Adrien Marchand

Cleo Tebby

Rémy Beaudouin

Audrey Catteau

Jean-Marc Porcher

Cyril Turiès

Anne Bado-Nilles

Reliability evaluation of biomarker reference ranges for mesocosm and field conditions: cellular innate immunomarkers in Gasterosteus aculeatus

Keywords: biomarker reference ranges, fish conditions, innate immunity, Highlights

Due to their sensitivity to environmental contamination and their link with fish health status, innate immunomarkers are of great interest for environmental risk assessment studies.

Nevertheless, the lack of knowledge about the effect of confounding factors can lead to data misinterpretation and false diagnostics. So, the determination of reference values was of huge interest for the integration of biomarkers in biomonitoring programs. Laboratory immunomarker reference ranges (including cellular mortality, leucocyte distribution, phagocytosis activity, respiratory burst and lysosomal presence) that consider three confounding factors (season, sex and body size) were previously developed in three-spined stickleback, Gasterosteus aculeatus, from our husbandry. Usefulness of these reference ranges in biomonitoring programs depends on how they can be transposed to various experimental levels, such as mesocosm (outdoor artificial pond) and field conditions.

Immunomarkers were therefore measured every two months over one year in one mesocosm and in one site assumed to uncontaminated (Houdancourt, field). Differences between immunomarker seasonal variations in mesocosm and field fish on one side and laboratory fish on the other side were quantified: in some cases, seasonal trends were not significant or did not differ between mesocosm and laboratory conditions, but overall, models developed based on data obtained in laboratory conditions were poorly predictive of data obtained in mesocosm or field conditions. To propose valuable field reference ranges, mesocosm and field data were integrated in innate immunomarker modelling in order to strengthen the knowledge on the effect of confounding factors. As in laboratory conditions, sex was overall a confounding factor only for necrotic cell percentage and granulocyte-macrophage distribution and size was a confounding factor only for cellular mortality, leucocyte distribution and phagocytosis activity. Confounding factors explained a large proportion of immunomarker variability in particular for phagocytosis activity and lysosomal presence. Further research is needed to test the field models in a biomonitoring program to compare the sensitivity of immunomarkers to the confounding factors identified in this study and the sensitivity to various levels of pollution.

Introduction

Biomarkers reflect the physiological state of an organism and can serve to detect exposures to xenobiotics or their early effects. They have therefore been proposed to determine effects of water quality on aquatic wildlife. Nevertheless, to be used currently in biomonitoring campaigns, some criteria, including natural variability [START_REF] Van Der Oost | Fish bioaccumulation and biomarkers in environmental risk assessment : a review[END_REF], should be considered in data analysis [START_REF] Flammarion | Marqueurs biochimiques de pollution dans les écosystèmes aquatiques continentaux. Exemple d'utilisation prospectives pour le gestionnaire[END_REF][START_REF] Van Der Oost | Fish bioaccumulation and biomarkers in environmental risk assessment : a review[END_REF] to reduce data misinterpretation. In fact, many confounding factors, including variations of environmental conditions (temperature, salinity, photoperiod or oxygen levels) and organism's morphology and life history traits (sex, age or sexual maturation) could greatly influence biomarker responses [START_REF] Bowden | Modulation of the immune system of fish by their environment[END_REF][START_REF] Bowden | Seasonal variation and the immune response: A fish perspective[END_REF][START_REF] Uribe | Innate and adaptive immunity in teleost fish: a review[END_REF]. For example, [START_REF] Shepherd | Characterization and evaluation of sex-specific expression of suppressors of cytokine signaling (SOCS)-1 and -3 in juvenile yellow perch (Perca flavescens) treated with lipopolysaccharide[END_REF] showed differences between sexes in the involvement of the SOCS genes in the yellow perch immune response. Since the immune system interacts in a bidirectional way with the endocrine system, variations in hormone concentrations due to the reproductive cycle of wild fish may be a major confounding factor for the measurement of immune responses. In fact, gonadal sex hormones affect innate immune capabilities by acting directly on hormonal receptors or indirectly through non-hormonal receptor mediated mechanisms [START_REF] Ahmed | The immune system as a potential target for environmental estrogens (endocrine disrupters): a new emerging field[END_REF]. In the same way, sex steroids are known to modulate greatly the immune response of fish [START_REF] Slater | Testosterone alters the immune response of chinook salmon, Oncorhynchus tshawytscha[END_REF][START_REF] Yamaguchi | Effects of estradiol, progesterone and testosterone on the function of carp, Cyprinus carpio, phagocytes in vitro[END_REF][START_REF] Cabas | Role of estrogens in fish immunity with special emphasis on GPER1[END_REF]. Regarding the effect of fish body size on immune response, little information was available. Even so, the attractiveness of fish to parasites was appears to be higher in larger fish notably due to increased contact with water flow. To stop this phenomenon, the host could increase their immune responses [START_REF] Lo | Parasite diversity/host age and size relationship in three coral-reef fishes from French Polynesia[END_REF]. All these sources of variability may disturb biomonitoring results, limit the interpretation of variation between sites, and may lead to incorrect site classification.

To limit the risk of false positive and false negative results, many authors suggest comparing biomonitoring results to robust reference values [START_REF] Barrick | Baseline levels of biochemical biomarkers in the endobenthic ragworm Hediste diversicolor as useful tools in biological monitoring of estuaries under anthropogenic pressure[END_REF][START_REF] Barrick | A novel methodology for the determination of biomarker baseline levels in the marine polychaete Hediste diversicolor[END_REF][START_REF] Burgeot | Acethylcholinesterase : Methodology development of a biomarker and challenges of its application for biomonitoring[END_REF][START_REF] Kumari | Seasonal variation in the innate immune parameters of the Asian catfish Clarias batrachus[END_REF][START_REF] Mohanty | Reference ranges and seasonal variations in innate immune responses of kalbasu , Labeo calbasu ( Hamilton )[END_REF][START_REF] Sanchez | Assessment of seasonal variability of biomarkers in three-spined stickleback (Gasterosteus aculeatus L.) from a low contaminated stream: Implication for environmental biomonitoring[END_REF]. Unfortunately, most of the developed reference values ignore confounding factor effects by providing either only one mean with a confidence interval or a range of means over a sampling period [START_REF] Kumari | Seasonal variation in the innate immune parameters of the Asian catfish Clarias batrachus[END_REF][START_REF] Mohanty | Reference ranges and seasonal variations in innate immune responses of kalbasu , Labeo calbasu ( Hamilton )[END_REF][START_REF] Sanchez | Assessment of seasonal variability of biomarkers in three-spined stickleback (Gasterosteus aculeatus L.) from a low contaminated stream: Implication for environmental biomonitoring[END_REF]. To better determine effects of confounding factors on biomarker responses, two methods were proposed. The first method, suggested by the Convention for the Protection of the Marine Environment of the North-East Atlantic (OSPAR) and by the International Council for the Exploration of the Sea (ICES), consists in collecting organisms on a same reference site during several years to integrate effects of many confounding factors [START_REF] Barrick | Baseline levels of biochemical biomarkers in the endobenthic ragworm Hediste diversicolor as useful tools in biological monitoring of estuaries under anthropogenic pressure[END_REF][START_REF] Barrick | A novel methodology for the determination of biomarker baseline levels in the marine polychaete Hediste diversicolor[END_REF][START_REF] Burgeot | Acethylcholinesterase : Methodology development of a biomarker and challenges of its application for biomonitoring[END_REF]. Nevertheless, this method was only representative of the water quality of the site used and it could be difficult to separate specific effects of each confounding factor. Within the second method, the influence of each confounding factor is evaluated separately before being integrated in a larger model of biomarker responses [START_REF] Coulaud | In situ feeding assay with Gammarus fossarum (Crustacea): Modelling the influence of confounding factors to improve water quality biomonitoring[END_REF][START_REF] Hanson | Using biological data from field studies with multiple reference sites as a basis for environmental management: The risks for false positives and false negatives[END_REF][START_REF] Krell | An estuarine mudsnail in situ toxicity assay based on postexposure feeding[END_REF][START_REF] Maltby | Evaluation of the Gammarus pulex in situ feeding assay as a biomonitor of water quality: Robustness, responsiveness, and relevance[END_REF]. Among all developed biomarkers, due to its sensitivity to environmental contaminations and its direct link with individual health status, the immune response is considered as an attractive non-specific marker for environmental biomonitoring [START_REF] Bols | Ecotoxicology and innate immunity in fish[END_REF] which could help to better identify risks associated to an ecosystem contamination [START_REF] Bado-Nilles | Applications in environmental risk assessment of leucocyte apoptosis, necrosis and respiratory burst analysis on the European bullhead, Cottus sp[END_REF]). Among all the measurable parameters composing innate and acquired components of the immune system, biomarkers related to innate immune functions were highly relevant due to their response being non-dependent of previous exposure to foreign antigens [START_REF] Monserrat | Pollution biomarkers in estuarine animals: Critical review and new perspectives[END_REF]. In teleosts, the innate mechanism of phagocytosis appears to be the central cellular immune process. Mainly two leucocyte sub-populations, the granulocytes and the monocytes/macrophages ("granulocyte-macrophage percentage"), are responsible for phagocytosis. This process consists in the adhesion to the leucocyte membrane and the engulfment of the target particle, followed by its destruction by a combined action of aerobic destruction pathway by respiratory burst and anaerobic destruction pathway by lytic enzymes contained in lysosomes [START_REF] Ellis | Immunity to bacteria in fish[END_REF][START_REF] Magnadóttir | Innate immunity of fish (overview)[END_REF]. In ecotoxicology, each part of this phagocytosis mechanism has been described as an attractive biomarker of field pollution in fish [START_REF] Betoulle | Lindane increases in vitro respiratory burst activity and intracellular calcium levels in rainbow trout (Oncorhynchus mykiss) head kidney phagocytes[END_REF][START_REF] Bols | Ecotoxicology and innate immunity in fish[END_REF][START_REF] Reynaud | The effects of polycyclic aromatic hydrocarbons on the immune system of fish: a review[END_REF]. For example, the phagocytic efficiency was repressed in rainbow trout (Oncorhynchus mykiss) after one week of exposure to Montreal municipal sewage effluent [START_REF] Salo | Effects of Montreal municipal sewage effluents on immune responses of juvenile female rainbow trout (Oncorhynchus mykiss)[END_REF]). In the same way, phagocytic efficiency and capacity were reduced in rainbow trout coming from industrial waste incineration site [START_REF] Benchalgo | Immunotoxic effects of an industrial waste incineration site on groundwater in rainbow trout (Oncorhynchus mykiss)[END_REF]. Modifications of respiratory burst index and lysosomal presence were affected by many pollutants [START_REF] Arnold | Simultaneous exposure of fish to endosulfan and disulfoton in vivo: ultrastructural, stereological and biochemical reactions in hepatocytes of male rainbow trout (Oncorhynchus mykiss)[END_REF][START_REF] Roméo | Cadmium and copper display different responses towards oxidative stress in the kidney of the sea bass Dicentrarchus labrax[END_REF][START_REF] Ahmad | Enzymatic and nonenzymatic antioxidants as an adaptation to phagocyte-induced damage in Anguilla anguilla L. following in situ harbor water exposure[END_REF][START_REF] Santos | Responses of European eel (Anguilla anguilla L.) circulating phagocytes to an in situ closed pulp mill effluent exposure and its association with organ-specific peroxidative damage[END_REF]) and were currently associated to cell death [START_REF] Holtzman | Lysosomes[END_REF][START_REF] Zapata | Photooxidative damage to lysosomes of cultures macrophages by acridine orange[END_REF][START_REF] Risso-De Faverney | Cadmium induces apoptosis and genotoxicity in rainbow trout hepatocytes through generation of reactive oxygene species[END_REF][START_REF] Guicciardi | Lysosomes in cell death[END_REF][START_REF] Krumschnabel | Oxidative stress, mitochondrial permeability transition, and cell death in Cu-exposed trout hepatocytes[END_REF][START_REF] Kurz | Lysosomes and oxidative stress in aging and apoptosis[END_REF]. For example, copper induced both necrotic and apoptotic cell death on rainbow trout hepatocytes by stimulation of ROS production, essentially into mitochondria, which favors destabilization of lysosomal membranes [START_REF] Krumschnabel | Oxidative stress, mitochondrial permeability transition, and cell death in Cu-exposed trout hepatocytes[END_REF]. In the same way, an increase of ROS production in Nile tilapia (Oreochromis niloticus) splenocytes by endosulfan could induce at term lysosomal destabilization and cellular mortality [START_REF] Tellez-Bañuelos | Oxidative stress in macrophages from spleen of Nile tilapia (Oreochromis niloticus) exposed to sublethal concentration of endosulfan[END_REF]. Induction of cellular mortality and decrease of phagocytosis activity could contribute to an increased susceptibility to opportunistic infections in animals [START_REF] Arkoosh | Effect of Pollution on Fish Diseases: Potential Impacts on Salmonid Populations[END_REF][START_REF] Misumi | p,p'-DDE depresses the immune competence of chinook salmon (Oncorhynchus tshawytscha) leukocytes[END_REF][START_REF] Bado-Nilles | Immune effects of HFO on European sea bass, Dicentrarchus labrax, and Pacific oyster, Crassostrea gigas[END_REF][START_REF] Kreutz | Exposure to sublethal concentration of glyphosate or atrazine-based herbicides alters the phagocytic function and increases the susceptibility of silver catfish fingerlings (Rhamdia quelen) to Aeromonas hydrophila challenge[END_REF][START_REF] Danion | Effects of in vivo chronic hydrocarbons pollution on sanitary status and immune system in sea bass (Dicentrarchus labrax L.)[END_REF][START_REF] Marchand | Evaluation of chlorpyrifos effects, alone and combined with lipopolysaccharide stress, on DNA integrity and immune responses of the three-spined stickleback, Gasterosteus aculeatus[END_REF]. Nevertheless, as previously explained, use of these immunomarkers in biomonitoring campaigns depends on applicability of reference values. Few studies provide information on their natural variability, which partly explains why they are scarcely used by managers in biomonitoring contexts.

Reference ranges of some innate immunomarkers were first modelled in function of season, sex, and body size in the three-spined stickleback (Gasterosteus aculeatus) [START_REF] Marchand | Modelling the effect of season, sex, and body size on the three-spined stickleback, Gasterosteus aculeatus, cellular innate immunomarkers: A proposition of laboratory reference ranges[END_REF]). In the present paper, the previously established laboratory reference ranges are evaluated at various experimental levels, ranging from the laboratory to the field. In this way, to evaluate the relevance of immunomarker reference ranges previously developed [START_REF] Marchand | Modelling the effect of season, sex, and body size on the three-spined stickleback, Gasterosteus aculeatus, cellular innate immunomarkers: A proposition of laboratory reference ranges[END_REF], data obtained in stickleback in other conditions, (outside artificial pond: mesocosm; Houdancourt reference site: field) were compared to the laboratory reference ranges.

II.

Material To make the mesocosm reference ranges, 112 sticklebacks were maintained in one outside artificial pond containing sediments, natural vegetation, phytoplankton, periphyton, and a macroinvertebrate community which were well-suited to stickleback [START_REF] Jones | Imaging flow cytometry and confocal microscopybased examination of F-actin and phosphoinositide dynamics during leukocyte immunetype receptor-mediated phagocytic events[END_REF]. Every two months, from February 2016 to December 2016, 15 -20 fish were sampled (Table I). Fish were anaesthetized using tricaine methanesulfonate (100 mg/L, Sigma) then sacrificed, measured, weighed, and the spleen was removed to measure immune responses.

Field reference ranges were developed using sticklebacks sampled at Houdancourt (60, Oise, France -Table I), an uncontaminated site located immediately upstream of a watercress exploitation, from October 2016 to April 2018. At each sampling date, temperature, dissolved oxygen, pH and conductivity were monitored and 20 adult fish were caught by electrofishing, anaesthetized (tricaine methanesulfonate, 100 mg/L, Sigma), sacrificed, measured, weighed, and the spleen was removed to measure immune responses.

Mesocosm and laboratory sampling were made strictly at the same time, whereas field sampling was sometimes a few weeks before or after.

b. Leucocyte isolation and innate immune biomarker analysis

The spleen filters plasma, traps blood-borne substances, and enriches blood with new immune cells [START_REF] Press | The morphology of the immune system in teleost fishes[END_REF], and was therefore selected to assess immunological characteristics. A splenic leucocyte suspension was obtained by gently pressing the spleen through sterilized nylon mesh (40 µm, Sigma) with 5 mL Leibovitz 15 (L15) medium (Sigma) containing heparin lithium (100 mg/L, Sigma), penicillin (500 mg/L, Sigma), and streptomycin (500 mg/L, Sigma). To eliminate any bias due to stressful conditions of the fish before sacrifice, samples were stored during 12 hr at 4°C [START_REF] Bado-Nilles | Applications in environmental risk assessment of leucocyte apoptosis, necrosis and respiratory burst analysis on the European bullhead, Cottus sp[END_REF]. Then, a

Malassez haemocytometer was used to adjust leucocyte concentration in samples to 10 6 cell/mL in L15 medium. Analyses were carried out by flow cytometry, on whole leucocytes, using a CyAn TM ADP (Beckman coulter) flow cytometer. A total of 10,000 events per sample were analyzed after cell excitation by a 488 nm-argon laser.

Percentage of each leucocyte sub-population (lymphocyte and granulocyte-macrophage) were determined by measuring size (forward scatter, FSC) and complexity (size scatter, SSC) [START_REF] Bado-Nilles | Applications in environmental risk assessment of leucocyte apoptosis, necrosis and respiratory burst analysis on the European bullhead, Cottus sp[END_REF]. For ease of reading, only the granulocyte-macrophage percentages among the total leucocyte populations are presented in figures and tables.

A double labelling, with Yo-PRO®-1 (Invitrogen, final concentration: 3.14 mg/L) and propidium iodure (Invitrogen, final concentration: 5.01 mg/L) probes, was used to assess the cellular mortality percentages. After 10 minutes of incubation on ice and in the dark, cellular fluorescence parameters were measured. Apoptotic and necrotic cells expressed respectively green (FL1) and red (FL3) fluorescences [START_REF] Bado-Nilles | Applications in environmental risk assessment of leucocyte apoptosis, necrosis and respiratory burst analysis on the European bullhead, Cottus sp[END_REF].

The measurement of leucocyte respiratory burst was performed using a modification of the [START_REF] Chilmonczyk | Flow cytometry as a tool for assessment of the fish cellular immune response to pathogens[END_REF] technique, adapted for stickleback. Determination of reactive oxygen species (ROS) production in unstimulated cells depends upon the cell incorporating 2',7'-dichlorodihydrofluorescein diacetate acetyl ester (H2DCF-DA, Invitrogen, final concentration: 29.30 mg/L), a stable non-fluorescent molecule which was hydrolysed to dichloro-dihydro-fluorescein diacetate (DCFH) by cytosolic enzymes. When leucocytes were stimulated with phorbol 12-myristate 13-acetate (PMA, Invitrogen, final concentration: 9.25 mg/L), the most specific inductor of respiratory burst [START_REF] Ambrozova | The effect of lipid peroxidation products on reactive oxygen species formation and nitric oxide production in lipopolysaccharidestimulated RAW 264.7 macrophages[END_REF][START_REF] Chadzinska | Adrenergic regulation of the innate immune response in common carp (Cyprinus carpio L.)[END_REF], H2DCF-DA was hydrolysed by H2O2. Finally, the DCFH obtained was oxidized to the fluorescent dichlorofluorescein (DCF) to enable quantification, after 30 min of incubation at room temperature, by flow cytometry of unstimulated and stimulated cells in FL1. The respiratory burst index was determined as the ratio of fluorescence of PMA stimulated cells (H2DCF-DA plus PMA) to that of unstimulated cells (H2DCF-DA).

Intracellular lysosomal presence was determined by incubation of spleen leucocyte suspension with 0.3 mg/L of acridine orange (AO, Sigma), a lysosomotropic weak base, for 20 min in the dark at room temperature. Then, fluorescence was measured in FL3 [START_REF] Bado-Nilles | Flow cytometry detection of lysosomal membrane integrity in the three spined stickleback (Gasterosteus aculeatus L.) immune cells: applications in environmental aquatic immunotoxicology[END_REF].

Phagocytosis activity was evaluated after 1 h of incubation in the dark and at room temperature of spleen leucocyte suspension with fluorescent microsphere at a concentration of 2.7 x 10 7 particles/mL (Fluorospheres® carboxylate-modified microsphere, diameter 1 µm, Invitrogen) [START_REF] Gagnaire | In vitro effects of cadmium and mercury on Pacific oyster, Crassostrea gigas (Thunberg), haemocytes[END_REF]. Phagocytosis activity was characterized by two biomarkers, phagocytic capacity (capacity of leucocyte plasma membrane; fluorescence of at least one bead currently attached to the membrane), and phagocytic efficiency (percentage of leucocytes that had engulfed microspheres; fluorescence of at least three beads).

c. Statistical analysis

Statistical analysis was performed with the software R v.2.14.1 (R Core Team, 2014).

Normality was checked using Shapiro's tests and homogeneity of variance was tested using Levene's test (p ≤ 0.05). When the normality assumption was not fulfilled, data were logtransformed if this improved normality.

To evaluate differences in biomarker levels between "experimental levels", three different statistical analyses were made:

-First, at each sampling period, the differences between laboratory and mesocosm or field data were tested either by an ANCOVA followed by a Dunnett test (parametric data, p ≤ 0.05) or by a Kruskal-Wallis test followed by a Dunn test with comparison to the control (non-parametric data, p ≤ 0.05, "dunn.test.control" function of R package "PMCMR").

-To determine whether mesocosm or field data fitted within the laboratory reference ranges, the percentage of data outside the reference ranges were calculated for each condition. Unilateral binomial tests (p ≤ 0.05) were performed at each sampling period to detect if there was a significant increase of data out of the ranges between laboratory data and mesocosm or field data.

-The predictive power of the previously-built models, which were based on data collected in laboratory conditions, towards mesocosm and field data was also evaluated using the predictive squared correlation coefficient (Q²) as previously described [START_REF] Marchand | Modelling the effect of season, sex, and body size on the three-spined stickleback, Gasterosteus aculeatus, cellular innate immunomarkers: A proposition of laboratory reference ranges[END_REF]).

The differences in biomarker levels between the three "experimental levels" highlighted the need for specific field reference ranges. These were determined with the following steps:

-First, a linear model that included all confounding factors (period, experimental level, sex and size) and their binary and ternary interactions was adjusted for each biomarker.

Period, experimental level and sex were included as qualitative variables whereas size was included as a quantitative variable. Though the aim was to build field reference ranges, data obtained in mesocosms were included in order to consolidate confounding effects if appropriate, for example by confirming relationships between size and biomarker levels.

-The effects of each confounding factor were evaluated using a type II analysis of covariance (ANCOVA) [START_REF] Shaw | Anova for Unbalanced Data: An Overview[END_REF] ("Anova" function of R package "car", p ≤ 0.05), owing for the fact the data was slightly unbalanced due to postmortem identification of sex. The ANCOVA model for each biomarker was selected using a downwards stepwise procedure based on the AIC and then by removing effects that were not statistically significant according to the F-test, starting from the highest order interactions.

Reference ranges were estimated using the 95% prediction interval.

-The model was evaluated using the coefficient of determination (R²), and the predictive squared correlation coefficient determined by Leave-One-Out (Q²LOO).

III. Results

a. Normality and heteroscedasticity

The results of normality tests (Shapiro test) and heteroscedasticity tests (Levene's test) made on residuals of the linear models were presented in the Table II. Briefly, after logtransformation of biomarker values, the normality conditions were complete in most of the groups. Nevertheless, some deviations from the normality assumption were found at specific periods, mainly for the respiratory burst index (Table II). Levene's test revealed differences in variance between groups mainly for respiratory burst index, capacity and phagocytosis efficiency (Table II). Observation of the variance in each group showed higher variances in laboratory data regarding respiratory burst index in June, capacity in February, and phagocytosis efficiency in December (data not shown).

b. Comparison between mesocosm and laboratory data

The results of the comparisons of means between "experimental levels" (Table III) were compared to results based on percentages of mesocosm data outside the laboratory reference ranges (Figure 1): the two methods provided complementary results for all biomarkers.

The results presented hereafter are based on statistical significance levels of 5%. Since the calculating Q² were low for granulocyte-macrophage (-1,54) and apoptotic cell (-1,07)

percentages, the models predict some discrepancy between laboratory and mesocosm data in December (40% for apoptotic cell percentage, binomial test: p = 2.86 x 10 -6 ; 20 % for granulocyte-macrophage percentage, binomial test: p = 0.0159; Figure 1) and in June (27.8 % for apoptotic cell percentage, binomial test: p = 0.0155; Figure 1, Table IV) whereas the comparison of means test does not (Table III). Only the granulocyte-macrophage percentage difference observed in June, with drastically highest values in mesocosm condition was observed with both methods (88.8 % of values out of range, binomial test: p = 1.31 x 10 -21 , Figure 1; Dunn test, p = 3.2 x 10 -8 , Table III).

A better predictivity of the models was shown for phagocytic activity, including efficiency (Q² = -0.40) and capacity (Q² = -0.36), and necrotic cell percentage (Q² = -0.22). The significant high decrease of phagocytic efficiency shown in February (Dunnett test: p = 1.71 x 10 -6 , Table III) and June (Dunnett test: p = 1.71 x 10 -6, Table III) were also detected with the predictive model, with 25 % (binomial test: p = 2.57 x 10 -3 ) and 27.8 % (binomial test: p = 0.00155) of deviation respectively (Figure 1). In the same way, no significant impact was shown with both methods in August and October. On the other hand, in December, the predictive model shown dissimilarity between laboratory and mesocosm efficiency percentages (60% of deviation, binomial test: p = 2.11 x 10 -11 ; Figure 1) without statistical significance (Table III). On the opposite, in April, statistical test shown a significant light decrease of efficiency percentage in mesocosm condition (Dunnett test: p = 8.5 x 10 -18 , Table III) without increase of error percentage with the model (Figure 1). Concerning the phagocytic capacity, a good projection of mesocosm data on laboratory reference ranges was observed in February, April, August and October (Figure 1). Nevertheless, the significant light modulation of capacity percentage observed in December (Dunnett test: p = 1 x 10 -6 , light increase in mesocosm condition, Table III) and June (Dunnett test: p = 1 x 10 -6 , light decrease in mesocosm condition, Table III III), the projection of mesocosm data on laboratory reference ranges was good for this biomarker (Figure 1).

The more important deviations between mesocosm and laboratory data were observed in June. One the other hand, in August no significant differences were identified whatever the immunomarker tested and the method used.

Overall, for all immunomarkers except lysosomal presence, there were statistically significant differences in seasonal trends between mesocosm and laboratory data (F-test between nested regression models, p<0.05). When data collected in June was removed from the analysis, no statistically significant differences were identified either regarding necrotic cell percentage.

The proportion of total variance explained by period and all related interactions is compared to the proportion of total variance explained by the differences in seasonal trend in Table VI.

Seasonal trends were strongest for phagocytosis efficiency and although differences in seasonal trend between mesocosm and laboratory data were comparatively weak (5.54 % of total variance), they represented significant contributions to the model (F-test, p=6.44 x 10 -11 ).

Differences in seasonal trends were also particularly marked for necrotic cell percentage (due to June, 19.2 % of total inertia, F-test, p=5.86 x 10 -14 ) and macrophage-granulocyte percentage (18.4 % of total inertia, p =6.66 x 10 -16 ). Lysosomal presence, for which no differences in seasonal trend were noted between mesocosm and laboratory data, was the immunomarker with the weakest seasonal trend in mesocosm and laboratory data (26.5 % of total inertia).

c. Comparison between field and laboratory data

Globally, projection of field data on laboratory reference ranges revealed high discrepancy to the model predictions (Figure 1). Like for mesocosm data, when these results were compared to classic statistical analysis (Table III), some dissimilarities were found.

With a Q² value highly negative, equal to -5.26, the phagocytosis efficiency was the biomarker that fit the less to the laboratory model. Indeed, differences between laboratory and field experimental conditions were found at each period, except in December, with higher values for field data in August (Dunnett test: : p =1 x 10 -7 ), but lower values in February (Dunn test: p = 7.11 x 10 -15 ), April (Dunnett test: p = 1.51 x 10 -18 ) and October (Dunn test: p = 1.51 x 10 -18 ) (Table III). These differences were observable by projection of field data on laboratory reference ranges in February (85 % of deviation, binomial test: p = 7.52 x 10 -20 )

and April (65 % of deviation, binomial test: p = 6.79 x 10 -13 ) but not in August (5 % of deviation, binomial test: : p = 1 and October (10 % of deviation, binomial test: : p = 2.64).

Moreover, the model predicts a difference in the December period (27.8 % of deviation, binomial test: p = 0.00155), not detected by statistical tests (Figure 1).

Low predictive power was also found for the granulocyte-macrophage percentage model (Q² = -1.1), for the respiratory burst index model (Q² = -0.72), for the phagocytic capacity model (Q² = -0.51), for the necrotic cell percentage (Q² = -0.44), and for apoptotic cell percentage (Q² = 0.016). The significant increase of granulocyte-macrophage percentages shown in December (Dunnett test: p = 0.01, Table III), February (Dunn test: p = 1 x 10 -4 , Table III) and October (Dunn test: p = 1 x 10 -6 , Table III) were also detected with the predictive model, with 33.3 % (binomial test: p = 1.72 x 10 -4 ), 50 % (binomial test: p = 1.13 x 10 -8 ) and 25 % (binomial test: : p = 2.57 x 10 -3 ) of deviation, respectively (Figure 1). On the contrary, the significant increase in April (Dunn test: p = 1 x 10 -6 ) was not detected by the statistical model

(5 % of deviation, binomial test: p = 1.00). Even if the respiratory burst index was higher in February (Dunn test: p = 2.1 x 10 -4 ), April (Dunn test: p = 0.00015) and October (Dunn test: p = 3.8 x 10 -5 ), but lower in August (Dunn test: p = 2.95 x 10 -5 ) in field compared to laboratory data (Table III), the proportion of values out of range was not significantly different from the expected 5%. As it was detected by the statistical model (100 % of values out of range, binomial test: p = 9.54 x 10 -27 ), strong decrease of capacity was observed in August (Dunnett test: p = 1 x 10 -6 ). On the contrary, the low capacity decreases observed in February (Dunn test: p = 4 x 10 -5 ) ) and in October (Dunnett test: p = 5 x 10 -4 ) was not detected by data projection on reference ranges (15 % of deviation and 0 % of deviation respectively; Figure 1). For the necrotic cell percentage, the same effects were detected using both methods in February (Dunnett test: p = 1.5 x 10 -6 ; 40 % of deviation, binomial test: p = 2.86 x 10 -6 ) and August (Dunn test: p = 3 x 10 -4 ) but not in December (Table III). Considering apoptotic cell percentage, significant higher values were found in field experimental condition in April (Dunnett test: p = 2 x 10 -4 ) that matched with the model predictions (35 % of deviation; binomial test: p = 3.39 x 10 -5 ).

With a Q² equal to 0.23, the model designed for the lysosomal presence was the most predictive for field data. Compared to laboratory data, lysosomal presence was significantly higher in field experimental condition in February (Dunnett test: p = 1 x 10 -4 ) and April (Dunnett test: p = 2.7 x 10 -16 ) but lower in August (Dunnett test: p = 1 x 10 -5 ) and October (Dunnett test: p = 0.02; Table III). These effects were found again using projection on reference ranges in February (25 % of values out of range; binomial test: p = 0.002), April 80 % of values out of range; binomial test: p < 2.2 x 10 -11 ) and August (50 % of values out of range; binomial test: p = 1.13 x 10 -8 ) but not in October (Figure 1). Statistically significant differences in seasonal trends between field and laboratory data for all immunomarkers (F-test between nested regression models, p<0.05). Seasonal trends were strongest for lysosomal presence, capacity, and phagocytosis efficiency (F-test, p<10 -16 ), for which differences in seasonal trends between laboratory and field data represented 26.6 %, 23.8 %, and 18.8 % of total variance respectively. Differences in seasonal trends were less strong for macrophage-granulocyte percentage (3.09 % of total inertia, p =2.17 x 10 -2 ).

d. Evaluation of the impact of the "experimental level" factor on the proposed reference ranges

The models designed for respiratory burst index and for apoptotic cells explained only a small proportion of the data variability with R² equal to 0.29 and 0.33 respectively. The necrotic cells percentage was slightly better (R² = 0.43) explained by the confounding factors ("month", "experimental level", "sex", "size") and their interactions (Table IV). The global type-II ANCOVA revealed that "month", "experimental level" (for apoptotic cell percentages, respiratory burst and necrotic cell percentages) and "size" (for respiratory burst index, apoptotic cell percentages, and necrotic cell percentages) all had a statistically significant effect on these immunomarkers. In addition to the "month:experimental level" interaction which influenced these three immunomarkers, the apoptotic cell percentages were also impacted by "month:size" interaction and the necrotic cell percentages by "month:sex" interaction (Table IV). The predictivity of the models including all the significant factors and interactions was relatively low for the immunomarkers cited above. With a Q²LOO almost equal, the apoptotic cell percentages (Q²LOO = 0.23) and the respiratory burst index (Q²LOO = 0.24) were the less well predicted biomarkers by the model. Higher, but still low, predictive power was found for the necrotic cell percentages, with a Q²LOO equal to 0.43 (Table VI).

Granulocyte-macrophage percentages (R² = 0.52), lysosomal presence (R² = 0.54), and capacity (R² = 0.63) presented better correlation with these factors (Table IV). Indeed, the "month", the 'experimental level" and their interaction have a great influence on these three immunomarkers. With granulocyte-macrophage percentages and capacity the interaction "period:size" was strongly significant. Effects of "size" were more considerable for capacity than for granulocyte-macrophage percentages and lysosomal presence. Granulocytemacrophage percentages were also influenced by "period:sex" and "experimental level:size" interactions (Table IV). Medium-high level of predictivity was found for theses markers, with Q²LOO equal to 0.51 for lysosomal presence, 0.60 for phagocytic capacity and 0.46 for granulocyte-macrophage percentages.

The phagocytic efficiency was highly correlated to the factors studied here (R² = 0.80). The "period" and the "experimental level" factors have a strong influence while the "size" factor influence was less important (Table IV). This marker was also highly influenced by "period:size", "period:experimental level" and "size:experimental level" interactions. This good correlation with confounding factors was associated with a high predictive power of the model including significant factors and interactions (Q²LOO = 0.77) (Table IV).

IV. Discussion

Use of robust reference values for biomarkers is important in biomonitoring programs to minimize data misinterpretation. Nevertheless, due to the influence of environmental factors on biomarkers [START_REF] Hanson | Spatial and annual variation to define the normal range of biological endpoints: An example with biomarkers in perch[END_REF][START_REF] Jolly | Multi-biomarker approach in wild European bullhead, Cottus sp., exposed to agricultural and urban environmental pressures: Practical recommendations for experimental design[END_REF][START_REF] Sanchez | Fish biomarkers for environmental monitoring within the Water Framework Directive of the European Union[END_REF], the addition of confounding factors in the statistical model should improve predictions of biomarker reference values in function of site characteristics [START_REF] Coulaud | In situ feeding assay with Gammarus fossarum (Crustacea): Modelling the influence of confounding factors to improve water quality biomonitoring[END_REF][START_REF] Maltby | Evaluation of the Gammarus pulex in situ feeding assay as a biomonitor of water quality: Robustness, responsiveness, and relevance[END_REF]. In this way, many authors developed laboratory reference values comprising confounding factor effect for field evaluation [START_REF] Barrick | Baseline levels of biochemical biomarkers in the endobenthic ragworm Hediste diversicolor as useful tools in biological monitoring of estuaries under anthropogenic pressure[END_REF][START_REF] Barrick | A novel methodology for the determination of biomarker baseline levels in the marine polychaete Hediste diversicolor[END_REF][START_REF] Burgeot | Acethylcholinesterase : Methodology development of a biomarker and challenges of its application for biomonitoring[END_REF][START_REF] Coulaud | In situ feeding assay with Gammarus fossarum (Crustacea): Modelling the influence of confounding factors to improve water quality biomonitoring[END_REF][START_REF] Krell | An estuarine mudsnail in situ toxicity assay based on postexposure feeding[END_REF][START_REF] Maltby | Evaluation of the Gammarus pulex in situ feeding assay as a biomonitor of water quality: Robustness, responsiveness, and relevance[END_REF]. In case of stickleback innate immunomarkers, the first step was performed in a previous study [START_REF] Marchand | Modelling the effect of season, sex, and body size on the three-spined stickleback, Gasterosteus aculeatus, cellular innate immunomarkers: A proposition of laboratory reference ranges[END_REF] by assessing the effect of sampling period, fish size and sex in laboratory conditions.

Nonetheless, before using these reference values on the field, the variations due to natural conditions should be included. Natural modulation of the immune response could be due to variations of environmental conditions, especially temperature and photoperiod [START_REF] Bly | Temperature and teleost immune functions[END_REF][START_REF] Bowden | Seasonal variation and the immune response: A fish perspective[END_REF][START_REF] Dittmar | Heat and immunity: An experimental heat wave alters immune functions in three-spined sticklebacks (Gasterosteus aculeatus)[END_REF][START_REF] Morgan | The effect of seasonality on normal haematological and innate immune parameters of rainbow trout Oncorhynchus mykiss L[END_REF][START_REF] Porter | Temperature, light intensity and plasma melatonin levels in juvenile Atlantic salmon[END_REF] and to reproductive status [START_REF] Hou | Changes in Immunoglobulin Producing Cells in Response to Gonadal Maturation in Rainbow Trout[END_REF][START_REF] Kortet | Breeding-related seasonal changes in immunocompetence, health state and condition of the cyprinid fish, Rutilus rutilus[END_REF][START_REF] Slater | Season and physiological parameters modulate salmonid leucocyte androgen receptor affinity and abundance[END_REF][START_REF] Zapata | Photooxidative damage to lysosomes of cultures macrophages by acridine orange[END_REF]. During the one-year sampling, temperature and photoperiod variations were similar in all three experimental conditions and the influence of sexual hormones during breeding period was expected to be similar.

To determine if laboratory reference values could predict other experimental levels, new data was compared to data obtained in laboratory conditions either using reference ranges obtained with a regression model or by simply comparing means at given periods. The results using both methodologies differed for several biomarkers. The laboratory reference ranges developed previously took the effect of fish size into account on most of these immunomarkers, except respiratory burst index and lysosomal presence [START_REF] Marchand | Modelling the effect of season, sex, and body size on the three-spined stickleback, Gasterosteus aculeatus, cellular innate immunomarkers: A proposition of laboratory reference ranges[END_REF] for which the models developed in this paper are therefore not expected to be an improvement over comparisons of means at a given period. For the other five immunomarkers, since fish body size did not cover the same range in mesocosm and field data as in the laboratory data, statistically significant differences in means could actually be due to the confounding effect of fish size. For example, the efficiency possessed a strong "size" effect in December and only the comparison with the laboratory reference ranges showed differences between experimental levels. However, this strong effect of size in December was based on laboratory data where two distinct fish sizes were analysed. Thus, this point had to be confirmed with additional data. Sex had also been identified as a confounding factor of necrotic cell percentage and granulocyte-macrophage distribution values.

Integration of more data in the regression models of immunomarkers can strengthen the knowledge on the effect of confounding effects of size and sex, even when the new data is not obtained in the same conditions. First, the immunomarkers that had been log-transformed in the first study were again log-transformed in the present study. Secondly, the additional data obtained in mesocosm and field conditions also confirmed that including sex was only relevant for necrotic cell percentage and granulocyte-macrophage distribution. No significant interactions between sex and experimental conditions were observed, which tends to confirm that the effect of sex is independent of the experimental conditions. Thirdly, size had not been included in the regression models based on laboratory data for respiratory burst index and lysosomal presence, but when mesocosm and field data were added, a linear effect of size on respiratory burst index (see Figure 2 in supplementary data) improved model quality, independently of experimental conditions and sampling period. The additional data did not however confirm the strong effect of size on efficiency in December, which appeared to be specific to the set of data obtained in laboratory.

Even so, regarding percentages of data outside laboratory reference values, Dunn/Dunnett statistical tests, and Q², the laboratory reference ranges developed in the precedent work [START_REF] Marchand | Modelling the effect of season, sex, and body size on the three-spined stickleback, Gasterosteus aculeatus, cellular innate immunomarkers: A proposition of laboratory reference ranges[END_REF] does not fit perfectly with mesocosm and even less with field data.

Seasonal trends varied significantly between experimental levels. Furthermore, although lysosomal presence showed little seasonal variation between mesocosm and laboratory data, large differences were observed in field conditions. Conversely, although the seasonal trends in macrophage-granulocyte percentages were similar in field and laboratory conditions, they were different in mesocosms. All these statistical aspects demonstrated that the "experimental level" factor have a strong effect on all the studied immunomarkers. Furthermore, as demonstrated by the "experimental level:period" interaction, the "experimental level" factor was also dependent of the season for almost all immunomarkers which implies that seasonal variations were not the same in the various experimental conditions. As previously discussed, the immune response could be influenced by many abiotic [START_REF] Bowden | Modulation of the immune system of fish by their environment[END_REF] and biotic [START_REF] Kortet | Breeding-related seasonal changes in immunocompetence, health state and condition of the cyprinid fish, Rutilus rutilus[END_REF][START_REF] Robertson | Measuring the immune system of the three-spined stickleback -investigating natural variation by quantifying immune expression in the laboratory and the wild[END_REF] factors, which explain this discrepancy between experimental conditions. Since the fish used in the different experimental condition originated from the same regional ecotype and the temperature and photoperiod were quite similar, differences in pathogen diversity and abundance were likely causes. In fact, pathogen presence induces specific adaptations which may lead to differences in immune profiles of populations living in separate environments [START_REF] Scharsack | Effects of environmental variation on host???parasite interaction in three-spined sticklebacks (Gasterosteus aculeatus)[END_REF]. Nevertheless, even if this specific adaptation of fish to their pathogenic environment can be responsible for a decrease in immunity variability [START_REF] Hablützel | Changing expression of vertebrate immunity genes in an anthropogenic environment: a controlled experiment[END_REF], the ANCOVA regression model relies on the assumption that residual variability is the same in each condition. The differences in variability observed at specific periods for some immunomarkers imply that at some periods, the reference ranges will be narrower (e.g. respiratory burst index in June) than expected

given the variability at that period, and in some cases, they will be slightly larger (e.g.

respiratory burst index at periods other than June). Food allocation and intestinal microbiota also impact the fish immune response [START_REF] Gómez | A review on the interactions between gut microbiota and innate immunity of fish: Table 1[END_REF][START_REF] Kosiewicz | Relationship between gut microbiota and development of T cell associated disease[END_REF][START_REF] Waagbo | The impact of nutritional factors on the immune system in Atlantic salmon, Salmo salar L.: a review[END_REF]. In laboratory and mesocosm conditions, fish were daily fed with bloodworms unlike in field conditions where the food may have been more diversified. This parameter may also be an explicative factor in the differences between conditions. Another factor fixed in laboratory experimental level but not in mesocosm and field experimental levels was the fish age. Since this factor was able to modulate fish immunity, in both innate and acquired component, they should be considered [START_REF] Robertson | Measuring the immune system of the three-spined stickleback -investigating natural variation by quantifying immune expression in the laboratory and the wild[END_REF].

As for laboratory reference ranges [START_REF] Marchand | Modelling the effect of season, sex, and body size on the three-spined stickleback, Gasterosteus aculeatus, cellular innate immunomarkers: A proposition of laboratory reference ranges[END_REF], the newly created models including the three type of data (laboratory, mesocosm and field) integrated especially the "size" effect. In fact, each immunomarker were differently influenced by the "size", with more impact on necrotic cell percentages and capacity and no statistically significant effect shown on respiratory burst index and lysosomal presence. Likewise, as shown by the coefficient of determination (R²) of the models, the part of data variability explained by these confounding factors was also variable between immunomarkers. Thus, whereas the efficiency, the capacity and the granulocyte-macrophage percentages were well-explained by the considered confounding factors, the respiratory burst index and the apoptotic cell percentages were poorly explained. In some cases, high values of the coefficient of determination are due to large effects of experimental conditions. This can be observed for example with lysosomal presence: in our previous paper the R² reported was 13 % [START_REF] Marchand | Modelling the effect of season, sex, and body size on the three-spined stickleback, Gasterosteus aculeatus, cellular innate immunomarkers: A proposition of laboratory reference ranges[END_REF] [START_REF] Maltby | Evaluation of the Gammarus pulex in situ feeding assay as a biomonitor of water quality: Robustness, responsiveness, and relevance[END_REF].

V. Conclusions

This study focused on the evaluation of laboratory-based reference ranges, created on threespined stickleback immunomarkers, for mesocosm and field conditions. The "experimental level" factor influences in a seasonal-dependent pattern all the studied immunomarkers, therefore care must be taken when extrapolating reference values or ranges between different experimental conditions. Finally, this study argued for considering the influence of specific field confounding factors. In this way, field reference ranges were developed. Nevertheless, before using them for large scale biomonitoring programs, these field reference ranges should be tested on multiple uncontaminated and contaminated sites in order to identify the biomarkers which offer the best compromise between sensitivity to contamination, and either reproducibility in similar conditions or well-characterized effects of abiotic confounding factors.

  and Method a. Experimental level of sticklebacks for each experimental level reference range During this study, all mature three-spined sticklebacks used for laboratory and outside artificial pond experiments were obtained from the INERIS husbandry (Verneuil-en-Halatte, France). The laboratory reference ranges used in the present work had been developed based on data obtained in 282 fish in a previous experiment, from December 2015 to August 2016 (Marchand et al. 2019).

Figure 1 :

 1 Figure 1: Projection of mesocosm (black) and field (grey) data on the laboratory reference ranges. For apoptotic cells, respiratory burst index, lysosomal presence, phagocytosis capacity and efficiency, the lines represent the values predicted by the model and the prediction interval at 95 %. For necrotic cells percentage and granulocyte-macrophage percentage, the full lines represent prediction intervals for females and the dotted lines represent prediction intervals for males. With MFI =Mean Fluorescent Intensity and e.u = experimental units.

  October, the significantly low increase detected with Dunn test (p = 0.0021, TableIII) was not observed using projection on laboratory reference ranges (0 % of deviation, Figure1).

	Moreover, positive calculating Q² values were shown concerning respiratory burst index (Q² =
	0.03) and lysosomal presence (Q² = 0.17) suggesting better projection deviations for these
	biomarker models. Lower values of respiratory burst index were found in mesocosm
	compared to laboratory experimental level at June period with both methods (27.7 % of
	deviation, binomial test, p = 0.0001; Dunn test, p = 1.3 x 10 -6 , Table III). Even if the
	lysosomal presence in mesocosm was slightly lower in December (Dunnett test p = 0.001) and

) was not detected by the predictive model (10%, binomial test: p = 0.264, and 0%, respectively). The necrotic cell percentage present a quite good projection of mesocosm data on laboratory reference range compared to statistical test, with significant highest values in mesocosm condition detected in June using both methods (77.7 % of deviation, binomial test: p = 2.12 x 10 -19 ; Dunn test, p = 3.2 x 10 -8 ). Nevertheless, in slightly higher in February (Dunn test, p = 0.00019) and in April (Dunn test, p = 0.034) in mesocosm compared to laboratory data (Table

  , whereas it is 53 % in the model including all data. The total variability due to experimental level when data from mesocosms or data from field conditions were compared to laboratory conditions is reported in TableVI: when data from field and laboratory are compared, as much as 40.8 % of variability is due to the experimental level (capacity). The importance of experimental level on immunomarkers may hinder extrapolation to other field conditions, since our experiments were designed to limit differences in photoperiod, temperature and fish population characteristics. Further research is need in similar filed conditions with the least possible contaminations. The fact that, for some immunomarkers, confounding factors only have a small effect on an immunomarker, as for respiratory burst index with only 28 % of data variability explained by the four confounding factors, can have several causes and implications. This may indicate either high inter-individual variability, high measurement uncertainty, unidentified inter-individual confounding factors such as reproductive or health status, or a lack of sensitivity to environmental factors. At the present, no studies have established a relationship between sensitivity to environmental confounding factors and sensitivity to various types and levels of pollution: further research is needed to compare values of immunomarkers in similar environmental conditions with different levels of pollution in a biomonitoring context. Depending on the sensitivity of these immunomarkers to various types of contamination, the field reference ranges derived in these models could help to define robust reference values useful for large scale biomonitoring programs

Table I :

 I Description of the two data sets used to perform the comparison to laboratory reference ranges in this study. Laboratory data set used to create laboratory reference ranges was described in[START_REF] Marchand | Modelling the effect of season, sex, and body size on the three-spined stickleback, Gasterosteus aculeatus, cellular innate immunomarkers: A proposition of laboratory reference ranges[END_REF].

	MESOCOSM				FIELD
	Sampling period	n (M ; F)	Fish body size (mm)	Sampling period	n (M ; F)	Fish body size (mm)	pH	Water parameters T O2 Conductivity (°C) (%) (µS/cm²)
	October 2015	20 (7 ; 13)	45-57	October 2015	20 (8 ; 12)	30 -46	7.07 12	7.3	544
	December 2015	20 (12 ; 8)	44-60	August 2017	20 (6 ; 14)	41 -60	*	*	*	*
	February 2016	20 (9 ; 11)	37-47	December 2017	20 (6 ; 14)	40 -53	7.14 11.6 68.3	719
	April 2016	15 (6 ; 9)	33-46	February 2017	20 (11 ; 9)	41 -60	7.42 11.3 75.2	706
	June 2016	18 (12 ; 6)	40-51	April 2017	20 (10 ; 10)	42 -52	7.7 13.9 79	799
	August 2016	19 (12 ; 7)	39-55
	TOTAL	112 (58 ; 54)	33-60	TOTAL	100 (41 ; 59)	30 -60

* Water parameters were not monitored at this period due to technical issues.

Table II :

 II p-values from Shapiro tests inside each group and Levene tests for each immunomarker. Analysis were performed on residuals of the full linear model and biomarkers with * were log transformed before performing statistical tests.

								SHAPIRO
		LEVENE	Sex	December Mesocosme Field	February Mesocosme Field	April Mesocosme Field	June Mesocosme Field	August Mesocosme Field	October Mesocosme Field
			Female	0.53	0.39	0.74	0.72	0.33	0.14	0.87	-	0.80	0.26	0.63	0.07
	Apoptotic cells*	4.54 x 10 -2
			Male	0.39	0.54	0.27	0.72	0.89	0.58	0.04	-	0.38	0.38	0.77	0.21
	Necrotic cells*	2.74x 10 -2	Female Male	0.55 0.21	0.81 0.93	0.96 0.89	0.86 0.07	0.77 0.01	0.53 0.14	0.19 0.53	--	0.59 0.03	0.59 0.35	0.82 0.94	0.30 0.37
			Female	0.44	0.72	0.30	0.82	0.71	0.06	0.92	-	0.78	0.22	0.25	0.99
	Granulocyte-macrophage	0.16
			Male	0.20	0.41	0.91	0.41	0.74	0.03	0.86	-	0.64	0.48	0.02	0.28
	Respiratory burst index*	6.32 x 10 -7	Female	0.09	0.12	0.22	0.66	0.33	0.028	0.26	-	0.28	0.17	0.64	0.31
			Male	0.64	0.82	0.01	0.006	0.009	0.25	0.68	-	0.85	0.16	0.41	0.11
	Lysosomal presence	1.84 x 10 -2	Female	0.40	0.08	0.009	0.15	0.32	0.88	0.61	-	0.46	0.73	0.85	0.94
			Male	0.79	0.32	0.84	0.94	0.32	0.40	0.92	-	0.23	0.48	0.81	0.89
			Female	0.55	0.81	0.96	0.86	0.77	0.53	0.19	-	0.59	0.59	0.82	0.30
	Phagocytosis capacity*	3.37 x 10 -6
			Male	0.21	0.93	0.89	0.07	0.01	0.14	0.53	-	0.03	0.35	0.94	0.37
			Female	0.30	0.38	0.44	0.67	0.55	0.71	0.44	-	0.87	0.94	0.82	0.30
	Phagocytosis efficiency*	7.64 x 10 -8	Male	0.58	0.76	0.73	0.08	0.26	0.64	0.28	-	0.46	0.49	0.06	0.38

Table III :

 III Values correspond to means ± standard deviations (with n = 15-20) for each innate immunomarker tested. Statistical differences between laboratory and mesocosm or field data were tested by a Dunnett test (parametric data, p ≤ 0.05) or by a Kruskal-Wallis test followed by a Dunn test (non-parametric data, p ≤ 0.05). With *, ** and *** indicates significant difference at p ≤ 0.05, p ≤ 0.01 and p ≤ 0.001 levels respectively.

			DECEMBER			FEBRUARY		APRIL			JUNE			AUGUST			OCTOBER
		Laboratory Mesocosme	Field	Laboratory Mesocosme	Field	Laboratory Mesocosme	Field	Laboratory Mesocosme Field	Laboratory Mesocosme	Field	Laboratory Mesocosme	Field
	Apoptotic cells (%)	3.84 ± 2.21	3.73 ± 1.19	2.65 ± 1.15	4.79 ± 2.37	5.01 ± 1.73	4.94 ± 3.16	4.02 ± 1.39	5.61 ± 2.02	8.58*** ± 5.87	9.15 ± 6.36	6.75 ± 5.15	-	5.27 ± 2.50	4.63 ± 1.84	4.91 ± 2.51	5.22 ± 3.25	5.64 ± 2.66	4.68 ± 3.73
	Necrotic cells (%)	1.77 ± 0.54	1.67 ± 0.67	1.25** ± 0.40	2.39 ± 1.81	1.88 ± 0.54	1.20*** ± 0.60	2.36 ± 0.92	1.99 ± 0.42	2.41 ± 1.06	3.68 ± 8.87	7.31*** ± 2.62	-	1.86 ± 1.05	1.60 ± 0.51	0.79*** ± 0.67	2.08 ± 1.90	2.61** ± 0.94	1.75 ± 0.88
	Granulocyte-macrophage (%)	25.90 ± 10.32	25.30 ± 6.65	32.80* ± 9.66	27.20 ± 10.00	19.40 ± 4.93	39.30* ± 8.17	18.50 ± 6.21	17.70 ± 8.47	27.20*** ± 5.78	8.19 ± 13.26	34.00*** ± 6.33	-	28.60 ± 9.33	32.20 ± 9.83	32.40 ± 9.82	26.20 ± 7.40	28.40 ± 6.21	41.20*** ± 10.37
	Respiratory burst index (experimental unit)	2.11 ± 1.09	3.27 ± 4.67	1.99 ± 0.85	1.27 ± 0.73	1.33 ± 0.71	1.57*** ± 0.74	1.09 ± 0.32	0.66 ± 0.31	1.90*** ± 1.08	3.62 ± 3.71	1.36*** ± 0.77	-	1.98 ± 0.91	1.80 ± 0.43	1.34*** ± 0.36	1.27 ± 0.77	1.07 ± 0.26	2.13*** ± 1.06
	Lysosomal presence (Mean Fluorescence Intensity)	2.25 ± 0.27	1.99** ± 0.27	2.30 ± 0.42	2.31 ± 0.28	2.56*** ± 0.26	2.64*** ± 0.42	2.40 ± 0.29	2.67* ± 0.21	3.30*** ± 0.36	2.16 ± 0.26	2.18 ± 0.17	-	2.17 ± 0.34	2.15 ± 0.24	1.52*** ± 0.23	2.03 ± 0.35	2.17 ± 0.24	1.81* ± 0.29
	Phagocytosis capacity (%)	55.90 ± 4.09	65.90*** ± 4.21	55.10 ± 3.18	63.40 ± 13.11	59.10 ± 2.84	52.40*** ± 4.56	62.30 ± 6.78	63.60 ± 4.52	62.40 ± 4.27	65.00 ± 5.64	60.10*** ± 6.25	-	64.90 ± 4.00	66.20 ± 4.29	40.90*** ± 2.31	69.50 ± 4.54	67.70 ± 4.28	64.70*** ± 4.37
	Phagocytosis efficiency (%)	21.70 ± 11.25	24.10 ± 3.81	25.20 ± 3.06	45.70 ± 16.78	28.00*** ± 2.60	18.70*** ± 3.62	42.40 ± 6.95	35.80* ± 5.08	23.90*** ± 4.60	26.40 ± 5.13	19.90*** ± 5.17	-	12.80 ± 2.44	14.30 ± 2.28	18.10*** ± 1.71	32.30 ± 6.65	32.20 ± 7.67	23.00*** ± 3.41

Table IV :

 IV Effects of sampling period, experimental level (laboratory, mesocosm or field data), sex and fish size (and interactions) on innate immune parameters of the three-spined stickleback, Gasterosteus aculeatus. Results were obtained by performing a type II ANCOVA (p ≤ 0.05). With *, ** and *** indicates statistically significant effect at p ≤ 0.05, p ≤ 0.01 and p ≤ 0.001 levels respectively in F-tests. R² were calculated on the model including only the significant explicative variables and interactions. Q²LOO corresponds to the squared cross-validated correlation coefficient obtained using a leave-on-out procedure.Biomarkers with * were log transformed before performing statistical tests.

						Explicative variables				R²	Q 2 LOO
		Period Experimental level Sex Size Period:experimental level Period:sex	Experimental level:sex	Period: size	Experimental level:size	Experimental level:Period:size
	Apoptotic cells*	***			***			***	*	***	0.33	0,24
	Necrotic cells*	***	***	**	***	**					0.43	0,43
	Granulocyte-macrophage	***	***	**	***	*		***	*		0.52	0,46
	Respiratory burst index*	***	***	*	***						0.29	0,24
	Lysosomal presence	***	**		***						0.53	0,51
	Phagocytosis capacity*	***	***	***	***			***			0.63	0,60
	Phagocytosis efficiency*	***	***	***	***			***	*	***	0.80	0,77

Table V :

 V Proportion of total variability A) explained by period and its interactions (binary and ternary) with other confounding factors B) explained by the period:experimental level interaction and its interactions (ternary) with other confounding factors, C) explained by experimental level and its interactions (binary and ternary) with other confounding factors, for each immunomarker, with the data collected in mesocosms, with and without June, and for the data collected in field, compared to data collected in laboratory conditions. Biomarkers with * were log transformed before performing statistical tests

		Laboratory reference ranges
				Tested in field/mesocosm sampling
				Mesocosm			Mesocosm without June			Field
	Log(Phagocytosis efficacity %)	Apoptotic cells* Necrotic cells* Granulocyte-macrophage Respiratory burst index* Lysosomal presence Phagocytosis capacity*	Period (%) 33.4 32.1 45.1 32.5 26.5 40.0	Period: experimental level (%) 7.38 19.2 18.4 6.64 5.36 15.3	Experimental level (%) 7.71 22.2 21.8 8.20 6.89 16.3	Period (%) 24.0 18.4 33.9 39.6 26.9 41.2	Period: experimental level (%) 8.42 5.57 7.80 4.73 5.81 13.8 Development of Experimental level (%) 9.58 6.80 11.0 5.19 7.64 16.2 new reference ranges	Log(Phagocytosis efficacity %)	Period (%) 28.3 31.0 27.1 30.8 58.1 45.6	Period: experimental level (%) 11.4 13.3 3.09 11.5 26.6 23.8	Experimental level (%) 13.3 26.2 19.1 14.8 27.9 40.8
		Phagocytosis efficiency*	79.5	5.54	6.32	81.3	4.89	5.21		77.8	18.8	23.1
		Not adapted to field and mesocosm data						Site-specific field reference ranges

Acknowledgment

This work was funded by the French Ministry of Ecology and Sustainable Development (Programme 190 Ecotoxicology and Programme 181 DRC50).