Living through changing climates: Temperature and seasonality correlate with population fluctuations among Holocene hunter-fisher-gatherers on the west coast of Norway

Abstract

The use of archaeological proxy records representative of population dynamics is paramount for a richer understanding of prehistoric cultural change, but its use require a dialectic assessment between proximate climatic drivers and ultimate cultural responses. Focusing on the Stone Age archaeological record of Western Norway (11,500–4300 cal. BP), this paper presents an exhaustive empirical curation and statistical testing between changing climates and demographic responses among coastal hunter-fisher-gatherers. The results connect long-term demographic fluctuations with changes in annual mean temperatures and seasonality and the results are discussed in relation changes in technology, subsistence and mobility. The paper also highlights the process of population decline and cultural loss towards the end of the Late Mesolithic (ca. 7000–6000 cal. BP) and emerging cultural novelties and population re-growth during the Early and Middle Neolithic (ca. 6000–4300 cal. BP). However, despite its strong correlation, the archaeological record of Western Norway lacks sufficient detail to ascribe an exclusive explanatory role to climate change, especially in episodes of significant population decline. This helps to emphasise that changing climates, while evidently central, form but a part of a larger system of interactions leading to demographic fluctuations and cultural change, the substantiation of which requires significant empirical improvements to the archaeological record.

Keywords

cultural evolution climate change Holocene Thermal Maximum hunter-fisher-gatherers palaeodemography SPD

Introduction

Humans make up the dynamo responsible for the diverse patterns and processes of biological and cultural evolution (Boyd and Richerson, 1985; Jordan, 2015; Metcalf and Pavard, 2007; Morin, 2015; Shennan, 2000; Sperber, 1996; Zhang and Mace, 2021). By and large, these patterns and processes vary as a result of demographic oscillations in population size or connectedness which, in itself, is driven by the many successful or unsuccessful ways that humans extract energetic resources from the environment (Binford, 2001; French, 2021; Morin, 2008; Smith and Winterhalder, 1992; Tallavaara and Seppä, 2012; Tallavaara et al., 2018; Tremayne and Winterhalder, 2017). However, significant re-configurations in the spatio-temporal distribution of resources can come about over both shorter and longer time-scales as a result of changing climates and social norms, and it forces humans to constantly and adaptively construct cultural solutions to help ensure their survival (Laland and Brown, 2006, 2011; Odling-Smee et al., 2003). Considering then that this dynamic resembles both current and predicted trends for the future (Vollset et al., 2020), a richer understanding of the interplay between climate change, population dynamics and societal change can be gained by situating such trends in much deeper (pre)historic contexts (Degroot et al., 2022). This is done by teasing out if, how, or why climate change affected long-term fluctuations in human populations and if those, in turn, occurred simultaneously to the sudden and significant changes that the archaeological record attests to (Ordonez and Riede, 2022). In recent years, a particularly fruitful analytical inroad to these concerns has been paved through explicit palaeodemographic modelling of archaeological proxy records (French, 2015), the relative changes of which are often leveraged to explain significant episodes of cultural change (French and Chamberlain, 2021; Ghirlanda and Enquist, 2007; Riede, 2009; Roth, 2004; Shennan and Sear, 2021; Strimling et al., 2009; Timpson et al., 2014).

In this paper, I will explore how Stone Age coastal hunter-fisher-gatherers in western Norway (ca. 11,500–4300 cal. BP) lived through changing climates as demonstrated through long-term demographic fluctuations and discuss their subsequent cultural responses. Drawing inspiration from previous work (Bergsvik et al., 2021; French, 2015; Jørgensen, 2020; Palmisano et al., 2017; Solheim and Persson, 2018; Tallavaara and Pesonen, 2020), I do this by presenting the most up-to-date data curation of four palaeodemographic proxy records and assess their degree of statistical co-variation between five different palaeoclimatic time-series. The archaeological proxies are radiocarbon dates, estimates on site size, stray finds, and site counts. The palaeoclimatic time-series account for annual and seasonal changes in temperature and precipitation from the marine and terrestrial realm. Results demonstrate a strong connection between demographic fluctuations and changing climates. The paper also highlights the process of population decline and cultural loss towards the end of the Late Mesolithic (ca. 7000–6000 cal. BP) and emerging cultural novelties and population growth during the Early and Middle Neolithic (ca. 5925–4300 cal. BP). However, despite its strong correlation, this paper falls short in ascribing an exclusive causal role of climate, inviting the possibility that climate formed but a part of a larger system of socio-ecological interactions that lead to cultural change (Mandryk, 1993), the substantiation of which requires significant empirical improvements to the archaeological record within and outside of Western Norway.

The role of palaeodemography in Scandinavian archaeology

In Scandinavian archaeology in general, and in Norwegian archaeology in particular, the connection between palaeodemography and cultural change has been central over the last five decades. While unquestionably important and laudable, pioneer work by Cullberg (1975), Welinder (1979) and Olsen and Alsaker (1984) pale in comparison to the numerous palaeodemographic studies undertaken within just the last decade. This strong trend has arguably been driven by increasing availability of large archaeological data sets and an increased use of advanced computational approaches to modelling (Apel et al., 2018; Bergsvik et al., 2021; Hinz et al., 2012; Lundström and Riede, 2019; Lundström et al., 2021; Manninen et al., 2023; Nielsen, 2021; Nielsen et al., 2019; Riede, 2009; Solheim, 2021; Solheim and Persson, 2018; Tallavaara and Pesonen, 2020; Tallavaara et al., 2015; Tallavaara and Seppä, 2012). What follows is in no way intended to detract from all previous work, but I nonetheless wish to highlight some significant methodological shortcomings that has prompted the analysis put forward in this paper.

In Norway, the work done by Olsen and Alsaker (1984) was the earliest attempt at providing any form of palaeodemographic inference through the Norwegian archaeological record. By analysing the distribution of axes for the latter half of the Stone Age on the west coast of Norway (Olsen and Alsaker, 1984: 99), absolute population numbers and densities were derived through the use of ethnographic reference data. However, due to the coarse resolution of the data, diachronic estimates could not be obtained, effectively curtailing any discussion relating to long-term trends and potential climatic co-variation. Much more detailed were the insights provided by Bergsvik (2001), who argued for population increase from the Late Mesolithic (ca. 8500–6000 cal. BP) to the Early and Middle Neolithic (ca. 6000–4300 cal. BP) resulting from a broadened subsistence basis, and from the analysis of shore-line dated sites from the early Preboreal by Breivik (2014: 1485) who assessed diachronic changes in relation to changing climates, albeit without explicitly addressing the notion of demographic change. Continuing the trend towards obtaining estimates of higher resolution, Bjerck and Åstveit (2008:549) produced histograms of radiocarbon dates within 500-year bins. Their estimates demonstrated how populations experienced a typical boom-and-bust pattern, but did not assess if these patterns were underscored by climate change.

Following the large-scale work performed by Shennan et al. (2013), Solheim and Persson (2018) provided long-term estimates on human population dynamics for the south-eastern parts of Norway using summed probabilities of radiocarbon dates and shoreline dated site counts but without any comparison of climatic data. The same analytical approach can also be identified in more recent work by Nielsen et al. (2019) and Bergsvik et al. (2021). And while the latter two have significantly pushed the envelope in palaeodemographic modelling by increasing the sample size of various archaeological proxies, little discussion has been directed towards long-term trends and their potential connection to climate change. In this regard, Jørgensen (2020), and Jørgensen et al. (2023) provide the only Norwegian examples of extensive testing of demographic estimates in relation to climate change, demonstrating climatically induced boom-and-bust cycles in the former (but see Brown et al., 2022 for a contrary interpretation) and the adoption of pottery through migrations in the latter. However, these latter examples utilise only one source of demographic proxy data (¹⁴C), creating a potential ‘blind spot’ for conflicting patterns and interpretations that could emerge if additional and complementary sources of data were used. Such an alternative approach will form the foundation for the results and discussion presented throughout the remainder of this paper.

Material and methods

The regional setting of Western Norway

Ever since 1825, the university museum of Bergen has collected and excavated prehistoric remains from the former administrational counties of Sunnmøre, Sogn og Fjordane and Hordaland (Figure 1).

Figure 1.

Overview of the study area highlighting the three former administrational counties from which all data have been collected. The mini-map in the upper left corner situates the study area in its wider geographical context (red square) and pinpoints the location of the MD99-2284 marine sediment core (black dot).

The complex topography of the region contributes to highly variable climate and landscape types. The outer coast consists of smaller to larger islands and skerries with mild temperatures and high degrees of precipitation. It also comprises areas of kelp forests, strong tidal current channels, and high biological productivity. The islands of the outer coast provide shelter from winds and waves on the inner parts of the archipelago, allowing for easy maritime travel along a north-south trajectory, as well as to the east where large systems of fjords permeate the landscape. The eastern parts have traditionally been more suitable for agricultural practices due to less rugged topography and more favourable climatic conditions, even if the amount of land for arable farming in Norway amounts to only 3% (Solheim, 2021). These fjord systems eventually connect the western parts with vast ranges of high-altitude mountains in the east with tundra-like biomes and in combination, the ecology of western Norway is highly variable. In what follows is a presentation of the palaeodemographic proxies applied, as well as a justification for their use. Their respective abbreviations have been summarised in Table 1.

Table 1.

Overview of the respective palaeodemographic and palaeoclimatic proxies used and their abbreviations as mentioned throughout the text.

Archaeological proxies	Abbreviation
Radiocarbon-dates	SPD
Site counts	SC
Site size	m²
Stray finds	SF
Palaeoclimatic time-series	Abbreviation
Annual Mean Temperature	AMT
Ocean Temperature	OT
Annual Precipitation	APR
Precipitation Seasonality	PS
Temperature Seasonality	TS

Archaeological proxies

Radiocarbon dates and their summed probabilities

To use large sets of radiocarbon dates as a proxy for population intensity was first proposed by Rick (1987), and the method has seen numerous statistical refinements ever since (Crema and Bevan, 2021; Shennan et al., 2013). This proxy was chosen as it is commonly sampled in most investigations and has, over time, been collected at such volumes to tease out robust long-term trends while suppressing short-term statistical noise. Anchored in absolute time, the resulting population estimates also become comparable to palaeoclimatic records at both inter-regional and global scales. This is a tall order for remains otherwise dated by typological means, making radiocarbon dates an indispensable proxy for palaeodemographic modelling. I therefore accept the premise that, other things being equal, varying intensities of dateable organic remains, over time, reflect changes in human population levels. The current data set of 1142 radiocarbon dates was sourced from excavation reports, regional databases, and previous work (Bergsvik et al., 2021). All dates come from a total of 345 unique archaeological sites, were all treated as unnormalised to avoid steep artificial spikes in the calibration curve and were binned at 100-year intervals to suppress intra-site sampling intensity and to align the estimates with the temporal resolution of the palaeoclimatic time-series. Furthermore, in order to assess the most likely trajectory of population growth, the models of uniform, linear, exponential, and logistic growth were assessed through 1000 simulations of each respective growth model as well as evaluating their resulting global p-values. However, the protocol of null-hypothesis testing, as performed through Markov Chain Monte-Carlo simulations, does not take into account the potential loss of datable radiocarbon as a result of taphonomic erosion. I therefore also decided to apply the mathematical correction as suggested previously (Bluhm and Surovell, 2019; Pelton et al., 2022; Surovell and Brantingham, 2007; Surovell et al., 2009) in order to qualitatively assess moments where the simulations might converge or diverge. The analysis was carried out with the statistical programming language R via RStudio (R Core Team, 2020) and can be reproduced with the code and data included in the article’s supplementary information.

Site counts, stray finds, and site size

Counts of sites and stray finds refers to any site or artefact that has not been dated by radiocarbon, and they are also hypothesised to fluctuate as a result of changing population levels. All sites and stray finds were retrieved from the national sites and monuments database of Norway, known as ‘Askeladden’, as well as an internal database called ‘MUSIT’ constructed for the university museum of Bergen. Dating of sites, unless already provided, relied on regional shoreline chronologies if available, as well as qualitative assessments on the composition of artefacts considered diagnostic for the respective time periods based on Bergsvik (2002: 288 and references within). The same procedure was carried out for stray finds and the same dating framework outlined in Bergsvik et al. (2021) was used. The quantification of site counts did not concern itself with what type of site that was counted, requiring only an indication of human activity, most often through scatters of worked lithics. Issues of cultural or temporal overlap was solved by binning sites accordingly: sites dating to ca. 11,500–10,000 cal. BP were placed into an ‘Early Mesolithic’ bin. Large degrees of trait overlap between the Middle and Late Mesolithic (Table 1) resulted in a ‘Middle/Late Mesolithic’ bin and taphonomic erosion caused by the tapes transgression (Fjeldskaar and Bondevik, 2020) reduced the duration of this bin to ca. 8000–6000 cal. BP. Finally, a merged bin was made for the Early (6000–5300 cal. BP) and Middle Neolithic (5300–4300 cal. BP). It is important to keep in mind that while this might be a useful compromise for generating time-series of large sets of archaeological data, the spatio-temporal distribution of different cultural traits make for such discrete and tidy bins highly unrealistic. Moreover, the uneven duration between each bin require standardisation in order for them to compared properly. This was done by using aoristic weights (Lawrence et al., 2021; Palmisano et al., 2017) and their calculation was performed in the same manner as in previous work (Bergsvik et al., 2021). This procedure was repeated for stray finds, with the additional binning across geographical space to suppress uneven sampling intensity. The coordinates of each site from which stray finds had been collected helped create a unique ID. Multiple counts per site were then reduced into counts of one using the dissolve function in QGIS 3.22.14. Just like the radiocarbon proxy, aoristic frequencies were corrected for potential taphonomic erosion. Finally, estimates on site size (m²) is deployed under the premise that the size of any given site should be, all else being equal, directly proportional to population size, even if it by no means is an unproblematic proxy on its own (French, 2015: 197). Estimates were retrieved from excavation reports or the sites and monuments registry. Sites with mention of disturbance were excluded as it may have exaggerated the horizontal extent of the site, and sites with several occupational phases were excluded unless separate phases were accurately delineated.

Palaeoclimatic time-series and correlation testing

Five palaeoclimatic records relevant to marine and terrestrial ecosystems were chosen for correlation testing. It is important to keep in mind that these are not intended to function as proxies for human population levels, but as potential drivers. For the marine realm, foraminifera-based temperature reconstructions of the sea were sourced from the MD99-2284 sediment core, extracted at a depth of 1500m right underneath the north-west Atlantic Current (Eldevik et al., 2014; Risebrobakken et al., 2011). Changes in OT correlate with community structure of marine ecosystems (Antão et al., 2020), and is therefore considered a highly relevant time-series. For the terrestrial realm, AMT, APR, TS and PS were chosen for the potentially positive and negative effects on resource availability and population densities (Ordonez and Riede, 2022; Tallavaara and Seppä, 2012). All terrestrial time-series were sourced from the CHELSA-TraCE21k data set (Karger et al., 2021) that provides palaeoclimate-data at 100-year intervals for the last 21,000 years in raster format. This is a globally downscaled data set based on the Community Climate System Model Version 3 (CCSM3). As such, it is important to keep in mind that time-series derived from this data set might not be as accurate as more regionally derived data sets (e.g. Bjune et al., 2005). However, its strength lies in the availability of several so-called ‘blioclimatic’ variables (seasonality, isothermality, temperature annual range etc.), many of which might be relevant to the geographical distribution, and temporal fluctuation, of human populations. Moreover, by relying on palaeoclimatologies that are reconstructed in space and time in the form of raster grids, time-series can be generated through more extensive sampling strategies (e.g. Barton et al., 2018). Such sampling strategies would simply not be possible through conventional methods, at least not from a logistical standpoint. Considering then that the performance of the CHELSA-TraCE21k data set has been thoroughly examined and verified through additional sources of evidence, I consider it a robust approach for the purpose of this paper. All rasters were downloaded, reprojected, and cropped according to the spatial extent of the study area and then sampled with two thousand random points with a 1 km spacing. Pixels intersecting with the Fennoscandian icesheet (Stroeven et al., 2016) at any given time or place were excluded. A custom R-script for downloading, cropping, and reprojecting the rasters, as well as a custom graphical model designed in QGIS 3.22.5 to sample them (provided in the supplementary) was designed to automate the workflow (but see Leonardi et al., 2023 for an alternative approach).

To establish the relationship between climate change and fluctuations in human population levels, correlation testing was performed in two ways. The first test was done with a multiple regression analysis (henceforth MLR). The advantage of performing an MLR (Carlson, 2017), as opposed to the standard linear regression (LR), lies in its ability to distinguish the relative contribution of several independent variables (palaeoclimate time-series) to the dependent variable (palaeodemographic proxies). Two hypotheses were put forward: (H₀ & H₁). H₀ represents the null hypothesis, namely that none of the climatic variables have any effect on changing population levels, indicated by a p-value > 0.05. H₁ on the contrary, suggests that one or several climatic variables affect changes in human population levels, indicated by a p-value of ⩽0.05. The second test draws on all demographic proxy records and palaeoclimatic records using a Pearson’s correlation matrix. This was done by extracting the central tendency for each variable in the form of median estimates, mainly due to the coarse temporal resolution for proxy records such as site counts, stray finds, and site size. For sake of comparison, the Pearson’s correlation was set up to mimic the two models performed with the MLR. However, it should be noted that the two methods rely on different measuring techniques. To start, negative values in the Pearson’s correlation are not insignificant unless the correlation is weak (<0.5, 0, <−0.5). Most importantly, however, is that by using median estimates for the Pearson’s correlation, as opposed to a century-by-century comparison as done in the MLR, a great deal of variation and nuance is lost. I do not wish to speculate as to what effects this could have on the resulting correlations, but its potential influence should be kept in mind.

Results

Results from the permutation test of the four theoretical growth models have been summarised in Figure 2. Aoristic frequencies of stray finds and site counts, as well as estimates on site size in Figure 3, a summary of the MLR is reported in Tables 2 and 3 and the results from a Pearson’s correlation matrix in Figure 4.

Figure 2.

A combined plot of four theoretical null models of human population growth (n_dates = 1142, n_sites = 351, n_bins = 746, n_sims = 1000). The grey band represents a 95% critical envelope for each growth model. Points in time when the summed probability (solid black) deviates from the 95% critical envelope is highlighted with vertical bars, with significant episodes of population growth in red and declines in blue (but see Crema and Bevan, 2021: 30 for interpretative issues).

Figure 3.

A stacked plot with aoristic frequencies of stray finds and site counts (top panel) and estimates on site size with a log10-scale (bottom panel).

Table 2.

Statistical summary of the multiple linear regression for model 1 & 2. p-values and R²-values have been truncated to the third decimal.

Model	Multiple R²	Adjusted R²	F-statistic	p-Value
1	0.787	0.764	34.75	1.072e-14 (<0.001)
2	0.783	0.775	90.59	2.2e-16 (<0.001)

Table 3.

Statistical summary of the coefficients for model 1 & 2.

Variable	Estimate	Std. error	t-value	Pr(>\|t\|)
AMT (model 1)	0.127	0.027	4.600	<0.001
PRA (model 1)	–0.000	0.000	–0.734	0.466
OT (model 1)	–0.008	0.014	–0.631	0.531
PRS (model 1)	–0.003	0.020	–0.164	0.870
TS (model 1)	0.145	0.079	1.837	0.072
AMT (model 2)	0.143	0.013	10.311	5.68e-14 (<0.001)
TS (model 2)	0.190	0.045	4.188	<0.001

p-Values and t-values have been truncated to the third decimal, and statistically insignificant variables are highlighted in bold.

Figure 4.

A Pearson’s correlation matrix highlighting how the various palaeodemographic proxies’ correlate with the palaeoclimatic record, ranging from a strong negative correlation (blue) to a strong positive correlation (yellow).

Permutation tests of four theoretical growth models

The results provide little to no support for any of the four null models. The closest, albeit still poorly supported (p-value: <0.05), fit is found in the model of exponential growth. However, exponential growth performs just as poorly as the models of linear and uniform growth, neither of which bears any resemblance to the shape of the observed SPD which, arguably, is much more akin to a typical logistic S-shaped growth curve that was found to provide a better fit for a comparable data set from neighbouring and interior regions to the east (see figure 3 in Manninen et al., 2023). It is possible that repeated peaks in population growth towards the last millennium of the analysis (ca. 5300–4300 cal. BP) that just barely surpassed previous levels of population growth in the Late Mesolithic (ca. 7200–6800 cal. BP) contributes to an edge effect where exponential growth is better supported. However, contrasting this result with the SPD that is corrected for taphonomic erosion (Figure 5) changes the picture considerably. Here, the inflection point in growth occurs during the population peak during the Late Mesolithic and it is never quite surpassed during the Early and Middle Neolithic (ca. 6000–4300 cal. BP). However, ‘eyeballing’ the simulation is not sufficient enough to determine the most likely mode of population growth, and thus the results should be treated with caution and as inconclusive until the problem can be resolved in the future, perhaps through exploration with other analytical protocols (e.g. Timpson et al., 2021). That being said, it is important to also keep in mind that the summed probability distributions for any given data set most likely does a rather poor job at capturing the dynamics suggested by any of the given theoretical growth models anyway. This is because the various theoretical trajectories of population growth are governed by highly complex and dynamic processes such as births, deaths, in and out-migration as well as environmental productivity, many of which the coarse archaeological record are simply unable to accurately capture (Tallavaara and Jørgensen, 2021).

Figure 5.

Stacked plot with an uncorrected and corrected summed probability distribution (a). Marine and terrestrial time-series (b–f) are plotted with a smoothed trendline (solid) and their 95% confidence interval (dashed). Vertical bars from the exponential permutation test highlight significant episodes of population growth (red) and decline (blue).

Comparison with the remaining archaeological proxies

Without any formal statistical test, the remaining population proxies have to be inspected qualitatively. Moreover, due to their much coarser temporal resolution, this has to be done with caution. However, it is clear from the frequency diagram on site counts and site stray finds (Figure 3) that population levels did not grow uniformly, nor linearly. Once again, regrettably, whether populations grew exponentially or logistically is hard to determine. Site counts and site size, for example, exhibits an arguably logistically S-shaped pattern whereas stray finds indicate an exponential trend. While it is not expected that the different proxy records should all converge on one particular trend (French, 2015), the resulting and conflicting statistical noise, while preferable (Hamilton and Tallavaara, 2021), prevents any conclusive interpretation of the time-series, at least in terms comparing them to the four theoretical models of population growth.

Correlation testing with palaeoclimatic time-series

Multiple linear regression

A first run of the MLR (model 1) indicate that the long-term changes in human population levels correlate to changes in climate (Table 2). However, it also demonstrates that variables such as PRA, PRS and OT have little to no effect (Table 3), and that the model can be improved by omitting them. Model 2 improved the performance significantly (Tables 2 and 3), highlighting that AMT has the greatest effect on the SPD while TS has slightly weaker, albeit complimentary effect.

Pearson’s correlation testing

While not comparable methodologically, the results are replicated similarly to the MLR. AMT exhibits the strongest positive correlation on the SPD as well as all the other demographic proxies, whereas TS indicates a strong negative correlation on the SPD and stray finds, but less so for site counts and site size. The Pearson’s correlation matrix also indicates that AMT and TS is highly negatively correlated with each other, even if the directionality of this relationship remains unclear.

Results from the MLR and the Pearson’s correlation (model 2 respectively) testing from the archaeological record of Western Norway thus supports results found not only in the ethnographic record (Tallavaara and Seppä, 2012: 217) but from more recent work with a much deeper time-perspective and wider geographical scope (Ordonez and Riede, 2022). Given that the two statistical tests converged on similar results, albeit with varying strengths, I consider the co-varying relationship between climate and the palaeodemographic proxies to be reliable and robust.

Discussion

Despite the firmly established relationship between climate and demography in the previous section, population growth is not a given. While climate undoubtedly shapes the spatio-temporal structure of ecosystems (Binford, 2001; Tallavaara et al., 2018), the environment does not create the cultural responses that lead to population growth, it selects for those that are able to successfully persist within any given socio-environmental regime. It is therefore important to interrogate the archaeological record for responses, or lack thereof, that contributed to the projected demographic development.

For the earliest part of the Stone Age in Western Norway, low AMT and high levels of TS supressed population growth. Frequently changing sub-zero temperatures (Figure 5) contributes to shortened growing seasons and an increased and predominant reliance on meat consumption in northern latitudes (Binford, 2001). This results in negative outcomes for forager population densities regardless of their reliance on aquatic resources, as recently demonstrated in a global assessment by Zhu et al. (2021). This is supported by the demographic proxies presented for western Norway (Figures 4 and 5) and more recent modelling results (Lundström et al., 2021) that emphasise the presence of small groups mainly subsisting along the coast in cold and highly seasonal environments (Breivik, 2014) just above demographically viable population levels (Smith, 2014). Such demographic fragility would have been made worse by a seemingly conformist and inflexible technological tradition (Berg-Hansen, 2018) and a pronounced ecological homogeneity caused by the extent of the Fennoscandian ice-sheet (Mangerud et al., 2016). Thus, a largely unchanged coastal tradition (Berg-Hansen, 2018; Burdukiewicz, 2011; Cullberg, 1996; Fredsjö, 1953; Kindgren, 1996; Schmitt et al., 2009) slowly became a cultural and dysfunctional by-product (Laland and Brown, 2011) that repeatedly positioned foraging communities within reach of repeated and stochastic cultural and demographic collapse (Lundström and Riede, 2019; Riede and Pedersen, 2018), rather than successful coastal adaptations as has been suggested by Bjerck (2008, 2016) and Breivik (2014).

Much of the transition to the Boreal time period cannot be commented on due to taphonomic erosion caused by the tapes transgression (Fjeldskaar and Bondevik, 2020), and it also prevents further comment on potential demographic impacts of extreme events at ca. 8200 cal. BP (Daley et al., 2011) and the Storegga tsunami at ca. 8150 cal. BP (Nyland et al., 2021). However, if allowed to speculate, continued uninterrupted population growth is reasonable to hypothesise at this time period. The meeting between small migrating groups (Manninen et al., 2021b) would have resulted in significant fitness benefits to increases in population size and densities (Allee and Bowen, 1932), all the while temperatures continuously rose, seasonality decreased (Figure 5) and the Fennoscandian ice-sheet continued to melt (Stroeven et al., 2016). Moreover, groups would have had the possibility to receive or imitate technological innovations such as the reduction of microblades from conical blade cores (Sørensen et al., 2013) or the slotted bone point (Manninen et al., 2021a) through increased inter-group contact (Damlien, 2015, 2016; Günther et al., 2018; Kashuba et al., 2019). Together, these new ideas would help prevent subsistence risk and allow access to new ecological niches, as testified in the use of the rock-shelter of Saevarhelleren (Bergsvik and David, 2015).

What can be commented on, however, is a synchronous shift towards increasing annual mean temperatures and decreasing levels of temperature seasonality following ca. 8000 cal. BP (Figure 5). In tandem with warming and stabilising climates, groups utilised new and improved technological implementations, such as fishhooks and sinkers (Bergsvik and David, 2015; Bergsvik and Ritchie, 2020; Ritchie et al., 2016) in order to shift their settlement patterns towards channels with strong tidal currents like Skatestraumen in the north (Bergsvik, 2002) and Fosnstraumen in the south, both of which demonstrate an intense clustering of sites with thick cultural layers (Bergsvik, 2001; Olsen, 1992). A lateral shift towards the east with use of rock-shelters is also noticeable, and together these trends testify to a broadening of the resource base, with fishing as an important component (Bergsvik and David, 2015; Bergsvik and Ritchie, 2020; Boethius et al., 2020; Ritchie et al., 2016). This raises the question if the weak and insignificant correlation between the marine temperature proxy and the palaeodemographic proxies is caused by a potential distance decay that simply does not capture important and potentially driving changes in demography (Table 3 and Figure 4), similarly to what has been noted with the use of the Greenland ice-cores for co-variation testing in northern Norway (Jørgensen, 2020).

This is an important point to make, considering that the above-mentioned patterns of mobility and subsistence practices appear to have supported significantly high population levels, as is clearly reflected in the SPD (Figure 5), the volume of site counts, stray finds, and increases in site size (Figure 4). However, population growth eventually reached a peak and decline between ca. 7000–6000 cal. BP (Figure 5). A similar and more or less synchronous trajectory is noticeable also in central Norway (Sletvold, 2021: 44), the interior of northern Scandinavia (Manninen et al., 2023) as well as central (Riede, 2009: fig. 9) and southern Sweden (Friman and Lagerås, 2023). This decline transpires towards the end of the Holocene Thermal Maximum (HTM), which is represented by global warming and regional cooling (Bader et al., 2020; Kalis et al., 2003; Renssen et al., 2009; Seppä et al., 2009). In Norway (Bjune et al., 2004, 2005), summer temperatures decline towards the end of the HTM, and prior and successive melting of glaciers contributed to more frequent glacial outburst floods between ca. 6900 and 6000 cal. BP (Ekblom Johansson et al., 2020; Røthe et al., 2019).

However, the archaeological record of western Norway provides no compelling evidence for the triggering effects of extreme climates (Abbott et al., 2021; Groucutt et al., 2022; Jørgensen and Riede, 2019). Estimates of tree cover in western Norway is at its highest during the Late Mesolithic (Bergsvik et al., 2021: Fig. 12), thus there is no reason to suspect any widespread ecosystem collapse. In light of this, Nielsen (2021) has argued for migrating Ertebølle groups as a cause for this population decline. While intriguing as an hypothesis, to infer causality requires exhaustive statistical testing of several competing sources of evidence (Kavanagh et al., 2018; Tallavaara et al., 2018), neither of which has been adequately carried out in Western Norway, nor peripheral areas for that matter (Nielsen, 2021). Methodological adequacy aside, technological traces of such a potential migration pulse, such as the apperance of transverse arrow heads along the west coast of Norway (Bjerck and Åstveit, 2008) were short-lived and went extinct in parallel to more long-standing cultural traits, such as fishhooks, soapstone sinkers, polished and pecked chubby adzes, conical microblade cores, slotted-bone points, and the brief appearance and sudden disappearance of rock art (Hjelle and Lødøen, 2017; Lødøen, 2014). In their place emerged cultural novelties such as blank production from cylindrical blade cores, as did projectile technologies made from slate, to name just a few (Bergsvik, 2006; Eigeland, 2015: 379). Unravelling the mode and tempo of these technological changes, as well as investigating whether or not they relate to this Late Mesolithic population decline has not been adequately addressed up until this point and thus calls for further research.

Unlike eastern Norway (Nielsen, 2021), Western Norway did not experience a population boom at the Early Neolithic transition (Bergsvik et al., 2021). Instead, decreasing annual mean temperatures and increasing seasonality co-occurred with an inflection point towards population stability with three peaks in population growth followed by a decline throughout the remainder of the Neolithic (Figure 5). As such, these results stand in contrast to that of Bergsvik et al. (2021) who argue for a gradual and continued demographic growth up until the Late Neolithic transition (<4300 cal. BP). In fact, activities dating between ca. 6000–4300 cal. BP, if anything, points to short-term growth, stability with minor fluctuations, but ultimately a population decline and bust at ca. 4400–4200 cal. BP. Granted, stray finds from the Middle Neolithic B (ca. 4650-4300 cal. BP) would indicate continued growth, but these estimates, as opposed to those presented in this paper, were not corrected for increased visibility over time, or uneven surveying intensity across space (Figure 3).

Bergsvik et al. (2021: 14) caution that the somewhat modest demographic projections for the Early and Middle Neolithic could be due to a lack of radiocarbon dates. While that is certainly possible, it would stand in stark contrast to other Norwegian (Jørgensen, 2020: 43; Nielsen et al., 2019; Solheim and Persson, 2018) as well as south-Scandinavian and European examples of stability and population decline towards the end of the Middle Neolithic (Apel et al., 2018; Friman and Lagerås, 2023; Hinz et al., 2012; Shennan et al., 2013; Tallavaara and Pesonen, 2020). This conflicting pattern is encouraging as it suggests that other regional population projections could need a reconsideration with the help of additional proxy records, or alternative methods for palaeodemographic reconstruction (Lundström et al., 2021; Ordonez and Riede, 2022; Schmidt et al., 2021). A pattern from Western Norway that does stand out, however, is that the population peak at ca. 6900 cal. BP (Figure 5 forms an anomaly to an otherwise stable population level, possible already at ca. 7900 cal. BP until ca. 4300 cal. BP (Figure 5). This is also reflected by the inflection point in site size and site counts starting at the Middle and Late Mesolithic (Figure 4). Even if this could suggest that the social and economic organisation of coastal communities were better able at gravitating more frequently towards demographic equilibrium, change will shortly ensue if a resource ceiling has been reached, as Shennan (2009) rightly points out.

It is therefore not surprising that the Early and Middle Neolithic of western Norway testifies to a continued broadening of the subsistence base (Hjelle et al., 2006:156) and a diversification of raw material acquisition patterns (Maier et al., 2022). While sites neither became much larger in size nor more frequent in numbers compared to previous time periods, larger sites in general became more frequent than smaller ones, suggesting an increased sedentism with potentially intensified settlement re-use at the expense of establishing new ones (Figure 4). Many of these shifts have previously been interpreted as resulting from social displays of prestige (Hjelle et al., 2006). While prestige certainly could have been the ultimate justification, its proximate motivation and archaeological correlates are difficult to define. It also does not explain very well why such behaviours emerged in the first place. More in line with the available evidence, is the emergence of a dynamic where an antecedent increase in population packing along the coast during the Late Mesolithic, coupled with increased territorial control (Bergsvik, 2006; Skjelstad, 2003), pushed individuals towards lower ranked resource patches that would have required additional subsistence means in order to raise their habitat suitability.

Such subsistence means would have been culturally transmitted via expanding networks of social contact and exchange (Kavanagh et al., 2018: 481), a process also seen with foraging groups around the Lower-Scheldt basin (Messiaen et al., 2023), and the archipelago of eastern Sweden and Åland (Vanhanen et al., 2019). Due to less favourable climatic conditions (Figure 5), low amounts of arable farmland (Solheim, 2021) and the fact that new and additional subsistence components appear to only have been partially appropriated among groups in Western Norway (Hjelle et al., 2006), the demographic benefits of a fully agricultural way of life were left untapped (Shennan, 2009), at least up until they were more decisively introduced at the onset of the Late Neolithic (Bergsvik et al., 2021).

Conclusion

The results of this study demonstrate how four palaeodemographic proxies correlates strongly with changes in annual mean temperature and temperature seasonality, a result that is in accordance with previous studies on continental and global scales (Ordonez and Riede, 2022; Tallavaara and Seppä, 2012; Tallavaara et al., 2015, 2018), as well as in northern Norway (Jørgensen, 2020). The long-term trend of growth and subsequent population decline throughout the Early and Middle Neolithic are also in agreement with studies conducted in other parts of Norway, southern Scandinavia and parts of continental Europe (Apel et al., 2018; Bergsvik et al., 2021; Friman and Lagerås, 2023; Jørgensen, 2020; Nielsen et al., 2019; Shennan et al., 2013; Tallavaara and Pesonen, 2020) and the results have been discussed in light of previous research pertaining to technology, subsistence, and mobility. By focusing exclusively on the role of climate change in population dynamics, this work has not addressed other factors such as conflict and violence (Fibiger et al., 2023), or the spread of diseases or other pathogens (Fournié et al., 2017; Susat et al., 2021), all of which undoubtedly shape long-term demographic trajectories in general (French, 2021). However, these additional pathways to population dynamics, intriguing as they may be, especially in the absence of any severe climatic deterioration, ultimately relies on empirical evidence that is currently unavailable through the archaeological record of Western Norway.

Future work would therefore do well to target strands of evidence that could allow access to such aspects of demography, because while the correlation between climate change and long-term population fluctuations was significant, the resolution of the archaeological record does not allow for any definitive causal inferences to be made, especially the kinds pertaining to its deteriorating effect on surrounding ecosystems. This is unfortunate considering how this work also highlighted significant episodes of population decline towards the end of the Late Mesolithic (7000–6000 cal. BP) and the Middle Neolithic (ca. 5200–4300 cal. BP) with simultaneous and significant re-configurations of the archaeological record, where the former highlights population decline and cultural loss, and the emergence of cultural novelties and parallel population growth and stability in the latter. In the case of the Late Mesolithic population decline, bayesian phylogenetics (Gjesfjeld and Jordan, 2019; Prentiss et al., 2022) applied to radiocarbon dated lithic assemblages from this region and time period would allow for a synchronised analysis of the results presented here. As for the Early and Middle Neolithic, eco-cultural niche models (Whitford, 2019) would allow for a better understanding of the potential limitations or opportunities of dispersal, and to what extent such might have been affected by changes in demography.

Moreover, conducting further and future cross-regional studies in other Scandinavian coastal regions where comparable studies have yet to be conducted would help to further develop the methodology, and either strengthen or contrast the results from Western Norway. Future work would also benefit from establishing protocols for data retrieval in the field, or the analysis of pertinent archaeological, palaeoecological, or even pathogenic traces, that could indicate the occurrence of large-scale socio-ecological deterioration with resulting demographic declines (Molloy, 2022).

Supplemental Material

sj-docx-1-hol-10.1177_09596836231185839 – Supplemental material for Living through changing climates: Temperature and seasonality correlate with population fluctuations among Holocene hunter-fisher-gatherers on the west coast of Norway

Supplemental material, sj-docx-1-hol-10.1177_09596836231185839 for Living through changing climates: Temperature and seasonality correlate with population fluctuations among Holocene hunter-fisher-gatherers on the west coast of Norway by Victor Lundström in The Holocene

Footnotes

Acknowledgements

I am tremendously grateful to my supervisors Knut Andreas Bergsvik, Daniela Hofmann and Felix Riede for their patient guidance during this work. This work would also not have been possible had not certain people granted me access to numerous databases. As such, I am tremendously grateful to colleagues such as Jostein Aksdal, Camilla Zinsli, Kim Darmark, Leif Inge Åstveit, Trond Klungseth Lødøen, Hanne Bente Årskog, Trond Eilev Linge and Kristoffer Dahle. I am also very grateful to Miikka Tallavaara who shared the necessary R-code for performing a taphonomic correction of the SPD, Bjørg Risebrobakken for proving time-series data on ocean temperatures and Alejo Ordonez for some last-minute consulting on the TraCE21k data set and Multiple Linear Regression. Finally, I would like to thank the two anonymous reviewers whose comments helped to significantly improve the final manuscript.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Victor Lundström

Supplemental material

Supplemental material for this article is available online:

References

Abbott

Niemeier

Timmreck

, et al. (2021) Volcanic climate forcing preceding the inception of the Younger Dryas: Implications for tracing the Laacher See eruption. Quaternary Science Reviews 274: 107260.

Allee

Bowen

(1932) Studies in animal aggregations: Mass protection against colloidal silver among goldfishes. The Journal of Experimental Zoology 61(2): 185–207.

Antão

Bates

Blowes

, et al. (2020) Temperature-related biodiversity change across temperate marine and terrestrial systems. Nature Ecology & Evolution 4(7): 927–933.

Apel

Wallin

Storå

, et al. (2018) Early Holocene human population events on the island of Gotland in the Baltic Sea (9200-3800 cal. BP). Quaternary International 465: 276–286.

Bader

Jungclaus

Krivova

, et al. (2020) Global temperature modes shed light on the holocene temperature conundrum. Nature Communications 11(1): 4726.

Barton

Aura Tortosa

Garcia-Puchol

, et al. (2018) Risk and resilience in the late glacial: A case study from the western Mediterranean. Quaternary Science Reviews 184: 68–84.

Berg-Hansen

(2018) Continuity and change in Late- and Post-Glacial social networks – knowledge transmission and blade production methods in Ahrensburg and Early Mesolithic Northwestern Europe. In: Knutsson

Knutsson

Apel

al.

(eds) Technology of early settlement in Northern Europe – Transmission of knowledge and culture. Sheffield: Equinox Publishing (The early settlement of Northern Europe, 2), p. 63–98.

Bergsvik

(2001) Sedentary and mobile hunterfishers in Stone Age Western Norway. Arctic Anthropology 38(1): 2–26.

Bergsvik

(2002) Arkeologiske undersøkelser ved Skatestraumen. Bind 1. Arkeologiske avhandlinger og rapporter fra Universitetet i Bergen 7. Bergen: Bergen Museum, University of Bergen.

10.

Bergsvik

(2006) Ethnic Boundaries in Neolithic Norway. Oxford: Archaeopress.

11.

Bergsvik

Darmark

Hjelle

, et al. (2021) Demographic developments in Stone Age coastal western Norway by proxy of radiocarbon dates, stray finds and palynological data. Quaternary Science Reviews 259: 106898.

12.

Bergsvik

David (2015) Crafting bone tools in Mesolithic Norway: A regional Eastern-related know-how. European Journal of Archaeology 18(2): 190–221.

13.

Bergsvik

Ritchie

(2020) Mesolithic fishing landscapes in western Norway. In: Schülke

(ed.) Coastal Landscapes of the Mesolithic. Human Engagement With the Coast From the Atlantic to the Baltic, 1st edn. Milton Park: Routledge, pp.229–263.

14.

Binford

(2001) Constructing Frames of Reference: An Analytical Method for Archaeological Theory Building Using Hunter-Gatherer and Environmental Data Sets (First Paperback Printing). Berkeley, CA: University of California Press.

15.

Bjerck

(2008) Norwegian Mesolithic trends. A review. In: Bailey

Spikins

(eds) Mesolithic Europe. New York: Cambridge University Press, pp. 60–106.

16.

Bjerck

(2016) Marine Ventures: Archaeological Perspectives on Human-Sea Relations. Sheffield: Equinox.

17.

Bjerck

Åstveit

(2008) Ormen Lange Nyhamna: NTNU Vitenskapsmuseets Arkeologiske Undersøkkelser. Forlag: Tapir.

18.

Bjune

Birks

HJB

Seppä

(2004) Holocene vegetation and climate history on a continental-oceanic transect in northern Fennoscandia based on pollen and plant macrofossils. Boreas 33(3): 211–223.

19.

Bjune

Bakke

Nesje

, et al. (2005) Holocene mean July Temperature and winter precipitation in western Norway inferred from palynological and glaciological lake-sediment proxies. The Holocene 15(2): 177–189.

20.

Bluhm

Surovell

(2019) Validation of a global model of taphonomic bias using geologic radiocarbon ages. Quaternary Research 91(1): 325–328.

21.

Boethius

Bergsvik

Nilsson

(2020) Knowledge from the ancient sea – A long-term perspective of human impact on aquatic life in Mesolithic Scandinavia. The Holocene 30(5): 632–645.

22.

Boyd

Richerson

(1985) Culture and the Evolutionary Process. (Paperback ed). Chicago, IL: University of Chicago Press.

23.

Breivik

(2014) Palaeo-oceanographic development and human adaptive strategies in the Pleistocene–Holocene transition: A study from the Norwegian coast. The Holocene 24(11): 1478–1490.

24.

Brown

Rijal

Heintzman

, et al. (2022) Paleoeconomy more than demography determined prehistoric human impact in Arctic Norway. PNAS Nexus 1(5): ac209.

25.

Burdukiewicz

(2011) Late glacial hunter-gatherer reactions to the Younger Dryas cooling event in the southern and eastern Baltic regions of Europe. Quaternary International 242(2): 302–312.

26.

Carlson

(2017) Quantitative Methods in Archaeology Using R. Cambridge: Cambridge University Press.

27.

Crema

Bevan

(2021) Inference from large sets of radiocarbon dates: Software and methods. Radiocarbon 63(1): 23–39.

28.

Cullberg

(1975) Prospecting the West Swedish Mesolithic. Norwegian Archaeological Review 8(1): 36–53.

29.

Cullberg

(1996) West Sweden: On the earliest settlements. In: Lars

(ed.) The Earliest Settlement of Scandinavia and its relationship with neighbouring areas. Stockholm: Almquist & Wiksell International, pp.177–189.

30.

Daley

Thomas

Holmes

, et al. (2011) The 8200yr BP cold event in stable isotope records from the North Atlantic region. Global and Planetary Change 79(3-4): 288–302.

31.

Damlien

(2015) Striking a difference? The effect of knapping techniques on blade attributes. Journal of Archaeological Science 63: 122–135.

32.

Damlien

(2016) Eastern pioneers in westernmost territories? Current perspectives on Mesolithic hunter–gatherer large-scale interaction and migration within Northern Eurasia. Quaternary International 419: 5–16.

33.

Degroot

Anchukaitis

Tierney

, et al. (2022) The history of climate and society: A review of the influence of climate change on the human past. Environmental Research Letters 17(10): 103001.

34.

Eigeland

(2015) Maskinmennesket i steinalderen. Endring og kontinuitet i steinteknologi fram motneolitiseringen av Øst-Norge. Oslo: Avhandling for graden Ph.d. Universitetet i Oslo.

35.

Ekblom Johansson

Bakke

Støren

, et al. (2020) Lake sediments reveal large variations in flood frequency over the last 6,500 years in south-Western Norway. Frontiers in Earth Science 8: 239.

36.

Eldevik

Risebrobakken

Bjune

, et al. (2014) A brief history of climate – The northern seas from the Last Glacial Maximum to global warming. Quaternary Science Reviews 106: 225–246.

37.

Fibiger

Ahlström

Meyer

, et al. (2023) Conflict, violence, and warfare among early farmers in Northwestern Europe. Proceedings of the National Academy of Sciences 120(4): e2209481119.

38.

Fjeldskaar

Bondevik

(2020) The Early-Mid holocene transgression (Tapes) at the Norwegian coast – Comparing observations with numerical modelling. Quaternary Science Reviews 242: 106435.

39.

Fournié

Pfeiffer

Bendrey

(2017) Early animal farming and zoonotic disease dynamics: Modelling brucellosis transmission in Neolithic goat populations. Royal Society Open Science 4(2): 160943.

40.

Fredsjö

(1953) Studier i Västsveriges Äldre Stenålder. Göteborg: Erlanders Boktryckeri Aktiebolag.

41.

French

(2015) The demography of the Upper Palaeolithic hunter–gatherers of southwestern France: A multi-proxy approach using archaeological data. Journal of Anthropological Archaeology 39: 193–209.

42.

French

(2021) Palaeolithic Europe: A Demographic and Social Prehistory, 1st edn. Cambridge: Cambridge University Press.

43.

French

Chamberlain

(2021) Demographic uniformitarianism: The theoretical basis of prehistoric demographic research and its cross-disciplinary challenges. Philosophical Transactions of the Royal Society of London 376(1816): 20190720.

44.

Friman

Lagerås

(2023) From Neolithic boom-and-bust to iron age peak and decline: Population and settlement dynamics in southern Sweden inferred from summed radiocarbon dates. European Journal of Archaeology 26: 168–188.

45.

Ghirlanda

Enquist

(2007) Cumulative culture and explosive demographic transitions. Quality & Quantity 41(4): 591–600.

46.

Gjesfjeld

Jordan

(2019) Contributions of Bayesian phylogenetics to exploring patterns of macroevolution in archaeological data. In: Prentiss

(ed.) Handbook of Evolutionary Research in Archaeology. New York: Springer International Publishing, pp.161–182.

47.

Groucutt

Prendergast

Riede

(2022) Editorial: Extreme events in human evolution: From the Pliocene to the Anthropocene. Frontiers in Earth Science 10: 1026989.

48.

Günther

Malmström

Svensson

, et al. (2018) Population genomics of Mesolithic Scandinavia: Investigating early postglacial migration routes and high-latitude adaptation. PLoS Biology 16(1): e2003703.

49.

Hamilton

Tallavaara

(2021) Statistical inference, scale and noise in comparative anthropology. Nature Ecology & Evolution 6(2): 122–122.

50.

Hinz

Feeser

Sjögren

K-G

, et al. (2012) Demography and the intensity of cultural activities: An evaluation of Funnel Beaker Societies (4200–2800 cal BC). Journal of Archaeological Science 39(10): 3331–3340.

51.

Hjelle

Hufthammer

Bergsvik

(2006) Hesitant hunters: A review of the introduction of agriculture in western Norway. Environmental Archaeology 11(2): 147–170.

52.

Hjelle

Lødøen

(2017) Dating of rock art and the effect of human activity on vegetation: The complementary use of archaeological and scientific methods. Quaternary Science Reviews 168: 194–207.

53.

Jordan

(2015) Technology as Human Social Tradition: Cultural Transmission Among Hunter-Gatherers. Berkeley, CA: University of California Press.

54.

Jørgensen

(2020) The palaeodemographic and environmental dynamics of prehistoric Arctic Norway: An overview of human-climate covariation. Quaternary International 549: 36–51.

55.

Jørgensen

Arntzen

Skandfer

, et al. (2023) Source-sink dynamics drove punctuated adoption of early pottery in Arctic Europe under diverging socioecological conditions. Quaternary Science Reviews 299: 107825.

56.

Jørgensen

Riede

(2019) Convergent catastrophes and the termination of the Arctic Norwegian Stone Age: A multi-proxy assessment of the demographic and adaptive responses of mid-Holocene collectors to biophysical forcing. The Holocene 29(11): 1782–1800.

57.

Kalis

Merkt

Wunderlich

(2003) Environmental changes during the Holocene climatic optimum in central Europe - human impact and natural causes. Quaternary Science Reviews 22(1): 33–79.

58.

Karger

Nobis

Normand

, et al. (2021) CHELSA-TraCE21k v1.0. Downscaled transient temperature and precipitation data since the last glacial maximum [Preprint]. Climate Modelling/Modelling only/Holocene. DOI: 10.5194/cp-2021-30

59.

Kashuba

Kırdök

Damlien

, et al. (2019) Ancient DNA from mastics solidifies connection between material culture and genetics of mesolithic hunter-gatherers in Scandinavia. Communications biology 2(1): 185.

60.

Kavanagh

Vilela

Haynie

, et al. (2018) Hindcasting global population densities reveals forces enabling the origin of agriculture. Nature Human Behaviour 2(7): 478–484.

61.

Kindgren

(1996) Reindeer or seals? Some Late Palaeolithic sites in central Bohuslän. In: Lars

(ed.) The Earliest Settlement of Scandinavia and its relationship with neighbouring areas. Stockholm: Almquist & Wiksell International, pp.191–205.

62.

Laland

Brown

(2006) Niche construction, human behavior, and the adaptive-lag hypothesis. Evolutionary Anthropology Issues News and Reviews 15(3): 95–104.

63.

Laland

Brown

(2011) Sense and Nonsense: Evolutionary Perspectives on Human Behaviour, 2nd edn. New York City: Oxford University Press.

64.

Lawrence

Palmisano

de Gruchy

(2021) Collapse and continuity: A multi-proxy reconstruction of settlement organization and population trajectories in the Northern Fertile Crescent during the 4.2kya rapid Climate Change event. PLoS One 16(1): e0244871.

65.

Leonardi

Hallett

Beyer

, et al. (2023) Pastclim 1.2: An R package to easily access and use paleoclimatic reconstructions. Ecography 2023(3): e06481.

66.

Lundström

Peters

Riede

(2021) Demographic estimates from the Palaeolithic–Mesolithic boundary in Scandinavia: Comparative benchmarks and novel insights. Philosophical Transactions of the Royal Society of London 376(1816): 20200037.

67.

Lundström

Riede

(2019) A spatially explicit model of Final Palaeolithic population densities for southern Scandinavia in the period between 14,000 and 12,700 cal BP. Journal of Archaeological Science Reports 26: 101886.

68.

Lødøen

(2014) På sporet av senmesolittiske døderiter: Fornyet innsikt i alderen og betydningen av bergkunsten i Ausevik, Flora, Sogn og Fjordane. Primitive Tider 16: 51–75.

69.

Maier

Sauer

Bergsvik

(2022) Transport patterns as heuristic testing variables for the social coherence of taxonomic units at different spatial scales. Journal of Paleolithic Archaeology 5(1): 8.

70.

Mandryk

(1993) Hunter-gatherer social costs and the nonviability of submarginal environments. Journal of Anthropological Research 49(1): 39–71.

71.

Mangerud

Aarseth

Hughes

ALC

, et al. (2016) A major re-growth of the Scandinavian ice sheet in western Norway during Allerød-Younger Dryas. Quaternary Science Reviews 132: 175–205.

72.

Manninen

Asheichyk

Jonuks

, et al. (2021a) Using radiocarbon dates and tool design principles to assess the role of composite slotted bone tool technology at the intersection of adaptation and Culture-History. Journal of Archaeological Method and Theory 28(3): 845–870.

73.

Manninen

Damlien

Kleppe

, et al. (2021b) First encounters in the north: Cultural diversity and gene flow in early Mesolithic Scandinavia. Antiquity 95(380): 310–328.

74.

Manninen

Fossum

Ekholm

, et al. (2023) Early postglacial hunter-gatherers show environmentally driven “false logistic” growth in a low productivity environment. Journal of Anthropological Archaeology (70): 1–13. DOI: 10.1016/j.jaa.2023.101497

75.

Messiaen

Vandendriessche

Crombé

(2023) The neolithization process in the lower-Scheldt Basin (Belgium, mid-6th to mid-4th Millennium cal BC) from a lithic technological perspective. Lithic Technology 48: 168–193.

76.

Metcalf

CJE

Pavard

(2007) Why evolutionary biologists should be demographers. Trends in Ecology & Evolution 22(4): 205–212.

77.

Molloy

(2022) Was there a 3.2 ka crisis in Europe? A critical comparison of climatic, environmental, and archaeological evidence for radical change during the Bronze age–Iron Age transition. Journal of Archaeological Research 31: 1–64. DOI: 10.1007/s10814-022-09176-6

78.

Morin

(2008) Evidence for declines in human population densities during the early Upper Paleolithic in western Europe. Proceedings of the National Academy of Sciences 105(1): 48–53.

79.

Morin

(2015) How Traditions Live and Die. New York City: Oxford University Press.

80.

Nielsen

(2021) A late Mesolithic forager dispersal caused Pre-Agricultural demographic transition in Norway. Oxford Journal of Archaeology 40(2): 153–175.

81.

Nielsen

Persson

Solheim

(2019) De-Neolithisation in southern Norway inferred from statistical modelling of radiocarbon dates. Journal of Anthropological Archaeology 53: 82–91.

82.

Nyland

Walker

Warren

(2021) Evidence of the Storegga Tsunami 8200 bp? An archaeological review of impact after a large-scale marine event in Mesolithic Northern Europe. Frontiers in Earth Science 9: 767460.

83.

Odling-Smee

Laland

Feldman

(2003) Niche Construction: The Neglected Process in Evolution. Princeton, NJ: Princeton University Press.

84.

Olsen

(1992) Kotedalen—En Boplass Gjennom 5000 År. Bind 1: Fangstbosetning Og Tidlig Jordbruk i Vestnorsk Steinalder: Nye Funn Og Nye Perspektiver. Bergen: University of Bergen.

85.

Olsen

Alsaker

(1984) Greenstone and diabase utilization in the stone age of western Norway: Technological and socio-cultural aspects of axe and adze production and distribution. Norwegian Archaeological Review 17(2): 71–103.

86.

Ordonez

Riede

(2022) Changes in limiting factors for forager population dynamics in Europe across the last glacial-interglacial transition. Nature Communications 13: 5140.

87.

Palmisano

Bevan

Shennan

(2017) Comparing archaeological proxies for long-term population patterns: An example from central Italy. Journal of Archaeological Science 87: 59–72.

88.

Pelton

Mackie

Kelly

, et al. (2022) Accurate population proxies do not exist between 11.7 and 15 ka in North America. Nature Communications 13(1): 4694.

89.

Prentiss

Walsh

Gjesfjeld

, et al. (2022) Cultural macroevolution in the middle to late holocene Arctic of east Siberia and north America. Journal of Anthropological Archaeology 65: 101388.

90.

R Core Team (2020) R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing, https://www.R-project.org/

91.

Renssen

Seppä

Heiri

, et al. (2009) The spatial and temporal complexity of the holocene thermal maximum. Nature Geoscience 2(6): 411–414.

92.

Rick

(1987) Dates as data: An examination of the Peruvian preceramic radiocarbon record. American Antiquity 52(1): 55.

93.

Riede

(2009) Climate and demography in early prehistory: using calibrated (14)C dates as population proxies. Human Biology 81(2-3): 309–337.

94.

Riede

Pedersen

(2018) Late glacial human dispersals in Northern Europe and disequilibrium dynamics. Human Ecology 46(5): 621–632.

95.

Risebrobakken

Dokken

Smedsrud

, et al. (2011) Early Holocene temperature variability in the Nordic Seas: The role of oceanic heat advection versus changes in orbital forcing. Paleoceanography 26(4): 2011PA002117.

96.

Ritchie

Hufthammer

Bergsvik

(2016) Fjord fishing in Mesolithic Western Norway. Environmental Archaeology 21(4): 309–316.

97.

Roth

(2004) Culture, Biology, and Anthropological Demography, 1st edn. Cambridge: Cambridge University Press.

98.

Røthe

Bakke

Støren

EWN

(2019) Glacier outburst floods reconstructed from lake sediments and their implications for Holocene variations of the plateau glacier Folgefonna in western Norway. Boreas 48(3): 616–634.

99.

Schmidt

Hilpert

Kretschmer

, et al. (2021) Approaching prehistoric demography: Proxies, scales and scope of the Cologne Protocol in European contexts. Philosophical Transactions of the Royal Society of London 376(1816): 20190714.

100.

Schmitt

Larsson

Burdukiewicz

, et al. (2009) Chronological Insights, cultural change, and resource exploitation on the west coast of Sweden during the late Palaeolithic/early Mesolithic transition. Oxford Journal of Archaeology 28(1): 1–27.

101.

Seppä

Bjune

Telford

, et al. (2009) Last nine-thousand years of temperature variability in Northern Europe. Climate of the Past 5(3): 523–535.

102.

Shennan

(2000) Population, culture history, and the dynamics of culture change. Current Anthropology 41(5): 811–835.

103.

Shennan

(2009) Evolutionary demography and the population history of the European Early Neolithic. Human Biology 81(2/3): 339–355.

104.

Shennan

Downey

Timpson

, et al. (2013) Regional population collapse followed initial agriculture booms in Mid-Holocene Europe. Nature Communications 4(1): 2486.

105.

Shennan

Sear

(2021) Archaeology, demography and life history theory together can help us explain past and present population patterns. Philosophical Transactions of the Royal Society of London 376(1816): 20190711.

106.

Skjelstad

(2003) Regionalitet i Vestnorsk Mesolitikum. Råstoffbruk og Sosiale Grenser på Vestlandskysten i Mellom-og Senmesolitikum [MA Thesis]. Department of Archaeology, University of Bergen.

107.

Sletvold

(2021) Store Data Som Inngang Til Fortidig Demografi—Variasjon i Demografi Og Bosetningsintensitet Langs Kysten Av Midt-Norge i Mesolittisk Tid. Trondheim: Norwegian University of Science and Technology.

108.

Smith

(2014) Estimation of a genetically viable population for multigenerational interstellar voyaging: Review and data for project Hyperion. Acta Astronautica 97: 16–29.

109.

Smith

Winterhalder

(1992) Evolutionary Ecology and Human Behavior. Hawthorne, NY: Aldine de Gruyter.

110.

Solheim

(2021) Timing the emergence and development of arable farming in southeastern Norway by using summed probability distribution of radiocarbon dates and a bayesian age model. Radiocarbon 63(5): 1503–1524.

111.

Solheim

Persson

(2018) Early and mid-Holocene coastal settlement and demography in southeastern Norway: Comparing distribution of radiocarbon dates and shoreline-dated sites, 8500–2000 cal. BCE. Journal of Archaeological Science Reports 19: 334–343.

112.

Sperber

(1996) Explaining Culture: A Naturalistic Approach. Oxford: Blackwell.

113.

Strimling

Enquist

Eriksson

(2009) Repeated learning makes cultural evolution unique. Proceedings of the National Academy of Sciences 106(33): 13870–13874.

114.

Stroeven

Hättestrand

Kleman

, et al. (2016) Deglaciation of Fennoscandia. Quaternary Science Reviews 147: 91–121.

115.

Surovell

Brantingham

(2007) A note on the use of temporal frequency distributions in studies of prehistoric demography. Journal of Archaeological Science 34(11): 1868–1877.

116.

Surovell

Byrd Finley

Smith

, et al. (2009) Correcting temporal frequency distributions for taphonomic bias. Journal of Archaeological Science 36(8): 1715–1724.

117.

Susat

Lübke

Immel

, et al. (2021) A 5,000-year-old hunter-gatherer already plagued by Yersinia pestis. Cell Reports 35(13): 109278.

118.

Sørensen

Rankama

Kankaanpää

, et al. (2013) The first eastern migrations of people and knowledge into Scandinavia: Evidence from studies of Mesolithic technology, 9th-8th Millennium BC. Norwegian Archaeological Review 46(1): 19–56.

119.

Tallavaara

Eronen

Luoto

(2018) Productivity, biodiversity, and pathogens influence the global hunter-gatherer population density. Proceedings of the National Academy of Sciences 115(6): 1232–1237.

120.

Tallavaara

Jørgensen

(2021) Why are population growth rate estimates of past and present hunter–gatherers so different? Philosophical Transactions of the Royal Society of London 376(1816): 20190708.

121.

Tallavaara

Luoto

Korhonen

, et al. (2015) Human population dynamics in Europe over the Last Glacial Maximum. Proceedings of the National Academy of Sciences 112(27): 8232–8237.

122.

Tallavaara

Pesonen

(2020) Human ecodynamics in the north-west coast of Finland 10,000–2000 years ago. Quaternary International 549: 26–35.

123.

Tallavaara

Seppä

(2012) Did the mid-Holocene environmental changes cause the boom and bust of hunter-gatherer population size in eastern Fennoscandia? The Holocene 22(2): 215–225.

124.

Timpson

Barberena

Thomas

, et al. (2021) Directly modelling population dynamics in the South American arid diagonal using ¹⁴C dates. Philosophical Transactions of the Royal Society of London 376(1816): 20190723.

125.

Timpson

Colledge

Crema

, et al. (2014) Reconstructing regional population fluctuations in the European Neolithic using radiocarbon dates: A new case-study using an improved method. Journal of Archaeological Science 52: 549–557.

126.

Tremayne

Winterhalder

(2017) Large mammal biomass predicts the changing distribution of hunter-gatherer settlements in mid-late Holocene Alaska. Journal of Anthropological Archaeology 45: 81–97.

127.

Vanhanen

Gustafsson

Ranheden

, et al. (2019) Maritime hunter-gatherers adopt cultivation at the farming extreme of Northern Europe 5000 years ago. Scientific Reports 9(1): 4756.

128.

Vollset

Goren

Yuan

C-W

, et al. (2020) Fertility, mortality, migration, and population scenarios for 195 countries and territories from 2017 to 2100: A forecasting analysis for the Global Burden of Disease Study. The Journal-Lancet 396(10258): 1285–1306.

129.

Welinder

(1979) Prehistoric demography, Acta archaeologica Lundensia. Series tertia in 8° minore: LiberLäromedel/Gleerup: Habelt, Lund: Bonn.

130.

Whitford

(2019) Characterizing the cultural evolutionary process from eco-cultural niche models: Niche construction during the Neolithic of the Struma River Valley (c. 6200–4900 BC). Archaeological and Anthropological Sciences 11(5): 2181–2200.

131.

Zhang

Mace

(2021) Cultural extinction in evolutionary perspective. Evolutionary Human Sciences 3: e30.

132.

Zhu

Galbraith

Reyes-García

, et al. (2021) Global hunter-gatherer population densities constrained by influence of seasonality on diet composition. Nature Ecology & Evolution 5(11): 1536–1545.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

10.50 MB