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Anthropic Activity Markers 2.0: A Shift Towards Compositional Data Analysis.

Abel Ruiz-Giralt¹, Stefano Biagetti^1,2,3, Carla Lancelotti^1,2

¹Culture, Archaeology and Socio-Ecological Dynamics Research Group, Universitat Pompeu Fabra, C. Ramon Trias Fargas 25-27, 08005 Barcelona, Spain.

Journal of Archaeological Method and Theory

|June 12, 2026

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Anthropic Activity Markers (AAMs) 2.0 offers a new statistical framework for analyzing human activities in sediments. This revised approach uses compositional data analysis and geostatistics for more reliable archaeological inference.

Area of Science:

Archaeological science
Geochemistry
Data analysis

Background:

Anthropic Activity Markers (AAMs) were developed to infer human activities from sediment signatures.
Previous methods using absolute geochemical values introduced statistical limitations.
A need existed for a more robust analytical framework for archaeological inference.

Purpose of the Study:

To introduce AAMs 2.0, a revised analytical framework for inferring human activities.
To integrate compositional data analysis (CoDA) and geostatistics for improved statistical rigor.
To demonstrate the application of CoDA principles to archaeological datasets.

Main Methods:

Theoretical framework development for CoDA and geostatistics in AAMs.
Application of CoDA principles to analyze geochemical signatures in sediments.

Keywords:

Anthropic Activity Markers Compositional Data Analysis Ethnoarchaeology Geostatistics Soil Geochemistry

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Revisiting an ethnoarchaeological dataset (Rondelli et al., 2014) as a test case.

Main Results:

Demonstration that treating geochemical concentrations as absolute values creates statistical artifacts.
Identification of meaningful compositional relationships using CoDA, independent of heuristic combinations.
Statistically robust results obtained through the revised framework.

Conclusions:

AAMs 2.0 provides a more rigorous foundation for archaeological inference.
Activity markers are reframed as relational, ratio-based spatial signals.
The integrated CoDA and geostatistics approach enhances analytical reliability.