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Abstract
The ancient polity of Tikal has been extensively studied by archaeologists and soil scientists, but more information is needed to determine the specific subsistence and ancient farming techniques that sustained its inhabitants for more than eight centuries. Recent settlement, soil resource, and vegetation surveys were completed during a re-evaluation of the earthworks of Tikal. The objective of this research was to combine non-parametric multiplicative regression, stable carbon isotopes, soil properties, and remote-sensing data for cost-effective, predictive modelling of ancient maize (Zea mays L.) agricultural areas. The study area is positioned within a 300 km2 region surrounding the Tikal site centre (17° 13′ 19.0″ N, 89° 27′ 25.2″ W). In the study area, ancient maize agriculture was determined using carbon (C) isotopic signatures of C4 vegetation that were incorporated into the soil humin fraction. Probability models predicting C isotopic enrichment were used to outline areas of potential long-term maize agriculture. A binomial model predicted areas mainly along the bajo margins, indicating that these areas may have been used for sustained ancient maize agriculture. Upland areas with shallow Rendolls soil were found to lack strong C4 isotopic signatures. They showed a long-term C3 vegetation signature that could have supported native forest, silvicultural activities, or C3 cropping practices (i.e. forest gardens). The mosaic of C3 and C4 signatures within the study site soils suggests that the Tikal area supported a variety of food production systems.
Acknowledgements
This portion of the re-evaluation of the earthworks at Tikal, Guatemala, project was funded by the National Science Foundation (Grant BCS-0443280) and by Brigham Young University. The Instituto de Antropología e Historia, the Consejo Nacional de Areas Protegidas, and the Parque Nacional Tikal of Guatemala granted permission for this research to take place. This project thanks all those who assisted with the collection, preparation, and analysis of the soil samples. Special thanks go to Ryan Sweetwood, Marco Alvarez, Horacio Martinez, and Kirk Straight for their contributions to this study. Thanks to the anonymous reviewers for their input.