# Variation in the number of radiocarbon (14C) dates does not equal change in Neolithic population size over time

Anders Högberg, Kristian Brink, Torbjörn Brorsson, Helena Malmström, Elisabeth Rudebeck, Miguel de Navascués

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Anders Högberg, Kristian Brink, Torbjörn Brorsson, Helena Malmström, Elisabeth Rudebeck, et al. Variation in the number of radiocarbon (14C) dates does not equal change in Neolithic population size over time. Journal of Archaeological Science: Reports, 2025, 67, pp.105436. 10.1016/j.jasrep.2025.105436. hal-05362578

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# Variation in the number of radiocarbon (14C) dates does not equal change in Neolithic population size over time

Anders Högberg<sup>a,b,*</sup>, Kristian Brink<sup>c</sup>, Torbjörn Brorsson<sup>d</sup>, Helena Malmström<sup>e,f</sup>, Elisabeth Rudebeck<sup>c</sup>, Miguel de Navascués<sup>f</sup>

<figure><img src="image_2.png" /></figure>

## ARTICLE INFO

**Keywords:**

Radiocarbon dating

Population dynamics

Lambda model

Old wood effect

Excavation results

Southwest Sweden

## ABSTRACT

This study investigates the relationship between radiocarbon (14C) dates and estimations of population size during the Neolithic period, approximately 5950–3650 cal BP, in the Malmö area of southwest Sweden. Utilising a dataset of 1025 radiocarbon samples, of which 732 were selected for detailed analysis, we applied the Lambda (λ) model approach to identify significant periods of change in the number of radiocarbon dates. We also address the complexities introduced by the old wood effect (OWE), which impacts the reliability of radiocarbon dating. Our findings highlight four distinct periods of increase and decrease in the amount of radiocarbon dates during the time period under study. By comparing these results with other data from over 45 years of extensive contract archaeological excavations, we demonstrate that the assumption of a correlation between the number of radiocarbon dates and population size is overly simplistic. Our analysis reveals that a high number of dates does not necessarily guarantee credible population size inferences. These findings suggest that similar conditions could hypothetically apply to other spatiotemporal contexts, thereby calling into question the reliability of radiocarbon dates as sole indicators of population dynamics.

## 1. Introduction

In recent years, numerous studies have utilised data from extensive sets of radiocarbon (14C) dates to examine Stone Age spatiotemporal variations in population size and dynamics (e.g., Shennan et al., 2013; Crema and Bevan, 2021; Crema and Shoda, 2021; Edinborough et al., 2021; Friman and Lagerås, 2022; Nielsen, 2022; Winther Johannsen et al., 2024, see also Appendix 1 in Becerra-Valdivia et al., 2020). Friman and Lagerås (2022: 169) describe this approach as based on the assumption that “there is a positive [...] relationship between the number of radiocarbon dates and population size, provided that the dates originate from archaeological features and that the number of dates is substantial” (see also Rick, 1987; Shennan et al., 2013). Similarly, Crema and Bevan (2021: 23) state that “given a sufficiently large set of 14C dates taken from anthropogenic samples, the changing

frequency of dates over time will reflect fluctuations in past human activity and, consequently, in human population”.

However, as Freeman et al. (2018: 454) assert, assuming a relationship between the number of radiocarbon dates and population size is “a poor assumption”. This is because population density is not the sole factor influencing the number of radiocarbon dates available from an area. Factors such as variations in prehistoric landscape use, excavation intensity, sampling strategies, sample processing, taphonomy, and the preservation status of archaeological features all impact on the number of radiocarbon dates from any given area (see discussions in Cooper and Green, 2017; Palmisano et al., 2017; Becerra-Valdivia et al., 2020; Crema and Bevan, 2021; Crema and Shoda, 2021; Nielsen, 2022; Clist et al., 2023; Arthur et al., 2024; de Navascués et al., 2025). This is acknowledged in several studies (e.g., Rick, 1987; Palmisano et al., 2017; Freeman et al., 2018; Crema and Bevan, 2021), and strategies to

<footnote><sup>a</sup> School of Cultural Science, Archaeology, Linnaeus University, Kalmar SE-391 82, Sweden</footnote>

<footnote><sup>b</sup> Palaeo-Research Institute, University of Johannesburg, P.O. Box 524, Auckland Park ZA-2006, South Africa</footnote>

<footnote><sup>c</sup> Sydsvensk Arkeologi, Erlandsrovägen 5, Vintrie 218 45, Sweden</footnote>

<footnote><sup>d</sup> Kontoret för Keramiska Studier, Rågåkravägen 145, Nyhamnsläge 263 75, Sweden</footnote>

<footnote><sup>e</sup> Human Evolution Program, Department of Organismal Biology, Uppsala University, Uppsala SE-752 36, Sweden</footnote>

<footnote><sup>f</sup> CBGP, INRAE, CIRAD, IRD, Institute Agro, University of Montpellier – Montpellier, France</footnote>

<footnote>* Corresponding author at: School of Cultural Science, Archaeology, Linnaeus University, Kalmar SE-391 82, Sweden.</footnote>

<footnote>E-mail addresses: anders.hogberg@lnu.se (A. Högberg), kristian.brink@sydsvenskarkeologi.se (K. Brink), torbjorn.brorsson@keramiskastudier.se (T. Brorsson), helena.malmstrom@ebc.uu.se (H. Malmström), elisabeth.rudebeck@sydsvenskarkeologi.se (E. Rudebeck), miguel.navascues@inrae.fr (M. de Navascués).</footnote>
address this “poor assumption” have been proposed. For instance, Shennan et al. (2013) and Arthur et al. (2024) balance variations in summed calibrated radiocarbon date distributions against climate change models. Palmisano et al. (2017) expand upon raw counts of archaeological sites, estimates of changing settlement sizes, and summed radiocarbon probability distributions to examine long-term population trends. Freeman et al. (2018) develop a model that considers not only changes in population size and dynamics but also variations in prehistoric energy consumption. Edinborough et al. (2017) test against known demographic events from written and oral history. Winther Johannsen et al. (2024) use quantitative modelling of radiocarbon dates, time series of material culture and palynological data to explore hypothetical relationships between population increase, intensified food production and cultural diversity. Several studies have also explored new software packages and computing technologies that enable researchers to use a range of statistical approaches to develop compensation models for inherent biases in the radiocarbon dates used for analysis (see discussions in Shennan et al., 2013; Crema and Shoda, 2021; Nielsen, 2022; Arthur et al., 2024; de Navascués et al., 2025).

In this study, we analyse a dataset of individual radiocarbon dates from the Malmö area in southwest Sweden to construct a model of population variation during the Neolithic period, approximately

5950–3650 cal BP in this region (Fig. 1, Table 1) (Högberg et al., 2022). We apply the recently introduced Lambda (λ) model approach (de Navascués et al., 2025) and develop a correction for samples affected by the old wood effect (OWE). Additionally, we draw upon over 45 years of extensive contract archaeological excavations in the Malmö area to investigate the spatiotemporal density of human activity across the landscape. By comparing the results of these approaches, we highlight the challenges involved in inferring variation in Neolithic population size over time from the analysis of abundant radiocarbon dates.

<caption>**Table 1**</caption>

Archaeological time periods within the Neolithic, with cal BP values as used in this study (based on Andersson, 2004; Iversen, 2015; Vandkilde, 2017).

<table><thead><tr><td>Period</td><td>Cal BP</td></tr></thead><tbody><tr><td>Early Neolithic I (EN I)</td><td>5950–5450</td></tr><tr><td>Early Neolithic II (EN II)</td><td>5450–5250</td></tr><tr><td>Middle Neolithic A (MN A)</td><td>5250–4750</td></tr><tr><td>Middle Neolithic B (MN B)</td><td>4750–4300</td></tr><tr><td>Late Neolithic I (LN I)</td><td>4300–3950</td></tr><tr><td>Late Neolithic II (LN II)</td><td>3950–3650</td></tr></tbody></table>

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** South Scandinavia with the area around present-day Malmö Municipality enlarged. The black dot on the small map to the upper right marks the location of Malmö in south-western Sweden. Within the Malmö area, more than 45 years of extensive, large-scale contract archaeological excavations have generated a wealth of prehistoric material that has been systematically investigated and analysed (Nilsson and Rudebeck, 2010). Yellow marks excavated areas, derived from GIS data curated by Sydsvensk Arkeologi AB. Blue to the left in the large map is Öresund. The black line marks the boundary of Malmö Municipality. Lidar data in background map © Lantmäteriet, I2014/00893.</figcaption></figure>
## 2. Radiocarbon dates, gathering our data

From a dataset comprising 1025 radiocarbon dates (from which 965 are from unique contexts) covering the time period in question, 732 were used for analysis. The large amount of radiocarbon dates from the Malmö area can be illustrated like this: Malmö Municipality covers circa 158 square kilometres, and of these approximately 10 square kilometres have been archaeologically investigated (6.3 %). The figures derive from GIS excavation data, curated by Sydsvensk Arkeologi AB (Fig. 1). Based on these numbers, the amount of radiocarbon dates per investigated square kilometre included in our study is circa 100. The dates were selected according to these criteria: In line with Friman and Lagerås (2022: 170), we only include dates from archaeological features or layers, excluding those from sediments or other natural deposits. In contrast to Friman and Lagerås (2022), we use 100 years as a threshold to distinguish dates with lower standard deviations from those with higher standard deviations (see Iversen, 2015: 21). Additionally, we include a variable to differentiate dated samples with a short-term lifetime (e.g. carbonized cereals) from those with a longer lifetime (e.g. charcoal from heartwood of old oak). Since Swedish contract archaeology has been research-based since the late 1990s (i.e., not merely 'rescue' or 'documentation' focused, see Nilsson and Rudebeck, 2010; Högberg, 2024 for discussion), we have not included a discriminating variable separating samples from contract archaeology and university-based research projects (see discussion in Friman and Lagerås, 2022). To ensure the inclusion of all dates from the time around the edges of our period of interest, the abundance of radiocarbon dates is modelled over a larger period (6500–2700 uncal BP, see Supplementary Material S1).

The radiocarbon dated samples included here were analysed by different laboratories over the past 70 years (Table 2, Supplementary Material S2). The samples were collected by archaeologists with varying levels of expertise in sampling protocols, working under radically different conditions in terms of available research resources, time, and

<table><caption>**Table 2**
Number of radiocarbon dates per laboratory and decade.</caption><thead><tr><th>Laboratories</th><th>Decade</th><th>Number of dates</th><th>Cerealia, number and %</th></tr></thead><tbody><tr><td>AAR (University of Aarhus, Denmark)</td><td>2010–2019</td><td>2</td><td>—</td></tr><tr><td rowspan="3">BETA (Beta Analytic, USA)</td><td>1980–1989</td><td>1</td><td>—</td></tr><tr><td>1990–1999</td><td>42</td><td>1 (2 %)</td></tr><tr><td>2000–2009</td><td>3</td><td>—</td></tr><tr><td>BM (British Museum, England)</td><td>1970–1979</td><td>1</td><td>—</td></tr><tr><td>GrA (Groningen AMS, Netherlands)</td><td>2000–2009</td><td>24</td><td>—</td></tr><tr><td rowspan="3">Lu (Lund, Sweden)</td><td>1970–1979</td><td>5</td><td>—</td></tr><tr><td>1980–1989</td><td>28</td><td>—</td></tr><tr><td>1990–1999</td><td>16</td><td>—</td></tr><tr><td rowspan="3">LuA (Lund, Sweden)</td><td>1990–1999</td><td>27</td><td>—</td></tr><tr><td>2000–2009</td><td>4</td><td>—</td></tr><tr><td>2000–2009</td><td>15</td><td>9 (60 %)</td></tr><tr><td rowspan="3">St (Stockholm, Sweden)</td><td>2010–2019</td><td>1</td><td>—</td></tr><tr><td>1960–1969</td><td>1</td><td>—</td></tr><tr><td>1980–1989</td><td>19</td><td>—</td></tr><tr><td rowspan="3">U (Uppsala University, Sweden)</td><td>1990–1999</td><td>3</td><td>—</td></tr><tr><td>1950–1959</td><td>1</td><td>—</td></tr><tr><td>1980–1989</td><td>4</td><td>4 (100 %)</td></tr><tr><td rowspan="5">Ua (Uppsala AMS, Sweden)</td><td>1990–1999</td><td>209</td><td>66 (32 %)</td></tr><tr><td>2000–2009</td><td>441</td><td>163 (37 %)</td></tr><tr><td>2010–2019</td><td>170</td><td>29 (17 %)</td></tr><tr><td>2020–</td><td>6</td><td>2 (33 %)</td></tr><tr><td>2010–2019</td><td>1</td><td>—</td></tr><tr><td>UBA (Belfast, Northern Ireland)</td><td>2010–2019</td><td>1</td><td>—</td></tr><tr><td>UGAMS (University of Georgia, USA)</td><td>2010–2019</td><td>1</td><td>—</td></tr><tr><td>Total</td><td></td><td>1 025</td><td>274 (27 %)</td></tr></tbody></table>

budget. Consequently, the samples were collected at different times, for diverse reasons and based on varied excavation rationales. They were also handled with different sample processing protocols (see discussion in Becerra-Valdivia et al., 2020). We have extracted our data from various sources: radiocarbon laboratory reports, archive documents, excavation reports, and published articles and books. As a result, there are variations in how the data was initially organised. Therefore, we standardized and made the data comparable through a manual quality checked for each post in our radiocarbon database (see Supplementary Material S2, S3). Note, intervals of radiocarbon values extracted from our database are listed in uncalibrated BP (i.e. 14C years). Results from our analyses are presented in calibrated BP (i.e. the number of years before 1950). Complimentary information for our tables can be found in Supplemental Materials.

The predominant material dated in our study is charcoal (51 %), followed by carbonised cereals (27 %) (Tables 2 and 3). As Table 2 shows, carbonised cereals have been a common material for dating over the past 35 years. The most common archaeological context for sampling for radiocarbon analysis is pits (41 %), followed by post-built houses (23 %) (Table 4).

## 3. The Lambda(λ) model approach

Variation in the abundance of radiocarbon dates through time was estimated with an Approximate Bayesian Computation analysis, using the Lambda(λ) model with methods developed in de Navascués et al. (2025). The abundance of radiocarbon dates per year are modelled with a Poisson distribution with parameter λ<sub>b</sub>, which represents the expected number of samples of dates for year t. Parameter λ changes through time according to a piecewise exponential model, i.e. the study period is divided in *m* sub-periods, within each period *x*, parameter λ changes exponentially from λ<sub>x-1</sub> to λ<sub>x</sub>, with λ<sub>0</sub> the initial value of λ.

Samples from 6500 to 2700 cal BP, were divided in *m* = 10 sub-periods of heterogeneous lengths. A symmetric Dirichlet distribution, Dir(1), was used as a prior for the relative lengths of the sub-periods. A log-uniform prior in the range (0.001, 2) was used for parameter λ<sub>0</sub> (λ at 6000 cal BP) and consecutive λ<sub>x</sub> values were determined as λ<sub>x</sub> = max (min(φ/λ<sub>x</sub> - 1, λ<sub>max</sub>), λ<sub>min</sub>) with φ taken from a log-uniform distribution in the range (0.1, 10), and λ<sub>min</sub> = 0.001 and λ<sub>max</sub> = 2 (as in Boitard et al., 2016). A reference table of 100 000 simulations was produced using these priors.

Age of simulated samples was transformed to Conventional Radiocarbon Age (CRA) and processed using IntCal20 calibration curve (Reimer et al., 2020) as implemented in rcarbon version 1.5.1 (Bevan et al., 2023). For each simulation, the following summary statistics were calculated on the ensemble of CRAs: total number of samples, number of samples at each consecutive interval of uncalibrated ages of size δ (with δ = 25, 50, and 100) and the difference in number of samples between those consecutive intervals. The same summary statistics were calculated for the ensemble of radiocarbon data from the Malmö area.

The value of λ<sub>t</sub> every 25 years, from *t* = 6000 to *t* = 3200, was inferred by training random forests on the reference table, in an approach known as Approximate Bayesian Computation via Random Forests (ABCRF; Raynal et al., 2019) and using the observed summary statistics to make parameter predictions with those random forests. The posterior distribution of each parameter λ<sub>t</sub> was summarized by the median (as point estimate) and the 95 % credibility interval.

### 3.1. Modelling old wood effect

Following Edinborough et al. (2021: Appendix A. Supplementary data), we include a variable related to the so-called old wood effect (OWE). As researchers rarely define a specific age of “old wood” (e.g. Persson, 1999; Ekholm, 2015, but see Allen and Huebert, 2014; Edinborough et al., 2021 for exceptions), we consider samples with OWE any sample that has not a short lifetime. Therefore, this category includes
<caption>**Table 3**</caption>

Dated material. Note, we only list the 693 dates in the table that are within the 6000–3000 uncalibrated BP range in our database. Hence, here we do not list dates that are included in our analysis to balance around the edges of our period of interest.

<table><caption>**Table 4**</caption><thead><tr><th>BP uncalibrated</th><th>Bone, Homo</th><th>Animal bone, domestic</th><th>Animal bone, game</th><th>Cereal</th><th>Hazelnut shell</th><th>Charcoal</th><th>Other</th><th>Total</th></tr></thead><tbody><tr><td>6000–5501</td><td>–</td><td>–</td><td>1</td><td>–</td><td>2</td><td>4</td><td>–</td><td>7</td></tr><tr><td>5500–5001</td><td>–</td><td>1</td><td>1</td><td>3</td><td>5</td><td>56</td><td>5</td><td>71</td></tr><tr><td>5000–4501</td><td>3</td><td>11</td><td>–</td><td>40</td><td>16</td><td>117</td><td>16</td><td>203</td></tr><tr><td>4500–4001</td><td>5</td><td>8</td><td>1</td><td>24</td><td>5</td><td>75</td><td>8</td><td>126</td></tr><tr><td>4000–3501</td><td>9</td><td>7</td><td>–</td><td>82</td><td>5</td><td>62</td><td>15</td><td>180</td></tr><tr><td>3500–3000</td><td>9</td><td>12</td><td>–</td><td>39</td><td>–</td><td>39</td><td>7</td><td>106</td></tr><tr><td>Total</td><td>26</td><td>39</td><td>3</td><td>188</td><td>33</td><td>353</td><td>51</td><td>693</td></tr></tbody></table>

Number of dates per context group. Note, we only list the 693 dates in the table that are within the 6000–3000 uncalibrated BP range in our database. Hence, here we do not list dates that are included in our analysis to balance around the edges of our period of interest.

<table><thead><tr><th>BP uncalibrated</th><th>Pit</th><th>Hearth</th><th>Layer</th><th>Grave</th><th>House</th><th>Hut</th><th>Flint mine</th><th>Palisade enclosure</th><th>Total</th></tr></thead><tbody><tr><td>6000–5501</td><td>4</td><td>1</td><td>2</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>7</td></tr><tr><td>5500–5001</td><td>30</td><td>9</td><td>15</td><td>1</td><td>1</td><td>2</td><td>13</td><td>–</td><td>71</td></tr><tr><td>5000–4501</td><td>115</td><td>8</td><td>24</td><td>13</td><td>8</td><td>10</td><td>25</td><td>–</td><td>203</td></tr><tr><td>4500–4001</td><td>68</td><td>2</td><td>19</td><td>7</td><td>12</td><td>2</td><td>4</td><td>12</td><td>126</td></tr><tr><td>4000–3501</td><td>36</td><td>7</td><td>18</td><td>18</td><td>98</td><td>2</td><td>–</td><td>1</td><td>180</td></tr><tr><td>3500–3000</td><td>28</td><td>3</td><td>20</td><td>8</td><td>43</td><td>2</td><td>2</td><td>–</td><td>106</td></tr><tr><td>Total</td><td>281</td><td>30</td><td>98</td><td>47</td><td>162</td><td>18</td><td>44</td><td>13</td><td>693</td></tr></tbody></table>

samples with a large lifespan (larger than 100 years), or charcoal of unidentified origin. Thus, the magnitude of the OWE is heterogeneous among samples.

Of the 732 samples, 406 were identified as materials susceptible to present an OWE. In those samples, the age of interest is the time those samples were associated to human activity, but radiocarbon dating will point to an older age due to the presence of older carbon in the sample (i. e. with lower 14C content). To ascertain the presence of OWE and quantify its influence on the analysis, we further analysed the dataset considering two categories of samples, materials with potential OWE and materials without OWE. Summary statistics were calculated separately for each category. In addition, we calculated summary statistics quantifying the temporal correlation and covariance between summary statistics of the two categories (following protocol in de Navascués et al., 2025).

Two models were considered for evaluating the relevance of the OWE in the analysis. First, in a model without OWE, we consider the number of samples at each year to be determined by the same piecewise exponential model described above, including the prior probability distributions for all parameters. In addition, each sample has a probability $\pi$ to be from a material with OWE. For parameter $\pi$ we use a beta distribution as a prior, with parameters $\alpha = 406$ and $\beta = 326$ (i.e. the observed values for the two categories of samples from short term human activity or potential old wood effect respectively). CRA for all samples from both categories were modelled from the calibration curve as presented above.

The second model considered includes OWE; it is identical to the first model except that a temporal lag is added to the age of samples belonging to the category of materials with OWE, before the modelling of CRA. This temporal lag was modelled with a negative binomial distribution which is characterized by parameters $r$ and $p$, NB($r, p$). The negative binomial distribution is used for its flexibility to describe the distribution of discrete variables. Under this model, the temporal lag for samples with OWE has a mean value of $\mu = r(1-p)/p$ and a variance of $v = r(1-p)/p^2$. We used a uniform prior between 0 and 2000 for $\mu$ and a log-uniform prior between 1 and $10^{10}$ for $v$, conditionally to $r \ge 0$ and $0 \le p \le 1$.

A reference table with 50,000 simulations for each model was produced and used to train a random forest for distinguishing between both models, as implemented in the ABCRF approach (Pudlo et al., 2016). This approach allows to choose the model that best explains the data and

estimates the posterior probability of the model. Then, the chosen model was used to estimate the value of $\lambda_t$ every 25 years, from $t = 6000$ to $t = 3200$, as in the first analysis described in Section 3. The mean and variance of the temporal lag under the model with OWE were also estimated using ABCRF. The analysis presented in this work were implemented in R and all scripts are available in de Navascués (2025).

## 3.2. Result from the Lambda ($\lambda$) model approach

Estimates of the abundance of dated radiocarbon samples through time are presented in Fig. 2. As a consequence of the methods used, the quality of the inference reduces dramatically near the limits of the studied period, within the ranges 6000–5900 and 3600–3200 cal BP. In the range 5900–3600 cal BP, three significant periods can be highlighted, a period of increasing number of samples between 5900–5600 cal BP followed by a period of decline between 5600–5200 cal BP. A second period of decline is identified between 3800–3500 cal BP. Apparent fluctuations between 5200 and 3900 cal BP present overlapping credibility intervals, which makes their significance more dubious.

Model comparison strongly favours a model with OWE, with a posterior probability of 0.91. The temporal lag induced by OWE on samples is estimated to have a mean of 641 years with 95 %CI (48–1866) and a variance of $1.6 \times 10^5$ with 95 %CI (500–7.8 $\times 10^7$). The inference of the abundance of radiocarbon samples through time, taking into account OWE, present an overall pattern similar to the results obtained in the previous analysis but provides a better resolution for the period between 5200 and 3900 cal BP: the decline after the peak at 5600 cal BP continues until 4700 cal BP and is followed by an increase 3900 cal BP (Fig. 2).

## 4. Results from analysis of data from more than 45 years of extensive contract archaeological excavations

In this section, we draw upon data from over 45 years of extensive contract archaeological excavations in the Malmö area to explore the density of human activity across the landscape. Our analysis is based on spatiotemporal archaeological data from excavated pits and post-built houses from the time period in question. In Table 5, we have compiled the number of pits, post-built houses, and sites with these features from the Malmö area. The pits have been grouped according to archaeological
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Abundance of radiocarbon dates through time. Point estimates (continuous line) of the expected number of dates per year (λt) and 95 % credibility intervals (dashed line) are presented from 6000 to 3200 cal BP every 25 years. Black line: model with all 732 samples in a single category, i.e. ignoring the presence of old wood effect. Blue line: model with two sample categories, with (406 samples) and without (326 samples) old wood effect (OWE). Archaeological time periods within the Neolithic with cal BP years added from Table 1.</figcaption></figure>

<caption>**Table 5**</caption>

Temporal variation illustrated using pits (typologically dated, mainly using pottery) and post-built houses (14C-dated and/or typologically dated). *Not necessarily solely to the Neolithic. **Note that site and settlement are not the same. Excavated sites might represent one settlement excavated at different times, more than one settlement within the same site or only partial excavated settlements (see Bilotti, 2024 for discussion).

<table><thead><tr><th></th><th>Number of pits, dated by typology</th><th>Of which are also 14C-dated, number and %*</th><th>Number of sites with pits**</th><th>Number of houses</th><th>Number of pits per house</th><th>Number of sites with houses</th></tr></thead><tbody><tr><td>EN I</td><td>66</td><td>16 (24 %)</td><td>24</td><td>—</td><td>—</td><td>—</td></tr><tr><td>EN II–MN A II</td><td>108</td><td>17 (16 %)</td><td>41</td><td>—</td><td>—</td><td>—</td></tr><tr><td>MN A III–MN A V</td><td>21</td><td>9 (43 %)</td><td>12</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Not specified</td><td>282</td><td>51 (18 %)</td><td>99</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Total</td><td>477</td><td>93 (19 %)</td><td>—</td><td>35</td><td>13,63</td><td>29</td></tr><tr><td>MN B, early</td><td>2</td><td>1 (50 %)</td><td>1</td><td>—</td><td>—</td><td>—</td></tr><tr><td>MN B, late</td><td>4</td><td>2 (50 %)</td><td>4</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Not specified</td><td>9</td><td>5 (56 %)</td><td>3</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Total</td><td>15</td><td>8 (53 %)</td><td>—</td><td>14</td><td>1,07</td><td>11</td></tr><tr><td>LN I</td><td>3</td><td>2 (67 %)</td><td>2</td><td>—</td><td>—</td><td>—</td></tr><tr><td>LN II</td><td>1</td><td>0</td><td>1</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Not specified</td><td>49</td><td>5 (10 %)</td><td>40</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Total</td><td>53</td><td>7 (13 %)</td><td>—</td><td>156</td><td>0,34</td><td>52</td></tr></tbody></table>

periods (see Table 1), mainly based on typological classification using pottery sherds. The remains of houses do not allow for the same level of detailed periodisation as for the pits. Therefore, the number of house-remains is summarised into three groups: Early Neolithic and Middle Neolithic A (EN–MN A); Middle Neolithic B (MN B); Late Neolithic (LN). The data presented in Table 5 has been aggregated from archaeological reports and a database on archaeological excavations first built by Malmö Museum, and since 2010 curated by Sydsvensk Arkeologi AB (Supplementary Material S4). Several pits lack precise dates. Instead, they have been categorised into three groups: Early Neolithic and Middle Neolithic A (EN–MN A); Middle Neolithic B (MN B); Late Neolithic (LN) (marked as 'Not specified' in Table 5).

Over the more than 2,000 years covered by our study, Stone Age communities utilised the landscape in different ways (Rudebeck et al., 2001). During the Early Neolithic and the first part of the Middle Neolithic, the deposition of materials in pits in various contexts was a significant aspect of people's lives. From the late Middle Neolithic,

people interacted with the landscape in ways that left few traces for archaeologists to recognise. By the Late Neolithic, pits are significantly less common in the archaeological record. Instead, we find multiple archaeological evidence of post-built houses from this period (Brink, 2009), something we have fewer examples of from the Early and Middle Neolithic (Table 5).

Several previous studies have used variations in the number of pits and post-built houses in the archaeological record as proxies for exploring changes in population size and density. Here we provide a well-curated sufficient sample size of pits and post-built houses for our analysis (see discussions in e.g. Schurr et al., 2021; Bilotti, 2024). Note, for the purposes of our study, we treat pits and post-built houses as broad conceptual categories, setting aside the variation these archaeological features exhibit in terms of function and duration of use during the Neolithic, preservation status and representational biases arising from differing excavation methods and research emphases (see discussions in e.g. Rudebeck et al., 2001; Björhem and Staaf, 2006; Garrow, 2006,
2012; Artursson, 2009; Brink, 2009, 2013; Rudebeck, 2010; Rudebeck and Macheridis, 2015).

Building on data in Table 5, Fig. 3 illustrates the spatiotemporal distribution of sites with pits and sites with post-built houses in the Malmö area (see also Supplementary Material S4). From the figure, we see that there are no significant differences in the utilisation of various parts of the landscape over time.

Fig. 4 is a schematic illustration of the data in Table 5. In terms of pits, we see a clear increase starting in EN I with a peak in MN A II. This is followed by an equally clear decrease from MN A III to LN. For the number of houses, we see a decrease in MN B and then a significant increase in LN.

## 5. Comparing the results from the Lambda (λ) model against other archaeological data

Using the Lambda (λ) model approach, incorporating OWE modeling, we identified four distinct periods of change: an increase in the number of samples of radiocarbon dates between 5900 and 5600 cal BP; a subsequent decline from 5600 to 4700 cal BP; a renewed increase around 3900 cal BP; and a final decline occurring between 3800 and 3500 cal BP.

Drawing on data from over 45 years of extensive contract archaeological excavations in the Malmö area, we identified variations in the number of pits and houses over the more than 2,000 years under study. Analysis of the number of pits reveals an increase in activity beginning in Early Neolithic I (5950–5450 cal BP), peaking during Middle Neolithic A II (4690–4590 cal BP), and followed by a pronounced decline from Middle Neolithic A III (4590–4490 cal BP) through to the Late Neolithic (4300–3650 cal BP) (cal BP values for MN A II and MN A III from Kirleis et al., 2012: 223, Fig. 2). From our analysis of house remains, we see a decrease during Middle Neolithic A (5250–4750 cal BP), followed by a significant rise in the Late Neolithic (4300–3650 cal BP).

These findings show that the results derived from the Lambda (λ) model and those obtained from the analysis of data from contract archaeological excavations do not fully support each other.

Since the 1990s Swedish contract archaeology has embraced a research-oriented approach (Nilsson and Rudebeck, 2010). This is evidenced by the development of comprehensive research programmes for excavation projects and archaeological institutions (e.g. Lindhé et al., 2001; Rudebeck et al., 2001). These programmes offer valuable insights into the rationale underpinning sampling methodologies, dating

strategies, and excavation planning. As a result, we possess a substantial body of material that allows us to explore reasons behind the discrepancies observed between the outcomes generated by the Lambda (λ) model and those derived from archaeological data analysis.

In the Early Neolithic, the increase indicated by the Lambda (λ) model corresponds with a rise in the number of pits. However, the subsequent decline suggested for Early Neolithic II by the Lambda (λ) model is not reflected in the archaeological record; instead, we observe a notable increase in pits during this period and into the early Middle Neolithic A. These discrepancies can largely be explained by sampling strategies. The Malmö area contains some of the earliest archaeological evidence of Neolithic lifeways in south Scandinavia (Sørensen, 2014; Högberg et al., 2023). For several decades, archaeologists working in the region have deliberately prioritised the collection of samples for radiocarbon dating from features that have been assumed to be from the Early Neolithic I (see e.g. Lindhé et al., 2001; Gidlöf et al., 2006). This approach stems from a strong research focus to identify the earliest evidence of farming practices. Hence, features that have been conceived as being from this period have been sampled for radiocarbon dating on an extensive scale.

The discrepancies between results obtained using the Lambda (λ) model and results from excavation data for the period spanning Early Neolithic II and the onset of Middle Neolithic A can mainly be attributed to typological dating practices. During the excavation of features from this period, archaeologists have not prioritised sampling for radiocarbon dating (see Gidlöf et al., 2006 for an example). This is largely because typological dating based on pottery sherds from Early Neolithic II and Middle Neolithic A has been considered sufficiently precise for chronological classification (Rostoványi, 2007), making sampling for radiocarbon dating redundant (see Palmisano et al., 2017 for similar discussions).

Consequently, the number of radiocarbon dates from Early Neolithic I is proportionally much higher than radiocarbon dates from Early Neolithic II to Middle Neolithic A. Moreover, the number of C14 dates from Early Neolithic II to Middle Neolithic A is significantly lower than it would have been had pottery typology for this period not been such a reliable dating method.

The increase in remains of post-built houses from the Late Neolithic observed in our results aligns with the findings of Bilotti (2024), who, based on an analysis of house floor sizes, demonstrates a population increase in the Malmö area during this period. However, we face challenges in typologically assigning these houses to either Late Neolithic I

<figure><img src="image_5.png" /><figcaption>Fig. 3. Spatio-temporal distribution of sites with pits (left) and houses (right). Lidar data in background map © Lantmäteriet, I2014/00893.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Fig. 4. Graphic illustration of the relation over time between the number (Y-axis) of pits and post-built houses, based on numbers from Table 5. Blue line = number of pits for each period. Green bars = number of houses for EN/MN A, MN B and LN.</figcaption></figure>

or Late Neolithic II. As a result, the observed increase around 3900 cal BP may pertain specifically to Late Neolithic I, while the subsequent decline indicated by the Lambda (λ) model may correspond to the final phase of the Late Neolithic and the Early Bronze Age, rather than the Late Neolithic as a whole. This interpretation is supported by Winther Johannsen et al. (2024), who identify a similar pattern based on quantitative modelling of radiocarbon dates, time series of material culture, and palynological data.

A population increase during the early part of the Late Neolithic is plausible. However, the rise observed around 3900 cal BP (Fig. 2) is likely influenced by both prehistoric demographic changes as well as archaeological practices and research interests. The Late Neolithic marks the emergence of architecturally distinctive and often robustly constructed two-aisled longhouses – a new building tradition compared to earlier periods. These structures are more readily identifiable in the archaeological record than earlier house types, which has made them a focal point of archaeological investigation. Since the 1980s, the evolution of long-houses from the Neolithic to the Iron Age have been a central focus of archaeological excavations and research in the Malmö area and beyond (e.g. Björhem and Säfvestad, 1989, 1993; Lindhé et al., 2001; Björhem and Staaf (2006); Artursson, 2009; Brink, 2013; Winther Johannsen et al., 2024). As a result, excavation efforts and dating strategies have focused on these structures, generating extensive radiocarbon datasets specifically related to this house type. This has also led to archaeologists becoming more skilled at identifying these remains during excavations. As a result, a higher proportion of post-built house remains have been identified over the past 30 years compared to older excavations conducted.

## 6. Conclusion

As noted in the introduction, Friman and Lagerås (2022: 169), along with others (e.g. Rick, 1987), argue that “there is a positive [...] relationship between the number of radiocarbon dates and population size, given that the dates derive from archaeological features and that the number of dates is high”. However, our findings suggest that the relationship is more complex. This aligns with Robbins (2013: 55), who

emphasises that “data collected by humans [i.e. archaeologists] is far from impartial [...] reasons for collecting the data, the methods used, and the choices behind which data to collect, all have an impact on the representativeness of the results.”.

Numerous studies justify the abundance of radiocarbon dates by arguing that a high number of dates enhance the reliability of the results (see discussion in, for example, Crema and Bevan, 2021). In this study, we worked with a substantial dataset – approximately one hundred radiocarbon dates per investigated square kilometre. Our findings demonstrate that a large number of dates does not necessarily ensure reliable results pertaining to population size. We show that the frequency of radiocarbon dates from the Malmö area is not directly proportional to the scale of past human activity, nor, by extension, to population size, as it also is related to archaeological sampling strategies, dating practices and research focus.

Unlike most previous studies, which are characterised by low spatial resolution and a high degree of generalisation (see discussion in Palmisano et al., 2017), our study is based on high chronological resolution and precision within a smaller area. We conclude that prehistoric population dynamics, archaeological practices, and research priorities are deeply intertwined in shaping the datasets used for analysis. Although our study only focuses on a relatively small area, it is reasonable to assume that similar conditions could hypothetically apply to other spatial and temporal contexts (see, for example, Nielsen et al., 2019).

Drawing on our findings, we stress the importance of accounting for a range of factors when using radiocarbon dating to analyse demographic shifts (see also Rudebeck et al., 2001; Palmisano et al., 2017; Becerra-Valdivia et al., 2020). Although not the focus of this paper, we suggest that future research could benefit from multi-proxy analyses that consider how variation in prehistoric landscape use may introduce biases into the archaeological record, how excavation strategies, and research priorities influence the available data, and how dating practices affect the distribution of radiocarbon dates across different archaeological periods. As Palmisano et al. (2017) highlight, cross-comparison of data derived from diverse approaches provides a productive pathway for analysing changes in prehistoric population size.
Declaration of generative AI and AI-assisted technologies in the
writing process

During the preparation of this work the authors used CoPilot for language proof reading. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.

CRediT authorship contribution statement

**Anders Högberg:** Writing – review & editing, Writing – original draft, Visualization, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. **Kristian Brink:** Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Torbjörn Brorsson:** Writing – review & editing, Writing – original draft. **Helena Malmström:** Writing – review & editing, Writing – original draft. **Elisabeth Rudebeck:** Writing – review & editing, Writing – original draft, Data curation. **Miguel de Navascués:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis.

Funding

This work was supported by the Swedish Research Council (grant no. 2021-01522).

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

We thank two anonymous reviewers for their useful comments.

**Appendix A. Supplementary data**

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jasrep.2025.105436.

Data availability

Data available in supplementary files

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