Edward J Anthony
email: anthony@cerege.fr

Antoine Gardel

Christophe Proisy

François Fromard

Erwan Gensac

Christina Peron

Romain Walcker

Sandric Lesourd

The role of fluvial sediment supply and river-mouth hydrology in the dynamics of the muddy, Amazon-dominated AmapáeGuianas coast, South America: A three-point research agenda

a b s t r a c t

The morphology and sediment dynamics of the 1500 km-long coast of South America between the mouths of the Amazon and the Orinoco Rivers are largely dependent on the massive suspendedsediment discharge of the Amazon, part of which is transported alongshore as mud banks. These mud banks have an overwhelming impact on the geology, the geomorphology, the ecology and the economy of this coast. Although numerous field investigations and remote sensing studies have considerably enhanced our understanding of the dynamics of this coast over the last three decades, much still remains to be understood of the unique functional mechanisms and processes driving its evolution. Among the themes that we deem as requiring further attention three come out as fundamental.

The first concerns the mechanisms of formation of individual mud banks from mud streaming on the shelf off the mouth of the Amazon. An unknown quantity of the fluid mud generated by offshore estuarine front activity is transported shoreward and progressively forms mud banks on the Amapá coast, Brazil. The volume of each mud bank can contain from the equivalent of the annual mud supply of the Amazon to several times this annual sediment discharge. The mechanisms by which individual banks are generated from the Amazon turbidity maximum are still to be elucidated. Areas of research include regional mesoscale oceanographic conditions and mud supply from the Amazon.

The second theme is that of variations in rates of migration of mud banks, which influence patterns of coastal accretion. Research emphasis needs to be placed on the analysis of both regional meteorologicalhydrodynamic forcing and distant Atlantic forcing, as well as on the hydrology of the large rivers draining the Guyana Shield. The rivers appear to generate significant offshore deflection of mud banks in transit alongshore, through a hydraulic-groyne effect. This may favour both muddy accretion on the updrift coast and downdrift mud liquefaction with probably lessened muddy deposition.

The third theme concerns sand supply by the Guiana Shield rivers. The rare sand deposits are important in providing sites for human settlements and routes and for nesting by marine turtles. The limited presence of sand bodies on this coast may reflect 'mud blanketing', a hypothesis that requires verification through high-resolution seismic analyses of shelf deposits and coring operations. The large Guiana Shield rivers, especially in Surinam and Guyana, have supplied sand for the construction of significant bands of cheniers, probably enhanced by the afore-mentioned downdrift hydraulic-groyne effect on hindered mud deposition. In all the three themes of this future research agenda, two central elements are the sediment input of the rivers of the Amazon basin, starting with the massive mud supply from the Amazon catchment itself, followed by sand inputs by the Guiana Shield rivers and their rivermouth effects on mud banks.

Introduction

The Atlantic coast of South America between Amapá, in Brazil, and Venezuela (Fig. 1a), constitutes the terminus of rivers draining the Andes, the Amazon basin and the Guiana Shield. By far the most important of these rivers is the Amazon itself. The objectives of the FrencheBrazilian Observatory of Research on the Environment of the Amazon Basin (ORE HYBAM (http://www.ore-hybam.org)), as stated by [START_REF] Cochonneau | The environmental observation and research project, ORE HYBAM, and the rivers of the Amazon basin[END_REF], are to foster a better understanding of the hydrology, geomorphology and sediment budgets of these rivers. These objectives have met with success over the last few years, especially as regards the geomorphology, sediment storage patterns, and sediment budgets, notably of the Amazon itself [START_REF] Filizola | Suspended sediment yields in the Amazon basin: an assessment using the Brazilian national data set[END_REF][START_REF] Martinez | Increase in sediment discharge of the Amazon River assessed by monitoring network and satellite data[END_REF][START_REF] Wittmann | Sediment production and delivery in the Amazon River basin quantified by in-situ-produced cosmogenic nuclides and recent river loads[END_REF]. However, the links between, on the one hand, the hydrology, sediment budgets and overall source-to-sink sediment dynamics of these rivers and, on the other, the 1500 km-long coast from Amapá to Venezuela are still poorly understood, notwithstanding the fact that this coast is characterized almost exclusively by terrigenous, fluvially transported sediment inputs.

The large supply of mud by the Amazon River has dominated the geological development of this coast, via a system of mud banks that migrate from the mouth of the Amazon to that of the Orinoco (Fig. 1b). This mudebank system appears to be unique in terms of both the magnitude of mud migration alongshore and the mud dynamics, due to the extremely large and pervasive supply of mud by the world's largest river. This supply involves mud concentration processes within an atypical shelf-based estuarine turbidity maximum, and conservative onshore-alongshore mud transport along an energetic inner shoreface belt along the AmapáeGuianas coast. The significant mud accumulation has led to the growth of a shelf clinoform structure at the mouth of the Amazon and to more or less important coastal progradation throughout the coast of the Guianas and into Venezuela. The spatial (and temporal) imprint of the waxing and waning of mudebank activity is characterized by 'bank' and 'inter-bank' phases, and locally by 'transitional' phases. Over time, the rhythmic nature of these alternating phases has an overwhelming impact on the coast, inducing rapid shoreline accretion and/or erosion, as well as important ecological changes involving the development and destruction of mangrove forests. This high mud supply also has strong impacts on the coastal economy of all the countries between the mouths of the Amazon and the Orinoco: Brazil, French Guiana, Surinam, Guyana and Venezuela.

There has been significant progress in understanding the development and morphosedimentary dynamics of this coast. Studies have notably focussed on processes of mudebank migration, mudebank interaction with the shore, and the influence of mud banks on mangrove dynamics. Progress accomplished over the last three decades has been summarized recently by [START_REF] Anthony | The Amazon-influenced muddy coast of South America: a review of mud bank-shoreline interactions[END_REF], to which the reader interested in a comprehensive review of the Amazon mudebank system is referred. The findings examined in this research review were based on a wide range of methodological procedures and techniques, including new field and remote sensing approaches. Field approaches involve considerable logistical difficulties, including the challenge of accessing experimental sites, and have included measurements of cross-shelf wave dissipation on mud beds, seismic profiling, high-resolution in situ mapping of mudebank topography, monitoring of mudebank sedimentation rates through ultrasonic altimetry, in situ instrumented monitoring, time-lapse photography to monitor changes in the surface properties of mud banks, and especially remote sensing, which, given the problems of field access, has been widely used for the analysis of coastal forms, physical processes, and mangrove ecology at various scales. In particular, topographic data-extraction from temporal series of satellite images and Lidar-derived digital terrain modelling have been used with success to highlight patterns of medium-(months) to long-term (years) mudebank dynamics and coastal evolution.

The review depicted a highly complex system that has still left many questions unanswered regarding the dynamics of this coast. The objectives of the present paper are to highlight three themes that provide a full research agenda in the coming years. These themes will require more insight into the role of fluvial sediment supply from the Amazon drainage system, both as regards the Amazon River itself, and the smaller rivers that debouch on this Amazon-dominated coast, and should help in bridging the gap between advances on catchment hydrology and sediment budgets and that have been largely fostered by the ORE HYBAM and our understanding of the dynamics of the coastal terminus of the Amazon and Guiana Shield rivers. These themes are: (1) Amazon mud supply, and processes and timescales of mudebank formation; (2) mechanisms and timescales of mudebank migration; and

(3) sand supply by the Guiana Shield rivers debouching on this coast.

Amazon mud supply, and processes and timescales of mudebank formation

The influence of the Amazon on the sediment dynamics of the AmapáeGuianas coast essentially resides in the unique belt of mud banks that circulate alongshore. These mud banks constitute, however, only an estimated 15e20% of the muddy discharge from the Amazon [START_REF] Augustinus | The Changing Shoreline of Surinam (South America)[END_REF][START_REF] Wells | Longshore transport of mud by waves: northeastern coast of South America[END_REF], a percentage that needs to be more thoroughly verified. This discharge progressively forms highly turbid suspensions (>1 g l À1 ) in the vicinity of the mouth of the river that are the precursors of the mud banks. The formation of distinct mud banks that migrate alongshore as discrete sediment bodies suggests periodic (order of several years) and localized mud concentration mechanisms that are a major element of the future research agenda. Two areas of research are needed in order to gain a better insight on this theme. The first concerns the mud discharge from the Amazon, and the second the oceanographic context of mudebank formation.

The Amazon is the world's largest river system with a drainage basin of 6.1 Â 10 6 km 2 (Organization of American States, 2005). The mean annual water discharge at Óbidos (Fig. 1a), 900 km upstream of the mouth, has been estimated at 173,000 m 3 s À1 by [START_REF] Martinez | Increase in sediment discharge of the Amazon River assessed by monitoring network and satellite data[END_REF] from continuous gauging between 1995 and 2007. Estimations of sediment discharge published over the last four decades have varied widely, ranging from 5 to 13 Â 10 8 m 3 yr À1 , according to [START_REF] Martinez | Increase in sediment discharge of the Amazon River assessed by monitoring network and satellite data[END_REF]. Following careful monitoring of a suspended-sediment discharge network at Óbidos run by the ORE HYBAM between 1995 and 2007, [START_REF] Martinez | Increase in sediment discharge of the Amazon River assessed by monitoring network and satellite data[END_REF] computed a mean annual mud discharge of about 754 Â 10 6 tonnes yr À1 , with a coefficient of variation of 8.6%. They also highlighted a good agreement (to within 1.8e3%) between their suspended-sediment concentrations and data derived from a MODIS space-borne sensor. A budget analysis by [START_REF] Wittmann | Sediment production and delivery in the Amazon River basin quantified by in-situ-produced cosmogenic nuclides and recent river loads[END_REF], based on cosmogenic nuclides, suggests a similar value. [START_REF] Filizola | Suspended sediment yields in the Amazon basin: an assessment using the Brazilian national data set[END_REF] assessed the Amazon sediment yield from the Brazilian national data set comprising over 60 sites since the 1970s and have suggested a value of over 1000 Â 10 6 tonnes yr À1 . [START_REF] Martinez | Increase in sediment discharge of the Amazon River assessed by monitoring network and satellite data[END_REF] have shown that compared to the relatively regular liquid discharge of the Amazon, there was more significant inter-annual variability in sediment discharge but the large Amazon floodplain has also been shown to have a significant buffering role on sediment budget variability.

The way variations in mud supply by the Amazon may play a role in mudebank formation is not clear. An indication may, however, be derived from the sheer volume of an average bank and the large number of mud banks. In any year, the number of mud banks throughout the 1500 km-long AmapáeGuianas coast may be 15 or more. The banks are spaced at intervals of 15e25 km, are up to 5 m thick, 10e60 km long and 20e30 km wide. The volume of each mud bank can contain from the equivalent of the annual mud supply of the Amazon to several times this annual supply. This, combined with the large number of mud banks at any time, suggests that periodic bank formation may not be principally related to the hydrology of the Amazon, such as through seasonal variations in mud discharge.

The other area requiring research concerns the context and mechanisms involved in the formation of individual mud banks. In other words, how does the quasi-continuous Amazon mud load, albeit with seasonal variations, goes from streaming, away from the mouth of the Amazon, to discrete mud banks? The mud concentration processes that are a precursor to the formation of distinct mud banks start on the continental shelf seaward of the mouth of the Amazon in water depths of about 20e60 m, where much of the suspended-sediment discharged by the river is initially stored, via rapid and sustained fluid-mud concentration and trapping associated with fresh wateresalt water interaction and 'unconfined' estuarine front activity [START_REF] Geyer | Observations of currents and water properties in the Amazon frontal zone[END_REF][START_REF] Kineke | Fluid-mud processes on the Amazon continental shelf[END_REF]. The link, however, between these estuarine sediment concentration processes that presumably maintain a massive inner shelf turbidity maximum and the subsequent mudebank belt still remains elusive. Mud is remobilized and transported shoreward and then alongshore by a complex combination of wave forcing, tidal currents, and wind-induced coastal currents, but the contributions of these transport mechanisms and their temporal and spatial frames are still to be elucidated. [START_REF] Nikiema | Numerical modelling of the Amazon River plume[END_REF] have shown, from coupling of a 3D hydrodynamic model with the bathymetry and the coastline, that a strong coastal current associated with the mesoscale North Brazil Current generates permanent northwestward extension of the sediment-charged Amazon plume, confirming earlier observations that attributed this net northwestward plume, and ambient shelf water motion, to a largescale pressure gradient associated with this current system [START_REF] Geyer | Physical oceanography of the Amazon shelf[END_REF]. [START_REF] Molleri | Space-time variability of the Amazon River plume based on satellite ocean color[END_REF] have shown from satellite images of seawater salinity that the northwestward flow of the Amazon plume occurs in a narrow coastal band from January to April. This period also corresponds to the annual peak in trade winds and wave activity affecting the AmapáeGuianas coast. In addition to energetic forcing by waves, by near-resonant semi-diurnal tides and by large buoyancy flux from the Amazon River discharge, the shelf is also subject to stress from the northeasterly trade winds, resulting in strong along-shelf flow associated with the North Brazil Current [START_REF] Geyer | Physical oceanography of the Amazon shelf[END_REF]. [START_REF] Martinez | Increase in sediment discharge of the Amazon River assessed by monitoring network and satellite data[END_REF] showed a strong concentration of muddy discharge (51% on average over the period 1995e2007) between January and April, with little variability from year to year.

Sustained relaxations in the forcing conditions that lead to alongshore streaming of Amazon mud, such as in the North Brazil Current due to mesoscale changes in wind intensity, as hypothesised by [START_REF] Eisma | Recent and subrecent changes in the dispersal of Amazon mud[END_REF], and subsequently by [START_REF] Allison | Origin of Amazon mudbanks along the northeastern coast of South America[END_REF], could be responsible for relaxations in mud streaming that may be a mechanism leading to disruption of the mud belt, thus generating discrete mud banks. One area of future research here could concern the climatic, hydrological and meteorological connections implicated in both the Amazon basin discharge and in the hydrodynamic context at the mouth of the Amazon and its environs, where the mud banks are generated from the mud stream. [START_REF] Allison | Origin of Amazon mudbanks along the northeastern coast of South America[END_REF] have shown that the mud banks originate in the vicinity of Cabo Cassipore, a muddy cape 350 km northwest of Maracá Island at the mouth of the Amazon (Fig. 1a). These authors highlighted a zone of relative water-column mud 'deficit' close to the northwestern approaches of the mouth of the Amazon between Cabo Cassipore and Maracá Island, and showed that the proto-mud banks started forming in the vicinity of this muddy cape. Direct shoreward deflection of the Amazon mud-charged plume, with little influence by the Coriolis force in this equatorial setting, may be responsible for the mud-deficient coast between Maraca Island and Cabo Cassipore. [START_REF] Allison | Seasonal sediment storage on mudflats adjacent to the Amazon River[END_REF] have suggested that up to 150 Â 10 6 tonnes of mud (ca. 20% of the annual mud discharge monitored by [START_REF] Martinez | Increase in sediment discharge of the Amazon River assessed by monitoring network and satellite data[END_REF]) may be stored in a year in the Cabo Cassipore area.

Mechanisms and timescales of mudebank migration

Downdrift of the mouth of the Amazon, the dynamics of mud banks depend on their interactions with waves and currents, with the shoreline, and on in situ changes, including those associated with mangrove colonization and destruction. These interactions involve wave energy dampening, cross-shore and alongshore mud advection, mudebank consolidation, fluidization and liquefaction, and mangrove colonization and removal. These processes lead to constant variations in mud concentrations and consolidation [START_REF] Gratiot | Trade-wind waves and mud dynamics on the French Guiana coast, South America: input from ERA-40 wave data and field investigations[END_REF]. In bank phases, these stages range from very high suspended-sediment concentrations (1e10 g l À1 ), through fluid mud, to settled mud, which, in turn, ranges from underconsolidated ( 650 g l À1 ) to consolidated sediment beds (!750 g l À1 ). Inter-bank zones are generally associated with less turbid waters (SSC of <1e5 g l À1 ).

Under strong wave action, fluid mud pushed shoreward during the neap-to-spring cycle results in overall accretion and increase in bank elevation. The mobilized mud layers generally form mud bars. The most important element of waveemud interaction, however, is that of the migration of mud banks alongshore under the dominant influence of waves [START_REF] Wells | Longshore transport of mud by waves: northeastern coast of South America[END_REF][START_REF] Gratiot | Trade-wind waves and mud dynamics on the French Guiana coast, South America: input from ERA-40 wave data and field investigations[END_REF], a phenomenon of overwhelming geological, geomorphic, ecological and economic importance on the AmapáeGuianas coast. [START_REF] Gratiot | Trade-wind waves and mud dynamics on the French Guiana coast, South America: input from ERA-40 wave data and field investigations[END_REF] showed that notable phases of increased wave energy were accompanied by higher annual rates of longshore mudebank migration, but that the correlation was rather poor between the wave forcing parameter combining wave height (H) and period (T), H 0 3 /T 2 , and migration rates because of the contribution of other mechanisms to bank migration, including wave incidence angle and wind stress. These other mechanisms constitute another important research area for the future. Changes in wave conditions provide, however, only a partial answer. The mean mudebank migration rate from 1995 to 2000 was twice that from 1979 to 1984, for instance, even though the wave forcing parameter, H 0 3 /T 2 , identified by [START_REF] Gratiot | Trade-wind waves and mud dynamics on the French Guiana coast, South America: input from ERA-40 wave data and field investigations[END_REF] from a 44-yr record of the ERA-40 wave data set was only 33% higher. It may be inferred from these observations that differences in migration rates may also account in part for variations in the spacing between the banks, and possibly bank morphology as well, notably where mud banks are 'stretched' alongshore.

On the basis of the foregoing, emphasis in research on the causes of temporal variations in migration rates should be placed on the analysis of both regional meteorological-hydrodynamic forcing and distant Atlantic forcing. Regional sources of variability that need to be investigated include variations in the North Brazil Current [START_REF] Nikiema | Numerical modelling of the Amazon River plume[END_REF], density currents due to the Amazon freshwater plume, the effect of variations in trade-wind stress due to changes in wind speed and directions on the generation of compensatory currents from Amapá to the Orinoco. On a global scale, the role of storm tracks and storm intensity in the North Atlantic, such as those associated with the North Atlantic Oscillation and El Niño and La Niña events needs to be taken into account. These sources of hydrodynamic forcing provide scope for future studies. On a longer timescale, evidence for an overprint of the 18.6 yr nodal tidal cycle on bank migration rates and attendant changes in shoreline dynamics has been presented by [START_REF] Gratiot | Significant contribution of the 18.6 year tidal cycle to regional coastal changes[END_REF], emphasising the plausibility of a hypothesis proposed by [START_REF] Wells | Periodic mudflat progradation, northeastern coast of South America: a hypothesis[END_REF] on the role of this cycle in generating periodic phases of higher high-tide water levels that are favourable to more efficient wave energy incidence and bank migration. Another set of factors influencing migration rates involves local irregularities in the alongshore mudebank migration corridor such as island and nearshore bedrock outcrops and rocky headlands in French Guiana that trap mud [START_REF] Anthony | The influence of Amazon-derived mud banks on the morphology of sandy, headland-bound beaches in Cayenne, French Guiana: a short-to long-term perspective[END_REF].

It has become clear in the course of ongoing research that river mouths and river discharge patterns, initially invoked as potential sources of migration-rate variability by [START_REF] Gardel | A satellite image-based method for estimating rates of mud bank migration, French Guiana, South America[END_REF], may indeed influence migration rates. The migrating mud banks tend to imprint a northwestward deflection of the river mouths on the coast, generating mud capes (Fig. 1a). Exceptions to this are the larger rivers in Surinam and Guyana, such as the Maroni (drainage basin: 64,230 km 2 ). The large liquid discharge from these bigger rivers appears to generate a hydraulic-groyne effect such as described by [START_REF] Todd | Dynamic diversion: influence of longshore currentetidal flow interaction on chenier and barrier island plains[END_REF], [START_REF] Komar | Computer models of delta growth due to sediment input from rivers and longshore transport[END_REF] and [START_REF] Giosan | River delta morphodynamics: examples from the Danube delta. In: river deltas e concepts, models and examples[END_REF] that may be responsible for more persistent muddy accretion on coastal sectors updrift of these large river mouths by slowing down mudebank migration and encouraging updrift mud concentration, and associated hindered muddy sedimentation downdrift due to significant offshore deflection of mud banks in transit alongshore. Such deflection by the hydraulic groyne should favour mudebank liquefaction, which may be further enhanced by the larger waves offshore. Mud banks have been shown to evince a non-linear response to wave stress. Beyond a threshold forcing, the apparent mud viscosity decreases considerably, and this could, in turn, strongly affect mudebank migration rates [START_REF] Fiot | Structural effects of tidal exposure on mudflats along the French Guiana coast[END_REF]. A decrease in mud viscosity could also be induced by large local river discharge. The relationship between river mouths and bank characteristics along the Amapá-Guianas coast should, therefore, be an important part of the future research agenda.

Sand supply to the coast by the Guiana Shield rivers

The overwhelming mud concentrations associated with the AmapáeGuianas coast reflect the almost exclusively fine-grained sediment discharge of the Amazon, a product of intense tropical weathering of materials of dominantly Andean origin [START_REF] Guyot | Clay mineral composition of river sediments in the Amazon basin[END_REF]. Bedload estimates have been much more difficult to obtain and are shrouded in uncertainty. [START_REF] Strasser | Bottom structures geometry of the Amazon River[END_REF] computed a bedload discharge of 4.7 Â 10 6 tonnes yr À1 from bedform structures, a value corresponding to 6% of the suspended load computed by [START_REF] Martinez | Increase in sediment discharge of the Amazon River assessed by monitoring network and satellite data[END_REF]. The bedload contribution constitutes an area for future work. [START_REF] Pujos | Les minéraux lourds des sables du littoral de la Guyane française: bilan sur l'origine des dépôts de la plate-forme des Guyanes[END_REF] concluded from analyses of the heavy-mineral assemblages of the quartz-dominated beach sands in French Guiana that these sands are derived exclusively from local sources and not winnowed out from the migrating Amazon mud banks. The coast receives variable amounts of sand and mud from the local rivers west of the Amazon that drain the Quaternary coastal plain and adjacent crystalline Guiana Shield. [START_REF] Sondag | Suspended sediment and dissolved load budgets of two Amazonian rivers from the Guiana Shield: Maroni river at Langa Tabaki and Oyapock river at Saut Maripa (French Guiana)[END_REF] have shown that some of these rivers have very low suspended loads, the Maroni having among the world's lowest values.

Sand is an important economical and ecological asset on the muddy Amapá-Guianas coast because the relatively rare sandy deposits provide locations for human settlement and routes. The rare beaches on this part of the South American coast are also fundamental for the ecology of marine turtles (Lepidochelys olivacea, Chelonia mydas, Eretmochelys imbricata, Dermochelys coriacea) which require mud-free beaches for successful nesting [START_REF] Kelle | Monitoring of nesting leatherback turtles (Dermochelys coriacea): contribution of remote-sensing for real time assessment of beach coverage in French Guiana[END_REF][START_REF] Caut | Effect of tidal overwash on the embryonic development of leatherback turtles in French Guiana[END_REF].

It has been suggested that the limited presence of sand bodies on this coast reflects the diluting influence of the enormous mud supply from the Amazon during the Quaternary and limited sediment yield from the local, well-forested drainage basins despite high rainfall [START_REF] Anthony | The influence of Amazon-derived mud banks on the morphology of sandy, headland-bound beaches in Cayenne, French Guiana: a short-to long-term perspective[END_REF]. Such blanketing of relict fluvial sand by the cover of Amazon mud on the inner shelf has been deemed by these authors to preclude shoreward sand reworking to form the beach-ridge barrier systems that are common along many sand-rich wave-dominated shorefaces such as those of the southeast coast of Brazil [START_REF] Dominguez | Controls on Quaternary coastal evolution of the east-northeastern coast of Brazil: roles of sea-level history, trade winds and climate[END_REF] and the coast of West Africa [START_REF] Anthony | Beach-ridge progradation in response to sediment supply: examples from West Africa[END_REF]. Seismic data off French Guiana show that inner shelf mud overlies sandy deposits of presumably fluvial origin that crop out on the middle shelf at depths beyond 20 m [START_REF] Bouysse | Reconnaissance sédimentologique du plateau continental de la Guyane française (mission Guyamer 1975)[END_REF][START_REF] Pujos | Sources and distribution of heavy minerals in Late Quaternary sediments of the French Guiana continental shelf[END_REF]. In this area, the coastal progradational sequence comprises overconsolidated mud on the inner shelf (0 to À20 m) that forms a relict Pleistocene to Holocene bed surface over which modern mud banks migrate [START_REF] Allison | Origin of Amazon mudbanks along the northeastern coast of South America[END_REF], within a dynamic system of short-term to seasonal sedimentation and resuspension cycles. This dynamic system feeds the long-term inner shelf mud accumulation, which is characterised by offshore thinning of the mud wedge, a phenomenon typical of wave influence. Verification of the 'blanketing' hypothesis of sand by Amazon mud will, however, require more high-resolution seismic profiling of shelf deposits and coring operations probably in association with future petroleum exploration. Onshore in Surinam, the similarity of the Pleistocene deposits with the modern mudebank system, both in terms of alternations between bank and chenier-rich inter-bank sequences and of the forcing mechanisms in coastal sedimentation, has been highlighted by [START_REF] Wong | The influence of sea level changes on tropical coastal wetlands: the Pleistocene Coropina formation, Suriname[END_REF].

Coherent sand bodies present on the Amazon-influenced shores are either bedrock-bound embayed beaches, notably in the vicinity of Cayenne (Fig. 1a), in French Guiana, or, much more commonly, cheniers, which may form linear features, and are generally found in eroding, inter-bank areas. In Surinam and Guyana, however, where larger rivers debouching on the coast have supplied sand, significant bands of cheniers have been incorporated in the prograded muddy coastal plain. Cheniers are poorly developed in French Guiana and Amapá, probably because the debouching rivers are smaller, and, therefore, the sand supply to be expected from these rivers much smaller, and significantly blanketed by mud from the nearby Amazon which dominates the coastal progradational regime, inducing river-mouth deflection.

Discussion and conclusion

The three themes that form the research agenda set out in the preceding sections call for more detailed studies aimed at better elucidating the role of rivers of the Amazon basin in the morphosedimentary dynamics and evolution (Fig. 2) of the 1500 km-long AmapáeGuianas coast. This coast is subject to the massive mud inputs that stream from the mouth of the Amazon as mud banks, the alternation of which with inter-bank phases generates markedly contrasting space-and time-varying coastal conditions. While the fine-grained sediment budget near the terminus of the Amazon River is now well constrained by studies carried out within the framework of the ORE HYBAM project, there is a need for the joint application of remote sensing, field monitoring and modelling methods in order to identify the percentage range of fine-grained sediment conveyed alongshore as mud banks from the Amazon river mouth. Partial mudebank sediment budgets have been established for the French Guiana coast from SPOT image analysis [START_REF] Gratiot | Significant contribution of the 18.6 year tidal cycle to regional coastal changes[END_REF], but linking such budgets with the Amazon budget has still to be carried out. While the fine-grained budget at the mouth of the river has been considered as relatively stable over time due to the smoothing effects of the Amazon flood basin [START_REF] Wittmann | Sediment production and delivery in the Amazon River basin quantified by in-situ-produced cosmogenic nuclides and recent river loads[END_REF], it is not clear as to whether this relative stability is translated in terms of the amount of mud entering the mud bank belt, which is conditioned not only by the river sediment discharge itself but also by the oceanographic processes involved in mud streaming alongshore. An ancillary element in this approach concerns the relationship between fluvial sediment input and mangroves, which can be remarkable archives of coastal environmental changes [START_REF] Souza Filho | Using mangroves as a geological indicator of coastal changes in the Braganca macrotidal flat, Brazilian Amazon: a remote sensing data approach[END_REF]. Assessing the variability of the Amazon sediment inputs to this coast is a long-term research effort to which careful analysis of mangrove forests could also contribute, because of the capability of such intertidal ecosystems to cope with changes in hydro-sedimentary processes. Given the importance of mangroves on the AmapáeGuianas coast and the close relationship between extensive mangrove development and mud banks, there is a ground-breaking challenge in elucidating this archive role of mangroves in Amazon sediment-input changes.

On a more local level, the influence of the much smaller Guiana Shield rivers may dominate the coastal dynamics. Two themes of interest in this regard are the direct effect of river liquid discharge on nearshore mud bank properties, and the supply of bedload involved in sandy beach and chenier formation. The liquid discharge of the larger of these Guiana Shield rivers appears to induce a hydraulic-groyne effect that needs to be taken into account in the analysis of spatial differences in muddy shoreline progradation. These differences can be quite marked on either side of these big river mouths due to this 'groyne' effect. Downdrift mud deficit induced by this effect may go hand in hand with the more common sand chenier formation exhibited by the downdrift sectors of the coast. There is a need, therefore, for a better comprehension of Guiana Shield sand inputs to the coast and of river-mouth dynamics, especially as regards the interaction between the river-mouth hydraulic jet and mud banks migrating alongshore. These themes (Fig. 2), as an adjunct to the objectives of the ORE HYBAM, offer scope for significant future research involving innovative field and remote sensing approaches, complemented by modelling.

Fig. 1 .

 1 Fig. 1. (a) A 2006 JERS-1 satellite image of the muddy AmazoneOrinoco coast, the world's longest muddy coast. Mud banks start forming in the Cabo Cassipore area rather than along the coast nearer to the mouth of the Amazon. This is due to the significant offshore location of the non-confined estuarine turbidity maximum of the Amazon and westward along-shelf deflection of this maximum towards the coast near Cabo Cassipore, directly south of which the Amapá coast appears to be mud-starved (see text). (b) The oblique aerial photograph shows a typical mud bank in French Guiana partly colonized by mangroves and cut by drainage channels. The bare part of the mud bank in the background shows a series of linear mud bars. This mud bank is one of several (up to 15 or more) banks migrating at any time from the mouth of the Amazon River in Brazil to that of the Orinoco River in Venezuela.

Fig. 2 .

 2 Fig.2. Diagram summarizing a research agenda aimed at a better understanding of the relationship between the hydrology and sediment budgets of the Amazon and Guiana Shield rivers and the 1500 km-long AmapáeGuianas coast.


ORCID Severine Le Faucheur
email: severine.le-faucheur@univ-pau.fr

ORCID Peter G C Campbell

Séverine Le Faucheur

Jelle Mertens

Eric Van Genderen

Amiel Boullemant

Claude Fortin

QICARs to Address the Lack of TCEs Toxicological Data

Keywords: Platinum group elements, precious metals, Ge, In, Re, Quantitative Ion

recent industrial developments have resulted in an increase in use of so-called technology-critical elements (TCEs), for which the potential impacts on aquatic biota remain to be evaluated. In the present study, quantitative ion character activity relationships (QICARs) have been developed to relate intrinsic metal properties to their toxicity towards freshwater aquatic organisms. In total, 23 metal properties were tested as predictors of acute ec 50 values for 12 data-rich metals, for algae, daphnids and fish (with and without species distinction). Simple and multiple linear regressions were developed using the toxicological data expressed as a function of the total dissolved metal concentrations. The best regressions were then tested by comparing the predicted ec 50 values for the tces (germanium, indium, gold, rhenium and platinum group elements -pgeswhich include iridium, platinum, palladium, rhodium, and ruthenium) with the few measured values that are available. The eight 'best' QICAR models (adjusted r 2 > 0.6) used  m 2 r as the predictor. For a given metal ion, this composite parameter, also known as the covalent index, is a measure of the importance of covalent interactions relative to ionic interactions. The toxicity of the tces was reasonably well predicted for most of the TCEs, with values falling within the 95 percent prediction intervals for the regressions of the measured versus predicted ec 50 values. Exceptions included au(i) (all test organisms), au(iii) (algae and fish), pt(ii) (algae, daphnids), ru(iii) (daphnids) and rh(iii) (daphnids, fish). We conclude that qicars show potential as a screening tool to review toxicity data and flag 'outliers', which might need further scrutiny, and as an interpolating or extrapolating tool to predict TCE toxicity.

INTRODUCTION

Until the beginning of the 21 st century, industrial uses of nonferrous metals were mostly focused on 'base metals', i.e., elements such as copper, lead, nickel or zinc, that are found in the first four rows of the Periodic Table of the Elements. However, recent developments in areas such as electrical engineering, computing, renewable energy technologies and medicine have expanded this range of metals. Gold, indium, germanium, rhenium and the platinum group elements (PGEs) belong to this group of newly-used 'Technology-Critical Elements' (TCEs), where the term 'critical' refers to the increasing technological importance of these elements and their limited number of sources [START_REF] Gulley | China, the United States, and competition for resources that enable emerging technologies[END_REF]. Extensive ecotoxicological databases exist for the base metals, for example in the US-ECOTOX database (US EPA 2020) and the European ECHA database (ECHA 2020), and they are used in environmental risk assessments around the world. In the case of most TCEs, however, these data are still very scarce, limiting our ability to properly assess their possible environmental impacts.

When new organic molecules are synthesized and are poised to enter commerce, Quantitative Structure-Activity Relationships (QSARs) can be used to predict their environmental fate and toxicity. These models relate the properties of the new molecule (e.g., water solubility, octanol-water partition coefficient) to its biological reactivity (Boethling and Mackay 2000). For metals, the development of such models has been less comprehensive since far fewer 'new' inorganic compounds have been introduced commercially compared to new organic chemicals. However, there have been a number of successful efforts to relate the toxicity of various base metals to their ionic properties (Walker et al. 2013). These Quantitative Ion Character Activity Relationships (QICARs) This article is protected by copyright. All rights reserved.

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have used ion properties such as electronegativity, softness, ionization potential and electrochemical potential as predictors (Kinraide 2009;Wang et al. 2016;Wolterbeek and Verburg 2001). To date, these models have been developed for and tested with data-rich metals and target species such as algae [START_REF] Fujiwara | Evaluation of metal toxicity in Chlorella kessleri from the perspective of the periodic table[END_REF][START_REF] Hickey | Microtesting appraisal of atp and cell recovery toxicity end-points after acute exposure of Selenastrum capricornutum to selected chemicals[END_REF]Mu et al. 2014), daphnids [START_REF] Kaiser | Correlation and prediction of metal toxicity to aquatic biota[END_REF]Tatara et al. 1997;Wu et al. 2013) and fish (Ownby and Newman 2003;Wu et al. 2013). However, they have not been systematically tested with data-poor metals such as the TCEs.

The objective of the present study was to develop robust organism-specific QICAR models for a wide range of data-rich metals, and then apply them to data-poor TCEs (germanium, indium, gold, rhenium and platinum group elements -PGEs -which include iridium, platinum, palladium, rhodium, and ruthenium) to predict their acute aquatic toxicity (algae, daphnids, fish) on the basis of their ionic properties. Many of the TCEs exist in oxidation states of +III or higher, which is not the case for the majority of the data-rich elements. Our study was thus designed to test whether this difference in oxidation state would affect the ability of QICARs developed for data-rich elements to predict the toxicity of the TCEs.

The toxicological data used to construct the models and the values predicted with the models were expressed as dissolved EC 50 values (total dissolved metal concentration that elicits a 50% response in the studied population). Given the important influence of metal speciation on metal bioavailability and toxicity, a second study was also conducted to predict toxicity from free metal ion concentrations and ionic properties. These results will be published in a follow-up paper.

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MATERIALS AND METHODS

QICAR modeling involves establishing a mathematical relationship between two separate databases. Here, these databases are a compilation of the ion characteristics of the metals in the two groups (the training set of data-rich metals and the prediction set of data-poor TCEs), and a compilation of the toxicological data that are available for each group.

Building of the metal characteristics database

To construct the metal characteristics database, the most significant metal characteristics that had been previously reported in QICAR studies were compiled (Walker et al. 2013). These characteristics can be grouped into five categories, i.e. The first step of the database building was thus to compile the data and verify whether the metal characteristics were available for the metal training dataset, i.e., for Ag, Ca, Cd, Co, Cu, K, Mg, Mn, Na, Ni, Pb, Zn and for the studied TCEs, i.e. Au(I), Au(III), Ge (IV), Ir(III), In(III), Pd(II), Pd(IV), Pt(II), Pt(IV), Re(V), Re(VII), Rh(III), Ru(III), Ru(IV). When one of the ion characteristics was missing for a particular metal, the characteristic was eliminated from the modeling process. The second step was to perform a Principal Component Analysis (PCA) to determine correlations among metal characteristics, in order to reduce the number of characteristics to be included in the models. For the construction of the QICARs, we prioritized non-redundant metal characteristics that were available for the training metals and that best bracketed the characteristics for the data-poor TCEs.

Preparation of the toxicity database

Freshwater toxicological data were obtained from different sources, such as national curated databases and from specific searches of the recent literature for updated or missing data. Data were compiled for short-term exposures conducted with algae (48 h, 72 h or 96 h, depending on the species), daphnids (48 h) and fish (96 h). Their measured responses (growth inhibition; immobilization; mortality) were noted, along with the species that had been tested, the experimental conditions (exposure duration, temperature) and details regarding the composition of the exposure media. For model construction, we only retained EC 50 values that had been derived from toxicity tests where the metal concentrations had been measured (i.e., not just nominal metal This article is protected by copyright. All rights reserved.

Accepted Article concentrations). The compiled EC 50 values were analyzed for outliers using boxplot representations (data not shown). Values were considered as outliers when they fell outside the interval of three times the interquartile range [START_REF] Addinsoft | XLSTAT 2018: Data analysis and statistical solutions for Microsoft Excel[END_REF].

Growth inhibition was the only endpoint used for algae, whereas both immobility and survival were used as toxicity endpoints for daphnid tests. All the daphnid EC 50 values were pooled for a given metal, regardless of the endpoints, since no outliers were revealed with this approach. In the Cu and Cd fish databases, the toxicity tests were performed with different life stages (from larvae to adults). Since the study of outliers did not reveal any trends that would have justified the exclusion of certain life stages for any metals, results for these life stages were pooled.

Model building

Simple linear regressions (SLR) were constructed between each selected training metal characteristic (as well as its logarithm) and the logarithms of the mean measured EC 50 values, without species distinction. The log-transformation of the metal characteristics was additionally performed to reduce their variances as compared to that of the series of -log EC 50 values. A second set of SLRs was constructed with speciesspecific EC 50 s for the species for which the number of independent tests was the largest, i.e., Pseudokirchneriella subcapitata for algae, Daphnia magna and Ceriodaphnia dubia for daphnids, and Oncorhynchus mykiss and Pimephales promelas for fish.

In a second step, multiple linear regressions (MLR) were performed using a stepwise approach, with all the selected metal characteristics and their logarithms as input variables. To reduce the number of tested variables, we compared the variances of the - The chosen models were then analyzed to determine if the assumptions for linear regression were met. The normality of the residuals was tested using the Shapiro-Wilk test (α = 0.05) whereas their lack of autocorrelation was assessed using the Durbin-Watson (DW) test (Supporting information and Table SI-1). Results of both tests are provided directly in the text only when the assumptions were not verified. Otherwise, statistical results are given in Table SI-2. Once developed, the models were tested using the log-transformed average EC 50 values for the TCEs. Model building and correlation statistics were performed using XLSTAT 2018.2 and higher [START_REF] Addinsoft | XLSTAT 2018: Data analysis and statistical solutions for Microsoft Excel[END_REF]).

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RESULTS

Metal characteristics database

Almost all the metal characteristics were found for the training metals, with the exception of electron affinity (E*) values for Cd, Mg, Mn and Zn, which were indicated to be 'not stable' (Handbook of Chemistry and Physics -Rumble ( 2018)) (Table SI-3).

For the studied TCEs, no IP or ∆IP values were found for Au(III), Ir(III), Pd(IV), Pt(IV), Re(V), Re(VII) and Ru(IV), whereas E 0 and ∆E 0 were missing for Re(V) (Table SI-4).

However, the lack of data was the most acute for ∆β, σ p and log K OH , with data available for only 5 out of 17 for ∆β, 8 out of 17 for σp and 11 out of 17 for log K OH , which thus precluded the use of ∆β, σ p and log K OH in the subsequent model development. This situation reflects the lack of thermodynamic data describing the binding of these TCEs to inorganic ligands.

The metal characteristics that remained at this stage for building the QICAR models were AW, V, ρ, MP, α, AN, r, CR, Vdw,  m , z, z/r, z 2 /r and  m 2 r. Among these characteristics, only the values for V, AN, CR and Vdw bracketed those of the TCEs (Table SI-5), and they were prioritized over the other characteristics to construct the models in the subsequent steps. At this point, a PCA was performed with the 14 remaining metal characteristics to identify possible redundancies among them (Figure 1).

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constituted of AN, which correlated with AW (r = 0.999) and ρ (r = 0.908). AN was chosen because, as was the case for V, its range of values for the training metals included the AN values for the studied TCEs. In the third PCA group, z 2 /r strongly correlated with z (r = 0.984) and z/r (r = 0.937). The ratio z 2 /r was chosen due to its prominence in earlier QICAR models (Newman and McCloskey 1996;Van Kolck et al. 2008). The electronegativity,  m , correlates with ρ (r = 0.797), which had already been eliminated, and with  m 2 r (r = 0.777); both parameters were kept but were not used concurrently in the models. MP and r did not correlate with any other characteristics and were thus also kept. To summarize, V, AN, z 2 /r, χ m or χ m 2 r, MP and r were retained to construct the QICAR models.

Toxicity database

Algae. and Pd(II), were found to be the most toxic of the TCEs, with EC 50 values of 2.8 x 10 -8 M for Pd(IV) and (1.7 ± 1.2) x 10 -7 M for Pd(II) (mean ± standard deviation when n > 2).

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Discussion

Quality of the toxicological database

The quality of toxicological models such as those developed in the present work is intrinsically linked to the quality of the database used to construct them. Here, the toxicological database was based on acute toxicity data that complied with the pre-set conditions: (i) that the total dissolved metal concentrations had been measured, (ii) that the toxicological data were obtained, as much as possible, from several sources and (iii)

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that the tested species were among those recommended by OECD and USEPA in their guidance documents for aquatic toxicity testing to predict EC 50 values.

In the algal toxicological database, copper had the highest number of available tests ( 62) with 38 performed with the recommended OECD and US EPA algal species P.

subcapitata. In contrast, the Ni database (n=25) included a variety of algal species, 11 in total, the highest number among the algal databases. The least extensive databases were those for the major cations (Ca, K, Mg, Mn and Na). Although these elements have been

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since three of the five EC 50 values were reported as "higher than a certain value" and thus

were not usable for the present study. Data for the major cations were again challenging to find in the literature, mostly because the total dissolved concentrations in the exposure media were again rarely measured.

Statistical analyses of the EC 50 values for each class of organism revealed that the incidence of outliers was linked to the use of additional species other than P. subcapitata for algae, such as C. reinhardtii and C. vulgaris for Cd, Chlorella sp. for Ni and C.

kessleri for Pb, and additional fish species other than P. promelas and O. mykiss, such as Danio rerio, Lepomis macrochirus and Perca flavescens for Cd. In contrast, outliers in the daphnia database were not related to the use of particular species. Note that the outliers in the algal and fish databases were removed before the data were used in the QICAR modeling.

There are far fewer EC 50 values for the TCEs than for the training metals. In total, one or two algal EC 50 values were available for each of the TCEs (n=12), with a maximum of five data points for Pd(II); P. subcapitata was again the most studied algal species. Ceriodaphnia dubia, D. magna and D. magna straus were the only daphnia species for which toxicological data were available for the TCEs, but the database did include 10 elements; Pd(II) and Pt(IV) had the highest number of data points (n=6) whereas only one test result was available for Au(I), Ir(III), Ge(IV) and Ru(III). Few EC 50 values were available for fish exposed to TCEs since for several experiments, the results were reported as "higher than a certain value" and thus could not be used in the present study (Table SI -18). Nonetheless, seven elements were represented with one or This article is protected by copyright. All rights reserved.

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two EC 50 values; O. mykiss was the most studied species but no P. promelas data were available.

Best QICAR models Among the studied metal characteristics and based on the EC 50 values for the training metals, the best predictor of the metal acute toxicity towards algae, daphnids and fish is the composite value  m 2 r or its log transformed value (Figures 2, 3 and4). The  m term represents the electronegativity of the metal, e.g. its ability to attract electrons when it interacts with a ligand in solution or a membrane-bound metal transport site, whereas r is its ionic radius. Their combination as the composite  m 2 r parameter was introduced in such as Na (0.88) and Ca (1.00), the interaction with ligands is predominantly an ionic interaction between the cation and the donor atom(s) in the ligand, whereas metals with high  m 2 r values (class B) tend to form bonds with covalent character. This classification was completed with the use of z 2 /r, which describes "the ability of cations to form ionic bonds" and takes into account the charge on the cation and the ability of ions with small radii to approach ligands donor groups closely. Since publication of their landmark 1980

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paper,  m 2 r has been a recurrent parameter used in QICAR models, either alone or in combination with z 2 /r or log K OH (Walker et al. 2013).

In the present study, the best models used  m 2 r as the sole parameter to describe the toxicity of total dissolved metals to freshwater aquatic organisms, without the further need to add another characteristic or index such as z 2 /r. Moreover, this latter index showed very poor correlations with metal toxicity for all the studied organisms. Note that log K OH was not tested due to the lack of values for some data-poor elements.

Quality of the QICARs prediction

Very good correlations were found between  m 2 r and the toxicity of the training metals, with r 2 adj values higher than 0.8 for all organisms except for O. mykiss (r 2 adj = 0.62) (Figure SI-10). However, predictions for the TCEs were not as good, in particular for Au (algae, daphnia, fish), Pd(II) (fish), Pt(II) (algae, daphnia) and for Rh and Ru

CONCLUSION

Eight 'best' models (r 2 adj > 0.6) were constructed to predict acute EC 50 values for twelve data-rich metals and ten data-poor TCEs, using toxicity testing data for freshwater algae, daphnids and fish (Table 1). All of these models used the covalent index,  m 2 r, in simple linear regressions, illustrating that a metal's ability to form covalent bond is a good proxy for its toxicity.

QICAR models show potential as a screening tool that could be used to review This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

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  physical properties (atomic weight -AW; atomic volume -V; density -ρ; melting point -MP; polarizability -α; molar refractivity -MR); electronic structure (atomic number -AN; ionization energy and potential -IP and ∆IP, respectively; electron affinity -E*); redox properties (oxidation number -ox; standard electrode potential -E 0 and electrochemical potential, ∆E 0 ); binding properties (ionic radius -r; covalent radius -CR; Van der Waals radius -Vdw; electronegativity -χ m ) and various composite indices (ionic potential -z/r, where z = the charge on the ion; ionic index -z 2 /r; covalent index χ m 2 r; covalent binding stability -∆β; Pearson softness parameter -σ p ; absolute value of the first hydrolysis constant -log K OH ; AN/∆IP). These ion characteristics were mostly found using the Handbook of physics and chemistry (Rumble 2018). Exceptions included σ p (Ahrland 1968; Williams et al. 1982), log K OH (Academic Software 2001; Smith et al. 2004) and ∆β, which was either retrieved from Newman and McCloskey (1996) or This article is protected by copyright. All rights reserved. Accepted Article calculated from stability constants found in the IUPAC (Academic Software 2001) or NIST databases (Smith et al. 2004).

  log EC 50 values with the variances of the metal characteristics and their log-transformed This article is protected by copyright. All rights reserved. Accepted Article values, using the Fisher test (α = 0.05). When the original values of a given metal characteristic had a variance similar to that of the -log EC 50 values, the log transformed metal characteristics were not considered. The 'entry' parameter significance of the MLR was tested using a Student t test at α = 0.05 whereas the removal of a variable was tested at α = 0.1. Metal parameter multicollinearity was assessed using the Variance Inflation Factor (VIF; < 10 as an indication of negligible collinearity). The 'best' metal characteristics were then selected based on several goodness of fit statistics for the developed model: the highest adjusted r 2 (r 2 adj ); the lowest root mean square error (RMSE); the highest F value with a p value lower than 0.05 (the F and p values being obtained with an Analysis of Variance (ANOVA) to test the significance of the model to predict the -log EC 50 values based on the selected metal characteristics); the closeness of the Mallows Cp coefficient to the target value of Cp (Cp= k+1, k being the number of variables in the model), and the lowest AIC (Akaike's Information Criterion). As was the case for the SLRs, MLRs were constructed with and without species distinction.

A

  first group of metal characteristics was composed of V, which correlated with α (r = 0.889), CR (r = 0.931) and Vdw (r = 0.720). V was chosen because the range of its values for the training metals bracketed the V values for the studied TCEs and it represents two variables (V = AW/ρ). It was also chosen in preference to CR because of the potential difficulty in differentiating between CR and r. A second group of characteristics was This article is protected by copyright. All rights reserved.

  TCEs [Au(III), In(III), Rh(III), Ru(III), Ru(IV), Pt(IV)] have EC 50 values ranging between 1.1 x 10 -6 M [Ru(IV)] and 1.4 x 10 -5 M ([In(III)]. The least toxic elements among the studied TCEs are Au(I), Ge(IV), Pt(II) and Re(VII), with EC 50 values between 1.0 x 10 -4 M [Au(I)] and 1.2 x 10 -3 M [Ge(IV)]. Daphnids. The EC 50 values for the training metals used to construct the daphnia models are presented in TableSI-8; comparable data for the TCEs are presented in TableSI-9. Among the studied metals, the cations Ca, Mg, Mn and Na are the least toxic with average EC 50 values of about 1 x 10 -2 M. Zinc and Co have comparable toxicity, with average EC 50 values of (1.4 ± 1.1) × 10 -5 M for Zn and (4.6 ± 2.7) × 10 -5 M for Co.Cadmium, Cu and Pb toxicity values are in the µM range whereas Ag is the most toxic of the studied metals with EC 50 values ranging between 1.0 × 10 -9 M and 1.5 × 10 -6 M.Several daphnia species were examined for each metal; for example, Ni toxicity was assessed using 13 different species. Nevertheless, D. magna and C. dubia were the most studied species. Each metal data set was examined for outliers, first by taking into account all species, then only D. magna and finally C. dubia (TableSI-8). Among the studied TCEs, Au(I) has the lowest EC 50 value (6.9 x 10 -7 M), whereas the toxicity of Au(III), Pd(II) and Pt(IV) are in the µM range. The least toxic TCEs are Ir(III), Pt(II), Fish. The compiled toxicity test data are presented in Table and in Table. Among the training elements and taking into account all the fish species, Ag is the most toxic metal with an average EC 50 value of (2.0 ± 3.4) × 10 -7 M. The least toxic cation is Na with EC 50 values ranging from 9.6 to 11 x 10 -2 , followed by Mg, Ca and K, the EC 50 values of which are in the 1 x 10 -2 M range. Lead and Cu were found to be more toxic (µM range) than Mn, Cd, Ni and Zn, based on their average EC 50 values. However, a wide dispersion around the average EC 50 values was observed for Cd (five orders of magnitude) and Ni (four orders of magnitude), which suggests that such comparisons of metal toxicity should be undertaken with caution.Although many fish species (n=24) were present in the database, the most studied species were O. mykiss and P. promelas for each of the studied metals. Pd(IV) is the most toxic TCE for fish with an EC 50 value of 1.4 x 10 -6 M, followed by both Au species with EC 50 values of 2.0 x 10 -5 M for Au(I) and 4.6 x 10 -5 M for Au(III). Ge(IV), Mn(II), Pd(II), Rh(III) and Pt(IV) have EC 50 values within the concentration range between 1.3 x 10 -4 M [Pt(IV)] and 9.9 x 10 -4 M [Ge(IV)]. Modeling All-species Algae and P. subcapitata models (designated model A-and model PS) Fourteen SLRs and four MLRs were constructed with the metal characteristics and the -log EC 50 values compiled for algae (without species distinction) exposed to the studied training metals. The best model (Model A12) was the SLR built with log  m 2 r as the predictor, yielding a r 2 adj of 0.89 and a RSME of 0.722 (

Figure 2A ;

 2A Figure 2A; Figure SI-1). Among the constructed MLRs, Model A16 had a very high r 2 adj

  studied over the years, they were rarely measured in the exposure medium (i.e., nominal concentrations or indirect estimates based on conductivity measurements were used -Santos et al. (2007), Venancio et al. (2017)) and thus many of the earlier studies were not useful for the present study. The daphnia database included many more tests than the algal database. The largest individual database was again for Cu with 239 data points and seven different daphnia species. The Ni database had the highest number of studied species (13 in total) whereas in contrast EC 50 values for Co were only available for C. dubia. Although the database for the major cations was again the least extensive in terms of the number of experiments, one large data set was found in the literature, thanks especially to the work of Mount and colleagues who did extensive work on C. dubia (Mount et al. 2016). The Ag database contained only nine EC 50 values but they came from eight different sources, suggesting good representativeness of the data. The toxicological data concerning fish are numerous for Cd, Cu, Ni, Pb and Zn, with a total of 201 results for Cu with eight species represented. The US EPA (2016) report from which the Cd data were extracted provided responses for 25 different species, and a total of 100 data points. In contrast, the Co database had only two available results for P. promelas This article is protected by copyright. All rights reserved.

  the early 1980's by Nieboer and Richardson in order to classify metals into three subcategories, i.e., class A, class B and borderline metals (Nieboer and Richardson 1980). This classification was carried out in the context of trying to understand and predict metal toxicity. At that time, Nieboer and Richardson demonstrated that "the index  m 2 r is an estimate of the quotient obtained by dividing the valence orbital energy by the ionic energy". In other words,  m 2 r represents the importance of covalent interaction in metalligand binding, relative to ionic interaction. For elements with low  m 2 r values (class A)

(

  daphnia). Several factors may be contributing to the poorer predictive power of the models for these elements. First, the toxicity data for the TCEs were extracted from very few experimental tests, meaning that the EC 50 values are not as robust as those for the training metals. Secondly, the  m 2 r values for the training metals that were used to construct the models ranged between 0.88 (Na) and 4.28 (Ag). As a consequence, for elements for which the  m 2 r values lie outside this range, such as Au(I) (7.89) and Au(III) (4.90), the model predictions necessarily involve an extrapolation, rather than an interpolation, and this may contribute to the poorer performance of the model. Note, however, that the  m 2 r values of Pd, Pt, Rh and Ru do fall within the range within which the models have been constructed. Thirdly, the toxicity testing of some TCEs were often performed with compounds having a chemical structure much more complex than those This article is protected by copyright. All rights reserved.

  existing data and identify 'outliers', i.e. toxicity values that lie outside the QICAR prediction intervals. These outliers might be values that are abnormally low(and driving This article is protected by copyright. All rights reserved.Accepted Articleregulation setting), or values that are unusually high; in both cases, the outliers might reflect inaccurate toxicity test results or inadequate predictive tools. The QICARs could be used to flag values that merit further scrutiny and possible retesting.In the case of TCEs, QICARs might also be used as an interpolation or extrapolation tool, to estimate their toxicity. Indeed, both parameters,  m (electronegativity) and r (ionic radius) forming the covalent index are easily retrieved from the basic Handbook of physics and chemistry (Rumble 2018). Improvement in QICAR predictions of TCE toxicity should include consideration of metal speciation in the exposure media (for both training and prediction sets of metals) and concurrent measurements of metal accumulation in the test species. Since metal-ion specific probes are generally not available for the TCEs, consideration of metal speciation will necessarily involve the use of chemical equilibrium models to calculate the major metal species present in the exposure media. Such models are inherently sensitive to the quality of the thermodynamic data that are available for the metal, and this may well be a limiting factor for some of the TCEs, which are data-poor not only in the case of toxicity data, but also with respect to their behavior in aqueous solution. Concurrent measurements of metal bioaccumulation, with a distinction between surface bound and internalized metal, would help establish links between metal speciation, metal accumulation and toxicity. The lack of such information currently hinders attempts to explain the mode of action of the TCEs.Supplemental Data-The Supplemental Data are available on the Wiley Online Library at DOI: 10.1002/etc.xxxx.

Figure 2 :

 2 Figure 2: (A) Simple linear regression of the negative logarithm of the mean measured

Figure 3 :

 3 Figure 3: (A) Simple linear regression of the negative logarithm of the mean measured

Figure 4 :

 4 Figure 4: (A) Simple linear regression of the negative logarithm of the mean measured

  Accepted Articleof the training metals, which were usually added as dissociable chloride or nitrate salts, and this might have contributed to the deviations from the QICAR predictions. For example, Pt(II) was added as tetraammineplatinum hydrogen carbonate and tetrammineplatinum diacetate whereas Pt(IV) was added as dihydrogen hexahydroxoplatinate and platinum chloride. Similarly, Rh was added as diammonium sodium hexakis(nitrito-N) rhodate and as rhodium nitrate hydrate. In the present study, we have expressed the EC 50 values as total dissolved metal concentrations, but to know to what extent these complex forms remain intact in the aqueous exposure media within the timeframe of the toxicity test would be very helpful. This is also true for the training metals, the speciation of which can vary as a function of pH, carbonate and DOM concentrations in exposure media. Given the central role of the free metal ion activity in the prediction of metal toxicity towards aquatic organisms[START_REF] Campbell | Interactions between trace metals and organisms: a critique of the free-ion activity model[END_REF], ongoing projects are now focusing on developing QICAR models based on the free metal ion activity to determine if this improves the prediction of TCE toxicities.

Table 1 :

 1 Compilation of the best models to predict metal acute toxicity towards freshwater algae, daphnids and fish, with and without species-specificity (models based on total dissolved metal concentrations).

	Model (-log EC 50 )	r 2	adj

Acknowledgement--The authors declare no conflicts of interest and thank Metals Environmental Research Associations (MERA) for their financial support. The authors would like to thank Jasim Chowdhury (ILA), Emily Garmin (NiPERA), Paul Marsh (the Cobalt Institute), Robert Santore (Windward Environmental), Christian Schlekat (NiPERA) and William Stubblefield (Oregon State University) for providing many of the toxicity test data. The authors are very grateful to Pierre Marle and Dorothea Hug-Peter (University of Geneva), Kevin Brix (University of Miami) and Mike Bank (Norwegian Inst. Marine Research) for their helpful discussion about statistical approaches. Geneviève Rioux (INRS-ETE) is also warmly thanked for the preparation of the cadmium toxicological database.

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Data Accessibility Statement--We have compiled an extensive Supplementary Materials (SM) file that is included with this submission. The toxicological data for the training metals are available in the open literature and the sources are indicated in the SM file.

The comparable data for the TCEs are plotted in the figures in the manuscript and in the SM file. Readers are invited to contact the corresponding author for any additional information (severine.le-faucheur@univ-pau.fr).

Accepted Article