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Article

Diet, DNA, and the Mesolithic–Neolithic Transition in Western Scotland

by
Catriona Pickard
*,
Elizabeth Greenberg
,
Emma Smith
,
Andy Barlow
and
Clive Bonsall
School of History Classics and Archaeology, University of Edinburgh, Edinburgh EH8 9AG, UK
*
Author to whom correspondence should be addressed.
Humans 2025, 5(1), 8; https://doi.org/10.3390/humans5010008
Submission received: 10 January 2025 / Revised: 10 March 2025 / Accepted: 11 March 2025 / Published: 17 March 2025
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Abstract

:
In this paper, we revisit the Mesolithic–Neolithic transition in western Scotland and the links between early European farmers and middens in light of new aDNA, radiocarbon, and stable isotopic evidence. New carbon and nitrogen stable isotopic data for food sources (plant and animal remains) from a Mesolithic site are presented, and dietary FRUITS models are recalculated based on these data. We also respond to recent criticisms of the Bayesian approach to diet reconstruction. Results support the view that Neolithic people had at most a minimal contribution of marine foods in their diet and also point to a dual population model of transition in western Scotland. A significant aspect of the transition in coastal western Scotland is the co-occurrence of Neolithic human remains with shell-midden deposits, which appears to contradict stable isotopic evidence indicating a minimal contribution of marine resources to the diet of early farming communities in the region. Finally, we highlight the need for further research to fully address these issues, including (1) targeted isotopic analyses of potential plant and animal resources, (2) single-entity radiocarbon and ZooMS analyses of animal bones and artefacts from shell middens, and (3) further aDNA analyses of the remains of Late Mesolithic and Neolithic people.

1. Introduction

The precise nature and timing of the transition from hunting and gathering to farming in western Scotland has been the subject of longstanding debate (e.g., Bonsall et al., 2002; Schulting & Richards, 2002; Sheridan, 2010; Schulting & Borić, 2017; Sheridan & Whittle, 2023). This debate significantly reflects the wider archaeological discourse on the spread of farming across Europe. Various models have been proposed; however, these fall broadly into two categories: demic diffusion or indigenism/acculturation (see Ammerman (2020) and Schier (2022) for reviews). Indigenous development or adoption of agricultural practices (grouped here for simplicity, although the former has never been convincingly argued for western Scotland) should be evident in the archaeological record through a gradual and/or piecemeal transition to farming with the persistence, at least over the short to medium term, of some hunting and gathering behaviours (Zvelebil & Rowley-Conwy, 1984; Thomas, 1991, 2004). By contrast, a ‘revolution’ linked to demic diffusion (demographic expansion and the dispersal of farmers) should be evidenced by a sudden and significant break from traditional hunter-gatherer activities and material culture, with the uniform and wholesale appearance of the Neolithic ‘package’ (Stevens & Fuller, 2012).
One culturally determined behaviour—diet—has long been regarded as central to this debate for two reasons: it is one of the fundamental changes in behaviour associated with the introduction of farming and can be measured directly through stable isotope analysis.
Traditionally, the diets of prehistoric populations have been ‘reconstructed’ from carbon (δ13C) and nitrogen (δ15N) stable isotope ratios of human bone through visual parsing or ‘eyeballing’ (e.g., Tauber, 1981) or by employing linear mixing models (LMMs) (e.g., Schwarcz, 1991; Phillips & Koch, 2002). Over the past two decades, the limitations of linear mixing models (LMM) have been extensively discussed (e.g., Robbins et al., 2002; Fernandes et al., 2012; Fry, 2013). Since the early 2010s, the use of Bayesian mixing models (BMM), which mitigate some of the drawbacks of LMMs, has become commonplace: Parnell et al. (2010); Fernandes et al. (2014, 2015); and see Pickard and Bonsall (2022) for FRUITS modelling of the Neolithic diet in western Scotland. However, the use of Bayesian approaches for modelling past diets has not been met with universal acclaim. For instance, Schulting et al. (2023) questioned the precision and accuracy of Bayesian models of Neolithic diets in western Scotland.
Advances in biomolecular techniques now enable the rapid and cost-effective sequencing of ancient DNA (aDNA), providing an additional line of evidence through which the Mesolithic–Neolithic transition can be investigated. The aDNA analyses of Mesolithic and Neolithic human remains from western Scotland have revealed a genetic admixture between early European farmers and indigenous hunter-gatherers (Olalde et al., 2018; Brace et al., 2019). Mithen (2022) explored the nature of the interaction between hunter-gatherers and European farmers on the Inner Hebridean island of Islay, informed by the results of aDNA sequencing and excavation records. He concluded that a biocultural merger of the two groups was likely (Mithen, 2022, p. 72). Subsequently, Patterson et al. (2022) published an expanded dataset of aDNA sequences from west Scottish Neolithic individuals, making a reassessment of the evidence timely.
Here, we examine how the combined analyses of diet and aDNA can enhance our understanding of the Mesolithic–Neolithic transition in western Scotland and explore the utility of the Bayesian modelling software FRUITS (version 3.1 Beta) in this context. We also explore the relationship between Neolithic mortuary practices and shell middens in western Scotland.

2. Archaeological and Archaeogenetic Background

The debate surrounding the introduction of farming to Britain has followed a trajectory similar to the broader European discourse, with scholars favouring either demic diffusion or indigenism/acculturation as the primary driver of the transition to agriculture (see Sheridan and Whittle (2023) for a concise overview). The discussion has continued in western Scotland due to the equivocal nature of the archaeological record (Bonsall et al., 2002). Beginning around 4000 cal BCE (and possibly as early as 4300 BCE, based on the presence of a sherd of Armorican Castellic ware in the Achnacreebeag chambered tomb), there were changes in subsistence practices, material culture, technology, architecture and monumentality, and land use in many regions of Scotland (Fairweather & Ralston, 1993; Sheridan, 2012; Thomas, 2013; Sheridan & Schulting, 2020), which had parallels in Continental Europe, and therefore were consistent with demic diffusion as the cause of cultural change. However, in western Scotland, the evidence arguably supported the continuity of hunter-gatherer lifeways, with activity at some sites seemingly spanning the transition, along with later reuse of Mesolithic locations by Neolithic communities, the persistence of certain chipped stone and bone technologies, and the exploitation of wild resources (Bonsall et al., 2002; Mithen et al., 2007). Most relevant to the current discussion are the following: (i) the continued (but not necessarily continuous) accumulation of deposits of marine resources at shell-midden sites during the Neolithic, such as at An Corran on Skye and Carding Mill Bay 1 and 2 near Oban (Connock et al., 1991; Saville et al., 2012; J. M. Bownes et al., 2017); and (ii) finds of shells at megalithic sites, some of which may represent foundation deposits hinting at the continued symbolic significance of marine resources, such as at Crarae (Scott, 1963).
Since the early 2010s, aDNA studies have become the cornerstone of resolving the debate. The DNA of European Mesolithic hunter-gatherers (WHG) compared to early European farmers (EEF) provides overwhelming support for demic diffusion—the first farmers in Europe were genetically distinct from the last hunter-gatherers (e.g., Haak et al., 2010; Mathieson et al., 2015; Olalde et al., 2018, 2019). In Britain, generally, there was a demographic transformation, the direct consequence of demic diffusion, such that, by the Late Neolithic, little to no trace of indigenous hunter-gatherer ancestry remained (Brace et al., 2019; Brace & Booth, 2023). However, in western Scotland, the population ‘replacement’ was not necessarily rapid and need not have been brought about by mass migration (Patterson et al., 2022; Brace & Booth, 2023). Ammerman (2020) has argued that contact between indigenous hunter-gatherers and incoming farmers likely occurred in many regions of Europe. This assertion is supported not only by the archaeological record (e.g., Fort, 2012) but by genomic evidence (e.g., Olalde et al., 2019; Brunel et al., 2020). The indicators of admixture between indigenous hunter-gatherers and incoming farmers in western Scotland are clear (Olalde et al., 2018; Brace et al., 2019; Patterson et al., 2022). Of the maximum of twenty west Scottish Neolithic individuals whose DNA has been sequenced (Figure 1, and see Table 1), at least eight exhibit recent (within the last ten generations or so, i.e., indigenous) hunter-gatherer ancestry. Five of these individuals (those with good coverage or ‘number of reads’ and a high count of SNPs) have been identified as genetic outliers, distinct from the broader Early Neolithic community of western Scotland (see Table 1 and the extended data Figure 2 in Patterson et al. (2022)). Therefore, genetic evidence confirms that farming was introduced to Scotland by migrants from continental Europe. The question of the nature of the interactions between the first farmers and indigenous hunter-gatherers remains unresolved. Understanding the diets of both Mesolithic and Neolithic groups remains fundamental to this discussion.

3. Stable Isotope Analysis

Dietary changes have been used as a proxy measure for the timing and rapidity of the transition from hunting and gathering to farming in western Scotland and, thus, to distinguish between the two proposed models, acculturation versus demic diffusion. Diachronic patterns in dietary habits can be assessed in archaeological populations through stable isotope analysis. Diet reconstruction through the measurement of carbon (δ13C) and nitrogen (δ15N) stable isotope ratios is a well-established approach (e.g., Vogel & van der Merwe, 1977).
Tauber (1981, p. 333) performed the first comparative analysis of the δ13C values of Danish Mesolithic and Neolithic individuals, concluding that the hunter-gatherer groups had subsisted largely on marine resources, while the farmers consumed mainly terrestrial foods. The shift from Mesolithic to Neolithic consumption patterns appeared abrupt and uniform (Figure 2 in Tauber, 1981), in keeping with a demic diffusion model. A similar conclusion was later drawn from the isotope data for Scottish Neolithic individuals (Bonsall et al., 2002; Schulting & Richards, 2002). Schulting and Richards (2002), among others, championed a model of rapid and wholesale transition to dependence on terrestrial resources at the beginning of the Neolithic in western Scotland (e.g., Schulting, 1998; Schulting & Richards, 2002; Richards et al., 2003; Richards & Schulting, 2006; Schulting & Borić, 2017). Further, Schulting and Richards (2002, p. 155) argued that the relatively high human δ15N compared to the values of herbivores suggested that Neolithic diets were dominated by domesticated animal products and that the “animal protein (meat, blood or milk) is presumably from domestic stock, as it seems highly improbable that people would suddenly abandon marine foods in favour of wild terrestrial game at this juncture”. The focus on terrestrial resources was in sharp contrast to the diets of Scottish hunter-gatherers. Richards and Mellars (1998) estimated that individuals whose remains were recovered from the Mesolithic shell midden at Cnoc Coig on the Isle of Oronsay had derived over 90% of their dietary protein from marine food sources. In each of the aforementioned studies, the diet was interpreted through a visual analysis of scatterplots (e.g., Schulting & Borić, 2017) or ‘reconstructed’ using simple linear mixing models, based on a single dietary proxy (e.g., Richards & Mellars, 1998). These models focused solely on sources of dietary protein and did not account for the impact of the varying concentrations of protein in different foodstuffs. The development of Bayesian approaches to modelling diets, along with advances in understanding macronutrient routing in collagen synthesis (Fernandes et al., 2012, 2014), has offered us an opportunity to revisit the stable isotope data and investigate the whole diet.
J. M. Bownes et al. (2017) published the first Bayesian analysis of Neolithic diets in western Scotland, presenting data for four individuals from Carding Mill Bay 1. Bayesian mixing models, such as FRUITS (Food Reconstruction Using Isotopic Transferred Signals), offer several advantages over linear mixing models (see Fernandes et al., 2014). These include the ability to incorporate more food sources (relative to the number of proxies), uncertainties (e.g., seasonal variation in food source isotope values), and priors (e.g., health-related limitations to protein intake). Diets were modelled from bone collagen δ13C and δ15N using FRUITS. Their model indicated a ‘modest’ contribution of seafood to dietary protein of between 15 ± 11% and 21 ± 14%. J. M. Bownes et al. (2017, p. 1292) noted that their findings did not align with previously published interpretations by Richards et al. (2003) and Richards and Schulting (2006), which had “suggested a complete absence of marine resources in the Neolithic diet”. In response to the J. M. Bownes et al. (2017) paper, Schulting et al. (2023) claimed that their stance was misrepresented and that they had postulated a ‘minimal’ contribution of marine foods to diet.
Schulting et al. (2023) explored the discrepancies between published interpretations of the diets of the early farmers from Carding Mill Bay 1 and critiqued the following aspects of the J. M. Bownes et al. (2017) model:
  • The Δ15N diet–consumer diet consumer offset used, i.e., +5.5 ± 0.5‰
  • The Δ13C diet–consumer offset used, i.e., +1.0‰
  • The inclusion of marine foods in the dietary model
  • The omission of plant foods from the model
  • Whether the faunal samples used to establish food source isotope values were contemporaneous with the human remains from the site.
To highlight these limitations, Schulting et al. (2023, fig. 1b) generated a revised model of protein intake for Carding Mill Bay 1 using the same parameters employed in J. M. Bownes et al. (2017), altering only the Δ13C diet–consumer offset from +1.0‰ to 4.8‰. Schulting et al. (2023, p. 54) concluded from the data generated in the revised model that the ‘most probable’ contribution of marine resources to diet was between 0% and 1%. Schulting et al. (2023) then generated two new models for one individual, Carding Mill Bay 1 OxA-7890, which incorporated three food sources: cereals, terrestrial herbivores, and young herbivores (reportedly to account for the enrichment associated with nursing), while excluding marine resources. One model included a δ15N for manured plants (3.0 ± 1.0‰), while the other incorporated a δ15N for non-manured plants (5.0 ± 2.0‰).
The parameters employed by Schulting et al. (2023) for their revised models are summarized in Table 2. The models are expanded here using FRUITS (version 3.1 Beta) to encompass all four individuals included in the J. M. Bownes et al. (2017) study. The results are presented in Table 3 and Table 4. Both models indicated that, for three of the four individuals, the mean estimated contribution of plant foods to dietary protein exceeds that from animal sources, with individual OxA-7664 being the exception, consuming nearly equal proportions of animal and plant protein. Notably, there is little difference between the manured and non-manured cereal models, which likely reflects the large standard deviations associated with the mean δ13C and δ15N values.
We agree with Schulting et al. (2023) that the dietary model for Carding Mill Bay 1, published by J. M. Bownes et al. (2017), is flawed. However, we contend that only two of the criticisms highlighted by Schulting et al. (2023) are valid (see also Pickard and Bonsall (2022), where some of these issues were addressed).
Firstly, the assumption that 100% of dietary protein was derived from one of two sources, either the meat of terrestrial animals or the flesh of marine organisms, is problematic. J. Bownes (2018, p. 150) acknowledged that plant foods would have been exploited by both Mesolithic and Neolithic groups in Scotland but excluded this resource from dietary models on the basis that “plants contain very little … protein in relation to animal meat”. This assumption can be challenged. The macronutrient content of animal ‘meat’ and, consequently, the protein-to-lipid ratio, vary by species and also depend on the cut of meat or the part of the animal consumed. Some cereals and wild plants have protein-to-energy concentrations that are similar to those of certain animal products (see Pickard & Bonsall, 2022, tab. 8). Hazelnuts and barley, for instance, which are among the most commonly identified plant foods recovered from Scottish Neolithic sites (Bishop et al., 2010, 2014), are relatively high in protein.
Deriving dietary protein exclusively from animal products is atypical (cf. Hedges & Reynard, 2007). In modern developed countries, mean protein intake consists of 57% animal and 43% plant-derived sources. By contrast, developing countries have relative proportions of animal to plant protein at 30% and 70%, respectively. However, animal protein intake may be higher in groups that consume large quantities of dairy foods (Hedges & Reynard, 2007).
Secondly, the Δ13Cdiet–consumer offset of +1‰ used by J. M. Bownes et al. (2017) is inappropriate. Although an offset of 0‰ to +2‰ has been demonstrated between herbivore and carnivore bone collagen values (Bocherens & Drucker, 2003), this offset is not appropriate for modelling the whole diet from bone collagen stable isotope values. The food web for the Carding Mill Bay 1 human population presents a more complex scenario. Plant foods (such as cereals and wild nuts) and shellfish should both be included in the dietary models of this group. It is essential to determine the macronutrients δ13C for the tissues consumed from each of the food sources that potentially contributed to diet, and to employ an appropriate Δ13Cdiet–consumer offset, e.g., O’Connell et al. (2012).
There are also several problems with the approach of Schulting et al. (2023). These broadly fall into three categories: (i) the offsets used, (ii) the food sources included (or excluded), and (iii) the critique of Bayesian models.

3.1. Δ15Ndiet–Consumer Offset

Schulting et al. (2023) assumed a Δ15Ndiet–consumer of 4.6 ± 0.5‰ rather than the more widely used 5.5 ± 0.5‰ or higher offset, as suggested by Fernandes et al. (2014), Bickle (2018), and Pickard and Bonsall (2022). The lower offset is derived from O’Connell et al. (2012, p. 431), wherein it is described as “very conservative”. O’Connell et al. (2012) determined that the diet–collagen offset in their study population was c. 6‰, significantly higher than the offset used by J. M. Bownes et al. (2017) and lower than that observed in some other medium to large mammals (e.g., Caut et al., 2009). Schulting et al. (2023) argued that the conservative value was more realistic, in keeping with values determined in ecological food web studies. Schulting et al. (2023) cited Minagawa and Wada (1984), Post (2002), and Vanderklift and Ponsard (2003) in support of a small diet–consumer offset of 3‰, and Halffman et al. (2020), Newsome et al. (2004), and Piličiauskas et al. (2017) for an offset of 3−4‰. However, using ecological studies as comparators for human diet offsets is problematic. Vanderklift and Ponsard (2003) observed that diet–consumer offsets varied taxonomically. The mean offset reported by both Minagawa and Wada (1984) and Post (2002) was 3.4‰. However, both studies indicated large ranges in Δ15Ndiet–consumer of up to +9.2‰ (Minagawa & Wada, 1984). Furthermore, the diet–consumer food webs studied were not appropriate analogues for humans (large omnivores). Minagawa and Wada (1984) studied the food webs of a range of species, including wild fish, insects, and molluscs, as well as the controlled feeding studies of a guppy, shrimp, and a mouse. No large omnivorous mammals were included in the study. Post (2002) investigated offsets in freshwater mussels and snails. The Vanderklift and Ponsard (2003) offset was generated from a synthesis of published controlled feeding studies, but only two samples came from medium-sized terrestrial omnivores (pigs). Moreover, these specimens were part of a controlled feeding experiment and had diets that included only plant foods (Hare et al., 1991). In each of the studies mentioned, a variety of tissues were sampled and, in some instances, the data were derived from whole-body samples. These were then combined to generate a mean Δ15N offset, i.e., it is not directly comparable with the O’Connell et al. (2012) diet to bone collagen offset. The Δ15N offset used in Newsome et al. (2004) was derived from other studies. The Halffman et al. (2020) offset was derived from the Bocherens (2015) prey–predator offset determined for herbivore to carnivore collagen to collagen values. Similarly, Piličiauskas et al. (2017) used the Δ15N offset published in Bocherens and Drucker (2003). Ecological studies of diet–consumer Δ15N offsets in large omnivorous mammals are few. Black bear (Ursus americanus) diet–plasma offset values have been reported as +2.3, +4.1, and +4.2‰ (Hildebrand et al., 1996). This is consistent with a diet–collagen offset of 3.2–3.7‰, 5.0–5.4‰, and 5.1–5.5‰, respectively (cf. O’Connell et al., 2012). Notably, the diet−plasma offset in grizzly bears has been reported to be up to +6.30‰, i.e., a diet−collagen offset of 7.2–7.6‰ (Caut et al., 2009).
The 5.5 ± 0.5‰ offset proposed by Fernandes et al. (2014) encompasses the variation between the higher and lower offsets of O’Connell et al. (2012) and Schulting et al. (2023), respectively. Therefore, it is deemed appropriate to use this diet–consumer offset in modelling the diets of the study populations.

3.2. Food Sources

Ideally, when modelling human diet, the food source isotope data should be determined from animal and plant remains that originate from the same archaeological context/phase as the human remains. There are two confounding factors when modelling the diets of Neolithic people in western Scotland. Firstly, the potential introduction of Neolithic human remains into older contexts, which has been established in certain instances through 14C dating. Secondly, there is a relative scarcity of faunal remains from western Scottish sites that have been identified to species level. One solution has been to judiciously use Mesolithic faunal remains in dietary models (e.g., Pickard & Bonsall, 2022). Schulting et al. (2023) critiqued the use of fauna from the Carding Mill Bay 1 midden to model the diets of Neolithic people (J. M. Bownes et al., 2017), arguing that the animal remains might have originated from an earlier chronological or cultural phase. However, Milner and Craig (2009) encountered this issue, i.e., the potential non-contemporaneity of animal and human remains, in their investigation of diet at the midden site of An Corran, on the Isle of Skye. Milner and Craig (2009) measured the δ13C and δ15N of both faunal samples that dated much earlier than the human remains from the midden as well as those of bevel-ended tools made from animal bones that had been previously determined to be of Neolithic date, i.e., broadly contemporaneous with the human remains, and found little difference in the isotope values of the animals from the two distinct cultural phases. This implies that the stable isotope ratios of fauna from Mesolithic contexts provide appropriate food source data for modelling the Neolithic diet.
Schulting et al. (2023) presented a model limited to three food categories: young and mature terrestrial herbivores, and cereals. Although they stated that this three-source model was “a heuristic exercise” and made “no claim to have ‘revealed’ the actual proportion of these three food groups consumed by the Neolithic communities around Carding Mill Bay”, Schulting et al. (2023, p. 54) reported that the results of this food-source-restricted model were “more in keeping with what might be expected of British Neolithic farming communities, known to have practised both cereal cultivation and animal husbandry from the outset”. The argument is circular. Schulting et al. (2023) hold a preconceived notion of what an early farming community in western Scotland would have subsisted on and then generate a model that seemingly confirms this. Furthermore, it is questionable whether adding young herbivores as a food source is meaningful. One of the prerequisites for high-quality outputs from FRUITS modelling is that the food sources must be isotopically distinct (Fernandes et al., 2014). The isotope values for young herbivores are only slightly higher than those of the adults, and food sources with overlapping isotope ranges should be aggregated (Fernandes et al., 2014; Phillips et al., 2014).
Assuming a priori that marine resources did not contribute to diet—and therefore excluding them from subsequent modelling—is equally, if not more, problematic than including them in Bayesian (or indeed linear) mixing models. A more robust approach involves generating a model that includes all potential food sources appropriately grouped while ensuring that the credible interval is considered carefully when interpreting the data (Fernandes et al., 2014; Cheung & Szpak, 2021).

3.3. Critique of Bayesian Models

Schulting et al. (2023, p. 54) urged caution before accepting the output of Bayesian palaeodietary models, implying that such models do not reflect “the actual proportion… of food groups consumed”. However, this critique could apply to any form of dietary modelling that utilizes proxies, such as LMMs and visual parsing. Weaknesses in dietary ‘reconstructions’ or models arise not only from Bayesian modelling generally, or specifically FRUITS, but rather reflect the GIGO principle (output is related to the quality of the input). Limitations on the quality of input, and consequently output, arise from the partial nature of the archaeological record and constraints on research (i.e., lack of isotopic data for food sources and diet–consumer offsets), poorly considered selection of parameters, and misinterpretation or over-interpretation of the data generated. Regardless of the modelling system employed, ‘modellers’ make assumptions about which food sources are likely to have contributed to diet and select other variables or parameters that are incorporated into the models; for example, which archaeological specimens to sample to establish food source baselines or ‘dietary endpoints’, and which diet–consumer offset values to use. It is not the case that, when a food source is included in FRUITS, it is “assumed a priori to make a dietary contribution” (Schulting et al., 2023, p. 54). When a food source that was not consumed is included in the model, this is indicated by the credible interval, which will span 0. Bayesian models are not inherently more heuristic than other forms of dietary modelling, although their ability to accommodate a much broader range of food sources and to include more macronutrients and macronutrient concentrations, proxies, and a greater number of uncertainties and offsets, may make them appear so. As new data relating to one or more parameters become available, models should be updated. The capability of models to incorporate this breadth of inputs increases the accuracy of dietary models where inputs are carefully and critically evaluated. While Bayesian models may seem less precise than linear mixing models because they provide a credible interval or range rather than a unique solution or absolute value, the ‘true’ value or dietary contribution lies within this credible interval. Thus, the data are more accurate than simple linear mixing models or visual analysis of scatterplots (of course, this assumes high-quality input).

4. Discussion

Arguably, neither the J. M. Bownes et al. (2017) nor the Schulting et al. (2023) models are as robust or as detailed as current knowledge permits. Both used protein-routed models that assumed carbon in bone collagen was derived exclusively from dietary protein. While Schulting et al. (2023) pointed out that concentration-dependent modelling is necessary to model food source calorie contribution to diet, nutrient routing should also be incorporated. Fernandes et al. (2012) demonstrated that carbon from the three dietary macronutrients is routed to bone collagen (see also Hedges, 2003). This information, namely the routing of consistent proportions of carbon from protein (74 ± 4%) and energy (26 ± 4%), can be used to determine the source of dietary calories (Fernandes et al., 2012, 2014). Modelling calorie contributions of food sources can significantly enhance our understanding of diet and subsistence activities. A concentration-dependent, nutrient-routed model with appropriate food source values, offsets, uncertainties, and priors for west Scottish Neolithic populations was published by Pickard and Bonsall (2022). Using the Bayesian model FRUITS, the findings of that study were aligned with those of Schulting et al. (2023) on the contribution that marine resources made to diet. We concluded that marine resources “were, at most, a minor component of the ‘lifetime’ diet” (Pickard & Bonsall, 2022, p. 226). We also agreed with Schulting et al. (2023) that the diets of the individuals from Carding Mill Bay 1 were overwhelmingly derived from terrestrial foods. However, we suggested that, while terrestrial animals (including potentially dairy products) were a vital source of dietary protein, plant foods were likely the primary source of calories for west Scottish Neolithic people.
Since publishing our models in 2022, additional food source data have become available, as summarized in Table 5 (see Figure 2 for locations; and Figure 3 for visualization of the food source and human isotopic data). Therefore, the diets of Neolithic individuals from western Scotland are revisited here using updated food source data (with all food sources included and grouped where appropriate) but expanded to include three additional Neolithic individuals—one from Ulva Cave and two from Carding Mill Bay 2 (see Patterson et al., 2022). Table 6 presents the updated human isotope data, Table 7 shows the parameters used in our revised FRUITS models, and Table 8 displays the results of the FRUITS models. Wild and domesticated resources are included in the baseline food source isotope values. Both radiocarbon and botanical evidence suggest that dependence on cultivars to the total exclusion of wild plants is unlikely in Neolithic Scotland. Bishop et al. (2010) noted that no cereal grains have been recovered from Neolithic contexts in mainland coastal west Scotland or the Inner Hebrides. Stevens and Fuller (2012, 2015) demonstrated through radiocarbon summed probability distributions that wild plant foods, specifically hazelnuts, remained a crucial staple throughout the Neolithic in mainland Scotland, especially so from c. 3650 BCE, when a climatic downturn may have led to “a readjustment of existing subsistence systems in response to less predictable harvests, during which wild plant foods and pastoralism gained prominence” (Stevens et al., 2022, p. 11). A pilot study of modern and archaeological specimens from the Isle of Ulva suggests that hazelnuts have similar mean δ13C and δ15N values as unmanured Early Neolithic cereals. Although further work is necessary to confirm this and also to establish the variation in nutshell and kernel values (given that the number of samples analysed was small), the preliminary results suggest that mean stable isotope values of the two resources, namely cereals and hazelnuts, may be indistinguishable. The evidence for the continued exploitation of wild animals in Neolithic Scotland is limited. While remains of red deer and possibly roe deer were recovered (Bartosiewicz et al., 2010) from the upper midden deposit at Carding Mill Bay 2, in the absence of 14C dates, it is not certain that these originate from Neolithic contexts. Although ample evidence exists for stock raising, there are no published stable isotope data for domesticated mammals from western Scotland where identification to species is certain.

4.1. Revised Dietary Models

The outputs of the models, perhaps unsurprisingly given the narrow ranges of the δ13C and δ15N values, suggest that the diets of Neolithic individuals in western Scotland were homogeneous. The 68% credible interval for each food source is very similar across all the individuals analysed (see Figure 4). The model suggests that plant foods were likely the mainstay of dietary calories. The mean estimated contribution of plant calories is approximately 75 ± 11%, with the lowest estimated CI range of 62–64% and the highest range of 87–94%. By contrast, dietary protein was likely sourced predominantly from terrestrial animal products, including meat and dairy (e.g., Cramp et al., 2014).
The mean estimated calorie contribution of fish varied from 1 ± 1% to 2 ± 2%, while for shellfish it ranged from 1 ± 1% to 3 ± 3%. This implies that marine resources represented, at most, a minor component of dietary calories. The mean estimated protein contributions of both fish and shellfish show a wider range of values, from 2 ± 2% to 10 ± 6% and 4 ± 4% to 10 ± 8%, respectively. This may indicate some variation, individually or between groups, in the consumption of, and nutrients derived from, marine resources. However, the credible intervals for all but two samples suggest that marine resources were not consumed—that is, the lower range of the fish and shellfish calorie contribution credible intervals is 0%. The credible intervals (68%) for two samples from An Corran (AC.04 and AC.05) indicate a small contribution of marine resources to diet but, at most, this constitutes a 6% contribution of both fish and shellfish to calories. Notably, these two samples may originate from a single individual; both were extracted from fragmentary bones, and both specimens were identified as the remains of a ‘mature’ adult (Milner & Craig, 2009).

4.2. Diet, DNA, and the Mesolithic–Neolithic Transition

Radiocarbon chronologies of Mesolithic and Neolithic sites on Islay point to the likely co-existence of hunter-gatherers and farmers on the island between c. 3790 and 3640 cal BCE, and potentially into the latter half of the 4th millennium BCE (Mithen, 2022). Mithen (2022) proposed three scenarios of indigenous hunter-gatherer and migrant farmer interaction on Islay: (a) swift succession, (b) dual populations, and (c) biocultural merger. Although developed for Islay, the scenarios serve as a valuable starting point for exploring the nature of immigrant farmer/indigenous hunter-gatherer interactions across the wider region of western Scotland.
The swift succession scenario proposes a short period of contact with some admixture or acculturation before the ‘extinction’ of hunter-gatherer groups, which Mithen (2022) argued did not wholly align with the archaeological evidence. The dual population model envisioned Neolithic and Mesolithic people coexisting in the same area, possibly even using the same locations, but with little or no contact for an extended period, possibly into the 2nd millennium BCE (Mithen, 2022). The biocultural merger model proposed a genetic and cultural union with a mixed economy, including hunting, gathering, fishing, and pastoralism (Mithen, 2022). Of the three scenarios, Mithen (2022) cautiously favoured the biocultural merger model.
All the west Scottish Neolithic individuals sampled for stable isotope analysis had diets largely, if not entirely, based on terrestrial resources. Moreover, their diets were remarkably uniform. This dietary emphasis is indicated not only by the biochemistry of human remains but is supported by organic residue analysis of Neolithic pottery sherds from coastal locations in northern Britain, including the Northern and Western Isles, which show a “near-complete absence” of residues of aquatic origin (Cramp et al., 2014, p. 3). Arguably, therefore, the Neolithic individuals represent a single dietary population even though some individuals show mixed EEF and indigenous hunter-gatherer ancestry (cf. Bollongino et al., 2013). While modelling the relative proportions of wild terrestrial species versus domesticates in the diet is not feasible due to an overlap in δ13C and δ15N values of these food groups, the isotope data clearly indicate whether marine resources were exploited. At most, fish and shellfish contributed minimally to diet, marking a significant shift in dietary focus from the Mesolithic.
Mesolithic human remains are scarce in Scotland, comprising isolated finds of disarticulated and fragmented bones or teeth. Stable isotope data are available for only six samples from adults (modelling the diet of non-adults remains problematic), with five from Cnoc Coig and one from Caisteal nan Gillean II, both shell-midden locations on Oronsay. The diets of the individuals whose remains were recovered from these two sites comprised a mix of terrestrial and aquatic resources, typically with more than a third of calories drawn from marine resources (Pickard & Bonsall, 2020, 2022). The aDNA from only one Mesolithic individual from Cnoc Coig, Oronsay, has been sequenced (Figure 3)—a female who lived between c. 4450 and 4260 BCE (5492 ± 36 BP, SUERC-69249). She had western hunter-gatherer (WHG) ancestry and presumably was an indigenous hunter-gatherer (Brace et al., 2019). This individual had among the highest δ13C and δ15N ratios of any of the human remains from Cnoc Coig that have been analysed (δ13C = −12.8‰ and δ15N = +16.6‰, GU41836). However, the collagen was extracted from the petrous portion of the temporal bone, which may reflect a combination of maternal, breastfeeding, and weaning/post-weaning diets as it begins to form in utero and does not remodel after approximately two years of age (Jørkov et al., 2009). The complex interplay between physiological effects, nursing, and diet means that it is inadvisable to use these data to model ‘typical’ Mesolithic diets. However, it is entirely conceivable that the diet of the adult from whom the petrous sample was extracted was similar to that of the other individuals from Cnoc Coig, for whom adult diet proxies are available (see Pickard & Bonsall, 2022). While it is not certain that the diets of all west Scottish hunter-gatherers were similarly constituted, the broader evidence from Atlantic Europe (e.g., Richards & Hedges, 1999; Bergsvik & Ritchie, 2020; García-Escárzaga & Gutiérrez-Zugasti, 2021) suggests that marine resources likely constituted an important element of the subsistence economy for coastal groups. While diet is only one culturally determined behaviour, turning away from the sea represents a distinct break with past traditions and habitus and, therefore, is of social and symbolic significance. As Fornander (2011, pp. 80–81) aptly summarized, “social identity, is rooted in the practices of everyday life” and “is also about defining what one is not”. The dietary evidence strongly suggests that fishing was an infrequent activity; therefore, west Scottish Neolithic people likely had a distinct socially constructed identity compared to earlier Mesolithic groups. Diachronic changes in diet are more consistent with either the dual population model or the swift succession model than with the biocultural merger model.
As Brace and Booth (2023, p. 133) observed, the aDNA evidence is inconsistent with a complete biocultural merger—migrant farmers “must have lived adjacent to people descended from local Mesolithic groups for hundreds of years but only had children with them some of the time”. In Figure 5, the Mesolithic individual from Cnoc Coig establishes the benchmark for indigenous hunter-gatherers with fully western hunter-gatherer (WHG) ancestry, while the individual(s) from MacArthur Cave set the boundary for migrant farmer (or early European farmer) ancestry (see Patterson et al. (2022) and Brace and Booth (2023) for discussion of the MacArthur Cave samples and levels of WHG ancestry in Neolithic groups). It is striking that the median dates of those individuals with evidence for increased Mesolithic ancestry (notably never exceeding 30%) are clustered between c. 3700 and 3500 BCE, a timespan which some authorities would place at the end of the Early Neolithic and the beginning of the Middle Neolithic, after which the genetic contribution reverts to the Neolithic ‘baseline’ (see the downward trend in Figure 5). As noted by Brace and Booth (2023), increases in hunter-gatherer ancestry in Middle Neolithic populations have been observed in several regions of continental Europe. However, only in rare cases does this reflect recent admixture between indigenous hunter-gatherers and migrant farmers in the Middle Neolithic, e.g., Blätterhöhle Cave, Germany, and likely among the Brześć Kujawski group, Poland (Bollongino et al., 2013; Lipson et al., 2017; Fernandes et al., 2018; Rivollat et al., 2020; Brace & Booth, 2023). At Blätterhöhle, biomolecular evidence strongly suggests the presence of two groups of Neolithic people, one subsisting entirely on agricultural resources with EEF ancestry, the other by foraging and fishing with hunter-gatherer ancestry (Bollongino et al., 2013). This is quite distinct from the uniform diet of west Scottish Neolithic groups. Furthermore, there are no 4th millennium BCE individuals who display a high level (i.e., ≥60%) of Mesolithic heritage (Brace & Booth, 2023), suggesting asymmetric gene flow between hunter-gatherers and farmers in the region. According to the Hardy–Weinberg principle, allele proportions in a population should remain constant over time unless (i) there is a significant selective advantage (in the case of Neolithic alleles), (ii) marriage practices distort the proportions (e.g., Zerjal et al., 2003), or (iii) there is a continuous influx of DNA into a population. The latter is most likely in this case, and such an influx of migrant farmers would dilute hunter-gatherer DNA allele frequency. Thus, based on both the dietary and DNA evidence, we propose a model of subsumption of the Mesolithic genetic heritage along with cultural replacement followed by continued influx of Neolithic farmers, further diluting the Mesolithic signal to complete loss, i.e., a dual population model. This is suggested cautiously for three reasons. Firstly, there is still some uncertainty about where in continental Europe the first farmers originated (Sheridan & Whittle, 2023). Resolving this issue may result in a reconsideration of the extent to which hunter-gatherers and migrant farmers interacted within Britain (see Brace & Booth, 2023). Secondly, there may be ascertainment bias—individuals with high levels of Mesolithic ancestry may have had mortuary rituals that leave little archaeological trace. Thirdly, it is difficult to determine what proportion of the people that were interred at the sites have been sampled for aDNA analysis—the sites at Carding Mill Bay and Raschoille were damaged by the construction work that led to their discovery, and an unknown number of human remains may have been lost. Ultimately, in many instances, it is difficult to reconcile whether the individuals sampled for aDNA analysis are also those whose remains have undergone stable isotope analysis for dietary modelling.

4.3. Shell Middens and Neolithic Burials

Various explanations have been proposed to account for the co-occurrence of Neolithic human remains with shell middens. One explanation is the use of marine resources by farmers during times of hardship, such as crop failures. This has been suggested for an Early Neolithic site at Sumburgh (in Shetland), where the consumption of marine resources is evident only in dentine increments (which provide a record of diet during childhood and over short periods, i.e., weeks to months) rather than in bone collagen, which typically reflects the average dietary intake over longer timescales (e.g., Montgomery et al., 2013). Further, Mithen (2022, p. 19) proposed that the Middle Neolithic failure of farming (Stevens & Fuller, 2012) may have led to a renewed impetus for hunting and gathering, “for which they may have required Mesolithic-type knowledge and technology” and thus served as a catalyst for increased contact with native hunter-gatherers. The use of wild resources, whether terrestrial or marine, does not require Mesolithic-type technology or skills. Marine resources can be harvested with minimal technology and experience (Groom et al., 2019). Despite their proximity and accessibility for coastal groups in western Scotland, marine resources did not feature prominently in the diets of Neolithic communities. Indeed, Schulting and Borić (2017, p. 94) expressed their surprise that the Early Neolithic people of Sumburgh did not focus all their attention on fishing, given the marginality of farming in such a northerly location. They noted that this “speaks to the cultural importance of maintaining a farming identity”. Similarly, while wild plants were exploited in Neolithic Scotland and seem to have become a crucial staple during the Middle to Late Neolithic, this does not necessitate the continuance of hunter-gatherers or the direct transfer of knowledge; as Stevens and Fuller (2012, p. 719) noted, “migrating farmers from the Continent already had a subsistence strategy encompassing both cultivated and wild foods”.
Another possibility is that farming communities were not responsible for the 4th millennium BCE midden accumulations; rather, these could represent the activities of a coexisting hunter-gatherer population. While Mithen (2022, p. 18) suggested that population collapse “may have driven local communities to relax their marriage rules to allow interbreeding with local Mesolithic groups”, it is important not to assume that hunter-gatherers were passive bystanders in the transition to farming. There may have been some resistance to merger on both sides—hunter-gatherers may also have maintained strong connections to past customs, rituals, and social structures (see Zilhão, 2011). The co-existence of dual populations in western Scotland may have been facilitated during the Mesolithic–Neolithic transition by its unique and varied coastal topography and biogeography. Gron and Sørensen (2018) observed that in Early Neolithic Denmark the transition to farming and a shift in occupation patterns to inland settlements was abrupt in regions with long, straight coastlines and relatively exposed shores. By contrast, in areas characterized by heterogeneous coastlines with multiple ecotones (regions of high productivity), similar to western Scotland, the archaeological record has indicated the concurrent presence of inland farming communities and coastal hunter-gatherers for, at most, the first three centuries of the Early Neolithic (Gron & Sørensen, 2018).
This raises the question of why, if middens were associated with hunter-gatherer activities, Neolithic people chose to deposit the remains of their deceased in these locations. Brace and Booth (2023), highlighting similarities between the depositional contexts of human remains on Oronsay and at Carding Mill Bay, imply that the act of depositing human remains at shell midden sites signifies a continuation of hunter-gatherer mortuary traditions into the Neolithic. If that were indeed the case, it suggests the persistence of funerary customs shaped by hunter-gatherer social memory despite hunter-gatherer ancestry being at low levels or absent in the individuals whose remains have been recovered from these sites. None of the individuals from Raschoille Cave was genetically closely related (Patterson et al., 2022; Brace & Booth, 2023), which suggests that placing the deceased in caves did not reflect the collective memory or practices of a small bioculturally-merged group.
Placing the remains of the dead in caves was not a Mesolithic tradition in western Scotland (Bonsall et al., 2012; Sheridan & Schulting, 2020). However, this practice is observed throughout Neolithic Europe (e.g., Peterson, 2019; Silvestri et al., 2020; Sparacello et al., 2020; Rivero et al., 2021). At Raschoille Cave, the human remains were found in or beneath angular rock debris, stratigraphically separated from the shell-midden deposits in the cave. Distillery Cave was discovered during quarrying in the 19th century. The existence of the cave and its archaeological significance were only recognized after rock blasting disturbed the deposits; hence, the archaeological context of the human remains, and their stratigraphic relationship with the shell midden are uncertain (Turner, 1895). Similarly, at An Corran, the funerary practices and the relationship between the shell midden and the human remains are uncertain. While the remains may have been intentionally inserted into pre-existing midden deposits, it has been suggested they were incidental intrusions, perhaps originally placed on a ledge of the rock shelter (Saville et al., 2012). At MacArthur Cave, the human remains were recovered from the surface of, and from within, a ‘black earth’ layer that overlay the shell-bearing deposits (Anderson, 1895), thus, post-dating the midden. Moreover, an AMS 14C date on a bone artefact from the midden (Bonsall & Smith, 1989) is substantially older than the 14C dates obtained from Neolithic human remains from the cave.
At Ulva Cave, the entrance area featured a shell midden formed by the periodic deposition of food and other refuse over thousands of years, which had become highly compacted due to human and animal trampling. The midden dates back to before 7500 cal BCE (Russell et al., 1995) but was added to intermittently during the later Mesolithic and subsequent periods. A few disarticulated human bones (one dated to the Early Neolithic—see Table 1) were found among mammalian remains recovered through hand excavation and fine sieving, but it could not be determined whether the human remains had been intentionally “buried”.
Arguably, the most compelling evidence for the intentional burial of Neolithic human remains in shell-midden deposits comes from Carding Mill Bay in Oban, where disarticulated human bones were found in shell-midden deposits within two shallow fissure caves at the base of a raised marine cliff (CMB 1 and CMB 2). Both sites had suffered damage due to land development, prompting salvage excavations at Site 1 in 1988 (Connock et al., 1991) and at Site 2 from 1992 to 1996 (Bartosiewicz et al., 2010). At CMB 1, the radiocarbon dates for human bones were, on average, several centuries younger than those for ungulate bones and bone artefacts recovered from the shell-midden deposits (Connock et al., 1991; Bonsall & Smith, 1992; Schulting & Richards, 2002; J. M. Bownes et al., 2017). This evidence implies that Neolithic human remains were either deposited in an in situ Late Mesolithic shell midden or that the shell-midden material had been brought to the site from another location specifically to conceal the human remains. At CMB 2, human remains were recovered from two distinct shell-rich layers separated by a layer of talus. Bones from two individuals with early farmer (EEF) ancestry excavated from the lower midden deposit were dated to the Early Neolithic (Table 1). The contrasting pedological and microfaunal characteristics of the two sets of midden deposits suggest that at least one, and possibly both, had not accumulated in situ but had been imported for the purpose of burying the human remains.

5. Conclusions

This paper was conceived as a contribution to discussions on how we can accurately model past diets, focusing on the nature of the transition from hunting and gathering to farming in western Scotland. FRUITS generates accurate models of past diets when appropriate parameters are input. Our FRUITS models indicate that plant foods were the primary source of dietary calories, while animal products provided the principal source of dietary protein for west Scottish Neolithic individuals. The results presented here support the long-standing view that marine resources constituted, at most, a minor component of Neolithic diets in western Scotland. Collectively, the dietary and aDNA evidence suggests subsumption with further influx dilution, that is, the dual population model of the Mesolithic–Neolithic transition in western Scotland best fits the current evidence. Extensive reliance on terrestrial resources in Neolithic western Scotland suggests deep-rooted connections to farming traditions.
The interpretations presented here are offered cautiously. We recognize that there remain significant gaps in food source isotope data that need to be addressed. While no 4th millennium BCE individuals with predominantly WHG ancestry have been identified so far, this may reflect the small number of individuals whose aDNA has been extracted and sequenced. It may also reflect ascertainment bias—hunter-gatherers might have disposed of their dead in a way that left little archaeological trace, either due to the nature of mortuary rituals or the poor preservation of bones in the acidic soils that are prevalent throughout Scotland.
To address the limitations in current dietary models, further biomolecular analyses and radiocarbon dating are essential, including the following: (i) targeted isotope analyses of potential food sources, especially domestic animals and wild plants, such as hazelnuts, from sites in western Scotland; (ii) additional radiocarbon dates from sites like Ulva Cave and Carding Mill Bay 2 to enhance our understanding of shell midden chronology; and (iii) palaeoproteomics (ZooMS) of non-diagnostic large mammal remains, which also has the potential to identify remains of humans as well as domesticates, and expand both the isotope and aDNA datasets.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/humans5010008/s1, Methods Statement for Extraction and Stable Isotope Analysis of Bone Collagen; Methods Statement for Pre-Treatment and Stable Isotope Analysis of Carbonised Hazelnut Shells.

Author Contributions

Conceptualization, C.P., E.S. and C.B.; methodology, C.P., A.B. and E.G.; validation, C.P., E.G., E.S. and C.B.; formal analysis, C.P.; investigation, C.P., E.G., E.S., A.B. and C.B.; resources, C.P. and C.B.; writing—original draft preparation, C.P.; writing—review and editing C.P., E.S. and C.B.; supervision, C.P.; visualization, C.P. and C.B.; validation, C.P. and C.B.; funding acquisition, C.P. and E.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by The Moray Endowment Fund, The Munro Fund, and the School of History, Classics and Archaeology, University of Edinburgh.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of the School of History, Classics and Archaeology, University of Edinburgh (SHCA2020/EG/CP) on 30 September 2020.

Data Availability Statement

All data generated for, or utilized in, the paper is included in the manuscript and the Supplementary Materials.

Acknowledgments

Thanks are extended to the North West Mull Community Woodland Company who gave permission for the excavations on Ulva, and also to those in the Ulva Community who supported the field projects: Andy Primrose; Ruairidh Munro; Donny Munro. This work was supported by SUERC, University of Glasgow, and we thank Kerry Sayle. This work is dedicated to the memory of Andy Barlow. Thanks are also extended to two anonymous reviewers for providing helpful comments on this manuscript.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of this study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Figure 1. Locations of western Scottish Neolithic individuals whose aDNA has been sequenced (cf. Table 1). C1—Carding Mill Bay 1; C2—Carding Mill Bay 2; D—Distillery Cave; M—MacArthur Cave; R—Raschoille Cave.
Figure 1. Locations of western Scottish Neolithic individuals whose aDNA has been sequenced (cf. Table 1). C1—Carding Mill Bay 1; C2—Carding Mill Bay 2; D—Distillery Cave; M—MacArthur Cave; R—Raschoille Cave.
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Figure 2. Locations of sites providing the food source δ13C and δ15N data presented in Table 5. 1. Braes of Ha’Breck; 2. Skara Brae; 3. An Corran; 4. Balbridie; 5. Bornish; 6. Ulva Cave; 7. Aird’s Bay, Loch Etive; 8. Carding Mill Bay and Raschoille, Oban; 9. Caisteal-nan-Gillean and Cnoc Coig, Oronsay.
Figure 2. Locations of sites providing the food source δ13C and δ15N data presented in Table 5. 1. Braes of Ha’Breck; 2. Skara Brae; 3. An Corran; 4. Balbridie; 5. Bornish; 6. Ulva Cave; 7. Aird’s Bay, Loch Etive; 8. Carding Mill Bay and Raschoille, Oban; 9. Caisteal-nan-Gillean and Cnoc Coig, Oronsay.
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Figure 3. Bulk collagen stable carbon and nitrogen isotope data for Neolithic humans from nine sites in western Scotland compared against the ranges of stable isotope values (represented by ellipsoid hulls) for major food groups (see Table 5): MF—marine fish; MM—marine mammals; MS—marine shellfish; P—pigs; TH—terrestrial herbivores; TP—terrestrial plants.
Figure 3. Bulk collagen stable carbon and nitrogen isotope data for Neolithic humans from nine sites in western Scotland compared against the ranges of stable isotope values (represented by ellipsoid hulls) for major food groups (see Table 5): MF—marine fish; MM—marine mammals; MS—marine shellfish; P—pigs; TH—terrestrial herbivores; TP—terrestrial plants.
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Figure 4. FRUITS dietary models for Neolithic individuals from western Scotland grouped by food source. The solid line represents the median estimated food source contribution to dietary calories, the dotted line shows the mean estimated contribution, the box shows the 68% credible interval (CI); and the whiskers the 95% CI.
Figure 4. FRUITS dietary models for Neolithic individuals from western Scotland grouped by food source. The solid line represents the median estimated food source contribution to dietary calories, the dotted line shows the mean estimated contribution, the box shows the 68% credible interval (CI); and the whiskers the 95% CI.
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Figure 5. The proportion of hunter-gatherer (WHG)/early European farmer (EEF) ancestry vs. the median date of the Mesolithic individual from Cnoc Coig (filled square) and Neolithic individuals (open circles) from western Scotland. Median 14C dates were calculated using OxCal 4.4 (Bronk Ramsey, 2021) and the IntCal20 dataset (Reimer et al., 2020) and are the mean of the upper and lower boundaries for contextual dates.
Figure 5. The proportion of hunter-gatherer (WHG)/early European farmer (EEF) ancestry vs. the median date of the Mesolithic individual from Cnoc Coig (filled square) and Neolithic individuals (open circles) from western Scotland. Median 14C dates were calculated using OxCal 4.4 (Bronk Ramsey, 2021) and the IntCal20 dataset (Reimer et al., 2020) and are the mean of the upper and lower boundaries for contextual dates.
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Table 1. Radiocarbon dates of Neolithic individuals for whom aDNA has been sequenced (data from Patterson et al., 2022; see also Olalde et al., 2018). The DNA of a minimum number of 19 individuals has been sequenced. While there are 20 samples listed, I2658 and I2657 from MacArthur Cave may be duplicates or, alternatively, first-degree relatives. Individuals I12314 and I12313 may be first-degree relatives. Individual I12313, from Carding Mill Bay 2, is dated by association with two other individuals for whom 14C determinations are available (I12314 and I12317). Radiocarbon dates are calibrated to 2σ (95.4% CI) and rounded outwards to the nearest 10 years. The median age is as reported by OxCal for the 14C dates and is the mean of the upper and lower limits for contextual/associated dates. See Figure 1 for site locations.
Table 1. Radiocarbon dates of Neolithic individuals for whom aDNA has been sequenced (data from Patterson et al., 2022; see also Olalde et al., 2018). The DNA of a minimum number of 19 individuals has been sequenced. While there are 20 samples listed, I2658 and I2657 from MacArthur Cave may be duplicates or, alternatively, first-degree relatives. Individuals I12314 and I12313 may be first-degree relatives. Individual I12313, from Carding Mill Bay 2, is dated by association with two other individuals for whom 14C determinations are available (I12314 and I12317). Radiocarbon dates are calibrated to 2σ (95.4% CI) and rounded outwards to the nearest 10 years. The median age is as reported by OxCal for the 14C dates and is the mean of the upper and lower limits for contextual/associated dates. See Figure 1 for site locations.
SiteIDSexDate TypeLab Code14C (BP)Calendar Years (Cal BCE/BCE)Median (Cal BCE/BCE)Ancestry% WHG
MacArthur CaveI2657M14CSUERC-687015052 ± 303960–37703872Farmer21.8
MacArthur CaveI2658MContextualn/an/a4000–37003850Farmer21.5
Distillery CaveI2659F14CSUERC-687024914 ± 273770–36403686Farmer23.4
Ulva CaveI12312M14CPSUAMS-57714895 ± 253760–36303679Admixed farmer/hunter-gatherer40.4
Distillery CaveI2691M14CSUERC-687044881 ± 253710–36303653Admixed farmer/hunter-gatherer30.7
Raschoille CaveI5370FContextualn/an/a4000–33003650Farmer25.1
Raschoille CaveI5371FContextualn/an/a4000–33003650Admixed farmer/hunter-gatherer26.2
Carding Mill Bay 2I12314F14CPSUAMS-5772
PSUAMS-5773
PSUAMS-5774
PSUAMS-5776		4832 ± 143650–35303634Admixed farmer/hunter-gatherer48.8
Raschoille CaveI3135M14CPSUAMS-20684770 ± 303640–33803576Admixed farmer/hunter-gatherer37.0
Carding Mill Bay 2I12313FContextualn/an/a3700–33503525Admixed farmer/hunter-gatherer26.8
Raschoille CaveI3134M14CPSUAMS-21554730 ± 253630–33703524Farmer24.5
Carding Mill Bay 2I12317M14CPSUAMS-57754725 ± 253630–33703524Admixed farmer/hunter-gatherer30.0
Raschoille CaveI3133M14CPSUAMS-21544725 ± 203630–33703514Farmer24.3
Raschoille CaveI3041MContextualn/an/a3950–30303490Admixed farmer/hunter-gatherer31.0
Raschoille CaveI3136F14CPSUAMS-20694665 ± 303520–33703450Farmer20.5
ClachaigI2988F14CSUERC-687114645 ± 293520–33603458Farmer17.5
Distillery CaveI2660M14CSUERC-687034631 ± 293520–33503462Admixed farmer/hunter-gatherer25.0
Raschoille CaveI3137MContextualn/an/a3800–30003400Farmer23.6
Raschoille CaveI3139_dFContextualn/an/a3800–30003400Admixed farmer/hunter-gatherer45.0
Raschoille CaveI3138F14CPSUAMS-21564415 ± 253320–29203040Farmer23.4
Table 2. Parameters used by Schulting et al. (2023) to generate two dietary models (one using a manured cereal δ15N, the other using an unmanured cereal δ15N) for the Late Neolithic individual Carding Mill Bay 1 OxA-7890. The young herbivore value was calculated from the terrestrial herbivore value with a ‘nursing’ offset applied—see Schulting et al. (2023, p. 3).
Table 2. Parameters used by Schulting et al. (2023) to generate two dietary models (one using a manured cereal δ15N, the other using an unmanured cereal δ15N) for the Late Neolithic individual Carding Mill Bay 1 OxA-7890. The young herbivore value was calculated from the terrestrial herbivore value with a ‘nursing’ offset applied—see Schulting et al. (2023, p. 3).
ParameterModelDescription
Δ15Ndiet–consumerBoth4.6 ± 0.5‰
Δ13Cdiet–consumerBoth4.8 ± 0.5‰
Manured cereals δ13C and δ15NModel Aδ13Cprotein = 26 ± 1.0‰ and δ15Nmanured = 5.0 ± 2.0‰
Unmanured cereals δ13C and δ15NModel Bδ13Cprotein = 26 ± 1.0‰ and δ15Nmanured = 3.0 ± 1.0‰
Terrestrial herbivores δ13C and δ15NBothTerrestrial herbivores δ13Cprotein = − 24.7 ± 0.1‰ and δ15N = 3.4 ± 0.1‰
Young herbivores δ13C and δ15NBothYoung herbivores δ13Cprotein = − 23.7 ± 0.1‰ and δ15N = 5.4 ± 0.1‰
Table 3. Carding Mill Bay 1, Model A: Manured cereals. Protein sources routed to collagen (% mean estimate ± standard deviation and 68% credible intervals). Context information as presented by Connock et al. (1991).
Table 3. Carding Mill Bay 1, Model A: Manured cereals. Protein sources routed to collagen (% mean estimate ± standard deviation and 68% credible intervals). Context information as presented by Connock et al. (1991).
SiteContextIndividualTerrestrial Herbivore Young Herbivore Cereals
Mean %CI (68%)Mean %CI (68%)Mean %CI (68%)
CMB 1XXIIIOxA-789024 ± 167–4118 ± 145–3358 ± 1741–76
CMB 1VIIOxA-766522 ± 165–4017 ± 134–3160 ± 1643–78
CMB 1XVOxA-766435 ± 2211–5820 ± 155–3645 ± 2123–68
CMB 1XIVOxA-766326 ± 197–4616 ± 134–2958 ± 1938–77
Table 4. Carding Mill Bay 1, Model B: Non-manured cereals. Protein sources routed to collagen (% mean estimate ± standard deviation and 68% credible intervals). Context information as presented by Connock et al. (1991).
Table 4. Carding Mill Bay 1, Model B: Non-manured cereals. Protein sources routed to collagen (% mean estimate ± standard deviation and 68% credible intervals). Context information as presented by Connock et al. (1991).
SiteContextIndividualTerrestrial Herbivore Young Herbivore Cereals
Mean %CI (68%)Mean %CI (68%)Mean %CI (68%)
CMB 1XXIIIOxA-789017 ± 144–3127 ± 169–4456 ± 1741–76
CMB 1VIIOxA-766517 ± 144–3123 ± 154–3160 ± 1743–78
CMB 1XVOxA-766428 ± 207–4925 ± 159–4247 ± 1829–66
CMB 1XIVOxA-766322 ± 175–4120 ± 146–3557 ± 1740–76
Table 5. Food source δ13C and δ15N data used in the FRUITS models. The δ13C and δ15N of the archaeological plant specimens have been corrected for charring, and the modern samples have been corrected for the Suess effect (Keeling et al., 2017; see also Pickard & Bonsall, 2022). See Supplementary Material (Longin, 1971; Sayle et al., 2019) for collagen extraction and mass spectrometric methods statements for the new data.
Table 5. Food source δ13C and δ15N data used in the FRUITS models. The δ13C and δ15N of the archaeological plant specimens have been corrected for charring, and the modern samples have been corrected for the Suess effect (Keeling et al., 2017; see also Pickard & Bonsall, 2022). See Supplementary Material (Longin, 1971; Sayle et al., 2019) for collagen extraction and mass spectrometric methods statements for the new data.
SiteDateSpecies Common Nameδ13C ‰δ15N ‰C:NLab/Specimen IDReference
TERRESTRIAL MAMMALS
An Corran Mesolithic/NeolithicBosCattle−22.21.6 Milner and Craig (2009)
An Corran Mesolithic/NeolithicBosCattle−22.01.7 Milner and Craig (2009)
Carding Mill Bay 1NeolithicBos taurus (probable)Cattle−23.33.53.3GU39625 (XIV)J. Bownes (2018)
Carding Mill Bay 1NeolithicBos taurus (probable)Cattle−23.23.33.3GU39626 (XIV)J. Bownes (2018)
Carding Mill Bay 1NeolithicBos taurus (probable)Cattle−23.33.53.3GU39627 (XIV)J. Bownes (2018)
Carding Mill Bay 1NeolithicBos taurus (probable)Cattle−23.43.43.3GU39628 (XIV)J. Bownes (2018)
Carding Mill Bay 1NeolithicBos taurus (probable)Cattle−22.63.83.3GU39629 (XV)J. Bownes (2018)
Carding Mill Bay 1NeolithicBos taurus (probable)Cattle−23.34.23.3GU39630 (XV)J. Bownes (2018)
Carding Mill Bay 1NeolithicBos taurus (probable)Cattle−23.43.43.3GU39631 (XV)J. Bownes (2018)
Carding Mill Bay 1NeolithicBos taurus (probable)Cattle−22.03.03.3GU39632 (XV)J. Bownes (2018)
An Corran Mesolithic/NeolithicCervus elaphusRed deer−21.21.0 Milner and Craig (2009)
An Corran Mesolithic/NeolithicCervus elaphusRed deer−22.52.4 Milner and Craig (2009)
Carding Mill Bay 1NeolithicCervus elaphusRed deer−23.23.03.4GUsi3509 (XVII)J. Bownes (2018)
Carding Mill Bay 1NeolithicCervus elaphusRed deer−21.92.0 CVII:123Schulting and Richards (2002)
Carding Mill Bay 1NeolithicCervus elaphusRed deer−22.92.5 C XVII:4Schulting and Richards (2002)
Raschoille Cave7640 ± 80 BPCervus elaphusRed deer−21.82.9 OxA-8396Pickard and Bonsall (2022)
Raschoille Cave7575 ± 75 BPCervus elaphusRed deer−21.52.8 OxA-8397Pickard and Bonsall (2022)
Raschoille Cave7480 ± 75 BPCervus elaphusRed deer−21.62.6 OxA-8398Pickard and Bonsall (2022)
Ulva CaveMesolithic Cervus elaphusRed deer−2.82.63.4GUsi8894this study
Carding Mill Bay 1NeolithicCervus elaphus/Bos taurusRed deer/Cattle−23.23.73.3GUsi3505J. M. Bownes et al. (2017)
Carding Mill Bay 1NeolithicCervus elaphus/Bos taurusRed deer/Cattle−22.52.43.2GUsi3506J. M. Bownes et al. (2017)
Carding Mill Bay 1NeolithicCervus elaphus/Bos taurusRed deer/Cattle−23.22.33.2GUsi3508J. M. Bownes et al. (2017)
Carding Mill Bay 1NeolithicCervus elaphus/Bos taurusRed deer/Cattle−22.83.93.2GUsi3511J. M. Bownes et al. (2017)
Carding Mill Bay 1NeolithicOvis aries/Capreolus capreolusSheep/Roe deer−21.63.53.2GUsi3497J. M. Bownes et al. (2017)
Carding Mill Bay 1NeolithicOvis aries/Capreolus capreolusSheep/Roe deer−22.93.73.2GUsi3507J. M. Bownes et al. (2017)
Carding Mill Bay 1NeolithicOvis aries/Capreolus capreolusSheep/Roe deer−23.33.43.2GUsi3498J. M. Bownes et al. (2017)
Carding Mill Bay 1NeolithicOvis aries/Capreolus capreolusSheep/Roe deer−22.82.83.5GUsi3500J. M. Bownes et al. (2017)
Carding Mill Bay 1NeolithicOvis aries/Capreolus capreolusSheep/Roe deer−23.13.13.5GUsi3501J. M. Bownes et al. (2017)
Carding Mill Bay 1NeolithicOvis aries/Capreolus capreolusSheep/Roe deer−22.82.83.6GUsi3502J. M. Bownes et al. (2017)
Carding Mill Bay 1NeolithicOvis aries/Capreolus capreolusSheep/Roe deer−22.52.73.4GUsi3503J. M. Bownes et al. (2017)
Carding Mill Bay 1NeolithicOvis aries/Capreolus capreolusSheep/Roe deer−22.53.13.2GUsi3504J. M. Bownes et al. (2017)
Ulva CaveMesolithic Ovis aries/Capreolus capreolusSheep/Roe deer−22.82.33.4GUsi8895this study
An Corran Mesolithic/NeolithicSusPig−22.32.3 Milner and Craig (2009)
An Corran Mesolithic/NeolithicSusPig −22.63.3 Milner and Craig (2009)
An Corran Mesolithic/NeolithicSusPig −21.74.1 Milner and Craig (2009)
Carding Mill Bay 1NeolithicSusPig −21.93.2 C XXIV:2Schulting and Richards (2002)
Cnoc CoigMesolithic SusPig −21.24.3 Charlton et al. (2016)
Cnoc CoigMesolithic SusPig −21.04.6 Charlton et al. (2016)
Cnoc CoigMesolithic SusPig −18.810.2 Charlton et al. (2016)
Ulva CaveMesolithic SusPig −21.96.53.3GUsi8896this study
MARINE RESOURCES-ARCHAEOLOGICAL SPECIMENS
Ulva CaveMesolithicDicentrarchus labraxSeabass−12.613.43.2GUsi8897this study
An CorranMesolithic/NeolithicGadus morhuaCod−13.615.3 0055-rMilner and Craig (2012)
Bornish12th–13th C. ADGadus morhuaCod−12.914.5 703Barrett et al. (2011)
Bornish13th C. ADGadus morhuaCod−11.315.4 706Barrett et al. (2011)
Bornish13th C. ADGadus morhuaCod−13.113.8 708Barrett et al. (2011)
Bornish12th–13th C. ADGadus morhuaCod−13.213.8 713Barrett et al. (2011)
Ulva CaveMesolithicLabrus bergyltaBallan wrasse−16.011.83.4GUsi8900this study
Ulva CaveMesolithicLabrus sp.Wrasse−12.614.13.3GUsi8898this study
Ulva CaveMesolithicLabrus sp.Wrasse−12.114.63.2GUsi8902this study
Ulva CaveMesolithicPollachius virensSaithe−12.213.63.2GUsi8899this study
Ulva CaveMesolithicPollachius virensSaithe−13.412.13.2GUsi8901this study
Cnoc CoigMesolithicPinnipedSeal −11.618.8 10,502Charlton et al. (2016)
Cnoc CoigMesolithicPinnipedSeal −11.819.5 10,420Charlton et al. (2016)
Airds Bay, Loch EtiveModern fleshPatellaLimpet −14.16.3 GUsi3201/3208J. Bownes (2018)
Airds Bay, Loch EtiveModern fleshPatellaLimpet −15.06.7 GUsi3202/3209J. Bownes (2018)
Airds Bay, Loch EtiveModern fleshPatellaLimpet −15.16.3 GUsi3204/3211J. Bownes (2018)
Airds Bay, Loch EtiveModern fleshPatellaLimpet −15.07.1 GUsi3205/3212J. Bownes (2018)
Airds Bay, Loch EtiveModern fleshPatellaLimpet −14.07.0 GUsi3206/3213J. Bownes (2018)
Airds Bay, Loch EtiveModern fleshPatellaLimpet −15.26.7 GUsi3207/3214J. Bownes (2018)
Oban, Scotland Modern fleshPatellaLimpet −13.67.8 GUsi3215/3221J. Bownes (2018)
Oban, Scotland Modern fleshPatellaLimpet −15.56.9 GUsi3216/3222J. Bownes (2018)
Oban, Scotland Modern fleshPatellaLimpet −14.76.2 GUsi3217/3223J. Bownes (2018)
Oban, Scotland Modern fleshLittorinaPeriwinkle−15.311.7 GUsi3446/3598J. Bownes (2018)
Oban, Scotland Modern fleshLittorinaPeriwinkle−13.38.5 GUsi3451/3603J. Bownes (2018)
PLANTS
Ulva CaveMesolithicCorylus avellanaHazelnut (shell)−26.09.3 UA1this study
Ulva CaveMesolithicCorylus avellanaHazelnut (shell)−24.97.4 UA2this study
Ulva CaveMesolithicCorylus avellanaHazelnut (shell)−27.28.0 UA3this study
Ulva CaveMesolithicCorylus avellanaHazelnut (shell)−25.65.6 UA4this study
Ulva CaveMesolithicCorylus avellanaHazelnut (shell)−24.66.2 UA5this study
Ulva CaveMesolithicCorylus avellanaHazelnut (shell)−25.15.7 UMS1 RWthis study
Ulva CaveMesolithicCorylus avellanaHazelnut (shell)−23.90.9 UMS2 RWthis study
Ulva CaveMesolithicCorylus avellanaHazelnut (shell)−25.0−1.2 UMS3 RWthis study
Ulva CaveMesolithicCorylus avellanaHazelnut (shell)−27.9−1.0 UMS4 RWthis study
Ulva CaveMesolithicCorylus avellanaHazelnut (shell)−27.00.5 UMS5 RWthis study
Ulva CaveMesolithicCorylus avellanaHazelnut (shell)−25.03.4 UMS6 RWthis study
BalbridieNeolithicHordeum vulgareNaked barley−24.60.624.4BB F40 IS.1Bishop et al. (2022)
BalbridieNeolithicHordeum vulgareNaked barley−23.10.136.0BB F40 IS.2Bishop et al. (2022)
BalbridieNeolithicHordeum vulgareNaked barley−24.50.832.0BB F40 IS.3Bishop et al. (2022)
BalbridieNeolithicHordeum vulgareNaked barley−24.6−1.637.2BB F40 IS.4Bishop et al. (2022)
BalbridieNeolithicHordeum vulgareNaked barley−23.82.330.2BB F40 IS.5Bishop et al. (2022)
BalbridieNeolithicHordeum vulgareNaked barley−24.81.427.0BB F40 IS.6Bishop et al. (2022)
BalbridieNeolithicHordeum vulgareNaked barley−24.41.125.4BB F40 IS.7Bishop et al. (2022)
BalbridieNeolithicHordeum vulgareNaked barley−24.13.027.7BB F40 IS.8Bishop et al. (2022)
BalbridieNeolithicHordeum vulgareNaked barley−25.21.429.3BB F40 IS.9Bishop et al. (2022)
BalbridieNeolithicHordeum vulgareNaked barley−22.71.527.8BB F40 IS.10Bishop et al. (2022)
BalbridieNeolithicHordeum vulgareNaked barley−24.40.430.0BB F294 IS.32Bishop et al. (2022)
BalbridieNeolithicHordeum vulgareNaked barley−23.90.535.5BB F294 IS.33Bishop et al. (2022)
BalbridieNeolithicHordeum vulgareNaked barley−25.20.526.1BB F294 IS.34Bishop et al. (2022)
BalbridieNeolithicHordeum vulgareNaked barley−25.20.033.0BB F294 IS.35Bishop et al. (2022)
BalbridieNeolithicHordeum vulgareNaked barley−25.21.331.0BB F294 IS.36Bishop et al. (2022)
BalbridieNeolithicHordeum vulgareNaked barley−23.5−0.228.2BB F294 IS.37Bishop et al. (2022)
BalbridieNeolithicHordeum vulgareNaked barley−23.5−1.627.5BB F294 IS.38Bishop et al. (2022)
BalbridieNeolithicHordeum vulgareNaked barley−24.60.931.0BB F294 IS.39Bishop et al. (2022)
BalbridieNeolithicHordeum vulgareNaked barley−24.62.427.7BB F294 IS.40Bishop et al. (2022)
BalbridieNeolithicHordeum vulgareNaked barley−24.8−1.330.7BB F294 IS.41Bishop et al. (2022)
Skara BraeNeolithicHordeum vulgareNaked barley−21.64.018.8SB C.168 IS.1Bishop et al. (2022)
Skara BraeNeolithicHordeum vulgareNaked barley−23.02.525.9SB C.168 IS.2Bishop et al. (2022)
Skara BraeNeolithicHordeum vulgareNaked barley−22.72.519.5SB C.168 IS.3Bishop et al. (2022)
Skara BraeNeolithicHordeum vulgareNaked barley−23.14.017.7SB C.168 IS.4Bishop et al. (2022)
Skara BraeNeolithicHordeum vulgareNaked barley−22.74.123.3SB C.168 IS.5Bishop et al. (2022)
Skara BraeNeolithicHordeum vulgareNaked barley−21.03.112.3SB C.168 IS.6Bishop et al. (2022)
Skara BraeNeolithicHordeum vulgareNaked barley−22.82.221.7SB C.168 IS.7Bishop et al. (2022)
Skara BraeNeolithicHordeum vulgareNaked barley−22.45.317.0SB C.168 IS.8Bishop et al. (2022)
Skara BraeNeolithicHordeum vulgareNaked barley−22.34.526.0SB C.168 IS.9Bishop et al. (2022)
Skara BraeNeolithicHordeum vulgareNaked barley−23.83.219.7SB C.168 IS.10Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−23.17.317.0BOH S.506.8 IS1Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−22.10.224.7BOH S.506.8 IS2Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−24.01.122.7BOH S.506.8 IS3Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−24.31.227.0BOH S.506.8 IS4Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−24.13.819.8BOH S.506.8 IS5Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−24.31.530.8BOH S.506.8 IS6Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−23.43.917.8BOH S.506.8 IS7Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−23.35.920.1BOH S.506.8 IS8Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−25.35.928.7BOH S.506.8 IS9Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−24.13.318.7BOH S.506.8 IS10Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−24.01.731.7BOH S.124 IS1Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−23.25.128.1BOH S.124 IS2Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−22.54.027.6BOH S.124 IS3Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−23.23.525.2BOH S.124 IS4Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−23.92.631.3BOH S.124 IS5Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−23.23.022.8BOH S.124 IS6Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−23.94.130.5BOH S.124 IS7Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−24.22.222.1BOH S.124 IS8Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−23.91.021.8BOH S.124 IS9Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−24.02.721.9BOH S.124 IS10Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−23.15.714.0BOH S.168 IS1Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−22.93.821.4BOH S.168 IS2Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−24.26.119.3BOH S.168 IS3Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−24.94.527.3BOH S.168 IS4Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−243.511.6BOH S.168 IS5Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−24.53.822.7BOH S.168 IS6Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−23.87.615.7BOH S.168 IS7Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−25.14.128.8BOH S.168 IS8Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−22.92.327.9BOH S.168 IS9Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−23.91.729.7BOH S.168 IS10Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−24.10.323.6BOH S.24 IS1Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−23.13.927.5BOH S.24 IS2Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−23.23.921.4BOH S.24 IS3Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−23.94.518.7BOH S.24 IS4Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−25.22.719.9BOH S.24 IS5Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−24.16.330.2BOH S.24 IS6Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−25.47.028.2BOH S.24 IS7Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−24.13.629.4BOH S.24 IS8Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−22.48.025.0BOH S.24 IS9Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−22.91.925.9BOH S.24 IS10Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−25.24.520.3BOH S.41 IS1Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−23.64.226.7BOH S.41 IS2Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−24.78.331.3BOH S.41 IS3Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−23.42.825.6BOH S.41 IS4Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−23.813.720.4BOH S.41 IS5Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−22.38.824.2BOH S.41 IS6Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−22.51.424.1BOH S.41 IS7Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−23.82.924.0BOH S.41 IS8Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−23.72.628.0BOH S.41 IS9Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−23.44.228.6BOH S.41 IS10Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−23.13.414.2BOH S.112 IS1Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−24.31.232.2BOH S.112 IS2Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−24.04.722.0BOH S.112 IS3Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−22.63.720.2BOH S.112 IS4Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−22.83.119.0BOH S.112 IS5Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−25.72.833.7BOH S.112 IS6Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−24.22.422.2BOH S.112 IS7Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−24.53.119.9BOH S.112 IS8Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−23.32.919.6BOH S.112 IS9Bishop et al. (2022)
Braes of Ha’BreckNeolithicHordeum vulgareNaked barley−24.42.721.7BOH S.112 IS10Bishop et al. (2022)
Dubton FarmNeolithicHordeum vulgareNaked barley twisted grain−26.64.415.2BREW S.235 IS.11Bishop et al. (2022)
Dubton FarmNeolithicHordeum vulgareNaked barley twisted grain−25.13.324.7BREW S.235 IS.12Bishop et al. (2022)
Dubton FarmNeolithicHordeum vulgareNaked barley twisted grain−24.33.015.6BREW S.235 IS.13Bishop et al. (2022)
Dubton FarmNeolithicHordeum vulgareNaked barley twisted grain−24.92.426.1BREW S.235 IS.14Bishop et al. (2022)
Dubton FarmNeolithicHordeum vulgareNaked barley twisted grain−24.23.918.5BREW S.235 IS.15Bishop et al. (2022)
Dubton FarmNeolithicHordeum vulgareNaked barley twisted grain−24.8−0.919.2BREW S.235 IS.16Bishop et al. (2022)
Dubton FarmNeolithicHordeum vulgareNaked barley twisted grain−24.4−0.228.1BREW S.235 IS.17Bishop et al. (2022)
Dubton FarmNeolithicHordeum vulgareNaked barley twisted grain−23.93.122.2BREW S.235 IS.18Bishop et al. (2022)
Dubton FarmNeolithicHordeum vulgareNaked barley twisted grain−26.42.825.9BREW S.235 IS.19Bishop et al. (2022)
Dubton FarmNeolithicHordeum vulgareNaked barley twisted grain−25.03.426.1BREW S.235 IS.20Bishop et al. (2022)
Dubton FarmNeolithicHordeum vulgareNaked barley twisted grain−22.93.821.6BREW S.214 IS.69Bishop et al. (2022)
Dubton FarmNeolithicHordeum vulgareNaked barley grain−24.32.527.1BREW S.374 IS.56Bishop et al. (2022)
Dubton FarmNeolithicHordeum vulgareNaked barley grain−25.01.323.3BREW S.374 IS.57Bishop et al. (2022)
Dubton FarmNeolithicHordeum vulgareNaked barley grain−24.42.523.2BREW S.374 IS.58Bishop et al. (2022)
Dubton FarmNeolithicHordeum vulgareNaked barley grain−22.73.227.3BREW S.374 IS.59Bishop et al. (2022)
Dubton FarmNeolithicHordeum vulgareNaked barley grain−26.04.531.0BREW S.214 IS.68Bishop et al. (2022)
Dubton FarmNeolithicHordeum vulgareNaked barley grain−23.85.821.3BREW S.214 IS.70Bishop et al. (2022)
Dubton FarmNeolithicHordeum vulgareNaked barley straight grain−26.94.333.4BREW S.65 IS.51Bishop et al. (2022)
Dubton FarmNeolithicHordeum vulgareNaked barley straight grain−24.21.914.7BREW S.374 IS.60Bishop et al. (2022)
Dubton FarmNeolithicHordeum vulgareNaked barley straight grain−24.94.822.4BREW S.374 IS.61Bishop et al. (2022)
Dubton FarmNeolithicHordeum vulgareNaked barley straight grain−24.22.123.8BREW S.374 IS.62Bishop et al. (2022)
Dubton FarmNeolithicHordeum vulgareNaked barley twisted grain−25.52.523.4BREW S.65 IS.52Bishop et al. (2022)
Dubton FarmNeolithicHordeum vulgareNaked barley twisted grain−26.20.736.0BREW S.65 IS.53Bishop et al. (2022)
Dubton FarmNeolithicHordeum vulgareNaked barley twisted grain−24.93.325.5BREW S.65 IS.54Bishop et al. (2022)
Dubton FarmNeolithicHordeum vulgareNaked barley twisted grain−25.14.624.6BREW S.65 IS.55Bishop et al. (2022)
Dubton FarmNeolithicHordeum vulgareNaked barley twisted grain−23.71.618.3BREW S.374 IS.63Bishop et al. (2022)
Dubton FarmNeolithicHordeum vulgareNaked barley twisted grain−26.05.927.3BREW S.214 IS.67Bishop et al. (2022)
Dubton FarmNeolithicHordeum vulgareNaked barley twisted grain−25.25.826.2BREW S.214 IS.71Bishop et al. (2022)
BalbridieNeolithicTriticum dicocconEmmer wheat grain−23.71.428.8BB F40 IS.11Bishop et al. (2022)
BalbridieNeolithicTriticum dicocconEmmer wheat grain−23.20.927.3BB F40 IS.12Bishop et al. (2022)
BalbridieNeolithicTriticum dicocconEmmer wheat grain−23.61.023.7BB F40 IS.13Bishop et al. (2022)
BalbridieNeolithicTriticum dicocconEmmer wheat grain−23.2−0.724.4BB F40 IS.14Bishop et al. (2022)
BalbridieNeolithicTriticum dicocconEmmer wheat grain−23.40.526.0BB F40 IS.15Bishop et al. (2022)
BalbridieNeolithicTriticum dicocconEmmer wheat grain−22.0−0.827.0BB F40 IS.16Bishop et al. (2022)
BalbridieNeolithicTriticum dicocconEmmer wheat grain−23.21.034.0BB F40 IS.17Bishop et al. (2022)
BalbridieNeolithicTriticum dicocconEmmer wheat grain−23.10.625.6BB F40 IS.18Bishop et al. (2022)
BalbridieNeolithicTriticum dicocconEmmer wheat grain−23.2−0.525.8BB F40 IS.19Bishop et al. (2022)
BalbridieNeolithicTriticum dicocconEmmer wheat grain−23.43.125.1BB F40 IS.20Bishop et al. (2022)
BalbridieNeolithicTriticum dicocconEmmer wheat grain−23.10.625.1BB F294 IS.42Bishop et al. (2022)
BalbridieNeolithicTriticum dicocconEmmer wheat grain−23.41.028.7BB F294 IS.43Bishop et al. (2022)
BalbridieNeolithicTriticum dicocconEmmer wheat grain−22.70.827.1BB F294 IS.44Bishop et al. (2022)
BalbridieNeolithicTriticum dicocconEmmer wheat grain−24.0−0.627.5BB F294 IS.45Bishop et al. (2022)
BalbridieNeolithicTriticum dicocconEmmer wheat grain−23.40.724.3BB F294 IS.46Bishop et al. (2022)
BalbridieNeolithicTriticum dicocconEmmer wheat grain−22.91.123.8BB F294 IS.47Bishop et al. (2022)
BalbridieNeolithicTriticum dicocconEmmer wheat grain−22.61.424.4BB F294 IS.48Bishop et al. (2022)
BalbridieNeolithicTriticum dicocconEmmer wheat grain−23.7−0.624.8BB F294 IS.49Bishop et al. (2022)
BalbridieNeolithicTriticum dicocconEmmer wheat grain−23.5−0.228.0BB F294 IS.50Bishop et al. (2022)
BalbridieNeolithicTriticum dicocconEmmer wheat grain−23.51.122.1BB F294 IS.51Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−25.01.713.4BREW S.235 IS.1Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−24.15.516.2BREW S.235 IS.2Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−24.95.416.4BREW S.235 IS.3Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−25.15.015.4BREW S.235 IS.4Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−25.04.811.1BREW S.235 IS.5Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−24.64.215.5BREW S.235 IS.6Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−25.05.310.3BREW S.235 IS.7Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−24.31.713.4BREW S.235 IS.8Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−24.32.413.0BREW S.235 IS.9Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−24.73.915.0BREW S.235 IS.10Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−24.00.218.7BREW S.65 IS.21Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−24.10.815.7BREW S.65 IS.22Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−24.03.815.9BREW S.374 IS.31Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−24.73.425.3BREW S.374 IS.32Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−25.33.719.1BREW S.214 IS.65Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−25.21.622.7BREW S.65 IS.23Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−23.74.823.6BREW S.65 IS.24Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−23.54.916.0BREW S.65 IS.25Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−25.03.020.0BREW S.65 IS.26Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−24.61.524.4BREW S.65 IS.27Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−25.33.715.6BREW S.65 IS.28Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−22.86.517.4BREW S.65 IS.29Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−23.73.213.8BREW S.65 IS.30Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−24.21.613.4BREW S.374 IS.33Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−24.61.320.2BREW S.374 IS.34Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−25.02.617.4BREW S.374 IS.35Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−23.93.810.6BREW S.374 IS.36Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−24.41.517.3BREW S.374 IS.37Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−25.42.521.8BREW S.374 IS.38Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−25.32.816.2BREW S.374 IS.39Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−23.41.613.0BREW S.374 IS.40Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−24.82.916.7BREW S.214 IS.64Bishop et al. (2022)
Dubton FarmNeolithicTriticum dicocconEmmer wheat grain−25.45.316.8BREW S.214 IS.66Bishop et al. (2022)
Skara BraeNeolithicTriticum dicocconEmmer wheat grain−20.73.815.6SB C.168 IS.11Bishop et al. (2022)
Skara BraeNeolithicTriticum dicocconEmmer wheat grain−20.81.616.0SB C.168 IS.12Bishop et al. (2022)
Skara BraeNeolithicTriticum dicocconEmmer wheat grain−22.23.515.3SB C.168 IS.13Bishop et al. (2022)
Skara BraeNeolithicTriticum dicocconEmmer wheat grain−22.50.913.7SB C.168 IS.14Bishop et al. (2022)
Skara BraeNeolithicTriticum dicocconEmmer wheat grain−21.53.115.0SB C.168 IS.15Bishop et al. (2022)
BalbridieNeolithicTriticumFree-threshing wheat−24.10.532.2BB F40 IS.21Bishop et al. (2022)
BalbridieNeolithicTriticumFree-threshing wheat−22.70.225.0BB F40 IS.22Bishop et al. (2022)
BalbridieNeolithicTriticumFree-threshing wheat−22.7−0.325.6BB F40 IS.23Bishop et al. (2022)
BalbridieNeolithicTriticumFree-threshing wheat−23.10.126.8BB F40 IS.24Bishop et al. (2022)
BalbridieNeolithicTriticumFree-threshing wheat−22.91.229.0BB F40 IS.25Bishop et al. (2022)
BalbridieNeolithicTriticumFree-threshing wheat−23.8−0.625.8BB F40 IS.26Bishop et al. (2022)
BalbridieNeolithicTriticumFree-threshing wheat−23.10.628.2BB F40 IS.27Bishop et al. (2022)
BalbridieNeolithicTriticumFree-threshing wheat−23.50.229.6BB F40 IS.28Bishop et al. (2022)
BalbridieNeolithicTriticumFree-threshing wheat−22.8−0.730.8BB F40 IS.29Bishop et al. (2022)
BalbridieNeolithicTriticumFree-threshing wheat−22.42.027.9BB F40 IS.30Bishop et al. (2022)
BalbridieNeolithicTriticumFree-threshing wheat−22.50.828.4BB F294 IS.52Bishop et al. (2022)
BalbridieNeolithicTriticumFree-threshing wheat−23.50.530.6BB F294 IS.53Bishop et al. (2022)
BalbridieNeolithicTriticumFree-threshing wheat−22.90.821.6BB F294 IS.54Bishop et al. (2022)
BalbridieNeolithicTriticumFree-threshing wheat−23.0−0.323.5BB F294 IS.55Bishop et al. (2022)
BalbridieNeolithicTriticumFree-threshing wheat−22.7−0.730.3BB F294 IS.56Bishop et al. (2022)
BalbridieNeolithicTriticumFree-threshing wheat−22.20.321.9BB F294 IS.57Bishop et al. (2022)
BalbridieNeolithicTriticumFree-threshing wheat−22.9−0.224.2BB F294 IS.58Bishop et al. (2022)
BalbridieNeolithicTriticumFree-threshing wheat−22.60.022.8BB F294 IS.59Bishop et al. (2022)
BalbridieNeolithicTriticumFree-threshing wheat−24.53.724.1BB F294 IS.60Bishop et al. (2022)
BalbridieNeolithicTriticumFree-threshing wheat−22.92.333.8BB F294 IS.61Bishop et al. (2022)
Table 6. Neolithic human δ13C and δ15N data. The diets of only those individuals identified as adults are modelled.
Table 6. Neolithic human δ13C and δ15N data. The diets of only those individuals identified as adults are modelled.
IDSiteLab/Context IDSkeletal ElementSexAgeδ13C ‰δ15N ‰C/N14C Age BPReferences
AC.01An Corran Small adult−20.79.8 Milner and Craig (2009)
AC.02An Corran Mature adult (<35)−20.69.8 Milner and Craig (2009)
AC.03An Corran Small adult−20.59.4 Milner and Craig (2009)
AC.04An Corran Mature adult (>40)−20.210.2 Milner and Craig (2009)
AC.05An Corran Mature adult−19.410.7 Milner and Craig (2009)
AC.06An Corran n.d.−21.211.1 Milner and Craig (2009)
AC.07An Corran n.d.−21.19.9 Milner and Craig (2009)
AC.08An Corran n.d.−21.010.1 Milner and Craig (2009)
AC.09An Corran n.d.−20.710.3 Milner and Craig (2009)
AC.10An Corran n.d.−20.89.8 Milner and Craig (2009)
AC.11An Corran n.d.−20.510.8 Milner and Craig (2009)
AC.12An Corran n.d.−20.211.4 Milner and Craig (2009)
AC.13An Corran n.d.−20.110.1 Milner and Craig (2009)
AC.14An Corran n.d.−19.99.7 Milner and Craig (2009)
AC.15An Corran n.d.−19.79.0 Milner and Craig (2009)
AC.16An Corran n.d.−19.710.2 Milner and Craig (2009)
AC.17An Corran n.d.−19.610.6 Milner and Craig (2009)
AC.18An Corran n.d.−22.92.6 Milner and Craig (2009)
CMB.01Carding Mill Bay 1C XIV:1Phalanx Adult−21.59.03.2 Schulting and Richards (2002)
CMB.02Carding Mill Bay 1C XV:1Metacarpal Adult−21.08.93.1 Schulting and Richards (2002)
CMB.03Carding Mill Bay 1C VII:130Parietal Adult−21.59.63.2 Schulting and Richards (2002)
CMB.04Carding Mill Bay 1C XXIIIMetatarsus Adult−21.49.83.1 Schulting and Richards (2002)
CMB.05Carding Mill Bay 1C III:74Humerus Adult−21.38.83.2 Schulting and Richards (2002)
CMB.06Carding Mill Bay 1C IV:94Phalanx Adult−21.510.03.1 Schulting and Richards (2002)
CMB.07Carding Mill Bay 1C V:105Femur Adult−21.38.93.2 Schulting and Richards (2002)
CMB.08Carding Mill Bay 1C VII:112Metatarsal Adult−21.39.13.2 Schulting and Richards (2002)
CMB.09Carding Mill Bay 1C XVII:1Phalanx Adult−21.59.93.1 Schulting and Richards (2002)
CMB.10Carding Mill Bay 1C X:1Scapula Non-adult−21.39.53.1 Schulting and Richards (2002)
CMB.11Carding Mill Bay 2 Incisor Non-adult−21.28.53.3 Patterson et al. (2022)
CMB.12Carding Mill Bay 2PSUAMS-5772/5773/5774/5776PhalanxFAdult−21.79.63.34832 ± 14Patterson et al. (2022)
CLA.01ClachaigSUERC-68711/I2988PetrousFn.d.−21.611.23.34645 ± 29Olalde et al. (2018)
CRA.01Crarae45 NN 1186.1Pelvis Adult−21.89.03.3 Schulting and Richards (2002)
CRA.02Crarae45 NN 1186.2Phalanx Adult−21.39.53.3 Schulting and Richards (2002)
CRA.03Crarae59C NN 1123Patella Adult?−21.79.13.5 Schulting and Richards (2002)
DC.01Distillery CaveSUERC-68702/I2659 PetrousF n.d.−21.48.93.24914 ± 27Olalde et al. (2018)
DC.02Distillery CaveSUERC-68703/I2660 Petrous n.d.−21.79.13.24631 ± 29Olalde et al. (2018)
DC.03Distillery CaveSUERC-68704/I2691PetrousM n.d.−21.88.63.24881 ± 25Olalde et al. (2018)
MC.01MacArthur CaveSUERC-68701/I2657 PhalanxMAdult −21.49.03.35052 ± 30Olalde et al. (2018)
RC.01Raschoille CaveOxA-8537L Humerus 1–2 yr−21.811.93.34535 ± 50Bonsall (2000); Pickard & Bonsall (2022)
RC.02Raschoille CaveOxA-8434R Femur c. 3 yr−21.18.73.44720 ± 50Bonsall (2000); Pickard & Bonsall (2022)
RC.03Raschoille CaveOxA-8431L Femur ?3–5 yr−20.68.63.34930 ± 50Bonsall (2000); Pickard & Bonsall (2022)
RC.04Raschoille CaveOxA-8399/I3137Cervical vertebraM3–7 yr−21.410.23.44630 ± 65Bonsall (2000); Pickard & Bonsall (2022); Patterson et al. (2022)
RC.05Raschoille CaveOxA-8432R Humerus 8–10 yr−20.47.63.34980 ± 50Bonsall (2000); Pickard & Bonsall (2022)
RC.06Raschoille CaveOxA-8401L Femur ?10 yr−21.19.13.34565 ± 65Bonsall (2000); Pickard & Bonsall (2022)
RC.07Raschoille CaveOxA-8400Rib Adult−20.39.73.24640 ± 65Bonsall (2000); Pickard & Bonsall (2022)
RC.08Raschoille CaveOxA-8444R HumerusFAdult−21.19.63.54715 ± 45Bonsall (2000); Pickard & Bonsall (2022)
RC.09Raschoille CaveOxA-8443R Humerus Adult−20.49.43.24825 ± 55Bonsall (2000); Pickard & Bonsall (2022)
RC.10Raschoille CaveOxA-8433L HumerusMAdult−20.29.43.24920 ± 50Bonsall (2000); Pickard & Bonsall (2022)
RC.11Raschoille CaveOxA-8404R Humerus Adult−21.67.73.24850 ± 70Bonsall (2000); Pickard & Bonsall (2022)
RC.12Raschoille CaveOxA-8441R Humerus Adult?−21.29.13.34900 ± 45Bonsall (2000); Pickard & Bonsall (2022)
RC.13Raschoille CaveOxA-8435Humerus −22.510.33.44680 ± 50Bonsall (2000); Pickard & Bonsall (2022)
RC.14Raschoille CaveOxA-8442R Humerus −21.08.73.34890 ± 45Bonsall (2000); Pickard & Bonsall (2022)
RC.15Raschoille CavePSUAMS-2068/I3135PetrousM −21.59.63.24770 ± 30Olalde et al. (2018)
RC.16Raschoille CavePSUAMS-2069/I3136PetrousF −21.09.03.34665 ± 30Olalde et al. (2018)
RC.17Raschoille CavePSUAMS-2154/I3133PetrousM −21.79.63.04725 ± 20Olalde et al. (2018)
RC.18Raschoille CavePSUAMS-2155/I3134PetrousM −22.211.03.24730 ± 25Olalde et al. (2018)
RC.19Raschoille CavePSUAMS-2156/I3138PetrousF −22.19.43.24415 ± 25Olalde et al. (2018)
RC.20Raschoille CaveGU-40818/SB 513A2/I3041PetrousMAdult−21.98.43.54550 ± 29J. M. Bownes et al. (2017); Knight et al. (2021)
RC.21Raschoille CaveGU-40819/SUERC-67265/SB525A2 PetrousFAdult?−21.77.73.34738 ± 31J. M. Bownes et al. (2017); Knight et al. (2021)
RC.22Raschoille CaveGU-40820/SUERC-67266/SB526A PetrousF?Adult−21.67.73.34817 ± 31J. M. Bownes et al. (2017); Knight et al. (2021)
RC.23Raschoille CaveGU-40821/SUERC-67267/SB528A2/I5371PetrousFIndeterminate−22.18.83.34490 ± 29J. M. Bownes et al. (2017); Knight et al. (2021)
RC.24Raschoille CaveGU-40822/SUERC-67268/SB527A1/I5370PetrousFAdult?−21.59.23.34499 ± 29J. M. Bownes et al. (2017); Knight et al. (2021)
RC.25Raschoille CaveGU-40823 −21.99.53.34668 ± 29J. M. Bownes et al. (2017); Knight et al. (2021)
RC.26Raschoille CaveGU-40824 −22.310.23.24432 ± 31J. M. Bownes et al. (2017)
RC.27Raschoille CaveGU-40825/SUERC-67271Petrous Non-adult−22.410.43.34731 ± 29J. M. Bownes et al. (2017); Knight et al. (2021)
RC.28Raschoille CaveGU-40826/SUERC-67275Petrous Non-adult−22.29.33.34638 ± 31J. M. Bownes et al. (2017); Knight et al. (2021)
UC.01Ulva CavePSUAMS-5771Long boneMAdult−21.09.93.24895 ± 25Patterson et al. (2022)
Table 7. Parameters input to the FRUITS 4 food source model. Constraints on protein intake are based on modern reference intake guidelines (Berryman et al., 2018).
Table 7. Parameters input to the FRUITS 4 food source model. Constraints on protein intake are based on modern reference intake guidelines (Berryman et al., 2018).
ParameterDescription
Δ15Ndiet–consumer5.5 ± 0.5‰
Δ13Cdiet–consumer4.8 ± 0.5‰
Food source isotope values—Plantsδ13Cprotein = −26.4 ± 1.0‰; δ13Cenergy= −24.1 ± 1.0‰; δ15N = 2.8 ± 1.0‰
Food source isotope values—Terrestrial animalsδ13Cprotein = −24.4 ± 1.0‰, δ13Cenergy= −30.4 ± 1.0‰; δ15N = 3.3 ± 1.0‰
Food source isotope values—Fish δ13Cprotein = −14.0 ± 1.0‰, δ13Cenergy= −20.0 ± 1.0‰; δ15N = 15.4 ± 1.0‰
Food source isotope values—Shellfishδ13Cprotein = −14.6 ± 1.0‰, δ13Cenergy= −18.1 ± 1.0‰; δ15N = 7.4 ± 1.0‰
Food source nutrient concentrations—PlantsProtein–energy—10:90
Food source nutrient concentrations—Terrestrial animalsProtein–energy—70:30
Food source nutrient concentrations—Fish Protein–energy—75:25
Food source nutrient concentrations—ShellfishProtein–energy—90:10
Prior0.05 ≤ protein intake ≥ 0.35
Table 8. Mean estimates (ME Cal) and credible intervals (CI, 68%) of calorie intake and a mean estimate of protein contribution (ME Pro) of four food groups, plants, terrestrial animals, fish, and shellfish consumed by Neolithic people from western Scotland. Values are presented as % of whole dietary intake. Samples are labelled to be consistent with Pickard and Bonsall (2022). Individuals are represented by site initials and a number: AC—An Corran; CMB—Carding Mill Bay; CRA –Crarae; MC—MacArthur Cave; RC –Raschoille Cave; UC—Ulva Cave.
Table 8. Mean estimates (ME Cal) and credible intervals (CI, 68%) of calorie intake and a mean estimate of protein contribution (ME Pro) of four food groups, plants, terrestrial animals, fish, and shellfish consumed by Neolithic people from western Scotland. Values are presented as % of whole dietary intake. Samples are labelled to be consistent with Pickard and Bonsall (2022). Individuals are represented by site initials and a number: AC—An Corran; CMB—Carding Mill Bay; CRA –Crarae; MC—MacArthur Cave; RC –Raschoille Cave; UC—Ulva Cave.
Sampleδ13Cδ15NPLANTSTERRESTRIAL ANIMALFISHSHELLFISH
ME (Cal)CI (68%)ME (Pro)ME (Cal)CI (68%)ME (Pro)ME (Cal)CI (68%)ME (Pro)ME (Cal)CI (68%)ME (Pro)
AC.01−20.79.875 ± 1163–8834 ± 1722 ± 119–3454 ± 192 ± 10–35 ± 42 ± 20–36 ± 5
AC.02−20.69.875 ± 1262–9034 ± 2022 ± 127–3454 ± 212 ± 10–35 ± 42 ± 20–36 ± 5
AC.03−20.59.475 ± 1163–8734 ± 1721 ± 1010–3254 ± 192 ± 10–35 ± 42 ± 20–36 ± 5
AC.04−20.210.275 ± 1163–8935 ± 1720 ± 117–3150 ± 192 ± 21–47 ± 52 ± 20–48 ± 6
AC.05−19.410.773 ± 1063–8531 ± 1420 ± 99–2948 ± 174 ± 21–610 ± 63 ± 31–610 ± 8
CMB.01−21.59.077 ± 1263–9138 ± 2021 ± 127–3454 ± 221 ± 10–24 ± 31 ± 10–25 ± 4
CMB.02−21.08.976 ± 1263–9137 ± 2121 ± 126–3453 ± 221 ± 10–24 ± 31 ± 10–35 ± 5
CMB.03−21.59.677 ± 1263–9238 ± 2021 ± 126–3452 ± 221 ± 10–35 ± 41 ± 10–25 ± 4
CMB.04−21.49.876 ± 1263–9137 ± 2021 ± 117–3353 ± 212 ± 10–35 ± 41 ± 10–35 ± 4
CMB.05−21.38.878 ± 1364–9442 ± 2219 ± 124–3350 ± 231 ± 10–23 ± 31 ± 10–25 ± 4
CMB.06−21.510.076 ± 1263–9238 ± 2121 ± 126–3352 ± 222 ± 10–35 ± 41 ± 10–35 ± 4
CMB.07−21.38.977 ± 1264–9138 ± 2021 ± 127–3353 ± 211 ± 10–24 ± 31 ± 10–25 ± 4
CMB.08−21.39.176 ± 1264–9138 ± 1921 ± 117–3453 ± 201 ± 10–24 ± 31 ± 10–35 ± 4
CMB.09−21.59.976 ± 1263–9137 ± 2021 ± 127–3454 ± 211 ± 10–34 ± 41 ± 10–24 ± 4
CRA.01−21.89.077 ± 1363–9340 ± 2120 ± 126–3452 ± 221 ± 10–23 ± 31 ± 10–24 ± 4
CRA.02−21.39.575 ± 1263–9036 ± 2022 ± 118–3455 ± 211 ± 10–34 ± 31 ± 10–35 ± 5
CRA.03−21.79.177 ± 1264–9138 ± 2021 ± 127–3354 ± 211 ± 10–24 ± 31 ± 10–24 ± 4
MC.01−21.49.075 ± 1262–9036 ± 1922 ± 118–3456 ± 211 ± 10–24 ± 31 ± 10–25 ± 4
RC.07−20.39.775 ± 1164–8734 ± 1721 ± 109–3253 ± 192 ± 20–46 ± 42 ± 20–47 ± 6
RC.08−21.19.677 ± 1264–9138 ± 2021 ± 127–3352 ± 212 ± 10–35 ± 42 ± 10–35 ± 5
RC.09−20.49.475 ± 1164–8835 ± 1721 ± 109–3253 ± 192 ± 20–35 ± 42 ± 20–47 ± 6
RC.10−20.29.475 ± 1163–8834 ± 1721 ± 109–3253 ± 182 ± 20–35 ± 42 ± 20–17 ± 6
RC.11−21.67.777 ± 1363–9341 ± 2221 ± 125–3553 ± 231 ± 10–22 ± 21 ± 10–24 ± 4
RC.12−21.29.175 ± 1263–9036 ± 1921 ± 118–3355 ± 201 ± 10–24 ± 31 ± 10–35 ± 5
UC.01−219.974 ± 1163–8834 ± 1822 ± 119–3355 ± 192 ± 20–35 ± 42 ± 10–36 ± 5
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Pickard, C.; Greenberg, E.; Smith, E.; Barlow, A.; Bonsall, C. Diet, DNA, and the Mesolithic–Neolithic Transition in Western Scotland. Humans 2025, 5, 8. https://doi.org/10.3390/humans5010008

AMA Style

Pickard C, Greenberg E, Smith E, Barlow A, Bonsall C. Diet, DNA, and the Mesolithic–Neolithic Transition in Western Scotland. Humans. 2025; 5(1):8. https://doi.org/10.3390/humans5010008

Chicago/Turabian Style

Pickard, Catriona, Elizabeth Greenberg, Emma Smith, Andy Barlow, and Clive Bonsall. 2025. "Diet, DNA, and the Mesolithic–Neolithic Transition in Western Scotland" Humans 5, no. 1: 8. https://doi.org/10.3390/humans5010008

APA Style

Pickard, C., Greenberg, E., Smith, E., Barlow, A., & Bonsall, C. (2025). Diet, DNA, and the Mesolithic–Neolithic Transition in Western Scotland. Humans, 5(1), 8. https://doi.org/10.3390/humans5010008

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