1. Kuzitrin Lake and Twin Calderas:
an Example of Optimal Land Use in the Late Holocene
in Seward Peninsula, Alaska
By
Michael J. Holt
School of Archaeology and Ancient History
University of Leicester
Dissertation submitted for MA degree in Archaeology
October 2012

3. i
Table of Contents
1.0 INTRODUCTION 1
1.1 Background 1
1.2 Objectives 8
1.3 Theoretical Approach 11
1.4 Research Question 13
2.0 STATEMENT OF PROBLEM 14
2.1 Introduction 14
3.0 THEORETICAL FRAMEWORK & EXPECTATIONS 16
3.1 Human Behavioral Ecology and Decision Making 16
3.2 Foraging Theory 16
Middle Range Theory 17
Optimality Models 18
3.3 Time Allocation, Movement and Central Place Foraging 19
3.4 Expectations 21
4.0 CONTEXT 22
4.1 Regional Chronology 22
4.2 Archaeology of Kuzitrin Lake and Twin Calderas 23
4.3 Hunting in the North 25
Caribou Hunting Model for Seward Peninsula 25
4.4 Socioterritorialism on Seward Peninsula 30
Mobility 30
5.0 ANALYSES 32
5.1 Spatial Point and Cost-Distance Analyses 32
5.2 Spatial Point Analyses and Archaeology 32
Spider Diagram Analysis 34
Cluster Analysis 35
Nearest Neighbor Analysis 37

4. ii
5.3 Site Catchment and Cost-Surface Analyses in Archaeology 39
Cost-Distance Analysis 39
Least-Cost Path 40
5.4 Geographic Information Systems Science and Archaeology 40
6.0 METHODOLOGY & RESULTS 42
6.1 Introduction 42
6.2 Application of Spatial Point Analyses 42
Spider Analysis 43
Hierarchical Cluster Analysis 43
Nearest Neighbor Analysis 44
6.3 Application of Cost-Surface Analysis 45
6.4 Intercept Hunting and Ice/Snow Patches 45
Spider Analyses Results 46
Hierarchical Cluster Analyses Results 47
Nearest Neighbor Analysis Results 56
Student's T-Test Results 60
Summary of Feature Cluster and Ice/Snow Patch Results 62
6.5 Settlement and Socioterritorialism 63
Spider Analysis Results 63
Hierarchical Cluster Analyses Results 64
Nearest Neighbor Analysis Results 68
Summary of Spatial Analytical Results 73
Cost-Surface Results 73
Cost-Distance Results 75
Least-Cost Path Results 79
7.0 CONCLUSION 82
7.1 Temporal Affiliations and Palimpsests Nature of Stone Features and Settlements 82
7.2 Evaluation of Expectations and Hypothesis 83
Hunting Features and Ice/snow patches 83
Settlement Distribution Patterns 85
Least-Cost Paths in Resolving Socioterritorial Dominion over a Distant Patch 87
7.3 Discussion 88

5. iii
Caribou Hunting Tactics at Kuzitrin Lake and Twin Calderas 88
Late Holocene Model of Settlement and Subsistence 92
Bibliography 93
Appendices
A (Spider Database for Hunting Features and ice/snow patches at Kuztrin Lake and Twin Calderas)
Figures:
Figure 1. Map of study Area______________________________________________________________________ 7
Figure 2: Ice Patches contained within Twin Calderas _________________________________________________ 9
Figure 3: Regional Chronology (adapted from Fagan 2006)____________________________________________ 22
Figure 4: Archaeological Features Overview Map____________________________________________________ 24
Figure 5: Cairns on the Eastern Caldera ____________________________________________________________ 2
Figure 6: Western Arctic Caribou Herd Seasonal Distributions (ADFG 2003)—(red circle is the study area) _______ 4
Figure 7: East Caldera. Aerial photos of a) ice patch and b) exposed area near hunting blinds. The ice patch c)
shows evidence of caribou use and d) its position at the base of the caldera rim. The exposed area near the hunting
blinds e) looking SW and f) NW. __________________________________________________________________ 27
Figure 8: Aerial photos of a) ice patch and b) spillway with caribou trail near hunting blinds. Views of the Ice patch
c) from the caldera bottom, d) from the rim looking south, and e) from spillway (note caribou on the extreme right
side of patch). ________________________________________________________________________________ 28
Figure 9: Spider diagram of hunting features (green) and results of the Hierarchical Cluster analyis (red). ______ 46
Figure 10: Overview of the calderas. Stars (variably colored) represent the features lining each caldera rim. Dark
gray represents the steep walls of each caldera, while the light gray is the bottom. The blue shapes represents the
ice/snow patches. _____________________________________________________________________________ 48
Figure 11: Overview of reconciled macro clusters for the study area. Also shown is the spider diagram generated
for the micro clusters and their nearest ice/snow patch. ______________________________________________ 50
Figure 12: Plan view of West Caldera illustrating the spider diagrams generated for the micro clusters and ice/snow
patches. _____________________________________________________________________________________ 53
Figure 13: Plan view of East Caldera illustrating the spider diagrams generated for the micro clusters and ice/snow
patches. _____________________________________________________________________________________ 55
Figure 14: Results of the spider diagram combined with the hierarchical clustering analysis. _________________ 65
Figure 15: Slope cost-surface generated in GIS. _____________________________________________________ 74
Figure 16: Cost-distance based on non-winter modes of travel from settlements adjacent to the study area. (left) is
the total return to home base using river travel by boat. (Right) is the total return to base using only pedestrian
means. ______________________________________________________________________________________ 77
Figure 17: Cost-distance based on winter modes of travel from settlements adjacent to the study area. (left) is the
total return to home base using river travel by dog traction. (Right) is the total return to base using only pedestrian
(snow shoeing) means. _________________________________________________________________________ 78
Figure 18: Least-cost paths from adjacent settlements to the study area. Also noted are navigable river channels.
____________________________________________________________________________________________ 79
Figure 19: Model of hunting represented at each macro cluster. _______________________________________ 89
Figure 20: Model of hunting tactic employed at East Caldera.__________________________________________ 90
Figure 21: Model of hunting tactic employed at West Caldera _________________________________________ 91
Figure 22: Hueristic model of subsistence and settlement patterns centered on caribou exploitation at Kuzitrin Lake
and Twin Calderas. ____________________________________________________________________________ 92

6. iv
Tables:
Table 1: Forager Hunting Schemes (adapted from Rasic 2008: 20) ______________________________________ 26
Table 2: Summary of Pope's (1918) results with Ishi over a two year period (1914 and 1915). _ Error! Bookmark not
defined.
Table 3: Average duration for hunting expeditions for several ethnographic groups (Binford 2001). ___________ 31
Table 4: Results of nearest neighbor analysis on the macro and micro clusters.____________________________ 56
Table 5: Observed and expected mean distances used in the student's t-test______________________________ 59
Table 6: Student's t-test results for macro clusters___________________________________________________ 60
Table 7: Observed and expected mean distances used in the student's t-test ______________________________ 61
Table 8: Student's t-test results for micro clusters ___________________________________________________ 61
Table 9: Results of nearest neighbor analysis on the settlement clusters._________________________________ 69
Table 10: Time and caloric costs incurred by each mode of travel. ______________________________________ 75
Table 11: Least-cost path results. ________________________________________________________________ 80
Table 12: Total caloric cost for a six member hunting party. If dog traction is an option, then a team of five dogs
will incur caloric costs as well. ___________________________________________________________________ 80
Table 13: Quantity of processed caribou (48260 calories) needed to complete a journey to or from the study area.
____________________________________________________________________________________________ 81

7. 1
1.0 INTRODUCTION
1.1 Background
In the northern latitudes of the Western Hemisphere, a region dominated by tundra
environments and limited resource variability, human foragers adapted their hunting and
settlement strategies to gain advantage over an abundant and highly predictable terrestrial
resource (caribou) (Binford 1978, 1980; Heffley 1981; Kelly 1995; Nelson 1899; VanStone 1974).
The study area's unique landscape character and abundant resources attracted the region's
prehistoric inhabitants far from the power centers of their affiliated socioterritories. The first
objective of this research will analyze the correlation between hunting features and ice/snow
patches in order to illustrate whether or not intercept hunting tactics where employed by the
region's foraging groups during the summer months. The second objective is to analyze
prehistoric settlement distribution patterns in order to determine the level of dispersion among
socially relatable home bases or power centers. The third objective of this research is to
analyze prehistoric hunter-gatherer time and energy costs incurred by travelling to the study
area, as well as the amount of processed game (caribou) needed to balance those costs.
Ethnographic analogy will be used to infer prehistoric socioterritorial domains throughout the
late Holocene (5500 BP), which were characterized as socially relatable enclaves exploiting,
either, contiguous sections of coast or individual watershed systems, exclusively. Settlement
distribution data will be analyzed with an assortment of spatial point analyses to identify
prehistoric socioterritorial power centers and optimal home base networks. All ideas presented
in this research are based on human behavioral ecology. Previous and current
Ethnoarchaeological work are highlighted to develop a heuristic model of seasonal resource

8. 2
Figure 1: Cairns on the Eastern Caldera

9. 3
exploitation and transhumance for the study area. Data derived from past and present
research in the study area will be rigorously subjected to spatial point and cost-surface analyses
and tested for statistical validity.
In 1975, a team of archaeologists lead by Powers (1982) recorded the enigmatic
archaeological complex contained within the unique landscape at Kuzitrin Lake and Twin
Calderas. Since that time, there have been two notable studies (Schaaf 1988; Harritt 1994)
which have aided in characterizing the relationship between the environment and an atypical
clustering of culturally produced hunting features. Regional ethnohistoric literature has
contributed substantially to our understanding of lake-based community game drive tactics
(circa 1800 - 1850 AD)(Ingstad 1954; Hall 1975; Binford 1978; Koutsky 1981: III: 37). However,
there is currently no appropriate analog regarding the integration of ice and snow patches for
game drive or other intercept hunting tactics elsewhere in the region.
In summer 2011, National Park Service cultural resources staff visited the study area in
order to obtain feature distribution data for all of the monumental dry masonry features lining
both rims at Twin Calderas, as well as a rather extensive stone featured game drive line
(Inuksuit, 'looks like men') between the calderas and Kuzitrin Lake to the south. During this
very brief project the author and other staff observed small groups (2-5) and solitary caribou
utilizing several small to moderately-sized ice and snow patches located in the study area (Holt
2011). This implies, dispersed caribou would have been sufficiently available to prehistoric
hunters during the summer months, in addition to the relative abundance of caribou
aggregations migrating to and from their calving grounds.

10. 4
Figure 2: Western Arctic Caribou Herd Seasonal Distributions (ADFG 2003)—(red circle is the study area)

11. 5
A regional literature search revealed that there was no documented evidence of
intercept hunting in relation to ice/snow patches. There is, however, brief mention of these
patches used in encounter (stalking) hunting practices by the ethnohistoric Nunamiut
populating parts of Alaska and Canada (Binford 1978; Bowyer 2011). The search was expanded
to include all northern latitude areas of the Western Hemisphere, resulting in a wealth of
comparable studies in the United States (Andrews 2009, 2010; Benedict et al. 2008; Dixon et al.
2005, 2010; Galloway 2009; Lee et al. 2006, 2010; VanderHoek 2010) and Canada (Bowyer et al.
1999; Farnell et al. 2004: Hare et al. 2004; Helwig et al. 2008; Bowyer 2011). There are also
parallel studies noted in other regions of the globe (i.e., Norway for instance, Callanan and
Farbregd 2010; Farbregd 2009). Perhaps of most usefulness to this study, the Yukon 'Ice
Patches Research Project' has yielded information critical in understanding past biology, climate
and hunting activity throughout the Holocene (Bowyer et al. 1999; Kuzyk et al. 1999; Farnell et
al. 2004; Hare et al. 2004).
During the 2012 field season, National Park Service staff returned, briefly, to Kuzitrin
Lake and Twin Calderas in order to investigate the link between ice/snow patches and
archaeological features related to hunting. While surveying a narrow, exposed area within the
western caldera spillway, the team encountered a heavily worn caribou game trail flanked by
hunting blinds. Though the trail disappears into a boulder field, its orientation suggests it is
used by caribou to access the perennial ice patch located in the western caldera. Upon further
investigation the team recorded several low-lying hunting blinds on the periphery of the
caldera spillway channel and game trail. We conducted a brief, nonintrusive survey of the ice
patches, discovering dense concentrations of fresh caribou prints, dung pellets and urine

12. 6
staining. This evidence suggests the ice patches are targeted by small caribou groups (or
solitary) throughout the summer. An intensive survey concentrated on the exposed boulder
field adjacent to the retreating ice patches did not reveal any cultural constituents.
Ethnohistoric accounts describe the Eskimo societies (Iñupiat and Yup'ik) inhabiting
Seward Peninsula as being densely clustered near their power centers. These accounts also
depict these societies being heavily reliant upon caribou exploitation for survival (Ray 1975,
1983, 1984; Burch 1998, 2006, 2007). Their relatively high population densities, concentrated
power centers and small exploitation territories were seemingly atypical to other
contemporaneous societies inhabiting Northwest Alaska (Ray 1975, 1983, 1984; Burch 1998,
2006, 2007). Doubtless, food abundance was a prerequisite for sustaining such high population
densities from relatively small exploitation catchments. The high concentration of stone
features in the study area suggests it was a high ranking patch choice for the adjacent regional
groups. This research investigates the paths of least resistance from the nearest settlements of
the waters adjacent to the study area. In so doing, we will explore the total daily energy and
time expenditures required to travel to and from the study area. Thus, from a forager's
perspective, we can determine whether or not a trip would have been profitable (achieving net
energy optimality), or if the costs outweighed the benefits.
As previously mentioned, this study will explore the correlation between hunting
features and ice/snow patches to determine seasonality based on hunting tactics employed.
Additionally, this study will examine settlement dispersion patterns in order to illustrate
prehistoric socioterritorial power centers and other home base clusters which are ideally
configured for net energy optimality. This information will be used to identify the nearest

13. 7
Figure 3. Map of study Area

14. 8
settlement of each distinct foraging group, which from a logistical standpoint would be the
staging area with the closest access to the resource-rich patch at Kuzitrin Lake and Twin
Calderas. Then a careful analysis will reveal the path of least resistance to the study from the
nearest settlements in adjacent watersheds, from which the total daily energy and time costs
can be calculated for each route.
An ethnoarchaeological approach will prove useful for this study to correlate parallel
values (settlement and subsistence patterns) between the static archaeological record and
ethnohistoric analogs. This approach is premised by human behavioral ecology and optimal
foraging theory. Geographic Information Systems Science (GISci) will be used to model the
environmental friction which has the greatest influence on forager behavior and decision
making in the region, i.e., slope. Spatial point data will be subjected to rigorous statistical
validation via nearest neighbor analysis and one-tailed student's t-test. A model of caribou
hunting and transhumance are presented as a heuristic device that is premised by the tenets of
optimal foraging theory.
1.2 Objectives
This research examines the relationship between the environment and human
settlement and subsistence strategies throughout the late Holocene (5500 BP). Human census
estimates obtained from ethnohistoric accounts suggest Seward Peninsula socioterritories were
among the most densely populated in the region (Ray 1975, 1983, 1984). Based on the tenets
of optimal foraging, these relatively dense human aggregations would have required abundant
resources and the availability of high-ranking prey species to sustain population growth. The
environment sets parameters around which hunter-gatherers adapt a variety of settlement and

15. 9
Figure 4: Ice Patches contained within Twin Calderas

16. 10
subsistence strategies in order to survive. This study aims to identify the ecological factors that
shaped forager behaviors and compositions.
In the northern latitudes, foragers have focused on the exploitation of a reliable caribou
resource base throughout much of the Holocene. The problem here relates to how ecological
determinates (i.e., resource breadth, patch accessibility, terrain and weather)--in space and
time--affect the decisions hunter-gatherers made in order to achieve net energy optimality. To
address this problem, the following objectives are proposed:
1) identify a correlation between hunting features and ice/snow patches through
spatial point analyses in order to ascertain seasonality, which will be used to inform an
alternative heuristic model of caribou hunting and transhumance;
2) test the statistical validity of the correlation between hunting features and ice/snow
with nearest neighbor analysis to identify levels of dispersion among intercept hunting
features, and a student's t-test to determine the significance of the distances between
feature clusters and ice/snow patches;
3) Examine the spatial distribution patterns of settlements through spatial point
analyses in order to identify distinct settlement clusters which are interpreted to
represent distinct prehistoric socioterritories;
4) test the statistical validity and composition of the settlement clusters with nearest
neighbor analysis to determine statistical significance and levels of dispersion among
settlements within each cluster. This information will be used to identify prehistoric
power centers or optimally arranged home base networks. Finally, a major watershed
associated with a power center cluster will be characterized as a being under the

17. 11
exclusive domain of a distinct prehistoric socioterritorial group--based largely on
ethnohistoric settlement patterns in Seward Peninsula;
5) identify the least-cost path into the study area from the nearest settlements in
adjacent watersheds (socioterritories) with GISci cost-surface algorithms to estimate
time and energy expenditures imposed on foraging groups to complete such a journey;
6) discuss the implications this research has for understanding prehistoric hunter-
gatherer settlement and subsistence patterns in Seward Peninsula.
1.3 Theoretical Approach
Hunter-gatherers are integrally linked with the environments and resources to which
they are associated, exploiting through a combination of hunting, fishing, scavenging, gathering
or collecting (Sheehan 2004; Broughton and Bayham 2003; Byers and Broughton 2004; Hockett
2005; Lovis et al. 2005; Winterhalder 2001: 12). As such, an evolutionary ecological approach
provides the most appropriate framework for understanding prehistoric hunter-gatherer
settlement and subsistence patterns (or land use). However, it must be recognized that
sociocultural variables influence hunter-gatherer decision-making and land use (UL 2010: 0-3;
Byers and Broughton 2004; Byers and Hill 2009; Butzer 1990; Kim 2006; Lovis et al. 2005;
Sheehan 2004; Bowyer 2011: 6).
An ecological approach to identifying cultural behavior requires that such behavior be
assessed from within its associated natural context, which itself may vary in space and time
(Hildebrandt and McGuire 2005; Jochim 1981, 1989; Lovis et al. 2005; Bowyer 2011: 6). Under
this paradigm, environmental influences have significant influence on human behavior.
Ecosystems are comprised of a dynamic set of biological, physical and cultural processes

18. 12
(Moran 2006, 2008). Though emphasis will be made to underscore how prehistoric hunter-
gatherers were influenced by a suite of environmental determinates, there are sociocultural
pressures (i.e., ideology, social networks and organization, etc.) that impact human behaviors
(Bamforth 1988; Lovis et al. 2005; Broughton and Bayham 2003; Hildebrandt and McGuire
2002, 2003, 2005; Kim 2006; Trigger 1989).
Hunter-gatherer societies have adapted a multitude of strategies and coping
mechanisms to deal with environmental fluctuations in natural resources and climate change
variability throughout much of the Holocene, such as resource diversification, developing
external sociocultural relationships, mobility, and technological and informational diffusion
(Kim 2006; Mandryk 1993; Morgan 2009; Wiessner 1982). Contrary to the most widely held
notions of hunter-gatherer behaviors, recent paradigms indicate these behaviors are not
merely simple responses driven by the natural environment. Instead, land-use patterns are
derived from a variety of plausible options which are embedded within broader ideological
perceptions and social organization (Ives 1990, 1998; Kim 2006; Trigger 1989). This research is
grounded in Steward's cultural ecology, and an idea that culture and environment are an
interrelated and dynamic system of exchanges and feedback (Burch 2007; Moran 2006, 2008,
Steward 1955; Hardesty 1977; Kaplan and Manners 1972). There are social, ideological,
economic and political pressures that influence cultural behavior, the extent of which may be
widely varying and dependent upon societal weighting of those pressures (Trigger 1989). The
study area's environment is characterized as an interconnected system of physical landscape
variables (topography, geology, floral character and hydrology), seasonal weather variability,
and resource breadth. These environmental variables influence hunter-gatherer behaviors and

19. 13
decision making, of which the dichotomous relationship between energy returns (benefit)
versus time and energy expenditures (cost) is of paramount concern.
1.4 Research Question
The hypothesis of this study is that subsistence and settlement strategies employed by
prehistoric foraging groups were shaped by a drive to achieve net energy optimality. From this
framework, foraging groups would optimize energy and time costs to exploit the highest-
ranking prey resources within their limits of available travel modes. To thoroughly investigate
this hypothesis it will be necessary to review the wider theoretical perspective of evolutionary
ecology, relevant anthropological literature, prehistoric subsistence and settlement patterns.

20. 14
2.0 STATEMENT OF PROBLEM
2.1 Introduction
In the barren, upland tundra steppe of Seward Peninsula, lying at the northern base of
the Bendeleben Mountains, lies an enigmatic prehistoric caribou game drive system (Koutsky
1982: 4: 89; Schaaf 1988: I: 258-59) integrally linked to a unique landscape (UL 2010: 1-5)
providing tactical advantage over a reliable caribou resource base (Burch 1986: 632). Previous
research describes the system at Kuzitrin Lake as one that fits a regional model of community
game drive strategies near lakes (Burch 1988; Ray 1975; Koutsky 1981; Powers 1982; Schaaf
1988; Harritt 1994). However, some aspects of this system have remained a mystery, until
recent observation of ice/snow patches within both calderas and the southern shore of Kuzitrin
Lake (Holt 2011, 2012). This study has benefitted from current research centered on ice
patches in Yukon, Canada (Bowyer et al. 1999; Farnell et al. 2004: Hare et al. 2004; Helwig et al.
2008; Bowyer 2011) and Alaska (Andrews 2009, 2010; Benedict et al. 2008; Dixon et al. 2005,
2010; Galloway 2009; Lee et al. 2006, 2010; VanderHoek 2010), which have opened an
alternative line of inquiry with regard to prehistoric caribou hunting practices of northern
latitude of the Western Hemisphere.
Archaeological research and indigenous accounts have successfully established cultural
significance of selected Yukon ice patches; thus demonstrating a long-standing (at least 8000
years) relationship between caribou, ice patches and the people who patterned their
settlement and subsistence life ways around them (Farnell et al 2004; Hare et al 2004; Bowyer
2011). Contemporary biological studies and local observations of caribou seasonal migrations
on Seward Peninsula (ADFG 2003; Joly 2006) and behaviors associated with ice patch use in the
Yukon (Kuhn et al 2010; Kuzyk and Farnell 1997) provide useful context for this study. Caribou

21. 15
adhere to a predictable summer range migration centered on the availability of high quality
forage (lichens), as well as specifically targeting perennial ice and seasonal snow patches for
thermoregulation and insect harassment (Ion and Kershaw 1989).
Settlement and subsistence are integrally linked (Kelly 1995), especially in northern
latitudes where low resource variability forced late-Holocene inhabitants to adopt a caribou-
centric life way in order to survive (Binford 1978; Anderson 1988). Settlement systems research
have focused on the role human behavioral ecology plays in decision making (Binford 1980;
Gamble 1986; Jochim 1976, 1998; Thomas 1983; Willey 1953), which are influenced by varying
sociocultural factors (Gamble 1999; Oetelaar and Meyer 2006).
The research conducted in this study can be used to develop an alternative model of
prehistoric hunting and transhumance in Seward Peninsula. The ideas posited for this study will
be tested using a repertoire of spatial point analytical tools and statistical measures (nearest
neighbor and one-tailed student's t-test). The majority of data derived from this study were
generated in Geographic Information Systems (GIS) including spatial point, Voronoi
tessellations and cost-surface analyses.

22. 16
3.0 THEORETICAL FRAMEWORK & EXPECTATIONS
3.1 Human Behavioral Ecology and Decision Making
Human behavioral ecology (HBE) is an evolutionary analysis tool designed to elucidate
the influences social and ecological factors have on human behavior and decision making (Bird
and O'Connell 2006; Smith 1999). Rooted in Julian Steward's theory of cultural ecology from
the perspective of hunter-gatherer societies, HBE is further embedded with a functional neo-
Darwanism approach to understanding human behavior (Winterhalder and Smith 2000: 51).
Thus, HBE finds wide application in anthropological research centered on hunter gatherer
societies based on a common understanding that human behavior and decision making are
directly linked to a variety of social and ecological factors (Smith and Winterhalder 1992: 25;
Smith 1999). There has been productive research in HBE, focused on three major themes:
production and resource acquisition (Beck 2008; Byers and Ugan 2005), reproduction and life
history (Borgerhoff 1992; Voland 1998), and distribution and exchange (Orth 1987; Smith and
Bird 2000). HBE research commonly employs formal economic models to include prey choice,
patch choice, and central place foraging models (Rasic 2008: 10-11). Though, there is some
debate surrounding application of 'real-time' foraging models which require dynamic inputs
from a static archaeological record (Kelly 1995: 333-334; Barton et al. 2004: 139; Meltzer 2004).
3.2 Foraging Theory
Given that behavior requires the consumption of two key resources (i.e., time and
energy), foragers must weigh decisions based on the most efficient use time and energy (Cuthill
and Huston 1997: 97). A principal assumption is that people will make decisions in order to
enhance fitness and caloric returns (benefits) by implementing varied courses of action (costs),
which translates into reproductive advantages and survival. The best way to way examine cost

23. 17
and benefit and investigate their archaeological register is through use of optimality models
(Cuthill and Huston 1997: 97).
Foraging theory research has been productive, yielding an abundance of data relating to
the costs of resource acquisition and caloric benefit (Bird and O'Connell 2006; Broughton and
Grayson 1993). An assumption is that forager must make decisions based on maximizing the
outcome of a behavior, where benefits (resource acquisition) outweigh costs (time and energy).
This optimality approach argues that ".., direct and indirect competition for resources gives
advantages to organisms that have efficient techniques of acquiring energy and nutrients"--
translating into measures of survival and reproductive fitness (Winterhalder 1981: 15).
Ethnoarchaeological research has contributed greatly to foraging theory by studying "..,
contemporary peoples to determine how their behavior is translated into the archaeological
record," (Thomas 1998: 273). This sub-discipline gained momentum in the 1960s as essential
component of processual archaeology, which aimed at understanding site formation processes
in the archaeological record (Schiffer 1972). Based on the premise that hunter-gatherers
exhibit universal behaviors in as far as they are guided by simple economics (cost-benefit) and
sociocultural influences, ethnoarchaeological methods have wide applicability in foraging
model research (Binford 1978, 1980).
Middle Range Theory
Midde-Range Theory (MRT) is an inferential tool used to define past human behaviors
based on contemporary or historic correlates (Merton 1968). In this context, subsistence and
settlement patterns of Prehistoric humans can be inferred by direct ethnohistoric analogy using
and actualistic research mode (Binford 1981: 27). The method is a four stage process which

24. 18
involves: 1) documenting ‘causal relations’ between contemporary human actions (or
interactions) and static remains left behind by those actions; 2) recognition of patterns in those
static remains; and 3) inference of prehistoric human actions based on the observed patterns in
contemporary human actions and their static remains; and 4) evaluation of these inferences
(Pierce 1989: 2). The MRT finds appropriate application with this study in as far as
ethnographic analogy can be used to infer Prehistoric human behaviors and decision making,
such as hunting and socioterritorialism.
Optimality Models
In terms of optimal foraging, there are two categories of costs incurred in the
procurement of resources (Cuthill and Huston 1997: 105)--acquisition (activity preparation and
engagement) (Stevens and Krebs 1968: 7) and post-acquisition (processing, transport and
storage) (Lindström 2007: 232). Optimal foraging theory (OFT) models are used to analyze how
hunter-gatherers search plan and search for, encounter and intercept, and handle resources
(Martin 1983: 615; Stephens and Charnov 1982: 251). It is generally accepted that the most
relevant measure of optimal foraging in hunter-gatherer societies is the maximization of net
energy gain, which is sum result of the ".., energy maximization over a fixed time and time
minimization to a fixed energy gain," (Stephens and Charnov 1982: 261). This correlates
directly to forager selection of resources patches within a given exploitation area. Foragers
incur energy and time costs by travelling to and from these patches, which factor heavily in
cost-benefit decision making. Causally linked to time and energy expenditures is the
recognition that foragers must make decisions about when certain patches will yield the highest

25. 19
energy output (Charnov 1976: 129), which in northern latitude hunter-gatherer societies is
largely dependent on the behaviors of migratory game animals.
Models are simplified versions of complex and dynamic realities, providing a conduit
through which components of a problem can be comparatively tested against a set of
conditions and assumptions (Stephens and Charnov 1982: 262). Generally, foraging models are
comprised of three components, all of which are based on assumptions: decision, currency and
constraint. Essentially, foragers must make decisions based on the options available to them,
weigh and compare those options (currency), and evaluate factors that limit and define the
relationship between decisions and currency (Stevens and Krebs 1986: 5-10).
Optimality models have found wide acceptance in archaeological research to help
define prehistoric settlement and subsistence strategies (Broughton 1994; Byers and Ugan
2005). Most optimal foraging models (OFM) emphasize variables related to patch choice, diet
breadth, prey choice, patterns/rates of movement, settlement, time allocation, and groups size
(Martin 1983: 615-624; Pyke et al. 1977: 141-49).
This study will emphasize OFMs pertaining to patch choice, diet breadth, prey choice,
patterns/rates of movement and settlement. These variables play a vital role in shaping
behaviors of northern latitude hunter-gatherer societies associated with subsistence and
settlement.
3.3 Time Allocation, Movement and Central Place Foraging
Research pertaining to game movement/behavior patterns and time allocation has been
a productive line of inquiry in evolutionary ecology (Bayham et al. 2011; Beck 2008; Broughton
1994, 2002; Kelly 2005; Pyke et al. 1977; Stevens and Krebs 1986). An emphasis was placed on

26. 20
the likelihood foragers move over broad landscapes (or exploitation areas) in pursuit of high-
ranking prey, sparring development of the central place foraging (CPF) model (Orians and
Pearson 1976). A pattern of seasonal transhumance lies at the heart of CPF, as foragers make
repeat visits to resource-rich patches from strategically located home bases. In this vein, time
and energy variables (i.e., pursuit , preparation, and resource transport) factor prominently into
logistical decisions regarding foraging and hunting.
Hunter-gatherers participating in a CPF strategy will expend energy over three phases:
travel from home base to patch choice; foraging resources and hunting prey associated with
the patch; and return trip from patch choice to home base. As the distances increase between
home base and patch choice, foragers must make decisions that necessarily favor net energy
gains in relation to travel time and energy expenditures. A prey item's rank and value is also
influenced by the distances needed to travel between home bases and patches (Orians and
Pearson 1976: 166-67).
The expectations derived from the CPF model suggest that if a forager makes a
significant travel investment to use a specific resource patch, that forager must exploit the
highest ranked resource within a related patch. Distance travelled to patches factors
prominently into a forager's resource processing and transport decisions. In order to achieve
net energy optimality at patch that is a greater distance from a home base, foragers adapted a
community or group-oriented subsistence strategy. This amplified foraging success rates, and
added capacity to process and transport game back to a home base.

27. 21
3.4 Expectations
The expectations derived from this study also serve as a stepwise process to inform the
next expectation in the sequence: 1) hunting features at Kuzitrin Lake and Twin Calderas will
tend to cluster in proximity to ice/snow patches, which would be indicative of a collective
intercept hunting tactic that was employed in the summer; 2) settlements on Seward Peninsula
will cluster in patterns that can be recognized as socioterritorial power centers or optimally
arranged home base networks, which I expect will illustrate a prehistoric settlement model that
corresponds well with ethnohistoric literature (i.e., territorial control of a major watershed by a
socially relatable foraging group); and 3) that socioterritorial dominion over the study area can
be determined on the basis of optimal foraging, through a critical evaluation of the time and
energy expenditures incurred by an adjacent prehistoric hunter-gatherer group travelling to the
study area.

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4.0 CONTEXT
4.1 Regional Chronology
Figure 5: Regional Chronology (adapted from Fagan 2006)

29. 23
To preface this chapter it is necessary to place the study area's chronology in a regional
context. There have been several references thus far to the late Holocene, which is marked by
the start of the Neoglacial period approximately 5500 BP (or 3500 BC). This geological time
frame is appropriate because it encompasses all cultural sequences beginning with the Arctic
Small Tool tradition (ASTt). The ASTt brought with it changes in hunting technology, and is
widely seen as the genesis of bow and arrow technology in the Western Hemisphere (Blitz
1988) . Figure 5 is adapted from Fagan (2005) which compares the chronologies of multiple
regions, and includes the temporal span of the study area.
4.2 Archaeology of Kuzitrin Lake and Twin Calderas
There is a significantly high concentration of large dry masonry cairns within the study
area, dotting both caldera rims—especially the east caldera. Schaaf (1988: 233) describes six
varieties of cairns in the area: cylindrical, Truncated, globular, conical hollow, conical with
loosely stacked rocks, and rock piles. All cairns range in size from small (0.5 meter high, 1.0
meter diameter) to the largest of these, which is semi-lunate in shape and comprised of two
cylindrical “.., cairns, 3.5 meters high and 2.4 meters [diameter] with a 1 meter-wide, straight
wall, 1.37 meters long and 2.36 meters high,” (Schaaf 1988: 241-45). Cylindrical, truncated,
globular and conical hollow cairns are not described in the region’s archaeological record. The
conical cairn of loosely stacked rocks and other rock piles are somewhat more ambiguous and
are often assigned a variety of forms and functions (Schaaf 1988: 242-45; Balikci 1970: 41).
Typically all cairns varieties are “.., located on land prominences, river bluffs, ridges and

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Figure 6: Archaeological Features Overview Map

31. 25
volcanic cones,” with the exception of those found “.., between Joan and Erich Lakes (BEN-110),
as well as on the south shore of Kuzitrin Lake (below BEN-115),” (Schaaf 1988: 245).
Functional descriptions of these stone features are derived from Powers (1982), Schaaf
(1988) and Harritt (1994) initial forays into the study area, and there are no archaeological
equivalents noted in the region’s archives (AHRS 2012) from which to draw comparison. Cairns
and other stone features here have been portrayed as representative of “.., large communal
caribou hunting and meat storage strategies,” (Schaaf 1988: I: 257). This study aims to
investigate an alternative preshistoric hunting tactic, by evaluating the feature distribution
patterns in relation to ice and snow patches.
4.3 Hunting in the North
Generally, northern latitude hunting strategies and tactics can be separated into two
primary schemes: 1) encounter; and 2) intercept (Binford 1978, 1983; Blehr 1990; Campbell
1968; Driver 1990; Marean 1997; Enloe and David 1997; Churchill 1993; Rasic 2008: 19-24). The
principal determinants are not a matter of scale (i.e., caribou breadth and foraging group sizes),
but rather of prey predictability and breadth (migration routes and behaviors, and herd
aggregates) and by the measure of premeditation involved in hunter-gatherer tactics (e.g.,
planning, execution and processing) (Binford 1978). In both schemes, patch selection is a
primary consideration, which translates into hunting success and net energy optimality. Rasic
(2008: 20) provides a useful table to compare these divergent hunting schemes (table 1).
Caribou Hunting Model for Seward Peninsula
Caribou hunting models are predominately concerned with large-scale latitudinal
strategies (Bowyer 2011), which often integrate the use of game drive-line systems and employ

32. 26
communal and group-based tactics (Benedict 1996, 2005; Brink 2005; MacDonald 1985; Sturdy
1975). These models are corroborated by ethnohistoric literature in Alaska (Binford 1978;
Burch 1998, 2001) and Yukon (McClellan 1975; Greer 1984; Hare et al. 2004). Current research
has been unable to link the game drive hunting tactic with ice/snow patch hunting strategy
(Bowyer 2011: 234)--although this study suggests a strong correlation between the two.
Encounter and Intercept Hunting Strategies
(adapted from Rasic 2008: 20)
Encounter Hunting Intercept Hunting
Personnel Small groups or individual hunters,
with a tendency for these to be all male
groups.
Variably-sized groups that consist of males and
females. Roles may include driving prey,
harvesting, processing.
Setting Practiced in a variety of topographic
settings, both open and concealed,
flat and with much relief. Emphasis
on microscale topographic/vegetation
concealment and constraints on
animal movement.
Requires topographic constraints or constructed
facilities.
Labor and
Planning
No special advanced preparation, low
intensity harvest, processing.
High intensity preparation, hunting and
processing.
Relation to
Settlements and
Processing
Camps
May be close to or far from residential
base.
Settlements and/or harvesting camps will be
situated near the hunting locale.
Prey Distribution Dispersed. Solitary animals or small
groups--some of which may be
distributed along summer ice/snow
patches.
Aggregated during migration. Dispersed solitary
animals/small groups during summer ice/snow
patch use.
Archaeological
Signature
Kill sites will have little archaeological
visibility; known sites
associated with this strategy may
include hunting stands or observation
locations, small assemblages
representing single, brief site
occupations; evidence of small scale
tool repair, dispersed site distribution;
sites in open terrain more likely to
represent encounter hunting.
Kill sites and associated location archaeologically
visible and may contain facilities, storage features,
possible bone accumulations; associated hunting
stands or staging areas contain assemblages with
high
weaponry discard rates (batch tool repair);
regional site distribution signature includes
repeated use of key locations that result in dense
artifact accumulations, site clusters
associated with strategic locations (e.g., passes,
topographic constraints).
Table 1: Forager Hunting Schemes (adapted from Rasic 2008: 20)

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Figure 7: East Caldera. Aerial photos of a) ice patch and b) exposed area near hunting blinds. The ice patch c) shows
evidence of caribou use and d) its position at the base of the caldera rim. The exposed area near the hunting blinds e)
looking SW and f) NW.

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Figure 8: Aerial photos of a) ice patch and b) spillway with caribou trail near hunting blinds. Views of the Ice patch c) from
the caldera bottom, d) from the rim looking south, and e) from spillway (note caribou on the extreme right side of patch).

35. 29
Figure 9: Socioterritorial boundaries of the Inupiat and Yup'ik societies of Seward Peninsula (Harritt 1994).

36. 30
4.4 Socioterritorialism on Seward Peninsula
Ethnoarchaeological literature suggests that the study area was an important
subsistence home base to at least five regional groups, and that competition and territorial
disputes must have been commonplace (Ray 1975: 109; Koutsky 1981: IV: 39; Schaaf 1988: I:
255; Harritt 1994: 47). The groups are identified as being linguistically affiliated with either the
Iñupiat (Qaviazaġmiut, Pittaġmiut and Kaŋigmiiut) or Yup'ik (Kuuyugmiut and Kałuaġmiut)
cultural traditions.
Mobility
Socioterritorial limits are directly influenced by the rates and modes of travel available
to foraging groups. Ethnohistorically, winter travel between settlements and choice patches
was accomplished by pedestrian means (e.g., snow shoeing) and by dog traction (e.g., sledding)
(Burch 1998). It is difficult to determine the temporal origins genesis of dog traction (Bowers
2009), but varying estimates suggest it occurred between approximately 3000 BP to the historic
period. This study evaluates the caloric expenditures incurred by using the likely modes of
travel available to the late Holocene inhabitants of the study area, such as pedestrian, dog
traction, and unpowered boats (Binford 1980, 1982, 2001).
Modes are affected by seasonality and the presence, or lack thereof, of snow and ice.
Ethnographic analogs indicate that a typical daily time limit for central place foraging is
approximately ten hours (table 3). Though seasonality likely plays a critical role in determining
hunting time restrictions, this study assumes ten hours can be applied generally across all
seasons.

37. 31
Table 2: Average duration for hunting expeditions for several ethnographic groups (Binford 2001).
The rates of winter travel are dependent upon terrain characteristics and the amount of
accumulated snow as well as iced-over rivers, lakes and lagoons. However, dog traction
provides the quickest means of travel with snow shoeing being the least efficient of all.
Conversely, during the non-winter months (spring thaw, summer and fall freeze-up; mid-April
to early-November) travel was accomplished via pedestrian means or boating (umiak or kayak).
Non-winter rates of travel are largely dependent upon terrain characteristics and hydrological
factors. Ice-free rivers and lakes certainly facilitated efficient travel via boat.
Caloric costs of each physical activity are derived from following equation: TDEE = RMR
+ TEF + EEPA, where TDEE is the total daily energy expenditure and the summation of RMR
(resting metabolic Rate), TEF (thermic effect of food) and EEPA (energy expended during
physical activity) (Comana 2001). For this study we will assume hunting parties were a
balanced composition of active men and women of comparable weight (63 and 54 kilograms,
respectively), height (162 and 157 centimeters, respectively) and age (30). A dog pulling a
traction device behind can burn up to 10,000 calories per day (Dogsled 2012).

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5.0 ANALYSES
5.1 Spatial Point and Cost-Distance Analyses
This section outlines the analyses that were used to model and test the relationship
between hunting features and ice/snow patches in the study area. These analyses will also be
used to model and test prehistoric settlement distribution patterns in Seward Peninsula.
The ice/snow patches selected for this study are located in the western portion of the
study area, near the southwestern shore of Kuzitrin Lake and within both calderas at Twin
Calderas (62.57 km²). The first dataset used in analyses consists of 482 archaeological features
related to intercept hunting ( 404 game drive features [inuksuit; meaning looks like man in
Inupiat], two observation/staging blinds, 14 hunting blinds and 62 cairn-type structures whose
purpose are not fully understood in the regional literature) (Schaaf 1988; Holt 2011, 2012). The
second dataset used in analyses consists of 227 generalized prehistoric settlements spread
across Seward Peninsula (127,267 km²). Research expectations are as follows: hunting features
will tend to have a clustering pattern within proximity of ice/snow patches; and prehistoric
socioterritories can be interpreted from settlement distribution patterns along major
watersheds and coastal areas. Cost-distance will, later, aid in determining the least-cost paths
to the study area from each of the nearest settlements.
5.2 Spatial Point Analyses and Archaeology
Spatial point analyses have been used in other studies to illustrate the arrangement of
objects (or points) in a defined space through use of mathematical models. These analyses are
commonly used in archaeology for settlement, regional and landscape studies (Illian 2008: XI).
Interpretation of intersite and intrasite spatial patterning plays a vital role in understanding the

39. 33
relationships between archaeological manifestations and the surrounding landscape in which
they occupy (Banning 2002; Binford 1978; Gargett and Hayden 1991: 11; Kroll and Price 1991:
1).
Questions pertaining to spatial arrangement in archaeology have traditionally focused
on explaining intrasite structure and settlement patterns (Kroll and Price 1991: 2). In recent
decades, there has been a substantial increase in topics and methodologies used to answer a
wide-range of spatial questions in archaeology, such as sociopolitical organization, site
abandonment, subsistence, and hunting strategies (Kanter 2007: 43). Research using spatial
analyses have addressed several recurrent themes including, long distance trade and migration,
and the distribution of material remains to identify socioterritorial boundaries (Geib 2000;
Kulischeck 2003).
The application of spatial techniques and models in archaeology provides researchers
with a quantitative tool to understand the complexities of human interactions with one
another, as well as with the ecosystems to which they are associated (Kanter 2007: 38). The
recent coalescence of evolutionary theory with regional analyses in archaeology has brought
significant diversification to the traditional methods used by researchers to model spatial
relationships--perhaps fueled by the proliferation of geographical information systems science
(GISci) (Kanter 2007: 50). As a result, archaeological spatial studies have grown beyond the
limiting uses of basic mathematical and geographical measures into a diverse toolkit of intricate
techniques that can accurately inform the archaeological record (Kanter 2007: 37).

40. 34
For this study, spider diagrams were used for displaying the Euclidean distances
between points in the intra-hunting feature and inter-settlement datasets, respectively. Cluster
analysis was applied to the resulting spider diagrams based on the effective range of primitive
bow and arrow technology (6-36 meters) from hunting features, and then again on settlements
spaced 5-17 kilometers from one another based on the minimal to mean distance ranges of
prehistoric mobility options.
Spider Diagram Analysis
A spider analysis is an automated GISci process which produces a series of lines that
represent, either, Euclidean or Manhattan distances between all points in an analysis. The
process results in a spider diagram, which offers an effective way to display and evaluate data
points within an analysis. This procedure's capacity to collect distances has been invaluable for
those engaged in the development of marketing strategies and planning scenarios (Howse et al.
2000: 26).
The use of spider analysis in GISci is a relatively recent development, but there are
numerous scripts (i.e., statistical package extensions) available to automate the processing of
point based datasets. This study benefitted greatly from the script created by Laura Wilson in
2005, which is designed for Environmental Systems Research Institute (ESRI) ArcGIS software
(arcscripts.esri.com).
GISci based applications of spider analysis in archaeological research are still in their
infancy, but growing. For instance, Wood and Wood (2006) use a modified version of spider
analysis to evaluate the energy costs of prehistoric forager travel across a variety of terrains.

41. 35
The researchers diagramed the shortest and optimal paths to sixteen destinations, which were
then factored against variably weighted frictions and attributes, such as terrain's elevation and
slope, and traveler's body weight, sex, stride and rate of travel. The authors were able to
determine the most efficient routes of travel across a particular terrain (Wood and Wood
2006).
For this study, spider analysis will be used to diagram distances between hunting
features and ice/snow patches at Kuzitrin Lake and Twin Calderas, as well as settlements
throughout Seward Peninsula. While not solely illustrative of my hypotheses spider diagrams
are prerequisite to cluster and nearest neighbor analyses, which will produce statistically
derived clusters.
Cluster Analysis
Cluster analysis is defined as a suite of mathematical techniques that are used to
examine the relationships of objects in a dataset by grouping similarly attributed objects into
subgroups (or clusters) (Lorr 1983: 1; Romesburg 1984: 2, 15). The technique produces
classification systems in which the number and relationship of the data groupings are not
known prior to analysis (Lorr 1983: 1). There are hundreds of mathematical models available
for clustering analysis, with each one capable of generating divergent outcomes from the same
data (Aldenderfer 1982: 61; Lorr 1983: 3; Romesburg 1984: 2). Consequently, researchers must
choose the cluster techniques best suited for their analyses.
This research uses a hierarchical cluster analysis technique, which is the most widely
accepted and applicable cluster method (Cowgill 1968: 369; Romesburg 1984: 3). The

42. 36
application uses inter-object Euclidean distance to create a multilevel diagram (or dendrogram),
which illustrates a hierarchy of similarity among the data (Romesburg 1984: 3). The
dependence of spatial relationships and inter-object Euclidean distances for this study, make
hierarchical cluster analysis the most appropriate cluster technique.
Cluster techniques have seen wide spread use in archaeology for almost half a century
(Aldenderfer 1982: 61), though the division of data into subgroups must be done as objectively
as possible (Hodson 1970: 299). The statistical precision and accuracy characterizing cluster
analysis make it a valuable quantitative tool in archaeology.
Hierarchical cluster analyses group data based on the similarity of selected attributes.
This method of cluster analysis was performed on the results of the spider analyses in order to
ascertain patterns or clusters in the data based on the relative Euclidean distance of each
individual point to all others selected in the analysis.
SPSS 18 utilizes a process known as agglomerative hierarchical clustering (Norusis 2010:
363) to complete a hierarchical cluster analysis. This algorithm starts by placing each case into
its own cluster and then merges other cases into that cluster until only one cluster remains.
The parameters set for selected variables determine when a significant grouping (clustering)
has been achieved (Norusis 2010: 364).
The hierarchical cluster analysis used in this study will produce statistically derived
groupings of hunting features and settlements, which will be guided by optimal foraging theory
(group size model, prey and patch choice models, and central place foraging model). This
resulted in the generation of three separate cluster analyses in order to illustrate feature and

43. 37
settlement patterns. This includes: Study area hunting feature groups (macro) within 200
meters of one another and reconciled with local terrain in mind; caldera hunting feature
clusters (micro) within 36 meters of each other; and settlement clusters within 17 kilometers of
one another. The groups associated with each analysis will be analyzed and tested with nearest
neighbor analysis.
Nearest Neighbor Analysis
Nearest neighbor analysis is a technique for examining spatial patterns by comparing
the observed patterning (clustered, dispersed, or random) of a particular dataset to that of an
expected spatial randomness (Bailey 1994: 25). In essence, nearest neighbor analysis is a form
of cluster analysis, but is considered a single-level technique in which the relatedness of objects
is expressed through an index (ESRI 2009; Lorr 1983: 62). The nearest neighbor index
represents the ratio of observed distance divided by expected distance. The expected distance
is derived from the average distance between neighbors in a hypothetical random distribution.
If the index is less than one, the data exhibits some degree of clustering; however if the index is
greater than one, the data is considered dispersed (ESRI 2009).
Nearest neighbor analysis was first demonstrated by Clark and Evans (1954: 445) in
ecological research, as a method for interpreting plant and animal distributions in the natural
environment. Soon after, geographers and archaeologists employed the technique to study
contemporary and archaeological settlement patterns (Corley and Hagget 1965; Hodder 1972).
Today, nearest neighbor analysis is a preferred technique for many archaeologists, due to its

44. 38
simple mathematical calculations and an easily interpreted coefficient (Conolly and Lake 2006:
164).
There are several algorithms associated with nearest neighbor queries, which are all
essentially defined as techniques that facilitate the finding of the closest object (k) in space (S)
to a specific query object (q) (Hjaltason and Samet 2003: 529). Most studies use a tree-based
Euclidean distance technique for spatial indexing commonly referred to as quadtree. Quadtree
prioritizes objects in space by placing them into a series of spatially adjacent blocks (Tanin et al.
2005: 85). The area incorporated into an analysis is divided into four equal regions, each of
which is divided into four sub-regions, and so forth, until all objects have been indexed (Longley
et al. 2005: 235).
This research uses a GIS-based nearest neighbor algorithm and student’s t-test to
explore the statistical validity of the following null hypotheses: 1) hunting clusters are
randomly distributed near ice/snow patches and there is a less than 95% chance these features
are related. A student’s t-test will be conducted simultaneously to nearest neighbor analysis to
assess the statistical significance (5% confidence level) of the mean distances between all
hunting feature clusters and ice/snow patches; and 2) settlements on Seward Peninsula are
randomly distributed across Seward Peninsula and there is a less than 95% chance the
groupings are indicative of power centers or optimally arranged home bases. If the null
hypotheses with a greater than 95% confidence level, then the study asserts that objects were
not distributed by random chance, and instead show patterns of clustering and dispersal or the
distance variant used in student's t-test show a level of significant correlation.

45. 39
5.3 Site Catchment and Cost-Surface Analyses in Archaeology
Ducke and Kroefges (2007: 245-46) define territory as being comprised of several
elementary aspects such as “.., distance, hierarchy and network connectivity.” The Xtent
Model, developed by Renfrew and Level (1979) provides a simple formula to predict a zone of
political and territorial influence.
Site catchment analysis (Vita-Finzi and Higgs 1970), derived from optimal foraging
theory (MacArthur and Pianka 1966; Emlen 1966), has been used to model mobility and
socioterritorial boundaries based on distance, cost frictions (slope and terrain) (Wheatley and
Gillings 2002; Brevan 2008), watershed accessibility (Llobera 2011) and network connectivity
(e.g., home base clusters, trade networks, etc.) (Brevan 2008). Generally, site catchment
analyses utilize cost-distance models to factor the costs (time and energy) of human and animal
movements through a defined space (Brevan 2008: 4).
Cost-Distance Analysis
Cost-distance analysis is a method developed by Kvamme (1983, 1986, 1989, and 1990),
Kohler and Parker (1986), Savage (1989) and Warren (1990). Since its inception researchers
have attempted to reconstruct prehistoric settlement and exploitation by factoring real-time
frictions that influence forager mobility (Duncan and Beckman 2001). Creation of a model
relies on a combination of hypothetico-deductive decisions which are based on the
interpretation of cost-distance information generated in GIS.
This study uses cost-distance analysis in order to evaluate the influences slope
(Wheatley and Gillings 2002; Brevan 2008) and hydrology (Llobera 2011) have on forager

46. 40
mobility across the landscape. Use of this particular isotropic model, for this study, is based on
the notable variation in slope and major river systems of Seward Peninsula. The settlement
dataset will be subjected to multiple cost-distance GIS algorithms on the basis of prehistoric
mobility mode options (i.e., walking, unpowered boating, dog traction).
Least-Cost Path
The cost of traveling from point A to B over some distance must involve some positive
cost in time, i.e., CostDist(A,B) > 0, for all B≠A (Worboys et al. 2004: 215-26). Tobler’s hiking
function (Ducke and Kroefges 2007) is widely used in the estimation of least cost paths in
archaeology. The velocity of walking is given by V (s) = 6 e-3.5 |e+0.05|
, where s is the slope
(calculated by vertical change divided by horizontal change) (Herzog 2010: 431-32). Cost-
distance algorithms in GIS help automate this process, generating a Manhattan distance for
each least-cost path (Wheatley and Gillings 2002: 157). Manhattan distance is defined as the
“.., distance between two points in a grid based on a strictly horizontal and/or vertical,” as
opposed to Euclidean distance (ESRI 2009).
This study will incorporate a least-cost path algorithm generated in GIS to determine the
optimal paths to the study area from the adjacent settlements. The distances produced will be
incorporated into an energy expenditure formula to investigate: 1) caloric cost per mode of
travel per route; and 2) which foraging group(s) were likely to complete a journey to the study
area based on optimal foraging.
5.4 Geographic Information Systems Science and Archaeology
Recent research has successfully integrated GISci into archaeological theory (Chapman
2006: 9; Connolly and Lake 2003, 2006: 3; Lock 2003), perhaps prompted by the

47. 41
interdisciplinary nature of modern archaeology in addressing archaeological questions.
Regional archaeologies such as landscape archaeology and those engaged in evaluating
settlement patterns have benefitted substantially through the global geographic modeling of
environmental and archaeological variables (Chapman 2006: 128).
GISci is an essential tool for modeling archaeological theory and interpretation. In terms
of its analytical capabilities, GISci has the potential to change existing archaeological practices
and greatly enhance new ones (Lock 2003: 268). GIS offers a suite of statistical tools that play
an essential role in the quantitative capabilities of many archaeologists, such as spider
diagrams, cluster analysis, nearest neighbor analysis and cost-surface analyses (Wheatley and
Gillings 2002; Lock 2003: 166; Arroyo 2008: 31, 34; McGuire et al. 2007: 361, 363; Grimstead
2010; Morgan 2008: 247, 254; ).

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6.0 METHODOLOGY & RESULTS
6.1 Introduction
The following passages are separated into four main results sections (spider, hierarchical
cluster, nearest neighbor, and cost-surface), each of which outlines the results of a particular
analytical technique utilized in this study. Due to the overlapping nature of analyses for this
study, each section is partitioned in accordance with the research topics being analyzed. Each
section will demonstrate the relevance of a particular analytical method used in addressing
study objectives.
There will be a brief discussion to illustrate how spider and cluster analyses were
combined for, both, intra-feature and inter-settlement datasets. Then there will be an
explanation regarding the applicability of nearest neighbor analysis to this research as a cluster
validation technique and for assessing spatial patterns. The concluding remarks at the end of
this chapter provide an overview of analytical results.
6.2 Application of Spatial Point Analyses
Spatial point analyses find a high degree of utility for this study. In addressing the
hypotheses presented in this research, I must articulate which data are relevant and why. As
such, an assumption must be made that the distance between hunting features and ice/snow
patches is a meaningful measure of their relationship. Another assumption is that hunting
methodologies and modes of mobility on Seward Peninsula have remained constant
throughout the late Holocene (5500 BP) at least up until early historic times (1850 AD; or the
widespread distribution of firearms) (see the context in a previous chapter). Finally, this study

49. 43
concedes that due to the palimpsest nature of archaeology (UL 2010: 1: 10-12), the datasets
used in analyses may very well represent divergent temporal/cultural sequences.
Spider Analysis
Spider analyses were used to provide the spatial proximity from each point (case) to
other points subject to analysis. This research used a spider script developed by Wilson (2005),
which automated the creation of three distinct GIS line shapefiles with associated databases.
A spider diagram (Appendix A) is in a tabular format, which effectively summarize the results of
each spider analysis. The appendix tables are structured as follows: first column provides the
'feature of origin'; column two provides the 'destination feature'; column three provides the
associated length of each spider line; and column four provides the unique identifier of each
spider line. All spider analysis appendices have been sorted by ascending distances, which
allowed for more efficient cluster analyses.
Hierarchical Cluster Analysis
Application of hierarchical cluster analysis in this study was a relatively simple process,
with distance being the only variable needed to generate groupings. The process of defining
clusters in terms of distance is common and frequently referred to as proximity analysis
(Norusis 2010: 366). The hierarchical cluster analyses utilized the distances generated by spider
analyses to create dendrograms that placed each case into statistically groups. In this study,
the distances obtained from three distinct spider databases were subject to cluster analyses via
this approach.

50. 44
The final step in this process was to isolate and select each group out the modified
spider diagram shapefile to create individual cluster shapefiles in GIS. This was a necessary step
to obtain independent results from nearest neighbor analysis for east clusters.
Nearest Neighbor Analysis
The third phase of evaluation incorporated a nearest neighbor analysis. The first
objective of the nearest neighbor analysis was to validate the results of the hierarchical cluster.
This application was conducted independent of the spider and hierarchical cluster analyses.
The second objective was to determine intra-feature and inter-settlement distances between
the clusters generated by hierarchical cluster analysis.
The results of the nearest neighbor analysis are summarized in tabular format within the
corresponding sections. The first column provides the cluster number. Column two provides
the nearest neighbor ratio. A nearest neighbor ratio of less than one results in some level of
data clustering, while above one the data are considered dispersed. Column three provides the
probability value (p-value) associated with each cluster. The p-value is a measure of
consistency; it calculates the likelihood of a study’s results against the possibility of those more
extreme. The p-value for nearest neighbor is derived from the comparison of an observed
feature distribution with that of an expected mean in a random distribution. Column four
provides the standard deviation (z-score) associated with each cluster. The z-score is a test of
statistical significance that aids a researcher in deciding whether or not to reject a null
hypothesis. Objects with z-scores that fall outside of the normal range using a 95% confidence
level (p-value = 0.05) are likely too abnormally distributed to be an instance of random chance
(ESRI 2009). Column five provides the observed mean distance (in meters) to nearest neighbor

51. 45
within each cluster. Column six provides the expected mean distance (in meters) to nearest
neighbor within each cluster based on user defined area (usually an area encompassing a
population dataset). Column six provides the pattern interpreted for each cluster.
6.3 Application of Cost-Surface Analysis
Cost-surface plays an integral role in this research to determine how slope and
hydrological variables influence prehistoric mobility. This study will use GIS to generate a series
of cost-distance algorithms to produce a realistic model of prehistoric socioterritorialism based
on optimal foraging theory and ethnohistoric analogs. Additionally, the least-cost paths
generated in GIS will be used to determine the most optimal path from the nearest adjacent
settlement to the study area. The resulting Manhattan distances will be used in a comparison
of rates, time investments and caloric outputs for each route, based on the mobility options
available to prehistoric hunter-gatherer groups throughout the late Holocene.
6.4 Intercept Hunting and Ice/Snow Patches
A portion of this research is based on the distributions of 544 hunting features and their
potential relationship with three ice/snow patches across the 62.57 km² (15,465 acres) study
area at Kuzitin Lake and Twin Calderas. As noted in a previous chapter, the locations and
descriptions of each hunting feature used for this study were obtained through previous survey
efforts by Powers (1982), Schaaf (1988), Harritt (1994), and Holt et al. (2011, 2012). The bulk of
game drive line features (inuksuk) and the snow and ice patch locations were obtained in 2011
and 2012 (Holt et al.) with funding provided by the National Park Service List of Classified
Structures program.

52. 46
Spider Analyses Results
The preliminary step for this inquiry was to perform spider script algorithms on the
hunting feature dataset and on the ice/snow patches. The results of the scripts are prerequisite
for further analyses of spatial patterning among hunting feature clusters, and distances
between hunting features and ice/snow patches in the study area.
Figure 10: Spider diagram of hunting features (green) and results of the Hierarchical Cluster analyis (red).
First, a spider script was executed on the hunting feature dataset to diagram the
Euclidean distances between each hunting feature in the study area. This resulted in the
creation of a line shapefile and associated database comprising 26,624 unique distance
measurements (figure 9). The associated spider database tabulated information pertaining to

53. 47
point of origin (hunting feature) and destination item (hunting feature) for each of the lines,
including the sum distance for each line. The line shapefile serves as a graphic representation
of the distances between each of the 544 hunting features in GIS, while the associated
database contains their spatial proximities.
Secondly, a spider script was executed in order to diagram distances between each
ice/patch and each hunting feature. This resulted in the creation of a line shapefile comprising
1632 unique distance measurements (figure 10). The associated spider database tabulated
information pertaining to point of origin (ice/snow patch centroid) and destination item
(hunting feature) for each of the lines, including the sum distance for each line (Appendix A).
This data is used for obtaining the observed mean distance (1500 meters) between all hunting
features and ice/snow patches. This value (1500 meters) is later used as the expected mean
distance in a student's t-test to statistically validate the level spatial randomness exhibited by
hunting feature clusters in proximity to ice/snow patches.
In order to complete hierarchical cluster analyses the databases containing the results
of the spider analyses were exported from GIS and imported into the statistical package for
social sciences 18 (SPSS 18). It is important to note the line shapefiles produced by both spider
analyses will, later, be combined with the results of the cluster analyses in GIS.
Hierarchical Cluster Analyses Results
Hierarchical cluster analyses were performed on the hunting feature database produced
in spider analysis to ascertain grouping based on an arbitrary distance variant. That is, all
hunting features within 200 meters of one another (macro); and all hunting features within 36
meters of one another (micro). However, because spider diagrams represent solely the

54. 48
Euclidean distances between points, it was necessary to deductively reconcile the cluster
compositions of hunting features located on each caldera based on crucial aspects of the local
terrain.
Figure 11: Overview of the calderas. Stars (variably colored) represent the features lining each caldera rim. Dark gray
represents the steep walls of each caldera, while the light gray is the bottom. The blue shapes represents the ice/snow
patches.
This reconciliation is based principally on the steep and rugged topogeological character
of each caldera, which restricts access and mobility--tantamount to corrals. These features are

55. 49
assumed to be separate systems tied to each caldera rim top or spillway. Both calderas exhibit
moderate to sheer walls, which act as a natural inhibitor of mobility, except for the exposed
spillways, as well as a grassy exposure on the northeastern rim of east caldera. The feature
distribution map (figure 10) clearly illustrates the unique relationship between each caldera
with the hunting features (possible territorial markers) surrounding them.
Macro Clusters
The first hierarchical cluster analysis grouped 482 of the 544 hunting features into four
primary hunting feature concentrations in the study area, including: two game drive systems
along the shores of Kuzitrin Lake (north and south); and unique feature concentrations around
each of the calderas (east and west). These macro clusters range in size from 15 to 389 hunting
features, comprised of game drive line features (inuksuit), hunting blinds, observation/staging
blinds, and cairns/caches. All clusters are located in the western portion of the study area,
which is most certainly an influence of terrain as well as the abundance of basalt and granite
outcrops as a principal construction material. The cluster groupings are as follows: west
caldera contains 19 features; east caldera contains 59 features; southern game drive line
contains 15 features; and northern game drive line contains 389 features.
The functional definition of the northern game drive line system has been well
established in previous works (Powers 1982; Schaaf 1988; Harritt 1994), and this corresponds
well with regional ethnohistoric accounts of lake-based game drives. The hunting tactic
associated with this system is best employed by foraging groups as a form of communal

56. 50
Figure 12: Overview of reconciled macro clusters for the study area. Also shown is the spider diagram generated for the
micro clusters and their nearest ice/snow patch.

57. 51
hunting during the late spring and late fall caribou migrations--when the animals are migrating
in dense herd aggregations.
The southern game drive line is the southernmost grouping in the analysis. The cluster
is composed of 15 features (hunting blind or cache, and 14 inuksuit [game drive features]) on
the north slope of the Bendeleben foothills. The system spans a distance of 800 meters and is
oriented roughly west-east. The system is located upslope and parallels the lake shore and a
seasonal snow patch. Interestingly, the lines' orientation does not correspond well with
regional contexts regarding lake-based game drive systems, similar to the northern game drive
line.
The cluster around west caldera is the northwestern most grouping in the analysis. The
cluster is composed of 19 features (13 cairns/caches, and 6 hunting blinds) which are aligned on
the rim top and spillway channel of the western caldera. This cluster contains one micro cluster
(cluster 1 with 6 features) lining the spillway channel and an associated game trail (see next
section).
The cluster associated with east caldera is the northeastern most grouping in the
analysis. The cluster is composed of 59 features (50 cairns/caches, 2 observation/staging
blinds, and 7 hunting blinds) which are aligned on the rim top, spillway channel and exposed
intermixed grassy/lava boulder area of the eastern caldera. This cluster contains the highest
concentration of features in the study, comprised of four micro clusters (clusters 2 - 5 with 54
features) lining the southern and eastern portions of the rim as well as five other features

58. 52
(observation/staging blind, and four cairn/cache features) variably aligned on the rim and
spillway channel.
Micro Clusters
An additional hierarchical cluster analysis was conducted on the two macro clusters
located at Twin Calderas to produce feature groups that are composed of hunting features
spaced within 6 to 36 meters of each other. The reason for selecting this arbitrary distance
range is based on the effective range of primitive bow and arrows (Pope 1918: 124; Bergman
and McEwen 1997; Cattelein 1997: 231), which remained the principal hunting technology
available to prehistoric foraging groups throughout the late Holocene (Blitz 1988: 128). The 6-
meter minimum range was based on an assumption that hunting blinds which are too tightly
grouped would certainly be ineffective and even dangerous to members occupying blinds
opposite of a 'bad shot'.
The resulting analysis grouped 60 of the 78 combined hunting features at Twin Calderas
(or 60 of the 544 total hunting feature population dataset) into five distinct micro clusters.
Each micro cluster ranges in size from 6 to 25 hunting features, comprised of a mixture of
hunting blinds, observation/staging blinds and cairn/caches. All micro clusters are located on
the rim tops or spillways of each caldera. The micro cluster groups are characterized as: cluster
one is located within the west caldera macro cluster spillway, and contains six features; and
four clusters are located within the east caldera macro cluster, containing a combined 54
features.

59. 53
Figure 13: Plan view of West Caldera illustrating the spider diagrams generated for the micro clusters and ice/snow patches.

60. 54
Cluster 1 is the southernmost grouping in west caldera. The cluster is composed of six
features (6 hunting blinds), which are located adjacent to the caldera spillway and a well-worn
game trail--both of which are oriented SSW/NNE. The spatial arrangement observed among
these hunting blinds indicates there is an optimal degree of bow range overlap throughout this
portion of the spillway.
Cluster 2 is the southernmost group in east caldera. The cluster is composed of 11
features (cairns/caches), which are arranged in a tight clumped group approximately 50 meters
in diameter on the south side of the caldera rim top.
Cluster 3 is the southeastern most group in east caldera. The cluster is composed of 12
features (cairns/caches), which are arranged in a tight linear alignment spanning approximately
80 meters on the southwestern side of the caldera rim top.
Cluster 4 is the westernmost group in east caldera. The cluster is composed of 25
features (6 hunting blinds, 1 observation/staging blind and 18 cairns/caches, which are
arranged in predominately a north-south linear alignment spanning approximately 160 meters
on the western side of the caldera rim top. Another linear alignment of features comprising six
hunting blinds are located at the northern terminus of this group. The spatial arrangement
observed among these and cluster 5 hunting blinds indicates there is an optimal degree of bow
range overlap associated with the grassy exposure.
Cluster 5 is the northernmost group in east caldera. The cluster is composed of six
features (2 hunting blinds and 4 cairns/caches), which are arranged in a moderately spaced
group spanning approximately 50 meters on the caldera rim top, immediately north of the

61. 55
Figure 14: Plan view of East Caldera illustrating the spider diagrams generated for the micro clusters and ice/snow patches.

62. 56
grassy/lava boulder exposure. The spatial arrangement observed in these and cluster 4 hunting
blinds indicates there is an optimal degree of overlap associated with the grassy exposure. The
cairns are highly visible from the caldera floor and associated ice patch.
Nearest Neighbor Analysis Results
Macro Clusters
Initially, nearest neighbor was applied to the hunting feature dataset (macro population
dataset) for Kuzitrin Lake and Twin Calderas study area. The average observed mean distance
produced is 45 meters, with an expected mean distance of 272 meters. After this initial
application of nearest neighbor, the analysis was repeated on the four macro clusters
generated in the prior analyses. The process measured feature dispersion within each cluster,
and the mean distances between features.
Cluster
Nearest
Neighbor Ratio p-value z-score observed expected Pattern
All Hunting
Features 0.164398 0
-
38.198695 45 272 Clustered
Macro
West Caldera 1.071018 0.553708 0.592213 69 64 Random
East Caldera 0.293261 0
-
10.385225 8 27 Clustered
North Game Drive 0.212793 0 -29.70261 7 32 Clustered
South Game Drive 1.626457 0.000003 4.641599 26 16 Dispersed
Micro
Cluster 1 1.677148 0.001508 3.173148 14 9 Dispersed
Cluster 2 1.567292 0.000319 3.599428 5 3 Dispersed
Cluster 3 1.655764 0.000014 4.345794 4 3 Dispersed
Cluster 4 0.813371 0.074234 -1.785167 9 11 Clustered
Cluster 5 1.944183 0.00001 4.424483 7 4 Dispersed
Table 3: Results of nearest neighbor analysis on the macro and micro clusters.

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The northern game drive line group produced a nearest neighbor ratio of 0.021. The
value is considerably lower than 1 (by -29.70 standard deviations), which indicates the hunting
features that comprise this grouping are highly clustered. This result is statistically significant to
at least the 0.05 confidence level. The mean intra-feature distance for this grouping is 7
meters, with an expected mean distance of 32.
The southern game drive line group produced a nearest neighbor ratio of 0.016. The
value is considerably higher than 1 (by 4.64 standard deviations), which indicates the hunting
features that comprise this grouping are dispersed. This result is statistically significant to at
least the 0.01 confidence level. The mean intra-feature distance for this grouping is 26 meters,
with an expected mean distance of 16.
The west caldera group produced a nearest neighbor ratio of 1.07. The value is slightly
higher than 1 (by 0.59 standard deviations), which indicates the hunting features that comprise
this grouping are random. This result is not statistically significant to the 0.05 confidence level.
The mean intra-feature distance for this grouping is 69 meters, with an expected mean distance
of 64.
The east caldera group produced a nearest neighbor ratio of 0.29. The value is lower
than 1 (by -10.39 standard deviations), which indicates the hunting features that comprise this
grouping are highly clustered. This result is statistically significant to at least the 0.01 level. The
mean intra-feature distance for this grouping is 8 meters, with an expected mean distance of
27.

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Micro Clusters
A second run of nearest neighbor was applied to the calderas hunting feature dataset
(micro population dataset) for only the features associated with Twin Calderas to aid in testing
the statistical significance (student's t-test) of the proximities of clusters nearest to a
corresponding ice patch in each caldera. The average observed mean distance produced is 8
meters, with an expected mean distance of 6 meters. After this, the analysis was repeated on
the five micro clusters generated in the prior analyses. The process measured feature
dispersion within each cluster, and the mean distances between features.
Cluster 1 produced a nearest neighbor ratio of 1.68. The value is higher than 1 (by 3.17
standard deviations), which indicates the hunting features that comprise this grouping are
dispersed. This result is statistically significant to at least the 0.05 confidence level. The mean
intra-feature distance for this grouping is 14 meters, with an expected mean distance of 9
meters.
Cluster 2 produced a nearest neighbor ratio of 1.56. The value is higher than 1 (by 3.6
standard deviations), which indicates the hunting features that comprise this grouping are
dispersed. This result is statistically significant to at least the 0.05 confidence level. The mean
intra-feature distance for this grouping is 5 meters, with an expected mean distance of 3
meters.
Cluster 3 produced a nearest neighbor ratio of 1.66. The value is higher than 1 (by 4.35
standard deviations), which indicates the hunting features that comprise this grouping are
dispersed. This result is statistically significant to at least the 0.05 confidence level. The mean

65. 59
intra-feature distance for this grouping is 4 meters, with an expected mean distance of 3
meters.
Cluster 4 produced a nearest neighbor ratio of 0.81. The value is lower than 1 (by -1.79
standard deviations), which indicates the hunting features that comprise this grouping are
slightly clustered. This result is not statistically significant to the 0.05 confidence level. The
mean intra-feature distance for this grouping is 9 meters, with an expected mean distance of 11
meters.
Cluster 5 produced a nearest neighbor ratio of 1.94. The value is higher than 1 (by 4.42
standard deviations), which indicates the hunting features that comprise this grouping are
dispersed. This result is statistically significant to at least the 0.05 confidence level. The mean
intra-feature distance for this grouping is 7 meters, with an expected mean distance of 4
meters.
Macro Cluster Distances From Ice/Snow Patches
Feature Cluster Observed Expected
West Caldera 257 1500
East Caldera 238 1500
South Kuzitrin Lake 555 1500
*Expected mean distance derived from the observed mean distance of all hunting features to the
ice/snow patches distributed throughout the study area (62.57 km² or 15,465 acres).
Table 4: Observed and expected mean distances used in the student's t-test

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Student's T-Test Results
A student’s t-test (t-test) was used to determine the statistical significance of the
observed and expected mean distances between feature clusters and ice/snow patches. All
distances were obtained from the relevant spider database. In this particular case, the
expected mean distance used in the t-test is derived from the average distance (1,500 meters)
between each hunting feature and each ice/snow patches in the study area.
Macro Clusters
The result of the t-test returned a p-value of 0.015, indicates there is a less than 5%
chance these clusters are randomly distributed in relation to ice and snow patches. This rejects
the null hypothesis and allows for an alternative hypothesis to be posited.
Variable 1 Variable 2
Mean 350 1500
Variance 31609 0
Observations 3 1
Pooled Variance 31609
Hypothesized Mean
Difference 0
df 2
t Stat -5.601741887
P(T<=t) one-tail 0.0152
t Critical one-tail 2.91998558
P(T<=t) two-tail 0.0304
t Critical two-tail 4.30265273
Table 5: Student's t-test results for macro clusters
Macro clusters are non-randomly distributed around the ice/snow patches in the study
area, with over 95% confidence. All macro clusters are within considerable range of the
expected mean distance.

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Micro Clusters
Micro Cluster Distances From Ice/Snow Patches
Feature Cluster Observed Expected
Cluster 1 267 1500
Cluster 2 113 1500
Cluster 3 141 1500
Cluster 4 294 1500
Cluster 5 312 1500
*Expected mean distance derived from the observed mean distance of all hunting features to the
ice/snow patches distributed throughout the study area (62.57 km² or 15,465 acres).
Table 6: Observed and expected mean distances used in the student's t-test
Another t-test was performed on the spider database to test statistical significance of
observed and expected mean distances between the micro clusters and the nearest associated
ice patch in the calderas. The result of the t-test returned a p-value of 0.011, indicating there is
a greater than 99% chance micro clusters are purposefully grouped near the ice patches in each
caldera.
Variable 1 Variable 2
Mean 225.4 1500
Variance 8423.3 0
Observations 5 1
Pooled Variance 8423.3
Hypothesized Mean
Difference 0
df 4
t Stat -12.67774922
P(T<=t) one-tail 0.01115
t Critical one-tail 2.131846786
P(T<=t) two-tail 0.02229
t Critical two-tail 2.776445105
Table 7: Student's t-test results for micro clusters

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Micro clusters are within a statistically meaningful proximity of the ice/snow patches in
the study area (with greater than 99% confidence), while macro clusters (i.e., the southern
game drive line) are also near ice/snow patches (with greater than 95% confidence). All
observed mean distances of each case are well below their expected mean distances. The
functional relationship between clusters and their nearest respective ice patch cannot be
absolutely verified in the absence of physical evidence manifest in archaeofaunal material, and
the patches located in the calderas may very well be a natural coincidence, but spatial
proximities of these clusters to their respective ice/snow patches is certainly significant.
Summary of Feature Cluster and Ice/Snow Patch Results
The results of these analyses presented above correlate well with the expectations
developed for this study. The identification of four macro clusters suggests there were in fact
at least four distinct intercept hunting localities used by foraging groups in their pursuit of a
high-ranking prey item (caribou) in the study area. The clustering of hunting features in close
proximity to ice/snow patches within the study area strongly supports the supposition that
group-based (or communal) hunting tactics were employed in relation to ice/snow patches.
Though a version of the community hunting strategy (lake-based game drives) for the study
area has been well documented, this research contends that an alternative ice/snow patch
collective hunting tactic was employed at the unique macro clusters around each caldera as
well as at the southern game drive line. If true, this would be the first documented evidence of
a game drive hunting strategy associated with ice/snow patches in this region.

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6.5 Settlement and Socioterritorialism
This inquiry is based on the spatial distributions of 227 prehistoric settlements across
Seward Peninsula (127,267 km²; or 31,448,235 acres). The criteria used in the selection of
settlements for this study are quite generic and do not exhibit any level of temporal control. As
such, any settlement with a prehistoric component (which possibly represent a sequence of late
Holocene temporal/cultural sequences) was selected, provided there are at least ten
permanent house pit features. Though, this dataset does not account for the palimpsest nature
of archaeological manifestations, the dataset is based on the premise, 'a good place to camp, is
a good place to camp.' The study assumes foraging group settlements and subsistence practices
have remained largely consistent throughout the late Holocene (see context). The locations and
descriptions of the each settlements used in the study were obtained from the Alaska
Archaeological Heritage Resources Survey database (AHRS 2006).
The objective of this inquiry is to investigate prehistoric settlement distribution patterns
in order to illustrate socioterritorial power centers or optimally arranged home base networks.
This section provides the results of the spatial point analysis, and further investigates the use of
cost-distance algorithms in GIS to factor the environmental variables (slope and hydrology) with
the greatest influence on forager mobility.
Spider Analysis Results
The first step in addressing this inquiry was to perform a spider script on the settlement
dataset. The result of the script is a prerequisite to further spatial point analyses, which will use
the distances produced in the spider database.

70. 64
The spider script was executed on the settlement dataset to diagram the distances
between each of the selected settlements in Seward Peninsula. This resulted in the creation of
a line shapefile and associated database comprising 25,764 unique distance measurements.
The associated spider database tabulated information pertaining to the point of origin
(settlement) and destination object (settlement) for each of the lines, including the sum
distance for each line. The line shapefile is a graphic representation of the distances between
each of the 227 settlements in GIS, while the associated database contains their spatial
proximities.
Hierarchical Cluster Analyses Results
Hierarchical cluster analysis was performed on the spider settlement database to
illustrate clustering patterns of all settlements which are with a range of 5 to 17 kilometers.
This range was selected based on a hypothetical home base and the furthest resource patch
available to it, considering pedestrian and dog traction modes of travel.
Hierarchical cluster analysis grouped 213 of the 227 selected settlements into twelve
settlement clusters (or home base networks) distributed across Seward Peninsula. These
clusters range in size from 4 to 51 settlements (figure 14).
Settlement Clusters
Cluster 1 is westernmost group in Seward Peninsula (also the westernmost point of the
continent). The cluster is composed of 23 settlement, which are arranged in a linear pattern

71. 65
Figure 15: Results of the spider diagram combined with the hierarchical clustering analysis.

72. 66
along the coast. The pattern corresponds well with ethnohistoric accounts of settlement
strategies along coastal stretches, and represents the Kiŋikmiut (Wales).
Cluster 2 is located northeast of cluster 1 and is composed of nine settlements, which
are arranged mainly along the coast, but also the confluence of Serpentine River and
Shishmaref Lagoon. The pattern corresponds well with ethnohistoric accounts of settlement
strategies along coastal stretches and in the interior watersheds, and represents the
Tapqaġmiut (Shishmaref) socioterritory.
Cluster 3 is located on the northeast portion of Seward Peninsula. The cluster is
composed of 31 settlements, which are concentrated mainly along the coast, but up the major
drainages in the area. The pattern corresponds well with ethnohistoric accounts of settlement
strategies along coastal stretches and in the interior watersheds, and represents the Pittaġmiut
(Buckland) socioterritory.
Cluster 4 is located south of cluster 3 and is composed of 19 settlements, which are
predominately lining the coast. of settlement strategies along coastal stretches and in the
interior watersheds, and represents the Pittaġmiut (Buckland) socioterritory.
Cluster 5 is southeastern most group in Seward Peninsula. Cluster is composed of six
settlements, which predominately line the coast. The pattern corresponds well with
ethnohistoric accounts of settlement strategies along coastal stretches, and represents the
Kuuyuġmiut (Yup'ik) socioterritory.

73. 67
Cluster 6 is located west of cluster 5, and is composed of 24 settlements, which
predominately line the coast. The pattern corresponds well with ethnohistoric accounts of
settlement strategies along coastal stretches, and represents the Kałuaġmiut (Yup'ik)
socioterritory.
Cluster 7 is located west of cluster 6, and is composed of five settlements, which
predominately line the coast. The pattern corresponds well with ethnohistoric accounts of
settlement strategies along coastal stretches, and represents the Ayaasaġiaġmiut (Nome)
socioterritory.
Cluster 8 is located west of cluster 7, and is composed of 15 settlements, which
predominately line the coast. The pattern corresponds well with ethnohistoric accounts of
settlement strategies along coastal stretches, and represents the Ayaasaġiaġmiut (Nome)
socioterritory.
Cluster 9 is located north of cluster 8, and is composed of five settlements, which
predominately line the coast. The pattern corresponds well with ethnohistoric accounts of
settlement strategies along coastal stretches, but represents the settlements of two
socioterritories (Ayaasaġiaġmiut and Sinġaġmiut).
Cluster 10 is located east of cluster 9 and is highest concentration of sites in the interior
reaches of Seward Peninsula. The cluster is composed of 51 settlements, which are
predominately located along major rivers and wetlands, but some also around Imuruk Basin (a
large salt-water lagoon). The pattern corresponds well with ethnohistoric accounts of

74. 68
settlement strategies along major watersheds, but represents the settlements of two
socioterritories (Qaviazaġmiut and Sinġaġmiut).
Cluster 11 is located north of the Kuzitrin Lake and Twin Calderas study area. The
cluster is composed of four settlements, located within the Goodhope River watershed. The
pattern corresponds well with ethnohistoric accounts of settlement strategies along interior
watersheds, and represents the Pittaġmiut (Buckland) socioterritory dominion over this
exploitation area by the Iñupiat group.
Cluster 12 bisects the Kuzitrin Lake and Twin Calderas study area in a north-south
alignment. The cluster is composed of 21 settlements, located within the Kuzitrin, Kugruk,
Koyuk, Fish and Noxapaga River watersheds. The pattern corresponds well with ethnohistoric
accounts of settlement strategies along major watersheds in the interior, but represents the
socioterritories of three Iñupiat (Qaviazaġmiut, Pittaġmiut, and Kaŋinmiiut) and two Yupik
(Kuuyuġmiut and Kałuaġmiut) groups.
Nearest Neighbor Analysis Results
Settlement Clusters
Initially, nearest neighbor was applied to the entire settlement data (population data)
for Seward Peninsula. The average observed mean distance produced is 3.71 kilometers, with
an expected mean distance of 8.35 kilometers. After this initial application of nearest neighbor,
the analysis was completed on the 12 settlement clusters generated in the prior analyses. The
process measured patterns of settlement dispersion within each cluster, and the mean
distances between settlements.