The causes, consequences, and timing of the rise of moralizing religions in world history have been the focus of intense debate. Progress has been limited by the availability of quantitative data to test competing theories, by divergent ideas regarding both predictor and outcomes variables, and by differences of opinion over methodology. To address all these problems, we utilize Seshat: Global History Databank, a large storehouse of information designed to test theories concerning the evolutionary drivers of social complexity. In addition to the Big Gods hypothesis, which proposes that moralizing religion contributed to the success of increasingly large-scale complex societies, we consider the role of warfare, animal husbandry, and agricultural productivity in the rise of moralizing religions. Using a broad range of new measures of belief in moralizing supernatural punishment, we find strong support for previous research showing that such beliefs did not drive the rise of social complexity. By contrast, our analyses indicate that intergroup warfare, supported by resource availability, played a major role in the evolution of both social complexity and moralizing religions. Thus, the correlation between social complexity and moralizing religion seems to result from shared evolutionary drivers, rather than from direct causal relationships between these two variables.

Recent palaeoenvironmental, historical, and archaeological investigations, primarily consisting of site reconnaissance, in the Shahrizor region within the province of Sulaymaniyah in Iraqi Kurdistan are bringing to light new information on the region's social and socio-ecological development. This paper summarises two seasons of work by researchers from German, British, Dutch, and Iraqi-Kurdish institutions working in the survey region. Palaeoenvironmental data have determined that during the Pleistocene many terraces developed which came to be occupied by a number of the larger tell sites in the Holocene. In the sedimentary record, climatic and anthropogenic patterns are noticeable, and alluviation has affected the recovery of archaeological remains through site burial in places. Historical data show the Shahrizor shifting between periods of independence, either occupied by one regional state or several smaller entities, and periods that saw the plain's incorporation within large empires, often in a border position. New archaeological investigations have provided insight into the importance of the region as a transit centre between Western Iran and northern and southern Mesopotamia, with clear material culture links recovered. Variations between periods' settlement patterns and occupations are also beginning to emerge.

This communication article provides a call for unmanned aerial vehicle (UAV) users in archaeology to make imagery data more publicly available while developing a new application to facilitate the use of a common deep learning algorithm (mask region-based convolutional neural network; Mask R-CNN) for instance segmentation. The intent is to provide specialists with a GUI-based tool that can apply annotation used for training for neural network models, enable training and development of segmentation models, and allow classification of imagery data to facilitate auto-discovery of features. The tool is generic and can be used for a variety of settings, although the tool was tested using datasets from the United Arab Emirates (UAE), Oman, Iran, Iraq, and Jordan. Current outputs suggest that trained data are able to help identify ruined structures, that is, structures such as burials, exposed building ruins, and other surface features that are in some degraded state. Additionally, qanat(s), or ancient underground channels having surface access holes, and mounded sites, which have distinctive hill-shaped features, are also identified. Other classes are also possible, and the tool helps users make their own training-based approach and feature identification classes. To improve accuracy, we strongly urge greater publication of UAV imagery data by projects using open journal publications and public repositories. This is something done in other fields with UAV data and is now needed in heritage and archaeology. Our tool is provided as part of the outputs given.

Recent fieldwork and archival sedimentary materials from southern Iraq have revealed new insights into the environment that shaped southern Mesopotamia from the pre-Ubaid (early Holocene) until the early Islamic period. These data have been combined with northern Iraqi speleothem, or stalagmite, data that have revealed relevant palaeoclimate information. The new results are investigated in light of textual sources and satellite remote sensing work. It is evident that areas south of Baghdad, and to the region of Uruk, were already potentially habitable between the eleventh and early eighth millennia B.C., suggesting there were settlements in southern Iraq prior to the Ubaid. Date palms, the earliest recorded for Iraq, are evident before 10,000 B.C., and oak trees are evident south of Baghdad in the early Holocene but disappeared after the mid-sixth millennium B.C. New climate results suggest increased aridity after the end of the fourth millennium B.C. For the third millennium B.C. to first millennium A.D., a negative relationship between grain and date palm cultivation in Nippur is evident, suggesting shifting cultivation emphasising one of these crops at any given time in parts of the city. The Shatt en-Nil was also likely used as a channel for most of Nippur's historical occupation from the third millennium B.C. to the first millennium A.D. In the early to mid-first millennium A.D., around the time of the Sasanian period, a major increase in irrigation is evident in plant remains, likely reflecting large-scale irrigation expansion in the Nippur region. The first millennium B.C. to first millennium A.D. reflects a relatively dry period with periodic increased rainfall. Sedimentary results suggest the Nahrawan, prior to it becoming a well-known canal, formed an ancient branch of the Tigris, while the region just south of Baghdad, around Dalmaj, was near or part of an ancient confluence of the Tigris and Euphrates.

The looting of cultural heritage sites has been a growing problem and threatens national economies, social identity, destroys research potential, and traumatizes communities. For many countries, the challenge in protecting heritage is that there are often too few resources, particularly paid site guards, while sites can also be in remote locations. Here, we develop a new approach that applies deep learning methods to detect the presence of looting at heritage sites using optical imagery from unmanned aerial vehicles (UAVs). We present results that demonstrate the accuracy, precision, and recall of our approach. Results show that optical UAV data can be an easy way for authorities to monitor heritage sites, demonstrating the utility of deep learning in aiding the protection of heritage sites by automating the detection of any new damage to sites. We discuss the impact and potential for deep learning to be used as a tool for the protection of heritage sites. How the approach could be improved with new data is also discussed. Additionally, the code and data used are provided as part of the outputs.