This work presents a methodological alternative to the traditional study of objects arranged inside funerary bundles, with the aim of preserving the integrity of the bundle and optimizing the material resources and costs derived from the storage and unwrapping processes. The research employs computerized tomographic scans to study the metal artifacts chosen to accompany the individuals arranged inside funerary bundles. It is the first systematic characterization of funerary treatment to focus on the presence of metal objects as part of burial offerings and their relationship with the body of the deceased individuals in the Andean area. Analyzing a total of 85 funerary bundles from the central coast of Peru, the study identified 26 bundles, dating to between AD 1100 and 1532, that contained at least one metal object. The objects were recorded to identify their use, decoration, measurements, location within the bundle, and the presence of any other objects associated with the individual, which made it possible to discern metal objects present in bundles corresponding to female, male, and nonadult individuals. The research concludes that the presence of metal artifacts in a funerary bundle is an indicator of elevated status, although the choice of specific artifacts is determined by elements of an individual’s identity.

The logistics, costs, and capacity needed to complete extensive archaeological pedestrian surveys to inventory cultural resources present challenges to public land managers. To address these issues, we developed a workflow combining lidar-derived imagery and deep learning (DL) models tailored for cultural resource management (CRM) programs on public lands. It combines Python scripts that fine-tune models to recognize archaeological features in lidar-derived imagery with denoising QGIS steps that improve the predictions’ performance and applicability. We present this workflow through an applied case study focused on detecting historic agricultural terraces in the Piedmont National Wildlife Refuge, Georgia, USA. For this project, we fine-tuned pretrained U-Net models to teach them to recognize agricultural terraces in imagery, identified the parameter settings that led to the highest recall for detecting terraces, and used those settings to train models on incremental dataset sizes, which allowed us to identify the minimum training size necessary to obtain satisfying models. Results present effective models that can detect most terraces even when trained on small datasets. This study provides a robust methodology that requires basic proficiencies in Python coding but expands DL applications in federal CRM by advancing methods in lidar and machine learning for archaeological inventorying, monitoring, and preservation.

This study from the Luoxiao Mountains, southeastern China, combines historical information with paleoecological data from two wetlands, yielding a detailed reconstruction of landscape changes over recent centuries. The historical record suggests that people first settled in the region in the late Tang dynasty (618 to 907 CE), and wetland sediments show an increase in charcoal from about this time. During the Qing dynasty in the mid-seventeenth century, a temple complex and a Tea and Salt trade road were constructed near the study sites. Greater impacts are recorded in the wetland closer to the temple site. In the last few hundred years, pollen data show a regional decline of forest cover and the expansion of open vegetation as nearby lowland areas were settled and cleared for agriculture. Proxies for erosion show human impacts in the vicinity of the wetlands. Changes in charcoal inputs reflect regional fire activity, with elevated values around 1500 CE, low values during the Qing dynasty, and a subsequent peak during the twentieth century.

Measurements of the radiocarbon (14C) content of subannual wood cellulose samples over the 1963 bomb spike have revealed an apparent delay between the increase in atmospheric radiocarbon content and that of wood cellulose. This delay is apparent in both coniferous and deciduous tree species and is of a magnitude of approximately 4 weeks. The delay in wood cellulose 14C change as measured in a Sitka spruce from Washington state, USA, was previously used to estimate the relative influence of tree physiological effects contra environmental effects. We repeated the measurements with the increased measurement precision of today’s AMS systems and compare the new results to the ones of a Scots pine tree from Trondheim, central Norway and a white oak from Oregon state, USA. The results challenge the assumption that the 14C tree ring records directly show the atmospheric 14C concentration of a homogeneous, zonally well-mixed atmosphere. Instead, the apparent 1963 delay reflects local influences of the ecosystem and tree physiology. The 1963/1964 data allows for exploratory modeling of the effects of biospheric decay CO2 and local environmental influences assuming the absence of stored photosynthates from the previous year. Compared to the 10–30% contribution from biospheric CO2, the effects of delayed incorporation of carbon into the wood cellulose and the effect of stored photosynthate are small in the conifers. Highly detailed 14C records of stem cellulose can, in combination with stable isotope studies, contribute to our understanding of variability of the local carbon cycle, climate, and the environment.