Perdiz arrow points from Caddo burial contexts aid in defining discrete behavioral regions
Robert Z. Selden, Jr. 
John E. Dockall
“The question of questions for mankind—the problem which underlies all others, and is more deeply interesting than any other—is the ascertainment of the place which man occupies in nature, and of his relation to the universe of things.” –H. Thomas Henry Huxley, Man’s Place in Nature
Basis of inquiry
Recent research into Caddo bottle and biface morphology yielded evidence for two distinct behavioral regions, across which material culture from Caddo burials expresses significant morphological differences. This study asks whether Perdiz arrow points from Caddo burials differ across the same geography, which would extend the pattern of morphological differences to a third category of Caddo material culture. Perdiz arrow points collected from the geographies of the northern and southern Caddo behavioral regions were employed to test the hypothesis that morphological attributes differ, and are predictable, between the two communities. The analysis of linear metrics indicated a significant difference in morphology by behavioral region. Using the linear metrics combined with the tools of machine learning, a predictive model—support vector machine—was designed to assess the degree to which community differences could be predicted, achieving a receiver operator curve score of 97 percent, and an accuracy score of 94 percent. The subsequent landmark geometric morphometric analysis identified significant differences in Perdiz arrow point shape and size between the behavioral regions—one characterized by a comparatively smaller blade and larger stem (north), and the other by a comparatively larger blade and smaller stem (south)—coupled with significant results for modularity and morphological integration. These findings build directly upon recent investigations that posited two discrete Caddo behavioral regions defined on the basis of discernible morphological differences, which is expanded here to include a third category of Caddo material culture.
Keywords
American Southeast, Caddo, NAGPRA, computational archaeology, archaeoinformatics, machine learning, museum studies, digital humanities, non-Western art history, STEM, STEAM
Primary findings
Linear morphometrics
Sixty seven whole/intact Perdiz arrow points recovered from Caddo burial contexts in Camp, Nacogdoches, and Shelby counties comprise the basis of this study. A standard suite of linear metrics were collected for each specimen, including maximum length, width, thickness, stem length, and stem width. Following collection, data were imported to R (Team 2021), where boxplots were produced, along with a principal components analysis (PCA), followed by a permutational multivariate analysis of variance (perMANOVA) to test whether the morphology of Perdiz arrow points differs between the behavioral regions.
Boxplots illustrate the distribution and mean for each of the five linear variables, and the PCA illustrates over 92 percent of the variation in the sample among PC1 (84.65 percent) and PC2 (11.71 percent). The perMANOVA demonstrated that linear metrics for Perdiz arrow points differ significantly by behavioral region (permutations = 10,000; Rsq = 0.29485; Pr(>F) = 1e-04).
- Perdiz arrow point morphology differs significantly by Caddo community
Machine learning
A support vector machine is a supervised machine learning model regularly used in classifying archaeological materials (Bhatt and Patalia 2017; Monna et al. 2020; Febriawan et al. 2020; Kadhim and Abed 2021; Zhang 2013; Elliot et al. 2021), which has utility in comparing and classifying datasets aggregated from digital repositories, comparative collections, open access reports, as well as other digital assets. For this effort, linear data were imported and modeled using the scikit-learn package in Python (Pedregosa et al. 2011; Buitinck et al. 2013), and subsequently split into training (75 percent) and testing (25 percent) subsets. A standard scaler was used to decrease the sensitivity of the algorithm to outliers by standardizing features, and a nested cross validation of the training set was used to achieve unbiased estimates of model performance.
- Model achieved a cross validation score of 86 percent
- Model achieved a receiver operator curve score of 97 percent
- Model achieved an accuracy score of 94 percent
Geometric morphometrics
Each of the arrow points was imaged using a flatbed scanner (HP Scanjet G4050) at 600 dpi. The landmarking protocol developed for this study included six landmarks and 24 equidistant semilandmarks to characterize Perdiz arrow point shape, and were applied using the StereoMorph package in R (Olsen and Westneat 2015). The characteristic points and tangents used in the landmarking protocol were inspired by the work of Birkhoff (Birkhoff 1933).
Generalised Procrustes Analysis
Landmarks were aligned to a global coordinate system (Kendall 1981, 1984; Slice 2001), achieved through generalized Procrustes superimposition (Rohlf and Slice 1990), performed in R (Team 2021) using the geomorph package (Adams and Otarola-Castillo 2013; Baken et al. 2021). Procrustes superimposition translates, scales, and rotates the coordinate data allowing for comparisons among objects (Gower 1975; Rohlf and Slice 1990). The geomorph package uses a partial Procrustes superimposition that projects the aligned specimens into tangent space subsequent to alignment in preparation for the use of multivariate methods that assume linear space (Rohlf 1999; Slice 2001).
Principal Components Analysis
Principal components analysis (Jolliffe 2002; Revell 2009) was used to visualize shape variation among the arrow points. Shape changes described by each principal axis are commonly visualized using thin-plate spline warping of a reference image or 3D mesh (Klingenberg 2013; Sherratt et al. 2014).
Procrustes ANOVAs
A residual randomization permutation procedure (RRPP; n = 10,000 permutations) was used for all Procrustes ANOVAs (Adams and Collyer 2015; Collyer and Adams 2018), which has higher statistical power and a greater ability to identify patterns in the data should they be present (Anderson and Ter Braak 2003). To assess whether shape differs by group (region), Procrustes ANOVAs (Goodall 1991) were also run that enlist effect-sizes (z-scores) computed as standard deviates of the generated sampling distributions (Collyer, Sekora, and Adams 2015).
A Procrustes ANOVA was used to test for a difference in Perdiz arrow point (centroid) size by behavioral region (RRPP = 10,000; Rsq = 0.30681; Pr(>F) = 1e-04), followed by a second to test for a difference in arrow point shape (RRPP = 10,000; Rsq = 0.0536; Pr(>F) = 0.0161). While shape and size differ significantly between behavioral regions, the Rsq value for size is just under six times larger than that for shape (smaller in the north; larger in the south), suggesting that between-region differences in Perdiz arrow point size may be more visually apparent than differences in shape. A comparison of mean consensus configurations was used to illustrate shape differences from the northern and southern behavioral regions. Diacritical morphology is characterized by a comparatively smaller blade and larger stem in the north, and by a comparatively larger blade and smaller stem in the south. Further, the angle between the shoulder and base is more acute, with a base that is generally shorter and narrower in the southern behavioral region.
Modularity/Integration
The analysis of modularity compares within-module covariation of landmarks against between-module covariation to quantify the degree of modularity between modules (Adams and Collyer 2019; Adams and Peres-Neto 2016). A pairwise comparison of morphological integration was used to test the strength of integration between blade and basal morphology using a z-score (Bookstein et al. 2003; Collyer, Sekora, and Adams 2015; Adams and Collyer 2016, 2019). Procrustes variance was then used to discriminate between regions and compare the amount of shape variation (morphological disparity) (Zelditch et al. 2004), estimated as Procrustes variance using residuals of linear model fit (Adams et al. 2018).
The analysis of modularity demonstrated that Perdiz arrow point blades and bases are, in fact, modular, while the test for morphological integration was also significant, indicating that the blades and bases of Perdiz arrow points are integrated. These results demonstrate that blade and base shapes for Perdiz arrow points are predictable; a finding that will have utility in subsequent studies of Perdiz arrow point morphology that incorporate fragmentary specimens.
- Perdiz arrow point shape differs significantly by Caddo community
- Perdiz arrow point size differs significantly by Caddo community
- Perdiz arrow points exhibit significant modularity
- Perdiz arrow points exhibit significant blade/base morphological integration
Discussion
The shape boundary empirically delineates two discrete behavioral regions in the ancestral Caddo area. That the Perdiz arrow points recovered from Caddo burials north and south of the shape boundary were found to differ significantly, expands the scope of the behavioral regions to include three classes of artifacts (Caddo bottles, bifaces, and—now—arrow points) (Selden Jr. 2018a, 2018b, 2019, 2021; Selden 2022; Selden Jr., Dockall, and Dubied 2020; Selden Jr., Dockall, and Shafer 2018).
For material culture included in burial contexts, the Caddo were selecting for significant morphological differences in bottles, bifaces, and arrow points recovered from either side of the shape boundary. Results clearly illustrate that morphological differences among Perdiz arrow points found in the northern and southern behavioral regions are predictable, and can be disaggregated using a standard suite of linear metrics regularly collected for cultural resource management endeavors.
This study demonstrated that linear metrics and shape variables collected for Perdiz arrow points support the shape boundary posited in recent social network and geometric morphometric analyses, and determined that those same metrics can be used to predict regional membership. Morphological features that discriminate between Perdiz arrow points recovered from each behavioral region were identified using geometric morphometrics, with substantive differences found to occur in size and basal morphology.
Blade and base shape were found to be both modular and morphologically integrated, suggesting that blade and base shapes are predictable. While evidence from one category—Caddo bottles—supports discussions of Caddo production, the other two—bifaces and arrow points—may articulate with production activities outside of the region by non-Caddo makers. Such production activity is more likely to be localized than exchange systems, thus assumed to leave a clearer signature (Costin 1991).
Acknowledgments
We extend our gratitude to the Caddo Nation of Oklahoma, the Caddo Nation Tribal Council, Tribal Chairman, and Tribal Historic Preservation Office for their continued guidance and support of our work, as well as access to NAGPRA and previously repatriated collections. Thanks also to the Anthropology and Archaeology Laboratory at Stephen F. Austin State University for the requisite permissions and access to the NAGPRA objects from the Washington Square Mound site and Turner collections, and to Tom A. Middlebrook for brokering access to the Perdiz arrow points from burials at the Morse Mound site. We wish to thank Michael J. Shott and Casey Wayne Riggs for their useful comments and constructive criticisms on a presubmission draft, and extend our gratitude to Emma Sherratt, Kersten Bergstrom, Lauren Butaric, Julien Claude, Dean C. Adams, and Michael L. Collyer for their constructive criticisms and suggestions throughout the development of this research program. Additional comments from the editor and three anonymous reviewers aided in further refining the manuscript.
Funding
Components of the analytical workflow were developed and funded by a Preservation Technology and Training grant (P14AP00138) to RZS from the National Center for Preservation Technology and Training, as well as grants to RZS from the Caddo Nation of Oklahoma, National Forests and Grasslands in Texas (15-PA-11081300-033) and the United States Forest Service (20-PA-11081300-074). Additional funding and logistical support was provided by the Heritage Research Center at Stephen F. Austin State University.
Data management
All data and analysis code associated with this project are openly available through the GitHub repository, which is digitally curated on the Open Science Framework (DOI 10.17605/OSF.IO/VZHJR). Images of all Perdiz arrow points used in this study were made available in an open access comparative collection (https://scholarworks.sfasu.edu/ita-perdiz/), with permission from the Caddo Nation of Oklahoma. These supplementary materials include all analysis data and code used in the study, providing a means for others to reproduce (exactly) those results discussed and expounded upon in this article. The replicable nature of this undertaking provides others with the means to critically assess and evaluate the various analytical components of this study (Gray and Marwick 2019; Peng 2011; Gandrud 2014), which is a necessary requirement for the production of reliable knowledge.
Reproducibility projects in psychology and cancer biology are impacting current research practices across all domains. Examples of reproducible research are becoming more abundant in archaeology (Marwick 2016; Ivanovaitė et al. 2020; Selden Jr., Dockall, and Dubied 2020; Selden Jr. et al. 2021; Selden 2022), and the next generation of archaeologists are learning those tools and methods needed to reproduce and/or replicate research results (Marwick et al. 2019). Reproducible and replicable research work flows are often employed at the highest levels of humanities-based inquiries to mitigate concern or doubt regarding proper execution, and is of particular import should the results have—explicitly or implicitly—a major impact on scientific progress (Peels and Bouter 2018).
Colophon
This version of the analysis was generated on 2023-05-06 06:05:19 using the following computational environment and dependencies:
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Head: [9980e6c] 2023-01-16: <edit index>References
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