Catherine Berthelon
email: catherine.berthelon@ifsttar.fr

Guy Gineyt

Effects of alcohol on automated and controlled driving performances

Keywords: Alcohol, scenarios, driving, simulator, performance

Rationale: Alcohol is the most frequently detected substance in fatal automobile crashes, but its precise mode of action is not always clear.

Objective: The present study was designed to establish the influence of blood alcohol concentration as a function of the complexity of the scenarios. Road scenarios implying automatic or controlled driving performances were manipulated in order to identify which behavioral parameters were deteriorated.

Method: A single blind counterbalanced experiment was conducted on a driving simulator.

Sixteen experienced drivers (25.3 ± 2.9 years old, 8 men and 8 women) were tested with 0, 0.3 g/l, 0.5 g/l and 0.8 g/l of alcohol. Driving scenarios varied: road tracking, car following and an urban scenario including events inspired by real accidents. Statistical analyses were performed on driving parameters as a function of alcohol level.

Results: Automated driving parameters such as standard deviation of lateral position (SDLP) measured with the road tracking and car following scenarios were impaired by alcohol, notably with the highest dose. More controlled parameters such as response time to braking and number of crashes when confronted with specific events (urban scenario) were less affected by the alcohol level.

Conclusion:

Performance decrement was greater with driving scenarios involving automated processes than with scenarios involving controlled processes.

Introduction

Consumption of psychoactive drugs produces a decrease in the mental and behavioral functions involved in automobile driving [START_REF] Moskowitz | A review of the literature on the effects of low doses of alcohol on driving related skills[END_REF]Odgen and Moskowitz 2004). In particular, it has been shown for many years that impairment of driving due to ethanol is a main factor in road traffic accidents and that blood alcohol concentration (BAC) is clearly linked with the probability of being involved in a road accident [START_REF] Borkenstein | The role of the drinking driver in traffic accidents (The Grand Rapids Study)[END_REF]Robertson and Drummer 1994;[START_REF] Zador | Relative risk of fatal crash involvement by BAC, age and gender[END_REF][START_REF] Blomberg | The Long Beach/Fort Lauderdale relative risk study[END_REF][START_REF] Phillips | The relationship between serious injury and blood alcohol concentration (BAC) in fatal motor vehicle accidents: BAC = 0.01% is associated with significantly more dangerous accidents than BAC = 0.00%[END_REF]. Alcohol is thus the most commonly detected substance in fatally injured drivers, and epidemiological evidence indicates that the risk of accident increases with alcohol alone and when alcohol is combined with licit or illicit psychotropic drugs [START_REF] Abotnes | Drugged drivers in Norway with benzodiazepine detections[END_REF]Odgen and Moskowitz 2004;[START_REF] Vingilis | Review: drugs and traffic collisions[END_REF][START_REF] Walsh | Drugs and driving[END_REF][START_REF] Brady | Prevalence of alcohol and other drugs in fatally injured drivers[END_REF][START_REF] Mørland | Drugs related to motor vehicle crashes in northern European countries: A study of fatally injured drivers[END_REF]. The relative risk estimated using the Odds Ratio is of 5.6 for a BAC between 0.5 and 0.79 g/l and of 15.5 for alcoholization greater than or equal to 0.8 g/l [START_REF] Movig | Psychoactive substance use and the risk of motor vehicle accidents[END_REF]); the number of drivers responsible for accidents rises to 2.3 for cannabis alone, to 9.4 for alcohol alone, and to 14.1 for the alcohol-cannabis combination [START_REF] Biecheler-Fretel | SAM survey on "drugs and fatal accidents": search of substances consumed and comparison between drivers involved under the influence of alcohol or cannabis[END_REF].

The precise mode(s) of alcohol's action are not always clear; it does not uniformly impair all aspects of performance, but most experimental studies show a global behavioral decrement from 0.5 g/l of alcohol in the blood (Moskowitz and Firentino 2000). In case of multiple tasks, people under the influence of alcohol tend to favor just one task, resulting in a strong decrease in performance for the secondary task from 0.2 g/l; some cognitive capacities (memory, response time) are affected from near 0.5 g/l [START_REF] Parks | Effect of ethanol at a blood alcohol concentration of 0.4 g/l on actual driving and memory[END_REF] and can show a dose effect [START_REF] Bisby | Decreases in recollective experience following acute alcohol: a dose-response study[END_REF]. Vigilance level and eye tracking are also affected at very low doses of alcohol [START_REF] Moskowitz | Skills performance at low blood alcohol levels[END_REF] and motor functions appear to be more affected than cognitive functions [START_REF] Koelega | Alcohol and vigilance performance: a review[END_REF]. Concerning driving, studies on simulators and on the road show deterioration in behavior starting at low level of alcohol; this deterioration increases with task difficulty [START_REF] Ogden | Effects of alcohol and other drugs on driver performance[END_REF][START_REF] Lenné | The effects of cannabis and alcohol on simulated arterial driving: influences of driving experience and task demand[END_REF]. Decisionmaking processes appear to be less affected than drivers' ability to divide attention, and the impairing effects of alcohol on performances is thus intensified when the driver is required to divide attention among distractor tasks while driving [START_REF] Leung | Gap acceptance and risk-taking by young and mature drivers, both sober and alcohol-intoxicated, in a simulated driving task[END_REF]Verster et al. 2009;[START_REF] Harrison | Alcohol and distraction interact to impair driving performance[END_REF]. Moreover, some alcohol effects on driving performances and risky driving are more pronounced in the presence of a response conflict, for example by increasing deviations in lane positions and failures to stop at red lights [START_REF] Fillmore | Acute disinhibiting effects of alcohol as a factor in risky driving behaviour[END_REF]).

Alcohol has thus consistently been found to produce dose-dependent impairment in the performance of driving tasks and of driving relative cognitive tasks [START_REF] Ronen | The effect of alcohol, THC and their combination on perceived effects, willingness to drive and performance of driving and non-driving tasks[END_REF][START_REF] Leung | A comparison of the effect of mobile phone use and alcohol consumption on driving simulation performance[END_REF].

One of the most often used scenarios to evaluate driving behavior after the ingestion of psychotropic drugs was initiated in pharmacology procedures with antihistaminic drugs and benzodiazepine [START_REF] O'hanlon | Hypnotics and actual driving performance[END_REF][START_REF] Ramaekers | Acrivastine, terfenadine and diphenhydramine effects on driving performance as a function of dose and time after dosing[END_REF]. The highway driving test consists in registering driver's action parameters in monotonous driving scenarios such as motorway situations, causing a clear deterioration of different driving parameters. In particular, the primary outcome measured is the standard deviation of lateral position (SDLP) of the driver's vehicle. This behavioral variable reflects the capacity (or the inaptitude) to keep the vehicle from swaying from side to side, so it is a good indicator of vehicle control and increases in case of less control. SDLP is extremely sensitive to modifications involving psychotropic drugs and fatigue. It is frequently used to quantify the negative effects of these factors in natural or simulated driving [START_REF] O'hanlon | Diazepam impairs lateral position control in highway driving[END_REF][START_REF] Brookhuis | How to measure driving ability under influence of alcohol and drugs, and why[END_REF][START_REF] Arnedt | How do prolonged wakefull and alcohol compare in the decrements they produce on a simulated driving task?[END_REF][START_REF] Brookhuis | Criteria for driver impairment[END_REF][START_REF] Vermeeren | Residual Effects of Hypnotics: Epidemiology and Clinical Implications[END_REF][START_REF] Philip | Fatigue, sleep restriction and driving performance[END_REF][START_REF] Verster | The on the road driving test[END_REF]Verster andRoth 2012a, Helland et al. 2013). The highway driving test also shows a dose-dependent relationship between the alcohol dose and performance degradation [START_REF] Louwerens | The relationship between drivers' blood alcohol concentration (BAC) and actual driving performance during high speed travel[END_REF]. Automated driving performance, as measured by this test, is impaired by alcohol (0.4 g/l) and SDLP increases, while speed (SD) and the standard deviation of speed (SDSP) are sometimes increased (Arneth et al. 2001[START_REF] Lenné | The effects of cannabis and alcohol on simulated arterial driving: influences of driving experience and task demand[END_REF][START_REF] Ronen | The effect of alcohol, THC and their combination on perceived effects, willingness to drive and performance of driving and non-driving tasks[END_REF] and sometimes unaffected [START_REF] West | Effect of low and moderate level doses of alcohol on driving hazard perception latency and driving speed[END_REF][START_REF] Kuypers | MDMA and alcohol effects, combined and alone, on objective and subjective measures of actual driving performance and psychomotor function[END_REF]).

However, the monotonous scenarios used to obtain SDLP effectively measure lower level behavior functions but not higher level functions such as response to emergency situations and risk-taking behavior, although, for example, the detection of danger can be slower with alcohol [START_REF] West | Effect of low and moderate level doses of alcohol on driving hazard perception latency and driving speed[END_REF]. It is only more recently that research has systematically turned to behavioral modifications involving psychotropic drugs in more stimulating scenarios rather than monotonous ones, e.g. scenarios reflecting controlled modes of cognitive processing, such as car following scenarios and complex urban scenarios requiring set-shifts and cognitive strategies [START_REF] Leung | Gap acceptance and risk-taking by young and mature drivers, both sober and alcohol-intoxicated, in a simulated driving task[END_REF][START_REF] Lenné | The effects of cannabis and alcohol on simulated arterial driving: influences of driving experience and task demand[END_REF][START_REF] Simons | Effects of dexamphetamine with and without alcohol on simulated driving[END_REF].

According to Michon's three-level driving model (1985), automated driving behavior concerns immediate parameters like lateral and longitudinal control of the vehicle (e.g. speed and lateral position in the lane), while controlled driving behavior concerns driving actions such as maneuvers when faced with specific events. Recent results tend to show that under the influence of alcohol, automated driving performances such as speed and SDLP measured in monotonous situations are mainly influenced. Driving parameters such as response time or gap acceptance when crossing an intersection, which are involved in more complex situations such as car following and urban driving performances, are not affected, however [START_REF] Veldstra | Effects of alcohol (BAC 5‰) and ecstasy (MDMA 100 mg) on simulated driving performance and traffic safety[END_REF]. Thus, lane keeping performances deteriorate to some extent but drivers are still able to adequately react to road signs indicating a lane change [START_REF] Huemer | Alcohol-related impairment in the Lane Change Task[END_REF]. These results are not in line with others which postulate that controlled performances and simulator tasks would be degraded earlier with low doses of alcohol in the blood while automated performances would only be so later, e.g. that automated processes are less sensitive to alcohol-induced impairment (Fisk and Schneider 1982;[START_REF] Krüger | Effects of low alcohol dosages: a review of the literature[END_REF][START_REF] Holloway | Low-dose alcohol effects on human behaviour and performance[END_REF].

Based on the aforementioned alcohol results, we decided to test the effects of different BACs on automated and more controlled driving performances to establish their respective influences on behavioral decrement. Automated and controlled driving parameters were analyzed with various driving scenarios. The BAC limits used were the BAC levels at which the accident risk is increased as well as the BAC limits generally tolerated in European countries. The BAC levels of 0.5 g/l and 0.8 g/l were thus selected to remain consistent with the legal limits in most European countries, the 0.3 g/l level was selected relative to other countries which have already adopted a lower limit and in agreement with a consortium of researchers involved in the DRUID project1 such as [START_REF] Veldstra | Effects of alcohol (BAC 5‰) and ecstasy (MDMA 100 mg) on simulated driving performance and traffic safety[END_REF]. In the present research, it was predicted that performances would deteriorate with increasing blood alcohol concentrations and, in comparison with the results of driving studies, would be more affected for scenarios implying automated driving performances than for scenarios implying more controlled driving performances.

Method

Design

The study was conducted according to a balanced, single-blind, cross-over design. Each participant followed four experimental sessions balanced for order with a Williams's square design [START_REF] Williams | Experimental Designs Balanced for the Estimation of Residual Effects of Treatments[END_REF]. Experimental sessions began at 1 pm. Each session was separated from the following by a washout period of at least 2 days.

Before each session, participants drank a beverage (vodka 40% and/or orange juice) in order to obtain a BAC of 0 (placebo), 0.3, 0.5 or 0.8 g/l. Doses of ethanol were calculated for each volunteer using the following formulae (Tiplady et al., 2005):

[3.82 -(0.143 x A) + (0.168 x H) + (0.525 x W)] x T/100 for males [-3.67 + (0.187 x H) + (0.432 x W)] x T/100 for females Where A is age (years); H is height (cm); W is weight (kg); and T is the target concentration (mg/100 ml).

Duration of drinking was around 15 minutes (mn). Time between the end of alcohol intake and driving was around 20 mn and was preceded by a verification of the BAC. The alcohol level was measured using a breathalyzer (SD-400 DJP/LION).

Participants

Sixteen drivers (8 men and 8 women) aged from 21 to 29 years (m = 25.31, SD = 2.87) and with a minimum of two years of driving experience were included in this experiment. Before inclusion, participants had a medical check-up and it was verified that they had no prior problems with drug or alcohol abuse. First-time consumers of alcohol and excessive drinkers were not accepted. Participants were instructed to abstain from alcohol the day before the experimental session, to avoid caffeinated beverages and to eat just a light meal before each experimental session. For each session, participants were transported from home to the laboratory by IFSTTAR staff.

All participants gave written informed consent and signed informed commitment prior to inclusion in the study. They were compensated for their participation.

The study was granted ethical approval by the French local ethics committee and by the French Health Products Safety Agency.

Apparatus

The driving experiment was carried out on the SIM2-IFSTTAR fixed-base driving simulator.

The simulator consisted of a one-quarter vehicle and 3 video projectors. The drivers' visual environment was generated using the ARCHISIM (2011 release) software package (Espié and Auberlet, 2007). The vehicle had an automatic gearbox and was not equipped with rear view mirrors. The image projection surface underlay an angular opening that spanned 150° horizontally and 40° vertically (Fig. 1). Images were refreshed at the rate of 30 Hz. Drivers controlled the vehicle using the steering wheel, the brake and accelerator pedals. The acquisition frequency for the different signals (position, speed, acceleration, etc.) was around 30 Hz.

Driving scenarios

During each experimental session (0, 0.3, 0.5 or 0.8 g/l), participants were presented with three driving scenarios. The order of presentation of the three scenarios was randomized within each session, and the BAC level was measured before each of them. To avoid any learning effect during the experimental sessions, participants were extensively trained on the different scenarios the day of the medical examination.

Highway scenario (15 mn)

The participants drove on a highway, without any traffic. They were instructed to drive with a steady position within the right traffic lane while maintaining a constant speed of 110 km/h during the test. "They were free to choose their preferred position within the traffic lane" as in [START_REF] Verster | Standard operation procedures for conducting the on-the-road driving test, and measurement of the standard deviation of lateral position (SDLP)[END_REF], so they could drive as in their usual, natural position. The duration of this task is longer (more than twice as long) than in [START_REF] Veldstra | Effects of alcohol (BAC 5‰) and ecstasy (MDMA 100 mg) on simulated driving performance and traffic safety[END_REF]. Moreover, at the end of the circuit, they were confronted with a vehicle stopped in their lane with a time to collision of 2.5 s. The mean lateral position of the vehicle from the centre of the right lane (LP), standard deviation of lateral position (SDLP), mean speed (SD) and standard deviation of speed (SDSD) were calculated across all data points during one drive (excluding values obtained with a radius of curvature of less than 1000 m). The number of off-lane incidents was also calculated (number of times that the participant left the right lane). The number of collisions and response times (RT) by the participants when confronted with a stopped vehicle at the end of the test were also noted. RT corresponded to the time difference between the apparition of the obstacle vehicle and the first press on the brake pedal.

Car following scenario (10 mn)

The participants had to follow a lead car at a short distance, but one considered to be safe.

Their task was to keep this distance constant whereas the lead car was programmed to randomly accelerate or decelerate. The speed of the lead vehicle could vary from 70 to 90 km/h (± 10 or 20 km/h) but the vehicle did not continuously accelerate and decelerate as in the study by [START_REF] Veldstra | Effects of alcohol (BAC 5‰) and ecstasy (MDMA 100 mg) on simulated driving performance and traffic safety[END_REF]. Each speed change was separated from another by a period of stable speed. The duration of stable speed was 20, 30 or 40 s.

Mean SDLP, Inter Vehicular Time (IVT, distance from the rear of the leading vehicle to the front of the following vehicle/participant's speed), the number of adjustments of speed during stable speed of lead vehicle, e.g. the number of times the participant's speed was lower or higher than the lead vehicle's speed, and response time delay after the lead vehicle's speed modification were analyzed. Analysis of speed and speed deviation were not relevant as linked to the speed of the lead vehicle.

Urban scenario (10 mn)

The participants drove along a realistic urban circuit where a few independent vehicles with non-conflicting behavior were also driving. The participants' task consisted in driving at 50 km/h, as usual. Seven prototypical events, inspired by real accidents, were also included in a counterbalanced order in the circuit. These events were spatio-temporally implemented using data from the Detailed Accident Study collected by the Laboratory of Accident Mechanism Analysis (LMA) at IFSTTAR (Perrin et al. 2004). As our objective was to include difficult situations on the simulator and not situations that systematically produced accidents, we adapted the different values from the accident reconstitution so that drivers on the simulator could develop collision avoidance strategies [START_REF] Berthelon | Methodology to introduce scenarios of accident in driving simulators. Interest for the analysis of drivers' behavior[END_REF][START_REF] Damm | The evaluation of traditional and early driving learning with simulated accident scenarios[END_REF].

In [START_REF] Veldstra | Effects of alcohol (BAC 5‰) and ecstasy (MDMA 100 mg) on simulated driving performance and traffic safety[END_REF], only three urban events were used. They were not really constructed with spatio-temporal reconstitutions of real accidents. One event assessed risk-taking in traffic (the time interval accepted by participants to turn left as oncoming traffic arrived), another tested violations of traffic regulations using a traffic light scenario, and the last one was, as in the present study, a vehicle pulling out of a parking space. The events used here tested the participants' maneuvers and their efficiency. Three events (a, b and c) were unexpected and four could be foreseen (d, e, f and g): f) Braking by the vehicle ahead. The driver follows a lead vehicle whose speed depends on his/her own speed, so that the distance between the two vehicles is 25 m. Suddenly, the coupling is broken and the obstacle vehicle slows down to a speed of 10 km/h. g) Opposite vehicle crossing. A vehicle in front of the driver gets ready to turn left into a petrol station and starts to cross the opposite lane, when the subject's vehicle is at a distance of 25 m from the potential point of impact.

In case of crashes, the vehicle being driven passes through the vehicle with which it crashes and the participant can continue the circuit without interruption.

The number of collisions, SD at the origin of the urban events (for example, at the initiation of braking of the obstacle vehicle) and RT were analyzed.

Statistical analysis

A first ANOVA verified that the average level of alcohol was equivalent for the three driving tests. Then, four alcohol-level ANOVAs within-subjects factors were performed for each quantitative driving variable. In case of a significant effect of alcohol, Dunnett's post-hoc tests were applied, where necessary rounded out with an analysis of linear contrasts and linear regression.

For the vehicle stopped at the end of the highway circuit and for urban scenarios, the descriptive parameters of overall driver behavior were classified into obstacle avoidance (by changing lateral position and/or braking) or crash. Non-parametric Friedman ANOVAs were then performed on the number of crashes (highway and urban scenarios) and on the number of off-lane incidents (highway scenario). In case of a significant effect of alcohol, contrasts were explored with the Wilcoxon test.

The level of significance for all analyses was set at 0.05.

Results

Blood alcohol concentrations

No detectable BACs were observed under the placebo condition; the mean BACs obtained prior and following the simulated tasks are summarized in table 1.

The mean alcohol level obtained did not differ significantly between driving scenarios (F (2,30) = 2.03, NS) (Fig. 2).

Insert Table 1 andFig.2 around here

Highway scenario

Average performance scores and p values for all driving parameters analyzed are displayed in Table 2. LP values did not significantly vary as a function of BACs; the subjects always drove in the middle of the right lane, but as expected SDLP varied according to alcohol level. Post hoc analysis showed that only the highest alcohol level (0.8 g/l) involved SDLPs significantly higher than placebo (p < 0.003).

Insert Table 2 around here SP and SDSP increased dose-dependently, but this effect did not reach significance. However, Dunnett's post hoc test applied to the data showed a significant increase in these two variables with 0.8 g/l relative to placebo (respectively, p < 0.042 and p < 0.043). Finally, the number of off-lane incidents per BAC increased but did not reach significance; these incidents always occurred on road shoulders (right side of the lane). The Wilcoxon post hoc test, however, showed an increase in off-lane incidents with 0.3 (p < 0.08) and 0.8 g/l (p < 0.09) relative to placebo.

At the end of the circuit, participants were confronted with a vehicle stopped in their lane and 28 crashes were noted out of a total of 64 events. Crashes were regularly distributed between the different alcohol levels and were frequently linked only to braking. Conversely, participants who avoided collision (36 cases) combined braking and a left maneuver or made a left maneuver without braking. Mean response times to this unexpected obstacle did not significantly vary with the alcohol level (Table 2).

Car following scenario

Performance scores and p value are presented Table 3.

There was an overall effect of alcohol on SDLP. The highest alcohol dose (0.8 g/l) brought about highest SDLP (p < 0.001). IVT did not vary significantly as a function of BACs.

Insert Table 3 around here

The number of speed adjustments during the lead vehicle's stable speed period revealed a variation according to the BAC. Participants with 0.3 g/l and 0.8 g/l of BAC produced more adjustments than with placebo (respectively p < 0.017 and p = 0.025).

Mean response times to the acceleration/deceleration of the followed vehicle were faster when the followed vehicle decelerated than when it accelerated (respectively 1.99 s and 2.45 s) but did not significantly vary according to BACs (Table 3).

Urban scenario

A total of 19 crashes occurred and the number of crashes increased as the BAC increased, but this variation was not significant due to the low number of crashes (χ 2 = 2.29, p = 0.51) (Fig. 3). Mean response times did not reveal any significant difference according to alcohol level (F (3,45) = 0.69, NS; fig. 3).

Insert Fig. 3 around here

At initiation of the scenarios, mean speeds did not significantly vary as a function of alcohol level (F (3.45) = 0.6, NS) (placebo = 43.7 km/h; 0.3g/l = 43.8 km/h; 0.5g/l = 42.7 km/h; 0.8 g/l = 44.7 km/h).

Discussion and conclusions

The main objective of this study was to quantify the effects of BAC on different driving scenarios: a highway driving scenario (monotonous situation), a car following scenario, and an urban driving scenario with specific events inspired by real crashes. The highway scenario involved assessing automated driving performances such as SDLP and speed; the other two scenarios implied more complex driving performances and controlled processing [START_REF] Michon | A critical review of driver behaviour models: what do we know, what should we do[END_REF].

We first verified that mean alcohol levels did not significantly vary among the three scenarios tested despite the decrease of BAC from the beginning to the end of each experimental session.

Looking at lateral data, the results obtained for the highway and car following scenarios were homogeneous. BACs clearly influenced SDLP: the higher the alcohol level, the more the participants had difficulties in maintaining a stable trajectory on the road. Impairment is thus clearly alcohol dose-dependent for this automated driving parameter. This result is in accordance with past results; SDLPs are in fact known to be sensitive to the effects of psychotropic drugs, alcohol and fatigue, in natural and simulated driving situations [START_REF] Liu | Predicting driver drowsiness using vehicle measures: recent insights and future challenges[END_REF][START_REF] Verster | The on the road driving test[END_REF][START_REF] Owens | Drugs, driving, and models to measure driving impairment[END_REF][START_REF] Lenné | The effects of cannabis and alcohol on simulated arterial driving: influences of driving experience and task demand[END_REF][START_REF] Simons | Effects of dexamphetamine with and without alcohol on simulated driving[END_REF]. The increase in SDLP values obtained with 0.5 g/l of alcohol relative to placebo (+2 cm) is relatively close to the values obtained in other studies using similar monotonous scenarios and BACs, which confirms the validity of our results [START_REF] Vermeeren | Residual Effects of Hypnotics: Epidemiology and Clinical Implications[END_REF]Kuyper et al. 2006;[START_REF] Veldstra | Effects of alcohol (BAC 5‰) and ecstasy (MDMA 100 mg) on simulated driving performance and traffic safety[END_REF]. Otherwise, the duration of the driving task in these two scenarios was relatively short, so the distances traveled were also short, while SDLP is known to increase with the duration of the task, in connection with vigilance decrement (Verster and Roth 2012b). This could be why significant differences were not found between the zero alcohol level and 0.5 g/l, but the tendency toward significance showed that behavior decrement can occur relatively rapidly.

In the highway scenario, participants drove in the middle of their lane and a slight but not significant increase of off-lane incidents occurred on road shoulders with 0.3 and 0.8 g/l of alcohol in the blood. Note that off-lane incidents always occurred to the right of the lane, which is difficult to interpret. This result is, however, in accordance with the fact that off-road occurrences can significantly increase with alcohol [START_REF] Arnedt | How do prolonged wakefull and alcohol compare in the decrements they produce on a simulated driving task?[END_REF]. The difficulty in maintaining a stable trajectory was associated with a tendency to increase one's speed as a function of BAC, which demonstrates a decrease in inhibitory processes [START_REF] Kalmen | Alcohol and desinhibition[END_REF][START_REF] Fillmore | Acute disinhibiting effects of alcohol as a factor in risky driving behaviour[END_REF]. Reduction in speed is in fact known to provide drivers with additional time to adapt to changes in their environment and to maintain greater vehicular control. It thus seems here that drivers failed to reduce their speed to compensate for additional impairment due to alcohol. Moreover, in the car following scenario, participants' speed adjustments to the speed of the followed car tended to be more frequent with an increase in BAC and thus attested to difficulties in regulating and stabilizing their own speed.

The results obtained with the highway and car following scenarios thus demonstrate an influence of alcohol on automated driving performances, e.g. lateral control (SDLP) and longitudinal control (speed) [START_REF] Lenné | The effects of cannabis and alcohol on simulated arterial driving: influences of driving experience and task demand[END_REF][START_REF] Mets | Effects of alcohol on highway driving in the STISIM driving simulator[END_REF]. Contrary to these results, the car following scenario with constant acceleration and deceleration of the lead vehicle previously failed to show alcohol effects (Veldstra et al. 2011); it thus seems that it is notably in the period of stable speed by the lead vehicle that performance decrements occur.

Speeds at the initiation of the events (urban scenario) were not affected by BACs, but it should be pointed out that the prescribed speed was lower in this scenario than in the others.

In accordance with the results of past studies, response times were not altered by the alcohol dose, either in the urban scenario or in the highway scenario, when an unexpected vehicle appeared after 15 min of monotonous driving. Braking reaction times are generally not sensible to low doses of alcohol (Lenné et al. 2011), even when combined with sleep deprivation [START_REF] Vakulin | Effects of moderate sleep deprivation and low-dose of alcohol on driving simulator performance and perception in young men[END_REF].

Otherwise, in the car following scenario, participants' response times were faster after deceleration than after acceleration by the followed car. This could be due to the variation of the obstacle vehicle's size: after deceleration, the obstacle's angular size increases, which entails a higher risk of crash; conversely, after acceleration it decreased, posing less of a risk for the participant [START_REF] Andersen | Optical information for car following: the driving by visual angle (DVA) model[END_REF].

In the urban scenario, although 68% of the crashes were observed with 0.5 and 0.8 g/l of BAC, this result was linked to the fact that one woman was very sensitive to the effects of alcohol (her behavior only significantly differed from the other participants' behavior for this variable and this scenario) and systematically collided in four events with 0.5 and 0.8 g/l (i.e. 8 crashes out of the 19 observed). From a safety point of view, it would be interesting to know the exact percentage of drivers (male and female) who are extremely sensitive to the effects of alcohol in order to take this percentage into account in discussions of legislative changes to the legal limits adopted in the European Community.

Urban events did not demonstrate alcohol's effects on drivers' behavior (Veldstra et al. 2011).

In right crossroad, vehicle pulling out from a parking space and hidden pedestrian crossing events, the obstacle vehicle became perceptible late and was relatively unexpected; conversely, in the left crossroad, opposite vehicle crossing, braking by the vehicle ahead, and vehicle overtaking and merging back events, the participants could see the obstacle vehicle long before the situation became risky, and they could foresee a potential crash. It thus seems that with or without the possibility to foresee the evolution of the events, the BAC level had no effect on the behaviors adopted by the participants.

Confronted with these results, the hypothesis could be put forward that the urban scenario, where more complex performances were measured, involves less impairment by alcohol intoxication than the other two scenarios. However, to neutralize learning effects during our four experimental sessions, participants were extensively trained for the difficult situations included in the urban scenario. The effect of learning the driving circuits and foreseeing the events may have resulted in improved driving performances (e.g., better crash avoidance) over the course of the four drives, in which case the effects of alcohol would have been attenuated. Urban events thus may not be regarded as completely unexpected, probably increasing the participants' alertness which in turn could have improved their driving performance [START_REF] Meskali | Residual effects of hypnotics drugs in aging drivers submitted to simulated scenarios of accidents: an exploratory study[END_REF]). Thus, they were probably unable to induce a real-life reaction unlike for the highway scenario, which has been proved to involve behaviors that are relatively similar to those of natural driving. Finally, the weak sensitivity to the urban scenario may also be linked to the complexity and variability of possible avoidance strategies when faced with one event, unlike the automated driving tasks involved in the highway and car following tasks, where behavioral strategies are limited. These questions could be resolved with a specific protocol including matched groups subjected to just one level of alcohol and thus to just one session of urban driving.

In brief, lateral and longitudinal control of the trajectory seems to be altered more than the strategies adopted when confronted with critical or stimulating events in which attentional processes are completely involved. There thus appears to be a specific decrement of performances according to the driving task, a hypothesis that has already been put forward [START_REF] Huemer | Alcohol-related impairment in the Lane Change Task[END_REF], and a relatively simple task requiring automated behavior may be more affected by alcohol and drugs than complex tasks that require conscious, higher-level control [START_REF] Ronen | The effect of alcohol, THC and their combination on perceived effects, willingness to drive and performance of driving and non-driving tasks[END_REF][START_REF] Veldstra | Effects of alcohol (BAC 5‰) and ecstasy (MDMA 100 mg) on simulated driving performance and traffic safety[END_REF]. One limit of this study, however, is that many obvious dose-response relationships failed to reach statistical significance due to the large dispersion of responses and a limited sample size, which may have reduced the detection of real differences. Some effects could have reached significance with a larger group of participants [START_REF] Helland | Comparison of driving simulator performance with real driving after alcohol intake: A randomised, single blind, placebo-controlled, cross-over trial[END_REF]. It is also important to note that our findings are based on a group of young but relatively experienced subjects (more than two years with a driving license) and cannot be generalized to other populations. Our next objective is thus to replicate the alcohol experiment with larger groups of very young, novice drivers who seem more often involved in road crashes with low alcohol level than experienced drivers [START_REF] Zador | Relative risk of fatal crash involvement by BAC, age and gender[END_REF][START_REF] Keall | The influence of alcohol, age, and number of passengers on the night-time risk of driver fatal injury in New Zealand[END_REF][START_REF] Peck | The relationship between blood alcohol concentration (BAC), age, and crash risk[END_REF]. As road crashes are generally due to a combination of several factors, it is possible that a lack of experience combined with alcohol, even at a low level, could have a cumulative effect on performances [START_REF] Lenné | The effects of cannabis and alcohol on simulated arterial driving: influences of driving experience and task demand[END_REF].

List of figures

  a) Hidden pedestrian crossing. A pedestrian, initially hidden by a bus parked on the right-hand side of the carriageway, suddenly crosses the carriageway. The pedestrian appears in the driver's field of vision 2.4 seconds before the driver reaches his level. b) Right crossroads. The driver arrives at an intersection without visibility. A vehicle comes from the right at 8 km/h, and can be seen from only 25 meters. c) Vehicle pulling out from a parking space. A vehicle parked on the right-hand side of the carriageway pulls out of its parking space when the driver is at a distance of 20 m. d) Vehicle overtaking and merging back into the lane. A vehicle overtakes the participant's vehicle, then merges back ahead of him and slows down to a speed of 30 km/h. e) Left crossroads. A vehicle coming from the left of an intersection starts to slow down for 4 s, adopting a speed of 5 km/h when reaching the crossroads.

Fig. 1

 1 Fig. 1 Simulator used in the experiment

Fig. 2

 2 Fig.2

Fig. 3

 3 Fig. 3

Table 2

 2 Highway scenario, mean values (SD) of the driving parameters for each BAC

	BAC (g/l)	0	0.3	0.5	0.8	Main within-subjects
						effect
	LP (cm)	72.83	73.14	73.01	73.42	F (3,45) = 1.34, n.s
		(20.30)	(24.08)	(18.13)	(20.89)
	SDLP (cm)	26.44	25.85	28.42	31.89	F (3,45) = 5.27, p < 0.003
		(6.75)	(7.24)	(6.83)	(8.71)
	SP (km/h)	110.68	110.59	110.86	112.48	F (3,45) = 2.38, p = 0.082
		(1.79)	(2.15)	(2.91)	(4.16)
	SDSP (km/h)	1.56	1.85	1.91	2.42	F (3,45) = 1.65, n.s
		(1.24)	(0.99)	(1.58)	(2.31)
	Number of	5	14	7	44	χ 2 (3)= 5.8, n.s
	off-lane incidents	(0.47)	(1.26)	(0.89)	(5.73)
	Number of	5	8	7	8	χ 2 (3)= 1.71, n.s
	crashes
	RT (s)	0.47	0.51	0.54	0.57	F (3,45) = 1.60, n.s
		(0.08)	(0.11)	(0.16)	(0.16)

Table 3

 3 Car following scenario, mean values (SD) of the driving parameters for each BAC

	BAC (g/l)	0	0.3	0.5	0.8	Main within-subjects effect
	SDLP (cm)	18.35	19.21	19.74	23.30	F (3,45) = 5.77, p < 0.002
		(6.65)	(5.61)	(6.13)	(6.68)
	IVT (s)	2.55	2.25	2.47	2.22
		(0.89)	(1.02)	(1.13)	(0.80)	F (3,45) = 1.39, n.s
	Number of speed adjustments	44.5 (18.23)	57 (20.30)	52.44 (22.39)	56.25 (16.54)	F (3,45) = 2.79, p < 0.05
	RT (s)	2.42 (1.06)	1.9 (0.68)	2.23 (0.76)	2.34 (1.33)	F (3,45) = 1.82, n.s

In the DRUID project, the objective was to dispose of baseline impairments due to different levels of alcohol for comparison with impairments due to other licit and illicit drugs.

Acknowledgements

This research was partly funded by the TREN-05-FP6TR-SO7.61320-518404-Druid project and partly funded by IFSTTAR. We would like to thank the IFSTTAR simulation team (LEPSIS: Laboratoire Exploitation, Perception, Simulateurs et Simulations), notably Isabelle Aillerie and Fabrice Vienne, for designing the displays. We would also like to thank Sylvie Hirt, Claudine Nachtergaële and Mohamed Meskali for their help and assistance for the simulator-based data collection and processing.


Julia F Van Winden

Helen M Talbot

François De Vleeschouwer

Gert-Jan Reichart

Jaap S Sinninghe Damsté

Variation in methanotroph-related proxies in peat deposits from Misten Bog, Hautes-Fagnes, Belgium

Methane emissions from peat bogs are strongly reduced by aerobic methane oxidising bacteria (methanotrophs) living in association with Sphagnum spp. Field studies and laboratory experiments have revealed that, with increasing water level and temperature, methanotrophic activity increases. To gain a better understanding of how longer term changes in methanotrophic activity are reflected in methanotroph biomarkers, a peat record (0-100 cm) from the Hautes-Fagnes (Belgium) encompassing the past 1500 years, was analysed for methanotroph-specific intact bacteriohopanepolyols (BHPs) and the carbon isotopic composition of diploptene. A predominance of aminobacteriohopanetetrol (aminotetrol) over aminobacteriohopanepentol (aminopentol) indicated the prevalence of type II methanotrophs. Relatively high methanotrophic activity was indicated by all methanotroph markers between 20 and 45 cm depth, around the present oxic-anoxic boundary, most likely representing the currently active methanotrophic community. Comparing methanotrophic markers in the deeper part of the peat profile with environmental variables afforded, however, no clear correlation between change in water level and methanotrophic activity. This is potentially caused by a predominance of type II methanotrophs, a combination of sources for methanotrophic biomarkers or insufficient variation in climatic changes. A proposed way forward would include a study of a core covering a longer timescale, thereby involving greater variability.

Introduction

Peat bogs are situated at mid to high latitude, primarily in the Northern Hemisphere, and play a large role in the global carbon cycle as they sequester a third of the Earth's soil carbon [START_REF] Smith | Siberian peatlands a net carbon sink and global methane source since the early Holocene[END_REF]. They are also important in the global methane cycle, since they contribute approximately 10% of the total CH 4 flux to the atmosphere [START_REF] Gorham | Northern peatlands: role in the carbon balance and probable responses to climatic warming[END_REF]. Methane emission from peat bogs depends on a balance between archaeal methane production and subsequent bacterial methane oxidation. Aerobic methane oxidising bacteria (methanotrophs), living in association with Sphagnum spp., can retain up to 98% of the diffusive methane flux (van Winden et al., 2012a), but methane cycling strongly depends on environmental factors. Field studies and laboratory experiments have revealed that, with increasing water level and temperature, both methanogenic and methanotrophic activity increase [START_REF] Frenzel | CH 4 emission from a hollow-ridge complex in a raised bog: the role of CH 4 production and oxidation[END_REF][START_REF] Basiliko | Roles of moss species and habitat in methane oxidation in a northern peatland[END_REF][START_REF] Mcnamara | Gully hotspot contribution to landscape methane (CH 4 ) and carbon dioxide (CO 2 ) fluxes in a northern peatland[END_REF][START_REF] Larmola | The role of Sphagnum mosses in the methane cycling of a boreal mire[END_REF][START_REF] Kip | Global prevalence of methane oxidation by symbiotic bacteria in peat moss ecosystems[END_REF]van Winden et al., 2012a). Mesocosm experiments suggest, however, that methane oxidation is not able to keep up with increased production rate at increasing temperatures (van Winden et al., 2012a). However, such experiments cannot take into account long term adaptation of the microbial community. Hence, it remains unclear how methane cycling responds to changes in climate on longer timescales.

Insight into the adaptation on longer timescales can potentially be obtained by studying peat bog records when natural climate and environmental changes have resulted in differences in the extent of methane cycling. Variation in past methanotrophic activity is reflected in molecular proxies for methanotrophs (van Winden et al., 2012b), such as compound specific carbon isotopes of the bacterial marker diploptene, and methanotroph-specific intact bacteriohopanepolyols (BHPs, hopanoids with a polyfunctionalized side chain).

Methane is strongly depleted in 13 C, yet diploptene, a lipid derived from methanotrophs (but also from other bacteria; e.g. [START_REF] Rohmer | Distribution of hopanoid triterpenes in prokaryotes[END_REF], exhibits only limited 13 C depletion, with d 13 C values of ca. À30‰ and À40‰ in peat bogs [START_REF] Van Winden | Lipids of symbiotic methane-oxidizing bacteria in peat moss studied using stable carbon isotopic labelling[END_REF]. This is probably the result of a mixed origin, since methanotrophs are not the only bacterial source for diploptene. Furthermore, serine cycle methanotrophs (type II), which form part of the methanotrophic community, do not only use methane but also CO 2 as carbon source, thereby diluting the 13 C depleted, methanederived signal in methanotroph lipids. Nevertheless, in a previous mesocosm experiment, it was shown that the carbon isotopic depletion of diploptene strongly correlated with methane production and methane consumption (van Winden et al., unpublished data). This may be a result of an increased contribution of methanotroph-derived diploptene with enhanced methane cycling. Alternatively, it is possibly caused by enhanced expression of the enzymatic isotope effect associated with methane oxidation, with increased methane availability.

Methanotroph-specific BHPs may also serve as biomarkers for methanotrophy, potentially containing useful process-specific information.

Methanotrophs in peat bogs comprise two main groups, type I methanotrophs (c-Proteobacteria) and type II methanotrophs (a-Proteobacteria). Aminobacteriohopanepentol (aminopentol) is a marker for type I methanotrophs, while aminobacteriohopanetetrol (aminotetrol) is indicative for type II methanotrophs (Neunlist and Rohmer, 1985a,b;[START_REF] Cvejic | Bacterial triterpenoids of the hopane series from the methanotrophic, bacteria Methylocaldum spp.: phylogenetic implications and first evidence for an unsaturated aminobacteriohopanepolyol[END_REF][START_REF] Talbot | Analysis of intact bacteriohopanepolyols from methanotrophic bacteria by reversed-phase high performance liquid chromatography-atmospheric pressure chemical ionisation mass spectrometry[END_REF][START_REF] Coolen | Sources for sedimentary bacteriohopanepolyols as revealed by 16S rDNA stratigraphy[END_REF]van Winden et al., 2012b). Still, the latter may also be produced to a lesser extent by type I methanotrophs and some sulfate reducing bacteria (Neunlist and Rohmer, 1985a;[START_REF] Cvejic | Bacterial triterpenoids of the hopane series from the methanotrophic, bacteria Methylocaldum spp.: phylogenetic implications and first evidence for an unsaturated aminobacteriohopanepolyol[END_REF][START_REF] Talbot | Analysis of intact bacteriohopanepolyols from methanotrophic bacteria by reversed-phase high performance liquid chromatography-atmospheric pressure chemical ionisation mass spectrometry[END_REF][START_REF] Blumenberg | Biosynthesis of hopanoids by sulfate-reducing bacteria (genus Desulfovibrio)[END_REF]Blumenberg et al., , 2009a,b;,b;van Winden et al., 2012b). Methylocella-like methanotrophs, belonging to the Beijerinckiaceae family within the type II methanotrophs, have been speculated to be one of the most important methane oxidisers in peat bogs [START_REF] Dedysh | Exploring methanotroph diversity in acidic northern wetlands: Molecular and cultivation-based studies[END_REF]. They are not closely related to the other type II methanotrophs. The only Methylocella-like methanotroph analysed for BHPs did not contain aminotetrol, but mainly aminobacteriohopanetriol (aminotriol) and bacteriohopanetetrol (BHT), which are also produced by many other bacteria (van Winden et al., 2012b). BHPs can thus be used to trace contributions of methanotrophs in peat records but the results require careful interpretation.

To gain a better understanding on how longer term climatic and environmental changes affected methanotrophic activity, a peat record from the Hautes-Fagnes, Belgium, encompassing the past 1500 years, was investigated. The core was recovered from the edge of a hummock, to ensure maximum sensitivity to changes in hydrology (De Vleeschouwer et al., 2012). It was analysed for methanotropic biomarkers including intact bacteriohopanepolyols (BHPs) and for compound specific carbon isotope values of diploptene.

Experimental

Site description

The peat monolith was obtained using a plain titanium 15 Â 15 Â 100 cm Wardenaar corer [START_REF] Wardenaar | A new hand tool for cutting peat profiles[END_REF] in February 2008 from Misten Bog, Hautes-Fagnes, East Belgium (50°33 0 50 00 N, 06°09 0 50 00 E; Fig. 1) and has been described in detail elsewhere (De Vleeschouwer et al., 2012 and references therein). Briefly, the bog is ombrotrophic in nature and present day average precipitation rate is 1440 mm/yr at a mean annual air temperature of 6.7 °C. Currently, the surface Sphagnum hummocks and lawns are covered with abundant shrubs such as Erica tetralix, Calluna vulgaris and Vaccinium spp., reflecting the ongoing evolution of the bog into a peat moor. At the coring site, some Ericales rootlets extended up to ca. 30 cm depth.

Dating and age model

The methods used to establish the chronology of the core are derived from and reported in De Vleeschouwer et al. (2012). Briefly, the dates for the peat horizons were based on 14 C AMS dates of selected plant macrofossils, as reported by De Vleeschouwer et al. (2012). 210 Pb was also used to date the most recent layers (down to 18 cm) and an age model for 210 Pb derived according to the Constant Rate of Supply (CRS) model [START_REF] Appleby | Chronostratigraphic techniques in recent sediments[END_REF]). An age model was constructed using the 14 C dates and the modelled 210 Pb ages. Radiocarbon dates were translated to calendar ages using the IntCal09 calibration curve [START_REF] Reimer | IntCal09 and Marine09 radiocarbon age calibration curves, 0-50,000 years cal BP[END_REF].

Extraction and derivatisation

The monolith was sliced in to ca. 1 cm sections. Fourteen slices were selected from the 1 m profile (see Table 1 for depths), freeze-dried and extracted using a modified Bligh and Dyer procedure [START_REF] Cooke | Bacterial populations recorded in bacteriohopanepolyol distributions in soils from Northern England[END_REF]. An aliquot of the extract was used for hopene analysis and separated into an apolar and a polar fraction over an activated Al 2 O 3 column using hexane:dichloromethane (DCM; 9:1 v/v) and DCM:MeOH (1:1 v/v). To separate hopenes from acyclic and saturated hydrocarbons, the apolar fraction was eluted over an AgNO 3 -impregnated column using DCM, after elution with hexane, hexane:DCM (9:1), hexane:DCM (1:1) and DCM. The DCM fraction was dried, dissolved in hexane and analysed using gas chromatography (GC), GC-mass spectrometry (GC-MS) and GC-isotope ratio monitoring mass spectrometry (GC-IRMS).

Another aliquot of the extract was used for BHP analysis. After addition of the internal standard (5a-pregnane-3b,20b-diol) the aliquot was acetylated with Ac 2 O and pyridine (1:1 v/v; 50 °C, 1 h) and left to stand at room temperature overnight. The acetylated extract was dried at 50 °C under a continuous N 2 flow. After filtration with a 0.45 lm PTFE syringe filter, the extract was dissolved in MeOH/propan-2-ol (60:40 v/v), prior to high performance liquid chromatography (HPLC) MS.

GC, GC-MS and GC-IRMS

GC was performed with a HP 6890 gas chromatograph equipped with flame ionisation detection (FID) set at constant pressure (100 kPa). A fused silica column (30 m  0.32 mm i.d., film thickness 0.1 lm) coated with CP Sil-5CB was used with He as carrier gas. Samples were injected on-column at 70 °C and temperature was increased at 20 °C per min to 130 and 4 °C min À1 to 320 °C (held 20 min). Quantification was performed by integration of peak areas, using squalane as a co-injected internal standard.

Components were identified using a Thermo Trace GC Ultra (equipped with the same type of column and using the same temperature programme as for GC) coupled to a Finnigan Trace DSQ Single Quadrupole mass spectrometer (GC-MS).

Compound specific d 13 C values were determined using GC-IRMS; ThermoFisher Delta V; with the same type of column and using the same GC temperature programme as above. Carbon isotopic (d) values are reported relative to the Vienna Peedee Belemnite (VPDB) standard, with a reproducibility of, on average, 0.5‰.

HPLC-(atmospheric chemical ionisation (APCI)-MS

Reversed-phase HPLC analysis was carried out using a Surveyor HPLC system (ThermoFinnigan, Hemel Hempstead, UK) fitted with a Gemini (Phenomenex, [Macclesfield, UK] C 18 5 lm column (150 mm  3.0 mm i.d.) and a security guard column of the same material. Separation was achieved at a flow rate of 0.5 ml/min at 30 °C with the following gradient profile: 90% A and 10% B (0 min); 59% A, 1% B and 40% C (at 25 min), then isocratic (to 40 min) and returning to the starting conditions over 5 min and finally stabilizing for 15 min before the next injection (A = MeOH, B = water and C = propan-2-ol; all Fisher Scientific HPLC grade). HPLC-MS was performed using a ThermoFinnigan LCQ ion trap mass spectrometer equipped with an APCI source operated in positive ion mode. Analysis was carried out in data-dependent mode with three scan events: SCAN 1 -full mass spectrum (m/z 300-1300); SCAN 2 -data-dependent MS 2 spectrum of most abundant ion from SCAN 1; SCAN 3 -data-dependent MS 3 spectrum of most abundant ion from SCAN 2.

A semi-quantitative estimate of concentration was obtained based on the comparison of response factors of five authentic peracetylated BHP standards (provided by M. Rohmer, Strasbourg, France; [START_REF] Cooke | Bacterial populations recorded in bacteriohopanepolyol distributions in soils from Northern England[END_REF] relative to the internal standard 5apregnane-3b,20b-diol. Bacteriohopanetetrol (BHT) was the only available standard which does not contain N and gives a response 8Â that of the internal standard. Four N-containing BHPs, i.e. 3 tetrafunctionalized composite structures (aminotriol; BHT cyclitol ether VI and the related BHT glucosamine IX) and adenosylhopane (V) were available. Although these 4 N-containing BHPs individually show some slight variation in response factor over time, taken as an average the response factor remains consistent at 12Â that of the internal standard (van Winden et al., 2012b). As only a limited number of standards were available relative to the known variety of possible structures, this average value was used for all N-containing BHPs including penta-and hexafunctionalised structures for which no standards were available. Quantification was performed using m/z traces targeting the characteristic base peak, i.e.[M+H] + , ion for N-containing structures and [M+H-CH 3 COOH] + for those with no N relative to the base peak ion of the internal standard (m/z 345 = [M+H-CH 3 COOH] + ). Reproducibility was ±20% based on replicate injections of a range of samples on different days over time [START_REF] Cooke | The Role of Bacteriohopanepolyols as Biomarkers for Soil Bacterial Communities and Soil Derived Organic Matter[END_REF] and was comparable to that reported by other workers (Blumenberg et al., 2009a,b).

Results and discussion

BHPs as markers for methanotrophs

BHPs detected across the peat profile include BHT, aminotriol, aminotetrol and aminopentol (Fig. 2b-e), known to be produced by methanotrophs [START_REF] Rohmer | Distribution of hopanoid triterpenes in prokaryotes[END_REF]Neunlist and Rohmer, 1985a,b;[START_REF] Cvejic | Bacterial triterpenoids of the hopane series from the methanotrophic, bacteria Methylocaldum spp.: phylogenetic implications and first evidence for an unsaturated aminobacteriohopanepolyol[END_REF][START_REF] Talbot | Analysis of intact bacteriohopanepolyols from methanotrophic bacteria by reversed-phase high performance liquid chromatography-atmospheric pressure chemical ionisation mass spectrometry[END_REF][START_REF] Coolen | Sources for sedimentary bacteriohopanepolyols as revealed by 16S rDNA stratigraphy[END_REF]van Winden et al., 2012b). Other BHPs present throughout the profile are 2-methylBHT, adenosylhopane, BHT cyclitol ether, BHT glucosamine, 2-methylBHT cyclitol ether, BHpentol cyclitol ether and BHhexol cyclitol ether (Table 1). Aminotriol and BHT cyclitol ether are the most abundant BHPs in all the samples. The depth distribution of the summed BHP concentration is rather constant, with only elevated values around 70 cm (Fig. 2a). The concentration profiles of BHT and aminotriol closely resemble that of the summed BHPs (Fig. 2b, c and a, respectively). The concentrations of aminotetrol and aminopentol are lower than BHT and aminotriol and both show some variability with depth, with peaks in abundance between 20 and 45 cm and at around 70 cm (Fig. 2d ande). High aminotetrol and aminopentol abundances between 20 and 45 cm suggest enhanced methanotrophic activity (Fig. 2d ande). The present oxic-anoxic interface starts around 25 cm (De Vleeschouwer et al., 2012). Since methanotrophic activity is typically highest around the oxic-anoxic interface [START_REF] Basiliko | Roles of moss species and habitat in methane oxidation in a northern peatland[END_REF]van Winden et al., 2012a), the high aminotetrol and aminopentol concentrations between 20 and 45 cm probably reflect ongoing methanotrophic activity. The presence of aminopentol confirms the presence of type I methanotrophs, yet the abundance of aminotetrol is significantly higher than it (Fig. 2d ande). This indicates a prevalence of type II methanotrophs. [START_REF] Kip | Ultra-deep pyrosequencing of pmoA amplicons confirms the prevalence of Methylomonas and Methylocystis in Sphagnum mosses from a Dutch peat bog[END_REF] identified two main types of methanotroph, a type II Methylocystus sp. and a type I Methylomonas sp. as the dominating methanotrophs in several Sphagnum ecosystems. These groups produce primarily aminotetrol and aminopentol, respectively [START_REF] Talbot | Analysis of intact bacteriohopanepolyols from methanotrophic bacteria by reversed-phase high performance liquid chromatography-atmospheric pressure chemical ionisation mass spectrometry[END_REF]van Winden et al., 2012b), so our results are therefore largely in line with the study of [START_REF] Kip | Ultra-deep pyrosequencing of pmoA amplicons confirms the prevalence of Methylomonas and Methylocystis in Sphagnum mosses from a Dutch peat bog[END_REF].

The peak abundance in aminopentol is at shallower depth vs. that of aminotetrol (Fig. 2d ande), similar to the pattern observed in another active peat bog (van Winden et al., 2012b). The difference in depth for the peak abundances in the top part of the peat core possibly suggests a slight difference in oxygen and methane affinity between type I and type II methanotrophs, as observed by [START_REF] Crossman | Stable isotope pulse-chasing and compound-specific stable carbon isotope analysis to assess methane oxidising bacterial populations in landfill cover soil[END_REF]. Type I methanotrophs are situated at shallower depth, where oxygen concentration is higher and methane level lower, while type II methanotrophs prefer lower oxygen concentration and higher methane concentration [START_REF] Crossman | Stable isotope pulse-chasing and compound-specific stable carbon isotope analysis to assess methane oxidising bacterial populations in landfill cover soil[END_REF].

In the topmost sample, at 14 cm, aminopentol is below detection limit and aminotetrol concentration is low (Fig. 2d ande), in line with low methanotrophic activity. As the top 14 cm of the peat are well above the oxic-anoxic interface, methanotrophic activity should be limited.

Methanotrophs may also produce BHT and aminotriol, which are, however, less diagnostic as they are also rather common among other bacteria (e.g. [START_REF] Talbot | Cyanobacterial bacteriohopanepolyol signatures from cultures and natural environmental settings[END_REF]. Methylocella-like methanotrophs, hypothesised to be one of the most important methanotrophs in peat bogs [START_REF] Dedysh | Exploring methanotroph diversity in acidic northern wetlands: Molecular and cultivation-based studies[END_REF] do not, to the best of our knowledge, produce the diagnostic aminotetrol and amonopentol, but primarily aminotriol (van Winden et al., 2012b;Talbot et al., unpublished data). In the core, BHT and aminotriol largely reflect the pattern of the cumulative BHPs, albeit that levels are slightly elevated between 20 and 45 cm (Fig. 2a-c). Except for the peak abundance at 70 cm, they do not match the curve of aminotetrol or aminopentol. BHT and aminotriol therefore probably have a mixed origin, and most likely do not primarily represent methanotrophs here. This is in line with a low abundance of Methylocella-like methanotrophs in Sphagnum moss inferred from low or absent PCR product when using PCR primers targeting known Methylocella genes [START_REF] Kip | Global prevalence of methane oxidation by symbiotic bacteria in peat moss ecosystems[END_REF].

The high aminotetrol and aminopentol concentration in the shallow part of the core suggest relatively high present day methanotrophic activity compared with the lower concentration observed downcore. Alternatively, the overall downcore decrease in aminopentol and aminotetrol could be the result of on-going diagenetic decay as the zone of active production by methanotrophs is limited to the oxic (i.e. upper) part of the record.

Hopenes as markers for methanotrophs

Diploptene concentration generally decreases with depth while, the concentration of hop-17(21)-ene increases (Fig. 2f,g). The steady decrease in diploptene suggests that little to none is produced at depth. The decrease in diploptene and the concurrent increase in hop-17(21)-ene may reflect isomerisation diploptene to hop-17(21)-ene. This could not, unfortunately, be confirmed from the d 13 C values of hop-17(21)-ene, since hop-17(21)-ene co-elutes with a 2-methyl analogue, making it difficult to accurately assess its d 13 C values. In an experiment where 13 C labelled methane was added to incubated Sphagnum moss, hop-17(21)-ene concentration was higher than diploptene concentration [START_REF] Van Winden | Lipids of symbiotic methane-oxidizing bacteria in peat moss studied using stable carbon isotopic labelling[END_REF]. Both hopenes were strongly labelled and were therefore considered of (partial) methanotrophic origin. In a follow up field experiment using 13 C-labelled methane, diploptene concentration was relatively high and showed strong label incorporation, while hop-17(21)-ene was below detection limit (van Winden et al., unpublished data). Furthermore, pure cultures of isolated methanotrophs contained diploptene, but no hop-17(21)-ene (van Winden et al., unpublished data). It is likely that hop-17(21)-ene can be indirectly derived from methanotrophs through isomerisation of diploptene. Isomerisation of hopanoids probably occurs rapidly because of the acidic environment (cf. [START_REF] Pancost | Response of an ombrotrophic bog to a regional climate event revealed by macrofossil, molecular and carbon isotopic data[END_REF]. The peak in hop-17(21)-ene abundance at 64 cm, superimposed on the gradual increase, is not reflected in the diploptene profile and must therefore reflect another (unknown) origin.

Compound-specific d 13 C values of diploptene vary between À29‰ and À34‰ (Fig. 2h). These values are relatively heavy vs. Diploptene 13 C content is relatively depleted in the lowermost samples, subsequently shifting towards higher d 13 C values at 94 cm and returning towards lower d 13 C values around 70 cm (Fig. 2h). Above this depth, the 13 C content of diploptene remains low, only returning to higher values around 14 cm. At the top part of the profile, between 20 and 45 cm, diploptene d 13 C values are relatively low, in line with a high abundance of aminotetrol and aminopentol, thereby reflecting the currently active methanotrophic community (Fig. 2d, e andh). In the topmost samples, around 14 cm, diploptene is relatively high in 13 C, corresponding to low aminotetrol and aminopentol concentrations, in line with low methanotrophic activity above the oxic-anoxic interface .

d
The diploptene 13 C content in the deeper parts of the peat is high vs. recent values. Also, aminopentol and aminotetrol suggest relatively high present day methanotrophic activity, with relatively high abundances vs. the lower concentrations downcore. This can be explained in several ways. First, it could indicate that present-day methanotrophic activity is relatively high vs. past activity. Second, it could be the result of (enhanced) contribution of relatively 13 C enriched diploptene originating from a different bacterial source in the deeper -anoxic -part of the profile. Hopanoids have been found in anaerobic bacteria [START_REF] Sinninghe Damsté | The occurrence of hopanoids in planctomycetes: Implications for the sedimentary biomarker record[END_REF][START_REF] Fischer | Targeted genomic detection of biosynthetic pathways: anaerobic production of hopanoid biomarkers by a common sedimentary microbe[END_REF][START_REF] Blumenberg | Biosynthesis of hopanoids by sulfate-reducing bacteria (genus Desulfovibrio)[END_REF] and diploptene is, for example, a major neutral lipid of anammox bacteria [START_REF] Sinninghe Damsté | Structural identification of ladderane and other membrane lipids of planktomycetes capable of anaerobic ammonium oxidation (anammox)[END_REF]. However, diploptene concentration indicates little to no diploptene production at depth. Third, the overall downcore decrease in aminopentol and aminotetrol could be a result of on going decay as the zone of active production by methanotrophs is limited to the oxic (i.e. upper) part of the record.

Potential relationship between extent of methane cycling and water level

Over time, methanotrophic activity may have co-varied with water level or temperature. In the Misten Bog profile, two wet phases were recognised, marked by a shift in vegetation towards Sphagnum section Cuspidata (S. s. Cuspidata), which is restricted to wet bog surface conditions (Fig. 2; De Vleeschouwer et al., 2012). These wet phases occurred around the 8th-9th and around the 13th century AD (De Vleeschouwer et al., 2012). These periods are governed by high relative water level, the result of the balance between precipitation and evaporation. Field studies and laboratory experiments have revealed increasing methanotrophic activity with increasing water level [START_REF] Basiliko | Roles of moss species and habitat in methane oxidation in a northern peatland[END_REF][START_REF] Raghoebarsing | Methanotrophic symbionts provide carbon for photosynthesis in peat bogs[END_REF][START_REF] Kip | Global prevalence of methane oxidation by symbiotic bacteria in peat moss ecosystems[END_REF][START_REF] Larmola | The role of Sphagnum mosses in the methane cycling of a boreal mire[END_REF].

Indeed, aminotetrol shows elevated values coinciding with the wet phases; however, aminopentol and diploptene d 13 C values do not show this (Fig. 2). Therefore, no clear conclusions can be drawn from the combination of different methanotroph biomarkers.

A reason for the discrepancy between the methanotroph markers is possibly the predominance of type II methanotrophs. They use a combination of available CH 4 and CO 2 , potentially leading to a dispersed d 13 C signature. Additionally, the diploptene d 13 C values may be derived from a combination of sources, masking a possible methanotrophic activity related profile. Furthermore, type II methanotrophs do not produce aminopentol, which explains why the aminopentol concentration, close to the detection limit, does not show a clear trend with depth.

Alternatively, the climatic shifts during the formation of the peat bog may have been insufficient to cause significant variation in the methanotroph proxies, explaining the lack of consistency between the proxies. The peaks in aminotetrol may be coincidental, or caused by other bacteria also producing aminotetrol, for instance sulfate reducing bacteria (cf. [START_REF] Blumenberg | Biosynthesis of hopanoids by sulfate-reducing bacteria (genus Desulfovibrio)[END_REF]Blumenberg et al., , 2009a,b),b), although unlikely in a sulfate-poor environment, or unknown species.

In order to establish how methanotrophic activity correlates with climatic changes, a future study could combine a study of methanotroph biomarkers with additional proxies which may provide information on water level and the methane cycle, such as testate amoebae, macrofossils and archaeol (cf. [START_REF] Woodland | Quantitative dominated estimates of water tables and soil in Holocene peatlands from testate amoebae[END_REF][START_REF] Pancost | Archaeol as a methanogen biomarker in ombrotrophic bogs[END_REF]. Finally, to ensure sufficient variation in the methanotroph proxies, future research should focus on peat deposits reflecting larger shifts in climate.

Conclusions

Relatively high methanotrophic activity was recorded by the investigated methanotrophic markers between 20 and 45 cm, around the present oxic-anoxic boundary, most likely representing a currently active methanotrophic community. A predominance of aminotetrol over aminopentol indicated a prevalence of type II methanotrophs. In the deeper parts of the peat, methanotrophic markers did not show a clear correlation between the past change in water level and methanotrophic activity. This may be related to a predominance of type II methanotrophs, which do not produce aminopentol and which consume both CO 2 and CH 4 , resulting in a lower sensitivity of diploptene 13 C values, Furthermore, the lack of correlation could be caused by a mixture of sources for the biomarkers or insufficient variation in climatic change. A proposed way forward would include a study of peat deposits, showing greater climatic variability, with the application of multiple proxies.

Fig. 1 .

 1 Fig. 1. Misten Bog and core location (MIS-08-06W), after De Vleeschouwer et al. (2012).

Fig. 2 .

 2 Fig. 2. Depth distribution of (a) BHPs (b) BHT, (c) aminotriol, (d) aminotetrol, (e) aminopentol, (f) diploptene, (g) hop-17(21)-ene and (h) diploptene d 13 C (‰) in a 100 cm peat core from Misten Bog, Hautes-Fagnes, Belgium. On the left axis, the current catotelm-acrotelm boundary is indicated and on the right axis, an abstracted vegetation profile is shown, along with the approximate dates of the wet phases (dominated by S. s. Cuspidata), adapted from De Vleeschouwer et al. (2012).

Table 1

 1 Concentrations of BHPs (lg g À1 dry sediment), quantified using HPLC-MS (accuracy ±20%). at various depths in the peat core.

	Depth	BHT 2-MethylBHT Aminotetrol Aminopentol Aminotriol Adenosylhopane BHT	BHT	2-MethylBHT	BHpentol	BHhexol	Total
	(cm)							cyclitol	glucosamine	cyclitol ether	cyclitol ether	cyclitol
								ether				ether
	13.7	45 26	8	0	295	88	215	8	12	6	12	715
	21.5	74 21	13	4	366	92	400	10	12	6	8	1007
	30.7	80 14	22	5	393	94	403	9	34	11	11	1076
	43.4	97 15	35	4	414	65	421	13	26	9	9	1107
	50.5	89 22	15	1	310	81	379	8	27	4	3	939
	58.5	96 15	10	3	372	42	359	12	19	7	2	938
	60.9	81 13	12	3	413	52	337	7	19	6	3	945
	63.5	89 18	10	2	454	61	502	5	37	7	5	1190
	69.8	161 19	20	3	711	68	511	11	32	9	12	1558
	79.7	98 21	14	3	432	46	195	9	14	3	3	837
	86.8	81 16	6	1	497	32	317	5	27	6	2	990
	93.7	81	9	6	3	388	34	348	5	21	2	3	898
	96.4	76 12	7	2	323	33	314	5	24	5	5	808
	99.4	61 16	9	2	351	36	441	4	27	5	2	956

Acknowledgements

We thank M. Kienhuis and S. Schouten, NIOZ Texel, for technical assistance with GC-IRMS. The Darwin Center for Biogeosciences partially funded the project via a grant to J.S.S.D. We thank The Science Research Infrastructure Fund (SRIF) from HEFCE for providing funding (to H.M.T.) for purchase of a ThermoFinnigan LCQ ion trap mass spectrometer. W. Shotyk (University of Heidelberg) is thanked for the loan of a Wardenaar corer and for allowing use of the peat-cutting facilities at the IES (University of Heidelberg, Germany). M. Mathijs and M. Court-Picon were of great help in the field. P. Mertes (DNF-Belgian Forest and Nature Ministry) is also acknowledged for providing authorization for coring the Misten bog. We thank R.D. Pancost and an anonymous reviewer for constructive comments which helped to improve the manuscript.

Guest associate Editor -T. Wagner