Simon Pouil
email: simon.pouil1@gmail.com

Reza Samsudin

Jacques Slembrouck

Ahmad Sihabuddin

Gusnia Sundari

Khazaidan Khazaidan

Hari Anang

Brata Kristanto

Domenico Pantjara

Caruso

Hari Kristanto

Brata Pantjara

Domenico Caruso

Effects of shading, fertilization and snail grazing on the productivity of the water fern Azolla filiculoides for tropical freshwater aquaculture

Keywords: Aquaculture, Floating macrophytes, Light, Nutrients, Productivity, Grazing

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

Introduction

Water fern species (Azolla spp.) are considered as the most economically important floating aquatic macrophytes in the world [START_REF] Brouwer | Azolla domestication towards a biobased economy?[END_REF][START_REF] Kollah | Aquatic Azolla microphylla: a perspective paradigm for sustainable agriculture, environment and global climate change[END_REF]. Azolla spp. are among the fastest growing plants and can reach very high growth rates via asexual reproduction with a doubling time of only 2-5 days [START_REF] Wagner | Azolla: A review of its biology and utilization[END_REF][START_REF] Sadeghi | A review of some ecological factors affecting the growth of Azolla spp[END_REF].

These aquatic fern species are native to Central and South America and western North America [START_REF] Sadeghi | A review of some ecological factors affecting the growth of Azolla spp[END_REF]. Nevertheless, Azolla spp. are nowadays widely established in freshwater ecosystems of temperate and tropical regions all over the world [START_REF] Sadeghi | A review of some ecological factors affecting the growth of Azolla spp[END_REF], demonstrating high adaptability to various environmental conditions. This excellent adaptability is explained by specific physiological characteristics.

Due to their symbiosis with nitrogen (N)-fixing microorganisms (diazotroph cyanobacteria Nostoc (ex. Anabaena) azollae; [START_REF] Kahindi | Agricultural intensification, soil biodiversity and ecosystem functions in the tropics: the role of nitrogen-fixing bacteria[END_REF], Azolla growth is vigorous even in natural N-limited conditions [START_REF] Brouwer | Metabolic adaptation, a specialized leaf organ structure and vascular responses to diurnal N2 fixation by Nostoc azollae sustain the astonishing productivity of azolla ferns without nitrogen fertilizer[END_REF]. Physiologically, the Azolla-Nostoc complex is outstanding, because it can fix N at substantial rates -approximately the double of Rhizobia living in the root nodules of soybean [START_REF] Hung | Quantitative analysis of the initial transport of fixed nitrogen in nodulated soybean plants using 15 N as a tracer[END_REF][START_REF] Brouwer | Metabolic adaptation, a specialized leaf organ structure and vascular responses to diurnal N2 fixation by Nostoc azollae sustain the astonishing productivity of azolla ferns without nitrogen fertilizer[END_REF]. The two symbiotic organisms' light-harvesting pigments are complementary and can capture a wide range of wavelengths of light [START_REF] Wagner | Azolla: A review of its biology and utilization[END_REF]. These specific characteristics make Azolla spp. very suitable for many uses in agriculture and aquaculture [START_REF] Pabby | Biological significance of Azolla and its utilization in agriculture[END_REF][START_REF] Sithara | Formulation of low-cost feed using Azolla as a protein supplement and its influence on feed utilization in fishes[END_REF][START_REF] Kollah | Aquatic Azolla microphylla: a perspective paradigm for sustainable agriculture, environment and global climate change[END_REF].

Beyond its high productivity, and according to the environmental conditions, Azolla spp. can reach high protein contents (15-40% of its dry weight; [START_REF] Brouwer | Growing Azolla to produce sustainable protein feed: the effect of differing species and CO2 concentrations on biomass productivity and chemical composition[END_REF][START_REF] Feedipedia | Feedipedia: an on-line encyclopedia of animal feeds[END_REF][START_REF] Slembrouck | Choosing floating macrophytes for ecological intensification of small-scale fish farming in tropical areas: a methodological approach[END_REF] and can therefore be a valuable alternative to costly fish meal and soybean, especially in pond aquaculture systems [START_REF] Datta | Culture of Azolla and its efficacy in diet of Labeo rohita[END_REF][START_REF] Gangadhar | Effect of Azolla-incorporated diets on the growth and survival of Labeo fimbriatus during fry-to-fingerling rearing[END_REF][START_REF] Das | Evaluation of fresh Azolla pinnata as a low-cost supplemental feed for Thai silver barb Barbonymus gonionotus[END_REF]. Azolla may represent a valuable resource to meet the current challenge for aquaculture: improvement in production to match the growing demand for aquatic products and in a sustainable manner. Thus, experiments carried out on many herbivorous or omnivorous fish species have shown that reasonable inclusion rates (usually not exceeding 30%, [START_REF] Abdel-Tawwab | The preference of the omnivorous-macrophagous, Tilapia zillii (Gervais), to consume a natural free-floating fern, Azolla pinnata[END_REF][START_REF] Datta | Culture of Azolla and its efficacy in diet of Labeo rohita[END_REF][START_REF] Das | Evaluation of fresh Azolla pinnata as a low-cost supplemental feed for Thai silver barb Barbonymus gonionotus[END_REF] of Azolla (fresh or dry form) in the fish diet reduce production costs without affecting growth (for review, see [START_REF] Mosha | A review on significance of Azolla meal as a protein plant source in finfish culture[END_REF].

However, this use requires continuous mass production, which is generally not yet common on a large scale [START_REF] Brouwer | Growing Azolla to produce sustainable protein feed: the effect of differing species and CO2 concentrations on biomass productivity and chemical composition[END_REF]. Although, Azolla species have undeniable growth capacities, many factors can affect the mass production of these macrophytes.

Nutrient availability is often considered as the main bottleneck in the production of Azolla.

Phosphorus (P) is a major nutrient limiting the growth of Azolla spp., as in many other photoautotrophic aquatic organisms [START_REF] Sadeghi | A review of some ecological factors affecting the growth of Azolla spp[END_REF][START_REF] Temmink | Azolla along a phosphorus gradient: biphasic growth response linked to diazotroph traits and phosphorus-induced iron chlorosis[END_REF], because the N2-fixation rates are ensured by symbionts [START_REF] Cary | Growth and nutrient composition of Azolla pinnata R. Brown and Azolla filiculoides Lamarck as affected by water temperature, nitrogen and phosphorus supply, light intensity and pH[END_REF][START_REF] Kushari | Differential responses of Azolla to phosphorus deficiency[END_REF]. In experimental conditions, 2 to 10 μmol L -1 P are reported for the sustainable growth of Azolla [START_REF] Subudhi | Differential phosphorus requirements of Azolla species and strains in phosphorus-limited continuous culture[END_REF][START_REF] Kitoh | Effect of mineral nutrients and combined nitrogen sources in the medium on growth and nitrogen fixation of the Azolla-Anabaena association[END_REF][START_REF] Temmink | Azolla along a phosphorus gradient: biphasic growth response linked to diazotroph traits and phosphorus-induced iron chlorosis[END_REF], but field observations suggest that higher concentrations are necessary (10 to 33 μmol L -1 , [START_REF] Sadeghi | A review of some ecological factors affecting the growth of Azolla spp[END_REF]. Based on this information, P may be limiting for Azolla production even in fertilized ponds used for fish production, where P concentrations are usually between 1-17 μmol L -1 [START_REF] Green | Chemical budgets for organically fertilized fish ponds in the dry tropics[END_REF][START_REF] Pengseng | Assessment of fertilizer application intervals for giant gourami (Osphronemus goramy Lacepede) in ponds[END_REF][START_REF] Pouil | Nutrient budgets in a small-scale freshwater fish pond system in Indonesia[END_REF]. In addition, temperature and light intensity are important climate variables determining Azolla growth rates in the field [START_REF] Sadeghi | A review of some ecological factors affecting the growth of Azolla spp[END_REF]. Although different Azolla species or strains have different temperature sensitivities [START_REF] Uheda | Response of six Azolla species to transient high temperature stress[END_REF], the optimum temperature range for Azolla growth is about 18-26°C [START_REF] Sherief | Nutritive value of the water fern Azolla for fish[END_REF][START_REF] Hasan | Use of Algae and Aquatic Macrophytes as Feed in Small-Scale Aquaculture. Food and Agriculture Organization of the United Nations[END_REF]. Furthermore, high temperatures (>35°C) are known to inhibit Azolla growth [START_REF] Sadeghi | A review of some ecological factors affecting the growth of Azolla spp[END_REF]. Light intensity affects Azolla photosynthetic activity, growth and N2 fixation [START_REF] Watanabe | Azolla-Anabaena symbiosis -its physiology and use in tropical agriculture[END_REF][START_REF] Pabby | Azolla-Anabaena symbiosis-from traditional agriculture to biotechnology[END_REF]Sagedhi et al., 2013). Although several experimental studies showed a decrease of Azolla biomass under shading conditions (i.e. plants received 30 to 60% of full sunlight, [START_REF] Cary | Growth and nutrient composition of Azolla pinnata R. Brown and Azolla filiculoides Lamarck as affected by water temperature, nitrogen and phosphorus supply, light intensity and pH[END_REF][START_REF] Abduh | The influence of light intensity on the protein content of Azolla microphylla and pre-treatment with Saccharomyces cerevisiae to increase protein recovery[END_REF], slight shade (25-50% of full sunlight) can benefit Azolla mass production [START_REF] Hasan | Use of Algae and Aquatic Macrophytes as Feed in Small-Scale Aquaculture. Food and Agriculture Organization of the United Nations[END_REF]. However, field investigations are missing to support this statement. In addition, light interacts with temperature in influencing the growth of Azolla species [START_REF] Janes | Growth and survival of Azolla filiculoides in Britain.1. Vegetative reproduction[END_REF]. Despite knowledge mainly gained through laboratory experiments, only few studies investigated the productivity of Azolla under field conditions (e.g. [START_REF] Brouwer | Growing Azolla to produce sustainable protein feed: the effect of differing species and CO2 concentrations on biomass productivity and chemical composition[END_REF].

The growth of Azolla can also be negatively affected by biological factors. Arthropods such as Lepidoptera and Diptera, or crustaceans and gastropods can affect the growth of Azolla by grazing [START_REF] Sadeghi | A review of some ecological factors affecting the growth of Azolla spp[END_REF]. The golden apple snail Pomacea canaliculata is a freshwater gastropod, native to South America that has become a serious pest in Asian agriculture and is included in the world's 100 worst invasive alien species [START_REF] Lowe | 100 of the World's Worst Invasive Alien Species A Selection from the Global Invasive Species Database[END_REF]. These snails, present in aquaculture ponds, are herbivorous and can feed on floating macrophytes such as Azolla [START_REF] Cruz | Food preference and consumption of aquatic macrophytes submerged by snail Pomacea canaliculata[END_REF]. Golden apple snails have a strong negative effect on the biomass of all macrophyte species in Asian wetlands [START_REF] Carlsson | Size-dependent effects of an invasive herbivorous snail (Pomacea canaliculata) on macrophytes and periphyton in Asian wetlands[END_REF] but, to our knowledge, the potential effects of these snails on the production of Azolla in the field have not been investigated.

We aimed to promote culture of Azolla in fish ponds as a potential alternative source of feed for freshwater fish. Thus, the objectives of this study were to assess the combined effects of fertilization levels and shading, considered among the main parameters for plant production, on (1) productivity of Azolla filiculoides in a fish pond setting with complementary information on chemical composition and morphology, and (2) on the grazing of Azolla cultivated in two fertilizer conditions by the invasive golden apple snail P. canaliculata. We hypothesized that the use of shading should support mass production of Azolla in fish ponds by maintaining favourable light and temperature conditions while snails should cause nonnegligible losses of plant production.

Materials and Methods

Experiment 1: Effects of shading materials and fertilization on Azolla production

A six-week field experiment was carried out to assess the response of A. filiculoides to several culture conditions (3 fertilization levels x 3 light level conditions). The experiment was performed from August to September 2018 during the dry season in an artificial pond (400 m 2 , depth 0.50 m) without fish. The pond was located in a fish farm of the village of Babakan (6°28'S; 106°42'E; altitude 125 m), district Bogor, West Java, Indonesia. The experimental block design consisted of 27 plastic half drums (cut lengthwise; water volume: 50 L, water depth: 0.25 m, surface: 0.5 m²), oriented east-west, partially immersed in the pond and maintained in place with bamboo poles. Nine of these drums were covered by a 60% shade net, nine by a transparent polyethylene sheet, and nine were exposed to direct sunlight without any cover (control condition). Light intensity, water and air temperature were recorded continuously throughout the experiment using data loggers (HOBO Pendant Temperature/Light and HOBO Water Temperature Pro v2). Data loggers were placed in three drums per light condition at 10 cm depth for water temperature and at 5 cm above the water surface for air temperature and light intensity measurements.

The use of these low-cost and locally available shading materials resulted in a gradient of shading between the different experimental conditions. Thus, Azolla cultivated under the shade net and the transparent polyethylene sheet received 57% and 10% less light respectively compared to the plants exposed to direct sunlight (Table 1). The nine drums of each condition were filled with water from the pond (N: 33.5 ± 15.0 µmol L -1 , P: 5.8 ± 3.6 µmol L -1 , K: 31.7 ± 30.4 µmol L -1 ). Commercial aqueous solutions of inorganic multi-nutrient fertilizer (Rosasol ® -P hydroponic fertilizer with an NPK ratio of 1:2:1, see Table S1 for composition) was used as nutrients source. Since P is the most limiting nutrient for Azolla growth, fertilizer was added to obtain concentrations of 2 µmol L -1 of P (i.e., 3.2 and 1.5 µmol L -1 of N and K respectively; treatment "low") and 10 µmol L -1 of P (i.e., 16 and 7.5 µmol L -1 of N and K respectively; treatment "high"). The third series of drums was maintained without fertilizer and served as the control (treatment "no"). Each condition was triplicated.

Before starting the experiment, A. filiculoides maintained in an aquaculture pond was acclimated to experimental conditions by inoculating 50 g fresh weight (FW) into the 50 L drums (i.e. 100 g FW m -2 and surface cover of ~20%). Plants were acclimated for 11 days without harvesting to minimize P history effects [START_REF] Temmink | Azolla along a phosphorus gradient: biphasic growth response linked to diazotroph traits and phosphorus-induced iron chlorosis[END_REF]. During the experiment, water in drums was changed 6 times per week. Azolla biomass was transferred in a net and drum walls were cleaned during the water change. Fertilizer addition to the fresh pond water was according to treatment, as described above. In order to check nutrient concentrations, two times per week, water was sampled 30 min after the fertilizer addition (300 mL per drum) and the operation was renewed the next day (i.e. 24 hours later) just before to change again the water. This procedure allowed to keep nutrient concentrations in water as constant as possible (at least 70% of N, P and K remained in the water after 24 h whatever the experimental condition). Before adding fertilizer, water measurements were performed in each drum (08:00-10:00 AM, two times per week) to determine pH, dissolved oxygen (DO, mg L -1 ), temperature (T°C), and total dissolved solid matter (TDS, mg L -1 ) using a multi-parameter probe (HI 9829 Hanna).

Total plant biomass in each drum was determined at the beginning and at the end of the 11-d acclimation period and then one time per week during the 6-week experiment. Samples were rinsed, carefully drained and weighed (± 0.1g). After FW determination, a 50 g sample (100 g FW m -2 ) of A. filiculoides was returned into its original drum to avoid too high surface densities. Productivity (expressed as g FW m -2 d -1 ) was calculated as the total amount of Azolla harvested in each drum minus the initial seeding (50 g FW per drum). When productivity was negative (i.e. mortality of Azolla), plants from different stocks maintained under appropriate fertilizer and shading conditions were used to re-inoculate the drums.

Samples from each drum (each 10 g FW) were carefully weighted as described above and were taken to determine dry weight (DW). The remaining samples were stored at -18°C for further analyses.

Experiment 2: Quantification of Azolla ingestion by snails

The grazing of the invasive golden apple snail P. canaliculata on Azolla cultivated outdoors was assessed at two extreme nutrient concentrations (treatments "no" and "high"). The experiment was carried out indoors (light: <2 µmol PAR m -2 s -1 ) to prevent autogenic changes Azolla biomass (i.e. changes in biomass caused by endogenous factors and not snail grazing).

The experimental set-up consisted of thirteen plastic buckets (6 L, 34 cm diameter) covered with a 60% shade net to prevent snails from escaping. Twelve buckets were used to quantify the ingestion of Azolla (from the treatments "no" and "high") by snails (n = 6 per condition) and one basin served as the control, without snails (control). Water temperatures were recorded continuously throughout the experiment using a data logger (HOBO Water Temperature Pro v2) placed at the bottom of the bucket used as the control.

Twelve snails (FW: 20.1 ± 3.2 g, shell length: 3.8 ± 0.2 mm, shell width: 2.9 ± 0.2 mm) were randomly placed in the buckets (one snail per bucket) and acclimated for three days to the experimental conditions. Buckets were filled with water from the pond (see Section 1.2.1).

During the acclimation period, each snail was fed with 10 g of fresh Azolla from treatments "no" and "high". At the end of the acclimation period, 20 g of fresh Azolla coming from treatments "no" or "high" was added to each bucket (n = 6 per treatment). After 24 h, the remaining Azolla (i.e. non-ingested) was carefully drained, weighed and dried. The water was changed at the same time. Measurements were repeated for four consecutive days using the same procedure. At the end of the experiment, dry weight (DW) and the flesh-to-total FW ratio of each snail was determined. Thus, daily Azolla ingestion was calculated as the difference between quantity of Azolla added to the bucket and the quantity remaining (on a DW basis) and expressed as grams (DW) of Azolla eaten per gram of snail flesh (DW) per day.

The dietary preference of snails (FW: 18.0 ± 2.3 g, shell length: 3.8 ± 0.1 mm, shell width: 3.0 ± 0.2 mm) for one of the two Azolla growing conditions (treatments "no" and "high") was also tested through a multiple-choice experiment. A floating plastic separation was placed in three basins each containing one snail and 10 g each of Azolla coming from the treatments "no" and "high" were introduced on either side of the plastic separation. As for the previous experiment, another bucket served as the control without snail. After 72 h, the remaining Azolla on each side was carefully drained and weighed, and the percentage of ingestion of fresh Azolla was calculated.

Sample analysis

Total Kjeldahl nitrogen (TKN) in water was measured following the Indonesian National Standard (SNI) 06-6989.52-2005 using Macro-Kjeldahl apparatus coupled with titration Total N was then determined as the sum of TKN, NO₃ --N and NO2 --N. Phosphorus (P) in water was determined based on the procedures described in the SNI 06-6989.31-2005 adapted from APHA 4500-PE [START_REF] Apha | Standard Methods for the Examination of Water and Waste Water[END_REF]. The chemical composition analyses were performed after the 11-d acclimation period (n = 1 per condition). Proximate analyses in Azolla samples were performed following [START_REF] Cunniff | Official Methods of Analysis of AOAC International[END_REF]. Moisture was determined by weight loss upon drying at 105°C for 24 h. Crude protein was determined using the standard Kjeldahl procedure; lipid content after acid hydrolysis was determined using the Weibull-Stoldt method; crude ash was determined on residue after heating at 550°C for 4-5 h in a muffle furnace. Crude fibre was determined as follows: macrophytes were extracted with 1.25% H2SO4 and 1.25% NaOH, dried, weighed, incinerated and reweighed. Gross energy content was calculated using energy value coefficients of 9 kcal for crude fat and 4 kcal for crude protein and carbohydrates. For total P and K determination, plant samples were weighed and digested in nitric acid for 20 min at 190°C. Measurements were then performed using an inductively-coupled plasma optical emission spectrometer. Chlorophyll content of Azolla was measured after acetone extraction by spectrophotometry. The absorbance was measured at wavelength of 663 nm and 645 nm. Calculation of chlorophyll levels was carried out based on Arnon's equation [START_REF] Arnon | Copper enzymes in isolated chloroplasts. Polyphenoloxidase in Beta vulgaris[END_REF].

Data treatment and statistical analysis

Prior to analysis, temperature data recorded in lux were converted to µmol PAR m -2 s -1 using x54 as conversion factor [START_REF] Thimijan | Photometric, radiometric, and quantum light units of measure: a review of procedures for interconversion[END_REF]. Because of non-normal distribution of the data, statistical comparisons were done for the environmental parameters (air and water) monitored under the different shading conditions using the non-parametric Kruskal-Wallis and Siegel and Castellan tests [START_REF] Siegel | Nonparametric Statistics for the Behavioral Sciences[END_REF].

A linear mixed model (LMM) was used to analyse differences in Azolla productivity at the nine experimental conditions (3 shading conditions x 3 fertilization levels). Shading materials and fertilizer conditions (with the interaction term) were considered as fixed effects in the model. The individual plastic drums in which Azolla grew were considered as random effects.

Assumptions of normality and homoscedasticity were checked on residuals of the model.

Contrast analysis was then performed as the mean separation procedure with a Bonferronicorrected p-value (α of 0.05/36 = 0.0014).

Analysis of covariance (ANCOVA) was then used to test for effects of light and other environmental covariables (air and water temperatures) on the productivity of Azolla cultivated at the three fertilization levels ("no", "low" and "high").

Quantities of ingested Azolla (from treatments "no" and "high") by snails (expressed as g g -1 DW d -1 ) and feed preferences (i.e. % of ingestion) were statistically compared using the nonparametric Mann-Whitney U test because of the non-normal distribution of the data.

The level of significance for ANCOVA and Mann-Whitney U test was set at α = 0.05. All statistics were performed using R freeware version 3.3 (R Development Core Team, 2016).

Unless otherwise stated, values shown are means ± SD.

Results

Response of A. filiculoides to culture conditions

In our experiment, depending on the weather and the shading conditions (Table 1), we observed air and water temperatures ranging from 23 to 40°C and light intensity reached peaks of 278 µmol m -2 s -1 (Fig. 2).

Fertilization levels and shading materials affected the growth of Azolla (Fig. 1). Overall, in the absence of added fertilizer, the productivity of Azolla was limited to 0.15 ± 0.73 g DW m -2 d -1 (n = 54) over the experiment and Azolla cultures collapsed in 43% of measurements performed (23 out of a total of 54). Conversely, the productivity of Azolla cultivated at a higher fertilizer level (treatment "high") reached 3.80 ± 1.44 g DW m -2 d -1 (n = 54) with doubling times of <1 d in some cases and no observed production collapse (Fig. 1).

Our LMM model indicated that both shading conditions (p < 0.001) and fertilizer levels (p = 0.001) affected the growth of Azolla. Significant interactions were found between the shading conditions and the fertilization treatment indicating that the effects of shading conditions are dependent of the fertilization level. Indeed, for the two lowest fertilizer addition treatments ("no" and "low"), productivity of Azolla was similar between the three shading conditions (tratio = 4.065-0.87, p = 0.016-0.992). Nevertheless, for the treatment "high", productivity of condition (F = 16.805, p = 0.001) while there were positive significant effects of increasing air temperatures on productivity in the "low" and "high" fertilization conditions (F = 4.904, p = 0.044 and F = 13.422, p = 0.003 respectively) (Fig. 2).

We observed that morphology of A. filiculoides changed in response to different experimental treatments. Visual aspect indicated that plants from the treatment "no" (no fertilizer added) were smaller and more fragile than those from the "low" and "high" fertilizer treatments (lower breaking strength of the fronds). Furthermore, control plants turned brownish, whereas plants from treatments "low" and "high" stayed green (mg kg -1 FW of chlorophyll respectively, n = 3, Table 2). A slight difference (χ 2 = 6.057, p = 0.045) was observed in the chlorophyll concentrations between the treatments "no" and "low" (343 ± 50 and 383 ± 23 mg kg -1 FW of chlorophyll respectively, n = 3) and the treatment "high" (450 ± 17 mg kg -1 FW of chlorophyll, n = 3). Although the number of Azolla samples analysed was limited by the low productivity in the treatment "no" (n = 1 per condition), the chemical composition analyses indicated that, after the acclimation period, P and K concentrations increased with fertilizer concentrations from 121 to 331 and 616 to 1113 mg kg -1 FW respectively, Table 2.

Impacts of the golden apple snail on Azolla production

During the experiments, no biomass change was observed in Azolla from both the treatment "no" and the treatment "high" maintained without snail (i.e. control condition). The consumption of Azolla by the golden apple snail P. canaliculata reached 0.06 ± 0.04 g g -1 DW d -1 and 0.17 ± 0.07 g DW g -1 d -1 when snails (n = 6 snails x 4 d) were fed on Azolla from the treatment "no" and the treatment "high" respectively (U = 536, p < 0.001, Fig. 3). These results were confirmed by estimation of the dietary preference of snails between Azolla from the treatments "no" and "high". When snails (n = 3) were able to choose between Azolla from the two fertilizer conditions, they slightly preferred (U = 9, p = 0.045) Azolla coming from the treatment "high" (79%, 80% and 88% of consumption after 72 h) to Azolla from treatment "no" (16%, 21% and 30% of consumption after 72 h).

Discussion

Some authors found that Azolla is able to grow in shade conditions (50% sunlight) and partial shade may favour its growth in field conditions (see for review: [START_REF] Hasan | Use of Algae and Aquatic Macrophytes as Feed in Small-Scale Aquaculture. Food and Agriculture Organization of the United Nations[END_REF] contrasting with other experimental studies. [START_REF] Abduh | The influence of light intensity on the protein content of Azolla microphylla and pre-treatment with Saccharomyces cerevisiae to increase protein recovery[END_REF] highlighted that Azolla growth gradually decreased under shading conditions (up to 50% sunlight) while [START_REF] Cary | Growth and nutrient composition of Azolla pinnata R. Brown and Azolla filiculoides Lamarck as affected by water temperature, nitrogen and phosphorus supply, light intensity and pH[END_REF] found that biomass yields of plants receiving 30% of greenhouse sunlight were less than one-third those of plants receiving no shading. Nevertheless, comparisons between studies are complex. Indeed, shading materials used are likely to affect not only the intensity but also the spectrum of the light received. Although we did not measure light spectra, another study performed on a large number of shading materials, revealed that the photon irradiance is reduced at wavelengths from 260 to 500 nm (-42% for UVB: 280-315 nm, -30% for UVA: 315-400 nm and -13% for blue: 400-500 nm) by polyethylene sheets [START_REF] Kotilainen | Light quality characterization under climate screens and shade nets for controlled-environment agriculture[END_REF]. The same authors found that the light spectrum under 50-70% shade nets remained similar to ambient sunlight spectrum. In this field experiment, we experimentally demonstrated that there is no benefit of using such shading materials for Azolla growth in field conditions suggesting that outdoor fish ponds may be an appropriate culture structures for a mass production of Azolla.

The water fern A. filiculoides is known to be sensitive to air temperatures with optimal for growth ranging from 20 to 30°C [START_REF] Hasan | Use of Algae and Aquatic Macrophytes as Feed in Small-Scale Aquaculture. Food and Agriculture Organization of the United Nations[END_REF][START_REF] Sadeghi | A review of some ecological factors affecting the growth of Azolla spp[END_REF]. In our experiment, depending on the weather conditions and the shading materials used (Table 1), we observed air temperatures ranging from 23 to 40°C. As for light intensity, increasing temperatures had a positive effect on Azolla productivity, indicating that in our experimental conditions high temperature was not a limiting factor for Azolla growth. Our results suggest that light and temperature have positive effects on Azolla production within small-scale fish farms in tropical environments.

The influences of temperature and light depended on fertilizer concentrations. In the absence of added fertilizer (treatment "no"), light, air and water temperatures had no effect on growth.

In this condition, Azolla productivity was low and highly variable with regular production collapses. There were significant positive linear relationships between the environmental variables (light, air and water temperatures) and the productivity of Azolla cultivated at the high fertilization level (treatment "high") and, to a lesser extent, for Azolla grown at the intermediate fertilizer level (treatment "low"). Based on our results, we estimated, for Azolla, cultivated under direct sunlight and high fertilizer addition, yields of 30 ± 9 t DW ha -1 year -1 .

In the present study, Azolla was harvested every week, and a minimum of 100 g m -2 was maintained in culture. As suggested by [START_REF] Brouwer | Growing Azolla to produce sustainable protein feed: the effect of differing species and CO2 concentrations on biomass productivity and chemical composition[END_REF], regular harvests of Azolla can be used to maintain cultures in a linear growth phase and to obtain predictable and high biomass yields. Our results confirm that the biomass production of Azolla depends on fertilizer concentrations and presumably by P concentrations because this nutrient is the most limiting in Azolla culture [START_REF] Subudhi | Differential phosphorus requirements of Azolla species and strains in phosphorus-limited continuous culture[END_REF][START_REF] Kushari | Differential responses of Azolla to phosphorus deficiency[END_REF][START_REF] Temmink | Azolla along a phosphorus gradient: biphasic growth response linked to diazotroph traits and phosphorus-induced iron chlorosis[END_REF]. Although production varied with environmental conditions (light and temperature), productivity remained high (median values: 44, 73 and 79 g FW m -2 d -1 for Azolla maintained under a shade net, clear plastic or exposed to direct sunlight, respectively) when plants were cultivated at high fertilizer concentrations (addition of 16, 10 and 7.5 µmol L -1 of N, P and K respectively), regardless of the environmental conditions.

Here, we used an inorganic multi-nutrient fertilizer rich in P (NPK = 1:2:1, see Table S1).

Thus, other important nutrients for Azolla, such as iron (Fe), which may be limiting under certain conditions and cause chlorosis [START_REF] Temmink | Azolla along a phosphorus gradient: biphasic growth response linked to diazotroph traits and phosphorus-induced iron chlorosis[END_REF], were already incorporated in the fertilizer. Although no visible evidence of Fe deficiency has been observed, it is possible that other trace elements like Co, Cu, Mn, Mn and Zn required for Azolla growth, particularly in relation with its nitrogen fixation metabolism [START_REF] Sadeghi | A review of some ecological factors affecting the growth of Azolla spp[END_REF] might have been limiting in the experimental condition without fertilizer. The use of multi-nutrient fertilizers mitigated the risk of trace element deficiency. Overall, Azolla cultivated under direct sunlight and with a fertilization level equivalent to the "high" condition may presumably generate 6.9 ± 2.2 t protein ha -1 year -1 , about three-fold higher than soybean (Glycine max) without arable land use [START_REF] Brouwer | Growing Azolla to produce sustainable protein feed: the effect of differing species and CO2 concentrations on biomass productivity and chemical composition[END_REF].

The morphology and chemical composition of A. filiculoides also changed in response to different experimental treatments. We observed that plants from the control were smaller and more fragile than those from the intermediate and high fertilizer treatments. Furthermore, control plants turned brownish whereas plants from treatments "low" and "high" stayed green. The chemical composition analyses indicated that P and K concentrations in plants increased with fertilizer concentrations. Similar results were observed for crude protein (from 14 to 23% of DW) and energy content (from 8.7 to 10.9 MJ kg -1 FW) while the fibre content decreased from 21 to 15% of DW. These findings corroborate observations of [START_REF] Subudhi | Differential phosphorus requirements of Azolla species and strains in phosphorus-limited continuous culture[END_REF] on A. pinnata who found increased of N (from 2.78 to 4.53 % of DW) and P (from 0.03 to 0.11 % of DW) cultivated with 0, 1 and 2 µmol L -1 of P additions. All together, these results indicate that visual aspect of Azolla (size and colour) can be used as an indicator of its health and its nutritional quality.

Previous studies have shown that the amino-acid profile of Azolla is suitable for fish feed (see for review [START_REF] Feedipedia | Feedipedia: an on-line encyclopedia of animal feeds[END_REF]. Given that the fibre content in Azolla cultivated under direct sunlight was the lowest that we measured (13% of DW), we also suggest that the recommended culture conditions can also improve the digestibility of Azolla for fish [START_REF] Maina | Digestibility and feeding value of some feed ingredients fed to tilapia Oreochromis niloticus (L.)[END_REF]. However, culture conditions (CO2 concentrations) also affected the total polyphenol content of Azolla, thereby affecting digestibility (Browner et al. 2018). In addition, plant-based feed may contain other anti-nutritional factors such as anti-vitamins, phytic acid protease inhibitors and tannins [START_REF] Dersjant-Li | The use of soy protein in aquafeeds[END_REF][START_REF] Drew | A review of processing of feed ingredients to enhance diet digestibility in finfish[END_REF]. Such factors were not considered in our study and more research is needed to confirm the effects of the culture conditions on the concentrations of these compounds in Azolla.

Regarding the impacts of the golden apple snail on Azolla production, we found that snails were able to ingest daily up to 38% of their body weight (FW) of Azolla, with a preference for Azolla cultivated in high fertilization level. These results highlight the huge negative impact of the presence of the golden apple snail on Azolla productivity. The average biomass of P. canaliculata has been estimated at 0.07 kg m -2 (i.e. approx. 3-4 adult individuals m -2 ) in Javanese aquaculture ponds [START_REF] Pouil | Nutrient budgets in a small-scale freshwater fish pond system in Indonesia[END_REF] which is in accordance with density observed in Philippines rice paddies (i.e. 1-5 individuals m -2 , Cowie, 2002) although higher densities up to 150 individuals m -2 have been reported [START_REF] Halwart | The golden apple snail Pomacea canaliculata in Asian rice farming systems: present impact and future threat[END_REF][START_REF] Schnorbach | The golden apple snail (Pomacea canaliculata Lamarck), an increasingly important pest in rice, and methods of control with Bayluscid[END_REF]. Based on this finding, the yield loss of Azolla due to ingestion by snails can reach about 12.3 t DW ha -1 year -1 (i.e. 35% of the estimated average yield, see Section 3.2). Further large-scale investigations are needed to confirm our findings.

Based on our observations, even maintaining Azolla in optimal conditions for production (direct sunlight and addition of fertilizer with high P concentration), does not overcome the risk of drastic drops in yields or even total loss of Azolla due to snails. The golden apple snail is able to select among macrophytes and prefers macrophytes species with high N and Ca contents and low C:N ratios [START_REF] Zhao | Characteristics of feeding preference and nutrients utilization of golden apple snail (Pomacea canaliculata) on macrophytes in paddy fields[END_REF]. These facts may explain the preference of snails for Azolla cultivated in optimal culture conditions (i.e. with the highest nutritional content). Considering the high risk represented by snails in the mass culture of Azolla in aquaculture ponds, we recommend (1) cultivating Azolla in snail-free ponds, (2) installing protections (e.g. wire-mesh grills; Cowie, 2002) in association to snail predator fish such as common carp [START_REF] Sin | Evaluation of different species of fish for biological control of golden apple snail Pomacea canaliculata (Lamarck) in rice[END_REF] to prevent access of snails to ponds and/or (3) regularly trapping snails (e.g. using baited traps), which are traditionally used for other purposes, such as feeding poultry or certain fish [START_REF] Cary | Growth and nutrient composition of Azolla pinnata R. Brown and Azolla filiculoides Lamarck as affected by water temperature, nitrogen and phosphorus supply, light intensity and pH[END_REF].

The present study demonstrates some important requirements for the mass production of Azolla in aquaculture ponds. This aquatic plant crop is potentially useful to improve sustainability of tropical pond aquaculture requiring a limited production surface with only few inputs and no nitrogen fertilizer. Potential yields are very promising compared to terrestrial crops. However, several practices must be optimised, especially regarding fertilization and risk of grazing by snails to maintain yields over time and sustain a mass production.

Captions to figures

Figure 1 .

 1 Figure 1. Productivity of Azolla (g FW m -2 d -1 , n = 3 replicates x 6 weeks = 18 per treatment)

Figure 2 .

 2 Figure2. Relationships between light intensity (µmol m -2 s -1 ), air temperature (°C) and average productivity (g FW m -2 d -1 ) of Azolla cultivated at the three fertilization levels

Figure 3 .

 3 Figure 3. Daily ingestion (g g -1 DW d -1 , n = 6 per day) of fresh Azolla cultivated at

  Figure 1 581

  Figure 3 583

Table 1 .

 1 Summary of the environmental conditions measured throughout the experiment in the three shading conditions (n = 99 for discrete measurements): direct sunlight, polyethylene sheet and 60% shade net. Data are means ± SD. Letters denote significant differences.

			Shading condition
	Parameters
		direct sunlight	polyethylene sheet	60% shade net
	Continuous records (data loggers)
	Diurnal light intensity (µmol m -2 s -1 )	359 ± 403 a	324 ±288 b	153 ± 133 c
	Atmospheric temperature (°C)	33.25 ± 4.78 a	34.66 ± 5.19 b	31.84 ± 4.03 c
	Water temperature (°C)	29.17 ± 2.65 a	29.35 ± 2.35 b	28.72 ± 1.94 c
	Discrete measurements (performed between 08:00 and 10:00 AM)
	pH	7.09 ± 0.19 a	7.05 ± 0.20 ab	6.99 ± 0.22 b
	DO (mg L -1 )	5.95 ± 1.02 a	5.62 ± 0.65 b	5.22 ± 0.59 b
	Conductivity (µS cm -2 )	74.19 ± 10.05 a	74.48 ± 10.49 a	74.62 ± 10.29 a
	TDS (mg L -1 )	37.08 ± 5.03 a	37.17 ± 5.18 a	37.31 ± 5.15 a

Table 2 .

 2 Chemical composition of Azolla (n = 1 per experimental treatment) cultivated at the 588 three fertilization levels (treatments "no", "low" and "high") under three shading conditions: 589 direct sunlight, polyethylene sheet and 60% shade net. Analysis were performed after the 12-d 590

	591	acclimation period.
							Shading conditions
			direct sunlight			polyethylene sheet			60% shade net
	Fertilizer condition	"no"	"low"	"high"	"no"	"low"	"high"	"no"	"low"	"high"
	Proximate
	Humidity (% FW)	90.41	90.27	89.17	89.68	88.48	87.74	92.66	90.58	89.76
	Ash (% DW)	25.00	24.03	24.00	29.46	29.9	25.77	23.61	29.58	26.98
	Fibre (% DW)	20.28	16.16	13.00	20.02	15.71	15.02	21.45	18.75	15.06
	Lipid (% DW)	1.10	1.08	1.57	0.99	1.27	1.65	0.95	1.62	1.41
	NFE* (% DW)	39.96	43.07	38.50	35.44	36.45	35.72	38.49	31.94	34.95
	Protein (% DW)	13.66	15.66	22.93	14.09	16.67	21.84	15.50	18.11	21.60
	Energy (MJ kg -1 FW)	9.39	10.24	10.87	8.66	9.37	10.25	9.39	8.99	10.00
	Chlorophyll and nutrients
	Chlorophyll (mg kg -1 FW)	290	370	430	350	370	460	390	410	460
	K (mg kg -1 FW)	683.7	817.9	947.5	616.2	692.1	949.4	618.5	683.2	1112.8
	P (mg kg -1 FW)	121.4	169.3	318.5	129.0	168.8	280.6	131.3	153.7	330.9
	592	* Nitrogen Free Extract.

Acknowledgements

We thank the three anonymous reviewers for their very useful comments. This publication is a production of the COFASP ERA-NET project "IMTA-EFFECT" funded by the European Union and French National Research Agency. The authors acknowledge Mr. Mamat Maco, fish farmer from the village of Babakan, for sharing his experience and for his assistance throughout the duration of the observations. Authors also thank laboratory staff from the IPB (Department of Aquaculture, Bogor) and SIG (Saraswanti Indo Genetech, Bogor) laboratories who carried out the nutrient and chlorophyll analysis. This is publication ISEM XXXX-XXX.

Conflict of Interest and Ethical statement

The authors declare that they have no conflict of interest. This article does not contain any studies with animals performed by any of the authors.


Sulivan Jouanneau
email: sulivan.jouanneau@univ-nantes.fr

Emilie Grangé

Marie-José Durand

Gérald Thouand
email: gerald.thouand@univ-nantes.fr

Rapid BOD assessment with a microbial array coupled to a neural machine learning system

Keywords: BOD5, multivariate approach, bacterial biosensor, neural network, wastewater

The domestic usage of water generates approximately 310 km 3 of wastewater worldwide (2015, AQUASTAT, Food and Agriculture Organization of United Nations). This sewage contains an important organic load due to the use of this water; this organic load is characterized using a standard method, namely, the biological oxygen demand measurement (BOD5). The BOD5 provides information about the biodegradable organic load (standard ISO 5815). However, this measurement protocol is very time-consuming (5 days) and may produce variability in approximately 20% of results mainly due to variation in the environmental inocula.

To remedy these limitations, this work proposes an innovative concept relying on the implementation of a set of rigorously selected bacterial strains. This publication depicts the different steps used in this study, from bio-indicator selection to validation with real wastewater samples. The results obtained in the final step show a strong correlation between the developed approach and the reference method (ISO 5815) with a correlation rate of approximately 0.9. In addition, the optimization of the experimental conditions and the use of controlled strains (8 selected strains) allow significant reduction in the duration of the BOD5 analysis, with only 3 hours required for the proposed method versus 5 days for the reference method. This technological breakthrough should simplify the monitoring of wastewater treatment plants and provide quicker, easier and more coherent control in terms of the treatment time.

Introduction

The global production of domestic wastewater was assessed to be 315 km 3 in 2010 and will tend to increase in the future concomitant with population expansion [START_REF] Flörke | Domestic and industrial water uses of the past 60 years as a mirror of socio-economic development: A global simulation study[END_REF][START_REF] Mateo-Sagasta | Global Wastewater and Sludge Production, Treatment and Use[END_REF]. These wastewaters, which are mainly contaminated by organic materials (i.e., carbohydrates, fats, proteins, etc.) resulting from human body wastes (20%faeces and urine) and domestic uses (80% -personal washing, laundry, food preparation, etc.) [START_REF] Duncan | Domestic wastewater treatment in developing countries[END_REF][START_REF] Garcia | Guide for the Treatment of Urban Waste Water in Small Settlements[END_REF] present a real risk for the receiving environment and the exposed population. To limit the consequences of release of these wastewaters into the environment, they are treated to remove the organic load through bioprocesses, such as wastewater treatment plants (WWTPs). To measure the conformity of the released waters, the organic load in the treated water is tested weekly in average samples according to the reference method (ISO 5815-1:2003, 2003;ISO 5815-2:2003ISO 5815-2: , 2003) ) based on BOD (biological oxygen demand). This method dates from the beginning of the twentieth century (Great Britain. Royal commission on sewage disposal, 1908; [START_REF] Jouanneau | Methods for assessing biochemical oxygen demand (BOD): A review[END_REF], and its implementation is widespread worldwide. This method is based on assessment of the biochemical oxygen demand [i.e., the amount of oxygen consumed over 5 days by environmental microorganisms to biodegrade the organic matter under aerobic conditions (BOD5 in mg.L -1 of consumed O2)].

However, this method has several drawbacks:

-The method exhibits important intrinsic variability (higher than 20%) that is mainly due to the broad diversity of inocula used to carry out the measurements (Guyard, 2010).

-The 5-day period required for a measurement is longer than the retention time of water in WWTPs (close to 3 days).

-The measurement carries a significant risk of measurement error due to the available amount of oxygen in water. Indeed, the maximal load measured with this method cannot exceeded 6 mg/L BOD (ISO 5815-1:2003, 2003;ISO 5815-2:2003ISO 5815-2: , 2003)), whereas the BOD5 value of wastewater is close to 300 mg/L and thus requires predilution.

Over the last several decades, many research teams have focused on these issues to propose new measurement strategies with an aim of removing these drawbacks [START_REF] Jouanneau | Methods for assessing biochemical oxygen demand (BOD): A review[END_REF][START_REF] Sun | Microbial fuel cell-based biosensors for environmental monitoring: a review[END_REF]Yang et al., 2015). A specific focus on the biological aspect of the measurement highlights two strategies based on (i) the use of environmental inocula and (ii) the implementation of only one microbial strain. The first strategy has the advantage of ensuring great representativeness of the biological answers due to broad microbial diversity but induces significant variability (i.e., origin, density, and diversity) [START_REF] Liu | A biofilm reactor-based approach for rapid on-line determination of biodegradable organic pollutants[END_REF][START_REF] Rastogi | Development and characterization of a novel immobilized microbial membrane for rapid determination of biochemical oxygen demand load in industrial waste-waters[END_REF][START_REF] Rocher | New tools for the determination of PO4 3-and BOD in municipal wastewaters[END_REF] due to the intrinsic characteristics of the inocula.

Conversely, the second strategy, based on the use of one known strain, is very reproducible but is limited by the range of metabolic abilities of the selected strain [START_REF] Arlyapov | Biosensor analyzer for BOD index express control on the basis of the yeast microorganisms Candida maltosa, Candida blankii, and Debaryomyces hansenii[END_REF][START_REF] Raud | Comparative study of semi-specific Aeromonas hydrophila and universal Pseudomonas fluorescens biosensors for BOD measurements in meat industry wastewaters[END_REF]Yoshida et al., 2001), which consequently provides a truncated view of the biodegradable organic diversity.

In light of these issues, some researchers have proposed an alternative strategy based on the use of several known microbial strains. Two strategies were proposed based on either (i) an artificial mixture of 2-4 strains or (ii) a set of several strains used individually. The aims of these strategies were the same, namely, to enlarge the biodegradation spectra while ensuring a great level of reproducibility [START_REF] Catterall | Development of a Rapid Ferricyanide-Mediated Assay for Biochemical Oxygen Demand Using a Mixed Microbial Consortium[END_REF][START_REF] Jia | Co-immobilized microbial biosensor for BOD estimation based on sol-gel derived composite material[END_REF][START_REF] Jiang | Optical biosensor for the determination of BOD in seawater[END_REF][START_REF] Lin | Novel BOD optical fiber biosensor based on co-immobilized microorganisms in ormosils matrix[END_REF]Xin et al., 2007). In the case of multi-species mixtures (i), the stability of the initial properties of the consortia (i.e., cell density, diversity, ratio between strains, biodegradation abilities, etc.) was difficult to ensure long-term mostly due to competition for the carbon substrate. The second strategy (ii) [START_REF] Pitman | Biochemical oxygen demand sensor arrays[END_REF][START_REF] Raud | Bioelectronic tongue and multivariate analysis: A next step in BOD measurements[END_REF] also relies on several strains but is based on the use of several selected strains (not structured in the consortium). This strategy ensures good reproducibility while freeing the results from potential inocula drifts (this strategy was applied in this work). Nevertheless, this multivariate approach (based on several biological descriptors) increases the complexity of the data analysis (one value per strains). To overcome this limitation, [START_REF] Raud | Bioelectronic tongue and multivariate analysis: A next step in BOD measurements[END_REF] analysed the data with a PLS (partial least squares) regression method to simultaneously take into consideration all the information provided by the different strains. The results were very encouraging, but the validation was performed only on synthetic wastewaters and not on real samples.

Moreover, bio-element selection is a crucial parameter in these metrological approaches based on controlled strains and directly conditions the potential performances of the future analytical method (i.e., biodegradation spectra and response kinetics) Moreover, questions remain concerning how to ensure the representativity of the proposed analytical approach under these conditions.

The aim of the proposed study was to design a relevant analytical method for the assessment of the BOD5 to address these limitations. This strategy is based on the separate use (not structured in the consortium) of a set of 8 bacterial strains that have been rigorously selected using defined characteristics, such as the biodegradation potential, metabolic rate and toxicological robustness (Fig. 1). Coupled with an algorithm of data processing allowing to analyses the multivariate information provided by the bacterial set, the proposed method is able to assess BOD5 in only 3 hours with a high reliability (r²=0.85).

Experimental section

Bacterial strains

A total of 28 strains were preselected from the literature according to the following characteristics: (i) their representativity with regard to the microbial population of an activated sludge from a WWTP [START_REF] Kong | Structure and function of the microbial community in a full-scale enhanced biological phosphorus removal plant[END_REF][START_REF] Snaidr | Phylogenetic analysis and in situ identification of bacteria in activated sludge[END_REF][START_REF] Xia | Bacterial community structure in geographically distributed biological wastewater treatment reactors[END_REF], (ii) their weak pathogenicity (mainly group 1 risk according to the 2000/54/EC classification proposed by the European Directive), (iii) their broad biodegradation abilities (Gao et al., 2010) and (iv) their resistance to freeze-drying conditions (information collected from microbial collections, such as ATCC, DSMZ or CIP) (specification relative to an eventual industrial transfer). The full list of pre-selected strains at this step is described in the supplementary data (Table SD-1).

Growth medium of the strains and other solutions

The Luria Bertani medium (LB medium) was prepared in distilled water and supplemented with 5 g.L -1 NaCl (Merck, Germany), 5 g.L -1 tryptone (Biokar Diagnostics, France) and 1 g.L - 1 yeast extract (Biokar Diagnostics, France). 15 g.L -1 of agar type E (Biokar Diagnostics, France) was added to obtain the solid medium. The pH was adjusted to 7 using a solution of HCl (Sigma Aldrich, USA) or NaOH (Merck, Germany) before autoclaving at 120°C for 20 min.

The synthetic wastewater (SWW) solution was prepared in accordance with the OECD 209 standard 28 as follows: 1 L of distilled water was supplemented with 16 g of tryptone (Biokar Diagnostics, France), 11 g of meat extract (Biokar Diagnostics, France), 3 g of urea (Sigma-Aldrich, USA), 0.7 g of NaCl (Merck, Germany), 0.4 g of CaCl2 • 2H2O (Merck, Germany), 0.2 g of MgCl2 • 7H2O (Sigma-Aldrich, USA), and 2.8 g of K2HPO4 (Merck, Germany).

The mineral medium [MM, derived from M9 medium (Atlas, 1997)] without a carbon source used in this study was prepared in two steps. The first step consisted of preparation of several concentrated solutions (S1, S2, S3, St and Sb) in distilled water. S1 consists of 60 g.L -1 Na2HPO4 (Fluka, Germany), 30 g.L -1 KH2PO4 (Merck, Germany), 10 g.L -1 NH4Cl (Merck, Germany) and 5 g.L -1 NaCl (Merck, Germany). S2, S3, Sb and St are composed of 264.5 g.L -1 MgSO4 • 7H2O (Sigma-Aldrich, USA), 14.7 g.L -1 CaCl2 • 2H2O (Merck, Germany), 0.5 g.L -1 biotin (Sigma-Aldrich, USA) and 0.15 g.L -1 thiamine • HCl (Sigma-Aldrich, USA), respectively. S1, S2 and S3 were sterilized by autoclaving, and St and Sb were filtered through 0.2 µm cellulose acetate filters (146560, Dutscher, France) and stored at +4°C. The second step consisted of mixing these solutions according to the following ratio (for 1 L of MM): 100 mL of S1, 1 mL of S2/S3/St/Sb and if required 10 mL of a carbon source at the desired concentration. The mixture was adjusted to 1 L with sterile distilled water.

Glucose solutions (GSs) were prepared at several carbon concentrations in the MM medium and sterilized by filtration (cellulose acetate filters, 146560, Dutscher, France).

Glycerol solution (GlyS) at 50% (v/v) was prepared in distilled water and sterilized by autoclaving.

Growth and preparation of bacterial cells

All strains were stored and cultivated under the same conditions. The cells were stored at -80°C in a mixture (v/v) of LB medium (50%) and GlyS (50%). The cells were isolated on solid LB medium at +30°C during 12 to 24 hours and the obtained colonies were used to inoculated the first cultivations.

For the first cultures, the cells were grown overnight (14 hours) in LB medium at 30°C under shaking at 250 rpm. Then, the cells were diluted to an optical density of OD620nm = 0.2 in fresh LB medium and cultivated at 30°C under shaking at 250 rpm until an OD620nm = 0.6 (exponential growth stage) was reached. This OD620nm value (0.6) corresponds to a cell density varying between 10 8 and 10 9 CFU.ml -1 for the tested strains (results not shown). For the sake of simplicity, we considered that these cell densities were not significantly different from strain to strain.

The cultivated cells (OD620nm = 0.6) were refreshed in an ethanol bath at -20°C for 1 minute.

The aim of this step is not to freeze the cells but to decrease quickly the temperature in order to avoid any change of their physiological state. Then, the cells were centrifuged (6,400 x g, 5 min, +4°C) to remove the growth medium and resuspended in a cold washing solution of MgSO4 • 7H2O (10 -2 M). After three washes, the cells were resuspended in MM without a carbon source and adjusted to obtain the required cellular density (OD620nm = 0.43/0.86/1/1.72/2.57/3.43/4.29).

Optimization of the experimental conditions

We were interested in improving some of the experimental conditions through two strategies:

the replacement of oxygen (used as an activity marker of biodegradation in the reference method) with a fluorescent dye (resazurin) that was a priori better suited for this metrological context and the optimization of the cell density in the biodegradation assays. The aim of this second strategy was to define the condition that ensured both a reduced analysis duration and a measurement range that was as large as possible. The resazurin solution used in this work was a commercial solution (PrestoBlue ® , Invitrogen TM , USA). No information was available relative its concentration.

These steps were performed with the model strain E. coli K12 MG1655 in duplicate.

2.4.1. Comparison of two activity markers: oxygen versus resazurin (fluorescent dye)

Monitoring the biodegradation activity via the fluorescent marker.

The bacterial suspension was prepared as described above (see 2.3) at an OD620nm = 0.43. The assay solution was prepared by mixing the washed bacterial suspension, MM supplemented with glucose and the commercial resazurin solution (PrestoBlue ® , Invitrogen TM , USA).

Several glucose concentrations (final concentration) ranging from 0.1 mg.L -1 to 500 mg.L -1

were tested to define the limits of the measurement range. The mixtures were prepared directly in the microplate wells (Nunc™ F96 MicroWell™, 236108) in following the ratio

Monitoring of the biodegradation activity via oxygen consumption.

For the assay based on the oxygen marker, the resazurin solution was replaced by distilled water in the assay solution. In this case, the biological activity was monitored in an Oxodish ® OD24 microplate reader (24-well microplate with an integrated oxygen sensor, PreSens Precision Sensing, GmbH, Germany). Each well was filled with 3 mL of assay solution (same ratio as in 2.4.1.(a) -700 µL of bacterial suspension, 2 mL of MM with glucose but 300 µL of distilled water) and sealed using an AB-0558 adhesive PCR film (Thermo, ABgene, France)

to prevent atmospheric oxygen from entering the system (Cregut et al., 2013) during the test.

Oxygen was monitored at 30°C with the SensorDish Reader (PreSens Precision Sensing).

Optimization of the cellular density.

The impact of the cell density was evaluated as described in the previous paragraph (2.4.1.).

The same protocol based on resazurin as the biodegradation marker was used except for the final bacterial concentration, which ranged from OD620nm = 0.1 to OD620nm = 1.

Characterization of the strains

The characterization steps described below were performed for the 28 selected strains in parallel with the optimization phases. The cellular density after the washing step (see 2.3) was adjusted to OD620nm = 1 for all strains.

Metabolic profile and stress robustness

The objective was to characterize the selected strains according to two criteria: their biodegradation capabilities and their intrinsic robustness towards several environmental stresses (i.e., pH, salinity, antibiotics, reference toxic compounds, etc.). These two parameters were assessed simultaneously using GEN III microplates TM (Biolog Inc., USA -see Fig. The results obtained from the 71 carbon sources were used to establish biodegradation profiles specific to each strain and then compared and clustered using statistical approaches (agglomerative hierarchical clustering, XLStat, Addinsoft).

For each strain, an index of robustness was calculated from the number of stress conditions, conditions inducing a growth inhibition (none observed cellular development), divided by the overall number of tested conditions (n=23). This index aims to compare the sensitivity of the strains to potential environmental stresses.

Assessment of the biodegradation kinetics

The biodegradation kinetics of the 28 strains were compared in the SWW medium to evaluate their respective response times. First, 2.7 mL of SWW and 300 µL of the washed suspension (OD620nm = 1) were added to the wells of an Oxodish ® OD24 microplate (PreSens, Germany).

An AB-0558 adhesive PCR film (Thermo, ABgene, France) was applied on the microplates to limit atmospheric oxygen diffusion in the reaction medium. Measurements were performed with the SensorDish Reader (PreSens Precision Sensing) each minute at 30°C for 10 hours in triplicate. The kinetics values were calculated from the maximal slope of the measured oxygen consumption.

Environmental samples

The environmental samples (n = 104 -Table SD The reference analyses for BOD5 were performed according to the standard method (ISO 5815) by the INOVALYS NANTES Lab (Nantes, France), which is certified by the French Accreditation Committee (COFRAC).

Method developed in this work

The measurement protocol for the proposed BOD5 test was carried out in 96-well microplates (Nunc™ F96 MicroWell™, 236108) according to the following ratio: 35 µL of bacterial suspension at an OD620nm = 2.57 (final OD620nm in well = 0.6), 100 µL of sample and 15 µL of resazurin (PrestoBlue ® , Invitrogen TM , USA). The microplate was incubated for 3 hours at 30°C. These test conditions (incubation period, cell density, oxygen marker, and used strains)

were determined during the optimization step of the experimental conditions (see results in 3.1 and 3.2). Subsequently, the fluorescence signal relative to the biological activity of the strains was individually read with the TriStar LB941 Multimode Microplate Reader as described above (see 2.4.1).

All the samples (see 2.6) were tested in duplicate, and the collected values were exported to an overall database. This data set was used to establish the correlation algorithm between the biological values provided by the bacterial set and the BOD5 reference method (ISO 5815).

Data processing: design of correlation models by a statistical approach

The aim of the data processing was to design algorithms to correlate the biological data (fluorescence values) obtained from the selected strains with the BOD5 reference values; the final goal was to propose a mathematical model to predict the BOD5 value from only the data provided by these strains. All models were designed via a dedicated software (Neuro One 6.13.0.5, Netral, France).

2.8.1. Architecture of the model (number of biological descriptors and nature of the model)

First, the data were analysed using a mono-parametric approach. The information (fluorescence values) from each strain was individually correlated with the reference values by linear regression. Because the results were insufficient (low correlation rates), the data processing focused on a multi-component strategy (based on several strains), which was also based on a linear regression algorithm. This step allowed the determination of the number of biological descriptors (selected strain) required to assess the BOD5. The predictive abilities of these models are limited.

To improve the performance of the models for the assessment of the BOD5, non-linear algorithms by neural networks were deployed. These models are defined according to architectural characteristics, such as the number of hidden neurons, the activation modalities of the neurons, and the data standardization conditions. Several architectures were compared for the overall database to identify a priori the most relevant algorithms.

Model validation

The last data processing step consisted of comparing the preselected architectures based on their predictive abilities with new samples. For this purpose, the overall database was randomly separated into two groups: a learning set (n= 148 assays) and a validation set (n= 60 assays). The learning set was used to design new algorithms based on the preselected architectures (number of hidden neurons, activation mode, and data standardization). To assess the robustness of these new models and choose the most relevant model, the estimated BOD5 values obtained from the validation set were compared to the reference values. This step was also performed with Neuro One 6.13.0.5 (Netral, France).

Results and discussion

Improvement of biological activity monitoring

The reference method used to assess BOD5 is based on oxygen consumption as a marker of the biodegradation activity. This approach induces some technical constraints, such as a These two cell activity markers were compared during glucose biodegradation monitoring (MM supplemented with glucose as the sole carbon source) in E. coli. Several glucose concentrations (range from 0.1 to 500 mg.L -1 ) were tested to determine the highest measurable concentration according to the marker used (Fig. 2A). Comparative analysis of these results showed that use of resazurin as a marker could assess the glucose concentration up to 495 mg.L -1 versus only 125 mg.L -1 with oxygen. In light of these data, the fluorescent dye was selected for continuation of the study.

measurement
Subsequently, we were interested in the effect of the cell density of E. coli on the measurement duration with resazurin as cell activity marker. Figure 2B shows that an increase in the cell density substantially reduced the analysis duration; a cell density of 0.1 (OD620nm) resulted in a measurement duration of almost 12 hours versus 2 hours for an OD620nm = 1. The results also showed a reduction in the measurement range that was linked to the increasing cell density. This trend can be explained by an increase in the signal-to-noise ratio (see Fig.

SD-3). An important noise (fluorescence baseline) measured with the high cell densities

probably is induced by endogenous cellular respiration. At the same time, the saturation limit of the approach is reached at approximately 800,000 RFU. Consequently, the maximal measurable concentrations should not exceed 500 mg.L -1 for a cell density of 0.1 (OD620nm) versus 170 mg.L -1 for an OD620nm = 1.

To comply with the approach expectations (analysis limited in time but ensuring a relevant measurement range), the better compromise seemed to be a cellular density of 0.6 corresponding to an analysis duration of approximately 3 hours.

Selection of a representative set of strains

The objective of this step was to constitute a set of controlled bacterial strains that were able to reliably assess the BOD5 in a limited time and under reproducible conditions.

Pre-selection of bacterial candidates

The first step was dedicated to the preselection of bacterial strains that were likely to become a biological strain capable of assessing the BOD5. A literature review identified the major phyla present in the activated sludge of wastewater treatment plants (main targets of the Xanthomonadales, some Burkholderiales, some Bacteriodetes, etc.) or their unsuitable metabolism (strictly anaerobic strains, such as Desulfobacterales and Desulfovibrionales, and marine strains, such as Alteromonadales), which were incompatible with the intended use of the technology (Fig. 3).

Among the selected families, we focused on the identification of bacterial species that met the criteria of the approach. The pre-selected strains should preferably be isolated from activated sludge of WWTPs, should be largely represented in these environments, should have wide and complementary a priori biodegradation abilities, should not be pathogenic and should be available in the current strain banks (i.e., DSMZ or ATCC). In accordance with these five criteria, 31 strains (among more than 1200 identified OTUs) were pre-selected before a second characterization step based on their ease of use (quick growth and ease of cultivation).

From the obtained results, 3 strains were discarded (complexity of culture or too low a growth rate), which reduced the bacterial preselection to 28 strains (see Table SD-1). The overall preselection strategy is shown in Figure 3.

Selection of a representative set of strains

The simultaneous use of the 28 strains as individual descriptors of the BOD5 was extremely technically complex and expensive. Consequently, we needed to reduce the number of strains used in the bioassay. First, we focused on the biodegradation capabilities of the selected strains. The biodegradation profile of each candidate was determined using 71 carbon sources (see Fig. SD-1), and hierarchical clustering was performed according to their similarities (agglomerative hierarchical clustering) (Fig. 4). Eight clusters with comparable biodegradation capabilities were identified among the 28 preselected strains (similar profiles at 95% In parallel, all strains were characterized according to their robustness and biodegradation kinetics (Fig. 5). These criteria were taken into account to select a representative among the bacterial groups with similar biodegradation profiles. To limit the effect of the water toxicity on the metrological approach, strains with strong robustness were preferred because of their inherent capabilities to resist environmental stresses. Indeed, in the case of excessive water toxicity or too high sensitivity of strains, the bacterial cells will be not able to provide reliable information inducing erroneous results. However, strains with high biodegradation kinetics were a priori more likely to provide a biological signal quickly. Consequently, these strains were also preferred in view of the context of technological development of this study.

Figure 5A reports the specific robustness of each strain. These values were calculated based on 23 stress conditions and varied between 0.13 for B. mycoides (the most sensitive strain among the 28 tested) and 1 for E. coli. Figure 5B depicts the biodegradation kinetics of each strain for the synthetic wastewater (SWW). From these data that were calculated from the maximal measured slope of oxygen consumption, we noted significant differences between strains. For example, the biodegradation kinetics reached 0.33 mg BOD.L -1 .h -1 for B. vertrisii, whereas the kinetics achieved by P. putida (DSM 1868) were approximately 12 mg BOD.L -

The final selection process of representative strains among each cluster was based on these last two criteria in addition to some criteria relative to the utilization of the strains, such as the flocculation of cells during growth (complexity of the preparation step). For example, group A consists of 6 strains (B. funiculus, B. vertrisii, Z. resiniphila, P. pantrophus, B. aquatica and A. towneri). Among these 6 strains, B. aquatica was selected to represent this group because of its capabilities (highest robustness and fastest biodegradation kinetics).

Finally, among the 28 candidates, a representative set of 8 strains was definitively selected, consisting of B. aquatica, C. testosteroni, P. putida (DSM 1868), V. paradoxus, C. pseudodiphteriticum, P. mirabilis, E. coli and B. subtilis.

BOD5 assessment with the standard method and the bioassay.

The BOD5 values of the 104 real samples (collective or non-collective sanitation or stormwater) were determined according to the standard method (ISO 5815) (see Table SD-2).

As expected, the samples taken before treatment by a WWTP showed significant pollution levels, with an average BOD5 of nearly 120 mg.L -1 (min. 66 mg.L -1 ; max. 190 mg.L -1 ). The levels measured in the treated waters varied strongly according to their origins (collective or non-collective sanitation). The average BOD5 of stormwater was approximately 40 mg.L -1 (min. 12 mg.L -1 ; max. 54 mg.L -1 ); this pollution is probably due to runoff of water on roads and roofs, which transfers pollutants from support to water.

In parallel, each sample was subjected to the selected strains to determine the corresponding biological activity (fluorescence values obtained after 3 hours of exposure for the 104 analysed samples in duplicate). The final database consisted of 208 assays. Each sample is defined by 8 fluorescence values (one per strain) and a BOD5 reference value obtained using the standard method. The data analysis was performed from this database. The main objective of this supplementary step was to propose a reliable correlation model that allowed prediction of the BOD5 after only 3 hours. Nevertheless, we were focused on the number of strains required to assess the BOD5 before improving the correlation algorithm a second time (numerical model allowing calculation of the BOD5 from fluorescence values provided by the selected strains).

Determination of the number of biological descriptors needed to assess the BOD5

The data provided by each selected bacterial strain were individually correlated by linear regression with the BOD5 reference values (Fig. 6). The average correlation rate was approximately 0.11, with a standard deviation of 0.08. Among the 8 selected strains, the best correlation rate obtained with B. aquatica (r²=0.2638) was too low to be used as a reliable bio-indicator of the BOD5.

Nevertheless, each strain provided a portion of the overall information. Consequently, increasing the number of strains used to assess the BOD5 should improve the relevance of the approach. However, the number of strains required to ensure a reliable measurement is unknown.

Correlation models (linear regression) were designed based on the use of 1 to 8 strains. The obtained models are presented on Figure 7. The correlation rate between the predicted (calculated from fluorescence data provided by the selected strains) and reference values (standard method) tended to increase with the number of strains used. Indeed, the average rate obtained from a single strain was approximately 0.11, whereas this rate reached approximately 0.6 when the 8 strains were used.

Increasing the number of strains used to assess the BOD5 was associated with a significant increase in the correlation rate of the models. However, the predictive capabilities of the best model based on the 8 strains was not sufficient (r²=0.58). In view of these results, a second step was required to improve the predictive capabilities of the data analysis model.

overlined in grey). Ten models were generated based on the architecture. The robustness of these models was compared from unused data in the validation set based on the correlation rate between the predicted and reference values.

Among the 100 models, the most relevant was able to estimate the BOD5 of the validation samples (data not included during the learning process) with strong reliability (r²=0.851) (Fig. 8). This model consists of a hidden layer of three neurons activated by a hyperbolic tangent function. The input and output data are standardized with respect to a centred, reduced, normal distribution. From a global perspective, the BOD5 values calculated from the bacterial set are relatively consistent with the reference values. The model seems to be particularly reliable between 25 and 300 mg/L -1 BOD5 (according to the reference method) for wastewaters (collective or non-collective sanitation). Nevertheless, its performance decreases for samples with low biodegradable organic loads (inferior to 25 mg.L -1 according to the reference method) and for stormwaters. Two hypotheses can explain this result: (i) the current model is not sufficiently accurate to predict low BOD5 values (a specific model must be designed to analyse specifically these low values) and/or (ii) an organic composition of stormwater different from that of wastewaters (organic compounds collected during water runoff on roads or roofs) induces specific findings from the bacterial descriptors.

Similar results were obtained by [START_REF] Raud | Bioelectronic tongue and multivariate analysis: A next step in BOD measurements[END_REF]. Their strategy was based on a microbial set consisting of 7 bacterial strains selected from their intrinsic biodegradation properties. The processing of multivariate data was performed using a PLS algorithm. The validation was only performed on artificial samples (synthetic wastewater spiked with phenol, milk, fat or cellulose) and not on real wastewater samples. Nevertheless, the correlation rate between the calculated and reference values was close to 1 (r²=0.85 in our study). Opposition to real samples seems to decrease the correlation rate (calculated values versus reference values), because mimicking a real sample with synthetic effluents is difficult.

The reference values own an inherent variability close to 20% [START_REF] Jouanneau | Methods for assessing biochemical oxygen demand (BOD): A review[END_REF] notably due to the environmental variability of used inoculum. Conversely, the mean variability of data provided by the selected strains is 6.62% (results not showed). During the learning process, the model adjusts the model variables to match the data provided by the bacterial set with the reference data integrating at the same time its inherent variability (20%).

To improve prediction reliability, it is required to know the "true" reference value. For this, it would be necessary to carry out several reference analysis by samples to determinate these "true" values (statistical analysis) in order to design new predictive models.

Post analysis

In accordance with the selected architecture, a post analysis was performed to demonstrate the relevance of the use of several strains as bio-indicators associated with data processing by the neural network. Figure 9 shows the average correlation rates (linear least squares method) and the associated standard deviations depending on the number of strains used and according to the models used (linear regression or neural network). The results show a significant improvement in the reliability of the provided BOD5 values compared with those obtained with the linear regression models.

With this post-analysis, we demonstrated firstly that the number of biological descriptors was a crucial factor in the development of a method to assess the biodegradable organic load. The use of a single bio-indicator seems to significantly limit the measurement performance of the strategy. The review of [START_REF] Jouanneau | Methods for assessing biochemical oxygen demand (BOD): A review[END_REF] tended to reinforce this observation. Indeed, among the many cited publications, strategies based on a unique biological descriptor show lower abilities to assess the BOD5 in real samples (see Fig. and wider variability between studies. From the publications cited in this review, the average correlation rate was close to 0.81 (SD=0.24) with a unique biological descriptor (r²min = 0.09 / r²max = 1) versus 0.95 (SD=0.07) with a consortium (r²min = 0.81 / r²max = 1). Strategies based on consortium-related systems use microbial mixtures from natural or artificial origins in most cases. These strategies are in agreement the protocol described in the reference method (ISO 5815) but induce wide inherent variability due to the descriptors used.

Conclusion

In this work, we proposed an overall strategy to assess the BOD5 from the selection of bacterial indicators to the validation of real wastewater samples. By acting on some levers, such as the measurement conditions or the selection of 8 bacterial bio-indicators, we developed a bioassay that allowed reliable and accurate BOD5 measurement and required only 3 hours versus 5 days for the reference method. The measurement range being enlarged, no dilution is required to assess BOD5 in most wastewater, contrary to the reference method limited to 6 mg/L BOD. The obtained results are very encouraging and show a strong correlation with the reference BOD5 values (r²=0.85). Nevertheless the method seems to reach its limits regarding to the assessing of low BOD5 levels (inferior to 25 mg.L -1 according to the reference method). Further work will have to be carried out to improve the model in these cases.

This significant technological improvement should increase the monitoring level of wastewaters. Indeed, by reducing the analysis duration, it is easier to control the process of water treatment and, if necessary, to adjust some parameters, such as the oxygen intake or the water flow, due to the time scale that is consistent with the treatment duration (2-3 days in WWTPs).

This metrological strategy was filed with the National Institute of Industrial Property to be patented (deposit number: 1851496).

Xin, L., Wang, Xudong, Guo, G., Wang, Xiaoru, Chen, X., 2007. An optical biosensing film for biochemical oxygen demand determination in seawater with an automatic flow sampling system. Meas. Sci. Technol.

  The selection step, when included, generally is based on either the intrinsic properties of the strains (i.e., biodegradation range and growth conditions) (Kwok et al., 2005; Oota et al., 2010; Raud et al., 2012; Raud and Kikas, 2013) or isolation of strains from a specific environment (Chan et al., 2000; Kwok et al., 2005; Yoshida et al., 2000). Nevertheless, no characterization or comparison has been carried out to determine the relevance of this choice.

(

  final volume = 150 µL/well): 35 µL of bacterial suspension (final OD620nm = 0.1), 100 µL of MM with glucose and 15 µL of dye (PrestoBlue ® , Invitrogen TM , USA, in accordance with the provider protocol: 10% v/v). Monitoring of the biological activity via the fluorescent marker (Cregut et al., 2013; Dudal et al., 2006; Tizzard et al., 2006) was performed with the TriStar LB 941 Multimode Microplate Reader (Berthold Technologies GmbH, Germany) every ten minutes [λexcitation = 540 nm (bandwidth = 10 nm), λemission = 610 nm (bandwidth = 10 nm), counting time = 0.1 s and incubation temperature = 30°C].

  SD-1) with 71 different carbon sources and 23 stress conditions (test based on the growth inhibition induced by specific conditions such as osmotic and pH stresses or several reference toxics) supplemented with tetrazolium redox dyes (as a colorimetric respiratory marker) following the protocol provided by the manufacturer. Briefly, after the washing step (see 2.3), the bacterial suspension was diluted tenfold in an IF-A solution (Biolog Inc., No. 72401, IF-A, USA) to obtain a cellular density between 10 7 and 10 8 cell.mL -1 (OD620nm = 0.1). Then, 100 µL of this diluted suspension was added to each microplate well, and the plate was incubated for 40 hours at 30°C. The reading was performed at a wavelength of 590 nm (Preston-Mafham et al., 2002) with the TriStar LB 941 multimode microplate reader (Berthold Technologies, Germany). Each strain was tested in duplicate.

  range that is limited by the saturation rate of oxygen in water (Cregut et al., 2013) or the tightness of the system to air (contaminant oxygen). In this context, resazurin appears to be a relevant marker for monitoring biological activity (Cregut et al., 2013). This non-toxic non-fluorescent dye (λabsorbance max = 601 nm) is reduced in the cell cytoplasm to a fluorescent compound, resorufin (λabsorbance max = 571 nm, λexcitation = 535-560 nm, λemission = 590-615 nm -see Fig. SD-2), by the enzymatic chain involved in cellular respiration (Cregut et al., 2013; Fai and Grant, 2009; O'Brien et al., 2000).

  More than 1200 operational taxonomic units (OTUs) were identified as potential candidates in activated sludge from WWTPs. Several of these OTUs were directly rejected due to their strong pathogenicity (i.e., Campylobacteraceae, Helicobacteraceae,

Fig. 1 .BFig 2 .

 12 Fig.1. Strategy applied to the development of the BOD 5 assessment method (Isazadeh et al., 2016; Xia et al., 2010; Zhang et al., 2018).

  Fig 3. Overall preselection strategy of bacterial candidates.

  acid Gentobiose N-Acetyl-D-Glucosamine D-Fucose D-Glucose-6-PO4 L-Histidine Glucuronamide Keto-Glutaric-Acid Acetoacetic acid Sucrose N-Acetyl-D-galactosamine L-Fucose D-Fructose-6-PO4 L-Histidine Mucic Acid D-Malic Acid Propionic acid D-Turanose N-Acetyl-D-Galactosamine L-Rhamnose D

Fig. 8 . 1 Fig. 9 .

 819 Fig. 8. Correlation between the reference values (5 days required) and the BOD5 estimation

  PE, 030424, Dustcher, France) that were previously washed with distilled water. The samples were aliquoted into 2 mL tubes (PP, BiopurTM Safe-LockTM tubes, Eppendorf®, Germany) and stored at -20°C prior to use.

	Several parameters were measured to characterize the samples, including the BOD5 (see 2.7),
	chemical oxygen demand (NF T90-101/NF EN 872), and toxicity (ISO 11348, ISO 6341 and
	ISO 20666) (data shown in Table SD-2).
	2.7. BOD5 measurement
	2.7.1. Reference method (ISO 5815)
	-2) had several origins, including raw or
	treated waters from several WWTPs in the Nantes countryside (France, n = 72), stormwaters
	from the Bordeaux Region (France, n = 8) and treated waters collected from non-collective
	treatment units (autonomous sanitation system dedicated to the isolated private dwellings)
	installed in the Scientific and Technical Centre for Building of Nantes (CSTB, France, n =

24

). The sampling procedure used for this study follows the conditions described by the ISO 5667-2 and ISO 5667-3 standards. The samples were collected in 250 mL disposable plastic bottles (

  ,Z. resiniphila, B. vertrisii and B. funiculus), exhibited a modest range (only 6) of molecules that were likely to be biodegraded among the 71 proposed carbon sources.

	Conversely, group H offered a very wide set of biodegradation capabilities with ca. 85%
	biodegraded molecules (R. radiobacter, B. subtilis, Staphylococcus sp., S. yanoikuyae and A.
	aurescens).

). For example, group A, which consisted of 6 strains

(A. towneri, B. aquatica, P. pantrophus

  Fig 4. Average profiles obtained after clustering of the data collected from the 28 preselected bacterial strains (clustering carried out from 71 carbon sources). Black squares correspond to the carbon sources used as substrate by the bacteria strains.

		Bacterial
		strain
		A.aurescens
	Group H	S.yanoikuyae Staphylococcus sp.
		B.subtilis
		R.radiobacter
	Group G	E.helveticus E.coli
	Group F	P.mirabilis P.denitrificans
	Group E	C.pseudodiphteriticum S.auricularis
	Group D	V.paradoxus
		P.putida (DSM 84)
	Group C	P.putida (DSM 1868) C.necator
		P.aeruginosa (DSM 6279)
		P.aeruginosa (DSM 11634)
		R.rhodochrous
	Group B	B.mycoides C.testosteroni C.denitrificans
		T.linaloolentis
		A.towneri
		B.aquatica
	Group A	P.pantotrophus Z.resiniphila
		B.vertrisii
		B.funiculus
	Saccharic Acid Bromo-Succinic acid Formic Acid
	Carbon source

.h -1 . Consequently, the second strain should a priori provide a faster biological signal, as desired for our bioassay.

Improvement of the correlation model

To overcome this limitation and to propose a more reliable method for the assessment of the BOD5, the regression linear algorithms were compared to a neural network approach. The neural network is a framework for many different machine learning algorithms that can process complex data. Consequently, an exploratory step was performed to screen the architectures with the most potential. Several architectures were compared with the dedicated Neuro One software (Version 6.13.0.5, Netral, France). The differences between the tested architectures were based on the number of hidden neurons, the activation mode, and the data standardization. More than 800 models (80 architectures and 10 models by architecture obtained by iterations) were generated (see Table SD-3) by supervised learning from the overall database. The correlation rate obtained between the predicted and reference BOD5 values varied from 0.36 (below the model based on linear regression -0.58) to 0.93. Among the 80 tested architectures, several architectures had relevant predictive capacities with correlation rates superior to 0.8. The best model (r² = 0.93) was based on an orthonormalization (Gram Schmidt process) of inputs, 4 hidden neurons activated by a hyperbolic tangent algorithm and post-treatment of the outputs.

These models are iteratively designed from existing data (i.e., they modify the different variables that control the variables during the learning process according to the data taken into account). Consequently, over-learning is a significant risk, which leads to questions about the relevance and the robustness of the predicted BOD5 values obtained from new data. To remove this uncertainty, new models were designed using a fraction of the overall database.

Model validation

The global database was randomly separated into two sets: the learning (n=148) and the validation (n=60) sets (see 2.8.2). The learning set was used to design models with the 10 best a priori architectures determined during the previous step (see Table SD- Index of stress robustness Biodegradation kinetic (mg BOD.L -1 .h -1 )

A B