Patterns of Craniofacial Size and Shape Variation from Medieval to Contemporary Human Populations

Patterns of Craniofacial Size and Shape Variation from Medieval to Contemporary Human Populations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Patterns of Craniofacial Size and Shape Variation from Medieval to Contemporary Human Populations Anna Walczak, Sylwia Łukasik, Antonio Profico, Jacek Tomczyk, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9544747/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Craniofacial morphology is partially shaped by non-biological factors, such as diet and masticatory loading. While the relationship between dietary hardness and cranial morphology is well established, major cultural and dietary transitions, such as industrialization and post-technological advancements remain relatively underexplored. This study examines morphological changes across three temporally distinct populations representing different stages of socio-economic development: medieval Cedynia, early modern Radom (both Poland, Central Europe), and a contemporary sample from the New Mexico Decedents Image Database, using 3D geometric morphometrics. We placed 37 fixed landmarks, 260 surface semilandmarks, and three curves on 415 adult skulls to compare ectocranial morphology among populations. Our results indicate that among the three tested variables, sex, sample (representing the three chronological groups), and age, sample exerted the strongest influence on cranial variation, exceeding the effects of age and sex. Across both sex- and sample-related differences, size contributes more strongly than shape alone. The modern sample is characterized by larger, longer, and narrower crania, as well as increased morphological variability. These findings are consistent with the hypothesis that dietary softening associated with industrialization may play a role in shaping human cranial morphology. diachronic changes human cranium geometric morphometrics cranial morphology masticatory function 3D analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1. Introduction Craniofacial morphology reflects a complex interplay between genetic, epigenetic and environmental influences (Biehler-Gomez et al. 2025 ; Paschetta et al. 2010 ; Galland et al. 2016 ). Throughout human history, environmental and socio-economic transformations have influenced patterns of craniofacial growth and remodeling contributing to observable changes in skull morphology across populations and time periods (Lieberman et al. 2004 ; Lieberman 2011 ; Galland et al. 2016 ). These changes are widely understood as multifactorial in nature, involving a range of interacting processes including developmental, functional, and cultural factors (Martinez-Maza et al. 2013 ; Stansfield et al. 2018; Veneziano et al. 2018 ; Liang et al. 2023 ). Among them, diet has consistently been identified as a major contributing factor, particularly through its biomechanical consequences for the masticatory system (von Cramon-Taubadel 2014 ; Galland et al. 2016 ). A significant body of research has examined morphological changes in the human skeleton associated with the transition from hunter–gatherer lifestyle to agriculture (e.g. Galland et al. 2016 ; von Cramon-Taubadel 2017 ). These studies demonstrated that this process was accompanied by craniofacial gracilization and overall reduction in skull robusticity, often interpreted as a response to diet-driven reduction in masticatory loading (Pinhasi et al. 2008 ; Pokhojaev et al. 2019 ). Industrialization and the post-industrial technological advancement are listed alongside the Neolithic Revolution as three major dietary transitions in the humankind history. Each of these turning points profoundly altered dietary nutritional composition and physical properties. The still ongoing advancement in processing technologies have progressively softened human food, contributing to a reduction in masticatory demands in modern populations (Laudan 2013 ; Huebbe and Rimbach 2020 ; Pilcher 2012 ). Although these later transformations may have affected craniofacial morphology, they received comparatively limited attention. Most studies have focused on secular changes occurring over relatively short time spans, often without explicit reference to industrialization (see Jonke et al. 2007 ; Jellinghaus et al. 2018 ). Moreover, some analyses addressing this topic rely on conventional cephalometric approaches or linear measurements (see Rock et al. 2006 ), which are limited in their ability to capture complex shape changes and account for the integration of cranial structures (Sella-Tunis et al. 2018 ). The present study investigates patterns of craniofacial variation across populations representing different stages of socio-economic development. Using three-dimensional geometric morphometric (GM) approach we analyzed three samples of representing distinct chronological periods: a medieval sample from Cedynia, an early modern (18th − 19th century) sample from Radom (both from Central Europe, Poland), and a contemporary sample from New Mexico (USA). Given the geographic disparity of the contemporary sample, the observed differences are more appropriately understood as general temporal trends in morphology, rather than evidence of continuity in morphological change across populations. The inclusion of early modern and present-day samples, spanning the transition from pre-industrial dietary conditions to modern technological societies, enables an assessment of morphological trends potentially associated with progressive dietary softening and broader lifestyle transformations and provides novel insights into the long-term effects of dietary and cultural shifts on craniofacial morphology across historical and modern populations. 2. Materials and Methods 2.1. Materials We selected adult skulls from three samples spanning different historical periods; two osteological collections from Poland (Central Europe), Cedynia (medieval, 52°52'45″N, 14°12'08''E coordinates reported in Krenz-Niedbała 2017 ) and Radom (early modern, 51°24'17"N 21°08'43" coordinates derived from archaeological documentation) and a contemporary sample from the New Mexico Decedent Image Database (NMDID, deceased between 2010 and 2017) (Edgar et al. 2020). In total, 415 individuals were analyzed: 203 from Cedynia, 68 from Radom and 144 from NMDID (Table 1 ). Table 1 Descriptive data for the examined individuals. Age category Females (N) Males (N) Cedynia Radom New Mexico Cedynia Radom New Mexico Young adult (YA) 56 15 13 42 27 21 Middle adult (MA) 29 9 22 58 17 31 Old adult (OA) 6 0 22 12 0 35 TOTAL 91 24 57 112 44 87 To conduct the analyses in 3D environment, skulls from the osteological collections were digitized using a surface scanner, whereas for the NMDID sample, 3D models were reconstructed from CT data. Previous studies have demonstrated that while different digitization techniques may introduce minor variations, these do not significantly affect shape-based metrics, allowing reliable comparisons across methods (Waltenberger et al. 2021 ). Any residual methodological bias is therefore unlikely to account for the broad sample-level patterns reported here. The medieval site in Cedynia, dated to the 10th–14th centuries AD, is associated with a proto-urban population (Dembinska 1999 ). The Radom series, dated to the 18th–19th centuries AD, derives from an early modern urban population (Fig. 1 ). Both series represent individuals of European ancestry, with detailed discussions of their socio-economic backgrounds presented elsewhere (Krenz-Niedbała 2016; Fuglewicz 2011 ; Tomczyk 2018 ). Only individuals with sufficiently well-preserved skull and without complete ante-mortem tooth loss were included, as edentulism influences cranial morphology (Coello 2024 ; Okşayan et al. 2014 ; Williams and Slice 2014 ). Individuals exhibiting facial and dental trauma, as well as periodontal disease were excluded. For NMDID sample additional exclusion criteria were plate implant, dental implant, osteoporosis, facial surgery, or chromosomal abnormalities known to alter facial features, such as Down syndrome. To ensure comparability with the historical groups of the European origin, adult (20–70 years of age) only non-Hispanic White individuals born in the United States with parents also born in the USA, were included (see Table S1 ). This selection was intended to provide a comparative contemporary sample representing fully industrialized and technologically advanced dietary conditions rather than direct biological continuity with the historical Polish groups. Although geographically distinct from the Polish historical series, this approach reduces broad ancestral heterogeneity. Potential population differences are acknowledged as a limitation of the study. The selected material captures a long-term diachronic trend in human dietary structure from the coarse, mechanically demanding medieval diet, through the transitional pre-industrial diet of the early modern period, to the highly processed and increasingly soft diet characteristic of contemporary industrialized societies. 2.2. Methods 2.2.1. Biological profile Basic biological profile information was obtained using standard methods commonly applied in biological anthropology. Sex estimation was based on morphological features of the pelvis and skull (Buikstra and Ubelaker 1994 ). Age at death was assessed using the pubic symphysis morphology (Todd 1921 ) and cranial suture closure (Meindl and Lovejoy 1985 ), with dental wear (Lovejoy 1985 ) serving as a supplementary indicator. Transitional Analysis (TA2) (Boldsen et al. 2002 ) was also applied, but only to individuals in whom both skull and pelvis were preserved (see Simon and Hubbe 2021 ). The following age categories were applied: young adult (YA; 20–35 years), middle adult (MA; 35–50 years) and old adult (OA; 50 + years). Biological profiles for the modern sample were obtained from the New Mexico database. Based on the recorded age information, individuals were assigned to the same age categories as those used for the historical populations. 2.2.2. 3D models Osteological materials were 3D scanned using an Artec Space Spider scanner (Artec 3D, Luxembourg). The models were processed in Artec Studio 17 (Artec 3D, Luxembourg) and Geomagic Studio (Hexagon AB, Sweden). For the modern sample, 3D models were reconstructed from CT data using 3D Slicer (The Slicer Community, USA) (Fedorov et al. 2012) and MONAI Auto3DSeg (Diaz-Pinto et al. 2024 ; Cardoso et al. 2022 ), which automatically configures and trains deep learning segmentation models based on data characteristics, generating optimized 3D reconstructions from volumetric scans. Selected elements were used to generate 3D models, which were subsequently refined by trimming excess parts and removing isolated or floating particles (if needed) and then exported as .ply files. For models derived from CT scans, hole filling was performed using adjusted threshold parameters, whereas models obtained through surface scanning were processed with Geomagic Qualify software. Smoothing functions were deliberately avoided, as previous studies have shown that excessive smoothing can lead to the loss of shape-related features (Profico et al. 2016 ). 2.2.3. Geometric morphometrics 2.2.3.1. Landmark configuration The cranial configuration consisted of 37 fixed landmarks, four bilateral orbital curves (10 superior and 15 inferior semilandmarks per side) (Fig. 2 a; Table S2), and 260 surface semilandmarks (Fig. 2 b). Although the frontal bone is not anatomically part of the facial skeleton, the bregma and both stephanion points were included to allow the detailed shape quantification. Furthermore, their inclusion helps maintain surface continuity in the upper facial and orbital regions, stabilizes the orientation of the semilandmark patch, and reduces potential rotational drift during the sliding procedure. Moreover, these superior landmarks facilitate consistent alignment among specimens during Procrustes superimposition, particularly in cases of minor asymmetry or incomplete preservation (Gunz and Mitteroecker 2013 ; Gunz et al. 2005 ). Landmarks were acquired using Checkpoint software (Stratovan, USA), while curves were generated in Amira (Thermo Fisher Scientific, USA; Zuse Institute Berlin, Germany). The raw landmark and curve data were then imported into R (R Core Team 2025 )where all subsequent analyses were performed. 2.2.3.2. Surface semilandmarks The patch was defined to capture the facial skeleton, with landmarks serving as its boundaries. To accommodate landmarks located on the frontal bone and to enable analysis of the upper orbital rim, the patch was extended to include the frontal squama. The nasal bones were excluded from the analyzed patch due to their frequent partial damage in osteological collections (Fig. 2 b). For surface semilandmarking based on Procrustes distances, we identified the cranium more close to the mean shape calculated from the landmark data. This reference specimen (CE_M_MA_479), a middle adult male from the Cedynia population, was used to construct the facial patch. The patch was prepared for the right side only in Geomagic Qualify software and consisted of 130 points, which were then mirrored to cover the entire analyzed facial skeleton. In total, 260 bilateral surface semilandmarks were automatically projected onto all cranial 3D models (Fig. 2 b). 2.2.3.3. Semilandmarks on curves Points were collected separately for the upper right, lower right, upper left, and lower left orbital rims. After data import, the number of semilandmarks was standardized and semilandmarks were evenly spaced along the curve, with clearly defined start and end landmarks: the upper rims begin at maxillofrontale and end at frontomalare orbitale, while the lower rims start at frontomalare orbitale and end at maxillofrontale. The curves were then integrated into the complete set of landmarks and surface semilandmarks, which were subsequently slid, by using the Morpho R package (Schlager 2017 ) across the mesh surfaces while keeping the start and end points fixed. 2.2.4. Data processing and statistical analysis 2.2.4.1. Landmarking reproducibility To assess the intraobserver error in landmark placement, the observer performing the analyses re-digitized the same set of landmarks on 30 randomly selected 3D skull models. Landmarking reproducibility was evaluated using ANOVA by comparing the repeated sets of landmark coordinates. 2.2.4.2. Principal component analysis After the sliding, further step includes Generalised Procrustes Analysis (GPA) performed on the entire sample. Further analyses were carried out in both shape and size–shape space ( sensu Mitteroecker et al. 2004 ) which includes the logarithm of the centroid size as an extra variable. This approach allows the assessment of both size- and shape-related variation within the sample. Principal component analysis (PCA) was then applied to reduce dimensionality and to identify the major axes of morphological variation across individuals. Variance partitioning was performed using redundancy analysis (RDA) which allows the unique and shared contributions of sex, age, and sample to be quantified simultaneously. 2.2.4.3. Surface warping To visualise shape differences, we applied a surface warping approach based on the thin-plate spline algorithm, which allows the mean shape to be deformed along the minimum and maximum values of selected principal components. This produces a colour map highlighting local regions of expansion and contraction. The visualization was performed using the functions from the Arothron R package (Profico et al. 2021 ), which compute facet-by-facet surface differences between warped meshes and represent them through a continuous colour gradient on the reference model. 2.2.4.4. Local mapping Local variation associated with sex and sample was quantified using a landmark-wise variance mapping approach (modified after Del Bove et al. 2023). For each landmark, the proportion of variance (R²) explained by either sex, age or sample was computed using local multivariate regression. In each iteration, 5 nearest neighbouring semilandmarks were selected to estimate R² values on Procrustes-aligned shape coordinates. Analyses were performed in the shape, form, and size spaces, using 10 parallel computational cores to optimize performance. Sexual dimorphism in cranial shape was assessed separately for each sample (Cedynia, Radom, New Mexico) using Procrustes-aligned landmarks. Shape was regressed on sex with a permutation-based RRPP test (1000 iterations). Procrustes distances between male and female mean shapes (D) and the proportion of variance explained by sex (R²) were calculated. 2.2.4.5. Shape variation within the samples For each sample, shape variability was quantified with the parameter of Procrustes disparity, defined as the mean squared distance of individual specimens from the sample mean shape (Zelditch et al. 2004 ). This metric reflects morphological disparity within groups, with higher values indicating greater shape dispersion. We run the analyses separately for females and males. The mean and standard error (SE) of this metric were calculated to assess within-group variability. To evaluate whether the observed shape variation differed significantly between samples, a Welch’s ANOVA was performed. Additionally, post-hoc test was performed when needed. 3. Results 3.1. Landmarking reproducibility Repeated 37 landmark sets collected for 30 individuals were tested with Procrustes ANOVA. No significant effect of replication was detected (p = 0.67), indicating high landmarking reproducibility. 3.2. Morphology in relation to sexual dimorphism, and inter-sample variation In the analyzed sample, in size-shape space (Fig. 3a), both sex and sample significantly influence shape variation (p = 0.001), explaining 12.56% and 20.06% of the variance, respectively. Age was also statistically significant (p = 0.001) accounted for 5.99% of the variance. Results for pure shape space are presented in Fig. 3b and in Supporting Information (Table S3). Figure 3 PCA plots for the first two components (a) in size-shape space. (b) in pure shape space. Different samples are marked with colors and sex with symbols Variance partitioning showed that sex, age, and sample jointly explained 35.25% of total cranial shape variation and the remaining 64.75% variation was unexplained. Sample was the dominant factor, uniquely accounting for 16.6% variation followed by Sex, which explained 12.7%. In contrast, Age had a negligible independent contribution (0.3%) (Fig. 4 ). The first two PCs capture approximately 72.21% of the total variance (PC1 = 44.83%, PC2 = 13.75, PC3 = 6.74%, PC4 = 3.81%, PC5 = 3.09%) (Fig. 4 a). We focused on these PCs for further analyses as they best describe the data structure. PC3-PC5 description can be found in the Supplementary Text S1 together with figures (Fig. S1 -S3). PC1 is strongly related to both sex (R² = 0.24, p = 0.001) and sample (R² = 0.34, p = 0.001). It describes a transition from a shorter and broader craniofacial form to a longer and narrower one (Fig. 5 ). At low PC1 scores, found predominantly in females, if sex variable is considered and in historical populations, when sample effect included, the facial skeleton is shorter and wider, with moderate contraction across the measured surface. Higher scores found in the male group, if sex included and in New Mexico, when sample variable is considered, correspond to elongation of the facial skeleton, with the strongest expansion located beneath the piriform aperture, in the alveolar region, and in the glabellar area. Lateral portions of the upper orbital rims and the frontal bone also expand, whereas the central portion of the frontal bone exhibits relative narrowing (Fig. 5 and Fig. 6 ). PC2 is linked to sample differences (R² = 0.23, p = 0.001). It captures differences in craniofacial width, describing a transition from a more elongated facial form to a shorter, broader, and more rounded morphology (Fig. 5 and Fig. 6 ). Low PC2 scores, found in New Mexico and Cedynia, correspond to a more elongated facial skeleton with marked contraction across the central facial region. Higher scores, characteristic of the Radom sample, correspond to a broader and shorter craniofacial form, with the strongest expansion concentrated in the glabellar region, upper orbital area, and central frontal bone (Fig. 6 ). 3.3.1. Sex-related differences The local mapping results show that pure shape variation alone explains only a very small portion of the total variance (R² = 0.002–0.08), indicating minimal sex-related differences in shape when size is completely removed. When allometry is included, form variation increases (R² = 0.07–0.18), and the inclusion of size produces the strongest signal (R² = 0.008–0.34), suggesting that size-related differences are the dominant factor driving sex differences in morphology. The most dimorphic regions include the glabellar area together with the supraorbital ridges and the zygomatic bone, with these effects becoming more pronounced when we consider only centroid size (Fig. 7 ). Note that each map has its own color scale, so absolute R² values are not directly comparable; the maps illustrate relative patterns of age effects within each variable We also performed local mapping of sex-related differences separately for each sample; the results are shown in Fig. 8 . When both size and shape are considered, the New Mexico and Radom samples display broadly similar spatial patterns of sexual dimorphism, with the strongest signals concentrated in the zygomatic bones and the glabellar region. In the New Mexico sample, this pattern is further accentuated by a pronounced signal around the piriform aperture. In contrast, the Cedynia sample shows a more restricted distribution of sex-related variation, largely confined to the zygomatic region. When pure shape is analyzed, a consistent signal is observed in the glabellar region across all three samples, whereas size-related variation highlights the zygomatic bone and maxilla as the regions showing the strongest and most consistent sexual dimorphism across samples. In the medieval Cedynia sample, sexual shape differences were most pronounced (D = 137.57, R² = 0.019, p = 0.015). In the Radom (18th–19th century) and New Mexico samples, the signal was weaker and not statistically significant (Radom: D = 118.48, R² = 0.015, p = 0.386; New Mexico: D = 708.27, R² = 0.010, p = 0.227), suggesting that sexual dimorphism in cranial shape was stronger in the medieval sample compared to the later samples. Note that each map has its own color scale, so absolute R² values are not directly comparable; the maps illustrate relative patterns of age effects within each variable 3.3.2. Sample-related differences For sample-related variation, pure shape alone explains only a very small portion of the total variance (R² = 0.002–0.31), while form contributes more (R² = 0.02–0.26), indicating that shape combined with size matters. However, variation in size has the largest effect (R² = 0.004–0.33). When we only consider shape, a strong sample-related signal is observed in the central portion of the frontal bone, extending toward the superior orbital ridges. If we consider size, the sample-related differences are more localized, primarily in the lateral parts of the frontal bone, the superior orbital rims, the anterior nasal spine, and the anterior portion of the alveolar process of the maxilla. A similar pattern is observed in the combined shape-and-size space, although the changes are less pronounced, and the signal in the central frontal bone remains detectable (Fig. 9 ). 3.3.3. Age-related differences Local mapping for age, revealed that the strongest effect of this variable was present in pure size space (R² = 0.001–0.12), shape and size showed an intermediate effect (R² = 0.005–0.01), whereas the weakest association was found for shape alone (R² = 0.003–0.08), which indicates that for this variable morphological change is driven predominantly by size variation. Shape-related variation is primarily associated with the midline region of the frontal bone, whereas size- and form-related differences display a similar spatial pattern, concentrating mainly in the lateral portions of the frontal bone (Fig. 10 ). Note that each map has its own color scale, so absolute R² values are not directly comparable; the maps illustrate relative patterns of age effects within each variable. 3.4. Range of shape variation in the analyzed samples Among females, Procrustes variance (within-group shape disparity) differed significantly between populations. The modern New Mexico sample showed higher variability compared to Cedynia (p = 0.020), whereas Radom sample did not differ significantly from either group. In males, no statistically significant differences were found between populations, although a trend for higher variability in New Mexico sample was observed (Fig. 11 ). 3.5. Size differences We compared the centroid size in sex groups between the samples. Results of Welch's ANOVA indicated that the samples differ for both sexes (p < 0.001). In both females and males mean centroid size of the New Mexico sample is significantly bigger when compared to the Radom sample (p < 0.001 for females and p < 0.005 for males) and Cedynia (p < 0.001 for females and p < 0.005 for males) (Fig. 12 ). 4. Discussion The present results explore broad patterns of craniofacial variation across populations (samples) representing different temporal and socio-economic contexts using 3D geometric morphometrics. Sample-related differences accounted for a substantial proportion of cranial shape variation, whereas the effects of sex and age were comparatively weaker. The observed morphological patterns are likely influenced by multiple factors, including changes in masticatory function linked to dietary shifts. Most diachronic studies have relied either on traditional linear measurements (e.g. Biehler-Gomez et al. 2025 ) or 2D radiographic cephalometry (e.g. Jonke et al. 2007 ; Rock et al. 2006 ), while 3D geometric morphometric approaches have been based on a limited number of fixed landmarks (e.g. Cridlin 2018 ). In contrast, the dense configuration of semilandmarks with local mapping applied here enables high-resolution analysis of cranial morphology and identifies regions most strongly affected by the analyzed variables, providing new insight into the spatial patterns of craniofacial variation. Moreover, whereas most previous studies have focused on shorter time spans, typically addressing more recent changes, here we examine a long-term period spanning approximately 1000 years and explicitly consider the role of industrialization, which has often been overlooked in the literature. Together, this approach allows to obtain a more comprehensive assessment of both global and region-specific cranial variation over long-term patterns of change. 4.1. Sex-related differences We observed differences in the expression of sexual dimorphism among the analyzed samples. Overall, it was highest in the medieval Cedynia, followed by the early modern Radom, and lowest in the contemporary New Mexico sample. This gradual decrease suggests pattern consistent with diachronic change toward reduced expression of craniofacial sexual dimorphism over time. Despite differences in the intensity of expression, the overall pattern of sexually dimorphic traits remains consistent across samples. Previous studies have suggested that such variation may be influenced by non-biological factors, which could modulate the expression of sexual dimorphism (Cappella et al. 2022 ; Garvin et al. 2014 ; Legros et al. 2025 ). At the same time, it should be noted that differences in the expression of sexual dimorphism may also reflect inherent population-specific variation, rather than exclusively temporal change. In our study, the two historical samples (Cedynia and Radom) share a comparable ancestry, and previous craniometric analyses suggest that they can be considered largely genetically homogeneous (Tomczyk 2018 ). This reduces the likelihood that genetic factors are the primary drivers of the observed differences between these two groups. The New Mexico sample, even though we included only individuals of one ethnic group, might be genetically different from two Polish samples. However, similarities in the spatial pattern of sexual dimorphism between groups might suggest that environment and culture related factors likely contributed to shaping observed sample differences, acting alongside population-specific biological variation. Revealed pattern of sex-related differences in our analysis indicates that size is the dominant factor, which aligns with the majority of previous GM analyses (e.g. Cabo et al. 2012 ; Gonzalez et al. 2011 ; Toneva et al. 2022 ), although some authors reported higher contribution of shape (e.g. Chovalopoulou and Bertsatos 2018 ; Milella et al. 2021 ). Our results found that females exhibit smaller and shorter craniofacial morphology, whereas males have larger and longer faces, which is in line with previous geometric morphometric and craniometric studies(e.g. Cocilovo et al., 2013 ; Kimmerle et al., 2008 ). In our analysis, three regions have been established as predominantly sex-related: zygomatic bone, glabella, and piriform aperture. These cranial regions are consistently highlighted in the literature as key contributors to sexual dimorphism (da Silva et al. 2023 ; Del Bove et al. 2023), despite different methodological approaches. In our study, the zygomatic bone shows a strong size-related dimorphic signal, with males exhibiting larger and more robust bones. This pattern is consistent with previous reports (e.g. Chovalopoulou et al. 2016 ; Del Bove et al. 2023) and likely reflects the role of this region in masticatory muscle attachment and the generally stronger musculature of males (De Jong et al. 2011 ; Tokpınar and Alkan 2025 ). Glabellar dimorphism was primarily shape-driven, in line with Del Bove et al. (2023). Males generally exhibit a more robust and protruding glabella (e.g. Abdel Fatah et al. 2014 ; Bigoni et al. 2010 ) reflecting likely hormonal and biomechanical influences, including larger sinuses (Čechová et al. 2019 ) and greater facial muscle mechanical demands in males (da Silva et al. 2023 ). The piriform aperture exhibits marked sexual dimorphism in size, with males displaying larger dimensions, as in other analyses (e.g. Alves et al. 2015 ; Sarač-Hadžihalilović et al. 2022 ). It has been linked to higher airflow demands in males, greater body size and muscle mass (Ajanović et al., 2025 ; Ibrahim et al., 2019 ; Milella et al., 2021 , de-Araújo et al., 2018 ). Less pronounced dimorphism was observed in the lateral orbital rim and maxilla, with males showing larger, more robust maxillae, consistent with previous studies (Ajanović et al. 2025 ; Toneva et al. 2022 ). 4.2. Temporal differences Our analysis of medieval Cedynia, early modern Radom, and contemporary New Mexico demonstrates that overall size is the primary factor differentiating the facial skeleton among these groups, with the strongest signal localized in the anterior maxilla and frontal bone. This pattern is likely related to allometric effects on craniofacial shape variation, as previously suggested in the literature, where size was identified as a major driver of facial morphological differences(e.g. Eyquem et al. 2019 ). In addition to size-related differences, more recent samples exhibit a relatively more elongated and narrower facial morphology compared to the medieval sample. This transition toward leptoprosopy – longer and narrower face, reflects a trend reported across different populations and time periods(e.g. Cridlin 2018 ; Nakahashi 1993 , Weisensee and Jantz 2011 , Biehler-Gomez et al. 2025 ). In our analysis, we propose a spatial mechanism underlying this pattern, whereby this effect is achieved primarily through vertical expansion of the maxilla and the lateral portions of the frontal bone, accompanied by horizontal contraction in the midfacial region. The observed narrowing of the facial skeleton, including anterior maxilla, might be partially explained by the "masticatory-functional hypothesis" (Carlson 1976 ; Carlson and Van Gerven 1977 ), which states that the transition from coarse historical diets to soft, highly processed modern foods reduced the mechanical demands on the masticatory apparatus. Because bone is a dynamic tissue that responds to mechanical strains (mechanotransduction), the reduction in chewing stress during ontogeny leads to the "underdevelopment" or reduction of the maxillary complex (von Cramon-Taubadel 2017 ). Additionally, the narrowing of the dental arches in modern samples may be related to a plastic response to these shifted dietary patterns, which results in a higher prevalence of dental crowding and malocclusion. This phenomenon became so common, that it has been regarded as “jaw epidemics” and is referred to as a public health problem (Kahn and Ehrlich 2020 ). The relationship between chewing forces and frontal bone morphology is complex. Changes in facial breadth may partly reflect variation in masticatory loading, as forces generated during mastication are transmitted through the facial skeleton via the facial buttress system, indirectly affecting more distant cranial regions. However, while some studies report correlations between temporal muscle traits and supraorbital robusticity, these associations often are weakened when controlling for overall cranial shape (Nowaczewska et al. 2023 ). This suggests that broader cranial architecture, systemic growth patterns, and allometric scaling, including increases in cranial vault height related to somatic growth and secular trends in body height also contribute to observed variation (Grasgruber 2025 ; Jellinghaus et al. 2018 ; Rock et al. 2006 ; Fudvoye and Parent 2017 ; Cole 2003 ) Demographic processes including gene flow, migration, and post-industrial population restructuring may have introduced novel morphological variation, altering craniofacial proportions independently of functional demands (Biehler-Gomez et al. 2025 ; Jantz and Meadows Jantz 2000 ; Cridlin 2018 ). Researchers also mention systemic improvements in nutrition and healthcare as key drivers of these facial shifts. Improved childhood health and declining morbidity facilitate more uniform and expansive growth of the facial skeleton, allowing it to reach its full developmental potential (Jantz and Jantz 2016 ; von Cramon-Taubadel 2014 ; Biehler‐Gomez et al. 2025; Cridlin 2018 ). As suggested by Weisensee and Jantz ( 2011 ) the rapid rate of these changes, often occurring within just a few generations, indicates that phenotypic plasticity and epigenetic modulation are the primary mechanisms driving the emergence of the modern leptoprosopic face. However, it remains unclear which of these factors exerts the dominant influence, and the observed facial patterns likely result from the combined action of multiple interacting mechanisms. Although diachronic studies often show consistent trends, contrasting patterns exist; for example, Egyptian populations showed little change over a millennium (Rösing 1992 ) suggesting long-term cranial stability (Biehler-Gomez et al. 2025 ), though methodological differences should be also taken into consideration. Some research suggest that masticatory loading and dietary softening do not uniformly affect craniofacial morphology, and show localized or divergent changes (Cheronet et al. 2016 ; Paschetta et al. 2010 ; Eyquem et al. 2019 ), indicating that diet does not act in isolation. Taken together, the pattern identified in the present study most likely reflects a multifactorial process in which reduced masticatory loading associated with dietary change might act as the major contributing factor alongside broader growth-related, and population-level processes in shaping craniofacial form through time. 4.3. Range of cranial shape variation Our shape-variability analysis indicates that the contemporary New Mexico sample exhibits significantly greater cranial morphological variation than both the early modern Radom and medieval Cedynia groups. It has been suggested that increased morphological variability in modern human crania resulted from cultural-environmental factors, especially those associated with diet and masticatory function (Brachetta-Aporta and Toro-Ibacache 2021 ; Toro-Ibacache et al. 2016 ; Noback and Harvati 2015 ). However, it must be noted that this pattern might also be related to greater heterogenicity of the NMDID sample in comparison to two historical Polish populations. Reduced biomechanical loading resulting from the consumption of softer, highly processed foods has been proposed as a major contributing factor, as it relaxes functional constraints and allows greater expression of genetic and environmental variation (Flis et al. 2025 ; Noback and Harvati 2015 ). At the same time, these dietary effects likely interact with a complex mixture of non-biological and genetic influences. For example Biehler-Gomez (2025) reported “opposing directional trends”, with increased orbital breadth but mid-facial narrowing in modern sample, which they linked to increased morphological heterogeneity caused by a mixture of adaptive response to changed functional demands and living conditions, urbanization, nutrition, or migration-related admixture in the post-industrial period. From an archaeological and anthropological perspective, these diachronic patterns can be interpreted as embodied responses to significant cultural transformations, such as intensified food processing introduced by industrialization, urbanization, reduced biomechanical demands, and improved developmental conditions. Accordingly, craniofacial morphology might retain a biologically informative signal of long-term changes in human lifeways associated with industrialization. 4.3. Age-related differences In the present study, age-related cranial shape variation was limited and accounted for only a small proportion of the overall morphological variance. Contrary to previous reports indicating pronounced age-related remodeling in the orbital, maxillary, and piriform regions (Mendelson et al. 2007 ), no significant associations with age were observed in these elements in our samples. Instead, the detected age-related signal was primarily localized in the frontal bone. Effect of age partially overlapped with sample effect. This raises the possibility that the detected age-related signal is, at least in part, sample-specific rather than universal. It is also worth noting that although our previous analysis of age-related changes in the Cedynia sample found significant correlations (Walczak et al. 2023 ), it did not include the frontal bone and was based on linear measurements of specific elements rather than geometric morphometrics. It should be noted that in our analysis we applied the same age categories for all samples, however individuals in contemporary populations attain on average older ages than those represented in medieval skeletal series, which may limit the comparability of age-related patterns between populations due to differences in age-at-death distributions and survival. 4.4. Limitations and future research Limitation of this study lies in its cross-sectional design, which is inherent to research covering a broad temporal span, from the medieval period to the present day. The further limitation concerns the anatomical scope of the analysis. The present study focused on ectocranial part of the cranium rather than the entire skull. Future research would benefit from including the cranial vault and cranial base, as well as inner cavities such as the maxillary and frontal sinuses, endocasts, and the mandible, as these regions are known to differ in their relative sensitivity and response to genetic and non-biological influences (Durmaz and Bolatli 2024 ). Such an extension would allow for a more comprehensive assessment of cranial morphological change through time, together with mandible, which was not included in the present analysis. It constitutes an integral part of the facial skeleton and is considered particularly responsive to environmental factors, including dietary and functional influences (De Angelis et al. 2025 ; von Cramon-Taubadel 2009 ; Kiliaridis 1995 ). Given that many studies addressing the relationship between diet and craniofacial morphology focus specifically on the mandible (e.g. Bosman et al. 2017 ; De Angelis et al. 2025 ; Stansfield et al. 2018), its dedicated analysis should be included in the future studies. The principal limitation concerns the geographic heterogeneity of the comparative design, as the contemporary sample derives from the United States, whereas the historical groups originate from Central Europe. Although this design captures broad diachronic trends associated with industrialization, it does not allow full separation of temporal and population-specific effects. Therefore, future studies integrating temporally structured samples from the same geographic region will be essential to disentangle functional, demographic, and population-history effects with greater precision. Conclusions This study provides a three-dimensional geometric morphometric assessment of diachronic craniofacial variation across medieval, early modern, and contemporary samples. The results demonstrate that the sample itself constitutes the principal source of morphological differentiation, with size exerting a dominant influence on craniofacial form. Temporal trends are characterized by a shift toward a relatively longer and narrower facial morphology in more recent sample, primarily expressed through vertical expansion of the maxilla and frontal bone combined with transverse reduction of the midface. Patterns of sexual dimorphism were consistently identified across all samples, with the zygomatic region, glabella, and piriform aperture exhibiting the strongest sex-related signals. Although the overall configuration of sexually dimorphic traits remained stable through time, their intensity appears to decrease in more recent sample, suggesting a potential temporal change in the expression of craniofacial sexual dimorphism. Overall, the results demonstrate that craniofacial variation in recent human populations is structured primarily by population-level differences and size-related effects, with additional contributions from sex and age. The observed patterns are consistent with the influence of long-term cultural and environmental changes, including dietary shifts, but also reflect broader processes such as secular growth, developmental plasticity, and population history. Declarations Funding The work was supported by the National Science Centre grant (2024/53/N/NZ8/02043) and ID-UB Initiative of Excellence—Research University (102/13/SNP/0010). Ethics The authors have nothing to report. The contemporary CT-derived models were obtained from the New Mexico Decedent Image Database under its institutional access policy. Competing interests The authors declare no conflicts of interest. Authors contribution: AW: Conceptualization, Formal Analysis, Investigation, Methodology, Visualization, Writing – Original Draft Preparation SŁ: Conceptualization, Supervision, Writing – Review & Editing AP: Formal analysis (geometric morphometric analyses and statistical scripting), Visualization, Methodology, Writing – Review & Editing JT: Resources, Data for Sex and Age-at-death for Radom population MKN: Conceptualization, Resources, Supervision, Writing – Review & Editing Data availability R scripts will be deposited in open access repository upon acceptance. References Abdel Fatah EE, Shirley NR, Jantz RL, Mahfouz MR (2014) Improving Sex Estimation from Crania Using a Novel Three-dimensional Quantitative Method. 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Am J Phys Anthropol 145:548–559. https://doi.org/10.1002/ajpa.21531 Williams SE, Slice DE (2014) Influence of edentulism on human orbit and zygomatic arch shape. Clin Anat 27:408–416 Zelditch ML, Swiderski DL, Sheets HD, Fink WL (2004) Geometric Morphometrics for Biologists: A Primer. Academic Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.docx suplementcranium.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9544747","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":632867713,"identity":"4235221a-6c71-46f5-b12c-4bcf8e1a3ed8","order_by":0,"name":"Anna Walczak","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYFAC5gYgkQBhf4BQBkBsgUcLI0IL4wyEFgnitDDzEKPF4PjBxo8/atIY+Gf3HvxsU7MtsYG9eZsE4w48Ws4kNktIHMthkLhzLlk659jtxAaeY2USjGdwazE7kNjGYMBWwcBwI8dAOocNqEUix0yCsQ2PlvMP2xgS/lUwyN/IMf5t8Q+oRf4NAS03gLYcbMthMLiRYybN2AayhQe/FvsbD5slG/vSeAyBWix7+24bt/GkFVsk4vGLZH/ywY8/viXLyQEdduPHt9uy/eyHN974uMMGpxYY4IGz2EBEYgNBHeiAkXQto2AUjIJRMHwBAEFPVE00/FyjAAAAAElFTkSuQmCC","orcid":"","institution":"Adam Mickiewicz University in Poznan","correspondingAuthor":true,"prefix":"","firstName":"Anna","middleName":"","lastName":"Walczak","suffix":""},{"id":632867716,"identity":"31a41906-225e-4396-b59e-4d7a54572674","order_by":1,"name":"Sylwia Łukasik","email":"","orcid":"","institution":"Adam Mickiewicz University in Poznan","correspondingAuthor":false,"prefix":"","firstName":"Sylwia","middleName":"","lastName":"Łukasik","suffix":""},{"id":632867719,"identity":"e4407e7d-ce62-4843-8a61-ad9c7b18700b","order_by":2,"name":"Antonio Profico","email":"","orcid":"","institution":"University of Pisa","correspondingAuthor":false,"prefix":"","firstName":"Antonio","middleName":"","lastName":"Profico","suffix":""},{"id":632867723,"identity":"1453364b-8a95-4fa4-b1e8-cba8ec1dd53c","order_by":3,"name":"Jacek Tomczyk","email":"","orcid":"","institution":"Cardinal Stefan Wyszyński University","correspondingAuthor":false,"prefix":"","firstName":"Jacek","middleName":"","lastName":"Tomczyk","suffix":""},{"id":632867726,"identity":"fe691e9d-8521-475a-8949-034d5c4f597c","order_by":4,"name":"Marta Krenz-Niedbała","email":"","orcid":"","institution":"Adam Mickiewicz University in Poznan","correspondingAuthor":false,"prefix":"","firstName":"Marta","middleName":"","lastName":"Krenz-Niedbała","suffix":""}],"badges":[],"createdAt":"2026-04-27 17:10:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9544747/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9544747/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108841122,"identity":"9817a41c-e62e-4bee-a49e-8279a58077ad","added_by":"auto","created_at":"2026-05-09 01:02:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":239503,"visible":true,"origin":"","legend":"\u003cp\u003eLocations of the three study samples: Cedynia and Radom (Poland, Central Europe) and New Mexico (USA)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9544747/v1/76d70651eeb51f5a92ad1020.png"},{"id":108841126,"identity":"43f51d51-dada-448c-8a37-407f535d976e","added_by":"auto","created_at":"2026-05-09 01:02:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":488824,"visible":true,"origin":"","legend":"\u003cp\u003eLandmark and semilandmark protocol for the cranium. (a) Fixed landmarks are shown in red and curve semilandmarks in blue. (b) Template-based semilandmarking procedure. The left panel shows a cranium (grey) with an orange patch on the right side used as the template region for landmark projection. This area was projected across the entire examined surface and converted into a semilandmark configuration (middle panel; 260 semilandmarks). The right panel shows the final configuration of applied semilandmarks (orange)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9544747/v1/12fbf2c64a6da3ef1de2df48.png"},{"id":109083004,"identity":"40a907ae-b7ac-4836-a89d-c11f6bbac06a","added_by":"auto","created_at":"2026-05-12 12:46:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":325637,"visible":true,"origin":"","legend":"\u003cp\u003ePCA plots for the first two components (a) in size-shape space. (b) in pure shape space. Different samples are marked with colors and sex with symbols\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9544747/v1/b9ecc96ad3aba74ad4e9dacf.png"},{"id":108841123,"identity":"d6d5ce09-89a9-4dba-9241-47f65acf5c38","added_by":"auto","created_at":"2026-05-09 01:02:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":203352,"visible":true,"origin":"","legend":"\u003cp\u003eVariation Partitioning Diagram. Variation in cranium size and shape expressed as adjusted R2 explained by sex, sample, age and their intersections. Significance codes: *** \u0026lt; 0. 001. Outside the circles, total variance explained by each variable is reported, considering all interactions. Note that the sum of explained variance does not equal 100% due to the variance partitioning algorithm, which can produce negative adjusted R² values\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9544747/v1/58f643e9a186eecda377f72b.png"},{"id":108841125,"identity":"29514cee-9273-4b66-b41c-d343c94ed0a8","added_by":"auto","created_at":"2026-05-09 01:02:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":233551,"visible":true,"origin":"","legend":"\u003cp\u003eShape variation described by the first two PCs. Average shape warped along each PCs maximal and minimal values (magenta and green, respectively)\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9544747/v1/3fc25c6f36da5941f8c5e5ca.png"},{"id":108976855,"identity":"279f22e0-9c14-4fdb-af5e-f1128041a88c","added_by":"auto","created_at":"2026-05-11 11:29:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":304683,"visible":true,"origin":"","legend":"\u003cp\u003eSurface warping built along PC1 and PC2 maximal and minimum values\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9544747/v1/0f319ef241391737090c18cf.png"},{"id":108976977,"identity":"45148779-3509-4cee-a10a-d34fe96544d4","added_by":"auto","created_at":"2026-05-11 11:29:45","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1839376,"visible":true,"origin":"","legend":"\u003cp\u003eLocal variance explained by sex in the human cranium. Maps for shape, form (shape and size) and centroid size. Colors indicate the proportion of variance (R²) explained by sex at each landmark, with higher values highlighting the most sexually dimorphic regions\u003c/p\u003e\n\u003cp\u003eNote that each map has its own color scale, so absolute R² values are not directly comparable; the maps illustrate relative patterns of age effects within each variable\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-9544747/v1/58aec8439ba691ba020526a7.png"},{"id":108977003,"identity":"63107d20-c246-4468-a97e-f5a7147f8867","added_by":"auto","created_at":"2026-05-11 11:29:53","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2401158,"visible":true,"origin":"","legend":"\u003cp\u003eLocal variance explained by sex in the human cranium in three analysed samples. Colors indicate the proportion of variance (R²) explained by sex at each landmark, with higher values highlighting the most sexually dimorphic regions\u003c/p\u003e\n\u003cp\u003eNote that each map has its own color scale, so absolute R² values are not directly comparable; the maps illustrate relative patterns of age effects within each variable\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-9544747/v1/b6435f9219f78d39f31e1dc3.png"},{"id":109082682,"identity":"0b2684b1-2c08-4a62-ad0a-5c125189ca09","added_by":"auto","created_at":"2026-05-12 12:42:32","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":576048,"visible":true,"origin":"","legend":"\u003cp\u003eLocal variance explained by sample in the human cranium. Maps for shape, shape and size and centroid size. Colors indicate the proportion of variance (R²) explained by sample at each landmark, with higher values highlighting the most sample-related regions\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-9544747/v1/3f34070810989ee76a949e8e.png"},{"id":108841127,"identity":"77d3da27-5e4a-41ba-8958-a723b773bc9a","added_by":"auto","created_at":"2026-05-09 01:02:28","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":176704,"visible":true,"origin":"","legend":"\u003cp\u003eLocal variance explained by age in the human cranium. Maps for shape, form (size and shape) and centroid size. Colors indicate the proportion of variance (R²) explained by age at each landmark, with higher values highlighting the most age-related regions\u003c/p\u003e\n\u003cp\u003eNote that each map has its own color scale, so absolute R² values are not directly comparable; the maps illustrate relative patterns of age effects within each variable.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-9544747/v1/aa3a463959253de21855096a.png"},{"id":108841132,"identity":"46d3ccdd-7130-4eb7-977c-dc3a61dfe993","added_by":"auto","created_at":"2026-05-09 01:02:28","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":232501,"visible":true,"origin":"","legend":"\u003cp\u003eRaincloud plot of Procrustes variance by population and sex. Boxplots show the median and interquartile range, while jittered points represent individual specimens\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-9544747/v1/669206d71b0ddc5c2f9c728a.png"},{"id":108841131,"identity":"843fc3ec-6596-4836-8af9-6a8012112811","added_by":"auto","created_at":"2026-05-09 01:02:28","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":171318,"visible":true,"origin":"","legend":"\u003cp\u003eVariation in centroid size (CS) across samples and sexes. Violin plots show the distribution within each sample (orange: females, blue: males) with overlaid boxplots for the median and interquartile range. Samples are ordered Cedynia (CE), Radom (RA), and New Mexico (NM)\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-9544747/v1/73fe83cc3c07b730918be4ba.png"},{"id":109204774,"identity":"2a8461f5-f5d5-4b70-845d-690c5b873a09","added_by":"auto","created_at":"2026-05-13 15:02:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10276901,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9544747/v1/19eda5d4-ad00-45d7-a8e0-2486bff97f0a.pdf"},{"id":108977429,"identity":"3b0ce178-ce2d-4c81-95fa-44adf34cf5eb","added_by":"auto","created_at":"2026-05-11 11:31:45","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":13750,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-9544747/v1/edfe1933138b2fe7e22454fc.docx"},{"id":108977625,"identity":"8a8ad64e-2067-4411-979d-9636a5ce08ba","added_by":"auto","created_at":"2026-05-11 11:32:21","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":445703,"visible":true,"origin":"","legend":"","description":"","filename":"suplementcranium.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9544747/v1/c68457f8a9aee809ea21eac7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Patterns of Craniofacial Size and Shape Variation from Medieval to Contemporary Human Populations","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCraniofacial morphology reflects a complex interplay between genetic, epigenetic and environmental influences (Biehler-Gomez et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Paschetta et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Galland et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Throughout human history, environmental and socio-economic transformations have influenced patterns of craniofacial growth and remodeling contributing to observable changes in skull morphology across populations and time periods (Lieberman et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Lieberman \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Galland et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These changes are widely understood as multifactorial in nature, involving a range of interacting processes including developmental, functional, and cultural factors (Martinez-Maza et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Stansfield et al. 2018; Veneziano et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Liang et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Among them, diet has consistently been identified as a major contributing factor, particularly through its biomechanical consequences for the masticatory system (von Cramon-Taubadel \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Galland et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA significant body of research has examined morphological changes in the human skeleton associated with the transition from hunter\u0026ndash;gatherer lifestyle to agriculture (e.g. Galland et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; von Cramon-Taubadel \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These studies demonstrated that this process was accompanied by craniofacial gracilization and overall reduction in skull robusticity, often interpreted as a response to diet-driven reduction in masticatory loading (Pinhasi et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Pokhojaev et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Industrialization and the post-industrial technological advancement are listed alongside the Neolithic Revolution as three major dietary transitions in the humankind history. Each of these turning points profoundly altered dietary nutritional composition and physical properties. The still ongoing advancement in processing technologies have progressively softened human food, contributing to a reduction in masticatory demands in modern populations (Laudan \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Huebbe and Rimbach \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pilcher \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Although these later transformations may have affected craniofacial morphology, they received comparatively limited attention. Most studies have focused on secular changes occurring over relatively short time spans, often without explicit reference to industrialization (see Jonke et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Jellinghaus et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Moreover, some analyses addressing this topic rely on conventional cephalometric approaches or linear measurements (see Rock et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), which are limited in their ability to capture complex shape changes and account for the integration of cranial structures (Sella-Tunis et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe present study investigates patterns of craniofacial variation across populations representing different stages of socio-economic development. Using three-dimensional geometric morphometric (GM) approach we analyzed three samples of representing distinct chronological periods: a medieval sample from Cedynia, an early modern (18th \u0026minus;\u0026thinsp;19th century) sample from Radom (both from Central Europe, Poland), and a contemporary sample from New Mexico (USA). Given the geographic disparity of the contemporary sample, the observed differences are more appropriately understood as general temporal trends in morphology, rather than evidence of continuity in morphological change across populations. The inclusion of early modern and present-day samples, spanning the transition from pre-industrial dietary conditions to modern technological societies, enables an assessment of morphological trends potentially associated with progressive dietary softening and broader lifestyle transformations and provides novel insights into the long-term effects of dietary and cultural shifts on craniofacial morphology across historical and modern populations.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Materials\u003c/h2\u003e \u003cp\u003eWe selected adult skulls from three samples spanning different historical periods; two osteological collections from Poland (Central Europe), Cedynia (medieval, 52\u0026deg;52'45\u0026Prime;N, 14\u0026deg;12'08''E coordinates reported in Krenz-Niedbała \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Radom (early modern, 51\u0026deg;24'17\"N 21\u0026deg;08'43\" coordinates derived from archaeological documentation) and a contemporary sample from the New Mexico Decedent Image Database (NMDID, deceased between 2010 and 2017) (Edgar et al. 2020). In total, 415 individuals were analyzed: 203 from Cedynia, 68 from Radom and 144 from NMDID (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive data for the examined individuals.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eFemales (N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMales (N)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCedynia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRadom\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNew Mexico\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCedynia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRadom\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNew Mexico\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYoung adult (YA)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMiddle adult (MA)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOld adult (OA)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTOTAL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo conduct the analyses in 3D environment, skulls from the osteological collections were digitized using a surface scanner, whereas for the NMDID sample, 3D models were reconstructed from CT data. Previous studies have demonstrated that while different digitization techniques may introduce minor variations, these do not significantly affect shape-based metrics, allowing reliable comparisons across methods (Waltenberger et al. \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Any residual methodological bias is therefore unlikely to account for the broad sample-level patterns reported here.\u003c/p\u003e \u003cp\u003eThe medieval site in Cedynia, dated to the 10th\u0026ndash;14th centuries AD, is associated with a proto-urban population (Dembinska \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The Radom series, dated to the 18th\u0026ndash;19th centuries AD, derives from an early modern urban population (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Both series represent individuals of European ancestry, with detailed discussions of their socio-economic backgrounds presented elsewhere (Krenz-Niedbała 2016; Fuglewicz \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Tomczyk \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOnly individuals with sufficiently well-preserved skull and without complete ante-mortem tooth loss were included, as edentulism influences cranial morphology (Coello \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Okşayan et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Williams and Slice \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Individuals exhibiting facial and dental trauma, as well as periodontal disease were excluded. For NMDID sample additional exclusion criteria were plate implant, dental implant, osteoporosis, facial surgery, or chromosomal abnormalities known to alter facial features, such as Down syndrome. To ensure comparability with the historical groups of the European origin, adult (20\u0026ndash;70 years of age) only non-Hispanic White individuals born in the United States with parents also born in the USA, were included (see Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). This selection was intended to provide a comparative contemporary sample representing fully industrialized and technologically advanced dietary conditions rather than direct biological continuity with the historical Polish groups. Although geographically distinct from the Polish historical series, this approach reduces broad ancestral heterogeneity. Potential population differences are acknowledged as a limitation of the study.\u003c/p\u003e \u003cp\u003eThe selected material captures a long-term diachronic trend in human dietary structure from the coarse, mechanically demanding medieval diet, through the transitional pre-industrial diet of the early modern period, to the highly processed and increasingly soft diet characteristic of contemporary industrialized societies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Methods\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Biological profile\u003c/h2\u003e \u003cp\u003eBasic biological profile information was obtained using standard methods commonly applied in biological anthropology. Sex estimation was based on morphological features of the pelvis and skull (Buikstra and Ubelaker \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Age at death was assessed using the pubic symphysis morphology (Todd \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e1921\u003c/span\u003e) and cranial suture closure (Meindl and Lovejoy \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1985\u003c/span\u003e), with dental wear (Lovejoy \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1985\u003c/span\u003e) serving as a supplementary indicator. Transitional Analysis (TA2) (Boldsen et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) was also applied, but only to individuals in whom both skull and pelvis were preserved (see Simon and Hubbe \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The following age categories were applied: young adult (YA; 20\u0026ndash;35 years), middle adult (MA; 35\u0026ndash;50 years) and old adult (OA; 50\u0026thinsp;+\u0026thinsp;years). Biological profiles for the modern sample were obtained from the New Mexico database. Based on the recorded age information, individuals were assigned to the same age categories as those used for the historical populations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. 3D models\u003c/h2\u003e \u003cp\u003eOsteological materials were 3D scanned using an Artec Space Spider scanner (Artec 3D, Luxembourg). The models were processed in Artec Studio 17 (Artec 3D, Luxembourg) and Geomagic Studio (Hexagon AB, Sweden). For the modern sample, 3D models were reconstructed from CT data using 3D Slicer (The Slicer Community, USA) (Fedorov et al. 2012) and MONAI Auto3DSeg (Diaz-Pinto et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Cardoso et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which automatically configures and trains deep learning segmentation models based on data characteristics, generating optimized 3D reconstructions from volumetric scans. Selected elements were used to generate 3D models, which were subsequently refined by trimming excess parts and removing isolated or floating particles (if needed) and then exported as .ply files. For models derived from CT scans, hole filling was performed using adjusted threshold parameters, whereas models obtained through surface scanning were processed with Geomagic Qualify software. Smoothing functions were deliberately avoided, as previous studies have shown that excessive smoothing can lead to the loss of shape-related features (Profico et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3. Geometric morphometrics\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section4\"\u003e \u003ch2\u003e2.2.3.1. Landmark configuration\u003c/h2\u003e \u003cp\u003eThe cranial configuration consisted of 37 fixed landmarks, four bilateral orbital curves (10 superior and 15 inferior semilandmarks per side) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea; Table S2), and 260 surface semilandmarks (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAlthough the frontal bone is not anatomically part of the facial skeleton, the bregma and both stephanion points were included to allow the detailed shape quantification. Furthermore, their inclusion helps maintain surface continuity in the upper facial and orbital regions, stabilizes the orientation of the semilandmark patch, and reduces potential rotational drift during the sliding procedure. Moreover, these superior landmarks facilitate consistent alignment among specimens during Procrustes superimposition, particularly in cases of minor asymmetry or incomplete preservation (Gunz and Mitteroecker \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Gunz et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Landmarks were acquired using Checkpoint software (Stratovan, USA), while curves were generated in Amira (Thermo Fisher Scientific, USA; Zuse Institute Berlin, Germany). The raw landmark and curve data were then imported into R (R Core Team \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)where all subsequent analyses were performed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section4\"\u003e \u003ch2\u003e2.2.3.2. Surface semilandmarks\u003c/h2\u003e \u003cp\u003eThe patch was defined to capture the facial skeleton, with landmarks serving as its boundaries. To accommodate landmarks located on the frontal bone and to enable analysis of the upper orbital rim, the patch was extended to include the frontal squama. The nasal bones were excluded from the analyzed patch due to their frequent partial damage in osteological collections (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). For surface semilandmarking based on Procrustes distances, we identified the cranium more close to the mean shape calculated from the landmark data. This reference specimen (CE_M_MA_479), a middle adult male from the Cedynia population, was used to construct the facial patch. The patch was prepared for the right side only in Geomagic Qualify software and consisted of 130 points, which were then mirrored to cover the entire analyzed facial skeleton. In total, 260 bilateral surface semilandmarks were automatically projected onto all cranial 3D models (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section4\"\u003e \u003ch2\u003e2.2.3.3. Semilandmarks on curves\u003c/h2\u003e \u003cp\u003ePoints were collected separately for the upper right, lower right, upper left, and lower left orbital rims. After data import, the number of semilandmarks was standardized and semilandmarks were evenly spaced along the curve, with clearly defined start and end landmarks: the upper rims begin at maxillofrontale and end at frontomalare orbitale, while the lower rims start at frontomalare orbitale and end at maxillofrontale. The curves were then integrated into the complete set of landmarks and surface semilandmarks, which were subsequently slid, by using the Morpho R package (Schlager \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) across the mesh surfaces while keeping the start and end points fixed.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4. Data processing and statistical analysis\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section4\"\u003e \u003ch2\u003e2.2.4.1. Landmarking reproducibility\u003c/h2\u003e \u003cp\u003eTo assess the intraobserver error in landmark placement, the observer performing the analyses re-digitized the same set of landmarks on 30 randomly selected 3D skull models. Landmarking reproducibility was evaluated using ANOVA by comparing the repeated sets of landmark coordinates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section4\"\u003e \u003ch2\u003e2.2.4.2. Principal component analysis\u003c/h2\u003e \u003cp\u003eAfter the sliding, further step includes Generalised Procrustes Analysis (GPA) performed on the entire sample. Further analyses were carried out in both shape and size\u0026ndash;shape space (\u003cem\u003esensu\u003c/em\u003e Mitteroecker et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) which includes the logarithm of the centroid size as an extra variable. This approach allows the assessment of both size- and shape-related variation within the sample. Principal component analysis (PCA) was then applied to reduce dimensionality and to identify the major axes of morphological variation across individuals. Variance partitioning was performed using redundancy analysis (RDA) which allows the unique and shared contributions of sex, age, and sample to be quantified simultaneously.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section4\"\u003e \u003ch2\u003e2.2.4.3. Surface warping\u003c/h2\u003e \u003cp\u003eTo visualise shape differences, we applied a surface warping approach based on the thin-plate spline algorithm, which allows the mean shape to be deformed along the minimum and maximum values of selected principal components. This produces a colour map highlighting local regions of expansion and contraction. The visualization was performed using the functions from the \u003cem\u003eArothron\u003c/em\u003e R package (Profico et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which compute facet-by-facet surface differences between warped meshes and represent them through a continuous colour gradient on the reference model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section4\"\u003e \u003ch2\u003e2.2.4.4. Local mapping\u003c/h2\u003e \u003cp\u003eLocal variation associated with sex and sample was quantified using a landmark-wise variance mapping approach (modified after Del Bove et al. 2023). For each landmark, the proportion of variance (R\u0026sup2;) explained by either sex, age or sample was computed using local multivariate regression. In each iteration, 5 nearest neighbouring semilandmarks were selected to estimate R\u0026sup2; values on Procrustes-aligned shape coordinates. Analyses were performed in the shape, form, and size spaces, using 10 parallel computational cores to optimize performance. Sexual dimorphism in cranial shape was assessed separately for each sample (Cedynia, Radom, New Mexico) using Procrustes-aligned landmarks. Shape was regressed on sex with a permutation-based RRPP test (1000 iterations). Procrustes distances between male and female mean shapes (D) and the proportion of variance explained by sex (R\u0026sup2;) were calculated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section4\"\u003e \u003ch2\u003e2.2.4.5. Shape variation within the samples\u003c/h2\u003e \u003cp\u003eFor each sample, shape variability was quantified with the parameter of Procrustes disparity, defined as the mean squared distance of individual specimens from the sample mean shape (Zelditch et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). This metric reflects morphological disparity within groups, with higher values indicating greater shape dispersion. We run the analyses separately for females and males. The mean and standard error (SE) of this metric were calculated to assess within-group variability. To evaluate whether the observed shape variation differed significantly between samples, a Welch\u0026rsquo;s ANOVA was performed. Additionally, post-hoc test was performed when needed.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Landmarking reproducibility\u003c/h2\u003e \u003cp\u003eRepeated 37 landmark sets collected for 30 individuals were tested with Procrustes ANOVA. No significant effect of replication was detected (p\u0026thinsp;=\u0026thinsp;0.67), indicating high landmarking reproducibility.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Morphology in relation to sexual dimorphism, and inter-sample variation\u003c/h2\u003e \u003cp\u003e In the analyzed sample, in size-shape space (Fig.\u0026nbsp;3a), both sex and sample significantly influence shape variation (p\u0026thinsp;=\u0026thinsp;0.001), explaining 12.56% and 20.06% of the variance, respectively. Age was also statistically significant (p\u0026thinsp;=\u0026thinsp;0.001) accounted for 5.99% of the variance. Results for pure shape space are presented in Fig.\u0026nbsp;3b and in Supporting Information (Table S3).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;3\u003c/b\u003e PCA plots for the first two components (a) in size-shape space. (b) in pure shape space. Different samples are marked with colors and sex with symbols\u003c/p\u003e \u003cp\u003eVariance partitioning showed that sex, age, and sample jointly explained 35.25% of total cranial shape variation and the remaining 64.75% variation was unexplained. Sample was the dominant factor, uniquely accounting for 16.6% variation followed by Sex, which explained 12.7%. In contrast, Age had a negligible independent contribution (0.3%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe first two PCs capture approximately 72.21% of the total variance (PC1\u0026thinsp;=\u0026thinsp;44.83%, PC2\u0026thinsp;=\u0026thinsp;13.75, PC3\u0026thinsp;=\u0026thinsp;6.74%, PC4\u0026thinsp;=\u0026thinsp;3.81%, PC5\u0026thinsp;=\u0026thinsp;3.09%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). We focused on these PCs for further analyses as they best describe the data structure. PC3-PC5 description can be found in the Supplementary Text S1 together with figures (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-S3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePC1 is strongly related to both sex (R\u0026sup2; = 0.24, p\u0026thinsp;=\u0026thinsp;0.001) and sample (R\u0026sup2; = 0.34, p\u0026thinsp;=\u0026thinsp;0.001). It describes a transition from a shorter and broader craniofacial form to a longer and narrower one (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). At low PC1 scores, found predominantly in females, if sex variable is considered and in historical populations, when sample effect included, the facial skeleton is shorter and wider, with moderate contraction across the measured surface. Higher scores found in the male group, if sex included and in New Mexico, when sample variable is considered, correspond to elongation of the facial skeleton, with the strongest expansion located beneath the piriform aperture, in the alveolar region, and in the glabellar area. Lateral portions of the upper orbital rims and the frontal bone also expand, whereas the central portion of the frontal bone exhibits relative narrowing (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePC2 is linked to sample differences (R\u0026sup2; = 0.23, p\u0026thinsp;=\u0026thinsp;0.001). It captures differences in craniofacial width, describing a transition from a more elongated facial form to a shorter, broader, and more rounded morphology (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Low PC2 scores, found in New Mexico and Cedynia, correspond to a more elongated facial skeleton with marked contraction across the central facial region. Higher scores, characteristic of the Radom sample, correspond to a broader and shorter craniofacial form, with the strongest expansion concentrated in the glabellar region, upper orbital area, and central frontal bone (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. Sex-related differences\u003c/h2\u003e \u003cp\u003eThe local mapping results show that pure shape variation alone explains only a very small portion of the total variance (R\u0026sup2; = 0.002\u0026ndash;0.08), indicating minimal sex-related differences in shape when size is completely removed. When allometry is included, form variation increases (R\u0026sup2; = 0.07\u0026ndash;0.18), and the inclusion of size produces the strongest signal (R\u0026sup2; = 0.008\u0026ndash;0.34), suggesting that size-related differences are the dominant factor driving sex differences in morphology. The most dimorphic regions include the glabellar area together with the supraorbital ridges and the zygomatic bone, with these effects becoming more pronounced when we consider only centroid size (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNote that each map has its own color scale, so absolute R\u0026sup2; values are not directly comparable; the maps illustrate relative patterns of age effects within each variable\u003c/p\u003e \u003cp\u003eWe also performed local mapping of sex-related differences separately for each sample; the results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e. When both size and shape are considered, the New Mexico and Radom samples display broadly similar spatial patterns of sexual dimorphism, with the strongest signals concentrated in the zygomatic bones and the glabellar region. In the New Mexico sample, this pattern is further accentuated by a pronounced signal around the piriform aperture. In contrast, the Cedynia sample shows a more restricted distribution of sex-related variation, largely confined to the zygomatic region. When pure shape is analyzed, a consistent signal is observed in the glabellar region across all three samples, whereas size-related variation highlights the zygomatic bone and maxilla as the regions showing the strongest and most consistent sexual dimorphism across samples. In the medieval Cedynia sample, sexual shape differences were most pronounced (D\u0026thinsp;=\u0026thinsp;137.57, R\u0026sup2; = 0.019, p\u0026thinsp;=\u0026thinsp;0.015). In the Radom (18th\u0026ndash;19th century) and New Mexico samples, the signal was weaker and not statistically significant (Radom: D\u0026thinsp;=\u0026thinsp;118.48, R\u0026sup2; = 0.015, p\u0026thinsp;=\u0026thinsp;0.386; New Mexico: D\u0026thinsp;=\u0026thinsp;708.27, R\u0026sup2; = 0.010, p\u0026thinsp;=\u0026thinsp;0.227), suggesting that sexual dimorphism in cranial shape was stronger in the medieval sample compared to the later samples.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNote that each map has its own color scale, so absolute R\u0026sup2; values are not directly comparable; the maps illustrate relative patterns of age effects within each variable\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2. Sample-related differences\u003c/h2\u003e \u003cp\u003eFor sample-related variation, pure shape alone explains only a very small portion of the total variance (R\u0026sup2; = 0.002\u0026ndash;0.31), while form contributes more (R\u0026sup2; = 0.02\u0026ndash;0.26), indicating that shape combined with size matters. However, variation in size has the largest effect (R\u0026sup2; = 0.004\u0026ndash;0.33). When we only consider shape, a strong sample-related signal is observed in the central portion of the frontal bone, extending toward the superior orbital ridges. If we consider size, the sample-related differences are more localized, primarily in the lateral parts of the frontal bone, the superior orbital rims, the anterior nasal spine, and the anterior portion of the alveolar process of the maxilla. A similar pattern is observed in the combined shape-and-size space, although the changes are less pronounced, and the signal in the central frontal bone remains detectable (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3. Age-related differences\u003c/h2\u003e \u003cp\u003eLocal mapping for age, revealed that the strongest effect of this variable was present in pure size space (R\u0026sup2; = 0.001\u0026ndash;0.12), shape and size showed an intermediate effect (R\u0026sup2; = 0.005\u0026ndash;0.01), whereas the weakest association was found for shape alone (R\u0026sup2; = 0.003\u0026ndash;0.08), which indicates that for this variable morphological change is driven predominantly by size variation. Shape-related variation is primarily associated with the midline region of the frontal bone, whereas size- and form-related differences display a similar spatial pattern, concentrating mainly in the lateral portions of the frontal bone (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNote that each map has its own color scale, so absolute R\u0026sup2; values are not directly comparable; the maps illustrate relative patterns of age effects within each variable.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Range of shape variation in the analyzed samples\u003c/h2\u003e \u003cp\u003eAmong females, Procrustes variance (within-group shape disparity) differed significantly between populations. The modern New Mexico sample showed higher variability compared to Cedynia (p\u0026thinsp;=\u0026thinsp;0.020), whereas Radom sample did not differ significantly from either group. In males, no statistically significant differences were found between populations, although a trend for higher variability in New Mexico sample was observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Size differences\u003c/h2\u003e \u003cp\u003eWe compared the centroid size in sex groups between the samples. Results of Welch's ANOVA indicated that the samples differ for both sexes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In both females and males mean centroid size of the New Mexico sample is significantly bigger when compared to the Radom sample (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for females and p\u0026thinsp;\u0026lt;\u0026thinsp;0.005 for males) and Cedynia (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for females and p\u0026thinsp;\u0026lt;\u0026thinsp;0.005 for males) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe present results explore broad patterns of craniofacial variation across populations (samples) representing different temporal and socio-economic contexts using 3D geometric morphometrics. Sample-related differences accounted for a substantial proportion of cranial shape variation, whereas the effects of sex and age were comparatively weaker. The observed morphological patterns are likely influenced by multiple factors, including changes in masticatory function linked to dietary shifts.\u003c/p\u003e \u003cp\u003eMost diachronic studies have relied either on traditional linear measurements (e.g. Biehler-Gomez et al. \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) or 2D radiographic cephalometry (e.g. Jonke et al. \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e; Rock et al. \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e), while 3D geometric morphometric approaches have been based on a limited number of fixed landmarks (e.g. Cridlin \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). In contrast, the dense configuration of semilandmarks with local mapping applied here enables high-resolution analysis of cranial morphology and identifies regions most strongly affected by the analyzed variables, providing new insight into the spatial patterns of craniofacial variation.\u003c/p\u003e \u003cp\u003eMoreover, whereas most previous studies have focused on shorter time spans, typically addressing more recent changes, here we examine a long-term period spanning approximately 1000 years and explicitly consider the role of industrialization, which has often been overlooked in the literature. Together, this approach allows to obtain a more comprehensive assessment of both global and region-specific cranial variation over long-term patterns of change.\u003c/p\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Sex-related differences\u003c/h2\u003e \u003cp\u003eWe observed differences in the expression of sexual dimorphism among the analyzed samples. Overall, it was highest in the medieval Cedynia, followed by the early modern Radom, and lowest in the contemporary New Mexico sample. This gradual decrease suggests pattern consistent with diachronic change toward reduced expression of craniofacial sexual dimorphism over time. Despite differences in the intensity of expression, the overall pattern of sexually dimorphic traits remains consistent across samples. Previous studies have suggested that such variation may be influenced by non-biological factors, which could modulate the expression of sexual dimorphism (Cappella et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Garvin et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Legros et al. \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). At the same time, it should be noted that differences in the expression of sexual dimorphism may also reflect inherent population-specific variation, rather than exclusively temporal change.\u003c/p\u003e \u003cp\u003eIn our study, the two historical samples (Cedynia and Radom) share a comparable ancestry, and previous craniometric analyses suggest that they can be considered largely genetically homogeneous (Tomczyk \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). This reduces the likelihood that genetic factors are the primary drivers of the observed differences between these two groups. The New Mexico sample, even though we included only individuals of one ethnic group, might be genetically different from two Polish samples. However, similarities in the spatial pattern of sexual dimorphism between groups might suggest that environment and culture related factors likely contributed to shaping observed sample differences, acting alongside population-specific biological variation.\u003c/p\u003e \u003cp\u003eRevealed pattern of sex-related differences in our analysis indicates that size is the dominant factor, which aligns with the majority of previous GM analyses (e.g. Cabo et al. \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Gonzalez et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Toneva et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), although some authors reported higher contribution of shape (e.g. Chovalopoulou and Bertsatos \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Milella et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Our results found that females exhibit smaller and shorter craniofacial morphology, whereas males have larger and longer faces, which is in line with previous geometric morphometric and craniometric studies(e.g. Cocilovo et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kimmerle et al., \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e). In our analysis, three regions have been established as predominantly sex-related: zygomatic bone, glabella, and piriform aperture. These cranial regions are consistently highlighted in the literature as key contributors to sexual dimorphism (da Silva et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Del Bove et al. 2023), despite different methodological approaches.\u003c/p\u003e \u003cp\u003eIn our study, the zygomatic bone shows a strong size-related dimorphic signal, with males exhibiting larger and more robust bones. This pattern is consistent with previous reports (e.g. Chovalopoulou et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Del Bove et al. 2023) and likely reflects the role of this region in masticatory muscle attachment and the generally stronger musculature of males (De Jong et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Tokpınar and Alkan \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGlabellar dimorphism was primarily shape-driven, in line with Del Bove et al. (2023). Males generally exhibit a more robust and protruding glabella (e.g. Abdel Fatah et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Bigoni et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e) reflecting likely hormonal and biomechanical influences, including larger sinuses (Čechová et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) and greater facial muscle mechanical demands in males (da Silva et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe piriform aperture exhibits marked sexual dimorphism in size, with males displaying larger dimensions, as in other analyses (e.g. Alves et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Sarač-Hadžihalilović et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). It has been linked to higher airflow demands in males, greater body size and muscle mass (Ajanović et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ibrahim et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Milella et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e, de-Araújo et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLess pronounced dimorphism was observed in the lateral orbital rim and maxilla, with males showing larger, more robust maxillae, consistent with previous studies (Ajanović et al. \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Toneva et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Temporal differences\u003c/h2\u003e \u003cp\u003eOur analysis of medieval Cedynia, early modern Radom, and contemporary New Mexico demonstrates that overall size is the primary factor differentiating the facial skeleton among these groups, with the strongest signal localized in the anterior maxilla and frontal bone. This pattern is likely related to allometric effects on craniofacial shape variation, as previously suggested in the literature, where size was identified as a major driver of facial morphological differences(e.g. Eyquem et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to size-related differences, more recent samples exhibit a relatively more elongated and narrower facial morphology compared to the medieval sample. This transition toward leptoprosopy – longer and narrower face, reflects a trend reported across different populations and time periods(e.g. Cridlin \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Nakahashi \u003cspan class=\"CitationRef\"\u003e1993\u003c/span\u003e, Weisensee and Jantz \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e, Biehler-Gomez et al. \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). In our analysis, we propose a spatial mechanism underlying this pattern, whereby this effect is achieved primarily through vertical expansion of the maxilla and the lateral portions of the frontal bone, accompanied by horizontal contraction in the midfacial region.\u003c/p\u003e \u003cp\u003eThe observed narrowing of the facial skeleton, including anterior maxilla, might be partially explained by the \"masticatory-functional hypothesis\" (Carlson \u003cspan class=\"CitationRef\"\u003e1976\u003c/span\u003e; Carlson and Van Gerven \u003cspan class=\"CitationRef\"\u003e1977\u003c/span\u003e), which states that the transition from coarse historical diets to soft, highly processed modern foods reduced the mechanical demands on the masticatory apparatus. Because bone is a dynamic tissue that responds to mechanical strains (mechanotransduction), the reduction in chewing stress during ontogeny leads to the \"underdevelopment\" or reduction of the maxillary complex (von Cramon-Taubadel \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). Additionally, the narrowing of the dental arches in modern samples may be related to a plastic response to these shifted dietary patterns, which results in a higher prevalence of dental crowding and malocclusion. This phenomenon became so common, that it has been regarded as “jaw epidemics” and is referred to as a public health problem (Kahn and Ehrlich \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe relationship between chewing forces and frontal bone morphology is complex. Changes in facial breadth may partly reflect variation in masticatory loading, as forces generated during mastication are transmitted through the facial skeleton via the facial buttress system, indirectly affecting more distant cranial regions. However, while some studies report correlations between temporal muscle traits and supraorbital robusticity, these associations often are weakened when controlling for overall cranial shape (Nowaczewska et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). This suggests that broader cranial architecture, systemic growth patterns, and allometric scaling, including increases in cranial vault height related to somatic growth and secular trends in body height also contribute to observed variation (Grasgruber \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Jellinghaus et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rock et al. \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e; Fudvoye and Parent \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Cole \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eDemographic processes including gene flow, migration, and post-industrial population restructuring may have introduced novel morphological variation, altering craniofacial proportions independently of functional demands (Biehler-Gomez et al. \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Jantz and Meadows Jantz \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e; Cridlin \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Researchers also mention systemic improvements in nutrition and healthcare as key drivers of these facial shifts. Improved childhood health and declining morbidity facilitate more uniform and expansive growth of the facial skeleton, allowing it to reach its full developmental potential (Jantz and Jantz \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; von Cramon-Taubadel \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Biehler‐Gomez et al. 2025; Cridlin \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). As suggested by Weisensee and Jantz (\u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e) the rapid rate of these changes, often occurring within just a few generations, indicates that phenotypic plasticity and epigenetic modulation are the primary mechanisms driving the emergence of the modern leptoprosopic face. However, it remains unclear which of these factors exerts the dominant influence, and the observed facial patterns likely result from the combined action of multiple interacting mechanisms.\u003c/p\u003e \u003cp\u003eAlthough diachronic studies often show consistent trends, contrasting patterns exist; for example, Egyptian populations showed little change over a millennium (Rösing \u003cspan class=\"CitationRef\"\u003e1992\u003c/span\u003e) suggesting long-term cranial stability (Biehler-Gomez et al. \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), though methodological differences should be also taken into consideration. Some research suggest that masticatory loading and dietary softening do not uniformly affect craniofacial morphology, and show localized or divergent changes (Cheronet et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Paschetta et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Eyquem et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), indicating that diet does not act in isolation.\u003c/p\u003e \u003cp\u003eTaken together, the pattern identified in the present study most likely reflects a multifactorial process in which reduced masticatory loading associated with dietary change might act as the major contributing factor alongside broader growth-related, and population-level processes in shaping craniofacial form through time.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Range of cranial shape variation\u003c/h2\u003e \u003cp\u003eOur shape-variability analysis indicates that the contemporary New Mexico sample exhibits significantly greater cranial morphological variation than both the early modern Radom and medieval Cedynia groups. It has been suggested that increased morphological variability in modern human crania resulted from cultural-environmental factors, especially those associated with diet and masticatory function (Brachetta-Aporta and Toro-Ibacache \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Toro-Ibacache et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Noback and Harvati \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, it must be noted that this pattern might also be related to greater heterogenicity of the NMDID sample in comparison to two historical Polish populations.\u003c/p\u003e \u003cp\u003eReduced biomechanical loading resulting from the consumption of softer, highly processed foods has been proposed as a major contributing factor, as it relaxes functional constraints and allows greater expression of genetic and environmental variation (Flis et al. \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Noback and Harvati \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). At the same time, these dietary effects likely interact with a complex mixture of non-biological and genetic influences. For example Biehler-Gomez (2025) reported “opposing directional trends”, with increased orbital breadth but mid-facial narrowing in modern sample, which they linked to increased morphological heterogeneity caused by a mixture of adaptive response to changed functional demands and living conditions, urbanization, nutrition, or migration-related admixture in the post-industrial period.\u003c/p\u003e \u003cp\u003eFrom an archaeological and anthropological perspective, these diachronic patterns can be interpreted as embodied responses to significant cultural transformations, such as intensified food processing introduced by industrialization, urbanization, reduced biomechanical demands, and improved developmental conditions. Accordingly, craniofacial morphology might retain a biologically informative signal of long-term changes in human lifeways associated with industrialization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Age-related differences\u003c/h2\u003e \u003cp\u003eIn the present study, age-related cranial shape variation was limited and accounted for only a small proportion of the overall morphological variance. Contrary to previous reports indicating pronounced age-related remodeling in the orbital, maxillary, and piriform regions (Mendelson et al. \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e), no significant associations with age were observed in these elements in our samples. Instead, the detected age-related signal was primarily localized in the frontal bone.\u003c/p\u003e \u003cp\u003eEffect of age partially overlapped with sample effect. This raises the possibility that the detected age-related signal is, at least in part, sample-specific rather than universal. It is also worth noting that although our previous analysis of age-related changes in the Cedynia sample found significant correlations (Walczak et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), it did not include the frontal bone and was based on linear measurements of specific elements rather than geometric morphometrics.\u003c/p\u003e \u003cp\u003eIt should be noted that in our analysis we applied the same age categories for all samples, however individuals in contemporary populations attain on average older ages than those represented in medieval skeletal series, which may limit the comparability of age-related patterns between populations due to differences in age-at-death distributions and survival.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Limitations and future research\u003c/h2\u003e \u003cp\u003eLimitation of this study lies in its cross-sectional design, which is inherent to research covering a broad temporal span, from the medieval period to the present day. The further limitation concerns the anatomical scope of the analysis. The present study focused on ectocranial part of the cranium rather than the entire skull. Future research would benefit from including the cranial vault and cranial base, as well as inner cavities such as the maxillary and frontal sinuses, endocasts, and the mandible, as these regions are known to differ in their relative sensitivity and response to genetic and non-biological influences (Durmaz and Bolatli \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Such an extension would allow for a more comprehensive assessment of cranial morphological change through time, together with mandible, which was not included in the present analysis. It constitutes an integral part of the facial skeleton and is considered particularly responsive to environmental factors, including dietary and functional influences (De Angelis et al. \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; von Cramon-Taubadel \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Kiliaridis \u003cspan class=\"CitationRef\"\u003e1995\u003c/span\u003e). Given that many studies addressing the relationship between diet and craniofacial morphology focus specifically on the mandible (e.g. Bosman et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; De Angelis et al. \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Stansfield et al. 2018), its dedicated analysis should be included in the future studies. The principal limitation concerns the geographic heterogeneity of the comparative design, as the contemporary sample derives from the United States, whereas the historical groups originate from Central Europe. Although this design captures broad diachronic trends associated with industrialization, it does not allow full separation of temporal and population-specific effects. Therefore, future studies integrating temporally structured samples from the same geographic region will be essential to disentangle functional, demographic, and population-history effects with greater precision.\u003c/p\u003e \u003c/div\u003e "},{"header":"Conclusions","content":"\u003cp\u003eThis study provides a three-dimensional geometric morphometric assessment of diachronic craniofacial variation across medieval, early modern, and contemporary samples. The results demonstrate that the sample itself constitutes the principal source of morphological differentiation, with size exerting a dominant influence on craniofacial form. Temporal trends are characterized by a shift toward a relatively longer and narrower facial morphology in more recent sample, primarily expressed through vertical expansion of the maxilla and frontal bone combined with transverse reduction of the midface.\u003c/p\u003e\u003cp\u003ePatterns of sexual dimorphism were consistently identified across all samples, with the zygomatic region, glabella, and piriform aperture exhibiting the strongest sex-related signals. Although the overall configuration of sexually dimorphic traits remained stable through time, their intensity appears to decrease in more recent sample, suggesting a potential temporal change in the expression of craniofacial sexual dimorphism.\u003c/p\u003e\u003cp\u003eOverall, the results demonstrate that craniofacial variation in recent human populations is structured primarily by population-level differences and size-related effects, with additional contributions from sex and age. The observed patterns are consistent with the influence of long-term cultural and environmental changes, including dietary shifts, but also reflect broader processes such as secular growth, developmental plasticity, and population history.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe work was supported by the National Science Centre grant (2024/53/N/NZ8/02043) and ID-UB Initiative of Excellence\u0026mdash;Research University (102/13/SNP/0010).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have nothing to report. The contemporary CT-derived models were obtained from the New Mexico Decedent Image Database under its institutional access policy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contribution:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAW: Conceptualization, Formal Analysis, Investigation, Methodology, Visualization, Writing \u0026ndash; Original Draft Preparation\u003c/p\u003e\n\u003cp\u003eSŁ: Conceptualization, Supervision, Writing \u0026ndash; Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003eAP: Formal analysis (geometric morphometric analyses and statistical scripting), Visualization, Methodology, Writing \u0026ndash; Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003eJT: Resources, Data for Sex and Age-at-death for Radom population\u003c/p\u003e\n\u003cp\u003eMKN: Conceptualization, Resources, Supervision, Writing \u0026ndash; Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR scripts will be deposited in open access repository upon acceptance.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdel Fatah EE, Shirley NR, Jantz RL, Mahfouz MR (2014) Improving Sex Estimation from Crania Using a Novel Three-dimensional Quantitative Method. 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Clin Anat 27:408\u0026ndash;416\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZelditch ML, Swiderski DL, Sheets HD, Fink WL (2004) Geometric Morphometrics for Biologists: A Primer. Academic\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"diachronic changes, human cranium, geometric morphometrics, cranial morphology, masticatory function, 3D analysis","lastPublishedDoi":"10.21203/rs.3.rs-9544747/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9544747/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCraniofacial morphology is partially shaped by non-biological factors, such as diet and masticatory loading. While the relationship between dietary hardness and cranial morphology is well established, major cultural and dietary transitions, such as industrialization and post-technological advancements remain relatively underexplored. This study examines morphological changes across three temporally distinct populations representing different stages of socio-economic development: medieval Cedynia, early modern Radom (both Poland, Central Europe), and a contemporary sample from the New Mexico Decedents Image Database, using 3D geometric morphometrics. We placed 37 fixed landmarks, 260 surface semilandmarks, and three curves on 415 adult skulls to compare ectocranial morphology among populations. Our results indicate that among the three tested variables, sex, sample (representing the three chronological groups), and age, sample exerted the strongest influence on cranial variation, exceeding the effects of age and sex. Across both sex- and sample-related differences, size contributes more strongly than shape alone. The modern sample is characterized by larger, longer, and narrower crania, as well as increased morphological variability. These findings are consistent with the hypothesis that dietary softening associated with industrialization may play a role in shaping human cranial morphology.\u003c/p\u003e","manuscriptTitle":"Patterns of Craniofacial Size and Shape Variation from Medieval to Contemporary Human Populations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-09 01:02:18","doi":"10.21203/rs.3.rs-9544747/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"683b61c5-991e-4443-a7d4-13d7cd0bdb93","owner":[],"postedDate":"May 9th, 2026","published":true,"recentEditorialEvents":[{"type":"editorAssigned","content":"","date":"2026-05-01T08:23:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-30T17:24:56+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-09T01:02:19+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-09 01:02:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9544747","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9544747","identity":"rs-9544747","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}