The Demeter project. Eight millennia of agrobiodiversity changes in the northwest Mediterranean basin

The development of agricultural societies is closely entangled with that of domestic animals and plants. Local and traditional domestic breeds and varieties are the result of millennia of selection by farmers. DEMETER (2020-2025) is an international project which is aiming to characterize the changes in animal and plant agrobiodiversity (pigs, sheep, goats, and barley) in relation with environmental and socioeconomic factors in the northwestern Mediterranean basin since the beginnings of agriculture. The project is based on a combination of approaches including phenomics (through geometric morphometrics), databasing, zooarchaeology, archaeobotany, climate modeling, paleoproteins (ZooMs) and statistical analyses. Several hundreds of archaeological sites from the South of France and Catalonia will be studied, covering the maximum environmental, societal and cultural diversity of context over the course of the last eight millennia

Studying the current diversity of barley using geometric morphometrics on modern seeds: protocol and first results

International audienceGeometric morphometrics is a powerful method to explore intraspecies variation in cultivated plants (Terral et al., 2012 ; Bonhomme et al., 2017). Barley (Hordeum vulgare L.) is one of the staple crop of the Mediterranean since the Neolithic (Zohary et al., 2012). While morphological discrete characters are available to distinguish two-rowed from six-rowed barley, and naked from hulled barley (Jacomet et al. 2006), quantitative approaches still need to be developed at a large scale. The aim of this study is to explore the morphometric grain variation between barley varieties, six- and two-rowed types, naked and hulled types and spring and winter varieties. Size and shape of 2950 modern barley seeds from 84 current varieties provided by the Small grain cereals Biological Resources Centre (INRAE, Clermont Ferrand, France) were quantified using Elliptic Fourier Transforms (EFT) applied to gain outlines 2D coordinates. Results open interesting perspectives for investigating archaeological barley seeds and trace barley evolution in the western occidental Mediterranean basin since the Neolithic. This perspective will be realized in the framework of the ERC project DEMETER (grant agreement No. 852573)

La variation phénotypique documente la diversité moderne de l’orge : une application de la morphométrie géométrique à l’étude de la forme des graines

International audienc

Barley systematics and taxonomy foreseen by seed morphometric variation.

Since its Neolithic domestication in the Fertile Crescent, barley has spread to all continents and represents a major cereal in many modern agrarian systems. Current barley diversity includes thousands of varieties divided into four main categories corresponding to 2-row and 6-row subspecies and naked and hulled types, each of them with winter and spring varieties. This diversity is associated to different uses and allow cultivation in diverse environments. We used a large dataset of 58 varieties of French origin, (1) to assess the taxonomic signal in barley grain measurements comparing 2-row and 6-row subspecies, and naked and hulled types; (2) to test the impact of the sowing period and interannual variation on the grains size and shape; (3) to investigate the existence of morphological differences between winter and spring types; and finally (4) to contrast the relationship between the morphometric and genetic proximity. Size and shape of 1980 modern barley caryopses were quantified through elliptic Fourier Transforms and traditional size measurements. Our results indicate that barley grains record morphological diversity of the ear (89.3% classification accuracy between 2-row/6-row subspecies; 85.2% between hulled and naked type), sowing time of the grains (from 65.6% to 73.3% within barley groups), and environmental conditions during its cultivation and varietal diversity. This study opens perspectives for studying archaeological barley seeds and tracing the barley diversity and evolution since the Neolithic

Identification of archaeological barley grains using geometric morphometrics and experimental charring

International audienceBarley is one of the main cereals found in archaeological sites in the north-western Mediterranean basin, over the last 8 millennia. Grains are preserved in archaeological sediments by charring after or before dehusking. Morphological criteria for distinguishing 2-row from 6-row barley, but also hulled barley from naked barley, can be affected by charring and this can complicate identification of barley subgroups. In the last decade, geometric morphometrics applied to uncharred barley grains has shown the possibility of identifying barley subgroups, but its applicability to archaeological remains still needs to be ascertained. We used 3985 raw and charred grains of 113 current varieties to (1) assess charring effects on the shape of barley caryopses, depending on their subgroup and whether they were charred husked or dehusked and (2) select the best dataset for identifying barley subgroups. We also used 700 archaeological grains, from the Neolithic period to the end of the Middle Ages, to (3) compare the morphometric taxonomic assignment of the grains with their carpological identification based on discrete anatomical features in a series of 7 archaeological samples. Our results indicate size and shape differences between barley grains when charredhusked or dehusked. Although the charring process results in greater morphometric homogeneity, it allows subgroups of barley to be identified. For the 2-row vs. 6-row identification, more than 71% of the charred modern grains can be correctly identified. For the hulled vs. naked identification, the correct cross-validation percentages range from 70 to 73%. Finally, the good correlation between carpological and morphometric identifications of archaeological grains suggests that it is possible to identify archaeobotanical samples in the future

Deep learning<i>versus</i>geometric morphometrics for archaeobotanical domestication study and subspecific identification

Taxonomical identification of archaeological fruit and seed is of prime importance for any archaeobotanical studies. We compared the relative performance of deep learning and geometric morphometrics at identifying pairs of plant taxa. We used their seeds and fruit stones that are the most abundant recovered organs in archaeobotanical assemblages, and whose morphological identification, chiefly between wild and domesticated types, allow to document their domestication and biogeographical history. We used existing modern datasets of four plant taxa (date palm, barley, olive and grapevine) corresponding to photographs of two orthogonal views of their seeds that were analysed separately to offer a larger spectrum of shape diversity. On these eight datasets, we compared the performance of a deep learning approach, here convolutional neural networks (CNN), to that of a geometric morphometric approach, here outline analyses using elliptical Fourier transforms (EFT). Sample sizes were at minimum eight hundred seeds in each class, which is quite small when training deep learning models but of typical magnitude for archaeobotanical studies. Our objectives were twofold: i) to test whether deep learning can beat geometric morphometrics in taxonomic identification and if so, ii) to test which minimal sample size is required. We ran simulations on the full datasets and also on subsets, starting from 50 images in each binary class. For CNN networks, we deliberately used a candid approach relying on pre-parameterised VGG16 network. For EFT, we used a state-of-the art morphometrical pipeline. The main difference rests in the data used by each model: CNN used bare photographs where EFT used (x, y) outline coordinates. This "pre-distilled" geometrical description of seed outlines is often the most time-consuming part of morphometric studies. Results show that CNN beats EFT in most cases, even for very small datasets. We finally discuss the potential of CNN for archaeobotany, why outline analyses and morphometrics have not yet said their last word by providing quantitative descriptions, and how bioarchaeological studies could embrace both approaches, used in a complementary way, to better assess and understand the past history of species

Morphometric variation of seeds as a tool for tracing barley history: modern diversity and preliminary archaeological results in Lattara (France)

International audienceFollowing its domestication in the Fertile Crescent, barley spreads into the west of the Mediterranean basin during the Neolithic period and represents today a major cereal in many agrarian systems. Geometric morphometrics, a series of quantitative approaches for studying the size and shape variation of objects, has provided encouraging results for studying barley seed diversity. In that vein, the first aim of this study was to document the current barley morphometric diversity using 2950 modern barley seeds belonging to 64 varieties. We demonstrate that barley grains record morphological diversity of the ear (2-row/6-row, naked/hulled), winter and spring varieties and environmental factors during its cultivation. Using these results, the second aim was to document barley diversity at the archaeological site of the Gallo-Roman port of Lattara (Lattes, France) dated from the Iron Age to the Middle-Ages as an example of morphometric perspectives in archaeology. The large sampling carried out at the site allowed to analyse of 2000 archaeological seeds from 40 samples. Morphometric diversity from various sectors of the city and various periods (500 BC – 900 AD) can thus be contrasted and characterized in comparison to our modern reference collection. Its opens interesting perspectives for studying barley diversity over the eight millennia since the onset of agriculture in the whole of the northwest Mediterranean basin in the framework of the DEMETER project (ERC Starting Grant; PI A. Evin)

GMM vs CNN paper by Bonhomme et al. (sub) all scripts and data

Taxonomical identification of archaeological fruit and seed is of prime importance for any archaeobotanical studies. We compared the relative performance of deep learning and geometric morphometrics at identifying pairs of plant taxa. We used their seeds and fruit stones that are the most abundant recovered organs in archaeobotanical assemblages, and whose morphological identification, chiefly between wild and domesticated types, allow to document their domestication and biogeographical history. We used existing modern datasets of four plant taxa (date palm, barley, olive and grapevine) corresponding to photographs of two orthogonal views of their seeds that were analysed separately to offer a larger spectrum of shape diversity. On these eight datasets, we compared the performance of a deep learning approach, here convolutional neural networks (CNN), to that of a geometric morphometric approach, here outline analyses using elliptical Fourier transforms (EFT). Sample sizes were at minimum eight hundred seeds in each class, which is quite small when training deep learning models but of typical magnitude for archaeobotanical studies. Our objectives were twofold: i) to test whether deep learning can beat geometric morphometrics in taxonomic identification and if so, ii) to test which minimal sample size is required. We ran simulations on the full datasets and also on subsets, starting from 50 images in each binary class. For CNN networks, we deliberately used a candid approach relying on pre-parameterised VGG16 network. For EFT, we used a state-of-the art morphometrical pipeline. The main difference rests in the data used by each model: CNN used bare photographs where EFT used (x, y) outline coordinates. This “pre-distilled” geometrical description of seed outlines is often the most time-consuming part of morphometric studies. Results show that CNN beats EFT in most cases, even for very small datasets. We finally discuss the potential of CNN for archaeobotany, why outline analyses and morphometrics have not yet said their last word by providing quantitative descriptions, and how bioarchaeological studies could embrace both approaches, used in a complementary way, to better assess and understand the past history of species.</p

The Demeter project: eight millennia of agrobiodiversity changes in the northwest Mediterranean basin

International audienceThe development of agricultural societies is closely entangled with that of domestic animals and plants. Local and traditional domestic breeds and varieties are the result of millennia of selec- tion by farmers. DEMETER (2020-2025) is an international pro- ject which is aiming to characterize the changes in animal and plant agrobiodiversity (pigs, sheep, goats, and barley) in relation with environmental and socioeconomic factors in the northwes- tern Mediterranean basin since the beginnings of agriculture. The project is based on a combination of approaches including phe- nomics (through geometric morphometrics), databasing, zooar- chaeology, archaeobotany, climate modeling, paleoproteins (ZooMs) and statistical analyses. Several hundreds of archaeo- logical sites from the South of France and Catalonia will be stu- died, covering the maximum environmental, societal and cultural diversity of context over the course of the last eight millennia

The Demeter project: eight millennia of agrobiodiversity changes in the northwest Mediterranean basin

International audienceThe development of agricultural societies is closely entangled with that of domestic animals and plants. Local and traditional domestic breeds and varieties are the result of millennia of selec- tion by farmers. DEMETER (2020-2025) is an international pro- ject which is aiming to characterize the changes in animal and plant agrobiodiversity (pigs, sheep, goats, and barley) in relation with environmental and socioeconomic factors in the northwes- tern Mediterranean basin since the beginnings of agriculture. The project is based on a combination of approaches including phe- nomics (through geometric morphometrics), databasing, zooar- chaeology, archaeobotany, climate modeling, paleoproteins (ZooMs) and statistical analyses. Several hundreds of archaeo- logical sites from the South of France and Catalonia will be stu- died, covering the maximum environmental, societal and cultural diversity of context over the course of the last eight millennia