Benchmarking methods and data for the whole-outline geometric morphometric analysis of lithic tools

Full text

Received: 22 March 2023
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Accepted: 6 April 2023
DOI: 10.1002/evan.21981
NEWS
Benchmarking methods and data for the whole‐outline
geometric morphometric analysis of lithic tools
1|INTRODUCTION
Originally developed for the quantitative analysis of organismal
shapes, both two‐dimensional (2D) and 3D geometric morpho-
metric methods (GMMs) have recently gained some prominence
in archaeology for the analysis of stone tools
1–3
—unquestionably
the primary deep‐time data source for the earliest periods of
human cultural evolution.
4
The key strength of GMM rests in its
ability to statistically quantify and hence qualify complex shapes,
whichinturncanbeusedtoinfersocialinteraction,
5
function,
6,7
reduction,
8
as well as to assess classification systems and cultural
relatedness.
9–11
The methodological diversification that has accompanied the rise
in popularity of this particular suite of methods has, however, also
resulted in an increasing lack of comparability and interoperability,
which—ironically—works against the promise of GMM to provide a
tool for comparing artifact shapes that is not sensitive to interanalyst
variation. Standardized protocols, vetted datasets, as well as case‐
transferable and fully reproducible methods do not currently exist,
hampering the full utility of geometric morphometrics as an approach
to comparatively understand human behavior as reflected in these
lithic proxies. Additionally, the emerging issue of methodological
diversity in the geometric morphometric analysis of stone tools is
further compounded by issues related to landmark selection. When
applied to organisms, landmark selection is guided by a priori
knowledge about ontogeny, homology, and function. For stone tools,
however, only very few such evident landmarks suggest themselves.
2
Instead, many studies have used landmarks selected specifically to
highlight particular design features of a given tool class (e.g.,
stemmed points or leaf points). These cannot, however, be easily
compared across tool classes. Other studies have used sets of
equidistant landmarks measured perpendicularly from a given tool's
longest axis to its margins to describe overall shape.
In this context, whole‐outline geometric morphometrics offers an
alternative approach that circumvents landmark selection by describing
the entire outline of the recorded artifact. It is computationally tractable,
readily replicable, and well‐suited for 2D object representations such as
drawings and photographs, many of which exist in excavation reports,
catalogs, finds registers and the published literature at large. Further-
more, emerging approaches in paleobiology now allow such continuous
shape data to be used in phylogenetic applications, opening up the
possibility of effectively combining stone tool geometric morphometrics
with cultural phylogenetics in one workflow.
2|THE “CULTURAL EVOLUTIONARY
TOOLS FOR STONE TOOL SHAPE
ANALYSIS”WORKSHOP
From 26 to 30 September 2022, the authors convened for a
workshop with the title “Cultural evolutionary tools for stone tool
shape analysis: Geometric morphometrics and Bayesian phyloge-
netics”at the Aarhus Institute for Advanced Studies, in Aarhus,
Denmark. This workshop was held under auspices and with
funding from Cultural Evolution Society (https://culturale
volutionsociety.org/) and in direct continuation of the Society's
biannual conference. The aim was to stimulate and foster the use
and application of whole‐outline GMM to questions of cultural
evolution, and to begin assembling a data set of stone tools—
probable projectile points in the first instance, but other classes
of artifacts as well—that may be used to explore these methods
and benchmark interpretations.
The event was conceived in a hybrid format and brought
together 10 participants from 7 different countries (Argentina,
Belgium, Brazil, Canada, Denmark, Germany, and Spain; Figure 1).
This 5‐day meeting had the dual purpose of:
1. introducing the attendees to the application of reproducible
outline based GMM in the programming language R
12
for the
analysis of 2D stone tools; and
2. assembling an initial data set of lithic projectile point shapes from
different times and places that can subsequently be used by the
research community for comparative analyses using these or other
methods.
The specific outline based GMM approach applied in this workshop
follows the protocol recently published by Matzig
13,14
whoalsoledthe
workshop. This approach includes the semi‐automated extraction of
outlines from legacy data, such as drawings or photographs. Vitally, this
protocol relies entirely on open‐source software and is, beyond basic
image preparation, fully replicable and reproducible. Before the start of
the workshop, all participants had prepared their own individual sets of
photographs or drawings related to their expertise alongside associated
metadata such as geographical coordinates and dating.
During the workshop, focus rested initially on how to prepare the
images for the extraction of artifact outlines using the open‐source
imaging software GIMP (http://www.gimp.org)andR.Thereafter,
the outline datasets created in this way and ranging from Late Pleistocene
Evolutionary Anthropology. 2023;1–4. wileyonlinelibrary.com/journal/evan © 2023 Wiley Periodicals LLC.
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1

Europe and Northern Africa to Holocene North and South America
(Figure 2) were analyzed in a multivariate framework closely following the
approach of Matzig et al.
15
The performance of this methodology has
been directly compared to previous published analyses that use both
traditional typo‐technological attributes as well as those using landmark‐
based GMM and was shown to capture salient differences in artifact
forms where they exist.
On the first day of workshop, each participant presented their data
set and shared their assumptions regarding the cultural evolutionary
processes they sought to test; these hypotheses related variously to
chronological and spatial differentiation, or to cultural taxonomic
assessments of the material at hand. Each participant's data set and
research questions differed substantially in their geographical and
chronological scope, and the number of artifacts in each data set also
varied. Some datasets were best suited to analyses regarding their
diachronic, intra‐site patterns of cultural evolution, while for others,
patterns on a continental, and temporally deep scale were most pertinent.
After each participant's presentation of their datasets and objectives, they
completed their metadata sheets with all relevant information.
The second day was dedicated to image preparation to a common
standard so that these could be transferred into the automated outline
extraction protocol. The third and fourth days then focused on the main
analytical pipeline, the first steps of which consist of the quantification of
the extracted outlines using elliptic Fourier analysis
16
and principal
component analysis for initial visualization. Then, the resulting data are
further interrogated using both hierarchical clustering and disparity
analysis. The latter, implemented using the R package dispaRity,
17
represents a multivariate measure of variance within a morphometric
data set that is comparable to the coefficient of variation (CV) for linear
measurements. By quantifying variance, the CV is commonly used in
cultural transmission research to infer the dominant modes of social
learning related to ancient craft production, including stone tools.
18,19
but
see Premo.
20
The disparity measures, together with multivariate analyses
that reveal internal structurewithinthestonetoolshapedataathand,
facilitate interpretations of social transmission and cultural evolution
(Figure 3). On the fifth and last day, participants presented their results
and discussed them in relation to their aprioriexpectations. Furthermore,
all datasets were combined and analyzed together following the exact
same analytical pipeline.
3|WORKSHOP RESULTS AND FUTURE
PERSPECTIVES
With its focus on both conceptual issues as well as data wrangling and
analysis, this workshop was intense, productive, and collaborative.
Participants walked away with a set of tools to reproducibly analyze
2D lithic outlines. By the same token, the heterogeneity of the data
and research questions brought to the table by the participants
afforded the occasion to review the analytical workflow's strengths
and weaknesses. For most datasets, the hierarchical clustering proved
to be a useful tool to visualize the relations between artifact shapes
and compare the efficacy of existing classifications. As all analyses
were performed in the flexible computing environment of R, mapping
or others forms of downstream visualizations can be added in a
straightforward manner, all the while retaining reproducibility.
21
The
final day ended with a stimulating discussion concerning the suitability
of the methods to capture tool shape heterogeneity, and raising vital
issues such as the orientation criteria for asymmetrical tools, such as
backed pieces. Issues of sampling bias and analytical scale were also
raised, with the current workflow being best suited to macroarchaeo-
logical approaches.
FIGURE 1 Participants on the fifth day of workshop discussing
the results of the combined analysis composed of each member's
analytical results.
FIGURE 2 The geographic spread of datasets included in the
workshop. (Araujo/Okumura: Gruta do Marinheiro, Alice Boer
[Brazil]; Barrera: La Esparragosa, Jovades, Niuet, Cova del Retoret,
Montelirio, Los Millares, Arenal de la Costa, Cova dels Diablets, Igay,
Casa de la Viuda, Can Gambus [Spain]; Cardillo: Puna area [Argentina‐
Chile]; Leplongeon: E71K18, E‐78‐3, E‐83‐4, E‐81‐1 [Egypt];
Rabuñal: Cova de Les Borres, Cova Gran de Montserrat [Spain];
Riede: Abri Fuchskirche, Golßen, Kettig, Külte, Mühlheim‐
Dietesheim, Niederbieber, Reichwalde, Rietberg, Rissen,
Rothenkirchen, Rüsselsheim, Urbar, Zigeunerfels [Germany], Rekem
[Belgium]; Wren: Jimmy Camp Creek Park [USA]).
2
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Besides these important findings and the training of the
participants, the data set collated as part of this workshop is now
freely available (https://doi.org/10.5281/zenodo.7757171); relevant
metadata are available as Supporting Information alongside this
report. We hope that future studies will use, update, and add to these
data. In time, such a public repository would be a first step towards
the comparative study of cultural evolution at large geographic and
chronological scales.
The workshop's final discussion revolved around the potential
to couple whole‐outline GMM with the analysis of specific
technological traits, and how to integrate these into emerging
phylogenetic applications. So far, phylogenetic analyses of stone
projectile points have partitioned artifacts using different traits to
capture their key characteristics as well as their shape. Only such
trait‐and landmark‐basedGMMhaveofferedanintegrationwith
phylogenetic methods.
22
Yet, both BEAST
23
as well as RevBayes
24
in principle allow continuous characters to be used, not least
within a Bayesian statistical framework. Thanks to such recent
developments, a fuller integration between these powerful
quantitative methods for stone tool analysis looms on the horizon.
The potential thus emerges that both rich outline shape data can
be combined with technological traits under one analytical
protocol.
ACKNOWLEDGMENTS
The authors gratefully acknowledge the Cultural Evolution Society
for funding this workshop. Additional costs were covered by funds
made available by the Danish Agency for Higher Education and
Science (project COMPARCH, Grant Number 1113‐00015B).
David N. Matzig and Felix Riede's contributions are part of
CLIOARCH, an ERC Consolidator Grant project funded by the
European Research Council (ERC) under the European Union's
Horizon 2020 research and innovation program (Grant Agreement
No. 817564). Mercedes Okumura's research is funded by the São
Paulo Research Foundation (FAPESP, Grant No. 2018/23282‐5).
Astolfo G. M. Araujo's research is funded by the São Paulo
Research Foundation (FAPESP, Grant No. 2019/18664‐9). Renata
P. Araujo's research is funded by a doctoral grant from Brazilian
National Council for Scientific and Technological Development
(CNPq, Grant No. 142353/2019‐1). Alice Leplongeon's research is
funded by a postdoctoral grant from the Research Foundation in
Flanders (FWO #12U9220N). José R. Rabuñal is supported by
Margarita Salas fellowship (MARSALAS21‐22) funded by the
Spanish Ministry of Universities, the European Union‐Next
Generation EU and the University of Alicante. The authors also
thank the Aarhus Institute of Advanced Studies for hosting all
participants during this event.
DATA AVAILABILITY STATEMENT
The authors have nothing to report.
Renata P. Araujo
1
Felix Riede
2
Mercedes Okumura
3
Astolfo G. M. Araujo
1
Alice Leplongeon
4,5
Colin Wren
6
José R. Rabuñal
2,7
Marcelo Cardillo
8
María B. Cruz
9
David N. Matzig
2
1
Museum of Archaeology and Ethnology,
University of São Paulo, São Paulo, Brazil
FIGURE 3 Example results from one data set used during the workshop reported on here. This data set is composed of 70 bifacial projectile
points from south‐eastern Brazilian Holocene sites Alice Boer and Gruta do Marinheiro. The analysis grouped bifacial points in five clusters, with
the mean shapes shown for each cluster.
ARAUJO ET AL.
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2
Department of Archaeology and Heritage Studies,
Aarhus University, Aarhus, Denmark
3
Department of Genetics and Evolutionary Biology,
Biosciences Institute,
University of São Paulo, São Paulo, Brazil
4
Centre for Archaeological Research of Landscapes,
KU Leuven, Leuven, Belgium
5
UMR Histoire Naturelle de l'Homme Préhistorique (HNHP), Muséum
National d'Histoire Naturelle—CNRS—UPVD, Paris, France
6
Department of Anthropology,
University of Colorado Colorado Springs, Colorado Springs,
Colorado, USA
7
Instituto Universitario de Investigación en Arqueología y Patrimonio
Histórico (INAPH),
University of Alicante, Alicante, Spain
8
Consejo de Investigaciones Científicas y Técnicas (CONICET)—Instituto
Multidisciplinario de Historia y Cs. Humanas (IMHICIHU)—Facultad de
Filosofía y Letras de la Universidad de Buenos Aires (FFyL‐UBA), Buenos
Aires, Argentina
9
Departament de Prehistòria, Arqueologia i Història Antiga,
Universitat de València, València, Spain
Correspondence
Renata P. Araujo, Museum of Archaeology and Ethnology, University of São
Paulo, Av. Prof. Almeida Prado, 1466 Cidade Universitária, São Paulo/
SPCEP: 05508‐070, Brazil.
Email: rennyaraujo@usp.br
Felix Riede, Department of Archaeology and Heritage Studies, Aarhus
University, Aarhus, Denmark.
Email: f.riede@cas.au.dk
ORCID
Felix Riede http://orcid.org/0000-0002-4879-7157
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SUPPORTING INFORMATION
Additional supporting information can be found online in the
Supporting Information section at the end of this article.
How to cite this article: Araujo RP, Riede F, Okumura M, et al.
Benchmarking methods and data for the whole‐outline
geometric morphometric analysis of lithic tools. Evolutionary
Anthropology. 2023;1‐4. doi:10.1002/evan.21981
4
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ARAUJO ET AL.
15206505, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/evan.21981 by Universidad De Tarapaca, Wiley Online Library on [04/05/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License