Eva-Maria Geigl

Thierry Grange

Of cats and men: ancient DNA reveals how the cat conquered the Ancient World

Keywords: Cat dispersal, domestication, ancient DNA, genetics

Neither genetics and genomics of modern cats nor archeology could so far reconstruct the domestication and dispersal process of the cat. It was only known that all domestic cats belong to the subspecies Felis silvestris lybica, that their genomes are close to the ones of wildcats and that they were translocated to Cyprus by the Neolithic farmers who colonized this island roughly 9,500 years ago. The results of our large-scale paleogenetic study of the mitochondrial DNA of archeological cat remains fill the existing gaps in that they allowed us to reconstruct the history of the dispersal of the cat starting in southwest Asia during the Neolithic and achieving a new quality in Egypt during the 1 st millennium BCE. Together with those from southwest Asia, these mitochondrial lineages from Egypt showed up in samples from the following centuries all over southwest Asia, North Africa, and Europe, testifying of the cat's conquest of the ancient world. The dispersal pattern that we reconstructed from our data tells us that cats accompanied seafarers throughout history on their trading and raiding routes.

Introduction

Hundreds of millions of cats inhabit the world: They live as cherished companions and pets in urban households where they make families happy; as stray cats in cities; as village and barn cats in villages and farms, where they restrict rodent plagues; but also as feral cats in nature where they become a threat to wild birds and other vertebrates and, in some areas, an invasive species that exterminates the endemic fauna. Despite their popularity and widespread occurrence, however, little is known about their domestication.

Genetics of present-day domestic cats based on mitochondrial and microsatellite DNA data

Genetic studies showed that all domestic cats, including feral cats, are descendants from one of the five subspecies of the wildcat Felis silvestris lybica, the wildcat from north Africa (NA) and southwest Asia (SWA), while the other subspecies of Felis silvestris, such as the European wildcat Felis silvestris silvestris, did not make a major contribution to the gene pool of the domestic cat although hybridization between the two subspecies is clearly detectable [START_REF] Driscoll | The Near Eastern Origin of Cat Domestication[END_REF][START_REF] Oliviera | Hybridization vs conservation: are domestic cats threatening the genetic integrity of European wildcat (Felis silvestris silvestris) populations in Iberian peninsula? Philosophical Transaction fo the[END_REF][START_REF] Mattucci | European wildcat populations are subdivided into five main biogeographic groups: consequences of Pleistocene climate changes or recent anthropogenic fragmentation?[END_REF]) (Fig. 1). Apart from revealing monophyly for the domestic cat, mitochondrial DNA sequences and nuclear microsatellite DNA did not disclose the course of its domestication process since no population structure in the domestic cat population emerged.

Archeozoology of cat domestication

There are two other sources of information about this process. The first one is the archeological record, which is regrettably scarce. The cat has never been a subsistence species and is, therefore, rarely found as refuse in archeological sites. The wildcat being a solitary animal of elusive nature, it was probably never hunted and only occasionally killed for its fur in prehistoric times. This changed only during the Middle Ages in Europe when items made of the pelts and skins of domestic cats became popular among common people (Ewing 1981), as well as their flesh and other body parts (Von den Driesch 1992). There are, however, two archeological finds that stand out and hold a clue to the timing and location of the taming process of cats. The first one is a human burial in Cyprus dated to ca. 7,500 BCE in which a cat skeleton was found [START_REF] Vigne | Early taming of the cat in Cyprus[END_REF]). The most parsimonious although disputed [START_REF] Rothwell | Evidence for taming of cats -Response to J.-D. Vigne and[END_REF]) explanation of this find on a formerly cat-free island is that the cat had been transported by Neolithic farmers to the island on a raft or ship as it would not have been able to swim there on its own. This situation is suggestive of the beginning of the taming process [START_REF] Vigne | Early taming of the cat in Cyprus[END_REF]). The second important find are six cat skeletons, including those from four kittens, found in a pit in an elite cemetery of Predynastic Egypt around 3,700 BCE [START_REF] Van Neer | More evidence for cat taming at the Predynastic elite cemetery of Hierakonpolis (Upper Egypt)[END_REF].

Based on the age of the animals, the authors believe that these cats had been held in captivity, at least for some time [START_REF] Van Neer | More evidence for cat taming at the Predynastic elite cemetery of Hierakonpolis (Upper Egypt)[END_REF]. As in the case of the cat in Cyprus, this situation testifies to a change in the status of the animal with respect to humans, i.e., a tightening of their relationship. Thus, these early finds hint to the Fertile Crescent and Egypt as the areas where the cat first may have changed its behavior so that a co-existence with humans became possible.

Since genetic data of extant cats clearly show that all domestic cats descend from F. s. lybica, the wildcat from NA and SWA, archeological and genetic data are in agreement albeit without proposing a refined view of the sequence of events taking place during the domestication process.

The cat in ancient Egyptian iconography

The second and richest source of information about the important place that cats occupied in ancient societies is Egyptian iconography (Málek 1993(Málek -2006;;[START_REF] Engels | Classical Cats: The Rise and Fall of the Sacred Cat[END_REF]): cats were depicted in Egypt as early as 2200 BCE, first on ivory knives and as outlines carved in walls.

Later, during the Middle Kingdom, they were painted as bird hunters in marshes in company of Egyptian hunters ("cat in the marshes") (Fig. 2). The presence of other wild animals, such as the genet and the ichneumon (the Egyptian mongoose) suggest that these cats were wildcats (Von den Driesch 1992). Some of the cats that hunted together with humans might also have been trained jungle cats, F. chaus, [START_REF] Morrison-Scott | The mummified cats of ancient Egypt[END_REF], but these are slightly bigger, lack the typical striped fur pattern of wildcats and have tufted ears, discriminative features that would have been faithfully transcribed in Egyptian paintings. During the New Kingdom, in the second half of the 2 nd millennium BCE, another theme became more and more recurrent, i.e., the cat sitting under the chair of a noble person, mostly a woman, translating the growing bonds between cats and humans, and in particular women. Indeed, cats were a symbol of fertility and motherhood (Fig. 3A). By 1450 BCE cats were common in paintings of domestic scenes. Cats also assumed great importance in Egyptian religion from about 2000 BCE onwards (Málek 1993(Málek -2006;;[START_REF] Engels | Classical Cats: The Rise and Fall of the Sacred Cat[END_REF]). From about 1500 BCE it was believed that the sun god Ra could manifest himself in the form of a cat, the 'Great Tomcat'. Each night Ra would travel to the underworld, confront his enemy, the snake demon Apophis, kill the snake with a knife and thus ensure the return of the sun the following morning. Many ancient Egyptian paintings depict Ra in the form of a spotted cat slaying Apophis and thus defeating the forces of chaos. By 945 BCE the cat had become associated with a female divinity, the goddess Bastet, and with the funeral rituals. Indeed, sacred cats kept in temple catteries were worshipped as living embodiments or epiphanies of the goddess (Málek 1993(Málek -2006;;[START_REF] Engels | Classical Cats: The Rise and Fall of the Sacred Cat[END_REF][START_REF] Machon | Le culte du chat en Egypte ancienne[END_REF][START_REF] Warmenbol | Le chien et le chat en Egypte pharaonique: à la vie, à la mort. Chiens et chats dans la Préhistoire et l'Antiquité[END_REF].

The popularity of this cult of Bastet continued for over 1500 years into the Roman era (to 330 CE). Many beautiful bronze sculptures of cats survive from this period (Fig. 3B). Cat mummification became more and more important during the 1 st millennium BCE and in particular during the Ptolemaic period and millions of mummified cats had survived until the 19 th c. CE when large amounts of them were shipped to England where they were ground up and used as fertilizer (Málek 1993(Málek -2006)).

Thus, Egyptian iconography is the richest archive testifying to the domestication of the cat: without changing appearance, it entered the domestic context. The dichotomy of the situations in which the cats are depicted, on one hand as skilled hunters that killed dangerous snakes and scorpions, as well as intelligent rats and mobile birds, and on the other hand sitting quietly under the chairs of noble people and as guardians of the deceased, suggests that the cat was appreciated as both a most useful hunter and a companion animal. This dual role was maintained in Greek, Etruscan and Roman iconography [START_REF] Engels | Classical Cats: The Rise and Fall of the Sacred Cat[END_REF][START_REF] Luce | Coat color variation at the beginning of horse domestication[END_REF].

From the carvings, frescos and statues in Ancient Egypt, it is not possible to distinguish a domestic from a wildcat except for the context in which it is placed. To a certain degree this holds still true since the shape and appearance of wild-colored domestic cats are not radically different from those of wildcats [START_REF] Krüger | Evaluation of anatomical characters and the question of hybridization with domestic cats in the wildcat population of Thuringia, Germany[END_REF][START_REF] Müller | Körpermerkmale als Unterscheidungskriterien zwischen wildfarbenen Hauskatzen (Felis s. catus) und Wildkatzen (Felis silvestris silvestris, Felidae) aus Mitteleuropa[END_REF]. Most of the differences that can be noticed today are of very recent origin, such as the fancy breeds that were created from random-bred cat populations in a frenzy of innovation and experimental cross-breeding during the 19 th century in the Western World [START_REF] Kurushima | Variation of cats under domestication: genetic assignment of domestic cats to breeds and worldwide random bred populations[END_REF]). The analysis of genomes of modern wild and domestic cats indeed showed a few differences, most of which are attributable to the genes involved in the development of the neural crest suggesting mainly behavioral changes [START_REF] Montague | Comparative analysis of the domestic cat genome reveals genetic signatures underlying feline biology and domestication[END_REF]. The resemblance of size and shape between the domestic cat and the wild ancestor is also seen in the skeletal remains of cats: wild and domestic cat bones and teeth cannot be distinguished on the basis of their metrics, unless one has complete skeletons or at least skulls (Wim van Neer, pers. comm.). It is only the archeological context that gives a hint about a change in the relationship between humans and cats, such as in the two examples described above. Thus, from the three lines of evidence, i.e., mitochondrial DNA sequences and nuclear microsatellite DNA data from extant cats, the archeological record as well as Egyptian iconography, it was only possible to conclude that the cat must have been domesticated in SWA and/or NA from the subspecies F. s. lybica starting in the Early Neolithic, when huntergatherers became sedentary farmers. It was clear that in order to better characterize the domestication process of cats in time and space, DNA preserved in archeological cat remains had to be analyzed. Thus, we aimed at establishing the phylogeographic structure of the wildcat in North Africa, southwest Asia and Europe and to follow its evolution across time.

Paleogenetic analysis of cat remains

Our team collected 352 ancient and 28 modern cat samples from all around the Mediterranean Basin and from Africa dated from 9,000 BP to the beginning of the 20 th century (see supplementary tables of [START_REF] Ottoni | The palaeogenetics of cat dispersal in the ancient world[END_REF]). An example for such archeological remains is shown in Fig. 4. Knowing that we had to analyze archeological specimens from Egypt, the Levant and Syria, i.e., areas with a very hot climate where DNA is poorly preserved based on our previous experience analyzing other species, we developed a metabarcoding approach tailored to highly degraded, small DNA molecules that combines the sensitivity of PCR and the power of NGS for the analysis of many ancient samples at reasonable costs [START_REF] Guimaraes | A cost-effective high-throughput metabarcoding approach powerful enough to genotype ~44 000 year-old rodent remains from Northern Africa[END_REF]. The approach uses highly optimized multiplex PCR to increase sample throughput without losing sensitivity and specificity of the PCR and proved to be powerful in various experimental systems [START_REF] Cote | A New High-Throughput Approach to Genotype Ancient Human Gastrointestinal Parasites[END_REF][START_REF] Guimaraes | A cost-effective high-throughput metabarcoding approach powerful enough to genotype ~44 000 year-old rodent remains from Northern Africa[END_REF][START_REF] Librado | Ancient genomic changes associated with domestication of the horse[END_REF]). The experimental design was such that through the analysis of the ND5, ND6 and CYTB region of the mitochondrial DNA [START_REF] Ottoni | The palaeogenetics of cat dispersal in the ancient world[END_REF], we achieved a phylogenetic resolution that was comparable to the one that had been obtained previously in extant cats [START_REF] Driscoll | The Near Eastern Origin of Cat Domestication[END_REF].

DNA was extracted and purified in the high containment laboratories of the University of Leuven and the Jacques Monod Institute using high stringency and DNA contamination prevention procedures (Champlot et al. 2010;[START_REF] Ottoni | The palaeogenetics of cat dispersal in the ancient world[END_REF]). Multiplex-PCR set-ups were performed in the high containment laboratory of the Jacques Monod Institute in Paris dedicated to ancient DNA research, while the construction of DNA libraries from the PCR products as well as Ion Torrent sequencing were carried out there in various modern DNA laboratories [START_REF] Guimaraes | A cost-effective high-throughput metabarcoding approach powerful enough to genotype ~44 000 year-old rodent remains from Northern Africa[END_REF][START_REF] Ottoni | The palaeogenetics of cat dispersal in the ancient world[END_REF].

It was the aim of our study to establish the phylogeographic structure of the wildcat populations in Europe, the Middle East and North Africa and to see whether we can detect changes that would disclose the domestication process. To achieve this aim, we analyzed many samples from these regions in order to compensate for samples that would not yield results and to increase the chance of detecting the emergence of a change in the geographical and temporal distribution pattern of mitotypes. This rationale of our experimental design turned out to be justified since DNA in most samples from Egypt, the Levant and Syria was so poorly preserved that they did not yield results. Moreover, it was the sheer number of samples that allowed us to draw conclusions on the domestication history of the cat.

Knowledge gained from our paleogenetic analysis

Phylogeography of the wildcat

Our oldest samples originated from western Europe dating to the Mesolithic period prior to the arrival of the Neolithic farmers that colonized Europe coming from Anatolia (e.g., [START_REF] Lazaridis | Genomic insights into the origin of farming in the ancient Near East[END_REF])) and introduced agriculture and domestic animals (e.g., (Özdogan 2011)).

These cats as well as more recent ones belonged to the mitotype of the European wildcat F. s. silvestris (Fig. 5A). In southeast Europe, however, we detected in samples dating to 7700 BCE, i.e., before the arrival of the Neolithic farmers, a mitochondrial lineage of F. s. lybica, IV-A1, that differed from the one we found in Early Neolithic Anatolia named IV-A* (Fig. 5A). The split date of lineages IV-A1 and IV-A* has been estimated to roughly 20,000 years ago, i.e., the last glacial maximum (LGM), based on the calibration of the maximum likelihood tree obtained from about one third of the mitogenome (Fig. 6). At this time, the Bosphorus was still a land bridge and it is conceivable that the aridity of the climate in southeast Europe during the

LGM would have allowed F. s. lybica, which is adapted to open bushland and deserts, to colonize the Balkans. At the end of the Pleistocene and beginning of the Holocene, forests would have extended again into the area and with it the forest-dwelling F. s. silvestris. A mosaic of open bushland and forest environments would have allowed the two different ecotypes, F. s. silvestris and F. s. lybica, to co-exist in this region. And indeed, we found both mitotypes in later periods and they still co-exist there to date although in two independent subspecies (Wozencraft 2005).

In Anatolia, not a single specimen analyzed carried the mitotype of F. s. silvestris. This finding was unexpected since Anatolia was so far considered to be inhabited by the European wildcat [START_REF] Yamaguchi | Felis silvestris. The IUCN Red List of threatened species[END_REF]. Although we cannot exclude that these latter ones lived in the northern forests of Anatolia lining the Black Sea and had simply not been sampled, our data rather suggest that Anatolia was inhabited by F. s. lybica, at least up to the 13 th c. CE (Fig. 5A andB).

The cat during the Neolithic

Cat remains from the early Neolithic site of Asikli Höyük on the Konya plain in Anatolia that carry mitotype IV-A* testify to both the mitotype of the native Anatolian wildcat population, at least on the Anatolian highlands, and to cats in a human settlement suggestive of some change in the human-cat relationship (Fig. 5B). This view is reinforced through cat figurines found in Neolithic sites in Turkey, Syria and Israel [START_REF] Vigne | Early taming of the cat in Cyprus[END_REF]). In particular, the cat-like animals depicted on ceramic figurines of women from the Anatolian site Haçilar dated to ca. 8,000 BP could well be the first iconographic evidence of tamed cats, as proposed already in 1965 [START_REF] Brentjes | An efficient multistrategy DNA decontamination procedure of PCR reagents for hypersensitive PCR applications[END_REF]), but later questioned (e.g., [START_REF] Gautier | Fauna, domesticated. Encyclopedia of the Archaeology of Ancient Egypt[END_REF]. The Anatolian mitotype IV-A* appeared in those of our samples from southeast Europe that were dated to the time when the Neolithic farmers migrated into Europe (e.g., [START_REF] Lazaridis | Genomic insights into the origin of farming in the ancient Near East[END_REF])), but not in earlier European specimens (Fig. 5C). Once again, the most parsimonious interpretation of this finding is that the cats had been translocated to Europe by the early farmers when they crossed the Bosphorus by boat suggesting that the cats were tamed or at least familiar with the presence of people. We also found the Anatolian mitotype in more recent samples (2 nd -1 st millennium BCE) from the Caucasus, the Levant, Iran, and Egypt (Fig. 5C). Since cats are territorial animals, the most parsimonious conclusion is that these cats have spread following humans on their migrations, in contrast to those types carrying lineage IV-B that were and remained endemic to the Levant. Taken together our genetic, but also archeological and ethological data, it comes naturally to mind that the first steps of the domestication of cats must have happened in the Fertile Crescent. Indeed, it was there where hunter-gatherers established the first permanent settlements, cultivated, collected and stored large amounts of wild grain as early as 13,000 years ago [START_REF] Snir | The Origin of Cultivation and Proto-Weeds, Long Before Neolithic Farming[END_REF], and developed agriculture more than 10,000 years ago (e.g., (Bar-Yosef 1998; Belfer-Cohen and Bar-Yosef 2000; Asouti and Fuller 2012)). As a consequence, cereals in the field as well as grain accumulations must have attracted rodents that for their parts attracted the local wildcats. Wildcats that overcame their fear of humans and tolerated the presence of other cats would have found much better living conditions in or close to the settlements of the early farmers than in the wild, a situation still encountered nowadays in NA and Arabia [START_REF] Faure | An Archaeological and Historical Review of the Relationships between Felids and People[END_REF]. In this way, the cat would have become a commensal, just as the rodents it preyed on, and its tameness would have been the result of a higher reward rate in human company compared to the wild. The human-cat relationship must have satisfied both sides from the very beginning of its establishment. Humans must have benefitted from cats that lived in and around the settlements since cats were the "bulwark" against rodent pests that depleted and spoiled the grain in the fields and in the stores causing starvation, famines and economic loss. Moreover, the spread of dangerous diseases carried by rodents, and in particular rats, would have been reduced. Egyptian cats were described as very big (Boessneck and von den Driesch 1983) compared to the cats in SWA [START_REF] Davis | The Archaeology of Animals[END_REF][START_REF] Benecke | Der Mensch und seine Haustiere -Die Geschichte einer jahrtausendealten Beziehung[END_REF]), as if they had been selected for as ratters [START_REF] Faure | An Archaeological and Historical Review of the Relationships between Felids and People[END_REF], while present-day domestic cats are poor killers of rats selecting only juveniles [START_REF] Childs | Size-dependent predation on rats (Rattus norvegicus) by house cats (Felis catus) in an urban setting[END_REF]. The find of six rat skeletons in the skeleton of an Egyptian cat [START_REF] Boessneck | Ein Katzenskelett der Römerzeit aus Quscir (Koser) am Roten Meer[END_REF], supports this hypothesis.

Cats would also have fought venomous animals such as scorpions and snakes. Since the latter predate on rodents they must have come closer to human settlements where they became pests themselves. The depiction of cats as snake killers in Egyptian iconography might therefore be linked directly to the role of cats as adjuncts in early agriculture. The gradual nature of the move from the use of wild stands of cereal to cultivation of domesticated plants that took place at the transition from the hunter-gatherer to the agricultural life style could well constitute the context for an equally gradual development of the human-cat association.

The Egyptian cat

The cat mummies from the Ptolemaic period in Egypt that we analyzed carried two different mitochondrial lineages, the Anatolian lineage IV-A* and two lineages, IV-C1 and IV-C*, that we had not found in more ancient cats from other areas (Fig. 5D). These latter lineages were present also in Anatolian cats from the Roman-Byzantine period, where about half of the samples carried these IV-C lineages, a fourth carried the native lineage IV-A* and the rest lineages IV-E and F. s. ornata (Fig. 5E). We found them also in samples from the Levant and Iran dated to the Roman period and later even in a Viking port at the Baltic Sea (Fig. 5E).

This fast and massive spread of the "Egyptian cat" over large distances hints to sea vessels as dissemination means. Indeed, cats must have accompanied seafarers, from the Phoenicians (called "cat thieves by the ancient Egyptians [START_REF] Faure | An Archaeological and Historical Review of the Relationships between Felids and People[END_REF]) over Greeks, Etruscans to Romans, on their ships keeping the vessels rodent-free and taking advantage of fish as a food source that naturally is not readily available for cats. Rodents on ships are a threat not only to the food reserves but also to all organic material such as ropes that are being gnawed on. The usefulness of ship's cats can be deduced from the fact that until 1975 cats were compulsory on all the vessels of the British Navy [START_REF] Beadle | The Cat: History, Biology, Behavior[END_REF] and some of them have been immortalized in novels, poems and got statues erected to celebrate their memory. That this was a widespread custom at the latest during Roman times can be deduced from a cat sample from the Egyptian-Roman port of Berenike at the Red Sea that carried the mitotype of the Asian wildcat, F. s. ornata. This port is known for its lively trading connections with India and the sailors spent several months on either end of their voyage giving their ship's cats the opportunity to mix with the local Asian wildcat the offspring of which would sometimes make the voyage back to the area where their ancestors came from. Alternatively, or in addition, Asian wildcats boarded the sea vessels and were shipped to Egypt and to the Eastern Mediterranean such as Anatolia, where we also found this mitotype in a few samples from coastal sites. Thus, our ancient mitochondrial data suggest that the spread of the "domestic" cat over the Ancient World occurred through seafaring merchants and soldiers, although the Roman army must have played an important role for the spread of the cat within the European continent as evidenced from archeological finds (e.g. [START_REF] Bökönyi | History of Domestic Animals. Mammals in Central and Eastern Europe[END_REF][START_REF] Bökönyi | Animal Husbandry and Hunting in Tac-Gorsium: The Vertrebrate Fauna of a Roman Town in Pannonia[END_REF][START_REF] Clutton-Brock | A Natural History of Domesticated Animals[END_REF]).

During the 8 th and 9 th c. CE, the Vikings, traders as well as raiders, continued the dispersal of cats as evidenced by our finding of the Egyptian mitotype in the Baltic sea port of Ralswieck from the 8 th c. CE, and so did the returning crusaders during the 11 th and 12 th c. CE [START_REF] Lepetz | L'animal dans la société gallo-romaine de la France du Nord[END_REF][START_REF] Sunquist | Wild Cats of the World[END_REF].

Selection of coat patterns in the domestic cat

Ancient DNA analyses have shown for some animals such as horses that domestication led to an increase in the variety of coat colors as an early signature of the domestication process [START_REF] Luce | Coat color variation at the beginning of horse domestication[END_REF]. In order to analyse whether this was also the case in cats and thereby obtain some information about the physical changes that the domestic cat potentially underwent through the domestication process, we analysed the Transmembrane Aminopeptidase A (Taqpep) gene coding for the tabby coat pattern, the striped coat pattern of wildcats. A nonsense mutation (W841X) in exon 17 of the Taqpep gene causes the blotched pattern present in a large percentage of domestic cats [START_REF] Kaelin | Specifying and sustaining pigmentation patterns in domestic and wild cats[END_REF]) (Fig. 6). Indeed, the coat marking variations are used to discriminate wildcats and hybrids (e.g., [START_REF] Beaumont | Genetic diversity and introgression in the Scottish wildcat[END_REF][START_REF] Pierpaoli | Genetic distinction of wildcat (Felis silvestris) populations in Europe, and hybridization with domestic cats in Hungary[END_REF][START_REF] Oliviera | Hybridization vs conservation: are domestic cats threatening the genetic integrity of European wildcat (Felis silvestris silvestris) populations in Iberian peninsula? Philosophical Transaction fo the[END_REF])) while other coat color variations are described mainly in domestic cat breeds (for review see [START_REF] Lyons | DNA mutations of the cat[END_REF]).

We investigated in all of our samples three single nucleotide polymorphisms (SNPs) in this recessive allele and found it not earlier than just before the 13 th c. in SWA. It then increased in frequency in domestic cats in Europe, SWA and Africa suggesting that it was selected for. If we take this marker of appearance as a witness of the timing of selection for physical traits, this would represent a very late selection process in cats compared to other animals. This is not in contradiction to the genomic, archeological and iconographic evidence but only underlines the hypothesis that the cat has not been actively selected through breeding. Indeed, 85% of the 40 -50 extant cat breeds arose only in the past 75 years [START_REF] Kurushima | Variation of cats under domestication: genetic assignment of domestic cats to breeds and worldwide random bred populations[END_REF]. We would like to hypothesize that it was not necessary to "change" the cat since it was the perfect animal for human societies living in farms, villages and towns. From the very beginning it naturally performed the tasks that it was expected to do, i.e., hunting vermin and venomous animals, without being dangerous or trying to escape.

Conclusions and Future Perspectives

To conclude, we showed that two different clades of cats colonized subsequently the Ancient World, an Anatolian and an Egyptian one, confirming that both locations played an important role in the domestication history of cats. The dates and areas where these clades appeared in our extensive data set speaks for translocations as the mechanism of spread: first the somehow tamed cat followed humans on their migrations into new territories (Fig. 5G) and later it was transported as ship's cats with seafarers on routes of trading and warfare confirming a previous hypothesis [START_REF] Kirk | The everlasting Cat[END_REF].

Since the underlying cause of cat domestication, grain accumulations in human settlements attracting rodents and thereby attracting cats preying on them, was generalized when the Neolithic began to spread, it is likely that the domestication process had several foci.

Once cats interacted more closely with humans, they were translocated over much larger distances than they would have roamed on their own. Thus, any new genetic change affecting cats in one of the areas that would have been interesting to humans could then have been rapidly propagated to other locations. We believe that this is what happened with the Egyptian cats. To detect this process, as we did in our data set, we need to assume that, apart from Egypt, the bonds between cats and humans were probably still loose at the time of Classical Antiquity, and these cats were not abundant enough to dilute the genetic contribution of the newcomers. Later on, the process of spread of novel genetic variations must have continued, but becomes increasingly more difficult to detect with the cat-human relationship growing closer everywhere. We think that one should consider the domestication process of cats as a gradual spread over several thousands of years rather than a limited number of key founding domestication events. The simplified view of a limited number of key founding events is prone to be challenged by new archeological finds.

Further insights from the study of whole genomes of ancient cats at archeological key sites are expected to refine our knowledge of the domestication process of the cat. Indeed, additional information could be obtained if it was possible to sequence the genomes of cat remains predating the period when cats started to be translocated in order to establish baseline genomes, to which genomes from later stages of the cat domestication process can be compared. Such baselines are important since it is clear that hybridization between wild and tamed cats associated with the human niche was recurrent with the consequence of blurring the picture of the process that modified the genome of the house cats. The methodologies for this enterprise exist, but the task is not easy since DNA is poorly preserved in most skeletal remains from the presumed centers of cat domestication due to the high temperature in these regions that is detrimental to DNA preservation. Moreover, it is likely that many samples need to be analyzed to detect gradual changes in the frequency of a limited number of relevant alleles. Nevertheless, this analysis is necessary if one wants to confirm the hypothesis deduced from the diachronic analysis of the tabby coat color allele [START_REF] Ottoni | The palaeogenetics of cat dispersal in the ancient world[END_REF]) that selection of physical traits has been a late event in the domestication process, which in turn suggests that the domestication process of the cat followed the commensal pathway [START_REF] Zeder | Pathways to Animal Domestication[END_REF]) and was relaxed and long.

Fig. 1 :

 1 Fig. 1: Distribution of wildcats based on IUCN (Yamaguchi et al. 2015) and data from

Fig. 2 :

 2 Fig. 2: Detail of cat from the hunting scene (fowling scene) from the tomb of Nebamun,

Fig. 4 :

 4 Fig. 4: cat mandible from the archeological site of Entzheim-Geispolsheim, France, dated

FigFig. 5 :

 5 Fig. 5A: Before Neolithization

Fig. 6 :

 6 Fig. 6: Maximum-likelihood trees based on haplotype sequence data generated by

Fig. 7 :

 7 Fig. 7: Tabby mackerel coat pattern in wildcats (F. s. lybica, left, F. s. silvestris, right)

F

  . s. lybica -Tabby mackerel F. s. silvestris -Tabby mackerel Domestic cats -Tabby mackerel -Tabby blotched

Acknowledgments: We would like to thank an anonymous reviewer for his/her interesting propositions.


Mirna El Ghosh
email: mirna.elghosh@insa-rouen.fr

Automation of Legal Reasoning and Decision Based on Ontologies

Keywords: Legal Ontology, Rule-Based Reasoning, Modular Ontology, Ontology Reuse, Ontology Integration, Criminal Domain

The main goal of our research is to build a legal reasoning system that performs decision support functions in the criminal domain. The system is based on a rule-based reasoning model which is composed of a legal domain ontology, rule base and reasoning engine. The legal domain ontology is needed for modelling the legal norms of the criminal domain. For this purpose, a middle-out approach is proposed to modularize the ontology in order to reduce the complexity and the difficulties of ontology building process. The rule base contains set of logic legal rules formalized based on the ontology.

1

Research Question

Our research analyses the problem of building reusable legal domain ontologies for legal reasoning and decision support systems. Legal decision support systems, known as legal knowledge based systems (LKBS) [START_REF] Chorley | Developing Legal Knowledge Based Systems Through Theory Construction[END_REF], are capable of legal reasoning [START_REF] Popple | Legal expert systems: The inadequacy of a rule-based approach[END_REF], since they are based on a model that describe the norms operating in the legal system [START_REF] Popple | A pragmatic legal expert system[END_REF]. There are three main models for legal reasoning: rule-based, case-based and hybrid. For the current research, the scope is limited to rule-based legal reasoning. Generally, rule-based reasoning models are composed of two main parts: rule-based domain knowledge and reasoning engine [START_REF] Corsar | Developing knowledge-Based Systems using the Semantic Web, SSS-08 on Symbiotic Relationships between the Semantic Web & Knowledge Engineering[END_REF]. We motivate to develop a simple, but expressive, domain knowledge in order to produce useful reasoning. Legal domain ontologies are needed for developing such domain knowledge. They are used mainly for modelling the legal norms of the given legal domain.

Generally, Building ontologies from scratch is not an easy task. It is considered as a resource-intensive, time consuming and costly task. This is due to the difficulty and the complexity of capturing knowledge from legal sources which are mainly unstructured textual documents such as legislations and codes. In this regard, to reduce the complexity of building legal domain ontologies, a modular middle-out approach is proposed. This approach tends to simplify the ontology building process based on reusing existent foundational ontologies in a top-down strategy and on ontology learning process in a bottom-up strategy. Both strategies will be integrated to obtain the resulting ontology. In order to complete the domain knowledge of the rule-based legal reasoning model, a set of logic rules will be constructed based on the obtained legal domain ontology. In this context, an integration process will take place to combine the ontology and the rules.

The domain application of this research is the Lebanese criminal system and the Lebanese criminal code is considered as the main textual resource since it contains the legal norms of the Lebanese criminal domain.

Challenges

In this research, there are two main challenges concerning the building of the criminal decision support system mainly for the reasoning model components: building a reusable legal domain ontology for modelling the legal norms of the criminal domain and formalizing the logic rules of the legal reasoning model of the decision support system based on the resulting ontology.

It is commonly known that ontologies aim to capture consensual knowledge of a given domain in a generic and formal way, to be reused and shared across applications and by groups of people [START_REF] Corcho | Ontological Engineering: What are Ontologies and How Can We Build Them[END_REF]. Meanwhile, it is considerable in the literature that the number of ontologies has increased and they are becoming larger and more complex to manage and reuse [START_REF] D'aquin | Ontology modularization for knowledge selection: Experiments and evaluations[END_REF]. For this purpose, the challenge that we face is to build reusable ontology for modelling the norms of the criminal domain using modularization techniques as an ontology engineering principle. Ontology modularization and the problem of formally characterizing a modular representation for ontologies are great challenges in the ontological engineering domain.

Furthermore, based on the legal domain ontology, there is a need to formalize legal norms of the criminal domain. In this context, the challenges to be faced are how to integrate the obtained ontology with logic rules. The integration of ontologies and rules remains a challenging task in the knowledge engineering domain for building rule-based reasoning models.

3

State of the Art

Our research is composed in two main parts: the building process of the criminal domain ontology and the construction of the legal reasoning model of the rule-based decision support system. Concerning the ontology building process, in the literature, two main categories of approaches exist: top-down and bottom-up [START_REF] Francesconi | Integrating a bottom-up and top-down methodology for building semantic resources for the multilingual legal domain[END_REF]. The bottom-up approaches start from the most specific concepts and build a structure by generalization [START_REF] Gandon | Distributed artificial intelligence and knowledge management: Ontologies and multi-agent systems for a corporate semantic web[END_REF] where the building process of the ontology usually starts with linguistic study on existing data structures forms (documents, reports, etc.) in order to extract relevant concepts of the domain and relations among them with the semi-automatic support in document analysis.

Top-down approaches start from the most generic concept and build a structure by specialization [START_REF] Gandon | Distributed artificial intelligence and knowledge management: Ontologies and multi-agent systems for a corporate semantic web[END_REF]. In this approach, the building process of the ontology starts by an analysis and study of relevant information sources about the given domain and then modeling the top level concepts which will be refined in next steps.

Meanwhile, for the construction of the legal rule-based reasoning model, two main approaches are found in the literature for integrating ontologies and rules: homogeneous and hybrid [START_REF] Antoniou | Combining Rules and Ontologies -A survey[END_REF].

The homogeneous approaches define the integration between ontologies and rules over a tight semantic integration where ontologies and rules are embedded in a common logical language. The most typical homogeneous paradigms are: Combination of OWL ontologies with SWRL rules expressed in First Order Logic (FOL) and Description Logic programs (DLP) [START_REF] Grosof | Description logic programs: Combining logic programs with description logic[END_REF]. However, the hybrid approaches define the integration between ontologies and rules over a strict semantic separation where the ontology elements and the rules predicates are separated. In this strategy, rules are expressed in Logic Programming LP formalism. The most typical hybrid approaches are Answer Set programming (ASP) [START_REF] Gelfond | Classical negation in logic programs and disjunctive databases[END_REF], dl-programs [START_REF] Eiter | Combining answer set programming with description logics for the semantic web[END_REF] and DL+log [START_REF] Rosati | DL+log: Tight Integration of Description Logics and Disjunctive Datalog[END_REF].

Proposed Approaches

In our research we have proposed two main approaches: a modular middle-out approach for building the criminal domain ontology and a homogenous approach for integrating the resulting ontology with the logic rules in order to form the legal reasoning model of the legal decision support system.

Modular Middle-Out Approach for Building Criminal Domain Ontology

A modular middle-out approach has been proposed for building a criminal modular domain ontology [START_REF] El Ghosh | Towards a Middle-out Approach for Building Legal Domain Reference Ontology[END_REF]. The proposed approach tends to combine two complementary strategies: top-down and bottom-up. In the ontology building process the modularization techniques are used to split the ontology into four independent modules (upper, core, domain and domain-specific), which are themselves ontologies, that can be reusable. At the highest level, the upper module represents the most general concepts and relations that cover all the domains. The core module provides a definition of structural knowledge in the legal domain. The domain module, in turn, describes the conceptualization of the criminal domain. Finally, at the lowest level, the domain-specific module describes the most-specific knowledge of the Lebanese criminal domain.

Concerning the strategies: top-down consists of the definition of the highest part of the conceptual structure of the criminal domain which is modeled as ontology modules (upper and core). In this strategy, reusing existent ontologies, that capture similar or complementary knowledge (foundational and core-legal ontologies such as UFO [START_REF] Guizzardi | Ontological foundations for structural conceptual models[END_REF], LKIF-Core [START_REF] Hoekstra | The lkif core ontology of basic legal concepts[END_REF]- [START_REF] Hoekstra | Ontology representation: design patterns and ontologies that make sense[END_REF]) can help in building well-founded ontology.

Meanwhile, the bottom-up strategy consists of extracting the legal concepts and relations among them from textual resources (the Lebanese criminal code) by using Ontology Learning and NLP techniques and then modeling this knowledge as a domain and domain-specific modules.

Furthermore, the two strategies are combined together to form the complete architecture of the criminal domain ontology that is modularized into four modules: upper, core, domain and domain-specific.

Homogenous Approach for Ontology and Rules Integration

In order to build the legal reasoning model of the legal decision support system, there is a need to integrate the resulting criminal domain ontology with set of logic rules.

For this purpose, a homogenous approach is proposed to define a tight semantic integration where the ontology and the rules are embedded in a common logical language. In this approach, the ontology is treated as external sources of information accessed by rules. Ontology concepts and properties may be defined through the rules. The most typical homogeneous paradigm is the combination of OWL ontology with SWRL rules expressed in First Order Logic (FOL).

Results

In this section, we will discuss briefly the results obtained for the proposed approaches concerning the criminal modular domain ontology and the rule-base of the legal reasoning model.

Criminal Modular Domain Ontology

After applying the proposed middle-out approach, a criminal modular domain ontology, named CriMOnto. The proposed approach is defined by developing the modules independently and then combining them together to compose the whole CriMOnto (see Fig. 1) [START_REF] El Ghosh | Towards a Middle-out Approach for Building Legal Domain Reference Ontology[END_REF]. From this perspective, the different modules are in different subjects since they are in different conceptual levels. Therefore, an integration process is performed to combine them [START_REF] Bontas | Case studies on ontology reuse[END_REF].

Upper Ontology Module

The UOM consists of abstract concepts and relations which are effectively independent of any specific domain. For a well founded building of this module, the unified foundational ontology UFO [START_REF] Guizzardi | Using UFO as a Foundation for General Conceptual Modeling Languages[END_REF], proposed by Guizzardi and Wagner [START_REF] Melo | Applying Foundational Ontologies in Conceptual Modeling: A Case Study in a Brazilian Public Company[END_REF], is partially reused to facilitate and speeding up the ontology development process by preventing to reinvent the wheel concerning basic categories [START_REF] Rosa | Using events from UFO-B in an ontology collaborative construction environment[END_REF]. Therefore, UFO permits the building of an ontology reusing some generic concepts such as category, kind, subkind, relator, role, role mixin and Event, where the ontologist does not need to rebuild these concepts.

In order to make possible the activity of conceptual modeling via UFO, a conceptual modeling language, named OntoUML [START_REF] Guerson | OntoUML Lightweight Editor: A Model-Based Environment to Build, Evaluate and Implement Reference Ontologies[END_REF], is used. OntoUML uses the ontological constraints of UFO as modeling primitives and is specified above the UML 2.0 meta-model [23].

Core Ontology Module

The COM consists of concepts and relations that are common across the domains of law and can provide the basis for specialization into domain and domain-specific concepts. The same perspective is applied, as for upper module, for reusing partially the legal core ontology, LKIF-Core, to build this module (see Fig. 3).

Domain Ontology Module

The DOM is composed of categories that are related mainly to the criminal domain in general such as Criminal_Act, Penalty, Misdemeanor, Violation, etc. In order to build this module, two main strategies are applied:

Domain-Specific Ontology Module

The DSOM consists of concepts and relations of a specific subject domain such as the Lebanese criminal system. The bottom-up strategy helped to generate semiautomatically the domain-specific ontology. Unfortunately, the generated results were inexpressive and thus insufficient for practical use. For this reason, a reengineering process, inspired from the work of [START_REF] Caldarola | An approach to ontology integration for ontology reuse in knowledge based digital ecosystems[END_REF], is applied to correct, prune and enrich the extracted ontology and make it more expressive by transforming it to heavyweight or axiom-based ontology.

Integration Process of Ontology Modules

The integration phase is the final step after building the independent ontology modules to form the resulting ontology CriMOnto. The source ontologies (upper, core, domain and domain-specific modules) are aggregated, combined, and assembled together. In this context, a simple vertical mapping strategy is applied between the concepts of the different modules. In our experiments, since ontology modules are located on vertical conceptual levels from general (upper module) to specific (domainspecific module), the mappings will be based mainly on a parent-child hierarchical relationship [START_REF] Legat | Semantics to the Shop Floor: Towards Ontology Modularization and Reuse in the Automation Domain[END_REF]. At the end, all the modules are imported into the final ontology using the OWL:imports formalisms (see Fig. 6).

Rule-Base of the Legal Reasoning Model

The Rule base of the legal reasoning model of the decision support system stores the knowledge in form of rules. In the legal domain, a legal norm is represented by an obligation rule that denotes that the conclusion of the rule will be treated as an obligation 52 in the following form:

IF condition (operative facts) THEN conclusion (legal effect).

For representing the norms of the Lebanese criminal code, a modelling process is needed, as well as a rule language. The modeling process is based on a homogeneous integration of the obtained criminal domain ontology and SWRL selected as a rule language. In SWRL, rules are of the form of an implication between an antecedent (body) conjunction and a consequent (head) conjunction in the following form [START_REF] Antoniou | Combining Rules and Ontologies -A survey[END_REF]:

a 1 ∧ a 2 ∧… ∧ a n → b 1 ∧ b 2 ∧… ∧ b m ;
In Table 1, some examples of SWRL rules, expressed using CriMOnto elements. Article 213: "An accomplice to an offence shall be liable to the penalty prescribed by law for the offence".

Accomplice(?x), commit(?x, ?y), is_punishable_by(?y, ?z) -> is_liable_to_punished_by(?x, ?z)

Discussion

In our research, a modular middle-out approach is applied to build a criminal modular domain ontology (CriMOnto). The modularization direction is tracked, with the support of ontology reuse and integration, in order to reduce and simply the complexity of ontology building process. This approach is considered as useful track for the ontologists who seek to build domain ontologies based partially on existent valid ontologies. Furthermore, a legal reasoning model of a legal decision support system is designed based on CriMOnto which is used to model the legal rules of the criminal domain. For this purpose, a homogeneous approach is applied for the integration of the ontology and the rules to form the rule base of the reasoning model. In further works, a prototype for the rule-based decision support system will be constructed in order to validate the achieved results.

Fig. 1 .

 1 Fig.1. Middle-out approach for building CriMOnto.

Fig. 2 .

 2 Fig.2. Fragment of the upper module in OntoUML.

Fig. 3 .

 3 Fig.3. Excerpt of core concepts reused from LKIF-Core.

  (1) specialize the concepts and relations of the core module (Fig 4 (a)); (2) extract the knowledge from textual resources using ontology learning and NLP techniques [24] (Fig 4 (b)).

Fig. 4 .

 4 Fig.4. Excerpt of the domain concepts in Protégé.

Fig. 5 .

 5 Fig.5. Excerpt of the domain-specific ontology module represented in Protégé.

Fig. 6 .

 6 Fig.6. Hierarchical mapping in Protégé.

Table 1 .

 1 Excerpt of the Lebanese criminal code rules expressed in SWRL.

	Legal Norms	Rules expressed in SWRL
	Article 547: "Anyone who	Intentional_Homicide(killing), committed_towards(killing,
	intentionally kills another per-	?y), committed_by(killing, ?x) -> is_punished_by(?x,
	son shall be punishable by hard	hard_labour),	imposed_for_maximum(hard_labour,
	labour for a term of between 15	max_d_2), imposed_for_minimum(hard_labour, min_d_2),
	and 20 years".	term_value(max_d_2, 20), term_value(min_d_2, 15),
		term_type(max_d_2, "years"), term_type(min_d_2, "years")

Acknowledgements

This work has been supported by the CLASSE project (European Union with the European Regional Development Fund ERDF), the National Council for Scientific Research in Lebanon (CNRS) and Lebanese University.


L Mathieu
email: mathieu@cenbg.in2p3.fr

M Aïche

Simulations of a parallel plate Fission Chamber using the MCNPX simulation code

Keywords: parallel plate fission chamber, MCNPX simulations, alpha-FF discrimination

MCNPX in its latest version is able to simulate the transportation of Fission Fragments. It opens the door to Fission Chamber simulations. Such simulations are not straightforward and comparisons with experimental spectra often failed. A procedure is described in the present paper to perform such simulation and to process the result to obtain realistic spectra. Simulated spectra are compared with experimental ones in various different conditions to validate the method and to present its limitations.

Introduction

Ionization chambers containing a deposit of fissile material (later on referred to as Fission Chambers -FC) are very simple and versatile devices [1] used in nuclear physics fundamental research, as well as in nuclear industry applications and nuclear energy research [2-4]. In the field of nuclear data measurements, FCs are typically used to tag fission events in coincidence or in anti-coincidence with other observables. This makes possible to study either fission-related quantities, such as fission crosssections [5,6], prompt particle emission [7,8] or hardly observable phenomena, such as capture events [9]. FCs are also used as neutron flux monitors when the neutron-induced fission cross-section of the fissile deposit is very well known [10,11]. Fission events are identified by detecting at least one of the two Fission Fragments (FFs). These particles typically have to be discriminated from alpha particles arising from the natural radioactivity of the sample. The quality of this discrimination strongly depends on the characteristics of the FC itself and of the used deposit. Typically Frish grids are implemented in FCs to obtain pulse-height signals independent of the ionization track position inside the detector. This is not the case in parallel plate FCs when, due to compactness requirements, the gap between the electrodes is few millimeters. In these FCs, the gap is much smaller than the range of the particles to be detected. In the projecting phase of these latter detectors, the design and optimization of the device rely on the capability of simulations to predict the pulse-height spectrum of incident particles, and in particular of alpha particles and fission fragments. Calculations are typically based on energy loss calculations [12], such as for instance SRIM/TRIM [6,8] or the PHITS code [13]. In few cases [14] simulations make use of the GEANT4 [15] package. However, to the best of our knowledge, the particle transport code MCNPX [16] has never been used for this purpose. Nevertheless, only a handful of previous works presents simulated "pulse-height spectra" rather than "energy deposition spectra" [8,12], and fewer can withstand the comparison with experimental data. In this work, we present in Section 2 a simulation procedure to simulate the response to FFs and alpha particles of a parallel plate FC. In Section 3 and 4, simulated spectra for both a thin 252 Cf source and a thick 233 U fissile deposit respectively, are shown and compared to experimental data. The impact of different parameters (deposit characteristics, gap width, etc.) will be investigated and discussed in Section 5.

Method

The capability of the latest versions of MCNPX (2.6.0 and later) and of the more recent MCNP6 version to calculate the transport of heavy ions (i.e. nuclei heavier than helium) opened the possibility of using this code to simulate the detection of FF and alpha particles in fission chambers. Our parallel plate FC consists of two plates of aluminum separated by a 5mm gap acting as cathode and anode. The volume is filled with a 90%Ar -10%CH4 gas mixture (referred to as P10 in the following) at a pressure of 1050 mbar. A circular deposit is placed on the surface of the cathode. Its thickness is few hundreds of nanometers for the 233 U samples, and lower than a nanometer for the 252 Cf source. The deposits are assumed to be homogeneous and α-particles and FFs to be emitted isotropically from the deposit. The FF mass and energy distribution is given by the GEF code [17]. GEF is a semi-empirical model based on the observation of a common, general behavior of the fission properties of a broad variety of fissioning nuclei that is traced back to well-founded theoretical ideas. GEF calculates fission quantities with a precision that complies with the needs for applications in nuclear technology without specific adjustments to measured data of individual systems. The very good agreement of the predictions of GEF with evaluations or experimental data for the FF mass yields and FF kinetic energies of 252 Cf(SF) and 233 U(nth,f) and can be seen in figures 36, 127, 40 and 124 of [17], respectively. A multi-steps procedure allows one to obtain the simulated pulse-height spectrum, to be compared to experimental data. First, the induced charge signal is calculated based on the particle energy deposition. Second, events where both the FFs deposit energy in the FC are accounted for. And finally the pile-up between different events is included. These steps are described in the following Sub-Sections.

Induced-charge signal calculation

Electrons are generated by the passage of a charged particle in the gas. They drift towards the anode inducing an electric signal. According to the Shockley-Ramo theorem [18,19], the pulse-height, Ic, of the induced signal, is proportional to the number of electrons (and hence to the deposited energy Edep) and to the electron-drift distance x: Ic α Edep * x/d where d is the gap between the electrodes. To convert the deposited energy into a pulse-height, the gap between electrodes is (virtually) divided into several parallel layers (typically more than 10). For a given particle ionization track, the energy deposited in each layer, which corresponds to a given average x value, is retrieved from the MCNP PTRAC file and multiplied by the corresponding x/d ratio. The sum of the contributions of each layer gives the pulse-height for this track. The deposited-energy (in black) and the pulse-height (in red) spectra produced by a 252 Cf source are shown in Figure 1, for α-particles (a) and FFs (b). Spectra are normalized to unit. The signal pulseheight is expressed in MeV, rather than in V as it should, to allow its direct comparison with the deposited energy. x/d values averaged on a given track range from 0.5 (particle track straight to the anode) to ~1 (particle track parallel to the cathode). This allows one to establish the relationship between the pulse-height and the particle-emission polar-angle. In the present experimental conditions, the FF ranges are between 1.5 cm and 3 cm depending on their initial kinetic energy. As the gap is only 5 mm, a FF can only deposit all its kinetic energy if it is emitted along the cathode. The FF heavy and light peaks are observed on the right part of the FF spectra, where x/d are close to one. The different parts of these spectra will be discussed thoroughly in Section 3.

Simultaneous detection of the two fission fragments

In the fission process, two FFs are emitted back-to-back in the center-of-mass reference system, if we neglect eventual pre-scission neutron emission. Under certain conditions (no or negligible recoil of the fissioning nucleus) FFs are emitted back-to-back in the laboratory system. Usually, the FF emitted at backward angles (θ > 90° relative to the target normal) loses all its energy in the backing material of the deposit. However, if the FF is emitted at grazing angles (θ ∼ 90-91°), it may scatter in the backing and deposit some of its energy into the gas, inducing a signal. This signal adds to the signal of the FF emitted at the corresponding forward angle (θ = 89-90°). To implement this process in the final pulse-height spectrum, simulated FFs are divided into several cos(θ) bins (with narrower bins for cos(θ) ~ 0) depending on the FF initial emission direction. A randomly chosen pulse-height from one cos(θ) bin is added with a randomly chosen pulse-height of the cos(θ+π) bin. It should be noted that this procedure does not take into account real FF couples. As there is nearly no dependence of the backward-emitted spectrum to the FF properties (mass and energy) such approximation can be made. Results from all cos(θ) bins are then summed to obtain the final FF pulse-height spectrum. Figure 2 shows the pulse-height spectrum when taking into account the "simultaneous detection of the two emitted FFs", compared to the spectra obtained for the detection of only-forward (red, θ < 90°) and only-backward (green, θ > 90°) FFs. In the ''only-backward'' spectrum, events above and below 20 MeV correspond to events where FFs are emitted with 90° < θ < 91° and 90° < θ < 100°, respectively. Indeed, the probabilities of a FF being scattered outside the backing are around 60% and 15%, for the two polar angle ranges, respectively. FFs emitted at angles greater than 100° have a very low probability to be scattered out of the backing, namely 0.5‰. The two-FFs spectrum obtained shows a high-energy tail, above the energy of the light-FF peak (above 100 MeV). Figure 2: (color online) Simulated pulse-height spectra for FFs emitted in the only-forward (red) and only-backward (green) directions, and spectrum accounting for the "simultaneous detection of both FFs" (black).

Pile-up

Parallel plate ionization chambers are often used with highly α-active isotopes. In this case, the probability that the signal from an α-particles sums up in the ADC time window with the signal from a FF or another α-particle is not negligible. This effect has been taken into account for the correct simulation of pulse-height spectra, with the following procedure. One or several α-particle and/or FF events are randomly chosen accordingly to their activities and to the width of the time window. Gaussian or asymmetric triangular signals with given time constants are then randomly generated in the time window and summed. The maximum of the so-obtained signal gives the pulse-height of the event. Such procedure is obviously an approximation, since in reality the electronic signal processing is much more complex. In particular, for very high counting rates and/or slow electronic, baseline fluctuations due to non-ideal shape of signals can lead to a strongly reduced energy resolution. One to 100 billions events were simulated to properly account for low-probability events (highmultiplicity α pile-up, FF spectrum for highly radioactive sample, etc…).

Figure 3 shows α-particle (red) and FF spectra (green), normalized to their respective activities, as well as the α+FF spectrum, obtained accounting for the pile-up phenomenon. The pile-up effect mainly affects the high-energy tail of the α peak, around 10 MeV. One can notice the shape of the FF spectrum hidden under the α peak. This shape is in general agreement with the spectrum shown in [20] for the same 252 Cf source.

Energy resolution

As for other types of detectors, the same energy deposition along the same ionization track may lead to different pulse-height values, due to secondary electron production fluctuations, electron recombination fluctuations, electron screening effect, electronic noise, non-ideal electric field and gas contaminations. The energy resolution is often simulated as a Gaussian convolution with the spectrum, with a width that is function of the deposited energy. These parameters have to be measured experimentally. Contrarily to gamma-ray or proton/alpha detectors, it is very difficult to experimentally measure the energy resolution of a parallel plate ionization chamber at FF typical energies. Indeed, FFs are not mono-energetic, and, given the small dimensions of the gap, part of their kinetic energy is deposited in the opposite electrode itself. Moreover, due to the absence of a Frisch-grid the pulse-height spectrum of mono-energetic particles is broad (it depends on the position of the ionizing track in the detector). For these reasons, in this work it is decided not to include energy resolution to the simulated results.

Post-processing procedure

The procedure described in Section 2 is summarized in Figure 4. Three different simulations are needed, and represented as gray rectangles. Analysis steps are shown in ellipses, while results are presented in white rectangles. The impact of the different parameters in the simulation and in the pileup procedure is schematized as rounded rectangles. Figure 4: Steps of the simulation post-processing to infer the final FC spectrum including physics effects 3. Simulation of a 252 Cf source 3.1. Experimental spectrum Measurements were carried out with a 252 Cf source placed in a parallel plate fission chamber. The aluminum electrodes were 12 cm in diameter separated by a 5.0 mm-wide gap. A 90%Ar -10%CH4 gas mixture at 1050 mbar was used. The bias voltage was 100 V, corresponding to the maximum electron mobility for the used gas (~5 cm/µs). The source, located at the center of the cathode, is constituted of a 5.04 mm-wide disk of 20 ng electrodeposited californium oxide on a nickel-clad platinum foil. The deposit was fixed by diffusion-bonding at 1000°C and is covered with a 50 µg/cm²thick gold layer to prevent 252 Cf losses. The activity of the target was 153 kBq at the time of the measurements. The signal was collected on the anode via a CAMBERRA 2004 BT charge preamplifier. The experimental spectrum is shown in Figure 5a. In the insert, a spectrum with a lower threshold is presented to show the whole α peak. Thanks to information provided by simulations, the different parts of the spectrum can be explained unambiguously, for the FFs as well as the α-particles: -region a: lot of α particles emitted at low to medium polar angles, depositing only a small part of their kinetic energy in the gas, -region b: few α particles emitted at high polar angles, depositing all their kinetic energy in the gas, -region c: α particle pile-up with energies greater than the maximum α energy, -region d: α-FF valley, populated by FFs emitted at grazing angles (see region i), -region e: FFs emitted at very low polar angles and hitting the anode before having deposited all their kinetic energy into the gas. The two peaks corresponding to light and heavy FFs give rise to a shoulder in the simulated spectra (around 20 MeV), which is not visible in the experimental spectrum due to the poor energy resolution of the detector, -region f: FFs emitted at high polar angles, depositing all their kinetic energy into the gas; heavy and light peaks can be seen, -region g: FFs emitted at grazing angles, both simultaneously detected, -region h: pile-up of FF signals from uncorrelated events -region i: FFs emitted at grazing angles, losing part of their kinetic energy in the deposit itself. The residual energy deposited in the gas can be as low as zero. The shape of the simulated pulse-height spectrum of alpha particles presented in Figure 1a is in very good agreement with the experimental result shown in the insert of Figure 5a. A quantitative comparison cannot be carried out, as the experimental spectrum cannot be calibrated. Indeed, the maximum alpha-particle energy is the only calibration point known with a good accuracy. This is because alpha particles emitted at high angle are stopped in the gas; therefore, the maximum deposited energy is nearly independent of the FC features. On the contrary the light-and heavy-FF peaks are very broad, and their positions slightly depend on some unknown source features (as for instance the diffusion of sample atoms in the backing, see Section 3.2). Interestingly, the maximum of the FF spectrum (around channel 500) is quite narrow and independent of the source characteristic. But as FFs deposit only a part of their kinetic energy in the gas, the position of this peak depends on uncertain energy deposition calculation, which typically are affected by ~10% uncertainty. In addition, these calibration points are too close one to each other to obtain a reliable energy calibration. The maximum energy deposited by both FFs is more accurate and separated from the other points, but the determination of this calibration point suffers from strong experimental uncertainties.

Simulated spectrum

Following the steps detailed in Section 2, a simulated spectrum was calculated and it is shown as a black line on Figure 5b. Although there is a rather good global agreement between the simulated and experimental spectra, a strong discrepancy can be observed in the height of the FF peaks, which appear more prominent in the simulated spectrum. An additional step must be taken into account when dealing with very thin deposits, as the 252 Cf sources. Indeed, the average thickness of the californium deposit is lower than one atomic layer while that of the gold layer is 26 nm. These dimensions must be compared to the roughness of the backing. In addition, the 1000°C thermal treatment of the Cf deposit could have induced the diffusion of californium atoms into the backing. To characterize and simulate these effects is beyond the scope of this work. It was therefore assumed that 252 Cf nuclei are uniformly distributed over a given depth in the backing. Different geometrically increasing implantation depths have been tested. The spectra for a maximum implantation depth of 30, 100 and 300 nm are shown in Figure 5b. For higher implantation depths, the FF peaks are less pronounced and the slope of the "both FFs events" tail increases. This effect clearly reduces the discrepancies when compared to the experimental spectrum, although the agreement is not perfect. Other implantation profiles (for instance more 252 Cf atoms near the surface) have been simulated and lead to the same conclusion. The remaining discrepancy can be attributed to the non-ideal experimental device (homogeneity of the gold layer, electrodes parallelism...) compared to the simulated system. In the following, the presented simulations assume a maximum implantation depth of 30 nm with a uniform distribution. The aim of FC simulations can be to infer the FF detection efficiency. This parameter and its accuracy are crucial for some experiments using FCs [8,9,20,21]. The depth and the width of the α-FF valley are key parameters to determine the FC efficiency. Indeed, an experimental threshold is usually set in the α-FF valley and FFs below this threshold are treated as not-detected. The simulated efficiency significantly depends on experimental parameters, like the implantation depth, which are typically not known and introduce a large uncertainty. For instance, assuming a α-FF threshold at 10 MeV in the pulse-height spectrum, the obtained detection efficiencies are of 99.1%, 98.0%, 97.1% and 94.2% for implantation depths of 0, 30, 100 and 300 nm, respectively. These values have to be compared to the uncertainty associated to the experimental efficiency, measured via the prompt fission neutrons method [20], which is of (98.7±0.2)%. Therefore, it turns out that simulations cannot be used to obtain an accurate value of fission detection efficiency.

Simulation of a thick 233 U target

The procedure described in Section 2 was applied to thick targets. A 233 U target (U3O8 chemical form) produced by EC-JRC-Geel, with a thickness of about 250 µg/cm² and a diameter of 6 cm, was considered. The α-activity was 2.5 MBq. The irradiation of the target with a thermalized neutron flux at the AIFIRA facility is presented in Ref. [START_REF] Companis | [END_REF]. The fission rate was about 40 fission/s, which is a very low rate when compared to the one of the described 252 Cf source. The signal was treated with an analogic electronics and a shaping time of 0.25 µs. The experimental and the simulated spectra are shown in Figure 6 and Figure 7, respectively. The impact of the target thickness is important on the FF high-energy tail, which drops quickly. Structures can be seen in the simulated spectrum, but not in the experimental one. On the contrary, the sample thickness has little impact on the "low polar angle" FF peak at about 20 MeV. Indeed, a typical 233 U FF ( 89 Kr with a kinetic energy of 107 MeV) emitted in the forward direction deposits 0 to 3.5 MeV in the target, depending on the depth of the emission point, whereas the energy deposition in the gas only ranges from 24.7 MeV (for a surface emission) to 24.4 MeV (for a 250 µg/cm²-deep emission).

Given the high α counting rate, the α-FF valley is partly filled with α pile-up. Indeed, in the experimental spectrum as well as the simulated one, a smaller slope of the α tail can be observed when compared to the one of the 252 Cf source (see Figure 5). Moreover, the simulated FF spectrum below the α pic is rather flat, contrary to what was found for the 252 Cf case. This support the efficiency determination technique consisting in extrapolating to zero signal amplitude the floor of the α-FF valley, while it reject the assumption of a linear decrease of the FF yield down to 0 for zero energy deposition [12]. In Figure 7 the spectra obtained for a chemical pure and for the oxide U target are compared. The observed differences in the spectra arise from the presence of light but numerous oxygen atoms that participate to the particle energy loss in the target. It should be noted that the sensitivity of the spectrum to the target composition introduce an additional issue in the pulse-height spectrum simulation, as the real chemical composition of the target is not completely known. Indeed, other impurities (like O, C, N or F) may be present in the target in high quantity [START_REF] Sibbens | Proc. 28th World Conference of the International Nuclear Target Development Society[END_REF]. The target chemical composition also influences the simulated FF detection efficiency. For instance, setting a threshold in the α-FF valley, the simulated efficiencies are 94.2 and 96.9% for U3O8 and the pure 233 U sample, respectively.

Sensitivity analysis 5.1. Influence of the signal treatment

As mentioned in Section 2.3, the electronics signal treatment has an influence on the results of the pile-up effect. Combinations of FF-and α-simulated spectra for the 252 Cf source applying different electronic treatments (width of signals) have been carried out and are shown in Figure 8a. The different treatments can be schematically separated in fast and slow treatments, where the former are characterized by the use of shaping times of the order of 0.25 µs while the latter by shaping times of the order of 1 µs. A slow treatment of signals implies the presence of more events in the same time window, leading to an increase of α pile-up. This effect is observed both on the simulated and the experimental spectra (Figure 8b), as an increase of events on the right side of the alpha peak. A similar behavior is observed when the activity increases (more alphas in the same time window), as shown in Figure 9. This indicates the importance of using fast electronics, especially when the activity of the target is high (typically above 1 MBq). The high-energy part of the FF spectra, not shown in these figures, is not affected by the pile-up effect, even for high α-activities.

Influence of the bias

In typical PP FC, the sample to be studied is deposited on the cathode and electrons generated in the gas drift to the opposite electrode (anode). This implies that, closer the ionization track is to the cathode stronger is the induced signal. The opposite is true when reversing the FC polarity [12]: electrons produced by tracks close to the deposit (now the anode) will induce a weak signal. Simulated spectra with normal and reversed bias are shown in Figure 10a, while experimental spectra are shown in Figure 10b. It can be seen in both figures how reversed bias induce a less deep α-FF valley, which is filled with FF emitted at high polar angle. When the fissile material is deposited on both electrodes ("double-side coating" [START_REF] Taieb | A new fission chamber dedicated to Prompt Fission Neutron Spectra measurements[END_REF]), the FC spectrum is the sum of both spectra (grey line in Figure 10a).

Influence of the gas thickness

The pulse-height signal generated by a FC strongly depends on the amount of matter presents between the electrodes, as it determines the energy deposited by α-particles and FFs. Both the gas pressure and the gap dimension determine the "gas thickness", with roughly the same impact, provided that particles do not escape the active volume of the FC. Figure 11 shows the effect of the gas thickness (via a modification of the gap dimension) on the simulated and experimental FC spectra. A bigger gas thickness increases the energy deposited by α or FF before hitting the opposite electrode (i.e. emission at low polar angles), but has little influence on particles depositing all their energy in the gas (i.e. emission at high polar angles). As discussed in Section 3.2, the left side of the α-FF valley is determined α-particles depositing all their kinetic energy, while the right side by FFs depositing a small part of their kinetic energy. Therefore, a bigger gas thickness leads to a wider α-FF valley and thus to a better α-FF discrimination. The bottom of the α-FF valley is determined by FFs emitted at grazing angle and, therefore, it is not affected by the gas thickness. MCNPX simulations use tabulated stopping powers, which are poorly known quantities [START_REF] Filliatre | Stopping power of fission fragments of 252 Cf in argon: A comparison between experiments and simulation with the SRIM code[END_REF]. Adjustments are sometimes made on simulated spectra [8] to fit the experimental data. An error in stopping powers of FFs in the gas would be similar to a change in the gas thickness.

Influence of target thickness

The sample thickness is one of the main concerns when optimizing the design of a FC. Thicker targets give better statistics at the cost of an increased energy loss of FF and therefore a lower α-FF discrimination. No experimental data are available to validate the simulations, so these are variants of the 233 U simulation presented in Section 4. Figure 12a presents the simulated spectra obtained for different thicknesses of 233 U (U3O8 chemical form), namely 125, 250, 500 and 1000 µg/cm2. As discussed in Section 4, the sample thickness primarily affects the high-energy tail of the FF spectrum and the α-FF valley, since both regions are populated by FFs emitted at high or grazing angles. Structures in the FF spectrum also disappear as the sample thickness increases. The first FF peak around 20 MeV is only slightly affected, because FFs emitted at low angles do not lose a significant part of their kinetic energy in the target, independently of its thickness. The fission detection efficiencies are 96.8, 94.2, 88.0 and 76.3%, for thicknesses of 125, 250, 500 and 1000 µg/cm², respectively. The main part of undetected FFs stop in the deposit, with a non-exiting proportion of 3.0%, 4.5%, 8.2% and 17.0%, respectively. Real samples are often inhomogeneous. Spectra arising from thinner and thicker parts of the target mix up. This leads to a mixed high-energy tail, making FF structures to disappear meanwhile the first FF peak remains quite unchanged. The behavior of an inhomogeneous sample was calculated by averaging the spectra for different thicknesses (125, 250 and 500 µg/cm² with weights of 2, 1 and 0.5 respectively, resulting in an averaged thickness of 250 µg/cm²). The result is presented in Figure 12b. The obtained spectrum seems in very good agreement with the experimental data presented in Figure 6, especially for the absence of structures and for the slope change in the FF part of the spectrum.

Influence of the gas type

Several gases are suitable to be used in FC, and P10 is one of the most common. CF4 is also often used, for its very high electron mobility. Moreover, this gas has a molar mass of 88 g/mol to be compared to 37.6 g/mol of P10. This induces a very large α-FF valley due to the increased gas thickness. This effect can be clearly seen on the simulated spectra presented in Figure 13a. On the contrary, the two FF peaks are approximately at the same position. Figure 13b presents comparable experimental spectra (using different bias to obtain the same electron drift velocity), which are in clear disagreement with simulations. When using CF4, the valley is partly filled with FFs (αs are more or less unaffected), and FF peaks as well as high-energy tail are shifted to lower energies. It should be kept in mind that similar experimental spectra have been previously reported in literature [START_REF] Taieb | A new fission chamber dedicated to Prompt Fission Neutron Spectra measurements[END_REF][START_REF] Bacak | A compact multi-plate fission chamber for the simultaneous measurement of 233U capture and fission cross-sections[END_REF], although an efficient signal treatment allows for some improvements [START_REF] Taieb | Multi-layered parallel plate ionization chamber for cross-section measurements of minor actinides[END_REF]. Usually, the broad shape of the FF spectrum is attributed to the target thickness, however, as previously shown, it has nearly no influence on the position and narrowness of the first FF peak. The present experiment was carried out using the same 252 Cf source and changing the type of gas in the FC. It therefore proves the broad spectrum observed using the CF4 gas arise from the use of the gas itself. Several tests were carried out with different electron drift velocities with CF4 gas, as well as with P10 gas. It was observed that different shapes of the FC spectrum could be attributes to different gas mobilities. The electron drift velocity is not taken into account in our simulations. Moreover, spectra presented in literature obtained using different gases (90%Xe-10%CF4 [15], isobutene [28], methane [START_REF] Blain | A method to measure prompt fission neutron spectrum using gamma multiplicity tagging[END_REF]) show a behavior similar to the P10 gas spectrum. The unexpected shape of the spectrum obtained using CF4 gas is certainly related to the electron drift in the gas [START_REF] Christophorou | Electron Interactions with CF4[END_REF], therefore additional studies of CF4 properties would be required to obtain better simulated spectra.

Conclusion

Simulations have been carried out with MCNPX, and a post-processing procedure has been developed to reproduce parallel plate Fission Chamber spectra. The overall agreement between experimental and simulated spectra is rather good, as the global features of experimental spectra are reproduced. This provides unambiguous explanations on the physical processes involved, as the relationship between the spectrum shape and the particle emission polar angle, the energy lost in the target and the simultaneous detection of both the emitted FFs. The procedure has been used by the n_ToF collaboration with MCNPX and GEANT4 simulations [START_REF] Bacak | A compact multi-plate fission chamber for the simultaneous measurement of 233U capture and fission cross-sections[END_REF]. Nevertheless, the accuracy of the simulations is limited by the knowledge of the experimental conditions (especially the source/target characteristics). In particular, these simulations cannot be used to infer the fission detection efficiency with a good accuracy. Moreover, they fail to reproduce spectra using CF4 gas, due to specific electronic drift phenomena not implemented here. Multi-physics simulations, including FF-energy loss, electron production and drift in a specific gas may lead to more detailed and accurate results.

Figure 1 :

 1 Figure 1: (Color online) Simulated deposited-energy spectra (black) and pulse-height spectra (red) for α-particles (a) and FFs (b) emitted from a 252 Cf source.

Figure 3 :

 3 Figure 3: (color online) Simulated spectra for FFs (green) and α-particles (red) emitted by a 153kBq 252 Cf source, normalized by their activities (74 kBq in 2π for α emission and 4.7 kBq for spontaneous fission), and sum spectrum accounting for the pile-up effect (black) (gaussian signals, sigma: 0.1 µs, time window: 0.3 µs).

Figure 5 :

 5 Figure 5: (Color online) (a) Experimental spectrum of a 252 Cf source. The insert shows the α contribution obtained in a dedicated measurement with a lower threshold; (b) Simulated spectra of a 252 Cf source obtained assuming different maximum implantation depths into the backing: 0, 30, 100 and 300 nm (see text).

Figure 6 :

 6 Figure 6: Experimental pulse-height spectrum for a 250 µg/cm² 233 U target in thermal neutron flux.

Figure 7 :

 7 Figure 7: (color online) Simulated pulse-height spectra for a 250 µg/cm² 233 U target (296 µg/cm² of U3O8) as well as for a chemically pure 233 U target.

Figure 8 :

 8 Figure 8: (Color online) Simulated (a) and experimental (b) 252 Cf spectra obtained using three different shaping times, namely 0.25µs (black), 0.5µs (red) and 1µs (green).

Figure 9 :

 9 Figure 9: (Color online) Simulated spectra of a 252 Cf source with three different activities, namely 153kBq (black), 1530kBq (red) and 15 300kBq (green).

Figure 10 :

 10 Figure 10: (Color online) Simulated (a) and experimental (b) 252 Cf spectra obtained applying normal (black) or reversed (red) bias.

Figure 11 :

 11 Figure 11: (Color online) Simulated (a) and experimental (b) 252 Cf spectra for three different gas thicknesses, namely 3mm (red), 5mm (black) and 7mm (green).

Figure 12 :

 12 Figure 12: (Color online) Simulated spectra of 233 U targets (U3O8 form) (a) with different homogeneous thicknesses of 233 U, namely 125, 250, 500 and 1000 µg/cm²; (b) with homogeneous (black) or inhomogeneous (green) thicknesses.

Figure 13 :

 13 Figure 13: (Color online) Simulated (a) and experimental (b) 252 Cf spectra obtained using P10 (black) and CF4 (red) gases at about the same electron drift velocities.


domain: spi phys

text:
Guillaume Lassalle

Sophie Fabre

Anthony Credoz

Remy Hedacq

Pierre Borderies

Georges Bertoni

Dominique Dubucq

Arnaud Elger

Rémy Hédacq

Detection and quantification of total petroleum hydrocarbons in soils using vegetation optical properties

Keywords: hyperspectral remote sensing, oil, vegetation, PROSPECT, PROSAIL, pigment

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

In recent years, the use of hyperspectral remote sensing for detecting soil contamination and prospecting natural oil seepages sparked a growing interest by oil and gas companies [START_REF] Scafutto | Hyperspectral remote sensing detection of petroleum hydrocarbons in mixtures with mineral substrates: Implications for onshore exploration and monitoring[END_REF]. Several approaches have been proposed to detect and quantify oil from airborne and satellite images in the on-shore and off-shore domains. Although these approaches perform well on soils and oceans, they are ineffective in vegetated regions. However, because vegetation can be affected by oil exposure, recent studies proposed detecting oil by tracking alterations in vegetation biochemistry and optical properties [START_REF] Sanches | Assessing the impact of hydrocarbon leakages on vegetation using reflectance spectroscopy[END_REF]. Progress in this field showed that it is possible to detect and discriminate among various oil products in soils (crude oil, diesel, etc.) from leaf and canopy reflectances in the reflective domain, under experimental conditions [START_REF] Lassalle | Science of the Total Environment Detection and discrimination of various oil-contaminated soils using vegetation reflectance[END_REF]. The next step of this approach consists in quantifying oil concentration, especially Total Petroleum Hydrocarbons (TPH), which remains a major challenge. To achieve this, previous studies suggested tracking changes in leaf pigment contents using vegetation reflectance, since their alteration depends on the level of exposure to oil [START_REF] Sanches | Assessing the impact of hydrocarbon leakages on vegetation using reflectance spectroscopy[END_REF] [START_REF] Lassalle | Science of the Total Environment Detection and discrimination of various oil-contaminated soils using vegetation reflectance[END_REF]. However, the application of such approach under natural conditions, on airborne or satellite images, remains uninvestigated. In this study, we propose a method for detecting and quantifying soil TPH over vegetated areas using hyperspectral remote sensing, which can be applied to airborne or satellite images. This method relies on the retrieval of leaf pigment contents from vegetation optical properties using the PROSPECT and PROSAIL models [START_REF] Jacquemoud | PROSPECT + SAIL models: A review of use for vegetation characterization[END_REF].

MATERIALS AND METHODS

A field campaign was carried out on summer 2017 on an industrial brownfield contaminated by TPH and colonized by vegetation, mainly Rubus fruticosus L. (bramble). Spectral signatures of this species were acquired in the [400:2500] nm spectral domain using a leaf-clip connected to an ASD FieldSpec 4 Hi-Res spectroradiometer (Malvern Panalytical, Malvern, UK), on 23 points of the site (Fig. 1). 3 leaves per point were measured this way and soil TPH concentrations (16 -77 g.kg -1 ) were analyzed. Similar measurements were carried out on the same species on a control site, where no TPH were found. Leaf biochemical parameters were estimated from the spectral signatures by inverting the PROSPECT model, following the procedure described in [START_REF] Li | Retrieval of leaf biochemical parameters using PROSPECT inversion: A new approach for alleviating ill-posed problems[END_REF]. PROSPECT simulates the spectral signature of leaves from their anatomical and biochemical parameters (including pigment, water and dry matter contents) [START_REF] Lassalle | Science of the Total Environment Detection and discrimination of various oil-contaminated soils using vegetation reflectance[END_REF]. Inversion of the model allows retrieving these parameters from measured spectral signatures, using an iterative optimization approach. Chemical analyses were carried out on leaves to assess biochemical parameters estimated by the model, using the R 2 and the Root Mean Squared Error (RMSE) [START_REF] Li | Retrieval of leaf biochemical parameters using PROSPECT inversion: A new approach for alleviating ill-posed problems[END_REF]. The retrieved leaf pigment contents were compared between the two sites (brownfield and control) using ttest. Within the brownfield, pigment contents were thereafter linked to soil TPH concentrations. 50 % of the data ("train set") were used to calibrate univariate regression models, and the remaining 50 % ("test set") to assess TPH predictions. The predicted concentrations were compared to those of soil analyses on the test set using the R 2 and the RMSE. In addition, the method was assessed simultaneously at leaf and canopy scales on the same period. 10 plots of approximately 16 m 2 with known soil TPH concentrations (17 -39 g.kg -1 ) were defined. 18 and 9 spectral signatures were acquired on each plot at leaf and canopy scales, respectively. A 25 mm-wide fore optics placed 30 cm above the target at nadir was used for canopy measurements. Data were acquired in radiance and converted to reflectance using a white calibration panel [START_REF] Lassalle | Science of the Total Environment Detection and discrimination of various oil-contaminated soils using vegetation reflectance[END_REF]. Leaf biochemical parameters were estimated from leaf and canopy spectral signatures by inverting the PROSPECT and PROSAIL models, respectively. The PROSAIL model extends the use of PROSPECT to canopy measurements by accounting for the influence of vegetation structure and bare soil fraction. A planophile leaf inclination distribution function was used for inverting PROSAIL [START_REF] Balandier | Architecture, cover and light interception by bramble (Rubus fruticosus): A common understorey weed in temperate forests[END_REF]. The spectral signature of bare soil was also acquired and used in PROSAIL inversions. For both PROSPECT and PROSAIL inversions, the parameters (pigments, water, LAI, etc.) were estimated within bounds typically found in the literature [START_REF] Jacquemoud | PROSPECT + SAIL models: A review of use for vegetation characterization[END_REF]. Leaf pigment contents retrieved from leaf (PROSPECT) and canopy (PROSAIL) measurements were linked to soil TPH concentrations using the procedure described above. As a perspective of application, airborne hyperspectral images were acquired over the same study site on summer 2017. These acquisitions were carried out using HySpex VNIR-1600 and SWIR-320m-e cameras (Norsk Elektro Optikk AS, Lørenskog, Norway), with a spectral resolution of 3.7 and 6 nm, respectively. The images served for validating the proposed method over a large vegetated area.

RESULTS AND DISCUSSION

The PROSPECT model performed well for retrieving leaf biochemical parameters from the spectral signatures, especially chlorophylls (R 2 = 0.92 and RMSE = 2.65 µg.cm -2 ). Variations in these parameters explained the variability of reflectance observed within the brownfield (Fig. 1). Leaf mean chlorophyll content was significantly different between the brownfield and the control site (p < 0.05). A similar trend was observed on leaf carotenoid content. These alterations in leaf pigments and reflectance under exposure to TPH are consistent with previous work carried out under controlled conditions [2][3]. Our results show that these alterations also occur under natural conditions, which allow detecting TPH.

Leaf pigment contents were strongly related to TPH concentrations on the 23 points of the brownfield, especially chlorophylls (R 2 = 0.89) (Fig 2a). As a result, TPH were predicted with good accuracy (RMSE = 3.63 g.kg -1 ), especially for concentrations below 30 g.kg -1 (Fig. 2b). Predictions were less accurate using leaf carotenoid content (RMSE = 8.31 g.kg -1 ), because the different carotenoid pigments (ßcarotene, lutein, etc.) are not affected in the same way by exposure to TPH [START_REF] Lassalle | Science of the Total Environment Detection and discrimination of various oil-contaminated soils using vegetation reflectance[END_REF]. These results are consistent with previous work linking pigment-related vegetation indices to low TPH concentrations (< 1 g.kg -1 ) [START_REF] Zhu | Soil TPH Concentration Estimation Using Vegetation Indices in an Oil Polluted Area of Eastern China[END_REF]. The results obtained on the 23 points were confirmed at leaf and canopy scales on the 10 plots. Because TPH affect vegetation growth, the spectral signature measured at canopy scale strongly differed from that of leaves, as described in previous work [2][3]. However, because PROSAIL accounts for the influence of ground cover, leaf pigment contents estimated from the spectral signatures at canopy scale were consistent with those obtained at leaf scale using PROSPECT (Fig. 3a). Therefore, a similar relationship with TPH concentrations was observed at leaf and canopy scales (R 2 = 0.87 and 0.84, respectively) (Fig. 3b). This resulted in accurate estimations of TPH (RMSE = 2.62 and 2.85 g.kg -1 at leaf and canopy scales, respectively) (Fig. 3c). Previous work showed that TPH-induced alterations in pigments can be detected from leaf to canopy scale using vegetation reflectance, under controlled conditions [2][3]. Our method extends its use to the quantification of TPH under natural conditions and opens up promising perspectives for applications at larger scale.

CONCLUSION

This study demonstrates that soil TPH can be detected and quantified from vegetation reflectance by tracking alterations in leaf pigment contents through inversions of PROSPECT and PROSAIL models. The proposed method proved to be robust from leaf to canopy scale. This method is currently assessed on the airborne images with high spatial and spectral resolutions acquired over the same study site (Fig. 4). This step represents a major challenge for operational oil prospection and monitoring applications.

Figure 1 .

 1 Figure 1. Spectral signatures of bramble leaves measured on the 23 plots on summer 2017.

Figure 2 .

 2 Figure 2. (a) Relationship between leaf chlorophyll content and soil TPH concentrations (train set). (b) Comparisons between predicted and measured soil TPH concentrations (test set).

Figure 3 .

 3 Figure 3. (a) Comparisons between leaf chlorophyll contents predicted from leaf or canopy spectral signatures using PROSPECT and PROSAIL, respectively. (b) Relationship between leaf chlorophyll content estimated at canopy scale and soil TPH concentrations (train set). (c) Comparisons between predicted and measured soil TPH concentrations at canopy scale (test set).

Figure 4 .

 4 Figure 4. Subset of the airborne hyperspectral acquired over the study site on summer 2017 for assessing the proposed method.

AKNOWLEDGEMENTS

This work is part of the NAOMI project between TOTAL and the ONERA, with the support of Ecolab and Dynafor research units of Toulouse. Financial support of this work was provided by TOTAL.