Pierre Chassé
email: pierre.chasse@ens-lyon.fr

Céline Pelosi

Jean-Christophe Lata

Sébastien Barot

Impact of crop genetic diversity on a litter consumer

Keywords: balanced diet hypothesis, cultivar mixture, earthworms, genetic diversity, mixed diet hypothesis, sustainable agriculture

come

Introduction

It is now well documented that species richness, mostly through functional diversity, strongly influences ecosystem functioning [START_REF] Isbell | High plant diversity is needed to maintain ecosystem services[END_REF][START_REF] Cardinale | Biodiversity loss and its impact on humanity[END_REF]. While most studies focus on species richness, recent studies are showing that genetic diversity within species can also influence species interactions and ecosystem processes [START_REF] Hughes | Ecological consequences of genetic diversity: Ecological effects of genetic diversity[END_REF][START_REF] Cook-Patton | A direct comparison of the consequences of plant genotypic and species diversity on communities and ecosystem function[END_REF][START_REF] Prieto | Complementary effects of species and genetic diversity on productivity and stability of sown grasslands[END_REF] through variations in ecologically important traits within the species [START_REF] Mcgill | Rebuilding community ecology from functional traits[END_REF]. Due to the fact that phenotypic diversity can be as high within species as among species [START_REF] Bangert | Benefits of Conservation of Plant Genetic Diversity to Arthropod Diversity[END_REF], it would not be surprising if interspecific diversity and intraspecific diversity had the same type of ecological consequences. Some recent results are in fact pointing in that direction [START_REF] Creissen | Stabilization of yield in plant genotype mixtures through compensation rather than complementation[END_REF][START_REF] Schöb | Intraspecific genetic diversity and composition modify species-level diversity-productivity relationships[END_REF], even if underlying mechanisms have not yet been fully documented for intraspecific genetic diversity.

The effect of horizontal biodiversity, i.e. within a trophic group, on adjacent trophic levels is, however, less known and more complicated to study [START_REF] Duffy | The functional role of biodiversity in ecosystems: incorporating trophic complexity[END_REF]. Prey diversity, has been shown to influence ecosystem functioning through various mechanisms reviewed by [START_REF] Duffy | The functional role of biodiversity in ecosystems: incorporating trophic complexity[END_REF]. According to the balanced diet hypothesis, a more diverse resource assemblage leads to the consumption of more diverse nutritional resources, which improves the growth of the consumer [START_REF] Demott | Utilization of a cyanobacterium and a phosphorus-deficient green alga as complementary resources by daphnids[END_REF][START_REF] Pfisterer | Dietary shift and lowered biomass gain of a generalist herbivore in species-poor experimental plant communities[END_REF]. On the other hand, according to the variance in edibility hypothesis, a more diverse range of resources likely contains resources resistant to consumption or at least less valuable in terms of nutrient content, which could decrease consumer growth [START_REF] Leibold | Resource Edibility and the Effects of Predators and Productivity on the Outcome of Trophic Interactions[END_REF]. These two mechanisms are not incompatible and can operate simultaneously. The balanced diet hypothesis relies on the fact that animals have particular food requirements, called "intake target", for different nutrients such as proteins or carbohydrates [START_REF] Senior | An Overlooked Consequence of Dietary Mixing: A Varied Diet Reduces Interindividual Variance in Fitness[END_REF]. It has been shown that animals more easily reach their intake target with a diet composed of more than one type of food, i.e. a mixed diet [START_REF] Demott | Utilization of a cyanobacterium and a phosphorus-deficient green alga as complementary resources by daphnids[END_REF][START_REF] Simpson | Optimal foraging when regulating intake of multiple nutrients[END_REF][START_REF] Senior | An Overlooked Consequence of Dietary Mixing: A Varied Diet Reduces Interindividual Variance in Fitness[END_REF]. However, these experiments only manipulated species richness and not genetic intraspecific diversity.

Our goal is thus to test whether genetic diversity within a plant species can impact its consumer through mechanisms linked to the balanced diet and variance in edibility hypotheses.

The intensification of agriculture has reduced crop biodiversity at different scales during the 20 th century [START_REF] Fao | The second report on the state of the world's plant genetic resources for food and agriculture[END_REF]. In particular, genetically homogeneous crop cultivars have been selected and usually a single cultivar is planted within each field. However, more and more results are showing that mixing crop cultivars could be beneficial through an increase in production and a decrease in its temporal variability [START_REF] Kiaer | Grain yield increase in cereal variety mixtures: A meta-analysis of field trials[END_REF][START_REF] Barot | Designing mixtures of varieties for multifunctional agriculture with the help of ecology. A review[END_REF]. Among the identified mechanisms, a decrease in the sensitivity of crops to pathogens and parasites (Zhu et al., 2000) or to climate change [START_REF] Tilman | Biodiversity and stability in grasslands[END_REF] seems to be particularly important. However, mixing crop cultivars could also promote the activity of beneficial organisms such as earthworms through the improvement of belowground-aboveground interactions [START_REF] Postma-Blaauw | Soil biota community structure and abundance under agricultural intensification and extensification[END_REF]. The influence of within field plant diversity loss on earthworm abundance and diversity is not yet fully demonstrated [START_REF] Spehn | Plant diversity effects on soil heterotrophic activity in experimental grassland ecosystems[END_REF][START_REF] Laossi | Effects of plant diversity on plant biomass production and soil macrofauna in Amazonian pastures[END_REF]. Anecic earthworms in agricultural fields mostly feed on litter composed of crop residues that are relatively poor in terms of nutrient compare to forest litter for instance. Yet, it is likely that earthworms could not reach their intake target for an optimal growth through this single genetically poor food resource available in this environment. If the balanced diet hypothesis applies to earthworms, genetic crop diversity erosion could affect earthworm growth and survival leading to a decrease in their activities and in the ecosystem services they provide. This is an important issue because: (1) Decomposers and especially earthworms provide many services to agriculture [START_REF] Bertrand | Earthworm services for cropping systems. A review[END_REF]. Earthworms are known to contribute to physical, chemical and biological soil properties. Anecic earthworms accelerate the decomposition of plant litter and enhance water infiltration. (2) Current intensive agriculture has often deleterious effects on soil macrofauna [START_REF] Birkhofer | Long-term organic farming fosters below and aboveground biota: Implications for soil quality, biological control and productivity[END_REF][START_REF] Postma-Blaauw | Soil biota community structure and abundance under agricultural intensification and extensification[END_REF] and decrease earthworm abundance and diversity [START_REF] Postma-Blaauw | Soil biota community structure and abundance under agricultural intensification and extensification[END_REF] through widespread practices such as tillage [START_REF] Pelosi | Earthworm community in conventional, organic and direct seeding with living mulch cropping systems[END_REF] and pesticides [START_REF] Pelosi | Pesticides and earthworms. A review[END_REF].

It is now well recognized that our current agricultural model based on the use of huge amounts of inputs and a few selected cultivars is not sustainable [START_REF] Tilman | Agricultural sustainability and intensive production practices[END_REF][START_REF] Foley | Solutions for a cultivated planet[END_REF]. In order to maintain yields while developing a more sustainable agriculture, it is necessary to develop new alternative agricultural practices. The results of agroecological or ecological engineering experiments suggest that increasing biodiversity in agrosystems to mimic natural ecosystems could be a way to benefit from the services provided by biodiversity and consequently lower the use of chemical inputs [START_REF] Malézieux | Designing cropping systems from nature[END_REF]. In this line of thought, this study aims at investigating the influence of mixtures of wheat cultivars on the growth and the survival of an anecic earthworm, Lumbricus terrestris. This species was selected for its crucial role in the decomposer communities of agricultural and forest ecosystems in temperate zones. Because food diversity could affect juvenile and adult earthworms differently, the aerial biomass of a field experiment was harvested and used as food for earthworms at both the juvenile and adult life stage in a laboratory experiment. We assessed the effect of intraspecific genetic diversity of wheat cultivars and the effect of their functional diversity. We tested the general hypothesis that a high level of intraspecific diversity and functional diversity leads to high litter quality, providing more diverse nutrients to earthworms and consequently leading to higher growth and survival rates. This would support the balance diet hypothesis for earthworms. On the contrary, a negative effect of diversity would support the variance in edibility hypothesis.

Materials and methods

Wheat cultivars

The present experiment is based on a field experiment in small plots that is fully described elsewhere [START_REF] Dubs | An experimental design to test the effect of wheat variety mixtures on biodiversity and ecosystem services[END_REF]. Wheat, Triticum aestivum, was used in the experiment.

Twenty-four functional traits (See Appendix A: Table 1) were measured for 58 different cultivars. These cultivars were classified using their functional traits in a hierarchical cluster analysis, using Ward method into 4 "functional groups" (See Appendix A: Table 2). Four cultivars were selected from each of the 4 functional groups (16 cultivars in total). These 16 cultivars were chosen according to seed availability and in order to maximize the overall functional diversity, making sure that the clustering in four groups remained robust when the clustering process was applied to the 16 cultivars [START_REF] Dubs | An experimental design to test the effect of wheat variety mixtures on biodiversity and ecosystem services[END_REF]. Among these cultivars, 9 were modern winter cultivars, selected to have high yields, 5 cultivars were old landraces, locally selected by farmers, and 2 cultivars were selected within an INRA MAGIC population, i.e. a highly recombinant and multi-parental population (60 parents: see [START_REF] Thepot | Efficiently Tracking Selection in a Multiparental Population: The Case of Earliness in Wheat[END_REF] for more details). From these 16 different cultivars, 72 different combinations of wheat cultivars were created. Among these mixtures, 24, 28 and 20 mixtures respectively combined 2, 4 and 8 cultivars. Cultivars were selected in the mixtures in order to create, for each level of cultivar diversity, homogeneous mixtures with low functional diversity (28 and 30 mixtures combined cultivars from only one or two functional groups respectively) and heterogeneous mixtures with high functional diversity (16 and 14 mixtures combined cultivars from three and four functional groups respectively) (See Appendix A: Table 3). The 16 plots with a single cultivar and the 72 plots with mixtures of cultivars were sown in Versailles (48°48'26"N 2°05'13"E) on November 2014. Earthworms were fed with 88 different litters coming from the aerial biomass of the 88 wheat mixtures harvested in May 2015. The aboveground biomass was dried (30 °C for 3 days) and cut (3 min in a blender) into small pieces (from 0.1 cm to 1 cm long).

The nitrogen concentration of each litter was measured on a sub-sample using an elemental analyzer (Flash HT, Thermo Scientific). The calcium, copper, iron, magnesium, manganese, phosphorus, potassium, sodium and zinc concentrations were measured by dry mineralization and spectrometry (radial ICP-AES, Varian Vista RL).

Earthworm growth experiment

The soil used in the experiment was collected from the 0-30 cm soil layer of a wheat cultivated field. For technical reasons this was another field than the one used to grow the mixtures of cultivars. It was a sandy-loam soil made of 53% sand, 34% silt, 11% clay, and with 2.8% of organic matter. The soil was air-dried and manually homogenized.

L. terrestris were obtained from a commercial supplier (http://appats-michel.fr/). They were patted dry, weighed and classified into juveniles or adults based on the presence of a clitellum. 88 juveniles and 224 adults (average fresh weight 2.34g ± 0.14 g for juveniles and 4.25 g ± 0.11 g for adults) were used in the experiment. Each container (10 cm inner diameter, 15 cm height) was filled with 775 g of soil. The soil moisture was adjusted to 80% of the soil water holding capacity during the whole experiment (by weighing the pots every week). A single earthworm was placed in each container. The containers were covered with perforated lids to prevent earthworm escape. Each earthworm was fed with the litter of a single experimental plot, i.e. a single mixture of wheat cultivar. The 16 treatments with litters obtained from one cultivar were replicated 6 times, one time with a juvenile and five times with an adult. The 72 treatments with litters combining more than one cultivar i.e. mixtures of cultivars, were replicated 3 times, one time with a juvenile and two times with an adult.

The experiment was divided into two periods to simulate local field conditions in cereal cultivation. The first one, simulating the autumn period, started in October 2015 and ended in February 2016. During this period called "feeding period", all containers were kept at 15 °C and fed with 10 g of wheat litter by month (20 g of litter was added twice at the surface of the soil in October and December 2015). Earthworms were removed for weighing in February 2016 after little more than 3 months of experiment. Each earthworm was patted dry, weighed without voiding gut content (to avoid stress) and returned in its container. The second period, simulating winter and called "non-feeding period", occurred between February and March 2016. Earthworms were kept at 8 °C without any new food supply. They were weighed in March 2016 using the method described above. Earthworm survival was assessed in December, in February and in March. During autumn, a significant quantity of litter is available at the soil surface (at least before tillage in agricultural systems) and earthworms are active. On the contrary, during winter, there is almost no litter available, temperature falls and earthworms are less active. The second period, between February and March, gave us information about a potential "carryover effect" [START_REF] O'connor | Biological carryover effects: linking common concepts and mechanisms in ecology and evolution[END_REF], i.e. the capability of earthworms to survive and grow throughout winter depending on their previous feeding conditions. This experiment thus aims to study the influence of food genetic diversity on growth but also on the capability of earthworms to survive during winter.

Calculation of earthworm growth rate

We used the instantaneous growth rate (IGR, d -1 ) [START_REF] Daniel | Weight gain and weight loss of the earthworm Lumbricus terrestris L. at different temperatures and body weights[END_REF][START_REF] Whalen | Growth of Aporrectodea tuberculata (Eisen) and Lumbricus terrestris L. under laboratory and field conditions[END_REF], and calculated as, IGR = ln(Wt/Wi)/Δt, where Wt (g) is the earthworm mass in February for the feeding period and in March for the non-feeding period and Wi (g) the mass in October for the feeding period and the mass in February for the non-feeding period. Δt was measured in days and represented the time interval for the studied growth interval.

Statistical analyses

We used linear models to determine whether earthworm growth was influenced by the mineral nutrient concentrations, the wheat cultivar number (WCN), wheat functional diversity (as assessed by the number of cultivar functional groups in the mixtures of cultivars, F) and the presence of each functional group (the effect of one particular functional group). The number of cultivars and the number of functional groups within a mixture are not independent because the number of functional groups cannot be higher than the number of cultivars and conversely that mixtures of 8 cultivars have at least 2 functional groups. This is why we used two different ANCOVA models to the test the effect on earthworm growth of the developmental stage (juvenile or adult, A), the effect of the wheat cultivar number or wheat functional group, and the interaction between the two. We used an ANOVA model to test the effect on earthworm growth of the developmental stage, the effect of the presence of each functional group in the mixture and the interaction between the two. We considered the wheat cultivar number and the wheat functional diversity as continuous variables to increase the power of the tests and because we expected linear effects of diversity and tested their interactions with the development stage. We first used linear mixed effect models, using the composition of the cultivar mixture as a random effect, but as this random effect was never significant, we used standard models with only fixed effects. Homoscedasticity and normality of model residuals were tested to ensure the robustness of the results. Dead earthworms were removed from growth analyses. The influences of cultivar number, cultivar functional diversity and the presence of particular functional groups in the mixture on earthworm death were analyzed by logistic regressions (glm procedure using a logit link function with a binomial error) in three separate models to avoid any problem of dependence between tested factors. In these three models assessing the effect of wheat cultivar number, wheat functional diversity and the presence of particular functional groups in the mixture on earthworm growth, we also tested the effect of nutrient concentrations. Non-significant nutrients were removed manually from the final model. We used R software version 3.1.2 for all statistical analyses (R Development Core Team, 2008).

Results

General description of the results

By February, all juveniles were adults so that during the non-feeding period all earthworms were adults. However, the growth difference between young adults and adults was still significant during the October-February and the February-March periods so that we compared "juveniles" and "adults" in our statistical analyses. On average in all treatments, the biomass of juveniles increased by 309% (± 8%) with a mean IGR of 1.1 10 -2 day -1 (± 0.6 10 -3 day -1 ) in the feeding period. During the non-feeding period, juveniles lost 1% (± 1%) of their weight.

The biomass of adults increased by 168% (± 4%) with a mean IGR of 5.1 10 -3 day -1 (± 0.2 10 - adults during the non-feeding period. However, in February, young adults had a lower weight than adults (result of t-test, t148=2.58, P=0.01).

Influence of wheat cultivar number on earthworm growth During the feeding period, no general effect of wheat cultivar number on earthworm growth was found but a significant interaction between development stage and wheat cultivar number occurred (Table 1). During the feeding period, juvenile growth significantly increased and adult growth significantly decreased with the number of cultivars (Fig. 1). During the nonfeeding period, there was no significant effect of the cultivar number in feed mixtures that had been provided during the previous months on earthworm growth.

Influence of cultivar functional diversity on earthworm growth

During the feeding period, there was no general effect of cultivar functional diversity on earthworm growth (Table 2). During the non-feeding period, earthworms lost significantly less weight with an increasing functional diversity (Table 2, Fig. 2).

Influence of the presence of particular functional groups

Among the four functional groups, only one functional group had a significant influence on earthworm growth. Earthworms that ate wheat litter from functional group 3 gained less weight than the others (regression, P<0.01). When the mineral nutrient concentrations were added to the models, earthworm growth was no longer affected by cultivars from the functional group 3 but was positively affected by magnesium concentration (Table 3). When the negative effect of the functional group 3 was removed from the analyses, the growth of adult earthworms did not decrease anymore with increasing number of wheat cultivars or increasing wheat functional diversity (Table 3, Fig. 3). The growth of juvenile earthworms even increased significantly with an increasing number of wheat cultivars not belonging to the functional group 3 during the feeding period (Table 3, Fig. 3). This suggested that the negative effect of cultivar number on adult earthworm growth was due to the presence of wheat from functional group 3 when the number of cultivars increases. During the nonfeeding period, the effect of functional group 3 was the opposite: earthworms that had eaten litter from functional group 3 during the feeding period lost less weight than the others (Table 3).

Influence of nutrient concentrations on earthworm growth

Only nitrogen and magnesium had significant effects on earthworm growth (Tables 1 and2).

Nitrogen had a positive effect during the feeding periods and a negative effect during the nonfeeding period. Magnesium had a positive effect during the feeding period. We found no interactions between the development stage and the different nutrient concentrations (statistical details not shown).

We also analyzed the influence of the wheat cultivar number, wheat functional diversity and the presence of particular functional groups on the two influential nutrients.

There was no significant effect on nitrogen concentrations (result of linear models). There was a decrease in magnesium concentration with wheat cultivar (regression, P=0.001) number and wheat functional diversity (regression, P=0.005). Moreover, there was less magnesium in mixtures containing cultivars from the functional group 3 (ANOVA result, P<0.001, decrease in Mg content of 10% for mixtures with the functional group 3).

Influence of wheat cultivar number on earthworm mortality

Forty-one adults and 13 juveniles died during the October-December period. Between December and February, only 3% of the 258 remaining earthworms died: 8 adults and 1 juvenile. During the non-feeding period, only one juvenile died. Overall, over the feeding and non-feeding period, earthworm survival increased with the number of cultivars (logistic regression, P=0.0428, Fig. 4). We did not find any significant effect of cultivar functional diversity or nutrient concentrations on earthworm mortality (statistical details not shown).

Discussion

Effect of cultivar mixtures during the Feeding period During the feeding period, adult growth decreased with the number and the functional diversity of cultivars. This decrease could be explained by the variance in edibility hypothesis.

This hypothesis is based on the fact that a more diverse range of resources likely contains resources resistant to consumption or at least less valuable in terms of nutrient content [START_REF] Leibold | Resource Edibility and the Effects of Predators and Productivity on the Outcome of Trophic Interactions[END_REF]. In our experiment, increasing the number of cultivars in mixtures increased the likelihood to have a cultivar from functional group 3, which has a negative effect on adult earthworm growth. To support the hypothesis that cultivars from functional group 3 triggered the negative effect of the increasing number of cultivars on adult earthworm growth, we analyzed the effect of an increase in the number of cultivars not belonging to the functional group 3. Since no relationship between adult growth and the number of cultivars not belonging to the functional group 3 has been found, this suggests that the decrease in adult earthworm growth can only be explained by the presence of cultivars from the functional group 3. Our results on nutrient concentration suggest that the negative effect of the functional group 3 is due to the lower magnesium concentration of the cultivars belonging to this functional group. We indeed found that the magnesium content of the wheat biomass had a positive effect on earthworm growth during the feeding period.

During the feeding period, juveniles behaved differently from adults since their growth increased with the number of cultivars. This result could be explained by the balanced diet hypothesis. This hypothesis is based on the fact that a more diverse resource assemblage leads to the consumption of more diverse nutritional resources, which improves the growth of the consumer [START_REF] Demott | Utilization of a cyanobacterium and a phosphorus-deficient green alga as complementary resources by daphnids[END_REF][START_REF] Pfisterer | Dietary shift and lowered biomass gain of a generalist herbivore in species-poor experimental plant communities[END_REF]. Increasing the number of cultivars would lead to a better diet because cultivar diversity (apart from functional group 3) provides a more diverse range of nutrients for juvenile earthworms. To our knowledge, this is the first time intraspecific genetic diversity is shown to lead to the balanced diet or the variance in edibility hypothesis. Most of the experiments on the links between diet and animal growth and performance addressed the influence of interspecific resource diversity [START_REF] Demott | Utilization of a cyanobacterium and a phosphorus-deficient green alga as complementary resources by daphnids[END_REF][START_REF] Pfisterer | Dietary shift and lowered biomass gain of a generalist herbivore in species-poor experimental plant communities[END_REF][START_REF] Lefcheck | Physiological effects of diet mixing on consumer fitness: a meta-analysis[END_REF]. Most of them found positive effects of mixtures on the performance of consumers. DeMott (1998) suggested for instance that a diet composed of different prey species can be beneficial for its consumer. In this case, the different kinds of nutrients brought by each of the two prey species were complementary and increased consumer growth. A few studies observed negative effects of mixed diet on periphyton and amphibian biomass [START_REF] Stoler | Living in the litter: the influence of tree leaf litter on wetland communities[END_REF]. [START_REF] Swan | Preferential Feeding by an Aquatic Consumer Mediates Non-Additive Decomposition of Speciose Leaf Litter[END_REF] documented an overall negative effect of interspecific litter diversity on detritivore growth. These opposite results may be related to the breadth of the diet of the consumer. Because some species have high requirements for particular nutrients, specialist consumers can be negatively impacted by a mixed diet because of the lower concentration of the food they particularly need. Generalist consumers likely have the opposite response. Earthworms are detritivores and L. terrestris feed on a wide range of organic materials and are considered generalist feeders [START_REF] Curry | The feeding ecology of earthworms -A review[END_REF]. Our results support the idea that a more diverse intraspecific mix diet could provide a more diverse range of nutrients leading to a positive influence on generalist feeders.

Overall, we hypothesize that depending on the consumer needs and the composition of its diet, either the balanced diet or the variance in edibility mechanisms, which have opposite consequences, are more influential.

In our experiment, it is likely that the variance in edibility hypothesis occurred in both adults and juveniles and the balanced diet hypothesis for juveniles only. There is already evidence showing that food requirements are changing during an individual's life [START_REF] Raubenheimer | Nutrient-specific compensation following diapause in a predator: implications for intraguild predation[END_REF][START_REF] Simpson | The Nature of Nutrition: A Unifying Framework from Animal Adaptation to Human Obesity[END_REF]. For instance, [START_REF] Dussutour | Communal Nutrition in Ants[END_REF] showed that ants, depending on their developmental stage, need a proteinrich diet or a carbohydrate-rich diet in order to maximize their performance (survival and production of larvae). The major difference between juvenile and adult earthworms is the presence of the reproductive organs. It has been shown that juveniles use food resources for the formation of sexual organs rather than their growth [START_REF] Daniel | Weight gain and weight loss of the earthworm Lumbricus terrestris L. at different temperatures and body weights[END_REF]. We could assume that juvenile earthworms that ate a more diverse range of nutrients (e.g. amino acids) were able to save energy -that they would otherwise have had to use to metabolize them -and allocated it both to the formation of their sexual organs and their growth. The nutrient intake target would be lower for adults than juveniles and could be reached with only one cultivar in adults, which would explain why there was no balanced diet effect in adults. However, the balanced diet effect on juvenile earthworm growth cannot be explained by the mineral nutrients we measured. The only effects of nutrients on earthworm growth during the feeding period were the positive effect of nitrogen and magnesium concentrations (see Table 1 andTable 2) but there was no relationship between nitrogen and wheat cultivar number and a negative relationship between magnesium and wheat cultivar number. In this case, no nutrient can explain the positive effect of cultivar numbers on earthworm growth. This suggests that another undocumented feature of wheat biomass was involved. We tried to assess the effect of other wheat functional traits measured on the 16 wheat cultivars but results remained difficult to interpret (See Appendix B). The positive effect of nitrogen concentration on earthworm growth during the feeding period is consistent with previous experiments on earthworms [START_REF] Schonholzer | Effects of decomposition of leaves onbacterial biomass and on palatability to Lumbricus terrestris L[END_REF][START_REF] Eisenhauer | Earthworms as seedling predators: Importance of seeds and seedlings for earthworm nutrition[END_REF]).

We did not find any effect of an increase of functional diversity on earthworm growth. This result suggests that our approximation of functional diversity, i.e. the number of functional groups in the litter, does not reflect a diversity affecting earthworm growth.

Cultivars were classified using plant functioning and growth strategy traits [START_REF] Wright | The worldwide leaf economics spectrum[END_REF]. They might not be fully relevant to predict interactions with other trophic groups.

However, it cannot be excluded that the positive effect of the number of cultivars is caused by a specific group of cultivars that has not been identified but whose likelihood of presence would increase with the number of cultivars. The use of traits more closely linked to litter quality and its nutritive value could help us to interpret our results and support either the variance in edibility or the balanced diet effect. In any case, whatever the underlying mechanism, we have demonstrated a trophic impact of intraspecific genetic diversity.

Effects of functional diversity during the non-feeding period

For technical reasons, the non-feeding period simulating winter only lasted one month but we can assume that the effect would have been amplified if the period had lasted longer. During the non-feeding period, earthworm growth increased with litter functional diversity. This result is consistent with the balanced diet hypothesis predicting that a mixed diet is beneficial.

Moreover, we also found a positive effect of cultivars from functional group 3. Earthworms that ate cultivars from functional group 3 or litter composed of cultivars with a more diverse functional diversity during the feeding period lost less weight during the non-feeding period. [START_REF] Shipitalo | Effect of diet on the feeding and casting activity of Lumbricus terrestris and L. rubellus in laboratory culture[END_REF] showed that L. terrestris ingested more slowly low-quality food than high-quality food. One explanation would then be that cultivars from functional group 3, providing a low-quality food (low magnesium concentration), were ingested more slowly than cultivar mixtures not containing wheat from this function group. Consequently, food containing cultivars from functional group 3 remained more available for earthworms during the non-feeding period, allowing them to lose less weight. This would also explain the negative influence of nitrogen concentration on earthworm growth during the non-feeding period. The low nitrogen concentration of particular cultivars would have decreased their consumption rate during the feeding period and these low-quality cultivars (low nitrogen concentration) could still have been available during the non-feeding period for earthworms, allowing them to lose less weight.

Influence of the cultivar number on earthworm mortality

The mortality rate after December was similar or lower than in other earthworm laboratory studies despite the rather small size of the vials used in the present study [START_REF] Daniel | Weight gain and weight loss of the earthworm Lumbricus terrestris L. at different temperatures and body weights[END_REF][START_REF] Milcu | The response of decomposers (earthworms, springtails and microorganisms) to variations in species and functional group diversity of plants[END_REF]. The higher mortality between October and December is probably due to the fact that earthworms came from a commercial supplier and had to adjust to the laboratory conditions. The positive effect of the number of cultivars on earthworm survival during the whole experiment was mainly due to the high mortality rate between October and December and suggests that the adaptation to the laboratory environment was facilitated by a more diverse food. This result on mortality is consistent with our results on earthworm growth supporting the balanced diet hypothesis. However, it remains difficult to determine whether this effect is driven by the balanced diet hypothesis, i.e. the positive influence of the interactions between cultivars, or the variance in edibility hypothesis, i.e. the positive influence of a particular cultivar or functional group that we did not identify. Such effects of interspecific food diversity on mortality have already been documented [START_REF] Lefcheck | Physiological effects of diet mixing on consumer fitness: a meta-analysis[END_REF]. It has been shown for two bug species that larval survival was higher when they fed on several plants [START_REF] Di Giulio | The influence of host plant diversity and food quality on larval survival of plant feeding heteropteran bugs[END_REF]. Insect species likely depend on a specific nutrient balance, including amino acids, water content, minerals and trace elements that cannot be provided by a single-species diet but by the complementarity effect of a mixed diet.

In our case, none of the studied mineral nutrients had an effect on earthworm mortality. However, other unmeasured characteristics of the biomass could explain the positive effect of the number of cultivars on earthworm survival.

Consequences for agricultural systems and ecosystems

The effect of crop genetic erosion on belowground communities has so far hardly been investigated. For instance, there is evidence suggesting that reducing plant species richness reduces earthworm species abundance in particular cases [START_REF] Spehn | Plant diversity effects on soil heterotrophic activity in experimental grassland ecosystems[END_REF]. The underlying mechanism would be the reduction of primary production, which decreases the resources available to earthworms. Our results suggest that crop genetic erosion, besides its impact on crop production [START_REF] Kiaer | Grain yield increase in cereal variety mixtures: A meta-analysis of field trials[END_REF][START_REF] Borg | How to unfold the potential of cultivar mixtures in bread wheat: A meta-analysis perspective on diversification strategies and identification of lacks of knowledge[END_REF], decreases earthworm growth and increases their mortality through the quality of the provided food. The decrease in earthworm abundance in cropping systems could thus be amplified by the intra-field reduction in crop genetic diversity. Since, the importance of services provided by earthworms to agriculture has been widely demonstrated [START_REF] Bertrand | Earthworm services for cropping systems. A review[END_REF], mixing cultivars could be a way to support earthworm populations and increase the services they provide. However, because the effects of cultivar mixtures on earthworm growth and mortality that we have documented in controlled conditions are rather small, field trials and modeling would be required to better assess the real and long term influence of these effects on earthworm populations in cropping systems.

However, to increase earthworm abundances using mixtures of cultivars it would first be necessary to determine the cultivar functional traits affecting earthworms in order to choose the right cultivars to be mixed. Indeed, as we saw with cultivars from functional group 3, mixing cultivars could have deleterious effects on earthworm growth. Moreover, the impact of crop genetic diversity we have detected on earthworms could also impact the whole soil biodiversity because a high proportion of soil organisms are decomposers that feed on litter.

Hence, mixing cultivars could also affect the whole micro food-webs through the quality of the litter provided by mixtures. However, before using our results to improve agricultural practices, our results should be tested in field experiments and their robustness should be assessed for other earthworm and soil fauna species and other crop types. Moreover, our results also depend on potential interactions between specific wheat cultivars and earthworm genotypes and we did not control for these genotypes. Earthworm genetic diversity is likely higher in agricultural fields and could influence the impact of the genetic diversity of their food.

Table 1. Results of two linear models analyzing earthworm growth (IGR) as a function of the developmental stage (A), mineral nutrient concentrations (N: nitrogen, Mg: magnesium), wheat cultivar number (WCN) and their interactions during the two periods: FP for the feeding period and NFP for non-feeding period. The non-significant variables and interactions are removed from the models (e.g. other mineral nutrient concentrations). The negative effect "(-)" or the positive effect "(+)" of the variables are indicated in the P column for each period. In case of significant interaction between the maturity stage and the cultivar number the effect (+ or -) of cultivar number is indicated separately for adults (Ad) and juveniles (Juv). wheat functional diversity (F, number of functional groups in the mixtures of cultivars) and their interactions during the two periods: FP for the feeding period and NFP for non-feeding period. The non-significant variables and interactions are removed from the models (e.g.

FP NFP

other mineral nutrient concentrations). The negative effect "(-)" or the positive effect "(+)" of the variables are indicated in the P column for each period. In case of significant interaction between the maturity stage and the cultivar number the effect (+ or -) of cultivar number is indicated separately for adults (Ad) and juveniles (Juv). magnesium), the presence of cultivars belonging to particular functional groups (functional groups from C1 to C4, C: functional group), the number of cultivars not belonging to the third functional group (N≠C3) and their interactions during the two periods: FP for the feeding period with the effect of magnesium and FP (Mg-) for the feeding period without the effect of magnesium in the model, and NFP for the non-feeding period. Non-significant variables (especially the presence of cultivars from functional group 1, 2, 4 and other mineral nutrient concentrations) have been removed manually for the final model. The negative effect "(-)" or the positive effect "(+)" of the variables are indicated in the P column for each period. In case of significant interaction between the maturity stage and the cultivar number the effect (+ or -) of cultivar number is indicated separately for adults (Juv) and juveniles (Ad).

FP NFP

Fig. 1 .

 1 Fig. 1. Covariance analysis of earthworm growth as a function of the number of cultivars and

Fig. 2 .

 2 Fig.2. Effect of wheat functional diversity on earthworm growth during the non-feeding

Fig. 3 .

 3 Fig. 3. Effect of the number of wheat cultivars not belonging to the functional group 3 on

Fig. 4 .

 4 Fig. 4. Logistic regression between the mortality probability and the number of cultivars.

  Fig. 1

Fig. 4

 4 Fig. 4

Table 2 .

 2 Results of a linear model analyzing earthworm growth (IGR) as a function of the developmental stage (A), mineral nutrient concentrations (N: nitrogen, Mg: magnesium),

		DF	F	P	DF	F	P
	A	1	517.00	<0.001	1	17.0	<0.001
	N	1	4.28	0.040 (+)	1	4.2	0.040 (-)
	Mg	1	5.55	0.020 (+)			NS
	WCN	1	1.59	NS			NS
	A×WCN	1	13.20	<0.001			NS
			Ad: -; Juv: +
	Total	248			247
			R² = 0.68		R² = 0.07

Table 3 .

 3 Results of a linear model analyzing earthworm growth (IGR) as a function of the developmental stage (A), mineral nutrient concentrations (N: nitrogen, Fe: iron, Mg:

		DF	F	P	DF	F	P
	A	1	494.00	<0.001	1	17.0	<0.001
	N	1	4.09	0.044 (+)	1	4.3	0.040 (-)
	Mg	1	5.30	0.022 (+)			NS
	F	1	0.11	NS	1	7.0	0.008 (+)
	A×F	1	3.15	0.077			NS
		Ad: -; Juv: No effect
	Total	248			247
			R² = 0.67		R² = 0.09

day -1 ) in the feeding period. During the non-feeding period, adults lost 6% (± 1%) of their weight. These results illustrate the effect of the developmental stage (Tables1 and 2): juveniles thus grew faster than adults during the feeding period and lost less weight than

Acknowledgments

The wheat biomass was produced by the experimental unit of INRA Versailles directed by C.

Montagnier in an experiment coordinated by S. Saint-Jean, D. Tropée and P. Lusley and involving all the WHEATAMIX Consortium (www.inra.fr/Wheatamix).We thank M. Mendez from the Alysés analytical platform who has done the N analyses. We thank the INRA USRAVE laboratory for the other elemental analyses. This work was supported by the WHEATAMIX project, financed by the French National Research Agency (ANR-13-AGRO-0008).

Appendix A. Supplementary data

Supplementary data associated with this article can be found, in the online version, at XXXXX.


Dimitri Gominski
email: dimitri.gominski@ign.fr

Martyna Poreba
email: martyna.poreba@ign.fr

Valérie Gouet-Brunet

Liming Chen
email: liming.chen@ec-lyon.fr

Challenging deep image descriptors for retrieval in heterogeneous iconographic collections

Keywords: Information systems → Retrieval effectiveness;, Computing methodologies → Image representations CBIR, deep learning, image descriptors, evaluation

come   L'archive ouverte pluridisciplinaire

contents. For this purpose, we introduce a novel dataset, namely Alegoria dataset, consisting of 12,952 iconographic contents representing landscapes of the French territory, and encapsultating a large range of intra-class variations of appearance which were finely labelled. Six deep features (DELF, NetVLAD, GeM, MAC, RMAC, SPoC) and a hand-crafted local descriptor (ORB) are evaluated against these variations. Their performance are discussed, with the objective of providing the reader with research directions for improving image description techniques dedicated to complex heterogeneous datasets that are now increasingly present in topical applications targeting heritage valorization.

INTRODUCTION

Digital or digitized cultural collections of images offer the interesting challenge of managing a wide variety of images together, mixing photographs with hand-made content, old black and white images with color pictures, low-quality scans or blurry photographs with digital pictures, etc. These collections are usually hosted within various institutions (e.g. GLAM -Galleries, Libraries, Archives and Museums), then organized in silos, without any interconnection between themselves while parts of them may address the same content (e.g. old paintings and recent photographs of Notre-Dame). The annotations associated may be of variable quality, or applicationdriven, making the interconnection of the fragmented contents not always easy. However, a better overall organization of these funds would be profitable in many areas where the complementarity of the available resources improves the analysis, ranging from SSH to landscape ecology, including urban planning and multimedia. In addition, there is currently a significant interest in the massive digitization of these heritage collections, with a desire to make them accessible to as many people as possible for multiple uses, associated with relevant structuring and consultation tools.

In this context, content-based image retrieval (CBIR) offers a powerful tool for establishing connections between images, independently of their native organization in the collections. Because of the variety of such contents in terms of acquisition source, viewpoint and illumination changes, evolution of the content across time, the key-word characterizing these collections is heterogeneity. These constraints introduce difficulty in the determination of efficient and robust content-based descriptors, from pixel level (alterations due to the digitization process or the aging of photographic chemicals) to semantic level (is a place still considered the same place if every building has changed ?).

In this work, our objective is to establish an extensive evaluation of the most recent content-based descriptors, relying on deep features, for heterogeneous data such as those available in cultural collections, when considering an image retrieval task. This problem is sometimes referred to as "cross-domain" retrieval as in [START_REF] Shrivastava | Data-driven Visual Similarity for Cross-domain Image Matching[END_REF] or [START_REF] Bhowmik | Cross-domain image localization by adaptive feature fusion[END_REF], but we will refrain from using that term since it suggests well-defined domains with their specific characteristics. Instead, we propose to speak of "heterogeneous" content-based image retrieval.

The paper is organized as follows: in section 2, we revisit the characteristics of the main public image collections available and present the one we propose for experiments in this work, which is related to cultural heritage. Section 3 is dedicated to the presentation of recent deep learning based descriptors. They are experimented in section 4, where we discuss the impact of the photometric and geometric transformations available on the heterogeneous contents related to cultural collections, before concluding in the last section.

HETEROGENEOUS COLLECTIONS OF IMAGES

This section is dedicated to image datasets involving very heterogeneous contents, with discussions about their main characteristics and the way of exploiting the latter in the qualitative and quantitative evaluation of image analysis and indexing techniques. We focus on cultural collections which gather interesting properties that continue to challenge the most efficient state-of-the-art image descriptors. In section 2.1, we present the Alegoria benchmark, which is a new challenging image dataset characterized by several interesting intra-class types of variations, before explaining in section 2.2 how we model these variations in order to enable a sharp evaluation of state-of-the-art image descriptors. Section 2.3 revisits other classical benchmarks and positions the Alegoria one alongside them.

ALEGORIA dataset

To address the retrieval problem in heterogeneous collections, we propose a benchmark consisting of 12952 grey and color images of outdoor scenes. Each image (in JPEG format) has a maximum side length of 800 pixels. Street-view, oblique, vertical aerial images, sketches, or old postcards can be found, taken from different viewpoints, by different type of cameras at different periods and sometimes even under different weather conditions. These geographic iconographic contents describe the French territory at different times since the inter-war period to the present day. They contain multiple objects and cultural artifacts: buildings (also stadiums and train stations), churches and cathedrals, historical sites (e.g. palaces, the most important monuments of Paris), seasides, suburbs of large cities, countrysides, etc. Some example images are shown in Figure 1, each row represents the same geographical site.

To enable quantitative comparisons, a subset of 681 images of this database have been manually selected and labelled. There are 39 classes of at least 10 images, each one is associated with the same topics site, for example Eiffel Tower, Arc de Triomphe, Notre-Dame de Paris, Sacré-Coeur Basilica, Palace of Versailles, Palace of Chantilly, Nanterre, Saint-Tropez, Stadium Lyon Gerland, Perrache train station. This benchmark can be used for CBIR in several applications such as place recognition, image-based localization or semantic segmentation.

Annotation of the appearance variations

The Alegoria dataset is a good illustration of a highly multi-source, multi-date and multi-view dataset. This heterogeneity allows to highlight significant variations of appearance, such as landscape transformations (site development, seasonal changes in vegetation), perspective (significant change in angle of view) or quality (color, B&W or sepia old photos). In order to evaluate the impact of these variations on current approaches of image analysis and indexing, we annotated the Alegoria dataset by considering a set of the most common and important intra-class variations. A total of 10 variation types were taken into account, including the usual Scale, Illumination and Orientation changes, plus variations that are more specific to cultural heritage : Alterations (chemical degradations or damages on the photographic paper before digitization), Color domains (grayscale, sepia, etc.), Domain representation (picture, drawing, painting), Time changes (impact of large time spans) ; and general indicators of difficulty like Clutter, Positionning (when the main object of interest is not central to the picture) and Undistinctiveness (when the object of interest is not clear even to the human eye). Only variations presenting a high level of difficulty were counted: obviously there is always a degree of scale variation between two images of the same class, but we counted it only if it clearly adds difficulty when we visually compare two images.

To quantify this variation predominance, we use the following annotation process. For each class, we define a reference image, carefully chosen as the image depicting the object in the most common way in the class. For example, the second image of row a. on Figure 1 is a good reference, because most of the images in this class are in grayscale, with orientations between horizontal and vertical, with overall low quality, etc. We then get the variation predominance score by comparing all images in the class to the reference image, and measuring the frequency of occurrence: 0 when no image is concerned, 1 when anecdotal (one or less image from the class concerned), 2 when multiple occurrences are present, 3 when the variation is predominant (more than a third of the class). This also allows the computation of an overall difficulty score, that gives hints about the most difficult classes associated with multiple severe types of variations.

Relations to other benchmarks

The standard benchmarks for evaluation of content-based image retrieval techniques dedicated to landscapes are historically Ox-ford5k [START_REF] Philbin | Object retrieval with large vocabularies and fast spatial matching[END_REF], Paris6k [START_REF] Philbin | Lost in quantization: Improving particular object retrieval in large scale image databases[END_REF], and to a lesser extent Holidays [START_REF] Nister | Scalable Recognition with a Vocabulary Tree[END_REF] and UKBench [START_REF] Jégou | Hamming Embedding and Weak Geometry Consistency for Large Scale Image Search -extended version[END_REF]. Recently, Radenović et al. [START_REF] Radenović | Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking[END_REF] proposed a revised version of Oxford and Paris datasets (ROxford and RParis), correcting mistakes in the annotation and proposing three protocols of evaluation with varying levels of difficulty. The main variations in these small datasets (55 queries in 11 classes) arise due to image capture conditions like different viewpoints, occlusions, weather and illumination. Google also proposed its own dataset, namely Google-Landmarks [START_REF] Noh | Large-Scale Image Retrieval with Attentive Deep Local Features[END_REF], which contains around 5 million images of more than 200000 different landmarks. But this dataset is for now mainly used for training descriptors.

By introducing the Alegoria dataset, we aim at proposing a complementary benchmark, designed for precise evaluation of robustness on a broader panel of appearance variations. These variations bring into play challenging conditions such as long-term acquisitions (multi-date contents) as well as multi-source contents (drawings, engraving, photographs, etc.) that are not widely represented in the other popular datasets and have the additional interest of bridging cultural heritage and geographical information domains. We also generalize the content to a larger panel of geographical landscapes, including urban contents and landmarks as well as more natural landscapes such as mountains and rivers. The cathedral Notre-Dame de Paris is a good example of this complementarity: this landmark can be found in both Alegoria and Paris datasets, the difference being in what is evaluated. On Paris dataset, we assess the absolute performance of the retrieval method, whereas on Alegoria we can assess how the method reacts to different types of variations, including variations due to multi-date and multi-source contents.

DEEP FEATURES

Recently, convolutional neural networks (CNNs), aided with GPUs [START_REF] Krizhevsky | ImageNet Classification with Deep Convolutional Neural Networks[END_REF], have been proven to be powerful feature extractors. Contrary to the hand-crafted methods where descriptors were carefully designed to maximize invariance and discriminability (e.g. SIFT, ORB, SURF...), deep learning offers a way of letting an optimization algorithm to determine how to get these characteristics. In the seminal work of Babenko et al. [START_REF] Babenko | Neural Codes for Image Retrieval[END_REF], features were simply obtained at the output of the fully connected layers in early networks such as Alexnet. Babenko and Lempitsky [START_REF] Babenko | Aggregating Local Deep Features for Image Retrieval[END_REF] proved that aggregating features from the last convolutional layers produced better global descriptors. But these techniques lacked the core advantage of deep learning : optimizing the network directly for the retrieval task.

Arandjelović et al. [START_REF] Arandjelović | NetVLAD: CNN architecture for weakly supervised place recognition[END_REF] resolved this problem by proposing an end-to-end learning of deep features. Also declined in local [START_REF] Moo | LIFT: Learned Invariant Feature Transform[END_REF], or patch-based [START_REF] Xufeng Han | MatchNet: Unifying feature and metric learning for patch-based matching[END_REF] versions, these works completed the toolbox of deep features that was now ready to replace hand-crafted methods.

The backbone of recent image retrieval methods using deep features relies on a CNN, applying a function f i to the input image I (depending on what layer i is considered), and producing a tensor of activations T i : T i = f i (I ). Through the training process, we aim at optimizing f i so that this 3D tensor, with width W and height H depending on the dimensions of the input image, and depth D depending on i, contain discriminative information. However T i is too big to be indexed and compared during the retrieval process, it is thus mandatory to design a method reducing the memory cost of describing the images. The following methods will be presented along this guideline of how T i is handled, giving either a local or a global descriptor.

Local methods

Local methods use a set of carefully selected points on an image. This involves identifying points that maximize invariance, and describing small patches around these points to extract information. Early deep methods replaced parts of the historical hand-crafted pipeline by trainable pieces. Verdie et al. [START_REF] Verdie | TILDE: A Temporally Invariant Learned DEtector[END_REF] designed a learnable keypoint detector maximizing repeatability under drastic illumination changes. Yi et al. [START_REF] Moo | LIFT: Learned Invariant Feature Transform[END_REF] extended the architecture for full point detecting and describing. However these two methods aim at building a robust detector/descriptor, whereas we want to fit our features to the data.

Noh et al. [START_REF] Noh | Large-Scale Image Retrieval with Attentive Deep Local Features[END_REF] solved the issue with a pipeline producing a set of local descriptors in a single forward pass, and that can be trained directly on any dataset with image-level labels. In their method, T i is seen as a dense grid of local descriptors, where each position in the activation map is a D-dimensional local feature, whose receptive field in the input image is known. Additionally, an on-top function assigns a relevance score to each feature, and a threshold is set to only select most meaningful features. The output is a set of N DELF descriptors per image. See Figure 3a for a visual interpretation. This departs from the traditional detectthen-describe process by selecting points after describing them, but it is simple to train and has shown good results on standard benchmarks [START_REF] Radenović | Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking[END_REF]. Note that D typically ranges from 512 to 2048 in the last layers of the CNN, hence the PCA reduction to D = 40 proposed by the authors. Optimization on relevant data is done uniquely with a classification loss.

Dusmanu et al. [START_REF] Dusmanu | D2-Net: A Trainable CNN for Joint Detection and Description of Local Features[END_REF] expanded this work with a detect-and-describe approach where they enforce keypoint repeatability and descriptor robustness using a Structure-from-Motion dataset with correponding points on different images.

To perform the database similarity measure, local features must be aggregated. This is usually done with the learning of a dictionary (as in the well-known bag-of-words [START_REF] Sivic | Video Google: A Text Retrieval Approach to Object Matching in Videos[END_REF]), image are then described with a sparse vector suited for the inverted index structure.

Some local methods aggregate local descriptors into a compact representation, like the VLAD descriptor [START_REF] Jegou | Aggregating local descriptors into a compact image representation[END_REF]. Arandjelović et al. [START_REF] Arandjelović | NetVLAD: CNN architecture for weakly supervised place recognition[END_REF] mimic VLAD with a learnable pooling layer, giving NetVLAD. By replacing the hard assignment step with soft assignment to multiple clusters, they can train this layer. In this work, T i is considered, as in DELF, as a block containing W xH D-dimensional descriptors. The recent work of Teichmann et al. [START_REF] Teichmann | Detectto-Retrieve: Efficient Regional Aggregation for Image Search[END_REF] builds upon the same idea, they describe selected candidate regions in the query with VLAD, and then propose a regional version of the ASMK [START_REF] Tolias | Image Search with Selective Match Kernels: Aggregation Across Single and Multiple Images[END_REF] (another aggregation method) to aggregate these descriptors into a global descriptor.

Finally, the also recent work of Siméoni et al. [START_REF] Siméoni | Local Features and Visual Words Emerge in Activations[END_REF] proposes a new view, following the observation that shapes of objects of interest in the input image can be found in some channels of T i .

They perform detection and description of interest points in T i using a hand-crafted detector (MSER [START_REF] Matas | Robust wide-baseline stereo from maximally stable extremal regions[END_REF]), and then match images based on spatial verification. However, this method is not suitable for large-scale retrieval since it works on pairs of images.

Global methods

Global methods describe an image as a whole, embedding all important information in a single vector. This is conceptually closer to the classification task for which common architectures like Resnet [START_REF] He | Deep Residual Learning for Image Recognition[END_REF] or VGG [START_REF] Simonyan | Very Deep Convolutional Networks for Large-Scale Image Recognition[END_REF] were designed. Babenko et al. [START_REF] Babenko | Neural Codes for Image Retrieval[END_REF] indeed showed that simply taking intermediate features from a classification network and using them for retrieval yields good performance.

To handle varying sizes in images and allegedly get more invariance, a standard seems to have emerged in deep global descriptors : extracting information at one of the last layers with a pooling process giving one value per channel. Following our notation, here the tensor T i is seen as a set of D activation maps that contain each a different type of highly semantic information. To get a global descriptor, a straightforward approach is thus to get the most meaningful value per channel. We can then compare two images simply with dot-product similarity of their descriptors. See figure 3b for an example with the MAC descriptor detailed below. Babenko and Lempitsky [START_REF] Babenko | Aggregating Local Deep Features for Image Retrieval[END_REF] propose to sum the activations per channel, establishing the SPoC descriptor. This is equivalent to an average pooling operation. Differently, Kalantidis et al. [START_REF] Kalantidis | Cross-Dimensional Weighting for Aggregated Deep Convolutional Features[END_REF] tweak the SPoC descriptor with a spatial and channel weighting, while Tolias et al. [START_REF] Tolias | Particular Object Retrieval With Integral Max-Pooling of CNN Activations[END_REF] get better results by using the maximum value per channel (MAC descriptor). They also propose the regional version RMAC, by sampling windows at different scales and describing them separately. Radenović et al. [START_REF] Radenović | Fine-Tuning CNN Image Retrieval with No Human Annotation[END_REF] generalize the preceding approaches with a generalized mean pooling (GeM) including a learnable parameter.

Global methods allow efficient fine-tuning on relevant data, either with the triplet loss [START_REF] Hoffer | Deep Metric Learning Using Triplet Network[END_REF] or the contrastive loss [START_REF] Chopra | Learning a similarity metric discriminatively, with application to face verification[END_REF].

PERFORMANCE EVALUATION

This section is dedicated to the evaluation of the most efficient and recent approaches of image description for image retrieval, revisited in section 3, on the benchmark Alegoria presented in section 2, with the ambition of highlighting their behavior according to several types of appearance variations.

We evaluate the performance of DELF and NetVLAD for deep local-based descriptors, and GeM, MAC, RMAC and SPoC for global-based descriptors. We also include the hand-crafted descriptor ORB for reference.

Since there is no dataset for fully training a deep feature network with heterogeneous data involving all the types of variations we consider, we evaluate methods in an off-the-shelf manner, with no fine-tuning. However, all these methods were trained on contents close to the Alegoria contents: DELF was trained on the Landmarks dataset, which is a large-scale noisy dataset of landmarks with some typical variations such as Scale, Orientation and Occlusion. NetVLAD, GeM, MAC, RMAC and SPoC were all trained using the code provided by Radenović et al. [START_REF] Radenović | Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking[END_REF], on the retrieval SfM 120k dataset. This one also focused on specific objects, mostly landmarks, with also interior pictures and standard variations (mostly Scale, For fair comparison, we discard any post-processing step. Global methods are compared with simple dot-product. DELF (dimension 1024 as in the original paper without PCA) and ORB descriptors are matched one-to-one giving a number-of-inliers score assessing similarity, using a product-quantized index for efficient memory management.

To compare the image descriptors evaluated, we use the classical mAP score, computed per class. For each query q, the average precision (AP) is computed on the sublist of results from 1 to k, k being the index of the last positive result.

The mAP per class is obtained by averaging the AP over all query images from a single class.

Results

The reader can refer to table 3 for the full lists of mAPs computed per class and the associated evaluation of variation predominance. To give an indicator of the overall difficulty of each class, we summed the predominance score of all the variations in the last column. We also computed the correlation matrix (see table 1) between the results of each methods and the predominance scores, considering each column as a series of 39 observations. Lower values indicate negative correlation: this variation is highly correlated with a decrease in performance. This does not imply causality but gives insights on the correspondences between variations and the performance of the descriptor. Note that there are also positive values, notably for Time changes and Undistinctiveness, indicating that these variations are correlated with other factors that on the contrary improve performance. This might be a result of a bias in the selection of the pictures: when selecting pictures on a long time range for example, we tend to reduce the actual difficulty.

The Overall difficulty correlation score gives us a sanity check : overall difficulty indeed has a consistent negative correlation with the performance of all methods.

Discussion

In this section, we discuss the results obtained according to several criteria:

Local vs global description.

The local DELF descriptor yields the best results with a consistent and significant margin. Table 1 shows that it is particularly stronger than global methods against Occlusion and Representation domain changes. This highlights the well-known advantage of local methods: by focusing on a set of local areas, they avoid the semantic noise captured by global methods. They also avoid the usual centering bias of global methods, as shown with the better Positioning score. However, on classes consisting of aerial images (e.g.class 7) RMAC gets better results. We believe this is due to the training dataset of DELF: it does not contain much aerial images, and DELF enforces a selection of important keypoints with its learned attention mechanism. DELF thus fails to find enough discriminative points on this type of data, whereas RMAC captures information on a large part of the image, allowing better results. Figure 4a gives an example where DELF fails to find true correspondences between two images from class 8.

4.2.2

Pooling. NetVLAD, GeM, MAC and SPoC only differ in the way they pool the tensor T to get a single global descriptor. We note that GeM gets overall better results, this can be explained by the fact that it generalizes MAC and SPoC with a tunable parameter, getting the best of both methods. NetVLAD performs consistently worse than others.

Attention mechanisms.

RMAC has overall better performance than other global descriptors. We confirm the observation from the original paper [START_REF] Tolias | Particular Object Retrieval With Integral Max-Pooling of CNN Activations[END_REF] that the Region Proposal mechanism (which is basically an attention mechanism) of RMAC allows it to outperform its simpler version MAC, and we note that this is also true against other pooling methods.

See figure 4b for an example where RMAC gets better results because it focuses on the parts of the image considered to contain the object of interest, whereas GeM returns negative but visually similar images. As showed in the ablation studies in the original paper of DELF, its attention mechanism is also responsible for a performance boost, but we lack other deep local methods to highlight this fact on our data. 4.2.4 Types of variations. Table 1 shows that Scale, Illumination, Orientation and Color are consistently associated with worse results. This shows that the main problem of image retrieval, even with modern deep learning methods, is still about getting invariance against basic variations. To support this analysis, we propose to do a simple experiment: mapping all the images in the same color domain (grayscale) before performing the description and the matching. We compute the global mAP for DELF, GeM and RMAC for the original dataset and for the grayscale dataset; see table 2 for the results. This normalization step reduces intra-class variance, but also the discriminative power of the descriptors. The mild but noticeable improvement shows that the former prevails, and we argue that a careful fine-tuning [START_REF] Radenović | Fine-Tuning CNN Image Retrieval with No Human Annotation[END_REF] can maintain this discriminative power with reduced variance.

CONCLUSION

We proposed a new benchmark for the evaluation of deep features on heterogeneous data, and discussed how most recent and efficient features react to the panel of variations encountered. Our results show that there is still many difficult cases to be handled by image retrieval methods, we thus presented insights on how to gain robustness with attention mechanisms and intra-class variance reduction. We believe this evaluation is necessary to allow image retrieval knowledge to be applied to real-world situations, and encourage future research to include detailed robustness studies, and to carefully design deep learning architectures for robust feature extraction regarding these variations.

Figure 2 :

 2 Figure 2: Deep features extraction

  (a) DELF matching failure case on aerial images (b) RMAC vs. GeM descriptors on difficult cases. The top row shows the first 5 results for RMAC by decreasing order of similarity, the bottom row for GeM. The queries are in a white box, correct retrieved images in a green box and incorrect in a red box.

Figure 4 :

 4 Figure 4: Retrieval examples

  Best seen in colorSc.: Scale, Il.: Illumination, Or.: Orientation, Co.: Color, Do.: Domain representation, Oc.: Occlusion, Po.: Positioning, Cl.: Clutter, Un.: Undistinctiveness, Al.: Alterations, Ti.: Time changes, Overall: Overall difficulty. For each class, the 3 top mAPs are in green, ranging from dark (best result) to light green (3rd result). Column Avg. indicates the average performance of all methods. Column Overall gives an overall score of difficulty (sum of the variation scores).

Table 1 :

 1 Correlation between variations and performance

		DELF ORB NetVLAD GeM MAC RMAC SPoC
	Scale	-0.41 -0.36	-0.47 -0.45 -0.47	-0.50 -0.43
	Illumination	-0.32 -0.39	-0.53 -0.48 -0.45	-0.49 -0.45
	Orientation	-0.42 -0.37	-0.50 -0.46 -0.44	-0.49 -0.45
	Color	-0.28 -0.14	-0.09 -0.58 -0.58	-0.52 -0.54
	Representation domain	0.07 -0.23	-0.10 -0.22 -0.18	-0.15 -0.23
	Occlusion	0.05 -0.26	-0.38 -0.33 -0.37	-0.34 -0.32
	Positioning	-0.17 0.07	-0.13 -0.38 -0.41	-0.41 -0.33
	Clutter	-0.13 0.02	0.11 -0.10 -0.06	0.00 -0.10
	Undistinctiveness	-0.02 0.09	0.09 0.22	0.16	0.24	0.23
	Alterations	-0.31 -0.07	-0.15 -0.23 -0.22	-0.19 -0.23
	Time changes	0.05 0.23	0.35 0.19	0.13	0.13	0.22
	Overall difficulty	-0.42 -0.31	-0.41 -0.62 -0.64	-0.62 -0.58

Table 2 :

 2 Color experiment: influence on the intra-class color variation on several descriptors.

	Global mAP	DELF GeM RMAC
	Mixed color domains 0.402 0.277 0.294
	Grayscale only	0.421 0.282 0.299

Orientation, Illumination). We use Resnet101 as the backbone architecture, giving 2048-dimensionnal descriptors, except for NetVLAD, for which we use the standard parameters of the original paper (64 clusters * 512 channels in T i ).

Table 3 :

 3 Results: retrieval accuracy against class variationsDescriptorVariations Class # DELF ORB NetVLAD GeM MAC RMAC SPoC Avg Sc. Il. Or. Co. Do. Oc. Po. Cl. Un. Al. Ti.

	Overall

ACKNOWLEDGMENTS

This work is supported by ANR (French National Research Agency) and DGA (French Directorate General of Armaments) within the ALEGORIA project, under respective Grant ANR-17-CE38-0014-01 and DGA Agreement 2017-60-0048.


Bassam Moujalled
email: bassam.moujalled@cerema.fr

Sylvain Berthault

Andrés Litvak

Valerie Leprince

Damien Louet

Frances Gilles

Julien Chèdru

Onsite evaluation of building airtightness durability: Long-term and mid-term field measurement study of 61 French low energy single family dwellings

Keywords: Airtightness durability, field measurements, building envelope, low-energy dwelling

The increasing weight of building leakages energy impact on the overall energy performance of low-energy buildings led to a better understanding of the actual airtightness performance of buildings. However, low expertise is available today on the durability of airtightness products in mid-and long-term scales. The French ongoing research project "Durabilit 'air" (2016-2019) aims at improving our knowledge on the variation of buildings airtightness through onsite measurement and accelerated ageing in laboratory controlled conditions. This paper is issued from the second task of the "Durabilit'air" project. This task deals with the quantification and qualification of the durability of building airtightness of single detached houses. It is done through field measurement at mid-term (MT) and long-term (LT) scales. This paper first presents the field measurement protocol. For the MT campaign, a sample of 30 new singledetached dwellings has been selected nationwide. During the study, the airtightness of each building is to be measured once per year over a 3-year period. A part of this sample is to be also measured twice per year in order to investigate the impact of seasonal variations. The LT campaign is to be carried out with a second sample of 31 existing single-detached dwellings constructed during the last 10 years. The airtightness of each dwelling is to be measured once. A specific measurement protocol was defined after a detailed literature review. The protocol is mainly based on the standard ISO 9972 for the measurement method with additional requirements for the measurement conditions (same tester, same calibrated measurement device, same building preparation, same pressure difference sequences, same season…). It also includes a detailed qualitative leakage detection and questionnaire for occupants. The main challenge is to understand the variations of the airtightness and to identify whether it is related to the products/assembly ageing, the maintenance conditions or other factors such as the occupants' behaviour. Secondly, this paper presents the 61 dwelling samples construction characteristics. All dwellings were tested upon completion. The air flow rates at 4 Pa per envelope area excluding lower floor of both samples show the same mean value around 0.3 m 3 .h -1 .m -2 (n50 of 1.4 h -1 ), with larger variations among the LT sample. Finally, we discuss first measurement results. Regarding MT sample, results after 1-2 years show a slight increase of airflow rate at 50 PA (q50) with a median value of +6%. However, with exposed timber framing, q50 has increased by more than 100%. Regarding LT campaign, measurements results after 3-10 years show a more important increase of q50 with a median value of 28%. The measurement campaigns and data analysis will continue over 2018 to complete the work in order to better understand the in situ variations of the buildings' envelope airtightness.

INTRODUCTION

The increasing weight of building leakages energy impact on the overall performance of lowenergy buildings led to a better understanding and characterization of the actual airtightness performance of buildings. Several European countries have already included in their EPregulation mandatory requirements regarding the building airtightness. This is the case in France, where the EP-regulation requires a limit airtightness level for residential buildings that must be justified by measurement. However, low expertise is available today on the durability of building airtightness and its evolution in mid-and long-term scales. The French ongoing research project "Durabilit'air" is conducted since 2016 for a 42-month period, in order to improve our knowledge on the variation of buildings airtightness through onsite measurement campaigns and accelerated ageing in laboratory controlled conditions. As part of this project, a comprehensive literature review about building airtightness durability was realized by [START_REF] Leprince | Durability of building airtightness, review and analysis of existing studies[END_REF]. This review showed an important evolution over time of the air permeability in real buildings, with an increase of more than twice in some cases. The air permeability seems to increase in the 3 first years and then stabilise. This paper is issued from the second task of the "Durabilit'air" project. This task deals with the quantification and qualification of the durability of building airtightness of single detached houses. It is done through field measurement at mid-term (MT) and long-term (LT) scales. This paper presents the first results of both MT and LT measurements.

METHODOLOGY

In order to evaluate the durability of the building airtightness in real conditions at mid-and long-term scales, two field measurement campaigns were conducted: a mid-term (MT) campaign and a long-term (LT) campaign. The MT campaign aims at characterising the yearly evolution of building airtightness of new dwellings over a 3-year period. Therefore, a sample of 30 new single-detached low-energy houses, measured upon completion, has been selected nationwide. The following measurements are to be performed:

 The airtightness of each building is to be measured once per year over the 3-year period.  Five buildings of this sample are to be measured twice per year in order to investigate the impact of seasonal variations.  For six buildings of this sample, the airtightness of an installed window is to be measured once per year over a 3-year period. The LT campaign aims at characterising the evolution of building airtightness of existing dwellings over a longer period from 5 to 10 years. A second sample of 31 existing singledetached dwellings, measured upon completion, has been therefore selected. The dwellings have been constructed during the last 10 years. The airtightness of each dwelling was measured once. All dwellings were selected according to well-defined criteria to reduce uncertainties about main factors impacting building airtightness. In particular, all dwellings should be tested upon completion, and the test reports should be available and in accordance with the standard ISO 9972 [START_REF] Nf | Thermal performance of buildings -Determination of air permeability of buildings -Fan presurization method[END_REF] and its French implementation guide (FD P50-784, 2016). Information about the treatment of the building airtightness must also be available. In particular, dwellings of the MT sample must be certified according to French quality management approach, thus making available information about construction details.

The main challenge of this project is to understand the variation of the airtightness and to identify whether it is related to the products/assembly ageing, the maintenance conditions or other factors such as the occupants' behaviour. Therefore, a specific measurement protocol was defined after a detailed literature review [START_REF] Leprince | Durability of building airtightness, review and analysis of existing studies[END_REF]. The protocol is mainly based on the standard ISO 9972 and its French implementation guide for the measurement method with additional requirements for the measurement conditions in order to reduce uncertainty due to measurement procedure:

 Each dwelling is to be measured under the same conditions as the first measurement upon completion as far as possible (same tester, same calibrated measurement device, same building preparation, same pressure difference sequences, and same season). Measurements are to be performed both in pressurization and depressurization.

Deviations from the conditions of the first test are to be reported.  Detailed qualitative leakage detection is to be performed at each measurement according to the leaks categories of the French implementation guide of ISO 9972. In particular, an annual follow-up of leaks is to be performed for the dwellings of MT sample during the 3 years.  Questionnaires for occupants is to be filled at each measurement in order to identify the modifications of the building envelope due to the action of the occupants (i.e. drillings made in the air barrier after the first test, replacement of products…). At the total, 90 and 31 measurements of building airtightness are to be performed with MT and LT samples respectively, plus an extra of 10 measurements for the seasonal impact, and an another extra of 18 measurements for the airtightness of windows. This paper explores the measurements results of the first 2 years of MT sample (60 measurements) and the LT sample (31 measurements). The results presented here are expressed according to two indicators:

 the airflow at 50 Pa (q50);  and the air permeability French indicator Q4Pa-surf, which is the airflow rate at 4 Pa per unit of envelope surface area excluding lowest floor (m 3 .h -1 .m -2 ). As the measured air leakage rate at 50 Pa is more accurate than at 4 Pa [START_REF] Delmotte | Interlaboratory tests for the determination of repeatability and reproducibility of buildings airtightness measurements[END_REF], main analysis of the evolution of the airtightness will be based on "q50". The French indicator "Q4Pa-surf" will be used to check compliance with the mandatory requirement.

RESULTS

Main characteristics of buildings

Figure 1 shows the distribution of buildings of MT and LT samples depending on the year of construction and buildings main material and type of air barrier. The MT sample is composed of 30 new single-detached low-energy dwellings constructed mainly in 2014 and 2015, with 20 one-story houses and 10 two-story houses. The average floor area is 124.1 m² with a minimum of 87 m² and a maximum of 172.2 m², and the average volume is 217.1 m 3 with a minimum of 156.1 m 3 and a maximum of 363.9 m 3 . All houses are built of masonry with interior insulation (16 houses with concrete blocks and 14 with hollow bricks). The majority of roofs are made of light-frame wood truss (20 houses), against 8 houses with traditional wood frame and 2 houses with an exposed traditional wood frame. All houses are equipped with humidity sensitive single exhaust ventilation system. The airtightness of all houses is treated in the same way, through plasterboards and mastics at the inside facing of the walls (air barrier C).

All dwellings of the MT sample are certified according French quality management approach for building airtightness. Also they are constructed according to the current French Energy Performance (EP) regulation which requires a limit value of 0.6 m 3 .h -1 .m -2 for Q4Pa-surf in single-family houses. Besides, 9 dwellings had a target value lower than the mandatory requirement of 0.6 m 3 .h -1 .m -2 (6 houses with a target value of 0.5 m 3 .h -1 .m -2 and 3 houses with 0.4 m 3 .h -1 .m -2 ).

The LT sample is composed of 32 single-detached low-energy dwellings constructed between 2009 and 2015, with 7 one-story houses and 25 two-story houses. The average floor area is 147.9 m² with a minimum of 83.1 m² and a maximum of 269 m², and the average volume is 256.6 m 3 with a minimum of 138.9 m 3 and a maximum of 478.2 m 3 . The majority of houses are built of masonry with interior insulation (25 houses with hollow bricks and 3 with concrete blocks), against 6 wooden houses. The majority of roofs are made of light-frame wood truss (27 houses), against 5 houses with flat roof. Almost all houses are equipped with humidity sensitive single exhaust ventilation system (only one house with balanced ventilation system). The airtightness of masonry houses of the LT sample is mainly ensured by coating on the masonry (air barrier B), while the airtightness of wooden houses is ensured by the vapour barrier (air barrier A). Dwellings of the LT samples were all constructed before the current French EP regulation. However, the majority are certified according the French EP label "BBC EFFINERGIE" which has the same requirement of maximum air permeability of 0.6 m 3 .h -1 .m -2

. For MT sample, 1 st -year measurements (measurements n1) started in November 2016 and finished in May 2017, thus from 1 to 3 years after the measurements upon completion (measurements n0). The measurements were delayed due to the difficulty in finding occupants who agree to participate in 3 year-long measurement campaigns. The 2 nd year measurements (measurements n2) started in February 2017 and finished in June 2018, less than one year after n1 to compensate for delays. A house was excluded from the MT sample because of problems during measurement n1. For LT sample; all measurements (measurements nx) were conducted in 2017, from 3 to 8 years after n0. Only 9 houses aging more than 5 years were measured. This is due to the difficulty of finding houses corresponding to the selection criteria, and also to have the agreement of occupants.

Results of MT measurements

Figure 3 presents the deviations of the measured q50 between n0, n1 and n2 for the pressurization test, the depressurization test and the average between both tests. For the measurements n0, the dwellings were tested either in pressurization (26 houses) or in depressurization (3 houses), contrary to the measurements n1 and n2 performed both in pressurization and depressurization. The measurements are classified into four categories depending on the deviation of q50 between n1 and n0: the 1 st category with a significant decrease of q50 (less than -50 m 3 .h -1 ), 2 nd category with small variations (from -50 to +50 m 3 .h - 1 ), 3 rd category with a moderate increase of q50 (from +50 to +150 m 3 .h -1 ), and 4 th category with strong increase of q50 (more than +150 m 3 .h -1 ). In "n2" it is also the same categories determined by the deviation between n1 and n0 that are used.

for the pressurization test, the r the MT sample fo Results show that the deviations of q50 between n1 and n0 (for both pressurization and depressurization) vary in a wide range between -150.8 m 3 .h -1 (-42%) up to a maximum of 599.3 m 3 .h -1 (181%) with an average value of 52.7 m 3 .h -1 (16%). However, half of measurements present a deviation lower than 6% (median value of 13.0 m 3 .h -1 ). The value of q50 has moderately increased for 6 houses. It has strongly increased for 5 houses (in particular for houses tested in pressurization upon reception). In order to investigate the variations of q50, Figure 5 presents a comparison of the measured values during n1 and n2 against n0 depending on the type of roof and the number of levels. The impact of the type of roof or the number of levels is not clear, but it seems that the air leakage rates are, in average, more increasing for two-story houses than one-story houses. Also the air leakage rates increase slightly more for houses with traditional wood frame than houses with light-frame wood truss. We observe the particular case of a measurement that is largely above the others. It's a twostory house with an exposed wood frame roof where q50 has increased by +180%. For this house, it was observed a significant increase of the number of leakages at the junctions between the wood frame and the ceiling or the walls (Figure 6). As mentioned in paragraph 2, a detailed qualitative leakage detection is performed at each measurement. Figure 7 presents the comparison of the numbers of observed leakages between measurements n0, n1 and n2 for each category of leakages as defined in (FD P50-784, 2016).

Results show an increase in the number of leakages for doors and windows, electrical components, penetrations through envelope and finally junctions between walls and doors/windows. In particular, the numbers of leakages of doors and windows are largely higher than other categories. However, it's difficult to conclude about the impact of the number of leakages on the evolution of q50. Further analysis is needed in order to investigate the correlation between the leakages and q50.

Results of LT measurements

Figure 8 the deviations of the measured q50 between n0 and nx for the pressurization test, the depressurization test and the average between both tests. For n0, the dwellings were only tested in depressurization, contrary to the measurements nx performed both in pressurization and depressurization. The measurements are classified into four categories in the same way as for MT sample.

As for MT sample, results show that the deviations of q50 between nx and n0 vary in a wide range between -208.9 m 3 .h -1 (-30%) and 521.7 m 3 .h -1 (+292%) with an average value of 79.4 m 3 .h -1 (+28%). LT sample presents globally higher increase of q50 than MT sample, with half of measurements increasing by more than 29% (median value of 67.8 m 3 .h -1 ). Only 8 houses show small variations of q50, whereas 10 houses with moderate increase and 8 houses with strong increase. show also an increase in the number of leakages for doors and windows, electrical components, penetrations through envelope and junctions between walls and doors/windows. However, the numbers of leakages are much lower than MT sample. Indeed, the leakage detection of LT sample was performed in a less detailed manner than the MT sample as there is no annual follow-up of leakages to be performed.

CONCLUSIONS

The building airtightness durability of French low energy single family dwellings was assessed in real conditions at mid-and long-term scales, through two field measurement campaigns: a MT campaign with a sample of 30 new houses, and a LT campaign with a sample of 31 existing houses.

Regarding mid-term sample, results after 1-3 years show a slight increase of the air leakage rate at 50 Pa (q50) with an average increase of +16% and a median value of +6%. However, q50 has increased by more than +100% in the case of a 2 -story house with an exposed wood frame roof, due to many leaks at the junctions between the wood frame and the ceiling or the walls. For the LT sample, results after 3-8 years show a more important increase of q50 with an average and a median value around 28%. The increase in building air permeability is less important than in the previous studies [START_REF] Bracke | Durability and Measurement Uncertainty of Airtightness in Extremely Airtight Dwellings[END_REF])(ADEME, 2016). A significant increase of the number of leakages was observed for doors and windows, electrical components, penetrations through envelope and junctions between walls and doors/windows. However, it's difficult to conclude about the impact of the number of leakages on the evolution of q50.

The measurement campaigns and data analysis will continue over 2018 to complete the work in order to better understand the in situ variations of the buildings' envelope airtightness depending on the envelope characteristics. Further analyses will also be performed in order to investigate the impact of seasonal variation, the durability of airtightness of installed windows, and the correlation between the leakages and the air leakage rate.

Figure 1 :

 1 Figure 1: Distribution of buildings depending on the year of construction (left) and buildings main material and type of air barrier (right); Air barrier-A when the air barrier is ensured by vapour barrier, Air barrier-B by coating on the masonry, and Air barrier C by plasterboards and mastics at the inside facing of the walls

Figure 2

 2 Figure2presents the results of the measured air permeability Q4Pa-surf upon reception of all buildings. The figure shows also the target values of air permeability. Both samples present the same average and median values of air permeability, around 0.3 m 3 .h -1 .m -2 , however the LT sample shows more variations than the MT sample. All buildings of LT and MT samples meet their air permeability target values respectively, except one house of the MT sample with an air permeability of 0.42 m 3 .h -1 .m -2 slightly higher than 0.4 m 3 .h -1 .m -2 .

  categories determined by the deviation between n1 and n0.

Figure 4 :

 4 Figure 4: Boxplot of the measured Q4Pa-surf of the MT sample depending on the measurement year for the pressurization test, the depressurization test and the average between both tests Figure 4 presents the variations in term of the French indicator Q4Pa-surf. Compared to the target values. Results show very small variations with almost the same mean and median values, except of the three houses tested in pressurization upon reception. Therefore, the majority of houses remain below the target values during n1 and n2, except 2 houses exceeding the limit value of 0.6 m 3 .h -1 .m -2 .In order to investigate the variations of q50, Figure5presents a comparison of the measured values during n1 and n2 against n0 depending on the type of roof and the number of levels. The impact of the type of roof or the number of levels is not clear, but it seems that the air leakage rates are, in average, more increasing for two-story houses than one-story houses. Also the air leakage rates increase slightly more for houses with traditional wood frame than houses with light-frame wood truss.

Figure 5 :

 5 Figure 5: Comparison of q50 for MT sample between measurements n1 (rectangular points) and n2 (triangular points) against measurement n0 depending on the type of roof (left) and the number of levels (right)

Figure 6 :

 6 Figure 6: leakages at the junctions between the wood frame and the ceiling or the walls

Figure 7 :

 7 Figure 7: Comparison of the numbers of observed leakages of MT sample between measurements n0, n1 and n2 for each category of leakages

Figure 8 :

 8 Figure 8: Deviation of q50 between n0 and nx for the LT sample for the pressurization test, the depressurization test and the average between both tests

Figure 10 :

 10 Figure 10: Comparison of q50 for LT sample between measurements nx (rectangular points) against measurement n0 depending on the type of air barrier (left) and the number of levels (right) Figure 11 presents the comparison of the numbers of observed leakages between measurements n0, n1 and n2 for each category in the same way as for MT sample. Resultsshow also an increase in the number of leakages for doors and windows, electrical components, penetrations through envelope and junctions between walls and doors/windows. However, the numbers of leakages are much lower than MT sample. Indeed, the leakage detection of LT sample was performed in a less detailed manner than the MT sample as there is no annual follow-up of leakages to be performed.

Figure 11 :

 11 Figure 11: Comparison of the numbers of observed leakages of LT sample between measurements n0 and nx for each category of leakages

ACKNOWLEDGEMENTS

This work was supported by ADEME and French ministry for construction. The views and opinions of the authors do not necessarily reflect those of ADEME and French ministry. The published material is being distributed without warranty of any kind, either expressed or implied. The responsibility for the interpretation and use of the material lies with the reader. In no event shall authors, ADEME or French ministry be liable for damages arising from its use. Any responsibility arising from the use of this report lies with the user.