Adeline Tauran
email: adeline.tauran@univ-brest.fr

Julien Dubreuil
email: jdubreuil@bretagne-peches.org

Benjamin Guyonnet
email: b.guyonnet@tbm-environnement.com

Jacques Grall
email: jacques.grall@univ-brest.fr

Impact of fishing gears and fishing intensities on maerl beds: An experimental approach

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

INTRODUCTION

Maerl beds are formed by an aggregation of live and dead unattached and nongeniculated coralline algae, also called rhodoliths, which form highly complex threedimensional biogenic habitats [START_REF] Biomaerl Team | Final Report, BIOMAERL project[END_REF][START_REF] Foster | Rhodoliths and rhodolith beds[END_REF]. Considering their slow growth rate (~500 μm/year), maerl beds are regarded as non-renewable resources [START_REF] Bosence | Maerl growth, carbonate production rates and accumulation rates in the ne atlantic[END_REF], although they can be long-lived when undisturbed. Indeed, some maerl beds in the Glenan archipelago (France) are estimated to be more than ~8000 years old [START_REF] Birkett | Maerl (volume V). An overview of dynamic and sensitivity characteristics for conservation management of marine SAC[END_REF]. The main factors affecting the presence of maerl appear to be light penetration together with hydrodynamic conditions. The latter must be sufficiently strong to maintain low turbidity and allow the maerl to photosynthesize while also preventing burial of the maerl under fine particle sedimentation [START_REF] Grall | Fiche de synthèse sur les biocénoses: les bancs de maerl[END_REF]. Maerl beds can be locally structured in ripple marks from exposure to these strong hydrodynamic conditions, but those from sheltered areas lack such features (personal observations). These habitats are found worldwide, from the Arctic to the tropics. In Europe, they are especially present in the Mediterranean Sea and on the North-East Atlantic coast, particularly in Galicia, Brittany, Ireland, Scotland, and Norway [START_REF] Foster | RHODOLITHS: BETWEEN ROCKS AND SOFT PLACES[END_REF]. Maerl beds are highly ecologically important biogenic habitats with a complex three-dimensional structure involved in numerous ecosystem functions, such as habitat and shelter for many benthic species (some of them of economic value) and settlement and nursery areas for juveniles [START_REF] Kamenos | Nursery-area function of maerl grounds for juvenile queen scallops Aequipecten opercularis and other invertebrates[END_REF]. As a result, maerl beds are considered to be bioengineers and have been viewed as "biodiversity hotspots" for supporting high faunal and floral biodiversity and serving essential ecological functions in coastal ecosystems [START_REF] Biomaerl Team | Final Report, BIOMAERL project[END_REF][START_REF] Grall | Community structure and food web based on stable isotopes (δ15N and δ13C) analysis of a North Eastern Atlantic maerl bed[END_REF][START_REF] Peña | The diversity of seaweeds on maerl in the NE Atlantic[END_REF].

Considering their ecological value, maerl beds are under national, European, and international conservation legislation. At an international level, maerl beds are covered under Atlantic and parts of the Arctic ocean between 36°N to the North pole and from 44°W to 51°E (excluding the Baltic Sea and the area covering 44°W and 42°W to 36°N and 59°N) [START_REF] Dinter | Biogeography of the OSPAR Maritime Area[END_REF]. In this area, maerl beds are coastal habitats occurring in open or semi-enclosed spaces at depths ranging from 0 to 51 m [START_REF] Peña | The diversity of seaweeds on maerl in the NE Atlantic[END_REF]. [START_REF] Grall | Problems facing maerl conservation in Brittany[END_REF] and cover up to 64% of their surface (CRPMEM de Bretagne, 2020). In addition, the main Breton maerl beds are in these protected areas.

Maerl extraction was banned in

Marine ecosystems -and more specifically benthic habitats -are increasingly threatened by human activities and anthropogenic factors [START_REF] Glover | The deep-sea floor ecosystem: current status and prospects of anthropogenic change by the year 2025[END_REF][START_REF] Halpern | Evaluating and Ranking the Vulnerability of Global Marine Ecosystems to Anthropogenic Threats[END_REF]. Maerl beds are no exception because they are highly vulnerable to climate change and ocean acidification [START_REF] Amado-Filho | Rhodolith Beds Are Major CaCO3 Bio-Factories in the Tropical South West Atlantic[END_REF], eutrophication [START_REF] Biomaerl Team | Final Report, BIOMAERL project[END_REF], aquaculture and fish farms [START_REF] Hall-Spencer | Impact of fish farms on maerl beds in strongly tidal areas[END_REF], invasive species (such as the slipper limpet Crepidula fornicata) [START_REF] Grall | Problems facing maerl conservation in Brittany[END_REF], and the fishery industry.

The dredging activities associated with fishing have a well-documented negative impact on benthic ecosystems [START_REF] De Groot | The impact of bottom trawling on benthic fauna of the North Sea[END_REF][START_REF] Dayton | Environmental effects of marine fishing[END_REF][START_REF] Jennings | The Effects of Fishing on Marine Ecosystems[END_REF][START_REF] Blanchard | Fishing effects on diversity, size and community structure of the benthic invertebrate and fish megafauna on the Bay of Biscay coast of France[END_REF], including maerl bed habitats. The repercussions of bivalve dredging on maerl grounds have received considerable attention over the last decade, leading to a better understanding of the impact of mobile bottom-contacting gears on this fragile and unique habitat [START_REF] Barberá | Conservation and management of northeast Atlantic and Mediterranean maerl beds[END_REF][START_REF] Barberá | Maerl beds inside and outside a 25-year-old no-take area[END_REF][START_REF] Kamenos | Substratum heterogeneity of dredged vs un-dredged maerl grounds[END_REF]. These studies showed that the principal effects of bivalve dredging on maerl grounds are burial of live maerl, changes in maerl structure and complexity, and reduced biodiversity and changes in the benthic community.

Although the results of these and other studies [START_REF] Cabanellas-Reboredo | Morpho-demographic traits of two maërl-forming algae in beds with different depths and fishing histories[END_REF][START_REF] Coquereau | Marine soundscape shaped by fishing activity[END_REF] broadly illustrate how dredging affects maerl beds, they did not evaluate outcomes by intensity of dredging or type of dredge used, which depends on the targeted species. Here, we focus on the experimental assessment of specific gear types and the level of pressure applied to the habitat. Our aim was to distinguish how two fishing intensities and three dredge types (targeting clams, king scallops, and queen scallops) affect the habitat and macrofauna biodiversity of the maerl beds. For this work, we used an experimental Before -After -Control -Impact (BACI) design [START_REF] Stewart-Oaten | Environmental impact assessment: "pseudoreplication[END_REF], which has been commonly applied for benthic community impact assessments of anthropogenic disturbances, including dredging [START_REF] Currie | Effects of scallop dredging on a soft sediment community: A large-scale experimental study[END_REF][START_REF] Guerra-García | Short-term benthic recolonization after dredging in the harbour of Ceuta, North America[END_REF][START_REF] Clarke | Change in marine communities: an approach to statistical analysis and interpretation[END_REF]. This design involves simultaneous sampling of two groups of stations ("control" and "impacted") multiple times before and after an experimentally induced disturbance.

Based on current evidence, we tested the following hypotheses: (1) that dredging would lead to biodiversity loss that varies with pressure level and/or gear type; (2) that fishing pressure and gear type would affect habitat structure through reduced maerl complexity and vitality; and (3) that dredging intensity would variably affect the one-year temporal evolution of this habitat, depending on the pressure levels.

This work was called for by the fishing industry as part of the DECIDER program (Diagnostic of gear/habitat interaction: the dredge/maerl example) and strongly supported by managers. Permission was granted by the Armorique Regional Natural Park (Parc naturel régional d'Armorique, PNRA), which took part in the decision making regarding the location of the study area, which was within the Natura 2000 zone of the Bay of Brest.

METHODS

Study site

The Bay of Brest is a 180 km² semi-enclosed area located in Brittany, Western France.

The bay is connected to the Atlantic Ocean and the Iroise Sea by a narrow strait (~2.5 km) and is under the influence of freshwater run-off from two main rivers (the Aulne and Elorn) draining a 2800 km² watershed (Le [START_REF] Pape | Hydrodynamic prevention of eutrophication in the Bay of Brest (France), a modelling approach[END_REF]. Approximately half of the surface of the bay is <5 m deep [START_REF] Monbet | Bilan des connaissances océanographiques en rade de Brest[END_REF], which makes it a rather shallow bay. Maerl beds cover >30% of the bottom of the bay [START_REF] Martin | Comparison of Zostera marina and maerl community metabolism[END_REF] and consist mainly of Lithothamnion corallioides and Phymatolithon calcareum.

The fishery industry in the Bay of Brest mainly focuses on three species: king scallops (Pecten maximus, Linnaeus 1758), queen scallops (Mimachlamys varia, Linnaeus 1758), and clams (Venus verrucosa, Linnaeus 1758). In recent years, 10-30 tonnes of queen scallops, 150-300 tonnes of king scallops, and 100-150 tonnes of clams were fished in the bay all habitats considered (unpublished data from the Fishery Committee of Finistère (Comités des Pêches du Finistère)). The fishing fleet exploiting these species consists of about 35 fishing vessels of 11 m or less in length, with engines with a maximum power of 150 kW. Fishing occurs every year from October to March. For this study, we focused on a 0.164km² (≈4000 m²) area in the eastern part of the bay, selected for the presence of an unfished homogeneous maerl bed.

Experimental dredging

Eight 25×200m zones (three controls and five impacted) were delimited to test the impact of (1) three different dredges commonly used for bivalve dredging in the Bay of Brest and (2) different fishing intensities on the maerl habitat (Figure 1). Features of the dredges were as follows: (1) a clam dredge (CD), 70 to 90 kg, 1.5 m wide, 40 teeth of 11 cm each; (2) a queen scallop dredge (QSD), 120 kg, 1.8 m wide, with a blade; and (3) a king scallop dredge (KSD), 190 kg, 1.8 m wide, 18 teeth of 10 cm each every 9 cm (Figure 2). Penetration into the substratum was estimated for each gear based on diver observation, as follows: ~5 cm for the KSD, ~10 cm for the QSD, and ~15 cm for the CD (clams are difficult to access).

Pressure levels were measured as the number of dredge tows performed on the zone during the experimental dredging session, i.e., 0, 10, or 30 dredge tows. These levels of pressure can be expressed as the number of times a zone was totally dredged by considering the width of the zones and of each gear. With these factors, the zone would be totally dredged 0.6 times for the CD_10 treatment, 0.72 times for the QSD_10 and KSD_10 treatments, 1.8 times for the CD_30 treatment, and 2.16 times for the QSD_30 treatment. Each of the three dredges was tested with two levels of pressure, except for the KSD, which was tested only for the moderate pressure level because of funding limitations. The decision was made to conserve a third replicate of control instead of dedicating a zone to a KSD_30 treatment for three reasons: (1) to allow for maximum spatial variability in the control areas; (2) of the three fisheries being compared, the king scallop fishery is not the main fishery on maerl beds; and

(3) king scallops have large epibenthic shells that can be easily captured without a significant application of pressure.

The experimental pressures selected for this study are not equivalent to the most extreme fishing intensities recorded (~60 dredge tows) [START_REF] Bernard | Declining maerl vitality and habitat complexity across a dredging gradient: Insights from in situ sediment profile imagery[END_REF][START_REF] Pantalos | Clam dredging and maerl bed health in the south basin of the Bay of Brest[END_REF] but still offer a good representation of the situation in the Bay of Brest. Indeed, fishing intensities recorded in 50 m 2 cells on a nearby maerl bed for the 2015-2016 fishing season showed a maximum of one cell being totally dredge 2.15 times in the span of 6 months (unpublished data from the IMPECAPE program). Reproducing these levels of fishing intensity would require recapitulating a ~60 dredge tows using a 1.8m wide dredge on a 50m² cell. In comparison, the highest level of pressure selected in this study (30 dredge tows using a 1.8m wide dredge) would correspond to a 50 m 2 cell being totally dredged 1.08 times.

Sampling protocol

Samples were collected on four occasions from April 2016 to April 2017. The study area was monitored for a year according to the BACI approach: session 1 was sampled on April 15, 2016 (T0), just before dredging; session 2 on April 27, 2016 (T0 + 1 week); session 3 on May 25, 2016 (T0 + 1 month), and session 4 on April 6, 2017 (T0 + 12 months).

In this study, 24 stations (3 stations randomly located for each of the 8 zones with 3 replicates per station) were sampled each session with a Smith grab (0.1 m²) to study the macrofauna population (Figure 3). For each station, three replicates were sampled a few meters apart, but their position is not displayed in Figure 3 for clarity and readability purposes. In addition, 15 normalized 0.1 m² quadrats (1.5 m² total) were randomly selected and photographed in each zone and for each session (480 quadrats total) by professional scuba divers for investigation of how dredging affected habitat (Figure 4).

Characterization of the benthic community and the maerl habitat

Regarding the macrofauna community, each replicate was rinsed thoroughly in the laboratory using a 1mm mesh sieve to eliminate the formaldehyde used for preservation. All samples were sorted and the organisms isolated in 70% alcohol until identification at the lowest taxonomic level possible using a binocular and/or a microscope.

The quadrats were analyzed using geographic information system (GIS) software (ArcGis 10.3). Using a grid in which each cell was 0.33 cm², the surface occupied by maerl (live, dead, and broken), algae, sediment (mud), fauna, or "other" (eggs, dead shell) was digitally measured (for more details, see TBM Environnement, (2017)). All visible complex aggregations of pink/purple maerl were characterized as living maerl, pink/purple small maerl fragments on sediment (mud) or other maerl fragments were characterized as broken maerl, and all white/orange maerl fragments were characterized as dead maerl.

Statistical analysis

The effects of experimental dredging on the species richness and density of individuals were tested using R © software (R Core Team, 2019). One-way analyses of variance (ANOVAs) were performed when the ANOVA assumptions (normality of residuals and homogeneity of variance) were verified. Otherwise, non-parametric Kruskal-Wallis tests were used. When a significant effect was identified (p < 0.05), Tukey's post-hoc test (ANOVA) or the post-hoc Nemenyi test (Kruskal-Wallis) were used to characterize the differences. In addition, two-sample Wilcoxon Mann-Whitney tests were used on arcsine-transformed percentages of cover from the quadrats data. To assess the impact of dredging on the macrofauna population spatially (among the different zones) and temporally (among the different sessions), we performed non-metric multidimensional scaling (nMDS) on the nonstandardized, log-transformed mean abundance data, using the Bray-Curtis similarity [START_REF] Clarke | Non-parametric multivariate analyses of changes in community structure[END_REF]. For comparing the observed species richness to the real diversity calculated by the estimator, we computed the nonparametric diversity estimator Jackknife 2 nd order (Jack2; 1000 randomizations) using EstimateS version 9 [START_REF] Colwell | EstimateS: Statistical estimation of species richness and shared species from samples[END_REF] for each dredge and pressure level tested and each session. Jack2 is reported to yield the best results for different benthic communities and as such to be the most suitable non-parametric diversity estimator when dealing with marine habitats [START_REF] Canning-Clode | Estimation of regional richness in marine benthic communities: quantifying the error[END_REF]. In addition, the number of singleton and unique species were computed using the same software. The computation of Jack2 and of the number of singletons allowed us to consider transient and highly dispersed species that in situ measurements do not encompass. These species have an important role in the functional richness of a habitat [START_REF] Boyé | Trait-based approach to monitoring marine benthic data along 500 km of coastline[END_REF] and also rely on maerl beds, so their inclusion was expected to yield a better estimation of the actual diversity of the study area.

To distinguish the initial impact (t = 0) of the three dredges on the benthic community and the rate of habitat recovery following such perturbations, we applied a previously described method [START_REF] Sciberras | Response of benthic fauna to experimental bottom fishing: A global meta-analysis[END_REF]. We used the unadjusted log response ratio (ln(RR)) because the different intensities of fishing were of interest. As specified, we used a weighted linear mixed-effects model with the rma.uni function in the metaphor R package (Viechtbauer, 2010) with a restricted maximum-likelihood estimator. Model selection was guided by small sample-corrected Akaike information criterion (AICc) because our sample size was small. The different models tested are listed in the supplementary material.

Replicate samples were used as replicates for all analyses except for the nMDS analysis, in which stations were used as replicates (as the mean of the samples) for the purposes of clarity. In the present study, the level of replication was different between the controls and the impacted zones. For this reason, we considered each control as an independent zone for the analysis and figures to illustrate the variability that can exist among unaffected zones from the same area.

RESULTS

Impact of experimental dredging on the habitat

To investigate the impact of dredging on habitat structure, 15 photographs of quadrats, per zone and per session (480 photographs total) were digitally analyzed using GIS (Figure 5).

The results showed that during session 1, the live maerl was predominant in all zones and covered around 90% of the quadrats. Dead maerl, fauna, and mud in small percentages covered the remaining 10%. During session 2, mud covered around 20% of the surface at CD_10 and KSD_10 and >50% at CD_30, QSD_10, and QSD_30. The percentage of mud cover significantly increased in all zones compared to CTRL1 (p ≤ 0.02), in all zones (p ≤ 0.01) except CD_10 compared to CTRL2 (p = 0.08), and in all zones (p ≤ 0.01) except for KSD_10 compared to CTRL3 (p = 0.16) and CD_10 compared to CTRL3 (p = 0.62). The percentage of mud cover did not differ significantly between KSD_10 and the other dredged zones (p > 0.05) or among CD_30, QSD_10, and QSD_30 (p > 0.05). However, the percentage of mud cover significantly differed between CD_10 and CD_30 (p = 0.01), CD_10 and QSD_10 (p = 0.007), and CD_10 and QSD_30 (p = 0.02). In addition, the results showed a greater presence of broken maerl in the dredged zones (5%-15%) compared with very small percentages in the three controls (<1%).

During session 3, macroalgae covered from 28% to 76% of the surface in dredged zones and more than 80% in the three control zones. The surface area covered by algae was significantly higher in the three controls than the dredged zones (p ≤ 0.05), except for the comparison between KSD_10 and CTRL3 (p = 0.26), but this coverage did not differ significantly among CD_10, CD_30, QSD_10, and QSD_30 (p > 0.05). Mud still covered an important portion of the surface during session 3 in CD_10, CD_30, and QSD_30 (20%-33%). CD_10 and CD_30 significantly differed from KSD_10 (both p = 0.02) and QSD_30 (p = 0.001 and p = 0.003, respectively), and CD_10 also differed from QSD_10 (p = 0.02).

Results from session 4 showed that living maerl predominantly covered the surface (from 60% to 95% of the surface) and mud and dead maerl were present in variable proportions, especially in CD_30 (dead maerl = 26%). Mud was significantly more abundant in CD_30 (p = 0.02) and QSD_30 (p = 0.03) than in at least one of the three control zones and in CD_30 compared to QSD_30 (p = 0.004). In addition, the results showed a significant loss in the cover of live maerl at CD_30 and QSD_30 between sessions 1 and 4 (p = 0.03 and p = 0.02, respectively).

The results shown in Figure 5B may seem redundant with those already described, but they attest to the important variability observed among photoquadrats taken in the same zone, during the same session. Their position in relation to the dredge track influenced the proportion of mud, maerl, and algae observed, factors that are important to consider when using photoquadrats. Indeed, the cover of live maerl appeared to be extremely variable among the 15 analyzed quadrats, except in the three controls and in CD_10 and KSD_10 during session 3, where the variability was rather small. In contrast, the surface covered by dead maerl appear to be similar between quadrats during sessions 1 and 2 but tended to increase during session 3. There was an important heterogeneity in all zones in the surface covered by mud during session 2 (except for the three controls). This high variability was still present during session 3 in CD_10 and CD_30 only. Finally, the surface covered by algae during session 3 appeared extremely variable among quadrats in all zones, except for the three controls and KSD_10.

Impact of the experimental dredging on the benthic community

To investigate the impact of dredging on the benthic community, we performed several analyses, using an important dataset produced from 288 grab samples, with 238 identified species and 101446 individuals counted (see supplementary material), including the three target species in this study. Based on the general model (ln(RR) ~log2(t+1)), dredging (all gears and pressure combined) led to a significant reduction in benthic community abundance at t = 0 (mean response: -67%; 95% confidence interval [CI]: -73% to -53%; R² = 42%) and species richness (mean response: -30%; 95% CI: -40% to -18%; R² = 10%).

Predicted recovery rates were estimated at ~8 years for benthic community abundance and >100 years for species richness.

Based on AICc values, the optimal model (that with the lowest AICc value) for both abundance and species richness was ln(RR) ~ gear + log2(t+1). The reduction in benthic abundance was the most important for QSD (-70%; 95% CI: -81% to -52%), followed by CD (-65%; -73% to -54%) and KSD (-58%; -74% to -32%). Time to recovery was estimated at ~16 years for QSD, ~6 years for CD, and ~1.5 years for KSD. Regarding species richness, the reduction was steepest for QSD (-35%; -49% to -15%), followed by KSD (-27%; -45% to -4%) and CD (-26%; -36% to -12%). Time to recovery was estimated at ~17 years for CD, ~34 years for KSD, and ~900 years for QSD.

Figure 6 shows the significant effects of both gear characteristics and pressure levels during the course of a year on species richness and density of individuals. No significant differences were found regarding the species richness and density of individuals among the three control zones for sessions 1 to 4. Species richness (Figure 6A) also did not differ significantly between experimental zones during session 1 (ANOVA; p = 0.89), whereas we found a significant effect of dredging from sessions 2 to 4 (ANOVA; sessions 2 and 3: p < 0.001; session 4: p < 0.002). A significant decrease in species richness was visible during session 2 compared to one or more controls at CD_30 (pCD_30-CTRL1< 0.001; p CD_30-CTRL2 = 0.052; p CD_30-CTRL3 = 0.02), QSD_10 (p QSD_10-CTRL1 = 0.029), and QSD_30 (p QSD_30-CTRL1 < 0.001; p QSD_30-CTRL2 =0.02; p QSD_30-CTRL3 = 0.01). During session 3, three zones experienced a significant decrease in species richness compared to the three control zones (CD_30, p < 0.05; QSD_10, p < 0.01; QSD_30, p < 0.002), but CD_10 (p > 0.1) and KSD_10 (p > 0.1) did not.

A significant reduction in species richness was still detectable during session 4 at CD_30 (p CD_30-CTRL1 = 0.0085), KSD_10 (p KSD_10-CTRL1 = 0.004; p KSD_10-CTRL3 = 0.04), and QSD_30 (p QSD_30-CTRL1 = 0.001; p QSD_30-CTRL3 = 0.02).

The density of individuals (Figure 6B) did not differ significantly among zones during session 1 (ANOVA; p = 0.55), but a significant effect of dredging was detectable from sessions 2 to 4 (session 2 ANOVA, p < 0.001; session 3 ANOVA, p < 0.001; session 4 Kruskal-Wallis test, p = 0.0006). During session 2, all zones underwent a significant decrease in density of individuals compared to at least one of the three controls. The densities of individuals at CTRL1 and CTRL3 were significantly higher than in almost all impacted zones (p at least < 0.05 for CTRL1 and all p ≤ 0.002 for CTRL3), with the exception of CTRL3 compared with CD_10 (p = 0.18). The density of individuals in CTRL2 differed significantly from CD_30 only (p = 0.04). During session 3, almost all zones showed a significant decrease in density of individuals (all p ≤ 0.03) compared with the three control zones; the exception was CD_10, which differed only from CTRL2 and CTRL3. During session 4, QSD_30 still had a lower density of individuals than CTRL1 (p = 0.003), CTRL3 (p = 0.0015), and QSD_10 (p = 0.009).

Figure 7 shows the results from the computation of the nonparametric estimator Jack2 and the measure of the number of singletons and unique species for each session and each zone. The estimated and observed species richness findings are shown in Figure 7A. The results for each session show that the estimated species richness was always higher than the observed diversity. During session 1, there was no particular pattern, and both the estimated and observed species richness appeared to be similar among zones. During session 2, all of the dredged zones showed a decrease in both estimated and observed species richness.

CTRL3 had similar results between sessions 1 and 2, whereas CTRL1 and CTRL2 had lower levels of diversity between these two sessions. During session 3, almost all zones had a decrease in species richness, with the exception of CD_30, where both the estimated and observed species richness increased. A clear pattern was visible for the observed species richness during session 3 among the three groups: (1) CTRL3 and CD_30, (2) CTRL1, CTRL2, KSD_10, and CD_10, and (3) QSD_10 and QSD_30 had the lowest diversity.

During session 4, the results were similar to those for session 1 but with a lower diversity. QSD_10 and QSD_30, which had the poorest diversity in session 3, showed an increase in diversity in session 4.

Regarding the number of singletons (Figure 7B), there was no evident pattern in session 1. CTRL3 had the highest number of singletons during session 2, followed by CTRL2 and KSD_10. CD_30 had an important increase in singletons during session 3, whereas the three controls and KSD_10 had a decrease. During session 4, QSD_10 and QSD_30 had an important increase in number of singletons, but the other zones remained stable.

The findings regarding the number of uniques in the samples (Figure 7C) were similar.

There is no particular pattern during session 1. CTRL3 had an increase in the number of unique species during session 2 whereas CTRL1, CTRL2, and KSD_10 showed stable results.

The other zones showed decreasing curves during session 2, and CD_30 had a larger number of unique species than the other zones during session 3.

Temporal evolution of the habitat after the experimental dredging

The results of the nMDS performed on the log-transformed mean abundance data (stress = 0.13) are shown in Figure 8. The different zones during session 1 are gathered on the ordination plot. For sessions 2 and 3, there was differentiation among zones, with the appearance of a pressure gradient effect in increasing order as follows: control zones (CTRL 1, CTRL2, CTRL3), zones affected by 10 dredge tows (CD_10, KSD_10, QSD_10), and zones affected by 30 dredge tows (CD_30, QSD_30). In session 4, the position of the zones on the ordination plot still showed a clear separation from session 1. However, in the plot, the zones appear to be more closely gathered than during sessions 2 and 3, although the pressure gradient is still visible. In addition, the position of the objects on the ordination plot shows a different trajectory over time depending on the pressure levels, as indicated in the upper right corner of Figure 8.

DISCUSSION

The objectives of this study were to use an experimental BACI approach to characterize the effects on maerl habitat and benthic community structure of two fishing intensities and three dredge types commonly used in the Bay of Brest for bivalve fisheries.

The BACI design

The BACI design is one of many possible approaches to assessing environmental impact [START_REF] Underwood | Beyong BACI: Experimental Designs for Detecting Human[END_REF]. Underwood described how to implement a robust BACI sampling design to detect environmental changes, recommending the use of several control sites. In this way, our experimental design is consistent with Underwood's recommendations: we established three control sites, one in the middle of the study area and one each at the north and south extremities, thus allowing for consideration of spatial variability at the study site. In addition, we sampled these three control areas at each session to obtain temporal and spatial data from unaffected sites, as recommended by [START_REF] Green | Sampling design and statistical methods for environmental biologists[END_REF]. One could argue that sampling only one area per treatment (per fishing gear and intensity) was insufficient (Figure 1); however, we found clear differences in the temporal evolution of each zone (Figure 8), mostly based on fishing effort, which suggests that these results are valid even if replicate zones would have strengthened them.

Impact of dredging on the maerl habitat

We have illustrated the variability of the immediate impact of dredging on maerl beds, using an analysis of 288 grab samples and 480 in situ photographs of quadrats, sampled over a year from eight zones affected by three dredges and two fishing intensities. The consequences of dredging were visible starting from session 2 (one week after dredging) through the presence of broken maerl, an increase in mud-covered surface, and the loss of diversity (Figures 567). Broken maerl is visually distinguishable from unbroken maerl because the three-dimensional structure of the former is far less complex. The mechanical action of bottom-contacting gears (which are very destructive to such fragile habitats) fragment the maerl thalli into smaller pieces. Depending on the perturbation intensity, broken maerl can range from rectilinear to small round pieces with a heterogeneity similar to gravel [START_REF] Kamenos | Substratum heterogeneity of dredged vs un-dredged maerl grounds[END_REF], leading to reduced habitat complexity [START_REF] Hall-Spencer | Bivalve fishing and maerlbed conservation in france and the uk -Retrospect and prospect[END_REF][START_REF] Bernard | Declining maerl vitality and habitat complexity across a dredging gradient: Insights from in situ sediment profile imagery[END_REF]. Hall-Spencer and Moore (2000) reported that one immediate impact of dredging is increased suspended matter in the water column, reducing light availability and thus maerl photosynthesis. When these fine particles settle, they cover the maerl bed, fill its interstices, and limit light availability, thus reducing Lithothamnion sp. productivity by up to 70% [START_REF] Riul | Decrease in Lithothamnion sp. (Rhodophyta) primary production due to the deposition of a thin sediment layer[END_REF]. Our current observations are consistent with these previous findings, given that the significant differences between the control and dredged zones clearly link the surface covered by mud to dredging activities, with mud covering over 50% of the quadrats surface in the most damaged stations. We note, however, the quite high variability at each zone among the 15 photographed quadrats (Figure 5B), which precluded our using Kruskal-Wallis tests to evaluate the impact of dredging, despite previous observations (Figure 5A). Indeed, depending on the location of the quadrats regarding the dredge track, variable proportions of maerl (live, broken, or dead), mud, and algae could be observed (see Figure 4).

Severe impacts of dredging were recorded in terms of macrofauna diversity, with a significant decline in density and species richness observed during sessions 2 and 3 (Figures 6 and7). These results were expected because similar outcomes have already been reported previously. [START_REF] Kaiser | The Effects of Beam-Trawl Disturbance on Infaunal Communities in Different Habitats[END_REF] concluded that trawling was responsible for the disappearance of 50% of the species richness in their samples, a value slightly higher than our estimated reduction of 37% after dredging, but our results still confirm the severity of the impact. In addition, [START_REF] Bordehore | Comparative study of two maerl beds with different otter trawling history, southeast Iberian Peninsula[END_REF] compared two maerl beds with different trawling histories in the Iberian Peninsula (one preserved from all dredging activities and one dredged for 25 years) and concluded that macrofauna species richness, biomass, and density of individuals at the control site were significantly higher than at the impacted site. This substantial body of evidence thus illustrates the impact of dredging on habitat structure through ecological diversity declines and suggests consequences for ecosystem functioning related to the nursery role of maerl beds for fishes and bivalves [START_REF] Kamenos | Nursery-area function of maerl grounds for juvenile queen scallops Aequipecten opercularis and other invertebrates[END_REF], effects on nutrient cycling [START_REF] Martin | Community metabolism in temperate maerl beds. II. Nutrient fluxes[END_REF][START_REF] López-Acosta | Silicon consumption kinetics by marine sponges: An assessment of their role at the ecosystem level[END_REF], and benthic primary production [START_REF] Martin | Comparison of Zostera marina and maerl community metabolism[END_REF].

Our observations from session 3 may not be totally representative of the actual impact one month after dredging because of the seasonal development of macroalgae covering the maerl bed in abundance. This seasonal phenomenon is well known in this zone of the bay and appears to be linked to eutrophic conditions (personal observation). However, some interesting results emerged related to the percentages of surface covered by algae during session 3, which tended to decrease when the pressure level increased (Figure 5). The algae visible during season 3 were principally epiphytic macroalgae. Given these results, we hypothesize that the removal of maerl and the amount of surface covered by mud because of dredging prevented the algae from settling in a fixation spot, limiting their development. This effect would not only highlight the negative indirect impact of dredging on macroalgae but also indicate an alteration in natural ecosystem functioning and the natural dynamics of species that were not even present when dredging occurred.

After a year, the impacts of experimental dredging were still visible in the diversity parameters from the grab samples, suggesting the long-lasting nature of these effects, as previously reported [START_REF] Hall-Spencer | Scallop dredging has profound, long-term impacts on maerl habitats[END_REF], but they were not discernible on the quadrats. These authors noted no signs of recuperation in a previously unfished Scottish maerl bed, even 4 years after experimental dredging that had triggered a 70% decrease in the number of live thalli in core samples after 5 months. These results are more consistent with the literature than those from our study regarding the live maerl surface, especially considering the extremely slow growth rate of maerl-forming species, and indicate problems in our selected methodology. We do note that the previous study involved a thin (and thus vulnerable) layer of maerl, whereas the thickness of the maerl bed in our study area was estimated at ~5 m on muddy substratum [START_REF] Berthois | Les bancs de Saint Marc et du Moulin Blanc (Rade de Brest) et remarques sur la sédimentation du Maërl[END_REF]; thus, the thickness of the layer of live maerl can be expected to be important. In addition, external factors, such as storms, water currents, or wave actions, can be responsible for maerl movements [START_REF] Harris | Sand and rhodolith-gravel entrainment on the mid-to outer-shelf under a western boundary current: Fraser Island continental shelf, eastern Australia[END_REF]. Session 3 occurred in May 2016 and session 4 in April 2017, and we cannot exclude potential live maerl movements from adjacent areas that might have led to overestimation of the live maerl surface one year after dredging. Considering the existing literature and the in situ observations (Figure 4), these movements appear to be the most logical explanation for these findings.

According to the literature, sand habitats require ~100 days to recover from a single dredging event, meaning that these habitats can withstand three fishing events per year before being in a "permanent(ly) altered state" [START_REF] Collie | A quantitative analysis of fishing impacts shelf-sea benthos[END_REF]. Considering our observations, the fragility and low resilience of maerl beds, and the results of Hall-Spencer and Moore (2000), it seems that maerl beds could reach a "permanently altered state" more rapidly than sand habitats. Indeed, the estimated time needed to recover from the dredging session was ~8 years for abundance and >100 years for species richness. Even though this latter value seems to be overestimated, it still highlights the low resilience of maerl beds. Commercially fished maerl beds in the Bay of Brest are dredged every 6 months, as the fishing season runs from October to March each year. Traces of our single dredging event were still visible after a year, meaning that the maerl beds had not returned to their initial state by the time a new fishing period began, suggesting that dredging commonly occurs on an already altered habitat. As a result, recovery of long-term dredged maerl beds seems very unlikely.

The importance of the gear characteristics and pressure level

Although numerous studies based on the impact of dredging on maerl beds already exist, the objective of the present work was to characterize precisely the repercussions of different fishing gears and pressure levels on this particular habitat. We observed differences in the immediate impact of the three dredges and two pressure levels tested. For example, there was a significant difference in the percentage of mud during session 2 with use of the CD 10 or 30 times but no difference with use of the QSD. Moreover, the percentages covered by algae during session 3 differed between two groups of zones, the first one consisting of the three controls and KSD_10 and the second consisting of the remaining dredged zones (CD_10, CD_30, QSD_10, and QSD_30). Significantly less surface was covered by algae in the second group, suggesting that dredge features, along with the pressure level, may negatively affect algae development, as hypothesized previously.

The most damaged zones in terms of the macrofauna diversity appeared to be CD_30 and QSD_30, which showed significantly lower levels of diversity compared with one or more of the controls during session 4 (Figure 6). The absence of significant differences in species richness in CD_10 and QSD_10 compared with controls can be attributed to the important number of singletons and unique species present in the samples, biasing the results and inducing an overestimation of the total number of species.

After a year, none of the zones had returned to their initial state (Figure 8).

Observations prior to dredging (session 1) illustrated the natural variability of the maerl bed, whereas the different positions occupied by the three controls during session 1 and session 4 are indicative of the natural evolution of the undisturbed habitat. A clear gradient primarily attributed to fishing intensity appeared in session 2 and was still visible a year after the experimental dredging (Figure 8). The dredged zones showed different trajectories depending on fishing intensity, but as noted, none had returned to their original state after a year, suggesting that fishing intensity affects the temporal evolution of maerl beds and that dredging has a lasting impact on the habitat. These results, coupled with those depicted in Figure 6, are consistent with the findings of [START_REF] Hiddink | Indicators of the ecological impact of bottomtrawl disturbance on seabed communities[END_REF], who modeled recovery rates of biomass and production as a function of dredging intensity. In their study, for a single dredging event, recovery times in terms of macrofauna diversity were measured in years to reach initial biomass (1-41 years) and production (1-12 years) levels of the pristine maerl bed, suggesting that trawling intensity affects the long-term evolution of the habitat. Thus, the observed fishing intensity-based gradient we observed at one year after our single experimental dredging event is not unexpected.

Our results also echo those of [START_REF] Kaiser | Chronic fishing disturbance has changed shelf sea benthic community structure[END_REF], who studied the effects of chronic fishing disturbances on benthic community structure in gravel and coarse sand sediments and reported that repeated dredging led to significant changes. In their study, the removal of large organisms led to a population dominated by smaller and more resilient organisms after such physical disturbances. They concluded that if a habitat is altered to such an extent that it significantly differs from its original state, it might become unsuitable for species relying on it for their development, including commercial species, leading to decreased exploitable stocks.

Overall, our findings indicate that the QSD was the most destructive gear among the three dredges tested because we observed no significant difference between the effects of a pressure level of 10 or 30 with this gear. Conversely, the clam dredge may be less destructive if used with a moderate intensity because we observed clear differences between CD_10 and CD_30 regarding the percentages of surface covered by mud, species richness, and density of individuals. The KSD used for 10 tows appeared to be the least damaging of the five combinations of dredges and fishing intensity we tested, as the habitat seemed to recover more rapidly than with the other two dredges (~1.5 years to reach the same level of abundance as before dredging compared to ~6 years for CD and ~16 years for QSD). However, we cannot conclude that the KSD did not affect the habitat because the results clearly show a significant reduction in the density of individuals compared to controls and an increase in mud surface one week after dredging. In addition, the estimated depletion of species richness was similar between KSD and CD, whereas the estimated time to recovery was longer for KSD (~34 years) than for CD (~17 years), highlighting a severe impact on the benthic community. Indeed, a relatively moderate pressure in a short time period can still be responsible for significant long-term impacts, especially on repetitively dredged beds, as highlighted in the Bay of Brest with the IMPECAPE research project [START_REF] Bernard | Declining maerl vitality and habitat complexity across a dredging gradient: Insights from in situ sediment profile imagery[END_REF]. If the fishing activity recurs, the KSD would be responsible for the same habitat alteration as the other two dredges we tested.

These findings are in agreement with previous work by [START_REF] Thrush | Disturbance to Marine Benthic Habitats by Trawling and Dredging: Implications for Marine Biodiversity[END_REF], who reported that the physical impact of a fishing gear on a habitat depends on the gear's characteristics (e.g., mass) and the way it is used (e.g., the degree of contact with the seabottom, speed). In addition, [START_REF] Macdonald | Disturbance of benthic species by fishing activities: A sensitivity index[END_REF] reported that the severity of these effects will depend on the type of gear and how deeply it penetrates the sediment, the latter of which is directly linked to the gear's characteristics. Moreover, the differences between the QSD and the KSD are interesting considering that scallop dredges are more destructive than otter or beam trawling [START_REF] Foden | Recovery of UK seabed habitats from benthic fishing and aggregate extraction-towards a cumulative impact assessment[END_REF]. Our result implies that differences can exist between gears in the same category, depending on the targeted species. In comparison with the hydraulic dredge, which is currently considered one of the most destructive gears in bivalve fisheries, our results showed similarities with those of [START_REF] Hauton | An experimental study of the ecological impacts of hydraulic bivalve dredging on maerl[END_REF] (reduced live maerl cover, burial of maerl under fine particles, presence of broken maerl). Those authors estimated the penetration depth of their hydraulic dredge to ±10 cm, which was similar to the penetration depth estimated for the QSD in our study (~10 cm). A reduction by ~67% of the live maerl cover on photoquadrats after dredging with the hydraulic dredge compared to an average decrease of ~35% in our study (by ~57% with the QSD, ~27% with CD_10, ~61% with CD_30, and ~11% with the KSD) suggests that hydraulic dredges are more destructive than the three dredges we tested.

Limitations and the possible improvements

Experimental assessments of anthropogenic impacts such as dredging are commonly used because they allow researchers to work on data closely related to a disturbance and control factors such as intensity, time period, and gear employed [START_REF] Lokkeborg | Impacts of trawling and scallop dredging on benthic habitats and communities[END_REF]. In this paper, the experimental design we chose was intended to allow for the comparison of impacts on the maerl habitat resulting from different dredges and pressure levels. The study zone was not ideally located because it was subject to a seasonal phenomenon of eutrophication, introducing a bias in the results. In addition, the eight zones delimited for this study were (1) located on a previously unfished maerl bed and (2) relatively small. As a result, the experimental observations do not entirely reflect the reality of a repeatedly dredged bed or large-scale and long-term effects of such disturbances on the habitat [START_REF] Lokkeborg | Impacts of trawling and scallop dredging on benthic habitats and communities[END_REF]. As a matter of fact, a recent study based on a nearby maerl bed subject to annual commercial dredging for bivalves showed far more severe consequences than those we report here [START_REF] Bernard | Declining maerl vitality and habitat complexity across a dredging gradient: Insights from in situ sediment profile imagery[END_REF]. Those authors differentiated three levels of pressure (control, moderate, and high) based on the intensity of dredging over the past 5 years, for which they studied different proxies of the maerl condition. The vitality of the maerl appeared to be strongly affected by dredging and tended toward zero at the most severely dredged stations. Their results, based on a maerl bed dredged for over 5 years, indicate the long-term effect of dredging on maerl vitality in a way that the present approach was not designed to highlight. In addition, they recorded a modification of the vertical structure of the maerl bed resulting from the fragmentation and compaction of the maerl thalli and the filling of interstices by fine particles. All of these consequences of dredging led to the destruction of suitable ecological niches for the benthic macro-and megafauna and to the deterioration of the habitat, thus diminishing its role in ecosystem functioning.

As we have described, however, the present experimental design followed the recommendations of [START_REF] Underwood | Beyong BACI: Experimental Designs for Detecting Human[END_REF] and [START_REF] Green | Sampling design and statistical methods for environmental biologists[END_REF], and thus presents reliable results regarding the immediate impact of dredging based on fishing intensity and gear features. A potential improvement in the design would be to include replicates for each impacted zone to consolidate the present observations. The estimation of time to recovery could also be improved because the estimates seemed to be surprisingly long (>100 years for abundance, ~900 years for species richness). It would also be of interest to apply this method in future assessment along with in situ data on fishing pressure and different fishing histories because the impact of dredging is greater on previously unfished maerl beds. However, we found it useful to distinguish the initial impact of the dredges tested and to classify their impact on the maerl benthic community.

Implication for fishing activity management on maerl beds

Because we did find differences in impacts among the QSD, KSD, and CD, we suggest that these parameters be considered when dealing with management measures, both for maerl conservation and for fishing regulation. To preserve habitat richness and complexity, the priority should be on areas with intermediate levels of alteration because the most severely altered beds are not expected to recover from such intense and repeated pressures. Thus, it appears that non-destructive activities such as hand-collecting bivalves by diving should be favored when aiming to ameliorate maerl bed exploitation.

The conservation of maerl beds through the creation of protected areas would, in the long run, allow for a higher rate of recruitment and the development of a more diverse community of species (including species targeted by bivalve fisheries) in terms of genetics and age range, making them more resistant to natural environmental changes [START_REF] Roberts | The role of marine reserves in achieving sustainable fisheries[END_REF]. Ultimately, this conservation would allow for higher stock levels of targeted species with mature organisms that could potentially (re)colonize less vulnerable adjacent habitats through spawning. The fisheries would benefit with this version of "insurance" against stock collapse by the creation of new fishable grounds [START_REF] Hall-Spencer | Bivalve fishing and maerlbed conservation in france and the uk -Retrospect and prospect[END_REF].

CONCLUSION

Our aim with this study was to add new information about the impact of dredging on maerl bed by characterizing this impact in terms of different dredges and dredging intensities using a BACI approach. Overall, the impacts recorded were consistent with the literature (loss of diversity, increased mud cover, destruction of maerl thalli). We could differentiate the immediate and one-year impacts of three different dredges and two levels of pressure. In this context, the high-pressure level appeared to be the main factor affecting maerl habitat, whereas under low fishing intensity, the extent of damage depended on the type of gear used.

Impacts were still visible after one year whereas fishing occurs every 6 months in the Bay of

  France in 2012, and two of the main maerl-forming species Phymatolithon calcareum (Pallas) W.H.Adey & D.L.McKibbin ex Woelkering & L.M.Irvine, 1986 and Lithothamnion corallioides (P.Crouan & H.Crouan) P.Crouan & H.Crouan, 1867 are now included in Annex V of the EC Habitats Directive (Council Directive 92/43/EEC). Maerl beds occur in half of the Breton Natura 2000 areas

FIGURE CAPTIONS Figure 1 :

 CAPTIONS1 FIGURE CAPTIONS

Figure 2 :

 2 Figure 2: Photographs of the three gears used in this study: (A) the king scallop dredge (KSD), (B) the queen scallop dredge (QSD), and (C) the clam dredge (CD). Image credit: Fishery Committee of Finistère (Comités des Pêches du Finistère).

Figure 3 :

 3 Figure3: Location of the 24 stations (three stations randomly placed for each of the eight zones) sampled during the four sessions using a Smith grab (0.1 m²). For each station, three replicates were sampled a few meters apart (not displayed). The figure was created using QGIS 3.8.0 software (QGIS Development Team http://qgis.osgeo.org).

Figure 4 :

 4 Figure 4: Variability of the maerl bed structure one month after experimental dredging. (A-C) Control zone. (B-D) Dredged zone. Image credit: Erwan Amice, CNRS -Laboratoire des sciences de l'Environnement MARin (LEMAR).

Figure 5 :

 5 Figure 5: (A) Percentages of cover of maerl (live, dead, and broken), algae, sediment (mud), fauna, or "other" (eggs, dead shells) and (B) variability of the percentages of cover of the quadrats for each zone and for the four sessions. Percentages of cover were obtained by digital analysis of the quadrats.

Figure 6 :

 6 Figure 6: Boxplot of (A) the species richness and (B) the density of individuals for the eight zones and for each session in the Bay of Brest. The horizontal line in the box represents the median, the box indicates the interquartile range, and the black dots indicate outliers. ANOVA or Kruskal-Wallis results are indicated (p), and different letters indicate significant differences.

Figure 7 :

 7 Figure 7: (A) Species richness observed (dashed lines) and estimated by Jack2 (solid lines); (B) number of singleton and (C) unique species measured for each zone and for each session.

Figure 8 :

 8 Figure 8: Non-metric multidimensional scaling ordination for the eight zones and for the four sessions based on log-transformed abundance data using the Bray-Curtis similarity. Each session is represented by a different color (S1: red; S2: green; S3: blue; S4: yellow), and each fishing effort is indicated by a different symbol (circle: 10 dredge tows; square: 30 dredge tows; triangle: control). The schema in the upper right corner of the plot indicates the trajectory of the objects depending on the fishing effort over time.

Brest, raising questions about the recovery capacities of this habitat under repetitive dredging activity. This issue is particularly important considering the severity of the impacts highlighted in a field study (non-experimental) in an adjacent maerl bed. This questions the advantages and drawbacks of the experimental approach versus real-life observations and their reliability in assisting in the development of fishing policies related to the maerl habitat.

Acknowledgments

The authors are grateful to two anonymous referees for their valuable comments and corrections on this manuscript. The authors wish to thank the crew of the fishing boat CORTO and Marion Maguer for their work during sampling, Erwan Amice and Thierry Le Bec for diving and providing photographs, and Olivier Gauthier for help with statistical analysis. Environmental Impacts on Temporal Variations in Natural Populations. Aust. J. Mar. Freshw. Res. 42, 569-87. UNEP-MAP, 2015. Mediterranean action plan (UNEP(DEPI)/MED WG.408/5). Viechtbauer, W., 2010. Conducting meta-analyses in R with the metafor. J. Stat. Softw. 36, 1-48.

Funding

This work was supported by the Association du Grand Littoral Atlantique (AGLIA) and funded by France Filière Pêche, Région Bretagne, Région Pays de la Loire, and Région Nouvelle Aquitaine as part of the DECIDER program (Diagnostic of gear/habitat interaction: the dredge/maerl example).


Antonin Derinck
email: antonin.derinck@chu-rouen.fr

Alyssa Faradji

Salma Touleimat

Clothilde Hennetier

Mickaël Lair

Clothilde HENNETIER b , Mickaël LAIR a , Céline Savoye-Collet

Endométriome surinfecté fistulisé au sigmoïde

Keywords: endométriose pelvienne profonde, endométriose digestive, abcès, fistule colo-ovarienne deep pelvic endometriosis, bowel endometriosis, abcess, colo-ovarian fistula

Introduction

L'endométriose pelvienne est une maladie bénigne, fréquente, touchant environ 10% des femmes en période d'activité génitale, et est définie par la présence de tissu endométrial ectopique en dehors de l'utérus [START_REF] Manero | Endometriosis][END_REF]. La localisation sous péritonéale postérieure est l'atteinte pelvienne profonde la plus fréquente, tandis que la localisation digestive représente le premier site d'atteinte extragénitale [START_REF] Bazot | Deep pelvic endometriosis: MR imaging for diagnosis and prediction of extension of disease[END_REF][START_REF] Nezhat | Bowel endometriosis: diagnosis and management[END_REF].

La surinfection des lésions d'endométriose est rare, et la fistulisation digestive d'un endométriome surinfecté est encore plus exceptionnelle.

Observation clinique

L'IRM pelvienne a été réalisée avec une opacification vaginale et rectale par du gel d'échographie devant la symptomatologie évocatrice d'une atteinte digestive. L'examen retrouvait une masse centropelvienne complexe, uniloculaire, développée aux dépens de l'ovaire gauche (Figure 1). Elle présentait un signal T2 intermédiaire, hétérogène, avec un niveau hydroaérique. Son contenu restreignait la diffusion (Figure 2a), en rapport avec un contenu purulent, et sa paroi épaisse se rehaussait intensément après injection de produit de contraste (Figure 2b). Cet aspect était celui d'un abcès tubo-ovarien.

Cet abcès communiquait avec le colon sigmoïde sous forme d'un trajet fistuleux en hypersignal T2 (Figure 3).

Le reste de l'examen retrouvait une atteinte endométriosique profonde postérieure sévère avec un nodule du torus et de l'origine des ligaments utérosacrés, étendu au paramètre gauche (Figure 4). Des implants endométriosiques millimétriques de l'ovaire droit étaient présents (Figure 4b).

Il existait également une double atteinte du tube digestif chez cette patiente, l'une en aval de la fistule, en continuité avec l'atteinte du torus (Figure 5), l'autre en amont de la fistule (Figure 6). • soit une résection chirurgicale à proprement parler avec à nouveau la possibilité de réaliser une résection discoïde avec patch pour des lésions de petites tailles (inférieures à 3 cm et à 40% de la circonférence) ;

• soit une résection segmentaire si la lésion est plus volumineuse, ou multifocale, comme dans notre cas (10).

Conclusion

La fistulisation d'un endométriome surinfecté au sigmoïde est rarement observée.

La sémiologie de l'abcès tubo-ovarien doit être connue, correspondant à une masse annexielle uni ou multiloculaire complexe, hétérogène présentant un aspect « sale » sur les pondérations T2, une restriction de la diffusion, et une paroi épaisse intensément réhaussée après injection de produit de contraste.

L'atteinte digestive doit toujours être recherchée en cas d'endométriose. Une description précise de l'atteinte en IRM permet d'orienter vers le traitement chirurgical le plus approprié.

Légendes des figures

  La patiente a bénéficié d'une prise en charge chirurgicale coelioscopique, consistant en une annexectomie gauche, une urétérolyse bilatérale, une résection du nodule du torus étendu aux ligaments utérosacrés et au paramètre gauche. Un geste chirurgical sur le tube digestif a également été réalisé avec une résection colorectale segmentaire emportant la double atteinte digestive et le trajet fistuleux, retrouvé en peropératoire, avec anastomose latéroterminale.Les résultats anatomopathologiques ont confirmé l'atteinte décrite en IRM, avec des lésions d'hydrosalpinx et composante de pyosalpinx, associées à des lésions d'endométriose de l'ovaire gauche.La physiopathologie est mal connue. La surinfection de l'endométriome pourrait être secondaire à une altération de l'environnement immunitaire au sein des glandes endométriales et du stroma.L'accumulation de sang au cours des cycles dans un espace kystique, et la faiblesse de la paroi du kyste endométriosique en comparaison à un épithélium ovarien normal pourrait favoriser la prolifération bactérienne. La surinfection de l'endométriome et l'inflammation locorégionale engendrée peuvent entrainer des adhérences à la paroi digestive jusqu'à la fistulisation.La sémiologie habituelle de l'endométriome, en franc hypersignal T1 supérieur à la graisse, persistant sur les séquences avec saturation du signal de la graisse associé à un « shading » caractéristique en T2, est donc modifiée. Dans le cas présent, la masse avait les caractéristiques classiques d'un abcès tubo-ovarien.L'intérêt de ce dossier réside également dans la nécessité de rechercher une atteinte digestive multifocale dans l'endométriose afin de guider la prise en charge chirurgicale de façon optimale. Il est essentiel de décrire dans les comptes-rendus radiologiques plusieurs points clés de l'atteinte digestive : leur nombre (atteinte souvent multifocale), leur localisation (distance à la marge anale), leur étendue ainsi que la circonférence de l'atteinte et son caractère sténosant. La profondeur de l'atteinte est également utile, d'étude parfois difficile, et pouvant justifier le recours à l'échoendoscopie.Ces éléments déterminent trois attitudes possibles sur le plan chirurgical :• soit la réalisation d'un « shaving », c'est-à-dire passer entre la lésion et la paroi antérieure du rectum pour réséquer le nodule, mais cela implique l'absence d'extension en profondeur à la musculeuse interne ;

Figure 1 .

 1 Figure 1. IRM pelvienne chez une patiente atteinte d'un endométriome surinfecté fistulisé au sigmoïde : séquence pondérée en T2, coupe axiale, mettant en évidence une masse d'origine ovarienne gauche, avec un « signe du croissant ».

Figure 2 .

 2 Figure 2. IRM pelvienne chez une patiente atteinte d'un endométriome surinfecté fistulisé au sigmoïde. a : coupe axiale, séquence de diffusion à b élevé et cartographie du coefficient de diffusion apparent : restriction de diffusion de la masse ; b : coupe sagittale, séquence pondérée en T1 tardive avec saturation du signal de la graisse et injection de gadolinium mettant en évidence une paroi épaisse et intensément rehaussée après l'injection.

Figure 3 .

 3 Figure 3. IRM pelvienne chez une patiente atteinte d'un endométriome surinfecté fistulisé au sigmoïde. Coupe axiale T2 oblique dans le plan des ligaments utérosacrés, mettant en évidence la communication entre l'abcès tubo-ovarien et le sigmoïde sous forme d'un trajet fistuleux en hypersignal T2 (rectangle blanc).

Figure 4 .

 4 Figure 4. IRM pelvienne chez une patiente atteinte d'un endométriome surinfecté fistulisé au sigmoïde. a : coupe axiale, séquence pondérée en T2 mettant en évidence une infiltration nodulaire microkystique, centrée sur le torus et l'origine des ligaments utérosacrés étendue au paramètre gauche ; b : coupe axiale, séquence T1 Dixon Water mettant en évidence des microlésions hémorragiques en hypersignal T1 persistantes après saturation du signal de la graisse, au sein du nodule du torus, en faveur d'une atteinte active. Implant endométriosique millimétrique de l'ovaire droit (flèche creuse).

Figure 5 .

 5 Figure 5. IRM pelvienne chez une patiente atteinte d'un endométriome surinfecté fistulisé au sigmoïde. a : coupe coronale, séquence pondérée en T2 mettant en évidence un nodule du torus et de l'origine des ligaments utérosacrés étendu à la charnière rectosigmoïdienne ; b : coupe sagittale, séquence pondérée en T2 mettant en évidence une extension digestive de contiguité du nodule du torus à la charnière rectosigmoïdienne. Trajet fistuleux (flèche creuse).

Figure 6 .

 6 Figure 6. IRM pelvienne chez une patiente atteinte d'un endométriome surinfecté fistulisé au sigmoïde, mettant en évidence une seconde atteinte digestive en amont de la fistule sigmoïdienne : infiltration nodulaire sur les séquences axiale et coronale avec remaniement microkystique. a : coupe axiale T2 oblique ; b,c : coupes coronales T2.

Nous rapportons le cas d'une patiente de 35 ans adressée en IRM en raison d'écoulements rectaux dans un contexte d'endométriose connue. Une chirurgie lui avait été proposée il y a plusieurs années mais n'avait finalement pas eu lieu en raison de la survenue d'une grossesse spontanée. Sans contraception au moment de cette observation, elle rapportait une dysménorrhée, des dyspareunies profondes, une dysurie et une dyschésie.

.DiscussionLa surinfection d'endométriomes est une complication classiquement décrite mais rare. L'endométriose sévère serait un facteur de risque de développer un abcès tubo-ovarien avec une incidence de 2,3% chez des patientes atteintes d'un endométriome[START_REF] Kubota | A study of tubo-ovarian and ovarian abscesses, with a focus on cases with endometrioma[END_REF][START_REF] Chen | Increased occurrence of tubo-ovarian abscesses in women with stage III and IV endometriosis[END_REF]. Elle intervient le plus fréquemment comme complication de prélèvements ovocytaires dans le cadre d'assistance médicale à la procréation (6), mais peut aussi intervenir de novo[START_REF] Hameed | A rare case of de novo gigantic ovarian abscess within an endometrioma[END_REF].Des formes de fistules vésico-ovariennes compliquant des endométriomes ont été décrites (8).En revanche la fistulisation d'un endométriome surinfecté au tube digestif est exceptionnelle et seul un cas de fistule colo-ovarienne secondaire à une atteinte endométriosique ovarienne a été rapporté dans la littérature[START_REF] Lopez | A case of a colo-ovarian cyst fistula as a complication of endometriosis[END_REF].