One sea but many routes to Sail. The early maritime dispersal of Neolithic crops from the Aegean to the western Mediterranean.

This paper explores the first maritime westward expansion of crops across the Adriatic and the northern coast of the western Mediterranean. Starting in Greece at c.6500 cal BC and following the coastline to the Andalusian region of Spain to c.4500 cal BC, the presence of the main cereal, pulse, oil and fibre crops are recorded from 122 sites. Patterns in the distribution of crops are explored through ubiquity scores, correspondence analysis and Simpson's diversity index. Our findings reveal changes in the frequencies of crops as farming regimes developed in Europe, and show how different crops followed unique trajectories. Fluctuations in the diversity of the crop spectrum between defined areas are also evident, and may serve to illustrate how founder effects can explain some of the patterns evident in large-scale spatio-temporal evaluations. Within the broader westward expansion of farming, regionalism and multi-directional maritime networks described through archaeological materials are also visible in the botanical records

Middle Neolithic farming of open-air sites in SE France: new insights from archaeobotanical investigations of three wells found at Les Bagnoles (L'Isle-sur-la-Sorgue, Dépt. Vaucluse, France)

Previous reviews of Middle Neolithic agricultural practice (4400–3500 cal bc) in southern France have highlighted a change in crop assemblages after 4000 cal bc, with a reduction of naked wheat and an increase of emmer and partly of einkorn. The recent investigation of three wells from the site of Les Bagnoles (4250–3800 cal bc) in the periphery of the southern Rhône valley yielded an unprecedented amount of waterlogged uncharred and charred plant macro remains that offer new insights into crop diversity and its changes over time. The results from the wells at Les Bagnoles were compared with other dated sunken features from open-air sites (in contrast to caves and rock shelters), with the aim of identifying patterns sug-gesting changes in the crop spectra between the early (MN1) and late (MN2) Middle Neolithic phases from taphonomically comparable contexts. The results from Les Bagnoles demonstrate that oil crops and pulses are underrepresented in dry sites and that they were a significant part of Middle Neolithic agriculture. They also indicate an increase in the representation of einkorn (instead of emmer) during MN2 that is also visible in other open-air sites. The comparison of the archaeobotani-cal results with silo storage capacity values as a proxy for average production capacity per household leads us to propose a possible drop in naked wheat productivity and opens new questions in factors affecting crop choice at the beginning of the 4th millennium cal bc

First finds of Prunus domestica L. in Italy from the Phoenician and Punic periods (6th-2nd centuries BC)

Abstract During the archaeological excavations in the Phoenician and Punic settlement of Santa Giusta (Oristano, Sardinia, Italy), dating back to the 6th–2nd centuries bc, several Prunus fruitstones (endocarps) inside amphorae were recovered. The exceptional state of preservation of the waterlogged remains allowed morphometric measurements to be done by image analysis and statistical comparisons made with modern cultivated and wild Prunus samples collected in Sardinia. Digital images of modern and archaeological Prunus fruitstones were acquired with a flatbed scanner and analysed by applying image analysis techniques to measure 26 morphometric features. By applying stepwise linear discriminant analysis, a morphometric comparison was made between the archaeological fruitstones of Prunus and the modern ones collected in Sardinia. These analyses allowed identification of 53 archaeological fruitstones as P. spinosa and 11 as P. domestica. Moreover, the archaeological samples of P. spinosa showed morphometric similarities in 92.5% of the cases with the modern P. spinosa samples currently growing near the Phoenician and Punic site. Likewise, the archaeological fruitstones identified as P. domestica showed similarities with the modern variety of P. domestica called Sanguigna di Bosa which is currently cultivated near the village of Bosa. Currently, these findings represent the first evidence of P. domestica in Italy during the Phoenician and Punic periods. Keywords Archaeobotany · Image analysis · Morphometric features · Prunus · Sardini

Progress for research of grape and wine culture in Georgia, the South Caucasus

This communication will provide the latest information about the progress of the “Research Project for the Study of Georgian Grapes and Wine Culture”, managed by the National Wine Agency of Georgia since 2014. Local and foreign institutions continue to work together with the aim of stimulating multidisciplinary scientific research activity on Georgian viticulture and viniculture and to reconstruct their development from Neolithic civilizations to the present. The project is multidisciplinary in nature, merging contributions from archaeology, history, ethnography, molecular genetics, biomolecular archaeology, palaeobotany, ampelography, enology, climatology and other scientific fields

Du bois au charbon de bois : approche expérimentale des processus de réduction de masse et de fragmentation.

International audienc

A new combination of automated detection and classification of Mediterranean pollen grains from annual pollen traps

Pollen is a valuable proxy for reconstructing current and past vegetation. Automating identification and counting of pollen grains could greatly help palynologists, by increasing sample size, and thus their spatial and temporal resolutions, and standardizing methods and results. Several recent studies have already shown the potential of deep learning for automatic pollen recognition, especially for aeropalynology. Studies on pollen traps and fossil samples remain scarce, most probably because they contain many non-pollen particles and damaged pollen grains, increasing the difficulty of the task. Here, we test a new combination of last-generation deep-learning algorithms for automatic detection and classification of pollen from annual traps containing as many as 70 Mediterranean taxa, and many debris types. A total of 16 traps were collected each of three consecutive years in six locations in France. For each trap, one slide was mounted, and photographed partially with an automatic microscope. This operation produced 1,024 images of 204x204µm per slide, which contained a few pollen grains, that could be damaged, cut, or clumped, and many debris. We first trained YOLOv5 to detect the single category of pollen on 85% of 4,096 images (256 images per slide) containing 12,344 manually detected pollen grains. On the remaining 15% of the annotated images, the model left 0.7% pollen undetected, and falsely detected 12% of debris which are meant to be excluded by the subsequent classification. We then applied the model on the remaining 12,288 images and obtained 42,156 additional pollen grains. For the classification, we have trained so far ResNet50 on 85% of 8,000 manually identified pollen grains among 26 classes, made of a single or a few pollen taxa, and one extra class of debris. On the remaining 15% of the images, we obtained a class-mean accuracy of 0.73 with per-class accuracy ranging from 0.28 to 0.96. The best classification rates were obtained for taxa we had most images for (Pistacia sp., Quercus ilex, Lycopodium sp.). We are now improving the classification, e.g. by testing other algorithms, increasing the training dataset and/or through data augmentation

A new combination of automated detection and classification of Mediterranean pollen grains from annual pollen traps

Pollen is a valuable proxy for reconstructing current and past vegetation. Automating identification and counting of pollen grains could greatly help palynologists, by increasing sample size, and thus their spatial and temporal resolutions, and standardizing methods and results. Several recent studies have already shown the potential of deep learning for automatic pollen recognition, especially for aeropalynology. Studies on pollen traps and fossil samples remain scarce, most probably because they contain many non-pollen particles and damaged pollen grains, increasing the difficulty of the task. Here, we test a new combination of last-generation deep-learning algorithms for automatic detection and classification of pollen from annual traps containing as many as 70 Mediterranean taxa, and many debris types. A total of 16 traps were collected each of three consecutive years in six locations in France. For each trap, one slide was mounted, and photographed partially with an automatic microscope. This operation produced 1,024 images of 204x204µm per slide, which contained a few pollen grains, that could be damaged, cut, or clumped, and many debris. We first trained YOLOv5 to detect the single category of pollen on 85% of 4,096 images (256 images per slide) containing 12,344 manually detected pollen grains. On the remaining 15% of the annotated images, the model left 0.7% pollen undetected, and falsely detected 12% of debris which are meant to be excluded by the subsequent classification. We then applied the model on the remaining 12,288 images and obtained 42,156 additional pollen grains. For the classification, we have trained so far ResNet50 on 85% of 8,000 manually identified pollen grains among 26 classes, made of a single or a few pollen taxa, and one extra class of debris. On the remaining 15% of the images, we obtained a class-mean accuracy of 0.73 with per-class accuracy ranging from 0.28 to 0.96. The best classification rates were obtained for taxa we had most images for (Pistacia sp., Quercus ilex, Lycopodium sp.). We are now improving the classification, e.g. by testing other algorithms, increasing the training dataset and/or through data augmentation