Wen-Shen Liu

Antony Van Der Ent

Peter D Erskine

Jean Louis Morel

Guillaume Echevarria

Emmanuelle Montargès-Pelletier

Kathryn M Spiers

Rong-Liang Qiu

Ye-Tao Tang

Spatially Resolved Localization of Lanthanum and Cerium in the Rare Earth Element Hyperaccumulator Fern Dicranopteris linearis from China

Keywords: X-ray fluorescence microscopy, compartmentalization, necrosis, vein, manganese, silicon

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Introduction

Rare earth elements (REEs), which include 14 lanthanides and yttrium (Y), have a range of applications in modern technologies, such as high-strength magnets, electric vehicles and medical devices, and increasing demand for these technologies has resulted in a growing need for REEs [START_REF] Binnemans | Recycling of rare earths: a critical review[END_REF]. Consequently, mining activities and a subsequent release of wastes into the environment, may pose a threat to agricultural crops and human health (Migaszewski & Galuszka, 2015). Some REEs can be beneficial to plants at low concentrations [START_REF] Redling | Rare earth elements in agriculture with emphasis on animal husbandry[END_REF]), yet can be toxic to plants at higher concentrations [START_REF] Thomas | Rare earth elements (REEs): effects on germination and growth of selected crop and native plant species[END_REF][START_REF] Wang | Rare earth elements activate endocytosis in plant cells[END_REF]. However, hyperaccumulator plants are able to accumulate and tolerate high concentrations of potentially toxic elements in their living shoots [START_REF] Baker | Terrestrial higher plants which hyper accumulate metallic elements[END_REF][START_REF] Reeves | Tropical hyperaccumulators of metals and their potential for phytoextraction[END_REF][START_REF] Van Der Ent | Hyperaccumulators of metal and metalloid trace elements: facts and fiction[END_REF]. Thus far ~700 plant species have been reported globally to hyperaccumulate a large variety of metals and metalloids [START_REF] Reeves | A global database for hyperaccumulator plants of metal and metalloid trace elements[END_REF][START_REF] Reeves | Agromining: farming for metals[END_REF], but only 22 plant species are currently recognized as REEs (hyper)accumulators [START_REF] Liu | Agromining: farming for metals[END_REF]. The threshold concentration for REEs hyperaccumulation is set at 1000 mg kg -1 in the dry biomass of the aerial parts [START_REF] Wei | Research trends on rare earth element hyperaccumulator[END_REF]. This criterion is the same as used for other trace metals (such as Ni) and metalloids (As), which are typically two or three orders of magnitude higher than concentrations present in 'normal' plants (van der Ent et al., 2013).

Moreover, the bioaccumulation factor (BF), which is the quotient of REE concentration in shoots to that in soil, is typically >1 in hyperaccumulator plants; which is indicative of a high ability of soil-toplant metal(loid) transfer (van der [START_REF] Van Der Ent | Hyperaccumulators of metal and metalloid trace elements: facts and fiction[END_REF].

Hyperaccumulator plants potentially offer an environmentally-friendly and cost-effective option for phytoremediation of REE polluted soils and recovery of REEs from low-grade ores and mining wastes (van der Ent et al., 2015;[START_REF] Liu | Agromining: farming for metals[END_REF]. Dicranopteris linearis collected from ionadsorption REE mine tailings in Ganzhou, Jiangxi Province, can yield 10-15 t of dry biomass per ha per year containing 0.2 wt% REEs, yielding 20 to 30 kg REEs ha -1 (unpublished data). Knowledge on the ecophysiology of REE hyperaccumulator plants is important to better understand the mechanisms for uptake, translocation and sequestration of REEs into living shoots. To date, most studies have focused on the uptake of Ni, zinc (Zn), cadmium (Cd) and arsenic (As) in various hyperaccumulator plant models [START_REF] Hokura | Arsenic distribution and speciation in an arsenic hyperaccumulator fern by x-ray spectrometry utilizing a synchrotron radiation source[END_REF][START_REF] Tappero | Hyperaccumulator Alyssum murale relies on a different metal storage mechanism for cobalt than for nickel[END_REF][START_REF] Vogel-Mikuš | Spatial distribution of cadmium in leaves of metal hyperaccumulating Thlaspi praecox using micro-PIXE[END_REF][START_REF] Tian | Stem and leaf sequestration of zinc at the cellular level in the hyperaccumulator Sedum alfredii[END_REF][START_REF] Hu | Elemental distribution by cryo-micro-PIXE in the zinc and cadmium hyperaccumulator Sedum plumbizincicola grown naturally[END_REF], while much less is known about the ecophysiology of REE hyperaccumulator plants [START_REF] Li | Hyperaccumulator plants from China: A synthesis of the current state of knowledge[END_REF].

Elucidating the spatial distribution of metal(loid)s and their associations with other elements is key to understanding the mechanisms of tolerance in hyperaccumulator plants [START_REF] Zhao | Imaging element distribution and speciation in plant cells[END_REF][START_REF] Van Der Ent | X ray elemental mapping techniques for elucidating the ecophysiology of hyperaccumulator plants[END_REF]. Over the past decades, elemental distribution in tissues, cells and organelles of a selection of hyperaccumulator plants have been studied extensively (e.g. [START_REF] Van Der Ent | X ray elemental mapping techniques for elucidating the ecophysiology of hyperaccumulator plants[END_REF].

Excess metal(loid)s are typically concentrated in bio-inactive tissues of the leaves to minimize the damage to biological activities (e.g. [START_REF] Zhao | Imaging element distribution and speciation in plant cells[END_REF]. This includes Ni localised in foliar epidermal layers, vascular tissues and basal parts of trichomes of Alyssum murale [START_REF] Tappero | Hyperaccumulator Alyssum murale relies on a different metal storage mechanism for cobalt than for nickel[END_REF], As in the venules and the edges of the pinnae of Pteris vittata [START_REF] Hokura | Arsenic distribution and speciation in an arsenic hyperaccumulator fern by x-ray spectrometry utilizing a synchrotron radiation source[END_REF], Zn in the vascular and epidermal tissues of Sedum alfredii and Sedum plumbizincicola [START_REF] Tian | Stem and leaf sequestration of zinc at the cellular level in the hyperaccumulator Sedum alfredii[END_REF][START_REF] Hu | Elemental distribution by cryo-micro-PIXE in the zinc and cadmium hyperaccumulator Sedum plumbizincicola grown naturally[END_REF], and selenium (Se) in the tips and leaf edge of Astragalus bisulcatus and Stanleya pinnata [START_REF] Freeman | Spatial imaging, speciation, and quantification of selenium in the hyperaccumulator plants Astragalus bisulcatus and Stanleya pinnata[END_REF]. Meanwhile, vacuole compartmentalization is thought to be an indispensable component of metal(loid)s detoxification in leaves [START_REF] Sharma | Vacuolar compartmentalization as indispensable component of heavy metal detoxification in plants[END_REF]. The vascular system clearly has a critical role in transporting metal(loid)s through xylem and phloem loading and unloading [START_REF] Tappero | Hyperaccumulator Alyssum murale relies on a different metal storage mechanism for cobalt than for nickel[END_REF][START_REF] Kitajima | Observation of arsenic transfer in leaf tissue of hyperaccumulator fern by utilizing synchrotron radiation micro-XRF imaging[END_REF][START_REF] Tian | Stem and leaf sequestration of zinc at the cellular level in the hyperaccumulator Sedum alfredii[END_REF]. In the hyperaccumulator Iberis intermedia, thallium (Tl) is distributed in the vascular bundles of the leaves [START_REF] Scheckel | Synchroton x-ray absorptionedge computed microtograpy imaging of thallium compartmentalization in Iberis intermedia[END_REF], whereas in Phyllanthus balgooyi the phloem sap is extremely enriched in Ni (van der Ent and Mulligan, 2015) and in Pycnandra acuminata the latex is the main storage for Ni [START_REF] Jaffré | Sebertia acuminata: a hyperaccumulator of nickel from New Caledonia[END_REF].

Except for cerium (Ce) (+3 and +4 valences) and europium (Eu) (+2 and +3 valences), the REEs are a group of trivalent (+3) elements which show biochemical behavior that differs from other metals e.g. higher affinity to O-containing ligands but lower affinity to S-/N-containing ligands than Zn, Ni, Cd, etc. [START_REF] Nieboer | The replacement of the nondescript term 'heavy metals' by a biologically and chemically significant classification of metal ions[END_REF]. Thus, REEs in (hyper)accumulators may have distinct uptake and transport strategies yet to be fully understood.

Dicranopteris linearis (Gleicheniaceae) is a native pioneer fern common throughout the Old World (sub)tropics and Oceania (USDA, 2018). This species can grow in acidic (pH 4-5) and poor soils, and exhibits high phosphorus use efficiency [START_REF] Russell | The ecology of the climbing fern Dicranopteris linearis, on windward Mauna Loa, Hawaii[END_REF]Chen et al., 2016a). Moreover, D. linearis often forms patches under an open canopy and dominates the understory of many plant communities in Southern China (e.g. Pinus massoniana, Cunninghamia lanceolata, Eucalyptus robusta) (Fig. 1). This plant has a crucial role in the ecosystem, retaining nutrients and organic matter [START_REF] Cohen | Releasing rain forest succession: a case study in the Dicranopteris linearis fernlands of Sri Lanka[END_REF]; sustaining soil microclimates (Zhao et al., 2012); facilitating growth of Eucalyptus trees [START_REF] Wan | The understory fern Dicranopteris dichotoma facilitates the overstory eucalyptus trees in subtropical plantations[END_REF]; and controlling REE migration and gully erosion (Chen et al., 2016b). To date, only the D. linearis accessions from Southern China have been recognized as a REEs hyperaccumulator [START_REF] Wei | Rare earth elements in naturally grown fern Dicranopteris linearis in relation to their variation in soils in South-Jiangxi region (Southern China)[END_REF][START_REF] Wei | Subcellular and molecular localization of rare earth elements and structural characterization of yttrium bound chlorophyll a in naturally grown fern Dicranopteris dichotoma[END_REF][START_REF] Shan | Accumulation and uptake of light rare earth elements in a hyperaccumulator Dicropteris dichotoma[END_REF]. Field surveys confirm that D. linearis can accumulate up to 1240-1760 mg kg -1 REEs in the aboveground parts when growing on (ion-adsorption) REE mine tailings (Table S1). Dicranopteris linearis thus may be used for phytoremediation and phytomining at ion-adsorption REE mine tailings in Southern China, which can contain 409-1035 mg kg -1 REEs [START_REF] Chao | Structure, variation, and co-occurrence of soil microbial communities in abandoned sites of a rare earth elements mine[END_REF], and occupy an area of more than 100 km 2 [START_REF] Liu | Limiting factors for restoration of dumping sites of ion-adsorption rare earth tailing[END_REF]. Most of the REEs are accumulated in the cell walls in the pinnae of D. linearis, as shown by differential extractions; although some of the REEs are also accumulated within the protoplast, such as the organelles and cytosol, vacuoles and cell membranes [START_REF] Wei | Subcellular and molecular localization of rare earth elements and structural characterization of yttrium bound chlorophyll a in naturally grown fern Dicranopteris dichotoma[END_REF]. Light REEs (LREEs, i.e. La, Ce, Pr and Nd) deposits have been reported from the cell wall, intercellular space, plasmalemma, vesicles, and vacuoles of the root endodermis and stele cells of the adventitious root by scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS) [START_REF] Shan | Accumulation and uptake of light rare earth elements in a hyperaccumulator Dicropteris dichotoma[END_REF]. Other studies reported that chlorophyll La and chlorophyll Ce (i.e. La and Ce bound to chlorophyll) formed in the pinnae of D. linearis (Zhao et al., 1999;[START_REF] Wei | Subcellular and molecular localization of rare earth elements and structural characterization of yttrium bound chlorophyll a in naturally grown fern Dicranopteris dichotoma[END_REF], and the chlorophyll REE can partly replace chlorophyll Mg [START_REF] Wei | Off-line separation and determination of rare earth elements associated with chloroplast pigments of hyperaccumulator Dicranopteris dichotoma by normal-phase liquid chromatography and ICP-MS[END_REF]. Moreover, binding to proteins is likely one of the mechanisms against physiological toxicity of REEs in D. linearis pinnae [START_REF] Wang | Preliminary characterization of a light-rare-earthelement-binding peptide of a natural perennial fern Dicranopteris dichotoma[END_REF]. Overall, the different techniques and samples preparations used to unravel those different observations may be responsible for some artefacts. The high concentration of histidine reported in the pinnae of this plant [START_REF] Shan | Accumulation and uptake of light rare earth elements in a hyperaccumulator Dicropteris dichotoma[END_REF] may result from various physiological activities and its presence is not sufficient to prove or demonstrate the chemical status of REEs, i.e.

histidine complexes of REEs. The investigation of REE distribution and speciation at a submicrometric scale might provide key information about the mechanisms driving the uptake and storage of those elements by identifying the analogies and co-distributions with other elements, including nutrients.

Synchrotron-based X-ray fluorescence microscopy (XFM) is a non-destructive method that has been successfully used to study the in-situ distribution of trace elements in hyperaccumulator plants (for recent reviews see [START_REF] Lombi | Synchrotron-based techniques for plant and soil science: opportunities, challenges and future perspectives[END_REF][START_REF] Van Der Ent | X ray elemental mapping techniques for elucidating the ecophysiology of hyperaccumulator plants[END_REF][START_REF] Kopittke | Synchrotron-based X-ray fluorescence microscopy as a technique for imaging of elements in plants[END_REF]. The aim of this study was to better understand the ecophysiological mechanisms that enable tolerance to REEs in this fern. To that end, XFM elemental images of the sum of two light REEs (referred to as La + Ce and being the sum of La and Ce), of K, Ca and Mn in the tissues and cells of D. linearis frond were acquired.

Materials and methods

Collection of plant tissues

Live samples of D. linearis grown on the ion-adsorption REE mine tailings of Ganzhou, Jiangxi Province, China (24 57 N, 115 05 E), and the corresponding soil material were collected. The plants were transplanted in pots containing soil material and placed in a greenhouse, Sun Yat-sen University, Guangzhou, China. Three mature plants with rhizosphere soil were brought alive to the P06 beamline (PETRA III Synchrotron, DESY, Hamburg, Germany) for the experiments described below. In parallel, four mature live pinna and three standing litter pinna (the dead pinna was still connected to the stolon) samples from a D. linearis population on an ion-adsorption REE mine tailing were sampled for bulk chemical analysis (Fig. S1).

Chemical analysis of bulk tissue samples

Plant samples were washed with pure water (18Ω, 25°C), then dried in an oven at 105°C for 2 h and 60°C for 72 h. The analysis methods of total concentrations of Al, REEs, Si, and other trace elements (Mn, K, Ca, P) were adapted from [START_REF] Liu | Co-deposition of silicon with rare earth elements (REEs) and aluminium in the fern Dicranopteris linearis from China[END_REF].

X-ray fluorescence microscopy (XFM)

The X-ray fluorescence microscopy (XFM) experiment was undertaken at Beamline P06 at the PETRA III (Deutsches Elektronen-Synchrotron; DESY, Hamburg, Germany), a 6 GeV synchrotron (Boesenberg et al., 2016). The undulator beam was monochromatized with a cryogenically cooled Si(111) channel-cut monochromator to an energy of 12 keV with a flux of 10 10 photon/s. A Kirkpatrick-Baez mirror pair was used to focus the incident beam to 700 × 530 nm (hor × ver). The samples were scanned in fly-scan mode, with the resultant sample X-ray signal detected using the Maia 384C detector system, operated in backscatter geometry [START_REF] Kirkham | The Maia spectroscopy detector system: engineering for integrated pulse capture, low latency scanning and real time processing[END_REF][START_REF] Ryan | Elemental X-ray imaging using the Maia detector array: the benefits and challenges of large solid-angle[END_REF][START_REF] Ryan | Maia X-ray fluorescence imaging: Capturing detail in complex natural samples[END_REF][START_REF] Siddons | Maia X-ray microprobe detector array system[END_REF]. Typically, a quick 'survey scan' was first conducted to allow for the selection of the appropriate portion of the sample. For the survey scan, the resolution was 50-100 µm with a dwell of 1-2 ms and generally took ca. 5 min to complete. After that a 'detailed scan' was conducted, with a resolution of 2-10 µm and a dwell time of 8-20 ms. For the whole experiment, the incident energy of 12 keV was used in order that the fluorescence lines of the elements of interest are well below the inelastic and elastic scatter peaks.

Live/fresh pinnule samples were analysed whole, or as cross-sections which hand cut with a stainless-steel razor blade ('dry knife' method); whole or sectioned samples were then mounted between two sheets of 4 µm Ultralene thin film in a tight sandwich to limit evaporation, and analysed within 10 minutes after excision. The fresh samples mounted between two sheets of Ultralene thin film (4 µm) were stretched over a 3D-printed frame magnetically attached to the x-y-z sample stage at atmospheric temperature (~23°C). X-ray micro-fluorescence was performed using fast scanning to keep the scan time, and hence sample degradation, to a minimum.

Data processing and statistics

The XRF event stream was analysed using the Dynamic Analysis method [START_REF] Ryan | Dynamic analysis: on-line quantitative PIXE microanalysis and its use in overlap-resolved elemental mapping[END_REF][START_REF] Ryan | Quantitative trace element imaging using PIXE and the nuclear microprobe[END_REF] as implemented in GeoPIXE [START_REF] Ryan | Quantitative PIXE microanalysis of geological material using CSIRO proton microprobe[END_REF][START_REF] Ryan | Advances in dynamic analysis, PIXE imaging: correction for spatial variation of pile-up components[END_REF]. It was necessary to use a precise matrix file for the spectra fitting to account for X-ray fluorescence absorption of the relatively low energy of the REEs L-lines, and the matrix was based on the stoichiometry of the mean concentrations of major elements (>0.1 wt%) in dried D. linearis pinna samples (Table S2).

The matrix composition assumed hydration of the fresh samples, and was formulated as C 7 O 31 H 59 N 0.7 S 0.2 Al 0.3 Si 1.5 Ca 0.2 Mn 0.1 K 0.3 (on the basis of average concentrations of these elements in bulk samples) with a density of 0.90 g cm -3 , considering two layers of Ultralene foil (4 µm) and with varying sample thicknesses. Pinnule samples were considered to have uniform thickness. The rhodium coating of the KB mirror focusing optics available for this experiment results in a high-energy cutoff of 23 keV, well below that required to excite the K-lines of the REEs (33.44 keV for La), so therefore the experiment had to rely on exciting the L-lines (ranging from 4.65 keV for La to 7.41 keV for Yb). The L-lines of Al and Si were around 1.5-3 keV, thus could not be detected.

Although the K-line of Y could be reached at 14.96 keV, its concentration was too low (mean of 106 µg g -1 Y in pinna) to be reliably detected. The reported resolution of the Maia 384C detector is 220 eV and it is hence unable to distinguish between the numerous L-lines of various the REEs.

Moreover, the L-lines of Gd, Eu and Sm are in the range of the K-lines of Mn and thus these elements, at these relative concentrations, are not possible to distinguish reliably using the Maia detector. Consequently, only La and Ce were used to represent the REEs.

Results

Bulk elemental concentrations in Dicranopteris linearis

The bulk pinna concentrations of REEs, Al, Si, Mn, K, Ca, and P are given in Table 1. The concentrations of Al, Si and REEs in live pinnae are around half that of the standing litter pinnae -Al 2850 vs 4850 mg kg -1 , Si 14700 vs 33900 mg kg -1 and REEs 1900 vs 3500 mg kg -1 respectively.

Alternatively, the concentrations of Mn, P and K in live pinnae are always higher than the standing litter pinnae -Mn 1480 vs 310 mg kg -1 , P 211 vs 129 mg kg -1 and K 3000 vs 268 mg kg -1 respectively. The concentration of Ca has no significant difference between the two types of pinnae (Table 1). Among the 15 REEs, the sum of La and Ce accounts for ~50% of the total REEs in the pinnae, without significant differences between live and standing litter pinnae (Table S2).

Localization of REEs in pinna and pinnule

The X-ray fluorescence element maps evidenced a distinct mapping for K, Ca, Mn and the summed REEs La + Ce in the tissues of the pinna (Fig. 2; Fig. 3) and the pinnule (Fig. 4 linearis. The most significant enrichment of La + Ce and Mn is in the necrotic tissues, which are by definition the bio-inactive regions of the pinna. The preferential accumulation of REEs in these necrotic spots appear to in all of the tissue areas (pinna tips, margins and blades) of the necrosis.

Significant La + Ce and Mn enrichments are also in the midvein, secondary veins, tertiary veins and in the margins of the pinna and its pinnule. The La + Ce and Mn were not strictly restricted within the veins and necrotic tissues; their signals were also visibly and uniformly localized outside the veins and necrotic tissues. Calcium is mainly distributed in the necrotic tissues and blade, but low in the veins. Potassium has a distinctly different distribution in the pinna and pinnule, and is mostly localized in the blade, and absent in the necrotic tissues.

In order to compare the elements spatial co-occurrences and correlations, tri-color (Ce, Mn and Ca) composites maps and Ce-Mn frequency plots of a pinna and a pinnule are provided in Fig. S3. The results further confirm the co-localization of Ce and Mn both inside and outside the necrotic tissues, with higher concentrations inside the necrotic tissues and lower outside. However, tri-color elemental maps and element association frequency plots also show that Ce and Mn are not totally co-localized, e.g. there are some high Ce areas at the right of the pinnule (Fig. S2), where Mn is relatively low.

Localization of REEs in pinnule and midvein cross-section

In order to establish the localization of REEs at the cellular level, XFM mapping was performed on cross-sections of midvein and pinnule. The elemental maps revealed that La + Ce and Mn are colocalized in the epidermis (Fig. 6; Fig. 7). The La + Ce and Mn in the upper epidermis are much more concentrated than in the low epidermis. In the midvein cross-section, there is a "ring" shaped peak of La + Ce and Mn between the vascular bundle and the cortex, likely the pericycle. In the pinnule cross-section, the vascular bundle and cortex are difficult to differentiate. However, compared to La + Ce, Mn signals are more prominent in the cortex and mesophyll, while less marked in the vascular bundle. Potassium is low in the cortex, but concentrated in the mesophyll, epidermis and vascular bundle. Calcium is predominantly localized in the mesophyll and epidermis, but low in the vascular bundle and cortex. The subcellular elemental distribution could not be differentiated for any element.

Localization of REEs in petiole and stolon cross-section

The elemental XFM maps of the petiole and stolon cross-sections show La + Ce enrichment in the epidermis and vascular bundle, while in the cortex and the pericycle of the petiole cross-section prevailing concentrations are very low (Fig. 8 and Fig. S4). In contrast, in both the petiole and stolon cross-sections, the K, Ca and Mn are similar and mostly concentrated in the epidermis, pericycle and vascular bundle, with distinct localizations in the cortex. Within the vascular bundle of petiole crosssections, La + Ce, Mn, K and Ca are mainly localized in the protoxylem and metaxylem.

Discussion

Dicranopteris linearis accumulates the highest concentrations of REEs in necrotic tissues, which differs significantly from the behavior of most other metal hyperaccumulators. Potassium is an essential element that plays a critical role in cellular osmotic regulation in the young and fresh leaves, and the absence of K in necrotic tissues suggests that these areas are physiologically inactive. The markedly higher concentrations of REEs, Al and Si in the standing litter pinnae, as compared to the live pinnae further affirms that these elements are rather immobile as opposed to K which is strongly depleted in litter tissues (Fig. S1; Table 1). The concentrations of Mn in the pinna of D. linearis (Table 1) are much greater than what is typically toxic in most of the plants (e.g. < 200 mg kg -1 in maize) [START_REF] Shao | The key to Mn homeostasis in plants: regulation of Mn transporters[END_REF]. Therefore, the necrosis in these tissues is possibly, among other reasons, the result of Mn accumulation, oxidation and localised toxicity within the pinnules, which then induces cell death, necrotic spots, substantially larger necrotic tissue and then finally acts as a "dump site" for REEs. A similar phenomenon has been reported in the leaf of soybean and cowpea in response to Mn toxicity; the toxicity started with Mn pumped under the cuticle via the apoplast, or expelled via hydathodes towards the leaf tip, and then increasing concentrations of Mn leading to Mn oxidation (+2 to +3 and +4 valences) and necrotic lesions, which in turn stimulate more Mn translocation as a result of higher evaporation (Blamey et al., 2018a(Blamey et al., & 2018b)). However, Ce and Mn were not totally co-localized, it is therefore also possible that the necrotic tissues were induced by Ce accumulation and oxidation (+3 to +4 valences). It could be also interpreted as a tolerance mechanism in which some cells are sacrificed and used as a dump, while in the others, photosynthesis and normal activity can continue [START_REF] Küpper | Cadmium induced inhibition of photosynthesis and long term acclimation to cadmium stress in the hyperaccumulator Thlaspi caerulescens[END_REF]. The underlying mechanisms as to why REEs prefer to compartmentalize into the upper epidermis, margins and veins are not fully understood. Previous studies on hyperaccumulator plants suggest that it may reflect a physiological and/or defense-related response -protection of photosynthetically active tissues such as mesophyll, and defense from predation by herbivores and pathogens [START_REF] Martens | The ecological significance of nickel hyperaccumulation: a plant chemical defense[END_REF][START_REF] Cappa | Evolutionary aspects of elemental hyperaccumulation[END_REF].

The distinct localization in vascular bundles, while prevailing concentrations are low in the cortex of petiole and midvein, suggests an efficient REE transport system in this plant. The transport of REEs to the pinna probably occurs as mass flow through the vascular tissue, driven by transpiration. In non-accumulating species of beech and oak (Fagaceae), REEs transport within xylem was suggested to be associated with general nutrient flux [START_REF] Brioschi | Transfer of rare earth elements (REE) from natural soil to plant systems: implications for the environmental availability of anthropogenic REE[END_REF]. However, in the vascular bundles of D. linearis, we found both a small area of La + Ce peaks within vascular bundle and a very bright "ring" shaped peak between vascular bundle and cortex (Fig. 7). The significant La + Ce enrichment, with no obvious Ca and Mn enrichment within the vascular bundle of veins, suggests that REEs are translocated to the blade to a lesser extent than Ca and Mn. This might be due to the features of the pericycle tissue where cells are dense, preventing REEs translocation from the xylem into the blade via the intercellular space. It is possible that once the REEs enter into the vascular system of veins (xylem), they are partly fixed by the pericycle, and then transported to the D. linearis blades (especially to the epidermis and necrotic tissues) via transpiration flow. In this case, the transport and accumulation of REEs should mainly be passive convection processes. Thus, the distribution of REEs in D. linearis pinna could be influenced by various parameters that are associated with the evaporation (e.g. biological variety, seasonal and climatic changes) and Mn toxicity (e.g. light, pinnule age and height at sampling) [START_REF] Küpper | Tissue-and age-dependent differences in the complexation of cadmium and zinc in the cadmium/zinc hyperaccumulator Thlaspi caerulescens (Ganges ecotype) revealed by x-ray absorption spectroscopy[END_REF][START_REF] Fernando | Manganese phytotoxicity: new light on an old problem[END_REF][START_REF] Bartoli | Multiscale and age-dependent leaf nickel in the Ni-hyperaccumulator Leptoplax emarginata[END_REF]. The highest concentrations recorded within the necrotic tissues of pinna may be ascribed to a lack of wax coats at the surface of pinna, and/or the damage of the pericycle tissue or cells between vascular bundle and cortex in the veins, thus leading to a higher evaporation rate. The higher evaporation rate may also lead to higher concentrations of REEs in the upper epidermis, compared to the lower epidermis (Fig. 6; Fig. 7).

As vacuoles in necrotic tissues of the pinnae disintegrate, compartmentalization and accumulation in these areas indicate that the REEs are likely to be sequestrated by cell walls or exist under immobile forms. Many studies found that REEs tend to complex with phosphate and form deposits in the cell walls and more generally in the intercellular space of plant tissues [START_REF] Ding | Fractionation mechanisms of rare earth elements (REEs) in hydroponic wheat: an application for metal accumulation by plants[END_REF][START_REF] Ruíz-Herrera | Rare earth elements lanthanum and gadolinium induce phosphate-deficiency responses in Arabidopsis thaliana, seedlings[END_REF]. However, the molar ratio of P in D. linearis pinnae was much lower than that of REEs (Table 1). Moreover, we found that D. linearis also accumulates high concentration of Al and Si (Table 1; [START_REF] Chour | Recovery of rare earth elements from Dicranopteris dichotoma by an enhanced ion exchange leaching process[END_REF]. Detoxification of Al and other heavy metals by Si has been observed in many plants [START_REF] Liang | Mechanisms of silicon-mediated alleviation of abiotic stresses in higher plants: a review[END_REF]. For example, Al localization coincided with Si distribution in cell walls of the Al hyperaccumulator Rudgea viburnoides [START_REF] Malta | Rudgea viburnoides (Rubiaceae) overcomes the low soil fertility of the Brazilian Cerrado and hyperaccumulates aluminum in cell walls and chloroplasts[END_REF]. The co-deposition of Si and Cd in the cell walls as a [Si-wall matrix]Cd co-complex inhibited Cd ion uptake by rice cell [START_REF] Liu | Inhibition of cadmium ion uptake in rice (Oryza sativa) cells by a wall-bound form of silicon[END_REF]. In the Al and Si hyperaccumulator Faramea marginata, Al and Si are thought to be co- S2. oven for 48 hours after adding 3 mL HNO 3 (UP, 65%) and 1 mL HF, and then evaporated on a hot plate to dryness.

One mL 20% HNO 3 was added to the Teflon crucible and heated to 195 for 2 hours (1). The digestion solution was transferred to 15 mL tubes with 2% HNO 3 . The REEs were determined using an X Series

  ; Fig. 5; Fig. S2) of D.

  Zhao GW, Hong FS, Wei ZG, Gu YH, Hu TD, Xie YN, Liu T, Tao Y. 1999. Light rare earth element speciation of chlorophyll a in naturally grown fern Dicranopteris linearis by EXAFS. Progress in Natural Science 9: 1133-1135. Zhao J, Wan SZ, Li ZA, Shao YH, Xu GL, Liu ZF, Zhou LX, Fu SL. 2012. Dicranopterisdominated understory as major driver of intensive forest ecosystem in humid subtropical and tropical region. Soil Biology and Biochemistry 49: 78-87.

Fig. 1 .

 1 Fig. 1. Live Dicranopteris linearis grown at (a) the understory of Cunninghamia lanceolata; (b) an ion-adsorption REE mine tailing abandoned for about 10 years at Ganzhou, Jiangxi Province, Southern China.

Fig. 2 .

 2 Fig. 2. Elemental µ-XRF maps of fresh Dicranopteris linearis pinna. The maps measure 8.86 × 8.56 mm. The elemental image was acquired in step size 15 µm with dwell 15 ms per pixel, 12.0 keV, incident beam.

Fig. 3 .

 3 Fig. 3. Elemental µ-XRF maps of fresh Dicranopteris linearis pinna. The maps measure 13.1 × 8.58 mm. The elemental image was acquired in step size 25 µm with dwell 15 ms per pixel, 12.0 keV, incident beam.

Fig. 4 .

 4 Fig. 4. Elemental µ-XRF maps of fresh Dicranopteris linearis pinnule. The maps measure 10.2 × 4.26 mm. The elemental image was acquired in step size 20 µm with dwell 20 ms per pixel, 12.0 keV, incident beam.

Fig. 5 .

 5 Fig. 5. Elemental µ-XRF maps of fresh Dicranopteris linearis pinnule. The maps measure 12.95 × 2.88 mm. The elemental image was acquired in step size 8 µm with dwell 15 ms per pixel, 12.0 keV, incident beam.

Fig. 6 .

 6 Fig. 6. Elemental µ-XRF maps of fresh Dicranopteris linearis pinnule cross-section. The maps measure 2.25 × 0.86 mm. The elemental image was acquired in step size 5 µm with dwell 15 ms per pixel, 12.0 keV, incident beam.

Fig. 7 .

 7 Fig. 7. Elemental µ-XRF maps of fresh Dicranopteris linearis midvein cross-section of pinna. The maps measure 4.70 × 1.57 mm. The elemental image was acquired in step size 8 µm with dwell 15 ms per pixel, 12.0 keV, incident beam. The concave side represents the adaxial side in the figure.

Fig. 8 .

 8 Fig. 8. Elemental µ-XRF maps of fresh Dicranopteris linearis petiole cross-section of pinna. The maps measure 5.62 x 3.41 mm. The elemental image was acquired in step size 12 µm with dwell 15 ms per pixel, 12.0 keV, incident beam.

Fig. S1 .

 S1 Fig. S1. Live Dicranopteris linearis grown at (a) the understory of Cunninghamia lanceolata; (b) an

Fig. S2 .

 S2 Fig. S2. Dicranopteris linearis growing at an ion-adsorption REE mine tailings. (a) D. linearis

Fig. S3 .

 S3 Fig. S3. Elemental µ-XFM maps of hydrated Dicranopteris linearis pinna. The maps measure 13.1 ×

Fig. S4 .

 S4 Fig. S4. Elemental µ-XFM maps of hydrated Dicranopteris linearis pinnule. The maps measure 10.2

Fig. S5 .

 S5 Fig. S5. Elemental µ-XRF maps of hydrated Dicranopteris linearis pinnule. The maps measure

Fig. S6 .

 S6 Fig. S6. Tri-color elemental maps (red = Ce, green = Mn, blue = Ca) in a pinna (top left) and element

Fig. S7 .

 S7 Fig. S7. Elemental µ-XFM maps of hydrated Dicranopteris linearis petiole cross-section of pinna.

Fig. S8 .

 S8 Fig. S8. Elemental µ-XRF maps of hydrated Dicranopteris linearis stolon cross-section. The maps

   USA). The internal standard used was115 In. The external standard used during analysis was shrub branches and leaves (GBW07602) and yellow-red soil (GBW07405). The recovery of In during the testing was 95-105%. The measured values of the external standards were in the range of standard values of references.

  Different letters in the figure represent significant difference among each group(ANOVA, Duncan, p<0.05).accumulator and removed from the figure; (b) means the La concentration in the shoot, BF means bioaccumulation factor (i.e. La in the shoot / La in the soil); (c) the REE concentrations in the ash.

Table 1 .

 1 TABLES Bulk elemental concentrations in pinna of Dicranopteris linearis (mg kg -1 dry weight).

	Type	n	Al	Si	REEs*	Ca	Mn	P	K
		1	2860	13400	1540	1330	1350	232	3330
		2	3180	19500	2450	2160	1340	179	2600
	Live pinna
		3	1490	11800	1400	1160	1020	213	3390
		4	3860	14100	2190	2580	2230	220	2700
	Mean ± sd		2850±997a	14700±3310a	1900±505a	1810±676a	1480±523b	211±23.0b	3000±413b
		1	4910	36600	3620	2300	541	130	240
	Standing litter pinna	2	5280	36800	3750	988	241	156	432
		3	4370	28200	3130	908	149	100	131
	Mean ± sd		4850±455b	33900±4910b	3500±327b	1400±779a	310±205a	129±28.0a	268±152a

* The summed concentration of

La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu and Y,

see the concentration of each rare earth element in Table

Table S1 .

 S1 Rare earth elements concentrations in Dicranopteris linearis and rhizosphere soil collected from different sites.Dicranopteris linearis samples and rhizosphere soils were collected in non-mining area near ion-adsorption REE mine tailings (NM), ion-adsorption REE mine tailings (MT), edge of the ion-adsorption REE mine tailings (TE), high levels of REEs background (HB) from Ganzhou, Jiangxi province, and low levels of REEs background (LB)from Guangzhou, Guangdong province of China. Different letters in the figure represent significant difference among each group(ANOVA, Duncan, p<0.05). Plant samples were divided into aboveground part and underground part, thoroughly washed with deionized water, dried in an oven at 105 for 1 hour and then 60 for 48 hours, and then carefully crushed. Soil samples were air-dried and sieved to 0.15 mm. 50 ± 0.5 mg of dry plant samples and soil samples were introduced in a Teflon crucible equipped with cylinder sleeve, heated to 195 in an

	Type	Aboveground part / mg kg -1	Underground part / mg kg -1	Rhizosphere soil / mg kg -1	Aboveground part / Rhizosphere soil	Aboveground part / Underground part
	MT	1470±199 a	727±426 a	518±208 a	3.27±1.55 a	2.77±1.89 a
	TE	875±449 ab	198±66.0 bc	304±159 ab	4.09±3.57 a	5.12±3.49 a
	NM	1430±733 a	393±71.8 b	573±307 a	2.92±1.45 a	3.52±1.18 a
	HB	1160±806 ab	189±93.3 bc	363±81.9 ab	3.21±1.88 a	8.01±6.94 a
	LB	297±296 b	24.8±18.2 d	117±52.6 b	2.68±2.32 a	18.4±22.1 a

Table S2 .

 S2 Rare earth elements concentrations of live pinnae and dead standing litter pinnae collected from an ion-adsorption REE mine tailing in Southern China (mg kg -1 dry matter).

	Type	La	Ce	Pr	Nd	Sm	Eu	Gd	Tb	Dy	Ho	Er	Tm	Yb	Lu	Y
	Living pinna	686±2	355±2	121±30	366±63	82.9±6.	12.4±0.	80.0±6.	9.00±1.	37.6±8.	5.92±1.	15.2±3.	1.58±0.	8.11±3.	1.07±0.
	(n=4)	84 a	38 a	.2 a	.2 a	68 a	74 a	51 a	16 a	15 a	71 a	51 a	55 a	07 a

deposited as phytolith Al (Britez et al., 2002). Also, silicon may co-deposit with Mn at the apoplast and decrease the oxidation of Mn in cowpea, soybean and sunflower (Blamey et al., 2018a). Rare earth elements are a group of trivalent elements, which exhibit many chemical similarities to Al (Pletnev & Zernov, 2002). Considering the high concentration of REEs, Mn, Al and Si, a codeposition of REEs, Mn and Al with Si may be involved in the homeostasis of high concentrations of REEs, Mn and Al in D. linearis. Therefore, it would be crucial to study the localization of Al and Si in D. linearis tissues with other methods to confirm these hypotheses.

Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (41771343), and the 111 Project (B18060). This work is a contribution of the joint lab ECOLAND, established between Sun Yat-sen University, University of Lorraine and INRA. We acknowledge DESY (Hamburg, Germany), a member of the Helmholtz Association HGF, for the provision of experimental facilities. Parts of this research were carried out at PETRA III and we would like to thank Dr. Jan Garrevoet and Dr. Gerald Falkenberg for assistance in using beamline P06. The research leading to this result has been supported by the project CALIPSOplus under the Grant Agreement 730872 from the EU Framework Programme for Research and Innovation HORIZON 2020. The French National Research Agency through the national "Investissements d'avenir" program (ANR-10-LABX-21, LABEX RESSOURCES21) and through the ANR-14-CE04-0005 Project "Agromine" is acknowledged for funding support.

360 361

Author contributions

WSL conducted the fieldwork and collected the samples. AvdE, GE, PDE and KMS conducted the synchrotron XFM experiment. KMS, AvdE and EMP performed the XFM data processing and analysis. WSL conducted and bulk elemental analysis. All authors contributed to writing the manuscript.

Supporting Information

Spatially-resolved localization of lanthanum and cerium in the rare earth element hyperaccumulator fern Dicranopteris linearis from China Wen-Shen Liu 1,2,3 , Antony van der Ent 4,5 , Peter D. Erskine 4 , Jean Louis Morel 5 , Guillaume Echevarria 4,5 , Kathryn M. Spiers 6 , Emmanuelle Montargès-Pelletier 7 , Rong-Liang Qiu 1,2,3 , Ye-Tao Tang 1,2,3,* 1 School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China. (3) Dryopteris fuscipes 10; BF>1 (b) Aspleniaceae Asplenium filipes 25; BF>1 (b) Asplenium hondoense 14; BF>1 (b) Asplenium ruprechtii 40; BF>1 (b) Asplenium ritoense 12; BF>1 (b) Asplenium subnomale 14; BF>1 (b) Asplenium trichomanes 21; BF>1 (b) Blechnaceae Blechnum niponicum 9.7; BF>1 (b) Adiantaceae Adiantum monochlamys 11; BF>1 (b) The table is adapted from Liu et al. (10) with minor modifications. (a) The lanthanum (La) concentration in Glochidion triandrum (Phyllanthaceae) leaf < 1 mg kg -1 , thus not a REEs


Thomas Trancart
email: thomas.trancart@mnhn.fr

Alexandre Carpentier

Anthony Acou

Fabien Charrier

Virgile Mazel

Valentin Danet

Éric Feunteun

When "safe" dams kill: analyzing combination of impacts of overflow dams on the migration of silver eels

Keywords: acoustic telemetry, silver eels, migration, mortality, non-powered dams, turbines drinking water intake 1. Introduction

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

environments at a particular stage of their life cycle [START_REF] Mcdowall | Diadromy in fishes: migration between freshwater and marine environments[END_REF]. This breeding migration requires passing through narrow ecological pathways, called corridors, where anthropogenic (e.g., pollution and fishing) and ecological (e.g., predation) pressures may be strong. This complex life cycle leads to dramatic declines in most diadromous fishes, especially in areas in the North Atlantic ocean [START_REF] Limburg | Dramatic Declines in North Atlantic Diadromous Fishes[END_REF]. The European eel Anguilla anguilla is no exception to this rule. Its estimated recruitment rate is currently lower than 10% of the maximum level recorded in the late 1970s [START_REF] Ices | Report of the Joint EIFAAC/ICES/GFCM Working Group on Eels (WGEEL)[END_REF], and the species is now far outside its safe biological limits. Therefore, the International Union for Conservation of Nature (IUCN) consider European eels as a critically endangered species [START_REF] Jacoby | Anguilla anguilla[END_REF].

To protect the European eel, the European Commission has demanded that measures are taken to allow at least 40% escapement of reference silver eel biomass, relative to unexploited, unpolluted circumstances in unobstructed rivers [START_REF] Dekker | The 2003 Quebec Declaration of Concern About Eel Declines-11 Years Later: Are Eels Climbing Back up the Slippery Slope?[END_REF]European Commission, 2007). The European Commission recommends actions focused on reducing commercial fishing, limiting recreational fishing, restocking measures, increasing watershed connectivity and quality, catching and transporting of silver eels, predator control, hydroelectrical turbine shutdowns, and aquaculture measures. These actions are targeted to reduce the effects of parameters contributing to major decline in eel stock. Unfortunately, the relative importance of each cause was not clearly established. Overfishing represents one of the primary causes of decline, followed by mortality as a result of turbines and dams [START_REF] Feunteun | Management and restoration of European eel population (Anguilla anguilla): An impossible bargain[END_REF]. The impacts of hydroelectric complexes are well-known; specifically, they cause injuries [START_REF] Bruijs | Silver eel migration and behaviour, in: Spawning Migration of the European Eel: Reproduction Index, a Useful Tool for Conservation Management[END_REF], direct mortality [START_REF] Winter | Assessing the impact of hydropower and fisheries on downstream migrating silver eel, Anguilla anguilla, by telemetry in the River Meuse[END_REF][START_REF] Bruijs | Silver eel migration and behaviour, in: Spawning Migration of the European Eel: Reproduction Index, a Useful Tool for Conservation Management[END_REF], delays to the timing of migration [START_REF] Behrmann-Godel | A preliminary telemetry study of the migration of silver European eel (Anguilla anguilla L.) in the River Mosel, Germany[END_REF], and hinder downstream migration [START_REF] Durif | Behavioral study of downstream migrating eels by radio-telemetry at a small hydroelectric power plant[END_REF]. Dams that are not equipped with turbines also impact the migration of silver eels, leading to, for instance, delays in migration [START_REF] Larinier | Dams in Fish Migration[END_REF][START_REF] Larinier | Downstream migration: Problems and facilities[END_REF].

Because the relative contribution of each cause of decline has not been clearly established, the mitigation measures recommended by European Commission might be not be appropriately targeted. For instance, overflow dams with spillways are often considered a safe passage for migration (e.g., [START_REF] Larinier | Dams in Fish Migration[END_REF][START_REF] Larinier | Downstream migration: Problems and facilities[END_REF][START_REF] Watene | Selective opening of hydroelectric dam spillway gates for downstream migrant eels in New Zealand[END_REF][START_REF] Boubée | Downstream passage of silver eels at a small hydroelectric facility[END_REF][START_REF] Silva | Downstream Swimming Behaviour of Catadromous and Potamodromous Fish Over Spillways[END_REF], with both scientists and environmental managers recommending that silver eels are guided towards spillways. Therefore, mitigation measures for spillways effects is not cited among the actions recommended by the European Commission. Although managing the passage of eels through spillways (rather than turbines) is highly commendable, when considering all of the difficulties encountered by diadromous species during migration, dams without turbines might negatively impact downstream migration. Indeed, recent studies have shown the different, and sometimes unexpected, effects of these simple dams. Expected impacts include delays to or the complete blockage of migration. The Frémur River is a small waterway in northwestern France, where [START_REF] Besson | Disrupted downstream migration behaviour of European silver eels (Anguilla anguilla, L.) in an obstructed river[END_REF] showed that two major dams delay the migration of 75% silver eels, of which the passage of 65% was clearly stopped. However, other less expected effects also occur frequently. For example, in a highly important floodplain lake in France (Grand-Lieu), a small dam that was installed to regulate water level might contribute to the low escapement rate of silver eels (36%) by reducing their perception of environmental triggers and/or cues, and by disturbing them when gates are open at inappropriate times [START_REF] Trancart | Migration behaviour and escapement of European silver eels from a large lake and wetland system subject to water level management (Grand-Lieu Lake, France): New insights from regulated acoustic telemetry data[END_REF].

Furthermore, in the Rhine system in France, a dam was installed upstream of an old natural river (Vieux-Rhin) that might cause migration breaks and/or increased natural predation as a result of strongly reduced water flow in this section of the system, reducing motivation and orientation [START_REF] Trancart | Silver eel downstream migration in the River Rhine, route choice, and its impacts on escapement: A 6-year telemetry study in a highly anthropized system[END_REF].

The mitigation measures adopted to protect silver eels from the negative impacts of a dam should be considered over a wider context than just turbine mortality. The present study aimed to delineate all potential impacts of overflow dams on silver eels, including migration delay, migration stop, direct mortality, and loss of orientation. The behavior of eels was evaluated to highlight potential negative effects of spillway dams that have been less studied.

To accomplish this objective, we surveyed silver eels using acoustic telemetry during the 2017-2018 downstream migration period in the Frémur River. This river is representative of many short coastal rivers and tributaries of main rivers in Europe, as it is strongly constrained by non-powered dams to supply freshwater for human consumption and/or irrigation.

Methods

Study site

This study was performed in the Frémur River in northwestern France. The Frémur River is a small coastal waterway (17 km long, 60 km 2 of watershed area) that supported a dense population of eels, which has shown a major decline over the last 18 years (80% decrease between 1995 and 2018; [START_REF] Acou | Habitat carrying capacity is reached for the European eel in a small coastal catchment: evidence and implications for managing eel stocks[END_REF][START_REF] Charrier | Suivi des migrations d'anguilles et évaluations des stocks en place sur le Frémur en[END_REF][START_REF] Feunteun | Spatial distribution of an eel population (Anguilla anguilla L.) in a small coastal catchment of northern Brittany (France). Consequences of hydraulic works[END_REF]. The migration of eels along the Frémur River is severely impacted by six dams and weirs [START_REF] Acou | Migration pattern of silver eel (Anguilla anguilla, L.) in an obstructed river system[END_REF][START_REF] Feunteun | European eel (Anguilla anguilla): prediction of spawner escapement from continental population parameters[END_REF]. This study focused on the main dam, which is considered to have the greatest impact on silver eel migration [START_REF] Feunteun | Spatial distribution of an eel population (Anguilla anguilla L.) in a small coastal catchment of northern Brittany (France). Consequences of hydraulic works[END_REF], and two smaller dams. Three minor works-a culvert under a road, a flow gauging device, and a sill (step) beneath a bridge-are present upstream, but were considered to not hinder silver eel migration and were excluded from the present study.

The most upstream and important dam in this study is the "Bois-Joli Dam", which is 14 m-high with a 3.10 6 m 3 capacity reservoir to supply a drinking water treatment plant (Figure 1), hereafter called "Dam A". The downstream migration of silver eels is facilitated by an overflow crest and a pipe. This main pipe has two parts: a compensation pipe (0.04 m 3 .s -1 minimum flow) and a drinking water intake pipe (0.21 m 3 .s -1 ). The compensation pipe is designed to maintain a minimal instream flow within legal limits. The overflow crest is 28.20 m NGF (Niveau Général de la France; baseline mean sea level for France), and can be accessed by eels at the end of fall or the beginning of winter. A net was installed in front of the exit of the compensation pipe to prevent eel escapement. This net was frequently checked, and all eels that were caught alive were released downstream of the dam. A second net was installed at the exit of the drinking water intake pipe, and was checked in the same way as the first one. The entrance to the main pipe remains open year-round and is protected by a grid; however, this grid is not fully efficient, because eels were caught in the net.

The second dam ("Pont-es-Omnes", hereafter called "Dam B") is located 1 km downstream of dam A (Figure 1) and is smaller (3 m high and 50 m wide.) A 3 m-wide overflow crest facilitates eel escapement during overflow, and is equipped with a gutter to catch migrating eels, which are then channeled to a wolf trap. This trap was checked daily during the peak migration of silver eels. However, during the high-flow periods of the present study, the wolf trap might not have been fully effective because it overflowed. Any eels caught alive were released downstream of this second dam.

The third dam ("Pont-Avet", hereafter called "Dam C") is located 2.5 km downstream of dam B (Figure 1). It is similar in size to dam B (3 m high and 50 m wide), but the overflow crest spans the full width. This dam presents no obstacles to migration to the Frémur Estuary, which is 2 km downstream. However, compared to the other two dams, the depth (1-2 m) and width (3-4 m) of the final river section downstream of dam C is much narrower, generating small detection distances for telemetry surveys.

Collection and tagging of silver eels

Silver eels were captured with fyke nets during their migration period from September to October 2017. The nets were positioned upstream of the Frémur Reservoir, 2.5 km upstream of dam A (Figure 1), and were checked three times a week. Sixteen silver eels were selected using standard external characteristics of silvering [START_REF] Acou | Towards a standardized characterization of the potentially migrating silver European eel (Anguilla anguilla, L.)[END_REF]. They were then anesthetized with benzocaine (150 mg/l), and tagged with acoustic tags (ID-LP9-69 kHz Thelmabiotel, 9 mm large, 4 g in air), following the 2% tag/body mass rule [START_REF] Winter | Advances in underwater biotelemetry[END_REF].

Mean total length (TL) was 663 mm ± 127 (SD, range: 520-974 mm), and mean body weight (BW) was 566 g ± 358 (SD, range: 228-1515 g). All tagged eels were assumed to be female based on body length and sexually dimorphic features [START_REF] Tesch | The eel. Biology and management of anguillid eels[END_REF]. Their individual morphological characteristics are summarized in Table 1. The median lag between two successive acoustic transmissions was 60 s (uniform law from 30-90 s). Incisions were closed with absorbable sterile sutures (3-0 Ethicon Monocryl tm , Ethicon Ltd, Livingston, UK) and disinfected with bactericidal antiseptic (0.05% chlorhexidine). After a recovery period, and when all anesthetic effects had dispersed (~1h), the fish were released 500 m below the fishing site (Figure 1) to prevent a second capture. Previous survival tests with eels tagged using the same method showed no death or injury [START_REF] Trancart | Migration behaviour and escapement of European silver eels from a large lake and wetland system subject to water level management (Grand-Lieu Lake, France): New insights from regulated acoustic telemetry data[END_REF]; thus, based on the endangered status of European eels, we chose not to perform a survival test for this experiment. We also assumed low natural mortality, as study site is free of potential predators of large silver eels, such as catfish (e.g., Silurus glanis), while grey herons (Ardea cinerea) are unlikely to consume eels of this size, and are limited to foraging along the banks of this river [START_REF] Feunteun | Assessment of Grey Heron predation on fish communities: the case of the largest European colony[END_REF].

The collision of transmissions prevents receivers from detecting them. In this study, we were concerned that tagged eels would aggregate in front of the dams. Therefore, because of the size of the reservoirs, the power output of the acoustic tag, and acoustic receiver detection ranges. We restricted the number of tagged eels in the reservoir at any one time to 10 individuals. This allowed us to obtain precise and informative behavioral metrics.

Consequently, the individual number of periods when a given eel was not detected in the receiver array was very low-below 10% of total time for all tagged eels. An increase in the number of tagged eels would have certainly reduced the quality of detection.

Acoustic design

The silver eels were tracked using an array of 36 acoustic receivers (Vemco, VR2W) from the release site to dam C (Figure 1) from September 2017 to January 2018. At dam A, receivers were positioned at 50-100 m intervals in the main passage of the river. Three small tributaries were monitored by three supplementary receivers. To detect eel escapement from dam A, two additional receivers were positioned 500 m downstream of it. Two other receivers were positioned immediately before dam B. To detect eel escapement from dam C, two supplementary receivers were positioned 500 m downstream of it. Dam C was monitored by two receivers in the reservoir positioned immediately before the dam and by two receivers in the section of the Frémur River downstream of the dam. Detection ranges of all receivers were tested, and were of high quality to 300 m. Certain conditions, such as low depth and width, caused the two final receivers (downstream of dam C) to have low detection ranges (~15 m), which prevented the optimal detection of tagged eels.

Environmental data

Environmental data were collected periodically for this study. Atmospheric pressure (hPa), wind direction (°, 0° = from North, 180° = from South), wind force (kn), pluviometry (mm/ hour), and air temperature (°C) were measured every hour at the Pleurtuit Airport Weather Station, which was located 2 km from the study site. The data were provided by Meteo France for this study. Information on water level at the dam to the nearest centimeter was collected every 20 min using a HOBO 100-Foot Depth Fresh Water Level Data Logger. To remove errors in the log, this level was filtered using a 4 h moving average. Water temperature was continuously recorded (once an hour) by a thermic data logger placed in the river immediately before dam A. Water flow was estimated using three inclinometers (HOBO Pendant ® G Data Logger) placed in the main stream of the river, immediately before dam A, in the middle of the study site, and at the release site. Lunar phase was also recorded.

Data analyses

Dam A reservoir

Mean position of individuals

Receiver density was highest in dam A reservoir, and the mean position of each silver eel at 20-min intervals was calculated as the mean position of receivers receiving signals from the eel during a given interval weighted by the number of detections from each receiver.

Therefore, the calculated position did not reflect the exact position of the eel, rather the estimated short-term (20 min) centers of activity [START_REF] Simpfendorfer | Estimation of short-term centers of activity from an array of omnidirectional hydrophones and its use in studying animal movements[END_REF]. If the raw data had been used, the position would not have been that of the eel, but that of the receiver detecting the fish, which is less accurate. When no receivers recorded data at a given time interval, the position was estimated by linear regression between the last calculated position and the next calculated position using the R package "pastecs" [START_REF] Grosjean | pastecs: Package for Analysis of Space-Time Ecological Series[END_REF]) in R 3.3.1 (R Development Core [START_REF] Team | R: A language and environment for statistical computing[END_REF].

Factors triggering migration

To highlight the factors triggering the downstream migration of silver eels, we considered migration to have started when eels reached dam A. The relationships between the environmental data (predictor variables) and the presence of silver eels in front of dam A were explored using Boosted Regression Trees (BRTs) [START_REF] Elith | Novel methods improve prediction of species' distributions from occurrence data[END_REF][START_REF] Elith | A working guide to boosted regression trees[END_REF][START_REF] Buston | Determinants of reproductive success in dominant pairs of clownfish: a boosted regression tree analysis[END_REF]. Before implementing these models, all correlations between environmental descriptors were analyzed to remove variables, with a correlation level >50%. For modelling, we considered the environmental factors that occurred from the release time to the first detection in front of the dam. The analyses were fitted using the R "gbm" package [START_REF] Ridgeway | Generalized Boosted Regression models. Documentation on the R package 'gbm[END_REF] and "dismo" supplement functions [START_REF] Elith | A working guide to boosted regression trees[END_REF]. Model performance was assessed via the amount of cross-validated deviance explained, cross-validated correlation between model prediction and observed data, and the area under the Receiver Operating Characteristic (ROC). The ROC score ranged from 0 to 1, where a score of 1 indicates perfect discrimination, a score of 0.5 indicates predictive discrimination that is no better than a random guess, and a score of < 0.5 indicates performance worse than random [START_REF] Elith | Novel methods improve prediction of species' distributions from occurrence data[END_REF]. The best model was chosen using the gbm.simplify function in "gbm" package, assessing the potential (changes in deviance and its standard error) to remove predictors using k-fold cross validation.

Escapement rate from dam A

At the end of the experiment, the rate of escapement (%) was defined as the number of individuals passing through dam A divided by the total number of tagged eels. The number of individuals passing through the dam was calculated as the number of eels caught in the nets after the compensation and drinking water intake pipes plus the number of eels recorded by the two first acoustic receivers located immediately downstream of this dam.

Effects of dam A on eel passage

To obtain a precise description of silver eel migration and behavior during migration, the following metrics were used for each eel: 1) daily swimming distance; 2) daily number of attempts to pass through the dam, i.e., the number of times an eel was detected in front of the dam (in cases with a long presence in this area, this value was incremented when the eel left the area and returned to it); 3) daily time spent attempting to pass the dam, i.e., the total time that an eel was detected in the area immediately in front of dam A compared to the number of tracked days.

The impact of the dam was studied using two variables. The first was the additive distance, which was defined as the distance travelled from initial detection in front of the dam to initial detection downstream of the dam, using the previously defined mean individual position. The second variable was additive time, which was defined as the time an eel spent from initial detection in front of the dam to initial detection downstream of the dam.

Individual efficiency to pass the dam A

Time to pass, which differs from additive duration, was defined as the time difference between initial detection of an individual in front of the dam when it overflowed and initial detection of the same individual downstream of the dam. Periods with dam overflow were exclusively used, because the time that an eel was present in front of the dam when it dam was not overflowing did not represent a real estimation of the efficiency of a given individual to pass. Time to pass was subsequently analyzed to detect possible correlations with the other behavioral metrics, such as the daily distance swam, the daily number of attempts, and the daily time to try to pass, using a principal component analysis (PCA, in "FactoMineR" package).

Dam B and dam C reservoirs

Because of the low density of receivers, the analyses at these two dams were performed differently to those in dam A reservoir.

Escapement rate and effects of dam B

Eel escapement rate for this dam was evaluated considering the ratio between the number of eels observed in the wolf trap and the number of eels detected by the last upstream receiver.

However, considering the low efficacy of the wolf trap during high flow periods, eels observed at the first receiver downstream of the dam and not caught in the trap were added.

Because the stream immediately downstream of this dam was very small, and therefore not adequate for the acoustic telemetry survey, the downstream receivers were located at the mouth of dam C (500 m downstream of dam B), and were not relevant to evaluate the additional time to pass dam B. The impact of dam B was evaluated considering the additional time to pass it, which was defined as the time spent time from initial detection to final detection in front of the dam.

Escapement rate and effects of dam C

Considering the very low detection range of the receiver located downstream of dam C, eel escapement rate from this dam was only based on the final detection (number, time) of the last receiver located just upstream the dam, which was based on a number of assumptions. First, we expected escapement if the final detection occurred at night because silver eels are nocturnal species that avoid daylight, even though daily movement is possible. Thus, passage downstream of dam C is likely to occur if the final detection occurs very close to the final receiver. Therefore, we considered the mean lag between the final detections at the final receiver during the last 5-min of detection. A mean lag that was close to the theoretical mean lag given by the constructor (60 s) was assumed to indicate that an eel was located very close to the receiver. To improve readability, we used the ratio between the theoretical mean lag over the mean lag between the last detection, with a ratio close to 1 indicating eels were very close to the final receiver.

Thus, we considered that a silver eel successfully passed dam C when: (1) its final detection was at the receiver immediately in front of this dam; (2) the ratio observed/ theoretical number of transmissions during the last 5-min was ~1; and (3) the time of the probable passages occurred at night. For eels that probably passed dam C, the additional time to pass the dam was defined as the spent time from initial detection in front of the dam to final detection in front of the dam, but only if the eel was never detected in front of the dam again.

Effects of eel size and weight on migration metrics

The relationships of individual migration metrics (probability to start migration, i.e., to reach dam A; escapement success from the three dams;, time to pass the three dams; additive distance to pass dam A) with eel biometry data (length and weight) were tested. For the probability to start migration and escapement success (binomial variables), the size of groups was unbalanced; thus, a Kruskal-Wallis test was used. For the other variables (quantitative variables, time to pass, and additive distance), linear regressions were implemented using R software.

Results

Factors triggering migration

Out of the 16 tagged eels, three did not migrate. These three eels were smaller and lighter than the migrant eels (Table 1), although Kruskal-Wallis rank sum test was non-significant (p = 0.07 for weight and p = 0.12 for length).

Only four variables were selected in the final model: water level, 12 h increase in water level, wind direction, and atmospheric pressure. This final model had a cross-validation explained deviance of 55.58% and an AUC of 0.95. Therefore, it was evaluated as excellent.

The relative influence of water level, 12 h increase in water level, wind direction, and atmospheric pressure was 41.8%, 20.4%, 19.3%, and 18.5%, respectively (Figure 2). Water level had the strongest effect, with an abrupt threshold at around 25.7 m (Figure 2). Lower values indicated an absence of water in front of the dam, and that downstream migration was strongly suppressed. A 12 h increase in water level had a strong threshold of ~0.5 m/12 h, which favored silver eel migration (Figure 2). Wind direction had a positive effect when wind blew from 125° (east-southeast) to 170° (south-southwest). Finally, high atmospheric pressure enhanced migration (Figure 2).

Escapement rates

Out of the 16 tagged eels, three did not pass dam A. Two eels stayed at the release site and were detected by the receiver array for 8.8 and 118.6 days, and swam 9.44 and 62.9 km, respectively (Table 2). The first one, after this disappearance, was again observed in front of dam C the next year in another telemetry survey. The third eel was detected in front of the dam for 44.9 days and travelled 53.6 km; it failed to pass the dam after nine attempts during 560 minutes of activity near the dam (Table 2, Figure 3).

The eels that successfully passed dam A mainly travelled through spillways (81.2%, Table 3). Only three eels used the main pipe, but mortality was high, as two eels died (Table 3, Figure 3). No difference was observed in the length and weight of eels that successfully passed through spillways and those that successfully passed through the main pipe.

Out of the 13 eels that passed through dam A, two were found dead in the nets at the end of the main pipe; therefore, only 11 eels survived and were able to reach dam B. All of these eels passed through dam B (Figure 3). Seven eels were found in the wolf trap (of which three were not alive), and four were recorded by the receiver downstream of the dam, but were never found in the trap (Table 5, Figure 3).

Eight eels passed through dam B via the trap or over it, and were still alive afterwards. Two of these eels did not reach dam C and one did not pass it. Five other eels were recorded for the last time by the acoustic receiver near dam C. The ratio of mean theoretical lags between transmissions to the mean observed lags between transmissions at this receiver was ∼1, except for eel 24. For eel 24 a final position very close to this receiver was indicated, and then very close to the dam (Table 6). Thus, these four eels likely passed through dam C (Figure 3). No difference was observed in the length and weight of eels that successfully passed through dam C and the eels that not passed (Kruskal-Wallis rank sum test, p = 0.92 for weight and p = 0.93 for length).

Effect of the dams on eel passage

The mean additive time to pass dam A was 21.3 ± 19.6 days, and ranged from 4.3 to 65.9 days. The mean additional distance to pass dam A was 39.7 ± 19.9 km, and ranged from 9.7 to 77.2 km (Table 4). The additive time to pass through dam B varied for each individual, ranging from 15 min to 7.25 days. The additive time to passed through dam C also varied, ranging from 2.4 days to 4.9 days (mean: 3.4 days). No relationship was observed for eel length/weight and additive time/distance (linear regression, R² = 7% for length and 14% for weight).

Efficiency of individuals to pass through dam A

The efficiency in passing through dam A varied among eels. Three eels passed through the main pipe of dam A before it overflowed. The group denominated as 'fast' was composed of eels that passed through the dam in less than four days after it overflowed. The group denominated as 'slow' was composed of eels that passed through the dam 14-21 days after it overflowed. The group denominated as 'non-migrant' was composed of eels that never crossed the dam. Therefore, after overflow started, two groups were observed (Figure 4).

The factorial analysis showed high segregation among the three delineated groups.

The 'non-migrant' group was composed of three eels with the lowest values of daily time attempting to pass, daily distance swam, and daily number of attempts (Figure 5). The 'fast' group was formed of eight eels with a high number of attempts and a high daily distance swam, but with low daily time attempting to pass (Figure 5). The 'slow' group was composed of five individuals with the highest daily time attempting to pass, but with low daily number of attempts and daily swimming distance (Figure 5).

Discussion

Methodological limitations and the number of studied eels

Technological advances in acoustic telemetry have resulted in it becoming more widely used in recent years [START_REF] Hussey | Aquatic animal telemetry: A panoramic window into the underwater world[END_REF]. Some studies have used this technology to monitor thousands of animals simultaneously; however, the probability of signal collision increases, particularly when study sites are small or enclosed. Signal collision causes individuals (e.g., fish or eels) to become invisible to the receiver array, leading, for instance, to imprecise estimates of escapement. Second, telemetry data metrics might be misevaluated. Thus, these issues must be considered when designing telemetry surveys. A trade-off must be found between tagging as many individuals as possible and maintaining the quality of metrics for optimal evaluation and interpretation. In the current study, a number of behavioral metrics were used and refined. Tagging more eels in any one time period would certainly have hindered the quality of the studied metrics. In particular, cross validation between Capture Mark Recapture (CMR) and acoustic telemetry might provide the best compromise, but requires extensive time and technical effort.

Factors triggering migration

The factors triggering silver eel migration in river habitats have already been delineated (e.g., [START_REF] Trancart | Forecasting animal migration using SARIMAX: an efficient means of reducing silver eel mortality caused by turbines[END_REF]. Waterflow is the most frequently cited factor, presenting a strong positive effect (negative rheotaxis). The use of river flow to migrate represents selective stream transport, in which silver eels choose environmental conditions that limit effort and the consumption of fat reserves [START_REF] Verhelst | Selective tidal stream transport in silver European eel (Anguilla anguilla L.) -Migration behaviour in a dynamic estuary[END_REF]. In rivers that have dams, different factors appear to trigger migration. In our study, the main triggering factors were water level and a 12 h increase in water level. These triggers are similar to those described for lentic ecosystems [START_REF] Trancart | Silver eel downstream migration in the River Rhine, route choice, and its impacts on escapement: A 6-year telemetry study in a highly anthropized system[END_REF]. In addition to hydrological factors, wind direction from south-southeast also had a strong positive effect on eel migration. This wind direction is the same as the direction of flow of the Frémur River; thus, wind might generate a surface water current that triggers migration by eels. Yet, locally prevailing winds are north-northwest associated with low atmospheric pressure. In comparison, winds from the south are associated with high atmospheric pressure, which is the fourth main triggering factor. Luminosity is considered a primary triggering factor for silver eel downstream migration, and was excluded from the final model. The very high turbidity (resulting in low light penetration) of the river during the migration period might explain the absence of this effect.

The triggers commonly found in rivers were attenuated in the Frémur River, due to the presence of dams. This phenomenon might have a major impact on this endangered eel species, delaying or entirely stopping downstream migration. Moreover, the triggering factors described in the Frémur River were only documented in January, whereas classical migration in non-obstructed rivers of the same geographic area normally occur between September and November.

Escapement rate from dam A

The presence of dam A likely prevented two eels from initiating downstream migration due to their failing to interpret/detect the triggers. Both eels exhibited significant movement after release; thus, post-surgery death was not a reason. One eel (number 20) was observed for a short period (8.8 days) before disappearing. However, this eel was observed 12 months later in another study in front of dam C; thus, it moved to the most upstream part of the river, where no acoustic receivers were present. A third eel (number 32) attempted to pass through the dam nine times over a cumulative 9.3 h period, but then migrated upstream. At the end of the tracking period, this eel was again detected at the release site. These eels were smaller and lighter than the other eels, even if the low size of the group blocked to reach a significant difference between the two groups.

Effect of dam A on eel passage

Only three eels crossed dam A through the compensation pipe and the drinking water input pipe, but two of these individuals were found dead in the nets at the end of the pipes. High water pressure and the 90° angle of the main pipe likely explains this mortality. Yet, the third eel successfully passed through the pipe and was recorded crossing the third and most downstream dam (dam C), demonstrating that it was not injured and still able to migrate. The grid blocking the pipe does not seem to be effective at preventing eels from entering, with management measures being required to address this problem. We suggest that the design of the grid is improved to block any fish from passing through.

The dam adds time to the migration of eels, by delaying their passage. This was demonstrated through the measurement of additive time, which is the accurate measurement of the difference between the first detection of eels in front of the dam and the first detection of the same eels downstream of the dam. The measurement of additive distance of migration induced by the dam was based on the mean longitudinal position of animals at 20-min intervals. Thus, this measurement is actually a minimal estimate, because potential lateral movements cannot be measured. Additive distance was very low compared with the total distance eels normally migrate to their reproduction areas (6,000 km from Frémur River). However, while this distance was minimal for a single barrage, this value must be multiplied by the number of dams in a given river (70,000 dams in French Rivers). Considering that this long migration occurs without food intake, this additive energetic cost might have significant effects on reproductive success. Moreover, this additive time might negatively impact eel migration by increasing the risk of predation (natural and anthropic).

Efficiency of individuals to pass through dam A

Two different behaviors were observed in eels that succeeded in crossing the dam. The fastest eels exhibited exploratory behavior, moving extensively and searching for alternative escape passages. The slowest eels exhibited sedentary behavior, and did not fully explore the reservoir, but attempted to pass through the dam after a long time (~20 days). The dam overflowed for a long period during our study (from December to the end of the study), which potentially facilitated the successful passage of both fast and slow eels. If the dam overflows for only a short period, which is a potential scenario under global climate change, only eels from the 'fast' group would be able to pass through the dam. This phenomenon might lead to the indirect selection of the 'fast' phenotype, i.e., animals with bold behavior. [START_REF] Caudill | Slow dam passage in adult Columbia River salmonids associated with unsuccessful migration: delayed negative effects of passage obstacles or condition-dependent mortality? Can[END_REF] showed that slow passage across dams by adult salmon is associated with unsuccessful anadromous migration; thus, fast eels exhibiting bold behavior have a greater chance of exiting from their growth watershed and reaching the site for reproduction. In comparison, [START_REF] Andersen | Evolution of boldness and life history in response to selective harvesting[END_REF] showed that fishing has a greater negative effect on fish exhibiting bold behaviors than timid fish.

Impact of dam B

All eels recorded in dam B reservoir passed through dam B (the second dam), which was probably related to the low distance between the first and the second dam (i.e., there is a lower probability of eels becoming lost in a small area) and the small size of the second dam (i.e., higher probability of finding the exit passage). However, almost half of the eels (3 of 7) were found dead in the wolf trap. This mortality rate was higher than that usually observed in this trap (e.g., 17.7% in 2016-2017) even though high mortality events have been documented [START_REF] Charrier | Suivi des migrations d'anguilles et évaluations des stocks en place sur le Frémur en[END_REF]. The cause of death remains unclear, but might have occurred during the upstream passage of eels to dam A by spillways or the main pipe. For instance, two of the three dead eels found in the trap had the lowest transit times between dams A and B; thus, they might have died before entering the trap, and were carried by water flow into the traps. Alternatively, excessively high water discharge at dam B might induce trauma or mortality. Of note, some of the eels were able to pass over the wolf trap; thus, this trap is not completely efficient for measuring the passage of eels during migration when overflow is high, as observed in this study. These observations led to the formulation of important recommendations for managing the Frémur River, especially dams A and B. We strongly recommend adding soft coats to the spillways, and adding a larger reception area at the foot of dam A. For dam B, we strongly recommend extending the size of the trap and revising the design of the entrance to the trap. These actions would reduce the amount of trauma and mortality to eels. These recommendations have already been taken into consideration.

Impact of dam C

Eight eels escaped from dam B, after which they encountered dam C. The condition (presence of injuries) of the four eels that crossed dam B over the wolf trap was not known; however, all four individuals that were caught and released from the wolf trap appeared healthy, but with cutaneous erosion. Therefore, the reason why two of the eels stopped migration between these two last dams, which are only 2.5 km apart, was not known. Similar to that documented between dam A and B, the very low water flow these two dams might explain this migration break.

Out of the six eels detected in the reservoir of dam C, only one returned to the upper part of the reservoir. Because the detection of eels downstream of this final dam was not very efficient, only an estimate of successful passage was obtained. The final record of the five remaining eels was obtained by the last acoustic receiver immediately upstream the dam (four of the five eels were very close to it), and at night. These five individuals likely passed through the third dam; however, one of these eels was detected farther upstream and in daylight, so it is not clear whether it eventually passed the dam or not. Therefore, the escapement rate of eels from dam C was considered to be 4/6.

Cumulative effect of the three dams

Out of the 16 eels that were tagged in the reservoir upstream of the first dam, only four (25%) successfully reached the estuary. Considering the small size of the Frémur River and the type of the dams (one with spillways and two with overflow crests), which are usually regarded as safe, this rate was unexpectedly low.

The additive time to pass these successive dams (21.3 ± 19.6 days, 20.1 ± 51.8 h, and 83.72 ± 27.52 h for dam A, dam B and dam C, respectively, reaching 25.62 days in total) might be problematic for reaching the breeding sites in time to ensure reproduction. As expected, the time to pass through a dam was inversely related to the size of the dam and its respective upstream reservoir. Nevertheless, a difference was documented between dam B and C; thus, the location of the overflow crest might affect the time required to pass. For instance, at dam B, the overflow crest points in the direction of the river. In comparison, at dam C, the overflow crest points to the side of the river. Another difference was the width of the spillways. For instance, the spillway at dam B was very small (3 m), while the one at dam C was very large (50 m). Consequently, the velocity of the water current passing over the crest of dam C was very low. We strongly recommended that the managers of dam C reduce the width of the spillway and lower the threshold. These actions would make the total surface of the opening similar to dam B, but the water current would be greater, possibly favoring the passage of silver eels.

Other possible assumption to explain the difference in the observed escapement rates might be some heterogeneity in water quality between the three reservoirs. This has been observed in different sites from a lined river section, where [START_REF] Gomes | Concrete lined urban streams and macroinvertebrates: a Hong Kong case study[END_REF] showed that water quality could influence the species distribution. One can assume that these differences could be also induce variations in behavioural responses and then escapement rate difference. For example, a low oxygen rate could be penalizing for eels during their exploration phase to find the exit. Unfortunately, there was no monitoring for these data in the three dams from this study. Nevertheless, this phenomenon could potentially be verified by comparing the representative population fraction of eels from each reservoir in the biological dataset.

Because the density of acoustic receivers was low in dam B and C, it was not possible to obtain an accurate estimate of the additive distance covered by eels across the three dams.

Based on the total additive time of presence (i.e., 25.62 days) and the mean daily distances swam by eels in dam A (1.86 km.d -1 ), the additive distance for the three dams was estimated as 47.65 km. Travelling this additive distance has extra energetic costs, likely contributing to the various factors impairing the reproductive migration route of European eels in this highly disturbed river.

Individual variation

Although our sample size was small, no relationship was observed between the morphological characteristics of eels and their efficiency in passing through the three dams. For instance, longer and heavier individuals were not the fastest to pass. Moreover, there was no difference in the morphological traits of eels between the two ways available to pass dam A (spillways versus main pipe). Thus, the efficiency of individuals in migration did not appear to be linked to morphology, but to differences in behavior.

Comparing the impacts of dams with mechanical elements

In comparison to our estimate of 25.62 days for eels to pass three dams without turbines, [START_REF] Behrmann-Godel | A preliminary telemetry study of the migration of silver European eel (Anguilla anguilla L.) in the River Mosel, Germany[END_REF] showed that silver eels required 0 to 8 days to cross a hydroelectrical power dam in the Meuse River (Germany). [START_REF] Carr | Migration of silver American eels past a hydroelectric dam and through a coastal zone[END_REF] showed that the median time for eels to pass dam turbines in the USA was just 1 min at night and 8 min during the day, and 46.5 min for eels passing by bypass. In a small river in France, [START_REF] Travade | Behaviour and passage of European silver eels (Anguilla anguilla) at a small hydropower plant during their downstream migration[END_REF] detected five different behaviors with very different time to pass a small hydroelectrical dam, with a maximum delay of 30 h to pass the dam. Longfin (Anguilla dieffenbachia) and shortfin eels (A. australis), [START_REF] Brown | Behaviour and fate of downstream migrating eels at hydroelectric power station intakes[END_REF] in New Zealand required 0.93 h (55.9 min; range 1 min to 9.96 h) and 0.23 h (13.9 min; range 1 min to 4.92 h) to pass a hydroelectric dam, respectively. Time to pass seems to a major issue for non-hydroelectrical dams, maybe because energy production requires a constant high flow of water, which contrasts with dams created to generate reservoirs for water supply, which retain the main part of water discharge.

Conclusions

This study clearly showed that dams without turbines that are equipped with large spillways or overflow crests have a strong negative impact on the migration of silver eels. First, the triggering of migration might be delayed or entirely halted by the masking or attenuation of the effect of environmental triggers that have been previously identified, such as water flow.

This delay might extend several months, with serious implications on the timing of arrival of eels at supposed reproduction sites (Sargasso Sea) after the mean peak of arrival, which is estimated to be in February [START_REF] Righton | Empirical observations of the spawning migration of European eels: The long and dangerous road to the Sargasso Sea[END_REF]. Second, the presence of successive dams has clear negative and additive impacts, increasing the time and distance required by eels to exit the river. This increase, added to the that caused by the attenuation of triggering factors, further increases the final time of arrival at the reproduction site. This increase in distance also impacts fitness, as it reduces the energy that eels are able to allocate to reproduction.

Third, part of the population might never find a passage through the dam, contributing to the loss of the reproductive potential of this species. Fourth, a possible evolutionary effect might appear, leading to the possible selection of the fast/bold phenotype. Yet, this phenotype is very sensitive to human pressure caused by fishing. Finally, the rate of direct mortality caused by the successive dams strongly affects the silver eel population and impairs the 40% of escapement, reducing the abundance of the pristine stock required to sustain the population.

Based on these observations, strong recommendations have been made by the National Museum of Natural History, which have already been taken into consideration. Although the present study was limited to a single study site, global recommendations could be made for natural sites where eels are present. Indeed, many important rivers supporting eel populations have similar dams in some parts of their watersheds. These dams, considered as safe to date, must be revised as they actually represent an important potential threat to the persistence of

Figure captions Figure 1 .

 captions1 Figure captions

Figure 2 .

 2 Figure 2. Partial plots of the functions fitted for the final Boosted Regression Trees model describing the triggering of downstream movement. The relative contribution that explains the variance of each descriptor is shown in parentheses. Black lines represent raw data and red lines represent smoothed data.

Figure 3 .

 3 Figure 3. Overview of the fates of tagged European eels for each dam.

Figure 4 .

 4 Figure 4. Number of European eels and the time required to pass dam A. Time to pass is defined as the time difference between an individual first being detected in front of the dam, when the dam overflowed, and when the same individual was first detected downstream of the dam. Negative time to pass values indicate passage before overflowing, i.e., via the main pipe.

Figure 5 .

 5 Figure 5. Factorial map of the efficiency of individual European eels to pass through dam A based on behavioral metrics (indicated by arrows). Each eel is represented by a group symbol and a number, with a barycenter for each group.

  Figure 1

Figure 4

 4 Figure 4

Figure 5

 5 Figure 5

Table 1 .

 1 Morphological characteristics and silvering stage of tagged European eels

	Tables
	Eel number	Total	Total Weight	Sex
		Length
	17	794	788	f
	18	644	383	f
	19	629	360	f
	20	530	228	f
	21	640	506	f
	22	520	261	f
	23	660	597	f
	24	537	304	f
	25	561	320	f
	26	605	466	f
	27	776	689	f
	28	853	1247	f
	29	974	1515	f
	30	680	621	f
	31	635	441	f
	32	572	338	f

Table 2 .

 2 Activity descriptors for the three silver eels that did not pass dam A.

	Eel	Number of	Total	Daily	Number of	Time to attempts
	number	tracked	swimming	swimming	attempts to	to pass dam
		days	distance	distance (km)	pass dam	(min)
			(km)
	19	118.6	62.9	0.5	0	0
	20	8.8	9.4	1.1	0	0
	32	44.9	53.6	1.2	9	560

Table 3 .

 3 Summary of European eel escapement, passing strategies, and survival rates at dam A. "SW" = spill way; "MP" = main pipe; "-" = unknown. Survival rate was only evaluated for fish caught in nets close to the main pipe.

	Eel number	Passing strategy	Survival
	17	SW	-
	18	SW	-
	21	SW	-
	22	MP	Alive
	23	MP	Dead
	24	SW	-
	25	SW	-
	26	SW	-
	27	MP	Dead
	28	SW	-
	29	SW	-
	30	SW	-
	31	SW	-
	Total	76.9 % by SW

Table 4 .

 4 Impact of dam A on the migration of silver eels

	Eel number	Additive time (days) Additive distance (km)
	17	65.9	71.4
	18	57.1	30.4
	21	21.2	32.6
	22	9.1	9.7
	23	4.7	33.9
	24	24.8	77.2
	25	15.5	53.2
	26	30.7	39.4
	27	11.2	21.8
	28	10.7	47.3
	29	5.1	38.9
	30	16.2	46.8
	31	4.3	14.2
	Mean	21.3	39.7
	Standard deviation	19.6	19.9

Table 5 :

 5 Fate and behavior of the European eels that reached dam B. Survival was only evaluated for eels caught in the wolf trap. "Y" = yes; "N" = no; "-" = unknown.

	Eel number	Escapement	Passing strategy	Survival	Additive time
		(Y/N)			(hours)
	17	Y	Trap	Dead	29.3
	18	Y	Trap	Alive	14.4
	21	Y	Over the trap	-	0.4
	22	Y	Over the trap	-	174.1
	24	Y	Trap	Alive	0.4
	25	Y	Trap	Dead	0.2
	26	Y	Trap	Alive	1.5
	28	Y	Trap	Dead	0.2
	29	Y	Trap	Alive	0.3
	30	Y	Over the trap	-	0.4
	31	Y	Over the trap	-	0.3
	Total / Mean	100 %	64 % trap	43 % dead,	20.1 ± 51.9
				57 % alive

Table 6 .

 6 European eels escapement from dam C and additive migration time. "Y" = yes; "N" = no; "-" = unknown.

	Eel	Attemp	Last	Theoretical	Time of	Escapement	Additive
	num	ts to	detection	lag / last	last	(Y/N)	time
	ber	pass	location	observed lag	detection		(hours)
		the
		dam
		(Y/N)
	18	N	Downstream	-		N	-
			dam B
	21	Y	Last receiver	1.06	05:00 AM	Y	58.6
			upstream
			dam C
	22	Y	Last receiver	1.37	07:00 AM	Y	118.8
			upstream
			dam C
	24	Y	Last receiver	3.85	02:00 PM	N	-
			upstream
			dam C
	26	N	Downstream	-	-	N	-
			dam B
	29	Y	Last receiver	0.86	11:00 PM	Y	92.2
			upstream
			dam C
	30	Y	Downstream	-	-	N	-
			dam B
	31	Y	Last receiver	1.69	10:00 PM	Y	65.3
			upstream
			dam C
	Total	75%				50%	83.72 ±
	/	attempt				escaped	27.52
	Mea	ed
	n

Acknowledgements

This study was funded by the 'Agence de l'Eau Loire Bretagne,' the 'Region Bretagne,' and the 'Syndicat Eau du pays de Saint-Malo.' The study was conducted by the Museum National d'Histoire Naturelle. We thank Fish Pass (Yohan, Francois and Mathieu) and Museum National d'Histoire Naturelle teams, as well as everyone who helped with sampling and collecting data. Institutional and national guides for ethical care and use of laboratory animals were followed.

eel populations.