Floriane Larras

Philippe Usseglio-Polatera
email: philippe.usseglio-polatera@univ-lorraine.fr

Heterogeneity in macroinvertebrate sampling strategy introduces variability in community characterization and stream trait-based biomonitoring: influence of sampling effort and habitat selection criteria

Keywords: Assemblage structure, Bio-ecological traits, Habitat, Effort, Anthropogenic pressures, Risk assessment AQEM: Assessment System for the Ecological Quality of Streams and Rivers throughout Europe using Benthic Macroinvertebrates, IBGN: Normalized Invertebrate-Based Index, I2M2: Multimetric Invertebrate Index, ASPT: Average Species per Taxon, SPEAR: SPEcies At Risk, ODI: Diagnostic tool based on macroinvertebrate metrics

Benthic macroinvertebrate assemblages have been of major importance in the biomonitoring of rivers for many decades. To more closely fit with new regulatory purposes, standardized methods of macroinvertebrate field sampling have changed over time both in terms of sampling effort and criteria of habitat type sampling priority (e.g. according to a decreasing gradient of "bottom surface coverage" versus "substratum favorability"). Such methodological heterogeneity has rendered the exploitation of biomonitoring data far more complex because they are indeed not fully comparable over time and may induce bias in long term series studies. In this context, we assessed the influence of variation in field sampling strategy on (i) the taxonomical characterization of benthic macroinvertebrate assemblages (297 taxon abundances, taxonomic richness and diversity, EPT-related metrics), (ii) the response of sensitivity descriptors (ASPT, SPEAR), (iii) the bio-ecological trait description of assemblages and (iv) the resulting ecological diagnostic of river reaches, based on 10074 sampling events across France. Six sampling strategies were compared, corresponding to three levels of sampling effort (4, 8 and 12 individual samples) and different criteria for substratum selection; e.g. marginal (= less than 5% coverage at reach scale) vs. dominant; hosting capacity (= substratum favorability) vs. bottom coverage. A strong influence of the sampling strategy was observed on the characterization of the macroinvertebrate assemblages. Marginal substrata revealed a unique picture of the community at the reach scale and presented a higher taxonomic diversity and richness than dominant ones, as well as a higher number of sensitive EPT and SPEAR taxa. Both strategies based on dominant substratum sampling provided similar pictures of the taxonomical and bio-ecological characteristics of macroinvertebrate assemblages. The high hosting capacity of the most frequent marginal substrata has been identified as an important driver of the observed responses. When using a tool calculating the probability that sampled macroinvertebrate assemblages were impaired by several pressures related to water quality degradation, hydromorphological alteration or land-use, differences in sampling strategy affected the probability estimation. As a main trend, the results were especially reliable when they were obtained from an 8-sample strategy, even more if they included the information provided by the marginal substrata. If sampling effort definitely matters for environmental diagnostic, substratum selection criteria play also a crucial role on (i) the description of local assemblages and (ii) the reliability of calculated impairment risks, due to the strong relationship between stenotopic taxa and specific habitat conditions.

Introduction

World-wide intensification of the use of both land and aquatic resources has highlighted the importance of managing water quality as well as the critical role of innovative diagnostic tools for defining robust management responses to aquatic ecosystem degradation [START_REF] Davis | Multiple stressors and regime shifts in shallow aquatic ecosystems in antipodean landscapes[END_REF][START_REF] Dolédec | Responses of freshwater biota to human disturbances: Contribution of J-NABS to developments in ecological integrity assessments[END_REF][START_REF] Hering | Managing aquatic ecosystems and water resources under multiple stress-An introduction to the MARS project[END_REF][START_REF] Navarro-Ortega | Managing the effects of multiple stressors on aquatic ecosystems under water scarcity. The GLOBAQUA project[END_REF][START_REF] Sokolova | Energy-Limited Tolerance to Stress as a Conceptual Framework to Integrate the Effects of Multiple Stressors[END_REF]Van der Laan et al., 2013). Several directives, including the EU Water Framework Directive (European Commission, 2000) or the Clean Water Act (1977), aim at restoring and maintaining the integrity of water bodies. They have triggered the development of many new bioassessment methods, which evaluate the ecological status of water bodies, for example by comparing observed assemblages of a given Biological Quality Element (e.g., fishes, macroinvertebrates or diatoms) to reference ones.

Benthic macroinvertebrates have been probably the most widely used group for the biological assessment of human impacts in freshwater ecosystems (e.g., 27% of the 297 European methods listed in [START_REF] Birk | Three hundred ways to assess Europe's surface waters: An almost complete overview of biological methods to implement the Water Framework Directive[END_REF]. Many of the macroinvertebrate-based assessment methods aggregate a variable number of metrics (mean = 4 on 28 European countries, in [START_REF] Birk | Three hundred ways to assess Europe's surface waters: An almost complete overview of biological methods to implement the Water Framework Directive[END_REF] describing the taxonomic or trait structure of assemblages as well as the sensitivity of taxa. These multimetric indices are far more consistent in the detection of reach impairment than single metrics [START_REF] Hering | Cook book for the development of a Multimetric Index for biological condition of aquatic ecosystems: Experiences from the European AQEM and STAR projects and related initiatives[END_REF][START_REF] Urbanič | Hydromorphological degradation impact on benthic invertebrates in large rivers in Slovenia[END_REF].

For example, in France, the Multimetric Invertebrate Index (I2M2; [START_REF] Mondy | A new macroinvertebratebased multimetric index (I2M2) to evaluate ecological quality of French wadeable streams fulfilling the WFD demands: A taxonomical and trait approach[END_REF] has improved the detection of impaired river reaches by at least 17% for nitrogen compounds and up to 35% for organic micropollutants and clogging risk, when compared to the previously normalized macroinvertebratebased index (IBGN; [START_REF] Afnor | Détermination de l'indice biologique global normalisé (I.B.G.N.)[END_REF][START_REF] Afnor | Qualité de l'eau-Détermination de l'indice biologique global normalisé (IBGN)[END_REF].

Efficiently and robustly evaluating the global effect of anthropogenic pressures on river reach communities requires to aggregate a combination of generalist metrics (i.e., capable of efficiently detect a wide range of pressure categories), which are much more robust than specialist ones in stream ecological diagnostic especially when anthropogenic pressures interact [START_REF] Dedieu | A Multimetric Macroinvertebrate Index for the Implementation of the European Water Framework Directive in French Guiana, East Amazonia: A biotic index for East-Amazonian streams[END_REF][START_REF] Mondy | A new macroinvertebratebased multimetric index (I2M2) to evaluate ecological quality of French wadeable streams fulfilling the WFD demands: A taxonomical and trait approach[END_REF][START_REF] Touron-Poncet | Implementing the Water Framework Directive in overseas Europe: A multimetric macroinvertebrate index for river bioassessment in Caribbean islands[END_REF]. As a result, new multimetric indices often provide a pertinent evaluation of the global effect of anthropogenic pressures, but poorly feed the diagnostic in precise information on the exact nature of the pressures really impairing local communities [START_REF] Angradi | Multimetric macroinvertebrate indices for mid-continent US great rivers[END_REF][START_REF] Mondy | Using conditional tree forests and life history traits to assess specific risks of stream degradation under multiple pressure scenario[END_REF]. Many authors have thus underlined the need for cost-effective diagnostic tools, capable of disentangling the complex effects of interacting pressures [START_REF] Brucet | Biological assessment of European lakes: Ecological rationale and human impacts[END_REF][START_REF] Christensen | Multiple anthropogenic stressors cause ecological surprises in boreal lakes[END_REF][START_REF] Darling | Quantifying the evidence for ecological synergies[END_REF][START_REF] Downes | Back to the future: Little-used tools and principles of scientific inference can help disentangle effects of multiple stressors on freshwater ecosystems[END_REF]. In this context, we have developed a series of models individually able to estimate the risk of stream impairment by a given category of anthropogenic pressure using trait-based and/or taxonomy-based characteristics of macroinvertebrate assemblages (tool called in this study ODI=diagnostic tool based on macroinvertebrates metrics; [START_REF] Mondy | Using conditional tree forests and life history traits to assess specific risks of stream degradation under multiple pressure scenario[END_REF]. Several of these models have given good impairment risk assessment for various pressures related to water quality (e.g., organic matter, nitrates, other nitrogen compounds, phosphorous components, pesticides, metals) or hydromorphology and land-use alterations (e.g., riverine vegetation alteration, urbanization at the reach scale, clogging risk or hydrological instability), even at moderate risk level and in a multi-pressure context [START_REF] Desrosiers | Assessing anthropogenic pressure in the St. Lawrence River using traits of benthic macroinvertebrates[END_REF][START_REF] Mondy | Using conditional tree forests and life history traits to assess specific risks of stream degradation under multiple pressure scenario[END_REF]. Existing biotic indices and diagnostic tools have been calibrated on a precise, standardized sampling strategy. Variation in sampling can then induce detrimental effects on the precision of the individual metrics used in indices and tools to assess the ecological status and provide an ecological diagnostic of rivers (Clarke et al., 2006;[START_REF] Clarke | Errors and uncertainty in bioassessment methods -major results and conclusions from the STAR project and their application using STARBUGS[END_REF][START_REF] Wiederkehr | Experimental study of the uncertainty of the intrasubstrate variability on two French index metrics based on macroinvertebrates[END_REF]. Such variation can affect sampling effort or substratum selection.

Many studies, grounded upon the hypothesis that the habitat is a flexible templet for the selection of ecological strategies [START_REF] Southwood | Habitat, the templet for ecological strategies[END_REF][START_REF] Southwood | Tactics, Strategies and Templets[END_REF][START_REF] Townsend | Species traits in relation to a habitat templet for river systems[END_REF], have indeed demonstrated that macroinvertebrate assemblages exhibited quite different taxonomic composition (Beisel et al., 1998a[START_REF] Beisel | The spatial heterogeneity of a river bottom: A key factor determining macroinvertebrate communities[END_REF] or trait structure [START_REF] Demars | Linking biotopes to invertebrates in rivers: Biological traits, taxonomic composition and diversity[END_REF] at the mesohabitat (sensu [START_REF] Armitage | Impact assessment of regulation at the reach level using macroinvertebrate information from mesohabitats[END_REF] or biotope (sensu [START_REF] Demars | Linking biotopes to invertebrates in rivers: Biological traits, taxonomic composition and diversity[END_REF] scale, as a result of contrasted local heterogeneity, habitat stability and trophic potentialities (e.g., see fig. 5 in Beisel et al., 1998a). However it is rather difficult to estimate the potential impact of sampling variation in local habitats on the ecological diagnostic of rivers over a large set of sites corresponding to a wide range of environmental conditions (e.g. Strahler ranks from 1 to 8) and anthropogenic pressures, especially when using diagnostic tools simultaneously working with several hundred of metrics (e.g., 216 for the ODI).

Examining how metrics, biotic indices and diagnostic tools respond to variation in sampling is also important in the context of long-term ecological surveys, which are often suffering from abrupt modifications in standardized sampling methods. In Europe, the WFD implementation has contributed to variations in sampling methodologies over time in several countries, hampering the compilation of common and homogeneous datasets to objectively analyze pressure-impact relationships over long-term time series [START_REF] Blocksom | Comparison of macroinvertebrate sampling methods for nonwadeable streams[END_REF][START_REF] Cao | The comparability of bioassessments: A review of conceptual and methodological issues[END_REF][START_REF] Tronstad | Long-term trends in aquatic ecosystem bioassessment metrics are not influenced by sampling method: Empirical evidence from the Niobrara River[END_REF]. For example, in France, the IBGN protocol was used at the National scale over the 1992-2005 period (norm NF T 90-350 in AFNOR, 2004). It was based on eight sample units selected according to a decreasing gradient of hosting capacity of predefined habitat types; i.e. distinct units of habitat within the stream, easily recognizable from the bank and which are unique combinations of "substratum type x current velocity range", hosting a specific faunal assemblage (Beisel et al., 1998a). Due to severe inconsistencies of the IBGN with the WFD, a new multimetric index (I2M2) based on a new field sampling protocol (see section 2.1) with a different sampling effort and habitat selection strategy [START_REF] Afnor | Qualité de l'eau -Prélèvement des macro-invertébrés aquatiques en rivières peu profondes[END_REF][START_REF] Afnor | Qualité de l'eau -Traitement au laboratoire d'échantillons contenant des macroinvertébrés de cours d'eau[END_REF] has been defined for the macroinvertebrate-based ecological assessment of French shallow streams [START_REF] Mondy | A new macroinvertebratebased multimetric index (I2M2) to evaluate ecological quality of French wadeable streams fulfilling the WFD demands: A taxonomical and trait approach[END_REF].

The implementation of this new sampling strategy, gathering three sets of samples performed with different strategies of substratum selection, on about 2,000 sites of National routine survey networks, has offered the unique opportunity to test the impact of variation in macroinvertebrate sampling effort and substratum selection strategy on the results of metrics, indices and a diagnostic tool developed for shallow rivers, at large spatial scale. Based on a large set of data, we aimed at evaluating the potential impact of changes in field sampling strategy on (i) the taxon abundances, (ii) the trait-based and taxonomy-based characterization of macroinvertebrate communities, (iii) assemblage sensitivity descriptors to pesticides and organic pollution, and (iv) the reliability of risk assessment based on the ODI. We compared six strategies that differ in terms of (i) substratum sampling priority rule (i.e., differences in substratum type selection criteria) and (ii) sampled habitat number (i.e., variation in sampling effort). We also analyzed the methodological and ecological rationale underlying the main observed differences/deviations and provided guidance on the interpretation of biological quality element data when different sampling protocols were successively used during the survey.

Material and methods

Macroinvertebrate sampling design

Benthic macroinvertebrate sampling was managed by French regional environmental agencies between 2005 and 2013 within the European WFD context, and consisted of 10074 sampling events. The fieldwork design included 1860 sites (Fig. A.1). These sites belonged to three major national networks surveying (i) 404 reference sites (= Réseau de Référence, RCR), (ii) 185 sites severely impaired by specific anthropogenic pressures and surveyed until reaching again the "good" ecological status (= Réseau de Contrôle Opérationnel, RCO), and (iii) 1394 sites providing an objective idea of the mean ecological quality of French shallow streams, routinely surveyed and used for periodic reporting on their ecological status (Réseau de Contrôle de Surveillance, RCS; 123 of them are also belonging to the RCR), as required of Member States by the WFD.

The assemblages were sampled following the I2M2 standardized method (Norm AFNOR XP T90-388; AFNOR, 2010). For each sampling event, 12 elementary samples (Surber net; sampled area: 0.05 m 2 ; mesh size: 0.5 mm) were performed on benthic substrata (Tables A.1 and A.2). The sampling design consisted of three different steps (= "phases" in the Norm) called "A", "B" and "C", taking into account different combinations of criteria among (i) the relative benthic coverage of substratum types, (ii) their "marginal" (i.e. with an individual share of less than 5% coverage at reach scale) or "dominant" (i.e. with an individual share of at least 5% coverage) status, (iii) their hosting capacity (i.e. their ability to support a rich and diverse macroinvertebrate assemblage) and (iv) the superficial current velocity range in front of the substratum. Each phase was composed of 4 elementary samples. Phase A gathered samples performed on the first four marginal substrata (from bryophytes to artificial substrata; cf. Table A.2) selected according to a decreasing gradient of hosting capacity (criteria ii and iii). They were individually performed in the current velocity range (among four pre-defined categories) in which each substratum type was the most represented (criterion iv). If less than four marginal substratum types had been identified in the site, additional samples were made on the marginal substratum types already sampled, giving priority to the type with the highest individual share and the second most represented velocity range for this type. Phase B consisted of samples performed on the first four dominant substrata applying the same criteria than in phase A. Phase C was composed of four samples performed on dominant substrata, but in proportion to their individual benthic coverages, considering samples already performed on dominant substrata during phase B [START_REF] Afnor | Qualité de l'eau -Prélèvement des macro-invertébrés aquatiques en rivières peu profondes[END_REF]. The taxonomic levels of identification corresponded to the standardized French I2M2 protocol (Norm AFNOR XP T90-388; AFNOR, 2010), i.e. mainly the genus level, except for Diptera (family level) and Oligochaeta or Nematoda identified as such.

A total of 297 taxa were identified at this level.

Ecological rationale of the investigated sampling strategies

To summarize, we got at the beginning of the study three core taxonomic datasets (one from each of the phases "A", "B" and "C") providing the raw abundances of the 297 taxa over the 10074 sampling events.

Then, organism abundances in individual phases were added to establish the assemblages sampled (i) only in benthic substrata with the highest hosting capacity (A+B = "AB"), (ii) only in dominant substrata (B+C = "BC") and (iii) in the whole river reach (A+B+C = "ABC"). AB and BC could be related in terms of habitat selection to the philosophy of the IBGN and the AQEM strategies, respectively. The six resulting taxonomic datasets (A, B, C, AB, BC and ABC) corresponded to six potential sampling strategies exhibiting different sampling efforts and substratum selection strategies (Fig. 1). Strategy ABC, exhaustively gathering all the sampled habitats in a given site and exhibiting the highest sampling effort (= 12 elementary samples), corresponded to the strict application of the French norm (i.e. the National standardized strategy; [START_REF] Afnor | Qualité de l'eau -Prélèvement des macro-invertébrés aquatiques en rivières peu profondes[END_REF][START_REF] Afnor | Qualité de l'eau -Traitement au laboratoire d'échantillons contenant des macroinvertébrés de cours d'eau[END_REF]. Strategy AB corresponded to a moderate reduction of the sampling effort (= 8) with substratum types (four marginal and four dominant substrata) selected only according to a decreasing gradient of hosting capacity. Strategy BC corresponded to a moderate reduction of the sampling effort (= 8) with samples performed exclusively on dominant substrata, taking into account their bottom surface coverage. Finally, strategies A (only marginal substratum types), B (dominant substratum types according to a decreasing gradient of hosting capacity) and C (dominant substratum types according to a decreasing grading of bottom substratum coverage) were characterized by a high reduction of the sampling effort (= 4). We have to mention that, in this study, the effort effect cannot be completely disentangle from the mesohabitat effect due to the sampling design settings.

Metric calculation

The influence of the sampling strategy on the community characterization was assessed on a set of taxonomic and bio-ecological metrics. We investigated 101 trait-based metrics (gathered in 11 biological and 10 ecological traits, Table A.3) expressed as the relative frequencies of trait category utilization within each assemblage (in [START_REF] Mondy | Using conditional tree forests and life history traits to assess specific risks of stream degradation under multiple pressure scenario[END_REF]. The effect of sampling strategy was also assessed on pollution-sensitivity indices such as the Average Score Per Taxon (ASPT, Biological Monitoring Working Party, 1978) and four metrics related to the "SPEcies At Risk" towards pesticides (SPEAR, [START_REF] Liess | Analyzing effects of pesticides on invertebrate communities in streams[END_REF]. On the 297 taxa, 153 were retained for the ASPT score calculation.

The four SPEAR metrics designed to evaluate macroinvertebrate community sensitivity to pesticides were the raw number and the raw abundance of SPEAR in the assemblage and the relative number and the relative abundance of SPEAR in the assemblage. The total taxonomic richness (at the I2M2 level of identification), the Shannon diversity index, the richness of Ephemeroptera, Plecoptera and Trichoptera (EPT) taxa and their relative richness in the assemblage were also investigated.

Risk estimation for 15 pressure categories

We estimated the probability that each assemblage had been impaired by eight pressure categories related to water quality degradation (organic matter, nitrogen compounds (except nitrates), nitrates, phosphorous compounds, acidification, mineral micropollutants, pesticides and organic micropollutants excluding PAHs) and seven pressure categories related to hydromorphology and land use (transportation facilities, riverine vegetation degradation, clogging risk, hydrological instability, straightening, reach urbanization and watershed anthropization) using the ODI (Fig. A.2). This tool was run on each of the six taxonomic datasets ( § 2.2) and provided, for each macroinvertebrate assemblage (in a given dataset) and each pressure category, an impairment probability (IP) using a random forest model specifically designed and calibrated for this category of pressure [START_REF] Mondy | Using conditional tree forests and life history traits to assess specific risks of stream degradation under multiple pressure scenario[END_REF]. The provided probabilities range from 0 (no risk that the pressure category significantly impaired the assemblage) to 1 (maximal risk that the pressure category significantly impaired the assemblage).

Reliability of the risk estimation obtained for the different strategies

To assess the reliability of risk estimations, we compared the estimated IP obtained for each sampling strategy and each pressure category with the observed chemical or hydromorphological quality classes allocated to the sampling events (from field observations and measurements). The pressure categories related to water quality degradation were described by one (i.e. [NO3 -] for "nitrates") to 71 chemical parameters (i.e. 71 molecules for the pressure category "pesticides"). Each pressure category related to habitat degradation was described by one or two elementary parameters [START_REF] Mondy | A new macroinvertebratebased multimetric index (I2M2) to evaluate ecological quality of French wadeable streams fulfilling the WFD demands: A taxonomical and trait approach[END_REF]. The water quality of a given reach at the sampling date was described by averaging available chemical measures from this reach during the six months before macroinvertebrate sampling as suggested by [START_REF] Mondy | A new macroinvertebratebased multimetric index (I2M2) to evaluate ecological quality of French wadeable streams fulfilling the WFD demands: A taxonomical and trait approach[END_REF] for best describing environmental changes integrated by macroinvertebrate communities. The quality class of each individual parameter was defined by comparing the mean value of the parameter over this period of six months with the quality class boundaries specific to this parameter ("high/good", "good/intermediate", "intermediate/poor", "poor/bad"), proposed in the French water quality assessment system [START_REF] Oudin | Assessment system of streams quality (Système d'évaluation de la qualité de l'eau des cours d'eau (Seq-Eau))[END_REF]. The quality class allocated to a site for a given pressure category at the sampling date was the worst quality class allocated over all the individual parameters characterizing this pressure category.

If the observed quality class is "high" or "good", the model should detect a low risk of impairment and provide an IP value below 0.5. If the observed quality class is "intermediate", "poor" or "bad", the model should detect a significant risk and provide an IP value higher than 0.5.

Statistical analysis

The sampling strategy effect was assessed on (i) the taxonomic abundance of 297 taxa, (ii) the Shannon diversity, total taxonomic richness and EPT metrics, (iii) the polluo-sensitivity indices (ASPT and SPEAR indices) and (iv) the relative utilization of 101 modalities of traits within the assemblage. For the three first categories of these metrics, the influence of the sampling strategy was investigated by comparing metric value distributions between nine couples of strategies using Wilcoxon paired-tests with the R software (R Core Team, 2018). From those nine couples, four exhibited the same sampling effort and thus reflected the substratum effect (A vs. B, A vs. C, B vs. C, AB vs. BC). Five couples displayed a different sampling effort and thus reflected both the sampling effort and the substratum effect (A vs. ABC, B vs. ABC, C vs. ABC, AB vs. ABC, BC vs. ABC). As bio-ecological metrics were expressed as relative frequencies of trait category utilization, it made no sense to test the effort effect. Thus, the Wilcoxon paired-tests were performed on such metrics only between strategies with the same sampling effort.

To obtain more robust results, each Wilcoxon test was performed on 100 sub-sampled datasets composed of 5000 sampling events randomly selected among the 10074 sampling events. Each subset of data was composed of 20% of sampling events performed on least impacted river reaches and 80% on impacted river reaches, mirroring the original dataset. In brief, "least impacted river reach" means that no chemical pressure has been reported as belonging to the poor or bad quality class and that a maximum of three pressure categories could be in the moderate quality class, only one with missing information, and only one hydromorphological pressure in the poor quality class. At the end, we got for each couple of sampling strategies and each tested metric, 100 p-values (one p-value per Wilcoxon test performed on each subset of data). To synthetize the information obtained over the 100 tests, the 95 th percentile of the distribution of the 100 p-values was kept as the "representative p-value" of the corresponding comparison. To counteract the problem of multiple statistical decisions (9 couples of sampling strategies over several hundred of metrics) on the alpha error risk, a Bonferroni correction was used to adjust statistical significance for the number of tests that was performed.

Two heatmaps were built based on the arrays gathering the representative p-values obtained when comparing each couple of sampling strategies based on (i) the abundances of each of 297 taxa or (ii) the relative frequencies of utilization of each of 101 trait modalities within the macroinvertebrate assemblages.

A third heatmap was also built based on the relative frequencies of sampling of the 11 substratum types (cf. Table A.2) during the phases A, B and C of the sampling design, over the data available on reference sites (1275 sampling events) within the whole dataset. All the heatmaps were built using the Euclidian distance and the complete-linkage method with the gplot package [START_REF] Warnes | Gplots: Various R Programming Tools for Plotting Data[END_REF].

To assess if IPs showed significant differences between each of the five strategies A, B, C, AB, BC and ABC, a Friedman test was performed followed by a Nemenyi post-hoc test using the PMCMR package [START_REF] Pohlert | The Pairwise Multiple Comparison of Mean Ranks Package (PMCMR). R package[END_REF].

Results

Substrata sampled with each strategy

The heatmap separated the phase A from the cluster composed of the phases B and C (Fig. 2). Phase A was especially related to the hydrophytes, bryophytes, root mats and organic detritus. Phases B and C showed a low sampling frequency in those substrata. C exhibited a high sampling frequency in filamentous algae, sand, mud, rocks and helophytes. B appeared especially associated to gravel and boulders/cobbles/pebbles, but also to mud and helophytes in a lower extent.

Abundance of taxa in assemblages

The heatmap based on taxon abundances highlighted two main clusters of taxa reflecting contrasted responses to sampling strategy (Fig. 3). The first main cluster (cluster I), at the top of the heatmap, was composed of 87 taxa (detailed in table B.1) for which no significant difference (representative p-value > 0.05) was observed at least among strategies characterized by 4 samples (A, B and C). The abundances of the taxa belonging to Cluster I were not influenced by the substratum types selected during the three phases of the field sampling. Within Cluster I, Cluster I.1 gathered taxa (e.g. Lestes, Nemurella, Hydroscapha, Ptilocolepus) showing also no significant differences between the other strategies whatever their sampling effort (= no sampling effort effect). In contrast, Cluster I.2 was composed of taxa (e.g. Mesovelia, Procambarus, Siphlonurus, Sympecma) exhibiting significant differences between several strategies characterized by different sampling efforts, reflecting a potential higher affinity to A-related substrata (see the yellow dots on the right part of fig. 3). The taxa of Cluster I were also characterized by low abundances.

The second main cluster was composed of the remaining 210 taxa and was shared in three sub-clusters exhibiting different response profiles. Cluster II.1 included 63 taxa such as Stenelmis, Ephemera, Dreissena and Libellula showing in most cases no difference between strategies A, B and C but strong significant differences between almost all the strategies with different sampling efforts. Cluster II.2 gathered 137 taxa for which the abundance in assemblages was influenced by the sampling strategy in terms of substratum type selection as well as sampling effort. Most of them exhibited no significant difference between B and C, meaning that either 1/ they are more abundant in A than in B and C (most of the cases as shown by the dot plot at the right of the heatmap) like for example Platycnemis, Limnebius, Leptocerus, Hydrometra or 2/ they are more abundant in B and C than in A like for example Psychomyia, Glossosoma, Ephoron, Choroterpes, and Agriotypus. Whatever the trend, the criterion (hosting capacity vs. bottom coverage) applied to select the substratum types to sample has a clear effect on the abundance of these taxa in the three phases of the sampling design.

Finally, cluster II.3 was composed of 10 taxa such as Triaenodes, Ylodes, Plea and Somatochlora mainly caught in marginal substrata (i.e., for which the A-related substrata highly contributed to the taxon abundance at the site scale as demonstrated by the dot plot).

Taxonomy-related metrics

For Shannon diversity, total taxonomic richness and the richness of EPT taxa in assemblages (Fig. 4.A, 4.B and 4.C, respectively), an effect of substratum type selection and sampling effort was observed. The values mainly increased over the sampling strategies in the order C < B < A < BC < AB <ABC. In addition, the Wilcoxon paired-tests showed significant differences in these metric values between all the investigated strategies (Table B.2). The relative richness of EPT taxa in the assemblage (Fig. 4D) showed an opposite response with A, AB and ABC displaying lower values than B, C and BC with no significant difference observed for "B vs. C".

Sensitivity-related metrics

The number and the abundance of species considered at risk towards pesticides (Fig. 5A and5B, respectively) exhibited also an increasing gradient of values in the order C < B < A < BC < AB < ABC. For both metrics, significant differences were observed between all the couples of sampling strategies (Table B.3). The relative number of species at risk showed similar values over strategies (Fig. 5C) and no significant difference was detected between strategies of similar sampling efforts (A vs. B, A vs. C, B vs. C and AB vs. BC). In contrast, the relative abundance of species at risk in the assemblage (Fig. 5D) as well as the ASPT (Fig. 5E) exhibited an opposite response with A, AB and ABC displaying lower metric values than B, C and BC. Only B and C exhibited no significant difference in ASPT values.

Bio-ecological metrics

The influence of the sampling strategy on the utilization of 101 bio-ecological trait categories by macroinvertebrate assemblages was assessed by comparing trait category utilization on the basis of the representative p-value obtained for each of the four couples of strategies corresponding to similar sampling effort. The data were summarized by a heatmap (based on the p-values of the Wilcoxon-paired tests) and a dot plot representing, for each trait category and each phase, the median of the distribution of its relative frequencies of use over the 10074 sampling events (Fig. 6). Cluster I was composed by two sub-clusters gathering 88 metrics (detailed in table B.4) influenced by the sampled substrata. Cluster I.1 included 66 metrics with no-significant difference only between B and C, and exhibited a high number of trait-based metrics (i) more frequently used in A such as 'ovoviviparity' (reproduction trait), 'shredder' (feeding habits) and 'outside river system' (longitudinal distribution) or (ii) more related to B and C such as 'living macrophytes' (food), 'scraper' (feeding habits) and 'flag/boulder/cobble/pebble' (substrate preferences). Cluster I.2 gathered 22 metrics exhibiting mostly significant differences between all the couples of strategies. Some metrics such as 'null' (current velocity trait) and 'clutches/terrestrial/in vegetation' (reproduction) appeared more related to A, in contrast to the metrics 'medium' and 'fast' (current velocity) and 'river channel' (transversal distribution). The second cluster was composed by 13 metrics divided in two sub-clusters related to metrics such as 'asexual reproduction' (reproduction), 'nymph' and 'larva' (life-stage) or 'polysaprobic' (tolerance to organic contamination) for which mostly no significant differences were observed among A, B and C, but frequent significant differences between AB and BC in Cluster II.1 in contrast to Cluster II.2.

Predicted risk vs. observed chemical status

For each of the 15 pressure categories, the IP values derived from the A, B, C, AB, BC and ABC sampling strategies were compared to the corresponding observed chemical quality classes (i.e. high, good, moderate, poor, bad) allocated according to the French SEQ-eau2 system [START_REF] Oudin | Assessment system of streams quality (Système d'évaluation de la qualité de l'eau des cours d'eau (Seq-Eau))[END_REF] (Fig. 7 & B.1). In most of the cases, IP values remained in their range of expected values taking into account the corresponding quality class of water or habitat (i.e. IP < 0.5 for high and good status, IP ≥ 0.5 for moderate, poor and bad status). A few models like 'organic matter', 'nitrogen compounds (except nitrates)', 'nitrates' and 'phosphorous compounds' displayed contrasted IP values among quality classes (low IP values in high and good quality classes, high IP values for the others) and low IP median variability among strategies. For these models, the six sampling strategies provided rather similar risk estimation.

That was confirmed by the main linear relationship between the IPs obtained with each sampling strategy and the IPs obtained with ABC (Fig. B.2). Other models like 'acidification', 'pesticides', 'mineral' and 'organic micropollutants', 'riverine vegetation', 'clogging risk' and 'straightening' displayed also contrasting IP values among a few or all the quality classes but mostly between ABC and the other sampling strategies; while A, B and C provided similar IP value, around 0.5, whatever the quality class.

For most of those models, the relationship between IPs obtained by each strategy against ABC presented a Z-shape (Fig. B.2). The basis and the top of each Z shape correspond to assemblages for which one strategy provides an IP value highly above 0.5 while the other strategy provides an IP value clearly lower than 0.5. That means that different lists coming from the same site but obtained with different sampling strategies provided contrasted risk estimation leading either to impairment risk underestimation or overestimation.

The Friedman statistical tests (per pressure category and quality class) revealed most often significant differences in IPs between strategies except for the pressure acidification (good quality class), pesticides (bad quality class) and organic micropollutants (bad and poor quality classes) (data not shown). The Nemenyi post-hoc tests confirmed this observation and highlighted the most frequent, statistically significant differences in IP values between the sampling strategy ABC and the five other strategies (Table B.5).

Discussion

We compared six invertebrate sampling strategies differing (i) only in substratum selection criteria, or (ii) in both substratum selection criteria and sampling effort. Two of these strategies (i.e., AB and ABC) could be related to field-sampling protocols currently used to estimate the ecological status of river reaches based on standardized methods. The AB strategy has been associated to the French IBGN index applied since 1992 for evaluating the ecological status of French shallow river reaches [START_REF] Afnor | Détermination de l'indice biologique global normalisé (I.B.G.N.)[END_REF][START_REF] Verneaux | Une nouvelle méthode pratique d'évaluation de la qualité des eaux courantes[END_REF]. The ABC strategy is the current standardized method (AFNOR, 2009) explicitly related to the I2M2 index [START_REF] Mondy | A new macroinvertebratebased multimetric index (I2M2) to evaluate ecological quality of French wadeable streams fulfilling the WFD demands: A taxonomical and trait approach[END_REF], which will be used for the invertebrate-based reporting on the ecological status of the French shallow rivers during the third European water body management cycle (2021)(2022)(2023)(2024)(2025)(2026)(2027). Finally, the BC strategy mimics the philosophy of the AQEM sampling protocol in terms of habitat selection criteria [START_REF] Hering | Overview and application of the AQEM assessment system[END_REF] giving priority to the benthic coverage of substrata and focusing on the dominant ones [START_REF] Haase | Effects of sampling microhabitats with low coverage within the STAR/AQEM macroinvertebrate sampling protocol on stream assessment[END_REF]. However, the reference list of substratum types, the benthic mosaic description (i.e., including or not mineral substrata covered by organic ones in this description), the sampling effort (i.e., eight versus 20 sampling units), and the sampling device (Surber net versus hand net) are different.

Because sampling strategies have often progressed over time [START_REF] Moog | Biomonitoring and Bioassessment[END_REF], e.g. within a given country due to legislative compliance requirements, the characterization of macroinvertebrate assemblages may also vary over time independently of variations caused by changes in habitat condition.

Variations in sampling effort and/or in the selection of sampled substrata could indeed seriously alter the interpretation of long time-series in terms of taxonomy-or trait-based assemblage description as well as river ecological assessment [START_REF] Friberg | Comparison of macroinvertebrate sampling methods in Europe[END_REF][START_REF] Haase | Effects of sampling microhabitats with low coverage within the STAR/AQEM macroinvertebrate sampling protocol on stream assessment[END_REF][START_REF] Vlek | Influence of macroinvertebrate sample size on bioassessment of streams[END_REF]. Moreover, the different strategies used over the different European countries rely not necessarily on the same habitat sampling priority criteria, sampling effort, or sampling devices, inducing variability in biological assessment [START_REF] Birk | Three hundred ways to assess Europe's surface waters: An almost complete overview of biological methods to implement the Water Framework Directive[END_REF]Clarke et al., 2006;[START_REF] Friberg | Comparison of macroinvertebrate sampling methods in Europe[END_REF]. The following sections will point out how our results converge towards this statement.

Influence of sampling strategy on invertebrate community description

In bioassessment, macroinvertebrate communities are commonly characterized on the basis of taxonomic or functional metrics. Our study pointed out that sampling strategies not sharing the same substratum sampling priority criteria but also the same sampling effort can provide clearly different descriptions of the community due to different distributions of substratum types in marginal and dominant substrata of benthic mosaics, even when observed at National scale (cf. Fig. 2), leading to different compositions of invertebrate assemblages at mesohabitat scale. Our study has highlighted that marginal substrata appeared to host a greater diversity and richness of macroinvertebrate taxa than the dominant substrata (e.g., when comparing A to B, A to C and AB to BC). This result was in line with the significantly higher number of individuals obtained by the French IBGN method over the eleven European methods compared by [START_REF] Friberg | Comparison of macroinvertebrate sampling methods in Europe[END_REF] and despite its lowest normalized sampling effort. This greater taxonomic diversity was accompanied by a higher number of EPT and SPEAR taxa. Variation in taxonomic richness and taxonomic diversity of macroinvertebrate assemblages has been already observed between substrate-types (Beisel et al., 1998a[START_REF] Beisel | The spatial heterogeneity of a river bottom: A key factor determining macroinvertebrate communities[END_REF] or biotopes [START_REF] Demars | Linking biotopes to invertebrates in rivers: Biological traits, taxonomic composition and diversity[END_REF][START_REF] White | Macroinvertebrate Taxonomic and Functional Trait Compositions within Lotic Habitats Affected By River Restoration Practices[END_REF] with higher alpha (within-habitat) taxonomic diversity rather supported by organic (e.g., bryophytes, marginal plants, root mats, organic litter) than mineral (e.g., sand, silt, mud, flagstone) substrata or biotopes. These results are in accordance with the composition of habitat clusters and the related patterns of taxonomic metric values observed in our study, allowing to assume the strong driving role of marginal substrata on the taxonomic richness and diversity of macroinvertebrate assemblages at reach scale (Beisel et al., 1998b;[START_REF] Haase | Effects of sampling microhabitats with low coverage within the STAR/AQEM macroinvertebrate sampling protocol on stream assessment[END_REF]. These results are in agreement with the highest hosting capacity of the most frequent marginal substrata (i.e., bryophytes, hydrophytes, root mats and organic detritus in Fig. 2; table A.2), maximizing taxonomic richness and diversity -but also taxon sensitivity -in local assemblages (Beisel et al., 1998a[START_REF] Beisel | The spatial heterogeneity of a river bottom: A key factor determining macroinvertebrate communities[END_REF]. When the macroinvertebrate sampling protocol ignores the marginal substrata (e.g., the AQEM protocol), a lower taxonomic diversity and number of EPT taxa (at least at the family level) have been obtained in comparison to communities sampled using other methods on the same sites [START_REF] Friberg | Comparison of macroinvertebrate sampling methods in Europe[END_REF]. A similar trend has been observed for taxa sensitive to pesticides (SPEAR) and reflecting water quality (ASPT). However, when the EPT and SPEAR metrics were expressed as relative richness and abundance in the whole assemblage respectively (Fig. 4D and5D), between-sampling strategies differences in metrics values were lower.

Another assumption could be that marginal -mainly organic -substrata are characterized by greater beta (between-habitats) diversity than the dominant ones [START_REF] Verdonschot | The role of benthic microhabitats in determining the effects of hydromorphological river restoration on macroinvertebrates[END_REF][START_REF] White | Macroinvertebrate Taxonomic and Functional Trait Compositions within Lotic Habitats Affected By River Restoration Practices[END_REF].

However, even if the I2M2 sampling protocol provides a good overview of reach habitats, it gathers a maximum of four substrata per phase, not allowing to rigorously test this assumption on the whole range of habitat combinations in river reaches including often more than four marginal ones.

Most of the 101 investigated trait-based metrics were influenced by the substratum priority criterion of the selected sampling strategy. The three main patterns highlighted that (i) only a minority of trait-based metrics (e.g., >1-2cm (size), flier (locomotion), twigs/roots (substrate preference)) were independent of the substratum priority criterion and thus may provide the same information whatever the substrata sampled, (ii) several metrics (e.g. macrophytes (substrate preference), slow (current velocity preference)) depended on this criterion, providing a different picture of assemblage trait composition according to the sampled substratum types, and (iii) most of metrics indicated that marginal substrata with high hosting capacity have not the same functional characteristics than dominant ones. The two last patterns are in coherence with the habitat templet concept which has linked organisms with specific traits to specific habitat characteristics [START_REF] Southwood | Habitat, the templet for ecological strategies[END_REF][START_REF] Townsend | Species traits in relation to a habitat templet for river systems[END_REF]. The macroinvertebrate trait structure in an assemblage is known to be related to the substratum type [START_REF] Demars | Linking biotopes to invertebrates in rivers: Biological traits, taxonomic composition and diversity[END_REF]Usseglio-Polatera et al., 1999). Thus, it appears crucial to identify, prior to the sampling, the different habitats present at the reach scale in order to capture cautiously the diversity of traits of in-situ communities. A reliable definition of community trait-based metrics is especially important when they are included in index calculation for evaluating the ecological status of streams or for supporting the stakeholder decision-making process.

The ecological rationale of a sampling strategy and the way for describing a benthic mosaic at the reach scale have appeared as two very critical points. In order to prevent important bias in data interpretation, several general guidelines can be given. First, the hosting capacity of substrata is clearly an important determinant of the taxonomic and functional macroinvertebrate assemblage composition, confirming the important role of the rationale underpinning the sampling strategy (hosting capacity vs dominance). Some structural aspects of the community are indeed captured differently by different sampling strategies [START_REF] Friberg | Comparison of macroinvertebrate sampling methods in Europe[END_REF][START_REF] Rossini | Determining optimal sampling strategies for monitoring threatened endemic macro-invertebrates in Australia[END_REF], potentially leading to deliver to stakeholders different -or even divergent -messages depending on the selected substrata in the benthic mosaic and the selected metrics. In streams, the hydromorphological conditions are one of the drivers of the benthic mosaic [START_REF] Villeneuve | Direct and indirect effects of multiple stressors on stream invertebrates across watershed, reach and site scales: A structural equation modelling better informing on hydromorphological impacts[END_REF] which influence the biological characteristics of local macroinvertebrate assemblages as well as the ecological status assessment [START_REF] Buffagni | In-stream microhabitat mosaic depicts the success of mitigation measures and controls the Ecological Potential of benthic communities in heavily modified rivers[END_REF]. Thus, the definition of the benthic mosaic appears as a critical step for the ecological diagnostic. Second, the marginal substrata in a given reach are clearly important for macroinvertebrate assemblages and significantly influence macroinvertebrate community characterization at the reach scale. Third, the compositional structure of marginal substrata is more specific than that of dominant ones, as already observed by [START_REF] Leitão | Habitat-oriented sampling of macroinvertebrates affects the determination of ecological status in temporary mediterranean river systems[END_REF]. As a result, including or not marginal substrata in the reach macroinvertebrate sample could lead to a more or less important selectivity that has to be kept in mind when defining the stream sampling design with the major objective of surveying long-term effects of anthropogenic pressures or when analyzing data achieved with different successive sampling protocols. Potentially observed tipping points could be indeed not only related to objective variations in the taxonomy-or trait-based structure of local macroinvertebrate assemblages subjected to anthropogenic pressures but could be also linked to potential modifications in sampling strategies especially if successive selected strategies differently consider marginal substrata. Fourth, macroinvertebrate compositional metrics aggregated in multimetric indices should preferably be expressed as relative abundance or richness of the targeted taxa than with raw values of abundance or richness, for providing a more robust ecological assessment of river reaches.

Fifth, the highest richness of species sensitive to pesticides and organic pollution in marginal substrata should allow to explore the development of rapid, cost-effective methods especially focusing on macroinvertebrate assemblages of a limited number of marginal substrata, used as early warning indicators of corresponding categories of anthropogenic pressures, as already proposed by [START_REF] Piscart | Salinization consequences in running waters: Use of a sentinel substrate as a bioassessment method[END_REF] for pebbles, a sentinel substratum reflecting salinization.

Influence of sampling strategy on risk assessment

In Europe, different methods have been successively used over time to sample benthic macroinvertebrate assemblages. In France, the IBGN (1992IBGN ( -2004) ) has been replaced by the I2M2 since 2005 [START_REF] Mondy | A new macroinvertebratebased multimetric index (I2M2) to evaluate ecological quality of French wadeable streams fulfilling the WFD demands: A taxonomical and trait approach[END_REF]. Similarly, the European AQEM protocol has been routinely applied in many European countries since 2004, in place of older bioassessment methods [START_REF] Buffagni | The AQEM multimetric system for the southern Italian Apennines: Assessing the impact of water quality and habitat degradation on pool macroinvertebrates in Mediterranean rivers[END_REF][START_REF] Hering | Overview and application of the AQEM assessment system[END_REF][START_REF] Hering | Cook book for the development of a Multimetric Index for biological condition of aquatic ecosystems: Experiences from the European AQEM and STAR projects and related initiatives[END_REF]. Various other macroinvertebrate sampling methods presenting different rationales have been also used across European countries (Clarke et al., 2006;[START_REF] Friberg | Comparison of macroinvertebrate sampling methods in Europe[END_REF].

Consequently, a huge amount of information has been gained over years, but often heterogeneously.

Retrospectively investigating ecological risk assessment at large spatial and temporal scales using data obtained with different sampling strategies should be made cautiously to avoid potential bias related to between-sampling methods heterogeneity (this is especially obvious when the sampling effort is low).

The tool we developed to estimate the risk of a significant impact of different anthropogenic pressure categories on the local macroinvertebrate communities have clearly shown some limits when it was run on faunal assemblages not obtained with the sampling protocol (i.e., ABC) on which it had been calibrated. Both sampling effort and substratum selection criteria matter for impairment probability estimation by the ODI. Above all, a loss of reliability in risk estimation (Fig. 7B,B.1) was observed when the models were not running on the ABC list but this loss was more or less dramatic depending on the pressure type. In most of the cases, the models were not powerful enough to support the loss of biological information induced by the reduction of the sampling effort and/or the restricted biological picture of the assemblage (e.g., 'acidification', 'mineral' and 'organic' micropollutants, 'hydrological instability'). This can be due to the (only) fair accuracy of the model, a lack of homogeneity in the gradient pressure description in the training dataset or because the model relies on habitat-specific metrics, and then is sensitive to sampling strategy. For example, even if both marginal and dominant substrata are taken into account by the sampling strategy (e.g., the strategy AB), only slight differences in trait-based metric values calculated on the assemblages provided by the sampling strategies AB and ABC can be sufficient for inducing strong differences in estimated IP values (i.e., at the origin of the Zshapes on Fig. B.2), leading to divergent diagnostics (one sampling strategy detecting a clear site community impairment, the other not).

For other models, the loss of biological information induced by lower sampling effort and variation in substratum sampling priority induced only a moderate loss of reliability and the IP values remained in the expected range of values (IP < 0.5 for the high quality and good quality classes; IP ≥ 0.5 for the moderate, poor and bad quality classes) when they run on lists obtained via other strategies than ABC (organic matter, nitrogen compounds, nitrates and phosphorous compounds). Another trend is that the sampling strategies using hosting capacity as the main criterion of substratum selection tend to provide the IP values most similar to those provided by ABC in the moderate, poor and bad quality classes. Only a few models (i.e. those that not exhibited a Z-shape when comparing IPs values between sampling strategies) could be potentially used, but very cautiously, with data provided by sampling protocols slightly different from the calibration sampling protocol (ABC).

Conclusion

Differences in taxonomic and trait structure characterization as well as sensitivity descriptors of macroinvertebrate communities due to differences in sampling strategies clearly influence the macroinvertebrate-based environmental diagnostic of streams. If both sampling effort and sampled habitat selection criteria have an impact on community characterization and impairment risk assessment, an important component is undoubtedly the presence/absence of marginal habitats in the sampling design. Such methodological confounding effect undoubtedly represents a critical issue for the ecological interpretation of the provided results, for example in the context of long-term series surveys when the sampling strategy has changed over time. However, the effect is globally less important on trait-based metrics than on taxonomy-based metrics. Moreover, robust models assessing the risk of impairment by a given pressure category (e.g., organic matter, nitrogen compounds (except nitrates) and phosphorous compounds; AUC > 0.85) are not only able to run on data achieved with the sampling strategy on which they were calibrated (i.e., ABC), but also on data slightly differing in the number of samples performed or in the rules of sampled habitat selection (e.g., strategy AB (IBGN-like) or BC (AQEM-like)). Even if the results provided by such diagnostic tools should be interpreted cautiously (in particular when pressure effects are moderate), they could clearly facilitate the ecological interpretation of macroinvertebrate community dynamics over long periods, especially when sampling strategy has varied over time.

Figure 1 :

 1 Figure 1: Different combinations of the elementary phases (A, B and C) leading to the six sampling strategies tested and their specific characteristics regarding substratum sampling. The vertical location of

Figure 2 :

 2 Figure 2: Heatmap based on the relative frequencies of sampling of the 11 substratum-types during the phases A, B and C of the standardized sampling design, over the 404 reference sites of the whole dataset.3.2 Abundance of taxa in assemblages

Figure 3 :

 3 Figure 3: Responses of the abundances of 297 taxa to the sampling strategy. On the left, the heatmap reflects the p-values obtained from the Wilcoxon paired-tests for each taxon (in row). On the right, the dot plot is based on the total abundance of each taxon over the 10074 sampling events. In heatmap, grey = p.value > 0.05, red = p.value ≤ 0 .05 but ≥ 0.0005, dark-red = p.value < 0.0005. In dot plot, yellow = strategy A, purple = strategy B, black = strategy C. Horizontal lines indicate cluster limits. The taxonomic composition of each cluster is detailed in table B.1.

Figure 4 :

 4 Figure 4: Distribution of the values of A) total taxonomic richness, B) Shannon diversity index, C) richness of EPT taxa and D) relative richness (%) of EPT taxa, calculated over the 10074 sampling events, for each sampling strategy (A, B, C, AB, BC and ABC). Boxplots represent the minimum/Q25/median/Q75/maximum of the distributions of values over the 10074 sampling events. Outliers are out of the 1.5 interquartile range.

Figure 5 :

 5 Figure 5: Distributions of the values of A) the number of "SPEcies At Risk" (SPEAR), B) the abundance of SPEAR, C) the relative number of SPEAR, D) the relative abundance of SPEAR and E) the ASPT values; for each sampling strategy. Boxplots represent the minimum/Q25/median/Q75/maximum of the distributions of values over the 10074 sampling events. Outliers are out of the 1.5 interquartile range.

Figure 6 :

 6 Figure 6: Responses of 101 trait categories to the sampling strategy. On the left, the heatmap reflects the p-values obtained from the Wilcoxon paired-test results for each trait category (in row). On the right, the dot plot represents for each trait category and each phase the median of the distribution of its relative frequencies of use over the 10074 sampling events. In heatmap, grey = p.value > 0.05, red = p.value ≤ 0 .05 but ≥ 0.0005, dark-red = p.value < 0.0005. In dot plot, yellow = strategy A, purple = strategy B, black = strategy C. Horizontal lines indicate cluster limits. The composition of each cluster of trait categories is detailed in table B.4.

Figure 7 :

 7 Figure 7: Distribution of impairment probabilities (IPs) assigned to macroinvertebrate assemblages provided by the A, B, C, AB, BC and ABC sampling strategies for 10074 sampling events against their respective observed quality classes (based on chemical characteristics of water or hydromorphological characteristics of habitat) for the pressure categories: A) 'Nitrogen compounds' and B) 'Riverine vegetation'. The horizontal line (IP = 0.5) represents the threshold between a 'low' impairment risk and a 'significant' impairment risk. The bow and whisker plots represent the minimum/Q25/median/Q75/maximum values, respectively. Outliers (open circles) are outside the 1.5 interquartile range of the corresponding distribution. The number of observations for each sampling strategy and quality class is specified as N.