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. 2022 Nov 16;180(1):224–234. doi: 10.1002/ajpa.24657

Testing different 3D techniques using geometric morphometrics: Implications for cranial fluctuating asymmetry in humans

Trine Bottos Olsen 1,✉, Daniel García‐Martínez 2,3,4, Chiara Villa 1
PMCID: PMC10100329  PMID: 36790697

Abstract

This study aimed to test the performance of 3D digitizer, CT scanner, and surface scanner in detecting cranial fluctuating asymmetry. Sets of 32 landmarks (6 in the midline and 13 bilateral) were acquired from 14 archeological crania using a 3D digitizer, and from 3D models generated from a CT scanner and surface scanner using Viewbox 4. Levels of shape variation were analyzed in MorphoJ using Procrustes analysis of variance and Principal component analysis. Intra‐observer error accounted for 1.7%, 1.8%, and 4.5% of total shape variation for 3D digitizer, CT scanner, and surface scanner respectively. Fluctuating asymmetry accounted for 15%–16% of total shape variation. Variation between techniques accounted for 18% of total shape variation. We found a higher level of missing landmarks in our surface scan data than for both 3D digitizer and CT scanner data, and both 3D model‐based techniques sometimes obscured taphonomic damage. All three 3D techniques are appropriate for measuring cranial fluctuating asymmetry. We advise against combining data collected with different techniques.

Keywords: fluctuating asymmetry, geometric morphometrics, measuring error, principal component analysis, Procrustes ANOVA

When measuring the shape human crania, 3D digitizer, CT scanner, and surface scanner will measure precisely enough that individuals can be told apart in a principal component plot.

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1. INTRODUCTION

Geometric morphometrics (GMM) and 3D models are tools that assist in the examination of skeletal remains, enabling quantitative and objective analysis of the anatomy and variation of human bones (Slice, 2007). GMM is the study of form, that is, the size and shape of organisms or structures. The method expresses and analyzes variation in form using landmarks: points defined by Cartesian coordinates in 2D or 3D (Bookstein, 1986; Kendall, 1989). In humans, GMM has been used, for example, when evaluating sexual dimorphism (e.g., Garvin & Ruff, 2012; Gonzalez et al., 2011; Kimmerle et al., 2008; Pretorius et al., 2006; Shearer et al., 2012), age estimation (e.g., Braga & Treil, 2007; Martínez‐Abadías et al., 2011; Noble et al., 2019; San‐Millán et al., 2017), and population variation (e.g., Franklin et al., 2007, 2010; Nicholson & Harvati, 2006; Reyes‐centeno et al., 2017).

GMM is regularly utilized when studying fluctuating asymmetry (Klingenberg, 2015, Klingenberg et al., 2002). Fluctuating asymmetry describes deviations from an organism's bilaterally symmetrical body plan (Palmer & Strobeck, 1986; Van Valen, 1962), and can be used as a biomarker of developmental instability and environmental stress (Beasley et al., 2013; McGrath et al., 2022; Møller & Thornhill, 1997; Van Dongen, 2006; Van Dongen & Gangestad, 2011). Fluctuating asymmetry has been studied in humans—both on modern and archeological populations and on the cranium and the post‐cranial skeleton (Bigoni et al., 2013; Chovalopoulou, Papageorgopoulou, & Bertsatos, 2017; DeLeon, 2007; Gawlikowska et al., 2007; Gawlikowska‐Sroka et al., 2017; Mopin et al., 2018; Zurawiecka et al., 2019). Studying fluctuating asymmetry can be difficult since it is generally a small biological signal and is, therefore, vulnerable to measuring error (Greene, 1984; Lundström, 1960; Palmer & Strobeck, 1986). When studying fluctuating asymmetry, it is important to use a data collection technique that is able to pick up the biological signal. As technologies have evolved, data acquisition in morphometric studies has progressed from distance‐based measurements using calipers (e.g., Kujanová et al., 2008; Rossi et al., 2003) to 3D landmark data collected using various 3D technologies such as surface scanners (e.g., Friess, 2010; Hennessy & Stringer, 2002; Kuzminsky et al., 2016), 3D digitizers (e.g., Bigoni et al., 2013; Chovalopoulou, Bertsatos, & Papageorgopoulou, 2017; Jung & von Cramon‐Taubadel, 2018; Weisensee & Spradley, 2018), and CT scanners (e.g., Braga & Treil, 2007; Neubauer et al., 2020; Waltenberger et al., 2021). All these digital techniques are in theory very precise, but they can vary in many aspects, such as the resolution of 3D models, the inclusion of surface texture data, and tactile assistance (Friess, 2012; Sholts et al., 2011). These factors can affect how a technique performs and may introduce measuring error that exceeds the precision advertised by developers.

Earlier studies have tested technique measurement error (Brzobohatá et al., 2012; Ross & Williams, 2008; Shearer et al., 2017; Sholts et al., 2011; Stephen et al., 2015), and most studies on fluctuating asymmetry include a small measuring error pilot study in their methods (Bigoni et al., 2013; Chovalopoulou, Papageorgopoulou, & Bertsatos, 2017; Kujanová et al., 2008), as is the generally accepted approach described in earlier literature (Palmer & Strobeck, 1986). However, we have found no studies comparing technique performance when detecting cranial fluctuating asymmetry.

The aim of this study is to investigate the performance of three different techniques for collecting 3D landmarks on human crania when measuring cranial fluctuating asymmetry. Specifically, it compares 3D landmarks obtained from a 3D digitizer and 3D models generated from a CT scanner and a surface scanner. The applicability of the three techniques is tested using GMM methods.

2. MATERIALS AND METHODS

2.1. Sample

A sample of 14 adult crania was selected from the anthropological collection in the Laboratory of Biological Anthropology, Department of Forensic Medicine, University of Copenhagen (DK), which houses archeologically excavated individuals from all over Denmark. Crania were selected on grounds of completeness: we selected as well‐preserved crania as possible—they were intact but affected by taphonomic changes in some areas, where parts had broken off. Crania with visible taphonomic warping, surface damage, and signs of pathology were excluded. Twelve crania were sampled from two medieval sites from Jutland (Tjærby and Åbenrå), and two from the Danish post reformation era without precise geographical origin. The sample consisted of 10 males and 4 females. Of the 14 crania, eight had all landmarks present, while the rest were missing between one and six landmarks.

2.2. Landmarks

A 3D landmark list was taken from Chovalopoulou, Bertsatos, and Papageorgopoulou (2017), as the individual landmarks on this list have been tested for intra‐ and inter‐observer error. The list was then modified as follows:

  1. Exclusion of all landmarks of type III, which are at geometric extremes in relation to other landmarks or geometric entities (Bookstein, 1991)

  2. Exclusion of landmarks that did not appear in other relevant publications regarding cranial geometric morphometry (an overview can be found in Rupić et al., 2020)

  3. Exclusion of landmarks found in areas with high levels of taphonomic alteration (e.g., the zygomatic arch, the nasal spine, and the medial wall of the orbit)

The final list of 32 landmarks (6 in the midline and 13 bilateral is shown in Table 1 and illustrated in Figure 1).

TABLE 1.

List of the chosen landmarks, including name, a description of the landmark location, landmark type, and cranial region

Landmark number Name Description Type Cranial region
1 Bregma Intersection of the sutura coronalis and sutura sagittalis in the midsagittal plane 1 Calvarium
2 Nasion The middle of the sutura nasofrontalis in the midsagittal plane 1 Face
3L, 3R Maxillonasofrontale Intersection of the sutura frontonasalis, sutura frontomaxillaris and sutura nasomaxillaris 1 Face
4L, 4R Frontomalare orbitale Intersection of the sutura frontozygomatica and the lateral margin of the orbit 2 Face
5L, 5R Infraorbitale The most lateral point on the margin of the foramen infraorbitale 2 Face
6L, 6R Apertion The most lateral point on the nasal aperture 2 Face
7L, 7R Zygomaxillare The most inferior point on the sutura zygomaticomaxillaris 2 Face
8L, 8R Sphenion Intersection of the sutura coronalis, sutura sphenoparietalis, and sutura sphenofrontalis 1 Calvarium
9L, 9R Crotaphion Intersection of the sutura sphenosquamosa, sutura sphenoparietalis, and sutura squamosa 1 Calvarium
10L, 10R Jugale The point at the union of the processus frontalis and processus temporalis of the os zygomaticus 2 Face
11L, 11R Asterion Intersection of the sutura lambdoidea, sutura parietomastoidea and sutura occipitomastoidea 1 Calvarium
12 Lambda Intersection of the sutura sagittalis and sutura lambdoidea in the midsagittal plane 1 Calvarium
13 Opisthion The midpoint of the posterior margin of the foramen magnum in the midsagittal plane 2 Base
14 Basion Lowest midline point on the anterior margin of the foramen magnum 2 Base
15L, 15R Foraminolaterale The most lateral point on the margin of the foramen magnum 2 Base
16L, 16R Caroticum mediale The most medial point on the margin of the foramen caroticum externum 2 Base
17L, 17R Spinale mediale The most medial point on the margin of the foramen spinosum 2 Base
18L, 18R Ovale mediale The most medial point on the margin of the foramen ovale 2 Base
19 Hormion Intersection of the midsagittal plane and the line where the base of the vomer meets os sphenoidale 1 Base
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FIGURE 1.

FIGURE 1

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Location of landmarks shown on a sketched cranium (illustrated with red dots). 1: Bregma, 2: Nasion, 3: Maxillonasofrontale, 4: Frontomalare orbitale, 5: Infraorbitale, 6: Apertion, 7: Zygomaxillare, 8: Sphenion, 9: Crotaphion, 10: Jugale, 11: Asterion, 12: Lambda, 13: Opistion, 14: Basion, 15: Foraminolaterale, 16: Caroticum mediale, 17: Spinale mediale, 18: Ovale mediale, 19: Hormion. Copyright TBO

All landmarks were collected twice for all three techniques by the first author. Data were collected with at least 1 day between replicates.

3. TECHNIQUES AND SOFTWARE FOR DATA ACQUISITION

3.1. 3D digitizer

Landmarks were collected with a 3D digitizer using Revware Microscribe i+ and Microscribe Utility Software (Revware, 2020). The landmark coordinates were exported to an excel file. To keep crania stable during measuring, they were placed on a beanbag filled with sand. The landmarks were collected in two sessions: once with the cranium in anatomical position, and once with the cranium lying upside down. Two separate datasets were created: superior and basal. The two datasets were aligned using packages morphomap, morpho, and rgl in RStudio (Murdoch & Adler, 2021; Profico et al., 2020; RStudio Team, 2021; Schlager, 2017) using three corresponding landmarks collected in both cranial positions; we chose to use lambda and zygomaxillare left and right, as these landmarks are spread across the cranium, which minimizes the effects of rotation when superimposing the data sets (von Cramon‐Taubadel et al., 2007). If one of these landmarks could not be identified, another was chosen based on available structures.

3.2. Surface scanner

Surface scans were acquired using the Next Engine desktop 3D scanner (Next Engine, 2020). Scans were acquired using the following parameters: 360°, 12 divisions, 1.1 k points/inch. All crania were scanned in anatomical position and lying on their side. The 3D models were post‐processed in Next Engine Scan Studio (Next Engine, 2020): scans were aligned; outlier data were removed; scans were merged and fused; and texture data were applied. No smoothing was applied as advised by Veneziano et al. (2018). The 3D models were exported as .stl files to Viewbox 4 (Bastir et al., 2019; Halazonetis, 2021) where the landmarks were collected. The landmark coordinates were exported to an excel file.

3.3. CT scanner

CT scans were acquired using a Siemens Somatom Definition using the following parameters: 120 kV, 300 mAs, 0.6 mm slice thickness, 0.35 pitch, 0.3 mm slice increment, H70s reconstruction algorithm. 3D models were generated using the setting "optimal quality" in Mimics (Materialise, 2020). No further smoothing was applied as advised by Veneziano et al. (2018). The 3D models were exported as .ply files to Viewbox 4 (Halazonetis, 2021) where the landmarks were collected. The landmark coordinates were exported to an excel file.

3.4. Missing data estimation

Due to taphonomic alterations, we could not record all landmarks. To accommodate the need for a full data set when working with multivariate methods, missing landmarks were estimated in Viewbox 4 (Halazonetis, 2021) using thin‐plate spline interpolation to minimize the bending energy between a reference (complete) cranium and the target (incomplete) (Mitteroecker & Gunz, 2009).

3.5. Statistical analyses

MorphoJ (Klingenberg, 2011) and RStudio (R Core Team, 2021; RStudio Team, 2021) were used for the statistical analysis. R package ggplot2 (Wickham, 2016) was used for data visualization.

Coordinate data were aligned using generalized Procrustes analysis (GPA), and Procrustes analysis of variances (ANOVAs) were run on the superimposed datasets.

GPA isolates information on shape by removing position, size, and orientation from landmark configurations. This is done by:

  1. Scaling the individual data sets to a centroid size of 1.0

  2. Translating the individual data sets into a common coordinate system by setting their centroid to the point (0,0,0)

  3. Rotating the individual data sets to a best fit, by minimizing the sum of squares distances between corresponding landmarks

For more detailed descriptions of the underlying principles, see, for example, Dryden and Mardia (1998), Rohlf and Slice (1990), Slice (1996), and Small (2012).

With the Procrustes ANOVA we looked at variation in shape on multiple levels: intra‐observer error (replicates), variation between techniques (inter‐technique error), and variation between individuals (inter‐individual variation). These levels of shape variation were compared to levels of shape variation caused by fluctuating and directional asymmetry. Levels of shape variation were compared using Procrustes ANOVA: a nested ANOVA developed for shape analysis (Klingenberg et al., 2002; Klingenberg & McIntyre, 1998). In this ANOVA model, individual is treated as the random effect, while side (meaning the two mirrored sides of the cranium) is treated as the fixed effect. The fixed effect expresses directional asymmetry, while the interaction between the random and fixed effect expresses fluctuating asymmetry. The technique and replicates are treated as the error term and residuals respectively. The ANOVAs were run on both full datasets and datasets divided by technique. Each level in the model accounts for a percentage of the total shape variation, which was calculated by dividing each levels' sum of squares with the total sum of squares (Fruciano et al., 2017).

Principal component analysis (PCA) was run on the symmetrical component of the superimposed datasets in order to explore the morphological affinities between the different repetitions and measurement techniques of the 14 individuals.

4. RESULTS

Some landmarks were missing due to taphonomy since we worked with archeological materials. We collected 91.7% of all landmarks, while the remaining landmarks (8.3%) were estimated. We estimated 5.5% of the landmarks for the 3D digitizer, 6.1% for the CT scanner, and 13.5% for the surface scanner.

Figure 2 shows Procrustes distances between replicates, techniques, and individuals; it illustrates that intra‐observer error for all three techniques was lower than both inter‐technique error and inter‐individual variation. Inter‐individual variation in Procrustes distance ranged from 0.059 to 0.125, while measuring error ranged from 0.011 to 0.053, which indicates that measuring error for all three techniques had little influence on any measured variation in shape. Error between techniques ranged between 0.018 and 0.089 with data from 3D digitizer and CT scanner more similar than to the data from surface scanner.

FIGURE 2.

FIGURE 2

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Boxplot of Procrustes distances between replicates, techniques, and individuals

The Procrustes ANOVA was run on the full data set containing all techniques and replicates (Table 2). It shows that the variation on all levels of the analysis was statistically significant, confirming the findings in Figure 2. Additionally, the Procrustes ANOVA showed that any variation caused by measuring error was smaller than variation caused by fluctuating asymmetry.

TABLE 2.

Procrustes ANOVA of full data set, and data sets divided by technique

Full data set SS MS df F p (param.) % variation
Individual 0.202135 0.000331 611 4.84 <0.0001* 59.79
Side 0.027715 0.00066 42 9.65 <0.0001* 8.20
Ind*Side 0.037327 6.84 E‐05 546 2.75 <0.0001* 11.04
Error1 0.061844 2.48 E‐05 2492 10.26 <0.0001* 18.29
Residuals 0.009044 2.42 E‐06 3738 2.68
CT scanner SS MS df F p (param.) % variation
Individual 0.079985 0.000131 611 4.26 <0.0001* 71.42
Side 0.013173 0.000314 42 10.2 <0.0001* 11.76
Ind*Side 0.016784 3.07 E‐05 546 18.7 <0.0001* 14.99
Residuals 0.002048 1.64 E‐06 1246 1.83
3D digitizer SS MS df F p (param.) % variation
Individual 0.076588 0.000125 611 4.54 <0.0001* 80.60
Side 0.001782 4.24 E‐05 42 1.54 0.0187 1.88
Ind*Side 0.015074 2.76 E‐05 546 21.79 <0.0001* 15.86
Residuals 0.001579 1.27 E‐06 1246 1.66
Surface scanner SS MS df F p (param.) % variation
Individual 0.074883 0.000123 611 3.3 <0.0001* 61.75
Side 0.020662 0.000492 42 13.23 <0.0001* 17.04
Ind*Side 0.0203 3.72 E‐05 546 8.54 <0.0001* 16.74
Residuals 0.005424 4.35 E‐06 1246 4.47
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Note: Error1 and Residuals account for measuring error of technique and replicate measurements respectively.

Abbreviations: ANOVA, analysis of variance; df, degrees of freedom; Ind*side, interaction between individual and side effects (expresses fluctuating asymmetry); Individual, random effect; MS, mean squares; Side, fixed effect (expresses directional asymmetry); SS, sum of squares.

*

p < 0.0001.

When running the Procrustes ANOVA on the full data set, it was evident that some differences in shape were caused by variation between techniques. The calculated percentage of variation showed that different techniques (error 1) accounted for 18% of all variation, and the fluctuating asymmetry term (ind*side) accounted for 11%.

To account for this variation between techniques, Procrustes ANOVAs were run separately on the data sets for the three techniques. Here, we found that the variation due to measuring error accounted for 1.7%–4.5% of the total variation across all tests, while variation due to fluctuating asymmetry accounted for 15%–16%.

Figure 3 shows a PCA plot of PC1 and PC2, which accounted for 23.2% and 12.4% of variation, respectively. We can observe clustering of individuals and techniques, though overlap is present for some crania, especially close to the mean shape at the origin. For most crania, replicates from the same technique cluster together, which is expected based on the Procrustes distances found in Figure 2. Furthermore, the 3D digitizer and CT scanner replicates generally cluster more tightly than the surface scanner replicates, confirming the levels of measurement error found in the Procrustes ANOVA. We found no apparent connection between level of clustering and number of estimated data points.

FIGURE 3.

FIGURE 3

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Principal component analysis (PCA) plot of the full data set—14 crania measured twice with all three techniques

The results of the Procrustes distance boxplot, Procrustes ANOVAs and PCA show that intra‐observer error is negligible, and that the 3D digitizer, CT scanner, and surface scanner are all suitable for measuring fluctuating asymmetry.

5. DISCUSSION

When working with GMM and fluctuating asymmetry, it is important to be aware of what techniques best suit one's study design. Our results showed that the 3D digitizer, CT scanner, and surface scanner are all appropriate techniques to use when detecting cranial fluctuating asymmetry. We found that Procrustes distances between intra‐observer replicates are smaller than those among different techniques and different individuals. The Procrustes ANOVAs confirmed this and showed that measuring error accounts for 1.7%, 1.8%, and 4.5% of all measured shape variation for the 3D digitizer, CT scanner, and surface scanner, respectively. Earlier studies on craniofacial fluctuating asymmetry have shown similar results with intra‐observer measuring error accounting for 0.2%–18% of variation for data collected with 3D digitizers (Bigoni et al., 2013; Chovalopoulou, Papageorgopoulou, & Bertsatos, 2017; Weisensee, 2013) and 1.6% for data collected with a surface scanner (Meloro et al., 2019). While our results are similar to the results from other studies, it should be noted that comparing levels of measurement error should be approached with some caution. Measurement error in GMM is not exclusively explained by observer reliability, but is also dependent on the number of replicates; sample size; and variation among individuals in the sample (Bailey & Byrnes, 1990), all of which vary between studies.

Similar to previous studies, the Procrustes ANOVA results found in this study indicated that combining data from more than one technique could introduce unnecessary error, which will have influence on detecting fluctuating asymmetry (Fruciano et al., 2017; Robinson & Terhune, 2017; Shearer et al., 2017; Sholts et al., 2011).

Looking at the PCA plot, we found similar results to previous studies: a similar clustering of replicates and individuals as was found by Bigoni et al. (2010) using a 3D digitizer, Barbeito‐Andrés et al. (2012) using CT scans, and Garvin and Ruff (2012) using surface scans. Clustering of techniques comparable to this study was found by Marcy et al. (2018) and Robinson and Terhune (2017).

The results from this analysis do not guarantee that ME will not obscure FA in some instances. It may be the case that a cranium has such low levels of FA that a chosen measuring technique will not pick it up. The reason for this is that Procrustes ANOVAs operate on a population level, that is, FA is expressed as the variation of asymmetry around the mean asymmetry of the measured population. This leaves us with the knowledge that some individual FA may go unnoticed, and it is therefore a goal in GMM studies to keep ME as low as possible, as this is the only thing one can do to mitigate the problem. Procrustes ANOVA is, as previously mentioned, sensitive to number of replicates and sample size. In this study, we have kept these variables as basic as possible, with a small sample size and two replicates: this to ensure that the ME reported in this study would be the worst outcome one can expect. In essence, this should ensure that any studies using these techniques with larger samples or more replicates would obtain more precise results and thus, have an even lower risk of having FA obscured by ME.

6. MISSING DATA

Due to the archeological nature of the crania studied in this article, some accommodations had to be made with regards to missing data, that is, landmarks missing due to taphonomic alterations. The estimated landmarks comprised 8.3% of all data in this study, which one could argue introduced some bias. For this study, we chose to use thin‐plate spline interpolation to estimate data, as advised by Neeser et al. (2009). How much bias this data estimation introduced is difficult to quantify; however, given the underlying principles of thin‐plate spline interpolation, it follows that any missing data estimation will underestimate individual variation, rather than overestimate it. Other methods for estimating landmark data exist, like mirroring the skull, but when working with asymmetry, this would introduce a very obvious source of bias (Bastir et al., 2019; Gunz et al., 2004, 2009). Another method for limiting bias in landmark estimation is using semilandmarks (Gunz et al., 2009), which could be an interesting possibility to study further in the context of cranial FA.

While missing landmarks in archeological samples are almost unavoidable, it is possible to minimize the problem by using an appropriate technique. We found a large proportion of missing data in the surface scanner dataset compared to the 3D digitizer and CT scanner. The missing landmarks were generally found at suture intersections and small foramina on the cranial base. We expect this is due to multiple factors, such as the resolution of the scanner, inconsistent lighting, the color of the crania, shadowing of structures, and differences in the placement of crania relative to the scanner (Brzobohatá et al., 2012; Sholts et al., 2011; Villa et al., 2015; Vukašinović et al., 2010; Zaimovic‐Uzunovic & Lemes, 2010).

Last, we found that there was not always agreement between what landmarks were found using the 3D digitizer and 3D models. One must expect that a 3D digitizer will have the most accurate representation of which landmarks are present, and which are missing. This indicates that 3D models can obscure taphonomy to an extent that nonexistent landmarks are recorded. However, this phenomenon was not within the scope of the study to investigate.

7. THE TECHNIQUES IN PRACTICE

While all three techniques performed well in our tests, we did note some practical differences that could influence data collection.

The 3D digitizer data yielded the lowest measuring error and had the lowest number of missing landmarks. Measuring landmarks directly on the crania has the advantage of tactile assistance, and the 3D digitizer software is simple and easy to learn and use. Other studies have found that successful data collection using a 3D digitizer may depend on the experience of the observer, indicating that some preparatory work may be necessary when using the technique (Shearer et al., 2017; Sholts et al., 2011).

3D models from the CT scanner also yielded low measuring error in our tests, and we found that landmarks on these 3D models were easy to identify in most cases, despite the lack of surface texture. An advantage of this technique compared to the others is the opportunity to measure endocranial landmarks, which could be of interest in some studies. However, CT scanners are not largely available, and they require specific knowledge to be operated. Moreover, scanner type, scanner settings, and thresholding and segmentation of the CT scans can have an influence on the 3D models and thus, the measured shape (Brzobohatá et al., 2012; Colman et al., 2017, 2019; Fajardo et al., 2002).

The surface scanner 3D models yielded higher measuring error than the other techniques, though it is still low enough to be useful when measuring FA. The visualization aspect of keeping surface texture when using a surface scanner is practical for presenting results, but it did not aid in identifying landmarks, as was also found by Sholts et al. (2011). We found that identifying landmarks on surface scanner 3D models is more difficult than with other techniques and learning to use both the tools, and the post‐processing software can be time consuming.

As mentioned in the methods section, we did not apply a smoothing factor to the 3D models from the CT scanner and the surface scanner. Literature on smoothing 3D models of bone advises having high levels control over the choice of smoothing algorithm (Veneziano et al., 2018)—a level of control which our software did not offer. We therefore chose to forego the smoothing step to avoid possibly introducing error. Whether this would affect our measurements is interesting, though it is not within the scope of this study.

8. CONCLUSION

A 3D digitizer, CT scanner, and surface scanner are all appropriate data collection techniques when studying cranial fluctuating asymmetry. For all three techniques, we found intra‐observer measurement error was lower than all other shape variation and thus all three instruments are acceptable to use in studies of fluctuating asymmetry. We found that combining data collected with different techniques is not advisable. When working with 3D models, especially surface scans, it is important to be aware that some structures may be obscured. This can result in landmarks being difficult to identify, and even registration of non‐existent landmarks due to obscured taphonomic damage.

AUTHOR CONTRIBUTIONS

Trine Bottos Olsen: Conceptualization (equal); data curation (lead); formal analysis (lead); investigation (lead); methodology (lead); project administration (lead); visualization (lead); writing – original draft (lead); writing – review and editing (lead). Daniel García‐Martínez: Formal analysis (supporting); methodology (supporting); software (equal); supervision (supporting); validation (supporting); writing – review and editing (supporting). Chiara Villa: Conceptualization (equal); funding acquisition (equal); methodology (lead); project administration (supporting); software (equal); supervision (lead); validation (lead); writing – review and editing (equal).

ACKNOWLEDGMENTS

The authors would like to thank Marie Louise Schjellerup Jørkov and Niels Lynnerup from the Department of Forensic Medicine, University of Copenhagen for guidance and support. We also thank the Lundbeck foundation for the financial support.

Olsen, T. B. , García‐Martínez, D. , & Villa, C. (2023). Testing different 3D techniques using geometric morphometrics: Implications for cranial fluctuating asymmetry in humans. American Journal of Biological Anthropology, 180(1), 224–234. 10.1002/ajpa.24657

Funding information Lundbeck foundation

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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