Salma Benmokhtar
email: salma_benmokhtar@um5.ac.ma

Marc Robin
email: marc.robin@univ-nantes.fr

Mohamed Maanan
email: mohamed.maanan@univ-nantes.fr

Hocein Bazairi
email: hocein.bazairi@um5.ac.ma

Mapping and Quantification of the Dwarf Eelgrass Zostera noltei Using a Random Forest Algorithm on a SPOT 7 Satellite Image

Keywords: seagrass, remote sensing, random forest, vegetation indices, machine learning classification

The dwarf eelgrass Zostera noltei Hornemann (Z. noltei) is the most dominant seagrass in semi-enclosed coastal systems of the Atlantic coast of Morocco. The species is experiencing a worldwide decline and monitoring the extent of its meadows would be a useful approach to estimate the impacts of natural and anthropogenic stressors. Here, we aimed to map the Z. noltei meadows in the Merja Zerga coastal lagoon (Atlantic coast of Morocco) using remote sensing. We used a random forest algorithm combined with field data to classify a SPOT 7 satellite image. Despite the difficulties related to the non-synchronization of the satellite images with the high tide coefficient, our results revealed, with an accuracy of 95%, that dwarf eelgrass beds can be discriminated successfully from other habitats in the lagoon. The estimated area was 160.76 ha when considering mixed beds (Z. noltei-associated macroalgae). The use of SPOT 7 satellite images seems to be satisfactory for long-term monitoring of Z. noltei meadows in the Merja Zerga lagoon and for biomass estimation using an NDVI-biomass quantitative relationship. Nevertheless, using this method of biomass estimation for dwarf eelgrass meadows could be unsuccessful when it comes to areas where the NDVI is saturated due to the stacking of many layers.

Introduction

Seagrass meadows exist in most shallow, sheltered, soft-bottomed marine coastlines and estuaries throughout the world and rank among the most productive systems in the ocean [START_REF] Orth | A Global Crisis for Seagrass Ecosystems[END_REF][START_REF] Duarte | Seagrass Ecosystems: Their Global Status and Prospects[END_REF][START_REF] Short | Extinction risk assessment of the world's seagrass species[END_REF]. They fulfill important trophic and structural functions in the coastal ecosystems [START_REF] Duffy | Biodiversity and the functioning of seagrass ecosystems[END_REF][START_REF] Short | Global seagrass distribution and diversity: A bioregional model[END_REF][START_REF] Gutiérrez | Physical Ecosystem Engineers and the Functioning of Estuaries and Coasts[END_REF] and rank among the priority habitats classified by the Convention on Biological Diversity (1992). They have been listed as one of the five biological quality elements to be included in ecological quality assessments in marine waters by the European Water Framework Directive (2000/60/EC) [START_REF] Marbà | Diversity of European seagrass indicators: Patterns within and across regions[END_REF]. Therefore, due to the widespread decline of seagrass beds on a global scale [START_REF] Orth | A Global Crisis for Seagrass Ecosystems[END_REF], monitoring these ecosystems is a fundamental step for any program to manage these key habitats. Many techniques are used for monitoring seagrass beds, however the most used indices worldwide relate to their extent, fragmentation, bathymetric limits (colonization of the lower depth), abundance, recovery rate, biomass, density, canopy height and leaf biometry, specific composition and coverage, associated macroalgae, and associated benthic invertebrates [START_REF] Fontan | nc%2Fsystem%2Ffiles_ force%2Fdocuments%2Fmethode_de_cartographie_de_caracterisation_et_de_suivi_des_herbiers_marins[END_REF].

Monitoring of the extent of seagrass meadows is a useful approach to estimate the impacts of natural and anthropogenic stressors. Indeed, this index is the simplest way to study these habitats at both small and large scales [START_REF] Borum | European Seagrasses: An Introduction to Monitoring and Management; The EU Project Monitoring and Management of European Seagrasses (M&MS Project)[END_REF]; it has a minimal impact on the environment and can prove signs of degradation and suggest the presence of anthropogenic disturbances such as eutrophication, changes in the use of space, and increasing aquatic activities. In addition, the presence or absence of seagrass beds or their regional distribution defines the limits for the sampling of the cover, biomass, or density along depth gradients, which are supplementary indicators of health of seagrass beds [START_REF] Fontan | nc%2Fsystem%2Ffiles_ force%2Fdocuments%2Fmethode_de_cartographie_de_caracterisation_et_de_suivi_des_herbiers_marins[END_REF].

The dwarf eelgrass Z. noltei Hornemann, 1832, is one of the predominant global species in intertidal zones, thus representing the land-sea interface [START_REF] Short | Global Seagrass Research Method[END_REF]. The distribution of Z. noltei extends from the eastern Atlantic shores starting from Mauritania to southern Norway and the Kattegat Sea, and throughout the Mediterranean, Black, Azov, Caspian, and Aral Seas and the Canary Islands [START_REF] Green | World Atlas of Seagrasses[END_REF][START_REF] Moore | Biology, ecology and management[END_REF][START_REF] Diekmann | The possible origin of Zostera noltii in the Canary Islands and guidelines for restoration[END_REF]. Z. noltei beds represent a habitat which allows the spatial structuring of many biological communities and are considered the basis of exceptional trophic compartments for many primary consumers [START_REF] Dalloyau | Qualité Ecologique des Herbiers Intertidaux à Zostère Naine Zostera Noltei dans les Sites Fonctionnels des Réserves Naturelles Nationales Gérées par la LPO (Pertuis Charentais)[END_REF]. Moreover, Z. noltei beds can contribute quantitatively to the function of tidal flats, as they are an extended juvenile habitat for some of the extremely important species in the food web [START_REF] Polte | The contribution of seagrass beds (Zostera noltii) to the function of tidal flats as a juvenile habitat for dominant, mobile epibenthos in the Wadden Sea[END_REF]. Indeed, Bououarour et al. [START_REF] Bououarour | Effects of the Zostera noltei meadows on benthic macrofauna in North Atlantic coastal ecosystems of Morocco: Spatial and seasonal patterns[END_REF] showed that macrofaunal biomass has a significant positive relationship with the presence of dwarf eelgrass. They demonstrated that variations in the macrobenthic faunal structure are mainly due to the existence of Z. noltei. In semi-enclosed coastal areas, this species is particularly vulnerable to climate-change-derived effects and to anthropogenic pressures [START_REF] Cabaço | Population dynamics and production of the seagrass Zostera noltii in colonizing versus established meadows[END_REF]. Despite the fact that Z. noltei is listed as a species of minor concern by the International Union for the Conservation of Nature (IUCN), the overall population is reported to be declining. Z. noltei forms meadows mainly within the intertidal zone, which make it likely to be vulnerable, in particular to climate-change-derived effects, mainly the sea water temperature and increasing sea level rise (SLR), which will affect the state, composition, and evolution of occupied areas, ranging from homogeneous sectors to fragmented patches scattered on the foreshores [START_REF] Dalloyau | Qualité Ecologique des Herbiers Intertidaux à Zostère Naine Zostera Noltei dans les Sites Fonctionnels des Réserves Naturelles Nationales Gérées par la LPO (Pertuis Charentais)[END_REF][START_REF] Chust | Climate change impacts on coastal and pelagic environments in the southeastern Bay of Biscay[END_REF][START_REF] Massa | Temperature tolerance and survival of intertidal populations of the seagrass Zostera noltii (Hornemann) in Southern Europe (Ria Formosa, Portugal)[END_REF][START_REF] Short | The effects of global climate change on seagrasses[END_REF].

Studies over time have shown that remote sensing applications for seagrass ecosystems represent good tools for monitoring. This can be achieved at a primary level (detection of seagrasses, spatial coverage, species-level discrimination, biomass detection, growth patterns, and degradation) or at a secondary level (environmental variables influencing seagrasses, such as sea surface temperature (SST), salinity, sea level rise, pollution, detection of epiphytes, etc.) [START_REF] Chauvaud | Remote sensing techniques adapted to high resolution mapping of tropical coastal marine ecosystems (coral reefs, seagrass beds and mangrove)[END_REF][START_REF] Dekker | Remote Sensing of Seagrass Ecosystems: Use of Space-borne and Airborne Sensors[END_REF][START_REF] Ferwerda | Satellite-based monitoring of tropical seagrass vegetation: Current techniques and future developments[END_REF][START_REF] Hossain | Application of Landsat images to seagrass areal cover change analysis for Lawas, Terengganu and Kelantan of Malaysia[END_REF][START_REF] Hossain | Potential of Earth Observation (EO) technologies for seagrass ecosystem service assessments[END_REF]. The combination of remote sensing techniques with field surveys is essential when monitoring seagrass statuses and changes over long time scales in large regions [START_REF] Dingtian | Seagrass Distribution in China with Satellite Remote Sensing[END_REF][START_REF] Meyer | Seagrass resource assessment using remote sensing methods in St. Joseph Sound and Clearwater Harbor, Florida, USA[END_REF][START_REF] Roelfsema | An integrated field and remote sensing approach for mapping Seagrass Cover, Moreton Bay, Australia[END_REF]. Z. noltei meadows are the most dominant seagrass areas in semi-enclosed coastal systems (SECS) of the Atlantic coast of Morocco [START_REF] Bououarour | Spatial patterns of the Zostera noltei meadows across the Atlantic coast of Morocco: Is there a latitudinal gradient?[END_REF][START_REF] Boutahar | Biomonitoring environmental status in semi-enclosed coastal ecosystems using Zostera noltei meadows[END_REF]. Aspects related to mapping and long-term monitoring of dwarf eelgrass are very incomplete or even absent.

The present study aimed to map Z. noltei meadows in the Merja Zerga coastal lagoon using a SPOT 7 image provided by the Royal Center for Remote Sensing, along with field data. This is a first step towards future monitoring and an important tool for the management of Z. noltei meadows along the Atlantic coast of Morocco.

Materials and Methods

Study Area

The Merja Zerga lagoon, also called Moulay Bousselham lagoon, is a RAMSAR wetland. It is the most northern coastal lagoon along the Atlantic Moroccan coast and is located approximately 120 km north of Rabat, between 34 • 47 and 34 • 53 north and 6 • 13 and 6 • 19 west. This lagoon receives daily influence from the Atlantic tides, which are considered the main source of its volume water. It also receives fresh water from two main inflows-the Nador Canal, which is of artificial origin and includes drainage water, as well as the Drader River. Moreover, the lagoon undergoes significant anthropogenic changes [START_REF] Ahmed | Application of remote sensing to site characterisation and environmental change analysis of North African coastal lagoons[END_REF]. Indeed, the population bordering this lagoon has increased by 150% in the last 25 years [START_REF] Maanan | Evaluation of the anthropogenic influx of metal and metalloid contaminants into the Moulay Bousselham lagoon, Morocco, using chemometric methods coupled to geographical information systems[END_REF]. This lagoon's waters are characterized by temperatures ranging from 27 to 28 • C in summer and from 13 to 15 • C in winter [START_REF] Bazaïri | Spatial organisation of macrozoobenthic communities in response to envi-ronmental factors in a coastal lagoon of the NW African coast (Merja Zerga, Morocco)[END_REF]. The depth of the lagoon fluctuates between 0 and 2 m but does not exceed 50 cm on average according to the tidal cycle and the precipitation [START_REF] Kraiem | Some observations on the age and growth of thin-lipped grey mullet, Liza ramada Risso, 1826 (Pisces, Mugilidae) in three North African wetland lakes: Merja Zerga (Morocco), Garâat Ichkeul (Tunisia) and Edku Lake (Egypt)[END_REF][START_REF] Labbardi | Étude de la variation spatio-temporelle des paramètres physicochimiques caractérisant la qualité des eaux d'une lagune côtière et ses zonations écologiques: Cas de Moulay Bousselham[END_REF]. The tide has a mean tidal range of nearly 1.4 m, which increases to 3.60 m during the spring tide, while during the neap tide it decreases to about 0.80 m [START_REF] Alaoui | Metal fluxes to the sediments of the Moulay Bousselham lagoon, Morocco[END_REF].

The Merja Zerga lagoon provides important socioeconomic support to the local communities through many activities such as tourism and fisheries. Furthermore, it represents a significant ecological resource that has attracted the interest of many scientific researchers. This lagoon has been part of the CASSARINA and MELMARINA projects during the last two decades [START_REF] Flower | An overview of integrated hydro-ecological studies in the MELMARINA Project: Monitoring and modelling coastal lagoons-making management tools for aquatic resources in North Africa[END_REF]; thus, several studies have been based on observations and fieldwork in the Merja Zerga, mainly treating hydrological characteristics, environmental changes, contaminants in sediments, plankton, and fish fauna [START_REF] Kraiem | Some observations on the age and growth of thin-lipped grey mullet, Liza ramada Risso, 1826 (Pisces, Mugilidae) in three North African wetland lakes: Merja Zerga (Morocco), Garâat Ichkeul (Tunisia) and Edku Lake (Egypt)[END_REF][START_REF] Labbardi | Étude de la variation spatio-temporelle des paramètres physicochimiques caractérisant la qualité des eaux d'une lagune côtière et ses zonations écologiques: Cas de Moulay Bousselham[END_REF][START_REF] Appleby | Radiometrically determined dates and sedimentation rates for recent sediments in nine North African wetland lakes (the CASSARINA Pro-ject)[END_REF][START_REF] Peters | Recent environmental change in North African wetland lakes: A baseline study of organochlorine contaminant residues in sediments from nine sites in the CASSARINA Project[END_REF][START_REF] Ramdani | North African wetland lakes: Characterization of nine sites included in the CASSARINA Project[END_REF][START_REF] Ramdani | Environmental influences on the qualitative and quantitative composition of phytoplankton and zooplankton in North African coastal la-goons[END_REF][START_REF] Thompson | Hydrological charac-teristics of three North African coastal lagoons: Insights from the MELMARINA project[END_REF].

Field Data

Seagrass samples were taken from seven dwarf eelgrass stations on 4-5 November 2019, and on 11 November 2019 they were chosen randomly depending on the tidal conditions and scattered on all of the discernible aquatic vegetation areas (pass, east, west and center of the Merja Zerga lagoon) from field investigations and satellite image observations (Table 1, Figure 1). In order to define the spectral separability of the species lagoon and the mixtures, spectral measurements were performed in situ using a JAZA0478 spectroradiometer with a 348.95-1039.85 nm wavelength range, provided by the LETG laboratory of the University of Nantes. In each station, the reflectance of different types of habitats, in particular the dwarf eelgrass Z. noltei, macroalgae (chlorophyte and Rhodophyta), sand, and mud sediments, were recorded at 1 m above ground. For each type of habitat, an average of three to four spectral measurement were sampled in each station and their corresponding references were also taken. The white and dark references were calculated by measuring the reflectance of the white reference (Spectralon ® WS-1-SL) and dark reference (performed by covering the collimating lens with its cap).

In order to estimate the Z. noltei biomass in g/m 2 and its cover percentage, seven stations were sampled on 16 September 2018 in the intertidal area located in the downstream, upstream, and center of the lagoon during low tide. Biomass was assessed using hand cores (30 cm in length and 12 cm in diameter), with ten replicates for each station. The samples were randomly collected from Z. noltei meadows, were washed in situ and taken to the laboratory, then frozen at -20 • C before biomass measurements. The samples before being weighed were oven-dried at 60 • C for two days. As for estimating the cover percentage of the Z. noltei, in each station, five replicates of quadrats (0.5 × 0.5 m 2 ) were photographed and their geographic coordinates were saved for mapping validation. The quadrats photos were processed by ImageJ software according to Stationary Bed Monitoring Protocols for the Water Directive (WFD) Zostera marina (L.)-Z. noltei Version 3 [START_REF] Hily | Protocoles Suivi Stationnel des Herbiers à Zostères pour la Directive Cadre sur l'Eau (DCE) Zostera marina[END_REF]. When the use of quadrats was not possible, the cover percentage was estimated visually by assigning it to one of the following classes: 0-25%, 25-50%, 50-75%, 75-90%, 100%.

Satellite Images

To map the distribution of dwarf eelgrass Z. noltei in the Merja Zerga lagoon, we tried to obtain very good images with high resolution, at low tide and with a good tidal coefficient. This was achieved by making a list table with the dates of low tides, coefficients, and the number of images that contained little cloud. Unfortunately, the results were very limited; we must say that this added to the difficulty of achieving very good images. Consequently, we used one SPOT 7 satellite image acquired on 19 August 2019 at 10:54 a.m local time, matching with the falling tide. Indeed, 19 August 2019 corresponds to the lowest tide, which was 0.7 m at 11:46 a.m UTC+1, with a tidal coefficient equal to 72. Images were obtained during the summer, which is the period of increased density of eelgrass beds, culminating in August-September [START_REF] Wong | Ecological dynamics of Zostera marina (eelgrass) in three adjacent bays in Atlantic Canada[END_REF]. In fact, among many Sentinel 2 and Pleiades images, the SPOT 7 image presented the best conditions required for mapping dwarf eelgrass in the Merja Zerga lagoon. The SPOT 7 satellite has four multispectral bands-blue (450-520 nm), green (530-590 nm), red (625-695 nm), and near-infrared (NIR; 760-890 nm)-at a spatial resolution of 6 × 6 m, and one panchromatic band (450-745 nm) at a spatial resolution of 1.5 × 1.5 m [START_REF]8lBF8QFjABegQIAhAD&url=https%3A%2F% 2Fwww.spaceoffice[END_REF]. The scene was free from cloud cover and was provided by the CRTS (The Royal Center for Remote Sensing-Centre Royal de Télédétection Spatiale (crts.gov.ma, accessed on 25 February 2021)) in a bundle (PAN + XS) format, which was orthorectified and projected to World Geodetic System 1984 datum, EPSG CODE: 4326.

Typology

In our approach, we mapped the main habitats that form the Merja Zerga lagoon with reference to the floristic typology outlined by Hammada [START_REF] Hammada | Etudes sur la Végétation des zones Humides du Maroc: Catalogue et Analyse de la Biodiversité Floristique et Identification des Principaux Groupements Végétaux[END_REF] for this lagoon. This typology showed that there are two gradients that influence the vegetation distribution, namely submersion rhythms and salinity. In fact, the submersion gradient allowed the three types of species to be distinguished, which are: We aimed to discern between the aquatic species of the Merja Zerga lagoon marine environment. Hence, the classes that were used for the classification were shallow water, sand, mud, terrestrial plants, algae, mixture of algae and dwarf eelgrass, submerged dwarf eelgrass, and emerged dwarf eelgrass.

Image Processing

The SPOT 7 image used for this study was delivered in bundle (PAN + XS) format. Atmospheric correction for the four bands was achieved by using the GRASS algorithm in order to convert them to reflectance.

Therefore, the spectral and spatial resolutions were suitable for use with the 9 vegetation indices (Table 1). Indeed, mapping of dwarf eelgrass Z. noltei in the Merja Zerga lagoon was achieved by using a supervised classification approach applied with a random forest algorithm (Figure 2), which is a model that constructs and combines a large number of base decision trees. Before applying the random forest model, 117 polygons were created manually using ArcGIS 10.4.1 software based on personal field knowledge and were distributed over the eight classes, which be used for the classification: shallow water, sand, mud, terrestrial plants, algae, mixture of algae and dwarf eelgrass, submerged dwarf eelgrass, and emerged dwarf eelgrass. For this, we used Breiman and Cutler's random forests for classification and regression algorithm with the R package randomforest v.4.6-14. Known as a robust and consistent model, the random forest algorithm is the most popular ensemble-based model for classification problems, and in particular for seagrass mapping [START_REF] Ha | A Comparative Assessment of Ensemble-Based Machine Learning and Maximum Likelihood Methods for Mapping Seagrass Using Sentinel-2 Imagery in Tauranga Harbor[END_REF]. We empirically tuned the random forests model by testing two parameters (because it seems that there is no theory to guide the choice of the mtry number (number of predictors sampled for spliting at each node) or the number of trees [START_REF] Scornet | Tuning parameters in random forests[END_REF]. First, the mtry number was fixed at 3 (with the use of the tuneRF function of the randomForest package). Then, with this mtry number, and in order to find the optimal number of trees that correspond to a stable classifier without excessive computation time, we computed the random forests algorithm with different ntree values and recorded the OOB error rate to assess the number of trees at which the out of bag error rate stabilized and reached the minimum (in our case this was fixed at 500).

First of all, we returned the four raw bands of the SPOT 7 image and used them to calculate nine vegetation indices (Table 2). The resulting layers were then stacked onto a 13-band image, which was then implemented in the random forests model using the R software. The random forests algorithm was validated by the overall out-of-bag (OOB) model error and the confusion matrix (percentage of falsely and classified training pixels) and seemed to be robust (OOB = 3.14). Then, we used the predict function to compute the RF classification prediction with the use of the clusterR interface to speed up the computation (in the R package "raster"). In addition, a final estimation of the quality of the "accuracy" classification error was made using Erdas software by creating 120 equalized random points spread over the eight classes.

To estimate Z. noltei aboveground biomass in the Merja Zerga part of the Moulay Bousselham lagoon using remote sensing, we chose to use the quantitative experimental NDVI -Z. noltei biomass relationship, whereby biomass = 610.61 (NDVI)ˆ1.88 (n = 31, r2 = 0.97), as created by [START_REF] Barillé | Increase in seagrass distribution at Bourgneuf Bay (France) detected by spatial remote sensing[END_REF]. Depending on the presence or absence of algae and the emersion or submersion condition, the precedent equation was separated or combined for the estimated percentage cover (Figure 2).

Hence, a specific biomass estimation map was generated for each of the three classes of Z. noltei: two maps were created by directly applying the equation for both the submerged Z. noltei class and the emerged Z. noltei class, and one map was created by combining the equation with the percentage cover of Z. noltei for the mixed class of algae and Z. noltei.

Results

Spectral Results

According to the spectra, separability between classes was possible. Moreover, the equivalent image calibrated to reflectance also made discrimination between classes possible. The reflectance spectra of algae (n = 9) and Z. noltei (n = 13) sampled in the Merja Zerga lagoon presented different overall shapes in the visible and NIR ranges between 400 and 900 nm. Indeed, algae spectra were plotted above the dwarf eelgrass spectra. The distance between algae spectra and emerged dwarf eelgrass spectra was estimated using the spectral angle, which had a value of 0.936 radians. The reflectance spectra of Z. noltei were discernible from chlorophyta in the visible wavelengths. However, they had the same tendency (increasing) in the NIR, except for the submerged dwarf eelgrass spectra (decreasing) (Figure 3). The overall shapes of the spectra from the SPOT 7 image we used were similar to those sampled with the spectroradiometer in the field.

Biomass and Cover Percentage of Z. noltei

Our results showed that the stations S6, S4, and S7 had the highest biomass among the five stations sampled in the Merja Zerga lagoon (Table 3). According to the visual interpretation and to the quadrat analysis, station S7 had the lowest cover percentage of Z. noltei, which was limited to 50% (Table 4). The stations S5 and S4 came in the second position, which were mixed beds (Z. noltei and algae) with cover percentages of Z. noltei limited to 60% and 75%, respectively. On the contrary, the rest of the stations of the lagoon were homogeneous beds with the highest cover percentages of Z. noltei, which reached 96%. In fact, despite their small size, the dwarf eelgrass beds in stations "S3", "S2", and "S1" were homogeneous and denser, plus the presence of macroalgae species there was not very prevalent (Figure 4).

Satellite Image Processing

Our random forests model accurately predicted the distribution of all eight classes for the Merja Zerga lagoon. The model's internal class errors fluctuated between 0.00 and 0.25% and the OOB for the model was 3.14%. It turned out that the raw bands and indices that were most relevant for distinguishing between the eight classes according to the mean decrease Gini index were the NDVI and GNDVI indices, followed by the GVI, CVI, and EVI, then followed by the green and blue bands successively (Figure 5). Indeed, NDVI and GNDVI indices had the highest mean decrease Gini values, which reached 141.03 and 138.69, respectively.

Satellite Image Processing

Our random forests model accurately predicted the distribution of all eight classes for the Merja Zerga lagoon. The model's internal class errors fluctuated between 0.00 and 0.25% and the OOB for the model was 3.14%. It turned out that the raw bands and indices that were most relevant for distinguishing between the eight classes according to the mean decrease Gini index were the NDVI and GNDVI indices, followed by the GVI, CVI, and EVI, then followed by the green and blue bands successively (Figure 5). Indeed, NDVI and GNDVI indices had the highest mean decrease Gini values, which reached 141.03 and 138.69, respectively. The average curve of the pixels per class extracted from the SPOT 7 image showed that each class had a specific band or index which best discriminated it. For example, the IB index was the most relevant for distinguishing submerged dwarf eelgrass (Figure 6).

Image Classification

We managed to estimate the surface areas of Z. noltei meadows in the Merja Zerga lagoon, which was approximately equal to 160.76 ha (mixed beds included), of which 65.22 ha was the total area for emerged and submerged Z. noltei.

The classification results indicated that Z. noltei beds are more extensive in the center and in the upstream area of the lagoon than in it downstream area (Figure 7), in particular at station S6 (Table 5), which was in agreement with the field observations. Thus, the total surface areas for Z. noltei beds were 27.31 ha, 95.54 ha, and 37.91 ha for submerged, mixed, and emerged beds, respectively. Dwarf eelgrass biomass results were estimated using remote sensing ranges between 0 and 277.81 g DW/m 2 for the class "emerged Z. noltei" and between 0 and 156.509 g DW/m 2 for the class "mixed beds", of which 62% (which is the average of the three mixed station: "S5":0.77, "S4":0.60, "S7":0.50) corresponded to Z. noltei, i.e., was between 0 and 97.03 g DW/m 2 . Lastly, biomass values were between 0 and 160.79 g DW/m 2 for the class "submerged Z. noltei" (Figure 8).

Discussion

The overall shapes of the spectral signature of the algae and the dwarf eelgrass found in the field look like those obtained by Bargain et al. [START_REF] Huete | Development of vegetation and soil indices for MODIS-EOS[END_REF] and by Dehouck et al. [START_REF] Dehouck | Hyperspectral field database in support to coastal wetland mapping[END_REF]. In fact, Z. noltei reflectance is discernible from green algae in the visible wavelengths and even more in the NIR wavelength. In addition, emerged dwarf eelgrass and algae spectra increase in the NIR wavelength compared to the submerged dwarf eelgrass spectra, which have a decreasing slope due to the residual water layers in them. This is due to the influence of water. Dehouck et al. [START_REF] Dehouck | Hyperspectral field database in support to coastal wetland mapping[END_REF] indicated that in general the spectra of Z. noltei and algae are similar, however small differences are noticeable in narrow bands. They claimed that the spectra need to be analyzed and separated numerically to find out the best spectral bands.

We chose to use vegetation indices to map Z. noltei in the Merja Zerga lagoon because it exists in the form of monospecific seagrass beds. Indeed, the average curve for pixels per class extracted from the SPOT 7 image showed that each class has a specific band or index which best discriminated it. For example, the IB index is the most relevant for distinguishing submerged dwarf eelgrass (Figure 6). Our OOB and mean decrease Gini results showed that NDVI, GNDVI, GVI, and CVI are the best indices to use for this purpose (Figure 5). The mapping of seagrass meadows using multispectral and hyperspectral images has often been achieving by using the NDVI, which is the most widely used index to assess green biomass [START_REF] Lillesand | Remote Sensing and Image Interpretation[END_REF]. Indeed, Barillé et al. [START_REF] Barillé | Increase in seagrass distribution at Bourgneuf Bay (France) detected by spatial remote sensing[END_REF] calibrated SPOT 7 images to NDVI and tested the influence of Z. noltei aboveground biomass variations on spectral reflectance. Hence, they used field-measured hyperspectral data from seagrass and algae and then applied a quantitative experimental NDVI-biomass relationship to map the biomass distribution of Z. noltei. The latter displayed a characteristic steep slope from 700 to 900 nm, which increased with increasing biomass. Later, Bargain et al. [START_REF] Bargain | Seasonal spectral variation of Zostera noltii and its influence on pigment-based Vegetation Indices[END_REF] demonstrated that vegetation indices used to map these kinds of beds and their biomass with satellite or airborne imagery must be selected carefully to moderate all sources of variability affecting the index value, such as the pigment variations, or those related to background influences, such as the soil spectral contrast, albedo variations, or the specular reflectance at the leaf surfaces.

Our study is the first study of the mapping of Z. noltei beds using a random forest algorithm. This method showed satisfying results because it has been proven effective to estimate the surface distributions of this species using SPOT 7 satellite imagery. Our mapping results showed that Z. noltei beds in the Merja Zerga lagoon are more extensive than in the older studies [START_REF] Dakki | Cartographie des Habitats Naturels et de la Végétation de Merja Zerga[END_REF][START_REF] Elso | Genetic Description and Remote Sensing Techniques as Management Tools for Zostera noltii Seagrass Populations along the Atlantic Moroccan Coast[END_REF]. Indeed, their surface area reached 65.22 ha, and when including associated seaweed mats, this equaled 160.76 ha (Table 6). Usually, mapping Z. noltei meadows by using a handheld GPS instrument or by using transects is not enough when some areas are inaccessible due to the nature of the substrate, to the extent of the meadows, and to the tidal cycles. Generally, estimating seagrass distributions using traditional methods with pre-recorded grid patterns or a combination of transects and sampling points [START_REF] Qninba | Etude phénologique et variabilité interannuelle d'abondance des Charadriidés (Aves, Charadrii) dans un site Ramsar du Maroc: La Merja Zerga[END_REF] is time-consuming and has high associated costs [START_REF] Short | Global Seagrass Research Method[END_REF]. This can, therefore, be achieved thanks to remote sensing, either by interpretating aerial photographs from an unmanned aerial vehicle (UAV) combined with ground truthing measurements [START_REF] Duffy | Biodiversity and the functioning of seagrass ecosystems[END_REF][START_REF] Short | Global seagrass distribution and diversity: A bioregional model[END_REF][START_REF] Gutiérrez | Physical Ecosystem Engineers and the Functioning of Estuaries and Coasts[END_REF][START_REF] Marbà | Diversity of European seagrass indicators: Patterns within and across regions[END_REF] or by terrestrial, oblique, large-scale photography and satellite image classification [START_REF] Mckenzie | Methods for mapping seagrass distribution[END_REF][START_REF] Andrade | A method for monitoring shallow seagrass meadows (Zostera spp.) using terrestrial oblique large-scale photography[END_REF][START_REF] Calleja | Long-term analysis of Zostera noltei: A retrospective approach for understanding seagrasses' dynamics[END_REF][START_REF] Kohlus | Mapping seagrass (Zostera) by remote sensing in the Schleswig-Holstein Wadden Sea[END_REF].

Some studies also proved the efficiency of random forest approaches in mapping the extent of seagrass. Indeed, according to Traganos and Reinartz [START_REF] Traganos | Interannual Change Detection of Mediterranean Seagrasses Using RapidEye Image Time Series[END_REF], the random forest machine learning classifiers and support vector machines are gaining more interest for coastal habitat remote sensing. Indeed, they assessed the extent of change for Posidonia oceanica (L.) Delile and Cymodocea nodosa (Ucria) Ascherson following atmospheric and analytical water column correction and using a random forest algorithm for the RapidEye time series. Furthermore, Zhang [START_REF] Zhang | Applying data fusion techniques for benthic habitat mapping and monitoring in a coral reef ecosystem[END_REF] and Traganos and Reinartz [START_REF] Traganos | Mapping Mediterranean seagrasses with Sentinel-2 imagery[END_REF] proved that random forest algorithms generated promising results regarding classification of seagrasses, while Zhang et al. [START_REF] Zhang | Object-based benthic habitat mapping in the Florida Keys from hyperspectral imagery[END_REF] exhibited the advantages of al. [START_REF] Dehouck | Hyperspectral field database in support to coastal wetland mapping[END_REF] emphasizes that by carefully selecting multispectral bands or by combining them random forest algorithms over the maximum likelihood classifier using hyperspectral imagery.

In contrast to our study, several previous studies have mentioned the presence of Z. noltei in Merja Zerga lagoon, but without giving its overall distribution. First, Dakki et al. [START_REF] Dakki | Cartographie des Habitats Naturels et de la Végétation de Merja Zerga[END_REF] estimated that 4.78% of the lagoon's surface was occupied by dwarf eelgrass meadows, the surface area of which corresponded to 140 ha, including algae species. The presence of Z. noltei in the Merja Zerga lagoon was also reported by Boutahar et al. [START_REF] Boutahar | Biomonitoring environmental status in semi-enclosed coastal ecosystems using Zostera noltei meadows[END_REF], Gam et al. [START_REF] Gam | Impact de la présence d'herbiers à Zostera noltii sur l'infestation parasitaire des coques Cerastoderma edule dans la lagune de Merja Zerga (Maroc)[END_REF], Grignon-Dubois and Rezzonico [START_REF] Grignon-Dubois | Phenolic chemistry of the seagrass Zostera noltei Hornem. Part 1: First evidence of three infraspecific flavonoid chemotypes in three distinctive geographical regions[END_REF], Natij et al. [START_REF] Natij | A first attempt at seagrass repartitioning in the Moroccan coasts[END_REF], Touhami et al. [START_REF] Touhami | Study of the functional structure and bioassessment of the ecological quality of benthic communities[END_REF], and Touhami et al. [START_REF] Touhami | Vertical Distribution of Benthic Macrofauna in Intertidal Habitats Frequented by Shorebirds at Merja Zerga Lagoon[END_REF]. The dwarf eelgrass meadows in this lagoon have developed in muddy and sometimes deep substrates, which makes walking through them very difficult, so mapping these meadows on foot is not feasible. Lately, according to Zarranz Elso et al. [START_REF] Elso | Genetic Description and Remote Sensing Techniques as Management Tools for Zostera noltii Seagrass Populations along the Atlantic Moroccan Coast[END_REF], dwarf eelgrass has been mapped in the Merja Zerga lagoon through spectral characterization and a maximum likelihood classification of a World View 2 satellite image acquired on 3 November 2010, combined with field samples. Hence, the surface area of Z. noltei meadows in Merja Zerga lagoon was estimated to be 71.508 m 2 , however in this case the satellite image used did not cover all of the intertidal areas of the lagoon. However, this study missed the bigger beds located in the downstream area of the lagoon. When interpreting multispectral optical images, confusion persisted between dwarf eelgrass meadows, algae, and the cordgrass Spartina sp., which is an emergent halophilous species that boarders the lagoon with other saltmarsh species, especially Salicornia sp. (L.) and Juncaceae rushes [START_REF] Elso | Genetic Description and Remote Sensing Techniques as Management Tools for Zostera noltii Seagrass Populations along the Atlantic Moroccan Coast[END_REF][START_REF] Touhami | Study of the functional structure and bioassessment of the ecological quality of benthic communities[END_REF]. Dehouck et using a multitemporal database they could limit this confusion, and that algae seem to be discriminable only when using fine spectral bands. According to Proença et al. [START_REF] Proença | Potential of High-Resolution Pléiades Imagery to Monitor Salt Marsh Evolution After Spartina Invasion[END_REF], the mapping of Spartina anglica C.E. Hubb. and Spartina maritima (Curtis) Fernald using supervised classification of Pleiades images showed that these species were often confused with the vast areas vegetated by Z. noltei, in particular in October. In order to achieve better results and to avoid this confusion, it is better to mask the majority of the potential terrestrial vegetation from the satellite image before running the classification algorithm. In our case, this problem did not occur because we created a sufficient number of polygons (117) distributed across the eight classes used for the classification. In fact, based on the accuracy assessment results, the random forest classification generated a satisfactory Z. noltei meadow map, with a kappa index equal to 0.9429 and an overall accuracy equal to 95%.

Our classification results allowed the estimation of the overall extent of Z. noltei in the Merja Zerga lagoon and also allowed the estimation of the aboveground biomass of this species. As with the study by Barillé et al. [START_REF] Bargain | Seasonal spectral variation of Zostera noltii and its influence on pigment-based Vegetation Indices[END_REF], in our case the NDVI-biomass quantitative relationship was applied to all Z. noltei meadows successfully, because despite the stacking of many layers in certain areas, the NDVI was not saturated. Thus, this method was applied directly for the classes "emerged Z. noltei" and "submerged Z. noltei " and was combined with the cover percentage for the class "mixed Z. noltei and algae". Indeed, we observed during field investigations that Z. noltei was not the main living macrophyte in the mapped intertidal area-filamentous green macroalgae was observed in the mixed beds and a few macroalgae species (Cholorophyta and Rhodophyta) were occasionally present (Figure 9). Indeed, Valle et al. [START_REF] Valle | Mapping estuarine habitats using airborne hyperspectral imagery, with special focus on seagrass meadows[END_REF] claimed that the NDVI is useful for distinguishing between sparsely vegetated and dense Z. noltei beds, and therefore can be further applied to CASI (hyperspectral sensor) imagery to map the cover percentages of the species living in the area.

The calibration of the SPOT 7 image with the biomass-NDVI relationship showed a pattern of maximal biomass in the submerged and emerged classes, especially in the "S6" station, and lower biomass in the center zone ("S5" and "S4" stations). The average biomass measured in situ in September 2018 was not significantly different from the value obtained from remote sensing (Table 7). Lately, active remote sensing data from aircrafts or UAVs have been used to map seagrass cover, mainly LiDAR [START_REF] Valle | Mapping estuarine habitats using airborne hyperspectral imagery, with special focus on seagrass meadows[END_REF][START_REF] Tulldahl | Classification of aquatic macrovegetation and substrates with airborne lidar[END_REF][START_REF] Pan | Shallow water seagrass observed by high resolution full waveform bathymetric LiDAR[END_REF][START_REF] Zavalas | Habitat Classification of Temperate Marine Macroalgal Communities Using Bathymetric LiDAR[END_REF][START_REF] Ishiguro | Classification of Seagrass Beds by Coupling Airborne LiDAR Bathymetry Data and Digital Aerial Photographs[END_REF][START_REF] Parrish | Post-Sandy benthic habitat mapping using new topobathymetric lidar technology and object-based image classification[END_REF][START_REF] Webster | Optimization of Data Collection and Refinement of Postprocessing Techniques for Maritime Canada's First Shallow Water Topographic-bathymetric Lidar Survey[END_REF][START_REF] Webster | Results from 3 seasons of surveys in maritime Canada using the Leica Chiroptera II shallow water topo-bathymetric lidar sensor[END_REF][START_REF] Collings | Quantifying the discriminatory power of remote sensing technologies for benthic habitat mapping[END_REF]; hyperspectral sensors such as CASI-2, PHILLS, and HyMap [START_REF] Valle | Mapping estuarine habitats using airborne hyperspectral imagery, with special focus on seagrass meadows[END_REF][START_REF] Dierssen | Ocean color remote sensing of seagrass and bathymetry in the Bahamas Banks by high-resolution airborne imagery[END_REF][START_REF] Garcia | A Method to Analyze the Potential of Optical Remote Sensing for Benthic Habitat Mapping[END_REF][START_REF] Pan | Comparison of bathymetry and sea-grass mapping with hyperspectral imagery and airborne bathymetric lidar in a shallow estuarine environment[END_REF][START_REF] Castillo-López | The importance of atmospheric correction for airborne hyperspectral remote sensing of shallow waters: Application to depth estimation[END_REF]; or photographs [START_REF] Frederiksen | Spatial and temporal variation in eelgrass (Zostera marina) landscapes: Influence of physical setting[END_REF][START_REF] Lathrop | A Multi-scale Segmentation Approach to Mapping Seagrass Habitats Using Airborne Digital Camera Imagery[END_REF][START_REF] Fletcher | A Semiautomated Approach for Monitoring Landscape Changes in Texas Seagrass Beds from Aerial Photography[END_REF][START_REF] Young | Mapping intertidal eelgrass (Zostera marina L.) in three coastal estuaries of the Pa-cific Northwest USA using false colour near-infrared aerial photography[END_REF][START_REF] Uhrin | Improved seagrass mapping using linear spectral unmixing of aerial photographs[END_REF]. The efficiency of remotely sensed data has been proven through many studies involving the estimation of seagrass cover; nevertheless, most studies are still reliant on field measurements when estimating shoot density and canopy height (e.g., [START_REF] Gullström | Assessment of changes in the seagrassdominated submerged vegetation of tropical Chwaka Bay (Zanzibar) using satellite remote sensing[END_REF]).

Conclusions

In our study, the suitability of using remote sensing to firstly map the distribution of Z. noltei and secondly to estimate its biomass was confirmed. In fact, we classified a SPOT 7 satellite image using a random forest algorithm and field data to produce dwarf eelgrass habitat maps. This approach enabled us to define the distribution of Z. noltei beds and associated macroalgae in terms of surface coverage. Our results proved with an accuracy of 95% that GNDVI, NDVI, and EVI are potentially useful vegetation indices for discriminating dwarf eelgrass beds from other habitats in the lagoon. Older maps involving the spatial characterization of Z. noltei in the Merja Zerga lagoon could not determine the entire extent of this seagrass. Compared to our work, previous studies on Z. noltei mapping underestimated its extent due to the fact that they used images that did not fit all of the Merja Zerga lagoon or were taken at a higher tide. Indeed, the overall surface occupied by dwarf eelgrass in this wetland is 160.76 ha when considering mixed beds. Our approach was based on estimating the extent, which is a global index that can reveal the increase or loss of the area of seagrass beds at its limits; it does not consider the losses inside these limits, which are visible at the level of the fragmentation of these beds. Biomass estimation of dwarf eelgrass using the NDVI-biomass quantitative model applied to SPOT 7 images can be very useful as a non-destructive method for estimating the biomass of this seagrass species. In future studies of Z. noltei and in order to purchase the data obtained, the same methodology should be considered.

Figure 1 .

 1 Figure 1. Z. noltei sampling stations in the Merja Zerga lagoon: S1, S2, S3, S4, S5, S6, S7 (source: Image SPOT 7).

•

  Hygrophilous species, which colonize wetlands (wet lawns): Baldellia ranunculoides (L.) Parl., Scirpus lacustris (L.) Palla., Typha domigensis (Pers.) Steud., Mentha aquatica (L.), Nasturtium officinale (R.Br.); • Aquatic species, some of which thrive in marine environments (Z. noltei, Nitella sp. (C. A.) Agardh), while the others colonize calm or stagnant waters and can be floating (Lemna gibba (L.)) or fixed (Myriophyllum sp. (L.), Potamogeton pectinatus (L.) Börner); • Terrestrial species, which develop in generally humid edge habitats during the rainy period (Centaurea calcitrapa (L.), Euphorbia clementei Boiss., Solanum nigrum (L.)).

Figure 2 .

 2 Figure 2. Flowchart of data processing to map dwarf eelgrass Z. noltei and algae in the Merja Zerga lagoon.

Figure 3 .

 3 Figure 3. Spectral signatures for the eight different habitat types in the Merja Zerga lagoon: emerged Z. noltei, submerged Z. noltei, algae, mixed beds of Z. noltei and algae, sand, mud, and shallow water. Left: average spectra (n = 5) numbers degraded to the four SPOT 7 satellite image bands: 1 (blue; 450-520 nm), 2 (green; 530-590 nm), 3 (red; 625-695 nm), and 4 (NIR; 760-890 nm). Right: average spectra acquired at the spectral resolution of a JAZA0478 spectroradiometer.

  t. J. Geo-Inf. 2021, 10, x FOR PEER REVIEW 9

Figure 4 .

 4 Figure 4. Quadrats of Z. noltei in the homogeneous station (a) S3 and in mixed stations (b) S5 (dense) and (c) S7 (sparse, submerged), their respective bed photography (d-f) .

Figure 4 .

 4 Figure 4. Quadrats of Z. noltei in the homogeneous station (a) S3 and in mixed stations (b) S5 (dense) and (c) S7 (sparse, submerged), their respective bed photography (d-f).

Figure 5 .

 5 Figure 5. Random forest results: mean decrease Gini values for the 13 bands (L.1: band 1; L.2: band 2; L.3: band 3; L.4: band 4; L.5: GNDVI; L.6: NDVI; L.7: CVI; L.8: GVI; L.9: ICS; L.10: NLI; L.11: EVI; L.12: IB; L.13: SAVI).

Figure 6 .

 6 Figure 6. The average of 1371 pixels per class for each of the 4 Bands and 9 indices of the SPOT 7 image.

Figure 7 .

 7 Figure 7. (a) Random forest classification results and (b) the distribution of dwarf eelgrass Z. noltei and macroalgae in Moulay Bousselham lagoon.

Figure 8 .

 8 Figure 8. Aboveground biomass in g DW/m 2 per Z. noltei class (a-c) in the Merja Zerga lagoon.

Figure 9 .

 9 Figure 9. Macroalgae species associated with Z. noltei (a) and filamentous green macroalgae (b) in the mixed beds observed in the intertidal area of the Merja Zerga lagoon.

Table 1 .

 1 Z. noltei field sampling stations in the Merja Zerga lagoon.

	Station	S1	S2	S3	S4	S5	S6	S7
	Location of the station	Pass 1	Pass 2	Pass 3	Center 1	Center 2	East	West

Table 2 .

 2 SPOT 7 satellite image bands and the 9 indices used for random forests classification.

	Bands	Bands and Indices	Band Name and Full Form of Indices
	L.1	Band 1	Reflectance in Blue
	L.2	Band 2	Reflectance in Green
	L.3	Band 3	Reflectance in Red
	L.4	Band 4	Reflectance in Near Infra-Red (NIR)
	L.5	GNDVI	Green Normalized Vegetation Index [49]
	L.6	NDVI	Normalized Vegetation Index [50]
	L.7	CVI	Chlorophyll Vegetation Index = (Reflectance in Green-Reflectance in Red)
	L.8	GVI	Green Vegetation index [51]
	L.9	ICS	Normalized Difference Red/Green, Redness Index [52]
	L.10	NLI	Nonlinear Vegetation index [53]
	L.11	EVI	Enhanced Vegetation Index [54]
	L.12	IB	Brilliance Index [55]
	L.13	SAVI	Soil-Adjusted Vegetation Index [56]

Table 3 .

 3 The average biomass of Z. noltei in g/m 2 per station in the Merja Zerga lagoon (stations S3, S4, S5, S6, and S7).

	Stations	S5	S3	S4	S7	S6
	Average biomass of Z. noltei g D.W.m 2	36.17	84.749	96.48	106.448	142.81

Table 4 .

 4 Cover percentage of Z. noltei per station in the Merja Zerga (stations S1, S2, S3, S4, S5, S6, and S7; *: insufficient quadrats.

	Stations	September 2017	Cover Percentage of Z. noltei September 2018	November 2019
	S1	0.72	0.92	0.80
	S2	0.70	0.72	0.75
	S3	0.96	0.94	0.97
	S5	0.78	0.76	0.77
	S4	-*	0.61	0.60
	S7	-*	-*	0.50
	S6	-*	-*	0.97

Table 5 .

 5 The surface areas in ha estimated for Z. noltei meadows for each station in the Merja Zerga lagoon: S1, S2, S3, S4, S5, S6, S7.

	Classes of Z. noltei beds	S6	S7	S5	S4	S3	S1 and S2
	Submerged Z. noltei	14.08	5.83	1.23	4.58	0.0888	1.49
	Emerged Z. noltei	31.52	4.67	0.179	1.25	0.0094	0.286
	Mixed patches	50.26	24.74	12.26	7.93	0.0736	0.26

Table 6 .

 6 Surface area for Z. noltei at each station in the Merja Zerga lagoon: S1, S2, S3, S4, S5, S6, S7.

	Z. noltei Beds Surfaces (ha)	S6	S7	S5	S4	S3	S1 and S2
	Total surface area of Z. noltei and algae (ha)	95.86	35.24	13.68	13.76	0.17	2.05
	Cover percentage of Z. noltei	0.97	0.5	0.77	0.61	0.96	0.77
	Z. noltei surface area (ha)	92.98	17.61	10.53	8.39	0.16	1.57

Table 7 .

 7 The average biomass for Z. noltei in g/m 2 for each station in the Merja Zerga lagoon: S1, S2, S3, S4, S5, S6, S7.

	Z. noltei Classes	Mixed Beds	Submerged/Emerged Beds
	Maximum biomass of Z. noltei g
	D.W.m 2 (from SPOT 7 image	97.03	160.79/277.95
	calibration)
	Stations	S5	S4	S3	S6	S7
	Average biomass of Z. noltei g D.W.m 2 (from field samples)	36.17	96.48	77.62 to 84.749	142.81	106.448

Funding: This research received no external funding.

Institutional Review Board Statement: Not applicable.

Informed Consent Statement: Not applicable.

Data Availability Statement: Not applicable.

Acknowledgments:

The authors thank Ouassal Keltoum, Louizi Halima, Tifarouine Loubna, Boutahar Loubna, Boutoumit Soilam for providing their help during field data acquisition; El Mahsani Abderahman and Maanan Mehdi for their help in my initiation to GIS and remote sensing.

Author Contributions: Conceptualization, Salma Benmokhtar, Marc Robin, and Hocein Bazairi; metholodogy, Salma Benmokhtar, Marc Robin, and Hocein Bazairi; field data acquisition, Salma Benmokhtar, Marc Robin, Hocein Bazairi, and Mohamed Maanan; writing-review and editing, Salma Benmokhtar, Marc Robin, Hocein Bazairi, and Mohamed Maanan. All authors have read and agreed to the published version of the manuscript.

Conflicts of Interest:

The authors declare no conflict of interest.


Asri El

Fatima

Errhif Ahmed

Martin Daniel

Tamsouri Mohamed-Naoufal

Zidane Hakima

Community structure and spatial patterns of soft-bottom macrozoobenthos in Oualidia lagoon, Moroccan Atlantic

Keywords: Macroinvertebrates, Biodiversity, Environmental factors, spatial patterns, Oualidia lagoon, Morocco

The paper analyse the composition, structure and spatial organization of the soft-bottom macrozoobenthos inhabiting Oualidia lagoon (Moroccan Atlantic coasts), as well as their relationships with the main environmental variables. Material for the study was collected from 43 stations in winter 2013. A total of 56 species belonging to 6 phyla were recorded and identified. Species diversity was highest in molluscs, crustaceans and polychaetes. We used a Hierarchical Ascending Classification and a non-Metric Multidimensional Scaling to characterize the macrozoobenthos of the lagoon. We identified three groups of stations arranged from the outer -most to innermost areas of the lagoon, harbouring a Cerastoderma edule, a Tritia pfeifferi and a Tanais dulongii assemblage, respectively. The trophic structure of the macrozoobenthos was dominated in density by deposit-feeders (88.1%)and species richness by carnivores (20%) and detritivorous (20%). The BIO-ENV analysis showed that the combination of granulometry, salinity and temperature were the major factors controlling the spatial distribution of the macrozoobenthos in the Oualidia Lagoon.

Introduction

In marine ecosystems, soft-bottom macrofauna represents a main basal component of the food web. Most macrobenthic species represent an important nutritional resource for many higherlevel consumer, such as epibenthic crustaceans, fish and especially shorebirds (Jędruc et al, 2019). Shorebirds in particular are typically benthivorous and the most important consumers of intertidal benthic communities [START_REF] Herman | Ecology of estuarine macrobenthos[END_REF][START_REF] Goss-Custard | Intake rates and the functional response in shore-birds Charadriiformes eating macroinvertebrates[END_REF][START_REF] Platell | Inter-and intraspecific partitioning of food resources by six large and abundant fish species in a seasonally open estuary[END_REF].

Benthic communities are also widely used to monitor the effects of environmental change. Being relatively long-living, compared to meio-and micro-benthos, they integrate water and sediment quality conditions with time and thereby, indicate temporary and chronic disturbances [START_REF] Warwick | Detection of initial effects of pollution on marine benthos: an example from the Ekofisk and Eldfisk oilfields, North Sea[END_REF][START_REF] Borja | A marine biotic index to etablish the ecological quality of soft bottom benthos within european estuarine and coastal environnements[END_REF][START_REF] Blanchet | Use of biotic indices in semi-enclosed coastal ecosystems and transitional waters habitats -Implications for the implementation of the European water framework[END_REF]. The composition and structure of macrobenthic fauna are strongly correlated with the ecological conditions prevailing in the water-sediment interface, where multiple effects of organic enrichment and pollution occur [START_REF] Borja | A marine biotic index to etablish the ecological quality of soft bottom benthos within european estuarine and coastal environnements[END_REF][START_REF] Dauvin | Polychaete/amphipod ratios: An approach to validating simple benthic indicators[END_REF]. Thus, having a detailed knowledge of the macrobenthos appears essential to understand the functioning of coastal ecosystems including lagoons, and is a mandatory step to implement effective management and conservation measures.

Costal lagoons occupy around a 13% of the world coastline [START_REF] Kjerfve | Coastal lagoon processe[END_REF], playing a key ecological role [START_REF] Velasco | Ecosystem services and main environmental risks in a coastal lagoon (Mar Menor[END_REF]. They are among the marine habitats with the highest biological productivity [START_REF] Kennish | Coastal lagoons. Critical habitats of environmental change[END_REF], while being subjected to intense anthropogenic activities as they provide many goods and services. Among them, there are valuable social and economic activities such as traditional fisheries of fish and molluscs. Oualidia lagoon is not an exception and their natural resources supports many aspects of local people lives, including economic, cultural, and community relationships [START_REF] Maanan | A long-term record of land use change impacts on sediments in Oualidia lagoon, Morocco[END_REF]). In addition, its biological, ecological and landscape potential favoured the a growing development of recreational and tourist activities. Altogether, these various human activities contribute to generate pressures that may impact biodiversity and cause ecosystem malfunctioning [START_REF] Dauer | Relationships between benthic community condition, water quality, sediment quality, nutrient loads, and land use patterns in Chesapeake Bay[END_REF][START_REF] Borja | Assessing the environmental quality status in estuarine and coastal systems: comparing methodologies and indices[END_REF].

Moroccan coasts include a rich network of lagoons, one of them being Oualidia, which is located in its Atlantic coast. This lagoon provides a valuable environment for a rich variety of plants, birds, fish, and other wildlife [START_REF] Hamoumi | Fiche descriptive sur les zones humides Ramsar (FDR) : Complexe de Sidi Moussa-Walidia[END_REF]. It is currently considered a Natural Park and, being the most important wintering area for migratory birds in Morocco (El [START_REF] Hamoumi | Fiche descriptive sur les zones humides Ramsar (FDR) : Complexe de Sidi Moussa-Walidia[END_REF], it is also considered a Wetland of International Importance (under the RAMSAR Convention).

Despite its intrinsic interest, the studies on Oualidia lagoon mainly focused on oceanography [START_REF] Bennouna | Étude des populations phytoplanctoniques de la lagune de Oualidia (Maroc) ; dinoflagellés potentiellement nuisibles[END_REF][START_REF] Hilmi | Oualidia lagoon, Morocco: an estuary without a river[END_REF][START_REF] Damsiri | Spatio-temporal nutrients variability in the Oualidia lagoon ( Atlantic Moroccan coast )[END_REF][START_REF] Maanan | Environmental and ecological risk assessment of heavy metals in sediments of Nador lagoon, Morocco[END_REF] and wholesomeness [START_REF] Bellucci | Distribution of heavy metals in sediments of the Venice Lagoon: the role of the industrial area[END_REF][START_REF] Zourarah | Fifty-year sedimentary record of heavy metal pollution in the lagoon of Oualidia (Moroccan Atlantic coast)[END_REF][START_REF] Hassou | Spatial and temporal variation of faecal pollution indicators (Escherichia coli and faecal streptococci ) and physico-chemical parameters at the Oualidia lagoon and its watershed ( Morocco )[END_REF]. Only three approached the analysis of the biodiversity of benthic species [START_REF] Chbicheb | Organisation biogéologique d'un bassin paralique : la lagune d'Oualidia (côte atlantique marocaine)[END_REF][START_REF] El Asri | Taxonomic diversity and structure of the molluscan fauna in Oualidia lagoon (Moroccan Atlantic coast )[END_REF], 2017). The aim of the present paper is, thus, to provide an overview of the biodiversity of the whole soft bottom macroinvertebrate assemblages inhabiting Oualidia lagoon, with particular emphasis on their distribution patterns and community structure, as well as their relationships with the main environmental variables explaining their spatial distribution.

Materials and methods

Study area

Oualidia lagoon, located on the Atlantic coast of Morocco between El Jadida and Safi (32°44'42" N-09°02'50" W) (Figure 1), has a surface area of 3.5 km² and exchanges water with the ocean through a 150 m wide major inlet. During spring, the tidal regime gives rise to a secondary shallower inlet about 50 m wide. The lagoon is characterized by having lateral channels connected to a meandering main channel with an average depth of 2 m and a maximum depth during flood tides not exceeding 5 m [START_REF] Carruesco | Genèse et évolution de trois lagunes du littoral atlantique depuis l'holocène : Oualidia, Moulay Bou Salham (Maroc) et Arcachon[END_REF]. Flood tides cover more than 75% (2.25 km²) of the lagoon surface, bringing salt water up to the upstream reaches of the lagoon and into a saline marsh.

Sampling and processing

The soft bottom macrofauna was sampled at 43 stations (Figure 1) during winter 2013. Sediment samples were collected using a Van Veen grab, with two replicate samples in each station (0.125 m² in surface area). Samples were washed in situ through a 1 mm mesh sieve and fixed in a 10 % seawater formaldehyde mixture. Superficial water salinity and temperature were recorded at each station by a thermosalinometer. One additional sediment sample was also collected at each station to measure chlorophyll a concentration, grain size, and organic matter content. The chlorophyll 'a' content was determined according to the Lorenzen method [START_REF] Holm-Hansen | Fluorometric Determination of Chlorophyll[END_REF]. Grain size was measured with a laser granulometer (Malvern, Mastersizer) at the LETG (Littoral, Environnement, Geomatique, Teledetection) (UMR 6554, University of Nantes). The percentage of organic matter was obtained as the weight losses after ignition at 450°C for 4 hours.

Data analysis

Macrobenthic communities were described through the following indicators: species richness, abundance, Shannon diversity (H', as log2) [START_REF] Shannon | A mathematical theory of communication[END_REF] and evenness (J') [START_REF] Pielou | Shannon's Formula as a Measure of Specific Diversity: Its Use and measure[END_REF], calculated using the PAST software (Paleontological Statistics v2.14) [START_REF] Hammer | PAST: Paleontological statistics software package for education and data analysis[END_REF]. Hierarchical Ascending Classification (HAC) based on Bray-Curtis distance (Ward's method) and non-metric multidimensional scaling (nMDS) were used to analyse the structure of the sampling stations based on the density matrix, using the SYSTAT 11 program. Densities were transformed log10 (x+1) to limit the influence of the most dominant taxa, and the species with a single occurrence in the lagoon were omitted from the analysis. The IndVal index [START_REF] Dufrêne | Species Assemblages and Indicator Species : The Need for a Flexible Asymmetrical Approach[END_REF] was calculated for each species in each assemblage and the species showing the highest value were used to describe the respective assemblages. A multivariate correlation (BIOENV) [START_REF] Clarke | A method of linking multivariate community structure to environmental variables[END_REF] was used to test the observed patterns and to define the existing correlations between species abundance and abiotic parameters, which allowed us to quantitatively explore the relationship between the levels of different factors and the biotic patterns. The BIOENV analysis was based on the Euclidean distance for the abiotic dataset and on the Spearman rank correlation, and were carried out in the R computational software 3.4.3. The analysis of the functional structure of the assemblages was based on the trophic groups assigned to the different macrobenthic taxa identified, according to [START_REF] Fauchald | The diet of worms: a study of polychaete feeding guilds[END_REF] and notably modified by [START_REF] Grall | Using biotic indices to estimate macrobenthic community perturbations in the Bay of Brest[END_REF], [START_REF] Hily | Modifications of the specific diversity and feedingguilds in an intertidal sediment colonized by an eelgrass meadow (Zostera marina) (Brittany, France)[END_REF], [START_REF] Afli | Fluctuation à long terme des peuplementsmacrobenthiques de la partie orientale du golfe du Morhiban (Bretagne, France)[END_REF], [START_REF] Pranovi | Determination of food sources for benthic invertebrates in a salt marsh(Aiguillon Bay, France) by carbon and nitrogen stable isotopes, importance oflocally produced sources[END_REF] and [START_REF] Afli | Trophic organization of the macro-zoobenthic assemblages within coastal areas subjected to anthropogenic activities[END_REF].

Results

Environmental data

Most environmental descriptors of Oualidia lagoon showed a clear gradient from outer to inner parts of the lagoon: temperature increased from 16.9 to 19.9 °C (Figure 2), salinity decreased from 10.1 to 39.5‰ (Figure 2), , organic matter increased from 1.94 to 31.97 %, showing the maximum at station 20 and the minimum at station 39 near the sandpit where the hydrodynamic regime favours sediment instability (Figure 3) And the granulometry was mainly sandy in the outer area and varied from sandy-silt to silty-sand in the inner one (Figure 3). Chlorophyll a showed a heterogeneous distribution and ranged between 1.19 and 23.41 mg/m² (Figure 3).

Species composition and diversity

A total of 56 taxa were recorded, specifically 28 mollusks, 17 crustaceans, seven polychaetes, two echinoderms, one cnidarian and one insect larvae (Table 1). Molluscs were the most abundant taxa (95.3%), followed by polychaetes (2.7%), and crustaceans (1.2%).

The middle part of the lagoon showed the highest number of species. Up to 21 species were recorded at stations 35 and 36 and only 1 at stations 33 and 42 (Figure 4). The highestdensity was also found in the middle part of the lagoon. Up to 5,142 ind.•m-2 were recorded at station 23, while only 8 ind.•m-2 occurred a station 32 (Figure 4). The diversity varied from 0 to 3.8 bits (Figure 5) and did not show any defined gradient, ranging in most stations between 1 and 2 bits. The evenness varied from 0 to 0.9 (Figure 5), being of less that0.4 in several stations.

Trophic structure

The soft bottom microbenthic fauna of Oualidia lagoon can be grouped in eight feeding groups: carnivores, detritivorous, herbivorous, micrograzers, surface deposit feeders, subsurface deposit feeders, suspension feeders and scavengers,. In terms of the species richness carnivores (19%), detritivorous (19%), herbivorous (18%), suspension feeders (16%) and surface deposit feeders (14%) were dominant, while micrograzers, scavengers and subsurface deposit feeders exhibited the lowest values (Figure 6A). In terms of density, the deposit-feeders were dominant (88.1%), followed by the scavengers (5%) and by a weak representation of all other groups (Figure 6B).

Assemblage structure

Three main groups of stations were identified in Oualidia (HAC, distance = 20%, Figure 7A; stress level = 0.14, nMDS, Figure 7B) showing significant differences (PERMANOVA, Table 2).

The first group included the stations from the outer part of the lagoon. It was characterized by having sandy bottoms with low organic matter, high species richness (37 species), the lowest mean density (99.7±104.5 ind.•m-²), and the highest mean H′ and J′ ( 2.5±0.8 bit/ind. and 0.8±0.1, respectively). The most dominant species were the crustacean Tanais dulongii, the gastropods Peringia ulvae, Tritia pfeifferi and Tritia reticulate, and the bivalve Abra alba.

The second group included the stations from the middle part of the lagoon. It was characterised by showing the highest mean density (1726.8±1485.7 ind. m-²), a moderate species richness (31) and the lowest mean H′ and J′ (1.1±0.5 bit/ind. and 0.3±0.2, respectively). The most dominant species were the gastropods Tritia pfeifferi, Peringia ulvae and the bivalve Abra alba.

The third group included stations from the inner part of the lagoon. It was characterized by a low species richness (17 species), a density intermediate between the two previous groups (209.9±188.3 ind.m-²) and moderate H′ and J′ (1.1±0.7 bit/ind. and 0.4±0.2, respectively). The sediments were characterized by a fine-grained texture and high organic matter, while the temperature was the highest.. The dominant species were the bivalve Cerastoderma edule, the gastropods Peringia ulvae, Haminoea cf. japonica and the polychaetes Hediste diversicolor, Capitella sp.

Relationships between biotic and environmental patterns

The highest rank correlation (Bio-Env, rho = 0.541) in Oualidia lagoon showed granulometry, salinity and temperature forming the "best combination" of abiotic parameters playing a relevant role in structuring the macrobenthic assemblages (Table 4).

Discussion

The present study aimed to analyse the spatial variability of the soft bottom macrozoobenthic assemblages in Oualidia lagoon, assessing the relationships between their spatial distribution patterns and the environmental factors driving them. Taking into account that Oualidia lagoon is affected by numerous impacts from different anthropogenic sources of disturbance, and that there is an evident lack of previous studies analyzing its macrozoobentic assemblages as a whole, our study certainly provides a good baseline and will be a useful reference for future ecological research and monitoring of environmental impacts in this lagoon, as well as in similar coastal ecosystems.

Our results proves the existence of a very pronounced decreasing gradient of salinity from the outer to the inner parts of the lagoon, where there were massive inland freshwater inflows [START_REF] Hilmi | Oualidia lagoon, Morocco: an estuary without a river[END_REF]), which agrees with our finding of marked lower salinities in the inner part. However, inland freshwater also affected the outer station 43, as previously reported [START_REF] Hennani | Temporal and spatial distribution of faecal bacteria in the Oualidia lagoon (Morocco)[END_REF][START_REF] Hassou | Spatial and temporal variation of faecal pollution indicators (Escherichia coli and faecal streptococci ) and physico-chemical parameters at the Oualidia lagoon and its watershed ( Morocco )[END_REF][START_REF] El Asri | Taxonomic diversity and structure of the molluscan fauna in Oualidia lagoon (Moroccan Atlantic coast )[END_REF], 2017). The lower temperature at the entrance of the lagoon reflects the influence of the cold ocean waters. Being far from this influence, the mid and inner stations tended to be easily warmed due to their shallower depth and the loss of momentum [START_REF] Rharbi | Caractéristiques hydrologiques et écologiques de la lagune d'Oualidia : milieu paralique de la côte atlantique marocaine[END_REF][START_REF] Hilmi | Oualidia lagoon, Morocco: an estuary without a river[END_REF][START_REF] Hennani | Temporal and spatial distribution of faecal bacteria in the Oualidia lagoon (Morocco)[END_REF][START_REF] Damsiri | Spatio-temporal nutrients variability in the Oualidia lagoon ( Atlantic Moroccan coast )[END_REF]). The overall distribution of both granulometry and organic matter follows the dominat hydrodynamic characteristics of the lagoon, with the relatively calm inner area tending to have more fine sediments and organic matter [START_REF] Maanan | Impact des changements de l'occupation des sols sur l'état de[END_REF][START_REF] Zourarah | Les processus côtiers actuels et leur impact sur l'environnement littoral des Doukkala : Approches hydrodynamique, morphologique, sédimentologique et géochimique[END_REF][START_REF] El Asri | Taxonomic diversity and structure of the molluscan fauna in Oualidia lagoon (Moroccan Atlantic coast )[END_REF]2017).

The soft bottom macrofauna of Oualidia lagoon was mainly characterized by the presence of mollusks (especially gastropods and bivalves), followed by crustaceans (especially amphipods and isopods) and polychaetes (especially errant taxa). Despite the occasional presence of tunicates and cnidarians, the resulting taxonomic composition was typical of lagoon environments [START_REF] Bazaïri | Spatial organisation of macrozoobenthic communities in response to environmental factors in a coastal lagoon of the NW African coast (Merja Zerga, Morocco)[END_REF][START_REF] Mistri | Persistence of benthic communities: A case study from the Valli di Comacchio, a Northern Adriatic lagoonal ecosystem (Italy)[END_REF]. Our report of 56 taxa more than doubled the number of species (24) previously reported in the lagoon [START_REF] Chbicheb | Organisation biogéologique d'un bassin paralique : la lagune d'Oualidia (côte atlantique marocaine)[END_REF]. We suggest that this increase can be explained by the progressively increasing organic matter contents in the sediments of the lagoon, as well as by the changes in its hydrodynamic characteristics caused by the construction of a pit upstream, but we cannot discard the fact that we have sampled four times more stations (43 vs. 10 in 1996). Compared to other lagoons [START_REF] Mergaoui | Qualité des eaux et macrofaune benthique d'un milieu estuarien du Maroc : cas de l'estuaire de Sebou[END_REF][START_REF] Marchini | Spatial and temporal variability of hard-bottom macrofauna in a disturbed coastal lagoon (Sacca di Goro, Po River Delta, Northwestern Adriatic Sea)[END_REF][START_REF] Chaouti | Diversité taxonomique et structure de la macrofaune benthique des substrats meubles de la lagune de Smir[END_REF][START_REF] Touhami | Structure and spatial organization of macrobenthic fauna of intertidal habitats frequented by wintering shorebirds at Merja Zerga lagoon (Moroccan Ramsar Site)[END_REF]), Oualidia appears to be richer in terms of species, probably because the combined influence of a wide connection with the ocean with the progressive arrival of inland freshwaters. Accordingly, the partuclar environmental characteristics seemed to provide favorable habitats allowing the combined presence of marine, brackish and freshwater benthic organisms.

The presence of as much as eight trophic functional groups in the macrozoobenthos is an indicator of the availability of a wide panoply of trophic resources in the lagoon (Touhami et al.2017). However, the observed trophic structure is certainly comparable with that commonly observed worldwide in other coastal lagoons, as well as in estuaries [START_REF] Mclusky | The Estuarine Ecosystem; Ecology, Threats and Management[END_REF], particularly in what concerts to the dominance of deposit-feeders in density [START_REF] Bachelet | Seasonal changes in macrophyte and macrozoobenthos assemblages in three coastal lagoons under varying degrees of eutrophication[END_REF][START_REF] Gaudêncio | Trophic structure of macrobenthos in the Tagus estuary and adjacent coastal shelf[END_REF]Marchini et al. 2000;[START_REF] Ysebaert | Large-scale spatial patterns in estuaries : estuarine macrobenthic communities in the Schelde estuary, NW Europe[END_REF].

The particular combination of geological, hydrographical and environmental characteristics of Oaulidia lagoon generates a macrofaunal structure characterized by three assemblages that can be clearly distinguished in the HAC and nMDS plots (Figure 7A-B) and proved to be organized along a inner-to-outer lagoon gradient (Figure 8), in a similar was as previously reported for other lagoons (e.g. [START_REF] Bazaïri | Spatial organisation of macrozoobenthic communities in response to environmental factors in a coastal lagoon of the NW African coast (Merja Zerga, Morocco)[END_REF][START_REF] Lefrere | Macrobenthic community structure of soft-bottom sediments in the Khnifiss lagoon, South of Morocco[END_REF]: 1) the inner-lagoon, which was characterised by having highest fine sediment contents and harboured a Cerastoderma edule assemblage; 2) the mid-lagoon, which had medium grain-sized sediments and harboured a Tritia pfeifferi assemblage; and 3) the outer-lagoon, which was closer to the lagoon inlets, showed sandy sediments, and harboured a Tanais dulongii assemblage.

The spatial organisation of the lagoon and its associated benthic assemblages, as defined based on our data, agrees with the previous consideration of the lagoon as 'an estuary without a river' based on a physical oceanography approach [START_REF] Hilmi | Oualidia lagoon, Morocco: an estuary without a river[END_REF]. This also corresponded with the spatial arrangement typically characterising paralic environments, whose main descriptors responded to a gradient of confinement (Guelorget and Perthuisot 1983). Despite the absence of a river, the taxonomic organization of the macrozoobenthic assemblages of Oualidia lagoon closely resembled that of an estuarine system, where the tidal regime plays a key structuring role [START_REF] Cherkaoui | Organisation spatiale des peuplements macrozoobenthiques subtidaux d'un estuaire de la côte atlantique marocaine : l'estuaire du Bou Regreg[END_REF]. In Oualidia lagoon, however, the main driving factor appear to be the combined influence of granulometry, temperature and salinity, which seems also to be a common trend in lagoon systems [START_REF] Uwadiae | Spatial Patterns in Diversity and Distribution of Benthic Molluscs in a Weak Tidal Tropical Lagoon[END_REF]. However, the environmental factors controlling the species distribution may change from one paralic environment to another being, for example, vegetation and grain size in the Smir lagoon (Mediterranean coast of Morocco) [START_REF] Chaouti | Spatial patterns of soft-bottom macro-invertebrates and relationships with environmental conditions in a north African coastal lagoon (Smir lagoon, Morocco)[END_REF], dissolved oxygen, temperature and salinity in the Sacca di Goro (Adriatic coast of Italy) [START_REF] Mistri | Redundancy of macrobenthos from lagoonal habitats in the Adriatic Sea[END_REF], or temperature and depth in Monolimni lagoon (Mediterranean lagoon, Northern Aegean) [START_REF] Kevrekidis | Seasonal variation of the macrozoobenthic community structure at low salinities in a Mediterranean lagoon (Monolimni Lagoon, Northern Aegean)[END_REF]).

Conclusions

The present study proves the high diversity and abundance of the benthic macrofauna of Oualidia lagoon, and determines the main factor(s) controlling the species distribution to be a combined influence of granulometry, temperature and salinity. We also prove that the lagoon provides a high diversity of trophic resources to the macrozoobenthic organisms, as shown by the high number (up to eight) of trophic-functional groups identified. The assemblages of the lagoon were characteristically dominated by a few species, which appears to be a common trend in lagoon macrozoobenthos and reflects its very high vulnerability and fragility. Our study helps to compensate the lack of fundamental knowledge on the structure of the benthic macroinvertebrate assemblages of the lagoon, while providing a useful tool for further ecological assessment and environmental impact monitoring of these coastal ecosystems of Moroccan Atlantic. The Additional studies of the relationships between macrobenthic communities and abiotic parameters are needed to discuss spatial and seasonal variations.

Figure 1 .

 1 Figure 1. Geographical location of the Oualidia lagoon, showing the position of the sampling stations.

Figure 3 .

 3 Figure 3. Distribution of organic matter, chlorophyll a and granulometry in Oualidia.

Figure 4 .

 4 Figure 4. Spatial distribution of density and species richness in Oualidia.

Figure 5 .

 5 Figure 5. Spatial distribution of Shannon and evenness indexes in the Oualidia lagoon.

Figure 6 .

 6 Figure 6. Trophic structure of the macrofauna community in Oualidia, expressed as relative number of individuals (A) and species (B).

Figure 7 .

 7 Figure 7. Structure of the macrofaunal assemblages based on abundances. A. Dendogram inferred from HAC. B.MDS plot showing the groups inferred from HAC.

Table 1 .

 1 Comparative list the macrofauna collected in 2013 (our study) and 1996[START_REF] Chbicheb | Organisation biogéologique d'un bassin paralique : la lagune d'Oualidia (côte atlantique marocaine)[END_REF] in Oualidia.

	Family	Species	This study	Chbicheb (1996)
	Mollusca
	Solenidae	Solen marginatus Pulteney, 1799	*
	Cardiidae	Cerastoderma edule (Linnaeus, 1758) Cerastoderma glaucum (Poiret, 1789)	*	* *
	Semelidae	Abra alba (W. Wood, 1802) Scrobicularia plana (da Costa, 1778)	* *	*
	Lucinidae	Loripes orbiculatus Poli, 1791	*	*
	Mactridae	Spisula subtruncata Da Costa, 1778	*
	Veneridae	Ruditapes decussatus (Linnaeus, 1758) Venerupis Pullastra (Linné, 1758)	*	* *
	Mytilidae	Mytilus galloprovincialis Lamarck, 1819	*
	Haminoeidae	Haminoea cf. japonica Pilsbry, 1895	*
	Aplysiidae	Aplysia punctata (Cuvier, 1803)	*
	Runcinidae	Runcina coronata (Quatrefages, 1844)	*
	Onchidiidae	Onchidella celtica (Cuvier, 1817)	*
	Ellobiidae	Myosotella myosotis (Draparnaud, 1801)	*
	Discodorididae	Jorunna tomentosa (Cuvier, 1804)	*
	Dendrodorididae Dendrodoris sp.	*
	Epitoniidae	Epitonium clathrus (Linnaeus, 1758)	*
	Hydrobiidae	Peringia ulvae (Pennant, 1777)	*
	Muricidae	Hexaplex trunculus (Linnaeus, 1758)	*
		Tritia pfeifferi (Philippi, 1844)	*
	Nassariidae	Tritia reticulata (Linnaeus, 1758)	*	*
		Tritia mutabilis (Linnaeus, 1758)		*
		Phorcus sauciatus (Koch, 1845)	*
	Trochidae	Phorcus lineatus (da Costa, 1778) Gibbula umbilicalis (da Costa, 1778)	* *	* *
		Gibbula divaricata (Linnaeus, 1758)		*
	Phasianellidae	Tricolia sp.	*
		Patella rustica Linnaeus, 1758	*
	Patellidae	Cymbula safiana (Lamarck, 1819)	*
		Patella depressa Pennant, 1777	*
	Cerithiidae	Cerithium vulgatum (Bruguière, 1792)		*
	Lepidochitonidae Lepidochitona cinerea (Linnaeus, 1767)	*
	Octopodidae	Octopus vulgaris Cuvier, 1797	*
	Polychaeta
	Nereididae	Hediste diversicolor (O.F. Müller, 1776)	*	*
	Phyllodocidae	Phyllodoce sp.	*
	Glyceridae	Glycera alba (O.F. Müller, 1776)	*
	Nepthyidae	Nephtys hombergii Savigny in Lamarck, 1818 Nephtys caeca (Fabricius, 1780)	*	* *
	Onuphidae	Diopatra cf. marocensis Paxton et al, 1995	*
	Ampharetidae	Alkmaria romijni Horst, 1919	*
	Oweniidae	Owenia fusiformis Delle Chiaje, 1844		*
	Capitellidae	Capitella sp. Capitella capitata (Fabricius, 1780)	*	*
	Crustacea
	Gammaridea		*
	Melitidae	Melita palmata (Montagu, 1804)	*
	Corophiidae	Corophium sp. Corophium volutator (Pallas, 1766)	*	*
	Cirolanidae	Eurydice pulchra Leach, 1815	*
	Anthuridae	Cyathura carinata (Krøyer, 1847)	*
	Sphaeromatidae	Sphaeroma serratum (Fabricius, 1787)	*
	Idoteidae	Idotea balthica (Pallas, 1772)	*

* * Figure 2. of temperature and salinity in the Oualidia lagoon.

Acknowledgments

This paper is part of a research program on coastal resources of the LPRL/DRH-Casablanca (National Fisheries Research Institute) and a contribution of DM to the research project PopCOmics (CTM2017-88080), funded by the Agencia Estatal de Investigación and the European Funds for Regional Development and to the Consolidated Research Group on Marine Benthic Ecology of the Generalitat de Catalunya (2014SGR120). We are deeply grateful to all INRH-Oualidia staff for their human quality and help during sampling, especially to Mrs Brahim Moutaki and Mrs Ahmed Kaddioui who actively participates in the field surveys. We are grateful to anonymous editors for their corrections on the manuscript. We also thank the anonymous reviewers for their valuable comments.

Funding

None.

Data availability

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with animals performed by any of the authors.

Sampling and field studies

All necessary permits for sampling and observational field studies have been obtained by the authors from the competent authorities and are mentioned in the acknowledgements, if applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.