986, 206).
In the Sierra de Aralar permanent snowlines are
calculated at 1050m for the LGM, a depression of 1360m (Kopp 1965, 14). In the
Picos de Europa, Butzer (1973), placed the permanent Pleniglacial snowline
around the level of 1400 – 1500m above sea level, and mentioned that some
evidence of earlier (Middle Pleistocene) glaciations suggested a level of
around 1450m. Winter snowfall in the area today means that much of the terrain
at altitudes above c1000m is impassable (Gilbert and Beckinsale 1941, cited
Bailey 1983), but the estimation of lower limits for winter snowfall in the
past is problematic because of uncertainties about relative precipitation and
snowfall (see Bailey 1983, 151): clearly the 1350m descent in the permanent
snowline indicated by Kopp (1965, 14) for the Pleniglacial cannot simply be
applied to the line of snowfall; the ameliorating proximity of the sea would
have maintained the coastal plain as a relatively favourable winter zone (snow
rarely falls on the coastal plain today; (Altuna 1972, 17). Although 4.5 - 5m
of snow was probably common in lowland Vasco-Cantabria from early December to
late April in pleniglacial phases, the impression is one of a ‘moderately
thick snow cover that would only occasionally pose a problem for grazing
animals or hunting forays’ (Butzer 1986, 216). Sturdy et al., working in
|
Winter |
Spring |
Early
summer |
High
summer |
|
|
18-13kyr |
450m |
700m |
1000m |
1700m |
|
13-10kyr |
700m |
1000m |
1300m |
2000m |
Suggested Palaeolithic snowlines in
In terms of vegetational regime, variations
between warmer and colder paleoenvironmental phases tend to show up as
alternations between open and more forested conditions (van Andel, 1998, 491;
Kukla, 2002: 9). Conditions in the north were harsh during the coldest
Pleniglacial phases, with areas of almost barren polar desert and open tundra
communities.
However, reconstruction of palaeoenvironments
is not simply a case of making latitudinal shifts: Guipuzcoa (and
Warmer phases, in general, were characterised
by mixed (although probably mainly coniferous) forest with some thermophile
deciduous trees, arriving in familiar succession with birch and pine through
the elm, oak, hazel and hornbeam forests, gradually shifting back to pine,
spruce and other cold-tolerant species when colder conditions returned. Tree
cover, however, was probably always rather open to judge from avian faunas
(Adams & Faure 1998) and arboreal pollen (AP) values (Mellars 1996, 27),
although reforestation might have been quite significant during longer or warmer
phases – though still not attaining the dense forests recorded in the
area at the onset of the Holocene (d'Errico & Goni 2003, 777). Open
pinewoods and parkland probably dominated during ‘warm’ phases,
then, with isolated oak, hazel and birch groves in more sheltered spots, and
the coastal plain and river valleys providing rich grass pastures.
Although the generalised ‘warm’ and
‘cold’ phase regimes discussed in section 7.4. form the basis for
the reconstruction of palaeoenvironments in this analysis, these large-scale
patterns were subject to considerable variation during the different
palaeoenvironmental phases of the Vasco-Cantabrian Pleistocene
|
Site |
Level |
Industry |
Dating? |
Notes |
|
Amalda |
VII |
Mousterian |
- |
|
|
Lezetxiki |
VI |
Typical Mousterian |
Levels from the Early Glacial/OIS 5a or
c/St.German I or II
In many ways, as Mellars has commented,
the most striking feature of the early glacial
period is not the severity of the colder periods but the relative warmth of the
intervening “interstadial” periods represented by stages 5c (St.
Germain I) and 5a (St. Germain II) of the oxygen-isotope records (Mellars 1996,
21).
The total volume of global ice sheets during
these phases – although still much greater than that during fully
interglacial periods – was only around half of that of the cold stages of
5b and 5d.
Both OIS 5c (correlated with the pollen
Interstadial St. Germain I) and OIS 5a (correlated with St. Germain II) appear
to have been fairly marked and significant warming phases, marked by the
shrinking of the Scandinavian ice sheet during OIS 5c (van Andel & Tzedakis
1998, 490), and the retreat of the
North Atlantic polar front[16]
(Mellars, 1996). July temperatures are estimated to have ranged from c12
degrees C (approximately 6 degrees below present values) in southern
Scandinavia, to 18-20 degrees C (only 1-2 degrees C below present) in the
southern parts of
Lambeck et al. argue that global sea
levels were around 20-30m lower than at present during these two phases (2002).
However, European research suggests a figure at the lower end of this estimate,
with van Andel and Tzedakis suggesting a 20m drop (1998, 491) and Mellars
estimating around 10-20m (1996, 23). It seems likely that OIS5c levels were
slightly higher than those of 5a, as the ice volume curve shows a gradual
global increase in 5a beyond that of the previous Interstadial (van Andel &
Tzedakis 1998, 491), but this has yet to be quantified satisfactorily and in
this analysis I have used the figure of -20m for both phases.
With temperatures only a little lower than
those of today, permanent snowlines during OIS5a and 5c were certainly well
above the highest peaks above the study region; the ‘summer’
timeslice, therefore, does not include a snowline, and for ‘winter’
I have used Bailey’s altitude of 1000m as a functional upper limit for
human and animal activity (1983, 150).
While the climatic oscillations of OIS5, as
noted above, appear in the pollen records as alternations between expanding
open vegetation during the colder phases and returning forest conditions in the
warmer periods (van Andel & Tzedakis 1998), the exact ways in which the
interstadials of 5a and 5c were reflected in the local records varied
significantly across Europe (Mellars 1996, 22). South central Europe and France
were apparently rather rapidly covered by a mixed forest of birch and pine,
with some elm, oak, hazel and hornbeam during the warmer periods (ibid.;
van Andel & Tzedakis 1998), although fossil bird faunas suggest that the
Interstadial tree cover was always rather open (Adams & Faure 1998).
These data were combined into a broad-scale
reconstruction of the ecosystem of OIS 5a/c, and also the habitats with which
various animal species were associated
Having established some of the relevant
parameters of the palaeoenvironments, this section will consider how different
animal species might have behaved within it. A number of avenues are relevant
here, including:
·
Preferred feeding regimes – e.g., whether animal species
prefer to graze in more open conditions or browse in woodlands.
·
Accessibility of and preferences for different types of terrain
– the landscape offers e.g. ibex and horse very different kinds of
affordances in terms of elevation and slope.
·
Seasonal variation in behaviour; aggregation, dispersal and
patterns of movement, reproductive changes and variation in condition over the
course of the year.
Of course
species’ behaviour and habitat preferences today are not necessarily an
accurate guide to their behaviour in the past, especially where past
environments have no precise modern analogue (Sturdy, et al. 1997, 587-8). Some dimensions of behaviour are certainly
more predictable than others, and thus provide a more secure basis for
extrapolation back onto past palaeoenvironments, particularly those regarding
feeding behaviours. Animal species’ behaviour and habitats relevant to
their ‘placing’ in the ecosystem; reviews of these and other
species’ behavioural ecology given by, for example, Kurtén 1968;
Jochim 1976; Winterhalder & Smith 1981; Clark 1983; Boyle 1990; Mithen
1990; MacDonald & Barrett 1993; West 1997, and allow the construction of a
comprehensive picture of the characteristics of the major species represented
at faunal sites in the region.
These habitat preferences were used to
associate animals with the topographic/vegetational categories represented in
the timeslices illustrated.
The next step was to identify potential paths
of movement between the areas of species’ preferred habitats and the
particular sites at which their remains were recovered.
Such pathways are generated automatically in
GIS by calculating the cumulative ‘cost’ of moving between two
points. Calculation of such costs requires the specification of a cost surface, in which each individual
‘cell’ of information is associated with a number representing the
‘cost’ of traversing it, differing from previous methods of
geographical analysis such as catchment analysis (van Leusen 1999, 216), which
assumed that geographical space was ‘flat’ and homogeneous.
However, definitions of ‘cost’ inevitably vary widely (this is as
true of GIS computer systems as it is of Processual Archaeology), as do the
parameters and algorithms used to calculate the cost of movement through a
landscape (see ibid.: 216-7 for review).
Algorithms can be both isotropic (the same in
all directions) or anisotropic (where the cost of movement may differ with
direction – e.g. swimming upstream rather than down); the cost of travel
obviously combines components of both: ‘the former exemplified by costs
relating to the type of terrain (soil, vegetation, wetness), the latter by
costs relating to slope and streams’ (ibid. 217). However, there
are certain advantages to using isotropic calculations of cost in this
analysis. When traversing particularly rather rugged terrain, for example,
descent is often as tiring – if not more so – than ascent (see e.g.
Susta et al. 2000; Llobera 2000, fig 2; Wheatley & Gillings 2002,
156 fig 7.4.). In addition, while the faunal remains recovered from sites
clearly travelled there from the species’ preferred habitats, it is less
certain that hunters travelled to these hunting grounds from that
particular cave site; in the absence of evidence regarding the direction
of travel, it seems more prudent to use isotropic methods.
However, there are a considerable number of
ways to calculate even isotropic cost surfaces. Most studies have taken degree
of slope as the most significant factor for calculating the cost of movement,
although there are now several examples of more complex calculations based on
physiological measurements of actual energy expenditure, for example that of
Gorenflo & Gale (1990; cited van Leusen 1999), who specify the effect of
slope on travelling speed by foot as:
V = 6e –3.5
| s + 0.05 |
Where V = walking speed in km/h, s = slope of
terrain (calculated as vertical change divided by horizontal change, and e =
the base for natural logarithms).
A much simpler alternative is provided by Diez
(cited van Leusen 1999, 217), who recommends:
Effort = (percent
slope) / 10
This has several advantages, particularly its
very simplicity; more complex calculations tend to produce costs related to
actual physiological expenditure and derived from modern human observations:
even if we could assume that Palaeolithic ‘modern’ humans had
identical metabolic systems to our own (despite evidence that they may well
have been considerably more ‘robust’ than ourselves; e.g. Klein
1999), we certainly cannot assume this of Neanderthal populations. In addition,
most people do not generally base their daily activities on precise
calculations of the likely expenditure of energy and this does not seem a sound
basis for exploring the ways in which their movements reflect their
interactions with other aspects of the ecosystem. And lastly, given the coarse
temporal scale of the study and the inevitably high level number of unknown
variables and assumptions involved, any attempt to calculate actual physical
costs of the movement of individuals or populations in the Palaeolithic would
give a spurious accuracy to the results that would simply not be supported by
the data itself – more complex calculations of the cost of movement can
always be used in any subsequent, more detailed analyses.
However, as
thus surmounting a 45 degree slope is not
simply 45 times as difficult as moving on the level, a 0 degree slope. Taking
this to its logical conclusion would suggest that climbing a vertical slope of
90 degrees is ninety times as difficult as walking on the level, a 0 degree
slope, an absurd simplification which would not stand up to scrutiny (2000,
88).
Instead, they suggest that by taking the
tangent of the slope angle and then dividing the result by the base cost of
traversing entirely flat ground (1 degree, to avoid a division by 0), the relative
cost of movement across a landscape, rather than any absolute cost,
can be established. Using this equation, the relative cost of climbing a 60
degree slope is not 60 but 100 ‘units’.
Diez’s equation was therefore modified
slightly, and the equation used to generate the cost surface from the original
‘slope’ layer was:
Effort = tan
slope / tan 1
However, other factors than slope of course
play a role in the ‘cost’ (however defined) of movement through a
landscape, including barriers, transportation routes and the effects of
differential terrain types (flat grassland, for example, presents a very
different experience in terms of bodily movement to the dense undergrowth of
mature woodland):
The vast majority of archaeological applications
have so far accepted the simplification that energy expended or time taken to
move around in a landscape is a function of slope … this is a worrying
oversimplification (Wheatley & Gillings 2002, 155),
and further modification of this equation was
necessary to account for this. We have no real way of knowing if there were any
cultural ‘no-go’ zones in the area during the Palaeolithic, or even
any territorial boundaries that would have influenced movement: really the only
significant factors here are the potential relative costs of crossing rivers
(as river transport is unlikely to be an issue in the Palaeolithic) and of
moving through different forms of vegetation.
The cost of traversing rivers may be
considerable and was addressed by the addition of an extra variable t into the equation. A high cost (200,
arbitrarily chosen to be greater than the highest base cost derived solely from
slope) was added to cells in the timeslice maps categorised as ‘sea’ or ‘snow’
(altitudes below sea level and above the permanent snowline for the timeslice
in question), and for cells categorised ‘river’, t = 50
(also chosen arbitrarily relative to the ‘cost’ of sea/snow).
Of further concern is the effects of changing
(whether seasonally or climatically) vegetation on the cost of movement –
my own visits to the region have highlighted the fact that walking in summer
when undergrowth is thick consumes much more time and energy than that in
autumn and winter when it has died back[18].
Much of the seasonal variation should be
negated by the generation of ‘summer’ and ‘winter’
variants of the reclassified timeslice maps, and as the pathways will be
generated within specific timeslices, climatic variation should also not impact
negatively on the analysis. However, assessments of the differing relative
‘friction’ of different types of vegetation on movement have been
suggested by, for example, Glass et al. (1999) and are integrated into
this analysis by the addition of a further variable v. The
‘base’ cost of traversing terrain – represented by the
tangent of the slope gradient - is multiplied by this value: where vegetation
is moderately difficult to move through (e.g. grassland with stands of trees), v
= 1.5 – hence it is considered half as ‘costly’ again to
traverse than other terrain of a similar gradient. Where vegetation is more
difficult to travel through (open woodland and parkland environments), v
= 2 (the cost of movement is doubled). And in areas of denser vegetation (e.g.
dense woodland), v = 2.5.[19].
Travelling across open grassland, steppe and bare rock, it is assumed, incurred
no significant extra cost above the ‘base’ terrain cost derived
from the slope gradient.
The final equation used to derive the
‘costs’ of movement in each timeslice, therefore, was:
Effort = ((tan s/tan
1) v) + t
Where s = slope, v = vegetation and t
= terrain.
The map layer with the base costs derived from
slope tangents was thus amalgamated with the timeslice vegetation map[20]
and values re-calculated to reflect the addition of the terrain value t
and vegetation value v[21]
as described above.
The result was a raster map in which each cell
was associated with a cumulative ‘cost’ of traversing across it
from the specified starting point of a particular cave site[22]:
i.e. the landscape in the immediate vicinity of the specified cave site has a
low cumulative cost because it requires less effort to reach from the starting
point of the cave than points at a distance. This timeslice-specific cost
surface was then used to derive a least-cost pathway (defined by a sequence of
cells of lowest cumulative ‘cost’) of potential movement between
these two points using the GRASS module r.drain. This module is designed to
model the run-off patterns of water, and traces a path from the higher cost
areas of a user-defined starting point (within an area of habitat associated
with a particular species) to the ‘low’ cost of the cave site from
which the cost surface was generated (and where that species’ bones are
represented).
References:
Adams, J. &
Fauré, H. 1998.
Altuna, J.1972.
Fauna de Mamíferos de los Yacimientos Prehistóricos de
Guipúzcoa. Munibe 24: pp.
1-464.
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[2] UTM (zone 30) projection (ellipsoid: international, datum: ED50)
[3] using the module v.in.dxf
[4] using the module v.build.polylines
[5] using the module v.digit
[6] from the Instituto Geologico y Minero de España’s (IGME) 1:25,000 Mapa Topográfico Nacional de España sheets 63-I (Ondarroa) and 63-II (Eibar)
[7] using the GRASS module v.patch
[8] using the module v.surf.rst, which interpolates values between vector contour lines using the ‘regularised spline with tension’ algorithm (RST). Resolution 20x20 (maintained from source data).
[9] Using the module r.mask
[10] The interpolation process of transformation from vector to raster does not ‘stop’ at the edges of the map and thus produces an effect whereby raster data extends into adjacent areas of ‘null’ values.
[11] http://www.alexski.co.uk/mountainsafety/Avalanche_Hazard_Scale.htm
[12] using the v.to.rast command
[13] N, NE and NW: 45-135° - aspects are calculated in GRASS in degrees counter-clockwise from East
[14] S, SE and SW (203-248°); West-facing (136-224º) and East-facing (316-44º).
[15] This creates a new raster map layer showing buffer zones around any non-NULL category cells in an existing map layer. As the input map needs to be composed solely of cells with values of 1 and 0, the coastline of each timeslice was extracted as a separate thinned raster file before r.buffer was performed.
[16] Several hundred miles from latitude 50°N to above 60°N
[17] Using the GRASS module r.mapcalc
[18] Interestingly, this also has significant effects on the visibility of sites.
[19] In the study region, such vegetation occurred only on river valley floodplains, which are in and of themselves rather difficult to traverse, at least in temperate climates (Chambers and Hosfield pers comm.)
[20] Using the GRASS module r.cross
[21] Using the GRASS module r.reclass
[22] using r.cost, the ‘Knight’s move’ option
[23] from Ekain to the summit of Erlo; see also Altuna & Merino 1984.
[24] http://www.cannabisculture.com
[25] 1673.5 x 15 = 25102.5/2 = 12551.25
[26] 1673.5 x 8.5 = 14224.75/2 = 7112.38