Papach Anna

Richard Freddie-Jeanne
email: freddie.jeanne.richard@univ-poitiers.fr

Aupinel Pierrick
email: pierrick.aupinel@magneraud.inra.fr

Co-exposition effects on honey bee Apis meliffera olfactory learning performance

Acknowledgment I would like to thank Freddie-Jeanne Richard for suggesting me an interesting topic

Introduction

Pollination is an essential ecosystem service that sustains biodiversity and contributes to the global food security. More than 70% of all flowering plants depend on animal pollination; this value varies depending on the region and reaches its maximum of 94% in the tropical communities [START_REF] Ollerton | How many flowering plants are pollinated by animals?[END_REF]. Besides sustaining the biodiversity, pollinators support crop yields; around 35% of global crop production relies on insect pollination [START_REF] Klein | Importance of pollinators in changing landscapes for world crops[END_REF]) and around 84% of commercial crops in EU are pollinated by insects, mostly by honey bees [START_REF] Carreck | The economic value of bees in the UK[END_REF]. Still, insect pollination is an essential for one third of global food market, but this one third provides us with the majority of nutrients as mostly fruits and vegetables depend on animal pollination. In 2005 the estimated economic value of pollination worldwide was about €153 billion [START_REF] Gallai | Economic valuation of the vulnerability of world agriculture confronted with pollinator decline[END_REF]).

However, in last decades there was a decline in populations of wild and managed pollinators (honey bees in particular), while the demand for their services has significantly increased [START_REF] Breeze | Pollination services in the UK: How important are honeybees? Agriculture[END_REF][START_REF] Calderone | Insect pollinated crops, insect pollinators and US agriculture: Trend analysis of aggregate data for the period 1992-2009[END_REF]. Starting from 2006 beekeepers in USA reported great losses of adult bees in their colonies [START_REF] Vanengelsdorp | Colony collapse disorder: A descriptive study[END_REF]). The same decline of domesticated honey bee populations was observed in Europe (Neumann, [START_REF] Carreck | Honey bee colony losses[END_REF][START_REF] Potts | Global pollinator declines: Trends, impacts and drivers[END_REF]) which is characterized with high winter mortality. Unexplained losses in bee colonies were also observed in China and in some places in Africa [START_REF] Peduzzi | Global Bee Colony Disorder and Threats insect pollinators[END_REF]). Decline of pollinators has a negative impact on agriculture but also on landscapes [START_REF] Ricketts | Landscape effects on crop pollination services: Are there general patterns?[END_REF]) and plant diversity.

There are several factors that contribute to pollinator losses. Among the main pressures on pollinators are the land-use intensification that also includes habitat fragmentation and heavily usage of pesticides; climate change and also spread of alien species and diseases [START_REF] Potts | Global pollinator declines: Trends, impacts and drivers[END_REF][START_REF] Vanbergen | Threats to an ecosystem service: Pressures on pollinators[END_REF]. Population growth leads to the increase of food production that in turns leads to the land-use intensification. Therefore, natural habitats are being transformed into the monotonous crops and are being fragmented. This implies that less food sources are available to pollinators and monotonous crops can't provide them with all the necessary nutrients. Additionally, land-use intensification leads to the application of pesticides that sometimes also can affect non-target species. Climate change in turn leads to the decrease of floral resource availability to pollinators; models done showed that this reduction can reach about 17-50% [START_REF] Memmott | Global warming and the disruption of plant-pollinator interactions[END_REF]). Another problem is introduction of alien species and spread of diseases. New species out-compete native pollinators but also pests and pathogens are causing a big danger. Varroa destructor mite is a well-known pest that is spread across the world and besides feeding on honey bee hemolymph it is also the primary vector of many viruses (Le [START_REF] Conte | Varroa mites and honey bee health: can Varroa explain part of the colony losses?[END_REF]. Another widespread pest is microsporidian gut parasite Nosema apis and Nosema ceranae that can also lead to the colony losses [START_REF] Higes | How natural infection by Nosema ceranae causes honeybee colony collapse[END_REF][START_REF] Wolf | So near and yet so far: Harmonic radar reveals reduced homing ability of nosema infected honeybees[END_REF]). One of the most detrimental honey bee diseases is American Foulbrood. This disease is the only highly virulent disease that occurs in honey bees. It is caused by the spore-forming Paenibacillus larvae that infect honey bee larvae; larvae that are older than 53 hours after the egg has hatched cannot be infected [START_REF] Crailsheim | Honey bee age-dependent resistance against American foulbrood[END_REF]. American Foulbrood occurs throughout the world and can cause serious threats to the colonies; it is very hard to treat it. One of the available treatments is application of antibiotics; however they don't kill spores and just suppress clinical symptoms. In Europe use of antibiotics is prohibited as the residues can be stored in honey, they may affect the longevity of the bees and also Paenibacillus larvae gets more resistant after several applications [START_REF] Genersch | American Foulbrood in honeybees and its causative agent, Paenibacillus larvae[END_REF]. In many countries infected colonies should be destroyed, the bees are killed with poisonous gas and hives are burned along with the contaminated equipment; this method causes a big economic damage [START_REF] Ritter | Honey bee diseases and pests: a practical guide[END_REF]. Another method is artificial swarm method, but it is not always efficient and effective just during the early stages of disease [START_REF] Fries | Implications of horizontal and vertical pathogen transmission for honey bee epidemiology[END_REF][START_REF] Genersch | American Foulbrood in honeybees and its causative agent, Paenibacillus larvae[END_REF]).

As previously mentioned, application of pesticides is another factor that can lead to the decrease of honey bee colonies. Nowadays the most well-known and widely used group of Thiamethoxam along with other pesticides of this group causes higher mortality [START_REF] Laurino | Toxicity of neonicotinoid insecticides to honey bees: laboratory tests[END_REF]), impairment of learning, behavioral disturbances and also evokes problems with orientation [START_REF] Aliouane | Subchronic exposure of honeybees to sublethal doses of pesticides: effects on behavior[END_REF][START_REF] Williamson | Exposure to neonicotinoids influences the motor function of adult worker honeybees[END_REF]). Sub-lethal doses of thiamethoxam also induce homing failure in honey bees [START_REF] Henry | A Common Pesticide Decreases Foraging Success and Survival in Honey Bees[END_REF]. Further, in Europe in April 2013 application of thiamethoxam along with imidacloprid and clotianidin was forbidden on crops that attract pollinators (EFSA 2013). This ban came into force in December 2013 and was based on the conclusion of the European Food Safety Authority [START_REF] Food | Conclusion on the peer review of the pesticide risk assessment for bees for the active substance clothianidin[END_REF]. This year this ban comes to an end and reopens the debates about its acceptance again.

All mentioned above leads to the decrease of pollinators, doubting the food security and preservation of the ecosystem services. Nevertheless, none of these factors alone were identified as responsible for the observed great colony losses. Honey bees can be infected with different pathogens and parasites; on the same time they are exposed to pesticides and residues of different chemical treatments applied by beekeepers and their metabolites that are stored in pollen, nectar, wax [START_REF] Mullin | High Levels of Miticides and Agrochemicals in North American Apiaries: Implications for Honey Bee Health[END_REF]). However, the interaction between those factors is unknown and may have different effects. Therefore, it is believed that the current decrease of wild and managed pollinators has a multifactorial nature (vanEngelsdorp et al. Honey bees learn to associate the floral odor with a nectar reward and afterwards share this information with newly recruited foragers [START_REF] Von Frisch | The dance language and orientation in honey bees[END_REF]. Learned odors can be retrieved several days and weeks after [START_REF] Menzel | Memory dynamics in the honeybee[END_REF][START_REF] Beekman | How long will honey bees (Apis mellifera L.) be stimulated by scent to revisit past-profitable forage sites? Journal of comparative physiology[END_REF]. Therefore olfactory learning and memory are very important for the colony success and survival. One of the classical methods that is used for studying learning and memory processes in honey bees is olfactory proboscis extension response (PER) conditioning. During the conditioning the honey bee worker learns to associate the odorant with sucrose reward and therefore it learns to extend its proboscis after the presentation of the odor. The first well-known protocol belongs to Takeda [START_REF] Takeda | Classical conditioned response in the honey bee[END_REF] and lately it was improved by Bitterman [START_REF] Bitterman | Classical Conditioning of Proboscis Extension in Honeybees (Apis mellifera)[END_REF]) and in 2012 it was revised by

Sandoz and Giurfa (Giurfa & Sandoz 2012).

There is a gap of knowledge in co-exposition studies between pesticide and pathogen;

moreover, yet all the studies done on impact of pesticides on honey bees were done on workers and none of them looked at the impact that it might have after early exposure of the honey bee larvae.

Here we decided to test whether exposure of honey bee larvae to sub-lethal doses of thiamethoxam and American Foulbrood have an effect and also if the co-exposition modify the effect. To that end, we studied the following aspects, the effect of exposition and co-exposition on: 1) honey bee larvae survival and emergence; 2) impairment of learning and memory abilities.

Materials and Methods

We studied the effect of co-exposition between neonicotinoid and pathogen on honey bee survival and learning performances. The experiments were conducted in the entomological experimental unit of INRA, le Magneraud, France between March and August, 2015.

Larval rearing and exposure

The honey bee larvae were obtained from three different colonies of Apis mellifera ligustica. In our work we followed the method of artificial larval rearing that was improved by Aupinel and colleagues [START_REF] Aupinel | Improvement of artificial feeding in a standard in vitro method for rearing Apis mellifera larvae[END_REF]). To get the comb with first instar larvae, the queen was confined in the empty comb by means of the excluder cage and left there for less than 30 hours; the queen was confined four days before grafting. After 24-30 hours the queen was removed from the cage to prevent further egg laying in the comb; workers were able to freely move between the encaged comb and the rest of the hive. This method allowed us to obtain enough first instar larvae for the grafts. With the help of a thin paintbrush larvae were transferred from the comb to the artificial cells earlier filled with the special diet that consisted of royal jelly and aqueous solution of D-glucose, D-fructose and yeast extract (Annex 1). In total we performed two grafts per colony and we had 96 larvae per treatment for each graft. After grafting plates with larvae were transferred into desiccator ensuring relative humidity (RH) of about 95% that was placed in the incubator with the temperature of 34,5 0 C (Fig. 2).

In our work we used five different treatments to study the co-exposition impact on honey bee survival and learning performances. The first group was the control group and it had no treatment. The second group was subjected to sub-lethal dose of Paenibacillus larvae that is four spores per larva. The pretesting of determining the number of spores that doesn't induce Thiamethoxam was obtained from the Cluzeau Info Labo with purity of 99 %. Stock solutions of this pesticide were prepared in water; to get the solutions of the desired concentrations we prepared several aliquots from the stock solution. We performed chronic exposure to thiamethoxam, therefore starting from day three and until day six, larvae were fed with thiamethoxam that was added to their diet. In total each larvae received the full amount of 0.628 ng of thiamethoxam in treatment with 4 ppb and 0.314ng in the treatment with 2 ppb.

The mortality was controlled every day before feeding larvae.

On the 8 th day all larvae were transferred into the desiccator with a lower humidity of 80% (Fig. 2). At the end of the pupation stage larvae plates were transferred into the alimentary crystal polypropylene boxes with aerated lids. Each box contained the Queen Mandibular Pheromone and a piece of wax. The boxes were kept in incubator at 34.5 0 C and RH around 30-40%. After the emergence honey bees workers were provided with pollen powder and 50%

sugar solution ad libitum. Pollen was changed every two days. Adults were kept in these conditions for two weeks until the olfactory learning tests.

Olfactory Proboscis Extension Response conditioning

We used the classical olfactory proboscis extension response (PER) conditioning procedure for testing the exposition and co-exposition effect on honey bee workers' learning and memory abilities.

We used honey bee workers that were reared in vitro and exposed them to different treatments during the larval stage. For the conditioning we used workers that were 13-14 days old as at this age they show better responses to odour and better learning abilities compare to the younger bees (Laloi et

Absolute conditioning procedure

In the present study we used the absolute olfactory conditioning when the bee learns that the CS predicts the US and it associates odour with the sucrose reward. We tested the exposition and co-exposition effects on worker's middle and long term memory. To determine the impact on the middle term memory we performed three conditioning trials [START_REF] Decourtye | Imidacloprid impairs memory and brain metabolism in the honeybee (Apis mellifera L.)[END_REF]). Before the experiment all the bees were checked for the PER to sucrose and those individuals that didn't extend their proboscis were discarded from the test.

The conditioning site had a ventilation hood for the odour removal and workers were placed in front of it so it was at their back.

Middle-term memory

All bees were conditioned in a following way: a bee was placed to the conditioning site and left for 15 sec to habituate to the experiment conditions; after 15 sec we presented the odour for 4 sec with subsequent sucrose stimulation for 4 sec and 2 sec overlap. Therefore the inter-stimulus (ISI) interval was 2 sec (Fig. 3). After the CS-US pairing the bee was left in the conditioning place for another 15 sec. When the bee was extending it proboscis it was allowed to drink. We After the retention test we checked all the bees for the PER to sucrose, we stimulate the antennae with the sucrose solution and individuals that didn't show the response were discarded from the test.

Long-term memory

To test the influence of different treatments on the long-term memory we used the same olfactory PER conditioning procedure as described above. Bees were harnessed in the morning

Data analysis

Cumulative larval mortality was compared between each treatment and control by multiple two-by-two x 2 with 1 df. The same test was using to compare the adults' emergence rate between treatments and control. To compare the effect of our treatments on mortality between larval stage and pupation we also used multiple two-by-two x 2 with 1 df between each treatment and control.

In the experiment with conditioning of PER, we compared the number of reflex responses with the number of conditioned responses at each trial between the group that received treatment and the control group by multiple two-by-two x 2 with 1 df.

Results

Honey bee larvae survival and emergence

In total we grafted 576 first instar larvae per treatment. The mortality was controlled six times on day: three, four, five, eight, fifteen and twenty two. We observed mortality during the larval stage, pupation and until the emergence of adults (Annex 2). Fig. 4 shows the cumulative mortality rates for each treatment during the experiment. We observed an increase in mortality after day five in the treatment with 4 spores of Paenibacillus larvae and both treatments with co-exposition. Our results showed that at day 3 and at day 4 the difference between control and four treatments was not significant. However, at day 5 we observed significant differences of cumulative honey bee larvae mortality between control and Paenibacillus larvae 4 spores

Start of the pupation stage

To see the influence of our treatments on the honey bee larvae survival we compared each cumulative mortality rate with the control at the end of the larval stage, at day 8. We found significant differences between control and treatments with Paenibacillus larvae 4 spores (x 2 = 27.2, df:1, P<0.05) , Paenibacillus larvae 4 spores + thiamethoxam 4ppb (x 2 =30.4, df:1, P<0.05) , Paenibacillus larvae 2 spores + thiamethoxam 2 ppb (x 2 =17.8, df:1, P<0.05).

Another tested aspect was the level of mortality between two stages, larval and pupation.

We calculated the mortality rates for each treatment during both stages and compared number of dead larvae (D3-D8) with the number of dead pupae (D15) relative to the number of individuals at the beginning of the respective stage. We didn't detect significant difference in mortality between stages in any treatment, however we can see that in all the treatment that contained spores of Paenibacillus larvae the percentage of dead individuals was higher during the pupation (Fig. 5) while for control and group treated with thiamethoxam it is opposite. We also analysed the impact of applied treatments on the adult emergence rate. There was no effect of thiamethoxam 4 ppb alone on the workers' emergence rate (x 2 =1.75, df:1, P>0.05).

Significant decrease in adults' emergence rate in groups that received Paenibacillus larvae 4 We also found in the group that received 4 spores of Paenibacillus larvae + 4 ppb of thiamethoxam adults' emergence rate was lower compare to the group that was co-exposed to 2 spores of Paenibacillus larvae + 2 ppb of thiamethoxam (x 2 =7.35, df:1, P<0.05). The lowest adult emergence rate of 58% was observed in the group that was exposed to 4 spores of Paenibacillus larvae + 4 ppb of thiamethoxam while in the control group this value was 82% (Fig. 6). In groups that were treated with 4 spores of Paenibacillus larvae and group with 2 spores of Paenibacillus larvae + 2 ppb of thiamethoxam adults' emergence rate was 64% and 66% respectively.

Acquisition, middle-term memory

We subjected 13-14 days old honey bee workers that were exposed to different treatments the end of the third trial the lowest response was observed in the group that was subjected to thiamethoxam 4 ppb; the % of PER for this group was 48%, while for control group it was 66% (x 2 =3.46, df:1, P>0.05). However, we observed a tendency that workers from the treated groups had lower acquisition level compare to control (Fig. 7), especially during trial 2 we can see that in control group the response to the conditioned stimulus (CS) was about 58%, while in treated groups it lays within the range of 36-43%. We found significantly lower response rate compare to control in the groups that received 4 ppb of thiamethoxam (trial2: x 2 =4.22, df:1, P<0.05) and the group that was exposed to 2 spores of Paenibacillus larvae and 2 ppb of thiamethoxam (trial2:

x 2 =5.22, df:1, P<0.05).

Acquisition, long-term memory

The groups of honey bee workers that were trained for testing the long-term memory performance received five conditional trials (Fig. 8). We can see that the learning in all tested groups was rapid during the first three trials and after the level of PER to CS stayed almost the same (Fig. 8). We compared the level of acquisition at the end of the fifths and found significantly reduced level of responses compare to control in the group subjected to 4 ppb of thiamethoxam (x 2 =4.22, df:1, P<0.05), showing the negative effect of thiamethoxam at concentration of 4 ppb on the olfactory learning performance.

As we used the same conditioning procedure we decided to pull the data that we obtained for the third trial from both sets of groups (groups that were trained during three trials for testing middle-term memory and groups that were trained during five trials for testing the longterm memory) together. We found the reduction of olfactory learning performance in the group that was treated with 4 ppb of thiamethoxam (trial 3: x 2 =13.9, df:1, P<0.05) and in the group that received 2 spores of Paenibacillus larvae and 2 ppb of thiamethoxam (trial 3: Middle-term memory All groups showed significantly higher responses to the CS than to the novel odour (NOd) (control: x 2 =5.5, P<0.05; Paenibacillus larvae 4spores: x 2 = 7.26, P<0.05; thiamethoxam 4 ppb:

x 2 =25.29, P<0.05; Paenibacillus larvae 4 spores + thiamethoxam 4 ppb: x 2 =12.03, P<0.05;

Paenibacillus larvae 2 spore + thiamethoxam 2 ppb: x 2 =7.26, P<0.05). Our results confirmed that honey bee workers had a CS-specific memory and could distinguish between odours. We compared the number of PER to the CS one hour after conditioning between the control group and our treatments and we detected reduced level of responses in the group treated with thiamethoxam 4 ppb (x 2 =7.51, df:1, P<0.05) showing that thiamethoxam impairs the middleterm memory (Fig. 9).

Long-term memory

We also tested the influence of exposition and co-exposition on the long-term memory corresponding to 24 hours after the conditioning (Fig. 10).

We used two odours (same as with the middle-term memory for the memory retention test, CS and NOd) and found significant higher response level to the CS compare to NOd for all the groups (control:

Discussion

Effect of exposition and co-exposition on honey bee larval survival and emergence

First of all in our experiment honey bee larvae were reared in vitro and even after the emergence until we performed our olfactory conditioning adults were kept in the emergence boxes in the incubator. Our experimental conditions differ from the one in hives; in hives larvae are fed by nurse bees, they may receive different amount and quality of food. We didn't perform the study in the field as we used infectious disease. Moreover, laboratory conditions allowed us to control all the variables and rear all larvae in the same conditions.

There are several acceptance criteria's that must be fulfilled in order to validate the results of a toxicity test. As we performed chronic exposure to thiamethoxam we referred to these criteria's. According to them, in the control group, cumulative mortality at day 8 should be ≤15% and the adults' emergence rate in this group at day 22 should be ≥70% (OECD, 2014). In our experiment the observed cumulative mortality in the control group at day 8 was 6% and the adults' emergence rate at day 22 was 82%.

We didn't reveal differences in larval mortality and adults' emergence rate between the control group and the one that was subjected to the chronic exposure with thiamethoxam at concentration of 4 ppb. Therefore we confirmed the results of pretesting that were done before at INRA, le Magneraud (unpublished data) that at this concentration thiamethoxam doesn't induce honey bee larvae mortality and this dose is a sub-lethal dose for Apis mellifera ligustica larvae's. Up to now, there is just one study that showed the effect of thiamethoxam on the development of Africanized honey bee larvae's [START_REF] Tavares | In vitro effects of thiamethoxam on larvae of Africanized honey bee Apis mellifera (Hymenoptera: Apidae)[END_REF]. They determined the oral LC 50 (concentration of a chemical at which 50% of individuals die) of thiamethoxam (14.34 ng/µL of diet) for the larvae of Africanized honey bees and after they calculated the sub-lethal concentration as 1/10 of LC 50 that was 1.43 ng/µL of diet. They showed that at sub-lethal concentrations thiamethoxam induces alteration in the optic lobes of Africanized honey bee larvae. However, the concentration that were used in their study is much higher compare to one used in our experiment. The sub-lethal concentration they used was 42.9 ng/larvae by acute exposure while in our study we used 0.628 ng/larvae by chronic exposure. It suggests that sensitivity of honey bee larvae to thiamethoxam may depend on subspecies, strain, laboratory conditions.

Regarding treatments with Paenibacillus larvae, our results showed significant increase in mortality at day five in the treatments that contained 4 spores of Paenibacillus larvae. We infected first instar larvae that were less than 24 hours old as in this age they are the most susceptible to Paenibacillus larvae [START_REF] Crailsheim | Honey bee age-dependent resistance against American foulbrood[END_REF]. The cumulative mortality rate at the end of the larval stage (D8) in the group that received 4 spores of Paenibacillus larvae was 16% and the adults' emergence rate in this group was 64%,that was significantly lower compare to control. Hence, the amount of spores that we used in our test was not a sub-lethal as it induced increased level of mortality. The virulence of Paenibacillus larvae depends on the strain and sometimes it is genotype specific [START_REF] Brodsgaard | Response of in vitro reared honey bee larvae to various doses of Paenibacillus larvae larvae spores[END_REF][START_REF] Genersch | Strain-and genotype-specific differences in virulence of Paenibacillus larvae subsp. larvae, a bacterial pathogen causing American foulbrood disease in honeybees[END_REF][START_REF] Genersch | American Foulbrood in honeybees and its causative agent, Paenibacillus larvae[END_REF]). The number of spores that is considered as sub-lethal varies and should be determined for each strain. Another factor that determines the larval mortality is the genetic constitution of the exposed honey bees as some strains are more resistant compare to others and it depends on the innate larval tolerance (Genersch 2010). For our experiment the pretesting on determining the number of spores that doesn't induce mortality was done in February at INRA, Le Magneraud. Results of the pretesting showed that 4 spores of Paenibacillus larvae didn't cause significant increase in larval mortality compare to the control group. However, the tests were performed on "winter" larvae collected from the indoor hives, but with the same Apis mellifera ligustica strain. In our experiment we used larvae from the outdoor hives and they were collected between April and July, 2015. Possibly, the honey bee larvae that were used in our experiment were more susceptible to the pathogen.

Season may be one of the factors that influence larvae's susceptibility to a pathogen or a pesticide [START_REF] Crailsheim | Standard methods for artificial rearing of Apis mellifera larvae[END_REF] This can be explained with the fact that American Foulbrood kills honey bee larvae within 6 to 13 days post-infection, depending on the strain of Paenibacillus larvae used because some strains act rapidly while others slower [START_REF] Genersch | Strain-and genotype-specific differences in virulence of Paenibacillus larvae subsp. larvae, a bacterial pathogen causing American foulbrood disease in honeybees[END_REF]. For this reason, we can say that in groups that received spores of Paenibacillus larvae mortality was caused by the pathogen. And as we didn't find significant difference between the treatments that contained both, Paenibacillus larvae and thiamethoxam and the treatment with Paenibacillus larvae alone we can conclude that co-exposition of honey bee larvae to this pesticide and pathogen has no additive effect on the larval mortality and adults' emergence.

Similar study was done by Brodsgaard and colleagues [START_REF] Brødsgaard | Interactions among Varroa jacobsoni mites , acute paralysis virus , and Paenibacillus larvae larvae and their influence on mortality of larval honeybees in vitro[END_REF] where they examined the interaction between Paenibacillus larvae and Varroa jacobsoni along with acute paralysis virus on honey bee larval mortality. They conclude that American Foulbrood is severe infection and other viruses have no additive effect. Hence, our results also showed that mortality during the larval stage and pupation is caused by Paenibacillus larvae and thus we didn't find a synergistic effect with thiamethoxam.

Olfactory learning performance

In our work we grafted first instar larvae and exposed them to pesticide and pathogen during the larval stage. After emergence, we still reared them in the incubators until adult were 13-14 days old. We used classical olfactory proboscis extension response (PER) conditioning to study the influence of our treatments on their learning performances. PER conditioning was shown to be a useful tool for studying effects of pesticides on behavioral essays (Laloi et al. Olfactory learning performance showed that in the control group bees were learning fast and at the end of the third conditioning trial the percentage of PER was almost 70% . As we were using a syringe for odor delivery we had to use two odors for memory retention test in order to check whether tested bees learned the odor or the air puff. The results we obtained showed that tested bees (in all treatments) had an odor-specific memory and therefore they were really learning the odor. Considering all that, we affirm that our control group was correct.

The group that received 4 spores of Paenibacillus larvae did not differ from the control one;

consequently we assume that this pathogen does not influence the adults' learning performance.

We each larva and determined that imidacloprid at the concentration of 0,4 ng/larva doesn't induce larval mortality. On day 15 they removed pupae from the hives and placed in incubator until the emergence. Afterwards, they colour-marked every emerged adult to distinguish between different treatments and released them back to their home colonies. The olfactory learning performance was conducted when adults were 15 days old. The results of their experiment showed that chronic exposure to imidacloprid (0.4 ng/larva) during the larval stage leads to the impairment of associative behaviour.

The group with co-exposition that had higher concentrations of Paenibacillus larvae and thiamethoxam had higher acquisition levels than the group that was receiving 4 ppb of thiamethoxam alone. As in the group that received just 4 spores of Paenibacillus larvae we didn't detect any changes in the learning performance, but we showed impairment of learning in the group that received 4 ppb of thiamethoxam, we would have expect to see the same impairment of learning in the group that was subjected to 4 spores of Paenibacillus larvae + 4 ppb of thiamethoxam. However, even though we obtained lower acquisition levels for this group compare to control, the difference was not significant. We can explain it with the highest larval mortality rates and lowest adults' emergence in this group; under selection pressure just the stronger individuals had survived and therefore they have better performance.

In the group that was treated with 2 spores of Paenibacillus larvae + 2 ppb of thiamethoxam we found significantly lower response rate compare to control after the second trial, nonetheless there was no significant difference after trial three, four and five. But this allows us to suggest that this group tend to learn slower and we believe that it caused by thiamethoxam at concentration of 2 ppb alone. It shows that concentrations even lower than the sub-lethal one can negatively affect honey bees' learning performance.

However, it was demonstrated that thiamethoxam doesn't interact with the nicotinic receptors [START_REF] Nauen | Thiamethoxam is a neonicotinoid precursor converted to clothianidin in insects and plants[END_REF]) as all the other neonicotinoids, but it has the same biological effects. It is related to its rapid conversion to clotianidin, and this compound binds to insects' nAChRs with high affinity and it is considered as full agonist of these receptors. Nauen and colleagues (2003) showed that thiamethoxam is likely to be a neonicotinoid precursor for clothianidin, which is highly active open-chain neonicotinoid and is highly toxic to bees.

Memory retention

Our results showed that thiamethoxam impairs middle-tem and long-term memory. Results obtained by Decourtye and colleagues [START_REF] Decourtye | Imidacloprid impairs memory and brain metabolism in the honeybee (Apis mellifera L.)[END_REF]) showed that oral exposure to sub-lethal doses of imidacloprid, another pesticide from the group of neonicotinoids, impairs the honey bees' middle-term memory. In their study they applied sublethal doses of imidacloprid (12 ng/bee) on honey bee workers and subjected them to the conditioning trials half an hour later. Their results suggested that imidacloprid at sub-lethal doses has a negative effect on memory formation processes; it affects the consolidation process that insures transfer of information from the short-term to midlle-term memory.

Another study was done by [START_REF] Aliouane | Subchronic exposure of honeybees to sublethal doses of pesticides: effects on behavior[END_REF]) that showed that chronic treatment of honey workers with thiamethoxam induces changes in behaviour. Oral treatment with thimathoxam at concentrations of 1 ng/bee and 0.1 ng/bee causes decrease in learning performance and retention test. Topical application caused significant decrease of learning at concentration of 1 ng/bee. They showed that at concentration of 0.1 ng/bee thiamethoxam causes impairment of long-term memory that was checked 24 hours after learning trials. The concentration they used is lower compare to one used in our experiment, but it was suggested that honey bee larvae are more tolerant to some of neonicotinoids compare to adults [START_REF] Yang | Impaired Olfactory Associative Behavior of Honeybee Workers Due to Contamination of Imidacloprid in the Larval Stage[END_REF][START_REF] Tavares | In vitro effects of thiamethoxam on larvae of Africanized honey bee Apis mellifera (Hymenoptera: Apidae)[END_REF].

Neither middle-term nor long-term memory was impaired in the group treated with 4 spores of American Foulbrood. Thus, this pathogen doesn't influence the memory performance.

As we didn't find learning and memory impairment in the group that received 4 spores of Paenibacillus larvae, we suggest that in both treatments that received Paenibacillus larvae +and thiamethoxam, impairment of long-term memory was caused by thiamethoxam. However, neither group that received 4 spores of Paenibacillus larvae + 4 ppb of thiamethoxam, nor the group that received 2 spores of Paenibacillus larvae + 2 ppb of thiamethoxam showed any changes in the middle-term memory, but we found significantly lower response rate in this groups during the long-term memory testing.

It is known that neonicotinoids are widely used and persist in the environment and their residues can be found in the hives. Pohorecka and colleagues in 2012 showed that thiamethoxam was one of the most abundant neonicotinoids; it was present in 65% of nectar samples and 37% of pollen samples examined. They showed that in nectar collected from flowers, the concentration of thiamethoxam was about 3.2-12.9 ng/g. In the guttation fluid the concentration of thiamethoxam residues is about 11.9±3.32 mg/L [START_REF] Blacquière | Neonicotinoids in bees: A review on concentrations, side-effects and risk assessment[END_REF].

Mullin and colleagues [START_REF] Mullin | High Levels of Miticides and Agrochemicals in North American Apiaries: Implications for Honey Bee Health[END_REF] found thiamethoxam in pollen and wax in Northern American apiaries, the mean amount of thiamethoxam found in wax was 53.3 ppb. These findings show that this pesticide abundant in nature and honey bee larvae can be exposed to it in the hives, hence thiamethoxam and its residues can be accumulated in larvae and have negative impact on their adult life. It reduced learning and memory consequently they are not effective in learning and memorizing food locations and homing routes and that can cause the homing failure that earlier was shown by Henry and his colleagues [START_REF] Henry | A Common Pesticide Decreases Foraging Success and Survival in Honey Bees[END_REF]).

Conclusion

Our results confirmed the pretesting results that 4 ppb of thiamethoxam is the sub-lethal dose for larvae of Apis meliffera ligustica. We demonstrated that the co-exposition between American Foulbrood and thiamthoxam doesn't have a synergistic effect on honey bee larvae mortality and adults' emergence neither on their learning and memory performance. Our results indicated that Paenibacillus larvae alone doesn't induce changes in olfactory learning and memory performances. We showed that the exposition to sub-lethal doses of thiamethoxam during the larval stage can lead to the impairment of olfactory learning and memory in adults. Moreover, even doses lower than sub-lethal can cause this impairment. The knowledge about sub-lethal effects of neonicotinoids on honey bee larvae is poor and our study contributed to a better knowledge. A successful learning and memory performance ensures successful foraging behaviour which is an important for colony survival and thiamethoxam impairs this performance doubting the colony survival. Therefore, application of this pesticide should be careful and alternatives should be found.

  pesticides is neonicotinoids. This group of chemicals in 2012 shared 26% of the global insecticide market (Van der Sluijs et al. 2013). Although this group of chemicals also affects the non-target species, honey bees in particular. A lot of studies have been done showing their negative impact on honey bees and other pollinators (Peduzzi et al. 2010; Hopwood et al. 2013; Vanbergen 2013). Neonicotinoids reduce adult longevity and cause delayed development (Desneux et al. 2007; Wu et al. 2011), they impair the brain functions and have negative influence on the foraging behavior (Decourtye, Devillers, et al. 2004; Potts et al. 2010; Vanbergen 2013), they also impair olfactory learning and memory abilities (Decourtye, Armengaud, et al. 2004; Decourtye et al. 2005; Aliouane et al. 2009; Blacquière et al. 2012; Tan et al. 2015). Previous studies have shown that neonicotinoids like imidacloprid also act on hypopharyngeal gland of honey bee nurses and shift their nest activities to the field activities (Hatjina et al. 2013).

  2009; Potts et al. 2010; Vanbergen 2013; Food & Authority 2014). In scientific literature several studies have been done on co-exposition effects of pesticides and pathogen on honey bee and bumblebee survival (Alaux et al. 2010; Vidau et al. 2011; Aufauvre et al. 2012; Locke et al. 2012; Pettis et al. 2012; Di Prisco et al. 2013; Aufauvre et al. 2014; Baron et al. 2014; Doublet et al. 2014; Fauser-Misslin et al. 2014; Retschnig et al. 2014). It was shown that honey bees that are exposed to clotianidin have lower immune defenses and are more susceptible to viruses (Di Prisco et al. 2013) and exposure to sub-lethal concentrations of imidacloprid during the larval stage makes workers more sensitive to the gut parasite Nosema ceranae (Pettis et al. 2012). In general, exposure to neonicotinoids (imidacloprid, thiacloprid and fipronil) and to Nosema leads to the increased mortality; pathogen and pesticide can have synergistic effect (Alaux et al. 2010; Vidau et al. 2011; Aufauvre et al. 2012; Pettis et al. 2012; Di Prisco et al. 2013; Doublet et al. 2014; Retschnig et al. 2014). Honey bees exposure to neonicotinoids leads to the impairment of their olfactory learning and memory (Decourtye, Armengaud, et al. 2004; Decourtye et al. 2005; Aliouane et al. 2009; Blacquière et al. 2012; Tan et al. 2015). Though, olfactory learning is one of the key conditions of successful foraging.

Figure 1 .

 1 Figure 1. Key drivers of honey bee loss and interaction between them. Red arrows show direct pressures; green arrows represent interaction between drivers and blue within drivers (Potts et al. 2010 a).

  larval mortality was done earlier in February and INRA, le Magneraud (unpublished data). The third group received sub-lethal dose of thiamethoxam that is four ppb. Again, the sub-lethal concentrations of thiamethoxam were determined earlier with another test in the INRA, le Magneraud (unpublished data). The next two groups were subjected simultaneously to Paenibacillus larvae and thiamethoxam. The fourth group received four spores of Paenibacillus larvae and four ppb of thiamethoxam and the last group was subjected to the half dose used in the fourth group, making it two spores of Paenibacillus larvae and two ppb of thiamethoxam per cell. For the 2 nd , 4 th and 5 th group honey bee larvae were exposed to spores of Paenibacillus larvae that was added to the diet on day 1. We used a strain of Paenibacillus larvae that was obtained from the apiaries in Magneraud in 2010.

Figure 2 .

 2 Figure 2. Schematic representation of the main steps of the larval rearing and exposure.

  used 10 min Inter Trial Interval (ITI) (Matsumoto et al. 2012) as ITI that are less than 10 min show lower acquisition and retention levels (Menzel et al. 2001). In total we performed three conditional trials and at the end we had 50-54 individuals per treatment.

Figure 2 .

 2 Figure 2. Experimental procedure of absolute conditioning Memory retention test was performed 1 hour after the last conditioning trial. As was mentioned above we used two odours for this test; one odour was the novel one -2-hexanol and the second one was the conditioned odour -1-nonanol. The trial time was the same as for the conditioning experiment; the only difference was the absence of the sugar reward. To avoid the sequential effect the odours were presented in a random way.

  and the conditioning trials started four hours after. It was shown that five conditioning trials are enough for the formation of a robust long-term memory[START_REF] Menzel | Memory dynamics in the honeybee[END_REF][START_REF] Matsumoto | Revisiting olfactory classical conditioning of the proboscis extension response in honey bees: A step toward standardized procedures[END_REF] therefore we performed five conditioning trials instead of three that were done for the testing of the middle-term memory. After the last conditioning trial each worker was fed with sucrose solution to avoid excessive starvation and mortality. Bees were kept in the dark place with a room temperature overnight and the memory retention test was performed 24 hours after. At the end we had around 38-42 individuals per treatment.

Figure 4 .

 4 Figure 4. Evolution of mortality after application of treatments from day 3 to day 22. Error bars represent the 95% confidence interval.

(x 2

 2 =9.3, df:1, P<0.05) and also between control and Paenibacillus larvae 4 spores + thiamethoxam 4 ppb (x 2 =6.46, df:1, P<0.05). Paenibacillus larvae 4 sp + Thiamethoxam 4 ppb Paenibacillus larvae 2 sp + Thiamethoxam 2 ppb

Figure 5 .

 5 Figure 5. Percentage of mortality during the larval stage and pupation. The number of dead larvae was compared with the number of dead pupae for each treatment using a x 2 test with 1 df (*P<0.05).

  .8, df:1, P<0.05), Paenibacillus larvae 4spores + thiamethoxam 4 ppb (x 2 =77.2, df:1, P<0.05) and Paenibacillus larvae 2 spores + thiamethoxam 2 ppb (x 2 =37.3, df:1, P<0.05).

Figure 6 .

 6 Figure 6. Effects of treatments applied during the larval stage on adults' emergence; n=number of alive honey bee workers at day 22. The number of emerged workers was compared with the control group using x 2 test with 1 df (*P<0.05). Error bars represent the 95% confidence interval.

  during larval stage to the absolute olfactory conditioning of PER (N=53 control, N=53 Paenibacillus larvae 4 spores, N=52 thiamethoxam 4, N=54 Paenibacillus larvae 4 spores + thiamethoxam 4 ppb and N=50 Paenibacillus larvae 2 spores + thiamethoxam 2 ppb). All workers received three conditional trials during the training period (Fig.7). We can see that at

Figure 7 .

 7 Figure 7. Olfactory learning performance of restrained honey bee workers during the absolute olfactory conditioning of PER.

Figure 8 .

 8 Figure 8. Olfactory learning performance of restrained honey bee workers during the absolute olfactory conditioning of PER.

x 2 =4

 2 .45, df:1, P<0.05). No significant difference were found between control and Paenibacillus larvae 4 spores (trial 3: x 2 =0.54, df:1, P>0.05), between control and Paenibacillus larvae 4 spores + thiamethoxam 4ppb (trial 3: x 2 =1.73, df:1, P<0.05).

Figure 9 .

 9 Figure 9. Memory retention performance of honey bee workers subjected to olfactory conditioning of PER one hour after the conditioning. Bars represent % of PER for the. The number of conditioned responses was compared with the control group using a x 2 test with 1 df (*P<0.05).

  x 2 =12.2, P<0.05; Paenibacillus larvae 4 spores: x 2 = 18.4, P<0.05; thiamethoxam 4 ppb: x 2 =21.3, P<0.05; Paenibacillus larvae 4 spores + thiamethoxam 4 ppb: x 2 =20.9, P<0.05; Paenibacillus larvae 2 spores + thiamethoxam 2 ppb: x 2 =28.3, P<0.05). response rate (Fig.10) in groups that were exposed to Paenibacillus larvae and thiamethoxam at different concentrations (Paenibacillus larvae 2 spores+ thiamethoxam 2 ppb: x 2 = 6.96, df:1, P<0.05; Paenibacillus larvae 4 spores + thiamethoxam 4 ppb: x 2 =4.49, P<0.05) and the group that received 4 ppb of thiamethoxam (x 2 =4.36, P<0.05). The treatment with 4 spores of Paenibacillus larvae doesn't show impairment of the long-term memory (x 2 = 3.23, P>0.05).

Figure 10 .

 10 Figure 10. Memory retention performance of honey bee workers subjected to olfactory conditioning of PER 24 hours after the conditioning. Bars represent % of PER to the CS. The number of conditioned responses was compared with the control group using a x 2 test with 1 df (*P<0.05).

  . Comparison of mortality between larval stage and pupation didn't show any significant difference, however we had more dead individuals during the pupation stage in all the treatments with Paenibacillus larvae. In treatments with 4 spores of Paenibacillus larvae, 4 spores of Paenibacillus larvae + 4 ppb of thiamethoxam and 2 spores of Paenibacillus larvae + 2 ppb of thiamethoxam most of the individuals died between day 5 and day 15.

  2000; Pham-Delegue et al. 2002; Decourtye, Devillers, et al. 2004). The available data from previous studies confirmed that results obtained during the PER laboratory tests is consistent with the data obtained during the semi field experiments, therefore the individual effects of pesticides on learning and memory that are observed during the PER conditioning can be extrapolated on the colony level (Decourtye, Devillers, et al. 2004).

  al. 2000;[START_REF] Wang | Maturation of odor representation in the honeybee antennal lobe[END_REF]. We followed the revised olfactory classical conditioning of the proboscis extension response protocol by Matsumoto and colleagues[START_REF] Matsumoto | Revisiting olfactory classical conditioning of the proboscis extension response in honey bees: A step toward standardized procedures[END_REF]. In order to prepare the honey bee workers for the PER test

they were anesthetised by cooling on ice until they became motionless

[START_REF] Felsenberg | Behavioural pharmacology in classical conditioning of the proboscis extension response in honeybees (Apis mellifera)[END_REF][START_REF] Frost | The proboscis extension reflex to evaluate learning and memory in honeybees (Apis mellifera): Some caveats[END_REF]

. Every testing day we had five groups of ten individuals. Bees were individually harnessed with the adhesive tape in the metal tubes and tubes were marked for individual identification. Every honey bee worker was restrained in the tube in a way that the worker was able to extend its proboscis and freely move its mouth parts. After harnessing bees were left for 3-4 hours to recover in a dark place with the room temperature

(

Giurfa & Sandoz 2012). The 50% sucrose solution was used as the unconditioned stimulus (US). In most studies 30% sugar solution is used (Morgan et al. 1998; Laloi et al. 2000; Decourtye et al. 2005; Matsumoto et al. 2012

), but considering the fact that our bees were reared in vitro and fed with the 50% sugar solution during their life for our experiment we decided to use 50% sugar solution. For odor delivery we used 20 ml plastic syringe, containing a piece of a filter paper (10mm*30mm) soaked with 5 µl of odorant. 1-nonanol was used as a conditioning stimulus (CS) during the conditioning trials and two odors (1-nonanol and 2-hexanol) were used for the memory retention test that workers are able to distinguish from each other

[START_REF] Guerrieri | Perceptual and neural olfactory similarity in honeybees[END_REF]

). The odours were purchased from Sigma Aldrich.

  found decreased level of PER in the group treated with thiamethoxam 4 ppb. In this group we had the lowest rate of PER to the CS. It allows us to indicate that chronic exposure of honey bee larvae to thiamethoxam at concentration 4 ppb leads to the impairment of olfactory learning in adults. Results that we obtained are consistent with other studies that showed that

	neonicotinoids impair foraging behavior (Decourtye, Devillers, et al. 2004; Decourtye,
	Armengaud, et al. 2004; Decourtye et al. 2005; Aliouane et al. 2009; Potts et al. 2010;
	Blacquière et al. 2012; Vanbergen 2013; Tan et al. 2015). After acute exposure of honey bee
	workers to thiamethoxam at 1 ng/ bee no effect was detected on locomotor activity (El Hassani
	et al. 2008), but application of thiamethoxam at the amount of 0.1 ng/bee by contact reduced
	adults' learning.

Study by Oliveira and colleagues

[START_REF] Oliveira | Side-effects of thiamethoxam on the brain andmidgut of the africanized honeybee Apis mellifera (Hymenopptera: Apidae)[END_REF]

showed that sub-lethal doses of thiamethoxam applied on newly emerged Africanised bees cause impairment in the brain; they found changes in mushroom bodies and optical lobes. Similar study was done with imidacloprid

[START_REF] Yang | Impaired Olfactory Associative Behavior of Honeybee Workers Due to Contamination of Imidacloprid in the Larval Stage[END_REF]

where they did chronic exposure to imidacloprid during the larval stage directly in the hive. They applied 1µL solution of imidacloprid with different concentrations to

Annex 1

Composition of the diet fed to honey bee larvae during the experiment

Abstract

In the last ten years honey bee (Apis mellifera) colonies have been in decline. There are multiple causes that contribute to that rapid downtrend. Among them are pests and pathogens, land-use intensification, habitat loss and fragmentation. In most of the cases, the consequences of co-exposition are unknown as only few studies addressed these questions so far. Here we would like to investigate the impact of co-expositon between a pathogen and a neonicotinoid on honey bee survival and learning performances. Yet all the studies about the influence of pesticides on learning and memory abilities are conducted on honey bees that were exposed to pesticide after emergence. But it is known that neonicotinoids are also stored inside the hives in pollen, nectar and brood is exposed to it. In our work we exposed honey bee larvae to American Foulbrood and to sub-lethal doses of Thiamethoxam (chronic exposure). First we hypothesise that the application of two treatments may have synergistic effect and induce higher mortality rates on larvae. We also tested the impact of this co-exposition, during the larval stage, on impairment of learning and memory abilities as well as the social behaviour of the honey bee workers. We used the standard olfactory conditioning procedure that is based on Proboscis Extension Response (PER) to study the changes in learning and memory abilities caused by the co-exposition effect.

Key words: co-exposition, honey bee, proboscis extension response (PER), olfactory conditioning, Thiamethoxam, American Foulbrood.


Adapting the feed, the animal and the feeding techniques to improve the efficiency and sustainability of monogastric livestock production systems



  , the animal and the feeding techniques to improve the efficiency and sustainability of monogastric livestock production systems How did it all start?  INRA and WUR team up in some calls on animal production  INRA was to coordinate a consortium on "Sustainable terrestrial livestock production", focusing on monogastric animals  The coordinator was solicited in Nov. 2013 by INRA to lead this consortium  Support from INRA Transfert on project management from the very start  The call text was published in Dec. 2013 12/1/2015 Workshop Interactive Innovation in Motion 2 Adapting the feed, the animal and the feeding techniques to improve the efficiency and sustainability of monogastric livestock production systems How did it all start?  Discussion between INRA and INRA Transfert (and WUR) in Nov. And Dec. 2013  Detailed analysis of the call text  Identification of main impacts (→ structuring of WP)  Road map  Synopsis  Identification and invitation of consortium partners:  5-7 research partners (mostly of existing networks)  Industry (based on potential contributions to impact)  Extension services and interbranch organizations  Physical meeting in Paris in Jan. 2014 with committed partners 12/1/2015 Workshop Interactive Innovation in Motion 3 Adapting the feed, the animal and the feeding techniques to improve the efficiency and sustainability of monogastric livestock production systems The objectives  Based on the topic title "Genetics and nutrition and alternative feed sources for terrestrial livestock production"  A focus of monogastrics (INRA-WUR decision) but maintain the "diversity in production systems"  Work packages:  Genetics: better animals with poorer feeds  Alternatives feed sources: not or less in competition with food for humans  Nutrition: precision livestock feeding  New traits: monitoring and phenotyping  Modeling: understanding and accounting for interactions (animal x environment), perturbations, variation among individuals  Evaluation of the sustainability of the new management systems  Dissemination, training and technology transfer 12/1/2015 Workshop Interactive Innovation in Motion 4 Adapting the feed, the animal and the feeding techniques to improve the efficiency and sustainability of monogastric livestock production systems Building the consortium  Scientific partners:  Many more were willing to participate  Industrial partners:  A very industrialized sector (e.g., genetics, feed companies, enzyme producers, equipment manufacturers)  Invitations mostly through scientific networks  Quite a few industrial partners declined the invitation  Interbranch and farmers organizations:  Through scientific and industrial networks  Announcement through the Enterprise Europe Network  China: a bit difficult … 01/12/2015 Workshop Interactive Innovation in Motion 5 Adapting the feed, the animal and the feeding techniques to improve the efficiency and sustainability of monogastric livestock Adapting the feed, the animal and the feeding techniques to improve the efficiency and sustainability of monogastric livestock production systems Reach-out beyond the consortium  Involvement of the Stakeholder Advisory Board  Creation of a stakeholder platform: Communication/awareness  Dedicated stakeholder meetings once results become available Interactions with on-going FP7 projects (upstream) and on-going and new H2020 projects (downstream)

Budget EU funded Research project 2015 2020


pro-active position to prepare research supports to develop better adapted, productive and quality trees in diversified forests to resources and exchanges of expertise -To information (genetics -environment) International information systems (WP1, WP4, WP9) Standardisation (WP2) -To biological material (MTA) (ABS) -To diversified forestry research infrastructures (WP5, TNA) -To services from some key-laboratories (molecular, NIRS) or improved methodologies and tools -Better or new ways to generate data -Standardisation of methodologies (WP2) -High-throughput phenotyping methods (WP11) -Molecular markers for Traceability of FRM (WP7) -Better or new tools to explore data -Statistical/genetic analysis tools for genetic experiments (WP6) -Analysis of tree reaction to environment (WP3, WP8, WP11) -Planification of breeding/deployment zones (WP8) -Models to simulate impacts of environmental/human practices changes on forestry resources and services (

Used bioclimatic multivariate and ensemble models Douglas-fir: greater genetic adaptation and plasticity to cope with site types outside its niche Beech: net loss in area excepted in one bioclimatic zone

• Provenance trial design should be more thorough Test all material at all sites: survival, growth, phenology Pool empirical trial data in Europe -hence the need for a European Tree Breeding Centre


domain: sdv sde

text:
Frédéric Rees
email: f.rees@gisfi.fr

Aline Peregrina Puga

Luke Beesley

Claudia Kammann

Effects of "fresh" versus "aged" biochar on soil metal leaching  1 agricultural soil: contaminated by repeated sewage sludge amendments for 20 years PERSPECTIVES CONCLUSIONS

INTRODUCTION

* (solid product from biomass pyrolysis used as soil amendment)

In particular, an increase of soil pH induced by biochar has been shown to reduce metal solubility, while a release of labile compounds from biochar has resulted in an increased soil metal leaching [2][3][4] .

Different results may therefore be expected depending on the use of "fresh" or "aged" biochar, i.e. subject to weathering which can modify the surface and chemical properties of the biochar. Understanding the influence of ageing on biochar ability to immobilize metals in soils is crucial to assess biochar long-term effects. Biochar* has been proposed as an effective soil amendment for the management of metal(loid)s via a number of mechanisms [1] :



 by direct sorption on biochar's surface,  by the occlusion of soil particles,  by indirectly modifying soil chemistry.

Control experiment

(with soil alone)

« Amendment » experiment (with soil and fresh/aged biochar) Comparison of chemical analysis of liquid samples taken at different times show that:

Ageing of biochar can increase its metal retention capacity. Loss of minerals (e.g. carbonates) and labile organic matter associated with surface oxidation is likely to have increased both specific surface area and cationic exchange capacity of biochars.

Other processes of ageing should be tested, e.g. using litter bags filled with biochar in contact with a real soil environment. The specific design of this multi-column leaching experiment could be used for other purposes (e.g. nutrients mobility tests with biochar or compost).

  Column leaching experiments were conducted in August 2014 at the James Hutton Institute of Aberdeen (UK), in the frame of a Short Term Scientific Mission supported by COST Action TD1107. This work was done in order to:  Differentiate direct sorption on biochar from indirect effects of biochar on soil metal leaching  Assess the impact of ageing process on biochar's ability to modify soil metal mobility METHODS [1] Beesley et al. (2011.) A review of biochars potential role in the remediation, revegetation and restoration of contaminated soils. Environmental Pollution [2] Beesley & Dickinson (2011). Carbon and trace element fluxes in the pore water of an urban soil following […] amendments […]. Soil Biology & Biochemistry [3] Houben et al. (2013) Mobility, bioavailability and pH-dependent leaching of cadmium, zinc and lead in a contaminated soil amended with biochar. Chemosphere [4] Rees et al. (2014) Short-term effects of biochar on soil heavy metal mobility are controlled by intra-particle diffusion and soil pH increase. European Journal of Soil Science



  A new column leaching protocol was tested to mimic the new progressive equilibrium which takes place once biochar has been introduced in the soil, while keeping both materials separated.  Fresh biochar: (partially) saturated with Zn  Aged biochar: not saturated at the end  Ageing of biochar ↗ Zn retention capacity  Both biochars: not able to retain all Cu from soil  Aged biochar: less efficient than fresh biochar at the beginning, but not at the end Experiments were run in duplicates for 48h. Liquid samples were regularly taken for analysis in the reservoir R, at the outlet of C1 and at the outlet of C2.  Despite large initial differences, pH of leachates from soil or biochar columns were identical after 24h Fast equilibration between soil and biochar  Both fresh and aged biochar enabled to capture the metals released from the soil and to indirectly decrease this metal release by increasing soil pH  Higher impact of direct sorption on biochar than indirect effect on soil metal leaching  Both direct and indirect effects of biochar on Cu were lower than on Zn; Cu concentration was correlated to the concentration of dissolved organic carbon  Stabilization of Cu in solution with dissolved organic matter 1 Université de Lorraine/INRA, LSE, Nancy, France; 2 Instituto Agronômico de Campinas, Campinas, Brazil; Evolution of Cu leaching with time Evolution of Zn leaching with time [Following results are only shown for the first replicate of each leaching run.]

1.

  First column containing pure soil (C1) is leached by water stored in a reservoir (R) 2. Second column containing either fresh or aged biochar (C2) is associated in series with column C1, so that the leaching solution for C2 equals the leachate from C13. Loop-circulation is imposed: leachate from C2 goes back into reservoir R and progressively affects subsequent soil and biochar leaching Glass columns washed with diluted nitric acid were used associated to a multi-channel peristaltic pump.

This work is part of LORVER program and has been funded by Lorraine Region and FEDER program. We thank the STSM Committee of COST Action TD1107, e.g. . We also thank Samia for her technical assistance.


The egg-to-egg Stock/Recruitment series of the Nivelle R. Stock -> spawned eggs (/m² RR eq.) Recruitment -> returning eggs (/m² RR eq.)

The egg-to-egg Stock/Recruitment series of the Nivelle R. Stock -> spawned eggs (/m² RR eq.) Recruitment -> returning eggs (/m² RR eq.)

Median recruitment curve

The egg-to-egg Stock/Recruitment series of the Nivelle R. Stock -> spawned eggs (/m² RR eq.) Recruitment -> returning eggs

The egg-to-egg Stock/Recruitment series of the Nivelle R.

Maximum Sustainable Yield NASCO CL -> 1.8 egg/m² (RR eq.)

Troubles with NASCO CL definition

• Assume catches (at sea and in rivers) are well known or negligible

-Generally not true

• E.g. in the Nivelle: significant but unknown coastal exploitation

• Spawning stock producing MSY: is this relevant to conservation?

-Rather aims at fisheries exploitation optimization • Acknowledge recruitment stochasticity (uncertainty)

• Modified CL definition: spawning escapement threshold that ensure a low probability of wild juvenile recruitment falling too low

-Conservation aims at controlling the risk of low recruitment

• But what is low recruitment?

-A management decision -An operational proposal: set as a proportion of the carrying capacity (Rmax)

• And what is low probability?

-A management decision -Uses scientific and fisheries independent data only -Not sensitive to changes in density independent mortality (e.g. at sea)

-Transferable from Index Rivers to non-Index Rivers (as NASCO CL)

• CONs

-CL estimation sensitive to recruitment rate at low spawning escapement

  spawned eggs (/m² RR eq.) Recruitment -> returning eggs (/m² RR eq.) spawned eggs (/m² RR eq.) Recruitment -> returning eggs (/m² RR eq.)

•

  What if survival (e.g. at sea) drops? MORFISH Conference -Wareham (UK) -4th March 2015 MORFISH Conference -Wareham (UK) -4th March 2015 Evolution of NASCO CL if sea survival drops spawned eggs (/m² RR eq.) Recruitment -> returning eggs Nivelle current conditions NACSO CL: 1.8 egg/m² Nivelle new conditions NACSO CL: 1.4 egg/m² Sea survival drops by 25% Recruitment curve flattens → NACSO CL decreases MORFISH Conference -Wareham (UK) -4th March 2015 MORFISH Conference -Wareham (UK) -4th March 2015CL definition: time for a change?• NASCO CL: based on fisheries optimization principle -when times become harder for salmon CL should be revised downward• Fisheries optimization ≠ Conservation• But how can we define Conservation?• An operational (and partial) proposal: conservation should ensure that wild juvenile recruitment does not fall too low• Associated CL definition: spawning escapement threshold that ensure wild juvenile recruitment does not fall spawned eggs (/m² RR eq.) Recruitment -> 0+ parr (/m² RR eq.) spawned eggs (/m² RR eq.) Recruitment -> 0+ parr (/m² RR eq.)

-

  Based on a conservation principle/definition in a risk control context -Identify the risk of low recruitment level as a management decision • Easier to communicate to management stakeholders (?)

•

  A quantity usually difficult to estimate from SR data -Do not take into account potential density dependence of survival after the juvenile recruitment stage considered

4th March 2015 MORFISH Conference -Wareham (UK) -4th March 2015 Risk plot for 3 options of low recruitment level

					Risk: Proba(Recruitment < X% of Rmax)
	100%
	90%
	80%
	70%
	60% 50%										50% Rmax 75% Rmax
	40%										90% Rmax
	30%
	20%
	10%
	0%
	0	2	4	6	8	10	12	14	16	18	20
				Stock -> spawned eggs (/m² RR eq.)

• Scientific advice: produce (risk) plots that help management decision MORFISH Conference -Wareham (UK) -

Risk plot: an example of management decision

					Risk: Proba(Recruitment < X% of Rmax)
	100%
	90%
	80%
	70%
	60%
	40% 50%			Low recruitment: 50% Rmax Low probability: 25%	50% Rmax
	30%
	20%
	10%		CL: 2.8 egg/m²
	0%
	0	2	4	6	8	10	12	14	16	18	20
				Stock -> spawned eggs (/m² RR eq.)

MORFISH Conference -Wareham (UK) -4th March 2015 MORFISH Conference -Wareham (UK) -

4th March 2015 Risk plot: being too risk averse is not realistic

					Risk: Proba(Recruitment < X% of Rmax)
	100%
	90%
	80%
	70%
	60%
	50%										90% Rmax
	40%
	30%
	20%
	10%
	0%
	0	2	4	6	8	10	12	14	16	18	20

Stock -> spawned eggs (/m² RR eq.)

What about non-Index Rivers?

• A handful of Index Rivers but several 100's salmon rivers

• How can we transfer our knowledge about CLs from data-rich Index Rivers to data-poor non-Index Rivers?

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Scarlett Tannous
email: scarlett.h.tannous@gmail.com

Romain Besseau

Anne Prieur-Vernat

Julie Clavreul

Marie Payeur

Isabelle Blanc

Paula Perez-Lopez

A PARAMETERIZED MODEL FOR THE ESTIMATION OF LIFE-CYCLE ENVIRONMENTAL IMPACTS OF CRYSTALLINE PV SYSTEMS

Keywords: c-Si, CO2 footprint, photovoltaic, environmental effect, LCA

As part of global efforts to thwart the climate change challenge, the integration of renewable energy systems, including an increasingly dominant solar sector, would contribute by 36% to the reduction of carbon related emissions. Among current photovoltaic technologies, the focus is on silicon-based technologies dominating the photovoltaic market with a share of 95% of the total production in 2017. Despite nearly-zero emissions during the operation, non-negligible environmental impacts of these systems can be associated with the manufacturing stage, which contributes to 80 -90 % of the engendered impacts over their lifetime. The electricity mix is a key variable parameter among others affecting the environmental impact in particular manufacturing and recycling phases. In order to tackle this variability, the Life Cycle Assessment method along with comprehensive reviews of latest crystalline-silicon advancements are used in this work to develop a parameterized model. The approach does not only provide a scientific multi-criterion support monitored by more than 25 input variables but also includes updates to current databases for photovoltaic systems, dating from 2005. This parameterization shows the relevance of the model's modular aspect allowing simulations of various scenarios, ensuring up-to-date LCA data, and serving uncertainty and sensitivity analyses in the future.

INTRODUCTION

The electric power sector, responsible of 40% of today's energy-related carbon dioxide (CO2) emissions, is one of the main contributors to the climate change challenge [START_REF] Pavarini | Tracking the decoupling of electricity demand and associated CO2 emissions[END_REF]. Thus, this induced challenge which constitutes one of the main and most complex problems faced by the humanity and the Earth is calling for instant and effective solutions at the global level [START_REF] Constantino | Adoption of Photovoltaic Systems Along a Sure Path: A Life-Cycle Assessment (LCA) Study Applied to the Analysis of GHG Emission Impacts[END_REF]. These solutions should limit and mitigate the engendered environmental, social, and economic problems [START_REF] Constantino | Adoption of Photovoltaic Systems Along a Sure Path: A Life-Cycle Assessment (LCA) Study Applied to the Analysis of GHG Emission Impacts[END_REF]. This has led the International Energy Agency (IEA) to encourage and project, by 2050, a significant reduction for the power-sector-resulted emissions in order to reduce them from 13 to 1.4 gigatons of carbon dioxide CO2 per year [START_REF]Energy, climate change & environment[END_REF]. This reduction is interlinked with many substantial steps which should be implemented; one of them is the expansion of the integration of renewable energy systems (RES) such as the photovoltaic (PV) solar energy which is showing a significant development over the past years [START_REF] Constantino | Adoption of Photovoltaic Systems Along a Sure Path: A Life-Cycle Assessment (LCA) Study Applied to the Analysis of GHG Emission Impacts[END_REF]. RES are expected to contribute by 36% to this reduction by 2040 [START_REF] Pavarini | Tracking the decoupling of electricity demand and associated CO2 emissions[END_REF]. However, their quantified contribution to decrease the greenhouse gases (GHG) when compared to other energy sources is debatable with a high level of variability linked with geographical, technological, methodological, and temporal factors [START_REF] Marini | Towards prospective Life Cycle Assessment: how to identify key parameters inducing most uncertainties in the future? Application to photovoltaic systems installed in Spain[END_REF]. Other potential environmental impacts should also be accounted for to obtain a comprehensive environmental footprint.

It is common that the environmental impacts of the PV systems are considered negligible when addressing the use phase only. On the contrary, the assessment of the raw material extraction and manufacturing as well as the end-of-life stages may involve significant impacts [START_REF] Sherwani | Life cycle assessment of solar PV based electricity generation systems: A review[END_REF]. For instance, 80 -90 % of the environmental impacts of the PV systems can be associated with the manufacturing stage [START_REF] Wong | Review of life cycle analyses and embodied energy requirements of single-crystalline and multi-crystalline silicon photovoltaic systems[END_REF]. Hence, a global analysis accounting for all life cycle stages of the PV system is imperative and Life Cycle Assessment (LCA) methodology is one of the most mature methods to quantify the environmental impacts of products throughout their life cycle stages including the raw material extraction, the manufacturing, the use phase, the transport, and the end-of-life [START_REF]environmental managements-life cycle assessments-principles and framework[END_REF][START_REF]environmental management-life cycle assessment-requirements and guidelines[END_REF].

Addressing variability of the environmental performance of PV systems is one of the challenges encountered when quantifying the environmental impacts of these systems. For instance, based on a review of 400 LCA studies evaluating PV systems, documented by a report from the Intergovernmental Panel on Climate Change (IPCC) [START_REF] Edenhofer | Renewable energy sources and climate change mitigation: Special report of the intergovernmental panel on climate change[END_REF], the range of GHG emissions varies between 5 and 217 g-CO2-eq/kWh. The majority of the studies documented a value between 30 and 80 g of CO2 eq/kWh. These values express the carbon footprint of the systems in grams-carbon dioxide-equivalent/kilowatthour (g-CO2-eq/kWh). This range is considerably wide due to the variability of the system boundary, the technologies, the applications and the assumptions.

In order to address this variability related to various technological, geographical, and methodological approaches, an explicit parameterized model for crystalline silicon (c-Si) systems is developed. It is based on key variable parameters contributing to this environmental performance variability. The choice of the technology is justified by the dominance of c-Si one among the PV systems: 95% of the total PV production in 2017 [10]. They were extracted from intensive comprehensive review of technological and LCA studies which will be documented in the following paragraphs. These parameters were grouped and modeled within an LCA approach using advanced LCA tools and ecoinvent 3.4 database [START_REF] Wernet | The ecoinvent database version 3 (part I): overview and methodology[END_REF], as explained in the next sections. The results will quantify the impacts and contribution to the carbon footprint expressed in g-CO2-eq/kWh associated with the chosen parameters. These scenarios are then compared to a baseline scenario representing the original data dated from 2005.

PURPOSE OF THE WORK

The objective of this work is to provide a tool allowing the flexibility to analyze environmental impacts of a wide range of PV systems. To do so, an explicit LCA parameterized model has been developed to generate environmental results in an automatized way by simply specifying values for a set of key input variables associated with technological hotspots.

The model allows the user, for the first time, to monitor technological, geographical, and methodological parameters to generate tailored life cycle inventories accounting for the current and prospective technological advancements of silicon-based PV systems related to the whole systems' life cycle. One of the potential applications of this work is to provide a solution for the outdated PV data issue while always relying on wellknown and developed available databases. The integration of these updates is based on an explicit LCA model with variable input parameters. This parameterization allows a time-effective accessibility to vary the parameters' values across the model in order to elaborate representative environmental assessments. The integration of such parameterization may help the propagation of uncertainties across the model for sensitivity analyses in the future. It also enhances the accuracy and eases the comparisons with other systems and scenarios from an environmental assessment perspective.

METHODOLOGY

As the PV market grows, research centers and industries scrutinize the environmental impacts of these technologies. LCA methodology is one of the most widespread techniques to achieve this goal. The adopted cradle-to-grave LCA accounts for the environmental impacts of a product or system over its life cycle stages. In order to represent the models, inventories are used: they consist of lists of inputs and outputs for each unit process included in the life cycle. Then, the Life Cycle Impact Assessment (LCIA) phase translates the inventories into potential environmental impacts using characterization factors that describe the environmental mechanisms behind each impact category [START_REF]environmental managements-life cycle assessments-principles and framework[END_REF][START_REF]environmental management-life cycle assessment-requirements and guidelines[END_REF]. With the LCA methodology as a basis and taking into account the current technological improvements which influence the silicon photovoltaic (PV) industry as well as their prospective evolution, a parameterized modular PV model is developed based on a set of technological, geographical, and methodological parameters (Figure 1). As shown Figure 1, the multi-criteria LCA results are first expressed in terms of impact per kWp of installed capacity then expressed per kWh produced over the whole lifetime. Each of the boxes on the left represents a category of input variables which are exerted to control the modeling according to the user's choice and their system.

The open-source LCA framework Brightway2 [START_REF] Mutel | Brightway2 life cycle assessment framework[END_REF], relying on Python language, is used to develop the model able to generate the environmental footprint for a large set of PV systems and configurations. The inventories rely primarily on ecoinvent 3.4 database as a basis, then, are modified and updated according to the latest advancements proposed either by the experts of task 12 of the International Energy Agency's Photovoltaic Power Systems Programme (IEA PVPS) or issued from industrial data or other LCA data [START_REF] Bracquene | Taking Evolution into Account in a Parametric LCA Model for PV Panels[END_REF][START_REF] Stolz | Life cycle assessment of photovoltaic module recycling[END_REF][START_REF] Frischknecht | Life cycle assessment of future photovoltaic electricity production from residential-scale systems operated in Europe[END_REF][START_REF] Kumar | Diamond Wire Sawing of Solar Silicon Wafers: A Sustainable Manufacturing Alternative to Loose Abrasive Slurry Sawing[END_REF][START_REF] Yang | Recycling and reuse of kerf-loss silicon from diamond wire sawing for photovoltaic industry[END_REF][START_REF] Perez-Gallardo | A Multi-objective Framework for Assessment of Recycling Strategies for Photovoltaic Modules based on Life Cycle Assessment[END_REF][START_REF] Libby | Photovoltaic Recycling Processes[END_REF][START_REF]AG SMA Solar Technology AG -Inverter & Photovoltaics solutions[END_REF][START_REF] Manfredi | Product Environmental Footprint (PEF) Guide, European Commission[END_REF].

Regarding the functional unit (FU), the results are expressed per power capacity installed in kilowatt peak (kWp) or per kWh. In order to calculate the impact per kWh, geographical parameters are added to calculate the electricity production [START_REF] Bracquene | Taking Evolution into Account in a Parametric LCA Model for PV Panels[END_REF]. It is advantageous to separate the impacts related to the technology (manufacturing, end-of-life, etc.) and those related to the geographical aspects of the site inducing a large variability (i.e. irradiation, orientation, etc.) [START_REF] Marini | Towards prospective Life Cycle Assessment: how to identify key parameters inducing most uncertainties in the future? Application to photovoltaic systems installed in Spain[END_REF]. Hence, the environmental performance is calculated based on the following formula: the environmental Performance is equal to the environmental impacts divided by the electricity generation. A multi-criteria approach is considered based on 4 impact categories selected for this particular study after international standards and initiatives such as the Product Environmental Footprint (PEF) Guide, the ILCD 2011 Handbook, and IEA PVPS reports [START_REF] Manfredi | Product Environmental Footprint (PEF) Guide, European Commission[END_REF][START_REF]International Reference Life Cycle Data System (ILCD) Handbook-Recommendations for Life Cycle Impact Assessment in the European context[END_REF][START_REF] Frischknecht | Methodology guidelines on life cycle assessment of photovoltaic electricity[END_REF][START_REF] Wade | The Product Environmental Footprint (PEF) of photovoltaic modules-Lessons learned from the environmental footprint pilot phase on the way to a single market for green products in the European Union[END_REF]. The main characteristics of the analyzed case studies can be summarized as follows: -Technological scope: 3 kWp PV installation roofmounted with modules of multi-Si technology.

-Geographical scope: A South European case study (corresponding to specific energy production of 1300 kWh/kWp) with an electricity mix representative of the world electricity mix.

-Temporal scope: 2005 and 2019 technology horizon.

-Methodological scope: the greenhouse gas (GHG) emissions are addressed using the IPCC 2013 global warming potential (GWP) 100a method as recommended by the Product Environmental Footprint Category Rules (PEFCR) and Task 12 [START_REF] Frischknecht | Methodology guidelines on life cycle assessment of photovoltaic electricity[END_REF][START_REF]PEFCR Guidance document -Guidance for the 14 development of Product Environmental Footprint Category Rules (PEFCRs)[END_REF][START_REF] Stocker | Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change[END_REF]. Additionally, 3 of the impact categories recommended by the PEF were integrated to assess the impacts of PV cells which will be explained in Section 5. They include the cumulative energy demand (CED), the respiratory effects inorganics, and the freshwater ecotoxicity impact categories.

SCIENTIFIC INNOVATION AND RELEVANCE

This parameterized LCA model is the first PV model allowing such a vast flexibility and accessibility to operational parameters for the silicon technology where enormous advancements have been achieved lately. While Marini and Blanc (2014) PV parameterized model focused on the Cadmium Telluride (CdTe) technology based on 8 input parameters including both technological and site-specific aspects, this model accounts for more than 25 input parameters able to characterize the technological, geographical, and methodological aspects of the silicon PV technology. These parameters can be divided into subcategories such as PV manufacturing, electricity production, transport and end-of-life. The proposed model also allows the user to monitor a large number of operational parameters and to either specify their own values or use the default set values to calculate the environmental impacts using the LCA method without being limited to a set of less recent inventories. Such model is especially relevant for industrial stakeholders and academic researchers to face the timeconsuming modeling and accuracy limitations encountered using traditional LCA tools.

RESULTS AND DISCUSSION

By modifying some of the input variables of the parameterized model, represented in Table I, the impacts engendered by 1 m 2 multi-Si PV cell, are compared at the level of 4 chosen impact categories. As shown in Table I and Figure 2, two scenarios were compared: the original dataset of the multi-Si PV cells extracted from ecoinvent 3.4 (represented in red) and the adjusted multi-Si PV cell resulted from the variation of the input variables of the parameterized model (represented in green). The choice of showing the impacts of PV cells, among many other components, is encouraged by the significance contribution of the cells to the system's impacts and the numerous advancements, found in the literature, at this level, such as: the reduction of the electricity consumption between the modified Siemens process (110 kWh / kg of silicon) and the Fluidized bed reactor (FBR) (30 kWh / kg of silicon) for the silicon processing [START_REF] Bracquene | Taking Evolution into Account in a Parametric LCA Model for PV Panels[END_REF][START_REF] Fischer | ITRPV 9th edition 2018 report release and key findings[END_REF]; the tendency to reduce the thickness of the wafer; the reduction of the amount of silver used for the metallization paste; the shift from the Loose Abrasive Slurry (LAS) towards the diamond wiring (DW) cutting process for the wafer [START_REF] Fischer | ITRPV 9th edition 2018 report release and key findings[END_REF]; the reduction of cutting losses during processing; and the dependency of the impacts on the electricity mix used for the manufacturing [START_REF] Frischknecht | Life cycle assessment of future photovoltaic electricity production from residential-scale systems operated in Europe, Subtask 2.0 "LCA[END_REF][START_REF] Fischer | ITRPV 9th edition 2018 report release and key findings[END_REF].

The results show a reduction of 40-50 % of GHG emissions, cumulative energy demand, and human health respiratory effects, along with a reduction of 21-22% of the freshwater ecotoxicity impact. This is justified by the reduced amount of material (silver, silicon, etc.) involved in the manufacturing phase because of the reduction of the initial amount and the less cutting losses associated to the manufacturing phase. Additionally, the reduction of the electricity amount largely contributes to an impact reduction in the 4 impact categories, even after changing the mix from a European context to a less favorable RoW context. The electricity mix is changed from a European (RER) to a rest of the world (RoW) context for a better representation of the cell manufacturing market. This analysis reflects the altered impact associated with a component of the system: the cells. It helps to identify the main hotspots at the level of manufacturing. Moreover, in order to address the carbon footprint with respect to the chosen FU (1 kWh), Figure 3 represents the carbon footprint of the full system expressed in g-CO2-eq/kWh. The first bar represents the impact of a 3 kWp multi-Si PV system extracted from ecoinvent 3.4 [START_REF] Wernet | The ecoinvent database version 3 (part I): overview and methodology[END_REF]. The second bar represents the impact of the same system when analyzed using the parameterized model. It can be noticed that both have an impact of around 60-61 g-CO2-eq/kWh which validates the modeling aspect. Since a 210 Wp panel is not representative of the current technologies, a 280 Wp considered as a representative current panel is analyzed [START_REF] Bracquene | Taking Evolution into Account in a Parametric LCA Model for PV Panels[END_REF][START_REF] Gazbour | A path to reduce variability of the environmental footprint results of photovoltaic systems[END_REF]. This is equivalent to a better efficiency of 17.5%. As a result, the impact of such system with 280 Wp panel would reduce the impacts to around 47 g-CO2eq/kWh. As presented previously in Table I, since the silicon processing rely more on the FBR process which consumes less energy than the modified Siemens process, the impacts are to around 38 g-CO2-eq/kWh when FBR process is selected. Finally, in order to emphasize the impact associated with the electricity mix used for manufacturing, a low carbon mix is tested and represented in the bottom bar. This low carbon electricity mix can result in 31-32 g-CO2-eq/kWh carbon footprint. This significant overall reduction highlights the necessity to adopt an up-to-date database with representative technological data to avoid the over-estimation of the systems' impacts and accommodate for the rapid technological enhancement of the PV field with a timeeffective way. With the emergence of photovoltaic-based electricity generation systems and its competitiveness with other conventional systems and RES, it is important to have an easy, transparent, and explicit model allowing the users, researchers, and industrial stakeholders to better model and compare their systems in terms of their environmental performance. The proposed parameterized model answers this need and provides estimates of the impacts of a 3 kWp multi-Si PV cell and system, as an example, accommodating for the latest technological trends in the PV sector within a European context. Among the studies' finding, the following key points can be highlighted: -There is a significant decrease (21-50%) in the environmental impacts of the PV silicon cell resulting from the latest technological advancements associated with the electricity consumption, silicon manufacturing process, cutting processes, silver use, source of electricity, etc.

-This explicit parameterized model enabled to monitor 4 different multi-Si PV systems' scenarios which showed a significant reduction of the carbon footprint from 60-61 g-CO2-eq/kWh to 31-32 g-CO2-eq/kWh by directly modifying a set of input parameters. This overcomes the limitations of some traditional LCA software and less recent inventories that might reflect misconceptions in the environmental analyses.

-If users, aside from industries and researchers, are not aware of certain specifications of their system, there is a possibility to use the proposed default values based on the current industrial data and recommendations.

-An optimization could be integrated to the model, in the future, to address the uncertainties associated with these systems and the robustness of resulted LCA impacts.

It is worth mentioning that, although a 3 kWp multi-Si PV system is analyzed, this explicit model allows the user to investigate the environmental performance for representative systems ranging from few kilowatt to several Megawatt (MW) silicon-based PV systems along with different options of installations, balance of system (BOS), electricity mixes, etc.

Figure 1 :

 1 Figure 1: Explicit structure of the parameterized model (4 boxes on the left: technological input variables, dashed white boxes: intermediate parameters, orange box: geographical and production input variables).

Figure 2 :

 2 Figure 2: Comparison of the impacts of the original PV cell dataset (ecoinvent 3.4) and the adjusted one (produced by the parameterized model).

Figure 3 :

 3 Figure 3: Comparison of various scenarios for the PV system carbon footprint with an assumed productivity of 1300 kWh/kWp and a 30-years lifespan system.

Table I :

 I Input variables related to PV cell manufacturing.

	Parameter	Original	Adjusted	Unit or
		value (eco-	value	Type
		invent 3.4)
	Silicon electricity intensity 110	30	kWh / kg
	Wafer thickness	240	200	µm
	Silver amount	10.2	9.6	g / m 2
	Cutting Process	LAS	DW	Boolean
	Kerf loss	0.5	0.29	fraction
	Electricity dataset	RER	RoW	dataset

ACKNOWLEDGMENTS

This research has been undertaken with the framework and financial support of the 'Agence de l'Environnement et de la Maîtrise de l'Energie', ADEME (France) as part of the INCER-ACV Project -contract n°1705C004. The authors would like to thank treeze Ltd. for their valuable support.