Demographic fluctuations and the emergence of arctic maritime adaptations

Journal of Anthropological Archaeology 56 (2019) 101100

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Journal of Anthropological Archaeology journal homepage: www.elsevier.com/locate/jaa

Demographic fluctuations and the emergence of arctic maritime adaptations a,⁎

b

a

Shelby L. Anderson , Thomas Brown , Justin Junge , Jonathan Duelks a b

b

T

Portland State University, Department of Anthropology, P.O. Box 751, Portland, OR 97207, United States Department of Anthropology, University of British Columbia, 6303 NW Marine Drive, Vancouver, BC V6T 1Z1, Canada

ARTICLE INFO

ABSTRACT

Keywords: Demography Radiocarbon analysis Aquatic adaptations Arctic

Our goal is to study the role of demographic change in the development and spread of maritime adaptations in the North American Arctic over the last 6000 years. We compile and analyze a regional radiocarbon database (n = 935) for northern Alaska, using Oxcal to analyze demographic patterns in summed probability distributions. We find that northern Alaskan populations grew significantly over the last 4500 years, although growth was punctuated by three periods of decline from approximately 3700 to 3125 cal BP, 1000 cal BP, and 600 cal BP. We assess possible alternative explanations for the observed demographic patterns (e.g. calibration and taphonomic effects, investigator bias). Region-wide erosion and calibration effects likely contribute to the dearth of radiocarbon dates around 1000 cal BP, and sampling bias may contribute to the post-600 cal BP decline. However, we conclude that the overall pattern reflects regional population growth, decline, and recovery. Population growth predates intensification of marine resource procurement by at least 1200 years; we hypothesize that population growth was a possible driver for late Holocene marine intensification in the Arctic. These findings have further implications for understanding the process of intensification and the development of complexity in coastal hunter-gatherer societies.

1. Introduction

Arctic maritime traditions, demographic shifts, through analysis of an updated regional radiocarbon database for northern Alaska (Fig. 1).

Archaeologists studying the emergence of maritime adaptations in hunter-gatherers consider the interplay of several variables, including subsistence abundance and distribution (Ames, 1994, 2005; Butler and Campbell, 2004; Kennett and Kennett, 2000; Lepofsky et al., 2005; Matson, 1992), population size and density (Binford, 2001; Cohen, 1977; Kelly, 1995), territoriality (Kelly 1995), and environmental variability (Cashdan, 1989; Yesner, 1998). In the Arctic, the emergence and expansion of maritime adaptations during the mid-late Holocene is associated with increased population density and increased sedentism (Mason, 1998). The evolution of Arctic maritime traditions parallels similar developments in other maritime hunter-gatherer groups (Ames, 1994; Arnold, 1996; Erlandson, 2001; Fitzhugh, 2003; Mason, 1998; Yesner, 1998). Yet, despite decades of research on the origins of maritime adaptations in the north (e.g. Ackerman, 1998; Collins, 1940; Dumond, 1998; Ford, 1959; Larsen and Rainey, 1948), many questions remain about precisely when, how, and why Arctic peoples developed the complex social organization and technological expertise centered on marine mammal hunting that was well established by the 1800s in coastal northern Alaska (e.g. Burch, 1998, 2005, 2006; Ray, 1975). In this paper we focus on one variable in the development and spread of

⁎

Corresponding author. E-mail address: [email protected] (S.L. Anderson).

https://doi.org/10.1016/j.jaa.2019.101100 Received 21 November 2018; Received in revised form 28 August 2019 0278-4165/ © 2019 Elsevier Inc. All rights reserved.

2. Background 2.1. The development and spread of Arctic maritime adaptations Early research on Arctic maritime traditions was directed at establishing Arctic culture history and tracing the origins of North American Arctic peoples (e.g. Collins, 1940, Ford, 1959). Subsequent research focused on the role of environmental change and shifting resource distribution (Mason, 1998; Mason and Barber, 2003; Mason and Gerlach, 1995a, 1995b; Sheehan, 1985, 1997) driving the emergence and spread of maritime adaptations across the North American Arctic. The timing and nature of major mid-late Holocene environmental shifts may have caused changing resource abundance and distribution; subsequent concentration of people around good marine hunting grounds provided opportunities for both cooperation (Grier, 1999; Hill, 2011; Savelle, 2002) and competition (Mason, 1998). Cooperation or competition around hunting, processing, and distribution of surplus generated by procurement of large marine mammals (i.e. walrus and whales) may have driven the emergence of Arctic social complexity. Issues of

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S.L. Anderson, et al.

Fig. 1. Map of project area showing key locations mentioned in the text and the locations of sites included in radiocarbon analysis.

population size, population packing (or density), and demographic shifts, are key variables in recent models for the development and spread of Arctic maritime traditions (e.g. Mason, 1998). Continued population growth due to surplus resources could have led to denser packing of people in resource-rich locations along the coasts; increased competition and even conflict could have pushed some people into less desirable locations along interior river and tundra regions and, perhaps, across the North American Arctic (Gerlach and Mason, 1992; Mason, 1998, 2009a, 2009b; Mason and Barber, 2003; Mason and Gerlach, 1995a, 1995b). This idea is supported by Tremayne and Winterhalder’s (2017) recent analysis of the mid-late Holocene population of Alaska’s coasts by people associated with the Arctic Small Tool Tradition (ASTt); their results indicate that when ASTt people populated coastal Alaska, they first settled resource-rich coastal locations (i.e. places where large bodied marine and terrestrial mammals were available) including the northern and southern Bering Sea and Chukchi Sea coasts. As coastal population grew and competition for resources increased, people moved to less desirable habitats including interior tundra and boreal regions. Building on this work, we focus our analysis on northern Alaska, with the goal of further detailing regional demographic trends and understanding what happened after the initial introduction of maritime adaptations with the arrival of ASTt peoples approximately 4500 years ago. Unraveling the timeline for the development and spread of Arctic maritime adaptations is difficult, due to research history and to the complexities of precontact history at the crossroads of two continents. It is possible that a marine focus developed earlier than currently understood as Bering Strait sites dating to before 5500 years ago are now submerged by early Holocene sea level rise (Mason and Jordan, 2002). Recent research established that people were using marine resources soon after this, as early as 4500 years ago (Buonasera et al., 2015; Tremayne, 2015). Over time, seasonal use of the coast increased until some people lived the majority of the year on the coast, perhaps as early as 2000 years ago and certainly by 1500 years ago. Increasingly

specialized marine technologies emerged, examples include the development of toggling harpoon and float technology, critical for the successful capture of marine mammals (e.g. Ackerman, 1998; Bockstoce, 1973). By the 18th century ethnographic period, whaling and walrus hunting were widespread across northern Alaska. People lived in small family groups, in villages that included extended family; leadership was through influence, often achieved through success at prestigious subsistence activities, including whaling (Burch, 1998). We organized our analysis into five key periods of change in the development and spread of Arctic maritime adaptations. These are: Period I) seasonal use of coastal areas beginning around 4500 cal BP (Arctic Small Tool Tradition (ASTt)/Denbigh Phase); Period II) increased coastal sedentism beginning approximately 2800 cal BP (Choris, Norton/Near Ipiutak Phases); Period III) the initial development of whaling between about 2000 and 1000 cal BP by Bering Strait (Old Bering Sea, Okvik, and Birnirk Phases) and mainland Alaska (Ipiutak and Birnirk Phases) people; Period IV) emergence and spread of socially stratified and highly marine focused groups around 1000 cal BP (Punuk and Thule Phases); Period V) a possible shift in subsistence to less reliance on marine resources beginning around 550 cal BP (Late Thule Phase) (Table 1). While the temporal framework described here is generally accepted, archaeologists are unclear on the specific timing of major shifts in maritime adaptations; evolutionary relationships and level of interaction among western Alaskan cultures are also poorly understood (Dumond and Collins, 2000; Harritt, 2004). We set aside these evolutionary problems and turn our attention here to examining mid-late Holocene demographic trends in northern Alaska. 2.2. Previous western Alaskan demographic research Prior research focused primarily on addressing chronological issues that continue to hinder efforts to understand the evolution and spread of Arctic maritime traditions in the western Arctic. There have been several 2

2500–2000

Norton (Near Ipiutak in Northwest Alaska)

3

b

a

550 to contact era

950 to 550

Thule

Late Thule/Kotzebue (Arctic Woodland in interior areas of Northwest Alaska)

1150–550

1350–750

Birnirk

Punuk

1750–1150

1750–1550b

Okvik

Ipiutak

2150–750

a

Old Bering Sea

2400/2300–1000 cal B.P. in western and southwest Alaska. 2550–2350 cal B.P. in Chukotka.

V: Shift to fishing focus

IV: Emergence and spread of complex and highly marine focused groups

III: Increased marine focus

2750–2450

Choris

II: Increased coastal occupation and use of coastal resources

4500–2750

Denbigh

I: Initial coastal occupation

Approximate Established Age (cal B.P.)

Cultures

Time Period

Table 1 Key periods in the development of Arctic maritime traditions.

Coastal areas of Northwest Alaska

Western shore of Chukchi Sea and Bering Strait Islands, limited distribution in mainland northwest Alaska Bering Strait to Greenland

Western shore of Chukchi Sea and Bering Strait Islands Western shore of Chukchi Sea and Bering Strait Islands Norton Sound to Point Barrow, interior of Northwest Alaska and Brooks Range Eastern and western shores of Chukchi sea, Bering Strait Islands

Kotzebue Sound, Brooks Range, northern Yukon Territory Southern Alaska to western Canada.

Kotzebue Sound, Brooks Range

Geographic Range

Seal, walrus, whale, caribou, fishing (possibly with greater intensity than before)

Seal, walrus, whale, caribou, fishing

Seal, walrus, whale

Seal, walrus, caribou, possibly small whale (beluga) Seal, walrus, whale, caribou (north Alaska)

Seal, walrus, whale

Seal, walrus, whale

Seal, possibly small whale (beluga), caribou, possibly fish Seal, possibly whale, caribou, fish

Caribou, marine mammal

Subsistence Base

Giddings (1952a) and Schaaf (1988)

Mason (2009b)

Gerlach and Mason (1992) and Mason (2009b)

Mason (2009b)

Mason (2009b)

Mason (2009b)

Mason (2009b)

Dumond and Henry (2000) and Mason (2009b)

Mason (2009b)

Buonasera et al. (2015) and Tremayne (2015)

References

S.L. Anderson, et al.

Journal of Anthropological Archaeology 56 (2019) 101100

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important efforts to explicate and address these issues through the compilation and interpretation of northern Alaskan radiocarbon data. Gal (1982) compiled radiocarbon dates from across the region, primarily for the purpose of facilitating future research but also to make the point that context and association of dates is an important consideration when relying on radiocarbon determinations. Gerlach and Mason (1992) further synthesized dates from northern Alaska, focusing on the last 2000 years; their primary interest was in clarifying the evolutionary relationships and potential interactions between different groups of people by refining the chronology of cultural historical phases. Mason (1998) explored the relationship between population fluctuations, migration, and interaction in the evolution of western Arctic maritime cultures. Building on these efforts, Blumer (2002) conducted an analysis of radiocarbon data and associated diagnostic artifact styles from St. Lawrence Island, with the goal of refining the evolutionary sequence for late Holocene St. Lawrence Island occupations. This analysis identified a non-linear cultural succession of Bering Strait cultures during the last 2000 years and highlights problems, many of which have not yet been resolved, with the regional radiocarbon dataset (Blumer, 2002:94–95). Tremayne and Winterhalder (2017) refine the chronology for ASTt dispersal and settlement across Alaska through Bayesian analysis of an Alaska-wide radiocarbon database. Most recently, Tremayne and Brown (2017) examine the role of population change and cultural transition in the ASTt-Norton transition in Alaska around 3500 years ago; they note similar trends to ones we identify between 6000 and 1000 cal BP. In addition, their regional analysis suggests different population patterns across Alaska and different evolutionary trajectories in the development of maritime adaptations. Others (Blumer, 2002; Dumond, 1998; Dumond and Griffin, 2002; Gerlach and Mason, 1992) have already outlined many of the problems that continue to plague radiocarbon dating and interpretation of radiocarbon data in the Arctic. For example, some dates were obtained early in the development of radiocarbon dating methods, when sample sizes were large and equipment could not sample a high percentage of 14 C isotopes, resulting in higher levels of uncertainty in the dates (e.g. Arnold and Libby, 1951; Gfeller et al., 1961; Ralph and Ackerman, 1961; Rainey and Ralph, 1959). Calibration procedures have also changed considerably in the past 20 years and older dates require recalibration. Until recently, there were relatively few dates available for the region (Gerlach and Mason, 1992; Mason, 1998). Fortunately, numerous projects were undertaken in the years since Gerlach and Mason’s (1992) radiocarbon synthesis; dates resulting from these efforts remain largely unpublished or difficult to access in the regional gray literature. We cannot resolve some problems with existing radiocarbon dates (e.g. lack of details on sample context or material dated). However, we can synthesize new dates and data from northern Alaska in an effort to understand regional population trends and clarify the timing of key demographic shifts in the development of maritime hunting and fishing across the region. To that end we compiled and analyzed a comprehensive database of northern Alaskan radiocarbon dates and modeled demographic patterns for the last 6000 years.

approximately 1130 and 830 cal BP (T2) (Mason and Jordan, 1993; Mason et al., 1995; Mason and Ludwig, 1990; see also Anderson et al., in press). Processes of beach ridge erosion and deposition are thought to be caused by storm induced shifts in depositional regimes across the Chukchi Sea region (Mason and Jordan, 1993). The T1 erosional unconformity at Cape Krusenstern is correlated in time with unconformities at Cape Espenberg, Choris, and Wales; the T2 event is correlated with unconformities at Espenberg and Choris. Mason and Jordan (1993) also tentatively correlate narrow swales at Point Hope to frequent storms dating to 950–850 cal BP and 350–250 cal BP; they note a correlation between high ridges at Point Barrow to T1 and T2 events in Kotzebue Sound, and a major shift in coastal depositional processes after 850 cal BP (Mason and Jordan, 1993:64). Additional research at Point Barrow, Kotzebue, Deering, and Cape Espenberg (Mason et al., in press) refined local sequences by studying ridge stratigraphy and dating driftwood and other material from storm deposits. The authors identify two major storm episodes that they estimate took place in the early 5th to late 6th centuries AD (approx. 1550–1350 cal BP) (Mason et al., in press: 15) at Kotzebue and Barrow; a similar pattern and timing of storminess was identified at Deering and Cape Espenberg. It is not clear whether this work refined the dating for the second erosion episode identified previously by Mason and Jordan (1993) or if these are two, additional, episodes of erosion that overlap those identified by the previous study. Other proxies also indicate at least one period of erosion during the Little Ice Age, which may have begun as early as 550 cal BP in Northwest Alaska, and certainly by 300 cal BP (Bird et al., 2009; Calkin et al., 1998). Anderson et al. (in press) obtained and synthesized new archaeological and geological dates (on wood and driftwood collected from ridges/swales) from Krusenstern, refining the chronology of the local coastal environmental record and further correlating coastal change with the archaeology. Widespread erosion could potentially have several implications for the preservation and interpretation of radiocarbon data. First, erosional events, particularly the large-scale T1 and T2 erosional events, may have a dampening effect on our data from these time periods, due to potential loss of the archaeological record that dates to, and prior to, erosional events. Second, people may have avoided areas of the coast when and where active erosion was taking place; the same is likely possible for areas of active progradation. Last, while we know the geomorphology and archaeology of Cape Espenberg and Cape Krusenstern well, many other areas of the coast are not as closely studied. The paleoecology and storm history of the interior riverine regions is not well established. Prior research does suggest that paleoenvironmental conditions were variable across the region during the mid-late Holocene; when conditions were appealing on the coast, they may have been more challenging in the interior and vice versa (Minc and Smith, 1989). These problems cannot be easily resolved at this time. The first problem, the potential dampening effect of large scale erosion, cannot be resolved and must be taken into account in our interpretation of radiocarbon data. The second problem also cannot be resolved, since we are missing the archaeological record of eroded areas. At Cape Krusenstern, we (Anderson and Freeburg, 2013, 2014; also Anderson et al., in press) hypothesize that people may have avoided areas of the landform dating to approximately 2800–1000 cal BP because the local environment was wet, marshy, and low-lying at that time. While seasonal or short-term subsistence activities could have taken place in such environments, it is unlikely these marshy areas were inhabited for longs periods in the past as indicated by the dearth of archaeological sites (see Anderson et al., in press: Fig. 7, Unit II). There are nearby occupations dating to this same time period at Cape Krusenstern; we infer from this that while people were not spending much time in the less appealing environments, they were seasonally occupying other adjacent areas of the beach ridge complex. Overall, the

2.3. Potential taphonomic effects on demographic data: potential for widespread coastal and riverine erosion While there are various forces acting on the archaeological record (e.g. freezing, thawing, soil acidity, etc.), widespread episodes of erosion have the greatest potential to act on site preservation, and preservation of radiocarbon data, in Northwest Alaska. Study of beach ridge geomorphology in the Kotzebue Sound region has identified two periods of significant and widespread coastal erosion, between approximately 3530 cal BP and 1620 cal BP1 (T1) and again between 1 Published dates are in radiocarbon years B.P. We calibrated the dates using OxCal v4.3 (Bronk Ramsey, 2009a, 2009b) so as to make them comparable to other information presented in the text.

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site complex is occupied nearly continuously, even during periods of erosion. For example, our results support Mason and Jordan’s (1993) conclusion that the T1 erosional event (dated to approximately 3350–2800 cal BP) eroded older ridges. But, at the same time, people were occupying newly forming ridges (see Anderson et al., in press: Fig. 7, Unit I compared to Units II and III. Units II and III are separated by the T1 unconformity at the eastern area of the beach ridge complex). Again, we cannot resolve this issue of potential avoidance of eroding areas and must take it into account in interpreting our results. Lack of paleoecological and erosion data from the interior of the region also cannot be solved at this time; perhaps forthcoming research will better establish the record of interior erosion and mid-late Holocene environmental change. We do take steps in our analysis to further understand the potential role of erosion on our dataset by studying the distribution of sites on the landscape and by considering known patterns of erosion in our interpretation of results.

improves the accuracy of subsequent analysis. We excluded dates that were missing information (e.g. error terms or conventional ages), samples that yielded modern ages, and dates that were found to be problematic due to lab error in previous studies (Reuther and Gerlach, 2005). While the potential for obtaining older ages on driftwood is well known (Arundale, 1981; Friesen and Arnold, 2008; Giddings 1952b) we could not systematically exclude driftwood samples from the analysis, without dramatically reducing the sample size of the dataset, because information about wood species or details of sample context are often not reported. Additionally, because many dates were either run prior to standard adjustment of isotopic fractionation, authors like Dumond (1998) and Blumer (2002) created slightly different values for estimating 13C/12C values for correcting measured ages from the Bering Strait region. (see Dumond, 2002, 2006, 2008 for discussion); when available, we used Blumer’s (2002) corrected measured dates (i.e. estimated conventional ages) (See Supplemental Table 1). Summed probability distributions were constructed using the remaining 935 dates (687 dates from coastal sites and 248 from interior sites) from 254 sites (175 coastal and 80 interior sites) (Supplemental Table 2). In the future, more consistent reporting of sample details and results of radiocarbon dating would yield more information for analysis of radiocarbon data. More comprehensive dating of large sites that are most likely multi-component sites is also important; past practice of minimal dating is changing as researchers working in the region build radiocarbon databases for large sites or site complexes and as more work is done in Arctic Alaska (e.g. Anderson and Freeburg, 2013; Norman et al., 2017; Shirar, 2011).

3. Methods 3.1. Radiocarbon database compilation Site data were collected from the Alaska Heritage Resource Survey (AHRS) using the web based AHRS mapping program. Additional data were collected from the Canadian Archaeological Radiocarbon Database (CARD), recent publications, reports, theses, dissertations, and previously unreported dates from recent projects. The search boundary extended north to Cape Lisburne, west up the Kobuk River, and south to Shaktoolik (see Fig. 1). While our interest is in maritime traditions, the movement of coastally adapted people into the interior rivers of the region is well documented (e.g. Giddings 1952a); these movements may be associated with coastal demographic pressure (see Tremayne and Winterhalder, 2017) and are of interest with respect to our larger question about the evolution and expansion of Arctic maritime traditions. Therefore, we included dates from the interior of the study area in our analysis. We also incorporated published dates from the Chukotka Coast; these are outside of our core project area but pertinent to understanding regional demographic trends. We reviewed supporting publications and documents for additional detail related to radiocarbon sample characteristics and contexts. A total of 4515 AHRS sites were identified within the study area; this includes precontact, contact, and recent sites. Prior to conducting analysis, we excluded dates obtained on geologic deposits or from geologic contexts, and dates obtained on unknown sample materials (Table 2) (Supplemental Table 1). We also excluded dates on marine materials, as well as human remains, polar bear bone, dog bone, and ceramic encrustations due to the potential for high consumption of marine foods. The marine calibration for this region is not yet well established (although see Dumond and Griffin, 2002); excluding any dates with possible marine contribution thus

3.2. Radiocarbon analysis We use radiocarbon date densities in the form of SPDs as a proxy for past population fluctuations in northern Alaska between 6000 cal BP and the modern era. We incorporate dates before and after our period of interest (4500 cal BP to contact, approximately 250 cal BP) to eliminate the possibility of an edge effect in our analysis. This method relies on the basic assumption that there is a monotonic relationship between relative population sizes and the amount of dateable deposits, i.e., more people leave more dateable material behind (Rick, 1987), thus larger numbers of radiocarbon dates from a given time period indicate relatively larger populations. At the risk of oversimplification, the regional SPDs used throughout this paper can be loosely understood as plotting relative changes in the number of dated sites through time. Aggregated radiocarbon ages have been used as a proxy for past population for some time (e.g. Chatters, 1995; Rick, 1987; Weninger, 1986) and the method and applications have evolved considerably in recent years (e.g. Brown, 2015, 2017; Crema et al., 2016; Edinborough et al., 2017; Kelly et al., 2013; Palmisano et al., 2017). However, there continues to be debate about methods of SPD analysis (e.g. Contreras and Meadows, 2014; Timpson et al., 2015; see also Downey et al., 2014; Hinz et al., 2012, Timpson et al., 2014, and Woodbridge et al., 2014 for a more detailed discussion of the method and its validity) and the strength of resulting datasets, particularly when the goal is reconstructing past demography (e.g. Collard et al., 2010; Surovell and Brantingham, 2007; Williams, 2012). However, straightforward summing of calibrated radiocarbon date distributions can be very useful for projects such as ours, where the goal is to study general population and settlement pattern trends rather than developing robust estimates of past demography (sensu Barton et al., 2018; Lechterbeck et al., 2014). A model is only as good as data used to construct the model (Bronk Ramsey, 2009a) and the northern Alaskan radiocarbon data are problematic in some cases. For example, some dates obtained when the application of radiocarbon dating to archaeology was new were analyzed using solid carbon methods that have proven problematic. Had our demographic analysis been focused on a narrow spatial or temporal scale (e.g. demographic history of a small cluster of sites) the quality of radiocarbon data would have a profound impact on our results.

Table 2 Summary of samples excluded from analysis. Reason for Exclusion

Number of Samples

Unknown sample material Marine materials Human remains Lab error or contamination Geologic context or materials (Humic acids or sediments) Missing conventional age Polar bear bone Dog bone Ceramic encrustation Modern age

108 67 24 18 14 8 3 3 5 7

Total Dates Excluded

257

5

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However, the strength of using large datasets covering a large geographic and temporal span is that the noise generated from any single highly imprecise or inaccurate radiocarbon date is dwarfed by the effects of all the other dates in the dataset. Therefore, outside of regionwide systematic biases in the radiocarbon record the overall patterns visible in the SPD are too robust to be swayed by few problematic dates; further chronometric hygiene is not necessary. Following suggestions from various authors (e.g. Edinborough et al., 2017; Palmisano et al., 2017; Timpson et al., 2014 and many others) we normalized the representation of sites within the SPD by binning dates within individual sites. This was done by starting from the youngest conventional age at a site and combining all mean conventional ages that were within 200 years. After which a new bin was started and the process repeated. Bins were created using the R_Combine function in OxCal version 4.2.3 (Bronk Ramsey, 2009a, 2009b), which creates a weighted average of combined dates prior to calibration, giving us a total of 457 bins from an original 935 dates. Once finished, these bins were then used to construct the regional SPDs, which was also done using OxCal version 4.2.3 (Bronk Ramsey, 2009a). We admit that this method does have the effect of slightly distorting data by artificially eliminating variability within the dates’ error ranges. However, this method is necessary in order to offset the effects of a few, well-sampled sites disproportionately driving the shape of the overall SPD. Moreover, as shown in Figs. 3 and 4, SPDs created from individual sites using unbinned data demonstrate that patterns observed between ~1500 and 500 calBP are unlikely an artifact of the binning procedure. In order to smooth out spurious oscillations in the SPD caused by the shape of the calibration curve we then applied a 200-year moving average (Shennan et al., 2013; Williams, 2012). We used a generalized linear model (GLM) with a Poisson distribution and an exponential function to determine if growth in the SPD was statistically significant. Instead of using a linear regression model to test this relationship, we used a GLM as this model does not assume a continuous and linear relationship among variables (Pierce and Shafer, 1986). The use of the Poisson distribution was suggested because of the nature of radiocarbon data (see Shennan et al., 2013). Lastly, we chose an exponential curve because it weights the effects of data which appear later in the time series less than it does the effects of data from early on. This creates a more conservative model when looking for population growth and was chosen to mitigate (though not fully address) the effect of sites in later time periods being possibly overrepresented. Moving average and GLM procedure were completed using PAST v3.12 (Hammer et al., 2001). It is important to note that we do not apply the simulation and significance testing procedures, or a taphonomic correction as described in Shennan et al. (2013), Timpson et al. (2014) or most recently in Edinborough et al. (2017). We simply test whether the trend in our data statistically differs from the null hypothesis of a zero-trend using a GLM. This test allows us to assess if there was an overall signal of growth or decline in the data. We then take a more qualitative assessment of broad patterning within the SPD following comparative methods (e.g. Berger and Guilaine, 2009; Flohr et al., 2016; Goldberg et al., 2016; Lechterbeck et al., 2014; Wiener, 2014). The reasons for this decision are twofold; (1) we are only interested in the broad timing of population growth, not necessarily whether any individual period within the SPD shows a statistically significant deviation from the general pattern. In other words, we were interested in measuring whether growth was statistically significant between the period of ~4500–500 cal BP, not whether there were specific periods within this time range showed statistically significant oscillations. We do consider the effects of the calibration curve on the SPD by first comparing the overall SPD to a simulated set of uniformly distributed dates, illustrating areas where the calibration curve may be distorting the signal. Secondly, we use the Kernel Density Estimation Model (KDE) function in OxCal (see Bronk Ramsey (2017) and McLaughlin (2018) for detailed discussion) on the same dataset used to produce the SPD. As demonstrated in Bronk Ramsey (2017) and McLaughlin (2018), this KDE

procedure is useful for both smoothing out noise caused by sampling biases and the calibration curve, but also in modeling the actual uncertainty associated with these sources of error (Bronk Ramsey, 2017; McLaughlin, 2018). In addition, Northwest Alaskan research has historically been highly focused on certain times or culture phases (e.g. Thule) introducing an unknown bias for these periods. And as Porčić et al. (2016) have demonstrated, these biases can be profound and are not mitigated by the more advanced approaches discussed above; and (2) taphonomic forces such as erosion, sea-level changes are so poorly known for the region that we have no way to construct a model that systematically accounts for their effects. Thus, instead of introducing further distortion into the data we felt it more productive to leave it uncorrected until sampling and taphonomy forces unique to the coastal setting and Arctic landscapes within our study region are understood more fully. We do, however, use several other sources of information available to us to assess the potential role of sampling bias and taphonomy in shaping our regional SPD. Specifically, we consider the relationship between site density and site location on probable erosional landforms, and we expand on a prior analysis of site components (Anderson, 2011) to measure broad demographic trends using another method (i.e. number of phase designated sites). As the preceding discussion illustrates, the relationship between SPDs and demography is incredibly complicated and fraught with potential for bias and distortion. As such, subsequent studies focusing on better understanding of differential taphonomic biases within the study region, redating sites/site-components to improve precision/quality of 14C dataset, and more fine-grained exploration of how areas or sites within the study region compare to the overall trend, would undoubtedly improve the accuracy, precision and interpretation of SPDs. However, each of these require their own dedicated research project and thus are far beyond the scope of this paper. Lastly, we want to remind the reader that while the methods described above are helpful for better understanding and analyzing trends in the existing empirical record of radiocarbon dates sites, they cannot ‘fix’ the relationship between number of radiocarbon dated sites and population. As such, future work should also focus on better understanding this relationship, not just enhancing the empirical record. Despite these issues (which plague most archaeological analyses) we believe that the analysis presented here is a useful preliminary framework for the general demographic history of the region. 3.3. Accounting for possible taphonomic effects Preservation certainly reduces the number of possible samples and therefore dates (Surovell et al., 2009) but the sites included in this analysis are relatively recent, dating to the last 6000 years. Tremayne (2015; Tremayne and Brown, 2017) demonstrated that a correction for taphonomic bias had little effect on the shape of SPDs that resulted from analysis of an Alaska-wide radiocarbon dataset. Large scale issues of time-dependent preservation are unlikely to be driving the patterns observed here. Coastal and riverine erosion are the most plausible region-wide taphonomic processes that could affect radiocarbon sample preservation. To further assess the potential impact of widespread erosion on population, we examined the distribution of sites in relationship to possible erosional landforms. Fine-grained geologic data for this region is limited so we focused on the landforms most likely to erode: coasts, river shores, and lake shores. We created buffers around the existing coastline, rivers, and lake in ESRI ArcGIS 10.2. When sites were located on overlapping landforms, e.g. river mouths and coastlines, sites were assigned to the coastal landform rather than river landform. When sites were located where rivers and lakes overlap, they were assigned to the river landform (Table 3). We further explored the potential impact of widespread erosion on site preservation by analyzing radiocarbon data for individual sites on 6

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complexes in the study area. Examination of spatial and temporal bias were conducted graphically, through study of date distributions in a Geographic Information System (GIS). We also conducted site component analysis for a subset of our study area, using site information from National Park Service lands (n = 415). Each component of multi-component sites was counted and data were normalized to account for differential duration of various cultural phases. The counts corrected for differences in duration of cultural period or phase are a better measure for comparing number of sites across archaeological cultural units. There are problems inherent in this analysis as well (e.g. ambiguity in use of cultural historic designators, probable overlap between Arctic Woodland, Kotzebue, Late Thule, and Historic Iñupiat occupations, etc.). Nevertheless, the site component information is an alternate source of evidence about general demographic patterns. Large radiocarbon datasets from three sites or site complexes, Cape Krusenstern (n = 181), Cape Espenberg (n = 39), and Onion Portage (n = 39), could be driving the patterning observed in our SPD analysis. To assess this possibility, we removed dates from these three sites from the database and ran the SPD analysis again; we then compared the resulting SPD curves for these three sites and the region as a whole without the sites.

Table 3 Relationship between number of known sites and erosional landforms. Landform

Period I

Period II

Period III

Period IV

Period V

Total

Coast River Lake shore All other locations

12 4 11 13

15 0 11 10

36 1 18 26

46 5 8 22

47 13 17 28

156 23 65 99

Total

40

36

81

81

105

343

the coast and the interior; this analysis included all of the coastal sites for which there are 10 or more radiocarbon dates, and the 10 best dated interior sites. Comparison of individual sites was also done using OxCal v4.3 (Bronk Ramsey, 2009a) (Figs. 3 and 4). Dates for the individual site SPDs have not been binned or smoothed with a moving average. It is important to note that while the patterning of the SPDs is comparable among the sites, the actual heights of the curves are not. Furthermore, we would like to emphasize that we do not view these individual site SPDs as reflecting population fluctuations. They are intended to be illustrative for the purposes of looking at site occupation history. 3.4. Sampling bias Investigator bias and biases in archaeological sampling could also be contributing to the patterns observed in our radiocarbon analysis. Potential sources of bias include the distribution of different types of public lands, the increased visibility of more recent sites (larger sites, better preserved, continuity with contemporary people) and sites in tundra versus forest areas, the accessibility of certain regions versus others (e.g. coast is more easily accessed for fieldwork than highlands), and a history of archaeological research focused on maritime adaptations that leads investigators to focus on coastal regions. It is likely that archaeologists are not radiocarbon dating late pre-contact and contact era sites, relying instead on relative ages from historic artifacts. Lastly, only a small percentage of this large and remote region (approximately 43 million hectares) has been surveyed (e.g. Schaaf, 1988). We attempt to further understand the possible role of bias in the northern Alaska dataset through: (1) an examination of the spatial distribution of sites; (2) consideration of investigator bias in temporal patterns in our radiocarbon data set; (3) an analysis of demographic trends using a an independent source of data, site components; and (4) examination of the possible role of large radiocarbon datasets from three sites or site

4. Results and discussion 4.1. Radiocarbon analysis Results of the analysis indicate several interesting patterns in the radiocarbon dataset that align with episodes of cultural change identified by prior research (e.g., Mason, 2009a). First, results of the GLM analysis demonstrates that there is an overall signal of growth in the SPD and that this trend is statistically significant (P < .001), meaning that we can reject the null hypothesis of no change in the data. Visual inspection of the SPD suggests that this growth was particularly high during our analytic Period IV, between about 1000 and 600 cal BP (see Fig. 2). Second, the SPD indicates several periods of potential population decline, the most pronounced of which are between about 3700 and 3125 cal BP (during Period I), slightly before 1000 cal BP (end of Period III), and beginning around 600 cal BP (end of Period IV). Before considering these trends to be a real reflection of past population shifts, several alternative explanations for the observed patterns must be considered. Possible alternative explanations include taphonomic

Fig. 2. Overall northern Alaskan demographic trends created by analysis of regional radiocarbon database in OxCal version 4.2.3. 7

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effects, patterns in the calibration curve itself, and archaeological sampling biases.

various patterns, such as the decline at 1000 cal BP may be less intense than observed here. Or, this decline may be shifted temporally up to 200 years; it may have occurred closer to 800 cal BP. Moreover, this analysis indicates that the apparent post 400–500 cal BP demographic decline is likely being exaggerated by issues with old wood. However, the post 200–300 cal BP spike observed in our offset models (Supplemental Data 1) is also a product of the highly problematic nature of the calibration curve during this time, which tends to force calibrated ranges of radiocarbon dates from the past 300 years to either before CE 1750 or after CE 1850 (Ames and Brown, 2018: Fig. 9). The issue is currently unresolvable at the scale of our analysis. However, as shown in Ames and Brown (2018), future studies focusing on individual sites and using new dates from secure contexts and on short lived samples may help unpack demographic issues during this time period by using Bayesian chronological models in combination with artifactual and site history data.

4.2. Possible alternate explanations for apparent demographic patterns 4.2.1. GIS erosional landform analysis results We found that more than half of our sites are located on a probable erosional landform (Table 3). There is no way, of course, to know how many sites have actually eroded away, or to more carefully parse the geologic data any further, for example, to consider site location with respect to prograding versus eroding coastlines; geologic spatial data of this resolution do not yet exist for this region. But, our analysis suggests that many sites are preserved despite their location on landforms that could erode; erosion alone is not driving the patterns in our SPD results. 4.2.2. OxCal individual site analysis results If region-wide erosion was driving the patterns observed in our radiocarbon data, we would not expect the majority of sites to date through periods of erosion. Instead, we would expect a significant and dramatic drop in radiocarbon dates at individual sites as part of the record was lost to erosion. We considered the occupation of individual sites during the post-1000 cal BP and post-600 cal BP regional declines identified by SPD analysis; this analysis included all of the coastal sites for which there are 10 or more radiocarbon dates, and the 10 best dated interior sites. Out of 18 coastal sites, only six were occupied through the regional decline in dates beginning slightly before 1000 cal BP (Fig. 3); out of 10 interior sites, four were occupied through the 1000 cal BP regional decline. Some sites were not significantly occupied until after the 1000 cal BP decline (e.g. 49XPH45, 49XPH311, 49BEN52) (Fig. 4). Seven coastal sites and four interior sites were occupied during the regional 600 cal BP population decline; some of these sites saw population increases during this period. This site level analysis of occupation patterns indicates that the impact of region-wide erosion on site preservation is complex. But, erosion alone does not account for the shape of the SPD. Widespread coastal erosion (T1-event) could explain areas of low probability in our radiocarbon data between about 3800 and 2750 cal BP, and potentially episodically or unevenly after that up to the second major erosion event (T2) dated to approximately 1000 cal B.P. The T2 erosional event is roughly correlated with the decrease in population we identify beginning slightly before 1000 cal BP. The third period of demographic decline identified by our analysis, after about 600 cal BP, could be related to Little Ice Age erosive events. It is possible that the radiocarbon probability distribution for sites occupied across this period of storminess/erosion indicate fluctuations in coastal erosion (e.g. Fig. 3. Cape Krusenstern/NOA-2, NOB-2, XHP-8, XSL-1; Fig. 4. PSM-49, PSM-50, XHP-311). Or, we could be seeing a pattern of people occupying some areas more than others due to erosion/coastal storminess that made some patterns of the coast less appealing for habitation. As more archaeological dates emerge (many are forthcoming from an on-going project at Cape Espenberg; Alix et al., 2018), we will be able to explore sub-regional patterns in more detail than we currently can. While we cannot, as of yet, unpack all of these issues, this paper sets the stage for exploring sub-regional patterns of demography in relationship to erosion and other environmental shifts.

4.2.4. Results of calibration effect modeling As mentioned above, in order to assess potential distortions caused by the calibration curve we compared the overall SPD to a series of uniformly distributed data that was created by simulating uncalibrated radiocarbon dates for each for a series of calendar dates that were evenly spaced every 25 years. Each uncalibrated simulated date was given a standard error of 66 years to approximate the average of the dataset. This was then superimposed upon both the SPD modified with a 200-year moving average and without (Fig. 5; see Fig. S-3 in Supplemental Data). Six additional simulations were run and are shown in Fig. S-4 (see Supplemental Data). Visual inspection of the simulated SPDs shows that overall there seems to be little correlation between peaks and valleys within the real dataset and the simulated one. However, numerous simulation iterations (see Supplemental Data) showed dips similar to the real SPD a little prior to ~1000 cal BP indicating that the drop in the real SPD may be being driven partially by the calibration curve. It is important to note here that these simulations are purely illustrative, used only as a way to graphically demonstrate potential distortion caused by the calibration curve. As the spacing (e.g. 25, 35, 50 years etc.) between dates, their associated error and randomness of the simulation process can all have significant effects on how the shape of any SPD is effected by the calibration curve, simulations like these cannot be used by themselves, as a way to counteract or evaluate distortion from the calibration curve. As illustrated in the simulated examples, the observed dip at ~1000 cal BP may be caused or at least exacerbated by a corresponding fluctuation in the calibration curve. Therefore, we ran additional analyses using the KDE model function in OxCal with the same data used to create the overall SPD (Fig. 6). This function has shown to be a reliable way to counteract noise introduced by the calibration curve and sampling biases within the dataset when we have little a priori quantifiable knowledge of said distribution (see Bronk Ramsey, 2017 for detailed discussion). Unlike the straightforward summing of calibrated radiocarbon date probability densities, the KDE weights each point by the data surrounding it. This allows the KDE to remove noise (i.e. peaks and troughs) caused by the calibration curve and spurious oscillations caused by small sampling errors, as spikes in the data are weighed down by the data surrounding them. Thus, to create large peaks and valleys you need substantial amounts of data in a relatively small interval. Critically, the KDE function in OxCal uses a Markov Chain Monte Carlo process that resamples the dataset thousands of times, allowing the KDE to create a robust estimation of the underlying distribution as well as error associated with this estimate (see Bronk Ramsey, 2017). OxCal’s default kernel and bandwidth settings were used for this analysis, as they are recommended as most suitable in situations where little is known about the true underlying distribution of events (Bronk Ramsey, 2017). We chose to use the KDE method to estimate the underlying shape of the data as opposed to other methods which seek to identify statistically (not substantively) significant periodicities in SPDs

4.2.3. Results of old wood analysis Further complicating this relationship is that many dates from these sites are likely to be on ‘old-wood’. Thus, future work, either in the form of more detailed site-specific analyses or through re-dating of sites using short-lived species is needed to better clarify site occupation histories and their relationship to erosional episodes and larger regional trends. While not a solution to this issue, we provide analyses demonstrating potential effects that old-wood could be having on the patterning observed in the regional SPD as well as at well dated sites such as Cape Espenberg and Krusenstern (Supplemental Data 1). Overall, as expected, modeling of potential old-wood effects demonstrates that 8

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Fig. 3. Comparison of occupation history at all coastal sites with ten or more radiocarbon dates using OxCal 4.2.3. ‘+’ symbols denote the median of an individual radiocarbon date. Note the occupation history of each sites around 1000 cal BP, when we see a decline in radiocarbon dates in the regional SPD.

(e.g. Edinborough et al., 2017), because these methods are not usually appropriate in contexts where specific hypotheses are not being tested, or where we lack the ability to sufficiently quantify or understand underlying processes that filtered the dataset. Thus, for more exploratory cases (such as ours), where significant issues of filtering (i.e. sampling biases and taphonomic forces) are still poorly understood, we felt it more productive to try and describe underlying patterns in the data rather than test specific periods for ‘significance’. If the shape of the calibration curve is driving patterning in the SPD, the KDE of the dataset should look much different than the SPD in places where this is happening. However, if it is not driving patterning in the SPD, the KDE should produce similar peaks and troughs. As Fig. 6 shows, peaks and valleys are muted in the KDE model results, indicating that some of the patterning within the SPD is being driven by the calibration curve. Of note is that the dip beginning just prior to 1000 cal BP is much less pronounced in the KDE model, indicating that the calibration curve and sampling error is likely driving some of this phenomenon. Despite this, the dip at ~1000 cal BP is still the most

pronounced decrease in site density between ~3000 and 500 cal BP. Interestingly, this analysis also indicates that the post 500 cal BP decline may have been much more constant than the original SPD suggests, meaning that the stabilization of site density following ~300 cal BP is likely a product of the calibration curve. Importantly, both of these analyses demonstrate that the overall trend of long term, sustained growth in population between ~3000 and 500 cal BP is likely a real phenomenon. A claim supported by previous work using SPDs focusing on other parts of Alaska (Tremayne and Brown, 2017). Although the analyses above demonstrate that possible population decline around 1000 cal BP may be much less severe than indicated in the initial SPD, we still believe that this was likely a time of major reorganization on the landscape. Furthermore, even if this period on the SPD is being exaggerated, this does not mean that the apparent population decline is a complete artifact of the calibration curve. As already demonstrated (Figs. 3 and 4), multiple sites have dated components with high probabilities dating directly to within the 1000 cal BP period, indicating that the decline in radiocarbon dated site components during 9

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Fig. 4. Comparison of occupation history at the ten best dated interior sites using OxCal 4.2.3. ‘+’ symbols denote median of an individual radiocarbon date. Note the occupation history of each site around 1000 cal BP, when we see a decline in radiocarbon dates in the regional SPD.

this time is not simply an artifact of the calibration curve, but is instead likely associated with changes in local demography. This is further attested to when we look at patterns in when sites become occupied. The post 1000 cal BP spike in population is not caused by new components in previously occupied sites, but instead by a large series of newly occupied sites across the landscape. Moreover, many well dated sites or site complexes (e.g. NOB-2, XLS-1, XPH-4) with pre-1200 cal BP components cease to be occupied after this period. Therefore, even if the dip in the SPD around 1000 cal BP is partially a product of the calibration

curve (or of erosion as previously discussed), it does seem to be signaling a major shift in settlement and demographic activities. 4.2.5. Examination of potential sampling bias In order to further assess the potential effect of sampling biases on our dataset, we studied the spatial and temporal distribution of dates in GIS. We found that there is a somewhat even distribution of sites across the coasts and interiors of the study region (Fig. 7, Supplemental Table 3), although research is concentrated in public lands (particularly

Fig. 5. Comparison of simulated, evenly spaced SPD and actual SPD. Note that there is little correlation in the peaks and valleys of the two datasets. The exception is the period prior to 1000 cal BP. Unmodified SPD is also shown to illustrate where the effects of the calibration curve are most pronounced. 10

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National Park Service lands). It is highly likely that archaeologists are biased against radiocarbon dating post-250 cal BP and during the contact era because of issues with the calibration curve. These issues make it difficult to receive high probability calibrated ranges between ~CE 1500–1700 and CE 1800–1900 (Reimer et al., 2013) and historical records for this period tend to yield more precise dates. Regionally, this bias may explain the decline in dates after about 250 cal BP, but this is difficult to assess further. Future analysis of tree ring dates from this time period (see Giddings, 1952a; VanStone, 1955) could further our understanding of demography and intensity of site occupation during the late pre-contact and contact era. There are also many important known sites with multiple components that are not dated, or only minimally dated (e.g. Tikigaq, Shishmaref); these sites are under-represented in this analysis because they are not adequately dated. We cannot control for these research biases in our analysis. Results of site component analysis contradict the results of our SPD analysis during the post-600 cal BP period (Thule, Arctic Woodland,

Fig. 6. Results of the Oxcal Kernel Density Estimation Model (KDE), indicating little correlation between the calibration curve and SPD patterning, expect at prior to 1000 cal BP.

Fig. 7. Distribution of study sites across the study area for each analytical period. Note the relatively even distribution of sites across coast and interior locales. Also note that the total number of sites is obscured by scale of graphic. See Supplemental Table 3 for more data on number of dated site in during each ecoregion and time period. 11

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Fig. 8. Analysis of site components for National Park Service lands within the project area (adapted from Anderson, 2011). Historic Iñupiat sites are removed from graphic B to make overall demographic trends more visible.

and Kotzebue periods) (Fig. 8, A). However, the terms Thule, late Thule, Kotzebue, and Arctic Woodland, have been inconsistently applied so the results of component analysis for the Ancestral Iñupiat and Inuit period is questionable. The shape of the 5000–600 cal BP (Denbigh-Thule) site component curve (Fig. 8, B) is similar to the results of our demographic analysis. There is an initial increase in site components during the Period I (Denbigh, Choris, Norton), followed by an increase during the Ancestral Iñupiat and Inuit period (Birnirk, Thule, Arctic Woodland) and finally, a decrease during the late pre-contact period (Kotzebue); the site component analysis identifies a decrease during Period III (Ipiutak, OBS) that we do not see in our SPD analysis. Note that we ignore the Old Whaling components in this discussion simply because

Old Whaling is limited to a single site at the Cape Krusenstern site complex. The component analysis provides additional support for our interpretation of general demographic patterns from 5000 to 600 cal BP and suggests that the post-600 cal BP patterns should be further investigated; the post-250 cal BP (Historic Iñupiat) data should be treated with particular caution. It is also possible that large radiocarbon datasets from Cape Krusenstern (n = 181), Cape Espenberg (n = 39), and Onion Portage (n = 39) are driving the pattern we identify in our regional SPD. We removed the dataset from each site and re-analyzed the coastal radiocarbon dataset. Without the dates from these sites, the declines in the regional coastal SPD, particularly at 1000 cal BP, are less severe though 12

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Fig. 9. Comparison of Cape Krusenstern, Cape Espenberg, and regional coastal SPDs. The regional coastal SPD excludes Cape Krusenstern and Cape Espenberg dates so that the potential effect of the large datasets from these site complexes can be evaluated.

we propose that: (1) northern Alaskan populations grew significantly over the last 4500 years, particularly during our analytic Period IV, between about 1000 and 600 cal BP; and (2) population growth was punctuated by three periods of population decline between about 3700 and 3125 cal BP (during Period I), slightly before 1000 cal BP (end of Period III), and beginning around 600 cal BP (end of Period IV/beginning of Period V). These periods of population decline cannot be explained solely by calibration or taphonomic issues and reflect changes in regional demography to some extent. Several geographic trends are also apparent in our analysis. Specifically, more of the coast and interior river systems are occupied over time (Fig. 7). This pattern is particularly apparent on the Seward Peninsula where the barrier island system and interior of the peninsula is occupied more intensely over time, particularly during Period IV and V, after about 1200 cal BP. Evolution in the coastal landscape itself could be driving these geographic patterns. Investigation of barrier island systems around the region (Jordan, 1988; Mason and Jordan, 1993, 2002) shows that none of the systems date to before about 550 cal BP; it is possible that people inhabited new areas of the coast as new landforms became available. Alternatively, people may have moved settlements to new, uninhabited areas, as population increased over time and population centers became crowded. Mason (1998) proposed that initially, people occupied prime marine hunting access points (usually coastal promontories). As population increased and competition for resources at these locations increased, people moved to less desirable coastal and interior locations across Northwest Alaska. Tremayne and Winterhalder (2017) test a formal version of this model, the ideal free distribution model, for all of Alaska and find that initial Paleo Iñupiat and Inuit2 (ASTt) settlement was likely driven by access to large mammals, including marine mammals. Competition at prime

still present (Fig. 9); it is likely that the decline in the Cape Krusenstern SPD at 1000 cal BP is influencing the regional demographic pattern. As previously discussed, this is a known period of erosion at Cape Krusenstern and Cape Espenberg (Mason and Jordan, 1993); given this, our earlier observations about the role of erosion in shaping the SPD patterns stand. However, the persistence of occupation at other locations on the coast and interior (e.g. Figs. 3 and 4) indicate that there is likely a real decline in population at this time that cannot be solely attributed to widespread coastal erosion or intensive dating programs at some sites. Overall, we conclude that the impact of investigator biases against dating certain time periods is difficult to assess and we must assume that the overall size of the database minimizes the impact of any such bias. Our consideration of sampling biases does identify data gaps on which future investigators could focus research and dating efforts. 4.3. Exploration of identified demographic trends Our exploration of potential alternative explanations for observed demographic patterns indicated that widespread erosion and distortion from the calibration curve are likely exaggerating some of the patterning observed in the SPD, especially the decrease in the SPD at about 1000 cal BP. Widespread coastal erosion (the T1-event; Mason and Jordan, 1993) could have a dampening effect on our dataset and explain areas of low probability in our radiocarbon data, between about 3800 and 2750 cal BP, and potentially episodically or unevenly afterwards. The second major erosion event (T2) dated to approximately 1000 cal B.P. is a period of region-wide coastal erosion and roughly correlated with the decrease in population we identify beginning slightly before 1000 cal BP. The third period of demographic decline identified by our analysis, after about 600 cal BP, could be related to Little Ice Age erosive events. However, as discussed in more detail below, people’s movements in response to erosional periods and ridge progradation is not straightforward, hindering our ability to accurately model or even conceptualize the magnitude of potential effects erosive episodes had on settlement or demographic trends. Sampling bias may also be contributing to the post-600 cal BP decline in the SPD, but probably only for the contact era, after 250 cal BP. Given these caveats,

2 In accordance with terminology preferred by circumpolar Indigenous groups (ICC Resolution 2010–01, http://www.inuitcircumpolar.com/uploads/ 3/0/5/4/30542564/iccexcouncilresolutiononterminuit.pdf), we use the term Paleo Iñupiat and Inuit instead of Paleoeskimo, and Ancestral Iñupiat and Inuit instead of Neoeskimo (see Friesen (2015) and Hodgetts and Wells (2016) for further discussion). We include the term Iñupiat as well as Inuit, as Iñupiat is the preferred term for northern Alaskan Indigenous groups.

13

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hunting locations over time increased the appeal of more marginal habitats, and, as a result, people moved into new environments over time. A brief comparison of the Krusenstern and Espenberg site occupation patterns further reflects the complexities of demography, human response to erosion/storminess, and interpretation of preserved demographic data. Based on current geomorphological and paleoecological data, the cycles of erosion are thought to be somewhat synchronous at the Epsenberg and Krusenstern beach ridge systems (Mason and Jordan, 1993, Mason et al., in press: section 4.4). But, the radiocarbon records of occupation at these two places are different (Fig. 9). For example, between approx. 2100 and 1400 cal BP, occupation appears less intense at Espenberg and higher at Krusenstern. Then, as occupation decreases at Krusenstern around 1350 cal BP, it rises at Espenberg. Are there actually asynchronous storm events/erosion impacting the record at these two places? And/or could shifting local environment/subsistence resources have led people to move from the north to the south coast of Kotzebue Sound? These are questions that can be explored further, particularly with forthcoming data from Cape Espenberg. Comparable data from the interior is currently lacking, but a site to site regional study would be informative, especially as more coastal paleoenvironmental data emerges. The timing and nature of the approximately 1000 cal BP population decline has significance for understanding the relationship between mainland Alaskan residents (Ipiutak people) and migrants (Birnirk people) into the region at the beginning of the Ancestral Iñupiat and Inuit period (see Bockstoce, 1973, 1979; Mason, 1998, 2000, 2009a for further discussion of migration during this period). If the resident population declined significantly around this time, the migrant population would have inhabited a depopulated landscape. But, if resident populations remained in place, there would have been interaction and even competition or conflict between migrants and residents. And, it may explain why so many sites were either abandoned or newly occupied around this time. Further exploration of why these demographic patterns occurred requires more research but it is likely that the demographic fluctuations we identify were caused by a constellation of factors including environmental variability and both internal and external social forces. The mid-late Holocene is a period of significant environmental variability (e.g. Bird et al., 2009; Calkin et al., 1998; Graumlich and King, 1997; Overpeck et al., 1997); environmental shifts and associated changes in resource abundance, availability, and distribution could have factored into demographic change over time. For example, Bockstoce (1973, 1979) links a decline in fish and caribou populations to the 1000 cal BP decline and subsequent population increase as marine mammal focused Birnirk people populated Alaska. Mason and Barber (2003:72; see also Mason 2009a, 2009b) correlate the development of whaling around 2000 years ago with cooler temperatures, and the intensification and spread of whaling with a second period of cooling around 1150–750 BP. Little Ice Age cooling, beginning around 300 cal BP (Bird et al., 2009) may have led people to focus on other marine and aquatic resources (e.g., fish), as suggested by technological data and limited faunal analysis of post-550 cal BP archaeological sites (Giddings, 1952a; Giddings and Anderson, 1986). On the central Alaskan Peninsula, Vanderhoek (2009) and Barton et al. (2018) identify a population decline following the approximately 3700 cal BP eruption of the Aniakchak volcano; this eruption may have depressed culturally important caribou, salmon, and a variety of key near shore resources. Tremayne and Brown (2017:373) also identify a population decline at this time in southwest Alaska, but find that it was followed by a small occupation pulse in both the coast and interior of the region; these results suggest that the impact of the eruption was complex and should be further investigated. Together, these studies also demonstrate the value of analyzing radiocarbon data at different spatial scales in order to build knowledge about processes of culture change. Higher resolution paleoenvironmental data, and more zooarchaeological research,

are needed in Northwest region to better understand shifts in past resource abundance in relationship to demographic trends. Outside pressures could also have affected regional demography; pressure from social or environmental conditions in Chukotka or even further afield could have driven multiple migrations into Alaska (Tremayne and Winterhalder, 2017). Early, undocumented, epidemic disease passed through indirect contact with Europeans could have contributed to the post-600 cal BP demographic decline. Ethnohistoric data indicates that a poorly documented epidemic probably occurred in Northwest Alaska during the early 1800s (Burch, 1998:315); earlier epidemics are possible, particularly given the tight intercontinental ties of northern Alaskan groups with Chukotka and beyond (Burch, 2005). Indirect connections with market economies in the late precontact could have had a profound effect on western Alaskan populations (sensu Fitzhugh et al., 2016; McGhee, 1994). 4.4. Significance of demographic shifts for the development and spread of Arctic maritime adaptations The patterns we identify are similar to those identified by other recent analyses of Alaskan radiocarbon data (Barton et al., 2018; Tremayne and Brown, 2017; Tremayne and Winterhalder, 2017), although there are some differences in interpretation. Tremayne and Brown (2017) analyze an Alaska-wide radiocarbon dataset dating from 6000 to 1000 radiocarbon years BP. They are also interested in the relationship between population trends, culture change, and marine resource intensification. They focus on a critical transition from the earliest Paleo Iñupiat and Inuit populations, the ASTt groups (Denbigh in northern Alaskan) to later Paleo Iñupiat and Inuit groups (Norton or Norton Near Ipiutak in northern Alaska), which occurred between about 3700 and 2500 cal BP. Tremayne and Brown (2017:370) identify similar pan-Alaskan patterns as we do in our regional analysis; specifically, they see population growth beginning between 4200 and 4000 cal BP, followed by a rapid decline beginning around 3700 cal BP (see also Tremayne and Winterhalder, 2017). Population is low for several centuries, with a minor increase or pulse in population between 3000 and 2750 cal BP. This is followed by renewed population growth beginning around 2500 and 2400 cal BP, at the beginning of the Norton period. Tremayne and Brown also find that this general pattern plays out in slightly different ways across Alaska (2017: 371). For example, they see an increase in population in Western Alaska at the same time there is a decrease in Arctic Alaska around 3600 cal BP. Tremayne and Brown hypothesize that the ASTt terrestrial resource base, caribou, collapsed around 3600 cal BP, which led ASTt groups to abandon tundra regions and move to the coast in Northwest and southwest Alaska. ASTt culture is replaced by a more marine focused Norton culture after a period of low population density across Alaska. Tremayne and Brown hypothesize that low population density led to marine resource intensification; specifically, selective pressures acting on remnant late ASTt populations may have led to the development of new marine procurement technologies and practices. In this case, a population bottleneck resulted in technological innovation, specifically, increased technological complexity related to maritime adaptations. A major difference between our interpretation and that of Tremayne and Brown (2017) is their focus on technological change, increasing complexity, and marine resource intensification. Archaeologists link technological complexity to issues of prey choice and risk (i.e. the possibility of failure) (sensu Fitzhugh, 2001). For example, Torrence and Van der Leeuw (1989), Binford (2001) and Edinborough (2005) demonstrate that technological complexity increases as cost of failure increases; Binford (2001: 392) asserts that focusing more on marine resources increases technological complexity. Edinborough (2005) finds the same result in Mesolithic Scandinavia; technological complexity and change increases during times of population decline. Edinborough also suggests that this may be due to decreasing population levels leading to increasing risk of failure and thus promoting 14

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innovation or more investment in reliable/composite technology. In contrast, in this paper we are focused on the relationship between population growth and settlement or mobility change, not technological change. Since technological change and settlement change are governed by different forces and have been shown to relate to population in different ways, it is expected that we found different relationships between population change and “complexity” than those identified by Tremayne and Brown (2017). Furthermore, these differences highlight the problems in discussing ‘complexity’ in archaeology as we can be operating under fairly divergent and unrelated definitions of the term. Our results suggest several interpretations with respect to demographic shifts and the emergence of marine-focused subsistence. First, one could interpret our results as identifying a possible correlation between population growth and a transition toward increased sedentism, the development of social complexity, and the emergence and spread of maritime adaptations. This possible pattern is apparent when considering our evidence for overall population growth over the period of interest, 4500 cal BP to the contact era. Use of marine resources is apparent by 4500 cal BP (Buonasera et al., 2015), and it is likely people relied in part on coastal resources even earlier. Our analysis, however, shows that population growth began perhaps as early as 5000 years ago, and consistently by 3200 cal BP, well before subsistence focused primarily on marine hunting and fishing around 2000 cal BP. There is a brief decline in population around 1000 cal BP, but this period of decline is followed by a rapid increase in population and subsequent spread of people and marine-specific technology from western Alaska across the Arctic to Greenland. While marine resource use likely has deep roots in this region, marine subsistence became markedly more intense beginning around 2000 cal BP as measured by increased technological complexity and specialization (Tremayne, 2017), coastal sedentism (Anderson and Freeburg, 2013, 2014), and dietary shifts (Darwent and Darwent, 2005, 2016; Giddings and Anderson, 1986; Norman et al., 2017). From this we hypothesize that population increase was a potential driver for mid-late Holocene cultural and organizational change in the Arctic and a major factor in economic intensification that included increased marine focus and expansion of maritime adaptations. In this case, we are using the term intensification in a descriptive rather than explanatory, Boserupian, way (Morgan, 2015: 165). Specifically, here we mean that intensification is “the process by which one or more elements of production (e.g., labor, land, technology, skill, knowledge, organization) are increased relative to other elements in order to maintain or increase food production” (Ames, 2005:69). Our data provide empirical evidence of population growth preceding intensification of marine resource use in the Arctic. Population growth is sustained across the mid-late Holocene as technology, settlement patterns, and social organization rapidly evolved. Similar patterns of population increase, sedentism, a delayed return/food storage economy, and the emergence of complex social organization occurred on the Pacific Northwest Coast (Ames, 2003, 2005; Kelly, 1995; Matson and Coupland, 1995) and in other regions of western North America (e.g. California, southern Alaska), although the sequence of events vary from region to region and are debated (see Morgan, 2015 for summary). At a large temporal and regional scale, our results suggest that pursuit of large-bodied marine mammals was driven by a need for more food to support a growing population. Alternatively, low population densities at specific transitional time periods e.g. during the ASTtNorton transition (Period I: Table 1), and the later Paleo Iñupiat and Inuit – Ancestral Iñupiat and Inuit transition (Period III-IV: Table 1), and during the late pre-contact (Period V) suggests that there may be a relationship between population decline and technological innovation, or increased technological complexity, as proposed by Tremayne and Brown (2017). However, a more detailed consideration of this possibility requires more dates for key times periods, particularly the Choris (n = 15) and Norton/Norton Near Ipiutak (n = 23) Phases. At this point, our research raises interesting questions about how

population fluctuations make space for migration, new cultural interactions, and innovation. Additional research is required to explore the potential role of Arctic population growth in the development and spread of Arctic maritime adaptations. For example, were people isolated or connected during periods of low population density? What role did migration into the region, and into Alaska, play in bringing about innovation and cultural change? Did population actually decline after 600 cal BP or is it a results of investigator bias against radiocarbon dating late pre-contact sites? Is there a link between technological innovations at this time (e.g. more mass capture fishing technology), a hypothesized shift from focus on marine resources to fish, and population change? What is the role, if any, of changing environment and marine resource availability over time? In other words, though population growth may have acted as a prime driver for the proliferation of Arctic maritime adaptations, it does not in itself answer how this proliferation occurred. Nor did population growth happen in a vacuum. Simply adding more people does not intrinsically create new social organization and technology, even if it does create the need for them. Thus, future research needs to focus on the process of social, technological and economic complexification through the interplay of these variables, not just the reason for it. 5. Conclusions Our analysis identifies potential correlations between environmental and demographic shifts that require additional investigation. The timing and geography of the demographic shifts has implications for the interactions between groups of people whose territories may have expanded or contracted as population grew over time. Our compilation of radiocarbon dates for northern Alaska highlights major knowledge gaps in our understanding of northern Alaskan pre-contact history; sites and several time periods (e.g. Choris phase) are poorly dated. Despite these limitations, this analysis suggests a possible driver for cultural change: population growth. We find that significant population growth in the western Arctic predates the intensification of marine resource procurement by at least 1200 years. These results invite further investigation of patterns in regional demography and have implications for understanding the development and spread of aquatic adaptations in other parts of the world, and for studying the process of intensification in hunter-gatherer groups. Declaration of Competing Interest The authors declared that there is no conflict of interest. Acknowledgements Thank you to the National Park Service Anchorage Regional Office Cultural Resources and Curation staff Andrew Tremayne, Jane Workman, and Rhea Hood, National Park Service Fairbanks Office staff Adam Freeburg, and to Bureau of Indian Affairs ANCSA Office staff Matt O’Leary, Sean Mack, and Ken Pratt for providing access to radiocarbon data. Ken Ames provided helpful comments on an earlier version of this paper. Jennie Shaw provided advice on appropriate offsets for our old wood effects modeling. Rhiannon Held provided technical editing. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Data availability statement The radiocarbon data on which this analysis is based is included with this publication as supplemental data (Supplemental Tables 1 and 2). 15

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Appendix A. Supplementary material

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