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North American regionalization scheme employed in this investigation to test the effectiveness of alternative data and data-analysis methods at recovering 2D geographic shape-group distinctions within the sample of fluted projectile points. Orange, Eastern Rocky Mountain Front and Northern Plains (ERMF & NP); Red, Southwest; Green, Southern High Plains; Blue, Southeast; Black, Northeast; Cyan, Mid-Continent East; Purple, Northern Great Plains.
Source publication
Archaeologists often wish to distinguish between groups of cultural artifacts using information collected from descriptions or measurements of their morphological forms. Morphometric methods have played an increasingly large role in such quantitative assessments. However, standard approaches to morphometric analyses are often poorly suited to many...
Contexts in source publication
Context 1
... the broad dis- persal of the specimens used in this investigation, state-level data were organized into a categorization scheme that represents an extension of the six-re- gion scheme devised by Smith et al. (2015), with the addition of a seventh region (Northern Great Plains) to cover sites located in the states of Iowa, Wisconsin and Minnesota (Fig. 5). Since the pur- pose of this investigation lies primarily in the area of methods evaluation, no special claim is made for the appropriateness of this particular regional cat- egorization scheme other than to note it is broadly consistent with the schemes employed by previous ...
Context 2
... approach to the morphometric investigation of archaeological projectile points involves the collection of a se- quence of quasi-equally spaced semilandmark point coordinate locations around the artifact's periphery beginning at a common location (usually the point tip, e.g., Smith et al., 2015). In some cases (e.g., Buchanan et al., 2011; see Fig. 5B) investigators have modified this sampling scheme to take ad- vantage of the placement of landmarks at the two basal corners (= maxima of curvature, see Fig. 1) to improve the matching of semilandmark points across the entire dataset. This approach to the char- acterization of artifact outlines is essentially identical to that proposed ...
Citations
... Compositional data can be further divided between object composition (e.g., elemental or isotopic composition) and assemblage composition (counts of particular types of artifacts from a site, grave, level, or grid square). Quantitative typological analysis in archaeology is a method of formal typological classification using an explicit quantitative approach to investigate patterns of cultural change [31][32][33]. ...
The relationship between social factors and the formation of exported mug designs is blurred. This study addresses how they influence material design. Based on a quantitative typological analysis, this paper interprets the cultural relationships underlying the evolution of mug designs exported during the Ming and Qing dynasties. The study reveals: (1) the typology and handle styles of mugs can be categorized into six types, with a predominance of Cylindrical bodies and Ear-shaped handles. Notably, artistic emphasis is concentrated on Bulbous cup bodies and Tail outward curved handles; (2) the design of mugs in the eighteenth century exhibited diversity, morphological similarities, and feature continuity, evolving from representational (Ming Dynasty) to abstract and then to minimalistic styles (Qing Dynasty), particularly evident in the three-dimensional modeling of handles for ergonomic comfort (transitioning from a singular Outward curved form to Ear-shaped form, and then to Entwined branch form); (3) the body shape of mugs transformed from Arc-barrel bodies to Bulbous bodies (focusing on heat retention), and eventually to Cylindrical bodies (prioritizing heat retention, practicality, and cost-efficiency).; (4) the volume of mugs steadily increased from the early eighteenth century, generally classified into large volume (≥ 500 ml), medium volume (200-499 ml), and small volume (< 200 ml); (5) considering the extreme range of volume (11090 ml) and height (25 cm), it indicates that these two variables do not necessarily have a direct positive correlation; (6) the low center of gravity in handle design (average width of 3.4 cm against an average height of 9.9 cm for the body) reflects considerations for the distribution of liquid weight and operational convenience. The volume of sample-10 (11127 ml) notably exceeds the typical range for tea-drinking utensils, revealing the mechanism of wealth and status symbolism. Market demand orientation emerged as the primary driver for the evolution of export mug designs during the Ming and Qing Dynasties, with adjustments in the merchant system playing a secondary role. The transformation in mug design reflects the interplay of multiple factors, with the trend toward minimalist design being a response to market demands.
... No comparison was made between projectile point shapes (i.e., triangular versus side-notched) using landmark analysis, as initial experiments determined that it was best to use different landmark procedures for the different shapes. Perhaps machine learning may solve this problem (see Castillo Flores et al., 2019;MacLeod, 2018;Nash and Prewitt, 2016). Triangular projectile points require a different approach than side-notched points, and even side-notched and cornernotched/stemmed points require different procedures. ...
... Geometric morphometric (GM) methods have been applied in a wide range of archaeological studies since the beginning of the 21 st century (Lycett et al., 2006). With regard to projectile points, primarily lithics have been analyzed with the aim of retrieving either functional information or to identify geographical/chronological groups (e.g., Charlin and González-José, 2012;de Azevedo et al., 2014;Chacón et al., 2016;Fox, 2015;Serwatka and Riede, 2016;MacLeod, 2018;Serwatka, 2018;Buchanan et al., 2020;Matzig et al., 2021). In some cases, canonical variate analysis (CVA) was used to study patterns of between-groups lithic point morphological variation (Thulman, 2012;Smeyatsky, 2014;Eren et al., 2015;MacLeod, 2018;Smith et al., 2021;Smith and Reid, 2022). ...
... With regard to projectile points, primarily lithics have been analyzed with the aim of retrieving either functional information or to identify geographical/chronological groups (e.g., Charlin and González-José, 2012;de Azevedo et al., 2014;Chacón et al., 2016;Fox, 2015;Serwatka and Riede, 2016;MacLeod, 2018;Serwatka, 2018;Buchanan et al., 2020;Matzig et al., 2021). In some cases, canonical variate analysis (CVA) was used to study patterns of between-groups lithic point morphological variation (Thulman, 2012;Smeyatsky, 2014;Eren et al., 2015;MacLeod, 2018;Smith et al., 2021;Smith and Reid, 2022). ...
... Recent developments in methods of archaeological group classification (Sholts et al., 2017;MacLeod, 2018;Salili-James et al., 2022;) show that investigations of normativity, (i.e., shape conventions and the identification of specific shape variables) are possible under a variety of analytic approaches. In this study, we employed a specific exploratory, GM-based approach to the quantitative assessment of complex weapon points in terms of overall artifact structures and one of its sub-structures to identify both functional and non-functional information. ...
Mesolithic harpoons are structurally complex weapon points and significant sources of archaeological information. Nonetheless, separating different types of information inherent in harpoon point shapes (e.g., aspects relating to mechanical performance, personal or group craft variation and chronology) using descriptive approaches is difficult. In this study, we employed an exploratory geometric morphometric approach to the analysis of 28 Mesolithic harpoon points, and 144 undated harpoon points from the circum-Baltic Sea area in order to retrieve both functional and spatiotemporal information. By analyzing harpoon structure statistically through (i.) a shape proxy (harpoon silhouette) and (ii.) harpoon sub-structures (barbs), we have been able to reveal information related to both variation in shape convention and functional constraints. Barb shape results revealed statistically significant chronological and geographical groupings with spatiotemporal barb-shape trajectories made visible and objective evaluation of how barb-shape conventions impacted functional variation. In addition, harpoon silhouette shape distributions were shown to have potential as sources for robust artifact classifications in relation to functional constraints and raw-material engagement. These results suggest that morphometric approaches similar to the ones we have employed offer promising ways of addressing specific archaeological questions in the context of harpoon point shapes and, more generally, other complex weapon point forms.
... Data on individual points, including site name and type, haft-length, haft-width, blade-length, maximum-length, heuristic haft-size class, and the ratio of haft-length to maximum-length are in S1 Table. Fluted points, including Clovis points, have been analyzed with LGM frequently, although techniques vary [55,56,81,93,94,96,98,99,113]. Here we focus on two approaches, ours and those used by Buchanan and colleagues in several publications. ...
Landmark-based geometric morphometrics (LGM) is most often used in archaeology to characterize and differentiate groups of artifacts, but it can be used for much more. We demonstrate LGM's power to uncover new insights by exploring stone-tool allometry, modularity, and integration using a sample of 100 western North American Clovis points. Here, allometry concerns how stone tools change in shape as their size changes through their use-lives, and modularity and integration concern how the constituent parts of a tool work together. We show that Clovis points are surprisingly complex tools. When their blades and hafts are defined technologically, rather than arbitrarily, they unambiguously exhibit allometry, and their hafts and blades are modular and highly integrated. We use these analyses to further explore questions about Clovis points, including the differences between cache and non-cache points. Finally, we use heuristic haft-size categories to examine functional constraints on the shape and size of hafts and blades. This work illustrates the importance of using accurate measurements of point components rather than estimates or proxies, which can lead to unfounded inferences. These analytical approaches and accompanying R code are easily transferable to other research questions of stone-tool use.
... Canonical variates analysis (CVA) has been a popular DR tool used in the morphological analysis of lithic artefacts (e.g. Graham and Roe 1970;Webnan-Smith 1989;Costa et al. 2010;MacLeod 2018;Caruana 2020;Scerri et al. 2021;Méndez-Quintas 2022; inter alia), with some authors even stating CVA to be a more 'suitable and accurate' analysis, while PCA is 'incomplete and impractical' (Méndez-Quintas 2022). Unfortunately, however, there is very little mathematical and theoretical evidence to support this. ...
Morphological analysis is a critical component in the study of archaeological artefacts. Handaxes are some of the most iconic tools of the Palaeolithic era, and the study of their morphology can provide important insights into their creation, use, and development throughout early human evolution. While many studies exist for the study of handaxe morphology, little consensus exists as to what methods should be applied. Here the most reliable means of analysing handaxe morphology are explored; based on the use of simulated 2D toy datasets, we compare two widely used methods, geometric morphometrics, and elliptic Fourier analysis, and find that the latter is more reliable and powerful for differentiating between different handaxe groups. As a product of these analyses, a debate is proposed regarding the most reliable methods for studying handaxe morphology. In addition, the present study cautions against the use of certain statistical tests, such as canonical variates analysis, when sample sizes are too small. From this perspective, the research conducted highlights the importance of selecting the most appropriate methods for certain types of morphological analyses for lithic technology.
... Our protocol has consisted of the following steps, each of them applied separately for the geometric microliths of phase A and phase B. First, we have extracted the principal components from the outlines of the artefacts. Despite the recurrent use of PCAs to understand grouping patterns in scientific literature, it has been widely and convincingly discussed how this is not the appropriate method to understand sample clustering (Jombart et al., 2010;MacLeod, 2018). Essentially, PCA is just a method for dimensionality reduction. ...
... Middle: Solutrean points from Volgu cash (France) dated between 22,000 and 17,000 years ago(Kilby, 2019). Bottom: Clovis points from Virginia, Arizona and Wyoming (North America) dated between 13,500 and 12,900 years ago(MacLeod, 2018). ...
... The use of machine learning methods has gained some interest in lithic tool research, too, although the approaches taken do not necessarily fall under deep learning. Naïve Bayes was used in classifying Paleoindian fluted projectile points from their landmark-semilandmark and image pixel data (MacLeod, 2018), and both Nash and Prewitt (2016) and Grove and Blinkhorn (2020) used ANNs to create lithic classifiers based on typological information. In an attempt to create a more general archaeological object identifier, Resler et al. (2021) used pretrained deep learning CNNs in training a classifier capable of predicting the time period of the object from an image and created a query tool to detect culturally similar archaeological communities. ...
Archaeological object identifications have been traditionally undertaken through a comparative methodology where each artefact is identified through a subjective, interpretative act by a professional. Regarding palaeoenvironmental remains, this comparative methodology is given boundaries by using reference materials and codified sets of rules, but subjectivity is nevertheless present. The problem with this traditional archaeological methodology is that higher level of subjectivity in the identification of artefacts leads to inaccuracies, which then increases the potential for Type I and Type II errors in the testing of hypotheses. Reducing the subjectivity of archaeological identifications would improve the statistical power of archaeological analyses, which would subsequently lead to more impactful research. In this thesis, it is shown that the level of subjectivity in palaeoenvironmental research can be reduced by applying deep learning convolutional neural networks within an image recognition framework. The primary aim of the presented research is therefore to further the on-going paradigm shift in archaeology towards model-based object identifications, particularly within the realm of palaeoenvironmental remains. Although this thesis focuses on the identification of pollen grains and animal bones, with the latter being restricted to the astragalus of sheep and goats, there are wider implications for archaeology as these methods can easily be extended beyond pollen and animal remains. The previously published POLEN23E dataset is used as the pilot study of applying deep learning in pollen grain classification. In contrast, an image dataset of modern bones was compiled for the classification of sheep and goat astragali due to a complete lack of available bone image datasets and a double blind study with inexperienced and experienced zooarchaeologists was performed to have a benchmark to which image recognition models can be compared. In both classification tasks, the presented models outperform all previous formal modelling methods and only the best human analysts match the performance of the deep learning model in the sheep and goat astragalus separation task. Throughout the thesis, there is a specific focus on increasing trust in the models through the visualization of the models’ decision making and avenues of improvements to Grad-CAM are explored. This thesis makes an explicit case for the phasing out of the comparative methods in favour of a formal modelling framework within archaeology, especially in palaeoenvironmental object identification.
... It is, of course, always tempting to employ as much data as can be collected in attempts to resolve outstanding issues of controversy and/or develop comprehensive summaries of morphological trends. However, the wellknown "curse of dimensionality" often renders datasets, in which the number of variables greatly exceeds the number of samples or specimens available, difficult to analyze (see [43]), especially when the task is to achieve reliable between-groups discrimination ( [44], but see [45,46]). Related to this question are perennial concerns regarding whether it is better to focus analysis on the locations of landmark configurations that represent aspects of the wing's internal morphology, or the geometry of the wing outline. Then there is the question what to do about the coloration patterns that are an intrinsic part of the morphology of many insect wings and may have significant species characterization/identification and/or behavioral roles, but that resist attempts to characterize them consistently or accurately across even modestly sized samples via reference landmark or semilandmark data (Additional file 1). ...
... Some might be tempted to interpret this improvement to reflect the well-know tendency for high-dimensional datasets to yield artificially large apparent group distinctions when subjected to linear-discriminant analysis owing to the sparse distribution of data points in high-dimensional mathematical spaces (see [44,54]). If this was the correct interpretation of our results this should be revealed by a bootstrap analysis of between-group separation relative to within-group dispersion via any of a number of statistical test indices (see [45,46]). However, when this experiment was carried out using the well-known Hotelling's T 2 test, for each of the four comparisons shown in Fig. 8, observed values of the T 2 statistic fell well beyond the ranges of T 2 -value distributions obtained from 1000 random permutations of the data (see Additional file 2: Geometric Morphometrics Frequency histograms for Trithemis forewing and hindwing shape distinctions for species preferring different landscape and water body habitats based on the direct assessment of wing-image datasets. ...
... From the results we have presented above it is unquestionably clear that the classic GM approach, when applied to the analysis of Trithemis wing-shape data, was the one that performed least well in finding, summarizing, and testing sets of characteristics that could be used to answer the question of whether shape variance was distributed among Trithemis landscape and water-body ecological guilds in a continuous or disjunct manner. What is also clear is that this comparative finding is neither an unusual, nor an exceptional, result (e.g., [45,46,59,[65][66][67][68][69][70][71][72][73][74]). ...
Background
The phylogenetic ecology of the Afro-Asian dragonfly genus Trithemis has been investigated previously by Damm et al. (in Mol Phylogenet Evol 54:870–882, 2010) and wing ecomorphology by Outomuro et al. (in J Evol Biol 26:1866–1874, 2013). However, the latter investigation employed a somewhat coarse sampling of forewing and hindwing outlines and reported results that were at odds in some ways with expectations given the mapping of landscape and water-body preference over the Trithemis cladogram produced by Damm et al. (in Mol Phylogenet Evol 54:870–882, 2010). To further explore the link between species-specific wing shape variation and habitat we studied a new sample of 27 Trithemis species employing a more robust statistical test for phylogenetic covariation, more comprehensive representations of Trithemis wing morphology and a wider range of morphometric data-analysis procedures.
Results
Contrary to the Outomuro et al. (in J Evol Biol 26:1866–1874, 2013) report, our results indicate that no statistically significant pattern of phylogenetic covariation exists in our Trithemis forewing and hindwing data and that both male and female wing datasets exhibit substantial shape differences between species that inhabit open and forested landscapes and species that hunt over temporary/standing or running water bodies. Among the morphometric analyses performed, landmark data and geometric morphometric data-analysis methods yielded the worst performance in identifying ecomorphometric shape distinctions between Trithemis habitat guilds. Direct analysis of wing images using an embedded convolution (deep learning) neural network delivered the best performance. Bootstrap and jackknife tests of group separations and discriminant-function stability confirm that our results are not artifacts of overtrained discriminant systems or the “curse of dimensionality” despite the modest size of our sample.
Conclusion
Our results suggest that Trithemis wing morphology reflects the environment’s “push” to a much greater extent than phylogeny’s “pull”. In addition, they indicate that close attention should be paid to the manner in which morphologies are sampled for morphometric analysis and, if no prior information is available to guide sampling strategy, the sample that most comprehensively represents the morphologies of interest should be obtained. In many cases this will be digital images (2D) or scans (3D) of the entire morphology or morphological feature rather than sparse sets of landmark/semilandmark point locations.
... Geometric morphometric methods treat outline shapes as sets of points, assuming that the points identified on each shape are in correspondence and thus parametrize the shape [8,9,16]. One common approach is to treat the coordinates of each point as a statistical random variable in Euclidean space, assuming that the variations are small enough to be approximated linearly. ...
... Geometric morphometric methods have been used in fields such as evolutionary biology [8,28], medical image analysis [29,30] and archaeology [16,31,32] for many years. Diffeomorphic methods, by contrast, have been applied only recently (e.g. ...
We often wish to classify objects by their shapes. Indeed, the study of shapes is an important part of many scientific fields, such as evolutionary biology, structural biology, image processing and archaeology. However, mathematical shape spaces are rather complicated and nonlinear. The most widely used methods of shape analysis, geometric morphometrics, treat the shapes as sets of points. Diffeomorphic methods consider the underlying curve rather than points, but have rarely been applied to real-world problems. Using a machine classifier, we tested the ability of several of these methods to describe and classify the shapes of a variety of organic and man-made objects. We find that one method, based on square-root velocity functions (SRVFs), outperforms all others, including a standard geometric morphometric method (eigenshapes), and that it is also superior to human experts using shape alone. When the SRVF approach is constrained to take account of homologous landmarks it can accurately classify objects of very different shapes. The SRVF method identifies a shortest path between shapes, and we show that this can be used to estimate the shapes of intermediate steps in evolutionary series. Diffeomorphic shape analysis methods, we conclude, now provide practical and effective solutions to many shape description and classification problems in the natural and human sciences.
