domain: math info

text:
Diane Bailleul

Se ´bastien Ollier

Sylvie Huet

Antoine Gardarin

Jane Lecomte
email: jane.lecomte@u-psud.fr

Seed Spillage from Grain Trailers on Road Verges during Oilseed Rape Harvest: An Experimental Survey

Context: Anthropogenic vectors enhance the natural dispersal capacity of plant seeds significantly in terms of quantity and distance. Human-mediated seed dispersal (i.e. anthropochory) greatly increases the dispersal of crop species across agroecosystems. In the case of oilseed rape (OSR), spillage of seeds from grain trailers during harvest has never been quantified.

Methods: Our experimental approach involved establishing 85 seed trap-sites on the road verges of an agricultural area around the grain silo of Selommes (Loir-et-Cher, France). We recorded OSR spillage during harvest and applied a linear model to the data.

Results:

The amount of seed spilled was related positively to the area of the OSR fields served by the road, whereas the amount of seed spilled decreased with other variables, such as distance from the trap-site to the verge of the road and to the nearest field. The distance to the grain silo, through local and regional effects, affected seed loss. Local effects from fields adjacent to the road resulted in a cumulative spillage on one-lane roads. On two-lane roads, spillage was nearly constant whatever the distance to the silo due to a mixture of these local effects and of grain trailers that joined the road from more distant fields. From the data, we predicted the number of seeds lost from grain trailers on one road verge in the study area. We predicted a total spillage of 2.05610 6 seeds (64.76610 5 ) along the road length, which represented a mean of 404694 seeds per m 2 .

Conclusion:

Containment of OSR seeds will always be challenging. However, seed spillage could be reduced if grain trailers were covered and filled with less seed. Reducing distances travelled between fields and silos could also limit seed loss.

Introduction

Quantifying dispersal is crucial for understanding the population dynamics and spatial distribution of species [START_REF] Nathan | Spatial patterns of seed dispersal, their determinants and consequences for recruitment[END_REF]. In the case of annual plants, dispersal is governed in general by seed flow [START_REF] Bullock | Plant dispersal and colonization processes at local and landscape scales[END_REF]. Seed dispersal is often a complex phenomenon that involves different vectors [START_REF] Ozinga | Dispersal potential in plant communities depends on environmental conditions[END_REF], which can be characterised on the basis of their nature (biotic, abiotic or anthropogenic), their temporal action (primary or secondary dispersal), and by the range of distances covered (short or long distance dispersal).

Human-mediated seed dispersal (i.e. anthropochory) influences plant dispersal greatly in areas where human activities are intense [START_REF] Hodkinson | Plant dispersal: the role of man[END_REF][START_REF] Lu | Transgene escape from GM crops and potential biosafety consquences: An environmental perspective[END_REF]. A huge range of anthropogenic vectors disperses seed. For example, seeds can be trapped in mud on footwear [START_REF] Clifford | Seed dispersal on footwear[END_REF][START_REF] Wichmann | Human-mediated dispersal of seeds over long distances[END_REF] or on cars [START_REF] Clifford | Seed dispersal by motor vehicles[END_REF][START_REF] Schmidt | Plant dispersal by motor cars[END_REF][START_REF] Lonsdale | Tourist vehicles as vectors of weed seeds in Kakadu National Park, Northern Australia[END_REF][START_REF] Pauchard | Influence of elevation, land use, and landscape context on patterns of alien plant invasions along roadsides in protected areas of South-Central Chile Influencia de la altitud, uso de suelo y contexto del paisaje sobre invasiones de plantas exo ´ticas[END_REF][START_REF] Kalwij | Human activity facilitates altitudinal expansion of exotic plants along a road in montane grassland, South Africa[END_REF][START_REF] Hochrein | Corridors and plant invasions: a comparative study of the role of roadsides and hiking trails on plant invasions in Moorea, French Polynesia The[END_REF]. Cars can disperse seeds in different types of environment as they can travel very long distances [START_REF] Hodkinson | Plant dispersal: the role of man[END_REF][START_REF] Schmidt | Plant dispersal by motor cars[END_REF][START_REF] Zwaenepoel | Motor vehicles as vectors of plant species from road verges in a suburban environment[END_REF]. However, car-mediated dispersal is limited by the fact that, in general, cars carry only a small number of seeds. In contrast, in agroecosystems, agricultural machinery can disperse large quantities of seed. Seed can be scattered by mowing machinery [START_REF] Strykstra | Seed dispersal by mowing machinery in a Dutch brook valley system[END_REF][START_REF] Bullock | Habitat-specific dispersal: environmental effects on the mechanisms and patterns of seed movement in a grassland herb Rhinanthus minor[END_REF] and harvesters [START_REF] Yoshimura | Transgenic oilseed rape along transportation routes and port of Vancouver in western Canada[END_REF][START_REF] Ghersa | Seed dispersal, distribution and recruitment of seedlings of Sorghum halepense (L)[END_REF], either in situ or while being driven [START_REF] Yoshimura | Transgenic oilseed rape along transportation routes and port of Vancouver in western Canada[END_REF][START_REF] Mccanny | Spread of proso millet (Panicum miliaceum L.) in Ontario, Canada. II. Dispersal by combines[END_REF]. However, none of the above-mentioned studies, except for Bullock et al. [START_REF] Bullock | Habitat-specific dispersal: environmental effects on the mechanisms and patterns of seed movement in a grassland herb Rhinanthus minor[END_REF], have conducted precise measurements of the amounts of seed and dispersal distances, which are required to characterise seed dispersal [START_REF] Nathan | Spatial patterns of seed dispersal, their determinants and consequences for recruitment[END_REF].

Quantifying anthropochory is particularly necessary in agroecosystems, in which seed flow from crops could lead to the establishment of feral populations. Feral populations are crop populations which establish in uncultivated areas, such as road verges. Oilseed rape (OSR; Brassica napus L.) is one of the most commonly cultivated crops in the European Union (6.3610 6 ha of OSR was cultivated in 2009 [START_REF]CETIOM : Centre Technique Interprofessionnel des Ole ´agineux Me ´tropolitains[END_REF]), and feral populations of OSR show a strong capacity to persist and retain traits from varieties that are no longer grown [START_REF] Pessel | Persistence of oilseed rape (Brassica napus L.) outside of cultivated fields[END_REF]. In particular, the spread and persistence of genetically modified (GM) feral populations of OSR are expected to lead to ecological, agricultural, and economic issues, because feral GM plants could constitute reservoirs of transgenes [START_REF] Squire | Status of feral oilseed rape in Europe: its minor role as a GM impurity and its potential as a reservoir of transgene persistence[END_REF]. GM OSR feral populations emerged from seed import spilled during transportation [START_REF] Yoshimura | Transgenic oilseed rape along transportation routes and port of Vancouver in western Canada[END_REF][START_REF] Saji | Monitoring the escape of transgenic oilseed rape around Japanese ports and roadsides[END_REF]. Moreover the cultivation of GM OSR has been shown to lead to GM feral populations [START_REF] Schafer | The establishment of genetically engineered canola populations in the U[END_REF] and to GM volunteers, even after GM cultivars are no longer used [START_REF] Lutman | Persistence of seeds from crops of conventional and herbicide tolerant oilseed rape (Brassica napus)[END_REF][START_REF] Beckie | Persistence of an oilseed rape transgene in the environment[END_REF]. GM feral populations are able to exchange genes through pollen flow with other GM feral populations and with GM fields [START_REF] Schafer | The establishment of genetically engineered canola populations in the U[END_REF][START_REF] Knispel | Landscape-scale distribution and persistence of genetically modified oilseed rape (Brassica napus) in Manitoba, Canada[END_REF][START_REF] Aono | Detection of feral transgenic oilseed rape with multiple-herbicide resistance in Japan[END_REF]. Seed from OSR could also survive in feral soil seedbanks [START_REF] Pivard | Processus e ´cologiques implique ´s dans la pre ´sence d'une plante cultive ´e dans les milieux naturels et semi-naturels d'un agroe ´cosyste[END_REF] thus the survival of GM OSR seeds in feral soil seedbanks should as well increase the persistence of transgenes in the environment.

Some studies have attempted to determine the origins of feral OSR populations [START_REF] Colbach | GS: a model of the influence of cropping system on gene escape from herbicide tolerant rapeseed crops to rape volunteers: II. Genetic exchanges among volunteer and cropped populations in a small region[END_REF][START_REF] Pivard | a) Where do the feral oilseed rape populations come from? A large-scale study of their possible origin in a farmland area[END_REF][START_REF] Crawley | Spatially structured population dynamics in feral oilseed rape[END_REF][START_REF] Pivard | Characterizing the presence of oilseed rape feral populations on field margins using machine learning[END_REF][START_REF] Garnier | Measuring and modelling anthropogenic secondary seed dispersal along roadverges for feral oilseed rape[END_REF]. With the exception of the mechanism of pod shattering, which is a natural but spatially limited ballistic mode of seed dispersal [START_REF] Colbach | GS: a model of the influence of cropping system on gene escape from herbicide tolerant rapeseed crops to rape volunteers: II. Genetic exchanges among volunteer and cropped populations in a small region[END_REF], the origin of feral populations seems to be linked to agricultural machinery and vehicle-mediated seed dispersal. Pivard et al. [START_REF] Pivard | a) Where do the feral oilseed rape populations come from? A large-scale study of their possible origin in a farmland area[END_REF] found that feral populations originated mainly from seeds that were scattered from adjacent fields. Crawley & Brown [START_REF] Crawley | Spatially structured population dynamics in feral oilseed rape[END_REF] found that feral populations are rarely correlated with previous adjacent field presence. Furthermore, seed banks that originate from scattered seeds are responsible for the persistence of feral populations over time rather than local recruitment [START_REF] Pivard | Characterizing the presence of oilseed rape feral populations on field margins using machine learning[END_REF]. From road verges, seeds can be blown over many metres (maximum distance 21.5 m) by the air displacement caused by vehicle traffic [START_REF] Garnier | Measuring and modelling anthropogenic secondary seed dispersal along roadverges for feral oilseed rape[END_REF].

Surveys of OSR populations in various countries have demonstrated the major role of seed spillage from grain trucks in the spread of OSR which is correlated closely with the transport network. In France, 15% of feral populations were found to originate from the transport of seed by trailers and trucks [START_REF] Pivard | a) Where do the feral oilseed rape populations come from? A large-scale study of their possible origin in a farmland area[END_REF]. In Canada, the presence and persistence of feral populations of GM OSR were correlated with the transportation routes of trucks, for example, between fields and grain silos [START_REF] Yoshimura | Transgenic oilseed rape along transportation routes and port of Vancouver in western Canada[END_REF][START_REF] Knispel | Landscape-scale distribution and persistence of genetically modified oilseed rape (Brassica napus) in Manitoba, Canada[END_REF]. Similarly, in Great Britain, seed spillage from trucks was correlated with the persistence of feral OSR populations [START_REF] Crawley | Spatially structured population dynamics in feral oilseed rape[END_REF][START_REF] Crawley | Seed limitation and the dynamics of feral oilseed rape on the M25 motorway[END_REF]. In Japan, where no GM OSR is cultivated, feral GM populations were found between ports into which GM seed is imported and oilseed processing facilities [START_REF] Aono | Detection of feral transgenic oilseed rape with multiple-herbicide resistance in Japan[END_REF][START_REF] Saji | Monitoring the escape of transgenic oilseed rape around Japanese ports and roadsides[END_REF][START_REF] Nishizawa | Monitoring the occurrence of genetically modified oilseed rape growing along a Japanese roadside: 3-year observations[END_REF][START_REF] Kawata | Dispersal and persistence of genetically modified oilseed rape around Japanese harbors[END_REF].

Quantitative estimates of the spillage of OSR seed by grain trailers or trucks have been obtained indirectly through surveys of feral populations of OSR but have been rarely measured directly. To our knowledge, the only study that has quantified the dispersal of OSR seed by vehicles was by von der Lippe and Kowarik (2007a, 2007b, 2008), who conducted seed trapping experiments in tunnels on a motorway between Berlin (Germany) and its agricultural suburbs. They found 6252 viable seeds, of which the most abundant were wheat, cereal rye, and OSR. The type of seed trapped was associated with the direction of traffic flow through the tunnels [START_REF] Der Lippe | Crop seed spillage along roads: a factor of uncertainty in the containment of GMO[END_REF][START_REF] Von Der Lippe | Long-distance dispersal of plants by vehicles as a driver of plant invasions[END_REF][START_REF] Der Lippe | Do cities export biodiversity? Traffic as dispersal vector across urban-rural gradients[END_REF]. In general, few seed trapping studies have been conducted [START_REF] Cutway | The influence of urban land use on seed dispersal and wetland invasibility[END_REF] and even fewer studies have quantified long distance human-mediated dispersal [START_REF] Wichmann | Human-mediated dispersal of seeds over long distances[END_REF]. However, the quantification of OSR seed dispersal by grain trailers and trucks is necessary to estimate the risk of long distance seed dispersal from cultivated fields and, consequently, the establishment of feral populations.

To improve knowledge of the dispersal of OSR seed in agroecosystems, we performed an experiment using traps. We laid seed traps on road verges located near OSR fields to measure seed spillage from grain trailers during harvest. We then used statistical models to explain the amount of trapped seed according to several covariables that described the road network, as well as local and landscape elements. We therefore make the five following assumptions:

(1) The number of seeds spilled from grain trailers onto the road verge should be related positively to the total area of OSR fields served by the selected road. Indeed, the number of grain trailers needed to transport the harvest from the fields to the grain silo should be proportional to the area of OSR fields harvested. (2) Seed spillage from grain trailers should be greater on two-lane roads (main roads) than on one-lane roads. Drivers of grain trailers heading to the grain silo are expected to use more rapid and direct two-lane roads where possible. (3) The number of seeds spilled from grain trailers should decrease with distance from the nearest OSR field. Grain trailers that have been overfilled with seeds in the field would lose increasingly less seed on the roads as the excess of seed decreases. (4) The amount of seed spilled on road verges should decrease with increasing distance to the edge of the road because more seed is spilled on and near the road. (5) Seed spillage from grain trailers should increase near the grain silo. All grain trailers converge at the grain silo so seed spillage from trailers should also converge at the grain silo.

Results

We collected a total of 7710 seeds among the 85 trap-sites. We found seeds every day on every road. The amount of seed spilled during the 8-day experiment varied greatly among trap-sites: it ranged from 0 (for example, trap-site 26 on R8, see Figure 1) to 1567 seeds per trap-site (trap-site 50 on R12) with an average of 90.7 seeds per trap-site 6185.1. The spatial variability of seed deposition was higher at the landscape scale than at the road scale. Indeed, the between-roads variability (standard deviation of mean amount of seed per trap-site per road) was 188.9 seeds per trap-site per road and the within-roads variability (mean of standard deviation per trap-site per road) was 115.9 seeds per trap-site per road.

Spatio-temporal patterns

There was substantial temporal variability with respect to the number of seeds collected at a trap-site. The mean deposition of seed during the 8-day experiment ranged from 0 on R8 to 692.06794.9 seeds per trap-site on R12. Seven trap-sites on onelane roads did not collect any seed (R8 and R9 roads, see Figure 1). All the trap-sites on two-lane roads collected seeds during the survey. The maximum number of seeds collected at a trap-site was 1548 seeds on the third day at trap-site 50 on R12 (which collected a total of 1567 seeds during the 8-day survey). Only seven trapsites collected seeds on every day (trap-sites 36, 59, 80, 81, 103, 87, and 88). However, in general, trap-sites that were close to each other showed the same pattern of daily seed deposition (see, for example, trap-sites 54 and 55 on R4, Figure 1).

Almost all trap-sites collected seeds, regardless of the distance to the nearest OSR field: 92% of the trap-sites at 0 m received seeds, 90% of the trap-sites at 40 m, and 94% of the trap-sites at 400 m.

We did not investigate these spatiotemporal patterns any further because this was not the goal of our study, but they were highly informative in the case of roads R11 and R12, (Figure 1) which received substantial amounts of seed on the third day of the survey. This abundant spillage of seeds could not be related to any of the OSR fields in the study area (data not shown). Grain trailers seemed to be heading along these roads from the south to another grain silo in the north, outside our study area. Therefore, we decided to exclude the data from the five trap-sites on these two roads R11 and R12 from the subsequent statistical analyses.

The stepwise procedure based on the AIC criterium selected the best-fit linear regression model included all the principal variables and the interaction between Lanes and Dist_Silo (see Table 1). The referential for the qualitative variable Lanes was one-lane roads and for Dist_field, 0 m. Furthermore, with respect to Dist_field, the difference between 0 and 40 m was significant (P,0.05), and not significant between 0 and 400 m (P = 5.60610 22 , data not shown). Data for sites at 40 m and 400 m were thus combined into a new variable: faraway.

The amount of seed lost on road verges increased with the surface of OSR fields serviced and decreased with distance to the field, distance to the road and distance to the silo (see Figure S1). Indeed, the slope of the linear regression between ln(Seeds+1) and Surface_OSR was positive (9.86?10 23 , P,0.05) and negative with ln(Dist_road) (20.48, P,0.05, see Table 1). When Dist_field was faraway, the expected response decreased by 0.64 (P,0.05). As for the interaction between Dist_Silo and Lanes, these results were interpreted as followed: the number of lanes was not significant (P.0.05) thus Lanes had no effect on the origin of the regression line. As for the slope, on a one-lane road, when Dist_field was 0 m, the expected response decreased by 4.45610 24 with Dist_Silo (P,0.05) while on a two-lane road the slope was negative (21.49610 24 , P = 0.264, data not shown) but was not significant and thus interpreted as null.

The graphic of standardized residuals versus fitted values does not show any particular structure, confirming thus that the logarithm transformation stabilized well the variance of the observed response. Moran's test for regression residuals was not significant (0.41, P = 0.34): residuals of the model were spatially independent according to a neighborhood defined as the inverse of the shortest distance by the road network between two trap-sites and due to the introduction of Dist_Silo.

Predictions

We predicted the number of seeds spilled onto the verge of the two-lane paved road R2 owing to transport by grain trailer. A total spillage of 2.05610 6 seeds (1.09610 6 -3.00610 6 , 95% confidence interval) were predicted by our model, which represented the harvest of 26 m 2 of OSR (the average OSR harvest in situ is 78849 seeds per m 2 for an efficiency of 35 quintal per hectare, with 9.5% of harvest lost in situ and 15% humidity [START_REF]CETIOM : Centre Technique Interprofessionnel des Ole ´agineux Me ´tropolitains[END_REF]) and a mean dispersal of 404 seeds per m 2 694.

Emergence Rates

After planting, seeds emerged in nearly every plot (94.4%). The mean rate of emergence for the collected seeds was 80.7%. Some seeds (11.9%) were damaged: half broken or a portion of their tegument detached. Damaged seeds had a lower emergence rate than undamaged seeds (36.8% vs 90.8%).

Discussion

Spillage of OSR seed from grain trailers during harvest appears to be a very common phenomenon, affecting both main roads to the grain silo and other roads. We showed that seed spillage increased with the surface area of OSR fields served, whereas local elements (e.g. distance from the trap-site to the verge of the road and to the nearest field) decreased the amount of seed spilled at a given site. We found no effect of the number of lanes of the roads alone on the number of seeds lost. We detected an interaction between the number of lanes and the distance to the main silo that influenced the slope but not the origin of the regression line. Seed loss decreased with distance to the main silo on one-lane roads but was constant on two-lane roads. On a road that serviced 66 ha of OSR fields, we predicted the spillage on the verge in the direction of transportation to be nearly two million seeds. In the context of GM crops cultivation in agroecosystems, these results emphasise the need to introduce the landscape complexity in models predicting the presence and persistence of GM OSR feral populations.

To our knowledge, there have been no previous studies on the amount of crop seed lost on road verges from grain trailers. Few studies have quantified seed dispersal by vehicles. For example, a study of car-mediated dispersal of seeds stuck into mud attached to vehicles was conducted on Flemish roads [START_REF] Zwaenepoel | Motor vehicles as vectors of plant species from road verges in a suburban environment[END_REF], in which the carborne flora included seeds quite similar to OSR seeds (small, light seeds able to establish a persistent seedbank). The results predicted that an average of 2.6 million cars disperse 7.8 million seeds each year on Flemish roads. In contrast, our prediction of OSR seed spillage by grain trailers was much higher: nearly 2 million seeds lost in only 8 days on a single road of 5 km. Von der Lippe & Kowarik [START_REF] Von Der Lippe | Long-distance dispersal of plants by vehicles as a driver of plant invasions[END_REF] laid traps in tunnels in suburban Berlin to collect seed spilled from vehicles. They estimated an annual seed loss that ranged from 2 to 67 seeds per m 2 and 0.4 to 14.4 seeds per trap, whereas we obtained an average of 11 seeds per trap-site per day with smaller traps, so approximately 66 seeds per m 2 per day. Thus, if comparison is possible between these three studies, grain trailers appear to contribute more to seed dispersal than cars and seed dispersal seems to be more abundant in agricultural areas than in suburban areas.

On verges, seeds were spilled mostly near the road, regardless of the type of road, despite our preliminary hypothesis. No previous study on dispersal is dealing with road type and grain trailers or trucks to our knowledge either. From what we observed on field, the absence of interaction between the distance to the road and the type of road on the amount of seed collected could be explained by different styles of driving on the different types of road. Seed losses through cracks in the back doors are presumed negligible and must occur mostly from the top of the trailer, which was rarely covered in our study area. Wind turbulence across the top increased with speed and so, seeds spilled. On narrow one-lane roads, grain trailers were driving slower and nearer to the verge, whereas on two-lane roads, grain trailers drove faster and in the middle of the road (pers. obs.) but also seemed to allow seeds to disperse further.

As for the relationship between the number of seeds spilled and the distance to the field, our hypothesis was confirmed by the statistical analysis. Less seed was spilled at trap-sites located 40 m and 400 m from the field than at field borders. Indeed, grain trailers are commonly overfilled with seeds. The excess seed seems to be usually spilled within the first 10 m that the grain trailers drove on the road. When the excess had been lost and the seeds had been compacted in the trailer, spillage decreased. Given that the amount of seeds lost decreased with distance from the field, we presumed that at a certain distance from the field, spillage should cease.

Distance to the silo was a predominant variable that interacted with the number of lanes and the geography of the area. The result that seed spillage decreased with the distance to the silo on onelane roads was expected. However, seed spillage on two-lane roads was constant with the distance to the silo. Few grain trailers travelled on one-lane roads, and for short distances because the grain silo was at the centre of the area and one-lane roads were in general peripheral. Therefore, seed spillage on one-lane roads should be related only to local fields. Two-lane roads always led to the grain silo and thus received also seed spillage from all the neighbouring fields from other roads. This led to a non-structured and more heterogeneous cumulative pattern than on one-lane roads.

The unique event that spilled 1500 seeds (i.e. 20% of the total number of seeds collected during the eight days) at a single trapsite could not be correlated with any OSR field in the study area. The roads that were affected by the high levels of spillage on day 3 of the study period (R11 and R12) were on the edge of the study area and the grain trailers involved in the spillage probably originated from a farm outside the study area. This massive deposition of seeds also demonstrated that grain trailers can disperse seeds over distances greater than 400 m. Despite the large amounts of seed spilled and the high overall emergence rate under greenhouse conditions (80.7%), no feral populations were observed on road R12 in 2011, or during the previous 10 years. The road R12 is bordered by forest and low availability of light could explain the absence of feral OSR. Indeed, the deposition of seed is not necessarily related to adult recruitment, because the environmental conditions where seeds fall play a major role [START_REF] Schupp | Spatial patterns of seed dispersal and the unification of plant population ecology[END_REF].

We showed that grain trailers frequently dispersed seeds up to a distance of 400 m. This is the farthest dispersal distance for OSR seed ever quantified. Environmental conditions such as traffic [START_REF] Garnier | Measuring and modelling anthropogenic secondary seed dispersal along roadverges for feral oilseed rape[END_REF] and wind velocity favoured secondary dispersal (shown for winddispersed seeds [START_REF] Soons | Human effects on long-distance wind dispersal and colonization by grassland plants[END_REF][START_REF] Soons | Determinants of longdistance seed dispersal by wind in grasslands[END_REF]). Seed dispersal under these conditions could increase dispersal distances and trapped amounts of seeds. Inversely, owing to our experimental design, dispersal distances might have been highly underestimated because seeds could be dispersed further if not trapped [START_REF] Lemke | New opportunities for an old method: using fluorescent colours to measure seed dispersal[END_REF].

Synthesis and Perspectives

Despite its limitations, this study is the first to quantify the spillage of OSR seed from grain trailers, highlight the importance of this phenomenon, and explain seed loss on the basis of local and landscape elements.

According to Pivard et al. [START_REF] Pivard | a) Where do the feral oilseed rape populations come from? A large-scale study of their possible origin in a farmland area[END_REF], feral populations of OSR originate from the immigration of seed from fields (35-40%), from local recruitment (10%), from persistent seed banks (35-40%), and from seed transportation (15%). From all the potential sources of feral populations, spillage from grain trailers is the only one which can disperse OSR seeds farther than tens of metres. Although emergence rates of collected seeds in the field were high, conditions in situ will determine rates of seed germination, survival or secondary dormancy. In a context where GM plants are cultivated, GM OSR seeds will be dispersed farther than expected by actual estimations. Road verges management should take into account these dispersal distances. Although it might be impossible to eliminate the spillage of small seeds, such as OSR, during transportation, spillage from grain trailers could be reduced by the introduction of appropriate policies. For example, covering the top of grain trailers, not overfilling trailers, or reducing the distances travelled between fields and silos could limit the loss of seed.

In general, seed spillage from grain trailers is not considered or is underestimated in models for seed dispersal and the persistence of feral populations of GM OSR [START_REF] Middelhoff | GeneTraMP, a spatio-temporal model of the dispersal and persistence of transgenes in feral, volunteer and crop plants of oilseed rape and related species[END_REF][START_REF] Colbach | How to model and simulate the effects of cropping systems on population dynamics and gene flow at the landscape level: example of oilseed rape volunteers and their role for co-existence of GM and non-GM crops[END_REF]. To enable seed dispersal by grain trailers to be integrated into mechanistic models, further study of the phenomenon is required. To perform genotypic analyses, we collected mature leaves from the germinated seeds from the traps and from OSR fields cultivated in 2010. By determining genotypes in specific fields, we would be able to assign trapped seeds to particular sources and thus evaluate realistic dispersal distances. Our study has highlighted the role of the surface area of fields and transportation to grain silos in seed dispersal by grain trailers. Local and landscape elements and rare events of massive seed deposition should also be integrated into models evaluating GM crops escape and their persistence.

Materials and Methods

Study Area

The study area was a typical open-field agricultural landscape of 41 km 2 centred on the village of Selommes, Loir-et-Cher, France (47u459240N; 1u119340E) which contains a grain silo where most local farmers take their harvested grains (see Figure 1). Local farmers use grain trailers to transport their seeds from fields to silo. In 2010, 185 field plots of winter OSR were cultivated in this area over a total of 684 ha (16.7% of the area). Feral populations of OSR were found on 10-14% of the road verges each year over the last ten years [START_REF] Pivard | Characterizing the presence of oilseed rape feral populations on field margins using machine learning[END_REF]. Fields, populations, roads, and many landscape elements were recorded and mapped. Roads were categorised as paths (42% of roads) and one-lane (29%) or two-lane (29%) paved roads.

No specific permission was required for this study as french roads are public areas. Road verges are not privately-owned or protected. The DDE (Direction De ´partementale de l'Equipement, in charge of road verges management), town mayors and farmers were informed about the study before the experiment.

Experimental Design

We focused on the study of paved roads more than 600 m long as this length was sufficient to put several traps at various distances from fields. We used as reference a survey from local farmers about grain trailer trips between fields and the main silo (survey data, [START_REF]CETIOM : Centre Technique Interprofessionnel des Ole ´agineux Me ´tropolitains[END_REF]). Based on this survey, we computed the area of adjacent OSR fields along each road. Then, the 21 roads were classified on the basis of the area of the OSR fields that they served (four increasing categories of field areas) and the number of lanes (one or two). We randomly selected two roads when possible in each of the eight categories. Twelve roads were then selected, half in each lane category. The regular presence of feral populations along almost all the possible roads was a rough indication of seed loss from grain trailers.

Traps were placed at the beginning of the OSR harvest season in 2010. Over the subsequent eight days, all traps were checked daily (except for the final record, which was taken after a gap of two days) and emptied of seeds. At the end of the experiment, all the surrounding fields have been harvested. Grain trailers are the only seed transportation vehicles circulating during harvest.

Each trap consisted of two rectangular plastic saucers (1726425 mm) that were positioned side by side to form a 1-m long trap, parallel to the road, the nearest as possible to the road, and secured to the ground with two camping pegs each. A fine layer of cotton wool was placed in the base of each trap to prevent seeds from ricocheting out. Vegetation around the traps was cut back before installation to prevent it from disturbing the fall of the seeds into the traps [START_REF] Thiede | Intraspecific variation in seed dispersion of Lepidium campestre (Brassicaceae)[END_REF]. The two saucers were collected together because considered as single trap.

Data collection

We pooled the number of seeds collected in each trap over the eight days. Thus, the dependent variable in our model was the total number of seeds.

To explain the total number of seeds (Seeds, quantitative variable) found in the traps over the eight days, five explanatory variables were defined (see Table 2):

(1) Surface_OSR: the total area of OSR fields serviced by the selected road. We calculated these areas according to the survey of trailer trips. (2) Lanes: the number of lanes of the selected road. Two-lanes roads are generally straight and heading to the grain silo whereas one-lane roads are making path between two-lanes roads or fields and two-lanes roads. (3) Dist_field: distance between each trap-site and the nearest OSR field. We placed traps at 0 m, 40 m, and 400 m from OSR field, along the side of the road that was directed towards the main grain silo. This gave a total of 85 trap-site locations: 37 at 0 m, 30 at 40 m, and 18 at 400 m. In addition to grain trailers, combine harvesters also scatter crop seed in fields [START_REF] Lu | Transgene escape from GM crops and potential biosafety consquences: An environmental perspective[END_REF][START_REF] Price | Seed losses in commercial harvesting of oilseed rape[END_REF] and on the borders of fields [START_REF] Mccanny | Spread of proso millet (Panicum miliaceum L.) in Ontario, Canada. II. Dispersal by combines[END_REF]. To measure only spillage from grain trailers, trap-sites located at 0 m from fields were put adjacent to the fields, with a gap of about 5 m. (4) Dist_road: distance from the edge of the road to the trap-site on the road verge. Even if traps were placed the nearest as possible to the road, theirs positions differ according to the road or the soil nature. We also tested the interaction between this variable and Lanes. Given that one-lane roads are narrow and two-lane roads are straight and wide, grain trailers are expected to drive differently on these roads, which should affect the pattern of seed spillage on road verges. (5) Dist_Silo: distance between each trap-site and the main Silo of Selommes. As each trap was mapped, the distance to the main silo was computed through the road network. We tested also the interaction between Lanes and Dist_Silo.

Statistical Modelling

The relation between the observed amounts of seeds and the covariates is modeled via a linear model where the dependent variable is ln(Seeds+1). The model included all the variables (see Table 1) and the interactions between Lanes and Dist_Silo, and between Lanes and Dist_road. The dependent variable Seeds was lntransformed to stabilise the variance to fulfil the assumptions of the linear model. Dist_road was also considered through its logarithm as ln(Dist_road). We chose the model that fit our data best according to Akaike's information criteria (AIC) and analysis of residuals and partial residuals of the explanatory variables.

Model analysis

To evaluate the spillage of OSR seeds from grain trailers along road verges, predictions were made using the best linear model identified previously. This model was used to predict the total number of seeds that dispersed along the verge in the direction of traffic flow to the silo on a given road; road R2 was selected (Figure 1). R2 is 5078 m long and services 28 OSR fields with a total area of 66.4 ha. Road verges were approximately one metre wide. To make predictions on a squared metre at a time, as trapsites were one metre long and 17.2 cm wide, we predicted seed amounts at 6 different distances from the edge of the road to obtain a metre wide, for every metre of the R2 road.

The predict function calculated the predicted amounts of seed and we approximated a prediction interval at the road scale.

Emergence Rate of Collected Seeds

To test for seed viability and identify OSR, we germinated every seed collected in rich soil under greenhouse conditions. All the seeds collected on one day from one trap-site were grown in the same plot. For one sample that comprised more than 1500 seeds, we only planted 140 seeds. Given that the emergence rate for this plot was 100%, we did not test any more seed from this sample. We surveyed the seedlings until their first leaves appeared.

We used R software [START_REF]R: A language and environment for statistical computing[END_REF] for all the analysis.

Table 2. The five explanatory variables for the number of seeds recorded in trap-sites.

Modalities Description

Surface_OSR (ha) From 2.8 to 147.5 Total area of all OSR fields adjacent to the road and connected to the road by network 1 Based on a farmer survey concerning trips between fields and silos (from CETIOM data [START_REF]CETIOM : Centre Technique Interprofessionnel des Ole ´agineux Me ´tropolitains[END_REF], 1999).

2

Only four trap-sites were placed at more than 1 m.

3 Calculated along a given road from georeferenced data with R software. doi:10.1371/journal.pone.0032752.t002

Figure 1 .

 1 Figure 1. Global map of the Selommes area with daily barplots of OSR seed collected at each trap-site location. Seed amounts are ln(x+1) transformed; all barplot axes have the same scale between 0 and 8 (which indicates 0 to 2980 seeds). Each trap-site is associated with a number. Each road is represented by an R-number code. D1 is the first day of the survey and D8 the last day. doi:10.1371/journal.pone.0032752.g001

Lanes 1 or 2

 2 Number of lanes on the road Dist_field (in m) 0, 40 or 400 then 0 or faraway Distance between the trap-site and the nearest OSR field Dist_road (in m) from 2.4610 22 to 1.7 2 Distance between trap-sites and the edge of the road Dist_Silo (in m) From 1073 to 8848 Distance between the trap-site and the grain silo at Selommes 3 1

Table 1 .

 1 Summary of the linear model describing relationships between the natural logarithm of seeds lost +1 and landscape elements.

		Estimate	Standard error P-value
	(Intercept)	4.259	0.820	1.81610 26 ***
	Surface_OSR	9.93610 23	4.23610 23	2.35610 22 *
	Dist_field_faraway 20.644	0.236	7.84610 23 **
	ln(Dist_road)	20.470	0.183	1.21610 22 *
	Lanes_2	20.617	0.800	0.442
	Dist_Silo	24.45610 24	1.13610 24	1.97610 24 ***
	Dist_Silo:Lanes_2	2.95610 24	1.71610 24	8.83610 22 .
	#10%;
	*#5%;
	**#1%;
	***#0.1%.
	On a two-lane road, when Dist_field is 0 m, the expected response equals
	(4.25920.617)+9.93610 23 ?Surface_OSR20.4706ln(Dist_road)+(24.45610 24 +
	2.95610 24 )?Dist_Silo = 3.642+9.93610 23 ?Surface_OSR20.4706ln(Dist_road)2
	1.491610 24 ?Dist_Silo.
	doi:10.1371/journal.pone.0032752.t001

.

PLoS ONE | www.plosone.org

March 2012 | Volume 7 | Issue 3 | e32752

Acknowledgments

We are grateful to A. Garnier for her wise and helpful advice on the manuscript, to F. Loesch for her essential help during fieldwork, to C. Pelle and L. Saunois for their work in the greenhouse, and to the CETIOM for collaboration and the information it provided. We also thank the two reviewers for their valuable comments.

This work was funded by the Agence Nationale de la Recherche (project GMBioImpact, ANR-06-POGM-04). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Supporting Information


V Maire

G Alvarez

J Colombet

A Comby

R Despinasse

E Dubreucq

M Joly

A.-C Lehours

V Perrier

T Shahzad

S Fontaine

Extracellular respiration

The respiratory release of CO 2 from soils is a major determinant of the global carbon cycle. It is traditionally considered that this respiration is an intracellular metabolism consisting of complex biochemical reactions carried out by numerous enzymes and co-factors. Here we show that the intracellular enzymes released from dead organisms are stabilized in soils and have access to suitable substrates and co-factors to permit function. These enzymes reconstitute an extracellular oxidative metabolism (Exomet) that may substantially contribute to soil respiration (16 to 48 % of CO 2 released from soils in the present study). Exomet and respiration from living organisms should be considered separately when studying effects of environmental factors on the C cycle because Exomet shows specific properties such as resistance to high temperature and toxics.

Introduction

Knowledge of the metabolic pathways through which organic carbon is oxidized into CO 2 is fundamental to understand the global carbon cycle and its interactions with climate [START_REF] Fontaine | Stability of organic carbon in deep soil layers controlled by fresh carbon supply[END_REF][START_REF] Heimann | Terrestrial ecosystem carbon dynamics and climate feedbacks[END_REF]. Outside the metabolic environment of a cell, organic molecules are generally highly stable. For instance, the physical oxidation of one molecule of glucose requires a temperature greater than 500 • C. In contrast, a complex cascade of biochemical reactions (i.e. glycolysis and the Krebs cycle) mediated by numerous intracellular enzymes makes the oxidization of organic C possible at low temperature within living cells. The function of these respirationcarrying enzymes depends on various co-factors (e.g. NAD + ), on being located adjacent to other enzymes, and on physiological properties of the cell (e.g. redox potential) [START_REF] Krebs | Introductory remarks, philosophical transactions of the Royal Society of London[END_REF][START_REF] Burns | Enzyme-activity in soil -location and a possible role in microbial ecology[END_REF][START_REF] Rich | The molecular machinery of Keilin's respiratory chain[END_REF]. Given this complexity and the fragility of respiratory enzymes, it is traditionally considered that respiration is strictly an intracellular metabolism. However, some studies have shown persistence of substantial production Figures

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Full of CO 2 in soils where microbial life has been strongly reduced by exposition to toxics (ClCH 3 , orange acridine) or irradiation [START_REF] Peterson | Respiration of soil sterilized by ionizing radiations[END_REF][START_REF] Ramsay | Effects of sterilization and storage on respiration, nitrogen status and direct counts of soil bacteria using acridine-orange[END_REF][START_REF] Lensi | Dynamics of residual enzyme-activities, denitrification potential, and physicochemical properties in a gammasterilized soil[END_REF]Trevors, 1996;[START_REF] Kemmitt | Mineralization of native soil organic matter is not regulated by the size, activity or composition of the soil microbial biomass -a new perspective[END_REF]. This production of CO 2 cannot be explained by the activity of extracellular enzymes previously secreted by soil microorganisms since these enzymes only hydrolyze organic matter and do not release CO 2 . To date, the cause of CO 2 production in soils where microbial life has been minimized is unknown and questions our basic knowledge of biology.

Our findings challenge the idea that respiration is strictly an intracellular metabolism, demonstrating the existence of an extracellular oxidative metabolism (Exomet) reconstituted by respiratory enzymes released from dead organisms in soils. The manuscript is organized in four key parts. We first demonstrate the possibility of an Exomet by incubating a cell-free yeast-extract containing respiratory enzymes with glucose in sterilized water and soil. The Exomet induced by the yeast-extract was quantified by measuring CO 2 and O 2 fluxes in water and soil microcosms. Second, the protective role of soil particles (minerals and humus) for respiratory enzymes is demonstrated by incubating three enzymes involved in glycolysis and the Krebs cycle in five top soils sampled from different regions of the world (Table 1). The third part of the manuscript is devoted to the quantification of Exomet contribution to the CO 2 emissions from the living (non-sterilized) studied soils. This contribution is quantified with a method combining modeling and incubations of sterilized and non-sterilized soils. Finally, we study some Exomet properties including resistance to high temperature and toxics.

Materials and methods

Soil sampling and sterilization

For each of the five studied sites (Table 1), twenty independent soil samples were collected from the 0-20 cm soil layer. Soil samples from each site were pooled to make a composite sample per site. Soil was sieved at 2 mm and was then used to determine Figures pH, texture and organic matter content and to conduct incubation experiments. The soils of Bugac (Hungary), Laqueuille (France), Ponta Grossa (Brasil), Soro (Denmark) and Theix (France) presented textures from sandy-silted to silty-clay soils, pH from 4.3 to 8.6 and three types of land use (grassland, forest and crops). The soil of Theix was used for all investigations whereas the other soils were used to generalize the key findings of this study. Some incubation experiments involved the use of sterilized soils. We tested different methods of soil sterilization (γ-irradiation, autoclaving and dry heating) during preliminary investigations. Irradiation was chosen among other sterilizing methods for its efficiency to kill soil micro-organisms and for its moderate effect on soil enzymes (see S1 for details of investigations on soil sterilization). The preservation of soil enzymes was important for quantifying the Exomet contribution to soil CO 2 emissions (see the Sect. 2.4). Soils were sterilized by γ-irradiation at 45 kGy ( 60 Co, Ionisos, ISO14001, France).

Demonstration of Exomet by incubating a cell-free yeast-extract in sterilized

water and soil

Production of cell-free yeast-extract

Pichia pastoris X33 (Invitrogen, [START_REF] Clare | High-level expression of tetanus toxin fragment c in Pichia pastoris strains containing multiple tandem integrations of the gene[END_REF] cells were cultured at 28

• C in a 1.5 l capacity Applikon bioreactor containing synthetic medium described by [START_REF] Boze | High-level secretory production of recombinant porcine follicle-stimulating hormone by Pichia pastoris[END_REF]. This medium contained 80 µg l -1 D-biotin, 40 g l -1 glycerol and mineral solutions (FM21 and PTM1). The pH of the medium was regulated at 5 using a NH 4 OH solution (15 %, v/v) that also served as a nitrogen source. Dissolved oxygen was measured using a polarographic probe and was maintained at over 30 % saturation by stirring up to 1800 rpm and aeration (an injection of 1 l of air per litre of medium per minute). Data acquisition and bioprocess control were carried out using the Applikon The yeast culture was harvested at the end of the growth phase by centrifugation at 25 000 × g for 10 min at 5

• C to yield a hard pellet. To remove the culture media, the sediment was washed and re-decanted three times in potassium phosphate buffer (0.1 M, pH = 6.5 which corresponded to the pH of the Theix soil used for the experiment, V yeast /V buffer = 1/10). The sediment was re-suspended in a small volume of potassium phosphate buffer (V yeast /V buffer = 2/1) before cells were disrupted with a French pressure cell press (100 MPa). Unbroken cells and large cell debris were removed from the yeast extract by fourteen successive centrifugations at 25 000 × g and 5 • C. The yeast extract was filtered under sterile conditions at 0.2 µm to obtain a cell-free extract. Direct microscopic observation confirmed sterility of the yeast extract (S1). The cell-free yeast extract was immediately incorporated into water and soil microcosms under sterile conditions. The cell-free yeast extract contained 28.5 mg ml -1 protein (Biuret method) and 0.77 U MDH ml -1 .

Yeast extract incubation in sterilized water and soil

The cell-free yeast extract (YE) was incubated in sterilized water (W) or in the irradiated-soil (S) of Theix (Table 1). Experimental microcosms consisted of 5 ml of cell-free yeast extract and 1 ml of 13 C labeled glucose with or without 20 g of γirradiated-soil (S + G + YE and W + G + YE treatments, respectively) placed in 250 ml flasks. A second dose of glucose was applied after twenty days of incubation in order to determine the persistence of the Exomet. Water (5 ml) with 13 C labeled glucose solution (1 ml) and irradiated-soil (20 g) with or without 13 C labeled glucose solution (1 ml) were incubated as controls (W + G, S and S + G treatments, respectively). The sterile conditions and the sterility of microcosms was verified after 13 and 53 days of incubation through direct fluorescence microscopic observations and TSA-FISH method (S1).

Soil stabilization of respiratory enzymes

Enzyme incubation

In order to quantify the protective role of soil particles (minerals, humus) for respiratory enzymes, three microbial enzymes involved in glycolysis (GHK: glucose hexokinase EC.2.7. Soils without enzyme amendment were also incubated as controls. Two sets of each treatment (control soil without enzyme and soil with enzyme) were prepared in order to separately quantify the activity of soluble and soil-immobilized enzymes. Immobilized enzymes were assumed to be protected from denaturation and proteolysis [START_REF] Burns | Enzyme-activity in soil -location and a possible role in microbial ecology[END_REF][START_REF] Sarkar | Immobilization of enzymes on clays and soils[END_REF] and expected to maintain their activity in the long-term (>ten days). Different independent subsets of soil samples were prepared to measure the activity of enzymes at different times between 20 min and 35 days of incubation. The protective role of soil on respiratory enzymes was generalized to the four other soils varying in texture and organic matter content using the G6PI as a model enzyme (S2).

Enzyme activity measurement

The principle of the enzymatic activity measurement was to set up a system of enzymatic reactions where the studied enzyme (red in Fig. 1) was the limiting factor (Nardi Figures Back Close

Full , 2007). To this end, substrates, cofactors and intermediary enzymes of the enzymatic reaction system were added in excess in soil samples (green in Fig. 1). The activity of the studied enzymes (GHK, G6PI, MDH) was quantified by measuring the formation or the consumption of NADH by spectrometry following [START_REF] Nardi | Relationship between molecular characteristics of soil humic fractions and glycolytic pathway and krebs cycle in maize seedlings[END_REF], with some protocol modifications for soil conditions as opposed to leaves. In particular, concentrations of buffer and Mg ++ solution were increased in order to better control soil pH and precipitate humic acids that could hamper the quantification of NADPH by spectrometry (both humic acids and NADPH absorb at 340 nm). For each of the three enzymes, the enzymatic reaction system and the method of enzyme activity measurement were as follows:

-GHK (Fig. 1A): soil with or without GHK was amended with 300 µl of solution containing buffer (Bicine-NaOH, 100 mM, pH = 8.5), glucose (1.5 mM), NADP -G6PI (Fig. 1B): soil with or without G6PI was amended with 300 µl of solution containing buffer (Bicine-NaOH, 100 mM, pH = 8.5), fructose-6-phosphate (1.5 mM), NADP + (1.5 mM), Mg ++ (32 mM) and glucose-6-phosphate dehydrogenase (2.5 U l -1 ). Production of NADPH following the chemical transformation of fructose-6-phosphate to phosphoglucono-δ-lactone was measured by spectrometry at 340 nm.

-MDH (Fig. 1C): soil with or without MDH was amended with 300 µl of solution containing buffer (TRIS 100 mM, pH = 6.

Relative contribution of soluble and soil-immobilized enzymes to total enzyme activity

After 5 min of incubation with substrates, co-factors and intermediate enzymes, the set of soil samples prepared for quantifying the activity of soluble enzyme was centrifuged at 11 000 × g during 3 min. The supernatant containing the soluble enzymes and substrate was transferred to a micro-plate where the production of NADPH (for GHK and G6PI) and the consumption of NADH (for MDH) were measured by spectrophotometry (340 nm). This measurement corresponded to the activity of soluble enzymes (Soluble Enz). The set of soil samples prepared for quantifying the activity of soil-immobilized enzymes was centrifuged at different times between 5 and 45 min after incubation with substrates, co-factors and intermediate enzymes. The concentration of NADPH and NADH in the supernatant was then measured by spectrophotometry. In the latter case, production of NADPH or consumption of NADH was carried out in the presence of soluble and soil-immobilized enzymes (Total Enz). Activity of enzymes immobilized on soil particles was calculated as the difference (Total Enz -Soluble Enz). Enzyme activity with time (A j ) was expressed as % of initial activity (A 0 ) of the incorporated enzymatic solution.

Kinetic analysis of total enzyme activity

Different exponential models were tested to fit the decrease in total enzyme activity (Total Enz) with time. The best-fit model considers three pools of enzymes:

Y (t) = a • exp (-b • t) + c • exp (-d • t) + f • exp (-g • t)
Where

Contribution of Exomet to soil respiration

A method based on the measurement of CO 2 emissions from irradiated and nonirradiated soils (see the Sect. 2.4b) was developed to quantify Exomet and the respiration of living organisms in the five studied soils. Gamma irradiation, by killing the soil organisms, was considered to stop living respiration whilst preserving soil enzymes (S1) responsible for Exomet. Therefore, CO 2 emissions from irradiated soils were used to estimate the Exomet. However, γ-irradiation also released a large quantity of intracellular enzymes from the killed organisms thereby artificially increasing the Exomet in the irradiated-soils. Taking this effect into account, a model of carbon flux was used to quantify Exomet and living respiration.

Model of carbon flux

In the model, CO 2 emission from non-irradiated soil (R ni ) is represented by the sum of living respiration (R l ) and Exomet (R x ) (Eq. 1). After irradiation, a fraction k of R l is converted to Exomet via the soil stabilization of respiratory enzymes released by the killed organisms. As a result, the sum of Exomet (R x ) and k.R l determines CO 2 emission from irradiated soil (R i ) (Eq. 2). The model reads as follows

R ni = R l + R x (1) R l = k • R l + R x (2)
By fixing the same R x in the irradiated and non-irradiated soils, the model assumes that irradiation has no effect on the pre-existent Exomet R x . We discuss here two examples where irradiation could modify R x . First, the γ-irradiation by denaturing part of soil enzymes could decrease R x . It is easy to show in this case that the Exomet contribution to soil CO 2 emissions is underestimated by the model. Nevertheless, this underestimation of Exomet is likely to be moderate since the effect of γ-irradiation on soil enzymes is typically low (see review of [START_REF] Mcnamara | Effects of acute gamma irradiation on chemical, physical and biological properties of soils[END_REF]. Second, the irradiation by suppressing the microbial uptake of organic substrates could increase the availability of Figures these substrates for Exomet increasing R x . In this case, the current model would overestimate the Exomet contribution to soil CO 2 emissions. However, Exomet and living respiration are not likely to be in competition for organic substrates. Indeed, Exomet may have preferential access to organic substrate since Exomet-carrying enzymes are adsorbed on soil particles including organic matter. Moreover, most of the soil sites

where Exomet can proceed are deprived of microorganisms. Indeed, enzymes responsible for Exomet may diffuse in most soil pores whereas living soil microorganisms due to their size occupy less than 0.5 % of the soil pore space [START_REF] Paul | Soil Microbiology and Biochemistry[END_REF].

Parameter k varies between 0-1 and depends on the fraction of respiratory enzymes released by dead organisms that is stabilized in irradiated-soils. In order to estimate k, we incubated the glucose-6-phosphate isomerase (G6PI) in the irradiated-soils of Theix, Ponta Grossa, Laqueuille, Soro and Bugac for 14 days. This enzyme was selected because it showed the lowest stabilized fraction among the three respiratory enzymes tested in the soil of Theix (Fig. 2). The total G6PI activity was measured throughout the incubation as described in the Sect. 2.3.b. For each soil, we fixed the parameter k equal to the fraction of stabilized G6PI activity assuming that the reaction sustained by the G6PI was the limiting reaction for the Exomet. Other enzymatic reactions could be more limiting for extracellular oxidative metabolism. In this case, k would have a lower value and calculated Exomet would increase. This signifies that the model underestimates the contribution of the Exomet to soil CO 2 emissions. Moreover, parameter k has a limited bearing on the calculation of Exomet. For example, for the soil of Theix, we calculated that a 50 % variation of k caused a 6 % deviation in Exomet.

Soil incubation experiment

Experimental microcosms consisted of 30 g (dry mass basis) samples of fresh sieved soils placed in 250 ml flask. Sets of irradiated and non-irradiated soils were prepared for the five studied soils. Soils were γ-irradiated as previously described. Soils were incubated at 30

• C and a water potential of -100 kPa for 21 days. Sufficient soil mi- treatment. Microcosms were sampled after 2, 6, 13 and 21 days of incubation to determine CO 2 emissions. The sterility of irradiated-soils was verified throughout the incubation by a combination of methods including electron microscopy and molecular tracing of functional RNA-producing microorganisms (S1). The C flux model was constrained with CO 2 emissions from irradiated (R i ) and non-irradiated-soils (R ni ) corresponding to the incubation period 13-21 days. The 0-13 day period was excluded because soil stabilization of respiratory enzymes required several days (Fig. 2 andS3).

Some Exomet properties

Given that we consider Exomet to be carried out by soil-protected slow-cycling enzymes rather than microbe-dependent living respiration with tight physiological constraints, we predicted that Exomet would show specific properties. First, we suggest that Exomet persist in the long-term (>100 days) without microbial production of new enzymes. To test this hypothesis, the irradiated soil of Theix was incubated at 30

• C and a water potential of -100 kPa for 332 days. The CO 2 emission rate was regularly measured during the incubation period in order to calculate the Exomet half-life. Second, we suggest that Exomet resist high temperature, pressure and toxics. To test this hypothesis, we exposed the irradiated-soil of Theix to additional treatments: 150

• C for two hours, autoclaving (137 • C and 2.4 × 10 5 Pa for 45 min) or chloroform vapors for 24 h. All microcosms were then incubated at 30 • C and a water potential of -100 kPa for 21 days. Four replicates were prepared for each treatment. The CO 2 emission from soils exposed to temperature, pressure or toxics were compared to that of control soil (only irradiated) in order to quantify treatments effects on Exomet.

Flux measurements

In the incubation experiments where gas fluxes were studied (Sects. 2.2, 2.4 and 2.5), two sets of microcosms were prepared in order to quantify gas exchange and to de- was trapped in NaOH. The 13 C abundance of CO 2 was analyzed by IRMS after precipitating carbonates with an excess of BaCl 2 . CO 2 and O 2 gas concentrations in the other set of microcosms were measured by gas spectrometry.

Data analysis

All statistical tests were performed with the Statgraphics Plus software (Manugistics, Rockville, MD, USA). General linear model (GLM) procedures, using the LSD method in post ANOVA multiple mean comparison tests, were employed to test effects of soil, treatment (irradiation, substrate and yeast-extract amendment, exposure to high temperature, autoclaving and toxics) and time factors on CO 2 emission and O 2 consumption. When model residuals did not follow a normal distribution, the variables were log-transformed. Non linear regressions were used to analyze kinetics of CO 2 , O 2 concentration and enzymatic activity.

Results and discussion

Demonstration of Exomet by incubating a cell-free yeast-extract in sterilized water and soil

The sterility of microcosms was maintained throughout the experiment (S1). The emission of CO 2 was null in water with glucose (W + G, data not shown). Despite sterilization, soils with and without glucose (S and S + G) still emitted CO 2 throughout the incubation (Fig. 3) confirming previous observations [START_REF] Peterson | Respiration of soil sterilized by ionizing radiations[END_REF][START_REF] Ramsay | Effects of sterilization and storage on respiration, nitrogen status and direct counts of soil bacteria using acridine-orange[END_REF][START_REF] Lensi | Dynamics of residual enzyme-activities, denitrification potential, and physicochemical properties in a gammasterilized soil[END_REF]Trevors, 1996). The supply of glucose had no ef- represented 725 and 72 times that of control soil (after eighteen hours of incubation, Fig. 3). The released CO 2 originated from added 13 C labeled glucose and unlabelled substrates present in yeast-extract and soil (S4). Moreover, CO 2 emissions mirrored the consumption of O 2 for all treatments and dates, with the exception of W + G + YE treatment at eighteen hours of incubation (respiratory quotient = 2, Fig. 3a). In this particular case, the availability of O 2 was reduced, metabolic activity was intense and the pressure in microcosms rose to 1.25 atm, all of which indicated the presence of fermentative metabolism.

Our results confirm the idea that the enzymatic cascade leading to the oxidation of organic carbon (i.e. glycolysis and the Krebs cycle) can occur in an extracellular context in soil and water. This indicates that respiratory enzymes can maintain their activity outside the cell and have access to substrate and co-factor flux to function. It is worth noting that the electron transfer to O 2 can also occur in an extracellular context since the emission of CO 2 was coupled to the consumption of O 2 in incubated mesocosms (Fig. 3a). This electron transfer, probably carried out by the cytochromes of cellular debris, permits the regeneration of electron acceptors (i.e. NAD + , [START_REF] Trevors | Measurement of electron-transport system (Ets) activity in soil[END_REF] explaining the persistence of Exomet over 53 days (Fig. 3). Moreover, our results show that fermentative metabolism is another intracellular process that can be reconstituted outside the cell when the availability of O 2 for the Exomet is low.

The Exomet was significantly higher in water than in soil during the first eighteen hours of incbation (W + G + YE versus S + G + YE, Fig. 3b), which can be explained by enzyme inhibitors present in soil [START_REF] Burns | Enzyme-activity in soil -location and a possible role in microbial ecology[END_REF]. However, Exomet continuously decreased with time in water whereas it stabilized in soil at a rate representing 247 % of CO 2 emissions from control soil at day 20. The higher Exomet in soil than in water after 20 days (Fig. 3b) cannot be explained by an exhaustion of C-substrate in water since the second glucose dose had no effect (S5). These results indicate that the soil particles permit Exomet persistence, possibly by preserving the respiratory enzymes from denaturation and proteolysis.

BGD Figures Back Close

Full

Soil stabilization of respiratory enzymes

Kinetic analysis of total enzyme activities indicated the existence of fast-, intermediateand slow-cycling pools of enzymes in the soil of Theix (Fig. 2, Table 2). Between 36 and 66 % of the initial enzymatic activity was lost within minutes following enzyme addition to the soil (the difference between initial activity and that at the first measurement 20 min after enzyme addition, Fig. 2). The half-life of this fast pool was too fast to be determined precisely but is <14 min (Table 2). Given the rapidity of their inactivation, enzymes of the fast-cycling pool were likely denatured by physico-chemical processes [START_REF] Burns | Enzyme-activity in soil -location and a possible role in microbial ecology[END_REF]. A second pool of enzymes representing 29-58 % of the initial activity was inactivated more slowly with a half-life of 9-26 h. Soil proteolytic activity may have contributed to the degradation of this pool [START_REF] Sarkar | Immobilization of enzymes on clays and soils[END_REF]. Finally, 5-14 % of the initial enzymatic activity was retained in a highly stable form with a half-life of 32-495 days. Figure 2 shows that this long-term persistence of enzymatic activities exclusively relied on enzymes immobilized on soil particles (humus and minerals). These results confirm the protective role of soil particles for respiratory enzymes against physico-chemical denaturation and proteolysis. Our investigations in the other soils gave consistent results with 0.8-3.3 % of the initial enzymatic activity that was stabilized (S2).

Contribution of Exomet to soil respiration

Despite sterilization by γ-irradiation, the five studied soils released large quantities of CO 2 throughout the incubation period (Fig. 4). After 21 days of incubation, cumulated CO 2 emissions from the irradiated-soil represented 17 to 59 % of that measured in non-irradiated-soil depending on soil type (Fig. 4). The value of model parameter k was determined for the soils of Theix, Ponta Grossa and Laqueuille but not for the soils of Soro and Bugac where large release of humic acids did not permit the measurement of G6PI activity (Table 3 andS3). the non-irradiated-soils (Table 3). Exomet was responsible for 16, 21 and 48 % of CO 2 emissions from the soils of Ponta Grossa, Laqueuille and Theix, respectively. These unexpected contributions of Exomet indicate the presence of a large quantity of respiratory enzymes in soils outside cells. This enzymatic pool may result from the long-term accumulation of enzymes released from dead organisms and stabilized on soil particles (Fig. 2).

Some Exomet properties

Significant CO 2 emissions were maintained throughout the 332 day incubation of irradiated-soil from Theix (Fig. 5). We calculated that the half-life of Exomet was 165 days. This result confirms the idea that Exomet can persist in the long-term without microbial production of new enzymes. This persistence reflects soil stabilization of respiratory enzymes released by ancient generations of microbial populations.

Although the treatments applied to the irradiated-soil of Theix (high temperature, pressure and chloroform) are known to denature unprotected enzymes and be lethal for most microorganisms [START_REF] Koffler | Molecular basis of biological stability to high temperatures[END_REF][START_REF] Kashefi | Extending the upper temperature limit for Life[END_REF][START_REF] Rainey | Extensive diversity of ionizing-radiation-resistant bacteria recovered from Sonoran Desert soil and description of nine new species of the genus Deinococcus obtained from a single soil sample[END_REF][START_REF] Lopez-Garcia | Habitability: the point of view of a biologist[END_REF] significant CO 2 emissions persisted during the 21 day incubation period. Based on soil CO 2 emissions (Fig. 6), we estimated that 50, 20 and 10 % of Exomet were resistant to chloroform, 150

• C and autoclaving, respectively. Thus, soil particles not only protect enzymes against denaturation (e.g. L ähdesm äki and [START_REF] Pnspanen | Soil enzymology: role of protective colloid systems in the preservation of exoenzyme activities in soil[END_REF] but also allow the maintenance of complex oxidative metabolism in conditions where life is generally impossible.

Conclusion and perspectives

Our findings indicate that CO 2 emissions from soils are driven by two major oxidative metabolisms: (1) the well-established respiration of soil biota, (2) an Exomet carried out by enzymes released from dead organisms and stabilized by soil particles. These two Figures

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Full metabolic pathways should be considered separately when studying effects of environmental factors on the C cycle because Exomet shows specific properties including resistance to high temperature. Further experiments are necessary to better understand the molecular mechanisms at play and predict Exomet in different soils and ecosystems. Finally, the reconstitution of intracellular metabolism outside the cell could occur in different environments (water, sediments) and may concern other metabolisms (fermentation, denitrification) since it only requires the presence of dead cells releasing intracellular enzymes.

Our results have several implications. First, the resistance of Exomet to high temperature (150 • C) indicates that the respiratory release of CO 2 from soils is more resistant to extreme temperatures than photosynthetic fixation (photosynthesis in most plants stops at temperature >40

• C). Given that the frequency of extreme temperatures is expected to increase with global warming (IPCC, 2007), these temperatures may shift the photosynthesis-respiration equilibrium towards greater ecosystem CO 2 emissions. Second, the long-term persistence of Exomet-carrying enzymes (Fig. 2 and5) signifies that current CO 2 emissions from soils depend on past microbial activities. This memory of soils suggests a delay between the modification of microbial activities and its consequence on soil respiration. Such a delay could explain why many short-term experiments manipulating the diversity of soil microorganisms fail to demonstrate links between soil biodiversity and soil function [START_REF] Griffiths | An examination of the biodiversity-ecosystem function relationship in arable soil microbial communities[END_REF]. Thus, the functional redundancy of soil microbial species concluded from these experiments should be reevaluated. Finally, our results support the idea that soils have played a key role in the origin of life (Bernal, 1949;[START_REF] Hazen | Mineral surfaces and the prebiotic selection and organization of biomolecules[END_REF]. Previous studies have shown the role of soil minerals in the concentration and polymerization of amino-acids and nucleic-acids provided during the prebiotic period (Bernal, 1949). Our results show that, when all relevant organic molecules are present, complex biochemical reactions underpinning bioenergetics of life (respiration) can occur spontaneously in the soil and without any cellular organization. Thus, the first ancestral oxidative metabolism may have occurred in soil before the presence of the first cells.

(t) = a•e -b•t + c•e -d•t + f•e -g•t GHK G6PI MDH Model: r 2 = 0.99*** Y(t) = a•e -b•t + c•e -d•t + f•e -g•t Model: r 2 = 0.99*** Y(t) = a•e -b•t + c•e -d•t + f•e -g•t

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  biocontroller software. Biomass concentration of culture samples was determined by weighing washed cells after drying at 105 • C to constant weight. Discussion Paper | Discussion Paper | Discussion Paper |

  3712‰) contained 60 mg Cglucose ml -1 and was sterilized by filtration at 0.2 µm. Three replicates per treatment were prepared. Soils were incubated at a water potential of -100 kPa. Water and soil microcosms were incubated at 30 • C for 53 days. The Exomet induced by the cell-free yeast extract was quantified by measuring the concentration of CO 2 , 13 CO 2 and O 2 in water and soil microcosms throughout the 53 days of incubation. All manipulations were done under Discussion Paper | Discussion Paper | Discussion Paper |

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+( 1

 1 .5 mM), Mg ++ (32 mM) and glucose-6-phosphate dehydrogenase (EC 1.1.1.49, Sigma-Aldrich ref G5885). Production of NADPH following the chemical transformation of glucose in phosphoglucono-δ-lactone was measured by spectrometry at 340 nm.

  7), oxaloacetate (1.5 mM), NADH (1.5 mM) and Mg ++ (32 mM). Consumption of NADH following the chemical transformation of oxaloacetate to L-malate was measured by spectrometry at 340 nm. Discussion Paper | Discussion Paper | Discussion Paper |

  t is the time of incubation, a, c and f represent the sizes and b, d and g the decay rates of fast, intermediate and slow pools, respectively. The half life of enzyme pools were calculated as (ln 2)/(decay rate). Discussion Paper | Discussion Paper | Discussion Paper |

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  crocosms were prepared to permit four destructive harvests and four replicates per Discussion Paper | Discussion Paper | Discussion Paper |

  termine the 13 C content of released CO 2 . In one set of microcosms, the released CO 2 Discussion Paper | Discussion Paper | Discussion Paper |

  fect on soil CO 2 emissions (S versus S + G). In contrast, the combined supply of glucose and cell-free yeast-extract containing respiratory enzymes triggered sudden and enormous respiration fluxes in water and soil (W + G + YE and S + G + YE, respectively). The CO 2 emission from W + G + YE and S + G + YE treatments, respectively Discussion Paper | Discussion Paper | Discussion Paper |

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  For the three soils where k could be determined, modeling results indicated that Exomet subtancially contributed to CO 2 emissions from Introduction Discussion Paper | Discussion Paper | Discussion Paper |

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Fig. 1 .

 1 Fig.1. Enzymatic reactions mediated by the two enzymes involved in glycolysis (A. GHK: glucose hexokinase, B. G6PI: glucose-6-phosphate isomerase) and the enzyme involved in the Krebs cycle (C. MDH: malate dehydrogenase) used to quantify the protective role of soil particles on respiratory enzymes. The principle of the enzymatic activity measurement was to set up a system of enzymatic reactions where the studied enzyme (red) was the limiting factor. For doing this, we added all substrates, cofactors and intermediary enzymes in excess (green). The activity of the studied enzyme (GHK, G6PI, MDH) was quantified by measuring the formation or the consumption of NADH by spectrometry. Reactions surrounding the studied enzymatic (grey) are given for information.

Fig. 2 .

 2 Fig. 2. Activity of three specific enzymes involved in glycolysis and the Krebs cycle following their incorporation in non-irradiated-soil of Theix. Enzyme activity along time (A j ) is expressed as % of the initial activity of enzymatic solution (A 0 ) applied to the soil. The activities of total (dark circles), soluble (white circles) and immobilized enzymes (grey circles) are distinguished. Full, dashed and dotted lines represent the fit of the kinetic model on total enzyme activity, its confidence and predictive error intervals at 5 % P -level, respectively. (A) G6PI: glucose-6phosphate isomerase; (B) GHK: glucose hexokinase; (C) MDH: malate dehydrogenase.

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Fig. 4 .

 4 Figure 4

Fig. 5 .Fig. 6 .

 56 Fig. 5. CO 2 emission rate from irradiated-soil of Theix incubated during 238 days. Half-life (t 1/2 ) was calculated from a simple exponential model.

Supplementary material related to this article is available online at: http://www.biogeosciences-discuss.net/9/8663/2012/ bgd-9-8663-2012-supplement.pdf. Introduction

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Table 1 .

 1 Site characteristics and soil properties.

				Sites
		Theix	Laqueuille	Soro	Ponta Grossa	Bugac
	Country	France	France	Denmark	Brazil	Hungary
	Land use	Grassland Grassland Beech forest	Crops	Grassland
	Clay/Silt/Sand (%)	26/25/49	19/55/26	10/22/68	14/6/80	10/5/85
	pH (-)	6.2	5.3	4.3	7.0	8.6
	OM content (g C kg -1 )	39.0	121.9	33.7	14.8	44.0
	CEC (C mol kg -1 )	21.5	25.1	9.5	3.9	10.2

Table 2 .

 2 Size and half-time of fast, intermediate and slow pools of respiratory enzymes. GHK: glucose hexokinase, G6PI: glucose-6-phosphate isomerase, MDH: malate dehydrogenase. Pool size and half-time have been obtained by fitting the kinetic model presented in Sect. 2.3 to total enzyme activity (Fig.2).

			Fast pool	Intermediate pool		Slow pool
	Enzyme Size (%) Half-life (min) Size (%) Half-life (h) Size (%) Half-life (days)
	GHK	35.8	<14	57.6	8.9	6.6	43.3
	G6PI	66.4	<14	28.8	26.4	4.8	31.5
	MDH	38.3	<14	47.6	17.1	14.1	495.1

Table 3 .

 3 Activity of glucose-6-phosphate isomerase (G6PI) and model quantification of CO 2 emissions from living organisms (R l ) and extracellular oxidative metabolism (R x ) for the five studied soils. R ni and R i represent CO 2 emissions from non-irradiated and irradiated soil respectively, for the incubation period 13-21 days. k is the fraction of R l converted in extracellular oxidative metabolism (Exomet) after irradiation. Contribution (%) of Exomet to soil CO 2 emission was calculated as R x /R ni • 100.

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Malate dehydrogenase KREBS CYCLE Mg 2+ B. Glucose-6-phosphate isomerase (G6PI EC 5.3.1.9) A. Glucose hexokinase (GHK E.C. 2.7.1.1) C. Malate dehydrogenase (MDH EC 1.1.1.37)

[53][54][55][56][57][58][59] 1996. Vanderheiden, G. J., Fairchild, A. C., and Jago, G. R.: Construction of a laboratory press for use with the French pressure cell, Appl.Microbiol. 19, 875-877, 1970.

Acknowledgements. We thank D. Tourvieille and the "Plant and Pathogen" team of the UMR-INRA 1095 for their technical support. We thank P. Ambus, L. da Silveira Pontes, and L. Horv áth who sampled the soil in Denmark, Brazil and Hungary, respectively; C. Mallet for culture analysis; S. Revaillot, O. Delfosse and J.-L. Ollier for chemical and isotopic analyses; L. Genestoux, C. Chabalier, F.-X. Sauvage, N. Duchatau for respiratory enzyme activity analyses, G. Borrel for gas spectrometry; and V. Allard, P. Barr é, S. Barot, R. Pilon and our favorite native English speakerine J. Bloor for their constructive comments on previous versions of the manuscript.

The research leading to these results received funding from the French Institutes INRA-INRIA (ARC-VitelBio), the French national agency ANR (DIMIMOS), le Conseil G én éral d'Auvergne (CPER 2007(CPER -2013) ) and the European Community's 6th and 7th Framework Programs (FP6 and FP7) under grant agreement n

• 017841 (Nitro-Europe) and n

• 226701 (CARBO-Extreme). All authors have refused to apply for the research bonus scheme set up by the French government in 2010 and have consequently no competing financial interest in this work. This work is dedicated to Waniya Shahzad and Gabin Maire. This work originated from an idea of SF; VM, GA, ED and SF designed experiments; RD and MJ conducted soil incubations and counting of cultivable microorganisms; JC conducted electron microscopic observations of soils, ACL, RD, VM and SF performed gas spectrometry; ACL and VM performed fluorescence in situ hybridization (TSA-FISH); VM and SF conducted the incubation experiment with intracellular enzymes; GA, VP, ED and SF conducted the incubation experiment with yeast-extract; SF built the model for estimating Exomet in soils; VM, GA, TS and SF wrote the manuscript; and all authors took part in the interpretation of the results.