About

Martha Tappen is an Associate Professor in Biological Anthropology at the University of Minnesota's Department of Anthropology. Her research focuses on human evolution and adaptations, with particular interest in out of Africa migrations, paleoenvironments, stone age archaeology, zooarchaeology, and taphonomy. She contributes to understanding human biological and cultural evolution through her work in these areas, advancing knowledge of our species' origins and adaptations.

Research topics

• Computer Science
• Geography
• Archaeology
• Geology
• Ecology
• Optics
• Biology
• Physics
• Paleontology

Selected publications

• Using machine learning on new feature sets extracted from three-dimensional models of broken animal bones to classify fragments according to break agent

  Journal of Human Evolution · 2024-02-01 · 5 citations

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• An ancient cranium from Dmanisi: Evidence for interpersonal violence, disease, and possible predation by carnivores on Early Pleistocene Homo

  Journal of Human Evolution · 2022-03-31 · 11 citations

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• Using machine learning on new feature sets extracted from 3D models of broken animal bones to classify fragments according to break agent

  arXiv (Cornell University) · 2022-05-20 · 4 citations

  preprintOpen access

  Distinguishing agents of bone modification at paleoanthropological sites is at the root of much of the research directed at understanding early hominin exploitation of large animal resources and the effects those subsistence behaviors had on early hominin evolution. However, current methods, particularly in the area of fracture pattern analysis as a signal of marrow exploitation, have failed to overcome equifinality. Furthermore, researchers debate the replicability and validity of current and emerging methods for analyzing bone modifications. Here we present a new approach to fracture pattern analysis aimed at distinguishing bone fragments resulting from hominin bone breakage and those produced by carnivores. This new method uses 3D models of fragmentary bone to extract a much richer dataset that is more transparent and replicable than feature sets previously used in fracture pattern analysis. Supervised machine learning algorithms are properly used to classify bone fragments according to agent of breakage with average mean accuracy of 77% across tests.

  PublisherOA PDFDOI

• Life and death at Dmanisi, Georgia: Taphonomic signals from the fossil mammals

  Journal of Human Evolution · 2022-09-15 · 24 citations

  article1st authorCorresponding
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• Early Pleistocene stratigraphy, sedimentary environments, and formation contexts at Dmanisi in the Georgian Caucasus

  Journal of Human Evolution · 2022-09-15 · 6 citations

  articleOpen access
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• The virtual goniometer: demonstrating a new method for measuring angles on archaeological materials using fragmentary bone

  Archaeological and Anthropological Sciences · 2021 · 21 citations

  • Computer Science
  • Computer Science
  • Optics
  PublisherDOI

• Archaeology and formation processes in the M6 Block at Dmanisi, Georgia

  2020-01-01 · 1 citations

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• Spatial patterning of the archaeological and paleontological assemblage at Dmanisi, Georgia: An analysis of site formation and carnivore-hominin interaction in Block 2

  Journal of Human Evolution · 2020 · 23 citations

  • Geography
  • Paleontology
  • Geology
  PublisherDOI

• The Virtual Goniometer: A new method for measuring angles on 3D models of fragmentary bone and lithics

  arXiv (Cornell University) · 2020-11-10 · 1 citations

  preprintOpen access

  The contact goniometer is a commonly used tool in lithic and zooarchaeological analysis, despite suffering from a number of shortcomings due to the physical interaction between the measuring implement, the object being measured, and the individual taking the measurements. However, lacking a simple and efficient alternative, researchers in a variety of fields continue to use the contact goniometer to this day. In this paper, we present a new goniometric method that we call the virtual goniometer, which takes angle measurements virtually on a 3D model of an object. The virtual goniometer allows for rapid data collection, and for the measurement of many angles that cannot be physically accessed by a manual goniometer. We compare the intra-observer variability of the manual and virtual goniometers, and find that the virtual goniometer is far more consistent and reliable. Furthermore, the virtual goniometer allows for precise replication of angle measurements, even among multiple users, which is important for reproducibility of goniometric-based research. The virtual goniometer is available as a plug-in in the open source mesh processing packages Meshlab and Blender, making it easily accessible to researchers exploring the potential for goniometry to improve archaeological methods and address anthropological questions.

  PublisherOA PDFDOI

• Differentiable Geometric Invariants for Bone Fragment Refitting

  2019-01-01

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Recent grants

• Analysis of the Vertebrate Taphonomy at Dmanisi, Georgia, and Its Bearing on Out of Africa I

  NSF · $110k · 2010–2014

Frequent coauthors

• David Lordkipanidze

  Georgian National Museum

  21 shared

• Reed Coil

  Nazarbayev University

  20 shared

• Reid Ferring

  University of North Texas

  19 shared

• Katrina Yezzi-Woodley

  17 shared

• Peter J. Olver

  15 shared

• Jeff Calder

  University of Minnesota

  15 shared

• Maia Bukhsianidze

  11 shared

• Jordi Agustı́

  Universidad Rovira i Virgili

  11 shared

Awards & honors

• McKnight Land-Grant Professorship, University of Minnesota (…
• Fulbright Senior Scholar (2003)
• L.S.B. Leakey Foundation (2002)
• National Science Foundation: "Stratigraphy, Archaeology, Tap…
• NSF: Analysis of the Vertebrate Taphonomy at Dmanisi, Georgi…