Juliette Pelle

MD Florence A Castelli

PhD Marika Rudler

Imen Alioua

MS Benoit Colsch

PhD François Fenaille

PhD Christophe Junot

PhD Dominique Thabut

MD, PhD Nicolas Weiss
email: nicolas.weiss@aphp.fr

Metabolomics in the understanding and management of hepatic encephalopathy

Keywords: metabolomics, mass spectrometry, hepatic encephalopathy, cirrhosis, encephalopathy

Word count: 3139 Table : 1 Figure : 1 Reference: 42

Disclosures:

Nicolas Weiss declares having perceived consultant fees from MedDay Pharmaceuticals and Owkin.

Highlights :

• Metabolomics refers to the study of biological components below 1000 Daltons (Da) involved in metabolic pathways as substrates, products or effectors. • Untargeted metabolomics provide potential associations of metabolites to metabolic pathways and biochemical insights which can be further associated to a biochemical phenotype • According to the pathophysiology of HE made from a close interaction between systemic inflammation and metabolic disturbances, especially hyperammonemia, metabolomics appears as a promising technique • Recent studies suggest that metabolomics could unravel new HE pathophysiological pathways, enable the identification of new biomarkers and help in the prognostication and the prediction of treatment response • Its wide acceptability and use needs to address some shortcomings: standardization in samples preparation and analysis, identification of the main confounders • Some portable devices are in development and could lead to bring metabolomics to the bedside of HE patients.

Introduction

Hepatic encephalopathy (HE) is defined as the neurological or the neuropsychological symptoms caused by an acute or chronic liver disease and/or a portosystemic shunt [START_REF]American Association for the Study of Liver Diseases, European Association for the Study of the Liver, Hepatic encephalopathy in chronic liver disease: 2014 practice guideline by the European Association for the Study of the Liver and the American Association for the Study of Liver Diseases[END_REF]. Symptoms range from mild neuropsychological disturbances in the particular setting of minimal HE (MHE), only detected by extensive neuropsychological testing, or covert HE (MHE and also HE detected by caregivers), to confusion and coma in the setting of HE in its overt form (overt HE-OHE) [START_REF] Rose | Hepatic encephalopathy: Novel insights into classification, pathophysiology and therapy[END_REF].

Although clinical HE is estimated to be present in about 10 to 15% of patients at the time of cirrhosis diagnosis, it is probably present in up to two-third of them during the disease course. The presence of HE is independently associated to increased mortality, and altered quality of life [START_REF] Jepsen | Clinical course of alcoholic liver cirrhosis: a Danish population-based cohort study[END_REF][START_REF] Bustamante | Prognostic significance of hepatic encephalopathy in patients with cirrhosis[END_REF].

The two key features that have been recognized for years in participating in HE pathogenesis are hyperammonemia and systemic inflammation (see for review [START_REF] Romero-Gómez | Hepatic encephalopathy in patients with acute decompensation of cirrhosis and acute-on-chronic liver failure[END_REF][START_REF] Shawcross | Role of ammonia and inflammation in minimal hepatic encephalopathy[END_REF][START_REF] Weiss | Understanding hepatic encephalopathy[END_REF]). Due to altered liver function and portosystemic shunts, ammonia level increases in the systemic circulation. This increase could become even more important once the muscle mass decreases due to sarcopenia, as muscle cells express glutamine synthetase able to convert ammonia into glutamine. Ammonia can cross passively the blood-brain barrier and access the central nervous system where astrocytes, also expressing glutamine synthetase, convert it to glutamine from glutamate. Elevated levels of intracerebral glutamine probably largely contribute to astrocyte dysfunction and secondary altered neurotransmission [START_REF] Cordoba | Glutamine, myo-inositol, and organic brain osmolytes after portocaval anastomosis in the rat: implications for ammonia-induced brain edema[END_REF][START_REF] Chavarria | Magnetic resonance of the brain in chronic and acute liver failure[END_REF]. The role of concomitant alteration in gut metabolism with an increased glutaminase activity in the intestinal border cells and the possibility of increased translocation have been proposed as contributing factors for both hyperammonemia and systemic inflammation [START_REF] Romero-Gómez | Variations in the promoter region of the glutaminase gene and the development of hepatic encephalopathy in patients with cirrhosis: a cohort study[END_REF][START_REF] Iebba | Combining amplicon sequencing and metabolomics in cirrhotic patients highlights distinctive microbiota features involved in bacterial translocation, systemic inflammation and hepatic encephalopathy[END_REF]. More recently, modification in gut microbiota composition has been unveiled [START_REF] Bajaj | Microbiota changes and intestinal microbiota transplantation in liver diseases and cirrhosis[END_REF]. Briefly, it appears that intestinal microbiota diversity is decreased in cirrhosis and becomes even poorer in decompensated cirrhosis and in patients with HE, with an increase in pathogenic bacterial species, such as Proteobacteria, responsible for hyperammonemia, systemic inflammation and an increase in secondary to primary bile acids ratio.

Important clues in favor of the major role of gut microbiota modifications have been brought up by two recent randomized controlled trials where the authors described that fecal microbiota transplantation could be able to reverse these phenotypes but also prevent further HE episodes [START_REF] Bajaj | Fecal microbiota transplant from a rational stool donor improves hepatic encephalopathy: A randomized clinical trial[END_REF][START_REF] Bajaj | Fecal Microbial Transplant Capsules Are Safe in Hepatic Encephalopathy: A Phase 1, Randomized, Placebo-Controlled Trial[END_REF]. Despite these recent advances, many areas of the pathophysiology of HE are still not elucidated.

The "-omics" revolution has rapidly gained a widespread diffusion in the different domains of biology [START_REF] Evans | Designer science and the "omic" revolution[END_REF]. In fact, the impact of these new biological techniques in the "-omics" era is huge and undeniable. The concept of "-omics" refers to technologies enabling the simultaneous measure, quantification and analysis of sets of genes (genomics), mRNA (transcriptomics), proteins (proteomics), or metabolites (metabolomics) in biological media thanks to data production tools such as nucleic acid sequencing, nuclear magnetic resonance spectroscopy or mass spectrometry, combined with data mining tools. It was first developed around genetic studies aiming at investigating genetic information as a whole. It comes with a renewed vision on biological questions and in particular pathophysiological mechanisms, aiming at an exhaustive picture of a biological phenomenon for a deeper understanding of the complex underlying biochemical mechanisms. Among these -omic approaches, the metabolome is thought to be the closest representation of the metabolic phenotype;

and its large-scale study without any specific target constitutes the untargeted metabolomics. Untargeted metabolomics provide potential associations of metabolites to metabolic pathways and biochemical insights which can be further associated to a biochemical phenotype. Therefore, metabolomics appears particularly interesting for studying HE owing to the interlinked metabolic disturbances.

This review will focus on recent advances in metabolomics in the field of HE. Thus, after a brief overview of the general principles of metabolomics, we will discuss metabolomics for getting deeper insight into HE pathophysiology, as well as a valuable tool for identifying biomarkers of disease progression, outcome prognosis or treatment efficacy. Lastly, future perspectives will be discussed.

General principles of metabolomics

Metabolomics refers to the large-scale study of biological components below 1000 Daltons (Da) involved in metabolic pathways as substrates, products or effectors [START_REF] Fenaille | Data acquisition workflows in liquid chromatography coupled to high resolution mass spectrometry-based metabolomics: Where do we stand?[END_REF][START_REF] Junot | High resolution mass spectrometry based techniques at the crossroads of metabolic pathways[END_REF][START_REF] Wishart | Metabolomics for Investigating Physiological and Pathophysiological Processes[END_REF]. Its value to the field of HE can be anticipated owing to the aforementioned interlinked metabolic disturbances. It is based on the simultaneous analysis of hundreds to thousands of metabolites found in a biological sample, whether they are naturally present or produced by other biological systems such as microbiota in humans, or foreign to the biological system for example environmental compounds, xenobiotics or drugs (Figure 1). An overview of a typical metabolomic workflow is given in Figure 1. It starts with sample preparation and data acquisition steps, followed by data preprocessing, statistical analyses. An additional metabolite annotation/identification step is then performed as a prerequisite to further metabolite repositioning into corresponding metabolic pathways. Detailing all these steps is beyond the scope of this review. For greater detail, the reader is referred to recent review articles [START_REF] Junot | High resolution mass spectrometry based techniques at the crossroads of metabolic pathways[END_REF][START_REF] Letertre | Combined Nuclear Magnetic Resonance Spectroscopy and Mass Spectrometry Approaches for Metabolomics[END_REF][START_REF] Rampler | Recurrent Topics in Mass Spectrometry-Based Metabolomics and Lipidomics-Standardization, Coverage, and Throughput[END_REF].

Metabolite detection essentially relies upon two types of techniques: nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS). NMR spectroscopy is a non-destructive technique that can also provide accurate quantitative results, enabling the quantification of multiple metabolites with a single internal or external reference [START_REF] Letertre | Combined Nuclear Magnetic Resonance Spectroscopy and Mass Spectrometry Approaches for Metabolomics[END_REF]. In contrast, MS is a destructive but highly sensitive technique, therefore detecting many more metabolite-derived signals than NMR. Nowadays, liquid chromatography (LC) coupled to high-resolution and highmass accuracy mass spectrometry (HRMS) with electrospray ionization (ESI)

represents the most widely implemented approach for metabolomic purposes [START_REF] Perez De Souza | Ultra-high-performance liquid chromatography high-resolution mass spectrometry variants for metabolomics research[END_REF].

LC offers efficient and flexible separation of metabolite mixtures while HRMS enables successful detection and identification thanks to high mass accuracy measurement of a large range of metabolites present at minute amounts in complex biological samples such as human biofluids [START_REF] Junot | High resolution mass spectrometry based techniques at the crossroads of metabolic pathways[END_REF].

One of the greatest challenges in untargeted metabolomics lies in the identification of metabolites. The identification process starts with data set annotation. Overall, such annotation process consists of associating a feature (i.e., a NMR chemical shift, an accurate m/z ratio measured by HRMS, or a [m/z, retention time] combination from LC-HRMS experiments) with those of a pure authentic standard analyzed under similar conditions. This can be achieved by interrogating in-house and publicly available biochemical, metabolomic and also spectral databases. For instance, in MS-based metabolomics, it is common practice to perform either MS/MS or sequential MSn experiments in order to confirm MS annotations. Combining NMR (1D, 2D, 1 H/ 13 C) and LC-HRMS approaches can also greatly benefit to successful metabolite identification [START_REF] Letertre | Combined Nuclear Magnetic Resonance Spectroscopy and Mass Spectrometry Approaches for Metabolomics[END_REF]. Identified and/or annotated metabolites have then to be replaced into metabolic pathways to further translate analytical data into valuable biological information [START_REF] Ivanisevic | From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data[END_REF].

Once pertinent metabolites and pathophysiological pathways have been identified or when specific hypotheses need to be addressed, targeted metabolomics enable simultaneous analysis of a large number of components and their study in regards to known pathophysiological pathways or clinical and paraclinical features.

Unraveling new HE pathophysiologic pathways

Both NMR-and MS-based metabolomics, by their unique characteristics without any previously established hypothesis, have been successfully applied in HE with promising results in animal models and in patients (Table 1). In animals, metabolomics has been performed in murine models of acute liver failure (ALF) and chronic liver disease (CLD). Several studies have suggested an impairment in energy metabolism with altered tricarboxylic acid cycle, glycolysis and ketogenesis [START_REF] Murphy | Carnitine, acylcarnitine and amino acid profiles analyzed by tandem mass spectrometry in a surfactant/virus mouse model of acute hepatic encephalopathy[END_REF][START_REF] Bak | Metabolic fate of isoleucine in a rat model of hepatic encephalopathy and in cultured neural cells exposed to ammonia[END_REF][START_REF] Pathania | 1H NMR-Based Metabolic Signatures in the Liver and Brain in a Rat Model of Hepatic Encephalopathy[END_REF].

This seems to be a common feature of ALF and CLD models. These data also suggest the presence of regional metabolic differences in the brain that could explain differential clinical presentation [START_REF] Barba | A 1H nuclear magnetic resonance-based metabonomic approach for grading hepatic encephalopathy and monitoring the effects of therapeutic hypothermia in rats[END_REF]. In humans, metabolomics also provides interesting pathophysiological insights. [START_REF] Mcphail | Urinary metabolic profiling by 1H NMR spectroscopy in patients with cirrhosis may discriminate overt but not covert hepatic encephalopathy[END_REF].

No data are available on brain tissue from humans but our group was able to analyze plasma and cerebrospinal fluid (CSF) samples of ICU hospitalized cirrhotic patients with or without HE [START_REF] Weiss | Cerebrospinal fluid metabolomics highlights dysregulation of energy metabolism in overt hepatic encephalopathy[END_REF]. Interestingly, the analysis of CSF of cirrhotic patients presenting with HE has shed the light on some remarkable metabolic pathways involved in brain dysfunction. We found altered ammonia and amino-acids metabolism, encompassing increased glutamine levels, but also significant changes in energy metabolism. Clues in favor of an increase in the involvement of β-oxidation, an energetic pathway used by astrocytes but marginal in the brain in normal conditions, have been found. This could be secondary to the increase in ammonia and reduction of the Krebs cycle activity and could intervene in astrocyte dysfunction leading to altered neurotransmission regulation and neuronal homeostasis [START_REF] Weiss | Cerebrospinal fluid metabolomics highlights dysregulation of energy metabolism in overt hepatic encephalopathy[END_REF].

Another key finding was the important amount of bile acids in the CSF, especially secondary bile acids, that were several hundred times higher compared to healthy controls. This was since confirmed in animal models of HE by others [START_REF] Hadjihambi | Impaired brain glymphatic flow in experimental hepatic encephalopathy[END_REF] and by us (unpublished data). Bajaj et al. found very similar results in a less severe population of cirrhotic outpatients by performing MS-based metabolomics on urine and blood samples [START_REF] Bajaj | Serum and urinary metabolomics and outcomes in cirrhosis[END_REF]. Abnormalities were found in key metabolic pathways such as energy metabolism, urea cycle, aromatic amino-acids and fatty acids metabolism that proved associated to outcome especially HE occurrence. Unexpectedly, about one third of the patients in our study displayed also several xenobiotics, i.e. drugs, in their CSF.

While some substances like benzodiazepines or anticonvulsivants were somehow expected, the presence in the CSF of antimicrobial agents was unexpected. Indeed, those were given at a normal dosage and encompassed norfloxacine, fluconazole, 10/19 piperacillin or piperacillin/tazobactam. It could thus be discussed that, due to increased blood-brain barrier permeability in cirrhotic patients [START_REF] Weiss | Modification in CSF specific gravity in acutely decompensated cirrhosis and acute on chronic liver failure independent of encephalopathy, evidences for an early blood-CSF barrier dysfunction in cirrhosis[END_REF], some patients would display drug-induced encephalopathy in addition to classical HE.

Metabolomics has also been used to study further modifications of stool microbiota in cirrhotic patients with or without HE. Indeed, marked dysbiosis has been described in cirrhotic patients compared to healthy controls, and in cirrhotic patients with HE relatively to cirrhotic patients without HE [START_REF] Bajaj | Modulation of the metabiome by rifaximin in patients with cirrhosis and minimal hepatic encephalopathy[END_REF]. One of the main hypotheses is that this microbiota modification leads to a functional shift in microbiota metabolic pathways with especially a decline of bacterial autoregulation and network interactions [START_REF] Iebba | Combining amplicon sequencing and metabolomics in cirrhotic patients highlights distinctive microbiota features involved in bacterial translocation, systemic inflammation and hepatic encephalopathy[END_REF].

Moreover, Iebba et al. showed, by combining an analysis of stool and cecal microbiota through 16S-RNA on the one hand and metabolomics in peripheral and portal blood samples of cirrhotic patients on the other hand, that specific metabolic profiles (increase in faecal methanol and threonine and a decrease in faecal nbutyrate) as well as bacterial species and inflammation markers such as IL6 were significantly associated with the development of HE [START_REF] Iebba | Combining amplicon sequencing and metabolomics in cirrhotic patients highlights distinctive microbiota features involved in bacterial translocation, systemic inflammation and hepatic encephalopathy[END_REF]. Inflammatory disturbances in OHE and MHE patients were also found in a recent study combining transcriptomic, metabolomic and cytokine profiling in cirrhotic patients [START_REF] Rubio | Multi-omic analysis unveils biological pathways in peripheral immune system associated to minimal hepatic encephalopathy appearance in cirrhotic patients[END_REF]. Indeed, the authors were able to show in MHE patients that their adaptative immunophenotype tended to shift towards an auto-immune profile, that serum samples exhibited alterations of phospholipid metabolism and that inflammatory cytokines such as IL-6, IL-15 and IL-22 were significantly higher than in cirrhotic patients without MHE.

Identifying new biomarkers

The diagnosis of HE is frequently difficult. Whereas in overt HE the challenge is not to misdiagnose HE in the presence of a differential diagnosis and to grade appropriately the severity of the disease, in MHE, the challenge is more to identify HE with very few complaints from the patients and/or the caregivers. Metabolomics enables to identify specific metabolic profiles according to disease severity in an animal model of ALF [START_REF] Barba | A 1H nuclear magnetic resonance-based metabonomic approach for grading hepatic encephalopathy and monitoring the effects of therapeutic hypothermia in rats[END_REF]. In humans, several studies showed the ability of metabolic profiles to distinguish cirrhotic patients without HE from cirrhotic patients with HE and this for both overt HE and MHE [START_REF] Jiménez | Serum metabolic signature of minimal hepatic encephalopathy by (1)H-nuclear magnetic resonance[END_REF][START_REF] Weiss | Cerebrospinal fluid metabolomics highlights dysregulation of energy metabolism in overt hepatic encephalopathy[END_REF][START_REF] Takado | Association between Brain and Plasma Glutamine Levels in Healthy Young Subjects Investigated by MRS and LC/MS[END_REF]. Recently, metabolomics was applied to identify MHE in cirrhotic patients [START_REF] Jiménez | Serum metabolic signature of minimal hepatic encephalopathy by (1)H-nuclear magnetic resonance[END_REF] [START_REF] Barba | A 1H nuclear magnetic resonance-based metabonomic approach for grading hepatic encephalopathy and monitoring the effects of therapeutic hypothermia in rats[END_REF]. We obtained very similar results by using CSF-metabolomic data from plasma samples, to distinguish cirrhotic patients with or without overt HE [START_REF] Weiss | Cerebrospinal fluid metabolomics highlights dysregulation of energy metabolism in overt hepatic encephalopathy[END_REF]. Nevertheless, this clear difference between patients with or without HE was mainly found by using supervised partial least-squares -discriminant analysis (PLS-DA) and less frequently by using unsupervised principal component analysis (PCA). Furthermore, all those distinctions take into account tens of metabolites that are not easily transferable to simple biochemical routine dosages. Another limitation is the presence of potential confounding factors in those profiles, especially external factors such as diet, intoxications, medication, and age. It should be noticed also that, in the most severe patients, the metabolomic signature of OHE could be confounded by an ACLF signature. In the future, more extensive data on large amounts of well characterized patients, in blood, urine but also CSF samples, could confirm these findings and allow to restrict the list of metabolites to a "short-list" of metabolites candidates assessable in routine. Another opportunity would be the development of portable devices able to bring metabolomic techniques to bedside (see below Perspectives).

Prognostication and prediction of treatment response

Another promising application of metabolomics could be prognostication, i.e.

predicting in a cohort of patients those who are at high risk of developing HE or those that will respond, or not, to a given treatment option. Two recent studies showed very promising results in patients with liver decompensation regardless of the presence of HE [START_REF] Bajaj | Serum and urinary metabolomics and outcomes in cirrhosis[END_REF][START_REF] Mcphail | Multivariate metabotyping of plasma predicts survival in patients with decompensated cirrhosis[END_REF]. Using [START_REF] Bajaj | Serum and urinary metabolomics and outcomes in cirrhosis[END_REF]. Furthermore, urine and serum metabolomic profiles tended to be better predictors than MELD score [START_REF] Bajaj | Serum and urinary metabolomics and outcomes in cirrhosis[END_REF]. Thus, metabolomics could help in the early identification of "at high-risk" patients, consequently selected for an intensified follow-up.

Finally, some preliminary data suggest that metabolomics could help in the prediction and the evaluation of HE treatment response. Barba [START_REF] Bajaj | Modulation of the metabiome by rifaximin in patients with cirrhosis and minimal hepatic encephalopathy[END_REF]. They found no significant modification in microbiota but an increase in serum saturated and unsaturated fatty acids after treatment with rifaximin.

Perspectives

The main limitation of the wide use of metabolomics is still the accessibility to the technique and the expertise associated with its use and data interpretation. This could be resolved in a near future. Indeed, LC/MS is more and more accessible in clinical routine in tertiary care centers especially for toxicology [START_REF] Roux | Applications of liquid chromatography coupled to mass spectrometry-based metabolomics in clinical chemistry and toxicology: A review[END_REF]. The technique itself relies upon necessary standardized procedures given the complexity of analyses. However, standardized procedures would facilitate large-scale reproducible studies and thus diffusion in routine analysis [START_REF] Martin | Can we trust untargeted metabolomics? Results of the metabo-ring initiative, a large-scale, multiinstrument inter-laboratory study[END_REF]. Even though the "best" sample has long been considered the invasive one such as blood, CSF or ascite, several data suggest good performance for urine, sweat and even exhaled air.

Some authors suggest a "real-time" bedside use of the technique in order to achieve direct implication in diagnosis, therapeutic response and prognosis. Portable devices able to analyze exhaled air are under development, like the "electronic-nose" [START_REF] Wilson | Noninvasive Early Disease Diagnosis by Electronic-Nose and Related VOC-Detection Devices[END_REF].

Such devices could really enable widespread use of the technique, although their sensitivity and quantitative performances has still to be established. We previously saw that metabolomics was able to unravel dysfunctional metabolic pathways in HE.

Even if these findings rely mainly on observational correlations and not causality, the data provide some clues for both drug repositioning and specific drug development.

Indeed, the functionality of some of these metabolomic pathways can be affected by drugs that are already available. It is also possible to identify, in silico, drugs able to modulate these metabolic pathways. Finally, those in vitro metabolomic techniques could be complementary to other in vivo techniques like magnetic resonance spectroscopy (MRS). Thus, Braissant et al. showed, in an animal model of HE, that by using high field MRS they were able to identify and distinguish between metabolites inside the brain of the animals [START_REF] Braissant | Longitudinal neurometabolic changes in the hippocampus of a rat model of chronic hepatic encephalopathy[END_REF]. These techniques are available in routine for humans at lower fields (3T) and showed promising results [START_REF] Hermann | Magnetic resonance spectroscopy: A surrogate marker of hepatic encephalopathy?[END_REF].

Combination of these different techniques is a promising approach.

Conclusion

In summary, metabolomics provides numerous solutions to complex interrogations in the understanding, diagnosis, treatment and prognosis of HE. It remains a research technique but exponential increase of publications on its clinical applications reflects the possibilities of a shift towards routine uses. Indeed, metabolomics offers an original snapshot of pathophysiological processes in HE at a given moment.

Moreover, metabolomic profiles are relevant in non-invasive samples such as urine, due to the systemic distribution of metabolic products and effectors and the systemic consequences of metabolic disturbances. In this context, an interesting perspective could be to use data mining and statistical tools to combine metabolomics data obtained from different kinds of biofluids (i.e., blood, urine and CSF) on the same patients, and to compare them to results obtained on HE animal models, the objective remaining the distinction between molecular signatures coming from liver injury, associated systemic inflammation, and those related to alteration of brain metabolism. It should however be noted that in the future, metabolomic studies should be ideally prospective and encompass very precise selection of the patients, detailed protocols for biofluid collection and storage in order to minimize confounders.

Easily obtainable samples, rapidly obtained results and help from robust statistical tools characterize metabolomics, as well as the systematic biological relevance of what is highlighted. We foresee in this the possibility of personalized and precise medical care at the patient's bedside.

Legends:

Table

Table 1

 1 McPhail et al. included a very wide clinical spectrum of cirrhotic patients with patientswithout HE, patients with covert HE and with OHE and studied urine metabolites

depicts the main original studies on metabolomics in HE in both animal models and humans. Whereas the first major study by Jimenez et al. (

27

) used 1 H-NMR, almost all further studies involved MSbased approaches to obtain a broader metabolome coverage

[START_REF] Rubio | Multi-omic analysis unveils biological pathways in peripheral immune system associated to minimal hepatic encephalopathy appearance in cirrhotic patients[END_REF][START_REF] Bajaj | Serum and urinary metabolomics and outcomes in cirrhosis[END_REF]

. Several studies focused on MHE patients who were well phenotyped by thorough neuropsychological testing. These patients presented with blood altered energy metabolism, decreased levels of branched-chain amino acids (BCAA) and fatty acids and increased levels of lactate and trimethylamin-N-oxide. In an original study,

  et al. were able to show a correlation between metabolomic profiles and HE outcome in an experimental model of therapeutic hypothermia in ALF (26). More recently, Bajaj et al. used metabolomic and stool microbiota analysis to evaluate the effect of rifaximin in HE in cirrhotic patients

: Main original studies on metabolomics in hepatic encephalopathy in both animal models and humans.

  Increased CSF levels of amino acids, acylcarnitines, bile acids and xenobiotics (drugs) in HE patients. Accumulation of acetylated compounds could be due to a defect of the Krebs cycle in HE patients.Different metabolic profiles enabled to distinguish patient with overt HE from patient without HE but could not distinguish patient according to different HE grades or covert HE from patient without HE.Glutamine levels determined by1 H NMR in the brain (posterior cingulate cortex, i.e. a major hub of the default mode network) & LC/MS in the blood were correlated whereas glutamate levels were not.

			acetoacetate, alpha-acid glycoproteins and lipids.
		Technic(s)	Study design and main findings	Key topic
	Bajaj et al. 2013	GC&LC/MS	MHE cirrhotic patients, n=20	Pathophysiology
			Baseline and 8 weeks after rifaximin	Treatment follow-up
	Animal			(rifaximin)
			Increase in serum saturated (myristic, caprylic, palmitic,
	Meredith et al. 1986	Gaz ionization /MS *	Rat model of ALF/CLD (portosystemic bypass model), palmitoleic, oleic and eicosanoic) and unsaturated (linoleic, linolenic, gamma-linolenic and arachnidonic) fatty acids n=n.a., brain samples Increased levels of octopamine and tyramine in the brain. after treatment with rifaximin, no modification in microbiota.	Pathophysiology
	Murphy et al. 2007 Weiss et al. 2016	MS-MS LC/MS	Mice model of ALF (surfactant/virus model), n=9-11 per Increased levels of both medium-and long-chain cirrhotic patients with HE and 9 controls group, blood samples CSF samples in 14 HE patients and 27 controls Serum samples in 13 cirrhotic patients without HE, 12	Pathophysiology Pathophysiology Biomarker
			acylcarnitines (C6:0-C12:0 and C14:0-C20:0,
			respectively).
			Clues in favor of diffuse mitochondrial dysfunction.
	Barba et al. 2008	1 H NMR	Rat model of ALF, n=6 per group, brain samples	Grading (biomarker)
	* corresponds not exactly to metabolomics $ reanalysis of previously acquired data using a specific tool Different profiles in the brain according to HE grades metabolic deterioration (brainstem earlier affected than (No HE=17, covert HE=7, overt HE=21) enabling severity scoring. Regional discrepancies in Treatment follow-up 45 patients were well-characterized for the presence of HE (hypothermia) McPhail et al. 2017 1 H NMR Cirrhotic patients, n=52 and 17 healthy controls Biomarker
			cortical regions). Lesser abnormalities in treated animals.
	Abbreviations: 1 H, proton; ALF, acute liver failure; BLD, biliary duct ligation; BCAA, brain samples branched chain amino acids; CLD, chronic liver disease; CSF, cerebrospinal fluid; Bak et al. 2009 MS * Rat model of CLD (BDL), n=n.a., blood, liver, muscles and Pathophysiology
	GC, gas chromatography; HE, hepatic encephalopathy; HPLC, high performance liquid chromatography ; LC, liquid chromatography; LT, liver transplantation; MHE, Increased metabolism of isoleucine in muscles compared to the brain suggesting that therapeutic effect of BCAA is Lebba et al. 2018 1 H NMR Cirrhotic patients, n=46 and 14 healthy controls Pathophysiology
	minimal hepatic encephalopathy; MS, mass spectrometry; NMR, nuclear magnetic mainly due to an effect in the muscle and seems not able to bypass tricarboxylic acid cycle in the brain. Fecal methanol and threonine levels were positively resonance. associated with HE whereas n-butyrate levels were
	Marini et al. 2017	HPLC &	Rat model of CLD (ammonium enriched diet) negatively associated with HE.	Pathophysiology
		targeted	n=5 per group, brain dialysates
	biofluids of interest has to be collected and then prepared in adapted samples ; 2, the samples have to be analyzed by GC/MS, LC/MS or RMN ; 3, a preprocessing step compared to control animals. Prognostic value better than MELD hyperammonemic animals and different time-course serum Higher increase in adenosine after sleep deprivation in HE associated with 284 metabolites in urine and 228 in animals and higher levels of putrescine. Altered energy metabolism and bile acids. General principles of metabolomics are based of several successive steps: 1, the proline, and acetylcarnitine in hyperammonemic Figure: General principles of metabolomics. Bajaj et al. 2019 LC/MS Cirrhotic patients, n=211 (hospitalized=64, HE=19, LT=13, Pathophysiology LC/MS Lower baseline levels of adenosine, taurine, hydroxy-death=11) Outcome
	enables to select relevant signals among automatically detected signals and to compare them to internal and public databases ; 4, a statistical analysis step will Pathania et al. 2020 1 H NMR Rat model of CLD (BDL) n=5 per group, liver and brain samples Takado et al. 2019 1 H NMR & Healthy adults, n=20 Biomarker Pathophysiology LC/MS Glutamine challenge Treatment follow-up
	enable to distinguish difference in signals or signatures according to different Increased levels of lactate, acetate, succinate, citrate, and predetermined groups ; 5, annotations have to be confirmed and unknowns malate in the brain whereas the levels of glucose, creatine,
	isoleucine, leucine, and proline were decreased. Increased characterized in order to identify all the metabolites ; 6, a final step will enable to levels of lactate, BCAA, glutamate, and choline in the liver, visualize findings in metabolic maps. Abbreviations: GC, gas chromatography; gp, group ; LC, liquid chromatography; MS, mass spectrometry; NMR, nuclear magnetic resonance ; RT, retention time. whereas levels of glucose, phenylalanine, and pyridoxine analysis $ metabolic fingerprint of HE glycolysis, and ketogenesis) in both the liver and the brain. secondary alpha-ketoglutaramate is proposed to be added as a Altered energy metabolism (tricarboxylic acid cycle, tool for were decreased. Frainay et al. 2019 MetaboRank Reanalysis of samples of Weiss et al. with MetaboRank Pathophysiology
	Human Rubio et al. 2021	approach Multi-omic	Cirrhotic patients, n=11 (no MHE=5, MHE=6)
	Amuro et al. 1981	GC/MS * LC/MS-MS	Cirrhotic patients, n=13 (2 HE patients, comatose) Suggested link between long-chain unsaturated phospholipids and the increased fatty acid transport and	Pathophysiology
			Possible abnormal keto bile acids levels in urine and stool prostaglandin production.
	Goldberg 1981	GC/MS *	HE patients, n=18 (13 grade 1-2 and 5 grade 3-4)	Pathophysiology
			compared to control patients, n=20 (6 healthy controls, 5	Grading (biomarker)
			patients hospitalized for colonoscopic exploration, 4
			cirrhotic patients without HE, 5 with portocaval shunts)
			Increased levels of 3-methylbutanal in HE patients
			compared to controls that correlated to grades of HE.
	Jimenez et al. 2010	1 H NMR	Cirrhotic patients, n=101 (no MHE=62, MHE=39) vs 69	Pathophysiology
			healthy controls	Biomarker
			Increased serum levels of glucose, lactate, methionine,
			trimethylamin-N-oxide, and glycerol in MHE patients and
			decreased levels of choline, BCAA, alanine, glycine,

Pathophysiology

Table:

Main original metabolomics-based studies in hepatic encephalopathy in both animal models and humans.

  correspond not exactly to metabolomics $ reanalysis of previously acquired data using a specific tool Abbreviations: 1 H, proton; ALF, acute liver failure; BLD, biliary duct ligation; BCAA, branched chain amino acids; CLD, chronic liver disease; CSF, cerebrospinal fluid; GC, gazeous chromatography; HE, hepatic encephalopathy; HPLC, high performance

* liquid chromatography; LC, liquid chromatography; LT, liver transplantation; MHE, minimal hepatic encephalopathy; MS, mass spectrometry; n.a., not available ; NMR, nuclear magnetic resonance.s

Acknowledgements:

The authors would like to thank the Fondation pour le Recherche Médicale for their support (FRM (EQU202003010517)) and all the team of the Brain-Liver Pitié-Salpêtrière Study Group team, especially Dr Philippe Sultanik, Dr Sara Mouri, Dr Charlotte Bouzbib and Lyes Kheloufi.


Romaissa Harid
email: r.harid@enssmal.dz

Hervé Demarcq
email: demarcq@ird.fr

Mehdia-Asma Keraghel
email: keraghel@enssmal.dz

Malik Ait-Kaci

Mohamed Zerrouki
email: m.zerrouki@enssmal.dz

Nour-El-Islam Bachari
email: bechari@usthb.dz

Fouzia Houma

Spatio-Temporal variability of a Chlorophyll-a based biomass index and influence of coastal sources of enrichment in the Algerian Basin

Keywords: Ocean colour, alongshore variability, wadi, anthropic enrichment, MODIS. 1

A specific fortnightly climatology of 1-km resolution Chlorophyll-a has been generated over the Algerian basin from MODIS data to explore the coastal domain.

• The Algerian Basin is characterised by two extreme seasons of high and low biomass, separated by sharp transitions, while the coastal area reveals a separated dynamic.

• Discontinuous and intense coastal cross-shore gradients reveal specific coastal sources of enrichments.

• A Chl-a based integrated index was defined to determine the importance of coastal enrichments.

• The Chl-a biomass in Algerian coastal waters is mainly associated with enrichments from wadis and cities with a pronounced seasonal effect.

Introduction

Chlorophyll-a (Chl-a) biomass is associated with the net primary production in marine ecosystems, and marine physical and biochemical processes strongly influence its variability. A precise description of the spatio-temporal variability of Chl-a biomass is necessary to understand coastal marine systems functioning. The Chl-a concentration in the southwestern Mediterranean Sea (Med) is closely related to winter mixing and summer stratification. The Med ecosystem is increasingly threatened by human activities in coastal areas, as well as by a continuing warming trend [START_REF] Vargas-Yáñez | How much is the western Mediterranean really warming and salting?[END_REF]. A recent study conducted by [START_REF] Keraghel | Carbonate system properties and anthropogenic carbon inventory in the Algerian Basin during SOMBA cruise (2014): Acidification estimate[END_REF] highlights that the southwestern Med is a net sink of carbon dioxide, even compared to the Med as a whole, which is a significant contributor at the global level [START_REF] Khatiwala | Global ocean storage of anthropogenic carbon[END_REF].

Scientists are still trying to understand the evolution of the Med ecosystem to better assess current and future changes and consider solutions to mitigate some impacts of global warming.

The Algerian Basin (AB) is classified as mesotrophic [START_REF] D'ortenzio | On the trophic regimes of the Mediterranean Sea: a satellite analysis[END_REF][START_REF] Harid | Seasonal and Interannual Variability of Primary Production and Chlorophyll Concentrations in the Algerian Basin: Application of Ocean Color[END_REF][START_REF] O'reilly | Chlorophyll algorithms for ocean color sensors -OC4, OC5 & OC6[END_REF]. Water exchange across Gibraltar has a significant influence on its general circulation [START_REF] Béranger | Seasonal variability of water transport through the Straits of Gibraltar, Sicily and Corsica, derived from a high-resolution model of the Mediterranean circulation[END_REF][START_REF] Millot | La circulation générale en Méditerranée occidentale : Aperçu de nos connaissances et projets d'études[END_REF][START_REF] Peliz | Filament generation off the Strait of Gibraltar in response to Gap winds[END_REF] and controls its nutrient content [START_REF] Bethoux | Deep water in the western Mediterranean: peculiar 1999 and 2000 characteristics, shelf formation hypothesis, variability since 1970 and geochemical inferences[END_REF][START_REF] Crispi | Long-term numerical evolution of the nitrogen bulk content in the Mediterranean Sea[END_REF][START_REF] Elbaz-Poulichet | A Reassessment of Trace Metal Budgets in the Western Mediterranean Sea[END_REF][START_REF] Huertas | Anthropogenic and natural CO2 exchange through the Strait of Gibraltar[END_REF], with a direct influence on the Chl-a based phytoplanktonic biomass. In winter, the presence of distinct water bodies indicates the eastward movements of anticyclonic eddies [START_REF] Olita | Pelagic primary production in the Algero-Provençal Basin by means of multisensor satellite data: focus on interannual variability and its drivers[END_REF], generated by instabilities in the Algerian current [START_REF] Millot | Analysis of variations in ocean color1: Ocean color analysis[END_REF], which enrich the surface water and increase its primary production.

The Atlantic water flow is characterised by a transit time of two to four months between Gibraltar and the Algerian coasts [START_REF] Millot | Circulation in the Western Mediterranean Sea[END_REF], strongly influencing the seasonal Chl-a signal [START_REF] Salgado-Hernanz | Trends in phytoplankton phenology in the Mediterranean Sea based on ocean-colour remote sensing[END_REF]. In summer, the stability of water masses limits the nutrient input [START_REF] Moutin | Influence of anticyclonic eddies on the Biogeochemistry from the Oligotrophic to the Ultraoligotrophic Mediterranean (BOUM cruise)[END_REF], leading to a decrease in phytoplankton production.

The continental shelf of the AB is very narrow (15 km on average) and is neglected in most studies. Nonetheless, the shelf is the richest domain of the AB and shelter more complex interactions than in the offshore domain. Ocean colour remote sensing has provided high-quality observations in this respect for over twenty years on the abundance and distribution of Chl-a concentration, which is considered a proxy for phytoplankton biomass [START_REF] Cullen | The Deep Chlorophyll Maximum: Comparing Vertical Profiles of Chlorophyll a[END_REF][START_REF] Strickland | Production of organic matter in primary stages of themarine food chain[END_REF]. Turbid waters in the Med are rare compared to other seas [START_REF] Millot | Analysis of variations in ocean color1: Ocean color analysis[END_REF].

According to [START_REF] Antoine | Algal pigment distribution and primary production in the eastern Mediterranean as derived from coastal zone color scanner observations[END_REF], the coastal case-2 waters (where other constituents as mineral particles are also present) in the whole Med are estimated to be 5%. Currently, ocean colour analysis coupled with in-situ data could be used to characterise and monitor phytoplankton blooms [START_REF] Barale | Near-Coastal Features of the NW Mediterranean Sea -Space and Time Heterogeneity of Atmospheric Forcing, Vertical Mixing and Algal Blooming, from Satellite Observations and Model Simulations[END_REF][START_REF] Cerino | Phytoplankton temporal dynamics in the coastal waters of the north-eastern Adriatic Sea (Mediterranean Sea) from 2010 to 2017[END_REF][START_REF] Groom | Satellite Ocean Colour: Current Status and Future Perspective[END_REF]Lavigne et al., 2015;[START_REF] Mayot | Interannual variability of the Mediterranean trophic regimes from ocean color satellites[END_REF][START_REF] Palmiéri | The Mediterranean subsurface phytoplankton dynamics and their impact on Mediterranean bioregions[END_REF]. In previous studies, a significant limitation was related to the poor representation of coastal patterns due to Level-3 data at 4-km resolution (as in [START_REF] Lavigne | On the vertical distribution of the chlorophyll a concentration in the Mediterranean Sea: a basin scale and seasonal approach[END_REF], [START_REF] Marañón | Deep maxima of phytoplankton biomass, primary production and bacterialproduction in the Mediterranean Sea during late spring (preprint)[END_REF], [START_REF] Mayot | Interannual variability of the Mediterranean trophic regimes from ocean color satellites[END_REF], [START_REF] Volpe | Seasonal to interannual phytoplankton response to physical processes in the Mediterranean Sea from satellite observations[END_REF], [START_REF] Volpe | The Mediterranean Ocean Colour Level 3 Operational Multi-Sensor Processing[END_REF]).

To this end, our study proposes a practical approach to improve the quality of the standard MODIS-Aqua Level-2 1-km resolution (swatch) Chl-a product, specifically in coastal environments, where the higher data resolution provides more detailed information. A comparison between simultaneous in-situ and satellite Chl-a was performed to assess the accuracy of MODIS Chl-a data in AB. In addition, a cumulative Chl-a biomass index (IB) was developed to synthesise the spatial patterns and variability of Chl-a. This paper describes the seasonal climatology of IB in AB from 16 years of data (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018), focusing on offshore and coastal areas separately. Finally, a discussion on the influence of different sources of coastal enrichment on the Chl-a biomass in the AB is presented.

Methods

Study area

The Algerian Basin (Fig. 1) is a major energetic area for mesoscale activity throughout the Med [START_REF] Amores | Vertical structure and temporal evolution of an anticyclonic eddy in the Balearic Sea (western Mediterranean): DESCRIPTION OF AN EDDY (BALEARIC SEA)[END_REF][START_REF] Pessini | Mesoscale eddies in the Algerian Basin: do they differ as a function of their formation site?[END_REF]. [START_REF] Millot | Circulation in the Mediterranean Sea[END_REF] described the East flowing Algerian current, which carries Atlantic surface water, is 50-100 km wide and 100-200 m thick with a speed of some 10s cm s -1 (El-Geziry and Bryden, 2010). It generally follows the continental slope and generates small eddies of 10-100 km diameter, lasting a few weeks or months. Periodically, this current forms a growing meander of 50-100 km; it can detach to form an anticyclonic eddy of 100-200 km diameter that encompasses the entire thickness of the Med water [START_REF] El-Geziry | The circulation pattern in the Mediterranean Sea: issues for modeller consideration[END_REF][START_REF] Fani | Fibrocapsa japonica (Raphidophyceae) occurrence and ecological features within the phytoplankton assemblage of a cyclonic eddy, offshore the Eastern Alboran Sea[END_REF][START_REF] Millot | La circulation générale en Méditerranée occidentale : Aperçu de nos connaissances et projets d'études[END_REF]. Some oceanic eddies persist for up to three years, circulating in the AB in a cyclonic circuit [START_REF] Millot | Circulation in the Mediterranean Sea[END_REF]. Thus, the AB acts as a reservoir where Atlantic waters accumulate before flowing either eastwards (surface waters) or northwards (deep waters) of the Med [START_REF] Millot | Circulation in the Western Mediterranean Sea[END_REF]. Indeed, this buffer zone decouples the inflow and outflow of Med surface waters.

Satellite data sources

We used daily Level-2 Chl-a concentration data from the MODIS-Aqua sensor from 2003 to 2018 at 1-km nominal resolution. The data set consists of 5844 daily observations from 15020 individual orbits acquired from NASA's Ocean Color website (NASA's Ocean Color Web, 2019).

Each daily data field was remapped over the AB, between 35°N-40°N and 6°W-10°E (Fig. 1), at a spatial resolution of 96 pixels per degree of latitude and longitude. The equivalent daily MODIS Level-3 mapped data set at 4-km spatial resolution was obtained from the NASA Ocean Data Processing System. This data set was compared to the MODIS Level-2 data set to demonstrate permanent coastal Chl-a patterns. The climatological period from 1 to 15 January 2003-2018 (Fig. 2) illustrates the differences between both spatial resolutions of 1-km and 4-km.

In this work, a corrected version of the MODIS Level-2 data at 1-km was used to adequately describe the AB Chl-a variability in the coastal and offshore areas.

Cloud masking improvement of MODIS Level-2 Chl-a data

This section describes the specific processing steps applied for the first time to the standard cloud-corrected Level-2 Chl-a fields to detect and remove spurious patterns that affect data quality, even in fortnightly averages (Fig. 2b). Specifically, we noticed the presence of (i)

partially cloudy pixels at the edge of the cloud mask, resulting in spurious high Chl-a values and

(ii) noisy pixels. Three criteria were used to discriminate these contaminated pixels: 1) a maximum allowable value associated with a realistic Local Chl-a Gradient (LG), 2) a maximum value of daily Chl-a change during 3-day periods (Temporal Variation, "TV"), and 3) their position as Isolated Pixels (IP) inside the cloud mask. The pixel values corresponding to any of these criteria cited here are selected and replaced by the missing value.

Local Gradient (LG) criteria

First, we applied a 3x3 Gaussian filter (Eq. 1) followed by a Sobel Gradient filter [START_REF] Sobel | An Isotropic 3x3 Gradient Operator, Machine Vision for Three Dimensional Scenes[END_REF] as follows:

, = (1)
where, k(x,y) is the matrix of the kernel used to convolute the original image; x and y are the pixel's position along the abscissa and ordinate axes respectively. is the variance of the 3x3 pixel matrix.

The horizontal and vertical components of the Sobel gradient (Eq. 2 and Eq. 3, respectively)

were separately computed and combined into the final gradient (Eq. 4):

= , * -1 -2 -1 0 0 0 1 2 1 (2) = , * -1 0 1 -2 0 2 -1 0 1 (3) !"# $ % = & + (4)
As spurious gradients are mainly associated with atmospheric perturbations in the vicinity of clouds, a maximum threshold value of the local Sobel gradient of Chl-a was considered to detect outlier pixels (Fig. 3a), at a maximum distance of 5 km from cloud borders (black areas in Fig. 3b). After several tests, two thresholds were defined: 0.4 mg m -3 km -1 and 1 mg m -3 km -1 respectively for the coastal zone (distance from the coast <5 km) (Fig. 3c) and the offshore area.

Temporal Variability (TV) criteria

Each daily individual Chl-a pixel value of a given day (Day0) was compared to the average value of the previous day (Day-1) and the following day (Day+1) when one or both values are available to detect anomalous Chl-a variations through time, as given by Eq. 5:

() = *+ , -- ./ 01 ./ 21 3 (5)
This difference was then compared with the maximum threshold of temporal variability set at 2 mg m -3 . Furthermore, this test was applied in the offshore domain only (distance from coast >20 km) to consider the higher spatio-temporal variability of the coastal environment (Fig. 3e).

Isolated Pixel (IP) removal

This criterion is used to eliminate pixels closely associated with a cloud structure. We considered that Chl-a pixels bordered by more than five cloudy pixels (including those isolated in a cloud)

belong to the same atmospheric structure and should be eliminated (Fig. 3f).

Combination of criteria

The three criteria mentioned above were cumulated, and the pixels marked by at least one criterion were removed. The resulting daily data are significantly less noisy, as shown in Fig. 3h.

A fortnightly climatology of Chl-a at 1-km resolution is then computed for the whole AB.

Comparison between in-situ and satellite Chl-a data

We compared the resulting 1-km satellite Chl-a data with an initial set of 70 high-performance liquid chromatography (HPLC) measurements of surface Chl-a concentration obtained during the SOMBA (Système d'Observation à la Mer dans le Bassin Algérien) cruise in the AB between August 13 and September 10, 2014 [START_REF] Mortier | SOMBA-GE-2014 cruise,Téthys II R/V[END_REF]. Further details about the cruise are available at https://doi.org/10.17600/14007500. Calibration precision was estimated to be 0.4% from the HPLC 1200 instrument used for the Chl-a measurements. A total of 34 measurements were retained according to their correspondence with satellite passes within ± 6 hours. The nearest pixel at 1-km resolution was considered. A representativity error (RE) was also considered, based on the proximity of a high Chl-a gradient, which we defined at a value of 0.01 mg m -3 km -1 , compared with the effective Chl-a gradient measured in a 5x5 pixel matrix (Error bars in Fig. 4). In contrast, this spatial variability is generally low for pixels far from eddies (Fig. 4b and4e). We also checked the proximity of the HPLC measurements to the clouds (less than 5 km); only two measurements deviate from this criterion (Fig. 4c and4d).

Biomass index

A modified version of the coastal Chl-a-based index developed by [START_REF] Demarcq | Application of a chlorophyll index derived from satellite data to investigate the variability of phytoplankton in the Benguela ecosystem[END_REF] was applied. Based on the Chl-a concentrations, a threshold (β) of Chl-a concentration was set to compute the proposed integrated Chl-a biomass index (IB) in AB. This threshold was chosen as a value observed during all seasons (Fig. 7b). A value of 0.5 mg m -3 was selected according to this criterion for the present study. The integrated Chl-a biomass index was calculated at each coastal point with the following formula:

4 5 = ∑ 7ℎ$+
.9:; <, =>? @9:;ADist =FG × *IJK L,M/ (6)

where, the average Chl-a is calculated by the formula:

7ℎ$+ = ∑ 7ℎ$+ N <,=>? 9A O L,M/ ⁄ (7)
where, O L,M/ is the position of the most distant pixel of the cross-shore transect.

The cross-shore distance associated with the index*IJK L, M/ is the most distant position where Chl-a ≥ β with the constraint*IJK L, M/ ≤ max, is the maximum allowable distance. When this distance is determined, all pixel values from*IJK min to*IJK L,max are considered, regardless of their value; sometimes slightly <β. If no data ≥β was found within a profile, the computation of IB was performed only for the first valid pixel at the only*IJK min position, regardless of its value.

The maximum distance for*IJK L, M/ in km was chosen at 10 km for the coastal area and 80 km for the next offshore area, according to the average structure of the cross-profiles (Fig. 6b). It is important to note that for continuity reasons, the first (inner) pixel of the offshore area was considered the pixel immediately offshore of the last of the coastal areas. The integrated coastal and offshore biomass indices were then calculated for the entire Algerian coastline or part of it (Table 1).

To estimate the relationships between Chl-a biomass (Chl-a or IB) classes and factor variables representative of the different sources of coastal enrichment, we applied the Generalized Linear Model (GLM) approach. All data analysis was done using the "stats" package version 3.4.4 of the R software.

Physical oceanographic data

Altimetry data

Geostrophic ocean currents and total kinetic energy (TKE) were extracted from the CMEMS (Copernicus Marine Environmental Service) database of the SEA-LEVEL GLO PHY L4 REP OBSERVATIONS 008 047 altimetry data product (http://marine.copernicus.eu, last accessed February 27, 2019), for the same period, and remapped in the AB at a spatial resolution of 0.25° (Fig. 1 for geostrophic currents and Fig. S2b for TKE).

Mixed Layer Depth

The mixed layer depth (MLD) has been defined in previous studies (as [START_REF] Lavigne | On the vertical distribution of the chlorophyll a concentration in the Mediterranean Sea: a basin scale and seasonal approach[END_REF] and Volpe, (2012)) using in-situ data in the Med (AB included). In this study, the monthly climatology (1969 to 2013) of the MLD was used as defined by [START_REF] Houpert | Seasonal cycle of the mixed layer, the seasonal thermocline and the upperocean heat storage rate in the Mediterranean Sea derived from observations[END_REF] (data from https://www.seanoe.org/data/00354/46532/).

Wadis outflows

Outflow data measured by the Algerian National Agency for Hydraulic Resources (Agence Nationale des Ressources Hydrauliques, ANRH, http://www.anrh.dz/) were used to evaluate the possible influence of wadis (temporary rivers) on the Chl-a variability along the Algerian shelf.

Results

We show that a high-resolution fortnightly climatology very significantly improves the description of the spatio-temporal variability of Chl-a (including abrupt seasonal changes) and a spatio-temporal view of the enrichment sources. We explore here the coastal and offshore domains along meridional and cross-shore transects, focusing on the coastal domain, to understand the main seasonal dynamics of these enrichments.

Impact of data resolution on the description of seasonal patterns

Firstly, we compared the standard (original) MODIS data (1-km) to the corrected (this work)

MODIS data (1-km). The improvement is particularly high during winter (characterised by a large cloud cover) with better detection of atmospherically contaminated pixels (Fig. 5). An example of the impact of Chl-a outlier values in the spatial distribution of the time series averages is represented in Fig. 5a and Fig. 5b for a fortnightly, and in Fig. 5c and Fig. 5d for a monthly climatological average. This correction produces a moderate decrease in Chl-a mainly during the productive season, reinforcing the descripting cross-shore profile. The resulting fortnightly climatology of MODIS Level-2 Chl-a data (at 1-km resolution) in the AB can be found online at https://doi.org/10.5281/zenodo.5390383 .

The new fortnightly climatology at 1-km resolution was compared to the 4-km resolution used in all previous studies. Seasonal variability of Chl-a from the corrected MODIS 1-km Level 2 data (Fig. 6b) was explored along average cross-shore transects and compared to the MODIS 4-km Level-3 data (Fig. 6a). A closer look at the shorter distances (0-10 km) (Fig. 6c and6d) shows that, as expected, the improvement is very significant and highlights much stronger cross-shore patterns (Fig. 6e and 6f for the most contrasted months of March and August), both in terms of Chl-a concentration average and seasonal patterns. The coastal Chl-a (0-10 km) from the 1-km data is 37% higher than that from 4-km data (49% and 46% respectively at distances of 2 and 4km from the coast). The 4-km product cannot detect a significant part of the coastal enrichment, representing 44% of the production of the AB from 1-km data, while only 25% are detected from 4-km data. Consequently, the spatial resolution impacts the scale of the description and more importantly the high contribution of the coastal area in the regional marine productivity.

MODIS Level-2 data validation in the AB

The in-situ Chl-a data range between 0.062 and 0.307 mg m -3 (Fig. 4). These values are typical for the AB offshore area during the oligotrophic season in the surface layer. Both sources of Chla data span nearly the same magnitude. The HPLC data have a slightly lower mean and median (respectively 0.100 mg m -3 and 0.094 mg m -3 ) than the satellite data (0.105 mg m -3 and 0.104 mg m -3 ). It should be noted that the satellite has a vertically integrating effect (exponentially decreasing) on the estimated Chl-a value. In contrast, the in-situ measurements represent exclusively punctual surface values at 1 m depth and the remaining ones at 2 m depth. The final error associated with the Chl-a satellite data was estimated at 0.025 (8% of the average) by the RMSD (Root Mean Square Deviation) between Chl-a HPLC and MODIS Level-2 data.

No in-situ measurements were available in the coastal areas of the AB. Nevertheless, [START_REF] Pieri | Assessment of three algorithms for the operational estimation of [CHL] from MODIS data in the Western Mediterranean Sea[END_REF] have found that the OC3M standard algorithm (used in our work) gives valid results in the Western Mediterranean Sea when the Chl-a concentration does not exceed 1 mg m -3 . In our case, the Chl-a exceeds 1 mg m -3 generally in the three first kilometers from coast (i.e. the 3 first pixels) and only during the high production season (December to March, as shown in Fig. 6 and7). To estimate the importance of the likely overestimation of the values >1 mg m -3 , we apply an empirical correction model with two levels of intensity, by reducing the values >1 mg m -3 by a factor of two and by a factor of three. The results show a relatively modest overestimation of respectively 6% and 9% of the Chl-a in these two extreme cases. This allows us to assume that a likely overestimation of the Chl-a values in the very coastal area does not significantly impact our conclusions.

Cross-shore and seasonal variability of Chl-a

Chl-a cross-shore sections (Fig. 6b) indicate that the lowest Chl-a concentrations are observed from May to October at all locations, both in coastal and offshore areas. The highest Chl-a concentrations, representing the productive season, are observed from November to April. In the coastal area, the maximum Chl-a can exceed 2 mg m -3 in winter (Fig. 7a) and 0.5 mg m -3 in summer (Fig. 7a). However, in the offshore area, beyond 10 km from the coast, the average minimum reach 0.5 mg m -3 in winter and 0.2 mg m -3 in summer (Fig. 6b and Fig. 7b). Beyond 10 km, the Chl-a concentration becomes stable during all seasons (Fig. 6b and Fig. 7a). Indeed, we chose the distance of 10 km from the coast as the shortest distance at which Chl-a seasonality becomes weak and stops increasing towards the offshore (Fig. 6b and7b).

The cross-shore gradient is well pronounced throughout the year and increases exponentially towards the coast (Fig. 6b). The intensity of this gradient is maximum during the productive season and is always maximal at the coast and regularly decreases with increasing distance from the coast (Fig. 6b), ranging from 0.2 to 2 mg m -3 onshore (Fig. 6b) and 0.1 to 0.5 mg m -3 offshore (Fig. 6b).

Meridian variability and seasonality of Chl-a biomass index

The integrated cross-shore biomass index (Section 2.6) was computed from each coastal point northwards to integrate the Chl-a concentration up to a variable distance (Fig. 8b) where Chl-a drops below the predefined value of 0.5 mg m -3 (see methods). The aim was to explore and quantify the integrated coastal Chl-a biomass along the Algerian coastline (Fig. 8d). The value was carefully chosen as the best threshold β = 0.5 mg m -3 that intersects the different average cross-shore climatological profiles over the year (Fig. 7b). The red line in Fig. 8a represents the maximum distance effectively reached during the productive season (we considered Chl-a <0.5 mg m -3 as oligotrophic and does not represent an enrichment). The resulting distance (Fig. 8b)

shows that the productive area varies over time from a few kilometres in summer (cyan area in Fig. 8b) with a minimum of 1 km (when only one sea pixel is considered) to a maximum of 80 km in winter, mainly reached near the Alboran Sea in the west. The longitudinal gradient along the coastline shows an apparent decrease of this distance eastward, which is well in line with the decreasing influence of the enrichment of Atlantic waters entering the Alboran Sea and moving eastwards.

The average Chl-a along the same transect (Fig. 8c) shows extreme variability between regions, from values <0.5 mg m -3 in summer (this is possible when a minimum of one sea pixel is considered) to values >2 mg m -3 between April and November, i.e., during low-biomass season.

The resulting biomass index IB (Fig. 8d) is defined as the product of the previous distance by the average Chl-a concentration along the same transect (Eq. 6). This index represents the spatial integration of the most elevated Chl-a values along the cross-shore transect, while the vertical dimension is partially considered by the attenuation depth of the satellite measurement.

Nevertheless, this depth represents a variable fraction of the euphotic layer, according to the shape of the vertical Chl-a profile.

The Chl-a (Fig. 8c) represents a proxy of the average phytoplankton biomass over the crossshore profile, while the spatially integrated index (IB, Fig. 8d) is predominantly determined by the length of the profile (Fig 8b). The IB index is expressed in g m -2 and varies between 20 and 50 g m -2 during the productive season (Fig. 8d) with a regular eastward decrease. Several peaks in phytoplanktonic biomass are observed (IB is >45 g m -2 ; between 2.2°W and 0.5°E from January to March, between 5.1°E to 5.5°E in January and February, and at 7.7°E from January to March-April). These peaks correspond to pronounced extensions of Dist0.5mg (Fig. 8b) as near the Alboran region or mostly to higher Chl-a values in the central and eastern parts of the AB (Fig. 8c). IB is <10 g m -2 everywhere during the low-biomass season, except in the Algiers and Annaba bays (Fig. 8d). IB is, in fact, practically zero from June to September in many locations where the Chl-a concentration barely exceeds 0.5 mg m -3 (Fig. 8c).

Discussion

The construction of a data set at 1-km spatial resolution allows investigating and extracting the specific enrichments of coastal origin, distinguishable from the large-scale seasonal cycle.

Previous descriptions of the climatological cycle in the AB were based on monthly averages (instead of fortnightly in this study) and at a much higher spatial resolution. Therefore, our description of the cross-shore gradient of Chl-a and its variability along a longitudinal gradient becomes much more realistic and highlights the high importance of the coastal domain (<10 km) in the whole basin.

Processes governing Chl-a variability in AB

Seasonal variability

AB is characterised by two contrasting seasons (Fig. It is well known that winter and spring blooms in the region are almost exclusively driven by the nutrient input following autumn and winter vertical mixing [START_REF] Fani | Fibrocapsa japonica (Raphidophyceae) occurrence and ecological features within the phytoplankton assemblage of a cyclonic eddy, offshore the Eastern Alboran Sea[END_REF][START_REF] Huertas | Atlantic forcing of the Mediterranean oligotrophy: ATLANTIC FORCING OF MEDITERRANEAN OLIGOTROPHY[END_REF][START_REF] Lazzari | Seasonal and inter-annual variability of plankton chlorophyll and primary production in the Mediterranean Sea: a modelling approach[END_REF][START_REF] Millot | Analysis of variations in ocean color1: Ocean color analysis[END_REF][START_REF] Pasqueron De Fommervault | Seasonal variability of nutrient concentrations in the M editerranean S ea: Contribution of B io-A rgo floats[END_REF], as reflected by our biomass index (Fig. 8d). Moreover, the results show that high Chl-a values dominate several well-defined coastal areas outside the productive season, from April to November (Fig. 8c). On a large scale, the variability of Chl-a concentration in the AB is known to be driven by the inflow of nutrient-rich Atlantic waters that enter the Alboran Sea through the Gibraltar strait (Taupier-Letage and [START_REF] Taupier-Letage | Surface circulation in the Algerian basin during 1984[END_REF] and progress eastwards along the AB. The same conclusions have also been drawn more recently by several authors [START_REF] Fani | Fibrocapsa japonica (Raphidophyceae) occurrence and ecological features within the phytoplankton assemblage of a cyclonic eddy, offshore the Eastern Alboran Sea[END_REF][START_REF] Huertas | Atlantic forcing of the Mediterranean oligotrophy: ATLANTIC FORCING OF MEDITERRANEAN OLIGOTROPHY[END_REF][START_REF] Lazzari | Seasonal and inter-annual variability of plankton chlorophyll and primary production in the Mediterranean Sea: a modelling approach[END_REF][START_REF] Pasqueron De Fommervault | Seasonal variability of nutrient concentrations in the M editerranean S ea: Contribution of B io-A rgo floats[END_REF]. Consequently, the eastward propagating eddies modulate the circulation of water masses beyond the continental shelf [START_REF] Pessini | Life history of an anticyclonic eddy in the Algerian basin from altimetry data, tracking algorithm and in situ observations[END_REF], generating intense vertical mixing [START_REF] Millot | Analysis of variations in ocean color1: Ocean color analysis[END_REF]. Its positive influence on productivity is perceptible up to the eastern part of the country and is reinforced by nutrient enrichments from the bottom [START_REF] Millot | Analysis of variations in ocean color1: Ocean color analysis[END_REF]. The offshore vertical mixing is considered to be the main factor influencing winter-spring enrichments before the summerautumn stratification period.

In addition to these two well-known potential sources of enrichment, we identified a third coastal source: the presence of nutrients of coastal origin, generally associated with bays or gulfs, which enhance local phytoplankton growth. The integrated Chl-a biomass index (IB, Fig. 8d) is used in this study as a proxy for the primary production dynamic in the AB. Better than local Chl-a averages, it adequately describes the longitudinal variability of spring blooms due to its crossshore integrative capability. In other words, the Chl-a averages (Fig. 8c) gives a clear view of the origin of the enrichment effects without considering their spatial importance. In contrast, the IB (Fig. 8d) integrates both components.

However, this index in Fig. 8d (and the associated average Chl-a) does not distinguish between coastal and offshore sources of enrichment. We, therefore, divided it into an inshore and an offshore component, as detailed in Section 2.6. The coastal area is defined as the distance between the coast and the 0.5 mg m -3 isopleth position, with a maximum distance of 10 km (Fig. 6b). The offshore component is then defined as the area beyond this variable spatial limit up to a maximum distance of 80 km. The maximum 10 km limit was chosen to best separate the coastal and offshore signals, from the Chl-a signature (Fig. 9a-b) and the corresponding integrated biomass index (Fig. 9c-d).

Coastal enrichment

Beyond the spatially averaged seasonal signal computed in both domains (Fig. 7a), the results

give precise insights about their regional alongshore variability, which is well distinguished by the biomass index (Fig. 9c-d). The coastal biomass index (IB, Fig. 9c) highlights the increase in the duration of the productive season varies, that from 4 to 6 months from East to West and from 4 to 8 months (and more) in the coastal areas in the form of spatially distinct peaks of values >20 g m -2 . Some locations, such as the Algiers Bay (3.2°E) and the Annaba Bay (7.9°E), show high index values almost yearly. In contrast, no Chl-a peaks are visible in the offshore area (Fig. 9d), even in the locations where the highest coastal peaks are observed (Fig. 9c). The offshore area exhibits a much more homogeneous spatial pattern with high cumulated biomass during the central part of the productive season, with a maximum between January and February, except near the Alboran Sea, where the maximum occurs one month later. The duration of the productive season considering the IB at 10 g m -2 limit, varies from 5 months (December to April)

west of 1°E with maximum values constantly between 30 g m -2 and 40 g m -2 , then is 4 months in most of the area up to 7°E, to a minimum duration <2 months (mid-January to February) with maximum values <25 g m -2 . Between 5°E and 6°E longitude, a distinct offshore maximum in the biomass index (Fig. 9d and Fig. 11, blue curve) is observed during the productive season. This feature probably corresponds to the relative permanence of the mesoscale cyclonic EAG (Eastern Algerian Gyre, Fig. 1) as described by [START_REF] Testor | The mean circulation of the southwestern Mediterranean Sea: Algerian Gyres[END_REF] and where high Chl-a concentrations are regularly found [START_REF] Taupier-Letage | Biological response to mesoscale eddies in the Algerian Basin[END_REF].

The same processing was applied to compare these results with the equivalent information from the 4-km Level 3 data and presented in Supplementary Fig. S2. As previously shown, the coastal fraction of the enrichment is here only 25% (44% from 1-km data), and only two areas with Chla peaks would be partially detected, regardless of the season. This clearly shows that 1-km data are a minimum requirement to adequately explore the relative importance of the different sources of enrichment in the coastal regions.

Coastal and offshore averages of the biomass index were calculated for all areas where high biomass index values were observed, hereafter referred to as High-Biomass Coastal Zones (or HBC), and outside these zones referred to as Low-Biomass Coastal Zones (or LBC). The aim is to separate the specific effects of local (coastal) enrichments from the large-scale enrichments (Table 1, left-hand side). Compared to LBC, HBC logically dominate the total biomass by a 4fold ratio (+300%) in summer, and almost double by +80% in winter (Table 1, and Fig. S3a). The importance of HBC is still high in the offshore domain in summer (+97%), while almost no difference is observed (+7%) in winter during the productive season (see Fig. S3b for more details). The annual cumulative biomass index associated with the HBC represents 88% of the coastal domain, despite a corresponding coastline fraction of 42%. Because of the relative importance of the winter period (December to March) in the productive season, we summarised the overall significance of the coastal sector by its ratio to the offshore sector (Table 1, right-hand side). This ratio shows that the coastal domain slightly dominates from January to February in both LBC and HBC (respective values of 0.64 and 0.73). Nevertheless, this ratio strongly increases in the adjacent months (December and March), with values of 2.57 and 4.14 for LBC and HBC respectively, highlighting the importance of HBC in coastal areas.

Large scale longitudinal variability

We specifically examined three profiles of the average biomass index and the MLD and TKE (Fig. 10), both averaged from the coast to 80 km offshore, to explore the longitudinal variability of the phytoplankton biomass over the year. Three cross-shore transects from the coastline to 80 km offshore were examined at three longitudes (1°W, 4.5°E and 7.5°E, blue dashed line in Fig. 8a). They were chosen because of their independence from the observed enrichments in the coastal domain (Fig. 8c).

Firstly, the results show a substantial eastward decrease in the intensity of the productive season, as shown by the biomass index (Fig. 10, orange bars), with annual cumulative values of 285, 152 and 86 g m -2 from West to East. This quantifies well the observations of a progressive decrease in the influence of the rich Atlantic waters eastward, following its progressive nutrient depletion.

The shape of the productive season is stable at 1°W and 4.5°E, with a variable maximum centred at the first fortnight of February. A precise computation of this chronology along the entire Algerian coast (data not shown) shows that this central position is relatively stable from 1°W to 8.7°E (with minor variability <1 fortnight). In contrast, there is a positive shift of almost a fortnight (first fortnight of March) between 3°W and 1°W near the Alboran Sea, where a maximum shift of two fortnights is observed (data not shown). The productive season is more extended, due to the higher nutrient content of Atlantic waters.

We know that winter mixing due to wind and currents is the main cause of nutrient availability in the euphotic layer of the region [START_REF] Conan | Observing Winter Mixing and Spring Bloom in the Mediterranean[END_REF][START_REF] Fernandez | Validation and intercomparison of two vertical-mixing schemes in the Mediterranean Sea[END_REF][START_REF] Kessouri | Vertical Mixing Effects on Phytoplankton Dynamics and Organic Carbon Export in the Western Mediterranean Sea[END_REF][START_REF] Millot | La circulation générale en Méditerranée occidentale : Aperçu de nos connaissances et projets d'études[END_REF]. A close relationship was effectively observed between the seasonality of the MLD and, to a lesser extent, the TKE (see Fig. S1 for spatial mapping of these two parameters) and the dynamics of the productive season captured by the biomass index. The MLD deepens eastwards with a simultaneous winter maximum (45 m to 60 m, see Fig. S1a) from January to February while the Modified Atlantic Waters (MAW) are drifted eastwards [START_REF] Font | The drift of Modified Atlantic Water from the Alboran Sea to the eastern Mediterranean[END_REF]. The productive season follows the winter mixing (average MLD >20 m, blue line) by about one month in the western and central part of the region (Fig. 10a-b). At the same time, a relative synchrony is progressively reached further East (Fig. 10c). The TKE (computed from geostrophic currents, i.e., including eddy energy and permanent currents) is almost permanently high at 1°W near the Alboran Sea (Fig. 10a) and not in phase with the biomass index.

It is very likely that, along with the significant enrichment effect of the MAW, water mixing is an important factor in the initiation of surface productivity but is probably not a limiting factor near the Alboran Sea with high-energy levels related to both Alboran gyres. Lower energy levels are observed further East at 4.5°E (Fig. 10b) with a slight relationship to the biomass index, while a higher association is observed at 7.5°E (Fig. 10c). This well-defined seasonal pattern explains that the higher MLD (50 to 60 m) observed in January-February, induces a rapid mixing of surface waters and a rapid increase in planktonic biomass during the second half of February.

Average wind speed (data not shown) do not show any relationship with the surface water mixing, with low winter wind values from October to February. The MAW trajectory determines the large-scale variability of planktonic biomass in the AB. It is strongly constrained by nutrient availability, while the different sources of coastal enrichment represent a significant contribution throughout the year and unexpected relative importance of about two-thirds during the central part of the productive season and progressively higher during the rest of the year.

Sources of coastal enrichment

Previous studies using Chl-a variability as a proxy of the phytoplankton biomass variability along the Algerian basin have focused on large spatial scales, mainly over the continental shelf [START_REF] Mayot | Interannual variability of the Mediterranean trophic regimes from ocean color satellites[END_REF][START_REF] Pieri | Assessment of three algorithms for the operational estimation of [CHL] from MODIS data in the Western Mediterranean Sea[END_REF][START_REF] Salgado-Hernanz | Trends in phytoplankton phenology in the Mediterranean Sea based on ocean-colour remote sensing[END_REF]. They have generally ignored small scales and coastal waters, except locally for sanitary purposes or risk assessment.

The annual average of the IB averaged spatially from all seasons was calculated in the coastal domain (0-10 km, green curve in Fig. 11) and in the offshore domain (beyond 10 km, blue curve in Fig. 11). The ratio (IB Coastal/IB Offshore) is considered a relative indicator of the local coastal enrichment (red curve in Fig. 11). The IB coastal peaks are variable in space and time (Fig. 11) and indicate many distinct anomalies. In the Arzew, Bou-Ismaïl, Algiers and Annaba bays, the IB average is >20 g m -2 throughout the seasons (Fig. 11). These anomalies correspond mainly to sandy coasts (orange bars in Fig. 11). It is important to note that many microphytobenthos species (some diatoms, cyanobacteria, chlorophyceae and/or flagellates) prefer shallow sandy coastal environments for their development [START_REF] Cook | Advective relief of CO2 limitation in microphytobenthos in highly productive sandy sediments[END_REF][START_REF] Hassan | Modern Diatom Assemblages in Surface Sediments from Estuarine Systemsin the Southeastern Buenos Aires Province, Argentina[END_REF].

Nevertheless, the origin of these high production areas remains unclear.

Several bays are associated with wadis (temporary rivers) in many locations along the coast (in blue in Fig. 11). These wadis are characterised by shallow flows (Fig. 12), generally <15 m 3 s -1 during all year seasons. For example, in the Bou-Ismaïl Bay (Fig. 12a), the Mazafran flow varies from <4 m 3 s -1 in summer (June to October) and from 4 to 13 m 3 s -1 in winter. In Algiers Bay (Fig. 12b), the El-Harrach flow presents a very similar pattern. The Algiers city is affected by intensive urbanisation (~70% of the coastline up to 800 m inland is urbanised), which has caused significant environmental degradation of the coastal area and impacted the coastal morphology [START_REF] Rabehi | La baie d'Alger, un espace côtier prisé, entre pressions d'urbanisation et gouvernance territoriale[END_REF]. In Annaba Bay, the Seybouse wadi (Fig. 11, wadi 1) input is highly concentrated in PO4 and NH4 compared to Mediterranean rivers [START_REF] Ounissi | Variability of the dissolved nutrient (N, P, Si) concentrations in the Bay of Annaba in relation to the inputs of the Seybouse and Mafragh estuaries[END_REF], and presents a potential risk of eutrophication [START_REF] Ziouch | Transport of nutrients from the Seybouse River to Annaba Bay (Algeria, SW Mediterranean)[END_REF]. The observed peaks of Chl-a (and IB) are associated with different contributions (Table 2): the type of coast (sandy and rocky), the presence of wadis and large cities, and the presence of bays.

In many cases, coastal enrichments are not the result of a single factor: for example, a biomass peak is observed at 7.3°E associated with a wadi (wadi 2). The peak disappears shortly at 7.5°E, despite the presence of sandy coast. In Oran Bay, a coastal peak is observed despite the absence of a sandy coast and wadis (Fig. 11). However, Oran is a large city (>100,000 inhabitants) (Fig. 11), that induces a significant marine pollution due to wastewater discharged into the sea, increasing nutrients in coastal waters. Another enrichment is observed at approximately 1.3°W, where floating aquaculture cages are installed (Fig. 11). These aquaculture facilities are considered a significant source of local enrichment [START_REF] Cao | Environmental Impact of Aquaculture and Countermeasures to Aquaculture Pollution in China[END_REF]. The low flows of the wadis on the Algerian coast suggest that suspended matter (SM) from terrestrial inputs is weak in coastal waters. In contrast, a significant source of SM may originate from local sediment resuspension, especially on sandy coasts, where sediment resuspension generates a considerable flux of nutrients (Robinson and Hill, 2005).

It is also known that the inner part of gulfs and bays trap nutrients from territorial inputs by modifying local hydrodynamics that limit nutrient dispersal, thus maintaining high coastal production with little influence on offshore production. It is important to note that, all previous studies [START_REF] Colella | Mediterranean Ocean Colour Chlorophyll Trends[END_REF][START_REF] Okubo | Effect of shoreline irregularities on streamwise dispersion in estuaries and other embayments[END_REF][START_REF] Pingree | The tidal physics of headland flows and offshore tidal bank formation[END_REF][START_REF] Signell | Transient eddy formation around headlands[END_REF][START_REF] Taillandier | Profiling float observation of thermohaline staircases in the western Mediterranean Sea and impact on nutrient fluxes[END_REF][START_REF] Wolanski | Topographically Controlled Fronts in the Ocean and Their Biological Influence[END_REF] never considered potential coastal influences. In winter and early spring, the richer coastal waters are often mixed with the offshore waters and therefore contribute to the production beyond the continental shelf up to 10 km from the coast, as shown by our biomass index.

Modelling approach

As previously mentioned, the Algerian coastal waters were divided into two classes: HBC (High-Biomass Coastal Zones) and LBC (Low-Biomass Coastal Zones) (Fig. 11). The HBCs consequently refer to the highest values of Chl-a and IB, and the LBCs to the lowest values.

Their separation into two classes was visually optimised by defining specific thresholds for Chla and IB variables, summarised in Table 3 (response variables). We defined three qualitative variables: the presence of wadis, the type of coast (sandy/rocky), and the existence of a Bay. The City is defined as a quantitative variable with four levels: 0 (no-city), 25 k, 75 k, and 200 k inhabitants.

Table 2 summarises the respective characteristics of LBC (numbered 1-16) and HBC (numbered 1-15) as manually selected in Fig. 11. The presence of Cities, Wadis and Bays are positively related to the detection of High-biomass coastal areas. At the same time, the type of coast appears to be irrelevant, mainly compared to the presence of a Bay.

Linear qualitative models (General Linear Models) were performed to evaluate the interactions between either the coastal Chl-a concentration or the biomass index (IB) and the four explanatory variables, as shown in Table 3. The two specific areas HBC-1 and HBC-8 were excluded from the modelling because these two areas are specifically influenced by aquaculture floating cages that are not associated with the explanatory variables. A general model (m1 in Tab. 3) is first tested by combining all seasons to test the separation between HBC and LBC, as presented in Fig. 11. This model explains 67% of the variability, with a unique City effect. The Wadi effect is absent, probably because of its association with City. On the opposite, in winter, the Chl-a response variable (m2 model, 79%) is primarily associated with the presence of a Wadi, then to City. In contrast, the IB response variable (m3 model) is mainly related to a Bay and City presence. The winter IB model (m3) is very similar in explaining the biomass variability (77%), with a dominance of City presence (as previously with Chl-a) as well as to a significant Bay effect. In these winter models, the Wadi effect is only evidenced by the Chl-a variable that most reflects the influence of local enrichments rather than their spatial extension, associated with the biomass index (IB), highlighting the Bay effect.

During summer (low-biomass season), the Chl-a based model (m4 in Tab. 3) shows only 57% of explanation, with a unique Wadi effect despite the generally low flow of wadis in winter (Fig. 12). The equivalent model for the biomass index (m5) explains 78% of the variability, with a dominance of Bay presence, while the Wadi effect is still present. The lower biomass variability in summer (not shown) is better explained by the spatially integrated biomass index (IB). The later highlights a Bay effect, even if the main variability of the coastal enrichments is probably dominated by wadis and underneath by the influence of cities sewage. Therefore, we can argue that anthropic effects (presence of a City and a Wadi) dominate the biomass variability in the coastal areas along the Algerian coast, much more than "natural" effects such as the coast type and the presence of a bay. However, the presence of Bay is also of primary importance for trapping enriched water within the coastal domain. Another significant point is undoubtedly the strong positive effect of aquaculture cages in two specific country locations (Fig. 11). It is noteworthy that marine aquaculture has developed considerably over the last decade, with a national initiative plan whose objective was to produce 100,000 tonnes of fish and shellfish by 2020 horizon (FAO, 2019).

Conclusion

Satellite-based Chl-a is an important proxy of phytoplanktonic biomass that allows us to disentangle very different dynamics between the coastal and offshore domains of the Algerian Basin (AB), characterised by a very narrow continental shelf. We show that a specific fortnightly climatology of 1-km resolution Chl-a generated from MODIS data makes possible this identification. The AB is characterised by two extreme high and low biomass seasons, separated by short 2-month transition periods. The offshore variability is closely related to large-scale processes governed by the influence of Atlantic waters and a progressive eastward decrease in biomass. The coastal domain reveals a very distinct dynamic associated with highly productive hotspots rather than a well-defined seasonality. The irregular morphology and nature of the Algerian coast (bays, gulfs, rocky or sandy coasts) is shaped by numerous terrestrial and temporary inputs that affect its local productivity. A Chl-a based spatially integrated index allows us to quantify the importance of these coastal enrichments. At the same time, a modelling approach shows that seasonal wadis and city sewages, along with the presence of a bay, explain up to 79% of the presence of these productive hotspots. A separate source of enrichment is undoubtedly associated with the recent presence of aquaculture cages. Finally, considering phytoplanktonic communities and the in-situ determination of water quality would be beneficial to understand the biological consequences of these enrichments. Zonally averaged cross-shore Chl-a transect from the coast to 50 km for the High-Biomass (green), the transition (yellow), and the Low-Biomass (blue) seasons. The horizontal line shows the 0.5 mg m -3 limit used to compute the integrated index, detailed in Fig. 8 and Fig. 9.

  7a): an early 3.5-month high-biomass season (mid-December to March) and a 4.5-month low-biomass season (June to mid-October), characterised by intense stratification. The two seasons are separated by quasi symmetrical and sharp 2-month transition periods in spring and autumn (Fig.7a). The extreme precocity of the productive period (between October and November), i.e. during a low sun elevation, clearly shows that light is not the main limiting factor in the occurrence of winter blooms. The seasonality of Chl-a is closely related to the dynamics of the Mixed Layer Depth (MLD, Fig.7a, brown line and Fig.S1in supplementary material), which is maximum (40 m to 60 m) in winter between December and February and very low (15 m) in summer from June to September.

Figure 1 .

 1 Figure 1. March climatological average of the Chl-a concentration (mg m -3 ) between 2003 and 2018 in the Algerian Basin (Mediterranean Sea). The average current velocity (m s -1 ) for the same period (black arrows), the 800 m isobath (red line), and the 0.5 mg m -3 isopleth (dark green line) is superimposed. The stations of the SOMBA-2014 cruise are also superimposed (white dots). The large eddies in black (Alg. WG and Alg. EG) are deduced from the average sea level anomaly between 2003 and 2018. The average position of the Alboran eddies (Alb. WG and Alb. EG) are added.

Figure 2 .

 2 Figure 2. Comparison of Chl-a concentration for the first fortnight of January (2003-2018 climatology) from (a) MODIS Level-3 data at 4-km resolution and (b) MODIS Level-2 (uncorrected data) at 1-km resolution.

Figure 3 .

 3 Figure 3. Definition of the outlier pixels criteria for improving the cloud masking (example of 07 January 2014). (a) Local (3x3 matrix) Sobel gradient (mg m -3 km -1 ). (b) Distance (D1) from the cloud borders, in km (clouds are in white). (c) Distance (D2) from the shoreline, in km. (d to f): Pixels identified as cloudy from,: (d) the gradient criteria, (e) the temporal variation (TV) criteria, (f) from isolated pixels within cloud (IP) criteria. The result of the combination of all three criteria is shown in (h) versus the original MODIS Level-2 data (g).

Figure 4 .

 4 Figure 4. Linear relationship (red line) (a) between MODIS satellite corrected data (Y axis) and in-situ Chl-a data (X axis) during the SOMBA cruise (14 August to 10 September 2014) in the Algerian Basin. (b), (c), (d), and (e): spatial variability of the daily Chl-a field associated with four selected stations. The stations represented with a grey cross in (c) and (d) were removed from the comparison on the basis of a gradient threshold criteria or because their proximity to clouds.

Figure 5 .

 5 Figure 5. Results of the elimination of outlier pixels from the original uncorrected MODIS Level-2 data (left column), respectively for a fortnightly average (example of 1-15/01/2014) (a and b), and for a monthly climatological average (example of January 2003-2018) (c and d).

Figure. 6 .

 6 Figure. 6. Hovmöller diagrams of the cross-shore seasonality (from the coast line to 120 km offshore) of the Chl-a concentration in the Algerian Basin averaged between 2.2°W and 8.7°E, from fortnightly climatologies (2003-2018) computed from (a) MODIS Level-3 data at 4-km resolution, (b) MODIS Level-2 corrected data at 1-km resolution. The black line separates the 10 km distance from the coast. (c) and (d) highlight the data for this coastal area. The average crossshore profiles in March (green line) and August (blue line) are shown in (e) and (f).

Figure 7 .

 7 Figure 7. Average seasonality of the Chl-a concentration from 2003 to 2018 in the Algerian Basin from fortnightly Chl-a averages (a) in the 0-10 km coastal sector (green plain line) and offshore (blue plain line). The corresponding 4-km resolution Chl-a MODIS data (dotted lines) as well as the depth of the mixed Layer (MLD, orange dasher line) are superimposed. (b)

Figure 8 .

 8 Figure 8. Time-space diagrams of the longitudinal variability (2.2°W to 8.7°E) of three Chl-a associated indices in the Algerian Basin from August to July. The four distances considered (a) were: the distance of 10 km from the coast (green line), the average maximum distance of the 0.5 mg m -3 isopleth, the*IJK ,.VMW M/

Figure 9 .Figure 10 .

 910 Figure 9. Time-space diagrams of the alongshore variability (2.2°W 8.7°E) of the Chl-a and biomass index IB: (a) the Chl-a averaged in the coastal area (0-10 km) and (b) in the offshore area (10-*IJK ,.VMW M/ km), the biomass index (IB) integrated from (c) the coastline to a maximum distance of 10 km, and (d) from 10 km to the maximum distance*IJK ,.VMW M/ . Figure 10. Average seasonality of the integrated biomass index (orange bars) at three locations : 1°W (a), 4.5°E (b), and 7.5° (c) situated outside coastal influences (see Fig. 8a for precise locations). The climatological values of the Mixed Layer Depth (MLD, blue line), and the Total Kinetic Energy (TKE, black line) at the same locations are superimposed.

Figure 11 .

 11 Figure 11. Longitudinal variability of the offshore (blue line) and coastal (green line) yearly averages of the integrated biomass index, with the "IB coastal / offshore" ratio superimposed (red

Figure 12 .I

 12 Figure 12. Seasonal variability of the Mazafran and the El-harrach wadis flows (in m 3 s -1 , orange line) and the corresponding Chl-a concentration (green line) averaged at the isobath <50 m from 2003 to 2012 : (a) in the Bou-Ismaïl bay and (b) in the Algiers bay. The maps (a' and b') show the Chl-a yearly average at each location.

Acknowledgements

We applied the SDC (Sequence Determines Credit) approach for the sequence of authors. We would like to thank the space agency NASA for providing the MODIS satellite images used in this paper (https://oceancolor.gsfc.nasa.gov/). The authors are grateful to the whole team of the SOMBA-2014 cruise for providing the in-situ data used in this work. We thank the three anonymous reviewers for their helpful suggestions that greatly improved this manuscript. The combination of three funding sources supported this research: a PhD scholarship from the MESRS (Algerian government), a PhD scholarship from the Algerian-French program PROFAS B+ 2018-2019 (MERS-Algeria) and a partnership project (IRD-French) Fellowship.