M Vaïtilingom

L Deguillaume

V Vinatier

M Sancelme

P Amato

N Chaumerliac

A M Delort
email: marie.delort@univ-bpclermont.fr

Potential impact of microbial activity on the oxidant capacity and organic carbon budget in clouds

Within cloud water, microorganisms are metabolically active and, thus, are expected to contribute to the atmospheric chemistry. This paper investigates the interactions between microorganisms and the reactive oxygenated species that are present in cloud water because these chemical compounds drive the oxidant capacity of the cloud system. Real cloud water samples with contrasting features (marine, continental and urban) were taken from the puy de Dôme mountain (France). The samples exhibited a high microbial biodiversity and complex chemical composition. The media were incubated in the dark and subjected to UV radiation in specifically designed photo-bioreactors. The concentrations of H 2 O 2 , organic compounds and the ATP/ADP ratio were monitored during the incubation period. The microorganisms remained metabolically active in the presence of • OH radicals that were photo-produced from H 2 O 2 . This oxidant and major carbon compounds (formaldehyde and carboxylic acids) were biodegraded by the endogenous microflora. This work suggests that microorganisms could play a double role in atmospheric chemistry; first, they could directly metabolize organic carbon species, and second, they could reduce the available source of radicals through their oxidative metabolism. Consequently, molecules such as H 2 O 2 would no longer be available for photochemical or other chemical reactions, which would decrease the cloud oxidant capacity.

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Introduction

The cloud system is an ideal medium for the development of complex multiphase chemistry, in which chemical species from the gas, solid and aqueous phases are transformed. This perturbs the homogeneous gas phase chemistry through the dissolution of various chemical compounds that undergo efficient photochemical processing. During a cloud's lifetime, cloud chemistry can lead to the formation of new, low volatile compounds, such as organic and inorganic acids, that modify the physical and chemical properties of aerosols after cloud evaporation and can also contribute to the formation of secondary aerosols (1)(2). The formation of clouds is, consequently, modified, and this process remains one of the major uncertainties in climate models that assess the earth's radiative balance (3).

Within this framework, the presence of free radicals and oxidants in the cloud system leads to aqueous phase oxidations, transforming both inorganic and organic compounds. Cloud chemistry models predict that the HO • radicals represent the most important oxidant in the cloud aqueous phase (4). This oxidant can either be transferred from the gas phase or produced in situ in the aqueous phase through photochemical processes and/or related reactions with hydrogen peroxide and transition metal ions, such as iron (5). Multiple other oxidants that are produced in clouds can also oxidize chemicals, and these oxidation processes must be better understood because they impact atmospheric chemical cycles and radiation. Indeed, the resulting aerosols increase or decrease the scattering albedo, thus modifying the radiative forcing by clouds.

Many volatile organic compounds from secondary formations are associated with moderately high Henry's law constants and are, consequently, dissolved into the tropospheric aqueous phase (6). Additionally, organic compounds constitute a significant mass fraction of tropospheric aerosol particles, which can also be transferred into cloud water. Hence, the dissolved organic matter is able to interact directly or indirectly with the aqueous chemistry of radicals, radical anions, non-radical oxidants and transition metal ions. A large proportion of the dissolved organic matter is still not characterized, but carboxylic acids could represent a significant proportion of this soluble matter. Among these acids, the formic and acetic acids are the most abundant (mainly produced in the gaseous phase), oxalic acid is commonly the third dominant species and the main di-carboxylic acid, followed by succinic, malonic and maleic acids (predominantly dissolved from organic particles) (7)(8)(9)(10).

Cloud water also hosts microbial populations that are primary biological aerosols and the dominant living aerosols that are present in the atmosphere (11)(12)(13)(14). They can be integrated into clouds because they can serve as cloud condensation nuclei for droplet formation (13)(14)(15). This environment is stressful for airborne microorganisms (low temperature, desiccation, oxidation, UV radiation, acidic pH in the aqueous phase, etc.) (13,[START_REF] Jones | The effects of meteorological factors on atmospheric bioaerosol concentrations: a review[END_REF]. Low temperatures appear to represent one of the major obstacles for cellular activity because they are directly linked to decreased molecular motion and reaction rates. However, bacteria can sustain growth in cloud water at temperatures at or below 0°C [START_REF] Sattler | Bacterial growth in supercooled cloud droplets[END_REF], and they are capable of maintaining metabolic activity at sub-zero temperatures down to -20°C [START_REF] Christner | Incorporation of DNA and Protein Precursors into Macromolecules by Bacteria at -15 o C[END_REF][START_REF] Junge | Bacterial incorporation of leucine into protein down to -20 °C with evidence for potential activity in sub-eutectic saline ice formations[END_REF][START_REF] Rivkina | Metabolic activity of permafrost bacteria below the freezing point[END_REF]. Cultivable microorganisms (fungal spores, yeasts and bacteria) have been found in fog and cloud water [START_REF] Bauer | The contribution of bacteria and fungal spores to the organic carbon content of cloud water, precipitation and aerosols[END_REF][START_REF] Ahern | Fluorescent pseudomonads isolated from Hebridean cloud and rain water produce biosurfactants but do not cause ice nucleation[END_REF][START_REF] Vaïtilingom | Long-term features of cloud microbiology at the puy de Dôme (France)[END_REF]. [START_REF] Bauer | The contribution of bacteria and fungal spores to the organic carbon content of cloud water, precipitation and aerosols[END_REF] demonstrated that the majority of bacteria that are present in cloud water are viable (up to 95% in two samples) [START_REF] Bauer | The contribution of bacteria and fungal spores to the organic carbon content of cloud water, precipitation and aerosols[END_REF]. The adenosine-5'-triphosphate (ATP) concentrations in cloud water that were measured by Amato et al. (2007) [START_REF] Amato | An important oceanic source of micro-organisms for cloud water at the Puy de Dôme (France)[END_REF] suggest that a significant fraction of the microorganisms that are present in these environments are metabolically active.

The discovery of microbial activity in this environment has indicated that there are biologically mediated processes in the chemistry of clouds. In the recent past, researchers investigated the microbial activity in cloud water containing organic compounds, such as carboxylic acids, formaldehyde and methanol [START_REF] Amato | A fate for organic acids, formaldehyde and methanol in cloud water: their biotransformation by microorganisms[END_REF][START_REF] Ariya | Microbiological degradation of atmospheric organic compounds[END_REF]. Inferred estimates indicated that the activity of microorganisms was likely to affect the chemistry of these compounds in warm clouds and could even drive their reactivity during the night [START_REF] Husárová | Biotransformation of methanol and formaldehyde by bacteria isolated from clouds. Comparison with radical chemistry[END_REF][START_REF] Vaïtilingom | Contribution of microbial activity to carbon chemistry in clouds[END_REF][START_REF] Vaïtilingom | Atmospheric chemistry of carboxylic acids: microbial implication versus photochemistry[END_REF].

All of these studies are pertinent, but they were conducted under conditions that were different from those in real clouds. In particular, the presence of reactive oxygenated species, such as hydrogen peroxide (H 2 O 2 ) and free radicals, was ignored. Although these compounds are toxic to cell life, the survival of microorganisms in clouds strongly suggests that cloudborne microorganisms can resist the high concentrations that are found in the atmosphere. This is likely to be due to the efficient, anti-oxidative stress metabolism that involves specialized enzymes (such as catalases, peroxidases and superoxide dismutase) and nonenzymatic compounds [START_REF] Kreiner | Oxidative stress response of a recombinant Aspergillus niger to exogenous menadione and H 2 O 2 addition[END_REF][START_REF] Sigler | Oxidative stress in microorganisms -I. Microbial vs. higher cells-damage and defenses in relation to cell aging and death[END_REF].

We investigated the metabolic activity of microorganisms in microcosms that were more similar to the real cloud environment. We used cloud water samples that were collected at the puy de Dôme mountain, which is a reference site in France for cloud observations and is part of the European ACTRIS (Aerosols, Clouds, and Trace gases Research InfraStructure Network) project. The samples contained complex mixtures of organic and inorganic compounds, oxidants, such as iron complexes and H 2 O 2 , and the endogenous microflora. The cloud water samples were either kept intact or were sterilized by filtration and were then incubated in the dark or subjected to UV-light radiation in specially designed photobioreactors (Figure S1 in Supplementary Information, SI). This procedure resulted in the separation of biologically driven processes and other chemical phenomena (including photochemistry) that occurred in the cloud water under controlled conditions. The biological and chemical characterizations were conducted throughout the incubation. Under these conditions, the microorganisms were exposed to H 2 O 2 and potential • OH radical photoproduction. This study had four specific objectives: i) to study the possible biodegradation processes of H 2 O 2 , ii) to determine the energetic state (ADP/ATP ratios) of cells under these stressful conditions; iii) to investigate the impact of the presence of reactive oxygenated species on the biodegradation rates of organic acids (acetate, formate, oxalate, malonate and succinate) and formaldehyde; and (iv) to compare abiotic and biotic processes.

The major result of this work helps to answer two questions: i) do microorganisms interact with reactive oxygenated species? and ii) consequently, does microbial activity control the oxidant capacity and the organic carbon budget in natural clouds?

Results and discussion

Three cloud events were sampled in June 2010 at puy de Dôme mountain (1465 m a. s. l.).

The backward trajectories from the NOAA Hysplit model were plotted for the various sampled air-masses and are displayed in Figure S2 (SI). Three cloud types were selected for their contrasting features: Cloud 1 had a northwestern marine origin; Cloud 2 was from the continental southwest; Cloud 3 was from the continental northeastern flux but was influenced by anthropogenic emissions. The average temperatures during sampling were 10°C for Clouds 1 and 3 and 13.5°C for Cloud 2 (see Table 1 for additional physico-chemical parameters).

Chemical and biological content of cloud water samples

The chemical and biological data from the 3 cloud water samples are summarized in Table 1.

The chemical properties of these 3 samples are consistent with their respective origins. For example, Cloud 3, collected from an "urban" air-mass, was more acidic and oxidant (pH = 3.9 and [H 2 O 2 ] = 57.7 µM) than Cloud 1, which had a "marine" origin (pH = 6.1 and [H 2 O 2 ] = 3.6 µM). Cloud 2, from the "continental" air-mass, represented an intermediate condition.

The five most abundant carboxylic acids that are usually found in cloud water (i.e., formic, acetic, oxalic, succinic and malonic acids (7)(8)(9)) and the most abundant aldehyde (i.e., formaldehyde) were present in Clouds 2 and 3, while succinic and malonic acids were not detected in Cloud 1. The contribution of carboxylic acids and formaldehyde to dissolved organic carbon was approximately 19, 25 and 23% in Clouds 1, 2 and 3, respectively. The total number of microbial cells was of the same order of magnitude as the typical previous measurements at the puy de Dôme site [START_REF] Vaïtilingom | Long-term features of cloud microbiology at the puy de Dôme (France)[END_REF][START_REF] Amato | Microbial population in cloud water at the Puy de Dôme: Implications for the chemistry of clouds[END_REF] and was very similar to samples from Mt. Rax (1644 m a. s. l., Austria) [START_REF] Bauer | The contribution of bacteria and fungal spores to the organic carbon content of cloud water, precipitation and aerosols[END_REF].

Hydrogen peroxide biotransformation in real cloud water microcosms

The 3 cloud water samples were incubated at 17°C under 4 incubation regimes: unfiltered and in the presence or absence of UV radiation ("Microorganisms + Light" and "Microorganisms", respectively) and filtered and in the presence or absence of UV radiation ("Light" and "Reference", respectively). H 2 O 2 concentrations were measured periodically over the incubation period and are plotted in Figure 1; the corresponding degradation rates are reported in Table 2.

In the absence of UV radiation in filtered cloud water ("Reference"), a slow degradation of H 2 O 2 was observed in Clouds 2 and 3. This phenomenon can be explained by the reactivity of H 2 O 2 with chemical species, such as transition metal ions (a.k.a. "Fenton reactions") or sulphite [START_REF] Gunz | Atmospheric chemistry of peroxides: a review[END_REF][START_REF] Deguillaume | The role of transition metal ions on HO x radicals in clouds: a numerical evaluation of its impact on multiphase chemistry[END_REF]. The zero order kinetic constants were the same for the 2 clouds, most likely because of the very similar iron concentrations. In Cloud 1, the degradation of H 2 O 2 was even slower, most likely because the species responsible for the radical reactions (S and Fe) were present at lower concentrations than in Clouds 2 and 3 (Table 1). Under UV light, the first order kinetics had similar constants, indicating that H 2 O 2 was photolyzed, producing • OH radicals. Interestingly, in the presence of microorganisms without UV light, H 2 O 2 was also efficiently degraded in Cloud 2 (v c = 28.5  10 -11 M s -1 ) and Cloud 3 (v c = 30.0  10 -11 M s -1 ).

In Cloud 1, the microbial degradation of H 2 O 2 was very slow until 72 h but then increased (v c = 2.9  10 -11 M s -1 ). In darkness, the presence of microorganisms enhanced the rate of H 2 O 2 degradation by a factor of 2.9 and 4.2 for Clouds 2 and 3, respectively. Combining UV light and microorganisms resulted in higher degradation rates for H 2 O 2 than either individual condition alone. For Clouds 2 and 3, the degradation rates for H 2 O 2 appeared to be additive: "Light" and "Microorganisms" ≈ "Microorganisms + Light" (see Furthermore, it was possible to quantify the relative impact of biotic activity vs. abiotic H 2 O 2 transformations (see Table S2 andFigure S3). During the day, three types of H 2 O 2 degradation mechanisms were active in Clouds 2 and 3. Photodegradation was the major process (54% and 76%, respectively), followed by biodegradation (30% and 18%, respectively). Other abiotic reactions accounted for 16% in Cloud 2 and 6% in Cloud 3. In Cloud 1, only light was involved initially. During the night, photodegradation was no longer involved, and microbial activity was the major process, accounting for 66% (Cloud 2) and 76% (Cloud 3) of H 2 O 2 degradation, while non-photochemical abiotic reactions only accounted for 34 % (Cloud 2) and 24 % (Cloud 3). These reactions were too slow to be measured in Cloud 1 during the first few hours. Clearly, the impact of microbial activity controlling the oxidant capacity in warm clouds may be important, particularly at night, when it may be dominant.

In addition, the results obtained during the combined photo-biodegradation processes indicate that photo-produced radicals are not toxic to cloud microflora; this statement is consistent with the oxidative stress metabolism of microorganisms. This type of metabolism involves not only catalases but also peroxidases and superoxide dismutase, as well as other nonenzymatic antioxidants [START_REF] Sigler | Oxidative stress in microorganisms -I. Microbial vs. higher cells-damage and defenses in relation to cell aging and death[END_REF].

Microbial energetic states in real cloud water microcosms

In the previous section, we noted that microorganisms were exposed to photo-produced radicals, which are known to be potentially toxic to microbial cells. Therefore, we investigated the ability of cloud microorganisms to resist these oxidative stresses by quantifying the ADP/ATP ratios during the same incubations. Essentially, growing bacteria present a ratio of ~ 0.25, whereas dead cells have a ratio > 6 [START_REF] Koutny | Acquired biodegradability of polyethylenes containing prooxidant additives[END_REF]. The plots presented in In all cases, the microbial energetic state improved over time as the ADP/ATP ratios decreased. The microorganisms in Clouds 2 and 3 had similar initial energetic states (ADP/ATP ratios of 0.3 and 0.5, respectively) that decreased slightly over the incubation period and reached 0.1 and 0.2, respectively, after 72 hours. The initial energetic state of bacteria from Cloud 1 was lower (ADP/ATP ratio 1.4) than the other cloud samples. This difference could be due to some specific, unfavorable stress that was encountered by the microorganisms during the air-mass history. The ADP/ATP ratio increased over time and attained an energetic state that was similar to Clouds 2 and 3 after 72 h. This recovery of a higher energetic state after 72 h reflects an activation of the microbial metabolism.

Consequently, the biodegradation rate of H 2 O 2 increased, as displayed in Figure 1 (Cloud 1), when microorganisms were present in the incubation medium.

The ADP/ATP ratios during the microbial incubations under dark and UV light conditions were similar. Clearly, photodegradation in the presence of H 2 O 2 , notably the production of • OH radicals under light conditions, did not affect the cells' energy metabolism. This result is in agreement with the additive nature of the photochemical and biological processes that are involved in H 2 O 2 degradation (Table 2).

Impact of the presence of reactive oxygen species on the biotransformation of carboxylic acids and formaldehyde in real cloud water microcosms

Formate, acetate, succinate, oxalate, malonate and formaldehyde were also measured in the 4 incubation regimes that were used for H 2 O 2 . Focusing on Cloud 2, the evolution of the concentrations of carboxylic acids and formaldehyde are plotted in Figure 3, and the production and degradation rates of carboxylic acids and formaldehyde are presented in Table 3. Because they are very similar to Cloud 2, the results for the other clouds are presented in SI (Figures S4,S5). Acetate, formate and succinate were only degraded in the presence of microorganisms, while oxalate was only degraded in the presence of UV light. Malonate and formaldehyde were photo-produced during the experiment and degraded in the presence of microorganisms. The biotransformation was not delayed for acetate, succinate, malonate and formaldehyde, while in the case of formate, we observed a lag time (up to 48 h) before the biodegradation began. The degradation of oxalate by UV light in the absence of microorganisms indicated that photochemical reactions were involved. This conclusion is consistent with the concomitant H 2 O 2 photolysis under the same conditions (see section 3.2).

It is also important to note that the degradation rates in the "reference" sample were close to zero, demonstrating that the major processes were photodegradation and biodegradation.

The most interesting results clearly show that the endogenous microflora were not inhibited by the presence of reactive oxygen species (H 2 O 2 and photo-produced • OH radicals). In the case of acetate, formate and succinate, the degradation rates that were measured for "Microorganisms" (15.5, 17.5 and 4.5 × 10 -11 M s -1 , respectively) and "Microorganisms + Light" (15.6, 16.1 and 3.5 × 10 -11 M s -1 , respectively) were very similar. Therefore, the presence of light, and, thus, of • OH radicals, had no influence on microbial carbon metabolism. In the case of formaldehyde, the photo-production rate was 0.2 × 10 -11 M s -1 ("Light"), while the biodegradation rate ("Microorganisms") was 0.3 × 10 -11 M s -1 . When the two processes were combined ("Microorganisms + Light"), the resulting rate of transformation was null, indicating that the addition of the processes occurred without any inhibition. The case of malonate is rather similar to that of formaldehyde; the rate observed during the combined photo-biodegradation processes after 36 hours (4.3 × 10 -11 M s -1 ) corresponded to the addition of the photo-production rate (0.3 × 10 -11 M s -1 ) and the biodegradation rate (4.2 × 10 -11 M s -1 ). The conclusions related to Cloud 2 can be extended to Clouds 1 and 3 (see comments in the SI text); they are straightforward in the case of Cloud 3, while the case of Cloud 1 is more complex to interpret. This difficulty in interpretation could be due to the unusually low concentration of organic and inorganic compounds in Cloud 1 (Table 1) and to the lower initial energetic state of the microorganisms (see Figure 2), which explains why biodegradation only began after 72 hours. Global comments about the 3 clouds are provided in the SI section and are related to Figures S4 andS5 and Table S3, which lists all of the transformation rates.

The principle of the non-inhibition of reactive oxygen species to the biodegradation process was observed in all of the study clouds, and the addition of the various photo-and biotransformation rates remains valid (see SI). This result has major consequences when considering the impact of microbial activity on carbon budgets. It suggests that microorganisms could play a role not only in cloud chemistry at night, as previously indicated [START_REF] Husárová | Biotransformation of methanol and formaldehyde by bacteria isolated from clouds. Comparison with radical chemistry[END_REF][START_REF] Vaïtilingom | Contribution of microbial activity to carbon chemistry in clouds[END_REF][START_REF] Vaïtilingom | Atmospheric chemistry of carboxylic acids: microbial implication versus photochemistry[END_REF], but also during the daytime, when • OH radicals are photo-produced. The implication of microorganisms in carbon flux in the atmosphere at the global scale was estimated (see SI and Table S4). This rough calculation results in a global release of 51 to 215 million tons of CO 2 per year through microbial respiration.

Conclusions

We studied microbial activity in real cloud samples that represent the 3 major categories of air-masses on the puy de Dôme mountain (marine, continental and urban origins). Their physical and chemical compositions thus represented varied experimental scenarios, and the endogenous microflora in each sample were likely to be different. Indeed, the microbial composition of cloud waters greatly depends on the sources of microbial aerosolization (vegetation, oceans, urban areas, etc.) that are present on the air-mass trajectories and also on the chemical composition of clouds, which can favor the survival of some species over others (11). During their transport over long distances, these microorganisms have been subjected to numerous stresses, including evaporation-condensation cycles; nevertheless, they remained active, as shown by their good energetic states (ADP/ATP ratios range to 0.3 to 1.4) and their efficiency at bio-degradation. Microorganisms can adapt very easily to changing conditions and stresses in the atmosphere, as observed in other extreme environments [START_REF] Roszak | Survival strategies of bacteria in the natural environment[END_REF].

Our experiments were conducted in innovative microcosms that were designed to mimic more realistic environmental cloud conditions. Using unfiltered samples in a homemade photobioreactor, we investigated the activities of microorganisms that were exposed to UV light and H 2 O 2 , which is a major source of • OH radicals in cloud waters. The experiments that combined both the photo and biodegradation processes were compared to experiments with bio-degradation (absence of UV light) or photo-degradation alone (filtered sample).

First, our results indicate that the microorganisms that were present in the cloud samples and exposed to UV light remained metabolically active in the presence of • OH radicals that were photo-produced from H 2 O 2 due to the oxidative stress metabolism of the cells. This phenomenon is clearly demonstrated by the similar ADP/ATP ratios that were measured when the microorganisms were exposed or not exposed to UV light, indicating that the microbial energetic state was unchanged. This result was also clearly demonstrated by the degradation rates under combined conditions ("Microorganisms + Light"), where photo-degradation and biodegradation are additive processes. There was no inhibition of microbial activities towards the organic, biodegradable compounds (acetate, formate, succinate, malonate, and formaldehyde) that were tested in the presence of reactive oxygen species. This information is particularly important when considering the potential role of microorganisms in cloud chemistry and the resulting carbon balance. Previously, studies were conducted in the absence of such reactive oxygen species, and the resulting biodegradation rates were subject to debate.

The results obtained here reinforce the hypothesis that the actual activity of microorganisms in clouds is an alternative route in photochemistry. These two transformation processes could coexist in cloud droplets and be modulated by the bio-physico-chemical conditions that are encountered in natural warm clouds.

Second, and most importantly, we have demonstrated that H 2 O 2 , a precursor to oxidant species in clouds, is biodegraded by the endogenous microflora through the actions of catalases. To our knowledge, this is the first report of such an effect in the atmospheric environment. Moreover, the biodegradation process is significant compared to the photochemical process. This finding has major consequences for atmospheric chemistry because it shows that microorganisms may have an impact on the oxidant capacity of clouds. This concept is clearly new and should be considered in much greater detail. However, we are aware that our microcosms are still unlike real cloud systems, which are polydisperse, with highly variable spatial and temporal parameters. However, this work suggests that microorganisms could play a double role in atmospheric chemistry and, more specifically, on the carbon budget of the atmosphere. First, they could directly metabolize organic carbon species. Second, they could destroy a portion of the source of radicals due to their oxidative metabolism, and, as a result, these molecules, such as H 2 O 2 , would no longer be available for photochemical or other chemical reactions.

Materials and methods

Three cloud water samples from 3 different origins were collected at the puy de Dôme station in 2010 and were analyzed chemically and biologically. They were incubated in photobioreactors at 17°C under 4 incubation regimes for 7 days: unfiltered and in the presence or absence of UV radiation ("Microorganisms + Light" and "Microorganisms", respectively), and filtered and in the presence or absence of UV radiation ("Light" and "Reference", respectively). We recorded the concentrations of formate, acetate, oxalate, succinat, malonate, formaldehyde, H 2 O 2 , and the ADP/ATP ratio during the incubation period. Additional details about the methodology are provided in the SI Text. (aerobic condition, 110 rpm) under a fluorescent tube that emitted radiation with wavelengths between 340-λ max =365 nm and total light energy: 33 J s -1 m -2 ). For incubation in darkness, two brown 250 mL Erlenmeyer flasks were used. For incubation under UV light emission, two photobioreactors were used (see Figure S1). These photo-bioreactors consisted of a cylindrical Pyrex crystallizer (300 mL, 95 mm diameter) covered with a Pyrex ® glass filter ( 3.3 mm thick and 80 mm Ø)

Figure captions

Figure captions

and mounted on a nylon lid that was equipped with 8 vents (Teflon ® tubes of 8 mm Ø, plugged with sterile cotton wool to avoid microbial contamination) to prevent water condensation on the filter and to maintain continuous oxygenation in the tested medium. Preliminary tests in artificial cloud water solutions have shown that there was no effect of the incubation flask type on the transformation rates. Bio-and chemical analyses were conducted after the cloud water collection and at various times during the incubation, as indicated in Table 1.

Chemical analyses.

Conductivity, pH, total and dissolved organic carbon. Conductivity and pH were measured on-site with a portable multi-parameter pH-meter that was equipped with a temperature sensor. The total organic carbon (TOC) and dissolved organic carbon (DOC) were measured with a TOC analyzer (TOC 5050A, Shimatzu © ). Five mL of filtered (filter porosity 0. and Fe(Total), 2×1 mL samples were necessary. The uncertainty in the measurements was less than 10%, and the detection limit (DL) was 0.1 µM (calculated as three times the standard deviation of the field blanks).

Ionic species. The ion concentrations in the cloud water samples were measured using ionic chromatography (3,6) units: Dionex DX320 for anions (column AS11, eluent KOH) and Dionex ICS1500 for cations (column CS16, eluent hydroxymethanesulfonate acid). Samples were thawed 15 min before their dilution by a factor of 10 in ultrapure water and transferred into vials (5 mL)

previously rinsed with ultrapure water. No chemical transformations were observed in our samples after one freeze/thaw cycle. The accuracy of the ion chromatographic analyses was 5% (3).

Formaldehyde. The formaldehyde concentration was measured using a miniaturized fluorimetric assay that was adapted from Li et al., (2007) (7). The reaction medium included 60 µL of ammonium acetate solution (4 M), 60 µL of acetoacetanilide (0.2 M in ethanol solution 50% v/v), 60 µL of ethanol (96°) and 120 µL of the cloud water sample. The solutions were mixed and incubated on a plate with ninety-six black, flat bottomed wells at roo te perature for i efore readi g λ ex = a d λ em = 490 nm). The uncertainty in the measurements was less than 5%, and the DL was 0.1 µM (calculated as three times the standard deviation of the field blanks). Previous tests indicated that the fluorescent dimeric compound remained stable after this freeze/tha pro ess. Fluores e e readi gs λ ex = a d λ em = 390 nm) were made in a 96 well format. The uncertainty of the measurements was less than 5%, and the detection limit was 0.07 µM.

To determine the concentration of organic peroxides, samples were treated with catalase. After 30 seconds, 200 µL of the reagent solution was added, and the previous protocol was applied. For all cloud waters, the organic peroxide concentrations were lower than the limit of detection (0.07 µM).

Biological analyses. A direct enumeration of cells (duplicates, 10 mL) was performed with epifluorescence microscopy, as described by Amato et al., (2005) (9). The main difference in methods was in the use of a phosphate buffering solution (pH = 7) to improve the fluorescence efficiency.

The ATP and ADP concentrations were measured in cloud water samples (0.2 mL) using the ATP Biomass Kit HS (Biothema © ) and a Biolumineter (Lumac Biocounter M2500). The analytical protocol was described in (10).

The cloud water samples (0.2 mL) were strongly mixed in situ in a sterile microtube with an equal volume of extractant B/S, from the ATP Biomass Kit HS (Biothema © ). This mixture was used for ATP determination and was stored frozen prior to the analysis. The ATP concentrations were determined by bioluminescence (11-12) using a Biolumineter (Lumac Biocounter M2500). The ADP concentration was determined after the direct transformation of ADP to ATP in the luminometer tube, in the presence of pyruvate kinase and phosphoenolpyruvate; this analytical protocol is described in (10). A small volume of the mixture (sample + extractant B/S: 60 µL) was used to determine the ATP or ADP concentration.

Calculations of the transformation rates. To calculate the initial transformation rates, the temporal evolution of the concentration of each compound was plotted. Then, the pseudo-first order decay k s -1 ) was determined (only for r²>0.8) using the following linear regression:

    t k t f ] C /[ ] C [ ln 0    
The transformation rate of the compound C (v c ) was determined as follows:

t c C k v ] [   [M -1 s -1 ]
Where [C] t is the concentration of the chemical compound C (mol L -1 ) at time t (s), and k is the pseudo-first order decay (s -1 ).

H 2 O 2 degradation: relative contribution of biotic and abiotic processes. To quantify the relative impact of biotic activity compared to abiotic H 2 O 2 transformations, we corrected some of our data.

We used the initial degradation rate of H 2 O 2 in the absence of microorganisms and light (reference sample) to quantify the impact of the radical reactions that were non-photochemically induced. For photodegradation and biodegradation, we have subtracted this value from the initial rates that were measured in the presence of light, or microorganisms, respectively (Table S2). From these corrected values, it was possible to calculate the relative contribution of each type of reaction (Figure S3). S3.

Bio-and phototransformation of carboxylic acids and formaldehyde

Under dark conditions with the endogenous microbial population ( Microorga is s case), the acetate and formate biodegradation rates were much higher than those of succinate, malonate and formaldehyde for the 3 cloud events. For formate, a lag time was observed in Clouds 1 and 2, as already noted for Cloud 2. For malonate, 2 kinetic steps took place in Clouds 2 and 3: until 36 h, the rates were slow but then increased dramatically. Oxalate was not biodegraded in any of the samples; this lack of degradation was also observed with selected bacterial strains that were isolated from cloud water (13).

U der UV light o ditio s Light ) with filtered cloud water, only the succinate concentrations did not evolve. Formaldehyde was continuously photo-produced, due to the production of concentrations in the 3 cloud waters (see Tables 1 and2). The end of these transformations was, consequently, linked to the total consumption of H 2 O 2 . In summary, from the observations that were made during incubations under UV light, the temporal evolutions of organic compound concentrations were highly variable, resulting from both the photo-production and photodegradation processes. For Cloud 1, the photo-degradation rate was very low because the H 2 O 2 concentration was 10 to 20 times lower than that in Clouds 2 and 3, respectively.

In the presence of UV light and microorganisms ( Microorga is s + Light case), we observed the sum of photochemical and microbial activities without synergy or inhibition. In Clouds 2 and 3, the degradation rates of acetate, formate and succinate, were similar with and without UV light because these compounds were not involved in any photochemical processes. For oxalate, the degradation rates were the same for light alone and light plus microorganisms; this observation is in agreement with the non-biodegradability of this compound (Clouds 2 and 3). Finally, for malonate, production and destruction are competitive (Clouds 2 and 3). During the first part of the kinetic (up to 48 h), photo-production dominated. Then, after 36 h, biodegradation began and became dominant. A similar behavior was noted for the degradation of acetate in Cloud 1. The first photo-production

(1)

(2)

(3) (4) Table S1. Bio-physicochemical measurements in cloud water samples. Table S4. Estimates of the amount of CO 2 released by microbial respiration in clouds. The cell concentrations are listed in Table 1, and the degradation rates from Table 3 were converted into gC h -1 cell -1 . The total mass of clouds follows the conclusions of Pruppacher and Jaenicke, (1995) (14).

Figure 2

 2 Figure2reflect the evolution of the energetic states of these microorganisms over time.

Figure 1 :

 1 Figure 1: Temporal evolution of H 2 O 2 concentrations (µM) in the presence or absence of UV light and/or microorganisms during incubation of cloud water (Clouds 1, 2, 3). Cloud water samples were incubated at 17°C under 4 incubation regimes for 7 days: unfiltered and in the presence or absence of UV radiation ("Microorganisms + Light" and "Microorganisms", respectively), filtered and in the presence or absence of UV radiation ("Light" and "Reference", respectively). Error bars represent the standard errors of the enzymatic assay (5%).

Figure 2 :

 2 Figure 2: [ADP] / [ATP] ratios of microbial cells in the presence and absence of UV-light during the incubation of unfiltered cloud water samples (Clouds 1, 2, 3).

Figure 3 :

 3 Figure 3: Temporal evolution of carboxylic acids and formaldehyde concentrations during the incubation of Cloud 2. The Cloud 2 water sample was incubated at 17°C under 4 incubation regimes for 7 days: unfiltered and in the presence or absence of UV radiation ("Microorganisms + Light" and "Microorganisms", respectively), filtered and in the presence or absence of UV radiation ("Light" and "Reference", respectively). Error bars represent the standard errors of the chemical analysis (5%).

Figure 1 :

 1 Figure 1: Temporal evolution of H 2 O 2 concentrations (µM) in the presence or absence of UV light and/or microorganisms during incubation of cloud water (Clouds 1, 2, 3). Cloud water samples were incubated at 17°C under 4 incubation regimes for 7 days: unfiltered and in the presence or absence of UV radiation ("Microorganisms + Light" and "Microorganisms", respectively), filtered and in the presence or absence of UV radiation ("Light" and "Reference", respectively). Error bars represent the standard errors of the enzymatic assay (5%).

Figure 2 :

 2 Figure 2: [ADP] / [ATP] ratios of microbial cells in the presence and absence of UV-light during the incubation of unfiltered cloud water samples (Clouds 1, 2, 3).

Figure 3 :

 3 Figure 3: Temporal evolution of carboxylic acids and formaldehyde concentrations during the incubation of Cloud 2. The Cloud 2 water sample was incubated at 17°C under 4 incubation regimes for 7 days: unfiltered and in the presence or absence of UV radiation ("Microorganisms + Light" and "Microorganisms", respectively), filtered and in the presence or absence of UV radiation ("Light" and "Reference", respectively). Error bars represent the standard errors of the chemical analysis (5%).

Figure 1

 1 Figure 1.

  22 µm) and non-filtered samples were used for DOC and TOC measurements, respectively, as described in Parazols et al. (2007) (4).Fe(II), Fe(III). The iron (Fe) concentrations (Fe(II) and Fe(III)) were measured with a spectrophotometric assay with a colorimetric complexant (ferrozine) (Stokey (1970) (5)) that was used for cloud water analyses by Parazols et al. (2007) (4). To determine the concentrations of Fe(II)

H 2 O 2 .

 22 The hydrogen peroxide concentration was measured using an accurate enzymatic fluorimetric assay with a 4-Hydroxyphenylacetic acid that produced a fluorescent dimeric compound with hydrogen peroxide (microassay adapted from[START_REF] Lazrus | Automated fluorimetric method for hydrogen peroxide in atmospheric precipitation[END_REF] (8)). 1.5 mL of a solution of 4-Hydroxyphenylacetic acid (1.5 mM) in a phosphate buffering solution (0.1 M, pH 7.4) was mixed with 10 µL of a horse radish peroxydase solution (380 units mL -1 ) to constitute the reagent solution; this solution was kept at 5°C for less than 12 h. After sampling (less than 5 min), 10 and 50 µL of the cloud water sample were mixed on-site with 200 µL of the reagent solution in duplicates and incubated at room temperature (>17°C) for 5 min before freezing at -25°C. A new calibration was systematically performed before each analysis session, with a normalized H 2 O 2 solution with a different concentration (0 to 200 µM) using the same reagent lot. Before the analysis, the mixed samples (cloud water + reagent) were thawed at ambient temperature for 10 min, and 200 µL were analyzed.

Figure*

  Figure S3.

Table 2 ). These results demonstrate

 2 that cloud microorganisms can metabolize H 2 O 2 into O 2 and H 2 O using catalases, which are ubiquitous oxidoreductase enzymes.

Table captions Table 1 :

 captions1 Initial bio-physico-chemical characteristics for the 3 cloud events, sampled at the pdD station.

Table 2 :

 2 Initial degradation rates of H 2 O 2 in the presence and absence of UV light and/or microorganisms during the incubation of natural cloud waters. A negative value indicates the disappearance of H 2 O 2 from the medium. In the case where a non-continuous transformation occurred, the time period used for the linear regression is indicated in brackets.

Table 3 :

 3 Initial transformation rates of carboxylic acids and formaldehyde in the presence and

absence of UV-light and/or microorganisms during the incubation of cloud 2. A negative value indicates the disappearance of the organic compounds from the medium. Values in bold represent production of the compounds in question. In the case where a non-continuous transformation occurred, the time period used for the linear regression is indicated in brackets.

Table 1 .

 1

						Cloud 1	Cloud 2	Cloud 3
	Air-mass origin	North-western	South-western	North-earstern
	Air-mass type	Marine	Continental	Urban
	Date of sampling	6/1/10 8:20 PM 6/8/10 12:05 PM	6/18/10 11:15 AM
	Duration of sampling	6:30	11:20	19:45
	Temperature	10 °C	13.5 °C	10 °C
	pH	6.1	5.2	3.9
	Conductivity (µS cm	-1 )	3.5	37.6	78.6
	TOC (DOC) (mg L	-1 )	1.1 (1.1)	6.8 (6.7)	6.9 (6.8)
	Compound	Concentration (µM)
	Acetate	4.5	25.4	23.2
	Formate	4.9	42.7	33.2
	Succinate	-	3.1	3.8
	Oxalate	1.0	9.7	9.3
	Malonate	-	3.1	3.5
	Cl	-	3.0	7.7	11.3
	NO 3 -	4.5	70.6	228.7
	SO 4 2-	1.8	46.1	64.0
	Na	+	2.2	10.1	8.8
	NH 4 +	8.5	100.3	122.3
	K	+	-	1.5	2.2
	Mg 2+	1.0	2.1	2.7
	Ca	2+	1.7	3.8	3.8
	Fe (total)	0.9	1.1	1.3
	Fe (II)	0.3	0.5	0.5
	Formaldehyde	1.5	2.7	6.1
	H 2 O 2	3.6	33.4	57.7
	ATP (pmol mL	-1 )	0.8	2.3	2.1
	ADP (pmol mL	-1 )	1.1	0.7	1.1

Table 2 .

 2

		Cloud 1	Cloud 2	Cloud 3
		Rate of H 2 O 2 transformation ( × 10	-11 M s -1 )
	Reference*	0	-9.8	-7.2
	Light	-0.6	-48.6	-105.1
	Microorganisms	-2.9 (72 h to end)	-28.5	-30.0
	Microorganisms + Light	-0.9	-68.4	-126.9
	* Reference: sterilized sample (filtration 0.22 µm) incubated in darkness

Table 3 .

 3

		Acetate	Formate	Succinate	Oxalate	Malonate	Formaldehyde
				Rate of transformation ( × 10	-11 M s -1 )
	Reference*	0	0	0	0		0	0
	Light	0	0	0	-4.0 (0 h to 60 h) 0.3 (0 h to 60 h)	0.2
	Microorganisms	-15.5	-17.5 (48 h to end)	-4.5	0	-4.2 (36 h to end)	-0.3
	Microorganisms + Light	-15.6	-16.1 (48 h to end)	-3.5	-2.7 (0 h to 60 h) -4.3 (36 h to end)	0
	* Reference: sterilized sample (filtration 0.22 µm) incubated in darkness

in cloud samples.

  The concentrations of formate, acetate, succinate, oxalate, malonate and formaldehyde were recorded Microorganisms + Light , Light , Microorga is s , "Refere ce ) for the 3 cloud water samples (see Figure3, S4 and S5). The transformation rates that were measured in these experiments are listed in Table

	at	°C u der	i u atio	o ditio s

  • OH from H 2 O 2 photolysis. Malonate was photo-produced up to a stationary concentration (60 h) (Clouds 2 and 3). Oxalate was photo-degraded up to a stationary state (60 h) that corresponded to the consumption-time of H 2 O 2 in these sample (Clouds 2 and 3). Formate was slowly degraded in Cloud 3, up to 48 h, and was not degraded in Cloud 2. The behavior of Cloud 1 was somewhat different; a slow photo-production of acetate and formate was observed, while oxalate was degraded continuously until the end of the experiment and was correlated with the presence of H 2 O 2 . The variations in the rates of organic compound photo-transformation were related to the initial H 2 O 2

Table S2 .

 S2 Calculated values of the initial transformation rates of H 2 O 2 , linked to biotic and abiotic processes occurring during the incubation of Clouds 1, 2 and 3. "Light" corresponds to pure photochemical processes, "other" corresponds to non-photochemically induced radical processes, and "microorganisms" corresponds to pure biodegradation processes. Negative values indicate the disappearance of H 2 O 2 from the medium.

Acknowledgments

The research was funded by the CNRS/INSU and the French Ministry of Research under the LEFE-CHAT program. M. Vaïtilingom thanks the French Ministry of Research for Ph.D. scholarships. Bruce Moffett is gratefully acknowledged for correcting the manuscript. We thank warmly C. Bernard and M. Ribeiro for the technical support.

Supporting information "Potential impact of microbial activity on the oxidant capacity and organic carbon budget in clouds"

Vaïtilingom et al.

SI Text

Cloud water collection. Cloud water was sampled from the top of the puy de Dôme mountain (1465 a.s.l., ° ' North, ° ' East, Fra e , as des ri ed o the e site: http://wwwobs.univbpclermont.fr/SO/beam. The study location is frequently covered by clouds and is disconnected from local pollution. Two Cloud Water Samplers, developed by [START_REF] Kruisz | A cloud water sampler for high wind speeds[END_REF] (1), were used with an estimated cut-off diameter of 7 µm, for an approximately debit rate of 80 m 3 h -1 (2-3). The collection mechanisms on the CWS were sterilized using an autoclave (20 min at 121°C) before sampling, while the body parts were sterilized on site by washing them with ethanol (70% v/v) and extensively rinsing them with sterile, ultrapure water. After 30 min, to assess the microbial sterility and chemical purity of the freshly cleaned body parts, 50 mL of sterilized, ultrapure water were poured into the sampler a d re o ered i the olle tio parts la k sa ples . For the loud sa pli g e e ts prese ted i this paper, o i ro ial or he i al o ta i atio as o ser ed i the la k sa ples.

During sampling, the cloud water was recovered from the collector under sterile conditions when the volume in each collector reached 100 mL. A minimum volume of 600 mL of cloud water was necessary for the biological and chemical analyses listed in Table 1. After collection, samples were immediately frozen or kept at 4°C. Laboratory experiments were performed less than 2 h after the end of sampling.

Cloud water incubation.

Half of the sample was sterilized by filtration (filter porosity 0.22 µm, nylon filter) to eliminate microorganisms. The filtered and unfiltered samples were incubated for 7 days in the dark or under UV light. The experiments were carried out at a constant temperature of 17°C, a value slightly different from the temperature during the cloud sampling events (10°C, 13.5°C and 10°C, for Clouds 1, 2 and 3, respectively). The incubator chamber was equipped with a stirring plate occurred before 72 h, and biodegradation then took over the relay. Formaldehyde was photoproduced and biodegraded. The apparent non-transformation of formaldehyde in the cloud waters in the presence of UV light and microorganisms (especially for Clouds 2 and 3) revealed the competition between these two processes.

For all experiments, the organic compound bio-transformation rates were similar between incubation under UV light or in darkness, and we did not observe an inhibition effect of ROS over the microbial carbon metabolism.

Estimation of the global consequences of microorganisms in carbon flux in the atmosphere. From the number of cells (Table 1) and the degradation rates that were measured in the unfiltered microcosms that were incubated in the dark (i.e., the biodegradation rates, converted into gC h -1 cell - 1 ) (Tables 3 andS4), we inferred the consumption of dissolved organic carbon species by microorganisms in clouds at the global scale (Table S4). Assuming a total mass of clouds of 1.94 × 10 17 g on earth (14), microorganisms would constitute between 13 and 60 million tons of dissolved organic carbon, originating from formate, acetate, oxalate, malonate, succinate and formaldehyde each year, on a global scale (results are based on Clouds 1 and 2, respectively, in which the extreme lower and upper levels of microbial activity were measured). This is a conservative estimate of the total dissolved organic carbon biodegradation in clouds because other carbon compounds not measured here were most likely also consumed by cells. If we assume a bacterial growth efficiency on those compounds of 0 (i.e., these carbon sources are not used by microorganisms for producing biomass but are completely respired into CO 2 ), which is close to reality at low carbon concentrations (15), then the microbial respiration would lead to a global release of 51 to 215 million tons of CO 2 per year. (in red) corresponds to non-photo he i ally i du ed radi al pro esses, a d Mi roorga is s i green) corresponds to a pure biodegradation processes. Percentages were calculated from the initial rates of degradation that were reported in Table S2.

Figure S4. Temporal evolution of carboxylic acids and formaldehyde concentrations during the incubation of Cloud 1. The Cloud 1 water sample was incubated at 17°C under 4 incubation regimes for 7 days: unfiltered and in the presence or absence of UV radiation ("Microorganisms + Light" and "Microorganisms", respectively), filtered and in the presence or absence of UV radiation ("Light" and "Reference", respectively).

Error bars represent the standard errors of the chemical analysis (5%).

Figure S5.

Temporal evolution of carboxylic acids and formaldehyde concentrations during the incubation of Cloud 3. The Cloud 3 water sample was incubated at 17°C under 4 incubation regimes for 7 days: unfiltered and in the presence or absence of UV radiation ("Microorganisms + Light" and "Microorganisms", respectively), filtered and in the presence or absence of UV radiation ("Light" and "Reference", respectively).

Error bars represent the standard errors of the chemical analysis (5%). -No tra sfor atio s o ser ed for the refere e sa ple i.e., sterilized sa ple i u ated i darkness).

Table S3. Initial transformation rates of carboxylic acids and formaldehyde in the presence and absence of UV light and/or microorganisms during the incubation of cloud 2. A negative value indicates the disappearance of the organic compounds from the medium. Values in bold represent production of the study compounds. In the case where a non-continuous transformation occurred, the time period used for the linear regression is indicated in brackets.


Anne-Kristel Bittebière
email: anne-kristel.bittebiere@univ-lyon1.fr

M Garbey
email: garbey@cs.uh.edu

M Smaoui-Feki

Bernard Clément
email: bernard.clement@univ-rennes1.fr

Cendrine Mony
email: cendrine.mony@univ-rennes1.fr

A K Bittebiere

Structural blueprint and ontogeny determine the adaptive value of the plastic response to competition in clonal plants: a modelling approach

Keywords: Adaptive value, Competition intensity, Ontogeny, Plasticity, Structural blueprint

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

Introduction

Local interactions such as competition are one of the principal drivers of plant community assemblages and determine plant spatial and temporal dynamics [START_REF] Rees | Quantifying the impact of competition and spatial heterogeneity on the structure and dynamics of a four-species guild of winter annuals[END_REF][START_REF] Gibson | Designs for greenhouse studies of interactions between plants[END_REF][START_REF] Warren | Competitive relationships in a fertile grassland community -does size matter?[END_REF]Wilson 2007). Depending on its intensity (i.e., the extent to which it decreases the quality of the individual environment), competition impacts plant individuals in terms of survival, growth or reproduction [START_REF] Begon | [END_REF][START_REF] Weigelt | Indices of plant competition[END_REF].

Nevertheless, many studies have demonstrated that plants can actively modify their architecture [START_REF] Weijschedé | Variation in petiole and internode length affects plant performance in Trifolium repens under opposing selection regimes[END_REF][START_REF] Herben | Fight or flight: plastic behavior under self-generated heterogeneity[END_REF]Bittebiere et al 2012a), based on environmental signals in order to limit the effects of the presence of competitors on their performance [START_REF] Sultan | Phenotypic plasticity and plant adaptation[END_REF][START_REF] Novoplansky | Picking battles wisely: plant behaviour under competition[END_REF]. The nature of these architectural modifications (e.g., internode elongation or shortening) can strongly influence the outcome of competitive interactions (Bittebiere et al 2012b).

Growth in clonal plants occurs through horizontal iterations that generate a network of ramets (potential descendants) [START_REF] Van Groenendael | Comparative ecology of clonal plants[END_REF]. This growth is regulated by a trade-off between space occupation (producing aggregated ramets limiting competitor intrusions within the clonal territory and thus supporting the resistance to competition) and exploration (investing in long connections to infiltrate the surrounding vegetation) [START_REF] Lovett-Doust | Population dynamics and local specialization in a clonal perennial (Ranunculus repens). I. The dynamics of ramets in contrasting habitats[END_REF]. In heterogeneous habitats, clonal plants are able to forage for the best patches through variations in their horizontal growth traits. In the presence of competitors, this foraging capacity may enable them to develop in the direction of least interference [START_REF] Ross | Occupation of biological space during seedling establishment[END_REF][START_REF] Richards | Improving the effectiveness of angular dispersion in plant neighbourhood models[END_REF]. We would expect similar response strategies to result in similarities among architectural features in different plant species (similar mean traits). For instance, growing far from competitors (i.e., avoidance behaviour) may imply an elongation of connections. However, experimental or field studies that compare plant architecture in the presence or absence of competitors report a large range of effective (i.e., observed) responses to competition (e.g., either an increase or a decrease in the inter-ramet distance or branching frequency) or intensities in these responses (from low to high variation in these traits) [START_REF] Cheplick | Clonal growth and storage in relation to competition in genets of the rhizomatous perennial Amphibromus scabrivalvis[END_REF]Markuvitz and Turkington 2000;[START_REF] Novoplansky | Picking battles wisely: plant behaviour under competition[END_REF]Bittebiere et al 2012a;[START_REF] Oborny | From virtual plants to real communities: A review of modelling clonal growth[END_REF].

We argue that this gap between expected and observed architectures should be explained by the interaction between plasticity and two other processes ultimately defining the architecture: the structural blueprint and ontogeny [START_REF] Huber | Spatial structure of stoloniferous herbs: an interplay between structural blue-print, ontogeny and phenotypic plasticity[END_REF]. The structural blueprint specifies the basic structural organisation or growth form of the species [START_REF] Bell | Dynamic morphology: a contribution to plant population ecology[END_REF] and can be viewed as a combination of phylogenetically fixed traits characterising, for example, the location and number of connection buds or the degree of apical dominance.

Ontogeny includes processes that either activate or inhibit connection buds during clone development. They may both constrain the expression of the plastic avoidance response, consequently altering its adaptive value. We suggest that certain values of traits linked with the plant structural blueprint or with ontogeny may well increase or decrease this adaptive value and contribute to the selection of plastic features in clonal plants. Depending on their basic architectural traits, plants may thus evolve a variety of plastic behaviours that may be more advantageous in certain species than others. This process may have strong implications for the understanding of the reasons that certain species may be more plastic than others. These hypotheses were addressed through a modelling approach, as trait variation effects are difficult to disentangle experimentally. Virtual clonal plant populations were simulated to manipulate the values of individual traits linked with the structural blueprint and ontogeny and to subsequently determine the resulting success of plastic and non-plastic clones under contrasted situations of competition intensity. We used an Individual Based Model (IBM) that enabled us to disentangle the effects of the different traits studied on clone success. Model simulations were performed through volunteer computing, facilitating the extensive browsing of model parameters through large-scale simulations [START_REF] Mony | Large scale parameter study of an individualbased model of clonal plant with volunteer computing[END_REF].

More precisely, we aimed to determine (i) under which conditions of competition intensity plastic avoidance was adaptive and (ii) which traits linked with the structural blueprint and with ontogeny promoted the adaptive value of plasticity.

Materials and methods

Principles of the model

The model simulates the growth of a clonal plant population for one growing season (colonisation of bare soil) without mortality or sexual reproduction. The elementary units of the model are clones modelled as a branched network of ramet units linked through rhizome units (non-photosynthetic connections) without any fragmentation (Fig. 1). Ramets allow the acquisition of resources, which may be considered to be equivalent to biomass, whereas rhizome units are only used for resource storage and translocation. Space is represented by a hexagonal grid offering six growing directions and balanced competitive interactions between ramets [START_REF] Birch | Rectangular and hexagonal grids used for observation, experiment and simulation in ecology[END_REF]. Plant growth is a function of the time step t. The model comprises two levels: the population model PRAIRIE, which defines interaction rules and is based on the model CLONAL, simulating the growth of the clone (for a detailed description of the model see [START_REF] Mony | Large scale parameter study of an individualbased model of clonal plant with volunteer computing[END_REF].

Model structure

The entire model structure and simulation process is summarised in Fig. 2. Among all plasticity model versions detailed in Bittebiere et al (2012b), we chose this model version because it corresponds to exploitative behaviour based on surrounding environmental cues.

 PRAIRIE model

The initialisation phase of PRAIRIE first defines the dimensions of the virtual grassland, and the duration of the simulation in terms of the number of time steps. Second, it provides the number of elementary units (clones) to sow on the grassland and the random position of their initial ramets. At each time step, PRAIRIE applies the growing processes of CLONAL to each clone, selected individually in random order. Once all clones have grown, a new time step begins until the entire run is completed.

 CLONAL model CLONAL governs the two-dimensional growth of each clonal plant through 19 input parameters and nine stochastic growth rules relative to the metabolism and storage of resources (Appendix A) and to the form and spatial colonisation strategy of the plant. Each combination of input parameter values defines a species.

Clonal colonisation of space relies on two processes: the elongation of existing rhizomes and the addition of new rhizomes from ramet units by branching (Fig. 1). At each time step, the spatial colonisation thus begins by determining which of these two processes occurs, following (1):

(1)

 P br el  / , α ϵ [0; 1],
where P el/br is the probability of elongation and α is a random variable between 0 and 1. We fixed P el/br(0) as the threshold value for elongation vs. branching processes.

If P el/br > P el/br(0) , an elongation process occurs rather than a branching process.

Equation ( 2) is then used to determine which rhizome (R) elongates. A new growth unit is added to the rhizome (R) characterised by the highest elongation probability P el , calculated as follows:

(2)

  N g l el F L(R)) E G(R)) ( E ( β R P            1 1 1 ) ( , β ϵ [0; 1],
where G(R) and L(R) are the generation level and the length of the rhizome, respectively. These components were both included in (2) to reflect that plants usually first invest in the growth of older structures [START_REF] Huber | Spatial structure of stoloniferous herbs: an interplay between structural blue-print, ontogeny and phenotypic plasticity[END_REF]. β is a random variable ranging from 0 to 1, E g and E l express the dependence of elongation on generation level and the length of the rhizome, respectively, and F N represents a plasticity function (described below and see Fig. 1a). The type of growth unit created to elongate the rhizome is defined in relation to the interramet distance D r attributed to each ramet unit (r). If the distance along the rhizome from (r) is lower than the actual inter-ramet distance, a rhizome unit is created; otherwise, a ramet unit is produced. D r follows a stochastic law:

(3)

1 0 d d D r    , µ ϵ [0; 1],
where d 0 and d 1 are integers representing a number of rhizome units.

If P el/br < P el/br(0) , a branching process occurs rather than an elongation, and CLONAL consequently first defines which rhizome should branch. This selection is based on the branching probability of each clone rhizome P br (R), calculated as follows:

(4)

           )) ( 1 ( )) ( 1 ( ) ( R G B R L B R P g l br  , γ ϵ [0; 1] if G(R) < 3 0 ) ( R P br  if G(R) ≥ 3,
where γ is a random variable ranging from 0 to 1, G(R) and δ(R) are the generation level and the rhizome length, respectively, and B l and B g express the dependence of branching on the length and the generation level of the rhizome, respectively. We implemented branching limitation for tertiary rhizomes because previous experiments had demonstrated that clonal plants do not develop connections at higher generation levels if cultivated on a short-term basis (for a few months) [START_REF] Benot | Responses of clonal architecture to experimental defoliation: a comparative study between ten grassland species[END_REF]. The rhizome with the highest P br is selected. This result synchronises branching decisions over the clone via physiological integration. Second, CLONAL determines which ramet (r) of the previously selected rhizome should hold the new branch and in which direction, based on Equation ( 5). The new branch may develop, starting with a rhizome unit, in two possible directions, k = 1 or k = 2. The branching probability of each ramet, P br (r), is thus calculated for these two k values:

(5)

  (k) F r d B e k r P N p br ) ( 1 ) , (            , δ ϵ [0; 1],
where is a random variable ranging from 0 to 1, e is a constant, d(r) is the distance between ramet (r) and the base of the rhizome to which it belongs, B p is the variable expressing the dependence of the branching process on the distance from the ramet to the base of the branch, and F N represents a plasticity function (described below and see Fig. 1b) calculated for the two possible branching directions k. The highest P br (r, k) indicates which ramet of the previously selected rhizome should hold the branch and in which direction.

Two clonal populations were tested separately: (i) a control population, within which clones display no active response to competition and for which F N = 1, and (ii) a plastic population composed of clones that actively orient their growth in the direction of the least competitive pressure (avoidance behaviour) [START_REF] Ross | Occupation of biological space during seedling establishment[END_REF][START_REF] Richards | Improving the effectiveness of angular dispersion in plant neighbourhood models[END_REF] (Fig. 1). In this second case, the avoidance response was defined through the plasticity function F N as follows:

(6) 6 1 N F N  
, where N is the number of ramet units present in the six neighbouring cells of the target cell (i.e., the cell that will be colonised if a branching or an elongation process occur). This definition of plastic avoidance is based on the assumption that clones can detect the presence of surrounding competitors, as demonstrated by previous experimental studies [see, e.g., [START_REF] Novoplansky | Picking battles wisely: plant behaviour under competition[END_REF] for a review]. Plasticity costs are not implemented in the model, as experimental studies have demonstrated that they are negligible [START_REF] Van Kleunen | Quantifying the effects of reciprocal assimilate and water translocation in a clonal plant by the use of steam-girdling[END_REF][START_REF] Van Kleunen | Clonal integration in Ranunculus reptans: by-product or adaptation?[END_REF].

At each time step, the growth process of each clone ends with the calculation of the probability P g of creating the new growth unit given the amount of resource available. This calculation is performed according to (7):

(7)               g IPU g c R P log 1  , ϵ [0; 1] if R IPU ≥ c g 0  g P if R IPU < c g ,
where P g = P ramet or P rhizome depending on the nature of the growth unit created, c g is the production cost of one ramet (c g = 1) or one rhizome unit (c g = 0.5), is a random variable ranging from 0 to 1, and R IPU is the total amount of available resource within the 10 growth units sharing them, i.e., composing the integrative physiological unit (IPU). The growth unit is created if P g > 0.6. These parameter values were chosen according to previous results [START_REF] Mony | Large scale parameter study of an individualbased model of clonal plant with volunteer computing[END_REF].

Competition prevents the creation of ramets in cells already occupied by ramets but does not affect rhizome units, which can be created regardless of the cell status (empty or already occupied by a ramet or a rhizome unit). If this competition rule is fulfilled, the new growth unit is created, and the IPU is depleted of c g ; otherwise, the growth unit is not produced. A single growth unit per clone is created per time step.

Simulations

The simulated system was a virtual grassland of 200 × 200 cells. The boundary effect was avoided by the addition of an external frame of 99 × 99 cells sown at the same density as the focal grassland and where identical processes were simulated. The individual grid size was adapted to avoid the clone reaching its boundaries while growing: 199 × 199 cells for a clone without competition and 99 × 99 cells for competing clones. Three sowing densities were tested to simulate various levels of competition intensity: low (one clone, intra-clonal competition), intermediate (50 clones), and high (300 clones). As all clones on the virtual grassland belonged to the same species, competition was intraspecific. For each sowing density, the two plastic responses were tested successively. Six modalities of competition intensity × plasticity were thus tested.

We tested the effect of 11 input parameters characterising plant space colonisation on plant performance, distinguishing between plant structural blueprint and ontogeny. Two to four input parameter values (each value corresponding to one input trait) were selected from the literature or experiments [START_REF] Benot | Responses of clonal architecture to experimental defoliation: a comparative study between ten grassland species[END_REF] (Table 1), but instead of testing all combinations of parameter values for each treatment, 2,000,000 combinations of parameter values for each competition intensity × plasticity modality were randomly selected using a Monte Carlo method. Each simulation ran for 300 time steps and was replicated 1000 times for the low-density grassland and 20 times for the two others. These replicate numbers were determined by a previous analysis of the convergence of the model and are a function of the clone densities. Indeed, the output measures are means calculated per simulation on all clones growing on the virtual grassland. Because the clone number increases with the tested density, the variability of the mean result due to external noise decreases, as well as the number of required simulations. The large simulation campaigns were run on the shared software platform BOINC [START_REF] Anderson | BOINC: a system for public-resource computing and storage[END_REF]) using volunteer computing [START_REF] Smaoui-Feki | Parallel genetic algorithm implementation for BOINC[END_REF].

Five output measures were calculated to characterise the clone performance (biomass and number of ramets) and architecture (number of rhizomes, mean lengths of the primary and secondary rhizomes). Primary rhizomes started from the initial ramet and branched in the secondary rhizomes.

Data analysis

All analyses were performed on a data subset consisting of the simulations in which clones performed best in terms of biomass production and reached 75% of the maximal per-clone biomass recorded during a simulation for each competition intensity × plasticity modality. This threshold was fixed by a preliminary analysis per competition intensity × plasticity modality of the curve describing the maximal per clone biomass produced in all simulations and corresponded to the threshold at which the curve slope drastically decreased.

Using this data subset, we calculated, for each parameter and each competition intensity × plasticity modality, the percentage of best-performing clones sharing each parameter value (trait). For example, at low competition intensity and for non-plastic clones, we found the following distribution of percentages: 20.2% of the best-performing clones had n 0 = 2; 39.1% had n 0 = 4; and 40.7% had n 0 = 6. Traits shared by 100% of the best-performing clones are considered determinant for their success. Chi-square tests were then performed per parameter to compare the distributions of trait percentages (i) among the three competition intensities for plastic and non-plastic clones and (ii) among plastic and non-plastic clones for the three competition intensities. If the effect of competition intensity was significant, partial Chi-square tests were performed to detect differences among competition intensity levels. A Bonferroni correction was used to control for multiple comparisons.

Results

Adaptive value of plasticity depends on competition intensity

Clone performance decreased along the gradient of competition intensity, with mean decreases of 12% to 43% (biomass) or 22% to 43% (number of ramets) for the intermediate and high competition intensities, respectively, compared to the low competition treatment (Figs. 3a,b). Plastic avoidance compensated for these competitive effects by biomass increases of 3% and 10% for the low and intermediate competition intensities, respectively, compared with the non-plastic clones. However, at high competition intensity, biomass production per plastic clone was not maintained and even decreased by 13%. Similarly, the number of ramets produced increased 3% and 11% in the low and intermediate competition intensity treatments, respectively, and then decreased 20% under high competition intensity.

Determinant input traits in plastic and non-plastic clone success

The effects on clone performance of seven of the 11 structural and ontogenetic parameters tested were independent of the competition intensity and plastic avoidance response: d 0 , d 1 , n 0 , B l , B g , e, and B p (Tables 2 and3, Figs. 4 and5). Nevertheless, particular values of these traits promoted plant performance without being involved in resistance to competition: the same trait was selected in plastic and non-plastic clones regardless of competition intensity. Indeed, 100% of the best-performing clones displayed a short and fixed inter-ramet distance The other four traits (n b , P el/br(0) , E l , E g ) significantly depended on competition intensity (Table 2) and were modulated by the plastic response of the plant (Table 3, Figs. 4 and5). Competition intensity increased selection for the highest number of buds (n b = 2) at rhizome nodes, which was similar at low and intermediate competition intensities compared with high competition intensity in non-plastic individuals. A plastic avoidance response increased the importance of having two buds per node at intermediate competition intensity (Fig. 4). In non-plastic clones, the predominance of elongation over branching was a strong determinant for low and intermediate competition intensities (100% of performing clones had P el/br(0) = 0.2) but was less significant at higher competition intensities (75% of the bestperforming clones had P el/br(0) = 0.2 and 25% had P el/br(0) = 0.5). The same pattern was observed in plastic clones at low and intermediate competition intensities. At higher competition intensities, however, plastic avoidance reduced the importance of P el/br(0) = 0.2 (60% of clones displayed this trait, and 40% had P el/br(0) = 0.5) (Fig. 4). Competition intensity affected the dependence of the elongation process on the rhizome length (E l ) independently of plasticity (Tables 2 and3). The percentage of best-performing clones with an intermediate E l value (0.2) increased with competition intensity (Fig. 5). At low competition intensity, most non-plastic clones had an extremely low dependence of elongation on the generation level of the rhizome (lowest E g value) compared with plastic clones. In contrast, high competition intensity showed strong selection of intermediate E g values for non-plastic clones and balanced selection between intermediate and high E g values for plastic clones (Fig. 5).

Discussion

The resistance to competition depended primarily on the traits linked with the structural blueprint, with the ontogenetic traits playing a less strongly determining role. These traits also determined the adaptive value of plastic avoidance, which only modulated their importance for clone success.

Plastic avoidance is adaptive only under low and intermediate competition intensities

Competition induced a decrease in clone performance, which plastic avoidance only buffered at low and intermediate competitor densities. These situations generally occur in early successional stands or in highly disturbed habitats. Clone success for the intermediate competitor density was promoted by an adaptive effective architecture that differed between plastic and non-plastic clones. If both displayed relatively long rhizomes, plastic competitor avoidance induced a clumping (more rhizomes of shorter lengths, Fig. 3). This observation is consistent with experimental studies demonstrating that ramet aggregation increases clone success under competitive conditions [START_REF] Cheplick | Responses to severe competitive stress in a clonal plant: differences between genotypes[END_REF][START_REF] Humphrey | Demographic and growth responses of a guerrilla and a phalanx perennial grass in competitive mixtures[END_REF], although this phenomenon does not appear to be general (Stoll and Prati 2001;Bolker et al 2003). In such environments, plasticity promotes space preemption, limiting interspecific contacts [START_REF] Lovett-Doust | Population dynamics and local specialization in a clonal perennial (Ranunculus repens). I. The dynamics of ramets in contrasting habitats[END_REF][START_REF] Schmid | Spatial dynamics and integration within clones of grassland perennials with different growth form[END_REF], and it lowers intraclonal interference via a better spatial arrangement of clone connections.

At higher competition intensity, plastic avoidance appears maladaptive. This outcome may be explained by two possible and non-exclusive reasons. First, clones may experience higher intraclonal interference due to their even more highly clumped architecture (Fig. 3) [START_REF] Lovett-Doust | Population dynamics and local specialization in a clonal perennial (Ranunculus repens). I. The dynamics of ramets in contrasting habitats[END_REF][START_REF] Schmid | Spatial dynamics and integration within clones of grassland perennials with different growth form[END_REF][START_REF] Humphrey | Demographic and growth responses of a guerrilla and a phalanx perennial grass in competitive mixtures[END_REF]. Indeed, local space availability for the development of new ramets was reduced (the birth rate was spatially constrained), and clones may have been unable to reach more favourable patches because of their shorter rhizomes. In these situations, plastic responses may involve traits that may not be linked with horizontal colonisation (e.g., height, growth rate) [START_REF] Goldberg | Neighborhood competition in an old-field plant community[END_REF][START_REF] Grime | Plant strategies, vegetation processes, and ecosystem properties[END_REF]. [START_REF] Novoplansky | Picking battles wisely: plant behaviour under competition[END_REF] recognised two other competitive responses in plants: (i) confrontation, maximising the negative effects of plants on their neighbours; and (ii) tolerance, showing no active response but maximising plant performance under the worsened conditions generated by neighbours. Such strategies may have higher adaptive values in crowded habitats [START_REF] Herben | Fight or flight: plastic behavior under self-generated heterogeneity[END_REF][START_REF] Oborny | From virtual plants to real communities: A review of modelling clonal growth[END_REF]. Moreover, these authors suggested that plants can switch plastically between these strategies, from avoidance to tolerance, depending on their environmental competitive conditions. Second, it has been emphasised that the adaptive nature of a growth response also depends on the predictability of the environment [START_REF] Oborny | Growth rules in clonal plants and environmental predictability --A simulation study[END_REF][START_REF] Alpert | The relative advantages of plasticity and fixity in different environments: when is it good for a plant to adjust?[END_REF]. More densely populated environments may be less predictable because their density increases more rapidly, and this characteristic may be less favorable to plastic behaviours.

Competition intensity was approximated by clone density with no implementation of competition effects on plant photosynthesis (no biomass and density-dependent reduction of plant growth). Nevertheless, a study by Bittebiere et al (2012b) on competition modelling has demonstrated that even the simplest model yields realistic results. The implementation of the plastic response alone was of primary importance to obtain relevant results. The three competition intensities tested may have differed for plastic and non-plastic clones, as they compete against themselves (intraspecific competition) rather than against a standard competitor. The spatial occupation of grasslands may have been more efficient in the early time steps of simulations in the plastic population, temporarily reducing competition intensity.

Nevertheless, the duration of the simulation was chosen to allow the saturation of the grassland surface. This approach should have reduced differences in spatial occupation and competition intensity between plastic and non-plastic populations over time.

Importance of ontogeny and of the structural blueprint for clone success

Clone performance was enhanced, regardless of the competition intensity and plastic ability, by a star-like architecture supported by an initial branching in six directions, by a low interramet distance, and by weak effects of ontogeny on the branching process. This result confirms that in homogeneous and productive environments, the local exploitation strategy is the most efficient (Lovett Doust 1981; [START_REF] Humphrey | Demographic and growth responses of a guerrilla and a phalanx perennial grass in competitive mixtures[END_REF][START_REF] Herben | Physiological integration affects growth form and competitive ability in clonal plants[END_REF]). Under heterogeneous environmental conditions, traits linked with an exploratory strategy may have been selected.

Resistance to competition (i.e., maximisation of clone success under competition) depended on (i) the plant structural blueprint through the selection of a high number of potentially active axillary buds and a weaker apical dominance; it also depended on (ii) plant ontogeny, with an increasing or a decreasing dependence of the elongation process on the branch generation level or length, respectively, along the competition gradient. Ontogenetic processes influence the ramet positioning within the clone and thus the intensity of intraclonal competition. Favouring the elongation of primary rhizomes limits new ramet settlement near the clone base (i.e., the initial ramet) and thus favours the spatial expansion of the clone. This mechanism allows the clone to first reach available spaces and then consolidate the occupation of these spaces via a high branching process based on a sympodial development.

This tactic limits competitor invasions within the clone territory. Nevertheless, the number of axillary buds tested was limited by the grid used to model the clone environment because this grid constrains the number and angle of branches. Further studies using continuous-space models would be needed to test a wider range of number of buds (structural trait) and consequently analyse the effective architecture. Surprisingly, competition intensity had no effect on the role of the inter-ramet distance in clone success. Clone success was always favoured by a low inter-ramet distance, although previous studies have shown that clones with long inter-ramet distances are more successful because of their higher potential to explore space at low competitor densities [START_REF] Schmid | Clonal growth in grassland perennials: I. Density and patterndependent competition between plants with different growth forms[END_REF]Winkler and Schmid 1995;[START_REF] Cheplick | Responses to severe competitive stress in a clonal plant: differences between genotypes[END_REF][START_REF] Humphrey | Demographic and growth responses of a guerrilla and a phalanx perennial grass in competitive mixtures[END_REF]. This divergence may be linked to the spatial distribution of resources. Under our homogeneous conditions, investing in long connections is not necessary to reach favourable sites (Lovett Doust 1981; Humphrey and Pike 1998) and would have been made at the expense of local resource exploitation and biomass production by ramets. In contrast, we would have expected a greater allocation of biomass to exploration-related organs in heterogeneous environments.

The effects of ontogenetic and structural traits on plant fitness can be altered by their own interactions and by contingent relationships with other traits [START_REF] Wildová | Architectural and growth traits differ in effects on performance of clonal plants: an analysis using a field-parameterized simulation model[END_REF][START_REF] Oborny | From virtual plants to real communities: A review of modelling clonal growth[END_REF]. For this reason, further studies are needed to investigate the consequences for the adaptive value of plasticity.

The adaptive value of the plastic avoidance response depends on the plant species

The adaptive value of the plastic avoidance response to competition depended on the same ontogenetic and structural traits as those promoting resistance to competition. The avoidance response only modulated the importance of these traits for clone success. At low density, the best-performing plastic individuals displayed a strong development of the primary rhizomes involved in the exploration of space at the expense of the secondary and tertiary rhizomes generally used for local habitat exploitation (i.e., an increase in the dependence of the elongation process on the rhizome generation level, an ontogenetic trait) [START_REF] Huber | Spatial structure of stoloniferous herbs: an interplay between structural blue-print, ontogeny and phenotypic plasticity[END_REF].

At intermediate competition intensity, plastic competitor avoidance stimulated branch production by increasing the importance of having two buds per node (a structural trait), thus enhancing the local exploitation of favourable patches (i.e., less densely colonised) [START_REF] Cain | Consequences of foraging in clonal plant species[END_REF][START_REF] De Kroon | On plastic and non-plastic variation in clonal plant morphology and its ecological significance[END_REF]. At high competition intensity, however, plastic avoidance decreased the selectivity of competition for high apical dominance, which decreased interference with other clones but promoted intraclonal competition [START_REF] Lovett-Doust | Population dynamics and local specialization in a clonal perennial (Ranunculus repens). I. The dynamics of ramets in contrasting habitats[END_REF][START_REF] Schmid | Spatial dynamics and integration within clones of grassland perennials with different growth form[END_REF]). This effect may explain the relatively poor performance of plastic individuals.

Under the modelling conditions of our study, the results obtained showed that plastic competitor avoidance was maladaptive at high competition intensity regardless of the ontogenetic and structural traits of plant species. However, such plasticity was adaptive at low and intermediate competition intensities but only for certain combinations of these traits.

These findings have strong implications for evolutionary ecology, as they may explain why clonal plants develop many plastic behaviours in a species-specific manner. Indeed, [START_REF] Bradshaw | Evolutionary significance of plasticity in plants[END_REF] has emphasised that plasticity is a character in itself that evolved under the pressure of three forces: selection, drift, and disruption of the genetic system [START_REF] Schlichting | The evolution of phenotypic plasticity in plants[END_REF]. During evolution, the basic architectural traits of species may have altered the selection of plasticity by competitive interactions. Accordingly, our results help to determine in which species (characterised as the combination of genetically fixed traits linked with ontogeny and the structural blueprint as described above) plastic avoidance should be selected as an additional feature supporting the species' competitive ability. εore precisely, horizontal competition avoidance should be observed in species with sympodial development (at least two buds of connection per node). This prediction tends to be supported by previous studies based on experiments or field studies. This type of competitor avoidance has indeed been observed in Portulaca oleracea [START_REF] Novoplansky | How Portulaca seedlings avoid their neighbors[END_REF] and in Aechmea nudicaulis [START_REF] Sampaio | Directional growth of a clonal bromeliad species in response to spatial habitat heterogeneity[END_REF], which both display sympodial branching.

Concluding remarks

Our study contributed to the knowledge of the relative importance of the structural blueprint and ontogenetic processes for the adaptive value of plant responses. Previously, this topic was poorly understood (but see [START_REF] Geber | Genetic differences in the clonal demography in Eichhornia crassipes[END_REF]. The difficulty of addressing certain questions through experimentation emphasises the value of modelling approaches coupled with volunteer computing, especially those based on IBMs, which focus on local interaction between individuals. As illustrated here, modelling approaches are particularly powerful for determining and disentangling the effects of different traits on plant fitness under specified environmental conditions.

Significance

Label Values

Structural blueprint

Maximum number of branches developing from the initial ramet n 0 2; 4; 6 Maximum number of branches developing from non-initial ramets n b 1; 2 Minimum inter-ramet distance (3) d 0 1; 2; 3; 4 Variability in the inter-ramet distance (3) d 1 0; 2 Threshold probability for the elongation process compared with the branching process (apical dominance) (1) P el/br(0) 0.2; 0.5; 0.8

Ontogeny

Elongation process

Dependence of elongation on the length of the rhizome (2) E l 0.02; 0.2; 2 Dependence of elongation on the generation level of the rhizome (2) E g 0.001; 0.01; 0.1

Branching process

Dependence of branching on the rhizome length (4) B l 0.001; 0.01; 0.1 Dependence of branching on the generation level of the rhizome (4) B g 0.02; 0.2; 2 Constant in (5) e 0; 1 Dependence of the branching location on the distance from the ramet to the base of the rhizome (5) B p 0.001; 0.01; 0.1

Table 2. Comparison of trait distributions between the three competition intensities in plastic

and non-plastic clones (see Data analysis for trait distribution assessment). Trait distributions were compared using Chi-square tests. If significant differences were detected, partial Chisquare tests were performed (results are given in Fig. 4). For d 0 and d 1 , no Chi-square tests were performed as their distributions remained constant regardless of competition intensity.

Traits

  (d 0 = 1 and d 1 = 0). Most performing clones (approximately 70%) also displayed intermediate B g , B l and B p values, indicating that clone success tend to rely on an intermediate dependence of the branching process on the rhizome length and generation level and on the branching position. However, variations in the n 0 and e values had no consequences for clone performance.

Figure legends Figure 1 .

 legends1 Figure legends

Figure 2 .

 2 Figure 2. Virtual grassland simulation. Shaded stages are governed by the PRAIRIE model,

Figure 3 .

 3 Figure 3. Mean (± SD) performance and architectural traits of plastic and non-plastic clones

Figure 4 .

 4 Figure 4. Structural blue-print trait percentages in the best-performing plastic and non-plastic

Figure 5 .

 5 Figure 5. Ontogenetic trait percentages in the best-performing plastic and non-plastic clones

Table 1 .

 1 Input parameters of the CLONAL plant model determining clone architecture. The numbers within brackets correspond to the equation in which the parameter was involved. The parameter values tested were determined through experimentation or literature surveys.

Table 3 .

 3 Asterisks indicate significant differences: ns not significant; * P < 0.05; ** P < 0.01; *** P < 0.001. Comparison of trait distributions with and without plasticity under low, intermediate, and high competition intensity (see Data analysis for trait distribution assessment). Trait distributions were compared using Chi-square tests. For d 0 , d 1 , and P el/br(0) at low and intermediate competition intensity, no Chi-square tests were performed as their distributions remained constant regardless of competition intensity. Asterisks indicate significant differences: ns not significant; * P < 0.05; ** P < 0.01; *** P < 0.001.

		Non-Plastic		Plastic
		χ²	df P-value	χ²	df P-value
	Structural blueprint
	n 0	5.62	4	ns	3.80	4	ns
	n b	16.28 2	***	29.53	2	***
	P el/br(0)	54.27 2	***	92.42	2	***
	Ontogeny
	E l	13.85 2	***	21.58	2	***
	E g	145.0 2	***	53.03	2	***
	B l	0.57	2	ns	0.05	2	ns
	B g	0.93	2	ns	0.24	2	ns
	e	3.29	2	ns	2.30	2	ns
	B p	0.25	2	ns	0.22	2	ns
	Note:

Acknowledgements

This project benefited from the grant ANR-08-SYSC-012 provided by the Agence Nationale de la Recherche (France). We thank the volunteers worldwide that contributed to the calculations.

Appendix A. Metabolic rules of the CLONAL model.

At each time step t, the total amount of resource of the clone R T (t) was calculated according to equation (A1):

where R g (t) is the net gain of resource (biomass accumulation for ramets or storage for rhizome units) of each growth unit g, C g is the cost of creating one growth unit (C g equals 1 for a ramet and 0.5 for a rhizome unit), and a is equal to zero if no new unit is added to the clone (otherwise, a equals one).

Biomass accumulation occurs at the ramet scale and was assumed to follow a logistic law at each time step t:

, where B r (t) is the biomass status of ramet r, r p is a constant (r p = 0.3) providing the biomass supplied to a ramet by photosynthesis, 1-r s is the fraction of biomass allocated to long-term reserve formation (r s = 0.1), and r mr (r mr = 20) is the maximum biomass achieved by a ramet.

The resource available for the growth of a ramet r was calculated as the overall growth units belonging to the same integrative physiological unit (IPU) according to the following equation:

, where R g is the resource of the growth unit g and n IPU (equal to 10) is the number of growth units in the IPU. R g is null for a rhizome unit, which stores only long-term resources unavailable for growth.